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title: The anti-inflammatory activity of GABA-enriched Moringa oleifera leaves produced
by fermentation with Lactobacillus plantarum LK-1
authors:
- Long Zheng
- Xuli Lu
- Shengtao Yang
- Ying Zou
- Fanke Zeng
- Shaohao Xiong
- Yupo Cao
- Wei Zhou
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC10034114
doi: 10.3389/fnut.2023.1093036
license: CC BY 4.0
---
# The anti-inflammatory activity of GABA-enriched Moringa oleifera leaves produced by fermentation with Lactobacillus plantarum LK-1
## Abstract
### Introduction
Gamma-aminobutyric acid (GABA), one of the main active components in *Moringa oleifera* leaves, can be widely used to treat multiple diseases including inflammation.
### Methods
In this study, the anti-inflammatory activity and the underlying anti-inflammatory mechanism of the GABA-enriched *Moringa oleifera* leaves fermentation broth (MLFB) were investigated on lipopolysaccharide (LPS)-induced RAW 264.7 cells model. The key active components changes like total flavonoids, total polyphenols and organic acid in the fermentation broth after fermentation was also analyzed.
### Results
ELISA, RT-qPCR and Western blot results indicated that MLFB could dose-dependently inhibit the secretions and intracellular expression levels of pro-inflammatory cytokines like 1β (IL-1β), interleukin-6 (IL-6), interleukin-8 (IL-8) and tumor necrosis factor-α (TNF-α). Furthermore, MLFB also suppressed the expressions of prostaglandin E2 (PGE2) and inducible nitric oxide synthase (iNOS). Moreover, the mRNA expressions of the key molecules like Toll-like receptor 4 (TLR-4) and nuclear factor (NF)-κB in the NF-κB signaling pathway were also restrained by MLFB in a dose-dependent manner. Besides, the key active components analysis result showed that the GABA, total polyphenols, and most organic acids like pyruvic acid, lactic acid as well as acetic acid were increased obviously after fermentation. The total flavonoids content in MLFB was still remained to be 32 mg/L though a downtrend was presented after fermentation.
### Discussion
Our results indicated that the MLFB could effectively alleviate LPS-induced inflammatory response by inhibiting the secretions of pro-inflammatory cytokines and its underlying mechanism might be associated with the inhibition of TLR-4/NF-κB inflammatory signaling pathway activation. The anti-inflammatory activity of MLFB might related to the relative high contents of GABA as well as other active constituents such as flavonoids, phenolics and organic acids in MLFB. Our study provides the theoretical basis for applying GABA-enriched *Moringa oleifera* leaves as a functional food ingredient in the precaution and treatment of chronic inflammatory diseases.
## 1. Introduction
Inflammation is a natural immunological response that the immune system reacted to outside stimulus like pathogen [1]. However, the inordinate and uncontrolled inflammation will also lead to apoptosis of immune cells and immunologic derangement which resulting in the chronic degenerative diseases and cancer [2, 3]. Although various steroids and non-steroidal anti-inflammatory drugs (NSAIDs) like azathioprine and aspirins have been used to treat these acute and chronic inflammatory diseases, long-term use of these drugs may also lead to various adverse effects to human health [1]. Therefore, the development of natural anti-inflammatory drugs with greater efficacy and minimal toxicity has attracted more and more attention. Besides, with the improvement of consumption level, natural anti-inflammatory functional foods are more and more popular in humans [4].
Gamma-aminobutyric acid (GABA) is a non-protein amino acid that can be found in plants, bacteria and animals [5]. It's an important inhibitory neurotransmitter that presented in mammal central nervous system with multiple physiological functions including anti-inflammation [6]. For example, GABA showed obvious anti-inflammatory effect on streptozotocin-treated mice by decreasing the synthesis of inflammatory mediators like IL-1β, TNF-α, IFN-γ, IL-12, and increasing the production of anti-inflammatory mediator TGF-β1 [7]. Besides, GABA restrained the expression of inflammatory cytokines (L-6, IL8, and TNF-α) and alleviated inflammatory response of MAC-T cells induced by LPS via the TLR4-MyD88-NFκB signaling pathway [8]. Except for anti-inflammation, GABA has also been reported to possess hypotensive, antidiabetic, immunity enhancement, and sedative effects, and has been applied to alleviate sleeplessness, depression and improve visual cortical function [6, 9]. Despite its various physiological functions, GABA content in natural animal- and plant-based food products is pretty low which cannot meet people's needs [6]. Therefore, GABA enrichment has attracted increasing attention and a variety of related functional foods have been developed like GABA-enriched tea, soft sweets, beverages, dairy products and so on. At present, microbial fermentation is the most common method to enrich GABA because of its ability to produce lactic acid, flavonoids, polyphenols and other active substances beneficial to human health [5, 10]. Up to now, microbial fermentation has been widely applied in the GABA enrichment in strawberry juice [6], green tea [11], water dropwort [10], etc.
Moringa oleifera, also known as “drumstick tree” or simply Moringa in English, is a perennial deciduous tropical plant with a variety of bioactive compounds [12]. Because of its nutritional and medicinal value, *Moringa oleifera* has been regarded as one of the most economically valuable plants and is widely used in food, industry, agriculture and medicine in the developing countries [13]. Moringa oleifera leaves contain multiple active constituents like protein, amino acids, polysaccharides, dietary fiber, phenols, flavonoids, phytosterols, glycosides [2], and are reported to possess multiple pharmacological activities like antioxidant, anti-inflammatory, anti-hypertensive, hypoglycemic, hypolipidemic, liver and kidney protective, as well as anti-cancer effects (14–16). Previous studies have found that GABA as well as other active constituents like flavonoids, polyphenols, most amino acids, oligosaccharides, organic acids, nucleosides of *Moringa oleifera* leaves are significantly enhanced after fermentation [12, 17], which suggested that *Moringa oleifera* leaves were ideal candidate for GABA enrichment. Therefore, GABA enrichment in *Moringa oleifera* leaves may provide new idea for its application in functional foods. However, to our knowledge, there are still no studies on the enrichment of GABA using *Moringa oleifera* leaves as raw material. As a consequence, we tried Lactobacillus fermentation to enrich GABA in *Moringa oleifera* leaves and the result showed that the GABA content of the fermented *Moringa oleifera* liquid increased to 209 mg/L under the optimal fermentation conditions, which was 1.45 times higher than that of the unfermented [18]. However, the other key active components changes in the fermentation broth after fermentation has not been further studied. Besides, the anti-inflammatory activity of the fermentation broth and the underlying anti-inflammatory mechanism is still unclear, which greatly limited its application in functional foods.
Therefore, in order to exploit the application value of GABA-enriched *Moringa oleifera* leaves fermentation broth (MLFB) in functional foods, the key active components changes like total flavonoids, total polyphenols and organic acid in the fermentation broth after fermentation was analyzed, and the anti-inflammatory activity as well as the underlying anti-inflammatory mechanism of the fermentation broth were investigated on LPS-induced RAW 264.7 cells model. This study may provide theoretical basis for the application of MLFB on the anti-inflammatory functional foods.
## 2.1. Materials
Moringa oleifera leaves powder was provided by Henan Jinlamu Bio-technology (Hebi, Henan, China). Lipopolysaccharide (LPS), 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), Dulbecco's modified *Eagle medium* (DMEM) and dimethyl sulfoxide (DMSO) were from Solarbio Science and Technology (Beijing, China). Lactobacillus plantarum LK-1 was isolated from pickles in our laboratory [19]. Fetal bovine serum (FBS) and ethylene diamine tetraacetic acid (EDTA) digestive juice were from Gibco (Carlsbad, CA, USA). IL-6, IL-8, IL-1β, TNF-α detection kits (enzyme-linked immunosorbent assay) were from Nanjing Jiancheng Biotechnology (Nanjing, Jiangsu, China). Nucleic Acid Stain was from Beijing Dingguo Changsheng Biotechnology (Beijing, China). Reverse transcription kit and fluorescent dye kit were from Guangzhou Jisai Biotechnology (Guangzhou, Guangdong, China).
## 2.2. Preparation of MLFB and its key active components analysis before and after fermentation
GABA-enriched *Moringa oleifera* leaves fermentation broth was obtained using *Lactobacillus plantarum* LK-1 as fermentation strain according to the previous study [18]. In detail, the *Moringa oleifera* leaves powder was added to distilled water at 1:25 (w/w), $3\%$ (w/w) of glucose was added and monosodium glutamate was then added at the final concentration of 5 g/L. After autoclaving at 105°C for 20 min, the *Moringa oleifera* solution was fermented with *Lactobacillus plantarum* LK-1 at 35°C for 72 h. After that, the fermentation mixture was centrifuged at 6,000 g for 30 min, then the supernatant was collected and named as MLFB for the following anti-inflammatory activity evaluation. The GABA content of MLFB was detected to be 209 mg/L by high performance liquid chromatography (HPLC) with pre-column o-phthaldialdehyde (OPA) derivatization [18]. The result of GABA content analysis in MLFB and *Moringa oleifera* leaves solution (MLS) along with their HPLC spectrograms can be seen in the Appendix. The total flavonoids, total polyphenols and organic acid content in MLFB and MLS were detected according to the methods of Li et al. [ 17].
## 2.3. Cell culture
The macrophages RAW 264.7 were provided by the Procell Life Technology (Wuhan, Hubei, China). The cells were cultured in DMEM with $10\%$ FBS at 37°C in an atmosphere of $5\%$ CO2, as the cell provider required.
## 2.4. Cell viability assay
The cell viability was determined by MTT assay [2]. Cells in logarithmic phase was dispersed in DMEM containing $10\%$ FBS. Then the cell suspension was evenly transferred to 96-well plates at 5 × 103 cells/well. After cultivating for 12 h, the culture medium was removed and 100 μL fresh culture medium with different concentrations of MLFB (31.25, 62.5, 125, 250 and 500 μg/mL) was added to each well. 12 h later, 10 μL 5 mg/mL MTT was added to each well, and the supernatant was gently removed after 3 h of culture. Then 150 μL DMSO was added to each well and the mixture was oscillated for 10 min. The absorbance of the mixture was detected at 570 nm using a microplate reader (Bio-Tek, Winooski, VT, USA). The relative activity of RAW 264.7 cells was calculated considering the activity of cells treated without MLFB as $100\%$.
## 2.5. Enzyme-linked immunosorbent assay (ELISA)
The concentrations of inflammatory factors (IL-1β, IL-6, IL-8 and TNF-α) in cell supernatants were detected using ELISA kits referring to the manufacturer's descriptions. RAW 264.7 cells suspension (1 × 105 cells/mL) was inoculated into 12-well plate at 1 × 105 cells/well. After cultivating for 12 h, the supernatant in each well was removed and the cells were treated with different concentrations of MLFB (125, 250, 500 μg/mL) for 1 h. After incubating with 1 μg/mL LPS for 24 h, the mixture was collected and centrifuged for 20 min at 3,000 g. The secretion levels of IL-1β, IL-6, IL-8, and TNF-α in supernatants of each group were measured and the standard curves of these inflammatory factors were made referring to the manufacturer's descriptions in ELISA kits.
## 2.6. Real-time reverse transcription quantitative polymerase chain reaction
After RAW 264.7 cells were treated with LPS and different concentrations of MLFB for 24 h, the mRNA expressions of IL-1β, IL-6, TNF-α, NF-κB, PGE2, TLR-4 in RAW 264.7 cells were determined by RT-qPCR [6]. Firstly, TRIzol reagent was used to extract the total RNA of cells. Then RNA was synthesized into cDNA by cDNA reverse transcription kit referring to the manufacturer's descriptions. Finally, the RT-qPCR was proceeded in 20 μL reaction system containing 2 μL cDNA, 1 μL primer pairs (10 μmol/L), 10 μL SYBR Green PCR Master Mix, 0.4 μL 50 × ROX Reference Dye 2 and 6.6 μL ultra-pure distilled water. The PCR conditions were as followings: initial denaturation at 95°C for 5 min, 40 cycles at 95°C for 10 s, 60°C for 34 s, and the melting curve was obtained at 95°C for 15 s, 60°C for 1 min and 95°C for 15 s in the ABI 7500 real-time fluorescent quantitative PCR System (Applied Biosystems, Foster, CA, USA). β-actin was used as internal control and the primers used here are shown in Table 1.
**Table 1**
| Primer name | Primer sequence | Product size/bp |
| --- | --- | --- |
| β-actin | Forward: 5′-GCTTCTAGGCGGACTGTTAC-3′ | 100.0 |
| β-actin | Reverse: 5′-CCATGCCAATGTTGTCTCTT-3′ | 100.0 |
| IL-1β | Forward: 5′-GTGTCTTTCCCGTGGACCTT-3′ | 121.0 |
| IL-1β | Reverse: 5′-CGTCACACACCAGCAGGTTA-3′ | 121.0 |
| IL-6 | Forward: 5′-CCACTTCACAAGTCGGAGGC-3′ | 117.0 |
| IL-6 | Reverse: 5′-TTTCTGCAAGTGCATCATCGTT-3′ | 117.0 |
| NF-κB | Forward: 5′-ACACCTCTGCATATAGCGGC-3′ | 152.0 |
| NF-κB | Reverse: 5′-GGCACCACTCCCTCATCTTC-3′ | |
| PGE2 | Forward: 5′-CACCTTCGCCATATGCTCCT-3′ | 154.0 |
| PGE2 | Reverse: 5′-GACCGGTGGCCTAAGTATGG-3′ | 154.0 |
| TLR-4 | Forward: 5′-AGATCTGAGCTTCAACCCCTTG-3′ | 137.0 |
| TLR-4 | Reverse: 5′-AGAGGTGGTGTAAGCCATGC-3′ | 137.0 |
| TNF-α | Forward: 5′-CCACCACGCTCTTCTGTCTA-3′ | 105.0 |
| TNF-α | Reverse: 5′-TGAGGGTCTGGGCCATAGAA-3′ | 105.0 |
## 2.7. Western blot
After treated with LPS and different concentrations of MLFB for 24 h, the protein expression levels of IL-1β, IL-6, TNF-α and iNOS in RAW 264.7 cells were determined by Western blot [20]. Firstly, the cells were lysed by 400 μL cell lysis buffer containing protease inhibitors. Then the lysate was stirred on ice for 30 min and transferred to a 100°C water path for 10 min. After centrifuging the lysate at 6,000 g for 6 min at 4°C, the supernatant was gathered and the protein content was determined by a BCA protein assay kit (Solarbio Science and Technology Crop., Beijing, China). Then the proteins were analyzed by $10\%$ SDS-PAGE and transferred to a PVDF membrane. After blocking with $5\%$ skimmed milk for 60 min, the PVDF membrane was incubated with the primary antibody at 4°C for a night. Then the PVDF membrane was washed with TBST (50 mmol/L Tris-HCl, 150 mmol/L NaCl and $0.1\%$ v/v Tween-20) for three times before incubating with the secondary antibody for 40 min at room temperature. The bands on the membrane were quantified by an automatic gel imaging analysis system (Peiqing science and technology Crop., Shanghai, China). β-actin was used as the internal control, and the results were displayed referring to the control.
## 2.8. Statistical analysis
All tests were performed at least in triplicate and the experimental results were presented as mean ± standard deviation (SD). SPSS Version 17.0 (SPSS Inc., Chicago, IL) was used to analyze the significant differences between samples and $P \leq 0.05$ was regarded as statistically significant. All the figures were drawn by origin 8.0 (OriginLab Corp., Northampton, USA).
## 3.1. Cytotoxicity of MLFB on RAW 264.7 cells
The cytotoxic effect of MLFB on RAW 264.7 cells was determined by MTT assay. As shown in Figure 1, after treated with different concentrations of MLFB, the viability of RAW 264.7 cells showed no significant changes ($P \leq 0.05$). After treated with 500 μg/ml MLFB for 12 h, the cell viability was measured to be $99.33\%$. The result indicated that the MLFB with concentration under 500 μg/mL had no obvious toxicity to cells. The result was consistent with the study result of Lin et al. [ 20] who stated that the brown seaweed *Laminaria japonica* fermented by *Bacillus subtilis* also showed no significant cytotoxic effects on RAW 264.7 cells. According to the result of cytotoxic effect, concentrations of 125, 250 and 500 μg/mL of MLFB were selected as the standard test concentrations in the follow-up experiments.
**Figure 1:** *Effects of MLFB on the viability of RAW 264.7 cells.*
## 3.2. MLFB inhibits the productions and expressions of pro-inflammatory cytokines in LPS-induced RAW 264.7 cells
IL-1β, IL-6, IL-8, and TNF-α are typical pro-inflammatory cytokines that secreted by activated macrophages, and are important in inflammatory responses [21]. Dysregulation of these pro-inflammatory cytokines is related to systemic inflammatory disorder and may lead to various inflammatory diseases like rheumatoid arthritis and inflammatory bowel disease [21]. The effects of MLFB on the productions, mRNA and protein expressions of LPS-induced pro-inflammatory cytokines including IL-1β, IL-6, IL-8 and TNF-α were determined by ELISA, RT-qPCR and Western blot, respectively. As shown in Figure 2, the productions of IL-1β, IL-6, IL-8 and TNF-α in LPS-induced inflammatory model group were obviously more than those in normal group, which indicated that the cellular inflammatory model was successfully established. Compared with the inflammatory model group, the productions of pro-inflammatory factors IL-1β, IL-6, IL-8 and TNF-α in cells were dose-dependently decreased after treated with a series concentrations of MLFB. When the concentration of MLFB reached 500 μg/mL, the productions of IL-1β, IL-6, IL-8 and TNF-α in the MLFB treatment groups decreased by 63.22, 35.71, 59.89, and $60.77\%$, respectively. The mRNA expression levels of pro-inflammatory cytokines IL-1β, IL-6 and TNF-α in RAW 264.7 cells were shown in Figure 3. As expected, the mRNA expressions of IL-1β, IL-6 and TNF-α in LPS-induced inflammatory model group were obviously increased when compared with normal group. Despite the treatment of MLFB at concentrations of 125 μg/mL showed no significantly inhabitation effects on the mRNA expression of these pro-inflammatory cytokines induced by LPS, when the concentrations of MLFB increased to 250 and 500 μg/mL, the mRNA expression of these pro-inflammatory cytokines were obviously inhibited. Similarly, compared with LPS-induced inflammation model group, the protein expression levels of pro-inflammatory factors IL-1β, IL-6, TNF-α were decreased in different degrees after treated with different concentrations of MLFB, despite the decrease of IL-1β protein expression was not significant (Figure 4).
**Figure 2:** *Effects of MLFB on the secretions of IL-1β (A), IL-6 (B), IL-8 (C), and TNF-α (D) in LPS-induced RAW 264.7 cells. #Means statistical difference compared with blank control group (###P < 0.001). *Means statistical difference compared with LPS-induced inflammatory model group (*P < 0.05, **P < 0.01, ***P < 0.001).* **Figure 3:** *Effects of MLFB on the mRNA expressions of IL-1β (A), IL-6 (B), TNF-α (C) in LPS-induced RAW 264.7 cells. #Means statistical difference compared with blank control group (###P < 0.001). *Means statistical difference compared with LPS-induced inflammatory model group (*P < 0.05, **P < 0.01, ***P < 0.001).* **Figure 4:** *Effects of MLFB on the protein expressions of IL-1β (A), IL-6 (B), TNF-α (C) in LPS-induced RAW 264.7 cells. #Means statistical difference compared with blank control group (#P < 0.05, ##P < 0.01). *Means statistical difference compared with LPS-induced inflammatory model group (**P < 0.01, ***P < 0.001).*
The above results indicated that the MLFB could down-regulate the expressions of pro-inflammatory cytokines in macrophages activated by LPS, thus further inhibit the secretions of these pro-inflammatory cytokines and exerts anti-inflammatory effect. Previous studies have showed that GABA could promote proliferation of T-cells, inhibit apoptosis in β-cells, decrease the synthesis of inflammatory mediators like IL-1β, TNF-α, IFN-γ, IL-12, increase the production of anti-inflammatory mediator TGF-β1, and thus played important roles in anti-inflammatory responses [7, 8]. Besides, the ability of GABA to restrain the secretions of TNF-α, COX-2 and iNOS, IL-6 and IL-12 by LPS-induced macrophages was also reported [22, 23]. Therefore, the anti-inflammatory activity of MLFB may be attributed to its high content of GABA. Our study result is similar to the study result of Chang et al. [ 24] who reported that GABA-enriched pepino extract obtained after fermentation can inhibit the expression of TNF-α in LPS-induced RAW 264.7 macrophages.
## 3.3. MLFB inhibits the expressions of PGE2 and iNOS in LPS-induced RAW 264.7 cells
Prostaglandin E2 (PGE2), an important prostanoid metabolite mainly produced by cyclooxygenase-2 (COX-2), is involved in multiple inflammatory processes including activating immune cell and promoting the secretion of pro-inflammatory cytokines [25]. A variety of stimulations of inflammation or tissue injury will lead to overproduction of PGE2, thus inhibiting the expression of PGE2 is also an effective measure to treat inflammation [20]. The effect of MLFB on the mRNA expression of PGE2 was shown in Figure 5A. The result showed that the mRNA expression of PGE2 in cells were significantly increased after treated with LPS, and 125 μg/mL of MLFB displayed no significant effect on the mRNA expression of PGE2 compared with LPS-treated group. Notably, the mRNA expression of PGE2 in cells treated with 250 μg/mL of MLFB was significantly decreased. The result indicated that MLFB at a certain dose could inhibit the expressions of PGE2 and therefore control the overproduction of PGE2 induced by LPS.
**Figure 5:** *Effects of MLFB on the expressions of PGE2
(A) and iNOS (B) in LPS-induced RAW 264.7 cells. #Means statistical difference compared with blank control group (###P < 0.001). *Means statistical difference compared with LPS-induced inflammatory model group (**P < 0.01).*
Inducible NOS (iNOS), an isoform of nitric oxide synthase (NOS), is especially expressed during inflammatory response and involved in the synthesis of pro-inflammatory mediator nitric oxide (NO) [25]. The over expression of iNOS will produce excess amount of NO, thus leading to tissue damage, septic shock, and further resulting in other complication during persistent chronic inflammatory response [21]. The effect of MLFB on the protein expression of iNOS in RAW 264.7 macrophages was exhibited in Figure 5B. The intracellular protein expression of iNOS in LPS-induced inflammatory model group was obviously higher than that in normal group. Compared with the inflammatory model group, the protein expression of iNOS in cells was dose-dependently decreased after treated with different concentrations of MLFB. The result indicated that MLFB could inhibit the expression of iNOS, which may further suppress the production of NO, thus alleviate LPS-induced inflammatory response.
COX-2 and iNOS are primary inflammatory mediators expressed in macrophages which are involved in the synthesis of PGE2 and NO, and the production of PGE2 and NO are closely related with initiation of the early stage of inflammatory pathways [1, 26]. In the present study, pretreatment with MLFB effectively inhibited the mRNA expression of PGE2 and protein expression level of iNOS in LPS-stimulated RAW 264.7 macrophages at different degree, which indicated that MLFB involved in the suppression in the early stage of inflammatory pathways. Previous studies illuminated that ethyl acetate fraction, concentrate and isothiocyanates of *Moringa oleifera* could effectively down-regulate the expression of COX-2 and iNOS, and concomitantly inhibited the production of NO and PGE2 in LPS-stimulated RAW 264.7 macrophages [1, 13]. In addition, GABA is also reported to have the ability of inhibiting immune cells activation by diminishing the production of COX-2 and iNOS [6]. Therefore, the GABA as well as other active constituents contained in *Moringa oleifera* leaves may play roles in the inhibition of PGE2 and iNOS expressions.
## 3.4. MLFB suppresses inflammation mediated by the TLR-4/NF-κB signaling pathway
Toll-like receptors (TLRs) are important transmembrane signaling receptors mediating signal transductions, and TLR-4 is one of the most studied receptors involved in inflammatory response, immune response and other processes [27, 28]. It has been reported that LPS could interact with TLR-4 and following activate several intracellular signaling pathways including NF-κB signaling pathway [29, 30]. NF-κB, mainly composed of p50 and p65 subunits, is a transcription factor that plays an important role in regulating innate and adaptive immunity, including inflammation signal transduction of macrophages [31, 32]. In the normal state, NF-κB exists in cytosol and is combined with inhibitory protein IκB. When the cells were stimulated by LPS, IκB is phosphorylated and rapidly degraded by proteasomes in the cytoplasm, thus NF-κB is released [28]. The free NF-κB transfers into the nucleus and turn on gene expression of pro-inflammatory mediators [33, 34]. In a word, TLR-4/NF-κB signaling pathway participates in the regulations of various inflammatory cytokines like IL-1β, IL-6, IL-8, and TNF-α, and plays an important role in both immune and inflammatory response [8, 35]. Interdicting the TLR/NF-κB signaling pathway will be an effective method for treatment of chronic inflammatory diseases. Therefore, the key molecules (TLR-4 and NF-κB p65) in the TLR-4/NF-κB signaling pathway were detected to explore the anti-inflammatory mechanism of MLFB on LPS-induced RAW 264.7 cells. As shown in Figure 6, comparing to the normal group, the mRNA expression level of NF-κB p65 and TLR-4 in RAW 264.7 cells were significantly increased after stimulated by LPS, indicating that the TLR-4/NF-κB signaling pathway was activated by LPS. When the cells were pretreated with different concentrations of MLFB, the mRNA expression levels of NF-κB p65 and TLR-4 in cells were obviously decreased in a dose-dependently manner. The result demonstrated that MLFB suppressed the mRNA expression levels of the key molecules in the NF-κB signaling pathway induced by LPS in RAW 264.7 cells. Therefore, MLFB may exert anti-inflammatory activity by inhibiting the TLR-4/NF-κB signaling pathway.
**Figure 6:** *Effects of MLFB on the mRNA expressions of NF-κB p65 (A) and TLR-4 (B) in LPS-induced RAW 264.7 cells. #Means statistical difference compared with blank control group (#P < 0.05, ###P < 0.001). *Means statistical difference compared with LPS-induced inflammatory model group (*P < 0.05, **P < 0.01).*
## 3.5. The key active components analysis of MLFB and its anti-inflammatory mechanism elaboration
The changes of key active components like total flavonoids, total polyphenols and organic acid content in MLFB after fermentation was shown in Table 2. After fermentation, the total flavonoids content decreased from 48.66 mg/L to 31.81 mg/L, while total polyphenols content increased from 21.43 mg/L to 26.02 mg/L, which was increased by $21.42\%$ compared with the unfermented MLS. The content changes of flavonoids and polyphenols during fermentation may be due to their continuous degradation, convertation, and synthesis during the growth and metabolic processes of microorganisms [36]. For instance, it is reported that microbial degradation may cause the cleavage and hydroxylation of the aromatic ring in flavonoids and polyphenols [37]. Their precipitation or oxidation during the fermentation process may also lead to decrease of these compounds [38]. Conversely, some lactobacillus like L. rhamnosus are reported to have the ability of releasing esterases which can hydrolyze ester bonds between phenolic acids and cell wall substances, thus resulting in the release of phenols [39]. Previous studies also showed that lactobacillus can produce glycosyl hydrolases which can convert the bound phenolics linked with glycosides into free phenols during fermentation [40]. As shown in Table 2, the contents of citric acid and succinic acid in MLFB decreased to < 1 mg/L after fermentation, but the content of pyruvic acid, lactic acid and acetic acid were increased obviously ($P \leq 0.05$). The content of lactic acid greatly increased to 303.84 mg/L and became the main organic acid in MLFB. However, the contents of oxalic acid showed no significantly changes after fermentation. The content changes of organic acids in MLFB may be associated with glycolytic pathway and tricarboxylic acid (TCA) cycle during the growth and metabolic processes of lactobacillus. During fermentation process, lactobacillus can catabolize sugars into lactic acid and acetic acid through the glycolytic pathway [38, 40]. Besides, citric acid, pyruvic acid and succinic acid are important intermediate metabolites of the TCA cycle and the contents of these organic acid change dynamically with the involved biochemical reactions [40, 41]. For example, citric acid is used as the second carbon source by lactobacillus and can be converted into acetic acid and oxaloacetic acid by a citrate lyase, while the oxaloacetic acid can be further translated into pyruvic acid which is eventually reduced to lactic acid by the lactate dehydrogenase [42]. In addition, in the oxidative branch pathway of the TCA cycle, oxaloacetate can also be converted into succinic acid through a serious of enzymes like aconitase, isocitrate dehydrogenase, and succinic acid can be further converted to fumarate by succinate dehydrogenase [43]. The changes of total polyphenols as well as citric acid, succinic acid, lactic acid and oxalic acid contents were similar to our previous study [17], in which the contents of polyphenols and lactic acid increased, citric acid and succinic acid decreased, and oxalic acid showed no obvious changes after the *Moringa oleifera* leaves were fermented with *Lactobacillus plantarum* S35. However, the changes of total flavonoids, pyruvic acid and acetic acid content in this study showed opposite trend with our previous study [17], which might due to the difference of fermentation condition and the used fermentation strain. Furthermore, in the study of Jin et al. [ 11], except for GABA content, the lactic acid and acetic acid contents in GABA-enriched green tea were also significantly increased after fermentation with Levilactobacillus strain GTL 79, which was similar with present study result.
**Table 2**
| Active components | MLS | MLFB |
| --- | --- | --- |
| Flavonoids (mg/L) | 48.66 ± 3.93 | 31.81 ± 2.35* |
| Polyphenols (mg/L) | 21.43 ± 1.48 | 26.02 ± 0.33* |
| Organic acids | Organic acids | Organic acids |
| Oxalic acid (mg/L) | 14.14 ± 0.70 | 13.71 ± 0.53 |
| Citric acid (mg/L) | 26.44 ± 0.39 | < 1.00* |
| Pyruvic acid (mg/L) | 4.28 ± 0.04 | 12.19 ± 0.52* |
| Succinic acid (mg/L) | 19.16 ± 1.68 | < 1.00* |
| Lactic acid (mg/L) | 0 | 303.84 ± 8.13* |
| Acetic acid (mg/L) | 0.81 ± 1.76 | 5.36 ± 0.08* |
It has been reported that GABA suppressed the mRNA expression levels of key mediators in the NF-κB signaling pathway induced by LPS in MAC-T cells, such as TLR-4, myeloid differentiation 88 (MyD88), NF-κB p65 and TNF receptor-associated factor 6 (TRAF6), which indicated that GABA might suppresses inflammation by inhibiting the activity of the NF-κB signaling pathway [8]. Furthermore, previous studies also showed that *Moringa oleifera* extract pretreatment could block LPS-induced activation of NF-κB p65 subunit in MAC-T cells and thus protect bovine mammary epithelial cells against inflammation [35]. Several reports indicated that the high content of flavonoids and phenolic compounds contained in *Moringa oleifera* leaves contributed to its anti-inflammatory activity [15, 44, 45]. In addition, it has been reported that the enhanced effect of fermentation on the anti-inflammatory activity of *Laminaria japonica* was possibly due to the productions of more bioactive constituents after fermentation, such as fucoidan, alginate and phenols [20]. Zhang et al. [ 3] also verified that the enhancement of antioxidant and anti-inflammatory activities of rape bee pollen after fermentation were mainly due to the increase of phenolics, flavonoids, fatty acids, and amino acids. Previous studies showed that except for the increase of GABA content, other active constituents like flavonoids, polyphenols, oligosaccharides, organic acids of *Moringa oleifera* leaves were also significantly increased after fermentation [12, 17]. In our study, the GABA [18], total polyphenols, and most organic acids like pyruvic acid, lactic acid as well as acetic acid were increased obviously after fermentation. Though the total flavonoids content in MLFB decreased after fermentation, there were still 32 mg/L of flavonoids remained. Therefore, the anti-inflammatory activity that MLFB showed on LPS-induced cell inflammatory model might be associated with GABA and other active constituents such as flavonoids, phenolics and organic acids contained in the fermentation broth.
The possible anti-inflammatory mechanism of MLFB was summarized in Figure 7. In detail, the high content of GABA and other active constituents like flavonoids, polyphenols contained in MLFB inhibited the expression of the main transmembrane signaling receptor TLR-4, and therefore disturbed its interaction with LPS. As a result, the stimulation of LPS to the macrophages was reduced and the NF-κB signaling pathway was inhibited, which further led to the secretion decrease of pro-inflammatory mediators and cytokines like iNOS, PGE2, IL-1β, IL-6, IL-8 and TNF-α, eventually the inflammation was alleviated.
**Figure 7:** *The underlying anti-inflammatory mechanism of MLFB.*
## 4. Conclusion
In this study, the anti-inflammatory activity of MLFB was investigated and its underlying anti-inflammatory mechanism was clarified on LPS-induced RAW 264.7 cells model combining with its key active components analysis before and after fermentation. The results showed that MLFB suppressed the secretions and expressions of pro-inflammatory cytokines such as IL-1β, IL-6, IL-8 and TNF-α in LPS-induced RAW 264.7 cells. In addition, the expressions of PGE2 and iNOS were inhibited by MLFB, which might contribute to the decrease of pro-inflammatory cytokines and NO productions. Moreover, the mRNA expressions of the key molecules (TLR-4 and NF-κB p65) in the NF-κB signaling pathway were also restrained by MLFB in a dose-dependent manner, which indicated that the activity of the NF-κB signaling pathway was inhibited. In conclusion, MLFB can effectively ameliorate LPS-induced inflammation by inhibiting the secretions of pro-inflammatory cytokines and its underlying mechanism may be associated with the inhibition of TLR-4/NF-κB inflammatory signaling pathway activation. Besides, the anti-inflammatory activity of MLFB might related to the relative high contents of GABA as well as other active constituents such as flavonoids, phenolics and organic acids in MLFB. Our research provides a theoretical basis for the follow-up development of GABA-enriched *Moringa oleifera* functional foods with anti-inflammatory functions. However, the present study about the anti-inflammatory mechanism of MLFB was preliminary. Its in-depth mechanisms against inflammation should be further clarified and the active ingredients contained in MLFB also need to be further clarified.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.
## Author contributions
LZ: conceptualization, methodology, and validation. XL: data curation, writing—original draft preparation, and visualization. SY: methodology and data curation. FZ: funding acquisition and validation. YZ: resources and writing—review and editing. SX: formal analysis and validation. YC: conceptualization and funding acquisition. WZ: writing—review and editing, funding acquisition, and supervision. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.1093036/full#supplementary-material
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|
---
title: Pharmacokinetics and tissue distribution of bleomycin-induced idiopathic pulmonary
fibrosis rats treated with cryptotanshinone
authors:
- Xiangjun He
- Zhi Zhong
- Quan Wang
- Zhenmao Jia
- Jing Lu
- Jianwen Chen
- Peiqing Liu
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10034131
doi: 10.3389/fphar.2023.1127219
license: CC BY 4.0
---
# Pharmacokinetics and tissue distribution of bleomycin-induced idiopathic pulmonary fibrosis rats treated with cryptotanshinone
## Abstract
Introduction: Cryptotanshinone(CTS), a compound derived from the root of Salvia miltiorrhiza, has been linked to various of diseases, particularly pulmonary fibrosis. In the current study, we investigated the benefit of CTS on Sprague-Dawley (SD) rats induced by bleomycin (BLM) and established high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) methods to compare pharmacokinetics and tissue distribution in subsequent normal and modulated SD rats.
Methods: The therapeutic effect of CTS on BLM-induced SD rats was evaluated using histopathology, lung function and hydroxyproline content measurement, revealing that CTS significantly improved SD rats induced by BLM. Additionally, a simple, rapid, sensitive and specific HPLC-MS/MS method was developed to determine the pharmacokinetics of various components in rat plasma.
Results: Pharmacokinetic studies indicated that CTS was slowly absorbed by oral administration and had low bioavailability and a slow clearance rate. The elimination of pulmonary fibrosis in 28-day rats was slowed down, and the area under the curve was increased compared to the control group. Long-term oral administration of CTS did not accumulate in vivo, but the clearance was slowed down, and the steady-state blood concentration was increased. The tissue distribution study revealed that CTS exposure in the lungs and liver.
Discussion: The lung CTS exposure was significantly higher in the model group than in the control group, suggesting that the pathological changes of pulmonary fibrosis were conducive to the lung exposure of CTS and served as the target organ of CTS.
## 1 Introduction
Pulmonary Fibrosis (PF) is a chronic, progressive and irreversible lung disease common in clinical practice. In the early stage, it is characterized by alveolar epithelial cell injury, interstitial lung inflammation and interstitial lung edema. In the end stage, a large amount of Extracellular Matrix (ECM) deposition, abnormal proliferation, activation of fibroblasts and destruction of tissue structure destruction occur (Urban et al., 2015; Barratt et al., 2018; Sgalla et al., 2018). The lung tissue thickens, scar tissue forms and lung function decrease significantly, eventually developing organ dysfunction and respiratory failure (King et al., 2011). Pulmonary fibrosis is prevalent in the elderly, and its incidence increases yearly. The average life expectancy after diagnosis is approximately 2.8 years, and the salvage rate is lower than for most tumors (Chanda et al., 2019). The etiology of pulmonary fibrosis is complex. Many factors are known to cause pulmonary fibrosis, such as smoking, environmental pollution, lung injury, virus and drugs (Rangarajan et al., 2016). Idiopathic pulmonary fibrosis (IPF), the most severe form of pulmonary fibrosis, has a high mortality rate and a poor prognosis (Richeldi et al., 2017). Pulmonary fibrosis is treated primarily with glucocorticoids, anti-inflammatory drugs, immunosuppressants, and antifibrotic drugs. Although drug therapy can alleviate disease symptoms and improve respiratory function, long-term use is prone to adverse reactions (du Bois, 2010) and cannot significantly improve the survival rate of patients (Spagnolo et al., 2015). It is important to obtain more effective drugs due to adverse reactions and the limited effectiveness of existing drugs in preventing and treating fibrosis. Additionally, recent studies (Li et al., 2022) reveal that various natural small-molecule compounds have certain therapeutic effects on pulmonary fibrosis, so developing natural small-molecule compounds is important.
Cryptotanshinone (CTS) is a diterpenoid quinone lipid-soluble compound extracted from the root of S. miltiorrhiza. It benefits from abundant sources, low toxicity and low relative molecular weight. It has high biological activity and high content among the extracts of S. miltiorrhiza. Recently, CTS has been proven to have anti-inflammatory, antioxidant, anti-angiogenic and anti-proliferative activities, and play a role in various malignant tumors (Wang et al., 2017; Qi et al., 2019; Chen et al., 2020; Luo et al., 2020), cardiovascular diseases (Zhang et al., 2021), neuroprotection (Kwon et al., 2020) and other diseases. Our laboratory has conducted multiple studies on CTS, and discovered that CTS could significantly improve pulmonary fibrosis in rats induced by bleomycin (BLM), and reverse the fibrosis level of human fetal lung fibroblasts (HLF) induced by factor-beta 1 (TGF-β1) by inhibiting the STAT3 and Smad$\frac{2}{3}$ phosphorylation (Zhang et al., 2019). Furthermore, our laboratory revealed that treatment with CTS attenuates adult rat cardiac fibroblasts and cardiac fibrosis rats induced by angiotensin Ⅱ (Ma et al., 2014). However, the comparative pharmacokinetics and tissue distribution of CTS under normal and model conditions remain unclear.
Pharmacokinetic (PK) based studies are considered a reliable approach for identifying and screening potential bioactive components that contribute to the pharmacological effects of natural compounds and to better elucidate their mechanisms of action (Sun et al., 2012). Numerous factors, including species, age, sex, mode of administration, dose of administration, and disease (Labrecque and Bélanger, 1991; Lin, 1995; Meibohm et al., 2002; Shi and Klotz, 2011), affect drug absorption (A), distribution (D), metabolism (M), and excretion (E). Diabetes (Pass et al., 2002; Wang et al., 2003; Lam et al., 2010), liver injury (Adawi et al., 2007; Li et al., 2010), chronic heart failure (Huang et al., 2021), inflammatory diseases (Gong et al., 2009; Cressman et al., 2012) and fever (Gao et al., 2014) may cause significant changes in the body’s drug metabolic enzymes, transporters, cell permeability and intestinal microbiota, affecting the ADME process of drugs. Therefore, studying animal or human pharmacokinetic parameters under pathophysiological and normal conditions may help us better understand the mechanism of pharmacodynamic action. According to pharmacokinetic studies, CTS is widely distributed in fat and mucosal tissues, accumulating most in rat lungs after oral or intravenous injection (Pan et al., 2008).
This study examines the pharmacokinetics of A in rats using the LC-MS/MS method established by Song et al. [ 2005]. This method has the advantages of sensitivity and high efficiency. It plays an important role in a pharmacokinetic study. This study may promote the CTS for the first time based on a study of the pharmacodynamics of rats with pulmonary fibrosis and normal rats lavage for drug pharmacokinetics and reveal the CTS in the dynamic change law of pulmonary fibrosis in rats in vivo. Tissue distribution study may discuss the distribution of the CTS in the body, and lung targeting intends to elucidate the relationship between distribution and pharmacodynamics in vivo.
## 2.1 Reagents and chemicals
Cryptotanshinone standard (HPLC) was purchased from Aladdin, and a Loratadine (LTD) standard (HPLC) from China Institute for Pharmaceutical and Biological Products. Methanol (HPLC) was acquired from Amethyst. Ethyl acetate (HPLC), purchased from Kermel and formic acid (HPLC) from Aladdin. Ultrapure water was attained from made by laboratory, Bleomycin from Macklin, Hydroxyproline test box from Nanjing Jiancheng Bioengineering Institute and sodium carboxymethylcellulose cellulose from Tianjin Zhiyuan Chemical Reagent Co., LTD., Normal saline was attained from Jiangxi Kelun Pharmaceutical Co., LTD., Sodium pentobarbital, purchased from Beijing Huayye Huanyu Chemical Co., LTD. and $4\%$ paraformaldehyde from Sevier Bio.[1] Bleomycin sulfate solution: The molding dose was 5 mg/kg, and the volume of trachea infusion was 0.1 mL/100 g. The concentration of bleomycin solution prepared with normal saline was 5 mg/mL.[2] Sodium carboxymethyl cellulose slurry: Weigh 5.0 g sodium carboxymethyl cellulose powder, sprinkle it in a beaker containing 1,000 mL distilled water, stir well, and place it overnight, make it fully expanded, get $0.5\%$ sodium carboxymethyl cellulose slurry.[3] Cryptotanshinone suspension: The administration dose was 60 mg/kg, and the administration volume was 1 mL/100 g by gavage, that is, the concentration of prepared cryptotanshinone suspension was 6 mg/mL. The powder was fully ultrasonic to form a stable suspension for use.[4] $1\%$ sodium pentobarbital: The anesthetic dose of rats was 45 mg/kg and intraperitoneally injected. Check whether any crystals precipitate before use. Heat it in a 37°C water bath to dissolve it completely.
## 2.2 Animals and treatments
This study followed the Guide for the Care and Use of Laboratory Animals (NIH Publication No. 85-23, revised 1996). Specific Pathogen Free (SPF) male Sprague-Dawley (SD) rats weighing 220–260 g were provided and raised in an SPF environment in by the Animal Experiment Center of Sun Yat-sen University East Campus (license number: SCXK 2011-0029). Animal quarantine observation was 3–5 days. The animals’ appearance and physical signs, behavioral activities, body weight, diet and other indicators were observed during this period. Animals in good condition with no abnormal behavior and activity can be tested.
## 2.2.1 Model establishment and evaluation
Thirty male SD rats were randomly divided into six groups ($$n = 5$$ rats per group), A to F: (A) 14-day control group; (B) 14-day model group; (C) 28-day control group; (D) 28-day model group; (E) 28-day control group; and (F) 28-day model group. Pulmonary fibrosis was induced through the tracheal infusion of 5 mg/kg bleomycin in the model group, but not in the control group. E and F were administrated 60 mg/kg CTS from the second day after modeling for 28 days, and other groups were not administrated CTS. Lung function and pathology tests were performed in groups A and B on the 15th day after modeling, and in group C-F on the 29th day after modeling.
## 2.2.2 Single-dose pharmacokinetic study
Twenty male SD rats were randomly divided according to body weight into four groups ($$n = 5$$ rats per group), 1 to 4: [1] 14-day control group; [2] 14-day model group; [3] 28-day control group; [4] 28-day model group. The rat model of pulmonary fibrosis induced by tracheal infusion of bleomycin was established in the model group, while the control group was not interfered. The 14-day group rats underwent blood were collected at 0.25, 0.5, 1, 1.5, 2, 3, 4, 6, 8, 12, and 24 h after administration CTS on the 14th day after modeling. The 28-day group rats underwent blood were collected at 0.25, 0.5, 1, 1.5, 2, 3, 4, 6, 8, 12, and 24 h after administration CTS on the 28th day after modeling.
## 2.2.3 Multi-dose pharmacokinetic study
Ten male SD rats were randomly divided into two groups according to body weight ($$n = 5$$ rats per group): [5] multi-dose control group and [6] multi-dose model group. The model group was induced pulmonary fibrosis by tracheal drip bleomycin, while the control group did not interfere. The rats in the two groups were administrated 60 mg/kg CTS by gavage on the second day after modeling for 28 days. Blood were collected at the 26th and 27th day and 0, 0.25, 0.5, 1, 1.5, 2, 3, 4, 6, 8, 12, and 24 h on the 28th day after modeling after administration.
## 2.2.4 Tissue distribution study
Forty male SD rats were randomly divided into two groups according to body weight ($$n = 20$$ rats per group): [7] control group and [8] model group. The model group was induced pulmonary fibrosis via tracheal drip bleomycin, while the control group did not interfere. After 28 days of modeling, they were randomly divided into four subgroups according to body weight: 0.5 h group, 3 h group, 10 h group and 24 h group. At 0.5, 3, 10, and 24 h after administration on the 28th day after modeling, the corresponding subgroups of heart, liver, spleen, lung, kidney, and brain tissues were collected separately.
## 2.3 Pulmonary function assay
Lung function indicators of each rat were collected using a small animal pulmonary function instrument (EMKA, France) on the 14th day after modeling for groups A and B and on the 28th day after modeling for groups C–F. The rats were placed into the whole body plethysmography system, and the environment was kept quiet and the ambient air flow rate was stable. After the rats had reached a state of calm, data were collected continuously for more than 5 min. The indexes of lung function can be obtained: inspiratory time, expiratory time, relaxation time, maximum inspiratory volume, maximum expiratory volume, ventilatory volume per min, respiratory rate, end-inspiratory apnea, end-expiratory apnea, and mid-expiratory flow rate.
## 2.4 Histology and morphological analysis
SD rats in groups A and B were sacrificed on the 14th day after modeling, and rats in groups C–F were sacrificed on the 28th day after modeling; their whole lungs of rats were quickly removed. Immediately fixed with $4\%$ paraformaldehyde for 24 h, left lung tissue embedded paraffin and cut into slices of 5 μm. Sections were stained with hematoxylin and eosin (HE), and Masson’s trichrome and lung histopathological changes were evaluated. Sections were photographed using a light microscope (EVOS FL Auto Cell Imaging System, United States).
## 2.5 Measurement of hydroxyproline (HYP) assay
HYP, a unique distribution in connective tissue collagen, is a post-translational product of proline hydroxylation. The hydroxyproline content reflects collagen metabolism and regulation. In this study, HYP content in lung tissue was determined using alkaline hydrolysis (da Silva et al., 2015). Fresh right lung tissue (weight of 80–100 mg) was chopped in vitro, add 1 mL of hydrolyzed was added to a test tube. After cooling the tube to room temperature with tap water, the pH of the lysate was adjusted to 6.0–6.8 and 10 mL of double distilled water was added. Then, centrifuged at 3,500 rpm for 10 min, and suck on 1 mL to new test tubes. The following steps of HYP test Kit instructions (Nanjing Jianchen Bioengineering Institute, China, #A030-2) were followed. Each sample was measured at 550 nm using the following formula to calculate HYP content: Hydroxprolinecontent μg/mg=Measured OD value−Blank OD valueStandard OD value−Blank OD value×Standard content×Lysate total volume mLOrganization wet weightmg
## 2.6 Sample processing
The whole blood was extracted quickly from SD rats through main abdominal vein, and the blood was placed in the heparinized collection vessel, centrifuged at 3,000 rpm for 10 min, and the upper plasma was obtained. Precise measurement of 100 uL plasma sample, add 10 μL of loratadine working solution (plasma, heart, spleen, kidney, brain samples use 200 ng/mL LTD working solution; liver and lung samples use 500 ng/mL LTD working solution) and 500 μL ethyl acetate for liquid-liquid extraction. The mixture was vortexed for 1 min and centrifuged at 12,000 × g for 4 min. The organic phase of the upper layer was 400 μL and dried in a vacuum for 2 h at room temperature. The mobile phase [100 μL; methanol-$1\%$ formic acid water (90:10, V/V)] was added to redissolve, vortex for 1 min, and centrifuged at 12,000 × g for 3 min at low temperature. Add 100 μL mobile phase [methanol-$1\%$ formic acid water (90:10, V/V)] to redissolve, vortex for 1 min, and centrifuge at 12,000 × g for 3 min at low temperature. An aliquot (80 μL) of the supernatant was transferred into a sample vial for HPLC-MS/MS analysis.
## 2.7 HPLC-MS/MS conditions
The injection volume was 5 μL and the flow rate was kept at 0.2 mL/min. The separation was performed using a HyPURITY C18 (I.D. 2.1 mm × 50 mm, 3 μm, Thermo Scientific, US) column. The mobile phase consisted of methanol and water with $1\%$ formic acid (90:10, V/V). The column temperature was 30°C.
Liquid chromatography-mass spectrometry (Thermo Finnigan, TSQ Quantum) was used to detect CTS in biological samples. The sub-source was electrospray ionization (ESI) with positive ion scanning mode, and the scanning mode was Selected Reaction Monitor (SRM). Spray voltage: 4,000 V; Sheath gas: 35 psi; Auxiliary gas: 10 psi; Capillary temperature: 350°C; Peak width of color filter: 20.0 s; Collision gas pressure: 1.9 mtorr; Scanning width: 0.7 m/z; Scanning time: 0.1 s. Our optimized SRM parameters for the analyte and internal standards (ISs) detection are shown in Table 1.
**TABLE 1**
| Compounds | Precursor ion (m/z) | Product ion (m/z) | Mode | CE (V) |
| --- | --- | --- | --- | --- |
| Cryptotanshinone | 297 | 251 | Positive | 21 |
| Loratadine | 383 | 266 | Positive | 31 |
## 2.8 Preparation of stock solutions, working solutions, and quality control samples
CTS and LTD standard substances were precisely weighed and dissolved in $50\%$ methanol to prepare 1 mg/mL CTS and LTD reserve solutions. The working solution was marked into blank rat plasma to generate calibration curves, and the following concentrations were obtained: [1] The first set (for plasma, heart, spleen, kidney and brain samples): the CTS reserve solution was diluted step by step with $50\%$ methanol to obtain the CTS standard curve working solution with the concentration of 10–2,000 ng/mL. CTS Quality control (QC) samples were prepared at low, middle and high concentrations of 20, 200, and 2,000 ng/mL, respectively.
The LTD reserve liquid was diluted step by step with $50\%$ methanol to obtain 200 ng/mL LTD internal standard working liquid.
[2] The second set (for liver and lung samples): the CTS reserve solution was diluted with $50\%$ methanol step by step to obtain the CTS standard curve working solution with a the concentration of 20–5,000 ng/mL. CTS QC samples were prepared at low, middle and high concentrations of 50, 500, and 3,000 ng/mL, respectively.
The LTD reserve liquid was diluted step by step with $50\%$ methanol to obtain 500 ng/mL LTD internal standard working liquid.
All samples were stored at 4°C before UPLC-MS/MS analysis.
## 2.9 Method validation
The developed HPLC-MS/MS method was validated in terms of specificity, linearity, lower limit of quantification, precision, and accuracy.
Specificity was assessed by comparing chromatograms of drug-free blank samples, low-concentration CTS quality control samples, and biological samples treated with 60 mg/kg CTS. The weighted least square regression method was used for linear regression analysis. The horizontal coordinate was the concentration of CTS drug (ng/mL), and the vertical coordinate was the ratio of chromatographic peak area between CTS and LTD. Intraday accuracy and precision were assessed progressively using five replicates of the high, medium, and low QC samples over a single day, whereas intraday accuracy and precision were assessed using five replicates over three consecutive days. The lower limit of quantitation was assessed by substituting the peak area ratio of CTS and LTD into the standard curve and comparing it with the standard concentration.
## 2.10 Pharmacokinetic data and statistical analysis
The experimental data were statistically processed using GraphPad Prism 9.0 biostatistics software (GraphPad Prism 9.0, San Diego, CA, United States). The date was analyzed using one-way Analysis of Variance (ANOVA) combined with Dunnett’s multiple comparison method. The pharmacokinetic software DAS 2.0 was used to calculate the main pharmacokinetic parameters according to the non-av model: the calculated parameters were the maximum plasma concentration (Cmax), elimination half-life (t$\frac{1}{2}$), time to reach maximum plasma concentration (Tmax), area under the plasma concentration curve (AUC) versus time (AUC0−t) from time zero to the time of last measured concentration, AUC from time zero to infinity (AUC0−∞), and total body clearance (CL), mean residence time (MRT), Apparent clearance rate (CL/F), Apparent distribution volume (Vd/F), Mean drug concentration at steady state (Cav), Steady-state plasma concentration (AUCss) and degree of fluctuation (DF).
## 3.1 Therapeutic effects of CTS on BLM-induced SD rats
Pharmacokinetic studies in model animals are based on the assumption that the animal models are stable and that the pathological changes are consistent with clinical disease characteristics. Endotracheal administration of a single dose of BLM (5 mg/kg) resulted in decreased lung function, implying successful induction of pulmonary fibrosis in SD rats. The maximum inspiratory capacity (PIF, Figure 1B), mid-expiratory flow rate (EF50, Figure 1C), ventilation volume per minute (MV, Figure 1H), maximum expiratory volume (PEF, Figure 1I) and respiratory rate (f, Figure 1I) of rats in each model group were significantly increased. End expiratory apnea (EEP, Figure 1D), expiratory time (TE, Figure 1E), relaxation time (RT, Figure 1F) and inspiratory time (TI, Figure 1G) were significantly reduced compared to the corresponding control group. Nevertheless, the variation trend of all model groups is consistent (Figures 1A–J). The 14-day model group displayed a significant change, followed by the 28-day model group, while the 28-day model group revealed the least change.
**FIGURE 1:** *Effect of CTS treatment on BLM-induced SD rats. (A) Animal Experiment Diagram for Pharmacodynamic Study (Study Ⅰ) and Animal experiment diagram for pharmacokinetic Study (Study Ⅱ). (B–J) Measurement of pulmonary function parameters. (K) Determination of hydroxyproline in lung tissue. (L,M) H&E and Masson staining of heart tissue in each group; a: 14-day control group; b: 14-day model group; c: 28-day control group; d: 28-day model group; e: control group after 28 days of CTS administration; f: Model group after 28 days of CTS administration; Scale bar = 200 µm. One-way analysis of variance combined with Dunnett’s multiple comparison method was used for analysis, and all data were expressed as mean ± SD. *p < 0.05; **p < 0.01.*
At 14 days after BLM treatment, the pulmonary morphology and structure were obviously disordered, with numerous pulmonary bullae and increased infiltration of inflammatory cells, but the pulmonary tissue fibrosis was mild (Figures 1K, L). After 28 days of BLM treatment, numerous collagen fibers appeared in lung tissue, and the thickness of the fibrous scar increased, and the degree of fibrosis aggravated (Figures 1K, L). All indicators were remission in the rat model group treated with CTS for 28 days (Figures 1K, L). Additionally, consistent with MASSON staining results, HYP did not significantly increase after 14 days of BLM treatment, but significantly increased after 28 days. The HYP level was significantly downregulated after CTS treatment (Figure 1M). These results indicated that severe inflammatory reactions destroyed the alveolar structure and seriously affected the respiratory function of rats in the early stage of the model. The alveolar structure was repaired and respiratory function improved with the self-repair of lung tissue. However, the abnormal repair resulted in the production of many collagen fibers and irreversible structural changes in lung tissue. In conclusion, we have successfully constructed pulmonary fibrosis SD rats induced by BLM, and continuous treatment with CTS has a good therapeutic effect on the pathological changes of pulmonary fibrosis SD rats induced by BLM.
## 3.2 HPLC-MS/MS method optimization
Different chromatographic columns were tested to develop an efficient HPLC-MS/MS method for quantitative analysis of CTS. The HyPURITY C18 (I.D. 2.1 mm × 50 mm, 3 μm, Thermo Scientific, US) was compared to Hypersil BDS C18 (I.D. 2.1 mm × 150 mm, 5 μm, Elite HPLC, Dalian, China). The chromatographic column has the advantages of small particle size, short length, high separation potency and high efficiency. After gradient modification of the mobile phase, CTS and LTD exhibited a relatively long retention time, a good peak shape and a high degree of separation when the methanol-$1\%$ formic acid water ratio was 90:10 (V/V). The CTS and LTD response and separation were better when the injection volume was 5 μL. Furthermore, spray voltage, capillary temperature, sheath gas and auxiliary gas were optimized for CTS and LTD standard solutions to improve the charged rate of the compounds. The CTS and LTD were completely decomposed into stable daughter ions by optimizing collision energy and collision gas pressure. Appropriate collision conditions and daughter ions were selected respectively to conduct quantitative analysis on ion pairs.
## 3.3 Validation of the HPLC-MS/MS method for simultaneous quantitative analysis of CTS
The CTS and INTERNAL standard LTD specific chromatograms obtained from blank plasma samples, low-concentration CTS quality control samples, and 60 mg/kg CTS after oral administration were detected. The peak times of CTS and internal standard LTD were about 1.54 and 1.38 min, respectively. There was no interference with each other and no interference from endogenous substances. It demonstrated that the analytical method is specific.
The linear range of CTS concentration in plasma, heart, spleen, kidney, and brain samples was 1–200 ng/mL, whereas the linear range of CTS concentration in liver and lung samples was 2–500 ng/mL. Analysis of plasma quality control samples from the same batch demonstrated RE of −$9.2\%$ to −$3.0\%$ and RSD of $2.7\%$–$8.6\%$. Plasma quality control samples were analyzed for three consecutive days with RE of −$5.8\%$ to −$1.5\%$ and RSD of $4.0\%$–$7.2\%$. Additionally, the RE of the same batch and the quality control samples of each organization for three consecutive days were all in the range of −$20\%$ to $20\%$, with RSD of less than $20\%$, all in line with the guidelines. According to the HPLC-MS/MS analysis established in this study, the lower limit of quantification of CTS in plasma samples was 1 ng/mL. The lower limit of quantification was 1 ng/mL in heart, spleen, kidney, and brain samples, while 2 ng/mL in liver and lung samples. The RE and RSD of six LLOQ samples in plasma were $0.0\%$ and $7.1\%$ respectively. The RE of LLOQ samples in each tissue was in the range of −$20\%$ to $20\%$, with RSD less than $20\%$.
The results demonstrated that the experiments were consistent and reproducible, that the method provided sufficient exclusivity, and that the HPLC-MS/MS method was sensitive and efficient enough to be used for routine analysis and pharmacokinetic studies of analyses.
## 3.4 Pharmacokinetic study
Normal rats and pulmonary fibrosis models were given a single dose of 60 mg/kg CTS on days 14 and 28, and normal rats and pulmonary fibrosis models were given a continuous dose of 60 mg/kg CTS for 28 days. The validated assay was successfully applied to both groups. Simultaneously, the concentration data of plasma CTS at different time points were measured for pharmacokinetic study. The mean plasma concentration-time curves based on the data and the major pharmacokinetic parameters calculated based on the non-AV model are provided in the Figure/Table.
Figure 2 and Table 2 illustrate the 14-day and 28-day data of CTS single dose, whereas Figure 2 and Table 3 depict the 28-day data of multiple doses.
**FIGURE 2:** *Mean blood concentration-time curve of CTS in SD rats. (A) *Mean plasma* concentration-time curves of 14-day single dose CTS in control and model SD rats ($$n = 5$$). (B) *Mean plasma* concentration-time curves of 28-day single dose CTS in control and model SD rats ($$n = 5$$). (C) *Mean plasma* concentration-time curves of 28-day multi-dose CTS in control and model SD rats ($$n = 5$$).* TABLE_PLACEHOLDER:TABLE 2 TABLE_PLACEHOLDER:TABLE 3 The results of the t-test demonstrated no significant differences in major pharmacokinetic parameters between the control and the model groups at 14 days of a single dose ($p \leq 0.05$). Based on the average blood concentration-time curve, the model group exhibited faster absorption and an earlier peak time. The control group and model groups indicated a bimodal phenomenon with low oral bioavailability. After a single dose of 28 days, the elimination half-life and mean dwell time were significantly different between the control and the model groups ($p \leq 0.05$), but there was no significant difference in other parameters ($p \leq 0.05$). The elimination half-life and average dwell time of the model group were significantly longer than those of the control group. The area under the curve was larger than that in the control group, but there was no statistical difference between individuals. The control group rats and the model group rats also exhibited a bimodal phenomenon.
After 28 days of continuous administration, there were significant differences in the curve area and clearance rate between the control and the model groups ($p \leq 0.05$), and there was no significant difference in other parameters ($p \leq 0.05$). The elimination half-life, average dwell time, average steady-state concentration and steady-state drug administration curve area of the model group were also significantly increased compared to the control rats, but there was no statistical difference due to the large individual differences. The blood concentration values demonstrated that the three steady-state blood concentrations were all low.
The model group had a higher blood concentration than the control group at each time point. At the last blood collection point, the plasma concentration of CTS in the model group was still higher. The average blood concentration-time curve observation revealed that the bimodal phenomenon also exists in multi-dose administration. Additionally, the individual differences of model group rats were significantly greater than those of control group rats.
## 3.5 Tissue distribution study
After a single oral administration of 60 mg/kg CTS, the concentrations of CTS in various tissues (heart, liver, spleen, lung, kidney, and brain) at different time points were measured (Figure 3). CTS was widely distributed in various organs, including the lung and liver. In the control and the model groups, the distribution trend of CTS was consistent: lung > liver > heart > spleen > kidney > brain at 0.5 h, lung > liver > heart > kidney > spleen > brain at 3 h, lung > liver > heart ≈ kidney > spleen > brain at 10 h, lung > liver > kidney > heart > spleen > brain at 24 h. The amount of CTS exposure in the heart, liver, spleen, kidney, and brain of the two groups of rats did not differ significantly. However, the exposure amount in the lungs of the model group was significantly higher than that in the control group.
**FIGURE 3:** *Tissue distribution of CTS in SD rats. (A–F) The concentration of SD rats in the control group and model group at different time points after oral administration of 60 mg/kg CTS. (G) The concentration of SD rats in the control group at different time points after oral administration of 60 mg/kg CTS. (H) The concentrations of 60 mg/kg CTS in each tissue at different time points in the model group. One-way analysis of variance combined with Dunnett’s multiple comparison method was used for analysis, and all data were expressed as mean ± SD. *p < 0.05; **p < 0.01.*
## 4 Discussion
Pharmacokinetic research is an important part of the process of new drug development, because it guides the entire process and serves as a foundation for pharmacodynamics, toxicology and drug preparation research. Pharmacokinetic studies in animal models of disease can better reflect the dynamic changes of drugs and explain the pharmacokinetic basis of the pharmacological effects of drugs. BLM is a basic glycopeptide anticancer antibiotic. It is frequently used as an intratracheal infusion in experimental animals to cause severe inflammation and pulmonary fibrosis (Della Latta et al., 2015) due to its strong pulmonary toxicity (Moeller et al., 2008). In the previous pharmacodynamic study of CTS in pulmonary fibrosis rats treatment, the high-dose group of 60 mg/kg exhibited a good pharmacodynamic effect and no side effects (Zhang et al., 2019). Additionally, the amount of oral CTS inhaled into the systemic circulation was small (Song et al., 2007; Wang et al., 2020), so choosing a higher dose may be beneficial to compare the pharmacokinetic characteristics of CTS in normal and pulmonary fibrosis rats.
After intragastric administration of CTS to rats, the absorption was slow, the amount absorbed into the systemic circulation was low, the CTS was widely distributed in the body, and the elimination rate was relatively slow. At 14 days, the pharmacokinetic characteristics of the control and the model groups were the same. The faster absorption of the rats in the model group may be due to the increased permeability of vascular endothelial cells caused by the inflammatory reaction (Wautier and Wautier, 2022). The 28-day model group had slower elimination and longer dwell time, and the area under the curve was significantly higher than in the control group. This may be due to the decline of lung function or even the overall physiological function of rats; the speed of CTS clearance metabolism is reduced. It is also possible that the drug accumulation in the substantively diseased lungs increased, and CTS released slowly from the lungs into the blood. Changes in intestinal microbiota under pathological conditions may lead to slower metabolic rates (Morgan et al., 2018; Parvez et al., 2021). However, the steady-state blood concentration and the area under the curve of the model group were slightly higher than those of the control group, and the area under the curve and clearance rate of the model group were significantly higher than those of the control group, which was further amplified by long-term administration. These results suggest that in rats with advanced pulmonary fibrosis, the substantial lung lesions may greatly influence the pharmacokinetic behavior of CTS, resulting in the slow elimination of CTS. Moreover, a bimodal phenomenon occurred in all experimental groups, a common phenomenon in non-intravenous drug injection, possibly because CTS is widely distributed in vivo and tissue redistribution occurs. Gastrointestinal imbalance (Hatton et al., 2019) and liver-intestinal circulation also caused the bimodal phenomenon.
The tissue distribution study revealed that CTS could be detected in all tissues 0.5 h after administration of 60 mg/kg CTS administration by gavage, indicating that CTS could be rapidly and widely distributed in various tissues and organs after oral administration. The peak concentration of all tissues reached 3 h after administration, and high concentrations of CTS could still be detected at 10 h after administration. CTS could be detected at 24 h after administration except in brain tissue, indicating that CTS was retained in all tissue and organs for a long time. After gavage of CTS to rats, the concentration of CTS in lungs and liver was higher than in other tissues and organs, indicating that they are likely to be the effector organs or toxic organs of CTS. This result is consistent with literature reports: CTS has therapeutic effects in liver cancer, ethanol-induced liver injury (Nagappan et al., 2019), liver failure (Jin et al., 2014), liver fibrosis (Han et al., 2019), lung cancer (Vundavilli et al., 2020) and pulmonary fibrosis (Zhang et al., 2020), among other diseases (Liu et al., 2021), and is metabolized through in the liver (Zeng et al., 2018). Compared to the control group, there was no significant difference in the amount of CTS exposure in the heart, liver, spleen, kidney, and brain of rats in the model group after the gavage of CTS. However, the amount of CTS exposure in the lung tissue was significantly increased, indicating that the pathological changes of pulmonary fibrosis are conducive to the targeted distribution of CTS in the lung. This phenomenon may be caused by pulmonary vascular hyperplasia accompanied by increased permeability of vascular endothelial cells. It is also possible that components of lung dysplasia (collagen, collagen fibers, aminoglycans, or cytokines) have a better binding effect on CTS.
## 5 Conclusion
This was the first study to investigate the pharmacokinetic characteristics and tissue distribution of CTS in normal and pulmonary fibrosis rats, and to compare the pharmacokinetic and tissue distribution differences between the two rat groups. The results demonstrated that the distribution and metabolism of CTS and the targeted distribution of CTS in the lungs were affected by the pathological conditions of pulmonary fibrosis, as evidenced by the increase of the area under the curve, clearance half-life, average dwell time and high exposure of CTS in the lungs of pulmonary fibrosis rats.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.
## Ethics statement
The animal study was reviewed and approved by Animal Ethics Committee of Sun Yat-Sen University.
## Author contributions
PL, JL, and JC were corresponding authors and reviewed the manuscript; XH and ZJ wrote the manuscript; ZZ and QW conducted animal experiments.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2023.1127219/full#supplementary-material
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|
---
title: 'Causal relationships between gut microbiota and programmed cell death protein
1/programmed cell death-ligand 1: A bidirectional Mendelian randomization study'
authors:
- Yu-Feng Huang
- Wei-Ming Zhang
- Zhi-Song Wei
- Huan Huang
- Qi-Yan Mo
- Dan-Li Shi
- Lu Han
- Yu-Yuan Han
- Si-Kai Nong
- Guo-Xiang Lin
journal: Frontiers in Immunology
year: 2023
pmcid: PMC10034163
doi: 10.3389/fimmu.2023.1136169
license: CC BY 4.0
---
# Causal relationships between gut microbiota and programmed cell death protein 1/programmed cell death-ligand 1: A bidirectional Mendelian randomization study
## Abstract
### Background
Multiple clinical studies have indicated that the gut microbiota influences the effects of immune checkpoint blockade (ICB) therapy comprising PD-1/PD-L1 inhibitors, but the causal relationship is unclear. Because of numerous confounders, many microbes related to PD-1/PD-L1 have not been identified. This study aimed to determine the causal relationship between the microbiota and PD-1/PD-L1 and identify possible biomarkers for ICB therapy.
### Method
We used bidirectional two-sample Mendelian randomization with two different thresholds to explore the potential causal relationship between the microbiota and PD-1/PD-L1 and species-level microbiota GWAS to verify the result.
### Result
In the primary forward analysis, genus_Holdemanella showed a negative correlation with PD-1 [βIVW = -0.25; $95\%$ CI (-0.43 to -0.07); PFDR = 0.028] and genus_Prevotella9 showed a positive correlation with PD-1 [βIVW = 0.2; $95\%$ CI (0.1 to 0.4); PFDR = 0.027]; order_Rhodospirillales [βIVW = 0.2; $95\%$ CI (0.1 to 0.4); PFDR = 0.044], family_Rhodospirillaceae [βIVW = 0.2; $95\%$ CI (0 to 0.4); PFDR = 0.032], genus_Ruminococcaceae_UCG005 [βIVW = 0.29; $95\%$ CI (0.08 to 0.5); PFDR = 0.028], genus_Ruminococcus_gnavus_group [βIVW = 0.22; $95\%$ CI (0.05 to 0.4); PFDR = 0.029], and genus_Coprococcus_2 [βIVW = 0.4; $95\%$ CI (0.1 to 0.6); PFDR = 0.018] were positively correlated with PD-L1; and phylum_Firmicutes [βIVW = -0.3; $95\%$ CI (-0.4 to -0.1); PFDR = 0.031], family_ClostridialesvadinBB60group [βIVW = -0.31; $95\%$ CI (-0.5 to -0.11), PFDR = 0.008], family_Ruminococcaceae [βIVW = -0.33; $95\%$ CI (-0.58 to -0.07); PFDR = 0.049], and genus_Ruminococcaceae_UCG014 [βIVW = -0.35; $95\%$ CI (-0.57 to -0.13); PFDR = 0.006] were negatively correlated with PD-L1. The one significant species in further analysis was species_Parabacteroides_unclassified [βIVW = 0.2; $95\%$ CI (0-0.4); PFDR = 0.029]. Heterogeneity ($P \leq 0.05$) and pleiotropy ($P \leq 0.05$) analyses confirmed the robustness of the MR results.
## Introduction
Immune checkpoint blockade (ICB) therapy has been a significant breakthrough in cancer research discovery in recent years; it offers a highly effective method for enhancing anticancer effects against aggressive cancers [1]. Programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) is one of the most high-profile target protein pairs in ICB. PD-1/PD-L1 can inhibit the proliferation and differentiation of effector T lymphocytes and prevent the presentation of neoantigens. PD-1 is mainly expressed by activated T cells, dendritic cells (DCs), B cells, and natural killer cells (NKs). However, many tumors have been shown to elevate PD-1 expression, which further helps the tumor escape from the immune system [2]. Numerous cell types express PD-L1, but its expression is significantly elevated in most malignancies, which are the primary source of PD-L1 in the blood [3]. PD-1/PD-L1 inhibitors can relieve the limitation of PD-1/PD-L1 and restore exhausted T cells to resume the antitumor immune reaction [4]. In clinical practice, however, ICB has limited efficacy in many patients; thus, there is an urgent need to find an auxiliary treatment to promote the effect of ICB.
In recent years, numerous investigations have shown that the gut microbiota influences the efficacy of ICB [5, 6]. The gut microbiota consists of approximately 4 × 1013 symbiotic bacteria, protozoa, fungi, archaea, and viruses. It can affect numerous physiological systems, such as metabolism, inflammatory processes, and immune responses [7, 8]. Previous research links the microbiota to the toxicity and efficacy of cancer treatments and the processes of carcinogenesis with specific taxa of bacteria [9]. Identifying microbial taxa that directly or indirectly produce anticancer activities is essential for developing a microbiome-based combinatory therapy that can enhance the overall rate of response to anti–PD-1/PD-L1 therapy. A few bacterial genera/species are enriched in patients with favorable clinical outcomes [6, 10]. However, due to the influence of reverse causality and confounders, the causal association between PD-1/PD-L1 and the microbiota has not been verified and many potentially related microbes associated with PD-1/PD-L1 therapy have not been explored [8]. Therefore, it is essential to initiate a relevant genetic-level study.
Mendelian randomization (MR) is a genetic epidemiology method that utilizes human genetic variation known to influence modifiable exposures as instrumental variables (IVs) to infer the causal effect of an exposure on an outcome; it can eliminate confounding bias and is advantageous for separating the causal pathways of phenotypically grouped risk variables that are hard to randomize or susceptible to measurement error [11].
Here, we conducted a two-sample bidirectional Mendelian randomization at two distinct thresholds to determine the causal relationship between PD-1/PD-L1 and the gut microbiota and explore potential biomarker microbes (Figure 1).
**Figure 1:** *Workflow of this MR analysis. SNPs, single-nucleotide polymorphisms; MR, Mendelian randomization; LD, linkage disequilibrium; eQTL, expression quantitative trait loci; pQTL, protein quantitative trait loci.*
## Univariable forward MR
We used the loose threshold as the primary reference to investigate further potential linkages.
The F statistic of any single genetic instrument that was used in the analysis was >10 to avoid weak instrument bias. In the forward MR with a relaxed threshold ($$P \leq 1$$ × 10-5, R2 = 0.01, LD = 10000), we discovered important microbes. For instance, genus_Holdemanella showed a negative correlation with PD-1 [βIVW = -0.25; $95\%$ CI (-0.43 to -0.07); PFDR = 0.028] and genus_Prevotella9 showed a positive correlation with PD-1 [βIVW = 0.2; $95\%$ CI (0.1 to 0.4); PFDR = 0.027]; order_Rhodospirillales [βIVW = 0.2; $95\%$ CI (0.1 to 0.4); PFDR = 0.044], family_Rhodospirillaceae [βIVW = 0.2; $95\%$ CI (0 to 0.4); PFDR = 0.032], genus_Ruminococcaceae_UCG005 [βIVW = 0.29; $95\%$ CI (0.08 to 0.5); PFDR = 0.028], genus_Ruminococcus_gnavus_group [βIVW = 0.22; $95\%$ CI (0.05 to 0.4); PFDR = 0.029], and genus_Coprococcus_2 [βIVW = 0.4; $95\%$ CI (0.1 to 0.6); PFDR = 0.018] were positively correlated with PD-L1; and phylum_Firmicutes [βIVW = -0.3; $95\%$ CI (-0.4 to -0.1); PFDR = 0.031], family_Clostridiales_vadin_BB60_group [βIVW = -0.31; $95\%$ CI (-0.5 to -0.11), PFDR = 0.008], family_Ruminococcaceae [βIVW = -0.33; $95\%$ CI (-0.58 to -0.07); PFDR = 0.049], and genus_Ruminococcaceae UCG014 [βIVW = -0.35; $95\%$ CI (-0.57 to -0.13); PFDR = 0.006] were negatively correlated with PD-L1. MR-PRESSO detected no horizontal pleiotropy (P Presso Gable>0.05). The weighted median and the weighted mode yielded similar patterns of effects or directions except for MR-Egger in some exposures, and the difference possibly due to the power of the MR-Egger method was smaller [11]. According to Cochran’s Q statistic, there was no evidence of pleiotropy across instrument effects (Cochran’s QIVW >0.05). Analysis of MR-Egger intercepts revealed no indication of directional pleiotropy (P Intercept >0.05). The leave-one-out analysis identified all taxonomic groups exhibiting robustness under the loose threshold, except for genus_Prevotella_9 (Figure 2). More information is available in Supplemental Table 1 (Table S1).
**Figure 2:** *Forest plot of causal relationships estimated and sensitivity analysis for genus-level microbes and PD-1/PD-L1, the significant result (PFDR <0.05) by the IVW method in forward two-sample MR analysis (includes two thresholds). The words in bold type indicate significant results. CI, confidence interval; F, F-statistics; R2, the genetic variants for instrument; IVW, inverse variance weighted.*
## Univariable reverse MR
In the reverse two-sample MR with the loose threshold ($$P \leq 1$$ × 10-5, R² = 0.01, window = 10,000), PD-1 was negatively related to genus_Terrisporobacter [βIVW = 0.2; $95\%$ CI (0 to 0.3); PFDR = 0.029], PD-L1 was positively related to family_Peptococcaceae [βIVW = 0.15; $95\%$ CI (0.05 to 0.25); PFDR = 0.019], and PD-L1 was negatively correlated with family_Porphyromonadaceae [βIVW = -0.1; $95\%$ CI (-0.2 to 0); PFDR = 0.017], genus_Odoribacter [βIVW = -0.1; $95\%$ CI (-0.2 to 0); PFDR = 0.022], and genus_Parabacteroides [βIVW = -0.1; $95\%$ CI (-0.18 to -0.02); PFDR = 0.042]. The sensitivity analysis showed no evidence of pleiotropy or heterogeneity. Other taxa in addition to the genera Parabacteroides and Odoribacter had robust results in the leave-one-out analysis (Table 1).
**Table 1**
| Exposure | Outcome | F | Variants | Variants.1 | IVW | IVW.1 | IVW.2 | IVW.3 | Weighted Median | Weighted Median.1 | Weighted Mode | Weighted Mode.1 | MR Egger | MR Egger.1 | MR Egger.2 | Cochran’s QIVW | Presso Gable P |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Exposure | Outcome | F | nSNP | Outlier | βIVW 95%CI | P | PFDR | Beta | P | Beta | P | Beta | P | Beta | P Intercept | Cochran’s QIVW | Presso Gable P |
| PD-L1 | PD-L1 | PD-L1 | PD-L1 | PD-L1 | PD-L1 | PD-L1 | PD-L1 | PD-L1 | PD-L1 | PD-L1 | PD-L1 | PD-L1 | PD-L1 | PD-L1 | PD-L1 | PD-L1 | PD-L1 |
| | Genus_Odoribacter | 19.6-37 | 5 | 1 | -0.1 (-0.2 - 0) | 0.005 | 0.022 | -0.127 | 0.036 | -0.132 | 0.183 | -0.131 | 0.717 | 0.067 | 0.316 | 0.492 | 0.566 |
| | Genus_Parabacteroides | 19.6-37 | 6 | 0 | -0.1 (-0.18 - -0.02) | 0.010 | 0.042 | -0.104 | 0.245 | -0.062 | 0.597 | -0.041 | 0.090 | -0.317 | 0.196 | 0.335 | 0.391 |
| | Family_Peptococcaceae | 19.6-37 | 5 | 1 | 0.15 (0.05 - 0.25) | 0.005 | 0.019 | 0.151 | 0.019 | 0.163 | 0.171 | 0.167 | 0.947 | 0.016 | 0.565 | 0.689 | 0.720 |
| | Family_Porphyromonadaceae | 19.6-37 | 6 | 0 | -0.1 (-0.2 - 0) | 0.007 | 0.017 | -0.102 | 0.009 | -0.124 | 0.099 | -0.141 | 0.239 | -0.192 | 0.535 | 0.823 | 0.837 |
| PD-1 | PD-1 | PD-1 | PD-1 | PD-1 | PD-1 | PD-1 | PD-1 | PD-1 | PD-1 | PD-1 | PD-1 | PD-1 | PD-1 | PD-1 | PD-1 | PD-1 | PD-1 |
| | Genus_Barnesiella | 19.9-88 | 7 | 0 | 0 (-0.1 - 0.1) | 0.430 | 0.440 | 0.037 | 0.376 | 0.051 | 0.364 | 0.092 | 0.440 | 0.268 | 0.497 | 0.200 | 0.228 |
| | | 20.9-88 | 4 | 0 | 0.13 (0.03 - 0.23 | 0.014 | 0.028 | 0.127 | 0.059 | 0.119 | 0.317 | 0.099 | 0.412 | 0.425 | 0.543 | 0.804 | 0.824 |
| | Genus_LachnospiraceaeUCG001 | 19.9-88 | 7 | 0 | -0.1 (-0.2 - 0.1) | 0.359 | 0.927 | -0.052 | 0.793 | -0.018 | 0.890 | -0.016 | 0.927 | 0.039 | 0.830 | 0.162 | 0.188 |
| | | 20.9-88 | 3 | 1 | -0.2 (-0.3 - 0) | 0.030 | 0.047 | -0.157 | 0.035 | -0.194 | 0.206 | -0.231 | 0.470 | 0.537 | 0.387 | 0.351 | – |
| | Genus_Terrisporobacter | 19.9-88 | 6 | 1 | 0.2 (0 - 0.3) | 0.015 | 0.029 | 0.151 | 0.013 | 0.177 | 0.162 | 0.205 | 0.962 | 0.523 | 0.377 | 0.918 | 0.929 |
| | | 20.9-88 | 3 | 1 | 0.2 (0 - 0.3) | 0.049 | 0.090 | 0.175 | 0.068 | 0.202 | 0.235 | 0.215 | 0.609 | 0.433 | 0.744 | 0.807 | – |
| | Genus_Veillonella | 19.9-88 | 7 | 0 | -0.1 (-0.2 - 0) | 0.128 | 0.406 | -0.077 | 0.203 | -0.085 | 0.413 | -0.094 | 0.956 | -0.021 | 0.883 | 0.353 | 0.373 |
| | | 20.9-88 | 4 | 0 | -0.17 (-0.29 - -0.04) | 0.009 | 0.019 | -0.167 | 0.054 | -0.148 | 0.380 | -0.110 | 0.419 | -0.523 | 0.561 | 0.737 | 0.770 |
## The stability of validation MR
To confirm the resulting stability, we conducted strict threshold analyses using the threshold ($$P \leq 5$$ × 10−6, R² = 0.001, window = 10,000) in bidirectional MR analyses. In forward MR, the family_ClostridialesvadinBB60 group [βIVW = -0.34 $95\%$ CI (-0.56 to -0.12); PFDR = 0.012] and genus_RuminococcaceaeUCG014 [βIVW = -0.46; $95\%$ CI (-0.8 to -0.12); PFDR = 0.034] were negatively correlated with PD-L1, through both sensitivity analysis and leave-one-out analysis (Figure 3).
**Figure 3:** *The significant (PFDR <0.05) and robust results (Family_ClostridialesvadinBB60group and Genus_RuminococcaceaeUCG014) in forward MR analysis with two different thresholds. Scatter plot of microbe-related SNP effects on PD-L1, with the slope of each line corresponding to the estimated MR effect per method. Vertical and horizontal black lines around each point show the 95% confidence interval for each polymorphism exposure association and polymorphism outcome association. Forest plot lists single and combined (IVW and MR egger) SNP MR-estimated effect sizes; the effect estimates represent the β for PD-L1 per one-s.d. increase in mean microbes. The one-sided leave-one-out and symmetric funnel plots meant that the results were stable without outliers. Family_Clostridiales_vadin_BB60_group: (A) Forest plot at the loose threshold. (B) MR scatter at the loose threshold. (C) Leave one out at the loose threshold. (D) Funnel plot at the loose threshold. (E) MR scatter at the strict threshold. Genus_Ruminococcaceae_UCG014: (F) Forest plot at the loose threshold. (G) MR scatter at the loose threshold. (H) Leave one out at the loose threshold. (I) Funnel plot at the loose threshold. (J) MR scatter at the strict threshold.*
## Species-level MR
Species_Parabacteroides_unclassified was positively related to PD-L1 [βIVW = 0.2; $95\%$ CI (0-0.4); PFDR = 0.029], and no causal relationship was found between other genetically determined gut microbes. Species_Parabacteroides_unclassified was identified through sensitivity analysis but did not pass leave-one-out analysis (Figure 4).
**Figure 4:** *In the species-level bidirectional two-sample MR analysis, microbial features were prefixed with species(s). The selection of species-level microbes is based on the significant result of genus-level microbes. The MR estimates and 95% CI values are shown in the plot. The point of the plot indicates the P value of IVW, and red indicates significance (PFDR <0.05). The “+” and “-” in the legend indicate the direction of the estimate effect (beta).*
## Discussion
In this work, we revealed associations between the relative abundance of the gut microbiota and the concentration of PD-1/PD-L1 by employing genetic variations as unconfounded proxies. To examine more potential associations and verify the outcome’s dependability, we utilized two alternative thresholds in two-sample bidirectional Mendelian randomization. Moreover, we conducted an expanding MR analysis based on the species-level dataset from another GWAS to explore potential relationships.
Phylum_Firmicutes is the largest of the four bacterial phyla [12], making up approximately $90\%$ of the human genome. We discovered that phylum_Firmicutes reduced the blood’s PD-L1 level at the loose threshold. In the context of ICB therapy, phylum_Firmicutes was identified in a prior study as response-associated [13]. Our investigation supports this causal connection. In our work, the family_Ruminococcaceae and family_Clostridiales_Vadin_BB60 group, members of phylum_Firmicutes, both led to decreased PD-L1 levels. A prior study found that family_Ruminococcaceae increased SCFA production [14], CD8 T-cell infiltration into the tumor microenvironment, and effectiveness of anti-PD-L1 therapy against colon cancer in mice [15]. It exhibits beneficial effects in various races, malignancies, and geographic areas and has received substantial clinical validation [China; HCC [16];/America; melanoma [12];/America; melanoma [10];]. SCFA production seems to activate T cells against malignant cells and further decrease PD-L1. Famliy_Ruminococcaceae includes genus_Ruminococcaceae_UCG005, genus_Ruminococcaceae_UCG014, and genus_Ruminococcus_gnavus_group. Interestingly, they have varying effects on the PD-L1 level. Although not depicted in the prior investigation, genus_Ruminococcaceae_UCG014 has a detrimental effect on PD-L1 and is confirmed at two distinct thresholds. Genus_Ruminococcus_gnavus_group and genus_Ruminococcaceae_UCG005 exhibited a trend of a positive influence on PD-L1. The genus_Ruminococcus_gnavus group includes iso-bile acid-producing organisms. The iso-bile acid route detoxifies deoxycholic acid, causes DNA damage by the formation of free radicals, and has been linked to multiple cancers [17]. It also favors the growth of the important genus_Bacteroides [18]. Bacteroides fragilis has a positive association with PD-L1 expression and the PD-1 checkpoint pathway [19]. Genus_Ruminococcaceae_UCG005 is abundant and deemed a biomarker taxon in HCC patients with hepatitis B or C virus infection [20] and lung adenocarcinoma patients [21]. Genus_Coprococcus_2 is a subspecies of phylum_Firmicutes, and a prior study found that it was enriched in a high-fat-induced liver cancer model in male rats; it produces butyrate [14]. SCFAs, such as butyrate or propionate, impact intestinal immunological homeostasis, affecting Tregs, γδ T cells, and effector T cells and participating in immunomodulatory and anti-inflammatory properties [5]. However, its use in cancer immunotherapy remains contentious. Both positive and negative impacts are possible [5]. Intriguingly, our study also shows that the SCFA producer may have a different effect on cancer, and these effects require further research. Despite the lack of pertinent analysis of family_Clostridiales_Vadin_BB60_group, two different thresholds support it as a protective factor against PD-L1, making it a possible target gut microbe for subsequent investigation. Genus_Holdemanella is a member of the family Erysipelotrichaceae; it produces SCFAs that, in humans, modulate intestinal immune homeostasis [5]. Considering that it also lowered the expression of PD-1 in our study, it is possible that it affects PD-1 expression and further prevents immune escape in cancer cells and against cancer. Genus_Prevotella_9 is enriched in patients with advanced, unresectable hepatocellular carcinoma, according to a study [22]. Curiously, another study revealed that genus_Prevotella_9 is deficient in bladder cancer tissue [23]. Since genus_Prevotella_9 promoted PD-1 in our study, it may have various effects on different tumors, and its function requires further study. Studies on the relevance of order_ Rhodospirillales and family_Rhodospirillaceae to the human body are lacking, and additional investigation is required to investigate their potential relationship with the human immune system.
Early research shows that genus_Parabacteroides in colorectal cancer (CRC) [24] and early HCC versus cirrhosis [25] has the potential to become a biomarker, and we found that PD-L1 has a negative correlation with it. Genus_Odoribacter, a butyrate producer, has been found at lower levels in patients with breast cancer and rectal carcinoma [26], and it is essential in several related taxonomic models of CRC [27]. Bacteroidetes (phylum) includes genus_Odoribacter and genus_Parabacteroides. Genus_Odoribacter is positively involved in non-ribosomal peptide structures and negatively involved in the metabolism of phenylalanine and cyanoamino acids. Family_Porphyromonadaceae has been identified as a potential biomarker for CRC recurrence and patient prognosis [28]; case-control study results showed that healthy controls had a higher relative abundance of family_Porphyromonadaceae than primary liver cancer patients [29], and combined analysis with our study confirmed its potential as a biomarker in cancer. Family_ Peptococcaceae and genus_Terrisporobacter have been lacking in studies of the human immune system, so it is possible that we found microbes that can be applied as new biomarkers for PD-1/PD-L1 therapy or precancerous diagnosis.
In the species-level study, only species_Parabacteroides_unclassified demonstrated significant species-level verification results; it is possible that this is due to the lack of complete related species-level GWAS data and the variability of different region samples.
Overall, we demonstrated the potential causal relationships between microbiomes and PD-1/PD-L1. We have not found any similar research that has been published previously. This study expands the possibility of antineoplastic therapy and immunotherapy. We expanded the study to the species level, which enhanced the comprehensiveness of this study. We used two different thresholds in the MR analysis. First, the loose threshold widely used in the previous study allowed more potential microbes to be analyzed. Its significant result included many microbes considered as impact factors in a relevant ICB therapy study, which improves the credibility of this analysis. The strict threshold is approximated with the traditional MR threshold setting. Combining its result with the result of loose thresholds, we found two microbes that were never identified as impact factors for ICB therapy in a prior study. The newly identified microbiota will require further study. In reverse MR analysis, we investigated several microbes that can potentially be biomarkers in cancer therapy. Our study has a few limitations. First, while utilizing the largest single-cohort multiethnic GWAS to date, the sample size was quite limited and requires expansion, similar to prior research on the heritability of the microbiome [7, 30]. Traditional GWAS and MR research frequently employ cohorts with hundreds of thousands of people, thereby enhancing power and lowering false associations; because of the low power and small sample sizes, many legitimate signals were unlikely to reach statistical significance at the study-wide level. Second, the GWAS we used to conduct MR combines many races, although the majority of individuals were of European descent (>$72.3\%$). However, mixed races inevitably introduce bias into the results. Third, the heterogeneity of the makeup of the gut microbiota also results in a loss of strength. Similar to all research on the human gut microbiota, the sample variation is substantial, so its effect on the predicted heritability should not be overestimated.
## Method
In our study, two-sample bidirectional MR analysis was undertaken at two distinct thresholds to determine the causal relationship between PD-1/PD-L1 and the gut microbiota. We utilized two distinct thresholds: one to explore the possibility of a relation and the other to validate the precision of the test (Figure 5).
**Figure 5:** *When sample 1(exposure) and sample 2(outcome) are used for causal estimates in MR inference, three assumptions must be satisfied (11). ① Relevance assumption: the genetic variations are highly related to the exposure, ② independence assumption: the genetic variants are not associated with any putative confounder of the association between exposure and result, and ③ exclusion restriction: the variants do not alter the outcome independently of exposure.*
## Data sources and methods
This study relied on publicly accessible summary-level data; ethical approval was acquired for all original investigations.
## Gut microbiota
Genetic variants of the gut microbiota were found in a large-scale association study involving 24 cohorts (18,340 participants) [7]. Populations from Canada, the USA, Israel, South Korea, Denmark, Germany, the Netherlands, Belgium, Sweden, the UK, Finland, and Denmark were included in the cohorts. Twenty cohorts had samples of single ancestry, and most subjects (16 cohorts, $$n = 13$$,266) were of European ancestry. In 17 ($$n = 13$$,804) of the 24 cohorts, the participants’ mean ages ranged between 50 and 62. The microbiome quantitative trait locus (mbQTL) mapping study for each cohort only included taxa present in >$10\%$ of the samples, totaling 211 taxa (131 genera, 35 families, 20 orders, 16 classes, and 9 phyla). The study of binary trait locus mapping (mbQTL) covered the taxa that comprised $10\%$–$90\%$ of the included samples. There were 196 taxa included in our analysis (excluding 15 taxa that cannot be definitively classified and named) Strain Categorization, the microbiota that we take into analysis (phylum-level to genus-level) listed in Supplemental Figure 5.
For species-level analysis, we used another GWAS dataset that included 7,738 Dutch Microbiome Project (DMP) participants whose microbiota data were quality-controlled with LifeLines [30]. A total of $58.1\%$ of its members were women, and their ages ranged from 8 to 84 years (mean, 48.5 years). Data from 15 subordinate species taxa, whose genera were confirmed as significant (IVWFDR <0.05) in the MiBioGen GWAS, were included in our study.
## PD-1 and PD-L1
We identified genetic predictors of cis-protein quantitative trait loci [cis-pQTLs] of PD-L1 based on summary statistics from the INTERVAL study, which recruited 3,301 healthy participants of European descent with an average age of 44 years and $48.9\%$ women [31]. Concerning trans-pQTLs, the functional genetic variations influence protein abundance with little or no attenuated effect on messenger RNA or ribosome levels [31, 32].
## Selection of the instrumental genetic factors
In the MR investigation of the link between the microbiota and PD1/PD-1, two thresholds were used to choose the IVs. For MR, the genetic variations that were representative of the microbiota trait were required to be sufficient; therefore, we decided on a locus-wide significance threshold $$P \leq 1$$ × 10-5 [7, 30], which was commonly utilized in prior microbe MR analyses, clustered for independence using PLINK in the two-sample MR tool [33] and the 1000 Genomes *European data* as the reference panel, using a looser cutoff of R2 < 0.01 and a window of 10,000-kb clumping. Another set of SNPs was fewer than the generally used threshold of 5 × 10-6 for emphasizing “suggestive” genetic variants [34] and clustering under the tighter cutoff of R2 < 0.001 and a 10,000-kb window. We supplemented the effect of allele frequency prior to clumping using data from the 3DSNP database [35]. To avoid any confounding, we queried each SNP in the PhenoScanner database [36] for any past associations ($$P \leq 5$$ × 10-8) with probable confounders (that is, cancer and tumors).
In the MR investigation of the association between PD1/PD-1 and the microbiota, we selected pQTLs associated with genetically predicted PD-L1 or PD-1 from the INTERVAL study of the log-transformed relative fluorescence unit [log(RFU)] using the same threshold as described before. To limit random variability, only annotated pQTLs [i.e., those with proper identification and description [37]] from the RegulomeDB database were included. We selected cis-pQTLs by eliminating pQTLs that express quantitative trait loci (eQTLs) [32] from the RegulomeDB and VannoPortal databases [38, 39].
The effects of SNPs on exposure and outcome were then harmonized to ensure that the β values were signed for the identical alleles. After harmonizing the data, we eliminated SNPs with intermediate allele frequencies (>0.42). Radial-MR [40]and MR-PRSSO [41]were also performed to identify IVs with the best contribution to heterogeneity (alpha = 0.05/nSNP) and hence identify probable outliers. These outliers were removed from the IVs. Radial-MR was also utilized to determine whether the independence and exclusion restriction assumptions were violated. We eliminated the trait combination from the analysis for IVs < 3, and the MR analysis was conducted using the remaining SNPs.
## Testing instrument robustness and statistical validity
In the initial proteomic GWAS, it was found that the sentinel cis-pQTL explained $2\%$ of the variation in circulating programmed death-1-ligand 2 levels [31]. Using the web MR power calculation tool [42](https://sb452.shinyapps.io/power/), when the causal effect achieves 0.345, there is $80\%$ power if all detected cis-pQTLs explain $2\%$ of the variation in PD-L1. The individual SNP effect size was estimated as the explainable variance with the formula [2 f(1 − f)β2], where f is the allele frequency and β is the regression coefficient [43]. The formula [F statistic = beta2/se2] was used to calculate the F statistic [44]. If the F statistic was ≥ 10, it implied a low probability of instrument bias in MR analysis [45].
## Statistical analysis
Cochran’s Q statistic was employed to assess the heterogeneity of the IVW meta-analysis; $P \leq 0.10$ indicates significant heterogeneity in the SNP effect estimates. When all IVs are valid instruments, the IVW method provides the most accurate estimate of the causal effect. However, because there are so few variants, the heterogeneity between the variant-specific estimates cannot be reliably estimated [11]. Therefore, we conducted both random-effects IVW and fixed-effects IVW. When SNP >4 or without heterogeneity, we used random-effects IVW as the primary method; otherwise, we used fixed-effects IVW. The IVW method aggregated the Wald ratio estimates of each SNP into a single causal estimate for each risk factor, with each estimate derived by dividing the SNP–outcome association by the SNP–exposure association [46]. The results of the IVW test with a P threshold corrected by FDR (PFDR) <0.05 are classified as significant. Considering that the FDR corrected by the number of microbes would be too stringent, we corrected the P threshold by the number of MR analysis methods. Since the IVW estimates can be biased if pleiotropic IVs are introduced, a series of sensitivity analyses were conducted to account for pleiotropy in the causal estimates. We examined the probable presence of horizontal pleiotropy using MR-Egger regression based on its intercept term, where the divergence from zero ($P \leq 0.05$) was interpreted as evidence of the presence of directional pleiotropic bias [47]. In the presence of horizontal pleiotropy, the slope coefficient from MR-Egger regression provides a reliable estimate of the causal influence. As sensitivity analyses, we also conducted MR-Egger, weighted median, and weighted mode analyses based on varying hypotheses. Briefly, MR-*Egger* generally adheres to the Instrument Strength Independent of Direct Effect (InSIDE) and negligible measurement error (NOME) assumptions [47, 48]. The weighted median method assumes that at least half of the instruments are valid (the weighted median method assumes the causal effect from the median of the weighted empirical density function of individual SNP effect estimates and permits up to $50\%$ of information from variants to violate MR assumptions in the presence of horizontal pleiotropy) [49]. The mode method is assumed to apply to the vast majority of genetic instruments (clusters the SNPs based on the similarity of causal effects and estimates the causal effect on the basis of the cluster with the most significant number of SNPs, thus providing an unbiased estimate if the SNPs contributing to the largest cluster are valid) [50]. Leave-one-out analysis was performed to determine the impact of individual variations on the observed connections.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author.
## Author contributions
Y-FH and G-XL conceived and designed the research. Y-FH analyzed the data and wrote the paper. W-MZ, Z-SW, HH, Q-YM, D-LS, LH, Y-YH and S-KN assisted in completing this research. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1136169/full#supplementary-material
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|
---
title: Dietary supplementation of coated sodium butyrate improves growth performance
of laying ducks by regulating intestinal health and immunological performance
authors:
- Tao Zeng
- Hanxue Sun
- Manman Huang
- Rongbing Guo
- Tiantian Gu
- Yongqing Cao
- Chengfeng Li
- Yong Tian
- Li Chen
- Guoqin Li
- Lizhi Lu
journal: Frontiers in Immunology
year: 2023
pmcid: PMC10034168
doi: 10.3389/fimmu.2023.1142915
license: CC BY 4.0
---
# Dietary supplementation of coated sodium butyrate improves growth performance of laying ducks by regulating intestinal health and immunological performance
## Abstract
### Introduction
This study was conducted to assess the effects of dietary supplementation of coated sodium butyrate (CSB) on the growth performance, serum antioxidant, immune performance, and intestinal microbiota of laying ducks.
### Methods
A total of 120 48-week-old laying ducks were randomly divided into 2 treatment groups: the control group (group C fed a basal diet) and the CSB-treated group (group CSB fed the basal diet + 250 g/t of CSB). Each treatment consisted of 6 replicates, with 10 ducks per replicate, and the trial was conducted for 60 days.
### Results
Compared with the group C, the group CSB showed a significant increase in the laying rate ($p \leq 0.05$) of the 53-56 week-old ducks. Additionally, the serum total antioxidant capacity, superoxide dismutase activity and immunoglobulin G level were significantly higher ($p \leq 0.05$), while the serum malondialdehyde content and tumor necrosis factor (TNF)-a level were significantly lower ($p \leq 0.05$) in the serum of the group CSB compared to the group C. Moreover, the expression of IL-1b and TNF-a in the spleen of the group CSB was significantly lower ($p \leq 0.05$) compared to that of the group C. In addition, compared with the group C, the expression of Occludin in the ileum and the villus height in the jejunum were significantly higher in the group CSB ($p \leq 0.05$). Furthermore, Chao1, Shannon, and Pielou-e indices were higher in the group CSB compared to the group C ($p \leq 0.05$). The abundance of Bacteroidetes in the group CSB was lower than that in the group C ($p \leq 0.05$), while the abundances of Firmicutes and Actinobacteria were higher in the group CSB compared to the group C ($p \leq 0.05$).
### Conclusions
Our results suggest that the dietary supplementation of CSB can alleviate egg-laying stress in laying ducks by enhancing immunity and maintaining the intestinal health of the ducks.
## Introduction
For a long time, animal feeds were supplemented with antibiotics to promote growth and disease resistance. However, this practice has since been banned, due to the discovery of the complications associated with the unregulated use of antibiotics [1], thereby necessitating the discovery of green and safe feed additives for poultry to improve their antioxidant capacity and reduce disease incidence [2]. Oxidative Stress (OS) is a state of imbalance between oxidative and antioxidant effects in the body, and it is clear from previous studies that OS can also lead to follicular atresia and ovarian senescence, reducing animal reproductive performance [3, 4]. It has been shown that OS can affect nutrient metabolism through deleterious effects on intestinal function, gut microbial flora and altered dynamic homeostasis in poultry [5, 6]. At the peak of egg laying, ducks lay eggs almost every day, and in order to meet the nutritional needs of the output ducks themselves, the intake increases accordingly, resulting in OS, which creates egg laying stress thus affecting egg production [7].
Short Chain Fatty Acids (SCFAs) are organic fatty acids with a carbon chain length of 1-6, and sodium butyrate (SB), an SCFA produced during fermentation, is an important nutrient for the intestinal cells, providing for >$70\%$ of the energy requirements [8, 9]. SB has been widely used as a feed additive for pigs [10] and recently for poultry [11]. However, SB is less palatable as a feed additive due to its unpleasant odor and volatility. Coating SB in palm oil (coated sodium butyrate, CSB) improves its palatability, allowing its use as a feed additive [12]. SB-supplementation is thought to improve the development [13, 14] and morphological structure of the intestinal mucosa and regulate the growth of commensal intestinal flora. In addition, butyric acid (BA) can mediate the immune response, inhibit the growth of harmful bacteria, induce the proliferation and differentiation of intestinal epithelium, and protect epithelial cells [15, 16]. Moreover, CSB can regulate the development of many immune cells, such as macrophages [17], and the expression of inflammatory cytokines, including interleukin (IL)-6, IL-8, interferon (IFN)-γ, transforming growth factor (TGF)-β, and IL-1β [18]. Several studies have reported the promoting effects of SB in improving immunity and small intestinal structure, to regulate intestinal flora in poultry (19–21).
However, studies on the potential effects of CSB on laying animals, especially laying ducks, are limited. Considering the previous reports on the beneficial effects of CSB on poultry, we suspect that adding CSB in diet can increase immunity and antioxidant capacity of laying ducks and reduce laying stress. To test this hypothesis, we studied the effects of CSB on growth performance, immune performance and intestinal health of laying ducks.
## Animals and experimental design
The CSB ($30\%$ SB covered with palm oil) was purchased from Hangzhou King Techina Feed Co., Ltd., (Hangzhou, China). Shendan No. 2 laying ducks (obtained by a ternary cross between Shaoxing, Jinyun, and Youxian duck varieties), used for commercial duck egg production, were provided by Hubei Shendan Health Food Co., Ltd. (Hubei, China).
A total of 120 48-week-old Shendan No. 2 laying ducks were randomly divided into 2 treatment groups: the control group (group C), which was fed a basal diet and the CSB-treated group (group CSB), which was fed the basal diet supplemented with 250 g/t of CSB. Each treatment consisted of 6 replicates with 10 ducks per replicate. The feeding trials were conducted in a closed duck house at the welfare duck farm of Hubei Shendan Health Food Co., Ltd. The ducks were housed in four layers of cages (50 ×50 ×60 cm) at 2 ducks/cage, and the replicates of the same treatment were evenly distributed in the duck house. During the experimental period, the duck house was maintained at 20-25 °C, 65-$75\%$ relative humidity, and 16:8 h light/dark cycle (4:00-20:00 h). The basic feed of the experimental ducks combined with the needs of ducks during the egg-laying period met the nutritional requirements of the Chinese egg and duck standard (GB/T 41189-2021), and the basic composition of the feed was shown in Table 1. The ducks were fed twice a day (8:00 and 14:00) by hand, from 48 to 56 weeks of age.
**Table 1**
| Ingredients | Content | Nutrient levels | Content.1 |
| --- | --- | --- | --- |
| Corn | 42.0 | ME/(MJ/kg)2 | 11.72 |
| Soybean meal | 29.0 | CP | 18.8 |
| Rice bran | 4.0 | Lys | 1.06 |
| Wheat bran | 2.1 | Met | 0.48 |
| Soybean oil | 2.5 | Met+Cys | 0.82 |
| Flour | 10.0 | Trp | 0.24 |
| Limestone | 5.8 | Ca | 3.36 |
| Fine gravel | 2.0 | TP | 0.59 |
| CaHPO4 | 1.1 | AP | 0.31 |
| Chaff | 0.2 | | |
| NaCl | 0.3 | | |
| Premix1 | 1.0 | | |
| Total | 100.0 | | |
## Bird slaughter and sample collection
The number of eggs laid and the weight of the feed, in each replicate, were recorded daily, and the weight of the leftover feed in each replicate was recorded weekly, to evaluate the growth performance of laying ducks. For sampling, 6 56-week-old ducks were chosen per treatment (one duck from each replicate), after 12 h of fasting. All the experiments and methods were designed to minimize animal suffering. Blood collected from the wing vein was dispensed into 5 mL procoagulation tubes for 3-4 h and centrifuged at 3,000 rpm for 5 min. The serum was collected and transferred to 1.5 mL EP tubes and stored at -20 °C. After slaughter, spleen, ileum tissues were collected immediately, placed into sterilized freeze tubes, and stored at -80 °C after flash-freezing in liquid nitrogen. Thereafter, 12 ducks were randomly selected per treatment (2 ducks per replicate, with 6 previously sampled ducks), and duodenum, jejunum, and ileum segments were collected in EP tubes and fixed using $4\%$ paraformaldehyde, and cecal contents were collected immediately, the treatment is the same as the above organization.
## Growth performance
As mentioned earlier, the number of eggs laid and the feed weight, per each replicate, were recorded daily, and the leftover feed per each replicate was recorded weekly, to evaluate the average daily feed intake (ADFI), laying rate, and feed-to-egg ratio (F/E), as follows:
## Serum antioxidant status and cytokine analysis
Total antioxidant capacity kit, No. HY-60021 was used to detect total antioxidant capacity (T-AOC), activities of superoxide dismutase kit, No. HY-M0001 was used to detect the activities of serum superoxide dismutase (SOD), catalase kit, No. HY-M0018 was used to detect the catalase (CAT), content of malondialdehyde kit, No. HY-M0003 was used to detect the malondialdehyde (MDA) were determined according to the manufacturer’s instructions accompanying the assay kit (Beijing Huaying Biotechnology Research Institute, Beijing, China).
Immunoglobulin G kit, No. bs-0293G was used to detect the immunoglobulin G (IgG), immunoglobulin A kit, No. bs-0360G was used to detect the immunoglobulin A (IgA), immunoglobulin M kit, No. bs-0314P was used to detect the immunoglobulin M (IgM), interferon gamma kit, No. bs-0481P was used to detect the interferon gamma (IFN-γ), interleukin-6 kit, No. bs-0379P was used to detect the interleukin-6 (IL-6), Interleukin-1β kit, No. bs-0812P was used to detect the Interleukin-1β (IL-1β), tumor necrosis factor-α, No. bs-0078P was used to detect the tumor necrosis factor (TNF)-α in serum segments were determined by absorbance changes at 450 nm with ELISA kits (Beijing Huaying Biotechnology Research Institute, Beijing, China) according to the manufacturer’s protocol. Concentrations of immunoglobulin and cytokine were calculated with the standard curve.
## RNA Extraction
RNA was extracted using the Total RNA kit I [22] R6834-01 (Omega, Norcross, Georgia, USA). Approximately 20 mg of spleen tissue (or ileum tissue), 500 mL of lysis buffer, and 2 sterilized steel beads were added to a 1.5 mL EP tube and homogenized using an automatic sample grinder (Shanghai Jingxin Technology Co., Ltd., Shanghai, China). Thereafter, the supernatant was transferred to a centrifuge column for 3 min, and the filtrate was transferred to a 1.5 mL EP tube with 2x (v/v) of $70\%$ ethanol. After shaking with a vortex (Vortex Genie2, Scientific Industries Inc, NY, USA) the liquid was transferred to a HiBind RNA microcolumn and centrifuged for 3 min and the supernatant was discarded. After adding Buffer I, the samples were centrifuged for 2 min and the supernatant was discarded. Thereafter, this step was repeated twice with Buffer II. Lastly, the eluent was added to the centrifuge for 2 mins to obtain the extracted RNA. The quality of the extracted RNA was measured using a Nanodrop (NanoDrop One, Thermo, USA) by measuring the OD $\frac{260}{280}$ values, and the RNA was stored at -80°C for subsequent analyses.
## Reverse transcription of total RNA
Genomic DNA was isolated and added to RNase-free EP tubes. Thereafter, the sample was subjected to reverse transcription using the HiScript® II Q RT SuperMix for qPCR (+gDNA wiper) kit (R223-01, Nanjing Novozymes Biotechnology Co., Ltd., Nanjing, China).
## Fluorescent quantitative polymerase chain reaction
Fluorescent quantitative PCR was conducted using the ChamQ Universal SYBR qPCR Master Mix (Q711-$\frac{02}{03}$, Nanjing Novozymes Biotechnology Co., Nanjing, China). β-actin was used as an internal control, and the relative gene expression was calculated by the 2-△△Ct method. The primer sequences were obtained from the previous literature or primer design software (Table 2), and they were synthesized by Shanghai Jereh Bioengineering Co. (Shanghai, China).
**Table 2**
| Gene | Primer Sequence (5’-3’) | Product size/bp |
| --- | --- | --- |
| IL-1β | F:GCTTCATCTTCTACCGCCTGGAC | 159 |
| IL-1β | R:TTAGCTTGTAGGTGGCGATGTTGAC’ | 159 |
| IL-6 | F:TCTGGCAACGACGATAAGG | 154 |
| IL-6 | R:TGAAGTAAAGTCTCGGAGGATG | 154 |
| IFN-γ | F:ATACCCTTTCCAATGACT | 130 |
| IFN-γ | R:GTCTCCACCAGTTTCTGT | 130 |
| TNF-α | F:AAATCTGCTGCTGGTCTT | 235 |
| TNF-α | R:CCATCATCGTCCTCACTA | 235 |
| ZO-1 | F: GGGGAAGACAACTGATGC | 159 |
| ZO-1 | R: TTGTGATGTGCTGGGAGA | 159 |
| Claudin-1 | F: TGATGGTGGCTGCGATAC | 218 |
| Claudin-1 | R: AACAGGCGTGAAAGGGTC | 218 |
| Occludin | F: GCAGGATGTGGCAGAGGAATA | 119 |
| Occludin | R: TGCGCTTGATGTGGAAGAGTT | 119 |
| β-actin | F:CCCCATTGAACACGGTATTGTC | 151 |
| β-actin | R:GGCTACATACATGGCTGGGG | 151 |
## Small intestinal morphometric traits
Each intestinal segment was fixed in $4\%$ paraformaldehyde for more than 24 h (EG1150h, LEIC, Germany) and smoothed using a scalpel, in a fume hood. The tissues were then subjected to dehydration and embedded in wax using an embedding machine (rotating microbodies, RM2225, LEIC, Germany). Thereafter, the sections were dewaxed and stained with hematoxylin and eosin (H&E) [23]. The villus height (VH) and crypt depth (CD) were observed under a light microscope (S4E, LEIC, Germany) and analyzed using an Image analyzer (Image-Proplus 5.0).
## Cecum microbiome
After the above-mentioned cecal content DNA was extracted, 16S rRNA sequencing technology was used to analyze the diversity composition spectrum of the cecal content microbial community in Shanghai Personal Biotechnology Co., Ltd. (Shanghai, China). The detailed steps and analysis process are described in the previous research article [24].
## Statistical analysis
Statistical analysis was conducted using the SPSS software package (SPSS version 22.0; IBM Corp, Armonk, NY, United States). The Shapiro-Wilk test was used for the phenotypic analyses, and the Student’s t-test was used to analyze the differences after compound normal distribution conditions.
## Determination of growth performance
The effects of dietary CSB supplementation on the growth performance, including laying rate, F/E ratio, and ADFI of the 48-56 week-old laying ducks are shown in Figures 1A-C. The results showed that dietary CSB supplementation improves ADFI and F/E of the ducks, although there is no significant difference between the groups C and CSB ($p \leq 0.05$; Figures 1B, C). In contrast, the laying rate of the group CSB was significantly higher than that of the group C ($p \leq 0.05$; Figure 1A).
**Figure 1:** *Effects of dietary supplementation of coated sodium butyrate (CSB) on the growth performance of laying ducks. (A) Laying rate of different times laying ducks. (B) Feed-to-egg ratio (F/E) of different times laying ducks. (C) Average daily feed intake (ADFI) of different times laying ducks. Values are presented as means ± SEM (n = 6). *p<0.05. group C, Control group, which was fed a basal diet; CSB group, CSB-treated group, which was fed the basal diet supplemented with 250 g/t of CSB.*
## Determination of serum antioxidant index
The effect of dietary CSB supplementation on the serum antioxidant indices of laying ducks is shown in Figures 2A-D. The SOD and T-AOC activity were significantly higher, while the MDA content was significantly lower in the serum of the group CSB compared to that of the group C ($p \leq 0.01$; Figures 2A, B, D). The results showed that dietary CSB supplementation improves CAT of the ducks, although there is no significant difference between the groups C and CSB ($p \leq 0.05$; Figure 2C).
**Figure 2:** *Effects of dietary supplementation of coated sodium butyrate (CSB) on the serum antioxidant status of laying ducks. (A) Superoxide dismutase (SOD). (B) Total antioxidant capacity (T-AOC). (C) Catalase (CAT), and (D) Malondialdehyde (MDA). Values are presented as means ± SEM (n = 6). *p<0.05 and **p<0.01. group C, Control group, which was fed a basal diet; CSB group, CSB-treated group, which was fed the basal diet supplemented with 250 g/t of CSB.*
## Serum Ig and inflammatory cytokine analysis
The effect of dietary CSB supplementation on the serum Igs and inflammatory cytokines of laying ducks is shown in Figures 3A-G. The results showed that dietary CSB supplementation increases the serum IgA and IgM levels, although there was no significant difference between the groups C and CSB ($p \leq 0.05$; Figures 3A, C). In contrast, compared to the group C, the group CSB showed a significant increase in serum IgG levels ($p \leq 0.05$; Figure 3B). The serum TNF-α content was significantly lower in the group CSB compared with that in the group C ($p \leq 0.05$; Figure 3F).
**Figure 3:** *Effects of dietary supplementation of coated sodium butyrate (CSB) on the serum immunoglobulin (Ig) and inflammatory cytokine levels of laying ducks. (A) IgA. (B) IgG. (C) IgM. (D) Interferon (IFN)-γ. (E) Interleukin (IL)-B. (F) Tumor necrosis factor (TNF)-α. (G) Interleukin (IL)-6. Values are presented as means ± SEM (n = 6). *p<0.05. group C, Control group, which was fed a basal diet; CSB group, CSB-treated group, which was fed the basal diet supplemented with 250 g/t of CSB.*
## Analysis of spleen immune-related gene and ileal tight junction protein gene expression
The effect of dietary CSB supplementation on the expression of immune-related genes in the spleen and ileal TJ protein of laying ducks is shown in Figures 4A-G. The expression of IL-1β and TNF-α in the spleen of the group CSB was significantly lower compared to that in the group C ($p \leq 0.05$; Figures 4B, D). In contrast, there was no significant difference between the expression of IFN-γ and IL-6 in the spleen of the groups C and CSB ($p \leq 0.05$; Figures 4A, C). The expression of Occludin in the ileum of the group CSB was significantly higher than that in the group C ($p \leq 0.05$; Figure 4F). Furthermore, ZO-1 and Claudin-1 expression were increased in the ileum of the group CSB, although the difference between the two groups was not significant ($p \leq 0.05$; Figures 4E, G).
**Figure 4:** *Effects of dietary supplementation of coated sodium butyrate (CSB) on the expression of immune-related genes in the spleen and the ileal tight junction protein gene of laying ducks. (A)
Interferon-γ (IFN-γ). (B)
Interleukin-1β (IL-1β). (C)
Interleukin-6 (IL-6). (D) Tumor necrosis factor alpha (TNF-α). (E)
Claudin-1.
(F)
Occludin.
(G)
ZO-1. Values are presented as means ± SEM (n = 6). *p<0.05. group C, Control group, which was fed a basal diet; CSB group, CSB-treated group, which was fed the basal diet supplemented with 250 g/t of CSB.*
## Small intestinal morphology examination
The jejunal VH in the group CSB was significantly higher ($p \leq 0.05$) than that in the group C (Figure 5A). However, there was so significant difference between the villus morphology of the duodenum and ileum of the groups C and CSB ($p \leq 0.05$; Figures 5B, C). H&E stained images of the small intestines (duodenum, jejunum, and ileum) of the groups C and CSB are shown in Figure 6.
**Figure 5:** *Effects of dietary supplementation of coated sodium butyrate (CSB) on the morphometric characteristics of the duodenum, jejunum, and ileum of laying ducks. (A) Villus height (VH) of small intestines. (B) Crypt depth (CD) of small intestines. (C) VH/CD ratio of the small intestines. Values are presented as means ± SEM (n = 12). *p<0.05. group C, Control group, which was fed a basal diet; CSB group, CSB-treated group, which was fed the basal diet supplemented with 250 g/t of CSB.* **Figure 6:** *Effects of dietary supplementation of coated sodium butyrate (CSB) on the intestinal (duodenal, jejunal, and ileal) morphology of laying ducks. Intestinal sections were stained with hematoxylin and eosin (H&E). group C, Control group, which was fed a basal diet; CSB group, CSB-treated group, which was fed the basal diet supplemented with 250 g/t of CSB.*
## Cecal microbiome analysis
A total of 1,229,199 bases were obtained from 24 samples, which were quality filtered and chimera removed, to obtain a total of 30,423 amplicon sequence variants (ASVs). Rarefaction analyses were performed to gauge adequate sequencing depth per sample (Figure 7A). The Venn diagram in Figure 7B shows that the ducks in the groups C and CSB had 10,051 and 11,716 unique ASVs (Figure 7B), respectively. The α-diversity assessed using Chao1, Faith’s PD, Good’s coverage, Shannon, and Pielou-e indices is shown in Figure 7C. The Chao1, Shannon, and Pielou-e indices were higher in the group CSB than in the group C ($p \leq 0.05$), while the Good’s coverage index was higher in the group C compared to the group CSB ($p \leq 0.05$). These results suggest that compared to the group C, cecal microflora richness and diversity were higher in the group CSB.
**Figure 7:** *Effects of dietary supplementation of coated sodium butyrate (CSB) on cecum microbiota in laying ducks. (A) Rarefaction curves. (B) Venn diagram of the amplicon sequence variants (ASVs). (C) Chao1, Faith’s phylogenetic diversity (PD), Good’s coverage, Shannon, and Pielou’s evenness (Pielou-e) α-diversity indices. (D) principal coordinate analysis (PCoA) of taxonomical classifications of the cecal bacterial communities. (E) weighted_unifrac distance. (F) linear discriminant analysis effect size (LEfSe) analysis (LDA threshold = 3). Values are presented as means ± SEM (n = 12). *p<0.05. group C, Control group, which was fed a basal diet; CSB group, CSB-treated group, which was fed the basal diet supplemented with 250 g/t of CSB.*
The PCoA plot was used to visualize the microbial trends and outliers to determine the difference in the gut microbial composition of the groups C and CSB (Figure 7D). Analysis of similarities (ANOSIM) indicated a clear difference between the two groups (Figure 7E). Analysis of the differentially abundant taxa between the two groups revealed that c:Bacteroidia, p_Bacteroidetes, o_Bacteroidales, f_Bacteroidaceae, and g_Bacteroides were enriched in the group C ($p \leq 0.05$), while p_Firmicutes, c_Clostridia, o_Clostridiales, and f_Lachnospiraceae were enriched in the CBS group ($p \leq 0.05$; Figure 7F).
The cecal microflora community structure of the groups C and CSB was nearly identical at the phylum level (Figures 8A-F). In the group C, Bacteroidetes were the most abundant phyla ($59.41\%$), followed by Firmicutes ($36.77\%$), Proteobacteria ($2.12\%$), and Actinobacteria ($0.57\%$). In contrast, in the group CSB, Firmicutes were the most abundant phyla ($48.44\%$), followed by Bacteroidetes ($45.19\%$), Proteobacteria ($3.16\%$), and Actinobacteria ($1.2\%$). Furthermore, based on the variance analysis, we found that the abundance of Bacteroidetes in the group CSB was lower than that in the group C ($p \leq 0.05$; Figure 9A), while the abundances of Firmicutes and Actinobacteria were higher in the group CSB than in the group C ($p \leq 0.05$; Figures 8B, E). In contrast, at the genus level, both groups C and CSB showed similar trends (Figures 9A-F), with Bacteroides being the most abundant ($46.65\%$ and $33.50\%$), followed by Faecalibacterium ($6.10\%$ and $7.74\%$), Megamonas ($1.36\%$ and $4.22\%$), and Subdoligranulum ($1.95\%$ and $3.17\%$). However, the abundance of Bacteroides in the group CSB was lower than that in the group C ($p \leq 0.05$; Figure 9A).
**Figure 8:** *Effects of dietary supplementation of coated sodium butyrate (CSB) on the relative abundance of cecal microbiota of laying ducks at the genus level. (A)
Bacteroides.
(B)
Faecalibacterium.
(C)
Megamonas.
(D)
Subdoligranulum.
(E)
Desulfovibrio.
(F)
Prevotellaceae_Ga6A1_group. Values are presented as means ± SEM (n = 12). *p<0.05. group C, Control group, which was fed a basal diet; CSB group, CSB-treated group, which was fed the basal diet supplemented with 250 g/t of CSB.* **Figure 9:** *Effects of dietary supplementation of coated sodium butyrate (CSB) on the relative abundance of the cecal microbiota of laying ducks at the phylum level. (A)
Bacteroidetes.
(B)
Firmicutes.
(C)
Proteobacteria.
(D)
Deferribacteres.
(E)
Actinobacteria.
(F)
Fusobacteria. Values are presented as means ± SEM (n = 12). *p<0.05. group C, Control group, which was fed a basal diet; CSB group, CSB-treated group, which was fed the basal diet supplemented with 250 g/t of CSB.*
## Discussion
Cultural backgrounds and dietary habits have led to an increase in the demand for meat and duck eggs in Asia, and duck egg, along with hen, has become an important component of the food industry owing to its excellent nutritional and functional properties [25]. Asia is the world’s largest producer of duck meat and eggs [26], with China being the primary producer [27]. However, with the transformation of the breeding models and increasing market demand, healthy breeding has become the top priority in the animal husbandry sector. Some studies have shown that SB is beneficial to the reproductive performance of the hens [28, 29]. Furthermore, CSB was found to increase the laying rate and the daily egg weight, while decreasing the F/E ratio of yellow-feathered breeder hens [30]. This is consistent with the results of the present study, in which the laying rate of the CSB-supplemented ducks increased significantly. According to Ghosh and Cox [31], SB induced follicle-stimulating hormone secretion, stimulated follicle growth and development, and increased the laying rate. Moreover, CSB supplementation improved the ADFI and F/E ratio of laying ducks, although there was no significant variation between the groups C and CSB, suggesting that the effect of butyrate was too low in the pre-test period to make a significant difference.
OS often occurs in animals during production, and the main reason for its production is the production and accumulation of reactive oxygen species (ROS) [32].In order to fight this oxidative stress, the body uses antioxidant enzymes to limit the accumulation of ROS and reduce the stress response [33]. T-AOC is a comprehensive indicator of the antioxidant function of the body, and the serum MDA level reflects the degree of FR-mediated lipid peroxidation [34]. Numerous studies have shown that butyrate has a powerful antioxidant effect both inside (35–38). Some studies have shown that dietary CSB supplementation can increase SOD and T-AOC activities and reduce MDA levels in laying hens [19]. In our study, the serum T-AOC and SOD activity were significantly higher, while the MDA content was significantly lower in the group CSB compared to the control group, indicating that dietary CSB supplementation can improve the serum antioxidant capacity of laying duck.
Immunoglobulins produced by B lymphocytes bind specifically to the corresponding antigen [39]. Immunoglobulins have the largest proportion of IgG in serum, probably because of their specific immune activity [40]. Dietary SB supplementation increases the serum IgG content in meat-type chickens [41] and weaned piglets [42]. However, only limited studies have been published on the effects of SB on the humoral and cellular immune status [43, 44], and further research is required on this aspect. In our study, the serum IgG content of the group CSB was significantly higher than that of the control group, which improved the humoral immune capacity of the group CSB. The pro-inflammatory cytokines, including IL-1β, IL-6, IFN-γ, and TNF-α, are closely associated with the immune system and are influenced by the highly-automated process (HAP) axis activity [45]. BA has been shown to play an important role in maintaining the integrity of the intestinal mucosa and exert potent anti-inflammatory effects in broilers [46]. Previous studies have also reported that butyrate modulates the immune system by stimulating the release of inflammatory cytokines [47]. Zou et al. [ 48], found that TNF-α and IL-1β were significantly decreased in chickens supplemented with 300 mg/kg SB, which is consistent with the results of this study, in which dietary CSB supplementation significantly decreased serum TNF-α levels in laying ducks.
Furthermore, previous studies have demonstrated that supplementation of BA or SB reduces the expression of pro-inflammatory cytokines and affects immune response by inhibiting the activation of nuclear factor-κB (NF-κB) and decreasing the release of IL-1β, IL-6, and TNF-α [49]. Moreover, dietary supplementation of CSB can significantly suppress the intestinal gene expression levels of pro-inflammatory cytokines, TNF-α and IL-6, in laying hens [19]. This is consistent with the results of our study, in which compared with the control group, the expression of IL-1β and TNF-α was significantly lower in the spleens of the group CSB. These results suggest that CSB can affect cellular inflammatory factors and thus the immune capacity of the organism. Homeostasis in the intestines can be maintained by a physical barrier consisting of epithelial cells and intercellular TJs [50]. TJs are the most important intercellular junctions and consist of cytoplasmic protein ZO family and transmembrane proteins, including Occludin and Claudin [22]. Previous studies demonstrated that SB increased TJ expression, reduced gut permeability, and enhanced the intestinal physical barrier [20, 21]. This is consistent with the results of the present study, in which dietary CSB supplementation significantly increased the expression of Occludin in the ileum of laying ducks. Intestinal permeability is closely associated with cytokines [51], and TNF-α and IL-6 are enriched in the inflamed gut and contribute to gut damage [52]. The results of our study suggest that CSB can inhibit cellular inflammatory factors, thereby enhancing the physical barrier of the intestines and maintaining intestinal integrity.
In addition to the barrier, intestinal tract histology also plays an important role in laying ducks. The absorption capacity of the small intestine is measured by VH, CD, and, VH/CD ratio [53]. The longer villi correspond to a shallower crypt and greater VH/CD ratio, which increases the absorptive capacity of the small intestines [54]. Previous studies found that SB supplementation could improve feed utilization by increasing the length of the small intestinal villi [41, 55], which is consistent with the results of this study, in which CSB supplementation significantly increased the VH in the jejunum of the group CSB compared to the control group. Furthermore, BA expands the absorption area of the small intestines, thereby increasing the VH [56].
The cecum contains majority of the intestinal flora and plays a key role in the digestion and absorption process of poultry [57]. Some studies have shown that changes in the intestinal flora can affect intestinal health [58], and the cecal microbiota composition significantly impacts the growth and health of poultry [59, 60]. A study found that the addition of *Clostridium butyricum* to the feeds of early Muscovy ducks improved the cecal microflora richness [61]. In this study, we found that CSB supplementation increased the Chao1, Shannon, and Pielou-e indices in laying ducks, suggesting higher cecal microflora richness and diversity in the group CSB compared to the control group. Higher microbiota diversity indicates stronger intestinal health, as the rich flora can resist the invasion of pathogenic bacteria [62]. Whether or not to add CSB to the ration the dominant flora of the cecum of ducks are Bacteroidetes and Firmicutes, this is consistent with the results of previous studies [59, 63]. C. butyricum can increase the abundance of Firmicutes in the cecum of yellow-feathered breeder hens [30]. In our study, we found that the abundance of Bacteroidetes was lower in the group CSB than in the control group, while the abundances of Firmicutes and Actinobacteria were higher in the group CSB compared to the control group. Firmicutes are involved in the energy absorption activities of the intestines [64] and include a variety of bacteria that can decompose cellulose in the intestinal tract [65], Bacteroidetes can degrade polysaccharides in the large intestine to produce butyrate [66]. Due to CSB supplementation, Bacteroidetes were not required for butyrate production to maintain the normal activity of the organism, which explains the significantly lower abundance of Bacteroidetes in the group CSB compared to the control group, which is consistent with the results of our previous studies [67, 68]. Furthermore, in our previous study, we found that CSB increased the abundance of Actinobacteria in the ileum of suckling pigeons [12], which is consistent with the results of this pilot study. Actinobacteria have great economic importance owing to their secondary metabolites, which have antibiotic properties [69]. CSB increases egg production by affecting the intestinal flora of the ducks, thereby increasing their immune and antioxidant capacity.
## Conclusions
Dietary supplementation of CSB enhanced the immune function and intestinal barrier of laying ducks by increasing the antioxidant capacity of the body, increasing the secretion of humoral immune factors, upregulating the expression of TJ-related genes, and improving intestinal morphology. Moreover, CSB supplementation improved the abundance of cecal microflora in laying ducks, reduced laying stress, and increased egg production rate, which indicates the positive effect of CSB supplementation on laying ducks.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://ngdc.cncb.ac.cn, PRJCA014034.
## Ethics statement
The animal care and use protocol was approved by the Ethics Committee for the Welfare of Animals of Zhejiang Academy of Agricultural Sciences (NO. 2022ZAASLA59).
## Author contributions
LL and TZ designed the experiments. HS and MH performed the experiments and carried out the data summarizing. RG and TG were mainly responsible for experimental animal feeding and result analysis. TZ, HS, YC, CL, YT, CL and GL wrote and revised the main manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
Author CL was employed by Hubei Shendan Health Food Co., Ltd.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Retinal vein changes after treatment with aflibercept and PRP in high-risk
proliferative diabetic retinopathy
authors:
- Hui Zhao
- Jundong Wang
- Shuting Li
- Ying Bao
- Xiaoxia Zheng
- Yuan Tao
- Hong Wang
journal: Frontiers in Medicine
year: 2023
pmcid: PMC10034169
doi: 10.3389/fmed.2023.1090964
license: CC BY 4.0
---
# Retinal vein changes after treatment with aflibercept and PRP in high-risk proliferative diabetic retinopathy
## Abstract
### Objective
The objective of the study was to investigate the effectiveness of aflibercept and panretinal photocoagulation (PRP) in the treatment of proliferative diabetic retinopathy (PDR).
### Methods
A retrospective analysis was performed on 59 patients (59 eyes) with high-risk PDR who were treated with aflibercept and PRP between January 2018 and December 2019. The best corrected visual acuity (BCVA), central foveal thickness (CFT), and retinal vein diameter post-treatment were compared to those before the treatment.
### Results
The best corrected visual acuity (BCVA) at 6 months (0.49 ± 0.14 logMAR), 12 months (0.54 ± 0.15 logMAR), 18 months (0.48 ± 0.15 logMAR), and 24 months (0.51 ± 0.15 logMAR) post-treatment were superior to the pre-treatment measurement (0.65 ± 0.18 logMAR). The central foveal thickness (CFT) at 6 months (310.67 ± 52.53 μm), 12 months (295.98 ± 45.65 μm), 18 months (282.56 ± 43.57 μm), and 24 months (281.53 ± 51.16 μm) post-treatment were lower than the pre-treatment measurement (456.53 ± 51.49 μm); the retinal vein diameter at 12 months (310.13 ± 24.60 μm), 18 months (309.50 ± 31.58 μm), and 24 months (317.00 ± 27.54 μm) post-treatment were lower than the pre-treatment measurement (361.81 ± 30.26 μm).
### Conclusion
Aflibercept intravitreal injection and panretinal photocoagulation may morphologically reverse retinal vein diameter and venous beading in high-risk proliferative diabetic retinopathy.
## Introduction
Diabetic retinopathy (DR) is a complication of diabetes and manifested as retinal microangiopathy. It occurs in many diabetic patients 5 to 10 years after the onset of the condition (1–3). As the most common complication of diabetes [4], diabetic retinopathy (DR) can lead to preventable blindness in working-aged adults [2, 3]. However, many patients are not promptly diagnosed or treated until the development of high-risk proliferative DR. The high-risk proliferative DR is the late stage of DR progression and has been associated with poor outcomes and blindness [5].
Diagnostic criteria for high-risk proliferative DR were 1. Optic disk neovascularization of ≥$\frac{1}{4}$ to $\frac{1}{3}$ of the optic disk diameter, with or without preretinal hemorrhage or vitreous hemorrhage; 2. Preretinal hemorrhage or vitreous hemorrhage with optic disk neovascularization or retinal neovascularization of ≥$\frac{1}{4}$ to $\frac{1}{3}$ of the optic disk diameter. The diabetic retinopathy study recommended immediate panretinal photocoagulation (PRP) for eyes with high-risk PDR since the risk of severe vision loss in this population within 5 years was greater than $50\%$ if the condition was untreated [6]. In the past decade, anti-vascular endothelial growth factor (VEGF) agents were primarily used for the treatment of diabetic macular edema [7]. The Diabetic Retinopathy Clinical Research (DRCR) Network Protocol S aimed to evaluate the effectiveness of ranibizumab compared to PRP in eyes with PDR. In this study, patients were randomized to ranibizumab 0.5 mg intravitreal injection monthly for 3 months. Any patient who developed progressive retinopathy despite monthly injections was allowed to receive PRP. At 2 years, ranibizumab provided better visual acuity outcomes, less visual field loss, fewer vitrectomies were required, and less development of center-involved DME when compared with the PRP group. The advantages of PRP were fewer visits, fewer injections, and greater cost-effectiveness in eyes without DME initially. Figueira et al. demonstrated that intravitreal injection of anti-VEGF agents was safe and was considered an option for high-risk PDR eyes in a study with a follow-up of 1 year. The outcome from intravitreal injection monotherapy or combination therapy was comparable or superior to that from PRP [8]. The combination treatment of PRP plus an anti-VEGF drug may be the treatment of choice for PDR [9].
Previous retrospective studies elucidated peripheral reperfusion in ischemic areas of the retina in patients receiving anti-VEGF intravitreal injections, suggesting the potential of anti-VEGF therapies in reversing DR [10]. This study aimed to determine the effectiveness of aflibercept combined panretinal photocoagulation in alleviating high-risk proliferative diabetic retinopathy, specific to retinal venous beading (VB), retinal vein diameter, best corrected visual acuity (BCVA), and central macular thickness (CFT).
## General information
The data were collected from patients who were diagnosed with high-risk proliferative DR by fundoscopy, fundus fluorescein angiography (FFA), and optical coherence tomography (OCT) between January 2018 and December 2019 (Table 1). Diagnostic criteria for high-risk proliferative DR were 1. Optic disk neovascularization of ≥$\frac{1}{4}$ to $\frac{1}{3}$ of the optic disk diameter, with or without preretinal hemorrhage or vitreous hemorrhage; 2. Preretinal hemorrhage or vitreous hemorrhage with optic disk neovascularization or retinal neovascularization of ≥$\frac{1}{4}$ to $\frac{1}{3}$ of the optic disk diameter [11]. Inclusion criteria were [1] Patients who were diagnosed with high-risk proliferative DR; [2] Type 2 diabetes patients with adequately controlled blood sugar, glycated hemoglobin (GHb) of less than $10\%$, blood pressure of less than $\frac{160}{90}$ mm Hg (1kpa = 7.5 mm Hg); [3] Patients who did not receive prior fundus therapy such as retinal photocoagulation, anti-vascular endothelial growth factor intravitreal injections, or hormones. Exclusion criteria were: [1] Patients with type 1 diabetes; [2] Patients with poor imaging quality due to refractive interstitial opacity; [3] Patients with non-diabetic retinal vascular disease (these patients were excluded if fundus observation was affected by refractive interstitial opacity due to massive vitreous hemorrhage). All patients provided informed consent and were aware of the possible risks associated with the treatment (see Figure 1).
## Methods
Upon review of medical records, the data regarding age, sex, laterality of the eye. BCVA, central foveal thickness (CFT), retinal venous beading and retinal vein diameter were collected. The BCVA, CFT, retinal venous beading and retinal vein diameter at 6-, 12-, 18-, and 24 months post-treatment were compared with their pre-treatment measurements. All results were reviewed by the same senior ophthalmologist.
Intravitreal injection of aflibercept (2 mg) was administered to all patients monthly for the first 3 months adopting a 3 + PRN regimen. All injections were administered by the same senior physician. Eye drops of $0.5\%$ levofloxacin were administered four times daily, starting 3 days before the surgery. Intravitreal injections of aflibercept and optical coherence tomography (HD-OCT) (Carl Zeiss AG) were performed by the same physician. Specifically, the eyes received aflibercept intravitreal injection + PRP at 0, 1, and 2 months. If NV (neovascular) persisted and/or if it recurred, combination therapy was administered for at least 4 weeks. For all study groups, the treatment standard was followed according to the ETDRS protocol for diabetic macular edema. Panretinal photocoagulation was performed 1 week after the first intravitreal injection (see Figure 2).
**Figure 2:** *The fundus angiography image of the patient’s right eye 6 months after treatment, and the retinal laser spot is clear.*
The affected eye was dilated with $1\%$ tropicamide 15 min prior to examination. Fundus fluorescein angiography (FFA) was performed to obtain images of each patient.
The measurements of all venules passing through a zone with 1–1.5-disk diameters from the optic disc margin were taken from the macula-centered and optic disk-centered photographs. The calibers of the largest six veins were considered as the central retinal vein equivalent (CRVE) using the formula developed by Parr and Hubbard and revised by Knudtson [12]. These equivalents were considered projected calibers for the central retinal veins. The intra-class reproducibility of retinal vascular measurements was excellent (the intra-class correlation coefficients for CRVEs all >0.98) in this study.
Fundus fluorescein angiography images were performed using a Heidelberg Spectralis HRA fundus camera and video angiography. The OCT images were acquired with the ZEISS Cirrus HD-OCT and measurements were taken using the built-in image processing software. The images with prominent retinal vein contours in the prevenous phase were used in fundus fluorescein angiography to avoid the interference of neovascular leakage with the measurements. All patients provided informed consent for the diagnostic and clinical procedures.
**Figure 3:** *The fundus angiography image of the patient’s right eye 18 months after treatment, and the beading of the superior temporal branch retinal vein is significantly improved.*
## Panretinal photocoagulation
Wide-angle panretinal photocoagulation (PRP) was carried out. Briefly, a frequency-doubling 532 laser photocoagulator was used at a laser setting of 200 μm spot diameter, 200 ms pulse duration, and 230 mW power with a 165° retinoscope. The PRP protocol followed the guidelines formulated by the diabetic retinopathy photocoagulation group. The pupils were dilated enough before treatment. The laser started from the posterior pole with photocoagulation applied to the vicinity of the optic disc, 1PD nasally at the center of the macula, and beyond 2PD from the temporal side and anterior to the equator. The laser spots were aimed at 1 spot diameter apart. A total of 1,200–1,600 laser spots were delivered causing a level 3 burn and completed in four stages.
## Statistical analysis
All statistical analyzes were performed using SPSS version 21.0 statistical software. Continuous variables were expressed as mean ± standard deviation, whereas categorical variables were expressed as percentages (%). The BCVA, CFT, and retinal vein diameter pre-treatment vs. post-treatment were compared using paired t-tests for the measurement data that conformed to normal distribution. The nonparametric test was used for the measurement data that did not conform to normal distribution. The ratios were tested using the Chi-Squared test. A value of p of less than 0.05 was considered statistically significant.
## Results
The best corrected visual acuity (BCVA) at 6 months (0.49 ± 0.14 logMAR), 12 months (0.54 ± 0.15 logMAR), 18 months (0.48 ± 0.15 logMAR), and 24 months (0.51 ± 0.15 logMAR) post-treatment were superior to the pre-treatment measurement (0.65 ± 0.18 logMAR) (Table 2); the central foveal thickness (CFT) at 6 months (310.67 ± 52.53 μm), 12 months (295.98 ± 45.65 μm), 18 months (282.56 ± 43.57 μm) and 24 months (281.53 ± 51.16 μm) post-treatment was lower than the pre-treatment measurement (456.53 ± 51.49 μm) (Table 3); the retinal vein diameter at 12 months (310.13 ± 24.60 μm), 18 months (309.50 ± 31.58 μm), and 24 months (317.00 ± 27.54 μm) post-treatment was lower than the pre-treatment measurement (361.81 ± 30.26 μm) (Table 4). The retinal venous beading changed significantly at 18 months post-treatment, with decreased beading (see Figure 3).
## Discussion
Diabetic retinopathy occurs in two forms, non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR). This study elucidated the changes in retinal veins (venous beading) and the changes in retinal vein calibers in high-risk PDR patients. Fundoscopy, OCT, and fundus fluorescein angiography (FFA) techniques were used for observing the changes. Filho et al. compared intravitreal 0.5 mg ranibizumab withPRP versus PRP alone for the treatment of high-risk PDR in 40 patients. They found significant reduction in fluorescein angiography leakage in both groups through week 48, but the reduction was significantly greater in the combination group, along with significant improvement in visual acuity and central retinal thickness [13], which is the same as our results in terms of visual acuity and central foveal thickness.
CLARITY was a multi-center phase 2b, single-blind, randomized, noninferiority trial that compared aflibercept to PRP. At 52 weeks, aflibercept was not only noninferior to PRP but also superior to PRP in terms of visual change. New-onset centers involved DME, vitreous hemorrhage, need for vitrectomy, and visual loss were more likely to occur in eyes treated with PRP than with aflibercept [14]. The special feature of our study is to observe the changes of retinal vein diameter and vein beading during treatment.
Baseline Retinopathy and Clinical Features Predict Progression of Diabetic Retinopathy showed that baseline signs and initial DR were prognostic from report 3 of the 2017 United Kingdom Diabetic Retinopathy Electronic Medical Record Users Group. It concluded that IRMA increases the risk of PDR whereas 4Q DBH increases the risk of VH. Venous beading was not a critical variable as the other two features in predicting PDR or VH [15] *In this* study, a significant remission of retinal venous beading and a reduction in retinal venous diameter by fundus photography and fundus fluorescein angiography in patients with PDR following intravitreal injections of anti-VEGF agents were noticed.
As the DR progressed, the death of pericytes and the thickening of the basement membrane resulted in impaired perfusion and retinal ischemia. The increased ischemia led to the formation of VB [16] A previous domestic study showed that VB was the chronic reactive expansion of the retinal vein in response to retinal ischemia or other abnormal stimuli [17]. The ancillary studies of CLARITY revealed that aflibercept reduced retinal hemorrhages and intravitreal microvascular abnormalities but not venous beading at week 52, suggesting that VEGF would not have been involved in the pathophysiology of vein changes, or these anatomical changes may not have been improved in a relatively short period of 1 year [18].
The retinal blood flow and hydrostatic pressure in the retinal vessels were increased in diabetic retinopathy [19]. The increased hydrostatic pressure in the retinal vessels may have been responsible for the small retinal vessel expansion. In this study, significant changes in the retinal venous beading were noted in PDR patients at 18 months, which may have been due to the anatomical changes in the retinal vein which may have required a sufficiently long time for the vein remodeling. On the other hand, retinal veins had no venous valves and the venous beading may have improved after remission of retinal ischemia and reduction of hydrostatic pressure in the retinal vessels.
A 12-month prospective clinical trial found that the calibers of both retinal arterioles and venules were reduced by the intravitreal anti-vascular endothelial growth factor (VEGF) treatment in DME, and the eyes that did not even receive PRN aflibercept after the loading phase had sustainable venous constriction at 12 months. The favorable effect of anti-VEGF therapy on retinal thickness in DME treatment might have been at least in part attributable to the reduction of pathologically increased vessel calibers to normal levels and a subsequent decrease in hydrostatic pressure [20]. Abnormal diameter in diabetes due to the changes in perfusion pressure might have been attributed to a lack of vascular tone and changes in the vessel walls. Additionally, endothelial dysfunction may have led to impaired endothelial vasodilatation and an imbalance of retinal vessel diameter regulation in diabetic patients [21]. Recent evidence also found that retinal glial cells were capable of sensing the reduction in perfusion pressure and contributed to the maintenance of vessel diameters. The glial cells are affected early in the diabetic retina and continue to degenerate along with retinal ganglion cells.
We speculate that the inflammatory response is also an important part of the mechanism of the effect of intravitreal injection of aflibercept on the retinal vein diameter and venous beading.aflibercept, by binding also to PlGF, could exert an anti-inflammatory action in the diabetic retina [22].
This study has the following limitations. First, an untreated control group was not included in the analysis for ethical reasons. Second, the effect of aging on retinal vascular remodeling was not ruled out (an elderly population was elucidated), although the blood pressure parameters were well controlled.
Furthermore, wider retinal vein calibre was considered an independent risk factor for the subsequent occurrence and development of DR [23].
In high-risk PDR patients who received anti-VEGF treatments, statistically significant differences in the BCVA and CFT were noticed at 6 months, 12 months, 18 months, and 24 months post-operation compared with the pre-operative measurements ($p \leq 0.05$). Statistically, significant differences in the retinal vessel diameter were observed at 12 months, 18 months, and 24 months post-operation compared with the pre-operative measurements ($p \leq 0.05$). The retinal venous beading improved significantly at 18 months. The anatomical changes of the retinal vein required a sufficiently long time for vein remodeling. Studies with a longer follow-up are warranted for further investigation in the future.
## Conclusion
Aflibercept intravitreal injection and panretinal photocoagulation may reverse the retinal vein diameter and venous beading in high-risk proliferative diabetic retinopathy.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.
## Ethics statement
This study was conducted after approval by the Ethics Committee of Qilu Hospital of Shandong University. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
HZ, HW, and YT carried out the conception and design of the research and drafted the manuscript. HZ and SL participated in obtaining funding. JW and YT participated in the acquisition of data. YB and XZ carried out the analysis and interpretation of data. HW and YT participated in the design of the study and performed the statistical analysis. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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13. Filho JA, Messias A, Almeida FP, Ribeiro JA, Costa RA, Scott IU. **Panretinal photocoagulation (PRP) versus PRP plus intravitreal ranibizumab for high-risk proliferative diabetic retinopathy**. *Acta Ophthalmol* (2011) **89** e567-72. DOI: 10.1111/j.1755-3768.2011.02184.x
14. Sivaprasad S, Prevost AT, Vasconcelos JC, Riddell A, Murphy C, Kelly J. **Clinical efficacy of intravitreal aflibercept versus panretinal photocoagulation for best corrected visual acuity in patients with proliferative diabetic retinopathy at 52 weeks (CLARITY): a multicentre, single-blinded, randomised, controlled, phase 2b, non-inferiority trial**. *Lancet* (2017) **389** 2193-203. DOI: 10.1016/S0140-6736(17)31193-5
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---
title: The neighborhood context and all-cause mortality among older adults in Puerto
Rico
authors:
- Catherine García
- Marc A. Garcia
- Mary McEniry
- Michael Crowe
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10034172
doi: 10.3389/fpubh.2023.995529
license: CC BY 4.0
---
# The neighborhood context and all-cause mortality among older adults in Puerto Rico
## Abstract
### Background
Recent efforts have been made to collect data on neighborhood-level attributes and link them to longitudinal population-based surveys. These linked data have allowed researchers to assess the influence of neighborhood characteristics on the health of older adults in the US. However, these data exclude Puerto Rico. Because of significantly differing historical and political contexts, and widely ranging structural factors between the island and the mainland, it may not be appropriate to apply current knowledge on neighborhood health effects based on studies conducted in the US to Puerto Rico. Thus, we aim to [1] examine the types of neighborhood environments older Puerto Rican adults reside in and [2] explore the association between neighborhood environments and all-cause mortality.
### Methods
We linked data from the 2000 US Census to the longitudinal Puerto Rican Elderly Health Conditions Project (PREHCO) with mortality follow-up through 2021 to examine the effects of the baseline neighborhood environment on all-cause mortality among 3,469 participants. Latent profile analysis, a model-based clustering technique, classified Puerto Rican neighborhoods based on 19 census block group indicators related to the neighborhood constructs of socioeconomic status, household composition, minority status, and housing and transportation. The associations between the latent classes and all-cause mortality were assessed using multilevel mixed-effects parametric survival models with a Weibull distribution.
### Results
A five-class model was fit on 2,477 census block groups in Puerto Rico with varying patterns of social (dis)advantage. Our results show that older adults residing in neighborhoods classified as Urban High Deprivation and Urban High-Moderate Deprivation in Puerto Rico were at higher risk of death over the 19-year study period relative to the Urban Low Deprivation cluster, controlling for individual-level covariates.
### Conclusions
Considering Puerto Rico's socio-structural reality, we recommend that policymakers, healthcare providers, and leaders across industries to [1] understand how individual health and mortality is embedded within larger social, cultural, structural, and historical contexts, and [2] make concerted efforts to reach out to residents living in disadvantaged community contexts to understand better what they need to successfully age in place in Puerto Rico.
## 1. Introduction
The twenty-first century in the Commonwealth of Puerto Rico (hereafter, Puerto Rico)—an unincorporated United States (US) territory—is an era characterized by rapid population aging, reductions in social and economic resources, rampant disparities in access to adequate healthcare, and the ongoing reconstruction of the built environment post-Hurricanes Irma and María (1–6). The constellation of these factors infers that many older Puerto Rican adults may lack access to resources, services, and contexts considered necessary for promoting healthy aging.1 In order to understand contemporary conditions in Puerto Rico, it is important to consider how historical contexts contribute to health inequities over time, particularly for older adults at increased risk for poor health and mortality.
Researchers have argued that social, political, and economic inequalities in Puerto Rico derive from the impacts of US colonialism—a structural and social determinant of health [7, 8]. One significant impact of US colonialism was the transition of Puerto Rico from a rural agricultural society to an urban industrial society in the early twentieth century [9]. This transition brought public health benefits, including improved sanitation practices and housing conditions, the creation of local health boards and hospitals, and increased access to primary education. However, urbanization in Puerto Rico also led to widening economic and racial disparities that resulted in unfavorable neighborhood and living conditions among socially marginalized individuals (e.g., poor and Black Puerto Ricans) [10].
For example, San Juan, the capital of Puerto Rico, has a long history of continuous urban growth and economic development. Under US control, San Juan experienced substantial modernization, including changes in land use efficiency and aggregation of local areas that connected land use with global-scale factors. Notably, new and growing opportunities in the San Juan wage labor market were a major driver for rural-dwelling Puerto Ricans to relocate in the early twentieth century; this rural-to-urban migration affected the subsequent development and preservation of several neighborhoods in the metropolitan area [11]. Due to their lower socioeconomic position, Puerto Ricans from rural areas were forced to reside in poor and disadvantaged, communities in San Juan, such as La Perla [12]. In addition to rural-urban migration patterns, rapid population growth and efforts to mirror the US model of suburbanization were additional factors that influenced variations in the investment of resources across neighborhoods in San Juan throughout the twentieth century that contributed to contemporary residential segregation patterns [13]. For example, a study examining residential segregation in the San Juan-Bayamón metropolitan area, the most racially diverse metropolitan area in Puerto Rico, found that neighborhoods with a higher percentage of Black residents were associated with lower socioeconomic status [14].
In addition, a study focusing on the socioeconomic features of neighborhoods to assess health disparities in Puerto Rico found that municipalities (considered county-equivalents by the US Census) with a low socioeconomic position (SEP) were linked to higher cancer-related mortality rates [15]. Importantly, the study showed that more deprived municipalities of Puerto Rico were in the island's central region.2 In contrast, less deprived municipalities were concentrated in the San Juan metropolitan area. This suggests that residents living in municipalities with lower SEPs may lack access to healthcare services and health-promoting resources due to economic, environmental, and physical barriers that impact health and increase the risk of mortality. However, these findings are conditional based on the assumptions made regarding area-based socioeconomic status. Better inference of neighborhood effects would require a more nuanced approach on how specific constructs of the neighborhood environment are measured (e.g., census tract vs. census block group) and how they influence health and the risk of mortality [16].
Recent efforts have been made to collect data on neighborhood-level attributes and link them to longitudinal population-based surveys [e.g., the Health and Retirement Study Contextual Data Resource (HRS-CDR)] [17]. These linked data have allowed researchers to assess the influence of neighborhood characteristics on the health of older adults in the US. However, these data only include the contiguous US and exclude Alaska, Hawai'i, and the five permanently inhabited US territories, including Puerto Rico. Because of significantly differing historical and political contexts, and widely ranging structural factors between Puerto Rico and the US mainland, it is not appropriate to apply current knowledge on neighborhood health effects based on studies conducted in the contiguous US to Puerto Rico. In addition, despite Puerto Rico's status as an unincorporated US territory, its social and economic contexts are more like Latin American and Hispanic-Caribbean countries than the US, which may lead to substantially different risk factors for poor health and mortality.
In this study, we aim to highlight multilevel perspectives and analyses of social determinants of health among older adults residing in Puerto Rico. We address a gap in the literature by using longitudinal data from the Puerto Rican Elderly Health Conditions Project (PREHCO) linked with 2000 US *Census data* to [1] examine the types of neighborhood environments older Puerto Rican adults reside in and, [2] explore the association between neighborhood environment and all-cause mortality.
## 2. Background
It is widely recognized that physical and social environments influence health behaviors, health outcomes, and mortality in the US. Although the neighborhood environment affects the health of people of all ages, the effects of the neighborhood environment may be accentuated among older adults as they are more likely than younger adults to have spent decades in the same community, have decreased physical mobility and cognitive functioning, and rely more on community resources for social integration and support [18]. The combination of these factors may result in an early onset of age-related diseases [19], reduced life expectancy [20], and an increased risk of all-cause mortality (21–23). Notably, a vast array of research has shown that individuals residing in neighborhoods with greater deprivation have poorer health behaviors [24], lack access to preventive health services [25], are exposed to chronic stress and pollutants [26], experience greater biological weathering [27], have worse health outcomes [28], and experience higher mortality rates [29]. In many of these studies, neighborhood deprivation is based on socioeconomic contextual variables or indices related to income, education, employment, and housing, typically at the census tract level. Although these socioeconomic indicators have different meanings for older adults, it is noteworthy that the influence of socioeconomic deprivation persists in the oldest ages [30]. Indeed, several studies suggest a cumulative effect of disadvantage across the lifespan that results in poor health and an increased risk of mortality [31]. However, there is limited knowledge of how these multilevel processes influence population health and mortality in Puerto Rico due to the lack of data infrastructure to support these inquiries.
## 2.1. Neighborhood socioeconomic context
Various theoretical perspectives and conceptual frameworks have been put forth to explain why the neighborhood socioeconomic context (NSEC) plays a vital role in poor health outcomes and mortality risk. For instance, the ecological framework with a life course perspective would suggest that individuals living in disadvantaged NSECs are more likely to have a low socioeconomic position themselves due to constrained opportunity structures [22, 32, 33]. Individuals who spend their early life in lower-income neighborhoods have less access to quality education than their peers residing in higher-income communities. This limits opportunities to obtain higher levels of education and marketable job skills and reduces lifetime earnings [34, 35]. Thus, the importance of neighborhood context as a fundamental cause of mortality cannot be overlooked [36], particularly given the vast literature documenting how education shapes access to resources that promote better health and an individual's exposure to multiple health risks [37].
Another theoretical consideration is the systemic perspective, which infers that the NSEC affects the social, service, and physical environments of communities shared by residents. Namely, neighborhoods characterized by low socioeconomic levels are linked to underinvestment in health-promoting resources, such as lack of green and recreational spaces, adequate public transportation, affordable and high-quality grocery stores, and access to medical and social services [23]. For example, individuals residing in high-poverty neighborhoods are less likely to have access to recreational opportunities to walk and exercise and are more likely to live in food swamps3 [38, 39]. Not being able to engage in healthy behaviors due to these structural challenges can increase the likelihood of early disease onset, reduce active life expectancy (e.g., physical mobility), and increase the risk of mortality. Overall, the emphasis of the NSEC on health is important from a public health perspective since resource-poor environments can be potentially addressed through community-level interventions, including investments in public education, transportation, expansion of door-to-door services (e.g., Meals on Wheels), and affordable and quality housing to name a few.
Although research has overwhelmingly demonstrated that the NSEC is a crucial determinant of health, other neighborhood-related factors interplay with the NSEC, such as a neighborhood's age structure, racial composition, residential stability, and family structure that shape opportunities and health-enhancing resources made available for residents across communities. We provide a summary of how each of these neighborhood-level determinants potentially influences health outcomes and the risk of mortality.
## 2.2. Neighborhood age structure
The age structure of a neighborhood may be particularly important to older adults who age in place as it may influence the provision of health services and facilities (including Medicaid reimbursements), perceptions of neighborhood safety, and opportunities for social engagement [40]. Previous research has shown that neighborhoods with a high concentration of older adults are associated with better health among older adults, including those who are socioeconomically disadvantaged [41, 42]. Evidence suggests that the presence of older adults in the community facilitates social integration and cultivates social ties, mutual support, social cohesion, and perceived safety [43], which is independently associated with various population-level health outcomes, including mortality [44]. Several pathways have been hypothesized on how aspects of the social environment may influence health and mortality, including the impact of health behaviors and physiology (e.g., allostatic load) [44, 45]. Specifically, individuals with positive social ties are less likely to engage in smoking and drinking and are more likely to receive preventive health screenings (e.g., cancer screenings). In contrast, socially isolated individuals are more likely to have weakened immune function, cardiovascular disease, and cognitive impairment. Older adults with chronic health conditions, disabilities, who live alone, and have reduced social networks are at an increased risk of social isolation, which has been shown to negatively impact health and mortality.
With the population of Puerto *Rico is* rapidly aging—due to a combination of outmigration among younger cohorts of adults, declining fertility, and increased longevity—these demographic changes will challenge the ability of Puerto Rico and local communities to meet the growing demands of older adults, including care and quality of life, that may further strain the collective (and scarce) resources available [1, 3, 5, 46]. Specifically, increases in poverty and declining economic conditions across the archipelago, changes in the family structure, and the limited availability (and proximity) of individuals and/or services to provide long-term care for older adults in Puerto Rico (due in part to out migration of family and professionals) may result in poor health and an increased risk of mortality. Older Puerto Ricans, cognizant of these social realities have expressed concerns with loss of family cohesion and intergenerational support due to their children's search of economic opportunities outside of Puerto Rico [47]. This suggests that places in Puerto Rico that have a larger concentration of older adults, particularly in rural areas, may not have the resources necessary for older adults to successfully age in place.
## 2.3. Neighborhood racial composition
Neighborhood racial composition has been shown to be associated with poor health and an increased risk of mortality among older adults due in part to exposure of institutionalized and systemic anti-Black racism across the life course (48–50). A large body of research shows that Black (including African American and Afro-Latino) individuals in the US overwhelmingly reside in residentially segregated neighborhoods that are characterized by concentrated economic disadvantage, which is often associated with disinvestment of municipal resources (e.g., high-quality medical care), poorly maintained infrastructures (e.g., sidewalks and green spaces), and densely populated and subpar housing quality (51–53). These conditions stem from racial capitalism and environmental racism that intentionally create the underdevelopment of non-White spaces [54]. The purposeful underdevelopment of these communities results in unequal exposure to contextual health-related risks that over time exact wear and tear on the body, which contributes to a process of “weathering,” leading to physiological dysregulation, the early onset of disease and disability, and ultimately mortality [55].
Although Puerto Rico appears to have a more flexible attitude toward race (i.e., the concept of “racial democracy”) than the US, there is ample evidence documenting that racial minorities, immigrants (e.g., Dominican immigrants), and phenotypically dark-skinned individuals in Puerto Rico are stigmatized, discriminated against, and experience more socioeconomic disadvantage than their more socially advantaged counterparts (14, 56–59). Notably, Black communities in Puerto Rico4 are largely located along the coastal regions of the Puerto Rican archipelago—a legacy of plantation slavery—and are regions that exhibit lower levels of education, lower median household income, lower median housing values, and higher rates of poverty and unemployment relative to predominantly White communities in Puerto Rico [60]. Indeed, for Black Puerto Ricans, systemic and institutional racism across generations and across the lifespan have led to the inequitable access of social, educational, and material resources that have direct (e.g., access to health care) and indirect (e.g., stress and psychosocial resources) effects on health and mortality.
A community-based study of Puerto Rican adults aged 25–55 years in Guayama, Puerto Rico (a southeastern coastal town) found that respondents that are culturally defined as negro (Black) have higher systolic blood pressure (SBP) and diastolic blood pressure (DBP) than those who are classified as blanco (White) or trigueño (racially mixed)5 [61]. Additionally, Black Puerto Ricans who occupy higher socioeconomic status (SES) positions exhibit higher SBP and DBP relative to their Black counterparts in low SES contexts [61]. The authors posit that Black Puerto Ricans' chronic exposure to institutional and interpersonal discrimination may be linked to their adverse cardiovascular responses (i.e., high blood pressure). Thus, deeply embedded, and multiple dimensions of racism in Puerto Rico are associated with the pronounced residential segregation of Black Puerto Ricans that results in constrained access to resources and opportunities which affect health and mortality.
## 2.4. Neighborhood residential stability
Living in residentially stable neighborhoods is theorized to promote the health and wellbeing of its residents as it facilitates the development of interpersonal bonds and ties (i.e., social cohesion) that individuals can draw on in times of need (i.e., social support) and may encourage healthy behaviors, and extend longevity. However, a study by Ross et al. [ 62] found that residential stability was only associated with enhanced psychological wellbeing among residents in affluent neighborhoods. In contrast, residential stability did not benefit the mental health of residents in impoverished communities. Ross et al. posit that living in a poor, stable neighborhood does not confer mental health advantages since residents of these environments do not have the instrumental and material resources needed to mitigate the high levels of disorder in their communities. For example, the chronic stress associated with living long-term in a neighborhood where the streets are dirty, noisy, and dangerous repeatedly activates the stress response, which can contribute to blood pressure and brain changes associated with mental and physical health outcomes [63]. Thus, the effects of residential stability need to be considered in the context of a neighborhood's economic resources available.
Data from the U.S. Census and Puerto Rican Community Surveys show that Puerto Rican have high residential stability [64]; however, no study, to our knowledge, has examined whether neighborhood-level variation in residential stability is beneficial or detrimental to the health of older adults in Puerto Rico. The scant research that does exist on island-born Puerto Ricans residing in the mainland U.S. has shown that living in ethnically dense, low NSECs reported worse physical health than island-born Puerto Ricans living in other types of NSECs [65]. Individuals residing in ethnic enclaves tend to share common sociocultural characteristics (e.g., language and cultural background) and have strong social ties with community members, which have been found to be beneficial for health and mortality. However, enclaves that are formed involuntarily due to housing discrimination may not offer opportunities necessary for economic development at the individual and community levels. Given the high rates of poverty across the archipelago, we can infer that residential stability may not confer health benefits for Puerto Ricans who are living in disadvantaged NSECs.
## 2.5. Neighborhood family structure
Research on the association between neighborhood family structure and mortality is scarce; however, neighborhood family structure is related to the formation of social ties, which has been shown to have a robust association with extended longevity [66]. For example, residents in neighborhoods with high family dissolution (e.g., single-parent households) have lower participation rates in formal voluntary organizations and local affairs. These forms of participation provide opportunities for individuals to integrate within the larger community—additionally, neighborhoods with a high percentage of individuals living alone present opportunities for crime. Individuals who live alone are more likely to go outside alone, which increases the likelihood of a targeted crime (e.g., robbery). These incidents are more likely to instill perceptions of neighborhood disorder that may contribute to the dissolution of social ties and an increased risk of mortality.
Traditionally, Puerto Ricans are very family oriented, embody familism,6 and their families encompass extended and non-blood relatives (e.g., godparents and informally adopted children). The traditional structure of family dynamics in Puerto Rico has historically benefited older family members who often rely on family-based care. Recent research shows that intergenerational co-residence (e.g., children living with their older parents) is associated with increased functional and health support among older adults in Puerto Rico [67]. However, the outmigration of younger Puerto Ricans to the US mainland, has led to a significant reduction in the number of family members available to provide care for older adult family members. Moreover, with increasing numbers of Puerto Ricans migrating in search of economic and educational opportunities, we can expect a higher risk of social isolation and lower social participation among older adults, which may be detrimental to mental and physical health [3]. Thus, we can expect that communities in Puerto Rico with a high proportion of older adults that live alone and have a high proportion of single-parent households may be associated with worse health and an increased risk of mortality.
## 2.5.1. The present study
There is compelling theoretical and empirical evidence illustrating how various dimensions of the neighborhood environment co-occur and/or interact to influence the risk of mortality. Given the limited knowledge on the types of residential environments that older Puerto Ricans reside in, it is important to characterize the places where they live based on the factors discussed above. Previous research has shown that using latent class (or profile) models offers an efficient and statistically robust means of summarizing many indicators that constitute neighborhood risks and resources that are not captured by continuous scales or indices [68, 69]. We intend to employ this method to classify how various neighborhood characteristics cluster together to create distinct neighborhood typologies that capture risk for all-cause mortality.
## 3.1.1. Individual-level data
This study used data from the Puerto Rico Elderly Health Conditions Project (PREHCO), a representative longitudinal cohort study of community-dwelling Puerto Ricans aged 60 and older residing on the archipelago's main island that began in May 2002, with follow-up interviews completed in 2006–2007 and 2021–2022 (the data and documentation are not yet publicly available) [70]. Response rates for the first two waves of PREHCO are high (>$90.0\%$). The 4,291 respondents included in the PREHCO baseline sample were derived from a multistage, stratified sample of older adults, including oversampling in regions heavily populated by Afro-descendant individuals (e.g., residents in Loíza) and individuals over 80 years of age. Face-to-face interviews were conducted with each respondent in Spanish or with a proxy if a respondent had cognitive limitations. Additional information on the study and its design is provided elsewhere (71–73).
PREHCO obtained mortality information on respondents using a combination of the National Death Index (NDI) mortality data and PREHCO-identified deaths using reports by family members or the Puerto Rican death registry. Respondents were matched to the National Death Index (NDI) from their first PREHCO interview in 2002–2003 to December 2020, using the available matching variables in the PREHCO study, including social security number (SSN), name (first, middle, father's last name and/or mother's last name), birth date (month and year), and sex (female or male). We would like to note that many Puerto Ricans use two surnames, which adds to the difficulty in NDI matching. Thus, the investigators examined different combinations of respondents' last names to increase the likelihood of a positive match for those with two last names. Additional deaths were identified through November 2021 using family reports or the Puerto Rican death registry. The data file comprising the currently restricted PREHCO mortality database contains the PREHCO respondent's case identification number, the mortality status of the respondent (presumed dead or alive), year of death, month of death, day of death (for some), and cause of death (for most respondents). Two thousand eight hundred and thirty-two all-cause presumed deaths were identified from the cohort of 4,291 PREHCO respondents.
## 3.1.2. Neighborhood-level data
Data on baseline neighborhood characteristics were constructed from the 2000 Decennial US Census at the block-group level downloaded from Social Explorer and were linked with the PREHCO data [74]. Census block groups typically include 600 to 3,000 people and is the smallest geographical unit for which the US Census Bureau publishes sample data. PREHCO respondents were linked to their affiliated census block group by linking their records in the public-use PREHCO to the restricted-use PREHCO geographic data file. These data were then merged with the 2000 US *Census data* using Federal Information Processing Standard (FIPS) codes to link the files. Out of the 2,477 unique census block group identifiers for Puerto Rico in 2000,7 we identified 233 unique census block groups in which PRECHO respondents resided at the time of the baseline interview, with 1–47 observations in each block group.
## 3.1.3. Sample selection
The baseline PREHCO cohort sample consisted of 4,291 unique respondents aged 60 and older. Given the design of the present study, we focused on individuals who were able to complete the full interview at baseline ($$n = 3$$,713). Respondents that needed proxies to do the interview were not asked health-related questions relevant to the present study ($$n = 578$$). Furthermore, we excluded respondents ($$n = 24$$) in neighborhoods with <5 individuals in any given block group to minimize statistical bias [75]. Lastly, we excluded ~$6\%$ of participants ($$n = 220$$) due to missingness on baseline covariates. The variables with the highest prevalence of missing values were body mass index (BMI; $5\%$) and receipt of government-related income and services ($1\%$). The final analytical sample included 3,469 participants.
Participants excluded from the analytical sample were more likely to be older (76.8 vs. 70.3 years), less likely to be married or partnered (38.8 vs. $53.2\%$), reported lower levels of education (6.1 vs. 8.3 years), and were more likely to receive government-related income and services (see Supplementary Table 1). Additionally, excluded participants were less likely to be obese, current smokers, and physically active. Excluded participants were also more likely to report chronic conditions and disability. We caution readers that the health profiles of excluded participants may be underestimated since proxy interviews were not asked all of the health-related questions. Thus, our analytical sample of community-dwelling older Puerto *Ricans is* relatively healthier than the general population of older adults in Puerto Rico. A detailed scheme showing the exclusion criteria and the analytic sample inclusion is provided in Supplementary Figure 1.
Additionally, given that measures included in our analysis are time varying, we briefly describe changes in sample characteristics for Wave 2 of PREHCO. From our analytical sample of 3,469 participants identified in Wave 1, 941 respondents ($27\%$) did not have information reported in Wave 2 relevant to the analysis, including 226 proxy interviews, 27 respondents that became institutionalized, 319 that were lost at follow-up, and 369 respondents that were reported dead. To keep all respondents in our analysis, we conducted multiple imputation using chained equations (MICE) for missing data at Wave 2 using the mi suite of commands in Stata [76]. We used the distribution of the observed data from Waves 1 and 2 to estimate a set of plausible values for the missing data in Wave 2. We then used Bodner's approach of generating the number of imputed data sets equivalent to the total percent missing and Rubin's rule for combining across data sets (77–82).
## 3.2.1. Mortality
The outcome of interest is all-cause mortality from May 2002 to November 2021. We calculated the time to censoring or death from the year of the interview to the year of death or censoring. For those who did not die in the interval, the censoring date was November 2021. We used years as the time metric.
## 3.2.2. Individual-level characteristics
Three groups of individual-level variables were considered as potential confounders in examining the role of neighborhood context and all-cause mortality—demographic, socioeconomic, and health characteristics.
## 3.2.2.1. Demographic variables
Age is measured in continuous years. We also included an age squared term, so we can model more accurately the effect of age rather than assuming the effect is linear for all ages. Sex was dichotomized as male or female. Marital status was dichotomized as married or partnered vs. never married, widowed, separated, or divorced. A dichotomous indicator for whether the respondent had moved from their main residence reported at baseline was also included.
## 3.2.2.2. Socioeconomic variables
Educational attainment is measured as continuous years of education completed. Given that PREHCO has limited variables for assessing individual income (e.g., not having combined household annual income or poverty thresholds) and wealth (e.g., not having a standardized measure of all assets and debt), we used indirect measures of income, including whether a respondent reports having difficulty paying for their daily necessities (categorized as never, sometimes, and often), whether they receive income from social welfare or the department of the family8 (yes/no), whether they receive income from the nutritional assistance program (yes/no), and whether they have government-sponsored health insurance (excluding Medicare; yes/no). Given the strong association between individual-level socioeconomic position and mortality, it is crucial to adjust for individual socioeconomic measures to ensure the validity of neighborhood-level factors [83].
## 3.2.2.3. Health characteristics
We included indicators related to the morbidity process such as health behaviors, health conditions, and disability [84]. Health behaviors included obesity, current smoking status, and physical activity. Dichotomous indicators were used to classify respondents as obese (i.e., body mass index of ≥30 kg/m2), for whether the respondent reported being a current smoker at the time of the interview (yes/no), and whether they engaged in either moderate or vigorous physical activity at least three times per week (yes/no).
Health conditions included cardiometabolic diseases, other chronic conditions, and severe depression. Cardiometabolic diseases were a sum of whether the respondent self-reported heart problems (e.g., coronary heart disease, congestive heart failure, and heart attack), stroke, hypertension (including medication use) and diabetes (including medication use), ranging from 0 to 4. Other chronic conditions were a sum of self-reported cancer, lung disease, and arthritis, ranging from 0 to 3. We used the geriatric depression scale in its 15-item version (GSD-15) to classify respondents as having severe depression [85]. Possible scores range from 0 (no depression) to 15 points (severe depression). Respondents were classified as having severe depression if they scored ≥10 points.
Disability was based on whether a respondent reported limitations in activities of daily living (ADLs) and instrumental activities of daily living (IADLs). ADLs are a continuous measure ranging from 0 to 6 and included difficulty with bathing, eating, dressing, walking across a room, getting in and out of bed, and using the toilet [86]. IADLs are a continuous measure ranging from 0 to 7 and included difficulty with using the telephone, managing transportation, buying food or clothing, preparing meals, doing household tasks, taking medications, and managing finances.
## 3.2.3. Neighborhood-level characteristics
We included variables at the block group level that are theoretically related to and have been identified in previous studies as being associated with all-cause mortality. Neighborhood characteristics included 19 indicators related to the neighborhood constructs of socioeconomic status, household composition, minority status, and housing and transportation. These indicators included the proportion of the population living in a rural area,9 Black residents, residents aged ≥65 years, older adults living alone, residents that lived in the same house past 5 years (residential stability), residents with <9 years of education, residents aged ≥16 years unemployed, residents aged ≥16 years employed in management, professional, and related occupations, households with income ≥$40,000, households with interest, dividend, or rental income, households with public assistance income, residents below $150\%$ of the poverty threshold, single-parent households with children <18 years of age, renter-occupied housing units, residents living in crowded housing units, occupied housing units without complete plumbing, occupied housing units without a telephone, occupied housing units without a motor vehicle, and homes valued ≥150 k.
## 3.3. Statistical analysis
A latent profile analysis (LPA) was conducted using the gsem feature on Stata to characterize the types of neighborhood environments that older Puerto Ricans resided in at baseline [88]. LPA is a semi-parametric finite mixture model that identifies homogenous subgroups based on common characteristics, creating mutually exclusive and exhaustive latent classes. LPA sorts data using posterior probabilities that calculate the probability of membership in each latent class given. Unlike other agglomerative approaches, such as cluster analysis and factor analysis, LPA is a non-parametric statistical technique that relaxes assumptions about normality and linearity in the variables used in the analyses, making LPA ideal for analyzing neighborhood-level characteristics since they do not have normal distributions. We selected the class solution that best represented the data using a combination of model fit statistics, the interpretability of the classes that emerged, and sample size per class once combined with the PREHCO data. When comparing class solutions based on model fit statistics, generally, lower values of the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) are preferred [89]; and entropy with values approaching 1, indicating a clear delineation of classes, are preferred [90].
Next, we described the characteristics of the PREHCO analytic sample by each neighborhood cluster that emerged. Means and percentages were calculated using the xtsum and xttab features in Stata to account for the multilevel design and repeat observations.
Lastly, we estimated hazard ratios (HRs) and $95\%$ confidence intervals (CIs) for all-cause mortality by applying a multilevel mixed-effects parametric survival model with a Weibull distribution and Berndt–Hall–Hall–Hausman (BHHH) optimization algorithm using the mestreg feature in Stata. We modeled our data with a three-level hierarchical structure: respondents (level 1) nested within each wave (level 2) and census block groups (level 3). Time-to-event was defined as the elapsed time, in years, from the baseline interview to the date of death or the end of the study follow-up, whichever came first. When we fitted a model, we included the neighborhood clusters and controlled for individual-level demographic variables: sex, age, age squared, and marital status (Model 1). Next, we proceeded to add individual-level socioeconomic indicators: education, income from social welfare, income from the nutritional assistance program, and government-sponsored health insurance (Model 2). Lastly, we added individual-level health characteristics: obesity, smoking, physical activity, cardiometabolic conditions, other chronic conditions, severe depression, and disability (i.e., ADLs and IADLs; Model 3).
All data wrangling, visualization, and analyses were conducted in Stata/MP version 17.0 [91]. The data were weighted using PREHCO-provided sampling weights to ensure the representativeness of the PREHCO survey and to account for the sampling design to get reliable statistical estimates. The study protocol was deemed exempt by the Institutional Review Board at Syracuse University.
## 4.1. Neighborhood clusters derived from the LPA
Latent profile models were fit based on 19 block-group level indicators using the 2,477 observations (i.e., unique block groups) available in the 2000 US Census for Puerto Rico, ranging from two to seven classes. Based on the model fit statistics, sample size, and accounting for interpretability, we chose the five-class model as having the best fit for further analysis (see Supplementary Tables 2–7). A summary of the five-class solution of neighborhood clusters is presented in Table 1.
**Table 1**
| Unnamed: 0 | Unnamed: 1 | Urban low deprivation | Urban low-moderate deprivation | Rural moderate deprivation | Urban high-moderate deprivation | Urban high deprivation |
| --- | --- | --- | --- | --- | --- | --- |
| | All block groups | | | | | |
| Probability (class) | | 0.079 | 0.323 | 0.059 | 0.47 | 0.068 |
| Probability of | | | | | | |
| Rural | 0.055 | 0.0 | 0.005 | 0.679 | 0.029 | 0.0 |
| Black | 0.082 | 0.04 | 0.083 | 0.04 | 0.086 | 0.129 |
| Adults ≥65 years of age | 0.124 | 0.172 | 0.135 | 0.102 | 0.118 | 0.081 |
| Older adults living alone | 0.328 | 0.323 | 0.302 | 0.299 | 0.338 | 0.417 |
| Lived in same house past 5 years | 0.727 | 0.666 | 0.714 | 0.77 | 0.746 | 0.688 |
| <9 years of education | 0.259 | 0.072 | 0.175 | 0.382 | 0.321 | 0.336 |
| Unemployed | 0.207 | 0.061 | 0.137 | 0.265 | 0.244 | 0.404 |
| Employed in management and professional occupations | 0.252 | 0.516 | 0.3 | 0.208 | 0.196 | 0.143 |
| Households with ≥ $40,000 income | 0.147 | 0.49 | 0.201 | 0.067 | 0.078 | 0.04 |
| Households with interest, dividend, or rental income | 0.048 | 0.188 | 0.053 | 0.024 | 0.029 | 0.02 |
| Households with public assistance income | 0.205 | 0.044 | 0.117 | 0.29 | 0.249 | 0.425 |
| Population living below 150% of the poverty threshold | 0.262 | 0.076 | 0.153 | 0.349 | 0.314 | 0.569 |
| Single-parent households with children <18 years of age | 0.190 | 0.117 | 0.168 | 0.131 | 0.189 | 0.436 |
| Renter-occupied housing units | 0.288 | 0.226 | 0.249 | 0.19 | 0.271 | 0.751 |
| Living in crowded housing | 0.194 | 0.095 | 0.156 | 0.259 | 0.217 | 0.269 |
| Homes without complete plumbing | 0.054 | 0.011 | 0.025 | 0.08 | 0.071 | 0.094 |
| Homes without a telephone | 0.241 | 0.047 | 0.137 | 0.346 | 0.305 | 0.422 |
| Homes without a motor vehicle | 0.302 | 0.158 | 0.215 | 0.294 | 0.339 | 0.635 |
| Homes valued ≥ $150,000 | 0.124 | 0.556 | 0.108 | 0.081 | 0.072 | 0.099 |
| Number of census block groups | 2477 | 236.0 | 783.0 | 143.0 | 1149.0 | 166.0 |
We labeled the first cluster Urban Low Deprivation (Class 1), representing $7.9\%$ of census block groups in Puerto Rico ($$n = 236$$). This cluster was characterized by block groups that were almost all urban, had the lowest proportion of Black individuals present, the highest proportion of older adults present, very favorable socioeconomic conditions, stable family structure, and favorable housing features relative to the other classes.
The second cluster was labeled Urban Moderate-Low Deprivation (Class 2) and represented $32.3\%$ census block groups in Puerto Rico ($$n = 783$$). This cluster was characterized by block groups that were like the previous neighborhood cluster but notably had lower socioeconomic conditions, family structures that were somewhat less stable, and less favorable housing conditions compared to the first neighborhood cluster.
We labeled the third cluster Rural Moderate Deprivation (Class 3), representing $5.9\%$ of census block groups in Puerto Rico ($$n = 143$$). This cluster was characterized by block groups that were predominantly rural, had a low proportion of Black individuals present, the lowest proportion of older adults living alone, unfavorable socioeconomic conditions, stable family structure, and unfavorable housing conditions relative to previous classes.
We labeled the fourth cluster Urban Moderate-High Deprivation (Class 4), representing $47.0\%$ of census block groups in Puerto Rico ($$n = 1$$,149). This cluster was characterized by block groups that were predominantly urban, a higher proportion of older adults living alone, less favorable socioeconomic conditions, family structures that were less stable, and less favorable housing conditions relative to previous classes.
The final cluster represented $6.8\%$ of census block groups in Puerto Rico ($$n = 166$$) and was labeled Urban High Deprivation (Class 5). This cluster was characterized by block groups that were urban, had the highest proportion of Black individuals present, the lowest proportion of older adults present yet the highest proportion of older adults living alone, very unfavorable socioeconomic conditions, unstable family structure, and unfavorable housing conditions relative to the other classes.
To better contextualize where these neighborhood clusters are geographically located in Puerto Rico, we provide a map of the neighborhood clusters identified in Puerto Rico by census block group (Figure 1). Neighborhoods that were classified as Urban Low Deprivation and Urban High Deprivation were mainly found in the municipalities of San Juan (the largest municipality), Ponce (the largest municipality outside the San Juan area), and Mayagüez (the largest municipality on the west side of the island). Neighborhoods characterized as Urban Low-Moderate Deprivation tended to be clustered outside larger municipalities (e.g., outside of San Juan). Neighborhoods characterized as Urban High-Moderate Deprivation and Rural Moderate Deprivation were distributed across the archipelago. Notably, neighborhoods in the Rural Moderate Deprivation cluster tended to be in the mountainous regions of the archipelago (i.e., the central part of Puerto Rico).
**Figure 1:** *The distribution of neighborhood clusters by year 2000 census block groups in Puerto Rico. Data source: 2000 U.S. Decennial Census.*
## 4.2. Characteristics of older Puerto Ricans by neighborhood cluster
The summary statistics of the PREHCO study sample by neighborhood cluster are presented in Table 2. We find that PREHCO respondents who resided in neighborhoods classified as Urban Low Deprivation (Class 1; $$n = 224$$), Urban Low-Moderate Deprivation (Class 2; $$n = 1$$,153), and Rural Moderate Deprivation (Class 3; $$n = 153$$) had a lower proportion of deaths over the study period relative to those residing in more disadvantaged neighborhood contexts. Older Puerto Ricans residing in the most advantaged neighborhood contexts included a higher proportion of female respondents, were older, less likely to move residences between waves, more educated, did not suffer from economic deprivation, and were less likely to report cardiometabolic conditions and disability. In contrast, respondents residing in the Urban High Deprivation (Class 5; $$n = 281$$) cluster had a higher proportion of individuals who died over the study period. The composition of this neighborhood cluster included a lower proportion of females, were younger, were the least likely to be married or partnered, more likely to move residences between waves, were less educated, suffered from economic deprivation, and were more likely to be classified with severe depression. Respondents in the Rural Moderate Deprivation (Class 3; $$n = 153$$) and Urban High-Moderate Deprivation (Class 4; $$n = 1$$,658) neighborhood clusters had similar demographic compositional profiles. However, respondents in the Rural Moderate Deprivation cluster had the lowest years of education attained relative to the other neighborhood clusters and had worse socioeconomic profiles relative to the Urban High-Moderate Deprivation cluster. Moreover, respondents in the Rural Moderate Deprivation cluster had relatively healthier behavioral profiles (e.g., more physically active, and lower proportion of obese individuals and current smokers) compared to the Urban High-Moderate Deprivation cluster.
**Table 2**
| Unnamed: 0 | Unnamed: 1 | Neighborhood cluster of residence | Neighborhood cluster of residence.1 | Neighborhood cluster of residence.2 | Neighborhood cluster of residence.3 | Neighborhood cluster of residence.4 |
| --- | --- | --- | --- | --- | --- | --- |
| | | Urban low deprivation | Urban low-moderate deprivation | Rural moderate deprivation | Urban high-moderate deprivation | Urban high deprivation |
| | Full sample | Class 1 | Class 2 | Class 3 | Class 4 | Class 5 |
| | % or mean ± SD | % or mean ± SD | % or mean ± SD | % or mean ± SD | % or mean ± SD | % or mean ± SD |
| Individual-level demographic variables | Individual-level demographic variables | Individual-level demographic variables | Individual-level demographic variables | Individual-level demographic variables | Individual-level demographic variables | Individual-level demographic variables |
| Presumed dead | 62.3 | 62.5 | 58.6 | 59.5 | 63.8 | 69.4 |
| Female | 59.7 | 70.5 | 61.9 | 51.0 | 57.1 | 61.9 |
| Age (years) | 73.4 ± 8.3 | 76.4 ± 9.1 | 73.1 ± 8.3 | 73.1 ± 8.3 | 73.1 ± 8.2 | 74.1 ± 8.3 |
| Married or partnered | 40.9 | 32.2 | 43.7 | 45.4 | 42.0 | 27.0 |
| Moved from baseline residence | 9.7 | 8.7 | 9.6 | 11.1 | 9.0 | 14.1 |
| Individual-level socioeconomic variables | Individual-level socioeconomic variables | Individual-level socioeconomic variables | Individual-level socioeconomic variables | Individual-level socioeconomic variables | Individual-level socioeconomic variables | Individual-level socioeconomic variables |
| Education (years) | 8.1 ± 4.6 | 12.0 ± 3.8 | 9.4 ± 4.4 | 6.0 ± 3.9 | 7.0 ± 4.3 | 7.2 ± 4.4 |
| Difficulty with daily needs being met | | | | | | |
| Never | 52.8 | 69.6 | 56.8 | 39.0 | 50.3 | 45.5 |
| Sometimes | 34.8 | 23.1 | 33.0 | 45.7 | 35.9 | 38.7 |
| Often | 12.4 | 7.3 | 10.2 | 15.3 | 13.8 | 15.9 |
| Receives income from social welfare/department of the family | 3.4 | 1.7 | 1.7 | 4.2 | 3.9 | 8.5 |
| Receives income from the nutritional assistance program | 29.0 | 10.6 | 19.6 | 48.4 | 33.8 | 43.6 |
| Has government-sponsored health insurance | 50.2 | 13.0 | 35.0 | 67.3 | 61.2 | 68.0 |
| Individual-level health variables | Individual-level health variables | Individual-level health variables | Individual-level health variables | Individual-level health variables | Individual-level health variables | Individual-level health variables |
| Obese (BMI ≥ 30 kg/m2) | 27.4 | 27.9 | 29.5 | 21.3 | 26.5 | 26.5 |
| Current smoker | 6.9 | 4.5 | 5.0 | 7.2 | 8.5 | 6.9 |
| Physically active | 57.4 | 63.0 | 60.1 | 61.5 | 54.7 | 55.6 |
| Cardiometabolic diseases (0–4) | 1.1 ± 0.9 | 0.9 ± 0.8 | 1.1 ± 0.9 | 1.2 ± 1.0 | 1.1 ± 0.9 | 1.1 ± 0.9 |
| Other chronic conditions (0–3) | 0.4 ± 0.6 | 0.5 ± 0.6 | 0.4 ± 0.6 | 0.4 ± 0.6 | 0.4 ± 0.6 | 0.4 ± 0.6 |
| Severe depression (GDS ≥ 10) | 7.6 | 6.2 | 6.5 | 6.4 | 8.1 | 11.1 |
| Activities of daily living (0–5) | 0.3 ± 0.9 | 0.2 ± 0.8 | 0.3 ± 0.9 | 0.3 ± 0.8 | 0.4 ± 0.9 | 0.4 ± 1.0 |
| Instrumental activities of daily living (0–5) | 0.7 ± 1.3 | 0.6 ± 1.2 | 0.6 ± 1.2 | 0.7 ± 1.3 | 0.7 ± 1.3 | 0.8 ± 1.3 |
| N | 3469 | 224 | 1153 | 153 | 1658 | 281 |
## 4.3. Association of neighborhood clusters with all-cause mortality
The results of the fitted multilevel survival models are summarized in Table 3. Hazard ratios (HR) are presented with $95\%$ confidence intervals (CI). Hazard ratios >1 indicate that the mortality hazard is increasing, whereas hazard ratios <1 indicate that the mortality hazard is decreasing. The results of Model 1 (our base model) show that neighborhood clusters are associated with an increased hazard in all-cause mortality among older Puerto Ricans. Older adults that resided in the Urban Low-Moderate Deprivation [HR: 2.94; $95\%$ CI (1.33, 6.49)], Rural Moderate Deprivation [HR: 2.60; $95\%$ CI (1.10, 6.13)], Urban High-Moderate Deprivation [HR: 3.55; $95\%$ CI (1.58, 7.94)], and Urban High Deprivation [HR: 5.59; $95\%$ CI (2.24, 13.96)] clusters at baseline had higher mortality rates over the study period relative to the Urban Low Deprivation cluster. We also observed that female and married or partnered respondents had lower mortality rates over the study period, and that increasing age was associated with higher mortality rates, which is consistent with results from studies in high- and middle-income countries.
**Table 3**
| All-cause mortality | Model 1 | Model 1.1 | Model 1.2 | Model 2 | Model 2.1 | Model 2.2 | Model 3 | Model 3.1 | Model 3.2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| All-cause mortality | HR | HR | 95% CI | HR | HR | 95% CI | HR | HR | 95% CI |
| Neighborhood-level variables | Neighborhood-level variables | Neighborhood-level variables | Neighborhood-level variables | Neighborhood-level variables | Neighborhood-level variables | Neighborhood-level variables | Neighborhood-level variables | Neighborhood-level variables | Neighborhood-level variables |
| Neighborhood clusters (ref = urban low deprivation) | | | | | | | | | |
| Urban low-moderate deprivation | 2.94 | ** | [1.33, 6.49] | 2.30 | * | [1.07, 4.97] | 1.86 | | [0.92, 3.78] |
| Rural moderate deprivation | 2.60 | * | [1.10, 6.13] | 1.83 | | [0.78, 4.30] | 1.76 | | [0.80, 3.88] |
| Urban high-moderate deprivation | 3.55 | ** | [1.58, 7.94] | 2.80 | * | [1.24, 6.33] | 2.16 | * | [1.02, 4.56] |
| Urban high deprivation | 5.59 | *** | [2.24, 13.96] | 4.74 | ** | [1.82, 12.30] | 3.45 | ** | [1.39, 8.54] |
| Individual-level demographic variables | Individual-level demographic variables | Individual-level demographic variables | Individual-level demographic variables | Individual-level demographic variables | Individual-level demographic variables | Individual-level demographic variables | Individual-level demographic variables | Individual-level demographic variables | Individual-level demographic variables |
| Female (ref = male) | 0.53 | *** | [0.42, 0.66] | 0.53 | *** | [0.42, 0.67] | 0.51 | *** | [0.39, 0.67] |
| Age | 1.37 | *** | [1.24, 1.52] | 1.47 | *** | [1.29, 1.66] | 1.47 | *** | [1.29, 1.68] |
| Age squared | 1.00 | *** | [1.00, 1.00] | 1.00 | *** | [1.00, 1.00] | 1.00 | *** | [1.00, 1.00] |
| Married or partnered | 0.75 | ** | [0.63, 0.90] | 0.78 | ** | [0.67, 0.92] | 0.80 | ** | [0.69, 0.93] |
| Moved from baseline residence | 1.36 | | [0.84, 2.21] | 1.29 | | [0.80, 2.07] | 1.20 | | [0.80, 1.78] |
| Individual-level socioeconomic variables | Individual-level socioeconomic variables | Individual-level socioeconomic variables | Individual-level socioeconomic variables | Individual-level socioeconomic variables | Individual-level socioeconomic variables | Individual-level socioeconomic variables | Individual-level socioeconomic variables | Individual-level socioeconomic variables | Individual-level socioeconomic variables |
| Education (years) | | | | 0.97 | * | [0.94, 0.99] | 0.98 | | [0.96, 1.01] |
| Difficulty with daily needs being met (ref = often) | | | | | | | | | |
| Sometimes | | | | 0.90 | | [0.68, 1.18] | 1.01 | | [0.78, 1.30] |
| Never | | | | 1.20 | | [0.85, 1.69] | 1.31 | | [0.95, 1.80] |
| Receives income from social welfare/department of the family | | | | 0.98 | | [0.63, 1.50] | 1.10 | | [0.72, 1.68] |
| Receives income from the nutritional assistance program | | | | 0.78 | * | [0.63, 0.97] | 0.88 | | [0.73, 1.06] |
| Has government-sponsored health insurance | | | | 1.36 | ** | [1.12, 1.65] | 1.19 | | [0.99, 1.44] |
| Individual-level health variables | Individual-level health variables | Individual-level health variables | Individual-level health variables | Individual-level health variables | Individual-level health variables | Individual-level health variables | Individual-level health variables | Individual-level health variables | Individual-level health variables |
| Obese (BMI ≥ 30 kg/m2) | | | | | | | 0.95 | | [0.80, 1.13] |
| Current smoker | | | | | | | 1.53 | *** | [1.29, 1.81] |
| Physically active | | | | | | | 0.72 | ** | [0.59, 0.89] |
| Cardiometabolic diseases (0–4) | | | | | | | 1.19 | *** | [1.09, 1.29] |
| Other chronic conditions (0–3) | | | | | | | 1.33 | ** | [1.09, 1.63] |
| Severe depression (GDS ≥ 10) | | | | | | | 0.75 | | [0.53, 1.07] |
| Activities of daily living (0–5) | | | | | | | 1.08 | | [0.96, 1.21] |
| Instrumental activities of daily living (0–5) | | | | | | | 1.12 | ** | [1.03, 1.21] |
Controlling for individual-level socioeconomic characteristics (Model 2) reduced the HR gradient of all the neighborhood clusters associated with all-cause mortality observed in Model 1. For example, adjusting for individual-level socioeconomic characteristics decreased the HR by ~32–$37\%$ for the Urban Low-Moderate Deprivation, Urban High-Moderate Deprivation, and Urban High Deprivation clusters but they remained significantly associated with all-cause mortality. Conversely, adjusting for individual-level socioeconomic characteristics reduced the Rural Moderate Deprivation cluster to non-significance [HR: 1.83; $95\%$ CI (0.78, 4.30)]. Furthermore, our results indicate that higher levels of education and receiving nutritional assistance was associated with lower mortality over the study period, whereas reporting government-sponsored health insurance was associated with higher mortality over the study period.
Additionally controlling for individual-level health characteristics (Model 3) further reduced (changed) the HR for all the neighborhood clusters. The Urban High-Moderate Deprivation and Urban High Deprivation clusters exhibited an ~20–$25\%$ decrease (change) in the HR and were still significantly associated with all-cause mortality. For the Urban Low-Moderate Deprivation cluster, adjusting for individual-level health characteristics reduced the association to non-significance [HR: 1.86; $95\%$ CI (0.92, 3.78)]. We also found that current smoker status, and reporting affirmative to individual items for cardiometabolic disease, other chronic conditions, and IADL limitations increased the hazard by 53, 19, 33, and $12\%$, respectively. In contrast, respondents that reported engaging in physical activity decreased the hazard by $28\%$.
Post-estimation tests of coefficients from the final model indicated that older Puerto Ricans residing in the Urban High Deprivation cluster were at the highest risk of death over the study period compared to all the other neighborhood clusters in Puerto Rico. Smoothed hazard estimates of the risk of mortality by neighborhood cluster demonstrating this are shown in Figure 2.
**Figure 2:** *Smoothed hazard estimates of all-cause mortality by neighborhood cluster.*
## 5. Discussion
Using a population-based sample of community-residing individuals aged 60 and older in Puerto Rico, this study builds on prior literature documenting the effect of neighborhood environments on all-cause mortality among older adults. Using latent profile analysis to classify neighborhoods based on indicators related to the constructs of socioeconomic status, household composition, minority status, and housing and transportation resulted in five neighborhood clusters with varying patterns of social (dis)advantage: Urban Low Deprivation, Urban Low-Moderate Deprivation, Rural Moderate Deprivation, Urban High-Moderate Deprivation, and Urban High Deprivation. Our results show that older Puerto Ricans residing in neighborhoods classified as Urban High Deprivation and Urban High-Moderate Deprivation in Puerto Rico (over half of our analytical sample) exhibited an increased risk of mortality over the 19-year study period after adjustment for individual-level covariates. This suggests that a high concentration of unsupportive contexts for healthy aging increases the risk of premature death. This finding is consistent with other studies in the US and Latin America that have found exposure to disadvantaged neighborhood contexts to be a robust predictor of poor health outcomes and increased risk of mortality [27, 28, 92].
In contrast, residing in neighborhoods classified as Rural Moderate Deprivation and Urban Low-Moderate Deprivation was associated with all-cause mortality among older adults, however the association was attenuated once individual-level socioeconomic factors and health-related characteristics were accounted for, respectively. Previous research has shown that individuals residing in rural communities in the US tend to be less educated, have higher rates of poverty, and are less likely to have health insurance than those residing in urban communities [93]. In the case of older Puerto Ricans that reside in Rural Moderate Deprivation contexts, our results indicate that the socioeconomic composition of individuals residing within these communities is a more important risk factor for all-cause mortality than the deprivation that exists at the community level. Furthermore, we can infer that older adults with socioeconomic or material advantages living in these communities can alleviate some of the adverse effects and exposures associated with these environments, which may be a family-level social selection mechanism that is unaccounted for in this study [94]. It is possible that individuals with economic advantages residing in rural areas in Puerto Rico have been there for generations and chose to stay for reasons related to social, cultural, human, and financial capital [95]. For older adults in Urban Low-Moderate Deprivation neighborhood contexts, we can infer that these individuals may self-select into neighborhoods with access to a wealth of social and material resources, such as having access to preventive health care services, and access to medical care that allows for the management of age-related diseases, which can increase longevity.
With the combination of rapid aging and compounding disasters in Puerto Rico, it is imperative to document and account for multilevel determinants of mortality for older adults across later stages of the life course. From a risk environment perspective, there is a need to delineate the environmental factors associated with the risk of mortality, such as the types of environments (e.g., physical, social, economic, and policy) and level of environmental influence (micro and macro), because understanding the places in which harm is produced and reduced offers a broader vision for intervention [96]. For instance, a recent review found that the long-term impacts of air pollution, heavy metals, chemicals, ambient temperature, noise, radiation, and urban residential surroundings are associated with increased mortality [97]. Since aging is an active response to “weathering,” we must consider how these environmental exposures are related to increases in inflammation, metabolic dysregulation, and genetic damage across the life span, increasing mortality risk. Specific to older adults, as their biological capacity declines with normal aging, the effects of deleterious environmental exposures may be exacerbated among individuals who enter the later stage of the life course with pre-existing health conditions and disabilities [98]. Indeed, the biophysiological mechanisms underlying the neighborhood-mortality association are just beginning to be elucidated. Nonetheless, evidence does show that there are links between social factors, physiological dysregulation, and adult mortality [99]. Future data collection efforts of older adults in Puerto Rico should include measures that represent multiple regulatory physiological systems (e.g., cardiovascular, metabolic, and immune) to comprehensively capture neighborhood influences on biology, and their contribution to health and mortality risks.
Considering Puerto Rico's socio-structural reality—including high levels of poverty, a deficient infrastructure, a fragile healthcare system, the dismantling of the public education system, and hazardous environmental exposures—a health disparities framework was established to reflect historical and sociocultural influences of the Puerto Rican population [100]. We can draw on this framework to highlight how present disparities are rooted in historical, cultural, political, and economic factors that influence biology and behaviors and to illustrate the complex relationship between the neighborhood environment and mortality. For example, a recent study found that Puerto Rican adults residing in San Juan had multiple lifestyle risk factors and cardiometabolic conditions and recommended targeted efforts to improve the health care system and material resources among socially disadvantaged populations [101]. While increasing material resources among older residents in the most disadvantaged neighborhood contexts may ease some of the challenges of aging in place, it does not get at the systemic causes of these challenges. For instance, the ports of Puerto Rico are controlled by mainland US agencies, leading to the high costs of (healthy) food on the archipelago [100]. As a result, some older adults may forgo eating foods that may improve or better manage their health and decrease their mortality risk since they must make constrained choices on what to spend their limited incomes on. Thus, we recommend that policymakers, health care providers, and leaders across industries to [1] understand how individual health and mortality is embedded within larger social, cultural, structural, and historical contexts, and [2] make concerted efforts to reach out to residents living in disadvantaged community contexts to understand better what they need to successfully age in place in Puerto Rico. A study of residents in La Perla (an informal shantytown in San Juan with a high proportion of older adults) found that despite living in socially and economically disadvantaged residential environments, the residents reported high residential satisfaction because they built their neighborhood environment according to their community needs and have a network of support [102]. This suggests that community engagement is essential to identify the health and social needs of Puerto Rican older adults and improve health in neighborhoods directly affected by inequities [103].
## 5.1. Limitations
Several limitations of this research should be acknowledged. First, we must recognize the physical resilience and robustness of Puerto Ricans who survived to older ages (i.e., aged ≥ 60 years) who were able to participate in the PREHCO study. Previous research has found that survival bias (or, selective survival) can attenuate associations between harmful exposures and age-related diseases, suggesting that the effects of harmful neighborhood environments may not be as pronounced among older adults and are likely underestimated [104].
Second, there are limitations associated with the operationalization of neighborhoods. We selected the smallest census unit for which we could obtain data—census block groups—to conceptualize neighborhoods in this study, an improvement from previous studies that have used census tracts as a neighborhood unit. However, recent research has emerged on the importance of activity spaces—defined as the places individuals encounter due to their day-to-day activities, which may not necessarily include their residential areas [105]. Older adults may have activity spaces in more favorable or less advantageous environments relative to their residential settings that affect resources, exposures, benefits, and risks that have multifaceted effects on health and mortality. Future data collection efforts should consider capturing mobility and location information on older adults in Puerto Rico.
Third, using LPA to classify neighborhood clusters depends on the measures included to identify class types. Our findings may be biased by the exclusion of neighborhood characteristics important for distinguishing underlying neighborhood clusters, such as the built environment (e.g., availability of green spaces), availability of health care (e.g., number of physicians and number of facilities), neighborhood crime (e.g., violent offenses), and air pollution (e.g., PM2.5), which we lacked data on, to determine whether the identification of neighborhood clusters is improved. Nonetheless, we included multiple neighborhood variables across multiple neighborhood constructs that have been used in previous studies of all-cause mortality.
Fourth, as with any observational study, this study has unmeasured potential confounders that limit causal inference. For example, due to the limited measures related to income and wealth available in PREHCO, we could not examine if the influence of the neighborhood context differed by individual-level socioeconomic status (SES; e.g., low vs. moderate vs. high SES). Previous research has shown that death rates were higher among low SES individuals residing in high SES neighborhoods [92, 106]. This suggests that there are potentially other subpopulations not captured in this study who are at higher risk for death.
Finally, we did not examine residential trajectories over time, which is especially relevant for Puerto Rico given the budget crisis, the great recession, the debt crisis, and Hurricanes Irma and María that may have resulted in increases in spatial inequality. PREHCO has publicly available data for two waves (2002–2003 and 2006–2007). The third wave of surviving respondents of PREHCO will be publicly available soon, and the fourth wave of data collection will begin later this year. These data will allow the creation of a longitudinal database to examine residential trajectories over time and their association with mortality.
Despite these limitations, our study makes several contributions on the role of neighborhoods on older adult mortality. First, we focus on older adults in Puerto Rico—a segment of the US and Latino population that is overlooked in US-based neighborhoods research and aging research more broadly. Second, we used latent profile analysis to summarize multiple indicators that constitute neighborhood risks and resources that are not captured by continuous scales or indices. Using this approach to identify neighborhood clusters associated with an increased risk of death in late life may help inform “upstream” points for structural interventions that can extend healthy years of life among older adults who have had adverse experiences throughout their life course. Third, we used longitudinal data to help establish causal inference. Using multilevel methods and longitudinal data, we assessed the temporal relationship of the association between the neighborhood context at baseline and 19-year all-cause mortality, controlling for possible confounders, allowing for more robust causal inferences. This investigation serves as a foundation to highlight a multilevel perspective of social determinants of health in Puerto Rico. Collectively, we must reframe the narrative on the aging process in Puerto Rico to understand the interplay that historical, environmental, social, behavioral, and biological factors have on health and mortality in this rapidly aging population. Through these efforts, we can identify opportunities to assess and improve the health and wellbeing of older Puerto Rican adults.
## Data availability statement
Publicly available datasets were analyzed in this study. This data can be found at: https://doi.org/10.3886/ICPSR34596.v1 for PREHCO and https://www.socialexplorer.com/ for the 2000 US Decennial Census.
## Ethics statement
The original two waves of the PREHCO study complied with all the IRB requirements at the University of Wisconsin-Madison and the University of Puerto Rico. The use of the NDI mortality data with the PREHCO study complied with IRB requirements at the University of Alabama-Birmingham. The use of the geographic data with the PREHCO study complied with IRB requirements at Syracuse University.
## Author contributions
CG conceptualized and designed the study, organized and conducted the statistical analysis, interpreted the results and findings, prepared all data visualization, and wrote the manuscript. MG assisted with the interpretation and validation of results and revised the manuscript critically for important intellectual content. MM created the mortality data for PREHCO for use in this study and revised the manuscript. MC assisted with the conceptualization of the study, revised the manuscript, and acquired financial support for PREHCO. All authors approve the submitted version of this manuscript and agree to be accountable for the content of the work.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.995529/full#supplementary-material
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|
---
title: 'Associations between media use, self-efficacy, and health literacy among Chinese
rural and urban elderly: A moderated mediation model'
authors:
- Yebo Yu
- Yibo Wu
- Zhen Huang
- Xinying Sun
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10034173
doi: 10.3389/fpubh.2023.1104904
license: CC BY 4.0
---
# Associations between media use, self-efficacy, and health literacy among Chinese rural and urban elderly: A moderated mediation model
## Abstract
### Objectives
The influence of media use on health literacy among urban and rural elderly has been unknown in China. This study aims to examine the association between media use and health literacy and to explore the mediating role of self-efficacy and the moderating role of urban-rural residency.
### Methods
Based on the cross-sectional study of the Psychology and Behavior Investigation of Chinese Residents (PBICR) in 2022, a total of 4,070 Chinese old people aged 60 years and above were included. We adopted the simplified New General Self-Efficacy Scale (NGSES) and the simplified Health Literacy Scale-Short Form (HLS-SF) to measure self-efficacy and health literacy. Media use was measured using a self-administrated questionnaire.
### Results
Results showed that Chinese urban elderly had a higher frequency of media use than rural ones in the aspects of social activities, self-presentation, social action, leisure and entertainment, information acquisition, and business transactions through media ($P \leq 0.001$). Among all participants, self-presentation ($B = 0.217$, $95\%$ CI: 0.040, 0.394), leisure and entertainment ($B = 0.345$, $95\%$ CI: 0.189, 0.502), and information acquisition ($B = 0.918$, $95\%$ CI: 0.761, 1.076) were significantly associated with health literacy. Self-efficacy partially mediated the effect of media use on health literacy (Bindirect = 0.045, $95\%$ CI: 0.032, 0.058), accounting for $18.37\%$ of the total effect. Urban-rural residency ($B = 0.049$, $95\%$ CI: 0.024, 0.075) moderated the relationship between media use and self-efficacy significantly.
### Conclusion
The urban-rural gap in health literacy requires more attention. The promotion of media use and self-efficacy may play a role in eliminating health disparities.
### Limitations
As a cross-sectional study, it could not establish cause-effect relationships.
## 1. Introduction
In recent years, the rapid growth of informatical technology has brought about wide popularity of media use, not only traditional media, such as broadcast and newspaper, but also social media. Social media means the internet-based channels of mass personal communication [1], which allows the creation and exchange of user-generated content [2]. In 2021, over 4.26 billion people were using social media across the world, and the number of media users is expected to reach 6 billion in 2027 [3]. People take advantage of media functions to enrich their lives, such as communicating with friends and browsing the latest news [4].
Although some obstacles exist for old people to learn and use media, the trends of media usage among the elderly seem to be increasing worldwide with a growing aging population [5]. A survey showed that $40\%$ of Amerian aged 65 and older used social media in 2019 [6]. In China, 119 million internet users aged 60 and older in 2021, with an internet penetration rate of $43.2\%$ [7]. This high rate indicates the popularity of media use among Chinese older adults. Nevertheless, as a huge economic gap between urban and rural areas in China, the media use of elderly living in cities and the countryside may show different conditions.
Health literacy is defined as the ability to obtain, process, understand, and communicate health-related information, which helps to make health decisions and to manage health status [8, 9]. According to Sørensen et al. [ 10], health literacy included three domains: health care (access medical information or clinical issues), disease prevention (learn and protect against health risk factors), and health promotion (access information on determinants of health for regularizing healthy behaviors). Previous studies suggested that health literacy played an essential role in preventing and managing chronic diseases [11, 12]. It's suggested that health literacy inequality was attributable to income inequalities [13], and inadequate health literacy was associated with a low socioeconomic position [14]. Some studies reported that Chinese older adults have a relatively low level of health literacy [15], especially those living in rural areas [16]. Therefore, it's necessary to investigate the urban-rural gap in health literacy and to explore potential factors influencing health literacy, for urban and rural elderly, respectively.
The process of media use is filled with information generating, exchanging, and assimilating. The users of media could obtain health-related information or skills through the process of media use. Therefore, it's reasonable to assume that the frequency of media use is associated with levels of health literacy [17]. But whether the relationship above is positive or negative, experts haven't reached a uniform idea. Some studies found that the usage of media, such as television, the internet, and smartphones, is beneficial for the improvement of health literacy [18, 19]. While some scholars hold the opinion that it was difficult to evaluate the authenticity of health-related information via media channels [20], which may play a negative impact on health literacy. As to the difference between urban and rural residents, the two groups may adopt diverse media use behaviors that exert different effects on health literacy. Pitifully, little literature has discussed this issue. As a result, it's worthwhile to test the influence of different types of media use on health literacy among Chinese older adults, stratified for urban and rural residents.
Self-efficacy is one's belief in their ability to execute activities that will access satisfactory outcomes [21]. Self-efficacy could be regarded as an important indicator that evaluates people's confidence, which has been widely used as a mediator in health promotion programs [22]. Previous literature has found an association between media use and self-efficacy [23, 24]. And several studies have supported the effect of self-efficacy on health-related knowledge and behaviors [25]. In this research, self-efficacy may mediate the relationship between media use and health literacy. On the one hand, the usage of media is a novel thing for Chinese old adults, as they were born and grew up in an era with low economy and technology, and most of them have low education levels. Generally, learning media use is a challenge for the Chinese elderly [26]. Once they can use the functions of the media, such as chatting with friends living far away, their self-efficacy would increase. On another hand, people with high self-efficacy could grasp useful health-related skills and distinguish between true and false information, which promotes the improvement of their health literacy [27]. Hence, it's suggested that self-efficacy is a potential mediator of the association between media use and health literacy.
Moreover, urban-rural residency may have the potential to moderate the association between media use and self-efficacy. There are gaps between urban and rural areas in China in aspects of income levels and entertainment facilities [28]. Compared with old adults living in the countryside, seniors living in cities have more chances to contact and make use of media. For example, the service of buying medicine online through applications on smartphones exists in most cities in China [29]. However, this service is rarely available in rural areas of China. Under this circumstance, urban old people could obtain more achievement from media use, which further improves their self-efficacy. In addition, the acquaintances of the urban elderly are also likely to use media in their daily life, which could give urban elderly more positive feedback and guidance on media use [30]. Consequently, urban old people are supposed to have a higher level of self-efficacy than rural aged, with the same frequency of media use, which could lead to greater urban-rural health inequalities.
So far, very little literature has discussed the influence of different types of media use on health literacy for urban and rural older adults, or taken self-efficacy and urban-rural residency into consideration. In light of the above concerns, four hypotheses would like to be proposed for Chinese old people (Figure 1).
**Figure 1:** *Conceptual framework.*
Hypothesis 1: There are differences between urban and rural elderly in terms of media use and health literacy.
Hypothesis 2: The health literacy of urban and rural elderly is associated with different types of media use.
Hypothesis 3: Self-efficacy will mediate the association between media use and health literacy.
Hypothesis 4: Urban-rural residency will moderate the effect of media use on self-efficacy. This means that urban elderly have a stronger relationship between media use and self-efficacy than rural aged.
## 2.1. Study design
The cross-sectional study, the Psychology and Behavior Investigation of Chinese Residents (PBICR) [31], was conducted in China between 20th June and 31st August 2022. Stratified sampling and quota sampling were adopted. A total of 148 cities were included, from the 23 provinces, 5 autonomous regions, and 4 municipalities directly under the central government (Beijing, Tianjin, Shanghai, and Chongqing). Electronic questionnaires were distributed and collected through face-to-face or real-time video guidance by investigators. Informed consent was obtained before participants were voluntarily involved in our research, which contained the study's purpose, anonymity, confidentiality, and other related rights. Excluding ineligible respondents whose surveys had extremely short completion times or inconsistent answer patterns, 21,916 samples entered our research. We selected 4,070 old people aged 60 years old or above. The study was approved by the Ethics Committee of the Health Culture Research Center of Shaanxi (JKWH-2022-02).
## 2.2.1. Media use
After reviewing related literature systematically [32, 33], media use was measured by a self-administrated questionnaire. Six items correspond to six categories of media use behaviors, including social communication (e.g., communicating with others), self-presentation (e.g., regarding the virtual community as a self-display platform, recording daily life and sharing personal feelings on it), social action (e.g., defending their rights or standing up for the justice of others), leisure and entertainment (e.g., playing games, listening to music or watching short videos), information acquisition (e.g., browsing the news and searching for various information), and business transactions (e.g., online shopping or online payment) via media. All items were rated on a five-point Likert scale (1 = never use, 5 = always use). The total score ranges from 6 to 30 and a higher score indicates more frequent usage of media. The Cronbach's alpha was 0.870 in this study.
## 2.2.2. Self-efficacy
New General Self-Efficacy Scale (NGSES) was developed by Chen, Gully, and Eden [34]. Self-efficacy in this study was measured using a simplified version of NGSES. With two random datasets including 11,031 participants, classical test theory and Mokken model of item response theory were used to simplify NGSES from 8 items to 3 items, which showed high reliability and validity. The three items (“When facing difficult tasks, I am certain that I will accomplish them,” “I will be able to successfully overcome many challenges,” and “I am confident that I can perform effectively on many different tasks”) rating with five responses (1 = very disagree, 5 = very agree). The total score was the sum of the scores of all three items, and a higher score means a higher level of self-efficacy. The Cronbach's alpha was 0.912 in this study.
## 2.2.3. Health literacy
Health Literacy Scale-Short Form (HLS-SF) was developed by Duong et al. [ 9] and has shown high reliability and validity [35]. We used the simplified version of HLS-SF to measure health literacy in this study. A total of 7,449 participants were included and two datasets were generated randomly for the simplified process of HLS-SF. Based on classical test theory, a 9-item version of the scale (HLS-SF 9) was obtained through simplification, which has high reliability and validity for Chinese residents [36]. The total scale includes three domains (health care, disease prevention, and health promotion), and each dimension was measured by three items. Each item was rated with four responses (1 = very difficult, 4 = very easy). Accordingly, the higher the scores, the greater level of health literacy that the participants own. The total score was calculated by summing all items. The Cronbach's alpha was 0.923.
## 2.2.4. Moderating and confounding variables
As the potential factor moderating the association between media use and self-efficacy, urban-rural residency refers to living in an urban or rural area in the last 3 months.
Potential confounders included age, gender (male/female), ethnicity (Han nationality/else), education (primary school or below/middle school/undergraduate or above), occupation status (retired/on a job/ else), per capita monthly household income (3,000 and below/3,001–5,$\frac{000}{5}$,001–9,$\frac{000}{9}$,001 and above, yuan/RMB), marital status (married/else), living alone (yes/no), chronic disease (yes/no), smoking habit (never smoking/used smoking/smoking nowadays), and drinking habit (never drinking/used drinking/drinking nowadays).
## 2.3. Statistical analysis
Descriptive analysis was used to describe the background characteristics of the samples. Mann-Whitney U test was adopted to test the difference in 11 items of media use and health literacy between urban and rural residents. After applying Bonferroni correction, here we choose $P \leq 0.0045$ ($\frac{0.05}{11}$) as the significance level to protect findings from type-I errors [37]. In the subsequent statistical analyses, the significance levels were set at $P \leq 0.05.$ We used multivariable linear regression models to detect the association between media use behaviors and health literacy stratified for urban-rural residency. Mediating and moderating effects were tested through PROCESS 4.1 invented by Hayes [38]. Model 4 of PROCESS was used to explore the mediating role of self-efficacy on the association between media use and health literacy, adjusted for age, gender, residency, ethnicity, education level, current marital status, monthly personal income, occupational status, living alone, chronic disease, smoking, and alcohol. Control for the same variables except for residency, model 7 of PROCESS was used to test the moderating role of urban-rural residents on the relationship between media use and self-efficacy in the moderated mediation model. Then we included three aspects of health literacy (health care, disease prevention, and health promotion) into the moderated mediation model separately. The number of bootstrap samples was 5,000 in this study. All the above statistical analysis processes were implemented through SPSS version 26.0.
## 3.1. Descriptive analysis
The mean age of participants was 68.77 years old. $50.2\%$ of them were males, and $90.7\%$ of them were of Han nationality. Urban residents occupied $55.9\%$ and rural residents accounted for $44.1\%$. Most participants have married ($83.3\%$). $47.8\%$ of them had primary school or below education level, $43.9\%$ had 3,000 yuan or below income per month, and $55.3\%$ were retired. The proportion of living alone was $14.7\%$. Some of them reported chronic disease ($57.9\%$), smoking ($17.5\%$), and drinking ($14.2\%$) currently. The means (SD) scores of total media use, self-efficacy, and health literacy were 24.00 (9.6), 15.00 (10.5), and 36.00 (25.1), respectively (Table 1).
**Table 1**
| Unnamed: 0 | N/mean | %/SD |
| --- | --- | --- |
| Age (mean, SD) | 68.77 | 6.3 |
| Gender | Gender | Gender |
| Male | 2044 | 50.2 |
| Female | 2026 | 49.8 |
| Residency in the last 3 months | Residency in the last 3 months | Residency in the last 3 months |
| Urban | 2276 | 55.9 |
| Rural | 1794 | 44.1 |
| Ethnicity | Ethnicity | Ethnicity |
| Han nationality | 3693 | 90.7 |
| Else | 377 | 9.3 |
| The highest educational level attained | The highest educational level attained | The highest educational level attained |
| Primary school or below | 1946 | 47.8 |
| Middle school | 1787 | 43.9 |
| Undergraduate or above | 337 | 8.3 |
| Current marital status | Current marital status | Current marital status |
| Married | 3389 | 83.3 |
| Else | 681 | 16.7 |
| Monthly personal income (¥) | Monthly personal income (¥) | Monthly personal income (¥) |
| 3,000 and below | 1786 | 43.9 |
| 3,001–5,000 | 1295 | 31.8 |
| 5,001–9,000 | 723 | 17.8 |
| 9,001 and above | 266 | 6.5 |
| Occupational status | Occupational status | Occupational status |
| Retired | 2251 | 55.3 |
| On a job | 569 | 14.0 |
| Else | 1250 | 30.7 |
| Living alone | Living alone | Living alone |
| No | 3472 | 85.3 |
| Yes | 598 | 14.7 |
| Chronic disease | Chronic disease | Chronic disease |
| No | 1715 | 42.1 |
| Yes | 2355 | 57.9 |
| Smoking | Smoking | Smoking |
| Now | 713 | 17.5 |
| Ever | 261 | 6.4 |
| Never | 3096 | 76.1 |
| Alcohol | Alcohol | Alcohol |
| Now | 579 | 14.2 |
| Ever | 570 | 14.0 |
| Never | 2921 | 71.8 |
| The total score of media use (mean, SD) | 24.00 | 9.6 |
| The score of self-efficacy (mean, SD) | 15.00 | 10.5 |
| The score of health literacy (mean, SD) | 36.00 | 25.1 |
Table 2 shows that urban elderly had higher scores of all types of media use than rural old people ($P \leq 0.001$), including social communication, self-presentation, social action, leisure and entertainment, information acquisition, business transactions, and total media use. Urban-aged people also had higher scores in health literacy, including health care, disease prevention, health promotion, and total health literacy, compared with rural old ones ($P \leq 0.001$).
**Table 2**
| Unnamed: 0 | Unnamed: 1 | Mean ±SD | Z | P -value |
| --- | --- | --- | --- | --- |
| Media use | Media use | Media use | Media use | Media use |
| Social communication | Urban | 2.14 ± 1.07 | −5.886 | < 0.001 |
| Social communication | Rural | 1.91 ± 1.15 | | |
| Self-presentation | Urban | 1.41 ± 1.26 | −5.645 | <0.001 |
| Rural | 1.18 ± 1.19 | | | |
| Social action | Urban | 1.52 ± 1.22 | −6.241 | <0.001 |
| Social action | Rural | 1.29 ± 1.23 | | |
| Leisure and entertainment | Urban | 1.75 ± 1.25 | −5.835 | <0.001 |
| Leisure and entertainment | Rural | 1.52 ± 1.25 | | |
| Information acquisition | Urban | 1.97 ± 1.18 | −9.672 | <0.001 |
| Information acquisition | Rural | 1.59 ± 1.23 | | |
| Business transactions | Urban | 1.46 ± 1.27 | −6.186 | <0.001 |
| Business transactions | Rural | 1.22 ± 1.25 | | |
| Total score of media use | Urban | 10.25 ± 5.57 | −8.547 | <0.001 |
| Total score of media use | Rural | 8.70 ± 5.76 | | |
| Health literacy | Health literacy | Health literacy | Health literacy | Health literacy |
| Health care | Urban | 8.77 ± 1.89 | −13.883 | <0.001 |
| Health care | Rural | 7.93 ± 1.82 | | |
| Disease prevention | Urban | 8.91 ± 1.82 | −14.472 | <0.001 |
| Disease prevention | Rural | 8.05 ± 1.84 | | |
| Health promotion | Urban | 8.52 ± 2.01 | −14.094 | <0.001 |
| Health promotion | Rural | 7.62 ± 1.99 | | |
| Total score of health literacy | Urban | 26.20 ± 5.24 | −15.192 | <0.001 |
| Total score of health literacy | Rural | 23.59 ± 5.09 | | |
## 3.2. Multiple linear regression analysis
The relationships between media use and health literacy, stratified for urban-rural residency, are presented in Table 3. Among all participants, self-presentation ($B = 0.217$, $95\%$ CI: 0.040, 0.394), leisure and entertainment ($B = 0.345$, $95\%$ CI: 0.189, 0.502), and information acquisition ($B = 0.918$, $95\%$ CI: 0.761, 1.076) were significantly associated with health literacy. For urban old people, health literacy was positively related to leisure and entertainment ($B = 0.295$, $95\%$ CI: 0.084, 0.506) and information acquisition ($B = 0.780$, $95\%$ CI: 0.561, 0.999). For rural elderly, their health literacy had positive associations with self-presentation ($B = 0.334$, $95\%$ CI: 0.059, 0.609), leisure and entertainment ($B = 0.435$, $95\%$ CI:0.206, 0.664), and information acquisition ($B = 0.989$, $95\%$ CI: 0.762, 1.216), and had a negative correlation with social communication (B = −0.247, $95\%$ CI: −0.475, −0.019). With health literacy as independent variables and media use as dependent variables, shown as Supplementary Table 1, the associations between health literacy and media use were positively significant, both among urban and rural old people.
**Table 3**
| Unnamed: 0 | Model 1: all participants | Model 1: all participants.1 | Model 2: urban elderly | Model 2: urban elderly.1 | Model 3: rural elderly | Model 3: rural elderly.1 |
| --- | --- | --- | --- | --- | --- | --- |
| | B (95% CI) | β | B (95% CI) | β | B (95% CI) | β |
| Social communication | −0.111 (−0.272, 0.049) | −0.023 | 0.064 (−0.159, 0.288) | 0.013 | −0.247 (−0.475, −0.019) | −0.056* |
| Self-presentation | 0.217 (0.040, 0.394) | 0.050** | 0.115 (−0.115, 0.346) | 0.028 | 0.334 (0.059, 0.609) | 0.078* |
| Social action | 0.098 (−0.067, 0.263) | 0.023 | 0.165 (−0.057, 0.387) | 0.038 | 0.028 (−0.218, 0.274) | 0.007 |
| Leisure and entertainment | 0.345 (0.189, 0.502) | 0.081*** | 0.295 (0.084, 0.506) | 0.070** | 0.435 (0.206, 0.664) | 0.107*** |
| Information acquisition | 0.918 (0.761, 1.076) | 0.210*** | 0.780 (0.561, 0.999) | 0.176*** | 0.989 (0.762, 1.216) | 0.240*** |
| Business transactions | −0.015 (−0.186, 0.155) | −0.004 | 0.065 (−0.159, 00.288) | 0.016 | −0.121 (−0.385, 0.142) | −0.030 |
## 3.3. Mediation model
Table 4 shows that the path coefficients of “media use→ self-efficacy” ($B = 0.053$, $95\%$ CI: 0.040, 0.067) and “self-efficacy→ health literacy” ($B = 0.842$, $95\%$ CI: 0.784, 0.899) and “media use→ health literacy” ($B = 0.200$, $95\%$ CI: 0.175, 0.226) were all significant. Self-efficacy partially mediated the effect of media use on health literacy, with an effect value of 0.045, accounting for $18.37\%$ of the total effect (Table 5).
## 3.4. Moderated mediation model
With self-efficacy as the dependent variable, media use ($B = 0.027$, $95\%$ CI: 0.007, 0.046), urban-rural residency ($B = 0.589$, $95\%$ CI: 0.435, 0.743), and the interaction of media use and urban-rural residency ($B = 0.049$, $95\%$ CI: 0.024, 0.075) significantly predicted self-efficacy (Table 6). Figure 2 revealed that compared with rural elderly, the association between media use and self-efficacy was stronger for urban old people. As shown in Table 7, the indirect effect of media use on health literacy was stronger among urban aged ($B = 0.066$, $95\%$ CI: 0.049, 0.083) compared with rural elderly ($B = 0.023$, $95\%$ CI: 0.005, 0.041). The confidence intervals of moderated mediation index did not contain zero ($B = 0.043$, $95\%$ CI: 0.019, 0.067), so this moderated mediation model was found to be significant (Figure 3).
Instead of the total score of health literacy, we included the three facets of health literacy (health care, disease prevention, and health promotion) into the moderated mediation models, respectively. The results showed that the path-coefficients of the three moderated mediating models were all significant ($P \leq 0.01$) (Supplementary Table 2). Compared with rural elderly, urban aged people had a stronger indirect effect of media use on health care (rural: $B = 0.007$, $P \leq 0.05$; urban: $B = 0.021$, $P \leq 0.05$), disease prevention (rural: $B = 0.008$, $P \leq 0.05$; urban: $B = 0.023$, $P \leq 0.05$), and health promotion (rural: $B = 0.008$, $P \leq 0.05$; urban: $B = 0.022$, $P \leq 0.05$). The moderated mediation indexes of the three models were all found to be significant ($P \leq 0.05$) (Supplementary Table 3).
## 4. Discussion
In this study, we assessed the difference in media use between urban and rural elderly in China. For urban residents, health literacy was positively related to leisure and entertainment and information acquisition via media; for urban old people, their health literacy was associated with four media use behaviors, including leisure and entertainment, information acquisition, self-presentation, and social action. Furthermore, self-efficacy mediated the relationship between media use and health literacy, and urban-rural residency played a moderating role in the indirect influence of media use on health literacy.
The results of this study supported hypothesis 1. Urban elderly had higher frequencies than rural old people in all six types of media use behaviors, including social communication, self-presentation, social action, leisure and entertainment, information acquisition, and business transactions. That is consistent with a survey conducted in China before [39], which concluded that urban children had higher media exposure and usage than rural children. And Lariscy and his colleagues found that social media was more important for rural adolescents than urban ones [40], as there were fewer health information channels for rural residents to contact to. The urban-rural gap in media use could be mainly attributed to the difference in economic development between urban and rural areas [41]. In the meantime, the health literacy score of urban older adults was also higher than that of rural aged. A systematic review had the same results [42], and they found that the urban-rural gap in health literacy was more likely to occur in developing countries. A study conducted in China also indicated that urban citizens had better health literacy than rural residents, and rural people aged 65 and above had the worst level of health literacy [43]. As health literacy is related to health outcomes [14], the large health literacy gaps between urban and rural areas are likely to lead to greater health inequalities. As a result, we ought to pay more attention to the health literacy and media use performance of Chinese rural elderly and try to narrow the urban-rural gaps in the future.
Hypothesis 2 was also confirmed. Health literacy was associated with the frequency of media use, and urban and rural elderly had different types of media use behaviors that predicted their health literacy. For urban old people, leisure and entertainment via media and acquiring information online helped them to improve their health literacy. Mass information is provided through multiple media channels, which contain much health-related information [44]. Hence, the frequency of acquiring online information could contribute to their health literacy. In addition, with the boosting of media for scientific knowledge popularization [45], the elderly can learn health-related knowledge and skills when they watch videos or listen to music. Therefore, the process of interacting with media content related to health for leisure and entertainment benefits older adults' health literacy. For the rural elderly, the same positive associations between health literacy with leisure and entertainment and acquiring information existed. Besides, their health literacy was also related to self-presentation positively and social communication negatively. Social media provides plenty of virtual platforms for users to present themselves, such as an application called Wechat in China. People would like to create or strengthen their ideal selves through posting photographs or recording personal feelings [46], to influence how others perceive them [47]. It's supposed that when some rural elderly want to create good online self-images online, they would like to perform well on health-related knowledge, which could bring them more appreciated attention. As to social communication, it showed a negative relationship with the health literacy of the rural elderly. This phenomenon can be ascribed to the low health literacy of the rural resident population [42]. Older adults are likely to access false or inaccurate health-related information through their communication with friends online, which may decrease their health literacy level. Nevertheless, urban citizens had more chances to create their social images among their acquaintances [48], who have a relatively high level of health literacy [43]. This may weaken the importance of media self-presentation for urban seniors, and reduce their contact with wrong health knowledge. Consequently, rural elderly, rather than urban old people, had a significant association between self-presentation and social communication with health literacy. Among all participants in this study, a higher frequency of media use predicted a higher level of health literacy. Health equality can be destroyed by the digital divide, which refers to inequitable access, use, and outcomes of technology use [49]. Urban residents who have higher socioeconomic status than rural people are more likely to master the ability to take use of technology [50] and improve their health further. As a result, media use is a potential factor that may reduce the growing health literacy gaps between urban and rural elderly. In the meanwhile, we found that old people with higher levels of health literacy have more frequent use of media. As better health literacy helps them to search health-related information online and to seek solutions to health problems from others via media. Whether there is a mutual relationship between health literacy and media use requires further exploration with longitudinal study design.
Moreover, hypothesis 3 was confirmed, which indicated that a high frequency of media use was indirectly associated with high levels of health literacy, with increasing self-efficacy as the mediator. The mediating effect of self-efficacy has been discussed in previous studies, on the relationships between external factors and health-related problems [51, 52]. According to social-cognitive theory, self-efficacy determines an individual's willingness to execute specific activities, which could be influenced by self-performance and others' feedback [53]. In this study, as a special behavior, media use contains personal using performance on novel things and interpersonal communication and feedback. Therefore, the self-efficacy of Chinese older adults improved through frequent media use, which further elevated the effect of learning health literacy. These findings enriched the understanding of the potential mechanism between media use and health literacy.
Furthermore, corresponding to hypothesis 4, our results suggested that the indirect effect of media use on health literacy via self-efficacy is moderated by urban-rural residency. That is to say, urban elderly had a stronger relationship between media use and health literacy compared with rural ones. Urban old adults performed better than rural elderly in all types of media use behaviors, which indicated that they make use of media more skillfully and deeply. It's supposed that urban elderly could obtain more enjoyment from media use in turn, which benefits their self-efficacy and then helps them to learn health-related knowledge online. In contrast, the rural elderly haven't mastered media use well, so they couldn't get enough self-satisfying and self-identity, which explains their relatively lower self-efficacy [54]. As a result, with the same frequency of media use, urban elderly had higher levels of self-efficacy to improve health literacy than rural ones. Some previous studies discussed the urban-rural gap of self-efficacy [55] and the moderating effect of urban-rural residency [56], which was similar to our findings.
Several limitations of this study should be considered. Firstly, as a cross-sectional study, we could not conclude causal relationships. In the future, related longitudinal research would be conducted to establish cause-effect inference. Moreover, we used self-reported questionnaires to access the frequency of media use of participants. Therefore, reporting bias may exist, which influences the accuracy of results. We will adopt better methods to collect media use data in future work. Besides, unequal probability sampling was adopted at the community/village level, which makes samples less representative of the whole population.
Based on the above results, several essential implications could be concluded. Firstly, old people living in the countryside in China need more attention to their media use. The types of media use that impacted health literacy between urban and rural elderly were different. Therefore, health promotion programs for improving health literacy and narrowing the urban-rural gap should pay more attention to particular media use behaviors among specific groups. Secondly, health practitioners should consider self-efficacy as a short-term target in the schemes of health literacy improvement. We ought to motivate their self-efficacy first, and then make strategies to improve their health literacy, which may be more effective and scientific. Finally, this study also provides new insight to eliminate health inequality. The disparities in media use could explain the health literacy inequality, and urban-rural residency and self-efficacy play important roles in the potential mechanism of the influence of media use on health literacy.
## 5. Conclusion
This research revealed the difference in media use between urban and rural elderly in China and found that the health literacy of urban and rural elderly was associated with different types of media use behaviors. Furthermore, self-efficacy played a mediating role in the relationship between the frequency of media use and health literacy. And urban elderly had a stronger association between media use and self-efficacy in the moderated mediation model, compared with rural older adults. These findings suggested that we should make more efforts to increase the media use of rural elderly and shrink the urban-rural gap of health literacy among old people to promote health equality.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by the Ethics Committee of the Health Culture Research Center of Shaanxi [JKWH-2022-02]. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
XS designed this study. YY wrote the original manuscript, prepared the analysis, and interpreted the data. YW and ZH helped with the analysis and gave essential comments on multiple versions. All authors approved the final version of the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1104904/full#supplementary-material
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|
---
title: 'A risk prediction model based on machine learning for early cognitive impairment
in hypertension: Development and validation study'
authors:
- Xia Zhong
- Jie Yu
- Feng Jiang
- Haoyu Chen
- Zhenyuan Wang
- Jing Teng
- Huachen Jiao
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10034177
doi: 10.3389/fpubh.2023.1143019
license: CC BY 4.0
---
# A risk prediction model based on machine learning for early cognitive impairment in hypertension: Development and validation study
## Abstract
### Background
Clinical practice guidelines recommend early identification of cognitive impairment in individuals with hypertension with the help of risk prediction tools based on risk factors.
### Objective
The aim of this study was to develop a superior machine learning model based on easily collected variables to predict the risk of early cognitive impairment in hypertensive individuals, which could be used to optimize early cognitive impairment risk assessment strategies.
### Methods
For this cross-sectional study, 733 patients with hypertension (aged 30–85, $48.98\%$ male) enrolled in multi-center hospitals in China were divided into a training group ($70\%$) and a validation group ($30\%$). After least absolute shrinkage and selection operator (LASSO) regression analysis with 5-fold cross-validation determined the modeling variables, three machine learning classifiers, logistic regression (LR), XGBoost (XGB), and gaussian naive bayes (GNB), were developed. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, and F1 score were used to evaluate the model performance. Shape Additive explanation (SHAP) analysis was performed to rank feature importance. Further decision curve analysis (DCA) assessed the clinical performance of the established model and visualized it by nomogram.
### Results
Hip circumference, age, education levels, and physical activity were considered significant predictors of early cognitive impairment in hypertension. The AUC (0.88), F1 score (0.59), accuracy (0.81), sensitivity (0.84), and specificity (0.80) of the XGB model were superior to LR and GNB classifiers.
### Conclusion
The XGB model based on hip circumference, age, educational level, and physical activity has superior predictive performance and it shows promise in predicting the risk of cognitive impairment in hypertensive clinical settings.
## 1. Introduction
Hypertension has been recognized as a significant risk factor for cognitive impairment, which may increase the risk of vascular dementia and Alzheimer's events [1]. According to recent evidence, hypertension was associated with a 1.86-fold and 1.62-fold increased risk of dementia and mild cognitive impairment in the Chinese population [2]. Although the mechanism of these deleterious effects is poorly supported by conclusive evidence, preclinical investigations have provided potential mechanistic evidence for better insight. Chronic hypertension can continuously damage the structure and function of cerebral vessels, challenge the integrity of the blood-brain barrier through inflammatory pathologic pathways [3], and also promote the formation of atherosclerotic plaques and evolve into ischemic stroke [4], which is an important pathological basis for cognitive impairment [5]. Although these possible mechanisms have given promising hints for the prevention and treatment of hypertensive cognitive impairment, this requires rigorous investigation to be confirmed in the future. Well-developed preventive procedures can significantly reduce the treatment burden of cognitive impairment in hypertensive populations. Maintaining cognitive health and preventing early cognitive impairment in hypertensive individuals is a critical public health priority. Therefore, it is necessary to investigate the risk factors of early cognitive impairment in the hypertensive population, establish an early risk prediction model, and explore its pathogenesis, to provide better decision-making for early cognitive impairment.
The traditional approach to the diagnosis of cognitive impairment in hypertension focuses on cognitive and neuropsychological assessment but is often criticized for its lag [6]. More recently, although amyloid proteins, tau proteins, and several structural magnetic resonance imaging (MRI) indicators have been recognized as promising pathologic markers [7, 8], high costs and complex inspection procedures still limit their widespread use. Considering the multi-factorial characteristics of hypertensive cognitive impairment, it is necessary to combine multiple parameters to better reflect its pathological development. In recent years, relevant influencing factors of early cognitive impairment, including age, education, chronic disease, and modifiable life factors, as well as their independent effects and interactions, have been considered. Several observational studies have identified several potentially modifiable risk factors for cognitive decline, including hypertension, dyslipidemia and obesity, diabetes mellitus, alcohol consumption, smoking, physical inactivity, dietary habits such as sodium intake [9], and sensory function. Several previous studies have reported strong associations between plant-based diets [10], age-related central auditory processing disorder (CAPD) [11], and antihypertensive medications [12, 13] with cognitive decline.
In recent years, many researchers, especially Chinese, have studied cognitive impairment with hypertension, but most of them are limited to risk factors. The conclusion is controversial, and the number of prediction models is limited. A recent community survey from China showed that hypertension grade, smoking, sleep disorder, and duration of hypertension were risk factors, while education, exercise, reading, social support, and medication adherence were protective factors; AUC, sensitivity, and specificity of the model developed based on influencing factors were 0.765, 0.630, and 0.877 [14]. Another study based on hypertensive patients from China showed that duration of hypertension, SBP, homocysteine (Hcy), and SUA were risk factors for developing cognitive dysfunction, and duration of education was a protective factor for developing cognitive dysfunction [15]. In addition, Zhang et al. [ 16] conducted a study based on the hypertensive population in plateau areas of China indicated that plateau environment, age, abdominal circumference, and SUA are independent risk factors affecting hypertensive cognitive impairment. Ma et al. [ 17] reported that low education attainment and elevated BMI, WHR, and homeostasis assessment model for insulin resistance index (HOMA-IR) are independent risk factors for cognitive impairment in elderly patients with hypertension. Qu et al. [ 18] performed a cohort study to reveal that intestinal microbiota dysbiosis may be an important predictor of cognitive impairment with hypertension.
With the development of artificial intelligence, machine learning techniques have been used in cardiovascular event risk prediction models to improve accuracy and other performance (19–21), providing a new paradigm for cardiovascular monitoring. However, the risk prediction model for early cognitive impairment in hypertensive populations based on machine learning has never been reported. Accordingly, we developed a predictive machine learning model that considers the independent effects and interactions of influencing factors to assess the risk of early cognitive impairment in the Chinese hypertensive population, which would conduct early risk screening strategies and interventions for hypertensive cognitive impairment. Here, we hypothesized that machine learning could be used to diagnose early cognitive impairment based on the clinical characteristics of individuals with hypertension.
## 2.1. Study design and participants
We conducted a multicenter observational study of hospitalized hypertensive patients, which considered geographic region, urbanization, gender, and age distribution. For this cross-sectional study, we randomly selected 5 prefecture-level cities in Shandong Province by stratified cluster sampling, including Jinan, Yantai, Weifang, Dongying, and Jining, and then randomly selected 8 hospitals in the selected prefecture-level cities. All patients with hypertension in hospitals were selected for this study, and 787 individuals were recruited from May 2022 to December 2022. This study was approved by the Institutional Review Committee (IRB) of Affiliated Hospital of the Shandong University of Chinese Medicine and obtained the informed consent of all study participants. All participants signed informed consent. Individuals over 30 years of age with essential hypertension were included in this study. Meanwhile, we excluded patients >85 years of age with a history of stroke, Parkinson's disease, brain trauma, brain tumor, epilepsy, vision or hearing impairment, dementia, mental or psychiatric illness, severe impairment of heart, liver, or kidney function, combined with severe infection, tumor, hyperthyroidism, heart failure, arrhythmia, or cardiac surgery. In addition, we excluded 16 patients with missing data, 8 patients with abnormal data, and 6 patients with MMSE scores <18 points, leaving 733 samples for analysis.
## 2.2. Sample size calculation
According to previous reports [22, 23], the incidence of cognitive impairment in the Chinese hospitalized hypertensive population π0 = 0.25, α = 0.05, β = 0.10, allowable error (δ) = 0.10, Zβ = 1.282 beta, Zα = 1.960, n = ((Zβ+Zα)/δ)2 × π0 × (1- π0), two-tailed test. According to the formula, the calculated sample size was at least 197 patients. Considering the loss of follow-up rate, a total of 733 patients were finally included in this study.
## 2.3. Predictors
Two sets of predictors (easy to collect variables, including socio-demographics, lifestyle factors, family history, laboratory test parameters, imaging parameters, and drug information) were considered for machine learning model development. Socio-demographic, lifestyle factors, family history, and medication information for all patients were obtained through questionnaires. Data collected included sex, age, marital status, educational level, smoking status, drinking status, type of work, estimated duration of hypertension, average salt intake per month, and medication information. Sleep parameters including night sleep onset time, night sleep duration, night sleep latency, and PSQI score were obtained by the PSQI questionnaire. PSQI is a reliable self-report tool used to assess patients' sleep quality over the past month [24], and its results involve scores on seven components, including sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disorders, sleep medications, and daytime sleep disorders [25]. Physical activity was obtained through the international physical activity questionnaire (IPAQ). The IPAQ (long form) consists of 27 questions about subjects' activities during the last 7 days as follows [26]: [1] professional sports activities; [2] transportation sports activities; [3] housework, house maintenance, and family care; [4] recreation, sports, and leisure sports activities; [5] sitting time. Blood pressure measurements for all participants were taken during a single visit. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured at 2-min intervals, and the average of the three measurements was calculated consecutively [27]. Anthropometric variables, including height (in centimeters), weight (in kilograms), waist circumference (in centimeters), and hip circumference (in centimeters) measurements of all participants were measured using standardized techniques and equipment by two trained interviewers; body mass index (BMI) was calculated by dividing weight in kilograms by height in meters squared (kg/m2) [28]. Fasting blood glucose (FBG), triglyceride (TG), total cholesterol (TC), low-density lipoprotein-cholesterol (LDL-C), and serum creatinine (SCr) were collected from laboratory tests by professional physicians. Right atrial diameter (RAD), left atrial diameter (LAD), right ventricular diameter (RVD), and left ventricular diameter (LVD) were measured by experienced cardiac color ultrasound physicians.
## 2.4. Diagnostic criteria
Two experienced cardiologists assessed hypertension diagnosis using the following criteria [29]: SBP ≥ 140 mmHg, DBP ≥90 mmHg, and/or the use of antihypertensive drugs. Two other trained investigators used MMSE to assess the diagnosis of early cognitive impairment within 5–10 min. The Chinese version of MMSE has been used for the early cognitive assessment of all individuals, which has been shown to be effective and reliable in the Chinese population [30]. MMSE covers simple task areas: time and place, repetitive words, arithmetic, language, and motor skills, with a total of 30 scores [31]. MMSE scores above 18 and below 27 were defined as early cognitive impairment, and MMSE scores above 27 were considered normal cognitive function [32, 33].
## 2.5. Outcomes
A total of 122 ($16.64\%$) participants had a diagnosis of cognitive impairment in 733 hypertensive individuals. 16 core variables were selected from 35 conventional variables for LASSO regression analysis, including 4 sociodemographic factors, 6 lifestyle factors, 1 laboratory test parameter, 3 imaging parameters, and 2 medication factors. Finally, four predictive variables, including age, hip circumference, education levels, and physical activity, were selected for the development of machine learning models.
## 2.6. Statistical methods
All statistical analyses in the current study were performed using R version 3.6.3 and Python version 3.7. Continuous variables were expressed by mean [standard deviation (SD)] or median [25th, 75th], and categorical variables were expressed by number (percentage%). To ensure the simplicity of the model, we performed T-tests, Mannwhitney-U tests, and Chi-square tests to screen for variables with statistical differences between the non-MCI group and the MCI group, and further least absolute shrinkage and selection operator (LASSO) analysis with 5-fold cross-validation was performed for dimension reduction to filter the most suitable predictors to build the machine learning model. The selected individuals in the current study were randomly divided into a training set and a validation set (7:3), and analyzed by three classifiers (LR, XGB, and GNB). By comparing their AUC, accuracy, sensitivity, specificity and F1 score, the prediction model with the most perfect prediction performance was selected. The ROC curve was developed to obtain the AUC of the predictive model, and its predictive power was further evaluated by calibrating the curve. Shape Additive Explanation (SHAP) analysis was applied to investigate the model's feature importance, while the DCA curve was developed to evaluate the model's clinical applicability. If a p-value in 2-sided tests is <0.05, it is considered statistically significant.
## 2.7. Machine learning models
Figure 1 illustrates the machine learning model development pipeline. The current models were developed by three classifiers, including logistic regression (LR), XGBoost (XGB), and gaussian naive bayes (GNB). Finally, we select the model with the best predictive performance according to the predictive performance of the three classifiers. Based on selected variables, we randomly divided the individuals into two groups: $70\%$ training for model development and hyperparameter tuning and $30\%$ verification for model evaluation. The model was trained and verified for 10 repetitions using five-fold cross validation (CV). AUC, accuracy, specificity, sensitivity, and F1 scores were used to evaluate the performance of the machine learning models. The classification confusion matrix definition is that individuals with cognitive decline are considered true positive (TP) and true negative (TN) if they are accurately predicted by the machine learning model; In contrast, it is considered false positive (FP) or false negative (FN) [6]. AUC, the area under the ROC curve, the larger the value, the better the classification effect. Accuracy is defined as the proportion of correctly classified samples to total samples for a given data, which can be calculated by the following formula: Accuracy = (TP+TN)/(TP+TN+FP+FN). Sensitivity refers to the percentage of samples that are positively determined to be positive, which can be calculated by the following formula: Sensitivity = TP/(TP+FN). Specificity refers to the percentage of samples that are actually negative that are determined to be negative, which can be calculated by the following formula: Specificity = TN/(TN+FP). Precision and recall are two commonly used evaluation indexes for the binary classification problems, in which precision refers to the proportion of real class in the predicted positive class sample, and recall refers to the proportion of predicted positive class in all the predicted positive class samples. F1-score is the evaluation standard to measure the comprehensive performance of classifiers, which can be calculated by the following formula: F1 score = 2 x precision x recall/(precision + recall).
**Figure 1:** *Study flow diagram. Flowchart illustrating patient selection and machine learning model development pipeline. Following standard inclusion and exclusion procedures, a total of 733 individuals were selected, including 122 patients with cognitive impairment and 611 NCI. We developed machine learning models using three classifiers, LR, XGB, and GNB, and synthesized them into an integrated model. All individuals were randomly assigned to one of two groups: 70% training and 30% verification. Five-fold cross-validation (CV) was used to train and verify the model for 10 repetitions. MMSE, mini-mental state examination; NCI, no cognitive impairment; LR, logistic regression; XGB, XGBoost; GNB, gaussian naive bayes; ROC, receiver operating characteristic; SHAP, shape additive explanation.*
## 3.1. Comparison of demographic and clinical characteristics between early cognitive impairment and NCI
Table 1 shows the demographic and clinical characteristics of all participants. The mean age of the individuals was 66.37 (10.88) years and $48.98\%$ of the individuals were male. A total of 122 ($16.64\%$) participants had a diagnosis of early cognitive impairment. Compared with NCI, patients with early cognitive impairment were found to be older [mean age 74.60 (7.28 years)], lower educational attainment, longer duration of hypertension, waist circumference, hip circumference, poorer sleep quality, less physical activity, higher levels of Scr, larger RAD, LAD, LVD, less likely to use ACEI/ARBs and beta-blockers (all $p \leq 0.05$). However, preliminary analysis showed no statistical difference between the two groups in gender, smoking, alcohol consumption, marital status, average salt intake per month, blood pressure level, BMI, night sleep duration, FBG, lipid profiles, RVD, family history, CCBs, and diuretic use (all $p \leq 0.05$).
**Table 1**
| Variables | Overall (N = 733) | NCI (N = 611) | Early cognitive impairment (N = 122) | P-value |
| --- | --- | --- | --- | --- |
| Age, years, mean (SD) | 66.37 (10.88) | 64.72 (10.74) | 74.60 (7.28) | <0.001* |
| Sex (Male), n (%) | 359 (48.98) | 300 (49.10) | 59 (48.36) | 0.881 |
| Current smoker, n (%) | 225 (30.70) | 194 (31.75) | 31 (25.41) | 0.166 |
| Current drinker, n (%) | 189 (25.78) | 163 (26.68) | 26 (21.31) | 0.216 |
| Marital status, n (%) | Marital status, n (%) | Marital status, n (%) | Marital status, n (%) | Marital status, n (%) |
| Married | 713 (97.27) | 594 (97.22) | 119 (97.54) | 0.841 |
| Unmarried, divorced or widowed | 20 (2.73) | 17 (2.78) | 3 (2.46) | |
| Educational levels, n (%) | Educational levels, n (%) | Educational levels, n (%) | Educational levels, n (%) | <0.001* |
| Primary school or below | 285 (38.88) | 200 (32.73) | 85 (69.67) | |
| Junior high school or senior high school | 402 (54.84) | 365 (59.74) | 37 (30.33) | |
| University or above | 46 (6.28) | 46 (7.53) | 0 (0.00) | |
| Type of work, n (%) | Type of work, n (%) | Type of work, n (%) | Type of work, n (%) | <0.001* |
| Manual labor | 392 (53.48) | 297 (48.61) | 95 (77.87) | |
| Mental labor | 101 (13.78) | 98 (16.04) | 3 (2.46) | |
| Both manual and brain labor | 240 (32.74) | 216 (35.35) | 24 (19.67) | |
| Estimated duration of hypertension, months, median [IQR] | 115 [59.00,160.00] | 107 [57.00,156.00] | 131 [67.00,196.00] | 0.018* |
| Average salt intake per month, g, median [IQR] | 300 [180.00, 600.00] | 300 [180.00, 600.00] | 300 [240.00, 480.00] | 0.152 |
| SBP, mmHg, mean (SD) | 142.76 (14.73) | 142.24 (14.08) | 145.39 (17.49) | 0.063 |
| DBP, mmHg, mean (SD) | 83.69 (12.94) | 83.88 (13.42) | 82.77 (10.19) | 0.301 |
| Waist circumference, cm, mean (SD) | 84.21 (15.32) | 83.35 (15.74) | 88.51 (12.18) | <0.001* |
| Hip circumference, cm, mean (SD) | 97.67 (12.34) | 96.78 (12.35) | 102.14 (11.35) | <0.001* |
| BMI, kg/m2, mean (SD) | 24.99 (3.31) | 24.90 (3.32) | 25.47 (3.23) | 0.082 |
| Sleep parameters | Sleep parameters | Sleep parameters | Sleep parameters | Sleep parameters |
| Night sleep initiation time, hour, mean (SD) | 21.68 (0.91) | 21.75 (0.94) | 21.38 (0.73) | <0.001* |
| Night sleep duration, hours, mean (SD) | 7.11 (0.89) | 7.14 (0.86) | 6.98 (1.04) | 0.113 |
| Night sleep latency, minutes, median [IQR] | 20 [10.00, 30.00] | 20 [10.00, 30.00] | 30 [20.00, 30.00] | <0.001* |
| PSQI score, points, mean (SD) | 6.11 (3.87) | 5.79 (3.97) | 7.75 (2.83) | <0.001* |
| Physical activity, n (%) | Physical activity, n (%) | Physical activity, n (%) | Physical activity, n (%) | <0.001* |
| Light | 143 (19.51) | 101 (16.53) | 42 (34.43) | |
| Moderate | 399 (54.43) | 330 (54.01) | 69 (56.56) | |
| Vigorous | 191 (26.06) | 180 (29.46) | 11 (9.02) | |
| Laboratory testing parameters | Laboratory testing parameters | Laboratory testing parameters | Laboratory testing parameters | Laboratory testing parameters |
| FBG, mmol/L, mean (SD) | 6.54 (2.10) | 6.50 (2.09) | 6.71 (2.16) | 0.314 |
| TG, mmol/L, mean (SD) | 1.68 (1.28) | 1.70 (1.34) | 1.54 (0.85) | 0.090 |
| TC, mmol/L, mean (SD) | 4.64 (1.27) | 4.68 (1.28) | 4.44 (1.22) | 0.057 |
| LDL-C, mmol/L, mean (SD) | 2.73 (1.02) | 2.76 (1.03) | 2.57 (0.95) | 0.060 |
| SCr, μmoI/L, median [IQR] | 66 [55.00, 78.30] | 65 [54.70, 77.00] | 71 [60.30, 88.00] | <0.001* |
| Imaging parameters | Imaging parameters | Imaging parameters | Imaging parameters | Imaging parameters |
| RAD, mm, mean (SD) | 33.13 (5.75) | 32.84 (5.57) | 34.57 (6.38) | 0.006* |
| LAD, mm, mean (SD) | 36.65 (5.92) | 36.24 (5.72) | 38.73 (6.50) | <0.001* |
| RVD, mm, mean (SD) | 21.97 (3.18) | 21.85 (3.00) | 22.57 (3.91) | 0.056 |
| LVD, mm, mean (SD) | 47.51 (6.51) | 47.15 (6.58) | 49.29 (5.85) | 0.001* |
| Family history, n (%) | Family history, n (%) | Family history, n (%) | Family history, n (%) | Family history, n (%) |
| Family history of hypertension | 446 (60.85) | 380 (62.19) | 66 (54.10) | 0.094 |
| Family history of CHD | 203 (27.69) | 173 (28.31) | 30 (24.59) | 0.401 |
| Family history of hyperlipemia | 169 (23.06) | 145 (23.73) | 24 (19.67) | 0.331 |
| Medication information, n (%) | Medication information, n (%) | Medication information, n (%) | Medication information, n (%) | Medication information, n (%) |
| CCBs use | 443 (60.44) | 366 (59.90) | 77 (63.12) | 0.508 |
| ACEI/ARBs use | 472 (64.39) | 407 (66.61) | 65 (53.28) | 0.005* |
| Beta-blockers use | 139 (18.96) | 124 (20.30) | 15 (12.30) | 0.040* |
| Diuretics use | 128 (17.46) | 106 (17.35) | 22 (18.03) | 0.856 |
## 3.2. Comparison of demographic and clinical characteristics between training and verification sets
Table 2 shows the demographic and clinical characteristics between the training set and the verification set. A total of 513 people were included in this study as the training set and 220 as the test set, with a ratio of 7:3. Current results indicate no statistical difference in most predictive variables between the training set and verification set (all $P \leq 0.05$).
**Table 2**
| Variables | Training set (N = 513) | Verification set (N = 220) | P-value |
| --- | --- | --- | --- |
| Age, years, mean (SD) | 66.07 (10.70) | 67.06 (11.29) | 0.259 |
| Sex (Male), n (%) | 236 (46.00) | 123 (55.91) | 0.014 |
| Current smoker, n (%) | 156 (30.41) | 69 (31.36) | 0.797 |
| Current drinker, n (%) | 134 (26.12) | 55 (25.00) | 0.751 |
| Marital status, n (%) | Marital status, n (%) | Marital status, n (%) | 0.620 |
| Married | 498 (97.08) | 215 (97.73) | |
| Unmarried, divorced or widowed | 15 (2.92) | 5 (2.27) | |
| Educational levels, n (%) | Educational levels, n (%) | Educational levels, n (%) | 0.274 |
| Primary school or below | 192 (37.43) | 93 (42.27) | |
| Junior high school or senior high school | 285 (55.56) | 117 (53.18) | |
| University or above | 36 (7.02) | 10 (4.55) | |
| Type of work, n (%) | Type of work, n (%) | Type of work, n (%) | 0.029 |
| Manual labor | 260 (50.68) | 132 (60.00) | |
| Mental labor | 80 (15.60) | 21 (9.55) | |
| Both manual and brain labor | 173 (33.72) | 67 (30.46) | |
| Estimated duration of hypertension, months, median [IQR] | 113 [57.00, 179.00] | 117 [62.00, 152.00] | 0.784 |
| Average salt intake per month, g, median [IQR] | 300 [180.00, 600.00] | 300 [180.00, 600.00] | 0.570 |
| SBP, mmHg, mean (SD) | 142.78 (14.98) | 142.72 (14.17) | 0.960 |
| DBP, mmHg, mean (SD) | 83.38 (12.82) | 84.41 (13.22) | 0.324 |
| Waist circumference, cm, mean (SD) | 83.51 (15.44) | 85.85 (14.93) | 0.058 |
| Hip circumference, cm, mean (SD) | 97.67 (12.37) | 97.68 (12.30) | 0.992 |
| BMI, kg/m2, mean (SD) | 24.84 (3.30) | 25.35 (3.32) | 0.056 |
| Sleep parameters | Sleep parameters | Sleep parameters | Sleep parameters |
| Night sleep initiation time, hour, mean (SD) | 21.70 (0.91) | 21.65 (0.91) | 0.496 |
| Night sleep duration, hours, mean (SD) | 7.11 (0.88) | 7.11 (0.92) | 1.000 |
| Night sleep latency, minutes, median [IQR] | 20 [10.00, 30.00] | 20 [10.00, 30.00] | 0.510 |
| PSQI score, points, mean (SD) | 6.10 (3.85) | 6.14 (3.93) | 0.898 |
| Physical activity, n (%) | Physical activity, n (%) | Physical activity, n (%) | 0.588 |
| Light | 104 (20.27) | 39 (17.73) | |
| Moderate | 280 (54.58) | 119 (54.09) | |
| Vigorous | 129 (25.15) | 62 (28.18) | |
| Laboratory testing parameters | Laboratory testing parameters | Laboratory testing parameters | Laboratory testing parameters |
| FBG, mmol/L, mean (SD) | 6.49 (2.01) | 6.65 (2.30) | 0.371 |
| TG, mmol/L, mean (SD) | 1.67 (1.21) | 1.70 (1.41) | 0.783 |
| TC, mmol/L, mean (SD) | 4.66 (1.28) | 4.59 (1.24) | 0.494 |
| LDL-C, mmol/L, mean (SD) | 2.75 (1.02) | 2.70 (1.02) | 0.543 |
| SCr, μmoI/L, median [IQR] | 65 [54.70, 78.00] | 67.30 [56.00, 80.56] | 0.206 |
| Imaging parameters | Imaging parameters | Imaging parameters | Imaging parameters |
| RAD, mm, mean (SD) | 33.13 (5.80) | 33.11 (5.64) | 0.966 |
| LAD, mm, mean (SD) | 36.90 (6.20) | 36.08 (5.19) | 0.066 |
| RVD, mm, mean (SD) | 22.00 (3.28) | 22.89 (2.94) | 0.001* |
| LVD, mm, mean (SD) | 47.50 (6.37) | 47.54 (6.85) | 0.939 |
| Family history, n (%) | Family history, n (%) | Family history, n (%) | Family history, n (%) |
| Family history of hypertension | 309 (60.23) | 137 (62.27) | 0.604 |
| Family history of CHD | 141 (27.49) | 62 (28.18) | 0.847 |
| Family history of hyperlipemia | 120 (23.39) | 49 (22.27) | 0.742 |
| Medication information, n (%) | Medication information, n (%) | Medication information, n (%) | Medication information, n (%) |
| CCBs use | 315 (61.40) | 128 (58.18) | 0.414 |
| ACEI/ARBs use | 333 (64.91) | 139 (63.18) | 0.654 |
| Beta-blockers use | 100 (19.49) | 39 (17.73) | 0.576 |
| Diuretics use | 84 (16.37) | 44 (20.00) | 0.236 |
## 3.3. Screening of modeling variables based on LASSO regression analysis
Here, we used LASSO regression to screen and reduce the dimension of the 16 variables with statistical differences in Table 1, including age, educational level, type of work, duration of hypertension, waist circumference, hip circumference, night sleep initiation time, night sleep latency, PSQI score, physical activity, Scr, RAD, LAD, LVD, ACEI/ARB use, and beta-blockers. As log (λ) increases, the average standard error increases, and the normalization coefficients of the 16 candidate variables are compressed to varying degrees until all of them become zero [34]. Current results show that when the lambda of the minimum standard error was 0.05, the continuous variables of the gaussian model were selected as hip circumference and age; when the lambda of the minimum standard error was 0.038, the classification variables of the binomial model were physical activity and educational levels. Finally, we determined four predictive variables for machine learning modeling, including hip circumference, age, education level, and physical activity.
## 3.4. Development of a predictive machine learning model
Table 3 shows the performance of the prediction model. Current analysis shows that the best performance model was the XGB model, with an AUC of 0.88, accuracy of 0.81, sensitivity of 0.84, specificity of 0.80, and F1 score of 0.59. The second model was the LR model, with an AUC of 0.83, accuracy of 0.740, sensitivity of 0.78, specificity of 0.73, and F1 score of 0.50. Compared to the XGB model and LR model, the GNB model had poor performance, with an AUC of 0.816, accuracy of 0.74, sensitivity of 0.75, specificity of 0.74, and F1 score of 0.50.
**Table 3**
| Classifiers/performance | AUC (95%CI) | Accuracy | Sensitivity | Specificity | F1 score |
| --- | --- | --- | --- | --- | --- |
| LR model | 0.83 (0.79–0.87) | 0.74 | 0.78 | 0.73 | 0.5 |
| XGB model | 0.88 (0.85–0.91) | 0.81 | 0.84 | 0.8 | 0.59 |
| GNB model | 0.82 (0.78–0.86) | 0.74 | 0.75 | 0.74 | 0.5 |
## 3.5. Evaluation of machine learning prediction model based on XGB
Further results suggest that the XGB model was superior with an average AUC of 0.89 (Figure 2A) and 0.79 (Figure 2B) based on training set data and verification by 5-fold cross-validation. Meanwhile, the probability of cognitive impairment predicted by the predictive model was positively correlated with the actual probability of cognitive impairment, and the model had a good degree of calibration ($P \leq 0.05$).
**Figure 2:** *ROC curve for the XGB model. (A) ROC analysis results of the XGB model based on training set data by 5-fold cross-validation. (B) ROC analysis results of the XGB model based on 5-fold cross-validation of verification set data. ROC curve, receiver operating characteristic curve; AUC, area under curve; XGB, XGBoost.*
## 3.6. SHAP analysis of the model
Figure 3 shows the SHAP values for the combination of feature importance and feature effects for all individuals based on the XGB model. Each point in the diagram represents a feature and Shapley value, which represents the contribution of each feature to the predicted model output. Feature values are shown in color, and feature importance is arranged from top to bottom along the Y axis. Current SHAP results suggest that hip circumference is the most important feature in predicting Shapley value. Increased hip circumference was positively correlated with Shapley value, and larger hip circumference was more likely to be predicted as cognitive decline. Secondary to the hip circumference is age. Having an older age (colored in pink) was associated with Shapley values and was a positive predictor of early cognitive decline. Having a lower educational level and physical activity (colored in blue) was related to Shapley values and were negative predictors of cognitive decline. Overall, SHAP analysis showed that hip circumference and age were positive predictors of cognitive impairment, while educational levels and physical activity were negative predictors of cognitive impairment.
**Figure 3:** *Feature importance based on SHAP results. The vertical axis shows the features, the horizontal axis represents SHAP observations. Points were colored differently with reference to their eigenvalues, pink indicating a positive correlation with early cognitive decline, and blue indicating a negative correlation with early cognitive decline.*
Figure 4 shows the SHAP force plot for predicting individual early cognitive impairment. We presented several random cases, including correct prediction and incorrect prediction. Figure 4A shows the SHAP force plot to correctly predict cognitive decline; the predictive model was supported by the Shapley value of larger hip circumference, older age, lower physical activity, and educational levels, and had a predictive probability of 0.760. Figure 4B shows the SHAP force plot to correctly predict NCI; the prediction model was supported by the Shapley value of larger hip circumference, younger age, and more vigorous physical activity, with a prediction probability of 0.990. Figure 4C shows the SHAP force plot of mispredicted early cognitive decline; the prediction model was supported by the Shapley value of higher education levels and older age, with a prediction probability of 0.431. Figure 4D shows the SHAP force plot of mispredicted NCI; the prediction model was supported by the Shapley value of more vigorous physical activity and older age, with a prediction probability of 0.900.
**Figure 4:** *SHAP force plot for predicting early cognitive decline. (A) SHAP forces plot to correctly predict early cognitive decline. (B) SHAP forces plot to correctly predict NCI. (C) SHAP force plot of mispredicted early cognitive decline. (D) SHAP force plot of mispredicted NCI. Pink represents predictors of early cognitive decline, while blue represents predictors of NCI. Bold values show the likelihood of early cognitive decline in the ensemble model.*
## 3.7. DCA modeling analysis
Figure 5 shows the DCA analysis results based on the XGB model. DCA analysis indicates that the XGB model had significant net benefits for threshold probabilities at different time points, suggesting the model's potential clinical benefit.
**Figure 5:** *DCA analysis was performed to evaluate the clinical usefulness of the XGB model. The y-axis indicated the net benefit; the x-axis indicated the threshold probability. The solid red line shows the net benefit rate of the XGB forecast model. Within a certain threshold range, the XGB model has a higher net benefit. DCA, Decision curve analysis.*
## 3.8. Visualization of the prediction model
As shown in Figure 6, we developed a nomogram to predict the risk of early cognitive impairment in hypertension using four predictors, including hip circumference, age, educational level, and physical activity. The longer the line length, the greater the risk factors for early cognitive impairment. In the nomogram, each predictor has corresponding “points”, and add the points of four predictors to get the total score. Based on the total score, we can obtain the corresponding percentage risk value to determine the risk of early cognitive impairment in hypertension.
**Figure 6:** *Nomogram construction for early cognitive impairment in hypertension. We established a nomogram based on the four high-risk predictors for early cognitive impairment in hypertension. In this plot, to use the nomogram model, a single node value is loaded on each variable axis and the line is drawn upwards to determine the number of points. Then, the sum of these numbers is located on the total point axis, and the line is drawn downwards to the risk of early diagnosis of cognitive impairment.*
## 4. Discussion
The main findings of this study indicate that hip circumference, age, education, and physical activity were core predictors of early cognitive impairment in hypertensive individuals. Three machine learning predictive models (LR, XGB, and GNB) for early cognitive impairment based on multiple predictors in hypertensive individuals were developed and evaluated. The XGB model has the most superior predictive performance, with AUC (0.880), F1 score (0.589), accuracy (0.806), sensitivity (0.835), and specificity (0.798). Therefore, this study may provide a useful perspective for individualized accurate prediction of early cognitive impairment in the hypertensive population.
In the current study, we used LASSO regression to model feature selection. Compared to ordinary least squares regression, LASSO regression provides better control for multicollinearity and overfitting between variables and is considered holoholic to help select effective predictors of early cognitive impairment in hypertension [35]. Current findings demonstrate that hip circumference, age, educational levels, and physical activity were significant predictors of early cognitive impairment in hypertension and were determined for use in machine learning model development. The SHAP analysis further determined the feature importance of four influencing factors and explained the variables involved in modeling.
Current findings suggest that hip circumference was considered the most important predictor of cognitive impairment. In this regard, we know that while the effects of obesity on the risk of cognitive impairment are well established [36], specific obesity-related indices and their relationships are still a subject of debate. Interestingly, BMI, waist circumference, and several lipid parameters were removed from the LASSO models and baseline comparison in our study. While there is evidence that these removed markers are associated with the development of cognitive impairment, several studies have reported findings similar to ours. Three previously published studies have indicated that BMI is associated with cognitive function (37–39); specifically, for every 1 kg/m2 increase in BMI, the prevalence of cognitive impairment increased by $3\%$ [40], while another study reported the opposite result [41]. A previous study based on large population data revealed a potential relationship between several obesity-related indicators, including WC, waist-to-hip ratio (WHR), BMI, LDL-C, and cognitive impairment [42]. Although WC has been reported in two studies as an important indicator of cognitive ability [43, 44], current data analysis is more inclined to recommend hip circumference as a predictor of early cognitive impairment in hypertension. Although the relationship between WHR and cognitive impairment has been studied [45], the association between hip circumference and cognitive function has not been reported. Anatomically, hip circumference not only represents fat distribution, but also reflects changes in gluteal muscle, bone structure (pelvic width), and subcutaneous gluteal fat [46], which may be influenced by lifestyle-related factors such as alcohol consumption, smoking, and physical activity and other factors [47]. Aging is a secondary risk factor for predicting cognitive impairment in hypertension and is considered a natural and uncontrollable factor. Moreover, it has been shown that advanced age is the main independent risk factor for cognitive impairment [48]. A cross-sectional study from Shandong Province, China, showed that age was associated with cognitive impairment, but was considered a protective factor [32]. This is different from our current results. Heterogeneity of the study population is the most likely reason. Our study population was a more center-based cohort of hypertensive hospitalized patients aged 30–85, and included older community adults 65 and older in the analysis. In fact, it is currently accepted that there is a consistent link between increased blood pressure and cognitive decline in middle age, but the link between blood pressure and cognitive ability is less consistent in older adults [49]. Evidence that educational attainment and long-term education have a positive effect on cognitive function, especially in adulthood [50], provides a plausible explanation for the current finding that low educational attainment is a significant risk factor for early cognitive impairment in hypertension. A recent multicenter observational study based on Japanese hospital data also reported similar results to our research [51]. Hypertension is often accompanied by lifestyle changes, and in the current study design, we focused on more lifestyle factors in the study population, such as sleep quality and physical activity. Surprisingly, however, the LASSO regression chose physical activity over sleep quality as a predictor of early cognitive impairment, although new evidence is emerging regarding sleep interventions in MCI and Alzheimer's disease (AD) [52]. In addition, high dietary salt [53], excessive smoking, and drinking [48] may also increase the risk of cognitive impairment, but this is different from our current findings. There is accumulated evidence that physical activity may delay the progression of MCI to dementia [54], but there is also evidence that moderate to high-intensity physical activity is not beneficial in patients with early dementia [55]. Herein, we observed that physical activity, as a modifiable risk factor, was analyzed as a fourth important factor in modeling early cognitive impairment in hypertension and that low-intensity physical activity was associated with the risk of developing early cognitive impairment in hypertension, supported by other earlier studies [56]. Notably, SBP and DBP did not enter our model. A Mendelian randomization (MR) study noted that in middle age, high blood pressure, especially SBP, is causally associated with cognitive decline [57]; it may be speculated that this result may be related to the larger number of elderly patients with hypertension included. Collectively, risk factors for cognitive impairment remain controversial. Therefore, further longitudinal analyses are needed to investigate the relationship between hip circumference, age, educational level, physical activity, and early cognitive impairment, and to further verify its ability to predict early cognitive impairment of hypertension.
Machine learning, a sub-field of artificial intelligence, is a systematic process of learning and training from data and accurately predicting the occurrence of future events [58]. Recently, some scholars have developed predictive models based on machine learning for cognitive impairment, but not for hypertensive individuals. Casanova et al. [ 59] recommended predictors of cognitive impairment were education level, age, sex, stroke, neighborhood socioeconomic status (NSES), diabetes, APOEε4 carrier status, and BMI; distinguishing the highest and lowest grades produced the best radio frequency performance: accuracy = $78\%$ ($1.0\%$), sensitivity = $75\%$ ($1.0\%$), specificity = $81\%$ ($1.0\%$). Kang et al. [ 60] developed and validated the Aβ positive predictive model for amnestic mild cognitive impairment (aMCI) using two-stage modeling based on machine learning with good accuracy (AUC: 0.892). Tan et al. [ 6] used three classifiers (logistic regression, support vector machine, and gradient enhancer) to construct a set model for predicting cognitive impairment, with F1 score (0.87), AUC (0.80), accuracy (0.83), sensitivity (0.86), and specificity (0.74). In this study, we used three classifiers (LR, XGB, and GBN) to develop machine learning predictive models for early cognitive impairment in hypertension for the first time and obtained stable predictive performance. Current results suggest that the XGB model had the best predictive effect, which was better than the LR model and GBN model, with AUC (0.88), F1 score (0.59), accuracy (0.81), sensitivity (0.84), and specificity (0.80). Compared to the studies that have been reported, it seems that the elements of our model are more economical, convenient, and suitable for popularization. In addition, we further performed SHAP analysis to identify predictors that contribute most to early cognitive impairment in hypertension prediction and enhanced the interpretability and transparency of the current machine learning model. Finally, DCA analysis shows that the machine learning model has good clinical practicability and acceptability in hypertensive clinical settings.
## 5. Limitations and strengths
We have recognized the following limitations of current research. First, the main limitation of this study is the small number of samples, which is not conducive to the partitioning of the data set used to develop the model; cognitive ability may be affected by different age groups, and no age-stratified follow-up design was performed due to the small sample size. Second, participant selection procedures may be biased, which may lead to uneven distribution of data for analysis; some of the data came from self-reports collected through questionnaires, which may also lead to bias. Third, there appears to be a bidirectional association between hypertension and cognitive decline, with elevated blood pressure being both a risk factor for and a symptom of cognitive impairment; the data supporting model development is based on cross-sectional collection, which makes it difficult to derive potential causalities. Fourth, the Montreal Cognitive Assessment (MoCA), which was developed specifically for screening for MCI, appears to be more sensitive than MMSE in diagnosing early cognitive impairment [61]. However, the machine learning prediction model developed based on *Korean data* shows that MMSE's cognitive impairment prediction algorithm also has a good prediction effect [62]. Finally, we missed some possible features affecting cognitive function, such as individual genetic profiles [63], anxiety, and depression [64], which could have skewed the results. Richer dietary data are also needed, although we analyzed alcohol intake and average monthly salt intake. However, the current work also has several strengths. First, this work is the first to demonstrate the feasibility of using machine learning models to predict early cognitive impairment in individuals with hypertension. Second, we analyzed as fully as possible the economic and non-invasive development model of relevant variables. There are many candidate factors for auxiliary modeling, including sociodemographic factors, lifestyle factors, laboratory test parameters, imaging parameters, and drug information. Third, the current prediction model developed contains only four simple, non-invasive and cost-effective variables that are readily available even in poorly equipped clinical settings. Finally, the multi-center population data collection also reduces the bias to a certain extent and increases the reliability and universality of the machine model. Collectively, despite several limitations of the current study, it did provide a non-invasive and cost-effective way to predict the risk of early cognitive impairment in hypertension. Certainly, we warmly suggest future longitudinal studies that better confirm the predictive power of the model.
## 6. Conclusion
The XGB model based on hip circumference, age, educational level, and physical activity has good performance and may improve the outcome of early cognitive impairment in hypertensive clinical settings by providing early prediction and actionable feedback. In future studies, we will further develop and validate the current machine learning model based on other large-scale, multi-center population data.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Ethics Committee of the Affiliated Hospital of Shandong University of Chinese Medicine. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
## Author contributions
HJ was the main coordinator of the project and was responsible for the design of the study. JY and XZ drafted the manuscript of this paper. FJ and HC were involved in the supervision of data collection and stratification. XZ, ZW, and JT contributed to data compilation and analysis. All authors contributed intellectually to this manuscript and have approved this final version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
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|
---
title: 'Responsiveness and minimal clinically important difference of EQ-5D-5L in
patients with coronary heart disease after percutaneous coronary intervention: A
longitudinal study'
authors:
- Yu Zheng
- Lei Dou
- Qiang Fu
- Shunping Li
journal: Frontiers in Cardiovascular Medicine
year: 2023
pmcid: PMC10034178
doi: 10.3389/fcvm.2023.1074969
license: CC BY 4.0
---
# Responsiveness and minimal clinically important difference of EQ-5D-5L in patients with coronary heart disease after percutaneous coronary intervention: A longitudinal study
## Abstract
### Background
Although the five-level version of the EuroQol five-dimensional questionnaire (EQ-5D-5L) has been validated in various diseases, no empirical study has evaluated the responsiveness and minimal clinically important difference (MCID) of the instrument in patients with coronary heart disease (CHD), which limits the interpretability and clinical application of EQ-5D-5L. Therefore, this study aimed to determine the responsiveness and MCID of EQ-5D-5L in patients with CHD who underwent percutaneous coronary intervention (PCI) and identify the relationship between the MCID values and minimal detectable change (MDC).
### Methods
Patients with CHD were recruited for this longitudinal study at the Tianjin Medical University’s General Hospital in China. At baseline and 4 weeks after PCI, participants completed the EQ-5D-5L and Seattle Angina Questionnaire (SAQ). Additionally, we used the effect size (ES) to assess the responsiveness of EQ-5D-5L. The anchor-based, distribution-based, and instrument-based methods were used in this study to calculate the MCID estimates. The MCID estimates to MDC ratios were computed at the individual and group levels at a $95\%$ CI.
### Results
Seventy-five patients with CHD completed the survey at both baseline and follow-up. The EQ-5D-5L health state utility (HSU) improved by 0.125 at follow-up compared with baseline. The ES of EQ-5D HSU was 0.850 in all patients and 1.152 in those who improved, indicating large responsiveness. The average (range) MCID value of the EQ-5D-5L HSU was 0.071 (0.052–0.098). These values can only be used to determine whether the change in scores were clinically meaningful at the group level.
### Conclusion
EQ-5D-5L has large responsiveness among CHD patients after undergoing PCI surgery. Future studies should focus on calculating the responsiveness and MCID for deterioration and examining the health changes at the individual level in CHD patients.
## Introduction
Coronary heart disease (CHD) is one of the most common cardiovascular diseases worldwide. Despite tremendous efforts in the prevention, treatment, and rehabilitation of CHD, which has led to a decline in mortality over the last few decades, CHD remains the leading cause of death and disability in adults worldwide, including in China [1, 2]. It is expected to account for $30.5\%$ of all deaths worldwide by 2030 [2]. In China, it is estimated that approximately 11 million people have CHD, and the morbidity and mortality of CHD are still increasing annually [3].
Several factors are thought to increase the likelihood of CHD development. The main traditional risk factors are hypertension, smoking, hyperlipidemia, diabetes, old age, gender, obesity, lack of exercise, and family history of CHD [4]. Often, these risk factors do not exist in isolation. Therefore, CHD patients commonly present with multiple comorbidities and are often accompanied by psychological problems such as anxiety and depression [5]. Percutaneous coronary intervention (PCI) guided by coronary physiology can relieve patients’ clinical symptoms and has been regarded as a standard and effective treatment for CHD [6, 7]. Previous studies have found that PCI can improve the health-related quality of life (HRQoL) of patients with CHD, but the effects do not last [8].
HRQoL refers to different health domains, including physiological, psychological, and social components [9]. It is increasingly accepted as an important outcome, especially for chronic conditions, including CHD [8, 10]. HRQoL in patients with CHD can be quantified using generic or disease-specific instruments [11]. CHD-specific HRQoL instruments, such as the Seattle Angina Questionnaire (SAQ) and the Minnesota Living with Heart Failure Questionnaire (MLHF), can examine the specific impact of CHD [11]. Generic instruments, such as SF-36 and EQ-5D, enable the comparison of HRQoL between CHD and other conditions [11].
The EuroQoL five-dimensional questionnaire (EQ-5D) is a generic preference-based HRQoL instrument developed by the EuroQoL group that has been widely used and validated in populations with various diseases [12]. The original version of EQ-5D has three response levels [13]. However, studies have found that the three-level version has an obvious ceiling effect and cannot capture small changes sufficiently [14, 15]. Therefore, a five-level version of EQ-5D was developed [16]. Furthermore, health state utility (HSU) generated by the EQ-5D can support the calculation of quality-adjusted life years (QALYs), which will allow for pharmacoeconomic evaluation [17].
However, the interpretation of HRQoL instrument scores faces enormous challenges, particularly in defining what constitutes a trivial or an important change in patients’ quality of life (18–20). The results of HRQoL scores are usually analyzed and interpreted using statistical tests in clinical research. Although statistical tests can reflect statistical changes in the measured outcomes, they do not always indicate that the changes are clinically relevant [18, 21]. Therefore, to explain the clinical relevance of score changes, the concept of minimal clinically important difference (MCID) was developed [19]. Calculating the MCID values of HRQoL instruments can not only assess clinically meaningful score changes for patients in clinical trials but also supports the interpretability of the measuring instrument [22].
The minimal detectable change (MDC) is the smallest change in scores that can be detected after considering the measurement error [21]. For a reliable instrument, the MCID should be greater than MDC [23, 24]. Therefore, we can use the ratio between the MCID and MDC to judge whether the calculated MCID is a real change or just a meaningless measurement error, which can support the application of MCID in clinical research. Furthermore, MCID and MDC are related to responsiveness. The former two indicators are more clinically oriented, and the responsiveness is regarded as an indicator reflecting the ability of the instrument to detect changes over time [25, 26].
Previous studies have shown that the responsiveness and MCID are affected by clinical setting, and their use requires calculations in specific disease contexts [27, 28]. Therefore, although the responsiveness and MCID of the EQ-5D-5L HSU have been calculated in patients with various diseases, the results vary. For example, previous studies have found small-to-moderate responsiveness in patients with stroke [29] and large responsiveness in venous leg ulcers [30]. The MCID of the EQ-5D-5L HSU in patients with cervical intraepithelial neoplasia (CIN) was 0.039 [31], while in patients with COPD was 0.051 [32]. Furthermore, a study evaluating the relationship between MCID and MDC in patients with CIN showed that when the MCID of the EQ-5D HSU was 0.039, it could not be distinguished from measurement error at the individual level [31]. Another study in patients after hip or knee replacement found that when the MCID of EQ-5D HSU was 0.32, it could be distinguished from measurement error at both individual and group levels [33]. All of these studies support the interpretability and applicability of EQ-5D in different clinical settings. Nevertheless, according to our knowledge, no empirical study has evaluated the responsiveness and MCID of EQ-5D-5L in CHD patients after PCI, and none has analyzed the relationship between MCID and MDC in CHD patients.
Therefore, our study aimed to [1] evaluate the responsiveness of EQ-5D-5L in CHD patients who underwent PCI, [2] calculate the MCID estimates of EQ-5D-5L HSU, and [3] identify the validity of MCID by using the ratios between MCID and MDC.
## Study design and population
This prospective cohort study was conducted at the General Hospital of Tianjin Medical University, China, between April and September 2019. Inclusion criteria were [1] recruited from the cardiology inpatients, [2] one or more lesions with ≥$50\%$ stenosis as shown by coronary angiography and met the requirements of the “Chinese Guidelines for Percutaneous Coronary Intervention” [34], [3] aged 18 years or older, and [4] will undergo PCI within 1 or 2 days. The exclusion criteria were [1] unwillingness to provide informed consent, [2] inability to understand the questionnaire, [3] serious comorbidities (such as severe liver and kidney insufficiency or cirrhosis, malignant tumor), [4] a history of mental illness, and [5] hearing or vision impairment.
The sample size was considered sufficient if the number of patients was at least 5–10 times the number of items in the main outcome (EQ-5D-5L in this study). Depending on the questionnaire used in the study, the estimated sample size was at least 25–50 cases. We anticipated a potential $20\%$ loss to follow-up, so at least 60 patients were required. All patients who completed the questionnaire and provided informed consent were enrolled in this study. Ethical approval was obtained from the Ethics Review Board of the School of Health Care Management, Shandong University (No. ECSHCMSDU20191002). The study was carried out in accordance with the Declaration of Helsinki.
## Study procedure and quality control
Before the survey, we provided homogeneous training to all investigators, addressing the survey process and questionnaires, the significance of questionnaire items, and points for attention during the survey. During the survey process, uniform terminology was adopted to explain the research purpose and questionnaire requirements to all participants in detail. Participants filled out the questionnaire anonymously after they provided their informed consent.
All eligible participants completed the questionnaires at baseline (before PCI surgery) and a follow-up point (4-week after PCI). At baseline, socio-demographic characteristics, including age, gender, marital status, education level, and occupation, were collected through face-to-face interviews with eligible patients on-site. Additionally, clinical data, including CHD types, duration time, disease status, and comorbidities, were collected from their clinicians. Well-trained investigators conducted a follow-up survey over the telephone. The participants’ phone numbers were randomly and anonymously assigned to different investigators. After confirming the free time of each participant, the investigator conducted a one-to-one telephone follow-up. At both time points, the participants were asked to complete the SAQ and EQ-5D-5L to evaluate their health status. The questionnaire was completed within 10–20 min. The data were entered uniformly. After the original data were entered, another member checked them to ensure the correctness of the data entry.
## Instruments
EQ-5D-5L includes a multi-attribute health description system based on five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression [16]. Each dimension has a five-point response option: no problems, slight problems, moderate problems, severe problems, and extreme problems [35]. The Chinese version of the EQ-5D-5L has been verified, and the Chinese-specific tariff was used in this study [35, 36]. EQ-5D-5L HSU calculated by Chinese-specific tariff ranges from −0.391 to 1.000, where 1 indicates “full health” and 0 means “dead.” A negative HSU represents a certain health status that is worse than death.
Seattle Angina *Questionnaire is* a disease-specific instrument used to measure the health status of CHD patients. It consists of 19 items divided into five domains: physical limitation, angina stability, angina frequency, treatment satisfaction, and disease perception [37]. The SAQ was scored by assigning each response an ordinal value, with 1 being the option with the lowest level of health and 5 being the option with the best level of health. All domain scores can be calculated by summing the scores of items in each domain and can be transformed to 0–100 points. Higher scores indicated a better quality of life [38]. The Chinese version of the SAQ has been proven to be a valid and reliable instrument [39].
## Statistical analysis
Descriptive statistics were used to describe the socio-demographic characteristics, clinical characteristics, and distribution of EQ-5D-5L HSU and SAQ scores. Characteristics were presented as means and standard deviations (SD) for continuous variables and numbers and percentages (%) for categorical variables. For comparing scores between two-time points, the paired t-test was used for parametric data, and the Wilcoxon signed-rank test was used for nonparametric data. Statistical analysis was conducted using the SPSS software (IBM SPSS Statistics 25.0). All statistical tests were two-tailed, with a significance level of 0.05.
## Responsiveness
Researchers have no consensus regarding the best method for calculating responsiveness [40, 41]. Therefore, we used effect size (ES) to examine the responsiveness in this study. ES was defined as the difference in scores between baseline and follow-up divided by the SD of the baseline scores [21, 29, 42]. According to Cohen’s d criteria, ES can be categorized as small (<0.5), moderate (0.5 ~ 0.8), or large (>0.8) [43].
Table 3 reports the responsiveness results of the EQ-5D-5L. Among all patients, the EQ-5D HSU increased by 0.125 ($p \leq 0.001$) after PCI. The ES of EQ-5D-5L HSU was 0.850, indicating large responsiveness in all patients. Among patients who responded to the anchor transition as “improvement” (including minimal improvement and much improvement), the ES was 1.152, suggesting a large effect size. Additionally, for the patients who were “no change,” the change of HSU was 0.028 ($p \leq 0.05$). ES showed no responsiveness among the “no change” group.
**Table 3**
| Variables | EQ-5D-5L HSU (Mean ± SD) | EQ-5D-5L HSU (Mean ± SD).1 | EQ-5D-5L HSU (Mean ± SD).2 |
| --- | --- | --- | --- |
| Variables | All (n = 75) | No change (n = 12) | Improved (n = 63) |
| Baseline score | 0.850 ± 0.147 | 0.898 ± 0.233 | 0.841 ± 0.125 |
| Follow-up score | 0.975 ± 0.102 | 0.927 ± 0.239 | 0.985 ± 0.040 |
| Change score | 0.125 ± 0.117* | 0.028 ± 0.044 | 0.144 ± 0.118* |
| ES | 0.850 | 0.120 | 1.152 |
## Minimal clinically important difference
Anchor-based method: The anchor-based approach uses external criteria to anchor minimal but important change scores for participants [44]. Correlation ≥|0.3| can be used as a threshold to assess the usefulness of the anchor [44]. Therefore, Spearman’s correlation coefficient was used to quantify the association between changes in EQ-5D-5L HSU and SAQ scores. We used half the SD of anchor scores at baseline as the lower cut-off of minimal change and twice the lower cut-off as the upper cut-off [45, 46]. Specifically, participants were categorized into three groups according to the change scores of the anchor: no change (<0.5 baseline SD), minimal change (≥0.5 baseline SD and ≤1 baseline SD), and large change (>1 baseline SD). To obtain the MCID for improvement, the mean change score for the “minimal improvement” group was subtracted from the average change score for the “no change” group [47]. Likewise, the deteriorative MCID was the difference between the mean change score for the “minimal deterioration” group and the “no change” group [47].
A receiver operating characteristic (ROC) curve was constructed to estimate the MCID in this study, and the area under the curve (AUC) was used to represent the ability of the instrument to distinguish patients who underwent a clinically meaningful change. The Youden index was calculated to determine MCID estimates with the highest sensitivity and specificity using the following formula: Youden index=sensitivity−1−specificity. The cut-off point corresponding to the maximum Youden index was the optimal cut-off value of the ROC curve and was the MCID [48, 49].
Instrument-based method: Instrument-based method is based on the average difference in the EQ-5D HSU between the baseline health states and single-level transitions to other health states [50]. According to the direction of single-level transitions, MCID can be categorized into three groups: only transitions to better health states, only transitions to worse states and all transitions to other health states [46]. In this study, we only focused on the better transitions. In addition, because the conversion parameter between Lever 3 (moderate problem) and Level 4 (severe problem) exceeds other adjacent levers at least 1.4 times in all dimensions according to the Chinese scoring algorithm, we excluded the interconversion between these two levels to avoid overestimating MCID [31, 46]. Finally, when calculating the improved MCID, we excluded the baseline health state “11,111” because it could no longer be improved.
Distribution-based method: Based on previous studies, half the SD of baseline scores and one-SEM were considered to approximate values of MCID [45, 51, 52]. Therefore, we calculated half the SD of the EQ-5D-5L HSU at baseline as the MCID. Additionally, the standard error measurement (SEM) was calculated using the following formula: SEM=SD(1−r), where r is the test–retest reliability or Cronbach’s α coefficient [51]. The Cronbach’s α coefficient of the EQ-5D-5L was calculated at baseline and follow-up in this study. The SEM was computed for the baseline and follow-up scores, and the mean was calculated to provide an MCID estimate.
## Minimal detectable change
The MDC is derived from SEM and is calculated using the formula: MDC=SEM∗Z−score∗2 [33]. In this study, a $95\%$ confidence level was established, corresponding to a Z-score of 1.96. This MDC was considered MDC$95\%$(ind), representing the smallest detectable change after considering the measurement error at the individual level. According to Boer, MDC$95\%$(group) is equal to MDC$95\%$(ind) divided by n, where n represents the sample size [53]. Finally, we used the ratio of MCID to MDC for comparisons at the individual and group levels. If the ratio of MCID to MDC > 1, the calculated MCID can be used to reflect the real minimal important change. Otherwise, the calculated MCID represents the measurement error of the questionnaire and is not a valid value [54].
## Descriptive analysis
Seventy-nine patients were included at baseline. Three patients were lost to follow-up during the study period, and one died. The socio-demographic and clinical characteristics of the 75 patients who completed the questionnaire at both time points are presented in Table 1. The mean age of the patients was 64.6 ± 9.1 years. Most of them were male ($62.7\%$), retired ($73.3\%$), and married ($96.0\%$). More than four-fifths ($86.7\%$) of them were diagnosed with unstable angina, and the mean duration of the disease was 1.6 ± 2.0 months. The proportion of participants with hypertension, diabetes, and hyperlipidemia was 65.3, 37.3, and $28.0\%$, respectively. Most patients ($61.3\%$) had at least two comorbidities. Additionally, $84\%$ of patients reported an improved health status after PCI, and their mean age was 64.5 ± 8.5 years. Most were male ($58.7\%$), retired ($74.6\%$) and married ($96.8\%$). Furthermore, $16\%$ of patients reported no change in their health status, and their mean age was 62.2 ± 10.9 years, and they were mostly male ($83.3\%$), retired ($66.7\%$), and married ($91.7\%$).
**Table 1**
| Characteristic | Overall (n = 75) N (%) or Mean ± SD | Improved (n = 63) N (%) or Mean ± SD | No change (n = 12) N (%) or Mean ± SD |
| --- | --- | --- | --- |
| Socio-demographic | | | |
| Gender | | | |
| Male | 47 (62.7) | 37 (58.7) | 10 (83.3) |
| Female | 28 (37.3) | 26 (41.3) | 2 (16.7) |
| Age (years) | | | |
| Mean ± SD | 64.6 ± 9.1 | 64.5 ± 8.5 | 62.2 ± 10.9 |
| Range | 39–84 | 41–84 | 39–82 |
| Educational level | | | |
| Illiteracy or primary school | 8 (10.7) | 8 (12.7) | 0 (0) |
| Secondary school | 33 (44.0) | 26 (41.3) | 7 (58.3) |
| High school or technical secondary school | 22 (29.3) | 18 (28.6) | 4 (33.3) |
| University degree and above | 12 (16.0) | 11 (17.5) | 1 (8.3) |
| Occupation | | | |
| Workinga | 20 (26.7) | 16 (25.4) | 4 (33.3) |
| Retired | 55 (73.3) | 47 (74.6) | 8 (66.7) |
| Marital status | | | |
| Married | 72 (96.0) | 61 (96.8) | 11 (91.7) |
| Unmarriedb | 3 (4.0) | 2 (3.2) | 1 (8.3) |
| Monthly income (Chinese Yuan, CNY) | | | |
| ≤4,000 | 37 (49.3) | 32 (50.8) | 5 (41.7) |
| >4,000 | 38 (50.7) | 31 (49.2) | 7 (58.3) |
| Smoking | | | |
| Yes | 13 (17.3) | 11 (17.5) | 2 (16.7) |
| No | 62 (82.7) | 52 (82.5) | 10 (83.3) |
| Drinking | | | |
| Yes | 13 (17.3) | 12 (19.0) | 1 (8.3) |
| No | 62 (82.7) | 51 (81.0) | 11 (91.7) |
| Exercise | | | |
| Yes | 26 (34.7) | 22 (34.9) | 4 (33.3) |
| No | 49 (65.3) | 41 (65.1) | 8 (66.7) |
| Clinical characteristics | | | |
| CHD type | | | |
| Stable angina | 1 (1.3) | 0 (0) | 1 (8.3) |
| Unstable angina | 65 (86.7) | 55 (87.3) | 10 (83.3) |
| Acute myocardial infarction | 9 (12.0) | 8 (12.7) | 1 (8.3) |
| Duration of CHD (months) | | | |
| Mean ± SD | 1.6 ± 2.0 | 1.7 ± 2.1 | 0.8 ± 1.0 |
| Range | 0–12 | 0–12 | 0–1.5 |
| Disease state | | | |
| First episode | 41 (54.7) | 32 (50.8) | 9 (75.0) |
| Relapse | 34 (45.3) | 31 (49.2) | 3 (25.0) |
| Comorbidities | | | |
| Yes | 67 (89.3) | 56 (88.9) | 11 (91.7) |
| No | 8 (10.7) | 7 (11.1) | 1 (8.3) |
| Number of comorbidities | | | |
| ≤1 | 29 (38.7) | 27 (42.9) | 2 (16.7) |
| 2 | 23 (30.7) | 19 (30.2) | 4 (33.3) |
| ≥3 | 23 (30.7) | 17 (27.0) | 6 (50.0) |
| Prevalence of comorbidities | | | |
| Hypertension | 49 (65.3) | 42 (66.7) | 7 (58.3) |
| Diabetes | 28 (37.3) | 21 (33.3) | 7 (58.3) |
| Hyperlipidemia | 21 (28.0) | 18 (28.6) | 3 (25.0) |
Table 2 describes the score distribution of the EQ-5D-5L HSU and SAQ at both time points. The mean EQ-5D-5L HSU scores at baseline and follow-up were 0.850 and 0.975, respectively, with an average change of 0.125 ($p \leq 0.001$). For the SAQ, the mean scores were 56.414 and 72.077 at the two time points, respectively, with an average change score of 15.663 ($p \leq 0.001$).
**Table 2**
| Instrument scale | Baseline | Baseline.1 | Baseline.2 | Baseline.3 | Follow-up | Follow-up.1 | Follow-up.2 | Follow-up.3 | Average change |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Instrument scale | Mean ± SD | Median | LQ | UQ | Mean ± SD | Median | LQ | UQ | Mean ± SD |
| EQ-5D-5L HSU | 0.850 ± 0.147 | 0.897 | 0.779 | 0.951 | 0.975 ± 0.102 | 1.000 | 1.000 | 1.000 | 0.125 ± 0.117* |
| SAQ | 56.414 ± 10.149 | 56.322 | 48.280 | 64.368 | 72.077 ± 6.377 | 73.563 | 68.966 | 75.862 | 15.663 ± 9.387* |
## Anchor-based analysis
There was a moderate correlation between the change scores of the EQ-5D HSU and SAQ, with a correlation coefficient of 0.533 (Table 4). The MCID estimates of the EQ-5D HSU based on the SAQ-anchored method are listed in Table 5. Based on the SAQ scores, no participant reported a worsening health state from baseline to four-week after PCI; therefore, in this study, we only focused on the improved MCID. As shown in Table 5, an increase of 0.052 points ($95\%$ CI: −0.061, 0.165) in the EQ-5D HSU corresponded to a minimally important change for patients anchored by the SAQ.
Receiver operating characteristic analysis was also performed to identify improved MCID only (Figure 1). The MCID estimate derived from the ROC curve was 0.098, corresponding to a sensitivity of $69.8\%$ and a specificity of $91.7\%$. The AUC anchored by the SAQ was 0.845 ($95\%$ CI: 0.753, 0.936), suggesting that EQ-5D can excellently distinguish patients whose health states improved and those whose health states did not change.
**Figure 1:** *Receiver-operating characteristic (ROC) curve of EQ-5D-5L HSU change score in patients whose health states improved.*
## Instrument- and distribution-based analysis
Based on the instrument-defined method, the MCID of EQ-5D HSU was 0.055 ($95\%$ CI: 0.054, 0.58). An MCID estimate of 0.074 was produced using half the SD of HSU at baseline. The MCID derived from the SEM value was 0.078 (Table 6).
**Table 6**
| EQ-5D-5L HSU | Distribution-based method | Distribution-based method.1 | Distribution-based method.2 | Distribution-based method.3 | Instrument-based method |
| --- | --- | --- | --- | --- | --- |
| EQ-5D-5L HSU | SD | Half SD | One-SEM | Cronbach’s α | Instrument-based method |
| Baseline | 0.147 | 0.074 | 0.080 | 0.707 | |
| Follow-up | | | 0.076 | 0.731 | |
| MCID | | 0.074 | 0.078 | | 0.055 |
## Validity of the MCID estimates
Table 7 shows ratios of MCID estimates calculated by all methods to MDC$95\%$(ind) and MDC$95\%$(group). The ratios of MCIDs computed by all methods to MDC$95\%$(ind) were all less than 1, indicating that on individual levels, the MCIDs cannot be discriminated from measurement error to reflect a real meaningful change. However, the ratios of MCIDs to MDC$95\%$(group) all exceeded 1, which means that the MCID can detect the smallest important improvement in the group of 75 CHD patients with $95\%$ confidence.
**Table 7**
| Variables | EQ-5D-5L HSU | EQ-5D-5L HSU.1 | EQ-5D-5L HSU.2 | EQ-5D-5L HSU.3 | EQ-5D-5L HSU.4 | EQ-5D-5L HSU.5 |
| --- | --- | --- | --- | --- | --- | --- |
| Variables | MDC | half SD | One-SEM | Anchor-based method | ROC curve | Instrument-defined method |
| MCID | | 0.074 | 0.078 | 0.052 | 0.098 | 0.055 |
| MDC95%CI | | | | | | |
| Ind | 0.216 | | | | | |
| Group | 0.025 | | | | | |
| Ratio | | | | | | |
| Ind | | 0.340 | 0.361 | 0.241 | 0.453 | 0.254 |
| Group | | 2.948 | 3.124 | 2.083 | 3.926 | 2.203 |
## Discussion
This study estimated the responsiveness and MCID of the instrument in CHD patients after PCI surgery to support score interpretation and clinical application of the EQ-5D-5L. This study showed that the EQ-5D-5L was largely responsive to changes and provided evidence that the MCID of the EQ-5D-5L HSU in patients with CHD ranged from 0.052 to 0.098. The MCID calculated in this study can distinguish significant changes in the HSU at the group level but not at the individual level.
Responsiveness has been suggested as an additional criterion for evaluating HRQoL instruments, which can reflect the ability of an instrument to respond to changes and is essential for longitudinal validity [55]. Various methods can be used to assess responsiveness, such as ES, SRM, relative efficiency (RE), and regression models. However, there is no consensus on the preferred method, and different methods provide different results [40, 41]. According to Husted et al. [ 42] ES is one of the most frequently used approaches for assessing responsiveness. Therefore, this method was used in this study, and a large responsiveness of the EQ-5D-5L was found in all participants and improved participants.
The responsiveness of EQ-5D-5L HSU has been explored in several diseases. In patients with acute asthma who self-reported improvement, researchers found that the EQ-5D-5L had moderate to large responsiveness [56]. Chen et al. [ 29] found that the EQ-5D-5L had small-to-moderate responsiveness in subacute and chronic stroke patients. Golicki et al. [ 57] found moderate to large responsiveness of the EQ-5D-5L in acute stroke patients whose health status improved from baseline. Although the above studies all found that EQ-5D-5L is responsive, this is quite different from our finding of large responsiveness. The source of inconsistency may be attributed to differences in patients’ disease stage or severity at baseline. In this study, $98.7\%$ of patients had unstable angina or acute myocardial infarction, and $61.4\%$ had at least two comorbidities, making patients have a stronger perception of the change in their health status compared with patients with chronic or stable disease stages, leading to large responsiveness. Furthermore, EQ-5D is more responsive to large treatment effects [28]. Compared with other treatments, PCI surgery can greatly improve patients’ perceived HRQoL in the short term [58].
There is no “one-size-fits-all” method and no consensus on the best method to calculate the MCID of HRQoL instruments [59]. Anchor-based and distribution-based methods are the main methods for evaluating the MCID [60]. The anchor-based approaches use an external and independent indicator to calculate the MCID by comparing the scores in anchor-based groups, including the change difference method, ROC curve, regression model, etc. [ 44]. In contrast, the distribution-based methods rely on measures of outcome variability, using the statistical property of the data set to identify the MCID [61], including the SD, SEM, and ES methods. Usually, these two methods are used together to ensure the accuracy of MCID estimates because each has its advantages and disadvantages [47]. In this study, we chose the most commonly used anchor-based methods (i.e., the change difference method and ROC curve) and distribution-based methods (i.e., 0.5 SD and SEM) to calculate the MCID of the EQ-5D-5L [27, 62]. In addition to these two methods, Luo et al. [ 50] first used an instrument-based method to evaluate MCID for the EQ-5D-3L, and the results showed that it is feasible to use this method to evaluate the MCID of preference-based HRQoL instruments. Subsequently, the instrument-based method has been widely used for calculating the MCID of the EQ-5D [46, 63, 64]. Therefore, we used the above three methods simultaneously to ensure the accuracy of the MCID and found that the average MCID was 0.071, with a range from 0.052 to 0.098.
The ratios of MCIDs to MDC$95\%$(group) were all greater than 1, indicating that the MCIDs were valid at the group level and could be distinguished from measurement errors. However, the ratios of MCIDs to MDC$95\%$(ind) were all less than 1, which means that the calculated MCIDs would not be useful at the individual level. One possible explanation may be that most patients had longer CHD duration, and nearly half of them were not first-episode CHD, which made them adapt to this disease and improved baseline scores. Another explanation may be that most patients have at least two comorbidities, resulting in lower psychological expectations of health changes.
Previous studies commonly used the Global Rating of Change Questionnaire (GRCQ) as an anchor. However, the GRCQ contains only one question, which makes it difficult or impossible to capture changes in participants’ HRQoL [49]. Moreover, its validity and reliability are uncertain [18]. Studies have shown that disease-specific questions have higher construct validity than global transition questions as anchors for determining MCID [65]. The GRCQ was not used or adopted in this study because of these limitations. In contrary, we used a valid, reliable, multi-attribute, and disease-specific instrument to anchor the EQ-5D-5L HSU as in previous studies [32, 66]. Furthermore, it has been illustrated that responsiveness and MCID may be affected by the direction of changes (60, 67–69). However, no patients in this study reported that they experienced health deterioration after PCI anchored by SAQ change scores. The outcome of clinical interest lies in surgery-associated health improvement and the resulting effect on CHD patients’ quality of life rather than exacerbation. Therefore, deteriorative responsiveness and MCID were not evaluated in this study.
The MCID values may vary according to disease, interventions, and baseline characteristics, including socio-demographic and clinical characteristics; therefore, MCID may vary by gender, education level, type of CHD, and different comorbidities in this study [47, 70]. In particular, studies found that the prognosis of male patients with CHD after PCI was better than that of female patients; the mortality, incidence of major adverse cardiovascular events, and bleeding events after PCI in female patients were higher than those in male patients; and the health status (such as HRQoL, mental health) of female patients after PCI was worse than that of male patients (71–73). Therefore, these differences in prognosis between genders may affect their perception of health and cause the MCID to vary among genders. However, due to the limited sample size within anchor-defined categories for the different stratified subgroups, it was impossible to explore whether the resultant MCID estimates based on these clinical/demographic factors in patients with CHD were different. We suggest exploring this issue using a larger sample size in the future.
This is the first study to evaluate the responsiveness and MCID of the EQ-5D-5L HSU in CHD patients after PCI. This study has several advantages. First, in addition to commonly used anchor-based and distribution-based methods, we adopted the instrument-based method to ensure the accuracy of the MCID estimates. Second, we used a multidimensional disease-specific scale as an anchor, which can reflect the changes in patients’ HRQoL from multiple dimensions. Third, we analyzed whether the MCID calculated by each method could reflect meaningful changes at both the individual and group levels, which allowed us to avoid false interpretations of the MCID.
This study also has few limitations. First, the patients were recruited from one Tianjin city hospital, which may not represent all the CHD patients across China. Second, the small sample size of our study may have affected the accuracy of the results, although it met the basic requirements for calculating the MCID [26]. Third, the responsiveness and MCID may differ depending on the research setting, including interventions and patients’ characteristics. Therefore, the results of this study may not be applicable under other conditions. Fourth, this was a longitudinal real-world study, and randomized controlled trials are needed to verify the MCID among CHD patients after PCI in the future.
## Conclusion
The EQ-5D-5L was largely responsive to patients with CHD undergoing PCI surgery. The MCID of EQ-5D-5L was 0.071, with a range between 0.052 and 0.098 in this study, and the calculated MCIDs could only determine whether patients experienced meaningful changes at the group level. Future studies should focus on the calculation of deteriorative responsiveness and MCID and examine the validity of MCID in CHD patients at individual levels.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by the Ethics Review Board of the School of Health Care Management, Shandong University (Reference No. ECSHCMSDU20191002). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
LD and SL designed the study. QF performed the data collection. LD and YZ performed the data analyses, and all authors contributed to interpreting the data. YZ drafted the manuscript, which was critically revised by all others. All authors read and approved the final manuscript.
## Funding
Financial support for this project was provided by NHC Key Lab of Health Economics and Policy Research (Shandong University; no. NHC-HEPR2018003) and National Natural Science Foundation of China (no. 71403056).
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Prognostic nomograms integrating preoperative serum lipid derivative and systemic
inflammatory marker of patients with non-metastatic colorectal cancer undergoing
curative resection
authors:
- Dimei Huang
- Shaochu Zheng
- Fang Huang
- Jingyu Chen
- Yuexiang Zhang
- Yusha Chen
- Bixun Li
journal: Frontiers in Oncology
year: 2023
pmcid: PMC10034181
doi: 10.3389/fonc.2023.1100820
license: CC BY 4.0
---
# Prognostic nomograms integrating preoperative serum lipid derivative and systemic inflammatory marker of patients with non-metastatic colorectal cancer undergoing curative resection
## Abstract
### Background
Lipid metabolism and cancer-related inflammation are closely related to the progression and prognosis of colorectal cancer (CRC). Therefore, this study aims to establish novel nomograms based on the combined detection of preoperative blood lipids and systemic inflammatory indicators to predict the overall survival (OS) and cancer-specific survival (CCS) of CRC patients.
### Methods
A total of 523 patients with stage I-III CRC in our institute were collected from 2014 to 2018. The independent predictors for OS and CCS were determined by forward stepwise Cox regression for the establishment of prognostic models. The superiorities of different models were compared by concordance index (C-index), Akaike information criterion (AIC) and integrated discrimination improvement analysis. The performance of the nomograms based on the optimal models was measured by the plotting time-dependent receiver operating characteristic curves, calibration curves, and decision curves, and compared with the tumor-node-metastasis (TNM) staging system. The cohort was categorized into low-risk, medium-risk and high-risk groups according to the risk points of the nomogram, and analyzed using Kaplan–Meier curves and log-rank test.
### Results
Preoperative TG/HDL-C ratio (THR) ≥ 1.93 and prognostic nutritional index (PNI) ≥ 42.55 were independently associated with favorable outcomes in CRC patients. Six (pT stage, pN stage, histological subtype, perineural invasion, THR and PNI) and seven (pT stage, pN stage, histological subtype, perineural invasion, gross appearance, THR and PNI) variables were chosen to develop the optimal models and construct nomograms for the prediction of OS and CCS. The models had lower AIC and larger C-indexes than other models lacking either or both of THR and PNI, and improved those integrated discrimination ability significantly. The nomograms showed better discrimination ability, calibration ability and clinical effectiveness than TNM system in predicting OS and CCS, and these results were reproducible in the validation cohort. The three risk stratifications based on the nomograms presented significant discrepancies in prognosis.
### Conclusion
Preoperative THR and PNI have distinct prognostic value in stage I-III CRC patients. The nomograms incorporated the two indexes provide an intuitive and reliable approach for predicting the prognosis and optimizing individualized therapy of non-metastatic CRC patients, which may be a complement to the TNM staging system.
## Introduction
Colorectal cancer (CRC) is one of the most common malignant tumors worldwide, and its incidence rate on the rise in recent years. It is reported that CRC is the cancer with the third highest incidence rate and the second highest mortality rate, which is second only to lung cancer [1]. Surgical resection is currently the primary treatment for non-metastatic CRC, and about $50\%$ of patients have recurrence or metastasis [2]. Therefore, adjuvant therapy is recommended for CRC patients with high-risk factors. Despite continuous progress in treatment, the long-term survival rate of CRC patients is still not optimistic, with a 5-year survival rate of only $60\%$ for those undergoing radical surgery [3].
At present, the criteria widely used to predict postoperative risk and develop treatment strategy of CRC patients is still the classification system of tumor-node-metastasis (TNM) approved by the American Joint Cancer Committee. Moreover, higher histological grading, positive lymphovascular invasion, positive perineural invasion, preoperative intestinal obstruction or perforation, elevated levels of preoperative carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) have been generally recognized to be associated with recurrence, metastasis and short survival [4, 5]. However, due to the heterogeneity of CRC, its clinical course is not always predictable, and patients with the same disease stage and similar pathological features may have different outcomes. Thus, it is always necessary for clinicians to identify new and effective factors related to the poor prognoses of CRC patients, so as to optimize personalized treatment.
A large number of studies have shown that lipid metabolism plays a key role in carcinogenesis and the invasive or metastatic procedure of neoplasms [6]. For instance, abnormal triglyceride (TG) metabolism regulates tumor cell proliferation through adenosine monophosphate-activated protein kinase (AMPK) and mechanistic target of rapamycin (mTOR) pathway [7]; cholesterol promotes tumor progression and chemotherapy resistance by altering cytoskeleton, angiogenesis and apoptosis [8]; low-density lipoprotein cholesterol (LDL-C) promotes tumor progression through accumulating more reactive oxygen species and mitogen-activated protein kinase (MAPK) signaling pathway [9]; and high-density lipoprotein cholesterol (HDL-C) may be associated with increased levels of anti-inflammatory cytokines such as interleukin (IL)-10, which reduces the production of pro-inflammatory cytokines such as IL-6 and tumor necrosis factor (TNF)-α, thereby inhibiting the growth and proliferation of tumor cells and promoting their apoptosis [10]. It was reported that some routinely measured blood lipid parameters were effective prognostic factors for many solid malignant tumors, including CRC, prostate cancer, and breast cancer (11–13). In addition, serum lipid derivatives, including the ratio of TC minus HDL-C to HDL-C which is known as the atherosclerotic index (AI), the ratio of TG to HDL-C (THR), and the ratio of LDL-C to HDL-C (LHR), have been widely considered to be associated with cardiovascular and cerebrovascular diseases, and their prediction for prognosis is better than that of individual blood lipid indicators [14, 15]. Previous studies have shown that serum lipid derivatives have significant predictive ability for postoperative survival of patients with malignant tumors such as breast and gastric cancer (16–18).
Tumor-related inflammatory response is closely related to tumor cell proliferation, angiogenesis, metastasis, anti-tumor immune disorder, and drug resistance of anti-cancer therapy [19]. As markers of the systemic inflammatory, neutrophils and platelets secrete pro-inflammatory cytokines such as vascular endothelial growth factor, tumor necrosis factor, interleukin-2, and interleukin-6 to affect the development of tumors [20, 21]. It is found that monocytes and lymphocytes play an anti-tumor role by enhancing the immune response to neoplasms [22, 23]. Recently, some immune prognosis scores that can only be obtained by calculating the whole blood cell count and/or preoperative nutritional indicators, such as neutrophil/lymphocyte ratio (NLR) [24], platelet/lymphocyte ratio (PLR) [25], neutrophil/white blood cell ratio (NWR) [26], lymphocyte/monocyte ratio (LMR) [27], C-reactive protein (CRP)/albumin (Alb) ratio (CAR) [28], modified Glasgow prognostic score (mGPS) [29], systemic immune inflammation index (SII) [30] and prognostic nutritional index (PNI) [31, 32], have been proved to be able to predict the prognoses of a variety of malignant tumors including CRC.
Researchers have begun to pay attention to the subtle relationship between plasma lipids and inflammation in patients with malignancies. Blood lipids may affect the development of tumors by up-regulating or suppressing immune responses [11, 33]. On the contrary, malignant tumors may also trigger low-grade acute phase response through systemic inflammatory responses, leading to changes in lipid metabolism [34]. It is reasonable to propose a hypothesis that the combination of circulating serum lipids and immune indexes may be helpful to identify CRC patients with poor prognosis. In this study, we aim to explore the prognostic value of preoperative blood lipids and inflammatory indexes in patients with non-metastatic CRC undergoing radical surgery, and attempt to develop and validate novel and promising prognostic nomograms to complement TNM staging system and to optimize individualized prediction of such populations.
## Patient selection
We collected the medical records of CRC patients admitted to the Affiliated Tumor Hospital of Guangxi Medical University from January 2014 to December 2018. Patients who meet the following criteria will be included study: 1) patients who received radical surgery (surgical R0 excision) and were confirmed as CRC by histopathology; 2) without distant metastasis before operation; 3) patients did not receive any preoperative anti-tumor treatment. The exclusion criteria included: 1) patients who suffered from other malignant tumors in the past or at the same time; 2) patients with two or more primary tumors; 3) patients who diagnosed with familial hereditary CRC such as Lynch syndrome and familial adenomatous polyposis; 4) patients who have taken any drug known to affect blood lipids level, such as lipid-lowering drugs, glucocorticoids, and metformin within six months before collecting serum information; 5) patients who have any clinical evidence of acute infectious disease such as pneumonia and urinary tract infection, liver or kidney dysfunction, severe cardiovascular or cerebrovascular disease, and other serious diseases before surgery; 6) patients receiving less than 3 months of follow up; 7) patients lacking relatively complete and available clinicopathological, laboratory information and follow-up data. Eligible patients constitute the overall cohort ($$n = 523$$) and were randomly assigned to a training cohort ($$n = 418$$) or validation cohort ($$n = 105$$) in 4:1 ratio. The flow chart for cohort selection was described in Figure 1.
**Figure 1:** *Flow diagram of patient selection.*
This study was approved by the Ethics Committee of the Affiliated Tumor Hospital of Guangxi Medical University (LW2023012). Individual consent from patients for this retrospective study was waived.
## Data collection and definition
All data were obtained from the electronic medical record system of the Affiliated Tumor Hospital of Guangxi Medical University. Patients’ clinical information included gender, age at diagnosis, body mass index (BMI), presence of preoperative intestinal obstruction or perforation, operation type, and the number of harvested lymph nodes in surgery. Among them, BMI was further divided into underweight (<18.5 kg/m2), normal weight (18.5-23.9 kg/m2), overweight (24.0-27.9 kg/m2) and obesity (≥28 kg/m2) in accordance with the diagnostic criteria in China [35]. Tumor condition included TNM stage, pT stage, pN stage, histological subtype, differentiation degree, perineural invasion, lymphovascular invasion, tumor location, tumor size, and gross appearance. The pathological staging was performed according to the 8th edition of the American Joint Committee on Cancer (AJCC) TNM staging system.
Laboratory indicators included serum lipid indexes, inflammatory indexes, and tumor markers. All blood samples were obtained by drawing fasting venous blood from the participants within two weeks before surgery. Serum lipid indexes included total cholesterol (TC), TG, HDL-C, LDL-C, apolipoprotein A1 (apoA1), apolipoprotein B (apoB), lipoprotein(a) [Lp(a)], and three blood lipid derivatives such as AI, THR, and LHR. Among them, the levels of TC and TG were assayed with enzymatic methods, while HDL-C and LDL-C were detected with direct methods. Serum levels of apoA1, apoB, and Lp(a) were measured using immunoturbidimetry. The measurements were conducted on the Siemens ADVIA 2400 automatic biochemical analyzer, and the kits were purchased from commercial sources.
Inflammatory indexes included CAR, mGPS, SII, LMR, NLR, PLR, NWR, and PNI. Notably, mGPS was evaluated in light of the previously reported formula [36]: score 0: no increase in CRP (≤ 10mg/L); score 1: increase in CRP (> 10mg/L), but normal level of Alb (≥ 35g/L); score 2: increase in CRP (> 10mg/L), and decrease in Alb (< 35g/L). SII and PNI were calculated according to the previously reported formulas [30, 37]: SII = (platelet × neutrophil count)/lymphocyte count; PNI = 10 × serum Alb level (g/dL) + 0.005 × peripheral blood lymphocyte count (mm3). In addition, serum tumor markers such as CEA and CA19-9 were also collected.
## Follow-up
The Follow-up was performed every 3 to 6 months for the first 2 years after surgery, every 6 months for the next 3 years, and annually thereafter until patient’s death or March 2022. The primary endpoints were overall survival (OS) and tumor-specific survival (CCS). OS was defined as the interval from the date of surgery to death or the last follow-up, and CCS was defined as the interval from the date of surgery to death due to CRC or the last follow-up.
## Statistical analysis
Categorical variables were expressed as frequencies (percentages) and compared using the chi-square test or Fisher’s exact test. Cut-off values for tumor markers were defined according to conventional reference ranges. And for lipid and inflammatory markers, the optimal cut-off values were obtained in the overall cohort to predict OS by using the maximum x2 method in the R language “maxstat” package [38].
All variables were consistent with the proportional hazard assumption. Univariate Cox regression analysis was used to assess the impact of each variable on OS and CCS in the total cohort, and the variables with $P \leq 0.10$ were regarded as potential predictors. Multivariate Cox regression analysis with forward stepwise selection was performed to identify independent prognostic factors. First, we identified prognostic factors that independently predict OS and CCS among the clinicopathological variables, which were defined as basic risk factors. Then, these factors were entered into stepwise regression together with laboratory indicators for variable screening. All selected independent prognostic factors with $P \leq 0.05$ were used to establish predictive models for OS and CCS based on the training cohort. Meanwhile, in order to prove the superiorities of the models, a few models containing different combinations of independent candidate variables were constructed, such as blood lipid models including serum lipids and clinicopathological factors, inflammatory models including inflammatory indicators and clinicopathological factors, and basic risk models with only clinicopathological factors. Akaike information criterion (AIC) and concordance index (C-index) were used for model comparison. The smaller the AIC value and the larger the C-index, the better the model. In addition, the integrated discrimination improvement (IDI) was applied to measure the improvement in forecasting ability of models. Nomograms for the likelihood of OS and CCS at 3- and 5-year were developed separately based on the optimal models. The predictive performance of the nomograms was assessed in the training cohort and compared with the 8th AJCC TNM classification. Time-dependent receiver operating characteristic (ROC) curves were used to evaluate the predictive discrimination of the nomograms. Calibration curve and decision curve analysis were applied to assess the clinical consistency and effectiveness of the nomograms. The boot-strap resampling strategy was applied to validate the nomograms internally. Furthermore, the nomograms were evaluated utilizing the same method in the validation cohort. The differences in the survival curves of patients stratified into low-risk, medium-risk and high-risk categories according to the risk points calculated from the nomograms were analyzed by applying Kaplan-Meier (K-M) survival analysis and log-rank test.
All statistical analyses were performed by using SPSS software, version 25.0 (SPSS Inc., Chicago, IL, USA) and R 4.1.2 software (Institute of Statistics and Mathematics, Vienna, Austria). All tests were two-sided and P values less than 0.05 were considered statistically significant.
## Patient characteristics
A total of 523 CRC patients including 310 ($59.3\%$) males and 213 ($40.7\%$) females were included in this study. The median age at diagnosis was 60 [39, 68] years. Among them, lesions of 122 patients ($23.3\%$) were located in the right colon, 145 patients ($27.7\%$) were located in the left colon and 252 patients ($48.2\%$) were located in the rectum. There were 109 ($20.8\%$), 189 ($36.1\%$), and 225 ($43.0\%$) patients with TNM stage I, II, and III, respectively. The median follow-up time was 53 months, ranged from 3 to 97 months. The total cohort included 115 patients who died during the follow-up, of which 104 died of CRC and 11 died of other diseases. There was no statistically significant difference in baseline features between the training ($$n = 418$$) and validation cohorts ($$n = 105$$). Detailed characteristics in the cohorts were summarized in Table 1.
**Table 1**
| Characteristics | Categories | Overall cohort(N = 523) | Training cohort(N = 418) | Validation cohort(N = 105) | P-value |
| --- | --- | --- | --- | --- | --- |
| Sex | Female | 213 (40.7) | 172 (41.1) | 41 (39.0) | 0.779 |
| Sex | Male | 310 (59.3) | 246 (58.9) | 64 (61.0) | |
| Age (years) | < 60 | 251 (48.0) | 199 (47.6) | 52 (49.5) | 0.809 |
| Age (years) | ≥ 60 | 272 (52.0) | 219 (52.4) | 53 (50.5) | |
| BMI (kg/m2) | < 18.5 | 53 (10.1) | 42 (10.0) | 11 (10.5) | 0.467 |
| BMI (kg/m2) | 18.5-23.9 | 334 (63.9) | 268 (64.1) | 66 (62.9) | |
| BMI (kg/m2) | 24-27.9 | 111 (21.2) | 91 (21.8) | 20 (19.0) | |
| BMI (kg/m2) | ≥ 28 | 25 (4.8) | 17 (4.1) | 8 (7.6) | |
| TNM stage | I | 109 (20.8) | 91 (21.8) | 18 (17.1) | 0.418 |
| | II | 189 (36.1) | 146 (34.9) | 43 (41.0) | |
| | III | 225 (43.0) | 181 (43.3) | 44 (41.9) | |
| pT stage | T1/T2 | 134 (25.6) | 113 (27.0) | 21 (20.0) | 0.126 |
| | T3 | 150 (28.7) | 123 (29.4) | 27 (25.7) | |
| | T4 | 239 (45.7) | 182 (43.5) | 57 (54.3) | |
| pN stage | N0 | 300 (57.4) | 238 (56.9) | 62 (59.0) | 0.88 |
| | N1 | 155 (29.6) | 126 (30.1) | 29 (27.6) | |
| | N2 | 68 (13.0) | 54 (12.9) | 14 (13.3) | |
| Differentiation degree | High/moderate-high | 56 (10.7) | 45 (10.8) | 11 (10.5) | 0.27 |
| | Moderate | 393 (75.1) | 319 (76.3) | 74 (70.5) | |
| | Low/low-moderate | 74 (14.1) | 54 (12.9) | 20 (19.0) | |
| Histological subtype | Non-mucinous | 430 (82.2) | 349 (83.5) | 81 (77.1) | 0.168 |
| | Mucinous | 93 (17.8) | 69 (16.5) | 24 (22.9) | |
| Perineural invasion | Negative | 279 (53.3) | 222 (53.1) | 57 (54.3) | 0.915 |
| | Positive | 244 (46.7) | 196 (46.9) | 48 (45.7) | |
| Lymphovascular invasion | Negative | 373 (71.3) | 299 (71.5) | 74 (70.5) | 0.926 |
| Lymphovascular invasion | Positive | 150 (28.7) | 119 (28.5) | 31 (29.5) | |
| Tumor location | Right-side colon | 122 (23.3) | 90 (21.5) | 32 (30.5) | 0.099 |
| | Left-side colon | 145 (27.7) | 115 (27.5) | 30 (28.6) | |
| | Rectum | 256 (48.9) | 213 (51.0) | 43 (41.0) | |
| Tumor size (cm) | < 5cm | 283 (54.1) | 232 (55.5) | 51 (48.6) | 0.244 |
| | ≥ 5cm | 240 (45.9) | 186 (44.5) | 54 (51.4) | |
| Gross appearance | Protruded | 222 (42.4) | 185 (44.3) | 37 (35.2) | 0.118 |
| | Infiltrating/ulcerative | 301 (57.6) | 233 (55.7) | 68 (64.8) | |
| Intestinal obstruction or perforation | No | 512 (97.9) | 411 (98.3) | 101 (96.2) | 0.326 |
| Intestinal obstruction or perforation | Yes | 11 (2.1) | 7 (1.7) | 4 (3.8) | |
| Harvested lymph nodes (no.) | < 12 | 139 (26.6) | 111 (26.6) | 28 (26.7) | 1.0 |
| Harvested lymph nodes (no.) | ≥ 12 | 384 (73.4) | 307 (73.4) | 77 (73.3) | |
| Operation type | Open surgery | 90 (17.2) | 77 (18.4) | 13 (12.4) | 0.186 |
| | Laparoscopic surgery | 433 (82.8) | 341 (81.6) | 92 (87.6) | |
| CEA (ng/mL) | ≤ 5 | 354 (67.7) | 289 (69.1) | 65 (61.9) | 0.194 |
| | > 5 | 169 (32.3) | 129 (30.9) | 40 (38.1) | |
| CA19-9 (U/mL) | < 37 | 458 (87.6) | 368 (88.0) | 90 (85.7) | 0.631 |
| | ≥ 37 | 65 (12.4) | 50 (12.0) | 15 (14.3) | |
| TC (mmol/L) | < 5.61 | 417 (79.7) | 330 (78.9) | 87 (82.9) | 0.45 |
| | ≥ 5.61 | 106 (20.3) | 88 (21.1) | 18 (17.1) | |
| TG (mmol/L) | < 1.63 | 415 (79.3) | 328 (78.5) | 87 (82.9) | 0.391 |
| | ≥ 1.63 | 108 (20.7) | 90 (21.5) | 18 (17.1) | |
| HDL-C (mmol/L) | < 1.47 | 439 (83.9) | 351 (84.0) | 88 (83.8) | 1.0 |
| | ≥ 1.47 | 84 (16.1) | 67 (16.0) | 17 (16.2) | |
| LDL-C (mmol/L) | < 3.98 | 437 (83.6) | 349 (83.5) | 88 (83.8) | 1.0 |
| | ≥ 3.98 | 86 (16.4) | 69 (16.5) | 17 (16.2) | |
| AI | < 4.49 | 455 (87.0) | 362 (86.6) | 93 (88.6) | 0.708 |
| | ≥ 4.49 | 68 (13.0) | 56 (13.4) | 12 (11.4) | |
| THR | < 1.93 | 454 (86.8) | 362 (86.6) | 92 (87.6) | 0.909 |
| | ≥ 1.93 | 69 (13.2) | 56 (13.4) | 13 (12.4) | |
| LHR | < 2.96 | 320 (61.2) | 258 (61.7) | 62 (59.0) | 0.696 |
| | ≥ 2.96 | 203 (38.8) | 160 (38.3) | 43 (41.0) | |
| ApoA1 (g/L) | < 1.19 | 309 (59.1) | 250 (59.8) | 59 (56.2) | 0.573 |
| | ≥ 1.19 | 214 (40.9) | 168 (40.2) | 46 (43.8) | |
| ApoB (g/L) | < 0.91 | 264 (50.5) | 211 (50.5) | 53 (50.5) | 1.0 |
| | ≥ 0.91 | 259 (49.5) | 207 (49.5) | 52 (49.5) | |
| ApoA1/ApoB | < 0.89 | 69 (13.2) | 54 (12.9) | 15 (14.3) | 0.835 |
| | ≥ 0.89 | 454 (86.8) | 364 (87.1) | 90 (85.7) | |
| Lpa (mg/L) | < 586.00 | 452 (86.4) | 363 (86.8) | 89 (84.8) | 0.691 |
| | ≥ 586.00 | 71 (13.6) | 55 (13.2) | 16 (15.2) | |
| CAR | < 0.26 | 429 (82.0) | 341 (81.6) | 88 (83.8) | 0.697 |
| | ≥ 0.26 | 94 (18.0) | 77 (18.4) | 17 (16.2) | |
| mGPS (Score) | 0 | 435 (83.2) | 346 (82.8) | 89 (84.8) | 0.474 |
| | 1 | 39 (7.5) | 34 (8.1) | 5 (4.8) | |
| | 2 | 49 (9.4) | 38 (9.1) | 11 (10.5) | |
| SII | < 317.37 | 80 (15.3) | 64 (15.3) | 16 (15.2) | 1.0 |
| | ≥ 317.37 | 443 (84.7) | 354 (84.7) | 89 (84.8) | |
| LMR | < 4.70 | 333 (63.7) | 259 (62.0) | 74 (70.5) | 0.131 |
| | ≥ 4.70 | 190 (36.3) | 159 (38.0) | 31 (29.5) | |
| NLR | < 1.95 | 230 (44.0) | 181 (43.3) | 49 (46.7) | 0.609 |
| | ≥ 1.95 | 293 (56.0) | 237 (56.7) | 56 (53.3) | |
| PLR | < 190.59 | 361 (69.0) | 296 (70.8) | 65 (61.9) | 0.1 |
| | ≥ 190.59 | 162 (31.0) | 122 (29.2) | 40 (38.1) | |
| NWR | < 0.64 | 341 (65.2) | 275 (65.8) | 66 (62.9) | 0.653 |
| | ≥ 0.64 | 182 (34.8) | 143 (34.2) | 39 (37.1) | |
| PNI | < 42.55 | 96 (18.4) | 76 (18.2) | 20 (19.0) | 0.949 |
| | ≥ 42.55 | 427 (81.6) | 342 (81.8) | 85 (81.0) | |
## Identification of basic risk factors for predicting OS and CCS
As classic prognostic factors, Clinicopathological variables such as pT stage, pN stage, gross appearance, differentiation degree, histological subtype, perineural invasion and lymphovascular invasion were all associated with the OS and CCS by univariate analysis in the overall cohort (all $p \leq 0.10$) (Tables 2, 3). With further selection by forward stepwise Cox regression analysis, pT stage, pN stage, histological subtype, and Perineural invasion were determined as independent prognostic factors of OS (all $p \leq 0.05$) (Table 2), and pT stage, pN stage, gross appearance, histological subtype and perineural invasion were determined as independent predictors of CCS (all $p \leq 0.05$) (Table 3). All factors above were considered as the fundamental risk factors and will be used as the cornerstones for further variable screening.
## Association between preoperative laboratory indicators and prognosis
In the entire cohort, univariate analysis showed that preoperative blood lipid indexes such as TC, HDL-C, LDL-C, THR, LHR, ApoA1, ApoB and Lp(a) were considered to be potential prognostic indicators for OS (all $p \leq 0.10$), while TC, TG, HDL-C, THR, LHR, ApoA1, and Lp(a) were potential prognostic indexes for CCS (all $p \leq 0.10$) (Table 4). Among the preoperative inflammatory indicators, univariate analysis showed that PNI was potentially associated with both OS and CCS (all $p \leq 0.10$) (Table 4). Besides, tumor markers such as CEA and CA19-9 also showed a correlation with prognosis in univariate analysis (Table 4). Further, both the identified basic risk factors and the candidate variables in preoperative laboratory indexes were collectively incorporated into the Cox regression analysis with stepwise forward. After multivariate analysis, only THR and PNI of blood indexes were finally selected (all $p \leq 0.05$) (Table 5). Our results showed that high THR level in patients was not only correlated with better OS (HR: 0.39, $95\%$ CI: 0.19‐0.80, $$P \leq 0.010$$), but also correlated with better CCS (HR: 0.31, $95\%$ CI: 0.14‐0.72, $$P \leq 0.006$$), and high PNI level in patients was associated with better OS and CCS (HR: 0.56, $95\%$ CI: 0.36‐0.87, $$P \leq 0.010$$; HR: 0.50, $95\%$ CI: 0.31‐0.81, $$P \leq 0.004$$, respectively) (Table 5).
## Development and comparison of novel prognostic models
According to the independent prognostic variables previously determined by multivariate regression, the blood lipid and inflammation models (model A and E), blood lipid models (model B and F), inflammation models (model C and G), and basic risk models (model D and H) were developed respectively to predict OS and CCS based on the training cohort (Table 6). Among them, model A predicting OS included pT stage, pN stage, histological subtype, perineural invasion, THR, and PNI, while model E predicting CCS included pT stage, pN stage, gross appearance, histological subtype, perineural invasion, THR, and PNI. In the comparison of different prognostic models (Table 6), the models with both THR and PNI had lower AIC values and higher C-indexes (with 1000 boot-strap resampling adjustments) (Model A: AIC: 1006.232; adjusted C-index: 0.741. Model E: AIC: 882.210; adjusted C-index: 0.763) than did models with other variables combination. And compared with the C-indexes of other models, the difference is statistically significant (all $p \leq 0.05$). The IDI analysis illustrated that the addition of PNI parameter could improve the integrated discrimination ability of blood lipid models (Model A vs model B: IDI = 0.027, $$p \leq 0.022$$; model E vs model F: IDI = 0.035, $$p \leq 0.022$$). Similarly, the predictive capability of inflammatory models was improved by adding THR parameter (Model A vs model C: IDI = 0.021, $$p \leq 0.018$$; model E vs model G: IDI = 0.026, $$p \leq 0.004$$). Compared with the basic risk model, the models combined with THR and PNI significantly improved comprehensive prediction ability (Model A vs model D: IDI = 0.050, $$p \leq 0.004$$; Model E vs model H: IDI = 0.063, p = < 0.001), especially more significant than the TNM staging models (Model A vs TNM: IDI = 0.116, $p \leq 0.001$; Model E vs TNM: IDI = 0.153, $p \leq 0.001$).
**Table 6**
| Prognostic models | AIC | C-index | C-index.1 | P-value b | IDI c | P-value d |
| --- | --- | --- | --- | --- | --- | --- |
| Prognostic models | AIC | unadjusted | adjusted a | P-value b | IDI c | P-value d |
| OS | OS | OS | OS | OS | OS | OS |
| Model A | 1006.232 | 0.761 | 0.741 | - | – | - |
| Model B | 1012.556 | 0.746 | 0.728 | 0.004 | 0.027 | 0.022 |
| Model C | 1013.467 | 0.750 | 0.731 | 0.002 | 0.021 | 0.018 |
| Model D | 1020.590 | 0.733 | 0.716 | < 0.001 | 0.050 | 0.004 |
| TNM stage model | 1039.352 | 0.661 | 0.660 | < 0.001 | 0.116 | < 0.001 |
| CCS | CCS | CCS | CCS | CCS | CCS | CCS |
| Model E | 882.210 | 0.786 | 0.763 | - | – | - |
| Model F | 890.254 | 0.770 | 0.748 | 0.002 | 0.035 | 0.022 |
| Model G | 892.247 | 0.768 | 0.746 | < 0.001 | 0.026 | 0.004 |
| Model H | 901.469 | 0.750 | 0.731 | < 0.001 | 0.063 | < 0.001 |
| TNM stage model | 922.730 | 0.670 | 0.669 | < 0.001 | 0.153 | < 0.001 |
## Construction and validation of novel nomograms
Novel nomograms were constructed on the strength of the optimal models (Figures 2A, B). The predicted area under the curve (AUC) values for 3- and 5-year OS and CCS in the training cohort utilizing the nomograms were 79.0 ($95\%$ CI: 71.9-86.1) and 78.6 ($95\%$ CI: 72.0-85.3) (Figures 2C, D), and 81.3 ($95\%$ CI: 74.1-88.6) and 81.7 ($95\%$ CI: 75.2-88.3) (Figures 2E, F), respectively, all of which were superior to the AUC values predicted by TNM stage. The calibration curve adjusted by 1000 times boot-strap resampling also indicated that the prediction probability of the nomograms for 3- and 5-year OS and CCS were consistent with the actual observation (Figures 2G–J). Finally, we draw decision curves to illustrate the clinical applicability of the nomograms. The decision curves showed that the clinical effectiveness of the nomograms is better than that of TNM staging system within the actual threshold probability range (Figures 2K, L).
**Figure 2:** *Construction of nomograms based on the training cohort to predict OS (A) and CCS (B) at 3- and 5-year in patients with non-metastatic CRC after receiving radical surgery. ROC curves for assessing the discrimination ability of the nomograms and TNM staging system for OS (C, D) at 3- and 5-year and for CCS (E, F) at 3- and 5-year in the training cohort. Calibration curves for evaluating the clinical consistency of the nomograms in predicting 3- and 5-year OS (G, H) and 3- and 5-year CCS (I, J) in the training cohort. Decision curves of the novel nomograms and TNM classification for predicting 3- and 5-year survival of OS (K) and CCS (L) in the training cohort.*
In the validation cohort, the AUC values for predicting 3- and 5-year OS and CCS using the nomograms were 91.3 ($95\%$ CI: 83.2-99.5) and 83.3 ($95\%$ CI: 69.2-97.4) (Figures 3A, B), and 92.2 ($95\%$ CI: 85.0-99.5) and 87.3 ($95\%$ CI: 74.2-100.0) (Figures 3C, D), respectively, all of which were also higher than those of TNM stage. Similarly, the calibration curves (Figures 3E–H) and decision curves (Figures 3I, J) showed that the nomograms had favorable calibration capacity and clinical efficacy in predicting 3- and 5-year OS and CCS in the validation set.
**Figure 3:** *ROC curves for assessing the discrimination ability of the nomograms and TNM staging system for OS (A, B) at 3- and 5-year and for CCS (C, D) at 3- and 5-year in the validation cohort. Calibration curves for evaluating the clinical consistency of the nomograms in predicting 3- and 5-year OS (E, F) and 3- and 5-year CCS (G, H) in the validation cohort. Decision curves of the novel nomograms and TNM classification for predicting 3- and 5-year survival of OS (I) and CCS (J) in the validation cohort.*
According to the gross risk score assigned to each patient by nomograms, the cases in the training cohort were ranked in ascending order and divided into low-risk, medium-risk, and high-risk groups with $50\%$ and $80\%$ percentiles as the cut-off values (For OS: 190.48, 260.99; for CCS: 212.01, 287.03). The K-M survival curves revealed that the differences of survival rate among the groups were statistically significant (all $p \leq 0.001$) (Figures 4A, B).
**Figure 4:** *K-M curves depicting OS (A) and CCS (B) of the training cohort stratified by 50% and 80% percentiles of risk points calculated based on the nomograms. K-M survival curves describing OS (C) and CCS (D) of the training cohort categorized into 3 groups according to the combination of THR and PNI at different levels. Time-dependent AUC curves showing the AUC values of THR combined with PNI compared to serum lipid or systemic inflammatory indexes alone to predict OS (E, F) and CCS (G, H).*
## The prognostic value of combined THR and PNI in patients with CRC following radical surgery
We divided the training cohort into three groups: both high THR and high PNI, either low THR or low PNI, and both low THR and low PNI. The K-M survival curves showed that patients with low THR and low PNI had the shortest OS and CCS (all $p \leq 0.05$) (Figures 4C, D). In addition, we also plotted time-dependent AUC curves of each indicator. The results showed that the combination of THR and PNI could achieve higher AUC values in predicting OS and CCS during 20-60 months than using blood lipid or inflammatory parameters alone (Figures 4E–H).
## The relationship between THR and PNI and clinicopathological characteristics
To better understand the role of THR and PNI in CRC prognosis, we further analyzed the correlation between them and clinicopathological features in the entire cohort (Table 7). Patients with higher BMI and tumor located in the left colon or rectum had higher THR levels, indicating that THR level may be influenced by BMI and tumor location. Patients with higher PNI level tended to have younger age, have more advanced pN stage, have a higher proportion of in the left colon or rectum tumors, and have smaller tumor. These results suggested that the PNI level may be influenced by the age of patient, pN stage of the tumor, tumor location, and tumor size.
**Table 7**
| Variables | THR | Unnamed: 2 | Unnamed: 3 | PNI | Unnamed: 5 | Unnamed: 6 |
| --- | --- | --- | --- | --- | --- | --- |
| Variables | < 1.93 | ≥ 1.93 | P-value | < 42.55 | ≥ 42.55 | P-value |
| Variables | (N = 454) | (N = 69) | | (N = 96) | (N = 427) | |
| Sex | Sex | Sex | Sex | Sex | Sex | Sex |
| female | 188 (41.41) | 25 (36.23) | 0.494 | 37 (38.54) | 176 (41.22) | 0.713 |
| male | 266 (58.59) | 44 (63.77) | | 59 (61.46) | 251 (58.78) | |
| Age | Age | Age | Age | Age | Age | Age |
| <60 | 213 (46.92) | 38 (55.07) | 0.257 | 27 (28.12) | 224 (52.46) | < 0.001 |
| ≥60 | 241 (53.08) | 31 (44.93) | | 69 (71.88) | 203 (47.54) | |
| BMI | BMI | BMI | BMI | BMI | BMI | BMI |
| < 18.5 | 51 (11.23) | 2 (2.90) | 0.009 | 15 (15.62) | 38 (8.90) | 0.067 |
| 18.5-23.9 | 295 (64.98) | 39 (56.52) | | 64 (66.67) | 270 (63.23) | |
| 24-27.9 | 89 (19.60) | 22 (31.88) | | 15 (15.62) | 96 (22.48) | |
| ≥ 28 | 19 (4.19) | 6 (8.70) | | 2 (2.08) | 23 (5.39) | |
| pT stage | pT stage | pT stage | pT stage | pT stage | pT stage | pT stage |
| T1 | 9 (1.98) | 1 (1.45) | 0.723 | 1 (1.04) | 9 (2.11) | 0.252 |
| T2 | 111 (24.45) | 13 (18.84) | | 16 (16.67) | 108 (25.29) | |
| T3 | 130 (28.63) | 20 (28.99) | | 32 (33.33) | 118 (27.63) | |
| T4 | 204 (44.93) | 35 (50.72) | | 47 (48.96) | 192 (44.96) | |
| pN stage | pN stage | pN stage | pN stage | pN stage | pN stage | pN stage |
| N0 | 258 (56.83) | 42 (60.87) | 0.100 | 65 (67.71) | 235 (55.04) | 0.033 |
| N1 | 141 (31.06) | 14 (20.29) | | 25 (26.04) | 130 (30.44) | |
| N2 | 55 (12.11) | 13 (18.84) | | 6 (6.25) | 62 (14.52) | |
| Differentiation degree | Differentiation degree | Differentiation degree | Differentiation degree | Differentiation degree | Differentiation degree | Differentiation degree |
| High/moderate-high | 49 (10.79) | 7 (10.14) | 0.985 | 11 (11.46) | 45 (10.54) | 0.495 |
| Moderate | 341 (75.11) | 52 (75.36) | | 68 (70.83) | 325 (76.11) | |
| Low/low-moderate | 64 (14.10) | 10 (14.49) | | 17 (17.71) | 57 (13.35) | |
| Histological subtype | Histological subtype | Histological subtype | Histological subtype | Histological subtype | Histological subtype | Histological subtype |
| Non-mucinous | 371 (81.72) | 59 (85.51) | 0.550 | 78 (81.25) | 352 (82.44) | 0.899 |
| Mucinous | 83 (18.28) | 10 (14.49) | | 18 (18.75) | 75 (17.56) | |
| Perineural invasion | Perineural invasion | Perineural invasion | Perineural invasion | Perineural invasion | Perineural invasion | Perineural invasion |
| Negative | 238 (52.42) | 41 (59.42) | 0.339 | 47 (48.96) | 232 (54.33) | 0.401 |
| Positive | 216 (47.58) | 28 (40.58) | | 49 (51.04) | 195 (45.67) | |
| Lymphvascular invasion | Lymphvascular invasion | Lymphvascular invasion | Lymphvascular invasion | Lymphvascular invasion | Lymphvascular invasion | Lymphvascular invasion |
| Negative | 324 (71.37) | 49 (71.01) | 1.000 | 67 (69.79) | 306 (71.66) | 0.809 |
| Positive | 130 (28.63) | 20 (28.99) | | 29 (30.21) | 121 (28.34) | |
| Location | Location | Location | Location | Location | Location | Location |
| Right-side colon | 116 (25.55) | 6 (8.70) | 0.005 | 38 (39.58) | 84 (19.67) | < 0.001 |
| Left-side colon | 119 (26.21) | 26 (37.68) | | 28 (29.17) | 117 (27.40) | |
| Rctum | 219 (48.24) | 37 (53.62) | | 30 (31.25) | 226 (52.93) | |
| Tumor size | Tumor size | Tumor size | Tumor size | Tumor size | Tumor size | Tumor size |
| < 5cm | 246 (54.19) | 37 (53.62) | 1.000 | 26 (27.08) | 257 (60.19) | < 0.001 |
| ≥ 5cm | 208 (45.81) | 32 (46.38) | | 70 (72.92) | 170 (39.81) | |
| Gross appearance | Gross appearance | Gross appearance | Gross appearance | Gross appearance | Gross appearance | Gross appearance |
| Protruded type | 198 (43.61) | 24 (34.78) | 0.211 | 49 (51.04) | 173 (40.52) | 0.077 |
| Infiltrating/ulcerative type | 256 (56.39) | 45 (65.22) | | 47 (48.96) | 254 (59.48) | |
## Discussion
Based on the single center retrospective cohort data, we investigated the effects of clinicopathological factors, blood lipid indexes, and systemic inflammatory indexes on the prognoses of non-metastatic CRC patients undergoing curative excision. Through multivariate analysis with forward stepwise, novel nomograms were established according to the optimal models containing THR and PNI, which could effectively predict the OS and CCS of the population at 3- and 5-year. Compared with traditional TNM classified system, nomograms showed better differentiation, accuracy and clinical applicability.
Given the role of lipid metabolism in carcinogenesis and the invasive or metastatic procedure in CRC, researchers are always keen to develop lipid parameters as new and convenient biomarkers for prognoses. THR is one of the derivatives of blood lipids. Previous studies proposed that its high level is associated with poor postoperative prognosis of breast cancer and gastric cancer [16, 17]. However, there are still few studies on the predictive role of three lipid derivatives including THR in CRC patients. This study excluded people with non-cancerous factors that may affect blood lipid level, and adjusted other variables, indicating that higher THR level is independently associated with reduced risk of death. As far as we know, this study is the first time to propose THR as a meaningful prognostic marker for non-metastatic CRC patients. The contradictory assessment of the prognostic role of THR may be caused by different tumor types, study populations, and cut-off values. Although it remains unclear why a higher THR level is associated with a better prognosis of CRC, several speculations may explain this phenomenon. Firstly, it is speculated that such association is related to the higher level of TG and the corresponding lower HDL-C concentration. In fact, the existing studies on the relationship between these two lipid indexes and the prognoses of CRC is not sufficient, and the results are inconsistent. Yang et al. reported that dyslipidemia, including high serum level of TG and low level of HDL-C, was independently associated with the improvements of OS and recurrence-free survival in patients with colon cancer [12]. Yin et al. found that increased adipose triglyceride lipase is negatively correlated with the OS of CRC patients, and in vivo experiments showed that it could promote the progression of CRC by enhancing lipid mobilization or lipolysis [40], which also reflects that high serum TG level are related to the improvement of prognosis. Other studies showed that increased TG or decreased HDL-C are associated with poor prognosis in CRC [11, 41], or not [13, 42]. However, although some studies seem to support THR as an independent protective factor for CRC patients, further exploration is needed. Secondly, in the correlation analysis between THR and clinicopathological factors, we found that the high BMI was significantly related to the high levels of THR. Since TG is one of the prime lipid metabolites involved in energy supply, our findings support the hypothesis that an elevated level of THR may be mainly driven by TG concentration, which represent a better nutritional status and is related to a good prognosis of CRC. Thirdly, we also found that THR level in patients with tumors located in the right colon are significantly lower than those in the left colon or rectum. Despite no statistical significance has been observed in predicting survival in our study, previous studies have shown that the prognoses of patients with left-sided neoplasms are better than those of patients with right-sided neoplasms [43]. It will be meaningful to further explore how the pathways affected by blood lipid profile interact with the carcinogenic pathways of different site [44].
Although indexes of systemic inflammation have been reported to predict cancer prognosis in recent years, it is still uncertain which marker has the greatest clinical application value. In this study, PNI was screened to be an independent protective factor affecting the postoperative survival of non-metastatic CRC patients by stepwise forward multivariate analysis. Onodera et al. firstly calculated PNI based on serum Alb level and peripheral blood lymphocyte count [37], and a large number of studies have confirmed its prognostic value in various cancers [31, 32]. The activation of immune response can promote the protective response of cancer patients, which mainly depends on the levels of lymphocytes. A possible mechanism is that circulating lymphocytes may promote cytotoxic cell death to exert anti-tumor effect by secreting cytokines such as interferon-gamma and tumor necrosis factor-alpha [23]. Alb, another component of PNI, is a common index to evaluate nutritional status in clinic. Malnutrition can inhibit the immune response by regulating the production of some cytokines and hormones which mainly affect T-lymphocytes metabolism and function [45]. Moreover, poor nutritional status may delay surgery or adjuvant treatment for patients. However, there was also evidence that hypoproteinemia in CRC patients is associated with serum Alb degradation caused by systemic inflammatory response during tumor progression, rather than reduced synthesis caused by malnutrition alone [46]. Therefore, low preoperative Alb level is usually associated with poor prognosis in patients with solid tumors. In summary, increased PNI levels may indicate that patients have a valid protective immune response and better nutritional status, so as to achieve longer survival.
Some studies have revealed the potential relationship between blood lipid derivatives and systemic inflammation. Blood lipid derivatives could serve as surrogate biomarkers of insulin resistance (IR) which is identified as a chronic subclinical inflammation in various chronic diseases including cancer [47, 48]. The immune pathway mediated by pro-inflammatory cytokines such as IL-6 and TNF-α interferes with the biological effects of the insulin receptor downstream signaling and results in IR [49], which is also found to be closely involved in cancer development [50]. Chronic IR is present in malignancies, and is speculated to contribute to tumor-related cachexia due to chronic exposure of pro-inflammatory cytokines and insulin growth factor binding protein [51]. Because of the close interaction between IR and systemic inflammation in cancer patients, serum lipid derivatives combined with systemic inflammatory indicators may have an important predictive effect on cancer prognosis, as highlighted by a multicenter prospective study from China [18].
To our knowledge, THR and PNI were first applied together as independent prognostic factors for patients with non-metastatic CRC. While the combination of THR and PNI showed relatively higher AUC values and better predictive ability compared to using serum lipid or inflammatory indicators alone in predicting OS and CCS, it should be noted that the AUC values of the combined use of these two markers did not reach the clinically recommended level, which may contribute to their limited application in clinical practice. In our study, the combined detection of THR and PNI could help to screen patients with high risk of death, i.e. patients with both low THR and low PNI display the worst postoperative survival, indicating that there may be a synergistic effect between the two indexes in predicting CRC prognosis. Moreover, compared with the models including other combinations of variables or the TNM staging model, the models containing THR and PNI showed greater superiorities in the comparison of AIC and C-index. IDI analysis also revealed that the performance of the new prognostic models was significantly improved with the addition of THR and PNI. As discussed above, the combination of these two indicators may reflect a tumor-related metabolic and inflammatory state of the host, which could provide additional information for prognostic prediction of cancer. Although external data validation is still needed to confirm our findings, we believe that incorporating THR and PNI with traditional clinicopathologic features such as established TNM stage into an integrated system may lead to a more comprehensive and accurate prediction of survival outcomes for CRC patients.
In view of tumor heterogeneity and individual differences in nutrition and metabolism, there is no clear cut-off point for serum lipids and systematic inflammatory indicators to predict the prognosis of cancer patients. In this study, we used the maximum x2 method to obtain the optimal cut-off value of the above indicators, which could divide the cohort into two groups with maximum discrepancy based on log-rank statistics. Compared with the cut-off values obtained by arbitrary number method, median value method or ROC curves, our method could appropriately reflect the correlation between binary independent variables and dependent variables in time-to-event data. It is worth noting that the cut-off values of THR and PNI in this study were 1.93 and 42.55, respectively. Several reports [31, 39, 52, 53] on CRC utilized ROC curves analysis or classification and regression tree analysis to determine the best threshold value of preoperative PNI as 42.4 or higher, and found that the high-level group was associated with the lower incidence of postoperative complications and improved prognosis, which was consistent with our results. Nevertheless, there is still no large-scale cohort evidence to determine the cut-off value of THR for predicting postoperative survival of CRC patients, and further exploration is needed to facilitate clinical promotion and application.
The nomograms in this study contained pathological prognostic factors that have been widely recognized and utilized, such as pT stage, pN stage, histological subtype, and Perineural invasion. Furthermore, it is worth mentioning that gross morphology of tumors was identified as an independent candidate for predicting the clinical outcomes of stage I-III CRC patients undergoing radical operation. This is consistent with previous study which showed that patients with protruded type CRC have a lower risk of cancer-specific death [54]. As a parameter that could be obtained directly by endoscopy or surgery, the predictive role of macroscopic morphology of CRC should not be underestimated.
The present research has several merits. First of all, through the detailed review of electronic medical records, the interference of non-CRC factors on blood lipids and inflammatory parameters was excluded with stricter criteria, which made the prognostic significance of above indexes more convincing and the models more robust. On the other hand, compared with some models based on large sample data obtained from the Surveillance, Epidemiology, and End Result (SEER) database, although the number of cases enrolled in this study is comparatively finite, we have obtained more detailed laboratory indicators than those found in tumor registration. Finally, our nomogram contains risk factors that could be easily collected from clinical practice. Easy accessibility, low cost and clinical applicability are prospects of the nomograms.
This study still has some limitations: 1) A retrospective study based on a single-center samples only, which may lead to selection and memory bias; 2) Lack of diet and lifestyle information of the surveyed population, which may affect the measurement of preoperative blood lipids and lead to potential deviations; 3) We only analyzed the relationship between preoperative THR and PNI and prognosis, and failed to monitor their dynamic variation in the disease process, which need to be further explained; 4) Given the screening conditions of this study, the application scenarios of constructed nomograms are limited; 5) The nomograms were only internally validated using data from a single center, and its generalizability needs further external data validation. Therefore, large-scale, multicenter prospective research is still needed in the future.
## Conclusion
Our study suggests that preoperative THR and PNI are independent predictors for survival of patients with stage I-III CRC. We have successfully established and verified the novel nomograms integrating preoperative THR and PNI, which will help clinicians to conveniently and accurately evaluate the prognosis of these patients and identify high-risk groups, so as to formulate individualized therapeutic regimens and follow-up strategies in time.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
This study was approved by the Ethics Committee of the Affiliated Tumor Hospital of Guangxi Medical University (LW2023012). Individual consent from patients for this retrospective study was waived.
## Author contributions
DH designed the study, performed the statistical analysis, and wrote the manuscript. SZ participated in the manuscript revision. FH, JC, YZ and YC participated in the clinical data collection and assembly. BL conceived of the research and engaged in the supervision and critical review. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
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|
---
title: Serum neurofilament light chain levels are associated with early neurological
deterioration in minor ischemic stroke
authors:
- Jie Li
- Ping Zhang
- Yalan Zhu
- Yong Duan
- Shan Liu
- Jie Fan
- Hong Chen
- Chun Wang
- Xingyang Yi
journal: Frontiers in Neurology
year: 2023
pmcid: PMC10034185
doi: 10.3389/fneur.2023.1096358
license: CC BY 4.0
---
# Serum neurofilament light chain levels are associated with early neurological deterioration in minor ischemic stroke
## Abstract
### Objectives
Patients with minor ischemic stroke (MIS) frequently suffer from early neurological deterioration (END) and become disabled. Our study aimed to explore the association between serum neurofilament light chain (sNfL) levels and END in patients with MIS.
### Methods
We conducted a prospective observational study in patients with MIS [defined as a National Institutes of Health Stroke Scale (NIHSS) score 0–3] admitted within 24 h from the onset of symptoms. sNfL levels were measured at admission. The primary outcome was END, defined as an increase in the NIHSS score by ≥2 points within 5 days after admission. Univariate and multivariate analyses were performed to explore the risk factors associated with END. Stratified analyses and interaction tests were conducted to identify variables that might modify the association between sNfL levels and END.
### Results
A total of 152 patients with MIS were enrolled, of which 24 ($15.8\%$) developed END. The median sNfL level was 63.1 [interquartile range (IQR), 51.2–83.4] pg/ml on admission, which was significantly higher than that of 40 age- and sex-matched healthy controls (median 47.6, IQR 40.8–56.1 pg/ml; $p \leq 0.001$). Patients with MIS with END had a higher level of sNfL (with ND: median 74.1, IQR 59.5–89.8 pg/ml; without END: median 61.2, IQR 50.5–82.2 pg/ml; $$p \leq 0.026$$). After adjusting for age, baseline NIHSS score, and potential confounding factors in multivariate analyses, an elevated sNfL level (per 10 pg/mL) was associated with an increased risk of END [odds ratio (OR) 1.35, $95\%$ confidence interval (CI) 1.04–1.77; $$p \leq 0.027$$). Stratified analyses and interaction tests demonstrated that the association between sNfL and END did not change by age group, sex, baseline NIHSS score, Fazekas' rating scale, hypertension, diabetes mellitus, intravenous thrombolysis, and dual antiplatelet therapy in patients with MIS (all p for interaction > 0.05). END was associated with an increased risk of unfavorable outcomes (modified Rankin scale score ranging from 3 to 6) at 3 months.
### Conclusion
Early neurological deterioration is common in minor ischemic stroke and is associated with poor prognosis. The elevated sNfL level was associated with an increased risk of early neurological deterioration in patients with minor ischemic stroke. sNfL might be a promising biomarker candidate that can help to identify patients with minor ischemic stroke at high risk of neurological deterioration, for reaching individual therapeutic decisions in clinical practice.
## Introduction
Minor ischemic stroke (MIS) is fairly common and accounts for about $30\%$ of all strokes [1]. Although most patients with MIS have favorable outcomes, a small but significant proportion of individuals suffer neurological deterioration in the early stages of acute ischemic stroke (AIS) and become disabled [2, 3]. It also has been demonstrated that early neurological deterioration (END) after ischemic stroke is an independent predictor of poor prognosis (4–6). Several hypotheses have been proposed regarding the mechanisms of END, including the propagation of thrombus in situ, inflammation, excitotoxicity, oxidative stress, and cortical spreading depolarization [7]. However, until recently, the underlying pathophysiology of END in patients with MIS is still unclear [8]. Once END occurs, there are no effective therapies to arrest it. Thus, the early identification and rational prevention of END are essential for this ominous event.
Neuronal damage and loss are the pathological basis of disability caused by cerebral infarction. As a part of the neuronal cytoskeleton that is exclusively expressed in neurons, neurofilaments are suitable candidate biomarkers for neuronal injury [9]. When ischemic damage occurs, neurofilament light chain (NfL) protein is released into the extracellular fluid, the cerebrospinal fluid, and to a lower concentration in the peripheral blood [10]. With the application of single-molecule array (SiMoA) assays that enabled a sensitive detection of NfL in blood samples, neurofilaments are gaining increasing attention in various neurologic diseases such as traumatic brain injury, multiple sclerosis, dementias, and different neurodegenerative diseases [9]. In patients with ischemic stroke, serum NfL (sNfL) levels have been correlated with initial stroke severity assessed by the NIHSS score on admission (11–14). Meanwhile, the baseline NIHSS score has been shown to be a good predictor of the course of END (15–17). Some studies have suggested that baseline sNfL is a valuable biomarker of the functional outcome at 3 months after cerebral infarction [12, 14], but others have reached a different conclusion [11, 13]. sNfL has also been shown to be associated with active small vessel disease [18]. Until recently, whether sNfL levels are associated with END in patients with MIS has not been elucidated.
Therefore, the current study aimed to explore the potential association of sNfL levels with END in Chinese patients with MIS.
## Study design and subjects
Patients with AIS admitted to Deyang People's Hospital were prospectively and consecutively registered from 1 March 2020 to 31 June 2021. Patients with MIS who were admitted within 24 h from the symptom onset and with magnetic resonance diffusion-weighted imaging (DWI) diagnoses of cerebral infarction were eligible for this observational study. MIS was defined as having a National Institutes of Health Stroke Scale (NIHSS) score of ≤3 points at admission [19]. All patients received an extensive stroke etiologic workup (computed tomographic angiography or magnetic resonance angiography, color duplex ultrasound, Holter monitoring, echocardiography, and blood sampling) and were routinely followed up after 3 months by telephone interview or by mail. We excluded cases with incomplete hospital records or missing imaging that would prevent complete data collection. We also excluded subjects with a preexisting score of more than 2 on the modified Rankin scale (mRS, a scale of 0 to 6, with 0 indicating no symptoms and 6 indicating death) and lived dependently [20]. Meanwhile, cases with comorbid disorders that could lead to neuronal damage, such as traumatic brain injury, multiple sclerosis, dementia, and other neurological diseases were excluded. The study was approved by the Ethics Committee of Deyang People's Hospital (Reference No. 2019-01-142-K01) and was carried out under the principles expressed in the Declaration of Helsinki. Written informed consent was obtained from all patients before they were enrolled. The study described here is registered at http://www.chictr.org/ (unique identifier: ChiCTR2000029902). The date of trial registration was 16 February 2020. All methods in the present study were performed according to relevant guidelines and regulations.
## Data collection
Baseline data on age, sex, onset to admission time, baseline NIHSS score, systolic and diastolic blood pressure on admission, baseline serum glucose, vascular risk factors, and potential stroke etiology were recorded, which has been described in our previous study [21]. Results of routine laboratory tests such as triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), uric acid, fibrinogen, d-dimer, and C-reactive protein (CRP) were also collected. The white matter lesions (WMLs) were visually evaluated by experienced neuroradiologists using the modified Fazekas scale [22, 23]. The Fazekas scale is a 4-point rating scale: 0 (no WML), 1 (mild WML), 2 (moderate WML), and 3 (severe WML). White matter changes were divided into two groups: 0–1 (absent or mild) or 2–3 (moderate or severe). In-hospital treatments analyzed in our study included intravenous thrombolysis and antiplatelet therapy. Intravenous thrombolysis was performed according to the Chinese guidelines, which had similar inclusion and exclusion criteria compared with the American guidelines [24, 25]. The final treatment decision was made in consultation with the neurologist and the patient's family. Antiplatelet therapies were administered at the physicians' discretion. Patients enrolled in the present study received either [1] aspirin or clopidogrel only or [2] clopidogrel plus aspirin (dual antiplatelet therapy) at admission.
## Measurement of sNfL levels
Whole blood samples (4 ml) were drawn from all patients with MIS at admission, and serum samples were isolated following centrifugation for 20 min at 2,000 g at room temperature. Then, serum samples were stored at −80°C until analysis. At the same time, 40 age- and sex-matched healthy controls were selected, and their serum samples were obtained after their enrollment in the study. Serum NfL (sNfL) concentrations were measured using a SiMoAplatform (Quanterix, Lexington, MA, United States) as described [26]. All serum samples were analyzed in duplicates for inter-test validation, and the two results were averaged to determine the mean concentration. The mean intra-assay variability (the coefficient of the variation of concentrations) was <$10\%$, and the inter-assay coefficient of variation was <$15\%$.
## Assessment of clinical outcomes
The primary outcome of the present study was END, which was defined as an increase in NIHSS score by 2 or more points within 5 days after admission, after excluding the hemorrhagic transformation of the brain infarct or a new infarct in another vascular territory [27]. Trained neurologists assessed the neurological severity of the patients daily between admission and discharge from the stroke unit. The secondary outcome measures in our study were 3-month death and an unfavorable outcome [defined as having an mRS score of 3–6 [20]].
## Statistical analyses
Continuous variables are presented as mean with standard deviation (SD) or median with interquartile range (IQR), and categorical variables are presented as frequencies with percentages. The normality of data was tested using a Shapiro–Wilk test. Baseline characteristics, laboratory values, and in-hospital treatment were compared between patients with MIS with or without END. The χ2 tests or Fisher's exact tests were used for differences in categorical data, while Student's t-tests or the Mann–Whitney U-test were used for differences in continuous data. Multivariate logistic regression analysis was performed using the forced entry method, including variables with a p-value of <0.1 in univariate analyses, to identify the association between sNfL levels and END in patients with MIS. Then, stratified analyses and interaction tests were conducted to identify variables that might modify the association between sNfL levels and END. All statistical analyses were performed using SPSS v21.0 (IBM, Chicago, IL, USA), the statistical software packages R (http://www.R-project.org, The R Foundation, version 3.4.3), and EmpowerStats (http://www.empowerstats.com, X&Y Solutions, Inc., Boston, MA, USA), which have been described in our previous study [28]. A two-sided p-value of < 0.05 denoted statistical significance.
## Results
During the study period, 798 patients with AIS were registered. Of those patients, 152 ($19.0\%$) patients with MIS admitted within 24 h were enrolled in the present study (median baseline NIHSS score: 2, IQR: 1–3). A flow diagram of included and excluded patients is provided in Figure 1. Their age varied from 32 to 95 years (mean age 67.7 and standardized difference 11.1), and 104 ($68.4\%$) were men. The median onset to admission time was 8.5 h (IQR: 3.0–20.8 h). On admission, the median sNfL levels of enrolled patients with MIS were 63.1 pg/ml (IQR: 51.2–83.4 pg/ml). We did not observe a significant correlation between the baseline NIHSS score and sNfL values at hospital admission (Spearman correlation analysis, rho = 0.150, $$p \leq 0.066$$) (Supplemental Figure 1). A total of 30 ($19.7\%$) cases were treated with intravenous thrombolysis, and 109 ($71.7\%$) were treated with dual antiplatelet therapy at admission (Table 1). A total of 24 ($15.8\%$) patients experienced END within 5 days after admission. The median time from the stroke onset to the development of END was 48 h (IQR: 30–49.75 h). In 22 out of the 24 patients ($91.7\%$), END was observed on days 2 and 3 after the stroke onset (Figure 2). All enrolled cases completed 3-month follow-up. In total, three ($2.0\%$) patients died, and 10 ($6.6\%$) patients had unfavorable outcomes at 3 months (Table 1).
**Figure 1:** *Flow diagram of included and excluded patients.* TABLE_PLACEHOLDER:Table 1 **Figure 2:** *Time from the stroke onset to the development of END.*
## Baseline characteristics and clinical outcomes between patients with MIS with and without END
Baseline characteristics and clinical outcomes were compared between patients with MIS with and without END (Table 2). The median sNfL levels of enrolled patients with MIS were significantly higher than the levels of 40 age- and sex-matched healthy controls (patients with MIS: median 63.1; IQR: 51.2–83.4 pg/ml; healthy controls median: 47.6; IQR: 40.8–56.1 pg/ml; $p \leq 0.001$), while patients with MIS with END had a higher level of sNfL compared with those patients without END (with END median: 74.1; IQR: 59.5–89.8; without END median: 61.2, IQR: 50.5–82.2; $$p \leq 0.026$$) (Figure 3). Meanwhile, the END group had a higher systolic blood pressure level on admission (168.1 ± 28.5 vs. 156.0 ±2 4.5 mmHg, $$p \leq 0.033$$) and less frequently received dual antiplatelet therapy (54.2 vs. $75.0\%$, $$p \leq 0.038$$). There was no difference in the age, sex, onset to admission time, baseline NIHSS score, diastolic blood pressure, vascular risk factors, stroke etiology, moderate–severe WML (defined as Fazekas' rating scale score 2–3), and other laboratory values between the two groups (all $p \leq 0.05$). Although there was no difference in the 3-month death between the two groups, the incidence rate of 3-month unfavorable outcome was significantly higher in patients with MIS with END (with END: $25.0\%$; without END: $3.1\%$; $p \leq 0.001$). After adjusting for age, sex, and baseline NIHSS score, END was still associated with an increased risk of the unfavorable outcome at 3 months [odds ratio (OR) 12.4, 95 % confidence interval (CI) 2.7 to 56.3, $$p \leq 0.001$$].
## Multivariate analyses for the association between sNfL and END in patients with MIS
Variables that potentially affect END in patients with MIS ($p \leq 0.1$) were included in multivariate logistic regression analyses; the results are shown in Table 3. After adjusting for the baseline NIHSS score and potential confounding factors (Model 1), an elevated sNfL level (per 10 pg/mL) was associated with an increased risk of END (OR 1.36, $95\%$ CI 1.04–0.77; $$p \leq 0.026$$) in multivariate analyses. When age was included in the multivariate logistic regression (Model 2), an elevated sNfL level (per 10 pg/mL) remained an independent risk factor for END (OR 1.35, $95\%$ CI 1.04–1.77; $$p \leq 0.027$$). Moreover, systolic blood pressure on admission (OR 1.25, $95\%$ CI 1.03–1.51) and dual antiplatelet therapy (OR 0.22, $95\%$ CI 0.08–0.63) were independently associated with END in patients with MIS in the two multivariate logistic regression models (both with $p \leq 0.05$). An increased baseline NIHSS score also tended to be associated with a higher risk of END (OR 1.66, $95\%$ CI 0.93–2.99; $$p \leq 0.090$$).
**Table 3**
| Unnamed: 0 | Model 1 | Model 1.1 | Model 2 | Model 2.1 |
| --- | --- | --- | --- | --- |
| Variables | OR (95%CI) | P- value | OR (95%CI) | P- value |
| Serum NfL, per 10pg/ml | 1.36 (1.04–1.77) | 0.026 | 1.35 (1.04–1.77) | 0.027 |
| SBP on admission, per 10 mmHg | 1.25 (1.03–1.51) | 0.024 | 1.25 (1.03–1.51) | 0.023 |
| Dual antiplatelet therapy | 0.22 (0.08–0.63) | 0.005 | 0.22 (0.08–0.63) | 0.005 |
| Age, year | – | – | 1.00 (0.97–1.05) | 0.840 |
| Baseline NIHSS score | 1.67 (0.93–3.00) | 0.089 | 1.66 (0.93–2.99) | 0.090 |
## Stratified analyses and interaction tests to identify factors that might modify the association between sNfL and END
Stratified analyses and interaction tests were further employed to explore the association between sNfL levels and END in patients with MIS. Stratified logistic regression analyses demonstrated that the association between sNfL and END did not change by age group, sex, baseline NIHSS score, Fazekas' rating scale, hypertension, diabetes mellitus, intravenous thrombolysis, and dual antiplatelet therapy in patients with MIS (all $p \leq 0.05$) (Table 4).
**Table 4**
| Variable* | Adjusted OR (95%CI) | P-value | P for interaction |
| --- | --- | --- | --- |
| Age | | | 0.334 |
| < 70 | 1.6 (1.1–2.5) | 0.026 | |
| ≥70 | 1.1 (0.7–1.6) | 0.605 | |
| Sex | | | 0.463 |
| Male | 1.3 (0.9–1.9) | 0.201 | |
| Female | 1.4 (0.9–2.0) | 0.112 | |
| Baseline NIHSS score | | | 0.999 |
| Score 0 | Reference | | |
| Score 1 | 1.3 (0.6–2.9) | 0.445 | |
| Score 2 | 1.4 (1.0–2.0) | 0.074 | |
| Score 3 | 1.3 (0.7–2.2) | 0.414 | |
| Fazekas' rating scale | | | 0.657 |
| Score 0–1 | 1.4 (1.0–2.0) | 0.08 | |
| Score 2–3 | 1.2 (0.8–1.9) | 0.365 | |
| Hypertension | | | 0.933 |
| Yes | 1.4 (1.0–1.8) | 0.022 | |
| No | 1.3 (0.5–3.7) | 0.596 | |
| Diabetes mellitus | | | 0.777 |
| Yes | 1.3 (0.9–2.0) | 0.19 | |
| No | 1.4 (0.9–2.1) | 0.097 | |
| Intravenous thrombolysis | | | 0.866 |
| Yes | 1.3 (0.7–2.6) | 0.446 | |
| No | 1.4 (1.0–1.9) | 0.026 | |
| Dual antiplatelet therapy | | | 0.953 |
| Yes | 1.4 (1.0–1.9) | 0.042 | |
| No | 1.3 (0.8–2.2) | 0.34 | |
## Discussion
Epidemiological studies showed that there are approximately 3 million new-onset strokes every year in China and approximately 1 million are MIS [29, 30]. Since the baseline NIHSS score can strongly predict outcomes after stroke, the outcomes for patients with MIS are generally favorable [2]. Yet, prospective data suggest that 4.5–$26.4\%$ of patients with MIS are also affected by early neurological worsening and become disabled [3, 27, 31, 32]. The incidence rate of END in patients with MIS is $15.8\%$ in our cohort. Differences in the incidence rate of END in patients with MIS may reflect heterogeneity in demographics (age, sex, and ethnicity) of the enrolled patients, the definition of MIS, and the way END was defined and measured, highlighting the need for a standardized definition of MIS and END. The median time from the stroke onset to the development of END in our cohort was 48 h (IQR: 30–49.75 h), similar to previous studies [3, 27, 32]. Although the association between END and the outcome of patients with MIS remains to be established, our study suggested that MIS patients with END had a significantly higher rate of the 3-month unfavorable outcome than those without (25.0 vs. $3.1\%$). END was also associated with an increased risk of a 3-month unfavorable outcome in multivariate analysis after adjusting for age, sex, and baseline NIHSS score, as found in many other studies conducted in patients with ischemic stroke (4–6, 33, 34) and is consistent with our results. Until recently, the mechanisms underlying END in patients with MIS are still unclear, and no consensus has been reached on the risk factors of END [8, 15]. Understanding the mechanisms underlying END in patients with MIS could provide valuable insights for rational prevention of END in patients with MIS. Moreover, the early targeting of patients at higher risk of END is of great importance for improving the outcome of MIS.
In a previous study, NfL was shown to be higher in patients with ischemic stroke than in healthy controls, whereas NfL in patients with transient ischemic stroke (TIA) was comparable to those in healthy controls [35]. In patients with ischemic stroke, NfL levels have been correlated with initial stroke severity (11–14). In the present study, we found that the sNfL levels of patients with MIS were significantly higher than that of age- and sex-matched healthy controls, while patients with MIS with END had a higher level of sNfL compared with those patients without END. Although it has been demonstrated that the baseline NIHSS score could strongly predict the course of END (15–17), multivariate analyses adjusting for confounders, including age and baseline NIHSS score, also suggested that an elevated sNfL level was independently associated with END in patients with MIS. Stratified analyses and interaction tests demonstrated that the association between sNfL and END did not change by age group, sex, baseline NIHSS score, Fazekas' rating scale, hypertension, diabetes mellitus, intravenous thrombolysis, and dual antiplatelet therapy in patients with MIS. Some studies have suggested an association between baseline sNfL levels and final infarct size on MRI [13, 26, 36], but others have reached a different conclusion [11, 12]. Therefore, our results could not be explained by the effect of final infarct volume on the sNfL levels. Neuronal damage and loss are the pathological substrates of disability caused by an AIS. As a part of the neuronal cytoskeleton that is exclusively expressed in neurons, neurofilaments are suitable candidate biomarkers of ischemic neuronal injury [9, 37]. It has been demonstrated that sNfL levels increased during the first few days after the stroke onset and remained increased over 3–6 months [26, 36]. Experimental studies suggest that synaptic NfL plays an essential role in controlling synaptic function, neurotransmission, and stabilizing NMDA receptors in the neuronal cell membrane (38–40). According to the currently available evidence, elevated sNfL levels after AIS seem to reflect the extent of neuronal injury, persistent blood–brain barrier breakdown, and ongoing post-ischemic immunological or inflammatory processes. Meanwhile, elevated sNfL levels may act as a biomarker of neural plasticity and a positive predictor of functional improvement [9, 41]. All these findings suggest a potential molecular mechanism that links the sNfL with the risk of neurologic worsening and functional disability. The present study is the first to report a positive correlation between sNfL levels and END in Chinese patients with MIS. Therefore, sNfL might be a promising biomarker candidate that can help identify MIS patients at high risk of END, for reaching individual therapeutic decisions in clinical trials. Further studies with large sample sizes are needed to determine the optimal cutoff value of sNfL as an indicator for END and validate sNfL as a biomarker for END in patients with MIS.
It is also worth noting that dual antiplatelet therapy was associated with decreased risk of END in patients with MIS in our cohort (OR 0.22, $95\%$ CI 0.08–0.63), which are in line with previous studies conducted in patients with AIS (42–44). In addition, higher baseline systolic blood pressure (OR 1.25, $95\%$ CI 1.03–1.51) was associated with increased END risk in patients with MIS. These results support that platelet aggregation might be an important mechanism of END [45]. Our results also support the view that an impaired cerebral hemodynamic response due to hypertension might be another contributor to END [45]. Therefore, dual antiplatelet therapy in the acute phase of MIS and premorbid personalized antihypertensive treatment may potentially reduce END and subsequently improve the outcome of patients with MIS. However, the present study was not specifically targeted at this treatment effect, thus, our results should be interpreted cautiously. Further studies targeting patients with MIS at high risk of END are warranted to determine the usefulness of different acute therapy strategies.
## Limitations
The results of the present study should be interpreted with caution, given its limitations. First, it was a single hospital-based study conducted in China, with limited generalizability. Second, the sample size of our study was relatively small, and only 24 cases suffered END. We could not determine the optimal cutoff value of sNfL as an indicator for END in patients with MIS. Third, sNfL levels may change dynamically after acute ischemic stroke. In the present study, sNfL levels were tested one time on admission. We did not have longitudinal data on sNfL levels. Further studies are needed to evaluate the association between dynamic changes in sNfL levels and END in patients with MIS. In addition, the number and location of the infarcts were not assessed in MRI imaging, as well as the infarct volume, which might have an association with END in patients with ischemic stroke. Meanwhile, the renal function might affect the level of sNfL. However, due to a lack of data, we did not include the eGFR levels in the multivariate analyses. In addition, although non-neurological complications, such as infections, might cause clinical deterioration and an increase in the NIHSS score by causing a confusional state and decreasing the level of consciousness, we did not include medical complications in the analyses because of a lack of data. Moreover, we performed the follow-up by telephone interview or a mailed questionnaire instead of a clinic visit which may result in reporting bias. Finally, our study was an observational study. No causal link could be drawn. Thus, well-designed studies with large sample sizes are needed to validate our findings in the future.
## Conclusion
We conducted a prospective observational study in Chinese patients with acute MIS admitted within 24 h from the symptom onset. We identified that END is common in patients with MIS and associated with 3-month unfavorable outcomes. An elevated sNfL level was independently associated with END in patients with MIS. sNfL might be a promising biomarker candidate that can help identify patients with MIS at high risk of END, for reaching individual therapeutic decisions in clinical practice.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Ethics Committee of Deyang People's Hospital (Reference No. 2019-01-142-K01). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
JL and XY: conceived the study, analyzed and interpreted the data, as well as drafted the manuscript. HC and CW: contributed to study supervision. PZ, YZ, YD, SL, and JF: participated in data collection. JL and PZ: participated in statistical analysis, data interpretation, and revised the manuscript. All authors critically revised the manuscript for important intellectual content and approved the final manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fneur.2023.1096358/full#supplementary-material
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|
---
title: Alterations in bacterial community dynamics from noncancerous to Gastric cancer
authors:
- Xuan Peng
- Siqi Yao
- Jing Huang
- Yiming Zhao
- Hao Chen
- Liyu Chen
- Zheng Yu
journal: Frontiers in Microbiology
year: 2023
pmcid: PMC10034189
doi: 10.3389/fmicb.2023.1138928
license: CC BY 4.0
---
# Alterations in bacterial community dynamics from noncancerous to Gastric cancer
## Abstract
Gastric microbiome has been shown to contribute to gastric carcinogenesis, understanding how alterations in gastric microbiome is helpful to the prevention and treatment of gastric cancer (GC). However, few studies have focused on the change of microbiome during the gastric carcinogenesis. In this study, the microbiome of gastric juice samples from healthy control (HC), gastric precancerous lesions (GPL) and gastric cancer (GC) was investigated by 16S rRNA gene sequencing. Our results showed that the alpha diversity of patients with GC was significantly lower than other groups. Compared to other groups, some genera in GC group were shown to be up-regulated (e.g., Lautropia and Lactobacillus) and down-regulated (e.g., Peptostreptococcus and Parvimonas). More importantly, the emergence of Lactobacillus was closely related to the occurrence and development of GC. Moreover, the microbial interactions and networks in GPL exhibited higher connectivity, complexity and lower clustering property, while GC showed the opposite trend. Taken together, we suggest that changes in the gastric microbiome are associated with GC and perform a key function in maintaining the tumor microenvironment. Therefore, our findings will provide new ideas and references for the treatment of GC.
## Introduction
Gastric cancer (GC) is the fifth most frequently diagnosed cancer and the fourth-leading cause of cancer-related deaths worldwide (Sung et al., 2021). Although the incidence and mortality rates are declining with the advancement of therapeutic, GC is still a global medical burden (Thrift and El-Serag, 2020). Therefore, it is necessary to understand the mechanism of GC to better treat and prevent the occurrence of this disease. The development of GC can be described as a series of sequential stages: from chronic superficial gastritis, chronic atrophic gastritis, intestinal metaplasia, dysplasia to GC (Correa, 1992). The microbiome is involved in the development of GC and the composition of gastric microorganisms varies in individuals at different stages of the stomach (Stewart et al., 2020). Previous studies have suggested that many factors are associated with the development of GC. In addition to genetic predisposition, environmental factors including microbial community interactions have been shown to contribute to GC (Kwon et al., 2021).
Microbiome is considered a significant component of the tumor microenvironment. The human gastrointestinal tract contains a large number of microbiomes, including bacteria, fungi and viruses. Helicobacter pylori (HP) infection is a fundamental factor for gastric lesions (Vinasco et al., 2019). However, the presence of HP is not the only factor involved in gastric carcinogenesis, as only a minority of HP infected individuals develop GC, implying that other factors also perform a key function (Bjorkholm et al., 2003). Naturally, more attention has been paid to the function of gastric microbiome diversity in the development of GC. Experiments in the insulin-gastrin transgenic (INS-GAS) mouse model have demonstrated that the synergy among bacterial communities promotes gastric tumorigenesis (Lertpiriyapong et al., 2014; Whary et al., 2014). Later, gastric microbiome was also found to produce carcinogenic N-nitroso compounds and secondary amines by metabolizing food, which strengthened the role of gastric microbiome in the development of GC (Tseng et al., 2016). A study found the relative abundance and diversity of microbiome in the GC group decreased compared to patients with chronic gastritis (Ferreira et al., 2018). In addition, changes in gastric microbiome in patients with GC have been detected by using 16S rRNA gene sequencing in several studies recently, and gastric carcinogenesis was revealed to be associated with microbial dysbiosis (Chen et al., 2019; Gantuya et al., 2020; Zhang et al., 2021). Together, these findings highlighted the potential role of microbial community structure diversity in the development of GC.
Some studies have been devoted to revealing the differences in the microbial composition from noncancerous to GC. However, there is no consensus on the microbiome changes in the pathological stages of GC. In this study, gastric juice samples from patients with healthy control (HC) gastric precancerous lesions (GPL) and GC were collected, aiming to explore the differential distribution profile of microbiome in different stages of gastric lesions by using 16S rRNA gene sequencing. Our findings will help explore the role of gastric microbiome in carcinogenesis.
## Study subjects and sample collection
Total 60 participants, including 22 of HC, 22 of GPL and 16 of GC, were recruited at the Xiangya Hospital, Changsha, Hunan, China from October 2015 to November 2016. The demographic characteristics of participants were shown in Table 1. Among them, the HC group mainly consisted of patients with gastritis. Gastritis and gastric precancerous lesions patients were diagnosed by the gastrointestinal endoscopes Department of Xiangya Hospital according to the clinical practices (Chinese Medical Association, 2006; Medical Secretary, M.O.H., People's Republic of China, 2012). In addition, GPL refers to a kind of pathological change of gastric mucosa liable to cancerization. The inclusion criteria of GPL were atrophic gastritis, intestinal metaplasia or adenomatous polyp. The study was approved by the independent Ethics Committee of Xiangya Hospital of Central South University following the ethical guidelines of the Declaration of Helsinki (No. 038, 2015). Participation was voluntary and written informed consent was obtained from all participants.
**Table 1**
| Variables | HC (n = 22) | GPL (n = 22) | GC (n = 16) | p-value |
| --- | --- | --- | --- | --- |
| Age [year, median(range)] | 49.5 (32–60) | 48.5 (32–59) | 59.5 (44–81) | 8e-05 |
| Gender, n (%) | | | | 0.516 |
| Female | 9 (40.9%) | 12 (54.5%) | 6 (37.5%) | 0.516 |
| Male | 13 (59.1%) | 10 (45.5%) | 10 (62.5%) | 0.516 |
| Smoking history, n (%) | | | | 0.393 |
| Yes | 5 (22.7%) | 4 (18.2%) | 1 (6.3%) | 0.393 |
| No | 17 (77.3%) | 18 (81.8%) | 15 (93.8%) | 0.393 |
| Drinking history, n (%) | | | | 0.216 |
| Yes | 2 (9.1%) | 5 (22.7%) | 0 (0%) | 0.216 |
| No | 20 (90.9%) | 17 (77.3%) | 16 (100%) | 0.216 |
Exclusion criteria were as follows: age under 18 years; the presence of a serious illness such as severe cardiopulmonary, renal, or metabolic diseases; prior medication history of antibiotics, acid drugs (proton pump inhibitors and H2 receptor antagonists), probiotics, or anti-inflammatory drugs (aspirin, nonsteroidal and steroids) for past one month; A large amount of alcohol and smoke for past a month. All patients were subject to endoscopy or biopsy.
The patients were treated according to clinical requirements and then undergo gastrointestinal endoscopy. Approximately, 10 mL of gastric juice was collected from the gastroscope with sterile syringe, then filtered by the double sterile gauze to remove food debris and stored in sterile 10–15 mL tubes. The tubes were kept at 0°C for no more than 12 h prior to DNA isolation. DNA isolation of the bacterial sediments was performed using the QIAamp® FAST DNA Stool Mini Kit (QIAGEN) according to the manufacturer’s protocol after centrifuging at 12,000 rpm, 4°C, for 10 min.
## Polymerase chain reaction and high-throughput sequencing of 16S rRNA gene
The V4 region of the 16S rRNA gene was amplified by polymerase chain reaction (PCR) with a universal forward primer and a unique barcoded fusion reverse primer (515\u00B0F: 5′-gtgccagcmgccgcggtaa-3′ and 806 R: 5′-ggactachvgggtwtctaat-3′). PCR was performed using 30 ng of genome DNA, V4 Dual-index Fusion PCR Primer Cocktail and PCR Master Mix (NEB Phusion High-Fidelity PCR Master Mix) for per reaction. The melting temperature is 56°C and PCR cycle is 30. The PCR products were purified with AmpureXP beads (AGENCOURT) to remove the unspecific products. The library was quantitated by determination of the average molecule length using the Agilent 2100 bioanalyzer instrument (Agilent DNA 1000 Reagents) and quantification of the library by real-time quantitative PCR (EvaGreenTM). Then, the qualified libraries were sequenced by the way of pair-end on the Illumina MiSeq System with the sequencing strategy PE250 (MiSeq Reagent Kit) by Beijing Genomics Institute (BGI, Wuhan, China).
After sequencing, the reads were de-multiplexed into samples according to the barcodes. Fitter sequences were imported to the Quantitative Insights into Microbial Ecology (QIIME2, 2022.2)1 (Bolyen et al., 2019). The raw data were filtered to eliminate the adapter pollution and low-quality reads to obtain clean reads, and then use DADA2 denoise-paired to dereplicate sequence data and create a feature table and feature representative sequences (Callahan et al., 2016). Taxonomic classifiers use plugin feature-classifier, classify-consensus-blast, based on Silva 138 reference sequence (MD5: a914837bc3f8964b156a9653e2420d22) and taxonomy files (MD5: e2c40ae4c60cbf75e24312bb24652f2c) (Camacho et al., 2009; Bokulich et al., 2018; Robeson et al., 2021). Use plugin taxa removal of non-bacterial sequences and mitochondrial chloroplast contamination.
## Statistical analysis
All statistical analyses were performed using the R V4.1.2 environment (R Core Team, 2022). No special instructions, the statistical results were visualized using the “ggplot2” package (Wickham, 2009). Alpha diversity was measured using the function diversity in the package “Vegan” based on a flat taxonomy table. Gini-Simpson diversity index was obtained by subtracting the value of the classical Simpson index from 1. Beta diversity was compared using principal coordinate analysis (PCoA). Bacterial community composition across all samples based on Bray-Curtis distances. Redundancy analysis (RDA) was also conducted using Vegan (Lai et al., 2022). Package rdacca.hp. was used to obtain conditional effect base on Hierarchical Partitioning (Oksanen et al., 2022). Beta diversity across sample groups was compared by PERMANOVA with permutations of 999. ANOSIM was chosen to test for significance between groups (Wang et al., 2008), R > 0, $p \leq 0.05$ was considered significant. The DEseq2 package was used to analyze abundance difference and *Marker genus* (Topper et al., 2017). The differential expression matrix and the p-value matrix of species composition were obtained through the function DESeqDataSetFromMatrix. The significant level was $p \leq 0.05$ and the absolute FoldChange value greater than 2. The coexistence network of three groups was established based on Spearman correlation matrix and corrected p-value matrix using the igraph package; the Benjamini and Hochberg false discovery rate (FDR) were used to correct the p value; modules were divided according to the high intra-module connectivity and the low inter-module connectivity; Spearman correlation coefficient and corrected p values were 0.8 and 0.05, respectively, (Yuan et al., 2021). Gephi software2 is used to calculate the network topological properties and the hub network. Among them, clustering coefficient is a measure of the degree of clustering property of microorganism. Abundance ratio greater than 0.05 genus abundance heatmap was created using the pheatmap package, and the abundance information was transformed by adding one, then taking the logarithm of ten (Kolde, 2019). Phylum level Manhattan plot computed using edgeR package based on taxonomy information (Robinson et al., 2010).
In addition, biomarkers of sample groups were discovered by Linear Discriminant Analysis (LDA) Effect Size (LEfSe)3 (Segata et al., 2011). The strategy for multi-class analysis was set one-against-all, and the threshold on the logarithmic LDA score for discriminative features was set to 2.0.
## Clinic characteristics of study subjects
The demographic characteristics of the HC, GPL, and GC groups are shown in Table 1. There is no difference in gender, smoking history, and drinking history, but difference in median age was detected ($p \leq 0.05$) among the three groups. On the one hand, RDA analysis showed that gender and age of all participants accounted for only $3.37\%$ of the composition and distribution of microorganisms in gastric juice (Supplementary Figure S1A). On the other hand, the conditional effect of age on microorganisms in gastric juice is $0.70\%$ (Supplementary Figure S1B). Moreover, the clinical data of the GC group population were shown in Table 1. The condition of patients with HP infection was shown in Supplementary Tables S1, S2.
## The diversity of gastric juice microbiome
Diversity rarefaction curves of all sample species tended to be parallel to the X-axis, indicating that there is a significant difference (Supplementary Figure S2). The alpha diversity indices Richness (Figure 1A), Gini-Simpson (Figure 1B) and Shannon Wiener (Figure 1C) decreased gradually with disease progression among HC, GPL, and GC groups, there are significant differences among Richness groups ($p \leq 0.05$). Compared by using principal coordinate analysis (PCoA), and the ANOSIM test ($R = 0.077$, $p \leq 0.05$), beta diversity demonstrated significant differences among groups. The PCoA results showed that clustered within groups with a smaller area in the HC group than GPL and GC groups, where the Bray-Curtis distances between HC and GPL were closer than HC and GC groups (Figure 1D; Supplementary Figure S3). The difference between groups was greater than that within groups, implying a significant difference in diversity among HC, GPL and GC groups (Figure 1D).
**Figure 1:** *The microbial diversity of microbial communities. Alpha diversity index analysis richness (A), Gini-Simpson (B), and shannon-wiener (C) (kruskal–wallis test, *p < 0.05, **p < 0.01). (D) Beta diversity PCoA analysis (ANOSIM R = 0.077, p < 0.05), the ellipse contains 85% of the samples in each group. The difference of composition in phylum (E), genus (F) level between three groups. HC, healthy control; GPL, gastric precancerous lesions; GC, gastric cancer.*
## Gastric juice microbiome composition
There were commonalities and differences in bacterial composition among the HC, GPL and the GC group. At the phylum level, 14 phyla were in the HC and GC group while 13 phyla were in the GPL group (Figure 1E). At the genus level, 157 genera were in the HC group, 150 in the GPL group, 131 in the GC group, only 95 genera common to all groups (Figure 1F). The differences in composition at the genus level among the three groups were greater than those at the phylum level. To clarify the bacterial differences among the groups, we compared the differentially abundant bacteria among the three groups at the phylum and the genus level, respectively. At the phylum level, the results indicated that the composition of the three groups was similar, but the relative abundance of components varied (Figures 2A,B). We analyzed the bacterial alterations between the GPL, GC, and HC groups. Comparison with the HC group, the decreased abundance of bacterial species was greater than the increased abundance in the GPL and GC groups (Figure 2C). Similar patterns of variation were also found in the comparison of GC and GPL groups (Supplementary Figure S4). Further analysis of bacterial abundance differences at the phylum level showed the same results. With the course of gastric disease intensified (HC, GPL, and GC), phylum Proteobacteria (Figure 2D) and Spirochaetota (Figure 2F) showed a significant down-regulation of gastric juice microbiome, while *Firmicutes phylum* (Figure 2E) showed a significant up-regulation ($p \leq 0.05$). Genus level abundance heatmap showed the distribution of abundance at the genus level for each sample. Prevotella, Alloprevotella, Haemophilus, Neisseria, Fusobacterium were the high-abundance genus (Supplementary Figure S5).
**Figure 2:** *The relative abundances of taxonomy at the phylum level. (A) The relative abundances of taxonomy at the phylum level in all samples (60). (B) The relative abundances in phylum level in three groups. (C) Demonstration of precancerous lesions gastric cancer (GPL/GC) versus bacterial group with healthy control (HC). X-axis for ASVs, alphabetically ordered by taxonomic phylum level; Y-axis p value values for the comparison of the two groups, taken as loge (P), i.e., natural logarithmic transformation; the size of the nodes in the graph represents the relative abundance of that ASV, taken as log2 (CPM), the logarithm of 2; CPM is an abbreviation for count per million, which being fractions of a million; different nodes colors represent different phylum; the shape of the nodes in the graph marks the type of its change, whether it is up-regulated enriched (positive solid triangle), down-regulated depleted (inverted hollow triangle), or no significant difference change nosig (solid nodes). Differences abundance at the phylum level among groups (Wilcox. test, * p < 0.05, ** p < 0.01), Proteobacteria (D), Firmicutes (E), Spirochaetota (F).*
## Biomarkers of sample groups discovered by LEfSe, DESeq2, and Metastats
To better elucidate the gastric juice microbial biomarkers among the HC, GPL, GC groups, we used three methods to analyze the markers. The results of LEfSe showed that the identified intergroup biomarker genera echoed the previous intergroup differences at the phylum level (Figure 3A), and Treponema, Campylobacter, Neisseria, Sphingomonas, Vulcaniibacterium, and Lactobacillus were biomarker genera (Figure 3B). Furthermore, GC and GPL showed decreased abundance of the genera Treponema, Campylobacter, and Neisseria, while Lactobacillus abundance increased compared to the HC group. Among them, genera Vulcaniibacterium, Sphingomonas were commonly found in the HC group but largely undetectable in the GC and GPL groups (Figure 3C). Lautropia was up-regulated and Escherichia-Shigella was down-regulated in both GC and GPL compared to the HC group, and the same result was observed in the GC group compared to the GPL group (Figures 4A,B). Compared with the GPL group, the abundance of Mycoplasma, Treponema was down-regulated in the GC group. The metastatic showed the GC group had significantly higher abundance in *Streptococcus and* Lactobacillus compared with the GPL and HC (Figure 4C). To further compare the commonalities and differences of the biomarkers discovered by several methods, we plotted the upsets. Interestingly, we found the biomarkers jointly identified based on LEfSe, DESeq2 and *Metastats analysis* methods have an intersection, which is Lactobacillus (Figure 4D).
**Figure 3:** *LEfSe analysis of taxonomy with significant differences in abundance among groups. (A) Evolutionary branching diagram. The circles radiating from the inside to the outside represent taxonomic levels from the phylum to the genus. Each small circle at different taxonomic levels represents a taxon at that level, and the diameter size of the small circles is proportional to the relative abundance size; species without significant differences are uniformly colored in chartreuse, and the difference species Biomarker follows the group for coloring, red nodes indicate microbial taxa that play an important role in the HC group, green nodes indicate microbial taxa that play an important role in the GC group, purple nodes indicate microbial taxa that play an important role in the GPL group. The names of the species indicated by letters in the figure are shown in the legend on the right. (B) Histogram of LDA value, taxon with significantly different abundances in different groups are shown, and the length of the bar graph represents the effect size of the significantly. (C) Comparison of the abundance of biomarkers in each sample among HC, GPL and GC groups. HC, healthy control; GPL, gastric precancerous lesions; GC, gastric cancer.* **Figure 4:** *Differential abundance analysis at the species level. The volcano shows significantly up-regulated and down-regulated in the gastric precancerous lesions (GPL) and gastric cancer (GC) group compared with the healthy control (HC) group (A); GC compared with the GPL group (B). The color of the nodes in the volcano marks the type of its change, blue indicates down-regulation, red indicates up-regulation, and gray indicates no significant difference between groups. (C) Differential abundance analysis at the genus level base on meta, LDA, DESeq2 and hub genus in co-occurrence network. The different relative abundance among three groups based on metastats. Significant differences among groups are indicated by alphabetic letters above the bars, determined by multiple comparison LSD-T test (p < 0.05). (D) This upset diagram shows the biomarkers found based on Metastats, LDA and DESeq2.*
## The microbial interactions and networks between gastric juice microbiome
We performed a network co-occurrence analysis to unravel the relationships among microorganisms. With the same network construction parameters, the HC group network (Figure 5A) had 88 nodes and 177 edges, the GPL group (Figure 5B) had 91 nodes and 329 edges, while 84 nodes and 157 edges in GC group network (Figure 5C). In addition, HC group and GC groups are all clustered into 23 modules, while GPL group has only 19 modules (Table S3). The results suggested that microbial networks were composed of tightly connected nodes and formed a kind of “small-world” topology (Supplementary Figure S6; Supplementary Table S3). Compared with the topological properties of the random network with the same number of nodes and edges (Supplementary Figure S6; Supplementary Table S4), the network of the HC, GPL and GC groups exhibited a scale-free characteristic ($p \leq 0.001$, Supplementary Figure S7), indicating that the network structure was non-random. Correspondingly, we also analyzed the network properties for each group of networks. The average degree of the HC and GC groups were 3.078 and 4.023, which were lower than that of the GPL group (7.231, $p \leq 0.001$, Figure 5D), and the number of sides forming triangles was also lower ($p \leq 0.001$, Figure 5E). This suggests that total connectivity and complexity between gastric juice microbiome was higher in the GPL group than in the HC and GC group. The network Cluster of the GC group was significantly higher than that of the GPL group ($p \leq 0.01$, Figure 5F). These results manifested that the average “clustering property” of the whole network between gastric microorganisms in the GPL group was lower than that in the HC and GC groups. To understand each network in three groups deeply, we extracted the microbial hub network. Among the three groups of hub networks, Anaerococcus was the highest abundance in the HC group (Figure 5G), Cupriavidus was the highest abundant in the GPL group (Figure 5H), and in the GC group hub network, Moraxella was the highest abundant genus (Figure 5I). Overall, the GPL group hub network has the highest number of nodes and the highest agglomeration. The above findings can be concluded that there are differences in the gastric juice microorganism interaction network among the HC, GPL, and GC groups. Compared with the HC and GC groups, the GPL group network has the highest connectivity and complexity and the lowest clustering property.
**Figure 5:** *Co-occurrence network in three groups. (A) healthy control (HC) network, (B) gastric precancerous lesions (GPL) network and (C) gastric cancer (GC) network. Comparison of network topology properties among groups, weighted degree (D), triangles (E), cluster (F). Wilcoxon test, **p < 0.01; ***p < 0.001. Co-occurrence hub network, HC (G), GPL (H), GC (I).*
## Discussion
Gastric juice as samples were investigated in the present study, which was different from other studies using gastric mucosa (Yu et al., 2017; Ferreira et al., 2018; Png et al., 2022). Compared to gastric mucosa, gastric juice had two advantages in the study of gastric microbiota its homogeneity and noninvasiveness.
Previous studies have demonstrated that the gastric microbiome structure changes during carcinogenesis (Lam et al., 2017). It has been thought that HP was closely related to the development of GC, but the results of the available studies indicate that the risk of GC was not proportional to the degree of HP infection (Diaz et al., 2018). HP penetrates the gastric mucosa through flagella and mainly adheres to the surface of gastric epithelial cells. Therefore, other microbiome in gastric juice may be of greater importance for GC development (Hessey et al., 1990; Ruch and Engel, 2017). Our study pointed out that the gastric microbial composition and network of patients with GC have changed compared to the patients with HC and GPL. We identified the differences in the gastric microbiome among GC group and other groups, explored potential correlations, and also revealed the interaction between the gastric microbiome. The alpha diversity of GC group was significantly lower than other groups, and the alpha diversity indexes decreased gradually with the progression of gastric lesions. The microbial composition of the patients with GC gradually deviates from other groups. In addition, in the microbial interactions of the three groups, we found that networks of the GPL group exhibited higher connectivity, complexity and lower clustering property, which in the GC group showed the opposite.
The occurrence of GC starts from superficial gastritis, but the key factors promoting the development of GC are still unclear (Correa, 1992; Li et al., 2022). The microbial community is generally recognized as an important biological factor in the development of GC. Previous studies have shown that the structure of gastric microbial diversity was constantly changing in the process of gastric lesions (Wang et al., 2018). In the case of GC patients, the microbial community diversity was quite different from other groups (Coker et al., 2018). Our results were consistent with these showed that the alpha diversity of patients with GC was significantly lower than other groups. In addition, the PCoA analysis of the microbiome composition revealed that there were significant differences in the community structure between the GC group and other groups. This is in keeping with previous findings that the composition of the microbial community changed as the stomach disease gradually deepened (Wang et al., 2018). Therefore, our findings suggested that changes in microbial diversity were critical to the development of GC. The dysregulation of the gastric microbial community may increase the likelihood of developing cancer. Focusing on microbial diversity contributes to a better understanding of GC course and supports GC prevention and treatment.
Helicobacter pylori is the earliest microorganism reported to be associated with GC. Apart from HP, there are many other microbiome in the gastric, and their interactions are related to the development of GC (Duan et al., 2022). A Portuguese study showed that bacteria such as Streptococcus, Prevotella, Clostridium, and Lactobacillus were significantly increased in GC patients compared with patients with chronic gastritis (Ferreira et al., 2018). In addition, a recent animal study showed that the increasing of Lactobacillus abundance contributed to the development of GC (Dai et al., 2021). In this study, analysis of group differences at the phylum level showed that while the abundance of Firmicutes increased in the GC group, Proteobacteria, and Treponema decreased. To search for potential taxonomies that may be associated with carcinogenesis, we used different methods to identify the most significantly correlated taxa among the three groups. Through LEfSe analysis, we found that Treponema, Campylobacter, Vulcaniibacterium and Neisseria were increased in HC group at the genus level, while Lactobacillus and *Streptococcus were* significantly enriched in GC group. Neisseria is symbiosis bacteria in the mouth and esophagus, but the translocation and expansion of oral bacteria may be involved in the development of inflammation (Han and Wang, 2013). Campylobacter is closely related to HP, and despite a normal oral bacteria, numerous studies have found that its high transcriptional activity in gastric acid verified its role as a potential pathogen in the gastric (von Rosenvinge et al., 2013; Liu et al., 2018; Cui et al., 2019). Its ability to cause tissue damage and disease has been attributed to the production of virulence factors, leading to changes in epithelial permeability and local tissue destruction (Istivan and Coloe, 2006; Hess et al., 2012). Although there is no direct evidence that the occurrence of gastritis is associated with Campylobacter infection, chronic pathological changes ultimately result from the adhesion and colonization of Campylobacter. The presence of oral microbiome in the HC group suggested that our oral microbiome gradually play a cautionary role in the early stages of gastric lesions. The oral cavity severs as the beginning of the digestive system, its microbiome affects the health of the digestive system. Vulcaniibacterium, a moderate thermophilic bacterium (Yu et al., 2013; Niu et al., 2020), was first found in the stomach, suggesting that it may be closely related to the occurrence of gastritis. Meanwhile, we also observed enrichment of *Streptococcus in* GC group, which was consistent with previous findings (Sun et al., 2018; Huang et al., 2021). The abundance of *Streptococcus has* also been reported in several types of cancer, such as colorectal adenocarcinoma (Abdulamir et al., 2011). Taken together, the results suggest that *Streptococcus may* be involved in gastric carcinogenesis. More attention can be paid to the relationship between this bacteria and GC in the future.
Then, we further observed that compared with the HC group by differential abundance analysis. The up-regulated bacteria in cancer patients are Rotobacter, while down-regulated bacteria include *Streptococcus pepticus* and Micromonas, which are common oral microflora. Lautropia has been reported to be more abundant in patients with periodontal disease (Papapanou et al., 2020). It is also the predominant microorganism isolated from the sputum of cystic fibrosis patients (Ben Dekhil et al., 1997) and the oral cavity of children infected with human immunodeficiency virus (Rossmann et al., 1998). However, the possible pathogenic mechanism of the bacteria is still unclear. Our study found that this bacteria is up-regulated in GC patients, and its pathogenic mechanism may serve as a key target for future research on the relationship between oral bacteria and human health. In addition, a study on salivary microbiome found that Parvimonas was shown to be inversely associated with the development of GC (Huang et al., 2021), the bacteria were also found in CRC patients (Nakatsu et al., 2015). In the comparison between GPL and GC, we found that Lactobacillus also showed up-regulation while Mycoplasma and Treponema were down-regulated. First, Mycoplasma in gastric juice is down-regulated, which may be due to the ability of its lipoprotein P37 periplasmic transport system to promote cell motility and invasion (Gong et al., 2008; Gomersall et al., 2015). It allows Mycoplasma to colonize on the gastric mucosa by breaking through the mucosal barrier of gastric juice, eventually leading to Mycoplasma decreased. Secondly, Treponema can weaken MMPs’ regulation to tumor cell tissue invasion and exocytosis because the mucosal invasiveness of its major virulence factor chymotrypsin-like protease (Td-CTLP) and the hydrolysis of host-derived matrix metalloproteinases (MMPs), which has major impact on the tumor microenvironment (Kessenbrock et al., 2010; Marttila et al., 2014). Our results reconfirm the relationship between Treponema and gastric cancer.
Lactobacillus is a common probiotic that converts lactose to lactic acid, leading to acidification of the gastric mucosa, which can adapt to growth in gastric juice due to its acidophilic properties (Han et al., 2015). The up-regulation of Lactobacillus in GC has been verified in previous studies on gastric mucosa. Interestingly, we found that several differential analyses indicated that Lactobacillus presents closely related to the occurrence and development of gastric carcinogenesis. Therefore, the existence of Lactobacillus can be regarded as the key bacteria involved in the occurrence of GC. Although Lactobacillus acts as a probiotic, in the context of cancer, its metabolite lactic acid can perform an energy source function for tumor cells. It is also a key participant in many carcinogenesis processes, such as metastasis, angiogenesis, metabolism, and immunosuppression (Hirschhaeuser et al., 2011; Hayes et al., 2021). After the Warburg effect was proposed (Warburg et al., 1927), subsequent studies found that lactate, as an immune destroyer of the tumor microenvironment, could directly mediate its effects on cells, such as by blocking cytotoxicity, motility. Its effects can also be indirectly mediated on cells by inducing immunosuppressive cell types such as Tregs, TAMs, and MDSCs. Immune escape driven by lactate within the tumor microenvironment is a major contributor to cancer growth, progression and metastasis (Brand et al., 2016; Corbet and Feron, 2017). In tumor microenvironment, Lactobacilli do not end up with the Warburg effect of lactic acid production, but as lactate is continuously released from transformed cells to initiate carcinogenesis in susceptible cells and tissues (Brizel et al., 2001; San-Millan and Brooks, 2017). Therefore, Lactobacillus showed an upward trend in the GC group, revealing that it plays an important role in GC. A study using network co-occurrence analysis pointed out that there is an interaction between Lactobacillus and other gastric microbiome (Wang et al., 2020). The increase of Lactobacillus may lead to ecological dysregulation of the gastric fluid by interaction with other bacteria, thus enhancing the carcinogenic potential of the gastric microbiome. Therefore, we can state that *Lactobacillus is* an important factor for the next studies on gastric cancer prevention and treatment.
Nowadays, a wealth of evidence suggests that changes in the microbiome were related to carcinogenesis. Dysbiosis of the gastric microbiome was reflected not only at the level of changes in the abundance of microbiome members but also in the altered relationships of microbial interactions (Chen et al., 2020; Zhang et al., 2020). Many studies have pointed out that there is widespread competition between bacteria instead of cooperation in networks (Palmer and Foster, 2022). Our network analysis showed that the GPL group had higher network connectivity and complexity and lower aggregation, while the GC group had significantly sparser network connectivity. The high degree of cooperation of GPL group microbial community was closely related to Cupriavidus. It was a multifunctional microorganism found in soil and water, which is resistant to heavy metals and has been studied from environmental samples as well as human samples (Vaneechoutte et al., 2004). In previous studies, Cupriavidus has been reported associated with invasive human infections, such as bacteremia, pneumonia, immunocompromised patients and cystic fibrosis (CF) patients (Coenye et al., 2005; Kobayashi et al., 2016; Bianco et al., 2018). Here, we found that Cupriavidus played a key role in the stage of GPL. We hypothesize that Cupriavidus was associated with the development of GC. Moreover, *Pathogenic bacteria* breaching the mucosal protective barrier may make contributions to the decreased aggregation in the GC group, thereby promoting a decrease in the degree of microbial interaction in gastric juice (Krishnan et al., 2020). Compared with HC, the gastric microbiome in the GPL and GC groups changed in composition, ecological network, and function. These changes may be used as relevant factors for predicting carcinogenesis in the future.
In conclusion, our research provides evidence for the imbalance in gastric microbiome in GC patients, clarifies the differences in gastric juice microbiome among HC, GPL, and GC groups, elucidates the changes of gastric microbiome during the development of GC. Our study shows that *Lactobacillus is* an important indicator strain in the course of gastric cancer. The changes in gastric juice microbial community interactions are indicative of the development of gastric cancer. For now, gastric cancer is still a difficult clinical problem. The genus *Lactobacillus is* composed of more than 100 species. The biological behavior of different species varies greatly. At the same time, the interactions between Lactobacillus and other microbiome should not be underestimated. Although we propose a key role for Lactobacillus, its specific mechanism of action on gastric cancer remains to be further investigated, this will lay the foundation for the study of gastric cancer.
## Data availability statement
The 16S rRNA gene data reported in this paper have been deposited in the Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra), under accession number PRJNA849572 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA849572/).
## Ethics statement
The studies involving human participants were reviewed and approved by the independent Ethics Committee of Xiangya Hospital of Central South University following the ethical guidelines of the Declaration of Helsinki (No. 038, 2015). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
LC and ZY designed the experiments. SY carried out experiments. XP analyzed data prepared the figures. XP and SY drafted the manuscript. YZ, HC, and JH participation in discussion and revised the manuscript. All authors contributed to this manuscript, read, and approved the final manuscript.
## Funding
This work was funded by the National Natural Science Foundation of China (32170071 and 32000054).
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2023.1138928/full#supplementary-material
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|
---
title: 'Community participatory learning and action cycle groups to reduce type 2
diabetes in Bangladesh (D:Clare): an updated study protocol for a parallel arm cluster
randomised controlled trial'
authors:
- Carina King
- Malini Pires
- Naveed Ahmed
- Kohenour Akter
- Abdul Kuddus
- Andrew Copas
- Hassan Haghparast-Bidgoli
- Joanna Morrison
- Tasmin Nahar
- Sanjit Kumer Shaha
- AKAzad Khan
- Kishwar Azad
- Edward Fottrell
journal: Trials
year: 2023
pmcid: PMC10034243
doi: 10.1186/s13063-023-07243-x
license: CC BY 4.0
---
# Community participatory learning and action cycle groups to reduce type 2 diabetes in Bangladesh (D:Clare): an updated study protocol for a parallel arm cluster randomised controlled trial
## Body
The D:Clare trial (Diabetes: Community-Led Awareness, Response and Evaluation) was designed as a cluster randomised stepped-wedge trial, in Alfadanga Upazilla, Faridpur District, Bangladesh (ISRCTN42219712) [1]. The trial aims to evaluate the impact of a scaled-up community-based participatory learning and action (PLA) cycle intervention to prevent type 2 diabetes (T2DM) in a population of 120,000 people. The study began in January 2020, with a public consent and randomisation ceremony including community and Ministry of Health and Family Welfare representatives, on the grounds that all communities in the Upazilla would eventually receive the intervention in line with the stepped-wedge approach.
Bangladesh reported its first confirmed cases of SARS-CoV-2 on the 8th of March 2020. Due to concerns about infection risk to both staff and communities, we made a decision to suspend all field-based project activities on the 20th of March 2020 (Fig. 1). Early in the pandemic, evidence emerged that uncontrolled hyperglycaemia and T2DM were risks for severe COVID-19 infections and mortality, alongside older age, obesity and heart disease [2–4]. Given the focus and nature of our PLA intervention, we were therefore particularly conscious that continuing the trial may have increased risks amongst vulnerable populations with non-communicable diseases. The status of the trial at the point of suspension is summarised in Table 1. Bangladesh subsequently entered into a nationwide government-declared lockdown from the 23rd of March to the 30th of May 2020, and restrictions on mass gatherings continued until the 1st of September 2020 [5]. The second serious COVID-19 wave began in March 2021, and lockdowns were again implemented between 5th April–21st April 2021 and 1st July–11th August 2021.Table 1Status of the D:Clare stepped-wedge trial at the point of COVID-19 field activity suspension on the 20th of March 2020MilestoneStatusAdministrationEthical approvalsApprovals received from University College London ($\frac{07}{11}$/22) and the Diabetic Association of Bangladesh ($\frac{03}{12}$/19)Trial registrationRegistered on $\frac{31}{10}$/19Community entry, consent and public randomisation. Meeting held $\frac{16}{01}$/20EvaluationCommunity census for development of sampling frameData collection completed $\frac{04}{02}$/20Recruitment and training of survey field staffTraining completed $\frac{10}{02}$/20Baseline cross-sectional survey (target sample=1320 across the 12 study clusters)Interrupted. $72\%$ of the survey completed, with data gathered across all clusters, by $\frac{20}{03}$/20. Follow-up data cleaning was conducted by phone. InterventionRecruitment and training of PLA community group intervention facilitators and supervisorsCompletedFormation of PLA community groups in 6 clustersCompleted This short article summarises the changes to our original trial design, in line with the CONSERVE 2021 Statement recommendations that trials impacted by extenuating circumstances should report on modifications [6]. We detail the considerations and rationale for these changes, which may be of relevance to other randomised controlled trials underway in dynamic contexts.
## Abstract
The “Diabetes: Community-led Awareness, Response and Evaluation” (D:Clare) trial aims to scale up and replicate an evidence-based participatory learning and action cycle intervention in Bangladesh, to inform policy on population-level T2DM prevention and control.
The trial was originally designed as a stepped-wedge cluster randomised controlled trial, with the interventions running from March 2020 to September 2022. Twelve clusters were randomly allocated (1:1) to implement the intervention at months 1 or 12 in two steps, and evaluated through three cross-sectional surveys at months 1, 12 and 24. However, due to the COVID-19 pandemic, we suspended project activities on the 20th of March 2020. As a result of the changed risk landscape and the delays introduced by the COVID-19 pandemic, we changed from the stepped-wedge design to a wait-list parallel arm cluster RCT (cRCT) with baseline data. We had four key reasons for eventually agreeing to change designs: equipoise, temporal bias in exposure and outcomes, loss of power and time and funding considerations.
Trial registration ISRCTN42219712. Registered on 31 October 2019.
## Change in trial design
As a result of the changed risk landscape and the delays introduced by the COVID-19 pandemic, we decided to change from a stepped-wedge (SW-RCT) to a wait-list parallel arm cluster RCT (cRCT) with baseline data. Conceptually, our wait-list design is a parallel arm cRCT but with a commitment to implement the intervention to control clusters at the end of the trial evaluation. As detailed in Table 2, this differs from our stepped-wedge trial design in terms of the timing of roll-out of the intervention across all clusters, timing of cross-sectional data collection for evaluation, and in terms of how clusters are exposed over time, i.e. the allocated exposure (intervention or control) does not change during the trial evaluation. Our original SW design had two steps and was planned to take 30 months, with cross-sectional surveys done at months 1, 12 and 24 of intervention implementation (Fig. 1) [1]. The SW design should be resilient to temporal changes within a population, and so our original approach remained valid. However, the interruption of activities and the nature of the COVID-19 pandemic meant this design was no longer the most efficient and appropriate to meet project goals, and we presented alternative options to our Trial Steering Committee for consideration. We also engaged with community and government stakeholders to check that the proposed adaptions would be acceptable. We had four key reasons for eventually agreeing to change designs. Table 2Summary of key changes from original stepped-wedge design to parallel arm wait-list trialTrial componentOriginal stepped-wedge designParallel arm waitlist designIntervention implementationDelivery to all clusters in two steps. $50\%$ of clusters to receive the intervention from month 1 and the remaining $50\%$ to receive the intervention from month 12.Delivered to all clusters in two steps. $50\%$ of clusters (i.e. intervention arm) to receive the intervention from month 1 and the remaining $50\%$ (i.e. control arm) to receive the intervention after the trial end at approx. month 30.Up to 216 PLA community groups meeting on a monthly basis to progress through a schedule of 18 meetings over approx. 18 months. Up to 216 PLA community groups meeting twice per month to progress through a schedule of a minimum of 13 meetings over a period of approx. 30 months. Total duration of implementation across all 12 clusters to be approx. 24 months. Total duration of implementation across all 12 clusters to be approx. 30 months (including periods of ‘lockdown’ where intervention was paused. Timing of survey data collectionAt baseline (month 1), month 12, and month 24.At baseline (month 1) and post intervention implementation in intervention clusters (approx. month 30).Cluster exposure over timeDepends on timing - all clusters contribute data to both control and intervention exposure. No change over time, i.e. intervention arm contributes to intervention exposure only, control arm contributes to control only. Total duration of project36 months54 monthsFig. 1Planned D:Clare project timeline and COVID-19 interruptions
## Equipoise
The D:Clare PLA intervention was shown to be effective in reducing both the 2-year cumulative incidence and prevalence of T2DM in a rural Bangladesh population during the D-Magic trial [7]. This was part of the justification for us using an SW-RCT originally, as evidence of population benefit existed, and our aim was to determine effectiveness at scale in a similar but new population. However, with the considerable change in context and the potential need to adapt the intervention components and delivery, the lack of equipoise around the PLA intervention we had previously argued was less clear. Specifically, our intervention encourages groups to meet, encourages participation from those with T2DM and NCDs and encourages collective action. In a context where COVID-19 preventive measures focused on restricting inter-household interactions, we hypothesised that PLA’s mechanism of action may be affected.
Further, if COVID-19 cases were not being diagnosed in this community setting, then group meetings had the potential to cause harm. However, by the end of 2020, there was evidence that outdoor environments posed a lower risk of transmission than indoor, crowded spaces, especially if this can be combined with the use of face masks, hand hygiene and physical distancing. Given the potential for our intervention to improve T2DM management (a key risk for poor COVID-19 outcomes), the ability to deliver in a way that would reduce transmission, and the inclusion of new stop/start rules (Fig. 2), we felt this risk could be sufficiently mitigated. We therefore decided we met the criteria for equipoise around the intervention needed to do a parallel arm cRCT.Fig. 2D:Clare trial stop, pause and start rules for COVID-19 adaptation
## Temporal bias in exposure and outcomes
We also hypothesised that health literacy, care-seeking, dietary and physical activity behaviours, and the epidemiology of diabetes could be vastly different after lockdown restrictions were lifted — and therefore from our baseline survey. This in itself should not invalidate the SW-RCT design, but could make the interpretation and communication of the intervention impact on primary and secondary outcomes more complicated. The timing of intervention delivery relative to lockdown and social distancing measures was also likely to have an important influence on the uptake, delivery and effectiveness of the intervention. This may result in variable intervention effects between the two steps of SW implementation, which could be assessed through process evaluation, but again would complicate interpretation.
## Loss of power
Our power calculation was based on achieving at least an $80\%$ response in the first cross-sectional survey. However, we only achieved $72\%$ recruitment at the time of interruption and saw variation in rates between clusters (49–$89\%$). In order to then ensure the SW-RCT was sufficiently powered, we would have had to increase the sample size of all the subsequent surveys. Switching to a parallel arm trial which uses both a new baseline and endline data (assuming an autocorrelation of 0.4), we could achieve $78\%$ power for a $30\%$ reduction in the primary outcome and considered this a feasible alternative. The change to the number and timing of surveys and the inclusion of baseline data in outcome evaluation are notable changes to our original protocol (Table 2).
## Time and funding
Finally, there was a very practical issue that we no longer had enough time to complete the SW-RCT design within the overall 36-month funded project period, using our 12-month staggered two-step design. By switching to a parallel cRCT we could complete the effectiveness evaluation within the funded project timeline, however, recognising that the parallel arm design would determine the intervention impact on a smaller population scale than we had originally intended. We then planned to source project extensions and explore the reallocation of resources to ensure that at a minimum the intervention could be delivered in control clusters as was promised to communities, but without incurring ongoing concurrent process, economic and impact evaluation costs.
## Protocol updates
We made changes to three key areas of the trial protocol: study design, intervention and sample size; no amendments were made to the trial procedures for population eligibility, sampling, randomisation, blinding, data collection, or analysis of the primary or secondary outcomes. A list of registered trial protocol amendments in our ISRCTN record is summarised in Table 3.Table 3Summary of registered trial protocol revisions in the D:Clare ISRCTN record$\frac{17}{11}$/20211. Publication reference added.2. The individual participant data (IPD) sharing statement has been updated$\frac{.17}{12}$/20201. Ethics approval details added.2. The study design was changed from ‘Stepped-wedge cluster randomized trial’ to a ‘Cluster randomized controlled trial’, with scale-up to control clusters after trial completion (‘wait-list’).3. The interventions and primary and secondary outcome measures were updated.4. The target number of participants measured across the baseline and endline surveys was changed from ‘12 clusters; 440 individuals per cluster’ to ‘12 clusters; 211 individuals per cluster’.5. The recruitment start date was changed from $\frac{07}{12}$/2019 to $\frac{04}{01}$/$\frac{2020.06}{03}$/20201. Ethics approval and secondary outcome measures updated.
We also set out COVID-19 standard operating procedures, with new stop-start rules (Fig. 2), and a COVID-19 safety protocol for staff and study participants, and consulted with a Data Monitoring and Safety Board on these infection prevention measures. For the intervention, we made the following modifications to incorporate COVID-19 measures: holding two meetings per village per month to allow for smaller groups but with the same coverage; inclusion of COVID-19 health information; re-organised meeting content to be delivered over a minimum of 13 instead of the planned 18 meetings (Table 2).
## Current trial status
As of $\frac{22}{04}$/2022: We completed a new baseline survey on $\frac{25}{02}$/2021, with a response rate of 1,392 from 1,584 ($87.9\%$) sampled participants, which forms the parallel arm cRCT baseline data. A total of 213 PLA groups have been formed in 6 of the 12 study clusters, and have completed 11 of a minimum of 13 planned meetings. The endline survey will be completed between August and October 2022.
Protocol version 3.0 ($\frac{16}{06}$/2021)
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---
title: Effect of sarcopenia on postoperative ICU admission and length of stay after
hepatic resection for Klatskin tumor
authors:
- Hyun Eom Jung
- Dai Hoon Han
- Bon-Nyeo Koo
- Jeongmin Kim
journal: Frontiers in Oncology
year: 2023
pmcid: PMC10034314
doi: 10.3389/fonc.2023.1136376
license: CC BY 4.0
---
# Effect of sarcopenia on postoperative ICU admission and length of stay after hepatic resection for Klatskin tumor
## Abstract
### Background
Hepatic resection of Klatskin tumors usually requires postoperative intensive care unit (ICU) admission because of its high morbidity and mortality. Identifying surgical patients who will benefit most from ICU admission is important because of scarce resources but remains difficult. Sarcopenia is characterised by the loss of skeletal muscle mass and is associated with poor surgical outcomes.
### Methods
We retrospectively analysed th.e relationship between preoperative sarcopenia and postoperative ICU admission and length of ICU stay (LOS-I) in patients who underwent hepatic resection for Klatskin tumors. Using preoperative computed tomography scans, the cross-sectional area of the psoas muscle at the level of the third lumbar vertebra was measured and normalised to the patient’s height. Using these values, the optimal cut-off for diagnosing sarcopenia was determined using receiver operating characteristic curve analysis for each sex.
### Results
Of 330 patients, 150 ($45.5\%$) were diagnosed with sarcopenia. Patients with preoperative sarcopenia presented significantly more frequently to the ICU ($77.3\%$ vs. $47.9\%$, $p \leq 0.001$) and had longer total LOS-I (2.45 vs 0.89 days, $p \leq 0.001$). Moreover, patients with sarcopenia showed a significantly higher postoperative length of hospital stay, severe complication rate, and in-hospital mortality.
### Conclusions
Sarcopenia correlated with poor postoperative outcomes, especially with the increased requirement of postoperative ICU admission and prolonged LOS-I after hepatic resection in patients with Klatskin tumors.
## Introduction
Klatskin tumors, also called perihilar cholangiocarcinomas, constitutes 50–$70\%$ of all biliary tract malignancies [1]. Klatskin tumors originate from the biliary ductal epithelium and are located between the bifurcation of the cystic duct junction and the second-order intrahepatic bile duct branches [2]. As the tumor progresses, the mass blocks the biliary tract, and patients typically present with cachexia, fatigue, and obstructive jaundice [3, 4]. Although complete surgical resection is the only curative treatment for Klatskin tumors, less than half of the cases are resectable [5]. In addition, this procedure is technically demanding to achieve a histologically negative margin and has high postoperative morbidity owing to the local anatomy [6, 7]. Consequently, the risk of postoperative morbidity and mortality in Klatskin tumors remains high, despite many advances in surgical techniques and perioperative management [8].
Postoperative admission to the intensive care unit (ICU) in high-risk patients is effective for the prevention, early recognition, and management of severe complications, thereby reducing the mortality risk [9, 10]. However, routine ICU admission after major surgery is not beneficial to all patients because of increased expenses and limited resources [11, 12]. Therefore, it is important to determine the need for ICU admission in high-risk patients, as it can provide more efficient medical resources. However, identifying surgical patients who will benefit the most from intensive care remains difficult, and the identification method is not fully established. Prediction of ICU admission and length of stay has traditionally focused on the presence of comorbidities using scoring systems such as the Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS), or Surgical Apgar scores [13, 14]. To date, patients’ nutritional and functional parameters have not been routinely evaluated or considered in the decision-making process [15].
Sarcopenia refers to the loss of skeletal muscle mass and strength associated with wasting and aging [16]. Cancer patients are vulnerable to sarcopenia, as malnutrition, cancer-mediated inflammation, and inactivity may lead to loss of muscle mass and strength [17, 18]. Identifying preoperative sarcopenia using computed tomography (CT) analysis is widely accepted because of its practicality [19]. Cross-sectional views of the trunk provide an objective method for estimating body composition, such as muscle mass and intramuscular proportions of adipose tissue in Hounsfield units (HU), and it has many clinical implications in cancer patients [20, 21]. Generally, abnormalities in these indices are associated with poor postoperative outcomes. Sarcopenia is reported as an independent risk factor for poor overall survival and disease-free survival [22, 23]. A negative effect of sarcopenia on short-term outcomes in hepatic resection has been reported [24].
To date, only few studies have investigated the impact of sarcopenia on predicting ICU admission and length of ICU stay (LOS-I) for patients with Klatskin tumors who underwent hepatic resection. We retrospectively analysed the relationship between preoperative sarcopenia and postoperative ICU admission and LOS-I in patients who underwent hepatic resection for Klatskin tumors. We also investigated whether other factors, including body composition and nutritional status, had an impact on postoperative ICU admission and LOS-I. We hypothesised that preoperative sarcopenia is related to poor surgical outcomes, including increased ICU requirements.
## Materials and methods
The electronic medical records of patients who underwent elective hepatic resection for Klatskin tumors at Severance Hospital in Seoul, Korea, between November 2005 and June 2022 were retrospectively reviewed. This study was approved by the Internal Review Board of Severance Hospital (approval number: 4-2022-0343). The need for informed consent was waived owing to the retrospective anonymized data of the study. The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki.
## Patient characteristics
We collected data including patients’ demographics, preoperative clinical characteristics, preoperative laboratory test results, pathologic, and surgical results. Patients received the appropriate preoperative treatment, such as endoscopic biliary stenting, percutaneous transhepatic biliary drainage (PTBD), chemotherapy (CTx), and portal vein embolisation (PVE), to augment remnant liver volume. Hepatic resection included segmental resection, left or right lobectomy, and extended hepatectomy. If patients presented severe obstructive jaundice with cholangitis, we evaluated the need for preoperative biliary drainage, including PTBD, endoscopic retrograde biliary stent, and endoscopic naso-biliary drainage, depending on the specific condition. Some patients underwent neoadjuvant treatment with the purpose of downstaging in the case of borderline resectable (suspicious tumor invasion to the contralateral portal vein or hepatic artery, and a tumor located over the U-point or P-point) or locally advanced (suspicious local lymph node metastasis) Klatskin tumor [25]. PVE was performed to augment the remnant liver volume, and the decision to resect the Klatskin tumor was made using a multidisciplinary approach considering vessel invasion, possibility of achieving negative margins, and future remnant liver volume [26]. We collected data on surgical and oncological outcomes, including postoperative morbidity, mortality, ICU admission, length of hospital stay (LOS-H) and LOS-I, overall survival, and recurrence. Criteria for postoperative ICU admission are for patients who need intensive monitoring, treatment, and ventilator care due to medical or surgical complications such as shock, heart failure, respiratory failure, renal injury, and massive bleeding. Postoperative ICU admission was arranged during the total perioperative period with flexibility including the intraoperative situation and patients’ status in the recovery room. Patients who were not directly admitted to the ICU from the operation room but underwent delayed admission or re-admission within 7 days from the surgery date because of an emergent reason, such as complications or re-operation requiring ICU care, were also included. Therefore, the LOS-I was the sum of the total length of ICU stay if admitted within 7 days after surgery. The grade of postoperative morbidity was assigned according to the Clavien–Dindo classification (CDC) within 90 days of surgery or until the discharge date, and severe complications were defined as complications with a CDC ≥ 3 [27]. Postoperative mortality was recorded during the same period. To evaluate the preoperative nutritional status of cancer patients, the prognostic nutritional index (PNI), suggested as a clinical predictor of prognosis, was calculated using collected laboratory data [28]. PNI was defined as 10 × serum albumin value (g/dL) + 0.005 × lymphocyte count (/mm³), with a cut-off value for low PNI of less than 1.39 indicating nutritional impairment. Cancer recurrence and survival were determined from the time of surgery to the time of event or the most recent follow-up date.
## Radiologic body composition evaluation
Preoperative sarcopenia was evaluated according to the obtained preoperative abdominal and pelvic CT images within 90 days before surgery which were routinely obtained to diagnose and plan treatment. The cross-sectional surface (cm²) of both psoas muscle areas (PMA) was automatically quantified at the third lumbar (L3) vertebra level using the Aquarius iNtuition Viewer (ver. 4.4.13, TeraRecon Inc., San Mateo, CA, USA) imaging server platform [29]. Using this software, each surface can be automatically quantified using a particular CT attenuation range. Non-contrast-enhanced CT images were used to measure the radiation attenuation density (HU) of the muscle and adipose tissue. Using these values, muscle steatosis was evaluated using intramuscular adipose tissue content (IMAC) by dividing the CT attenuation value of the multifidus muscle (HU) by that of the subcutaneous fat HU [30]. A higher IMAC indicates that skeletal muscles contain a greater amount of adipose tissue and may result in poor prognosis in several cancers (31–33). Cut-off values for high IMAC (-0.358 for men and -0.229 for women) were defined in a previous study including healthy donors for liver transplantation [34].
The measured PMA was then normalised by patient height squared (cm²/m²), which was termed the psoas muscle mass index (PMI). Optimal cut-off points for PMI data in the maximal predictive value for postoperative ICU admission were determined using receiver operating characteristic (ROC) curve analysis. Sarcopenia was determined when the patients’ PMI was lower than the sex-specific cut-off. The study population was categorised into two groups: sarcopenia and non-sarcopenia patients, and postoperative short- or long-term outcomes were compared.
## Statistical analysis
Categorical variables are expressed as frequencies and percentages. Continuous variables are summarised as means with standard deviations (SDs). Categorical variables were analysed using the χ2 or Fisher’s exact test, and continuous variables were compared using the independent t-test or Mann–Whitney U test. Spearman’s correlation tests were used to assess the relationships between risk factors and continuous outcomes. Multivariate analysis was performed to investigate statistically significant risk factors affecting ICU admission and LOS-I using variables with p-values < 0.05. Survival estimates were obtained using the Kaplan–Meier survival method, and differences in overall survival (OS) and recurrence-free survival (RFS) between the groups were determined using the log-rank test. All results with p-values < 0.05 were statistically significant. All statistical analyses were performed using IBM SPSS Statistics for Windows, version 26.0 (IBM Corp., Armonk, NY, USA).
## Study population
A total of 330 patients with pathologically confirmed Klatskin tumors who underwent curative hepatic resection were identified during the study period. Among them, 13 patients were excluded because they underwent hepatic wedge resection ($$n = 2$$) or cooperation with other organs ($$n = 11$$). Ultimately, 317 patients were included in the analysis. The T stage was unmeasured in three patients owing to post-CTx changes in pathological specimens. The mean age of the study population was 65.6 ± 9.0 years, and 202 men ($63.7\%$) and 115 women ($36.3\%$) were included. The PMI (men: 6.91 vs. women: 3.78, $p \leq 0.001$) and IMAC (men: -0.44 vs. women: -0.32, $p \leq 0.001$) showed significant differences between sexes. Among them, 196 patients ($61.8\%$) were provided ICU care within seven postoperative days, and most of them ($$n = 193$$/196, $98.4\%$) were directly transported from the operating theatre to the ICU. During the same period, 11 patients ($3.5\%$) were readmitted to the ICU after being discharged from the ICU to the general ward. Among them, 4 ($36.4\%$) developed septic shock, 2 ($18.2\%$) underwent hepatic artery embolization or operation due to bleeding, 2 ($18.2\%$) underwent thrombus aspiration and stent insertion due to portal vein thrombosis, 1 ($9.1\%$) had hepatic encephalopathy, 1 ($9.1\%$) had ventilator care due to pulmonary edema, and 1 ($9.1\%$) experienced liver failure. Severe complications (CDC ≥ 3) were observed in 125 ($39.4\%$) patients within 90 days of surgery, with 82 patients requiring surgical, endoscopic, or radiological intervention due to pleural effusion, pericardial effusion, bile leakage, abdominal abscess, ascites or bleeding (CDC = 3). 21 required intensive care or ventilator due to organ dysfunction like renal failure, hepatic failure, cardiogenic shock, respiratory distress, sepsis or stroke (CDC = 4). 22 patients ($6.9\%$) died owing to postoperative mortality (CDC = 5). Table 1 summarises patient characteristics and postoperative outcomes of the study population.
**Table 1**
| Unnamed: 0 | All patients(n = 317) | Men(n = 202) | Women(n = 115) | p-value |
| --- | --- | --- | --- | --- |
| Preoperative | Preoperative | Preoperative | Preoperative | Preoperative |
| Age (years) | 65.6 ± 9.0 | 65.4 ± 9.0 | 65.9 ± 8.9 | 0.652 |
| Body mass index (kg/m²) | 23.42 ± 2.69 | 23.29 ± 2.43 | 23.62 ± 3.09 | 0.287 |
| Psoas muscle index (cm²/m²) | 5.78 ± 2.13 | 6.91 ± 1.72 | 3.78 ± 1.04 | <0.001* |
| Sarcopenia | 150 (47.3%) | 106 (52.5%) | 44 (38.3%) | 0.015 * |
| Intramuscular adipose tissue content | n = 287 | n = 184 | n = 103 | |
| Intramuscular adipose tissue content | -0.40 ± 0.11 | -0.44 ± 0.10 | -0.32 ± 0.09 | <0.001* |
| Prognostic nutritional index | 44.86 ± 6.84 | 44.93 ± 6.81 | 44.73 ± 6.91 | 0.798 |
| Postoperative | Postoperative | Postoperative | Postoperative | Postoperative |
| Intensive care unit-admission | 196 (61.8%) | 127 (62.9%) | 69 (60.0%) | 0.613 |
| Intensive care unit-re-admission | 11 (3.5%) | 7 (3.5%) | 4 (3.5%) | 1.000 |
| Intensive care unit-delayed admission | 3 (0.9%) | 2 (1.0%) | 1 (0.9%) | 1.000 |
| Length of stay-intensive care unit (days) | 1.6 ± 3.4 | 1.6 ± 2.9 | 1.7 ± 4.1 | 0.886† |
| Postoperative length of stay- hospital (days) | 24.5 ± 20.2 | 22.8 ± 14.2 | 27.4 ± 27.5 | 0.094† |
| Clavien–Dindo Classification ≥ 3 | 125 (39.4%) | 78 (38.6%) | 47 (40.9%) | 0.693 |
| In-hospital mortality | 22 (6.9%) | 16 (7.9%) | 5 (4.3%) | 0.492 |
## Association between sarcopenia and postoperative short-term outcomes
The mean ± SD for PMI in men was 6.91 ± 1.72 cm²/m², whereas that in women was 3.78 ± 1.04 cm²/m² ($p \leq 0.001$). Sex-specific PMI cut-off values for sarcopenia were determined at 6.74 cm²/m² for men [sensitivity = $65.4\%$, specificity = $69.3\%$, area under the curve (AUC) = 0.700] and 3.39 cm²/m² for women (sensitivity = $47.8\%$, specificity = $76.1\%$, AUC = 0.609) by ROC curve analysis. Using these cut-offs, preoperative sarcopenia was observed in 106 men ($52.5\%$) and 44 women ($38.3\%$). The sarcopenia group had a significant lower BMI (23.0 vs. 23.8 kg/m², $$p \leq 0.004$$) and higher preoperative CTx incidence ($12.7\%$ vs. $4.8\%$, $$p \leq 0.015$$) than the non-sarcopenia group. Patients with sarcopenia underwent longer surgeries (550.7 vs. 506.7 min, $$p \leq 0.023$$) with more intraoperative bleeding (1,375 vs. 976 ml, $$p \leq 0.006$$), packed RBC transfusion events ($44.7\%$ vs. $28.1\%$, $$p \leq 0.002$$), and transfusion volumes (427 vs. 210 ml, $p \leq 0.001$) than those without sarcopenia.
Patients with preoperative sarcopenia presented with significantly more frequent ICU admissions within 1 week after surgery ($77.3\%$ vs. $47.9\%$, $p \leq 0.001$) and longer total LOS-I (2.45 vs. 0.89 days, $p \leq 0.001$) than patients without sarcopenia. Furthermore, the sarcopenia group showed significantly higher postoperative LOS-H (27.8 vs. 21.4 days, $$p \leq 0.006$$), severe complication rates ($48\%$ vs. $31.7\%$, $$p \leq 0.003$$), and in-hospital mortality ($11.3\%$ vs. $3.0\%$, $$p \leq 0.004$$) within postoperative 90 days than the non-sarcopenia group. Postoperative re-admission or delayed admission in ICU showed no difference between the two groups. Detailed comparisons of baseline characteristics and short-term outcomes between the sarcopenia and non-sarcopenia groups are presented in Table 2.
**Table 2**
| Unnamed: 0 | Sarcopenia(n = 150) | Non-sarcopenia(n = 167) | p-value |
| --- | --- | --- | --- |
| Preoperative | Preoperative | Preoperative | Preoperative |
| Men | 106 (70.7%) | 96 (57.5%) | 0.015* |
| Age (years) | 66.5 ± 8.2 | 64.7 ± 9.5 | 0.080 |
| Body mass index (kg/m²) | 22.96 ± 2.75 | 23.83 ± 2.58 | 0.004* |
| American Society of Anesthesiologists score | 2.4 ± 0.6 | 2.3 ± 0.6 | 0.185 |
| Charlson comorbidity index | 2.6 ± 1.3 | 2.5 ± 1.0 | 0.499 |
| Psoas muscle index | 4.76 ± 1.48 | 6.69 ± 2.22 | <0.001* |
| Intramuscular adipose tissue content | -0.39 ± 0.10 | -0.41 ± 0.12 | 0.161 |
| Chemotherapy | 19 (12.7%) | 8 (4.8%) | 0.015* |
| Portal vein embolization | 43 (28.7%) | 53 (31.7%) | 0.553 |
| Laboratory findings | Laboratory findings | Laboratory findings | Laboratory findings |
| Haemoglobin (g/dL) | 11.8 ± 1.4 | 12.2 ± 1.6 | 0.020* |
| Platelet count (10³/uL) | 293.4 ± 101.7 | 301.8 ± 107.5 | 0.481 |
| Prothrombin time(International normalized ratio) | 1.04 ± 0.16 | 1.03 ± 0.11 | 0.421 |
| Creatine (mg/dL) | 0.80 ± 0.35 | 0.76 ± 0.22 | 0.343 |
| Albumin (g/dL) | 3.5 ± 0.4 | 3.7 ± 0.5 | 0.001* |
| Alanine aminotransferase (IU/L) | 41.3 ± 24.6 | 48.6 ± 44.3 | 0.077 |
| Aspartate aminotransferase (IU/L) | 34.9 ± 27.9 | 45.1 ± 44.4 | 0.053 |
| Total bilirubin (mg/dL) | 1.54 ± 1.23 | 1.65 ± 1.98 | 0.552 |
| Carbohydrate antigen 19-9 (U/mL) | 918.7 ± 3118.2 | 566.9 ± 1743.6 | 0.739† |
| Prognostic nutritional index | 44.09 ± 6.43 | 45.55 ± 7.14 | 0.058 |
| Bismuth type | | | 0.471 |
| 1 | 5 (3.3%) | 5 (3.0%) | |
| 2 | 14 (9.3%) | 26 (15.6%) | |
| 3a | 64 (42.7%) | 72 (43.1%) | |
| 3b | 24 (16.0%) | 20 (12.0%) | |
| 4 | 43 (28.7%) | 44 (26.3%) | |
| Intraoperative | Intraoperative | Intraoperative | Intraoperative |
| Operation type | | | 0.188 |
| Liver lobectomy | 119 | 118 | |
| Extended hepatectomy | 22 | 37 | |
| Central lobectomy or segment resection | 9 | 12 | |
| Tumor stage (n = 314) | n = 148 | n = 166 | 0.281 |
| T1 | 17 (11.5%) | 14 (8.4%) | |
| T2 | 97 (65.5%) | 125 (75.3%) | |
| T3 | 26 (17.6%) | 22 (13.3%) | |
| T4 | 8 (5.4%) | 5 (3.0%) | |
| Lymph node metastasis | 51 (34.0%) | 57 (34.1%) | 0.637 |
| Resection margin negative | 116 (77.3%) | 118 (70.7%) | 0.286 |
| Mass size (cm) | 2.8 ± 1.4 | 2.9 ± 1.3 | 0.576 |
| Operation time (min) | 550.7 ± 172.7 | 506.7 ± 169.7 | 0.023* |
| Estimated blood loss (cc) | 1375 ± 1423 | 976 ± 1119 | 0.002*† |
| Packed RBC transfusion | 67 (44.7%) | 47 (28.1%) | 0.002* |
| Transfusion amount (cc) | 427 ± 838 | 210 ± 577 | <0.001† |
| Postoperative | Postoperative | Postoperative | Postoperative |
| Intensive care unit admission | 116 (77.3%) | 80 (47.9%) | <0.001* |
| Intensive care unit re-admission | 8 (5.3%) | 3 (1.8%) | 0.124 |
| Intensive care unit delay-admission | 2 (1.3%) | 1 (0.6%) | 0.605 |
| Length of stay-intensive care unit (days) | 2.5 ± 4.5 | 0.9 ± 1.4 | <0.001*† |
| Length of stay-hospital (days) | 27.8 ± 25.4 | 21.4 ± 13.1 | 0.006*† |
| Clavien–Dindo classification ≥ 3 | 72 (48.0%) | 53 (31.7%) | 0.003* |
| Pleural effusion requiring intervention | 25 (16.7%) | 23 (13.8%) | |
| Reoperation | 9 (6.0%) | 7 (4.2%) | |
| Bile leakage or ascites requiring intervention | 8 (5.3%) | 6 (3.6%) | |
| Sepsis | 3 (2.0%) | 4 (2.4%) | |
| In-hospital mortality | 17 (11.3%) | 5 (3.0%) | 0.004* |
Multivariate analysis showed that sarcopenia was significantly associated with postoperative ICU admission [adjusted odds ratio (OR): 2.461, $$p \leq 0.006$$] and prolonged LOS-I (B: 0.957, $$p \leq 0.008$$). Other factors associated with postoperative ICU admission in the multivariate analysis were serum carbohydrate antigen (CA) 19-9 levels, operation time, and intraoperative packed RBC transfusion events. Alternatively, BMI, Charlson comorbidity index, T stage 3 or 4, operation time, and intraoperative packed RBC transfusion events were significantly associated with LOS-I in multivariate analysis (Tables 3, 4).
## Impact of the nutritional status and body composition on short-term outcomes
Non-contrast-enhanced CT images were available for 285 patients, and a high IMAC was observed in $12.0\%$ ($$n = 38$$/285) of the patients. A strong linear correlation was noted between PMI and IMAC (correlation coefficient; -0.414, $p \leq 0.001$), and a high IMAC was more frequent in sarcopenia patients ($17.4\%$ vs. $9.4\%$, $$p \leq 0.046$$) than in patients without sarcopenia. Preoperative nutritional assessment via laboratory data revealed that patients with sarcopenia had lower serum haemoglobin and albumin levels than patients without sarcopenia. A low PNI was noted in 74 ($23.3\%$) patients and was significantly more frequent in the sarcopenia group ($28.7\%$ vs. $18.6\%$, $$p \leq 0.034$$) than in the non-sarcopenia group. A high IMAC ($81.6\%$ vs $58.6\%$, $$p \leq 0.007$$) and low PNI ($73.0\%$ vs $58.4\%$, $$p \leq 0.$$ 024) were significantly associated with an increased postoperative ICU admission rate.
## Survival analysis
In total, the median follow-up duration among the living patients was 44.3 months, and 181 ($57.1\%$) patients experienced recurrence during the study period. The 1-, 3-, and 5-year OS rates of the patients were $84\%$, $57\%$, and $44\%$, respectively, while RFS rates were $74\%$, $39\%$, and $25\%$, respectively. The median RFS was 27.8 months in patients with sarcopenia and 20.3 months in patients without sarcopenia, but the difference was not statistically significant ($$p \leq 0.609$$). However, sarcopenia patients showed a trend towards a lower OS than non-sarcopenia patients, with borderline significance (median 38.7 vs 57.3 months, $$p \leq 0.075$$). In the subgroup analysis, male patients with sarcopenia had significantly lower OS than those without sarcopenia (median 31.8 vs 57.2 months, $$p \leq 0.024$$, Figure 1). Further analysis of patients’ IMAC and PNI did not yield significant impacts on RFS and OS, except that patients with a low PNI were associated with lower OS (median 29.7 vs 57.2 months, $$p \leq 0.006$$, Figure 2) than patients with a normal PNI.
**Figure 1:** *Kaplan–Meier curves for overall survival after hepatic resection of Klatskin tumors for male patients stratified by presence of preoperative sarcopenia. Overall survival for male patients with sarcopenia following surgery of Klatskin tumors was shorter than for those without sarcopenia.* **Figure 2:** *Kaplan–Meier curves for overall survival after hepatic resection of Klatskin tumors stratified by prognostic nutritional index (PNI) Preoperative PNI lower than 1.39 was associated with a shorter overall survival for patients with Klatskin tumors following surgery.*
## Discussion
We aimed to investigate the clinical impact of sarcopenia on the surgical outcomes of patients who underwent hepatectomy for Klatskin tumors. The results showed that preoperative sarcopenia increased the ICU admission rate and prolonged ICU stay after hepatic resection in patients with Klatskin tumors. Additionally, sarcopenia patients showed significantly higher postoperative severe morbidity and mortality than non-sarcopenia patients. Our study implies that sarcopenia patients present a higher necessity of being admitted to the ICU postoperatively and should be given priority among high-risk patients who underwent major surgeries.
Sarcopenia, a physiological syndrome characterised by a combination of low muscle mass and low muscle function, has been proposed as a factor that increases perioperative risk for adverse clinical outcomes [35]. In particular, frequent obstructive jaundice in patients with Klatskin tumors makes them vulnerable to poor oral intake and decreased activity, which consequently induce sarcopenia [36, 37]. Among many previous definitions, the cut-offs for diagnosing sarcopenia for Klatskin tumor differed depending on the studies (range of PMI cut-off for men: 4.77 – 8.60 cm²/m² and for women: 3.38 – 6.04 cm²/m²) [38]. We used the normalised PMA which is widely used to diagnose sarcopenia, and the cut-off value was determined by ROC curve analysis by taking the rate of ICU admission as an indicator for predictive validity to determine the optimal cut-off value for each sex (6.74 cm²/m² for men and 3.39 cm²/m² for women). Sarcopenia was present in $47.4\%$ of our population, which is consistent with previous reports stating that sarcopenia was common among patients with Klatskin tumors, occurring in more than $30\%$ of patients [29].
In addition, we analysed other parameters, such as body composition and nutritional status. Through image analysis, we observed that muscle steatosis determined by a high IMAC was associated with postoperative ICU admission following hepatic resection of Klatskin tumors. This also corresponds with our earlier observations which showed that a poor preoperative nutritional status of patients based on the calculated low PNI increased ICU requirements. Furthermore, a high IMAC and low PNI were more frequent among patients with sarcopenia than among those without sarcopenia, which indicates their poor nutritional status and low muscle density. This result suggests that preoperative sarcopenia is strongly related to muscle steatosis and poor nutritional status with weight loss, indicating increasing requirements for postoperative ICU care.
Our findings also support those of previous studies, as we showed that sarcopenia was associated with increased operation time, intraoperative blood loss, packed RBC transfusion, and poor overall short-term prognosis [29, 39]. Moreover, sarcopenia showed poor OS which was consistent with findings of previous studies, while we did not observe any relevance to RFS [38]. It can be interpreted that our cut-off was based on the ROC curve analysis of ICU admission. However, intraoperative transfusion and longer operation time were significantly related to both postoperative ICU admission and LOS-I in multivariate analysis. Poor nutritional status and intraoperative packed RBC transfusion are also associated with poor OS in cholangiocarcinoma [40, 41]. In addition, prolonged operation time is a known risk factor for postoperative complications, which can help to interpret our results [42].
To the best of our knowledge, the current study is the first to analyse the impact of preoperative sarcopenia on postoperative ICU care frequency and duration. Postoperative ICU care is beneficial for the management and early detection of severe complications. However, unnecessary postoperative ICU admission only for surveillance is not appropriate considering low cost-effectiveness and increased ICU-related complications, such as infection or delirium [43, 44]. These issues have been considered during the COVID-19 pandemic which induced a shortage of critical care beds and staff [45]. Likewise, unpredicted, prolonged ICU stays can negatively affect ICU bed resources and may alter other operational schedules [46]. Therefore, filling surgical requests for ICU preparation should consider objective triage to maximise patient outcomes and medical resources [47]. Although some scoring systems have been developed to aid the preoperative determination of surgical candidates for ICU care, they are insufficient to provide adequate information regarding the risk for an individual patient [13, 48]. We suggest that physicians diagnose sarcopenia using preoperative CT images and use it as a parameter in predicting postoperative ICU requirements for patients with Klatskin tumors.
The current study has some limitations. 1) Our study has the possibility of temporal or selection bias. Our data were collected from a single institution, and the study population was limited to East Asians (Koreans) who can undergo surgery. Furthermore, we could not measure other functional parameters of sarcopenia, such as handgrip strength, walking speed, and low physical activity, owing to the retrospective nature of the study. 2) Chronological improvements in surgical techniques and diversity of surgeons were not considered during the long-term study period. 3) Possible differences as a consequence of using different CT scanners and scanning protocols in various periods or hospitals could not be precluded. In the future, multicentre, large, prospective studies may be required to verify this result. Together, skeletal muscle mass and function should be evaluated to diagnose sarcopenia. Further randomised controlled trials should be conducted to investigate whether improving muscle mass and quality before surgery through rehabilitation or nutritional support has the potential to reduce postoperative ICU admission and length of stay [49, 50].
In conclusion, this study showed that sarcopenia was correlated with poor postoperative outcomes, especially with the increased requirement of postoperative ICU admission and prolonged LOS-I after hepatic resection for patients with Klatskin tumors. The evaluation of preoperative sarcopenia could help predict the postoperative outcomes of such patients.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Internal Review Board of Severance Hospital. The ethics committee waived the requirement of written informed consent for participation.
## Author contributions
All authors contributed to conception and design of the study. HJ: data collection, data analysis, and writing of the first draft. DH: patient recruitment, manuscript review and critique. BK: patient recruitment, and data collection. JK: computational analysis methodology, manuscript review and critique. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Prediction model for gestational diabetes mellitus using the XG Boost machine
learning algorithm
authors:
- Xiaoqi Hu
- Xiaolin Hu
- Ya Yu
- Jia Wang
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10034315
doi: 10.3389/fendo.2023.1105062
license: CC BY 4.0
---
# Prediction model for gestational diabetes mellitus using the XG Boost machine learning algorithm
## Abstract
### Objective
To develop the extreme gradient boosting (XG Boost) machine learning (ML) model for predicting gestational diabetes mellitus (GDM) compared with a model using the traditional logistic regression (LR) method.
### Methods
A case–control study was carried out among pregnant women, who were assigned to either the training set (these women were recruited from August 2019 to November 2019) or the testing set (these women were recruited in August 2020). We applied the XG Boost ML model approach to identify the best set of predictors out of a set of 33 variables. The performance of the prediction model was determined by using the area under the receiver operating characteristic (ROC) curve (AUC) to assess discrimination, and the Hosmer–Lemeshow (HL) test and calibration plots to assess calibration. Decision curve analysis (DCA) was introduced to evaluate the clinical use of each of the models.
### Results
A total of 735 and 190 pregnant women were included in the training and testing sets, respectively. The XG Boost ML model, which included 20 predictors, resulted in an AUC of 0.946 and yielded a predictive accuracy of 0.875, whereas the model using a traditional LR included four predictors and presented an AUC of 0.752 and yielded a predictive accuracy of 0.786. The HL test and calibration plots show that the two models have good calibration. DCA indicated that treating only those women whom the XG Boost ML model predicts are at risk of GDM confers a net benefit compared with treating all women or treating none.
### Conclusions
The established model using XG Boost ML showed better predictive ability than the traditional LR model in terms of discrimination. The calibration performance of both models was good.
## Introduction
Gestational diabetes mellitus (GDM) is the most common metabolic complication to occur during pregnancy and is classed as a mild form of diabetes. It is normally diagnosed at 24–28 weeks’ gestation, and is characterized by hyperglycemia [1]. The global prevalence of hyperglycemia during pregnancy is approximately $15.8\%$, and over $80\%$ of cases are due to GDM [2]. With the growth of the economy and the transition to a more sedentary lifestyle, the prevalence of GDM in Chinese women continues to increase, and ranges from $14.8\%$ to $24.24\%$ (3–5). Over time, China has loosened its fertility restrictions, most recently with the replacement of the two-child policy with the three-child policy. Thus, this increase in GDM prevalence can be attributed mainly to the rising rates of pregnant women who are of advanced maternal age.
Hyperglycemia brings about both short- and long-term outcomes, resulting in a significant impact on the health of both pregnant women and their offspring. Several studies in mothers have reported that GDM is associated with adverse pregnancy complications, including pre-eclampsia, the need for delivery by cesarean section, as well as type 2 diabetes and cardiovascular disease after delivery [6]. GDM can also affect their offspring, being associated with a higher prevalence of macrosomia, shoulder dystocia, birth trauma, stillbirth, and, in later life, obesity and metabolic syndrome [7]. According to the Developmental Origins of Health and Disease framework for GDM, exposure to intrauterine hyperglycemia before GDM screening at 24–28 weeks’ gestation is associated with the abnormal growth and development of the fetus [8]. which includes smaller fetuses at 24 weeks’ gestation increased abdominal circumference growth rates [9], and hyperinsulinemia [6]. Lifestyle interventions during early pregnancy can reduce the risk of GDM by $18\%$–$62\%$ [10, 11], but are not effective if initiated at a later stage [12]. Thus, we concluded that a hysteretic diagnosis of GDM in the second or third trimester of pregnancy might lead to a narrow time frame for sufficient intervention. Therefore, it is imperative to establish a prediction model for women at risk of GDM to provide early intervention prior to the diagnosis of the condition at 24–28 weeks’ gestation.
There is accumulating evidence indicating that models based on multiple risk factors can improve predictive abilities [9]. Machine learning (ML) algorithms, as an artificial intelligence technology, have the advantage of presenting high-dimensional predictors constructed to model relatively small datasets with reduced overfit, and demonstrate a powerful selflearning ability to find complex relationships between predictors [9, 13]. As major predictors of GDM, demographic characteristics and clinical features contribute to improving the predictive ability of models combined with biomarkers [14, 15]. Consequently, we aim to present the results of prediction models for GDM based on demographic characteristics, clinical features, and laboratory parameters to make full use of the available variables. In addition, we compare and evaluate the performance of ML and logistic regression (LR) models to show the advantages of each.
## Participants
This case–control study of pregnant women was conducted at the Shenzhen Hospital of the Southern Medical University, Shenzhen, China. Pregnant women were eligible to participate in the study if they met all of the following inclusion criteria: [1] they were aged ≥ 18 years; [2] they had undergone all routine antenatal assessments; [3] they had taken a 75-g oral glucose tolerance test (OGTT) at 24–28 weeks’ gestation; and [4] they were willing to participate in this study and to sign the informed consent form. The exclusion criteria were as follows: [1] pre-existing type 1 or type 2 diabetes; [2] a history of severe diseases, such as hypertension or heart disease; and [3] taking medications affecting insulin and blood glucose levels.
## Data collection
Information on participants’ demographic characteristics was collected by using a structured questionnaire. Clinical features and laboratory parameters in the first trimester were collected from the hospital’s electronic medical record system (EMRS).
## Diagnosis of GDM
GDM was diagnosed at 24–28 weeks’ gestation when any one of the 75-g OGTT values met or exceeded 5.1 mmol/L at 0 h, 10.0 mmol/L at 1 h, and 8.5 mmol/L at 2 h, in accordance with the recommendations set out at the International Association of Diabetes and Pregnancy Study Groups Consensus Panel 2010 (IADPSG).
## Statistical analysis
All analyses were performed using IBM® SPSS® Statistics version 26.0 software (IBM Corporation, Armonk, NY, USA). Continuous variables of two groups were expressed as means and standard deviations, and analyzed by Student’s t-test for normally distributed variables. Categorical variables were described as frequencies (percentages), and evaluated by a chi-squared test. Test results with a p-value of less than 0.05 were considered statistically significant. Results from these tests, clinically relevant findings, and previous literature were used to preliminarily screen the set of variables for potentially meaningful predictors of GDM. Multiple imputations were used to deal with missing data, to avoid selection bias. The prediction model using LR was carried out in R (The R Foundation, Vienna, Austria) using the rms package, and XG Boost ML was carried out by R package (XG Boost, XG Boost Explainer, and MLR).
## Prediction models
In this study, we included variables with a p-value of < 0.05 in the univariate analysis, whereas variables indicated in previous literature and clinically meaningful variables were included in the LR analysis (stepwise). ML can present novel or complex combinations of multidomain variables, and also has features that weigh variable importance and reduce overfit [16]. Therefore, we incorporated all variables of the univariate analysis into the model using XG Boost ML.
The model for GDM, trained on the training set, was validated in the testing set with the optimal hyperparameters using 10-fold cross-validation.
## Model evaluation
The discrimination of the models was assessed using the receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC). The calibration plots and the Hosmer–Lemeshow (HL) test were used to evaluate the calibration of each model. Decision curve analysis (DCA) was introduced to evaluate the clinical use of the models.
## Participant characteristics
In total, 925 pregnant women were included in this study (735 in the training set; 190 in the testing set). The alternative 33 variables were collected for each pregnant woman. Table 1 shows the univariate analysis of the demographic characteristics, clinical features, and laboratory parameters of participants with GDM (cases) and participants without GDM (controls) in the training set. Participants with GDM were significantly older and had higher pre-pregnancy body mass index (BMI) and mean arterial pressure (MAP) than participants without GDM. The average time since the last pregnancy was also longer in this group than in the control group. The percentage of women who had previously GDM and the number with a family history of diabetes mellitus were also significantly higher in the GDM group, but participants in this group were also markedly younger at menarche than those in the non-GDM group (all p-values were < 0.05). Laboratory parameters, including platelet count, white blood cell count, and the levels of glucose in urine, ketone in urine, alanine aminotransferase, thyroid hormone T3, fasting plasma glucose, and glycated hemoglobin (HbA1c), were also higher in women with GDM than in control participants. The demographic characteristics, clinical features, and laboratory parameters of participants in the training and testing sets are compared in Table 2. Good consistency in the data between the training data set and the testing data set is shown for the majority of the variables.
## Predictors of models
Four predictors, previous GDM, age, HbA1c level, and MAP, were used to construct the predictive model using LR (Table 3). Twenty predictors were finally included to build the model using XG Boost ML. Figure 1 shows the relative importance of the 20 variables included in the predictive model for GDM using XG Boost ML.
## Accuracy of prediction models
For the data from the training set, the AUC of the prediction model for GDM using stepwise LR is 0.752, whereas the AUC of the model using XG Boost ML is 0.946; these are shown in Figures 2, 3, respectively. The accuracy of the two models for the data from the training set is 0.786 and 0.875, respectively. The specificity of the model using XG Boost ML was higher than that of the model using traditional LR for the data from both the training and testing sets. However, the sensitivity of the model using XG Boost ML was lower than that of the model using traditional LR, as shown clearly in Table 4.
**Figure 2:** *The AUC of the prediction model for GDM by stepwise LR. AUC, area under the receiver operating characteristic curve; GDM, gestational diabetes mellitus; LR, logistic regression.* **Figure 3:** *The AUC of the prediction model for GDM by XG Boost ML. AUC, area under the receiver operating characteristic curve; GDM, gestational diabetes mellitus; XG Boost ML, extreme gradient boosting (XG) machine learning (ML).* TABLE_PLACEHOLDER:Table 4
## Calibration of different models
The calibration plots demonstrate the consistency between the predicted values and the real outcomes, which are shown in Figures 4 – 7. The Hosmer–Lemeshow (HL) test p-values were 0.288 and 0.402 for the training set and testing sets, respectively, in the model using LR, and 0.831 and 0.556 for the training set and testing sets, respectively, in the model using XG Boost ML.
**Figure 4:** *The calibration plots of the training set by LR. LR, logistic regression.* **Figure 5:** *The calibration plots of the testing set by LR. LR, logistic regression.* **Figure 6:** *The calibration plots of the training set by XG Boost ML. XG Boost ML, extreme gradient boosting (XG) machine learning (ML).* **Figure 7:** *The calibration plots of the testing set by XG Boost ML. XG Boost ML, extreme gradient boosting (XG) machine learning (ML).*
## Clinical use
The DCA results for the two models are presented in Figures 8, 9. Compared with treating all women and none of the women, the prediction models using LR provide a net benefit between a threshold probability of $6\%$–$63\%$ and $87\%$–$90\%$. The DCA plot indicated good positive net benefits in the model using XG Boost ML with a threshold probability of between $5\%$ and $92\%$.
**Figure 8:** *The DCA of the model using LR. DCA, decision curve analysis; LR, logistic regression.* **Figure 9:** *The DCA of the model using XG Boost ML. DCA, decision curve analysis; XG Boost ML, extreme gradient boosting (XG) machine learning (ML).*
## Discussion
Early screening and prediction of the likelihood of pregnant women developing GDM are imperative to the prevention and treatment of this condition [17]. We compared two models and found that XG Boost ML models had better performance in terms of discrimination and achieved a larger AUC, which was as high as 0.946. Our results are concordant with a previous study showing that ML algorithms can be more accurate than traditional LR methods [18]. The HL test shows that the observed probability is largely consistent with the predicted probability, which implies that both models had good calibration.
Given evidence indicates that, in the situation of no overfitting, a prediction model with a greater number of predictors has an improved prediction ability compared with a model with fewer predictors [19]. Similarly, in our study, the XG Boost ML model presents 20 predictors with a higher predictive accuracy than the LR model with four predictors. Furthermore, linear models, such as LR models, highlight a clear linear contribution of each variable for GDM models, making them available for clinical implementation, whereas XG Boost ML models can weight the importance of factors and assess their complex non-linear relationships by boosting, integrating multiple factors, assess their complex non-linear relationships by boosting, and clearly demonstrate the relative contribution of each variable to GDM [18].
A recent relative study has indicated that hematologic and biochemical parameters measured during routine antenatal examination can be used in ML models to predict GDM [20]. However, it has not until now been possible to weigh the relative importance of each variable. In this study we have shown that it is possible quantify the likelihood of individual independent risk factors leading to GDM. Another related study [18] developed a ML prediction model based on a large population and weighed the importance of risk factors, but there was no exploration of biomarkers in early pregnancy in this study; by contrast, this was explored in our study.
In the two models, previous GDM was the most classical predictor, and LR analysis showed that pregnant women with previous GDM are 7.8 times more likely to develop GDM (OR = 7.822; $p \leq 0.05$). Furthermore, other model studies have shown [9, 21] that previous GDM increases the risk of GDM in a current pregnancy 13.7- to 21.1-fold ($p \leq 0.05$). One review also found that having GDM in a previous pregnancy is the strongest risk factor for GDM, with reported recurrence rates of up to $84\%$ [22]. In addition to previous GDM, age, HbA1c level, and MAP were considered independent factors for GDM in the LR analysis. Previously, age and HbA1c level have been strongly associated with an elevated risk of GDM [17, 21]. With increasing age, the fertility and organ function of pregnant women are reduced, and insulin sensitivity and pancreatic β-cell function are decreased, which in turn lead to insulin resistance (IR) and an increased risk of hyperglycemia. HbA1c level, an identified risk factor, can diagnose the severity of GDM and reflects the average blood glucose level in the past 2 to 3 months, which is significantly related to the degree of IR [23]. A previous study revealed that HbA1c level is a reliable predictor of GDM(OR = 3.11; $p \leq 0.05$)and that HbA1c levels are elevated in women with GDM, although still within the normal range [24], which is consistent with our results. MAP was calculated from one-third systolic blood pressure (SBP) and two-thirds diastolic blood pressure (DBP), both of which are considered to be predictors of GDM [18, 25, 26]. MAP can probably predict GDM because IR is the involved in the pathogenesis of both gestational hypertension (GH) and GDM, and the level of MAP, which can reflect the severity of GH, also stimulates a certain degree of GDM [27].
Another 16 predictors, comprising pre-pregnancy BMI and 15 laboratory parameters routinely measured during antenatal assessment, were confirmed as risk factors by XG Boost ML. Pre-pregnancy BMI, despite being considered an established predictor of GDM [28], has the lowest predictive ability, probably because of the low frequency of overweight and obesity (among our sample affecting approximately $11.700\%$ and $14.700\%$ of women in the training and testing sets, respectively). Another explanation is that the relationship between BMI and GDM is complex, with women with GDM and a high BMI having IR and women with GDM and a low BMI having defective insulin secretion [29].
Existing studies have identified that several laboratory parameters are independent predictors of GDM, such as glycemic markers (e.g., fasting glucose and HBA1c levels), alanine aminotransferase (ALT) levels, and thyroid function (levels of the thyroid hormones T3 and T4) [9, 18, 20]; all of these are available clinically in the first trimester of pregnancy. The possible link between these variables and GDM could be explained by the fact that hyperglycemia can change the hemodynamics of the body, and that these variables can reflect the inflammation and immune responses that are highly associated with IR [30]. Prior research has identified several blood potential biomarkers, such as platelet count, white blood cell count, and red blood cell count, which were positively correlated with the development of GDM [30]. Consistent with a previous study [9], high T3 and low T4 levels were identified as being predictors of GDM in our study, strongly confirming the existence of a close relationship between thyroid function and GDM. ALT and AST (aspartate aminotransferase), as markers of hepatocellular damage, were also examined as predictors of GDM in our study. The pathogenesis of GDM is linked with IR, which may in turn be caused by mild ALT and AST elevations [15, 31]. In summary, the laboratory parameters support the hypothesis that pregnancy blood routine examination is conducive to GDM screening.
## Limitations
This study has several limitations. Firstly, this study has limited sample size. Secondly, the fact is that a time external verification was used to verify the extrapolation in a single center. Lastly, there is a lack of complete data for all laboratory parameters and a comparison of multiple ML models. Variables such as clinical features and laboratory parameters are based on retrospective data from the EMRS that may have inevitable selection biases. Further multicenter prospective studies should be carried out to update and validate the models based on a large, population-based sample. Models constructed from more variables that are available from EMRS are often the most feasible option.
## Conclusion
In conclusion, a model with four predictors and using traditional LR and a model with 20 predictors and using XG Boost ML were successfully built and used to predict GDM. Compared with traditional LR, the XG Boost ML model can improve the discrimination of a prediction model for GDM and make full use of more predictors. The common laboratory parameters from pregnant women’s antenatal assessments can be used to predict the likelihood of their developing GDM.
## Data availability statement
The datasets presented in this article are not readily available because the generated datasets belong to hospital. Requests to access the datasets should be directed to XH, 731538045@qq.com.
## Ethics statement
This study was approved by the corresponding Hospital Ethics Committee (No.: NYSZYYEC20200032). The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
## Author contributions
XH and XiaolH contributed to the conception and design of the study. XH organized the database. XH and YY performed the statistical analysis. XH wrote the first draft of the manuscript. XH, XiaolH, YY, and JW wrote sections of the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Bioactive nutraceuticals oligo-lactic acid and fermented soy extract alleviate
cognitive decline in mice in part via anti-neuroinflammation and modulation of gut
microbiota
authors:
- Hamid M. Abdolmaleky
- Yin Sheng
- Jin-Rong Zhou
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC10034322
doi: 10.3389/fnut.2023.1116278
license: CC BY 4.0
---
# Bioactive nutraceuticals oligo-lactic acid and fermented soy extract alleviate cognitive decline in mice in part via anti-neuroinflammation and modulation of gut microbiota
## Abstract
### Introduction
Cognition decline is associated with aging and certain diseases, such as neurodegenerative or neuropsychiatric disorders, diabetes and chronic kidney disease. Inflammation/neuroinflammation is considered an important causal factor, and experimental evidence suggests that anti-inflammatory natural compounds may effectively prevent cognitive decline. The goal of this study was to evaluate the effects of two natural bioactive agents, oligo-lactic acid (LAP) and fermented soy extract (ImmunBalance, IMB), on cognition in an adenine-induced cognitive impairment mouse model and to investigate the modulation of related biomarkers.
### Methods
Male C57 black mice were randomly assigned into the following experimental groups and received the corresponding treatments for 2 weeks before the use of adenine for model development: [1] negative control; [2] model control: injection of adenine at 50 mg/kg daily for 4 weeks; [3, 4] IMB groups: adenine injection and IMB oral gavage at 250 and 1,000 mg/kg BW, respectively; and [5] LAP group: adenine injection and LAP oral gavage at 1,000 mg/kg BW. One week after the model was developed, mice were evaluated for cognitive performances by using Y maze test, novel object recognition test, open field test, and Barnes maze tests. At the end of the experiment, brain tissues and cecum fecal samples were collected for analysis of gene expression and gut microbiota.
### Results
Mice treated with LAP or IMB had significantly improved spatial working memory, spatial recognition memory (LAP only), novel object recognition, and spatial learning and memory, compared with those in the model group. Gene expression analysis showed that, among a panel of cognition related genes, six of them (ELOVL2, GLUT4, Nestein, SNCA, TGFB1, and TGFB2) were significantly altered in the model group. LAP treatment significantly reversed expression levels of inflammatory/neuroinflammatory genes (SNCA, TGFB1), and IMB significantly reversed expression levels of genes related to inflammation/neuroinflammation, neurogenesis, and energy metabolism (ELOVL2, GLUT4, Nestin, TGFB1, and TGFB2). The altered microbiome was attenuated only by IMB.
### Discussion
In conclusion, our data showed that LAP improved cognition associated with regulating biomarkers related to neuroinflammation and energy metabolism, whereas IMB improved cognition associated with regulating biomarkers related to neuroinflammation, energy metabolism, and neurogenesis, and modulating gut microbiota. Our results suggest that LAP and IMB may improve cognitive performance in mice via distinct mechanisms of action.
## Introduction
Cognition is regarded as a higher function of the central nervous system (CNS) and includes attention, learning, memory, and executive processes [1]. Cognition decline is associated with aging and certain diseases, such as neurodegenerative or neuropsychiatric disorders, diabetes, and chronic kidney disorder (CKD) [2]. Despite efforts to develop novel therapeutic agents to improve cognitive function, few viable treatment options exist. However, several lines of evidence indicate that natural bioactive agents may provide effective preventive and/or therapeutic strategies to delay cognitive decline [3]. More recent research data also provided strong evidence that nutritional compounds not only mediate their effects though their natural bioactive agents, their influence on gut microbiome alterations may also have significant beneficial or harmful consequences [4].
The science of microbial colonization of gut was limited before the development of microbial 16S rRNAs sequencing technology. Now, it has been shown that the mammals gut contains diverse bacterial elements which their total number is 10 times more than their body cells. Surprisingly, the gut microbial population collectively contain 100 times more genes than human which are involved in the digestion of many nutritional compounds or production of nutrients and vitamins (Reviewed in Alam et al. [ 4]). There is also evidence that nutritional elements and food compounds affect the gut microbiome composition and thus their function as well. For example, it has been shown that in mice fed with high-fat diet, silybin decreases Firmicutes, Lactobacillus, Lachnoclostridium, and Lachnospiraceae_UCG-006, and increases the gut level of Bacteroidetes, Bacteroides, Blautia, and Akkermansia [5], while epigallocatechin-3-gallate (EGCG) increases Verrucomicrobia, Enterococcaceae, and Verrucomicrobiaceae, but decreases Deferribacteres, Lachnospiraceae, Desulfovibrionaceae, Bacteroidaceae, Proteobacteria, Prevotellaceae, Deferribacteraceae, and Rikenellaceae [6].
Furthermore, it has been shown that gut microbiota not only plays an important role in normal brain development, but also has significant impacts on animal behavior and cognitive status. For example, metabolomic analysis showed that four metabolites linked to neuropsychiatric disorders were down-regulated in germ-free mice [7]. Additionally, gut microbiota manipulation in germ-free mice impacts fear extinction learning that is associated with gene expression alterations in single-nucleus RNA sequencing analysis of glia, excitatory neurons, and other brain cells in the medial prefrontal cortex [7]. These mice also exhibit postsynaptic dendritic spines remodeling and hypoactivity of cue-encoding neurons in transcranial two-photon imaging analysis. However, selective microbiota re-establishment could restore normal extinction learning [7]. Notably, increases in plasma tryptophan and hippocampal concentration of serotonin and its metabolite (5-hydroxyindoleacetic acid) was also shown in germ-free mice compared to the control mice. While the brain neurochemical changes remained stable until adulthood, restoring microbial colonization post weaning could reverse behavioral alterations in affected mice [8].
It has been reported that mice treated with adenine showed depressed locomotor activity associated with cognitive impairment, depleted brain norepinephrine, dopamine and serotonin, disrupted blood–brain barrier, and increased brain inflammation (9–12). In our previous study applying this mouse model to investigate the effect of bioactive components on CKD [13], we found that the adenine-treated mice had increased inflammation in circulation and in kidney tissues. Bioactive components, an oligo-lactic acid product (LAP) and/or a fermented soy extract (ImmunBalance, IMB), alleviated adenine-induced CKD associated with decreased circulating inflammatory cytokines and tissue inflammation. Our preliminary observation also indicated that mice in the model group had cognitive problems. Interestingly, LAP- and IMB-treated groups showed improved cognition and were more energetic and healthier than positive control mice (adenine treated). These results and observations suggest that LAP and/or IMB may have beneficial effects on improving cognition/alleviating inflammation-associated cognitive impairment.
In this study, we propose to investigate the effects of LAP and/or IMB on improving cognition in adenine-induced mouse model of cognitive impairment, and to determine if improved cognition is associated with modulation of gut microbiota using and expression of inflammation related genes in brain tissue.
## Materials
A soy extract, IMB was prepared by a koji fermentation of defatted soybeans with *Aspergillus oryzae* and lactic acid bacteria (*Pediococcus parvulus* and Enterococcus faecium) according to a proprietary fermentation technology, followed by water extraction and purification of Koji polysaccharides®. IMB was provided by Nichimo Biotics Co., Ltd., Japan. Oligo-lactic acid product (LAP) was a condensate of about nine ester-linked molecules of L-lactic acid that was purified from fermentation products of sugar beet and corn with Lactobacilli according to a proprietary process. LAP was provided by LifeTrade Co., Ltd., Japan.
## Animal study
Male C57BL/6 mice (8–9 weeks of age) were purchased from Taconic (Germantown, NY, United States), housed in a room at a temperature of 22 ± 2°C, relative humidity of about $60\%$, with a 12 h light–dark cycle, and free access to an AIN-93 M diet. After an acclimatization period of 1 week, mice were randomly assigned into the following five groups ($$n = 8$$/group) and receive the corresponding treatment for 2 weeks before the use of adenine for model development: [1] negative control (NC): PBS injection and PBS oral gavage daily; [2] model control (MC): adenine injection intraperitoneally (i.p.) at a dose of 50 mg/kg daily for 28 days and PBS oral gavage daily; [3] IMB-low treatment (IMB-L): adenine injection, oral gavage of IMB at 250 mg/kg BW; [4] IMB-high treatment (IMB-H): adenine injection, oral gavage of IMB at the 1,000 mg/kg BW; and [5] LAP treatment: adenine injection, oral gavage of LAP at 1,000 mg/kg BW. Our previous animal study indicated that IMB had a dose-dependent effect, whereas LAP did not show clear dose-dependent effect [13]. Therefore, we used two doses of IMB (250 and 1,000 mg/kg BW) and one dose of LAP (1,000 mg/kg BW) in this study to evaluate the effect of treatments on cognition improvement and biomarkers alterations. Body weight and food intake were measured weekly. The cognition/memory evaluations were started 1 week after adenine injection was finished. A diagram of experimental protocol is included as Figure 1.
**Figure 1:** *Diagram of experimental schedule.*
## Y maze test
The Y maze was used to measure spatial working and recognition memory by making use of a rodent’s natural exploratory instincts [14]. The Y-maze consists of three arms of equal length interconnected at 120°. The Y Maze test includes two sessions. The first session measures spatial working memory, and the second session measures spatial recognition memory. The experimental procedures are described as follows: Spatial working memory test: During the first session of the experiment to measure spatial working memory, all three arms of the maze were open. Specifically, mice were placed onto the end of one arm and allowed to explore freely for 5 min. The sequences of the arms entries were recorded. The spontaneous alternation behavior was calculated as the number of triads (three different arms, ABC not ABB) that contain entries into all three arms divided by the total visits. The first session represents a classic spontaneous alternation test of spatial working memory.
Spatial recognition memory test: The second session measures spatial recognition memory. During the testing phase, one of the arms of the maze was blocked while the mouse was allowed for a 5-min exploration of only two arms of the maze. After a 30 min break, the partition was removed and the mouse was allowed for another 5-min exploration. Since the first 2 min of activities were the most sensitive to measure spatial preference for a new arm, the time and number of visits to the new arm were calculated for the first 2 min of testing. The first 5 min of activities were also recorded and the motor activity was calculated as the number of arms visited for the whole period of the test. This experiment takes advantage of the innate tendency of mice to explore novel unexplored areas (e.g., the previously blocked arm). Mice with intact recognition memory prefer to explore a novel arm over the familiar arms, whereas mice with impaired spatial recognition memory enter all arms randomly. Thus, this experiment represents a classic test for spatial recognition memory.
Data were recorded and analyzed by using the Smartsuper software (Harvard Apparatus, Holliston, MA, United States) for the time spent in each arm overall, and the time spent in each arm for the first 2 and 5 min.
## Novel object recognition test
This test was used to examine memory in animals. The mouse was presented with an object during acclimation and then in testing presented with a novel (new) object, and the amount of time the animal spends exploring the novel object was recorded. The mouse was then challenged further with a subsequent test where the novel object was moved. Here instead of a three-chamber apparatus, which was used in previous experiments (thus mice were familiar with), a rectangle chamber was used. The experimental protocols are described as follows: Acclimation: Each animal was placed in a rectangle chamber without objects and was allowed to explore for 10 min. The procedures were repeated 24 and 48 h later, thus the total acclimation was 3 days.
Experiment setup-day 4: In the rectangle chamber apparatus, two identical objects were placed in the test chamber. The animal activity was recorded to track how much time was spent to interact with each of the objects, the “right object” and “left object,” for a fixed session length of 10 min. The animal’s nose must be within 1 cm of the object and directed at the object, or actually touch the object in order to be considered time spent exploring the object. After 10 min, we removed the animal was putted back in its home cage and the chamber and objects were cleaned with $70\%$ ethanol to remove any smell.
Experiment setup-day 5: One old object and one novel (new) object were putted in the chamber, and the animal activity was recorded for 5 min instead of 10 min. After an intertrial interval (ITI) of approximately 2 h, the location of the old object was moved so that it was located on a different wall and the steps were repeated, again using a 5 min trial length.
## Open field test
Open field test was used to evaluate mouse fear and anxiety levels. In this test, a mouse was placed in rectangle apparatus and its activity was recorded for 10 min. Mouse instinct drives it to search in the corners and angles of the chamber instead of the middle part (open field) of the chamber. In this test, we also placed two identical objects in the chamber, one in a corner and another one in the middle of the chamber to assess the frequency and time the mouse spent with each object. The mouse was expected to spend more time with the object placed in the corner than that in the middle.
## Barnes maze for spatial learning and memory test
Mice spatial learning and memory were assessed using Barnes maze test (Harvard Apparatus). Barnes maze is one of the best and ideal apparatus for testing spatial learning and memory in mice [14, 15]. The Barnes maze motivates animals to hide from the bright, open platform by finding the small, dark “escape box.” Animals learn the escape box’s location without the stress of swimming or food deprivation, and false escape boxes remove the possibility of inadvertent cues. With a variety of color options, Barnes maze adjusts to fit a wide range of experiments. In Barnes test, mice are trained in 3–5 sessions to find the escape box and after 5–7 days mice are tested for the time spent to find the escape box. We tested the mice for two successive days after 6 days interval. The data were collected and analyzed using the Smartsuper software (Harvard Apparatus).
At the end of Barnes maze test, mice were sacrificed, and biological samples (blood, brain, and cecum) were collected for analyses. A piece of mouse brain sample from the left frontal lobe (~30 mg) was used for RNA and DNA extraction. The fecal sample of mouse cecum was used for DNA extraction and microbiome profiling.
## Gene expression analysis by quantitative real-time PCR (qRT-PCR)
An aliquot of hippocampus-containing brain tissues was used for gene expression analysis. In brief, following RNA extraction using Direct-Zol DNA/RNA MiniPrep kit (Zymo Research, Irvine, CA, United States) and cDNA synthesis using Bio-Rad iScript cDNA synthesis kit (Hercules, CA, United States), expression levels of genes related to neurotransmission, inflammation, metabolism, and neuronal protection/regeneration were determined using CFX384 Touch real-time PCR detection system and Power SYBR™ Green PCR Master Mix (Applied Biosystems, Bedford, MA, United States) by following the established procedures [13]. *These* genes included brain-derived neurotrophic factor (BDNF), dopamine beta-hydroxylase (DBH), dopamine receptor D2 (DRD2), ELOVL fatty acid elongase 2 (ELOVL2), glucose transporter 4 (GLUT4), Huntington-associated protein 1 (HAP1), 5-hydroxytryptamine receptor 2A (HTR2A), interleukin 1β (IL1B), IL4, IL6, NESTIN, phosphoinositide 3-kinase (PI3K), solute carrier family 6 member 4 (SLC6A4), synuclein alpha (SNCA), transforming growth factor beta 1 (TGFB1), TGFB2, and TSC complex subunit 1 (TSC1). All mRNA quantification data were calculated using the 2∆∆Ct method and normalized to GAPDH, presented as folds to the negative control (NC). The primer sequences of genes were taken from Harvard Primer Bank (listed in Table 1) designed for real-time PCR analysis and tested to assure that they generated a single PCR product.
**Table 1**
| Genes | Forward | Reverse |
| --- | --- | --- |
| BDNF | TCATACTTCGGTTGCATGAAGG | AGACCTCTCGAACCTGCCC |
| DBH | GAGGCGGCTTCCATGTACG | TCCAGGGGGATGTGGTAGG |
| DRD2 | ACCTGTCCTGGTACGATGATG | GCATGGCATAGTAGTTGTAGTGG |
| ELOVL2 | CCTGCTCTCGATATGGCTGG | AAGAAGTGTGATTGCGAGGTTAT |
| GAPDH | AGGTCGGTGTGAACGGATTTG | GGGGTCGTTGATGGCAACA |
| GLUT-4 | ATCATCCGGAACCTGGAGG | CGGTCAGGCGCTTTAGACTC |
| HAP1 | AGGTGAACCTGCGAGATGAC | TGCTGGTCTTGATCCCTCTGT |
| HTR2A | TAATGCAATTAGGTGACGACTCG | GCAGGAGAGGTTGGTTCTGTTT |
| IL1B | GCAACTGTTCCTGAACTCAACT | TGGATGCTCTCATCAGGACAG |
| IL4 | GGTCTCAACCCCCAGCTAGT | GCCGATGATCTCTCTCAAGTGAT |
| IL6 | CCAAGAGGTGAGTGCTTCCC | CTGTTGTTCAGACTCTCTCCCT |
| Nestin | CCCTGAAGTCGAGGAGCTG | CTGCTGCACCTCTAAGCGA |
| PI3K | TTATTGAACCAGTAGGCAACCG | GCTATGAGGCGAGTTGAGATCC |
| SLC6A4 | GTCATTGGCTATGCCGTGGA | CACCCATTTCGGTGGTACTG |
| SNCA | GACAAAAGAGGGTGTTCTCTATGTAG | GCTCCTCCAACATTTGTCACTT |
| TGFB1 | CTCCCGTGGCTTCTAGTGC | GCCTTAGTTTGGACAGGATCTG |
| TGFB2 | CTTCGACGTGACAGACGCT | TTCGCTTTTATTCGGGATGATGT |
| TSC1 | ATGGCCCAGTTAGCCAACATT | CAGAATTGAGGGACTCCTTGAAG |
## Microbiome analysis of cecum fecal samples
DNA was extracted using Quick-DNA Fecal/Soil Microbiome Miniprep kit (Zymo research, cat # D6010) from 30 mg of fecal sample from the mouse cecum for microbiota analysis. After quantitation and quality control using NanoDrop One (Thermo Scientific), the extracted DNA (each ~100 ng/μl, in total volume of 100 μl) was diluted (10 ng/μl) and subjected to examination with one set of universal and one set of Lactobacillus specific primers (Table 2) [16] to reconfirm DNA quality. Then, DNA samples were sent to CoreBiome, Inc.1 for microbiome profiling using amplicon sequencing targeting variable region 4 of the bacterial 16S ribosomal RNA gene (16S rRNA). All quality control measures by CoreBiome experts confirmed the reliability of data presented in the result section.
**Table 2**
| Lac-F | AGCAGTAGGGAATCTTCCA | Lactobacillus genus |
| --- | --- | --- |
| Lac-R | CACCGCTACACATGGAG | Lactobacillus genus |
| Uni331F | TCCTACGGGAGGCAGCAGT | All bacteria |
| Uni797R | GGACTACCAGGGTATCTATCCTGTT | All bacteria |
The details of the methods are presented in www.corebiome.com and elsewhere by the developers [17].
## Statistical analysis
Data were expressed as the group mean ± standard error and analyzed by one-way analysis of variance (ANOVA) test, followed by multiple comparison of least-significant difference (equal variances assumed) or Dunnett’s T3-test (equal variances not assumed) to evaluate the difference of parametric samples among groups. A p-value of <0.05 was considered statistically significant.
## Effects of treatments on body weight and food intake
There was no significant difference in the baseline body weight among experimental groups (Figure 2A). As expected, model induction via daily adenine injection reduced body weight (Figure 2B) in all modeled groups, compared with the negative control group, whereas there were no significant differences of body weight among the modeled groups. Similarly, mice in the modeled groups reduced food intake, compared with the NC group, but there were no significant differences of food intake among the modeled groups (data not shown).
**Figure 2:** *Effects of treatments on body weights (BW). (A) Baseline BW; (B) BW after 4 weeks of model induction. Values are expressed as mean ± standard error. Within each panel, values with a superscript symbol are significantly different from the NC. *p < 0.05; **p < 0.01.*
## Effects of LAP and IMB treatments on spatial working memory and recognition memory
Spatial working memory and recognition memory tests were performed using Y maze as described in methods. There were no significant changes in the total activity (numbers of arm entry) among different groups (Figure 3A). The model development significantly impaired spatial working memory (Figure 3B, $p \leq 0.05$) but did not significantly affect recognition memory (Figure 3C). Compared with the MC, mice treated with LAP had significantly better spatial working memory (Figure 3B, $p \leq 0.05$) and recognition memory (Figure 3C, $p \leq 0.05$). IMB treatment improved spatial working memory in a dose-dependent manner and mice treated with the high dose IMB had significantly increased spatial working memory (Figure 3B, $p \leq 0.05$).
**Figure 3:** *Effects of treatments on spatial working memory and spatial recognition memory, as measured by Y maze test. (A) Total activity measured as the total numbers of arm entries in 5 min; (B) Spatial working memory measured as the correct trios (ABC or ACB) vs. total arm entry in 5 min; (C) Spatial recognition memory measured as the average number of entries to new arm in 5 min. Values are expressed as mean ± SE (n = 8). Within each panel, values with a superscript symbol “*” are significantly different from that of the MC. *p < 0.05.*
## Effects of LAP and IMB treatments on novel object recognition and spatial learning and memory
As shown in Figure 4A, the MC mice had non-significant impairment of novel object recognition memory; mice treated with LAP significantly improved novel object recognition by over $30\%$ ($p \leq 0.05$). IMB showed a dose-dependent effect on novel object recognition memory, and the high dose IMB treatment showed a significant effect ($p \leq 0.05$).
**Figure 4:** *Effects of treatments on novel object recognition memory and spatial learning and memory. Mouse novel object recognition memory was tested 24 h after training (A); mouse spatial learning and memory was evaluated in Barnes maze test (B). Values are expressed as mean ± SE (n = 7–8). Within each panel, values with a superscript symbol “*” is significantly different from that of the MC (*p < 0.05; ***p < 0.001); and the value in the MC with a superscript symbol “#” is significantly different from that of the NC (#p < 0.05).*
Barnes maze test was applied to determine spatial learning and memory. Compared with the NC, mice in the MC showed significantly impaired spatial learning and memory (Figure 4B, $p \leq 0.05$). This impaired memory was significantly improved by the treatment of LAP ($p \leq 0.05$) or IMB ($p \leq 0.05$ for IMB-L; $p \leq 0.001$ for IMB-H). Overall, IMB treatment showed a suggestive dose-dependent effect.
The results of open field test did not show significant alterations among any experimental groups (data not shown).
## Effects of LAP and IMB treatments on gene expression levels of related biomarkers in brain tissues
We explored the expression levels of a panel of genes related to neurotransmission, inflammation, metabolism, and neuronal regeneration at RNA levels (including, BDNF, DBH, DRD2, ELOVL2, GLUT4, HAP1, HTR2A, IL1B, IL4, IL6, NESTIN, PI3K, SLC6A4, SNCA, TGFB1, TGFB2, and TSC1) aiming to identify candidate genes whose expressions were significantly altered in the model group and were significantly reversed by LAP and/or IMB treatments. Six candidate genes (ELOVL2, GLUT4, SNCA, Nestein, TGFB1, and TGFB2) were identified. As shown in Figure 5, compared with the NC, the model development (MC) significantly decreased the expression levels of GLUT4, NESTIN, TGFB1, and TGFB2 genes by 75, 80, 60, and $50\%$, respectively (p at least <0.05), and significantly increased the expression levels of ELOVL2 and SNCA genes by 60 and $55\%$, respectively ($p \leq 0.05$). Expression of TGFB1 was significantly increased by LAP (>$100\%$, $p \leq 0.05$) and IMB-H (>$150\%$, $p \leq 0.05$; Figure 5E); the decreased expression of TGFB2 in model mice trended to be normalized by IMB treatment (Figure 5F). The reduced expression levels of GLUT4 (Figure 5B) and NESTIN (Figure 5C) in the MC mice were increased significantly by IMB-H treatment (>100 and $150\%$, respectively, $p \leq 0.05$). The increased expression of ELOVL2 in the MC mice was reversed only by IMB-L or IMB-H treatment (Figure 5A, ~$40\%$, $p \leq 0.05$), whereas the increased expression of SNCA in the MC mice was significantly altered by the LAP treatments (Figure 5D, $p \leq 0.05$).
**Figure 5:** *Effects of treatments on expression levels of genes that were significantly altered by model development and recovered by LAP and/or IMB treatment. ELOVL2 (A), GLUT-4 (B), Nestin (C), SNCA (D), TGFB1 (E), and TGFB2 (F). Values are expressed as mean ± SE (n = 7–8). Within each panel, values with a superscript symbol “*” are significantly different from that of the MC (*p < 0.05; **p < 0.01); and the value of the MC with a superscript symbol “#” is significantly different from that of the NC (#p < 0.05; ##p < 0.01).*
## Effects of LAP and IMB treatments on gut microbiota
*In* general, the abundance of almost $60\%$ (30 out of 51) of bacterial species exhibited significant changes in the MC, compared with that in the NC. As shown in Figure 6A the diversity of bacterial species decreased in the MC group which was recovered in part by IMB-H. Figure 6B shows the abundances of three common bacteria that were significantly altered in the MC group and were also reversed/normalized in the IMB-H group. Compared with the NC, model development (MC) significantly decreased the abundance of *Lachnospiraceae bacterium* 28-4 ($p \leq 0.05$), but significantly increased abundances of *Bifidobacterium pseudolongum* and *Faecalibaculum rodentium* ($p \leq 0.001$). *In* general, IMB treatments recovered aberrant alterations in a dose-dependent manner and the high dose IMB (IMB-H) treatment had significant effects. On the other hand, LAP treatment did not show significant effects on reversing the abundance of these common bacteria altered in the MC group (Figure 6B).
**Figure 6:** *Effects of treatments on gut microbiota. (A). A portray of cecum bacterial changes; (B). Effects of treatments on the abundance of three common bacteria; (C). Effects of treatments on the abundance of three phyla. Values are expressed as mean ± SE (n = 3–7). Within each panel (B or C), values with a superscript symbol “*” are significantly different from that of the corresponding MC (*p < 0.05; **p < 0.01; ***p < 0.001); and the value of the MC with a superscript symbol “#” is significantly different from that of the corresponding NC (#p < 0.05; ##p < 0.01; ###p < 0.001).*
The abundances of two phyla (out of seven phyla) were also altered significantly in the MC group (Figure 6C). Compared with the NC, the MC significantly increased the abundance of Actinobacteria ($p \leq 0.01$) and significantly decreased the abundance of Bacteroidetes ($p \leq 0.001$) (Figure 6C). Those alterations were significantly reversed by the treatment of high dose IMB (IMB-H), but not LAP or IMB-L.
We also found that the model development significantly altered the abundances of several rare (Figure 7A) and very rare (Figure 7B) bacteria, and some treatments significantly reversed these alterations. *In* general, IMB treatment showed a dose-dependent effect on reversing model-induced alterations and the high dose IMB treatment effects were statistically significant. On the other hand, LAP treatment did not show significant effect on the abundances of those bacteria.
**Figure 7:** *Effects of treatments on abundance of rare (A) and very rare (B) bacteria in mice ceca. Within each panel, values with a superscript symbol “*” are significantly different from that of the corresponding MC (*p < 0.05; **p < 0.01; ***p < 0.001); and the value of the MC with a superscript symbol “#” is significantly different from that of the corresponding NC (#p < 0.05; ##p < 0.01; ###p < 0.001).*
## Discussion
In this study, we evaluated the effects of two nutraceutical components, LAP and IMB, on cognitive status of adenine-induced cognitive impairment mouse model. Mice treated with LAP had significantly improved spatial working memory (Figure 3B), spatial recognition memory (Figure 3C), novel object recognition (Figure 4A), and spatial learning and memory (Figure 4B), compared with those in the MC group. Similarly, IMB treatment significantly improved spatial working memory (Figure 3B), novel object recognition (Figure 4A), and spatial learning and memory (Figure 4B) in a dose-dependent manner. Gene expression analysis showed that, among a panel of genes, six of them (ELOVL2, GLUT4, Nestein, SNCA, TGFB1, and TGFB2) were significantly altered in the model group, and their expression levels were significantly reversed by LAP (SNCA and TGFB1) and/or IMB (ELOVL2, GLUT4, Nestin, TGFB1, and TGFB2). On the other hand, the altered microbiome was attenuated only by IMB-H.
Our study showed that LAP could significantly alleviate adenine-induced cognitive impairment. While the mechanisms of LAP actions may remain elusive, it is possible that reduction of inflammation/neuroinflammation may be one of the important mechanisms. The causal role of inflammation/neuroinflammation in cognitive impairment is well recognized. Our previous study showed that LAP reduced inflammatory lesions in kidney tissues, circulating levels of proinflammatory cytokines (IL-6 and IL-12p70), and mRNA levels of proinflammatory cytokines (IL-6, TLR-4, F4/F80, and IL-1ß) in kidney tissues [13]. While histopathological evaluation of inflammation in brain tissues was not performed (in part due to insufficient tissue availability), we found that adenine significantly increased SNCA mRNA level and decreased TGFB1 mRNA level, and these alterations could be significantly reversed by LAP treatment. SNCA has recently been shown to be associated with decline of cognitive functions in older adults including Alzheimer’s disease patients possibly via mediation/induction of neuroinflammation [18, 19], supporting that SNCA may serve as an additional biomarker for determining poor cognitive functions [19]. TGFB1 is an important anti-inflammatory cytokine. It has been shown that its lower production may predict a longitudinal functional and cognitive decline in oldest-old individuals [20]. Cumulatively, these results strongly suggest that one of the mechanisms by which LAP alleviates adenine-induced cognitive impairment may via modulating SNCA- and/or TGFB1- mediated neuroinflammation. Further research on studying how LAP may alter SNCA and/or TGFB1 expression and function is warranted.
LAP is a chain of nine lactate molecules, it is likely that LAP is broken to lactate and then lactate affect gene expression pattern and cognitive functions. Lactate has long been considered a byproduct of glycolysis (glucose breakdown) in anaerobic metabolism and a product of oxygen-limited metabolism. However, subsequent studies revealed that lactate is formed under both aerobic and anaerobic conditions [21]. It was shown that lactate is taken up by liver and is converted to pyruvate by LDH to be used in Krebs cycle. Nevertheless, other studies proposed the lactate shuttle hypothesis and demonstrated that lactate generated in peripheral tissue is transferred to other tissues to be used as fuel. For instance, lactate produced in muscles (during physical exercise) could be taken by heart and other tissues (e.g., brain) and used as fuel [22, 23]. Therefore, it is also likely that LAP exerts its effects though these mechanisms.
Our study showed that IMB significantly alleviated adenine-induced cognitive impairment. It is also possible that reduction of inflammation/neuroinflammation may be one of the important mechanisms. IMB, to the less extend than LAP, reduced inflammatory lesions in kidney tissues, circulating levels of proinflammatory cytokines, and mRNA levels of proinflammatory cytokines in kidney tissues [13]. In this study, we found that IMB significantly reversed adenine-induced alterations of mRNA levels of ELOVL2, GLUT4, Nestin, TGFB1, and TGFB2 genes. ELOVL2 improves synaptic functionality and regulates/mitigates brain inflammatory activity [24]. It also inhibits apoptosis in pancreatic beta cells supporting its involvement in metabolic processes [25]. GLUT4 is an insulin-regulated glucose transporter. Animal models of insulin resistance showed memory deficit and a decrease in GLUT4 and hippocampal insulin signaling [26]. The reduced GLUT4 expression in brain tissues of adenine-treated mice indicated that cognitive impairment is associated with impaired glucose utilization/energy metabolism in brain. The reduced GLUT4 expression in brain tissues of adenine-treated mice indicated impaired neural stem cell production and reduced neurogenesis in brain. While the primary function of *Nestin is* related to neurogenesis from neural stem cells, neuroinflammation is also involved in Nestin-mediated neurogenesis [27]. The significantly reduced Nestin expression in adenine-treated mice indicated impaired neurogenesis, which could be reversed by IMB treatment. In addition to TGFB1, TGFB2 also has anti-inflammatory functions [28]. Similar to TGFB1, the reduced expression level of TGFB2 in adenine-treated mice was significantly increased by IMB. These cumulative experimental results strongly suggest that IMB may improve cognition in part via inhibiting neuroinflammation, increasing energy metabolism, and increasing neurogenesis processes.
IMB was prepared by a koji fermentation of defatted soybeans with *Aspergillus oryzae* and lactic acid bacteria (*Pediococcus parvulus* and Enterococcus faecium), followed by water extraction and purification of koji polysaccharides. Hydrolysis analysis showed that this polysaccharide was mainly consisted with arabinose ($41.4\%$), galactose ($23.7\%$), and xylose ($10.4\%$) [13]. The functional roles of polysaccharides in modulating gut microbiota have been well-established. In this study, our microbiome analysis revealed that adenine treatment drastically altered the microbiome profile of the fecal samples of mice cecum. In fact, the abundance of almost $\frac{2}{3}$ of bacterial species was changed at a significant level. Interestingly, IMB (especially IMB-H), but not LAP, could normalize the abundance of $30\%$ of the altered bacteria.
We found that mice in the MC had significantly increased abundance of *Actinobacteria phylum* and significantly decreased abundance of Bacteroidetes phylum. Previous research has suggested that Actinobacteria (mainly Bifidobacterium) may play a functional role in cognition. Patients with cognitive impairment had a higher abundance of Actinobacteria than that of the controls [29]. Mice with depression also had increased abundance of Actinobacteria [30]. Ovariectomized (OVX) mice fed a high-fat diet experienced impaired object recognition and spatial memory associated with increased Bifidobacteriales; administration of epigallocatechin gallate (EGCG) significantly improved cognition and memory and inhibited the increase of Bifidobacteriales [31]. The results in our current study showed that IMB supplementation significantly improved cognition associated with significant reduction of Actinobacteria abundance. Further analysis also showed that some Bifidobacterium species in the Actinobacteria phylum, such as the common bacteria s_Bifidobacterium_pseudolongum (Figure 6B), and the very rare bacteria s_Bifidobacterium_sp._AGR2158 and s_Bifidobacterium_animalis (Figure 7B), had significantly increased abundances in MC mice, which were reversed/normalized by IMB supplementation. These results suggest that IMB may improve cognition in part via inhibition of the abundance/function of bacteria species in Actinobacteria phylum.
Our results showed that the abundance of *Bacteroidetes phylum* (which includes notably Bacteroides and *Prevotella* genera) was significantly decreased in mice of adenine-induced cognitive impairment, and IMB treatment improved cognition associated with increased abundance of Bacteroidetes. It is well recognized that cognitive impairment is correlated to decreased abundance of Bacteroidetes and the increased Firmicutes/Bacteroidetes ratio. Alzheimer’s disease patients had significantly increased fecal abundance of Firmicutes but decreased abundance of Bacteroidetes compared to normal controls [32]. Fecal microbiota transplantation improved cognition in patients with cognitive decline associated with enrichment of Bacteroidetes [33]. Administration of young blood plasma significantly diminished the gut Firmicutes/Bacteroidetes ratio in middle-aged rats [34]. Again, these results suggest that IMB may improve cognition in part via inhibition of the function of bacteria species in Bacteroidetes phylum.
In conclusion, our data demonstrated that LAP and IMB could improve cognitive performance in mice via distinct mechanisms of action. LAP may improve cognition in part via inhibiting inflammation/neuroinflammation and modulating metabolic process, whereas IMB may improve cognition in part via inhibiting inflammation/neuroinflammation, enhancing energy metabolism, increasing neurogenesis, and modulating gut microbiota (Figure 8).
**Figure 8:** *A diagram of suggested mechanisms of IMB and LAP actions. The results of circulating inflammatory cytokines are from our previous study (13).*
## Data availability statement
The data presented in the study are deposited in the Harvard Dataverse, accession number (https://doi.org/10.7910/DVN/XUJYVB).
## Ethics statement
The animal study was reviewed and approved by Beth Israel Deaconess Medical Center.
## Author contributions
HA and YS contributed to the animal study, sample analysis, and manuscript preparation. HA and J-RZ contributed to experimental design and data analyses. J-RZ contributed to supervision and project administration. All authors contributed to the article and approved the submitted version.
## Funding
This research was funded by Nichimo Biotics Co., Ltd., Japan and LifeTrade Co., Ltd., Japan. The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.
## Conflict of interest
The authors declare that this study received funding from Nichimo Biotics Co., Ltd and LifeTrade Co., Ltd. The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'Sex-specific non-linear associations between body mass index and impaired
pulmonary ventilation function in a community-based population: Longgang COPD study'
authors:
- Hao Huang
- Xueliang Huang
- Jiaman Liao
- Yushao Li
- Yaoting Su
- Yaxian Meng
- Yiqiang Zhan
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10034327
doi: 10.3389/fphar.2023.1103573
license: CC BY 4.0
---
# Sex-specific non-linear associations between body mass index and impaired pulmonary ventilation function in a community-based population: Longgang COPD study
## Abstract
Aim: To investigate the prevalence of pulmonary airflow limitation and its association with body mass index (BMI) in a community-based population in Shenzhen, China.
Methods: Study participants were recruited from Nanlian Community in Shenzhen, China, and spirometry was performed to assess lung function including forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), FEV1/FVC ratio, and FEV1 divided by predicted value. Pulmonary airflow limitation was determined by the Chinese Guideline of Pulmonary Function Examination. Multivariable logistic regression models were used to examine the association between BMI and pulmonary airflow limitation. Age, sex, educational attainment, occupation, and current cigarette smoking were used as potential confounders.
Results: Of the 1206 participants, 612 ($50.7\%$) were men and 594 ($49.3\%$) were women with the average age being 53.7 years old. After adjusting for age, sex, educational attainment, occupation, and current cigarette smoking, higher BMI was associated with lower odds (odds ratio: 0.98, $95\%$ confidence interval: 0.97, 0.99) of pulmonary airflow limitation by assuming a linear relationship. Further investigation of the interaction terms, we found that the magnitudes of the associations differed in men and women. A U-shaped relationship was observed in women, while the association was almost linear in men.
Conclusion: The relationship between BMI and pulmonary airflow limitation was U-shaped in women and linear in men.
## Introduction
Chronic obstructive pulmonary disease (COPD) is one of the most common respiratory diseases including emphysema and chronic bronchitis (Safiri et al., 2022). The prevalence of COPD was estimated to be $10.3\%$ among people aged 30–79 years (Adeloye et al., 2022). It is characterized by irreversible airflow blockage and long-term respiratory symptoms. It progresses slowly with daily activity becoming harder. While COPD is difficult to cure and recover, it is treatable and modifiable from the public health perspective.
The causes of COPD pathogenesis are thought to be long-term exposures to harmful particles that can irritate the lung and lead to inflammation. For example, the most significant and outstanding risk factor is tobacco smoking which is estimated to contribute to almost half of COPD (Hikichi et al., 2019; Adeloye et al., 2022). Other risk factors include pollution, older age, being man, metabolic profiles, and genetics (Hooper et al., 2012; Sakornsakolpat et al., 2019; Silverman, 2020). While the disease burden of COPD can be reduced by mitigating exposures to smoking and pollution, the role of obesity in COPD pathogenesis is largely controversial. A recent meta-analysis and systematic review summarizing previous results found that being underweight was associated with higher risks of COPD and being overweight was associated with lower risks (Zhang et al., 2021). While the relationships between obesity and COPD reported in these studies were assumed to be linear, they were seldom tested in the analyses (Melo et al., 2014; Tang et al., 2022). Indeed, one study found that the relationship between obesity and COPD seemed to be U-shaped in Canadian (Chen, 2000) and British cohorts (Garcia Rodriguez et al., 2009). Further investigations of this relationship in other ethnic populations are warranted.
In the present study, we aimed to revisit the relationship between obesity, as measured by body mass index (BMI), and impaired pulmonary ventilation function in a community-based population in Shenzhen, China. Further, we sought to examine if there were interactions between BMI and gender. We made the hypothesis that a higher BMI was related to lower risks of impaired pulmonary ventilation function and gender could modify the observed associations.
## Study population
Study participants were recruited from local residential communities using a convenience sampling method in Longgang District, Shenzhen, China. The primary aim of this survey was to investigate the prevalence of COPD and related risk factors as well as long-term health outcomes in this population. Citizens or local permanent residents (people who are registered as Shenzhen citizens, but not those who lived outside of Shenzhen for no less than 6 months, and non-registered Shenzhen citizens who have temporary residence permits and have lived in Shenzhen for no less than 6 months) who were 18 years or older were recruited. Data collection was carried out from Jan to December 2021. At the beginning of the survey, our administrative staff collaborated with local administrative heads and told them our aim and methods of this survey. Because of their collaboration, we could share and inform them of our study design through social media and printed handouts. All families in the communities were informed and only one member of a house was invited to our study. An onsite questionnaire was administered by trained staff members and health professionals at the corresponding community health service centers. In total, 1206 participants aged 18 years and over completed the questionnaires.
## Spirometry
Trained health professionals performed spirometry using a U-Breath PF680 spirometer (Wang et al., 2022) (e-LinkCare Meditech Co., Ltd. Zhejiang, China). Study participants were instructed to lift their chins, lengthen their necks slightly, and place a nose clip on their noses during testing in order to prevent air leaks. They were asked to practice these procedures twice before carrying out a couple of formal maximal forced expiratory tests. In each maneuver, the participant took the deepest breath possible to fill the lungs with air, then put the mouthpiece into his/her mouth making a tight seal, and then blew the air out as hard and fast as possible.
Forced vital capacity (FVC), the first second of forced expiration volume (FEV1), FEV1/FVC ratio, percentage of predicted FEV1 value (FEV$1\%$predicted), and lower limit of normal (LLN) were recorded. According to the Chinese Guideline of Pulmonary Function Examination (Asthma, 2013), pulmonary airflow limitation was graded as any one of the following: minor (FEV$1\%$predicted ≥$70\%$, but < LLN or FEV1/FVC ratio < LLN), moderate (FEV$1\%$predicted: $60\%$–$69\%$), moderate-severe (FEV$1\%$predicted: $50\%$–$59\%$), severe (FEV$1\%$predicted: $35\%$–$49\%$), and extremely severe (FEV$1\%$predicted <$35\%$).
COPD was defined as FEV1/FVC <0.7 and was graded as mild, moderate, severe, and very severe according to The Global Initiative for Chronic Obstructive Lung Disease (GOLD) system: GOLD 1 - mild: FEV1 ≥$80\%$ predicted; GOLD 2 - moderate: $50\%$ ≤ FEV1 <$80\%$ predicted; GOLD 3 - severe: $30\%$ ≤ FEV1 <$50\%$ predicted; GOLD 4 - very severe: FEV1 <$30\%$ predicted.
## Potential confounders
Weight and height were measured by trained staff using a calibrated instrument. Height was recorded to the nearest 0.5 cm wearing no foot wares, and weight was assessed to the nearest 0.1 kg. Body mass index (BMI) was then estimated as weight (kg) divided by the square of height (m). Educational attainment was recorded as primary school and below, middle school, and college and above. Cigarette smoking was assessed as current smoking and non-smoking. Comorbidity was defined as any of the following diseases: heart failure, chronic pulmonary heart disease, diabetes, or respiratory failure.
## Statistical analysis
Descriptive statistics were presented in men and women separately. Mean and standard deviations were presented for continuous variables, while counts and percentages (%) were calculated for categorical phenotypes. Multivariable logistic regression models were employed to estimate the associations and magnitudes between BMI and the risks of pulmonary airflow limitation. Results are described as odds ratio (OR) and $95\%$ confidence intervals (CIs) after controlling for potential confounders. A potential non-linear relationship between BMI and pulmonary airflow limitation was tested and investigated by treating BMI as restricted cubic splines. We further carried out gender-specific analyses to test the potential effect modification of sex. These analyses were also conducted for COPD. All the analyses were two-tailed and $p \leq 0.05$ was taken as statistically significant. All analyses were performed using R 4.1.
## Demographic Characteristics of the study population
Among the 1206 participants, 612 ($50.7\%$) were men and 594 ($49.2\%$) were women with the average age being 53.7 (standard deviation: 9.0) years old as shown in Table 1. The prevalence of current cigarette smoking was $31\%$ while more than half of the participants ($52.0\%$) had a BMI over 24 kg/m2 and only $10.8\%$ of the participants had a college degree and above.
**TABLE 1**
| Variables | Variables.1 | N | Pulmonary airflow limitation, n (%) |
| --- | --- | --- | --- |
| Gender | Men | 612 | 259 (42.3) |
| Gender | Women | 594 | 159 (26.8) |
| Age Groups | 18–44 | 206 | 70 (34.0) |
| Age Groups | 45–59 | 686 | 218 (31.8) |
| Age Groups | 60- | 314 | 130 (41.4) |
| BMI (kg/m2) | <18.5 | 35 | 17 (48.6) |
| BMI (kg/m2) | 18.5–24 | 544 | 209 (38.4) |
| BMI (kg/m2) | >24 | 627 | 192 (30.6) |
| Educational Attainment | College and above | 130 | 41 (31.5) |
| Educational Attainment | Middle School | 773 | 269 (34.8) |
| Educational Attainment | Primary School and below | 303 | 108 (35.6) |
| Current Cigarette Smoking | Yes | 375 | 184 (49.1) |
| Current Cigarette Smoking | No | 831 | 234 (28.2) |
| Comorbidity | Yes | 276 | 140 (64.8) |
| | No | 930 | 278 (30.0) |
## Associations between body mass index and pulmonary airflow limitation
The prevalence of pulmonary airflow limitation was $34.7\%$ (minor: $27.9\%$, moderate: $3.9\%$, moderate-severe: $1.2\%$, severe: $1.0\%$, and extremely severe: $0.7\%$). Multivariable logistic regression analysis revealed that higher BMI was associated with lower risks of pulmonary airflow limitation with the OR ($95\%$ CI) being 0.95 (0.90, 0.97) per one unit increment in BMI when assuming a linear relationship and adjusting for age, sex, and current cigarette smoking. Compared with people with a normal BMI (18.5–24 kg/m2), those with a BMI <18.5 had a higher risk of pulmonary airflow limitation (OR:1.41, $95\%$ CI: 0.68, 2.92), and those with a BMI >24 had a lower risk of pulmonary airflow limitation (OR: 0.63, $95\%$ CI: 0.46, 0.87) when categorizing BMI into three groups. Further adjusting for educational attainment yielded comparable results (Table 2).
**TABLE 2**
| BMI | Model 1 | Model 2 |
| --- | --- | --- |
| <18.5 | 1.41 (0.68, 2.92) | 1.42 (0.70, 2.89) |
| 18.5–24 | Reference | Reference |
| >24 | 0.63 (0.46, 0.87) | 0.61 (0.47, 0.79) |
| Continuous, 1 kg/m2 increment | 0.95 (0.90, 0.97) | 0.98 (0.97, 0.99) |
We further tested if sex could modify the observed associations and found that the interaction term between sex and BMI was statistically significant ($$p \leq 0.03$$). We also examined if there were non-linear associations between BMI and pulmonary airflow limitation using restricted cubic splines (Figure 1). We further found that the relationship was almost linear in men (Figure 2), while a U-shaped relationship was observed in women (Figure 3).
**FIGURE 1:** *Association between body mass index and pulmonary airflow limitation in men and women.* **FIGURE 2:** *Association between body mass index and pulmonary airflow limitation in men.* **FIGURE 3:** *Association between body mass index and pulmonary airflow limitation in women.*
In total, 126 participants could be classified as COPD and the prevalence of COPD was $10.4\%$ ($14.4\%$ in men and $6.4\%$ in women) in the study population, while that of COPD was 16.9 in current smokers and $7.2\%$ in non-smokers. Among the 126 participants with COPD, 37 ($29.4\%$), 71($56.3\%$), 12 ($9.5\%$), and 6 ($4.8\%$) were graded as mild, moderate, severe, and very severe according to the GOLD criteria. We performed additional analysis to examine the relationship between BMI and COPD and found similar patterns (Figures 4–6).
**FIGURE 4:** *Association between body mass index and COPD in men and women.* **FIGURE 5:** *Association between body mass index and COPD in men.* **FIGURE 6:** *Association between body mass index and COPD in women.*
## Discussion
In the present study, we investigated the prevalence of pulmonary airflow limitation and its association with BMI in a community-based sample in China and found that the relationship between BMI and pulmonary airflow limitation was non-linear and sex-specific. A higher BMI was associated with lower risks of pulmonary airflow limitation in a linear fashion in men, while a U-shaped relationship was revealed in women.
Several previous studies reported the associations between obesity and pulmonary function or COPD in diverse populations. A recent large epidemiological survey of more than 200,000 Korean participants found that being underweight was associated with a higher risk of impaired pulmonary function (predicted FEV$1\%$ < $80\%$) in comparison with normal weight after taking into account a few potential confounders (Do et al., 2019). And a recent meta-analysis pooled thirty studies involving 1,578,449 participants also found that obesity had a higher risk of COPD with ORs ($95\%$ CIs) being 1.96 ($95\%$ CI: 1.78–2.17) for being underweight, 0.80 ($95\%$ CI: 0.73–0.87) for being overweight, and 0.86 ($95\%$ CI: 0.73–1.02) for being obese (Zhang et al., 2021). Further, another meta-analysis summarizing results from clinical trials found that the estimated rate of FEV1 decline decreased with increasing BMI, supporting that a higher BMI was associated with decreased risks of impaired pulmonary ventilation function (Sun et al., 2019). However, the results were not always consistent (Galesanu et al., 2014; Svartengren et al., 2020; Celli et al., 2021; Shin et al., 2022). For example, an earlier investigation in adolescents and adults found that there was a negative linear relationship between BMI and pulmonary function, implying that being overweight or obese was associated with higher risks of impaired pulmonary ventilation function (Davidson et al., 2014). The obesity paradox and discrepancies are not completely clear. One of the explanations may lie in the fact that some studies used data from the general population while others collected data only from patients with COPD. Further studies using longitudinal study design, repeated measurement of both obesity and pulmonary function as well as large samples are needed.
Our study has a few strengths. Firstly, we used validated spirometry to assess pulmonary function. The instrument has been validated and used in a couple of settings including epidemiological research. Secondly, we employed an interaction analysis to examine the potential effect modification effects of sex, and we found significant differences between men and women. Thirdly, we examined the potential non-linear relationships in the associations between BMI and the risks of impaired pulmonary ventilation function that were seldom investigated using restricted cubic splines. We found the relationship is almost linear in men and U-shaped in women.
A few limitations should also be acknowledged. Our survey employed a cross-sectional design. The built-in limitation of the cross-sectional study design implies that we cannot draw a firm causal conclusion for the associations between BMI and the risk of impaired pulmonary ventilation function. Longitudinal data with longer follow-up durations could strengthen our confidence in causal inference. Second, we are unable to employ a multistage sampling method to recruit participants because of the logistics and difficulties during the pandemic. The convenience sampling approach may underestimate the prevalence of impaired pulmonary ventilation function or COPD in this area. However, our sampling approach is much better than social media-based approaches which are commonly used during the pandemic nowadays. Moreover, it has been shown that obesity is a dynamic process and should be assessed repeatedly. Future research could incorporate both subjective and objective assessments of obesity levels.
In summary, our present analysis of this survey found that the relationship between BMI and pulmonary airflow limitation was U-shaped in women and linear in men. Future studies are warranted to clarify the biological mechanisms and shed light on these associations.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by the Longgang Central Hospital Ethic Committee. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
HH and YZ designed this study, HH and XH drafted the manuscript. All other authors provided critical contributions and approved the final version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'Factors influencing medication adherence in multi-ethnic Asian patients with
chronic diseases in Singapore: A qualitative study'
authors:
- Sungwon Yoon
- Yu Heng Kwan
- Wei Liang Yap
- Zhui Ying Lim
- Jie Kie Phang
- Yu Xian Loo
- Junjie Aw
- Lian Leng Low
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10034334
doi: 10.3389/fphar.2023.1124297
license: CC BY 4.0
---
# Factors influencing medication adherence in multi-ethnic Asian patients with chronic diseases in Singapore: A qualitative study
## Abstract
Background: Poor medication adherence can lead to adverse health outcomes and increased healthcare costs. Although reasons for medication adherence have been widely studied, less is explored about factors affecting medication adherence for patients in non-Western healthcare setting and from Asian cultures. This study aimed to explore cultural perspectives on factors influencing medication adherence among patients with chronic diseases in a multi-ethnic Asian healthcare setting.
Methods: We conducted a qualitative study involving in-depth interviews with patients with chronic conditions purposively recruited from a community hospital in Singapore until data saturation was achieved. A total of 25 patients participated in this study. Interviews were transcribed and thematically analyzed. Themes were subsequently mapped into the World Health Organization (WHO) Framework of Medication Adherence.
Results: Participants commonly perceived that sides effects (therapy-related dimension), poor understanding of medication (patient-related dimension), limited knowledge of condition (patient-related dimension), forgetfulness (patient-related dimension) and language issues within a multi-ethnic healthcare context (healthcare team and system-related dimension) as the main factors contributing to medication adherence. Importantly, medication adherence was influenced by cultural beliefs such as the notion of modern medicines as harms and fatalistic orientations towards escalation of doses and polypharmacy (patient-related dimension). Participants made various suggestions to foster adherence, including improved patient-physician communication, enhanced care coordination across providers, use of language familiar to patients, patient education and empowerment on the benefits of medication and medication adjustment.
Conclusion: A wide range of factors influenced medication adherence, with therapy- and patient-related dimensions more pronounced compared to other dimensions. Findings demonstrated the importance of cultural beliefs that may influence medication adherence. Future efforts to improve medication adherence should consider a person-centered approach to foster more positive health expectations and self-efficacy on medication adherence, supplemented with routine reviews, development of pictograms and cultural competence training for healthcare professionals.
## 1 Introduction
Medication adherence is defined as “the extent to which a person’s behavior in terms of taking medications corresponds to agreed recommendations from a healthcare provider” (WHO, 2003). Medication adherence can be also viewed as a dynamic process that changes over time and involves three components: initiation, implementation and persistence (Vrijens et al., 2012). Despite the importance of medication adherence, non-adherence to medication remains to be a significant challenge in managing chronic conditions. Studies have shown that up to $50\%$ of patients with chronic diseases fail to take their medications as instructed (WHO, 2003; Kleinsinger, 2018; Foley et al., 2021). In Singapore where this study was conducted, around $60\%$ percent of adults are non-adherent to their chronic disease medication regimen (Lee et al., 2017; Chew et al., 2021). Medication non-adherence creates a considerable economic and clinical burden to individuals and health systems. According to a systematic review by Cutler et al., the annual disease-specific economic cost associated with non-adherence ranged from US$949 to US$44,190 per person (Cutler et al., 2018). In addition, a previous report estimated that poor medication adherence resulted in 200,000 premature deaths in Europe each year (Khan and Socha-Dietrich, 2018). The economic and clinical repercussions of medication non-adherence underscore the importance of developing an optimal mechanism to foster adherence to medication regimen.
Medication adherence is a complex behavior with multiple factors in play. The World Health Organization (WHO) framework for medication adherence categorizes the factors affecting adherence into five major dimensions (WHO, 2003). Social and economic factors include the cost of medication and the impact of medicines on patients’ work and social lives. Healthcare team and system-related factors deal with healthcare workers’ knowledge, the distribution of medication, and the rapport between doctors and patients. Condition-related factors consider the impact of the severity and progression of the illness on medication adherence. Therapy-related factors relate to the complexity of the medication regime and possible adverse effects of medication. Finally, patient-related factors involve forgetfulness on the part of the patients, perceptions of their conditions, and their attitudes and mindsets, among many things.
The extent of and reasons for medication adherence have been empirically studied in different patient groups using various methods of measurement (Krass et al., 2015; Yap et al., 2016; Durand et al., 2017; Cheen et al., 2019). Literature reported that socioeconomic status and social support were found to be positively associated with medication adherence in adult patients with chronic diseases (Gast and Mathes, 2019; Peh et al., 2021). There have also been several meta-syntheses of qualitative studies exploring patients’ perspectives of perceived barriers to adherence (Marshall et al., 2012; Brundisini et al., 2015; Kvarnström et al., 2021). They highlighted that health beliefs, lay understanding of medication and life context contributed medication adherence. Although these factors are well documented and may apply to Asian patient populations, cultural and healthcare system specificities make it difficult to directly extrapolate from literature. Studies on international variations in medication adherence highlight the need to examine health system context of medication adherence (Rabia Khan, 2018). A recent systematic review also underlines the importance of personal and cultural beliefs that shape the adherence behavior (Shahin et al., 2019; McQuaid and Landier, 2018). However, with a few exceptions (Chen et al., 2009; Abdul Wahab et al., 2021), qualitative studies assessing medication adherence of Asian patients tended to focus on Asian minorities within the Western healthcare context (King-Shier et al., 2017; Jalal et al., 2019; Greenhalgh et al., 2015; Kumar et al., 2016; King-Shier et al., 2018).
Building on existing literature, this study aimed to explore factors influencing medication adherence among patients with chronic diseases in multi-ethnic Asian healthcare setting of Singapore. The healthcare system in Singapore employs a mixed model of public and private funding to ensure the optimum balance between individual responsibility and social protection. Although approximately $80\%$ of the population obtain their healthcare services from the public health system that offers subsidized healthcare services (Khoo et al., 2014), the country’s primary care sector is being dominated by private general practices (Foo et al., 2021). The annual healthcare spending ($4.08\%$ of GDP) is lower than countries in developed Western economies such as the US ($16\%$) and the United Kingdom ($10\%$), yet health outcomes of Singapore are largely comparable with these countries (WHO, 2022). Recently, Singapore has announced the HealthierSG initiative in which residents are encouraged to anchor themselves with a primary care doctor for regular check-ins for chronic disease management (Abraham et al., 2022). Like many of its neighboring countries in Asia, *Singapore is* facing an increasingly aging population (Department of Statistics Singapore, 2021), and medication non-adherence is found to be associated with an increased risk of hospital admission and healthcare expenditure (Ling et al., 2020). Understanding patients’ perspectives will therefore enable an identification of challenges of medication adherence faced by multi-ethnic Asian patients with chronic diseases and inform the intervention to foster medication adherence.
## 2.1 Study design and participant recruitment
We conducted a qualitative study involving in-depth interviews. Patients were eligible if they were 21 years and above (a minor is defined as a person who is below 21 years of age in Singapore) and having one or more of the following chronic diseases: diabetes mellitus, heart disease, cancer, osteoarthritis, asthma, COPD, anxiety or depression, stroke, migraine, vision disorders, or adult-onset hearing loss. These diseases were selected based on the top 20 causes of the total burden of disease in Singapore (Ministry of Health Singapore, 2019). Eligible participants were purposively recruited based on gender, years of medication use, chronic condition, language spoken from inpatient wards at a large Community Hospital in Singapore. Eligible patients were identified from the administrative databases by two of the study team members (medical doctors). Potential participants were approached by a research staff who explained the purpose of the study. Patients in community hospitals generally stay for a few weeks for rehabilitation and discharge planning. Inpatient recruitment enabled the study team to facilitate the process from identification of eligible patients to recruitment to interviews.
## 2.2 Data collection
A semi-structured interview guide was developed based on existing literature (Marshall et al., 2012; Krass et al., 2015) with open-ended questions to probe the participants on the important aspects of medication adherence and reasons behind non-adherence (Supplementary Material). Topics included the extent and reasons for non-adherence, goals and motivations for medication adherence and chronic disease management, barriers to and facilitators of medication adherence and suggestions for improving medication adherence. Interviews were conducted in either English or Chinese by a female interviewer (ZL) who had credentials in Bachelor of Science and ample experience in in-depth interviews and focus group discussions in various clinical settings. The interviewer had no prior relationship with the participants. The interviews lasted 35 min–65 min in duration. A couple of participants had caregivers in attendance, but caregivers did not participate in the interviews. We originally planned to conduct face-to-face interviews. However, due to the safe distancing measures enforced during the COVID-19 pandemic, we conducted remote interviews via online video conferencing or phone call after the 12th interview. Field notes were taken by the study team member (WY).
## 2.3 Data analysis
All interviews were audio-recorded with the consent of the participants and transcribed verbatim. Transcriptions were not returned to participants due to time constraints. Interviews in Chinese were translated into English and verified for accuracy by the bilingual study team members. Data analysis involved a hybrid approach of data-driven inductive coding (Boyatzis, 1998) and template-based deductive coding (Crabtree and Miller, 1999), supported by iterative study team meetings. Inductive coding was first performed independently by two coders (WY, ZL). The coders first familiarize themselves with the data. The interview data was then coded through open and line-by-line coding by naming or defining concepts through close examination of the data. The data was grouped according to various provisional categories and subcategories of factors influencing medication adherence. For example, provisional categories such as being difficult to reconcile work and medication due to long working hours, caregiving responsibility, and house chores were later merged into a category of competing priorities. Each category was constantly reviewed and refined, with any discrepancies being iteratively discussed and resolved with the third study team member (SY).
The categories and sub-categories identified were then mapped into the WHO Framework of Medication Adherence to explore similarities and differences (WHO, 2003). WHO framework is useful for understanding factors contributing medication adherence in the context of chronic disease management in a holistic manner as it recognizes that issues surrounding medication adherence are not limited to a personal dimension but include a wider environment such as healthcare systems. Hence, this study used the framework as a reference point to explore how inductive results were aligned with different dimensions of adherence and whether there were any categories that did not fit one of the dimensions. The WHO framework provides descriptions of five dimensions of medication adherence which facilitated comparing of inductive coding categories with the framework’s dimensions and subsequently assigning them to these dimensions. Consensus meetings involving all study team members were held in an iterative manner to conduct peer review and validation of the overarching dimensions. The study team employed member checking, detailed audit trail and reflexivity at each step to ensure credibility and improve trustworthiness of the data (Lincoln and Guba, 1985). All data analysis was performed using a data management software, NVivo 12. For rigor and transparency, we anchored our methodology according to the Consolidated Criteria for Reporting Qualitative Research (COREQ) (Supplementary Material) (Tong et al., 2007). Participant feedback was not sought due to difficulty contacting patients after discharge.
## 2.4 Patient and public involvement
Patients or the public were not involved in the design, conduct, reporting or dissemination plans of our research.
## 2.5 Ethics
This study was approved by the SingHealth Centralized Institutional Review Board (Ref: $\frac{2020}{2200}$). Written consent, which included publication of anonymized responses from the participants, was obtained prior to each interview.
## 3.1 Participant characteristics
A total of 25 interviews were conducted. Data saturation was reached with 23 participants with no new themes emerging from subsequent interviews. We undertook two additional interviews to ensure that point of information redundancy was achieved. Of 33 patients purposively approached, eight patients rejected participation for reasons of speech impairment, time constraints and physical discomfort due to chronic conditions. The mean age was 66.4 years and $76\%$ were Chinese. Participants had multiple chronic conditions: the majority of the participants had hypertension ($68\%$), followed by hyperlipidemia ($60\%$), diabetes mellitus ($40\%$) and ischemic heart disease ($20\%$). More than half of the participants ($61.3\%$) had been on medication for more than ten years. ( Table 1).
**TABLE 1**
| Unnamed: 0 | N (%) |
| --- | --- |
| Age (Mean ± SD) | 66.4 ± 11.7 |
| Sex | |
| Female | 10 (40) |
| Male | 15 (60) |
| Race | |
| Chinese | 19 (76) |
| Malay | 4 (16) |
| Indian | 1 (4) |
| Other | 1 (4) |
| Education | |
| Tertiary and above | 1 (4) |
| Secondary | 11 (44) |
| Primary and below | 13 (52) |
| Primary language spoken | |
| English | 16 (64) |
| Chinese | 9 (36) |
| Years of taking medication | |
| < 5 | 5 (20.8) |
| 5–10 | 5 (20.8) |
| 11–20 | 7 (28.0) |
| > 20 | 8 (33.3) |
| Condition a | |
| Hypertension | 17 (68) |
| Hyperlipidemia | 15 (60) |
| Ischemic Heart Disease | 5 (20) |
| Diabetes Mellitus | 10 (40) |
| Others | 9 (36) |
## 3.2 Factors influencing medication adherence
The factors influencing medication adherence, grouped according to the five dimensions of the WHO framework, are summarized in Table 2. Below, selected quotes were presented based on the representativeness of the themes identified and their informative nature.
**TABLE 2**
| Item | Representative quotes |
| --- | --- |
| Social and economic dimension | Social and economic dimension |
| Competing priorities (Family commitments and busy work schedules) | “Sometimes I miss the medication because of my work. I have long working-hours so when I am really busy, I can’t even manage to go down to do the insulin. And oftentimes, I totally forget the timing. I miss the insulin time.” (#22, M, 58) |
| Financial constraints (Cost of medication) | “Familywise, because I’m from a single parent family so if let’s say I have any flare or relapse right, my mom is the only one who’s supporting the family. And the medications are not cheap. In the long-term I don’t think I can afford it” (#11, F, 25) |
| Social demands | “If I’m out, you know, like social gathering, even after the meal, when you chat with your family or your friends, you will also forget the Metformin.” (#9, F, 60) |
| Healthcare team and system-related dimension | Healthcare team and system-related dimension |
| Long waiting time for refills | “Obtaining medication in the hospital is very difficult for me. I have to wait for 1–2 h. It takes so long to get my medication. It’s not only refills. When I was discharged, I had to wait for 2 h to get medication. My child sometimes gets irritated by having to wait so long.” (#18, M, 73) |
| Lack of communication skills in healthcare professionals (HCPs) | “When we got blood pressure, we have to recognize our medicine. One time I got very high BP. The doctor gave me medicine. He said, ‘take twice a day’. The BP never came down. I went back and he said, ‘take four times a day.’ I kept on taking it four times a day. Went again, he said, ‘now you take six times a day’ When it’s six times a day, I got a shock. I came home and talked to my daughter. My daughter and I went to talk to the doctor asking about doses. He then said ‘it doesn’t matter. this is a very low dose’ By then I was already scared, so I never take the medicine.” (#15, F, 77) |
| Inadequate patient-HCPs relationship | “…during my post-operation hospital admission, for a few weeks, the nurse didn’t give me a high blood pill. I asked, ‘where is my high blood pill?’. The nurse said, ‘doctor said no need because your high blood pressure is stable.’ ‘are you sure?’ Then I waited for the doctor to come. He said, ‘don’t worry. We always monitor you.’ But when I ever asked polyclinic doctors, they said, “don’t miss! this is long-term’. I am confused, I want to make sure nothing will happen to me.” (#12, F, 58) |
| Language barrier (Difficulty comprehending language(s) used for dispensing labels and instructions) | “Some of them didn’t help me write in Chinese instead. I can’t recognize the English words. My wife’s literacy is also not as good. My children live by themselves. My second daughter-in-law lives next door. I have to bring the medication over to let her or my grandchild help me. It’s cumbersome. So, I just ask them to help me write how many tablets I should take in the morning and in the evening.” (#5, M, 74) |
| Condition-related dimension | Condition-related dimension |
| Comorbidities (polypharmacy) | “I think due to my age and additional conditions that I was facing then after surgery, because I had a total knee replacement surgery, there were times when the medications had to be titrated again. You know, medications had to be adjusted again to the right dose so that my pressure can be controlled. So that’s the challenge.” (#10, F, 67) |
| Deterioration of conditions | |
| Deterioration of conditions | “Initially, my condition was still good, but the hypoglycaemic episodes started happening in the last couple of years after my health had deteriorated. I think it’s because of the injection I am getting aside from the oral medication. It’s difficult to continue.” (#16, M, 66) |
| Therapy-related dimension | Therapy-related dimension |
| Adjustment to new medication routine | “It’s just that when I started on this medication, I was not used to it because everything came too sudden, so you had to maintain medications, to make sure that you take the medication regularly so it will not flare up. Because there’s no one there to remind me hey you have to take x and y medications, it was very challenging” (#11, F, 25) |
| Discomfort caused by self-injection (pain, bleeding) | “Maybe because of the pain, because when I self-inject myself, I was told that over long term in the stomach, I have to, I don’t self-inject into the same places, if I keep poking into the same places, the skin might be hardened, then I might experience pain.” (#02, M, 57) |
| Administration requirements (dosing at specific times) | “Oh, actually I’m not very good at managing it, to be very frank. Because you know sometimes you are so eager to eat, you forget the medication especially the one before meal. The only thing about Letrozole is you have to take it exactly 24 h later, Yesterday I actually forgot so I take it like 2 h later. It’s not easy” (#06, F, 63) |
| Adverse effects | “Doctor’s instruction is to drink as much as I can, but I can’t [drink] because it will cause diarrhoea the whole day. This medicine is meant for those who want to cleanse their stomach, I cannot take too much. It’s a kind of laxative.” (#01, M, 57) |
| Patient-related dimension | Patient-related dimension |
| Poor knowledge of medication dosage and frequency | “When you take your medication, it is alright to miss one day as long as you don’t miss it every day. You have to take your medication but missing one to two days will be alright.” (#21, F, 78) |
| Forgetfulness | “It’s not that I have never forgotten. I might take them a little late. For example, for a medication that I’m supposed to take at 9, I might sometimes take it only at 10 plus. When I realized that I had not taken my medication, I would quickly take them. I don’t really know whether I took the medications or not. I also don’t know whether I took a medication twice on some occasions.” (#08, M, 72) |
| Personal belief | “My children are grown up so, I should say I fulfilled my duties. And life is not very important to me because the basic daily necessity is all the same. With all these chronic diseases and medications, I feel very fed-up of life…medication is a waste of money.” (#25, M, 70) |
| Misconception of medication | “It’s only when the hospital changes my medication, I’m not sure whether I should start taking the new medication. Like they would change from this type of medication to another type. I will ask others to help me search online for the medication. If they tell me, it’s the same as my previous medication. I will keep my old medication and continue to finish old ones. I would be alright with starting the new medication later.” (#24, M, 77) |
| Poor understanding of one’s chronic condition(s) | “In fact, at one time, after taking the cholesterol medicine for about a year or so, I have ideal readings, very good readings, so I thought then I shall skip it and then 6 months later when I went back to see a doctor, the readings were really bad.” (#06, F, 63) |
| Absence of perceived health benefits of medications | “Maybe just on a very psychological level, why people don’t adhere to taking long term medication regularly, I think it’s the poor outcomes. Sometimes, I’ve been taking this medication for years, and yet my sugar level keeps on going up, the doctor keeps on increasing, so there is this giving up of hope.” (#06, F, 63) |
## 3.2.1 Social and economic dimension
Participants mentioned that they had missed their medications due to competing priorities such as busy work schedules and family commitments. Having to attend social gatherings was also cited by participants. These activities served as an inadvertent distraction from a daily medication routine and resulted in failure to take the medication as prescribed. For example, participants described that social gathering could be considered as a barrier to medication non-adherence.
Although financial issues were not mentioned as a factor substantially hindering medication adherence, a minority of participants expressed that paying for regular prescriptions could be a burden for the family in the long term and hence would likely to impede medication adherence. As one participant described:
## 3.2.2 Healthcare team and system-related dimension
The most common factors under the healthcare team and system-related domain included a perceived lack of communication skills in healthcare professionals (HCPs), long waiting times for refills and inadequate relationship between patients and healthcare professionals. Participants commonly stated having to wait for a long time to refill their medication or limited communication skills of HCPs to communicate with patients to elicit their needs and challenges in managing medications. As one participant highlighted, a lack of coordination across multiple physicians prescribing medications along with suboptimal quality of communication made it difficult to manage medications which could result in poor adherence: Another item emerged from the data was the language-related issue. In the multi-ethnic society of Singapore, the primary working language in the healthcare system is English and thus written information is typically generated in English alone. Many older participants mentioned that they had difficulty understanding the instructions because they were not proficient in the language used in the prescription medication labels.
## 3.2.3 Condition-related dimension
The main challenges under condition-related dimension were the deterioration of conditions and presence of comorbidities. Participants commonly described the complexity of adherence to multiple medications with their comorbid health conditions. For example, a participant spoke of the medications having to be constantly titrated, which she found it challenging to keep up with the changes.
## 3.2.4 Therapy-related dimension
The most prominent item mentioned under therapy-related dimension was the presence of adverse effects. Those who experienced adverse effects from medications tended to stop their medications. Participants noted that often, they were not informed about the adverse effects and what they should do to counter the medication’s adverse reactions. A participant described: Other participants mentioned that they did not adhere to their medications when they did not see any improvement in their chronic conditions. Participants also experienced difficulties when they first started a new medication or had to take medication at specific times.
## 3.2.5 Patient-related dimension
Across five WHO dimensions for medication adherence, patient-related dimension appeared to be markedly salient in our data. They included poor knowledge of medication dosage and frequency, forgetfulness, personal perception, misconception of medication, poor understanding of one’s chronic condition(s) and absence of perceived health benefits of medications. For example, lack of perceived improvement in one’s condition represented giving up of hope, resulting in cessation of medication intake.
Some participants expressed a fatalistic outlook by stating that they had achieved everything they needed in life and did not see the need to continue taking medications. Similarly, ‘Karma’ was highlighted by a non-adherent participant who believed that having to manage multiple medications was a punishment for his past wrongdoings.
Others had cultural beliefs about Western medication as ‘harm’, making decisions to stop medications or lowering doses at their own discretion.
## 3.3 Suggestions for improving medication adherence
Participants were asked what could help them to adhere to medication. Most of the suggestions were centered around four dimensions of the WHO framework. A summary of factors influencing medication adherence and suggestions to improve medication adherence based on WHO Framework of Medication Adherence can be found in Supplementary Material. For the social and economic dimension, participants would like to receive more subsidies from the government to help cover the cost of medical expenses. Participants also felt that reminders from family and friends and assistance from patient support groups would improve their adherence to medications.
Under the healthcare team and system-related dimension, there was agreement that adequate education by HCPs would be important in improving medication adherence. Other suggestions made by participants included improved patient-doctor relationships and enhanced care coordination across the healthcare systems and providers.
The use of a language that is familiar with patients would be critical for medication adherence.
For the therapy-related dimension, an adjustment to the medication regimen in accordance with one’s lifestyle was found to be helpful.
Under the patient-related dimension, participants felt that increasing patient motivation and a good understanding of one’s own conditions could help achieve better medication adherence.
## 4.1 Principal findings
This study sought to understand the factors contributing to medication adherence among multi-ethnic Asian patients with chronic diseases, using the WHO framework of medical adherence. We found that a broad range of factors influenced medication adherence, with therapy- and patient-related dimensions more pronounced compared to other dimensions. In particular, findings from this study demonstrated the importance of cultural beliefs that may contribute to medication adherence. Participants made several suggestions to improve adherence. They included improved patient-HCP relationships and enhanced care coordination across levels, use of language familiar to patients, patient education and empowerment on the benefit medication and medication adjustment.
## 4.2 Implications for research and practice
In our study, therapy-related dimension was the most prominent. In particular, the fear of adverse effects when taking medication was cited as the main barrier. The adverse effects ranged from gastrointestinal discomfort to joint pain and epistaxis; participants commonly reported that they stopped taking medications to prevent unpleasant or harmful reactions to medications. This may be an important factor to consider in a multi-ethnic society since adverse effects can vary with ethnicity (Hsu et al., 2010). Previous studies have found that adverse effects were one of the important reasons for lower rates of medication adherence (Marshall et al., 2012; Ju et al., 2018). A possible strategy to address non-adherence arising from adverse effects would be a benefit-risk counselling whereby physicians evaluate counseling needs of patients, educate steps needed to minimize risks and support patients in the treatment decision making (Kooy et al., 2014). Past research suggests that patients benefit greatly from routine medication reviews through re-evaluation of the patient’s experience at subsequent patient-physician interactions (Zaugg et al., 2018; Xu et al., 2020; Schindler et al., 2020). In addition, a collaborative effort between physicians and pharmacists was found to help patients cope with adverse effects of their medications (Shim et al., 2018). Further research is needed to determine the value of such programs and the most feasible way to implement them in a multi-ethnic Asian healthcare setting to enhance patient adherence.
Patient-related dimension was equally a leading cause of medication non-adherence in our study. Specifically, patient’s cultural perception regarding medication significantly influenced their decision for non-adherence. A specific belief brought up by participants was that Western medications were detrimental to one’s health in the long-term; for this reason, participants often intentionally self-adjusted medication dose or discontinued medications without consulting their doctors. However, we did not observe cultural beliefs on the superiority of traditional medication over Western medication or concurrent intake of both Western and complementary medications, a common belief and practice found in studies of Asian patients in a Western healthcare setting (Li et al., 2006; Eh et al., 2016; Jamil et al., 2022). Our findings are also in contrast with studies in Western healthcare settings where spirituality and religious beliefs were culturally significant factors that were shown to have a considerable effect on medication adherence (Albargawi et al., 2017; McQuaid and Landier, 2018; Shahin et al., 2019). Our finding, together with results from other studies, underlines the importance of understanding cultural perceptions in medication adherence. HCPs should consider the impact of cultural beliefs on medication adherence in diverse ethnic patients when implementing appropriate education strategies (Saha et al., 2013; Bosworth et al., 2017). Developing a program to train HCPs in cultural competence might serve to further promote communication with diverse patients, engaging them as collaborative partners in their care in a multi-ethnic and multi-cultural healthcare setting. Studies have shown that targeted interventions to address specific concerns of cultural groups can enhance adherence and health outcomes (Zolnierek and Dimatteo, 2009; Zeh et al., 2012). Indeed, effective patient education and empowerment were desired by many of our participants. It is therefore timely to implement ongoing support and culturally sensitive educational programs to achieve better medication adherence.
Another culturally relevant factor under the patient-related dimension was patients’ belief about fatalism in our multi-ethnic Asian setting. We found that for some patients, the escalation of medication doses or having to take multiple medications was viewed as meaningless or losing hope, and consequently, there was a gradual sense of aversion to medication intake. Destiny, that seems to be more acceptable in Asian culture, may account for this non-adherent behavior (Heiniger et al., 2015; Kim and Lwin, 2021). Clinicians and healthcare teams should consider how cultural background can impact on health beliefs about medication to ensure that more sensitive care is provided to diverse ethnic patient groups. We also suggest that patients with fatalistic cultural beliefs may benefit from a person-centered approach to foster more positive health expectations, self-efficacy, and health locus of control (extent of an individual’s perceived control over health outcomes on medication adherence) (Bosworth et al., 2017; Gerland and Prell, 2021).
When comparing our results on healthcare team and system-related dimension to recent reviews which assessed factors associated with medication non-adherence (Konstantinou et al., 2020; Kvarnström et al., 2021), we found that language issue was more prominent in our healthcare settings. Having difficulty in understanding the language used by HCPs or written instructions used for medication labels can result in poor communication and suboptimal clinical outcomes. Prior studies reported that limited language proficiency leads to poor medication adherence in migrant or less acculturated minority patients in a Western setting (Kumar et al., 2016; Jalal et al., 2019), *What is* different in our study is that the language barriers were commonly reported across the board, indicating that patients particularly of older age experienced language issues regardless of ethnic minority status. Greater effort is therefore required to ensure that a language familiar with the patient is used to deliver messages regarding instruction, a suggestion also made by our participants. A combination of pictograms and bilingual text should also be considered to assist communication of medication use as evidenced by previous intervention literature (Berthenet et al., 2016; Malhotra et al., 2019).
Lastly, under social and economic dimensions, financial constraints were acknowledged by some participants who suggested the extension of government subsidies to offset cost concerns. Yet, it should be noted that the majority of participants did not see financial commitments as a determining factor influencing medication non-adherence. This is in contrast to previous studies where cost of medications was a commonly cited factor affecting medication adherence (Briesacher et al., 2007; Khera et al., 2019). A key reason for this difference may be that many medications for common chronic diseases in Singapore are heavily subsidized in public healthcare institutions through various schemes available to patients (e.g., Medisave, MediShield, MediFund and the Community Healthcare Assistance Scheme) that help ease their financial burden (Ministry of Health Singapore, 2021). However, physicians should be aware that a small number of patients might still face cost-related adherence issues so that prescription planning could be done to minimize the cost barriers.
## 4.3 Strengths and limitations
All participants interviewed in this study had at least one disease that falls under the top 20 causes of the total burden of disease in Singapore. Our participants also represented a broad spectrum of the patient population with chronic diseases in terms of the level of education, number of years of taking medications and language spoken. Hence, by obtaining their perspectives on medication adherence, the results of this study provided a meaningful insight into the complexity of factors influencing medication adherence in multi-ethnic Asian patients with chronic diseases.
This study is not without its limitations. First, as participants were recruited from a single intermediary healthcare institution, their experiences might not be generalizable to the spectrum of healthcare services in Singapore. However, many participants recounted their experiences of public primary care services in relation to the topic of medication adherence and hence their perspectives would be relevant. The format of interviews used in this study was limited to individual interviews due to difficulty in scheduling a group discussion. While the questions asked in individual interviews facilitated in-depth open discussions, focus groups would have allowed for a better exchange of views and experiences amongst participants that could have brought about other unique themes (Kitzinger, 1995). While the WHO framework has the benefits of considering a diverse range of factors related to medication adherence, use of other relevant frameworks could have engendered unique insights on medication adherence (e.g., ABC taxonomy for process-oriented medication adherence behavior) (Vrijens et al., 2012). Therefore, more research should be done using different frameworks to explain complex pattens of medication behavior and associated factors. Lastly, despite our efforts to recruit a balanced sample, there was a limited representation of Indian participants in this study, which might have introduced a selection bias in the findings. Nevertheless, the proportion of ethnicity was similar to the distribution of ethnicity in Singapore although Indian participants were slightly under-represented (Department of Statistics Singapore, 2021).
## 5 Conclusion
This study identified a wide range of factors that contributed to medication adherence. Our findings shed light on the importance of cultural beliefs that may affect medication adherence in a multi-ethnic Asian healthcare setting. The factors identified in this study can enable clinicians to understand multiple underlying determinants that promote or hinder medication adherence and incorporate patient concerns and perspectives into appropriate strategies to enhance adherence. healthcare professionals should pay more attention to addressing language barriers and patient’s cultural beliefs through patient education by language-concordant healthcare professionals, succinct written instructions and involvement of trained medical interpreters. In addition, interventions aimed at improving medication adherence will benefit from considering a person-centered approach to foster positive health expectations, locus of control and self-efficacy, along with routine reviews, development of pictograms and cultural competence training for healthcare professionals.
## Data availability statement
The datasets presented in this article are not readily available because of participant confidentiality and participant privacy. Requests to access the datasets should be directed to low.lian.leng@singhealth.com.sg.
## Ethics statement
The studies involving human participants were reviewed and approved by SingHealth Centralized Institutional Review Board (Ref: $\frac{2020}{2200}$). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
SY, YK, and LL contributed to the conception of the study. SY and YK designed the work. WY, ZYL, YL, JA, and LL contributed to the acquisition of the data. All authors contributed to the analysis and interpretation of data. SY drafted the manuscript. All authors revised the manuscript critically for important intellectual content and approved the final version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2023.1124297/full#supplementary-material
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|
---
title: Prognostic impact of white blood cell counts on clinical outcomes in patients
with chronic renal insufficiency undergoing percutaneous coronary intervention
authors:
- Wei Yan
- Mengyao Li
- Yumeng Lei
- Shuaiyong Zhang
- Fengfeng Lv
- Jiawang Wang
- Qian Yang
- Na Yu
- Ming Chen
- Xufen Cao
- Liqiu Yan
journal: Frontiers in Cardiovascular Medicine
year: 2023
pmcid: PMC10034344
doi: 10.3389/fcvm.2023.1027107
license: CC BY 4.0
---
# Prognostic impact of white blood cell counts on clinical outcomes in patients with chronic renal insufficiency undergoing percutaneous coronary intervention
## Abstract
### Objective
To determine whether the inclusion of white blood cell (WBC) counts in the SYNTAX score (SS) or SS II models could improve the models’ performance for risk stratification in individuals with chronic renal insufficiency (CRI) following percutaneous coronary intervention (PCI).
### Methods
In total, 2,313 patients with CRI, who were subjected to PCI and had data available on in-hospital WBC (ih-WBC) counts, were recruited. Patients were divided into 3 groups as per their ih-WBC counts (low, medium, and high). The primary endpoints were all-cause mortality (ACM) and cardiac mortality (CM). The secondary endpoints incorporated myocardial infarction, stroke, unplanned revascularization, and major adverse cardiovascular and cerebrovascular events (MACCEs).
### Results
During a median follow-up of 3 years, the high WBC group had the highest incidences of CM ($2.4\%$ vs. $2.1\%$ vs. $6.7\%$; $p \leq 0.001$), ACM ($6.3\%$ vs. $4.1\%$ vs. $8.2\%$; $p \leq 0.001$), unplanned revascularization ($8.4\%$ vs. $12.4\%$ vs. $14.1\%$; $p \leq 0.001$), and MACCEs ($19.3\%$ vs. $23.0\%$ vs. $29.2\%$; $p \leq 0.001$) among the three groups. Multivariable Cox regression analysis depicted that the risk of ACM and CM in the high WBC group was 2.577 ($95\%$ confidence interval [CI]: 1.504–4.415, $p \leq 0.001$) and 3.850 ($95\%$ CI: 1.835–8.080, $p \leq 0.001$) times that in the low WBC group after adjusting for other confounding factors. A combination of ih-WBC counts with SS or SS II significantly improved the risk assessment and prediction of ACM and CM.
### Conclusion
The ih-WBC counts was associated with the risk of occurrence of ACM, CM, unplanned revascularization, and MACCEs in individuals with CRI following PCI. It provides an incremental predictive value for the occurrence of ACM and CM when included in SS or SS II models.
## Introduction
Inflammation exerts a critical function in the progression as well as plaque destabilization in atherosclerosis [1]. Studies have found that downstream biomarkers of inflammation, such as interleukin-6 and the high-sensitivity C-reactive protein (hs-CRP) are linked to a greater risk of cardiovascular events [2, 3].
The white blood cell (WBC) counts are a widely-used and easily available marker of inflammation in clinical practice. Its predictive value as a marker for mortality in individuals with acute coronary syndrome (ACS) is well-established (4–6). Recently, it has also been demonstrated that total, as well as differential in-hospital white blood cell (ih-WBC) counts, are independent prognostic factors for long-term deaths and major adverse cardiovascular and cerebrovascular events (MACCEs). Including ih-WBC counts in SYNTAX score (SS) or SYNTAX score II (SS-II) models can improve mortality predictions in individuals with triple-vessel coronary artery disease (CAD) [7]. However, there have been no assessments of the predictive value of ih-WBC counts in patients with CRI after percutaneous coronary intervention (PCI). Chronic renal insufficiency (CRI), which is defined as the presence of kidney damage or reduced kidney function (estimated glomerular filtration rate (eGFR) <90 mL/min/1.73 m2) for ≥3 months [8], is high-risk comorbidity that increases the risk of cardiovascular mortality and morbidity and is known to be associated with poorer clinical outcomes in patients following PCI [9]. In this investigation, we sought to evaluate the utility of including ih-WBC counts as a factor in SS or SS II models for anticipating long-term clinical outcomes in individuals with CRI following PCI.
## Study population
The study design of the risk evaluation of CRI patients following PCI has been described previously [10]. Briefly, a total of 2,468 patients with creatinine clearance rates (CrCl) <90 mL/min per 1.73 m2 who underwent PCI between January 2014 and September 2017 in Cangzhou Central Hospital, Hebei Medical University, were retrospectively enrolled in the study. After excluding 155 patients for whom ih-WBC counts data were not available, 2,313 patients were included in this investigation. Patients were classified into three groups as per tertiles of ih-WBC counts as follows: low WBC group [WBC counts ≤6.18*109/L ($$n = 776$$)], medium WBC group [WBC counts >6.18*109/L but ≤8.14*109/L ($$n = 768$$)], and high WBC group [WBC counts >8.14*109/L ($$n = 769$$)] (Figure 1). The study protocol was subjected to approval by the ethics committee of Cangzhou Central Hospital. Written and informed consent was obtained for all subjects.
**Figure 1:** *Study flow chart. PCI, percutaneous coronary intervention; eGFR, estimated glomerular filtration rate; CABG, coronary artery bypass graft; WBC, white blood cell.*
The clinical and interventional data of the participants were collected from the electronic medical record (EMR) system. The CrCl values were calculated utilizing the simplified Modification of Diet in Renal Disease (MDRD) equation. The ih-WBC counts were defined as the first WBC counts value from the EMR system. Two of three trained cardiologists (who were blinded to the clinical data as well as outcomes) calculated the SS [11] and SS II [12] through the dedicated website.1 In case of any disagreement, the opinion of a third observer was obtained and resolved by consensus.
## Study endpoints and follow-up
Clinical follow-up was performed via clinic visits or telephone conversations. The primary endpoints included all-cause mortality (ACM) as well as cardiac mortality (CM). Deaths that could not be attributed to non-cardiac causes were considered CM. The secondary endpoints included myocardial infarction (MI), stroke, unplanned revascularization, and major adverse cardiovascular and cerebrovascular events (MACCEs), defined as a composite of ACM, MI, stroke, and unplanned revascularization. MI was defined following the consensus document on the fourth universal definition [13]. Stroke was defined as neural dysfunction due to a sudden rupture or blockage of a blood vessel, and was diagnosed based on signs of brain dysfunction or imaging evidence [14]. Revascularization of PCI or coronary artery bypass grafting (CABG) driven by ischemic symptoms or cardiac events was defined as unplanned revascularization. All endpoints were confirmed by two independent clinicians.
## Statistical analysis
Continuous variables are reported as mean ± standard deviation (SD) or medians with interquartile ranges (IQR). Categorical variables are presented as frequencies and percentages. Continuous variables were compared utilizing one-way ANOVAs or Kruskal-Wallis tests, when necessary. Chi-square or the Fisher exact tests, on the other hand, carried out comparisons of categorical variables. Patients who were lost to follow-up were deemed at risk until they were censored at the date of the last contact. The cumulative event rates were measured utilizing Kaplan–Meier curves and compared across groups via the log-rank test. We assessed the prognostic value of ih-WBC grouping for predicting clinical outcomes using multivariable Cox regression models. While Log[−logS(t)] plots were used to test for proportional hazard assumption. All potential confounders (with $p \leq 0.1$ in the univariate analyses) were incorporated in the multivariate analyses. By combining ih-WBC counts with SS or SS II values, we assessed the improvements in model performance, risk classification, and discrimination; this was done by comparing the AUC of the two nested models employing the nonparametric deLong approach and computing the net reclassification improvement (NRI) as well as the integrated discrimination improvement (IDI) indices. A two-sided value of $p \leq 0.05$ was statistically significant. SPSS 24.0 (IBM Corp., Armonk, NY, United States) and R Software Version 3.6.0 (The R Foundation for Statistical Computing, Vienna, Austria) conducted all the statistical analyses of this investigation.
## Patients’ baseline characteristics
The ih-WBC counts ranged from 2.6*109/L to 32.7*109/L. The SS values ranged from 1.0 to 47.0, and the SS-II values ranged from 9.7 to 59.6. Patients with high ih-WBC counts were younger and more likely to have current smoker status; clinical presentation of MI; reduced eGFR; lower left ventricular ejection fraction (LVEF); higher left ventricular end-diastolic diameter (LVEDD); and elevated levels of serum creatinine, blood glucose, total cholesterol, triglyceride, and low-density lipoprotein ($p \leq 0.05$ or $p \leq 0.001$) than those with low and median ih-WBC counts. Patients with high ih-WBC counts also had higher baseline SS values and were more likely to have thrombus lesions and undergo primary PCI ($p \leq 0.001$ for all) (Tables 1, 2).
## Association of ih-WBC counts with clinical outcomes
The median follow-up period was 3 years (IQR = 1.5–5.0). Among the three groups, the high ih-WBC counts group had the highest 5-year cumulative event rates of ACM ($6.3\%$ vs. $4.1\%$ vs. $8.2\%$; $p \leq 0.001$), CM ($2.4\%$ vs. $2.1\%$ vs. $6.7\%$; $p \leq 0.001$), unplanned revascularization ($8.4\%$ vs. $12.4\%$ vs. $14.1\%$; $p \leq 0.001$), and MACCEs ($19.3\%$ vs. $23.0\%$ vs. $29.2\%$; $p \leq 0.001$) (Table 3 and Figure 2). Univariate Cox regression analyses for different clinical outcomes were shown in Supplementary Table 1. Multivariate Cox regression analyses affirmed that the risk of ACM and CM in the high WBC group was 2.577 ($95\%$ confidence interval [CI]: 1.504–4.415, $p \leq 0.001$) and 3.850 ($95\%$ CI: 1.835–8.080, $p \leq 0.001$) times that in the low WBC group after adjusting for other confounding factors (Table 4).
## Combination of ih-WBC counts with SS or SS II values for prediction of ACM and CM
The analyses of time-dependent AUCs for ACM showed that the AUCs for SS in combination with ih-WBC counts were significantly larger than those of SS alone ($p \leq 0.05$) during the 30-month follow-up period. However, the degree of increase tended to decrease with time. There was no remarkable difference between the predictive value of the SS plus ih-WBC counts model and the SS model ($p \leq 0.05$) when the follow-up times were longer than 30 months. A similar result was observed for the SS-II models, albeit with a cutoff value of 28 months instead of 30 months. In the models used to predict the incidence of CM, though the extent of increase also tended to decrease with time, there was a remarkable difference between the predictive value of the SS plus ih-WBC counts model and the SS model ($p \leq 0.05$) during the whole follow-up period, and for the SS-II models, the cutoff value was 33 months (Figure 3).
**Figure 3:** *Comparisons of time-dependent AUCs of different models for discrimination of all-cause death and cardiac death. AUC, area under curve; SS, SYNTAX score; SS II, SYNTAX score II; WBC, white blood cell; SS II+WBC: SYNTAX score II plus white blood cell.*
Furthermore, by combining the ih-WBC counts with SS or SS II models, the metrics for risk reclassification and discrimination were significantly improved. The respective NRIs of the SS plus ih-WBC counts model over the SS model were 0.121 for ACM and 0.188 for CM; the NRIs of the SS-II plus ih-WBC counts model over the SS-II model were 0.025 for ACM and 0.135 for CM. The IDI indices of the SS plus ih-WBC counts model over the SS model were 0.019 ($p \leq 0.01$) for ACM and 0.022 ($p \leq 0.001$) for CM and the IDI indices of the SS-II plus ih-WBC counts model over the SS-II model were 0.025 ($p \leq 0.001$) for ACM and 0.032 ($p \leq 0.001$) for CM (Table 5).
**Table 5**
| Unnamed: 0 | Discrimination | Discrimination.1 | Risk reclassification | Risk reclassification.1 | Risk reclassification.2 | Risk reclassification.3 | Risk reclassification.4 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | IDI [95% CI] | P-value | Events | Events | Non-events | Non-events | NRI [95% CI] |
| | IDI [95% CI] | P-value | Risk up | Risk down | Risk up | Risk down | NRI [95% CI] |
| All-cause mortality | | | | | | | |
| SS vs. SS + WBC | 0.019 (0.004–0.058) | <0.01 | 0.243 | 0.092 | 0.113 | 0.083 | 0.121 (−0.003–0.232) |
| SS II vs. SS II + WBC | 0.025 (0.005–0.060) | <0.001 | 0.119 | 0.106 | 0.056 | 0.069 | 0.025 (−0.051–0.239) |
| Cardiac mortality | | | | | | | |
| SS vs. SS + WBC | 0.022 (0.006–0.060) | <0.001 | 0.256 | 0.033 | 0.089 | 0.045 | 0.188 (0.023–0.322) |
| SS II vs. SS II + WBC | 0.032 (0.009–0.060) | <0.001 | 0.209 | 0.077 | 0.060 | 0.057 | 0.135 (−0.051–0.266) |
## Discussion
This study shows that the ih-WBC counts is associated with the risk of occurrence of ACM, CM, unplanned revascularization, and MACCEs in individuals with CRI following PCI. The ih-WBC counts can be utilized for risk reclassification, especially in secondary prevention among patients with CRI post-PCI. Integrating ih-WBC counts into SS or SS II models also improves the predictive performance of these models and facilitates the identification of patients at risk for future ACM and CM. Therefore, the ih-WBC counts may also be used to flag patients at risk for adverse cardiac events post-PCI, who may warrant more intensive follow-up and preventive treatment.
Inflammation is known to exert an important function not only in atherogenesis but also in atherosclerotic plaque rupture resulting in acute coronary syndrome (ACS) (15–18). Studies have found that biomarkers of inflammation including hs-CRP and interleukin-6 are independent risk factors for cardiovascular events [19]. Preprocedural hs-CRP elevation has been linked to a greater risk of adverse cardiac events in people undergoing PCI (20–22). The WBC counts, which can be easily and repeatedly obtained in clinical practice, are one of the most viable inflammatory biomarkers. Cannon et al. [ 4] have reported that WBC counts are linked to an increased rate of mortality after 30 days and 10 months in individuals with acute MI and unstable angina pectoris. The TACTICS-TIMI 18 [Treat Angina with Aggrastat and Determine Cost of Therapy with an Invasive or Conservative Strategy (TACTICS) Thrombolysis in Myocardial Infarction (TIMI)] sub-study demonstrated that an increased WBC counts predicted extensive CAD and increased mortality at 6 months in people with ACS [5]. More recently, Alkhalfan et al. [ 23] observed that elevated WBC counts were linked to increased major or minor hemorrhage and ischemic events (such as cardiovascular death, MI, and stroke) in patients with ACS.
The SS model is a well-established tool used to predict adverse clinical outcomes to help clinicians decide on optimum revascularization strategies in individuals with complex CAD [11, 24, 25]. The SS-II model incorporates the anatomical variables in the SS model with other clinical variables (age, sex, LVEF, CrCl, chronic obstructive pulmonary disease, and peripheral vascular disease), and can predict 4-year mortality with higher accuracy. The SS-II model is also a better guide than the SS model for decisions on PCI and CABG in complex CAD cases [12]. Subsequently, several studies have demonstrated the predictive value of the SS-II model for predicting outcomes in different cohorts such as three-vessel and/or unprotected left main coronary artery disease (ULMAD) following PCI [26, 27], ACS [28, 29], and cardiogenic shocks after primary PCI [30].
The SS-II model was created according to a Cox proportional hazards model utilizing the SYNTAX trial findings [12]. The baseline features that were strongly associated with 4-year mortality were added to the model. However, the ih-WBC counts, a marker of the inflammatory state, were not included in the SS-II model. A recent study observed that a combination of differential WBC (eosinophil, monocyte, and lymphocyte) counts enhanced the success of risk prediction and reclassification for mortality when combined with SS or SS II models in patients with triple-vessel CAD [7]. Patients with CRI have a hallmark feature of persistent, low-grade inflammation, which is involved in the development of ACM [31]. Inflammation plays an important role in the initiation and progression of kidney disease. Recent studies have reported that plasma proinflammatory biomarkers, such as soluble TNF receptors 1 and 2 (TNFR-1 and TNFR2) were associated with the increased risk of progression of diabetic kidney disease, even after adjustment for established clinical risk factors [32, 33]. However, not much is known about the predictive value of ih-WBC counts for predicting clinical outcomes in patients with CRI post-PCI. This study demonstrated that elevated ih-WBC counts were associated with increased incidence of ACM, CM, unplanned revascularization, and MACCEs in individuals with CRI following PCI. Furthermore, the addition of ih-WBC counts to the SS or SS II models also improved the predictive performance of these models in predicting ACM and CM events in individuals with CRI following PCI, although the degree of improvement in predictive performance tended to decrease with time.
## Limitations
Despite these promising results, our results should be viewed in the light of multiple limitations. First, this study is based on data from a single center and is retrospective and observational in nature; therefore, it can only identify associations and cannot ascribe causality to related events. Second, differences in blood collection periods from the occurrence of the index event were not controlled for in this analysis. Third, we did not collect information on differential WBC counts and hs-CRP levels, both of which may be important for clinical outcomes. Finally, in some patients, we believe that the ih-WBC counts data may have been affected by undetected infections or other conditions for which we have no information.
## Conclusion
In patients with CRI following PCI, an elevated ih-WBC counts was found to be associated with the risk of occurrence of ACM, CM, unplanned revascularization, and MACCEs. A combination of ih-WBC counts with SS or SS II models significantly improved these models’ performance in predicting ACM and CM.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by the Ethics Committee of Cangzhou Central Hospital, Hebei Medical University. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
WY, LY, and ML provided the conception of the idea for the study and analyzed the acquired data. LY, WY, ML, SZ, and YL contributed to the development of the methodology and wrote the manuscript. FL, JW, NY, and MC were responsible for the interpretation of statistical results. XC revised the manuscript. All authors contributed to the article and approved the submitted version.
## Funding
This work was supported by the Natural Science Foundation of Hebei Province, China (H2021110008) and Hebei Provence Key Research Projects [172777163].
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcvm.2023.1027107/full#supplementary-material
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|
---
title: Peripheral blood mononuclear cells reactivity in recent-onset type I diabetes
patients is directed against the leader peptide of preproinsulin, GAD65271-285 and
GAD65431-450
authors:
- Rita D. Jores
- Davide Baldera
- Enrico Schirru
- Sandro Muntoni
- Rossano Rossino
- Maria F. Manchinu
- Maria F. Marongiu
- Cristian A. Caria
- Carlo Ripoli
- Maria R. Ricciardi
- Francesco Cucca
- Mauro Congia
journal: Frontiers in Immunology
year: 2023
pmcid: PMC10034372
doi: 10.3389/fimmu.2023.1130019
license: CC BY 4.0
---
# Peripheral blood mononuclear cells reactivity in recent-onset type I diabetes patients is directed against the leader peptide of preproinsulin, GAD65271-285 and GAD65431-450
## Abstract
### Introduction
T cell reactivity against pancreatic autoantigens is considered one of the main contributors to the destruction of insulin-producing cells in type 1 diabetes (T1D). Over the years, peptide epitopes derived from these autoantigens have been described in NOD mice and in both HLA class II transgenic mice and humans. However, which ones are involved in the early onset or in the progressive phases of the disease is still unclear.
### Methods
In this work we have investigated, in early-onset T1D pediatric patients and HLA-matched controls from Sardinia, the potential of preproinsulin (PPI) and glutamate decarboxylase 65 (GAD65)-derived peptides to induce spontaneous T cell proliferation responses of peripheral blood mononuclear cells (PBMCs).
### Results
Significant T cell responses against PPI1-18, PPI7-19 and PPI31-49, the first two belonging to the leader sequence of PPI, and GAD65271-285 and GAD65431-450, were found in HLA-DR4, -DQ8 and -DR3, -DQ2 T1D children.
### Conclusions
These data show that cryptic epitopes from the leader sequence of the PPI and GAD65271-285 and GAD65431-450 peptides might be among the critical antigenic epitopes eliciting the primary autoreactive responses in the early phases of the disease. These results may have implications in the design of immunogenic PPI and GAD65 peptides for peptide-based immunotherapy.
## Introduction
Type 1 diabetes mellitus (T1D) is the result of a slow progressive multistep autoimmune destruction of pancreatic insulin-producing cells (1–3). Early studies in European and derived populations have shown that susceptibility to T1D is strongly associated with HLA-DR3, -DQ2 and HLA-DR4, -DQ8 haplotypes, while protection is associated with HLA-DR2, -DQ6 haplotypes [4, 5]. Subsequent studies have shown that within these haplotypes certain HLA-DQB1, and -DRB1 alleles and residues confer susceptibility, while others provide resistance to the disease (6–11). For instance, several studies have shown that differences of a few amino acid residues in HLA-DRB1*04 alleles are per se sufficient to modify the risk of developing T1D conferred by the high-risk HLA-DQB1*03:02 allele (12–15). Indeed, the spectrum of HLA-DQB1 and -DRB1 association, which constitutes the major component of T1D risk, is more complex than initially outlined and can be primarily grouped into very high risk, intermediate and very low risk haplotypes which, in turn, are the result of the structure of the peptide-binding pockets [15, 16].
In recent years, the intricacy and the molecular nature of immunogenic T cell epitopes of pancreatic autoantigens have been further elucidated [17]. It is generally believed that T cell responses against autoantigens may become less evident with the progressive destruction of the islets of Langerhans [18, 19]. Moreover, the spreading of T cell reactivity to other not primarily involved regions, of preproinsulin (PPI) or glutamate decarboxylase 65 (GAD65) autoantigens or to other pancreatic proteins/neoantigens generated from post-translational modifications may be an important confounding factor complicating the identification of pathogenically relevant T cells (19–22). Secondly, autoreactive immune responses are not completely disease-specific, since T cell reactivity against autoantigenic proteins is detected also in control subjects who carry disease-associated HLA-DR and -DQ molecules [18, 23, 24].
A useful tool to identify immunodominant T cell epitopes from pancreatic autoantigens is the use of humanized HLA transgenic mice [25]. Indeed, numerous PPI and GAD65 T cell epitopes have been identified in triple HLA, human CD4 (hCD4) and IA knock-out transgenic mice (25–29) and confirmed in T1D patients (30–32). The majority of these studies have been conducted using triple HLA-DRB1*04:01, hCD4, IA knock-out mice (25–29), in HLA-DQ8 transgenic mice [33], whilst literature regarding studies on patients carrying different HLA-DR4 subtypes, such as the HLA-DRB1*04:05-DQA1*03:01-DQB1*03:02 haplotype is lacking. Interestingly, this is the most frequent HLA-DR4 haplotype found in Sardinian T1D patients [13, 16, 34].
Thus, we tested in children with recent-onset T1D and HLA-matched healthy controls from *Sardinia a* set of PPI and GAD65 peptides derived from previous studies in both HLA-DR4 and -DQ8 transgenic mice and humans (26–28, 33, 35).
## Sample selection
Twelve HLA-DR4, -DQ8 positive and four HLA-DR4, -DQ8 negative T1D patients with recent onset disease have been studied. In all T1D patients the diagnosis was performed 3 days before enrollment. Fourteen HLA-DR4, -DQ8 positive and four HLA-DR4, -DQ8 negative healthy blood donors without history of autoimmune diseases served as controls. The 18 healthy individuals were HLA typed in connection with other studies [36]. All T1D patients with recent-onset disease were recruited from our clinic in the Microcitemico Hospital A. Cao, ASL8, Cagliari. The mean age was 7.4 ± 3.3 years, in line with the mean age of T1D onset in Sardinia [37]. The mean age of healthy controls was 43.6 ± 5.4. Table 1 shows the HLA-DR, -DQ typing of T1D patients and controls. An ethics committee approval was obtained for this study (authorization no. PG/$\frac{2016}{7815}$).
**Table 1**
| T1D patients | T1D patients.1 | T1D patients.2 | T1D patients.3 | T1D patients.4 | T1D patients.5 | T1D patients.6 | T1D patients.7 | Healthy controls | Healthy controls.1 | Healthy controls.2 | Healthy controls.3 | Healthy controls.4 | Healthy controls.5 | Healthy controls.6 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| ID | DRB1 | DQA1 | DQB1 | DRB1 | DQA1 | DQB1 | DQB1 | ID | DRB1 | DQA1 | DQB1 | DRB1 | DQA1 | DQB1 |
| 01 | *0405 | *0301 | *0302 | *0405 | *0301 | *0302 | *0302 | 01 | *0405 | *0301 | *0302 | *1601 | *0102 | *0502 |
| 02 | *0405 | *0301 | *0302 | | | | | 02 | *0405 | *0301 | *0302 | | | |
| 03 | *0405 | *0301 | *0302 | | | | | 03 | *0402 | *0301 | *0302 | | | |
| 04 | *0402 | *0301 | *0302 | *0301 | *0501 | *0201 | *0201 | 04 | *0405 | *0301 | *0302 | | | |
| 05 | *0405 | *0301 | *0302 | *0301 | *0501 | *0201 | *0201 | 05 | *0405 | *0301 | *0302 | *0301 | *0501 | *0201 |
| 06 | *0405 | *0301 | *0302 | *0405 | *0301 | *0302 | *0302 | 06 | *0402 | *0301 | *0302 | | | |
| 07 | *0405 | *0301 | *0302 | *0101 | *0101 | *0501 | *0501 | 07 | *0405 | *0301 | *0302 | | | |
| 08 | *0405 | *0301 | *0302 | | | | | 08 | *0403 | *0301 | *0302 | *0301 | *0501 | *0201 |
| 09 | *0405 | *0301 | *0302 | *0301 | *0501 | *0201 | *0201 | 09 | *0405 | *0301 | *0201 | | | |
| 10 | *0405 | *0301 | *0302 | *0405 | *0301 | *0302 | *0302 | 10 | *0405 | *0301 | *0201 | | | |
| 11 | *0405 | *0301 | *0302 | *0301 | *0501 | *0201 | *0201 | 11 | *0404 | *0301 | *0302 | *1101 | *0501 | *0301 |
| 12 | *0402 | *0301 | *0302 | *0301 | *0501 | *0201 | *0201 | 12 | *0405 | *0301 | *0302 | | | |
| 13 | *0301 | *0501 | *0201 | *1601 | *0102 | *0502 | *0502 | 13 | *0405 | *0301 | *0302 | | | |
| 14 | *0301 | *0501 | *0201 | *1601 | *0102 | *0502 | *0502 | 14 | *0405 | *0301 | *0302 | | | |
| 15 | *0301 | *0501 | *0201 | *1601 | *0102 | *0502 | *0502 | 15 | *0301 | *0501 | *0201 | *0301 | *0501 | *0201 |
| 16 | *0301 | *0501 | *0201 | *0301 | *0501 | *0201 | *0201 | 16 | *0301 | *0501 | *0201 | *1601 | *0102 | *0502 |
| | | | | | | | | 17 | *0301 | *0501 | *0201 | *0301 | *0501 | *0201 |
| | | | | | | | | 18 | *0301 | *0501 | *0201 | *0301 | *0501 | *0201 |
## HLA class II typing and haplotype analysis
HLA-DRB1, -DQA1 and -DQB1 genotypes were determined by polymerase chain reaction with sequence-specific primers (SSP-PCR) using Olerup SSP typing kits (Olerup SSP AB, Stockholm, Sweden). The HLA class II haplotypes were predicted on the basis of the known linkage disequilibrium in Sardinians [12, 13].
## Proliferation assays
Blood was drawn from recent-onset T1D patients within two to three days after diagnosis. Peripheral blood mononuclear cells (PBMCs) were isolated by density gradient. Blood was diluted 1:1 with complete medium (RPMI 1640, Life Technologies Italia, Monza, Italy) containing $2\%$ heat-inactivated fetal calf serum, 2 mM L-glutamine, 100 U/ml penicillin, 100 μg/ml of streptomycin, 50 μM 2-mercaptoethanol and HEPES. Diluted blood was then layered 1:1 on a Lymphoprep™ gradient (Stem Cell Technologies, Monza, Italy) and centrifuged at 400 g for 30 min at room temperature.
Cells were harvested and washed in complete medium containing 10-$15\%$ autologous human serum, and 2 x 105 cells/200 µl per well were incubated in the presence or absence of 20 µg/ml of PPI or GAD65 peptides. Proliferative responses against antigens were determined in 6 replicate cultures in round-bottomed 96 well plates by [3H]-thymidine incorporation after 6 days of culture, following a 6-hour pulse with 0.5 µCi [3H]-thymidine. T cell reactivity was attributed using a relative ratio (RR) of counts per minute (cpm) of radioactivity incorporated by PBMCs plus peptide, compared to PBMCs alone. A RR of 3 or higher was considered positive and used in the statistical calculation. T cell reactivity has been controlled by stimulation with concanavalin A (data not shown).
## PPI and GAD65 peptides
Eleven peptides from PPI and 16 from GAD65 (Table 2) were purchased from Life Technologies Italia (Monza, Italy). These peptide epitopes were derived from research in humans and from previous studies in HLA-DR4 or -DQ8 transgenic mice (21, 25–33, 35). The purity of all these peptides was verified by reverse-phase HPLC and mass spectroscopic analysis. Before use, peptides were suspended in sterile PBS at a concentration of 2 mg/ml and stored at -80° C.
**Table 2**
| Peptides | Sequences | References |
| --- | --- | --- |
| PPI1-18 | MALWMRLLPLLALLALWG | (27, 33) |
| PPI7-19 | LLPLLALLALWGP | (33) |
| PPI11-26 | LALLALWGPDPAAAFV | (27, 33) |
| PPI13-25 | LLALWGPDPAAAF | (27, 33) |
| PPI19-31 | PDPAAAFVNQHLC | (27) |
| PPI31-49 | CGSHLVEALYLVCGERGFF | (32) |
| PPI40-59 | YLVCGERGFFYTPKTRREAE | (32) |
| PPI52-67 | PKTRREAEDLQVGQVE | (35) |
| PPI58-70 | AEDLQVGQVELGG | (31, 35) |
| PPI73-90 | GAGSLQPLALEGSLQKRG | (27, 32) |
| PPI85-101 | SLQKRGIVEQCCTSICS | (27) |
| GAD6576-90 | DQKPCSCSKVDVNYA | (35) |
| GAD6581-95 | SCSKVDVNYAFLHAT | (30, 35) |
| GAD65101-115 | CDGERPTLAFLQDVM | (28, 35) |
| GAD65116-130 | MNILLQYVVKSFDRST | (26, 35) |
| GAD65206-220 | TYEIAPVFVLLEYVT | (28) |
| GAD65271-285 | PRLIAFTSEHSHFSL | (26, 35) |
| GAD65356-370 | KYKIWMHVDAAWGGG | (26, 35) |
| GAD65376–390 | KHKWKLSGVERANSV | (26, 35) |
| GAD65431-450 | KHYDLSYDTGDKALQ | (28) |
| GAD65481-495 | LYNIIKNREGYEMVF | (26, 35) |
| GAD65511-525 | PSLRTLEDNEERMSR | (26, 35) |
| GAD65526-540 | LSKVAPVIKARMMEY | (30) |
| GAD65536-550 | RMMEYGTTMVSYQPL | (28, 35) |
| GAD65546-560 | SYQPLGDKVNFFRMV | (26, 35) |
| GAD65551-565 | GDKVNFFRMVISNPA | (26, 35) |
| GAD65556-570 | FFRMVISNPAATHQD | (26, 35) |
## Statistical analysis
A Chi-square test was used to compare PPI and GAD65 proliferative responses between T1D and controls. In the case of samples size lower than 5, a two-tailed Fisher’s exact test was used. A p value lower than 0.05 was considered statistically significant.
## T cell reactivity against PPI peptides
PBMCs from patients and HLA-matched healthy individuals were cultured with or without peptides and their proliferations measured. The eleven peptides tested cover the PPI protein sequence almost completely. Data were analyzed as a whole and after grouping patients and control individuals according to HLA-DR4 haplotype. Tables S1, S2 summarize the proliferative responses and RR obtained in T1D patients and controls respectively.
We have found that $75\%$ of T1D patients and $33.3\%$ of controls did respond to PPI peptides ($$p \leq 0.0204$$). Response to more than one PPI peptide was found in $62.5\%$ of T1D patients and in $11.1\%$ of controls ($$p \leq 0.0033$$).
Next, we evaluated which epitopes accounted for this reactivity by comparing the frequency of responses against specific peptides of PPI in T1D patients and controls. Figure 1A illustrates the frequency of responses against PPI peptides, while Figure 1B provides an example of responses against PPI peptides in a T1D patient (n. 11, Table S1). Tables S1, S2 summarize the proliferative responses and the RR obtained in T1D patients and controls respectively. Significant differences between patients and controls were found for PPI1-18 ($$p \leq 0.0348$$; Figure 1A), PPI7-19 peptides ($$p \leq 0.0348$$; Figure 1A) and PPI31-49 ($$p \leq 0.0392$$; Figure 1A). The statistical significance for PPI peptides was lost after stratification for DR4 haplotypes.
**Figure 1:** *(A) Percentage of T1D patients (solid bars) and controls (hatched bars) responding to PPI peptides. Significant p values (<0.05) are indicated above the corresponding bars. Statistical analysis was performed using the Fisher’s exact test. (B) The figure shows cpm response with different PPI peptides in a T1D patient (n. 11,
Table S1
).*
In terms of global reactivity, our results indicate a higher response against peptides of the PPI protein and less reactivity against GAD65 peptides. In T1D patients, we observed 46 out of 176 responses for PPI peptides versus 40 out of 224 responses for GAD65 ($$p \leq 0.045$$).
## T cell reactivity against GAD65 peptides
Sufficient PBMCs for measurement of proliferative responses against GAD65 peptides were available from 14 T1D patients and 18 control individuals. Tables S3, S4 summarize the proliferative responses and the RR obtained in T1D patients and controls respectively.
Fifty-six percent of T1D patients and $27.7\%$ of controls did respond against GAD65 peptides (p: NS). The responses to more than one GAD65 peptide did not differ significantly between T1D patients ($37.5\%$) and controls ($16.6\%$). Only for GAD65271-285 and GAD65431-450 peptides a significant difference between patients and controls was observed ($$p \leq 0.0278$$; Figure 2). The statistical significance for these two GAD65 peptides was lost after stratification for DR4 haplotypes.
**Figure 2:** *Percentage of T1D patients (solid bars) and controls (hatched bars) responding to GAD65 peptides. Significant p values (<0.05) are indicated above the corresponding bars. Statistical analysis was performed using the Fisher’s exact test.*
## Discussion
Here, we report the capacity of PPI and GAD65 derived peptides obtained from humans and from HLA-DR4 and -DQ8 transgenic mice (21, 25–33, 35) to induce spontaneous T cell proliferation responses in PBMCs from recent-onset T1D pediatric patients.
In terms of global reactivity, our results indicate a higher response against peptides of the PPI protein and less reactivity against GAD65 peptides.
Analysis of proliferative responses to PPI showed that reactivity is mainly directed against PPI1-18, and PPI7-19 peptides of the leader sequence. These 2 peptides are included in the immunodominant peptide PPI1-24 described as HLA-DQ8-restricted in HLA-DQ8 transgenic mice [33]. However, we found responses against these 2 peptides in both HLA-DR4, -DQ8 and -DR3, -DQ2 positive T1D patients compared to HLA-matched controls (Tables S1, S2; Figure 1A).
Since we found significant responses for both peptides, we hypothesize that the sequence responsible for reactivity in T1D patients is included in the PPI7-19 shared sequence LLPLLALLALWG. Prevalent reactivity of T1D PBMCs against the leader sequence may indicate that precursors of insulin, normally confined to the ER of the β-cells, are involved in targeting T cell autoreactivity to the islets. Interestingly, the same region of PPI is recognized by CD8+ T cells from recent-onset T1D patients [38, 39], which may suggest that the leader sequence of PPI is targeted by both CD4+ and CD8+ autoreactive T cells. In addition to HLA-DQ8, the leader sequence of PPI (LALLALWGPDPAAAFV) may be presented also by HLA-DRB1*04:01 as previously shown in HLA-DRB1*04:01 transgenic mice [27]. Therefore, in analogy to other autoantigens such as GAD65 [26, 28], the leader sequence of PPI may show overlapping peptide sequences that are presented by both HLA-DR and -DQ molecules.
Also for PPI31–49 peptide, including the B chain peptide PPIB9-23, reported immunodominant in both humans and NOD mice (40–42), significant proliferative responses were observed (Table S1; Figure 1A).
Interestingly, CD8+ T cells in long-standing patients have been shown to recognize the B-chain peptide PPI33–42 [38], that is also targeted by both human and NOD mice CD4+ T cells [41, 42].
Among the 16 GAD65 peptides tested, only GAD65271-285 and GAD65431-450 peptides induced significant proliferative responses in T1D patients. These patients carried both HLA-DR4 and HLA-DR3 haplotypes (Tables S3, S4; Figure 2). These findings are in line with previous data showing that GAD65271-285 was found immunodominant in HLA-DRB1*04:01 transgenic mice and humans [26], whilst a peptide similar to GAD65431-450 (GAD65431-445) is recognized by HLA-DQ8 molecules in HLA-DQ8 transgenic mice [28]. Interestingly, GAD65431-450 has been also reported as an inducer of IL-13 in CD4+ T cell lines derived from HLA-DR3, -DQ2 homozygous individuals [43].
Noteworthy, the GAD65 region 245-450 that includes both GAD65271-285 and GAD65431-450 has been identified as the main target of the earlier anti-GAD65 response in pre-diabetic, healthy high-risk subjects and early onset T1D patients (44–46). Finally, in genetically at-risk T1D patients, autoantibodies against GAD65 are found mainly in HLA-DR3, -DQ2 and less commonly in HLA-DR4, -DQ8 patients [19, 47].
Our findings demonstrate the crucial importance of validating in T1D patients the prediction of immunogenic epitopes. Indeed, even epitopes that are immunodominant in HLA class II transgenic mice may be not immunogenic in humans. As an example, GAD65206-220 peptide is an immunogenic/immunodominant T cell epitope in HLA-DQ8 transgenic mice [28, 48] and in NOD mice after immunization with murine GAD65 [49] and, as such, could be considered a good pathogenic candidate for T1D in mice and humans. Instead, T cell receptor transgenic mice specific for GAD65206-220 have been demonstrated to be protected against T1D development [50]. In accordance, our data show that this peptide elicits proliferative responses not only in T1D patients but also in controls (Tables S3, S4; Figure 2), making a pathogenic role highly unlikely also in humans.
Our study has several limitations that we tried to mitigate. The large number of potential T cell epitopes of pancreatic autoantigenic proteins that need to be tested challenges the relatively small amount of blood that can be drawn from children. To circumvent this limitation, we restricted the number of epitopes by prior analysis of the T cell responses in transgenic mice carrying human HLA haplotypes. Another limitation is the T cell spreading in long standing T1D patients (19–21), which is why we focused on early onset T1D pediatric patients recruited within three to four days after diagnosis. Discrepancies in the interpretation of T cell responses from PBMCs, the usage of diverse experimental methodologies among laboratories and the variability in the frequencies of HLA-DR4 subtypes in different T1D populations, may lead to different results. In this work we tried to reduce these variables taking advantage of the genetically homogenous Sardinian population, less prone to both genetic and clinical confounds that are present in more cosmopolitan collections [13, 51]. Finally, a further possible limitation of this work was the significant age difference between T1D patients and controls. However, the 18 controls were healthy blood donors who were selected for a negative history of autoimmune diseases, thus making spontaneous not specific responses less likely than what could have been derived from age matched random pediatric controls attending the outpatient clinic for different disorders.
In summary, we identified for both PPI and GAD65 specific proliferative responses addressed to the PPI leader sequence and to GAD65271-285, GAD65431-450 in T1D patients. We suggest that these could be primary epitopes involved in the early phase of the clinical onset of T1D. Finally, these data hint towards the possibility that the leader sequence of PPI is targeted by both CD8+ and CD4+ T cells, with HLA class I and class II presenting slightly different overlapping peptide epitopes and thus inducing a stronger pathogenic immune response involved in the β-cell destruction.
These results may have implications in the design of immunogenic PPI and GAD65 peptides for peptide-based immunotherapy. Finally, this work could pave the way to test these epitopes in early stage T1D patients (stage 1 or 2) who have islet autoantibodies but no overt clinical diabetes requiring treatment with exogenous insulin.
## Data availability statement
The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by Comitato Etico Indipendente AOUCA. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.
## Author contributions
RJ and MC conceptualized, designed and led the research. DB, ES, SM contributed to data interpretation and drafting the manuscript. RR provided HLA typing. MMan, MMar and CC collected and processed PBMC. CR and MR provided T1D patients. FC critically revised the manuscript. MC is the guarantor of this study. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1130019/full#supplementary-material
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|
---
title: Artificial oocyte activation using Ca2+ ionophores following intracytoplasmic
sperm injection for low fertilization rate
authors:
- Kazuhiro Akashi
- Mitsutoshi Yamada
- Seung Chik Jwa
- Hiroki Utsuno
- Shintaro Kamijo
- Yasushi Hirota
- Mamoru Tanaka
- Yutaka Osuga
- Naoaki Kuji
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10034378
doi: 10.3389/fendo.2023.1131808
license: CC BY 4.0
---
# Artificial oocyte activation using Ca2+ ionophores following intracytoplasmic sperm injection for low fertilization rate
## Abstract
This large multi-center retrospective study examined whether artificial oocyte activation (AOA) using Ca2+ ionophore following ICSI improves the live birth rate for couples with previous ICSI cycles of unexplained low fertilization rate. In this large-scale multi-center retrospective study conducted in Japan, data were collected from Keio University and 17 collaborating institutions of the Japanese Institution for Standardizing Assisted Reproductive Technology. Between January 2015 and December 2019, 198 couples were included in this study. Oocytes for both the intervention and control groups were procured from the same pool of couples. Oocytes obtained from ICSI cycles with no or low fertilization rate (<$50\%$) with unknown causes were included in the control (conventional ICSI) group while oocytes procured from ICSI cycles followed by performing AOA were assigned to the intervention (ICSI-AOA) group. Those fertilized with surgically retrieved sperm were excluded. ICSI-AOA efficacy and safety were evaluated by comparing these two groups. Live birth rate was the primary outcome. The ICSI-AOA group (2,920 oocytes) showed a significantly higher live birth per embryo transfer rate ($18.0\%$ [$\frac{57}{316}$]) compared to that of the conventional ICSI group with no or low fertilization rate (1,973 oocytes; $4.7\%$ [$\frac{4}{85}$]) (odds ratio 4.5, $95\%$ confidence interval 1.6–12.6; $P \leq 0.05$). A higher live birth rate was observed in younger patients without a history of oocyte retrieval. Miscarriage, preterm delivery, and fetal congenital malformation rates were similar between the two groups. ICSI-AOA may reduce fertilization failure without increasing risks during the perinatal period. AOA may be offered to couples with an ICSI fertilization rate < $50\%$.
## Introduction
The introduction of intracytoplasmic sperm injection (ICSI) in 1992 revolutionized the treatment of male infertility, enabling couples facing infertility issues due to severely impaired sperm characteristics to have children [1]. Currently, the fertilization rate after ICSI reportedly exceeds $65\%$ [2], though total fertilization failure occurs in 1–$3\%$ of all ICSI cycles [3, 4].
Oocyte activation deficiency (OAD) is considered to be a major cause of fertilization failure, accounting for 40–$70\%$ of the causes of fertilization failure after ICSI (4–6). In normal fertilization, phospholipase C zeta is released into the oocyte when the sperm enters the oocyte, causing Ca2+ oscillation in the oocyte to activate it, which results in fertilization. Meanwhile, if Ca2+ oscillation does not occur due to either oocyte- or sperm-related factors, inadequate oocyte activation leads to fertilization failure. At least $68\%$ of OAD is caused by sperm-related factors [7]. In contrast, other studies using the mouse oocyte activation test (MOAT) have indicated that only about $16\%$ to $18\%$ of OAD is caused by sperm-related factors alone, suggesting that the majority of OAD is attributable to oocyte-related factors alone or in combination with sperm-related factors [8, 9].
To overcome fertilization failure after ICSI, artificial oocyte activation (AOA) has been developed. Although the efficacy and safety of AOA have not yet been established, AOA is widely performed in clinical practice with a variety of techniques, such as the use of Ca2+ ionophores (e.g., ionomycin and calcimycin [A23187]) and strontium chloride (SrCl2), mechanical stimulation, and electrical stimulation [10]. Among the AOA methods, oocyte activation with SrCl2 is considered to be the most invasive, with a high frequency of oocyte degeneration (11–14). In contrast, Ca2+ ionophores are expected to cause minimal damage to oocytes [11]. In a survey conducted by the Ministry of Health, Labour and Welfare in Japan, $30.8\%$ of facilities report the use of Ca2+ ionophores for AOA [15]. AOA protocols throughout facilities are diverse with respect to the ionophore concentration and exposure duration, the timing of ionophore exposure following ICSI, and the number of exposure settings [16].
Therefore, to establish the efficacy and safety of AOA using Ca2+ ionophores, it is necessary to examine the reagents used and the protocol in detail. To answer these questions, we compared AOA using Ca2+ ionophores following ICSI, with ICSI alone by conducting a multicenter retrospective cohort study of couples with a fertilization rate of ≤ $50\%$ after ICSI, identifying live birth rate as the primary outcome.
## Study design
This retrospective multicentre study was conducted by Keio University with support from 17 collaborating institutions affiliated with the Japanese Institution for Standardizing Assisted Reproductive Technology. We collected the clinical data of all couples who underwent AOA between January 2015 and December 2019 at the participating facilities.
## Participants
Oocytes of couples whose fertilization rate with conventional ICSI was < $50\%$ at the last oocyte retrieval and those who subsequently underwent oocyte aspiration with AOA using Ca2+ ionophores were considered eligible for the analysis. Thus, oocytes for both the intervention and control groups were retrieved from different cycles of the same pool of couples. Oocytes obtained from conventional ICSI cycles were included in the control (conventional ICSI) group while oocytes procured from ICSI cycles followed by performing AOA were assigned to the intervention (ICSI-AOA) group. As of now, a consensus about the definition of fertilization failure has not yet been established. Although the *Vienna consensus* defined an $80\%$ fertilization rate after ICSI as the benchmark value [2], we set a cut-off value of < $50\%$ fertilization rate after referring to previous papers [17, 18]. The exclusion criteria were as follows: oocyte retrieval cycles with less than two oocytes retrieved; fertilization rate > $50\%$; a case of microsurgical epididymal sperm aspiration or testicular sperm extraction; women aged over 42 years at the time of initial visit; other AOA methods such as electrical stimulation or SrCl2; and cases of two-step embryo transfer (ET).
## Setting and definition of primary and secondary outcomes
The primary outcome in this study was live birth rate (number of live births/ET cycles). Secondary outcomes were defined as follows: number of retrieved oocytes; number of retrieved matured oocytes (number of metaphase II [MII] oocytes at the time of stripping); fertilization rate (number of embryos with two pronuclei and two polar bodies within 24 h after ICSI/number of MII oocytes that were used for ICSI); embryo cleavage rate (number of eight-cell stage embryos on day three/number of normally fertilized oocytes); developmental rate of blastocyst embryo (number of blastocysts on day five/number of normally fertilized oocytes); developmental rate of good blastocyst embryos (number of good blastocysts on day five/number of normally fertilized oocytes); degeneration rate (number of oocytes damaged or degenerated after ICSI/number of oocytes that were used for ICSI); ET cancel rate (number of cycles that did not reach fresh ET or embryo freezing/number of cycles in which at least two oocytes were obtained); biochemical pregnancy rate (number of ET cycles with positive pregnancy response/number of ET cycles); clinical pregnancy rate (ET cycle in which the gestational sac is found in the uterus by ultrasound computed tomography/number of ET cycles); miscarriage rate (number of miscarriages/number of clinical pregnancy); preterm birth rate (number of preterm births/number of clinical pregnancies); and congenital malformation rate (number of congenital malformations/number of clinical pregnancies). The embryo cleavage rate was determined 68 ± 1 h after ICSI; the number of blastocysts on day five was determined 116 ± 2 h after ICSI; and the number of good blastocysts was determined as Stage 3 or higher according to Gardner’s classification [19], not including grade C inner cell mass or trophectoderm.
## Ovarian stimulation
An ovarian stimulation protocol was selected and performed according to each institution’s criteria. The stimulation protocols were classified into the following seven categories: natural cycle, clomiphene citrate (CC), human menopausal gonadotropin/recombinant follicle-stimulating hormone with CC, gonadotropin-releasing hormone (GnRH) agonist, GnRH antagonist, progestin-primed ovarian stimulation, and other protocols. Both fresh and freeze-thaw ET cycles were included.
## AOA procedure
AOA was performed according to each institution’s protocol. All retrieved oocytes were incubated with a solution containing Ca2+ ionophores (A23187 or ionomycin) within 30 min after ICSI. The following information about the AOA protocol was obtained from each institution: type of reagent used, timing of AOA administration after ICSI, reagent concentration, and exposure time.
## Statistical analysis
The primary and secondary outcomes were compared between the ICSI-AOA and conventional ICSI groups to examine the embryological and clinical outcomes of AOA. In a subgroup analysis, both groups were compared according to further classifications according to the women’s age, history of oocyte retrieval, and fertilization rate of the previous ICSI. The chi-square test, Fisher’s exact test, and Student’s t-test were used for comparison among variables. Odds ratios (ORs) were obtained from a generalized estimating equation model with binomial response and a log link, assuming independent working correlation. The results were adjusted for the other covariates (female age, BMI, history of live births, history of smoking, indication of ART) by using multiple logistic regression analysis. Adjusted odds ratios were also calculated by multiple logistic regression analysis. Statistical analyses were performed using the IBM SPSS Statistics software, version 28 (IBM, Armonk, NY, USA), and GraphPad Prism 9 (GraphPad Software, Inc., La Jolla, CA, USA), as appropriate. P-values < 0.05 were considered to be statistically significant.
## Ethical approval
This study was approved by the institutional research ethics board of Keio University School of Medicine (approval number: 20211097). The collaborating institutions received approval to participate in the study from their own institutional ethics committees. The need for informed consent was waived by the institutional research ethics board owing to the retrospective nature of the study. Opt-out options were provided for the participants through the website of the Department of Obstetrics and Gynaecology, Keio University School of Medicine, and each collaborating institution.
## Role of the funding source
The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The funders-approved committee members discussed the interpretation of the results with the authors.
## Participant characteristics
The data of 15,212 oocytes (826 couples and 2,903 oocyte retrieval cycles) were collected from 18 institutions. A total of 4,893 oocytes (198 couples and 649 oocyte retrieval cycles) were finally included in the analysis (Figure 1). All embryos were used for frozen-thawed ETs; no fresh ETs were performed. Although no restrictions were placed on the method of freezing, all embryos were frozen by vitrification. The average age (± standard deviation) of women at the initial visit was 35.32 ± 0.27 years, and the average infertility duration was 38.28 ± 3 months (Supplementary Table 1). Female age at the time of oocyte retrieval was significantly higher in the ICSI-AOA group (37.74 ± 0.23 years vs. 36.72 ± 0.24 years, $P \leq 0.001$), and the number of mature oocytes was significantly lower in the ICSI-AOA group (4.82 ± 0.26 vs. 5.70 ± 0.27, $P \leq 0.05$) (Table 1).
**Figure 1:** *Study flow diagram.* TABLE_PLACEHOLDER:Table 1
## Embryological and clinical outcomes including live birth after AOA
The fertilization rate was significantly higher in the ICSI-AOA group ($53.7\%$ [$\frac{1016}{1893}$]) than in the conventional ICSI group ($20.8\%$ [$\frac{285}{1372}$]) (OR 4.4, $95\%$ confidence interval [CI] 3.8–5.2; $P \leq 0.001$). The ICSI-AOA group had significantly higher developmental rates of good cleavage stage embryos, blastocysts, and good blastocysts per fertilized embryos ($36.9\%$ [$\frac{375}{1016}$], $30.1\%$ [$\frac{306}{1016}$], and $12.3\%$ [$\frac{125}{1016}$], respectively) compared with the conventional ICSI group ($22.1\%$ [$\frac{63}{285}$], $17.2\%$ [$\frac{49}{285}$], and $6.3\%$ [$\frac{18}{285}$], respectively) (OR 2.1, $95\%$ CI 1.5–2.8, $P \leq 0.001$; OR 2.1, $95\%$ CI 1.5–2.9, $P \leq 0.001$; and OR 2.1, $95\%$ CI 1.2–3.5, $P \leq 0.01$, respectively).
The biochemical and clinical pregnancy rates per ET cycle were significantly higher in the ICSI-AOA group ($35.4\%$ [$\frac{112}{316}$] and $28.2\%$ [$\frac{89}{316}$], respectively) compared with those in the conventional ICSI group ($11.8\%$ [$\frac{10}{85}$] and $11.8\%$ [$\frac{10}{85}$], respectively) (OR 4.1, $95\%$ CI 2.0–8.3, $P \leq 0.001$ and OR 2.9, $95\%$ CI 1.5–5.9, $P \leq 0.01$, respectively). Overall, ICSI-AOA treatment significantly increased the live birth rate ($18.0\%$ [$\frac{57}{316}$]) compared with ICSI alone ($4.7\%$ [$\frac{4}{85}$]) (OR 4.5, $95\%$ CI 1.6–12.6; $P \leq 0.01$). ICSI-AOA treatment also significantly decreased the ET cancellation rate ($25.6\%$ [$\frac{103}{402}$]) compared with ICSI alone ($57.5\%$ [$\frac{142}{247}$]) (OR 0.3, $95\%$ CI 0.2–0.4; $P \leq 0.001$). The rates of oocyte degeneration, miscarriage, preterm delivery, and fatal congenital anomalies were similar between the two groups. After adjusted by multiple regression analysis, similar significant differences were confirmed for these outcomes.
The ovarian stimulation protocol during the oocyte retrieval cycle varied between couples, as well as the cycles; however, there were no significant differences in clinical pregnancy or live birth rates among the ovarian stimulation protocols (Supplementary Table 2).
Nine protocols of AOA were used at each institution in terms of the type of reagents, timing of AOA after ICSI, concentration of the reagent, and drug exposure time. No significant differences in clinical pregnancy and live birth rates were observed among the protocols Either A23187 or ionomycin was used in each institution. They are known to differ in length and strength with respect to Ca2+ release, which can lead to heterogeneity between protocols [20]. The total 316 transfer cycles following ICSI-AOA were classified into two groups: the A23187 group and Ionomycin group. The clinical pregnancy and live birth rates were compared, with no significant differences observed between the two groups (Supplementary Table 3).
## Populations that benefit from ICSI-AOA
It is known that in vitro fertilization results are better in younger age groups [21]. Accordingly, we evaluated the effectiveness of ICSI-AOA treatment by dividing the couples into two groups according to their age at the first visit: a younger (≤ 35 years) and an older group (≥ 36 years) (Table 2). In the younger group, the ICSI-AOA subgroup had significantly higher clinical pregnancy ($32.1\%$ [$\frac{52}{162}$]) and live birth ($19.1\%$ [$\frac{31}{162}$]) rates compared with those in the conventional ICSI subgroup ($5.9\%$ [$\frac{3}{51}$] and $0.0\%$ [$\frac{0}{51}$], respectively) (OR 7.6, $95\%$ CI 2.3–25.4, $P \leq 0.001$; OR not applicable [NA], $95\%$ CI NA, $P \leq 0.001$), while no differences were observed in the older subgroup.
**Table 2**
| Unnamed: 0 | Female age at the oocyte retrieval: ≤ 35 years | Female age at the oocyte retrieval: ≤ 35 years.1 | Female age at the oocyte retrieval: ≤ 35 years.2 | Female age at the oocyte retrieval: ≤ 35 years.3 | Female age at the oocyte retrieval: 36–41 years | Female age at the oocyte retrieval: 36–41 years.1 | Female age at the oocyte retrieval: 36–41 years.2 | Female age at the oocyte retrieval: 36–41 years.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Conventional ICSI | ICSI-AOA | P-value | Odds ratio (95% CI) | Conventional ICSI | ICSI-AOA | P-value | Odds ratio (95% CI) |
| ICSI cycles | 115 | 195 | | | 132 | 207 | | |
| Female age at the oocyte retrieval (years) | 34.1 ± 3.8 | 35.2 ± 3.6 | <0.05 | | 39.0 ± 2.0 | 40.1 ± 1.6 | <0.001 | |
| FSH/hMG dose (IU) | 2294.5 ± 1164.8 | 2246.8 ± 1266.8 | NS | | 2833.3 ± 3171.6 | 2229.8 ± 1547.1 | NS | |
| Oocytes retrieved | 9.1 ± 5.9 | 8.3 ± 6.2 | NS | | 7.0 ± 5.4 | 6.3 ± 5.2 | NS | |
| Matured oocytes | 6.4 ± 4.4 | 5.4 ± 4.5 | <0.05 | | 5.1 ± 4.1 | 4.3 ± 3.7 | NS | |
| Fertilization rate (%)* | 23.5 (170/722) | 53.0 (549/1036) | <0.001 | 3.7 (3.0–4.5) | 17.7 (115/650) | 54.5 (467/857) | <0.001 | 5.6 (4.3–7.1) |
| Developmental rate of good cleavage stage embryo (%)** | 21.8 (37/170) | 34.6 (190/549) | <0.01 | 1.9 (1.3–2.9) | 22.6 (26/115) | 39.6 (185/467) | <0.001 | 2.2 (1.4–3.6) |
| Developmental rate of blastocyst embryo (%)** | 15.9 (27/170) | 27.5 (151/549) | <0.01 | 2.0 (1.3–3.2) | 19.1 (22/115) | 33.2 (155/467) | <0.01 | 2.1 (1.3–3.5) |
| Developmental rate of good blastocyst embryo (%)** | 7.1 (12/170) | 12.9 (71/549) | <0.05 | 2.0 (1.0–3.7) | 5.2 (6/115) | 11.6 (54/467) | <0.05 | 2.4 (1.0–5.7) |
| Degeneration rate (%)* | 11.8 (85/722) | 12.4 (128/1036) | NS | 1.1 (0.8–1.4) | 15.1 (98/650) | 10.9 (93/857) | <0.05 | 0.7 (0.5–0.9) |
| ET cycles | 51 | 162 | | | 34 | 154 | | |
| ET cancellation rate (%)*** | 47.0 (54/115) | 25.1 (49/195) | <0.001 | 0.4 (0.2–0.6) | 66.7 (88/132) | 26.1 (54/207) | <0.001 | 0.2 (0.1–0.3) |
| Biochemical pregnancy rate (%)**** | 5.9 (3/51) | 35.8 (58/162) | <0.001 | 8.9 (2.7–29.9) | 20.6 (7/34) | 35.1 (54/154) | NS | 2.1 (0.8–5.1) |
| Clinical pregnancy rate (%)**** | 5.9 (3/51) | 32.1 (52/162) | <0.001 | 7.6 (2.3–25.4) | 20.6 (7/34) | 24.0 (37/154) | NS | 1.2 (0.5–3.0) |
| Live birth rate (%)**** | 0.0 (0/51) | 19.1 (31/162) | <0.001 | | 11.8 (4/34) | 16.9 (26/154) | NS | 1.5 (0.5–4.7) |
| Miscarriage rate (%)***** | 100.0 (3/3) | 30.8 (16/52) | | | 47.9 (3/7) | 27.0 (10/37) | | |
| Preterm birth rate (%)***** | 0.0 (0/3) | 5.8 (3/52) | | | 0.0 (0/7) | 8.1 (3/37) | | |
| Congenital malformation rate (%)***** | 0.0 (0/3) | 0.0 (0/52) | | | 0.0 (0/7) | 2.7 (1/37) | | |
When oocytes were divided based on the couples’ history of oocyte retrieval (Table 3), ICSI-AOA treatment increased the live birth rate from $6.1\%$ ($\frac{4}{66}$) to $18.4\%$ ($\frac{49}{266}$) in oocytes from couples without a history of oocyte retrieval (OR 3.5, $95\%$ CI 1.2–10.0; $P \leq 0.05$) and from $0.0\%$ ($\frac{0}{19}$) to $16.0\%$ ($\frac{8}{50}$) (not significant [NS]) in those from couples with a history of at least one oocyte retrieval.
**Table 3**
| Unnamed: 0 | History of oocyte retrievals: 0 | History of oocyte retrievals: 0.1 | History of oocyte retrievals: 0.2 | History of oocyte retrievals: 0.3 | History of oocyte retrievals: ≥1 | History of oocyte retrievals: ≥1.1 | History of oocyte retrievals: ≥1.2 | History of oocyte retrievals: ≥1.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Conventional ICSI | ICSI-AOA | P-value | Odds ratio (95% CI) | Conventional ICSI | ICSI-AOA | P-value | Odds ratio (95% CI) |
| ICSI cycles | 173 | 285 | | | 74 | 117 | | |
| Female age at the oocyte retrieval (years) | 36.2 ± 4.1 | 37.7 ± 3.9 | <0.001 | | 38.0 ± 2.7 | 38.0 ± 3.1 | NS | |
| FSH/hMG dose (IU) | 2575.0 ± 2842.1 | 2196.3 ± 1465.0 | NS | | 2590.5 ± 1211.1 | 2334.6 ± 1302.9 | NS | |
| Oocytes retrieved | 8.6 ± 6.1 | 7.5 ± 5.9 | <0.05 | | 6.6 ± 4.4 | 6.8 ± 5.4 | NS | |
| Matured oocytes | 6.0 ± 4.6 | 5.0 ± 4.1 | <0.05 | | 5.0 ± 3.3 | 4.5 ± 4.2 | NS | |
| Fertilization rate (%)* | 21.1 (215/1019) | 54.5 (742/1362) | <0.001 | 4.5 (3.7–5.4) | 19.8 (70/353) | 51.6 (274/531) | <0.001 | 4.3 (3.2–5.9) |
| Developmental rate of good cleavage stage embryo (%)** | 22.8 (49/215) | 39.2 (291/742) | <0.001 | 2.2 (1.5–3.1) | 20.0 (14/70) | 30.7 (84/274) | NS | 1.8 (0.9–3.4) |
| Developmental rate of blastocyst embryo (%)** | 18.1 (39/215) | 31.8 (236/742) | <0.001 | 2.1 (1.4–3.1) | 14.3 (10/70) | 25.5 (70/274) | <0.05 | 2.1 (1.0–4.2) |
| Developmental rate of good blastocyst embryo (%)** | 5.6 (12/215) | 13.7 (102/742) | <0.01 | 2.7 (1.5–5.0) | 8.6 (6/70) | 8.4 (23/274) | NS | 1.0 (0.4–2.5) |
| Degeneration rate (%)* | 12.4 (126/1019) | 10.8 (147/1362) | NS | 0.9 (0.7–1.1) | 16.1 (57/353) | 13.9 (74/531) | NS | 0.8 (0.6–1.2) |
| ET cycles | 66 | 266 | | | 19 | 50 | | |
| ET cancellation rate (%)*** | 54.3 (94/173) | 23.5 (67/285) | <0.001 | 0.3 (0.2–0.4) | 64.9 (48/74) | 30.8 (36/117) | <0.001 | 0.2 (0.1–0.4) |
| Biochemical pregnancy rate (%)**** | 13.6 (9/66) | 33.1 (88/266) | <0.01 | 3.1 (1.5–6.6) | 5.3 (1/19) | 48.0 (24/50) | <0.001 | 16.6 (2.1–134.2) |
| Clinical pregnancy rate (%)**** | 13.6 (9/66) | 26.3 (70/266) | <0.05 | 2.7 (1.1–4.8) | 5.3 (1/19) | 38.0 (19/50) | <0.01 | 11.0 (1.4–89.5) |
| Live birth rate (%)**** | 6.1 (4/66) | 18.4 (49/266) | <0.05 | 3.5 (1.2–10.0) | 0.0 (0/19) | 16.0 (8/50) | NS | |
| Miscarriage rate (%)***** | 55.6 (5/9) | 22.9 (16/70) | | | 100.0 (1/1) | 52.6 (10/19) | | |
| Preterm birth rate (%)***** | 0.0 (0/9) | 7.1 (5/70) | | | 0.0 (0/1) | 5.3 (1/19) | | |
| Congenital malformation rate (%)***** | 0.0 (0/9) | 0.0 (0/70) | | | 0.0 (0/1) | 5.3 (1/19) | | |
The number of retrieved oocytes per IVF cycle can affect IVF results [21]. Accordingly, we compared the developmental outcomes of the three groups (10 or less, 11-15, 16 or more) according to the number of oocytes retrieved per cycle. As a result, we found that AOA increased the fertilization rate and preimplantation embryo developmental rate regardless of the number of retrieved oocytes per IVF cycle (Supplementary Table 4).
To clarify the relationship between fertilization rates of conventional ICSI and improvement with AOA, the oocytes were further divided into three groups based on the fertilization rate in previous conventional ICSI cycles (Figure 2): $0\%$, >$0\%$–$30\%$, and >$30\%$–$50\%$ groups. ICSI-AOA treatment improved the live birth rate from $0.0\%$ ($\frac{0}{3}$) to $14.4\%$ ($\frac{19}{132}$) in the $0\%$ group (NS), $6.5\%$ ($\frac{2}{31}$) to $21.2\%$ ($\frac{24}{113}$) in the >$0\%$–$30\%$ group (NS), and $4.4\%$ ($\frac{2}{45}$) to $19.7\%$ ($\frac{14}{71}$) in the >$30\%$–$50\%$ group (OR 5.3, $95\%$ CI 1.1–24.5; $P \leq 0.05$). Because fertilized oocytes were not obtained in the $0\%$ fertilization rate group, p-values could not be calculated for the comparison of developmental outcomes with ICSI-AOA group.
**Figure 2:** *Embryological and clinical outcomes of intracytoplasmic sperm injection (ICSI) and subsequent artificial oocyte activation (AOA) treatment. The patients were distributed into different groups based on their fertilization rates from an initial ICSI cycle. The different categories were: (A) all couples (n=198), (B) total failed fertilization (0%, n=84), (C) low fertilization (>0% to <30%, n=64), and (D) moderate fertilization (30% to <50%, n=50). In the total failed fertilization group, p-values could not be calculated for the comparison of developmental outcomes with the ICSI-AOA group because fertilized eggs were not obtained in the 0% fertilization rate group. CSE, cleavage stage embryo; BL, blastocyst stage embryo. *p<0.05, **p<0.01, ***p<0.001.*
## Discussion
To the best of our knowledge, this multi-center study is the first to use such a large number of oocytes to evaluate the efficacy and safety of ICSI followed by AOA using Ca2+ ionophores to increase the low fertilization rates of individual ICSI. Our findings indicate that AOA combined with ICSI improved live birth rate more significantly than ICSI alone. AOA improved all outcomes from fertilization rate to live birth rate, with no known significant adverse perinatal effects. These results are consistent with previous reports [17, 18]. Our findings are derived only from couples who experienced fertilization failure, thus excluding male factor infertility that requires surgical treatment. This setting reduced the heterogeneity which was observed in previous studies [22].
Considering that the AOA procedure can be invasive to the oocytes, its indications need to be strictly defined. Factors that affect pregnancy outcome include maternal aging, number of retrieved oocytes, and fertilization rate [21]. The search for populations for whom AOA is effective suggested that AOA is particularly likely to be effective in individuals aged ≤ 35 years. In contrast, improvements were reduced in individuals aged ≥ 36 years, suggesting that both sperm and oocyte factors are involved in oocyte activation and that activation factors may decrease during the quality decline that occurs as part of normal oocyte aging [23]. In mice in their reproductive age, oocytes required two times more AOA to be activated for parthenogenesis [11]; therefore, stronger stimulation with AOA may be effective for older human oocytes.
The efficacy of AOA can be recognized in all groups with fertilization rates <$50\%$ after conventional ICSI. In the subgroup analysis divided by fertilization rate of conventional ICSI, there was a trend toward equal embryological and clinical efficacy of ICSI-AOA with fertilization rates ranging from 0-$50\%$.
Previous studies on the efficacy and safety of AOA have focused on male factors [22, 24, 25]. In the present study, AOA improved the fertilization and live birth rates, as well as the developmental rate of a good embryo. These results suggest that the mechanism of AOA may be involved in the process of embryonic development and implantation after fertilization (26–28). The signal transduction pathway related to Ca2+ stimulation remains largely unexplored [29], requiring further molecular investigation. One possible explanation for these improvements is that AOA may support preimplantation development by induction of zygotic genome activation (ZGA) after fertilization. In fact, somatic cell nuclear transfer studies have shown that inducing ZGA with histone deacetylase inhibitors significantly improves blastocyst development rates, whereas the eight-cell arrest occurs when ZGA does not take place [30, 31]. Our results might contribute to the elucidation of the mechanism of the Ca2+ signal transduction pathway and of the subsequent embryonic development.
In the current study, regarding the safety of AOA, no congenital abnormalities were observed (61 infants in total), which is consistent with a previous report [32]. In contrast, there are concerns that prolonged exposure to A23187 can cause cytoplasmic disruption [33], and that non-physiological increases in Ca2+ levels might unexpectedly activate proteins in signal transduction pathways, leading to abnormal gene expressions in long-term prognosis [28, 34]. While Capalbo et al. [ 35] indicated that the use of 10-fold higher concentrations of A23187 does not increase the ratio of chromosome segregation aberrations, the safety of the AOA protocol is still controversial, as lack of proper timing or insufficient duration of AOA may result in premature activation of the oocyte and chromosome segregation aberrations [36]. Moreover, it is important to recognize that AOA is an invasive technique involving intervention in fertilized oocytes; as such, it is necessary to establish safe and effective AOA protocols.
Previous reports have made little mention of comparisons between AOA protocols. Our current study showed no significant differences pertaining to pregnancy outcomes among the different AOA protocols. Currently, there is no standard AOA protocol, resulting in a variety of institutions using their own protocols, even in recent studies (17, 37–39). Excessive Ca2+ ionophore exposure can be toxic to fertilized oocytes [33]; thus, exposure time and Ca2+ ionophore concentration should be minimized as much as possible. Further investigation is required to determine appropriate standard AOA protocols.
Some strengths of our present study include the large sample size (the largest at present) and homogeneity in protocols by limiting AOA to the use of Ca2+ ionophores. In addition, the oocytes compared in the control and intervention groups were retrieved from the same couples, and a series of treatments from oocyte retrieval to ET was performed at the same institutions, making it possible to compare isogenic populations.
This study has some limitations. First, this was a retrospective cohort study, and a prospective randomized controlled trial is needed to confirm its efficacy. Second, as this study focused on fertilization failure in couples, we did not specify whether the factors of fertilization failure were in the sperm or oocyte. Thus, determining for which factors AOA was most effective in couples experiencing fertilization failure was not possible. A previous study in mice, using the MOAT procedure, reported that ICSI-AOA treatment improved the live birth rate for fertilization failure caused by sperm-related factors greater than that for failure caused by oocyte-related factors [8]. Technology that can detect the cause of fertilization failure may allow for more personalized treatment [40]. Meanwhile, it has been suggested that AOA may be effective for both sperm and oocyte factor fertilization failure [8]. It is necessary to explore a wider range of couples who may benefit from AOA, not limited to sperm-related factors. Thirdly, some of the outcomes in the subgroup analysis were limited by the small sample size (ex. miscarriage rate, preterm birth rate or congenital malformation rate in the conventional ICSI group). When divided based on the fertilization rate in previous conventional ICSI cycles, p-values could not be calculated for the comparison of developmental outcomes with ICSI-AOA group because fertilized eggs were not obtained in the $0\%$ fertilization rate group. Even with the selection of an appropriate statistical analysis, a relatively small sample size may result in a lack of statistical power. Finally, as the information was obtained from multiple institutions, there were variations in ovarian stimulation, AOA protocols, and the stage and grade of transferred embryos among the institutions. Although a previous study reported that live birth rate is not affected by gonadotropin dose or duration of ovarian stimulation, and that there is no significant difference in clinical pregnancy rate or live birth rate between the different AOA protocols [41], the possibility of some bias cannot be ruled out.
In clinical practice, AOA may be the final option for couples experiencing fertilization failure. Despite the results of many outstanding studies, the efficacy and safety of AOA have not yet been established. We showed that ICSI-AOA using Ca2+ ionophores significantly improved the live birth rate of couples whose previous fertilization rate in conventional ICSI was ≤ $50\%$. Women who are younger and have no history of oocyte retrieval were especially more likely to benefit from AOA. Those who receive AOA treatment might expect similar improvements regardless of the fertilization rate with former conventional ICSI. This study may be an important decision-making tool when considering the implications of AOA. The results of our study could lead to increasing clinical applications of AOA using Ca2+ ionophores following ICSI, with a subsequent increase in live births. The efficacy and safety of AOA should be evaluated using a unified AOA protocol, and the population for whom AOA is most effective requires further investigation.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by the institutional research ethics board of Keio University School of Medicine (approval number: 20211097). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
KA and MY designed the study and performed data analysis and interpretation with HU, SK, and NK. KA and MY wrote the manuscript with input from all authors. KA and SJ performed the statistical analysis. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1131808/full#supplementary-material
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|
---
title: Pericytes modulate islet immune cells and insulin secretion through Interleukin-33
production in mice
authors:
- Guzel Burganova
- Anat Schonblum
- Lina Sakhneny
- Alona Epshtein
- Tomer Wald
- Mika Tzaig
- Limor Landsman
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10034381
doi: 10.3389/fendo.2023.1142988
license: CC BY 4.0
---
# Pericytes modulate islet immune cells and insulin secretion through Interleukin-33 production in mice
## Abstract
### Introduction
Immune cells were recently shown to support β-cells and insulin secretion. However, little is known about how islet immune cells are regulated to maintain glucose homeostasis. Administration of various cytokines, including Interleukin-33 (IL-33), was shown to influence β-cell function. However, the role of endogenous, locally produced IL-33 in pancreatic function remains unknown. Here, we show that IL-33, produced by pancreatic pericytes, is required for glucose homeostasis.
### Methods
To characterize pancreatic IL-33 production, we employed gene expression, flow cytometry, and immunofluorescence analyses. To define the role of this cytokine, we employed transgenic mouse systems to delete the *Il33* gene selectively in pancreatic pericytes, in combination with the administration of recombinant IL-33. Glucose response was measured in vivo and in vitro, and morphometric and molecular analyses were used to measure β-cell mass and gene expression. Immune cells were analyzed by flow cytometry.
### Resuts
Our results show that pericytes are the primary source of IL-33 in the pancreas. Mice lacking pericytic IL-33 were glucose intolerant due to impaired insulin secretion. Selective loss of pericytic IL-33 was further associated with reduced T and dendritic cell numbers in the islets and lower retinoic acid production by islet macrophages.
### Discussion
Our study demonstrates the importance of local, pericytic IL-33 production for glucose regulation. Additionally, it proposes that pericytes regulate islet immune cells to support β-cell function in an IL-33-dependent manner. Our study reveals an intricate cellular network within the islet niche.
## Introduction
Insulin secretion is a complex and tightly regulated process that is dependent on both systemic and local signals. β-Cells respond to a variety of inputs from the islet microenvironment. The islet microenvironment comprises various cell populations, such as neuronal, immune, and vascular cells, which play a crucial role in guiding the development, replication, maturation, and function of β-cells (1–4). The activity of these cell populations needs to coordinate effectively to promote glucose regulation through insulin secretion. However, the molecular and cellular interactions between the diverse cell types within the islet microenvironment and how they orchestrate to ensure insulin secretion are still not fully understood.
While islet inflammation can contribute to diabetes, it can also play a beneficial role in glucose homeostasis by supporting β-cell mass and function (1, 5–15). Islets of healthy humans and mice contain immune cells, primarily macrophages, but also T, B, dendritic (DCs), and type 2 innate lymphoid (ILC2s) cells [7, 16]. These cells form a tightly-regulated network that supports glucose regulation. Macrophages and DCs were shown to support β-cell function and mass [1, 8, 13, 17, 18], but the role of islet lymphocytes in glucose regulation is currently unclear [16, 19]. Additionally, how islet inflammation is regulated to support glucose homeostasis is largely unknown.
IL-33 is a member of the interleukin-1 (IL-1) family of cytokines primarily localized in the nucleus [20, 21]. Studies have shown that the localization of IL-33 in the nucleus does not affect gene transcription and that the primary function of its binding to chromatin is to regulate its own extracellular release (22–24). Thus, the biological activity of IL-33 depends on its secretion and subsequent binding to the ST2 receptor (IL-1RL1), which is mainly expressed by immune cells [21, 25]. This cytokine has been dubbed “Alarmin” due to its role in activating the immune response; however, recent research has also implicated IL-33 in tissue homeostasis [26], indicating that it plays a complex role in both physiological and pathological conditions such as inflammation, tissue repair, and homeostasis.
The administration of recombinant IL-33 (rIL-33) has been shown to improve mice glucose response by affecting both adipose tissue and the pancreas [7, 27]. In agreement, IL-33 null mice display glucose intolerance upon obesity [7]. Additionally, rIL-33 administration has been shown to attenuate insulitis in mouse models of type 1 diabetes [28]. A recent study by Dalmas et al. demonstrates that in the islets, the expression of ST2 is restricted to ILC2s, which, in response to IL-33 administration, recruit and activate macrophages and DCs [7]. Subsequently, macrophages and DCs promote insulin secretion by producing retinoic acid (RA), which has various effects on β-cell function, including the induction of glucose-stimulated insulin secretion, insulin production, and glucokinase activity [7, 29]. Thus, rIL-33 activates a cellular network to promote insulin secretion. Although suggested to be cells of mesenchymal origin, the pancreatic source of IL-33 is currently unknown [7]. Further, the role of locally produced IL-33 in insulin secretion and glucose homeostasis remains to be elucidated.
Pericytes are contractile mural cells that wrap around capillaries and microvessels in various tissues and organs. Depending on the tissue, these cells have different origins and gene expression patterns [30, 31]. In the islets, pericytes have been shown to play an essential role in regulating β-cell function and mass (2, 4, 32–38). Further, this activity can be mediated by secreted factors, including NGF, BMP4, and ECM components, independently of blood flow regulation (32, 34–37). Studies suggest that pericytes may have an immunoregulatory role in various tissues, including the brain and kidneys, by secreting cytokines to control immune cell infiltration and activation (39–44). However, whether pericytes in the islets also have this function has yet to be determined.
Here, we provide evidence for the immunoregulatory role of pancreatic pericytes. First, we identified pericytes as the predominant source of IL-33 in the pancreas. We used transgenic mouse models to selectively delete pericytic IL-33 expression and found that loss of this cytokine caused glucose intolerance due to impaired insulin secretion. Further analyses revealed that pericytic IL-33 regulates the number of immune cells in the islets and their production of RA, indicating that pericytic IL-33 plays an essential role in maintaining glucose homeostasis by regulating immune cells in the islets. Our study indicates that pancreatic pericytes are immunomodulators, revealing an additional regulatory layer of islet inflammation and insulin secretion.
## Mice
Mice were maintained on a C57BL/6 background. Nkx3.2-Cre (Nkx3-2tm1(cre)Wez) mice [45] were a generous gift from Warren Zimmer (Texas A&M University, College Station, TX), and R26-EYFP (Gt(ROSA)26Sortm1(EYFP)Cos) and Il33 flox/flox-eGFP (Il33tm1.1Bryc) [46] mice were obtained from the Jackson Laboratory (Table S1). Wild-type mice were purchased from Envigo Ltd. (Jerusalem, Israel). Animals were housed under specific pathogen-free conditions. All experimental protocols were approved by the Tel Aviv University Institutional Animal Care and Use Committee (IACUC). The study was carried out in compliance with the ARRIVE guidelines.
## Treatments
For glucose tolerance tests, mice were fasted overnight before the i.p. injection of dextrose (2 mg/gr). For insulin tolerance tests, mice were fasted 6 hours before i.p. insulin administration (0.5 U/kg). Tail vein blood glucose levels were measured at indicated time points using glucose meters (Bayer). When indicated, mice were i.p. injected every other day with either 500 ng murine rIL-33 (BioLegend; Cat #580502) or PBS to obtain a total of three doses. Mice were analyzed 24 hours after the last injection.
## Islet isolation
For islet isolation, collagenase P solution (0.8 mg/ml; Roche) was injected through the common bile duct into the pancreas of the euthanized mouse, followed by 11 minutes of incubation and a density gradient (Histopaque-1119, Sigma) separation. Islets were collected from the gradient interface and hand-picked.
## Flow cytometry
To obtain single cell suspension from whole pancreas, dissected tissue was incubated in HBSS supplemented with collagenase P (0.4 mg/ml; Roche) and DNase I (0.1 ng/ml; Sigma-Aldrich). For analysis of islet cells, double-hand-picked islets were gently dispersed with Accutase (Sigma-Aldrich) solution for 5 minutes. For analysis of blood immune cells, tail vein blood was collected into EDTA-containing tubes, followed by a density gradient separation with Lymphoprep (StemCell Technologies). For analysis of splenic cells, dissected spleens were injected with collagenase D (1 mg/ml; Roche), sliced, and incubated at 37°C for 45 minutes. Then dissociated spleen was minced, strained, and washed. After supernatant aspiration, the pellet was resuspended in hypertonic ACK buffer to lyse erythrocytes for 2 minutes at room temperature.
For staining with surface markers, dispersed cells were incubated with Fc blocker (anti-CD16/CD32 antibody) for 15 minutes, followed by staining with the appropriate antibodies (Table S2) for 30 minutes on ice and incubation with DAPI (Sigma-Aldrich) to label dead cells.
Aldehyde dehydrogenase (ALDH) activity was determined using the ALDEFLUOR kit (StemCell Technologies) according to the manufacturer’s protocol. Briefly, dispersed cells from 200 islets were equally divided into two tubes, control and experimental. Control cells were incubated with ALDH inhibitor diethylaminobenzaldehyde (DEAB) for 15 minutes at 37°C to define the background fluorescence. Then, control and experimental tubes were incubated with activated fluorescent ALDH reagent for 35 minutes at 37°C. Following incubation and washes, cells were stained with appropriate antibodies.
Cells were analyzed by Cytoflex (Beckman Coulter) and analyzed with Kaluza software (Beckman Coulter).
## Cell isolation
For pancreatic pericytes isolation, pancreatic cells were collected from adult YFPPericytes (Nkx3.2-Cre;R26-EYFP) mice when pericytes were identified based on the yellow fluorescence [32, 34]. Pancreatic cells were isolated according to a previously published protocol [47]. Briefly, dissected pancreatic tissues were incubated in HBSS supplemented with collagenase P (0.4 mg/ml; Roche) and DNase I (0.1 ng/ml; Sigma-Aldrich) for 30 minutes at 37°C with mild agitation. Ice-cold HBSS buffer was added to stop digestion. Tubes were centrifugated at 300 g for 5 minutes at 4°C, washed, and strained through a 70 µm cell strainer (Miltenyi Biotec) to collect single cells. Cells were resuspended in PBS (without calcium chloride and magnesium chloride) supplemented with $5\%$ fetal bovine serum and 5 mM EDTA and re-strained through a 35 µm cell strainer (Corning). When indicated, single-cell suspension from wild-type or YFPPericytes mice were incubated with Fc blocker (anti-CD16/CD32 antibody; Table S2) for 30 minutes followed by 30 minutes incubation on ice with anti-PECAM1/CD31 or anti-CD45 antibodies (Table S2) to identify endothelial and immune cells, respectively. Prior to sorting, cells were incubated with DAPI (Sigma-Aldrich) to mark and exclude dead and late apoptotic cells. Pancreatic pericytes, endothelial, and immune cells were collected using FACSAria II or FACSAria III cell sorters (BD).
## Glucose-stimulated insulin secretion
For in vivo analysis, dextrose (2 mg/gr) was i.p. injected after an overnight fast. At indicated time points, tail vein blood was collected, and serum was separated. For ex vivo analysis, freshly isolated islets were pre-incubated for 30 minutes in RPMI medium with low glucose (1.67 mM). For each experiment, ten size-matched islets were hand-picked under a stereotaxic microscope and transferred in 5 µl volume to the well of 96-well U-bottom untreated plate, containing 250 µl of either low (1.67 mM) or high (16.7 mM)-glucose media, followed by 1-hour incubation at 37°C degrees and $5\%$ CO2. Alternatively, islets were pre-incubated for an hour in Krebs-Ringer buffer containing glucose (1.67 mM), followed by a 1-hour incubation with or without KCl (30 mM). Islet insulin content was extracted by $1.5\%$ HCl in $70\%$ ethanol solution, followed by lysis using TissueLyser II (Qiagen). Hormone levels were measured using a mouse ultrasensitive Insulin ELISA kit (Alpco).
## Immunofluorescence
Dissected pancreatic tissues were fixed in $4\%$ paraformaldehyde, followed by embedding in either O.C.T compound (Scigen) or paraffin. For paraffin-embedded tissues, heat-induced antigen retrieval in Citra buffer (BioGenex) was performed before the staining. Tissue sections were immunostained with indicated antibodies (Tables S3, S4) when control and transgenic tissues were stained in parallel. For TUNEL (Terminal deoxynucleotidyl transferase dUTP nick end labeling) assay, the Fluorescein In Situ Cell Death Detection Kit (Roche) was used according to the manufacturer’s protocol. Images were acquired using BZ-9000 BioRevo (Keyence) and SP8 confocal (Leica microsystems) microscopes.
## Morphometric quantification
For analysis of functional vasculature, fluorescently labeled tomato lectin (1 mg/ml; Vector Laboratories) was intravenously injected. After 5 minutes, mice were euthanized, and their pancreas was extracted and fixed in $4\%$ paraformaldehyde. Tissue cryosections were stained for insulin and imaged. For analysis of islet pericyte coverage, fixed pancreatic tissues of untreated mice were analyzed. Cryo-sections were immunostained for the pericytic marker Neuron-glial antigen 2 (NG2) and insulin and imaged. At least 50 islets per mouse, defined by their insulin expression, were analyzed to determine NG2- or lectin-positive areas per islet. For measurement of β-cell mass, fixed pancreatic tissues of untreated mice were analyzed. Paraffin-embedded sections were immunostained for insulin and counterstained with HCS CellMask Stain (Invitrogen) to mark the section area. Whole tissue sections, at least 100 µm apart, were automatically imaged. For each mouse, the acquired insulin-positive area was divided by the pancreatic area and multiplied by its weight. For analysis of the α-/β-cell ratio, glucagon- and insulin-positive areas were calculated for each islet when 50 islets per mouse were analyzed. For cell proliferation analysis, pancreatic tissues from postnatal day 5 pups were analyzed. Cryo-sections of at least 50 µm apart were stained for insulin and marker of proliferation Ki67 (Table S3). After imaging, at least 300 insulin-positive cells per pup were analyzed manually and the portion of the Ki67-positive cells out of the total number of insulin-positive cells was calculated. Images were acquired using Keyence BZ-9000 (Biorevo) and analyzed using ImageJ software (NIH).
## Gene expression
Gene expression levels were detected with Taqman (Applied Biosystems) or SYBR green assays (Applied Biosystems) using indicated primers (Table S5) and normalized to GAPDH and Cyclophilin, respectively. Expression levels were determined using the StepOne cycler (Applied Biosystems). Published mouse pancreatic pericytes RNA sequencing [34] was deposited in ArrayExpress (https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-5325/). Published islet genomic data [48] was accessed in http://www.gaultonlab.org/pages/Islet_expression_HPAP.html.
## Statistical analysis
Analysis was carried out using an unpaired two-tailed Student’s t-test (Prism software v.9; GraphPad). P values of 0.05 or less were considered to be statistically significant. Statistically significant outliers were identified according to the Grubbs’ method and excluded from the analyses.
## Pericytes are the pancreatic source of IL-33
IL-33 was shown to be produced by mesenchymal cells in the pancreas [7, 49]. As pericytes are the predominant mesenchymal cell population in the islets [34, 50], we set to characterize their IL-33 expression. First, we analyzed the published transcriptome of pancreatic pericytes of naïve, healthy mice [34]. As shown in Figure 1A, mouse pancreatic pericytes express IL-33 but no other members of the IL-1 family of cytokines. To define IL33 expression in human islets, we employed a published islet cell transcriptome [48]. Indeed, IL33 is expressed by pericytes, identified by the pericytic markers PDGFRB, ACTA2, ENG, and CD248 [30, 48](Figure S1).
**Figure 1:** *Pericytes are the primary source of IL-33 in the mouse pancreas and islet. (A) Analysis of IL-1 cytokine family ligands expression in isolated pancreatic pericytes and islets, employing a previously published RNAseq analysis (34). Heat maps show mean expression levels (as fragments per kilobase of exon per million aligned fragments [FPKM]) of indicated genes. N = 3. (B, C) Bar diagram (mean ± SD) shows relative levels of Il33
(B) and Il1rl1 (encodes ST2; C’) transcripts in different pancreatic cell populations, analyzed by qPCR (normalized to GAPDH). RNA was extracted from bulk pancreatic tissue, isolated islets, pancreatic endothelial cells (FACS-purified based on their PECAM1 expression) and pancreatic immune cells (FACS-purified based on their CD45 expression) of adult wild-type mice, and pancreatic pericytes (FACS-purified from Nkx3.2-Cre;R26-EYFP mice based on their yellow fluorescent labeling). N = 3-5. (D) Flow cytometry analysis of pancreatic tissue of Il33
flox-eGFP mice. Green fluorescence intensity was analyzed in bulk pancreatic cells, endothelial cells (defined as PECAM1+ cells), immune cells (defined as CD45+ cells), and pericytes (defined as PDGFRβ+ cells). Left, histogram showing representative fluorescence intensities of bulk pancreatic cells (gray) and pericytes (blue). Right, bar diagram shows the relative mean fluorescence intensity (MFI) when the average of bulk pancreatic cells was set to ‘1’. N = 4-5. (E) Immunofluorescence analysis of pancreatic tissue sections of non-transgenic (top) and ΔIl33
Pericytes (bottom) mice. Sections were stained for IL-33 (green) and Neuron-glial antigen 2 (NG2; red) to label pericytes and counterstained with DAPI (blue). Representative islets (defined by morphology) are shown. Right, higher magnification of the area framed by the white box in the middle panel. (F) Immunofluorescence analysis of pancreatic tissue sections of non-transgenic mice. Sections were stained for αSMA (red) to label vSMCs, PECAM1 (white) to label endothelial cells, and IL-33 (green) and counterstained with DAPI (blue). Shown is a representative field. (G) Bar diagram (mean ± SD) showing lower Il33 transcript levels in Il33-deficient pericytes. RNA was extracted from FACS-purified pancreatic pericytes from 3-month-old YFPΔIl33
Pericytes (Nkx3.2-Cre;Il33
flox/flox;R26-EYFP; green) and YFPPericytes (Nkx3.2-Cre;R26-EYFP; gray; the average was set to ‘1’) mice. Gene expression was analyzed by qPCR. N =3-5. ***p<0.005 (Student’s t-test). Each dot represents a single mouse.*
Next, we compared Il33 transcript levels in various mouse pancreatic cell populations: bulk pancreatic tissue (contains mainly acinar cells), isolated islets (contains mainly endocrine cells), and purified pancreatic endothelial cells, immune cells, and pericytes. As shown in Figure 1B, our qPCR analysis revealed that Il33 expression in pancreatic pericytes was two orders of magnitude higher than in other analyzed cell populations. Similarly, IL33 expression in the human islets was restricted to the pericytic cell cluster (Figure S1) [48].
The expression of the IL-33 receptor ST2, encoded by Il1rl1, was previously shown to be restricted to ILC2s [7]. Indeed, we detected its expression in immune cells but not in pericytes, endocrine, and endothelial cells of the mouse pancreas (Figure 1C).
To define pancreatic IL-33 expression further, we employed a transgenic mouse line in which GFP is expressed under this gene promoter, Il33 flox-eGFP [46]. In agreement with our qPCR analysis, pericytes, but no other analyzed pancreatic cell populations, expressed IL-33/GFP (Figure 1D). Additionally, we performed immunofluorescence analysis to determine IL-33 protein expression. As shown in Figure 1E, we detected IL-33 protein in pericytes but not in other islet cells. Notably, although vascular smooth muscle cells (vSMCs) are closely related to pericytes, these cells did not express IL-33 (Figure 1F). Thus, pericytes constitute a predominant source of IL-33 in the islets.
## Pericytic IL-33 is required for glucose regulation
To elucidate the role of IL-33 in pancreatic pericytes, we specifically deleted this gene in these cells. To this end, we crossed Il33 flox-eGFP mice, which allow Cre-dependent deletion of the *Il33* gene [46], with Nkx3.2-Cre mice [45] to generate ΔIl33 Pericytes (Nkx3.2-Cre;Il33 flox/flox-eGFP) mice. As previously established, the expression of the Nkx3.2-Cre in the pancreas is restricted to the mural cell lineage (i.e., pericytes and vSMCs) and does not target epithelial or endothelial cells [32, 34, 50]. Furthermore, Nkx3.2-Cre does not target immune cells in the pancreas, spleen, and blood (Figure S2). Immunofluorescence and qPCR analyses verified the loss of IL-33 expression in pancreatic pericytes of ΔIl33 Pericytes mice (Figures 1E, G).
To determine if pericytic IL-33 is required for glucose regulation, we analyzed ΔIl33 Pericytes and non-transgenic (Cre negative; Il33 flox/flox-eGFP) age- and sex-matched mice. Of note, it was previously shown that mice carrying the Nkx3.2-Cre allele alone display normal glucose response [34]. ΔIl33 Pericytes and non-transgenic control mice had comparable blood glucose levels (Figure S3). However, i.p. glucose tolerance test (GTT) analysis revealed a significantly impaired response to glucose challenge of ΔIl33 Pericytes male mice (Figure 2A). We did not detect glucose intolerance in transgenic female mice (Figure S3). As Il33 is expressed at similar levels in pancreatic pericytes of female and male mice (Figure S3), these differences likely reflect sex-dependent differences in glucose response [51]. Correlating with their glucose intolerance, ΔIl33 Pericytes male mice had significantly lower serum insulin levels after a glucose challenge than control mice (Figures 2B).
**Figure 2:** *Impaired glucose response in mice lacking pericytic IL-33. 4-month-old ΔIl33
Pericytes male mice (Nkx3.2-Cre;Il33
flox/flox; green), which lack Il33 expression in their pancreatic pericytes, and non-transgenic littermates (Cre-negative; ‘Non tg’; gray) were analyzed. (A) i.p. glucose tolerance test (GTT). Left, mean (± SEM) blood glucose levels at indicated time points following glucose administration. Right, the area under the curve (AUC) of the response as shown on the left panel, when each dot represents a single mouse. N = 12. (B)
In vivo GSIS. Left, mean (± SEM) serum insulin levels at indicated time points following glucose administration. Right, AUC of the response shown on the left panel, when each dot represents a single mouse. N = 8. (C) i.p. GTT after treatment of transgenic mice with exogenous IL-33. ΔIl33
Pericytes mice were either i.p. injected with rIL-33 (500 ng/dose; dashed green line and empty squares) or PBS (solid green line and filled squares) every other day to obtain a total of three doses. In parallel, non-transgenic mice were i.p. injected with PBS. After an overnight fast, and 24 hours after the last treatment, mice were i.p. injected with dextrose (2 mg/g body weight), and tail vein blood glucose levels were measured at indicated times. Left, mean (± SEM) blood glucose levels at indicated time points following glucose administration. Right, AUC of the response as shown on the left panel, when each dot represents a single mouse. N = 6-14. *p<0.05; **p<0.01; NS, not significant, as compared with non-transgenic mice (Student’s t-test).*
The Nkx3.2-Cre mouse line has non-pancreatic expression in the gastrointestinal mesenchyme and skeleton [45]. We, therefore, analyzed for potential non-pancreatic phenotypes that may contribute to the glucose intolerance of ΔIl33 Pericytes mice. Transgenic and control mice showed comparable body weight, indicating normal food uptake and digestion (Figure S3). Further, ΔIl33 Pericytes male mice were insulin sensitive (Figure S3). Thus, our analysis demonstrated that pericytic IL-33 is required for proper glucose response by regulating insulin secretion.
## Exogenous IL-33 rescues the glucose intolerance of ΔIl33
Pericytes mice
The nuclear localization of IL-33 raised the possibility that this cytokine has a dual function and may also act cell autonomously in a receptor-independent manner [20, 21, 26]. However, evidence accumulates that this cytokine acts primarily as a secreted ligand to induce signal transduction in ST2-expressing cells (22–24, 26). Notably, the loss of IL-33 affected neither islet pericytes abundance nor induced their activation (Figure S4). Further, IL-33 deficient islets had a comparable density of functional capillaries to control (Figure S4). To define if pericytic IL-33 acts as a secreted cytokine, we tested whether exogenous IL-33 rescues glucose intolerance of ΔIl33 Pericytes mice. To this end, we i.p. injected rIL-33 into transgenic mice. As shown in Figure 2C, rIL-33 administration improved the glucose response of ΔIl33 Pericytes mice, making it comparable to that of non-transgenic control mice. To conclude, our analysis suggests that pericytic IL-33 acts paracrine to regulate glucose response.
## Insufficient insulin secretion in the absence of pericytic IL-33
To define the underlying cause(s) of impaired insulin secretion of ΔIl33 Pericytes mice, we analyzed their β-cells. The pancreatic mass of transgenic mice was comparable to the control when their β-cell mass was mildly, but non-significantly, lower (Figures 3A, S5). In agreement, we observed neither cell death nor expression of stress-related genes (i.e., Chop, Atf4) in ΔIl33 Pericytes islets (Figure S5). Establishment of the β-cell mass relies on the proliferation of these cells in the neonatal period [52, 53]. When analyzing β-cells of ΔIl33 Pericytes and control pups (at postnatal day 5; p5), we observed comparable proliferation rates (Figure S5). Further, islet morphology and their β-/α-cell ratio were unaffected by the loss of pericytic IL-33 (Figures 3B, C). Thus, loss of pericytic IL-33 affected neither β-cell death nor their proliferation.
**Figure 3:** *Impaired β-cell mass and islet insulin secretion in ΔIl33
Pericytes mice. Islets and pancreatic tissues of ΔIl33
Pericytes transgenic (green) and non-transgenic (‘Non tg’; gray). 4-month-old male mice were analyzed. (A) Bar diagram (mean ± SD) showing estimated β-cell mass. N = 4. (B) Pancreatic tissues of non-transgenic (left) and transgenic (right) mice were stained for insulin (red) and glucagon (green) and counterstained with DAPI (blue). Representative islets are shown. (C) Bar diagram (mean ± SD) showing the ratio between α- and β-cells (identified as glucagon and insulin-expressing cells, respectively) in adult pancreatic tissues of non-transgenic and transgenic mice. N = 3. (D) Bar diagram (mean ± SD) showing insulin content of size- and number- matched isolated islets. N = 5-8. (E)
Ex vivo GSIS. Bar diagrams (mean ± SD) showing levels of insulin secreted from size-matched groups of ten isolated islets in response to 1.67 and 16.7mM glucose. N = 5-8. (F) Bar diagrams (mean ± SD) showing KCl-stimulated insulin secretion. Islets grouped into groups of ten islets were incubated with 1.67 mM glucose, either supplemented or not. N= 5-7. (G–I) Bar diagrams (mean ± SD) showing islet gene expression. Average levels in control islets were set to ‘1’. N = 6-10.*p<0.05; NS, not significant, as compared with non-transgenic control mice (Student’s t-test). Each dot represents a single mouse.*
Next, we defined the islet insulin secretion ex vivo to determine blood flow-independent glucose-stimulated insulin secretion (GSIS). ΔIl33 Pericytes islets displayed an impaired GSIS despite having comparable insulin content to non-transgenic islets (Figures 3D, E). Furthermore, transgenic islets secreted less insulin in response to KCl-mediated membrane depolarization (Figure 3F), pointing to abnormal islet insulin secretion in the absence of pericytic IL-33.
Gene expression analyses indicated slightly lower levels of Ins1 and Pdx1 in ΔIl33 Pericytes islets but comparable levels of genes encoding other islet hormones or transcription factors (Figures 3G, H, S6). Further, we observed similar expression of genes encoding components of the GSIS machinery (Figure S6), but Snap25. *This* gene, which encodes a component of a SNARE complex that mediates the docking of insulin secretory granules to the plasma membrane [54], was slightly lower in ΔIl33 Pericytes islets (Figure 3I). The moderately lower levels of Ins1, Pdx1, and Snap25 in IL-33-deficient islets may thus contribute to their abrogated insulin exocytosis.
Overall, our analysis indicates a requirement for IL-33 for proper insulin secretion.
## Deletion of pericytic IL-33 reduced islet T and dendritic cell numbers
Administration of rIL-33 affects islet immune composition [7]. Thus, we aimed to define changes in immune cell numbers in ΔIl33 Pericytes mice. First, we analyzed these mice for potential systemic effects on their immune cells by analyzing their spleen and blood. ΔIl33 Pericytes and control mice had comparable portions of T cells in these tissues (Figure S7). Similarly, the levels of splenic DCs and blood monocytes (the precursors for DCs and some macrophages) were comparable in transgenic and control mice (Figure S7). Thus, we observe no systemic effect on analyzed immune cell populations in ΔIl33 Pericytes mice.
Next, to define a dependency of islet immune cells on pericytic IL-33, we analyzed ΔIl33 Pericytes and non-transgenic islets (Figure 4A). We found no change in the number of islet macrophages or their ratio between cells with M1 and M2-like phenotypes (Figures 4B, S7). However, the loss of pericytic IL-33 caused a significant reduction in the number of T cells and DCs in the islets (Figures 4C, D). As these cell populations do not express ST2, they are unlikely to respond to IL-33 directly but depend on ILC2s activation [7]. However, the scarcity of ILC2s in islets of C57BL/6 mice hindered their reliable analysis. To conclude, our analysis indicated that pericytic IL-33 is required to establish proper islet immune cell composition locally.
**Figure 4:** *Loss of pericytic IL-33 affects islet immune cell number and functionIslets isolated from ΔIl33
Pericytes (green) and non-transgenic (‘Non tg’; gray). 4-month-old male mice were analyzed by flow cytometry. (A) Representative dot plots indicating gate used to identify immune cells (Left panel, CD45+ cells), macrophages (MФ; Middle panel, CD45+CD11c+CD64+ cells), DCs (Middle panel, CD45+CD11c+CD64- cells), and T cells (Right panel, CD45+CD90.2+ or CD45+CD3+ cells) out of dispersed islet cells. (B–D) Bar diagrams (mean ± SD) showing the total number of macrophages (MФ; B’), T cells (C’), and DCs (D’) in 100 isolated islets. N = 5-9. (E, F) Islet cells were analyzed with ALDEFLOUR to define the frequency of DC (E’) and macrophages (F’) with ALDH activity. Islets treated with the ALDH inhibitor diethylaminobenzaldehyde (DEAB; “with inhibitor”; empty bars and histograms) were used to define the background fluorescence for each sample, as indicated in the manufacturer’s protocol. Left, representative histograms. Right, bar diagrams (mean ± SD) showing the frequencies of cells with ALDH activity. N = 3-4. *p<0.05; ** p<0.01; NS, not significant, as compared with non-transgenic control mice (Student’s t-test). Each dot represents a single sample.*
## Impaired retinoic acid production capacity of islet macrophages in the absence of pericytic IL-33
rIL-33 was shown to indirectly promote islet RA synthesis to influence insulin secretion [7]. RA is produced in two sequential oxidative steps: first, retinol is oxidized reversibly to retinaldehyde, and then retinaldehyde is oxidized irreversibly to RA; the latter is catalyzed by aldehyde dehydrogenases (ALDHs) [55]. rIL-33 treatment enhanced ALDH activity by islet macrophages and DCs [7]. To determine if pericytic IL-33 has a similar function, we measured the activity of ALDH in islet macrophages and DCs of control and ΔIl33 Pericytes islets. As shown in Figure 4E, transgenic and control islets had a comparable portion of DCs with active ALDH. In contrast, the percentage of macrophages with active ALDH in ΔIl33 Pericytes islets was a quarter of their portion in control islets (Figure 4F), implying that pericytic IL-33 is required for adequate RA production by these cells. Together with the overall decrease in DCs (Figure 4D), our analysis points to fewer RA-producing cells in ΔIl33 Pericytes islets.
**Figure 5:** *A schematic model of the relay effect of pericytic IL-33 on β-cells. From left to right: Islet pericytes (green) secrete IL-33. ILC2s (yellow) respond to this cytokine to affect other islet immune cells (yellow) (7). Islet T and dendritic cell numbers, as well as RA production by islet macrophages (7), are all sensitive to IL-33. IL-33-dependent RA secretion, and potentially other factors, promotes insulin secretion from β-cells.*
## Discussion
β-Cell function relies on the islet niche. Here, we provide evidence for pericyte-regulated islet inflammation and its role in glucose homeostasis. We showed that pericytes produce IL-33 and are the predominant cell type to do so. Pericyte-selective deletion of the *Il33* gene resulted in glucose intolerance due to impaired β-cell function. Pericytic IL-33 deficiency was further associated with fewer T cells and DCs in the islets. In addition, islet macrophages displayed lower ALDH activity in the absence of pericytic IL-33, implicating an impaired production of RA, which was associated with insulin secretion, by these cells. Thus, our study proposes that a cellular network comprised of pericytes and immune cells modulates β-cell function and insulin secretion (illustrated in Figure 5).
Administration of IL-33 was shown to improve glucose tolerance [7, 27]. Dalmas et al. reported that systemic treatment with rIL-33 promotes insulin secretion [7]. Despite some differences, our study indicated that administrated rIL-33 highly mimics the activity of the endogenous cytokine. Both exogenous and pericytic IL-33 promoted insulin secretion [7]. Further, the number of islet DCs was affected by both rIL-33 and pericytic IL-33. However, while increasing IL-33 levels did not significantly affect islet T cell number [7], this cell population depends on the endogenous pericytic cytokine (Figure 5). Moreover, both endogenous and exogenous IL-33 affected RA production by islet macrophages, while RA production in DCs was only affected by increased cytokine levels [7]. Thus, systemic administration of rIL-33 does not fully recapitulate its endogenous activity. Of note, systemic knockdown of Il33 did not affect the glucose response of lean mice but only of diet-induced obese animals [7]. These differences highlight the importance of pancreatic-specific production of IL-33 for glucose homeostasis.
The various cell populations that make the islet microenvironment interact to ensure proper insulin secretion. Neurons were shown to regulate islet blood flow and glucose uptake by activating pericytes [38, 56]. Under stress, endothelial cells regulate the number and activity of macrophages, which produce factors promoting β-cell proliferation [1, 17, 18]. The various islet immune cells cooperate to induce insulin secretion when ILC2s promote recruitment and RA production by islet macrophages and DCs [7]. Our study introduces an additional player in this multi-cellular network: pericytes. As pericytes express other cytokines than IL-33, these cells may modulate islet inflammation and, thus β-cell functionality by regulating the number and activity of multiple immune cell populations. Further studies into the immunoregulatory activities of pancreatic pericytes are required to decipher their role in shaping islet inflammation and subsequent glucose homeostasis.
Cells of the islet niche, including pericytes and immune cells, support postnatal β-cell development, including functional maturation and proliferation [8, 32, 33, 36, 56]. We observed no significant effect on β-cell mass and neonatal proliferation in the absence of pericytic IL-33. In contrast, this deletion impaired insulin secretion from β-cells, with little effect on their mature phenotype. While the expression levels of Ins1 was slightly lower in islet lacking pericytic IL-33, this did not translate to reduced hormone levels. The expression of the transcription factor Pdx1, required for β-cell maturity, was reduced by $15\%$ in ΔIl33 Pericytes islets. While the lower Pdx1 transcript levels may point to β-cell abnormalities, it is unlikely to significantly affect β-cell gene expression or functionality. Indeed, we did not observe changes in the expression levels of genes encoding proteins required for glucose sensing and insulin production. Thus, we suggest that pericytic IL-33 affects β-cell insulin secretion with little influence on these cells’ maturation and proliferation.
Throughout the body, pericytes were shown to regulate the immune system (39–44, 57, 58). In response to tissue damage, pericytes modulate the local immune response to promote repair. Brain pericytes modulate neuroinflammation by regulating immune cell recruitment and activation [39, 41, 42, 44, 57, 58]. Kidney pericytes secrete cytokines in response to tissue damage [40, 43]. Pericytes may also contribute to pathologies through immune cell activation. In the tumor stroma, pericytes produce IL-33, which recruits and activates tumor-associated macrophages, ultimately promoting metastasis [59, 60]. Our study ascribes an immunoregulatory ability to pancreatic pericytes mediated by IL-33 production.
The clinical onset of type 2 diabetes (T2D) occurs when pancreatic β-cells fail to secrete sufficient insulin to maintain normoglycemia in the face of insulin resistance. A growing body of evidence has established T2D as an inflammatory disease associated with deleterious islet inflammation (1, 3, 17, 61–66). In particular, T2D is associated with an increased number of immune cells, predominantly macrophages, in the islets [11, 67]. The shift from beneficial to harmful islet inflammation has been suggested to play a role in the progression of diabetes [61, 68]. While obesity, a leading risk of T2D, increases IL-33 production in both human and mouse islets, it also interferes with the IL-33-ILC2 axis and its ability to promote insulin secretion [7]. Islet pericytes are affected by genetic and metabolic risks of T2D [34, 38, 69, 70]. Thus, by highlighting their immunoregulatory role, our study proposes that pericytes may contribute to the transformation of islet inflammation associated with diabetes. However, further research is needed to understand the interplay between pericyte function and T2D fully.
## Resource Identification Initiative
We take part in the Resource Identification Initiative and use the corresponding catalog number and RRID in our manuscript.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: E-MTAB-5325 (EBI).
## Ethics statement
The animal study was reviewed and approved by Tel Aviv University Institutional Animal Care and Use Committee.
## Author contributions
GB, AS, LS, AE, TW, and MT conducted experiments and acquired and analyzed data. LL designed and supervised research and wrote the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1142988/full#supplementary-material
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|
---
title: Model-interpreted outcomes of artificial neural networks classifying immune
biomarkers associated with severe infections in ICU
authors:
- Gustavo Sganzerla Martinez
- Ali Toloue Ostadgavahi
- Abdullah Mahmud Al-Rafat
- Alexis Garduno
- Rachael Cusack
- Jesus Francisco Bermejo-Martin
- Ignacio Martin-Loeches
- David Kelvin
journal: Frontiers in Immunology
year: 2023
pmcid: PMC10034398
doi: 10.3389/fimmu.2023.1137850
license: CC BY 4.0
---
# Model-interpreted outcomes of artificial neural networks classifying immune biomarkers associated with severe infections in ICU
## Abstract
### Introduction
Millions of deaths worldwide are a result of sepsis (viral and bacterial) and septic shock syndromes which originate from microbial infections and cause a dysregulated host immune response. These diseases share both clinical and immunological patterns that involve a plethora of biomarkers that can be quantified and used to explain the severity level of the disease. Therefore, we hypothesize that the severity of sepsis and septic shock in patients is a function of the concentration of biomarkers of patients.
### Methods
In our work, we quantified data from 30 biomarkers with direct immune function. We used distinct Feature Selection algorithms to isolate biomarkers to be fed into machine learning algorithms, whose mapping of the decision process would allow us to propose an early diagnostic tool.
### Results
We isolated two biomarkers, i.e., Programmed Death Ligand-1 and Myeloperoxidase, that were flagged by the interpretation of an Artificial Neural Network. The upregulation of both biomarkers was indicated as contributing to increase the severity level in sepsis (viral and bacterial induced) and septic shock patients.
### Discussion
In conclusion, we built a function considering biomarker concentrations to explain severity among sepsis, sepsis COVID, and septic shock patients. The rules of this function include biomarkers with known medical, biological, and immunological activity, favoring the development of an early diagnosis system based in knowledge extracted from artificial intelligence.
## Introduction
Sepsis and septic shock are life-threatening syndromes that are associated with dysregulation in the host immune responses to infection [1]. They can lead to organ failure and consequently death [2]. As an example, in 2017, more than 11 million deaths associated with sepsis were reported worldwide [3], which represented a mortality rate of approximately $22\%$. Moreover, a considerable share of viral sepsis patients (i.e., sepsis COVID) meet the definition for sepsis-3 (bacteria-induced sepsis) [4]. A more severe manifestation of sepsis is septic shock, in which patients meet all sepsis-3 criteria and require the use of vasopressor [1].
The fact that sepsis (of viral and bacterial sources) and its severe subset (i.e., septic shock) meet the criteria for sepsis-3 definition allows the employment of biomarkers as an early diagnostic tool [5]. We selected a consortium composed of the following 30 biomarkers that are responsible for reflecting signals of specific moments during an immune response towards a pathogen that causes sepsis and/or septic shock: Angiopoietin 2 (ANG2), C-C Chemokine Ligand 2 (CCL2), C-X-C Motif Chemokine Ligand 10 (CXCL10), D-dimer, E-selectin (E-SEL), ferritin, Granulocyte Colony-Stimulating Factor (G-CSF), Granulocyte Macrophage Colony-Stimulating Factor (GM-CSF), Granzyme B (GRANB), Intercellular Adhesion Molecule 1 (ICAM-1), Interferon ɣ (IFNɣ), Interleukin 1 β (IL1β), Interleukin 1 receptor antagonist (IL1ra), Interleukin 2 (IL2), Interleukin 4 (IL4), Interleukin 6 (IL6), Interleukin 7 (IL7), Interleukin 10 (IL10), Interleukin 12 (IL12), Interleukin 15 (IL15), Interleukin 17 (IL17a), Lipocalin-2 (LIPO), Myeloperoxidase (MPO), Programmed Death-Ligand 1 (PDL1), Soluble glycoprotein 130 (sGP130), Soluble interleukin 6 receptor (sIL6R), Surfactant Protein (SPD), Tumor Necrosis Factor-alpha (TNF-ɑ), Vascular Cell Adhesion Molecule 1 (VCAM), and Vascular Endothelial Growth Factor C (VEGFC). Further details on the impact of the dysregulation of these biomarkers are shown in Supplementary Material S1.
The inflammatory pathway of the diseases explored here leaves traces behind that might be employed in profiling the severity of the diseases themselves [6, 7]. Many of these traces are depicted through the analysis of biomarkers (i.e., cytokines and chemokines) associated with the host immune system. For example, VCAM-1, ICAM-1, and VEGFC are recruited when there is damage to vascular tissue [8]. Moreover, CCL2 orchestrates the recruitment of immune cells to sites of inflammation [9]. In addition, PDL1 functions as a suppressor of the adaptive immune system as it binds to the Programmed Cell Death Protein 1 (PD1) [10]. Finally, MPO is mainly expressed in neutrophil granulocytes, granting antipathogenic activity to the immune cells expressing them [11]. Each biomarker has an individual and grouped function in inflammatory pathways [12]; therefore, a systematic analysis using data mining to determine the key items involved in sepsis/septic shock syndrome is appreciated [13, 14].
It has been stated [15] that a dataset comprised of too many dimensions (i.e., biomarkers) might slow, mask, and reduce the efficiency of machine learning approaches. Therefore, the selection of the most important features of a dataset is an essential step within data pre-processing frameworks. Moreover, Explainable Artificial Intelligence (XAI) has arisen as a manner to promote comprehension for the decision pattern employed by machine learning approaches, especially with the solid ethical standards required by the medical sciences. Therefore, decision-making processes benefit of a mathematically evidenced procedure [16].
In this paper, we hypothesize that the severity of sepsis (bacterial and viral) and septic shock patients is a function of the concentration of biomarkers. For that, we aim to use feature selection algorithms that will isolate biomarkers as candidates for distinguishing the severity of multi-organ failure in sepsis, sepsis COVID, and septic shock patients. With subsets of the selected biomarkers, we aim to evaluate these subsets through interpretable Artificial Neural Networks, so the biomarker concentration that defines the severity of patients with sepsis, sepsis COVID, and septic shock can be used as an early diagnostic tool.
## Study design
All the samples used in this study were obtained from a critically ill cohort of Intensive Care Unit (ICU) sepsis, sepsis COVID, and septic shock patients at St James’s Hospital in Dublin, Ireland. Institutional Research Board approval was granted by the SJH/TUH Joint Research Ethics Committee and The Health Research Consent Declaration Committee (HRCDC) under the register number REC: 2020-05 List 17 and project ID 0428. Biological samples, clinical findings, and laboratory data were collected at days 0, 3, and 14 after presentation of severe infection to monitor the progression and sepsis-induced immune-paralysis state at different stages of the disease. Sample collection took place from September 2020 to March 2021. Sequential Organ Failure Assessment (SOFA) score was obtained on admission to the ICU and at the matching collection timepoints for samples. The clinical variables for white blood cell (WBC) count (worst record of day 0), neutrophils (day 0), positive culture, and up to five comorbidities were attributed to each patient.
## Biomarker immunoassays
The concentration of biomarkers with potential altered functions in sepsis, sepsis COVID, and septic shock [10] patients was quantified in the Laboratory of Emerging Infectious Diseases at Dalhousie University in Halifax, Nova Scotia, Canada. The following biomarkers were quantified through the Ella SimplePlex Immunoassay™ (San Jose, California): ICAM-1, LIPO, MPO, VCAM-1, D-Dimer, E-SEL, Ferritin, SPD, PDL1, G-CSF, IL-1b, VEGFC, ANG2, CXCL10, GM-CSF), Interleukin 10 (IL-10, IL-17A, IL-1ra, IL-6, IL-7, CCL2, GRANB, IFNg, IL-12, IL-15, IL-2, IL-4, and TNF-α. The biomarkers were selected due to their potential as characterizing the patients’ inflammation [12].
The plasma concentration of both sIL6R and sGP130 was evaluated and quantified with Enzyme-Linked Immunosorbent Assay (ELISA) kits (BMS214TEN for sIL-6R and EHIL6STX10 for sGP130; ThermoFisher Scientific). These biomarkers were selected due to their crucial role in the IL-6 inflammatory pathway [17]. Finally, each sample was assayed and quantified following the kit manufacturer’s instructions. All samples were obtained from patients already admitted to the intensive care unit (ICU). We included the concentrations for all biomarkers at day zero of ICU in Supplementary Material S2. We provide the dataset in Supplementary Material S2.
## Binarization of SOFA score into different degrees of multi-organ failure
Each patient in our cohort was assigned a Sequential Organ Failure Assessment (SOFA) score. For classification purposes, we binarized the SOFA score into two groups, i.e., High Degree Multi-Organ Failure (HDMOF) and Low Degree Multi-Organ Failure (LDMOF). We employed a cut-off value of 8, as reported by [18] to binarize the groups; thus the HDMOF group is characterized by a SOFA score equal to or higher than 8 while the LDMOF group has a SOFA score less than 8. In their paper, upon binarizing patients with a SOFA score cut of (>=8, and<8) Martin-Loeches et al. [ 2017] [18] found different mortality rates and antibodies levels that well explained the severity of sepsis patients.
## Statistical analyses
All statistical procedures were performed using R. We used the Shapiro-Wilk test to find data distribution. To compare averages between groups, the Kruskal-Wallis test, t test, and ANOVA test were used through the Rstatix package (version 0.7.0). The Kaplan-Meier method under the survival R package (version 3.3-1) was used for calculating survival probabilities for each group (i.e., sepsis, sepsis COVID, and septic shock). The level of significance was set to 0.05.
## Feature selection
To systematically choose biomarkers that are associated with high and low MOF, a Feature Selection (FS) step was applied, and the outcomes were benchmarked. The FS algorithms chosen for this study are representatives of three distinct classes of these methods. For a wrapper algorithm, we selected Boruta and applied the parameters specified in [19]; the filter algorithm we chose is Information Gain (IG); and a combination of both FS classes, which results in an embedded algorithm has as its representative the Lasso Regression (LR) under the parameters specified in [20]. Both Boruta and LR were implemented in R through the packages Boruta (version 7.0.0) and Glmnet (version 4.1-4), respectively. IG was implemented in Python using the class mutual_info_classif found in the sklearn.feature_selection library (version 1.1.0). Both the R and Python scripts that performed the feature selection process are available at https://github.com/gustavsganzerla/covid-biomarker/blob/main/XAI/feature_selection.R and https://github.com/gustavsganzerla/covid-biomarker/blob/main/XAI/information_gain.py, respectively.
## Classification of severity with artificial intelligence
We selected the algorithms Support Vector Machines (SVM), Random Forest (RF), Classification and Regression Trees (CART), K-Nearest Neighbors (KNN), and deep learning Artificial Neural Networks (ANN) to classify patients’ severity with immune biomarkers as input. For that, we performed a 10-fold cross-validation process on the input data. The test dataset encompassed $20\%$ of the whole data. The first five classification algorithms were implemented in R through the Caret package (version 6.0-9) and their code is available at https://github.com/gustavsganzerla/covid-biomarker/blob/main/XAI/classific.R.
The ANN approach was developed in Python using the Tensorflow library (version 2.8.0). A sigmoid function was used in the output layer to return a probability of a given patient having MOF or not. The outcome was binarized through a confusion matrix built under the default 0.5 decision threshold applied in the outcome of the output neuron. The classes obtained were categorized as follows: True Positives (TPs), True Negatives (TNs), False Positive (FPs), and False Negatives (FNs). The ANN performance was evaluated in terms of accuracy, AUC, FPs, FNs, TPs, TNs, all of which can be found in the module tf.keras.metrics. The architecture of the ANN has input layers that vary according to the set of biomarkers entered. Next, the model has two fully connected layers with 10 hidden neurons each. We also increased the epochs of the ANN until the error (binary cross entropy) kept dropping. The scripts containing the ANN simulation are available at https://github.com/gustavsganzerla/covid-biomarker/blob/main/XAI/SHAP-ANN.
## Explanation of the classification model
We used Shapley Additive Explanations (SHAP) [21] to provide interpretability to the successful classification models. SHAP will assign a score (either positive or negative) for each input variable in assigning a label to an observation. If the SHAP score of a given feature is positive, it is positively correlated with the assigning of a label; otherwise, it is negatively correlated with the target label.
The SHAP approach is defined as a solid theoretical foundation that may be used to explain any predictive model locally and globally. From this, we employed the kernel.explainer method in the SHAP module. As our classifier is not a tree-based algorithm, the Kernel SHAP is applied. Kernel SHAP will measure the contribution of each input feature to the outcome of the model, it consists of five steps: In conclusion, A flowchart describing the data analytical process employed in this study is available in Figure 1.
**Figure 1:** *Overview of the data analysis procedure employed in this study. In
Figure 1
, we show the three stages of the data analytical process employed in this study. First, 30 biomarkers from sepsis, sepsis COVID, and septic shock patients were obtained. Secondly, to reduce the number of variables, three algorithms are applied in the Feature Selection stage, i.e., Boruta, Lasso Regression, and Information Gain. The full outcome of the three algorithms is classified into an Artificial Neural Network (ANN). A second filter is applied to promote more reduction to the data using Exhaustive Search, whose outcomes are yet fed into ANNs in to compare their performance with the full outcomes of Boruta, Lasso Regression, and Information Gain. After running multiple ANNs, the prediction model is evaluated with SHAP.*
## Clinical characteristics and survival analysis
We provide Table 1 to clinically depict the population that composes the cohort ($$n = 112$$). We identify that most patients in the cohort are male ($60\%$) with an average age of 64.7 years old. The average ICU stay was 38.3 days. Finally, $63.7\%$ of all patients survived while the lowest reported mortality was in sepsis ($30\%$) patients followed by sepsis COVID ($32.4\%$) patients and septic shock patients ($38\%$). We also report that the patients with septic shock have a higher count of neutrophils. Differences in the neutrophil counts were found to be associated with the severity of the disease; i.e., in the severe form of sepsis, the leukocytes count increased 1.62-fold and the increase in sepsis patients was 1.41-fold, while the leukocytes count remained more stable in septic shock (1.005-fold) patients. Next, we report the differential WBC count in sepsis, sepsis COVID, and septic shock (1.44, 1.15, and 1.08, respectively). Patients were assessed according to positive microbiological culture. We report that the COVID patients showed a smaller proportion of patients with viral and bacterial co-infection, i.e., superinfection (9 patients, 7 in LDMOF and 2 in HDMOF). The positive culture results for patients without COVID was found stable across both groups in sepsis and septic shock. Finally, we found the most common comorbidities to be hypertension, affecting $45\%$ of the entire population of the cohort, followed by obesity ($18\%$), and chronic obstructive pulmonary disease ($15\%$). At the time of the study (i.e., September 2020 to March 2021), the circulating COVID-19 variant in the British Isles was B.1.1.7.
**Table 1**
| Unnamed: 0 | Sepsis | Sepsis.1 | Sepsis COVID | Sepsis COVID.1 | Septic shock | Septic shock.1 | All patients |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | LDMOF | HDMOF | LDMOF | HDMOF | LDMOF | HDMOF | |
| n (%) | 21 | 9 | 28 | 22 | 18 | 14 | 112 |
| Age (mean ± standard deviation) | 58.2 ± 10 | 65.2 ± 15 | 62.4 ± 10 | 68.3 ± 10 | 63 ± 15 | 69.1 ± 16 | 64.7 ± 12.8 |
| Female (n) | 7 | 4 | 12 | 10 | 8 | 3 | 44 |
| Male (n) | 11 | 8 | 11 | 17 | 8 | 13 | 68 |
| ICU stay (days, mean) | 27 | 25 | 25 | 53 | 16 | 19 | 38.3 |
| Survived [n (%)] | 18 (85%) | 3 (33.3%) | 21 (75%) | 10 (45.4%) | 13 (72.2%) | 6 (42.8%) | 71 (63.3%) |
| SOFA score (mean) | 4.5 | 10.4 | 4.6 | 10.2 | 2.9 | 12 | 7.6 |
| White blood cells | 13.1 | 18.8 | 11.7 | 13.5 | 23 | 24.9 | 16.5 |
| Neutrophil count (mean) | 9.6 | 15.6 | 9 | 12.7 | 18.6 | 18.7 | 13.8 |
| Lymphocyte count (mean) | 1.2 | 0.9 | 0.8 | 0.8 | 2.1 | 2.7 | 1.3 |
| Positive culture (%)1 | 66.6% | 66.6% | 27% | 9% | 66.6% | 57% | 49% |
| Coinfection (sars-cov-2 + bacteria) (%) | – | – | 21% | 36% | – | – | – |
| Comorbidities (n [%]) | | | | | | | |
| Hypertension | 9 (43%) | 2 (22%) | 8 (28.5%) | 12 (54.5%) | 7 (39%) | 3 (21.5%) | 39 (35%) |
| Cancer | 2 (9.5%) | 1 (11%) | 1 (3.5%) | 2 (9%) | 3 (17%) | 6 (43%) | 15 (13.5%) |
| Asthma | 1 (5%) | 0 (0%) | 6 (21.5%) | 4 (18%) | 1 (5.5%) | 2 (14%) | 14 (12.5%) |
| Diabetes mellitus | 1 (5%) | 0 (0%) | 4 (14%) | 6 (27%) | 0 (0%) | 4 (28.5%) | 15 (13.5%) |
| Obesity | 2 (9.5%) | 2 (22%) | 8 (28.5%) | 3 (13.5%) | 5 (27.5%) | 0 (0%) | 20 (18%) |
| Chronic obstructive pulmonary disease | 5 (24%) | 3 (33%) | 5 (18%) | 2 (9%) | 0 (0%) | 2 (14%) | 17 (15%) |
We also employed a survival analysis by days 28 and 90 (Figure 2) to assess the mortality probability of sepsis, septic shock, and sepsis COVID patients. First, in Figure 2A, up to 28 days, the three groups of patients did not present significant differences in their survival probability ($$p \leq 0.051$$); however, the low p value indicates a trend among the three groups’ survivability rate, placing septic shock as the highest mortality rate after 28 days. When a 90-days analysis was considered (Figure 2B), the survival rate among the patients showed statistical significance ($$p \leq 0.044$$), where the septic shock group presented the lowest survival probability and sepsis the highest.
**Figure 2:** *Kaplan-Meier survival rate. Kaplan-Meier survival probabilities were identified. In (A), we show the Kaplan-Meier survival rate of sepsis, sepsis COVID, and septic shock patients after 28 days. Firstly, the data was identified as non-parametric (Shapiro-Wilk p = 1.377e-08) and the Kruskal-Wallis test was chosen to compare the averages (p = 0.051). In (B), we show the Kaplan-Meier curve for sepsis, sepsis COVID, and septic shock after 90 days, the data also follows a non-parametric distribution (Shapiro-Wilk = 6.036e-11) and the same Kruskal-Wallis test was employed to compare the averages (p = 0.044).*
Finally, to validate the binarization we performed, described in [18], we selected clinical parameters in our data that each, individually, represent the failure of a single organ. By following a logical expression that considers vasopressor as an exclusive (conjunction, AND) variable and platelets, bilirubin, creatinine, and P/F ratio as inclusive (disjunction, OR), our binarization resulted in an AUROC score of 0.91 followed by a Youden index of 0.71 (Supplementary Material S3).
## Outcome of feature selection algorithms
To select a subset of biomarkers that explain the target variables (i.e., LDMOF and HDMOF), we performed a feature selection process. We chose three algorithms of distinct classes, namely Boruta, LR, and IG. Each application returned a different subset of biomarkers with varied lengths. We show in Figure 3, through panels A, B, and C, the outcomes of the Boruta, LR, and IG algorithms, respectively. The subsets of biomarkers obtained are as follows: i) PDL1, IL15, IL6, VCAM, IL1ra, IL1b, IL10, and CCL2 in Boruta; ii) MPO, VCAM, IL1b, VEGFC, IL17a, GMCSF, ANG2, CCL2, IL12, GRANB, and SGP130 in LR; and iii) PDL1, GRANB, IL15, ICAM, and IL1ra in IG.
**Figure 3:** *Biomarker selection using three feature selection algorithms. (A) indicates the feature selection process using the Boruta algorithm. The first set of obtained variables was submitted to a tentative fix method to deliver a more reliable subset. In the plot, the columns shown in green are the ones confirmed by the algorithm to be statistically significant and have higher importance in describing the data’s label. (B) shows the results obtained by the feature selection using Lasso Regression. The x-axis of the figure indicates the log of lambda. Since Lasso Regression might be used as a classification model, the y-axis shows the AUC when including the number of variables shown at the top of the plot. The table below shows the variables stored in the lasso$lambda.min object, which corresponds to the variables with the best lambda values. The more distant a value is from zero, the more relevant it is for the predictor once that Lasso Regression sets the lambda = 0 to unimportant variables. (C) conveys the information gain derived from dataset entropy reduction achieved by the Information Gain algorithm. The vertical dashed line represents a threshold that considered the five most impactful biomarkers.*
## Assessing the classification capacity of groups of biomarkers with different algorithms
We assessed the classification feasibility with five different machine learning algorithms, i.e., SVM, RF, CART, KNN, and ANN. We fed the classification models with the input variables identified by each one of our three FS algorithms (Table 2). From that, we identified that the ANNs presented the most satisfactory predictability as its accuracy score outperformed the other methods.
**Table 2**
| Unnamed: 0 | Boruta (%) | Lasso Regression (%) | Information Gain (%) |
| --- | --- | --- | --- |
| CART | 58.0 | 57.6 | 64.66 |
| KNN | 58.33 | 58.0 | 56.66 |
| SVM | 57.33 | 58.0 | 58.33 |
| RF | 63.66 | 59.33 | 64.66 |
| ANN | 96.0 | 96.2 | 78.3 |
The results displayed in Figure 4 indicate the detailed performance of the ANN models. To identify the best performing subset of biomarkers, we selected the model that presented the best error drop rate after 100 epochs, area under the curve (AUC), accuracy, precision, specificity, and recall. Next, the selected model was trained and tested with the subsets of biomarkers obtained in the FS stage. From that, the IG algorithm did not produce satisfactory results due to the imbalance of its prediction capacity, in which a low recall value was found ($67.1\%$), indicating that it identified too many FNs in proportion of TPs, which is explained by the high error drop rate of the ANN model. Conversely, the ANNs trained/tested with the outcomes of LR and Boruta yielded satisfactory results, which is observed by the proximity of AUC, accuracy, and error drop rate (Figures 4A–C) as well as the balanced metrics provided for the models in Figure 5D. Therefore, ANNs successfully distinguished the severity of patients with the consortium of input biomarkers: i) PDL1, IL15, IL6, VCAM, IL1ra, IL10, IL1b, IL4, and ii) MPO, VCAM, IL1b, VEGFC, IL17a, GMCSF, ANG2, CCL2, IL12, GRANB, and SGP130.
**Figure 4:** *ANN classification performance. Three ANN models fed with different input biomarkers had their classification performance assessed over 100 learning epochs (Boruta in green, Lasso regression in orange, and information gain in blue). In (A), we compare the area under the curve of the three models; in (B) we show the accuracy of each model; and in (C) we show the error drop rate (represented by binary cross entropy). Finally, in (D), we show at the 100th epoch the accuracy, precision, recall, and specificity of each model.* **Figure 5:** *Decision pattern of ANNs classifying patients’ severity with different input biomarkers. The importance of each input biomarker determined by SHAP is displayed in (A, B) for the ANN derived of Boruta and Lasso Regression, respectively. In (C, D) we track the decision of two ANN models in classifying patients. For that, each biomarker is included in the y axis; each patient is represented by a dot in the plot, which has a high or low value corresponding to the biomarker concentration. Finally, the longitudinal location of the dots in the x axis indicates the impact in the SHAP value. Positive SHAP values are used to explain our target class, i.e., LDMOF while negative SHAP values represent the opposite class (i.e., HDMOF).*
## Model interpretation
To interpret the decision pattern of each ANN model, a SHAP approach was applied to the training dataset. Since the ANN trained/tested with the biomarkers obtained by the Information Gain approach did not produce satisfactory classification metrics, we opted not to interpret this erroneous classification model. For both Boruta and Lasso regression, we analyzed the SHAP value for each input biomarker. We targeted the LDMOF class out of our train dataset and checked the contribution of each biomarker in predicting the class. First, in Figures 5A, B, we show that both PDL1 and MPO were the biomarkers that mostly contributed to predictions in their models.
Next, in Figures 5C, D, we demonstrate the concentration of each biomarker in assigning the LDMOF label for each patient. From that, we see that there is a clear division between patients with high/low concentrations of input biomarkers (blue and red dots, representing each patient of the train set) getting negative and positive SHAP values, which directly affects the label assignment. To provide individual explanations for each biomarker, we targeted the ones that are positively correlated with LDMOF (i.e., VCAM, IL1ra, and IL4) and the biomarkers that are negatively correlated with HDMOF (i.e., PDL1, MPO, IL17a, and VEGFC). The remaining biomarkers did not have a clear separation between our target variables. Additionally, our two ANN models did not produce the same results regarding IL1b since it had different behaviors in each model. Therefore, the XAI approach of our tool enabled us to locate biomarkers with pro- and anti-inflammatory activities.
## Conserved programmed death Ligand-1 and myeloperoxidase signals across patients with distinct manifestations of organ failure syndromes
We selected the two biomarkers flagged by the ANN (i.e., MPO and PDL1) to look for statistical differences between biomarker concentration and i) the three diseases of our cohort (sepsis, septic shock, and sepsis COVID-19) and ii) the two severity levels our binarization considered (LDMOF and HDMOF) (Figure 6). Statistical significance ($$p \leq 0.007$$) was found only in the distinction of the MPO concentration among HDMOF and LDMOF patients (Figure 6A), while the severity groups could not be statistically explained in terms of their PDL1 concentration (Figure 6B). We only found statistical difference ($$p \leq 0.045$$) in using MPO as a distinguisher of sepsis and sepsis COVID (Figure 6C), while the PDL1 concentration could not statistically differentiate sepsis, sepsis COVID, and septic shock (Figure 6D).
**Figure 6:** *Statistical tests employed for PDL1 and MPO as characterizers of severity and disease model. Statistical significance tests comparing the averages of different groups of patients. In (A, B), we show the mean comparison of MPO and PDL1 (respectively) in distinguishing each one of the target labels (i.e., HDMOF and LDMOF). In (C, D), we employ the mean concentration MPO and PDL1 (respectively) in distinguishing sepsis, sepsis COVID, and septic shock.*
## Discussion
In our study, we could explain the severity of sepsis, sepsis COVID, and septic shock patients as a function of an unbalanced concentration of biomarkers. The input parameters of our function are subsets of biomarkers that explain a dysregulated host immune response. Moreover, we could isolate both MPO and PDL1 as the key contributors to the function.
The output parameters of our function are high and low degree of multi-organ failure (i.e., severity) of subgroups of patients that all meet sepsis-3 criteria, clinically placing the patients into a wider group, converging with past evidence [4]. Our findings allowed us to immunologically place the patients from different disease models together as we failed to find major statistical significances in the concentration of PDL1 and MPO (Figures 6C, D) that distinguish sepsis, sepsis COVID, and septic shock all together. The only differences we could observe were in the MPO concentration between sepsis COVID and sepsis patients, and we argue that this is a result of the generally higher neutrophil count in bacterial induced sepsis [22]. Finally, the SOFA score-based binarization we achieved is on par with results previously reported [18].
Next we showed that the WBC count increased with severity. Neutrophils, the most abundant WBC, were higher in the severe manifestations of the diseases we assessed. Moreover, the WBC and neutrophil count was lower in COVID sepsis, matching previous references of neutrophils being one of the most responsive cells toward bacterial infection [22]. We argue that the lower occurrence of bacterial infection found in COVID (i.e., superinfection) patients contributed to their lower count of WBC and neutrophils. Nonetheless, neutrophils remained an important immune cell to express cytokines, which might explain their response toward infection. Furthermore, no records of comorbidities influencing severity were found, except from cancer; we found the proportion of patients with cancer was higher in the severe form of the three disease models we assayed. The two key biomarkers we found (i.e., MPO and PDL1) are parts of important pathways in cancer immunotherapies [23] providing an opportunity for future studies of data science approaches for biomarkers involved in cancer.
Statistically, no significant differences were found that distinguished our patients based on their severity (Figures 6A, B). It was previously reported that when statistics do not reach a satisfactory classification performance, Machine Learning (ML) might be a valid approach [24]. We tried different ML approaches to classify our data. From five algorithms tested, only ANNs yielded a satisfactory classification. In fact, the robustness of this method was previously reported [24] as a solid way to find patterns in tabular data, among others. Additionally, the appropriate selection of the input variables is a key process in obtaining satisfactory results [25]. In many cases, classification and regression models derived from lower-dimensional datasets benefit the downstream decision-making process [26, 27]. We further address this discussion by linking the appropriate selection of biomarkers with easily interpretable results in an information curation step. In this study, we were able to reduce a total of 30 biomarkers using three FS algorithms into subsets that conveyed satisfactory classification results in two instances. The three algorithms we selected belong to diverse classes of FS methods. In fact, they all have successfully been applied in reducing the complexity of ML inputs (20, 28–32) without creating synthetic datasets. The results we obtained highlight the reduced complexity in employing data-preprocessing (DP) techniques. In fact, DP accounts for most of the workload involved in ML applications [33]. We link the lack of success of the information gain algorithm in producing satisfactory predictability to the fact that this algorithm will only look for the association between input variables with a label (i.e., biomarkers and LDMOF/HDMOF). The other two FS algorithms will fit a model to determine the importance of each individual variable in predicting a label, granting them mathematical robustness. Therefore, we argue that a systematic evaluation of DP techniques such as the one here proposed is highly beneficial for developing in-silico models.
After selecting optimal input biomarkers, we applied them in a classification system that uses this immunological information to predict the severity trajectory of critically ill patients. For that, our model, when fed with distinct subsets of biomarkers, could predict patients’ severity. In fact, biomarkers have themselves been proposed as good predictors of a plethora of medical conditions (34–36); on the immunological side, they have been associated with pro- and anti-inflammatory responses [37] and we gathered evidence in support of our model succeeding to capture this. There are hundreds of biomarkers containing valuable information about the organic systems of the body and their functions. For their capacity to be interpreted, and consequently aid decision making and drug development, we argue that biomarkers related to a specific condition be systematically selected as we have proposed in this study.
We interpret our ANN model by linking the concentration of biomarkers in explaining LDMOF. First, Multiple Organ Dysfunction and even death have been reported to be associated with increased levels of VCAM-1 in adults and neonates diagnosed with sepsis (38–40). Similarly, our model shows a negative correlation between increased VCAM levels and LDMOF development. Next, IL1ra was used in a recombinant treatment and was successful in reducing levels of mortality [41]. Our last biomarker positively associated with LDMOF is IL4, which was reported to act together with IL6 to induce Th2 cells and macrophage differentiation [42]. Lower concentrations of this biomarker were associated with lower mortality of severe sepsis patients [43].
We spotted four biomarkers that are negatively correlated with LDMOF, granting them a negative effect on a patient’s favorable outcome. First, low concentrations of IL17a have been reported as a good predictor for mortality in sepsis caused by distinct pathogens [44, 45]. Next, septic shock and sepsis can lead to hypoxia due to tissue hypoperfusion [1]. A transcription factor named hypoxia-inducible factor 1-alpha (HIF-1α) accumulates in cells under hypoxic conditions and can upregulate the expression of VEGF and PD-L1 [46, 47]. The activated HIF pathway can also trigger the activation of innate immune cells, including macrophages, dendritic cells, neutrophils, and natural killer cells [48]. Furthermore, increased levels of PD-L1 will suppress the adaptive immune response by inhibiting the proliferation and activity of the CD4+ effector T cells and enhancing the differentiation of Tregs [49, 50]. This persistent inflammatory condition caused by innate immune activation along with suppressed adaptive immune response contributes to hypoxia-induced organ failure. It has also been reported that PD-L1 knock-out animals show a better survival rate after a septic challenge compared to wild-type animals [51]. VEGF is also known to induce VCAM-1 expression, and its upregulation is correlated with damaged vascular endothelium and organ dysfunction [40, 52]. Thus high levels of PD-L1 and VEGF can be considered key markers of multi-organ failure. Finally, myeloperoxidase (MPO), an enzyme produced by neutrophils, is found to be increased in severe patients suffering from septic shock, despite having no significant difference in neutrophil count [53]. In another study, MPO levels during the early stages of sepsis were found to be negatively correlated with patient survival [54].
The mapping of the XAI model provided meaningful information on the course of dysregulated immune responses and also converged with clinical interpretations regarding the neutrophil count of the disease models (i.e., neutrophils tend to increase with severity). A compelling example is the pro-inflammatory function our model attributed to MPO. This enzyme is primarily produced by the granulocytes of neutrophils. The overexpression of MPO generates harmful chemicals that have a detrimental effect on organ inflammation [11]. Our model also identified PDL1 as a pro-inflammatory protein. The blockade of the binding between PDL1 and PD1 might inhibit lymphocytes from apoptosis. The upregulation of PDL1 by neutrophils is increased in sepsis as the higher migration of these cells might allow them to be trapped in the lung vasculature [55, 56]. Therefore, with more neutrophils expressing PDL1, immunosuppressant effects start to occur with the death of neutrophils. The highest counts of neutrophils were found in the severe manifestations of the three disease models we investigated. Therefore, we can track the course of the multi-organ failure syndrome of our patients with increased neutrophils leading the overexpression of MPO and PDL1.
In-silico models are an efficient paradigm of experimentation. Compelling examples are found in big data-based applications that have been assisting in several areas in the medical sciences, such as predicting heart attacks [57], telediagnosis [58], and preventing disease outbreaks [59], among others. A guideline proposed by the Center for Drug Evaluation and Research (CDER) [60] shows that the development of therapeutics might initiate with screening characteristics that indicate biological processes. Here we propound to employ data science as an initial step for screening biomarkers, enabling the gathering of solid mathematical evidence for linking concentrations of biomarkers with patients’ severity.
## Conclusions
In the present study, we built a function whose input is the concentration of biomarkers and the output is the level of severity of a patient. For our goal to be achieved, a systematic data mining procedure enabled us to identify the upregulation of PDL1 and MPO as good predictors of severity in sepsis (viral and bacterial induced) and septic shock patients. After interpreting the results both clinically and immunologically, we found that there is solid medical and biological evidence for why the upregulation of PDL1 and MPO is a major driver of severity. To this extent, we posit that data mining routines such as the one we proposed be used to identify the biomarkers that can function as part of an early diagnosis system.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material.
## Ethics statement
The studies involving human participants were reviewed and approved by Health Research Consent Declaration Committee (HRCDC) under the register REC: 2020-05 List 17 and project ID 0428. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
GM, DK, IM-L, AG and AO designed the study. GM worked on the machine learning approach. GM prepared the figures. AO linked the machine’s decision pattern with immunological activity. AO, AA-R, and DK performed the immunoassays. AG, RC, and IM-L managed the clinical aspect of the study. GM, AO, AA-R prepared the draft version. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1137850/full#supplementary-material
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|
---
title: 'Polycystic ovary syndrome and 25-hydroxyvitamin D: A bidirectional two-sample
Mendelian randomization study'
authors:
- Nana Zhang
- Yan Liao
- Hongyu Zhao
- Tong Chen
- Fan Jia
- Yue Yu
- Shiqin Zhu
- Chaoying Wang
- Wufan Zhang
- Xinmin Liu
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10034407
doi: 10.3389/fendo.2023.1110341
license: CC BY 4.0
---
# Polycystic ovary syndrome and 25-hydroxyvitamin D: A bidirectional two-sample Mendelian randomization study
## Abstract
### Background
Accumulating observational studies have indicated that vitamin D deficiency (serum 25-hydroxyvitamin D (25OHD) < 50 nmol/L) is common in women with polycystic ovary syndrome (PCOS). However, the direction and causal nature remain unclear. In this study, we aimed to investigate the causal association between PCOS and 25OHD.
### Methods
A bidirectional two-sample Mendelian randomization (MR) study was used to evaluate the causal association between PCOS and 25OHD. From the publicly available European-lineage genome-wide association studies (GWAS) summary statistics for PCOS (4,890 cases of PCOS and 20,405 controls) and 25OHD ($$n = 417$$,580), we selected 11 and 102 single nucleotide polymorphisms (SNPs) as instrumental variables (IVs), respectively. In univariate MR (uvMR) analysis, inverse-variance weighted (IVW) method was employed in the primary MR analysis and multiple sensitivity analyses were implemented. Additionally, a multivariable MR (mvMR) design was carried to adjust for obesity and insulin resistance (IR) as well.
### Results
UvMR demonstrated that genetically determined PCOS was negatively associated with 25OHD level (IVW Beta: -0.02, $$P \leq 0.008$$). However, mvMR found the causal effect disappeared when adjusting the influence of obesity and IR. Both uvMR and mvMR analysis didn’t support the causal effect of 25OHD deficiency on risk of PCOS (IVW OR: 0.86, $95\%$ CI: 0.66 ~ 1.12, $$P \leq 0.280$$).
### Conclusion
Our findings highlighted that the casual effect of PCOS on 25OHD deficiency might be mediated by obesity and IR, and failed to find substantial causal effect of 25OHD deficiency on risk of PCOS. Further observational studies and clinical trials are necessary.
## Introduction
Polycystic ovary syndrome (PCOS) is a common and complex endocrinopathy affecting approximately $4\%$-$21\%$ of women of reproductive age around the world [1]. Characterized mainly by ovulation disorder, clinical and/or biological hyperandrogenism and/or an ultrasound aspect of polycystic ovaries [1, 2], it has been confirmed that PCOS is associated with certain metabolic disorders, such as obesity, insulin resistance(IR) and sometimes even type 2 diabetes mellitus (T2DM) [3].
Vitamin D, as an easy to supplementation and fat-soluble vitamin, has aroused great interest in the link with PCOS in recent decades. 25-hydroxyvitamin D (25OHD) is the main circulating form to evaluate the status of vitamin D. Accumulating observational studies have indicated that vitamin D deficiency [serum 25OHD < 50 nmol/L [4]] is common in women with PCOS. Convincing evidence has shown that 25OHD deficiency is associated with obesity [5, 6] and IR [7]. Metabolic disorders, such as obesity and IR, has been proposed as the possible confounding factors between PCOS and 25OHD deficiency [8]. In fact, due to confounding factors and reverse causation, the real relationship between PCOS and 25OHD remains unclear.
Mendelian randomization (MR) can strengthen inferring causality and give a robust estimation between a risk factor and an outcome of interest [9]. Compared with observational studies, MR, in particular, multivariable MR (mvMR), uses single nucleotide polymorphisms (SNPs) identified from the genome-wide association study (GWAS) as instrumental variables (IVs) and is less susceptible to confounding, as genetic variants are randomly allocated at conception [10, 11]. Therefore, we integrated the univariable MR (uvMR), mvMR and bidirectional MR approaches to investigate the possible causal association between PCOS and 25OHD.
## Study design
The bidirectional two-sample MR study was conducted in the framework shown in Figure 1. In order to have a valid interpretation for the MR analysis, it is necessary that the following three assumptions [10] hold: Assumption 1, IVs are robustly correlated with the exposure; Assumption 2, IVs are independent of any confounding factors between the relationship of exposure and outcome; Assumption 3, IVs influence outcome only via exposure. In this study, SNPs were employed as IVs to perform bidirectional two-sample MR to explore the causal association between PCOS and 25OHD.
**Figure 1:** *The framework of the bidirectional two-sample Mendelian randomization study between polycystic ovary syndrome and 25-hydroxyvitamin D. SNP, single nucleotide polymorphism; BMI, body mass index; WHR, waist-to-hip ratio; WC, waist circumference; HC, hip circumference; LD, linkage disequilibrium.*
## GWAS summary statistics of PCOS
The GWAS summary statistics of PCOS were obtained from the latest and largest published GWAS meta-analysis, including 4,890 patients with PCOS and 20,405 health controls of European ancestry, and were adjusted for age [12] (Supplemental Material S1). Diagnosis of PCOS was based on National Institutes of *Health criteria* [13], *Rotterdam criteria* [2], or self-report questionnaire [14].
## GWAS summary statistics of 25OHD
The GWAS summary statistics of 25OHD were obtained from 417,580 European UK Biobank participants with available serum 25OHD levels [6], and adjusted for age at assessment, sex, assessment month, assessment center, first four ancestry principal components, genotyping batch, supplement intake (variable with four levels, namely: “no information”, “never taken”, “other supplements”, “25OHD supplements”) [6] (Supplemental Material S1).
## Ethical approval
Our MR study was performed using publicly published studies or shared datasets, which had acquired ethical approval and informed consent. No additional ethics statement or consent was required.
## Instrumental variable selection
SNPs were chosen as IVs, which sanctified the genome-wide significance threshold of $p \leq 5$ × 10–8, and none of which surpassed the limited value (r2 < 0.001 within a clumping window of 10,000 kb) in linkage disequilibrium (LD) analysis [15]. Palindromic SNPs with regardless of allele frequency were removed from the analyses [15]. Only the SNPs that existed in the GWAS summary statistics of outcome were included as IVs, and the proxy SNPs were not included in the analysis [16, 17]. Each SNPs’ power was evaluated by the F statistics (F = Beta2/SE2) [18]. Finally, SNPs with less statistical power would be removed (F statistics<10). The proportion of variance of the exposure explained by SNPs was evaluated by R2. R2 of each SNP was calculated by the following formula: 2 × EAF × (1-EAF) × Beta2, and summed them up to calculate the overall R2, where EAF represents the effect allele frequency of the SNP [19].
Eventually, 11 SNPs for PCOS and 102 SNPs for 25OHD were separately extracted from the GWAS summary statistics. The F statistics of all selected SNPs ranged separately from 30.84 to 57.66 and 30.07 to 1,468.55, demonstrating that all selected SNPs had sufficient validity. Moreover, the total variance explained by the genetic instruments was $6.23\%$ and $2.71\%$, respectively. The detailed information of all selected SNPs of PCOS and 25OHD was presented in Supplemental Material S2-S3.
## Mendelian randomization estimates
Fixed-effect inverse-variance weighted (IVW) method was conducted as the primary methods for uvMR estimates [20]. The IVW method requires all instrumental variants to be valid, and will return an unbiased estimate if the horizontal pleiotropy is balanced [15]. MR-Egger, weighted median, MR-pleiotropy residual sum and outlier (MR-PRESSO) [21] were further employed to control for horizontal pleiotropy as complementary methods [20]. MR-Egger regression has a lower statistical power with a wide range of causality estimates [22]. It adapts the IVW analysis by allowing a non-zero intercept, allowing the net-horizontal pleiotropic effect across all SNPs to be unbalanced, or directional [15, 22]. If more than $50\%$ of the weight derived from effective IVs, the weighted median method may yield a more robust estimate of causality [23]. MR-PRESSO considered the horizontal pleiotropy and could detect and correct for outlier SNPs reflecting pleiotropic biases [21]. In addition, leave-one-out sensitivity analysis, Cochran’s Q statistic and MR-Egger intercept test [22, 24] were also used to test for heterogeneity and pleiotropy. MvMR was adopted to estimate the direct effect of exposure on outcome independent of important confounders including obesity and IR. Body mass index (BMI), waist-to-hip ratio (WHR), waist circumference (WC) and hip circumference (HC) were selected as indicators to access obesity. IR was represented by fasting insulin. The GWAS summary statistics of BMI, WHR, WC, HC and fasting insulin were all from the publicly available IEU Open GWAS Project database (https://gwas.mrcieu.ac.uk/), for which details were shown in Supplemental Material S1.
MR analyses were performed using the packages “TwoSampleMR” [15] (version 0.5.6) and “MRPRESSO” (version 1.0) through R Software (version 4.1.2). Statistical significance was set at $P \leq 0.05.$
## Causal association of PCOS on 25OHD via forward MR
The results of the uvMR analyses were shown in Table 1. The IVW method showed that genetically determined concentrations of PCOS were negatively associated with 25OHD. In the main IVW analysis, per standard deviation increase in PCOS was associated with decrease in 25OHD concentration of 0.02 nmol L−1 (OR = 0.98, $95\%$ CI = 0.97~1.00, $$P \leq 0.008$$). All other MR estimates were consistent with the direction of the main IVW estimate ($P \leq 0.05$), and no outlier SNPs were observed in the MR-PRESSO analysis. However, there were some SNPs crossing the zero line in leave-one-out sensitivity analysis, which suggested potential heterogeneity (Figure 2A). Cochran’s Q statistics showed there remained little evidence of heterogeneity among SNPs of PCOS. No pleiotropy was detected (MR-Egger intercept = 0.070, $$P \leq 0.710$$).
Adjusting the influence of confounders including obesity (BMI, WHR, WC and HC) and IR (represented by fasting insulin) in mvMR analysis, we found that it was disappeared for the causal relationship between genetically predicted PCOS and 25OHD ($P \leq 0.05$) (Figure 3; Supplemental Material S4). In addition, we found that BMI decreased 25OHD concentration (Beta: -0.09, $P \leq 0.001$), which was in keeping with previous MR study [6, 25].
**Figure 3:** *Comparisons of Mendelian randomization results. (A) Comparisons of Mendelian randomization results for PCOS on 25OHD; (B) Comparisons of Mendelian randomization results for 25OHD on PCOS. PCOS, polycystic ovary syndrome; 25OHD, 25-Hydroxyvitamin D; OR, odds ratio; 95% CI, 95% confidence interval; BMI, body mass index; WHR, waist-to-hip ratio; WC, waist circumference; HC, hip circumference.*
## Causal association of 25OHD on PCOS via reverse MR
As shown in Table 1, the IVW method showed OR = 0.86, $95\%$ CI = 0.66~1.12, $$P \leq 0.280$$, and the other uvMR methods also obtained generally consistent results ($P \leq 0.05$) with the main method in the opposite direction. There was no evidence suggested the possible causal relationship between genetically predicted 25OHD and PCOS. Leave-one-out sensitivity analysis (Figure 2B) did not identify any leverage points with high influence, suggesting the stability and reliability of MR results. Cochran’s Q statistics showed there was no heterogeneity among SNPs of 25OHD. The MR-Egger intercept test demonstrated no evidence of directional pleiotropy ($P \leq 0.05$). Besides, no outlier SNPs were observed in the MR-PRESSO analysis.
Adjusting the influence of confounders including obesity and IR in mvMR analysis, we still found no causal relationship between genetically predicted 25OHD and PCOS ($P \leq 0.05$), which was consistent with the results of univariable MR (Figure 3; Supplemental Material S4). In addition, when 25OHD and BMI were assessed together using mvMR method, we found that genetically predicted BMI increased the risk of PCOS (OR = 2.98, $95\%$ CI = 2.12~4.19, $P \leq 0.001$), so did fasting insulin (OR = 2.30, $95\%$ CI=1.02~5.15, $$P \leq 0.044$$).
## Discussion
This study explored the relationships between PCOS and 25OHD using a bidirectional two-sample MR design for the first time, which adopted the strong IVs from the largest GWAS of respective phenotypes in European populations. Our study found that genetically predicted PCOS was weakly inversely associated with deficiency of 25OHD. However, the casual association may be unstable and would disappear after correcting the influence of obesity and IR. The effects of PCOS on 25OHD might be accounted for obesity and IR. Meanwhile, our study didn’t support the causal effect of 25OHD on PCOS.
Previous observational studies have found that there seems to be a link between PCOS and 25OHD, while the causality is still not yet fully clear. Some studies reported that PCOS was a risk factor for vitamin D insufficiency (26–30). Recently, a retrospective cross-sectional study involving Chinese with PCOS also discovered that the low level of 25OHD was prevalent in PCOS women [31]. On the contrary, a systematic review and meta-analysis found that lower concentrations of serum 25OHD lead to a greater risk of developing PCOS [32]. Besides, Panidis D et al. ’s study grouped the patients with PCOS and controls according to BMI [33]. They found that increased BMI had a significant negative effect on 25OHD, but didn’t support the association between PCOS on 25OHD [33]. Some studies showed convincing data that low 25OHD levels were associated with obesity and IR, but not with PCOS per se [26, 32, 33], which was consistent with our outcome.
The real relationship between PCOS and 25OHD might be interpreted as follows: An increasing body of clinical evidence [27, 28, 34, 35] suggested that insufficient 25OHD were associated with hyperandrogenism, metabolic syndrome, IR and increased BMI, body fat percentage and WC. Furthermore, a few MR studies provided more evidence that BMI had causal effect on 25OHD deficiency [6, 25], which was also found in our mvMR study. For one thing, most people with obesity and overweight would have lower outdoor physical activity and would cover their body with more clothes, so they might not get sufficient sun exposure [36]. For another, being sequestered in adipose tissue, the bioavailability of vitamin D could get reduced in obesity patients [37]. Many published articles [38] suggested the association of vitamin D deficiency and IR. It cannot be ignored is that most existing studies emphasized vitamin D played a significant role in the pathogenesis of IR [32], but the pathological mechanism of IR effect on vitamin D is not yet clear. Meanwhile, obesity and IR (even in the absence of obesity) are common and important feature of PCOS [39, 40]. The above may account for the phenomenon of 25OHD deficiency in PCOS patients and the reason why this phenomenon disappears after adjusting for obesity and IR in our study. Anyway, our study provides new evidence that the effects of PCOS on 25OHD might be accounted for by obesity and IR.
Seeing that observational design is susceptible to reverse causality and potential confounders, current results of clinical studies between PCOS and 25OHD may bias the true relationship. For instance, most of the included studies were lack of control of BMI and IR. Obesity and IR have been confirmed to be important pathogenic factors of PCOS [41] and could lead to the decrease of 25OHD [6, 38, 42]. Consequently, BMI and IR may be important mediators and confounders for the PCOS-25OHD relation, resulting that the independent PCOS-25OHD relation could not be assessed effectively. More importantly, no prospective large-scale longitudinal cohort studies have been conducted so far, it is still not sufficient to draw a clear conclusion on the causal relationship between PCOS and 25OHD. Unlike traditional observational epidemiological studies, our study reduced the impact of reverse causality and potential confounders, and explored the relationship between PCOS and 25OHD more accurately.
Nonetheless, several limitations in our study cannot be ignored. Firstly, the proportion of PCOS cases was relatively low and could bring compromised statistical power, failing to detect true causal relationship. Secondly, we failed to evaluate the causality between different clinical phenotypes of PCOS on 25OHD, due to the lack of GWAS data of PCOS phenotypes. We were not able to further stratify our outcomes and identify the risk of each phenotype, although there was great heterogeneity between different phenotypes of PCOS. Therefore, without further stratification, the causal relationship between PCOS and 25OHD obtained may be mixed and inaccurate. Thirdly, although many methods were used to control and evaluate the pleiotropy, the bias caused by gene pleiotropy could not be completely ruled out. Finally, the participants involved in our study were all from European ancestry. Therefore, it should take care when extending our conclusions to people of other ancestry.
## Conclusion
In summary, our results highlighted that the causal effect of PCOS on 25OHD may be mediated by the negative effect of obesity and IR on 25OHD. Future studies with larger MR studies, clinical trials and further observational studies are highly warranted to confirm the results of our present study.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.
## Author contributions
Conception and design: NZ; data curation: HZ and FJ; analysis: SZ and YY; software and visualization: CW and WZ; writing—original draft, review and editing: NZ and YL; funding acquisition: TC and XL. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1110341/full#supplementary-material
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|
---
title: FDA-approved Abl/EGFR/PDGFR kinase inhibitors show potent efficacy against
pandemic and seasonal influenza A virus infections of human lung explants
authors:
- Robert Meineke
- Sonja Stelz
- Maximilian Busch
- Christopher Werlein
- Mark Kühnel
- Danny Jonigk
- Guus F. Rimmelzwaan
- Husni Elbahesh
journal: iScience
year: 2023
pmcid: PMC10034449
doi: 10.1016/j.isci.2023.106309
license: CC BY 4.0
---
# FDA-approved Abl/EGFR/PDGFR kinase inhibitors show potent efficacy against pandemic and seasonal influenza A virus infections of human lung explants
## Summary
Influenza viruses (IVs) cause substantial global morbidity and mortality. Given the limited range of licensed antiviral drugs and their reduced efficacy due to resistance mutations, repurposing FDA-approved kinase inhibitors as fast-tracked host-targeted antivirals is an attractive strategy. We identified six FDA-approved non-receptor tyrosine kinase-inhibitors (NRTKIs) as potent inhibitors of viral replication of pandemic and seasonal IVs in vitro. We validated their efficacy in a biologically and clinically relevant ex vivo model of human precision-cut lung slices. We identified steps of the virus infection cycle affected by these inhibitors and assessed their effect(s) on host responses. Their overlapping targets suggest crosstalk between Abl, EGFR, and PDGFR pathways during IAV infection. Our data and established safety profiles of these NRTKIs provide compelling evidence for further clinical investigations and repurposing as host-targeted influenza antivirals. Moreover, these NRTKIs have broad-spectrum antiviral potential given that their kinase/pathway targets are critical for the replication of many viruses.
## Graphical abstract
## Highlights
•Five of six NRTKIs that inhibited IAV replication in vitro were validated ex vivo•These NRTKIs inhibited the replication of the H3N2 and H1N1 IAV subtypes•NRTKIs targeting EGFR, PDGFRα, and Abl displayed the most potent IAV inhibition•Tested NRTKIs showed a high genetic barrier to the emergence of resistance mutations
## Abstract
Virology; Cell biology
## Introduction
Influenza viruses (IVs) cause respiratory tract infections in humans and are responsible for substantial annual morbidity and mortality, especially in individuals at high risk, like young children, the elderly, or immunocompromised patients. The most important preventative measure of protection from IV infections is vaccination. However, accumulation of genomic mutations contributes to reduced vaccine efficacy and evasion of virus-neutralizing antibodies; necessitating annual update vaccines against seasonal IVs.1,2,3,4 Moreover, genetic reassortment that leads to the emergence of novel IVs in human populations, that largely lack virus-neutralizing antibodies, can result in pandemic outbreaks. As with previous influenza pandemics and the current SARS-CoV-2 pandemic, effective vaccines are not readily available at early stages of a pandemic.
In absence of efficacious vaccines, virus-targeted antivirals offer some protection if administered within the therapeutic window. Until recently, only two classes of influenza antivirals were available, adamantanes targeting the viral M2 ion channel protein and neuraminidase inhibitors (NAI). However, adamantanes are ineffective due to resistance mutations in currently circulating strains and are no longer clinically used.5 In contrast, only ∼4–$5\%$ of currently circulating viruses carry resistance mutations to NAIs (like oseltamivir).6 Favipiravir (T705), baloxavir and pimodivir are recently developed antivirals targeting the viral PA, PB1, and PB2 polymerase proteins, respectively, and can inhibit adamantane- and NAI-resistant viruses.7,8 Although baloxavir was approved for the treatment of acute “uncomplicated” influenza infections in the United States in 2018, recent studies have already observed ∼$10\%$ of isolates from otherwise healthy adults and adolescents carried baloxavir resistance mutations; this may potentially be higher in immunocompromised patients.9,10,11,12 Sustained circulation of virus variants resistant to current antivirals and low genetic barrier for resistance highlight the need for host-targeted therapeutics; that do not suffer from these limitations. Because all viruses rely on cellular machinery for their replication, several host proteins are known to be required for efficient viral replication and pathogenesis.13,14,15,16,17 Host kinases regulate signaling pathways that are critical for replication of many viruses including influenza by direct phosphorylation of viral proteins or their cellular interaction partners.13,14,18,19,20 Although the human kinome consists of >550 kinases, only 62 small-molecule kinase inhibitors (SMKIs) are FDA-approved, primarily used to treat cancers and mostly target tyrosine kinases.21 Non-receptor tyrosine kinases (NRTKs) are cytoplasmic/membrane-anchored kinases that closely associate with cellular receptors or receptor complexes to mediate outside-in signaling.21,22,23,24,25,26 NRTKs like most kinases contain a catalytic kinase domain, and several protein-protein interaction motifs (e.g., SH2, SH3, and PH domains) necessary to relay cell signals. NRTKs including Abl, FAK, JAK, Src, and BTK have all been reported to play a role in IAV infections. Previous studies demonstrated that NRTK inhibitors (NRTKIs) modulate pro- and anti-viral signaling in vitro resulting in reduced viral pathogenesis and increased survival in vivo.13,16,27,28,29,30,31,32 However, no NRTKIs or SMKIs are approved for clinical use against IVs. Here, we identified, validated, and characterized five currently available FDA-approved NRTKIs as a potent inhibitor of IAV replication in vitro and ex vivo human lung tissue.
## NTRKI treatment inhibits IAV replication in vitro
We first identified non-toxic SMKI concentrations (≥$90\%$ relative to DMSO-treated cells) using CellTiter-Glo (CTG), an ATP-based cell-viability assay. The highest concentration with ≥$90\%$ relative viability is defined as 1x concentration ([1x]max) (Figure 1A/Table 1). Next, A549 cells were infected with either pandemic A(H1N1)pdm09 A/Netherlands/$\frac{602}{09}$ (NL09) or seasonal A(H3N2) A/Netherlands/$\frac{241}{11}$ (NL11) strains at a multiplicity of infection (MOI) of 1 in presence or absence of [1x, 0.5x and 0.25x]max of respective NRTKIs following inoculation. We observed dose-dependent viral titer reductions (from 2- to 1,000-fold) by six of eight NRTKIs (Figures 1B and S1). We observed variability in the magnitude and duration of titer reductions with more pronounced effects in NL11 (H3N2) infected cells than in NL09 (H1N1) infected cells. Although we observed only a transient reduction at 24 hpi with pan JAK (JAK1/⅔) inhibitor Tofacitinib (TF), we did not observe any significant reduction with the JAK$\frac{1}{2}$ selective inhibitor Ruxolitinib (RX). The highest and most sustained level of reduction (≥1,000-fold or 3-log10) was observed with Nilotinib (NI) (Abl/PDGFRa inhibitor). Bosutinib (BO) (Abl/Src/Btk inhibitor and Saracatinib (SA) (Src inhibitor) also showed inhibition (SA ∼5 to 25-fold; BO ∼10 to 1,000-fold). Acalabrutinib (AC) (Btk inhibitor) had minimal effect on NL09 replication but greater and longer inhibition (5- to 25-fold) of NL11 replication at higher concentrations (0.25 and 0.5 μM). Ibrutinib (IB) (Btk/EGFR inhibitor) had minimal effects on NL09 replication but a more appreciable and sustained (5- to 100-fold) reduction of NL11 replication at the highest concentration used (0.5 μM). Defactinib (DF) (FAK/Pyk2 inhibitor) treatment resulted in robust viral titer reduction (10- to 1,000-fold) in both NL09 and NL11 at higher concentrations (2.5 and 5.0 μM). In DF-treated NL09 infected cells, a reduction was more robust at earlier times (24 and 48 hpi), but in NL11 infected cells a larger reduction was observed at 24 and 72 hpi. Figure 1Effect of NRTKI treatment on IAV replication, infectivity, and viability(A) Table 1. Main targets of FDA-approved NRTKIs used in this study.(B) A549 cells were infected with NL09 or NL11 at MOI = 1 +/− indicated NRTKIs at [0.25x, 0.5x, or 1x]max concentration for 72 h. Viral titers were quantified by TCID50/mL assay at 24, 48, and 72 hpi and visualized by Heatmap of fold-change in viral titers relative to DMSO treated ($$n = 4$$/condition). Also seeFigure S1.(C and D) A549 cells were infected with NL09 or NL11 at MOI = 1 and incubated for 48 h in the presence of SMKIs ([0.5x]max concentration). Fluorescence microscopy images were acquired from cells stained for infection by anti-IAV NP antibody (red), and nuclei by using NucBlue Live ReadyProbes (blue). Data visualized by Heatmap are % infectivity (C) and % cell viability (D) relative to untreated infected cells or mock-infected treated cells, respectively ($$n = 4$$/condition) Also seeFigure S2. Images quantified by ImageJ software. p values were determined by Mann-Whitney tests compared to untreated cells.
## NRTKIs affect IAV infectivity and cell viability during IAV infection
Next, we determined the effect of NRTKIs on viral infectivity (number of infected cells) and cellular viability (total number of cells) in infected A549 cells by quantitative immunofluorescence microscopy. Treatment with most inhibitors caused a significant increase in infectivity despite reduction of viral titers. We observed robust significant increases in relative infectivity in cells treated with TF (NL09 = $217\%$, NL11 = $112\%$), RX (NL09 = $203\%$, NL11 = $116\%$), and DF (NL09 = $180\%$, NL11 = $139\%$). Among BO- and NI-treated cells, we only observed marginal increases in relative infectivity ($105\%$) in cells treated with BO or NI in NL09-infected cells. We observed marked decreases in relative infectivity in cells treated with AC (NL09 = $78\%$, NL11 = $74\%$) and SA (NL09 = $90\%$). We observed opposite effects on NL09-infected cells following treatment with IB (NL09 = $116\%$) (Figures 1C and S2).
Next, we determined whether NRTKI treatment improved cell viability during IAV infection. In mock-infected A549 cells treated with tested NRTKIs at [0.5x]max, <$5\%$ reduction in cell viability was observed (Figure 1D). However, IAV infection caused significant synergistic cytotoxicity and decreased viability with TF (NL09 = $40\%$, NL11 = $63\%$), RX (NL09 = $66\%$, NL11 = $89\%$), or DF (NL09 = $84\%$, NL11 = $92\%$) treatment. However, cell viability was increased by treatment of IAV-infected cells with AC (NL09 = $112\%$, NL11 = $109\%$), IB (NL09 = $102\%$, NL11 = $109\%$) BO (NL09 = $111\%$, NL11 = $114\%$), NI (NL09 = $110\%$, NL11 = $110\%$) or SA (NL09 = $150\%$, NL11 = $127\%$) (Figure 1D). Notably, this effect was only statistically significant for the treatment with SA. Therefore, neither decreased infectivity nor cell viability alone does not account for the observed NRTKI-induced reduction in viral titers.
## The antiviral effect of NRTKIs is MOI-independent
We excluded RX and TF from subsequent studies due to reduced viability and limited effects on viral titers. To determine if the NRTKI’s antiviral effects were MOI-dependent, we infected A549 cells with either a high MOI (MOI = 3) or a low MOI (MOI = 0.01) in the presence or absence of NRTKIs [0.5x]max. The effect on the seasonal H3N2 (NL11) strain was more pronounced compared to that on the pandemic H1N1 (NL09) strain; presumably due to the faster growth kinetics of NL11 compared to NL09 (Figure 2). However, NRTKI treatment of cells infected at high [3] or low (0.01) MOI resulted in viral titer reductions of ≥10-fold (1-log10) (Figure 2). Treatment with DF, BO, or NI had larger effects on early (24 hpi) viral replication, especially in NL11 infected cells. Although peak titers of untreated cells infected with NL11 were similar at either MOI, cells infected at MOI = 0.1 exhibited the greatest reduction (∼1,000-fold or 3-log10). AC, IB, and SA treatment had less of an impact on viral titers than DF, BO, or NI, but viral titer reductions were still up to 100-fold (2-log10) (Figure 2).Figure 2MOI-independent effect of NRTKIs on IAV infectionA549 cells were infected with NL09 and NL11 at (MOI = 0.1 or 3) and incubated for 72 h in presence of NRTKIs at [0.25x]max (gray) and [0.5x]max (white) concentrations. At 24, 48, and 72 hpi, supernatants were collected and viral titers quantified by TCID50/mL assay ($$n = 4$$). L.o.d.: limit of detection. Means ± SD are shown. ∗, $p \leq 0.05$; ∗∗, $p \leq 0.01$; ∗∗∗, $p \leq 0.001$; ∗∗∗∗, $p \leq 0.0001.$ p values were determined by Mann-Whitney tests compared to untreated cells.
## Validation of NRTKIs as IAV antivirals in hPCLS
We utilized human precision-cut lung slices (hPCLS) as a biologically relevant ex vivo model that more faithfully represents lung tissues than either 2D monolayer or 3D air-liquid-interface (ALI) cultures.33,34 We used hPCLS cultured up to 4 weeks; no gross alterations in cell type or morphology were observed and cilial beating was observed. We infected hPCLS with 104, 105, or 106 TCID$\frac{50}{200}$ μL of NL09 or NL11 to identify an infectious dose where peak titers of NL09 or NL11 are synchronized; these strains have different replication kinetics in vitro. To limit donor-heterogeneity effects, we used hPCLS from 8 donors ($$n = 24$$/virus). Highest peak titers were achieved at 48 hpi following infection with either 104 or 105 doses of NL11, but only in the 105 dose of NL09 (Figure 3A); therefore, the 105 dose was used in all subsequent hPCLS infections. Figure 3NRTKIs inhibit ex vivo IAV infection(A) hPCLS were infected with NL09 and NL11 (104, 105 or 106 TCID$\frac{50}{200}$ul). Viral titers were quantified by TCID50/mL assay at 2, 16, 24, 48, 72, 96, and 144 hpi (8 donors; $$n = 24$$/virus).(B) Heatmap visualization of NRTKI cytotoxicity in hPCLS treated with [1x]max and [10x]max concentration up to 144 h. At each time point, LDH release was measured using LDH-Glo Cell Viability Assay and normalized to DMSO control and relative to $1\%$ Triton X-100 treated cells (positive control) (8 donors/$$n = 24$$).(C) hPCLS were infected with NL09 or NL11 (105 TCID$\frac{50}{200}$ul) and incubated for 120 h with NRTKIs (Defactinib 50uM; Acalabrutinib 5uM; Ibrutinib 5uM; Bosutinib 5uM; Nilotinib 10uM; Saracatinib 0.125uM). Virus was quantified by TCID50/mL assay at 2, 12, 24, 48, 72, and 120 hpi (3 donors; $$n = 6$$/condition).(D) NL11 infected hPCLS were fixed 120 hpi and PFA-fixed paraffin-embedded (PFPE) PCLS were cut into 2 μm thick section. H&E staining and viral anti-NP staining (brown) at 10× magnification (Scale bar: 200 um).(E) Semi-quantitative analysis of virus infection (anti-NP staining) was performed for tested NRTKIs and normalized to tissue-area in each section using FIJI image-analysis software. Means ± SEM are shown. l.o.d.: limit of detection. ∗, $p \leq 0.05$; ∗∗, $p \leq 0.01$; ∗∗∗, $p \leq 0.001$; ∗∗∗∗, $p \leq 0.0001.$ Significance (p values) was determined by Mann-Whitney tests compared to untreated cells.
Next, we determined the tolerability of our hPCLS to either [1x or 10x]max NRTKI concentrations by measuring lactate dehydrogenase (LDH) release into the culture supernatant as an indicator for cytoxicity. As a positive control for cytotoxicity, hPCLS were treated with $0.1\%$ Triton X-100; DMSO-treated hPCLS were used as a vehicle control (Figure 3B). Our cytotoxicity cut-off was $20\%$ of the positive control treatment; none of the NRTKIs surpassed this cut-off at [1x]max. However, 10xmax concentrations of DF (50 μM), BO (50 μM), and SA (1.25 μM) showed significantly higher cytotoxicity (>$20\%$); we therefore, only used 1x concentrations of these NRTKIs in subsequent hPCLS experiments (Figure 3B).
Next, hPCLS from 3 donors ($$n = 6$$/virus/condition) were infected with 105 TCID50 of NL09 or NL11 and treated with NRTKIs (DF 5 μM; AC 5 μM; IB 5 μM; BO 5 μM; NI 10 μM; SA 0.125 μM). All tested NRTKIs reduced viral titers by at least 10-fold or 1-log10 (DF treatment) to more than 1,000-fold or 3-log10 (IB and NI treatments) (Figure 3C). However, while this effect was significant throughout the infections with either NL09 or NL11 following IB, BO, and NI treatments, AC and DF treatment was only significant at 24 and 48 hpi. Moreover, unlike what we observed in A549 cells, IB-, BO-, and NI-mediated IAV inhibition was observed within 12 hpi and maintained at 120 hpi; which was after the times of peak titers (48–72 hpi) (Figure 3C).
Next, we assessed NRTKI effects on the viral spread and associated damage to the epithelium. At 120 hpi, mock- and IAV-infected hPCLS ($$n = 3$$/virus/condition) were fixed and paraffin-embedded. H&E staining did not suggest gross alterations in cell composition or epithelium in mock-infected cells indicating acceptable viability of hPCLS; typical morphological changes associated with IAV infections were observed. Only data for NL11 is shown as we did not observe significant differences in titer reductions between NL09 and NL11 (Figure 3D). We observed IAV-specific staining in all observed cell types including type I/II pneumocytes and endothelial cells in consecutive sections from those H&E stained. Additionally, staining intensity and quantity were reduced in NRTKI-treated hPCLS compared to untreated hPCLS (Figure 3D). Semi-quantitative analysis of acquired images indicated that DF, AC, BO, and SA treatment significantly reduced infectivity by >$50\%$, whereas IB and NI treatment had limited effects on infectivity (Figure 3E).
## Stability of NRTKI inhibition
Host-directed antivirals/therapeutics likely have a higher barrier of resistance than their virus-targeted counterparts. To determine the stability of NRTKI antiviral effect, we phenotypically assessed the emergence of resistant escape variants.35,36 We passaged both NL09 and NL11 viruses (MOI = 0.001) in the presence of NRTKIs [1x]max in MDCK cells for 5 passages. Untreated virus stocks were also passaged as a control. In untreated passages, virus titers for both NL09 and NL11 were similar from passages 1 to 5 and were significantly lower in all treated passages (Figure 4A); this reduction was comparable to that observed in A549 cells. Viral titers were stable at all passages indicating no resistance mutations were acquired. Next, we ruled out NRTKI virucidal activity or direct NRTKI-virus interactions that may inhibit attachment or entry. Pre-treatment of virus stocks with NRTKIs [1x]max for 2 h prior to A549 infection had no effect on viral titers, indicating that the observed effects are due to host-cell effects (Figure 4B).Figure 4NRTKI inhibition of IAV is stable(A) Stability of SMKI treatment on NL09 and NL11 replication was determined by serial passaging (5 times) using MDCK cells infected at MOI = 0.001 for 72 h in the presence of the [1xmax] NRTKI concentrations ($$n = 4$$) at each passage. At each passage, virus titers were quantified to inoculate the next passage at MOI = 0.001 again. Means ± SD are shown.(B) NL09 and NL11 virus stocks were pre-incubated with control (DMSO) or the [1xmax] concentration of respective NRTKI for 4 h at 37°C and A549 cells were then infected using a 1:1000 dilution and incubated for 72 h, after which, virus titers were determined by TCID50/mL assay ($$n = 3$$). Means ± SD are shown. p values were determined by Welch t-tests compared to untreated cells; ns, not significant.
## Selected NRTKIs inhibit viral entry
Kinases regulate every step of the infection cycle and a single kinase can affect multiple steps.28,29 To determine the effect of our NRTKIs on viral entry, A549 cells were pretreated for 2 h, then infected with NL09 or NL11 (MOI = 10). At 0.5 hpi, cells were fixed, stained to detect viral NP, F-actin, and nuclei, and analyzed by confocal microscopy. We observed significant retention at the membrane and cell periphery following DF and IB treatment (Figure 5). Surprisingly, no virus was detected in response to BO treatment and the F-actin network was not detectable. Given the sustained viability of BO-treated cells, it is likely that the altered actin dynamics were tolerated. No significant changes were detectable in AC-, NI-, or SA-treated cells suggesting they did not affect viral entry (Figure 5).Figure 5NRTKI-specific effects on viral entryA549 cells were pretreated with NRTKIs for 2 h then infected with NL09 or NL11 strains (MOI = 10) for 0.5 h +/− NRTKIs [1xmax]. Cells were fixed and permeabilized and virions detected by anti-NP (green) antibody, F-Actin detected by ActinRed-555 (red), and nuclei detected using NucBlueLive ReadyProbes (blue). Virion localization was visualized by confocal microscopy ($$n = 2$$) (Scale bar: 25um).
## NRTKIs exert differential effects on IAV polymerase activity
We next assessed the effect of our NRTKIs on viral polymerase activity. The pPOLI-358-FFLuc reporter plasmid, which encodes a firefly luciferase gene under the control of the viral nucleoprotein (NP) promoter, and luciferase activity is a surrogate for viral polymerase activity.37,38,39 We first compared the effect of NRTKIs on polymerase activity during infection. We observed a significant reduction in polymerase reporter activity in response to AC (NL11 only), IB (NL09 only), BO, NI, and SA (NL11 only) (Figure 6A). Although the magnitude of reduction was higher in NL09-infected than NL11-infected cells, a significant reduction was more readily observed in NL11-infected cells at lower NRTKI concentrations. DF reduced reporter activity (NL09 = $20\%$, NL11 = $13\%$); however, this reduction was not statistically significant. At 24 hpi, a 3-fold increase in reporter activity could be observed in untreated NL11-infected cells over untreated NL09-infected cells; this is in line with faster replication kinetics of NL11 compared to NL09 (Figure 6B).Figure 6NRTKIs affect IAV RNA replication(A) A549 cells were transfected with pPOLI-358-FFluc and pmaxGFP plasmids. At 24 hpt, cells were infected with NL09 or NL11 at MOI = 1 +/− indicated NRTKIs at [0.5x or 1x]max concentrations. At 48 hpt (24 hpi), luciferase activity was measured and normalized to GFP expression (MFI).(B) GFP-normalized polymerase activity of untreated NL09-or NL11-infected cells is shown.(C) A549 cells were transfected with pPOLI-358-FFluc and pmaxGFP plasmids and co-transfected with NL09 or NL03-minigenome plasmids. At 6 hpt, NRTKIs were added to the medium. At 30 hpt (24 h of treatment), luciferase activity was measured and normalized to GFP MFI. Bars indicate values relative to untreated cells normalized to GFP.(D) GFP-normalized polymerase activity of untreated NL09 or NL03 minigenome transfected cells is shown. Triplicate measurements from triplicate samples ($$n = 3$$); error bars indicate ±standard deviation (SD). ∗, $p \leq 0.05$; ∗∗, $p \leq 0.01$; ∗∗∗, $p \leq 0.001$; ∗∗∗∗, $p \leq 0.0001.$ p-values determined by Brown-Forsythe and Welsh ANOVA compared to untreated.
To better dissect the direct effect on viral polymerase activity in absence of NRTKI-effects on viral entry and host responses, we determined polymerase activity using an established minigenome system that only expresses the viral replication complex proteins (NP, PA, PB1, and PB2). We used the minigenome from the related H3N2 strain NL03 as we did not have access to the NL11 minigenome plasmids; both strains are known to exhibit similar replication kinetics. In this context, AC (NL09 only), IB, NI, and SA treatments significantly reduced polymerase activity (Figure 6C). In contrast to infected cells, the magnitude of reduction in polymerase activity was comparable in NL09- and NL03-minigenome transfected cells. Interestingly, polymerase activity was significantly higher in untreated H1N1 (NL09) than in H3N2 (NL03) minigenome-transfected cells (Figure 6D).
## NRTKIs do not affect innate immune responses during IAV infections
STAT3 is a regulator of inflammatory responses that is activated by phosphorylation of Y705 (STAT3pY705) to upregulate anti-apoptotic factors. This activation is less efficient in H1N1 infections than in H5N1 infections which delays apoptosis more efficiently. Therefore, we determined if NRTKI-mediated inhibition of viral entry and/or replication affected STAT3pY705. We assessed STAT3pY705 in A549 cells infected with either NL09 or NL11 (MOI = 1) in the presence or absence of NRTKIs at [1x]max. As expected, decreased STAT3pY705 was observed in untreated NL09-infected cells, and to a lesser degree in NL11-infected cells (Figure 7A), compared to mock-infected cells (18 $h = 159$%, 48 $h = 197$%). Surprisingly, only DF treatment resulted in a significant reduction of pSTAT3 relative to untreated infected cells (NL09 = $5\%$–$8\%$, NL11 = $5\%$–$14\%$ of untreated).Figure 7Effect of NRTKIs treatment on STAT3 and NFkB activationA549 cells were infected with NL09 or NL11 at MOI = 1, treated with NRTKIs at [1xmax] concentration and total proteins isolated from whole cell lysate at 18 and 48 hpi. Immunoblot assay was performed for phospho/total STAT3, IAV-NP and bActin (A), and phospho/total NFkB, IAV-NP and bActin (B). See alsoFigure S3. All measurements were taken from two Western blots from two-independent experiments ($$n = 2$$). All values are relative to untreated virus-infected cells. P = phosphor, T = total. Error bars indicate ±standard deviation (SD). ∗, $p \leq 0.05$; ∗∗, $p \leq 0.01$; ∗∗∗, $p \leq 0.001$; ∗∗∗∗, $p \leq 0.0001$; ns, not significant ($p \leq 0.05$). p-values determined by Student’s t test compared to untreated virus-infected cells.
NFkB Activation (NFkBpS536) serves opposing functions in early vs later times of infection and is therefore tightly regulated during IAV infection. We did not detect a significant increase in relative NFkBpS536 in NL09- or NL11-infected cells compared to mock-infected cells at 18 or 48 hpi following treatment with any of the NRTKIs (Figure 7B). We did observe a reduction in NFkB activation following DF treatment of NL11-infected cells (18 $h = 64$%, 48 $h = 68$% of untreated); however, this reduction was not statistically significant. We also confirmed that NFkB signaling is not impaired in our system as treatment with the synthetic dsRNA activator, poly(IC), induced NFkBpS536 in mock-, NL09-,and NL11-infected cells at 18 and 48 hpi (Figure S3).
## Discussion
Despite their clear susceptibility to rapidly arising resistance mutations, virus-targeted antivirals are still the only available class of antivirals against respiratory viruses like influenza. In this study, we screened FDA-approved NRTKIs currently in clinical use against cancers and autoimmune diseases for their antiviral potential against IAV infections. Six of eight tested NRTKIs showed potent in vitro inhibition of pandemic (H1N1) and seasonal (H3N2) IAV strains with little to no impact on cell viability in vitro. The robust potency was further validated and confirmed for five of six NRTKIs using a faithful ex vivo model of human PCLS. We identified the step(s) of the viral replication cycle affected by each compound. In doing so, we provide valuable information on the interplay of signaling pathways regulating these steps and the likely kinases involved.
We initially used A549 cells (ATII lung adenocarcinoma); however, due to potential biases associated with aberrant expression and kinase activity of cancerous cell lines, we validated NRTKI candidates using human PCLS (hPCLS) from 11 donors in total. Unlike 2D monolayers or 3D well-differentiated air-liquid interface (ALI) cultures, PCLS preserve native lung tissue architecture, cellular composition including endothelial, ATI and ATII epithelial cells, fibroblasts, and maintain native extracellular matrix.33,34,40 Moreover, tissue tropism and infectivity of certain viruses may not be accurately represented in vitro due to the absence of relevant cell-cell interactions that influence infectibility and host responses.41 Accordingly, we observed a similar discrepancy in which strain-dependent variances observed in NRTKI-treated A549 cells were not observed in hPCLS, suggesting that the differences between IAV strains in A549 might be an in vitro artifact. Moreover, we observed wide tissue tropism in hPCLS which suggests that while ATII cells may support more efficient infection, other cell types of the lung are readily infectible as well. Nevertheless, robust viral titer reductions in both systems following NRTKI treatment were observed that were not readily explained by reduction in infectivity or cell viability only. Defactinib (DF) < Acalabrutinib (AC) < Saracatinib (SA) < Bosutinib (BO) treatment had the smallest effect on viral titers, but each of these NRTKIs reduced hPCLS infectivity by ∼$50\%$. Similarly, Ibrutinib (IB) and Nilotinib (NI) had the largest effect on viral titers and actually increased infectivity (NI:∼$22\%$, IB = ∼$7\%$). Together our data indicate that the reduction in infectivity of either A549 cells or hPCLS does not fully account for the potent reduction in viral titers. The rate at which viruses infect cells can indeed influence viral titers. However, the correlation between infectivity and titers is not absolute, and may only be readily observed when using a compound that targets the virus itself. Infectivity involves viral entry and replication at the very least, and in our assays, we carried out the infections at an MOI of 1 to allow for effects on virus spread. While the same number of cells may be infected, compound-specific effects on viral entry, polymerase activity, and/or virion assembly/egress could vastly differ and result in an uncoupling of the virus infectivity-titer correlation. Therefore, we do not believe that our findings regarding infectivity/viral titers are mutually exclusive or contradictory and support a bona fide effect of NRTKIs on viral entry and/or RNA replication.
Host kinases play a critical role in IAV entry, replication, and release as well as viral evasion/suppression of hosts’ immune responses; processes often requiring phosphorylation of viral proteins by mostly still unidentified kinases.42,43,44,45,46,47,48,49,50,51,52,53 Although there is a growing body of in vitro and in vivo evidence to support the therapeutic potential of kinase inhibitors, not a single SMKI has been approved or licensed for the treatment of influenza virus infection so far.1,13,14,17,48 SMKI selectivity remains a contentious topic and has been a hurdle to the pursuit of kinase inhibitors as antivirals. While compounds still in pre-clinical development phases require target validation, the selectivity of compounds in clinical use has been heavily investigated.21,54,55,56 Phosphorylation or activation of proteins/pathways beyond the intended target are often regarded as “off-target effects”. However, kinase-substrate interactions highlight extensive crosstalk between signaling pathways. Therefore, inhibition of an “off-target” kinase or pathway, should not be oversimplified and attributed to promiscuity of the SMKI in question; rather it is more likely evidence of an interaction between the intended target and the affected “off-target” signaling node. Two seminal studies collectively examined the selectivity of over 170 SMKIs against more than 440 kinases, covering ∼$80\%$ of the human kinome. Davis et al. compared inhibitor-kinase binding affinities, whereas Anastassiadis et al. used functional kinase inhibition assays. These studies suggest that while classes of SMKIs can inhibit multiple kinases within a single subfamily, inhibitors are selective against kinases outside that subfamily.55,56 Moreover, these studies likely overestimated “off-target” effects due to the use of truncated or fused recombinant proteins that may adopt altered conformations in the absence of regulatory domains (i.e., regulatory subunit of PI3K) or binding partners that affect substrate or ATP binding sites availability21; suggesting even greater selectivity than was proposed by those studies. Likewise, selectivity of clinically approved SMKIs was validated by super-resolution microscopy (dSTORM) to show the superior specificity and selectivity of fluorescently labeled SMKIs like gefitinib (EGFR inhibitor), over either fluorescent EGF ligand or EGFR monoclonal antibody.57 Therefore, SMKIs can be used as molecular beacons to probe host signaling pathways and delineate how they are regulated by kinases.
NRTKIs which target known effectors of actin reorganization and endocytosis have a significant effect on IAV entry. Indeed, we previously showed that targeting FAK using the pre-clinical inhibitor Y15, led to inhibition of PI3K-mediated endosomal trafficking of virions.28 Using the FDA-approved FAK inhibitor DF, we saw comparable effects on actin reorganization and viral entry as we previously observed using Y15.28 *This is* consistent with DF having the most effect at earlier time points in both A549 and hPCLS when reduction in viral entry may have a larger impact than at later time points. Indeed, we observed a reduction in infectivity in response to DF treatment which is consistent with the observed reduction of viral entry.
Cell-specific Bruton’s tyrosine kinase (BTK) isoforms are implicated in PI3K and PLCγ signaling that are either pro- or anti-apoptotic.58 IB and AC are high-affinity irreversible inhibitors of BTK; IB also inhibits EGFR activity.59,60 IB treatment reduces excessive neutrophil infiltration, acute lung injury, and subsequent ARDS; ultimately resulting in increased survival of mice severely infected with IAV.27 Given that IB inhibits both EGFR and BTK whereas AC selectively inhibits BTK, the IB-specific reduction in viral entry we observed, suggests this effect is mediated largely through inhibition of EGFR signaling. This is consistent with IAV-induced EGFR signaling which facilitates viral entry and activation of downstream pathways (Src, PI3K, and ERK) that promote efficient replication.61 BO, along with NI and SA, are second-generation Src inhibitors. BO also inhibits Abl kinase and to a lesser extent BTK. Growth factor RTKs like PDGFR and EGFR induce Src-mediated activation of PI3K/AKT, Ras-Raf-MEK-ERK, FAK, and STAT3.62 Src’s mostly proviral role during IAV infections is modulated by the viral NS1 protein.13,63 Abl inhibition by some avian IAVs results in significant pathology in vitro and in vivo; however, Abl’s role in human IAV infections is not fully understood.64,65 The direct interactions of a specific avian NS1 motif with Abl are necessary for the disruption of Abl kinase activity and result in significant cytopathic effect.64,65 Interestingly, this motif was present in the pandemic 1918 IAV strain when it first crossed into humans but was quickly lost during human adaptation so recent circulating human IAVs do not carry this motif suggesting that Abl kinase activity is important for human adaptation of IAVs. In the context of cellular functions, *Abl is* activated by both Src-dependent and -independent EGFR and PDGFR signaling. Among its functions is cytoskeletal reorganization which is at least partially mediated through Src signaling. Interestingly, Abl activity seems to positively regulate EGFR receptor endocytosis66; establishing a possible link for Abl to EGFR-mediated IAV entry. This is consistent with the significant reduction in viral titers we observed following BO treatment of hPCLS and A549 cells. BO treatment also resulted in a stark reduction in viral entry, concurrent to a complete absence of detectible actin filaments despite an increase in cell viability during infection. This is consistent with reports that show BO inhibition of Src activity can lead to altered actin dynamics or enhanced depolymerization of F-actin due to retention of alpha- and β-catenin at the cell membrane67,68; a process that may be influenced by Abl activity which is also affected by BO treatment.
We used a polymerase activity reporter and mini-genome systems to better dissected the effect of our NRTKIs on RNA replication and polymerase activity.39 Interestingly, in the context of viral infections, we detected higher polymerase reporter activity in NL11 (H3N2)-infected cells than in NL09 (H1N1)-infected cells. In contrast, the opposite was true when using the minigenome. Faster replication kinetics may be more susceptible to NRTKIs as a reduction in replication rate leads to exponential differences with time. This suggests that polymerase activity of NL09 is higher than NL11 and the faster kinetics in virus replication observed in NL11 infected cells may be due to more efficient virus entry, release, or immune evasion than NL09, and not polymerase activity. We previously demonstrated that FAK, in addition to its role in viral entry, also regulates in vitro polymerase activity of multiple IAV strains using the selective FAK inhibitor Y15 or dominant-negative kinase mutants.29 However, in contrast to our previous studies, we only observed a modest and non-significant effect on polymerase activity following DF treatment. O’Brien et al. showed that Y15 was a significantly more potent and selective inhibitor of FAK activity than DF which also targets the FAK-related kinase Pyk2.69 The disparity between a given SMKI’s binding affinity (Kd) and its functional inhibitory concentrations can also be observed in the case of a single inhibitor targeting multiple kinases. For instance, the Kd of the multi-kinase inhibitor sunitinib for TrkC is 5.1 μM, but a 10-fold lower concentration (0.5 μM) is sufficient to inhibit >$97\%$ of its activity. In contrast, sunitinib’s Kd for PAK3 is 16 nM, but not even a 30-fold higher concentration (0.48 μM) has an effect on its activity.54 Therefore, the difference in potency of FAK inhibition by DF vs Y15 may account for the limited effect of DF treatment on IAV polymerase activity we observed.
Inhibition of Src by SA, BTK, and EGFR by IB, and BTK by AC had less of a significant effect on IAV polymerase activity indicating that the contribution of these kinases to host innate immune signaling does not directly affect IAV RNA replication. In contrast, inhibition of Abl and PDGFRα by NI treatment had the most significant reduction in IAV polymerase activity that was also strain independent. These data point to a role of PDGFRα in facilitating efficient IAV polymerase activity. This is consistent with previous findings that show inhibition of PDGFR by the RTK inhibitor A9, blocks RNA synthesis of all viral RNA species (vRNA, cRNA, and mRNA) independently of NFkB signaling.44 However, A9 also inhibits EGFR and is not selective for PDGFR isoforms; whereas NI is >25-fold more selective for PDGFRα than PDGFRβ.70 Several reports indicate that IAVs modulate antiviral NFkB activity to facilitate viral replication. Inhibition of NFkB results in reduced viral titers partly due to a disruption of vRNP nuclear export.71,72,73 Although we observed induction of NFkB activation by poly(IC) treatment in mock- and IAV-infected cells at 18 h but not 48 h, we did not observe a robust induction in NFkB phosphorylation in IAV-infected cells without poly(IC) treatment. This is consistent with published data and most likely due to the immuno-suppressive role of the viral NS1 protein.74 FAK also modulates cellular immune responses by regulating T cell-, B cell-, and macrophage-functions as well as RIG-I-Like antiviral signaling.75,76,77,78 We previously demonstrated FAK-dependent regulation of NFkB signaling and polymerase activity in vitro and NFkB-dependent proinflammatory responses in vivo. 30 In that study, FAK inhibition increased survival, reduced viral load and pathogenesis in a severe infection model. However, DF treatment did not affect NFkB phosphorylation in this study; likely due to the difference in FAK inhibition potency between Y15 and DF. Similarly, none of the other NRTKIs influenced NFkB activation suggesting that the mechanism by which these NRTKIs inhibit virus replication is independent of the NFkB-pathway. Considering the transient and biphasic nature of NFkB activation, we cannot rule out that strain-dependent differences in kinetics did not affect the magnitude or duration of NFkB activation we observed as has previously been described by others.79,80,81,82,83,84 STAT3 is an emerging regulator of IFN and inflammatory responses. A wide range of cytokine, growth factors, and RTKs activate STAT3 via JAK$\frac{1}{2}$/3 and Tyk2-dependent phosphorylation at Y705 (STAT3pY705).85 The role of STAT3 is not fully understood with opposing functions dependent on pathway partners; IL-6 mediated STAT3 activation is proinflammatory while IL-10 mediated STAT3 activation is anti-inflammatory.85,86 Although STAT3 is dispensable for IFN signaling, it is activated by IFN-I and serves as a negative regulator to fine-tune the IFN response (reviewed in86). Recent studies suggest that STAT3 activation is likely IAV subtype/strain-dependent as well as host/tissue-specific.87,88,89 Because STAT3 activation upregulates anti-apoptotic factors, H5N1-mediated STAT3pY705 allows prolonged viral production through a delay of apoptosis; H1N1 is less efficient at STAT3pY705 and triggers apoptosis earlier.88,89 Although the mechanism of differential suppression of STAT3 activation by IAV is not clear, it has been suggested to be mediated by NS1 74. Interestingly, EGFR activation can result in Src/FAK/BTK mediated activation of STAT3, thereby modulating the IFN and proinflammatory responses. Moreover, EGFR/Src-mediated STAT3pY705 requires Pyk2 kinase activity, which can also mediate full STAT3 transcriptional activity via JNK, p38, or ERK activation.90 As expected of H1N1 and H3N2 infections,88,89 we observed limited levels of STAT3pY705 in untreated cells that were comparable to that observed following treatment with most NRTKIs. However, we observed significant suppression of STATpY705 following DF treatment (7–$14\%$ of untreated infected cells). This is consistent with the fact that DF inhibits both FAK and Pyk2 and suggests that IAV-induced STAT3pY705 requires FAK and/or Pyk2 activity.
In summary, we demonstrate that NRTKIs target kinases required for efficient IAV replication and represent promising drugs for the development of the next generation of antivirals. It is tempting to speculate on the molecular mechanisms and contribution of individual kinases to the antiviral effects observed following NRTKI treatment. The NRTKIs with the greatest effect had overlapping targets that mainly included EGFR, PDGFRα, and Abl. In contrast, NRTKIs that mainly target BTK or Src or the more distant FAK/PyK2 family had less robust, but still biologically significant, effects on viral replication. This data could be further used to fine-tune selectivity and potency for next-generation antiviral SMKIs and provide a rationale for combination therapy options to maximize SMKI antiviral efficacy. Surprisingly, our tested NRTKIs directly affected steps of the virus replication cycle with limited effects on proinflammatory host responses. Nicholas et al. [ 2015] used human cubic lung explants from young healthy donors to show the efficacy of a single pre-clinical vATPase inhibitor (TVB024) against IAV infection.91 Our study builds on and extends their findings by using precision-cut lung slices from 11 older donors to validate 6 FDA-approved NRTKIs. Given that most of our PCLS were obtained from lung cancer tumor resections, our donors tend to be older, are often smokers and suffer from either COPD or other respiratory pathologies. Although at first glance this may be perceived as a limitation of our model, we believe that these donors represent the “at risk” populations that would most benefit from IAV antivirals. Therefore, our data obtained from donor PCLS using these already FDA-approved NRTKIs as IAV antivirals are highly applicable to clinical settings. It should be noted however, that because these inhibitors target host factors, their therapeutic window is likely to be different from that of virus-targeted antivirals and pre/clinical studies must take this into account to establish efficacy.
In contrast to virus-directed IAV antivirals which are susceptible to resistance mutations, our tested NRTKIs data have a high genetic barrier for resistance based on their stability of IAV inhibition after 5 passages in the presence of each of our six NRTKIs. Although we cannot rule out NRTKIs-selected mutations, we did not detect a significant change in the magnitude of viral titer reductions across 5 passages, suggesting that no mutations conferring resistance accumulated in viruses passaged in the presence of SMKIs. Additionally, their established safety and bioavailability data further warrants clinical evaluation of these compounds as potential influenza treatments. Given that IAV infections are typically restricted to the respiratory tract, localized delivery of kinase inhibitors can limit potential cytotoxic effects. Finally, the local microenvironment must be considered to elicit balanced immune responses and avoid opposite or unintended consequences of promising SMKIs on resident and infiltrating immune cells. Because many viruses utilize the same (or related) host kinases to facilitate replication and transmission, our studies have broader implications for the potential use of these SMKIs to treat infections by other viruses.
## Limitations of study
Due to the dependence on available hPCLS, human lung samples of older (>58 years old) mainly male patients (10 male/1 female) undergoing tumor resection (8 tumor resections/3 IPF) were overrepresented. Also, A549 cells were originally derived from a 58 years old male patient with lung cancer. Although the female and the IPF patient samples showed no differences in their susceptibility or response to the NRTKI treatment, we cannot exclude potential bias of our data based on sex, medical condition, or age. Since older adults are at high risk for influenza, our findings are particularly relevant for this age group. IAV has multiple mechanisms to actively suppress p65 phosphorylation at specific times of infection. However, it is apparent that p65 phosphorylation in IAV-infected cells is not straightforward and a matter of debate. Differences in experimental conditions like strains used, multiplicities of infection and kinetics, as well as methods of measuring p65 activation (e.g., p65 phosphorylation only, phosphor/total p65 ratio, p65 nuclear translocation), could be at the basis of the discrepancies between studies. Addressing these discrepancies is well beyond the scope of the current study.
## Key resources table
REAGENT or RESOURCESOURCEIDENTIFIERAntibodiesMouse monoclonal anti-IAV NP (Clone HB65)ATCCCat#ATCC-HB-65Rabbit polyclonal anti-IAV NP (Clone PA5)ThermoScientificCat#PA5-32242; RRID:AB_2549715Rabbit monoclonal anti-phospho-NF-κB p65 (Ser536) (Clone 93H1)CellSignalingCat#3033; RRID: AB_331284Rabbit monoclonal anti-phospho-Stat3 (Tyr705) (Clone D3A7) XP®CellSignalingCat#9145; RRID:AB_2491009Mouse monoclonal anti-NF-κB p65 (Clone L8F6)CellSignalingCat#6956; RRID:AB_10828935Mouse monoclonal anti-Stat3 (Clone 124H6)CellSignalingCat##9139; RRID:AB_331757Mouse monoclonal anti-bActin (Clone BA3R)ThermoScientificCat#MA5-15739; RRID:AB_10979409Goat polyclonal anti-Rabbit IgG (H + L) Cross-Adsorbed Secondary Antibody, Alexa Fluor 594ThermoScientificCat#A-11005; RRID:AB_2534073Goat polyclonal anti-Mouse IgG (H + L) Cross-Adsorbed Secondary Antibody, Alexa Fluor 488ThermoScientificCat#A-11001; RRID:AB_2534069Goat polyclonal anti-Mouse IgG (H + L) Cross-Adsorbed Secondary Antibody, HRPThermoScientificCat#G-21040; RRID:AB_2536527Goat polyclonal anti-Rabbit IgG (H + L) Cross-Adsorbed Secondary Antibody, HRPThermoScientificCat#G-21234; RRID:AB_2536530Bacterial and virus strainsA/Netherlands/$\frac{602}{09}$ErasmusMCN/AA/Netherlands/$\frac{241}{11}$ErasmusMCN/ABiological sampleshPCLS #183 (Age: 67yo/Sex: m/Condition: tumor)Pathology MHHGLR#183hPCLS #184 (Age: 74yo/Sex: m/Condition: tumor)Pathology MHHGLR#184hPCLS #189 (Age: 77yo/Sex: m/Condition: tumor) centralPathology MHHGLR#189ChPCLS #189 (Age: 77yo/Sex: m/Condition: tumor) peripheryPathology MHHGLR#189PhPCLS #189 (Age: 77yo/Sex: m/Condition: tumor) airwayPathology MHHGLR#189AWhPCLS #191 (Age: 60yo/Sex: f/Condition: tumor)Pathology MHHGLR#191hPCLS #195 (Age: 66yo/Sex: m/Condition: tumor)Pathology MHHGLR#195hPCLS #766 (Age: 59yo/Sex: m/Condition: IPF) centralPathology MHHGLE#766ChPCLS #766 (Age: 59yo/Sex: m/Condition: IPF) peripheryPathology MHHGLE#766PhPCLS #766 (Age: 59yo/Sex: m/Condition: IPF) airwaysPathology MHHGLE#766AWhPCLS #205 (Age: 65yo/Sex: m/Condition: tumor)Pathology MHHGLR#205Chemicals, peptides, and recombinant proteinsDefactinib (VS-6063)SelleckchemCat#S7654Bosutinib (SKI-606)SelleckchemCat#S1014Nilotinib (AMN-107)SelleckchemCat#S1033Saracatinib (AZD0530)SelleckchemCat#S1006Acalabrutinib (ACP-196)SelleckchemCat#S8116Ibrutinib (PCI-32765)SelleckchemCat#S2680Ruxolitinib (INCB018424)SelleckchemCat#S1378Tofacitinib (CP-690550)SelleckchemCat#S2789NucBlue™ Live ReadyProbes™ThermoScientificCat#R37605ActinRed™ 555 ReadyProbes™ThermoScientificCat#R37112Critical commercial assaysCellTiter-Glo® 2.0 Cell Viability AssayPromegaCat#G9241LDH-Glo™ Cytotoxicity AssayPromegaCat#J2380ONE-Glo™ Luciferase AssayPromegaCat#E6110Pierce™ Detergent Compatible Bradford Assay KitThermoScientificCat#23246Experimental models: Cell linesHuman: A549ATCCCCL-185Dog: MDCKATCCCCL-34Recombinant DNApMAX GFPLonzaCat#V4XC-2012pPOLI-358-FFLuc reporter plasmidAzzeh et al., 200137; Deng et al., 200638; Hoffmann et al., 200839N/AA/Netherlands/$\frac{602}{09}$ PB2ErasmusMCRF1239PB2A/Netherlands/$\frac{602}{09}$ PB1ErasmusMCRF1240PB1A/Netherlands/$\frac{602}{09}$ PAErasmusMCRF1241PAA/Netherlands/$\frac{602}{09}$ NPErasmusMCRF1243NPA/Netherlands/$\frac{213}{03}$ PB2ErasmusMCRF600PB2A/Netherlands/$\frac{213}{03}$ PB1ErasmusMCRF601PB1A/Netherlands/$\frac{213}{03}$ PAErasmusMCRF602PAA/Netherlands/$\frac{213}{03}$ NPErasmusMCRF604NPSoftware and algorithmsImageJSchneider et al., 201292https://imagej.nih.gov/ij/Fiji image processing package for ImageJSchindelin et al., 201293https://imagej.net/software/fiji/Prism 9.0GraphPadhttps://www.graphpad.com/support/faq/prism-900-release-notes/Image Studio™Li-Corhttps://www.licor.com/bio/image-studio/cellSens SoftwareOlympushttps://www.olympus-lifescience.com/de/software/cellsens/CellCounting MacroGrishagin, 201594https://doi.org/10.1016/j.ab.2014.12.007
## Lead contact
Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Husni Elbahesh (husni.elbahesh@gmail.com).
## Materials availability
This study did not generate new unique reagents.
## Cell lines
A549 cells were derived from ardenocarcinomic lung tissue from a 58 year old Caucasian male. Madin-Darby canine kidney (MDCK) cells were derived from epithelial cells from the kidney tubule of an adult Cocker Spaniel dog. MDCK cells were cultured in Dulbecco’s modified *Eagle medium* (DMEM; Gibco) supplemented with $10\%$ fetal bovine serum (FBS) (ThermoScientific), 100 IU/mL penicillin (Gibco), 100 μg/mL streptomycin (Gibco), 2 mM glutamine (Gibco), and $1\%$ nonessential amino acids (NEAAs) (Gibco). A549 cells were cultured in F-12 K-Nut Nutrient *Mix medium* (Gibco) supplemented with $10\%$ FBS, 100 IU/mL penicillin, 100 μg/mL streptomycin, and 2 mM Glutamax (Gibco). All cells were incubated at 37°C and $5\%$ CO2.
## Ex vivo hPCLS model
Use of our human PCLS for ex vivo studies was previously described.95 Briefly, hPCLS were generated from lung tissues obtained from patients undergoing surgical operations at Hannover Medical School. Tissues used for PCLS generation that were obtained from lung tumor resections were confirmed as tumor-free by an experienced pathologist. The freshly obtained lung tissues were processed into circular slices that were 300 microns thick and 8 mm in diameter as previously described.34 All donors provided informed consent as approved by the Hannover Medical School Ethics Committee (Ethics vote #8867_BO_K_2020). PCLS were maintained in DMEM/F12 medium (Gibco) supplemented with 2 mM of HEPES (Gibco), 1× GlutaMAX, 100 U/ml penicillin and 100 μg/mL streptomycin in a humidified 37°C and $5\%$ CO2 incubator.
## Viruses
The pandemic H1N1 strain A/Netherlands/$\frac{602}{09}$ (NL09) and seasonal strain H3N2 A/Netherlands/$\frac{241}{11}$ (NL11) influenza viruses were obtained from the Repository of the National Influenza Center at the Erasmus Medical Center in Rotterdam, the Netherlands, and were grown on MDCKs for 48 h at 37 °C. Virus stocks and culture supernatants were stored at −80°C until further use. Virus yields were titrated on MDCK cells by $50\%$ tissue culture infectious dose (TCID50)/mL method as described by Reed and Muench.96
## Inhibitors
Small molecule kinase inhibitors (SMKI) were all purchased from Selleckchem (TX, USA). Inhibitors were diluted in DMSO to a stock concentration of 10 mM and stored at −20°C upon usage.
## In vitro cytotoxicity assays
In vitro cytotoxicity of SMKIs on mock-infected A549 cells was determined using CellTiter-Glo 2.0 (CTG) Cell Viability Assay (Promega). Roughly $80\%$ confluent A549 cells cultured in a 96-well were washed twice with phosphate-buffered saline containing Mg2+/Ca2+ (PBS+/+) (Gibco). The cell were replenished with infection medium (F12K (Gibco) containing $0.1\%$ [vol/vol] bovine serum albumin [BSA] (Sigma) and 50 ng/μL TPCK-treated trypsin (Sigma)) supplemented with small-molecule kinase inhibitor (SMKI) dilution series. The cell were incubated at 37°C and $5\%$ CO2 for 72 h. At 72h cell-viability was evaluated using CTG according to manufacturer protocols. We defined the [1x]max as the highest concentration resulting in > $95\%$ cell viability following treatment.
## Ex vivo cytotoxicity assays
Cytotoxicity of SMKIs on mock-infected PCLS was determined using the LDH-Glo Cytotoxicity Assay (Promega). hPCLS were placed in 48-well plates (1 hPCLS/well) and washed twice with PBS+/+ (Gibco). hPCLS were replenished with prewarmed culture medium (DMEM/F12 medium (Gibco) supplemented with 2 mM of HEPES (Gibco), 1 × GlutaMAX (Gibco), 100 U/ml penicillin (Gibco) and 100 μg/mL streptomycin (Gibco)) supplemented with SMKI [1x]max and [10x]max. Supernatants of SMKI-treated and untreated hPCLS were collected and completely replaced with fresh pre-warmed infection medium containing SMKIs at the indicated concentrations. LDH levels were evaluated according to manufacturer’s protocol and calculated relative to the positive control (treated with $1\%$ triton-X 100 for 30 min at 37°C).
## Virus infections
A549 cells were plated on the day prior to infection so they were 80–$90\%$ confluent on the day of infection. For infections, viruses were diluted in infection medium (F12K medium (Gibco) containing $0.1\%$ [vol/vol] bovine serum albumin [BSA] (Sigma) and 50 ng/μL TPCK-treated trypsin (Sigma)). The cells were inoculated with the virus at the indicated multiplicity of infection (MOI) for 1h at 37°C. The cells were washed twice with PBS+/+ (Gibco) to remove unbound virus and incubated in infection medium at 37°C in the presence or absence of SMKIs at the indicated concentrations. Supernatants were collected at 0, 24, 48, 72 h post-infection (hpi), and viral titers were determined by TCID50 assay in MDCK cells.96 Prism 9.0 (GraphPad) Heatmap function was used for visualization. The assay’s lower limit of detection (LoD) is 101 TCID50/mL, and its upper LoD is 109.5 TCID50/mL.
## Immunofluorescent staining and imaging
To visualize virus infection, infected cells were fixed with $4\%$ paraformaldehyde ($4\%$ PFA/PBS) (Roth) for 30 min at room temperature (RT), permeabilized with $0.1\%$ Triton X-100 for 15 min at RT, washed with PBS and blocked with heat inactivated $5\%$ horse serum (Sigma) in PBS (PBS-HS) at RT for 1h. Cells were then incubated with mouse monoclonal antibodies to IAV nucleoprotein (clone HB65, ATCC) diluted in PBS-HS at 0.2 μg/mL overnight at 4°C under constant agitation. Cells were washed and incubated with AlexaFluor-594 conjugated goat anti-mouse IgG antibody (0.2 μg/mL; ThermoScientific) and NucBlue Live ReadyProbes Reagent (ThermoScientific) for 1h at RT under constant agitation. Cells were washed 3 times with PBS (Gibco), images were captured using a Leica DMi8 fluorescence microscope and quantitative analysis was performed using ImageJ Threshold, Watershed, and Particle Analyser.
A previously described ImageJ Toolbox counting macro,94 was used to quantify the number of nuclei and the number of separate infected cells by analyzing the RAW image data for each channel ($$n = 4$$). The nucleus count was used to define the total cell number per 0.6 mm2. The NP staining was used to define the number of infected cells per 0.6 mm2. The ratio of infected to total cells was used to calculate Relative Infectivity. The total number of cells based on nuclei detected relative to mock-infected cells treated with the respective NRTKI was used to determine Relative Viability. Prism 9.0 (GraphPad) Heatmap function was used for visualization.
## Immunohistochemistry staining
Mock- and virus-Infected PCLS were inactivated by fixation in $4\%$ PFA/PBS (Roth) and paraffin-embedded into blocks. Tissue sections (2 μm thick) were cut from the paraffin-embedded blocks and subjected to Hematoxylin & Eosin (HE) staining using standard protocols. Immunostaining for IAV antigen was done using an HRP-conjugated anti-IAV NP antibody. Histological analysis was performed by an experienced pathologist blinded to clinical data and experimental setup using a routine diagnostic light microscope (BX43, Olympus). Representative images were acquired with an Olympus CS50 camera using Olympus CellSens software (Olympus). Semi-quantitative analysis of IAV NP signal was performed for all tested NRTKIs using FIJI image-analysis software.
## Polymerase activity assay
Semi-confluent (∼70–$80\%$) A549 cells (8×104 cells in 24-well plates) were transfected using Lipofectamine LTX with the pPOLI-358-FFLuc reporter plasmid, which encodes a firefly luciferase gene under control of the viral nucleoprotein (NP) promoter (kindly provided by Megan Shaw)37,38,39; the Lonza pmaxGFP™ expression vector, was used as a transfection control.
For minigenome polymerase activity, a mix of plasmids encoding the PB2, PB1, PA, and NP genes of A/Netherlands/$\frac{602}{09}$ (H1N1) or A/NL/$\frac{213}{03}$ (H3N2) IAVs in quantities of 0.35, 0.35, 0.35, and 0.5 μg, respectively, were co-transfected with the reporter and control plasmids. At 6h post-transfection (hpt), the indicated SMKIs were added at [1x]max and [0.5x]max concentrations (see Figure 1A/Table 1) and at 30 hpt (24h of treatment), luciferase reporter activity was detected using the One-Glo luciferase assay system (Promega). GFP mean fluorescence intensity (MFI) and luciferase luminescence were measured using a Tecan multi-mode plate reader.
To measure polymerase activity during IAV infection, cells were infected at an MOI of 1 with NL09 or NL11 at 24h post-transfection (hpt) of the pPOLI-358-FFLuc reporter and the GFP plasmids in the presence or absence of SMKIs at the indicated concentrations as described above. At 48 hpt (24 hpi), luciferase reporter activity was detected using the One-Glo luciferase assay system (Promega). GFP mean fluorescence intensity (MFI) and luciferase luminescence were measured using a Tecan multi-mode plate reader.
## Viral entry assay and confocal microscopy
A549 cells were seeded on 12.5-mm coverslips in 24-well plates. On the day of infection, cells were washed 3 times with PBS+/+ (Gibco) and incubated in infection medium in the presence or absence of kinase inhibitors for 2h. The cells were chilled on ice for 15 min and inoculated with virus (MOI = 10) in the presence or absence of the indicated SMKI concentrations at 4°C and on ice for 30 min. To limit receptor activation due to continuous viral-receptor engagement/internalization following the 4°C adsorption and to gently warm up the cells, unbound/noninternalized virus was removed by washing the cells twice with RT PBS+/+ (Gibco). The cells were then incubated with prewarmed infection medium containing the respective SMKIs at 37°C for 30 min. Cells were then fixed in $4\%$ PFA for 30 min, permeabilized with $0.1\%$ Triton X-100 at RT for 15 min, washed in PBS (Gibco), and incubated overnight at 4°C in blocking buffer (PBS-HS). The cells were then incubated with anti-IAV NP antibody (clone HB65, ATCC) diluted in blocking buffer for 1h at RT, washed 3 times with PBS (Gibco), and incubated for 1h at RT with AlexaFluor488-conjugated goat anti-mouse IgG secondary antibody (0.2 μg/mL; ThermoScientific) diluted PBS-HS. Cell nuclei and F-Actin were stained with NucBlue Live ReadyProbes (ThermoScientific) and ActinRed-555 ReadyProbes Reagent (ThermoScientific), respectively. Coverslips were mounted with Prolong mounting medium (Invitrogen), and cell images were acquired with a Leica TSC SP5 laser-scanning confocal system mounted on an upright Leica DM6000 CFS using a 63× oil immersion objective. The images were merged and analyzed with Leica LAS software using identical imaging settings across all experiments.
## NRTKI resistance analysis
To assess the resistance barrier for our NRTKIs, we passaged our viruses five times in the presence or absence of submaximal inhibitor concentrations ([0.5x]max; see Figure 1A/Table 1). The parental viruses were also passaged under the same culture conditions in parallel in the absence of NRTKIs. Semi-confluent MDCK cells (∼106 cells/well in 6-well plates) were infected with the pandemic H1N1 strain A/Netherlands/$\frac{602}{09}$ (NL09) and seasonal strain H3N2 A/Netherlands/$\frac{241}{11}$ (NL11) at MOI 0.001. At each passage, the cultures were maintained in 3 mL MDCK infection media at 37°C for 72h, in the presence or absence of the [0.5x]max (see Figure 1A/Table 1) of respective candidate NRTKIs. Supernatants were harvested, clarified by centrifugation at 500 x g for 5 min at 4°C, and stored at −80°C until titration by TCID50 assay on MDCK cells. For the subsequent passage, cells were infected by using virus from the previous passage at MOI = 0.001.
## SDS-PAGE and immunoblotting
Proteins were isolated from whole cell lysates using the M-PER Mammalian Protein Extraction Reagent (ThermoScientific). Proteins were quantified by Pierce Detergent Compatible Bradford Assay (ThermoScientific), separated by $8\%$ SDS-PAGE, transferred onto a PVDF membrane and blocked overnight in blocking solution (TBS pH7.6, $0.05\%$ Tween 20, and $5\%$ w/v of nonfat dry milk). Primary antibodies were diluted in blocking solution overnight at 4°C: phos-NFkB p65 (Ser536) (93H1) Rabbit mAb (1:1000) (Cell Signaling), phos-Stat3 (Tyr705) (D3A7) rabbit mAb (1:1000) (Cell Signaling), Influenza A virus NP Antibody (PA5-32242) rabbit pAb (1:20,000) (ThermoScientific). Beta-Actin (BA3R) mAb (1:5000) was used as a loading control. HRP-conjugated secondary anti-rabbit or anti-mouse antibodies (1:20,000) (ThermoScientific) were diluted in blocking solution for 1 h at room temperature. Proteins were detected by chemiluminescence using the Super-Signal West Pico Plus (ThermoScientific) and SuperSignal West Femto Maximum Sensitivity Substrate (ThermoScientific) using a Li-Cor C-DiGit scanner. Band density was quantified using ImageJ to determine Phospho/Total ratios after b-actin normalization. When necessary, the imaged membrane was subsequently stripped using a mild water-based stripping solution ($1.5\%$ glycine; $0.1\%$ SDS; $1\%$ Tween 20; pH 2.2) and restained for total proteins using NFkB p65 (L8F6) mouse mAb (1:1000) (Cell Signaling) or Stat3 (124H6) mouse mAb (1:1000) (Cell Signaling).
## Quantification and statistical analysis
Statistical analyses with GraphPad Prism 9.0 included multiple t test, Brown-Forsythe and Welsh’s ANOVA tests and Dunnett’s T3 test for multiple comparisons. Values are represented as means standard deviations (SD) or standard error of the mean (SEM), with a p value of 0.05 considered statistically significant (ns = $p \leq 0.05$; ∗ = p ≤ 0.05; ∗∗ = p ≤ 0.01; ∗∗∗ = p ≤ 0.001; ∗∗∗∗ = p ≤ 0.0001). The performed tests and given significances are provided in the figure legends.
## Supplemental information
Document S1. Figures S1–S3
## Data and code availability
•*All data* has been included in main figures or supplementary information. All data reported in this paper will be shared by the lead contact upon reasonable request.•This paper does not report original code.•Any additional information required to reanalyze the data reported in this work paper is available from the lead contact upon request.
## Author contributions
H.E. conceived the project. H.E., R.M., and G.R. designed the experiments and supervised the project. R.M., S.S., and M.B. performed the experiments. H.E. and R.M. conducted data analysis. C.W., M.K., and D.J. generated and provided human PCLS. C.W. and M.K. provided histopathological and immunohistochemical evaluation of PCLS. R.M., G.R., and H.E. wrote the manuscript with input from all the authors.
## Declaration of interests
The authors declare no conflict of interest.
## Inclusion and diversity
We support inclusive, diverse, and equitable conduct of research.
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|
---
title: Mitochondria-associated membrane protein PACS2 maintains right cardiac function
in hypobaric hypoxia
authors:
- Jie Yang
- Mengjia Sun
- Renzheng Chen
- Xiaowei Ye
- Boji Wu
- Zhen Liu
- Jihang Zhang
- Xubin Gao
- Ran Cheng
- Chunyan He
- Jingyu He
- Xuhong Wang
- Lan Huang
journal: iScience
year: 2023
pmcid: PMC10034453
doi: 10.1016/j.isci.2023.106328
license: CC BY 4.0
---
# Mitochondria-associated membrane protein PACS2 maintains right cardiac function in hypobaric hypoxia
## Summary
Hypobaric hypoxia (HH) is the primary challenge at highland. Prolonged HH exposure impairs right cardiac function. Mitochondria-associated membrane (MAM) plays a principal role in regulating mitochondrial function under hypoxia, but the mechanism was unclear. In this study, proteomics analysis identified that PACS2, a key protein in MAM, and mitophagy were downregulated in HH. Metabolomics analysis indicated suppression of glucose and fatty acids aerobic oxidation in HH conditions. Cardiomyocyte Pacs2 deficiency disrupted MAM formation and endoplasmic reticulum (ER)-mitochondria calcium flux, further inhibiting mitophagy and energy metabolism in HH. Pacs2 overexpression reversed these effects. Cardiac-specific knockout of Pacs2 exacerbated mitophagy inhibition, cardiomyocyte injury, and right cardiac dysfunction induced by HH. Conditional knock-in of Pacs2 recovered HH-induced right cardiac impairment. Thus, PACS2 is essential for protecting cardiomyocytes through ER-mitochondria calcium flux, mitophagy, and mitochondrial energy metabolism. Our work provides insight into the mechanism of HH-induced cardiomyocyte injury and potential targets for maintaining the right cardiac function at the highland.
## Graphical abstract
## Highlights
•PACS2 is essential for cardiac function maintenance at highland•*Hypobaric hypoxia* decreases PACS2, mitophagy and energy metabolism•PACS2-mediated MAM calcium flux is required for mitophagy and energy metabolism•PACS2 supplement restores mitophagy, energy metabolism, and right cardiac function
## Abstract
Cardiovascular medicine; Physiology; Molecular biology; Proteomics; Metabolomics
## Introduction
High-altitude areas cover much of the total geographical area worldwide, and human exposure to high altitudes is increasing for various reasons. Hypobaric hypoxia (HH) caused by exposure to increasing altitude is the main physiological challenge in such conditions and has long been recognized as a cause of cardiac stress. Acute exposure to high altitudes induces an increase in the right ventricular (RV) afterload, leading to the alteration of the RV filling patterns.1 Prolonged exposure to high altitude further results in chronic remodeling of the cardiac structure and function, ultimately leading to right heart failure.2,3 Among these cardiac adaptive and/or pathological alterations, cardiomyocyte responses, particularly in intracellular homeostasis maintenance during hypoxia, are the critical molecular basis that determines the adaptive cardiac outcomes.4,5 Cardiomyocytes consume the majority of oxygen in the mitochondria as an electron donor for oxidative phosphorylation (OXPHOS).6,7,8 Thus, mitochondria are highly sensitive to decreases in oxygen levels in cardiomyocytes. Hypoxia increases oxidative stress and mitochondrial DNA mutation and causes mitochondrial dysfunction.9 Hypoxia also suppresses mitochondrial OXPHOS and leads to the accumulation of aerobic metabolic substrates and anaerobic metabolites, which result in cardiomyocyte injury and cardiac dysfunction.10 These damaged mitochondria and excessive metabolic substrates could be removed via mitophagy.11,12 However, mitophagy is suppressed in some cardiac diseases, such as diabetic cardiomyopathy and ischemic cardiomyopathy,13 which leads to mitochondrial dyshomeostasis and cardiac dysfunction. Therefore, an appropriate level of mitophagy serves as a protective mechanism to maintain the mitochondrial function in response to cardiac stress.14 In recent years, several mitochondrial membrane receptors containing the LC3-interacting region (LIR) motif have been found to mediate mitophagosome formation under acute hypoxic conditions.11,15 However, there is a paucity of information regarding cardiomyocyte mitophagy during chronic HH exposure, and the precise mechanism has not been fully elucidated.
Recent studies indicated that cardiomyocyte mitochondrial function is regulated by the mitochondria-associated membrane (MAM) structure, tethering two organelles by proteins located on opposing membranes.16,17,18 Stable contact between the ER and the mitochondria integrates the two organelles’ functions. Dysfunctional communications between organelles are implicated in cardiovascular disorders, neurodegeneration,19 diabetes20 and cancer.21 Phosphofurin acidic cluster sorting protein 2 (PACS2) is a crucial physical linkage protein on the MAM, connecting the ER with the mitochondria and maintaining MAM integrity and function. It plays important roles in many cellular activities, such as lipid synthesis, calcium signaling, autophagy, and apoptosis. Simmen et al. first found that PACS2 depletion causes extensive mitochondrial fragmentation, and fragmented mitochondria uncouple from the ER.22 Subsequently, researchers have also demonstrated that PACS2 depletion induced severe cardiovascular diseases.19 For example, downregulation of PACS2 by miRNA-182 remarkably inhibited cardiomyocyte apoptosis in the progress of heart failure.23 In diabetic cardiomyopathy, hyperglycaemia caused distortion of MAM formation via PACS2, IP3R2, FUNDC1, and VDAC1 and decreased mitochondrial biogenesis, fusion and OXPHOS, which contributes significantly to fibrosis and hypertrophy in the heart.24 Meanwhile, PACS2 also regulates autophagic and mitophagic flux. The knockdown of PACS2 results in a reduction in the autophagosome marker LC3 II in starved cells, which is associated with the inhibition of STX17-dependent ATG14-recruitment to MAM sites.25 Removal of PACS2 interrupts mitophagosome formation in MAMs, which subsequently impairs mitophagy and involves the process of cell apoptosis in response to stress.26 Notably, the re-expression of PACS2 could reverse these changes. As a protein with the potential function of autophagic and metabolic regulation, whether PACS2 changes in MAM are related to cardiomyocyte mitophagy and metabolism under HH condition is still worth studying.
In this study, we established HH conditions to closely simulate high-altitude exposure and focused on PACS2-mediated mitophagy and mitochondrial energy metabolism, regarding calcium flux across the MAM as the core mechanism. This study provides insights into the cardiomyocyte response to HH conditions. We also interpreted the mechanism underlying high-altitude-induced right cardiac dysfunction.
## Hypobaric hypoxia exposure induces the downregulation of PACS2 and affects mitophagy and mitochondrial energy metabolism in the right myocardium
C57BL/6J mice were assigned to an HH chamber for 6 weeks for the simulation of high-altitude conditions (Figure 1A). We first performed proteomics and metabolomics analyses accordingly in the right myocardium of mice that had been allowed to develop hypoxia-induced pulmonary hypertension after the 6-week chronic HH exposure. The different candidates were defined using a criterion of ≥1.2 log2 fold change and a significant difference between the groups. In the proteomics analysis, we identified 217 downregulated proteins and 82 upregulated proteins in the right myocardium of HH-exposed mice when compared with the respective levels in the normobaric normoxia (NN) counterparts (Figure 1B and Table S1). In the metabolomics analysis, we identified that 53 endogenous metabolites increased and 22 decreased (Table S2). Hierarchical clustering analysis of metabolomics indicated markedly altered cardiac metabolic pathways under HH exposure (Figure 1C). Among the differentially expressed proteins, we identified PACS2 as being significantly downregulated by 3.16-fold (Figure 1D). In addition to PACS2, MAP1LC3A, MAP1LC3B, autophagy-related 16-like 1, and sequestosome 1 (SQSTM1/p62) were remarkably downregulated; these were involved in phagophore formation and mitophagy induction (Figure 1E). To gain insight into the possible biological effect of HH exposure, we subjected proteins that were down- or upregulated to the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) pathway enrichment analysis. Glycolysis, the HIF-1 signaling pathway, and focal adhesion were upregulated during HH, while OXPHOS, the citrate cycle, fatty acid beta-oxidation, and mitophagy were downregulated (Figures 1F and 1G). The above-mentioned results indicated that both mitophagy and mitochondrial energy metabolism were impaired in the right myocardium of the mice owing to HH exposure. Figure 1Proteomics and metabolomics assessment of the right myocardium in response to hypobaric hypoxia(A) Protocol for establishing chronic HH-induced mice model and related omics analysis.(B) Volcano plot indicating the number of significantly upregulated (red, $$n = 82$$) and downregulated (blue, $$n = 217$$) proteins via proteomics analysis. Significantly downregulated PACS2 is indicated by arrows (fold of change = −3.16).(C) Sample cluster heatmap performed with hierarchical clustering in HH mice heart vs. NN controls are assessed by metabolomics analysis (adjusted p value <0.05). Red: up-regulation and blue: downregulation. The purple and green labels at the bottom of the heatmap represent the separation between the HH and NN groups.(D) Downregulation of PACS2 (fold change = −3.16) and mitophagy-related proteins in the HH group compared with NN controls.(E) Mechanism diagram of phagophore and mitophagosome formation with the participation of PACS2 and mitophagy-related proteins.(F and G) Bar graphs of the significantly upregulated (red) and downregulated (blue) terms by KEGG (F) and GO (G) pathways analysis. HH, hypobaric hypoxia; NN, normobaric normoxia; KEGG, Kyoto Encyclopedia of Genes and Genomes; GO, gene ontology.
## Cardiac Pacs2 ablation exacerbated the right cardiac dysfunction and structure impairment induced via hypobaric hypoxia exposure
To determine the role of PACS2 in maintaining right cardiac function and structure, we generated cardiomyocyte-specific Pacs2 cKO (Pacs2flox/flox/CreαMHC+/−) mouse models (Figure 2A). The Pacs2 cKO mice and their littermate controls (Pacs2+/+/CreαMHC+/−) underwent echocardiography and right heart catheterization (RHC) evaluation after a 6-week HH exposure. Mice subjected to HH conditions showed a markedly lower RV fractional area change (FAC) (Figures 2B and 2C), concurrent with an increase in the Tei index (Figures 2D and 2E), mPAP (Figures 2F and 2G), and max dP/dt (Figure 2H) and lower velocity time integral (VTI) (Figure 2I), indicating impaired right cardiac function. During exposure to HH, Pacs2 cKO mice exhibited a significantly lower FAC (Figures 2B and 2C) and higher Tei index (Figures 2D and 2E) than their littermate controls. Interestingly, we observed that other parameters reflecting the afterload, including the mPAP, max dP/dt, and RV VTI, were not further aggravated in Pacs2 cKO mice during HH exposure (Figures 2G–2I). These data suggest that Pacs2 ablation exacerbates right cardiac impairment partly independent of the RV afterload during HH exposure. Additionally, hypobaric hypoxic mice showed increased heart mass (Figures 2J and 2K) and Fulton’s index (Figures 2L and 2M); however, the heart weight was not further changed in Pacs2 cKO mice (Figure 2K). hematoxylin-eosin (HE) staining further revealed increased RV chamber thickness, decreased RV chamber size, and disordered arrangement of RV myocardium caused by cardiac Pacs2 ablation (Figure 2N). Masson’s trichrome staining showed significant collagen deposition in the right myocardial interstitial space after HH exposure (Figures 2O and 2P). The mean cross-sectional area (CSA) of the RV cardiomyocytes in the HH group was significantly larger than that in the NN group (Figures 2Q and 2R). Such substantial cardiac remodeling caused by HH was significantly more serious in the hearts of Pacs2 cKO mice. The cardiomyocyte injury was also evidenced by plasma markers (B-type natriuretic peptide [BNP], TnI, and CK-MB), which were much higher in Pacs2 cKO mice than in the remaining two groups (Figures 2S–2U). Our results indicated that HH-induced right cardiac impairment phenotype became more noticeable following Pacs2 ablation. Figure 2Cardiac Pacs2 ablation exacerbated the right cardiac dysfunction and right myocardium structure impairment after hypobaric hypoxia(A) Schematic of cardiomyocyte-specific Pacs2 cKO model.(B and C) FAC measurement of the RV in the NN group (FAC = $57.60\%$), HH group (FAC = $23.36\%$), and HH + Pacs2 cKO group (FAC = $18.47\%$). Statistics data show mean ± standard error of mean (SEM) in (B). Representative images were acquired at end-diastole (up) and end-systole (down) ($$n = 6$$ hearts per group).(D and E) Tei index is measured in the NN (Tei index = 0.35), HH (Tei index = 0.50), and HH + Pacs2 cKO groups (Tei index = 0.68) by tissue Doppler imaging. Statistics data show mean ± SEM in (E) ($$n = 6$$ hearts per group).(F and G) mPAP was measured by RHC in the NN (mPAP = 13.27 mmHg), HH (mPAP = 27.66 mmHg), and HH + Pacs2 cKO groups (mPAP = 24.67 mmHg). Statistics data show mean ± SEM in (G) ($$n = 6$$ hearts per group).(H) Statistics of max dP/dt in the three groups are calculated respectively ($$n = 6$$ hearts per group).(I) Statistics of RV VTI in the three groups are calculated respectively ($$n = 6$$ hearts per group).(J) Gross appearance of the whole body of the NN, HH, and HH + Pacs2 cKO groups.(K) Bar graphs showing the heart weight to body weight ratio of the three groups.(L) Gross appearance of the whole heart of the NN, HH, and HH + Pacs2 cKO groups ($$n = 6$$ hearts per group).(M) Bar graphs showing Fulton’s index of the three groups.(N) Representative HE staining images of RV myocardium in the NN group, HH group, and HH + Pacs2 cKO group ($$n = 6$$ hearts per group). Scale bar: 100 μm.(O) Representative Masson’s trichrome staining images of RV myocardium in NN, HH, and HH + Pacs2 cKO groups. Scale bar: 100 μm.(P) Quantification revealing myocardial fibrosis area (blue) in the three groups.(Q) Representative images of RV tissue stained with WGA (red) to delineate sarcolemma and DAPI (blue) ($$n = 6$$ hearts per group). Scale bar: 20 μm.(R) Quantification of relative cardiomyocyte CSA in the three groups ($$n = 6$$ hearts per group).(S-U) Statistics of plasma concentrations of BNP, TnI, and CK-MB of the mice in the three groups. Cardiac function indexes are obtained from six mice per group. Data was shown by mean ± SD, ∗$p \leq 0.05$, ∗∗$p \leq 0.01.$ BNP: brain natriuretic peptide; CK-MB: creatine kinase MB; CSA: cross-sectional area; DAPI: FAC: fractional area change; HE: hematoxylin-eosin; HH: hypobaric hypoxia; max dP/dt: maximum positive time derivative of left ventricular pressure; mPAP: mean pulmonary artery pressure; NN: normobaric normoxia; RHC: right heart catheterization; RV: right ventricular; TnI: troponin I; VTI: velocity time integral; WGA: wheat germ agglutinin.
To verify the separate role of Pacs2 ablation, we evaluated the right cardiac function under NN conditions. Compared with littermate controls, Pacs2 cKO mice showed a normal mPAP (Figures S1C and S1F), which was accompanied by impaired right cardiac function, as revealed by the lower RV FAC (Figures S1A and S1D) and an increased Tei index (Figures S1B and S1E). Pacs2 cKO mice in NN exhibited significantly increased myocardial disorder and fibrosis (Figures S2A–S2C). Additionally, cardiomyocyte injury was also evident in NN conditions in the presence of Pacs2 cKO (Figure S2D–S2H). *In* general, Pacs2 ablation exacerbated the cardiomyocyte injury and right cardiac dysfunction; however, it did not act on the RV afterload during HH exposure.
## Cardiac Pacs2 ablation exacerbated mitochondria-associated membrane disruption and mitophagy reduction induced via hypobaric hypoxia
To determine how PACS2 responds to HH, we compared the subcellular localization of MAM-associated proteins in isolated right myocardium under HH or NN conditions. As shown in Figure 3A, different fractions were identified with the following organelle markers: FACL4, VDAC1, MFN2, FIS1, CNX, and TOMM20. The level of PACS2 in MAM significantly decreased in the HH group compared with its levels in the NN counterparts, although a small amount of PACS2 could also be found in the cytosol. However, the levels of other MAM-related proteins were not noticeably altered in MAM fractions isolated from the HH group. Notably, the *Pacs2* gene expression was also significantly reduced in HH hearts when compared with NN controls (Figure 3B). To assess whether PACS2 affects MAM integrity, we examined the ER-mitochondrial contacts in Pacs2 cKO mice myocardium. As illustrated by the TEM images (Figures 3C and 3D), the proportion of ER in close contact with mitochondria relative to the total ER content was lower in the HH group than in the NN group and further decreased in the Pacs2 cKO mice. Consistent with the TEM images, immunofluorescence analysis clearly showed a lower level of co-localization of the ER with mitochondria in the Pacs2 cKO mice than in the remaining groups (Figures 3E and 3F).Figure 3Cardiac Pacs2 ablation exacerbated MAM disruption and mitophagy reduction induced by hypobaric hypoxia(A) Western blot analysis of PACS2 and MAM-related protein (FACL4, VDAC1, MFN2, FIS1, CNX, and TOMM20) levels in NN and HH conditions.(B) *Pacs2* gene expression in right myocardium in NN and HH conditions ($$n = 6$$ hearts per group).(C) Representative TEM images of MAM structure in NN, HH, and HH + Pacs2 cKO groups (Scale bar: 2 μm) and then analyzed at higher magnification (Scale bar = 1 μm). ER are marked by red and mitochondria are marked by green. The black arrowheads indicate the ER-mitochondria contacts (<30 nm).(D) Quantification of ER and mitochondria contact in right myocardium ($$n = 6$$ hearts per group). Approximately 10–20 random fields with 50–100 mitochondria were analyzed in each experimental group.(E) Mitotracker Deep *Red is* used to mark mitochondria and ERP72 (green) is employed to mark ER in the right myocardium (Scale bar: 50 μm) and then analyzed at higher magnification (Scale bar: 25 μm).(F) Pearson’s overlap coefficient analysis indicates the lesser co-localization of ER with mitochondria ($$n = 6$$ hearts per group).(G and H) Representative western blots and statistical analysis ($$n = 6$$ hearts per group) of MAP1LC3B-I and MAP1LC3B-II in the three groups.(I) LSCM images of the right myocardium sections which were marked with Mitotracker Deep Red and MAP1LC3B (green). Scale bar: 25 μm.(J) Pearson’s overlap coefficient analysis of the co-localization of MAP1LC3B and mitochondria ($$n = 6$$ hearts per group).Data are shown as mean ± SD, ∗$p \leq 0.05$, ∗∗$p \leq 0.01.$ CNX: calnexin; CYTO: cytosol; ER: ER FACL: fatty acid CoA ligase 4; FIS1: mitochondrial fission 1; HH: hypobaric hypoxia; LSCM: laser scanning confocal microscopy; MAM: mitochondria-associated membranes; MFN2: mitofusin 2; MITO: mitochondria; NN: normobaric normoxia; PNS: post-nuclear supernatant; TEM: transmission electron microscopy; TOMM20: translocase of the outer mitochondrial membrane member 20; VDAC1: voltage-dependent anion channel 1.
Further, we determined whether PACS2 alteration affected mitophagy. Decreased mitophagy markers confirmed impaired mitophagy induced by HH exposure in the right myocardium. The blotting results indicated that the level of MAP1LC3B-II was lower in the Pacs2 cKO mice than in controls during HH conditions (Figures 3G and 3H). Moreover, immunostaining analysis revealed that Pacs2 deletion further reduced the co-localization of MAP1LC3B puncta and mitochondria induced by HH exposure (Figures 3I and 3J). Combined, cardiac Pacs2 ablation exacerbated MAM disruption and mitophagy reduction induced by HH.
## Hypobaric hypoxia reduced mitochondria-associated membrane formation and mitophagy in vitro
To explore the effect of HH on the MAM structure and biological function, we also measured the levels of MAM-related proteins in H9C2 cardiomyocytes exposed to simulated HH in vitro. As depicted in Figure 4A, in line with the in vivo results above, the expression of PACS2 in the MAM from HH-treated cells was lower than that in the MAM from NN-treated cells. Consistently, confocal imaging showed a decreased association between the ER and mitochondria in simulated HH-treated cardiomyocytes compared with that in the NN cells (Figures 4B and 4C). TEM imaging showed swollen mitochondria and fewer mitochondria adjacent to the ER after HH exposure (Figure 4D). With respect to the mitochondria-associated ER membranes, we also found a decrease in the ratio of close MAM contacts and relative length (Figure 4E). These data suggested that HH decreases the MAM junction structure in cardiomyocytes. Furthermore, we evaluated mitophagy levels in H9C2 cardiomyocytes. We found decreased MAP1LC3B-II transfer (Figures 4F and 4G) and co-localization with mitochondria after HH exposure (Figures 4H and 4I). To further verify the impaired mitophagy, we transfected the pH-dependent mitochondrial protein Keima into the cardiomyocytes; this can shift from green to red as mitochondria are delivered to lysosomes. Laser scanning confocal microscope (LSCM) monitoring showed that HH induced a markedly decreased mitophagy index in the cardiomyocytes (Figures 4J and 4K), indicating that HH decreased the number of mitophagosomes and impaired the mitophagy flux. The above results in the H9C2 cell lines as well show that HH reduces MAM formation and mitophagy. Figure 4Hypobaric hypoxia reduced PACS2 expression, MAM formation, mitophagy, ER-mitochondria calcium flux, and mitochondrial oxidative phosphorylation in vitro(A) Western blot analysis of PACS2 and MAM-related proteins (CNX, TOMM2O, VDAC1) in H9C2 cardiomyocytes.(B) Cardiomyocytes marked by Mitotracker Deep Red and ERP72 (green) and then analyzed at higher magnification (Scale bar: 10 μm) and then analyzed at higher magnification (Scale bar: 5 μm).(C) Pearson’s overlap coefficient analysis indicates the lesser co-localization of ER with mitochondria in the HH group ($$n = 5$$ hearts per group).(D) Representative TEM images in HH-treated cardiomyocytes. Scale bar: 1 μm. ER are marked by red and mitochondria are marked by green. The black arrowheads indicate the ER-mitochondria contacts (<30 nm).(E) Quantification of the ratio (%) of mitochondria adjacent to ER (upper) and the average ER-mitochondria contacts (<30 nm) length per mitochondrion (below).(F and G) Representative western blots and statistical analysis ($$n = 6$$ hearts per group) for MAP1LC3B-I and MAP1LC3B-II under HH and NN exposures.(H and I) LSCM images of cardiomyocytes marked with Mitotracker Deep Red and MAP1LC3B (green). Merged images (H) and Pearson’s overlap coefficient analysis (I) revealed that HH exposure reduced the co-localization of MAP1LC3B and mitochondria ($$n = 5$$ hearts per group). Scale bar: 25 μm.(J and K) H9C2 cardiomyocytes were transfected by mtKeima-red for 12 h before HH exposure. LSCM images (J) and statistical analysis (K) showed decreased mitophagy index after HH exposure ($$n = 5$$ hearts per group). Scale bar: 25 μm. Data are shown as mean ± SD, ∗$p \leq 0.05$, ∗∗$p \leq 0.01.$ CNX: calnexin; ER: ER HH: simulated hypobaric hypoxia; LSCM: laser scanning confocal microscopy; MAM: mitochondria-associated membranes; NN: simulated normobaric normoxia; TEM: transmission electron microscopy; TOMM20: translocase of the outer mitochondrial membrane member 20; VDAC1: voltage-dependent anion channel 1.
## hypobaric hypoxia reduced endoplasmic reticulum-mitochondria calcium flux and mitochondrial oxidative phosphorylation in vitro
PACS2 was reported to maintain the junction of the MAM and regulate mitochondrial calcium flux.22 *In this* study, we found that [Ca2+]m in the H9C2 cardiomyocytes after HH treatment was markedly lower than in the NN group (Figures S4A and S4B). To determine the origin of mitochondrial calcium, we incubated H9C2 cardiomyocytes under HH or NN with a cytoplasmic Ca2+ chelator, BAPTA-AM (10 μM), in calcium-free Hanks' balanced salt solution (HBSS) for 10 min. Mitochondrial calcium was labeled using a Rhod2-AM probe, and the cells were observed and measured under LSCM. TG- (a calcium pump inhibitor, Figures 5A and 5B) and ATP- (an indirect IP3R agonist, Figures 5C and 5D) elicited ER-mitochondria calcium flux was lower in HH-treated cells than in NN-treated cells. Inositol trisphosphate receptors (IP3R) are important ER calcium-release channels.27 Therefore, we added 2-APB, which blocked the release of calcium from IP3R. Comparable mitochondrial calcium to HH exposure was observed when treated with 2-APB (Figures 5E and 5F), suggesting that IP3R is required for maintaining the physiological mitochondrial calcium levels under NN conditions. Next, we detected the effect of HH on the expression of two calcium transporters majoring in conveying calcium flux in the contact sites of ER and mitochondria, IP3R, and the mitochondrial calcium uniporter (MCU). As shown in Figures S3A and S3B, the expression of both two calcium channel proteins did not significantly change between NN and HH conditions. It is not the calcium transporters that limit the calcium flux across MAM, but the disruption of MAM contact was more likely to affect the calcium flux. Figure 5Hypobaric hypoxia reduced ER-mitochondria calcium flux and regulated mitochondrial energy metabolism in vitro(A-D) Cardiomyocytes from NN (black line) and HH groups (red line) are treated with BAPTA-AM (10 μM) for 30 min prior to and during exposure. Calcium in the mitochondria is stained by Rhod-2 AM probe. Images showing TG (2 μM) (A) and ATP (10 μM) (C) elicited calcium fluorescence signals. ( B, D) *Statistical analysis* shows relatively lower ER-mitochondria calcium transfer in the HH group (cells in the NN group were used as controls).(E) Quantification of changes in mitochondria calcium concentration in cardiomyocytes pre-treated with 2-APB (50 μM) in NN condition.(F) *Statistical analysis* showing decreased mitochondrial calcium flux (cells without 2-APB treatment are used as controls).(G) The OCR and ECAR are determined with the Seahorse XF96 Extracellular Flux Analyzer. OCR is recorded at baseline and after the sequential injection of each compound (oligomycin, FCCP, and rotenone) at the indicated concentration.(H) Basal respiration, ATP production, maximal respiration, and spare respiratory capacity are calculated.(I) ECAR is recorded after the sequential injection of each compound (glucose, oligomycin, and 2-DG) at the indicated concentration.(J) Non-glycolytic acidification, glycolysis, glycolytic capacity, and glycolytic reserve are calculated.(K) OCR analysis of cells treated with BSA-conjugated palmitate substrate and after the sequential injection of each compound (oligomycin, FCCP, and ETO) at the indicated concentration.(L) Basal respiration, ATP production, maximal respiration, and spare respiratory capacity are calculated with FAO substrate. H9C2 cardiomyocytes are obtained from eight mice per group. Cells subjected with or without HH exposure before loading. Data are shown as mean ± SD, ∗$p \leq 0.05$, ∗∗$p \leq 0.01.$ 2-APB: 2-aminoethoxydiphenyl borate; ATP: adenosine triphosphate; BAPTA-AM: bis-(aminophenolxy) ethane-N,N,N′,N′-tetra-acetic acid acetoxyme-thylester; ECAR: extracellular acidification rate; ETO: etomoxir; FCCP: trifluoromethoxy carbonyl cyanide phenylhydrazone; HH: simulated hypobaric hypoxia; NN: simulated normobaric normoxia; OCR: oxygen consumption rate; TG: thapsigargin.
MAM formation and ER-mitochondrial calcium flux is essential for mitochondrial energy metabolism. Thus, we evaluated the effects of HH on cardiomyocyte mitochondrial energy metabolism using a Seahorse XF analyzer to measure mitochondrial respiration and glycolytic flux. We found that cardiomyocytes exhibited significant decreases in basal and maximal cellular oxygen consumption rate (OCR) in response to HH. ATP production and spare respiration capacity were also significantly lower after HH exposure (Figures 5G and 5H). ECAR results indicated an increase in glycolysis and glycolytic capacity owing to insufficient oxygen (Figures 5I and 5J). In addition, OCR measurement with a medium containing BSA-conjugated palmitic acid significantly decreased after HH exposure (Figures 5K and 5L). The above-mentioned results show that after HH exposure, the cardiomyocytes displayed metabolic reprogramming, represented by the restriction of FAO-related OXPHOS and a tendency to rely more on anaerobic than aerobic glycolysis for adapting to the HH condition. Since HH caused a decline in ER-mitochondria calcium flux, one mechanism that potentially accounts for the metabolic shift may be associated with the regulation of mitochondrial calcium.
## Endoplasmic reticulum-mitochondria calcium flux is involved in PACS2-mediated mitophagy and mitochondrial energy metabolism
We next determined the contributions of ER-mitochondria calcium in PACS2-induced mitophagy and mitochondrial energy metabolism. We obtained cardiomyocytes with stable overexpression of Pacs2 through lentiviral vectors (LVVs) infection (Figures 6A and 6B). ER-mitochondria contacts increased in cells where Pacs2 was overexpressed (Figures 6C and 6D). We found reversed [Ca2+]m in LVVs-infected cardiomyocytes (Figures S4A and S4B). Similar results were observed with TG (Figures 6E and 6F) or ATP (Figures 6G and 6H) treatment in Pacs2-overexpressed cultured cells in the dynamics of mitochondrial calcium flux, indicating a source of calcium flux released from the ER. The regulation of calcium flux between the ER and mitochondria via IP3R is a major function of the MAM.28 As depicted in Figures 6I–6L, the restored calcium levels caused by the overexpression of Pacs2 were partly blocked by 2-APB, suggesting that Pacs2 overexpression promoted ER calcium release in the MAM through IP3R. Additionally, higher MAP1LC3B-II levels were observed after LVV-overexpression of Pacs2 (Figures 6M and 6N), which could also be blocked by 2-APB (Figures 6O and 6P), suggesting that PACS2-mediated ER-mitochondria calcium flux was required for mitophagy. Figure 6ER-mitochondria calcium flux is required for PACS2-mediated mitophagy(A and B) Western blot and statistical analysis showed that Pacs2 is stably overexpressed in LVVs infected cardiomyocytes ($$n = 6$$ hearts per group).(C) Cardiomyocytes are co-immunostained for mitotracker (red) and ERP72 (green, Scale bar: 10 μm) and then analyzed at higher magnification (Scale bar: 5 μm).(D) Pearson’s overlap coefficient is employed to analyze the co-localization ($$n = 7$$ hearts per group).(E-H) Calcium in the mitochondria marked by Rhod-2 AM indicating reversed TG (E and F) and ATP (G and H) -mediated ER-mitochondria calcium fluorescence signals in Pacs2 overexpression cells (green line) in HH condition (cells without 2-APB treatment are used as controls).(I-L) *Mitochondria calcium* evoked by LVVs overexpression of Pacs2 (red line) in the presence of TG (I and J) and ATP (K and L) were blocked by 2-APB (blue line).(M and N) Western blots show significantly increased MAP1LC3B-II turnover in the LVVs-Pacs2 group ($$n = 5$$ hearts per group). ( M and N) Western blots show significantly increased MAP1LC3B-II turnover in the LVVs-Pacs2 group ($$n = 5$$ hearts per group). ( O and P) Western blots showing impaired PACS2-mediated MAP1LC3B-II turnover with the treatment of 2-APB ($$n = 5$$ hearts per group).Data are shown as mean ± SD, ∗$p \leq 0.05$, ∗∗$p \leq 0.01.$ 2-APB: 2-aminoethoxydiphenyl borate; ATP: adenosine triphosphate; ER: ER HH: simulated hypobaric hypoxia; LVVs: lentiviral vectors; NN: simulated normobaric normoxia; TG: thapsigargin; NC: negative control.
With the supplementation of PACS2, more MAP1LC3B puncta co-localized with mitochondria (Figures 7A and 7B) and an increased mitophagy index were observed in HH conditions (Figures 7C and 7D). These data showed that PACS2 restored impaired mitophagy through enhanced ER-mitochondria calcium flux. To investigate whether ER-mitochondria calcium flux was also involved in PACS2-mediated mitochondrial energy metabolism alteration, we compared real-time changes in OCR and ECAR in the H9C2 cardiomyocytes with or without overexpression of PACS2 under HH treatment. With PACS2 supplementation, the decreased basal respiration, ATP production, and maximal respiration (Figures 7E and 7F) and increased basal and maximal ECAR (Figures 7G and 7H) induced by HH were significantly reversed. The recovery in mitochondrial respiration was also blocked by 2-APB treatment (Figures 7G and 7H). To extend the hypothesis that PACS2 helped recover OCR, which is supported by FAO, we further measured OCR in a medium containing palmitate-BSA as an exogenous FAO substrate. Notably, the cardiomyocytes showed a reversed OCR after the supplementation of PACS2 when compared with an empty vector control (Figures 7I and 7J), which was significantly blocked on adding 2-APB. This indicates that PACS2 enabled HH-treated cardiomyocytes to switch from glycolysis to an increased reliance on FAO for ATP production. This metabolic reprogramming at least partly depends on the calcium flux across MAM. Together, these data suggested that ER-mitochondria calcium flux was essential for PACS2-mediated mitophagy maintenance and mitochondrial energy metabolism after HH exposure. Figure 7PACS2 supplementation alleviated impaired mitophagy and mitochondrial energy metabolism induced by HH(A and B) Mitotracker Deep Red and GFP-MAP1LC3B were used to mark H9C2 cardiomyocytes. Scale bar: 10 μm. Merged images (A) and Pearson’s overlap coefficient analysis (B) revealed that the overexpression of Pacs2 increased the co-localization of MAP1LC3B-II and mitochondria during HH exposures ($$n = 5$$ hearts per group).(C and D) All three groups were infected by mtKeima plasmid for 12 h before HH exposure and observed under an LSCM. ( C) Representative images showing puncta formation in three groups (Scale bar: 50 μm) and then analyzed at higher magnification (Scale bar: 25 μm). ( D) *Quantitative analysis* of the fluorescent area showing increased mitophagy index in the LVVs-Pacs2 group ($$n = 5$$ hearts per group).(E) Measurement of the OCR of cardiomyocytes (blue), negative controls (red), LVVs-Pacs2 (green), and LVVs-Pacs2+2-APB (orange) in HH exposure.(F) Basal respiration, ATP production, maximal respiration, and spare respiratory capacity are calculated.(G) Measurement of the ECAR of cardiomyocytes (blue), negative controls (red), LVVs-Pacs2 (green), and LVVs-Pacs2+2-APB (orange) in HH exposure.(H) Non-glycolytic acidification, glycolysis, glycolytic capacity, and glycolytic reserve are calculated.(I) OCR analysis of cells treated with BSA-conjugated palmitate substrate and after the sequential injection of each compound (oligomycin, FCCP, and ETO) at the indicated concentration.(J) Basal respiration, ATP production, maximal respiration, and spare respiratory capacity are calculated with FAO substrate. H9C2 cardiomyocytes are obtained from eight mice per group. Data are shown as mean ± SD, ∗$p \leq 0.05$, ∗∗$p \leq 0.01.$ 2-APB: 2-aminoethoxydiphenyl borate; ATP: adenosine triphosphate; BAPTA-AM: bis-(aminophenolxy) ethane-N,N,N′,N′-tetra-acetic acid acetoxyme-thylester; ECAR: extracellular acidification rate; ETO: etomoxir; FCCP: trifluoromethoxy carbonyl cyanide phenylhydrazone; HH: simulated hypobaric hypoxia; LSCM: laser scanning confocal microscopy; NN: simulated normobaric normoxia; OCR: oxygen consumption rate; TG: thapsigargin.
## Cardiac Pacs2 knock-in alleviated hypobaric hypoxia-induced right cardiac dysfunction
LVVs-overexpression of Pacs2 significantly reversed MAM formation, mitophagy, and mitochondrial energy metabolism in cardiomyocytes in vitro. To verify the contributions of PACS2 in maintaining RV function during HH exposure in vivo, we generated cardiomyocyte-specific Pacs2 knock-in mouse models (Pacs2 cKI; Figure 8A). Histological analysis of the hearts from the Pacs2 cKI mice showed significantly decreased right cardiac hypertrophy (Figure 8B), cardiac fibrosis area (Figures 8C and 8D), and cardiomyocytes CSA (Figures 8E and 8F) with HH exposure. In addition, the Pacs2 cKI mice had lower plasma levels of BNP, TnI, and CK-MB (Figures 8G–8I) than their littermate controls, indicating that PACS2 supplementation reduced the HH-induced myocardial damage. Compared with their littermate controls, the Pacs2 cKI mice exhibited a higher RV FAC (Figures 8J and 8M) and lower Tei index (Figures 8K and 8N). As expected, Pacs2 overexpression did not significantly alter mPAP, max dP/dt, and RV VTI (Figures 8O–8Q) during HH exposure. Pacs2 cKI did also fail to completely reverse the impaired cardiac function to the baseline level as the NN group, as shown in Figure S5. Additionally, in the NN condition, Pacs2 cKI had little effect on the normal right cardiac function, representing by no significant difference in FAC, Tei index, mPAP, et al. when compared with their littermate controls. The survival of mice without HH exposure were higher than those in HH exposure groups. Compared with WT mice without HH exposure, survival rate was significantly increased in the Pacs2 cKI group while decreased in the Pacs2 cKO group (Figure 8R). These data suggested that conditional Pacs2 cKI reduced cardiomyocyte injury and partially recovered RV cardiac function after HH exposure without significantly influencing the RV afterload. With the progressive right cardiac injury caused by HH, the left cardiac function in mice has also been affected significantly. We observed a significant increase in the left ventricular dimensions (end-systolic diameter and end-diastolic diameter) and a significant decrease in the systolic function (left ventricular ejection fraction and left ventricular fractional shortening) in the mice with HH exposure compared to the NN group (Figures S6A–S6D). Moreover, the effect of PACS2 genetic manipulation appeared to be significant in altering the left cardiac function, as shown by aggravated left cardiac dysfunction caused by Pacs2 cKO and alleviated HH-induced left cardiac dysfunction in Pacs2 cKI mice (Figures S6A–S6D).Figure 8Cardiac Pacs2 knock-in alleviated HH-induced right myocardium injury and right cardiac dysfunction(A) Schematic of Pacs2 cKI mice model.(B and C) Representative photographs of HE staining (B) and Masson’s trichrome staining (C) of the right myocardium in Pacs2 cKI mice heart and littermate controls. Scale bar: 100 μm.(D) Quantification of fibrotic area revealing less myocardial fibrosis area (blue) in Pacs2 cKI mice ($$n = 6$$ hearts per group).(E) Representative images of the right myocardium stained with WGA (red) to delineate sarcolemma and DAPI (blue). Scale bar: 50 μm.(F) Bar graphs revealing cardiomyocytes CSA in the two groups ($$n = 6$$ hearts per group).(G-I) Statistics of plasma concentration of BNP, TnI, and CK-MB of Pacs2 cKI mice and controls during HH exposure ($$n = 6$$ hearts per group).(J) FAC measurement of the RV in the control group (FAC = $28.51\%$) and Pacs2 cKI mice group (FAC = $50.68\%$) in HH exposure. Representative images acquired at end-diastole (left) and end-systole (right).(K) Tei index was measured in the control group (Tei index = 0.57) and Pacs2 Pacs2 cKI mice group (Tei index = 0.34).(L) mPAP measured by RHC and the ECG of the control mice group (mPAP = 22.58 mmHg) and Pacs2 cKI mice group (mPAP = 21.67 mmHg).(M-Q) Statistics of FAC (M), Tei index (N), mPAP (O), max dP/dt (P), RV VTI (Q) in the two groups ($$n = 6$$ hearts per group).(R) Survival rate of the control and modeling mice with or without HH exposure. Right myocardium is obtained from six mice per group, data are shown as mean ± SD, ∗$p \leq 0.05$, ∗∗$p \leq 0.01.$ BNP: brain natriuretic peptide; CK-MB: creatine kinase MB; CSA: cross-sectional area; FAC: fractional area change; HH: hypobaric hypoxia; mPAP: mean pulmonary artery pressure; NN: normobaric normoxia; RHC: right cardiac catheterization; RV: right ventricular; TnI: troponin I; VTI: velocity time integral; WGA: wheat germ agglutinin.
## Discussion
This is the first study to reveal the underlying mechanism of PACS2 in HH-mediated cardiomyocyte injury and right cardiac dysfunction. The core process was the downregulated PACS2 localized in the MAM after HH exposure. PACS2 reduction further suppressed MAM formation and resulted in decreased calcium flux from the ER to the mitochondria via the IP3R calcium channel. The reduced mitochondrial calcium influx further inhibited mitophagy and mitochondrial energy metabolism, inducing cardiomyocyte injury and right cardiac dysfunction. Moreover, cardiomyocyte-specific knock-in of Pacs2 reversed right cardiac dysfunction and RV fibrosis. Of note, neither conditional cKO nor cKI of the Pacs2 in cardiomyocyte influenced the RV afterload, highlighting an independent role of PACS2-directed cardiomyocyte responses in maintaining right cardiac function. Thus, our results provided potential therapeutic targets for high-altitude-induced right cardiac impairment.
Sufficient oxygen supply is the most essential condition for the survival and function of cardiomyocytes. Hypoxia induces pulmonary vasoconstriction and increases pulmonary vascular resistance. Prolonged hypoxia further results in right cardiac function impairment and even right heart failure.29 Thus, in this study, we focused on the right rather than on the left cardiac function. Hypoxia is usually generated by normobaric hypoxia (NH) or HH in the experiments. NH lowers the partial pressure of inspired oxygen (PiO2) by reducing the fraction of inspired oxygen by adding exogenous nitrogen without altering the barometric pressure. Conversely, HH lowers the PiO2 by reducing the barometric pressure. Previous studies have suggested that NH and HH induced similar cardiac adaptations over a short duration, although lower SpO2 and worse right cardiac function emerged during long-term exposure.30,31 Thus, more complicated mechanisms may exist in HH than in NH, including intravascular bubble formation, increased alveolar dead space, altered fluid permeability, and a mismatch in ventilation and perfusion.32 Our study established a long-term HH exposure model to simulate real cardiac function alteration and cardiomyocyte response in high-altitude environments. Previous studies on cardiomyocyte injury caused by hypoxia mainly focused on the increase in oxygen free radicals and anaerobic metabolites, eventually leading to cardiomyocyte apoptosis, myocardial fibrosis, and irreversible cardiac remodeling.33 Our study indicated that both mitophagy and mitochondrial energy metabolism were involved in cardiomyocytesurvival under HH conditions. Our results revealed a mechanism that results in right cardiac dysfunction at high altitudes.
Previous studies reported that PACS2 is closely associated with the onset and progression of tumors, such as colorectal and liver cancer.34,35 Owing to their infinite proliferation ability, tumor cells survive in relative hypoxic conditions. Therefore, it is essential to understand the biological function of PACS2 in regulating cell fate in response to hypoxic conditions. Accordingly, after HH exposure, PACS2 expression was found to be markedly reduced in the MAM, although with a moderate increase in PACS2 in the cytosol both in vivo and in vitro (Figures 4A and 5A). This implied a dynamics translocation from the MAM to the cytosol, which may partly explain the decreased PACS2 in the MAM. The free form of PACS2 in the cytosol contributed to nuclear gene expression and membrane trafficking rather than calcium flux, thus exhibiting the suppression of downstream mitophagy and energy metabolism. Besides PACS2, recent studies have found that the FUN14 domain containing 1 (FUNDC1), a new protein in the MAM, is responsible for the release of calcium from the ER to the mitochondria and mitophagy induction in mouse cardiomyocytes.36,37 Notably, our results revealed that PACS2 served as a mitophagic regulator in the MAM and modulated calcium release from ER to mitochondria in cardiomyocytes. However, the precise molecular mechanism of how PACS2 cooperates with FUNDC1 to regulate calcium flux, mitophagy, and cardiac function remains unknown. We considered that a few special proteins in the MAM may interact to form a protein complex or “protein machine” and involve themselves in the above-mentioned process. PACS2 may be the key protein and serves as a scaffold to sponge other proteins.
Mitophagy is essential for mitochondrial homeostasis and quality control in cardiomyocytes.38 During hypoxia, mitophagy is the sole mechanism through which cardiomyocytes eliminate superfluous or damaged mitochondria.14 However, the mechanisms underlying mitophagy remain largely unknown. Previous studies on mitophagy have focused on several protein receptors on the mitochondrial membrane, including BCL2 interacting protein 3 (BNIP3), BNIP3-like, and FUNDC1. Most of them have a classic LIR motif to directly bind MAP1LC3B for mitophagy activation.39 *In this* study, we found a new protein in the MAM without this classic LIR motif, although it was closely associated with HH-mediated mitophagy. PACS2 did not directly link to autophagy-associated proteins, such as ATG5, ATG7, Beclin1, and MAP1LC3B; however, it acted as a calcium channel to promote calcium influx into the mitochondria.22 Usually, intracellular calcium is considered an activator of autophagy.40,41,42 To date, the role of calcium signaling in autophagy regulation is highly controversial. Most studies considered that calcium works as an autophagy activator because calcium mobilizing agents and calcium ionophores promote autophagy by elevating intracellular calcium concentration.43 *In this* study, we detected that PACS2-mediated calcium influx was required for HH-induced mitophagy in cardiomyocytes, which further verified the effect of intracellular calcium on mitophagy regulation. However, the mechanism through which mitochondrial calcium is involved in mitophagy activation requires further exploration. Mitochondrial calcium uptake occurs mostly through MAM, which closely contacts with the ER and renders a micro-domain with a sufficiently high calcium concentration.44 A recent study reported that mitochondrial calcium influx inhibition decreased ATP production, enhanced mitophagy, and provided cardioprotection in cardiac failure.45 Conversely, we found that HH decreased ER-mitochondria calcium flux and calcium-mediated mitophagy. Similar to our results, Böckler and Zou demonstrated that ER-mitochondria contact and calcium flux across the MAM were required for autophagic removal of mitochondria since artificially tethering ER and mitochondria rescued mitophagy defects.18,33 Besides mediating calcium flux, the ER-mitochondria encounter structure may also supply the growing phagophore with lipids synthesized in the ER, which then enclose the impaired mitochondria to form a mitophagosome. Hence, ER-mitochondria-mediated calcium flux is required for mitophagy induction.
In addition to the above-mentioned role of calcium in mitophagy, mounting evidence suggests that calcium also dynamically regulates the aerobic energy metabolism by stimulating mitochondrial OXPHOS.46,47 In highly energy-consuming tissues, such as the heart, OXPHOS in the mitochondria provides a major source of cellular ATP through the oxidation of substrates, including fatty acids, glucose, and ketones.48 We found that to adapt to the HH condition, cardiomyocytes mainly rely on the glycolytic pathway rather than the OXPHOS pathway. As a critical signaling molecule in mitochondrial energy conversion, sufficient mitochondrial calcium concentration is required to activate mitochondrial dehydrogenases, including the pyruvate dehydrogenase complex (PDHC), NADH-isocitrate dehydrogenase (ICDH), and α-ketoglutarate dehydrogenase (α-KGDH).49,50 Other components within the energy-producing pathways besides NADH generation, such as downstream ATPase and the cytochrome chain, were also significantly stimulated by calcium.51,52 Current studies supported the proposed physiological metabolic role of calcium entry into the mitochondria matrix through the mitochondrial calcium uniporter (MCU) complex.53 Together with some recent reports, we indicated that the IP3R channels were also associated with alterations in mitochondrial calcium flux, especially in cardiomyocytes under HH exposure. IP3R-mediated calcium signaling required quantification of PACS2 and proximity of the ER and mitochondria. The supplement of PACS2 could improve mitochondrial respiration efficiency during HH exposure. Our previous randomized double-blinded clinical trial proposed that cardiac function could be recovered by optimizing myocardial energy metabolism.54 Combined with those results, our present results provide a therapy for improving cardiac function at high altitudes targeted on energy metabolic reprogramming based on calcium flux across the MAM in cardiomyocytes. Moreover, linking reprogramming of energy metabolism induced by the PACS2 supplement was associated with enhanced mitophagy. We considered that mitophagy may, at least partly, provide relatively efficient substrates such as fatty acids for maintaining energy demand. However, the exact role of calcium in cardiomyocyte energy metabolism reprogramming requires confirmation using accurate techniques such as isotope tracing analysis.
In conclusion, we described acardiomyocyte injury mechanism during HH exposure in high-altitude environments. HH downregulated the expression of PACS2 in the MAM. Decreased PACS2 disrupted MAM formation and calcium transfer from the ER to the mitochondria, leading to mitophagy inhibition and mitochondrial energy metabolism impairment, which induced cardiomyocyte injury and right cardiac dysfunction during HH exposure. Our study identified a potential target for the prevention and treatment of cardiovascular diseases caused by high-altitude exposure.
## Limitations of the study
Due to the limitations in the present study, we did not explore the possible molecular mechanisms that contribute to the downregulation of PACS2 in cardiomyocytes under HH conditions. Besides, MAM is also abundant and important in the left ventricular,19,36 therefore further studies are warranted to investigate the different chamber’s expression of PACS2 response to HH exposure and its role in modulating the cardiac function. Finally, there were no human samples from patients. The clinical relevance of the above findings needs further validation in the future.
## Key resources table
REAGENT or RESOURCESOURCEIDENTIFIERAntibodiesRabbit polyclonal anti-PACS2AbcamCat# ab222316Rabbit monoclonal anti-Mitofusin 2AbcamCat# ab124773Mouse monoclonal anti-VDAC1AbcamCat# ab14734Rabbit monoclonal anti-FACL4AbcamCat# ab155282Rabbit monoclonal anti-TTC11/FIS1AbcamCat# ab156865Rabbit monoclonal anti-CalnexinAbcamCat# ab133615Rabbit monoclonal anti-LC3BAbcamCat# ab192890Rabbit monoclonal anti-TOMM20AbcamCat# ab186735Mouse monoclonal anti-beta ActinAbcamCat# ab8226Rabbit polyclonal anti-ERp72AbcamCat# ab155800Bacterial and virus strainsLVVs carrying Pacs2 RNA systemGene Pharma TechnologyN/AChemicals, peptides, and recombinant proteinsBafilomycin A1Sigma-AldrichCat#B1793TGSigma-AldrichCat#T9033adenosine triphosphateSigma-AldrichCat# A18522-bis (2-aminophenoxy) ethane-N,N,N’,N’-tetraacetic acid tetrakisMolecular probesCat#B12052-aminoethoxydiphenyl borateAbcamCat#ab120124Tris-buffered salineBoster Biological TechnologyCat#AR0031Tween-20SolarbioCat#T8220Dulbecco’s modified Eagle’s mediumSigma-AldrichCat#0030034DJFetal bovine serumHy Clone LaboratoriesCat#SV30208.03Rhod2 AMAbcamCat#ab142780MitoTracker Deep RedInvitrogenCat#M22426Critical commercial assaysBNP ELISA kitJiangsu Jingmei Biological TechnologyCat#JM-02343M2TnI ELISA kitJiangsu Jingmei Biological TechnologyCat#JM-02662M2CK-MB ELISA kitJiangsu Jingmei Biological TechnologyCat#JM-03084M2XF Cell Mito Stress Test KitSeahorse BioscienceCat#103015-100Deposited dataiTRAQ proteomics analysis/nanoUHPLC-MS/MS analysis dataThis paperTable S1, iProX database; https://www.iprox.org (Project ID: IPX0005958000)LC-MS metabolomics dataThis paperTable S2Experimental models: Cell linesRat: H9C2 cardiomyocytesChinese Academy of SciencesBFN60804388Experimental models: Organisms/strainsMouse: C57BL/6J, Pacs2 cKO(Pacs2flox/flox/CreαMHC+/−)Cyagen BiosciencesN/AMouse: C57BL/6J, Pacs2 cKICyagen BiosciencesN/AMouse: PACS2fl/flCyagen BiosciencesN/AMouse: Cre transgenic miceCyagen BiosciencesN/ARecombinant DNAPlasmid: mitochondria-targeted monomeric Keima-Red-hygMedical and Biological LaboratoriesCat#AM-V0251HMSoftware and algorithmsImage-Pro Plus 5.0GE Healthcarehttps://imagej.nih.gov/ijLAS X softwareLeicahttps://www.leica-microsystems.com/products/microscope-softwareLabChart 7 softwareADInstructionhttps://www.adinstruments.com.cn/products/labchartSeahorse XF Extracellular Flux analyzer softwareAgilenthttps://www.agilent.com.cn/zh-cn/product/cell-analysis/real-time-cell-metabolic-analysis/xf-software/seahorse-wave-desktop-software-740897
## Lead contact
Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Lan Huang (lhuang@tmmu.edu.cn).
## Materials availability
This study did not generate new unique reagents.
## Development of a chronic HH-induced mouse model
All animal procedures were approved by the Experimental Animal Ethics Committee of the Army Medical University and conformed to the regulations of the Guide for the Care and Use of Laboratory Animals. Male C57BL/6J mice (6–8 weeks old) were housed in a temperature-controlled environment with a 12-hour light/dark cycle and had free access to water and food. For HH exposure, mice were subjected to a high-altitude low-pressure chamber (Fenglei Aviation Ordinance Co., Ltd, Guizhou, China) with reduced ambient air pressure to simulate an environment of high altitude of 5000 m (HH, approximately 424 mmHg, or the equivalent of $11.0\%$ O2) for 6 weeks. Control mice were raised at an altitude of the sea level (NN, approximately 760 mmHg, or the equivalent of $20.9\%$ O2) out of the hypobaric chamber for 6 weeks (Figure 1A).
## Generation of cardiomyocyte-specific Pacs2 knockout mice
Cardiomyocyte-specific Pacs2 knockout (Pacs2 cKO) mice were generated on a C57BL/6J background by the CRISPR/Cas9 system at Cyagen Biosciences. The gRNA to mouse *Pacs2* gene, the donor vector containing loxP sites, and Cas9 mRNA were co-injected into fertilized mouse eggs to generate targeted conditional knockout offspring. Pacs2 flox/flox mice in which the *Pacs2* gene was flanked by loxP sites within introns 1 and 3 (KO region: approximately 1842 bp) were crossed with α-myosin heavy chain (αMHC) promoter-Cre transgenic mice (Cyagen Biosciences) to obtain Pacs2flox/+/CreαMHC+/− mice. F0 founder animals were identified by polymerase chain reaction (PCR) followed by sequence analysis, which were bred to wild-type mice to test germline transmission and F1 animal generation. F1 founders, including Pacs2 cKO (Pacs2flox/flox/CreαMHC+/−) mice, were genotyped by tail genomic PCR.
## Generation of cardiomyocyte-specific Pacs2 knock-in mice
The Pacs2 cKI in C57BL/6J mice was created using CRISPR/Cas-mediated genome engineering (Cyagen Biosciences). The Hipp11 locus is located within an intergenic region between the Eif4enif1 and *Drg1* genes on mouse chromosome 11. The mouse *Pacs2* gene (NCBI Reference Sequence: NM_001291444.1) is located on mouse chromosome 12. For the KI model, the ‘alphaMHC_long promoter-Kozak-Mouse Pacs2 CDS-rBG pA’ cassette was inserted into the Hipp11 locus (approximately 0.7 kb 5' of Eif4enif1 gene and 4.5 kb 3' of the *Drg1* gene). To engineer the targeting vector, homology arms were generated by PCR using a BAC clone as the template. Cas9 and gRNA were co-injected into fertilized eggs with a targeting vector for mice production. The pups were genotyped by PCR followed by sequencing analysis.
## Blood preparation and enzyme-linked immunosorbent assay
Detection kits for mouse plasma BNP, troponin I (TnI), and creatine kinase MB (CK-MB) were purchased from Jiangsu Jingmei Biological Technology Co., Ltd. (Jiangsu, China). Approximately 1.5 mL of blood was drawn from each mouse and stored in procoagulant tubes. Plasma was separated by centrifugation (3000×g, 20 minutes) after coagulation at room temperature for 10 minutes. The plasma levels of BNP, TnI, and CK-MB were measured using a commercially available BNP enzyme-linked immunosorbent assay (ELISA) kit (JM-02343M2, 210727B8), TnI ELISA kit (JM-02662M2, 210727I4), and CK-MB ELISA kit (JM-03084M2, 210727C6), respectively, following the manufacturer’s instructions.
## Hemodynamic monitoring
RHC was performed using a pressure detecting device (ADInstruments Mikro-Tip®, MPVS Ultra RSBMIL002/M) after a 6-week HH or NN exposure. The mice were placed on a heated pad and anesthetized with $2\%$ isoflurane. The right jugular vein was exposed, and a 1F needle (ADInstruments Mikro-Tip®, SPR-1000) was slightly bent inwards to conduct the cannula containing the catheter into the jugular vein. The cannula was maneuvered to the right ventricle, with its tip pointing toward the heart until an RV pressure curve could be identified using LabChart 7 software. Subsequently, the cannula tip was manipulated to the left and superiorly. The catheter was advanced into the main pulmonary artery, passing through the pulmonary valve. When the catheter enters the main pulmonary artery, the diastolic pressure rises on the monitor, and a pulmonary artery pressure curve appears. When the curve was constant, the related indices, such as the mean pulmonary artery pressure (mPAP), the maximum positive time derivative of left ventricular pressure (max dP/dt), and RV VTI and electrocardiograms were measured.
## Evaluation of RV hypertrophy
After the hemodynamic measurement, the mice were sacrificed by cervical dislocation, and their hearts were removed quickly and weighed. The free wall of the right ventricle was dissected from the left ventricle and interstitial septum. Whole heart weight (normalized by body weight) and Fulton’s index (right ventricle / [left ventricle + interstitial septum]) were used as indices of cardiac hypertrophy.
## Histological analysis
The hearts from the mice exposed to NN and HH were excised, placed in $4\%$ paraformaldehyde, dehydrated in graded concentrations of ethanol, immersed in xylene, and embedded in paraffin. Sections of 5-μm thickness were cut on a microtome with a disposable blade, stained with HE and Masson’s trichrome stains, and examined by light microscopy. The cardiomyocyte CSA was analyzed by staining the heart sections with a wheat germ agglutinin–Alexa Fluor® 647 conjugate (W32466, Invitrogen). Six mice from each group were included in the histological analysis. A minimum of five cross-sections of each heart were examined, and the measurements were averaged for statistical analysis. ImageJ software (RRID:SCR_003070) was used to quantify all the histological endpoints.
## Echocardiography
Cardiac geometry and function were examined using ultrasonography (GE Vivid 7 Dimension, L$\frac{15}{6}$-MHz transducer). The mice were anesthetized with $2\%$ isoflurane while maintaining proper body temperature (36–37°C) and heart rate (450–550 beats/ minute). The temporal frame rate in the echo mode was set to 60 Hz. A 1.0-mm sampling gate was used to obtain the inflow and outflow velocities, and the maximal sweep speed was 200 mm/s. RV end-diastolic (ED), and end-systolic (ES) areas were measured using ImageJ from the apical or basal four-chamber views at end-diastole or end-systole. The RV FAC was calculated as follows: FAC = ([ED RV area – ES RV area] / ED RV area) × $100\%$. For Tei index calculation, the tricuspid closure opening time (TCO) and ejection time (ET) were measured from tissue Doppler myocardial velocity images, as follows: Tei index = (TCO – ET) / ET. Data were collected from six mice per group and represented the average of a minimum of five separate scans in a random blind fashion. To avoid bias, the researcher performed all echocardiography procedures blinded to the experimental treatments.
## Western blotting
Cardiomyocyte MAM fractions in vitro and MAM fractions of the hearts were isolated following a previously described protocol. Western blotting was used to evaluate protein expression in different fractions. Briefly, the protein concentrations of different fractions after isolation were detected using the bicinchoninic acid (BCA) assay (Beyotime Biotechnology, P0012). The same mass of total protein was separated by sodium dodecyl sulfate–polyacrylamide gel electrophoresis and transferred to polyvinylidene fluoride membranes (Millipore). The membranes were blocked with $5\%$ non-fat milk in Tris-buffered saline (Boster Biological Technology, AR0031) containing $0.5\%$ Tween-20 (Solarbio, T8220), and membrane-bound proteins were probed with primary antibodies purchased from Abcam against the following antigens: PACS2 (ab222316, Abcam), mitofusin 2 (MFN2; ab124773, Abcam), voltage-dependent anion-selective channel protein 1 (VDAC1; ab14734, Abcam), acyl-CoA synthetase 4 (FACL4; ab92501, Abcam), mitochondrial fission 1 (FIS1; ab156865, Abcam), calnexin (ab133615, Abcam), microtubule-associated protein 1 light chain 3 beta (MAP1LC3B; ab192890, Abcam), translocase of outer mitochondrial membrane 20 (TOMM20; ab186735, Abcam), and actin beta (ACTB; ab8226, Abcam). Protein bands were visualized by chemiluminescence detection and quantified using the Image QuantTL software (GE Healthcare, Sweden).
## Cell culture and RNA transfection
Rat H9C2 cardiomyocytes (BFN60804388) were purchased from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). Cardiomyocytes were cultivated in Dulbecco’s modified Eagle’s medium (Sigma-Aldrich, Louis, MO, USA) and $10\%$ fetal bovine serum (Hy Clone Laboratories, PA, USA) and supplemented with $1\%$ antibiotic-antimycotic (1000 U/mL penicillin and 100 μg/mL streptomycin). H9C2 cardiomyocytes in the NN group were incubated at 37°C with $5\%$ CO2. HH conditions were achieved by the hypobaric chamber (Billups-Rothenberg) -simulated high-altitude environment, which was flushed with a pre-analyzed gas mixture of $1\%$ O2, $5\%$ CO2, and $94\%$ N2. To maintain cardiomyocyte cultures, the medium was changed every two days. LVVs carrying Pacs2 RNA system were constructed by Gene Pharma Technology (Shanghai, China). The LVVs were added to the cells at a multiplicity of infection of 100. The transfection medium was changed two days later, and the cells were continuously cultured in fresh medium. Real-time quantitative reverse transcription-PCR and western blotting were used to detect the efficiency of Pacs2 overexpression in cardiomyocytes.
## Measurement of mitochondrial calcium in intact cells
Cardiomyocytes were seeded on glass-bottomed cell culture dishes and incubated with 1 μM of the calcium indicator Rhod2-AM (ab142780, Abcam) at 37°C in the dark for 30 minutes, as per the manufacturer’s guidelines. Subsequently, the cells were washed twice with calcium-free HBSS and imaged under a LSCM, Leica TCS-SP5). The fluorescence intensity (F) was normalized to the baseline fluorescence value F0 (F/F0) and expressed as mitochondrial calcium concentration ([Ca2+]m). We measured Fmax and Fmin, as previously described. Fmax was obtained by perfusion with 10-μM ionomycin and 5-mM CaCl2; Fmin was measured by perfusion with 10-mM ethylene glycol-bis (β-aminoethyl ether) -N,N,N′,N′-tetraacetic acid (EGTA) and 20-μM 1,2-bis (2-aminophenoxy) ethane-N,N,N′,N′-tetraacetic acid tetrakis (acetoxymethyl ester) (BAPTA-AM; B1205, Molecular probes) in HBSS. Further, 2-aminoethoxydiphenyl borate (2-APB; ab120124, Abcam), TG (T9033, Sigma-Aldrich), and adenosine triphosphate (ATP; A1852, Sigma-Aldrich) were added to the external solution at a proper final concentration. The fluorescence intensity of Rhod2-AM was measured using LSCM. The fluorescence intensity was converted to [Ca2+] using the following formula: [Ca2+]m = Kd × (F − Fmin) / (Fmax − F), where *Kd is* the equilibrium dissociation constant of Rhod2 for Ca2+, which was 570 nM.
## Immunofluorescence
Cardiomyocytes were stained with MitoTracker Deep Red FM (500 nM; M22426, Invitrogen) and fixed in $4\%$ paraformaldehyde (P0099, Beyotime Institute of Biotechnology) at room temperature for 10 minutes. They were then permeabilized with $0.1\%$ Triton 100-X (P0096, Beyotime Institute of Biotechnology) at room temperature for 30 minutes. Cells were washed with phosphate-buffered saline (PBS) three times and blocked with blocking buffer (P0102, Beyotime Institute of Biotechnology) for immunostaining at 37°C for 30 minutes. Samples were incubated with anti-MAP1LC3B antibody (1:100) or anti-ERP72 antibody (ab155800, Abcam; 1:100) at 4°C overnight and then washed in PBS twice before staining with the secondary antibody (31561, Invitrogen; 1:500) at 37°C for 2 hours. Co-localization of fluorescence was measured at 100–400 Hz under the LSCM. Samples without primary antibodies were used as negative controls. Images were analyzed using LAS X software (Leica) and Image-Pro Plus 5.0 (Media Cybernetics). Co-localization represented in Pearson’s correlation coefficient was measured using automatic thresholding, as previously described.55
## Measurement of mitochondrial bioenergetics and FAO metabolism
The cellular OCR and extracellular acidification rate (ECAR) were measured using a Seahorse XF96 extracellular flux analyzer (Seahorse Bioscience, North Billerica, USA). Briefly, cells with/without HH exposure or transfected with LVVs-Pacs2 were plated in XF96-well microplates (6000 cells/well, Seahorse Bioscience). After reaching the proper cell density, the cells were incubated with an XF assay medium without CO2 at 37°C for 1 hour. For OCR measurement, cells were then serially exposed to 1-μM oligomycin (mitochondrial/ATP synthase inhibitor), 2-μM trifluoromethoxy carbonyl cyanide phenylhydrazone (FCCP, a mitochondrial uncoupler), and 0.5-μM rotenone/antimycin A (respiratory chain inhibitor), provided in the XF Cell Mito Stress Test Kit (Seahorse Bioscience). Three measurements were performed for each cycle (4-minute mixing, followed by 3-minute detection). The data on basal respiration, maximal respiration, proton respiration, and coupled respiration were collected using Seahorse XF Extracellular Flux analyzer software following the manufacturer’s protocol. To measure the FAO, cells were cultured and replaced by an FAO assay medium containing palmitate-BSA according to the manufacturer’s instructions. Other conditions were consistent with the normal OCR measurement. OCR or ECAR experiments were conducted at 37 °C with adjusted pH of 7.4. Following an XF assay, the number of cells was determined and used to normalize OCR and ECAR.
## Measurement of mitophagy levels using the mitochondria-targeted Keima reporter
We used the mtKeima reporter to measure the mitophagy levels. Cardiomyocytes were transfected with mitochondria-targeted monomeric Keima-Red-hyg (mtKeima; AM-V0251HM, Medical and Biological Laboratories Co., Ltd.), which contained the hygromycin B-resistance gene. Hygromycin B infection was used to screen and obtain cardiomyocytes stably expressing mtKeima; cardiomyocytes were seeded on glass-bottom dishes and observed under an LSCM to evaluate mitophagy levels. The wavelengths of excitation and emission filters used were as follows: cytoplasmic Keima: 488 nm, 650–760 nm, and lysosomal Keima: 561 nm, 570–630 nm. Images were analyzed using ImageJ software. Briefly, the cardiomyocytes and mtKeima were segmented, and the areas of cytoplasmic and lysosomal mtKeima were determined. The mitophagy index was calculated as the ratio of the total area of lysosomal mitochondria to the total area of cytoplasmic mitochondria per well.
## Transmission electron microscopy
The right myocardium or H9C2 cardiomyocytes were fixed in $2.5\%$ glutaraldehyde for 2 hours and immersed in $1\%$ osmic acid for 2 hours at 4°C. The fixed samples were then washed in PBS and dehydrated in a graded series of ethanol. Subsequently, the samples were embedded in Epon 812 (SPI Supplies, West Chester, PA, USA) and placed in a model for polymerization. After the semi-thin section was used for positioning, the ultrathin section was made and collected for microstructure analysis, followed by counterstaining with $3\%$ uranyl acetate and $2.7\%$ lead citrate. Subsequently, we observed the sections using an HT7800 TEM (HITACHI, Tokyo, Japan) operating at 100 kV.
## LC-MS metabolomics analysis
We weighed 60 mg of sample and added 20 μL of 2-chloro-l-phenylalanine (0.3 mg/mL, dissolved in methanol) and 0.6 mL of mixed solution (methanol/water = $\frac{7}{3}$ [v:v]) into the 1.5-mL EP tube. The samples were homogenized for 2 minutes and then extracted for 30 minutes by sonication. They were then placed at −20°C for 20 minutes and centrifuged at 13000 g for 15 minutes (4°C). LC-HRMS was performed on a Waters UPLC I-class system equipped with a binary solvent delivery manager and a sample manager, coupled with a Waters VION IMS Q-TOF Mass Spectrometer equipped with an electrospray interface (Waters Corporation, Milford, USA). The injection volume was 3.00 μL, and the column temperature was set at 45°C. The mass spectrometric data were collected using a Waters VION IMS Q-TOF Mass Spectrometer equipped with an electrospray ionization source operating in either positive or negative ion mode. The source and desolvation temperatures were set at 120°C and 500°C, respectively, with a desolvation gas flow of 900 L/h. Centroid data was collected from 50 to 1000 m/z with a scan time of 0.1 s and an interscan delay of 0.02 s over a 13-minute analysis duration.
## iTRAQ proteomics analysis/nanoUHPLC-MS/MS analysis
Proteomics analyses were performed by Sinotech Genomics Inc. (Shanghai, China) according to the standard procedure and raw data were submitted to the integrated proteome resources (iProX) database (Project ID: IPX0005958000). Briefly, lysis buffer ($1\%$ SDS, 8-M urea, 1x Protease Inhibitor Cocktail [Roche Ltd. Basel, Switzerland]) was added to the samples and vibrated and milled for 400 s thrice. The samples were then lysed on ice for 30 minutes and centrifuged at 15000 rpm for 15 minutes at 4°C. The protein concentration of the supernatant was determined using the BCA protein assay; we then transferred 100 μg of protein/condition into a new Eppendorf tube and adjusted the final volume to 100 μL with 8-M urea. We added 2 μL of 0.5-M TCEP and incubated the sample at 37°C for 1 hour; subsequently, 4 μL of 1-M iodoacetamide was added to the sample, and the incubation lasted 40 minutes, protected from light at room temperature. Five volumes of −20°C pre-chilled acetone were then added to precipitate the proteins overnight at −20°C. The precipitates were washed by 1-mL pre-chilled $90\%$ aqueous acetone solution twice and then re-dissolved in 100-μL 100-mM TEAB. Sequence grade modified trypsin (Promega, Madison, WI) was added at the ratio of 1:50 (enzyme: protein, weight: weight) to digest the proteins at 37°C overnight. The peptide mixture was desalted by C18 ZipTip, quantified by Pierce™ Quantitative Colorimetric Peptide Assay [23275], and lyophilized by SpeedVac.
The resultant peptide mixture was labeled with iTRAQ 8Plex labelling kit (Sciex) following the manufacturer’s instructions. The labeled peptide samples were then pooled and lyophilized in a vacuum concentrator. The peptide mixture was re-dissolved in the buffer A (20-mM ammonium formate in water, pH 10.0, adjusted with ammonium hydroxide) and fractionated by high pH separation using Ultimate 3000 system (ThermoFisher Scientific, MA, USA) connected to a reverse-phase column (XBridge C18 column, Waters Corporation, MA, USA). High pH separation was performed using a linear gradient, starting from $5\%$ B to $45\%$ B in 40 minutes (B: 20-mM ammonium formate in $80\%$ ACN, pH 10.0, adjusted with ammonium hydroxide). The peptides were re-dissolved in $5\%$ ACN aqueous solution containing $0.5\%$ formic acid and analyzed by on-line nanospray LC-MS/MS on Q Exactive™ HF-X coupled to EASY-nLC 1200 system (Thermo Fisher Scientific, MA, USA). The column flow rate was maintained at 250 nL/min. The electrospray voltage of 2 kV versus the inlet of the mass spectrometer was used.
## Bioinformatics data analysis
The UPLC–Q-TOF/MS raw data were analyzed using progenesis QI (Waters CorporationMilford, USA) software. The parameters used were retention time (RT) range 0.5–14.0 minutes, mass range 50–1000 Da, and mass tolerance 0.01 Da. Isotopic peaks were excluded from the analysis, noise elimination level was set at 10.00, minimum intensity was set to $15\%$ of base peak intensity, and RT tolerance was set at 0.01 minute. The excel file was obtained with three-dimensional data sets including m/z, peak RT, and peak intensities; RT–m/z pairs were used as the identifier for each ion. The resulting matrix was further reduced by removing any peaks with missing values (ion intensity = 0) in > $60\%$ of samples. The internal standard was used for data quality control (reproducibility). The positive and negative data were combined to yield a combined data set imported into SIMCA-P + 14.0 software package (Umetrics, Umeå, Sweden). Principle component analysis and (orthogonal) partial least-squares-discriminant analysis ([O] PLS-DA) were performed to visualize the metabolic alterations among the experimental groups, after mean centering and unit variance scaling. Tandem mass spectra were processed by PEAKS Studio version X (Bioinformatics Solutions Inc., Waterloo, Canada). Differentially expressed proteins were filtered if they contained ≥ 1 unique peptide with P ≤ 0.05 and fold change ≥ 1.2. The pathway analysis was performed using GO and the KEGG database.
## Quantification and statistical analysis
All statistical analyses were performed with SPSS 20.0 software (Inc., USA). The measurement variables were presented as mean ± standard deviation (SD) in minimum triplicates. Statistical significance was determined using Student’s t-test between two groups and corrected for multiple comparisons (least-significant difference) for more than two groups. Mann–Whiney U test or nonparametric analysis of variance (Kruskal–Wallis) followed by Dunn’s multiple comparison post-hoc test was used when one or more datasets showed non-normal distribution. Imaging experiments and animal tests were assessed in a blinded fashion. Sample sizes were similar between experimental groups and replicates of experiments. The number of biological replicates and observations are described in the figure legends. Statistical significance was considered at $P \leq 0.05$, with ∗$P \leq 0.05$, ∗∗$P \leq 0.01.$ For graphs, all data were analyzed using GraphPad Prism software (version 8.4.0; GraphPad Software Inc, San Diego, CA).
## Supplemental information
Document S1. Figures S1–S6 and Data S1 Table S1. The fold changes of differential expression proteomics proteins in the right myocardium of hypobaric hypoxia exposed mice compared with the control mice, related to Figure 1 Table S2. The fold changes of main metabolites in the right myocardium of hypobaric hypoxia exposed mice compared with the control mice, related to Figure 1
## Data and code availability
•Proteomics and metabolomics analysis data have been provided in the Tables S1 and S2. The raw data of proteomics were submitted to the integrated proteome resources (iProX) database (Project ID: IPX0005958000).•This paper does not report original code.•*All data* supports the main and supplemental figures are either available online or available from the corresponding authors upon reasonable request.
## Author contributions
LH contributed to the conception and design of the research. JY, MS, and RC performed experiments and drafted the article. XY and BW helped with data acquisition and discussion. RC, CH, and ZL analyzed the data with guidance from JZ and XG. All authors read and approved the final submission of the article.
## Declaration of interests
The authors declare no competing interests.
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|
---
title: Naturally derived cytokine peptides limit virus replication and severe disease
during influenza A virus infection
authors:
- Christopher M Harpur
- Alison C West
- Mélanie A Le Page
- Maggie Lam
- Christopher Hodges
- Osezua Oseghale
- Andrew J Gearing
- Michelle D Tate
journal: Clinical & Translational Immunology
year: 2023
pmcid: PMC10034483
doi: 10.1002/cti2.1443
license: CC BY 4.0
---
# Naturally derived cytokine peptides limit virus replication and severe disease during influenza A virus infection
## Abstract
Novel host‐targeted therapeutics could treat severe influenza A virus infections, with reduced risk of drug resistance. Our studies provide the first evidence identifying LAT8881 and LAT9991F compounds, as novel host‐protective therapies that improve survival, limit viral replication and reduce local inflammation during severe influenza virus infection.
### Objectives
Novel host‐targeted therapeutics could treat severe influenza A virus (IAV) infections, with reduced risk of drug resistance. LAT8881 is a synthetic form of the naturally occurring C‐terminal fragment of human growth hormone. Acting independently of the growth hormone receptor, it can reduce inflammation‐induced damage and promote tissue repair in an animal model of osteoarthritis. LAT8881 has been assessed in clinical trials for the treatment of obesity and neuropathy and has an excellent safety profile. We investigated the potential for LAT8881, its metabolite LAT9991F and LAT7771 derived from prolactin, a growth hormone structural homologue, to treat severe IAV infection.
### Methods
LAT8881, LAT9991F and LAT7771 were evaluated for their effects on cell viability and IAV replication in vitro, as well as their potential to limit disease in a preclinical mouse model of severe IAV infection.
### Results
In vitro LAT8881 treatment enhanced cell viability, particularly in the presence of cytotoxic stress, which was countered by siRNA inhibition of host lanthionine synthetase C‐like proteins. Daily intranasal treatment of mice with LAT8881 or LAT9991F, but not LAT7771, from day 1 postinfection significantly improved influenza disease resistance, which was associated with reduced infectious viral loads, reduced pro‐inflammatory cytokines and increased abundance of protective alveolar macrophages. LAT8881 treatment in combination with the antiviral oseltamivir phosphate led to more pronounced reduction in markers of disease severity than treatment with either compound alone.
### Conclusion
These studies provide the first evidence identifying LAT8881 and LAT9991F as novel host‐protective therapies that improve survival, limit viral replication, reduce local inflammation and curtail tissue damage during severe IAV infection. Evaluation of LAT8881 and LAT9991F in other infectious and inflammatory conditions of the airways is warranted.
## Introduction
The coronavirus disease (COVID‐19) pandemic has demonstrated the global impact of the emergence of a novel respiratory virus in humans. There is also a constant threat that another influenza A virus (IAV) pandemic will occur. Therapeutic strategies for treating severe IAV infections have largely focussed on antivirals, which target viral proteins and have shown limited efficacy, 1 because of the emergence of viral resistance and the need to administer them prophylactically or within the first 2 days of symptomatic infection. 2 Severe IAV infections in humans are associated with hyperinflammation leading to the development of acute respiratory distress syndrome. 3, 4 *There is* an urgent need to develop new host‐targeted immunotherapies that limit hyperinflammation‐driven morbidity and mortality, a common feature of severe respiratory virus infections.
Growth hormone (GH) is a member of the 4α‐helical cytokine family that regulates several biological processes acting via its cognate GH receptor. 5 Growth hormone, like its structural homologue prolactin, is also known to be processed in vivo by several proteases at sites of tissue damage or pathology resulting in new active peptides. At least two major biologically active fragments of GH have been identified, a large fragment comprising the three N‐terminal α‐helices that has proposed anti‐angiogenic properties and a smaller C‐terminal fragment containing a di‐sulphide constrained loop. 6, 7 LAT8881 is a synthetic form of the C‐terminal fragment of human GH, with the sequence YLRIVQCRSVEGSCGF, in which the two cysteines are linked by a disulphide bond. 8, 9 Interestingly, LAT8881 (formerly identified as AOD9604) was found to not act via the GH receptor. 10 Chronic oral and injected dosing of LAT8881 has been shown to reduce body weight, affect lipolysis and lipogenesis in fat tissue of obese rodents. 8, 10, 11, 12 Intra‐articular injection has also been shown to reduce inflammatory damage in a rabbit collagenase model of osteoarthritis, resulting in an accelerated repair of the affected joint. 13 Importantly, LAT8881 has been shown to have a promising profile in preclinical toxicology and in human safety studies. 14, 15 The lanthionine synthetase C‐like protein (LANCL) family has recently been identified as putative targets for LAT8881. 16 LANCL1 and the closely related family member LANCL2 are thought to be peptide‐modifying enzymes that act to protect cells and promote survival in the face of oxidative stress. 17, 18, 19, 20, 21 Additionally, activation of LANCL2 by either natural or synthetic ligands reduced the severity of H3N2 and H1N1 IAV infection in mice, ameliorating inflammatory lung damage and accelerating recovery via immunoregulatory mechanisms. 22, 23 Thus, based on its safety profile, tissue protective properties and potential association with LANCL protein signalling, we investigated the therapeutic effects of LAT8881 and related compounds in a preclinical model of severe IAV infection.
Here, we report that LAT8881 promotes cell viability and protects stressed cells from death in vitro via a mechanism that requires either LANCL1 or LANCL2. Critically, intranasal administration of LAT8881 or its metabolite, LAT9991F following IAV infection of mice, correlated with reduced viral loads and pro‐inflammatory cytokines in the lung, as well as evidence of reduced pulmonary injury. Moreover, LAT8881 treatment increased numbers of alveolar macrophages (AM), primary defenders of the airways and a key target for infection by IAV. In contrast, LAT7771 derived from the C‐terminus of the related prolactin protein had limited activity in vivo. Finally, we demonstrate markers of disease severity were further curtailed when LAT8881 was co‐administered with oseltamivir phosphate (OP), an approved influenza antiviral.
## LAT8881 promotes cell viability and protects against chemical‐induced death in vitro
LAT8881 is a synthetic C‐terminal fragment derived from human GH (Figure 1a). To begin to elucidate a potential role for LAT8881 in mediating cell protection, we treated mouse fibroblast‐like L cells with 200 μM LAT8881 or vehicle alone (dimethyl sulfoxide (DMSO)). We observed that L cell viability was enhanced in the presence of LAT8881 following 24 and 48 h in culture relative to vehicle control (Figure 1b). Furthermore, 10 μM LAT8881 improved the viability of murine primary peritoneal exudate cells, suggesting its effects are not limited to nonmyeloid cells (Figure 1c). Interestingly, the prosurvival effect of LAT8881 was even more stark in the presence of cytotoxic stress, with human A549 alveolar epithelial cells displaying improved resistance to cell death induced by paclitaxel (Figure 1d) or hydrogen peroxide (H2O2; Supplementary figure 1) in a LAT8881 dose‐dependent manner. Recently, we identified that LAT8881 associates with LANCL1 in neuronal tissue from rats. 16 Consistent with the reported role of LANCL proteins in mediating cell survival, 17, 19, 20, 21 L cell viability markedly decreased 72 and 120 h following transfection with siRNA targeting Lancl1 or Lancl2 (Figure 1e). Finally, we observed that the ability of LAT8881 to improve the resistance of A549 cells to paclitaxel was lost in the presence of LANCL1 or LANCL2 siRNA (Figure 1f). Together, these data suggest LAT8881 has a prosurvival effect on various cell types in vitro via either LANCL1 or LANCL2 protein‐dependent pathways.
**Figure 1:** *Human growth hormone‐derived LAT8881 improves in vitro cell survival in either a LANCL1 or LANCL2 protein‐dependent manner. (a) Schematic of human growth hormone structure (green, http://www.rcsb.org/structure/1HGU). The synthetic compound LAT8881 comprises the short C‐terminal region (red) and is cyclised by a disulphide bond between two cysteine residues as shown. (b) Viability of murine L cells (1000 or 5000 cells) relative to vehicle control samples following 24‐ or 48‐h incubation with LAT8881 (200 μM) or vehicle (DMSO) alone, as determined by luminescent ATP detection ± SD. ****P < 0.0001 vs vehicle control, Student's t‐test. Data are pooled from three independent experiments. (c) Viability of murine peritoneal exudate cells relative to media control in culture at 24 h following LAT8881 (10 μM) or vehicle (DMSO) alone, as determined by luminescent ATP detection ± SD. *P < 0.05, **P < 0.01 vs media control, two‐way ANOVA. Data are pooled from two independent experiments. (d) Viability of human A549 cells 16 h following 350 μM paclitaxel or vehicle (DMSO) alone ± LAT8881 (1–100 μM), as determined by luminescent ATP detection ± SD. ****P < 0.0001 vs paclitaxel without LAT8881, two‐way ANOVA. Data are representative of two independent experiments. (e) Viability of murine L cells transfected with 100 nM siRNA against Lancl1 or Lancl2 relative to control siRNA samples at 72 or 120 h, as determined by luminescent ATP detection ± SD. *P < 0.05, ***P < 0.001, ****P < 0.0001 vs control siRNA, one‐way ANOVA. Data are pooled from three independent experiments. (f) Viability of human A549 cells transfected with 100 nM siRNA specific to LANCL1, LANCL2 or control siRNA 16 h following 350 μM paclitaxel ± LAT8881 (1–100 μM) or vehicle, as determined by luminescent ATP detection ± SD. **P < 0.001, ****P < 0.0001 vs paclitaxel + control siRNA without LAT8881, two‐way ANOVA. Data are representative of two independent experiments.*
## Intranasal treatment with LAT8881 or LAT9991F promotes resistance to severe IAV infection
There is an urgent need to develop new therapeutics for respiratory viral infections, and an attractive approach is to target the host immune response, thus providing broad protection against multiple infectious agents without eliciting antiviral resistance. Development could be expedited by evaluating existing compounds that already have a demonstrated safety profile in humans. Therefore, we investigated the therapeutic effects of LAT8881 and related compounds in a preclinical mouse model of severe IAV infection. Male mice were infected with 104 plaque‐forming units (pfu) of HKx31 (H3N2) IAV and subsequently received daily, intranasal (i.n.) treatments initiated 1‐day postinfection (dpi) with 20 mg kg−1 LAT8881 or its shorter metabolite LAT9991F (Figure 2a). Mice were also treated with 20 mg kg−1 of LAT7771, derived from the C‐terminus of prolactin, a cytokine hormone that is shares a common ancestral protein with GH (Figure 2a). Mice were monitored daily and euthanised either upon losing $20\%$ of their initial body weight or displaying severe clinical signs of disease (see Methods). Treatment with LAT8881 reduced IAV infection‐induced weight loss, resulting in a significantly higher survival rate than mice that received LAT7771 or PBS vehicle (Figure 2b and c). Treatment with LAT9991F also prolonged the survival of mice, displaying similar efficacy to LAT8881 (Figure 2d and e). In contrast, LAT7771 derived from prolactin had no impact on weight loss or survival of mice. These data suggest that localised, therapeutic treatment with either of the two cyclised peptides derived from the C‐terminus of GH, LAT8881 and LAT9991F, can limit influenza disease symptoms.
**Figure 2:** *LAT8881 and its metabolite LAT9991F improves resistance to IAV infection in vivo. (a) Schematics of human growth hormone (GH; green, http://www.rcsb.org/structure/1HGU) and prolactin (green, http://www.rcsb.org/structure/1RW5) structures, which share a common four alpha helical bundle and a C‐terminal C‐C constrained loop. The synthetic compounds LAT8881, its metabolite LAT9991F from GH and LAT7771 from prolactin are cyclised peptides comprising short C‐terminal regions of the respective cytokines (red). Groups of male C57BL/6 mice (n = 8) received daily i.n. treatment with 20 mg kg−1 of LAT8881, PBS (vehicle control), 20 mg kg−1 LAT7771 (b, c) or 20 mg kg−1 LAT9991F (d, e) from 1 dpi with 104 pfu of HKx31 IAV. (b, d) Mouse weight was recorded daily, and results are expressed as mean percent weight change ± SD. (c) Survival curves are shown. ****P < 0.0001 HKx31 vs HKx31 + LAT8881, Mantel–Cox log‐rank test. (e) Survival curves are shown. **P < 0.01 HKx31 vs HKx31 + LAT8881, ****P < 0.0001 HKx31 vs HKx31 + LAT9991F, Mantel‐Cox log‐rank test.*
## Intranasal treatment with LAT8881 or LAT9991F following IAV infection limits viral dissemination and inflammation
Host strategies to cope with influenza can involve either efficient clearance of the virus, or tolerance of the infection by reducing potential immunopathology and tissue damage. 24 Severe IAV infections are characterised by excessive inflammation (also known as hyperinflammation) and cytokine storm, observed at both the site of infection and systemically. Therefore, we examined lung viral titres, as well as immune cells, damage markers and inflammatory cytokines in bronchoalveolar lavage (BAL) fluid in addition to circulating cytokine levels in male mice at 3 dpi with IAV (Figure 3). As per Figure 2, mice were infected with HKx31 IAV and subsequently received daily i.n. treatment with 20 mg kg−1 LAT8881, LAT9991F or LAT7771. Both IAV‐infected and MOCK‐infected control cohorts received i.n. PBS daily as a vehicle control.
**Figure 3:** *LAT8881 treatment during severe IAV infection reduces viral loads, inflammation and damage in the lung. Groups of male C57BL/6 mice received daily i.n. treatment with 20 mg kg−1 of LAT8881, LAT9991F or LAT7771 from 1 dpi with 104 pfu of HKx31 IAV. BAL fluid and lung tissues were collected at 3 dpi. MOCK‐infected and IAV‐infected control mice received PBS alone. (a) Lung viral loads (pfu/lung) measured by a standard plaque assay. BAL fluid concentrations of IL‐6 (b), MCP‐1 (c) and TNF (d) determined by cytokine bead array. Levels of LDH (e) and ATP (f) in BAL fluid relative to the PBS‐treated, IAV‐infected control mice determined by colorimetric and luminescent assays, respectively. (g) Representative flow cytometry gating strategy for BAL fluid immune cells. Numbers (#) of total viable cells (h), NK cells (i), DC (j) and AM (k) in the BAL fluid as determined by flow cytometry. Data are presented as the mean, pooled from at least two independent experiments, with each data point representing an individual animal. n = 9–14. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, one‐way ANOVA with Dunnett's multiple comparisons test.*
Lung infectious viral burden (pfu/lung) was significantly reduced (> 2‐fold) 3 dpi by treatment with both LAT8881 and LAT9991F but not LAT7771 compared with infected PBS controls (Figure 3a). Interleukin‐6 (IL‐6), monocyte chemoattractant protein‐1 (MCP‐1) and tumor necrosis factor (TNF) are pro‐inflammatory cytokines commonly associated with influenza‐induced cytokine storms. 25 Both LAT8881 and LAT9991F treatment regimens significantly reduced levels of IL‐6, MCP‐1 and TNF in BAL fluid, while only the concentration of TNF had slightly decreased with LAT7771 administration (Figure 3b–d). Of note, the concentrations of these key inflammatory mediators were still above their respective levels in the BAL fluid taken from MOCK‐infected control mice, which received PBS alone (MOCK). By contrast, no significant differences were observed in BAL fluid levels of interferon gamma (IFNγ), IL‐10 or IL‐12p70 (Supplementary figure 2a–c). Systemic levels of all assessed cytokines were also largely unchanged by treatment with LAT8881, LAT9991F or LAT7771, with only a slight, yet significant increase in circulating anti‐inflammatory IL‐10 observed in LAT8881‐treated mice compared with infected PBS controls (Supplementary figure 2d–i).
Extracellular lactate dehydrogenase (LDH) and adenosine triphosphate (ATP) are released by dead or dying cells, making them indicators of tissue damage. LAT8881 treatment was observed to significantly reduce LDH in BAL fluid relative to infected PBS controls (Figure 3e), with levels of the shorter‐lived danger‐associated molecular pattern (DAMP) ATP also trending lower (Figure 3f). LAT9991F also significantly reduced LDH in the BAL fluid but had a more negligible effect on ATP. As inflammation and tissue damage appeared to diminish with these treatments, we investigated whether there had also been any changes in the innate immune cell composition of the airways. Accordingly, we enumerated total cells, neutrophils, natural killer (NK) cells, T cells, inflammatory macrophages (IM), dendritic cells (DC) and AM within the BAL fluid at 3 dpi using flow cytometry (Figure 3g). All IAV‐infected mice had comparable total airway cellularity (Figure 3h), as well as similar numbers of T cells, IM and neutrophils (Supplementary figure 3a–c). As expected, the BAL fluid of MOCK controls that subsequently received daily i.n. treatment with PBS had relatively few cells (Figure 3h). Only LAT8881 treatment induced a significant decrease in airway NK cells and increase in DC numbers 3 dpi (Figure 3i and j).
Resident in the lumen of the alveoli, AM are susceptible to infection by HKx31 IAV and play an important protective role in limiting lung viral loads. 26, 27, 28 Both i.n. administration of 20 mg kg−1 LAT8881 and LAT9991F resulted in greater numbers of AM in the BAL fluid, compared with infected mice that received i.n. PBS alone, yet the increase was only statistically significant in mice treated with LAT8881 (Figure 3k). Of note, while i.n. LAT8881 treatment almost doubled AM abundance in the BAL of IAV‐infected mice, the number of AM was still fewer than in MOCK controls. Therapeutic LAT8881 and LAT9991F treatment may therefore maintain AM in vivo potentially by protecting them against cell death. Epithelial cells lining the airways are the major cell type that supports IAV replication. Having established that i.n. LAT8881 administration limits IAV loads in vivo, we examined the ability of LAT8881 to limit viral replication in human primary bronchial epithelial cells (PBECs). Indeed, the addition of 100 μM LAT8881 1 h after infection of PBECs with HKx31 IAV resulted in decreased levels of infectious virus in cell culture supernatants at 24 h (Supplementary figure 4a). PBECs treated with LAT8881 were more viable than those treated with the vehicle control, suggesting the decreased level of infectious virus was not because of increased cell death (Supplementary figure 4b). Collectively, these data suggest that the increase in influenza disease resistance provided by local administration of LAT8881 and to a lesser degree, LAT9991F, correlates with decreases in tissue damage, inflammation and IAV titres in the lung, as well as the retention of greater numbers of AM, a key mediator of early defence against infection. Furthermore, our in vitro results suggest that LAT8881 can limit viral replication in epithelial cells, which may also influence the amount of AM we observe in the airways during IAV infection of mice.
## Dose‐dependent modulation of virus dissemination and the innate immune response to IAV infection by LAT8881
To confirm that the observed changes in IAV‐infected mice treated with 20 mg kg−1 LAT8881 were indeed the result of the localised administration of the compound, we examined the effect of different doses of LAT8881 on lung infectious viral loads, as well as the inflammatory cytokines and immune cells within the BAL fluid. Male mice were infected with HKx31 IAV and administered 5, 10 or 20 mg kg−1 LAT8881 i.n. each day from 1 dpi. An additional IAV‐infected control cohort received i.n. PBS daily as a vehicle control. The reduction in viral burden in the lung and the concentration of IL‐6, MCP‐1 and TNF in the airways inversely correlated with increasing dose of LAT8881 (Figure 4a–d). The effects were most marked with the two highest doses, although only IL‐6 was reduced to a significant degree by 10 mg kg−1 LAT8881 compared with treatment with PBS. Total cell numbers in the BAL fluid were not significantly affected by any treatment, while both the amount of DC and AM trended higher with increasing LAT8881 dose (Figure 4e–g). Congruent with this, the frequency of dying (Annexin V+ PI−) AM decreased with increased LAT8881 dose (Figure 4h). Importantly, analysis of multiple readouts of disease severity in IAV‐infected female mice treated with 20 mg kg−1 LAT8881 (Supplementary figure 5) revealed similar trends to that of male mice (Figures 3 and 4).
**Figure 4:** *Effectiveness of LAT8881 treatment of severe IAV infection is dose‐dependent. Groups of male C57BL/6 mice received daily i.n. treatment with 5, 10 or 20 mg kg−1 LAT8881 from 1 dpi with 104 pfu of HKx31 IAV. BAL fluid and lung tissues were collected at 3 dpi. IAV‐infected control mice received PBS alone. (a) Lung viral loads (pfu/lung) were measured by a standard plaque assay. BAL fluid concentration of IL‐6 (b), MCP‐1 (c) and TNF (d) determined by cytokine bead array. Numbers (#) of total viable cells (e), DC (f) and AM (g) in the BAL fluid as determined by flow cytometry. (h) Percentage of Annexin V+ PI− (AnxV+ PI−) AM of total AM cells in the BAL fluid. Data are presented as the mean from a single experiment, with each data point representing an individual animal. n = 8 per group. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, one‐way ANOVA with Tukey's multiple comparisons test.*
## LAT8881 reduces pulmonary pathology during severe IAV infection
Having established that 20 mg kg−1 LAT8881 limits LDH levels in BAL fluid (Figure 3e, Supplementary figure 5f), suggestive of reduced lung tissue damage, we performed further analysis on an additional cohort of animals. At 3 dpi, the marked reductions in LDH and ATP levels in BAL fluid were reproduced following 20 mg kg−1 LAT8881 treatment of IAV‐infected mice (Figure 5a and b). Furthermore, the concentration of another DAMP molecule, S100 calcium‐binding protein A10 (S100A10) significantly decreased in BAL fluid with LAT8881 administration (Figure 5c). Interestingly, histopathological analysis of lung tissue sections (Figure 5d) indicated that peribronchial inflammation was significantly diminished in LAT8881‐treated mice at 3 dpi, while a more moderate reduction in alveolitis was observed (Figure 5e and f). Finally, treatment with LAT8881 significantly reduced epithelial damage compared with infected PBS controls (Figure 5g). Together, these data demonstrate LAT8881 treatment curtails IAV‐induced pulmonary tissue damage.
**Figure 5:** *LAT8881 treatment of severe IAV infection reduces pulmonary immunopathology. Groups of male C57BL/6 mice received daily i.n. treatment with 20 mg kg−1 of LAT8881 or PBS alone from 1 dpi with 104 pfu of HKx31 IAV. BAL fluid was collected at 3 dpi. Levels of LDH (a), ATP (b) and S100A10 (c) in BAL fluid determined by colorimetric (OD; optical density), luminescent (RLU; raw luminescence units) assays or ELISA. Data are presented as the mean with each data point representing an individual animal. n = 8 per group from one experiment. **P < 0.005, ***P < 0.001, Student's t‐test. Groups of male and female C57BL/6 mice received daily i.n. treatment with 20 mg kg−1 of LAT8881 or PBS alone from 1 dpi with 104 pfu of HKx31 IAV. Lungs underwent formalin fixation at 3 dpi and histological analysis of H&E‐stained lung tissue sections were performed. (d) Representative images at 10× magnification (scale bar = 100 μm). Lung sections were randomised and scored blind by 3 readers for (e) peribronchial inflammation (scale 0–5), (f) alveolitis (scale 0–5) and (g) epithelial damage (scale 0–4), as described in the Methods. Data are presented as the mean with each data point representing an individual animal. n = 4–8 per group from one experiment. *P < 0.05, **P < 0.01, only PBS vs LAT8881 shown, one‐way ANOVA with Dunnett's multiple comparisons test.*
## Commencement of LAT8881 treatment at the onset of severe influenza disease reduces inflammation in the airways
As patients often only present to hospital with severe influenza disease pathology, we examined the impact of commencing LAT8881 closer to the previously observed onset of severe disease in our IAV infection model (Figure 2). 29 Therefore, HKx31 IAV‐infected male mice were administered with either 20 mg kg−1 LAT8881 or PBS vehicle control i.n. on 3 and 4 dpi rather than 1 dpi. At 5 dpi, lung viral loads were markedly lower than at 3 dpi (Figure 3a), as the infection started to resolve. 28 Indeed, infectious virus titres at 5 dpi were less than half of what we observed in infected PBS control mice at 3 dpi, and while lung viral burden in LAT8881‐treated mice trended lower, this was not significantly different to PBS controls (Supplementary figure 6a). LAT8881 had negligible effects on the concentration of IL‐6 in BAL fluid but did induce reductions in the concentrations of MCP‐1 and TNF, significantly so for the latter (Supplementary figure 6b–d). Modest reductions in the levels of ATP and LDH in BAL fluid were also observed following LAT8881 treatment (Supplementary figure 6e and f). Total cell numbers in the BAL fluid were comparable with PBS controls following LAT8881 treatment; however, NK cell and T cell numbers were significantly reduced (Supplementary figure 6g–i). Finally, AM abundance was significantly elevated in LAT8881‐treated mice (Supplementary figure 6j) despite the more delayed treatment, which is consistent with results obtained when treatment began 1 dpi (Figures 3k and 4g). Collectively, these data suggest local administration of LAT8881 following the development of severe influenza disease still limits inflammation and maintains the critical AM population in the airways.
## LAT8881 treatment has a comparable therapeutic efficacy to the IAV antiviral oseltamivir phosphate
Having established that intranasal treatment with LAT8881 limits viral spread and potentially deleterious inflammation, we compared its therapeutic efficacy against the existing prescription antiviral oseltamivir phosphate (OP), which inhibits the influenza neuraminidase (NA) protein. 30 Although the clinical effectiveness of OP in reducing mortality is in question, 1 particularly in the context of the emergence of OP‐resistant strains of influenza, 31 oral administration of OP has been shown to limit IAV disease in animal models using controlled infection parameters. 23, 32, 33 As such, we evaluated therapeutic daily i.n. treatment with 20 mg kg−1 LAT8881 or PBS vehicle, as well as oral gavage delivery of 10 mg kg−1 OP or PBS vehicle in our model of IAV infection. We additionally co‐administered LAT8881 and OP to look for any potential additive therapeutic benefit. As before, infected mice treated with PBS alone were used as a vehicle control cohort.
Interestingly, we observed a significant decrease in lung viral titres at 3 dpi in mice that received OP alone commensurate with its ability to limit IAV replication (Figure 6a). LAT8881 + PBS treatment alone almost halved the amount of infectious virus in the lung, when compared to mice that received PBS alone. Importantly, co‐treatment with LAT8881 + OP reduced lung viral titres to a similar or even lower level than PBS + OP or LAT8881 + PBS. An analogous pattern was observed for concentrations of IL‐6, MCP‐1 and TNF in BAL fluid at 3 dpi, with PBS + OP and LAT8881 + PBS treatment regimens inducing similar decreases in these pro‐inflammatory cytokines compared with PBS + PBS control mice, with LAT8881 + OP co‐treatment promoting even greater reductions (Figure 6b–d).
**Figure 6:** *LAT8881 treatment of severe IAV infection has similar efficacy to antiviral oseltamivir phosphate. Groups of male C57BL/6 mice received daily i.n. treatment with either PBS or 20 mg kg−1 LAT8881 concurrent with either PBS or 10 mg kg−1 oseltamivir phosphate (OP) via oral gavage from 1 dpi with 104 pfu of HKx31 IAV. Mice were sacrificed on 3 dpi and BAL fluid and lung tissues were collected. (a) Lung viral loads (pfu/lung) were measured by a standard plaque assay. BAL fluid concentration of IL‐6 (b), MCP‐1 (c) and TNF (d) determined by cytokine bead array. Numbers (#) of total viable cells (e), NK cells (f), neutrophils (g), inflammatory macrophages (IM) (h), DC (i) and AM (j) in the BAL fluid as determined by flow cytometry. Data are presented as the mean, pooled from two independent experiments, with each data point representing an individual animal. n = 10–11 per group. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, one‐way ANOVA with Tukey's multiple comparisons test.*
Consistent with our previous results, total cellularity in the airways in IAV‐infected mice was similar regardless of treatment (Figure 6e). NK cell abundance had decreased with all experimental treatment regimens; however, this was most pronounced in treatments including OP (Figure 6f). Interestingly, while LAT8881 + PBS or PBS + OP had minor effects on airway neutrophil numbers, mice that received LAT8881 + OP had significantly fewer neutrophils than the PBS + PBS control cohort (Figure 6g). This decrease in neutrophils may reflect a further dampening of the inflammatory milieu and diminished viral burden. The infiltration of IM was seemingly unaffected by any experimental treatment regimen (Figure 6h). Finally, mice treated with LAT8881 or OP individually or in combination had significantly elevated DC and AM numbers, although the increase was most notable in mice that received LAT8881 + OP (Figure 6i and j). In sum, these data suggest i.n. LAT8881 has similar efficacy as a therapeutic treatment for severe IAV infection, to an antiviral medication already in use in the clinic. Additionally, when used in combination, LAT8881 and OP had improved effects on several established correlates of disease severity, chiefly decreased pro‐inflammatory cytokines levels, a reduction in infiltrating neutrophils and an increase in resident AM numbers.
## Discussion
Severe and fatal IAV infections are associated with hyperinflammation, cytokine storm and tissue damage. Without novel host‐targeted therapeutics to limit the development of severe disease, we are ill‐prepared for an inevitable IAV pandemic. Identifying existing host‐targeted compounds that help defend against these viral pathogens and/or ameliorate disease symptoms is a high priority, as they could be more rapidly introduced into the clinic, thereby reducing the healthcare and economic burden, while limiting the potential drug resistance associated with antiviral medication.
LAT8881 is a synthetic version of the C‐terminal fragment of GH, the activity of which is not dependent on a functional interaction with the GH receptor. 8, 9, 10, 11 LAT8881 can undergo further N‐terminal cleavage in vivo to a peptide CRSVEGSCGF (LAT9991F) that retains its disulphide bond and ostensibly its cyclic 3D structure. 9, 34, 35 Proteolytic processing of GH at sites of tissue damage and inflammation (e.g. in viral lung infection or osteoarthritis) to release C‐terminal fragments may represent an endogenous pathway to help limit inflammatory damage and promote recovery. Furthermore, LAT8881 does not have any of the systemic hormonal effects of GH, including no changes in insulin‐like growth factor 1 production, which may exacerbate IAV‐induced lung damage and increase disease susceptibility. 14, 36 We demonstrated that LAT8881 promotes the viability of a range of cell types in vitro including in the presence of inducers of cytotoxic stress (Figure 1). Additionally, LAT8881 treatment of IAV‐infected mice reduced the proportion of AMs displaying the early cell death marker Annexin V in a dose‐dependent manner (Figure 4h). Together these data suggest a role for LAT8881 in stimulating cellular pathways responsible for survival particularly under various conditions of stress. We recently observed that LAT8881 interacts with the LANCL1 in rat neurons. 16 In line with this, we found siRNA targeting of LANCL1 or LANCL2 limited cell viability (Figure 1e) and abolished the LAT8881 effects (Figure 1f, Supplementary figure 1). The ability of LANCL proteins to promote cell survival is consistent with the literature. 17, 19, 20, 21 In direct comparison with our data, Wang et al. demonstrated that while overexpression of LANCL1 in human prostate cancer cell lines enhanced cell viability and conferred resistance to cell death induced by hydrogen peroxide, LANCL1 knockdown sensitised cells to oxidative stress. 20 The involvement of LANCL2 in cell survival has been associated with Akt signalling, whereby LANCL2 knockdown in HepG2 liver cells leads to increased rates of apoptosis. 21 Along with roles in promoting cell survival, the LANCL1 and LANCL2 proteins have proposed immunomodulatory functions. Tan et al. demonstrated central nervous system‐specific deletion of LANCL1 heightened mRNA levels of TNF and IL6, alongside increased neuronal apoptosis and neurodegeneration. 19 Recovery from infection can result from efficient virus elimination or by tolerance of the infection via limiting the associated immunopathology. 24 Influenza disease outcomes may therefore be dictated by pathogen virulence and host resistance to and/or tolerance of the infection. Crucial to this is striking a balance between swift virus elimination and regulation of the immune response and potential hyperinflammation. In our present study, therapeutic treatment with LAT8881 or its metabolite LAT9991F successfully reduced lung viral replication and pro‐inflammatory mediators during the acute phase of infection, which correlated with lessened disease symptoms and significantly improved survival (Figure 2). We see significant rapid reductions, but importantly not total depletion, of pro‐inflammatory cytokines IL‐6, TNF and MCP‐1 in the airways (Figure 3b–d) elicited by treatment with LAT8881 and LAT9991F. This may have tuned the local innate immune response to better counteract the infection, while also limiting excessive tissue damage that can exacerbate inflammation, as evidenced by reduced levels of LDH and the DAMP molecule S100A10 in BAL fluid (Figures 3e and 5a–c). Indeed, histological analysis of lung tissue sections from LAT8881‐treated mice revealed reduced peribronchial inflammation and features associated with epithelial damage (Figure 5d–g).
In the context of influenza, LANCL2‐deficient mice have higher levels of IL‐6 and MCP‐1 in their lungs, while the oral treatment of infected mice with selective LANCL2 ligands abscisic acid and NSC61610 reduced the levels of TNF and MCP‐1 in the lung during IAV infection. 22, 23 The anti‐inflammatory effect of LAT8881 and LAT9991F during IAV infection, therefore, is consistent with these previous reports of LANCL2‐targeted treatment in vivo; however, in contrast to those studies, LAT8881 and LAT9991F treatment also decreased viral titres in the lung (Figures 3a and 4a). Indeed, a reduction in IAV propagation was observed in vitro in primary human bronchial epithelial cells (PBECs) treated with LAT8881 (Supplementary figure 4a), suggesting LAT8881 may act on epithelial cells lining the airways to limit IAV replication. In this experiment, LAT8881 was added 1 h following IAV infection, indicating the observed reduction in viral replication was not because of direct interaction with the virus itself. However, a limitation with this approach is that the PBECs were not grown in an air liquid interface and therefore more closely emulate basal cells rather than epithelial cells of the bronchus.
Alveolar macrophages are the sentinels of the airways and a primary target of IAV infection which results in cell death without release of infectious virus, 26, 27, 28 and loss of AM has also been observed in hospitalised patients with moderate and severe COVID‐19. 37 HKx31 IAV infection of mice reported here resulted in a rapid reduction of AM in the airways of PBS‐treated controls (Figure 3k). Whereas the loss of AM following IAV infection of LANCL2‐deficient mice is even more profound than in wildtype controls, 23 LAT8881 and LAT9991F treatment was associated with greater numbers of AM in the airways (Figures 3k and 4g), suggesting both compounds may limit their IAV infection‐induced depletion. Significantly, delaying commencement of LAT8881 treatment until 3 dpi, the onset of severe disease, also resulted in greater numbers of AM (Supplementary figure 6j). Indeed, it appears that LAT8881 treatment may promote AM longevity during IAV infection as the proportion of AM displaying the early cell death marker Annexin V, decreased with increased dose of LAT8881 (Figure 4h). Pneumonia is a frequent influenza complication as either a direct consequence of IAV infection or, more commonly, secondary bacterial infections. 38, 39 As such, LAT8881‐ and LAT9991F‐mediated reductions in lung viral burden, modulation of inflammation in the airway microenvironment and maintenance of a denser population of AM at the site of infection, could help protect against additional viral infections, as well as the development of viral or bacterial pneumonia by promoting quicker resolution of primary virus infections via effective and proportionate immune responses. 27, 40, 41 Influenza A virus infection triggers a rapid influx of leukocytes from the circulation into the lung, dominated by IM and neutrophils and sustained by their release of chemokines. 29, 42 Interestingly, we observed no major changes in these key infiltrating leukocytes (Supplementary figure 3) with LAT8881 or LAT9991F treatment alone; however, neutrophils have significant cytotoxic potential and dysregulated neutrophil activation can contribute to lung injury and lethal disease. 43, 44, 45 Specific antibody‐mediated depletion of neutrophils reportedly results in worse survival, weight loss and increased extrapulmonary spread in mouse models of IAV infection. 46, 47, 48 Meanwhile, antibody‐mediated reduction, but not complete depletion, of neutrophils during infection with PR8 H1N1 IAV improved disease. 43 This suggested that a measured reduction in lung infiltrating neutrophils could limit neutrophil‐associated immunopathology while retaining their contribution to early host defence against the virus. In line with this, significantly fewer neutrophils were observed in the BAL fluid from IAV‐infected mice co‐treated with LAT8881 and OP, which was greater than that elicited by each individual treatment where neutrophil abundance was unchanged compared with PBS controls (Figure 6g). Whether this reflected reduced neutrophil infiltration or longevity has not been established yet; however, this reduction correlated with a further reduction in IL‐6 (Figure 6b), a cytokine that can protect neutrophils from influenza‐induced cell death. 46 In sum, we report that LAT8881 and its truncated metabolite LAT9991F provided a therapeutic benefit in a preclinical IAV infection model, while LAT7771, derived from a homologous C‐terminal region of prolactin, had negligible effects. This suggests that the protective properties of LAT8881 and LAT9991F are not shared by structurally similar peptides from one of growth hormone's close relatives, nor by cyclic peptides in general. Based on its established safety profile in animals and humans, 14, 15 LAT8881 shows promise as a potential treatment for severe IAV infection, and we also intend to extend our future investigations of this compound, and the derivatives thereof, to alternate inflammatory lung diseases including asthma, chronic obstructive pulmonary disease (COPD) and COVID‐19.
## Compound generation
LAT8881 is a 16‐amino‐acid synthetic form of the C‐terminal fragment of human GH (H‐YLRIVQCRSVEGSCGF‐OH), which contains an additional N‐terminal tyrosine residue and two cysteine residues linked by a disulphide bond. LAT9991F is a 10‐amino‐acid synthetic peptide (H‐CRSVEGSCGF‐OH), which is a truncated form and a known metabolite of LAT8881. 34 LAT7771 (H‐CRIIHNNNC‐OH) is the 9‐amino‐acid structural homologue of LAT9991F, derived from prolactin and was included as a control peptide. The cysteines in both LAT9991F and LAT7771 were also disulphide linked. All peptides were synthesised by Auspep Pty Ltd (Melbourne, Australia).
## In vitro treatment of cells with compounds
The mouse fibroblast L cell line (RRID:CVCL_8887) was grown in RPMI (Thermo Fisher Scientific, Waltham, USA, Cat #11875119) supplemented with $10\%$ (v/v) heat‐inactivated FBS and 2 mM L‐glutamine (Thermo Fisher Scientific, Waltham, USA, Cat #25030–081) and seeded into 96‐well plates, at the indicated densities. Peritoneal exudate cells (PECs) were obtained from untreated C57BL/6J mice by flushing the peritoneal cavity with 5 mL PBS and adherence to 96‐well plates overnight in DMEM (Thermo Fisher Scientific, Waltham, USA, Cat #11965118) supplemented with $10\%$ (v/v) heat‐inactivated FBS, $1\%$ (v/v) penicillin/streptomycin (Thermo Fisher Scientific, Waltham, USA, Cat #15140122) and 2 mM L‐glutamine (Thermo Fisher Scientific, Waltham, USA, Cat #25030–081), at the indicated densities. Triplicate wells of L cells and PECs were treated with LAT8881 (10–200 μM as indicated) or an equivalent volume of DMSO vehicle. In some experiments, L cells were transfected with 100 nM siRNA specific for mouse Lancl1 (Ambion In Vivo, Life Technologies, Carlsbad, USA, Cat #4457308), Lancl2 (Ambion In Vivo, Life Technologies, Carlsbad, USA, Cat #4457308) or control siRNA (Ambion In Vivo Negative Control #1; Cat #4457289) using Lipofectamine 3000 (Thermo Fisher Scientific, Waltham, USA, Cat #L3000015). Cell viability was assayed at the indicated time points using a CellTiter‐Glo 2.0 Cell Viability Assay (Promega, Madison, USA, Cat #G9242), according to the manufacturer's instructions.
The A549 human lung adenocarcinoma cell line (RRID:CVCL_0023) was grown in DMEM (Thermo Fisher Scientific, Waltham, USA, Cat #11960044) supplemented with $10\%$ (v/v) heat‐inactivated FBS and $2\%$ (v/v) penicillin/streptomycin (Thermo Fisher Scientific, Waltham, USA, Cat #15070063) and seeded into 96‐well plates. The following day, triplicate wells of A549 cells were treated with 350 μM paclitaxel from Taxus ravioli (Sigma Aldrich, St. Louis, USA, Cat #T7402) or 5 mM H2O2 (Sigma Aldrich, St. Louis, USA, Cat #H1009) in combination with LAT8881 (0.01–100 μM) or DMSO vehicle alone for 16 h. Cell viability was assayed using a CellTiter‐Glo Cell Viability Assay (Promega, Madison, USA, Cat #G7571), according to the manufacturer's instructions. In some experiments, A549 cells were transfected with 100 nM siRNA specific for human LANCL1 (Horizon Discovery, Waterbeach, UK, Cat #L‐012166‐00‐0005), LANCL2 (Origene, Rockville, USA, Cat #SR324535) or control siRNA (Origene, Rockville, USA, Cat #4457289) using Lipofectomine RNAiMAX (Thermo Fisher Scientific, Waltham, USA, Cat #13778100) 16 h prior to paclitaxel/LAT8881 treatment.
## Influenza virus
The IAV strain used in this study was HKx31 (H3N2), which is a high‐yielding reassortant of A/PR/$\frac{8}{34}$ (PR8; H1N1) that carries the surface hemagglutinin (HA) and neuraminidase (NA) glycoproteins of A/Aichi/$\frac{2}{1968}$ (H3N2). HKx31 was grown in 10‐day embryonated chicken eggs by standard procedures and titrated on Madin–Darby Canine Kidney (MDCK) cells (RRID:CVCL_0422).
## Influenza virus infection of mice
C57BL/6J male and female mice (6–8 weeks of age) were maintained in the Specific Pathogen‐free Physical Containment Level 2 (PC2) Animal Research Facility at the Monash Medical Centre. All experimental procedures were approved by the Hudson Animal Ethics Committee and experimental procedures carried out in accordance with approved guidelines. For virus infection studies, C57BL/6J mice were randomised. Mice were lightly anaesthetised with isoflurane and intranasally inoculated with 104 pfu of HKx31 (H3N2) IAV in 50 μL PBS, which induces severe disease. 29, 49 Mice were treated at the time points indicated with LAT8881, LAT9991F or LAT7771 (5, 10 or 20 mg kg−1; as indicated) in 25 μL PBS via the intranasal route. Control mice were treated with an equivalent volume of PBS alone. Mice were weighed daily and assessed for visual signs of clinical disease, including inactivity, ruffled fur, laboured breathing and huddling behaviour. Animals that lost $20\%$ of their original body weight or displayed severe clinical signs of disease (reduced mobility and rapid breathing) were euthanised. At the indicated time points, mice were sacrificed via intraperitoneal injection of sodium pentobarbital and BAL immediately performed by flushing the lungs three times with 1 mL of PBS. Lungs were then removed and frozen immediately in liquid nitrogen. Titres of infectious virus in lung homogenates were determined by standard plaque assay on MDCK cells (RRID:CVCL_0422).
## Quantification of cytokines in mouse BAL fluid and sera
To detect cytokines, BAL fluid was isolated following centrifugation, and serum was collected and stored at −80°C. Levels of IL‐6, MCP‐1, IFNγ, IL‐10, IL‐12p70 and TNF proteins were determined by cytokine bead array (CBA) using the mouse inflammation kit (BD Biosciences, San Jose, USA, Cat #552364, RRID:AB_2868960).
## Flow cytometry on BAL cells
Cells in the BAL fluid were isolated by centrifugation and treated with red blood cell lysis buffer (Sigma Aldrich, St. Louis, USA, Cat #R7757) for 5 min. The reaction was quenched by washing the cells in FACS buffer (PBS containing $2\%$ (v/v) FBS and 2 mM EDTA). Bronchoalveolar lavage cells were then incubated with fluorescently labelled antibodies at 4°C for 20 min in the presence of Fc receptor blocking monoclonal antibody against CD16/CD32 (clone 93, Thermo Fisher Scientific, Waltham, USA, Cat #139311, RRID:AB_468898) to limit nonspecific antibody binding. Bronchoalveolar lavage cells were stained in FACS buffer with monoclonal antibodies to Siglec‐F (clone E50‐2440, BD Biosciences, San Jose, USA, Cat #565527, RRID:AB_2732831), NK1.1 (clone PK136, BioLegend, San Diego, USA, Cat #108727, RRID:AB_2132706), CD3ε (clone 145‐2C11, BioLegend, San Diego, USA, Cat #100355, RRID:AB_2565969), CD11c (clone HL3, BD Biosciences, San Jose, USA, Cat #564080, RRID:AB_2738580), CD64 (clone X54‐$\frac{5}{7.1}$, BioLegend, San Diego, USA, Cat #139311, RRID:AB_2563846), Ly6C (clone AL‐21, BD Biosciences, San Jose, USA, Cat #562727, RRID:AB_2737748), Ly6G (clone 1A8, Cat #551461, RRID:AB_394208, BD Biosciences, San Jose, USA), and I‐Ab (clone AF6‐120.1, BD Biosciences, San Jose, USA, Cat #562823, RRID:AB_2737818) and the Zombie Aqua viability dye (Cat #423102; BioLegend, San Diego, USA). Total live cells (Zombie Aqua viability dye−), neutrophils (Ly6G+ Ly6Cint), NK cells (NK1.1+ CD3−), T cells (NK1.1− CD3+), IM (Ly6G− Ly6C+), AM (CD11c+ Siglec‐F+) and DCs (CD11c+ I‐Ab+) were quantified by flow cytometry using a BD LSRFortessa™ X‐20 (BD Biosciences, San Jose, USA, RRID:SCR_019600) or Aurora flow cytometer (Cytek Biosciences, Fremont, USA, RRID:SCR_019826) and FlowJo™ 10 analysis software (BD Biosciences, San Jose, USA, RRID:SCR_008520). Cells were enumerated using a standard amount of blank calibration particles (ProSciTech, Kirwan, Australia, Cat #QBCP‐60‐5) as determined using a haemocytometer.
For flow cytometric analysis of AM death, BAL cells were treated with Fc receptor blocking monoclonal antibody against CD16/CD32 (clone 93, Thermo Fisher Scientific, Waltham, USA, Cat #139311, RRID:AB_468898) to limit nonspecific antibody binding, followed by staining with fluorochrome‐conjugated monoclonal antibodies (BD Biosciences, San Jose, USA) to Siglec‐F (clone E50‐2440, Cat #565527, RRID:AB_2732831) and CD11c (clone HL3, Cat #553801, RRID:AB_396683). Cells were then incubated with Annexin V (Cat #640912; BioLegend, San Diego, USA) in binding buffer (10 mM HEPES pH 7.4, 150 mM NaCl, and 2.5 mM CaCl2) and 5 μg mL−1 propidium iodide (PI; Thermo Fisher Scientific, Waltham, USA; Cat #P1304MP). Cells were analysed using a BD FACS Canto II flow cytometer (BD Biosciences, San Jose, USA, RRID:SCR_018056) and FlowJo software (BD Biosciences, San Jose, USA, RRID:SCR_008520).
## Assessment of lung damage
Levels of LDH in BAL fluid supernatant were determined using a CytoTox 96 Non‐radioactive Cytotoxicity Assay (Cat #G1780; Promega, Madison, USA), according to the manufacturer's instructions. Levels of ATP in BAL fluid supernatant were determined by using a CellTiter‐Glo 2.0 Cell Viability Assay (Cat #G9242; Promega, Madison, USA), according to the manufacturer's instructions. Levels of S100A10 in BAL fluid were determined using ELISA (precoated 96 well format; Cat #SEC046Mu‐96 T, Cloud‐Clone Corporation, Katy, USA), according to the manufacturer's instructions.
In the indicated experiments, mice were sacrificed via intraperitoneal injection of sodium pentobarbital, and lungs were immediately inflated and fixed in $10\%$ formalin for at least 24 h, and then processed in paraffin wax. Longitudinal tissue sections (4 μm) were prepared and stained with haematoxylin and eosin (H&E). Tissues were graded for alveolitis and peribronchial inflammation on a subjective scale of 0–5 (0 = no inflammation, 1 = very mild, 2 = mild, 3 = moderate, 4 = marked and 5 = severe inflammation). Sections were also scored for features of epithelial damage such as presence of debris in the airspace, epithelial denudation and thickening of the epithelial wall (0 = no obvious damage, 1 = mild, 2 = moderate, 3 = marked and 4 = severe). Sections were blinded and randomised, and samples corresponding to the least severe and most severe were assigned scores of 0 and $\frac{4}{5}$, respectively. All other samples were graded in five random fields by three independent readers. Lung sections were viewed on an Olympus BX60 microscope and photographed at ×10 magnification with a Olympus DP74 colour camera running from Olympus cellSens Dimension software.
## In vitro IAV infection
Human primary bronchial epithelial cells (PBECs) were obtained via bronchial lavage as previously described, 50 from normal subjects who were non‐smokers or had not smoked for > 15 years and had not been diagnosed with asthma or COPD (normal FEV1 measurements). Studies were approved by the Monash Health and Monash Medical Centre Human Research Ethics Committee. Consent was obtained from all subjects, and studies were conducted in accordance with the approved guidelines. Human primary bronchial epithelial cells were cultured under submerged conditions on collagen‐coated flasks (Cat #A1064401; Thermo Fisher Scientific, Waltham, USA) in supplemented bronchial epithelial growth medium (BEGM; Cat #CC‐3170; Lonza, Basel, Switzerland) and were used within four passages. Human primary bronchial epithelial cells were plated into collagen‐coated 24‐well plates in BEGM medium without hydrocortisone. The following day, cell monolayers were infected with HKx31 IAV in BEGM media for 1 h at a multiplicity of infection (MOI) of 3. Cell monolayers were then washed and incubated with 100 μM LAT8881 or an equivalent volume of DMSO vehicle alone. Levels of infectious virus in primary cell culture supernatants were determined at 24 h by standard plaque assay on MDCK cells (RRID:CVCL_0422).
## Data and statistical analysis
Data were tested for normality and analysed by GraphPad Prism version 9 software (Graphstats Technologies, RRID:SCR_002798). When comparing three or more sets of values, a one‐way analysis of variance (ANOVA) was used with either a Tukey's or a Dunnett's (when comparisons were only made to IAV‐infected PBS control group) multiple comparisons post hoc test. A Student's t‐test was used when comparing two values (two‐tailed, two‐sample equal variance). Survival proportions were compared using the Mantel–Cox log‐rank test. A P‐value < 0.05 was considered statistically significant.
## Author contributions
Christopher M Harpur: Conceptualization; data curation; formal analysis; investigation; methodology; validation; visualization; writing – original draft; writing – review and editing. Alison C West: Conceptualization; data curation; formal analysis; investigation; methodology; validation; visualization; writing – original draft; writing – review and editing. Mélanie A Le Page: Data curation; formal analysis; methodology. Maggie Lam: Data curation; formal analysis; methodology. Christopher Hodges: Data curation; investigation. Osezua Oseghale: Data curation. Andrew J Gearing: Conceptualization; methodology; project administration; resources; supervision; writing – review and editing. Michelle D Tate: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; resources; supervision; validation; visualization; writing – original draft; writing – review and editing.
## Conflict of interest
The authors of this manuscript have several competing interests. MDT received research funding from Lateral Pharma Pty Ltd. AJG receives consultancy fees from Lateral Pharma Pty Ltd and has patent ownership.
## Data availability statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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|
---
title: 'Experiences of Filipino Americans with Type 2 Diabetes during
COVID-19: A Qualitative Study'
authors:
- Dante Anthony Tolentino
- Rey Paolo Ernesto Roca
- Joey Yang
- Josephine Itchon
- Mary E. Byrnes
journal: Western Journal of Nursing Research
year: 2023
pmcid: PMC10034559
doi: 10.1177/01939459231162917
license: CC BY 4.0
---
# Experiences of Filipino Americans with Type 2 Diabetes during
COVID-19: A Qualitative Study
## Body
Type 2 diabetes (T2D) disproportionately affects historically and contemporary marginalized groups, 1 including Filipino Americans. T2D among Filipino *Americans is* prevalent ($10.2\%$ to $19.4\%$)2,3 with evidence suggesting that Filipino Americans have suboptimal self-management behaviors on healthy eating, medication taking, and blood glucose testing. 4 With many struggling to cope with self-management under normal circumstances, the novel SARS Coronavirus 2019 (COVID-19) pandemic added to an already complex management of T2D. Filipino Americans are disproportionately dying from COVID-19, accounting for $30\%$ of deaths in California. 5 *Diabetes is* also associated with a higher risk of a more severe course of COVID-19 and long-term complications. 6 Despite the high prevalence of T2D and increased mortality rates among Filipino Americans during COVID-19, they continue to be understudied in the United States. 7
## Abstract
Little is known about the experiences of Filipino Americans with type 2 diabetes regarding their self-management during the early phase of the COVID-19 pandemic. We conducted a qualitative research study using semistructured interviews. In total, 19 interviews were recorded, transcribed, and analyzed by 4 independent coders. We situated our understanding of these results using three concepts from an indigenous Filipino knowledge system called Sikolohiyang Pilipino: Kapwa (shared identity), Bahala Na (determination), and Pakikibaka (spaces of resistance). The following three main themes emerged: [1] stressors of the pandemic, [2] coping behaviors (with two subthemes: emotional and lifestyle-focused responses), and [3] diabetes self-management outcomes. Participants experienced stresses, anxiety, and loneliness during the pandemic magnified by the complexities of self-management. Although many admitted the pandemic brought challenges, including burnout, they coped by using existing resources—support from family, friends, the use of technology, and various emotional coping mechanisms. Many said that they made few diabetes self-management changes.
## Epistemic and Ontologic Understanding of Filipino American Health
While experiences of individuals with T2D during the pandemic have been studied in the last two years,8-12 many focused on populations outside of the United States and overlooked the perspectives of Filipino Americans. Much of the extant literature on marginalized groups has also used Western epistemic and ontologic understandings of health and illness, wherein health is built on a biomedical model that addresses health deficits and disease symptoms and often gives little regard to sociocultural influences. Due to the lack of T2D studies focused on Filipino Americans, health outcomes and experiences are often invisible or misunderstood. For studies that include Filipino Americans, many undermine the influence of generational trauma, racism, and inaccurate risk factors. 7 Given the multifaceted impact of sociocultural and historical factors, and with the increase in decolonizing health research that requires addressing effects of colonization, structural oppression, and disenfranchisement, 7 we examine Filipino American experiences with T2D during the early phase of the pandemic with a strength-based lens using an indigenous knowledge system called Sikolohiyang Pilipino.13-15Sikolohiyang Pilipino (Filipino psychology) refers to the psychology drawn from the Filipino people’s own experiences, thoughts, and perspectives.
A dominant narrative among Filipino American health studies is the concept of resiliency.16-18 Although essential, the resiliency narrative is often grounded in Western health conceptualizations and detracts from understanding how inherent Filipino values impact diabetes self-management. We grounded our discussion of the results using Sikolohiyang Pilipino to reframe this narrative. 13 We used this critical approach using the concepts of kapwa, pakikibaka, and bahala na and how they relate to the experiences of Filipino Americans with T2D during the early phase of the pandemic.
## Overview of Sikolohiyang Pilipino
Due to the American colonization of the Philippines, Filipinos are typically characterized using Western philosophical conceptions. Influenced by colonialist worldviews, these Western concepts reinforce the idea of Filipino inferiority to colonizers, many of which are not culturally concordant. 19 For instance, many studies of chronic diseases, including T2D, focus on biomedical factors of diagnosis, management, and outcomes, neglecting sociocultural dimensions. 20 These Western views fail to recognize how Filipinos define themselves (i.e., the true spirit of Filipinos) and contribute to the formation of negative legacies of historical trauma, colonial mentality, and poor physical and mental health. 21 Sikolohiyang Pilipino frames and addresses these challenges. It examines historical and sociocultural facts about Filipinos, an understanding of the indigenous language, the uncovering of Filipino practices and values, and the interpretation of such characteristics from the standpoint of the indigenous Filipino conscience, knowledge, habits, and behaviors.13,15 We used three main concepts from Sikolohiyang Pilipino in this study. First is the concept of kapwa, or shared identity. In kapwa, individuals recognize that they share a fundamental nature with others. 13 The unity of one’s self with others implies inclusiveness and a sense of shared space and humanity by establishing a connection. The second concept is pakikibaka, or spaces of resistance. Pakikibaka is the ability of Filipinos to undertake internal and external challenges and the effort to adapt and work to bring about necessary changes in systems. 13 Finally, we used the concept of bahala na, or determination. Although often referred to as a fatalistic attitude, in Sikolohiyang *Pilipino bahala* na is an attitude of “determination and risk-taking.” 13 *Bahala na* is not passively accepting what is to come; rather, it is a conviction that they can overcome challenges or barriers.
## Purpose
This study aimed to understand Filipino Americans’ behaviors, values, perceptions, and challenges related to T2D self-management during the early phase of the COVID-19 pandemic, grounding the discussion of the results in Sikolohiyang Pilipino, an indigenous Filipino psychological framework.
## Study Design
We used interpretive description, a noncategorical qualitative methodology to understand the experiences of Filipino Americans with T2D, including their values, perceptions, and challenges with T2D self-management during the first wave of COVID-19.22,23 Interpretive description allows us to understand how Filipino Americans experience their health and illness by considering the constructed and contextual circumstances allowing for shared realities. The study was approved by the University of Michigan Institutional Review Board (HUMID #00194036). Informed consent was obtained from all study participants.
## Researcher Characteristics
The first author, D.A.T., is a PhD-prepared Filipino American nurse who emigrated from the Philippines at the age of 18. The first author has more than a decade working as a nurse informaticist, with a specific research interest in T2D among Filipino Americans. He is trained in qualitative research. The second author, R.P.E.R., is also a Filipino American nurse with research experience in illness perception and T2D among Filipino Americans. He is a seasoned nurse educator, has worked alongside the Filipino American community in the last 15 years, and is currently finishing his PhD studies. Authors J.Y. and J.I. are undergraduate students and are learners of qualitative research who added reflexivity into the analysis. The senior author, M.B., is a scholar of sociology with content expertise in intersectionality and inequalities and methodological expertise in qualitative methods.
## Study Population
To reach a broad range of Filipino Americans with T2D, we leveraged the strengths of social media (e.g., Facebook, Twitter, Instagram) to purposively sample potential participants from March to August 2021. Participants self-identified as Filipino American, >18 years old, understood English, and were diagnosed with T2D > 1 year (self-identified in the eligibility survey). In total, 37 participants completed the eligibility survey, and 19 participants completed the study and were compensated with a $25 gift card.
## Data Collection
We collected demographic information using an online survey (Qualtrics, Provo, UT) with potential participants consenting to a virtual interview. The first and last authors (D.A.T. and M.E.B.) conducted independent one-on-one semistructured virtual interviews over a secure video conference platform from March 2021 to August 2021. Informed consent was obtained verbally prior to the start of the interview. The interview guide was intended to elicit in-depth responses on their T2D self-management experiences during the pandemic. Interviews ranged from 35 to 90 minutes and were digitally recorded, professionally transcribed verbatim, and redacted for identifying information. D.A.T. reviewed the transcripts for accuracy and anonymity. Unique identifiers were applied to each participant for referencing purposes and to protect confidentiality.
## Data Analysis
We approached our initial coding through an iterative, inductive process. The initial codebook was created by three authors (D.A.T., J.I., and J.Y.) reviewing transcripts to identify an initial set of codes. The codes were refined through discussions, adding a fourth author (R.P.E.R.). Four members independently coded the transcripts deductively utilizing the initial codes, and then independently performed emotions and values coding inductively. 24 The team then organized the coded segments into categories, subthemes, and themes. We reached information power with a sample size of 19, as determined by our narrow aim and quality of dialogue that produced information-rich cases. 25
## Sikolohiyang Pilipino
To shift the deficit-based paradigm of discussing Filipino American health, we used Enriquez’ Sikolohiyang Pilipino (Filipino psychology) as a form of a framework to interpret the findings 26 using the indigenous concepts of Filipino behavior patterns and value structure. 13 We used three concepts described in Sikolohiyang Pilipino: the core value of [1] kapwa and confrontative surface values of [2] bahala na and [3] pakikibaka, to situate the results of our findings. 13
## Participant Characteristics
Table 1 displays the sociodemographic characteristics of the participants. About $90\%$ of the 19 participants were born in the Philippines, $69\%$ identified as women, and $5\%$ were nonbinary. The mean age was 57.3 years (SD = 13.8). More than $68\%$ had a bachelor’s degree. Over $70\%$ lived in California, and $53\%$ had an annual income of > $60,000. All participants had health insurance, a primary care provider, and no hospitalizations in the previous year. Many self-reported comorbidities include obesity, sleep apnea, hypertension, hypercholesteremia, gout, and arthritis.
**Table 1.**
| Characteristic | n (%) |
| --- | --- |
| Age | Mean = 57.32 (SD = 14.22) |
| <40 years old | 1 (5.3%) |
| 41-50 years old | 3 (15.8%) |
| 51-60 years old | 6 (31.6%) |
| 61-70 years old | 3 (15.8%) |
| >71 years old | 5 (26.3%) |
| Birthplace | Birthplace |
| Philippines | 17 (89.5%) |
| United States | 2 (10.5%) |
| Education | Education |
| Some college | 2 (10.5%) |
| Associate degree | 3 (15.8%) |
| Bachelor’s degree | 6 (31.6%) |
| Master’s degree | 6 (31.6%) |
| Doctoral degree | 1 (5.3%) |
| Gender | Gender |
| Nonbinary | 1 (5.3%) |
| Woman | 13 (68.4%) |
| Man | 5 (26.3%) |
| Income | Income |
| <$30K | 2 (10.5%) |
| $31K-$60K | 3 (15.8%) |
| $61K-$100K | 5 (26.3%) |
| $76K ≥ $100K | 7 (36.8%) |
| Location | Location |
| California Nevada | 14 (74.0%)1 (5.2%) |
| Georgia | 1 (5.2%) |
| Indiana | 1 (5.2%) |
| New York | 1 (5.2%) |
| Ohio | 1 (5.2%) |
| Job | |
| Nurse | 6 (31.6%) |
| Administrative (e.g., banking) | 5 (26.3%) |
| Sales and marketing | 1 (5.3%) |
| Retired | 6 (31.6%) |
| Academia | 1 (5.3%) |
| Has primary care provider | 19 (100%) |
| Has health insurance | 19 (100%) |
| Last HbA1C | Last HbA1C |
| <7% | 10 (52.7%) |
| 7-8% | 6 (31.6%) |
| >8.1% | 3 (15.8%) |
| Years with type 2 diabetes | Years with type 2 diabetes |
| 1-10 years | 12 (63.2%) |
| 11-20 years | 4 (21.0%) |
| >20 years | 1 (5.3%) |
## Themes
Figure 1 displays the themes identified from the analysis. Key themes are as follows: [1] stressors of the pandemic; [2] coping behaviors related to the pandemic; and [3] self-management outcomes. The three concepts from Enriquez’ Sikolohiyang Pilipino overlap the five categories that emerged from the coping behaviors theme.
**Figure 1.:** *Key Themes and Subthemes and How Select Tenets of Sikolohiyang Pilipino
Overlays with the Coping Behaviors Subthemes/Categories.*
## Stressors of the pandemic
Participants described everyday pandemic stressors, regardless of their occupation during the early phase of the pandemic. While many were essential workers (e.g., nurses and frontline workers), a common theme emerged in that they talked about work-related issues that brought stress or anxiety, including changes in work structure (e.g., irregular hours, remote work), job duties, and insufficient work training/resources. A nurse described her stress related to hospital work and the inability to go home to her family: “I wasn’t managing [referring to T2D] because the stress was all the way up there. Plus, you can’t go home” (P19, 60 years old). Some participants mentioned how changes in their job structure contributed to their stress throughout the early phase of the pandemic. One participant said, “I’ve worked through the pandemic. I...never had time off because I work at a bank, so we have to support finance, which was hard...now I’ve been taking sick days, like mental days” (P11, 51 years old).
Many described different emotions, (i.e., feelings of depression, loneliness, isolation, or anxiety). Pandemic restrictions, like lockdowns, contributed to depression. One participant shared: “Something COVID brought to me, which I must confess. It may have added to my depression. If I am a depressed person—totally you’re like in prison. I could hardly move and see the sun, and you’re limited” (P8, 82 years old). Others felt lonely because they worked from home while their significant others had to go to the office. Due to loneliness, a participant turned to food as comfort: “I still eat the same amount of rice. I don’t know why. Maybe because of COVID. There was a time that—I don’t wanna say—I get sad...food was my comfort” (P6, 44 years old).
The inability to go out and exercise also contributed to feelings of sadness. One simply desired to go outside, saying "I was actually in a gym, health club before COVID started, and I just felt myself just wearing down, just gloomy from lack of exercise” (P18, 54 years old).
Other pandemic stressors magnified the complexities of T2D self-management. These stressors included school, family, and comorbidities. When asked about how COVID-19 impacted their self-management, one participant expressed, “divorce, finals, midterms. Those are the things that stress me out, but not COVID-19. I will not get stressed out with that” (P4, 55 years old).
## Coping behaviors during the pandemic
The pandemic evoked feelings of isolation, shock, and confusion; consequently, participants developed patterned responses to cope with the scale and unpredictability of the pandemic. The following two subthemes emerged: [1] lifestyle-focused coping and [2] emotion-focused coping.
We used three indigenous concepts from Sikolohiyang Pilipino that overlapped with the theme of coping behaviors to explain these findings. First is the concept of kapwa, a core value of *Filipino indigenous* psychology that stresses the interconnectedness with one another. 13 *Second is* the indigenous concept of pakikibaka. This concept refers to an internal conflict with a concerted effort to adapt which can be accomplished by thriving in a challenging environment or for some, failing to adapt. Finally, the concept of bahala na is a Filipino sociocultural value translated to “whatever will be, will be.” It is, however, more than a fatalistic attitude; it is about determination in the face of uncertainty. 13
## Lifestyle-focused coping
Lifestyle-focused coping centered around participants’ adaptation to their changing environment by using social/family support and resources and adjusting their lifestyle related to diet and exercise.
## Utilizing social and familial support and resources
In this study, kapwa was represented by their family’s role in their T2D self-management to cope during the pandemic. Many relied on their family as their social support. One participant said of her family, “They were very concerned...so they encouraged us to have our groceries delivered, so we don’t go out” (P17, 69 years old). Some family members also encouraged them to exercise. A participant recalled that their husband said, “Come on, you need to walk. We haven’t walked for a day” (P16, 70 years old).
Technology also played a role in helping participants find new ways to connect with their social support and needed resources (e.g., health care needs) by turning to video communications technology (e.g., Zoom®). Some noted using telehealth to meet with their care provider, while others used virtual platforms for their physical activities. One participant spoke about using online Zumba, “I just look for Zumba classes.... It worked out okay. I could do it whenever...it fitted my schedule very well” (P17, 69 years old). Another talked about her husband buying her a smartwatch, saying “My husband bought this for me [pointing to smart watch], so I can monitor my lifestyle. We did go to the gym but ever since the pandemic, we couldn’t” (P16, 70 years old).
## Adapting to a changing environment by adjusting their
lifestyle
The concept of pakikibaka closely aligns with this category. This relates to the pandemic-induced changes in participants’ eating patterns and physical activities—some experienced positive changes in their eating habits, while some may have had negative adaptations. Before the pandemic, some shared that they would always eat out. However, the lockdowns during the pandemic helped them manage their unhealthy eating habits. One participant said, “It helps me manage eating out, like a bad eating habit, there’s a lot of fast food [prior to COVID]...I barely ate out because I couldn’t eat out” (P3, 68 years old). Another remarked that the pandemic forced them to change their poor eating habits: “Well, this pandemic has some good effect on this diabetes thing. If there is no pandemic, we’ll be traveling and eat, eat, eat...we cannot do that because of the pandemic. So it helps” (P9, 71 years old).
Others, however, failed to make some positive changes in their eating habits. This was because they would eat what was available, even if it was unhealthy. This form of pakikibaka was noted by a participant: I’m at home with my family...my dad, who cooks whatever food he wants, then I will eat with them. So we had a lockdown, like [in March]. So around December, I was already gaining weight, although my blood sugar was still okay (P15, 54 years old).
Others spoke about missing the opportunity to do in-person exercises because of lockdowns. However, this did not stop them from making healthy choices. Instead, they resisted being sedentary by acknowledging deficiencies and making adjustments. One said, “When I used to go to the office during my lunch or break, I would walk around. But due to the pandemic, just staying home, I only exercise during my lunchtime. I don’t think it’s enough, the 30 minutes cardio” (P6, 44 years old).
Virtual events helped many adapt. One Silver Sneakers participant detailed how she continued her fitness regimen during the pandemic: “I Googled Zumba online and I was just able to do it. Nobody cares about how you look...so I just look for Zumba classes.... Not the real fancy ones but just ones that are more like less jumping” (P17, 60 years old).
## Emotion-focused coping
Most participants exhibited emotion-focused coping during the pandemic. These were strategies that regulated their emotional reactions to the pandemic. There was a spectrum of growth and control, values and beliefs reliance, and emotional responses. The concepts of pakikibaka and bahala na were exhibited in many of the emotion-focused coping strategies manifested by the participants, particularly in growth and in relying on their values and beliefs.
## Growth and locus of control
This reflects pakikibaka, the capacity to thrive despite the pandemic due to their internal locus of control, believing that their self-management behaviors are determined by their own decisions and efforts. A participant talked about her experience wearing a mask—a behavior that she could control during the pandemic: “I’m very careful not just because of the diabetes, but I also have asthma. I wear a mask all the time.... Even if they say stop using it, I’m still using it” (P19, 60 years old).
Despite the pandemic’s stressors, numerous participants noted self-growth, including learning new skills like cooking and gardening. One said, “I learned to cook something. If I’m craving something...I have to research the internet, and then there you go, there’s the recipe” (P7, 51 years old).
## Relying on their values and beliefs
Participants turned to their values and beliefs as coping strategies during the pandemic. Many described their belief in determination and risk-taking (bahala na). One participant explained that while he had friends who smoked, they have not been diagnosed with lung cancer. He explained that it is about living life and that medicine will have an answer. He described it as, If you are going to look at each statistic, the lifespan of man has increased almost threefold from just, uh. Now, people can live longer...and so, the diabetes I think, uh, modern medicine will be able to cure diabetes. ( P8, 82 years old) Similarly, another talked about how they put their lives in God’s hands after contracting COVID-19: “I am not really afraid to transition. You know? Whatever happens to me, it is what it is. It’s God’s will” (P1, 42 years old). The participant later explained that he thinks about the present rather than the future. “ I’m worried about is what’s now, what’s happening now rather than what’s gonna happen in the near future” (P1, 42 years old).
## Emotional responses
Many exhibited emotional reactions related to their pandemic experiences, including being scared, anxious, frustrated, and cautious. One said, “The initial scare of COVID is because I am a higher risk so going to work...I was very anxious” (P5, 47 years old). Many were cautious in meeting people outside of their household, and some took extra sanitizing measures. One said, “When my daughter had her vaccine—every time she come to my house, I said, You have to take a bath first. I wear a mask when some come in” (P3, 68 years old).
Others expressed their frustration because of the imposed restrictions. Another lamented not being able to return to the Philippines, saying “the only thing might be my frustration. We cannot travel. We were supposed to go to the Philippines about earlier part of this year” (P13, 86 years old).
## Self-management outcomes
Despite the pandemic’s stresses, more than half acknowledged they made minor adjustments to their T2D self-management activities. Many said, "no changes” (P2, 36 years old), "not much changed” (P13, 86 years old), or "same thing” (P12, 65 years old). One admitted that "nothing has really changed as far as the COVID. I’m pretty much doing the same thing I’ve been doing pre-COVID” (P18, 54 years old).
Although many admitted no changes in behaviors, some expressed T2D self-management burnout (i.e., struggling with blood sugar and weight management), admitting exhaustion. One said, “With the remote work, I gained weight like everybody else” (P15, 54 years old). Another talked about how feelings of loneliness have led to burnout. She said, “I would feel sad like if my sugar’s elevated like I don’t care. And then I stop checking my glucose every day...it’s just like why am I doing this?” ( P6, 44 years old).
Some missed medical appointments due to the pandemic. One participant described the challenges of making appointments during the pandemic, saying “I choose not to go. Like last year, I missed the physical because of the whole COVID thing” (P11, 51 years old). Some found the shift in their routine demanding. One participant who worked half a day to care for their children said self-management was impaired: It changed, actually. Because last year, with the pandemic, I had to take a half-day off at work, for the kids" and because of the challenges with the pandemic she was not able to “do the right diet, it’s hard with three kids goin’ to work and then this pandemic” (P2, 36 years old).
Another person admitted that the pandemic was an unexpected change that shifted their way of thinking and behaving related to T2D self-management. They said, It wasn’t easy, because you know, at my age, you have to do a lot of behavior modification. We have to figure out what can we do differently? We thought this was gonna go away in a couple months (P17, 69 years old).
## Discussion
Filipino Americans in our study described shared experiences of living with T2D during the early phase of the COVID-19 pandemic, including pandemic stressors, emotional responses and coping, and the consequence of COVID-19 on T2D self-management outcomes. Similar to prior studies,9,11,12,27,28 negative emotions compounded the stress many people felt due to the pandemic’s uncertainty, which led to difficulty in T2D self-management. However, framing these results exclusively using a deficit-based lens (e.g., negative emotions and outcomes brought by the pandemic) fails to recognize the complex sociocultural realities of Filipino Americans and diminishes their indigenous values. 13 Therefore, we situated this discussion using the core value of kapwa and the confrontative surface values of pakikibaka and bahala na to explain our findings.
As a core value, kapwa (translation: “together with the person” or “shared identity”) is about strengthening and preserving relationships. 14 It maintains a connection and sense of community, which lies deep within Filipinos’ psyche. 13 *Kapwa is* a predominant Filipino value in which family or friends are considered hindi ibang tao (one of us). Unlike other studies where lack of social/family support was one of the barriers during the pandemic, 28 this community connection was evident in that family and social support played a critical role in their daily management of T2D. Many family members functioned as their care providers and health coaches, providing advice and urging them to exercise and eat better. Due to social distancing measures, many were also forced to turn to collaboration technology for continuity as a lifeline, which enabled them to still share a space with their loved ones and connect with needed resources (such as seeing a health care provider). The value of kapwa is reflected as a way of support (kaakbay) by one’s kin (kaanak) and by others (kasama).
As a confrontative surface value, pakikibaka, or “resistance,” was evident as a coping mechanism for many participants when dealing with T2D self-management changes. Many resisted in the form of adapting and thriving in a challenging environment. Recent research shows significant declines in physical activity among individuals with T2D during the pandemic. 29 Yet, in this study, many adapted to the pandemic challenges by being ingenious, such as using previously neglected resources. They also resisted the pandemic challenges by exhibiting control and growth. Many acquired new habits and skills as positive deviance to cope with the pandemic. Another form of adaptation was using technology. People were able to maintain some form of normality in their social lives and received up-to-date information about COVID-19 without placing themselves or their families in danger using digital technology. Despite the severe situation, new technologies presented significant hope for the future by reducing traditional barriers to maintaining social engagement, support exchange, and knowledge collecting. 30 We found coping behaviors aligned to bahala na. Bahala na’s pervasive interpretation of fatalistic resignation originated from Bostrom’s work of American fatalism. 14 When taken in isolation, automatic resignation may seem apparent in this study; however, participants’ bahala na attitude was about determination and risk-taking. Filipinos were assuring themselves that they were prepared to handle the difficult situation before them and would do all possible to achieve their goals by saying bahala na. It is not about pessimism but rather a positive affirmation 31 —showing an internal control that they are ready to deal with the challenges of the pandemic—taking the risk even with uncertainty and possible failure.
Filipino Americans are historically understudied, and to the best of our knowledge, this study is the first to examine the experiences of Filipino Americans with T2D during the pandemic. Many of the participants described the challenges and struggles they experienced in T2D self-management during the pandemic, including feelings of stress and anxiety which are echoed in other COVID-19 studies. Filipino Americans adapted to the changing pandemic environment by seeking different ways of exercising (e.g., using technology) and eating (e.g., cooking at home). Many valued the support from their family as a source of kapwa in preserving their physical and mental health. Many, however, perceived no changes in most of their self-management behaviors, despite the burden of the pandemic. We also situated our discussion using Sikolohiyang Pilipino to ground our understanding of these results as a strength-based rather than a deficit-based model.
There were several limitations to this study. The study was primarily conducted in English, limiting some participants’ expressions of opinions and values. Due to the pandemic, all interviews were conducted online, which may have hampered our ability to assess nonverbal cues that would have provided deeper context for the participants’ responses. The study was also conducted during the early phase of the pandemic; consequently, perceptions and experiences of individuals may have been different from subsequent waves of the pandemic. Although important constructs within a Filipino American context such as acculturative stress and intergenerational family relationships did not emerge as themes in this study, more research is required to uncover the dynamics of these concepts to T2D self-management.
The pandemic subjected different types of stresses among Filipino American individuals with T2D, which has amplified the complexities of T2D self-management. Anxiety, loneliness, and even depression, compounded by the pandemic’s uncertainty, have led to self-management issues. However, many made adaptations in response to the evolving nature of the pandemic. They unraveled how kapwa, bahala na, and pakikibaka can help frame deficit-like experiences as strengths. Clinical implications of this research involve the understanding of how Filipino Americans with T2D manage their self-management behaviors during crisis situations like a pandemic. Examining these values and perceptions could aid in the development of support strategies and standards that are culturally specific. Many of the participants also admitted the feeling of stress and mental health strain associated with the pandemic and living with T2D. With the pervasiveness of stigma surrounding mental health in Filipino American communities, many individuals often avoid seeking help. This study highlights that Filipino Americans can be open to talking about mental health, and clinicians should offer targeted mental health services or interventions to Filipino Americans with T2D. We hope that future research will include more historically marginalized populations in their studies, focus on situating research using culturally relevant theories, and develop interventions that consider lived experiences of their population of interest.
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|
---
title: Extracellular vesicle microRNA and protein cargo profiling in three clinical-grade
stem cell products reveals key functional pathways
authors:
- Ramana Vaka
- Sandrine Parent
- Yousef Risha
- Saad Khan
- David Courtman
- Duncan J. Stewart
- Darryl R. Davis
journal: Molecular Therapy. Nucleic Acids
year: 2023
pmcid: PMC10034570
doi: 10.1016/j.omtn.2023.03.001
license: CC BY 4.0
---
# Extracellular vesicle microRNA and protein cargo profiling in three clinical-grade stem cell products reveals key functional pathways
## Abstract
The cell origin-specific payloads within extracellular vesicles (EVs) mediate therapeutic bioactivity for a wide variety of stem cell types. In this study, we profiled the microRNA (miRNA) and protein cargos found within EVs produced by three clinical-grade stem cell products of different ontogenies being considered for clinical application, namely bone marrow-derived mesenchymal stromal cells (BM-MSCs), heart-derived cells (HDCs), and umbilical cord-derived MSCs (UC-MSCs). Although several miRNAs [757] and proteins [420] were found in common, each producer cell type expressed unique miRNA profiles when the most highly expressed transcripts were compared. Differential expression analysis revealed that BM-MSCs and HDCs were quite similar, while UC-MSCs had the greatest number of unique miRNAs and proteins. Despite these differences, all three EVs promoted cell adhesion/migration, immune response, platelet aggregation, protein translation/stabilization, and RNA processing. EVs from BM-MSCs were implicated in apoptosis, cell-cycle progression, collagen formation, heme pigment synthesis, and smooth muscle differentiation, while HDC and UC-MSC EVs were found to regulate complement activation, endopeptidase activity, and matrix metallopeptidases. Overall, miRNA and protein profiling reveal functional differences between three leading stem cell products. These findings provide a framework for mechanistic exploration of candidate therapeutic molecules driving the salutary effects of EVs.
Comparison of three clinical-grade stem cell products (bone marrow-derived or umbilical cord mesenchymal stromal cells and heart-derived cells) highlights the differences in their extracellular vesicle microRNA and protein cargo, and the bioinformatic analysis on this cargo reveals important insights on the mechanistic understanding of these cell products.
## Graphical abstract
## Introduction
Cell therapies are increasingly being investigated as novel treatments in disease models and patients. Thus far, clinicaltrials.gov reports 35,000+ clinical trials that assess cell therapies in various disease conditions. Recent work has shown these therapeutic benefits are driven by extracellular vesicles (EVs) secreted by transplanted cells.1,2,3,4,5 EVs are small vesicles secreted by nearly every cell in the body. Initially, vesicles were considered to be only a waste disposal system, but accumulating evidence has shown that EVs carry functional cargo (i.e., proteins, RNA, and lipids) that mediate cell-to-cell signaling.6 Since the initial publication of reports outlining the physiological significance of EVs,7,8,9,10,11,12 the number of studies characterizing cargo and functional relevance in preclinical disease models has increased.
The payloads within EVs are thought to be dependent upon tissue cell source as transcripts and proteins differ between studies. But how much of that variability can be attributed to biology as opposed to methodological issues (such as different media/EV collection conditions or protein/transcript quantification + analysis) is not clear. To date, there have been very few direct comparisons between EVs of different origins that use the same methodology.13,14 Despite these apparent differences, EV treatment often has similar salutary effects that are usually attributed to different anti-fibrotic or anti-inflammatory moieties. If true, these cargo differences represent an opportunity to engineer more potent EV pharmaceuticals that can tackle the myriad of known and unknown pathways underlying different diseases or, at the very least, build in additional redundancy to target critical drivers of pathology.
To address this challenge, we profiled the cargo within EVs isolated from three different producer cell lines being considered for clinical applications, namely bone marrow-derived mesenchymal stromal cells (BM-MSCs), heart-derived cells (HDCs), and umbilical cord-derived MSCs (UC-MSCs). To enhance reproducibility and insight into eventual clinical translation, all cell lines were produced in a clinical-grade cell manufacturing facility to good manufacturing practice (GMP) standards using sourced materials. For over 20 years, BM-MSCs have been studied, with multiple reports supporting utility in several diseases.15,16,17,18,19,20,21,22 The BM-MSC lines used in this study were sourced from qualified normal donors, thus avoiding potential adverse effects of medical comorbidities on therapeutic efficacy. Compared with BM-MSCs, UC-MSCs display a more consistent phenotype, higher yields, more rapid growth, and potentially higher therapeutic efficacy (potency).23,24,25 Although there are no head-to-head studies demonstrating therapeutic efficacy, the origin and physiological function of UC-MSCs likely lead to very different EV cargo. HDCs represent an emerging CD45−/CD105+ cell type culture directly from small pieces of heart tissue that confer protection in heart failure26,27,28 and possibly systemic diseases.29 *Forensic analysis* has shown that HDCs have no detectable contribution from extra-cardiac sources,30 thus providing the opportunity to identify unique transcripts and proteins that might be more cardiac specific.
Therefore, we profiled the microRNA (miRNA) and protein cargo within EVs collected from three clinical-grade cell lines using multiplex fluorescent oligonucleotide-based miRNA detection and liquid chromatography-mass spectrometry (LC-MS), respectively. Functional and pathway enrichment analysis was used to probe for important and unique differences attributable to EV cell source.
## Cell culture and EV characterization
A schematic of the experimental methodology is shown in Figure 1A. All three cell products were manufactured to clinical release standards and have been previously characterized for their surface marker identity31,32 and tri-lineage differentiation.33,34 Cell characterization was not repeated in the current study. The quality control parameters of cell products are shown in Table S1. EVs were isolated from conditioned media collected from cultured cells using differential ultracentrifugation. The sizes of EVs isolated from all 3 cell lines were representative of accepted definitions for EV identity35 with no differences attributable to producer cell line (Figure 1B). While BM-MSCs and HDCs produced similar amounts of EVs, UC-MSCs yielded 2-fold more EVs ($p \leq 0.05$ vs. BM-MSCs or HDCs). All 3 EV preparations expressed common EV surface markers (ALIX, ANXAS, CD81, CD63, EPCAM, FLOT-1, TSG101, ICAM) without evidence for cellular contamination (GM130; Figure 1C).Figure 1EV characterization(A) Schematic of experimental methodology utilized in the study. ( B) Representative plots of nanoparticle tracking analysis of EVs. Nanoparticle tracking analysis showed that the concentration and size of EVs isolated from all 3 cell types were representative of accepted definitions for EV identity. While the EV concentration was similar in BM-MSCs and HDCs, UC-MSCs produced 2-fold higher EVs in comparison. ( C) Proteomic antibody array showed the presence of 8 known EV markers (ICAM, ALIX, CD81, CD63, EPCAM, ANXAS, TSG101, FLOT-1) and the absence of bands for cis-Golgi marker (GM130) suggesting that EV preparations were free of cellular contaminants. All data are presented as individual and mean values ± SEM, $$n = 3$$ biological replicates; each filled circle on the bar graph represents one data point from one unique biological replicate. Differences between cell types were analyzed by one-way ANOVA. ∗$p \leq 0.05$ compared with BM-MSC or HDC EVs. BM-MSC, bone marrow-derived mesenchymal stromal cells; HDC, heart-derived cells; UC-MSC, umbilical cord-derived mesenchymal stromal cells.
## EV miRNA and protein cargo profiling within BM, heart, and UC EVs
As shown in Figure 2A, 757 miRNAs were commonly expressed by all 3 EVs. Interestingly, UC-MSCs were found to express the most distinct EVs with 18 unique miRNAs. Within the highly expressed miRNAs (i.e., expression above the 99th percentile), 4 miRNAs were found to be commonly expressed among all 3 EVs (miR-199a+miR-199b, miR-23a, miR-4454+miR-7975, miR-125b-5p). Two miRNAs (let-7a, let-7b) were shared between BM-MSC and HDC EVs, while 1 miRNA (miR-100) was found in both HDC and UC-MSC EVs (Figure S1A).Figure 2MicroRNA and proteomic profiling of EVsNanoString miRNA and LC-MS protein LFQ data were processed for background subtraction, normalization, and log2 transformation using ROSALIND and Perseus (https://maxquant.net/perseus/), respectively. The normalized mean data were visualized using Venn diagrams and rank order plots. The highly abundant (above the 99th percentile) miRNAs and proteins are highlighted (blue filled circles) on the rank order plots. ( A) Venn diagram and rank order plots showing distinct number of miRNAs identified in three stem cell types. ( B) Venn diagram and rank order plots showing distinct number of proteins identified in three stem cell types. The distinct highly abundant (above the 99th percentile) miRNAs and proteins between cell types are visualized using in Venn diagrams shown in Figure S1. Data are presented as mean values, $$n = 3$$ biological replicates; each circle on the rank order plots represents one distinct miRNA or protein. miRNA, microRNA; LFQ, label-free quantitation; BM-MSC, bone marrow-derived mesenchymal stromal cells; HDC, heart-derived cells; UC-MSC, umbilical cord-derived mesenchymal stromal cells.
As shown in Figure 2B, 420 proteins were expressed in all 3 cell types. In contrast to the few miRNAs uniquely expressed, all 3 EVs were found to contain several unique proteins (i.e., 100+), with the greatest number of shared proteins found in BM-MSCs and HDCs. UC-MSCs expressed the greatest number of unique proteins. When ranked as highly expressed proteins, only ACTG1 protein was shared above the above 99th percentile in EVs from 3 cell types, while ANXA2 and FN1 were shared between BM-MSC and HDC EVs (Figure S1B).
## Differential expression of miRNAs or proteins within BM, heart, and UC EVs
To probe the miRNA stoichiometry for differences in expression patterns, we compared differential miRNA expression using a 1.5-fold log2 threshold (Figure 3; Tables S2, S3, and S4). Expression of miRNA in BM-MSC and HDC EVs was largely similar with only 56 differentially regulated transcripts. In contrast, UC-MSC EV expression was markedly different with 176 and 314 differentially expressed miRNAs compared with BM-MSC and HDC EVs, respectively. Analysis of protein expression showed a similar pattern, with only 23 proteins showing differential expression when BM-MSC EVs were compared with HDC EVs (Figure 4; Tables S5, S6, and S7). UC-MSC EVs had 305 and 260 differential expressed proteins when compared with BM-MSC and HDC EVs, respectively. Figure 3Differential expression of microRNAs from EVsDifferential expression analysis of miRNAs between cell types was performed using ROSALIND t test method. p value adjustment was performed using the Benjamini-Hochberg method of estimating false discovery rates (FDRs). miRNAs were considered differentially expressed with a log2 fold change ≥ or ≤1.5 and $p \leq 0.05.$ Log2 fold change and p values were exported from ROSALIND to construct volcano plots and heatmaps using GraphPad Prism v.9.1 and RStudio (pheatmap package), respectively. ( A) Volcano plot showing 20 downregulated and 36 upregulated miRNA transcripts, and heatmap showing the significant differentially expressed miRNAs in HDC vs. BM-MSC EVs. ( B) Volcano plot showing 12 downregulated and 164 upregulated miRNA transcripts, and heatmap showing the significant differentially expressed miRNAs in BM-MSC vs. UC-MSC EVs. ( C) Volcano plot showing 15 downregulated and 299 upregulated miRNA transcripts, and heatmap showing the significant differentially expressed miRNAs in HDC vs. UC-MSC EVs. The list of differentially expressed miRNAs is provided in Tables S1, S2, and S3. $$n = 3$$ biological replicates. BM-MSC, bone marrow-derived mesenchymal stromal cells; HDC, heart-derived cells; UC-MSC, umbilical cord-derived mesenchymal stromal cells. Figure 4Differential expression of proteins from EVsDifferential expression analysis of proteins between cell types was performed using Perseus (https://maxquant.net/perseus/). Proteins identified in at least 2 of 3 replicates were considered for analysis. Two-sample two-tailed Student’s t test with permutation-based FDR (FDR = 0.05, no. of randomizations = 250) was used to calculate statistical significance between cell types. The proteins were considered differentially expressed with a $p \leq 0.05.$ Log2 difference and p values were exported from Perseus to construct volcano plots and heatmaps using GraphPad Prism v.9.1 and RStudio (pheatmap package), respectively. ( A) Volcano plot showing 9 downregulated and 14 upregulated proteins, and heatmap showing the significant differentially expressed proteins in HDC vs. BM-MSC EVs. ( B) Volcano plot showing 198 downregulated and 107 upregulated proteins, and heatmap showing the significant differentially expressed proteins in BM-MSC vs. UC-MSC EVs. ( C) Volcano plot showing 141 downregulated and 119 upregulated proteins, and heatmap showing the significant differentially expressed proteins in HDC vs. UC-MSC EVs. The list of differentially expressed proteins is provided in Tables S4, S5, and S6. $$n = 3$$ biological replicates. BM-MSC, bone marrow-derived mesenchymal stromal cells; HDC, heart-derived cells; UC-MSC, umbilical cord-derived mesenchymal stromal cells.
## Functional analysis of miRNA expression within BM, heart, and UC EVs
As shown in Figure 5, HDC EVs expressed miRNA transcripts implicated in apoptosis, collagen formation, osteoblast differentiation, and cell proliferation when compared with EVs from BM-MSCs. Gene Ontology (GO) enrichment analysis demonstrated that miRNA-mRNA targets were significantly enriched in pigment biosynthesis, mRNA catabolism, and RNA processing. miRNAs highly expressed in HDC EVs were associated with differentiation, cardiac regeneration, and differentiation, while miRNAs highly expressed in BM-MSC EVs were involved in collagen formation, cellular proliferation, and cell death. Figure 5Functional enrichment analysis of EV miRNA cargoTAM 2.0 (http://www.lirmed.com/tam2/) was used to perform miRNA functional enrichment analysis. The list of mature miRNA names from normalized ROSALIND data of all differentially expressed miRNAs and up- or downregulated miRNAs was used as input (overrepresentation, p ≤ 0.05) for enrichment analysis. TAM 2.0 data output: enriched term, fold enrichment, p values, and miRNA count are graphed as bubble plots using GraphPad Prism v.9.1. Further, Gene Ontology (biological process) enrichment analysis on experimentally validated mRNA targets of differentially expressed miRNAs was performed. The differentially expressed miRNA list was used as an input in the miRWalk v.3 (http://mirwalk.umm.uni-heidelberg.de/) (interaction probability score = 0.95, miRTarBase, 3′ UTR) to obtain the target information (Tables S8–S10) and then imported to Cytoscape to visualize networks (Figure S2), and enrichment analysis was performed using Cytoscape plugin BINGO (overrepresentation, hypergeometric test with Benjamini-Hochberg FDR correction, $$p \leq 0.05$$). ( A–C) The top 10 significantly enriched biological functions and Gene Ontology biological processes of all differentially expressed miRNAs among three cell products. ( D–I) The top 10 significantly enriched biological functions of enriched miRNAs in one cell type compared with other cell types. Significantly enriched terms related to transcription factors and tissue specificity are presented in Figure S3. The list of differentially expressed miRNA-mRNA targets is provided in the Tables S7–S9. $$n = 3$$ biological replicates. Activ, activity; BM-MSC, bone marrow-derived mesenchymal stromal cells; Dev, development; Diff, differentiation; HDC, heart-derived cells; MET, mesenchymal-epithelial transition; UC-MSC, umbilical cord-derived mesenchymal stromal cells.
EVs from BM-MSCs were enriched in transcripts implicated in apoptosis, hematopoiesis, and retinal development when compared with EVs from UC-MSCs. GO analysis of transcripts from BM-MSC EVs suggest involvement in glucose metabolism, protein transport, RNA processing, and vacuole transport. miRNAs upregulated in BM-MSC EVs were involved in apoptosis, cell migration, and osteogenesis, while miRNAs enriched within UC-MSCs were involved in aging, cardiac regeneration, and proliferation.
When HDC EVs were compared with UC-MSCs EVs, miRNAs were implicated in development, immune regulation, and proliferation. GO analysis of transcripts from HDC EVs were implicated in glucose metabolism, protein transport, RNA processing, and vacuole transport. Highly expressed miRNAs in HDC EVs were involved in mesenchymal-to-epithelial transition, regulation of nuclear factor κB (NF-κB), and development, while highly expressed miRNAs in UC-MSCs were involved in aging, inflammation, and proliferation. The downstream transcription factors and tissue associated with these functions for EVs from all 3 producer cell lines are shown in Figure S3.
## Functional analysis of protein expression in BM, heart, and UC EVs
Enrichment analysis of the protein cargo within EVs revealed several terms related to biological processes, cellular components, and molecular functions for each cell source (Table S11). The top 10 biological processes of whole proteome for each source are shown in Table S12. The top 10 biological processes, cellular components, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of differential expressed proteins are shown in Figures S4 and S5. Unsurprisingly, many of the top terms were shared and related to EV biology or cellular binding (i.e., cell adhesion, extracellular exosome, focal adhesion, cadherin binding, and protein binding). When EVs from different cell types were compared, each producer cell line imparted several unique terms, with the greatest degree of homology shared between BM-MSC and HDC EVs. When HDC EVs were compared with BM-MSC EVs, functional pathways were largely involved in calcium ion binding, cell binding, and extracellular matrix assembly (Figure 6). Unsurprisingly, proteins overexpressed in EVs produced by BM-MSCs were more apt to be implicated in heparin or integrin binding. When BM-MSC EVs were compared with UC-MSC EVs, differentially expressed proteins were involved in actin organization, cadherin binding, and cellular adhesion. Proteins highly expressed within BM-MSC EVs were involved in protein + RNA binding, while UC-MSC EVs’ highly expressed proteins were in extracellular matrix binding/structure and low-density lipoprotein (LDL) receptor biology. When HDC EVs were compared with UC-MSC EVs, proteins were implicated in calcium and protein binding. Figure 6Functional enrichment analysis of EV protein cargoFunctional enrichment analysis of the whole proteome and differentially expressed proteins was performed using DAVID v.6.8 (https://david.ncifcrf.gov/) (Homo sapiens proteome as a background, Fisher’s exact test with multiple testing by the Benjamini-Hochberg method with adjusted $$p \leq 0.05$$). The significantly enriched functional Gene Ontology (GOP) terms of biological process (BP), cellular component (CC), and molecular function (MF) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were extracted and graphed as bubble plots using GraphPad Prism v.9.1. ( A–C) The top 10 significantly enriched molecular functions of all differentially expressed proteins among three cell products. ( D–I) The top 10 significantly enriched molecular functions of enriched proteins in one cell type compared with other cell types. The top 10 significantly enriched biological processes, cellular functions, and KEGG pathways in differentially expressed proteins and in enriched proteins in one cell type compared with other cell types are presented in Figures S4 and S5. $$n = 3$$ biological replicates. BM-MSCs, bone marrow-derived mesenchymal stromal cells; ECM, extracellular matrix; GDP, guanosine diphosphate; GTP, guanosine triphosphate; HDC, heart-derived cells; LDL, low-density lipoprotein; Macromol.cmpx, macromolecule complex; MHC, myosin heavy chain; Protein. Ppase, protein phosphatase; UC-MSC, umbilical cord-derived mesenchymal stromal cells.
## Protein-protein interactions imparted by BM, heart, and UC EVs
Proteins regulate biological processes through functional or physical interactions with other proteins. Using protein-protein interaction network analysis, we probed for potential interactions between differentially expressed proteins. As shown in Figure S6, this analysis revealed several nodes and interaction pairs, with the highest degree of homology shared between HDC and BM-MSC EVs. Key nodes were then used to extract hub genes for cluster analysis which yielded 1 cluster from the HDC vs. BM-MSC EV network, 9 clusters from the BM-MSC vs. UC-MSC EV network, and 7 clusters from the HDC vs. UC-MSC EV network.
Using a topological scoring system,36 we found that the HDC vs. BM-MSC EV network did not contain significant clusters, indicating a high degree of homology.
Two significant clusters were found within the comparison between BM-MSC and UC-MSC EVs (topological score = 19.36, 20 nodes, 184 interaction pairs). Analysis of cluster 1 revealed hub genes related to chaperon-containing proteins (CCT3, degree = 32; CCT7, degree = 34; CCT8, degree = 26) and ribosomal protein subunits (RPS3, degree = 28; RPL11, degree = 25; Figure 7A). String enrichment analysis indicated that the top biological processes were related to protein localization to telomeres (GO: 1904851), protein localization to Cajal bodies (GO: 1904871), and telomerase localization to Cajal bodies (GO: 1904874). Cluster 2 contained 57 nodes and 668 interaction pairs, with the top 5 hub genes being related to chaperon containing proteins (CCT3, degree = 32; CCT4, degree = 33; CCT7, degree = 34), nucleotide-binding protein (GNB2L1, degree = 34), and translation elongation factor (EEFF2, degree = 39). The top 2 biological process terms identified related to cell localization (GO: 0051649) and interspecies interaction between organisms (GO: 0006810).Figure 7Protein-protein interaction (PPI) network and GO-BP enrichment analysis of differentially expressed proteins in EVsSTRING v.11.5 (https://string-db.org/cgi/input.pl) was used to analyze protein-protein interaction (PPI) analysis of differentially expressed proteins (medium confidence score = 0.4). STRING PPI network was used for cluster and GO (BP) enrichment analysis using Cytoscape plugin MCODE (https://apps.cytoscape.org/apps/MCODE) (degree cutoff = 2, cluster finding: haircut, node score cutoff = 0.2, K-core = 2, maximum [max.] depth = 100). CytoHubb plugin was used to extract to hub genes in the PPI network clusters. ( A) Significant clusters from the PPI network and enriched GO-BP terms of BM-MSC vs. UC-MSC differentially expressed proteins. ( B) Significant clusters from the PPI network and enriched GO-BP terms of HDC vs. UC-MSC differentially expressed proteins. The orange diamonds indicate nodes, gray lines indicate edges, and the blue diamonds indicate hub node genes. The PPI networks of differentially expressed proteins among cell types are presented in Figure S6.
The comparison between HDC and UC-MSC EVs yielded 2 clusters, with cluster 1 (score = 17.6) containing 31 nodes and 264 interaction pairs (Figure 7B). The top 5 hub genes within cluster 1 were related to copper binding (CP, degree = 22), iron biding transport (TF, degree = 22), thyroid hormone-binding (TTR, degree = 22), and vitamin D transport (GC, degree = 22). The top biological process terms within cluster 1 were implicated in exocytosis (GO: 0045055), platelet degranulation (GO: 0002576), and vesicle transport (GO: 0016192). Cluster 2 contained 30 nodes and 169 interaction pairs. The top hub genes within this cluster were related to collagen (COL1A1, degree = 8; COL1A2, degree = 17), glycoprotein (THBS1, degree = 17), and matrix metallopeptidases (MMP2, degree = 19; MMP14, degree = 16). String enrichment analysis within this cluster showed that proteins mediated cell adhesion (GO: 0007155), extracellular matrix organization (GO:0030198), and formation of the primary germ layer (GO: 0001704).
## Discussion
Recent work suggests that the therapeutic effects of cell treatment are partially attributable to delivery of the biological cargo within EVs secreted by transplanted cells.37 *In this* study, we analyzed the miRNA transcriptome and proteome from three leading stem cell products under clinical investigation for several diseases, including cardiovascular diseases and inflammation-mediated pathologies.
Interestingly, we found that the cargo within EVs was often much more similar than different. Several miRNAs and proteins commonly expressed in all three cell types were related to cell adhesion, cell proliferation, immune response, platelet aggregation, and protein translation/stabilization. Although many of the same miRNAs and proteins were present in EVs from all three cell types, stoichiometry between different EVs was often markedly different. For example, miR-29b is found to be highly expressed in HDC EVs (ranked #7) compared with other cell types: BM-derived stromal cell EVs (ranked #51) or UC-derived stromal cell EVs (ranked #42). Despite these differences, EVs from all three cell types highly expressed six miRNAs (miR-23a, -125b, -199a, -199b, -4454, and -7975). Functional enrichment analysis of these six miRNAs (data not shown) reveals that they are involved in endocytosis, immune response, inflammation, osteogenesis, osteoblast differentiation, and cell proliferation. Previous studies have shown that stromal cells of mesenchymal origin and their EVs regulate inflammation and bone formation and angiogenesis.38,39,40,41 These salutary effects can partially be attributed to abundant expression of these 6 common miRNAs in these cells. All three EVs were found to contain high levels of the ubiquitous protein actin gamma 1 (ACTG1), which is involved in cytoskeleton organization. When the differential expression of miRNAs and proteins was analyzed, BM-MSC and HDC EVs appeared to be more similar than UC-MSC EVs, a finding that is consistent with their similar physiologic purpose (i.e., organ repair vs. reproductive biology).
There were also transcripts and proteins characteristic of each EV type. HDC EVs were the only source for miR-29b, EGF-like repeats and discoidin domains 3 (EDIL3), Fibulin 1 (FBLN1), haptoglobin (HP), and Versican (VCAN). BM-MSC EVs were marked by miR-155, miR-877, annexin A6 (ANXA6), enolase 1 (ENO1), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), hemoglobin subunit alpha 1 (HBA1), and vimentin (VIM). UC-MSC EVs were the only source for miR-765, miR-28, miR-223, α-2-macroglobulin (A2M), clusterin (CLU), fibrinogen β chain (FGB), and gelsolin (GSN).
When compared with the few transcriptional profiling reports in the literature,42,43,44,45 our findings mirrored those studies. For example, a recent miRNA profiling study of UC-MSC EVs found several highly abundant commonalities (miRs-16, -21, -23b, -25, -34, -146a, and -222) that predicted involvement in immune regulation and proliferation.42 Baglio et al. showed that BM-MSC EVs contained several miRNA species (miRs-21, -22, −26a, -10b, -99b, -125b, and -148a)46 that were found in the top miRNAs expressed in BM-MSC EVs in our study. Interestingly, a recent study by Liao et al. highlighted the functional role of BM-MSC EV-derived miR-122 in promoting osteoblast proliferation.47 We found miR-122 within the top miRNAs in our BM-MSC EVs, and functional enrichment analysis confirmed overrepresentation of osteoblast and osteogenesis terms.
In terms of proteomic comparisons, Wang et al. recently performed a comprehensive proteomic analysis of EVs from adipose, BM, and UC-MSCs. In this report, BM-MSC EVs expressed 771 proteins that were largely associated with cadherin binding and protein biosynthesis, while UC EVs expressed 431 proteins that were associated with calcium ion binding, extracellular matrix formation, and integrin signaling/adhesion.48 Sixty proteins were found in both EV sources. While their functional enrichment findings are largely in line with our results, one distinction is that our analysis revealed slight variations in protein abundance that are likely attributable to differences in donor selection, cell manufacturing, and EV isolation.
It is challenging to speculate which producer cell line might offer superior therapeutic efficacy given the complexity of several (magnitude of 100s) biologically active molecules found within EVs. The functional comparison between different subtypes suggests that some EVs may be better suited toward specific applications (e.g., anti-fibrotic effects by HDC EVs in reducing post-infarct scarring). Understanding the differential expression of proteins or transcripts opens the possibility for engineering producer cell lines to boost functional effects (e.g., increasing miR-146a in BM-MSCs to improve BM-MSC EV pro-angiogenic effects). Akin to using a mixture of different grass seeds to reseed a lawn, combining different EV subtypes as a single therapy to boost efficacy might be the most practical and cost-effective way to improve treatment outcomes. This would ensure that different signaling pathways are recruited to maximize benefit. From our analysis, the combination of UC-MSC EVs with either BM-MSC EVs or HDC EVs would be the most logical choice given the miRNA and protein similarities between BM-MSC and HDC EVs.
Our study has several limitations that include [1] a small sample size, limited to three biological replicates from each cell product; [2] reliance on candidate miRNA profiling that includes only commonly expressed miRNAs; [3] target network and enrichment analysis confined to experimentally validated 3′ UTR targets, although recent work has shown that targeting may expand to the 5′ UTR and coding sequence regions of mRNA; [4] no evidence for other non-coding RNAs, which may play important roles in mediating EV effects; [5] no functional tests to confirm if the observed differences in protein or transcripts translate into meaningful differences in function; and [6] inclusion of $1\%$ platelet lysate during generation of conditioned media by UC-MSCs, which may have led to trace contamination of platelet-derived vesicles. Future work will be needed to expand this work to other non-coding RNAs, to other targeting regions, and to confirmatory validation in functional tests using up- or downregulation of candidate targets.
In summary, we profiled miRNA and protein cargo from three leading EV products under investigation. We identified several common miRNA transcripts and proteins and some unique that were in each donor cell type. Further, we found that these unique cargos display distinct functional enriched categories and pathways that offered mechanistic understanding of existing studies showing EV-mediated benefit. Overall, our study characterized EV miRNA and proteome and displayed functional significance of this cargo, which will further a great mechanistic understanding of the EV therapeutic effects in various models of disease.
## Cell culture and EV isolation
BM-MSCs were isolated from BM samples collected from healthy volunteers enrolled in the Cellular Immunotherapy for Septic Shock (CISS) trial under protocols approved by the Ottawa Hospital Research Ethics Board.33 HDCs were isolated from atrial appendage tissue collected from patients undergoing clinically indicated surgery under protocols approved by the University of Ottawa Heart Institute Research Ethics Board.32,49,50 A protocol was developed to isolate and culture UC-MSCs from UCs collected during scheduled C-sections performed at The Ottawa Hospital under protocols approved by the Ottawa Hospital Research Ethics Board.33 All cell products were manufactured to clinical-grade release standards in Biospherix units at The Ottawa Hospital Cell Manufacturing Facility. BM-MSCs were cultured in NutriStem XF media (Sartorius) under $21\%$ oxygen conditions.33 HDCs were cultured in NutriStem XF media at $5\%$ oxygen conditions.32 UC-MSCs were cultured in Dulbecco’s modified *Eagle medium* (Thermo Fisher Scientific) with $10\%$ clinical grade platelet lysate (Mill Creek Life Sciences) at $5\%$ oxygen conditions. When cells reached $70\%$ confluency, culture media were replaced with condition media (BM-MSCs and HDCs: NutriStem XF basal media; UC-MSCs: Dulbecco’s modified *Eagle medium* with high glucose and $1\%$ platelet lysate). After 48 h of conditioning at $1\%$ oxygen, media were collected for EV isolation. EVs were isolated using ultracentrifugation (10,000g × 30 min and 100,000g × 3 h).51,52
## Nanoparticle tracking system
The size and concentration of EV preparations were analyzed using NanoSight LM10 equipped with a blue laser (488 nm, 70 mW) with an sCMOS camera. Briefly, 1 μL final pellet suspension was diluted at 1:1,000 in saline, and 500 μL was loaded into the sample chamber. Three videos of 60 s were recorded for each sample. Data analysis was performed with NTA 3.0 software (Nanosight).
## EV proteomic antibody array
EV markers were characterized by using proteomic array as per the manufacturer’s recommendations (EXORAY200A; System Biosciences). In brief, 50 μg EV lysate was incubated with labeling reagent for 30 min, followed by incubation with membrane precoated antibodies for 8 known EV markers. After overnight incubation, detection buffer added before membranes were washed and scanned with X-ray imager.
## miRNA expression assay
miRNA was isolated from EVs using the appropriate miRNA isolation kit (Qiagen). 100 ng miRNA was used for the nCounter miRNA sample preparation reactions. Sample preparation was performed according to the manufacturer’s instructions. All hybridization reactions were incubated at 65°C for a minimum of 18 h. Hybridized probes were purified and counted on the nCounter Prep Station and Digital Analyzer. For each assay, a high-density scan (600 fields of view) was performed. The miRNA count data obtained from NanoString were analyzed by ROSALIND (https://rosalind.bio/). Background subtraction was performed based on POS_A probe correction factors, and normalization was performed using the geometric mean of each code set from the positive control normalization and code set normalization.
After normalization, fold changes were calculated, and comparisons between two EV types were assessed using the ROSALIND t test method. p value adjustment was performed using the Benjamini-Hochberg method of estimating false discovery rates (FDRs). Log2 fold changes, p values, and normalized log2 count data were exported from ROSALIND to construct volcano plots and heatmaps using GraphPad Prism v.9.1 and RStudio (pheatmap package), respectively.
miRNA functional enrichment analysis was analyzed using TAM 2.0 (http://www.lirmed.com).53,54 The list of mature miRNA names from the normalized ROSALIND data was used as input (overrepresentation, p ≤ 0.05). Three functional enrichment categories were extracted (i.e., function, tissue specificity, and transcription factor) to ascribe the functional significance of the miRNA cargo found within EVs. To delineate functional differences pertaining to miRNA cargo across all 3 EV types, we performed enrichment analysis using the list of all differentially expressed miRNAs and all upregulated miRNAs using TAM 2.0.
Target network and enrichment analysis was confined to validated 3′ UTR targets. The list of differentially expressed miRNAs was used as input to miRWalk v.3 (http://mirwalk.umm.uni-heidelberg.de/)55 using an interaction probability score of 0.95 (miRTarBase) to obtain the miRNA-mRNA target network. Cytoscape was then used to visualize networks and perform enrichment analysis.55 GO enrichment analysis was performed using the Cytoscape plugin BINGO (overrepresentation, hypergeometric test with Benjamini-Hochberg FDR correction, $$p \leq 0.05$$).
## EV proteomic analysis
EV isolates containing 25 μg protein were lysed using a solubilization buffer consisting of 8 M urea, 100 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES), $5\%$ glycerol, and $0.5\%$ n-dodecyl β-d-maltoside (DDS). Samples were reduced using Tris(2-carboxyethyl) phosphine (1.6 mM) and then alkylated with iodoacetamide (8 mM) for 55 min at room temperature. Proteins were digested using 0.45 μg trypsin/Lys-C solution (Promega) at room temperature for 20 h. 2 μL formic acid was then added to the samples, which were then desalted using C18 TopTips (Glygen) columns and finally vacuum dried. 5 μg protein were then analyzed by Orbitrap Fusion MS (Thermo Fisher Scientific).56 Peptides were separated by an in-house packed column (Polymicro Technology) using a water/acetonitrile/$0.1\%$ formic acid gradient. Samples were loaded onto the column for 105 min at a flow rate of 0.30 μL/min. Peptides were separated using successive rounds of acetonitrile at concentrations $2\%$–$90\%$ in a stepwise manner every 10 min. Peptides were eluted and sprayed into a mass spectrometer using positive electrospray ionization at an ion source. Peptide MS spectra (m/z 350–2,000) were acquired at a resolution of 60,000. Precursor ions were filtered according to monoisotopic precursor selection, and dynamic exclusion (30 s ± 10 ppm window). Fragmentation was performed with collision-induced dissociation in the linear ion trap. Precursors were isolated using a 2 m/z isolation window and fragmented with a normalized collision energy of $35\%$.
Differential protein expression analysis was performed using Perseus (https://maxquant.net/). Label-free quantitation values were log2 transformed for visual inspection using histogram distribution plots for each sample. Proteins identified in at least 2 of the 3 replicates were considered for analysis. A two-tailed Student’s t test with permutation-based FDR was used to calculate statistical significance between the two EV types. The log2 fold difference, p values, and log2 label-free quantitation values were used to make volcano plots and heatmaps using GraphPad Prism v.9.1 and RStudio (pheatmap package), respectively.
Functional annotations and pathway enrichment analysis of the whole proteome and differentially expressed proteins was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID; v.6.8, https://david.ncifcrf.gov/) with Homo sapiens proteome as a background.57,58 Fisher’s exact test with multiple testing by the Benjamini-Hochberg method with an adjusted p value of 0.05 was used to extract significantly enriched terms. The top 10 functional GO terms of biological process, cellular component, molecular function, and KEGG pathways were extracted, with enrichment analysis performed on all proteins and upregulated proteins separately.
Protein-protein interaction analysis of differentially expressed proteins was performed using Search Tool for the Retrieval of Interacting Genes (STRING; v.11.5, https://string-db.org/cgi/input.pl) with a medium confidence score of 0.4.59 Cytoscape was then used to perform cluster and enrichment analysis. The Cytoscape plugin MCODE was used to perform cluster and enrichment analysis (degree cutoff = 2, cluster finding = haircut, node score cutoff = 0.2, K-core = 2, maximum depth = 100), while the cytoHubb plugin was used to identify to hub genes.
## Statistical analysis
All statistical tests and graphical depictions of data are defined within the respective materials and methods sections. Unless otherwise stated, all data are presented as mean ± standard error of the mean. To determine if differences existed in EV size and concentration between cell types, the data were analyzed by a one-way analysis of variance (ANOVA; GraphPad Prism v.9.1) with post hoc testing using Tukey’s multiple comparisons test. A final value of p ≤0.05 was considered significant for all analyses.
## Data availability
Data are available upon reasonable request.
## Supplemental information
Document S1. Figures S1–S6 and Tables S1, S2, S5, S11, and S12 Data S1. Tables S3, S4, and S6–S10 Document S2. Article plus supplemental information
## Author contributions
Conception and design, R.V. and D.R.D.; collection of data, R.V., S.P. and Y.R.; data analysis, R.V., S.P., Y.R., S.K., and D.R.D.; data interpretation, R.V., D.C., S.K., D.J.S., and D.R.D.; manuscript writing, R.V. and D.R.D.; final approval of manuscript, R.V., D.C., D.J.S., and D.R.D.; provision of study material, S.K. and D.R.D.; financial support, D.R.D.
## Declaration of interests
D.R.D., D.C., and D.J.S. are co-inventors for a patent regarding serum-free xenogen-free culture of HDCs.
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|
---
title: Engineered probiotics Clostridium butyricum‐pMTL007‐GLP‐1 improves blood pressure
via producing GLP‐1 and modulating gut microbiota in spontaneous hypertension rat
models
authors:
- Xin‐liang Wang
- Wen‐jie Chen
- Rui Jin
- Xuan Xu
- Jing Wei
- Hong Huang
- Yan‐hua Tang
- Chang‐wei Zou
- Ting‐tao Chen
journal: Microbial Biotechnology
year: 2022
pmcid: PMC10034621
doi: 10.1111/1751-7915.14196
license: CC BY 4.0
---
# Engineered probiotics Clostridium butyricum‐pMTL007‐GLP‐1 improves blood pressure via producing GLP‐1 and modulating gut microbiota in spontaneous hypertension rat models
## Abstract
Hypertension is a significant risk factor of cardiovascular diseases (CVDs) with high prevalence worldwide, the current treatment has multiple adverse effects and requires continuous administration. The glucagon‐like peptide‐1 receptor (GLP‐1R) agonists have shown great potential in treating diabetes mellitus, neurodegenerative diseases, obesity and hypertension. Butyric acid is a potential target in treating hypertension. Yet, the application of GLP‐1 analogue and butyric acid in reducing blood pressure and reversing ventricular hypertrophy remains untapped. In this study, we combined the therapeutic capability of GLP‐1 and butyric acid by transforming *Clostridium butyricum* (CB) with recombinant plasmid pMTL007 encoded with hGLP gene to construct the engineered probiotics Clostridium butyricum‐pMTL007‐GLP‐1 (CB‐GLP‐1). We used spontaneous hypertensive rat (SHR) models to evaluate the positive effect of this strain in treating hypertension. The results revealed that the intragastric administration of CB‐GLP‐1 had markedly reduced blood pressure and improved cardiac marker ACE2, AT2R, AT1R, ANP, BNP, β‐MHC, α‐SMA and activating AMPK/mTOR/p70S6K/4EBP1 signalling pathway. The high‐throughput sequencing further demonstrated that CB‐GLP‐1 treatments significantly improved the dysbiosis in the SHR rats via downregulating the relative abundance of Porphyromonadaceae at the family level and upregulating Lactobacillus at the genus level. Hence, we concluded that the CB‐GLP‐1 greatly improves blood pressure and cardiomegaly by restoring the gut microbiome and reducing ventricular hypertrophy in rat models. This is the first time using engineered CB in treating hypertension, which provides a new idea for the clinical treatment of hypertension.
Clostridium butyricum‐pMTL007‐GLP‐1 improved blood pressure, cardiac markers, and dysbiosis in SHR models. The therapeutic effects are mainly contributed to GLP‐1 and butyrate secreted by the engineered probiotics.
## INTRODUCTION
Hypertension is a serious chronic disease affecting $23.2\%$ of adults in China (Wang et al., 2018) and more than 1.38 billion people worldwide (Mills et al., 2016). The high and ongoing prevalence and medication expenditures of hypertension had been causing a heavy burden to society and patients themselves. The most important clinical feature of hypertension is systolic blood pressure (BP) above 140 mmHg and/or diastolic BP above 90 mmHg (Poulter et al., 2015). The prolonged hypertensive state will lead to functional alteration of a variety of organs and tissues thus, in turn, increasing the risk of multiple diseases (Poulter et al., 2015). Hypertension has been identified as one of the major risk factors for many cardiovascular diseases such as myocardial infarction (MI), coronary heart disease (CHD) and apoplexy (van Oort et al., 2020). Moreover, apart from renal diseases and ocular hypertension, recent studies have also found correlation between hypertension with certain extra‐cardiovascular diseases including Alzheimer's disease (Carnevale et al., 2020; Kivipelto et al., 2018) and diabetes mellitus (Joseph et al., 2021; Zhang, Hou, et al., 2020; Zhang, Nie, et al., 2020).
However, hypertension is a multifactorial disease with vague understanding of its aetiology. The pathogenesis of hypertension is shown to be highly related to lifestyle, genetics, environment and mental state (Poulter et al., 2015). In accordance with the clinical feature and pathophysiology, the treatment of hypertension usually involves balanced diet, appropriate exercise and drug medication (Whelton et al., 2018). Current first‐line antihypertensive drugs are mainly α/β receptor antagonists, angiotensin‐converting enzyme inhibitors (ACEi), angiotensin II receptor blockers (ARBs), calcium channel blockers (CCB), diuretics, direct renin inhibitors and direct vasodilators (Whelton et al., 2018). Nevertheless, the aforementioned drugs require long‐term administration and have many adverse effects (Tsioufis & Thomopoulos, 2017).
Many studies indicated the significance of intestinal microecology in regulating BP (Lynch & Pedersen, 2016). Improvement of cardiovascular function mediated by gut microbiota is mainly due to neuro‐regulation via brain‐gut axis, immunoregulation and bioactive metabolite (Avery et al., 2021; Santisteban et al., 2016; Touyz & Camargo, 2019). Most notably, butyric acid, a representative of short‐chain fatty acids (SCFAs), is a bioactive metabolite generated by beneficial bacteria and is highly active in regulating BP, dilating vessels and reducing inflammatory reactions (Avery et al., 2021). Recent studies have shown that BP normalisation induced by change of lifestyle is attributed to elevated butyric acid from intestinal microecological restoration (Maifeld et al., 2021; Touyz, 2021). The microbial alteration of hypertension patients is manifested by a reduction of Akkermansia strains as well as overgrowth of Prevotella and Klebsiella strains at the genus level, which leads to marked reduction of serum butyric acid (Li et al., 2017; Naqvi et al., 2021). Another research about Australian people had been demonstrated that hypertensive patients were associated with increased *Clostridium and* Prevotella while normal people with lower BP have more Alistipesfinegoldii and Lactobacillus at genus level (Dinakis et al., 2022). It is suggested that butyrate has much stronger therapeutic effect than other SCFAs (Muralitharan et al., 2020). Clostridium butyricum (CB) is a probiotic bacterium with great ability to pass gastrointestinal tract and secret butyric acid, which has been preclinically applied to treat obstructive sleep apnoea‐associated hypertension (Ganesh et al., 2018). Yet, the direct effect of CB on hypertension remained unclear.
Glucagon‐like peptide‐1 (GLP‐1), a low half‐life molecule secreted by enteral L cells, has been cultivating its anti‐diabetic and weight‐reducing function (Drucker, 2018). Furthermore, studies have pointed out the potential of GLP‐1 in improving BP (Li et al., 2017). Currently, GLP‐1 receptor (GLP‐1R) agonists and dipeptidyl peptidase‐4 inhibitors (DPP‐4i) are approved clinically. However, GLP‐1 analogues such as liraglutide, exenatide, exenatide microspheres and duratide require periodic injection, leading to low compliance. In addition, DPP‐4i may lead to severe pancreatitis and certain types of DPP‐4i could cause cardiac and hepatic deterioration (Lee et al., 2019; Scheen, 2018; Sinha & Ghosal, 2019). Thus, developing highly compliant GLP‐1‐targeted drugs with minor adverse effects is demanded. In our previous studies, we presented the potency of using GLP‐1‐expressing engineered bacteria in the treatment of multiple diseases (Chen et al., 2018; Fang et al., 2019, 2020; Luo et al., 2021; Wang et al., 2021; Wu et al., 2022). Whereas we did not fully investigate the possible role of butyrate and neither single use of GLP‐1 nor the combination of two candidate antihypertensive substances butyric acid and GLP‐1 was investigated in cardiovascular diseases.
Herein, we constructed engineered bacteria Clostridium butyricum‐GLP‐1 that is capable of secreting GLP‐1 and butyric acid to treat spontaneous hypertensive rats (SHR) via oral administration of probiotic suspension and assess the cardiac improvement effect of the engineered probiotic strain on hypertension. The regulatory effectiveness of the engineered probiotic strain is quantified by sphygmomanometer, western blotting and histopathology. The restoration of dysbacteriosis is determined by 16S ribosomal DNA (rDNA) high‐throughput sequencing.
## Construction and evaluation of the engineered bacteria in vitro
The recombinant plasmid pMTL007‐GLP‐1 was synthesised by Suzhou GENEWIZ Co., Ltd. by integrating the hGLP‐EGFP gene into the 5′ HindIII to 3′ BsrGI site of pMTL007 plasmid (GenBank: EF525477.1), a specialised plasmid for Clostridium (Heap et al., 2007). Then, the receptor bacteria CB was transformed with the recombinant plasmid pMTL007‐GLP‐1 by heat shock. The growth curves, plasmid stability, acid resistance, bile salt resistance and anti‐oxidative capability were evaluated.
## Experimental design and treatment of animals
The experimental animals include 24 spontaneously hypertensive rats (SHR) and six Wistar rats were all aged 8 weeks, purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. and fed freely with standardised diet and water under consolidated conditions (humidity 50 ± $15\%$, temperature 22 ± 2°C, $\frac{12}{12}$ light–dark cycle) in the specific pathogen‐free (SPF) experimental animal barrier system of the Institute of Translational Medicine of Nanchang University (Nanchang, Jiangxi Province, China). After adaptation for 1 week, the experimental animals are randomly divided into five groups: [1] C group, a control group that contains six Wistar rats; [2] M group, a model group that contains six SHR rats; [3] CB group, a group that contains six SHR rats treated with 109 CFU/ml CB via gavage every 2 days until the end of the treatment; [4] CB‐GLP‐1 group that contains six SHR rats treated with 109 CFU/ml CB‐GLP‐1 via gavage every 2 days until the end of the treatment; and [5] EX group, a group that contains six SHR rats treated with 0.4 mg/kg exenatide injected intraperitoneally every 2 days until the end of the treatment. The rats were euthanised by qualified laboratory technicians by injecting $1\%$ sodium pentobarbital (40 mg/kg) intraperitoneally following dissection at the end of the terminal treatment session for later experiments. The flow chart of the experimental animals' treatment is shown in Figure 2A.
## Blood pressure tests
The initial systolic and diastolic BP of all rats were measured by Mouse and Rat Tail Cuff Blood Pressure Systems from IITC Life Science Inc. following instructions from the provider. During the treatment session, the blood pressure of each rat was measured weekly. To reduce experimental errors, the BP was measured every Monday and the average value is obtained by measuring three times in each rat until the end of the experiment.
## Histology and histopathology
The euthanised rat was immediately dissected, and the heart was selected at random. Then, after fixation using $4\%$ paraformaldehyde, the heart tissues were embedded in paraffin. Next, the heart tissues embedded in paraffin were sectioned by microtome into 2–4 μm serial cuts, following dewaxing, staining with haematoxylin and eosin (H&E) or Masson, dehydration and clearing. The stained section of the heart tissues is then sealed with Permount for observation under the light microscope.
## Western blotting
Take an appropriate amount of heart tissue into the centrifuge tube and add an appropriate amount of RIPA lysis buffer (Beijing Solarbio Science & Technology Co., Ltd.) and protease inhibitor. After tissue homogenate treatment on ice, the supernatant was collected by centrifugation at 12,000 g at 4°C for 10 min, and the protein concentration was measured. Then, the cell protein was isolated by $10\%$–$12\%$ gel electrophoresis (SDS‐PAGE) and transferred to a polyvinylidene fluoride (PVDF) membrane. At room temperature, $5\%$ non‐fat milk dissolved in Tris buffer saline Tween (TBST) was used to block the non‐specific binding site for 1 h. The PVDF membrane was then incubated with appropriately diluted primary antibody at 4 °C overnight (Table S1), and after washing with TBST, the secondary antibody diluted with $1\%$ dry milk TBST was incubated at room temperature for 60 min. The western blotting results were obtained by adding the proper amount of enhanced chemiluminescence to the PVDF membrane and exposing on an automatic gel imaging system.
## High‐throughput sequencing of 16S rDNA amplicon
The bacterial genomic DNA amplifying and sequencing was performed by Realbio Technology (RBT) Co., Ltd. The target of the fragment of the 16S subunit of bacterial ribosomal RNA V3‐V4 region in faecal samples was amplified using bacterial universal primers 341F (5′‐CCTACGGGRSGCAGCAG‐3′) and 802R (5′‐GGACTACVVGGGTATCTAATC‐3′) containing index and adapter sequence. The amplified products of 425 bp (excluding index and adapter sequence) were harvested to compile library. After library quality inspection with agarose gel electrophoresis, library quantification was performed using Qubit and proportionally mixed according to the amount of data required for each sample. Next, the colony DNA fragments of the amplified products were sequenced using the Ilumina NovaSeq PE250 platform.
## Data analysis
High‐throughput sequencing analysis including chimerism and cluster analysis was performed by Usearch software platform. During Usearch clustering, reads were first sorted according to their abundance from large to small, and then clustered by $97\%$ similarity to obtain operational taxonomic units (OTUs). The α Diversity (OTU, Chao1, Shannon, Simpson and Alpha Diversity indices) and β diversity (Anosim analysis, MRPP analysis, PCoA and NMDS) were analysed. Statistical analysis was performed using GraphPad Prism8.0 (Graph Pad Software). Numerical data are presented as means ± standard deviation (SD). Statistical significance was evaluated by one‐way ANOVA (and nonparametric or mixed) followed by Tukey's multiple comparison tests. Statistical significance was set at *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$ and ****$p \leq 0.0001.$
## Evaluation of probiotic characteristics of CB‐GLP‐1 in vitro
The growth curve assay has indicated no difference in growth characteristics between CB and CB‐GLP‐1 strains (Figure 1A). The plasmid stability of CB‐GLP‐1 was then assessed and suggested that viable CB‐GLP‐1 still reached 5 × 108 CFU/ml after 19 days of passaging once per day (Figure 1B). In addition, the resistance of CB and CB‐GLP‐1 to high concentrations of acid and bile salts was evaluated, respectively, and showed that both CB and CB‐GLP‐1 showed great resistance to acid and bile salts (Figure 1C,D), which suggested that both CB‐GLP‐1 and CB had the ability to resist and survive the gastrointestinal environment. Finally, the antioxidative properties were evaluated, both CB and CB‐GLP‐1 showed good antioxidative capacity, especially in 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH) radical and O2−O reducing capacity, with CB‐GLP‐1 scavenging capacity being superior to CB (Figure 1E, $p \leq 0.05$).
**FIGURE 1:** *Evaluation of the probiotic characteristics of CB‐GLP‐1. Values are presented as means ± SD (n = 3); (A) Growth curves of wild‐type CB and engineered stain CB‐GLP‐1; (B) Plasmid stability test of CB‐GLP‐1; (C) The acid tolerance of CB and CB‐GLP‐1; (D) The bile salt tolerance of CB‐GLP‐1; (E) The antioxidant capability of CB‐GLP‐1; *p < 0.05 and **p < 0.01.*
## Engineered bacteria ameliorated blood pressure and decreased ventricular hypertrophy in rat models
In order to observe the effects of CB and engineered bacteria CB‐GLP‐1 on blood pressure in SHR rats, we monitored the changes in blood pressure after probiotics treatment (Figure 2B–E). It was found that compared with the SHR group, systolic and diastolic blood pressure decreased significantly 6 weeks after bacterial and exenatide intervention (Figure 2C,E, $p \leq 0.05$). After that, we detected the expression of related proteins in the renin‐angiotensin‐aldosterone (RAAS) system by western blotting at the molecular level (Figure 2F). It was found that with the GLP‐1 intervention, the level of angiotensin II (detected by ELISA, data not shown) and the expression of angiotensin II type‐1 receptor (AT1R) were decreased, while the expression of angiotensin II type‐2 receptor (AT2R) and angiotensin‐converting enzyme 2 (ACE2) was increased (Figure 2H–J). We also found that the therapeutic effect mediated by butyric acid depended on increasing the expression level of G‐protein‐coupled receptor 109 A (GPR109A) (Figure 2G). Therefore, we conclude that GLP‐1 and butyric acid have the effect of treating the blood pressure in spontaneously hypertensive rats.
**FIGURE 2:** *CB‐GLP‐1 treatment reduces blood pressure in rats. Values are presented as means ± SD (n = 3); (A) A schedule of animal experiments; (B) Systolic pressure of rats; (C) Systolic pressure of rats at the end of treatment session; (D) Diastolic pressure of rats; (E) Diastolic pressure of rats at the end of treatment session; (F) Western blotting of GPR109A, ACE2, AT1R and AT2R expression in heart tissues, the relative expressions of the detected protein were quantified by ImageJ. β‐actin was used as an internal control. The activity of (G) GPR109A, (H) ACE2, (I) AT1R, (J) AT2R and (K) AT1R/AT2R, *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001.*
Next, to study the effect of GLP‐1 on myocardial hypertrophy, we intervened in hypertensive rats with bacteria and exenatide for 6 weeks and observed the changes before and after the intervention at the molecular and histological levels (Figure 3). At the molecular level, we detected the expression of cardiac hypertrophy markers by western blotting (Figure 3A). Compared with the control group, the levels of cardiac hypertrophy markers including atrial natriuretic peptide (ANP), brain/B‐type natriuretic peptide (BNP) and β‐myosin heavy chain (β‐MHC) increased significantly ($p \leq 0.05$) in CB‐GLP‐1 group, while ANP and BNP decreased markedly after GLP‐1 treatment, β‐MHC levels ($p \leq 0.05$) (Figure 3B–E). At the histological level, the cross‐section of cardiomyocytes in the SHR group increased and improved after bacterial administration and exenatide intervention compared with the control group in H&E staining (Figure 3H). The Masson staining is used to evaluate the deposition of collagen, and a large amount of collagen deposition between the myocardium and around the blood vessels in the SHR group was found, which was improved after the intervention (Figure 3H). These changes were consistent with the expression of markers of myocardial hypertrophy. Interestingly, we found merely slight changes in α‐smooth muscle actin (α‐SMA) among groups (Figure 3F,G).
**FIGURE 3:** *CB‐GLP‐1 reduced cardiac hypertrophy. Values are presented as means ± SD (n = 3). (A) Western blotting of ANP, BNP and β‐MHC expression in heart tissues. The relative expressions of (B) ANP, (C) BNP and (D) β‐MHC were quantified by ImageJ, and β‐actin was used as an internal control; (E) Heart weight to body mass ratio (HW/BM) of rats; (F) Western blotting of α‐SMA expression in heart tissues; (G) The relative expressions of α‐SMA quantified by ImageJ, and β‐Actin was used as an internal control; (H) H&E and Masson staining image of heart tissue (400×), *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001.*
## CB‐GLP‐1 exerts antihypertensive effects via activating AMPK/mTOR/p70S6K/4EBP1 signalling pathway in rats
It has been found that AMP‐activated protein kinase (AMPK) plays an important role in regulating cell proliferation and apoptosis, and activation of AMPK can inhibit cardiac hypertrophy (Gélinas et al., 2018), and GLP‐1 can inhibit vascular smooth muscle cell proliferation through activation of AMPK (Jojima et al., 2017). Thus, we are interested in whether CB‐GLP‐1 exerts an antihypertensive effect via activating the AMPK‐associated pathway. The western blotting confirmed that the engineered anaerobe CB‐GLP‐1 activated of AMPK/mTOR/p70S6K/4EBP1 signalling pathway in rats (Figure 4). The activation of AMPK inhibits mTOR/p70S6K/4EBP1. Activation of mTOR promotes phosphorylation of eukaryotic translation initiation factor 4E‐binding protein 1 (4EBP1) and ribosomal 40S subunit S6 protein kinase (p70S6K), which are involved in protein translation initiation and elongation (Morita et al., 2015). Studies have shown that the mechanistic target of the ribosomal protein 70 S6 kinase (p70S6K) pathway is involved in stimulating protein synthesis and regulating cardiac hypertrophy (Heineke & Molkentin, 2006). Exenatide, as a GLP‐1R agonist, can regulate cell proliferation and apoptosis seems also by activating AMPK/mTOR/p70S6K/4EBP1 signalling pathway (Zhou et al., 2015). It can also regulate myocardial hypertrophy through this pathway.
**FIGURE 4:** *CB‐GLP‐1 exerts antihypertensive effects via activating the AMPK/mTOR/p70S6K/4EBP1 signalling pathway in rats. Values are presented as means ± SD (n = 3). (A) Western blotting of GLP‐1R, AMPK, p‐AMPK, mTOR, p‐mTOR, 70S6K, p‐70S6K, 4EBP1 and p‐4EBP1 expression in heart tissues. The relative expressions of (B) GLP‐1R, (C) p‐AMPK/AMPK, (D) p‐mTOR/mTOR, (E) p‐70S6K/70S6K and (F) p‐4EBP1/4EBP1 were quantified by ImageJ, β‐actin was used as an internal control, *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001.*
## Effect of engineered bacteria treatment on the gut microbiota of hypertension rat models
The alteration of intestinal microbiota of SHR rats was determined by high‐throughput 16S rDNA. A total of 29 samples generated a total of 1144 OTUs, and 471 common OTUs were identified from all groups using the Venn method (Figure 5A). The unique OTUs numbers in C, M, EX, CB and CB‐GLP‐1 were 51, 41, 60, 48 and 50 respectively (Figure 5A). To better analyse the effect of CB‐GLP‐1 intervention on intestinal microbiota in the group CB‐GLP‐1, α diversity analysis of Observed index (used for species diversity) and Shannon index (used for community diversity) was performed (Figure 5B–D).
**FIGURE 5:** *CB‐GLP‐1 improved intestinal microbiota in SHR rats. Values are presented as means ± SD (n = 5). (A) Venn map representation of OTUs; (B) The observed species diversity index; (C) The Shannon diversity index; (D) PCoA of β diversity index; (E) Microbial composition at the family level; The relative abundance of (F) Porphyromonadaceae, (G) Lachnospiraceae, (H) Bacteroidaceae and (I) Lactobacillus were analysed, ns: no significance, *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001.*
Our results showed that no obvious changes were identified between group C and group M. While compared with group C, group CB and CB‐GLP‐1 have reduced microbial diversity and abundance (Figure 5E). PCoA analysis showed that dots aggregated in the M group and dispersed in the C group and EX group, while CB‐GLP‐1 treatment promoted the aggregation of dots (Figure 5B–D). In addition, the samples in CB and CB‐GLP‐1 groups showed close similarity, which was far different from that in the C group, which meant that the microbial diversity in CB and CB‐GLP‐1 groups was substantially different from that in the C group (Figure 5B–D).
At the family level, the first 20 microbial populations were analysed. Porphyromonadaceae, Prevotellaceae, Ruminococcaceae and Bacteroidaceae constitute the common dominant bacteria in group C (0.243 vs. 0.178 vs. 0.198 vs. 0.023), group M (0.307 vs. 0.221 vs. 0.232 vs. 0.026), group CB (0.194 vs. 0.291 vs. 0.229 vs. 0.021), group CB‐GLP‐1 (0.226 vs. 0.321 vs. 0.253 vs. 0.016) and group EX (0.269 vs. 0.193 vs. 0.239 vs. 0.087) (Figure 5E). Moreover, CB and CB‐GLP‐1 treatments immensely decreased the relative abundance of Porphyromonadaceae, while there was no significant difference in the relative abundance of Prevotellaceae and Ruminococcaceae between groups, and the abundance of Bacteroidaceae was not quite different from group C after the intervention of CB and CB‐GLP‐1 (Figure 5E).
## DISCUSSION
Hypertension is one of the world's major health problems, defined as systolic blood pressure above 140 mmHg or diastolic blood pressure above 90 mmHg (Mills et al., 2016). Hypertension affects $25\%$ of the general population and is the number one risk factor for serious diseases affecting the brain, heart and kidneys (Messerli et al., 2007). Most hypertensive patients need to take three or more antihypertensive drugs continuously, and most of them are still hypertensive because they cannot be cured (Carey et al., 2018). However, long‐term hypertension can cause pressure on the blood vessel wall, increase the thickness of the blood vessel wall and decrease blood flow, so that the endothelial cells of the blood vessel will be damaged, and the smooth muscle of the blood vessel will also start to proliferate, and the heart will also cause cardiac hypertrophy and diastolic dysfunction, which may eventually lead to heart failure (Poulter et al., 2015). GLP‐1 is a peptide produced by distal intestinal mucosal endocrine cells, apart from its effect on glucose metabolism, GLP‐1 has anti‐inflammatory and antioxidant effects in cell types associated with atherosclerosis formation, as well as direct cardioprotective effects (Krasner et al., 2014; Shiraki et al., 2012). The widespread distribution of GLP‐1 receptors in the human body underlies the role of GLP‐1 in organs other than the pancreas. So far, there has been evidence that GLP‐1 can effectively reduce systolic and diastolic blood pressure, and has attracted much attention for its cardioprotective effect (Wang et al., 2013). However, the therapeutic mechanism of GLP‐1 on hypertension and its inducing cardiac hypertrophy has not been fully explained. Therefore, studying the effect of GLP‐1 on the treatment of hypertension and clarifying the therapeutic value of GLP‐1 in hypertension‐related diseases can provide a new treatment idea for the treatment of hypertension.
In this study, we proposed the treatment of hypertension with engineered bacteria and conducted several experiments to verify its therapeutic function and explore its potential mechanism. In vitro growth curve test, plasmid stability test and GLP‐1 protein expression test showed that CB‐GLP‐1 was successfully constructed and had good plasmid stability and GLP‐1 protein expression ability. Acid tolerance and bile salt tolerance test showed that CB‐GLP‐1 had the ability to tolerate gastric acid and oral potential. The following experimental results showed that there was no significant difference between CB‐GLP‐1 and CB in the scavenging ability of OH− radical and O2− radical, and there was no significant difference between CB and CB‐GLP‐1 in the antioxidant ability (Figure 1). Next, we used the spontaneous hypertensive rat (SHR) models to explore the antihypertensive effect of CB‐GLP‐1 on hypertension. Two to five weeks after CB‐GLP‐1 intervention, the systolic and diastolic blood pressure of SHR was significantly decreased, with the reduction of systolic blood pressure more obvious (Figure 2B–E). The result may attribute to GLP‐1, expressed by CB‐GLP‐1, binding to GLP‐1R in the kidney and then regulating the RAAS system. In this study, we found that the expression of angiotensin II (detected by ELISA, data not shown) and AT1R increased in SHR rats, while the expression of AT2R decreased, resulting in increased secretion of angiotensin II (Figure 2I–K). These results were reversed 6 weeks after the drug intervention. Previous studies have shown that angiotensin II binds to its receptors to contract blood vessels, raise blood pressure and promote cell hypertrophy, which plays an important role in myocardial remodelling and the development of hypertension (Banks et al., 2022). In addition, we also found that with the down‐regulation of AT1R, the expression of ACE2 increased significantly (Figure 2H,I). Therefore, we believed that GLP‐1 could activate ACE2 by inhibiting angiotensin II synthesis and the expression of its receptor, thus playing a role in lowering blood pressure. These results were consistent with the conclusions of the previous studies (Morrell & Stenmark, 2013). Some studies have shown that butyrate regulates the occurrence and development of hypertension through immune response, and also induces the differentiation of regulatory T cells in vivo. When butyrate was applied to its receptor GPR109A, macrophages showed a higher ability to induce juvenile cells to differentiate into regulatory T cells. In our study, the level of receptor GPR109A was substantially increased after CB‐GLP‐1 intervention (Figure 2G). These results suggest that the antihypertensive effect of CB‐GLP‐1 may be partly derived from the secretion of butyric acid in the treatment of hypertension by activating receptor GPR109A.
Many studies have shown that hypertension can produce significant hemodynamic changes, increasing vascular inflammation and myocardial load, inducing cardiac hypertrophy and causing cardiac dysfunction. However, these anti‐inflammatory and cardiac protective effects have been observed in natural GLP‐1 or GLP‐1R agonists (Marso et al., 2016). Therefore, the improvement effect of engineering bacterium CB‐GLP‐1 on myocardial hypertrophy in spontaneous hypertension was investigated in this study (Figure 3). Western blotting was used to detect the levels of myocardial hypertrophy markers ANP, BNP and β‐MHC, and the results showed that the expression levels of myocardial hypertrophy markers were significantly increased in the SHR model (Figure 3B–D). After CB, CB‐GLP‐1 and EX intervention, the expression levels of ANP, BNP and β‐MHC were all decreased (Figure 3B–D). And the effect of CB‐GLP‐1 and EX is better than CB (Figure 3B–D). AMPK, as an energy sensor and regulator of energy homeostasis in eukaryotic cells, is activated when AMP/ATP and ADP/ATP ratios increase in the body, restoring energy balance by inhibiting anabolic processes that consume ATP and promoting catabolic processes that produce ATP (Garcia & Shaw, 2017). It has been found that AMPK also plays an important role in regulating cell proliferation and apoptosis, and activation of AMPK can inhibit cardiac hypertrophy (Gélinas et al., 2018), and GLP‐1 can inhibit vascular smooth muscle cell proliferation through activation of AMPK (Jojima et al., 2017). Activation of AMPK inhibits various anabolic pathways, including mTOR/p70S6K/4EBP1. Activation of mTOR promotes phosphorylation of eukaryotic translation initiation factor 4E‐binding protein 1 (4EBP1) and ribosomal 40S subunit S6 protein kinase (p70S6K), which are involved in protein translation initiation and elongation (Morita et al., 2015). Studies have shown that the mammalian target (mTOR)/P70 ribosomal S6 protein kinase (p70S6K) pathway is involved in stimulating protein synthesis and regulating cardiac hypertrophy (Heineke & Molkentin, 2006). Exenatide, as a GLP‐1R agonist, can regulate cell proliferation and apoptosis by activating AMPK/mTOR/p70S6K/4EBP1 signalling pathway. It can also regulate myocardial hypertrophy through this pathway. Therefore, to confirm our hypothesis, we detected the expression of signalling pathway‐related proteins. In this study, western blotting was used to detect the phosphorylation levels of AMPK, mTOR, p70S6K and 4EBP1 (Figure 4). After the intervention of engineered bacteria and exenatide, AMPK was activated, and mTOR while phosphorylation levels of p70S6K and 4EBP1 were reduced, suggesting that GLP‐1 may be involved in the treatment of hypertensive hypertrophy through activation of AMPK/mTOR/p70S6K/4EBP1 signalling pathway (Figure 4).
Due to the rise of 16S rDNA technology, an increasing number of studies have shown a strong link between gut microbiome and hypertension. Shannon's index in alpha diversity and PCoA results in beta diversity suggest that CB‐GLP‐1 appears to promote the conversion of the SHR gut microbiota to normal control rats (Figure 5). The results at the top 10 family‐level and genus‐level species composition indicated that CB‐GLP‐1 intervention had an inhibitory effect on the Porphyromonadaceae and enhance the relative abundance of probiotic bacteria, including the Lactobacillus and Lachnospiraceae (Figure 5). Among them, Lactobacillus has the effect of degrading sugar substances to regulate intestinal function, enhance immunity and exert antioxidation ability (Zhang, Hou, et al., 2020; Zhang, Nie, et al., 2020). In addition, the family Lachnospiraceae belongs to the *Clostridium cluster* XIVa of Firmicutes, and many studies have shown that *Lachnospiraceae is* the main producer of short‐chain fatty acid in the human intestine mediated by hydrolyses starch and other sugars to produce butyrate, propionate and other short‐chain fatty acids to improve intestinal inflammation, and provides energy to the intestinal epithelium, maintains an acidic environment in the intestine and inhibits the growth of harmful acid intolerant flora, while acid‐tolerant Lactobacillus, *Clostridium and* other beneficial bacteria are able to proliferate (Chen et al., 2017). The increase in the abundance of beneficial bacteria in the host gut microbiota is accompanied by an increase in the content of SCFAs, which are coupled to butyric acid secreted by CB‐GLP‐1, thereby activating the SCFAs receptor GPR109A in the kidney to reduce hypertension (Felizardo et al., 2019). It was found that SCFAs and their metabolites interacted with the RAAS system in the host kidney and downregulated RAAS in the kidney of experimental rat under the influence of a high‐fibre diet, greatly downregulating systolic and diastolic blood pressure (Marques et al., 2017). In addition, the intervention of CB‐GLP‐1 improved the gut microbiota disorder caused by hypertension, suggesting that CB‐GLP‐1 may regulate the gut microbiota through the probiotic properties of the host bacterium CB, further suggesting that another mechanism of CB‐GLP‐1 treatment of hypertension is the regulation of the gut microbiota through the host bacterium CB (Figure 5).
Collectively, this study shows that CB‐GLP‐1 treatment of hypertension is mediated by the expression of GLP‐1 and secretion of butyric acid to regulate the RAAS system and GPR109A in the kidney and that CB‐GLP‐1 activates the AMPK signalling pathway to regulate myocardial proliferation and apoptosis and ameliorate cardiomyocyte hypertrophy and ventricular wall fibrosis, in addition, CB‐GLP‐1 treatment through the host bacterial CB increased the abundance and biodiversity of probiotic bacteria, transforming the hypertension‐affected microbial ecology to normal levels (Figure 6). This experiment is the first to investigate the potential role of the engineered bacterium CB‐GLP‐1 in regulating hypertension as well as ameliorating myocardial hypertrophy.
**FIGURE 6:** *The underlying mechanism of therapeutic effect of CB‐GLP‐1.*
In conclusion, our results showed that genetically engineered bacteria CB‐GLP‐1 have great antihypertensive potential in treating SHR hypertension rat models (Figure 6). The plausible mechanism of the BP‐reducing ability of the CB‐GLP‐1 strain may be attributed to elevating short‐chain fatty acid levels in blood and increasing intestinal microbial diversity of the hypertensive rat. Our findings suggest that the genetically engineered bacteria expressed GLP‐1 and short‐chain fatty acids are putative therapeutics for the treatment of hypertension. Nevertheless, the miss of vector‐carrying strain and the limited number of hypertension rats used in our present study is a non‐negligible influence for us to deduce a better statistical conclusion.
## AUTHOR CONTRIBUTIONS
Xin‐liang Wang: Conceptualization (equal); data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); software (lead); validation (equal); visualization (lead); writing – original draft (equal); writing – review and editing (supporting). Wen‐jie Chen: Investigation (supporting); methodology (supporting); software (supporting); validation (supporting); visualization (supporting); writing – original draft (equal); writing – review and editing (equal). Rui Jin: Investigation (supporting); methodology (supporting); validation (supporting). Xuan Xu: Investigation (supporting); methodology (supporting); validation (supporting). Jing Wei: Investigation (supporting); supervision (supporting). Hong Huang: Funding acquisition (supporting); supervision (supporting); validation (supporting). Yan‐hua Tang: Supervision (supporting); validation (supporting). Chang‐wei Zou: Supervision (equal); validation (equal). Ting‐tao Chen: Conceptualization (lead); funding acquisition (lead); supervision (lead); validation (lead); writing – review and editing (lead).
## FUNDING INFORMATION
This work was supported by grants from the National Natural Science Foundation of China (No. 82060638 to T. T. Chen and No. 42265011 to H. Huang) and Double thousand plan of Jiangxi Province (High End Talents Project of Scientific and Technological Innovation to T. T. Chen).
## CONFLICT OF INTEREST
The authors declare that there is no conflict of interest regarding the publication of this paper.
## ETHICS STATEMENT
The ethical investigation of the experimental animals in our study was approved by the animal experimental ethical inspection of Nanchang Royo Biotech Co., Ltd (RyE2021070911) on August 2 2022.
## DATA AVAILABILITY STATEMENT
The datasets used and/or analysed during the present study are available from the corresponding author on reasonable request.
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---
title: Investigating the induction of polyphenol biosynthesis in the cultured Cycolocarya
paliurus cells and the stimulatory mechanism of co-induction with 5-aminolevulinic
acid and salicylic acid
authors:
- Li-Juan Ling
- Meng Wang
- Chuan-Qing Pan
- Dao-Bang Tang
- En Yuan
- Yuan-Yuan Zhang
- Ji-Guang Chen
- Da-Yong Peng
- Zhong-Ping Yin
journal: Frontiers in Bioengineering and Biotechnology
year: 2023
pmcid: PMC10034720
doi: 10.3389/fbioe.2023.1150842
license: CC BY 4.0
---
# Investigating the induction of polyphenol biosynthesis in the cultured Cycolocarya paliurus cells and the stimulatory mechanism of co-induction with 5-aminolevulinic acid and salicylic acid
## Abstract
Background: Plant cell culture technology is a potential way to produce polyphenols, however, this way is still trapped in the dilemma of low content and yield. Elicitation is regarded as one of the most effective ways to improve the output of the secondary metabolites, and therefore has attracted extensive attention.
Methods: Five elicitors including 5-aminolevulinic acid (5-ALA), salicylic acid (SA), methyl jasmonate (MeJA), sodium nitroprusside (SNP) and Rhizopus Oryzae Elicitor (ROE) were used to improve the content and yield of polyphenols in the cultured *Cyclocarya paliurus* (C. paliurus) cells, and a co-induction technology of 5-ALA and SA was developed as a result. Meanwhile, the integrated analysis of transcriptome and metabolome was adopted to interpret the stimulation mechanism of co-induction with 5-ALA and SA.
Results: Under the co-induction of 50 μM 5-ALA and SA, the content and yield of total polyphenols of the cultured cells reached 8.0 mg/g and 147.12 mg/L, respectively. The yields of cyanidin-3-O-galactoside, procyanidin B1 and catechin reached 28.83, 4.33 and 2.88 times that of the control group, respectively. It was found that expressions of TFs such as CpERF105, CpMYB10 and CpWRKY28 increased significantly, while CpMYB44 and CpTGA2 decreased. These great changes might further make the expression of CpF3′H (flavonoid 3′-monooxygenase), CpFLS (flavonol synthase), CpLAR (leucoanthocyanidin reductase), CpANS (anthocyanidin synthase) and Cp4CL (4-coumarate coenzyme A ligase) increase while CpANR (anthocyanidin reductase) and CpF3′5′H (flavonoid 3′, 5′-hydroxylase) reduce, ultimately enhancing the polyphenols accumulation Conclusion: The co-induction of 5-ALA and SA can significantly promote polyphenol biosynthesis in the cultured C. paliurus cells by regulating the expression of key transcription factors and structural genes associated with polyphenol synthesis, and thus has a promising application.
## 1 Introduction
Polyphenols are one of the most diverse and widely distributed secondary metabolites in the plants (Grajeda-Iglesias et al., 2022). Catechins, anthocyanins, flavonoids, and procyanidins are four common categories of polyphenols with a diversity of bio-activities such as anti-oxidation, anti-senescence, anti-tumor and anti-diabetic, and therefore have a promising application prospect in foods, cosmetics and pharmaceuticals (Cao et al., 2017; Khan and Siddiqui, 2017; Zong et al., 2020).
At present, polyphenols are mainly obtained from plant materials such as leaves, stems, flowers and fruits by extraction and purification (Cui et al., 2014). There are lots of plants that can be used in plant cell culture to produce polyphenols, but plants have a long growth cycle and a vulnerability to the natural environment and pests (Liu et al., 2022). It is also costly to get polyphenols of high purity because of a considerable variety of components in the differentiated plant organs like leaves and flowers (Silva et al., 2015), which restricts the availability of natural plant polyphenols (Dea et al., 2017). Over the past century, plant cell and tissue culture technologies have developed rapidly and shown outstanding advantages in the plant secondary metabolites production, such as short production cycle, independence of natural conditions and conveniences in extraction and purification (Davies et al., 2013; Regine et al., 2018). The output of shikonin derivatives reached 2,300 mg/L by cell suspension cultures of Lithospermum erythrorhizon (Yasuhiro and Yasuhiro, 1985). Amacha cells were cultivated in a 15L jar fermentor to bio-synthesize polyphenols such as skimmin and hydrangenol-8-O-β-D-glucose (Suzuki et al., 2014). These reports indicate that using plant cell culture to produce valuable secondary metabolites has promising application.
Although there are many reports about secondary metabolites production by plant cell culture, only a few of them have been produced in large-scale, such as paclitaxel (Li et al., 2009), ginseng (Hu et al., 2001) and shikonin (Sormeh et al., 2016) etc. Other species, however, cannot be utilized on a large scale probably due to the low contents and yields of active compounds. For instance, the yields of catechin, caffeic acid, kaempferol and apigenin in the cultured date palm cells were only 155.9, 162.7, 89.7, and 242.7 μg/L, respectively (Naik and Al-Khayri, 2018). Kikowska et al. [ 2019] found the contents of polyphenols in the shoot cultures of *Eryngium alpinum* L like caftaric acid, caffeic acid, and chlorogenic acid were only 48.6, 27.1 and 139 μg/g, respectively. Consequently, it is urgent to improve the target compound yields of the cultured plant cells.
In order to promote polyphenol production by plant cell culture, researchers have been searching for effective methods to improve the target polyphenol output, such as optimization of culture conditions including light (Curtin et al., 2003), carbon source (Ochoa-Villarreal et al., 2015) and medium composition (Naik and Al-Khayri, 2018). Precursor feeding, for example, phenylalanine (Li et al., 2021), and elicitors like 5-ALA (Zheng et al., 2018) and 24-Epibrassinolide (Tassoni et al., 2012). Among the above-mentioned promotion strategies, elicitation is regarded as one of the most effective ways, and now has attracted extensive attention (Rs et al., 2020). According to the report of Jeandet et al. [ 2016], resveratrol yield reached 7 g/L in grape suspension cells under the co-stimulation of MeJA and acyclodextrin. Li et al. [ 2021] also found that under the co-induction of SA and Phe, the total polyphenol content and yield were as high as 41.36 mg/g and 752.93 mg/L, respectively.
Cycolocarya paliurus is a unique and endangered plant with a sporadic distribution in many provinces of southern China (Zhao et al., 2020). Researches have shown that the leaves of this plant contain a wealth of bio-active compounds such as tirterpenoids, polyphenols, and polysaccharides, and therefore display multiple physiological activities and pharmacological functions, including anti-diabetes (Lin et al., 2020), anti-hyperlipidemia (Yao et al., 2015), and anti-oxidation (Yao et al., 2015). Polyphenols are the important bio-active components in the leaves of C. paliurus, which endow it with various pharmacological activities (Xiong et al., 2016). However, polyphenols in C. paliurus have not aroused much interest and therefore not been fully utilized at present. This may be due to the great difficulties in seed germination and asexual propagation of this plant that limit the availability of C. paliurus resources.
C. paliurus cell culture is a potential way to bio-synthesize these bio-active polyphenols. Our laboratory has been engaged in the researches related to the cell and tissue culture of C. paliurus for more than 15 years, screened and cultivated six categories calluses with unique characteristics like crisp light yellow-green callus (CYG), compact green callus (CG), wet grayish brown callus (WGB), crisp white callus (CW), crisp light pink callus (CPC), and red callus (CR), among which CYG was the most common and typical line, while CR was extremely rare (Zhao et al., 2020). CR was found accidentally during our long-term continuous sub-culturing and screening of C. paliurus callus, and its growth, morphology and secondary metabolites were all significantly different from the most common CYG. Our previous studies showed that CR was rich in polyphenols, including anthocyanins, catechin, procyanidin B1 and its content were much higher than that in CYG and other four typical callus (Zhao et al., 2020). Relatively speaking, our C. paliurus callus also displayed a higher polyphenol content than that induced from some other reported plants (Naik and Al-Khayri, 2018), and therefore has the potential to produce polyphenols with high antioxidant activity. Consequently, a stable cell suspension culture was successfully developed using CR to bio-synthesize polyphenols.
In the present paper, five elicitors including 5-ALA, SA, MeJA, SNP, and ROE were used to increase the content and yield of polyphenols in the cultured C. paliurus cells, meanwhile the co-induction mechanism of 5-ALA and SA was further investigated based on the integrated analysis of transcriptome and metabolome. This paper provides a theoretical basis for the regulation of polyphenol production by C. paliurus cell suspension culture.
## 2.1 Materials and chemicals
C. paliurus suspension cells were cultured and preserved in the Jiangxi Key Laboratory of Natural Products and Functional Food, Jiangxi Agricultural University (Nanchang, China). ROE was obtained from Guangdong Microbial Culture Collection Center. Murashige and Skoog (MS) medium (Murashige and Skoog, 1962) was bought from Hope Bio-Technology Co., Ltd. (Qingdao, China). Kinetin (KT), 1-naphthylacetic acid (NAA), 5-Aminolevulinic acid (5-ALA) and 2,4 -dichlorophenoxy acetic acid (2,4 -D) were offered by Aladdin Co., Ltd. (Shanghai, China). Other chemicals (purity: AR) were supplied by Sigma-Aldrich Co., Ltd. (Shanghai, China).
## 2.2 Cultivation of C. paliurus cells
C. paliurus calluses with excellent appearance and loose texture were inoculated into a 100 mL culture flask containing 40 mL MS medium supplemented with 1.0 mg/L kT, 0.3 mg/L NAA, 0.3 mg/L 2,4 -D, 30 g/L sucrose and $0.7\%$ agar. The cultures were cultivated in a rotary orbital shaker at 115 rpm and (25 ± 2)°C under constant light, and sub-cultured with an inoculation amount of 6.0 g per culture bottle every 7 days.
## 2.3 Biomass determination of the cultured cells
The cultured cells were collected on the 6th day using suction filtration, and then dried in an oven at 50°C until a constant weight was achieved. The weight of the harvested cells (dry weight, and DW) was measured after being cooled down to the room temperature, which was recorded as a biomass increment indicator (DW).
## 2.4.1 Polyphenol extraction and standard solution preparation
Polyphenol extraction was performed according to Zhao et al. [ 2020]. 1 g dried cell powder was extracted by 30 mL ethanol supplemented with $1\%$ hydrochloric acid (v/v) in an ultrasonic extractor at 50°C for 60 min. The extracts were centrifuged at 6,000 r/min for 10 min, and then the supernatant was collected and filtered through a 0.22 μm filtration membrane. The standards (procyanidin B1, (+)-catechin, cyanidin-3-O-galactoside) were dissolved in $1\%$ (v/v) hydrochloric acid water to prepare 1.0 mg/mL of standard solution, which were used to prepare series of standard solutions with concentrations from 10 to 400 μg/mL, 20–400 μg/mL, 5–200 μg/mL, respectively.
## 2.4.2 Polyphenol determination
Polyphenol extraction and standard solution were determined with HPLC (1260 HPLC system, Agilent Technologies, United States) on a C18 column (4.6 × 250 mm, 5 μm). The assay procedure was performed in accordance with that described by (Zhao et al., 2020). In this paper, the sum content and yield of the three main components (procyanidin B1, (+) - catechin, cyanidin-3-O-galactoside) was calculated and used as total polyphenol content (TPC) and total polyphenol yield (TPY) of the cultured C. paliurus cells. The standard curves for the detection of polyphenols were shown in Supplementary Table S1.
## 2.5 Preparation of elicitors
Referring to Li et al. [ 2021], MeJA and SA were dissolved in ethanol while SNP and 5-ALA were dissolved in distilled water, which were further filtrated through a 0.22 μm membrane and used as elicitors to stimulate the biosynthesis of polyphenols. ROE was prepared according to the method described by O-Hernández et al. [ 2017]. The collected mycelium pellets were fully ground with phosphate buffer solution (PBS, pH = 6.0), and then the ground mixture was centrifuged at 4,000 r/min for 10 min. Afterward the supernatant was collected and sterilized at 121°C for 25 min, which was used as ROE in the following induction experiments. The concentration of ROE was characterized with total sugar content, which was determined by Anthrone-sulfuric method using glucose as standard (Dona et al., 2011).
## 2.6 Single-factor experiment for elicitor screening
In the evaluation of the promotion effects of each elicitor on the polyphenol biosynthesis in the cultured cells, the effects of concentration and addition time of the elicitors were all taken into consideration, which were designed and described in the Supplementary Table S2.
## 2.7 Optimization experiment of co-induction parameters
The co-induction concentrations of 5-ALA and SA were optimized by comprehensive test (completely random design), using TPC as the evaluation indicator. The test concentration of 5-ALA and SA were designed and described in Supplementary Table S2. The addition and harvest time of the elicitors were as mentioned in the Supplementary Table S2.
## 2.8.1 Extraction of metabolites
The cultured fresh cells of the control group and treated group co-induced by 5-ALA and SA were sampled on the 6th day, and these samples were frozen with liquid nitrogen quickly and then stored at −80°C for the subsequent detection. Each group was sampled in six replicates, and the treated and untreated cells were named CPTG and CPCG, respectively. Sample cells were fully ground in liquid nitrogen with 400 µL cooled methanol/acetonitrile/aqueous solution (4:4:2, v/v). After vortex oscillation mixing, the extracted samples were stored at −20°C for 60 min, and then centrifuged at 14,000 g for 20 min under 4°C (Heraeus Fresco 17, Thermo Fisher Scientific). Afterward the supernatant was recovered, and then evaporated in a vacuum rotary evaporator at 55°C until a constant weight was observed. The recovered extracts were re-dissolved in the acetonitrile aqueous solution (the ratio of acetonitrile to water was 1 to 1 (v/v)), followed by another centrifugation at 14,000 g for 15 min under 4°C. Thereafter, the collected supernatant was filtered through PTFE filters (0.22 μm) and then kept in an auto-sampler vial for the LC-MS/MS analysis (http://personalbio.bioon.com.cn/).
## 2.8.2 Chromatographic conditions
All the detection was performed in a 1,290 series UHPLC (Dionex, CA, United States). Chromatographic separation was carried out in a ACQUITY UPLC BEH C18 column (100 mm × 2.1 mm, 1.7 μm, Waters, United States), and the column temperature was maintained at 40°C. The flow rate was typically set at 0.3 mL/min. The acidified water ($0.1\%$ formic acid (v/v), solvent A) and acetonitrile (solvent B) were used as mobile phases. The elution gradient was programmed as following: 0–0.5 min, $5\%$ B; 0.5–1.0 min, $5\%$ B; 1.0–9.0 min, 5–$100\%$ B; 9.0–12.0 min, $100\%$ B; 12.0–15.0 min, $5\%$ B. During the whole analysis process, the samples were placed in a 4°C automatic injector, and the volume of each injection was 5 µL (Benton et al., 2015).
## 2.8.3 Mass spectrum conditions
The samples were separated by UHPLC and analyzed by q-exact-four-stage rod electrostatic field orbital trap high-resolution mass spectrometer (Thermo Fisher Scientific, United States). Electrospray ionization (ESI) mode of positive and negative ions were both adopted for the MS detection. MS detection conditions were as follows: ion source gas1 (gas1): 60, ion source gas2 (gas2): 60, curtain gas (cur): 30, source temperature: 320°C, ionsapary voltage floating (isvf) ± 3,500 V. Declustering potential (DP): ±60 V, collision energy: 35 ± 15 eV, exclude isotopes within 4 Da, candidate ions to monitor per cycle 6 (Julijana et al., 2013).
## 2.8.4 Metabolomic data analysis
Qualitative and quantitative analyses were all carried out in Shanghai Personalbio Technology Co., Ltd. (http://personalbio.bioon.com.cn/). Multi-dimensional statistical analyses such as PCA (principal components analysis), orthogonal partial least squares discriminant analysis (OPLA -DA) were conducted. And single-dimensional statistical analysis like volcanic map was drawn by R software (https://www.r-project.org/).
## 2.8.5 Differential metabolites analysis
The variable weight for the project (VIP) and fold change obtained by OPLA -DA model were combined to analyze and screen differential metabolites. VIP >1, p-value <0.05 and fold change >2 were used as criteria to screen the significantly changed metabolites (Luo et al., 2020).
## 2.9.1 Preparation of the sequencing samples
Cell samples were prepared according to the method described in Section 2.8.1. The treated and untreated cells were sampled in three replicates, which were named CPTG and CPCG, respectively. These samples were then sent to Shanghai Personalbio Technology Co., Ltd. for transcriptome detection.
## 2.9.2 RNA extraction, cDNA library construction and high throughput sequencing (RNA-seq)
Total RNA was extracted from cells in a total RNA Extractor (SanGon, Shanghai, China) following the supplier’s instructions. The quality and quantity of extracted RNA were evaluated using agarose gel electrophoresis and 2,100 Bioanalyzer (Agilent, Germany). The quality-checked RNA was purified and then used as a template to synthesize cDNA first strand followed by cDNA second strand with Primscript™ First-Strand cDNA Synthesis Kit (Takara Bio, United States). PCR amplification was subsequently conducted to establish cDNA libraries. RNA-sequencing was performed on the IlluminaHiseq™ 2,500 platform (http://www.personalmedicine.cn/TechnicalPlatform) in Shanghai Personalbio Technology Co., Ltd.
## 2.9.3 Processing of RNA-seq data and gene function annotation
The high-quality fragment reads (quality value ≥20) were spliced by software Trinity (software version number: 2.4.0) (Jonathan et al., 2022). All the assembled unigenes were searched against public protein databases, such as NCBI non-redundant protein sequences (NR) and Swiss-Prot for homology comparison. Only those matches with E-value below 10–5 were deemed significant (Park et al., 2021; Jonathan et al., 2022). Furthermore, gene function prediction and classification were assigned based on the database of the Gene Ontology (GO) and Eukaryotic Orthologous Groups of proteins (KOG). Moreover, secondary metabolic pathways were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.
## 2.9.4 Differentially expressed genes (DEGs) analysis
DESeq was adopted to analyze DEGs with filtering conditions including |log2(Fold Change)| ≥ 1 and q-value <0.05. In order to analysis the metabolic and signal transduction pathways involved in DEGs, the DEGs enrichment of KEGG (https://www.genome.jp/kegg/genome) was performed by comparing DEGs with the entire background of all KEGG-associated unigenes with a hypergeometric test ($p \leq 0.05$).
## 2.10 QRT-PCR analysis
The DEGs expression levels were validated by QRT-PCR (quantitative real time polymerase chain reaction) assay. The RNA and cDNA required for this analysis were obtained according to the method in Section 2.9.2. and the primers were listed in Supplementary Table S3. Using the stable UBQ gene as the internal reference gene, QRT-PCR was performed using Ace-Q® qPCR SYBR® Green Master Mix (Vazyme). The reaction system was 20 μL, including 10 μL 2 × SYBR real-time PCR premixture, 1 μL cDNA and 0.8 μL primers. The qPCR procedures were as follows: initially denatured at 95°C for 5 min, followed by 40 cycles of 15 s at 95°C and 30 s at 60°C (Hazman, 2022). Gene relative expressions were calculated by 2−ΔΔCT and geometric mean method (Zhao et al., 2020).
## 2.11 Integration analysis of transcriptome and metabolome
To reveal the inducement mechanism of the co-induction of 5-ALA and SA on the polyphenol biosynthesis, the differential metabolites and DEGs between the treated and untreated C. paliurus cells were analyzed through KEGG database (https://www.genome.jp/kegg/).
## 2.12 Statistical analysis
All the experiments were performed in triplicate and the results were given as the mean ± standard deviation (SD). The data was analyzed by DPS statistical software. Statistical analysis was performed with ANOVA followed by Duncan’s multiple range test (p ≤ 0.05).
## 3.1 Effects of five elicitors on the cell growth and polyphenol biosynthesis
In the present paper, five elicitors including 5-ALA, SA, MeJA, SNP and ROE were used to improve the biosynthesis of polyphenols in the cultured C. paliurus cells. In terms of biomass (As shown in Figure 1), both 5-ALA and ROE showed no obvious effect on the growth of the cultured cells within the experimental concentration range, but MeJA displayed a negative effect, whenever they were added. Unlike above-mentioned three elicitors, SNP and SA hardly affected cell biomass when added on the 4th and 6th day while appeared adverse effect when added on the 2nd day. With regard to polyphenol content and yield, all five elicitors showed promotion effects, but their increments differed from each other greatly (Figures 2, 3). The results showed that the promotion effect of the five elicitors on polyphenol content was in the order of SA, 5-ALA, SNP, ROE, and MeJA from best to worst, meanwhile SA and 5-ALA also displayed the most significant improvement on polyphenol yield of the cultured cells. It was recorded that when 50 μM 5-ALA was added on the 1st day and 50 μM SA added on the 4th day, the content reaching 4.05 and 4.3 mg/g, respectively, and the yield reaching 66.89 and 78.236 mg/L, respectively. Therefore, 5-ALA and SA were chosen for the further co-induction experiments.
**FIGURE 1:** *Effects of 5-ALA (A), MeJA (B), SNP (C), SA (D), ROE (E) on the biomass of the cultured C. paliurus cells.* **FIGURE 2:** *Effects of 5-ALA (A), MeJA (B), SNP (C), SA (D), ROE (E) on polyphenol content of the cultured C. paliurus cells.* **FIGURE 3:** *Effects of 5-ALA (A), MeJA (B), SNP (C), SA (D), ROE (E) on polyphenol yield of the cultured C. paliurus cells.*
## 3.2 Optimization of 5-ALA and SA concentration under co-induction
Comprehensive test was used to confirm whether the co-induction of 5-ALA and SA had a better induction effect and found the optimal concentration combination of 5-ALA and SA. The results suggested that the co-induction of 5-ALA and SA within the test concentration had no obvious effect on the biomass, but showed a significant influence on TPC and TPY (Supplementary Tables S4, S5). As shown in Supplementary Table S1, the highest content and yield appeared in A2B2, followed by A2B1 and A1B1. The highest TPC and TPY reached 7.945 mg/g and 129.213 mg/L, respectively. Multiple comparison results in Supplementary Table S6 indicated that both factor A (SA) and B (5-ALA) exhibited significant effect on TPC and TPY.
## 3.3 Comparison of the effects of 5-ALA, SA and the co-induction of 5-ALA and SA on the cell growth and polyphenol biosynthesis
As shown in Table 1, all of 5-ALA, SA and the co-induction of 5-ALA and SA had no obvious impact on the biomass, but showed a great promotion effect on the total polyphenol accumulation. With the addition of 50 μM 5-ALA at the beginning of cultivation, the yield of (+) - catechin, cyanidin-3-O-galactoside and procyanidin B1 rose from 31.74, 0.47 and 9.64 to 38.08, 3.04 and 27.11 mg/L, respectively, while with the addition of 50 μM SA on the 4th day, the yield of (+) - catechin, cyanidin-3-O-galactoside and procyanidin B1 reached 49.28, 4.92 and 24.21 mg/L, respectively (Table 1). Relatively speaking, the co-induction of 5-ALA and SA had a much better improvement effect on polyphenol biosynthesis. Under the optimal co-induction parameter, the yields of (+) - catechin, cyanidin-3-O-galactoside and procyanidin B1 were 2.88, 28.83 and 4.33 times that of the control group, which were as high as 91.67, 13.55, and 41.83 mg/L, respectively. The above-mentioned results suggested that the co-induction of 5-ALA and SA could greatly promote the polyphenol biosynthesis of the cultured C. paliurus cells, and therefore have a promising application prospect.
**TABLE 1**
| Compounds | Item | Blank group | 5-ALA treatment | SA treatment | Co-induction of 5-ALA and SA |
| --- | --- | --- | --- | --- | --- |
| (+) - Catechin | Content (mg/g) | 1.79 ± 0.73b,c | 2.30 ± 0.08b | 2.65 ± 0.11b | 5.00 ± 0.22a |
| (+) - Catechin | Yield (mg/L) | 31.74 ± 1.28c | 38.08 ± 1.38bb | 49.28 ± 2.1b | 91.67 ± 4.16a |
| Procyanidin B1 | Content (mg/g) | 0.54 ± 0.01c | 1.64 ± 0.13b | 1.30 ± 0.30b | 2.28 ± 0.30a |
| Procyanidin B1 | Yield (mg/L) | 9.64 ± 0.13c | 27.11 ± 2.20b | 24.21 ± 5.6b | 41.83 ± 5.52a |
| Cyanidin 3-O-galactoside | Content (mg/g) | 0.026 ± 0.02c | 0.18 ± 0.01b | 0.26 ± 0.16b | 0.74 ± 0.12a |
| Cyanidin 3-O-galactoside | Yield (mg/L) | 0.47 ± 0.29c | 3.04 ± 0.077b | 4.92 ± 3.16b | 13.55 ± 2.32a |
| Biomass (g/L, DW) | Biomass (g/L, DW) | 17.75 ± 0.56a | 16.50 ± 0.06a | 18.63 ± 1.20a | 18.31 ± 0.16a |
| Total polyphenol content (mg/g) | Total polyphenol content (mg/g) | 2.35 ± 0.01c | 4.05 ± 0.28b | 4.2 ± 0.25b | 8.00 ± 0.56a |
| Total polyphenol yield (mg/L) | Total polyphenol yield (mg/L) | 41.05 ± 0.85c | 66.89 ± 4.66b | 78.23 ± 2.6b | 147.12 ± 14.32a |
## 3.4 Metabolome analysis
To explore the effect of co-induction of 5-ALA and SA on the secondary metabolite synthesis in the cultured C. paliurus cells, especially polyphenols, the metabolome detection of the treated and untreated cells was performed using UPLC-MS/MS, which provided metabolic information for the interpretation of co-induction mechanism by dual omics analysis. A total of 970 compounds were found by UPLC-MS/MS determination with positive ion mode from the extracts of cell samples, including 138 upregulated metabolites and 39 downregulated metabolites in CPTG (Supplementary Figure S1A). Meanwhile, totally 958 compounds were identified under negative ion mode, among which 110 were upregulated and 34 were downregulated in the cells under the co-induction (Supplementary Figure S1B). Further analyses on the metabolite function and pathway were conducted to fully understand the metabolic changes caused by the co-induction. A total of 256 differential metabolites were enriched in 70 metabolic pathways using KEGG database (Figure 4A), including five significantly changed polyphenol biosynthesis pathways, and six samples were well clustered into CPCG and CPTG (Figure 4B). Furthermore, a total of 21 differential polyphenol metabolites were screened out from above-mentioned five pathways, of which 15 were upregulated and six were downregulated (Supplementary Table S7). For example, the content of vitexin 2″-O-p-coumarate, cyanidin-3-O-galactoside and catechin of CPTG were 25.10, 22.06 and 15.45 times that of CPCG, respectively (Supplementary Table S7). The above results and analyses once again indicated that the co-induction could greatly change the secondary metabolism and increase the polyphenol biosynthesis in the cultured C. paliurus cells.
**FIGURE 4:** *(A) Histogram of the top ten enriched biosynthetic pathways. (B) Cluster heat-map based on the analysis of 21 differential metabolisms in six samples.*
## 3.5.1 Functional annotation
In order to further understand the regulatory mechanism of co-induction on polyphenol biosynthesis in cells at the genetic level, we conducted transcriptome sequencing and analysis. Total RNA was extracted and sequenced. A total of 234,653,324 transcripts and 72,795,281 unigenes were generated by splicing and assembling using Trinity software (version number: 2.4.0) (Supplementary Table S8). All of the 72,795,281 unigenes (e-value) < 1 × E−5) were aligned with six public databases, and 45.36, 21.58, 15.99, 21.35, $44.13\%$ and $30.10\%$ of these unigenes had been annotated in NR, GO, KEGG, Pfam, eggNOG and Swiss-prot through BlastX, respectively (Figure 5A). It suggested that *Vitis vinifera* showed the highest homology with C. paliurus ($21.44\%$) (Figure 5B). A total of 71,287 GO notes were divided into three categories such as Cellular Component ($38.75\%$), Biological Process ($37.62\%$) and Molecular Functions ($23.62\%$) (Figure 5C). When blasting the unigenes of the sample cells against KEGG database, a total of 11,379 unigenes were annotated. The annotated information related to metabolic pathway was presented in Figure 5D.
**FIGURE 5:** *Functional annotations of unigenes of suspension-cultured C. paliurus cells. (A) Summary of unigenes annotation of C. paliurus suspension cell against six public databases. (B) Homologous species distribution of C. paliurus based on the annotation from NR database. (C) Histogram of unigene categories and secondary functional groups according to the GO annotation messages. (D) Unigene classifcation based on KEGG annotation.*
## 3.5.2 General analysis of DEGs
A total of 5,233 genes changed significantly under the co-induction of 5-ALA and SA (Supplementary Figure S2A). As shown in Supplementary Figure S2B, KEGG pathway enrichment analysis showed that the expression of structure genes of the pathways related to the polyphenol biosynthesis changed notably, including “Phenylpropanoid biosynthesis,” “Flavonoid biosynthesis,” and “Phenylalanine metabolism” with 30, 5 and 7 DEGs, respectively. Meanwhile, other pathways like signal transduction pathways such as MAPK signaling pathway-plant (15DEGs), Linoleic acid metabolism (4DEGs) and Plant hormone signal transduction (23DEGs) were also involved in the co-induction of 5-ALA and SA (Supplementary Figure S2B). In conclusion, the improvement of polyphenol biosynthesis could be attributed to the DEGs of polyphenol biosynthesis pathway under the co-induction. Therefore, the differentially expressed transcription factors (DETFs) and structural genes related to polyphenol biosynthesis pathway were further explored in detail.
## 3.5.3 Analysis of DETFs related to polyphenol biosynthesis
A total of 7,351 DETFs were identified between CPCG and CPTG (Supplementary Figure S3), among which 17 were involved in SA bio-synthetic pathway (Figure 6A), and 15 were related to polyphenol biosynthesis pathway (Figure 6B). Further analysis revealed that the expression of two CpMYB44s, one CpMYB12, two CpERF105s, three CpMYB10s, four CpWD40s, one CpORG2 and two CpMYC2s changed significantly under the co-induction of 5-ALA and SA. Among these significantly changed DETFs, CpMYB10 and CpERF105 showed a remarkable upregulation expression, on the contrary CpMYB44 and CpMYC2 presented a downregulation expression. TGA family is an important regulator of the signal transduction pathway related to SA biosynthesis. Results indicated that CpTGA1, CpTGA2, CpTGA4, and CpMYC2 were all downregulated under co-induction, among which CpTGA1 and CpTGA2 decreased significantly, while CpWRKY18, CpWRKY28, CpWRKY70, and CpTGA6 were upregulated, especially CpWRKY28 (Figure 6A). Based on our determination results and the above analyses, it could be deduced that the co-induction of 5-ALA and SA might improve polyphenols accumulation by changing the expression of TFs related to polyphenol biosynthesis.
**FIGURE 6:** *The DETFs and DEGs in suspension-cultured C. paliurus cells under the co-induction of 5-ALA and SA. (A) 17 DETFs related to SA signal transduction and biosynthesis pathway in the cultured C. paliurus cells under the co-induction of 5-ALA and SA. (B) 15 DETFs related to polyphenol biosynthesis pathway in the cultured C. paliurus cells under the co-induction of 5-ALA and SA. (C) DEGs related to SA signal transduction and biosynthesis pathway in the cultured C. paliurus cells under the co-induction of 5-ALA and SA. (D) DEGs related to polyphenol biosynthesis pathway in the cultured C. paliurus cells under the co-induction of 5-ALA and SA.*
## 3.5.4 Analysis of DEGs related to polyphenol biosynthesis
As shown in Figure 6C, three CpPALs, two CpICS2s (isochorismate synthase 2) and two CpAIM1s (recombinant Absent In Melanoma 1) were annotated in SA biosynthesis pathway, meanwhile seven CpNPR1 genes (non-expresser of PR genes 1) and three CpPR1 genes (PR genes 1) were annotated in SA signal transduction pathway (as shown in Figure 6C). Under the co-induction of 5-ALA and SA, the expressions of two annotated CpPALs were significantly downregulated, while the expressions of all other annotated SA synthesis pathway genes did not show significant changes, which suggested that co-induction might inhibit SA synthesis by suppressing the expression of CpPAL. We speculated that the exogenous addition of 50 μM SA downregulated the expression of CpPAL, a key gene for SA endogenous biosynthesis, through feedback inhibition during co-induction. Furthermore, the exogenously added SA markedly enhanced the expressions of three annotated CpPR1s and one annotated CpNPR1 (Figure 6C), which made the SA signaling pathway more active, and eventually activated the polyphenol synthesis pathway. As shown in Figures 6A, D total of nine upregulated enzyme genes and five downregulated enzyme genes of the polyphenol biosynthesis pathway were found between CPCG and CPTG. Under the co-induction of 5-ALA and SA, the expressions of CpANS, CpLAR, CpF3′H, CpFLS, Cp4CL, CpF3H, and Cp3GalT were significantly increased, meanwhile the expressions of CpPAL, CpF3′5′H, and CpANR genes were significantly decreased (Figure 6D), which led to a notable improvement of polyphenol synthesis.
## 3.6 QRT-PCR verification of DEGs
The expressions of the identified 8 DEGs (LAR, FLS, F3′5′H, ANR, TGA2, TGA6, NPR1, and PR1) were further verified by QRT-PCR. The determination results (as shown in Figure 7) suggested that the expressions of all candidate genes were consistent with the transcriptome data except FLS, indicating that our RNA-seq detection and analysis were relatively reliable.
**FIGURE 7:** *QRT-PCR determination results of 8 DEGs between CPCG and CPTG. (A)
CpTGA2 relative expression. (B)
CpNPR1 relative expression. (C)
CpPR1 relative expression. (D)
CpTGA6 relative expression. (E)
CpFLS relative expression. (F)
CpF3′5′H relative expression. (G)
CpANR relative expression. (H)
CpLAR relative expression. Note: Significant difference analysis was conducted by independent samples t-test with a p-value of at least less than 0.05.*
## 3.7 Interpretation of co-induction mechanism by integrated analysis of transcriptome and metabolome
According to the above transcriptome analysis, we found that the expression of most upstream key structural genes in the polyphenol biosynthesis pathway were downregulated, while the expressions of downstream key structural genes were overall upregulated (Figure 8). It could be speculated that the upstream genes in the polyphenol biosynthesis pathway firstly responded to the stimulation at the beginning of co-induction and led to the accumulation of the upstream metabolites, afterwards the expression of midstream and downstream genes upregulated successively, and finally resulted in the improvement of the content and yield of polyphenols in the treated cells. Because our samples for RNA-seq were collected on the 6th day (i.e., 2 days after the SA addition and 6 days after 5-ALA addition), at this moment the sample cells were in the middle and late stage of the co-induction, in which the expression of upstream genes such as CpPAL, CpC4H, and CpCHI had already retreated, and the midstream and downstream genes like CpANS, CpLAR, CpF3′H, CpFLS, Cp4CL, CpF3H, and Cp3GalT were highly expressed (Figure 8), and therefore polyphenol accumulations were significantly enhanced. Consequently, our metabolome data showed that the content of dihydrokaempferol, quercetin, quercetin-3β-D-glucoside, cyanidin-3-O-galactoside and catechin in the elicited cells were significantly upregulated, whose content were 13.15, 2.36, 15.45, 22.06, and 2.21 times higher than those in the control group, respectively (Supplementary Table S7).
**FIGURE 8:** *Integration analysis figure of transcriptome and metabolome of the polyphenol biosynthesis in the C. Paliurus cells under the co-induction of 5-ALA and SA.*
Due to the increase of metabolic flow along the whole pathway, other polyphenol substances in the bypasses of polyphenol biosynthesis pathway were also significantly increased accordingly. For example, the content of benzoic and phloretin derived from phenylalanine and 4-coumaryl were greatly raised, which were 9.07 and 5.16 times that in the untreated cells, respectively (Supplementary Table S7). Besides, the content of prunin, hesperetin, vitexin 2″-O-p-coumarate converted from naringin were 1.47, 5.88, 24.10 times higher than those in the control group, respectively, which might also be one of the reasons for the reduce of naringin and eriodictyol (Supplementary Table S7).
In summary, the transcriptome data were generally consistent with the metabolome analysis. The co-induction stimulated the expression of key genes related to polyphenol biosynthesis pathway, and finally promoted the accumulation of polyphenols in the cultured cells (as shown in Supplementary Figure S4).
## 4 Discussion
Elicitation is generally regarded as one of the most effective ways to stimulate plant secondary metabolite synthesis. In the present research, five elicitors such as 5-ALA, SA, SNP, MeJA, and ROE were chosen to improve polyphenol biosynthesis in our cultured C. paliurus cells. We found that there were significant different promotion effects among the five elicitors, meanwhile both addition time and concentration also showed a strong influence on the polyphenol synthesis. Firstly, all the selected five elicitors obviously improved the TPC and TPY of the cultured cells, among which 5-ALA and SA were much more effective in comparison. TPY of the cultured cells stimulated by SA and 5-ALA reached 78.23 mg/L and 66.89 mg/L, respectively. We speculate that different elicitors have different physic-chemical and biological properties, and therefore show different promotion effects in our cultured C. paliurus cells. According to Yu et al. [ 2019], SA, NaCl, and AgNO3 exerted different influences on primary metabolites and secondary metabolites synthesis in the suspension culture of *Salvia miltiorrhiza* Bunge cells. Secondly, addition time plays an important role in the elicitation. Our results suggested that 5-ALA showed a stronger promotion effect when added on the 1st day, while SA worked better when added on the 4th day, which led to a TPY increment of $63\%$ and $90\%$ in our cultured cells, respectively. Li et al. [ 2021] also found that rosmarinic acid yield basically remained unchanged when 200 μM SA was added on the 12th day (cells in plateau phase), while increased 3.3 times when added on the 10th day (cells in mid-exponential phase) in suspension culture of Origanum vulgare cells. Consequently, we deduce that cells in different culture phase have different physiological states, and therefore might present different responses to the external stimulus. Thirdly, elicitor concentration has a profound effect on the induction, which may act on cell growth or on the regulation of polyphenol synthesis. Our experiment data indicated that both 50 μM 5-ALA and SA showed the best stimulatory effect on polyphenol synthesis in the series of our test concentrations. Similar results had been reported by Kara et al. [ 2019]. In addition, our results showed that MeJA with a concentration more than 10 μM had an adverse effect on polyphenol synthesis, however, Liu et al. found that MeJA at a concentration of 200 μM enhanced the biosynthesis of chlorogenic acid and its derivatives in Gardenia jasminoides Ellis cells (Kara et al., 2019), suggesting that elicitors might display species-specific and metabolite-dependent stimulatory effects in plant secondary metabolite synthesis.
To further enhance the synthesis of polyphenols in the cultured cells, a co-induction technology of 5-ALA and SA were developed. With the developed co-induction technology, TPC and TPY were 2.4 and 2.58 times than those of the control group, respectively. It is interesting to note that the content of cyanidin-3-O-galactoside, catechin, dihydrokaempferol and caffeic acid in the co-induced cells increased 21.06, 14.45, 12.15, and 9.60 times. The search for synergistic inducers and the establishment of co-induction techniques to obtain high yields of plant secondary metabolites is now a novel technique that has attracted the attention of many scholars. Li et al. [ 2021] found a synergistic effect between Phe and SA and accordingly established a co-induction technology for O. vulgare cells to obtain a high yield of polyphenols. Under the developed co-induction technology, the polyphenol yield reached 752.93 mg/L, which was respectively 2.32 and 1.13 times that of Phe and SA elicitation individually. According to the report of Zheng et al., 5-ALA and 24-EBL significantly stimulated anthocyanin accumulation in the cultured apple callus, however the promotion effect of co-induction was much stronger than that of 5-ALA and 24-EBL separately (Zheng et al., 2018). With the optimized co-induction technique, a 28.51-fold increase of anthocyanin content was obtained. From the reports available so far, although there have been many studies on co-induction to promote the synthesis of secondary metabolites in plants, the mechanisms of induction signaling and regulation are not yet well understood, which need to be further explored.
In the present paper, integration analysis of transcriptome and metabolome was conducted to reveal the promotion mechanism of 5-ALA and SA co-induction on polyphenol synthesis in cultured C. paliurus cells. The results showed that the expression of CpPAL in the treated cells were significantly downregulated, while CpPR1 and CpNPR1 were significantly upregulated. Zhang and Li [2019] had already demonstrated that there are mainly two pathways to bio-synthesize SA in plants, namely, iso-branched acid pathway and shikimic acid pathway. PAL was a key structural gene in shikimic acid pathway. An increase in SA concentration can inhibit PAL gene expression through negative feedback. Therefore, we deduced that exogenous addition of SA inactivated the shikimic acid pathway by suppressing PAL gene expression, which caused the inhibition of SA synthesis in the co-induced cells. PR1 and NPR1 were two critical genes in SA signal transduction pathway (Saleh et al., 2015). The upregulation of CpPR1 and CpNPR1 expression suggests that co-induction of 5-ALA and SA activated the SA signaling pathway. We inferred that the exogenous addition of SA led to an increase in intracellular SA concentration, which further caused a stress response and consequently activated the SA signaling pathway. Previously, it had been reported that exogenous addition of SA could promote the expression of PR1 gene in *Solanum lycopersicum* (Bozbuga, 2020). In addition, researches had validated that SA could affected the transcriptional activity of NPR1 through various protein modifications, thereby regulating downstream gene expression (Saleh et al., 2015; Withers and Dong, 2016). When SA concentration in plants is low, NPR1 forms multimers and locates in the cytoplasm, meanwhile serine at positions 55 and 59 of NPR1 are phosphorylated, which results in the inhibition of transcriptional activation of NPR1 (Withers and Dong, 2016). When the concentration of SA rises to a certain level, NPR1 changes from a multimer to a monomer and transfers to the nucleus. In the nucleus, NPR1 is ubiquitinated and phosphorylated at positions 11 and 15, which further enhance the transcriptional activation of NPR1 and promote the expression of SA downstream gene (Withers and Dong, 2016).
DETFs and DEGs analysis are commonly used to interpret the mechanisms of secondary metabolic regulation in plants. Our transcriptome sequencing data showed that the expression of TFs such as CpMYB10, CpERF105, and CpWRKY28 in the co-induced cells were significantly upregulated, while the expression of CpMYB44 was downregulated (Figure 6B). In addition, the expression of structure genes in polyphenol synthesis pathway, including CpANS, CpLAR, CpF3′H, CpFLS, Cp4CL, CpF3H, and Cp3GalT, were notably increased. It has been confirmed that MYB, bHLH, ERF were the critical TFs regulating the polyphenol biosynthesis pathway in most plants, especially phenylalanine metabolic pathway (Blanco et al., 2013; Enrique et al., 2017). MYB44 was reported an important negative regulator in the regulation of anthocyanin synthesis. According to Liu et al. [ 2019], StMYB44 repressed anthocyanin accumulation in leaves of N. tabacum by suppressing the activity of DFR promoter. Lin-Wang et al. [ 2014] reported that FvMYB10 co-expressed with FvbHLH33 strongly activated the AtDFR, FvDFR, and FvUFGT promoters, which further significantly promoted the anthocyanin biosynthesis in strawberry (Fragaria vesca). When treated by low temperature, ERF105 expression was positively correlated with the accumulation of polyphenols in *Arabidopsis thaliana* (Bolt et al., 2017). Therefore, we could conclude that co-induction promoted the expression of key genes such as CpDFR in the anthocyanin synthesis pathway by down-regulating CpMYB44 expression and up-regulating CpMYB10 and CpERF105 expression, thus activated the anthocyanin metabolic pathway and ultimately significantly enhanced anthocyanin synthesis in the cultured red C. paliurus cells. In addition, based on our results of integration detection and analysis of transcriptome and metabolome, it could be inferred that co-induction significantly increased the expression levels of CpF3H, CpFLS, and CpLAR, thereby led to a 12.15-, 1.46-, and 14.45-fold increase in the synthesis of dihydromyricetin, quercetin and chitosan, respectively.
The lignin synthesis pathway is also an important branch of the phenylpropane metabolic pathway (Supplementary Figure S5), which is closely related to the flavonoid synthesis pathway, and its expression status may affect the synthesis of flavonoids and anthocyanins, which in turn influence the accumulation of polyphenols. From our experimental results, lignin synthesis was inhibited under co-induction of 5-ALA and SA. As shown in Supplementary Figure S6, the expression levels of CpMYB1, CpMYB6, CpMYB46, CpMYB58, and CpMYB63 were decreased, especially CpMYB1 and CpMYB63 were significantly decreased, while CpMYB31 was upregulated, which may lead to a decrease in the expression levels of downstream structural genes such as CpCCoMT, CpCOMT, CpCCR, and CpCAD. It was reported that AtMYB58 and AtMYB63 are the first identified true lignin-specific transcript factors in Arabidopsis, and act as activators that regulate most monolignol genes to synthesize lignin (Perkins et al., 2020). Fornalé et al. [ 2006] found that the over-expression of ZmMYB31 may affect the expression of COMT gene and produced a decrease in lignin content in Zea mays. These changes in the expression levels of genes ultimately inhibited the entire lignin biosynthesis pathway. Therefore, we could deduce that co-induction may reduce the metabolic flow of the phenanthrene metabolic pathway to the bypass in order to promote the flavonoid and anthocyanin biosynthesis pathways, and eventually enhance the accumulation of polyphenols.
At present, there are two major obstacles for industrial production of secondary metabolites by plant cell culture, i.e., low yields and high costs. As a result, only a few plant secondary metabolites have been successfully industrialized by plant cell culture, which are either of high value or of high content in the cultured plant cells. Frankly speaking, polyphenol content of the cells is not much high, so in order to create a foundation for large-scale production in the future, it is necessary to study the technical means to increase its content. However, the polyphenol yield of our cultured C. paliurus cells was 147.12 mg/L under the co-induction of 5-ALA and SA, which is currently not enough to meet the requirements of industrialized production. Consequently, we will consider the following three ways to further improve polyphenol yield in our future researches. Firstly, cell lines with high polyphenol content can be obtained through mutagenesis and screening. Secondly, screening novel inducers is a feasible approach. Thirdly, optimization of the co-induction technology is another potential way.
However, the production of polyphenols by our red C. paliurus cell culture had the outstanding advantage of a relatively short production cycle, which was only 6 days. Currently, most production cycles for secondary metabolite production by plant cell culture are about 10–15 days (Ten Hoopen et al., 1994; Liu et al., 2020). For example, the production cycle of paclitaxel with Taxus suspension cells was 15 days (Li et al., 2009). Relatively speaking, the production of polyphenols by red C. paliurus cell culture had a shorter cycle, which could reduce the possibility of contamination and lower production costs to a certain extent.
## 5 Conclusion
Five elicitors were used to stimulate the polyphenol synthesis in the cultured C. paliurus cells. 5-ALA and SA showed better promotion effects and therefore were selected to develop a co-induction technology. Under the optimized co-induction technology (50 μM 5-ALA and 50 μM SA were added on the 1st and 4th day after inoculation, respectively), the content and yield of total polyphenols in the treated cells reached 8.0 mg/g and 147.12 mg/L, respectively. ( +)-Catechin was the most abundant polyphenol, whose content and yield were as high as 5 mg/g and 91.67 mg/L, respectively. Integration detection and analysis of transcriptome and metabolome showed that the co-induction of 5-ALA and SA significantly changed the expressions of 14 pathway genes and 7 TFs related to polyphenol biosynthesis. Under the co-induction, the expressions of TFs such as CpERF105, CpMYB10, and CpWRKY28 increased significantly, which might further upregulated the expressions of CpANS, CpLAR, CpF3′H, CpFLS, Cp4CL, CpF3H, and Cp3GalT and downregulated the expressions of CpPAL, CpANR, and CpF3′5′H, and finally enhanced the polyphenol accumulation.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.
## Author contributions
Conceptualization, D-BT, J-GC, and Z-PY; Data curation, L-JL and Y-YZ; Formal analysis, EY; Funding acquisition, Z-PY; Investigation, L-JL, C-QP and D-YP; Methodology, L-JL, D-BT, J-GC, D-YP, and MW; Project administration, Z-PY; Supervision, J-GC; Validation, L-JL and EY; Visualization, Z-PY; Writing—original draft, L-JL; Writing—review and editing, L-JL, MW, and Z-PY.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fbioe.2023.1150842/full#supplementary-material
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|
---
title: Effect of metformin on sepsis-associated acute lung injury and gut microbiota
in aged rats with sepsis
authors:
- Youdong Wan
- Shuya Wang
- Yifan Niu
- Boyang Duo
- Yinshuang Liu
- Zhenzhen Lu
- Ruixue Zhu
journal: Frontiers in Cellular and Infection Microbiology
year: 2023
pmcid: PMC10034768
doi: 10.3389/fcimb.2023.1139436
license: CC BY 4.0
---
# Effect of metformin on sepsis-associated acute lung injury and gut microbiota in aged rats with sepsis
## Abstract
### Background
Recent studies reported the association between the changes in gut microbiota and sepsis, but there is unclear for the gut microbes on aged sepsis is associated acute lung injury (SALI), and metformin treatment for the change in gut microbiota. This study aimed to investigate the effect of metformin on gut microbiota and SALI in aged rats with sepsis. It also explored the therapeutic mechanism and the effect of metformin on aged rats with SALI.
### Methods
Aged 20-21 months SD rats were categorized into three groups: sham-operated rats (AgS group), rats with cecal ligation and puncture (CLP)-induced sepsis (AgCLP group), and rats treated with metformin (100 mg/kg) orally 1 h after CLP treatment (AgMET group). We collected feces from rats and analyzed them by 16S rRNA sequencing. Further, the lung samples were collected for histological analysis and quantitative real-time PCR (qPCR) assay and so on.
### Results
This study showed that some pathological changes occurring in the lungs of aged rats, such as hemorrhage, edema, and inflammation, improved after metformin treatment; the number of hepatocyte death increased in the AgCLP group, and decreased in the AgMET group. Moreover, metformin relieved SALI inflammation and damage. Importantly, the gut microbiota composition among the three groups in aged SALI rats was different. In particular, the proportion of E. coli and K. pneumoniae was higher in AgCLP group rats than AgS group rats and AgMET group rats; while metformin could increase the proportion of Firmicutes, Lactobacillus, Ruminococcus_1 and Lactobacillus_johnsonii in aged SALI rats. Moreover, Prevotella_9, Klebsiella and Escherichia_Shigella were correlated positively with the inflammatory factor IL-1 in the lung tissues; Firmicutes was correlated negatively with the inflammatory factor IL-1 and IL-6 in the lung tissues.
### Conclusions
Our findings suggested that metformin could improve SALI and gut microbiota in aged rats, which could provide a potential therapeutic treatment for SALI in aged sepsis.
## Introduction
Sepsis refers to life-threatening organ dysfunction caused by the dysregulation of the host’s response to infection (Singer et al., 2016), and it’s mortality as high as $33\%$, which affects millions of people worldwide (Angus et al., 2001; Dellinger, 2003; Buchman et al., 2020). Furthermore, studies showed more than $60\%$ septic patients are elderly people (>65 years), and an exponential increase in the incidence and mortality of sepsis in the elderly patients (Milbrandt et al., 2010; Mankowski et al., 2020). The lung is the first organ involved in the progress of sepsis. According to epidemiological data, about $50\%$ of septic patients complicated with acute lung injury (ALI) or acute respiratory distress syndrome (ARDS) in the intensive care unit(ICU) (Sevransky et al., 2009; Aziz et al., 2018). As high mortality of the sepsis-related lung injury (SALI) patients and a poor prognosis in SALI suggest a lack of clinically feasible therapeutic methods. Therefore, studying the pathogenesis of SALI to search a new effective therapy method is necessary.
The gut microbiota is the the chief regulator in maintaining homeostasis of the host (Honda and Littman, 2016). The gut microbial composition changes with age (Yatsunenko et al., 2012), as such from infant to adult shows from dominant Bififidobacterium to Bacteroidetes and Clostridia genus, and to age shows the Centenarians’ gut microbiota with pathogenic microbiota increasing, which different from the infant and adults (Mariat et al., 2009; Claesson et al., 2012; Rampelli et al., 2013). Furthermore, the above gut microbial composition varies indicates the decreased short-chain fatty acid (SCFA) production, leading to the intestinal inflammation and the mucosa reduction and gut permeability increasing. More importantly, the Centenarians’ gut microbiota, Specifically, the relative abundance of the Faecalibacterium, Eubacteriaceae, Lactobacillus and Clostridia decreased and the relative abundance of the Proteobacteria and *Bacilli is* reduction, resulting the productive SCFA gut microbiota is decreasing, and yet changed pathogen is increasing in the elderly (Rampelli et al., 2013). Additionally, the previous study showed that chronic mild inflammation in the elderly, which might lead to an abnormal increase in intestinal wall permeability (Buford et al., 2018). Therefore, maintaining the balance of the gut microbiota and improving the gut permeability may be effective measures for treating age-related SALI.
Metformin is the first-line treatment in patients with type 2 diabetes (Flory and Lipska, 2019). Studies confirmed that metformin had an anti-inflammatory effect on the expression of inflammatory factors (Cameron et al., 2016; Wu et al., 2018). Furthermore, some studies showed that metformin delayed aging by reducing the production of reactive oxygen species (Valencia et al., 2017). Metformin is taken orally and absorbed mainly in the small intestine (Buse et al., 2016; Honda and Littman, 2016). Metformin also affects the resident intestinal microbiota and can correct intestinal damage by augmenting the intestinal microbiota (Lee et al., 2018; Maniar et al., 2018; Brandt et al., 2019). This study was performed to investigate the effect of metformin on the intestinal microbiota of senile rats with sepsis and assess whether metformin could be an effective drug for treating SALI.
## Animals
Twenty-four male Sprague–Dawley (SD) rats (6–8 weeks) were purchased from Beijing Vital River Laboratory Animal Technology (Beijing, China) and housed until 20–21 months. All rats were housed in a specific pathogen-free animal laboratory. The rats were fed a standard diet and purified water under controlled laboratory conditions (12-h light/dark cycle). They were randomly divided into three groups: sham-operated rats (AgS group, $$n = 4$$), rats with cecal ligation and puncture (CLP)-induced sepsis (AgCLP group, $$n = 12$$), and rats treated with oral metformin (100 mg/kg) 1 h after AgCLP treatment (AgMET group, $$n = 8$$). The AgCLP model was used in this study, and anesthesia was provided by injecting hydantoin ($10\%$, 3–4 mL/g) into the peritoneal cavity. Microsurgery was performed in the midline of the abdomen and involved ligating one half of the free end of the cecum, puncturing the cecum with a 21-gauge needle in two locations at the ligature site, and applying gentle pressure until the feces were extruded. The bowel was then placed back into the abdominal cavity, and the incision was closed. The procedure was performed by the same person to minimize the impact of different ligation and puncture sites on the results. After closing the incision, the rats were injected subcutaneously with normal saline (1 mL/100 g) at 37°C and placed back in the cage to rewarm for 1 h. In the AgS group, the rats underwent only open surgery and did not undergo ligation or puncture of the cecum. The rats in the metformin group were given metformin (25 mg/kg) intragastrically 1 h after the surgery. All rats were sacrificed 24 h after CLP treatment. Only five aged rats with SALI survived in the AgCLP group, and four aged rats with SALI survived in the AgMET group after 24 h of CLP treatment. This animal experiment was approved by the Life Science Ethics Review Committee of Zhengzhou University.
## Histological analysis
The tissues from rat lungs were analyzed using hematoxylin and eosin (H&E) staining. The lung and kidney tissues were fixed in $4\%$ paraformaldehyde for 24h and embedded in paraffin. The tissues were then stained and observed under a light microscope. The degree of alveolar congestion, hemorrhage, infiltration and aggregation of neutrophils or leucocytes, and alveolar wall thickness was observed. The lung sections were scored using the aforementioned indicators, with a maximum score of 16.
## Terminal deoxynucleotide transferase d-UTP nick-end labeling assay
The lung tissues were paraffin-embedded and fixed, and the proportion of apoptotic cells was determined using a terminal deoxynucleotide transferase d-UTP nick-end labeling (TUNEL) assay and fluorescence microscopy.
## 16S rRNA gene sequencing for gut microbiota analysis
First, extraction of genome DNA: we used the CTAB/SDS method to extract the genome DNA, and its’ concentration and purity was monitored on $1\%$ agarose gels. And then we diluted the DNA to 1ng/μL by using sterile water based on the concentration. Next, the primers 341F (5’-CCTAyGGRBGCasCAG-3’) and 806R (5’-GGA CTA CNN GGG TAT CTA AT-3’) were used to amplify the 16S rRNA genes of 16SV3–V4 regions with the barcode. All PCR reactions were carried out with Phusion® High-Fidelity PCR Master Mix (New England Biolabs) is used to performed all PCR reactions. Then, PCR Products quantification and qualification analysis. Mix same volume of 1X loading buffer (contained SYB green) with PCR products and operate electrophoresis on $2\%$ agarose gel for detection. Samples with bright main strip between 400-450bp(16S) and ITS (100-400bp) were chosen for further experiments. PCR products was mixed in equidensity ratios. Then, the Qiagen Gel Extraction Kit was used to purify the mixture PCR products (Qiagen, Germany).Sequencing libraries were generated using TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, USA) following manufacturer’s recommendations and index codes were added. The library quality was assessed on the Qubit® 2.0 Fluorometer (Thermo Scientific) and Agilent Bioanalyzer 2100 system. At last, the library was sequenced on an Illumina NovaSeq 6000 platform and 250 bp paired-end reads were generated. According to $97\%$ similarity, we used the Usearch (version 11.0.667) with no ambiguous bases to cluster. The Mothur v1.42.1 and the vegan package in R-package were used to calculated the Alpha diversity and beta diversity, respectively. The PICRUSt2 software package (https://github.com/picrust/picrust2) used to calculate the pathway enrichment.
## Quantification of mRNA using qRT–PCR
We used TRIzol reagent (TaKaRa, Tokyo, Japan) to extract total RNA from the lungs. The concentration and purity of RNA were quantified using ultraviolet spectroscopy. The corresponding cDNA was synthesized by reverse transcription of mRNA using a TaqMan reverse transcription kit (UE, Suzhou, China). All qRT-PCR was used 40 cycles for amplification, and the results were analyzed by the 2-ΔΔCT method. *The* gene expression was normalized using reduced glyceraldehyde 3-phosphate dehydrogenase (GAPDH) expression. *The* gene primers were chemokine (C-C motif) ligand 7 (CCL7) forward primer: CTTCTGTGTGTGCTGCTCAAC, reverse primer: CTATGGCCTCCTCAACCCAC; interleukin (IL)-6 forward primer: AGAGACTTCCAGCCAGTTGC, reverse primer: AGTCTCCTCTCCGGACTTGT; CCL3 forward primer: TGCTGTTCTTCTCTGCACCA, reverse primer: CAGGTCCTTTGGGGTCAGC; IL-1β forward primer: GCAACTGTTCCTGAACTCAACT, reverse primer: ATCTTTTGGGGTCCGTCAACT; chemokine (C-X-C motif) ligand 1 (CXCL1) forward primer: CGCTCGCTTCTCTGTGCA, reverse primer: TTCTGAACCATGGGGGCTTC; and GAPDH forward primer: TGTGAACGGATTTGGCCGTA, reverse primer: GATGGTGATGGGTTTCCCGT.
## Statistical analysis
The GraphPad Prism (Version 6.0; GraphPad Software Inc., USA) R-package were used to statistical analyses. The quantitative data was assessed by mean ± standard deviation. The unidirectional or bidirectional analysis of variance (ANOVA) was used for multiple groups, and an unpaired-sample Student t test was used for the statistical analysis in two groups. Furthermore, the Mothur v1.42.1 and the vegan package in R-package were used to calculated the Alpha diversity and beta diversity, respectively. The PICRUSt2 software package (https://github.com/picrust/picrust2) used to calculate the pathway enrichment. P value <0.05 indicated a statistically significant difference.
## Metformin alleviated the inflammation and lung injury in aged rats with SALI
A previous study (DeFronzo et al., 2016) reported that metformin may induce kidney failure result of lactic acidosis. Hence, the histological analysis to check whether metformin could induce kidney injury. The results showed that metformin did not cause kidney damage, confirming that the dose of metformin was safe to administer (Figure S1). We next performed H&E and TUNEL assays on lung tissues to assess the effect of metformin on SALI in aged rats. The results showed that lung tissue destruction, inflammatory infiltration, and alveolar wall thickening in the aged rats in the AgCLP group compared with the rats in the AgS group. However, lung tissue destruction and inflammatory infiltration were significantly improved in the AgMET group (Figures 1A, B, $P \leq 0.05$). The effect of metformin on apoptotic cells of lung tissues in aged rats with sepsis was determined by assessing the percentage of apoptotic cells through the TUNEL staining. The apoptotic cells of lung tissues significantly increased in aged rats with sepsis compared with that in AgS rats, and metformin treatment attenuated sepsis-induced apoptotic cells of lung tissues (Figures 1C, D, $P \leq 0.05$). Further, the mRNA expression of the inflammatory factors CCL3, CCL7, CXCL1, IL-1, and IL-6 substantially increased in aged rats with SALI compared with the AgS group. However, metformin reversed the expression of these inflammatory factors induced by sepsis (Figures 1E, I, $P \leq 0.05$). Metformin improved the inflammatory response in rats with sepsis, which was consistent with our previously reported results (Liang et al., 2022). Hence, these data suggested that metformin attenuated lung injury and inflammation in aged rats with SALI.
**Figure 1:** *Metformin reduces SALI in aged rats with sepsis. (A) H&E staining showed edema and an increased amount of hemorrhage in the lung tissue of rats in the AgCLP group compared with the control group. These changes could be reversed after metformin treatment. (B) The level of lung injury was assessed semi-quantitatively based on the lung injury score (P < 0.05). (C) TUNEL results showed an increased apoptosis in the AgCLP group and a decreased apoptosis in the AgMET group. (D) Three sections were taken from each group, and the number of apoptotic cells was measured (P < 0.05). (E-I) ccl3, ccl7, cxcl1, IL-1 and IL-6 expression increased in AgCLP group and metformin could decreased these factors’ expression. *p<0.05; **p<0.01; ***p<0.001.*
## Effects of metformin on the intestinal microbiota in aged rats
16S rRNA metagenomic analysis was used to assess the composition of gut microbiota, evaluating the effect of metformin on gut microbiota in aged rats with SALI. Alpha diversity (ACE, Chao1, Shannon, and Simpson indexes) can reflect the abundance of bacteria in the community. We used ACE and Chao1 to assess the abundance of microbiota. The results showed that metformin could improve the SALI induced the decreased Alpha diversity (Figure 2A). Next, we analyzed the β-diversity between bacterial populations. β-diversity assessed the differences of gut microbiota between multiple samples and the changes of microbiome under different factors. The results of β-diversity showed that compared with the AgCLP group, the gut microbiota in AgMET group was similar to that in the AgS group (Figure 2B).
**Figure 2:** *Effect of metformin on intestinal microbiota diversity in aged rats with sepsis. (A)16S rRNA metagenomic analysis for α-diversity (ACE, Chao1); (B) β-diversity reflected microbial richness in groups and between groups.*
We explored the effect of metformin on the abundance of gut microbiota. The results showed that compared with the AgS and AgCLP groups, the AgMET group had a higher Firmicutes/Bacteroidetes ratio (Figure S2). At the genus level, the abundance of Lactobacillus slightly increased, while the abundance of Ruminococcus-1 decreased in the AgCLP group compared with the AgS group, and increased after metformin treatment. Furthermore, the increased relative abundance of Prevotella-9 in the AgCLP group compared with the AgS group, which related with inflammation, while metformin could reverse this change. At the species level, the abundance of *Lactobacillus johnsonii* slightly increased, and *Escherichia coli* and K. pneumoniae increased in the AgCLP group compared with the AgS and AgMET groups (Figures 3A, B). In the AgCLP group, the increase in the abundance of these opportunistic pathogens increased the inflammatory response of the host, which was consistent with our previous findings (Liang et al., 2022). Furthermore, the abundance of Romboutsia and Ruminococcus-1 decreased and the abundance of Proteobacteria, Escherichia coli, and K. pneumoniae increased in AgCLP group compared with that in the AgMET group, indicating a decrease in the abundance of intestinal microbiota associated with SCFA production. In addition, we analyzed the correlations between inflammatory factors and gut microbiota (Figure 4), the results show that Klebsiella pneumoniae, Escherichia coli, Prevotella-9 and Proteobacteria were positively correlated with iL-6, while Romboutsia, Latobacillus, Latobacillus_johnsonii,Firmicutes were negatively correlated with IL-1 and IL-6. Therefore, metformin administration could improve the gut microbiota disorder in aged rats with SALI.
**Figure 3:** *Effects of metformin on intestinal microbiota. (A) Three figures show the differences between the microbiota in the AgMET, AgS, and AgCLP groups at the phylum, genus, and species levels, respectively. The abundance of opportunistic pathogenic bacteria increased in the AgCLP group. (B) Histogram showing the differential microbiota, highlighting the changes in intestinal microbiota between the experimental groups. *p<0.05; **p<0.01.* **Figure 4:** *Correlation analysis. Correlation analysis between microbiota and inflammatory parameters.*
## Discussion
This study assessed the effect of metformin for alleviating inflammation, lung injury and gut microbiota disorder in aged rats with sepsis. Metformin treatment reversed the pathological changes including lung tissue damage, hemorrhage, and edema in aged rats with CLP-induced sepsis. Meanwhile, significant apoptotic cells of lung tissues in the AgCLP group but with a considerable improvement in the AgMET group. The gut microbiota composition in the AgMET group varied from such as an increasing relative abundance of some opportunistic pathogen such as E. coli and K. pneumoniae, relating with LPS production and inflammation to anti-inflammation, which exerted the protection of aged rats with SALI. These results showed that metformin is a therapeutic alternative for treating aged SALI.
Metformin is a clinical first-line hypoglycemic drug as its specific antihyperglycemic properties with excellent safety profile. Metformin also has other functions such as anti-aging and anti-tumor effects (Shafiee et al., 2014; Pryor et al., 2019). Furthermore, enhanced the lifespan of Caenorhabditis by affecting the metabolism of microbial folate and methionine by modifying the gut microbiota (Cabreiro et al., 2013). Metformin could inhibit IκB kinase/nuclear factor-κB activation to suppress the expression of senescence-related factors (Moiseeva et al., 2013). It was later discovered that fecal microbial transplantation decreased the expression of IL-18 (Lee et al., 2019). Metformin improved the intestinal barrier function by modulating the gut microbiota, thereby increasing the number of mucus-producing goblet cells (Hur and Lee, 2015). Additionally, metformin inhibited apoptosis via the phosphoinositide 3-kinase/Akt signaling pathway, which was found to be effective in brain injury caused by sepsis (Tang et al., 2017). These studies also supported our findings.
The Firmicutes and *Bacteroidetes is* the main gut microbiota in human, accounting for $75.9\%$ and $10.83\%$, respectively (Pan et al., 2018), which was consistent with the results of this study. We found that the abundance of Firmicutes decreased in the AgCLP group compared with the AgS group, and this change was reversed after metformin treatment. Firmicutes are mainly Gram-negative bacteria, consisting of specialized anaerobes or parthenogenic anaerobes, of which *Faecalibacterium is* involved in the formation of butyric acid, while *Dialister is* engaged in the final phase of propionic acid production (Nava et al., 2011; Tanca et al., 2017). The abundance of Firmicutes decreased noticeably in type 2 diabetes. In this study, AgCLP caused variations in the abundance of intestinal microbiota, such as a decline in the abundance of lactic acid–producing bacteria and probiotics and an increase in the abundance of opportunistic pathogenic bacteria associated with inflammation, such as E. coli and K. pneumoniae. Metformin treatment reversed these changes, resulting in an increase in the abundance of L. johnsonii and a decline in the abundance of E. coli and K. pneumoniae. Bacteroides thetaiotaomicron and L. johnsonii reduced the infiltration of intestinal inflammatory cells, alleviated edema, disrupted the cell wall mannans of Candida albicans, and inhibited the development of C. albicans (Charlet et al., 2020). In sepsis, intestinal barrier dysfunction and increased permeability contribute to the pathological transfer of intestinal bacteria or endotoxins, worsening sepsis (Hassoun et al., 2001; Meng et al., 2017). Metformin further enhances intestinal barrier function by increasing the number of villi through the modulation of intestinal microbiota, such as Firmicutes and lactic acid–producing bacteria. Prevotella interacts with the immune system and enhances mucosal inflammation mediated by TH17, stimulating epithelial cells to produce inflammatory factors such as IL-8 and IL-6 (Zeng et al., 2019), which agreed with our results.
This study had some limitations. First, the sample size in the experimental groups was small, and this findings should be confirmed by other similar studies, in the other hand, in the next study, we could be further verify the findings. Second, we did not include the metformin-alone group, reducing the rigor of the study. However, after metformin treatment, the kidney injury had no difference between AgCLP group and AgMET group, which suggested the dose of metformin is safe. More importantly, our previous study (Liang et al., 2022) search the effect of metformin alone group on septic aged rats.
## Conclusions
This study indicated that metformin could relieve inflammation, lung injury and gut microbiota in aged rats with SALI. More importantly, metformin reversed the imbalance of gut microbiota such as as increasing relative abundance of opportunistic pathogen such as E. coli and K. pneumoniae. in aged rats with sepsis, which could provide a potential treatment for aged SALI.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: NCBI, PRJNA938428.
## Ethics statement
The animal study was reviewed and approved by the Life Science Ethics Review Committee of the Affiliated Hospital of Qingdao University.
## Author contributions
RZ and YW designed the study. SW, YN, BD, YL and ZL performed the experiments and collected the animal sample, and they also conducted the data analysis. YW wrote the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcimb.2023.1139436/full#supplementary-material
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|
---
title: Acceptability and feasibility of home-based hypertension and physical activity
screening by community health workers in an under-resourced community in South Africa
authors:
- Mark Stoutenberg
- Simone H. Crouch
- Lia K. McNulty
- Andrea Kolkenbeck-Ruh
- Georgia Torres
- Philippe J. L. Gradidge
- Andy Ly
- Lisa J. Ware
journal: Zeitschrift Fur Gesundheitswissenschaften
year: 2023
pmcid: PMC10034884
doi: 10.1007/s10389-023-01873-w
license: CC BY 4.0
---
# Acceptability and feasibility of home-based hypertension and physical activity screening by community health workers in an under-resourced community in South Africa
## Abstract
### Background
Low–middle-income countries (LMICs) face increasing burdens from non-communicable disease (NCDs) requiring primary care task shifting to community health workers (CHWs). This study explored community members' perceptions of NCD-focused, CHW-led home visits in a historically disadvantaged township of South Africa.
### Methods
Trained CHWs visited community member homes, performing blood pressure and physical activity (PA) screenings, followed by brief counselling and a satisfaction survey. Semi-structured interviews were conducted within 3 days of the visit to learn about their experiences.
### Results
CHWs visited 173 households, with 153 adult community members consenting to participate ($88.4\%$). Participants reported that it was easy to understand CHW-delivered information ($97\%$), their questions were answered well ($100\%$), and they would request home service again ($93\%$). Twenty-eight follow-up interviews revealed four main themes: 1) acceptance of CHW visits, 2) openness to counselling, 3) satisfaction with screening and a basic understanding of the results, and 4) receptiveness to the PA advice.
### Conclusion
Community members viewed CHW-led home visits as an acceptable and feasible method for providing NCD-focused healthcare services in an under-resourced community. Expanding primary care reach through CHWs offers more accessible and individualized care, reducing barriers for individuals in under-resourced communities to access support for NCD risk reduction.
## Background
The burden of non-communicable diseases (NCDs) in sub-Saharan Africa has increased in recent years (Gouda et al. 2019). Primary hypertension, defined in South Africa as a resting systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg and/or use of antihypertensive medication to control blood pressure (Rayner et al. 2019), is a leading cause of cardiovascular-associated mortality (Olsen et al. 2016; Zhou et al. 2021). The prevalence of hypertension has increased significantly in the last 30 years due to poor detection and blood pressure control and management, with low- and middle-income countries (LMICs) experiencing the highest burden (NCD Risk Factor Collaboration 2021). Several modifiable risk factors, including obesity, poor quality diets with excess sodium consumption, and physical inactivity, contribute to the development of hypertension (Mirmiran et al. 2018; Masilela et al. 2020). While knowledge about hypertension and associated behavioural risk factors is increasing, there is still a need for efforts to increase health awareness (Jongen et al. 2019).
Community health workers (CHWs) have become a critical part of healthcare service infrastructure in many countries (LeBan et al. 2021). CHWs provide additional workforce where resources are limited, increase access to basic services in underserved and vulnerable communities, and allow for task shifting to alleviate overstretched health systems (Perry et al. 2014). In their role supporting HIV patients in sub-Saharan Africa, CHWs reduced staff workload and patient wait times at healthcare facilities, expanded reach of health services in communities, increased uptake and quality of HIV services, and improved patient retention in care (Mwai et al. 2013). CHWs can also play a beneficial role in improving health outcomes and reducing community burden of NCDs, while improving equity in health care delivery and service (Jeet et al. 2017)]. A systematic review by Kim et al. [ 2016] reported positive outcomes for CHW interventions and cardiovascular disease risk reduction, as well as blood pressure and type 2 diabetes control. Previous work also demonstrates the potential of CHW-led interventions in increasing physical activity levels (Costa et al. 2015).
Following international guidelines for the expansion of activities performed by CHWs (Singh and Sachs 2013), the role of the CHW in primary care has expanded greatly over the last decade. In 2010, the South African National Department of Health launched a national primary health care initiative, which called upon CHWs to serve on ward-based primary health outreach teams providing education, promoting healthy behaviours, and supporting community member linkage to health services and health facilities (Mhlongo and Lutge 2019). This has led to CHWs providing greater levels of health and social services to community members for a range of conditions, including the screening, referral, and management of NCDs, such as type 2 diabetes and hypertension (le Roux et al. 2015; Morris-Paxton et al. 2018; Ramukumba 2020). Another unique CHW role is the ability to bring healthcare services to patients in their own homes. CHW-led home visits have proven beneficial for multiple health-related activities including immunization efforts, healthy development of children, and maternal health (Tripathi et al. 2016; Stansert Katzen et al. 2021).
Despite these advances, little is known about the role of CHWs in overcoming barriers and expanding the reach of NCD prevention efforts, particularly in historically disadvantaged communities. Outside of a few efforts in North America (Vidoni et al. 2019), efforts to investigate CHW-led, home-based NCD prevention and management activities are relatively sparse. Therefore, the goal of this study was to examine the feasibility and acceptability of CHW-led home visits for NCD risk reduction in a low-resource community.
## Study overview
This study took place in conjunction with a series of home visits by CHWs to conduct health screening assessments in Soweto, South Africa in September 2021. The home visits consisted of: 1) CHWs greeting residents, introducing themselves, and explaining the purpose of their visit, 2) obtaining permission to enter the home, 3) performing the informed consent process, 4) conducting a standardised health assessment protocol, and 5) completing a brief satisfaction survey at the end of the visit. Residents who gave consent to be recontacted for a follow-up interview were then contacted within 3 days to participate in a semi-structured interview to gain a deeper understanding of their experience with the CHW-led health assessment conducted in their home setting.
## Description of community health workers (CHWs)
The CHWs were young adults (age 18–30 years) undertaking training in health promotion, health behaviour change support, and basic community health screening as part of an accredited community health work qualification (South African National Qualification Framework Health Promotion Officer/Community Health Worker). Standard minimum entry requirements for community health work applied. The training programme formed part of a youth employment initiative and research programme operated by the Wits Health Hub (www.witshealthhubb.org) with funding support from government. Therefore, youth were eligible for this program only if they were from the local community and not already in employment, education, or training. Selection for the programme involved basic language and mathematics competency tests and interviews to assess core competencies for the role.
## Study design and setting
The home-based health screening was conducted in Soweto within a 5-km radius of the CHW training facility. The training facility, focused on youth development, is in an area of historical deprivation characterized by high-density housing (hostels and flats) that has been targeted in recent years for economic urban development and infrastructure investment. In 2021 alone, there were 398 reported assaults, 50 murders, 20 attempted murders, 92 sexual crimes, and 1,137 total contact crimes reported for this area (Institute for Security Studies issafrica.org/crimehub 2022). Twenty CHWs went in pairs door-to-door in the selected community during September 2021 to conduct health screening assessments, which are 20–35 minutes in duration. During the visit, household members received details of the study and were assessed for their eligibility to participate. Eligibility criteria included being: 1) at least 18 years old, 2) willing to provide written informed consent, 3) fluent in English, and 4) not displaying any symptoms of COVID-19 (determined by an infrared, non-contact thermometer reading of ≤ 37.5°C or the presence of symptoms). Before proceeding with any study procedures, household members were asked to provide their written informed consent, as well as consent to be contacted for a follow-up interview.
## Procedures for home visit
Eligible household members that provided informed consent had their height and waist circumference measured to the nearest 0.1cm using a disposable tape measure following standardized World Health Organization measurement protocols (de Onis et al. 2004). Thereafter, seated brachial blood pressure was measured using an automated device (M10-IT; Omron Healthcare, Kyto, Japan) following International Society of Hypertension (ISH) guidelines (Unger et al. 2020). Participants with a blood pressure of $\frac{140}{90}$ mmHg or above were provided with a referral to the local clinic for follow-up. The participants were then asked to complete a health questionnaire to obtain self-reported medical history (known hypertension, current hypertension treatment, known type 2 diabetes mellitus, previous heart attack/stroke, past COVID-19 infection, COVID-19 vaccination status) and health behaviour (tobacco and alcohol usage). Thereafter, physical activity was assessed using the Physical Activity Vital Sign (PAVS) questionnaire (Sallis et al. 2016; Stoutenberg et al. 2017) asking participants, on average, how many days per week they engage in moderate to strenuous exercise and for how long.
## Satisfaction survey
Following the health screening, the household members were asked to compete a short satisfaction survey regarding their experiences with the CHWs and the home visit. The satisfaction survey queried them on their general attitudes towards receiving home-based screening and guidance from the CHWs, their perceived monetary value of the visit, and how they felt about their health [using a scale from 0 (worst) to 100 (excellent)] prior to and after the visit from the CHW. Satisfaction surveys were administered through the tablets and completed in English. Study data were collected and managed using REDCap electronic data capture tool (Harris et al. 2009) hosted at the University of the Witwatersrand.
## In-depth interviews
After completing the measurements and the satisfaction survey, participants were asked if they would be interested in participating in an in-depth interview in the future to share their general attitudes towards the home health visit, the blood pressure and physical activity screening, and the guidance received from the CHWs. The in-depth interviews were conducted within 3 days of the initial home visit to promote adequate recall. All interviews were recorded and transcribed verbatim. Interviews were conducted in English when possible, or in the participant’s preferred language with transcription and translation of audio recordings by transcribers checked by the CHW and the research team.
## Health data and satisfaction surveys
Central obesity was categorized as a ratio of waist circumference to height of ≥ 0.5 (Ashwell and Gibson 2014). Hypertension was defined as a blood pressure ≥ 140 mmHg systolic or ≥ 90 mmHg diastolic or currently taking anti-hypertensive medication (Unger et al. 2020). For those not on anti-hypertensive medication, elevated blood pressure (prehypertension) was defined as 130–139 mmHg systolic or 85–89 mmHg diastolic, following the Hypertension Practice Guidelines (Unger et al. 2020). For assessments captured as continuous variables (i.e., age, years of education, height, waist circumference, systolic and diastolic blood pressure), visual inspection of histograms informed the normality of data, and the mean and standard deviation were reported for normally distributed data. Median and interquartile ranges were reported for non-normally distributed data, while absolute numbers and percentages were reported for categorical variables. To test for differences between men and women in the sample, an independent t-test, Mann–Whitney U test, or chi-square test was used. The Wilcoxon signed-rank test was used to compare repeated measures within the same sample. Statistical data analysis was conducted in SPSS 24.0.
## Follow up interviews
Interview transcripts were analysed using thematic analysis, as outlined by Braun and Clarke [2006]. A combination of deductive and inductive analyses was used to assess participant experiences engaging with the CHW home visits. Two research team members (the raters) read a subset of transcripts to develop an initial codebook based on participant responses. The raters (LM & AL) coded two transcripts together to refine the codebook. The research team met regularly to discuss codebook changes, verify that codes were applied systematically, and reach consensus on discrepant ratings. Five transcripts were selected to assess the percentage of agreement between raters, calculated by the number of times both raters assigned the same code to a text segment. Initially, the two raters agreed on $96.4\%$ of the independently coded data, but $100\%$ consensus was reached through further discussion. Coded text was entered into Dedoose (version 7.0.23) to perform the content analysis and to extract coded participant responses. Codes were sorted into categories derived from the interview guide. The research team reviewed all codes and categories to identify meaningful themes.
## Ethics
Ethical approval was grant by the Human Research Ethics Committee (Medical) at the University of Witwatersrand [Ref. M200941 and M170334] for all study materials and procedures.
## Study sample
The CHWs visited 173 community households during the study period (Fig. 1). Individuals at three homes ($2\%$) did not come to the door and a further nine individuals ($5\%$) were not interested in participating. Of the remaining 161 community members, 153 ($95\%$) gave their consent to take part in the study. Eighteen participants were either unable to complete (due to time commitments) or withdrew during the assessments. A final sample of 135 participants (56 men, 79 women), ranging from 18–83 years of age (median age: 38 years) agreed to participate (Table 1). More than half of the participants reported having 7–12 years of education, with approximately one third reporting > 12 years of education. The prevalence of central obesity was higher in women than men ($81\%$ vs $36\%$, $p \leq 0.001$). One in five individuals reported never having checked their blood pressure previously. Based on the readings taken during the home visit, $36\%$ of the participants were identified as having hypertension, compared to $11\%$ of participants who reported knowing they had hypertension. There were significantly more male smokers compared to female smokers ($55\%$ vs $19\%$, $p \leq 0.001$), while $21\%$ of participants reported regularly consuming alcohol (daily or weekly). More than half of the sample ($56\%$) reported not achieving 150 minutes per week of physical activity. Fig. 1Study flow diagram. * Participants were unable to complete the measurements and/or questionnaire due to previous scheduled appointments or voluntary withdrawalTable 1Distribution and characteristics of the study populationCharacteristicTotal sample($$n = 135$$)Males($$n = 56$$)Females($$n = 79$$)P-valueSocio-demographic Age, years (median, IQR)38 [29]35 (29.5)39 [28]0.345Number years education No education: n (%)6 (4.4)2 (3.6)4 (5.1)0.087 1-6 years: n (%)6 (4.4)5 (8.9)1 (1.3) 7-12 years: n (%)77 (57.0)27 (48.2)50 (63.3) > 12 years: n (%)46 (34.1)22 (39.3)24 (30.4)Anthropometry Height, cm164.7 ± 9.6170.6 ± 7.3160.5 ± 8.8< 0.001* Waist circumference, cm91.0 ± 18.086.1 ± 19.994.4 ± 15.80.008* Waist-to-height-ratio0.56 ± 0.120.50 ± 0.110.59 ± 0.11< 0.001*Central obesity (WHtR > 0.5)#: n (%)84 (62.2)20 (35.7)64 (81.0)< 0.001* Systolic blood pressure, mmHg124 ± 16123 ± 13124 ± 180.711 Diastolic blood pressure, mmHg84 ± 1082 ± 1085 ± 110.078 Hypertension status: n (%)39 (32.0)12 (27.9)27 (34.2)0.198 Normotensive66 (48.9)29 (51.8)37 (46.8)0.198 Elevated blood pressure20 (14.8)11 (19.6)9 (11.4) Hypertensive49 (36.3)16 (28.6)33 (41.8)Self-reported medical history Hypertension prior diagnosis*: n (%)15 (11.1)5 (7.1)11 (13.9)0.217 Diabetes mellitus: n (%)5 (3.9)0 [0]5 (6.3)- Previous heart attack: n (%)2 (1.5)2 (3.6)0 [0]- Previous stroke: n (%)2 (1.5)1 (1.8)1 (1.3)- Previous COVID-19 positive: n (%)6 (4.5)3 (5.5)3 (5.5)0.66 COVID-19 vaccinated: n (%)42 (31.1)14 (25.0)28 (35.4)0.258Behavioural health factors Tobacco use: n (%) Current use46 (34.1)31 (55.4)15 (19.0)< 0.001* Past use21 (15.6)8 (14.3)13 (16.5) Never used68 (50.4)17 (30.4)51 (64.6)Alcohol consumption: n (%) Daily1 (0.7)1 (1.8)00.076 1-6 times per week27 (20.1)15 (26.8)12 (15.4) 1-3 times per month50 (37.3)23 (41.1)27 (34.6) Never/rarely56 (41.8)17 (30.4)39 (50.0) Physical activity less than 150 min/week: n (%)75 (55.6)26 (46.4)49 (62.0)0.072Last BP check: n (%) Never29 (21.5)19 (33.9)10 (12.7)0.007 Over 12 months ago30 (22.2)13 (23.2)17 (21.5) Within the last 12 months76 (56.3)24 (42.9)52 (65.8)BP, blood pressure; cm: centimeter; IQR, interquartile range; WHtR, waist-to-hip ration* = statistically significant
## Post-home visit satisfaction survey
At the conclusion of the home testing, participants were asked if CHWs had visited their home previously to conduct similar health checks. Of the 135 participants, 130 ($96\%$) reported not receiving a similar home health check in the past. When asked about their experience with the home visit, $97\%$ of participants reported that it was ‘very easy’ or ‘easy’ to understand the information during the visit, $91\%$ reported that the CHWs seemed very knowledgeable, and $100\%$ reported that the CHWs answered their questions ‘very well’ or ‘well”. Additionally, $93\%$ of the participants reported that they would be ‘very likely’ or ‘likely’ to request this home service again if it were available. Participants reported feeling average (median score = 50) about their health prior to the visit, which significantly increased to 88 by the end of the visit ($p \leq 0.001$). Participants estimated the value of the home visit at 68 Rands (IQR 42 Rands), which was equivalent to approximately $4.30 USD at the time of this study.
## Follow up individual interviews with community members
Seventy-nine community members (27 men, 52 women) agreed to be contacted again for follow-up interviews, with $62\%$ expressing a preference for in-person rather than telephone interviews. Upon recontact, only 29 participants were accessible for the interviews. One recording was inaudible, resulting in a total of 28 individual interviews (22 in-person, six by telephone) for analysis (five men, 23 women). The median age of the individuals interviewed was 39 years (range: 18–76 years) with $21\%$ ($$n = 6$$) having > 12 years of education, $64\%$ ($$n = 18$$) having 7–12 years of education, and $14\%$ ($$n = 4$$) having <7 years of education. Nine individuals ($32\%$) reported engaging in 150 minutes or more of moderate to vigorous activity per week, while seven ($25\%$) of individuals were normotensive, seven ($25\%$) were prehypertensive, eight ($29\%$) of individuals had undiagnosed hypertension, and six ($21\%$) individuals were hypertensive, but receiving treatment. Four main themes, described below, were identified from the interview transcripts relating to the CHW engagement with the participants, with sample quotes presented in Table 2.Table 2Sample of quotes organized by themeThemeSub-themesQuotesReceptivity to home visits from health advocatesFirst impressions“…so I thought maybe it is regarding COVID. But then they made it clear that they here for high blood [colloquial term for hypertension] and continued to say they here to see what really troubles me. ”“The way they were talking to me, it was very respective and they were just open in general. They were just talking to me and they made me very comfortable. That’s why I allowed them to come in. ”“Yes, when I looked at him, I saw a trustworthy person. ”“Oh, when they mentioned that they will be checking my high blood, you see, because it’s not something that you check as often. And there’s too many diseases now. ( I: That’s right.). That’s the reason I decided to let them in. ”Accessibility“What I’ve learnt or what I’ve loved is getting visitors from people who are from the health, if this can continue to other communities it could help a lot of people, especially those who can’t go by themselves to the clinics, if maybe healthcare workers can go around checking people, like high blood, sugar diabetes all those things. ”“It makes me very happy, I feel very glad, I truly wish that you would check on us every second day…”“I think they should come more often and do other things so we don’t go to the clinic because it’s always full…”Receptivity to advice receivedEmpowerment from advice“I feel good because most of the time at the clinic they just pump us and they don’t explain anything (I: Ohhh…) so now I got to understand what it means when its like this and when its like that. ”Recollection of advice received“He asked me to reduce significantly the amount of fats I consume, not that I should not eat fats at all, but I should try introducing vegetables to my diet, I should sometimes just try to buy and eat vegetables like potatoes, cabbage and spinach especially for dinner without meat or pap. And eat a lot of fruits as well. He told me that he isn’t saying I’m sick. ”“I learnt that a person should always take care of themselves, and that life is too short. We must always check what’s happening in our bodies so that you can identify things that are not ok so that you can be able to fix it while you still have time. ”Acting on advice“I’ll start going to clinic, start taking my high blood treatment. I was lazy, but now I will go and take the pills if I have. ”Experience with blood pressure screeningExperience“She said she’s not supposed to take my BP while I’m standing, I should be seated. Then I sat, and she then sanitized the cuff, then she sanitized me then herself. Then she put the cuff on me and then she started taking my BP.”“They pulled out the machine to check my blood pressure, and their finding was that it is high. ”“He said I must just relax, not panic, put my hand straight and calm down because there’s nothing hard I must just calm down and just relax. ”“What I remember is that they stated that I mustn’t put my legs together and I must not panic. So that the numbers are accurate, because some of the time when you panic, it goes up and it would be like I am sick, while in am not sick. ”“No, they did a good job; there is nothing I can say needs to be added because everything went well. ”Comprehension“It was easy because of they explain, they explain very well, that even if you don’t understand you can ask question. ”“Yes, I understood well because he explained, he showed me on the machine. ”“They explained everything well. ”Receptivity to physical activity advicePhysical activity advice received“They said I must exercise, then I said to them but as I’m cleaning doing the house chore I’m exercising and they laughed, they said I should gym and stretch my legs and I told them okay I will do them in my bedroom. ”Preferences for exercise modalities“Just walking maybe from normal walking to brisk walking, and just exercises that feel like you are opening overhead cupboards over and over, you see, I wouldn’t like jumping up and down because I’m not young like you anymore. ”“Just exercises so I lose weight, stomach exercise you see and get a diet on what to eat so my body will be fine and not get sick. ”Barriers and facilitators to referral to a communitycenter“The only thing that would stop me would definitely be when I am not feeling well, or these boys are not there *demonstrating* (money I guess). Because we use that to survive. ”“Yes, I would like to join but now the problem is there’s a joining fee and when you not working it’s hard to join things like that, like me where would I get the money?”“*It is* a good thing; it will be within reach for us; the community and exercise is part of health; we have to exercise and minimize our visits to the clinics saying, ‘my BP is high’ or ‘my sugar levels are high.’”
## Receptiveness to home visits
In most cases, community members spoke about the need to go to local health clinics for check-ups, medication, and illness evaluation. For these people, having healthcare workers come to their door was unusual. Their initial thought in seeing the professionally dressed CHWs with uniforms and nametags were that they were ‘knocking on their front door’ for something pertaining to the COVID-19 pandemic and vaccine administration. Once the CHWs explained the purpose of their visit, community members expressed feeling safe and comfortable letting them into their homes. Community members described the CHWs as respectful and knowledgeable and expressed how much they appreciated the home visit, stating that they were very satisfied with their visit and learned a lot from the CHWs. Many conveyed the difficulties of visiting the local clinic including the long wait times, unfriendly staff, and a lack of explanation of medical issues. One interviewee commented that people will be very sick, “but they are too scared to go to the clinic.” The community members were grateful that people cared for their health, and wished the home health checks could be offered more frequently and to everyone in the ‘hood’. One interviewee shared, “I was so happy, because it shows that we also matter”, demonstrating the rarity of the home visits and the need for more accessible health care.
## Receptivity to advice received
Community members expressed an appreciation for receiving health advice from the CHWs. They were given the opportunity to learn how to measure their own blood pressure, advised on their dietary intake of salt and fat, and consulted on their general physical activity. Those interviewed described generally feeling empowered by the home service and care. There was no negative feedback from the community members regarding the advice they received. Overall, community members described feeling motivated to follow the CHW’s advice: “I’ll start going to clinic, start taking my high blood treatment. I was lazy, but now I will go and take the pills if I have.”
## Experience with blood pressure screening
While recalling their experience with the home visit, community members remembered being told by the CHWs to remain calm, relaxed and still so that they could get an accurate measurement of their blood pressure. Some community members also recalled the CHWs telling them that their blood pressure was elevated over the normal value, leading to an increased awareness of their health status. Overall, community members expressed satisfaction with the experience of having their blood pressure measured and there was little feedback as to how the process could be improved. Community members also reported they had no issues comprehending what the CHWs were doing, asking, and explaining. Community members reported easy, clear communication, being able to ask questions, and receiving thoughtful, understandable responses.
## Receptivity to physical activity advice
Those interviewed were receptive to the advice they received regarding their physical activity level. Older participants expressed a preference for engaging in lower intensity activities that fit within their daily routines, such as walking and stretching, while younger participants voiced a desire for exercising to lose weight. Several women discussed finances and childcare as barriers. The interviewees were receptive to the idea of receiving referrals to join exercise programs at a local community centre, and commented on the presence of a local exercise/training facility as a positive addition to their community: “*It is* a good thing; it will be within reach for us; the community and exercise is part of health; we have to exercise and minimize our visits to the clinics saying, ‘my blood pressure is high’ or ‘my sugar levels are high.’”
## Discussion
Innovative strategies are needed to address NCDs in LMICs. One emerging global strategy is using CHWs to reach vulnerable populations in under-resourced settings (Khetan et al. 2018; Long et al. 2018; Rawal et al. 2020; Bysted et al. 2022). Reviews have found that over $60\%$ of CHWs in urban settings performed some sort of home-based care; however, most of these efforts focused on HIV/AIDS, maternal, new born and child health, geriatric care, mental health, substance abuse, and immunization administration (Perry et al. 2014; Ludwick et al. 2020). Expanding on this previous work, our study investigated the feasibility and acceptability of CHWs conducting standardized, home-based health screening assessments and brief counselling for blood pressure and physical activity.
Overall, our home-visit approach proved to be highly feasible in a low-resource community with > $95\%$ of households consenting to participate in the health screenings. This level of participation is similar to a home-based screening program in rural India in which nearly $90\%$ of targeted households were covered (Basu et al. 2019). We also found high levels of acceptance towards the home visits, responsiveness to the advice provided, and a willingness to learn about their cardiovascular health. This is both a milestone for NCD implementation research and an important finding in a community where typically only one in every five individuals has sufficient health literacy (Calvert et al. 2022).
Household members expressed a high level of comfort with the CHW-led home visits, a surprising finding given the high levels of crime reported in the area, but similar to previous work conducted in South African settings (Medina-Marino et al. 2021; Ngcobo and Rossouw 2022), suggesting the trusted role that CHWs occupy in the community. Home-based tuberculosis testing, lasting as long as 2 hours, was acceptable due to its convenience (i.e., not having to make multiple trips to the health clinic) and receiving reliable information on the spot (Medina-Marino et al. 2021). In another study, community members expressed greater receptivity for the home visits when informed of the role, age, and gender of the CHWs prior to the visit (Ngcobo and Rossouw 2022). Our finding is particularly meaningful given that the home visits were conducted during the height of the COVID-19 pandemic, amidst societal lockdowns and physical distancing. Despite these barriers, household members welcomed CHWs into their homes. This may be due to the high level of professionalism displayed by the CHWs, who had recently completed rigorous training that provided them with the knowledge and skills to give accurate and meaningful advice, as well as their use of uniforms and nametags.
Follow-up interviews revealed high levels of community member satisfaction with the individualized care that they received, something many remarked that they were not receiving at the local health clinic. While previous work has found high levels of patient satisfaction with healthcare services in South Africa (Jacobsen and Hasumi 2014) and in local health clinics in the same region as the current study (Nunu and Munyewende 2017), our study participants expressed that clinics were very busy, forcing staff to move quickly between patients, limiting individual patient interactions. This is similar to other work that found the most common problems experienced with healthcare services in South Africa included long wait times, unfriendly staff, and being turned away from clinics (Hasumi and Jacobsen 2014). Home visits may alleviate concerns about going to health clinics, while providing high quality, accessible, and individualized care in communities facing numerous health issues and a lack of resources.
Community members in the current study expressed appreciation for the individualized care that they received, including an explanation of their blood pressure reading and receiving advice on improving their overall cardiovascular health. CHWs are effective in providing community members with hypertension guidance and helping residents improve chronic disease conditions (Kangovi et al. 2017). Further, home-based blood pressure screening allows for identifying potential blood pressure problems and achieving population-level blood pressure improvements in South Africa (Sudharsanan et al. 2020). A home-based blood pressure screening in Kenya proved to be a feasible strategy for screening a broad array of community members for hypertension and diabetes and identifying a large pool of high-risk individuals (Pastakia et al. 2013).
Community members were also highly receptive to the consultation they received regarding their dietary and physical activity habits. While previous work has demonstrated that CHWs are effective in promoting physical activity (Costa et al. 2015), physical activity screening and promotion in the home setting has not been widely examined. One study that utilized CHW home visits for individuals with type 2 diabetes and low incomes in the United States found that physical activity levels and dietary behaviours significantly improved in those randomized to the self-management intervention (Gray et al. 2021). This suggests that behavioural counselling by CHWs may be an effective strategy for improving NCD-related risk facts. Community members were also open to the idea of being connected to community-based physical activity resources. A systematic review found that in 38 of 114 studies involving hard-to-reach populations, CHW outreach efforts included referring and linking community members to health services (Ludwick et al. 2020), although none involved a referral to physical activity programs. Access to facilities that are safe and able to provide space for activities from a trusted source or community member can be a key factor to promote physical activity and health behaviours for members of this community (Ware et al. 2019). Future work should investigate community member referral to local health-promoting resources, such as physical activity and healthy eating programs, and strategies for overcoming noted barriers, such as cost of the programs and childcare resources.
This study has several notable strengths. First, we systematically sampled many households in a predefined geographical area in an under-resourced community. Second, the home-based health assessments were rigorously conducted by trained CHWs supervised by professional staff following a standardized protocol. Finally, we explored community members acceptability of the CHW home visit immediately after through a satisfaction survey, as well as through more in-depth interviews. At the same time, this study has several limitations. First, this work was conducted during the height of the COVID-19 pandemic, leading to the possibility that individual behaviours may have been affected by recent community lockdowns and curfews. The study sample (i.e., $82.1\%$ of follow-up surveys were conducted with women) may not be sufficiently representative to identify sex differences. Further, home visits were conducted during weekdays, which may exclude individuals engaged in full-time employment or academic study. While work-from-home policies were more common during the COVID-19 pandemic, low-income communities were frequently least able to implement this practice (Garrote Sanchez et al. 2021). Finally, since the study engaged CHW trainees, they had a lesser workload than full-time employed CHWs, allowing them more time and care with each household member. However, this research fills an important gap in understanding how to embed CHW programs into a community, identified as a key success factor across a range of health service provision in multiple countries and across the lifecourse (Scott et al. 2018).
## Conclusion
Although there have been ongoing efforts to improve primary care in South Africa, progress is often tempered by challenges, such as resource distribution, management, and leadership struggles (Maphumulo and Bhengu 2019). The success of the home visits in this study highlights the potential to expand accessible, community-based health care opportunities for NCD prevention and management, as the blood pressure and physical activity screening and brief counselling were well-received and understood by the community members. With the continued expansion of CHW roles and responsibilities assigned through the task shifting of health-care services in under-resourced communities, CHWs in LMICs may be the critical force needed to address the growing tide of NCDs through primary prevention and health promotion.
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|
---
title: Genetic loci and prioritization of genes for kidney function decline derived
from a meta-analysis of 62 longitudinal genome-wide association studies
authors:
- Mathias Gorski
- Humaira Rasheed
- Alexander Teumer
- Laurent F. Thomas
- Sarah E. Graham
- Gardar Sveinbjornsson
- Thomas W. Winkler
- Felix Günther
- Klaus J. Stark
- Jin-Fang Chai
- Bamidele O. Tayo
- Matthias Wuttke
- Yong Li
- Adrienne Tin
- Tarunveer S. Ahluwalia
- Johan Ärnlöv
- Bjørn Olav Åsvold
- Stephan J. L. Bakker
- Bernhard Banas
- Nisha Bansal
- Mary L. Biggs
- Ginevra Biino
- Michael Böhnke
- Eric Boerwinkle
- Erwin P. Bottinger
- Hermann Brenner
- Ben Brumpton
- Robert J. Carroll
- Layal Chaker
- John Chalmers
- Miao-Li Chee
- Miao-Ling Chee
- Ching-Yu Cheng
- Audrey Y. Chu
- Marina Ciullo
- Massimiliano Cocca
- James P. Cook
- Josef Coresh
- Daniele Cusi
- Martin H. de Borst
- Frauke Degenhardt
- Kai-Uwe Eckardt
- Karlhans Endlich
- Michele K. Evans
- Mary F Feitosa
- Andre Franke
- Sandra Freitag-Wolf
- Christian Fuchsberger
- Piyush Gampawar
- Ron T. Gansevoort
- Mohsen Ghanbari
- Sahar Ghasemi
- Vilmantas Giedraitis
- Christian Gieger
- Daniel F Gudbjartsson
- Stein Hallan
- Pavel Hamet
- Asahi Hishida
- Kevin Ho
- Edith Hofer
- Bernd Holleczek
- Hilma Holm
- Anselm Hoppmann
- Katrin Horn
- Nina Hutri-Kähönen
- Kristian Hveem
- Shih-Jen Hwang
- M. Arfan Ikram
- Navya Shilpa Josyula
- Bettina Jung
- Mika Kähönen
- Irma Karabegović
- Chiea-Chuen Khor
- Wolfgang Koenig
- Holly Kramer
- Bernhard K. Krämer
- Brigitte Kühnel
- Johanna Kuusisto
- Markku Laakso
- Leslie A. Lange
- Terho Lehtimäki
- Man Li
- Wolfgang Lieb
- Lars Lind
- Cecilia M. Lindgren
- Ruth J. F. Loos
- Mary Ann Lukas
- Leo-Pekka Lyytikäinen
- Anubha Mahajan
- Pamela R. Matias-Garcia
- Christa Meisinger
- Thomas Meitinger
- Olle Melander
- Yuri Milaneschi
- Pashupati P. Mishra
- Nina Mononen
- Andrew P. Morris
- Josyf C. Mychaleckyj
- Girish N. Nadkarni
- Mariko Naito
- Masahiro Nakatochi
- Mike A. Nalls
- Matthias Nauck
- Kjell Nikus
- Boting Ning
- Ilja M. Nolte
- Teresa Nutile
- Michelle L. O’Donoghue
- Jeffrey O’Connell
- Isleifur Olafsson
- Marju Orho-Melander
- Afshin Parsa
- Sarah A. Pendergrass
- Brenda W. J. H. Penninx
- Mario Pirastu
- Michael H. Preuss
- Bruce M. Psaty
- Laura M. Raffield
- Olli T. Raitakari
- Myriam Rheinberger
- Kenneth M. Rice
- Federica Rizzi
- Alexander R. Rosenkranz
- Peter Rossing
- Jerome I. Rotter
- Daniela Ruggiero
- Kathleen A. Ryan
- Charumathi Sabanayagam
- Erika Salvi
- Helena Schmidt
- Reinhold Schmidt
- Markus Scholz
- Ben Schöttker
- Christina-Alexandra Schulz
- Sanaz Sedaghat
- Christian M. Shaffer
- Karsten B. Sieber
- Xueling Sim
- Mario Sims
- Harold Snieder
- Kira J. Stanzick
- Unnur Thorsteinsdottir
- Hannah Stocker
- Konstantin Strauch
- Heather M. Stringham
- Patrick Sulem
- Silke Szymczak
- Kent D. Taylor
- Chris H. L. Thio
- Johanne Tremblay
- Simona Vaccargiu
- Pim van der Harst
- Peter J. van der Most
- Niek Verweij
- Uwe Völker
- Kenji Wakai
- Melanie Waldenberger
- Lars Wallentin
- Stefan Wallner
- Judy Wang
- Dawn M. Waterworth
- Harvey D. White
- Cristen J. Willer
- Tien-Yin Wong
- Mark Woodward
- Qiong Yang
- Laura M. Yerges-Armstrong
- Martina Zimmermann
- Alan B. Zonderman
- Tobias Bergler
- Kari Stefansson
- Carsten A. Böger
- Cristian Pattaro
- Anna Köttgen
- Florian Kronenberg
- Iris M. Heid
journal: Kidney international
year: 2022
pmcid: PMC10034922
doi: 10.1016/j.kint.2022.05.021
license: CC BY 4.0
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# Genetic loci and prioritization of genes for kidney function decline derived from a meta-analysis of 62 longitudinal genome-wide association studies
## Abstract
Estimated glomerular filtration rate (eGFR) reflects kidney function. Progressive eGFR-decline can lead to kidney failure, necessitating dialysis or transplantation. Hundreds of loci from genome-wide association studies (GWAS) for eGFR help explain population cross section variability. Since the contribution of these or other loci to eGFR-decline remains largely unknown, we derived GWAS for annual eGFR-decline and meta-analyzed 62 longitudinal studies with eGFR assessed twice over time in all 343,339 individuals and in high-risk groups. We also explored different covariate adjustment. Twelve genome-wide significant independent variants for eGFR-decline unadjusted or adjusted for eGFR-baseline (11 novel, one known for this phenotype), including nine variants robustly associated across models were identified. All loci for eGFR-decline were known for cross-sectional eGFR and thus distinguished a subgroup of eGFR loci. Seven of the nine variants showed variant-by-age interaction on eGFR cross section (further about 350,000 individuals), which linked genetic associations for eGFR-decline with age-dependency of genetic cross-section associations. Clinically important were two to four-fold greater genetic effects on eGFR-decline in high-risk subgroups. Five variants associated also with chronic kidney disease progression mapped to genes with functional in-silico evidence (UMOD, SPATA7, GALNTL5, TPPP). An unfavorable versus favorable nine-variant genetic profile showed increased risk odds ratios of 1.35 for kidney failure ($95\%$ confidence intervals 1.03–1.77) and 1.27 for acute kidney injury ($95\%$ confidence intervals 1.08–1.50) in over 2000 cases each, with matched controls). Thus, we provide a large data resource, genetic loci, and prioritized genes for kidney function decline, which help inform drug development pipelines revealing important insights into the age-dependency of kidney function genetics.
## INTRODUCTION
Glomerular filtration rate (GFR) is accepted as best overall index of kidney function1. A GFR<60 mL/min/1.73m2 defines chronic kidney disease (CKD)2, which affects about $10\%$ of adults3. A decline in GFR over time is characteristic for CKD-progression, which can lead to kidney failure4 requiring dialysis or kidney transplantation with a high risk of premature mortality5. In population studies on kidney function, estimated GFR (eGFR) is usually derived from serum creatinine6 and annual eGFR-decline as the difference between two such assessments divided by the years between these assessments. Decline in eGFR is age-related, with a physiological loss of ~1 mL/min/1.73m2 per year2 generally and 3 mL/min/1.73m2 per year in the presence of diabetes mellitus (DM), a major risk factor for CKD-progression7,8. Therapeutic options to decelerate kidney function decline are limited. In addition to pharmacological inhibitors of the RAAS-system9, the recent introduction SGLT2 inhibitors show promising reno-protective effects10,11. An understanding of the mechanisms of kidney function decline and the developing of new therapeutic options is thus of high clinical and public health relevance7,12.
Genes underneath genome-wide association study (GWAS) loci for diseases and biomarkers help identify new therapies13. Open access GWAS summary statistics from large sample sizes are a highly queried resource, also for causal inference studies14. Hundreds of loci and genes are identified by cross-sectional GWAS for eGFR, i.e. GWAS for eGFR based on a single serum creatinine measurement15–18, which help explain population variability. However, the mechanisms underlying a genetic variant association with lower but stable eGFR over time might not always be disease-relevant. GWAS on parameters more directly linked to disease progression are thought to better inform drug development19.
Current evidence from GWAS on annual eGFR-decline is limited, owed to substantial logistics in conducting longitudinal studies and thus small sample sizes. Only one variant, in the UMOD-PDILT locus, has been identified at genome-wide significance20 (n~60,000). With an estimated heritability of $38\%$ for annual eGFR-decline20, comparable to $33\%$−$39\%$ estimated for cross-sectional eGFR in general populations21,15, much more can be expected in larger sample sizes. Further three loci were genome-wide significant in an extreme phenotype approach, comparing individuals with large eGFR-decline or steep drop into CKD with respective controls22. While these are important binary clinical endpoints, methodological literature supports the use of regression methods on undichotomized variables23.
The limited availability of longitudinal GWAS is not only an issue for kidney function decline, but also generally: e.g. change in lung function ($$n = 27$$,24924), glucose ($$n = 13$$,80725), or blood pressure ($$n = 33$$,72026); consequently, locus findings on biomarker change are few and often unstable14. A challenge beyond power is limited experience in longitudinal GWAS with regard to covariate adjustment: clinical trials for disease-related biomarker change require control for differences in baseline levels between therapy groups27. However, covariate adjustment in GWAS requires a careful choice28: it can reveal important mediator effects (e.g. DM adjusted for BMI29), alter the phenotype (e.g. waist-to-hip ratio “unexpected” by body-mass-index28,30), yield artefacts from heritable covariates (collider bias28) or non-sense association (e.g. sex adjusted for height31). The impact of covariate adjustment on longitudinal GWAS on eGFR-decline, and biomarker change generally, is not well explored.
We thus aimed to identify genetic loci associated with annual eGFR-decline and CKD-progression (defined as eGFR-decline among individuals with CKD at baseline) and to prioritize genes that may inform drug development for slowing down eGFR-decline and CKD-progression. We also aimed to fill the gap of large-data genome-wide SNP summary statistics for annual eGFR-decline and CKD-progression, to help future meta-analyses and Mendelian randomization studies. Finally, we wanted to understand the impact of different covariate adjustment and whether a SNP associated with eGFR-decline showed an age-dependent association on eGFR cross-sectionally (i.e. SNP-by-age interaction on eGFR cross-sectionally). By this, we aimed to contribute to a better understanding of the interpretation of genetic findings for eGFR-decline and other progression traits.
To achieve these aims, we (i) increased sample size for GWAS on annual eGFR-decline to >340,000 individuals based on the CKDGen consortium32 and UK Biobank33, (ii) applied a suite of covariate adjustment models, (iii) analyzed SNP-by-age interaction on eGFR cross-sectionally in >350,000 individuals independent of the GWAS on decline, and (v) conducted genetic risk score (GRS) analyses for acute kidney injury (AKI) and end-stagekidney disease (ESKD).
## METHODS
We conducted GWAS meta-analysis based on study-specific summary statistics. Each study utilized data on two measurements of serum creatinine over time and genome-wide SNP-information imputed to 1000 Genomes34 phase 1 or phase 3, the Haplotype Reference Consortium35 v1.1 or similar (Table S1&S2). Serum creatinine measured at baseline and follow-up were used to estimate eGFR at baseline and follow-up, respectively, according to the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation6. Annual eGFR-decline was defined as “-(eGFR at follow-up - eGFR at baseline) / number of years of follow-up”. GWAS analyses were conducted separately by ancestry (if applicable), where ancestry was defined by genetic principal components or participants’ self-report. GWAS were based on linear regression with different covariate adjustment conducted overall and focused on individuals with DM or CKD at baseline.
Study-specific genome-wide summary statistics and detailed phenotype information were transferred to the meta-analysis center. For each SNP, summary statistics were pooled and genomic control corrected. *Significant* genetic variants were identified and respective locus regions selected.
Additionally, we investigated identified SNPs for SNP-by-age interaction on cross-sectional eGFR (based on creatinine or cystatin C, eGFRcrea, eGFRcys) using UK *Biobank data* that was independent of the SNP identification step (excluding the individuals in the decline GWAS). We computed the GRS and its association on eGFR-decline in the HUNT study via linear regression and provided odds ratios (OR) for GRS association in case-control studies on AKI and ESKD via logistic regression.
Detailed methods are provided in the Supplementary Methods.
## Overview across studies and models for GWAS
This GWAS meta-analysis included 343,339 individuals from 62 studies (Supplementary Table S1&S3, Supplementary Figure S1, Methods) and 12,403,901 analyzable SNPs. Most studies were population-based ($76\%$) and of European ancestry ($74\%$). Study-specific median annual eGFR-decline was independent of sample size and follow-up length (Supplementary Figure S2A&S2B) and the median across studies was 1.32 mL/min/1.73m2 per year; follow-up length was 1–21 years (median [25th, 75th] = 5 years [4,7]); median age ranged from 33 to 77 years (Supplementary Figure S2C).
All analyses were adjusted for age-, sex, and study-specific covariates, which is not mentioned further from here on (stable across different modes of age-adjustment, Supplementary Figure S3). We had five GWAS results for eGFR-decline (Methods): (i) “unadjusted”, (ii) “DM-adjusted”, (iii) “adjusted for eGFR-baseline”, (iv) restricted to individuals with DM at baseline (unadjusted), and (v) restricted to individuals with CKD at baseline (unadjusted).
## Similarities and differences across different model adjustments
There is, to date, no standard conduct for GWAS on eGFR-decline with regard to covariate adjustment. We explored the impact of two potentially important covariates additional to age and sex: (i) DM, as an important risk factors for eGFR-decline and potential mediator, and (ii) eGFR at baseline, as adjustment for baseline levels in analyses of change over time has noted pros (larger effects, better detectability) and cons (biased effects)36,37.
With regard to DM-adjustment, this model was computed in all studies ($$n = 343$$,339; 62 studies) and compared to unadjusted results for a subset of studies of varying scope ($$n = 103$$,970). DM-adjusted SNP-associations on eGFR-decline were precisely the same as unadjusted, in terms of beta-estimates and standard errors (Supplementary Figure S4A, Supplementary Note S1). We therefore did not distinguish these two models further.
In contrast, adjustment for eGFR-baseline altered SNP-associations on eGFR-decline (Supplementary Figure S4B). Therefore, results from both eGFR-decline unadjusted and adjusted for eGFR-baseline were evaluated in the following. GWAS summary statistics for eGFR-decline adjusted for eGFR-baseline were formula-derived from GWAS summary statistics for unadjusted eGFR-decline and for eGFR-baseline together with study-specific phenotypic information (Supplementary Note S2). In a subset of studies ($$n = 103$$,970), we validated that the formula-approach worked very well in our setting (Supplementary Note S3, Supplementary Figure S4C&D). Meta-analysis yielded GWAS results for eGFR-decline adjusted for eGFR-baseline for 320,737 individuals (50 studies, Supplementary Figure S1).
## Twelve variants identified for eGFR-decline unadjusted or adjusted for eGFR-baseline
First, our genome-wide screen for eGFR-decline unadjusted for eGFR-baseline ($$n = 343$$,339) identified two genome-wide significant independent variants near UMOD-PDILT (PDECLINE<5×10−8; Figure 1A, Table 1A): rs34882080, highly correlated with rs12917707 identified previously for this phenotype (r2=1.00)20, and rs77924615, known for altering UMOD expression and urine uromodulin15 and genome-wide significant for eGFR-decline for the first time.
Second, we evaluated the 263 additional lead variants known for cross-sectional eGFR GWAS15 for association with baseline-unadjusted eGFR-decline (candidate approach); we had a prior hypothesis that cross-sectionally known variants might also show association with eGFR-decline. We identified two additional variants for eGFR-decline near PRKAG2 and SPATA7, both new loci for this phenotype, at Bonferroni-corrected significance (PDECLINE<$\frac{0.05}{263}$=1.90×10−4; Table 1A).
Third, our genome-wide screen for eGFR-decline adjusted for eGFR-baseline ($$n = 320$$,737) identified 12 independent variants across 11 loci (PDECLINE_adj−BL<5×10−8, Figure 1B), including the four variants already identified by the baseline-unadjusted analyses (directly or via high correlation, r2≥0.9). The 8 variants additionally identified pointed to novel loci for this phenotype. Of these, 5 variants also showed directionally consistent, significant association for eGFR-decline unadjusted for eGFR-baseline (Bonferroni-corrected, PDECLINE<$\frac{0.05}{12}$=4.17×10−3; near FGF5, OVOL1, TPPP, C15ORF54, and ACVR2B; Table 1B), but 3 variants did not (PDECLINE from 0.156 to 0.710; near GATM, CPS1, SHROOM3, Table 1C).
Overall, we found 12 variants across 11 loci with genome-wide significant association for eGFR-decline unadjusted and/or adjusted for eGFR-baseline (PDECLINE or PDECLINE_adj_BL<5×10−8). All but one variant/locus were novel for this phenotype. All resided in loci known for eGFR cross-sectional GWAS15, but none was associated with DM-status (Supplementary Table S4).
The 12 variants’ associations showed no between-ancestry heterogeneity, stable statistics in various sensitivity analyses, and no impact by DM-adjustment (Supplementary Table S5&S6). Meta-analysis restricted to African American ($$n = 9$$,038) did not identify associations for published APOL1 risk variants38, but two other suggestive variants (Supplementary Table S7).
The 12 variants included 9 variants with non-zero effects on eGFR-decline unadjusted for eGFR-baseline (i.e. Bonferroni-corrected significant, i.e. PDECLINE<4.17×10−3).
## SNP-effects for eGFR-decline were larger when baseline-adjusted than baseline-unadjusted
Several interesting aspects emerged when comparing genetic effect sizes of the 12 identified variants across models. First, we observed consistently larger effects for eGFR-decline baseline-adjusted than baseline-unadjusted (Figure 2A), also when restricting to studies where the baseline-adjusted model was directly computed (inserted small panel, Figure 2A). This, together with the smaller standard errors (Supplementary Figure S4B), explained the larger yield of genome-wide significant loci in the baseline-adjusted GWAS.
Second, we contrasted effect sizes for eGFR-decline unadjusted for eGFR-baseline with those for cross-sectional eGFR15 (Figure 2B). Three variants showed relatively extreme cross-sectional effects and no effect on decline (near GATM, SHROOM3, CPS1). For the other 9 variants, the faster-decline allele was always the cross-sectional eGFR-lowering allele (Spearman correlation coefficient=−0.32). A similar more schematic presentation (Figure 2C) illustrates the mathematical relationship between baseline-adjusted and baseline-unadjusted effect sizes (Supplementary Note S4). This yields a corollary on the directionality of baseline-adjusted effect sizes: when the faster-decline allele (i.e. β^DECLINE>0) coincides with the baseline eGFR-lowering allele (i.e. β^BL<0), then the baseline-adjusted eGFR-decline effect size is larger than baseline-unadjusted (i.e. β^DECLINE_adj_BL>β^DECLINE) – in theory. Our data confirmed this empirically (Figure 2A). The larger genetic effect sizes for eGFR-decline adjusted for eGFR-baseline are thus a direct consequence of the phenotypic and genetic correlation between eGFR-decline and eGFR-baseline. *The* genetic effect for eGFR-decline unadjusted for eGFR-baseline provides the relevant effect size for further use and to distinguish between a “genuine association with eGFR-decline” (9 variants) and a pure “collider bias” effect (3 variants).
## Four genes with compelling biological in-silico evidence mapped to novel eGFR-decline loci
All 11 identified loci for eGFR-decline coincided with loci detected for cross-sectional eGFR: among the 12 identified variants, 11 variants were genome-wide significant for cross-sectional eGFR15 and the variant near TPPP showed $$P \leq 7.63$$×10−6 cross-sectionally with genome-wide significant variants nearby (Supplementary Figure S5A-C, Supplementary Note S5).
The 8 loci with genuine association for eGFR-decline included the well-known UMOD-PDILT locus. Biological evidence at the other seven loci was summarized using the Gene PrioritiSation tool18 generated from GWAS data on cross-sectional eGFR including evidence for SNP-modulated gene expression (eQTL, false-discovery-rate < 0.05): four lead variants or highly correlated proxies were eQTLs in tubule-interstitial kidney tissue with upregulating effects for SPATA7 and GALNTL5 (in PRKAG2 locus, kidney-tissue specific), a downregulating effect for FGF5 (kidney-tissue specific), and an upregulating effect for TPPP using NEPTUNE39. This supported these four genes in novel loci for eGFR-decline as kidney-tissue relevant and potentially causal genes for the association signals.
## SNPs for eGFR-decline showed SNP-by-age interaction on cross-sectional eGFR
In the absence of birth cohort effects, we hypothesized that a SNP associated with eGFR-decline might also show an age-dependent association on cross-sectional eGFR, which is SNP-by-age interaction on cross-sectional eGFR. Of note, the age-effect on eGFR should reflect the age-effect on filtration rate, not on creatinine metabolism, within limits of uncertainty of the CKD-EPI formula6. To empirically assess this hypothesis, we tested the identified 12 SNPs for SNP-by-age interaction on cross-sectional eGFRcrea or eGFRcys in UK Biobank data, which was independent from and similarly-sized as the decline GWAS ($$n = 351$$,462 or 351,601 for eGFRcrea or eGFRcys, respectively; Methods). For 8 of the 12 SNPs, we found SNP-by-age interaction for eGFRcrea and/or eGFRcys at Bonferroni-corrected significance (PSNPxage<$\frac{0.05}{12}$=4.17×10−3, Table 2). Interaction effect sizes were similar between eGFRcrea and eGFRcys (Figure 3A), except for the SNP near GATM.
The age-dependency of all SNP-effects and main age-effects were approximately linear (Supplementary Figure S6, Supplementary Note S6). The SNP-by-age interaction effect size can also be interpreted as the genetically modified age-effect on eGFR. This effect was large: e.g., 5 unfavorable alleles decreased eGFRcys by −0.136 mL/min/1.73m2 per year, which was ~$10\%$ of the overall age-effect on eGFRcys (−1.024 mL/min/1.73m2per year, Supplementary Note S6). SNP-by-age interaction effects on eGFRcys were highly correlated with SNP-effects on eGFR-decline (both in units of mL/min/1.73m2 per allele and year: “per year of age-difference between individuals” and “per year of person’s aging”, respectively; Figure 3B).
There was a noteworthy pattern with regard to presence and direction of SNP-by-age interaction: (i) among the 9 variants with genuine association for eGFR-decline, 7 variants showed significant SNP-by-age interaction on cross-sectional eGFRcys (Table 2A&B). All interaction effects were negative, i.e. the cross-sectional SNP-effect became larger (in absolute value) with older age. ( ii) Among the three SNPs without genuine association for eGFR-decline, two showed no SNP-by-age interaction; the third (near GATM) showed SNPby-age interaction, but only for eGFRcrea and with positive direction (β^SNPxage=+0.138, PSNPxage=9.71×10−5). Thus, the GATM SNP-effect on cross-sectional eGFRcrea gets smaller (in absolute value) by higher age. This might be explained by GATM being the rate-limiting enzyme in creatine synthesis in muscle, age-related loss of muscle mass, and thus decreased creatinine production with increasing age - in line with the lack of interaction with eGFRcys, which is unrelated to muscle mass.
## A concept of three classes of SNPs for cross-sectional eGFR distinguished by their eGFR-decline association
Our results suggested that SNPs for eGFR-decline were found among SNPs associated with eGFR cross-sectionally. This motivated the idea of, in theory, three classes of SNP-associations on cross-sectional eGFR (intercept) distinguished their eGFR-decline association unadjusted for eGFR-baseline (slope; Figure 4): no association with slope (class I), association of the eGFR-baseline lowering allele with flatter slope (class II), or association of the eGFR-baseline lowering allele with steeper slope (class III).
In our data, we found (i) three of the 12 SNPs as class I, in line with the lack of SNP-by-age interaction on eGFR cross-sectionally (judged for eGFRcys). ( ii) No variant was class II, consistent with the lack of positive SNP-by-age interaction on eGFRcys. ( iii) The 9 variants with genuine eGFR-decline association were class III, and 7 of these showed negative SNP-by-age interaction on eGFR. Thus, our data supported two classes of genetic effects on eGFR: no association with slope or steeper slope for the eGFR-lowering allele.
## Larger SNP-effects for eGFR-decline were observed in high-risk subgroups
Individuals with DM and/or CKD (defined as eGFR<60 mL/min/1.73m2) are at higher risk for CKD-progression and kidney failure, prompting us to quantify SNP-effects on eGFR-decline in these high-risk subgroups (meta-analysis for eGFR-decline unadjusted for eGFR-baseline restricted to DM or CKD at baseline, $$n = 37$$,375 or 26,653 respectively, Methods). For the 9 variants with genuine eGFR-decline association, we found almost all effects to be two- to four-fold larger in DM or in CKD compared to the overall analysis (Table 3, average effect size [mL/min/1.73m2/year and allele]: 0.061 in DM, 0.079 in CKD, compared to 0.030 overall).
To get an idea of the magnitude, we scaled the effects to “per 5 unfavorable average alleles” resulting in a decline of 0.305 in DM, 0.395 in CKD, compared to 0.150 mL/min/1.73m2/year overall. This compared well to the 9-variant weighted GRS effect on eGFR-decline per 5 unfavorable average alleles in the HUNT study ($$n = 2$$,235 with DM, $$n = 502$$ with CKD, $$n = 46$$,328 overall; Methods): 0.219 in DM, 0.262 in CKD, and 0.102 mL/min/1.73m2/year overall (one-sided $$P \leq 1.57$$×10−5, $$P \leq 0.0193$$, and $$P \leq 1.06$$×10−34, respectively).
*The* genetic effect sizes were also larger in the two subgroups when viewed relative to the phenotype variance (on the example of HUNT, Methods): rs77924615 variant (UMOD-PDILT locus) explained $0.38\%$ of the eGFR-decline variance in DM, $0.47\%$ in CKD, and $0.22\%$ overall; the 9-variants jointly explained $1.14\%$, $1.48\%$, and $0.51\%$, respectively. Of note, the explained variance of eGFR-decline overall was comparable to the explained variance of cross-sectional eGFR (rs77924615: $0.21\%$; 9 variants: $0.62\%$), but narrow-sense heritability was smaller (Supplementary Note S7).
## GALNTL5, SPATA7, and TPPP were identified as candidates for CKD-progression
Variants associated with CKD-progression and mapped genes might help identify drug targets against disease progression19. We queried the 9 SNPs with genuine association for eGFR-decline for significant association with CKD-progression, i.e. whether they still showed significant association with eGFR-decline when focusing on individuals with CKD at baseline (judged at $P \leq 0.05$/9=5.56×10−3, n up to 26,547). We found five such SNPs: (i) two in the UMOD-PDILT locus, which confirmed UMOD for a role in CKD-progression, (ii) three SNPs in novel loci for eGFR-decline, which mapped to three genes with eQTL in kidney tissue (GALNTL5 in PRKAG2 locus, kidney-tissue specific; SPATA7, and TPPP), making these compelling candidates as CKD-progression genes.
## Unfavorable GRS increased the risk for ESKD and AKI
Finally, we wanted to understand the cumulative impact of the 9 genuine eGFR-decline variants for severe clinical endpoints. We thus evaluated the 9-variant weighted GRS in cases-control studies for ESKD and AKI via logistic regression (ncases=2,068 and 3,878, ncontrols=4,640 and 11,634, respectively; Methods). The GRS effect per 5 unfavorable average alleles showed a significant OR=1.12 for ESKD ($95\%$CI=0.99–1.23; one-sided $$P \leq 0.033$$) and OR=1.18 for AKI ($95\%$ CI=1.09–1.27; one-sided $P \leq 0.0001$ Table 4). When comparing the individuals with GRS ≥90th versus ≤10th percentile (i.e. ≥14.6 unfavorable alleles versus ≤8.3 in UK Biobank), we found a significant OR=1.35 for ESKD ($95\%$CI=1.03–1.77, one-sided $$P \leq 0.0157$$) and OR=1.27 ($95\%$CI=1.08–1.50, one-sided $$P \leq 0.002$$, Table 4).
## DISCUSSION
Here, we provide data and results on a large longitudinal GWAS on annual eGFR-decline with >340.000 individuals from mostly population-based studies – to our knowledge the largest GWAS on annual eGFR-decline so far and probably one of the largest longitudinal GWAS of any trait. We identified 12 variants across 11 loci as genome-wide significant for annual eGFR-decline unadjusted and/or adjusted for eGFR-baseline (Figure 5). These included 9 variants across 8 loci with non-zero association unadjusted for eGFR-baseline, which we termed “genuinely” associated with eGFR-decline. Seven of these 9 variants also showed SNP-by-age interaction on cross-sectional eGFR in independent data of >350,000 individuals, while the three variants without genuine association did not. *We* generated and provide genome-wide summary statistics for eGFR-decline, CKD-progression, and eGFR-decline in DM. This data resource is informative for future meta-analyses, causal inference studies via Mendelian Randomization40, and drug development pipelines.
Clinically very important is our finding of the two-to four-fold larger genetic effects of almost all identified variants when focusing on individuals with DM or CKD at baseline, since these individuals are already at higher risk of kidney failure. This observation is in line with a “horse-racing effect”41 (“a faster horse is more likely observed up front”): individuals with an accumulation of faster eGFR-decline alleles are more likely observed with low eGFR at a given point in time, implying that these genetic effects might partly explain lower eGFR at baseline. A part of the larger eGFR-decline effect among CKD individuals might reflect collider bias. However, DM-status does not fulfill the characteristics of a collider for the SNP-associations with eGFR-decline (no impact by adjusting for DM-status, no SNP-association with DM-status), rendering the higher eGFR-decline effects in DM genuine.
The clinical relevance is further underscored by the 9-variant GRS being associated with increased risk of AKI and ESKD. This observation requires further analyses in future larger data. If substantiated, this may indicate a genetic risk of incomplete kidney function recovery after AKI and a genetic predisposition for ESKD.
The 9 identified variants across 8 loci included the UMOD-PDILT locus associated with eGFR-decline and CKD-progression, which is largely confirmatory but serves as proof-of-concept. A variant near MIR378C previously identified for CKD-progression42 (n~3000) was not confirmed here. Our other 7 loci are novel for eGFR-decline (near/in PRKAG2-GALNTL5, SPATA7, FGF5, OVOL1, TPPP, C15ORF54, and ACVR2B). These included at least three loci associated with CKD-progression (defined as eGFR-decline in individuals with CKD at baseline), mapping to the genes GALNTL5, SPATA7 and TPPP by SNP-modulated expression in tubolo-interstitium15,18. These associations and genes for CKD-progression are in strong demand as genetic information on a disease progression phenotype, in order to help identify treatment19. Our data particularly flags TPPP by its locus’ large effect on eGFR-decline and CKD-progression, making it second only after UMOD. This also documents the value of longitudinal GWAS in revealing relevance of genes like TPPP: the TPPP locus was one of hundreds of small effect loci cross-sectionally, but among the few loci longitudinally.
Our results highlight some overlap of quantitative eGFR-decline genetics with binary extreme decline genetics22, but also distinction. All loci identified here were directionally consistent, nominally significant with “rapid3” and/or “CKDi25” (one-sided $P \leq 0.05$) and two were genome-wide significant for rapid3 or CKDi25 (UMOD-PDILT, PRKAG2-GALNTL5). Particularly the loci identified here for CKD-progression, which is among individuals with CKD at baseline, complement the previously reported associations with CKDi25, which is among individuals without CKD at baseline. Methodologically, regression applied to a quantitative rather than dichotomized outcome has larger power and statistical advantages.
While all variants identified for eGFR-decline captured loci known from cross-sectional eGFR15, these associations are important on various accounts. First, the mere fact that eGFR-decline genetics is a subgroup of cross-sectional eGFR genetics is informative for future searches. Second, the finding that the full genetic signals were the same enabled the use of fine-mapping results from cross-sectional GWAS in >1 million individuals18 to prioritize genes also for longitudinal eGFR-decline. Third, all faster-decline alleles were the cross-sectional eGFR-lowering alleles. Together, this supported two classes of genetic variants for cross-sectional eGFR, distinguished by lack or presence of a slope effect, with steeper slope for the cross-sectional eGFR-lowering allele. The data rendered the third theoretical option, i.e. presence of a slope effect with flatter slope for the cross-sectional eGFR-lowering allele, void.
Some limitations warrant mentioning. Although this GWAS is currently the largest GWAS on eGFR-decline so far, more loci for eGFR-decline and CKD-progression might be detectable upon further increased sample size. The yield of eGFR-decline loci in >340,000 individuals was comparably low considering older GWAS for cross-sectional eGFR having already detected >50 loci in 170,000 individuals43. We used the CKD-EPI formula containing an ancestry term (Levey et al., Ann Intern Med), accounted for by ancestry-specific GWAS; future work should utilize the new ancestry-term-free CKD-EPI formula 2021 (Inker et al., NEJM). Evaluating the potential existence of sex-specific genetic effects on eGFR-decline is of interest, but was not addressed in this project. The target population is primarily population-based, including kidney diseases proportional to respective prevalence, and primarily European ancestry. Larger all-ancestry meta-analyses on eGFR-decline will open up opportunities to also utilize differential linkage disequilibrium between ancestries to help narrow down causal variants and genes. The interpretability of the SNP-by-age interaction on cross-sectional eGFR is limited to the age spectrum in the data (40–70 years) and by the power given the sample size; still, the sample size used was large and the age range typical also for most eGFR-decline GWAS studies. Two aspects need mentioning regarding the phenotype definition: uncertainty in eGFR-decline may be larger for studies with shorter follow-up, which decreases power, but measurement error in the outcome does not induce bias in linear regression44. By defining annual eGFR-decline from two eGFR assessments over time, our SNP associations capture only the linear component of decline. Serial eGFR assessments are better to characterize eGFR-trajectories, but at the cost of limiting sample size, since such studies are few and typically small. Furthermore, generalized additive mixed models for nonlinear eGFR-trajectories are complex and require particularly large sample sizes. The linear modelling of eGFR-decline is a reasonable approximation of monotonous decline, maintaining large sample sizes and limiting model complexity to be applicable for GWAS. Overall, the choice of the adjustment, target population, and phenotype definition are important to consider when interpreting results. While some modelling aspects are addressed here, other covariate adjustment or relative decline as phenotype might reveal further or other genetic loci. Future work is warranted to quantify effects in different target populations and the genetically determined shape of the decline, which requires more – and larger – longitudinal studies, ideally with more than two eGFR assessments over time.
Methodologically unique is our contrasting of GWAS SNP-associations on eGFR-decline for different covariate adjustment, which fills an important gap and helps design future studies. This is highly relevant, since covariate adjustment can alter GWAS findings and interpretation28–31,45. Adjusting for baseline DM-status had no impact, but genetic effects for eGFR-decline were larger when restricting to DM-individuals; this suggests DM-status as modulator for the SNP-association with eGFR-decline rather than mediator (i.e. in the causal pathway from SNP to eGFR-decline) or collider (i.e. generating biased association). Adjustment for eGFR-baseline yielded larger eGFR-decline effects and more genome-wide significant variants. Glymour et al. highlight that adjustment for baseline levels in analyses of change may help detect effects, but can induce spurious associations when the rate of change observed after baseline reflects a rate of change experienced in the past36. This might reflect the situation here rendering the larger genetic effects adjusted for eGFR-baseline - and the larger genetic effects when restricting to individuals with CKD at baseline – reflective of collider bias. Glymour et al. recommend the documentation of change effects without baseline adjustment36. In line with this, we considered a variant’s association with eGFR-decline genuine, when the variant reached genome-wide significance baseline-unadjusted or baseline-adjusted and Bonferroni-corrected significance baseline-unadjusted. The baseline-unadjusted model provides the relevant genetic effect sizes for eGFR-decline.
Interestingly, two of the three associations without genuine eGFR-decline association may relate to biomarker generation rather than kidney function: GATM and CPS1, known for a role in creatine biosynthesis41 and urea cycle42, respectively, reside in loci without supporting association with cross-sectional cystatin-based eGFR18. Conversely, the SHROOM3 locus was associated with cystatin-based eGFR18,15 and experimental studies support a role of SHROOM3 in kidney pathology46–48; thus, SHROOM3 appears to have an effect on cross-sectional kidney function, but not on kidney function decline within the limits of detectability by sample size.
A further unique aspect of our work is the empirical evidence for a link between SNP-effects on eGFR-decline with SNP-by-age interaction effects on cross-sectional eGFR. By this, we provide important insights into the age-dependency of kidney function genetics as well as into the genetic dependency of aging eGFR in adult general populations, where “aging” includes onset of age-related diseases as they develop in populations. Considering the much broader availability of cross-sectional than longitudinal data, the further parallel exploitation of SNP-by-age interaction might be a promising route to help improve our understanding of the mechanisms of kidney function decline over time.
In summary, we provide GWAS summary statistics, identified genetic loci, and prioritized genes for kidney function decline and CKD-progression. While UMOD has drawn attention already, GALNTL5, SPATA7, and TPPP may now receive more focus as therapeutic targets for disease progression. Our exploration of different covariate adjustment and the comparison to age-dependency of SNP-effect on eGFR cross-sectional provides important insights into the interpretation of these effects. With the emerging large biobank data linking medical records, longitudinal GWAS will become very important in the future. Our methodological framework is informative and applicable also generally for longitudinal phenotypes.
## Availability of data and materials
To support future work, we provide genome-wide summary statistics on eGFR-decline unadjusted for eGFR-baseline (adjusted for age, sex and DM-status) overall and restricted to individuals with DM or CKD at baseline (all adjusted for age and sex) (https://www.uniregensburg.de/decline and http://ckdgen.imbi.uni-freiburg.de). The summary statistics on eGFR-decline in individuals with CKD at baseline can be considered genetic effects on CKD-progression. We also provide genome-wide summary statistics on eGFR-decline adjusted for eGFR-baseline (additionally to adjustment for age and sex), but these summary statistics should be used with great care and an understanding that beta-estimates are subject to collider bias. For quantification of the genetic effect on eGFR-decline, the results unadjusted for eGFR-baseline should be utilized.
## DISCLOSURE STATEMENT
JÄ reports personal fees from AstraZeneca, Boehringer Ingelheim and Novartis, outside the submitted work. Sanofi Genzyme currently employs KeH. WKo reports modest consultation fees for advisory board meetings from Amgen, DalCor, Kowa, Novartis, Pfizer and Sanofi; modest personal fees for lectures from Amgen, AstraZeneca, Novartis, Pfizer and Sanofi, outside the scope of this work. CL received Grants/ Research Support from Bayer Ag/ Novo Nordisk, Husband works for Vertex. KBS, LMY, DMW and MAL are full-time employees of GlaxoSmithKline. MLO received grant support from GlaxoSmithKline, MSD, Eisai, AstraZeneca, MedCo and Janssen. BMP serves on steering committee of the Yale Open Data Access Project funded by Johnson & Johnson. PR received fees to his institution for research support from AstraZeneca and Novo Nordisk; for steering group participation from AstraZeneca, Gilead, Novo Nordisk, and Bayer; for lectures from Bayer, Eli Lilly and Novo Nordisk; and for advisory boards from Sanofi and Boehringer Ingelheim outside of this work. LWal received institutional grants from GlaxoSmithKline, AstraZeneca, BMS, Boehringer-Ingelheim, Pfizer, MSD and Roche Diagnostics. HW received grants and non-financial support from GlaxoSmithKline, during the conduct of the study, grants from Sanofi-Aventis, Eli Lilly, the National Institute of Health, Omthera Pharmaceuticals, Pfizer New Zealand, Elsai Inc. and Dalcor Pharma UK; honoraria and non-financial support from AstraZeneca; and is on advisory boards for Sirtex/ Acetilion and received personal fees from CSL Behring and American Regent outside the scope of this work. GS, DG, HH, IO, KStef, PS and UT are employees of deCODE/Amgen Inc.
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|
---
title: 'Association between clustering of unhealthy behaviors and depressive symptom
among adolescents in Taiwan: A nationwide cross-sectional survey'
authors:
- Chung Bui
- Li-Yin Lin
- Chun-Ji Lin
- Ya-Wen Chiu
- Hung-Yi Chiou
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10035074
doi: 10.3389/fpubh.2023.1049836
license: CC BY 4.0
---
# Association between clustering of unhealthy behaviors and depressive symptom among adolescents in Taiwan: A nationwide cross-sectional survey
## Abstract
### Background
Among Taiwanese adolescents, how the clustering of unhealthy behaviors, including insufficient physical activity, screen-based sedentary behavior and frequent sugar-sweetened beverage consumption affecting depressive symptom remains unclear. This study aims to examine the cross-sectional association between clustering of unhealthy behaviors and depressive symptom.
### Methods
We analyzed 18,509 participants from the baseline survey of the Taiwan Adolescent to Adult Longitudinal Survey in 2015. The outcome was depressive symptoms, and the main exposures were insufficient physical activity, screen-based sedentary behaviors and frequent sugar-sweetened beverage consumption. Generalized linear mixed models were performed to find key factor associated with depressive symptom.
### Results
Depressive symptoms were common among participants ($31.4\%$), particularly in female and older adolescents. After adjustments for covariates including sex, school type, other lifestyle factors and social determinants, individuals exhibiting clustering of unhealthy behaviors were more likely (aOR = 1.53, $95\%$ CI: 1.48–1.58) to exhibit depressive symptoms than those who have no or only one unhealthy behavior.
### Conclusions
Clustering of unhealthy behaviors is positively associated with depressive symptom among Taiwanese adolescents. The findings highlight the importance of strengthening public health interventions to improve physical activity and decrease sedentary behaviors.
## Background
According to the World Health Organization (WHO), the term adolescents were defined as individuals aged between 10 and 19 years old. Adolescence basically includes two periods i.e., young adolescent (aged between 10 and 14 years) and older adolescent (aged between 15 and 19 years) [1]. Adolescence is referred to the life period between childhood and adulthood, which is an important stage for human development (i.e., rapid physical growth, sexual maturation, emotional, social, and cognitive development). It is also an essential period to ensure good health status [2]. Among the WHO guidelines for the health services and interventions for adolescents, mental health is certainly one of the key components to focus on. Globally, suicide ranks third among the causes of mortality in the during adolescence, whereas depression can lead to disability. Nearly half of the mental health problems start occurring by the age of 14, however, a majority of mental disorders are neglected and untreated, which can lead to serious consequences in the long-term [3].
Depression is a major risk factor for suicide, which is the second-to-third leading cause of death among adolescents [4]. Depression can also lead to social, educational, and behavioral impairments such as low self-esteem, irritability, smoking, substance misuse, disordered eating (5–7). Depression is often being missed if the primary symptoms are unexplained physical symptoms, anxiety, skipping school, decline in academic participation, eating disorders, substance misuse, or behavioral problems [8]. The pathogenesis of depression is complicated due to the diverse causes of this illness. For example, inherited factors, hormonal changes, familial and genetic factors, environmental factors, and brain and neuroendocrine functioning are all critical factors contributing to the incidence of depression (9–11). Similar to many health disorders, usually more than one risk factors interact and contribute the development of depression [8]. These risk behaviors may frequently co-exist in a complex pathway [12]. Previous studies reveal the association between depressive symptoms with overconsumption of sugar-sweetened drinks [13], inadequate physical activity [14] and sedentary behaviors [15]. Meanwhile, feeling sad and hopeless are the most significant indicators for depression. Another key indicator of depression is low self-esteem, where he or she may feel foolish, unattractive, or worthless [16]. *In* general, health risk behaviors frequently occur together to contribute to increased risk of chronic diseases and premature mortality [17]. Multiple unhealthy behaviors are often more detrimental to one's health than a single behavior [18]. Two studies have examined the clustering of health risk behaviors and mental health status among college samples: one from the UK [19] and one from Canada [20] revealed that students with greater numbers of unhealthy behaviors had increased risks for depression and anxiety as well as greater self-perceived psychological stress (19–21). At present, research on clustered behaviors associated with depressive symptoms remains limited.
In Taiwan, a recent Taiwan's National Epidemiological Study of Child Mental Disorders showed that the weighted lifetime prevalence rate of overall mental disorders was $31.6\%$ among children and young adolescents. The lifetime prevalence rates of suicide-related problems: $8.2\%$ suicidal ideation, $3.6\%$ suicidal plans, and $0.7\%$ suicidal attempts [22]. Other previous study also found a reciprocal relationship between unhealthy eating behaviors and depressive symptoms from childhood to adolescence [23]. A quasi-experimental study demonstrated the effectiveness of a 12-week brisk walking program on improving self-esteem, anxiety, and depression [24]. Another large-scale project called the Health of Adolescents in Southern Taiwan examined the relationship between depression and self-esteem and social determinants in 2004 [25, 26]. To date, there has been limited study investigating the co-occurrence of clustering of unhealthy behaviors among adolescents in Taiwan.
In this study, we hypothesized that the clustering of unhealthy behaviors, such as insufficient physical activity, screen-based sedentary behaviors, and frequent consumption of sugar-sweetened beverages, is positively associated with depressive symptoms. After adjusting for other factors such as social determinants and other behavioral factors, each cluster's individual associated with depressive symptoms would be assessed. As a result, this study aims to analyze the association between the clustering of unhealthy behaviors and depressive symptoms and to identify key risk factors for future intervention suggestions.
## Methods
This study followed the Strengthening the Reporting of Observational studies in Epidemiology (STROBE) guidelines [27] (Supplementary File 1).
## Sample and study design
The study is a cross-sectional secondary analysis use data from the baseline survey of Taiwan Adolescent to Adult Longitudinal Study (TAALS) in 2015. TAALS is a longitudinal nation-wide study which employed a multistage, stratified area, probability sampling design to recruit participants from both junior high schools and senior high schools in Taiwan. The methodology of the study has been described in details in another study conducted by Chien et al. [ 28]. There were three levels of the data structure including geographic area, township-level unit, and school. The final sample contained 173 schools in 19 county/city in North, Central, East, and South Taiwan. Researchers selected 6,903 seventh-grade students (aged 13 years) in junior schools and 11,742 tenth-grade students (aged 16 years) in senior high schools, and vocational high schools. Students who failed to provide the informed consent form with their parents' signatures were excluded from the survey. For this study, data collected from those 18,509 participants who responded to all five depressive symptoms questions were used for analysis. During an interview, information was collected on sociodemographic factors, lifestyle and physical activity, substance use, dietary behaviors, mental health status, violence-related behaviors and experiences, sexual behaviors and attitudes, and social support. Ethics approval was obtained from the Joint Institutional Review Board of Taipei Medical University, Taiwan (TMU-JIRB-201410043) [28].
## Depressive symptoms
Information regarding depressive symptoms was collected during an on-site, self-administered survey assisted with a class-based interview system. From the Chinese version of the Center for Epidemiological Studies Short Depression Scale (CES-D) [28, 29], five questions used to investigate depressive symptoms during the past seven days were: [1] I did not feel like eating; my appetite was poor; [2] I had trouble keeping my mind on what I was doing; [3] I felt depressed; [4] I felt that everything I did was an effort.; [ 5] I felt lonely. The five-item scale had a high internal consistency with a Cronbach's α of 0.76. Each item was rated on a 4-point Likert-type scale ranging from 0: Rarely or none of the time (< 1 day); 1: Some or a little of the time (1–2 days); 2: Occasionally or a moderate amount of time (3–4 days); 3: All of the time (5–7 days). The sum of five responses ranged from 0 to 15. Participants who obtained a score ≥ 5 were considered having depressive symptoms [29, 30].
## Definitions on clustering of unhealthy behaviors
Clustering of unhealthy behaviors was dichotomized by the co-existence of two or more factors including frequent sugar-sweetened beverage consumption (FSSBC), insufficient physical activity (IPA), and screen-based sedentary behaviors (SBSB) [12, 31]. The reference group was participants with no unhealthy behavior or with just one unhealthy behavior. In addition, a categorical variable was generated to identify eight groups based on the amount and co-occurrence of unhealthy behaviors from no unhealthy behavior to all unhealthy behaviors (Supplementary File 2).
## Insufficient physical activity
Similar to the Global School-based Student Health Survey [32, 33], the question “During the past seven days, how many days were you physically active for a total of at least 60 minutes per day?” was used to determine physical activity. Insufficient physical activity was defined as participants who were not physically active for a total of at least 60 min per day on five or more days during the past 7 days [34].
## Screen-based sedentary behaviors
The question “How long have you spent watching TV, playing video games, using a computer, and playing with your mobile phone on average per day during the past seven days?” measured SBSB. A cutoff value of 2 h or more per day indicates the presence of SBSB as “yes” (≥ 2 h per day) and “no” (<2 h per day) [35].
## Frequent sugar-sweetened beverage consumption
The question “How many times did you drink sugar-sweetened beverages (i.e., bubble milk tea, fruit juice drinks, soft drinks, sports drinks, sugary tea drinks, Yakult) during the past seven days?” indicated FSSBC. We adopted a cutoff value of three times or more to classify the frequency of consumption as “high” (≥3 times per week) and “low” (<3 times per week) [36].
## Controlling variables
Sex, school type, body mass index (BMI), personal behaviors, school support, peer support and parental education were controlled in the predicted models of depressive symptom. We recorded sex and school type from the sampling database. Participants' weight (kg) and height (cm) were retrieved directly from school-based health record and Body Mass Index (BMI) was calculated. The Taiwan Ministry of Health and Welfare classifies BMI status as “underweight” (BMI < 18.5), “normal” (18.5 ≥ BMI < 24), and “overweight or obese” (BMI ≥ 24) [37] according to a WHO Expert Consultation for Appropriate Body-Mass Index for Asian Populations [38]. Personal behaviors integrated into regression models were eating behaviors, smoking, and binge drinking. The frequency of emotional eating, skipping breakfast, eating while performing other activities, and reading nutrition labels was being divided into two categories: “low” (0–2 times/week) and “high” (3 times/week) depending on how many times the participant reported these actions during the past week. Cigarette smoking was identified (yes/no) based on self-reported frequency during the last month [33]. We applied the criterion of 5 drinks on one occasion for at least 1 day during the past month to define binge drinking behavior in adolescents [39].
School support was determined by six questions including: “My teachers respect me,” “My teachers are fair,” “Teachers in my school are nice people,” “When students break rules at my school, they are treated fairly,” “My school is a good place to be,” “My school is important to me.” The six-item scale had a high internal consistency with a Cronbach's α of 0.88. Each item was rated on a 4-point Likert-type scale ranging from 1 (strongly disagree) to 4 (strongly agree). The sum of six responses ranged from 6 to 24. Participants who obtained a score ≥ 12 were considered to have school support. Four questions used to measure peer support were: “My classmate/friend really care about what happens to me,” “My classmate/friend are there for me whenever I need help,” “My classmate/friend can be trusted a lot,” “My classmate/friend care about my feelings” (a Cronbach's α of 0.90). Each item was rated on a 4-point Likert-type scale ranging from 1 (strongly disagree) to 4 (strongly agree). The sum of four responses ranged from 4 to 16. Participants who obtained a score ≥ 8 were considered to have good peer support. Being bullied experiences were investigated by “I was pushed, shoved, slapped, or kicked by other students,” “I was teased by other students,” “I was ignored or felt left out of activities or games on purpose,” and “Some pictures or words were posted online, (through email, computer text message, or Facebook), by someone else to make others laugh.” Each question was rated on a 5-point scale ranging from 1 (Never) to 5 being (Always). The sum of four responses ranged from 4 to 20. Participants who obtained a score ≥ 5 were considered to have being bullied experiences [40]. Representative socio-economic factors were the mother's and fathers' highest level of education attained: [1] elementary school graduate and below [2] junior high school graduate, [3] senior high school graduate, [4] university graduate.
## Statistical analysis
The TAALS study team assigned weights to weight variables in order to reduce bias caused by survey design and differences between respondents and non-respondents. We employed sample weights to obtain unbiased estimators (e.g., mean and standard error) and minimize the discrepancy between the distribution structure of the sample and that of the population [28]. Participants missing depressive symptoms records were being removed (Supplementary File 3). We used chi-square tests for categorical variables to analyze the characteristics of participants, depressive symptoms, and clustering of unhealthy behaviors by sex and school type. Generalized linear mixed models were used to assess the association between binary clustering of unhealthy behaviors and depressive symptoms. Models stratified by sex and school type were employed to determine the association between categorical clustering of unhealthy behaviors and depressive symptoms in each sub-population. Generalized linear mixed models were adjusted for BMI, unhealthy behaviors and socioeconomic determinants. We used SPSS software (version 25; IBM SPSS Statistic) to conduct all statistical analyses. A p-value of 0.05 indicates statistical significance.
## Results
Overall, among the 18,509 participants, 8,964 ($48.4\%$) were male, and 9,545 ($51.6\%$) were female. In addition, 6,846 ($37.0\%$) participants were junior high school students, 4,783 ($25.8\%$) were high school students, and 6,880 ($37.2\%$) were vocational high school students.
## Proportion of depressive symptoms and clustering of unhealthy behaviors
Table 1 shows the proportion of critical variables and the variations across sex and school type. Almost one–third of participants had depressive symptoms ($31.4\%$). As for unhealthy behaviors, over three-fourths of participants were IPA ($77.7\%$). Those who engaged in FSSBC and SBSB were dominant (60.2 and $54.3\%$, respectively). Over two-thirds of participants exhibited clustering of unhealthy behaviors ($68.5\%$). When stratified by sex, females were more likely than males to report depressive symptoms (34.7 vs. $28.0\%$, respectively; $p \leq 0.001$). Females were also more likely than males to participate in IPA (85.7 vs. $69.1\%$, respectively; $p \leq 0.001$). However, male participated in FSSBC (64.7 vs. $56.0\%$, respectively; $p \leq 0.001$) and SBSB (55.5 vs. $53.4\%$, respectively; $$p \leq 0.002$$) more than females. There was no significant difference between males and females in terms of clustering of unhealthy behaviors (67.9 vs. $69.2\%$, respectively; $$p \leq 0.057$$). When stratified by school type, it was shown that vocational high school students were more likely to report depressive symptoms than those from senior high school and junior high school (35.6 vs. $34.0\%$ vs. $25.4\%$, respectively; $p \leq 0.001$). Vocational high school students were the most likely ones among three school types to participate in IPA, SBSB, and clustering of unhealthy behaviors (Supplementary File 4).
**Table 1**
| Variables | Total (n = 18,509) | Sex | Sex.1 | Sex.2 | School type | School type.1 | School type.2 | School type.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | Male (n = 8,964) | Female (n = 9,545) | p | Junior (n = 6,846) | Senior (n = 4,783) | Vocational (n = 6,880) | p |
| Depressive symptom, n (%) | | | | <0.001 | | | | <0.001 |
| Yes | 5,816 (31.4) | 2,506 (28) | 3,310 (34.7) | | 1,742 (25.4) | 1,624 (34) | 2,450 (35.6) | |
| No | 12,693 (68.6) | 6,458 (72) | 6,235 (65.3) | | 5,104 (74.6) | 3,159 (66) | 4,430 (64.4) | |
| Sex, n (%) | | | | | | | | <0.001 |
| Male | | | | | 3,350 (48.9) | 2,054 (42.9) | 3,560 (51.7) | |
| Female | | | | | 3,496 (51.1) | 2,729 (57.1) | 3,320 (48.3) | |
| Age, n (%) | | | | 0.151 | | | | |
| 16 years old | 11,663 (63) | 5,614 (62.6) | 6,049 (63.4) | | | 4,783 (41) | 6,880 (59) | |
| 13 years old | 6,846 (37) | 3,350 (37.4) | 3,496 (36.6) | | 6,846 (100) | | | |
| School type, n (%) | | | | <0.001 | | | | |
| Senior | 4,783 (25.8) | 2,054 (22.9) | 2,729 (28.6) | | | | | |
| Junior | 6,846 (37) | 3,350 (37.4) | 3,496 (36.6) | | | | | |
| Vocational | 6,880 (37.2) | 3,560 (39.7) | 3,320 (34.8) | | | | | |
| Body mass index, n (%) | | | | <0.001 | | | | <0.001 |
| Underweight | 5,380 (29.6) | 2,548 (29) | 2,832 (30.3) | | 2,670 (40) | 1,091 (23) | 1,619 (24) | |
| Normal | 9,648 (53.1) | 4,345 (49.4) | 5,303 (56.7) | | 3,023 (45.3) | 2,875 (60.7) | 3,750 (55.6) | |
| Overweight or obese | 3,125 (17.2) | 1,901 (21.6) | 1,224 (13.1) | | 977 (14.6) | 773 (16.3) | 1,375 (20.4) | |
| FSSBC, n (%) | | | | <0.001 | | | | <0.001 |
| ≥3 times/week | 11,125 (60.2) | 5,792 (64.7) | 5,333 (56) | | 3,853 (56.5) | 2,906 (60.8) | 4,366 (63.5) | |
| 0–2 times/week | 7,353 (39.8) | 3,156 (35.3) | 4,197 (44) | | 2,968 (43.5) | 1,875 (39.2) | 2,510 (36.5) | |
| IPA, n (%) | | | | <0.001 | | | | <0.001 |
| Yes | 9,819 (53.3) | 3,707 (41.6) | 6,112 (64.3) | | 2,834 (41.6) | 2,843 (59.6) | 4,142 (60.6) | |
| No | 8,600 (46.7) | 5,200 (58.4) | 3,400 (35.7) | | 3,978 (58.4) | 1,926 (40.4) | 2,696 (39.4) | |
| SBSB, n (%) | | | | 0.002 | | | | <0.001 |
| Yes | 10,047 (54.4) | 4,960 (55.5) | 5,087 (53.4) | | 2,933 (43) | 2,182 (45.7) | 4,932 (71.8) | |
| No | 8,427 (45.6) | 3,980 (44.5) | 4,447 (46.6) | | 3,894 (57) | 2,597 (54.3) | 1,936 (28.2) | |
| Clustering of behaviors, n (%) | | | | <0.001 | | | | <0.001 |
| ≥2 behaviors | 10,776 (58.7) | 5,010 (56.5) | 5,766 (60.8) | | 3,129 (46.2) | 2,733 (57.4) | 4,914 (72) | |
| 0–1 behavior | 7,584 (41.3) | 3,862 (43.5) | 3,722 (39.2) | | 3,644 (53.8) | 2,031 (42.6) | 1,909 (28) | |
## Association between clustering of unhealthy behaviors and depressive symptoms
Table 2 shows the results of generalized linear mixed models with binary clustering of unhealthy behaviors and after adjustment for BMI, other unhealthy behaviors (adjusted model 1), and social determinants (adjusted models 2) (Supplementary File 5). In the unadjusted model, depressive symptoms were more likely to be found among participants who exhibited clustering of unhealthy behaviors (cOR = 1.81, $95\%$ CI: 1.73–7.90). This association became slightly decreased after adjusted for other unhealthy behaviors (adjusted model 1, aOR = 1.58, $95\%$ CI: 1.52–1.65) and further decreased after adjusted for socioeconomic determinants (adjusted model 2, aOR = 1.53, $95\%$ CI: 1.48–1.58).
**Table 2**
| Variables | Crude model* | Unnamed: 2 | Adjusted model I** | Unnamed: 4 | Adjusted model II*** | Unnamed: 6 |
| --- | --- | --- | --- | --- | --- | --- |
| | OR (95% CI) | p | OR (95% CI) | p | OR (95% CI) | p |
| Clustering of unhealthy behaviors | Clustering of unhealthy behaviors | Clustering of unhealthy behaviors | Clustering of unhealthy behaviors | Clustering of unhealthy behaviors | Clustering of unhealthy behaviors | Clustering of unhealthy behaviors |
| ≥2 behaviors | 1.81 (1.73–1.90) | < 0.001 | 1.58 (1.52–1.65) | < 0.001 | 1.53 (1.48–1.58) | < 0.001 |
| 0–1 behavior | Ref | | Ref | | Ref | |
| Sex | Sex | Sex | Sex | Sex | Sex | Sex |
| Male | 0.74 (0.71–0.76) | < 0.001 | 0.72 (0.70–0.74) | < 0.001 | 0.66 (0.64–0.67) | < 0.001 |
| Female | Ref | | Ref | | Ref | |
| School type | School type | School type | School type | School type | School type | School type |
| Senior | 0.97 (0.92–1.03) | 0.324 | 1.03 (1.00–1.06) | 0.041 | 1.10 (1.09–1.11) | < 0.001 |
| Junior | 0.69 (0.64–0.72) | < 0.001 | 0.77 (0.73–0.81) | < 0.001 | 0.7 (0.67–0.73) | < 0.001 |
| Vocational | Ref | | Ref | | Ref | |
## Generalized linear mixed models stratified by sex
The associations between categorical clustering of unhealthy behaviors and depressive symptoms stratified by sex were shown in Table 3. Overall, depressive symptoms were most likely to occur among the triple unhealthy behavior subgroup (aOR = 2.27, $95\%$ CI: 2.13–2.42). Among the dual unhealthy behavior subgroup, IPA combined SBSB subgroup (OR = 1.93, $95\%$ CI: 1.81–2.01) was more likely than IPA combined FSSBC subgroup (OR = 1.82, $95\%$ CI: 1.70–1.94) and SBSB combined FSSBC subgroup (OR = 1.85, $95\%$ CI: 1.66–2.05) to report depressive symptoms. All single unhealthy behavior significantly associated with depressive symptoms, in detail, IPA (OR = 1.45, $95\%$ CI: 1.37–1.54), SBSB (OR = 1.36, $95\%$ CI: 1.27–1.46), FSSBC (OR = 1.35, $95\%$ CI: 1.24–1.47).
**Table 3**
| Variables | All | Unnamed: 2 | Female | Unnamed: 4 | Male | Unnamed: 6 |
| --- | --- | --- | --- | --- | --- | --- |
| | OR (95% CI) | p | OR (95% CI) | p | OR (95% CI) | p |
| Clustering of unhealthy behaviors * | Clustering of unhealthy behaviors * | Clustering of unhealthy behaviors * | Clustering of unhealthy behaviors * | Clustering of unhealthy behaviors * | Clustering of unhealthy behaviors * | Clustering of unhealthy behaviors * |
| FSSBC** | 1.35 (1.24–1.47) | <0.001 | 1.77 (1.62–1.95) | <0.001 | 1.10 (0.99–1.22) | 0.066 |
| SBSB** | 1.36 (1.27–1.46) | <0.001 | 1.46 (1.41–1.51) | <0.001 | 1.35 (1.19–1.53) | <0.001 |
| IPA** | 1.45 (1.37–1.54) | <0.001 | 1.61 (1.27–2.03) | <0.001 | 1.32 (1.11–1.66) | 0.002 |
| FSSBC+SBSB | 1.85 (1.66–2.05) | <0.001 | 2.14 (1.91–2.41) | <0.001 | 1.63 (1.35–1.96) | <0.001 |
| IPA + FSSBC | 1.82 (1.70–1.94) | <0.001 | 1.87 (1.67–2.10) | <0.001 | 1.86 (1.76–1.97) | <0.001 |
| IPA+SBSB | 1.93 (1.81–2.01) | <0.001 | 2.12 (2.05–2.19) | <0.001 | 1.77 (1.55–2.04) | <0.001 |
| All unhealthy behaviors | 2.27 (2.13–2.42) | <0.001 | 2.38 (2.25–2.51) | <0.001 | 2.19 (1.86–2.57) | <0.001 |
| No unhealthy behavior | Ref | | Ref | | Ref | |
## Discussion
This study investigates the association between the clustering of unhealthy behaviors and depressive symptoms among Taiwanese adolescents in 2015. Our findings highlighted three significant predictors of depressive symptoms along with a complex pattern of health behaviors, individual factors, and social determinants. Our research findings contributed to the current knowledge of health consequences of unhealthy behaviors among adolescents [3].
Similar to previous studies [22, 25, 26], our main findings indicated that depressive symptoms were common in Taiwanese adolescents. During 2015–2017, another nationwide surveillance of DSM-5 mental disorders also highlighted the high prevalence rates of overall mental disorders among Taiwanese children (aged 10–13 years) [22]. These rates were higher than other previous analyses from the Project for the Health of Adolescents (aged 13–18 years) in Southern Taiwan which was conducted in 2004 [25]. In addition, these results might be considerably higher than the global estimation of mental health burden among adolescent [41]. Our study and various other studies on mental health matters in Taiwan [22, 26] raise the issue of the importance of preventing depression and suicide among Taiwanese children and adolescents.
We found that clustering of unhealthy behaviors was positively associated with depressive symptoms. Another study also indicated the positive association between physical activity and depressive symptoms [14]. Furthermore, other studies suggest to consider physical activity as a practical approach to improve mental health among children and adolescents [24, 42]. There was additional evidence of the dose-response relationship between screen-based sedentary behavior and depression [15, 35]. A variety of previous studies highlighted that overconsumption of sugar-sweetened beverages was a risk factor for depressive symptoms [23, 43]. Diet, physical activity and sedentary behavior often referred to as “energy balance-related behaviors” were frequently considered as the critical risk factors for overweight and obese [12, 44]. Meanwhile, our study addressed the correlation of diet, physical activity and sedentary behavior on mental health conditions in adolescents, which were rarely assessed in previous studies [45].
From children and adolescent mental health standpoint, risk factors for depressive symptoms are frequently related to psychological and social factors such as cognition, interpersonal skills, low self-esteem, self-emotion regulation [16, 46]. Recent evidence based on RCT study results demonstrated the efficacy of physical activity intervention for improving depressive symptoms [42]. However, larger RCT studies targeting whole adolescent populations rarely assessed outcomes of physical activity, sedentary behavior and diet interventions on mental health [47]. Therefore, assessment on depressive symptoms should be integrated into interventions on improving energy-balance related behaviors such as diet, physical activity and sedentary behaviors among children and adolescents.
Consistent with previous studies in other countries [8, 18], we found that multiple unhealthy behaviors were commonly co-existed among Taiwanese adolescents. A 5-years follow-up cohort study in Australia concluded lifestyle risk behaviors are prevalent among young adults, and that risk behaviors concurrent with another one [45]. Another study consisted of nationally representative U.S children and adolescents indicated excessive screen time and poor diet were commonly co-occurred. The multiple unhealthy behaviors were observed to be increased with age [31]. A systematic review on clustering of unhealthy behaviors among children and adolescents demonstrated insufficient physical activity and sedentary behavior cluster were frequently observed, and that the cluster relationship was influenced by age, sex and socio-economic status [12].
In Taiwan, there were a few studies investigating the clustering of unhealthy behaviors among adults. For instance, a prospective cohort study demonstrated the positive association between unhealthy behaviors and all-cause mortality [48]. Our study contributed to one of the first evidence that clustering of unhealthy behaviors is positively associated with depression among adolescents in Taiwan. Therefore, our study findings may have several implications on policy. For instance, our results suggest that the prevention interventions should target multiple behaviors at the same time for a better outcome. The outcomes of reducing IPA, SBSB, FSSBC are not only improving obesity but also mental health condition among adolescents. Prevention approaches that address multiple behaviors may be more cost-effective [17]. On the other hand, mental health prevention should be integrated into the Health Promotion School Program [49], especially for senior high schools and vocational high schools.
This study has several methodological limitations. First, because this is a cross-sectional study, the findings cannot be used to indicate the causal effect between clustering of unhealthy behaviors and depressive symptoms. Second, based on the questionnaire responses, measurements of both behaviors and depressive symptoms may contain information bias. Participants' responses may be influenced by their ability to recall events in the past, and by their social desirability [31]. Third, some factors significantly related to depressive symptoms, for instance, self-emotion regulation [47], were not adjusted in our analysis. A prior meta-analytic review highlighted the negative association between self-regulation and depressive symptom, obesity, substance abuse among young adolescents [50]. Finally, this analysis was not able to explain the relationship pattern of depressive symptoms, emotional eating and FSSBC. Previous studies demonstrated emotional eating as a significant mediator between depression, anxiety and weight gain [51]. Among Taiwanese adolescents, the association between emotional eating and FSSBC has been indicated elsewhere [36].
Our research has several strengths. The nationwide sampling offered a representative and consistent sample of Taiwan's adolescent population. Furthermore, our data revealed a variety of unhealthy behaviors among vocational high school students, senior high school students, and junior high school students, including IPA, SBSB and FSSBC as well as adolescent mental health issue. Adolescent demographics showed diverse lifestyles, social determinants, and unhealthy behaviors including smoking and binge drinking when stratified by school types, all of which likely to be significant driven factors of an adolescent's depressive symptoms. The stratified results enable the development of customized health promotion strategies for each subgroup. The critical variables associated with depressive symptoms have been controlled in our adjusted model.
## Conclusion
Our study findings show that depressive symptoms are common in Taiwanese adolescents and positively associated with unhealthy behaviors in a complex behavioral pattern. Therefore, conducting an assessment on risk behavior clustering is essential for directing the development of mental health prevention strategies because it provides insights into which risk behaviors should be targeted together. Furthermore, it becomes crucial to develop school-based health promotion interventions tailored to different subgroups for better intervention results.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Taipei Medical University—Joint Institutional Review Board (TMU—JIRB-201410043). Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.
## Author contributions
CB, Y-WC, and H-YC: conceptualization. CB, L-YL, and H-YC: methodology. L-YL, Y-WC, and H-YC: validation, supervision, and writing—review and editing. CB and C-JL: formal analysis and investigation. H-YC: resources. CB and H-YC: data curation. CB and L-YL: writing original draft preparation. All authors have read and agreed to the published version of the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1049836/full#supplementary-material
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|
---
title: Homocysteine levels correlate with AVSS-RigiScan test parameters in men with
erectile dysfunction
authors:
- Xin Qian
- Xing Tao
- Yangyang Gong
- Can Ran
- Yougang Feng
- Hongjian Liu
journal: Basic and Clinical Andrology
year: 2023
pmcid: PMC10035114
doi: 10.1186/s12610-022-00181-9
license: CC BY 4.0
---
# Homocysteine levels correlate with AVSS-RigiScan test parameters in men with erectile dysfunction
## Abstract
### Background
Although elevated homocysteine levels have been shown to affect penile erection, the relationship between homocysteine and erection at the tip or base of the penis has not been extensively studied.
### Results
We found that homocysteine levels were negatively correlated with the average event rigidity of the base (r = -0.2225, $$p \leq 0.0142$$). Homocysteine levels were also negatively correlated with the average maximum rigidity of the base (r = -0.2164, $$p \leq 0.0171$$). In particular, homocysteine levels were negatively correlated with ∆ Tumescence of the tip (r = -0.1866, $$p \leq 0.0404$$). Similarly, homocysteine was negatively correlated with ∆ Tumescence of the base (r = -0.2257, $$p \leq 0.0128$$).
### Conclusion
Our data showed that homocysteine inhibits penile erection. At the same time, homocysteine levels were negatively correlated with the parameters of the AVSS-RigiScan test.
## Contexte
Bien qu’il ait été démontré que des niveaux élevés d’homocystéine affectaient l’érection pénienne, la relation entre homocystéine et érection à l’extrémité ou à la base du pénis n’a pas été étudiée de manière approfondie.
## Résultats
Nous avons constaté que les niveaux d’homocystéine étaient négativement corrélés avec la rigidité moyenne de la base (r = -0,2225, $$p \leq 0$$,0142). Les taux d’homocystéine étaient également négativement corrélés avec la rigidité maximale moyenne de la base (r = -0,2164, $$p \leq 0$$,0171). En particulier, les taux d’homocystéine étaient négativement corrélés avec la tumescence Δ de l’extrémité (r = -0,1866, $$p \leq 0$$,0404). De même, l’homocystéine était négativement corrélée avec la tumescence Δ de la base (r = -0,2257, $$p \leq 0$$,0128).
## Conclusions
Nos données ont montré que l’homocystéine inhibe l’érection pénienne. Dans le même temps, les niveaux d’homocystéine étaient négativement corrélés avec les paramètres du test AVSS-RigiScan.
## Introduction
Erectile dysfunction (ED), one of the most common types of male sexual dysfunction, is the consistent inability of the penis to achieve or maintain an erection sufficient for satisfactory sexual intercourse and is a chronic condition that affects men's physical and mental health [1]. According to the Massachusetts Male Aging Study (MMAS), ED affects half of men aged 40–70 years and up to $70\%$ of older men [2]. The largest European multicentre population-based study of older men (40–79 years) reported that the prevalence of erectile dysfunction ranged from 6 to $64\%$, depending on the age group, and that the prevalence increased annually with age, with an average prevalence of $30\%$ [3]. ED also has a high prevalence in China, and a study based on a five-item International Index of Erectile Function questionnaire showed that the prevalence of ED was $40.56\%$ at an age of at least 40 years in 5210 men from 30 provinces and autonomies [4]. There are many risk factors for ED, including age, physical diseases, medications, lifestyles, and living conditions [5]. According to the available studies, ED is usually associated with systematic diseases such as diabetes mellitus, hyperlipidaemia, coronary artery disease, peripheral vascular disease, and cerebrovascular disease [6]. Atherosclerosis is a systemic pathological change of the blood vessels that also affects the cavernous arteries and may lead to altered blood flow at the penile level. Among the cardiovascular risk factors affecting the development of atherosclerosis, hyperhomocysteinemia (HHcys) plays a core role and is associated with oxidative stress and endothelial dysfunction [7].
RigiScan, a monitor of penile rigidity, has 2 testing modes: nocturnal and proactive. The nocturnal mode is mainly used for continuous monitoring during the nighttime sleep state, and the proactive mode is mainly used for monitoring by audio-visual sexual stimulation (AVSS) during the waking state. The NRT-RigiScan test and AVSS-RigiScan test have their own advantages in clinical application, and the correlation between the two tests is high [8]. The current majority view is that the AVSS-RigiScan test is simple, practical, quick and inexpensive and is suitable for initial aetiologic screening of ED patients in general. There is no standardized normative reference standard for the AVSS-RigiScan test, and most clinical centres refer to the NPTR-RigiScan diagnostic criteria. Wang et al. studied 1169 ED patients aged 18–67 years by the AVSS-RigiScan test and found that a basal rigidity of over $60\%$ was sustained for at least 8.75 min, with an average event rigidity of the tip reaching at least $43.5\%$ and that of the base reaching at least $50.5\%$; an average maximum rigidity of the tip reaching at least $62.5\%$ and that of the base reaching at least $67.5\%$; ∆tumescence (increase in tumescence or maximum − minimum tumescence) of the tip measuring at least 1.75 cm and that of the base measuring at least 1.95 cm; and a total tumescence time measuring at least 29.75 min [9]. The number of times attaining total tumescence at least once can be used as a reference criterion for normal erectile hardness by AVSS, and AVSS testing after oral PDE5i is more objective and accurate in identifying psychological and organic ED [9].
Studies have shown that patients with high levels of Hcys are at increased risk of developing ED [10]. HHcys leads to reduced expression and activation of endothelial nitric oxide enzyme (NOS). A study by Giovannone concluded that the Hcys level is an early predictor of ED that is superior to Doppler ultrasound [11]; that study revealed that increased Hcys levels in patients with mild ED were present before abnormal Doppler ultrasound values were observed. Al-Hunayan et al. compared 97 patients exhibiting type 2 diabetes mellitus with vascular ED and 97 type 2 diabetic patients without ED in a case‒control study, demonstrating that HHcys is a major determinant of ED in diabetic patients [12]. A cross-sectional study by Salvio et al. who collected clinical data, Hcys levels, and penile ultrasound *Doppler data* from 126 patients with ED of arterial origin for analysis showed that Hcys levels were associated with penile blood flow velocity parameters in basal penile duplex ultrasound, demonstrating the role of Hcys levels in vasculogenic ED [13].
Therefore, the present study aimed to investigate the correlation between serum homocysteine levels and audio-visual sexual stimulation and RigiScan™ (AVSS-RigiScan) test parameters in men with erectile dysfunction to further clarify the relationship between serum homocysteine levels and erectile dysfunction in men and to provide reliable data support for clinical application.
## Inclusion and exclusion criteria
Ninety-nine patients with a diagnosis of erectile dysfunction who attended the male outpatient clinic of Suining Central Hospital from October 2021 to March 2022 were selected for inclusion in the case group. Twenty-two men without sexual dysfunction who underwent a health examination or premarital examination during the same period were included in the control group. Participants were grouped and analysed retrospectively. All participants provided informed consent (which included a statement of confidentiality of responses and the right to stop answering at any time) and were given answers to any questions regarding the significance of the survey items. The inclusion criteria for participants were as follows: age from 40 to 75 years; regular sexual activity within the last six months; and International Index of Erectile Function (IIEF-5) ≤ 21. Subjects were excluded from the study according to the following criteria: abnormal sex hormones, cardiovascular accident within the last 6 months, medication affecting serum homocysteine levels or testosterone levels within the last 3 months, history of genital surgery, and history of genital trauma.
## Study design
A detailed history, physical examination, laboratory evaluation, and erectile function examination (i.e., audio-visual sexual stimulation (AVSS) test) was administered to all participants in the study. All patients' general information included age, IIEF-5 score, and Erection Hardness Scale (EHS) score. The following biochemical parameters were considered: glucose, total cholesterol (TC), triglycerides (TGs), high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), lipoprotein (a), fructosamine (FMN), homocysteine (Hcys), and testosterone (T). Normal ranges for adults were 3.88–6.38 mmol/L (glucose), < 5.17 mmol/l (cholesterol), < 1.70 mmol/L(TG), > 1.04 mmol/L (HDL), < 3.12 mmol/L (LDL), 1.00–1.60 g/L (ApoA1), 0.6–1.1 g/L (ApoB), ≤ 300 mg/l (lipoprotein(a)), 1.10–2.15 mmol/L (FMN), < 15 µmol/l(Hcys), 4.94–32.01 nmol/l (T).
The audio-visual sexual stimulation (AVSS) test was monitored by the RigiScan™ plus device using the RigiScan™ plus proactive mode. Participants were monitored in a quiet, comfortable, warm, softly lit room without external disturbances, with a calm and comfortable mood, free of all distractions. The patient was given 20 mg of oral vardenafil, and after 30 min, the RigiScan™ plus was fixed on the patient's right thigh according to the instructions. The 3D VR glasses were put on and adjusted, and the basal values were recorded for 15 min with quiet rest. A 30-min erotic video was shown to each patient individually, followed by stimulation of rigidity and tumescence for the next 30 min. All recorded data were transferred to a computer and analysed using RigiScan software.
## Statistical analysis
The Statistical Package for Social Sciences (SPSS) version 18.0 (SPSS Inc., Chicago, IL) for Microsoft Windows was used for the statistical analysis of the data. Data baselines are expressed as the mean ± standard deviation (SD). The Mann‒Whitney U test was used to compare the case group and control group between groups. The data were divided into subgroups 1 (Hcys ≥ 15 µmol/l) and 2 (Hcys < 15 µmol/l) according to homocysteine levels. The Mann‒Whitney U test was performed for comparisons between subgroups. Pearson's test or Spearman's test (normal or nonnormal distribution) was used to test the correlation between two variables. The threshold for significance was p value < 0.05.
## Results
All participants underwent general data collection and biochemical parameter assessments and completed the AVSS-RigiScan test. In the baseline analysis, the ED and non-ED groups were divided according to IIEF-5. The clinical characteristics of participants with ED and participants with normal erectile function are shown in Table 1. The mean age of the ED patients was 49.38 ± 0.66 years, and the mean homocysteine value was 14.03 ± 0.50 µmol/l. The mean age of the 22 healthy individuals in the control group was 37.14 ± 1.86 years, and the mean homocysteine value was 11.70 ± 0.53 µmol/l. The non-ED group had higher homocysteine levels than the ED group, with a statistically significant difference between the two groups ($$p \leq 0.0098$$). The data were divided into subgroup 1 (Hcys ≥ 15 µmol/l) and subgroup 2 (Hcys < 15 µmol/l) according to homocysteine levels. Table 2 shows a comparison of the parameters of the AVSS-RigiScan test between the two groups. The results for average event rigidity of the tip, average maximum rigidity of the tip, ∆ tumescence of the tip, and ∆ tumescence of the base were all lower for subgroup 1 than for subgroup 2 but were not significantly different. The average event rigidity of the base was lower in subgroup 1 than in subgroup 2, with a statistically significant difference ($$p \leq 0.0034$$). The average maximum rigidity of the base was lower in subgroup 1 than in subgroup 2, and the difference was statistically significant ($$p \leq 0.0007$$).Table 1Comparison of clinical and laboratory data between the non-ED group and the ED groupNon-ED group ($$n = 22$$)ED group ($$n = 99$$)Mann‒Whitney UpAge (years)37.14 ± 1.8649.38 ± 0.62326.5 < 0.0001IIEF-522.91 ± 0.2311.83 ± 0.490 < 0.0001EHS2.82 ± 0.112.12 ± 0.07505.5 < 0.0001Glucose (mmol/L)5.59 ± 0.186.53 ± 0.23708.50.0107TC (mmol/L)5.38 ± 0.235.63 ± 0.119300.2868TG (mmol/L)2.38 ± 0.572.03 ± 0.1210280.6843HDL (mmol/L)1.08 ± 0.041.18 ± 0.029865.50.1339LDL (mmol/L)3.25 ± 0.173.44 ± 0.09953.50.3643ApoA1 (g/L)1.49 ± 0.041.51 ± 0.0310660.8798ApoB (g/L)0.88 ± 0.040.96 ± 0.02878.50.1581lipoprotein(a) (mg/L)137.1 ± 20.58193.7 ± 18.999970.5386FMN (mmol/L)1.84 ± 0.041.96 ± 0.037600.0273T (mmol/L)21.50 ± 1.4818.77 ± 0.748260.0777Hcys (µmol/L)11.70 ± 0.5314.03 ± 0.507040.0098Data are expressed as the mean ± standard deviation; P values for the ED group and non-ED group were derived from Mann‒Whitney U test (for continuous dependent variables)ED Erectile function, IIEF-5 International Index of Erectile Function, EHS Erection Hardness Scale, TC Total cholesterol, TG Triglycerides, HLD High-density lipoprotein cholesterol, LDL Low-density lipoprotein cholesterol, ApoA1 apolipoprotein A1, ApoB Apolipoprotein B, FMN Fructosamine, T Testosterone, Hcys HomocysteineTable 2Instrumental data (AVSS-RigiScan) of patients with normal versus high levels of homocysteineSubgroup 1 (hcy ≥ 15 µmol/L)Subgroup 2 (hcy < 15 µmol/L)Mann‒Whitney Upaverage event rigidity of the tip (%)30.80 ± 2.7035.33 ± 1.6710830.0904average event rigidity of the base (%)39.87 ± 1.9248.90 ± 1.68876.50.0034average maximum rigidity of the tip (%)58.23 ± 2.6364.15 ± 1.8610610.0679average maximum rigidity of the base (%)63.07 ± 2.3873.49 ± 1.58797.50.0007∆Tumescence of the tip (cm)1.93 ± 0.152.27 ± 0.1010510.0593∆Tumescence of the base (cm)2.32 ± 0.112.58 ± 0.0810530.0611Data are expressed as the mean ± standard deviation; P values for Subgroup 1 and Subgroup 2 were derived from the Mann‒Whitney U testAVSS Audio-visual sexual stimulation, hcy Homocysteine, ∆Tumescence Increase in tumescence or maximum − minimum tumescence Table 3 shows the correlation analysis between homocysteine and the basic clinical information and biochemical parameters. It can be concluded that homocysteine was positively correlated with age ($r = 0.2366$, $$p \leq 0.009$$) and negatively correlated with the IIEF-5 score (r = -0.2123, $$p \leq 0.0194$$). Table 4 shows the relationship between homocysteine values and AVSS-RigiScan test results. It can be seen that the current data do not allow for a linear correlation between homocysteine and the average event rigidity and average maximum rigidity of the tip. We found that homocysteine levels were negatively correlated with the average event rigidity of the base (r = -0.2225, $$p \leq 0.0142$$) (Fig. 1a). Homocysteine levels were negatively correlated with the average maximum rigidity of the base (r = -0.2164, $$p \leq 0.0171$$) (Fig. 1b). In particular, homocysteine levels were negatively correlated with ∆Tumescence of the tip (r = -0.1866, $$p \leq 0.0404$$) (Fig. 1c). Similarly, homocysteine was negatively correlated with ∆ Tumescence of the base (r = -0.2257, $$p \leq 0.0128$$) (Fig. 1d). From Table 5, we can conclude that the average event rigidity of the base was negatively correlated with age (r = -0.2088, $$p \leq 0.0215$$), glucose values (r = -0.202, $$p \leq 0.0263$$), and homocysteine levels (r = -0.2225, $$p \leq 0.0142$$). With the current data, we cannot draw a linear correlation between average event rigidity of the base and cholesterol, triglycerides, HDL, LDL, lipoprotein A1, lipoprotein B, lipoprotein (a), fructosamine, and testosterone at this time. Table 3Correlation coefficients of HCY values with clinical parametersr$95\%$ CIpAge (years)0.2370.061–0.3980.009IIEF-5-0.212-0.377- -0.0350.020HCY Homocysteine, IIEF-5 International Index of Erectile FunctionTable 4Correlation coefficients of HCY values with AVSS-RigiScan test parametersr$95\%$ CIpaverage event rigidity of the tip (%)-0.158-0.327–0.0210.084average event rigidity of the base (%)-0.223-0.386—-0.0460.014average maximum rigidity of the tip-0.165-0.334–0.0140.071average maximum rigidity of the base-0.216-0.380—-0.0400.017∆ Tumescence of the tip-0.187-0.353—-0.0080.040∆ Tumescence of the base-0.226-0.389—-0.0490.013HCY Homocysteine, AVSS Audio-visual sexual stimulation, ∆Tumescence Increase in tumescence or maximum − minimum tumescenceFig. 1Correlation between Hcys levels and AVSS-RigiScan test parameters. a Hcys levels were negatively correlated with the average event rigidity of the base (r = -0.2225; $$p \leq 0.0142$$). Hcys: Homocysteine. b Hcys levels were negatively correlated with the average maximum rigidity of the base (r = -0.2164; $$p \leq 0.0171$$). Hcys: Homocysteine. c Hcys levels were negatively correlated with ∆Tumescence of the tip (r = -0.1866, $$p \leq 0.0404$$). Hcys: homocysteine. ∆Tumescence: increase in tumescence or maximum − minimum tumescence. d Hcys levels were negatively correlated with ∆ Tumescence of the base (r = -0.2257, $$p \leq 0.0128$$). Hcys: Homocysteine. ∆Tumescence: increase in tumescence or maximum − minimum tumescenceTable 5Correlation coefficients of the average event rigidity of the base by clinical parameterr$95\%$ CIpAge-0.209-0.373—-0.0310.022glucose-0.202-0.367—-0.0240.026TC0.095-0.085–0.2690.299TG0.021-0.159–0.1980.823HDL-0.075-0.251–0.1050.412LDL0.115-0.065–0.2880.208ApoA1-0.109-0.282–0.0710.233ApoB0.095-0.085–0.2690.301lipoprotein(a)-0.028-0.205–0.1520.764FMN-0.047-0.223–0.1330.612T-0.029-0.206–0.1500.752Hcys-0.2225-0.3857 to -0.045790.0142TC Total cholesterol, TG Triglycerides, HLD High-density lipoprotein cholesterol, LDL low-density lipoprotein cholesterol, ApoA1 Apolipoprotein A1, ApoB Apolipoprotein B, FMN Fructosamine, T Testosterone, Hcys Homocysteine
## Discussion
The relationship between homocysteine and erectile dysfunction was discussed as early as 2004 when Jones R. W et al. established a rabbit model of erectile dysfunction due to HHcys [14]. The results of several previous studies have shown a significant correlation between HHcys and erectile dysfunction. The association between homocysteine, vitamins, and folic acid and erectile dysfunction was evaluated in a cross-sectional study based on 1318 participants by Chen et al., which showed a significant association between homocysteine and erectile dysfunction, most significantly in men over 60 years old and in those living alone (single) [7, 15]. A recent study by Wang et al. based on 119 patients with erectile dysfunction showed that patients with erectile dysfunction with HHcys were 13.42 times more likely to develop vasogenic erectile dysfunction than patients without HHcys [16]. Giovannone et al. included 431 participants in their study and showed that plasma homocysteine levels were associated with the severity of erectile dysfunction and that the homocysteine level was expected to be a predictor of the development of erectile dysfunction [11]. The results of this study were consistent with previously reported findings, with a statistically significant difference in the erectile dysfunction group compared to the control group ($$p \leq 0.0098$$).
Since the invention of the RigiScan by Bradley et al. in 1985 [17], nocturnal penile erectile rigidity tests and audio-visual sexual stimulation tests have been widely used in the field of urology and andrology. The RigiScan, which is produced by GOTOP in the United States, is used to assess the aetiology and severity of ED and can objectively and effectively identify psychological and organic ED and has been included in the official ED treatment guidelines of the American and European Association of Urology [18]. The use of RigiScan™ in the diagnosis of nocturnal penile erection and rigidity has been recognized as an effective tool for distinguishing psychological erectile dysfunction from organic erectile dysfunction [9]. In the previously reported literature on the association between homocysteine and erectile dysfunction, the diagnostic application of RigiScan™ in nocturnal penile tumescence and rigidity (NPTR), was used in most studies to evaluate erectile function, and few studies used AVSS-RigiScan for erectile function evaluation. There are no AVSS-RigiScan evaluation criteria in any of the guidelines, including the EAU. Therefore, few researchers have used AVSS-RigiScan to evaluate erectile function in literature reports. In the diagnosis of ED, AVSS-RigiScan is a more widely used tool than NPTR-RigiScan. Compared to NPTR testing, penile erection during AVSS testing is more similar to erotic and reflex erection activity, which is relatively simple, economical, less time-consuming, more physiologically consistent, and unaffected by sleep [8, 19–21]. In a study by Wang et al., standardized Chinese AVSS-RigiScan evaluation criteria were established by the AVSS-RigiScan test used in combination with oral phosphodiesterase-5 inhibitors [9]. It has been shown that administration of phosphodiesterase-5 inhibitors before AVSS-RigiScan testing not only avoids or reduces the shortcomings of the routine AVSS-RigiScan but also improves its diagnostic properties [20, 22, 23]. Therefore, in this study, the AVSS-RigiScan test was performed to evaluate erectile function, and the participants were given 20 mg of oral vardenafil 30 min before the test. In our study, the aim was to investigate the correlation between serum homocysteine levels and AVSS-RigiScan test parameters in men with erectile dysfunction. The results of the correlation analysis in Table 3 also show that homocysteine levels were negatively correlated with the average event rigidity of the base (r = -0.2225, $$p \leq 0.0142$$) and the average maximum rigidity of the base (r = -0.2164, $$p \leq 0.0171$$). Table 5 also shows that the average event rigidity of the base was negatively correlated with age (r = -0.2088, $$p \leq 0.0215$$), glucose (r = -0.202, $$p \leq 0.0263$$) and homocysteine (r = -0.2225, $$p \leq 0.0142$$).
According to the present study, oxidative stress is the main biochemical mechanism leading to homocysteine-induced cell damage and endothelial dysfunction. HHcys induces ED by altering virtually every component of NO metabolism, including NOS expression, localization, activation, and activity. HHcys significantly reduces the expression of endothelial nitric oxide synthase (eNOS) protein in a dose-dependent manner. Initially, endothelial cells can increase NO synthesis and release to protect themselves and detoxify HHcys, which in turn leads to the formation of the S-nitroso homocysteine, a potent vasodilator. However, this defence mechanism is limited, and prolonged exposure to HHcys eventually leads to impaired basal NO production, free radical formation, and subsequent endothelial damage [24]. Reduced NO production and decreased cGMP concentrations lead to decreased vascular smooth muscle diastolic function, as well as impaired endothelium-dependent vasodilatory responses, causing endothelial dysfunction, which leads to various vascular diseases, including ED [25].
## Limitations of the study
We should also be concerned about some limitations of this study. First, this study was a single-centre study, which may have implications for external validation. Second, the relatively small sample size may limit the generalizability of the results. Third, repeated AVSS-RigiScan tests were performed to ensure diagnostic accuracy. In addition, a comprehensive series of blinded validation studies are necessary to determine the relationship between homocysteine and AVSS-RigiScan test parameters. What we also need to acknowledge is the lack of psychometric tests to assess erectile function, which is a study limitation. For participants with abnormal AVSS-RigiScan test results, we did not perform further examinations, including the NPTR-RigiScan test and noninvasive arterial Doppler ultrasonography.
## Conclusion
We can conclude that homocysteine levels are inhibitory to penile erectile function and negatively correlate with average event rigidity of the base and average maximum rigidity of the base in the AVSS-RigiScan test. This also confirms that homocysteine is a potential indicator for the diagnosis of ED.
## References
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4. Zhang X, Yang B, Li N, Li H. **Prevalence and Risk Factors for Erectile Dysfunction in Chinese Adult Males**. *J Sex Med* (2017) **14** 1201-1208. DOI: 10.1016/j.jsxm.2017.08.009
5. Yafi FA, Jenkins L, Albersen M, Corona G, Isidori AM, Goldfarb S. *Erectile dysfunction Nat Rev Dis Primers* (2016) **4** 16003. DOI: 10.1038/nrdp.2016.3
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11. Giovannone R, Busetto GM, Antonini G, De Cobelli O, Ferro M, Tricarico S. **Hyperhomocysteinemia as an Early Predictor of Erectile Dysfunction: International Index of Erectile Function (IIEF) and Penile Doppler Ultrasound Correlation With Plasma Levels of Homocysteine**. *Medicine (Baltimore).* (2015) **94** e1556. DOI: 10.1097/MD.0000000000001556
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|
---
title: 'Etanercept for patients with juvenile idiopathic arthritis: drug levels and
influence of concomitant methotrexate: observational study'
authors:
- Tiina Levälampi
- Johanna Kärki
- Katariina Rebane
- Paula Vähäsalo
- Merja Malin
- Liisa Kröger
- Minna-Maija Grönlund
- Maria Backström
- Heini Pohjankoski
- Hannu Kautiainen
- Sakari Jokiranta
- Kristiina Aalto
journal: Pediatric Rheumatology Online Journal
year: 2023
pmcid: PMC10035115
doi: 10.1186/s12969-023-00801-2
license: CC BY 4.0
---
# Etanercept for patients with juvenile idiopathic arthritis: drug levels and influence of concomitant methotrexate: observational study
## Abstract
### Background
Etanercept (ETN) is widely used tumour necrosis factor (TNF) blocker in the treatment of juvenile idiopathic arthritis (JIA) when traditional synthetic disease modifying antirheumatic drug (sDMARD) therapy is not sufficient. There is limited information about the effects of methotrexate (MTX) on serum ETN concentration in children with JIA. We aimed to investigate whether ETN dose and concomitant MTX would effect ETN serum trough levels in JIA patients, and whether concomitant MTX have an influence on the clinical response in patients with JIA receiving ETN.
### Methods
In this study, we collected the medical record data of 180 JIA patients from eight Finnish pediatric rheumatological centres. All these patients were treated with ETN monotherapy or combination therapy with DMARD. To evaluate the ETN concentrations, blood samples of the patients were collected between injections right before the subsequent drug. Free ETN level was measured from serum.
### Results
Ninety-seven ($54\%$) of the patients used concomitant MTX, and 83 ($46\%$) received either ETN monotherapy or used sDMARDs other than MTX. A significant correlation was noted between ETN dose and drug level [$r = 0.45$ ($95\%$ CI: 0.33–0.56)]. The ETN dose and serum drug level were correlated ($$p \leq 0.030$$) in both subgroups – in MTX group [$r = 0.35$ ($95\%$ CI: 0.14–0.52)] and in non-MTX group [$r = 0.54$ ($95\%$ CI: 0.39–0.67)].
### Conclusion
In the present study, we found that concomitant MTX had no effect on serum ETN concentration or on clinical response. In addition, a significant correlation was detected between ETN dose and ETN concentration.
## Background
Juvenile idiopathic arthritis (JIA) is the most common chronic inflammatory arthritis in childhood [1]. In Finland, with a population of 5.5 million, including 922 000 children under 16 years of age, nearly 200 children are diagnosed as having JIA every year [2] according to International League of Associations for Rheumatology (ILAR) criteria [3]. Treatment of JIA is usually initiated with conventional, synthetic disease-modifying antirheumatic drugs (sDMARDs), typically methotrexate (MTX) [4]. More than half of patients with JIA benefit from this treatment and achieve remission. Nearly all of those who do not achieve remission with sDMARDS benefit from biological disease-modifying antirheumatic drug (bDMARD) treatment [5]. According to the American College of Rheumatology (ACR) recommendations [4, 6], when traditional sDMARD therapy is not sufficient for treating JIA, a tumour necrosis factor (TNF) blocker, including etanercept (ETN), can be added. The treatment of JIA in *Finland is* based on the ACR treatment recommendation and is in line with European care practices [7].
ETN, a dimeric fusion protein that comprises two extracellular portions of the TNF receptor 2 linked to the Fc portion of human immunoglobulin G1, was introduced nearly 30 years ago for treating rheumatoid arthritis (RA) [8] and for treating JIA [9]. In Finland, ETN has been used for JIA since February 2000, and the normal procedure is subcutaneous administration once a week, occasionally twice a week, according to the manufacturer’s instructions https://www.ema.europa.eu/en/medicines/human/EPAR/enbrel.
In a clinical trial simulation, subcutaneous ETN injections 0.8 mg/kg weekly and 0.4 mg/kg twice a week produced overlapping steady-state time-concentration profiles and corresponding clinical outcomes [10]. Similar results were reported by Langley et al. in their study of pediatric patients with psoriasis who received ETN 0.8 mg/kg weekly and pediatric patients with arthritis who received ETN 0.4 mg/kg twice weekly [11]. ETN can be administered alone or in combination, usually with MTX. Nevertheless, the effect of MTX on the serum trough concentration of ETN remains unclear [12].
In this study, we aimed to investigate whether concomitant MTX and ETN doses affect ETN serum trough levels in patients with JIA and whether concomitant MTX affects clinical response in patients with JIA receiving ETN.
## Patients and methods
This observational retrospective study collected the medical record data of patients from eight Finnish pediatric rheumatological centres: five university hospitals and three within secondary referral hospitals. Patients who received ETN regularly from July 2014 to November 2017 for at least two weeks and were under 18 years old were included in the study. ETN treatment was accomplished by the decision of the pediatric rheumatologist. Serum samples for the concentration measurement were taken for clinical reasons, mainly to assist in dose adjustment to optimise the use of ETN and/or verification of individual compliance. Pharmacological treatment comprised ETN monotherapy or combination therapy, with or without sDMARD. All analysed patients were diagnosed as having JIA according to ILAR criteria [3].
The following patient data were collected: ETN initiation date, dose of the drug (mg/kg), body surface area using Mosteller modulation [13], concomitant sDMARDs, previous bDMARDs, height, weight, age, sex, diagnosis date, and type of JIA. Basic clinical disease information included the following: antinuclear antibody (ANA), human leucocyte antigen B27 (HLA-B27) result, rheumatoid factor (RF) level, cyclic citrulline peptide antibody (CCP-ab), patient’s global assessment of wellbeing (PaGA), measured on a visual analogue scale (VAS) from 0 to 100, physician's global assessment of disease activity (PhGA) on a VAS from 0 to 100, 10-joint juvenile disease activity score (JADAS10) at the time of ETN concentration measurements, and possible comorbidities (uveitis or inflammatory bowel disease).
To evaluate the ETN concentrations of the patients, blood samples were collected between injections right before the subsequent drug dose to enable trough concentration measurement. This was the first ETN concentration measurement. Free ETN level was measured from serum with the ELISA method by Sanquin Diagnostics (Amsterdam, the Netherlands) [14] subcontracted by the United Medix Laboratory (Helsinki, Finland). The target value for residual ETN concentrations was above 1.5 µg/mL [15–17].
## Ethics
This register-based study was performed by collecting clinical data from patient records. Therefore, according to Finnish legislation, no approval by an ethical committee or informed consent was required. Each hospital granted permission to collect the patient data.
## Statistics
Data are presented as means with standard deviation (SD), medians with interquartile range (IQR), or counts with percentages. Statistical significance between groups was evaluated using t test or chi-square test. When adjusting for confounding factors, an analysis of covariance or logistic regression model was applied. Relationship between ETN dose and concentration estimated according to the use of MTX by tuota moni ei mut intissäusing two separate univariate regression models. In the case of violation of the assumptions (e.g., non normality) for continuous variables, a bootstrap-type method or Monte Carlo p-values (small number of observations) for categorical variables were used. Correlation coefficients were calculated using the Spearman method, using Sidak-adjusted (multiplicity) probabilities. ETN dose adjusted (partial) correlation between dose of MTX and ETN serum trough level was calculated by the Pearson method. The normality of the variables was evaluated graphically and by using the Shapiro–Wilk W test. All analyses were conducted using Stata 17.0 (StataCorp, College Station, TX, USA).
## Results
Overall, 182 patients with JIA receiving ETN were eligible in the study. Two patients with inadequate compliance were excluded. Finally, 180 patients were included: 109 ($61\%$) girls and 71 ($39\%$) boys. The mean patient age was 8.0 years (range: 2–17 years).
The characteristics of the patients are presented in Table 1. Ninety-seven ($54\%$) of the patients used concomitant MTX, and 83 ($46\%$) received either ETN monotherapy or used sDMARDs other than MTX. Twenty-three patients used leflunomide, eight used sulfasalazine, and three used hydroxychloroquine (Table 2). Compared with the non-MTX group, patients in the MTX group were younger and had shorter disease duration at ETN treatment initiation. No significant difference was observed between the groups in body composition measures, disease activity, neither in the presence of ANA nor HLA-B27 antigen. CCP-ab was positive in all patients with RF-positive polyarthritis. Table 1Clinical and demographic characteristics of the patients at the time of ETN measurementMTX groupn = 97non-MTX groupn = 83p valueFemale (%)62 [64]47 [57]0.32Age (years), mean (SD)7.5 (3.6)8.6 (3.8)0.037Height (cm), mean (SD)122 [23]128 [239]0.11Weight (kg), mean (SD)26.5 (13.0)28.6 (13.7)0.27BMI, kg/ m216.6 (2.8)16.5 (2.5)0.77BSA (m2), mean (SD)0.94 (0.31)1.00 (0.33)0.17Disease duration (years), mean (SD)2.3 (2.4)3.2 (2.8)0.019Diagnosis0.74 Oligoarthritis, persistent17 [18]18 [22] Oligoarthritis, extended15 [15]14 [17] Polyarthritis, RF-negative57 [49]40 [48] Polyarthritis, RF-positive2 [2]1 [1] Enthesitis related arthritis4 [4]7 [8] Psoriatic arthritis1 [1]2 [2] Undifferentiated arthritis1 [1]1 [1]Uveitis, n (%)9 [9]2 [2]0.066Inflammatory bowel disease, n (%)1 [1]1[1]0.99Previous bDMARD, n (%)10 [10]15 [18]0.13 Etanercept710 Adalimumab34 Infliximab24 Tocilizumab02Concomitant treatment n (%) Other sDMARDs6 [6]32 [39]< 0.001 Prednisolone4 [4]7[8]0.23ESR (mm/h), mean (SD)13.2 (14.0)12.1 (12.1)0.61CRP (mg/l), mean (SD)4.9 (12.6)5.9 (13.5)0.63JADAS10, mean (SD)10.0 (5.6)9.7 (5.8)0.56PaGA, mean (SD)3.3 (2.6)2.6 (2.2)0.10PhGA, mean (SD)3.1 (1.8)2.7 (1.9)0.14HLA-B27 positive, n (%)27 [28]25 [30]0.58ANA, n (%)31 [32]23 [28]0.54Erosions, n (%)21 [22]16 [19]0.60ETN etanercept, MTX methotrexate, BMI Body mass index, BSA Body surface area, RF Rheumatoid factor, bDMARD biological disease-modifying antirheumatic drug, sDMARD synthetic disease-modifying antirheumatic drug, ESR Erythrocyte sedimentation rate, CRP C-reactive protein, JADAS10 10-joint Juvenile Arthritis Disease Activity Score, PaGA Patient’s global assessment of wellbeing measured on a linear analogue scale (VAS), PhGA Physician’s global assessment of wellbeing measured on a VAS scale, HLA Human leucocyte antigen B27, ANA Antinuclear antibodyTable 2Other sDMARDs of the patients at the time of ETN measurementsDMARDMTX groupn = 97non-MTX groupn = 83p valueLeflunomide, n (%)1[1]23[28]< 0.001Hydroxychloroquine, n (%)5[5]3[4]0.73Sulfasalazine, n (%)2[2]8[10]0.046Azathioprine, n (%)0[0]1[1]0.46Prednisolone, n (%)4[4]7[8]0.35sDMARD synthetic disease-modifying antirheumatic drug, MTX Methotrexate Median (Q1, Q3) time point for the measurement of ETN concentration was 12 [4, 30] months after ETN initiation. At that time point, median (range) MTX dose was 13.0 mg/m2 (5.5–24.2 mg/m2) and median ETN dose was 0.75 (0.49–1.47) mg/kg/week and median ETN concentration was 1.60 (0.40–6.30) µg/mL in the MTX group and 1.70 (0.60–4.90) µg/mL in the non-MTX group ($$p \leq 0.52$$ after adjusted ETN dose). Correlation between MTX dose and ETN concentration adjusted with ETN dose was 0.01 ($95\%$ Cl: -0.16 to 0.19).
A significant correlation was revealed between ETN dose and drug level [$r = 0.45$ ($95\%$ CI: 0.33–0.56)] (Fig. 1). The ETN dose and serum drug level were correlated ($$p \leq 0.03$$) in both subgroups – in MTX group [$r = 0.35$ ($95\%$ CI: 0.14–0.52)] and in non-MTX group [$r = 0.54$ ($95\%$ CI: 0.39–0.67)]. No correlation was detected between ETN concentration and patients’ weight or body surface area. Fig. 1Relationship between ETN (etanercept) dose and concentration according to MTX (methotrexate) use. The grey area represents $95\%$ confidence intervals of linear prediction No significant correlation was found between disease duration and ETN concentration when ETN dose was adjusted, neither in the MTX group $r = 0.01$ ($95\%$ CI: -0.15 to 0.15) nor in the non-MTX group r = -0.03 ($95\%$ CI: -0.23 to 0.18). Neither was significant correlation observed between disease activity and ETN concentration (Table 3).Table 3Correlations (Spearman) between ETN concentration and disease activityETN concentrationMTX groupr ($95\%$ CI)non-MTX groupr ($95\%$ CI)ESR-0.04 (-0.24 to 0.16)-0.01 (-0.23 to 0.21)CRP-0.20 (-0.39 to -0.01)-0.06 (-0.28 to 0.15)PaGA0.02 (-0.18 to 0.22)-0.25 (-0.44 to -0.04)PhGA0.19 (-0.01 to 0.37)-0.09 (-0.30 to 0.12)JADAS100.13 (-0.07 to 0.32)-0.22 (-0.42 to -0.01)No significant correlations after Sidak adjustmentETN etanercept, MTX methotrexate, ESR Erythrocyte sedimentation rate, CRP C-reactive protein, PaGA Patient’s global assessment of wellbeing measured on a linear analogue scale (VAS), PhGA Physician’s global assessment of wellbeing measured on a VAS scale, JADAS10 10-joint Juvenile Arthritis Disease Activity Score
## Discussion
To our knowledge, this is the first study to analyse ETN treatment and the effects of concomitant MTX usage on serum ETN concentration in pediatric patients with JIA receiving ETN with or without concomitant MTX. The main findings of this study are that concomitant MTX had no effect on serum ETN concentration and significant correlation was observed between ETN dose and ETN concentration. We did not observe any positive influence on clinical response in ETN-treated patients in MTX group compared with non-MTX group.
When sDMARDs are insufficient to provide remission in patients with JIA, bDMARDs are regularly used. TNF inhibitors, such as ETN, are the first choice of bDMARDs [18]. ETN has been used in JIA for over 30 years, and it has been shown to be effective and safe for long-term use [19, 20]. In a pilot study of 40 JIA patients treated with ETN, there was a clear association between circulating ETN levels, and the dose received [21], consistent with our results: increase in ETN dose was associated with increase in ETN concentration. Similarly to our study, Alcobendas et al. [ 21] did not find any relationship between ETN concentration and disease activity. Results of the study by Bader-Meunier et al. support these findings [22]. Also in adult patients with RA, ETN concentration did not correlate significantly with good clinial response [12].
Variation in the response to drug treatment among patients with JIA has awaken expectations to get support from therapeutic drug monitoring for decision-making during bDMARD treatment. Similar to other drugs, serum ETN concentration can be affected by several factors. ETN is administered subcutaneously, when the absorption and bioavailability is not necessarily complete. The injection site might have a minor effect on absorption accompanied by factors affecting ETN metabolism [23–25]. Moreover, it remains unclear whether body mass affects ETN concentrations, whether patients with higher body mass have higher volume on distribution [26], and whether obese patients with JIA may have difficulties in achieving remission [27]. In the present study, we did not find any correlation between ETN concentration and patients’ weight or body surface area, consistent with the results of Langley et al. [ 11].
ETN is a nonimmunogenic TNF inhibitor. Although antibodies are generated, they are nonneutralising and do not influence drug efficacy or safety [11, 22]. In the present study, considering the above, we did not measure anti-etanercept antibodies.
Apparently, drug concentrations in general vary widely within patients on the standard treatment dose. This intrapatient variability (IPV) is common during bDMARD treatment. Higher ETN doses might lower IPV by generating higher serum ETN concentrations and thus ensuring constant drug levels [28]. Parallel results have been reported in patients with JIA treated with ETN [29].
To our knowledge, no study has evaluated pediatric patients receiving ETN or the possible effect of concomitant MTX dosing on serum ETN concentration. In adult patients with RA receiving ETN treatment, concomitant MTX did not increase ETN concentration [12]. Deng et al. reported the influence of higher TNF-alpha concentration on ETN clearance in adult patients with ankylosing spondylitis [30], but another study revealed no association between circulating ETN concentration and concomitant MTX usage [31]. If concomitant MTX does not improve treatment outcome, it is worth of consider to taper off MTX in such patients.
In a case of a treatment failure, the problem can be that drug is ineffective and should be changed or that drug is effective, but the dose or frequency is too low. This can be determined by measuring drug concentrations. Drug trough level measurements can help in the decision of dose and frequency, and drug selection, as well as in situations where the patient is in remission, but it remains unknown whether continuing the drug administration is feasible. If the drug trough level is under the recommended level, it would be sensible to discontinue the treatment.
This study has some limitations. First, the present study was a register-based study, and clinical data were collected retrospectively from the patients’ records. On the other hand, this kind of data is valuable real-life data for clinicians. Second, considerable variation existed between the time of diagnoses of JIA and the initiation of ETN.
In conclusion, in a case of uncertainty of drug effectiveness in patients with increase disease activity, it is critical to determine whether to increase the drug dose or frequency or whether the drug is ineffective and should be altered. One possibility is to add sDMARD to the therapy if not added earlier. In the present study, we observed that MTX did not affect serum ETN concentration, but increase of the ETN dose increased its serum concentration. We found that ETN concentration did not correlate with disease activity. This might be explained by patients’ lower disease activity, when a lower ETN dose may be sufficient, or even a drug-free period. Moreover, based on the results of this study, it seems that concomitant MTX do not improve the treatment outcome. Further studies are needed to confirm our findings.
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---
title: Anti-human immunodeficiency virus-1 activity of MoMo30 protein isolated from
the traditional African medicinal plant Momordica balsamina
authors:
- Mahfuz Khan
- Amad Diop
- Erick Gbodossou
- Peng Xiao
- Morgan Coleman
- Kenya De Barros
- Hao Duong
- Vincent C. Bond
- Virginia Floyd
- Kofi Kondwani
- Valerie Montgomery Rice
- Sandra Harris-Hooker
- Francois Villinger
- Michael D. Powell
journal: Virology Journal
year: 2023
pmcid: PMC10035133
doi: 10.1186/s12985-023-02010-5
license: CC BY 4.0
---
# Anti-human immunodeficiency virus-1 activity of MoMo30 protein isolated from the traditional African medicinal plant Momordica balsamina
## Abstract
### Background
Plants are used in traditional healing practices of many cultures worldwide. Momordica balsamina is a plant commonly used by traditional African healers as a part of a treatment for HIV/AIDS. It is typically given as a tea to patients with HIV/AIDS. Water-soluble extracts of this plant were found to contain anti-HIV activity.
### Methods
We employed cell-based infectivity assays, surface plasmon resonance, and a molecular-cell model of the gp120-CD4 interaction to study the mechanism of action of the MoMo30-plant protein. Using Edman degradation results of the 15 N-terminal amino acids, we determined the gene sequence of the MoMo30-plant protein from an RNAseq library from total RNA extracted from Momordica balsamina.
### Results
Here, we identify the active ingredient of water extracts of the leaves of *Momordica balsamina* as a 30 kDa protein we call MoMo30-plant. We have identified the gene for MoMo30 and found it is homologous to a group of plant lectins known as Hevamine A-like proteins. MoMo30-plant is distinct from other proteins previously reported agents from the Momordica species, such as ribosome-inactivating proteins such as MAP30 and Balsamin. MoMo30-plant binds to gp120 through its glycan groups and functions as a lectin or carbohydrate-binding agent (CBA). It inhibits HIV-1 at nanomolar levels and has minimal cellular toxicity at inhibitory levels.
### Conclusions
CBAs like MoMo30 can bind to glycans on the surface of the enveloped glycoprotein of HIV (gp120) and block entry. Exposure to CBAs has two effects on the virus. First, it blocks infection of susceptible cells. Secondly, MoMo30 drives the selection of viruses with altered glycosylation patterns, potentially altering their immunogenicity. Such an agent could represent a change in the treatment strategy for HIV/AIDS that allows a rapid reduction in viral loads while selecting for an underglycosylated virus, potentially facilitating the host immune response.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12985-023-02010-5.
## Introduction
Plants are the source of treatments for a wide variety of diseases worldwide [1–4]. This includes viral infections such as human immunodeficiency virus type 1 (HIV-1) [3, 5]. Many medicinal plants can be found in the family Cucurbitaceae [6, 7], including the genus Momordica. The best-known medicinal plant in *Momordica is* the species Momordica charantia, commonly called "bitter melon." Bitter melon is used to treat a variety of medical conditions, from chronic inflammation [8] and diabetes [9–11] to cancer [12, 13]. It has also been reported to have various inhibitory effects on viruses, bacteria, and parasites (see [14]). Momordica anti-HIV protein (MAP30), a ribosome-inactivating protein (RIP) isolated primarily from the seeds of Momordica charantia, has been shown to have anti-HIV activity [15, 16]. RIPs are N-glycosidases that depurinate ribosomal ribonucleic acid (rRNA) [17]. This depurination irreversibly inactivates the ribosome, blocking protein synthesis [18]. Another species within the *Cucurbitaceae is* Momordica balsamina. It also has been reported to produce a RIP with anti-HIV-1 activity [19, 20]. The anti-HIV-1 activity of RIPs has been attributed to their interaction with viral nucleic acid and interaction with a post-reverse transcription step in replication [19]. Momordica balsamina is also used to treat other medical conditions, such as gastric ulcers [21, 22].
Proteins that bind to carbohydrate residues are collectively called carbohydrate-binding agents (CBAs) [23]. CBAs can block the binding of envelope glycoproteins with their target receptors on cells. The ability of CBAs to bind to glycoproteins and block their interaction with receptors has been proposed as a potential means to inhibit enveloped viruses like HIV-1 [23–26]; however, to date, no agent has become commercially available. A common type of CBA found in plants is lectins, which can bind to glycoproteins and cause red blood cells to agglutinate [26, 27]. Plant Lectins are reportedly more resistant to heat denaturation than animal proteins [28]. Indeed, banana and other plant lectins have been proposed to have various anti-microbial functions, including the ability to inhibit HIV-1 and Influenza viruses [27]. It has been theorized that treatment of HIV-1 with CBAs could select for mutations that leave "holes" in the carbohydrate layer and allow for broadly neutralizing antibodies to be induced [29]. Therefore, there has been significant interest in finding plant CBAs that could potentially be used to treat HIV/AIDS. Chitinases are another ubiquitous group of plant proteins that catalyze the breakdown of chitin found in the cell walls of fungi and can provide protection against fungal infection [30]. Some plant chitinases have evolved the ability to hemagglutinate red blood cells and are sometimes called chi-lectins [31]. Therefore, some of the many chitinases from plants could function as CBAs and potentially be used as antiviral treatments. Hevamine A-like proteins are a type of chitinase that generally act as plant defensins protecting the plant from pathogens with chitin in their cell walls, such as fungi [32].
This report details the characterization of a 30 kDa protein we have previously reported [33] from the medicinal plant *Momordica balsamina* used by traditional healers in certain African regions. Its N-terminal sequence has homology to Hevamine A-like proteins in other plants. It can bind to HIV-1 gp120 and inhibit the virus. It is a CBA and could represent a potential new type of therapy against HIV that would allow a short-term treatment to induce long-term viral suppression.
## The active agent of plant extracts is a 30 kDa protein
We have previously shown that extracts of *Momordica balsamina* made in water contain antiviral activity [33]. The active agent is a 30 kDa protein we call MoMo30. Hereafter called MoMo30-plant to denote plant-derived protein. A representative extract is shown on a 4–$20\%$ SDS-PAGE gel. It shows a single band of approximately 30 kDa in size (see Fig. 1A) in the original extract and after concentration on a 30 kDa cutoff filter (Fig. 1A, Purified). Surprisingly, wedetected no other significant bands on the SDS-PAGE gel. We used molecular weight cutoff filters to separate MoMo30-plant from lower molecular weight contaminants and to concentrate the protein. The N-terminal sequence of the first 15 amino acids was determined by Edman degradation and was found to be: GPIVTYWGQNVXEGEL. Western blot analysis with an antibody directed against the N-terminal peptide confirmed that the protein subjected to Edman degradation is indeed the 30-kDa band detected by Coomassie Blue staining of polyacrylamide gels (Fig. 1A, Ab 1). MoMo30-plant was also reactive with a second peptide antibody made from an internal region of the predicted gene sequence for Hevamine A-like protein (Fig. 1A, Ab 2).Fig. 1Plant extracts primarily contain a 30 kDa protein as visualized by Coomassie stain on a 4–$20\%$ SDS-PAGE gel. A A Coomassie blue stained gel of the extract and extract passed through a 30kD cutoff filter shows a predominantly single band. The band is reactive with an N-terminal antibody to the MoMo30-plant (Ab 1) and a second antibody made from a sequence from the gene of the predicted MoMo30-plant (Ab 2) B. The IC50 of the protein was determined by exposing HIV-1NL43 (equivalent to 1 ng p24) to concentrations of MoMo30-plant from 1 to 100 nM and determining the percent infectivity by MAGI assay. The IC50 value was determined by curve-fitting using Dr. Fit software. The blue curve is MoMo30-plant, and the red curve is MoMo30-HEK. The green curve is the commercially available fusion inhibitor Enfuvirtide. C MoMo30-plant is adsorbed into the serum of Rhesus macaques. Two macaques were given herbal therapy in the same regimen as in the field for humans (adjusted for weight). Three microliters of serum were tested by MAGI assay (in triplicate) for antiviral effects from 0 to 183 days. The inset shows a western blot using N-terminal ab and 15 µL of the sample in crosshatched bars. D MoMo30-plant shows no cellular toxicity at therapeutic levels. MoMo30-plant was tested at concentrations from 1 to 1000 nm in an MTT assay. Control is untreated cells
## MoMo30-plant can inhibit both X4 and R5 tropic strains of HIV-1
We tested the purified MoMo30-plant protein for its ability to inhibit HIV-1 infection in a MAGI cell assay (see Fig. 1B). Purified protein was able to inhibit HIV-1NL4-3 in a dose-dependent fashion. We tested concentrations of MoMo30-plant from 1 to 1000 nM. The IC50 of the protein was determined by curve fitting using the program Dr. Fit as described [34] and was determined to be 2.8 nM (Fig. 1B). The plot was curve-fit from triplicate measurements of two independently isolated purified protein preparations (blue curve). For comparison, we also ran a dose-dependence curve on MoMo30-HEK (red curve). Triplicate sample analysis of the commercially available fusion inhibitor Enfuvirtide was also conducted in parallel (Fig. 1B green curve). The IC50 of Enfuvirtide was determined to be 44 nM, consistent with the previously published value of 26 nM [35].
To determine the ability of MoMo30-plant to inhibit strains of HIV-1 other than HIV-1NL4-3, we tested a panel of primary isolates from different clades and origins. The isolates included HIV-1IIIb, HIV-1JRFL, HIV-193/MW/965, HIV-197/ZA/012, HIV-194/UG/118, HIV-192/RW/024, and HIV-193/RW/002 and the results are summarized in Table 1 and the representative IC50 values are shown. The IC50 values ranged from 2.8 to 9.0 nM.Table 1Inhibition of primary isolatesStrainCladeIC50 (nM)HIV-1 IIIbB0.7 ± 0.03HIV-1 92/RW/024A4.6 ± 0.20HIV-1 93/RW/002A2.0 ± 0.06HIV-1 JRFLB1.7 ± 0.001HIV-1 93/MW/965C3.9 ± 0.001HIV-1 97/ZA/012C9.0 ± 0.02HIV-1 94/UG/118D8.1 ± 0.07HIV-1 NL43B2.8 ± 0.001
## Orally delivered MoMo30-plant can accumulate in the bloodstream of non-human primates
To determine if orally administered plant extract resulted in the presence of MoMo30-plant in the bloodstream, we administered extracts of the medicinal plants to two Rhesus macaques by mouth for six months. We did MAGI infectivity assays and Western blot to confirm the presence of MoMo30-plant in the serum of treated animals (Fig. 1C). Serum was tested from 0 to 183 days after ingestion. Analysis of samples of one of the animals by Western blot using the antibody specific for the MoMo30-plant N-terminus showed that MoMo30-plant remains in the bloodstream for at least 84 days Fig. 1C, inset). Neither animal had detectable protein present in their serum before ingestion. Neither animal exhibited inhibitory activity in their serum prior to ingesting the plant extract; however, sera from both possessed significant amounts of antiviral activity by 42 days post-ingestion (Fig. 1C).
## MoMo30-plant is not toxic to cells over its inhibitory range
We tested the cellular toxicity of the MoMo30-plant using the MTT assay. We tested concentrations of MoMo30-plant from 1 to 1000 nM. Over this range, the protein showed minimal cellular toxicity (see Fig. 1D).
## MoMo30-plant stability studies
We studied the heat stability of MoMo30-plant by testing the ability to inhibit HIV infectivity after incubation of the protein at temperatures from 25 to 120 °C (autoclaving). The activity of the MoMo30-plant was tested at concentrations of 1 nM (red line), sufficient to inhibit the virus by $70\%$, and 0.1 nM (blue line), sufficient to inhibit the virus by approximately $50\%$. The results are summarized in Fig. 2A. We found that over a range from 25 to 120 °C, the percent infectivity of the purified protein remained relatively unchanged (Fig. 2A). To determine the stability of MoMo30-plant complexes formed with HIV-1, we mixed 100 ng/mL of MoMo30-plant with HIV-1NL4-3 virus equivalent to 1 ng of p24. Typically 1 ng of p24 will produce 10,000 ± 150 blue cells/ng p24 using HIV-1NL4-3, and we include a virus-only control in each experiment. The virus plus MoMo30-plant was centrifuged through a $30\%$ sucrose cushion at 125,000×g to remove any unbound MoMo30-plant. The sucrose pellet containing virus plus bound MoMo30-plant was then tested for infectivity by MAGI cell assay at 5 min to 3 days intervals. The results are summarized in Fig. 2B. Complexes of MoMo30-plant and the virus retained $100\%$ of their antiviral activity for at least 72 h, suggesting that the complex of MoMo30-plant and virus remains stable once formed. Fig. 2MoMo30-plant is heat stable and stays bound to the virus for long periods. A MoMo30-plant was mixed with HIV-1NL43 (equivalent to 1 ng p24) at concentrations of 0.1 (red) and 1 (blue) nM sufficient to cause $50\%$ (blue) or $70\%$ inhibition (red). The mixture was then heated at temperatures from 25° to 120° for 30 min, allowed to cool to 25 °C. An aliquot of 100 µL was then tested in the MAGI infectivity assay, B MoMo30-plant (3 nM) was mixed with HIV-1NL43 (equivalent to 1 ng p24) and allowed to interact for 5 min before centrifuging the complex through a $40\%$ sucrose cushion to remove from free MoMo30-plant. The virus-MoMo30-plant complex (in the pellet) was removed from 5 min to 72 h at 4 °C before testing by MAGI cell assay. All measurements were done in triplicate
## Comparison of the N-terminal sequence of MoMo30-plant to other proteins
To help determine the identity of the MoMo30-plant protein, the sequence of the N-terminal 15 amino acids was determined by Edman degradation (GPIVTYWGQNVXEGEL). The Edman sequence was compared to other proteins in the NR (NCBI) database using the BLAST algorithm (Fig. 3A). The top ten hits on the database were all forms of the Hevamine A-like protein from Prosopis alba, the South American (white carob tree) plant defensin with chitinase activity similar to those described above [32]. We compared the N-terminal sequence to Momordica's previously reported RIP protein [15, 36] (Fig. 3B). The N-terminal sequence from MoMo30-plant had no homology to the RIP proteins from M. charantia both the α and β momorcharins. In contrast, the N-terminal sequence from MoMo30-plant had the N-terminal sequence from MoMo30-plant had significant homology with the Hevamine A-like protein from M. charantia (Fig. 3B). The high degree of homology suggests that MoMo30-plant could be an unreported Hevamine A-like protein from M. balsamina. Fig. 3MoMo30-plant is homologous to the Hevamine A-like protein. A The N-terminal sequence from MoMo30-plant was used to search protein BLAST. The top ten hits are shown. The yellow box highlights the signal sequence, and the blue highlights the homologous portion of the N-terminal sequence. B The N-terminal sequence from MoMo30-plant was compared to the Hevamine A-like protein from M. chantaria and the ribosome-inactivating protein (RIP) from M. chantaria both alpha and beta forms. Conserved amino acids are highlighted in blue
## RNAseq analysis of Momordica balsamina reveals significant homology to the Hevamine A-like gene
We obtained enough RNA from freshly grown M. balsamina plant cells to obtain RNAseq data from the de novo transcriptome. A Diamond BLAST search of open reading frames identified in the sequence revealed homology between the N-terminal sequence of the MoMo30-plant protein and a Hevamine A-like sequence translated from the RNAseq data though it was not identical. The complete gene sequence assembled from RNAseq reads is shown in Fig. 4. *The* gene was $93\%$ identical to the Hevamine A-like gene from M. chantaria. Therefore, we considered that MoMo30-plant is a previously unreported Hevamine A-like protein from M. balsamina. A second peptide antibody was made in rabbit using a peptide sequence derived from the Hevamine A-like gene sequence and was reactive with the MoMo30-plant protein (Fig. 1A; Ab2).Fig. 4Clustal omega alignment of DNA sequences from MoMo30-plant, Hevamine A-like protein from M. charantia. Nucleotides that differ from the MoMo30-plant sequence are highlighted in yellow. MoMo30-plant is $92\%$ identical to the M. charantia Hevamine A-like gene A translation of this gene sequence and secondary structure prediction (by the Phyre2 website) is shown in Fig. 5. The predicted secondary structure shows strong homology to a TIM β-barrel (a structure that is commonly found in Hevamine A-like proteins [37]. The TIM structure is reported to be a very heat-stable conformation [38], consistent with our observation that MoMo30-plant is heat-stable (see Fig. 3).Fig. 5Translation of the DNA sequence of MoMo30-plant, including structural prediction of the resulting protein. Amino acids highlighted in red are differences between the two sequences. Arrows denote the predicted beta-sheet structure, and hatched boxes denote predicted alpha-helical structure. The two yellow shaded boxes denote domains of conservation in this class of proteins. The asterisks denote the highly conserved catalytic residues
## In vitro transcription/translation of the Hevamine A-like MoMo30-plant gene produces an antiviral effect
We took the sequence of the MoMo30-plant gene derived from the RNAseq data and had the gene synthesized and cloned into the pGen-lenti vector expression plasmid. In vitro coupled transcription and translation were done on purified template using the TNT wheat germ extract system (Fig. 6A). The product of the reaction was tested by MAGI assay to determine if there was any antiviral effect. The synthesized product MoMo30-wheat (panel A) inhibited HIV-1 similarly to purified MoMo30-plant (see Fig. 6B). Western blot revealed an ~ 30 kDa protein in the reaction. We transfected HEK-293 cells with the MoMo30-plant plasmid and tested the cell-free supernatant and cell lysate by western blot using an N-terminal antibody (Fig. 6B). We detected a single 30 kDa band in the supernatant. It cannot be explained why no processed MoMo30 is present in the cell lysate. We tested the cell-free culture supernatant for antiviral activity by the MAGI assay (panel D) and found that tissue culture supernatants could inhibit HIV-1 infection significantly, and a dose–response curve was similar to that seen for the MoMo30-plant protein (Fib 1B, red curve). In addition, Hevamine A-like proteins are known to have chitinase activity. MoMo30-HEK had chitinase activity (see Additional file 1: Fig S1). Finally, MoMo30-HEK could retain its anti-HIV activity after 30 min of incubation at 100 °C (Fig. 1B, the red curve is tested on heated protein). Together, these data suggested that the MoMo30-HEK protein was a Hevamine A-like protein from M. balsamina. Fig. 6In vitro translation of the MoMo30-plant gene produces a 30 kD protein with antiviral activity. A An in vitro synthesized gene was inserted into a pGenLenti vector and used as a template for coupled transcription/translation. The reaction was run on a $20\%$ SDS-PAGE gel, and a western blot was probed with an N-terminal ab to MoMo30-plant. B One hundred µL of the reaction mix was tested in a MAGI assay. C The MoMo30-plant pGenLenti plasmid was used to transfect HEK 293 cells. Supernatant and cell lysates were run on a $20\%$ SDS-PAGE gel and probed with the N-terminal ab. A sample of purified MoMo30-plant is used as a marker. D One hundred µL of the cell-free conditioned medium as tested by MAGI assay
## MoMo30-plant can block the binding of gp120 to Jurkat cells
To determine the stage of viral replication inhibited by the MoMo30-plant, we did assays that modeled steps in replication. To assay the attachment of gp120 to susceptible cells, we used purified FITC labeled gp120 mixed with Jurkat cells. FITC-labeled gp120 ornaments the cell surface making it visible. In the absence of MoMo30-plant, the purified gp120 can attach to CD4 or CXCR4 on the surface of Jurkat cells (see Fig. 8A–C). A stock solution of 200 nM of MoMo30-plant (sufficient to completely inhibit virus equivalent to 1 ng of p24) was pre-incubated with the gp120 before adding the Jurkat suspension. Treatment with MoMo30-plant blocked the interaction of gp120 with Jurkat cells (compare Fig. 7A–D). This finding suggests that MoMo30-plant blocked the initial step in replication by binding to gp120 and blocking entry. Fig. 7MoMo30-plant can bind to purified FITC labeled gp120 and blocks its interaction with Jurkat cells. A Labeled gp120 was added to Jurkat cells and allowed to bind to the surface, making it visible. B The same cells were stained with Hoechst 33342 nuclear stain. C Phase contrast image. D Pre-incubation of fluorescent gp120 was done with a stock of 200 µg of MoMo30-plant, which blocks its interaction with the cell. E The same cells were stained with Hoechst 33342 nuclear stain. F Phase contrast image
## MoMo30-plant-plant binds to the glycan residues of gp120
We did surface plasmon resonance experiments to quantify the interaction of MoMo30-plant-plant with gp120 better. Purified gp120 was bound to a chip surface, and MoMo30-plant-plant was allowed to flow across the surface, allowing binding to be characterized (Fig. 8A). Concentrations of MoMo30-plant-plant from 1 to 100 nM were analyzed. Increasing concentration of MoMo30-plant-plant showed proportional increases in changes in reflectance of the chip (Fig. 8A). Three independent measurements in triplicate gave a kd of 2.05 × 10–3 1/s, KD of 3.0 nM, and ka of 6.923 × 105 1/Ms. The binding profile suggests that there is a fast on rate and a biphasic off rate. The initial dissociation is rapid, followed by a very slow dissociation. We speculate that the fast part of this off rate may be due to the dissociation of multimers (see Additional file 1: Fig. S2) followed by a slow dissociation of the MoMo30-plant complex. To further characterize the binding of MoMo30-plant-plant to gp120, we pre-treated purified gp120 with PNGase F, which removes N-linked glycans. After treatment with PNGase F, we saw a dramatic decrease in binding to the chip surface (Fig. 8B), suggesting that MoMo30-plant binds to gp120 through its glycan residues. Fig. 8MoMo30-plant binds to purified gp120. A Gp120 was bound to a Biacore chip surface, and MoMo30-plant was allowed to flow across the chip at concentrations from 1 to 100 nM. Changes in surface plasmon resonance monitored binding. B Gp120 pre-treated with PNGase F (an N-linked glycosylase) dramatically reduces binding. The three lines represent triplicate measurements. C Mannose can block the activity of MoMo30-plant. HIV-1NL4-3, 2 nM of MoMo30-plant, and different concentrations of D-mannose were added simultaneously and incubated for 5 min at 37 °C before testing by MAGI cell assay for inhibition of infection. Inhibition as a percentage of the untreated control (Infection in the presence of 2 nM Momo30-plant) is plotted
## Effect of monosaccharide mannose on inhibition of infection by MoMo30-plant-plant
The glycans on gp120 are a mixture of high mannose and complex glycans [39, 40]. To determine the effect of adding the monosaccharide mannose to the MoMo30-plant, we investigated inhibition by MoMo30-plant in the presence of different concentrations of mannose (Fig. 8C). We tested inhibition of HIV-1NL4-3 by MoMo30-plant (2 nM) in the presence of concentrations of mannose from 0 to 2 nM. We saw that the inhibition of HIVNL4-3 by MoMo30-plant was significantly reduced by mannose in a concentration of 2 nM, a 1:1 molar ratio with MoMo30-plant.
## Discussion
In this study, we report the anti-HIV-1 activity of water-soluble plant extracts from the medicinal plant Momordica balsamina. We find that the biological activity is contained in a 30 kDa protein we call MoMo30-plant. Anti-HIV-1 activity has been previously reported in extracts of *Momordica balsamina* [20]. A 30 kDa ribosome-inactivating protein (RIP) has been reported that acts at a post-reverse transcription step in replication. MoMo30-plant is distinguishable from RIPs in several ways; first, its mechanism of action appears to be at the stage of attachment and fusion, as evidenced by MoMo30-plant's ability to bind to gp120 and block its interaction with cells expressing CD4 receptors. Second, from studies we did using surface plasmon resonance, MoMo30-plant appears to react specifically with glycans on the surface of gp120 (see Fig. 8). We also find that MoMo30-plant has chitinase activity (see Additional file 1: Fig. S1). Finally, its N-terminal sequence does not share homology with the N-terminal region of known RIPs from Momordica (Fig. 3B). In the current study, MoMo30-plant was isolated from water-soluble extracts made from dried leaves. In contrast, RIPs were isolated primarily from the plant's seeds. The healers partially process the dried leaves of M. balsamina by making tea in boiling water. The exceptional heat stability of MoMo30-plant-plant may allow it to survive this process, while any other proteins in the solution would tend to denature and precipitate out of the solution. Heat stability could account for our observation that water-soluble extracts of M. balsamina contain a single protein of 30 kDa. Therefore, we consider that MoMo30-plant protein is a previously undescribed protein present in *Momordica balsamina* but distinct from RIP.
The MoMo30-plant protein has been difficult to study using typical proteomics techniques, and the genomic sequence of M. balsamina has not been reported. Attempts to do de novo sequencing of the protein were not successful. The exceptional stability of MoMo30-plant may contribute to its relative difficulty digesting with proteolytic digest by traditional enzymatic methods. Repeated attempts to analyze the protein sequence by LC/MS yielded very few or no peptides for analysis. Even intact mass measurements by MALDI/TOF were impossible as MoMo30-plant does not appear to be ionized using various matrix types. However, we obtained 15 amino acids from the N-terminus by Edman degradation (GPIVTYWGQNVXEGEL). Because of our difficulty with proteomics methods, our strategy was to isolate total cellular RNA from fresh plant tissue, perform RNAseq, and then search the transcriptome for proteins that match the N-terminal sequence.
We could germinate seeds and obtain enough growth to isolate total plant RNA. We used this to perform RNAseq (Illumina) and obtain a de novo assembly of the total transcriptome of M. balsamina. A Diamond BLAST search for translated proteins showed significant homology to the Hevamine A-like protein from M. charantia (see Fig. 4B). There are several possibilities why the match was not exact. The plants used for protein isolation were from Senegal and were dried. To have fresh plant material for RNA isolation, we needed to germinate seeds in our lab under artificial conditions to obtain enough plant cells for processing. Since the two sources of plant material were from different sources and conditions, this may have introduced variation from the mRNA to the original protein sequence. Another possibility is that the sequence identified by Edman determination is different than the gene identified by RNaseq. Both tissue-specific and temporal differences have been reported in the expression of defensin-like genes in Arabidopsis and Medicago [41]. The complete genome of *Momordica balsamina* has not been published to date so we can not rule out that such tissue-specific variants of the Hevamine A-like are present in Momordica balsamina. To help ensure we had isolated the correct gene sequence, we had the gene for MoMo30-plant synthesized (GenScript) and cloned into an expression vector. In vitro, transcription/translation by TNT wheat germ extract (Promega) was used to produce a 30 kDa protein that was reactive with an N-terminal antibody for MoMo30-plant and showed antiviral activity in a MAGI assay. Similarly, transfecting HEK293 cells with the expression plasmid produced a 30 kDa protein in the cell-free conditioned medium that was reactive with the N-terminal MoMo30-plant antibody, and the medium contained antiviral activity in a MAGI assay (see Fig. 7).
That MoMo30-plant could be a Hevamine A-like protein is consistent with its observed characteristics. Hevamine is one of several family members of plant chitinases and lysozymes produced as plant defensins against fungal infections [32]. Plants produce intracellular and extracellular chitinases of approximately 25–35 kDa [42]. We detected chitinase activity in MoMo30-plant (Additional file 1: Fig. S1). Hevamine is a 29 kDa protein that, according to the classification of Henrissat [43], is a class III chitinase from family 18. Members of this family have a conserved region of amino acids from residues 120 to 130 DGXDXDWEXP. The motif DDDE is highly conserved, as demonstrated by a psi BLAST search of over a thousand proteins with glycosyl transferase activity. One of the conserved aspartates (shown in bold) is replaced by arginine in Hevamine A-like proteins from both M. balsamina and M. charantia. This specific change was previously observed in the chitinase from Aridopsis thaliania, which maintains chitinase activity [44]. There are precedents for chitinases that can bind to glycoproteins. The lysine motif (LysM) is present in some chitinases from various sources [45]. A LysM motif is also present in the carbohydrate-binding protein CyanoVirin-N [46], which has been previously shown to have the ability to bind to gp120 on HIV and inhibit viral replication [47].
A hypothesis for the action of carbohydrate-binding agents (CBAs) has been proposed [23, 48]. Exposure of viruses to CBAs selects for variants expressing reduced numbers of carbohydrates on their surface (i.e., mutants with fewer binding opportunities for MoMo30-plant-plant). Selection of viruses with reduced glycosylated gp120 proteins allows for altered immunological responses and particles with impaired infectivity, which could result in longer-term suppression. MoMo30-plant appears to act as a CBA, and exposure to the protein may induce a change in glycan patterns on the surface of virions. Changes in gp120 induced by selection in the presence of MoMo30-plant may alter the antigenicity of gp120 and allow a robust neutralizing immune response to be mounted, as proposed in the CBA theory [26]. Moreover, it is generally believed that the lack of a robust immune response to HIV-1 is that gp120 is heavily glycosylated [50]. Furthermore, our observation that the monosaccharide mannose can block the antiviral effect of MoMo30-plant suggests that MoMo30-plant can bind to high-mannose glycans and select for viruses with reduced glycosylation on the surface [23].
Several other properties of MoMo30-plant make it a good candidate as a therapeutic for HIV-1. First, it has an IC50 of 2.8 nm, similar to the IC50 of approximately 20–40 nM for the commercially available fusion inhibitor Enfuvirtide [35]. Secondly, it acts directly on the gp120; inhibiting attachment negates the need for penetration into the cell. In addition, once MoMo30-plant is bound to gp120, it stays bound for extended periods (Figs. 8A, 2B). The biphasic nature of the Biacore sensorgram likely reflects our observation that MoMo30-plant tends to form multimers (Additional file 1: Fig. S2). The initial off rate could reflect the disruption of multimers.
In contrast, the longer off rate is the dissociation of MoMo30-plant in complex with gp120. This prolonged “off” rate could mean less protein needs to be delivered to the blood over time to reach and maintain therapeutic levels. In addition, since the virus is prevented from entering the cell, it minimizes the possibility of integration into the host genome, avoiding potential latent infection.
It has been suggested that fewer glycosylated amino acids on g120 might allow for an altered immune response, enhancing the opportunities for broad-based neutralizing antibodies to be produced. If so, our model for MoMo30-plant action predicts that individuals treated with plants containing MoMo30-plant should have high levels of broadly neutralizing antibodies. Such studies are underway and should provide a more complete understanding of the mechanism underlying this traditional African therapeutic approach.
## Conclusions
The medicinal plant *Momordica balsamina* contains a 30 kDa protein that we call MoMo30-plant-plant that has activity against strains of HIV-1. It acts as a fusion inhibitor by blocking the binding of gp120 to susceptible cells. MoMo30-plant binds to gp120 through its glycan residues. These data suggest MoMo30-plant-plant could represent a potential new inhibitor.
## Plant extracts
The preparation of water extracts from plants has been previously described [33]. One hundred grams of dried and milled leaves from *Momordica balsamina* were extracted in 1L of distilled water overnight at 4 °C. The liquid extract was separated from the solid material through centrifugation at 4000×g for 30 min. at 4 °C. The resulting supernatant was filtered through Whatman filter paper (Cat# 3030) to remove particulates. The extract was then filter-sterilized by passing it through a 0.45-micron filter (Celltreat Cat # 229703) and was kept frozen at − 80 °C prior to lyophilization overnight.
## Isolation of MoMo30-plant
The lyophilized powder was dissolved in nuclease-free water (Invitrogen Cat# AM9938) to create a 15 mg/mL solution. The solution was passed through a 30kD molecular weight cutoff filter by centrifugation at 4000×g for 10 min (Amicon ultra-15 cat# UFC903024) to remove low molecular weight contaminants. Once a retentate of 1 to 1.5 mL was obtained, the solution was passed through a 0.22-micron syringe filter (Celltreate Cat# 229747) and stored at 4 °C before use (− 20 °C for long term storage). The retentate contained one protein MoMo30-plant-plant that was > $95\%$ pure as determined by SDS-PAGE and stained with Coomassie Brilliant Blue R-250 (Bio-Rad Cat# 161-0400).
## Multinuclear activation of an indicator (MAGI) assay for infectivity
MAGI cell assays for infectivity were done as previously described [49, 50]. MAGI cells (U-373-MAGI-CXCR4CEM glioblastoma cells, AIDS reagent program cat# ARP-3596) were grown to $90\%$ confluence. Cells were infected with HIV-1NL4-3 (equivalent to 1 ng of p24; AIDS reagent program cat # 114). They were then fixed by the addition of $1\%$ Formaldehyde (F-79–500 Fisher Chemicals) and $0.2\%$ Glutaraldehyde (F-02957-1 Fisher Scientific) in PBS and stained in a solution that contained (14.25 mL PBS, 300 µL 0.2 M potassium ferrocyanide, 300 µL 0.2 M potassium ferricyanide, 15 µL 2 M MgCl2 and 150 µL X-gal stock (40 mg/mL in DMSO). Two mL solution was added to each well and incubated at 37 °C for 50 min. Cells were washed twice with PBS and counted using light microscopy. Infected cells were identified as those exhibiting the development of a blue color.
## Primary HIV-1 stocks
All HIV-1 strains (clade A to D) were obtained from the National Institutes of Health (NIH) AIDS Research and Reference Reagent Program (ARRRP) and were propagated at New Iberia Research Center by infecting human PBMCs 3 days after stimulation with 1.0 μg/mL ConA and culturing infected cells for 21 days after stimulation, replenishing media supplemented with 50 U/mL IL-2 twice a week. Viral supernatants were tested for HIV p24 antigen by ELISA kit (ABL Inc.) and supernatants with a high concentration were pooled. Virus-containing supernatants were clarified by centrifugation, sterile filtered and stored separately in 1-mL aliquots in liquid nitrogen.
## Neutralization assay in TZM-bl cells
The neutralization activity of MoMo30-plant-plant against each HIV-1 strain (clade A to D) was measured using a standard protocol of luciferase-based HIV-1 neutralization assay in TZM-bl cells (Montefiori, Duke University). Briefly, 50 µL of fivefold serial diluted MoMo30-plant-plant and 50 µL of 1 ng virus were preincubated for 1 h at 37˚C in a 96-well flat-bottom plate. Next, 100 µL of TZM-bl cells (1 × 104/well) in $10\%$ DMEM growth medium containing 15 µg/mL DEAE dextran (Sigma-Aldrich) were added to the preincubated each well, and the 96-well plates were incubated for 48 h. Assay controls included TZM-bl cells alone (cell control, no virus) and TZM-bl cells with virus only (virus control, no test reagent). At 48 h, the cells were lysed, and luciferase activity was measured using (Promega, Cat# E1501, 10 × 100 assays). on a BioTek Synergy HT multimode microplate reader The average background luminescence (RLU) from cell control wells was subtracted from the luminescence for each experimental well. The neutralization curves and $50\%$ inhibitory concentration (IC50) were calculated and generated using GraphPad Prism (v7.01) software.
## Determination of the effect of MoMo30-plant and Enfuvirtide on HIV-1NL4-3 infectivity
We performed a dose–response curve on MoMo30-plant and Enfuvirtide (Sigma SML0934). We used concentrations of MoMo30-plant from 0.314 to 78.25 nM. Stock solution of MoMo30-plant protein (782.5 nM) was diluted to final concentrations of 0.314 nM, 0.609 nM, 1.22 nM, 2.44 nM, 4.88 nM, 9.77 nM,19.55 nM, 39.06 nM,and 78.25 nM. For Enfurvirtide we diluted at stock solution of 782.5 nM to final concentrations of 2.2 nM, 4.43 nM, 8.86 nM, 17.73 nM, 35.44 nM, 70.91 nM, 142.05 nM, 272.73 nM, and 568.00 nM. To each 1 mL of diluted inhibitor we added 5 µL of HIV-1NL43 (equivalent to 1 ng p24) and 10 µL of DEAE. The mixture was than added to 2 × 104 MAGI cells and then incubated for 48 h at 37 °C. and blue cells were counted. The IC50 of MoMo30-plant was determined by curve fitting using the Hill equation and determined using the Dr. Fit program [34].
## Detection of MoMo30-plant in serum
To determine if the ingestion of plant extracts resulted in detectable levels of MoMo30-plant in the blood, two Rhesus macaques were given plant extracts using a scaled dosage to that typically given to humans. Basically, macaques were given the plant extracts with food. Two grams of plant was given twice a day for a period of six months. Blood samples were taken at 0 days up to 183 days. Plasma was tested by SDS -PAGE and Western blot.
## MTT assay of MoMo30-plant
To determine if MoMo30-plant had significant cellular toxicity at therapeutic levels, we exposed HEK 293 cells to concentrations of MoMo30-plant from 1 to 1000 nM and performed a mitochondrial toxicity test (MTT; Sigma Cat# CGD-1) according to the manufacturer's recommendations. Percent viability was determined by comparison to an untreated control.
## Stability studies on MoMo30-plant and its complex to gp120
To determine the heat stability of MoMo30-plant, we first subjected stock solutions of MoMo30-plant (4 ng/mL and 40 ng/mL) to temperatures from 25 to 120 °C for 30 min. After heating, the solution was mixed with 1 ng of HIV-1NL4-3 and was added to a MAGI cell assay, and blue cells were counted. In a separate study to determine the stability of complexes formed between virus and MoMo30-plant, we mixed virus equivalent to 1 ng p24 of HIV-1NL4-3 with a stock solution of MoMo30-plant (400 ng/mL) and kept the sample at 4 °C, we removed aliquots at time intervals of 5 min to 3 days, and centrifuged at 125,000 g through $20\%$ sucrose cushion to remove free MoMo30-plant and tested its effect on infectivity by MAGI cell assay.
## N-terminal sequencing of MoMo30-plant
Edman degradation was performed on the plant-derived MoMo30-plant protein in two separate labs (Biosynthesis, Lewisville, TX, and Creative Proteomics, New York, NY). The analysis was performed on an ABI Procise 494HT (Thermo Fisher). The procedure determines the N-terminal amino acid sequence of proteins and peptides by the Edman degradation chemistry.
## RNAseq to determine the MoMo30-plant gene sequence
To help determine the gene sequence of MoMo30-plant we used RNAseq (Azenta Total RNA (~ 4 µg) purified from M. balsamina cells by the Trizol method was used for RNAseq on the Illumnina platform and the de novo.de novo transcriptome was assembled using Trinity software. The mRNA corresponding to the MoMo30-plant protein was determined by searching for the N-terminal sequence as determined by Edman degradation. Once a candidate DNA sequence had been determined we had the gene synthesized (Genscript) and cloned into the pGen-lenti vector which contains both a T7 promoter and a CMV promoter for expression in mammalian cells.
## Software for gene assembly and translation
BLAST searches were done at the national center for biotechnology information (NCBI) website. Comparisons of homology to various proteins and DNA sequences were made in SnapGene 6.0.2. Prediction of secondary structure was made at the Phyre2 structure prediction web portal [51].
## Coupled transcription/translation of MoMo30-plant gene
The cloned version of the synthesized gene was expressed in the wheat germ coupled transcription/translation system (TNT T7 coupled Wheat Germ Extract System Promega Cat# L4140) according to the manufacturer's recommendations with the following modification. Instead of adding [35S] methionine, we added 1 mM of unlabeled methionine to the mixture and detected protein using Western blot. Ten µL of the product was resolved on a 4–$20\%$ SDS-PAGE gel, and a Western blot was done using an antibody to the N-terminal peptide of MoMo30-plant. Ten µL of product was also tested in a MAGI cell infectivity assay to determine any antiviral effect. Since the pGen-lenti vector also contained a CMV promoter, we transfected HEK293 cells with the plasmid using Lipofectamine 3000 (ThermoFisher Scientific Cat # L3000008) following manufacturer's protocol and grew cells for 48 h. Conditioned medium was harvested, and cells were collected and lysed by using (Pierce RIPA buffer Cat# 89901). Ten µL of cell pellet and 20 µL of conditioned medium were resolved on a 4–$20\%$ SDS-PAGE gel and blotted with N-terminal antibody to MoMo30-plant. 100 µL of conditioned medium was tested by MAGI cell assay for antiviral effects.
## Fluorescent gp120 binding assay
Recombinant HIV-1 IIIB gp120 conjugated to FITC (ImmunoDX cat# 1001-F) was added to a suspension of 1 × 106/mL Jurkat cells (AIDS reagent program ARP-177 E6-1 clone) allowed to interact for 2 h at 37 °C. Free gp120 was removed by centrifugation, and the cells were resuspended in PBS. A portion was stained with Hoechst stain, and the cells were viewed under a fluorescent microscope using a neutral density filter, a blue filter, and a FITC filter.
## Surface plasmon resonance (Biacore)
Surface plasmon resonance was done at the Biacore Molecular Interaction Shared Resource at Georgetown University. A Biacore T200 was used with a CM5 chip at 25 °C. Purified gp120-IIIB (Immuno Dx, 1 mg/mL) in 1 mM sodium acetate buffer at pH 5.5 was used as a ligand to immobilize onto FC2, FC3, and FC4 to the levels of 8850 RU, 283RU, and 2980RU, respectively. Standard amine coupling chemistry was used. HBS-P (10 mM Hepes, pH 7.4, 150 mM NaCl, $0.05\%$ v/v surfactant P20) was used as the immobilization running buffer. Overnight kinetics were performed for MoMo30-plant binding to the ligand. Injected compound concentrations were 1–100 nM. Three 15 s pulses of 1:250 H3PO4 (v/v, ddH2O: H3PO4) were injected to regenerate the chip surface. All analyses were done in triplicate. The sensorgrams were obtained from overnight kinetics using 1:1 model fitting. In some experiments, gp120 was pre-treated with PNGase F (removes N-glycans) for 30 min at 50 °C before linkage to the chip surface. A control reaction was done with buffer alone. Three independent assays were done, each in triplicate.
## MoMo30-plant inhibition in the presence of mannose
To determine the effect of the monosaccharide mannose on the activity of MoMo30-plant, we did infectivity assays with 2 nM of MoMo30-plant in the presence of mannose in concentrations from 0.002 to 2 nM final concentration. and determined relative inhibition using a MAGI cell assay as described. In brief, 2 nM MoMo30-plant and different concentrations of D-Mannose (Sigma cat# M6020) were mixed. After that added, virus equivalent to 1 ng of p24 of HIV-1NL4-3 was incubated at room temperature for five minutes, added to MAGI cells, and determined relative inhibition was using a MAGI cell assay as described.
## Immunoblot
A rabbit antibody was produced (Genscript) from a 15-amino acid peptide with the N-terminal sequence of MoMo30-plant (GPIVTYGQNVNGELC). A separate rabbit antibody was made using a portion of the predicted sequence of the MoMo30-plant gene (LGGRSTSLRPGDC). The antibodies (at a dilution of 1:2000 for N-terminal ab; 1:3000 for the predicted sequence ab) were used to perform an immunoblot on purified protein resolved on a 4–$20\%$ SDS PAGE gel. The gels were transferred to 0.2 µm Nitrocellulose membrane using Bio-Rad Trans-Blot Turbo for 20 min and blocked with $0.5\%$ skim milk made in Tris buffer saline with $0.1\%$ tween 20 (TBST) for 1 h. The membrane was then incubated with primary antibody 1:2000 or 1:3000 in TBST overnight at 4 °C. The membranes were subjected to ten-minute washes with TBST and a wash with distilled water between each wash. Afterward, a secondary antibody (GE Healthcare goat anti-rabbit Cat# NA934V) was added at a dilution of 1:25,000 containing precision protein StrepTectin-HRP (Bio-Rad Cat# 1610380) 1:10,000 and allowed to incubate for 1 h at room temperature. After this, the membrane was washed three times as previously, and chemiluminescent substrate (SuperSignal West Femto Thermo Scientific Cat# 34096) was added and incubated for 5 min. The blot was visualized by a chemiluminescent imager (ThermoFisher iBright 1500).
## Supplementary Information
Additional file 1: Fig. S1. MoMo30-plant has chitinase activity. To determine if MoMo30-plant had chitinase activity, 5 µL of a 200 nM solution of MoMo30-plant was added to three different substrates (Sigma; cat # CS1030) in 100 µL of total volume a positive control is included for each substrate: 4-Methylumbelliferyl N,N′-diacetyl-β-D-triacetylchitotriose (Endo; endochitinase activity), 4-Methylumbelliferyl N,N′-diacetyl-β-D-chitobioside (Chito; exochitinase activity), and 4-Methylumbelliferyl N,N′-diacetyl-β-D-glucosaminide (Gluc; exochitinase activity). The assay is performed in an acidic environment (pH ~ 5.0) at 37 °C for 30 min. The assays were done in triplicate. The activity was measured as fluorescence and converted to activity in mg/mL. Fluorecent substrates require the least amount of time and were the most sensitive of the common methods used for chitinase activity [52].Additional file 2: Fig. S2. MoMo30-plant forms multimers on a native PAGE gel (4–$20\%$ TGX gels from Bio-Rad). The gel was then stained with Coomassie brilliant blue.
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|
---
title: Predictive value of the systemic immune inflammatory index in cardiac syndrome
x
authors:
- Yusuf Akın
- Mehdi Karasu
- Abdulmelik Deniz
- Çetin Mirzaoğlu
- Hasan Ata Bolayır
journal: BMC Cardiovascular Disorders
year: 2023
pmcid: PMC10035139
doi: 10.1186/s12872-023-03157-3
license: CC BY 4.0
---
# Predictive value of the systemic immune inflammatory index in cardiac syndrome x
## Abstract
### İntroduction
Patients with normal coronary arteries in whom increased vasospasm cannot be detected with the stress test should be evaluated in terms of cardiac syndrome x (CSX). İnflammatory systems are effective in endothelial activation and dysfunction in CSX. The systemic immune inflammation index (SII) is thought to be an important factor in determining the course of diseases, especially in infectious diseases or other diseases, as an indicator of the inflammation process. The aim of this study is to determine the role of SII levels in the diagnosis of CSX disease.
### Methods
The study group included 80 patients who applied to the cardiology department of Fırat University with typical anginal complaints between October 2021 and April 2022, and were diagnosed with ischemia after the myocardial perfusion scan, and then coronary angiography was performed and normal coronary arteries were observed.
### Results
When the study and control groups were examined according to age, gender and body mass index, hypertension, smoking, diabetes mellitus, dyslipidemia and family history, no statistical significant difference was observed between the groups. It was observed that there was a significant difference between the high sensitive C- reactive protin levels of the individuals in the study and control groups ($$p \leq 0.028$$). SII levels measured in samples taken from patients were significantly higher than control subjects ($$p \leq 0.003$$). SII cutoff at admission was 582 with $82\%$ sensitivity and $84\%$ specificity (area under the curve 0.972; $95\%$ CI:0.95–0.98;$p \leq 0.001$).
### Conclusion
It has been demonstrated that systemic SII parameters, which can be simply calculated with the data obtained from the complete blood count and do not require additional costs, can contribute to the prediction of CSX disease.
## Introduction
In the angiography performed in the preliminary diagnosis of coronary artery diseases, $30\%$ of the patients are reported as normal coronary arteries. However, it has been reported that patients with normal coronary arteries in whom increased vasospasm cannot be detected with the stress test should be evaluated in terms of cardiac syndrome x (CSX) [1].
CSX was defined by Kemp et al. [ 1973] in patients with chest pain, ischemic exercise electrocardiography(ECG) responses, and normal angiographic procedures performed on the coronary arteries. It has been reported that since there is no clear information about the underlying causes of chest pain in patients with normal coronary arteries in the coronary angiography procedure, they defined this patient group as 'Syndrome X' [2]. Although there is no clear information about the etiology of CSX disease, it is estimated that the etiology of the disease is caused by microvascular dysfunction [3].
CSX is a clinical picture especially seen in elderly men and postmenopausal women. While chest pain increasing with effort is observed in $20\%$ of patients diagnosed with CSX, it is reported that the remaining patients have angina pectoris characterized by transient chest pain at rest and ST elevation on ECG [4].
It has been reported that women undergoing coronary angiography are classified as normal 3 times more than men, and $72\%$ of CSX patients are postmenopausal women [5].
Although these patients are generally stable, chest pain is significant and limiting at certain intervals. For this reason, it is stated that although patients are benign in terms of survival, they have a significant adverse effect on quality of life [6].
It has been stated that patients diagnosed with CSX have pathophysiological abnormalities due to many factors. These abnormalities are reported to be caused by many factors, including abnormal coronary flow reserve, decreased insulin resistance, abnormal autonomic control, increased sodium-hydrogen level, abnormal cardiac sensitivity, and microvascular spasm [6].
Tousoulis et al. [ 2001] determined that inflammatory systems are effective in endothelial activation and dysfunction in CSX. It has been determined that blood levels of adhesion molecules synthesized by active endothelial cells due to inflammatory stimuli increase in patients with a diagnosis of CSX [7]. In addition, it was determined that increased high sensitivity C-reactive protein (hs-CRP) levels in these patients were associated with more severe angina pectoris and more active disease. It has been determined that CSX patients with increased hs-CRP levels have more frequent episodes of ST segment depression in Holter-ECG monitoring and earlier and more pronounced ischemic ST segment changes during exercise stress testing [8].
Myocardial perfusion scanning(MPS) is one of the most commonly used non-invasive test procedures in the evaluation of known or suspected coronary artery disease (CAD) [9]. In recent years, new strategies have been increasingly advocated and investigated to reduce radiation exposure, reduce costs, and increase laboratory efficiency [10]. For this reason, the importance of diagnostic tests that will reduce the need for nuclear imaging on the path to differential diagnosis has increased.
The systemic immune inflammation index (SII) is thought to be an important factor in determining the course of diseases, especially in infectious diseases or other diseases, as an indicator of the inflammation process [11]. It is stated that the SII is a new generation inflammation biomarker created with whole blood parameters. This biomarker is the result of the product of the neutrophil count and platelet count divided by the lymphocyte count [12].
The aim of our study is to determine the role of SII levels in the diagnosis of CSX disease, which has been shown in many previous studies to be closely related to important cardiovascular events such as CAD, heart failure (HF) and hypertension (HT).
## Study design and measurements
The study group included 80 patients who applied to the cardiology department of Fırat University with typical anginal complaints between October 2021 and April 2022, and were diagnosed with ischemia after the MPS, and then coronary angiography (CAG) was performed and normal coronary arteries were observed. In the control group, a total of 80 volunteers with similar demographic characteristics, who came to the outpatient clinic for control purposes and whose cardiovascular stress test was evaluated as negative, were included. Patients in control group did not have active cardiac complaints as a result of the evaluation of another physician who was unaware of the study. The cardiovascular stress test were evaluated by another physician who was unaware of the first physician's evaluation and the study. Presence of unstable angina, history of previous myocardial infarction (MI), coronary vasospasm, moderate to severe heart valve disease, renal dysfunction(SCr > 1.5 mg/dL in men and > 1.4 mg/dL in women), hepatic dysfunction(more than double the upper limit of the lab ref.), presence of left ventricular systolic dysfunction(EF < 0.40), presence of malignant tumors, hematological disease, history of chronic or acute infection, and patients receiving steroid treatment were excluded from the study.
The diagnosis of HT was based on systolic blood pressure > 140 mm Hg, diastolic blood pressure > 90 mm Hg, or a history of antihypertensive drug use. Type 2 *Diabetes mellitus* (DM) was defined as the use of antidiabetic drugs or fasting blood sugar ≥ 126 mg/dl. Smoking history was defined as regular tobacco use. Participants in the patient and control groups did not have any antiischemic, antiarrhythmic, antiplatelet, statin and/or anticoagulant use.
## Myocardial perfusion scan (MPS)
Daily exercise approach or stress test (dipyridamole Tc-99 m MIBI protocol) was determined for MPS according to the standards determined by the Turkish Nuclear Society Working Group. The criteria set in this study were followed. For the stress test, the condition of 'no food or drink' for at least 4 h was determined. İf any calcium channel blocker or beta blocker drugs were discontinued 48 h before so that they did not cause a difference in heart rate or blood pressure and did not create any contraindications.
In the stress/rest test according to the modified Bruce protocol, 8–10 mCi was applied first, then 22–25 mCi. Target heart rate was calculated with the formula (220-age) × 0.85. The procedure was terminated when a situation that would constitute a contraindication for the continuation of the test developed in the patient during the effort. Intravenous administration of dipyridamole (0.14 mg/kg/min × 4 min) followed by Tc99m sestamibi (8–10 mCi as a stress dose) was performed when the patient reached heart rate (0.85 × peak) and above and developed clinical weakness for the treadmill. It was applied with 22 to 25 mCi (approximately 3 × 8–10 mCi) from the 30th minute of the protocol; Tc 99 m sestamibi application was repeated at the end of the resting phase, approximately three hours later. Finally, MPS was performed after 45–60 min.
## Coronary angiography(CAG)
The standard Judkins method was used during CAG, a femoral or radial catheter was used for CAG, and the test results were blindly examined by the person who performed two different angiography protocols. The channel blockers adenosine, nitroglycerin or calcium were not administered. Hyperventilation testing was performed to detect arterial spasm and exclude diagnosed patients. Coronary arteries were considered abnormal when illuminated narrowing or irregularity was detected.
## Laboratory measurements
Blood samples were taken from peripheral veins of patients who fasted overnight. Auto-analyzers were used to evaluate hematological results. Abbott Cell-Dyn 3700 device was used for total or differentiated leukocyte counts. Abbott Architect C16000 automated analyzer (Abbott Lab, Abbott Park, IL, USA) was used for total and specific high-density lipoprotein (HDL) cholesterol, triglycerides and fasting blood glucose levels. The Friedewald equation was used to calculate serum low-density lipoprotein (LDL) cholesterol concentrations. The participants in the patient group applied to the hospital in the early period after the onset of symptoms, and their laboratory parameters were checked on the day of the mps recording. The time between MPS and symptom onset was short. The laboratory parameters of the participants in the control group were checked at the time of application and cardiovascular stress test was performed on the same day.
## Statistical analysis
SPSS for Windows v 20.0 (SPSS, Chicago, IL, USA) program was used for statistical analysis. Descriptive statistical analyzes such as mean, standard deviation, rate and frequency data were performed. The validity of the normal distribution for permanent variables was determined using the Kolmogorov–Smirnov test. Student t-test and Mann–Whitney U test were used for parametric and non-parametric data, respectively. Intergroup comparisons were made for certain variables with the chi-square test. In addition, Pearson correlation analysis was performed with the established logistic regression model to find the effects of the parameters. With the calculation of the standard beta coefficient, $p \leq 0.05$ was considered statistically significant at the $95\%$ CI confidence interval.
## Results
Table 1 shows the baseline clinical and demographic characteristics of the Study population. When the study and control groups were examined according to age, gender and body mass index (BMİ), HT, smoking, DM, dyslipidemia and family history, no statistical significant difference was observed between the groups. Table 1Main characteristics of study groups ($$n = 160$$)ParametersControl group ($$n = 80$$)Patients with CSX ($$n = 80$$)PAge [years]52.9 ± 9.554.6 ± 8.90,566BMİ [kg / m2]27.5 ± 3.327.3 ± 4.00,752Woman32 ($40.0\%$)33 ($42.0\%$)0,853DM12 ($15.0\%$)10 ($12.5\%$)0,503HT20 ($25.0\%$)18 ($22.5\%$)0,745Dyslipidemia20 ($25.0\%$)18 ($36.7\%$)0,745family history7 ($8.75\%$)10 ($12.5\%$)0,206Smoking12 ($15.0\%$)10 ($12.5\%$)0,745Data are given as mean ± standard deviation or number (percent), CSX Cardiac syndrome x Table 2 shows the laboratory results of the study groups. It was observed that there was a significant difference between the hs-CRP levels of the individuals in the study and control groups ($$p \leq 0.028$$). SII levels measured in samples taken from patients were significantly higher than control subjects ($$p \leq 0.003$$).Table 2Laboratory Results of Study GroupsParametersControl group ($$n = 80$$)Patients with CSX ($$n = 80$$)PGlucose [mg/dL]118.4 ± 44.1124.1 ± 59.70,490Creatinine [mg/dL]0.88 ± 0.21.00 ± 0.40,266Uric acid [mg/dL]6.8 ± 2.16.2 ± 1.70,580WBC count [103/mm3]9.8 ± 2.410.3 ± 2.60,269Hemoglobin [g/dL]13.4 ± 1.713.7 ± 1.50,255Platelet count [10 [10³/mm³]242.4 ± 62.4278.2 ± 56.80,171Total cholesterol [mg/dL]172.0 ± 79.6180.1 ± 77.40,615Triglyceride [mg/dL]124.0 (80.0–190.0)123.5 (78.25–161.25)0,683LDL-cholesterol [mg/dL]113.1 ± 57.3116.0 ± 58.70,790HDL-cholesterol [mg/dL]41.0 (33.5–48.0)43.5 (35.0–49.0)0,820hs-CRP [mg/L]3.0 (1.1–4.7)4.8 (2.6- 6.6)0,028NLR1.52 (1.38–1.77)1.82 (1.66–2.01)0,084LVEF [%]60.0 ± 4.958.2 ± 5.10,442SII388 (309–477)611 (542–704)0,003Data are given as mean ± standard deviation, number (percentage), or median (interquartile range), HDL High-density lipoprotein, hs-CRP-high-sensitivity C-reactive protein, LDL Low density lipoprotein, LVEF Left ventricular ejection fraction, CSX cardiac syndrome X, WBC Leukocyte, SII İndex of systemic immune inflammation, NLR Neutrophil / lymphocyte ratio After univariate logistic regression analysis to reveal possible causes of CSX, there was a statistical significant correlation between hs-CRP and SII parameters and CSX. Following multiple linear regression analysis, a high SII level was able to predict CSX disease (OR = 0.982; $95\%$ CI:0.969–0.995; $$p \leq 0.005$$) (Table 3).Table 3Univariate and multiple linear regression analysis showing predictors for CSXVariablesUnivariate ($95\%$ Cl)PMultivariate ($95\%$ Cl)Phs-CRP1.127 (1.008–1.261)0,0361.099 (0.982–1.230)0,100SII0.980 (0.967–0.992)0,0020.982 (0.969–0.995)0,005CI Confidence interval, SII İndex of systemic immune inflammation, hs-CRP High-sensitivity C-reactive protein, OR Odds ratio To predict the presence of CSX in all subjects included in the study population on the basis of receiver operating characteristic (ROC) curve analysis, the SII cutoff at admission was 582 with $82\%$ sensitivity and $84\%$ specificity (area under the curve 0.972; $95\%$ CI:0.95–0.98;$p \leq 0.001$) (Fig. 1).Fig. 1Receiver operating characteristic (ROC) curve of the SII parameter for the diagnosis of CSX. SII cutoff at admission was 582 with $82\%$ sensitivity and $84\%$ specificity (area under the curve 0.972; $95\%$ CI:0.95-0.98; $p \leq 0.001$). SII: Systemic immune-inflammation index
## Discussion
As a result of our observations, our study is the first to examine the relationship between CSX and SII in MPS positive patients. It has been demonstrated that SII parameters, which can be simply calculated with the data obtained from the complete blood count and do not require additional costs, can contribute to the prediction of CSX disease.
It is thought that chronic inflammation has an effect on the formation of many important diseases such as cancer, cardiovascular disease [13], DM and metabolic syndrome [14–17]. It has been reported that inflammatory parameters increase in pulmonary embolism, acute renal failure, pulmonary arterial hypertension, peripheral arterial disease and cerebrovascular diseases in which endothelial dysfunction and inflammation play a role [18]. Recent studies in the literature have reported an increase in inflammatory parameters in CAD, HF, and acute MI [18, 19]. Again, in many studies, it has been observed that there is a strong relationship between endothelial dysfunction and systemic immunoinflammation [20].
Neutrophil / lymphocyte ratio (NLR) has been reported to be an independent predictor of cardiovascular events and mortality in ST-elevation MI [21]. It has also been reported that platelet /lymphocyte ratio (PLR) is an effective marker for severe atherosclerosis [13]. Li et al. showed that interleukin-6 and hs-CRP were significantly higher in patients with coronary artery ectasia(CAE) compared to patients with normal coronary arteries [22].
Recio-Mayoral A et al. showed that systemic inflammation plays an important role in CSX by causing microvascular dysfunction [23]. Okyay K. et al. showed that patients with CSX have high NLR levels, and that inflammation may be associated with impaired myocardial perfusion in these patients [24]. It has been shown that monocyte / HDL ratio and mean platelet volume, which are other markers of inflammation, increase in CSX patients [25, 26]. In a study conducted by Bozcali et al. [ 2014] to evaluate Galectin-3 serum concentrations in patients with a diagnosis of CSX, it was reported that the level of hs-CRP increased in individuals with CSX [27]. Although the hs-CRP levels of the individuals in the CSX group were higher than the hs-CRP levels of the individuals in the control group in our study, no significant difference was observed in the NLR levels between the two groups.
It is stated that SII is associated with markers such as NLR, PLR, and monocyte-lymphocyte ratio, which are determined as parameters of the SII [28, 29]. Several studies have suggested that SII may more comprehensively represent the inflammatory state compared with NLR as well as neutrophil and lymphocyte counts [30, 31].
Hu et al. first reported that SII was used in hepatocellular carcinoma [32]. First, Seo et al. reported the predictive value of SII in patients with chronic HF [33]. Esenboğa et al. [ 2022] determined the SII in the study in which individuals with isolated CAE, obstructive coronary artery disease and normal coronary anatomy were compared; high SII levels have been shown to be associated with CAE and occlusive coronary disease [34].
In the studies of Ya-Ling Yang et al. [ 2020] investigating the relationship between SII and clinical outcomes of CAD; SII has been shown to be more effective than traditional methods in predicting major cardiovascular events in patients undergoing coronary intervention [35]. In a recent study on this subject, Yaşar E. et al. revealed that there is a significant relationship between SII and microvascular dysfunction in patients with CSX.[18] In our study, unlike this study, only patients diagnosed with ischemia by MPS were included and compared with a completely normal control group in terms of symptoms and signs. Thus, it was tried to determine a cutoff value that would not require advanced imaging methods by providing a more sensitive comparison opportunity.
In our study, it was shown that SII is a parameter that can predict CSX disease in which inflammation is effective in its etiology. To predict the presence of CSX, the SII threshold at admission was 582 with $82\%$ sensitivity and $84\%$ specificity.
## Conclusions
As a result of our observations, our study is the first to examine the relationship between CSX and SII in MPS positive patients. It has been demonstrated that SII parameters, which can be simply calculated with the data obtained from the complete blood count and do not require additional costs, can contribute to the prediction of CSX disease. However, this relationship needs to be confirmed by future studies with larger populations and prospective studies in this area.
## Limitations
Our study has some limitations. First, it was a single-center, retrospective cross-sectional study and included a relatively limited number of patients. Second, we could not evaluate the real plaque load in patients without evidence of lumen narrowing on angiography, as there may be plaque load on the coronary vessel walls as well.
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title: Diabetic kidney disease induces transcriptome alterations associated with angiogenesis
activity in human mesenchymal stromal cells
authors:
- Xiaohui Bian
- Sabena M. Conley
- Alfonso Eirin
- Eric A. Zimmerman Zuckerman
- Anastasia L. Smith
- Cody C. Gowan
- Zachary K. Snow
- Tambi Jarmi
- Houssam Farres
- Young M. Erben
- Albert G. Hakaim
- Matthew A. Dietz
- Abba C. Zubair
- Saranya P. Wyles
- Joy V. Wolfram
- Lilach O. Lerman
- LaTonya J. Hickson
journal: Stem Cell Research & Therapy
year: 2023
pmcid: PMC10035152
doi: 10.1186/s13287-023-03269-9
license: CC BY 4.0
---
# Diabetic kidney disease induces transcriptome alterations associated with angiogenesis activity in human mesenchymal stromal cells
## Abstract
### Background
Therapeutic interventions that optimize angiogenic activities may reduce rates of end-stage kidney disease, critical limb ischemia, and lower extremity amputations in individuals with diabetic kidney disease (DKD). Infusion of autologous mesenchymal stromal cells (MSC) is a promising novel therapy to rejuvenate vascular integrity. However, DKD-related factors, including hyperglycemia and uremia, might alter MSC angiogenic repair capacity in an autologous treatment approach.
### Methods
To explore the angiogenic activity of MSC in DKD, the transcriptome of adipose tissue-derived MSC obtained from DKD subjects was compared to age-matched controls without diabetes or kidney impairment. Next-generation RNA sequencing (RNA-seq) was performed on MSC (DKD $$n = 29$$; Controls $$n = 9$$) to identify differentially expressed (DE; adjusted $p \leq 0.05$, |log2fold change|> 1) messenger RNA (mRNA) and microRNA (miRNA) involved in angiogenesis (GeneCards). Paracrine-mediated angiogenic repair capacity of MSC conditioned medium (MSCcm) was assessed in vitro using human umbilical vein endothelial cells incubated in high glucose and indoxyl sulfate for a hyperglycemic, uremic state.
### Results
RNA-seq analyses revealed 133 DE mRNAs (77 upregulated and 56 down-regulated) and 208 DE miRNAs (119 up- and 89 down-regulated) in DKD-MSC versus Control-MSC. Interestingly, miRNA let-7a-5p, which regulates angiogenesis and participates in DKD pathogenesis, interacted with 5 angiogenesis-associated mRNAs (transgelin/TAGLN, thrombospondin 1/THBS1, lysyl oxidase-like 4/LOXL4, collagen 4A1/COL4A1 and collagen 8A1/COL8A1). DKD-MSCcm incubation with injured endothelial cells improved tube formation capacity, enhanced migration, reduced adhesion molecules E-selectin, vascular cell adhesion molecule 1 and intercellular adhesion molecule 1 mRNA expression in endothelial cells. Moreover, angiogenic repair effects did not differ between treatment groups (DKD-MSCcm vs. Control-MSCcm).
### Conclusions
MSC from individuals with DKD show angiogenic transcriptome alterations compared to age-matched controls. However, angiogenic repair potential may be preserved, supporting autologous MSC interventions to treat conditions requiring enhanced angiogenic activities such as DKD, diabetic foot ulcers, and critical limb ischemia.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13287-023-03269-9.
## Background
Tissue hypoxemia from loss of microvascular capillary density is a major contributor to morbidity and mortality in individuals with diabetic kidney disease (DKD), one of the most common causes of kidney failure worldwide [1]. Sustained hyperglycemia and uremia alter vascular function and integrity in the kidney and limbs wherein disease pathogenesis is further fueled by microvascular rarefaction, tissue hypoxemia, sterile inflammation, cellular senescence, endothelial dysfunction, and extracellular matrix deposition [2–6]. These effects culminate in complications common in DKD including end-stage kidney disease (ESKD), cardiovascular disease, critical limb ischemia, and amputations of lower extremities [7]. Despite a 2-decade decline in lower extremity amputations between 2009 and 2015, rates rebounded by $50\%$ in adults with diabetes [8]. Furthermore, the odds of major amputations are nearly twofold higher in individuals with chronic kidney disease (CKD) or ESKD compared to persons without CKD/ESRD [9]. Therefore, therapeutics aimed at optimizing vascular repair and relieving endothelial dysfunction in the DKD patient population are urgently needed.
Regenerative cell-based therapies, such as mesenchymal stem/stromal cells (MSC), have considerable potential to boost endogenous repair of injured tissues. MSC release soluble factors, such as extracellular vesicles (EVs), growth factors, and cytokines, which enhance tissue regeneration and stimulate pro-angiogenic signaling [10–12]. In preclinical models of DKD, renovascular disease, and ischemic limb disease, MSC-based approaches improve kidney function, reduce hypoxemia, and restore microvascular density [2, 11, 13–15]. Collectively, these encouraging studies provided the basis for clinical application of MSC in phase I and II studies in DKD (NCT05362786, NCT02585622, NCT03840343, NCT04869761, NCT04125329, NCT04216849, NCT02008851, NCT03270956, NCT02836574) [16] and diabetic foot ulcers (NCT04464213, NCT04104451, NCT00955669, NCT04466007, NCT02375802, NCT01686139) [17].
Despite the promise of MSC in DKD, numerous challenges remain. Chronic hyperglycemia and uremia may damage endogenous repair systems like MSC, disturbing the coordinated network of pro- and anti-angiogenic factors activated by these cells. Autologous (versus allogeneic) MSC source may be preferable, given reduced risk of allosensitization, particularly for patients that might ultimately require kidney transplantation [18]. However, multiple DKD-related factors including age, hyperglycemia, uremia, cellular senescence abundance, or obesity may alter MSC functionality and number [13, 19–21]. We and others demonstrated that immunomodulatory and paracrine activities of MSC are maintained despite kidney impairment with and without diabetes [21–23]. However, it is unclear whether the microenvironment of DKD negatively influences the angiogenic potential of MSC. Elucidation of pro-angiogenic and tissue-repairing potential of DKD-MSC in the development of MSC-based regenerative medicine strategies for this population is crucial.
Our previous phase 1 clinical trial showed that intra-arterial infusion of autologous adipose-derived MSC (AD-MSC) improved kidney blood flow and reduced kidney tissue hypoxia as assessed through blood oxygen level dependent magnetic resonance imaging (BOLD-MRI) and improved glomerular filtration rate in atherosclerotic renovascular disease subjects [24]. These findings reflected AD-MSC angiogenic capabilities to form new vessels and restore the microcirculation, consistent with our experimental observations in swine atherosclerotic renal artery stenosis [2, 10, 25]. AD-MSC remain a viable cell option for clinical applications given their high abundance in adipose tissue and ease of harvest when compared to bone marrow-derived MSC (BM-MSC). Interestingly, AD-MSC proved more effective than BM-MSC in promoting neovascularization in animal models of ischemic diseases [26, 27]. Given the need for regenerative investigations in CKD, we are currently conducting two phase 1 clinical trials testing AD-MSC (NCT04869761) and BM-MSC (NCT05362786) in the treatment of DKD/CKD. Therefore, based on our prior experiences and the literature, we compared the transcriptome of AD-MSC obtained from DKD subjects and age-matched controls focusing on angiogenesis-related mRNA and miRNA targeting these genes. We further compared the angiogenic repair capacity in vitro of AD-MSC from DKD subjects and controls.
## Study participants
The expression of angiogenesis-related mRNAs and miRNAs and angiogenic potential was characterized in AD-MSC isolated from diabetic kidney disease (DKD) subjects. Eligible individuals age ≥ 18 years were recruited from Mayo Clinic in Rochester, Minnesota, in the Nephrology clinic between November 2015–2019 (IRB: 15-000933), as previously reported [21]. Informed consent was obtained by trained clinical research coordinators. DKD was defined as estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 and/or abnormal albuminuria with preserved kidney function (eGFR ≥ 60) in the setting of pharmacologically treated Type 1 or Type 2 diabetes mellitus. Controls were age-matched individuals, without diabetes or CKD, undergoing laparoscopic nephrectomy for kidney donation. Adipose tissue was collected from DKD ($$n = 29$$) participants in an outpatient surgical suite, while adipose tissue from controls ($$n = 9$$) was sampled during their surgical procedure. To minimize bias, those with dialysis dependency, kidney transplant, immunosuppressive therapy, hemoglobin A1c (HbA1c) > $11\%$ (97 mmol/mol), or malignancy were excluded. The Mayo Clinic Institutional Review Board approved all experimental study procedures and all participants provided written informed consent prior to participating.
## MSC harvest and characterization
Abdominal subcutaneous adipose tissue (0.5–2.0 g) was collected from control and DKD subjects to isolate MSC as previously described [20, 21, 25]. MSC were cultured in standard conditions (37 °C with $5\%$ CO2) in advanced minimum essential medium supplemented with $5\%$ platelet lysate [28] (PLTmax, Mill Creek Life Sciences, Rochester, MN). Primary MSC at passages 3–5 were phenotyped in both groups to confirm cell surface marker expression by imaging flow cytometry (Amnis® FlowSight®, Austin, TX) [19–21]. The following fluorescently labeled antibodies were used: cluster of differentiation (CD)73 (ab106677; Abcam, Waltham, MA), CD90 (ab124527; Abcam), and CD105 (ab53321; Abcam). Conversely, MSC lack expression of CD34 (340441; BD Biosciences, San Jose, CA) or CD14 (ab82012; Abcam). For surface marker expression, 0.25–1.0 × 106 cells were incubated with the antibodies for 30 min at 4 °C according to the manufacturer’s instructions. Following incubation, cell suspensions were washed, pelleted, and resuspended in staining buffer (00-4222-57; Thermo Fisher Scientific, Waltham, MA). Thereafter, samples were run on the FlowSight® and analyzed using IDEAS software version 6.2. Initial gates were selected for single cells (aspect ratio intensity versus brightfield), followed by gates of positively stained cells. Thresholds for positivity were established from scatterplots generated by single-stained compensation beads (A10497, Molecular Probes, Eugene, OR).
## Collection of MSC conditioned medium (MSCcm)
MSC (passages 3–5) were cultured to $70\%$ confluence in T-75 flasks, then washed extensively with phosphate-buffered saline, and replenished with serum-free advanced minimum essential medium for 36 h. After serum starvation, MSCcm was centrifuged at 2000×g for 20 min at 4 °C to remove cellular debris, aliquoted and stored at − 80 °C for in vitro studies. MSCcm was normalized by MSC density per sample (1 mL conditioned medium for every 0.13 × 106 MSC) [21]. Thrombospondin 1 (TSP1) protein was measured in MSCcm using a commercially available kit (ELISA; R&D DTSP10) following manufacturer’s protocol.
## MSC mRNA sequencing and data analysis
To explore MSC angiogenic regulation in DKD, high-throughput RNA sequencing was performed as previously reported [21]. Briefly, RNA libraries were generated from 1 µg of total RNAs using an Illumina TruSeq RNA Sample Prep Kit v2 and loaded onto flow cells (8–10 pM) to generate cluster densities of 700,000/mm2. MSC were sequenced on an Illumina HiSeq 2000. Comprehensive analysis including alignment, gene and exon quantification, and QC of raw RNA sequencing reads was performed using the Mayo Analysis Pipeline for RNA Seq (MAP-RSeq) version 3.1.3 [29], an in-house bioinformatics workflow. R-bioinformatics package (edgeR) was used for differential gene expression analysis between MSC from DKD and control participants. Genes with p adjusted value < 0.05 and |log2FC|> 1 were considered significantly differentially expressed (DE) genes. GeneCards® database (http://www.genecards.org/) was utilized to screen genes associated with angiogenesis. DE angiogenesis mRNAs-of-interest were visualized on volcano plots and heatmaps created using R package ggplot2.
## MSC miRNA sequencing and data analysis
Secondary analyses of high-throughput miRNA analyses on MSC were performed utilizing CAP-miRSeq version 1.1 workflow [30]. CAP-miRSeq generates raw and normalized expression counts for both known mature miRNAs and predicted novel miRNAs from unaligned reads (FASTQ). Differential miRNA expression analysis was performed using bioinformatics R package edgeR to identify miRNA upregulated in DKD-MSC compared to Control-MSC (|log2FC|> 1 | and Benjamini–Hochberg adjusted p value < 0.05).
## Integrated mRNA/miRNA analysis
The miRNA/mRNA interactions were performed by crossing DE MSC mRNAs and miRNAs to predict targets analyzed using microRNA-*Target analysis* module in Ingenuity Pathway Analysis software (Ingenuity® Systems, www.ingenuity.com).
## Validation of mRNA and miRNA by quantitative polymerase chain reaction (qPCR)
To validate the expression levels of representative upregulated and down-regulated mRNAs and miRNAs, qPCR was performed on MSC. RNA was isolated from MSC (0.5–1.0 × 106 cells) and ran using an Applied Biosystems ViiA7 Real-Time PCR system as previously mentioned [21]. Fold change of gene expression was calculated using 2−ΔΔCT method. All probes were from Thermo Fisher Scientific (bone morphogenetic protein 2/BMP2: Hs00154192, proenkephalin/PENK: Hs00175049, vascular cell adhesion molecule 1/VCAM1: Hs01003372, insulin-like growth factor-binding protein 2/IGFBP2: Hs01040719_m1, thrombospondin 1/THBS1: Hs00962908, integrin subunit beta 8/ITGB8: Hs00174456, glyceraldehyde 3-phosphate dehydrogenase/GAPDH: Hs02786624, miR-let-7a-5p: hsa-let-7a-5p, miR-30c-5p: hsa-miR-30c and U6: U6 small nuclear RNA/snRNA, 715680. mRNAs and miRNAs were normalized to GAPDH and U6, respectively.
## MSC angiogenic activity
Angiogenic potential of MSC was assessed in vitro. Briefly, commercially available human umbilical vein endothelial cells (HUVEC, 200K-05f, Cell Applications Inc., San Diego, CA) were grown in endothelial cell growth medium [20, 31]. HUVEC were divided into 4 groups for experimentation: group 1 cells were cultured under normal conditions (non-injured), while group 2–4 cells were co-incubated with high glucose (HG, 25 mmol) and indoxyl sulfate (IS, 1 mmol) for 6 h to simulate a uremic, diabetic milieu. Groups 3 and 4 HUVEC were simultaneously incubated with Control-MSCcm or DKD-MSCcm (medium from 1.3 × 105 cells), respectively, at the time of HG + IS injury. After 6 h, all HUVEC underwent functional assessments in vitro (migration, proliferation, or tube formation assay) or were lysed and prepared for qPCR analyses.
HUVEC (1.6 × 105/well) were seeded onto a 6-well plate and treated as described above. To assess proliferation of HUVEC in groups 1–4, the cells were fixed in $4\%$ paraformaldehyde for 15 min, then stained for Ki67 (Abcam, ab15580) according to the manufacturers protocol. Treated HUVEC were also assayed for migration capacity via the QCM 24-well Colorimetric Cell Migration (Millipore, ECM 508) following manufacturer’s protocol. Samples were observed and imaged using an EVOS M5000 imaging system and analyzed via Image J. HUVEC (1.4 × 104/well) were also seeded onto a 96-well plate coated with GelTrex™ matrix (A1413201; Thermo Fisher Scientific) to assess tube formation. Plates were placed into a 37 °C incubator with $5\%$ CO2. After 4 h, HUVEC were stained with calcein-AM (10 µg/mL, for 30 min at 37 °C) to assess capillary-like structures and observed under an EVOS M5000 imaging system (Objective 40×; lens EVOS_AMEP4683; Camera 3.2 megapixels, monochrome, CMOS; Detectors and filters model DAPI—AMEP4650, F1420-1404-0373, TX Red—AMEP4655, F1620-1475-0197). A total number of tubular structures were manually counted. Gene expression was evaluated in HUVEC using the following probes from Thermo Fisher Scientific (E-selectin/SELE: Hs00174057, VCAM1, intercellular adhesion molecule 1/ICAM-1: Hs00164932, VEGF: HS00900055_m1, and THBS1:HS00962908_m1). Expression was normalized to TATA-binding protein (TBP: HS00427620).
## Statistical analysis
Data analysis was performed using GraphPad Prism 9 statistical software. Shapiro–Wilk normality test was performed on data from each group before statistical evaluation. Normally distributed data were represented as mean ± standard deviation, and non-normal data as median and interquartile range. Comparisons between Control-MSC and DKD-MSC were performed using either a two-sample t test with a $5\%$ type-I error rate or Wilcoxon Rank Sum, as appropriate. One-way analysis of variance (ANOVA) and post hoc pairwise testing were employed to detect differences in co-culture experiments. Statistical significance was accepted at p ≤ 0.05.
## Baseline characteristics
In total, 38 participants were included ($$n = 9$$ controls and $$n = 29$$ DKD subjects). Clinical characteristics of each cohort are summarized in Table 1. No significant differences were evident between groups for age, sex, or race. Body mass index (BMI) was higher (35.4 ± 5.6 vs. 28.8 ± 3.6 kg/m2; $p \leq 0.0001$), and by design, eGFR was lower in DKD participants (38.9 ± 15.4 vs. 80.5 ± 13.3 mL/min/1.73 m2; $p \leq 0.0001$) compared to non-diabetic, non-CKD control subjects. Table 1Baseline characteristics in control and diabetic kidney disease subjectsControl ($$n = 9$$)DKD ($$n = 29$$)p valueDemographics Age, years64.3 ± 3.765.4 ± 8.00.59 Female sex$44.4\%$$31.0\%$0.69 White race$100\%$$82.8\%$0.31Clinical eGFR (ml/min/1.73 m2)79.0 (23.2)36.0 (22.4)< 0.0001 Glucose (mg/dL)111.0 (24.0)150.0 (82.3)< 0.001 HbA1c (%)–7.7 (1.0)– BMI (kg/m2)28.8 ± 3.635.4 ± 5.6< 0.0001Medications Insulin–$72.4\%$– Oral hypoglycemics–$55.2\%$– Insulin and oral–$31.0\%$–Data are represented as mean ± standard deviation, median (IQR) or %DKD diabetic kidney disease, eGFR estimated glomerular filtration rate, HbA1c hemoglobin A1c, BMI body mass index
## AD-MSC express putative surface markers
Undifferentiated AD-MSC obtained from 38 study participants produce a relatively homogenous cell population exhibiting plastic adherence, fibroblast-like morphology, and multi-lineage potential as previously shown [21]. Culture-expanded MSC demonstrated robust MSC-specific cell surface positivity to CD90, CD105, and CD73, while low expression of hematopoietic markers, CD34 and CD14, was observed in MSC from control and DKD subjects (Fig. 1). Flow cytometric analyses revealed that MSC from DKD subjects maintain surface marker characteristics meeting the criteria required for MSC [32].Fig. 1Flow cytometric characterization of passage 3 mesenchymal stromal cells (MSC) isolated from adipose tissue of diabetic kidney disease (DKD) subjects which were positive for MSC-specific surface markers cluster of differentiation (CD)90, CD105, and CD73 and were negative for hematopoietic cell markers, CD34 and CD14. BF, Brightfield
## mRNA alterations in DKD-MSC
A total of 4611 mRNAs associated with angiogenesis were identified from the GeneCards® database (https://www.genecards.org/). Of these mRNAs, a total of 133 DE genes were related to angiogenesis. The distribution of DE upregulated and down-regulated genes is displayed in a volcano plot (Fig. 2A). Among them, 77 mRNAs were upregulated (Fig. 2B) and 56 down-regulated in DKD-MSC (Fig. 2C). Among the top 15 DE upregulated mRNAs were pro-angiogenic genes, including IGFBP2, lysyl oxidase-like 4 (LOXL4) and VCAM1, and anti-angiogenic genes, including THBS1 and ITGB8. Among the top 15 DE down-regulated genes several were pro-angiogenic genes, including interleukin (IL)13 receptor subunit alpha-2, C-X-C motif chemokine ligand (CXCL)3, IL33, PENK, matrix metalloproteinase (MMP)10, CXCL8, BMP2, and MMP3 as well as an anti-angiogenic gene, MMP12. Expression patterns of several mRNAs were subsequently confirmed by qPCR (Additional file 1: Fig. S1A and C). In addition, DKD-MSCcm tended to release higher levels of the anti-angiogenic protein TSP1 compared to Control-MSCcm (Additional file 1: Fig. S1B).Fig. 2The volcano plot demonstrates the distribution of the top 30 differentially expressed (DE) upregulated (red) and down-regulated (green) angiogenesis-related genes (A). Heatmaps are shown for DE upregulated (B) and down-regulated (C) mRNAs in DKD-MSC. padj: adjusted p value
## miRNA alterations in DKD-MSC
A total of 575 annotated miRNAs were identified, of which 208 (119 upregulated and 89 down-regulated) were DE in DKD-MSC. Expression patterns of miRNAs were subsequently confirmed by qPCR (Additional file 1: Fig. S1D). DE miRNAs were further inspected to identify mRNA targets involved in angiogenesis. Target prediction analysis resulted in 14 unique DE miRNAs regulating 18 unique DE angiogenesis-related genes (Fig. 3A) and 25 mRNA-miRNA interactions (Fig. 3B). Surprisingly, only one miRNA was down-regulated (miR-148a-3p), which interacted with ribosomal protein S6 kinase A5 (RPS6KA5), involved in phosphorylating cAMP-response element-binding protein (CREB) that promotes angiogenesis by regulating vascular endothelial growth factor (VEGF) receptor-1(VEGFR1). Notably, let-7a-5p targeted five mRNAs (transgelin/TAGLN, THBS1, LOXL4, collagen 4A1/COL4A1, and collagen 8A1/COL8A1; Fig. 3C).Fig. 3Heatmap demonstrates 14 DE microRNAs (miRNAs) in DKD-MSC (A). Table lists differentially expressed miRNA and their potential angiogenesis-related mRNA targets analyzed by ingenuity pathway analysis (B). Font colors: upregulated (red), down-regulated (green) miRNAs. Diagram of let-7a-5p targeting of several mRNAs including TAGLN, THBS1, LOXL4, COL4A1, and COL8A1 in addition to other differentially expressed miRNA activities (C)
## DKD-MSCcm angiogenic functionality
Apart from the influence of hyperglycemia, uremic conditions present in DKD may further impact gene expression, intrinsic properties of MSC, and MSC functionality. Therefore, we assessed the reparative capacity of DKD-MSCcm on injured endothelial cells by establishing a uremic and diabetic microenvironment using HG + IS. Pre-incubation of HUVEC with HG + IS diminished tube formation capacity. Co-incubation of HUVEC with DKD-MSCcm prior to HG + IS exposure restored formation of tube-like networks (Fig. 4A, B), reflecting sustained pro-angiogenic properties. In other functional studies, incubation of DKD-MSCcm restored the migration potential of injured HUVEC, while proliferation activity was not different between groups (Additional file 2: Fig. S2A, B). Co-culture of MSCcm with injured HUVEC increased THBS1 mRNA expression, though DKD-MSCcm exhibited lower THBS1 levels when compared with Control-MSCcm ($$p \leq 0.07$$) (Additional file 2: Fig. S2C). Furthermore, exposure to HG + IS led to upregulation of adhesion molecules SELE, VCAM1, ICAM-1 in HUVEC, while incubation of DKD-MSCcm attenuated mRNA expression, suggesting that DKD-MSCcm prevents endothelial dysfunction (Fig. 4C). No differences were observed between DKD-MSCcm and Control-MSCcm groups. Furthermore, VEGF mRNA levels increased in HG + IS-exposed HUVEC, but MSCcm from DKD and Controls prevented the rise in VEGF expression (Fig. 4D).Fig. 4Representative images of capillary-like tubes formed by non-injured human umbilical vein endothelial cells (HUVEC) or in the presence of high glucose (HG) plus indoxyl sulfate (IS) injury and either Control-MSC conditioned medium (cm) or DKD-MSCcm. Images were acquired at 40× resolution and were not enhanced. ( A). Quantification of total number of tubes among groups (B). Selectin E (SELE), vascular cell adhesion molecule 1 (VCAM1), intercellular adhesion molecule 1 (ICAM-1) (C) and vascular endothelial growth factor (VEGF) (D) mRNA expression levels in HUVEC. ns, not statistically significant ($p \leq 0.5$); other non-significant p values ($p \leq 0.05$) are shown in this figure given trends in the relationships
## Discussion
Our study used comprehensive sequencing analysis to compare the enrichment and suppression of mRNA and miRNA expression in adipose tissue-derived MSC in DKD and control subjects. We further explored the pro-angiogenic potential of DKD-MSCcm in vitro. Compared to MSC from controls, DKD-MSC exhibited heterogeneity in the expression profiles of mRNA associated with angiogenic activity. Furthermore, 14 miRNAs targeted these angiogenesis-related genes modifying mRNA activity. In particular, miRNA let-7a-5p, which regulates angiogenesis, has anti-thrombospondin activity, and is associated with DKD pathogenesis, was upregulated and had the highest number of mRNA targets. In our in vitro studies mimicking the DKD microenvironment, DKD-MSCcm incubation restored endothelial cell function and halted overexpression of adhesion molecules associated with vascular insult. These observations suggest that despite alterations of the transcriptome, the pro-angiogenic reparative capacity of MSC in vitro appears relatively preserved in DKD subjects.
To limit potential immunological reactions and minimize the risk of allosensitization in subjects who may eventually desire kidney transplantation, an autologous MSC source is preferable. However, DKD patient factors such as advanced age, hyperglycemia, and uremia may diminish MSC reparative ability. Our previous ex vivo studies in MSC from patients with DKD showed that DKD-MSC anti-inflammatory, immunomodulatory, anti-apoptosis, and anti-fibrosis functionality remained intact compared with Control-MSC. However, the migratory capacity of DKD-MSC was lower than that of Control-MSC [21]. Therefore, we further explored functional differences in angiogenic capacity between DKD-MSC and Control-MSC in this study.
Angiogenic activity is dependent on the intricate balance between pro-angiogenic factors (such as VEGF, fibroblast growth factor-2/FGF2, transforming growth factor-β/TGF-β, and angiopoietins) and factors that inhibit angiogenesis (angiostatin, endostatin, thrombospondins) [33]. Diabetes is associated with angiogenesis dysregulation leading to structurally immature blood vessel formation after injury, impaired wound healing, and excessive angiogenesis—particularly in DKD and diabetic retinopathy [33, 34]. We previously found no difference in secretion of VEGF-A between DKD-MSC and Control-MSC, while hepatocyte growth factor (HGF), maintaining anti-apoptosis, anti-fibrotic, and pro-angiogenic activity, was substantially higher in DKD-MSC [21]. VEGF is the key mediator of angiogenesis and vessel repair. Xin et al. [ 35] found that the combination of HGF and VEGF increased neovascularization greater than either growth factor alone. In our studies, DKD-MSCcm restored HUVEC formation of tube-like networks and migration ability in a uremic, diabetic milieu, thus demonstrating that DKD-MSC maintain angiogenic repair capability in vitro. Previous studies identified MSC subsets possessing greater angiogenic paracrine activity. Du et al. [ 36] found that VCAM1+ expressing MSC had increased gene expression and release of pro-angiogenic factors and cytokines (VEGF, HGF, MMP2, and CXCL1) compared to VCAM1−-MSC. In in vivo studies, exogenous delivery of VCAM1+-MSC increased perfusion in the preclinical ischemic hindlimb model versus VCAM1−-MSC. Likewise, VCAM1 mRNA was upregulated in our DKD-MSC, which could potentially influence pro-angiogenic repair activity observed in vitro.
Contrarily, anti-angiogenic genes were upregulated (e.g. THBS1) and pro-angiogenic genes were down-regulated (e.g. BMP2) in DKD-MSC in our study, which could disturb angiogenic activity. Thrombospondin 1 (TSP1), encoded by the THBS1 gene, is a secreted anti-angiogenic protein that modulates cell migration and adhesion by regulating vascular nitric oxide signaling [37, 38]. In a study by Dzhoyashvili et al. [ 39], MSC harvested from subjects with type 2 diabetes and coronary artery disease showed impaired ability to stimulate angiogenesis in vitro with significant increase in mRNA level of THBS1, as well as closely negative correlation between its mRNA level and MSC angiogenic activity measured by tube formation assay. Similar to these findings, our MSC harvested from participants with DKD also had trends of higher THBS1 mRNA expression and protein release in condition media, but no statistical differences compared with Control-MSC for angiogenic capability to restore formation of tube-like networks in injured endothelial cells. Similarly, we found that DKD-MSCcm led to increased migration of HG + IS injured HUVEC versus injured controls with no difference observed between DKD- and Control-MSCcm groups. BMP2 mRNA expression was lower in DKD-MSC in our study. BMP2 has been proven to promote angiogenesis by facilitating endothelial migration [40], invasion, and proliferation [41] and induce HUVEC tube formation [42]. Overall, these observations suggest that the ultimate functional outcome may depend on a weighted effect of these genes and their inhibitors, in concert with the impact of the microenvironment.
Given that miRNA regulate gene activity, we further assessed the relationship between miRNA and angiogenesis-related mRNA in DKD-MSC. In our study, 208 differentially expressed miRNAs were identified among which 14 miRNAs targeted differentially expressed angiogenesis-related mRNA. Interestingly, let-7a-5p was upregulated in RNA-seq analysis (and had a trend toward higher gene expression, supplemental Fig. 1D) and targets the highest number of mRNAs, including TAGLN, THBS1, LOXL4, COL4A1, and COL8A1. Let-7a-5p has pro-angiogenic properties with increasing sprout formation of hypoxic HUVEC [43, 44] by directly targeting the 3′UTR of the THBS1 mRNA [45]. Hence, we speculate that DKD-MSC restore angiogenetic abilities partly because of increased let-7a-5p. Zhu et al. [ 44] conducted miRNA sequencing revealing that let-7 family (let-7a-5p) was enriched in EVs derived from human AD-MSC, and blocking of let-7 impaired tube formation of HUVEC in vitro. In addition, platelet-derived EVs can deliver let-7a into HUVEC in vitro with inhibition of HUVEC tube formation by down-regulating TSP1 protein expression [45]. Consistent with these findings, we used DKD-MSCcm, containing MSC-EVs and found trends of higher tube formation and lower THBS1 mRNA expression in HUVEC compared to Control-MSCcm. Further investigation is required to explore the mechanism and outcomes of altered let-7a-5p in vivo and in vitro. In addition, our studies found that TSP1 targets included: TAGLN, a regulator of angiogenesis [46], LOXL4, a contributor to the vascular permeability in diabetes [47], types of collagen (COL4A1 and COL8A1) that influence angiogenesis plus THSB1 an inhibitor of angiogenesis [48]. Further observations are needed to explore how let-7a-5p adjusts these mRNA. Taken together, these observations suggest that differentially expressed miRNAs, particularly let-7a-5p, in DKD-MSC might contribute to angiogenesis dysregulation.
We further examined MSC angiogenic repair capacity in vitro. After an in vitro injury mimicking a DKD microenvironment, endothelial cells lost tube formation capacity and increased adhesion molecule (SELE, VCAM, ICAM-1) expression, key molecules associated in early vascular injury [49–51]. These effects were reversed following co-incubation with DKD-MSCcm. Additionally, HUVEC migration was reduced after HG + IS treatment but improved with DKD-MSCcm co-culture but was not different versus Control-MSCcm. Angiogenic function impairment in vivo was shown by Kim et al. [ 52] wherein exogenous delivery of MSC from diabetic rats failed to improve blood flow compared to MSC from non-hyperglycemic rats in a model of hindlimb ischemia. Interestingly, higher expression levels of pro-angiogenic factors (HGF, FGF2, PGF, insulin-like growth factor-1, and angiopoietin-2 were evident in both control- and diabetic MSC infused into ischemic tissue 2 weeks-post administration, indicating that diabetic MSC still possess paracrine angiogenic repair capacities albeit limited. Hence, a therapeutic boost of autologous MSC infusion could still help enhance angiogenic repair capacity in patients with DKD.
This study has limitations. First, the predominantly Caucasian and relatively older study cohort may limit generalizability of our findings to other races and younger individuals with DKD, but remains reflective of the DKD population in the USA [53]. Secondly, AD-MSC may differ from other autologous cell sources, such as bone marrow or umbilical cord [54], though AD-MSC may possess more potent pro-angiogenic activity [26]. Furthermore, in vivo studies are needed to confirm these in vitro findings in DKD-MSC. Finally, future mechanistic studies employing loss of function (e.g., siRNA or miRNA inhibitors) are necessary to establish which DE genes are involved in modification of DKD-MSC angiogenic function.
## Conclusions
Compared to Control-MSC, AD-MSC harvested from DKD participants possess modified genetic messages related to angiogenesis. Yet, DKD-MSC maintained the intrinsic capacity to release pro-angiogenic paracrine factors to avert further endothelial dysfunction. Identification of disease-related dysfunction and interindividual variation in MSC may allow for development of regenerative strategies to improve MSC function and thwart barriers that prevent successful MSC therapeutic outcomes in individuals with DKD.
## Supplementary Information
Additional file 1: Fig. S1. Validation of upregulated (A) and down-regulated (C) differentially expressed (DE) messenger RNAs (mRNAs) in diabetic kidney disease (DKD)-mesenchymal stromal cells (MSC), as well as microRNAs (miRNAs) (D). ELISA of TSP1 in MSC conditioned medium (MSCcm) (B). BMP2, bone morphogenetic protein 2; PENK, proenkephalin; VCAM1, vascular cell adhesion molecule 1; IGFBP2, insulin-like growth factor-binding protein 2; THBS1/TSP1, thrombospondin 1; ITGB8, integrin subunit beta 8.Additional file 2: Fig. S2. Representative images of capillary-like tubes formed by non-injured human umbilical vein endothelial cells (HUVEC) or in the presence of high glucose (HG) plus indoxyl sulfate (IS) injury and either Control-MSC conditioned medium (cm) or DKD-MSCcm. Images were acquired at 40X resolution and were not enhanced. ( A). 4′,6-diamidino-2-phenylindole (DAPI) nuclear DNA (A). Nuclear staining (blue), proliferation marker Ki67 protein staining (red). Quantification of proliferation ability (A) and migratory function (B). Thrombospondin (THSB1) gene expression in HUVEC groups (C).
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|
---
title: 'Correlates of long duration of untreated illness (DUI) in patients with bipolar
disorder: results of an observational study'
authors:
- Gabriele Di Salvo
- Giorgia Porceddu
- Umberto Albert
- Giuseppe Maina
- Gianluca Rosso
journal: Annals of General Psychiatry
year: 2023
pmcid: PMC10035162
doi: 10.1186/s12991-023-00442-5
license: CC BY 4.0
---
# Correlates of long duration of untreated illness (DUI) in patients with bipolar disorder: results of an observational study
## Abstract
### Background
Despite a high number of studies investigating the correlation between long Duration of Untreated Illness (DUI) and poor course of Bipolar Disorder (BD), the results concerning the impact of DUI on some specific factors, such as suicidality and medical comorbidities, are still inconsistent. This cross-sectional observational study aimed at analyzing potential socio-demographic and clinical correlates of long DUI in a large cohort of real-world, well-characterized BD patients.
### Methods
The socio-demographic and clinical characteristics of 897 patients with BD were collected. The sample was divided for analysis in two groups (short DUI vs long DUI) according to a DUI cutoff of 2 years. Comparisons were performed using χ2 tests for categorical variables and the Kruskal–Wallis test for continuous variables. Logistic regression (LogReg) was used to identify explanatory variables associated with DUI (dependent variable).
### Results
Six-hundred and sixty patients ($75.5\%$) presented long DUI (> 2 years) and mean DUI was 15.7 years. The LogReg analysis confirmed the association of long DUI with bipolar II disorder (p: 0.016), lower age at onset ($p \leq 0.001$), depressive predominant polarity (p: 0.018), depressive polarity onset ($p \leq 0.001$), longer duration of illness ($p \leq 0.001$), lifetime suicide attempts (p: 0.045) and current medical comorbidities (p: 0.019).
### Conclusions
The present study confirms the association between long DUI and higher risk of suicide attempts in patients with BD. Moreover, an association between long DUI and higher rates of medical conditions has been found.
## Introduction
Bipolar disorder (BD) is a prevalent and severe psychiatric disease, included among the world’s ten most disabling conditions according to the World Health Organization [1].
Duration of untreated illness (DUI), defined as the time span from disorder onset to proper diagnosis and adequate treatment, has been increasingly investigated in several psychiatric disorders as a possible predictor of illness course specifier [2, 3], such as symptomatic severity, remission, response to treatment and global functioning in psychoses [4], but also in mood disorders, including BD [5].
A large number of studies show that the DUI, is long in BD [6–10]. The reasons for long DUI in BD have been widely addressed by clinical research. First, the onset of BD usually takes place in late childhood/early adulthood [11], a key developmental period which can partly explain delays in diagnosis and treatment, due to possible reluctance in reaching a definitive diagnosis [12]. Moreover, BD is characterized by a complex clinical picture and can be misdiagnosed: manic episodes with psychotic features may fail to be differentiated from psychotic disorders, acute depressive episodes often occur before (hypo)manic symptoms and many patients with ‘soft’ bipolar disorders can easily be misdiagnosed as patients with personality disorders [13–15]. Further barriers standing in the way of BD patients seeking adequate diagnosis and treatment include social stigma and lack of easily accessible treatment centers [13, 16, 49].
Despite a high number of studies investigating the correlation between long DUI and poor course of BD, the results concerning the impact of DUI on some specific factors, such as suicidality and number of lifetime affective episodes, are still inconsistent.
Indeed, some studies showed longer DUI to be associated with higher rates of suicide attempts [5, 6, 16, 17], a higher number of mood episodes [6, 18] and hospitalizations [13],conversely, other reports failed to find an association between long DUI and, respectively, rapid cycling [19] and number of suicide attempts [20, 21]. Likewise, studies analyzing the correlation between longer DUI in BD and detrimental outcomes such as poor response to treatment [22–24] and worse overall functioning [2–4, 12, 21, 22, 30], have yielded conflicting results.
The aim of this study is to contribute to the knowledge of DUI-associated variables by examining a large naturalistic sample of well-characterized BD patients.
## Study design and patients
Data derive from an independent cross-sectional observational study aimed at analyzing course characteristics, medical conditions and response to treatments in in- and out-patients with BD. Subjects were recruited from all patients with a principal diagnosis of BD consecutively referred to the Psychiatric Unit of San Luigi Gonzaga University Hospital in Orbassano (University of Turin, Italy), from January 2014 to February 2022.
To be enrolled, patients had to fulfill the following inclusion criteria: (a) main diagnosis of BD type I, II or Not Otherwise Specified (NOS)(DSM-IV-TR, DSM-5) [25, 26],(b) written consent to participate in the study, after being thoroughly informed about aims and study procedures.
The exclusion criteria were: (a) age < 18; (b) concomitant severe, unstable, active degenerative diseases; (c) refusal to consent participating in the study.
The protocol was approved by the local Ethical Committee. All subjects gave a written informed consent to have their clinical data potentially used for research purposes (provided that these data are anonymously treated).
## Assessment and procedures
Certified psychiatrists or residents in psychiatry supervised by senior psychiatrists performed the clinical assessment of patients.
All diagnoses were confirmed by means of the Mini-International Neuropsychiatric Interview (MINI) [27].
At study entry, general socio-demographic information and clinical data were collected for each subject through the administration of a semi-structured interview that we developed and used in regular clinical practice and in previous studies as well [28].
A retrospective life chart ranging from onset of first mood symptoms until study entry was reconstructed for each participant, resulting in a graphical representation of the past longitudinal course of illness.
DUI was defined as the time from the onset of the first affective episode to the start of the first adequate treatment, as in previous studies [6]. Adequate treatment is defined as the prescription of drugs labelled by European Medical Agency (EMA) for acute episode treatment and/or recurrence prevention of bipolar disorder: lithium, anticonvulsants (valproic acid, lamotrigine), atypical antipsychotics (aripiprazole, olanzapine, quetiapine, asenapine).
Furthermore, all subjects received a medical examination, included the assessment of metabolic parameters.
## Statistical analysis
Patients were divided into two groups according to the DUI length (short: ≤ 2 years vs. long: > 2 years): the cutoff was set at 2 years, as available data suggest that early intervention within this time span can yield a substantial improvement in long-term outcomes of BD [17, 19, 42, 52]. The association between DUI and socio-demographic and clinical factors was analyzed in the entire sample and in two sub-samples (patients with BD type I and patients with BD type II).
The normality of data distribution was evaluated using Kolmogorov–Smirnov test (KS). Since the distribution was not normal (KS: 0.145; p: < 0.001), comparisons were performed using χ2 tests for categorical variables and Kruskal–Wallis test for continuous variables.
Furthermore, binary logistic regression (LogReg) was performed setting DUI (categorical variable: long DUI vs short DUI) as the dependent variable. Independent variables were gender, type of BD, age at BD onset, bipolar cycle, predominant polarity, onset polarity, duration of illness, lifetime suicide attempts, comorbid current/lifetime medical conditions.
All statistical analyses were performed by SPSS software version 28.0.1
## Results
Nine-hundred and seven patients with BD were asked to participate; ten refused their consent. Among the 897 patients recruited, 23 ($2.6\%$) were excluded from the research due to lack of data about the DUI.
Ultimately, we completed the analysis using 874 subjects. The demographic and clinical characteristics of the total sample are given in Table 1. The sample is representative for the population of patients with BD: $61.0\%$ of the patients were females, the majority of the sample ($54.9\%$) had bipolar II disorder, the mean age at onset of BD was 29.2 ± 12.4 years, the mean duration of illness was 20.4 ± 14.1 years. The mean DUI of the total sample was 15.7 ± 23.5 years; six-hundred and sixty patients ($75.5\%$) showed a long DUI (> 2 years). The demographic and clinical differences between patients with long and short DUI are summarized in Table 1. Subjects with long DUI, compared with patient with short DUI, showed a higher rate of female gender ($62.9\%$ vs $55.1\%$; p: 0.044), a higher percentage of bipolar II disorder ($60.6\%$ vs $37.4\%$; p: < 0.001), a lower age at onset (28.0 ± 11.2 vs 32.9 ± 15.1; p: > 0.001), a lower rate of MDI bipolar cycle ($27.2\%$ vs $38.2\%$; p: 0.026) and a higher rate of irregular cycle ($53.8\%$ vs $43.0\%$; p: 0.026), a higher rate of depressive predominant polarity ($43.1\%$ vs $22.1\%$; p: < 0.001), a higher rate of depressive onset polarity ($73.3\%$ vs $37.4\%$; p: < 0.001), a longer duration of illness (22.8 ± 13.4 years vs 13.1 ± 13.6 years; p: < 0.001), more lifetime depressive episodes (4.5 ± 4.2 vs 3.6 ± 3.6; p: < 0.001), more lifetime affective episodes (9.4 ± 7.0 vs 7.3 ± 6.6; p: < 0.001), a higher rate of lifetime suicide attempts ($28.5\%$ vs $17.3\%$; p: 0.001), a lower rate of lifetime involuntary admissions ($22.4\%$ vs $47.4\%$; p: < 0.001), higher rates of current ($55.3\%$ vs $41.8\%$; p: < 0.001) and lifetime ($54.9\%$ vs $40.9\%$; p: < 0.001) medical comorbidities. Table 1Socio-demographic and clinical characteristics of the total sample ($$n = 874$$) and differences in socio-demographic and clinical characteristics between patients with long DUI ($$n = 660$$) or short DUI ($$n = 214$$)CharacteristicsTotal sample($$n = 874$$)Long DUI($$n = 660$$)Short DUI($$n = 214$$)F/χ2pAge at inclusion (years), mean ± sd47.5 ± 18.147.9 ± 18.346.1 ± 17.31.6310.202Education (years), mean ± sd13.6 ± 9.214.1 ± 10.311.8 ± 4.010.7070.001Sex, n (%) Male341 (39.0)245 (37.1)96 (44.9)4.0670.044 Female533 (61.0)415 (62.9)118 (55.1)Mood state, type, n (%) Euthymic144 (16.5)113 (17.1)31 (14.5) 27.494< 0.001 Depressive489 (55.9)387 (58.6)102 (47.7) Hypomanic70 (8.0)56 (8.5)14 (6.5) Manic171 (19.6)104 (15.8)67 (31.3)Bipolar disorder, type, n (%) Bipolar I370 (42.3)242 (36.7)128 (59.8) 46.927< 0.001 Bipolar II480 (54.9)400 (60.6)80 (37.4) Bipolar NOS24 (2.8)18 (2.8)6 (2.8)Bipolar cycle, type, n (%) MDI261(29.9)179 (27.2)82 (38.3)11.0440.026 DMI134 (15.4)101 (15.3)33 (15.4) Irregular446 (51.1)354 (53.8)92 (43.0) Rapid cycling13 (1.5)11 (1.7)2 (0.9) Continuous18 (2.1)13 (2.0)5 (2.3)Age of onset (years), mean ± sd29.2 ± 12.428.0 ± 11.232.9 ± 15.126.044 < 0.001Duration of illness (years), mean ± sd20.4 ± 14.122.8 ± 13.413.1 ± 13.684.514 < 0.001Duration of untreated illness (years), mean ± sd15.7 ± 23.520.5 ± 25.10.6 ± 0.8134.19 < 0.001Manic episodes (number), mean ± sd1.3 ± 2.51.1 ± 2.22.1 ± 2.930.381 < 0.001Hypomanic episodes (number), mean ± sd2.5 ± 3.52.9 ± 3.51.6 ± 2.824.623 < 0.001Depressive episodes (number), mean ± sd5.0 ± 4.25.4 ± 4.23.6 ± 3.631.134 < 0.001Affective episodes, total (number), mean ± sd8.9 ± 7.09.4 ± 7.07.3 ± 6.614.772 < 0.001Predominant polarity, type, n (%) Manic/hypomanic104 (11.9)59 (9.0)45 (21.1)41.276 < 0.001 Depressive331 (38.0)284 (43.1)47 (22.1)Onset polarity, type, n (%) Manic/hypomanic283 (32.5)158 (24.0)125 (58.4)92.863 < 0.001 Depressive562 (64.4)482 (73.3)80 (37.4)Lifetime suicide attempts, n (%)225 (25.7)188 (28.5)37 (17.3)10.5950.001Lifetime involuntary admissions, n (%)116 (28.4)70 (22.4)46 (47.4)22.738 < 0.001Lifetime psychiatric comorbidities, n (%)364 (43.2)268 (42.7)96 (44.9)0.3100.577Family history of mood disorders, n (%)505 (57.8)388 (58.9)117 (54.7)1.1710.279Current medical comorbidity, n (%)430 (52.1)348 (55.3)82 (41.8)10.8960.001Lifetime medical comorbidity, n (%)422 (51.6)343 (54.9)79 (40.9)11.4860.001Weight (kg), mean ± sd72.1 ± 16.372.6 ± 16.170.8 ± 16.61.6630.198BMI (kg/m2), mean ± sd31.2 ± 98.030.0 ± 82.834.8 ± 134.30.3220.571Waist circumference (cm), mean ± sd92.9 ± 17.393.5 ± 18.090.4 ± 14.22.8110.094Serum lipid levels (mg/dl), mean ± sd Tryglicerides132.4 ± 75.9131.3 ± 71.8136.5 ± 89.40.5650.452 HDL cholesterol52.0 ± 16.452.7 ± 16.549.3 ± 15.94.9560.026Glycemia (mg/dl), mean ± sd86.7 ± 23.086.7 ± 21.786.6 ± 26.70.0020.964Systolic arterial pressure (mmHg), mean ± sd122.6 ± 12.2122.7 ± 12.3122.3 ± 11.70.1520.697Diastolic arterial pressure (mmHg), mean ± sd79.0 ± 8.779.2 ± 8.778.3 ± 8.61.2090.272Metabolic syndrome, n (%)235 (32.6)186 (33.0)49 (31.0)0.2300.631Abdominal obesity, n (%)265 (48.1)220 (50.1)45 (40.2)3.5280.060Low HDL cholesterol, n (%)284 (41.0)216 (39.6)68 (46.3)2.1000.147Elevated blood pressure, n (%)352 (50.1)283 (51.6)69 (44.5)2.4540.117Impaired fasting glucose, n (%)124 (17.5)100 (18.0)24 (15.7)0.4510.502Hypertriglyceridemia, n (%)208 (29.8)160 (29.2)48 (31.8)0.3800.537NOS not otherwise specified, BMI body mass indexThe statistically significant results ($p \leq 0.005$) are in bold type The LogReg analysis (Table 2) confirmed long DUI to be significantly associated with bipolar II disorder (0.016), lower age at onset (p: < 0.001), depressive predominant polarity (p: 0.018), depressive polarity onset (p: < 0.001), longer duration of illness (p: < 0.001), lifetime suicide attempts (p: 0.045) and current medical comorbidity (p: 0.019).Table 2Relationship between potential explanatory variables and long DUI: results from the logistic regression analysisDependent variablesBS.EWaldpGender (Male)0.1800.1840.9520.329Bipolar disorder, type0.3340.1385.8430.016Age of onset−0.420.00829.592 < 0.001Bipolar cycle, type0.0870.0970.8170.366Predominant polarity0.2690.1135.6260.018Onset polarity1.1030.19033.873 < 0.001Duration of illness1.0560.13531.784 < 0.001Lifetime suicide attempts0.4480.2293.8270.045Current medical comorbidity0.5380.2295.5160.019Lifetime medical comorbidity0.3720.2262.7170.099The statistically significant results ($p \leq 0.005$) are in bold type Furthermore, the correlations between DUI and patients characteristics were analyzed in the subgroups of patients with BD type I (n: 370) and patients with BD type II (n: 480).
In the sub-sample of patients with BD I (Table 3), subjects with long DUI, compared with patient with short DUI, showed a higher rate of female gender ($64.9\%$ vs $53.1\%$; p: 0.028), a lower age at onset (25.9 ± 13.2 vs 29.4 ± 9.9; p: 0.005), a higher rate of depressive predominant polarity ($30.7\%$ vs $11.8\%$; p: < 0.001), a higher rate of depressive onset polarity ($58.1\%$ vs $24.2\%$; p: < 0.001), a longer duration of illness (23.3 ± 13.6 years vs 15.0 ± 14.1 years; p: < 0.001), more lifetime depressive episodes (4.7 ± 3.4 vs 3.4 ± 3.1; p: < 0.001), more lifetime affective episodes (8.9 ± 6.0 vs 7.7 ± 5.9; p: 0.046), a higher rate of lifetime suicide attempts ($25.6\%$ vs $14.1\%$; p: 0.010), a lower rate of lifetime involuntary admissions ($46.6\%$ vs $61.1\%$; p: 0.047), higher rates of current ($55.3\%$ vs $36.7\%$; p: < 0.001) and lifetime ($50.2\%$ vs $38.1\%$; p: 0.032) medical comorbidities. Table 3Socio-demographic and clinical differences of the BD I sample ($$n = 370$$) between patients with long DUI ($$n = 242$$) or short DUI ($$n = 128$$)CharacteristicsLong DUI($$n = 242$$)Short DUI($$n = 128$$)F/χ2pSex, n (%) Male85 (35.1)60 (46.9)4.8510.028 Female157 (64.9)68 (53.1)Age at onset (years), mean ± sd25.09 ± 13.229.4 ± 9.98.1500.005Predominant polarity, type, n (%) Depressive74 (30.7)15 (11.8)18.377 < 0.001Onset polarity, type, n (%) Depressive140 (58.1)31 (24.2)38.623 < 0.001Duration of illness (years), mean ± sd23.3 ± 13.615.0 ± 14.130.505 < 0.001Depressive episodes (number), mean ± sd4.7 ± 3.43.4 ± 3.111.022 < 0.001Affective episodes, total (number), mean ± sd8.9 ± 6.07.7 ± 5.94.0330.046Lifetime suicide attempts, n (%)62 (25.6)18 (14.1)6.5990.010Lifetime involuntary admissions, n (%)62 (46.6)44 (61.1)3.9300.047Current medical comorbidity, n (%)131 (55.3)44 (36.7)11.037 < 0.001Lifetime medical comorbidity, n (%)118 (50.2)45 (38.1)4.6100.032The statistically significant results ($p \leq 0.005$) are in bold type In the sub-sample of patients with BD II (Table 4), subjects with long DUI showed a lower age at onset (29.0 ± 11.6 vs 38.0 ± 16.5; p: < 0.001), a higher rate of depressive predominant polarity ($66.8\%$ vs $51.2\%$; p: 0.03), a higher rate of depressive onset polarity ($81.5\%$ vs $60.0\%$; p: < 0.001), a longer duration of illness (22.7 ± 13.3 years vs 10.8 ± 12.6 years; p: < 0.001), more lifetime depressive episodes (5.9 ± 4.6 vs 4.1 ± 4.2; p: 0.001) and more lifetime affective episodes (9.8 ± 7.6 vs 7.0 ± 7.8; p: 0.003).Table 4Socio-demographic and clinical differences of the BD II sample ($$n = 480$$) between patients with long DUI ($$n = 400$$) or short DUI ($$n = 80$$)CharacteristicsLong DUI($$n = 400$$)Short DUI($$n = 80$$)F/χ2pAge at onset (years), mean ± sd29.0 ± 11.638.0 ± 16.534.466 < 0.001Predominant polarity, type, n (%) Depressive267 (66.8)41 (51.2)7.0380.030Onset polarity, type, n (%) Depressive325 (81.5)48 (60.0)19.044 < 0.001Duration of illness (years), mean ± sd22.7 ± 13.310.8 ± 12.654.660 < 0.001Depressive episodes (number), mean ± sd5.9 ± 4.64.1 ± 4.210.7180.001Affective episodes, total (number), mean ± sd9.8 ± 7.67.0 ± 7.88.8750.003The statistically significant results ($p \leq 0.005$) are in bold type
## Discussion
The present study aimed at investigating potential socio-demographic and clinical correlates of long DUI in a large cohort of real-world, well-characterized BD patients.
We found a mean DUI of 15.7 years: this result is in line with a previous study conducted by our research group (14.1 years) [29], while other authors report lower DUI in BD [5, 6, 16, 17, 20, 21]. A possible reason for the lack of correspondence between our results and available data is that our Psychiatric *Unit is* located in a tertiary referral center within the University General Hospital and, in addition, is specialized in the treatment of Mood Disorders: patients suffering from high complexity mood disorders, and thus more likely to receive delayed diagnosis and/or inadequate treatments, are often referred from other centers for a consultation visit. A further possible explanation is that the majority of previous studies [3, 6, 7, 20], unlike ours, enrolled samples mostly represented by BDI patients, more likely presenting a manic onset and, therefore, showing a shorter latency between diagnosis and the prescription of mood-stabilizers as compared with BDII [30],conversely, the predominance of BDII patients in our sample may account for the longer DUI we found. Given the heterogeneity of our sample and the high number of patients with BDII, after carrying out a binary logistic regression (LogReg) in the total sample we also performed separate analyses on the two subsamples of patients with BDI and with BDII, to detect the potential confounding effect of BDII, especially on depressive variables (e.g., depressive onset polarity, depressive predominant polarity).
Concerning socio-demographic variables, our analysis found longer DUI in women: nevertheless this correlation, shown by the Kruskal–Wallis test to be significant, was not confirmed by LogReg analysis and, as already hypothesized by Drancourt and colleagues [2013], is probably to be attributed to clinical confounders, such as bipolar II type and depressive predominant polarity.
Age at onset proved to be significantly lower in the long DUI subsample, in line with previous studies [6–9, 13, 16, 17, 31]. The failure to diagnose BD in young patients can be explained by the highly polymorphous and complex clinical presentation of early onset cases [32], but it may also reflect a lack of awareness of the peak age of BD onset or a reluctance to make a diagnosis with lifetime implications and persistent social stigma [33]. This finding can also account for the longer duration of illness detected in the subgroup of patients with long DUI.
Our analysis found bipolar II subtype to be related to a longer DUI, in accordance with previous studies [3, 5, 6]. This was an expected result, because BD II frequently has a depressive onset [34], that can hamper BD diagnosis and consequently cause delays in prescribing the appropriate treatment. Moreover, hypomania is rarely reported spontaneously [35, 36], and even clinicians often fail to properly recognize it or to consider the clinical implications of such a presentation [37].
Unsurprisingly, our data showed in the subgroup of patients with long DUI higher rates of depressive predominant polarity course and of depressive polarity onset; these results are consistent with previous literature [5–7, 16, 17, 38] and were significant both in the BDI and BDII subgroups. A depressive onset and a subsequent depressive predominant polarity need to be carefully addressed in clinical practice. To improve the diagnosis of BD and thus shorten the DUI, special attention must be paid to some potential predictors of bipolarity, such as abrupt onset, post-partum episodes, treatment resistance and family history of BD or suicide [39],using specific screening tools designed to detect hypomanic symptoms, such as the Hypomania Check List (HCL-32), can also represent a valid aid [40–42].
An interesting result found by our study is the correlation between longer DUI and a higher rate of lifetime suicide attempts. The relationship between suicide risk and DUI is worthy of interest and deserves to be explored by further methodologically rigorous studies, given the crucial clinical implications and the inconsistency of available literature [5, 6, 16, 17, 20, 21]. Our result, if confirmed, could potentially contribute to improve the understanding of suicide attempts and suicide in people with BD and may be helpful in identifying specific targets for suicide prevention interventions, which are still one of the most difficult challenges in daily clinical practice [43]. In addition, our finding may offer indirect support to studies proposing that the introduction of mood stabilizers provides protection against attempted and completed suicide [44, 45], especially lithium because of its proven anti-suicidal effect [46].
A further remarkable result of this study is the significant correlation between longer DUI and a higher rate of concurrent medical comorbidities. This finding confirms, on a larger sample, findings from a previous study performed by our research group on an independent sample [29],to our knowledge, there are no other studies highlighting the relationship between DUI and medical conditions associated with BD. This reason behind this link could be that patients who have been undertreated for years have the tendency to adopt unhealthy lifestyles and often have limited access to care. In addition, patients not undergoing proper treatment could experience higher levels of stress, which could raise cortisol levels, increasing the risk of developing poor glucose tolerance, diabetes, and hypertension. Our results highlight the importance of a thorough assessment of medical conditions when dealing with BD: potentially severe medical conditions can be prevented by better integrating medical and psychiatric care, thus possibly increasing life expectancy for patients with BD [47].
The results of the study should be interpreted in light of several limitations. First, due to the cross-sectional design, many variables were collected retrospectively, making some data less accurate than in controlled studies. Furthermore, in our research, we analyzed lifetime suicide attempts, but the sample does not include completed suicides, meaning that we are unable to test whether the results are generalizable to suicide deaths; in addition, the period in which suicide attempts occurred was not reconstructed, making it difficult to evaluate the direct relationship with DUI. Moreover, in our study the impact of pharmacological treatment on BD course was not evaluated, due to the lack of data on medication (e.g., type of medication, treatment duration, response to treatment) prior to study entry. Another limitation concerns the heterogeneity of recruited patients in terms of clinical characteristics (e.g., medical conditions, pharmacological treatments), with potential confounding factors.
On the other side, our study has some strengths, mainly the large sample size and the thorough clinical characterization: available studies on DUI in BD enrolled relatively small samples or analyzed patients enrolled and treated in different Regions of Italy, with different psychiatric organizations and potential influences on DUI and relative outcomes [5].
## Conclusions
In conclusion, the present study confirms the association between long DUI and some clinical features of BD, such as bipolar II subtype, early age at onset, depressive predominant polarity and depressive polarity onset. The most remarkable finding is the correlation between longer DUI and two detrimental long-term outcomes in BD, such as suicide attempts and medical comorbidity: as abovementioned, this result may potentially contribute to implement early and tailored interventions for BD patients. Prospective studies examining the long-term clinical outcome of DUI in BD are warranted, to better clarify the causal associations between DUI and BD characteristics and course.
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|
---
title: 'Influence of CReatine supplementation on mUScle mass and strength after stroke
(ICaRUS Stroke Trial): study protocol for a randomized controlled trial'
authors:
- Juli Thomaz de Souza
- Marcos F. Minicucci
- Natália C. Ferreira
- Bertha F. Polegato
- Marina Politi Okoshi
- Gabriel P. Modolo
- Bethan E. Phillips
- Philip J. Atherton
- Kenneth Smith
- Daniel Wilkinson
- Adam Gordon
- Suzana E. Tanni
- Vladimir Eliodoro Costa
- Maria Fernanda P. Fernandes
- Silméia G. Zanati Bazan
- Leonardo A. M. Zornoff
- Rodrigo Bazan
- Sérgio A. Rupp de Paiva
- Paula Schmidt Azevedo
journal: Trials
year: 2023
pmcid: PMC10035196
doi: 10.1186/s13063-023-07248-6
license: CC BY 4.0
---
# Influence of CReatine supplementation on mUScle mass and strength after stroke (ICaRUS Stroke Trial): study protocol for a randomized controlled trial
## Abstract
### Background
Stroke is a leading cause of mortality and disability, and its sequelae are associated with inadequate food intake which can lead to sarcopenia. The aim of this study is to verify the effectiveness of creatine supplementation on functional capacity, strength, and changes in muscle mass during hospitalization for stroke compared to usual care. An exploratory subanalysis will be performed to assess the inflammatory profiles of all participants, in addition to a follow-up 90 days after stroke, to verify functional capacity, muscle strength, mortality, and quality of life.
### Methods
Randomized, double-blind, unicenter, parallel-group trial including individuals with ischemic stroke in the acute phase. The duration of the trial for the individual subject will be approximately 90 days, and each subject will attend a maximum of three visits. Clinical, biochemical, anthropometric, body composition, muscle strength, functional capacity, degree of dependence, and quality of life assessments will be performed. Thirty participants will be divided into two groups: intervention (patients will intake one sachet containing 10g of creatine twice a day) and control (patients will intake one sachet containing 10g of placebo [maltodextrin] twice a day). Both groups will receive supplementation with powdered milk protein serum isolate to achieve the goal of 1.5g of protein/kg of body weight/day and daily physiotherapy according to the current rehabilitation guidelines for patients with stroke. Supplementation will be offered during the 7-day hospitalization. The primary outcomes will be functional capacity, strength, and changes in muscle mass after the intervention as assessed by the Modified Rankin Scale, Timed Up and Go test, handgrip strength, 30-s chair stand test, muscle ultrasonography, electrical bioimpedance, and identification of muscle degradation markers by D3-methylhistidine. Follow-up will be performed 90 days after stroke to verify functional capacity, muscle strength, mortality, and quality of life.
### Discussion
The older population has specific nutrient needs, especially for muscle mass and function maintenance. Considering that stroke is a potentially disabling event that can lead the affected individual to present with numerous sequelae, it is crucial to study the mechanisms of muscle mass loss and understand how adequate supplementation can help these patients to better recover.
### Trial registration
The Brazilian Clinical Trials Registry (ReBEC) RBR-9q7gg4. Registered on 21 January 2019.
## Background
Stroke is an important public health problem and is one of the leading causes of death in Brazil [1]. Stroke is the main cause of disability in the adult population, and approximately two-thirds of patients experience an incomplete recovery. Malnutrition on admission, reduced level of consciousness, dysphagia, and enteral tube feeding use may impair the nutritional status of these individuals, who generally become more susceptible to involuntary weight loss at this stage [2, 3].
Studies on the acute phase of stroke have shown that patients who were malnourished at the time of ictus had more complications and higher mortality rates than patients with adequate weight, overweight, or obesity [4].
After stroke, appetite loss, dysphagia, depression, changes in mobility, and functional dependence can lead to inadequate food intake, which is associated with neurological damage. Moreover, bed rest compromises nutritional status, resulting in a negative effect on physical and cognitive abilities [5, 6].
A previous study on patients in the acute phase of stroke demonstrated that individuals with some degree of dysphagia at the time of hospital discharge had higher mortality rates, regardless of stroke severity and other clinical factors, which may indicate the importance of adequate nutritional management during hospitalization for stroke and monitoring after hospital discharge for patients with food intake difficulties [7].
Malnutrition has been associated with inflammation and metabolic stress, which are proportional to injury severity, and increased protein catabolism, hypermetabolism, and insulin resistance [8].
Sarcopenia is associated with aging and is commonly observed in patients after stroke. Sarcopenia is considered primary when the patient has skeletal muscle mass decline, reduced water content, and increased body fat, or secondary to chronic or disabling diseases such as stroke [8, 9].
Unlike age-related sarcopenia, stroke-related sarcopenia has specific characteristics, such as rapid muscle mass decline and structural changes. The type of stroke and location and size of the brain injury are important factors affecting the differences in physical and functional performance, depending on the side affected by stroke, which is independent of age. Moreover, catabolic signals from brain damage can result in an imbalance in the neurovegetative state. Therefore, there is a complex metabolic and systemic change in individuals with stroke that can lead to weight loss and reduced anabolism, worsening the prognosis [10]. Muscle changes occur within 4 h after stroke, and over 7 days, patients can develop muscle weakness on both the affected and contralateral sides of the body [11].
Daily intake of 1.2 to 1.5 g of protein per kg of body weight is recommended along with exercise for sarcopenia prevention in older adults, as this population is more susceptible to inadequate protein intake for several aging-related reasons leading to increased proteolysis and anabolic resistance [12, 13]. However, limited data are available regarding the prevention of sarcopenia or malnutrition in the acute phase after stroke.
Some nutrients and amino acids such as creatine appear to be potential compounds capable of promoting hypertrophy and improving muscle function. Creatine supplementation has been proven effective in improving muscle mass in healthy older individuals, even when only taken for a few days; in pathological conditions, these positive results are supported by studies in several populations [14–17].
A combination of resistance exercise and adequate protein intake is recommended to maintain healthy skeletal muscles. However, there is no recommendation for this type of multimodal therapy in older people after stroke. Evidence is scarce regarding the influence of protein supplementation and specific amino acids associated with physical activity on the functional capacity and quality of life among this population.
The aim of the study is to verify the effectiveness of creatine supplementation on functional capacity, strength, and changes in muscle mass during hospitalization for stroke compared to usual care. A descriptive and comparative evaluation between groups in order to verify the inflammatory and potential serum biomarkers behavior during hospitalization will be performed with all participants randomized. A follow-up 90 days after stroke to verify functional capacity, muscle strength, mortality, and quality of life will be carried out.
## Trial design
It will be a (randomization 1:1 to the intervention or control groups) double-blind (participants and researchers responsible for administering the supplement and carrying out the protocol assessments), unicenter, parallel-group trial including individuals with ischemic stroke in the acute phase to verify the superiority of the intervention compared to the control group. The duration of the trial for the individual subject will be approximately 90 days, and each subject will attend a maximum of three visits. The protocol was written according to the 2013 Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) guidelines. A flow diagram of the study is presented in Fig. 1.Fig. 1Flow diagram of the ICaRUS Stroke Trial
## Participants and eligibility criteria
Men and women aged 60 years and older who were diagnosed with ischemic stroke without previous disability (Modified Rankin Scale [mRs] – [mRs≤2]) and agree to sign the informed consent form will be recruited within 24 h of stroke.
Patients with any of the following will not be included: hemodynamic instability, mechanical ventilation, pacemakers and metallic prostheses, previous kidney disease, creatinine clearance ≤30 ml/min/1.73 m2, history of disabsorptive gastrointestinal surgery, parenteral nutrition, allergies or intolerance to any component of the study products, and patients who decline to participate.
The study will be conducted at the Stroke Unit of the Clinical Hospital of Botucatu Medical School, Brazil, and the follow-up visits at the Neurovascular Disease Outpatient Clinic of the same institution.
## Interventions
Creatine supplementation will be administered in the intervention group to avoid the muscle loss that is common in patients after stroke. The control group will receive maltodextrin, a complex carbohydrate with no direct effect on muscle loss. All participants will receive the same amount of protein per kg of body weight so that the groups are homogeneous in the consumption of this nutrient. The intervention will proceed as follows: Intervention group (15 participants)—Will be administered one sachet containing 10 g of creatine twice a day.
Control group (15 participants)—Will be administered one sachet containing 10 g of placebo (maltodextrin) twice a day.
All sachets will be the same color and size. All patients will receive assisted supplementation with powdered milk protein serum isolate, if necessary, to achieve a daily intake of 1.5 g/kg of body weight and standard physiotherapy care. Supplementation will be offered during the 7-day hospitalization.
Rehabilitation will start after the first 24 h of the ictus, and the intensity will be proportional to the benefit and tolerance of each individual. Standard physiotherapy will be performed according to the current rehabilitation guidelines for stroke patients and will consist of contracture prevention, limb and trunk strengthening, gait training, and independence [18, 19].
All individuals will be instructed at the time of hospital discharge on healthy eating habits, and adequate fractioning, and provided incentives for eating fruits, vegetables, and whole grains, and maintaining water intake in accordance with the Dietary Guidelines for the Brazilian Population by the Ministry of Health [20].
Participants who are intolerant to the supplements or who withdraw their consent will be excluded from the study. All patients will be followed up daily and encouraged to consume all meals and study products. If necessary, substitutions will be made to the patient’s diet with similar foods to promote supplementation acceptance during the intervention period.
## Outcomes
The primary outcomes will be functional capacity, strength, and changes in muscle mass after the intervention. The evaluations will be conducted in the first 24 hours after stroke and on the 7th day of hospitalization. The patients will be assessed for the following:Functional capacity according to the mRs and Timed Up and Go test scores. Muscle strength according to handgrip strength and lower limb strength according to the National Institutes of Health Stroke Scale (NIHSS) score. Muscle mass by muscle ultrasonography, electrical bioimpedance, and muscle degeneration markers by D3-methylhistidine.
A descriptive and comparative evaluation between groups in order to verify the inflammatory and potential serum biomarkers behavior during hospitalization will be performed with all participants randomized.
## Follow-up evaluations
A follow-up 90 days after stroke will be conducted to verify functional capacity, muscle strength, mortality, and quality of life. The following will be assessed in each patient:Functional capacity according to the mRs and Timed Up and Go test scores. Muscle strength based on handgrip strength and lower limb strength according to NIHSS score and the 30-s chair stand test score. Patient perception of quality of life according to The European Quality of life Scale Five Dimension (EuroQol-5D) score.
## Procedures (template)
All patients with ischemic stroke confirmed by imaging examinations who meet the study inclusion criteria will be invited to participate. After signing the informed consent form, the patients will be randomized to the intervention or control groups. All patients will undergo clinical and neurological evaluations conducted by the medical team using the NIHSS score, which is used to determine treatment and predict prognosis; Bamford classification according to brain injury location; and the Alberta Stroke Program Early CT Score (ASPECTS), which verifies the extent of the injury [1, 21].
The template for the recommended schedule of enrollment, interventions, and assessments is presented in Fig. 2. To verify the occurrence of mortality, we will utilize death certificates from medical records. Fig. 2ICaRUS Stroke Trial template of recommended content for the schedule of screening, interventions, and assessments
## Sample size
We reviewed a study that analyzed the effects of creatine supplementation in 18 healthy older people, in which 10 patients were allocated to the creatine group and 8 to the control group. The authors observed an improvement in muscle strength, weight, and fat-free mass in the group that received supplementation [22]. In addition, a meta-analysis based on this previous study included approximately 600 patients from 60 studies, with an average of 10 patients in each arm per study [16]. Considering that this is a study on creatine supplementation in patients after stroke, we decided to include $50\%$ more in the convenience sample, with a total of 15 patients in each group.
## Recruitment
Daily monitoring of admissions to the Stroke Unit of the Clinical Hospital of Botucatu Medical School, Brazil, will be carried out to refer individuals for screening and checking of inclusion criteria.
## Randomization and blinding
For all subjects, the screening visit and the enrollment visit were performed on the same day. Subjects were randomized 1:1 to the intervention or control groups. The randomization was performed using a cell phone application, which creates a random control and intervention sequence, and was performed in 10 out of 10 participants. No stratification factor was used.
To maintain the blindness of the study, randomization will be performed by a person uninvolved in data collection and data analysis. The participant, the professional responsible for administering the supplements, and the person responsible for conducting the protocol assessments will be blinded to the intervention. The professional in charge of the statistical analysis of the data will not be blinded to the intervention.
Breaking the blinding will be allowed if the participant has a serious adverse effect related to the supplementation, such as an allergic reaction. In this case, the principal investigator will be responsible for breaking the blinding and for informing the hospital’s medical staff about which supplement the participant was consuming.
## Data collection, management, and analysis
All collected data will be de-identified and stored in a safe place to protect the confidentiality of each participant. After 5 years from the end of the research, the data will be destroyed. Subjects in both groups will be evaluated in the first 24 h after the stroke, after 7-day hospitalization, and 90 days after stroke. All individuals who participate in the study will be instructed to complete the protocol during the 7 days of hospitalization in the stroke unit. The research team will carry out daily visits during the 7 days of hospitalization to guarantee the participant’s permanence in the study. After hospital discharge, the team will contact the participant by telephone to ensure the follow-up visit 90 days after the stroke. In case of loss of contact, the researchers will count on the social service of the hospital to carry out an active search for the participant.
All data will be double-checked by 2 team researchers to promote data quality (e.g., double data entry; range checks for data values).
## Anthropometric evaluation
Anthropometric evaluation will be performed by the same evaluator and will include weight, height, arm circumference, tricipital skinfold, and adductor pollicis muscle thickness. Subsequently, the body mass index will be calculated according to the Quetelet equation and the arm muscle area will be corrected using the formula proposed by Frisancho [23, 24].
For anthropometric measurements, the following equipment will be used: weight scale (Relaxmedic Your Way), portable stadiometer (InLab), inelastic and inextensible measuring tape with values in centimeters, and an adipometer (Lange®; Cambridge Scientific Industries, Watertown, MA, USA). Anthropometric assessments will be performed on both the affected and unaffected sides of the body.
The thickness of the rectus femoris and biceps brachii muscles, both affected and unaffected by the stroke, will be measured with a BodyMetrix BX-2000 A mode ultrasound device (Intelametrix, Livermore, CA, USA) with a 2.5-MHz transducer. Each location will be measured two or three times, based on the software’s compliance with these measurements, and the average will be used to represent the final thickness [25].
## Body composition assessment
Multifrequency electric bioimpedance will be performed with the SECA mBCA525 body composition analyzer, with the patients in a supine position, with their legs apart, hands open, palms down, and separated from the body. Eight tetrapolar electrodes will be connected to assess the impedance of the trunk, arms, and legs at six different frequencies (1, 5, 50, 250, 500, and 1000 kHz).
## Muscle strength assessment
Handgrip strength will be measured using a hydraulic hand dynamometer (Saehan Hydraulic Hand Dynamometer®, Model SH5001, Saehan Corporation, Korea), which measures the force produced by an isometric contraction applied to the handles, and the value will be recorded in kilogram force. Three measurements will be performed and the maximal value achieved will be recorded with a 15-s rest interval between measurements on the affected and unaffected sides [26].
Lower limb strength will be assessed according to the 30-s chair stand test using a chair placed against a wall; the test begins with the patient sitting in the chair, with their back straight, feet aligned with the shoulders, and the arms crossed at the wrists and held against the chest. At the “go” signal, the patient will stand up straight and then return to the initial seated position. The number of correctly executed cycles within 30 s will be counted, and the incorrect cycles will be discarded [27].
The NIHSS item 6 score will be used to verify lower limb strength. The patient will be placed in a supine position with the evaluated leg extended to 30°, and we will assess whether the limb falls within 5 s. Each limb is tested in turn, beginning with the nonparetic leg. The examiner will only record the score as untestable in cases of amputation or joint fusion at the hip. This item is scored as follows: 0 = no drift, the leg is held in the 30-degree position for the full 5 s; 1 = drift, the leg falls before the end of the 5-s period but does not touch the bed; 2 = some effort against gravity, the leg falls to the bed before 5 s although there is some effort made against gravity; 3 = no effort against gravity, the leg falls to the bed immediately; 4 = no movement; and UN = untestable owing to amputation or joint fusion at the hip [28].
## Functional capacity assessment
The mRs will be applied, with scores ranging from 0 to 6; the higher the value, the greater the degree of disability. Death is classified as a score of 6 [21].
The Timed Up and Go test will assess mobility and balance by measuring the time it takes the patient to get up from a chair, walk in a straight line for 3 m (at a comfortable and safe pace), turn around, walk back, and return to a seated position on the chair [29].
## Dependence degree assessment
The Barthel Index will be used to measure functional independence and mobility in chronically ill patients. The scale consists of 15 items, with scores ranging from 0 to 100 (0–20 indicating total dependence; 21–60 severe dependence; 61–90 moderate dependence; 91–99 light dependence, and 100 independence) [21].
## Quality of life assessment
The EuroQol-5D will be used to assess the individuals’ perceptions of quality of life through five domains of mobility, personal care, usual activities, pain/discomfort, and anxiety/depression; the higher the value, the worse the quality of life perception. At the end of the questionnaire, the patient must indicate their health status using an ordinal scale from 0 to 100; the closer to 0 the worse their health status and the closer to 100 the better their health status [17].
## Assessment of anxiety and depression
The Hospital Anxiety and Depression Scale will be used to screen for anxiety and depression and assess the severity of mood disorders [30].
## Biochemical dosages
In the first 24 h after stroke and on the 7th hospitalization day, the patient will have blood samples collected for the analysis of insulin and interleukin-6 levels by enzyme-linked immunoassay, in which the amount of protein in the extract will be determined using the Bradford method with the final concentration adjusted to 1 mg/ml. The activity of metalloproteases 2 and 9 will be obtained by zymography as described by Tyagi et al. [ 31].
## Stable isotope tracer analysis
Compound 3-methylhistidine is a product of the breakdown of contractile proteins and is a marker of muscle degeneration. In the first 24 h after stroke, and on the 7th day of hospitalization, the patient will have blood samples collected for residual 3-methylhistidine evaluation. The patient will then receive 50 mL of a solution containing 10 mg of $99.1\%$ pure D3-methylhistidine (Cambridge Isotope Laboratories, Andover, MA, USA). After 18 h, three 10-mL blood samples will be collected at 1-h intervals. Of 30 patients, 10 will be included in the analysis. The samples will be sent to the Centre for Musculoskeletal Aging Research & NIHR Nottingham BRC (Clinical, Metabolic, and Molecular Physiology research group, University of Nottingham, Royal Derby Hospital Centre, Derby, UK). The ratio between labeled and unlabeled 3-methylhistidine will be evaluated using liquid chromatography-mass spectrometry [32–34].
## Statistical analysis
The Full Analysis Set (FAS) will comprise all randomized subjects who have received at least one dose of creatine. The subjects will contribute to the analysis as randomized. The FAS will be used for efficacy analysis instead of intention-to-treat analysis.
The Per-Protocol (PP) population will comprise all subjects in the FAS who do not have any major protocol deviations (including but not limited to violation of inclusion and exclusion criteria), and who have at least good compliance as reported in the eCRF and for whom the primary endpoint can be derived. The PP will be used for the analysis of the primary endpoint.
The Safety Analysis Set will comprise all subjects who have received at least one dose of creatine or placebo. The subjects will contribute to the analysis as actually treated. The Safety Analysis Set will be used for the evaluation of safety endpoints and the primary endpoint.
Missing values will be treated mathematically by multiple imputation.
All data will be analyzed using Stata SE version 15.0 (StataCorp LLC., College Station, TX, USA).
## Planned statistical methods
Statistical Considerations *Baseline is* defined as the randomization visit and the end of the trial is defined as the final visit. Categorical data will be summarized by treatment group, using the number and percentages of subjects. For the calculation of percentages, the denominator will be the number of subjects in the analysis set. Continuous data will be presented using the number of subjects, mean, standard deviation, median, lower quartile, upper quartile, minimum, and maximum. Both the absolute values and the change from baseline will be presented. The treatment group and visit will present descriptive statistics for all endpoints (if applicable). All statistical tests will be carried out as two-sided and performed on a 5 % significance level. Estimated treatment differences and $95\%$ CIs will be presented together with the corresponding p-value. All safety endpoints will be presented using descriptive statistics and no formal statistical tests will be applied. The evaluation of continuous variables of the characteristics between the groups will be analyzed by the t-test or Mann-Whitney. The assessment of variation of serum levels of biomarkers, muscle mass, and strength between groups and time will be done by the mixed regression models. Kaplan-Meier survival curves will be used to show the survival in the groups within 90 days. Considering day zero as the day of randomization and entering into the study, patients related the final date they had each outcome. This is appropriate as it takes into account what happened during the course of the study. P-values were obtained through proportional Cox regression models and a value of $5\%$ was considered statistically significant. All adverse events will be summarized. The summaries will include the number of events, the number of subjects, and the proportion of subjects reporting these events and will be tabulated by system organ class.
## Sensitivity analysis
The primary analysis will be repeated using the PP and safety analysis set. We will perform analysis using only patients with specific symptoms to each symptoms.
## Data monitoring
The study follows the schedule of the Research Ethics Committee of Sao Paulo State University with the submission of partial reports to assess the progress and conduct of the study. At the end of the study, the final report will be sent with all the results. As this is an intervention with a low risk of serious adverse events, with a supplement known to be safe at the doses administered in the study, interim analyzes are not foreseen before the end of the study.
Non-serious adverse events such as diarrhea, dyspepsia, and abdominal pain related to supplementation will be analyzed individually and discussed with the assisting medical team to assess what action will be taken. Serious adverse events related or not to the intervention, such as death, acute kidney injury, infection with prolonged hospitalization, and hospitalization for any cause will be reported to the institution’s Research Ethics Committee within 24 h of their occurrence. All adverse events will be summarized. The summaries will include the number of events, the number of subjects, and the proportion of subjects reporting these events.
Monthly team meetings are held with the principal investigator to align the protocol, verify the progress of the study, and ensure compliance with deadlines. Additionally, the study will have an external endpoint adjudication committee composed of three physicians independent from investigators and the sponsor to investigate adverse events.
## Discussion
This clinical trial encompasses the use of creatine in the acute phase of stroke. Indeed, research about therapeutic strategies to mitigate the muscle mass decline after stroke is urgent. *In* general, the researches are focused on assessment, instead of treatment.
The limitation of the study is the sample size. However, the studies involving nutritional interventions to old people are a big deal, because of compliance and preferences. In addition, the stroke may add an extra difficulty because of the chance of conscience level, dysphagia, and tube feeding. In fact, studies published in a systematic review involving creatine supplementation in different scenarios included an average of 10 patients per group, showing that creatine might exert benefits for muscle mass and function [16]. On the other hand, the study will be rigorously conducted inside a stroke unit, with intake control, standardized care, and monitoring.
In addition, the trial may bring information for next larger studies, especially regarding side effects, compliance, and future sample size calculation. ICaRUS Stroke Trial will not be conducted as a feasibility study because we intend to make an entirely statistical analysis and follow-up.
## Trial status
This trial is ongoing. Recruitment and intervention phase started in March 2019 and recruitment is expected to end in June 2023.
## Roles and responsibilities
The sponsor CNPq played no part in the study design; collection, management, analysis, and interpretation of data; writing of the manuscript; and the decision to submit this protocol for publication. The sponsor has guaranteed financial support for the study and requires that the researchers send a report for accountability and study results. The sponsor is contacted by the website http://www.cnpq.br.
Unicenter academic study with a team strategically divided to perform the functions of collection, monitoring, and follow-up of participants.
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|
---
title: 'Effect of focused power ultrasound-mediated perirenal fat modification on
primary hypertension: protocol of a multicenter, randomized, double-blinded, sham-controlled
study'
authors:
- Menghuan Li
- Jing Shi
- Yanhui Sheng
- Yuqing Zhang
- Tingting Wu
- Jiaming Yang
- Kerui Zhang
- Wei Sun
- Xiangqing Kong
journal: Trials
year: 2023
pmcid: PMC10035202
doi: 10.1186/s13063-023-07249-5
license: CC BY 4.0
---
# Effect of focused power ultrasound-mediated perirenal fat modification on primary hypertension: protocol of a multicenter, randomized, double-blinded, sham-controlled study
## Abstract
### Background
Perirenal fat plays a key role in sustaining pathological high blood pressure. We aim to investigate the efficacy of intervention for perirenal fat mediated by focused power ultrasound (FPU) on primary hypertension.
### Methods
A multicenter, randomized, sham-controlled, double-blinded trial will be implemented in 200 participants with mild to moderate hypertension. All enrolled participants will be randomly allocated to perirenal fat modification (PFM) intervention using FPU or sham-procedure at a ratio of 1:1 and will be followed up at 24 h, 14 days, 30 days, and 90 days after the intervention. The primary endpoint is changes in office systolic blood pressure (SBP) at 30 days compared with baseline. The secondary endpoints include the changes in office SBP from baseline to 90 days, changes in 24-h mean SBP from baseline to 30 days and 90 days, and changes in heart rate from baseline to 30 days. Safety endpoint is defined as any severe adverse events related to the intervention.
### Discussion
The present study is the first to use noninvasive FPU to intervene in perirenal fat to achieve the goal of reducing blood pressure for patients with essential hypertension. Our study is expected to provide a new treatment strategy to control high blood pressure.
### Trial registration
ClinicalTrials.gov. NCT05049096. Registered on September 7, 2021.
Protocol version: Version 1.3.1, data 23 August 2021.
Sponsor: Prof. Xiangqing *Kong is* the principal investigator of this trial.
## Administrative information
Title {1}Effect of focused power ultrasound-mediated perirenal fat modification on primary hypertension: protocol of a multicenter, randomized, double-blinded, sham-controlled studyTrial registration {2a and 2b}.ClinicalTrials.gov Identifier: NCT05049096. Registered on September 7, 2021.https://clinicaltrials.gov/ct2/show/NCT05049096?term=05049096&draw=2&rank=1Protocol version {3}Version 1.3.1, data 23 August 2021Funding {4}This work was funded by the National Natural Science Foundation of China (No.82150002, No.81627802) and the Science Foundation of Gusu School (No. GSKY20220102).Author details {5a}1. Menghuan Li, M.D., The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.2. Jing Shi, Ph.D., The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.3. Yanhui Sheng, Ph.D., The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China; Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China.4. Yuqing Zhang, M.D., The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.5. Tinting Wu, M.D., The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.6. Jiaming Yang, M.D., The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.7. Kerui Zhang, M.D., Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China.8. Wei Sun, M.D., Ph.D., The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.9. Xiangqing Kong, M.D., Ph.D., The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China; Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China. Name and contact information for the trial sponsor {5b}Prof. Xiangqing Kong (principal investigator), The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China; Email: kongxq@njmu.edu.cn. Phone: 02568303130. Contact for public and scientific queries: Wei Sun, Email: shunwee@126.com. Role of sponsor {5c}Prof. Xiangqing Kong serves as the principal investigator of this trial and applied for scientific financial support. The funders played no role in the study design, implementation, data analysis, and results publication.
## Background and rationale
Hypertension is one of the most common cardiovascular diseases, whose prevalence exceeds $30\%$ adult population worldwide, and has become the leading cause of global health issues and economic burden [1, 2]. Blood pressure (BP) is deemed to be related to cardiovascular risk. Effective BP management is critical to decreasing the incidence of stroke, heart attack, and heart failure [3–5]. However, the rate of awareness, treatment, and control of hypertension were low, especially in developing countries [5, 6]. The control rate of hypertension in China was even as low as $5.7\%$, raising great concerns about the health of Chinese individuals [6].
Although pharmacological antihypertensive therapies are the main measures to reduce BP, the percentage of hypertensive patients whose BP attain the target guideline recommendation is unacceptably poor. Exploration of other options to reduce BP is urgent. Visceral fat is closely related to the occurrence of hypertension. Notably, perirenal fat is a unique connected tissue that is well vascularized and innervated [7]. Cumulating evidence showed that perirenal adipose tissue (PRAT) was significantly associated with high BP [7–9] Recently, it was revealed that perirenal adipose afferent nerves played a key role in sustaining pathological high BP in rats [10]. Long-term reduction of BP was observed in spontaneously hypertensive rats by bilateral PRAT ablation [10]. Thus, we hypothesize that the PRAT can be a potential target for lowing BP in clinical practice.
Focused ultrasound is increasingly used in the broad area of medicine, especially in the ablation of solid tissue, such as tumors [11]. This promising treatment could effectively ablate the target area in a non-invasive way with superb precision of energy delivery [12, 13].
Herein, we conducted a multicenter, randomized, sham-controlled, double-blinded study using focused ultrasound to modify the PRAT to explore the efficacy of this kind of modification for the treatment of essential hypertension.
## Objectives
This study is mainly to assess the efficacy of perirenal fat modification (PFM) therapy with focused ultrasound for hypertensive patients with uncontrolled blood pressure. In addition, the occurrence of adverse events (e.g., renal injury, intestinal perforation) will be recorded to assess the safety outcomes.
## Trial design
This is an investigator-initiated, multicentered, randomized, sham-controlled, double-blinded, and exploratory clinical trial with a 3-month duration, which primarily aims to assess whether PFM therapy can reduce blood pressure in patients with grade one to two hypertension.
Eligible participants will be randomized at a ratio of 1:1 into an intervention group and a control group. The participants in the intervention group will receive PFM therapy with focused ultrasound, while a sham procedure will be performed for the participants assigned to the control group. All participants will be assessed for efficacy and safety at 24 h, 2 weeks, 1 month, and 3 months after PFM or sham procedure. The main indicators to evaluate efficacy are the changes in office BP and 24-h ambulatory BP. Adverse events will be recorded at each visit. Blood samples and urine samples will be collected, and imaging examinations will be performed at baseline, 24 h, 1 month, and 3 months. Participants are required to measure their office BP at a 2-week visit. Except for the operator who is responsible for focused ultrasound, all staff, including researchers, statisticians, and participants, are blinded to the groups of randomizations.
This protocol report follows the SPIRIT reporting guidelines [14].
## Study setting
This trial is initiated by the Cardiology Department, The First Affiliated Hospital of Nanjing Medical University. Eligible participants with uncontrolled BP will be recruited at three implementation sites, including the First Affiliated Hospital of Nanjing Medical University (Jiangsu Province), Nanjing Jiangning Hospital (Jiangsu Province), and Suzhou Municipal Hospital (Jiangsu Province).
## Eligibility criteria
The inclusive criteria are as follows:• Individuals aged 18–65 years old• Individuals have uncontrolled BP when receiving a medication regimen of one, two, or three antihypertensive medication classes for at least 4 weeks; and uncontrolled BP is defined as office systolic blood pressure (SBP) ≥ 140 mmHg and < 180 mmHg, and 24-h ambulatory BP monitoring (ABPM) average SBP ≥ 135 mmHg, regardless of diastolic BP• Individuals have at least 20 mm in the anteroposterior, transverse, and axial diameters of inferior perirenal fat measured by ultrasound• Individuals are willing to sign the informed consent of the study The exclusive criteria are as follows:• Individuals are diagnosed with secondary hypertension (e.g., renal parenchymal hypertension, renal artery stenosis, primary aldosteronism, pheochromocytoma, Cushing’s syndrome, aortic coarctation, severe obstructive sleep apnea-hypopnea syndrome)• Individuals have a history of kidney and or kidney surrounding tissue surgery• Individuals have impairment of liver or kidney function (ALT, AST, or creatinine is greater than 3 times the upper limit of normal reference)• Individuals have a myocardial infarction, unstable angina pectoris, cerebrovascular accident, or transient ischemic attack within 6 months before enrollment• Individuals have type 1 diabetes or poorly-controlled type 2 diabetes• Individuals have uncontrolled thyroid dysfunction• Individuals have urinary calculi and/or hematuria• Individuals have atrial fibrillation• Individuals have severe structural heart disease (e.g., heart valve disease, cardiomyopathy, congenital heart disease)• Individuals have second-degree and above atrioventricular block• Individuals have abnormal coagulation function• Individuals have infected waist skin• Individuals have a malignant tumor• Individuals are pregnant, nursing, or planning to be pregnant• Individuals are unwilling to sign informed consent• Individuals fail to complete the screening period
## Intervention
Eligible patients will undergo either PFM with focused ultrasound or a sham procedure. It is expected that their prescribed antihypertensive medications should be unchanged in the 3 months follow-up period after the intervention.
## PFM procedure
Patients who are assigned to the PFM group will be placed in a lateral position on the treatment bed. Firstly, an ultrasonic probe is used to locate the rough position of inferior perirenal fat and the location needs to be marked on the waist skin. Secondly, a focused power ultrasonic probe is applied to determine the precise target area of perirenal fat. Sequentially, automatic physique measurement starts to calculate the optimal output power for individuals. Lastly, treatment initiates. Bilateral inferior perirenal fat shares the same procedure. The training sessions including recognition of inferior perirenal fat from ultrasonic images and operation of the equipment have been conducted at each study site by only one designated operator.
## Sham procedure
Patients who are assigned to the sham control group share the same procedure as PFM does except that the focused ultrasound will not transmit energy.
## Criteria for discontinuing
Participants may discontinue this trial for any of the following reasons:• They may choose to withdraw for any reason• They have severe side effects that require unmarking to get cured• Their blood pressure is within “escape criteria,” defined as office SBP ≥ 180mmHg or < 90 mmHg• They have a significant deviation from the study algorithm, e.g., changing the established antihypertensive drugs scheme• Any other rational reasons to withdraw from the study
## Strategies to improve adherence
Before enrollment, we repeatedly confirm the participants’ willingness and compliance with the study’s requirements, especially drug numbers and doses, and being able to follow up within the next 3 months. We make efforts to monitor the adherence to drug use which is prescribed before allocation for at least 4 weeks. Blood and urine samples will be collected to measure the concentration of the antihypertensive drugs (diuretic, calcium channel blocker (CCB), angiotensin-converting enzyme inhibitors (ACEI)/angiotensin receptor blocker (ARB), beta-blocker, and alpha-blocker) at baseline, 1 month, and 3 months.
## Relevant concomitant care and interventions
Though lifestyle modification should be recommended for all hypertensive patients according to the hypertension guidelines, we asked recruited participants to maintain their current lifestyle, including physical activities, diet, and regular rest habits. In order to assess the changes in lifestyle, weight, waist circumference, or hip circumference will be recorded at each follow-up time.
## Outcomes
We speculate that PFM will have a significant blood pressure lowering effects compared with the sham-control group. The endpoints in the present study include primary endpoints, secondary endpoints, and safety endpoints.
Primary endpoint measure:• Changes in office systolic blood pressure at 1-month compared with baseline Secondary outcome measures:• Changes in office blood pressure at 3 months compared with baseline• Changes in mean systolic blood pressure measured by 24-h ambulatory blood pressure monitoring at 1 month compared with baseline• Changes in mean systolic blood pressure measured by 24-h ambulatory blood pressure monitoring at 3 months compared with baseline• Changes in the heart rate at 1 month compared with baseline• Changes in the mean heart rate measured by 24-h ambulatory blood pressure monitoring at 1 month compared with baseline Safety endpoint measures:• Any severe adverse events (SAE) related to the intervention. The SAE was defined as acute renal failure, acute intestinal perforation, and thromboembolic events, etc.
## Participant timeline
The participant timeline is shown in Table 1. Briefly, all participants will undergo either PFM or sham-control therapy during 14 days screening period. They will be asked to follow up at 1 day, 14 days, 30 days, and 90 days after randomization. Table 1The schedule of enrolment, interventions, and assessmentsStudy periodEnrolmentAllocationPost-allocationClose-outTimepoint− 14 daysDay 0Day 1Day 14Day 30Day 90Enrolment: Eligibility screenX Informed consentXInterventions: PFMX Sham-controlXAssessments: Office BP measurementXXXXX 24-h ambulatory BP measurementXXXX History of diseasesX Physical examinationsXXX Drugs use recordXXXX Antihypertensive drugs concentration testsXXX Blood routineXXXX Biochemical testXXXX C-reactive proteinXX Biomarkers of acute renal injuryXX ECGXXXX Renal and renal artery ultrasoundXXXX Carotid ultrasoundXXXX Cardiac ultrasoundX MR imaging of perirenal fatXXX Functional MR imaging of hypothalamusXXXPFM, perirenal fat modification. BP, blood pressure, ECG, electrocardiogram MR, magnetic resonance
## Sample size
The PASS software (version 2011) was used to calculate the sample size. This study is a parallel group design, whose primary endpoint is the difference in office BP at 1 month compared with baseline. Our previous pilot study was a single-arm design recruiting 15 participants with mild-moderate hypertension for PFM therapy with results unpublished. Based on the outcome of the pilot study, the mean change in office SBP from baseline to 1 month was expected to 10 mmHg with a deviation of 13 mmHg in the PFM group. Meanwhile, we assumed that a mean difference of office SBP was 5 mmHg [15, 16] with a deviation of 13 mmHg in the sham control group. The sample size was calculated as 100 in each group with a power of $80\%$, a two-sided significance level of $5\%$, and a drop-out rate of $15\%$.
## Recruitment
Potential eligible participants will be screened at the outpatient clinics of each study center. Advertisements of the trial introduction will be posted onsite at the hospital and on social media platforms.
## Sequence generation
The randomized allocation sequence was generated at a 1:1 ratio in a completely random design among 200 participants by a statistician via the SAS 9.4 software (SAS Institute, Cary NC, USA) and integrated with a central computerized randomization system (Biomed Information Technology Co., Ltd. Beijing).
## Concealment mechanism
The randomization codes were generated by a statistician and embedded in the computerized randomization system. The main investigators will not have permission to view the allocation except for the principal investigator of each study site and operators who are responsible for PFM operation. The principal investigators are only authorized for urgent unblinding when a serious complication related to the PFM procedure occurs.
## Implementation
An independent randomization system account is created for investigators at each study site for the randomization implementation. Each investigator will log in to the system via a website to enter the randomization page, input the information of the participants, check the name and code of the study site, and confirm the randomization information. Then, the randomization code and allocated arm of the participant will be generated in the system background which is blinded for the investigators. Additionally, the operator at each study site will log in to the system using an independent account with the authority to view the allocated arms. At this time, the participants are successfully enrolled in this study.
## Blinding
Study participants and all staff, including investigators, clinical care providers, statisticians, and personnel who recruit, follow-up with participants, and collect data are blinded to the randomizations. The operators will be unblinded to implement the PFM procedure and required to keep blinded to all other staff. After completing the procedure, we will ask about the participants’ feelings or any discomforts and if they can tell the real intervention and sham intervention. Once severe adverse events (SAE) that might be related to the PFM procedure occur, the principal investigators will be able to disclose the arm allocation. The disclosure reason, date, and location will be recorded in detail.
## Data collection
The outcomes will be assessed at baseline, 1 day, 14 days, 30 days, and 90 days after the intervention. All staff involved in the collection of data were trained by the standard operating procedure of the study. The data will be recorded utilizing electronic CRFs and EDC systems.
## Baseline data collection
The following measures will be completed at baseline.• Written informed consent• Clinical questionnaire• Office BP and 24-h ambulatory BP• Anthropometric information including height, weight, waist circumference, and hip circumference• Electrocardiography and cardiac ultrasound• Arteries stiffness assessments (PWV)• Imaging examinations including ultrasonic examinations of renal and perirenal fat, carotid ultrasound, and magnetic resonance imaging for perirenal fat and hypothalamic function• Blood tests including routine blood tests, blood biochemical tests, and C-reactive protein• Urine tests including routine urine tests, urine biomarkers of acute renal injury (Ngal, TIMP2, IGFBP7)• Hypertensive drug concentration detection (serum and urine)
## Data collection at 1 day after intervention
• Office BP and 24-h ambulatory BP• Electrocardiography• Arteries stiffness assessments (PWV)• Imaging examinations including ultrasonic examinations of renal and perirenal fat and carotid ultrasound• Blood tests including routine blood tests, blood biochemical tests, and C-reactive protein• Urine tests including routine urine tests and urine biomarkers of acute renal injury (Ngal, TIMP2, IGFBP7)
## Data collection at 14 days after intervention
• Office BP
## Data collection at 30 days after intervention
• Office BP and 24-h ambulatory BP• Anthropometric information including height, weight, waist circumference, and hip circumference• Electrocardiography• Arteries stiffness assessments (PWV)• Imaging examinations including ultrasonic examinations of renal and perirenal fat, carotid ultrasound, and magnetic resonance imaging for perirenal fat and hypothalamic function• Blood tests including routine blood tests, and biochemical blood tests• Urine tests including routine urine tests• Hypertensive drug concentration detection (serum and urine)
## Data collection at the final visit
• Office BP and 24-hour ambulatory BP• Anthropometric information including height, weight, waist circumference, and hip circumference• Electrocardiography• Arteries stiffness assessments (PWV)• Imaging examinations including ultrasonic examinations of renal and perirenal fat, carotid ultrasound, and magnetic resonance imaging for perirenal fat and hypothalamic function• Blood tests including routine blood tests, and biochemical blood tests• Urine tests including routine urine tests• Hypertensive drug concentration detection (serum and urine) All the eligible participants will have comprehensive health examinations and receive PFM therapy or sham-procedure for free. They will have close relationships with physicians who can provide professional consultations and medication guidance for their health problems. Traffic and accommodation subsidies will provide for all participants to promote participant retention and complete follow-up. If participants discontinue follow-up, the reasons will be recorded in detail, and their clinical treatment and other rights shall not be affected.
## Data management
Study data will be recorded via the Electronic Data Capture (EDC) system (Nanjing Yike Valtai Information Technology Co., Ltd). Well-trained practitioners will log into the website and input data. The EDC system has the function of automatically checking data format, rules, and range so as to ensure the accuracy of data. Moreover, manual reconfirmation by a third staff is also required. Any change in data recording is permitted and tracked with reasons and dates in the EDC system. Additionally, data quality will be under the supervision of a third party. All documents and data will be stored at the institutional office of each study site.
## Statistical method
For the primary endpoint, a mixed-effect model for repeated measures (MMRM) adjusted with baseline office SBP will be used to compare the group difference of the office SBP changes from baseline to 1 month. For the secondary endpoint, we will also use the MMRM method adjusted with mean SBP or heart rate to assess the changes in the mean SBP or heart rate from baseline to each visit point. And LS-means method adjusted with baseline SBP or heart rate will be used to estimate the changes in SBP or heart rate and $95\%$ confidence intervals. Imputation for missing values of the main indicator will be conducted using the Markov chain Monte Carlo (MCMC) model which will be performed as the result of the sensitive analysis. We predefine an adverse event of laboratory index that is significantly abnormal at follow-up compared with baseline. Detailed presentation of adverse events will be described and explore the relationship with PFM products. The efficacy analyses will be carried out based on the full analysis set and the pre-protocol set. All baseline characteristics analysis will be conducted on the basis of the full analysis set. The safety evaluation will be analyzed from the safety analysis set. A two-sided $P \leq 0.05$ will indicate significance in all statistical analyses used by the SAS software V.9.4 (SAS Institute, Cary, NC).
An interim analysis is planned when 50 subjects (100 subjects in total) are enrolled in each group and completed 1 month follow-up, which will be conducted by an independent data monitoring committee (IDMC). The purposes of the interim analysis are as follows: [1] to evaluate the safety (a necessary disruption of the study is considered when there are serious or expected adverse events) and [2] to assess the efficacy. This trial will be expected to terminate in advance if the hypothesis test P value is less than the boundary value of the interim analyzed α for the primary endpoint. And if the P value is greater than interim analyzed α with statistical power less than $60\%$, re-estimating the sample size is needed to make the final statistical power greater than $80\%$. In accordance with O’Brien-Fleming method, the interim analyzed α = 0.005, and final α = 0.048.
## Data monitoring
In order to monitor the trial process and participants’ safety, a trial steering committee (TSC) was established to make sure the trial run well. The members of this committee are experts from different professional fields, including hypertension management, cardiovascular disease, biostatistics and other fields. All personnel from the committee are absolutely independent from and have no competing interests with the funders and sponsor. We invited the staff from the Clinical Trial Institution of Jiangsu Province Hospital, and School of Public Health, Nanjing Medical University to regularly monitor the data and report to the committee. The TSC will meet onsite at each study site every three months to track the trial process and monitor the participant’s safety.
## Harms and auditing
In this study, potential risks of PFM therapy are the thermal damage of local skin, subcutaneous tissue, and organs adjacent to perirenal fat. Any discomforts or abnormal indicators changes through energy emission path including skin, muscles, renal area, and intestine will be identified as adverse events (AEs) and will be recorded in eCRF and EDC system with details of onset time, severity, and duration. The severity of AEs and their correlations with PFM therapy will be carefully evaluated in accordance with the Common Terminology Criteria for Adverse Events [17]. Severe adverse events (SAEs) included deaths, life-threatening disease or injury, permanent damage to body structure or function, and diseases requiring hospitalization treatment that are determined by the investigator according to the principles of GCP. Investigators are responsible for dealing with the SAEs to protect the participants as much as possible and reporting the SAEs to the local ethics committee within 24 h. AEs will be reported within 3 days after each visit. All adverse events will be revealed in the trial publication. And the trial will be audited onsite periodically.
## Ethics and dissemination
This study protocol was comprehensively considered and will not be amended easily without written permission from the Ethics Committee of the responsible unit. The revised protocol will be inspected by the local ethical boards again. The informed consent approved by local ethical boards will be presented to the potential participants and their authorized surrogates. Investigators is responsible for introducing the study design, procedure, potential harms, and benefits, and answering the participant’s questions. The participant can only be enrolled after signing their name on the page of the informed consent form. Additional sample collection is also noted on the pages of informed consent. For each visit, 5 ml blood samples and 2 ml urine samples will be collected and stored. To protect participants’ confidentiality, private information, including names, addresses, contact, and identified numbers will be kept within each investigation center. Access by any third party will be prohibited. All data used for analysis and report shall not have personal identifiers except for unique screening codes or randomization codes. Once the AEs occur, medical care will be provided for participants promptly. In addition, appropriate assistance and management will be presented according to the relevant laws and regulations.
The study results will be published in peer-review journals, presented at scientific conferences, and shared with all participants. Access to all deidentified data will be permitted only for certificated researchers with written approval from the responsible investigators. Any data required to support the protocol can be supplied on reasonable request.
## Discussion
Epidemiological studies have demonstrated that perirenal fat is closely associated with elevated blood pressure [8, 9]. It is reported that accumulation of fat in the renal sinus may promote hypertension due to compression of the lymphatic and venous vessels by perirenal fat, leading to activation of the renin-angiotensin-aldosterone system [18]. However, observational studies failed to clarify the pathological mechanism of hypertension caused by perirenal fat. Recently, researchers found that bilateral perirenal fat ablation leads to a long-term reduction of high blood pressure in spontaneously hypertensive rats and has no influence on normal blood pressure in control rats; they further indicate that perirenal afferent nerves serve as a pathological node of hypertension that sustains elevated blood pressure via suppressing CGRP, thereby being a potential therapeutic target to tackle primary hypertension [10]. And the perirenal fat is so distinguished that no other fat pad such as inguinal, pararenal, or epididymal adipose tissue is associated with the maintenance of high blood pressure in their research. Moreover, perirenal fat, especially the fat pad in the lower pole of the kidney, is a relatively independent solid tissue, which is different from other visceral fat, making it a potential target for clinical intervention to lower blood pressure.
Long-term use of medical treatment is the essential method for hypertensive patients, which is bound to have increased risks of adverse events. Especially for those who are resistant to antihypertensive drugs, traditional medical therapy is of little help. Therefore, novel therapeutic methods are urgent for lowing blood pressure, notably the instrument treatment for hypertension. Renal denervation (RDN) is an intervention that uses an ablation catheter to destruct sympathetic nerves of renal arteries, which might be a promising way to reduce blood pressure for individuals with resistant hypertension. Several randomized clinical trials with unblinded designs showed remarkable effects of RDN on lowering blood pressure and few complications [19, 20]. However, further RCT with a sham-control design presented a similar blood pressure reduction between the RDN group and the sham control group, indicating the placebo effects of RDN [21]. Even though a series of RCTs confirmed the effectiveness of RDN on elevated blood pressure after optimizing the criteria of the enrollment and working mode of the ablation catheter [22, 23], physicians are still concerned about the safety of RDN since it is an invasive procedure. Hence, a noninvasive way is needed to reduce blood pressure. Perirenal fat has become an ideal target for noninvasive intervention using focused power ultrasound. We previously conducted a small sample size study revealing that PFM therapy was a safe and effective way to lower blood pressure for individuals with essential hypertension [24].
Traditional focused power ultrasound has limited use on adipose tissue in the abdominal cavity. In order to equip a focused power ultrasound dedicated to the intervention of perirenal fat, we designed and developed a novel machine that can be effectively and safely used for the modification of perirenal fat. This novel FPU machine has the strength of temperature monitoring of the target area and personalized output power according to the fat characteristics of the subjects to ensure that the target perirenal fat can be effectively and safely modified.
To our knowledge, the present study is the first to use noninvasive FPU to intervene in perirenal fat to achieve the goal of reducing blood pressure for patients with essential hypertension. Our study is expected to provide a new treatment strategy to control high blood pressure. At the initial stage, we are exploring the efficacy of this new strategy for hypertensive patients on medication with mild to moderate elevated blood pressure. In the future, we will expand the population for adaption of this new therapy, such as new onset hypertensive patients, hypertensive patients without taking medications, and even refractory hypertensive patients.
## Trial status
This paper is in accordance with the protocol (version 1.3.1, data 23 August 2021). The study recruitment began on November 1, 2021, with 74 participants recruited by February 8, 2022, and is estimated to complete the study on September 30, 2023.
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24. 24.CNKI database. https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C44YLTlOAiTRKibYlV5Vjs7iJTKGjg9uTdeTsOI_ra5_XT4rtxCnkikMWIvdRWGZG_6G8qv9eBE1Wlv-HOBXB4WR&uniplatform=NZKPT. Accessed 10 March 2023.
|
---
title: Rates of maternal weight gain over the course of pregnancy and offspring risk
of neurodevelopmental disorders
authors:
- Shuyun Chen
- Mengyu Fan
- Brian K. Lee
- Christina Dalman
- Håkan Karlsson
- Renee M. Gardner
journal: BMC Medicine
year: 2023
pmcid: PMC10035205
doi: 10.1186/s12916-023-02799-6
license: CC BY 4.0
---
# Rates of maternal weight gain over the course of pregnancy and offspring risk of neurodevelopmental disorders
## Abstract
### Background
Previous studies have suggested that gestational weight gain (GWG) outside an optimal range increases the risks of neurodevelopmental disorders (NDDs) in offspring including autism spectrum disorder (ASD), intellectual disability (ID), and attention deficit/hyperactivity disorder (ADHD). The sequential development of the fetal brain suggests that its vulnerability may vary depending on the timing of exposure. Therefore, we aimed to investigate the associations of not only gestational age-standardized total GWG (GWG z-scores) but also the rate of GWG (RGWG) in the second and third trimesters with risks of NDDs in offspring.
### Methods
In this population-based cohort study, we used maternal weight data from antenatal care records collected for 57,822 children born to 53,516 mothers between 2007 and 2010 in the Stockholm Youth Cohort. Children were followed from 2 years of age to December 31, 2016. GWG z-scores and RGWG (kg/week) in the second and third trimesters were considered as continuous variables in cox regression models, clustered on maternal identification numbers. Nonlinear relationships were accommodated using restricted cubic splines with 3 knots. RGWG were also categorized according to the 2009 US Institute of Medicine (IOM) guidelines for optimal GWG. According to the IOM guidelines, the optimal rate of GWG for the second and third trimesters for underweight, normal weight, overweight, and obese categories were 0.44–0.58, 0.35–0.50, 0.23–0.33, and 0.17–0.27 kg/week, respectively.
### Results
During a mean follow-up of 5.4 years (until children were on average 7.4 years old), 2205 ($3.8\%$) children were diagnosed with NDDs, of which 1119 ($1.9\%$) received a diagnosis of ASD, 1353 ($2.3\%$) ADHD, and 270 ($0.5\%$) ID. We observed a J-shaped association between total GWG z-score and offspring risk of NDDs, with higher total GWG (GWG z-score = 2) associated with $19\%$ increased risk of any NDD ($95\%$ CI = 3–$37\%$) and lower total GWG (GWG z-score = − 2) associated with $12\%$ increased risk of any NDDs ($95\%$ CI = 2–$23\%$), compared to the reference (GWG z-score = 0). In the second trimester, lower RGWG (0.25 kg/week) was associated with a $9\%$ increased risk of any NDD diagnosis ($95\%$ CI = 4–$15\%$) compared to the median of 0.57 kg/week, with no apparent relationship between higher RGWG and risk of NDDs. In the third trimester, there was no apparent association between lower RGWG and risk of NDDs, though higher RGWG (1 kg/week) was associated with a $28\%$ increased risk of NDD diagnosis ($95\%$ CI = 16–$40\%$), compared to the median (0.51 kg/week). When considering categorized RGWG, we found that slow weight gain in the second trimester followed by rapid weight gain in the third trimester most significantly increased the risk of ADHD (HRadjusted = 1.55, 1.13–2.13) and ID (HRadjusted = 2.53, 1.15–5.55) in offspring. The main limitations of our study are the relatively few years for which detailed GWG data were available and the relatively short follow-up for the outcomes, limiting power to detect associations and misclassifying children who receive an NDD diagnosis later in childhood.
### Conclusions
The relationship between maternal weight gain and children’s risk of NDDs varied according to timing in pregnancy, with the greatest risks associated with slow weight gain in the second trimester and rapid weight gain in the third trimester.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12916-023-02799-6.
## Background
Autism spectrum disorder (ASD), Intellectual disability (ID), and attention deficit/hyperactivity disorder (ADHD) are three common neurodevelopmental disorders (NDDs) in children that often co-occur [1–4]. Their relatively high prevalence and the often life-long need for social support in affected individuals can place great burdens on their families and society as a whole [4]. Although highly heritable and linked to both rare inherited and de novo mutations, their underlying etiologies do not appear to be completely explained by genetics, indicating contributions also from other biological, environmental, and social factors [1–4].
Though the main intention of the Institute of Medicine (IOM) guideline for optimal gestational weight gain is to provide clinicians with a basis for practice [5], evidence has emerged in the past decades for an association between maternal total GWG outside of the optimal range defined by the guidelines and children’s risk of NDDs, such as ASD [6, 7], ID [8], and ADHD [9]. One limitation of previous studies using total GWG was that they did not take length of pregnancy into consideration, which made it difficult to disentangle the effects of GWG on adverse NDD outcomes from the effects of the gestational duration [10]. There is a growing appreciation for using the trimester-specific rate of weight gain and z-score charts of maternal weight-gain-for-gestational-age as a measure of pregnancy weight gain [10–12].
The rapid growth of fetal brains makes them particularly vulnerable to damage by nutritional and metabolic disturbances compared to adult brains [13]. The sequential growth and development of structural and functional components of the fetal brain is a dynamic process [14], and the vulnerability of the fetal brain varies across specific periods of exposure to environmental stressors [13]. However, the effects of abnormal rates of GWG (RGWG) during specific gestational periods, especially in the second and third trimesters when most weight gain occurs [15], on the risk of NDDs in offspring remain unclear, as previous studies lacked longitudinal measures of maternal weight and relied only on total GWG.
In this Swedish population-based cohort study, we aimed to investigate the relationships of both Swedish gestational age-standardized total GWG z-scores and rate of GWG in the second and third trimesters, with risks of NDDs (i.e., ASD, ID, and ADHD) in offspring.
## Study population
We used data from the Stockholm antenatal care record system (Obstetrix) [11, 16] from January 1, 2007, to December 31, 2010, which was linked to the Medical Birth Register (MBR) and nested within the Stockholm Youth Cohort (SYC). Details of the SYC design have been described elsewhere [17, 18]. Information concerning exposures, outcomes, and covariates was extracted from national and regional health registers and administrative registers. Ethics approval was obtained from the Stockholm regional ethical review committee (DNR $\frac{2010}{1185}$-$\frac{31}{5}$, $\frac{2016}{987}$-32). Informed consent was not required for the analysis of anonymized register data.
We included all children born from January 1, 2007, to December 31, 2010, in Stockholm and with maternal weight measurements throughout pregnancy. All children were followed up from 2 years of age until December 31, 2016, or the date of NDD diagnosis, emigration, or death, whichever came first. We excluded children from multiple births or without maternal height information and further excluded children whose mothers did not have at least one weight recorded within each trimester (14 and 28 weeks as trimester cut-points). Children who received a diagnosis of an NDD or who emigrated or died before their second birthday were also excluded (Additional file 1: Fig. S1A). Our final study sample included 57,822 children born to 53,516 mothers. Excluded children had a slightly higher risk of ID diagnosis and were more likely to be born to migrant parents and low-income families (Additional file 1: Table S1).
## Case ascertainment
Cases of ASD, ADHD, and ID were ascertained using information gathered from all potential care pathways in Stockholm County (Additional file 1: Table S2) [17–19]. Briefly, the International Classification of Diseases, 10th revision (ICD-10; F84 for ASD, F90 for ADHD, and F70–F79 for ID) and additional information from the Prescription Drug Register (methylphenidate or atomoxetine for ADHD definition) were used to define the diagnostic groups. Our primary analysis considered any NDD diagnosis as an outcome, along with any diagnosis of ASD, ADHD, or ID, though individuals can be included in more than one outcome category (e.g., those diagnosed with “ASD with ID” would be included in both the ASD and ID outcomes). In secondary analyses, we considered mutually exclusive outcomes defined as follows: ASD only (no ADHD or ID), ADHD only (no ASD or ID), ASD with ADHD (no ID), ASD with ID (not excluding ADHD), and ID without ASD (no ASD, not excluding ADHD) (Additional file 1: Fig. S1B).
## Exposures: GWG and RGWG in different pregnancy stages
The Obstetrix record system contains maternal weight data measured by midwives during each antenatal visit throughout pregnancy, beginning in 2006. Weight observations < 30 kg or > 200 kg were censored, as values indicating a weekly weight gain or weight loss > 5 kg. A total of 318,487 serial maternal weight measurements from 57,822 pregnancies were included in the final sample. The number of weight measurements per pregnancy differed, with a median of 5 [interquartile range (IQR): 4–7]. The frequency of measurements increased over time in pregnancy, with a median of 1 (IQR: 1–1) in the first, 1 (IQR: 1–2) in the second, and 2 (IQR: 1–4) in the third trimesters.
The rate of weight gain (kg/week) during the second trimester (RGWG-T2) was calculated using the difference in the last weight measurement in the second trimester and the last weight measurement taken in the first trimester divided by the gestational week interval between the measurements. As the weight gain in the first trimester was usually small (i.e., ~ 1–2 kg) compared to the second and third trimesters [10, 20] and most women only had one measurement in the first trimester, we recoded the timing of measurement in the first trimester as 13 wkGA if the measurement was taken before 13 wkGA to avoid underestimating RGWG in the second trimester. The rate of weight gain (kg/week) during the third trimester (RGWG-T3) was similarly calculated, using the difference in the final weight measurement before delivery and the last weight measurement taken in the second trimester divided by the gestational week interval between the measurements.
Given that the total GWG (kg) is influenced by gestational duration, which is also associated with the risk of NDDs, we standardized the total GWG to z-scores according to Swedish standards [11] taking gestational week of birth into account. For comparison to the z-score analysis, total GWG in kilograms was calculated as the difference in maternal weight between the first antenatal visit (median 9.4, IQR: 8.1–10.7 weeks) and the last antenatal visit (median: 37.1, IQR: 36.0–38.3 weeks).
While our primary analysis relied on the continuous measures described above, we also created categories for “optimal,” “insufficient,” or “excessive” rates of weight gain in the second and third trimesters based on IOM recommendations for each BMI category [20] (optimal ranges for underweight 0.44–0.58 kg/week; normal BMI 0.35–0.50 kg/week; overweight 0.23–0.33 kg/week; obese 0.17–0.27 kg/week). We hypothesized a U-shaped association between RGWG and offspring risk of NDDs; values furthest from the optimal range may therefore represent the highest risk categories. Following from previous work [21], we further divided the “insufficient” and “excessive” categories at their respective medians (by BMI category) to create extended rate categories: “optimal,” “extremely insufficient,” “insufficient,” “excessive,” and “extremely excessive.” Slow or fast weight gain in the second trimester may induce either catch-up or reduced weight gain in the third trimester due to effective gestational weight management. Taking RGWG-T2 and RGWG-T3 together, we generated the following groups: [1] optimal at both time points (optimal/optimal, reference), [2] optimal/insufficient, [3] optimal/excessive, [4] insufficient/optimal, [5] insufficient/insufficient, [6] insufficient/ excessive, [7] excessive/optimal, [8] excessive/insufficient, and [9] excessive/excessive. Finally, three total GWG categories were defined for each BMI category: “optimal,” “insufficient,” or “excessive” (optimal ranges for underweight 12.5–18 kg; normal BMI 11.5–16 kg; overweight 7–11.5 kg; obese 5–9 kg) [20].
## Covariates
Maternal weight at the first antenatal visit was used to approximate baseline maternal BMI (in kg/m2), at a median of 9.4 (IQR: 8.1–10.7) weeks and was categorized as underweight (BMI < 18.5), normal BMI (18.5 ≤ BMI < 25), overweight (25 ≤ BMI < 30), or obese (BMI ≥ 30). The following covariates were considered as potential confounders and included in the study: child’s sex, birth year, household income quintiles at birth, maternal age at birth, maternal education level, parental birth region (i.e., maternal and paternal region of birth), interpregnancy interval (IPI), maternal smoking during pregnancy, and maternal psychiatric history prior to the birth of the child, parameterized as specified in Table 1. A directed acyclic graph describing the associations between covariates, exposures, and outcomes is presented in Additional file 1: Fig. S2.Table 1Characteristics of the study cohort by rate of gestational weight gain categoryCharacteristicRGWG-T2RGWG-T3OptimalInsufficientExcessiveOptimalInsufficientExcessiveN10,480764739,69513,52610,16334,133Weight measurements, median (IQR) 1st trimester1.0 (1.0, 1.0)1.0 (1.0, 1.0)1.0 (1.0, 1.0)1.0 (1.0, 1.0)1.0 (1.0, 1.0)1.0 (1.0, 1.0) 2nd trimester1.0 (1.0, 2.0)1.0 (1.0, 2.0)1.0 (1.0, 2.0)1.0 (1.0, 2.0)1.0 (1.0, 2.0)1.0 (1.0, 2.0) 3rd trimester2.0 (1.0, 4.0)3.0 (1.0, 4.0)2.0 (1.0, 4.0)2.0 (1.0, 4.0)2.0 (1.0, 4.0)2.0 (1.0, 4.0)Gestational week at the first weight measurement, median (IQR) 1st trimester9.4 (8.1, 10.7)9.3 (8.0, 10.6)9.1 (7.9, 10.4)9.1 (8.0, 10.4)9.3 (8.0, 10.6)9.1 (8.0, 10.4) 2nd trimester23.1 (20.0, 24.7)22.6 (19.6, 24.6)23.0 (19.9, 24.6)23.0 (19.9, 24.6)23.1 (20.0, 24.7)22.9 (19.9, 24.6) 3rd trimester30.9 (29.1, 34.6)30.6 (29.1, 33.9)30.9 (29.1, 34.7)30.9 (29.1, 34.9)30.9 (29.3, 34.6)30.9 (29.1, 34.6)Gestational week at the last weight measurement, median (IQR) 1st trimester9.6 (8.4, 11.0)9.4 (8.1, 10.9)9.3 (8.0, 10.6)9.4 (8.1, 10.7)9.4 (8.1, 10.7)9.3 (8.1, 10.7) 2nd trimester25.1 (24.0, 26.1)24.9 (24.0, 26.0)24.9 (24.0, 26.1)24.9 (24.0, 26.0)25.0 (24.1, 26.3)24.9 (24.0, 26.0) 3rd trimester37.1 (36.0, 38.4)37.1 (36.0, 38.3)37.1 (36.0, 38.3)37.1 (36.0, 38.4)37.0 (35.9, 38.3)37.1 (36.0, 38.3)GWG (kg), mean (SD)10.8 (2.9)7.8 (3.8)14.8 (4.2)11.6 (3.2)8.9 (3.7)15.0 (4.5)RGWG-T2 (kg/week), mean (SD)0.4 (0.1)0.2 (0.2)0.7 (0.2)0.6 (0.2)0.5 (0.3)0.6 (0.3)RGWG-T2, % Optimal10,480 ($100.0\%$)NANA3155 ($23.3\%$)2460 ($24.2\%$)4865 ($14.3\%$) InsufficientNA7647 ($100.0\%$)NA2046 ($15.1\%$)2085 ($20.5\%$)3516 ($10.3\%$) ExcessiveNANA39,695 ($100.0\%$)8325 ($61.5\%$)5618 ($55.3\%$)25,752 ($75.4\%$)RGWG-T3, kg/week, mean (SD)0.5 (0.2)0.5 (0.3)0.5 (0.2)0.4 (0.1)0.2 (0.2)0.7 (0.2)RGWG-T3, % Optimal3155 ($30.1\%$)2046 ($26.8\%$)8325 ($21.0\%$)13,526 ($100.0\%$)NANA Insufficient2460 ($23.5\%$)2085 ($27.3\%$)5618 ($14.2\%$)NA10,163 ($100.0\%$)NA Excessive4865 ($46.4\%$)3516 ($46.0\%$)25,752 ($64.9\%$)NANA34,133 ($100.0\%$)Child’s sex, % Male5256 ($50.2\%$)3748 ($49.0\%$)20,577 ($51.8\%$)6810 ($50.3\%$)5043 ($49.6\%$)17,728 ($51.9\%$) Female5224 ($49.8\%$)3899 ($51.0\%$)19,118 ($48.2\%$)6716 ($49.7\%$)5120 ($50.4\%$)16,405 ($48.1\%$)Maternal BMI at the first antenatal visit, % Normal (18.5–25 kg/m2) (optimal range 0.35–0.50 kg/week)8478 ($80.9\%$)4948 ($64.7\%$)25,549 ($64.4\%$)11,304 ($83.6\%$)7748 ($76.2\%$)19,923 ($58.4\%$) Underweight (> 18.5 kg/m2) (optimal range 0.44–0.58 kg/week)490 ($4.7\%$)420 ($5.5\%$)855 ($2.2\%$)521 ($3.9\%$)735 ($7.2\%$)509 ($1.5\%$) Overweight (25–30 kg/m2) (optimal range 0.23–0.33 kg/week)981 ($9.4\%$)1212 ($15.8\%$)10,071 ($25.4\%$)1209 ($8.9\%$)1106 ($10.9\%$)9949 ($29.1\%$) Obese (> 30 kg/m2) (optimal range 0.17– 0.27 kg/week)531 ($5.1\%$)1067 ($14.0\%$)3220 ($8.1\%$)492 ($3.6\%$)574 ($5.6\%$)3752 ($11.0\%$)Maternal age at birth (years), % < 251043 ($10.0\%$)973 ($12.7\%$)3795 ($9.6\%$)1067 ($7.9\%$)821 ($8.1\%$)3923 ($11.5\%$) 25–292657 ($25.4\%$)1972 ($25.8\%$)9957 ($25.1\%$)3105 ($23.0\%$)2128 ($20.9\%$)9353 ($27.4\%$) 30–344010 ($38.3\%$)2724 ($35.6\%$)15,029 ($37.9\%$)5325 ($39.4\%$)3858 ($38.0\%$)12,580 ($36.9\%$) 35–392311 ($22.1\%$)1605 ($21.0\%$)9098 ($22.9\%$)3391 ($25.1\%$)2708 ($26.6\%$)6915 ($20.3\%$) ≥ 40459 ($4.4\%$)373 ($4.9\%$)1816 ($4.6\%$)638 ($4.7\%$)648 ($6.4\%$)1362 ($4.0\%$)Maternal smoking during pregnancy, % No9940 ($94.8\%$)7112 ($93.0\%$)37,168 ($93.6\%$)12,836 ($94.9\%$)9479 ($93.3\%$)31,905 ($93.5\%$) Yes444 ($4.2\%$)491 ($6.4\%$)2132 ($5.4\%$)602 ($4.5\%$)608 ($6.0\%$)1857 ($5.4\%$) Missing96 ($0.9\%$)44 ($0.6\%$)395 ($1.0\%$)88 ($0.7\%$)76 ($0.7\%$)371 ($1.1\%$)Maternal birth region, % Nordic7985 ($76.2\%$)5390 ($70.5\%$)29,572 ($74.5\%$)10,317 ($76.3\%$)7451 ($73.3\%$)25,179 ($73.8\%$) Europe557 ($5.3\%$)405 ($5.3\%$)2469 ($6.2\%$)739 ($5.5\%$)556 ($5.5\%$)2136 ($6.3\%$) Africa459 ($4.4\%$)609 ($8.0\%$)1455 ($3.7\%$)544 ($4.0\%$)650 ($6.4\%$)1329 ($3.9\%$) Asia1203 ($11.5\%$)1009 ($13.2\%$)5126 ($12.9\%$)1585 ($11.7\%$)1278 ($12.6\%$)4475 ($13.1\%$) Others276 ($2.6\%$)233 ($3.0\%$)1069 ($2.7\%$)341 ($2.5\%$)228 ($2.2\%$)1009 ($3.0\%$) Missing0 ($0.0\%$)NA (< $1\%$)NA (< $1\%$)0 ($0.0\%$)0 ($0.0\%$)NA (< $1\%$)Paternal birth region, % Nordic7928 ($75.6\%$)5297 ($69.3\%$)28,796 ($72.5\%$)10,165 ($75.2\%$)7330 ($72.1\%$)24,526 ($71.9\%$) Europe597 ($5.7\%$)389 ($5.1\%$)2408 ($6.1\%$)793 ($5.9\%$)541 ($5.3\%$)2060 ($6.0\%$) Africa475 ($4.5\%$)621 ($8.1\%$)1699 ($4.3\%$)580 ($4.3\%$)676 ($6.7\%$)1539 ($4.5\%$) Asia1033 ($9.9\%$)952 ($12.4\%$)5066 ($12.8\%$)1475 ($10.9\%$)1167 ($11.5\%$)4409 ($12.9\%$) Others325 ($3.1\%$)277 ($3.6\%$)1288 ($3.2\%$)372 ($2.8\%$)314 ($3.1\%$)1204 ($3.5\%$) Missing122 ($1.2\%$)111 ($1.5\%$)438 ($1.1\%$)141 ($1.0\%$)135 ($1.3\%$)395 ($1.2\%$)Maternal education level, % Pre-highschool954 ($9.1\%$)996 ($13.0\%$)3947 ($9.9\%$)1148 ($8.5\%$)1082 ($10.6\%$)3667 ($10.7\%$) High-school3161 ($30.2\%$)2578 ($33.7\%$)13,413 ($33.8\%$)3996 ($29.5\%$)3127 ($30.8\%$)12,029 ($35.2\%$) Post-high school6315 ($60.3\%$)4012 ($52.5\%$)22,163 ($55.8\%$)8317 ($61.5\%$)5892 ($58.0\%$)18,281 ($53.6\%$) Missing50 ($0.5\%$)61 ($0.8\%$)172 ($0.4\%$)65 ($0.5\%$)62 ($0.6\%$)156 ($0.5\%$)Household income quintiles at birth, % First (lowest)831 ($7.9\%$)814 ($10.6\%$)2920 ($7.4\%$)961 ($7.1\%$)887 ($8.7\%$)2717 ($8.0\%$) Second1653 ($15.8\%$)1494 ($19.5\%$)6742 ($17.0\%$)2115 ($15.6\%$)1790 ($17.6\%$)5984 ($17.5\%$) Third1663 ($15.9\%$)1294 ($16.9\%$)6676 ($16.8\%$)2065 ($15.3\%$)1673 ($16.5\%$)5895 ($17.3\%$) Fourth2090 ($19.9\%$)1479 ($19.3\%$)8314 ($20.9\%$)2710 ($20.0\%$)1875 ($18.4\%$)7298 ($21.4\%$) Fifth (highest)4235 ($40.4\%$)2552 ($33.4\%$)14,981 ($37.7\%$)5649 ($41.8\%$)3926 ($38.6\%$)12,193 ($35.7\%$) Missing8 ($0.1\%$)14 ($0.2\%$)62 ($0.2\%$)26 ($0.2\%$)12 ($0.1\%$)46 ($0.1\%$)Interpregnancy interval (years), % First born4857 ($46.3\%$)3404 ($44.5\%$)18,550 ($46.7\%$)5885 ($43.5\%$)3987 ($39.2\%$)16,939 ($49.6\%$) < 1814 ($7.8\%$)678 ($8.9\%$)2654 ($6.7\%$)1033 ($7.6\%$)889 ($8.7\%$)2224 ($6.5\%$) 1–21774 ($16.9\%$)1271 ($16.6\%$)6024 ($15.2\%$)2375 ($17.6\%$)1820 ($17.9\%$)4874 ($14.3\%$) 2–51957 ($18.7\%$)1356 ($17.7\%$)7475 ($18.8\%$)2606 ($19.3\%$)2091 ($20.6\%$)6091 ($17.8\%$) 5–10454 ($4.3\%$)361 ($4.7\%$)2294 ($5.8\%$)713 ($5.3\%$)646 ($6.4\%$)1750 ($5.1\%$) > 10116 ($1.1\%$)128 ($1.7\%$)640 ($1.6\%$)213 ($1.6\%$)182 ($1.8\%$)489 ($1.4\%$) Missing508 ($4.8\%$)449 ($5.9\%$)2058 ($5.2\%$)701 ($5.2\%$)548 ($5.4\%$)1766 ($5.2\%$)Maternal psychiatric history, %1049 ($10.0\%$)878 ($11.5\%$)4123 ($10.4\%$)1303 ($9.6\%$)1167 ($11.5\%$)3580 ($10.5\%$)Hyperemesis gravidarum, %86 ($0.8\%$)140 ($1.8\%$)376 ($1.0\%$)122 ($0.9\%$)108 ($1.1\%$)372 ($1.1\%$)Pre-eclampsia, %301 ($2.9\%$)293 ($3.8\%$)1488 ($3.8\%$)241 ($1.8\%$)188 ($1.9\%$)1653 ($4.8\%$)Gestational diabetes mellitus, %22 ($0.2\%$)31 ($0.4\%$)127 ($0.3\%$)29 ($0.2\%$)48 ($0.5\%$)103 ($0.3\%$)
## Statistical analysis
All statistical analyses were performed using Stata (version 16.0; StataCorp). For all models, we used cox regression models, clustered on maternal identification numbers and with robust standard errors to account for clustering of observations within mothers, to calculate hazard ratios (HRs) and $95\%$ confidence intervals (CIs) for NDDs in offspring. We replaced the missing values in covariates as a dummy category for adjustment.
For continuous analyses, we fit models using restricted cubic splines models with 3 knots. The post-estimation command xbrcspline was used [22], with the reference value set as a z-score of 0 for the total GWG z-score analysis, 13.0 kg (median) for total GWG (kg), and 0.57 kg/week for RGWG-T2 and 0.51 kg/week for RGWG-T3, representing the median rates of GWG for each trimester. Analyses were repeated after stratification by maternal baseline BMI category. In model 1, HRs were adjusted for child’s sex and birth year. In model 2, we further adjusted for household income quintiles at birth, parental birth region, maternal age at birth, education level, IPI, baseline BMI, smoking during pregnancy, and psychiatric history. Each NDD outcome was modeled separately. P-values for analyses were calculated for a Wald test with a null hypothesis that all spline terms were jointly equal to 0, as a test of whether the exposure was generally associated with the outcome.
In categorical analyses, the “optimal” group was the reference group. Models were adjusted as above, with the exception of including maternal BMI in model 2, as the RGWG/GWG categories are conditioned on BMI. We assessed the proportionality assumption for Cox regression by including time/GWG category interaction terms in the fully adjusted models. When we found evidence showing hazard ratios changed over time with regard to NDDs in the cox regression models, we used flexible parametric survival models to plot the variance of HRs over time.
Multiple comparison adjustment with Bonferroni correction [23] was considered as the probability of identifying at least one significant result due to chance increases as more hypotheses are tested. The Bonferroni-adjusted significance level is 0.001 (based on 39 statistical comparisons in splines and categorical models).
We conducted several sensitivity analyses. Analyses of total GWG z-score and any NDD diagnosis were repeatedly stratified by offspring sex (given the theory that the high male:female ratios among those diagnosed with NDDs may relate to differing etiological pathways) and restricted to Nordic-born mothers (as ethnic groups represented among those who are immigrants to Sweden may differ in GWG patterns [24] and also have different patterns of NDD diagnoses). Since our observations indicated that the risk of NDDs was associated with elevated third trimester weight gain, we repeated our analyses of maternal RGWG-T3 after excluding women diagnosed with pre-eclampsia or gestational diabetes mellitus (GDM), as pre-eclampsia and GDM may induce rapid weight gain in later pregnancy and were also associated with offspring risk of NDDs [25, 26]. As hyperemesis gravidarum may induce slow weight gain in early pregnancy and was also associated with NDDs [27, 28], we repeated our analyses of maternal RGWG-T2 after excluding women diagnosed with hyperemesis gravidarum. As we observed $5.2\%$ of the children had missing values in maternal IPI, we repeated our analyses after excluding those with missing values in IPI. Furthermore, the number of antenatal visits may be influenced by factors such as pregnancy complications which could then influence the accuracy of the RGWG calculation. An accelerated fetal growth usually occurs in the late second trimester [29], which is also a component of maternal gestational weight gain. Therefore, we repeated our analysis between RGWG-T2 and NDDs by additionally adjusting for the number of antenatal visits in the second trimester and performed the stratification analyses among those with the last weight measured < 25 and ≥ 25 weeks of gestation in the second trimester. As we found that the excluded and included populations differed in several characteristics, we repeated our analyses after applying inverse probability weights (IPW) to correct the analysis by weighting the observations with the probability of being selected [30].
## Study sample
Of the total sample of 57,822 children (29,581 [$51.2\%$] male; mean [SD] follow-up time after 2 years of age, 5.4 [1.1] years), 2205 ($3.8\%$) received an NDD diagnosis by the end of the follow-up. The majority of children ($67.4\%$) were born to mothers with baseline BMI within the normal range, whereas $29.5\%$ of mothers were overweight or obese. Most mothers gained a total amount of weight outside of the optimal range: $33\%$ and $27\%$ of women gained excessive and inadequate total amounts of weight during pregnancy, respectively (Fig. 1).Fig. 1Distributions of total GWG (kg), RGWG-T2, and RGWG-T3 categories according to the IOM guidelines Compared with optimal RGWG groups, mothers who exceeded the GWG guidelines were more likely to be primiparous, carrying a male fetus, younger than 30 years, or born outside of Nordic countries; to have lower family income, lower education level, and a history of psychiatric history; and to report smoking in early pregnancy (Table 1). We observed a similar pattern for total GWG categories (Additional file 1: Table S3).
## Total GWG and risk of NDDs
Examining GWG z-scores (accounting for the length of gestation), we observed J-shaped associations of GWG z-scores with any NDDs and ADHD, with slightly stronger associations for higher GWG compared to a lower GWG (Fig. 2A). For example, a total GWG of two standard deviations above the referent of 0 (GWG z-score = 2) was associated with $19\%$ increased risk of any NDD diagnosis ($95\%$ CI = 1.03–1.37) and $31\%$ increased risk of any ADHD diagnosis ($95\%$ CI = 1.10–1.57), which were higher compared to the associations with a total GWG of two standard deviations below the referent of 0 (GWG z-score = − 2) ($12\%$ for any NDDs, $95\%$ CI = 1.02–1.23; $15\%$ for ADHD, $95\%$ CI = 1.05–1.27). To put this into context, a GWG z-score of 2 and − 2 in our cohort would correspond to a total weight gain of 25.9 and 6.8 kg for normal-weight women delivering at 40 weeks, respectively, compared to 14.2 kg corresponding to $z = 0$ for the same group. However, only the association with ADHD survived Bonferroni correction. When stratified by maternal baseline BMI, the associations between higher GWG z-scores and the risks for NDDs and ADHD remained (Fig. 2B), but results showed wide CIs for the associations with lower GWG z-scores in the normal BMI group (Fig. 2C). Among overweight and obese women, lower maternal GWG z-scores were associated with increased risks of any NDDs, ASD, and ADHD, but results showed wide CIs for the associations with higher GWG z-scores (Fig. 2C).Fig. 2Maternal z-score for gestational weight gain (GWG) and offspring risk for neurodevelopment disorders in the full cohort (A) and according to the category of maternal BMI at first antenatal visit (B, C). Histograms illustrate the distribution of GWG z-score for those included in each analysis. Adjusted estimates are shown for any NDD, ASD, ADHD, and ID. The curved solid black line represents the hazard ratio (HR) calculated through restricted cubic splines models with 3 knots. The grey bands represent the $95\%$ CI. A reference line is included for an HR of 1.00. P-values for analyses are shown for a Wald test with a null hypothesis that all spline terms were jointly equal to 0, as a test of whether the exposure was generally associated with the outcome. The model was adjusted for birth year, child’s sex, maternal age at birth, household income quintiles at birth, maternal education level, parental birth region, interpregnancy interval, maternal psychiatric history, maternal smoking during pregnancy, and maternal BMI at first antenatal visit (only in the full cohort analysis). Note that the y-scale differs for ID compared to the other outcomes We observed steeper U-shaped associations of maternal GWG with offspring risk of any NDDs and any ADHD when we used the original values of total GWG (in kilograms; without adjustment for length of gestation) (Additional file 1: Fig. S3), while analysis of categories based on IOM recommendations for total weight gain did not indicate any associations with offspring risk of NDDs after adjustment for confounders (Additional file 1: Table S4).
## Rates of GWG in the second trimester and risk of NDDs
In the continuous analyses, lower RGWG-T2 was associated with increased risk for any NDDs, ASD, and ADHD (Fig. 3A). For example, maternal weight gain of 0.25 kg/week was associated with a $9\%$ increased risk of any NDD diagnosis ($95\%$ CI = 1.04–1.15) compared to the median of 0.57 kg/week in the fully adjusted model. Only the associations with any NDDs and ADHD survived the Bonferroni correction. When stratified by baseline maternal BMI category, the associations remained largely similar, although with wider CIs (Fig. 3B, C) and with higher point estimates associated with lower RGWG-T2 among normal-weight mothers for risk of any ADHD. However, increasing RGWG-T2 above the median was associated with an increased risk of ADHD among children to normal-weight mothers and a marginally lower risk of ASD among children to overweight/obese mothers. Fig. 3Rate of gestational weight gain during the second trimester (RGWG-T2) and offspring risk for neurodevelopment disorders in the full cohort (A) and according to the category of maternal BMI at first antenatal visit (B, C). Histograms illustrate the distribution of RGWG-T2 for those included in each analysis. Adjusted estimates are shown for any NDD, ASD, ADHD, and ID. The curved solid black line represents the hazard ratio (HR) calculated through restricted cubic splines models with 3 knots. The grey bands represent the $95\%$ CI. A reference line is included for an HR of 1.00. P-values for analyses are shown for a Wald test with a null hypothesis that all spline terms were jointly equal to 0, as a test of whether the exposure was generally associated with the outcome. The model was adjusted for birth year, child’s sex, maternal age at birth, household income quintiles at birth, maternal education level, parental birth region, interpregnancy interval, maternal psychiatric history, maternal smoking during pregnancy, and maternal BMI at first antenatal visit (only in the full cohort analysis) In the 3-category RGWG-T2 analysis, compared to those with an optimal rate of weight gain during the second trimester, insufficient maternal RGWG-T2 was associated with increased risk of any ADHD diagnosis (1.30, 1.08–1.57) and specifically ASD with ADHD (1.75, 1.19–2.57) in fully adjusted models (Additional file 1: Table S5). However, RGWG-T2 was not associated with other NDDs or mutually exclusive diagnoses. In the 5-category RGWG-T2 analysis, extremely insufficient and insufficient RGWG-T3 were associated with $35\%$ (1.35, 1.07–1.70) and $26\%$ (1.26, 1.01–1.57), respectively, increased risk of any ADHD while none of them survived the Bonferroni correction. However, we did not observe any associations of excessive or extremely excessive RGWG-T2 with any NDD diagnoses (Table 2). We did not observe any indication of interaction between RGWG-T2 and follow-up time, with exception of models for ADHD, which indicated potential increases in risk associated with maternal excessive RGWG-T2 as children grew older (Additional file 1: Fig. S4).Table 2Associations between the rate of gestational weight gain at different stages of pregnancy and offspring risks of neurodevelopment disorders in the full cohortExtended categoryaRGWG–T2RGWG–T3N cases%bModel 1cModel 2dP-valueN cases%bModel 1cModel 2dP-valueeOptimal Any NDDs3803.631.00 (ref)1.00 (ref)–4693.471.00 (ref)1.00 (ref)– Any ASD2071.981.00 (ref)1.00 (ref)–2531.871.00 (ref)1.00 (ref)– Any ADHDf2172.071.00 (ref)1.00 (ref)–2782.061.00 (ref)1.00 (ref)– Any ID420.401.00 (ref)1.00 (ref)–510.381.00 (ref)1.00 (ref)–Extremely insufficient Any NDDs1604.801.35 (1.12–1.62)1.16 (0.96–1.40)0.121653.611.05 (0.88–1.26)0.99 (0.83–1.18)0.91 Any ASD762.281.18 (0.90–1.53)1.07 (0.82–1.39)0.64831.820.99 (0.77–1.27)0.95 (0.74–1.21)0.66 Any ADHDf1103.301.62 (1.29–2.04)1.35 (1.07–1.70)0.011072.341.16 (0.92–1.45)1.08 (0.86–1.35)0.52 Any ID230.691.76 (1.06–2.92)1.42 (0.85– 2.39)0.18150.330.88 (0.49–1.56)0.76 (0.43–1.35)0.35Insufficient Any NDDs1834.241.19 (1.00–1.42)1.12 (0.94–1.34)0.201783.180.91 (0.77–1.09)0.89 (0.75–1.06)0.18 Any ASD882.041.06 (0.82–1.36)1.02 (0.79–1.31)0.89911.630.87 (0.68–1.10)0.85 (0.67–1.08)0.17 Any ADHDf1202.781.36 (1.09–1.70)1.26 (1.01–1.57)0.041111.980.96 (0.77–1.19)0.93 (0.75–1.16)0.53 Any ID190.441.12 (0.65–1.92)1.02 (0.59–1.75)0.96250.451.18 (0.73–1.90)1.08 (0.67–1.73)0.77Excessive Any NDDs7193.650.99 (0.87–1.12)0.98 (0.86–1.11)0.706773.571.02 (0.91–1.15)0.98 (0.87–1.10)0.69 Any ASD3731.890.94 (0.79–1.12)0.93 (0.78–1.10)0.403501.840.98 (0.84–1.16)0.95 (0.81–1.11)0.51 Any ADHDf4202.131.01 (0.86–1.19)0.99 (0.85–1.17)0.953982.101.01 (0.87–1.18)0.96 (0.82–1.12)0.62 Any ID970.491.22 (0.85–1.75)1.21 (0.84–1.74)0.31900.471.26 (0.89–1.77)1.19 (0.84–1.67)0.33Extremely excessive Any NDDs7633.811.03 (0.91–1.16)0.97 (0.86–1.10)0.657164.721.34 (1.19–1.51)1.17 (1.04–1.32)0.01 Any ASD3751.870.93 (0.78–1.10)0.89 (0.75–1.05)0.163422.261.19 (1.01–1.40)1.08 (0.91–1.27)0.39 Any ADHDf4862.431.14 (0.97–1.34)1.07 (0.91–1.25)0.434593.031.46 (1.26–1.69)1.23 (1.06–1.43)0.01 Any ID890.441.10 (0.76–1.58)1.06 (0.73–1.52)0.78890.591.56 (1.10–2.20)1.44 (1.01–2.03)0.04Abbreviations: RGWG rate of gestational weight gain, Ref reference, NDDs neurodevelopmental disorders, ASD autism spectrum disorder, ADHD attention deficit/hyperactivity disorder, ID intellectual disabilityaFor normal weight women, the optimal rate of weight gain during the second and third trimesters was 0.35–0.50 kg/week, the extremely insufficient rate was < 0.27 kg/week, the insufficient rate was 0.27–< 0.35 kg/week, the excessive rate was > 0.50–0.68 kg/week, and the insufficient rate was > 0.68 kg/week. For underweight women, the optimal rate was 0.44–0.58 kg/week, the extremely insufficient rate was < 0.37 kg/week, the insufficient rate was 0.37–< 0.44kg/week, the excessive rate was > 0.58–0.72 kg/week, and the insufficient rate was > 0.72 kg/week. For overweight women, the optimal rate was 0.23–0.33 kg/week, the extremely insufficient rate was < 0.12 kg/week, the insufficient rate was 0.12–< 0.23 kg/week, the excessive rate was > 0.33–0.61 kg/week, and the insufficient rate was > 0.61 kg/week. For obese women, the optimal rate was 0.17–0.27kg/week, the extremely insufficient rate was < 0 (weight loss) kg/week, the insufficient rate was 0–< 0.17 kg/week, the excessive rate was > 0.27–0.51 kg/week, and the insufficient rate was > 0.51 kg/weekbCalculated as the number of cases observed when following children from 2 years of age for a mean [SD] of 5.4 [1.1] years, divided by the number of children at risk for developing the disordercModel 1: Cox regression model, clustered on the maternal identifier, adjusted only for birth year and child’s sex. Results are displayed as the hazard ratio ($95\%$ confidence interval)dModel 2: Cox regression model, clustered on the maternal identifier, adjusted for birth year, child’s sex, maternal age at birth, household income quintiles at birth, maternal education level, parental birth region, interpregnancy interval, maternal psychiatric history, and maternal smoking during pregnancy. Results are displayed as the hazard ratio ($95\%$ confidence interval)eP-values for model 2fAn interaction with time was observed for these categories, indicating that the HR changes over time (see Additional file 1: Fig. S4)
## Rates of GWG in the third trimester and risk of NDDs
In the continuous analysis, in contrast to findings for RGWG-T2, no association was apparent between lower maternal RGWG-T3 and offspring risk of NDD outcomes (Fig. 4), nor was there any indication that insufficient maternal RGWG-T3 was associated with offspring risk of NDDs in the categorical analysis (Table 2). A pattern of increasing risk with higher RGWG-T3 was observed for all outcomes (Fig. 4A), with a rate of 1 kg/week associated with a $28\%$ increased risk of any diagnosis ($95\%$ CI = 1.16–1.40), $24\%$ increased risk of ASD ($95\%$ CI = 1.08–1.43), $31\%$ increased risk of ADHD ($95\%$ CI = 1.16–1.48), and $44\%$ increased risk of ID diagnoses ($95\%$ CI = 1.17–1.77), compared to the median of 0.51 kg/week. However, only the associations for any NDD and for ADHD survive Bonferroni correction. Similar patterns were observed for women after stratification on baseline maternal BMI, though with wider confidence intervals for estimates among overweight/obese mothers. However, decreasing RGWG-T3 below the median was also associated with an increased risk of any NDDs and ADHD among women who were overweight or obese (Fig. 4B, C). In categorical analyses, compared to those with an optimal weight gain, extremely excessive RGWG-T3 was associated with an increased risk of any NDD diagnosis, any ADHD, and any ID (Table 2). We did not find any associations between excessive RGWG-T3 and any NDD diagnoses or mutually exclusive diagnoses (Additional file 1: Table S5). We did not observe any indication of interaction between RGWG-T3 and follow-up time, with exception of models for ADHD, which indicated potential increases in risk associated with maternal extremely excessive RGWG-T3 as children grew older (Additional file 1: Fig. S4).Fig. 4Rate of gestational weight gain during the third trimester (RGWG-T3) and offspring risk for neurodevelopment disorders in the full cohort (A) and according to category of maternal BMI at first antenatal visit (B, C). Histograms illustrate the distribution of RGWG-T3 for those included in each analysis. Adjusted estimates are shown for any NDD, ASD, ADHD, and ID. The curved solid black line represents the hazard ratio (HR) calculated through restricted cubic splines models with 3 knots. The grey bands represent the $95\%$ CI. A reference line is included for an HR of 1.00. P-values for analyses are shown for a Wald test with a null hypothesis that all spline terms were jointly equal to 0, as a test of whether the exposure was generally associated with the outcome. The model was adjusted for birth year, child’s sex, maternal age at birth, household income quintiles at birth, maternal education level, parental birth region, interpregnancy interval, maternal psychiatric history, maternal smoking during pregnancy, and maternal BMI at first antenatal visit (only in the full cohort analysis). Note that the y-scale differs for ID compared to the other outcomes
## Rates of GWG in the second and third trimesters and risk of NDDs
Compared to those with optimal rate of GWG in both second and third trimesters (Additional file 1: Table S6), insufficient maternal RGWG in the second trimester but excessive RGWG in the third trimester was associated with increased risk of ADHD (1.55, 1.13–2.13) and ID (2.53, 1.15–5.55).
## Sensitivity analyses
After stratification by sex, higher GWG z-scores were associated with increased risk for any NDDs and ADHD in male offspring, though the patterns for the point estimates were generally similar among females. Lower GWG z-scores were associated with any NDDs, ASD, and ADHD in female offspring (Additional file 1: Fig. S5). However, there was no evidence for interaction by sex (all P-values for interaction > 0.05). Similar patterns of associations were observed compared to the primary analyses when analyses were restricted to Nordic-born mothers (Additional file 1: Fig. S6). The association of higher RGWG-T3 with increased offspring risk of any NDDs and any ADHD remain unchanged when restricted to mothers without pre-eclampsia or GDM, and the associations of lower RGWG-T2 with any NDDs, any ASD, and any ID remained unchanged when restricted to mothers without hyperemesis gravidarum (Additional file 1: Fig. S7). Furthermore, excluding those with missing values in IPI did not change the main results (Additional file 1: Fig. S8). Moreover, we found the associations were similar to the main results when adjusting for the number of antenatal visits in the second trimester or restricting the population to those with the last weight measured < 25 weeks in the second trimester (Additional file 1: Fig. S9B&C). The relationship between lower RGWG-T2 and higher risk for any NDDs, ASD, and ADHD remained when restricting the population to those with last weight measured ≥ 25 weeks in the second trimester, while we found a higher RGWG-T2 was associated with higher risks for ADHD, even though after the adjustment for GDM and pre-eclampsia (Additional file 1: Fig. S9D&E). Finally, after applying the inverse probability weights (IPW) to correct the analysis by weighting the observations with the probability of being selected, we found the impact of selection bias was negligible (Additional file 1: Fig. S10).
## Discussion
In this population-based cohort study, we observed J-shaped associations between total GWG and offspring risks of any NDDs, particularly ADHD, using a z-score measure that accounted for length of gestation. The associations between rates of weight gain and NDDs in offspring varied by the timing of weight gain during pregnancy and differed with regard to specific NDD diagnoses. Lower RGWG during the second trimester was associated with an increased risk of any NDDs in offspring, particularly ASD and ADHD, while higher RGWG during the third trimester was associated with a higher risk of all three NDD diagnoses examined. When rates of weight gain in the second and third trimesters were considered together, we found that insufficient weight gain in the second trimester followed by excessive weight gain in the third trimester was most significantly associated with increased risks of ADHD and ID in offspring.
## Comparison with previous studies
The proportions of total gestational weight gain and rate of gestational weight gain in the second and third trimesters in our study were comparable to the findings in previous studies that also relied on the IOM guidelines [31, 32]. To our knowledge, two previous studies have investigated the relationship between the rate of GWG and the risk of NDD outcomes. In a cohort study including 12,556 children, Rodriguez et al. reported that rates of weekly weight gain (calculated using observations over the entire pregnancy) were not significantly associated with teacher-reported ADHD symptoms in offspring among normal weight or underweight women but were associated with increased offspring odds of ADHD symptoms among women with high-pregnancy BMI [33]. In a case-control study including 4409 children, Matias et al. calculated the rates of GWG for the second and third trimesters together and found that RGWG below or above the optimal range according to the IOM guidelines did not significantly increase the risks of ASD or developmental delay after adjusting for confounders, though point estimates for ORs for ASD and developmental delay were above one for excessive GWG categories [34]. A key difference between these studies and our current study is in the treatment of the rates of GWG. We observed different patterns when considering RGWG in the second and third trimesters separately, and we also took the non-linear associations with NDDs into consideration. However, previous studies considered only an overall rate of weight gain and assessed a linear relationship with NDDs. Such variations suggested different effects of weight gain on fetal neurodevelopment during specific timing of exposure.
Existing studies relating to total GWG and NDDs have used different definitions for GWG as well as outcomes, which in turn influences the comparability of their results. In previous studies, autism was the most commonly considered outcome, and IOM guidelines were most frequently used to identify non-optimal GWG, followed by treating total GWG as a continuous variable. In a recent meta-analysis, evidence from five cohort studies and four case-control studies (involving 323,253 participants) showed that both excessive and inadequate GWG (according to IOM guidelines 2009 [8 studies]/1990 [1 study]) were associated with a higher risk of ASD in offspring [7]. Matias et al. reported that the GWG z-score in the highest tertile was associated with $22\%$ higher odds of ASD after adjustment for confounders while no significant associations were found with regard to the lowest tertile of GWG z-scores [34]. We also observed a U-shaped pattern of association between total GWG (kg) and children’s risk of NDDs, but with wide confidence intervals for the outcome of ASD. The U-shape was attenuated when the length of gestation was taken into account (i.e., GWG z-score), especially at the left tail which represented insufficient GWG.
Few studies have focused on ADHD and ID in relation to total weight gain, and their results are often inconsistent. In a cohort study involving 331 children, Fuemmeler et al. reported that GWG below IOM recommendations was associated with hyperactive-impulsive symptoms in offspring and GWG above recommendation was associated with worsened working memory, planning and organizing behavior in offspring between 2 and 6 years old [9]. However, two other cohort studies (involving 12,556 children and 511 children respectively) found no significant associations between GWG (categorized according to the IOM guidelines 1990 or GWG z-scores) and ADHD symptoms [33, 35]. In our study, we observed an apparent U-shaped association between total GWG (kg) and ADHD, while the association between lower GWG and the risk of ADHD in offspring was largely attenuated when the length of gestation (i.e., GWG z-score) was accounted for.
Among 78,675 children, Mann et al. reported gestational weight change (gain or loss) was not significantly associated with the odds of ID [36]. However, in a Swedish register-based study involving 467,485 children, Lee et al. indicated that inadequate GWG (according to the IOM guidelines) may increase the risk of ID in offspring, regardless of maternal BMI and such associations remained after excluding children born preterm [8]. We did not observe an apparent association between total GWG (kg) and ID in offspring, though this may be due to our limited sample size. A novel finding in our study is that children’s risk of ID was most pronounced for women who experienced insufficient weight gain in the second trimester followed by excessive weight gain in the third trimester. While this finding requires confirmation with larger study samples, it suggests that studies of GWG in relation to children’s risk for ID may need to consider the rate of weight gain over time in addition to the total amount gained.
In this study, the associations between total GWG and NDDs from continuous analyses were more pronounced than categorical analyses based on the IOM guidelines. The results in the categorical analyses should be interpreted with caution as none of them survived Bonferroni correction. However, it should be noted that the Bonferroni adjustment may be overly conservative [37], as this approach decreases the risk of false positive results (type I errors) at the cost of increasing the risk of false negative results (type II errors). Our findings suggest that studying the full range of continuous GWG values might better capture the risk associated with NDDs, for both total GWG and rates of weight gain, in line with recent good practice recommendations for studies of GWG in observational studies [10]. The associations we observed between excessive total GWG with NDDs were generally consistent when comparing total GWG in kg to GWG z-scores accounting for pregnancy durations. However, the associations of insufficient GWG with NDDs were largely attenuated when considering GWG z-scores. This finding was in line with other studies investigating perinatal outcomes [38]. Since total GWG and NDD outcomes are highly correlated with gestational duration, the use of GWG z-score enabled us to disentangle the associations with pregnancy weight gain from the effects of the gestational duration.
## Potential mechanisms
The association between excessive GWG and fetal neurodevelopment may be related to the downstream effect of increased maternal/fetal adipose tissues. A number of plausible pathways to link increased maternal or fetal adiposity to alternations in neurodevelopment have been hypothesized, including dysregulated pro-inflammatory cytokine signaling; lipotoxicity; increased oxidative stress, dysregulated insulin, glucose, and leptin signaling; dysregulated serotonergic and dopaminergic signaling; and perturbations in synaptic plasticity [39, 40]. Furthermore, excessive or rapid GWG may also be related to gestational diabetes or pathological edema caused by preeclampsia, which has also been associated with increased risks of NDDs in offspring [25, 26]. Finally, excessive GWG is also associated with macrosomia and LGA fetuses, which are associated with greater risks of asphyxia-related complications during labor [41] and increased risk of NDDs [42, 43].
There are two potential hypotheses for linking insufficient GWG to NDDs in offspring [7]: [1] insufficient GWG may be considered a marker of maternal nutritional deficiency which in turn causes suboptimal nutritional states in the developing fetus, detrimentally influencing fetal brain development [44], and [2] insufficient GWG can be associated with co-morbidities during pregnancy such as anorexia nervosa, hyperemesis gravidarum, and intestinal malabsorption which could lead to maternal nutrient deficiencies and placental dysfunction-related complications [45, 46]. Insufficient GWG is also associated with higher risks for low birth weight and preterm birth [31] which are themselves associated with higher risks of NDDs.
We observed that insufficient RGWG during the second trimester and excessive RGWG during the third trimester were associated with increased risk of NDDs, in line with the notion that the effect of any obstetric-related factors (with regard to nutrient deficiency or overload) on fetal neurodevelopment depends on the timing of exposure [42, 44]. Considering RGWG-T2 and RGWG-T3 together, we found that insufficient RGWG during the second trimester and excessive RGWG during the third trimester were most significantly associated with increased risks of NDDs (especially for ADHD and ID), which could be related to a double jeopardy effect stemming from these perturbations. One potential condition related to this phenomenon could be hyperemesis gravidarum. Mothers with hyperemesis gravidarum (severe nausea and vomiting) usually have slower weight gain or lose weight in early pregnancy while a “catch-up” weight gain may occur later in pregnancy as this condition usually resolves after 20 weeks of gestation [27, 47]. Hyperemesis gravidarum has been associated with increased risks of ASD, ADHD, and cognitive impairment of offspring [28]. While hyperemesis gravidarum could plausibly be related to the associations that we observe, our sensitivity analyses indicate that the associations between low gestational weight gain in the second trimester and children’s risk of NDDs cannot be entirely explained by this condition.
In this study, we observed sex differences in the associations which suggest that fetal vulnerability to aberrant maternal metabolic and nutritional states may differ by fetal sex, with higher risk for any NDD and particularly autism among females associated with lower total maternal weight gain, though no interactions were detected in formal testing. Female fetuses have a higher survival rate than male fetuses during periods of maternal malnutrition, which has been observed under very harsh conditions, such as during the Dutch famine period [48]. We also noted that the proportion of female children is slightly higher among mothers with insufficient weight gain compared to other categories. In the Dutch famine cohort, sex differences in certain neurodevelopmental outcomes have been reported, with exposure to early prenatal famine associated with a higher incidence of spina bifida only in males, but more strongly associated with other neurodevelopmental conditions, such as epilepsy, cerebral palsy, and spastic diplegia, among females [49]. Our observations in the sex stratification analysis are in line with the notion that female fetuses generally have higher survival rates than male fetuses under stress conditions, though remain vulnerable to the influences of maternal undernutrition on neurodevelopment.
## Strengths and limitations
An important strength of our study is that we not only used the IOM guidelines, but also used gestational age-standardized GWG z-scores to define total GWG which disentangled the effect of gestational duration from that of GWG. This measure was developed specifically for the Swedish population using similar register resources as were available in this study [11] and provides z-score measures for all BMI categories, though other international methods to estimate GWG z-scores indicate similar weight gain patterns (e.g., the INTERGROWTH-21 charts indicate a weight gain of 24.6 kg for normal weight women at 40 weeks if $z = 2$ compared to 13.7 kg if $z = 0$) [50]. Using maternal weight data taken from multiple time periods during pregnancy, we were able to explore the critical windows of development during which non-optimal weight change may have the greatest detrimental effect on fetal neurodevelopment. For weight gain during the second and third trimesters, we calculated RGWG as weight gain divided by the number of interval weeks to reduce bias due to the length of observation [10, 51]. We used objectively measured, prospectively recorded data from Swedish registry data to define exposures, outcomes, and covariates to minimize the possibility of bias. Finally, important potential confounders, such as maternal BMI and maternal psychiatric history, were accounted for in the analyses.
Some limitations in this study should also be mentioned. First, maternal weight measured during the first antenatal visit is a pragmatic but insufficient proxy measure for pre-pregnancy BMI. This method may have overestimated pre-pregnancy BMI because of weight gain that occurred between conception and the first antenatal visit (median gestational age of 9 weeks in this study). However, weight gain within the first trimester is minimal in most cases [52]. Second, random errors in the measurement of weight may exist in our study because we used weight data collected across multiple clinics. These errors may have diminished the strength of the observed HRs. Third, we were unable to separately explore the association between GWG and the risk of NDDs among underweight and obese mothers because of limited sample sizes. However, metabolic or nutritional disturbances may be of greater importance in these populations. Fourth, limitations in sample sizes and follow-up time in this study could be a potential issue for investigating the relationships between maternal weight gain and offspring risks of NDDs due to the low prevalence of NDDs, especially for ASD and ID. Limited follow-up time compared to other register-based studies likely resulted in the misclassification of children who will eventually receive NDD diagnoses, biasing our estimates toward the null. Future studies with larger sample sizes and longer follow-up times are warranted to replicate our findings. Fifth, the baseline characteristics differed in the included and excluded population in our study which may indicate selection biases, though the impact of such selection bias appears to be negligible. Additionally, although we have accounted for many confounders, residual confounding may still exist, such as specific components of the maternal diet or genetic predisposition. We were unable to carry out sibling comparisons or other family-based study designs to address this issue, given the limited sample size and number of birth years for which GWG data were available. Furthermore, we did not have biomarkers of intermediate conditions (e.g., inflammation, endocrine alterations) that may help elucidate the underlying mechanisms connecting maternal GWG with offspring risk of NDDs. Finally, our study population was dominated by Nordic-born mothers. Therefore, our findings would need to be replicated in other populations to verify their generalizability.
## Conclusions
During pregnancy, most women gain weight outside of the optimal range commonly recommended by clinicians. Here, we report that insufficient rates of weight gain during the second trimester and excessive rates of weight gain during the third trimester were associated with a higher risk of NDD outcomes, suggesting that intensity (the rate of GWG) and timing of exposure (at different stages of pregnancy) also play an important role. In addition, by accounting for gestational durations, we showed J-shaped associations between total GWG and risks of NDDs in offspring, especially for ADHD. These results require replication in larger and more diverse populations. Future studies with more specific assessments of genetic and metabolic factors responsible for insufficient and excessive GWG during pregnancy are also warranted.
## Supplementary Information
Additional file 1: Table S1. Included and excluded individuals. The characteristics of the included and excluded population in our study. Table S2. Diagnostic codes. The codes for identifying exposures, outcomes, and covariates. Table S3. Study cohort description by RGWG category. The characteristics of the study sample according to different categories of RGWG. Table S4. Total GWG category and offspring risk of NDCs in full cohort. The association between the 3-category total GWG and offspring risks of autism, ADHD and ID, and the mutuality exclusive diagnoses of these three NDCs. Table S5. RGWG category and offspring NDCs in full cohort. The association between the 3-catgory total RGWG and offspring risks of autism, ADHD and ID, and the mutuality exclusive diagnoses of these three NDCs. Table S6. RGWG in second and third trimester together with offspring NDCs in full cohort. The association between RGWG in second and third trimester in combination and offspring risks of NDCs. Fig. S1. Sample derivation and outcome description. A description of sample derivation and NDC co-occurrence in offspring. Fig. S2. Directed acyclic graph. A directed acyclic graph describing how potential confounders influence the exposures and outcomes. Fig. S3. Total GWG (kg) and offspring NDCs. Spline models depicting the association between GWG (kg) and offspring NDCs. Fig. S4. Visualization for the change of hazard ratios over time. We observed evidence in the cox regression models that the hazard ratios may be time varying for the association between excessive RGWG-T2 and any ADHD, and the association between extremely excessive RGWG-T3 and any ADHD. We therefore visualized the hazard ratios over time by using flexible parametric survival models for non-linear time-dependent effects. Fig. S5. Total GWG z-scores and offspring NDCs (sex stratification). Spline models depicting the association between total GWG z-scores and offspring NDCs, stratified by child’s sex. Fig. S6. Total GWG z-scores and offspring NDCs (restricted to Nordic-born mothers). Spline models depicting the association between total GWG z-scores and offspring NDCs, restricted to Nordic-born mothers. Fig. S7. RGWG and offspring NDCs (restricted to non-hyperemesis gravidarum, non-preeclampsia or non-gestational diabetes mellitus). Spline models depicting the association between total RGWG and offspring NDCs, restricted to those without hyperemesis gravidarum, preeclampsia or gestational diabetes mellitus. Fig. S8. Total GWG z-scores and RGWG with offspring NDCs (excluding those without IPI information). Spline models depicting the association of total GWG z-scores and RGWG with offspring NDCs, excluding those without IPI information. Fig. S9. RGWG-T2 and offspring NDCs (with additional adjustments and timing specification). Spline models depicting the association between RGWG-T2 and offspring NDCs, with additional adjustment for number of antenatal visits in the second trimester, and stratified by those who had the last weight measured < 25 and ≥ 25 weeks of gestation in the second trimester. Fig. S10. Impact of selection bias. The potential impact of selection bias in the association of total GWG z-scores, RGWG-T2, and RGWG-T3 with offspring NDCs by applying the inverse probability weight method. Additional file 2. STROBE checklist. The STROBE checklist showing our study was reported according to the STROBE checklist for cohort studies.
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52. Fattah C, Farah N, Barry SC, O’Connor N, Stuart B, Turner MJ. **Maternal weight and body composition in the first trimester of pregnancy**. *Acta Obstet Gynecol Scand* (2010) **89** 952-955. DOI: 10.3109/00016341003801706
|
---
title: 'Lifestyle and the hypertensive disorders of pregnancy in nulliparous women
in the United States: a secondary data analysis of the nuMom2b'
authors:
- Elizabeth Mollard
- Constance Cottrell
journal: BMC Pregnancy and Childbirth
year: 2023
pmcid: PMC10035211
doi: 10.1186/s12884-023-05522-8
license: CC BY 4.0
---
# Lifestyle and the hypertensive disorders of pregnancy in nulliparous women in the United States: a secondary data analysis of the nuMom2b
## Abstract
### Background
Hypertensive disorders of pregnancy are a leading cause of maternal and fetal morbidity and mortality and a significant risk factor for future cardiovascular disease development in women. This study aimed to explore lifestyle wellness-related variables and how they impact the risk of hypertension in pregnancy.
### Methods
This is a secondary analysis of data from the prospective cohort study Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-To-Be (nuMoM2b). Data was collected through questionnaires, clinical evaluations, and medical records review at 8 academic medical centers in the United States. Four study visits were scheduled throughout the participant’s pregnancy (visits one–four): 60–136, 160–216, and 220–296 weeks gestation and birth. A series of statistical modeling and logistical regression were performed using 15 lifestyle variables related to sleep, nutrition, resilience, illness avoidance, and physical activity were selected as predictor variables with an outcome variable of hypertension.
### Results
Of 9289 nulliparous participants considered for inclusion in our analyses, 1464 had any HDP during study participation, and 554 participants had complete data available for the study and were included in our final sample. Results were statistically significant at a level of $p \leq 0.05.$ Of the sleep variables, snoring at visit 1 increased the risk of hypertension in pregnancy. Greater vegetable consumption reported at visit one decreased risks of hypertension in pregnancy. Physical activity reported at visit two and visit three were associated with decreased risk of hypertension. Physical activity reported at visit three combined with more hours of sleep each night, or through napping habit reported at visit one decreased hypertension risk. Increased fish oil consumption combined with more hours of sleep at visit one increased odds of hypertension in pregnancy.
### Conclusions
Our results support that lifestyle wellness-related variables relating to sleep, physical activity and nutrition affect hypertension in pregnancy. The studied variables and others should be considered in future research and intervention development to reduce hypertension in pregnancy and improve maternal wellness.
## Background
Hypertension is the most common medical complication experienced by pregnant women and is a leading cause of poor maternal and fetal outcomes. Hypertensive disorders of pregnancy (HDP) increase maternal morbidity, such as heart attack and stroke, pregnancy-related mortality, and future cardiovascular disease development [1, 2]. The number of women experiencing HDP has risen rapidly globally, and in the United States have a prevalence rate of over $15\%$, impacting one in seven hospital births [1, 3, 4].
Predisposing risk factors for HDP are similar to risk factors for hypertension outside of pregnancy [5, 6]. In the US, risks of HDP are higher in nulliparous women, women of advanced maternal age (≥ 35 and further at ≥ 40), who live in low-income areas, are unemployed, have low educational attainment, live in the South or Midwest, are non-Hispanic Black, non-Hispanic American Indian or Alaska Native race, overweight or obese body mass index, or who have underlying health conditions, including diabetes, kidney disease, and autoimmune disorders [3, 7].
Clinical research on HDP focuses heavily on pharmacological prevention and management of HDP and limited research is conducted on lifestyle and wellness behaviors as they relate to HDP during pregnancy [8–10]. Although pharmacological advances for HDP have improved outcomes and reduced rates of adverse events from HDP, lifestyle and wellness remain critical to overall physiological health and blood pressure [11, 12]. In addition, there is growing evidence that the increased prevalence of HDP is related to lifestyle, primarily due to increases in chronic hypertension, which has risen exponentially over the last 40 years [13].
Lifestyle and wellness recommendations focus on increased adoption of positive health-promoting behaviors such as physical activity, good nutrition, adequate sleep, preventative health measures, and improved resilience [14]. While there are recommendations on post HDP lifestyle modification for long term cardiovascular health, there are limited lifestyle wellness-based guidelines for pregnant populations beyond modifying what is recommended for the general public [6, 15]. Despite that most dietary interventions have been shown to improve pregnancy outcomes in research, nutritional pregnancy guidelines often focus on appropriate weight gain and restricting unsafe foods instead of identifying optimal nutrition strategies for pregnancy [16–18]. While exercise has been shown to decrease risks of some forms of HDP, pregnancy physical activity guidelines are similar to general recommendations of 150 min of moderate-intensity aerobic activity per week [19–22]. Even fewer recommendations are available regarding sleep or fostering or developing traits like resilience in pregnancy. As such, most women do not meet guidelines for the general population for dietary intake or physical activity in pregnancy and are unlikely to be counseled on wellness in areas where guidelines are not established [18, 23–25].
Despite an increase in HDP with an underlying etiology related to lifestyle, there is limited published research on lifestyle or wellness and its relationship to HDP in pregnancy. This study aimed to explore lifestyle wellness variables and their association and interaction with risk for HDP in nulliparous women. We hypothesized that protective lifestyle variables, specifically sleep, physical activity, nutrition, resilience, and preventive health, may be related to a lower risk of HDP in nulliparous women. By identifying lifestyle variables that lower the risk for HDP, future low-cost, easy-to-implement lifestyle interventions can be developed to prevent HDP and promote wellness.
## Study design
This is a secondary analysis of data from the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-To-Be (nuMoM2b), a prospective cohort study designed to evaluate contributors to poor birth outcomes (Clinicaltrials.gov identifier NCT01322529). The methods of this study were described in detail previously and will be described here in brief [26]. The parent study population included 10,037 first-time pregnant women at one of eight academic medical systems in seven states in the US (California, Indiana, Illinois, New York, Ohio, Pennsylvania, and Utah) from October 2010 to September 2013. Eligible women had a viable singleton pregnancy confirmed by ultrasound with a gestation less than 14 weeks at enrollment. Participants had never given birth (20 weeks gestation or greater) and were planning to give birth at a participating site. Exclusion criteria were individuals less than 13 years old, women who planned termination, participants with lethal fetal malformations and aneuploidies, donor oocyte pregnancy, history of three or more pregnancy losses, or inability to provide consent. Appropriate institutional review board approval was obtained at each participating site, and informed consent was collected from each participant.
Study information was obtained throughout participants’ pregnancies concurrently with their prenatal care to study pregnancy outcomes, including HDP. Four study visits were scheduled throughout the participant’s pregnancy. Visit one took place in approximately the first trimester (6 weeks- 13 weeks 6 days gestation). Visit 2 took place in approximately the early second trimester of pregnancy (16 weeks through 21 weeks 6 days gestation), Visit 3 took place in the late 2nd or early 3rd trimester (22 weeks through 29 weeks 6 days gestation), and Visit 4 took place at the time of birth. Data was collected through interviews, questionnaires, clinical evaluations, and medical record review.
## Variables
Fifteen variables related to wellness (nutrition, physical activity, resilience, avoidance of illness as preventative health measures, and sleep) were chosen as predictor variables in the model (Table 1). Sixteen other wellness-related variables were considered but removed due to potential issues with multicollinearity. Table 1Fifteen chosen lifestyle wellness variables and when they were collectedVariable numberVariable Instrument or QuestionVisit collectedNutrition 1Daily caloric intakeVisit 1 2Healthy Eating Index-2010 (HEI) total (overall)Visit 1 3Alternative Healthy Eating Index (AHEI) component scores for vegetable intakeVisit 1 4Dehydroepiandrosterone (DHEA) and eicosapentaenoic acid (EPA) fatty acid intakeVisit 1Physical Activity 5“During the past four weeks, did you participate in any physical activities or exercises like running, aerobics, gardening, ball games, or walking for exercise”?Visit 1 6Visit 2 7Visit 3Sleep 8“How many hours of sleep do you usually get per night (in hours)?”Visit 1 9“*During a* usual week, how many times do you nap for 5 minutes or more?”Visit 1 10“Do you snore?”Visit 1Resilience 11“Are you able to adapt to change?”Visit 2 12“Do you tend to bounce back after illness or hardship?”Visit 2Illness avoidance (presumed preventative health practices) 13“Have you had any 'flu-like illnesses,' 'really bad colds,' fever, a rash, or any muscle or joint aches since last study visit?”Visit 2 14Visit 3 15Visit 4 Four nutrition variables were taken from the modified Block 2005 Food Frequency Questionnaire (FFQ) administered at Visit 1 [12]. The FFQ focused on the foods and nutrients consumed in the three months following conception. Of nutrition variables, Healthy Eating Index-2010 (HEI) total (overall) score provided the level that foods aligned with key dietary recommendations, based on the Health and Human Services and the United States Department of Agriculture Dietary Guidelines for Americans [13]. The score ranges from 0 to 100, with a higher score reflecting more optimal alignment with key dietary recommendations. The Alternative Healthy Eating Index (AHEI) component scores for vegetable intake, dehydroepiandrosterone (DHEA) and eicosapentaenoic acid (EPA) fatty acid intake, and daily caloric intake were calculated.
The variable used to measure any physical activity in the past month at Visit 1–3 was: “During the past four weeks, did you participate in any physical activities or exercises like running, aerobics, gardening, ball games, or walking for exercise”? This variable was derived from the standardized physical activity questions adapted from the Behavior Risk Factor Surveillance System (BRFSS) [27, 28]. Data were available for each of the trimester visits.
Two variables for resilience were extracted from the Connor Davidson Resilience Scale using a 5-point Likert scale ranging from 0 (not true at all) to 4 (true nearly all of the time) with higher scores being indicative of resilience [29]. The questions from the Connor Davidson, collected at visit two included: “Are you able to adapt to change?”, and “Do you tend to bounce back after illness or hardship?” Sleep questionnaire data were collected at Visit 1 and included three items. The first sleep variable used in our analysis was related to overall hours of sleep per night asked as, “How many hours of sleep do you usually get per night (in hours)?” The second variable was about napping habits, asked as, “*During a* usual week, how many times do you nap for 5 min or more?” Finally, a sleep variable assessing whether the individual snores was a simple question, “do you snore?”. The illness avoidance via presumed preventative health practices was measured using the variable, “Have you had any 'flu-like illnesses,' 'really bad colds,' fever, a rash, or any muscle or joint aches since last study visit?” at visit two, three, and four.
## Data analysis
Statistical analysis was performed using R Statistical Software (version 4.0.3; R Foundation for Statistical Computing, Vienna, Austria). All participants with available data were considered for this analysis ($$n = 9289$$) which included 1464 participants with HDP. After removing observations with missing values for any of the 15 selected variables, we were left with 554 participants in the remainder of the analysis.
The correlations between these 15 variables were calculated to ensure multicollinearity would not be a problem (Table 2). No high pairwise correlations were observed, as all absolute correlation values were below 0.5. The distribution of each of these variables was also examined via histogram to check for potential outliers. Based on these distributions, no outliers were removed, and no data transformations were utilized. A logistic regression model was then fit to the data with these 15 variables. Yes/No variables were coded numerically with Yes = 1 and No = 0. All other variables were treated as numeric. The outcome variable was the presence of HDP (Yes = 1, 0 = No).Table 2Correlations among variables Variable CALHEIVEGFAPAV1PAV2PAV3SLPHRNAPSNORERES1RES2ILLV2ILLV3ILLV4CAL1.00-0.320.330.18-0.05-0.09-0.180.110.120.02-0.18-0.13-0.010.00-0.03HEI-0.321.000.380.230.220.280.27-0.04-0.13-0.010.260.21-0.040.000.03VEG0.330.381.000.360.150.150.020.020.050.050.080.11-0.04-0.02-0.07FA0.180.230.361.000.100.120.10-0.05-0.030.020.070.050.010.030.09PAV1-0.050.220.150.101.000.420.36-0.02-0.02-0.060.090.10-0.02-0.030.00PAV2-0.090.280.150.120.421.000.44-0.06-0.020.010.040.16-0.030.000.01PAV3-0.180.270.020.100.360.441.00-0.06-0.030.060.140.170.00-0.020.05SLPHR0.11-0.040.02-0.05-0.02-0.06-0.061.000.03-0.03-0.04-0.04-0.01-0.02-0.04NAP0.12-0.130.05-0.03-0.02-0.02-0.030.031.000.08-0.07-0.100.000.030.03SNORE0.02-0.010.050.02-0.060.010.06-0.030.081.00-0.040.01-0.010.050.02RES1-0.180.260.080.070.090.040.14-0.04-0.07-0.041.000.46-0.09-0.020.05RES2-0.130.210.110.050.100.160.17-0.04-0.100.010.461.00-0.10-0.070.02ILLV2-0.01-0.04-0.040.01-0.02-0.030.00-0.010.00-0.01-0.09-0.101.000.11-0.02ILLV30.000.00-0.020.03-0.030.00-0.02-0.020.030.05-0.02-0.070.111.000.10ILLV4-0.030.03-0.070.090.000.010.05-0.040.030.020.050.02-0.020.101.00Variable abbreviations: CAL Daily Caloric intake, HEI Healthy Eating Index-2010 total (overall), VEG Alternative Healthy Eating Index component scores for vegetable intake, FA Dehydroepiandrosterone (DHEA) and eicosapentaenoic acid (EPA) fatty acid intake; PAV1 (visit 1), PAV2(visit 2), PAV3(visit 3) for the question: “During the past four weeks, did you participate in any physical activities or exercises like running, aerobics, gardening, ball games, or walking for exercise”?; SLPHR: “How many hours of sleep do you usually get per night (in hours)?”; NAP: “*During a* usual week, how many times do you nap for 5 minutes or more? SNORE for the question: “Do you snore?”; RES1: “Are you able to adapt to change?”; RES2: “Do you tend to bounce back after illness or hardship?”; ILLV2 (visit 2), ILLV3 (visit 3), ILLV4 (visit 4) for the question: “Have you had any 'flu-like illnesses,' 'really bad colds,' fever, a rash, or any muscle or joint aches since last study visit?”
## Results
Five hundred and fifty-four participants had complete data available for the study and were included in our final sample. The initial run indicated that the following interaction terms were significant at a level of 0.05 (shown with p-values): DHEA and EPA intake with overall hours of sleep per night (variables 4:8) ($$p \leq 0.003$$); Physical activity in the past month at both visit two and visit three (variables 6:7) ($$p \leq 0.04$$); Physical activity in the past month at visit three and overall hours of sleep per night (variables 7:8) ($$p \leq 0.02$$), Physical activity in the past month at visit three and napping habit (variables 7:9) ($$p \leq 0.01$$). The following main effects that did not appear in a significant interaction but that were also found to be significant at a level of 0.05 were vegetable intake (variable 3) ($$p \leq 0.03$$) and Do you snore? ( variable 10) ($$p \leq 0.02$$).
The model was rerun with only these significant interaction terms (and main effects for the variables involved) and main effects (Table 3). Significant interactions and effects with coefficients of small absolute value were kept in the model to control for additional factors and improve model fit. The following are the interpretable main effects and significant interactions, along with parameter estimates and p-values: Do you snore? ( variable 10) (0.68, $$p \leq 0.01$$); Physical activity in the past month at visit two and visit 3 (varibles 6:7) (-1.49, $$p \leq 0.01$$), Physical activity in the past month at visit 3 with overall hours of sleep per night (variables 7:8) (-0.59, $$p \leq 0.00$$), Physical activity in the past month at visit three and napping habit (variables 7:9) (-1.41, $$p \leq 0.04$$).Table 3Parameter estimates and significance for final model Coefficients:EstimateStd. Errorz valuePr(>|z|)(Intercept)-2.271.54-1.470.1416VEG-0.120.05-2.190.0283FA-0.790.27-2.970.0029PAV21.020.472.190.0287PAV36.191.743.550.0004SLPHR-0.080.18-0.480.6329NAP1.460.592.450.0144SNORE0.680.262.570.0101FA:SLPHR0.100.033.090.0020PAV2:PAV3 -1.490.61-2.420.0157PAV3:SLPHR -0.590.19-3.030.0024PAV3:NAP-1.410.68-2.080.0375Variable abbreviations: VEG Alternative Healthy Eating Index component scores for vegetable intake, FA Dehydroepiandrosterone (DHEA) and eicosapentaenoic acid (EPA) fatty acid intake; PAV2(visit 2), PAV3(visit 3) for the question: “During the past four weeks, did you participate in any physical activities or exercises like running, aerobics, gardening, ball games, or walking for exercise”?; NAP: “*During a* usual week, how many times do you nap for 5 minutes or more;?”SLPHR: “How many hours of sleep do you usually get per night (in hours)?”; SNORE for the question: “Do you snore?” The logistic regression model was then fit to the data with these 15 variables. Because logistic regression is modeling log (p/(1-p)) where p is the probability of the outcome in question (in our case, the negative outcome of HDP), these results can be interpreted as the change in the log odds of a negative outcome for a one unit increase in the effect.
Simply reporting yes to ‘Do you snore?’ at visit one increased the log odds of HDP by 0.68. A higher intake of DHEA and EPA combined with a higher number of hours of reported sleep per night at visit one increased the log odds of HDP by 0.10. An increase in vegetable intake reported at visit one decreased the log odds of HDP by 0.12.
Reporting any physical activity in the past month at both visit two and visit three decreased the log odds of HDP by 1.49. Physical activity in the past month at visit three, combined with a higher number of hours of sleep per night at visit one, decreased the log odds of HDP by 0.59. Reporting yes to napping at least once a week at visit one and reporting any physical activity in the past month at visit three decreased the log odds of HDP by 1.41.
## Discussion
Hypertensive Disorders of Pregnancy cause significant maternal and fetal morbidity and mortality. We hypothesized that lifestyle wellness variables associated with sleep, nutrition, physical activity, resilience, and illness avoidance would impact HDP. Our final analysis of 554 nulliparous women included several findings related to lifestyle wellness variables and HDP risk.
One straightforward finding that indicated an increased risk of HDP in pregnancy was snoring at visit one. There are limited options to screen for hypertension risk in the first trimester and assessing whether a woman snores may be a simple strategy to identify HDP risk early in pregnancy. Research supports that self-reported snoring is related to higher blood pressure in the general population and our results are consistent with emerging research on pregnant populations associating self-reported chronic snoring with adverse pregnancy outcomes like HDP [30, 31]. Known sleep-disordered breathing is associated with HDP, but much of the literature speaks to the risks in later pregnancy when sleep-disordered breathing becomes more pronounced due to the anatomic and physiological changes of later pregnancy [32, 33]. Future research on HDP risk screening should consider snoring prepregnancy and in the first trimester as a variable of interest.
There are limited nutritional guidelines that are specific to pregnancy, although most nutritional guidelines for the general public highlight the importance of vegetables in a healthy diet. Greater vegetable consumption in the three months prior to pregnancy as reported at visit one was associated with decreased risk of HDP in our study. This finding is likely to be associated with the nutrient-dense nature of vegetables and an overall diet habit, established even before pregnancy, that includes more whole foods. Our findings are consistent with previous research that vegetable consumption before and during pregnancy reduces adverse pregnancy outcomes including HDP [34, 35].
One interaction that is difficult to interpret is that increased DHEA and EPA fatty acids in the three months prior to pregnancy combined with more hours of sleep at baseline were associated with HDP. Much research focuses on the health benefits of fish consumption and DHEA and EPA fatty acids for heart health, and some of this research support its reduction of preeclampsia [36, 37]. Typically, adequate sleep is associated with better cardiovascular health outcomes in women [38]. Further study is needed on these variables, their interaction, and their meaning.
Potentially of the greatest importance were the physical activity variables. Any reported physical activity in the four weeks prior to visit two (second trimester) and visit three (later second trimester to early third trimester) reduced the odds of HDP in the sample. This finding is important because it is not based on baseline activity level and includes an area for potential health promotion and intervention development for physical activity during pregnancy to reduce the risk of HDP. In addition, reporting physical activity at visit three with more hours of nightly sleep or with napping habit at visit one reduced risks of HDP. These findings signal that physical activity in later pregnancy is an important variable in HDP risk reduction and may be improved with other health promoting behaviors such as getting recommended sleep, whether through overnight sleep or nap supplementation.
We had hypothesized that resilience traits and avoidance of illness as a representation of preventative health practices would make participants more likely to engage in health promoting wellness lifestyle variables, and further reduce HDP. Our study showed no relationship between these variables and HDP.
## Limitations
Since this study was a secondary analysis, our findings were limited to the data collected in the nuMoM2b dataset. This data was collected during 2010–2013, and since this time HDP research, screening, diagnostics, and management have changed significantly making some of the data potentially irrelevant or less relevant to current day HDP. The sample only included nulliparous, American women who received care at academic medical centers and initiated prenatal care in the first trimester, limiting the generalizability of our findings to other birthing populations. The authors of this study were looking explicitly at select lifestyle and wellness variables and how they may relate to HDP risk, so did not explore in detail or limit for all variables including some relevant comorbidities, such as sleep apnea, which may have further impacted results.
## Conclusion
Hypertension in pregnancy negatively impacts maternal and fetal health. While certain types of HDP, such as preeclampsia, have decreased over the past several decades, chronic hypertension, which has a clear relationship to lifestyle have been on the rise. We hypothesized that wellness variables that often improve chronic diseases may also improve HDP.
Our findings indicate that snoring may be a risk indicator for HDP. Additionally, physical activity in later pregnancy may be an important variable in HDP risk reduction and may further reduce risk when combined with getting recommended sleep. Additionally, simple nutritional changes like increased vegetable consumption may reduce HDP. Our study supports the hypothesis that wellness variables relate to HDP risk reduction and that these variables can be used in interventions to reduce the risk of HDP. These variables should be considered in future research and intervention development to reduce HDP.
These findings should guide research and intervention development. Screening women for snoring and potentially identifying them as at risk may trigger other preventative strategies for HDP, such as aspirin prophylaxis [39]. Simple, low-cost interventions, such as promoting vegetable intake, physical activity and adequate sleep may have a much greater impact on pregnancy health and HDP than previously thought [40].
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|
---
title: 'Perirenal fat thickness and liver fat fraction are independent predictors
of MetS in adults with overweight and obesity suspected with NAFLD: a retrospective
study'
authors:
- Li Wang
- Yuning Pan
- Xianwang Ye
- Yongmeng Zhu
- Yandong Lian
- Hui Zhang
- Miao Xu
- Mengxiao Liu
- Xinzhong Ruan
journal: Diabetology & Metabolic Syndrome
year: 2023
pmcid: PMC10035216
doi: 10.1186/s13098-023-01033-w
license: CC BY 4.0
---
# Perirenal fat thickness and liver fat fraction are independent predictors of MetS in adults with overweight and obesity suspected with NAFLD: a retrospective study
## Abstract
### Background
Nonalcoholic fatty liver disease (NAFLD) has a multidirectional relationship with metabolic syndrome (MetS) and used to be considered a hepatic manifestation of MetS. Perirenal fat, as a part of visceral adipose tissue (VAT), was reported to be correlated with MetS components, but data for intraorgan fat are lacking. This study was undertaken to assess the value of peripheral and intraorgan fat to predict MetS in adults with overweight and obesity with suspected NAFLD.
### Methods
We studied 134 sequential adults (mean age, 31.5 years; $47\%$ female) with overweight and obesity with suspected NAFLD. All participants underwent abdominal magnetic resonance imaging (MRI) examination. Anthropometric and metabolic parameters and perirenal fat thickness (PRFT), subcutaneous adipose tissue thickness (SATT), liver fat fraction (LFF), pancreas fat fraction (PFF), and lumbar spine fat fraction (LSFF) were collected. MetS was defined according to the International Diabetes Federation (IDF) criteria. Statistical analyses included basic statistics, linear correlation and logistic regression analysis.
### Results
A total of 63 adults with MetS and 71 adults with advanced liver steatosis (grades 2 and 3) were included in our study. Patients with MetS had greater PRFT ($$p \leq 0.026$$) and LFF ($p \leq 0.001$), as well as greater homeostasis model assessment of insulin resistance (HOMA-IR), alanine transaminase (ALT), aspartate transaminase (AST), and decreased SATT. MetS patients had a higher proportion of advanced steatosis than those without MetS ($P \leq 0.001$). The MetS score was associated with PRFT and LFF. Logistic regression analysis showed that the PRFT and LFF were independent predictors of MetS after adjusting for age and sex. A cutoff of 9.15 mm for PRFT and $14.68\%$ for LFF could be predictive of MetS.
### Conclusions
This study shows that the absolute cutoff level of 9.15 mm for PRFT and $14.68\%$ for LFF may be clinically important markers for identifying patients who are at high risk of MetS among adults with overweight and obesity with suspected NAFLD, irrespective of sex and age. Moreover, ectopic fat levels in pancreas and lumbar spine are positively associated with PRFT.
### Trial registration
Not applicable.
## Background
MetS, variously known as syndrome X, insulin resistance, etc., is defined by WHO as a pathologic condition characterized by abdominal obesity, insulin resistance, hypertension, and hyperlipidemia [1]. The reported prevalence of MetS in China in 2015–2017 among Chinese residents aged 20 years or older was $31.1\%$ [2], which parallels the incidence of obesity. The total cost of the malady including the cost of health care and loss of potential economic activity is enormous. MetS patients have a high risk of cardiovascular diseases, type 2 diabetes, stroke, and other disabilities. The syndrome is now both a public health and a clinical problem, and individuals with MetS need to be identified effectively. The definition of MetS is slightly different according to various organizations. The three most commonly used definitions are the WHO 1999 criterion, National Cholesterol Education Program (NCEP) ATP3 2005 criterion and IDF 2006 criterion [3]. The IDF criterion for Chinese patients is as waist ≥ 90 cm (males) or ≥ 80 cm (females) along with the presence of two or more of the following: [1] Blood glucose ≥ 5.6 mmol/L or diagnosed diabetes; [2] High-density lipoprotein (HDL) cholesterol < 1.0 mmol/L in males, < 1.3 mmol/L in females or drug treatment for low HDL-C; [3] Blood triglycerides ≥ 1.7 mmol/L or drug treatment for elevated triglycerides; or [4] Systolic blood pressure ≥ 130 and/or diastolic ≥ 85 mmHg or drug treatment for hypertension.
NAFLD is one of the most significant comorbidities of obesity and presents a high degree of comorbidity with disorders of MetS, including type 2 diabetes and cardiovascular disease. Their prevalence has increased worldwide and can be characterized as a growing epidemic, increasing along with the incidence of obesity [4, 5]. NAFLD has a multidirectional relationship with MetS and used to be considered the hepatic consequence of MetS [6]. It was reported that the estimated prevalence of NAFLD in China was $23.8\%$ in the early 2000s, and it reached $32.9\%$ in 2018 in parallel with the rising trend of obesity in China [7].
The diagnosis of NAFLD requires more than or equal to $5\%$ hepatic fat accumulation and exclusion of other etiologies of liver disease, such as viral hepatitis, autoimmune liver disease, hemochromatosis, Wilson’s disease, drug-induced liver disease and significant alcohol consumption [8]. There are several noninvasive methods to quantitatively assess liver fat, including ultrasonography (US), controlled attenuation parameter (CAP), computed tomography (CT), hydrogen-1 magnetic resonance spectroscopy (MRS) and magnetic resonance imaging-estimated proton density fat fraction (MRI-PDFF) [9]. MRI-PDFF, regarded as the most accurate quantitative method for measuring liver fat content in clinical practice, was found to be correlated with histologic steatosis grade and provided reasonable accuracy for noninvasive classification of steatosis grades by Tang and colleagues [10, 11]. Moreover, it could also be used quantitatively evaluate the fat content of other organs, such as the pancreas, kidney, spine and muscle, at the same time.
Data from several studies over the past three decades have shown that MetS is more associated with visceral adipose and ectopic fat tissue than with overall and subcutaneous fat mass (SAT) [12–14]. Increasing visceral accumulation above the threshold is associated with decreased insulin sensitivity and cardiovascular risk independent of total body fat [15]. As a part of visceral adipose tissue, the adipose tissue surrounding the kidney, has been reported as an easily reproducible, indirect measurement of visceral fat, is considered a metabolically active tissue, and has been reported to be associated with hypertension [16] and atherosclerosis [17] in adults. Moreover, the accumulation of perirenal fat (PRF) was reported to correlate with MetS features in patients with obesity [18–20]and was also identified as an emerging cardiovascular risk factor. However, there have been few studies on the connection between MRI-measured PRF and intraorgan fat depots.
Therefore, we enrolled adults with overweight and obesity with suspected NAFLD and divided them into the MetS + and MetS-. Then, we investigated excessive fat depots and the intraorgan fat content, including the PRFT, SATT, and fat contents of the liver, pancreas, and lumbar spine, and their relationship with MetS to identify the subgroups of patients at high risk of MetS.
## Study population
In this monocentric cross-sectional study, we investigated patients with body mass index (BMI) ≥ 25 kg/m2 suspected to have NAFLD on the basis of clinical, laboratory and US data at the Department of Endocrinology, Ningbo First Hospital, Zhejiang, China, from April 2021 to December 2021. Criteria for inclusion were as follows: [1] age ≥ 18 years; [2] evidence of absent or minimal alcohol consumption: <20 g alcohol/day for females and < 30 g alcohol/day for males; [3] absence of confounding disease including acute and/or chronic viral hepatitis (hepatitis A, B, or C); and [4] exclusion of other forms of liver disease including autoimmune, drug-induced, cholestatic and metabolic liver diseases, as well as large liver cysts and hemangioma. The research flowchart is shown in Fig. 1. According to the IDF criteria [3], we divided participants into the MetS + and MetS- groups. The Ethics Committee of Ningbo First Hospital approved the study(022RS). The data are anonymous, and the requirement for informed consent was therefore waived. A total of 134 adults with overweight and obesity with suspected NAFLD were enrolled in the study.
Fig. 1Flowchart of the patient selection and demographics. Populations with overweight and obesity were defined according to World Health Organization BMI cutoffs. Patients were suspected of NAFLD on the basis of clinical, laboratory and US findings. NAFLD nonalcoholic fatty liver disease
## Clinical and laboratory assessments
All anthropometric parameters were obtained, including age, sex, height, weight, BMI, and waist circumference (WC). Patients were evaluated for all the features of MetS, including diabetes mellitus, hypertension, HDL and central obesity. A WC value ≥ 90 cm in Chinese males and ≥ 80 cm in Chinese females was considered central obesity. AST and ALT, fasting glucose, postprandial blood glucose (PPG), fasting insulin, total cholesterol, HDL cholesterol, LDL-cholesterol, triacylglycerol, uric acid and glycated hemoglobin (HBALC) were measured by our central laboratory. The MetS score is defined as the total number of MetS components present in an individual. Insulin resistance (HOMA-IR) was estimated by the homeostasis model assessment [HOMA-IR = fasting insulin (µU/mL) x fasting glucose (mmol/L)/22.5] [21].
## MRI assessments
The thickness of the SAT and PRF, as well as the MRI-PDFF of visceral organs and the lumbar spine, were measured through a 3.0 T MRI scanner (BioMatrix system, MAGNETOM Vida, Siemens Healthcare, Erlangen, Germany) equipped with an 18-channel array coil. The whole liver and organs in the upper abdomen were covered. The scanning parameters of axial liver acquisition with volume interpolated breath-hold examination (VIBE-Dixon) sequence were as follows: repetition time (TR) = 3.97 ms; echo time (TE) = 1.23 ms; thickness = 3 mm; field of view (FOV) = 420 mm; voxel size = 1.3 mm × 1.3 mm × 3 mm; flip angle = 9°; and averages = 1. The total acquisition time was 13 s. The Q-DIXON sequence of the program named “Liver Quant” was acquired to quantify the fat content of different organs. The parameters of this sequence were TR = 9 ms; TE = 1.05, 2.46, 3.69, 4.92, 6.15, and 7.38 ms; thickness = 3 mm; FOV = 420 mm; voxel size = 1.3 mm × 1.3 mm × 3 mm; flip angle = 4°; and averages = 1. The total acquisition time was 15 s.
Patients were instructed to hold their breath during examination. MRI measurement of SAT and PRF was obtained at the level of the exit of the left renal vein, which was easily measured on a transverse section MRI fat VIBE-Dixon image, with the adipose tissue having significantly high signal intensity, while other tissues had significantly low signal intensity (Fig. 2). The SATT was defined as the distance between the skin and external face of the linea alba. The PRFT was defined as the distance from the anterior margin of the quadratus lumborum muscle to the dorsal margin of the left kidney as previously described [22–24].
Fig. 2Measurement of PRFT, LFF, PFF and LSFF on MRI map. ( A) The assessment of PRFT of a 49 years old femalewith grade 3 NAFLD and MetS on axial MRI fat VIBE-Dixon map. PRFT was calculated by the distance from the anterior margin of the quadratus lumborum muscle to the dorsal margin of the left kidney. ( B) The two ROIs in hepatic segments VII-VIII on the PDFF map. LFF was calculated by averaging the results of 8 round ROIs in each of the hepatic segments. ( C) ROI in the pancreas body on the PDFF map. The PFF was calculated by averaging the results of three ROIs on the head, body and tail of the pancreas. ( D) ROI in the L1 vertebral body on the PDFF map. LSFF measurements were performed by averaging the results of two ROIs on L1-2. PRFT perirenal fat thickness, LFF liver fat fraction, PFF pancreas fat fraction, LSFF lumbar spine fat fraction Fig. 3ROC curves for perirenal fat thickness, liver fat fraction and their combination. Y-axis: sensibility; x-axis: 1-specificity. PRFT and LFF as predictive values for MetS. PRFT perirenal fat thickness, LFF liver fat fraction, MetS metabolic syndrome Quantitative assessments of the fat contents of the liver, pancreas, and vertebral spine were obtained on the PDFF maps of the Q-Dixon sequence. All MRI images were analyzed by two radiologists with 7 and 5 years of experience. They recorded the LFF by averaging the results of 8 round regions of interest (ROIs) that were more than 2 cm2 in each of the hepatic segments on the PDFF map (Fig. 2). The fatty liver grades were defined according to Jens-Peter Kühn’s study as follows: 0, none (PDFF ≤ $5.1\%$); 1, mild (PDFF > $5.1\%$); 2, moderate (PDFF > $14.1\%$); and 3, severe (PDFF > $28.0\%$) [25]. The PFF was calculated by drawing three ROIs that were more than 1 cm2 on the head, body and tail of the pancreas; this process was repeated three times to ensure that all slices showed the pancreas clearly on the postprocessing workstation. LSFF measurements were performed by placing ROIs on the L1-2 vertebral body with more than 2 cm2 at the level of the pedicle of the vertebral arch, and the mean PDFF was obtained as the final result by averaging them. All ROIs were placed within the tissue of interested by avoiding major vessels, ducts, and imaging artifacts and ensuring that the ROI was surrounded by the tissue of interest. Image analysis was performed on our hospital patient information system (PACS).
## Statistical analysis
Statistical analysis was performed using SPSS version 25 (IBM Corp, Armonk, Chicago, USA). Continuous variables are expressed as the means (SD); categorical variables are expressed as absolute and relative frequencies. Chi-square tests were used to test for proportions of the categorical variables. Differences between two groups were compared using the Mann‒Whitney U or Student’s t test, as appropriate.
Associations between the MetS score and peripheral and intraorgan fat parameters were studied by Pearson’s and Spearman’s method; logistic regression analysis was used to study independent associations of excessive fat depots, metabolic and anthropometric parameters and MetS after adjustment for potentially confounding factors. The odds ratio (OR) with $95\%$ confidence interval (CI) was determined. The sensitivity and specificity of the PRFT and LFF to predict MetS were assessed using receiver operating characteristic (ROC) curve analysis. Goodness-of-fit was assessed by calculating the area under the curve (AUC), and the optimal cutoff value was determined by the Youden index. A p value < 0.05 was considered to indicate statistical significance.
## General characteristics, PRFT and intra-organ fat of the whole population
Age ranged from 18 to 59 years old (mean age, 31.5 years). Of a total of 134 adults, $52.99\%$ were male and $47.01\%$ were female. Table 1 shows the baseline characteristics, visceral adipose tissue and intraorgan fat, separated by sex. Sixty-three patients ($47.01\%$) presented with MetS, of whom 33 were male and 30 were female, 15 patients hae fatty liver grade 0 and 1, and 48 patients had grade 2 and 3. Eighty-two patients ($61.2\%$) had advanced liver steatosis (grade 2 and 3), of whom 44 were male and 38 were female. No significant differences in LFF, MetS or advanced liver steatosis were found between males and females. The MetS + group had a higher proportion of advanced liver steatosis. Males showed higher WC, uric acid, PFF, and LSFF and thicker PRFT, whereas females had significantly larger HDL and thicker SATT ($p \leq 0.05$). The PRFT was significantly greater in males than in females (15.19 ± 7.06 mm vs. 7.72 ± 5.38 mm, $P \leq 0.001$).
Table 1Clinical features of all participants according to sexCharacteristicsOverall ($$n = 134$$)Male($$n = 71$$)Female ($$n = 63$$)p valueAge (y)31.5 (9.16)31.49 (9.90)31.51 (8.34)0.993BMI (kg/m2)33.96 (4.75)34.25 (4.37)33.63 (5.16)0.451Waist circumference (cm)106.84 (11.5)110.7 (10.03)102.51 (11.57)< 0.001Fasting glucose (mmol/L)5.77(1.62)5.58(1.21)5.99(1.98)0.142Systolic blood pressure (mmHg)134.27(14.93)135.38(13.08)133.02(16.79)0.362Diastolic blood pressure (mmHg)82.48(10.95)82.82 (10.52)82.10(11.50)0.705Uric acid (µmol/L)448.33 (108.13)500.2 (106.21)390.69 (76.98)< 0.001Triglycerides (mmol/L)2.19 (2.77)2.57 (3.70)1.76 (0.78)0.087HDL (mmol/L)1.12 (0.22)1.07 (0.23)1.18 (0.20)0.002LDL (mmol/L)3.7 (0.94)3.64 (0.93)3.78 (0.95)0.385ALT (U/L)70.58 (54.66)77.62 (51.41)62.65 (57.48)0.114AST (U/L)43.23(32.22)42.45(24.74)44.11(39.18)0.773HOMA-IR8.37 (5.96)8.57 (6.32)8.15 (5.57)0.689PRFT (mm)11.68 (7.33)15.19 (7.06)7.72 (5.39)< 0.001SATT (mm)29.47 (10.23)25.36 (9.23)34.1 (9.33)< 0.001LFF (%)17.25 (10.19)16.88 (9.33)17.66 (11.15)0.662PFF (%)5.9 (5.35)7.14 (6.07)4.45 (4.01)0.004LSFF (%)41.43 (9.7)44.11 (9.22)38.41 (9.40)0.001MetS (%)47.0146.4847.620.895Data are presented as mean (SD) or percentage. Results were based on analyses weighted towards the sex distribution of the general population. HDL high density lipoprotein, LDL low density lipoprotein, ALT alanine transaminase, AST aspartate transaminase, HOMA-IR homeostasis model assessment of insulin resistance, PRFT perirenal fat thickness, SATT subcutaneous adipose tissue thickness, LFF liver fat fraction, PFF pancreas fat fraction, LSFF lumbar spine fat fraction, MetS metabolic syndrome.
## The clinical and biochemical characteristics and excessive and intraorgan fat depots according to MetS and fatty liver grade
The MetS + group had a higher proportion of advanced steatosis ($58.54\%$ vs. $28.85\%$, $P \leq 0.001$) and higher LFF (20.7 ± $10.5\%$ vs. 14.18 ± $8.9\%$, $P \leq 0.001$) than those of the MetS- group. In comparison with those of the MetS- group, the PRFT of the MetS + group was significantly increased (13.17 ± 7.7 mm vs. 10.35 ± 6.78 mm, $P \leq 0.05$), whereas the SATT was significantly decreased (27.53 ± 10.07 mm vs. 31.19 ± 10.12 mm, $P \leq 0.05$) (Table 2). The MetS + group had higher HOMA-IR, ALT and AST levels. Patients with advanced liver steatosis (grade 2 and 3) had greater PRFT than those with grade 0 and 1 fatty liver (12.79 ± 7.60 mm vs. 9.92 ± 6.58 mm, $P \leq 0.05$), as well as higher WC, fasting glucose, triglycerides, HOMA-IR, ALT, AST, and glycated hemoglobin (Table 3).
Table 2Clinical and biochemical characteristics and ectopic fat depots according to MetSCharacteristicsnPatients with MetS($$n = 63$$)Patients without MetS($$n = 71$$)p valuePRFT (mm)13413.17 (7.70)10.35 (6.78)0.026SATT (mm)13427.53 (10.07)31.19 (10.12)0.038Age (y)13433.06(9.20)30.11(8.97)0.063BMI (kg/m2)13433.77(4.56)34.12(4.93)0.668Waist circumference (cm)134107.46(10.86)106.30(12.09)0.584Fasting insulin (pmol/L)134236.80(127.52)207.23 (117.51)0.165HOMA-IR1349.81 (6.86)7.09 (4.72)0.009ALT (U/L)13484.27 (60.72)58.44 (45.74)0.006AST (U/L)13451.00 (36.46)36.34 (26.32)0.008LFF (%)13420.70(10.50)14.18 (8.92)< 0.001PFF (%)1336.67 (6.25)5.20(4.39)0.114Pancreas head fat fraction (%)1334.02(3.44)3.05(3.06)0.086Pancreas body fat fraction (%)1335.96(7.20)4.51(4.31)0.157Pancreas tail fat fraction (%)13310.05(10.65)8.03(8.0)0.217LSFF (%)13442.12(9.82)40.82 (9.62)0.440Data are presented as mean (SD) or percentage. Results were based on analyses according to the MetS of the general population. MetS metabolic syndrome, PRFT perirenal fat thickness, SATT subcutaneous adipose tissue thickness, HOMA-IR homeostasis model assessment of insulin resistance, ALT alanine transaminase, AST aspartate transaminase, LFF liver fat fraction, PFF pancreas fat fraction, LSFF lumbar spine fat fraction.
Table 3The differences in clinical, biochemical characteristics, subcutaneous fat, perirenal fat thickness and intraorgan fat depots between fatty liver grade 0 & 1 and grade 2 & 3CharacteristicsGrade 0 & 1($$n = 52$$)Grade 2 & 3($$n = 82$$)P valueAge (y)30.75 (9.03)31.98 (9.26)0.453BMI (kg/m2)33.5 (4.18)34.25 (5.08)0.354Waist circumference (cm)104.04 (10.62)108.62 (11.74)0.024Fasting glucose (mmol/L)5.31 (0.63)6.06 (1.97)0.002Uric acid (µmol/L)432.18 (101.05)459.00 (111.58)0.15Triglycerides (mmol/L)1.59 (0.93)2.57 (3.41)0.045HDL (mmol/L)1.15 (0.22)1.10 (0.23)0.287ALT (U/L)38.33 (28.08)91.04 (57.60)<0.001AST (U/L)26.31 (15.66)53.96 (35.36)<0.001Fasting insulin (pmol/L)198.79 (142.45)235.99 (107.44)0.089HOMA-IR6.92 (5.83)9.29 (5.89)0.024HbAlc (%)5.37 (0.40)6.19 (1.39)<0.001PRFT (mm)9.92 (6.58)12.79 (7.60)0.027SATT (mm)30.09 (9.00)29.07 (10.97)0.577PFF (%)5.97 (4.78)5.85 (5.71)0.907LSFF (%)40.65 (11.19)41.92 (8.66)0.489MetS (%)28.8558.54< 0.001Data are presented as mean (SD) or percentage. Results were based on analyses weighted towards the fatty liver grade of the general populationHDL high density lipoprotein, ALT alanine transaminase, AST aspartate transaminase, HOMA-IR homeostasis model assessment of insulin resistance, HbAlc glycosylated hemoglobin, PRFT perirenal fat thickness, SATT subcutaneous adipose tissue thickness, PFF pancreas fat fraction, LSFF lumbar spine fat fraction, MetS metabolic syndrome
## The relationships of anthropometric and biochemical parametersand peripheral and intraorgan fat depots with MetS
We observed a significant positive correlation between PRFT and age ($r = 0.297$; $p \leq 0.0001$), BMI ($r = 0.244$; $$p \leq 0.004$$), WC ($r = 0.402$; $p \leq 0.0001$), uric acid ($r = 0.315$; $p \leq 0.0001$), ALT ($r = 0.176$; $$p \leq 0.041$$), fasting insulin ($r = 0.182$; $$p \leq 0.036$$), PFF ($r = 0.314$; $p \leq 0.0001$), and LSFF ($r = 0.225$; $$p \leq 0.009$$), whereas PRFT showed a negative correlation with SATT (r=-0.339; $p \leq 0.0001$), and HDL (r=-0.248; $$p \leq 0.004$$). There were no significant correlations between PRFT and LFF ($r = 0.077$; $$p \leq 0.378$$), fatty liver grade ($r = 0.147$; $$p \leq 0.090$$), and HOMA-IR ($r = 0.167$; $$p \leq 0.055$$). Fatty liver grade was positively correlated with fasting glucose ($r = 0.306$; $p \leq 0.0001$), fasting insulin ($r = 0.251$; $$p \leq 0.003$$), HOMA-IR ($r = 0.331$; $p \leq 0.0001$), ALT ($r = 0.584$; $p \leq 0.0001$), AST ($r = 0.576$; $p \leq 0.0001$), and uric acid ($r = 0.197$; $$p \leq 0.023$$). With regard to intraorgan fat depots, PFF showed a significant positive correlation with PRFT, age, and LSFF and was negatively correlated with SATT, and LSFF was positively correlated with PRFT, age, BMI, WHR, uric acid, and PFF and negatively correlated with SATT.
Univariable analysis showed that PRFT, HOMA-IR, ALT, AST and LFF in patients with MetS were significantly increased compared with those in patients without MetS, while SATT was significantly decreased in patients with MetS (Table 3). In Table 4, the association of the MetS score, ranging from zero to five, with peripheral and intraorgan fat parameters is shown. PRFT ($$p \leq 0.013$$) and LFF ($p \leq 0.001$) were significantly associated with the MetS score. The association between biochemical parameters, intraorgan fat, excessive fat depots and MetS was assessed by using logistic regression analysis. The results showed that the PRFT and LFF were significant and independent predictors for the presence of MetS (OR = 1.061, $95\%$ CI, 1.007–1.118; $$p \leq 0.026$$; and OR = 1.077; $95\%$ CI, 1.035–1.121; $p \leq 0.001$) after adjusting for confounding factors, i.e., age and sex, including the PRFT, SATT, HOMA-IR, ALT, AST, LFF, and pancreas head fat fraction as predictive values with MetS as the dependent value.
Table 4Relationship of peripheral and intraorgan fat parameters to metabolic syndrome scores (range 0–5)Peripheral and intra-organ fat parametersrp valueWaist circumference0.5210.056SATT-0.1610.063PRFT0.2150.013LFF0.324< 0.001PFF0.10.252LSFF0.0240.784SATT subcutaneous adipose tissue thickness, PRFT perirenal fat thickness, LFF liver fat fraction, PFF pancreas fat fraction, LSFF lumbar spine fat fraction.
## Predictive analysis
ROC curves analysis showed a cutoff point for PRFT of 9.15 mm to predict MetS with a sensitivity of 0.683 and specificity of 0.549 (AUC = 0.610, $$p \leq 0.028$$), and a cutoff point for LFF of $14.68\%$ to predict MetS with a sensitivity of 0.762 and specificity of 0.563 (AUC = 0.679, $p \leq 0.001$). Moreover, the combination of PRFT and LFF better predicted MetS with a sensitivity of 0.841 and specificity of 0.465 (AUC = 0.70 and $p \leq 0.001$) (Fig. 3).
## Discussion
In our study, we used PRFT as an easily reproducible method to measure visceral fat indirectly on MRI. Univariable analysis revealed that PRFT, SATT, HOMA-IR, ALT, AST and LFF were associated with MetS. Adjusting for many potential confounding variables, logistic regression analysis showed that PRFT and LFF were independent predictors of the presence of MetS in adults with overweight and obesity suspected of having NAFLD. ROC analysis showed the cutoff point for PRFT of 9.15 mm was an important indicator of MetS in our study. Previous studies showed that PRF as visceral fat was strongly associated with diastolic blood pressure level [16], the risk of developing of chronic kidney disease in diabetes [26], metabolic risk factors in patients with chronic kidney disease [27], and postoperative complications after laparoscopic distal gastrectomy for gastric cancer [24]. Anatomy studies have proven that perirenal fat is unique compared to other connective tissues in that it is well vascularized, innervated, and drains into the lymphatic system [28–30]. In Liu’s study, excessive perirenal adiposity increased the risk of coronary heart disease (CHD) and hypertension through adipokine secretion, fat–kidney interactions, and the neural reflex [31]. A recent study also showed that a cutoff of 22.5 mm (M)/12.5 mm (F) of perirenal fat measured by ultrasound could be predictive of later MetS onset [19]. Further studies are needed to evaluate the value of these cutoff measures as prognostic markers in larger studies.
Data from recent studies showed that the MRI measured liver fat content was significantly associated with an increased risk of MetS, and an increase in the amount of liver fat had a clear dose-response relationship with the presence of MetS and the number of MetS components among adults [32, 33]. NAFLD used to be considered the hepatic consequence of MetS. In this study, our results showed that LFF measured by MRI-PDFF was an independent predictor for the presence of MetS, even after adjusting for age and sex. We proposed a cutoff level of $14.68\%$ for LFF may be a clinically important marker for identifying NAFLD patients who are at high risk of MetS. ROC analysis showed that compared with PRFT, the area under the curve of LFF was larger. Furthermore, the combination of PRFT and LFF better predicted MetS with a sensitivity of 0.841, specificity of 0.465, and AUC = 0.70.
In this study, another significant correlation between the PRFT and intraorgan fat content has also been described, that is, the LFF, PFF and LSFF. To the best of our knowledge, this study, performed in Chinese adults with overweight and obesity suspected to have NAFLD, is the first to show a direct relationship between perirenal fat and intraorgan fat depots, such as the liver, pancreas and lumbar spine, measured by MRI. These results suggest that the perirenal fat and intraliver, intrapancreas and intralumbar spine fat depots were simultaneously increased in overweight or obese individuals suspected of having NAFLD, supporting the coexistence of excessive fat depots and intraorgan fat depots in such patients. In line with this hypothesis, our results agreed with the study of Cuatrecasas et al. [ 19] that patients with advanced liver steatosis showed larger amounts of perirenal fat compared to that of patients with fatty liver grade 0 and 1. Linear correlation analysis showed that there was no significant correlation between PRFT and LFF or liver steatosis grades. Further research with a large sample size is needed to examine the correlations between excessive fat depots and ectopic fat depots to identify the organs that are vulnerable to MetS.
With regard to the positive correlation between PRFT and PFF, as well as fasting insulin, our results agreed with those of previous studies that PRFT was independently associated with higher insulin resistance [34], and fatty pancreas was correlated with insulin resistance and β-cell dysfunction [35–38]. A study by Nadarajah and colleagues showed that there was a significant difference between the T2DM and control groups with respect to the fat fraction in the pancreatic head, body, and tail, and increased fat in the pancreatic tail may identify patients at risk of T2DM [39], while in our study, the value of the pancreas fat content in identifying MetS was not found. Few studies have investigated the relationship between PRFT and LSFF, but our results partly agreed with Bredella et al. [ 40] that ectopic fat was positively associated with bone marrow fat in adults with obesity. Further research is needed to elucidate the mechanisms linking these excessive visceral fat depots with intraorgan fat depots.
US has been widely used to measure visceral fat to find the association between abdominal fat layers and MetS features in a number of studies [16, 19, 20, 27]. Nevertheless, the evaluation is subjective and relies on the operator’s experience. Quantitative analysis of PRFT by MRI has a higher degree of precision and reproducibility than US [41]. Furthermore, PRFT/SATT and MRI-PDFF of different organs can be precisely quantified within 1 min. Thus, it is a promising, more accurate and reproducible method preferred over US. Controversy exists regarding the reference single axial slice that is used to assess VAT/SAT at baseline and predict changes. In our study, the level of exit of the left renal vein was near the level of the L1–L2 intervertebral disc, which was considered the optimized site that can be used for quantitating the amount of VAT/SAT on MR examinations [42], and the optimal site for assessing perirenal fat was near the central level of the left kidney. Since the assessment of fat distribution of VAT in clinical practice remains a challenge, PRFT and LFF measured by MRI may be a simple, convenient, easy-to-reproduce, clinically applicable tool to monitor changes in VAT and ectopic fat, implying their role as emerging MetS and cardiovascular risk factors.
Our study had several limitations. First, the data were derived from our single-center institution, and our sample size was relatively small and thus cannot represent the current state of the whole country. Second, it was an observational cross-sectional design, and longitudinal data and outcomes were lacking. Third, the causal effect of the association between PRFT and MetS cannot be elucidated. Finally, the cutoff value as a prognostic marker should be evaluated in larger studies.
## Conclusions
The present study, performed in adults with overweight and obesity with suspected NAFLD, showed that the absolute cutoff level of 9.15 mm for PRFT and $14.68\%$ for LFF may be clinically important markers for identifying patients who are at high risk of MetS, irrespective of sex and age. Moreover, ectopic fat levels in the pancreas and lumbar spines are positively associated with PRFT.
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|
---
title: CA916798 predicts poor prognosis and promotes Gefitinib resistance for lung
adenocarcinoma
authors:
- Jian He
- Xi Lan
- Xiayan Liu
- Caixia Deng
- Hu Luo
- Yan Wang
- Ping Kang
- Zhijian Sun
- Lintao Zhao
- Xiangdong Zhou
journal: BMC Cancer
year: 2023
pmcid: PMC10035219
doi: 10.1186/s12885-023-10735-3
license: CC BY 4.0
---
# CA916798 predicts poor prognosis and promotes Gefitinib resistance for lung adenocarcinoma
## Abstract
### Background
Our previous studies have identified CA916798 as a chemotherapy resistance-associated gene in lung cancer. However, the histopathological relevance and biological function of CA916798 in lung adenocarcinoma (LUAD) remains to be delineated. In this study, we further investigated and explored the clinical and biological significance of CA916798 in LUAD.
### Methods
The relationship between CA916798 and clinical features of LUAD was analyzed by tissue array and online database. CCK8 and flow cytometry were used to measure cell proliferation and cell cycle of LUAD after knockdown of CA916798 gene. qRT-PCR and western blotting were used to detect the changes of cell cycle-related genes after knockdown or overexpression of CA916798. The tumorigenesis of LUAD cells was evaluated with or without engineering manipulation of CA916798 gene expression. Response to Gefitinib was evaluated using LUAD cells with forced expression or knockdown of CA916798.
### Results
The analysis on LUAD samples showed that high expression of CA916798 was tightly correlated with pathological progression and poor prognosis of LUAD patients. A critical methylation site in promoter region of CA916798 gene was identified to be related with CA916798 gene expression. Forced expression of CA916798 relieved the inhibitory effects of WEE1 on CDK1 and facilitated cell cycle progression from G2 phase to M phase. However, knockdown of CA916798 enhanced WEE1 function and resulted in G2/M phase arrest. Consistently, chemical suppression of CDK1 dramatically inhibited G2/M phase transition in LUAD cells with high expression of CA916798. Finally, we found that CA916798 was highly expressed in Gefitinib-resistant LUAD cells. Exogenous expression of CA916798 was sufficient to endow Gefitinib resistance with tumor cells, but interference of CA916798 expression largely rescued response of tumor cells to Gefitinib.
### Conclusions
CA916798 played oncogenic roles and was correlated with the development of Gefitinib resistance in LUAD cells. Therefore, CA916798 could be considered as a promising prognostic marker and a therapeutic target for LUAD.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12885-023-10735-3.
## Background
Lung cancer is the most common malignant tumor and the leading cause of cancer-associated deaths worldwide [1, 2]. Small cell lung cancer and non-small cell lung cancer (NSCLC) are two histopathological types of lung cancer seen in patients clinically. NSCLC accounts for approximately $85\%$ of lung cancer and can be further classified into lung adenocarcinoma (LUAD, 40–$50\%$), lung squamous cell carcinoma (about $30\%$), and large-cell carcinoma according to histopathological features [3–5]. As the major histological subtypes of NSCLC, LUAD generally evolves from mucosal glands and related with chronic inflammation in lungs [6, 7]. LUAD is one of the most aggressive and rapidly fatal types of cancer with overall survival of less than 5 years for LUAD patients [8, 9]. Despite the progression of chemo-radio therapy, targeted therapy and immunotherapy, the prognosis of LUAD is still poor with 5-year survival less than $15\%$ [10, 11]. The low survival rate is mainly attributed to the primary or acquired resistance to treatments, resulting in progression, metastasis, and recurrence of LUAD [12, 13].
CA916798 gene is firstly identified by our group in a cisplatin-resistant LUAD cell line through suppression cut hybridization technology [14]. CA916798 is expressed at higher levels in the 12-week fetal lung, the 12-week fetal liver and embryonic skin tissues than in normal adult lung tissues in our previous experiments. These results indicate that the CA916798 may be related to embryonic development, which is a characteristic of most tumor-related genes. Indeed, the gene was expressed in a variety of tumor tissues and tumor cells, including lung cancer cells [15–18], prostate cancer cells [19, 20], renal cell carcinoma [21], and breast cancer [22]. Multiple roles of this gene have been reported. For example, our previous work indicated that high CA916798 gene expression was related to cisplatin resistance in LUAD and small cell carcinoma [15, 23, 24]. Accumulating evidence suggests that CA916798 has oncogenic roles. CA916798 is found to be regulated by PI3K/AKT and SHP2 pathways [17, 25, 26], which intensively promote proliferation and inhibit apoptosis in cancers. CA916798 promotes growth and metastasis of androgen-dependent prostate cancer cells [19]. In addition, C19orf48 encodes a minor histocompatibility antigen in renal cell carcinoma cells [21]. However, the clinical significance and biological function of CA916798 in LUAD remains unclear.
EGFR strongly stimulates cell proliferation, invasion, and metastasis via AKT/MTOR and MAPK/ERK signaling axes [27]. Constitutive activation mutations of EGFR have been frequently detected in LUAD patients, especially in east Asian countries, and these patients are shown to benefit from the first-generation tyrosine kinase inhibitors (TKIs), such as gefitinib and erlotinib [28]. Gefitinib is the most efficient treatment for blocking EGFR activation in clinical cases [29]. However, gefitinib resistance is inevitably developed in most patients [30, 31]. Although different mechanisms of acquired EGFR-TKIs resistance have been reported [32, 33], it remains poorly understood.
In this study, we further explored the functions of CA916798 in LUAD, which demonstrated critical involvements of CA916798 in poor prognosis of LUAD and revealed CA916798 as a promising target for in Gefitinib-resistant LUAD.
## Cell lines and reagents
A549, PC9 and HCC827 human LUAD cell lines (Shanghai Institute of Biochemistry and Cell Biology, Shanghai, China) were cultured in DMEM containing $10\%$ fetal bovine serum (FBS) (16,140,071, ThermoFisher Scientific, Shanghai, China) at 37 °C and $5\%$ CO2. Ro-33,066, Gefitinib, and sodium carboxymethyl cellulose (CMC-Na) was purchased from Selleck (Houston, TX, USA).
## Lentivirus, infection, and stable cell lines
Three short hairpin RNAs (shRNAs) targeting CA916798 and mock shRNA were inserted into lentivirus by GenePharma (Shanghai, China). The shRNA target sequences are listed in Table 1. Lentivirus containing human full-length CA916798 or empty vector as control were constructed by GenePharma (Shanghai, China). Lentivirus was used to infect cells following standard protocols and cells were selected using 5 µg/mL puromycin (Sigma, USA).
Table 1Sequences of CA916798 shRNA control shRNA.PlasmidSequenceLV3-CtrlTTCTCCGAACGTGTCACGTLV3-CA916798-Homo-1TGGAGGCCGTCTTGGAGATLV3-CA916798-Homo-2GACCCAACCCAGTGCACAAGGLV3-CA916798-Homo-3TCCATACGCCACCGTGAGA
## Immunohistochemistry (IHC)
LUAD tissue microarray (HLugA180Su06) was purchased from Shanghai Outdo Biotech CO., LTD. ( http://www.superchip.com.cn/index.html). IHC of tissue array and tumor xenografts was performed using the streptavidin-biotin peroxidase complex method and a rabbit polyclonal antibody against CA916798 (1:100, Invitrogen, USA). Photographs were obtained using an Olympus BX51 microscope and expression was scored in five random fields using the Image-Pro Plus 5.0 software. The area sum and integrated optical density (IOD) sum of the positive-stained sites (brown staining) were measured in pixels. CA916798 expression intensity was determined as the mean value of IOD sum/area sum in five images per slide. The same parameter settings were maintained for all images. Combined with the clinical prognosis of patients, the cut-off value for expression was 0.027609, as analyzed with SPSS 22.0. Expression > 0.027609 was defined as CA916798 high expression.
## RNA extraction and quantitative real-time RT-PCR
Total RNA was extracted using the RNAfast200 Kit (Feijie, Shanghai, China) and the PrimeScript RT Master Mix (Takara, Japan) was used for reverse transcription according to the manufacturers’ instructions. qRT-PCR reactions were conducted with the SYBR Premix Ex Taq II (Takara) on the Bio-Rad CFX96 Real-Time PCR Detection System (Bio-Rad, USA) in accordance with the manufacturers’ instructions. qRT-PCR was performed in triplicate. β-actin mRNA expression was used for normalization. The qRT-PCR primers are listed in Table 2.
Table 2Primer sequences used in the experimentsGene symbolPrimer sequenceCA916798Forward: 5’-TTCTCCGAACGTGTCACGT-3’CA916798Reverse: 5’-TGGAGGCCGTCTTGGAGAT-3’ACTBForward: 5’-CATGTACGTTGCTATCCAGGC-3’ACTBReverse: 5’-CTCCTTAATGTCACGCACGAT-3’
## Western blot analysis
Cells were lysed in RIPA buffer (Thermo, USA) containing $1\%$ protease and phosphatase inhibitors following the manufacturer’s protocol. Lysates were incubated with SDS-PAGE Sample Loading Buffer (Bio-Rad, USA) for 10 min at 95 °C. Proteins were separated by $10\%$ SDS-PAGE and transferred to a PVDF membrane (Bio-Rad, USA). After blocking in $5\%$ non-fat milk for 2 h, the membrane was incubated at 4 °C overnight with the following primary antibodies: CA916798 (1:100, Invitrogen, USA), β-actin (1:5000, CST, USA),WEE1 (1:500, CST, USA), CDK1 (1:1000, CST, USA), p-CDK1Y15 (1:500, CST, USA), and CCNB1 (1:500, CST, USA).
## Cell invasion and migration assays
Transwell inserts (8.0 μm pore; Millipore, USA) were precoated with Matrigel (for invasion assays) or left untreated (for migration assays) and placed into 24-well plates. Cells were seeded into the upper chambers (5 × 104/well) with 200 µL serum-free medium and 600 µL DMEM medium containing $10\%$ FBS was added to the lower chambers. Cells were incubated for 36 h and 24 h for invasion and migration assays, respectively. Cells were then fixed in $4\%$ paraformaldehyde and dyed with crystal violet. Five random fields were examined for each chamber, and the invaded or migrated cells were counted. Images were obtained using a light microscope at 100-fold magnification.
## Colony formation assay
Cells were seeded into 6-well plates at a density of 200 cells per well. Plates were then incubated at 37℃ with $5\%$ CO2 for 10 ~ 14 days until colonies were visible. Plates were washed with phosphate buffer saline (PBS) and $4\%$ paraformaldehyde was used to fix cells for 15 min. Colonies were stained with crystal violet solution for 15 min, followed by a wash in PBS and air-drying, five random fields were selected for each well and colonies were counted. Images were obtained using a light microscope at 100-fold magnification.
## Cell viability and flow cytometric analysis
Cells were seeded in a 96-well plate (1000 cells per well). Cell viability was measured over 7 days using the CCK8 Kit (Beyotime, Shanghai, China), according to the manufacture’s instruction. The Cell Cycle and Apoptosis Analysis Kit (Beyotime, Shanghai, China) was used to determine cell cycle distribution following the manufacturer’s instructions.
## Animal experiments
Six-week-old female NOD/SCID mice were obtained from Charles River Labs (Beijing, China) and kept in specific pathogen-free conditions with light/dark cycles of 12 h, $60\%$ humidity, 23 ± 3 ℃, and free access to water. The mice were randomly divided into two groups: shRNA-CA916798, shRNA-control ($$n = 5$$ per group). The mice were anesthetized with an intraperitoneal injection of pentobarbital (50 mg/kg). No signs of peritonitis, pain, or discomfort were observed after anesthesia. The indicated cells were subcutaneously injected into mice (1 × 106 cells per mouse). Tumor size was measured at days 15, 20, 24 and 28. For treatment, the Gefitinib and vehicle was given through gavage administration Qod for total 14 days. The mice were euthanized by cervical dislocation at 28 days after implantation. Tumors were fixed in formaldehyde and subjected to paraffin embedding for hematoxylin and eosin and IHC staining. The animal experiments were approved by the Laboratory Animal Welfare and Ethics Committee of Army Medical University (AMUWEC20182181).
## Statistical analysis
All experiments were repeated at least three times and each treatment was set up in triplicate, unless specially indicated otherwise. Data are presented as mean ± SD. Statistical analyses were performed using SPSS version 24.0 and GraphPad Prism 5, String Version 11.0 by a two-tailed Student’s t-test. The cut-off value of CA916798 IHC staining scores was analyzed with SPSS. Chi-square analysis was used to evaluate the relationship between CA916798 high rate and LUAD clinicopathological features. $P \leq 0.05$ indicated statistical significance. High and low expression of genes were defined using median value. TCGA_LUAD mRNA expression and methylation450K datasets were downloaded from https://xenabrowser.net. Gene Set Enrichment Assay (GSEA) ($P \leq 0.05$, FDR < 0.25) [34] were used to analyze data. The statistical significance was determined by Student’s t test and $P \leq 0.05$ was considered statistically significant. Kaplan-Meier survival plot sand log-rank statistics were used to evaluate the survival of patients. Pearson rank correlation was used to analyze the relationship between different genes or proteins.
## Results
CA916798 overexpression predicted poor prognosis for LUAD patients.
We first examined protein expression of CA916798 in 94 LUAD tumors and 86 matched non-tumor tissues by IHC. The result indicated that CA916798 was barely detected in non-tumor lung tissues (Fig. 1A) but strongly expressed in tumor cells of LUAD (Fig. 1B). Statistic data suggested that CA916798 expression was significantly higher in tumor tissues than in adjacent non-tumor tissues (Fig. 1C). The expression of CA916798 was correlated with tumor T-stage (Fig. 1D), but not with sex, age, N-stage, M-stage, and pathological grade (data not shown). Moreover, patients with high CA916798 expression showed significantly shorter overall survival time than those with low CA916798 expression (Fig. 1E). To examine the results afore mentioned, we analyzed TCGA_LUAD dataset (gene expression RNAseq) and consistently observed the upregulation of CA916798 in tumor tissues compared with non-tumor lung tissues (Fig. 1F) and worse prognosis of patients with high CA916798 than those with low CA916798 (Fig. 1G). Analysis on Lung Cancer database from Kaplan-Meier plotter (https://kmplot.com) also proved that high expression of CA916798 was associated with shortened overall survival time of LUAD patients (Fig. 1H). Since CpG island methylation in gene promoter region has been known to be critically regulate gene expression, we analyzed the TCGA_LUAD methylation 450 K dataset. The result showed that several methylation probes were localized in the promoter of CA916798 gene and the one closely next to transcription start site (probe cg22306691) had significantly negative correlation with mRNA level of CA916798 (Fig. 1I J). In addition, the methylation level of cg22306691 was also dramatically downregulated in LUAD tissues compared to adjacent non-tumor tissues (Table 3). Moreover, low methylation level of probe cg22306691 predicted poor prognosis (Fig. 1K). Therefore, the expression of CA916798, partially regulated by promoter methylation, was correlated with tumor progression and predicted poor prognosis of LUAD.
Fig. 1 CA916798 is upregulated in LUAD and predicts poor survival of patients A and B) Representative IHC images of CA916798 in matched non-tumor tissue (A) and LUAD tissue (B). Scale bar = 100 μm. Brown color represents CA916798 protein; blue color represents nucleiC) IHC score of CA916798 expression in matched non-tumor tissue (Normal) and LUAD tissue (Tumor) from Cohort-94. Data are shown as mean ± SD.D) IHC score of CA916798 expression in different stages of LUAD from Cohort-94. Data are shown as mean ± SD.E) Kaplan-*Meier analysis* on overall survival (OS) in LUAD patients with CA916798highvs. CA916798low from Cohort-94F) mRNA level of CA916798 in matched non-tumor tissue (Normal) and LUAD tissue (Tumor) from TCGA_LUAD dataset. Data are shown as mean ± SD.G) Kaplan-*Meier analysis* on 5-year overall survival (OS) in LUAD patients with CA916798highvs. CA916798low from TCGA_LUAD datasetH) Kaplan-*Meier analysis* on overall survival (OS) in LUAD patients with CA916798highvs. CA916798low from KM-PLOT-LUAD databaseI) Pearson correlation list of methylation levels with CA916798 mRNA expression in TCGA_LUAD mRNA and methylation450K datasetsJ) Pearson correlation of CA916798 with cg22306691 from TCGA_LUAD mRNA and methylation450K datasetsK) Kaplan-*Meier analysis* on 5-year overall survival (OS) in LUAD patients with cg22306691highvs. cg22306691low from TCGA_LUAD methylation450K dataset Table 3Average methylation level of LUAD vs. matched normal tissues in TCGA.ProbeAverage methylation levelLUAD:NormalPNormal ($$n = 32$$)LUAD ($$n = 456$$) cg22306691 0.1336 0.1122 0.8400 0.0009 cg261714690.14510.14250.98220.5224cg145285250.03310.03150.95220.3985cg059691480.14220.12750.89640.0003cg068757400.10590.08980.84790.1407cg272927770.02110.02090.99190.9088cg085674980.07680.07500.97580.5768cg091234440.05940.05680.95710.3367cg240313310.07650.08001.04590.0915cg140931250.05390.05080.94130.2544cg266536940.07010.06660.94940.3165cg119684360.06150.05790.94100.2271cg003356330.09570.09470.98990.7703cg183829470.12780.12530.98020.5676cg232373640.03060.03321.08600.2523cg015344160.27690.23270.84040.0001 CA916798 promoted growth of LUAD cellsin vitroandin vivo.
We then explored the biological function of CA916798 in LUAD cells. For this purpose, CA916798 was stably knocked down in A549 and PC9 cell lines using shRNA targeting CA916798 (A549-shCA916798 and PC9-shCA916798) with scramble shRNA as control (A549-shCtrl and PC9-shCtrl) (Fig. 2A). CCK8 assay and colony formation assay indicated that CA916798 knockdown significantly impaired cell viability and growth (Fig. 2B C). However, transwell assay suggested that CA916798 knockdown did not affect cell migration (seeding cells without Matrigel) and invasion (seeding cells with Matrigel) of A549 and PC9 cells (Fig. 2D and E). To further examine the effects of CA916798 on cell growth of LUAD in vivo, we generated a subcutaneous xenograft model using A549-shCA916798 cells and A549-shCtrl cells. The result suggested that CA916798 knockdown suppressed tumorigenicity of A549 cells in nude mice (Fig. 2F H). Hematoxylin-eosin (HE) staining and Ki67 staining confirmed that CA916798 deficiency markedly reduced growth of A549 cells in vivo(Fig. 2I). Together, these results suggested that interference of CA916798 expression impaired cell growth of LUAD in vitro and in vivo.
Fig. 2 CA916798 promotes growth of LUAD cells in vitro and in vivo A) The efficiency of CA916798 knockdown in A549 and PC9 cells with three individual shRNA sequence targeting CA916798. shCA916798-1 and − 3 are used in A549 and PC9, respectively, for the following experimentsB) Growth curve of CA916798-knockdown LUAD cells and mock cells as measured by CCK8 assay. Data are shown as mean ± SD ($$n = 3$$; *$P \leq 0.05$, ***$P \leq 0.001$)C) Colony formation of CA916798-knockdown LUAD cells and mock cells. Data are shown as mean ± SD ($$n = 3$$, *$P \leq 0.05$, ** $P \leq 0.01$)D and E) Measurement on migration (D) and invasion (E) in CA916798-knockdown cells and mock cells. Data are shown as mean ± SD ($$n = 3$$)F-H) Tumor image (F), tumor volume (G), and tumor weight (H) of xenograft models generated using A549-shCA916798 and mock cells. Data are shown as mean ± SD ($$n = 5$$, *$P \leq 0.05$)I) HE and IHC staining of tumor tissues from experimental mice obtained 28 days after implantation CA916798 was involved in regulation on G2/M phase transition in LUAD cells.
To further pursue the mechanism on the regulation of LUAD cell growth by CA916798, we examined the effect of CA916798 on cell cycle of LUAD cells through flow cytometric analysis. The result showed that CA916798 knockdown in two LUAD cell lines consistently induced G2/M phase arrest (Fig. 3A and B). GSEA [34, 35] on TCGA_LUAD dataset suggested that high expression of CA916798 significantly enriched HALLMARK_G2M_CHECKPOINT geneset (Fig. 3C). It is known that CCNB1 is the critical checkpoint for G2/M phase [36], and analysis on TCGA_LUAD dataset revealed that CCNB1 was the most correlated gene with CA916798 among all cyclin genes (Fig. 3D and Table 4). These findings indicated that CA916798 might regulate the G2/M phase transition in LUAD cells, and silencing CA916798 could suppress tumor growth by inducing G2/M phase arrest.
Fig. 3 CA916798 regulates G2/M-phase transition in LUAD cells A) Cell cycle analysis in CA916798-knockdown LUAD cells and mock cells (*$P \leq 0.05$)B) Cell cycle analysis in CA916798-overexpressing LUAD cells and mock cells (*$P \leq 0.05$)C) Geneset enrichment assay on TCGA_LUAD datasetD) Pearson correlation of CA916798 with CCNB1 from TCGA_LUAD dataset Table 4Pearson correlation of CA916798 with cyclin genesCorrelation of CA916798Pearson RP Value vs. CCNB1 0.703 < 0.0001 vs. CCNB20.6812< 0.0001vs. CCNA20.6256< 0.0001vs. CCNE10.5929< 0.0001vs. CCNE20.5215< 0.0001vs. CCNO0.286< 0.0001vs. CCNI20.1826< 0.0001vs. CCNC0.1711< 0.0001vs. CCNT2-0.005970.8863vs. CCNH-0.012910.7572vs. CCNG2-0.017840.6692vs. CCNA1-0.10750.0098vs. CCNT1-0.11180.0072vs. CCNG1-0.11450.006vs. CCND1-0.14270.0006vs. CCNI-0.1722< 0.0001vs. CCNB3-0.1939< 0.0001vs. CCND3-0.2588< 0.0001vs. CCND2-0.3642< 0.0001 CA916798 activated the WEE1/CDK1 axis to promote cell cycle progression.
It is well-known that the CDK1/CCNB1 complex promotes cell cycle progression from G2 phase to M phase and results in cell proliferation [37, 38]. WEE1 inactivates the CDK1/CCNB1 complex by phosphorylating CDK1 at Tyr15 amino acid. Conversely, CDC25C activates the CDK1/CCNB1 complex by dephosphorylate CDK1 at the Tyr15 [37, 38]. Pearson correlation on TCGA_LUAD dataset revealed significantly positive correlation of CA916798 and CDK1 (Fig. 4A). Moreover, protein levels of p-CDK1(Tyr15) and WEE1 were upregulated upon interference of CA916798 in A549 and PC9 cells (Fig. 4B). On the contrary, overexpression of CA916798 reduced protein levels of p-CDK1(Tyr15) and WEE1 (Fig. 4C). These findings suggested that CA916798 might promote proliferation of LUAD cells by inhibiting the expression of WEE1 and the phosphorylation of CDK1(Tyr15). Next, we treated A549 cells with CDK1 inhibitor Ro-33,066 (5 µM for 24 h) and the data indicated that CDK1 inhibitor leaded to G2/M phase arrest in both A549-Control and A549-CA916798 cells (Fig. 4D). Therefore, CA916798 could promote cell proliferation through regulating WEE1/CDK1 axis.
Fig. 4 CA916798 activates WEE1/CDK1 axis to promote cell cycle progression A) Pearson correlation of CA916798 with CDK1 from TCGA_LUAD datasetB) Western blotting of A549 and PC9 cells with CA916798 knockdown (shCA) vs. shControl (shCtrl)C) Western blotting of A549 and PC9 cells with CA916798 overexpression (CA) vs. empty vector (Ctrl)D) Cell cycle distribution measured by flow cytometry in cells treated with 0.1 µM Ro-3306 (CDK1 inhibitor) for 24 h CA916798 was correlated with Gefitinib sensitivity.
For LUAD, TKI targeting EGFR, such as Gefitinib, promotes G2/M phase arrest to inhibit tumor growth but fails to do so in TKI resistant cells. Thus, we asked whether CA916798 could facilitate resistance of LUAD cells against Gefitinib. To this aim, we developed Gefitinib-resistant PC9 (PC9/R) and HCC827 (HCC/R) cells through culturing the cells in the presence of Gefitinib with gradually increased concentrations. CCK8 assay showed that IC50 of parental PC9 and PC9/R cells for Gefitinib were 0.028 µM and 8 µM, respectively, and IC50 of parental HCC827 and HCC/R cells for Gefitinib were 0.141µM and 18.27µM, respectively (Fig. 5A), confirming successful establishment of Gefitinib-resistant cells. Through qRT-PCR, we found that the mRNA level of CA916798 was significantly upregulated in Gefitinib-resistant cells compared with parental cells (Fig. 5B), implying potential implication of CA916798 in acquired Gefitinib resistance. Consistent with our result, analysis on data from GEO dataset GSE34228 also showed that CA916798 expression was increased in PC9/R cells compared with PC9 parental cells (Fig. 5C). In GSE172002, the expression of CA916798 in HCC827/R cells was also higher than that in HCC827 parental cells (FPKM value 56.89 vs. 35.63). However, the analysis on GSE200894 indicated that the sensitivity of PC9 cells to the third-generation TKI, osimertinib, was not associated with CA916798 expression (Fig. 5D). To verify whether CA916798 participated in the regulation of LUAD response to Gefitinib, PC9/R and HCC/R cells were stably infected by lentivirus including shRNA targeting CA916798 (PC9/R-siCA and HCC/R-siCA) or control shRNA (PC9/R-siCtrl and HCC/R-siCtrl) (Fig. 5E F). Notably, knockdown of CA916798 in Gefitinib-resistant cells significantly decreased IC50 against Gefitinib from 10.34 µM to 3.92 µM ($P \leq 0.01$) in PC9/R cells and from 14.1 µM to 8.07 µM ($P \leq 0.05$) in HCC/R cells, respectively (Fig. 5E F). Furthermore, forced expression of CA916798 in parental PC9 and HCC827 cells increased IC50 of the cells against Gefitinib from 0.48 µM to 2.60 µM ($P \leq 0.01$) in PC9 cells and from 0.06 µM to 1.42 µM ($P \leq 0.001$) in HCC cells, respectively (Fig. 5G H). Therefore, CA916798 was tightly associated with development of Gefitinib resistance in LUAD cells.
Fig. 5 Correlation of CA916798 with Gefitinib sensitivity in LUAD cells A) Cell growth curves of parent and Gefitinib-resistant cells from PC9 and HCC827 measured via CCK8.B) mRNA level of CA916798 in parent and Gefitinib-resistant cells from PC9 and HCC827 measured via qRT-PCR. *** $P \leq 0.001$C) mRNA level of CA916798 in PC9 and PC9/GR (Gefitinib resistance) cellsD) mRNA level of CA916798 in PC9 and PC9/OR (Osimertinib resistance) cellsE and F) Cell growth curves with Gefitinib treatment and IC50 calculation of Gefitinib for PC9/Resistance (E) and HCC827/Resistance (F) cells stably transfected with control shRNA (shCtrl) or shRNA targeting CA916798 (shCA). ** $P \leq 0.01$G and H) Cell growth curves with Gefitinib treatment and IC50 calculation of Gefitinib for PC9 (G) and HCC (H) cells stably transfected with empty vector (Ctrl) or CA916798 (CA). ** $P \leq 0.01$, ***$P \leq 0.001$ CA916798 knockdown promoted inhibitory effects of Gefitinibin vivo.
Since overexpression of CA916798 dampened effects of Gefitinib on LUAD cells, we then examined whether interference of CA916798 expression would enhance Gefitinib inhibitory effects. For this purpose, we established a xenograft model through subcutaneously inoculated PC9/R-shCA916798 or PC9/R-shCtrl cells into NOD/SCID mice followed by treatment of Gefitinib or placebo (Fig. 6A). Consistent with in vitro results, CA916798 knockdown significantly inhibited the growth of xenograft (Fig. 6B). Both tumor volume and tumor weight were decreased with knockdown of CA916798 (Fig. 6C and D). Moreover, Gefitinib showed stronger inhibitory effects on PC9/shCA916798 cells than PC9/shCtrl cells (Fig. 6B and D). HE staining indicated that in Gefitinib treatment did not obviously change pathological features of PC9/shCtrl cells and only slightly decreased density tumor cells (Fig. 6E). However, Gefitinib treatment induced significant death of tumor cells and collapse of nuclei (Fig. 6E), confirmed the inhibitory effects of Gefitinib on LUAD cells with knock down of CA916798. Therefore, diminishing CA916798 might augment inhibitory effects of Gefitinib on LUAD cells.
Fig. 6 Targeting CA916798 enhances Gefitinib inhibitory effects on LUAD cells A) Flow chart of in vivo experiment. CMC-*Na is* used as control for GefitinibB-D) Tumor image (B), tumor volume (C), and tumor weight (D) of xenograft models generated using PC9/GR cells stably transfected with control shRNA (Mock) or shCA916798 treated with CMC-Na or Gefitinib. Data are shown as mean ± SD ($$n = 5$$, *$P \leq 0.05$, ***$P \leq 0.001$E) Representative HE staining of xenograft tumors. Scale bar = 100 μm (upper panels) or 25 μm (lower panels)
## Discussion
In this study, we explored the clinicopathological significance of CA916798 in LUAD and found that CA916798 expression was significantly correlated with poor prognosis of LUAD patients. Surprisingly, our study also identified a key methylation site in promote region of CA916798 gene as a mechanic regulator for CA916798 gene expression and clinical predictor for patient prognosis of LUAD. Through cell and animal experiments, we further delineated that CA916798 promoted proliferation and tumorigenesis of A549 cells. In addition, CA916798 was upregulated in Gefitinib-resistant LUAD cells and elimination of CA916798 facilitated Gefitinib effects on LUAD cells (Fig. 7).
Fig. 7 Targeting CA916798 enhances Gefitinib inhibitory effects on LUAD cells Schematic diagram of CA916798-mediated progression and Gefitinib resistance of LUAD. In normal lung epithelial cells, promoter of CA916798 gene is hypermethylated and the transcription level of CA916798 is low, which leads to suppression of CDK1 by WEE1 and cell cycle arrest (left panel). In LUAD cells, promoter of CA916798 gene is hypomethylated and the transcription level of CA916798 is dramatically elevated. In this context, CA916798 blocks the inhibition of CDK1 by WEE1 and facilitates cell cycle progression, resulting in LUAD progression and Gefitinib resistance (right panel) The occurrence and development of NSCLC is a complex process involving multiple abnormal gene expression and mutation. EGFR-tyrosine kinase inhibitors have opened up a new era of treatment in NSCLC [39, 40]. Subsequent drugs targeting ALK, ROS1, KRAS, BRAF, MET, RET, NTRK, and other genes have also been explored [41]. As more genes related to lung cancer are discovered, more targeted drugs will enter clinical application, expanding the treatment possibilities for lung cancer patients.
The CA916798 gene was identified more than 10 years ago, but the function of its encoded protein is still obscure. It is found that the CA916798 gene encodes a minor histocompatibility antigen, which is presented to cytotoxic T cells and initiates killing effects on tumor cells in patients with renal cancer [21]. The CA916798-encoded antigen is expressed in a variety of tumor cells, so it may be related to tumor immunity. However, this possibility remains to be investigated. We previously reported that the CA916798 gene is related to chemotherapy resistance in lung cancer; a higher the expression of this gene was associated with a higher chemotherapy resistance. Further studies showed that the gene is regulated by the PI3K/AKT signaling axis, which regulates the proliferation and apoptosis of malignant tumors. Therefore, CA916798 gene might be a lung cancer-related gene and represent a promising therapeutic target for LUAD treatment.
Cell proliferation is tightly regulated by a cell cycle network, including cyclin proteins, cyclin-dependent protein kinases, and cyclin-dependent protein kinase inhibitors. G1/S and G2/M boundaries are the most important checkpoints in cell cycle regulation. Our results showed that CA916798 influenced the expression of WEE1 and CDK1, which are key molecules that regulate the G2/M transition. It is clear that CDK1 binds to CCNB1 as a mitotic promoter that drives G2 to M phase transition. The mitotic promoter complex is inactive due to phosphorylation of Thr14 and Tyr15 of CDK1 by WEE1. We found that CA916798 inhibited the expression of WEE1 in LUAD cells, suggesting that CA916798 might be a transcriptional repressor of WEE1. Reduction of WEE1 promotes dephosphorylation and activation of CDK1 and eventually leads to cell cycle progression. These findings indicated that CA916798 promoted cell proliferation through regulation of WEE1/CDK1.
EGFR-TKI inhibits cell proliferation by competitively binding to ATP binding sites in the catalytic domain of tyrosine kinase, inhibiting its self-phosphorylation and blocking downstream signal transduction. In vivo, Gefitinib and other EGFR-TKI drugs widely inhibit the tumor growth of human tumor cell derived lines xenografted in nude mice, and improve the anti-tumor activity of chemotherapy, radiotherapy and hormone therapy. Our data firstly reported that knockdown of CA916798 improved the sensitivity of drug-resistant cells to EGFR-TKI and enhanced the growth inhibition of EGFR-TKI on NSCLC cells. LUAD cell derived xenograft in immunodeficient NOD/SCID mice further established that knockdown of CA916798 combined with TKI significantly inhibited tumor growth compared with single drug treatment. These findings suggested that the increased expression of CA916798 significantly promoted the development of EGFR TKIs resistance, so CA916798 might be an important molecule to promote the acquired EGFR TKIs resistance of LUAD cells. However, it should be mentioned that *Gefitinib is* a first-generation TKI, while *Osimertinib is* a third-generation TKI. Our current finding, in association with other reports, emphasized that CA916798 is associated with Gefitinib but not Osimertinib resistance. Since first-generation TKIs block EGFR activity through reversible interaction, CA916798 might lead to Gefitinib resistance through interference with the interaction between EGFR and Gefitinib. However, third-generation TKIs specifically target mutant EGFR, which could not be affected by CA916798 function. Thus, it would be worthy further pursuing the differences of CA916798 function and regulation in LUAD cells with wild-type EGFR versus mutant EGFR.
Together, our work herein demonstrated that CA916798 promoted LUAD cell proliferation and was related with poor prognosis of LUAD patients probably through inhibiting WEE1 expression and subsequent activation of CDK1. Thus, CA916798 could be considered as a promising prognostic marker and a therapeutic target for LUAD.
## Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1
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---
title: 'Impact of comorbidities on hospital mortality in patients with acute pancreatitis:
a population-based study of 110,021 patients'
authors:
- Nils Jimmy Hidalgo
- Elizabeth Pando
- Rodrigo Mata
- Nair Fernandes
- Sara Villasante
- Marta Barros
- Daniel Herms
- Laia Blanco
- Joaquim Balsells
- Ramon Charco
journal: BMC Gastroenterology
year: 2023
pmcid: PMC10035222
doi: 10.1186/s12876-023-02730-6
license: CC BY 4.0
---
# Impact of comorbidities on hospital mortality in patients with acute pancreatitis: a population-based study of 110,021 patients
## Abstract
### Background
The impact of pre-existing comorbidities on acute pancreatitis (AP) mortality is not clearly defined. Our study aims to determine the trend in AP hospital mortality and the role of comorbidities as a predictor of hospital mortality.
### Methods
We analyzed patients aged ≥ 18 years hospitalized with AP diagnosis between 2016 and 2019. The data have been extracted from the Spanish National Hospital Discharge Database of the Spanish Ministry of Health. We performed a univariate and multivariable analysis of the association of age, sex, and comorbidities with hospital mortality in patients with AP. The role of the Charlson and Elixhauser comorbidity indices as predictors of mortality was evaluated.
### Results
A total of 110,021 patients diagnosed with AP were hospitalized during the analyzed period. Hospital mortality was $3.8\%$, with a progressive decrease observed in the years evaluated. In multivariable analysis, age ≥ 65 years (OR: 4.11, $p \leq 0.001$), heart disease (OR: 1.73, $p \leq 0.001$), renal disease (OR: 1.99, $p \leq 0.001$), moderate-severe liver disease (OR: 2.86, $p \leq 0.001$), peripheral vascular disease (OR: 1.43, $p \leq 0.001$), and cerebrovascular disease (OR: 1.63, $p \leq 0.001$) were independent risk factors for mortality. The Charlson > 1.5 (OR: 2.03, $p \leq 0.001$) and Elixhauser > 1.5 (OR: 2.71, $p \leq 0.001$) comorbidity indices were also independently associated with mortality, and ROC curve analysis showed that they are useful for predicting hospital mortality.
### Conclusions
Advanced age, heart disease, renal disease, moderate-severe liver disease, peripheral vascular disease, and cerebrovascular disease before admission were independently associated with hospital mortality. The Charlson and Elixhauser comorbidity indices are useful for predicting hospital mortality in AP patients.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12876-023-02730-6.
## Background
Acute pancreatitis (AP) is a prevalent acute inflammatory disease that affects the pancreas, with an increased incidence in recent years [1, 2]. Most cases are mild with a self-limited course [3]. However, patients with severe acute pancreatitis have a high mortality rate (20–$50\%$) [4–6]. For this reason, many efforts have been made to find predictors of severity and mortality in patients with AP [7–11] to identify patients who need admission to an intensive care unit or specific treatment.
In clinical practice, systems such as the Ranson score, the Acute Physiology and Chronic Health Evaluation II (APACHE II) score, the Computed Tomography Severity Index (CTSI), the Bedside Index for Severity in Acute Pancreatitis (BISAP), and various biochemical markers are used to predict severe AP and mortality [3, 12–16]. However, hospital mortality in AP could also be related to intrinsic patient characteristics, such as individual comorbidities. Most classic scores do not consider comorbidities before admission, except for APACHE II, but are restricted to severe chronic diseases.
According to some previous studies, patients with certain comorbidities, such as obesity [17], hypertriglyceridemia [18], chronic renal failure [19], diabetes [20, 21], and systemic lupus erythematosus [22], are associated with a higher risk of AP severity and mortality. However, few studies currently evaluate the impact of comorbidities on AP severity and mortality.
Our study aimed to determine the relevance of comorbidities and their indexes (Charlson and Elixhauser) as predictors of hospital mortality in patients with AP.
## Study design
We carried out a retrospective observational study using the Spanish National Hospital Discharge (Registro de Actividad de Atención Especializada-Conjunto Mínimo Básico de Datos, “RAE-CMBD”). The RAE-CMBD collects all the administrative data from hospitals (public and private) in the country [23].
The information collected in this database comes from hospital discharge reports made by the physicians in charge of the patient. This information and record of coded diagnoses are automatically collected by the computer software of each center or by technical-administrative staff.
## Study population
The study population includes patients diagnosed with AP and admitted to the Spanish National Health System hospitals from 2016 to 2019. Since 2016, the RAE-CMDB has collected 20 diagnoses and 20 procedures from each patient based on the International Classification of Diseases Version 10 (ICD-10).
The inclusion criteria were: Patients with a primary or first registered secondary diagnosis of AP.
The exclusion criteria were: 1) Patients under 18 years of age, 2) Patients with a diagnosis primary or first registered secondary of pancreatic neoplasm, chronic pancreatitis, pancreatic cyst, pancreatic pseudocyst, extrahepatic bile duct neoplasm, and complications of the transplanted pancreas. We excluded the population under 18 years of age because the incidence of AP is lower in the pediatric population [24], and the etiologies distribution differs from that of adults [25].
## Variables analyzed
The variables included are demographic variables such as age and sex, and AP etiology. We used the ICD-10 diagnostic code from each patient to identify the etiology of AP, which includes six categories: biliary, alcohol, idiopathic, drug-related, other, and unspecified. Our clinical-administrative database does not have data on diagnostic tests such as ultrasound or magnetic resonance imaging that allow the identification of the etiology in patients with a diagnosis of "unspecified pancreatitis. In addition, we did not have data after hospital discharge that could expand the information on the etiology. Other variables assessed were clinical variables on diagnoses and procedures.
## Comorbidity assessment
Comorbidities were identified from the ICD-10 diagnosis codes of each patient. We have used the POA indicator (Present on registration) to identify comorbidities and differentiate them from diagnoses produced during hospital admission that could be secondary complications of AP. The ICD-10 codes used to identify specific comorbidities are described in the supplementary material (Additional file 1).
## Calculation of comorbidities indexes
We assessed comorbidity by calculating the Charlson [26] and Elixhauser [27] comorbidity indices. These two indices are used in medical practice to predict mortality. ICD-10 diagnosis codes described by Quan et al. [ 28] were applied to identify specific comorbidities from the Charlson and Elixhauser indices.
The Charlson index assigns weights for 17 specific diseases, and its value was calculated by adding the weights of each condition as described by Charlson et al. [ 26]. The Elixhauser index assigns weights for 30 specific diseases, and its value was calculated using the algorithm described by Walraven et al. [ 29].
## Outcomes
Outcomes analyzed included pancreatic necrosis length of hospital stay, admission to intensive care unit (ICU), length of ICU stay, and hospital mortality. Since 2018, the definition of pancreatic necrosis and pancreatic necrosis infection has been included in the ICD-10 diagnosis code for AP: AP without necrosis or infection (K85. × 0), AP with uninfected necrosis (K85. × 1) and AP with infected necrosis (K85. × 2). In addition, we did not have information on the percentage extension of pancreatic necrosis. Therefore, the data on pancreatic necrosis were only used for the descriptive analysis of the evolution of AP in the period analyzed.
## Statistical analysis
We used the Kruskal–Wallis test for continuous variables and the Linear-by-Linear association test for categorical variables to analyze the characteristics and results of patients with AP during the years evaluated (2016–2019).
The analysis of risk factors for hospital mortality was performed by applying the chi-square test for categorical variables and the Student's t-test or the Mann–Whitney U test for continuous variables.
Univariate and multivariable analysis of the factors associated with hospital mortality was performed using logistic regression. We performed three multivariable analyses of significant variables in the univariate analysis. The first analysis included: age ≥ 65 years, sex, and specific comorbidities. In the second analysis, the Charlson comorbidity index replaced the specific comorbidities. The third analysis replaced specific comorbidities with the Elixhauser comorbidity index.
Receiver-operating characteristic (ROC) curves were drawn to analyze the in-hospital mortality prediction capacity of the Charlson and Elixhauser comorbidity indices, and the area under the curve (AUC) was described. A Delong test [30] was performed to compare the AUC. We used the Youden's index to identify the best cut-off point for the Charlson and Elixhauser comorbidity indices.
Statistical analyses were performed using IBM SPSS 20.0 (IBM Corp. in Armonk, NY) and Stata version 16 (Stata, College Station, Texas, USA). Statistical significance was set at $p \leq 0.05.$
## Ethical aspects
Our study follows the principles of the Declaration of Helsinki for research on human beings. The data was extracted from the Spanish Ministry of Health register, which is anonymous following Spanish legislation. Identifying patients at the individual or reporting unit level with the data obtained is impossible.
## General population characteristics
Between January 1, 2016, and December 31, 2019, a total of 125,622 cases with the diagnosis of AP were identified. After applying inclusion and exclusion criteria, 110,021 patients were included (Fig. 1). The demographic and clinical characteristics of the population and its variations during the period are shown in Table 1. The mean age was 64.32 ± 17.94 years, with a slight progressive decrease throughout the years. The $53.3\%$ of patients were 65 years or older. Male sex prevalence was $53.1\%$, which significantly increased during the study period ($$p \leq 0.043$$). The most frequent etiologies of AP were biliary ($41.2\%$) and alcohol ($7.9\%$). The Charlson and Elixhauser comorbidity indices values progressively increased in the last years of the study period (Table 1).Fig. 1Case Selection Flow Chart. AP: acute pancreatitis IDC-10: 10th revision of the International Statistical Classification of DiseasesTable 1Characteristics of patients hospitalized with a diagnosis of acute pancreatitisTotal (N: 110,021)2016 (N: 26,952)2017 (N: 22,170)2018 (N: 29,785)2019 (N: 31,114)p valueAge, years Mean ± SD64.32 ± 17.9464.54 ± 17.9964.34 ± 17.8264.33 ± 17.9764.11 ± 17.960.017 Age ≥ 65 years, N (%)58,612 (53.3)14,679 (54.5)11,806 (53.3)15,832 (53.2)16,295 (52.4)< 0.001Sex, N (%) Male58,457 (53.1)14,178 (52.6)11,765 (53.1)15,899 (53.4)16,615 (53.4)0.043 Female51,564 (46.9)12,774 (47.4)10,405 (46.9)13,886 (46.6)14,499 (46.6)0.043Charlson Index Mean ± SD0.95 ± 1.470.88 ± 1.410.91 ± 1.430.97 ± 1.51.02 ± 1.53< 0.001Elixhauser Index Mean ± SD3.43 ± 5.873.24 ± 5.713.2 ± 5.663.49 ± 5.933.72 ± 6.08< 0.001Pancreatitis etiology, N (%) Biliary45,281 (41.2)9,909 (36.8)8,263 (37.3)13,354 (44.9)13,755 (44.2)< 0.001 Alcohol8,658 (7.9)1,708 (6.3)1,477 (6.7)2,625 (8.8)2,848 (9.2)< 0.001 Medications1,086 [1]197 (0.7)177 (0.8)332 (1.1)380 (1.2)< 0.001 Idiopathic2,456 (2.2)380 (1.4)366 (1.7)821 (2.8)889 (2.9)< 0.001 Other4,029 (3.7)947 (3.5)909 (4.1)1,038 (3.5)1,135 (3.6)0.761 Not specified48,511 (44.1)13,811 (51.2)10,978 (49.5)11,615 [39]12,107 (38.9)< 0.001 Pancreatic necrosis, N (%)-NRNR2,523 (8.5)2,873 (9.2) Infected necrosis, N (%)-NRNR918 (3.1)1,069 (3.4) ICU admission, N (%)5,155 (4.7)1,187 (4.4)1,093 (4.9)1,445 (4.9)1,430 (4.6)0.383 ICU stay (days) Mean ± SD13.05 ± 22.2713.15 ± 22.0512.65 ± 22.0613.35 ± 21.5713.97 ± 23.310.181Hospital stay (days) Mean ± SD9.38 ± 12.229.73 ± 12.119.43 ± 12.029.35 ± 12.359.08 ± 12.29< 0.001 Mortality, N (%)4,153 (3.8)1,095 (4.1)884 [4]1,097 (3.7)1,077 (3.5)< 0.001SD Standard deviation, NR Not reported, ICU Intensive care unit
## General outcomes
The proportion of patients who required ICU admission was $4.7\%$, with no differences in its prevalence by year studied. The mean length of hospital stay was 9.38 ± 12.22 days, showing a significant decrease over the period. Pancreatic necrosis was reported in $8.5\%$ in 2018 and $9.2\%$ in 2019 (Table 1).
## Mortality
Mortality was $3.8\%$ in all the population and significantly decreased over time, from $4.1\%$ in 2016 to $3.5\%$ in 2019 ($p \leq 0.001$) (Table 1).
## Impact of Age, sex, and etiology
Age and male sex were higher in the non-survivors compared to the group of survivors (78.02 ± 13.24 vs. 63.78 ± 17.89, p = < 0.001 and $51.1\%$ vs. $53.2\%$, $$p \leq 0.007$$, respectively). The prevalence of pancreatitis of biliary, alcoholic, and drug-related etiology was lower in the group of non-survivors ($p \leq 0.001$) (Table 2).Table 2Demographic characteristics of acute pancreatitis according to survivor and non-survivorsTotalSurvivorsNon-survivorsp valueAge, mean ± SD64.32 ± 17.9463.78 ± 17.8978.02 ± 13.24< 0.001Sex, N (%) Male58,457 (53.1)56,335 (53.2)2,122 (51.1)0.007 Female51,564 (46.9)49,533 (46.8)2,031 (48.9)0.007Comorbidities, N (%) Arterial hypertension51,532 (46.8)49,011 (46.3)2,521 (60.7)< 0.001 Heart disease18,696 [17]17,202 (16.2)1,494 (35.9)< 0.001 Chronic pulmonary disease9,234 (8.4)8,739 (8.3)495 (11.9)< 0.001 Renal disease8,193 (7.4)7,399 [7]794 (19.1)< 0.001 Moderate or severe liver disease1,498 (1.4)1,375 (1.3)123 [3]< 0.001 Diabetes mellitus20,259 (18.4)19,315 (18.2)944 (22.7)< 0.001 Obesity9,681 (8.8)9,327 (8.8)354 (8.5)0.523 Peripheral vascular disease3,361 (3.1)3,091 (2.9)270 (6.5)< 0.001 Cerebrovascular disease2,220 [2]2,023 (1.9)197 (4.7)< 0.001 Rheumatic disease1,335 (1.2)1,261 (1.2)74 (1.8)0.001 Charlson Index, mean ± SD0.95 ± 1.470.92 ± 1.431.76 ± 2.1< 0.001 Elixhauser Index, mean ± SD3.43 ± 5.873.29 ± 5.757.15 ± 7.38< 0.001Pancreatitis etiology, N (%) Biliary45,281 (41.2)44,041 (41.6)1,240 (29.9)< 0.001 Alcohol8,658 (7.9)8,533 (8.1)125 [3]< 0.001 Medications1,086 [1]1,070 [1]16 (0.4)< 0.001 Idiopathic2,456 (2.2)2,333 (2.2)123 [3]0.001 Other4,029 (3.7)3,768 (3.6)261 (6.3)< 0.001 Not specified48,511 (44.1)46,123 (43.6)2,388 (57.5)< 0.001 ICU admission, N (%)5,155 (4.7)3,684 (3.5)1,471 (35.4)< 0.001SD Standard deviation, ICU Intensive care unit
## Impact of comorbidities and indexes
Non-survivor patients presented a higher percentage in all comorbidities except for obesity (Table 2).
Median values of Charlson and Elixhauser indexes were significantly higher in the group of non-survivors compared with survivors (1.76 ± 2.1 vs. 0.92 ± 1.43, $p \leq 0.001$ and 7.15 ± 7.4 vs. 3.29 ± 5.8, $p \leq 0.001$ respectively) (Table 2). The analysis of the comorbidities included in the Charlson and Elixhauser indices according to hospital mortality is described in the supplementary material (Additional file 1).
## Multivariable analysis
Logistic regression analysis was performed using the best cut-off point obtained by Youden's index ($J = 1.5$ points for both Charlson and Elixhauser indices). After multivariable logistic regression analysis, we found that factors independently associated with mortality were age 65 years or older (OR 4.11, $95\%$ CI 3.75–4.5), heart disease (OR 1.73, $95\%$ CI 1.62–1.86), renal disease (OR 1.99, $95\%$ CI 1.74–2.07), moderate-severe liver disease (OR 2.86, $95\%$ CI 2.35–3.47), peripheral vascular disease (OR 1.43, $95\%$ CI 1.25–1.64), and cerebrovascular disease (OR 1.63, $95\%$ CI 1.4–1.9). Arterial hypertension has been found to be a protective factor in the population. The Charlson Index > 1.5 points (OR 2.03, $95\%$ CI 1.9–2.16) and Elixhauser Index > 1.5 points (OR 2.71, $95\%$ CI 2.53–2.9) were independently associated with mortality (Table 3).Table 3Multivariable analysis showing association of proposed risk factors with hospital mortality in acute pancreatitisMultivariable analysisOR ($95\%$ CI)p valueVariables of analysis 1 Age ≥ 65 years4.11 (3.75–4.5)< 0.001 Sex Female0.96 (0.9–1.03)0.236 Arterial hypertension0.89 (0.84–0.96)0.003 Heart disease1.73 (1.62–1.86)< 0.001 Chronic pulmonary disease1.09 (0.99–1.21)0.085 Renal disease1.99 (1.74–2.07)< 0.001 Moderate or severe liver disease2.86 (2.35–3.47)< 0.001 Diabetes mellitus0.94 (0.87–1.01)0.09 Peripheral vascular disease1.43 (1.25–1.64)< 0.001 Cerebrovascular disease1.63 (1.4–1.9)< 0.001 Rheumatic disease1.13 (0.89–1.44)0.317Variables of analysis 2 Age ≥ 65 years4.41 (4.04–4.8)< 0.001 Sex Female0.96 (0.9–1.03)0.962 Charlson Index ≥ 1.52.03 (1.9–2.16)< 0.001Variables of analysis 3 Age ≥ 65 years4.23 (3.88–4.61)< 0.001 Sex Female0.98 (0.92–1.05)0.581 Elixhauser Index ≥ 1.52.71 (2.53–2.9)< 0.001OR Odds ratio, CI Confidence interval
## AUC analysis
The Elixhauser comorbidity index exhibited a slightly higher AUC value in predicting hospital mortality (AUC: 0.666, $95\%$ CI 0.657 – 0.674) than the Charlson comorbidity index (AUC: 0.633, $95\%$ CI 0.623 – 0.641). When performing the Delong test to compare these AUC, it was observed that this difference is significant ($p \leq 0.001$). The ROC curves and AUC for the Charlson and Elixhauser comorbidity indices to predict hospital mortality are shown in Fig. 2.Fig. 2ROC curve and AUC (Area Under the Curve) of the Charlson Comorbidity Index, and the Elixhauser Comorbidity Index in predicting the hospital mortality rate in patients with acute pancreatitis
## Discussion
Our study found that pre-admission comorbidities such as heart disease, kidney disease, moderate-severe liver disease, peripheral vascular disease, cerebrovascular disease, and age ≥ 65 years were independently associated with mortality in AP. Charlson and Elixhauser comorbidity indices were independently associated with mortality.
Advanced age has been extensively studied as a marker of severity and mortality in AP. Most studies report longer hospitalization [31, 32] and higher overall mortality from AP in elderly patients [33–36]. However, other studies have observed that older patients may have a more severe course of AP but do not present increased mortality [37]. Likely explanations explaining advanced age as a risk factor for mortality include a proinflammatory state in older people [38] and increased production of cytokines in elderly patients with sepsis [39]. Other reasons would be delayed diagnosis and treatment due to less clinical and analytical expression [40, 41].
The increase in life expectancy and the aging of the population have been associated with the increase in patients with comorbidities [42, 43], so determining its impact on AP becomes more necessary. The importance of comorbidities in predicting outcomes in other diseases that require acute hospital admission is well known [44–46]. However, few studies analyze the impact of comorbidities on severity and mortality in AP patients [47, 48]. Additionally, few studies have incorporated comorbidities in their clinical models when evaluating determinants of AP severity. Our study is one of the first studies in the literature that put in relevance the role of comorbidities in AP.
In the last period of our study, we observed an increase in comorbidities and the values of the Charlson and Elixhauser comorbidity indices. These trends could be explained by the increase in life expectancy and the prevalence of chronic diseases in the European population in the last decades [49, 50]. However, despite the increase in comorbidities, hospital mortality has decreased in the period studied. The decrease in mortality is likely due to the advances in critical care medicine, step-up approach to treat infected necrosis, and surgical and endoscopic new approaches [3, 51].
Few studies had previously assessed comorbidity indexes' role in predicting mortality in patients with AP. Previous studies have observed that more comorbidities are associated with organ failure and mortality in patients with AP [47, 52]. In our study, we analyzed the Charlson and Elixhauser indices which are good predictors of mortality in other diseases [53, 54]. Our results showed that values > 1.5 points for both indices are independently associated with hospital mortality in AP after adjusting for age and sex. Future studies that expand knowledge of the effects of comorbidities on complications and mortality in patients with AP will improve the identification of patients at risk and their quality of care.
Regarding other comorbidities, our results align with previous studies stating that pre-existing heart and renal diseases predict mortality in patients with AP [19, 47, 55]. One hypothesis is that intravascular depletion and aggressive fluid resuscitation cause decompensation of previous heart and renal disease [55].
Similarly, to Frey et al. [ 47], we found an association between liver disease, peripheral vascular disease, and cerebrovascular disease with mortality. However, Murata et al. found no association between these diseases with mortality [55]. The worse results of AP in patients with liver diseases such as cirrhosis could be explained because they present an inflammatory syndrome with arterial vasodilation and release of proinflammatory cytokines that increase the severity of AP [56]. In addition, acute pancreatitis produces significant stress that could decompensate underlying chronic comorbidities and increase the risk of death.
Other major comorbidities before admission, such as chronic lung disease, were not independently associated with mortality, coinciding with the results of Murata et al. [ 55]. Similar controversial results were found regarding obesity, in which previous studies have observed that obesity is a risk factor for developing local and systemic complications and mortality in patients with AP [17, 57–59]. We did not find an association between hospital mortality and obesity, but this result has to be taken carefully due to the potential information bias because our results are based on the history of obesity recorded in the medical-administrative database and not on the BMI at hospital admission. In the same line, we did not find a relation between diabetes mellitus and mortality after the multivariable analysis. The literature remains controversial, with reports describing diabetes mellitus as a risk factor for mortality [20, 21]. However, Frey et al. found that diabetes increased the risk of multiple organ failure but was not associated with mortality [47].
Other risk factors classically related to mortality in AP patients failed to represent a risk factor in our population. This was relevant to the role of AP etiology, in which the literature reports controversial results, identifying a more severe course and higher mortality in alcoholic pancreatitis [58, 59]. In contrast, others observed greater severity in biliary pancreatitis [60, 61] or no relation with mortality between both aetiologies [37, 62–64]. Our study did not observe that acute pancreatitis's biliary or alcoholic etiology was associated with higher hospital mortality. However, in our study, $44.3\%$ of the patients were classified as "unspecified acute pancreatitis,” limiting the precision of our results and constituting a bias regarding the real impact of etiology in AP mortality. In addition, our database does not include other etiologies of acute pancreatitis such as those caused by hypercalcemia, after trauma, viral infections, anatomical variants, iatrogenic after endoscopic retrograde cholangiopancreatography or endoscopic ultrasound-guided interventions [65–67].
Our study is subject to some limitations. The data analysis from a clinical-administrative base has low level of granularity and does not include some clinical results of interest, such as severity or the evolution of the patient in the medium or long term after their hospital stay. Our study could not identify pancreatic necrosis in the first two years because it began to be considered in the ICD-10 in 2018. Another limitation is the potential underreporting of information because the hospital discharge report may be incomplete or poorly registered by the technical-administrative staff. In our study, we could not identify the etiology of AP in many patients due to a lack of precise coding.
No studies have been published to validate the use of ICD-10 codes for identifying patients with AP using the RAE-CMBD database. However, there are recent studies from Danish [68] and US [69] databases with PPV of $97.3\%$ and $87\%$, respectively. A recent meta-analysis recommends using ICD codes only in incident cases of AP in adults, where it reaches a PPV of $78\%$ [70]. However, this may be because the studies analyzed used ICD-8, ICD-9, and ICD-10 codes, and the PPV is higher when using the ICD-10 because it is more specific and includes the etiology of pancreatitis [70, 71]. In addition, the studies were carried out in different hospitals in several countries, contributing to the heterogeneity.
We used the primary and the first registered secondary diagnoses to reduce the bias of not including patients with an initial diagnosis of cholelithiasis/choledocholithiasis and AP. A recent study validating ICD codes using primary and secondary diagnoses observed a PPV of $97.3\%$ for AP [68].
The main strength of our study is its large sample size which provides strong statistical power. The RAE-CMBD database is a mandatory registry for the Spanish National Health System, which covers almost $100\%$ of admissions in Spain, reinforcing the external validity of our results. In addition, the database has several internal audit mechanisms and has proven its usefulness for health research [53, 72, 73].
## Conclusions
Comorbidities such as heart disease, kidney disease, moderate-severe liver disease, peripheral vascular disease, cerebrovascular disease, and advanced age were independently associated with mortality in AP. The Charlson and Elixhauser comorbidity indices are useful for predicting hospital mortality in these patients.
## Supplementary Information
Additional file 1: Table S1. The ICD-10 codes used to identify specific comorbidities. Table S2. Association of comorbidities of Charlson Comorbidity Index and hospital mortality in acute pancreatitis. Table S3. Association of comorbidities of Elixhauser Comorbidity Index and hospital mortality in acute pancreatitis.
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|
---
title: DNA methylation patterns at birth predict health outcomes in young adults born
very low birthweight
authors:
- Vicky A. Cameron
- Gregory T. Jones
- L. John Horwood
- Anna P. Pilbrow
- Julia Martin
- Chris Frampton
- Wendy T. Ip
- Richard W. Troughton
- Charlotte Greer
- Jun Yang
- Michael J. Epton
- Sarah L. Harris
- Brian A. Darlow
journal: Clinical Epigenetics
year: 2023
pmcid: PMC10035230
doi: 10.1186/s13148-023-01463-3
license: CC BY 4.0
---
# DNA methylation patterns at birth predict health outcomes in young adults born very low birthweight
## Abstract
### Background
Individuals born very low birthweight (VLBW) are at increased risk of impaired cardiovascular and respiratory function in adulthood. To identify markers to predict future risk for VLBW individuals, we analyzed DNA methylation at birth and at 28 years in the New Zealand (NZ) VLBW cohort (all infants born < 1500 g in NZ in 1986) compared with age-matched, normal birthweight controls. Associations between neonatal methylation and cardiac structure and function (echocardiography), vascular function and respiratory outcomes at age 28 years were documented.
### Results
Genomic DNA from archived newborn heel-prick blood ($$n = 109$$ VLBW, 51 controls) and from peripheral blood at ~ 28 years ($$n = 215$$ VLBW, 96 controls) was analyzed on Illumina Infinium MethylationEPIC 850 K arrays. Following quality assurance and normalization, methylation levels were compared between VLBW cases and controls at both ages by linear regression, with genome-wide significance set to $p \leq 0.05$ adjusted for false discovery rate (FDR, Benjamini-Hochberg). In neonates, methylation at over 16,400 CpG methylation sites differed between VLBW cases and controls and the canonical pathway most enriched for these CpGs was Cardiac Hypertrophy Signaling ($$p \leq 3.44$$E−11). The top 20 CpGs that differed most between VLBW cases and controls featured clusters in ARID3A, SPATA33, and PLCH1 and these 3 genes, along with MCF2L, TRBJ2-1 and SRC, led the list of 15,000 differentially methylated regions (DMRs) reaching FDR-adj significance. Fifteen of the 20 top CpGs in the neonate EWAS showed associations between methylation at birth and adult cardiovascular traits (particularly LnRHI). In 28-year-old adults, twelve CpGs differed between VLBW cases and controls at FDR-adjusted significance, including hypermethylation in EBF4 (four CpGs), CFI and UNC119B and hypomethylation at three CpGs in HIF3A and one in KCNQ1. DNA methylation GrimAge scores at 28 years were significantly greater in VLBW cases versus controls and weakly associated with cardiovascular traits. Four CpGs were identified where methylation differed between VLBW cases and controls in both neonates and adults, three reversing directions with age (two CpGs in EBF4, one in SNAI1 were hypomethylated in neonates, hypermethylated in adults). Of these, cg16426670 in EBF4 at birth showed associations with several cardiovascular traits in adults.
### Conclusions
These findings suggest that methylation patterns in VLBW neonates may be informative about future adult cardiovascular and respiratory outcomes and have value in guiding early preventative care to improve adult health.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13148-023-01463-3.
## Background
Very low birthweight (VLBW, < 1500 g) babies account for nearly $2\%$ of all births [1], with a large proportion of VLBW survivors remaining healthy as adults. However, being born VLBW or very preterm is a chronic condition, such that VLBW individuals are at risk of cardiovascular and metabolic disorders becoming manifest at an earlier age than their term-born peers [2–6]. Data from very large Scandinavian population registries show that preterm birth (< 37 weeks’ gestation), particularly very low gestational age (< 32 weeks), is associated with increased mortality in young adulthood [2, 6, 7], with respiratory, endocrine, and cardiovascular disorders among the main causes of death [2]. Recent publications of Mendelian randomization [8] and genome-wide association studies [9] have shown that lower birthweight is causally related to increased risk of cardiovascular disease and type-2 diabetes (DM2). Many studies, including meta-analyses, in VLBW or very premature cohorts have reported elevated blood pressures in young adulthood [10, 11]. Premature birth has been associated with risk of adult pulmonary hypertension with an odds ratio of 3.08 after adjusting for known risk factors [3, 12]. Moreover, VLBW and preterm individuals may have altered cardiac structure and function that persists into young adulthood and beyond. The Victorian Infant Collaborative Study (VICS) reported that former extremely preterm (< 28-week gestation) adolescents aged 18 years had decreased left ventricular (LV) mass and cavity size but preserved cardiac function [13]. Cardiac magnetic resonance imaging (MRI) studies have reported that relative to term-born peers, young adults (20–39 years) born preterm had increased LV mass but smaller LV cavities, associated with impaired LV systolic/diastolic function [14, 15], and smaller right ventricles (RV), with increased RV wall mass, resulting in reduced RV ejection fraction [16]. The mechanism leading to smaller heart chambers may be a consequence of the preterm birth prematurely halting cardiac maturation, with early termination of muscle cell proliferation within the myocardium [17], resulting in a reduction in the absolute number of cardiomyocytes and reduced functional reserve. Low birthweight is reportedly also associated with impaired endothelial function in young adults [18, 19], with greater right carotid intima-media thickness [20]. In short, cardiovascular features documented in the VLBW population consistently include elevated systolic blood pressure, smaller heart dimensions and impaired cardiac and vascular function. Crucially, in a registry-based study of over 2.6 million individuals born in Sweden between 1987 and 2012 [21] and other studies [22], preterm birth was linked to a substantial increase in the risk of early heart failure. Compared to those born at term (≥ 37-week gestation), adjusted relative risks for heart failure incidence were RR = 17.0 after extremely preterm birth (< 28 weeks) and 3.58 after very preterm birth (28–31 weeks) [21].
In addition to altered cardiovascular function, those born VLBW or very preterm have enduring abnormal respiratory function as young adults, with reduced expiratory airflow and an increased risk of future obstructive airways disease [23, 24]. The developmental origins of lung disease are well-recognized [25] and may interact with and compound adverse cardiovascular health outcomes in VLBW adults. Moreover, the impact of a “second hit” on the lung, such as tobacco smoking, may exacerbate both perinatal lung damage [25] and suboptimal cardiovascular health.
We have recently described the cardiovascular and lung function, as well as metabolic characteristics of young adults (mean age 28 years) in the 1986 NZ VLBW cohort born < 1500 g, compared with 100 age-matched controls of normal birthweight [1, 26–29]. The VLBW individuals were shorter and lighter in stature, and consistent with international studies, had higher systolic blood pressure, smaller hearts, reduced stroke volume and endothelial function, as well as higher LV and arterial elastance than their normal birthweight peers [26, 27]. Adult VLBW cases also showed a higher incidence of airflow obstruction and other features of impaired respiratory function compared with controls [29].
Among VLBW survivors, early detection of those most at risk of pulmonary hypertension and heart failure may provide an opportunity for preventative interventions. At present, there are no reliable markers to predict future risk of cardiovascular or respiratory disorders apart from birthweight and gestation. General approaches to prevention include optimizing postnatal nutrition, with preterm infants exclusively fed human milk showing beneficial effects on cardiac size and function in young adulthood compared to those fed formula [30]. As with the general population, cardiovascular health could also be promoted through supporting VLBW adults to adopt a healthy diet and engage in regular physical activity, and living smoke-free would benefit both cardiovascular and respiratory health [31]. In addition, more intensive monitoring and targeted pharmacological approaches for high-risk individuals may improve myocardial development beyond the critical postnatal window. Although the benefits of antihypertensive therapies, inhibitors of cardiac remodeling or of atherosclerotic disease progression in the VLBW population are unknown, in the general population lowering blood pressure by 2 mm Hg can reduce hypertension by $17\%$, heart attacks by $6\%$, and stroke by $15\%$ [32]. Endothelial dysfunction is potentially treatable before it leads irreversibly to atherosclerosis and clinical disease [33]. Thus, a greater understanding of cardiovascular risk stratification is important to inform preventive care for adults born VLBW.
The greater risk of chronic disease in later life results from the convergence of numerous clinical and physiological parameters of the infant and mother, along with life-long environmental exposures. However, the group of individuals born VLBW is very heterogeneous, and the literature is unclear as to which clinical parameters should be used to guide ongoing monitoring and support for the health of VLBW survivors into adulthood. For example, the NZ guidelines for Cardiovascular Disease (CVD) Risk Assessment and Management for Primary Care [2017] [34] make no mention of a patient’s birth history as a trigger for regular assessment of these at-risk individuals. Identification of new biomarkers that are surrogate markers, or even causal markers [35] of the cellular/physiological pathways that underlie susceptibility to chronic disease could lead to better identification of at-risk individuals.
There is now extensive evidence that susceptibility to a number of adult chronic diseases may be linked to the fetal and early postnatal environment through epigenetic processes, such as DNA methylation of cytosine-guanine dinucleotides (CpG) within regulatory elements of genes [36, 37]. Perturbations in DNA methylation have been robustly demonstrated for many disorders (e.g. coronary artery disease [35, 38–40]), physical traits (eg obesity [41–43] and type 2 diabetes [44–46]) and environmental factors (e.g. smoking [47]). DNA methylation patterns associated with heart disease have been used to generate DNA methylation risk scores to predict incident cardiovascular disease [38]. Measures of epigenetic age acceleration, such as DNA methylation (DNAm) GrimAge [48], have been associated with late-life chronic disease including ischemic heart disease [49]. Significantly, being born VLBW has been associated with altered patterns of peripheral blood DNA methylation [37, 50–53], with some methylation differences persisting to adolescence [50, 52] or adulthood [53].
These prior studies indicate that DNA methylation patterns associated with VLBW or preterm birth may persist into adulthood, raising the tantalizing possibility that methylation profiling might be useful to predict adult health and to guide preventative care. Here we aimed to identify methylation markers that differed between NZ VLBW cases and age-matched controls, either at birth or at 28 years, and to determine if these are associated with cardiovascular and respiratory health outcomes in the cohort as young adults. We aimed to identify methylation patterns that might ultimately assist in targeting interventions to mitigate adverse health outcomes in adulthood in those born VLBW.
## Methods
The aim of this study was to determine whether methylation differed between NZ VLBW cases and age-matched controls either at birth or at 28 years of age, and to investigate whether methylation at birth is predictive of cardiovascular and respiratory health outcomes in the VLBW cohort as young adults.
## The New Zealand 1986 VLBW cohort
The New Zealand (NZ) 1986 VLBW cohort originally consisted of all 413 VLBW (< 1500 g) live births admitted to a NZ neonatal unit in 1986, who were enrolled in a prospective audit of retinopathy of prematurity (ROP) with data collected on 173 perinatal variables [1]. Of this cohort 338 ($82\%$) survived to discharge home and were followed up at 7–8 years and at 22–23 years. In 2013–16 (26–30 years), 229 participants ($71\%$ of survivors) underwent a 2-day, multidisciplinary evaluation in Christchurch, NZ, as per the study protocol previously described [1, 26, 27, 29]. A flow diagram of inclusion and exclusion criteria is given in Additional file 1: Methods 1. In brief, of the VLBW cases who underwent clinical screening, 64 ($28\%$) were < 1000 g at birth, 57 ($25\%$) were < 28 weeks’ gestation, 72 ($31\%$) were small for gestational age, and 129 ($56\%$) had received antenatal steroids. A control group of 100 subjects were also assessed, who were born healthy at full term (≥ 37 weeks) in 1986 in NZ and had not been admitted to an intensive care unit (recruited through peer nomination by cohort member or random sampling from the electoral rolls, seeking balance with respect to sex, ethnicity, and regional distribution). Participants completed a standardized health questionnaire including self-reported morbidities, and height, weight, body mass index (BMI), and waist-hip measures were recorded. Systolic and diastolic blood pressure were measured in the non-dominant arm by a trained health professional with the participant seated, with the third of three readings over a 15-min period recorded. Transthoracic echocardiography was performed using an iE33 ultrasound machine (Philips Life Healthcare, Amsterdam, The Netherlands) for reporting of indices of cardiac size, structure and function [26, 27]. Endothelial function was assessed by peripheral arterial tonometry using an EndoPAT system (Itamar Medical, Caesarea, Israel), measuring reactive hyperemic index (log normal transformed, LnRHI) [27]. Lung function tests were undertaken as previously described [29] at the Respiratory Physiology Laboratory, Christchurch Hospital, which is accredited by the Thoracic Society of Australia and New Zealand. Peripheral blood samples were collected for subsequent biochemistry, white cell counts and DNA extraction, and whole blood or plasma stored at −80 C. The study was approved by the Southern Health & Disability Ethics Committee (URB/$\frac{12}{05}$/015), with optional consent obtained for DNA analysis.
## DNA extraction and methylation arrays
Genomic DNA was extracted from archived newborn heel-prick blood spots (3 × 3 mm card punches of “Guthrie” cards) for 109 VLBW and 51 controls, provided by The Neonatal Screening Unit of NZ following patient consent. Blood-spot DNA was extracted with a modified Dried Blood Spots protocol using the QIAamp® 96 Blood Kit (Qiagen, Hilden, Germany), optimized for maximum DNA yield (see Additional files 2: Methods). While DNA shows some degradation after long-term storage, this reportedly does not markedly impact DNA methylation arrays [54]. Genomic DNA was also extracted from peripheral blood at 28 years of age for 215 VLBW cases and 96 controls (those consenting for DNA analysis and for whom blood was available). DNA was isolated from 3 mL frozen whole blood using an automated KingFisher® Flex 24 instrument (ThermoFisher Scientific, Waltham, MA, USA) and Machery-Nagel DNA isolation kits (Machery-Nagel, GmbH &Co, Düren, Germany). All DNA samples underwent bisulfite conversion (EZ DNA Methylation Kit, Zymo, Orange, CA, USA) following the manufacturer’s instructions, prior to hybridization and scanning by an accredited provider (GenomNZ™, AgResearch, Invermay, NZ) on Illumina Infinium Human MethylationEPIC 850 K arrays (Illumina Inc, San Diego, CA, USA), covering 850,000 genome-wide, differentially methylated regions. Cases and controls were run together with balanced physical positions across each array.
## Methylation data analysis
Raw methylation output files underwent quality assurance (QA) filtering, background and batch normalization and Infinium probe type correction within the Illumina GenomeStudio methylation software module as previously described [55]. In brief, Illumina (Genome Studio) normalized beta values were analyzed for differential methylation using the Chip Analysis Methylation pipeline (ChAMP version 2.20). Any sample flagged as having poor intensity in either the red or green fluorescence channel, probes reported to be cross-reactive and non-specific probes were excluded from the analyses [56], leaving 822,741 CpG sites for analysis. Recorded single nucleotide polymorphism (SNP)-variable CpGs were flagged but not excluded. We subsequently scrutinized the individual distribution patterns of the top sites to ensure that differential methylation observations were not being influenced by obvious SNP effects. The normalized data were analyzed by linear regression to compare VLBW cases and controls, adjusting for sex, blood cell composition (Houseman extended method [57, 58]), EPIC array slide, array position, and CpG call rate (linear). Graphical representation of data with Manhattan plots or principal component analyses (PCA) was performed using Qlucore Omics Explorer bioinformatics software (version 3.4, Lund, Sweden). A shortlist of candidate CpGs for further follow up was generated for each age using a significance threshold after adjusting for false discovery rate (FDR-adj-p, Benjamini-Hochberg) of < 0.05. Because of the differences in storage time and method of DNA sample preparation for neonatal blood spots versus adult whole blood, and the potential differences in white cell profiles in newborns versus adults, raw methylation data files were not directly compared between ages. Rather, lists of candidate CpGs that differed between VLBW cases and controls were separately generated for each age, and the resultant lists were compared. In addition, the EWAS data were interrogated for differentially methylated regions (DMRs) using the Bioconductor package DMRcate (https://tinyurl.com/y3uex6bj). All CpGs shortlisted as significantly differentially methylated between VLBW and controls were examined using the UCSC Genome Browser (UCSC Genome Institute, University of California, Santa Cruz, CA, USA) to ensure that they were correctly annotated to their gene location. Gene lists were analyzed for pathway associations and gene networks using Ingenuity® Pathway Analysis (Qiagen, Aarhus, Denmark), after inversing positive/negative fold changes to account for inhibitory methylation effects in this gene expression software, with IPA software recognizing 6053 of 16,400 features in the neonate list and 388 of 1000 features in the adult list. Lists of differentially methylated CpG were also examined for localization relative to CpG islands and with transcription binding sites using DNA Methylation Interactive Visualization Database (DNMIVD) [59]. For the 28-year samples DNA methylation age scores, DNAm GrimAge and GrimAge Adjusted Age [48] were calculated by running the data through the algorithm at https://dnamage.genetics.ucla.edu/home. Comparisons of baseline characteristics between VLBW cases and controls were done using ANOVA and Chi-square tests within SPSS Statistical software for Mac, version 27 (IBM Corporation, Armonk, NY, USA). Association analyses between methylation at our top CpGs and adult cardiovascular and respiratory traits that we have previously reported to differ between VLBW and controls as young adults in this cohort [26, 27] were performed by univariate general linear regression models (GLM) with case–control status, sex and ethnicity as covariates, also using SPSS version 27.
## Characteristics of the VLBW cases and normal birthweight controls
Characteristics of the subset of VLBW cases and normal birthweight controls for whom DNA was available for methylation analysis at 28 years (215 VLBW, 96 controls) are shown in Table 1. The variables presented are those previously reported to differ significantly between VLBW and controls in the full NZ 1986 VLBW cohort [26, 27, 29]. Mean birthweights of the VLBW cases were approximately a third that of controls. The average gestation at birth in VLBW cases was 29.2 weeks, while all controls self-reported that they were born at term (> 37 weeks, taken as a mean of 38.5 weeks). There was no significant difference in the gender split between VLBW cases and controls, but the distribution of ethnicities differed, with a greater percent of Māori among VLBW cases and a greater proportion of Pacific Peoples and Europeans among controls. At 28 years of age, the VLBW young adults had similar rates of smoking to controls, and similar rates of diabetes and BMI profiles. Among cardiovascular variables at 28 years, VLBW cases had higher systolic BP and MAP, smaller hearts (even when indexed to body surface area, BSA) including: LV mass index (LVMI), right ventricular basal diameter (RV basal dia) and right atrial volume index (RAVI). The VLBW cases also had lower cardiac output (CO), lower stroke volume indexed to BSA (SVI), and lower RV global endocardial longitudinal strain (Endo-GLS), with higher LV elastance and arterial elastance. Table 1 also shows respiratory variables at 28 years, with VLBW having lower z-scores for forced expiratory volume in the first second (FEV1z), forced expiratory flow at 25 and $75\%$ of the pulmonary volume (FEF25–75) and FEV1z by FVCz (Tiffeneau-Pinelli index), higher residual volume (RVz) and RVz to total lung capacity (RVz by TLCz), lower diffusing capacity of the lungs for carbon monoxide (DLCOz), lower carbon monoxide transfer coefficient (KCOz), but cases and controls had similar values of VO2 Max. Because our study analyzed methylation levels of DNA extracted from peripheral blood mononuclear cells (PBMC), Table 1 also shows that white blood cell profiles were comparable between VLBW cases and controls except for higher neutrophil counts in VLBW young adults. Table 1Characteristics of VLBW Cases and Controls with DNA Available at 28 yearsVLBWnControlsNp valueAt birthBirthweight, g1135 ± 16.12143468 ± 59.982 < 0.001Female n (%)120 ($55.8\%$)21461 ($63.5\%$)960.219Gestation at birth, weeks29.22 ± 0.1721538.50 ± 0 *96 < 0.001Ethnicity214960.003 Māori60 ($27.9\%$)15 ($15.6\%$) Pacific4 ($1.9\%$)8 ($8.3\%$) European150 ($69.8\%$)73 ($76.0\%$)Ventilation, days10.69 ± 1.04215UnknownMaternal smoking in pregnancy, n (%)87 ($40.5\%$)211UnknownPreeclampsia-toxemia53 ($24.7\%$)214UnknownTreatment with antenatal steroids124 ($57.7\%$)215UnknownBreast feeding duration, wks4.15 ± 0.03215UnknownAt 28 yearsAge at Screening, years28.35 ± 0.0720828.18 ± 0.10940.169Current Smoking, n (%)65 ($30\%$)21420 ($21\%$)960.053Ever smoked, n (%)94 ($44\%$)21432 ($33\%$)960.051Diabetes Type 1, n (%)0 ($0\%$)2152 ($2.1\%$)960.095Diabetes Type 2, n (%)1 ($0.5\%$)2151 ($1.0\%$)960.523BMI, kg/m226.95 ± 0.4320528.23 ± 0.65940.101SBP, mmHg121.85 ± 0.97207117.23 ± 1.39940.008DBP, mmHg75.94 ± 0.7220774.02 ± 1.02940.133MAP, mmHg87.36 ± 0.6720284.76 ± 0.90940.026LVMI, g/m289.68 ± 1.321395.00 ± 2.31960.036LVEDV, mL/m258.09 ± 0.7421462.40 ± 1.27960.002LVESV, mL/m220.72 ± 0.3321422.60 ± 0.60960.003RV basal dia, cm3.06 ± 0.032033.23 ± 0.05960.003RAVI, cm3/m225.22 ± 0.4621228.36 ± 0.8096 < 0.001Endo-GLS%23.66 ± 0.2610824.95 ± 0.43560.008CO, L/min4.92 ± 0.092095.34 ± 0.12940.009SVI, mL/m237.38 ± 0.4821439.81 ± 0.82960.008LnRHI0.61 ± 0.021880.68 ± 0.03930.062LV Elastance, mmHg/mL3.38 ± 0.062082.84 ± 0.0894 < 0.001Arterial Elastance, mmHg/mL1.85 ± 0.032081.59 ± 0.0494 < 0.001FEV1 z-score− 0.66 ± 0.08210− 0.15 ± 0.1296 < 0.001FEV1 z-score by FVC z-score− 1.23 ± 0.08210− 0.62 ± 0.1096 < 0.001FEF25–75 z-score− 1.30 ± 0.09211− 0.51 ± 0.1296 < 0.001RV z-score− 0.81 ± 0.07212− 1.22 ± 0.0996 < 0.001RV by TLCz-score− 1.06 ± 0.07212− 1.49 ± 0.0996 < 0.001DLCO z-score− 0.69 ± 0.07210− 0.12 ± 0.0893 < 0.001KCO z-score− 0.60 ± 0.07210− 0.02 ± 0.1093 < 0.001VO2 Max, kg30.68 ± 0.5819131.23 ± 0.83890.590WBC, 109/L6.45 ± 0.122086.06 ± 0.17940.066Platelets 109/L244.7 ± 4.06208252.6 ± 5.90940.278Neutrophils, 109/L3.67 ± 0.092083.32 ± 0.13940.032Lymphocytes, 109/L2.02 ± 0.042082.01 ± 0.05940.893Monocytes, 109/L0.52 ± 0.012080.49 ± 0.02940.099Eosinophils, 109/L0.22 ± 0.012050.23 ± 0.02940.819Basophils, 109/L0.06 ± 0.002880.06 ± 0.002410.299Data are expressed as mean ± SEM or n (%). p values <0.005 indicated by bold fontBMI, body mass index; BP, blood pressure; SBP, systolic BP; DBP, diastolic BP; MAP, mean arterial pressure; BSA, body surface area; LVMI, left ventricular mass index; LVEDV, LV end diastolic volume indexed to BSA, LVESV, LV end systolic volume indexed to BSA; RV basal dia, right ventricular basal diameter; RAVI volume, right ventricular volume indexed to BSA; Endo-GLS, Global endocardial longitudinal strain; CO, cardiac output; SVI, Stroke Volume indexed to BSA; LnRHI, Ln Reactive Hyperemia Index; LV Elastance, left ventricular elastance; Z-scores for the following indices: FEV1, forced expiratory volume in the first second; FEF25–7 5, forced expiratory flow at 25 and $75\%$ of the pulmonary volume; FEV1 by FVC, ratio of FEV1 to forced vital capacity; RV, residual volume; RV by TLC, ratio of RV to total lung capacity; DLCO, diffusing capacity of the lungs for carbon monoxide; KCO, carbon monoxide transfer coefficient; WBC, white blood cell count*Controls were considered born at term (37 to 40 weeks) and mean gestation was set at 38.5 weeks Archived neonatal heel pricks for DNA methylation profiling were only available for a subset of the cohort (109 VLBW, 51 controls), and the characteristics of this subgroup (Additional file 3: Table S1) were similar to the overall cohort, except that differences between VLBW cases and controls in CO, RV basal dia and neutrophils did not reach statistical significance.
For DNA extracted from neonatal blood spots, $93\%$ of samples gave over $80\%$ call rates, with eleven samples having call rates < $80\%$, (indicating methylation data were obtained from < $80\%$ of the 822,741 valid CpGs on the EPIC arrays for that sample). A total of 12 neonate samples were excluded due to a combination of low call rates and poor red or green channel fluorescence. The remaining neonate samples included in the EWAS had an average call rate of $98\%$ (average 806,286 probes called). For DNA collected from adult peripheral blood samples, Illumina GenomeStudio software indicated all samples had > $99\%$ call rates and high fluorescence intensity on both red and green channels, and hence no samples were excluded due to low quality (822,741 CpG probes included in the EWAS).
## Methylation differences between VLBW cases and controls in neonate samples
Analysis of DNA extracted from neonatal blood spots identified over 16,400 CpGs with methylation levels that differed significantly between VLBW and controls (FDR-adjusted $p \leq 0.05$). The methylation profiles in neonatal samples are illustrated in the Manhattan Plot in Fig. 1, labeled with the names of the nearest gene for the top twenty loci (the full list of CpGs is given in Additional file 4: Table S2 and an interactive Manhattan plot is available at: https://my.locuszoom.org/gwas/506658/?token=2815c5e9b01d4f04a376efb17f9c2f27). The majority of these loci were hypermethylated (Additional file 5: Fig. S1), with a smaller cluster of CpGs being hypomethylated in VLBW cases versus controls. Fig. 1Manhattan Plot of differentially methylated CpGs between VLBW cases and normal birthweight controls in neonatal blood-spot samples (red is hyper-, green is hypomethylated in VLBW cases). Red dotted line indicates FDR-adjusted genome-wide significance $p \leq 0.05.$ Blue arrows indicate the 20 most significant CpGs, with nearest gene name Gene network analysis performed on the 16,400 CpGs that reached FDR-adjusted significance indicated the canonical pathway most highly enriched with these CpGs was Cardiac Hypertrophy Signaling ($$p \leq 3.44$$E−11, Additional file 6: Fig. S2), with predicted upregulation of this pathway in VLBW neonates. Physiological functional systems most enriched included Organismal Development and Function (p range 2.59E−07–3.87E−27), Embryonic Development (p range 2.04E−07–1.24E−19) and Cardiovascular System Development and Function (p range 1.31E−07–1.03E−18).
Relationships between neonatal methylation, birthweight and gestation are shown in Table 2 for the twenty CpGs most differentially methylated in VLBW cases versus controls. Nineteen of these CpGs were hypermethylated and one was CpG hypomethylated (cg09476997 in SLC9A3R2). The top twenty CpGs included several gene clusters: two CpGs in ARID3A (cg02001279 being the most significant CpG overall), two in SPATA33, (also known as C16orf55) and two in PLCH1. Additional CpGs within these same genes also appeared multiple times in the long list of FDR-significant CpGs (Additional file 4: Table S2). Methylation at the majority, but not all, of these CpGs demonstrated significant associations with birthweight and gestational age at birth. The DMR analysis (Additional file 7: Table S3) confirmed that ARID3A, SPATA33, and PLCH1, along with DMRs spanning MCF2L, TRBJ2-1 and SRC, topped the list of 15,000 DMRs reaching FDR-adj significance. Table 2Top 20 CpGs differentially methylated between VLBW versus controls and associations with birthweight or gestation, adjusting for study group, sex and ethnicity (full list of 16,408 CpGs FDR-adj $p \leq 0.05$ in Additional file 11: Table S2)CpG ID and nearest geneChromosome: locationp valueFDR-adj p value*Fold changeR statisticDirection in VLBWBirthweight p value and direction (full Cohort)Gestation p value and direction (VLBW cases)cg02001279ARID3A19:940,9671.01E−378.28E−321.1530.862Hypermethylated0.003+ < 0.001+cg07835443SPATA33/C16orf5516:89,734,9863.17E−351.30E−291.2170.847Hypermethylated < 0.001+ < 0.001+cg10461390LRBA4:151,324,2239.63E−302.64E−241.1540.808Hypermethylated < 0.001+ < 0.001+cg04347477NCOR212:125,002,0071.76E−293.62E−241.1770.805Hypermethylated0.008+ < 0.001+cg25975961KCNH27:150,600,8181.97E−283.24E−231.1440.797Hypermethylatednsnscg17307655WHRN/DFNB319:117,214,6722.42E−283.31E−231.1430.796Hypermethylated < 0.001+ < 0.001+cg16725984SPATA33/C16orf5516:89,735,1845.52E−286.49E−231.1270.793Hypermethylated0.015+ < 0.001+cg06870470DOCK619:11,315,7671.31E−271.35E−221.1670.789Hypermethylated0.004+ < 0.001+cg09476997SLC9A3R216:2,087,9328.45E−277.72E−220.815− 0.782Hypomethylated0.007–0.002–cg12713583ARID3A19:940,7249.54E−277.85E−221.1340.781Hypermethylated0.012+ < 0.001+cg19274030ST3GAL63:98,489,7452.12E−261.58E−211.1160.778Hypermethylated0.002+ < 0.001+cg00406098LINC0059813:40,716,1804.19E−262.65E−211.1230.775Hypermethylated0.001+0.006+cg06063190RNA5SP31810:55,163,5254.41E−262.65E−211.1190.775Hypermethylated0.025+0.007+cg11932158PLCH13:155,422,1294.50E−262.65E−211.0990.775Hypermethylatedns0.033cg19333758RAPGEF312: 48,135,5497.05E−263.87E−211.1660.773Hypermethylated0.003+ < 0.001+cg19744173FBLN72:112,913,1787.73E−263.98E−211.1020.773Hypermethylated < 0.001+ < 0.001+cg25715278OAT10:126,040,3053.21E−251.55E−201.0760.767Hypermethylated0.013+ < 0.001+cg26708048DLG417: 7,087,6551.08E−244.94E−201.1210.761Hypermethylated0.042+0.002+cg18623216PLCH13:155,421,9701.24E−245.12E−201.1020.761Hypermethylatednsnscg12405088PACSIN222:43,257,1201.24E−245.12E−201.1010.761Hypermethylated0.049+ < 0.001+* Benjamini–Hochberg FDR p value is corrected for 822,741 CpG sites. R statistic is correlation coefficient for continuous variables We also investigated relationships between methylation at birth and later health outcomes for the twenty most significant CpGs in neonates (using GLMs adjusted for case–control status, sex and ethnicity), with fourteen demonstrating associations with cardiovascular outcomes in young adults aged 28 years (Table 3). Remarkably, twelve of the top CpGs that were different between VLBW cases and controls at birth showed significant associations with adult LnRHI (noting that no associations were observed between methylation in adult samples and LnRHI at any CpG), and the association was especially strong for cg04347477 (NCOR2), cg17307655 (WHRN), cg19333758 (RAPGEF3), cg25715278 (OAT) and cg26708048 (DLG4). Several CpGs also showed associations between neonatal methylation and systolic BP or indices of cardiac size at 28 years, while methylation of two CpGs, cg25975961 (KCNH2) and cg26708048 (DLG4), showed associations with adult BMI. Fewer associations between neonatal methylation and adult respiratory traits were observed; the hypomethylated cg09476997 (SLC9A3R2) was associated positively with FEV1z by FVCz-score ($$p \leq 0.037$$) and negatively with RVz ($$p \leq 0.012$$), while the hypermethylated cg11932158 (PLCH1) was negatively associated with FEF25–75 z-score ($$p \leq 0.021$$), FEV1z-score ($$p \leq 0.024$$). Other traits not included in Table 3, including a history of smoking, arterial elastance and VO2 max, showed no associations between neonatal methylation and adult outcomes. Table 3CpGs differentially methylated at birth and associations with adult cardiovascular traits at 28 years, adjusting for study group, sex and ethnicityCpGNearest GeneBMISystolic BPLVMILVESVRV basal diaRAVICOEndo-GLSLnRHIcg02001279ARID3A0.023cg07835443SPATA330.034cg04347477NCOR20.0160.007cg25975961KCNH20.018†† 0.044*0.0410.042cg17307655WHRN0.0480.004cg06870470DOCK60.0240.027cg09476997SLC9A3R20.027††0.009††0.040cg12713583ARID3A0.0280.033cg00406098LINC005980.0460.0320.032cg06063190RNA5SP318†† 0.035*0.038cg19333758RAPGEF30.005cg19744173FBLN70.019cg25715278OAT0.001cg26708048DLG40.0250.017 < 0.001†† Preceding the p value indicates the interaction term of CpG x Study *Group is* significant $p \leq 0.05$; * CpG is significant in VLBW cases when interaction term is also significant in the model We tested whether potential confounders, including antenatal steroid treatment (ANS), maternal smoking in pregnancy, preeclampsia-toxemia or duration of breastfeeding were accounting for any of these associations with the top 20 CpGs. These variables were tested in neonate cases only (as these data were not available for controls) and weak correlations were identified between maternal smoking and methylation at a single CpG, with preeclampsia-toxemia at four CpGs, with treatment with ANS at three CpGs, and with breastfeeding duration at three CpGs. Adjusting GLM models for the respective potential confounders (in cases only due to lack of data in controls) replicated the previous pattern of associations, albeit with weaker significance due to having only VLBW cases in the model (data not shown). We concluded the associations between top CpGs and adult traits were largely independent of these factors.
## Methylation differences between VLBW cases and controls in adult samples
At 28 years of age, twelve CpGs demonstrated altered methylation between VLBW and controls at genome-wide significance (FDR-adj $p \leq 0.05$), as presented in the Manhattan Plot in Fig. 2 and in Table 4 (an interactive Manhattan *Plot is* also available at https://my.locuszoom.org/gwas/120785/?token=be8874a3d3374ffba76e7b99fddc2a26). Differentially methylated CpGs included clusters within two genes: four CpGs hypermethylated in EBF4 and three CpGs hypomethylated in HIF3A in VLBW relative to controls, with multiple other CpGs in these two regions also differentially methylated but below our threshold for genome-wide significance. The DMR analysis (Additional file 7: Table S3) confirmed that the multiple CpGs in HIF3A, EBF4 and GLI2 were classified as DMRs. Additional CpGs that reached the significance threshold were located in KCNQ1 (hypomethylated) and UNC119B, EVX1, GLI2 and CFI (all hypermethylated), although cg14486095 in UNC119B was determined to be polymorphic and was excluded from further analyses. Methylation levels at the majority of these sites were significantly associated with birthweight, especially CpGs within EBF4. Methylation at seven of the lead sites in adults were also weakly associated with gestational age at birth (Table 4, available for VLBW cases only). The DNAm GrimAge scores calculated from the 28-year samples were higher in VLBW cases versus controls (38.12 ± 4.83 versus 36.38 ± 4.87, respectively; $$p \leq 0.003$$) as were DNAm GrimAge Adjusted Age scores (0.47 ± 4.77 versus −1.09 ± 4.61, respectively; $$p \leq 0.007$$).Fig. 2Manhattan plot of CpGs differentially methylated between VLBW cases and normal birthweight controls in samples collected at 28 years of age. The red dotted line indicates the threshold for significance (FDR-adjusted $p \leq 0.05$), with blue arrows and gene names indicating the 12 CpGs that reached significance (red is hyper-, green is hypomethylated in VLBW cases)Table 4CpGs differentially methylated in adult VLBW versus controls and associations with birthweight or gestation adjusting for study group, sex and ethnicityCpG ID-nearest geneChromosome: locationp valueFDR-adj p value*Fold changeR statisticDirection in VLBWBirthweight p value and direction (Full Cohort)Gestation p value and direction (VLBW cases)cg13518079EBF420: 2,675,0721.31E−090.0011.0670.343Hypermethylated < 0.001–0.015–cg05857996EBF420: 2,675,4184.90E−090.0021.0650.332Hypermethylated0.002–0.009–cg22891070HIF3A19: 46,801,6421.25E−080.0030.942− 0.323Hypomethylated0.013+0.012–cg16672562HIF3A19: 46,801,6723.05E−080.0060.944− 0.315Hypomethylated0.015+0.013–cg24263062EBF420: 2,730,1914.20E−080.0071.0470.312Hypermethylated < 0.001††–0.011–cg26344859KCNQ111: 2,584,6291.08E−070.0150.992− 0.303Hypomethylatednsnscg14486095UNC119B**12: 121,147,2222.19E−070.0261.0440.296Hypermethylated0.028††+nscg04099095EVX17: 27,278,5703.19E−070.0291.0130.292Hypermethylated0.049–nscg27146050HIF3A19: 46,801,5573.35E−070.0290.978− 0.291Hypomethylated0.005+nscg20219891GLI22: 121,496,8753.52E−070.0291.0530.291Hypermethylated0.027–nscg05149986CFI4: 110,683,9206.41E−070.0481.0150.285Hypermethylatedns0.007–cg05825244EBF420: 2,730,4887.23E−070.0491.0680.283Hypermethylated < 0.001††–0.005–*Benjamini–Hochberg FDR p value is corrected for 822,741 CpG sites. R statistic is correlation coefficient for continuous variables**cg14486095 in UNC119B was found to be polymorphic and was excluded from further analyses††Following the p value indicates both the interaction term CpG x Study Group and CpG are significant $p \leq 0.05$ When pathway analysis was performed on the 1000 most differentially methylated sites in adults (including those below the FDR threshold), the top canonical pathway was Pigment Epithelium-derived Growth Factor (PEDF) signaling ($$p \leq 1.47$$E−04), but this had only a $9.5\%$ overlap with our list. Gene networks enriched with the adult CpG list included a network featuring upregulation of the gene HIF3A and downregulated Akt, which was enriched in genes associated with Hereditary Disorders, Neurological Diseases and Organismal Injuries and Abnormalities (Additional file 8: Fig. S3A). A further key network featured downregulation of EBF4, predicted inhibition of Erk$\frac{1}{2}$, and was associated with Cellular Development, Embryonic Development, Nervous System Development and Function (Additional file 9: Fig. S4A). The most enriched Physiological Functional System for the adult CpG list was Cardiovascular System Development and Function (p range 1.21E−03–6.58E−06) followed by Organ Morphology (p range 1.40E−02–8.78E−06).
Association between the leading 12 CpGs in adult samples plus Grim Age Adjusted Age score and their concurrent cardiovascular traits (Additional file 10: Table S4A) and respiratory measures (Additional file 10: Table S4B), adjusting for VLBW versus control status, sex and ethnicity in the GLM. Higher methylation levels at EBF4 CpGs were associated with higher arterial elastance, lower stroke volume (cg05857996 only), RV basal dia or RAVI. Other hypermethylated CpGs also showed associations with smaller indices of cardiac size. The only CpG demonstrating an association with systolic BP was cg05149986 in CFI, which was also associated with higher LV elastance and arterial elastance, and with smaller LVMI measurements. The hypomethylated CpGs in HIF3A and KCNQ1 showed associations in the opposite directions to other CpGs, being negatively associated with LV elastance and arterial elastance, and positively associated with (lower) CO and LVMI. Lower methylation at the KCNQ1 site cg26344859 was also positively associated with lower SVI and smaller LVEDV. Hypomethylation at HIF3A sites showed associations with a smaller BMI. DNAm GrimAge Adjusted Age was weakly associated with several cardiovascular measures (Additional file 10: Table S4A). As above, we looked for potential confounders in the 28-year-olds, finding neither treatment with ANS nor beast-feeding duration were correlated with methylation at any CpG, while maternal smoking was correlated with methylation at two CpGs and preeclampsia-toxemia correlated methylation at two CpGs. Adjusting the GLM with these measures in VLBW cases only (due to the lack of data in controls) indicated the associations described above were independent of these confounders.
Additional file 10: Table S4B displays the ten top CpGs that showed any associations with respiratory traits at 28 years. Hypermethylation at EBF4 was weakly associated with lower DLCOz-scores or lower RVz and RVz by TLC (cg05825244 only). The strongest associations with respiratory traits were between higher methylation at CFI and higher RVz by TLC, but lower DLCOz, KCOz and VO2 max. Hypomethylation at HIF3A generally showed associations with lower RVz and RVz by TLC and higher DLCOz and KCOz. Other CpGs showed associations with other scattered respiratory traits. Across the overall cohort, a history of having ever smoked was associated with methylation at only two CpGs (HIF3A, cg22891070 and cg16672562, data not shown, $$p \leq 0.042$$ and $$p \leq 0.023$$, respectively), both being hypomethylated in smokers. DNAm GrimAge Adjusted Age was strongly associated with several respiratory measures (Additional file 10: Table S4B), and these associations remained even when smoking history was included in the model (data not shown).
## Overlap of CpGs associated with VLBW in both neonate and adult samples
To identify if any CpGs were differentially methylated both at birth and at 28 years of age, regardless of direction, we compared extended lists of CpGs differentially methylated between VLBW and controls (dipping below genome-wide significance in adults). Four CpGs common to both neonate and adult sample lists were identified (Additional file 11: Table S5), including two CpGs in EBF4 and one in SNAl1 that were hypomethylated in neonates but hypermethylated in adults, while a CpG in LOC101928911 was hypermethylated at both ages. At both timepoints, robust associations were observed between these CpGs and several cardiovascular or respiratory variables in adults (see Additional file 11: Table S5), although associations with cardiac dimensions tended to be more marked in controls.
To visualize how age-related changes in methylation influenced the activity of gene pathways, we compared canonical pathways enriched for differentially methylated CpGs at each age using Ingenuity Pathway Analysis (IPA, Fig. 3), identifying 27 pathways regulated in opposite directions between birth and adulthood. Fig. 3Comparison of Canonical Pathways enriched with CpGs differentially methylated between VLBW cases and controls either in neonates or adults, arranged by hierarchical clustering We also used IPA to graphically display gene networks enriched for differentially methylated CpGs at each age and to overlay the neonatal methylation data onto the two gene networks identified in the adult samples. The top network from adult data (Additional file 12: Fig. S3B) featured upregulation of HIF3A and downregulation of Akt, and in neonates a similar pattern of activation and inhibition was demonstrated. In contrast, the second adult gene network featuring EBF4 (Additional file 13: Fig. S4B) switched direction between neonates and adults: the EBF4 gene expression changed from strongly upregulated in neonates to downregulated with age, and activation of Erk$\frac{1}{2}$ at the network center shifted to inhibition in adults, with associated de-activation of several growth hormone and IGF-binding protein signaling molecules in the network.
We then examined the full methylation dataset in both neonates and adults to identify CpGs showing VLBW-related methylation differences at both ages, irrespective of direction, and this highlighted total of 18 CpGs in EBF4 that reached genome-wide significance in neonates, with methylation reversing direction in adults although below the genome-wide significance threshold. Principal Component Analysis (PCA) was used to make aggregate methylation scores from these 18 CpGs, performed in both neonate and adult samples separately (Additional file 14: Fig. S5). Associations were observed between adult cardiovascular or respiratory traits and the EBF4 first and second principal components (PC1 and PC2) at each age (Additional file 15: Table S6). However, associations were generally stronger in controls than in VLBW cases and hence this approach did not add value in predicting outcomes in VLBW babies beyond the top individual CpGs.
## Localization relative to CpG islands and transcription binding sites
Many of our top CpGs were not represented in the DMNIV database, especially those identified in neonate samples, with none of those identified as overlapping enhancer sites. In adult samples the GLI2 CpG cg20219891 was reported to overlap enhancers, including six enhancers for the genes GLT2, RALB, TMEM185B. Of the top CpGs in neonates, six were located within a CpGs island, four in OpenSea, and the remainder spread across N_Shore, S_Shore or N_Shelf relative to CpG islands. Of the CpGs most significant in adults, $50\%$ of those were shown as being located on a S_Shore relative to a CpG island (including cg16426670 in EBF4 that was altered in opposite directions in adult and neonate samples), three were within a CpG island, and the remainder spread across N_Shore, S_Shelf or OpenSea.
## Discussion
Individuals in the NZ VLBW cohort are now in their fourth decade and at increased risk of cardiovascular disease and other health sequelae arising from their difficult start in life. Within NZ there is no systematic monitoring of the ongoing health and wellbeing of this population group, nor any mechanism to trigger interventions to prevent the onset of adverse health outcomes. The goal of the current study was to identify methylation markers in neonatal DNA that may be predictive of health outcomes in VLBW adults. This is, to our knowledge, the first report where altered methylation at birth in VLBW infants has been associated with health outcomes as adults, particularly their cardiovascular health. This was consistent with finding that the Cardiac Hypertrophy Signaling pathway was the canonical pathway most highly enriched for CpGs with altered methylation in VLBW neonates. This study is also the first genome-wide screen of DNA methylation sites in adulthood with sufficient statistical power to detect differences between VLBW individuals and controls, highlighting differentially methylated clusters within the EBF4 and HIF3A genes. Moreover, the observation that methylation at EBF4 CpGs reversed direction between birth and 28 years and was associated with adult outcomes leads us to propose that dynamic methylation of EBF4 may be associated with the trajectory of compensatory growth and development from birth to adulthood.
We noted many commonalities between our data and that of previous studies of birthweight and methylation, despite differing study designs, such as comparing preterm versus full-term birth rather than birthweight per se, sampling adipose tissue, saliva or cord blood versus peripheral blood, assaying methylation at various time points between birth and middle age, or using differing methylation assay platforms. The finding of analogous methylation sites to our study was particularly striking in a report from Gillberg et al. [ 60], who examined adult adipose tissue from low- versus normal-birthweight men after 5 days of high-fat diet versus a control diet. They observed 53 CpGs located on 40 genes where methylation differed between low-birthweight men and controls, and of these 40 genes, 21 also featured within our long list of differentially methylated CpGs in neonates (C17orf97, KAZALD1, SORBS2, DPP10, CPLX1, CACNA2D2, FADS2, LOC100271832, CUGBP2, TTYH3, HCCA2, PTPRN2, ACAT1, C7orf50, CASZ1, IGF2R, PDLIM4, CREB3L2, ARHGAP23, ARID1B, and UPK3A).
Findings convergent with the current study can also be seen in the report from Simpkin et al.[52], who performed a longitudinal study of DNA methylation profiles from cord blood and peripheral blood at birth and at ages 7 and 17 years in over 900 children from the Avon Longitudinal Study of Parents and Children (ARIES). They identified 224 CpG sites associated with gestational age (predominantly a negative association, as we also found), and 23 CpG sites associated with birthweight. Their supplementary list of probes where cord blood methylation was negatively associated with gestational age in ARIES showed a remarkable overlap with our most significant CpGs in VLBW neonates, including cg02001279 in ARID3A; cg07835443in C16orf55 (SPATA33); cg11932158 in PLCH1; cg04347477 in NCOR2; cg18623216 in PLCH1; cg12713583 in ARID3A. Some of the same CpGs were also identified by Cruikshank and co-workers [50], who compared 12 extremely preterm cases and 12 matched controls with DNA from archived blood spots collected from neonates and the same individuals at age 18 years. Although in 18-year-olds no sites achieved genome-wide significance, there was extensive overlap between those CpGs reported by Cruikshank [50] to be different between preterm and term birth and our top CpGs in neonates, including in C16Orf55 (SPATA33), ARID3A, DOCK6, NCOR2, SLC9A3R2, PLCH1 and FBLN7.
A meta-analysis of epigenome-wide association studies with birthweight by Küpers et al. [ 37], included 8825 neonates from 24 birth cohorts in the Pregnancy And Childhood Epigenetics Consortium and demonstrated that lower birthweight, even within the normal range, is related to altered DNA methylation at 914 sites. In additional analyses in 7278 participants, < $1.3\%$ of birthweight-associated differential methylation was also observed in childhood and adolescence, and this study could find no associations in adulthood (30–45 years). The top hits for association with birthweight included some minor overlap with gene families we observed associated with VLBW in neonates, such as ARID5B ARHGAP20, ARHGAP29, and ARHGAP45. However, Küpers and colleagues [37] also identified 147 birthweight-related methylation quantitative trait loci (mQTL, where genetic variants are associated with methylation), which included 23 CpGs from our neonate long list, most notably cg06870470 in DOCK6, one of our most significant CpGs at birth.
Other prior studies that did not replicate the current findings include a report from Tan et al. [ 61], who carried out DNA methylation profiling of preterm birth in 144 adult twins with a median age of 33 years (with 26 twin pairs of premature birth). They found three genomic regions associated with preterm birth annotated to the SDHAP3, TAGLN3 (both hypomethylated) and GSTT1 (hypermethylated) genes. While none of these CpGs reached genome-wide significance individually, combining multiple CpGs in each locus led to the clusters attaining significance. These differentially methylated regions replicated in an older, independent set of 175 twin pairs (median age 66 years) with 40 twins classified as preterm birth (at least 3 weeks before term). Finally, Wheater et al. [ 62] investigated the impact of low gestational age at birth on methylation patterns in neonatal saliva samples and also documented associations of methylation with brain white matter structure by diffusion magnetic resonance imaging of these infants. The most significant probes associated with gestation in the Wheater study showed no apparent overlap with CpGs identified in neonates in this current study, possibly related to their use of saliva samples versus peripheral blood.
In this current study, we observed age-related changes in methylation at EBF4. Between birth and 28 years, the CpG that showed the greatest reversal in direction of altered methylation in VLBW cases compared to controls (EBF4 cg16426670) was associated with cardiovascular traits in adulthood. The EBF gene belongs to a family of transcription factors associated with B-lymphocyte maturation and neuronal development, and also has a role in governing the differentiation of cardio-pharyngeal mesoderm into heart versus pharyngeal muscle fate [63]. Methylation of members of this gene family have been associated with a number of phenotypes, including EBF4 methylation associated with hematopoiesis and neuronal development in persons with Trisomy 21 [64], and EBF1 and EBF3 methylation to neurobehavioral development in very preterm infants [65]. Our observation of dynamic methylation of EBF4 between birth and adulthood extends the findings of Simpkin and colleagues [52], who reported dynamic EBF4 methylation in children up to 7 years of age. Their analysis of serial methylation from birth to adolescence suggested that methylation differences did not persist beyond early childhood, with the authors suggesting that methylation levels had largely stabilized by age 7. Notably, however, that study found that among 36 probes with increased methylation per week of gestation, the probe with the maximum increase during childhood was cg16426670 in EBF4, showing $6.7\%$ increase per year between birth and 7 years, the same CpG probe that we found to switch from hypomethylation in VLBW neonates to hypermethylation in VLBW adults compared with controls.
A recent study by Long et al. [ 66] investigated associations between methylation at copper-related CpGs and risk of acute coronary syndromes. They found higher methylation at cg05825244 in EBF4 (among our 12 CpGs differentially methylated in VLBW adults), which was associated with a $23\%$ increased risk of acute coronary syndromes. Further, mass spectrometry of sera from patients about to undergo coronary artery bypass graft (CABG) surgery identified that presence of EBF4 protein was more prevalent and helped distinguish patients with T2DM [67]. *Our* gene network analysis identified that in VLBW adults EBF4 is a key gene in a network centering on the serine/threonine protein kinase, Akt, which has key roles in many signaling pathways [68]. Together, these findings suggest that the reversal from EBF4 hypomethylation in VLBW neonates to hypermethylation in adulthood may be involved in the compensatory catch-up of growth and development of the cardiovascular system. It is plausible that overcompensation of the EBF4 pathway with age may even contribute to adult cardiovascular risk in VLBW cases, with lower EBF4 methylation levels at birth potentially associated with higher adult systolic blood pressure, smaller cardiac dimensions and lower cardiac output. Future functional studies in animal models may be needed to clarify the relationship between EBF4 methylation and phenotypic traits of adult VLBW survivors.
Methylation patterns have been used to generate epigenetic age estimates, which have now been established to predict chronic disease burden and time to death [48, 49]. Using the DNAm GrimAge calculator, we confirmed previous reports that VLBW survivors have significantly greater methylation age scores than age-matched controls. However, the GrimAge scores for both cases and controls (38.12 versus 36.38 years, respectively) indicate a greater age than the participants’ chronological age. We suggest the reason may be that these algorithms were derived to predict chronic disease mortality risk in middle age and they may not work as well in younger individuals. However, in this study we compared like-with-like, since the VLBW cases and controls were of equivalent ages at sample collection. We have provided the GrimAge data as a useful data reduction tool, to combine multiple methylation markers previously associated with mortality risk, rather than an accurate predictor of an individual’s biological age.
Van Lieshout et al.[53] compared epigenetic age estimates in adults aged 30 to 35 years born extremely low birthweight (ELBW) (< 1000 g), with a sample of age- and sex-matched controls. This study analyzed DNA from buccal cells and generated a methylation score consisting of 353 CpGs in Horvath’s epigenetic-clock algorithm. They demonstrated that ELBW men had a significantly older epigenetic age (an additional 4.6 years) than normal birthweight men, although women born ELBW were not found to be epigenetically older than their normal birthweight peers. A further study of 143 ELBW infants born 1991–1992 in Victoria, Australia, used DNA extracted from neonatal blood spots collected after birth to generate an algorithm to estimate DNAm-based gestational age [51]. The residual of DNAm gestational age and clinically estimated gestational age (referred to as “gestational age acceleration”) was used to assess developmental maturity. Infants with higher gestational age acceleration were less likely to have received surfactant or postnatal corticosteroids, had fewer days of assisted ventilation, and less frequently had bronchopulmonary dysplasia.
There are several limitations of our study. Firstly, DNA methylation analysis in neonates was performed on dried blood spots that had been archived for 30 years, while in the young adults we were able to extract DNA from recently collected whole blood. The differences in storage time and DNA sample preparation method meant that it would be inappropriate to directly compare raw methylation data files between ages; rather we analyzed the samples separately and compared the resultant lists of candidate CpGs with differing methylation between VLBW cases and controls. Secondly, data relating to pregnancy and the perinatal period was only available for VLBW cases, and not available for controls. Consequently, associations between methylation and potential confounders such as maternal smoking in pregnancy, preeclampsia-toxemia, treatment with antenatal steroids and duration of breast feeding could only be explored in VLBW cases, not analyzed over the entire cohort.
In future work, it would be of interest to relate our findings to current cardiovascular and respiratory health outcomes of the Victoria cohort [50, 51], and to examine the associations with their neonatal DNAm gestational age. Those infants were born 1991–1992 and would now be aged 29–30 years, similar to the age of the NZ VLBW cohort in the present study.
## Conclusions
Levels of DNA methylation in samples collected at birth from VLBW infants have altered methylation that is predicted to lead to perturbations of underlying gene signaling pathways involved in cardiovascular development and hypertrophy. Moreover, the altered methylation profiles at birth show associations with cardiovascular and respiratory health in adulthood, with dynamic methylation in EBF4 (especially cg16426670) potentially informative for cardiovascular outcomes in later life. Thus, methylation patterns in VLBW neonates and young adults, in combination with clinical data collected at birth beyond birthweight and gestational age, may provide predictive information of those individuals at risk of future adverse health outcomes.
## Supplementary Information
Additional file 1: Methods 1. Flow chart of the New Zealand 1986 VLBW Cohort follow-up study recruitment process, with inclusion and exclusion criteria for DNA methylation of samples collected at birth and at 28 years. Additional file 2: Methods 2. The modified protocol used for extraction of DNA from archived neonatal dried blood spots and the method for bisulphite conversion used prior to analysis on Human MethylationEPIC 850K arrays. Additional file 3: Table S1. Characteristics of the subset of VLBW cases and controls with neonatal DNA available. Additional file 4: Table S2. Full list of 16407 CpGs with methylation that differed between VLBW cases and controls after adjusting for false discovery rate (Benjamini-Hochberg FDR adj-$p \leq 0.05$).Additional file 5: Figure S1. Image of the CpGs with significantly different methylation between VLBW cases and controls in neonate samples; hypermethylated clustered on the right and hypomethylated CpGs clustered on the left (also enlarged in the inset, indicating CpGs within EBF4 that reached FDR-adj significance).Additional file 6: Figure S2. In neonatal DNA the top canonical pathway enriched with the CpGs different between VLBW cases and controls was Cardiac HypertrophySignaling ($$p \leq 3.44$$E−11).Additional file 7: Table S3. Differentially methylated regions (DMRs) that were significant (FDR adj-$p \leq 0.05$), with DMRs in neonatal samples on the first worksheet and DMRs identified in adult samples on the second worksheet. Additional file 8: Figure S3a. In adult samples, the gene network most enriched for CpGs differentially methylated in VLBW cases versus controls, which predicted upregulation of HIF3A gene expression and downregulation of Akt, was associated with Hereditary Disorders, Neurological Diseases and Organismal Injuries and Abnormalities. Additional file 9: Figure S4a. In adult samples, a further gene network enriched for CpGs differentially methylated in VLBW cases versus controls, which predicted downregulation of EBF4 and inhibition of Erk$\frac{1}{2}$, was associated with Cellular Development, Embryonic Development, Nervous System Development and Function. Additional file 10: Tables S4a and S4b. Associations between CpGs with differential DNA methylation in adult samples, DNAm GrimAge and cardiovascular variables (4A) or respiratory traits (4B) at 28 Years. Additional file 11: Table S5. From the lists of CpGs with differential DNA methylation in VLBW cases versus controls, four overlapped in both neonates and adults and showed several associations with adult cardiovascular and respiratory traits. Additional file 12: Figure S3b. Overlay of neonatal methylation data onto the top gene network identified in adult samples, showing a similar pattern of predicted upregulation of HIF3A gene expression and downregulated Akt in both neonate and adult samples for this gene network. Additional file 13: Figure S4b. Overlay of neonatal methylation data onto the second gene network identified in adult samples, showed this network switched direction with age: EBF4 gene expression changed from strongly upregulated in neonates to downregulated in adults, activation of Erk$\frac{1}{2}$ at the center of the network shifted to inhibition, with associated de-activation of several growth hormone and IGF-binding protein signaling molecules in the network. Additional file 14: Table S5. Principal Component Analysis (PCA) of the 18 CpGs in EBF4 with differential methylation in both neonate and adult datasets, irrespective of direction. Additional file 15: Table S6. After PCA was performed on the 18 EBF4 CpGs identified from both adult and neonate methylation data, associations were observed between the first and second principal components (PC1 and PC2) at each age and adult cardiovascular or respiratory traits.
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|
---
title: 'School climate and academic burnout in medical students: a moderated mediation
model of collective self-esteem and psychological capital'
authors:
- Wanwan Yu
- Wenjun Yao
- Ming Chen
- Hongqing Zhu
- Jing Yan
journal: BMC Psychology
year: 2023
pmcid: PMC10035231
doi: 10.1186/s40359-023-01121-6
license: CC BY 4.0
---
# School climate and academic burnout in medical students: a moderated mediation model of collective self-esteem and psychological capital
## Abstract
### Background
The study burnout of medical students is more and more serious, which directly affects the study style of university and the learning quality of students. This has aroused the high attention of researchers and universities. This study aimed to explore the mechanism of the influence of school climate on academic burnout among medical students in Chinese cultural context.
### Methods
2411 medical students ($50.52\%$ female; mean age = 19.55, SD = 1.41, rang = 17–24 years) were investigated with psychological environment questionnaire, collective self-esteem scale, psychological capital scale and academic burnout scale. The data were analyzed by using a moderated mediation model with SPSS and the Process 4.0 macro.
### Results
The results revealed that: [1] school climate had a significant negative predictive effect on academic burnout among medical students controlling for gender, grade and age (B = -0.40, $p \leq 0.001$). [ 2] Collective self-esteem played a partial mediating role in school climate and academic burnout (indirect effect = -0.28, $95\%$ CI = [-0.32,-0.25], accounting for $52.83\%$). [ 3] The first and second half of the indirect effect of school climate on medical students’ academic burnout were moderated by psychological capital ($B = 0.03$, $p \leq 0.01$; B = -0.09, $p \leq 0.001$).High level of psychological capital can enhance the link between school climate and collective self-esteem as well as the link between self-esteem and academic burnout.
### Conclusion
Creating a good school atmosphere and improving the level of collective self-esteem and psychological capital are beneficial to improve the academic burnout of medical students.
## Introduction
Academic burnout is a negative attitude and behaviour of students who are bored with learning due to pressure or lack of interest in learning [1]. Its negative effects are mainly reflected in physical and mental health (e.g. insomnia, weakness), emotional adaptation (e.g. anxiety, depression) and behaviour (e.g. aggression, dropping out of school) [2–4]. Medical students are more prone to academic burnout due to their long training cycles, course content and heavy study load as a reservoir of healthcare professionals [5]. A meta-analysis study showed that the detection rate of academic burnout among medical students was about $44.2\%$ [6]. Therefore, it is important to explore the factors influencing academic burnout among medical students and its mechanisms of action to promote the quality of learning and positive development of medical students.
## School climate and academic burnout
Schools are important micro-systems that influence the growth and development of individuals in addition to the family [7]. They are not only places where individuals learn and develop cognitively, but are also important contextual factors for the formation of positive social relationships and for their emotional and behavioral development [8]. As a result, a growing body of research has focused on the impact of school climate on the physical and psychological development of individuals [9–11]. School climate, also known as the school psychological environment, refers to the relatively persistent and stable features of the environment that students experience and influence their behaviour [12], including the norms, goals, values, interpersonal relationships, teaching practices and organizational structures of the school environment [13]. Numerous studies have shown that school climate is strongly associated with healthy adolescent development, with the more positive the perceived school climate, the less suicidal ideation, depression, bullying, etc. [ 14, 15]. According to the Stage-Environment Matching Theory, when the school climate meets the developmental needs of students, it strengthens the connection between individuals and the school which could promote good development; on the contrary, when the school climate does not meet the developmental needs of students, they are prone to psychological and behavioral problems [16, 17]. A positive school climate in terms of teacher-student relationships, peer relationships and student autonomy can lead to a strong attachment to the school in which students are studying, which is an important protective factor against problematic behaviour [18] and can push motivation to learn and less likely to develop academic burnout [19]. Empirical studies have also found that school climate is significantly and negatively associated with academic burnout in primary school students [20]. Previous study on the relationship between school climate and academic burnout has focused on primary and secondary school students, and less on medical students. However, there is an inherent consistency between university campuses and primary and secondary school campuses, mainly in terms of school norms and discipline, teacher-student relationships, peer relationships, and physical environment, which are all important components of school climate [21]. Accordingly, this study proposes hypothesis 1: School climate negatively predicts academic burnout among medical students.
Although the relationship between school climate and academic burnout has been partially verified by researchers, the exact mechanisms of the relationship are still largely unclear. Therefore, the question of how school climate “influences” medical student burnout needs to be further explored.
## The mediating role of collective self-esteem
The ego self-system process model suggests that external environmental resources influence developmental outcomes through an individual’s internal self-system [22]. Therefore, the influence of school climate on medical student burnout may be mediated by collective self-esteem. Self-esteem, as an important component of the ego system, includes both individual and collective self-esteem [23] and is an important protective resource for mental health [24]. The former is the overall positive evaluation and acceptance of individuals to themselves [25, 26], while the latter is the individual’s evaluation and perception of the value of the group he or she belongs to, which emphasizes a sense of collective value, respect and belonging [27]. In the context of Chinese collectivist culture, self-esteem, with its strong collective and social overtones, has also become a focus of research [28, 29]. Existing studies also indirectly support the mediating path of school climate - collective self-esteem - academic burnout. On the one hand, collective self-esteem helps to reduce academic burnout. Self-categorization theory suggests that the pursuit of self-improvement and self-esteem is one of the most basic motivations of people [30]. Since in-group evaluations are essentially evaluations of the self, when individuals identify with the group to which they belong, they would strive to enhance or protect the prestige and status of their group in relation to other groups, producing more adaptive outcomes [31]. Empirical studies have also shown that collective self-esteem significantly and negatively predicts academic burnout, individuals with low collective self-esteem tend to lack good motivation to learn and thus exhibiting more academic burnout [32, 33]. On the other hand, collective self-esteem is influenced by school climate. According to sociometric theory, a positive school climate, typically reflecting a supportive environment [34], enhances psychological and socio-emotional functioning and raises one’s self-esteem [35–37]. Empirical studies have also shown that school climate can be a significant positive predictor of collective self-esteem [38–40]. When students perceive that they are accepted and supported by important people (e.g. teachers, classmates), they also have a stronger sense of belonging and identification with school and show higher levels of collective self-esteem [41, 42]; conversely, a negative school climate decreases students’ overall evaluation of school and shows lower levels of collective self-esteem [43]. Accordingly, this study proposes hypothesis 2: collective self-esteem may mediate the relationship between school climate and academic burnout.
## The moderating role of Psychological capital
Although collective self-esteem may mediate the relationship between school climate and academic burnout, there may still be moderating variables, i.e. the process by which school climate influences academic burnout may be moderated by other factors. With the rise of positive psychology, the additive role of psychological capital on individual psychology and behaviour has received increasing attention from researchers [44–46]. According to mental resilience research, exposure to situational risk does not imply poor development of mental health, and certain positive qualities (e.g. psychological capital) help individuals overcome the adverse effects of severe adversity [47, 48]. Thus, psychological capital may play a moderating role in the process of school climate affecting academic burnout. Psychological capital refers to a positive psychological state that individuals demonstrate as they grow and develop, and includes four core elements: resilience (the ability to recover and grow positively from adversity and setbacks), optimism (positive beliefs about the present and future), hope (a positive state of motivation to strive to achieve goals in multiple ways) and self-efficacy (the belief in one’s ability to succeed when faced with challenging tasks) [49], which is an important protective factor for individual psychological well-being and facilitates resistance to the adverse effects of external stressors such as academic burnout [50, 51]. Previous research has shown that psychological capital is negatively associated with academic burnout in nursing students, increasing engagement in learning and reducing academic burnout [52]. All elements of psychological capital also positively predict collective self-esteem, high psychological capital has positive and effective coping strategies, which exhibiting higher levels of collective self-esteem [53]. Self-depletion theory suggests that human psychological resources are limited, and although individuals can mobilize internal resources to cope with various external risks, they are not replenished in time and are prone to negative consequences [54]. As a positive psychological quality, individuals with high psychological capital can cope with the negative effects of a negative school climate [55], and enhance the positive effects of a positive school climate on cognition, mood and behavior [56]. The individual-environment interaction model states that individual development is the result of the interaction between individual factors and the environment in which the individual lives [57]. This means that academic burnout and collective self-esteem are not only closely related to environmental factors (school climate), but may also be influenced by individual factors (psychological capital). This suggests that psychological capital may regulate the relationship between school climate and collective self-esteem or academic burnout.
Secondly, psychological capital and collective self-esteem, as protective factors, may work together to reduce academic burnout. Psychological capital has been found to enhance the positive impact of protective factors on individual school adjustment [56]. According to the protective factor-protective factor model, different protective factors may interact with each other to influence developmental outcomes [58]. Researchers have summarized two different hypotheses: the facilitation hypothesis and the exclusion hypothesis [59, 60]. According to the facilitation hypothesis, the effect of school climate on collective self-esteem or academic burnout and collective self-esteem on academic burnout is stronger as the level of psychological capital increases [61]. According to the exclusion hypothesis, as the level of psychological capital increases, the effect of school climate on collective self-esteem or academic burnout and collective self-esteem on academic burnout decreases [62]. At present, there has been on research point out the moderating mode of psychological capital in the relationship between school climate and academic burnout (direct and indirect pathways). Accordingly, this study only hypothesized that psychological capital may play a moderating role in the influence of school climate on academic burnout through collective self-esteem (Hypothesis 3), without making specific hypotheses about the specific moderating patterns.
In summary, this study constructs a moderated mediation model based on the Stage-Environment Matching Theory and the Protective Factor-Protective Factor Model (see Fig. 1 for the hypothetical model) to examine the mediating role of collective self-esteem in the relationship between school climate and medical students’ academic burnout, and the role of psychological capital in this mediating role. The model is designed to test the role of collective self-esteem in mediating the relationship between school climate and medical student burnout, and the moderating role of psychological capital in this mediating process, in order to provide guidance for the prevention and reduction of medical student burnout.
Fig. 1Hypothetical model
## Participants
A cluster random sampling method was used to select 2,600 medical students from seven medical colleges and universities in Anhui Province as the research objects. 189 invalid questionnaires with missing values and test results outside ± 3 standard deviations were excluded [63]. Finally, 2411 valid questionnaires were obtained, and the effective recovery rate was $92.73\%$. Among them, there were 1193 boys ($49.48\%$), 1218 girls ($50.52\%$); 641 freshmen ($26.59\%$), 673 sophomores ($27.91\%$), 585 juniors ($24.26\%$), and 512 seniors ($21.24\%$). The subjects were between 17 and 24 years old, with an average age of 19.55 years (SD = 1.41 years).
## School climate
The “School Psychological Environment Questionnaire” (SPEQ) developed by Xu and Zhong [64] was used to measure the subjects’ perceived school climate, and has been validated to have good reliability and validity [65]. The questionnaire has a total of 30 items, including 7 reverse questions, which is composed of six dimensions: teacher-student relationship (e.g., “teacher cares about me.”), classmate relationship (e.g.,“classmates care about each other.”), collective activities (e.g., “I participate in group activities.”), professional development (e.g. “I learned about possible careers in the future.”), resources (e.g., “The resources in the school do not meet my needs.”), and institutions and order (e.g., “The rules and regulations of the department are helpful for the development of students.”). A 5-point Likert scale was used, with 1–5 points for “Never” to “always” respectively. After reverse scoring, the average score for all items was calculated, with higher scores indicating more positive school climate perceived. In this study, the Cronbach’s α coefficient of this questionnaire was 0.93.
## Collective self-esteem
The collective self-esteem level of the subjects was measured using the “collective self-esteem scale” (CSES) developed by Luhtanen and Crocker [66], and has been validated to have good reliability and validity [67]. The scale has 16 items, including 8 reverse questions, which is composed of four dimensions: membership self-esteem (e.g.,“I am a valuable member of the class collective.“), private collective self-esteem (e.g., “I feel good about the school where I belong.”), public collective self-esteem (e.g., “generally, others respect our school.”), identity importance (e.g., “generally, the identity of school group members is an important part of my self-image.”). A 7-point Likert scale was used, with 1–7 points for “very inconsistent” to “very consistent” respectively. After the reverse scoring, the average score was calculated for all items, with higher scores indicating higher levels of collective self-esteem. In this study, the Cronbach’s α coefficient of this scale was 0.87.
## Psychological capital
The “Positive Psychological Capital Questionnaire” (PPQ) developed by Zhang et al. [ 68] was used to measure the psychological capital level of the subjects, and has been validated to have good reliability and validity [69]. The questionnaire consists of 26 items, consisting of four dimensions of optimism (e.g., “I always see the bright side of things.“), hope (e.g., “I pursue my goals with confidence.“), self-efficacy (e.g., “I can always do well Completion of tasks.”) and resilience (e.g., “When I encounter setbacks, I can quickly recover. ”).A 7-point scale was used, with 1–7 points for “strongly disagree” to “strongly agree” respectively. Calculate the average score of all items and higher scores indicate higher levels of psychological capital. In this study, the Cronbach’s α coefficient of this questionnaire was 0.94.
## Academic burnout
The academic burnout scale (LBS) developed by Lian et al. [ 1], and has been validated to have good reliability and validity [70]. The scale has 20 items in total, including 8 reverse questions, which are composed of three dimensions: low mood(e.g., “ I am tired of studying.“), low sense of accomplishment(e.g. ,“I am energetic when studying.“) and improper behavior(e.g., “ only I only read it when I take the test.”). A 7-point Likert scale is used, with 1–7 points for"very inconsistent” to “very consistent”. After reverse scoring, the average score for all items is calculated, with higher scores indicating higher levels of academic burnout. In this study, the Cronbach’s α coefficient of this scale was 0.89.
## Procedures and statistical analysis
After obtaining the informed consent of the subjects, group testing was conducted in a class as a unit. The main test was held by a strictly trained graduate student majoring in psychology, who explained the instructions in detail to the subjects and emphasized that the content was strictly confidential and answered anonymously according to their actual situation. The test time took 12 min in total and all the questionnaires were collected on the spot. SPSS 25.0 and PCOCESS3.3 developed by Hayes were used for data processing and analysis [71].
## Common method bias and multicollinearity test
Restricted by objective conditions, this study only used the method of self-report by the subjects to collect data, and the results may be affected by common method bias. According to the suggestions of Podsakoff et al. [ 72], control is carried out in terms of procedures, such as using an anonymous method for testing and using reverse questions for some items. After data collection was completed, HarmanOne-factor test was used to test for common method bias. The results showed that a total of 13 factors with eigenvalues greater than 1were extracted,and the unrotated variation of the first factor was $28.91\%$, which was lower than the critical standard of $40\%$. At the same time, the variance inflation factor values of all predictors are between 2.02 and 2.09 (less than 5 means there is no collinearity), and the tolerance is between 0.45 and 0.50 (more than 0.1 means there is no collinearity) [73]. Therefore, the influence of common method bias and multicollinearity on the results of this study is basically excluded.
## Descriptive statistics and correlation analysis
Table 1 lists the mean, standard deviation and correlation matrix for each variable. The results showed that academic burnout was significantly negatively correlated with school psychological environment (r = -0.54, $p \leq 0.001$), collective self-esteem (r = -0.60, $p \leq 0.001$) and psychological capital (r = -0.69, $p \leq 0.001$); School psychological environment was significantly positively correlated with collective self-esteem ($r = 0.68$, $p \leq 0.001$) and psychological capital ($r = 0.63$, $p \leq 0.001$); collective self-esteem was significantly positively correlated with psychological capital ($r = 0.67$, $p \leq 0.001$). Considering that gender, grade, and age were significantly correlated with the main research variables, the three were included as control variables in the subsequent analysis to improve the accuracy of the model.
Table 1Mean, standard deviation and correlation matrix of each variableVariable1234567891. Gender a12. Sophomoreb-0.0113. Junior b0.01-0.35***14. Senior b0.07***-0.32***-0.29***15. Age0.02-0.18***0.30***0.57***16. School climate-0.002-0.06***-0.07***-0.12***-0.19***17. Collective Self-Esteem0.06***-0.09***-0.13***-0.07***-0.20***0.68***18. Psychological Capital-0.05*-0.07***-0.10***-0.04-0.14***0.63***0.67***19. Academic burnout-0.010.10***0.07***-0.0040.07***-0.54***-0.60***-0.69***1Mean0.510.280.240.2119.553.414.774.772.71Standard deviation0.500.450.430.411.410.520.790.830.54Note: $$n = 2411$.$ a gender is a dummy variable, boys = 0, girls = 1, and the mean represents the proportion of girls. bgrade is a dummy variable, with freshman as the reference category, sophomore, junior and senior are relative to this category forms 3 dummy variables, the mean values of which represent the percentage of the population in that grade to the total population. *** $p \leq 0.001$, **$p \leq 0.01$, *$p \leq 0.05$, the same below
## Moderated mediation test
First, using Model4 in the SPSS macro program PROCESS (Model4 is a simple mediation model) to test the mediating effect of collective self-esteem in the relationship between school psychological environment and academic burnout under the control of gender, grade and age. The results are shown in Table 2, the school psychological environment significantly negatively predicted academic burnout (B = − 0.40, $p \leq 0.001$), and significantly positively predicted collective self-esteem ($B = 0.65$, $p \leq 0.001$); When predicting academic burnout at the same time, school psychological environment can still significantly negatively predict academic burnout (B = -0.25, $p \leq 0.001$), and collective self-esteem significantly negatively predicts academic burnout (B = -0.44, $p \leq 0.001$). Based on the bias-corrected percentile Bootstrap method, it was further found that collective self-esteem played a partial mediating role between the school psychological environment and academic burnout, and its $95\%$ CI was [-0.32, -0.25]. The mediating effect (-0.28) accounted for $52.83\%$ of the total effect (-0.53).
Table 2Mediation model test of collective self-esteemPredictor variableEquation 1 (efficacy criterion: academic burnout)Equation 2 (efficacy criterion: collective self-esteem)Equation 3 (efficacy criterion: academic burnout) B SE $95\%$ CI B SE $95\%$ CI B SE $95\%$ CI School climate-0.53***0.02[-0.57,-0.50]0.65***0.02[0.62,0.68]-0.25***0.02[-0.29,-0.20]collective self-esteem-0.44***0.02[-0.48,-0.39]Gender a-0.020.03[-0.09,0.05]0.13***0.03[0.07,0.19]0.040.03[-0.03,0.10]Sophomore b0.20**0.05[0.09,0.03]-0.28***0.04[-0.37,-0.19]0.070.05[-0.02,0.17]Junior b0.16*0.07[0.03,0.20]-0.32***0.06[-0.44,-0.21]0.020.06[-0.10,0.14]Senior b0.010.08[-0.16,0.15]-0.14*0.07[-0.27,-0.001]-0.070.07[-0.21,0.08]Age-0.030.03[-0.09,0.03]-0.030.02[-0.08,0.01]-0.040.03[-0.10,0.01] R 2 0.300.490.39 F 168.66***378.50***223.83***Note: All variables in the model are brought into the regression equation after standardization, the same below Secondly, Model59 in SPSS macro program PROCESS (adjusting the first half path,the second half path and the direct path, which is consistent with the hypothetical model)was used to test the moderating effect of psychological capital under the condition of controlling gender, grade and age. The results are shown in Table 3. After adding psychological capital, the interaction term between school psychological environment and psychological capital significantly positively predicted collective self-esteem ($B = 0.03$, $p \leq 0.01$), indicating that psychological capital modulates the relationship between school psychological environment and collective self-esteem; The interaction item of collective self-esteem and psychological capital significantly negatively predicted academic burnout (B = -0.09, $p \leq 0.001$), indicating that psychological capital moderates the relationship between collective self-esteem and academic burnout; while the interaction term between school psychological environment and psychological capital had no significant predictive effect on academic burnout (B = -0.02, $p \leq 0.05$), indicating that psychological capital could not moderate the relationship between school psychological environment and academic burnout. In conclusion, school psychological environment, collective self-esteem, psychological capital, and academic burnout constitute a moderated mediating model. Specifically, school psychological environment influences the first half path of academic burnout through collective self-esteem, and the second half path is regulated by psychological capital.
Table 3Moderated mediation model test of school climate on academic burnoutPredictor variableEquation 1 (efficacy criterion: collective self-esteem)Equation 2 (efficacy criterion: academic burnout) B SE $95\%$ CI B SE $95\%$ CI School climate0.40***0.02[0.36,0.43]-0.06***0.02[-0.10,-0.02]Psychological capital0.38***0.02[0.35,0.42]-0.50***0.02[-0.54,-0.46]School climate × Psychological capital0.03**0.01[0.01,0.05]-0.020.02[-0.05,0.01]Collective self-esteem-0.19***0.02[-0.24,-0.15]Collective self-esteem × Psychological capital-0.09***0.02[-0.13,-0.06]Gendera0.17***0.03[0.12,0.23]-0.07*0.03[-0.12,-0.01]Sophomore b-0.23***0.04[-0.31,-0.15]0.070.04[-0.02,0.15]Junior b-0.28***0.05[-0.38,-0.17]0.010.05[-0.09,0.12]Senior b-0.16**0.06[-0.29,-0.04]-0.030.07[-0.16,0.09]Age-0.020.02[-0.06,0.02]-0.050.02[-0.09,0.00] R 2 0.580.53 F 406.51***275.27*** In order to better explain the moderated mediation model, psychological capital is divided into high group and low group according to the mean plus or minus one standard deviation. simple slope analysis is performed and a simple effect analysis graph is drawn. As shown in Fig. 2, when the individual’s psychological capital level is low, the school psychological environment significantly positively predicts collective self-esteem (Bsimple = 0.37, $t = 16.91$, $p \leq 0.001$); when the individual’s psychological capital level is high, the school Mental environment still significantly positively predicted collective self-esteem and increased predictive power (Bsimple = 0.43, $t = 22.44$, $p \leq 0.001$). The results show that with the increase of psychological capital level, the predictive effect of school psychological environment on collective self-esteem is enhanced. It can be seen from Fig. 3 that when the individual’s psychological capital level is low, collective self-esteem significantly negatively predicts academic burnout (Bsimple = -0.10, t = -3.29, $p \leq 0.01$); when the individual’s psychological capital level is high, school Mental environment still significantly negatively predicted collective self-esteem and increased predictive power (Bsimple = -0.29, t = -10.77, $p \leq 0.001$). The results show that with the increase of psychological capital level, the predictive effect of collective self-esteem on academic burnout is enhanced.
Fig. 2The interaction between school climate and psychological capital on collective self-esteem Fig. 3The interaction between collective self-esteem and psychological capital on academic burnout
## Discussion
This study found that school climate significantly and negatively predicted medical students’ academic burnout, confirming research hypothesis 1. This suggests that a positive school climate is an important protective factor for medical students’ academic burnout, reducing the likelihood of academic burnout and providing good environmental conditions for medical students’ learning, which is consistent with previous research [18, 20]. The results also support the stage-environment match theory, which states that matching the school environment with student needs promotes more positive outcomes [16]. Self-determination theory suggests that autonomy, relationship and competence needs are basic psychological needs of individuals and are motivational in nature [74]. Medical students are largely mature in their physical and mental development and desire to have their basic needs met. Good teacher-student relationships, peer relationships, fair rules and order on campus can meet these needs, making students more inclined to align with the school’s goals and values, motivating intrinsic learning and actively seek academic progress [75]; Conversely, when school fail to meet this psychological need, students may turn to other environments for satisfaction, such as online games [76], which in the long run may lead to problems such as lack of interest in learning and reduced motivation to learn, which may lead to truancy or a sense of alienation from learning in turn.
Moreover, this study also found that school climate can indirectly influence medical students’ academic burnout through collective self-esteem, confirming hypothesis 2. This result also confirms the self-system process model [22], which suggests that collective self-esteem plays a key role in individual adaptation to the external environment and is a proximal factor in school climate influencing academic burnout. Firstly, positive campus characteristics such as good teacher-student relationships, sound infrastructures and fair systems promote a higher sense of belonging to the school, making students feel part of the school, more aware of their importance and value in their group, hold more positive evaluations of the school and show higher levels of collective self-esteem [65]. Secondly, anxiety is an important emotional manifestation of academic burnout, and self-esteem is an important force in reducing anxiety [26, 77]. Individuals with high collective self-esteem are more likely to have a positive emotional experience of school, maintain a good emotional state even when suffering from chronic academic stress, seek social support to alleviate anxiety, and engage in learning with a positive attitude, thereby reducing academic burnout [78]. The rationale for collective self-esteem as a mediating variable is also supported by the perspective of social control theory. The theory emphasizes that the emotional connection between the individual and the school is a protective factor for individual development, and that this connection causes the individual to strive to align with social expectations, thereby avoiding negative consequences [10]. A positive school climate can lead to students developing a positive emotional connection with school, enabling them to control their own behaviour, move towards the academic goals expected by the school, and become actively engaged in learning activities, thereby reducing academic burnout [79]; conversely, when students lack this emotional connection with school, they can become negative about school activities, even if they believe that academics are important to them. In contrast, when students lack this emotional connection to school, they may become resistant to school activities, even if they consider it important, and gradually lose interest and enthusiasm for learning, which can be detrimental to academic achievement [80].
Additionally, this study also found that psychological capital moderates the mediating process of “school climate → collective self-esteem → academic burnout”, in that the first and second halves of the mediating chain are moderated by psychological capital, which partially confirms research hypothesis 3. On the one hand, psychological capital moderates the relationship between school climate and medical students’ collective self-esteem. School climate moderates the relationship between school climate and the collective self-esteem of medical students, which is more influential on the collective self-esteem of medical students with high psychological capital than those with low psychological capital. This moderating model is consistent with the facilitation, rather than exclusion, hypothesis of the “protective factor-protective factor” model, which suggests that psychological capital, as a positive psychological quality, can mobilize its own resources to enhance self-perceptions in a positive school environment [81], and supports the individual-environment interaction model [57]. Resource conservation theory suggests that students become relatively vulnerable when they are under-resourced and are likely to experience greater psychological stress and negative emotions; when they are well resourced they have better coping skills and a greater sense of self-worth and competence [82, 83]. Medical students with high psychological capital possess positive psychological qualities such as self-efficacy, hope, resilience and optimism, and are more sensitive to the positive components of the school climate, and are able to adopt cognitive strategies to adjust their mindset without wavering in their positive perceptions and evaluations of school, even when experiencing stressful events in school life [84].
On the other hand, psychological capital moderates the relationship between collective self-esteem and academic burnout among medical students. Collective self-esteem had a greater impact on academic burnout among medical students with high psychological capital than low psychological capital. This moderating pattern is also consistent with the facilitation hypothesis of the “protective factor-protective factor” model. High psychological capital is closely related to positive emotions [85]. According to the extended construct of positive emotion, individuals with high psychological capital have more flexible cognitive and behavioral patterns and are more likely to receive energy from external sources in response to external risks, whereas individuals with low psychological capital are more vulnerable to external risks and have more difficulty replenishing their energy after attrition [86]. It is evident that high psychological capital medical students, even if they lack an emotional connection to school, compensate for or counteract this negative impact through the various internal and external resources they have, better allocating their attention to their studies and exhibiting lower levels of academic burnout. It is worth noting that psychological capital did not moderate the effect of school climate on academic burnout in medical students, which is inconsistent with previous hypotheses. Further evidence suggests that a good school climate is an important protective factor for individuals’ positive psychological, behavioral development and that their academic burnout is protected by the protective effect of a good school climate regardless of their level of psychological capital [54]. Therefore, to promote good academic adjustment in medical students, it is important to focus not only on the role of internal factors (core self-assessment) but also on the role of external environmental factors (school climate).
## Limitations and future directions
This study reveals the internal mechanism of school climate affecting medical students’ academic burnout, and has a certain reference value for improving school climate and reducing academic burnout. On the one hand, the current study suggests that school managers and educators should pay attention to the role of school climate in academic development, and strive to create a good school climate, which can be considered from the aspects of teacher-student relationship, classmate relationship, system and order, etc. On the other hand, under the circumstance that the school climate cannot be improved in the short term, it can promote the positive academic development by cultivating and improving students’ collective self-esteem and psychological capital level.
Additionally, the current study also has certain limitations that need to be improved in future studies. First, this study uses a cross-sectional study, which is difficult to reveal the causal relationship between variables. Future studies can use follow-up studies to further verify. Secondly, the data of the study comes from students’ self-reports, which may be affected by the social approval effect. Future research may consider adding more objective measurement methods such as experimental methods. Finally, whether the research results can be generalized to other groups needs to be further tested. The research may consider adding more groups (such as primary and secondary school students) to be tested.
## Conclusion
This study constructs a moderated mediation model to explore the process by which school climate acts on academic burnout in medical students. It enriches the theoretical framework of stage-environment fit and provides practical implications for the development of positive psychological resources. Positive school climate can mitigate medical students’ academic burnout levels through collective self-esteem levels, and the relationship between school climate and collective self-esteem and between collective self-esteem and academic burnout is moderated by psychological capital. Therefore, creating a good school climate and improving the level of collective self-esteem and psychological capital is conducive to improving the academic burnout of medical students.
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|
---
title: 'Comparison of clinical outcomes between aggressive and non-aggressive intravenous
hydration for acute pancreatitis: a systematic review and meta-analysis'
authors:
- Xiu-Wei Li
- Chien-Ho Wang
- Jhih-Wei Dai
- Shu-Han Tsao
- Po-Hsi Wang
- Cheng-Chen Tai
- Rong-Nan Chien
- Shih-Chieh Shao
- Edward Chia-Cheng Lai
journal: Critical Care
year: 2023
pmcid: PMC10035244
doi: 10.1186/s13054-023-04401-0
license: CC BY 4.0
---
# Comparison of clinical outcomes between aggressive and non-aggressive intravenous hydration for acute pancreatitis: a systematic review and meta-analysis
## Abstract
### Background
Current practice guidelines for optimal infusion rates during early intravenous hydration in patients with acute pancreatitis (AP) remain inconsistent. This systematic review and meta-analysis aimed to compare treatment outcomes between aggressive and non-aggressive intravenous hydration in severe and non-severe AP.
### Methods
This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We systematically searched PubMed, Embase and Cochrane Library for randomized controlled trials (RCTs) on November 23, 2022, and hand-searched the reference lists of included RCTs, relevant review articles and clinical guidelines. We included RCTs that compared clinical outcomes from aggressive and non-aggressive intravenous hydration in AP. Meta-analysis was performed using a random-effects model for participants with severe AP and non-severe AP. Our primary outcome was all-cause mortality, and several secondary outcomes included fluid-related complications, clinical improvement and APACHE II scores within 48 h.
### Results
We included a total of 9 RCTs with 953 participants. The meta-analysis indicated that, compared to non-aggressive intravenous hydration, aggressive intravenous hydration significantly increased mortality risk in severe AP (pooled RR: 2.45, $95\%$ CI: 1.37, 4.40), while the result in non-severe AP was inconclusive (pooled RR: 2.26, $95\%$ CI: 0.54, 9.44). However, aggressive intravenous hydration significantly increased fluid-related complication risk in both severe (pooled RR: 2.22, $95\%$ CI 1.36, 3.63) and non-severe AP (pooled RR: 3.25, $95\%$ CI: 1.53, 6.93). The meta-analysis indicated worse APACHE II scores (pooled mean difference: 3.31, $95\%$ CI: 1.79, 4.84) in severe AP, and no increased likelihood of clinical improvement (pooled RR:1.20, $95\%$ CI: 0.63, 2.29) in non-severe AP. Sensitivity analyses including only RCTs with goal-directed fluid therapy after initial fluid resuscitation therapy yielded consistent results.
### Conclusions
Aggressive intravenous hydration increased the mortality risk in severe AP, and fluid-related complication risk in both severe and non-severe AP. More conservative intravenous fluid resuscitation protocols for AP are suggested.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13054-023-04401-0.
## Introduction
Acute pancreatitis (AP) is one of the most frequent and critical gastrointestinal diseases resulting in hospital admissions worldwide [1–3]. The global incidence and mortality of acute pancreatitis is estimated at 34 cases per 100,000 person-years and 1.6 deaths per 100,000 person-years, respectively, and the incidence has been reported to be rising in recent years [4–6]. Appropriate initial management decisions for AP can significantly affect the disease course and hospitalization duration [5, 7–10].
According to a number of international guidelines, early fluid resuscitation with predominantly isotonic crystalloid (i.e., normal saline or Ringer’s lactate solution) is widely indicated for AP management to prevent hypovolemia and organ hypoperfusion, without waiting for hemodynamic worsening [7, 8, 11, 12]. However, the guidelines for the design of fluid resuscitation protocols remain inconsistent when it comes to the infusion rate [12–19]. For example, the American College of Gastroenterology (ACG) guidelines suggest that aggressive intravenous hydration (250–500 ml/hour) should be given to all patients with AP in the first 12–24 h unless cardiovascular and/or renal comorbidities exist [11]. For severe AP, the Italian Association for the Study of the Pancreas (AISP) suggests early aggressive hydration at 2 ml/kg/h, with an initial bolus of 20 ml/kg within 30–45 min in the first 24 h [20]. However, the guidelines of the American Gastroenterological Association (AGA) and experts from ACG’s Acute Pancreatitis Task Force suggest a goal-directed fluid therapy, but are unable to make specific recommendations on the optimal initial rate of fluid resuscitation in AP, due to the paucity of evidence [7, 21].
Three previous systematic review and meta-analysis studies, potentially with methodological flaws, yielded inconsistent findings on the effects of aggressive intravenous hydration in AP [22–24]. Gad MM et al. concluded that aggressive intravenous fluid therapy did not reduce mortality in AP, based on 9 included studies (3 randomized controlled trials (RCTs) and 6 cohort studies) [23], and Liao J et al. [ 22] concluded that aggressive hydration increases in-hospital mortality in AP by 1.66 times, based on 12 included studies (4 RCTs and 8 cohort studies). However, the certainty of evidence (CoE) from these reviews was compromised since the inclusion of observational studies with RCTs in meta-analyses frequently increases heterogeneity among the included studies and is therefore discouraged [25]. Another systematic review and meta-analysis from Di Martino M et al. [ 24] reported that high-rate fluid infusion increased mortality about threefold in AP, compared to moderate-rate fluid infusion, based on 4 RCTs. However, these previous meta-analyses did not separately analyze the severity of AP, so firm conclusions regarding specific AP populations cannot be drawn. Recently, interim findings from the WATERFALL trial indicated about threefold increased risks of fluid overload, and potentially threefold increased risks of mortality, in patients with non-severe AP receiving aggressive intravenous fluid resuscitation, compared to those receiving non-aggressive fluid resuscitation [26]. Since the heterogeneity, in terms of disease severity, of the population studied in previous RCTs has probably contributed to inconsistent findings, we conducted this systematic review and meta-analysis of RCTs, separately reporting and contrasting the benefits and harms of aggressive and non-aggressive intravenous hydration protocols for severe and non-severe AP.
## Methods
This systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Additional file 1: Appendix Table S1) [27], and the pre-defined study protocol has been published on the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY) with the registration number INPLASY2022110068.
## Search strategy
We searched PubMed, Embase and the Cochrane Library from database inception to November 23, 2022, with no limitation on language, to identify relevant RCTs. The search strategy was developed by an experienced evidence-based medicine researcher (SCS) in collaboration with a senior librarian (CCT) (Additional file 1: Appendix Table S2). Important keywords with MeSH terms included “acute pancreatitis”, “normal saline” and “Lactated Ringer’s solution.” To make our search more comprehensive, we also manually reviewed the reference lists of the included studies, previous review articles and published guidelines regarding AP.
## Inclusion criteria
After removing duplicate records from the different databases, two independent reviewers (XWL and CHW) selected the included studies based on the following PICOS criteria: [1] Participants: Adults with AP. The diagnosis of AP should be based on two of the following criteria developed by the Atlanta international symposium and revised Atlanta classification: a.) Abdominal pain consistent with AP (e.g., acute onset of persistent, severe, epigastric pain often radiating to the back); b.) Serum lipase or amylase activity at least three times greater than the upper limit of normal; or c.) Classic image findings from computed tomography, magnetic resonance imaging or abdominal ultrasonography [1, 28]. We defined the severity of AP based on the Atlanta international symposium and revised Atlanta classification [1, 28]. For example, mild AP is defined by the absence of organ failure and local or systemic complications, and moderately severe AP is defined by the presence of transient organ failure or local or systemic complications. We classified mild and moderately severe AP into the non-severe AP group, because severe AP, characterized by persistent organ failure, may pose a higher mortality risk [2]; [2] Interventions: aggressive intravenous fluid resuscitation, defined as a.) Fluid administration (predominantly normal saline or lactated Ringer’s solution) at a rate greater than 10 ml/kg/hour as the initial management [12]; b.) *Fluid bolus* 20 ml/kg for 2 h, then 2–3 ml/kg/hour in the first 24 h [20]; c.) Isotonic crystalloid > 500 ml/hour for the first 12–24 h [11]. If the RCTs did not report the fluid infusion rate in the study protocols, the crystalloid fluid administration should be greater than 4000 ml in the first 24 h [29]; [3] Comparisons: non-aggressive intravenous fluid resuscitation, defined as a.) Fluid administration at a rate lower than 10 ml/kg/hour; b.) *Fluid bolus* 10 ml/kg for 2 h; then, 1.5 ml/kg/hour in the first 24 h or c.) Isotonic crystalloid < 500 ml/hour for the first 12–24 h. If the RCTs did not report the fluid infusion rate in the study protocols, the crystalloid fluid administration should be less than 4000 ml in the first 24 h [29]; [4] Primary outcome: all-cause mortality. Other secondary outcomes, such as the rate of clinical improvement (based on the objective parameters, including systemic inflammatory response syndrome (SIRS) subsides and time period therefore, decrease in hematocrit (Hct), blood urea nitrogen (BUN) and creatinine from baseline, and subjective measurements, including decrease in epigastric pain degree, assessed by visual analogue scale (VAS) and tolerance of oral nutrition within 48 h) in non-severe AP and the changes of Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, Sequential Organ Failure Assessment (SOFA) scores and Multiple Organ Dysfunction (MOD) scores for severe AP [30–32], fluid-related complications, such as abdominal compartment syndrome, pulmonary/peripheral edema and any sign of volume overload (e.g., rapid weight gain, incident ascites or jugular vein engorgement), as defined by previous guidelines [20, 33], sepsis, acute respiratory failure, acute kidney injury, pancreatic necrosis, SIRS subsiding within 48 h [34, 35], SIRS persisting > 48 h [34, 35], persistent organ failure (any organ failure > 48 h), defined by the revised Atlanta classification [1] and total hospitalization days were also evaluated if they were reported in the included studies. Specifically, decrease in epigastric pain may be related to better prognosis of AP and better subjective perception of quality of life for patients [7, 36]. In addition, we included APACHE II score changes as the clinical prognosis parameter, based on the suggestions of the ACG guidelines [37], and the prediction performance of poor outcome in severe AP has been validated [37, 38]. We also evaluated the Hct and BUN changes within 48 h [39, 40], because these parameters have been considered as surrogate markers for successful hydration for AP [11]; [5] Designs: RCTs. In cases of disagreement over study selection, the senior author (SCS) made the final decision.
## Data extraction and risk-of-bias assessment
The data extraction and risk-of-bias assessment were performed by two independent reviewers (XWL and CHW). Data extracted included the study (e.g., first author with publication year and study period), participants (e.g., mean age and sex proportion, SIRS on admission and BUN levels) and intervention/comparison (e.g., fluid infusion protocols). We extracted data for the event number and mean with standard deviation (SD) in the intervention and comparison groups for categorical and continuous outcomes, respectively. In RCTs not reporting the SD for changes from baseline in continuous variables, we imputed a change-from-baseline SD using a correlation coefficient approach [41]. We used the Cochrane Collaboration's ROB tool 2.0, addressing the critical domains of randomization process, deviations from intended interventions, missing outcome data, measurement of the outcome, selection of the reported result and overall bias, to evaluate the methodological quality of the included RCTs [42]. Any disagreements between the two reviewers were resolved by the senior author (SCS).
## Data synthesis and statistical analysis
We used Review Manager Version 5.3 (Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2014) to conduct a random-effects meta-analysis due to the expected clinical heterogeneity among the included RCTs [43]. Considering the heterogeneity with regard to disease severity among trial participants with AP in the RCTs, we separately calculated the pooled risk ratio (RR) and mean difference (MD) with a $95\%$ confidence interval (CI) for categorical and continuous outcomes, respectively, for severe and non-severe AP. Where multiple scales were employed to measure the same continuous outcome, we used the standardized mean difference (SMD) to express the results. The I2 statistic was used to determine the extent of statistical heterogeneity among the included RCTs, and a value > $50\%$ was considered as significant heterogeneity [44]. Funnel plots were to be constructed to visually examine for the presence of publication bias if there were at least 10 RCTs included in the meta-analysis [45].
## Subgroup analysis
We assessed the heterogeneity of intravenous hydration effects on the primary and secondary outcomes of study interest based on the trial-level subgroups, including study countries (e.g., Asian or non-Asian), and mean or median age (e.g., < 50 or > 50 years).
## Sensitivity analyses
Recent practice guidelines from AGA and the expert consensus of the ACG Acute Pancreatitis Task Force have highlighted the importance of goal-directed fluid therapy for AP, defined as the titration of intravenous fluids to specific clinical and biochemical perfusion targets (e.g., heart rate, mean arterial pressure, central venous pressure, urine output, BUN and Hct levels) [7, 21]. We therefore replicated all analyses by including only RCTs with goal-directed fluid therapy as a sensitivity analysis to determine the robustness of our results.
In the sensitivity analyses, we included 7 RCTs (severe AP: 2 RCTs; non-severe AP: 5 RCTs) examining intravenous hydration protocols based on goal-directed fluid therapy after the initial fluid resuscitation therapy. The results also indicated an increased risk of mortality in the severe AP group receiving aggressive intravenous hydration (2 RCTs, 191 participants; pooled RR: 2.45, $95\%$ CI: 1.37, 4.40; I2: $0\%$) (Additional file 1: Appendix Fig. S12), and increased risks of fluid-related complications in both the severe (1 RCT, 76 participants; pooled RR: 2.22, $95\%$ CI: 1.36, 3.63; I2: not applicable) and non-severe AP group (3 RCTs, of which only 1 contributed outcome events, 353 participants; pooled RR: 3.25, $95\%$ CI: 1.53, 6.93; I2: not applicable) receiving aggressive intravenous hydration, compared to the non-aggressive intravenous hydration group (Additional file 1: Appendix Fig. S13). Other secondary outcomes from sensitivity analyses were consistent with those from the main analyses (Additional file 1: Appendix Figs. S12–S25 and Tables S20–33).
## Certainty of evidence of the study outcomes
Two independent reviewers (JWD and CHW) evaluated the CoE for each study outcome based on the Grading of Recommendations Assessment, Development and Evaluation (GRADE) criteria [46]. Any discrepancy between the review authors was resolved by discussion with the senior author (SCS).
## Characteristics of included studies
We retrieved a total of 239 records from the different databases, plus 7 records from additional reviews found on the reference lists of the included studies, previous review articles and published guidelines regarding AP. The study selection flowchart is presented in Additional file 1: Appendix Fig. S1, and the reasons for exclusion of records after full-text review are presented in Additional file 1: Appendix Table S3. We finally included 9 RCTs (severe AP: 2 RCTs; non-severe AP: 7 RCTs), covering a total of 953 trial participants in this systematic review and meta-analysis [26, 47–54]. We found both of the included RCTs focusing on severe AP were from China [47, 48], while 3, 1, 1, 1 and 1 of the included studies focusing on non-severe AP were from China, Thailand, Mexico, USA and multiple countries, respectively [26, 49–54]. Other important study characteristics of the included RCTs are shown in Table 1 and Additional file 1: Appendix Table S4. Table 1Study characteristicsFirst author (Ref)CountryStudy periodSeverityaAge, mean years ± SDMale, n (%)Participants, n (%)AggressiveNon-aggressiveAggressiveNon-aggressiveAggressiveNon-aggressivede‑Madaria [26]India, Italy, Mexico, Spain$\frac{2020}{05}$–$\frac{2021}{09}$Mild56 ± 1857 ± 1754 (44.3)68 (53.5)122127Angsubhakorn [51]Thailand$\frac{2019}{08}$–$\frac{2020}{10}$Mild46 ± 1545 ± 1518 (81.8)16 (72.7)2222Liu [52]China$\frac{2019}{01}$–$\frac{2020}{06}$Mild to moderately severe45 ± 1347 ± 1236 [75]44 (63.8)4869Wang [53]China$\frac{2017}{06}$–$\frac{2019}{06}$Mild43 ± 142 ± 129 (55.8)28 (53.8)5252Cuéllar [50]Mexico$\frac{2015}{05}$–$\frac{2016}{10}$Mild to moderately severe37 ± 1639 ± 1513 (30.2)18 [40]4345Li [54]China$\frac{2016}{01}$–$\frac{2017}{12}$Mild44 ± 1046 ± 1336 [72]27 [54]5050Buxbaum [49]U.S$\frac{2013}{04}$–$\frac{2015}{11}$Mild44 ± 1445 ± 1221 (77.8)24 (72.3)2733Mao [48]China$\frac{2006}{09}$–$\frac{2008}{12}$Severe50 ± 1549 ± 1034 (60.7)36 [61]5659Mao [47]China$\frac{2001}{03}$–$\frac{2007}{12}$Severe51 ± 1450 ± 12N/AN/A3640First author (Ref)Initial fluid protocolGoal-directed therapyFluid volume on day 1 after trial entryAggressiveNon-aggressiveAggressiveNon-aggressivede‑Madaria [26]20 ml/kg bolus then 3 ml/kg/hr10 ml/kg bolus then 1.5 ml/kg/hrYes5400 ml3310 mlAngsubhakorn [51]20 ml/kg bolus then 3 ml/kg/hr10 ml/kg bolus then 1.5 ml/kg/hrYes4886 ml3985 mlLiu [52]15 ml/kg bolus then 5 ml/kg/hr after vital sign stable10 ml/kg bolus then 5 ml/kg/hr after vital signs stableYes3618 ml3310 mlWang [53]20 ml/kg bolus then 3 ml/kg/hr for 1 week10 ml/kg bolus then 1.5 ml/kg/hr for 1 weekNoN/AN/ACuéllar [50]20 mL/kg bolus, then 3 mL/kg/hr for the first 24 h and then 30 mL/kg for the next 24 h1.5 mL/kg/ hr for the first 24 h and 30 mL/kg during the next 24 hNo~ 6400 mlb~ 2795 mlbLi [54]20 ml/kg bolus then 3 ml/kg/hr10 ml/kg bolus then 1.5 ml/kg/hrYesN/AN/ABuxbaum [49]20 ml/kg bolus then 3 ml/kg/hr10 ml/kg bolus then 1.5 ml/kg/hrYes5600 ml3900 mlMao [48]As referencecAs referencecYes4805 mld3883 mldMao [47]10–15 ml/kg/hr5–10 ml/kg/hrYes6855 mld5841 mldaSeverity, severity of the acute pancreatitisbCalculated from the given fluid protocolcFluid protocol of Mao et al. 2010: H0 × TBW (kg) × $5\%$ = Hg × V1; V1 = (H0/Hg) × TBW × $5\%$ (Liters); H0: Hematocrit on admission; Hg: Goal hematocrit in unit time; TBW: total body weight (kg); V1: Amount of fluid administered to meet the goal HCT; V = V1 + (2.5 L/24 h × unit time (h)). Goal-HCT within 48 h was $34\%$ and $35\%$ in rapid hemodilution and slow hemodilution, respectivelydThis study followed the mixed fluid type protocol (the ratio of crystalloid and colloid volumes: 2–3:1) as the intravenous hydration for acute pancreatitis. Only lists the amount of crystalloid fluid
## Risk of bias of included studies
We judged only 1 study with 249 participants to have a low risk of bias in all domains [26], and 8 studies as having non-low risk of bias (either “some concerns” or “high risk of bias” in at least one domain). The greatest sources of bias were unclear randomization process and the possibility of selective reporting of results (Additional file 1: Appendix Table S5).
## Primary outcome: all-cause mortality
A meta-analysis of 9 RCTs with a total of 953 participants with AP revealed an increased risk of mortality in the aggressive intravenous hydration group, compared to the non-aggressive intravenous hydration group (pooled RR: 2.42, $95\%$ CI: 1.41, 4.17; I2: $0\%$) (Fig. 1). However, the significantly increased risk of mortality in the aggressive intravenous hydration group was only observed in severe AP (2 RCTs; 191 participants; pooled RR: 2.45, $95\%$ CI: 1.37, 4.40; I2: $0\%$), while in non-severe AP the results remained similar but did not reach statistical significance (7 RCTs, of which only 3 contributed mortality events; 762 participants; pooled RR: 2.26, $95\%$ CI: 0.54, 9.44; I2: $0\%$). Fig. 1Mortality risk, comparing aggressive (intervention) and non-aggressive (control) hydration protocols for acute pancreatitis
## Secondary outcomes: fluid-related complications, clinical improvement, sepsis, acute respiratory failure, acute kidney injury, pancreatic necrosis, SIRS subsiding within 48 h, SIRS persisting > 48 h, persistent organ failure, BUN changes within 48 h, Hct changes within 48 h, and total hospitalization days
A meta-analysis of 5 RCTs (of which only 2 contributed outcome events) with a total of 517 participants with AP revealed an increased risk of fluid-related complications in the aggressive intravenous hydration group, compared to the non-aggressive intravenous hydration group (pooled RR: 2.49, $95\%$ CI: 1.65, 3.75; I2: $0\%$) (Fig. 2). In addition, an increased risk of fluid-related complications in the aggressive intravenous hydration group was found both in severe AP (1 RCTs; 76 participants; RR: 2.22, $95\%$ CI: 1.36, 3.63; I2: not applicable), and in non-severe AP (4 RCTs; 441 participants; pooled RR: 3.25, $95\%$ CI: 1.53, 6.93; I2: not applicable).Fig. 2Fluid-related complication risk, comparing aggressive (intervention) and non-aggressive (control) hydration protocols for acute pancreatitis A meta-analysis of 2 RCTs with a total of 104 participants with non-severe AP revealed no additional clinical improvement in the aggressive intravenous hydration group, compared to the non-aggressive intravenous hydration group (pooled RR: 1.20, $95\%$ CI: 0.63, 2.29; I2: $72\%$) (Fig. 3a). None of the included RCTs reported the changes of SOFA or MOD scores. For severe AP, results from the meta-analyses indicated significantly greater changes in APACHE II scores for the aggressive vs. the non-aggressive intravenous hydration group (2 RCTs, 191 participants; pooled MD: 3.31, $95\%$ CI: 1.79, 4.84; I2: $0\%$) (Fig. 3b). Another meta-analysis of 3 RCTs with a total of 440 participants with AP revealed an increased risk of sepsis in the aggressive intravenous hydration group, compared to the non-aggressive intravenous hydration group (pooled RR: 1.44, $95\%$ CI: 1.15, 1.80; I2: $0\%$) (Additional file 1: Appendix Fig. S2). However, an increased risk of sepsis in the aggressive intravenous hydration group was only found in severe AP (2 RCTs; 191 participants; RR: 1.44, $95\%$ CI: 1.15, 1.80; I2: $0\%$), but not in non-severe AP (1 RCTs; 249 participants; pooled RR: 1.73, $95\%$ CI: 0.42, 7.10; I2: not applicable).Fig. 3a Clinical improvement, comparing aggressive (intervention) and non-aggressive (control) hydration protocols for acute pancreatitis b APACHE II score changes within 48 h, comparing aggressive (intervention) and non-aggressive (control) hydration protocols for acute pancreatitis A meta-analysis of 3 RCTs with a total of 442 participants with AP revealed an increased risk of acute respiratory failure in the aggressive intravenous hydration group, compared to the non-aggressive intravenous hydration group (pooled RR: 1.49, $95\%$ CI: 1.18, 1.89; I2: $0\%$) (Additional file 1: Appendix Fig. S3). However, an increased risk of acute respiratory failure in the aggressive intravenous hydration group was only found with severe AP (1 RCT; 76 participants; RR: 1.45, $95\%$ CI: 1.14, 1.85; I2: not applicable), but not with non-severe AP (2 RCTs; 366 participants; pooled RR: 2.46, $95\%$ CI: 0.85, 7.15; I2: $0\%$).
No RCTs focusing on severe AP reported acute kidney injury, pancreatic necrosis, SIRS subsiding within 48 h, SIRS persisting > 48 h or persistent organ failure. For those with non-severe AP, results from the meta-analyses did not indicate significant differences between the aggressive and non-aggressive intravenous hydration groups with regard to acute kidney injury (3 RCTs, 441 participants; pooled RR: 0.83, $95\%$ CI: 0.32, 2.16; I2: $0\%$), pancreatic necrosis (2 RCTs, 337 participants; pooled RR: 1.82, $95\%$ CI: 0.92, 3.59; I2: $0\%$), SIRS subsiding within 48 h (4 RCTs, 441 participants; pooled RR: 1.09, $95\%$ CI: 0.70, 1.70; I2: $0\%$), SIRS persisting > 48 h (3 RCTs, 348 participants; pooled RR: 1.04, $95\%$ CI: 0.50, 2.16; I2: $30\%$) or persistent organ failure (5 RCTs, of which only 4 contributed outcome events, 558 participants; pooled RR: 1.34, $95\%$ CI: 0.65, 2.79; I2: $22\%$) (Additional file 1: Appendix Fig. S4–S8).
A meta-analysis of 2 RCTs with a total of 191 participants with severe AP revealed no significant differences in Hct changes within 48 h in the aggressive intravenous hydration group, compared to the non-aggressive intravenous hydration group (pooled MD: − 4.41, $95\%$ CI: − 15.97, 7.16; I2: $100\%$). No RCTs focusing on severe AP reported BUN changes within 48 h or total hospitalization days. For non-severe AP, we did not find significant differences between the aggressive and non-aggressive intravenous hydration protocols as regards BUN changes within 48 h (2 RCTs, 104 participants; pooled MD: − 1.84, $95\%$ CI: − 3.76, 0.08; I2: $0\%$), Hct changes within 48 h (2 RCTs, 104 participants; pooled MD: − 1.25, $95\%$ CI: − 3.90, 1.41; I2: $71\%$) and total hospitalization days (6 RCTs, 673 participants; pooled MD: − 0.43, $95\%$ CI: − 2.03, 1.17; I2: $98\%$) (Additional file 1: Appendix Figs. S9–S11).
## Subgroup analyses
These results from the subgroup analyses of primary (Additional file 1: Appendix Table S6) and secondary outcomes (Additional file 1: Appendix Table S7–S19), comparing aggressive and non-aggressive intravenous hydration protocols for severe and non-severe AP, were generally similar to those from the main analysis. For example, compared with non-aggressive intravenous hydration protocols, the increased mortality risk associated with aggressive intravenous hydration protocols was also observed in participants with severe AP from Asian countries (2 RCTs, 191 participants; pooled RR: 2.45, $95\%$ CI: 1.37, 4.40; I2: $0\%$), while inconclusive results were observed for non-severe AP.
## GRADE assessment
Since there was a serious risk of bias and imprecise results due to the small sample sizes in the included RCTs, we judged the CoE by the GRADE criteria for our primary and secondary outcomes to be low to very low (Additional file 1: Appendix Table S34).
## Discussion
In this systematic review and meta-analysis of 9 RCTs, the mortality risk from selecting aggressive intravenous hydration protocols over non-aggressive intravenous hydration protocols for fluid resuscitation therapy significantly increased 2.45-fold in severe AP, while in non-severe AP the results remained similar but did not reach statistical significance. This finding may be explained by our analyses of secondary outcomes, which indicated that aggressive intravenous hydration protocols did not decrease APACHE II scores in severe AP, or improve the clinical conditions in non-severe AP. In addition, aggressive intravenous hydration protocols increased the fluid-related complication risk in severe and non-severe AP by 2.22–3.25 times. Our findings suggested that aggressive intravenous hydration protocols should not be recommended for fluid therapy in severe and non-severe AP and may serve as an important reference not only for clinical practitioners but also for future practice guideline makers.
Determining an appropriate fluid therapy strategy for AP, especially as regards infusion rate, is critical but remains controversial. Previous animal studies have suggested that aggressive fluid therapy could improve splenic blood flow, correct pancreatic hypoperfusion and ultimately reduce pancreatic damage and mortality [55, 56], but may cause higher central venous pressure, leading to side effects of interstitial edema without significant changes in mean arterial pressure [57]. Conflicting treatment effects from aggressive intravenous fluid therapy have been found in previous RCTs and observational studies in patients with AP [23, 24, 29, 43, 44]. Nonetheless, ACG guidelines recommend aggressive intravenous hydration (defined as 250–500 ml per hour of isotonic crystalloid solution), to be administered early to most patients with AP [11], but the AGA guidelines and the ACG Acute Pancreatitis Task *Force consensus* make no recommendations on the hydration infusion rate [7, 21]. Our findings did not support the practice guidelines of the ACG or AISP and extended the knowledge gaps in the ACG/Acute Pancreatitis Task Force consensus. Our meta-analysis indicated a greater than twofold increased risk of mortality and fluid overload associated with aggressive intravenous hydration protocols in severe AP cases, and a similarly increased risk of fluid overload in non-severe AP cases.
AP induces interstitial edema and increases inter-capillary distance and therefore, leads to focal ischemia [14, 45]. In severe AP, in addition to interstitial edema, the production of free radicals and vasoconstriction of small arterioles with inflammatory cells adhering to the endothelial cells all contribute to a cytokine cascade [55, 56, 58], leading to multi-organ dysfunction syndrome, which is associated with higher mortality [13, 57, 59, 60]. Fluid overload itself may worsen the natural course of severe AP, with the accumulating fluid leading to abdominal compartment syndrome which then further compromises the kidneys, lungs and peritoneal viscera, which also potentially increases the risk of multi-organ failure and mortality [59, 61]. The aforementioned mechanisms could explain the poorer results in several of our secondary outcomes, such as APACHE II changes, acute respiratory failure and sepsis, that may derive from aggressive vs. non-aggressive intravenous hydration protocols in severe AP. Overall, aggressive intravenous hydration is not suggested for patients with severe AP because it not only increases the risk of mortality, but also the risk of fluid overload.
In contrast to patients with severe AP, this meta-analysis did not find a significantly increased mortality risk or clinical improvement rate from aggressive intravenous hydration in patients with non-severe AP. Mild AP is mostly self-limiting, causes only local inflammation and usually resolves within one week [10], and therefore is unlikely to increase mortality, affect the clinical progression of pancreatitis or cause multi-organ failure due to fluid-related complications. Our analyses of several secondary outcomes such as clinical improvement, sepsis, acute respiratory failure, acute kidney injury, pancreatic necrosis, BUN changes within 48 h, Hct changes within 48 h, SIRS subsiding within 48 h, SIRS persisting > 48 h, and persistent organ failure further supported this viewpoint. Our results were also consistent with previous large cohort studies [40, 62]. Specifically, the clinical improvement outcome included the subsiding of epigastric pain, which reflected overall clinical improvement and integrated the patient’s subjective symptoms, and our finding may imply that patients with AP receiving aggressive intravenous hydration, compared to non-aggressive intravenous hydration, may not achieve better quality of life in terms of pain relief [63]. Taken together, our findings suggest that aggressive intravenous hydration is not recommended for patients with non-severe AP, because it may increase the fluid overload risk while not actually improving clinical conditions.
Early goal-directed therapy has recently been suggested to titrate the intravenous fluid to specific clinical and biochemical targets of perfusion (e.g., heart rate, mean arterial pressure, central venous pressure, urine output, BUN, and Hct). The treatment benefits of goal-directed therapy in AP, compared to non-targeted therapy, remain inconsistent according to the findings of previous RCTs, but the AGA guidelines included a conditional recommendation suggesting the use of goal-directed fluid therapy versus other methods [7]. In this systematic review and meta-analysis, we replicated the analyses by only including RCTs using early goal-directed therapy in intravenous hydration protocols, and the results remained consistent to those from the main analyses. We found that aggressive intravenous hydration protocols were still associated with a higher risk of mortality and fluid-related complications in AP, even with the goal-directed fluid strategy. Our findings provided further robust evidence supporting the use of conservative intravenous hydration protocols for AP [7].
## Strength and limitations
To the best of our knowledge, this is the first and most comprehensive systematic review and meta-analysis of RCTs that examine and compare outcomes between aggressive and non-aggressive intravenous hydration protocols for fluid therapy to treat severe and non-severe AP. In contrast to previous reviews which included both RCTs and cohort studies [22–24, 64], we included only RCTs in order to achieve more homogeneity between studies, and in order to provide the highest quality of evidence to address the specific research questions of clinicians, researchers, and health policy makers [65]. More importantly, we critically examined every potential record to ensure eligibility of the trials for our review. For example, we excluded one RCT by Wu et al. which was frequently included in previous meta-analyses on this topic, because the comparison of this RCT did not receive the non-aggressive intravenous hydration protocol [22, 23, 66]. Furthermore, we performed more pre-specified subgroup analyses to examine the heterogeneity of treatment effects, and the results from these subgroup analyses were generally consistent with our main analysis. Finally, we replicated all analyses by including only RCTs with goal-directed fluid therapy for AP, as suggested by the AGA and ACG Acute Pancreatitis Task Force since 2018 [2], and these findings were also consistent with our main analysis. Overall, our findings highlighted the clinical importance of a more conservative intravenous hydration therapy.
Several limitations should be noted before final interpretation of our findings. First, due to the limited number of included trial participants, the statistical power to detect significant differences in the secondary outcomes and the results from subgroup analyses may be suboptimal. However, our review did include four Asian RCTs and one multi-country RCT that had never been included in previous reviews, thus providing greater precision around estimates of treatment effects. Second, no individual-level data were obtained to examine the heterogeneity of treatment effects with different etiologies of AP [67, 68]. However, our included RCTs had participants with various etiologies of AP, so our findings may be applicable to the overall AP population. Third, the included RCTs did not report the total volumes of resuscitation fluid for the trial participants during hospitalization, so the actual volume differences between the aggressive and non-aggressive intravenous hydration groups remain unclear. Fourth, we could not investigate publication bias since we only included 9 RCTs in this meta-analysis. Finally, we judged the CoE of all study outcomes as low to very low, largely due to methodological concerns and small sample sizes of the included RCTs. We suggest regularly updating systematic reviews to include newly published RCTs on this critical topic.
## Conclusion
This systematic review and meta-analysis of currently existing RCTs indicates that aggressive intravenous hydration protocols increased the mortality risk in severe AP and the fluid-related complication risk in both severe and non-severe AP. Our findings highlighted the clinical importance of a more conservative approach to fluid therapy for AP in order to mitigate excessive risk of avoidable side effects and mortality.
## Supplementary Information
Additional file 1. Supplementary tables and figures.
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|
---
title: 'Predictive factors for degenerative lumbar spinal stenosis: a model obtained
from a machine learning algorithm technique'
authors:
- Janan Abbas
- Malik Yousef
- Natan Peled
- Israel Hershkovitz
- Kamal Hamoud
journal: BMC Musculoskeletal Disorders
year: 2023
pmcid: PMC10035245
doi: 10.1186/s12891-023-06330-z
license: CC BY 4.0
---
# Predictive factors for degenerative lumbar spinal stenosis: a model obtained from a machine learning algorithm technique
## Abstract
### Background
Degenerative lumbar spinal stenosis (DLSS) is the most common spine disease in the elderly population. It is usually associated with lumbar spine joints/or ligaments degeneration. Machine learning technique is an exclusive method for handling big data analysis; however, the development of this method for spine pathology is rare. This study aims to detect the essential variables that predict the development of symptomatic DLSS using the random forest of machine learning (ML) algorithms technique.
### Methods
A retrospective study with two groups of individuals. The first included 165 with symptomatic DLSS (sex ratio 80 M/85F), and the second included 180 individuals from the general population (sex ratio: 90 M/90F) without lumbar spinal stenosis symptoms. Lumbar spine measurements such as vertebral or spinal canal diameters from L1 to S1 were conducted on computerized tomography (CT) images. Demographic and health data of all the participants (e.g., body mass index and diabetes mellitus) were also recorded.
### Results
The decision tree model of ML demonstrate that the anteroposterior diameter of the bony canal at L5 (males) and L4 (females) levels have the greatest stimulus for symptomatic DLSS (scores of 1 and 0.938). In addition, combination of these variables with other lumbar spine features is mandatory for developing the DLSS.
### Conclusions
Our results indicate that combination of lumbar spine characteristics such as bony canal and vertebral body dimensions rather than the presence of a sole variable is highly associated with symptomatic DLSS onset.
## Introduction
Degenerative lumbar spinal stenosis (DLSS) is the most common spine disease in the elderly population [1]. Moreover, this pathology is most closely correlated with lumbar spine surgery performed on this population [2]. It is well accepted that the radiological manifestations of DLSS are degeneration of the three-joint complex, ligamentum flavum thickening and osteophytes formation [3, 4], which ultimately decrease the space available for the neurovascular elements. However, the essential definition of DLSS combines the radiological picture with the presence of clinical criteria following the Spine Patient Outcomes Research Trial (SPORT) [5]. It is noteworthy that up to $30\%$ of older individuals (≥ 55 years) have at least moderate radiological stenosis without symptoms [6].
Many studies have previously reported that there are anthropometric elements as well as morphological spine characterizations that significantly associate with symptomatic DLSS. For example, greater BMI, vertebral body size and lesser anterior posterior bony canal diameters increase the risk for DLSS development [7, 8]. On the other hand, the precise pathogenesis of this phenomenon is still inconsistent and unclear. As a poor correlation between the clinical picture and the radiological signs has been observed, there is a vital need to identity those individuals who will later suffer from symptomatic DLSS.
Machine learning (ML) is defined as a series of computational tools that are capable of determining the association between material data and do not required a specific setup [9]. *In* general, ML is applied in fields requiring predictions or decisions to be made according to the training data. ML has three major subgroups: supervised learning, unsupervised learning and reinforcement learning [10, 11]. Supervised ML is the subgroup in which a learner describes the input–output relationship based on labeled input variables with a grounded truth [12]. It is also a model used to analyze the training data to synthesize the pattern between independent and dependent variables [13], then the testing dataset can to be predicted. One of the three common ML models is the decision tree (DT) learning whose classification and regression implements a grouping or a regression task, which is more visible and easier to understand than other modalities [13]. The tree comprises internal nodes (conditions), branches (decisions) and leaves (end) that are not computationally intensive and therefore suitable for big data [14, 15].
To the best of our knowledge, no previous study has dealt with big data and has examined all the variables that together could predict DLSS development in order to highlight the most associated one. In addition, using a specific and exclusive method such as ML is rare in this field. *In* general, there is a belief that ML may provide new insights into biomedical analyses and diseases [16].
In recent years, the potential role of ML in driving personalized medicine has become highly recognized, especially in the realm of spine care [17–20]. Moreover, ML models are common in rheumatology, where numerous classification algorithms have been developed [21–24].
Compared with conventional associated factors obtained by the logistic regression analysis, ML has numerous advantages [18]. Firstly, fewer restrictions exist on the number of variables or predictors used in the final model with ML, thus making large modern datasets more amenable to ML approach [25]. Secondly, it can capture non-linear relationships between the predictor variables and the outcome variable, which it can exhibit complex interactions and relationships that may not be captured by traditional statistical models such as logistic regression. Thirdly, ML is less sensitive to outliers than logistic regression, because it uses a combination of multiple decision trees, so the effect of outliers is dampened. Finally, the ability of this method to handle missing and big data, contrary to traditional statistical analysis, will considerably improve diagnostic accuracy and prognosis [26].
The aim of this study is to reveal the outstanding predictive variables for the development of symptomatic DLSS using the ML algorithm technique.
## Study design and participants
This is a retrospective study that includes two groups: individuals with DLSS ($$n = 165$$) and control ($$n = 180$$). The study groups were enrolled between 2008 and 2012 and included demographic (e.g., age and occupation) and health data (hypertension, diabetes mellitus) regarding the participants [7]. Details about the inclusion and exclusion criteria for the study groups could be obtained from the research of Abbas and colleagues [8] as it is the same sample of participants. One of the coauthor (KH) who is a spine surgeon has recruited the participants of the DLSS group following the SPORT recommendations [5]. The predominant symptom of individuals with DLSS was neurogenic claudication that usually improves in sitting or lumbar flexion and worsens with standing and lumbar extension. All participants underwent computer tomography (CT) (Brilliance 64, Philips Medical System, Cleveland, OH, thickness of the sections were 1–3 mm and MAS, 80–250) in the supine position with extended knees.
All the CT measurements were taken from L1 to S1 levels and included the vertebral body diameters (width, length and height), bony canal dimensions such as anterior- posterior (AP), medio-lateral, and cross-section area (CSA) [8]. We also addressed the facets orientation and tropism [27], pedicle width and height [28], spinous process orientation [29], laminar inclination and inter-laminar angle [8]. Spine pathology such as vacuum phenomenon, intervertebral disc height, and the presence of Schmorl’s nodes [30, 31] were also recorded. Dimensions of the para-vertebral muscles (psoas, multifidus and erector spinae) density and CSA [32] as well as the spino-transvers area were evaluated in the axial plane at the middle part of L3 vertebra. The presence of lumbosacral transitional vertebra, sacral slope and lumbar lordosis angles were also recorded [33]. It should be noted, that cases with LF hypertrophy, facet joints arthrosis, degenerative spondylolisthesis and intervertebral disc bulging, which are the main radiological manifestations of DLSS, were enrolled in our study, however, these variables, were not considered in the machine learning analysis. All the participants gave informed consent to participate in this study. The Departmental Research Ethics Committee, of the Carmel Medical Center (0083–07-CMC), approved this study.
## Statistical analyses
We used SPSS version 20, in order to check the normal distribution for all the metric parameters. Descriptive statistics (frequencies and number) were also used to present the distribution of age, BMI and CSAs of dural sac of the participants in the study groups.
## Supervised machine learning analysis
We have applied the supervised machine learning approach considering the random forest (RF) for the classification task [34] in view of the default parameters (Split criteria: Information Gain Ratio and number of trees 100). The RF also provides a score that expresses the significance of each variable or feature. Significant variables are essential for developing the final model, whereas those with a low score could be removed from the final model. For simple visualization, we have used the decision tree (DT) model with the default parameters (Quality measure is Gini index, Pruning method is Minimum Descriptive Length). DT can be expressed as a set of "if–then-else" decision rules. The DT is a tree consisting of root nodes and leaf nodes. A decision node has two or more branches. A leaf node represents a classification or decision [35] ("pos" or "neg" label) (Figs. 1 and 2). The peak of the DT corresponds to the best predictor variable called the root node. For using the DT as a classifier, one has to start from the root node, moving to the next node (branches) based on the decision rules. This process is repeated until reaching the leaf node (ends) that explains the prediction outcome ("pos" = DLSS or "neg" = Control) (Figs. 1 and 2). The RF classifier was trained and tested with a split into $90\%$ training and $10\%$ testing data. The features of the RF model were recorded over all the 100 iterations. The average scores were calculated to assign a final score to each feature. The higher score outcomes indicate a significant impact of the feature to the model. Fig. 1The decision tree (DT) of random forest obtained by the given variables in males. " AP- antero-posterior bony canal, Mdensity mult- mean density of multifidus, VA- vertebral height anterior, lamina ang—inter-laminar angle, Spine pr.—spine process inclination, M.facet Or.- mean facet orientation, Mspinotransverse- mean spinotransverse area, CSAbony- cross section area of bony canal VM- vertebral height middle, M.pedicle. H- mean pedicle height, VW- vertebral body width"Fig. 2The decision tree (DT) of random forest obtained by the given variables in females. " AP- anterio-posterior bony canal, VM- vertebral height middle, CSAbony- cross section area of bony canal, Mlamina- mean lamina inclination, VL- vertebral body length, MCSA.ES- mean cross section area of erector spina muscle, VA- vertebral height anterior" We evaluated the performance of RF classifier by the following measures: [1] sensitivity (SE) which represent the true positive (TP) rate, [2] specificity (SP) which represents the true negative (TN) rate (complement of sensitivity), and [3] precision (PR) which represents the ability to correctly predict the positive target condition to the total [28]. In addition, we assessed the accuracy (ACC) which represents the classifier ability to predict the target condition correctly, and the F-measure that illustrates the classifier ability to predict the target condition correctly (compared to ACC, it is more accurate in cases of imbalanced data set, since it considers both PR and SE) [35]. All reported performance measures refer to the average of 100-fold Monte Carlo cross validation (MCCV) [36].\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{Sensitivity}=\mathrm{TP}/(\mathrm{TP}+\mathrm{FN}),\;\mathrm{Specificity}=\mathrm{TN}/(\mathrm{TN}+\mathrm{FP}),\;\mathrm{Precision}=\mathrm{TP}/(\mathrm{TP}+\mathrm{FP}),\;\mathrm{Accuracy}=(\mathrm{TP}+\mathrm{TN})/(\mathrm{TP}+\mathrm{FN}+\mathrm{TN}+\mathrm{FP})$$\end{document}Sensitivity=TP/(TP+FN),Specificity=TN/(TN+FP),Precision=TP/(TP+FP),Accuracy=(TP+TN)/(TP+FN+TN+FP)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{F}-\mathrm{ Measure }= 2\mathrm{ x }(\mathrm{PR x SE})/(\mathrm{PR }+\mathrm{SE})$$\end{document}F-Measure=2x(PRxSE)/(PR+SE)
## Results
Information concerning age, body mass index (BMI) and CSAs of dural sac of the studied groups is presented in Tables 1 and 2. No significant differences were found in the mean-age of the control group compared to the stenosis group: 62.8 ± 12 (males), 62 ± 12 (females) vs. 66.2 ± 11 and 62 ± 8, respectively ($P \leq 0.05$). Individuals in the stenosis group manifested higher values of BMI compared to their counterparts in the control group: 28.9 ± 4 (males), 31.4 ± 5 (females) vs. 27.3 ± 4, 27.6 ± 5, respectively ($P \leq 0.05$).Table 1Data concerning age and body mass index (BMI) in the studied groups by genderVariablesControl ($$n = 180$$)Stenosis ($$n = 165$$)Males ($$n = 90$$)Females ($$n = 90$$)Males ($$n = 80$$)Females ($$n = 85$$)Age (%):40–60 years48 ($$n = 43$$)49 ($$n = 44$$)28 ($$n = 22$$)41 ($$n = 35$$)61–90 years52 ($$n = 47$$)50 ($$n = 45$$)72 ($$n = 58$$)59 [50] > 90 years01 ($$n = 1$$)00BMI (%):18.5—24.927 ($$n = 24$$)33 ($$n = 30$$)19 ($$n = 15$$)11 ($$n = 9$$)25- 29.950 ($$n = 45$$)35 ($$n = 31$$)41 ($$n = 33$$)33 ($$n = 28$$) ≥ 3023 ($$n = 21$$)32 ($$n = 28$$)40 ($$n = 32$$)56 [48]Table 2Percentage of subjects with cross section area (CSA) of dural sac below 70 mm2, between 70–99.9 mm2 and ≥ 100 mm2 in the studied groups by levelLevelsControl group ($$n = 180$$)Stenosis group ($$n = 165$$)Below 70 mm270–99.9 mm2 ≥ 100 mm2Below 70 mm270–99.9 mm2 ≥ 100 mm2L100100 ($$n = 180$$)7 ($$n = 11$$)9 ($$n = 15$$)84 ($$n = 139$$)L203 ($$n = 5$$)97 ($$n = 175$$)27 ($$n = 44$$)23 ($$n = 38$$)50 ($$n = 83$$)L33 ($$n = 5$$)9 ($$n = 16$$)88 ($$n = 159$$)61 ($$n = 101$$)26 ($$n = 43$$)13 ($$n = 21$$)L44 ($$n = 7$$)14 ($$n = 25$$)82 ($$n = 148$$)87 ($$n = 144$$)8 ($$n = 13$$)5 ($$n = 8$$)L53 ($$n = 5$$)14 ($$n = 25$$)83 ($$n = 150$$)32 ($$n = 53$$)27 ($$n = 44$$)41 ($$n = 68$$) The results showed that $50\%$ to $72\%$ of the participants in both groups are between the ages of 61and 90 years. We also found that almost half of the stenosis group ($48\%$) suffered from obesity (BMI ≥ 30) compared to $28\%$ in the control group. It is significant that an average of $74\%$ of the stenosis group at L3 and L4 levels manifested CSAs of the dural sac below 70 mm2 compared to $4\%$ in the control group.
Due to gender dimorphism as well as hormonal or lifestyle differences, we carried out the ML analysis for each gender separately. The DTs demonstrate the significant variables/features that predict the development of DLSS in both genders independently (Figs. 1 and 2). The results reveal 14 features for males as opposed to nine features in the female group (Figs. 1 and 2). The score averages of each variable that is involved in the DTs are presented in Table 3. The score is an indication of the importance of the variable in the model. The value of 0 means the variable is irrelevant, while value of 1 means that is mostly relevant. Table 3Scores of the features that were involved in the DTs for males and females independentlyGenderVariableScoreMaleAP L51L4 Vertebral height anterior0.496Mean density multifidus0.325Inter-laminar angle L50.197Spinous process inclination L30.202Mean pedicle height L40.117Mean facet orientation L2-30.125Spino-transverse area0.285L4 vertebral height middle0.113Spinous process inclination L40.150Mean facet orientation L4-50.292CSA of bony canal L50.533L2 vertebral width0.261L5 vertebral height anterior0.398FemaleAP L40.938L3 vertebral height middle0.095CSA of bony canal L50.674Mean lamina inclination L20.196L1 vertebral length0.232CSA of erector spinae0.242L3 Vertebral height anterior0.192Mean lamina inclination L10.223AP L10.096AP Anteroposterior, CSA Cross section area We found that the AP diameter of the bony canal (L5), density of Multifidus, anterior and/or middle vertebral height (L4, L5), inter-laminar angle (L5), spinous process inclination (L3, L4), pedicle height (L4), facet orientation (L2-3, L4-5), spino-transverse area and vertebral body width (L2) are the best predictors for DLSS development in males (Fig. 1). In females, those were the AP diameter of the bony canal (L4, L1), anterior and/or middle vertebral height (L3), bony CSA (L5), laminar inclination (L1, L2), vertebral body length (L1), and CSA of erector spinae muscle (Fig. 2).
It should be noted that the AP bony diameters of L5 and L4 (peak of the trees) in males and females are the variables most significantly associated with DLSS. As we descend from the tree root (peak) to its leaf (edge), the impact of these variables /features decreases; therefore, one can conclude that the AP bony canal diameter of L5 in males is more significant than the facet orientation at L2-3 for DLSS onset (Fig. 1). We also considered the path from the root to the leaf as rules connected by an "and" relationship. In females, for example, when the "AP diameter at L4 is ≤ 15.58 mm and the middle vertebral body height of L3 is < 28.30 mm and the lamina inclination of L3 < 31.03", we have 48 subjects with DLSS from the total of 49. On the other hand, we have also observed stenotic females whose AP bony canal value of L4 is greater than 15.58 mm. This will occur with the following conditions: (a) the AP diameter of the bony canal of L4 > 15.58 mm, (b) bony canal CSA of L5 > 338.5 mm2, (c) CSA of erector spinae muscles > 1718.5 mm2 and (d) the AP diameter of the bony canal of L1 > 18.15 mm. We observed seven individuals in this situation with DLSS ($$n = 7$$) (Fig. 2). Additionally,the DT indicates that the impact of L4 AP bony canal diameter in females for developing DLSS is extraordinary when its values are ≤ 15.85 mm (left branches) rather than value of > 15.85 mm. The reason is that we have a greater number of "pos"/DLSS of leaf nodes on the left branches: 53 compared to 29 subjects with DLSS. This situation is also true in relation to the DT of males (Fig. 1).
## Discussion
To the best of our knowledge, this is the first study that presents a predictive model for symptomatic DLSS using machine-learning algorithms. Furthermore, it is the first study utilizing big data combining comprehensive parameters of the spine as well as health and demographic information.
The potential role of ML in recent years in driving personalized medicine has been well accepted. The use of ML in spine care and for lumbar degenerative disease is still in its infancy. Huber and colleagues [17], for example, have recently reported that texture analysis with ML offers highly reproducible quantitative parameters that increase accuracy for detecting severe lumbar spinal stenosis. ML algorithms indicate that fewer comorbidities with certain sociodemographic factors increased the likelihood of achieving minimal clinically important differences, which assist surgeons in determining the relevance and timing of surgery [37]. Others have also used ML to detect the preoperative predictive factors that could promote recovery and personalized shared decision-making [38, 39].
The outcomes of this study show that lumbar spine characteristics, rather than the demographic (e.g., age and BMI) and health data, are far more important factors that lead to development of symptomatic DLSS. Our results indicate that the combination of these spine features is mandatory for the development of this phenomenon. For example, the presence of a sole variable such as decreased value of bony canal or vertebral height diameter is not sufficient for DLSS development. Compared with conventional logistic regression analysis, the ML has superior advantages for revealing the most important predictive factors for DLSS development. Therefore, we think that the ML technique of analyzing the effect of different parameters is far more comprehensive and conclusive than utilizing the traditional statistical analysis using the odds ratio. The results of DTs show that the AP diameter of the bony canal at L5 and L4 levels for males and females respectively, has the greatest impact (scores of 1 and 0.938) upon DLSS onset for both genders. Bony spinal canal dimensions (e.g., AP diameter) are determined by genetics and/or environmental factors [40]. It should be noted that the AP diameter of the bony canal (L4 and L5) combined with other spinal features lead to DLSS development regardless of their values. However, the influence of the AP diameter is greater when its values fall in the low range. This result is in agreement with our previous study, which reported that the AP diameter of the bony canal has a significant role for development of DLSS [8]. We believe that this result may suggest that (a) the current view of the AP bony canal in DLSS pathology should be modified and (b) this variable should be considered an essential trigger for DLSS development.
The fact that DT peaks such as the AP diameter of the bony canal for each gender are at different levels (L5 vs. L4) could be explained by the study of Hay and colleagues [2015], which reported that the shape and curve of the lumbar lordosis are different between males and females [41].
Our results also indicate that both anterior and posterior elements of lumbar vertebra as well as the paravertebral muscles are involved in DLSS development. The vertebral body length, width and height belong to the anterior part, whereas the pedicle height, canal dimensions (AP and CSA), and facets inclination share the posterior portion. The para vertebral muscles have a crucial role in maintaining the stability of the spine segment [42]. There is a consensus that DLSS pathophysiology is based mainly on the spinal destructive and re-constructive changes [43]. We believe that lumbar spine variations at any part of the vertebra irrespective of its location, shared with the surrounding muscle could alter the forces trajectories upon the spinal column. These forces may harm the spine segment instability leading eventually to three-joint complex degeneration and stenosis.
## Study limitations
As this is a retrospective study, no causal relationship is determined. This study did not address in detail the pathogenesis of symptomatic DLSS. In addition, the CT scans were performed in supine position ignoring the effect of posture/or dynamic elements on radiological features. Further research with larger number of participants and data regarding their clinical presentation could be essential to improve the outcomes of ML methods in this field.
## Conclusions
Our study showed, using the decision tree method of machine learning, that lumbar bony AP canal is the strongest feature associated with DLSS. We also indicate that the combination of this feature with other variables obtained in the RF (e.g., paraspinal muscles morphology, vertebral body size) rather than any single variable is required for the onset of symptomatic DLSS. We believe that intervention programs or strategies that could affect the characteristics of the lumbar spine, such as the paraspinal muscles morphology and the vertebral body height, should be considered for the middle-age population in order to minimize the possibility of late onset of DLSS.
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|
---
title: 'Joint effect of maternal pre-pregnancy body mass index and folic acid supplements
on gestational diabetes mellitus risk: a prospective cohort study'
authors:
- Minyu Li
- Lijiang Wang
- Zhanhui Du
- Qianqian Shen
- Lu Jiang
- Lun Sui
- Nan Zhang
- Hong Wang
- Guoju Li
journal: BMC Pregnancy and Childbirth
year: 2023
pmcid: PMC10035259
doi: 10.1186/s12884-023-05510-y
license: CC BY 4.0
---
# Joint effect of maternal pre-pregnancy body mass index and folic acid supplements on gestational diabetes mellitus risk: a prospective cohort study
## Abstract
### Background
The joint effect of folic acid (FA) supplements and maternal pre-pregnancy body mass index (BMI) on gestational diabetes mellitus (GDM) has not been fully addressed. This study aimed to examine the joint effect of FA supplements and pre-pregnancy BMI on GDM.
### Methods
Pregnant women at 4 to 14 weeks of gestation ($$n = 3186$$) were recruited during their first prenatal visit in Qingdao from May 1, 2019, to June 27, 2021. The main outcome was GDM at 24–28 weeks’ gestation. Screening was based on 75 g 2-hour oral glucose tolerance (OGTT), a fasting glucose ≥ 5.1 mmol/L, or a 1-hour result ≥ 10.0 mmol/L, or a 2-hour result ≥ 8.5 mmol/L. The interactive effect of FA supplements and pre-pregnancy BMI on GDM was examined using logistic regression analysis and ratio of odds ratios (ROR) was used to compare subgroup differences.
### Results
Overall, 2,095 pregnant women were included in the analysis, and GDM incidence was $17.76\%$. Compared with women with pre-pregnancy BMI lower than 25.0 kg/m2 and FA-Sufficient supplements ≥ 400 µg/day (FA-S) population, the adjusted odds ratios (aORs) of FA-S and FA-Deficiency supplements < 400 µg/d (FA-D) were 3.57 ($95\%$ confidence interval [CI]: 2.02–6.34) and 10.82 ($95\%$ CI: 1.69–69.45) for the obese women (BMI ≥ 30.0 kg/m2), and the aORs of FA-S and FA-D were 2.17 ($95\%$ CI: 1.60–2.95) and 3.27 ($95\%$ CI: 1.55–6.92) for overweight women (25.0 kg/m2 ≤ BMI < 30.0 kg/m2). However, the risk of GDM did not differ significantly between the FA-D and the FA-S group in pre-pregnancy obese women (ROR = 2.70, $95\%$CI: 0.47–2.30), or overweight women (ROR = 0.66, $95\%$CI: 0.30–1.49). After further stratification of FA supplementation time, F-D and FA-S in obese women showed an interaction when FA supplement intake time < 3 months. However, there was no significant difference between subgroups (ROR = 1.63, $95\%$ CI: 0.37–7.04).
### Conclusion
Maternal pre-pregnancy BMI was associated with the incidence of GDM, the dose of FA supplementation from pre-pregnancy to early pregnancy was not found to be related to the incidence of GDM. The dosage of FA supplement was not associated with GDM irrespective of maternal pre-pregnancy BMI.
## Introduction
Gestational diabetes mellitus (GDM) is one of the most common pregnancy complications worldwide [1–3]. Hyperglycemia during pregnancy is linked to adverse pregnancy outcomes, such as neonatal adiposity, macrosomia, large for gestational age, caesarean section and shoulder dystocia [4–6]. It also has long-term negative effects on women and their offspring [7–10]. Despite extensive public health efforts, the prevalence of GDM remains high around the world and continues to rise at an alarming rate, imposing an immense burden on global healthcare services [11–13]. This increase has been especially noticeable in China, and previous research showed the incidence of GDM had reached $17.42\%$ in 2018–2019[14]. In view of the impact of this condition, there is an urgent need to identify modifiable risk factors.
Folic acid (FA) supplements are recommended before and during pregnancy around the world. Periconceptional consumption of FA, or multivitamins that contain FA, reduces the risk of neural tube defects (NTDs)[15, 16]. Since the beneficial effect of FA was well established, the link between daily FA supplements and GDM remains controversial. Recent reports suggest that the incidence of GDM may be increased in women who take FA supplements during pregnancy [17, 18]. However, a cohort study showed that a higher intake of habitual FA from supplements before pregnancy was significantly associated with a lower risk of GDM [19]. Experimental studies have shown that high-dose FA supplements throughout pregnancy may lower blood homocysteine levels and therefore protect against oxidative stress [20], which is known to contribute to endothelial dysfunction and insulin resistance [21]. Homocysteine concentrations have also been strongly linked to risk of GDM among pregnant women [22].
Previous studies have identified pre-pregnancy body mass index (BMI) as an important risk factor for GDM [23]. The effect of FA supplements or dietary folate intake on GDM may vary with maternal pre-pregnancy BMI. Studies have reported that there is an inverse interaction between pre-pregnancy BMI and serum folate levels. Obese individuals may be at risk of folate deficiency even after controlling for dietary and supplementary intake of FA [24–27]. Several potential mechanisms have been suggested for relative folate deficiency in obese women, such as chronic inflammation and hyperinsulinemia [28].
However, the joint effect of pre-pregnancy BMI and FA supplements on GDM is unclear. To address this gap in knowledge, this cohort study aimed to examine the interaction between FA supplements and pre-pregnancy BMI on the risk of GDM. It also, considered the time of the supplementation, to provide favorable evidence for effective antenatal nutritional interventions.
## Data sources and cohort
This was a prospective cohort study of pregnant women at 4 to 14 weeks of gestation from May 1, 2019, to June 30, 2021. We recruited a total of 3,186 pregnant women. Details of the study have been described previously [29]. All the pregnant women in the study were from the Qingdao Women and Children’s Hospital Health Cohort, which is a prospective cohort designed to determine the impact of maternal dietary, environmental and lifestyle exposures on the health of pregnant women and their offspring. At registration, questionnaire-based interviews were used to gather information on social demographic status, reproductive variables, family history of diseases, the use of supplementation, lifestyle factors, and illnesses. Women responded to a semiquantitative food frequency questionnaire (FFQ) [30], where they reported the frequencies at which they had consumed a specific portion of each of the 25 food or food group items during the past six months. They also reported their use of dietary supplements, including the brand, dose, and frequency of use. The daily food consumption and nutrient intakes in the FFQ were calculated according to the China Food Composition Tables (6th edition) [31]. Throughout the follow-up visits during mid-pregnancy and late pregnancy, information on lifestyle, dietary intake, and the use of supplements was acquired. Participation in the study was entirely voluntary, and each study participant provided written informed consent.
## Assessment of FA supplements and pre-pregnancy BMI
Participants were asked for information about FA supplements at enrollment. This included brand, daily doses, and the time of supplements. In this study, taking sufficient FA supplements (FA-S) was defined as taking either an FA specific supplement or FA-containing supplements of at least 400 µg/day, deficiency of FA supplements was defined as taking either FA specific supplement or FA-containing supplements less than 400 µg/d (FA-D) [32]. Pregnant women were divided into underweight/normal (pre-pregnancy BMI < 25.0 kg/m2), overweight (25.0 kg/m2 ≤ pre-pregnancy BMI < 30.0 kg/m2) and obese (pre-pregnancy BMI ≥ 30.0 kg/m2) [33].
## Diagnosis of GDM
In line with the Ministry of Health of China’s Diagnostic Criteria for Gestational Diabetes Mellitus (WS311-2011), all participants were screened for GDM using a 75 g oral glucose tolerance test (OGTT) at 24 to 28 weeks’ gestation [34]. The criterion cut-off values were consistent with the International Association of Diabetes and Pregnancy Study Groups Consensus Panel recommendations [35]. A diagnosis of GDM can be made if any of the following values in the 75 g OGTT were met or exceeded: 0-hour (fasting plasma glucose) ≥ 5.1mmol/L, 1-hour ≥ 10.0 mmol/L, or 2-hour ≥ 8.5 mmol/L.
## Assessment of covariates
The questionnaire information included, social demographic characteristics, living environment, personal and family history of diseases, dietary content, and anthropometric information. When participants were enrolled, we measured weight, height, waist circumference, hip circumference, and blood pressure. The pre-pregnancy BMI was calculated by dividing self-reported weight before pregnancy in kilograms by the square of height in meters measured at enrollment. Smoking activities were divided into active and passive smoking (second-hand smoke exposure). Passive smoking was divided into pre-pregnancy contact, pregnancy contact, and pre-pregnancy to pregnancy exposure. Drinking was defined those consuming alcohol more than three times per week. We also identified whether participants took vitamin B12 supplements.
## Statistical analysis
Numerical variables were expressed as mean ± standard deviation (SD). Frequency of category variable expressed in percentage [n (%)]. Maternal characteristics were compared by FA supplements use status using ANOVA for continuous variables and Chi-square test for categorical data. Logistic regression analysis was performed and odds ratios (OR, with $95\%$ confidence intervals [CI]) were calculated to evaluate the risk associated with GDM. We used a binomial logistic regression model to estimate odds ratios (ORs) and $95\%$ CIs of incidence of GDM in by category of folate intake (sufficient and deficiency [≥ 400 ug/day and < 400 ug/day] for total and supplemental folate intake, and across quartiles of food folate intake). Linear trends of GDM risk across categories of folate intake were examined by fitting the models using the median intake of each category of folate intake as a continuous variable.
We examined interaction effects on the multiplicative scale. For multiplicative interaction, we calculated two-sided P- values to assess the significance of each product term in the logistic regression models and compared the ORs for pre-pregnancy BMI across FA supplement doses. To clarify the relationship further, we carried out a stratified analysis by the intake time for FA supplements to determine the joint effect of pre-pregnancy BMI and FA supplements level on GDM in different groups. We hypothesized that the effect estimates would be greater for the association of obesity with FA-D than FA-S, and tested this by computing the ratio of odds ratios (ROR). A P-value of 0.05 or less was considered significant, and an ROR > 1.00 and the $95\%$ CI does not contain 1.00 signified a statistically significant difference between two ORs [36]. All the data were analyzed using SAS 9.4 software.
## Results
In the cohort of Qingdao Women and Children’s Hospital, we recruited 3,186 pregnant women, 1,091 were excluded according to the exclusion criteria, and finally 2,095 pregnant women were included in the data analysis. The exclusion criteria were: [1] multiple pregnancy ($$n = 52$$); [2] termination or abortion ($$n = 126$$), loss to follow-up before 24–28 gestational weeks ($$n = 229$$), or no 75 g oral glucose tolerance test (OGTT) information ($$n = 409$$); [3] incomplete or missing information on height and weight before pregnancy ($$n = 14$$); [4] incomplete or missing information about FA supplements, with unclear doses and unclear duration($$n = 215$$); and [5] History of diabetes ($$n = 26$$) and with diabetes mellitus before pregnancy or within 20 weeks of gestation($$n = 30$$) (Fig. 1). The incidence of GDM among the 2,095 women with singleton births was $17.76\%$ ($$n = 372$$). Overall, 186 ($8.88\%$) of the participants had either not taken any FA supplements or their daily supplement consumption was less than 400 µg before pregnancy and in the first trimester. The proportion of women consuming less than 400 µg/day of FA supplements was higher among those with pre-pregnancy BMI ≥ 30.0 kg/m², but the difference was not significant ($P \leq 0.05$). Intake of vitamin B12 supplements was higher among pregnant women with FA ≥ 400 µg/day ($P \leq 0.05$) (Table 1).
Fig. 1Flow chart of the screening process for the selection of eligible participants Table 1Demographic characteristics of the FA supplement use status ($$n = 2095$$)FA-D($$n = 186$$,$8.88\%$)FA-S ($$n = 1909$$,$91.12\%$) p-value Age (years)0.374< 35160(86.02)1626(85.18)≥ 3526(13.98)283(14.82)Pre-pregnancy BMI (kg/m2)0.405< 25.0147(79.03)1573(82.40)25.0 ≤ BMI < 30.034(18.28)279(14.61)≥ 30.05(2.69)57(2.99)Education(years)0.748Under the high school26(13.98)288(15.09)High school and above160(86.02)1621(84.91)Monthly income (¥)0.320< 500046(24.73)554(29.02)≥ 5000140(75.27)1355(70.98)Parity0.0600123(66.13)1387(72.66)≥ 163(33.87)522(27.34)SmokingActive smoking0.606No175(94.09)1803(94.45)Yes11(5.91)106(5.55)Passive smoking0.850No159(85.48)1648(86.33)pre-pregnancy12(6.45)108(5.66)after pregnancy0(0.00)7(0.37)pre-pregnancy to pregnancy15(8.06)146(7.65)Drinking0.653No182(97.85)1849(96.86)Yes4(2.15)60(3.14)Family history of diabetes mellitus0.310No132(70.97)1433(75.07)Yes53(28.49)454(23.78)Unclear1(0.54)22(1.15)Family history of hypertension0.531No106(56.99)1007(52.75)Yes75(40.32)853(44.68)Unclear5(2.69)49(2.57)Fertilization way0.618Natural conception169(90.86)1711(89.63)Non-natural conception17(9.14)198(10.37)History of GDM0.677No181(97.31)1841(96.44)Yes5(2.69)68(3.56)Gestational weight gain (kg)1.18 ± 2.701.21 ± 2.780.811Vitamin B12 supplements < 0.001 No163(87.63)887(46.46)Yes23(12.37)1022(53.54)Data are presented as n (%), mean ± SD, P-values are determined by chi-square, independent t-test This study evaluated folate intake from supplements and food, both together (i.e., total folate) and separately, as the exposures of interest (Table 2). After adjustment for age, pre-pregnancy BMI, education level, monthly income, passive smoking, drinking, family history of diabetes mellitus, mode of fertilization, history of GDM, and the use of vitamin B12 supplements, the ORs of GDM across increasing quartiles of food FA intake were 1.00 (reference), 1.02 ($95\%$ CI: 0.72–1.44), 1.08 (0.77–1.52), and 0.86 (0.60–1.23), respectively (Ptrend = 0.980). The ORs of GDM across increasing quartiles of total FA intake were 1.00 (reference), 0.78 ($95\%$ CI: 0.56–1.10), 0.82 (0.58–1.17), and 0.84 (0.57–1.24), respectively (Ptrend = 0.919). Sufficient total folate intake (≥ 400 ug/day) was a OR of GDM of 1.25 ($95\%$ CI: 0.64–2.43) ($$P \leq 0.518$$) compared with a deficient intake (< 400 ug/day). Food folate intake was not associated with GDM risk.
Table 2Odds ratio ($95\%$ confidence interval) of GDM according to folate intakeGDM/pregnancyCrude ORAdjusted ORFood folate(ug/day)Q1(28–130)$\frac{116}{5241.00}$(ref.)1.00(ref.)Q2(131–206)$\frac{84}{5251.03}$(0.74–1.45)1.02(0.72–1.44)Q3(207–308)$\frac{88}{5231.16}$(0.83–1.62)1.08(0.77–1.52)Q4(308–1572)$\frac{84}{5230.94}$(0.66–1.33)0.86(0.60–1.23) P trend 0.8460.980Supplemental folate(ug/day)< $\frac{40035}{1861.00}$(ref.)1.00(ref.)≥ $\frac{400337}{19090.93}$(0.63–1.36)0.86(0.58–1.30) P 0.6920.483Total folate(ug/day)Q1(38–602)$\frac{102}{5241.00}$(ref.)1.00(ref.)Q2(603–833)$\frac{80}{5270.80}$(0.58–1.12)0.78(0.56–1.10)Q3(834–1001)$\frac{95}{5220.92}$(0.68–1.26)0.82(0.58–1.17)Q4(1001–2296)$\frac{95}{5221.01}$(0.73–1.39)0.84(0.57–1.24) P trend 0.7620.919Total folate(ug/day)< $\frac{40013}{781.00}$(ref.)1.00(ref.)≥ $\frac{400359}{20170.84}$(0.44–1.60)1.25(0.64–2.43) P 0.5950.518Q, quartile. Adjusted OR: adjusted for age, pre-pregnancy BMI, education level, monthly income, passive smoking, drinking, family history of diabetes mellitus, fertilization way, history of GDM, the use of vitamin B12 supplement Table 3 shows the effects of pre-pregnancy BMI and daily FA supplement on GDM. Compared with pre-pregnancy BMI < 25.0 kg/m2, pregnant women who were overweight (OR = 2.38, $95\%$ CI: 1.81–3.14)) and obese (OR = 3.61, $95\%$ CI: 2.13–6.12) had increased risk of GDM ($P \leq 0.05$). Table 3 also shows the aORs, and similar results were observed in the association between pre-pregnancy BMI and GDM in the adjusted model ($P \leq 0.05$). Compared with FA intake < 400 µg /day, FA intake ≥ 400 µg /day had no a significant association with GDM, regardless of adjustment ($P \leq 0.05$).
Table 3Odds ratio ($95\%$ confidence interval) of GDM according to pre-pregnancy BMI and FA supplement use doses as categoricalN(%)Crude OR P-value Adjusted OR P-value BMI (kg/m2)< 25.01720(82.10)Ref-Ref-25.0≤ BMI < 30.0313(14.94)2.38(1.81–3.14) < 0.001 2.28(1.71–3.03) < 0.001 ≥ 30.062(2.96)3.61(2.13–6.12) < 0.001 3.91(2.27–6.74) < 0.001 FA intake< 400 µg /day186 (8.89)1.12(0.77–1.65)0.5501.49(0.53–4.23)0.453≥ 400 µg /day1909(91.11)Ref-Ref-Adjusted OR: adjusted for age, education level, monthly income, passive smoking, drinking, family history of diabetes mellitus, fertilization way, history of GDM, the use of Vitamin B12 supplement. Variables with statistical significance were shown in boldface To further determine the joint effect of FA supplements and pre-pregnancy BMI on GDM risk (Table 4), we divided pregnant women into six groups by both pre-pregnancy BMI and FA supplement levels [Group 1: FA-S and BMI < 25.0 kg/m2; Group 2: FA-D and BMI < 25.0 kg/m2; Group 3: FA-S and BMI (25.0 kg/m2 -30.0 kg/m2); Group 4: FA-D and BMI (25.0 kg/m2 -30.0 kg/m2); Group 5: FA-S and BMI ≥ 30.0 kg/m2; Group 6: FA-D and BMI ≥ 30.0 kg/m2]. Compared with Group 1, the aOR of Group3, 4, 5 and 6 were 2.17 ($95\%$ CI: 1.60–2.95), 3.27 ($95\%$ CI: 1.55–6.92), 3.57 ($95\%$ CI: 2.02–6.34) and 10.82 ($95\%$ CI: 1.69–69.45) (all $P \leq 0.05$). The ROR value was 2.70 ($95\%$ CI: 0.47–2.30) for the two subgroups with different FA doses in the population with BMI ≥ 30.0 kg/m2 and 0.66 ($95\%$CI: 0.30–1.49) for those with BMI of 25.0 kg/m2 -30.0 kg/m2. The RORs and the corresponding lower boundaries of the confidence intervals were both greater than 1, and there is therefore no good evidence to support a different risk effect with different levels of FA supplementation.
Table 4Interaction analysis of pre-pregnancy BMI and FA supplement intake dose on the risk of GDMInteractionCrude ORP-valueAdjusted ORP-valueFA-S*BMI (< 25.0 kg/m2)RfRfFA-D*BMI (< 25.0 kg/m2)0.95(0.59–1.54)0.8310.99(0.60–1.66)0.997FA-S*BMI(25.0 kg/m2≤ BMI < 30.0 kg/m2) 2.29(1.71–3.07) < 0.001 2.17(1.60–2.95) < 0.001 FA-D*BMI(25.0 kg/m2 ≤ BMI < 30.0 kg/m2) 3.11(1.52–6.36) 0.002 3.27(1.55–6.92) 0.002 FA-S*BMI (≥ 30.0 kg/m2) 3.32(1.91–5.79) < 0.001 3.57(2.02–6.34) < 0.001 FA-D*BMI (≥ 30.0 kg/m2) 8.54(1.42–51.38) 0.019 10.82(1.69–69.45) 0.012 Adjusted OR: adjusted for age, education level, monthly income, passive smoking, drinking, family history of diabetes mellitus, fertilization way, history of GDM, the use of vitamin B12 supplement. Variables with statistical significance were shown in boldface To clarify the effect of FA supplements and pre-pregnancy BMI on GDM, we carried out stratified analyses by the time of FA intake (Figs. 2 and 3). We calculated the ROR = 3.35, $95\%$ CI: 0.68–16.49 between the FA-D (OR = 17.20, $95\%$ CI: 1.41–21.65) and FA-S (OR = 5.14, $95\%$ CI: 2.25–11.72) subgroups in obese (BMI > 30.0 kg/m2) women taking FA supplements < 3 months. There was no statistical difference between FA and the risk of GDM after stratification by FA intake time.
Fig. 2Interaction analysis of pre-pregnancy BMI and FA supplement intake dose on the risk of GDM (FA supplement intake time ≥ 3 months) Fig. 3Interaction analysis of pre-pregnancy BMI and FA supplement intake dose on the risk of GDM (FA supplement intake time < 3 months)
## Discussion
Maternal pre-pregnancy BMI was associated with the incidence of GDM, but the dose of FA supplementation from pre-pregnancy to early pregnancy was not related to the incidence of GDM, irrespective of maternal pre-pregnancy BMI. After subgrouping, the interaction was not statistically significant, but this may have been due to the limited number of FA-D individuals ($\frac{186}{2}$,095, $8.88\%$) in our cohort. This is probably mostly due to recent work by the Chinese government [37], including the provision of public health services and the distribution of free FA supplements, which have boosted the percentage of pregnant women who use FA supplements throughout pregnancy.
FA is an important pregnancy nutrient for its protective effects against birth defects.
The Chinese Government places a high priority on the prevention of congenital anomalies by promoting FA supplementation. Plenty of countries have proposed that flour be fortified with folate for the prevention of NTDs [38, 39]. However, China currently has no policies on mandatory folate fortification [37], and the prevention of birth defects in *China is* mainly achieved through the promotion of FA supplements for women of the right age, and the distribution of free folic acid supplements [40, 41].
An article published in the journal Diabetes Care shows that average total folate intake (i.e., ≥ 400 ug/day) was significantly associated with lower risk of GDM This association was entirely driven by folate from supplements, and food folate was not associated with GDM risk [19]. This consistent with our finding that folate intake from food was much lower than from supplements and thus may be insufficient to achieve an effect against GDM. Folate, in supplements is also more bioavailable than food folate [42]. Other studies have also reported that supplemental folate has stronger associations with relevant health outcomes than food folate [43, 44]. We therefore only considered the dosage of FA supplements in further analyses.
Previous studies evaluating the association of FA supplementation before or during pregnancy with GDM risk have conflicting results [18]. A large prospective cohort ($$n = 20$$,199) showed that habitual intake of FA supplements preconception was inversely associated with GDM risk in the United States [19]. However, a prospective Chinese study of 326 pregnant women showed that high-dose FA supplementation in early pregnancy was associated with an increased risk of GDM [45]. This discrepancy might be due to a smaller sample size in the latter study. Consistent with this, a prospective cohort study showed that daily FA supplementation in the first trimester was positively associated with GDM risk [17]. However, it is difficult to interpret this finding because details of the research methods and results were not reported. Cueto and colleagues [46] found no clear association between preconception FA use and diabetes diagnosis, and our result is consistent with this. The relationship between maternal FA intake and GDM therefore needs further examination through larger cohort studies.
Obesity affects short-term folate pharmacokinetics through diminished uptake of orally administered FA. The low serum folate status associated with obesity may be due to a volumetric dilution of the blood in obese individuals and/or low folate intake in the obese population [47]. Another explanation may be that adiposity influences folate uptake by the intestinal epithelium [48, 28]. This suggests that FA may not be distributed freely in adipose tissue. An alternative explanation is that the reduction of the ratio of surface area to volume of mast adipocytes may limit the penetration rate [49].
Obesity also affects the metabolism of serum folate. A retrospective case-control study found that higher BMI in the first trimester was negatively correlated with serum folate levels in the third trimester [50]. Another possible explanation is that obesity can increase estrogen, which has been reported to be associated with decreased serum folate availability [51]. It is therefore plausible that pathways related to metabolic regulation may underpin the associations between BMI and serum folate.
Previous studies have suggested that different BMI levels may influence the effect of FA supplementation on disease. A case–control study found that the association between FA supplements and the NTDs risk was weaker in overweight/obese mothers than in underweight/normal weight mothers. This suggested that maternal BMI could affect the association between FA supplementation and the NTDs risk in offspring [52]. A retrospective cohort study [53] reported that the protective effect of FA supplements against preterm delivery (PTD) was reduced in women whose BMI was equal to or greater than 24.0 kg/m2. However, few articles have examined the relationship between FA and BMI on GDM. This study was therefore important in analyzing the interaction between FA and BMI and the relationship between FA, BMI and GDM.
One of the most interesting observations of this study is that the risk of GDM was increased in obese women regardless of adequate folate intake. However, the subgroup analysis found that there was no heterogeneity between the two groups, which meant that different intake doses of FA supplements would not affect the incidence of GDM. Prospective cohort studies in China have assessed the impact of FA supplement use on GDM with consideration of both doses and durations. One showed a U-shape relationship between duration of FA supplements and risk of GDM [45], and another suggested that long-term use of high-dose FA increased GDM risk [19]. We therefore also compared the interaction between the FA supplements and pre-pregnancy BMI, dividing the groups into FA taken for at least 3 months and less than 3 months. Risk of GDM in obese women with both deficient and sufficient FA intake was still higher than women with BMI < 25.0 kg/m2 and FA-S. There was no statistical association between FA supplement and the risk of GDM after stratification by FA intake time.
The biological mechanisms that underlie the modified association are complicated and remain unclear. However, our hypothesis could be partly supported by the theory that FA could inhibit homocysteine production [54, 55]. A previous study found that homocysteine concentrations declined as FA concentrations increased, as did the prevalence of hyperhomocysteinemia [56]. High concentrations of homocysteine are associated with insulin resistance [57, 58]. These findings suggest that FA might have a protective effect on GDM by reducing homocysteine concentration and improving insulin resistance. However, a higher BMI might decrease the levels of serum folate or dietary folate intake [59–61]. The combine effect of high pre-pregnancy BMI and low dose FA intake leads to greater homocysteine concentrations and reduced insulin resistance, resulting in GDM. Therefore, we suggest that plans for FA supplementation should vary with women’s BMI category.
Epigenetics is defined as alterations in the gene expression profile of a cell that are not caused by changes in the deoxyribonucleic acid (DNA) sequence [62]. Folate may affect the incidence of GDM by influencing epigenetics. Epigenetics is critical to normal genome regulation and development. One-carbon metabolism is required for epigenetic modifications because it provides methyl groups for the methylation of DNA, associated proteins, which requires an adequate supply of folate [63]. Periconceptional FA supplementation has been linked to epigenetic changes [64]. These epigenetic modifications, particularly DNA methylation, have been proposed as plausible mechanisms underlying associations between folate and various disease outcomes, including NTDs, cardiovascular disease, and cancer [65, 66].However, so far, there is no direct evidence that high dietary folate or folate intake will lead to abnormal DNA methylation, or to diabetes in pregnancy. This is because DNA methylation is part of a complex, highly regulated system. Further research is needed to clarify the relationship between folic acid, DNA methylation and GDM.
Our study has several advantages. Firstly, it was a prospective cohort study, which reduces the effects of selection or recall bias. We excluded women with hypertension or established diabetes to avoid information bias. Secondly, previous studies also shown that vitamin B12 in multivitamin supplements has an impact on the risk of GDM [67, 68]. We collected sufficient data to include various confounders in the adjusted analyses and matched for vitamin B12 as a confounder. This allowed us to assess the effects of the interaction of FA supplements alone with pre-pregnancy BMI on GDM. However, the study also had several limitations. First, FA exposure was determined by self-reported FA supplement use rather than plasma folate levels. Misclassification is therefore a possibility. However, significant efforts were made to ensure that reliable FA supplement use data were collected on time by trained medical personnel with meticulous follow-up. Self-reported FA intake from supplements has also previously been found to be correlated with plasma folate and is therefore regarded as a reliable indicator of folate exposure [56]. Second, we mainly analyzed daily intake of FA by pregnant women from pre-pregnancy to first trimester. The FA intake during the whole pregnancy was not analyzed, but our study is consistent with the recommended folic acid intake time in the Nationwide Folic Acid Supplementation Program of China [3]. Third, the relatively small sample size in our study also limited our ability to investigate the relationship between FA supplements and pre-pregnancy BMI at different levels.
In future, our research group will consider collecting biochemical data and analyzing the effects of serum folate and erythrocyte folate on GDM to further enrich the literature on the relationship between FA and GDM. Our findings should provide new perspectives to support the development of prevention strategies, and further studies should consider larger sample sizes, total time from pre-conception to post-conception, and sophisticated statistical methods to examine the relationship between FA supplements, pre-pregnancy BMI, and pregnancy disorders.
## Conclusion
Maternal pre-pregnancy BMI was associated with incidence of GDM. However, the dose of FA supplements from pre-pregnancy to early pregnancy was not related to GDM, irrespective of maternal pre-pregnancy BMI.
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|
---
title: The healing effects of thymoquinone on experimentally induced traumatic tendinopathy
in rabbits
authors:
- Alireza Soltanfar
- Abdolhamid Meimandi Parizi
- Mohammad Foad-Noorbakhsh
- Mansour Sayyari
- Aida Iraji
journal: Journal of Orthopaedic Surgery and Research
year: 2023
pmcid: PMC10035262
doi: 10.1186/s13018-023-03706-8
license: CC BY 4.0
---
# The healing effects of thymoquinone on experimentally induced traumatic tendinopathy in rabbits
## Abstract
### Objectives
Thymoquinone is a major bioactive compound present in the black seeds of the Nigella sativa. Tendon injuries are almost $50\%$ of all musculoskeletal injuries. The recovery of tendon after surgery has become a significant challenge in orthopedics.
### Design
The purpose of this study was to investigate the healing effect of thymoquinone injections in 40 New Zealand rabbits tendon traumatic models.
### Materials and methods
Tendinopathy was induced by trauma using surgical forceps on the Achilles tendon. Animals were randomly divided into 4 groups: [1] normal saline injection (control), [2] DMSO injection, [3] thymoquinone $5\%$ w/w injection, and [4] thymoquinone $10\%$ w/w injection. Forty-two days after surgery, biochemical and histopathological evaluations were done, and biomechanical evaluation was conducted 70 days after surgery.
### Results
Breakpoint and yield points in treatment groups were significantly higher compared to control and DMSO groups. Hydroxyproline content in the $10\%$ thymoquinone receiving group was higher than all groups. Edema and hemorrhage in the histopathological evaluation were significantly lower in the thymoquinone $10\%$ and thymoquinone $5\%$ receiving groups compared to control and DMSO groups. Collagen fibers, collagen fibers with fibrocytes, and collagen fibers with fibroblasts were significantly higher in the thymoquinone $10\%$ and thymoquinone $5\%$ receiving groups compared to control groups.
### Conclusions
Thymoquinone injection in the tendon in the concentration of $10\%$ w/w is a simple and low-cost healing agent that could enhance mechanical and collagen synthesis in traumatic tendinopathy models in rabbit.
## Introduction
Tendon problems usually present chronically with symptoms of pain, decreased biochemical activity, and decreased exercise tolerance [1]. Unfortunately, this disease is common in horses and reduces their athletic performance, and even causes the termination of the animals’ careers. The superficial digital flexor (SDF) tendon injury is more frequent in horses compared to the other tendons [2]. $82\%$ of injuries and diseases of horses are related to musculoskeletal problems, of which $46\%$ of them are diseases and injuries to tendons and ligaments [3].
Overuse of tendons and mechanical stress can cause tendinopathy which has been estimated to account for 30–$50\%$ of all sports-related injuries. Achilles tendinopathy presented as the second highest injury incidence at 9.1–$10.9\%$. The major factor that induces tendinopathy is the loading of a tendon with physical activities without adequate time for tendon recovery (4–8). Pathoetiology of tendinopathies is multifactorial, but it is due to the degeneration of type I collagen as the result of the up-regulation of matrix metalloproteinases (MMPs) [9]. This change in structure causes the production of inflammatory cytokines that slow down the healing process [10]. Also, the healing process in the horse tendon is slow due to the lack of vascularity in the area [11]. Following tendon injury, the expression of the MMP-1 gene, which is related to collagenase production, increases, and the strength, flexibility, and mechanical properties of the tendon decrease [2]. These changes eventually cause varying degrees of pain and lameness in the animal [12]. After tendon repair, there is a possibility of re-injury to the same tendon, causing tendinopathy [13].
Based on several articles, inflammation is the main key pathogenesis of tendon injury in both humans and equines. In case of tendon injury, the animal is usually given rest for 6 months to a year, as any exercise will cause more tendon injury [2]. Available treatments for tendinopathy are pharmacological treatments such as non-steroidal anti-inflammatory drugs (NSAIDs) or in severe cases, surgery. But usually, these types of treatments rarely help the animal recover and return to normal sports activity [14].
Although regenerative medicine has received a great deal of attention today, and many articles have been published in the field of cell therapies by mesenchymal stem cells (MSCs), platelet-rich plasma (PRP), and autologous proteins or cells [15], there is still a need to study more compounds, especially compounds with anti-inflammatory and collagen production activity.
There are a variety of plant compounds that have anti-inflammatory, analgesic, and stimulating collagen production effects. Nigella sativa (NS) is a medicinal plant, and thymoquinone, (TQ), as its main active chemical component is reported to have analgesic, diuretic, antihypertensive, antidiabetic, anticancer, immunomodulatory, anti-inflammatory, and antioxidant properties [16]. Thymoquinone has hepatoprotective, antihypertensive, diuretics, digestive, anticancer, appetite stimulant, antidiarrheal, anti-inflammatory, nephroprotective, neuroprotective, analgesics, and antibacterial activities which can stimulate collagen production in injured tissue [16].
So far, the healing effects of thymoquinone on various musculoskeletal diseases have not been investigated, but limited research has been done on tendinopathy. Due to the anti-inflammatory properties and collagen production of thymoquinone, in this article, we have investigated the healing effects of thymoquinone on tendinopathy caused in the Achilles tendon of rabbits. Histopathology, biomechanical and biochemical evaluation were used to evaluate its healing effect on tendinopathy.
## Materials
Thymoquinone was purchased from Sigma-Aldrich (CAS number: 490-91-5); hydroxy-proline kit was purchased from Kiazist (Kiazist Co., Hamedan, Iran). Formaldehyde solution was purchased from Merck; cryotube was purchased from Bio Plas Inc., xylazine and ketamine were purchased from Nasr (Iran), and rabbits were purchased from the animal center of Shiraz University of Medical Science.
## Preparation of thymoquinone solution
Two different concentrations of thymoquinone were prepared to identify the best concentration for tendinopathy healing. For this purpose, thymoquinone was diluted in DMSO + distilled water to obtain $5\%$ and $10\%$ w/w solution.
## Study design and animal model
A total of 40 New Zealand rabbits with an average body weight of 2.25 kg were used to induce tendinopathy in the Achilles tendon with the approval of the Animal Ethical and Welfare Committee of Shiraz Veterinary Medicine University (grant number: 10171). Ethical approval was confirmed by the Animal Care Committee of Shiraz University (IR. REC ethical code: 10171). The authors followed up all institutional and international guidelines for animal care and use during this study. The Animal Research Reporting in Vivo Experiments guidelines (ARRIVE) were also followed up. Each rabbit was kept in an individual laboratory rabbit cage. Before the beginning of the experiment, rabbits were housed for two weeks to adapt to the environment. The animals were maintained under controlled conditions of 25 °C ± 1 and 12-h light–dark cycles and had free access to a standard chow diet and water throughout the study. All rabbits were anesthetized with ketamine-xylazine (ketamine 30 mg/kg and xylazine 6 mg/kg, IM).
The left hind limb of each animal was used for tendinopathy induction. First, the area of Achilles' tendons was identified, and then, a longitudinal skin incision was made over the tendon. The paratenon was identified and incised longitudinally as a separate layer to expose the Achilles tendon. Hemostatic forceps were placed on the tendon for 60 s. After forceps removal, the treatment protocol was done as mentioned below (Fig. 1):Fig. 1Surgical preparation and induced tendinopathy surgery in the rabbit Group 1: Tendinopathy was induced and intratendinous injection was followed by normal saline (0.2 ml). Also, 0.2 ml normal saline was injected 3 days after surgery ($$n = 10$$).
Group 2: Tendinopathy was induced and intratendinous injection was followed by DMSO (0.2 ml). Also, 0.2 ml DMSO was injected 3 days after surgery ($$n = 10$$).
Group 3 (TQ 5): Tendinopathy was induced and intratendinous injection was followed by $5\%$ w/w thymoquinone solution. Also, 0.2 ml $5\%$ w/w thymoquinone was injected 3 days after surgery ($$n = 10$$).
Group 4 (TQ 10): Tendinopathy induced and intratendinous injection was followed by $10\%$ w/w thymoquinone solution. Also, 0.2 ml $10\%$ w/w thymoquinone solution was injected 3 days after surgery ($$n = 10$$).
In each group for the second time under general anesthesia, the incision site was opened again and 0.2 ml substance was injected into the tendon.
After surgery and full recovery, animals were returned to individual cages. Enrofloxacin (5 mg/kg, IM) was administrated to rabbits after surgery and continued for three days. All animals were clinically evaluated every week until the end of the sixth week, and lameness, swelling, and tenderness were assessed. Animals that lost weight a few days after surgery were excluded from the study and replaced with new animals.
## Biomechanical evaluation
The tensile test was performed in the biomechanics Department of the Faculty of Agriculture of Shiraz University. At the end of the experiment, five rabbits in each group were euthanized and the tendon was obtained. Biomechanical testing was done according to the methods of Oryan et al. [ 2012]. After preparing the samples, they were wrapped with foil and stored at − 80 °C. The specimens were defrosted before the biomechanical examination. The samples were placed between two jaws of a tensile testing machine (Santam STM20) and stretched at a speed of 10 mm/min. Force–displacement graphs were drawn by the computer, and other information was obtained. The yield point and break point were read from the force–displacement curve to calculate yield stress and break stress. Also, the large and small diameter of the tendon at the lesion site was measured with a caliper (CALIPER) with an accuracy of 0.01 and calculated through the formula.
## Measurement of hydroxyproline
The amount of hydroxyproline in the tendon was measured using a hydroxyproline measuring kit (Kiazist Co., Hamedan, Iran). The protocol was performed according to the manufacturer’s catalog.
The samples taken for the pathology were divided into two equal parts exactly of the lesion repair site: the right-side samples were used for pathology and the other similar halves (weighing 20 mg) were used for measuring hydroxyproline. Briefly, the tissue samples were first taken out of the freezer; 20 to 40 mg of the sample was cut and placed in a microtube, and then, 100 μl of deionized water was poured into the microtubes and homogenized by the homogenizer. Then, 100 μl of HCl (12 M) was added to the microtube and incubated at 120 °C for 3 to 4 h. The sample was placed at 90 °C to evaporate the liquids. Fifty microliters of assay buffer was poured into each microtube and mixed. Next, 30 mg of activated charcoal was added to each sample and stirred thoroughly. It was then centrifuged at 12000 g for 15 min. The supernatant was used for further analysis. Each well of the kit was filled with 20 ml of the supernatant and detection solutions, and the absorption rate of the wells at a wavelength of 540–560 nm was read by Spectrophotometry Unicam UV vis (Thermo scientific, USA), and the concentration of the samples was obtained in comparison with a standard curve.
## Histopathology evaluation
Five animals in each group were euthanized at the end of the sixth week, and tendon samples were taken for histopathology evaluations. Samples were placed in $10\%$ neutral formalin-buffer solution for 24–48 h for fixation, and formalin was changed every 24 h. Automatic tissue processing was used to prepare the tissue (brand name of the device). In this device, tissue samples were first placed in containers with alcohol with ascending subtility degrees. This step helps to remove excess water from the tissue and also the extra intracellular reactions stop. Then, tissue samples were transferred to containers containing xylol solution for clarification and alcohol removal and were finally embedded in paraffin. Four-micron sections were taken and stained with Hematoxylin & Eosin and Masson’s trichrome staining. All slides were evaluated using a light microscope (Olympus, Japan) and photographed by a camera (Olympus, Japan) under 40×, 100×, 200× and 400× magnification.
In histopathological examination, the inflammation score, collagen filament, and congestion or bleeding were scored qualitatively (Table 1). The scoring system was done based on Stoll et al. [ 17], with a slight change in scoring grade and protocol. Table 1Tendon healing scoring system based on histopathological findingsParameterScoreInflammationAbsent inflammatory cell01–2 inflammatory cells (mild)1 (+)Inflammatory cells with giant cell (moderate)2(+ +)EdemaNo congestion0Congestion (mild)1 (+)Congestion (moderate)2(+ +)HemorrhageNo hemorrhage0Mild hemorrhage1 (+)Moderate hemorrhage2 (+ +)Collagen fibersFew thin collagen fibers0Few thick collagen fibers1 (+)Mild thick collagen fibers2 (+ +)Abundant thick collagen fibers3 (+ + +)Collagen fibers with fibroblastsNo fibroblasts0Mild fibroblasts1 (+)Moderate fibroblasts2 (+ +)Abundant fibroblasts3 (+ + +)Collagen fibers with fibrocytesNo fibrocytes0Mild fibrocytes1 (+)Moderate fibrocytes2 (+ +)Abundant fibrocytes3 (+ + +)
## Statistical analysis
For the analysis of histopathological evaluation, a nonparametric Kruskal–Wallis test was used. Statistical analyses of biomechanical and biochemical evaluation were performed with one-way ANOVA and post hoc Tukey's tests by GraphPad Prism 8(version 9- GraphPad). Any p-value less than 0.05 was considered to be statistically significant.
## Biomechanical findings
Yield points at week 6 were significantly different in the treatment group (TQ 5) than control and DMSO groups (Table 1, $$p \leq 0.0048$$). Break points in both treatment groups (TQ 5 and TQ 10) were significantly higher than in control and DMSO groups (TQ 5: control, DMSO $$p \leq 0.0005$$, $$p \leq 0.001.$$ TQ 10: control, DMSO $$p \leq 0.007$$, $$p \leq 0.016$$, respectively). Yield stress in both treatment groups (TQ 5 and TQ 10) was significantly higher than in control and DMSO groups (TQ 5: control, DMSO $$p \leq 0.0003$$, $$p \leq 0.0006.$$ TQ 10: control, DMSO $$p \leq 0.0007$$, $$p \leq 0.0014$$, respectively). Break stress in TQ 10 group was significantly higher than control and DMSO group ($$p \leq 0.0097$$, $$p \leq 0.032$$, respectively). Break stress in TQ 5 group was significantly higher than the control group ($$p \leq 0.016$$) (Table 2). Table 2Biomechanical parameters of the tendon in all groupsGroupsYield point (N)Breakpoint (N)Yield stress (Mpa)Break stress (Mpa)Control35.34 ± 22.82a40.42 ± 16.35c8.23 ± 1.80e9.050 ± 3.84 gDMSO30.75 ± 20.39a43.8 ± 17.79c8.4 ± 1.67e11.90 ± 4.83 gTQ 5110 ± 37.2b121.9 ± 32.03 d28.80 ± 5.95f32.00 ± 12.00iTQ 1080.0 ± 30.19ab101.0 ± 27.22 d27.01 ± 9.39f33.86 ± 15.46iResults are expressed as mean with S.D. In each column, different alphabet shows statistical difference against each other in different groups
## Hydroxyproline content
The hydroxyproline content per wet weight in TQ 5 was significantly higher than the control and DMSO groups (Fig. 2, $p \leq 0.05$). Also, $10\%$ w/w thymoquinone solution could significantly increase the hydroxyproline content compared to the control and DMSO group with a $p \leq 0.01.$Fig. 2Hydroxyproline content (μg/ml) in tendons. Results are expressed as mean with S.E.M, and $p \leq 0.05$ is considered a significant difference. (* :$p \leq 0.05$ vs. control and DMSO, **: $p \leq 0.01$ vs. control and DMSO)
## Histopathology findings
Six parameters were in histopathological evaluation. Three parameters consist of edema, inflammation, and hemorrhage (0–2 score), and three parameters consist of collagen fibers, collagen fibers with fibroblasts, and collagen fibers with fibrocytes (0–3 score). Histopathological sections of all groups are presented in Fig. 3. The number of fibroblasts and fibrocytes along the collagen fibers was higher in treatment groups than in control groups. In the control and DMSO groups, a cluster of round-shaped chondrocyte like cells were found at the repair zone (c,f) and collagen fibers were not organized in a regular orientation (b,d,e,f). In TQ 5 and TQ 10 groups, more density of collagen fibers that have aligned in a more orderly fashion was observed (i,l). Also, fibroblast migration increased in treatment groups than in the control and DMSO group. In edema parameters, there was a significant difference between treatment groups and control and DMSO groups ($p \leq 0.05$, Fig. 4a). Edema in TQ 5 group was significantly lower than control and DMSO groups. Edema in TQ 10 group was significantly lower than in the control group. There was no significant difference between the control and DMSO groups. In the hemorrhage parameter, there was a significant difference between TQ 10 and control groups (Fig. 4b). In inflammation cells, there was no significant difference between all groups (Fig. 4c). In the collagen fibers parameter, there was a significant difference between treatment groups and control and DMSO groups (Fig. 4d). Collagen fibers in TQ 10 and TQ 5 were significantly higher than DMSO group (Fig. 4e). Collagen fibers in the TQ5 group were significantly higher than in the control group. Collagen fibers with fibroblasts were significantly higher in TQ 10 and TQ 5 groups compared with the control group (Fig. 4e). Collagen fibers with fibrocytes were significantly higher in the TQ10 group than in the control group (Fig. 4f). Collagen fibers with fibrocytes were significantly higher in the TQ10 group than in the DMSO group (Fig. 4f).Fig. 3Histopathological image of all groups in 4, 10, and 40 × magnifications. H&E and Masson’s trichrome staining. * show dens and aligned collagen fiber and * shows unorganized collagen fiber in control and DMSO groupsFig. 4Histopathological parameters evaluation of tendons. Results are expressed as mean with S.E.M, and $p \leq 0.05$ is considered a significant difference
## Discussion
Tendon injuries are almost $50\%$ of all musculoskeletal injuries. Tendon recovery after surgery has become a significant challenge in orthopedics, so more investigation and better strategies for promoting tendon healing may provide new therapies for such patients. The purpose of this study was to investigate the healing effect of thymoquinone injections in rabbit tendon traumatic models. Although thymoquinone is well-known antioxidant, anti-inflammatory, and antibacterial effects [18], its injection effects on the traumatic tendon in rabbit models have not been studied yet. As a result, in the current study, the histopathological evaluation (H&E and mason trichrome), biomechanical, and biochemical evaluation was performed to investigate the healing effect of thymoquinone in the traumatic tendon in the rabbit model.
The tendon healing process consists of three major phases that overlap with each other: inflammation, formative, and remodeling phase [19]. For optimized and rapid tendon healing, high production of collagen type I is necessary [19]. In the current study, two-time injections three days apart of thymoquinone in the tendon demonstrated pronounced effects on collagen production and hydroxyproline content according to histopathological (Masson trichrome staining) and biochemical (hydroxyproline content) evaluations. In one study, the healing effect of polylactic acid/cellulose acetate along with thymoquinone on wounds was investigated. Their Masson’s trichrome staining showed significantly higher collagen deposition in the wound area [20]. Many studies used thymoquinone as a hepatoprotective agent due to its upregulating effect on the AMP-activated protein kinase pathway [21, 22]. Thymoquinone could increase the expression of the MAPKs, p-p38, p-ERK, p-JNK, and PI3K/pAkt to upregulate collagen I expression in a dose-dependent manner [23, 24].
The promising results of the current study could be due to not only the collagen production potential of thymoquinone but also due to the anti-inflammatory effects of thymoquinone that are certainly mentioned in many studies [25, 26]. Thymoquinone inhibits tumor necrosis factor-α, interleukin-1B, and IL-6 production. These cytokines decrease collagen synthesis and activate MMPs that degrade collagen [27, 28]. The canonical Wnt–wingless signaling pathway is well known and regulates many biologic processes by increasing the transcriptional activity and stability of β-catenin. Additionally, the Wnt pathway is important for wound healing because its key mediator β-catenin has a pivotal role in the proliferation phase of wound healing. β-catenin also participates in some phases of wound repair. First, phosphorylation occurs and it accumulates in the cytoplasm and then migrates into the nucleus. In the nucleus, the regulation of the target gene transcription occurs and this results in proliferation, migration, and accumulation in the collagen of fibroblasts (29–31). Pekmez and his colleagues showed that thymoquinone treatment increased β-catenin expression [32] which can result in proliferation, migration, and accumulation in the collagen of fibroblasts. Another reason for increased collagen and hydroxyproline content is due to the antioxidant activity of thymoquinone which could protect fibroblast from reactive oxygen species. Collagen type I is mainly produced by fibroblasts that replace the temporary fibrin-based matrix [33]. A study conducted by Rahmani-Moghadam et al. used thymoquinone and hydroxyapatite to investigate its osteogenesis and collagen synthesis on mesenchymal stem cells. A real-time-PCR study demonstrated that thymoquinone-treated MSCs expressed collagen type I at the early phase of the differentiation phase [34].
One of the major problems in tendinous and ligamentous injuries in larger animals like the equine is the moderate prognosis of the horse to return to their racing activity. Treatment carried only a moderate prognosis for return to racing ($62\%$), with a moderate rate of reinjury ($46\%$) after treatment with growth factors [35]. In rare cases, the tendon achieves functionality equal to that of the pre-injured state. Mostly, tensile strength is reduced up to $30\%$ after injury (Müller et al., 2015). Tendons are related to the flexion of toes and foot movement, and to maintain this flexor movement, it is important to keep it in normal mechanical properties such as tensile load and normal diameter [36]. Tendon healing after surgical repair generally progresses through a short inflammatory phase, which lasts about a week, followed by a proliferative phase, which lasts a few weeks, followed by a remodeling phase, which lasts many months [37]. During the inflammatory phase, vascular permeability increases and an influx of inflammatory cells enters the healing site. These cells produce several cytokines and growth factors that lead to the recruitment and proliferation of macrophages and resident tendon fibroblasts. During the proliferative and remodeling phases of healing, fibroblasts proliferate and begin to produce, deposit, orient, and crosslink fibrillar collagens. Tendon is a dense connective tissue composed of highly organized parallel and longitudinal collagen fiber bundles [20, 38]. The major ECM component in tendon tissues is type I collagen. Collagen contains at least one domain of repeated sequences of glycine (Gly)–X–Y, where X and Y are most frequently proline (Pro) and 4-hydroxyproline (Hyp), respectively [39]. Commonly collagen content in connective tissues has been estimated by measuring the hydroxyproline content and assuming that it is present in collagen in a specific proportion based on the amino acid composition of collagen. The determination of Hydroxyproline content is in proportion to collagen content [40]. Also, in 2015, Allahverdi et al. used the measurement of hydroxyproline in the evaluation of tendon repair [41]. In another research, Jiang et al. used quantification of hydroxyproline for tendon repair evaluation [42]. And also, Abd Al-Hussein et al. used the measurement of collagen content in the evaluation of tendon repair [43].
In our study, biomechanical evaluation showed an increase in yield point and break point in both treatment groups than DMSO and control groups. Biomechanical results are completely consistent with the hydroxyproline and histopathology results of our study. An increase in mechanical properties is due to the increase in collagen content, which has also been shown in our results. Based on histopathological results, collagen with fibrocyte and fibroblast was significantly higher in treatment groups, especially TQ 10 group than in control and DMSO groups. Rahmani–Moghadam study showed thymoquinone in the vicinity of mesenchymal stem cells induces a change in morphology after the second passage and cell morphology gradually changed to fibroblast-like cells [34]. Another important thing that should be noticed is the type of collagen distribution in tendon healing. In early tendon healing, type III collagen is mainly present throughout the tendon tissue, but normal tendon contains type I collagen [19]. Usually, after 40 days fibroblasts resorb collagen and produce new collagen with a more longitudinal orientation [19]. In our study in a group treated with $10\%$ w/w thymoquinone, more orientated fibers were seen in histopathological slides. So thymoquinone in a concentrate of $10\%$ w/w could enhance fibroblast and fibrocyte proliferation and subsequently enhance collagen production as well as improve the remodeling phase by better fiber orientation in tendon healing.
This study evaluates the short-term effect of thymoquinone on rabbit Achilles tendon healing. A long-term investigation is needed to assess long-term biomechanical evaluation and also re-injury rate in rabbits.
## Conclusion
In conclusion, the promising biological results of thymoquinone injection in the Achilles tendinopathy model suggest this natural product as an ideal non-invasive initial treatment after tendon injury. Histopathological evaluation showed more fibroblast and fibrocyte proliferation and also improved fiber orientation after 42 days of tendon injury. Therefore, thymoquinone intratendon injection can be a low-cost and useful therapy for tendon injury.
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|
---
title: 'Patient-tailored transcranial direct current stimulation to improve stroke
rehabilitation: study protocol of a randomized sham-controlled trial'
authors:
- Mia Kolmos
- Mads Just Madsen
- Marie Louise Liu
- Anke Karabanov
- Katrine Lyders Johansen
- Axel Thielscher
- Karen Gandrup
- Henrik Lundell
- Søren Fuglsang
- Esben Thade
- Hanne Christensen
- Helle Klingenberg Iversen
- Hartwig Roman Siebner
- Christina Kruuse
journal: Trials
year: 2023
pmcid: PMC10035265
doi: 10.1186/s13063-023-07234-y
license: CC BY 4.0
---
# Patient-tailored transcranial direct current stimulation to improve stroke rehabilitation: study protocol of a randomized sham-controlled trial
## Abstract
### Background
Many patients do not fully regain motor function after ischemic stroke. Transcranial direct current stimulation (TDCS) targeting the motor cortex may improve motor outcome as an add-on intervention to physical rehabilitation. However, beneficial effects on motor function vary largely among patients within and across TDCS trials. In addition to a large heterogeneity of study designs, this variability may be caused by the fact that TDCS was given as a one-size-fits-all protocol without accounting for anatomical differences between subjects. The efficacy and consistency of TDCS might be improved by a patient-tailored design that ensures precise targeting of a physiologically relevant area with an appropriate current strength.
### Methods
In a randomized, double-blinded, sham-controlled trial, patients with subacute ischemic stroke and residual upper-extremity paresis will receive two times 20 min of focal TDCS of ipsilesional primary motor hand area (M1-HAND) during supervised rehabilitation training three times weekly for 4 weeks. Anticipated 60 patients will be randomly assigned to active or sham TDCS of ipsilesional M1-HAND, using a central anode and four equidistant cathodes. The placement of the electrode grid on the scalp and current strength at each cathode will be personalized based on individual electrical field models to induce an electrical current of 0.2 V/m in the cortical target region resulting in current strengths between 1 and 4 mA. Primary endpoint will be the difference in change of Fugl-Meyer Assessment of Upper Extremity (FMA-UE) score between active TDCS and sham at the end of the intervention. Exploratory endpoints will include UE-FMA at 12 weeks. Effects of TDCS on motor network connectivity and interhemispheric inhibition will be assessed with functional MRI and transcranial magnetic stimulation.
### Discussion
The study will show the feasibility and test the efficacy of personalized, multi-electrode anodal TDCS of M1-HAND in patients with subacute stroke patients with upper-extremity paresis. Concurrent multimodal brain mapping will shed light into the mechanisms of action of therapeutic personalized TDCS of M1-HAND. Together, the results from this trial may inform future personalized TDCS studies in patients with focal neurological deficits after stroke.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13063-023-07234-y.
## Background and rationale
Ischemic stroke (IS) remains a global challenge and two-thirds of stroke patients show continued motor deficits which impact activities of daily living and quality of life [1]. Early-initiated rehabilitation training is central to recovery of motor function after IS [2, 3]. Transcranial brain stimulation (TBS) as an add-on to neurorehabilitation in the early subacute phase after IS (within the first 4 weeks after stroke onset) might result in faster and better recovery by optimizing the underlying neuroplastic processes, which may be more susceptible during the subacute phase post-stroke [4]. However, the use of a TBS technique to improve rehabilitation has to be feasible for patients and implementable in a clinical setting.
Transcranial direct current stimulation (TDCS) has been tested as a non-invasive tool to improve neurorehabilitation. The electric currents that TDCS can induce in the cortex through scalp electrodes result in a minor shift in the membrane potential and thereby a modification of the intrinsic neuronal network activity [5–7]. The pyramidal neurons in the area located under the anodal electrode are suggested to increase in excitability through depolarization of both the soma and the afferent axons while the pyramidal neurons located under the cathodal electrode decrease in excitability through hyperpolarization of the soma and afferent axons [8, 9]. These modulations in the membrane potential are thought to modulate behavior [10] and enhance neural plasticity by stimulating synaptic connections and long-term potentiation processes [11, 12].
Previous clinical trials have demonstrated that TDCS of the primary motor cortex (M1) may improve upper-extremity function in both subacute and chronic stroke patients, when applied concurrent with rehabilitation training. Ipsilesional anodal TDCS with the traditional montage of two square electrodes have been most widely examined [13–15], but contra-lesional cathodal TDCS [16, 17] and dual-TDCS [18] also has been studied. Across these studies, TDCS was applied to target the hand region of the primary motor cortex (M1-HAND) of either the healthy or the affected hemisphere, and current intensity is usually fixed between 1 and 2 mA across all subjects. However, according to meta-literature, up to $50\%$ of patients are non-responders to the intervention [19], and only limited evidence of a significantly increased effect of TDCS compared to sham regarding upper-extremity rehabilitation [20–22]. Such lack of effects may associate to the one-size-fits-all approach which might miss the area thought to be targeted by TDCS (e.g., M1) both regarding location and current strength necessary to induce shifts in the membrane potential of the neurons in the target area. In addition, TDCS is often combined with either robot-assisted rehabilitation [22] or virtual reality [23] which may further confound the interpretation of the results.
Using individual magnetic resonance imaging (MRI) scans, electric field modeling enables a precise estimation of the electric field distribution in the brain during transcranial electrical brain stimulation and an optimization of electrode placement and dosing of interventional TDCS [24–27].
Patients with post-stroke upper extremity disability show an impaired motor network structure including a reduced excitatory influence from pre-motor brain areas and disinhibition of the contra-lesional M1-HAND [28, 29]. The interhemispheric imbalance between precentral motor cortices tends to improve with motor recovery and is often completely restored in patients with full recovery [28–30]. It is however unclear whether this imbalance facilitates or hinders motor recovery [29, 31].
## Objectives
The main hypothesis of this study is that patient-tailored anodal TDCS targeting the ipsilesional M1-HAND during supervised upper extremity training will result in greater improvements in upper-extremity function, measured by difference in change in FMA-UE score, compared to sham stimulation. It is furthermore hypothesized that patient-tailored TDCS is feasible to use for stroke rehabilitation in a stroke unit at a hospital setting.
Additionally, it is hypothesized that motor improvements correlate with the degree of normalization of functional motor connectivity and interhemispheric inhibitory interaction as revealed by task-related functional MRI and TMS. We will also assess the degree of corticospinal tract (CST) integrity measured by transcranial magnetic stimulation (TMS) and diffusion-weighted MRI (DWI) to explore how structural impairment in the corticospinal tract relates to the efficacy of anodal TDCS.
## Trial design
The study will be a parallel double-blinded two-arm randomized sham-controlled trial. In addition to usual care (preventive medication, advice on self-managed lifestyle changes, and municipal rehabilitation) the intervention group will receive two times 20 min of patient-tailored TDCS concurrent with supervised upper-extremity training for three times per week for 4 weeks in a 1:1 allocation ratio. The framework applied is exploratory.
## Study setting
Patients will be recruited during their admission at the Stroke Units of three participating University Hospitals of the Capital Region of Denmark (Region Hovedstaden): Copenhagen University Hospital Herlev and Gentofte, Copenhagen University Hospital Rigshospitalet, Copenhagen University Hospital Bispebjerg and Frederiksberg. Recruitment started in August 2022.
Patients will undergo routine clinical examinations for stroke patients and stroke subtype will be classified according to the Trial or Org 10,172 in Acute Stroke Treatment (TOAST) classification [32]. Patients will be grouped according to a cortical- or subcortically located stroke lesion for follow-up analyses that will explore the effect of stroke location on the potential beneficial effects of real TDCS.
See Table 1 for routine clinical examinations and demographic information collected. Table 1Routine examinations and demographic informationRoutine examinationsDemographicsBlood samplesChest x-rayMagnetic resonance imaging of the brainCarotid ultrasoundComputerized tomography (CT) angiography or MRI time of flight (TOF) of the brain to screen for intracranial stenosisElectrocardiogram (ECG)Holter monitoring for 72 hTransthoracical ecocardiography (TTE) in patients < 65 yearsMedical historyPrior and concurrent medicationSmoking status (former, active never smoker)Alcohol consumption (weekly),Pre-morbid modified Rankin ScoreEducation level,Pre-morbid walking status,Pre-morbid living arrangementsMarital status,Stroke severityActive hand movement at stroke onset Ability to walk unassisted at stroke onsetAdministration of thrombolysis (IVT) or reperfusion therapy (EVT) prior to inclusion
## Eligibility criteria
Inclusion criteria for the patients are ischemic stroke lesion either located cortically or subcortically in the large hemispheres, symptoms presenting with any degree of arm paresis, age ≥ 18 years, able to speak and read Danish, and able to give informed consent and index stroke within 28 days of inclusion.
Exclusion criteria are > $50\%$ stenosis of extra- or intracranial vessels, > 1 cerebral infarct or stroke event during admission, cerebral aneurysms or cerebral arterio-venous malformations, stroke location outside the large hemispheres, cognitive dysfunction interfering with the ability to participate, history of seizures, epilepsy or epilepsy in first-degree family, anxiety, dementia, alcohol- and drug abuse, headaches > 16 days per month or migraine as these can be provoked by TDCS and TMS, current use of neuro-receptor/transmitter modulating medication, medication reducing seizure threshold or prior adverse effect to TDCS or TMS, contraindications to MRI, or claustrophobia.
## Intervention — rehabilitation training
A training program designed to meet the individual needs and challenges of the patient will be planned in a pretraining session. Based on the evidence-based practice within neurorehabilitation the training will be goal-directed, repetitive, and task-specific [33, 34] with a focus on reaching, grip, and fine motor skills. See Supplemental Materials S1 for a detailed description of the exercise framework.
The participant will be encouraged to remain physically active. Furthermore, the patients will be instructed in two to four individual home-based exercises which they will do between intervention days. The number of repetitions and time spent on home-based exercises will be recorded in a pen-and-paper log by the patient in order to record compliance (see Supplemental Material S1).
There are no concomitant care or interventions (including medications) prohibited during the trial for both arms.
Intervention sessions will take place at Copenhagen University Hospital Herlev supervised by a trained occupational- or physiotherapist. Each training session will consist of two sessions of 20 min exercise with concurrent TDCS separated by a small break (≈5 min). The type of TDCS, active or sham TDCS, will be determined by randomization after inclusion of the patient.
The exercises will advance in difficulty gradually as the patient improves which is consistent with earlier findings that progressive practice improves motor skill learning and increased corticospinal plasticity [35, 36].
## Intervention — transcranial direct current stimulation
Focal anodal TDCs of the ipsilesional M1-HAND will be given via a multiple-electrode 4 + 1 montage using 20 mm round rubber electrodes (Richardson et al. [ 37], Alam et al. [ 38]) and a DC-STIMULATOR PLUS connected to a neuroConn Equalizer Box (NeuroConn, Ilmenau, Germany). Electrodes will be fixed on the scalp using Ten20® conductive paste in an approximately 0.2 cm thick even layer and covered by a net cap. The scalp will be prepped prior to electrode placement with NuPrep® skin scrub and alcohol swaps. The target electrode will be positioned over the ipsilesional hand knob area and the four cathodal return electrodes will be positioned equidistant surrounding like a ring with 60 mm to the target electrode (see Fig. 1).Fig. 1NeuroConn DC-STIMULATOR PLUS and an example of TDCS 1 + 4 round electrode montage Active stimulation mode consists of 30 s ramp followed by 19 min of stimulation and 30 s ramp-down to 0 mA. On the sham mode, there will be 30 s ramp-up and 30 s ramp-down [10, 39] to a small 50-μA sinusoidal current followed by 30 s ramp-down to 0 mA. See Fig. 2. The 50-μA current does not induce any physiological effects but allows impedance testing whereby the display at the DC stimulator appears identical in active and sham mode. The sham mode was preprogrammed in the DC-STIMULATOR at neuroConn Technology. Fig. 2Diagram displaying the active and sham mode of transcranial electrical stimulation TDCS intensity will be calculated based on personalized electrical field models and adjusted to reach a field intensity of 0.2 V/m in the target area [40]. Both, the center electrode position, and current intensity will be individualized to each patient. We anticipate that personalized electrical field modeling will result in current intensities of 1.5–2.5 mA in most participants. TDCS intensity will not exceed a maximum current of 4.0 mA.
## Criteria for discontinuation
Patients not able to complete > $75\%$ of the intervention sessions, patients with side effects from TDCS (such as severe headache following each intervention session), patients not able to complete the MRI scans, readmission with recurrent stroke or admission with another condition that contradicts TDCS, or upper-extremity exercise will be discontinued from the study.
## Strategies to improve adherence to intervention
Study retention will be promoted by a phone call 6 weeks after intervention has ended. The content of the training program will change weekly as the patient improves to promote motivation.
## Magnetic resonance imaging
At baseline, after the 4-week intervention and 12 weeks later whole-brain MRI scans will be acquired with a 3 T Philips Achieva scanner (Philips, Amsterdam, The Netherlands). The structural MRI protocol includes T1-weighted, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) sequences and diffusion-weighted imaging (DWI). Functional brain mapping will include task-related blood oxygen level-dependent, functional MRI (BOLD-fMRI), and brain perfusion measurements with pulsed continuous arterial spin labeling (pcASL). Table 2 gives a detailed description of each sequence. Table 2MRI acquisition detailsSequenceT1WT2WFLAIRfMRI BOLDpcASLDWIVoxel size (mm)0.85 × 0.85 × 0.850.85 × 0.85 × 0.851.0 × 1.0 × 1.0 mm3.00 × 3.05 × 3.003.00 × 3.08 × 6.002.00 × 2.04 × 2.00FoV (mm)245 × 245 × 2081245 × 245 × 190256 × 256 × 202192 × 192 × 126240 × 240 × 126224 × 224 × 100TR (ms)700025004800249046077700TE (ms)3.3265314301864Flip angle8°35°40°80°90°90°/180°Acquisition time (min:sec)05:4006:3205:4005:06a09:2605:31 + 0:53Readout method (EPI, FEE, TFE, TSE)TFE 243TSE 133TSE 182FFE/EPIFFE/EPIPGSE/EPISENSE/TSE/halfscan factorSENSE 2 (AP)SENSE 2 (AP), 1.8 (RL)SENSE 1.8 (AP), 1.9 (RL)n.aSENSE 2.0 (AP)Halfscan factor 0.8SENSE 2 (AP)OtherInversion time: 1650 msMonitoring of respiration and pulse (PhysioLog)Post-label delay: 2 sb = 1000 s/mm2 (gradient ampl. = 62mT/m, duration = 12.5 ms, separation = 27.5 ms)40 gradient directions and 6 interleaved $b = 0$Additional 5 $b = 0$ scans in the AP-PA direction every five conditionaSequence repeated three times
## Task-related fMRI
Task-related fMRI will employ two runs featuring different manual motor tasks: The first fMRI paradigm records BOLD signal changes during a unimanual index-finger tapping task. Participants will produce irregular finger taps in response to a central visual cue at a pace of 0.5 Hz with a jitter of 0.25 s. The task will be performed in a single fMRI run, lasting 5 min. The second fMRI paradigm uses a block design to probe BOLD signal changes during a visually cued bimanual motor task. In the bimanual paradigm, participants have to generate bimanual responses with their index fingers in four visually cued conditions, “left before right finger,” “right before left finger,” “simultaneous index finger response,” or “no press” in a pseudorandomized order. Each task condition is presented in a pseudorandomized order in blocks of 20 s for a total of 5 min. In each condition, button presses will be triggered by a central visual cue at a pace of 0.5 Hz with a jitter of 0.25 s. The bimanual fMRI paradigm will be tested in two fMRI runs, lasting 5 min each. See Supplemental Figure S4 for an illustration of the visual cues.
All patients will be trained prior to each scan session to ensure an accurate and consistent performance of the motor tasks. Motor performance will be recorded with a four-button bimanual hand-held response pad (Cambridge Research System, Cambridge, UK). The visuomotor tasks are programmed with PsychoPy® [41] and visual cues were presented on an MRI-compatible screen behind the scanner on a mirror mounted on the head coil.
## Arterial spin labeling (ASL)
Whole brain perfusion maps will be measured by pulsed-continuous ASL (pcASL). See Table 2 for sequence details. Post-label delay is set for 2 s as recommended in an elderly population [42]. Patients are instructed to relax and stay awake during the ASL. We will use pcASL to compare changes in whole brain perfusion from baseline to follow-up between the patients receiving active vs. sham TDCS, respectively.
## Diffusion-weighted imaging (DWI)
DWI of the brain will be done to segment the CST and transcallosal motor fiber tract that connects the left and right M1-HAND and to characterize the microstructural damage of these motor white-matter tracts. The details of the MRI sequence used for DWI are described in Table 2. Using a diffusion tensor model, we will evaluate fractional anisotropy (FA) and mean diffusivity (MD) in the CST between the affected and unaffected hemispheres. Regional MD is acutely reduced after stroke but decreases to or below normal values in the weeks after infarct, reflecting a loss in cell density and tissue integrity [43–45]. Regional FA is a voxel-wise measure of the directionality of water diffusion and is sensitive to axonal alignment, density, and integrity [46]. A disruption of corticospinal tract integrity by a stroke lesion increases regional FA in the ipsilesional CST resulting in a FA asymmetry between the affected and the unaffected hemisphere [45, 46]. Tract segmentation will be performed with TractSeg [47] combined with custom MATLAB (MathWorks) scripts as a quantitative biomarker of microstructural white matter changes.
## Transcranial magnetic stimulation
Single- and paired-pulse TMS is performed at pre-interventional baseline and at either one or both follow-up visits to estimate the integrity of the CST as well as to evaluate corticomotor excitability. Specifically, the maximal amplitude of the motor evoked potential (MEP), cortico-motor conduction time (CMCT), contralateral silent period (cSP), ipsilateral silent period (iSP), and short intracortical inhibition (SICI) [48, 49] will be measured. TMS will be delivered with a hand-held figure-of-eight coil (MC-B70) connected to a MagPro 100 option stimulator (MagVenture, Farum, Denmark). The TMS evoked motor responses will be recorded with self-adhesive surface electrodes (Neuroline 700, Ambu, Ballerup, Denmark) attached to the left and right contralateral first dorsal interosseus (FDI) muscle using a belly-to-tendon montage. Electromyographic signals will be sampled at 5 kHz, band-pass filtered (5–2000 Hz) and amplified [1000], digitized, and stored using an eight-channel DC amplifier (1201 micro Mk-II unit, Digitimer, Cambridge Electronic Design) and Signal software version 4.11 (Cambridge Electronic Design, Cambridge, UK).
The cortical motor hotspot, the scalp position at which TMS produces the largest MEP, will be determined functionally and recorded using stereotactic neuronavigation (Localite, Bonn, Germany) throughout the experiment to ensure precise coil positioning. Resting motor threshold (RMT) will be determined as the stimulation intensity eliciting an MEP of > 50 mV in 5 out of 10 stimulations [50] and active motor threshold as an MEP of > 200 mV and a visible cortical silent period during a $10\%$ maximal voluntary contraction (MVC) (if possible).
We will evaluate the excitability of intracortical GABAergic inhibitory circuits by measuring the strength of short intracortical inhibition (SICI) with paired-pulse TMS, applying a conditioning stimulus at an intensity of $80\%$ RMT 2.1 ms prior to a test stimulus at an intensity of $120\%$ RMT [51]. Twenty conditioned MEPs and 20 non-conditioned MEPs will be recorded to obtain reliable estimates of MEP amplitude for each stimulation condition.
Transcallosal inhibition will be estimated by recording the iSP. To this end, 20 pulses at $150\%$ of RMT will be applied, while the patient performs a $50\%$ MVC of the FDI muscle ipsilateral to the stimulated hemisphere. Lastly, maximum MEP amplitude, corticomotor MEP latency, and cSP will be determined using 20 TMS pulses at $150\%$ RMT during $10\%$ MVC of the contralateral hand. Moreover, we will determine the M-wave and F-wave latencies from 20 supramaximal constant current ulnar nerve stimulations (Digitimer DS7A, Cambridge, UK) to determine CMCT [48].
## Field modeling and electrode positioning
The scalp location of the TDCS center electrode and current intensity will be individualized using a SimNIBS pipeline based on the T1- and T2-weighted images of the individual patient [25, 40].
A custom SimNIBS script (SimNIBS version 4.0) will determine both the current intensity necessary to reach a mean field strength of 0.2 mV/m as well as locate the position of the target electrode on the scalp of the patient using a mask drawn in fsaverage for the hand knob area on either left or right hemisphere depending on location of the stroke lesion [52].
The position of the target electrode will be visualized on a 3D head mesh using a custom SimNIBS script displaying the three nearest EEG positions corresponding to a 64-channel EasyCap® M10-layout EEG-cap (EasyCap_BC_TMS64_X21, EasyCap, Woerthsee-Etterschlag, Germany). See Fig. 3. An individual EasyCap EEG-cap will be fitted for each patient prior to starting the intervention on which the position of the target electrode is marked with a hole. This is used to mark and ensure consistent electrode positioning during the intervention period. Surround electrodes will be positioned in 60 mm equally distributed as a ring around the target electrode. Five landmark positions will be used to ensure consistent fitting of the cap between sessions (Cz, Fpz, and Iz for frontal alignment and T8 and T7 for left–right-alignment).Fig. 3The use of SimNIBS to determine the current strength of TDCS necessary to reach a field strength of 0.2 mV/m in the target area as well as the distance from the target point to EEG positions on a head mesh to transfer the target position to a position of the scalp in real life
## Primary outcome
The primary outcome is the difference in change of FMA-UE score at follow-up after 4 weeks of intervention compared to baseline between active TDCS vs. sham-stimulation.
The FMA probes five domains: motor function, sensation, balance, joint pain, and joint range of motion in the upper and lower extremities of hemiplegic stroke patients. It can be performed as either full-FMA or as upper or lower extremity FMA (FMA-UE or FMA-LE, respectively). In this study, FMA-UE will be assessed. Each of the five domains contains different items for assessment which are scored on a 3-point scale: 0 = cannot perform. 1 = performs partially, and 2 = performs fully. Impairment severity is based on FMA motor scores. Maximum score for FMA-UE is 66 points [53, 54].
## Motor function and activity
Difference in improvement in upper-extremity function from baseline to follow-up in the active and sham-group will also be assessed with Action Reach Arm Test (ARAT). ARAT is composed of 19 items categorized into four subscales (grasp, grip, pinch, gross movements) arranged hierarchically with decreasing difficulty Task performance is rated on a 4-score scale ranging from 0 = “no movements” to 3 = “movements performed normally” [55]. Additionally, we will register the time spend on each task because the time frame resulting in the score of 2 (“task completed”) is large covering from 5 to 60 s. The Action Research Arm Test (ARAT) is currently being validated to the Danish language in a separate study by co-author KLJ (ID: H-20046644).
Changes in stroke severity and daily activity will be assessed by the National Health Institutes Stroke Scale (NIHSS) score, Modified Rankin Scale (mRS) [56], 20-item Barthels Index (BI-20) [57], 10-m-walk-test [58]. See also Table 3 as well as S3.Table 3SPRIT-figure: schedule of study proceduresSDMT symbol digit modalities test, MoCA Montreal Cognitive Assessment, IQCODE Informant Questionnaire on Cognitive Decline in the Elderly, BDI-II Beck’s Depression Inventory II, FSS Fatigue Severity Scale, WHO-5 World Health Organization – Five Well-being Index, PAS2 Physical Activity Scale 2, UE-FMA Upper-extremity Fugl-Meyer Assessment, ARAT Action Reach Arm Test, BI-20 Bartel-20 Index, 10MWT 10-m walk-test, MEP motor evoked potential, SICI Short interval intracortical inhibition, iSP ipsilateral silent period, CMCT cortico-motor conduction time
## Cognition
Baseline cognitive level will be evaluated by Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) [59, 60]. Cognitive changes will be assessed by Montreal Cognitive Assessment (MoCA) [61] and Symbol Digit Modalities Test (SDMT) [62]. See also Table 3 as well as S3.
## Fatigue, mental well-being, and degree of depression
Fatigue will be examined by the Fatigue Severity Scale (FSS) [63]. Mental well-being will be evaluated by the EQ-5D-5L-test of health [64] and the World Health Organization – Five Well-Being Index (WHO-5) [65]. The degree of depression will be evaluated by Beck’s Depression Inventory-II (BDI-II) [66]. See also Table 3 as well as S3.
All tests and questionnaires used have been validated for stroke patients and are available in the Danish language.
## Motor network connectivity and interhemispheric imbalance
Changes in the interactions between the motor network structures of the affected and unaffected hemispheres are important as they correlate with motor recovery. They will be assessed by effective connectivity during task-related fMRI. Using dynamic causal modeling (DCM) the direct influence (either facilitatory or inhibitory) of one region of interest (ROI) over another can be estimated (Friston [67]). The ROIs of this study are the primary motor cortex (M1), the supplementary motor area (SMA), and the ventral and dorsal premotor cortex (vPMC and dPMC, respectively) as these areas are involved in the motor network disturbances after stroke [68, 69]. Furthermore, the imbalance in the degree of activation of the motor cortex in the affected vs unaffected hemisphere will be determined by the laterality index (LI), as this is also correlated with motor recovery and tends to change to a more balanced activation with recovery [70].
## Blood samples
A battery of routine blood samples (basic lab) will be collected at baseline and at each follow-up visit for safety and evaluation of cardiovascular risk factors (hemoglobin, hematocrit, leukocytes, C-reactive protein, blood platelets, international normalized ratio (INR), activated partial thromboplastin time (aPTT), fibrinogen, high-sensitive CRP (hsCRP) and apolipoprotein(a) (LP(a)). Additional blood will be collected at baseline for genetic analysis of brain-derived neurotrophic factor (BDNF) Val66Met genetic polymorphism, since this allele variant may influence the effect of TDCS [71] (*De la* Rosa 2019). Furthermore, blood for determination of plasma levels of cathepsin B will be collected at baseline and at each follow-up visit for measurements, as this lysosomal protein may relate to the cognitive effects of exercise [72]. See also Table 3 as well as S3.
## Participant timeline
All patients will be assessed at baseline directly after enrolment and at follow-up1 (after the 4 weeks of intervention has ended) and follow-up2 (12 weeks post-interventions).
See Table 3 for SPIRIT-figures of study procedures, Fig. 4 for a study flowchart, and Fig. 5 for a graphical synopsis of the study protocol. Fig. 4The PRACTISE-trial flow diagramFig. 5A graphical synopsis of the PRACTISE trial
## Sample size
The sample size is calculated based on the primary outcome. We expect an average difference in UE-FMA between groups of 5 points with a standard deviation of 3–4 points [23]. Assuming a Type I error of 0.05 and a Type II error of $80\%$ 12 participants per arm are needed to reach sufficient power. To account for dropouts, we will include a minimum of 15 patients in each arm. An interim analysis will be done after the inclusion of 24 patients. To consider any differences due to the location of the stroke lesion we aim to include 30 patients with a cortical stroke lesion and 30 patients with a subcortical stroke lesion. However, if recruitment is more challenging than expected a feasibility analysis will be done after the inclusion of 30 stroke patients stratifying for infarct location (cortical vs. subcortical) in the analysis.
## Recruitment
Eligible patients will be identified by daily screening of medical records, and they will be provided with both oral and written information about the study. In case of severe aphasia, a next of kin participating in the information meeting may co-sign the consent if the patient wishes to participate in the study.
## Allocation and sequence generation
Patients will be randomized consecutively into two groups, active or sham-stimulation, based on equal allocation. The allocation sequence is generated by SealedEnvelope™ (London, UK). Random-sized permuted blocks of participants will be applied at randomization. No stratification.
## Allocation concealment and implementation
At randomization, each patient is assigned a unique 5-digit code which will be typed into the DC-stimulator before each stimulation to apply either “active” or “sham” stimulation to maintain double blinding.
The randomization key containing the order of blocks and the allocation to either active or sham TDCS will be kept secured and unavailable to any delegates who are connected to the study until the trial is complete or in case an emergency unblinding is needed.
## Blinding
To remove any sensation of the TDCS the skin under the electrodes will be prepared with surface analgesics (Emla crème ®) 15 min prior to each stimulation. After each intervention session, the patient will be asked about any sensations from TDCS (such as skin itch, dizziness, headache).
At the last follow-up visit the patients will be asked: “Do you think you got active TDCS? Yes or no” for a quality assessment of the blinding. Furthermore, they will be asked: “Would you recommend TDCS and training for other stroke patients? Yes or no” for an assessment of feasibility.
## Data collection methods and management
All data recorded will be kept in the electronical report form (eCRF), REDCap™ (Vanderbilt University, TN, USA). All blood samples and lab results will be kept in the electronical medical file of the patient (see Fig. 1).
The collection of blood samples and the questionnaires regarding depression, quality of life fatigue, and activity level will be done by the study coordinator (MK). Assessment of motor function will be done by a study occupational- or physiotherapist that has not participated in the intervention sessions. All study therapists have received adequate training in all assessments of motor function prior to entering the study and will be supervised by an experienced coordinating therapist (KLJ). Clinical MRIs are described by an experienced neuroradiologist (KG). TMS data will be collected by co-investigators MJM and DH. MRI data will be collected by the study coordinator MK as well as sub-investigators MJM and DH. MJM is certified and experienced in TMS from several prior trials. MK has been certified in MRI and supervised by experienced senior researchers from DRCMR. MK has completed several pilot scans of both healthy and volunteering stroke patients independently prior to study initiation to ensure the quality of the MRI protocol. DH is certified in both TMS and MRI supervised MK and MJM and is experienced by several pilot sessions.
Checks for data entry errors and out-of-range errors are done in REDCap after each visit has been completed by each participant. Upper and lower limits are fixed for several data items in REDCap to ensure data entry within the normal range. After each TMS session inspection of the dataset for quality assessment and removal of outliers will be done. Furthermore, image quality is assessed during each MRI scan (e.g., appropriate field of view, movements, and other artifacts) and MRI data quality assessment will be done regularly along with regular testing of the MRI data analysis pipelines once a patient has completed both baseline MRI and follow-up visits. At the final follow-up visit missing data or any entry errors will be evaluated and handled.
## Statistical methods
All variables will be tested for normal distribution prior to analysis and logarithmically transformed if necessary. If data diverge from the normal distribution after transformation non-parametric testing will be performed. All tests will be two-sided and $P \leq 0.05$ will be considered significant. Data will be analyzed using Microsoft Excel 2010 (Microsoft Corporation, Redmond, WA, USA), R (version 3.6.1), and REDCap or similar software. Statistical planning is conducted in a corporation with a biostatistician.
All data will be analyzed for the intention-to-treat population. All patients with complete outcome data will be analyzed according to the group they were randomized to. All available data for each patient will be included in the analysis. Missing data will be analyzed using imputation. Estimated treatment effects will be calculated based on a constrained longitudinal data analysis (cLDA) which will provide a result unbiased by values missing at random.
At follow-up 1 and follow-up 2 immediately after and 12 weeks after the intervention, respectively, a linear mixed model analysis will be used for both primary and secondary outcomes using cLDA. No other independent variables will be included in the analysis. The effect size will be calculated as mean change from baseline to follow-up and given as mean estimates of differences with a $95\%$ confidence interval (CI). Patients that do not show up for a follow-up visit will be counted as missing values for the specific assessment point meant to be evaluated at that particular follow-up visit.
## Data monitoring
Confidential documents will be stored in a locked file, while the electronic information that can be traced to an identifiable person will be stored on a password-protected computer behind a secure “firewall” in accordance with the Danish Privacy Act. Data access will be limited to the study coordinator and sub-investigators involved in the study.
An interim analysis will be conducted after the inclusion of 24 patients.
## Adverse events monitoring and harms
Discomforts during the supervised training are expected to be muscle soreness and fatigue. All serious adverse events (SAEs) or side effects will be reported within 15 days to the sponsor and the Danish Medicines Agency (in Danish: Lægemiddelstyrelsen) and EUDAMED when implemented. A SAE is considered as an event resulting in considerable risk of or disability of the participant (or the offspring of the participant) including (but not limited by) death, permanent or severe disability/incapacity, hospitalization, or extension of hospitalization. All adverse events will be recorded in the electronic clinical report form.
## Plans for auditing and communication of amendments
Audits will be implemented on a yearly basis by the study sponsor. A protocol amendment was added for a 1-page patient information as a supplement for the detailed version of written patient information material (approved January 2021) and in October 2022 for repeating TMS at both follow-up visits after MRI as well as February 2023 for Bispebjerg Hospital as a recruitment site.
Any further protocol amendments will be communicated to ClinicalTrials.gov as well as collaborators.
The SPIRIT reporting guidelines were used for reporting the contents of this study protocol [73].
## Research ethics approval
The study has been approved by the Research Ethics Committee in the Capitol Region of Denmark in November 2020 (H-20036199) according to the Declaration of Helsinki of 1964, revised in 2008 and approved by The Danish Data Protection Agency (ID: P-2020–921). The study is registered at ClinicalTrials.gov (ClinicalTrials.gov ID NCT05355831).
Selected elements of the study protocol have been tested in volunteering stroke patients prior to study initiation to ensure that the design is feasible for a patient population.
## Consent
Patients will be informed about the study and its contents by both oral and written informed consent obtained by the study coordinator (MK). The patient is given 24 h to consider participation. There is no post-trial care or any anticipated harm and compensation for trial participation.
## Confidentiality
To ensure confidentiality all patients are assigned to an identifiable study ID and names will never be included in the dataset.
## Dissemination policy
Results will be published in peer-reviewed international journals as well as be presented at national- and international conferences. Results will be published adhering to the CONSORT guidelines [74]. Co-authorship will comply with the Vancouver rules.
A letter will be sent to all participants explaining the results of the study in layman’s language.
## Discussion
TDCS has been used as an add-on treatment to exercise in several previous randomized controlled trials (RCTs) in stroke patients targeting motor deficits as well as language deficits (aphasia) or dysphagia [18, 20, 75, 76]. The clinical effects of TDCS on upper-extremity motor recovery in subacute stroke patients are inconsistent and up to $50\%$ of the patients in the active group are non-responders. This suggests a need to examine the possible missing link between the application of TDCS and a clinical effect on the patient.
There is considerable heterogeneity in prior RCTs considering timing (before/during intervention), mode (anodal/cathodal) and duration of TDCS, current intensity, number of sessions, placement, shape, and size of electrodes as well as stage of stroke (acute, subacute, chronic) [20]. Few prior studies have investigated the use of the more focal HD-TDCS with a 1 + 4 electrode montage in stroke patients with aphasia [37, 77] and motor function in chronic stroke [78, 79]. Prior studies have used field modeling to simulate the optimal TDCS electrode placement but in chronic stroke patients [27, 80] and these studies did not individualize the current intensity of the stimulation. The largest recovery occurs within the first 12 months post-stroke. However, there is a “window of opportunity” within the first 30 days of the stroke onset in which neuroplasticity and thereby the potential for recovery is enhanced and the changes dramatic [2, 4]. It would therefore be appropriate to add TDCS already during this early phase of rehabilitation to faster achieve a better outcome which would allow the patient a faster return to a normal life with as minimal deficits as possible.
When searching MEDLINE, Scopus, and ClinicalTrials.gov there are no studies that combine anodal focal TDCS and field modeling to individualize treatment in a cohort of subacute stroke patients regarding upper-extremity motor function.
We suggest that this individualization is necessary to reach the full potential of TDCS as an add-on treatment for stroke rehabilitation in order to target clinically relevant areas for stimulation and use an appropriate current strength.
This study addresses these issues by individualizing TDCS for each subacute stroke patient using state-or-the-art MRI and field modeling techniques regarding both [1] the individual location of the ipsilesional hand knob area and [2] individual current strength to ensure a physiologically effective electric field distribution in the target area.
This study will further investigate the feasibility of patient-tailored TDCS for stroke rehabilitation in the daily clinical routine of a hospital stroke unit and if it is favorable to apply for the patient concurrent with rehabilitation. This is done both by drop-out rates, by tracking sensations during TDCS, and by asking the patient whether they would recommend TDCS for future stroke patients. We hope this study will also help clarify the process of upper-extremity motor recovery and the role of interhemispheric competition during this process.
In addition, it would be highly interesting to repeat the baseline measurements in a cohort of healthy age-matched individuals to compare the motor network organization and connectivity with stroke patients both in the subacute phase and in the later stages of motor recovery.
## Trial status
The current version of the protocol was approved by the ethics committee in November 2020. Recruitment at Herlev Hospital began in August 2022, recruitment at Rigshospitalet-Glostrup began in October 2022, and at Bispebjerg Hospital in February 2023. In February 2024 an interim study analysis of the first 30 included patients is expected to be conducted. Recruitment of patients is expected to be completed in late 2024.
## Supplementary Information
Additional file 1. Standard Operating procedures – Upper-extremity training. Additional file 2. Exercise log – translated version and the original Danish version. Additional file 3. Outcome measures, abbreviations, description and purpose. Additional file 4: Figure S4. Visual cues for the fMRI paradigms.
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|
---
title: 'Efficacy of Hyaluronic Acid in Relieving Post-implantation Pain: A Split-Mouth
Randomized Controlled Trial'
journal: Cureus
year: 2023
pmcid: PMC10035270
doi: 10.7759/cureus.36575
license: CC BY 3.0
---
# Efficacy of Hyaluronic Acid in Relieving Post-implantation Pain: A Split-Mouth Randomized Controlled Trial
## Abstract
Background Many patients suffer from some degree of pain following the surgical procedures of dental implantation. The fear of pain may be one reason for postponing such prosthodontic treatments. Many procedures have been suggested to control post-implantation pain. This trial evaluated the effectiveness of using hyaluronic acid (HA) during dental implantation on patients’ perceived pain during the postsurgical soft-tissue healing period.
Methodology A split-mouth randomized controlled trial (RCT) was conducted. The trial sample consisted of 22 dental implants in 11 patients (five males and six females). Patients were selected from those attending the Department of Oral Medicine at the Faculty of Dentistry, University of Damascus between February 2021 and May 2022. The implants were performed in similar bone quality and density for each patient as the implants were inserted in the same jaw on both sides to ensure the same physiological conditions. The study sample was divided into two groups. The first group (the experimental group) consisted of 11 implants in which the implant site was drilled, following which HA was placed inside the implant site and on the surrounding bone before the flap was returned and sutured. The second group (the control group) comprised 11 implants following the conventional procedure without applying any material to the implant socket. The main outcome measure was pain perception which was assessed using the visual analog scale (VAS). Patients were asked to record their perceived pain on the first, third, and tenth days. Two-sample t-tests were used to detect significant differences.
Results There were statistically significant differences in the mean pain intensity between the experimental and control groups on the first, third, and tenth days ($p \leq 0.05$). The mean values of perceived pain in the control group were 5.68, 1.72, and 0.56 on the first, third, and tenth days, respectively. In comparison, the mean values of perceived pain in the experimental group were 4.52, 1.14, and 0.18 on the first, third, and tenth days, respectively. The maximum perceived pain in the control group was 7.5 on the first day following implantation, whereas the maximum value recorded in the experimental group was 6.5. At the third assessment time (i.e., 10 days following the surgical intervention), the mean values were in the very mild category of pain intensity.
Conclusions This study showed that applying HA in the implant cavity and on the surrounding bone effectively reduced pain after dental implant surgery in comparison with the control group. Patients had lower mean pain scores at one, three, and ten days following surgery compared to the conventional method. HA is suggested to be an adjunctive method to control postsurgical pain after dental implantation.
## Introduction
Dental implantation is a surgical procedure in which a piece fixed to the jaw bones is placed to support a crown, bridge, or movable device [1]. The success of any implant depends on a series of factors related to the patient and the procedure itself, such as general health, bio-acceptance of the implant material, implant surface treatment, surgical procedure, local bone quality, and quantity [2]. Several studies have been conducted on the use of auxiliary materials that can be applied in the context of dental implants to ensure higher success rates and benefit from the additional properties of these materials [3]. For example, hyaluronic acid (HA), which effectively transfers some elements that direct bone growth and tissue regeneration and accelerate bone restoration, has been reported [4].
The body naturally produces HA which forms polysaccharide chains found in the extracellular space of connective tissue, synovial fluid, and other tissues [4]. It has many structural and physiological functions, including intra- and extracellular interactions, interactions with growth factors, and osmotic pressure regulation, as these functions help maintain tissue structure and integrity [4]. *In* general, all cells of the body, especially connective tissues, can produce HA, as it is synthesized in the cell membrane and then excreted into the extracellular matrix [5].
Studies have shown that a high concentration of low or moderate-molecular-weight HA molecules has the strongest effect in controlling germs compared to other elements [6]. It has an anti-inflammatory effect in the initial stages of inflammation and promotes the infiltration of inflammatory cells and the extracellular space into the wound site [7]. In addition to activating cell migration, reproduction, and differentiation, HA helps differentiate the cells responsible for rebuilding the damaged tissue and plays an important role in forming blood vessels [8]. It accelerates bone repair through chemotaxis, proliferation, and successive differentiation of mesenchymal cells, as well as supports the growth of fibroblasts, chondrocytes, and mesenchymal stem cells [7]. HA has been used as a direct injection into the articular space of the temporomandibular joint [9]. It has also been used to treat lichen planus [10]. HA has been used as an adjunctive therapy during the surgical correction of Class I Miller gingival recession [11]. Moreover, it has been used to relieve complications such as impacted mandibular third molar surgery pain, trismus, edema, and dry sockets [12].
When reviewing the literature, some studies have evaluated the use of HA in relieving pain and inducing new bone formation. A study by Yilmaz et al. aimed to evaluate the effectiveness of applying HA topically to relieve the complications of extraction of the impacted lower third molars [13]. Because the pain decreased in the test group, HA was suggested as an alternative to analgesics after extraction of the impacted mandibular third molars [13]. In another study by Alcantara et al., in which they studied the effect of $1\%$ HA on bone formation in the dental alveolar socket after extraction, it was found that there were significant differences in bone formation in favor of the group where HA gel was applied [14]. In a study by Mohammad and Al-Ghaban to study the effect of HA gel on osseointegration around titanium implants in rabbits, the authors concluded that HA was a bone-conducting substance that promoted and accelerated fusion around titanium implants by stimulating osteoblasts and early localization of bone tissue [15].
Few previous studies have used HA in the implant socket, and most used HA topically after the placement of dental implants in the form of ointments and sprays [16,17]. In these studies, the application was made following suturing. However, in this study, the intention was to inject HA in the implant socket and on the alveolar bone before implant placement and suturing to preserve HA for a long time. This study aimed to evaluate the effectiveness of using HA in dental implants in terms of pain relief that may accompany dental implant surgery using the visual analog scale (VAS) on the first, third, and tenth days following the surgical intervention.
## Materials and methods
Study design and settings The design of this study was a split-mouth randomized controlled trial. This trial was conducted between February 2021 and May 2022 at the Department of Oral Medicine, Faculty of Dentistry, University of Damascus. The Faculty of Dentistry at the University of Damascus Local Research Ethics Committee approved this study (UDDS-3255-13112021/SRC-1198). This trial was retrospectively registered at Clinical Trials.gov (NCT05776290).
Sample size The sample size was calculated using the G*power 3.1.7 program (the Heinrich-Heine University in Düsseldorf, Germany) with a significance level of 0.05 and statistical power of $80\%$. After applying the calculation assumptions, it was found that 22 implants were required (i.e., 11 patients having two opposite implants in their mouths).
Patient recruitment and follow-up After examining 47 patients attending the Department of Oral Medicine, Faculty of Dentistry University Damascus, 15 patients (aged 27 to 60) met the requirements for inclusion. Patients were informed about the research using standardized and well-detailed information sheets. Upon accepting to participate in the trial, informed consent forms were obtained from patients. Of the 19 patients who consented to participate in the trial, 11 (five men and six women) were chosen randomly. The inclusion criteria were [1] bilateral tooth loss with sufficient amount of bone volume; [2] no general problems; [3] good oral health; and [4] age between 20 and 60 years. The exclusion criteria were [1] the use of immunosuppressive drugs and corticosteroids for long periods; [2] the existence of serious systemic disorders; [3] contraindications for local anesthesia or oral surgery; [4] pregnant women and nursing mothers; [5] patients receiving chemotherapy or radiation; and [6] alcoholics and heavy smokers.
Experimental and control groups A total of 11 patients were included in this trial, with 22 implants implanted, two implants in each patient’s mouth through a split-mouth design. The experimental group consisted of 11 implant cases. The skin around the mouth was initially cleaned using a polyvidone iodine solution, and the surgical area was isolated. Then, local infiltration anesthesia was established. A full-thickness buccal mucoperiosteal flap was lifted, and the implant socket was drilled. HA was injected into the implant socket by syringe and on the alveolar bone in the experimental group (Figure 1). The implant was inserted at the level of the alveolar ridge (Figure 2), while the other socket of the control group on the opposite side was manipulated normally (Figure 3). Finally, suturing was performed (Figure 4). The principal researcher (WHA) performed all surgical interventions in both groups.
**Figure 1:** *Injection of hyaluronic acid into the alveolar socket before implantation on the experimental side.* **Figure 2:** *Inserting the implant into the alveolar socket and positioning it at the desired level.* **Figure 3:** *The opposite side being treated conventionally in the control group.* **Figure 4:** *Suturing performed at the end of the implantation procedure in both groups (i.e., on both sides).*
Randomization of the intervention side Each patient was asked to pick an opaque sealed envelope from a container to allocate the intervention side. The containers included six envelopes with the letter R indicating the right-hand side and six envelopes with the letter L indicating the left-hand side.
Outcome measures The pain was assessed using the VAS to determine post-implant pain intensity. Each patient was asked to mark their perception of pain at two o’clock in the afternoon on the first, third, and tenth day after implantation. The intensity of pain was classified according to the following categories: 0 = no pain, 1-3 = mild pain, 3-6 = moderate pain, and 6-10 = severe pain [18,19].
Statistical analysis All statistical analyses were performed using SPSS version 20 (IBM Corp., Armonk, NY, USA). The Kolmogorov-Smirnov test was used to check the normality of the distributions. Levene’s test was used to evaluate the equality of variances. An independent t-test was used to compare the results between the two groups. The significance level was set at 0.05.
## Results
Baseline sample characteristics The Consolidated Standards of Reporting Trials (CONSORT) flow diagram illustrates patient recruitment, assignment, follow-up, and inclusion in data analysis (Figure 5). In total, 11 patients with 22 implants were enrolled (five males ($45.45\%$) and six females ($54.54\%$)) in the study groups, with a mean age of 44 (±11.9) years (Table 1). A total of 11 implant cases were enrolled in the HA group, and 11 implant cases were enrolled in the control group. There were no dropouts.
**Figure 5:** *The Consolidated Standards of Reporting Trials (CONSORT) flow diagram of patient recruitment, assignment, follow-up, and inclusion in data analysis.* TABLE_PLACEHOLDER:Table 1 Perception of pain In the first assessment time (i.e., on the first day), the greatest VAS score was 6.5 in the HA group and 7.5 in the control group. The lowest VAS score in both study groups was 4. On the third day, the greatest VAS score for both study groups was 3, and the lowest VAS score was 0.5. On the 10th day, the highest VAS score was 0.5 in the HA group and 1 in the control group. The lowest VAS score was 0 in both study groups (Table 2).
**Table 2**
| Assessment time | Group | Minimum | Maximum | Mean | Standard deviation |
| --- | --- | --- | --- | --- | --- |
| First day | Control | 4.0 | 7.5 | 5.68 | 1.27 |
| Third day | Control | 0.5 | 3.0 | 1.72 | 0.84 |
| Tenth day | Control | 0.0 | 1.0 | 0.56 | 0.32 |
| First day | Experimental | 4.0 | 6.5 | 4.52 | 1.21 |
| Third day | Experimental | 0.5 | 3.0 | 1.14 | 0.96 |
| Tenth day | Experimental | 0.0 | 0.5 | 0.18 | 0.25 |
In the control group, the mean VAS score was 4.52 on postoperative day one. On the third day, it decreased to 1.14, and after 10 days, it further decreased to 0.14. In the control group, the mean VAS score was 5.68 on postoperative day one. On the third day, it decreased to 1.72, and on the 10th day, it further decreased to 0.56. It was observed that at all assessment times, the mean VAS scores in the control group were significantly greater than those in the HA group ($p \leq 0.05$) (Table 3).
**Table 3**
| Assessment times for the comparisons between the two groups | Assessment times for the comparisons between the two groups.1 | Levene’s test | Levene’s test.1 | T-test for equality of means | T-test for equality of means.1 | T-test for equality of means.2 | T-test for equality of means.3 | T-test for equality of means.4 | T-test for equality of means.5 | T-test for equality of means.6 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Assessment times for the comparisons between the two groups | Assessment times for the comparisons between the two groups | F | P-value | t-value | df | P-value (two-tailed) | Mean difference | SE | 95% CI of the difference | 95% CI of the difference |
| Assessment times for the comparisons between the two groups | Assessment times for the comparisons between the two groups | F | P-value | t-value | df | P-value (two-tailed) | Mean difference | SE | Lower | Upper |
| First day | Equal variances assumed | 0.001 | 0.919 | 0.085 | 2.0 | 0.021 | 1.15 | 0.52 | -0.65 | 1.56 |
| First day | Equal variances are not assumed | | | 0.085 | 1.995 | 0.025 | 1.15 | 0.53 | -0.65 | 1.56 |
| Third day | Equal variances assumed | 0.017 | 0.677 | 0.047 | 2.0 | 0.043 | 0.18 | 0.58 | -0.62 | 0.99 |
| Third day | Equal variances are not assumed | | | 0.047 | 1.969 | 0.043 | 0.18 | 0.58 | -0.62 | 0.99 |
| Tenth day | Equal variances assumed | 0.276 | 0.112 | 0.129 | 2.0 | 0.021 | 0.18 | 0.38 | -0.11 | 0.48 |
| Tenth day | Equal variances are not assumed | | | 0.129 | 1.704 | 0.024 | 0.18 | 0.38 | -0.12 | 0.48 |
## Discussion
The study results showed that HA had a positive effect in relieving pain after dental implants, with statistically significant differences between the experimental and control groups at the three assessment times. This may be due to the physiological and structural properties of HA, as it has an anti-inflammatory effect in the initial stages, an antibacterial effect, and activates cell migration, proliferation, and differentiation. As a result, it helps the differentiation of the cells responsible for rebuilding the damaged tissue and accelerates the healing of the bony space at the site of the wound; by stimulating angiogenesis, it also accelerates bone repair.
We found that the application of HA gel can reduce pain in the first and third days after the dental implant, which was also reported by Yıldırım et al. [ 20]. Their study was conducted on 36 patients requiring a free gingival graft from the palate. The patients were divided into two study groups with the application of HA gel at two different concentrations of $0.2\%$ and $0.8\%$, followed by covering it with a gingival pad, with nothing placed in the third control group. The pain was recorded using the VAS on days three, seven, 14, 21, and 42, and the results indicated lower pain values in test groups on days three and seven ($p \leq 0.001$ and $p \leq 0.001$, respectively).
Our results also agreed with the study by Abdelmabood and Eid, who studied the effect of HA gel in relieving pain after applying it to patients with odontogenic cysts [21]. The results showed statistically significant differences between the study groups, and HA reduced pain levels on the first, third, and seventh days after surgery.
Our study findings agreed with those of Yilmaz and colleagues, who used HA gel after extracting third molars [13]. Their results showed a significant decrease in pain in the HA groups according to the VAS ($$p \leq 0.001$$).
Our study differed from the study reported by Marin et al. which included 30 patients with uncontrolled type 2 diabetes who had symmetrical extractions in the mandible [22]. HA gel at a concentration of $0.8\%$ was applied to the alveolar socket of the test group. Pain intensity was recorded on the fifth, 10th, 15th, 20th, and 25th days, and no statistically significant differences were noted. This difference can be explained by applying HA in patients with uncontrolled diabetes prone to infections and delayed wound healing.
Study limitations There were some limitations of this study. The sample size did not allow for gender-based comparisons. In addition, discrimination between patient responses on the pain scale based on their age group was not done in this study. Therefore, future research work should consider gender- and possible age-related effects on the perception of pain following implantation. This trial evaluated the effect of HA on pain control; however, the effect on the quantity and quality of the newly formed bone around the inserted implants was not assessed. Furthermore, other materials should be compared with HA regarding pain control following dental implantation in future clinical trials.
## Conclusions
This study showed that applying HA in the implant cavity and on the surrounding bone effectively reduced pain after dental implant surgery in comparison with the control group. Patients had lower mean pain scores at one, three, and ten days following surgery compared to the conventional method. This material is suggested to be an adjunctive method to control postsurgical pain after dental implantation. More studies are required to assess the effect of this material on the quality of bone being formed in the post-healing period. Additionally, gender and age differences in pain perception should be evaluated in future research.
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|
---
title: Gender differences in premature mortality for cardiovascular disease in India,
2017–18
authors:
- Jhumki Kundu
- K. S. James
- Babul Hossain
- Ruchira Chakraborty
journal: BMC Public Health
year: 2023
pmcid: PMC10035272
doi: 10.1186/s12889-023-15454-9
license: CC BY 4.0
---
# Gender differences in premature mortality for cardiovascular disease in India, 2017–18
## Abstract
### Background
The present study tries to provide a comprehensive estimate of gender differences in the years of life lost due to CVD across the major states of India during 2017–18.
### Methods
The information on the CVD related data were collected from medical certification of causes of death (MCCD reports, 2018). Apart from this, information from census of India [2001, 2011], SRS [2018] were also used to estimate YLL. To understand the variation in YLL due to CVD at the state level, nine sets of covariates were chosen: share of elderly population, percentage of urban population, literacy rate, health expenditure, social sector expenditure, labour force participation, HDI Score and co-existence of other NCDs such as diabetes, & obesity. The absolute number of YLL and YLL rates were calculated. Further, Pearson’s correlation had been calculated and to understand the effect of explanatory variables on YLL due to CVD, multiple linear regression analysis had been applied.
### Results
Men have a higher burden of premature mortality in terms of Years of life lost (YLL) due to CVD than women in India, with pronounced differences at adult ages of 50–54 years and over. The age pattern of YLL rate suggests that the age group 85 + makes the highest contribution to the overall YLL rate due to CVD. YLL rate showed a J-shaped relationship with age, starting high at ages below 1 years, dropping to their lowest among children aged 1–4 years, and rising again to highest levels at 85 + years among both men and women. In all the states except Bihar men had higher estimated YLL due to CVD for all ages than women. Among men the YLL due to CVD was higher in Tamil Nadu followed by Madhya Pradesh and Chhattisgarh. On the other hand, the YLL due to CVD among men was lowest in Jharkhand followed by Assam. Similarly, among women the YLL due to CVD was highest in Tamil Nadu followed by Madhya Pradesh and Chhattisgarh. While, the YLL due to CVD among women was lowest in Jharkhand. Irrespective of gender, all factors except state health expenditure were positively linked with YLL due to CVD, i.e., as state health expenditure increases, the years of life lost (YLL) due to CVDs falls. Among all the covariates, the proportion of a state's elderly population emerges as the most significant predictor variable for YLL for CVDs ($r = 0.42$ for men and $r = 0.50$ for women).
### Conclusion
YLL due to cardiovascular disease varies among men and women across the states of India. The state-specific findings of gender differences in years of life lost due to CVD may be used to improve policies and programmes in India.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-023-15454-9.
## Background
Non-communicable diseases are the major public health and development concerns of the twenty-first century. The non-communicable diseases, which accounts for more than $60\%$ of all deaths globally, includes cardiovascular disease (CVD), various cancers, chronic respiratory disorders, and diabetes [1]. Although NCDs related morbidity and mortality were once prevalent mostly in developed countries, the morbidity and mortality due to NCDs have increased recently in many LMICs [2]. CVDs, such as ischaemic heart disease and stroke, account for 17.7 million deaths and are the leading cause of death [3]. CVDs are set to accelerate even more as a result of demographic shifts, epidemiological transition, and increasing urbanization, which are all linked to an increase in CVD risk factors i.e., smoking, sedentary lifestyle, obesity, hypertension, and hypercholesterolemia [4]. Cardiovascular disease (CVD) also continues to be a leading cause of premature mortality and rising healthcare expenses [5, 6]. By 2025, over 5 million premature CVD deaths in men and 2.8 million in women are projected globally [7], and the disease imposes a significant economic burden on both developed and developing countries [8]. Thus, understanding the impact of CVD related morbidity and mortality is important to understand the health of a country.
The proverb "women get sicker, but men die sooner" is widely supposed, Women have not always outlived males, according to historical demographic figures. However, historical demographic data demonstrate that women have not always outlived men [9]. Men and women are exposed to various social, psychological, and disease risk factors throughout their lives, which contributes significantly to gender differences in morbidity and mortality, including non-communicable disease (NCD). Thus, the seemingly counterintuitive gender disparities in morbidity and death within and across countries are neither universal nor unchanging [10]. Although, evidence from existing literature suggests that men have higher rates of mortality, while women have a higher prevalence of morbidity than men, this situation can be explained by using two broad theories: biological and psychosocial [11, 12]. According to biological theory, genetic and hormonal differences between men and women are the root cause of the majority of health-related outcomes [13]. On the other hand, the psychosocial theory, proposes that the difference is primarily due to the role of women in society and their attitude towards illness. This disparity exists in both developed and developing countries [11].
The long-held beliefs on the health difference between men and women have contributed to the under-recognition of NCDs in women [14]. For instance, there is a widespread belief that women's health is characterized solely by their reproductive capacity [15], despite the fact that chronic diseases, violence, and other injuries account for two-thirds of all deaths and impairments in women [16]. NCDs, particularly CVDs, have traditionally been thought of as exclusively male diseases and women’s non-communicable diseases (NCDs) are only a problem in high-income countries [17]. In fact, the majority of NCD deaths among women occur in low- and middle-income countries, with rates significantly higher than in developed countries [18]. Also, it was considered that NCDs related deaths were prominent exclusively among the elderly [18].
Looking at the most populated country, India, the age-standardized CVD death rate of 272 per 100,000 people, which is much higher than the global average of 235 [1]. According to the WHO's India report, males had higher age-adjusted CVD death rates than women (349 per 100,000 men and 265 per 100,000 women). These rates are two to three times higher than the United States, where men have a mortality rate of 170 per 100,000 and women have a rate of 108 per 100,000 [19]. Until now, Indian studies analysing age-pooled data have mainly reported male dominance in the overall prevalence of CVD. The gender difference in different ages have been reported by only one Indian study to our knowledge, which also showed greater rates in males in all age groups and no sign of reversal, contrary to evidence from high income nations [20].
Saying so, one of the well-established measures to understand the population health is premature mortality. Premature mortality has increased in recent years due to demographic and epidemiological changes that have altered death rates and morbidity patterns across age groups. Although new evidence indicates a decline in infant and child mortality, little is known about India's age and gender distribution of premature deaths. In India, premature mortality, as measured by years of life lost due to CVD, rose by $59\%$, from 23.2 million in 1990 to 37 million in 2010 [21]. In 2019, CVDs accounted for $38\%$ of the 17 million premature deaths (before age 70) caused by non-communicable illnesses [22]. Despite numerous studies on the trends, differentials, and risk factors for CVD mortality, studies that estimate premature mortality due to CVD in India are scarce [23, 24].*It is* necessary to count the dead to understand the influence of cardiovascular diseases on mortality, but it is also required to determine how early the deaths occur.
A number of measures have been developed and used to quantify the extent of premature mortality: the potential years of life lost (PYLL), the premature years of potential life lost (PYPLL), the working years of potential life lost (WYPLL) and the valued years of potential life lost (VYPLL). Many new indices have been developed to measure premature mortality such as the DALY (disability adjusted life years), HALY (health adjusted life years) and YLL (years of life lost). Although numerous approaches have been used to correctly analyze and report the prevalence and trends of premature mortality; many of them have severe shortcomings, especially when gathering real early fatalities [25]. The YLL is a versatile, accurate and comprehensive measure of premature mortality. It is able to reflect the mortality patterns dominated by underlying disease processes occurring at early deaths, majority of which could be delayed to older ages or could be prevented with effective public health interventions. It takes into account both the frequency of deaths and the age at which it occurs. YLL (years of life lost) is an important mortality statistic that complements the crude mortality indicators. As a measure of the premature mortality, YLL has the following advantages: (i) it avoids arbitrary age cut-offs, which are never methodologically justifiable, and exclusions of older population groups;(ii) all deaths imply the loss of some potential years of life, which means that deaths at all ages contribute to the quantification of the burden of premature mortality; & (iii) YLL gives more weight to deaths that happen at younger ages [25]. The YLL becomes a key indicator for evaluating and guiding the progress of public health policies and interventions due to its ease of calculation and comprehension. Thus, this research aims to estimate gender differences in years of life lost (YLL) due to cardiovascular diseases (CVDs) across the states of India.
## Data source
Three major data sources were used to estimate YLL. These include, the census of India for the years 2001 and 2011, Sample Registration System (SRS), 2018 and medical certification of causes of death Report (MCCD), 2018. The data on age-sex structure of the population of India and its states were taken from the C13 table of the Census of India. The age-specific death rates for the states for the year 2018 (latest available data) were taken from the SRS Statistical Report. Data on cause-specific deaths across the states of India were taken from the Medical Certification of cause of death (MCCD) reports. Although the MCCD report has the limitation of a lack of representativeness of information, we used it due to the unavailability of other sources of state-specific cause of death information in India. Therefore, we used the proportion of CVD deaths rather than the absolute number of CVD deaths from the MCCD reports.
Explanatory variables used to establish a correlation with YLL due to CVDs, were chosen through extensive literature study from different data sources. The share of the elderly population, literacy rate, percent urban and workforce participation rate were calculated from the Census of India, 2011. To adjust the potential chronic disorders, prevalence of obesity and diabetes for 15 to 49 years of women and 15 to 54 for men were taken from the National family health survey (NFHS-4, 2015–16). The data for state-wise social security-related expenditure was taken from RBI report of state finances whereas state-wise health expenditure data was taken from NSSO 75th round report. The data of state-wise human development Index (HDI) score was taken from the ministry of statistics and programme implementation, govt of India, for the year of 2017–18. Gender stratified analysis was carried out in this study.
## Methodology
Estimates of YLL (Years of Life Lost) were used in the analysis. YLL was calculated considering the discounting rate of $3\%$ [26–29].The discounting rate reflects the social preference for a healthy year now rather than in the future. The value of a year of life is generally decreased annually by a fixed percentage. Both the World Bank's Disease Control Priorities study and the Global Burden of Disease (GBD) project used a $3\%$ discount rate, and the US Panel on Cost-Effectiveness in Health and Medicine recently recommended that health economic analyses also use a $3\%$ real discount rate to adjust both costs and health outcomes [30, 31].The absolute number of YLL The absolute number of YLL is estimated as:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$YLL=\frac{N}{r}(1-{e}^{-rL})$$\end{document}YLL=Nr(1-e-rL)where N is the number of deaths, L is the life expectancy at the age of death, and r is the discount rate. This metric quantifies the absolute number of YLL due to premature deaths in certain population. Calculation of YLL rates YLL rate due to cause c, in the population of sex s and age a, and time t can be calculated by the formula:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$YLL\;rate \left(c,s,a,t\right)=\frac{YLL\left(c,s,a,t\right)}{P(s,a,t)}*100000\;population$$\end{document}YLLratec,s,a,t=YLLc,s,a,tP(s,a,t)∗100000populationwhere YLL (c, s, a, t) is the number of YLL due to cause c, in population of sex s and age a, and period t, P (s, a, t) is the population size at sex s, age a and period t.
YLL rate provides a relative quantification of the magnitude of the effect of diseases, injuries, and risk factors on premature mortality in the population, but it does not account for variations in the population's age distribution [25]. It is useful for the comparison of sex and age groups, for instance, age-sex-specific YLL rates, is helpful.
Estimation of YLL involves following steps-Step 1: age-sex specific population for 2017 was projected by the exponential growth rate method for the major states of India. The most frequently used mathematical model assumes that population growth will follow an exponential distribution, which is a generalization of the geometric function when time t is considered to be a continuous variable [32].
The exponential growth of population leads to the equation\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{Pt}=\mathrm P0\;\mathrm e^\wedge\mathrm{rt}$$\end{document}Pt=P0e∧rtWhere Pt = Population in 2017.
P0 = Population in 2011 r = Exponential rate of population growth as per person per year; and t = Period in years elapsed from year 0 to t (2017–2011 = 7 years) Step 2: The age-sex-specific death rate (ASDR) from SRS -2018 was multiplied by the projected 2017 population to get the age-sex-specific numbers of total deaths. Step 3: The proportion of the CVD deaths from MCCD reports (Medical Certification of Cause of death) were multiplied with the numbers of total deaths from step 2 to obtain the age-sex specific numbers of deaths due to CVDs. The total numbers of deaths and deaths due to CVD, thus obtained, were aggregated into ten age groups: below 1 year, 1–4 years, 5–14 years, 15 -24 years, 25–34 years, 35–44 years, 45–54 years, 55–64 years,65–69 years and 70 years or older. Step 4: Age-sex-specific life tables for all-cause were constructed for India (2017–18). Then, YLL due to CVD was calculated for the major states of India.
To establish the relationship between the independent [share of elderly population, literacy rate, percent urban, work force participation rate, prevalence of obesity and diabetes, social security related expenditure, Health expenditure & Human Development Index (HDI)] and dependent variable (YLL due to CVD), Pearson’s correlation had been calculated and to understand the effect of explanatory variables on YLL due to CVD, multiple linear regression analysis has been done.
The equation for the regression is as follows,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$y=a+{\beta }_{1}{X}_{1}+{\beta }_{2}{X}_{2}+{\beta }_{3}{X}_{3}+\dots +{\beta }_{n}{X}_{n}+\in$$\end{document}y=a+β1X1+β2X2+β3X3+⋯+βnXn+∈Where, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{1},{\beta }_{2},{\beta }_{3}$$\end{document}β1,β2,β3 are regression coefficients, shows the effect of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{1}, {X}_{2},{X}_{3}$$\end{document}X1,X2,X3 (e.g. share of elderly population, rate of urbanisation, the prevalence of diabetes etc.) on the value of YLL due to CVDs.
## Robustness check of YLL estimation using MCCD data
The estimated YLL was validated using GBD estimates. The comparison of our estimated YLL vis-à-vis GBD estimates as a strong robustness cheek for our estimates. For instance, Fig. 1 shows the comparison of our estimated YLL to the GBD estimated YLL. Our estimated YLL is lower compared to GBD estimated YLL (Fig. 1). The most notable discrepancies between GBD estimates and our estimated YLL for just a few conditions. We used Medical Certification of cause of death (MCCD) report due to the unavailability of other sources of state-specific cause of death information in India. However, the MCCD report is based primarily on urban hospital deaths (either public or private). Due to incomplete coverage and inadequate quality of data, use of this MCCD data has been compromised. Therefore, our estimated YLL is lower than GBD estimates. Our estimated YLL may lack the representative character in the strict sense, however, it may be sufficient to throw some valuable insights into YLL by CVD in India and its states. Fig. 1Comparing the YLL estimates between MCCD estimates and GBD estimates for robustness check
## Age and gender wise years of life lost (YLL) for India
Table 1 illustrates the estimated age-specific YLL rates due to CVD among men and women in India. The estimated YLL rate for all ages to be 3565.51 [3347.23–3812.96] among per 100,000 men and 2804.9 [2399.91–2970.82] among per 100,000 women in India (Table 1). The YLL distribution pattern by age and gender can be explored and analysed using age-and-sex-specific YLL rates. Figure 2 shows that age group 60–64, 65–69,70–74,75–79,80–84 and 85 + had the highest YLL rates, which is consistently higher in men compared with women across age groups. YLL rate showed a J-shaped relationship with age, starting high at ages below 1 years, dropping to their lowest among children aged 1–4 years, and rising again to highest levels at 85 + years among both men and women. The age pattern of YLL suggests that the age group 85 + makes the highest contribution to the overall YLL rate due to CVD.Table 1Years of life lost (YLL) rate due to CVD per 100,000 population by gender in India, 2017–18INDIAAge groupsMenWomenMenWomenCVD deathsYLL rateYLL rate $95\%$ CI (LL)YLL rate $95\%$ CI (UL)YLL rateYLL rate $95\%$ CI (LL)YLL rate $95\%$ CI (UL)0–1918598082062.351576.323658.812491.411620.943666.271–433692710223.23199.45297.12201.64199.77377.965–932313188155.51121.32191.83170.53139.72195.3910–1438773053168.70132.15194.71149.27101.48152.4515–1996445333372.30311.88434.07233.54240.45335.3120–24133437762501.97425.13594.63304.93287.69387.5325–29204889687792.76742.43941.83395.88370.37497.1030–342310710739994.53862.581099.07511.69479.37642.1735–3936395163661726.071726.071570.68841.35820.771041.1140–4445339225402294.142004.872459.421260.091218.511571.9545–4969531366513783.003411.714197.862215.731981.232498.8250–5082842532645017.574320.045252.733604.283287.824061.3655–59125188907308477.237398.999054.626543.585640.726920.2560–6416135210855910776.129036.4510892.367963.117225.018657.6865–6918641813320115069.4813509.8416180.6511718.6610598.5912896.5470–7417614015147819109.7616565.9420269.7416242.8714585.5718333.7275–7914487013484220759.9019166.7723494.3019487.9017722.7022394.7280–8411734911373225552.8421334.0825894.2622676.6421056.6127839.1385+11690312512036587.3428939.9136433.2130975.0626375.6633681.06Total134857010387653565.513347.233812.962804.92391.992970.82Fig. 2Age -specific YLL rates per 100,000 population by gender, in India,2017–18
## Gender difference in YLL rate across the states in India
There were substantial regional variations in the years of life lost (YLL) due to CVD in India. We also observed gender differences in the Years of life lost (YLL) due to CVD across the states in India. In all states except Bihar (1768.76 YLL per 100,000 men vs 2184.03 YLL per 100,000 women), men had higher estimated YLL due to CVD for all ages than women. The gender differences in Years of lost (YLL) due to CVD was higher in Tamil Nadu among the states. Among men the years of life lost (YLL) due to CVD was higher in Tamil Nadu (4842. 51 per 100,000 men) followed by Madhya Pradesh (4192.98 per 100,000 men) and Chhattisgarh (3992.86 per 100,000 men). On the other hand, the YLL due to CVD among men was lowest in Jharkhand (1011.59 per 100,000 men) followed by Assam (1167.13 per 100,000 women) (Fig. 3). Among women the years of life lost due to CVD was also highest in Tamil Nadu (3710.37 per 100,000 women) followed by Madhya Pradesh and Chhattisgarh. On the other hand, the years of life lost (YLL) due to CVD mong women was lowest in Jharkhand (957.68 per 100,000 women) (Fig. 3). The full results for YLLs by gender, and age for the major states were provided in the Supplementary Tables 1.Fig. 3YLL rates per 100,000 population by gender across the major states of India, 2017–18
## Association between years of life lost and correlates by gender
Correlations between the outcome variable of years of life lose (YLL) owing to CVDs and state-specific explanatory factors by gender were shown in Tables 2 and 3. To better understand the variation in YLL at the state level, nine sets of covariates were chosen: the share of the elderly population, the percentage of the urban population, the literacy rate, health expenditure, social sector expenditure, labour force participation, and the coexistence of other NCDs such as diabetes, obesity, and Human development Index (HDI)etc. The effect of explanatory variables on YLL owing to CVD was examined using multiple linear regression analysis (Table 4). The Variance Inflation Factor test was performed to determine if any regression model was multicollinear, and the mean result was found to be between 2.66 to 3.01, suggesting no multicollinearity. All factors included in a regression model may account for the connection between YLL and men ($64\%$) and YLL and women ($49\%$). Irrespective of gender, all factors except state health expenditure were positively linked with YLL due to CVD, i.e., as state health expenditure increases, the years of life lost (YLL) due to CVDs falls. Among all factors, the proportion of a state's elderly population emerges as the most significant predictor variable for YLL for CVDs ($r = 0.42$ for men and $r = 0.50$ for women). With rising rates of obesity and diabetes, the incidence of early mortality from CVDs also increased, resulting in a high YLL. The situation was similar for the labour force participation rate, social security spending, urban population share, and HDI score although the association is not as strong. Table 2Association between the years of life lost (YLL) due to CVD among men and socio-economic and health related variables, India, 2017–18Years of life lost (YLL) due to CVD among menObesityDiabetesShare of elderly populationPercentage of male population in Urban areaMale literacy RateHealth expenditureSocial sector expenditureLabour force ParticipationHuman development Index (HDI)Years of life lost (YLL) due to CVD1Obesity0.251Diabetes0.190.461The share of elderly population0.420.640.601Percentage of male population in urban area0.130.560.200.081Male literacy rate0.080.610.300.340.731Health expenditure-0.220.35-0.310.610.470.351Social sector expenditure0.390.250.070.210.130.120.281Labour force participation0.24-0.140.02-0.170.01-0.17-0.14-0.201Human development Index (HDI)0.310.830.270.380.650.830.47-0.420.141Table 3Association between the years of life lost (YLL) due to CVD among women and socio-economic and health rated variables, India, 2017–18Years of life lost (YLL) due to CVD among womenObesityDiabetesPercentage of elderly populationPercentage of Female population in urban areaFemale literacy rateHealth expenditureSocial sector expenditureLabour force ParticipationHuman Development Index (HDI)Years of life lost (YLL) due to CVD among women1Obesity0.351Diabetes0.240.621The share of elderly population0.500.690.621Percentage of female population in urban area0.160.550.280.311Female literacy rate0.130.680.630.580.621Health_ expenditure-0.310.34-0.150.410.470.321Social sector expenditure0.350.230.070.100.130.220.281Labour force participation0.32-0.280.12-0.210.18-0.23-0.19-0.011Human Development Index (HDI)0.210.790.390.530.640.880.47-0.420.291Table 4Regression analysis showing the effect of selected socio-economic and health-related variables on YLL Due to CVD, India, 2017–18Explanatory variablesMaleFemaleCoefficientConfidence IntervalCoefficientConfidence IntervalShare of elderly population4.401.31, 7.491.47-1.82,4.76Share of urban population1.21-0.01, 2.430.26-0.89,1.40Literacy rate-8.74-21, 3.53-3.01-9.67,3.64State-health expenditure-1.37-3.41, 0.680.56-1.92,3.05Obesity-0.30-1.29, 0.670.01-1.85,1.88Diabetes-1.01-2.5, 0.47-0.86-2.76,1.08Social sector expenditure0.05-0.66, 0.770.07-0.86,0.96Labour force participation3.06-3.31, 9.480.10-0.62,0.86HDI3.47-7.09,15.042.06-11.48,15.56Constant12.30-19.8, 44.407.18-6.48,22.48R square0.640.49
## Discussion
The present study provides the estimate of years of life lost (YLL) due to cardiovascular diseases (CVDs) by gender across the states of India (2017–18). The estimated YLL rate due to CVD was higher among men than women in India. According to the current study, YLL rates were higher in the age groups 60 and older up to 85 + due to CVD, with 85 + making the largest contribution. Men have a higher burden of premature mortality in terms of Years of life lost (YLL) due to CVD than women, with pronounced differences at adult ages of 40–44 years and above. We observed a remarkable gender differences in YLLs across the states of India. In all states except Bihar, men had higher estimated YLL due to CVD than women. The gender differences in Years of lost (YLL) due to CVD was higher in Tamil Nadu among the states. Irrespective of gender, all the covariates were positively correlated with YLL due to CVD except health expenditure. Among all the covariates, the proportion of a state's elderly population emerges as the most significant predictor variable for YLL for CVDs.
Men in India lost more years of life (YLL) than women in terms of years lost to CVD, supporting earlier findings that men had significantly higher age-standardized rates of cardiovascular disease (CVD) [33]. This finding is consistent with Zhang et al. ’s [2021] [34] study, which found that globally men had a premature death rate from cardiovascular diseases was nearly $35.6\%$ higher than that of women. However, the current study's finding is contradicting with Jie et al. ’s [2014] study, which found that women lost more life expectancy (LE) than men owing to CVD in China [35]. Around $5\%$ of WHO member states (eight countries in 2016, including Ghana, Mali, Sao Tome and Principe, Zimbabwe, Bhutan, Republic of the Congo, and Nigeria) also had a higher premature death rate from CVDs in women than men [34].
The present study found that Years of life lost (YLL) due to CVD varied significantly between Indian states, corroborating an earlier study that found that regardless of the country's level of development, stage of epidemiological transition, or age composition, cardiovascular mortality is not uniformly distributed across the country [36–42]. Jha [2009] also revealed that there are large regional differences in cardiovascular mortality in India among both men and women [43]. A previous study also highlighted that CVDs are geographically dependent and vary among areas and states due to differences in lifestyle and eating habits among residents of different states [44]. Our study revealed that the years of life lost (YLL) due to cardiovascular disease (CVD) were higher in the southern states of Tamil Nadu and the central states of Madhya Pradesh and lower in the eastern states of Jharkhand and the north-eastern states of Assam. Kaur et al. [ 2019] [45] in their study also revealed that Ischaemic heart disease was the leading cause of years of life lost due to premature mortality in Tamil Nadu in 2016. This might be as a result of Tamil Nadu being one of the southern Indian states with an advanced level of epidemiological transition [45]. The findings of the present study also corroborate the finding of an earlier study suggesting that there is considerable geographic variation in CVD mortality in India, with less developed regions, such as eastern and north-eastern states with low Human Development indices, experiencing lower proportionate CVD mortality than more developed states in the south and west [46].
Our study demonstrates that health expenditure is negatively correlated with Years of life (YLL) due to CVD. It can also be pointed out that with State’s intervention through increasing health expenditure the years of life lost due to CVD can be reduced [47]. This finding is further supported by the study of Farahani et al. [ 2010] which showed that a $10\%$ increase in public spending on health in India decreases the average probability of death by about $2\%$, with effects mainly on the young, the elderly, and women [48]. It is also noted from the present study that with rising rates of obesity and diabetes, the incidence of early mortality from CVDs also increased, resulting in a high YLL. Compared to age, sex, and county-matched controls, patients with type 2 diabetes (T2D) had a $15\%$ higher risk of premature all-cause mortality and a $14\%$ higher risk of cardiovascular death, however, the risk varied depending on the patient's age and glycemic control level [49]. The finding of the present study revealed that there is a positive association of obesity prevalence and Years of life lost due to CVD among women which is further supported by the study of Dikaiou et al. [ 2021] who stated that there is a significant increase in the risk for early CVD death in overweight young women, with a marked increase in obese women [50].
## Strengths and Limitation
This is the first ever study that provides estimates of Years of life lost (YLL) due to CVD by gender across the states of India. Some studies used GBD data to estimate YLL but GBD data and the modelling techniques are not in the public domain and hence have not been reproduced in other studies, However, it is not possible to determine how these data were used because changes in model specifications and variable data inputs are not public [51], leading to an inability to understand trends or to compare them with estimates using other methods.
Like every other data-based research this study is also bounded by some limitations; First, this study was unable to assess the YLL of CVDs in all Indian states due to a lack of comprehensive data, and hence 20 major states were selected for the present study. Second, quality of YLL will depend ultimately on the quality of mortality statistics, assessed by the level of registration coverage, timeliness, completeness and accuracy of underlying causes of death diagnosis and coding. The information on CVD deaths across the states of India was obtained for this study from the Medical Certification of Cause of Death (MCCD) reports in order to estimate the YLL due to CVD. MCCD is the only key source of cause specific death statistics across states in India. However the MCCD report is based on hospital deaths (public or private) that have received medical certification and that too mostly from urban areas, so at the national level, the reliability and quality of this information is a major issue. Therefore, our estimated YLL may lack the representative character in the strict sense, but it may be sufficient to throw some valuable insights into YLL by CVD in India and its states. Third, the observed state-specific years of life lost (YLL) due to cardiovascular disease were puzzling to explain, and the relationships between state-specific variables and years of life lost (YLL) due to cardiovascular disease may not have been evaluated conclusively due to data availability constraints.
## Conclusion
From the above findings it is clear that the years of life lost (YLL) due to CVD differs by gender across the states of India. The state-specific findings of gender differences in years of life lost due to CVD may be used to improve policies and programmes, allowing for more persuasive planning of cardiovascular disease prevention and treatment in each state of India, hence advancing progress toward meeting national and global objectives for cardiovascular disease reduction.
## Supplementary Information
Additional file 1: Supplementary Tables 1. YLL rates by 5 years age groups in selected states in India.
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|
---
title: Potential biomarkers of aortic dissection based on expression network analysis
authors:
- Junbo Feng
- Yuntao Hu
- Peng Peng
- Juntao Li
- Shenglin Ge
journal: BMC Cardiovascular Disorders
year: 2023
pmcid: PMC10035273
doi: 10.1186/s12872-023-03173-3
license: CC BY 4.0
---
# Potential biomarkers of aortic dissection based on expression network analysis
## Abstract
### Background
Aortic dissection (AD) is a rare disease with severe morbidity and high mortality. Presently, the pathogenesis of aortic dissection is still not completely clear, and studying its pathogenesis will have important clinical significance.
### Methods
We downloaded 28 samples from the Gene Expression Omnibus (GEO) database (Accession numbers: GSE147026 and GSE190635), including 14 aortic dissection samples and 14 healthy controls (HC) samples. The Limma package was used to screen differentially expressed genes. The StarBasev2.0 tool was used to predict the upstream molecular circRNA of the selected miRNAs, and Cytoscape software was used to process the obtained data. STRING database was used to analyze the interacting protein pairs of differentially expressed genes under medium filtration conditions. The R package "org.hs.eg.db" was used for functional enrichment analysis.
### Results
Two hundred genes associated with aortic dissection were screened. Functional enrichment analysis was performed based on these 200 genes. At the same time, 2720 paired miRNAs were predicted based on these 200 genes, among which hsa-miR-650, hsa-miR-625-5p, hsa-miR-491-5p and hsa-miR-760 paired mRNAs were the most. Based on these four miRNAs, 7106 pairs of circRNAs were predicted to be paired with them. *The* genes most related to these four miRNAs were screened from 200 differentially expressed genes (CDH2, AKT1, WNT5A, ADRB2, GNAI1, GNAI2, HGF, MCAM, DKK2, ISL1).
### Conclusions
The study demonstrates that miRNA-associated circRNA-mRNA networks are altered in AD, implying that miRNA may play a crucial role in regulating the onset and progression of AD. It may become a potential biomarker for the diagnosis and treatment of AD.
## Background
Aortic dissection (AD) is a cardiovascular disease with high mortality and risk; it is also one of the most challenging diseases in cardiovascular surgery [1]. The mortality rate increases by $1\%$ to $2\%$ per hour within 24 to 48 h and $75\%$ within 2 weeks of onset if prompt untreated [2]. There are many influencing factors of AD, but hypertension is the leading cause of AD in China [3]. There are nearly 300 million hypertensive patients in China, and hypertension awareness and control rates are lower than those in developed countries [4]. Therefore, the potential incidence of AD in *China is* enormous. In recent years, the incidence of AD has been increasing yearly, and it is getting younger and younger [5]. With the continuous improvement of surgical diagnosis and treatment, surgical treatment is still one of the essential methods for the treatment of AD [6]. The surgical treatment methods include Sun's procedure, endovascular repair, hybrid surgery and covered stent intervention [7–9]. Although the postoperative survival rate of patients is greatly improved, surgical treatment is only a palliative treatment [7]. The pathological process of the aortic wall in AD patients will not be terminated by partial aortic resection, and the residual false lumen will face a lifelong risk of long-term neoplasia and rupture [10]. Studies have shown that the incidence of long-term postoperative complications in AD patients is still high, such as new hairpin layer [11], aneurysm formation or rupture [12], endleakage [13], stent displacement and stent rupture, which seriously affect the quality of life of patients. Therefore, long-term and even lifelong early prevention after surgery are significant.
Aortic dissection is rapid, ferocious and has a high mortality rate [14]. Early diagnosis of aortic dissection is crucial since most patients wait until the onset of the disease, when mortality is significantly increased and the disease is often misdiagnosed when it first occurs [15]. Most current studies focus on the treatment and pathogenesis of aortic dissection, but there are few studies on the markers of aortic dissection. In recent years, more and more studies have shown that miRNAs negatively regulate protein translation by binding to complementary mRNA sequences. Zhang et al. found that lncRNA-miRNA-mRNA ceRNA regulatory network plays a regulatory role in the occurrence and development of AD [16]. Liu and colleagues found that circRNA networks mediated by circRNAs may be novel biomarkers for aortic dissection [17].
In conclusion, lncRNA, miRNA, mRNA and ceRNA all impact the occurrence and development of aortic dissection, especially the network constructed by them is of great significance to aortic dissection. However, the effect of circRNA-miRNA-mRNA regulatory network on AD regulation is rarely reported. This study is a supplement to this finding, in order to explore the relationship between miRNA and AD.
## Clinical samples of AD were retrieved from the GEO database
Use the GEO database screening of aortic dissection (AD) http://www.ncbi.nlm.nih.gov/GEO. The inclusion criteria were as follows: [1] the data set contained AD or healthy persons; [2] There are 6 or more specimens in the data set. Two eligible datasets were selected, including GSE147026 (expression profiling by high-throughput sequencing, GPL24676) and GSE190635 (expression profiling by array, GPL570). A total of 28 samples were collected from the data, including 14 AD and 14 healthy control (HC) samples. The RNA information of the selected samples was downloaded for further analysis. The sample information and data used in this part are downloaded from public databases and therefore do not require patient consent or ethics committee approval.
## The data processing
The original expression matrix is normalized. The Limma package was used to screen for differentially expressed genes. P-values for genes were calculated using the t-test, and adjusted P-values were calculated using the Benjamini and Hochberg methods. The following criteria selected differentially expressed genes: at least 1.0-fold change between healthy control and AD patient samples and adjusted P-value < 0.05.
## Analysis of enrichment
*The* gene names of differential genes (DEGs) are converted to gene ids by R package "org.hs.eg.db". Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) [18] analyses were implemented with the R "clustererProfiler" software package (version 3.14.3) to explore the possible functions of these DEGs further. Threshold $p \leq 0.05$ and Q-value & LT; 0.05 can screen out different GO terms and signal pathways. The results are visualized using R software packages RichhPlot and GGploT2.
## circRNA-miRNA-mRNA network
The TargetScan, DIANA-microT, miRDB, miRanda, Pita, MicroCosm, eimmo, PicTar databases were used to search for miRNAs that the mRNA might target. The predicted miRNAs that overlapped with at least three databases were selected as candidates. Then, the miRNA with the most binding difference mRNA was screened. On this basis, StarBase v2.0 tool is used to predict the upstream molecule circRNA of the selected miRNA, and Cytoscape software is used to process the obtained data to visualize the predicted results.
## Construction of protein–protein interaction network (PPI) and identification of Hub gene
STRING (https://STRING-db.org) was used to analyze the PPI network (score & GT; 0.4). Execute Perl to get the network files. The cellular Hubba plug-in of Cytoscape (V3.7.2) was used to score the top 10 algorithms for each node gene, namely maximal clique centrality (MCC), the density of maximum neighborhood component (DMNC), maximum neighborhood component (MNC). The five node genes with the highest score of the MCC algorithm were selected as screening genes for further analysis.
## Screening of differentially expressed genes
Two original datasets were studied, including 14 AD and 14 control groups (CON) selected for this study. Differential expression analysis of the data sets showed that compared with the control group, the DEG(GSE147026:total-2908/UP-1115/ Down-1793; GSE190635: total. 1004 / up. / 633 to the 371) significant differences of expression, the threshold for | log2FC | ≥ 0.25 and $p \leq 0.05.$ The expression of DEGs can be seen in the volcano map and heat map. Next, the expression of 200 genes between GSE147026 and GSE190635 was analyzed (Fig. 1).Fig. 1Differential gene analysis. A, C Volcano map showing significantly differentially expressed genes in AD and CON. Red dots represent raised genes, dot cut genes, said threshold for | log2FC |≥ 0.25 or higher, adjusted $p \leq 0.05.$ Figure A is GSE147026, and Figure C is GSE190635 (B, D) heat map, showing the expressions of DEG in AD and CON. Darker red squares indicate higher DEG expression, while darker blue squares indicate lower DEG expression. Figure B is GSE147026, and figure D is GSE190635. E Venn diagram of overlapping DEGs from GSE147026 and GSE190635. A consensus of 200 overlapping mRNAs was identified
## GO and KEGG enrichment analysis
The database was used for GO and KEGG enrichment analysis of 200 overlapping genes. For GO analysis, DEG was abundant in the "regulation of chemical synaptic transmission," "regulation of cross-synaptic signaling," and "muscle organ development" when Gene Ontology annotations of Biological Process (GO-BP) analysis was considered (Fig. 2A). The top three enrichment items for Gene Ontology annotations of Cellular Component (GO-CC) analysis were "cell–cell junctions," "cell cortices," and "asymmetric synapses" (Fig. 2B). For Ontology annotations of Molecular Function (GO-MF), the first enrichment item was "cargo receptor activity" (Fig. 2C). KEGG enrichment analysis showed that the two most critical pathways were "regulation of lipolysis in adipocytes" and "renin secretion" (Fig. 2D).Fig. 2Results of GO and KEGG. Genomic (KEGG) pathway enrichment analysis of DEGs. A ~ C Top 5 genes in GO-BP, 5 genes in GO-CC and 1 gene in GO-MF analysis item in DEGs. D The first two most abundant KEGG pathways (including up-regulated and down-regulated pathways) of DEGs
## Construction of miRNA-mRNA pairing, circRNA-miRNA pairing and circRNA-miRNA-mRNA network
Multimir predicted 2720 miRNAs across the eight databases using target gene overlap in at least three of the eight databases as the criterion (Fig. 3A, B). DEGs from GSE147026 and GSE190635 integrated with miRWalk target genes, and a total of 71,775 pairs of miRNA-mRNAs were identified. Among them, 97 mRNAs of hsa-miR-650, 95 mRNAs of hsa-miR-625-5p, 94 mRNAs of hsa-miR-491-5p and 91 mRNAs of hsa-miR-760 were obtained. Fig. 3CircRNA-miRNA-mRNA Network construction (A) MultimIR tool with eight databases was used to clutter the predicted mRNAs of four miRNAs (miR-650, miR-625-5p, miR-491-5p and miR-760). B Show the number of overlapping genes from different databases. C CircRNA-miRNA-mRNA regulatory network, including 7106 circRNA nodes, 4 miRNA nodes, 155 mRNA nodes and 7483 disease edges. Red triangle: miRNA; Green node: circRNAs; Blue nodules: mRNA By applying the starbase database to identify the corresponding circRNA for each potential miRNA, 7106 circRNA-miRNA pairs were obtained. Specifically, 2120 circRNAs of hsa-miR-650, 1350 circRNAs of hsa-miR-625-5p, 1632 circRNAs of hsa-miR-491-5p and 2004 circRNAs of hsa-miR-760 have been identified. As shown in Fig. 3C, a circRNA-miRNA-mRNA network was preliminarily constructed based on miRNA-mRNA and miRNA-circRNA pairs consisting of 7106 circRNA nodes, 4 miRNA nodes, 155 mRNA nodes and 7483 edges (Fig. 3C).
## PPI Network analysis
A PPI network based on 200 overlapping DEGs associated with four miRNAs miR-650, miR-625-5p, miR-491-5p, and miR-760) was established using Cytoscape software(Version 3.7.2). The original network consists of 86 nodes and 94 edges. Using the cytoHubba plugin, 10 genes (CDH2, AKT1, WNT5A, ADRB2, GNAI1, GNAI2, HGF, MCAM, DKK2, ISL1) were identified in this cluster (Table 1).Table 1Hub gene listRankNameScore1CDH2222AKT1213WNT5A134ADRB285GNAI165GNAI265HGF68MCAM59DKK249ISL14
## Identify potential circRNA-miRNA-mRNA regulatory axes
After calculating the degree of circRNA in the preliminary circRNA-miRNA-mRNA network, circRNAs exhibited the highest degree (degree = 4). By extracting relevant miRNAs and mRNAs, a secondary net consisting of 98 nodes and 358 edges was constructed (Fig. 4).Fig. 4CircRNA-miRNA-mRNA regulatory network with 84 circRNA nodes, 4 miRNA nodes, 10 mRNA nodes and 358 disease edges. Red triangle: miRNA; Green node: circRNAs; Blue nodules: mRNA
## Discussion
Our study attempted to investigate the potential pathogenic mechanism of AD through a comprehensive analysis of two GEO datasets containing AD and healthy samples. We identified two modules with higher retention rates in the two datasets in the analysis results. In addition, the differential miRNAs in the two groups were identified, including hsa-miR-650,hsa-miR-625-5p, hsa-miR-491-5p and hsa-miR-760. We analyzed these four miRNAs to establish a PPI network and obtained 10 DEGs (CDH2, AKT1, WNT5A, ADRB2, GNAI1, GNAI2, HGF, MCAM, DKK2, ISL1) related to these four miRNAs.
CDH2 is overexpressed in cardiac, smooth muscle cells after myocardial infarction [19]. Transverse coarctation of the aorta occurs in hypertrophy and heart failure under pressure overload, and Akt1 hyperactivation occurs in left ventricular cardiomyocytes [20]. TAC enhanced the expression and secretion of WNT5A or WNT11 in cardiomyocytes (CM), cardiac fibroblasts (CF) and cardiac microvascular endothelial cells (CMEC) [21]. ADRB2 agonist promoted the activity of BDNF/TrkB and cAMP/PKA signaling pathways and alleviated HG-aggravated H/R injury in H9C2 cells. Caveolin-3 protects the diabetic heart from I/R injury through GnAI$\frac{1}{2}$, cAMP/PKA and BDNF/TrkB signaling pathways [22]. They are involved in regulating G protein-coupled receptor (GPCR) signaling. Previous studies have shown that the production of HGF in bone marrow stromal cells (BMSCs) leads to IL-11, IL-10, IL-6, IL-8, stromal cell-derived factor (SDF)-1α and vascular endothelial growth factor (VEGF) [23]. The expression of melanoma cell adhesion molecule (MCAM) is increased in abdominal aortic aneurysms [24]. METTL3 positively regulates PRI-MIR$\frac{221}{222}$ maturation in an M6A-dependent manner and subsequently promotes Ang-II-induced cardiac hypertrophy by inhibiting DKK2 activation of Wnt/β-catenin signaling [25].
Unlike miRNAs, circRNAs have high stability and tissue specificity, form a continuous covalent closed cycle, have no 5' or 3' polyadenylation tail, and are resistant to RNAser degradation or RNA exonuclide digestion. Recent evidence has linked circRNAs to a variety of human diseases, such as cancer [26], Alzheimer's disease [27], and cardiovascular disease [28]. However, no studies have shown that circRNA and the associated ceRNA network can be used as diagnostic or prognostic markers for AD.
More and more evidence has confirmed that circRNAs can act as "miRNA sponges" to inhibit miRNA inhibition of their target genes [29]. Of note, the mechanism of the circRNA effect on AD has not been investigated. However, previous studies have found differential circRNA expression prevalent between human AD tissues and normal control tissues. Therefore, we hypothesized that the dysregulation of ceRNA expression would affect the pathogenicity and progression of AD and chose circRNA as the entry point to study the underlying mechanism of miRNA.
Our study has some limitations that need to be addressed. First, all microarray datasets are obtained from purely public data, and there are some unavoidable biases, such as gender and age differences. In addition, only a small number of data sets were analyzed in this study. In addition, further examination of genes altered with AD is needed to determine whether overexpression/knockdown of these genes plays a vital role in AAD in vivo, which may also motivate novel therapeutic approaches.
## Conclusions
Our study demonstrates that circRNA-associated miRNA-mRNA networks are altered in AD, implying that circRNA may play a crucial role in regulating the onset and progression of AD. It may become a potential biomarker for the diagnosis and treatment of AD.
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|
---
title: 'Associations of sleeping, sedentary and physical activity with phenotypic
age acceleration: a cross-sectional isotemporal substitution model'
authors:
- Mengying Han
- Jiaxin Fang
- Yixin Zhang
- Xingxu Song
- Lina Jin
- Yanan Ma
journal: BMC Geriatrics
year: 2023
pmcid: PMC10035275
doi: 10.1186/s12877-023-03874-6
license: CC BY 4.0
---
# Associations of sleeping, sedentary and physical activity with phenotypic age acceleration: a cross-sectional isotemporal substitution model
## Abstract
### Background
Physical activity was believed to be associated with reduced aging among adults, while the competing nature of the physical activity and sedentary behavior has mainly been neglected in studies. We aimed to estimate the association of sleeping, sedentary behavior, and physical activity with aging among adults, considering the competing nature between variables of activity status.
### Methods
A total of 5288 participants who were 20 years or older from the National Health and Nutrition Examination Survey were involved. The questionnaire was used to collect data regarding sociodemographics (age, sex, ethnicity/race, and education), and lifestyle behaviors (smoking, drinking). The Global Physical Activity Questionnaire was used to measure self-reported time for sedentary behavior, walking/bicycling, and moderate-to-vigorous physical activity (MVPA). The sleeping duration was obtained via interview. Phenotypic age acceleration (PhenoAgeAccel) was calculated as an aging index using nine chemistry biomarkers. Isotemporal substitution models using multivariable linear regression to examine the associations of sleeping, sedentary behavior, and physical activity with PhenoAgeAccel, stratified by MVPA (< 150 min/week, ≥ 150 min/week).
### Results
Thirty minutes per day spent on sedentary behavior was positively associated with PhenoAgeAccel (β = 0.07, $95\%$ CI: 0.04, 0.11), and 30 min/day spent on leisure-time MVPA was adversely associated with PhenoAgeAccel (β = − 0.55, $95\%$ CI: − 0.73, − 0.38). Replacing 30 min/day sedentary behaviors with 30 min/day of MVPA (β = -3.98, $95\%$ CI: -6.22, -1.74) or 30 min/day of walking/bicycling (β = -0.89, $95\%$ CI: -1.10, -0.68) was adversely associated with PhenoAgeAccel. Substituting 30 min/day of walking/bicycling for 30 min/day of leisure-time MVPA was positively associated with PhenoAgeAccel (β = 3.09, $95\%$ CI: 0.93, 5.25).
### Conclusion
Sedentary behavior was positively associated with aging. Replacing sedentary behaviors with walking/bicycling or MVPA was adversely associated with aging among adults.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12877-023-03874-6.
## Impact statement
We demonstrate that this work is novel: For the first time in the United States, isotemporal substitution models have been applied to study the association of sleeping, sedentary behavior, and physical activity with phenotypic age acceleration.
## Key points
30 min/day spent on sedentary behavior was positively associated with PhenoAgeAccel.30 min/day spent on leisure-time MVPA was adversely associated with PhenoAgeAccel. Replacing 30 min/day sedentary behaviors with 30 min/day of MVPA or 30 min/day of walking/bicycling was adversely associated with PhenoAgeAccel. Substituting 30 min/day of walking/bicycling for 30 min/day of leisure-time MVPA was positively associated with PhenoAgeAccel.
## Welfare of animals
This article does not contain any studies with animals performed by any of the authors.
## Study registration
This study was not formally registered.
## Analytic plan pre-registration
The analysis plan was not formally pre-registered.
## Background
The world's population is growing older due to life expectancy increasing and fertility levels decreasing. By 2050, the world's elderly population will reach one in six [1]. The aging process is associated with an increased risk of many chronic diseases [2, 3]. From the economic perspective, a delay of 2.2 years in aging would save seven trillion dollars over the next 50 years [4]. As time goes on, aging refers to changes in body composition, internal balance, energy, and brain health [5]. According to previous studies, aging may lead to degenerative loss of muscle mass and quality [6], neurodegeneration [7], cardiovascular homeostasis and metabolic disturbances [8], among others, and affects every organ in the body. However, Chronological age (CA) is not a perfect proxy for the true biological aging status of the body [9]. A new biological aging measure, PhenoAgeAccel, has been proved to identify the risk of morbidity and mortality among different subpopulations in the U.S, especially among adults who were healthy and free of diseases [10]. As a complex process, aging also involves the interaction of physiologic and lifestyle factors.
Based on the latest recommendations for Americans' Physical Activity are the following: active adults should do 150 to 300 min of moderate-intensity exercise every week, or 75 min of vigorous-intensity aerobic exercise every week, or an equivalent combination of exercise that is both moderate and vigorous intensity [11]. It is crucial to prevent the aging onset, perpetuation, and progression by enhancing physical activity, especially moderate-to-vigorous intensity [12, 13], which is defined as energy expenditure ≥ 3 metabolic equivalents (METs) [14].On the other hand, some evidence has identified that sedentary behavior is a significant predictive factor of aging [15, 16], with energy expenditure ≤ 1.5 METs [14]. Meanwhile, the research on aging and sleeping duration is scant, and results have been controversial [17–20], with shorter or longer sleeping duration is associated with accelerating cellular aging.
However, most studies [9, 17] failed to reflect the competitive relationship between sleeping, sedentary behavior, and physical activity within a fixed period. The increment of moderate-to-vigorous physical activity (MVPA) for one hour requires a corresponding decrease in one hour of other forms of physical activity, sedentary behavior, or sleeping. It is important to consider such relationships when we analyze the effects of certain types of physical activity on health outcomes. According to the seminal works of Mekary et al. [ 21], isotemporal substitution model (ISM) was put forward to evaluate the potential effect on health outcomes of substituting one specific type of activity with another [22].
To the best of our knowledge, little is known about the complex inter-relationships of sleeping, sedentary behavior, and physical activity with aging. In order to fill these gaps in the literature, the objective of this study is to use the isotemporal substitution model to explore the relationships between these health-related behaviors and aging. We hypothesized that substituting 30 min of sedentary behaviors with the same amount of sleep, walking/bicycling or MVPA would slow biological aging.
## Study population
The National Health and Nutrition Examination Survey (NHANES) is a cross-sectional survey of the US population. A two-year cycle of the NHANES was conducted, and a nationally representative sample was chosen using a multistage sampling design [23]. The survey is conducted annually among a national sample of about 5,000 people. These people are located in counties throughout the country and visit 15 of them each year. NHANES include interviews and physical examinations. NHANES interviews cover demographic, socioeconomic, diet and health-related issues; The latter includes clinical, physiological and laboratory assessments administered by trained medical personnel [24].
From 2007 to 2010, 12,153 participants aged 20 or older participated in the NHANES study. We excluded the participants who lacked PhenoAgeAccel information ($$n = 6842$$); and those with incomplete physical activity questionnaires ($$n = 23$$). Finally, 5288 participants were involved in our study (Fig. S1).
## Physical activity measures
Data were derived from questionnaire measurements; The Physical Activity Questionnaire (PAQ) is based on the Global Physical Activity Questionnaire (GPAQ) [25]. The Physical Activity questionnaire included 19 items of physical activity, providing information on walking/ bicycling time, vigorous and moderate-intensity activity, and sedentary activity. Relevant physical activities in the questionnaire included Vigorous work-related activity, Moderate work-related activity, Walking or bicycling for transportation, Vigorous leisure-time physical activity, Moderate leisure-time physical activity. In addition, participants were asked to answer a separate question about how much time they spent sitting each day. For each physical activity, you will be asked if you do it, then how many days a week you do it, and finally how long you do it each day. The number of days calculated was multiplied by the number of hours of exercise per day to determine how much time participants spent on different types of physical activity in a given week.
In this study, moderate leisure-time physical activity and vigorous leisure-time physical activity were combined to form MVPA. MVPA minutes per week was calculated using the formula: [moderate leisure-time activity minutes × moderate leisure-time days] + [vigorous leisure-time activity minutes × vigorous leisure-time days] = mvpa minutes per week. According to MVPA, participants were classified into two categories: MVPA < 150 min/week, MVPA ≥ 150 min/week [11]. [ walking/ bicycling minutes × walking/ bicycling days] = walking/ bicycling minutes per week [26].
## Sleeping
Assessments were conducted on sleeping duration during workdays and weekends. In response to the NHANES question: " How much sleep do you usually get at night on weekdays or workdays?" [ 27]. The sleep duration was categorized as short (< 7 h per night), normal (≥ 7 h per night), and normal used as the reference group.
## Phenotypic age acceleration
PhenoAgeAccel was calculated according to the formula proposed by Levine et al. [ 28]. It was calculated by age and nine biomarkers including albumin, creatinine, glucose, C-reactive protein, lymphocyte percentage, mean cell volume, red cell distribution width, alkaline phosphatase, white blood cell count (Supplementary methods) [29]. Biomarkers data data were extracted based on “Albumin & Creatinine—Urine, Plasma Fasting Glucose & Insulin, C-Reactive Protein (CRP), Complete Blood Count with 5-part Differential—Whole Blood, Standard Biochemistry Profile” from NHANES Laboratory Data. PhenoAgeAccel is the symbol of phenotypic aging after considering the effects of chronological age [30].
## Other covariates
In this study, we adjusted for sex, ethnicity/race, education, drinking, smoking, coffee, and body mass index (BMI) to control for confounding bias. Table S1 gives details about covariates. Sex, race and education were obtained from demographics data; Information on alcohol, smoking and coffee intake was collected from questionnaire data. To calculate BMI (kg/m2), weight (kg) was divided by height squared(m2). In addition, overweight was defined by a BMI of above 28 kg/m2 [31].
## Statistical analyses
For the description of the baseline characteristics of the sample, we computed means and standard deviation for continuous variables while frequencies were calculated for categorical variables. The associations between each PA and PhenoAgeAccel were estimated using multivariate linear regression analyses. To estimate associations of each activity with PhenoAgeAccel, separate models were used to estimate all exercise intensity levels (sleeping, SB, MVPA, and walking/ bicycling). Each of the covariates mentioned above was adjusted [32].
In an isotemporal substitution model, a defined duration of one physical activity intensity is replaced with the same duration of another physical activity intensity [21, 33]. The regression coefficients of these models describe the distinctions in PhenoAgeAccel with various types of physical activity for 30 min. Statistically significant differences were found in the regression model. Three of the four physical activity variables (sleeping, sedentary, walking/bicycling, MVPA) were continually included in models, and the total time and other covariates were adjusted. Results are presented as β-values and confidence intervals ($95\%$ CIs) [34]. There is a significant association between the outcome variable and a covariate if the regression coefficient, beta, is significantly different to zero.
As a result of the complex NHANES sampling design, nonresponse, and oversampling, all analyses were incorporated with interview weights provided by NHANES. Analyses of all statistical tests were conducted using IBM SPSS Statistics 21.0 software (IBM, Asia Analytics Shanghai).
## Characteristics of participants
A significantly lower PhenoAgeAccel occurred in participants who had MVPA ≥ 150 min/week compared to those who had MVPA < 150 min/week (Table 1). Moreover, participants who had MVPA ≥ 150 min/week engaged in more walking/bicycling, and fewer sleeping. Table 1The characteristics of the participants were divided by MVPACharacteristicMVPAMVPA < 150 min/week ($$n = 3845$$)MVPA ≥ 150 min/week ($$n = 1443$$)Weighted frequency143,288,97470,118,799Sex, % Male1737(45.1)807(55.9) Female2108(54.8)636(44.0)Ethnicity/race, % Non-Hispanic White1763(45.8)759(52.5) Others2082(54.1)684(47.4)Education, % Under high school1373(35.7)212(14.6) High school or above2464(64.0)1230(85.2)BMI (kg/m2) Under & health weight1015(26.3)498(34.5) Overweight2764(71.8)938(65.0)Smoking status, % Non-smoker2012(52.3)839(58.1) Ever-smoker943(24.5)374(25.9) Current-smoker887(23.0)230(15.9)Drinking, % Non-drinker528(13.7)124(8.5) Ever-drinker554(14.4)137(9.4) Current-drinker2406(62.5)1064(73.7)Coffee, % < 1 cup/day2723(70.8)1061(73.5) ≥ 1 cup/day482(12.5)183(12.6)Sleeping, 30 min/day13.63 ± 3.0613.60 ± 2.61Sedentary behavior, 30 min/day10.53 ± 6.7810.57 ± 6.51MVPA, 30 min/day0.09 ± 0.181.95 ± 1.77Walking/bicycling, 30 min/day0.40 ± 1.450.50 ± 1.41PhenoAgeAccel-3.88 ± 8.34-6.17 ± 6.80Values were means ± SD or n (percentages)BMI Body mass index, MVPA Moderate-to-vigorous physical activity, PhenoAgeAccel Phenotypic age acceleration
## Independent and partition models
The association of 30 min/day of sleeping, sedentary behavior, walking/bicycling, and MVPA with PhenoAgeAccel was depicted in Table 2. As shown in the total participants, 30 min/day of SB was positively related to PhenoAgeAccel (β = 0.07, $95\%$ CI: 0.04, 0.11), while 30 min/day of MVPA for leisure time was adversely related to PhenoAgeAccel (β = − 0.55, $95\%$ CI: − 0.73, − 0.38). After multivariate adjustments (Model 2 and Model 3), the above two associations remained (β = 0.07, $95\%$ CI: 0.05, 0.10; β = − 0.35, $95\%$ CI: − 0.51, − 0.19). In this study, the score for MET from walking or bicycling was close that of moderate leisure-time physical activity, resulting in MVPA results similar to those of walking or bicycling. Table 2Associations (β($95\%$ CI)) of 30 min/day of sleeping, sedentary behavior, walking/bicycling and MVPA with PhenoAgeAccel among adultsVariablesModel 1Model 2Model 3Sleeping-0.10(-0.19, -0.02)-0.03(-0.10, 0.05)-0.03(-0.11, 0.04)Sedentary behavior0.07(0.04, 0.11)0.08(0.05, 0.11)0.07(0.05, 0.10)Walking/bicycling-0.11(-0.24, -0.01)-0.14(-0.25, -0.03)-0.09(-0.21, 0.03)MVPA-0.55(-0.73, -0.38)-0.39(-0.55, -0.22)-0.35(-0.51, -0.19)Model 1 Adjusted for noneModel 2 Adjusted for sex, ethnicity/race, education, smoking, body mass indexModel 3 Adjusted for sex, ethnicity/race, education, smoking, body mass index, additionally adjusted for the other three variables of activity statusMVPA Moderate-to-vigorous physical activity, CI Confidence intervals *Associations analysis* of PhenoAgeAccel with sleeping, sedentary behavior, walking/bicycling, and MVPA of the study population stratified to the underlying MVPA are depicted in Table 3.Table 3Associations analysis of (β($95\%$ CI)) PhenoAgeAccel with sleeping, sedentary behavior, walking/bicycling and MVPA in different MVPA populationsVariablesMVPA < 150 min/weekMVPA ≥ 150 min/weekModel 1Model 2Model 3Model 1Model 2Model 3Sleeping-0.09(-0.21, -0.03)-0.03(-0.13, 0.07)-0.04(-0.14, 0.06)-0.11(-0.20, -0.02)-0.01(-0.13, 0.12)-0.01(-0.13, 0.12)Sedentary behavior0.11(0.07, 0.15)0.11(0.08, 0.13)0.10(0.08, 0.13)0.002(-0.06, 0.06)0.01(-0.05, 0.07)0.01(-0.05, 0.07)Walking/bicycling-0.21(-0.34, -0.08)-0.22(-0.35, -0.10)-0.21(-0.34, -0.08)0.21(-0.04, 0.46)0.15(-0.09, 0.39)0.16(-0.08, 0.41)MVPA-4.82(-6.03, -3.60)-3.15(-4.40, -1.91)-3.29(-4.58, -2.00)0.04(-0.15, 0.23)-0.02(-0.21, 0.16)-0.04(-0.23, 0.14)Model 1 Adjusted for noneModel 2 Adjusted for sex, ethnicity/race, education, smoking, body mass indexModel 3 Adjusted for sex, ethnicity/race, education, smoking, body mass index, additionally adjusted for the other three variables of activity statusMVPA Moderate-to-vigorous physical activity, CI Confidence intervals
## Isotemporal substitution models
The ISM analysis of the association of 30 min/day spent on sleeping, walking/bicycling, sedentary behaviors, and MVPA with PhenoAgeAccel was presented in Table 4. After adjusting all of the confounders, replacing 30 min/day spent on MVPA for leisure time with an equal amount of sleeping was positively related to PhenoAgeAccel (β = 0.32, $95\%$ CI: 0.16, 0.48). Moreover, replacing 30 min/day spent on MVPA for leisure time with an equal amount of sedentary behaviors was positively related to PhenoAgeAccel (β = 0.43, $95\%$ CI: 0.26, 0.60). In contrast, replacing 30 min/day spent on sedentary behavior with an equal amount MVPA for leisure time was adversely related to PhenoAgeAccel (β = -0.43, $95\%$ CI: -0.60, -0.26); replacing 30 min/day spent on sleeping with equal amount leisure time MVPA was adversely related to PhenoAgeAccel (β = -0.32, $95\%$ CI: -0.48, -0.16).Table 4Isotemporal substitution model of associations (β($95\%$ CI)) of 30 min/day of sleeping, sedentary behavior, walking/bicycling and MVPA with PhenoAgeAccel among adultsSleepingSedentary behaviorWalking/bicyclingMVPAOverall Replacing sleepDropped0.11(0.04, 0.18)-0.05(-0.19, 0.08)-0.32(-0.48, -0.16) Replacing sedentary-0.11(-0.18, -0.04)Dropped-0.16(-0.29, -0.04)-0.43(-0.60, -0.26) Replacing walking0.05(-0.08, 0.19)0.16(0.04, 0.29)Dropped-0.27(-0.49, -0.04) Replacing MVPA0.32(0.16, 0.48)0.43(0.26, 0.60)0.27(0.04, 0.49)DroppedMVPA < 150 min/week Replacing sleepDropped0.06(-0.10, 0.22)-0.83(-1.04, -0.62)-3.92(-6.12, -1.72) Replacing sedentary-0.06(-0.22, 0.10)Dropped-0.89(-1.10, -0.68)-3.98(-6.22, -1.74) Replacing walking0.83(0.62, 1.04)0.89(0.68, 1.10)Dropped-3.09(-5.25, -0.93) Replacing MVPA3.92(1.72, 6.12)3.98(1.74, 6.22)3.09(0.93, 5.25)DroppedMVPA ≥ 150 min/week Replacing sleepDropped0.01(-0.27, 0.29)0.26(-0.13, 0.64)0.004(-0.31, 0.32) Replacing sedentary-0.01(-0.29, 0.27)Dropped0.25(-0.19, 0.69)-0.01(-0.34, 0.33) Replacing walking-0.26(-0.64, 0.13)-0.25(-0.69, 0.19)Dropped-0.25(-0.76, 0.26) Replacing MVPA-0.004(-0.32, 0.31)0.01(-0.33, 0.34)0.25(-0.26, 0.76)DroppedAdjusted for sex,ethnicity/race, education, smoking, body mass index, additionally adjusted for the other three variables of activity statusMVPA Moderate-to-vigorous physical activity, CI Confidence intervals *In this* association, 30 min/day spent on SB was replaced by an equal amount MVPA (β = -3.98, $95\%$ CI: -6.22, -1.74) or walking/bicycling (β = -0.89, $95\%$ CI: -1.10, -0.68) was adversely related to PhenoAgeAccel; replacing 30 min/day spent on walking/cycling with equal amount leisure time MVPA was positively related to PhenoAgeAccel (β = 3.09, $95\%$ CI: 0.93, 5.25), still existed among those MVPA < 150 min/week. In the population of MVPA ≥ 150 min/week, no such association has been found (Table 4).
## Discussion
Our work provided evidence that walking/bicycling, sleeping, and the MVPA for leisure time were adversely related to PhenoAgeAccel. In contrast, sedentary behavior was positively associated with PhenoAgeAccel. Replacing SB with an equal amount of bicycling/walking, sleeping, or MVPA or substituting bicycling/walking with MVPA was adversely related to PhenoAgeAccel, particularly among those with no more than 150 min MVPA per week [35, 36]. Based on the sample size for most adults with MVPA < 150 min/week, perhaps the low PA group is driving the analysis for associations between sedentary, walking/bicycling, and MVPA.
According to the studies of Chunyu Xin et al., they discovered that the long nighttime sleep duration (≥ 8 h/night) was associated with a lower likelihood of aging [37]. Yi-Hsuan Lin et al. ’s study reported that physically active middle-aged and older adults were less likely to aging than sedentary adults [38]. Based on a sample of nationally representative adults and modeling isotemporal substitution with consideration of potential factors, our study on the relationship of PA with aging increased the validity and relevance of the evidence [39]. The modeling isotemporal substitution allows comparing normative exchanges of a fixed amount of one activity for the same amount of another PA based on empirically-derived data [21]. ISM shows the accuracy enhanced estimate of the relationships of physical activities and sedentary behaviors with PhenoAgeAccel compared to the conventional modeling [39]. Considering that a day has a finite amount of time and the intensity of physical activity, the isotemporal substitution modeling also estimates the effects of changing activity for another [24]. In epidemiological studies of physical activity, the ISM has been broadly used to replace PA effects with health. According to the studies of Martins et al. [ 40], they discovered that replacing the time spent sitting or sleeping with the same amount of MPA time may reduce frailty. It has been shown in these studies that physical activity, especially MVPA, can lead to positive health outcomes when replaced with time for sedentary behaviors [41]. Compared with studies without consideration of the nature of competition, a more solid basis for evidence can be found using isotemporal substitution models [29].
Based on a sample of large nationally representative adults, physical activity may be able to reduce the burden of aging in America, according to the results of our study [39]. In our study, physical activity is associated with a lower PhenoAgeAccel, and the benefits of MVPA are greater than bicycling/walking and sleeping. Modeling of isotemporal substitutions is scarcely supported by evidence on PhenoAgeAccel compared to other health outcomes among adults. Research by Saunders found that health benefits were most consistently associated with high levels of physical activity, especially MVPA [30]. The study from Sun et al. also found a preliminary connection between poor sleeping and frailty, one of the syndromes associated with aging [42]. PhenoAgeAccel represents an aging process that affects multiple systems characterized by complex biological mechanisms, though what we found complements the result. Many studies have used isotemporal models showing the replacement of lower-intensity behaviors with higher-intensity behaviors results in better health and lower mortality. In comparison, two isotemporal substitution studies suggested that engaging in a light physical activity instead of sedentary behavior was not projected to result in any benefits for either well-being indicator [27, 32]. Han et al. ’s study reported that the number of sleeping hours per day over 8 is positively correlated with an increased PhenoAgeAccel [43]. Participants in different studies may have different characteristics (e.g., country, race/ethnicity, lifestyle), which may account for the different findings [29].
According to our analysis, especially for participants with MVPA less than 150 min, walking/bicycling and sleeping displayed a trend of lower aging compared to sedentary behavior. Physical activity may play an essential role in improving adults' aging and preventing subsequent adverse health outcomes [29, 33]. As a result of the considerable burden on public health brought by aging in the present study, the cost-effectiveness of actively participating in sports, especially MVPA, should be considered to prevent aging [39, 44]. In longitudinal research, physical activities have been related to a lower burden of aging among adults, and future studies will likely consider the competing nature of variables related to physical activity [34]. In addition to MVPA, bicycling/walking and sleeping also contribute to aging.
Multiple underlying mechanisms may account for the benefits on PhenoAgeAccel from physical activity. Regular physical activity can prolong the average human life span by affecting the development of chronic diseases, alleviating aging degeneration and its impact on health, and maintaining physical function. PA promotes health by allocating energy away from potentially harmful overinvestments in fat storage and reproductive tissues and PA also stimulates energy allocation toward repair and maintenance processes [45]. Physical activity also increases longevity and survival. For middle-aged and older people, a dose–response relationship was found between physical activity and decrease in mortality. Compared with sedentary older people, physically active older adults were more likely to remain living independently. Physical activity in old age preserves the cognitive and physical functions [38].
One major limitation of our study is that the cross-sectional study design cannot make causal relationship inference [29]. The temporal relationships between sleeping, sedentary behavior, physical activity, and PhenoAgeAccel cannot be determined. And future longitudinal studies are still needed to better explore the causal relationship between different variables. Another limitation involves measuring physical activity: there is a possibility that GPAQ data may not be as accurate as accelerometer data, although GPAQ has been validated in various populations [46]. Different intensities (with METS < 4) of physical activity data were not collected by the GPAQ [39]. In addition, despite including many covariates, confounders that are unmeasured and residual may still exist. Moreover, a variety of lifestyle information was reported by themselves; therefore, recall bias may exist [47]. Despite these limitations, PhenoAgeAccel is a marker that may be used to monitor the aging process before symptoms of diseases appear [10]. A comprehensive quality control and quality assurance procedure are applied to NHANES to ensure that data is accurate and reliable. Furthermore, this study has a sample of nationally representative adults in America, and many samples were used, making the results more generalizable [29]. Therefore, in terms of public health, the findings have more significance regarding disease prevention.
## Conclusions
In conclusion, replacing SB with an equal amount of bicycling/walking, sleeping, or MVPA for leisure time is adversely related to PhenoAgeAccel in adults. The findings of our study emphasize the significance of adhering to physical activity, especially for adults exposed to MVPA, for no more than 150 min per week. It is necessary for future research on the relationship between sedentary behavior and PA with aging, considering the nature of competition and examining physical activity and its effectiveness in preventing aging.
## Supplementary Information
Additional file 1: Supplementary Methods. Figure S1. Flow chart of the population included in the final analysis of our study. Table S1. The Classifications of Covariates.
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|
---
title: 'Metabolically healthy obesity is associated with higher risk of both hyperfiltration
and mildly reduced estimated glomerular filtration rate: the role of serum uric
acid in a cross-sectional study'
authors:
- Hong Zhang
- Rui Chen
- Xiaohong Xu
- Minxing Yang
- Wenrong Xu
- Shoukui Xiang
- Long Wang
- Xiaohong Jiang
- Fei Hua
- Xiaolin Huang
journal: Journal of Translational Medicine
year: 2023
pmcid: PMC10035285
doi: 10.1186/s12967-023-04003-y
license: CC BY 4.0
---
# Metabolically healthy obesity is associated with higher risk of both hyperfiltration and mildly reduced estimated glomerular filtration rate: the role of serum uric acid in a cross-sectional study
## Abstract
### Background
The impact of metabolically healthy obesity (MHO) on kidney dysfunction remains debatable. Moreover, few studies have focused on the early stages of kidney dysfunction indicated by hyperfiltration and mildly reduced eGFR. Thus, we aimed to investigate the association between the MHO and early kidney dysfunction, which is represented by hyperfiltration and mildly reduced estimated glomerular filtration rate (eGFR), and to further explore whether serum uric acid affects this association.
### Methods
This cross-sectional study enrolled 1188 residents aged ≥ 40 years old from Yonghong Communities. Metabolically healthy phenotypes were categorized based on Adult Treatment Panel III criteria. Obesity was defined as body mass index (BMI) ≥ 25 kg/m2. Mildly reduced eGFR was defined as being in the range 60 < eGFR ≤ 90 ml/min/1.73m2. Hyperfiltration was defined as eGFR > 95th percentile after adjusting for sex, age, weight, and height.
### Results
Overall, MHO accounted for $12.8\%$ of total participants and $24.6\%$ of obese participants. Compared to metabolically healthy non-obesity (MHNO), MHO was significantly associated with an increased risk of mildly reduced eGFR (odds ratio [OR] = 1.85, $95\%$ confidence interval [CI] 1.13–3.01) and hyperfiltration (OR = 2.28, $95\%$ CI 1.03–5.09). However, upon further adjusting for uric acid, the association between the MHO phenotype and mildly reduced eGFR was reduced to null. Compared with MHNO/non-hyperuricemia, MHO/non-hyperuricemia was associated with an increased risk of mildly reduced eGFR (OR = 2.04, $95\%$ CI 1.17–3.58), whereas MHO/hyperuricemia was associated with an observably increased risk (OR = 3.07, $95\%$ CI 1.34–7.01).
### Conclusions
MHO was associated with an increased risk of early kidney dysfunction, and the serum uric acid partially mediated this association. Further prospective studies are warranted to clarify the causality.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12967-023-04003-y.
## Background
Chronic kidney disease (CKD) has emerged as a critical public health problem, affecting approximately $10\%$ of adults worldwide [1]. CKD may lead to end-stage renal disease (ESRD) and increased cardiovascular morbidity and mortality, and it is expected to be the fifth-leading cause of death by 2040 [2]. Therefore, early detection and prevention are of great significance to clinical practice. Glomerular hyperfiltration (GH), defined as an increased whole-kidney glomerular filtration rate (GFR) or an increased filtration per nephron [3], is considered an early and reversible stage of glomerular damage and a harbinger for subsequent development of CKD [4, 5]. Of note, a mildly reduced GFR, characterized as a GFR < 90 ml/min/1.73m2, is also an indication of early-stage CKD. Fox et al. report that patients with mildly reduced GFR, as assessed by estimated GFR (eGFR), were three times more likely to progress to CKD than those with normal GFR values [6].
Numerous studies have established that obesity is a risk factor for CKD [7, 8]. However, there has been no consensus as to whether the underlying mechanisms of obesity are definite risk factors for kidney dysfunction. Obesity is usually accompanied by metabolic abnormalities, including elevated blood glucose [9], elevated blood pressure [10], and lipid disorders [11]. Most studies declare that the metabolic abnormalities induced by obesity played a key role in kidney dysfunction [12, 13]. However, there is a unique subgroup of obese individuals who have a normal metabolic status, for instance, appropriate blood glucose and pressure levels and favorable lipid profiles. This obesity phenotype is termed metabolically healthy obesity (MHO) [14]. The impact of MHO on kidney dysfunction remains debatable. Some studies have reported that MHO is associated with a higher risk of incident CKD, suggesting that having a metabolically healthy status does not protect obese individuals from the onset of CKD [15, 16], but other studies have not found significant associations between MHO and incident CKD [17]. Furthermore, most studies have investigated the association between MHO and severe kidney disease, characterized as eGFR < 60 ml/min/1.73m2, whereas few studies have focused on the early stages of CKD indicated by hyperfiltration and mildly reduced eGFR.
Consequently, the main aim of the present study is to investigate the associations of MHO with hyperfiltration and mildly reduced eGFR, to clarify whether obesity without metabolic abnormalities is adversely associated with early kidney dysfunction. Additionally, we explore the possible mechanism between MHO and early kidney dysfunction. We hypothesize that individuals with MHO have a higher potential for early kidney dysfunction and that metabolic abnormalities only partially mediate this association.
## Study population
In the present study, all participants were enrolled from Yonghong Community, Zhonglou District, Changzhou [18, 19]. From December 2016 to December 2017, 1328 residents who had lived in the district for more than 6 months and were 40 years of age or above were recruited. The present analysis excluded participants who had missing body mass index (BMI) or eGFR values, previously diagnosed renal diseases, or an eGFR < 60 ml/min/1.73m2. After these exclusions, a total of 1188 participants remained. The flow chart of participants was shown as Fig. 1.Fig. 1The follow diagram of participants Each participant signed the written informed consent form. The content is that participants provided sociodemographic information and medical history to an interviewer through a standard questionnaire and provided blood samples for biochemical measurements. The study was approved by the Institutional Review Board of the Third Affiliated Hospital of Soochow University.
## Data collection
Trained interviewers obtained information on sociodemographic characteristics, lifestyle factors (including drinking and smoking habits and physical activity) and medical history (including diseases and use of medications) for a detailed series of standardized questions. Current smokers were defined as participants who smoked at least one cigarette per day or seven cigarettes per week, and current drinkers were defined as participants who consumed alcohol at least once per week. Physical activity was categorized as high physical activity or not, according to the International Physical Activity Questionnaire (IPAQ).
Trained staff conducted anthropometric measurements based on standard protocols defined in previous studies [18]. BMI was defined as weight (kg) divided by height squared (m2). Blood pressure was measured three times in one-minute intervals, following a five-minute rest (OMRON Model HEM-752 FUZZY, Omron Company, Dalian, China). The average systolic blood pressure (SBP) and average diastolic blood pressure (DBP) were used in the analysis.
After at least a 10-h overnight fast, blood samples were collected for serum lipid profiles, including total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), liver enzymes (aspartate aminotransferase, AST; alanine aminotransferase, ALT; γ-glutamyl transferase, GGT), serum creatinine, and uric acid (AU-5800 Chemistry System, Beckman, USA). Fasting plasma glucose (FPG) was also measured with an autoanalyzer using the glucose oxidase method (AU-5800 Chemistry System, Beckman, USA). The eGFR values were calculated with the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations for cystatin C alone: [1] for cystatin C ≤ 0.8 mg/l, eGFR = 133 × (cystatin C/0.8)−0.449 × 0.996age × [0.932 if female]; 2) for cystatin C > 0.8 mg/l, eGFR = 133 × (cystatin C/0.8)−1.328 × 0.996age × [0.932 if female] [20].
## Definitions
The eGFR was categorized into three groups: mildly reduced eGFR, normal eGFR, and hyperfiltration. A mildly reduced eGFR was defined as eGFR values between 60 and 90 ml/min/1.73m2. Hyperfiltration was defined as an eGFR > 95th percentile after adjusting for sex, age, weight and height [21]. Normal eGFR was defined as all eGFR values other than those signaling mildly reduced eGFR and hyperfiltration.
Previous studies have demonstrated that Asian populations have higher percentage of body fat at a given BMI than do white or European populations. Additionally, at the same BMI, Asian populations have higher prevalence of type 2 diabetes and increased cardiovascular risk factors [22]. Thus, we defined obesity as a BMI ≥ 25 kg/m2 and non-obesity as a BMI < 25 kg/m2 using the World Health Organization Asia Pacific guidelines [23]. We used the Adult Treatment Panel III (ATP III) criteria to define metabolically healthy status as meeting < 2 of the following criteria, excluding waist circumference: [1] elevated SBP (≥ 130 mmHg) and/or DBP (≥ 85 mmHg) or on antihypertensive treatment, [2] high TG (≥ 1.7 mmol/L) or on lipid-lowering medications, [3] high FPG (≥ 5.6 mmol/L) or on medications for diabetes, and 4) low HDL-c (< 1.04 mmol/L in men and < 1.29 mmol/L in women) [16]. The obesity phenotype was defined as: [1] metabolically healthy non-obesity (MHNO): BMI < 25 kg/m2 and < 2 metabolic risk factor, [2] MHO: BMI ≥ 25 kg/m2 and < 2 metabolic risk factor, [3] metabolically unhealthy nonobesity (MUNO): BMI < 25 kg/m2 and ≥ 2 metabolic risk factors, and [4] metabolically unhealthy obesity (MUO): BMI ≥ 25 kg/m2 and ≥ 2 metabolic risk factors.
Hyperuricemia was identified in participants in the sex-specific upper quartile of serum uric acid levels (≥ 372 μmol/l for males and ≥ 332 μmol/l for females).
## Statistical methods
Characteristics of the study population were presented according to obesity phenotypes. Means ± standard deviation (SD) and medians (interquartile ranges) were used to describe the normally distributed and skewed continuous variables. Numbers (proportions) were used to present categorical variables. TG, FPG, ALT, AST, and GGT were logarithmically transformed due to their skewed distributions. P values for the four obesity phenotypes were calculated using ANOVA for continuous variables and a Chi-squared test for categorical variables. Multiple comparisons among the four groups were made with the Bonferroni test.
We used multivariate-adjusted logistic regression to evaluate the impact of the obesity phenotype on mildly reduced eGFR and hyperfiltration. We established four models as follows: Model 1 was adjusted for age and sex; Model 2 was based on Model 1 and further adjusted for physical activity and current smoking and drinking habits; Model 3 was based on Model 2 and further adjusted for ALT, AST, GGT, TC and LDL-c; and Model 4 was based on Model 3 and further adjusted for uric acid. In addition, we stratified the four obesity phenotypes according to hyperuricemia status (present and not). The Bonferroni method was used to compare the prevalence of mildly reduced eGFR in the hyperuricemia and non-hyperuricemia groups according to obesity phenotype as well as the prevalence of hyperfiltration. With MHNO/non-hyperuricemia as the reference, multivariate logistic regression was used to assess the combined impact of obesity phenotype and uric acid on mildly reduced eGFR.
All statistical analyses were performed with SAS version 9.3 (SAS Institute Inc, Cary, NC). Two-tailed P values < 0.05 were considered statistically significant.
## Characteristics of the study population
Overall, the mean age of total participants was 67.0 ± 8.1 years old, and the proportion of males was $33.6\%$ ($$n = 399$$). In all, $35.3\%$ ($$n = 419$$) of participants were metabolically healthy and $52.6\%$ ($$n = 618$$) were obese. Overall, 152 participants were defined as having MHO, accounting for $12.8\%$ of total participants and $24.6\%$ of obese participants. Table 1 shows the sociodemographic and biochemical characteristics of the study population according to the four obesity phenotypes. Compared to participants in the MHNO group, those with MHO had higher BMI, SBP, and serum uric acid levels; comprised a higher proportion of current drinkers; and had lower levels of HDL-c (all P values < 0.05). However, participants in the MHNO and MHO groups had comparable eGFR values. Those who were metabolically unhealthy had higher levels of SBP, TG, and FPG and lower levels of HDL-c, while those with MUNO comprised a lower proportion of current smokers and drinkers than those with MHO (all P values < 0.05). Notably, serum uric acid levels were higher in participants with MHO and MUO than in their non-obese counterparts (all P values < 0.05). Additionally, characteristics of participants according to renal function were shown in Additional file 1: Table S1.Table 1Characteristics of study population according to obesity phenotypeVariablesMetabolically healthyMetabolically unhealthyP value*Non-obese (MHNO)($$n = 267$$)Obese (MHO)($$n = 152$$)Non-obese (MUNO)($$n = 303$$)Obese (MUO)($$n = 466$$)eGFR (ml/min/1.73 m2)96.3 ± 15.092.8 ± 15.192.7 ± 14.591.5 ± 15.1a0.0005Age (years)65.8 ± 8.267.6 ± 8.467.5 ± 8.067.1 ± 7.90.048Male, n (%)85 (31.8)69 (45.4)81 (26.7)164 (35.2)0.95BMI (kg/m2)22.2 ± 1.927.3 ± 2.1a23.0 ± 1.5a,b28.1 ± 2.8a,b,c < 0.0001Lifestyle factors Current smokers, n (%)35 (13.1)29 (19.1)28 (9.2)b56 (12.0)0.24 Current drinkers, n (%)9 (3.4)16 (10.5)a8 (2.6)b30 (6.4)0.40 High physical activity, n (%)160 (59.9)90 (59.2)186 (61.4)264 (56.7)0.41Blood pressure (mmHg) SBP127 ± 14132 ± 14a136 ± 14a138 ± 13a,b < 0.0001 DBP81 ± 882 ± 983 ± 885 ± 8a,b,c < 0.0001Lipid profiles (mmol/L) TC5.26 ± 0.895.08 ± 0.965.16 ± 1.015.04 ± 1.000.026 HDL-c1.57 ± 0.311.43 ± 0.25a1.29 ± 0.28a,b1.23 ± 0.24 a,b,c < 0.0001 LDL-c2.79 ± 0.682.70 ± 0.712.74 ± 0.692.67 ± 0.660.096 TG1.15 (0.89–1.45)1.34 (1.05–1.51)1.86 (1.41–2.43)a,b1.98 (1.52–2.61) a,b < 0.0001Liver enzymes (U/L) AST22.0 (19.0–26.0)22.0 (20.0–27.0)22.0 (19.0–27.0)24.0 (19.0–29.0)0.012 ALT16.0 (13.0–22.0)18.0 (14.0–23.0)18.0 (14.0–24.0)22.0 (16.0–31.0)a,c < 0.0001 GGT17.0 (13.0–25.0)19.0 (15.0–28.0)18.0 (15.0–29.0)24.5 (18.0–33.0)a0.004 FPG (mmol/L)4.87 (4.49–5.44)5.16 (4.63–5.60)6.13 (4.99–7.95) a,b6.37 (5.22–7.38)a,b < 0.0001 Serum uric acid (μmol/L)278 ± 65308 ± 79a292 ± 64321 ± 71a,c < 0.0001Data were presented as means ± SD for median (interquartile ranges) for continuous variables, and numbers (proportions) for categorical variableseGFR estimated glomerular filtration rate; BMI body mass index; SBP systolic blood pressure; DBP diastolic blood pressure; TC total cholesterol; HDL-c high-density lipoprotein cholesterol; LDL-c low-density lipoprotein cholesterol; TG triglyceride; AST aspartate aminotransferase; ALT alanine aminotransferase; GGT γ-glutamyltransferase; FPG fasting plasma glucose*ANOVA for continuous variables and Chi-square test for categorical variables were used to assess the differences among the four groupsaCompared with MHNO, $P \leq 0.05$ calculated by Bonferroni testbCompared with MHO, $P \leq 0.05$ calculated by Bonferroni testcCompared with MUNO, $P \leq 0.05$ calculated by Bonferroni test
## Obesity phenotype and the risk of mildly reduced eGFR and hyperfiltration
Figure 2 shows the prevalence of mildly reduced eGFR, normal eGFR, and hyperfiltration according to four obesity phenotypes. The crude prevalences of mildly reduced eGFR and hyperfiltration were $27.7\%$ and $5.6\%$ for the MHNO phenotype, $41.5\%$ and $10.5\%$ for the MHO phenotype, $38.6\%$ and $2.3\%$ for the MUNO phenotype, and $42.3\%$ and $4.5\%$ for the MUO phenotype. The Bonferroni test for multiple comparisons among the four phenotypes showed that the prevalence of mildly reduced eGFR in individuals with MHO was higher than individuals with MHNO ($$P \leq 0.0026$$, $41.5\%$ vs. $27.7\%$), but not statistically different from the prevalence in metabolically unhealthy individuals ($P \leq 0.05$). Conversely, the Bonferroni test found that the crude prevalence of hyperfiltration in individuals with MHO was higher than in individuals in the MUNO group ($$P \leq 0.002$$, $10.5\%$ vs. $2.3\%$), but found no statistical difference between the MHO and MHNO phenotypes ($P \leq 0.05$).Fig. 2The prevalence of mildly reduced eGFR, normal eGFR and hyperfiltration according to obesity phenotype. The Bonferroni test was used for multiple comparisons among the four phenotypes, †$P \leq 0.05$, the prevalence of mildly reduced eGFR in MHO compared to MHNO; ‡$P \leq 0.05$, the prevalence of hyperfiltration in MHO compared to MUNO; eGFR estimated glomerular filtration rate; MHNO Metabolically healthy non-obesity; MHO Metabolically healthy obesity; MUNO Metabolically unhealthy non-obesity; MUO Metabolically unhealthy obesity Multinomial logistic regression revealed an association between the MHO phenotype and prevalent mildly reduced eGFR (Table 2). The multi-adjusted odds ratio (OR) for mildly reduced eGFR associated with MHO was 1.85 ($95\%$ CI 1.13–3.01), referenced to MHNO (Model 3 in Table 2). However, adjusting for uric acid reduced the association between the MHO phenotype and prevalent mildly reduced eGFR to null (OR = 1.49, $95\%$ CI 0.90–2.48) (Model 4 in Table 2). Analogously, the MUNO and MUO phenotypes were significantly associated with a higher risk of mildly reduced eGFR after adjusting for confounding factors (Model 3 in Table 2). Adjusting for uric acid resulted in the disappearance of the associations (Model 4 in Table 2). In addition, the MHO phenotype had a 1.28-fold higher risk of hyperfiltration compared to the nonobese counterparts (OR = 2.28, $95\%$ CI 1.03–5.09) with multiple adjustments. After further adjusting for uric acid, the risk of hyperfiltration still remained significant (OR = 2.57, $95\%$ CI 1.14–5.77). In contrast, there were no significant associations found between hyperfiltration and metabolically unhealthy individuals. Table 2Odds ratios of mildly reduced eGFR and hyperfiltration according to obesity phenotypeMetabolically healthyMetabolically unhealthyNon-obese(MHNO)Obese(MHO)Non-obese(MUNO)Obese(MUO)For mildly reduced eGFR Case/number (%)$\frac{74}{267}$ (27.7)$\frac{63}{152}$ (41.5)$\frac{117}{303}$ (38.6)$\frac{197}{466}$ (42.3) Model 1 (adjusted for age and sex)1.00 (Ref.)1.77 (1.10–2.86)1.42 (0.95–2.11)1.80 (1.25–2.59) Model 2 (further adjusted for physical activity, current smoking and drinking habits)1.00 (Ref.)1.85 (1.14–3.00)1.42 (0.95–2.12)1.86 (1.29–2.68) Model 3 (further adjusted for ALT, AST, GGT, TC and LDL-c)1.00 (Ref.)1.85 (1.13–3.01)1.51 (1.01–2.27)2.00 (1.35–2.96) Model 4 (further adjusted for uric acid)1.00 (Ref.)1.49 (0.90–2.48)1.36 (0.89–2.06)1.46 (0.97–2.19)For hyperfiltration Case/number (%)$\frac{15}{267}$ (5.6)$\frac{16}{152}$ (10.5)$\frac{7}{303}$ (2.3)$\frac{21}{466}$ (4.5) Model 1 (adjusted for age and sex)1.00 (Ref.)2.11 (0.97–4.61)0.45 (0.18–1.16)0.95 (0.47–1.92) Model 2 (further adjusted for physical activity, current smoking and drinking habits)1.00 (Ref.)2.33 (1.06–5.13)0.45 (0.17–1.14)0.99 (0.49–2.01) Model 3 (further adjusted for ALT, AST, GGT, TC and LDL-c)1.00 (Ref.)2.28 (1.03–5.09)0.42 (0.16–1.10)0.93 (0.44–1.97) Model 4 (further adjusted for uric acid)1.00 (Ref.)2.57 (1.14–5.77)0.40 (0.15–1.06)1.11 (0.51–2.40)eGFR estimated glomerular filtration rate; TC total cholesterol; LDL-c low-density lipoprotein cholesterol; AST aspartate aminotransferase; ALT alanine aminotransferase; GGT γ-glutamyltransferase; MHNO Metabolically healthy non-obesity; MHO Metabolically healthy obesity; MUNO Metabolically unhealthy non-obesity; MUO Metabolically unhealthy obesityModel 1: adjusted for age and sex;Model 2: further adjusted for physical activity, current smokers (yes/no), current drinking (yes/no) on basis of model 1;Model 3: further adjusted for ALT, AST, GGT, TC and LDL-c on basis of model 2;Model 4: further adjusted for the level of uric acid on basis of model 3
## Combined effects of obesity phenotype and uric acid on the risk of mildly reduced eGFR and hyperfiltration
Figure 3 shows that the MHNO/non-hyperuricemia group had the lowest prevalence ($24.6\%$), and the MUO/hyperuricemia group had the highest prevalence ($52.7\%$), of mildly reduced eGFR. The Bonferroni test further found that the four obesity phenotypes with hyperuricemia had markedly higher prevalences of mildly reduced eGFR than the four corresponding non-hyperuricemia groups (All P values < 0.05). Figure 4 displays the multivariate-adjusted ORs of mildly reduced eGFR according to obesity phenotype and serum uric acid level. Compared to the MHNO group with non-hyperuricemia, the MHO group with non-hyperuricemia had a 1.04-fold higher risk of mildly reduced eGFR (OR = 2.04, $95\%$ CI 1.17–3.58). In addition, the MHO group with hyperuricemia had an observably increased risk (OR = 3.07, $95\%$ CI 1.34–7.01). The two metabolically unhealthy phenotypes were associated with an increased risk of mildly reduced eGFR, irrespective of the level of uric acid, except for the MUNO group with non-hyperuricemia. Moreover, we performed stratified analyses using MHNO in each uric acid group as the reference and found that the MHO phenotype and the metabolically unhealthy phenotypes with hyperuricemia no longer had a higher risk of mildly reduced eGFR, while the MHO and MUO phenotypes with non-hyperuricemia remained the significantly higher risk of mildly reduced eGFR (data shown in Additional file 2: Table S2).Fig. 3The prevalence of mildly reduced eGFR according to obesity phenotype and serum uric acid level. P values were calculated by Bonferroni method, *$P \leq 0.05$, each obesity phenotype with hyperuricemia vs. non-hyperuricemia counterparts; ‡ $P \leq 0.05$ the other groups vs. MHNO/non-hyperuricemia group. eGFR estimated glomerular filtration rate; MHNO Metabolically healthy non-obesity; MHO Metabolically healthy obesity; MUNO Metabolically unhealthy non-obesity; MUO Metabolically unhealthy obesityFig. 4Odds ratio of mildly reduced eGFR according to obesity and the level of serum uric acid. Odds ratios were calculated by multivariable logistic regression adjusting for age, sex, physical activity, current smokers (yes/no), current drinking (yes/no), ALT, AST, GGT, TC and LDL-c. eGFR estimated glomerular filtration rate; MHNO Metabolically healthy non-obesity; MHO Metabolically healthy obesity; MUNO Metabolically unhealthy non-obesity; MUO Metabolically unhealthy obesity. OR Odds ratio We also assessed the combined effect of obesity phenotype and uric acid on hyperfiltration (Additional file 4: Fig. S1). Non-obese individuals were non-hyperuricemic, irrespective of metabolic status. The prevalence of hyperfiltration in the MHNO/non-hyperuricemia and MUNO/non-hyperuricemia groups were $6.5\%$ and $2.9\%$, respectively. The MHO/non-hyperuricemia group did not have a statistically higher prevalence of hyperfiltration than its hyperuricemia counterpart ($12.2\%$ vs. $5.4\%$, $P \leq 0.05$), nor did it have a higher prevalence than the MUO/non-hyperuricemia group ($5.7\%$ vs. $2.4\%$, $P \leq 0.05$). Moreover, the MHNO/non-hyperuricemia group’s prevalence of hyperfiltration was not markedly different from that of any other group across the obesity phenotypes and uric acid levels (all P values > 0.05). However, multivariate logistic regression revealed that when referenced to the MHNO/non-hyperuricemia phenotype, a $139\%$ increased risk of hyperfiltration was found in the MHO/non-hyperuricemia phenotype (OR = 2.39, $95\%$ CI = 1.04–5.52), whereas the increased risk was not found in MHO/Hyperuricemia phenotype. The MUNO and MUO phenotypes did not have a significant association with prevalent hyperfiltration, with or without hyperuricemia present (Additional file 3: Table S3).
## Discussion
In this study, we found that obesity without metabolic abnormalities was adversely associated with early kidney dysfunction among middle-aged and elderly Chinese adults. Participants with MHO had a significantly higher risk of hyperfiltration and mildly reduced eGFR compared to the MHNO phenotype. Furthermore, we found that the significant association between MHO and mildly reduced eGFR attenuated to null after adjusting for serum uric acid level. Additionally, we found that different levels of serum uric acid affect the association between MHO and mildly reduced eGFR. These findings clarified that obesity per se increases the risk of early kidney dysfunction and metabolically healthy status cannot prevent obese patients from early kidney dysfunction. Interestingly, hyperuricemia might partly explain the association of MHO with mildly reduced eGFR.
Obesity has been considered a critical risk factor for chronic diseases, such as cardiovascular disease and mortality. Recently, an obesity paradox phenomenon has been reported among CKD patients [24], in which higher BMI levels are paradoxically associated with greater survival and lower BMI levels are associated with higher mortality, especially in ESRD patients [25, 26]. The potential mechanisms of the obesity paradox are indeterminate and may be partly attributed to discrepancies in metabolic status among obese patients. Better metabolic reserves may prevent obese patients from unfavorable metabolic dysregulation as compared to their non-obese counterparts [27]. Thus, we took metabolically healthy status into account in our obesity phenotypes. MHO is characterized by obesity without metabolic abnormalities and is considered to protect obese patients from the metabolic complications of obesity; furthermore, the risks of chronic diseases appear to be lower than that of the given BMI [28].
However, could the MHO phenotype actually indicate a favorable prognosis? In terms of type 2 diabetes [29], subclinical cardiovascular disease (CVD) [30], and long-term CVD risk [31, 32], the answer to this question has been widely debated in the literature. Meanwhile, individuals with MHO may be heterogeneous with respect to CKD. Recently, a retrospective cohort study based on a British primary care population declared that metabolically healthy overweight and MHO individuals had 1.30-fold and 1.66-fold risks of incident CKD compared to those with MHNO, respectively [15]. Another prospective study conducted in middle-aged and elderly Chinese adults found that those with MHO had a $65\%$ increased risk for incident CKD than those with MHNO [16]. Our results, which focused on MHO and early kidney dysfunction, discovered the same trends as these studies. Previous studies and our study suggest that individuals with MHO, regardless of ethnicity, have an increased risk of early and advanced kidney dysfunction. However, several studies have refuted these conclusions. The Reasons for Geographic and Racial Differences in Stroke (REGARDS) study enrolled 30,239 Black and white adults and followed up for 6.3 years, reported that a higher BMI was associated with a lower risk of ESRD in those without metabolic syndrome (hazard ratio was 0.70 (0.52–0.95) per 5 kg/m2 increase in BMI) but not in those with metabolic syndrome [33]. The different results from our study could be attributed to the study population and the definition of metabolic health. Most participants in the REGARDS study were African Americans aged ≥ 45 years, and the study included waist circumference in its definition of metabolically healthy status, while our study was conducted in a Chinese population and excluded waist circumference in the definition of metabolically healthy status. Alternatively, two prospective studies conducted in Asia found that metabolic health mediated the association between obesity and CKD and that the MHO phenotype was not associated with a higher risk of incident CKD [17, 34]. Younger participants, advanced kidney dysfunction and dissimilar definitions of eGFR may have contributed to the discrepancies with our study. In these two studies, the primary outcome was incident CKD defined as serum-creatinine-based eGFR < 60 ml/min/1.73m2. In contrast, the main outcome in our study was early kidney dysfunction-hyperfiltration and mildly reduced eGFR calculated with serum cystatin C, which is an earlier biomarker of kidney dysfunction.
The current study is the first to investigate the associations of MHO with both hyperfiltration and mildly reduced eGFR. We found that individuals with MHO had a 2.28-fold increase in risk of hyperfiltration and an $85\%$ increased risk of mildly reduced eGFR relative to MHNO individuals. Our results suggest that obesity is not harmless in terms of kidney dysfunction, even at an early stage. An analogous conclusion was also drawn by the Korean National Health and Nutrition Examination Survey, in which MHO indicated a $49\%$ increased risk of microalbuminuria compared to MHNO [35].
The association between obesity phenotype and early kidney dysfunction might be mediated by multiple mechanisms. Insulin resistance [36], chronic inflammation [16], and visceral adiposity [37] have been implicated in previous studies. In addition, uric acid might play a crucial role on kidney injury in obese patients. Prospective studies found that elevated baseline uric acid levels were associated with an increased risk of CKD and that changes in uric acid levels were inversely associated with renal function decline [38, 39]. Additionally, in individuals without metabolic abnormalities, increased uric acid levels have been strongly associated with albuminuria [40]. In accordance with these findings, we observed that the association of MHO with mildly reduced eGFR was statistically attenuated through adjustment for the uric acid level. Referenced to MHNO with non-hyperuricemia, the OR of mildly reduced eGFR was 2.04 ($95\%$ CI = 1.17–3.58) for MHO with non-hyperuricemia and 3.07 ($95\%$ CI = 1.34–7.01) for MHO with hyperuricemia. Furthermore, we did not find an increased risk of mildly reduced eGFR in MHO with hyperuricemia when referenced to MHNO with hyperuricemia, whereas MHO with non-hyperuricemia still had a 1.13-fold higher risk of mildly reduced eGFR when referenced to MHNO with non-hyperuricemia. The results imply that uric acid levels partly mediate the association between MHO and early kidney dysfunction, especially in individuals with hyperuricemia. Additional longitudinal studies are needed to elucidate the conclusive association between MHO and early kidney dysfunction and to determine the modifying effects of uric acid on this association.
As a cross-sectional study, this study had unavoidable limitations of note. First, the causal relationship between MHO and early kidney dysfunction could not be inferred. Hence, further prospective studies are warranted to clarify the role of MHO on the progression of kidney dysfunction. Second, early kidney dysfunction was defined based on eGFR (via the CKD-EPI equation) rather than directly measured. However, in a large epidemiological study, the CKD-EPI equation was used as a standard method of estimating GFR and were demonstrated to be better than the most frequently used Modification of Diet in Renal Disease (MDRD) equation in the estimation of GFR for both CKD and healthy individuals [41]. We would further measured the proteinuria and albuminuria levels to evaluate renal function of participants in longitudinal study. Third, our study’s focus on middle-aged and elderly Chinese adults limited the generalizability of the results to other ethnic groups. Fourth, we didn’t measure insulin levels and inflammation-related markers of participants in present study. We could not directly evaluate the influence of insulin resistance and inflammation on the association. However, we instead used the triglyceride-glucose (TyG) index to evaluate insulin resistance[42]. We further adjusted TyG index based on Model 3 in Table 2. Further adjusting TyG index caused little changes and the significant relationship still remained. Hence, we speculated that the insulin resistance of participants has no significant influence on the association of obesity phenotype with renal function. Finally, information on diet and urate-lowering therapy was not collected in the present study. A follow-up study would need to be designed to acquire this information.
## Conclusions
In conclusion, among the middle-aged and elderly Chinese population, we found that MHO was associated with an increased risk of early kidney dysfunction, including hyperfiltration and mildly reduced eGFR. Our results suggest that MHO is not a harmless condition and that a metabolically healthy status cannot protect obese patients from early kidney dysfunction. Additionally, we found that the significant association between MHO and mildly reduced eGFR attenuated to null after adjusting for serum uric acid level and that the association between MHO and mildly reduced eGFR was not significant among those with hyperuricemia. These results suggest that uric acid partially explains the association of MHO with mildly reduced eGFR, especially in individuals with hyperuricemia. Hence, uric acid should be taken into account when estimating the risk of early kidney dysfunction in individuals with MHO. Prospective studies are warranted to clarify the causality in the future.
## Supplementary Information
Additional file 1: Table S1. Characteristics of study population according to renal function. Additional file 2: Table S2. Odds ratios for mildly reduced eGFR according to obesity phenotype and serum uric acid level (MHNO as reference in each uric acid level).Additional file 3: Table S3. Odds ratio ($95\%$ CI) for hyperfiltration according to obesity phenotype and serum uric acid level. Additional file 4: Figure S1. The prevalence of hyperfiltration according to obesity phenotype and the serum uric acid level.
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|
---
title: Prospective Analysis of Antibody Diagnostic Tests and TTS1 Real-Time PCR for
Diagnosis of Melioidosis in Areas Where It Is Endemic
authors:
- Chawitar Noparatvarakorn
- Sineenart Sengyee
- Atchara Yarasai
- Rungnapa Phunpang
- Adul Dulsuk
- Orawan Ottiwet
- Rachan Janon
- Chumpol Morakot
- Mary N. Burtnick
- Paul J. Brett
- T. Eoin West
- Narisara Chantratita
journal: Journal of Clinical Microbiology
year: 2023
pmcid: PMC10035309
doi: 10.1128/jcm.01605-22
license: CC BY 4.0
---
# Prospective Analysis of Antibody Diagnostic Tests and TTS1 Real-Time PCR for Diagnosis of Melioidosis in Areas Where It Is Endemic
## ABSTRACT
Melioidosis is a tropical infectious disease caused by Burkholderia pseudomallei. Melioidosis is associated with diverse clinical manifestations and high mortality. Early diagnosis is needed for appropriate treatment, but it takes several days to obtain bacterial culture results. We previously developed a rapid immunochromatography test (ICT) based on hemolysin coregulated protein 1 (Hcp1) and two enzyme-linked immunosorbent assays (ELISAs) based on Hcp1 (Hcp1-ELISA) and O-polysaccharide (OPS-ELISA) for serodiagnosis of melioidosis. This study prospectively validated the diagnostic accuracy of the Hcp1-ICT in suspected melioidosis cases and determined its potential use for identifying occult melioidosis cases. Patients were enrolled and grouped by culture results, including 55 melioidosis cases, 49 other infection patients, and 69 patients with no pathogen detected. The results of the Hcp1-ICT were compared with culture, a real-time PCR test based on type 3 secretion system 1 genes (TTS1-PCR), and ELISAs. Patients in the no-pathogen-detected group were followed for subsequent culture results. Using bacterial culture as a gold standard, the sensitivity and specificity of Hcp1-ICT were $74.5\%$ and $89.8\%$, respectively. The sensitivity and specificity of TTS1-PCR were $78.2\%$ and $100\%$, respectively. The diagnostic accuracy was markedly improved if the Hcp1-ICT results were combined with TTS1-PCR results (sensitivity and specificity were $98.2\%$ and $89.8\%$, respectively). Among patients with initially negative cultures, Hcp1-ICT was positive in $\frac{16}{73}$ ($21.9\%$). Five of the 16 patients ($31.3\%$) were subsequently confirmed to have melioidosis by repeat culture. The combined Hcp1-ICT and TTS1-PCR test results are useful for diagnosis, and Hcp1-ICT may help identify occult cases of melioidosis.
## INTRODUCTION
Melioidosis is an often severe subtropical and tropical infectious disease that is endemic in northern Australia and Southeast Asia. The causative agent of melioidosis is the Gram-negative tier 1 select agent Burkholderia pseudomallei, which is naturally found in moist soils and surface waters in these areas [1]. The routes of infection in humans include inhalation of aerosols of contaminated soil and dust, percutaneous inoculation, and ingestion of contaminated food and water [1]. Worldwide, melioidosis cases have been estimated at 165,000 per year, with estimated deaths of 89,000 ($54\%$) [2]. In northeast Thailand, there are approximately 2,000 cases per year with a mortality rate exceeding $40\%$. Melioidosis has emerged in previously unaffected regions, such as North America and northeastern Brazil. This could be partially attributable to increased knowledge of the disease and improved diagnostic tools [1].
Melioidosis exhibits a broad spectrum of clinical symptoms that vary from localized cutaneous manifestations to severe sepsis and death [1]. Bacteremia occurs in 40 to $60\%$ of melioidosis patients, with approximately $20\%$ of patients developing septic shock, the most severe form of melioidosis [1]. Early diagnosis is required, since the optimal treatment requires intravenous administration of ceftazidime or a carbapenem, drugs that may not be widely available in regions where it is endemic. The median time for treatment response can be slow, up to 9 days [1].
For decades, diagnosis of melioidosis depended on isolation of bacterial culture, which requires microbiology facilities and can take several days. While bacterial culture is the diagnostic gold standard test for melioidosis, it is recognized to be imperfect, with Bayesian latent class modeling estimating the sensitivity to be $60\%$ and the negative predictive value to be $61.9\%$ [3]. Even after isolation of bacteria, it can take several additional days to positively identify the organism, delaying diagnosis and appropriate treatment [4]. Because of this, more rapid and non-culture-based diagnostics are desirable.
A point-of-care (POC) lateral flow assay for antigen detection was developed using a monoclonal antibody to B. pseudomallei capsular polysaccharide (CPS). However, the sensitivity was only $40\%$, based on evaluation with stored whole blood samples [5]. PCR with specific primers can provide more rapid results. However, these assays need to overcome several challenges in clinical specimens, including low numbers of bacteria in whole blood [4] and PCR inhibitors present in the samples, such as immunoglobulin G (IgG), heme, and human leukocyte DNA [6, 7]. Among several PCR targets, open reading frame 2 of the type three secretion system 1 (TTS1-orf2) gene cluster in B. pseudomallei is well-validated using real-time PCR for species-specific assays with clinical, animal, and environmental DNA samples from the Americas, Asia, Europe, Africa, and Oceania (8–11). However, performing the assay on buffy coat samples from Thai patients resulted in high specificity ($100\%$) but low sensitivity ($0\%$) [10]. The sensitivity of the assay in buffy coat samples from Australia improved from $36\%$ to $56\%$ by increasing the volume of DNA samples [11].
Several serological tests have been developed and evaluated for the diagnosis of melioidosis. The indirect hemagglutination assay (IHA), which detects rising antibody titers against B. pseudomallei, is widely used but has low sensitivity and specificity in areas of endemicity [12, 13]. Rapid enzyme-linked immunosorbent assays (ELISAs) targeting O-polysaccharide (OPS) and hemolysin coregulated protein 1 (Hcp1) associated with the B. pseudomallei type VI secretion system were developed and evaluated for serodiagnosis of melioidosis in an area of endemicity of Thailand [14, 15]. The diagnostic accuracy of both ELISAs was significantly higher than that with the IHA [15]. When anti-Hcp1 IgM and IgG antibodies were compared, an area under the receiver operating characteristic curve (AUROCC) was significantly greater for IgG (0.90) than for IgM (0.60) [16]. Based on these promising results, a rapid immunochromatography test for POC detection of IgG antibodies to Hcp1 was recently developed (Hcp1-ICT). The diagnostic characteristics of the Hcp1-ICT were initially evaluated using Thai and U.S. serum samples and compared to bacterial culture results as the gold standard. Results demonstrated $88.3\%$ sensitivity in *Thai melioidosis* patients, $86.1\%$ specificity in Thai healthy donors, and $100\%$ specificity in U.S. donors [17]. More recently the Hcp1-ICT was evaluated in patients admitted to hospitals in northeast Thailand with a febrile illness but negative blood cultures. The specificity was determined to be $60.2\%$, with $39.8\%$ of patients presenting Hcp1-ICT-positive results (unpublished data). This finding suggests that some Hcp1-ICT-positive but culture-negative patients may have undiagnosed melioidosis, previous exposure to B. pseudomallei, or cross-reactivity.
In the present study, we hypothesized that Hcp1 antibody detection might be a clinically useful tool for diagnosing melioidosis. To test this, we enrolled suspected melioidosis patients from northeast Thailand to validate the diagnostic accuracy of the Hcp1-ICT as a POC test for melioidosis using whole blood samples and compared the results with the results of bacterial culture, TTS1 real-time PCR, Hcp1-ELISA, and OPS-ELISA. We also evaluated the potential of Hcp1-ICT to identify occult cases of melioidosis. Occult cases were deemed likely to be melioidosis using combined results of repeated culture, real-time PCR, diagnostic imaging, and clinical investigations.
## Ethical approval.
The study protocol and related documents were approved by the Human Research Ethics Committees of the Faculty of Tropical Medicine, Mahidol University (approval number MUTM 2019-064-01) and of Mukdahan Hospital (MEC $\frac{03}{62}$). The study was conducted in accordance with the Declaration of Helsinki and the principles of good clinical practice. Written informed consent was obtained from all patients.
## Study design and participants.
A prospective observational study was conducted at Mukdahan Hospital in Mukdahan Province, northeast Thailand. Adult patients with suspected melioidosis were recruited within 24 h of hospital admission (day 1) between October 2019 and November 2020. Suspected melioidosis cases were identified and selected by clinicians, including patients admitted to the hospital who met the criteria documented in the medical record or with clinical suspicion of melioidosis based on the following criteria: (i) sepsis, defined as an infection with organ dysfunction in accordance with the Third International Consensus (Sepsis-3) guidelines for sepsis [18, 19]; (ii) patients without sepsis but with one of the following, fever (>38°C) or low temperature (<36°C) with any of the following diseases, diabetes mellitus (underlying disease or first diagnosis based on American Diabetes *Association criteria* [20, 21]), chronic kidney disease [22], or thalassemia. Exclusion criteria were admission to other hospitals with a total of admission time of >72 h, pregnancy, receiving palliative care, or incarceration. Vital status data were collected by follow-up phone calls conducted 28 days after admission. All blood and other clinical samples were collected on day 1 (the first day of hospital admission) for bacterial culture, Hcp1-ICT, TTS1 real-time PCR, Hcp1-ELISA, and OPS-ELISA.
Pus, sputum, urine, and body fluid samples obtained for culture were incubated for 2 days. Blood cultures were performed using BacT/Alert 3D (bioMérieux, Marcy l'Étoile, France) and routinely incubated for 5 days. Patients with negative cultures and no other infection identified by other standard testing were further investigated as potentially occult cases of melioidosis. Clinical samples were collected from these patients for a second time on day 4 to repeat bacterial culture, Hcp1-ICT, TTS1 real-time PCR, Hcp1-ELISA, and OPS-ELISA (Fig. 1). Blood culture bottles with no growth results after 5 days were further incubated for a total of 15 days, and any bacteria detected were identified.
**FIG 1:** *Diagnostic test evaluation flow chart. Clinical samples were collected from suspected melioidosis cases for assays on the day of admission (day 1). Patients whose day 1 cultures were negative underwent repeat blood and urine sampling at day 4 to investigate occult cases of melioidosis.*
Confirmed melioidosis was defined by culture of B. pseudomallei in any clinical sample. Other infection cases were defined as detection of other pathogenic microorganisms by culture and laboratory testing, including molecular tests, microscopy, lateral flow chromatographic immunoassay, or electrochemiluminescence immunoassay at the Mukdahan Hospital clinical laboratory. A no-pathogen-detected group was defined as no pathogenic microorganism identified by all laboratory tests.
## Clinical samples.
Clinical samples, including blood, pus, sputum, urine, and peritoneal dialysis fluid (PD fluid), were collected and processed at the study site. Five milliliters of blood was obtained in an EDTA tube. Whole blood was centrifuged at 1,500 × g for 15 min, and plasma and buffy coat fractions were collected. The buffy coat was lysed with an equal volume of sterile ultrapurified water. Pus was obtained using a disposable sterile cell harvester (Jiangsu Jianyou Medical Technology, JiangSu, China) and resuspended in 500 μL of sterile phosphate-buffered saline. Sputum was collected and mixed with an equal volume of sterile $4\%$ NaOH.
Ten milliliters of blood was cultured using BacT/Alert 3D (bioMérieux, Marcy l'Étoile, France), and urine was cultured on Ashdown agar [23], sheep blood agar, and MacConkey agar. Suspected B. pseudomallei colonies were tested with a B. pseudomallei-specific latex agglutination test [24]. Biochemical identification and antibiotic susceptibility testing were performed as described in ASM’s Clinical Microbiology Procedures Handbook [25]. All clinical samples, including urine, plasma, buffy coat, pus, sputum, and peritoneal dialysis fluid, were stored at −80°C until used for TTS1 real-time PCR, Hcp1-ELISA, and OPS-ELISA.
## Hcp1-ICT.
The Hcp1-ICT (lot number 19F1003) was performed with whole blood samples as previously described [17] (Fig. 2A). In brief, a 10-μL EDTA-blood sample was applied to the sample well, followed by 4 drops of running buffer. The result was read following 10 min of incubation at room temperature and interpreted by the presence of a control line. Prior to knowledge of culture results, the intensity of the test line color was assigned a score from 0 to 10 (Fig. 2B). Subsequently, receiver operating characteristic (ROC) curve analysis was performed to optimize the area under the curve. Based on this analysis, scores of 7 to 10 were interpreted as positive results. In contrast, the absence of any test line (score of 0) and scores of 1 to 6 were interpreted as negative. All scores were confirmed by 3 independent examiners.
**FIG 2:** *Hcp1-ICT tests. (A) Hcp1-ICT results with whole blood samples. (B) Color score of Hcp1-ICT results. (C) The receiver operating characteristic (ROC) curve was estimated using proportions of bacterial cultures of patient showing scores of 0 to 10 in the Hcp1-ICT. Hcp1-ICT tests with the color score at test bands 7 to 10 were considered positive. The absence of test bands and a color score of 1 to 6 were interpreted as negative. The red dot represents the cutoff score at 7. (D) Number of patients with melioidosis, other infections, and no pathogenic infection for each color score of Hcp1-ICT results.*
## DNA extraction.
DNA was extracted from 2 mL of plasma sample using a QIAamp DNA blood midi kit (Qiagen, Hilden, Germany) as recommended by the manufacturer with a final elution volume of 140 μL. Buffy coat obtained from 5 mL of EDTA-blood or 200 μL of other clinical samples, including uncentrifuged urine, peritoneal dialysis fluid, pus, and sputum, was DNA extracted using a QIAamp DNA Mini Kit per the manufacturer’s instructions (Qiagen, Hilden, Germany) with a final elution volume of 70 μL.
The control for DNA extraction was orf2 of the B. pseudomallei TTS1 [8]. The 115-bp PCR product was used as a template with the sequence modification at the probe hybridization location. This region was substituted with double-strand oligonucleotides of *Angiostrongylus vasorum* cytochrome c oxidase subunit I. The fragment was synthesized and ligated into pUC57 plasmid as TTS1-AVa (Bionic, South Korea). Plasmids were transformed into *Escherichia coli* JM110 for propagation. TTS1-AVa DNA was obtained using the QIAprep Spin Miniprep kit (Qiagen, Hilden, Germany). In each sample, 2.5 pg of TTS1-AVa DNA was added as an internal control prior to DNA extraction.
## Real-time PCR.
The real-time PCR assay targeted the 115-bp orf2 of B. pseudomallei TTS1 [8]. Each reaction mixture consisted of a total volume of 11 μL with 1× SensiFAST probe No-ROX (Bioline, Australia), 500 nM (each) BpTT4176F primer (5′-GCTCTCTATACTGTCGAGCAATGC-3′) and BpTT4290R primer (5′-CGTGCACACCGGTCAGTATC-3′), 200 nM BpTT4208P probe (5′–6-carboxyfluorescein [FAM]–CCGGAATCTGGATCACCACCACTTTCC–black hole quencher 1 [BHQ1]–3′) [8], 200 nM TTS1-AVa probe (5′-hexachlorofluorescein [HEX]-CTCCGTTGAGTAGTTTGGGTCATCCGG-BHQ1 3′), and 4 μL of total genomic DNA template. The thermal cycling conditions were 95°C for 5 min, followed by 95°C of 15 s and 59°C of 30 s for 45 cycles. The positive control was 100 pg of B. pseudomallei K96243 DNA. Real-time PCR was performed on a CFX96 Touch real-time PCR system (Bio-Rad, Hercules, CA).
Results of the Hcp1-ICT and TTS1 real-time PCR were analyzed as a combined test for antibody and DNA detection. A positive combined result was defined when either the Hcp1-ICT or TTS1 real-time PCR was positive, and a negative combined result was reported when both Hcp1-ICT and TTS1 real-time PCR results were negative.
## Hcp1-ELISA and OPS-ELISA.
ELISAs were performed on plasma samples at a dilution of 1:250, using recombinant Hcp1 [15, 26] at 2.5 μg/mL and OPS [27] at 1 μg/mL, with a 1:2,000 dilution of horseradish peroxidase-conjugated rabbit anti-human IgG as the secondary antibody [14]. All samples were performed in duplicate. The results were determined using a Sunrise microplate reader (Tecan, Männedorf, Switzerland) at an optical density of 450 nm (OD450). The samples were considered positive if the OD was ≥1.165 for Hcp1-ELISA and ≥0.875 for OPS-ELISA, as previously described [15].
## Statistical analyses.
Statistical analyses were performed using Stata version 14 (Stata Corp. LP, College Station, TX, USA) and Prism 8 statistics (GraphPad Software Inc., La Jolla, CA). The continuous variables and proportions for discrete data were presented as the median and interquartile range (IQR). IQRs were presented for 25th and 75th percentiles. Data were compared between groups using the Kruskal-Wallis test and the chi-square test for categorical data, continuous variables, and proportions. The data in the box plot demonstrate the 25th to 75th percentiles, with the middle line representing the median. The whiskers indicate the 10th and 90th percentiles. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of all tests were calculated using bacterial culture results as a gold standard. The McNemar test was used to compare the sensitivity and specificity between tests.
## Characteristics of participants.
A total of 180 adult patients with suspected acute melioidosis were screened in the study (Fig. 1). Seven individuals were excluded because they were repeat cases ($$n = 6$$) or referred from other hospitals after ≥72 h ($$n = 1$$). The final number of patients enrolled and analyzed was 173. Of these patients, initial diagnostic testing using bacterial culture identified 54 ($31.2\%$) confirmed melioidosis patients. Forty six ($26.6\%$) patients were diagnosed with infection due to other pathogens, and 73 ($42.2\%$) patients had no identified infection by bacterial culture or by testing for infections using other methods. The 73 patients without identified infection were subjected to further testing as described in Fig. 1. Extended incubation of blood culture samples to 15 days did not result in identification of B. pseudomallei; however, one additional patient had a bacterial culture that was positive for B. pseudomallei from the clinical sample collected on day 4. Day 4 sampling also identified three other infectious etiologies. In total, 55 ($31.8\%$) patients were classified as having melioidosis, 49 ($28.3\%$) patients were classified as having other infections, and 69 ($39.9\%$) patients had no diagnosed etiology of infection.
Table 1 shows the demographic and clinical characteristics of the patients. The median age was 58 years (IQR, 47 to 68 years) for all patients, 55 years (IQR, 45 to 62 years) for the melioidosis group, 64 years (52 to 71 years) for the other infections group, and 57 years (IQR, 48 to 67 years) for the no-infection group ($$P \leq 0.03$$). Seventeen melioidosis cases ($30.9\%$) were admitted to the intensive care unit (ICU). Patients in the melioidosis group had longer hospitalizations than patients from other groups. The median time of hospitalization in the melioidosis group was 14 days (IQR, 11 to 20 days), compared with 12 days for the other infections group (IQR, 5 to 14 days) and 5 days for the no-pathogen-detected group (IQR, 3 to 8.5 days) ($P \leq 0.001$).
**TABLE 1**
| Characteristic | Total (N = 173) | Melioidosis (N = 55) | Other infections (N = 49) | No pathogen detected (N = 69) | P valuea |
| --- | --- | --- | --- | --- | --- |
| Median age (IQR) | 58 yrs (47–68) | 55 yrs (45–62) | 64 yrs (52–71) | 57 yrs (48–67) | 0.03 |
| Gender | | | | | 0.37b |
| Male (%) | 92 (53.2) | 32 (58.2) | 22 (44.9) | 38 (55.1) | |
| Female (%) | 81 (46.8) | 23 (41.8) | 27 (55.1) | 31 (44.9) | |
| 28-day mortality | 14 (8.1%) | 5 (9.1%) | 3 (5.8%) | 6 (9.1%) | 0.35 |
| No. (%) with previous history of melioidosis | 10 (5.8%) | 6 (10.9%) | 1 (1.9%) | 3 (4.5%) | 0.15 |
| No. (%) admitted to ICU | 36 (20.8%) | 17 (30.9%) | 9 (18.4%) | 10 (14.5%) | 0.22 |
| Median no. of days hospitalized (IQR) | 9 (4.0–14.0) | 14 (11–20) | 12 (5–14) | 5 (3.0–8.5) | <0.001 |
## Optimization of the Hcp1-ICT test cutoff.
All Hcp1-ICT results performed on whole blood samples on admission were determined by assigning a score based on the color of the test band for patients before culture results were known (Fig. 2B). Combining this information and the subsequent identification of melioidosis patients using bacterial culture, ROC analysis was performed to identify the optimal cutoff for Hcp1-ICT interpretation on whole blood samples. Assigning a score of ≥7 as positive yielded a test sensitivity of $74.6\%$ and specificity of $83.9\%$, with AUC of 0.80 (Fig. 2C). Based on this, we decided to interpret Hcp1-ICT as positive at a color score of ≥7. A majority of melioidosis patients (41; $74.6\%$) were noted to be Hcp1-ICT positive, with scores of 7 to 10. In contrast, the absence of test bands was noted for most patients with other infections (28; $57.1\%$) or patients with no infection (36; $52.2\%$). However, some patients with other infections or no infection were scored as false positive by Hcp1-ICT at a color score of ≥7 (Fig. 2D).
## Diagnostic accuracy of Hcp1-ICT using whole blood samples.
Using the threshold of color score at test band 7 to dichotomize the Hcp1-ICT results as positive or negative, other measures of diagnostic accuracy (sensitivity, specificity, PPV, and NPV) were assessed (Table 2). The Hcp1-ICT was positive in 60 of 173 ($34.7\%$) patients with suspected melioidosis in the study. The sensitivity of Hcp1-ICT for 55 culture-confirmed melioidosis patients was $74.5\%$ ($95\%$ confidence interval [CI], $61.7\%$ to $84.2\%$). The specificity for 49 patients with other infections was $89.8\%$ ($95\%$ CI, $78.2\%$ to $95.6\%$) and for 69 patients with no pathogenic infection it was $79.7\%$ ($95\%$ CI, $68.8\%$ to $87.5\%$). Using all 173 patients, the PPV and NPV of Hcp1-ICT were $68.3\%$ ($95\%$ CI, $55.8\%$ to $78.7\%$) and $87.6\%$ ($95\%$ CI, $80.3\%$ to $92.5\%$), respectively.
**TABLE 2**
| Test | % Sensitivity (95% CI) | % Specificity for other infections group (95% CI) | % Specificity for no-pathogen-detected group (95% CI) | % PPV (95% CI) | % NPV (95% CI) |
| --- | --- | --- | --- | --- | --- |
| Hcp1-ICT | 74.5 (61.7–84.2) | 89.8 (78.2–95.6) | 79.7 (68.8–87.5) | 68.3 (55.8–78.7) | 87.6 (80.3–92.5) |
| TTS1 real-time PCR | 78.2 (65.6–87.1) | 100.0 (92.7–100.0) | 98.6 (92.2–99.9) | 97.7 (88.2–99.9) | 90.7 (84.4–94.6) |
| Combined Hcp1-ICT and TTS1 real-time PCR | 98.2 (90.4–99.9) | 89.8 (78.2–95.6) | 78.3 (67.2–86.4) | 73.0 (61.9–81.8) | 99.0 (94.5–99.9) |
| Hcp1-ELISA | 70.9 (57.9–81.2) | 85.7 (73.3–92.9) | 76.8 (65.6–85.2) | 62.9 (50.5–73.8) | 85.6 (77.9–90.9) |
| OPS-ELISA | 69.1 (56.0–79.7) | 89.8 (78.2–95.6) | 68.1 (56.4–77.9) | 58.5 (46.3–69.6) | 84.3 (76.2–89.9) |
The Hcp1-ICT was positive in 5 of 49 ($10.2\%$) of the patients with other infections (Table 3), as follows: *Escherichia coli* ($\frac{2}{17}$ patients), *Klebsiella pneumoniae* ($\frac{2}{4}$ patients), and *Pseudomonas aeruginosa* ($\frac{1}{1}$ patient). The patient infected with P. aeruginosa had a history of melioidosis 37 weeks prior to enrollment in this study.
**TABLE 3**
| Infectious agent | Total no. of patients | Hcp1-ICT positive | Hcp1-ELISA positive | OPS-ELISA positive | TTS1 real-time PCR positive |
| --- | --- | --- | --- | --- | --- |
| Acinetobacter baumannii | 3 | 0 | 0 | 0 | 0 |
| Aeromonas hydrophila | 1 | 0 | 0 | 0 | 0 |
| Brucella spp. | 1 | 0 | 0 | 0 | 0 |
| Corynebacterium spp. | 1 | 0 | 0 | 0 | 0 |
| Enterobacter cloacae | 2 | 0 | 0 | 0 | 0 |
| Enterococcus faecalis | 5 | 0 | 0 | 0 | 0 |
| Enterococcus faecium | 1 | 0 | 0 | 0 | 0 |
| Escherichia coli | 17 | 2 | 3 | 2 | 0 |
| Klebsiella pneumoniae | 4 | 2 | 2 | 1 | 0 |
| Pseudomonas aeruginosa | 1 | 1 | 1 | 1 | 0 |
| Staphylococcus aureus | 2 | 0 | 0 | 0 | 0 |
| Streptococcus pyogenes | 2 | 0 | 0 | 0 | 0 |
| Mixed infection | 9 | 0 | 1 | 1 | 0 |
| Total | 49 | 5 | 7 | 5 | 0 |
## Sensitivity of diagnostic tests in culture-confirmed melioidosis patients.
The performance of the Hcp1-ICT for diagnosis of melioidosis for 173 suspected cases was compared with the results of TTS1 real-time PCR, Hcp1-ELISA, and OPS-ELISA (Table 2). The real-time PCR was performed on DNA extracted from various clinical samples ($$n = 543$$) of 173 patients. Hcp1-ELISA and OPS-ELISA were performed on plasma samples from all 173 patients. All samples were collected from patients on the first day of admission. The sensitivities of TTS1 real-time PCR, Hcp1-ELISA, and OPS-ELISA in 55 culture-confirmed melioidosis patients were $78.2\%$ ($95\%$ CI, 65.6 to $87.1\%$), $70.9\%$ ($95\%$ CI, 57.9 to $81.2\%$), and $69.1\%$ ($95\%$ CI, 56.0 to $79.7\%$), respectively. The median OD values of the Hcp1-ELISA and the OPS-ELISA in culture-confirmed melioidosis patients were higher than for nonmelioidosis patients (Hcp1-ELISA: 2.669 with IQR of 0.482 to 3.302, compared to results in nonmelioidosis patients, 0.290 with IQR of 0.078 to 0.718; $P \leq 0.001$; OPS-ELISA: 1.622 with IQR of 0.620 to 3.468 versus 0.341 with IQR o 0.144 to 0.843; $P \leq 0.001$) (Fig. 3A and B).
**FIG 3:** *ELISA results for melioidosis and nonmelioidosis patients. (A) Hcp1-ELISA. (B) OPS-ELISA. The box plots represent OD450 readings and extend from the 25th to 75th percentiles, with the middle lines representing the medians. The whiskers indicate the 10th and 90th percentiles.*
The sensitivity of the Hcp1-ICT was not significantly different from that for the TTS1 real-time PCR ($74.5\%$ versus $78.2\%$; $$P \leq 0.839$$), the Hcp1-ELISA ($74.5\%$ versus $70.9\%$; $$P \leq 0.500$$), or the OPS-ELISA ($74.5\%$ versus $69.1\%$; $$P \leq 0.453$$). The combined results of the Hcp1-ICT and TTS1 real-time PCRs were analyzed, and the sensitivity increased to $98.2\%$ ($95\%$ CI, 90.4 to $99.9\%$), which was higher than that of the Hcp1-ICT alone ($74.5\%$; $P \leq 0.001$), TTS1 real-time PCR alone ($78.2\%$; $P \leq 0.001$), Hcp1-ELISA ($70.9\%$; $P \leq 0.001$), or OPS-ELISA ($69.1\%$; $P \leq 0.001$) (Table 2). TTS1 real-time PCR was positive in 13 of 15 ($86.7\%$) culture-confirmed melioidosis patients who were Hcp1-ICT negative.
## Specificity of the diagnostic tests used in this study in nonmelioidosis patients.
The specificities of the diagnostic tests used in this study were first calculated for results with the 49 patients with other infections. The specificity of TTS1 real-time PCR was the highest, at $100\%$ ($95\%$ CI, 92.7 to $100\%$) (Table 2). The specificity of the Hcp1-ICT was $89.8\%$ ($95\%$ CI, 78.2 to $95.6\%$), which was comparable to that for the TTS1 real-time PCR ($$P \leq 0.063$$), the Hcp1-ELISA ($85.7\%$, with $95\%$ CI of 73.3 to $92.9\%$; $$P \leq 0.500$$), and the OPS-ELISA ($89.8\%$, with $95\%$ CI of $95.6\%$; $$P \leq 1.000$$). The specificity of the combined Hcp1-ICT and TTS1 real-time PCR tests was $89.8\%$ ($95\%$ CI, 78.2 to $95.6\%$), which was comparable to that for Hcp1-ICT alone ($89.8\%$; $$P \leq 1.000$$), the Hcp1-ELISA ($85.7\%$; $$P \leq 0.500$$), or the OPS-ELISA ($89.8\%$; $$P \leq 1.000$$), but lower than that for the TTS1 real-time PCR alone ($100\%$; $$P \leq 0.063$$).
The specificity in the no-pathogen-detected group (69 patients) was highest for TTS1 real-time PCR at $98.6\%$ ($95\%$ CI, 92.2 to $99.9\%$). The Hcp1-ICT specificity for this group was $79.7\%$ ($95\%$ CI, 68.8 to $87.5\%$), compared to that for the TTS1 real-time PCR ($$P \leq 0.001$$), the Hcp1-ELISA ($76.8\%$, with $95\%$ of CI 65.6 to $85.2\%$; $$P \leq 0.625$$) or the OPS-ELISA ($68.1\%$, with $95\%$ CI of 56.4 to $77.9\%$; $$P \leq 0.057$$). The combined test (Hcp1-ICT + TTS1 real-time PCR) specificity was $78.3\%$ ($95\%$ CI, 67.2 to $86.4\%$), compared to $79.7\%$ for the Hcp1-ICT ($$P \leq 1.000$$), $76.8\%$ for the Hcp1-ELISA ($$P \leq 1.000$$), $68.1\%$ for the OPS-ELISA ($$P \leq 0.119$$), and $98.6\%$ for TTS1 real-time PCR ($P \leq 0.001$).
## Sensitivity of the diagnostic tests used in this study for melioidosis patients with bacteremia.
Of 55 melioidosis patients, 38 ($69.1\%$) patients were positive for B. pseudomallei from blood culture. TTS1 real-time PCR sensitivity was the highest ($78.9\%$, $95\%$ CI, 63.7 to $88.9\%$) among the four single tests used (Table 4). The sensitivity of the Hcp1-ICT, Hcp1-ELISA, and OPS-ELISA were $73.7\%$ ($95\%$ CI, 58.0 to $85.0\%$), $71.1\%$ ($95\%$ CI, 55.2 to $83.0\%$), and $63.2\%$ ($95\%$ CI, 47.3 to $76.6\%$), respectively.
**TABLE 4**
| Infection type | Sensitivity (%) of test (95% CI) | Sensitivity (%) of test (95% CI).1 | Sensitivity (%) of test (95% CI).2 | Sensitivity (%) of test (95% CI).3 | Sensitivity (%) of test (95% CI).4 |
| --- | --- | --- | --- | --- | --- |
| Infection type | Hcp1-ICT | TTS1 real-time PCR | Combined Hcp1-ICT and TTS1 real-time PCR | Hcp1-ELISA | OPS-ELISA |
| Bacteremia | 73.7 (58.0–85.0) | 78.9 (63.7–88.9) | 97.4 (86.5–99.9) | 71.1 (55.2–83.0) | 63.2 (47.3–76.6) |
| Abscess | 84.6 (57.8–97.3) | 69.2 (42.4–87.3) | 100 (77.2–100.0) | 76.9 (49.7–91.8) | 84.6 (57.8–97.3) |
The combined results of the Hcp1-ICT and TTS1 real-time PCR tests demonstrated a sensitivity of $97.4\%$ ($95\%$ CI, 86.5 to $99.9\%$) in bacteremic melioidosis patients. The sensitivity of the combined tests was greater than that for the Hcp1-ICT alone ($73.7\%$; $$P \leq 0.004$$), TTS1 real-time PCR alone ($78.9\%$; $$P \leq 0.016$$), Hcp1-ELISA ($71.1\%$; $$P \leq 0.002$$), and the OPS-ELISA ($63.2\%$; $P \leq 0.001$).
## Positivity rate of the diagnostic tests used in this study for melioidosis in patients with abscesses.
Of 21 patients with abscesses in their skin or internal organs, 13 were positive for melioidosis ($61.9\%$), 1 belonged to the other infections group ($4.8\%$), and 7 belonged to the no-pathogenic-infection group ($33.3\%$). Ten of 21 ($47.6\%$) patients had imaging for internal abscesses with a computed tomography (CT) scan of the abdomen or abdominal ultrasound. The abscesses in the 13 melioidosis patients included 9 skin abscesses ($69.2\%$), 3 splenic abscesses ($23.1\%$), and 1 hepatosplenic abscess ($7.7\%$). One patient from other infections group had a skin abscess. In 7 patients with no pathogenic infection, we detected 1 hepatosplenic abscess ($14.3\%$), 4 hepatic abscesses ($57.1\%$), 1 lung abscess ($14.3\%$), and 1 lymph node abscess ($14.3\%$).
The combined Hcp1-ICT and TTS1 real-time PCR test results presented the highest positivity rate, at $\frac{13}{13}$ ($100\%$) in any clinical sample from melioidosis patients with abscesses, including urine, plasma, buffy coat, sputum, and pus, followed by the Hcp1-ICT ($\frac{11}{13}$; $84.6\%$) and the OPS-ELISA ($\frac{11}{13}$; $84.6\%$), the Hcp1-ELISA ($\frac{10}{13}$; $76.9\%$), and TTS1 real-time PCR ($\frac{9}{13}$; $69.2\%$) (Table 4). The clinical specimens that were TTS1 real-time PCR positive for these patients included $\frac{6}{13}$ urine samples ($46.2\%$), $\frac{1}{13}$ plasma samples ($7.7\%$), $\frac{3}{13}$ buffy coat samples ($23.1\%$), $\frac{3}{3}$ sputum samples ($100\%$), and $\frac{4}{4}$ pus samples ($100\%$).
## Positivity of the diagnostic tests used in this study and number of days post-symptom onset in bacteremic melioidosis patients.
Thirty-eight patients were documented with bacteremic melioidosis. Of these, 25 ($65.8\%$) patients had symptoms for 1 to 3 days, 7 patients ($18.4\%$) had symptoms for 4 to 6 days, and 6 patients ($15.8\%$) had symptoms for ≥7 days. The combination of Hcp1-ICT and TTS1 real-time PCR tests presented the highest positivity rates, $96\%$ at 1 to 3 days and $100\%$ at more than 4 days, compared with any single test. At 1 to 3 days post-symptom onset, every single test presented a positivity rate ranging from $60\%$ to $80\%$. At 4 to 6 days post-symptom onset, the detection rate for single tests was $57.1\%$ for the Hcp1-ELISA, the OPS-ELISA, and TTS1 real-time PCR and $71.4\%$ for the Hcp1-ICT. The positivity rates of all tests were highest at ≥7 days, with $100\%$ for the Hcp1-ICT, TTS1 real-time PCR, and Hcp1-ELISA, followed by $85.7\%$ for the OPS-ELISA (Fig. 4).
**FIG 4:** *The positivity rate of the diagnostic tests used in this study at different periods post-symptom onset among bacteremic melioidosis patients.*
## TTS1 real-time PCR detection in melioidosis patients.
A total of 543 clinical samples were collected from 173 patients on the day of admission with a range of 3 to 4 samples each (median, 3 samples per patient). Of 55 melioidosis patients, 184 clinical samples were collected. The positivity rate of TTS1 real-time PCR in melioidosis patients was $34.6\%$ ($\frac{19}{55}$) in plasma samples, $47.3\%$ ($\frac{26}{55}$) in buffy coat samples, $45.5\%$ ($\frac{25}{55}$) in urine samples, $90.9\%$ ($\frac{10}{11}$) in sputum samples, $100\%$ ($\frac{6}{6}$) in pus samples, and $100\%$ ($\frac{2}{2}$) PD fluid samples. The melioidosis patients that were TTS1 real-time PCR positive included patients who were both Hcp1-ICT positive and negative (Fig. 5A).
**FIG 5:** *Positivity rate of TTS1 real-time PCR in melioidosis patients. (A) The positivity rate of TTS1 real-time PCR in different types of clinical samples. (B) Positivity rate of TTS1 real-time PCR associated with Hcp1-ICT results in melioidosis patients.*
Of 41 Hcp1-ICT-positive melioidosis patients, TTS1 real-time PCR was positive in $73.2\%$ ($\frac{30}{41}$) of any clinical samples (Fig. 5B). The positivity rate was $43.9\%$ ($\frac{18}{41}$) in urine samples, $26.8\%$ ($\frac{11}{41}$) in plasma samples, $41.5\%$ ($\frac{17}{41}$) in buffy coat samples, $87.5\%$ ($\frac{7}{8}$) in sputum samples, $100\%$ ($\frac{3}{3}$) in pus samples, and $100\%$ ($\frac{2}{2}$) in PD fluid samples.
Of 14 Hcp1-ICT-negative melioidosis patients, we found that TTS1 real-time PCR was $92.9\%$ ($\frac{13}{14}$) positive in any clinical sample (Fig. 5B). The positivity rate for each sample type was $50\%$ ($\frac{7}{14}$) in urine samples, $57.1\%$ ($\frac{8}{14}$) in plasma samples, $64.3\%$ ($\frac{9}{14}$) in buffy coat samples, $100\%$ ($\frac{3}{3}$) in sputum samples, and $100\%$ ($\frac{3}{3}$) in pus samples.
## Hcp1-ICT for identification of occult melioidosis patients.
Culture results of the first samples from 73 patients collected at admission were negative. Sixteen of these patients were Hcp1-ICT positive. Eight of 16 ($50\%$) culture-negative Hcp1-ICT-positive patients were either subsequently culture positive for B. pseudomallei infection ($$n = 5$$; $31.3\%$) or had clinical features that were especially suggestive of melioidosis ($$n = 3$$; $18.8\%$) (Table 5). Of the five patients with delayed confirmation of infection, one was culture-positive from a blood sample collected on day 4 of admission as per the study protocol. Four reported no significant illness recovery after discharge from the hospital and were readmitted to the same hospital for culture-confirmed B. pseudomallei infection 3 to 11 weeks after the first admission, which suggests that melioidosis caused the initial illness. Of the patients with suggestive clinical features, one patient with diabetes mellitus, alcoholism, and prolonged fever was diagnosed with probable melioidosis by the treating clinicians, based on diagnostic imaging, including CT of the upper abdomen and ultrasound of the whole abdomen, which showed liver and spleen abscesses with pleural effusion. Additionally, two patients were deemed to be possible occult cases of melioidosis, as they had previously been diagnosed with melioidosis 3 months and 15 months prior to enrollment in this study.
**TABLE 5**
| Hcp1-ICT result | No. (%) of occult melioidosis patients with indicated Hcp1-ICT result, by finding on follow-up | No. (%) of occult melioidosis patients with indicated Hcp1-ICT result, by finding on follow-up.1 | No. (%) of occult melioidosis patients with indicated Hcp1-ICT result, by finding on follow-up.2 | No. (%) of occult melioidosis patients with indicated Hcp1-ICT result, by finding on follow-up.3 |
| --- | --- | --- | --- | --- |
| Hcp1-ICT result | Confirmed melioidosis | Probable or possible melioidosis | Probable or possible melioidosis | Probable or possible melioidosis |
| Hcp1-ICT result | B. pseudomallei positive in repeated culture | Clinical findinga | Previous melioidosis | TTS1 real-time PCR |
| Positive (N = 16) | 5 (31.3%) | 1 (6.3%) | 2 (12.5%) | 0 |
| Negative (N = 57) | 0 | 0 | 0 | 1 (1.8%) |
| Total (N = 73) | 5 (6.9%) | 1 (1.4%) | 2 (2.7%) | 1 (1.4%) |
Fifty-seven of the 69 patients ($78.1\%$) in the no-pathogen-detected group were Hcp1-ICT negative. No patients converted to Hcp1-ICT positive on repeat assessment on day 4.
Of the 16 Hcp1-ICT-positive patients without evidence of infection upon enrollment sampling, 15 ($93.8\%$) had positive Hcp1-ELISA and 13 ($81.3\%$) had positive OPS-ELISA. None of these patients were positive by TTS1 real-time PCR performed on the buffy coat, plasma, or urine samples. Nine patients ($56.3\%$) underwent repeat Hcp1-ICT on day 4 and all were still positive.
## DISCUSSION
In melioidosis, early diagnosis is critical to initiate prompt treatment with effective antibiotics, prevent disease progression, and improve outcomes. While bacterial culture is the standard method for diagnosis, it is time-consuming and provides low sensitivity [3]. In this study, we evaluated the Hcp1-ICT as a point-of-care test, and we prospectively evaluated the diagnostic accuracy of antibody tests, including the Hcp1-ICT, the Hcp1-ELISA, the OPS-ELISA and TTS1 real-time PCR tests, performed within 24 h of admission of suspected melioidosis patients in northeast Thailand. This study demonstrated that a combination of the Hcp1-ICT and TTS1 real-time PCR tests could be useful tools for rapidly identifying B. pseudomallei with high sensitivity and specificity. The diagnostic accuracy of the combined tests was higher than that for any single test, and we determined that the Hcp1-ICT alone may help increase clinical suspicion for melioidosis in culture-negative individuals.
Reading the color intensity of the Hcp1-ICT test bands as scores ranging from 1 to 10 has provided an interpretation of IgG antibody levels in patients. Our evaluation using whole blood samples indicated a high potential when using scores of ≥7 as positive. However, the interpretation of the visible test bands could lead to an interpretation error, especially when difficult-to-read bands are nearly at the cutoff score. Our future studies will evaluate a mobile application to distinguish between positive and negative results. Artificial intelligence was recently applied to improve the interpretation of lateral flow assay for 2019 coronavirus disease detection [28]. Using whole blood samples is ideal for POC tests at the bedside, because this does not require centrifugation to separate serum or plasma. The sensitivity of the Hcp1-ICT evaluated with whole blood samples was $74.5\%$, lower than that previously reported using serum samples ($88.3\%$) [17]. The lower sensitivity was possibly due to differences in the designs of the two studies. In this study, we prospectively collected blood samples on the day of admission. The previous study collected samples at least 48 to 72 h after melioidosis patient identification by culture results. Our data showed improved sensitivity when the result of the Hcp1-ICT was combined with the result of TTS1 real-time PCR. The data in this study support the idea that TTS1 real-time PCR could identify melioidosis patients with slow or no IgG seroconversion when their specimens are collected early in infection and the Hcp1-ICT was negative.
The sensitivity of the TTS1 real-time PCR was high for sputum, pus, urine, and PD fluid samples, with high bacterial loads as previously reported [10, 11]. As expected, blood samples, including buffy coat and plasma, showed less sensitivity by TTS1 real-time PCR [10, 11]. A quantitative blood culture study showed that the median concentration of B. pseudomallei in blood samples was 1.1 CFU/mL [29]. The sensitivity of TTS1 real-time PCR in 200-μL blood samples evaluated in Thailand was $0\%$ [10]. In this study, we used a high-volume blood samples (5 mL) and eluted the DNA with a low buffer volume (70 μL for buffy coat and 140 μL for plasma), which improved sensitivity in the buffy coat to $47.3\%$ and $34.6\%$ in plasma. However, the sensitivity of TTS1 real-time PCR in human whole blood samples may be low due to the presence of PCR inhibitors, such as immunoglobulin G, heme, and human leukocyte DNA [6, 7]. Our study showed that the sensitivity of TTS1 real-time PCR using 2 mL of plasma was not as high as using buffy coat samples. Using a higher volume of plasma with a small volume of elution buffer might improve the sensitivity of testing blood samples. Uncentrifuged urine was used in this study because a previous study showed no difference in results between using 10 mL of centrifuged urine and 200 μL of uncentrifuged urine [30]. TTS1 real-time PCR detection with the samples collected from localized infections with B. pseudomallei, such as pus samples, might be useful, as the sensitivity of TTS1 real-time PCR in melioidosis patients was $100\%$. Unfortunately, we were not able to collect pus from all melioidosis patients with abscesses, because pus samples were not available for some patients, including those with internal organ infections. The TTS1 real-time PCR assay could lead to a false-negative result by using a single target for detection. Combining the TTS1-orf2 with other targets, such as BPSS0745, could potentially improve the sensitivity of these assays, as demonstrated previously in the detection of B. pseudomallei DNA in soil samples, where the sensitivity increased from $76.5\%$ to $90\%$ [9].
TTS1 real-time PCR-positive results in patients with symptom onset of ≥7 days might be related to several potential factors, including persistent infection. B. pseudomallei persistence has been reported to be associated with toxin-antitoxin systems, the ability of bacteria to survive under stressful conditions, and adaptive mutations [31]. Furthermore, the response of B. pseudomallei to initial intensive therapy could be slow, as median fever clearance time is 9 days [32]. A longer response time is observed in patients with deep-seated abscesses [1]. PCR detection could also be positive after the fever clearance phase due to DNA from dead cells.
The sensitivity and specificity of serological testing, including the Hcp1-ICT, the Hcp1-ELISA, and the OPS-ELISA were less than those reported in the previous study [15]. Lower accuracy could have been due to different times of sample collection. This study collected samples within the first 24 h of patient admission. Another explanation is that the two studies were conducted in different study populations. Our study enrolled one population of suspected melioidosis patients from Mukdahan Hospital, while various populations, including Thai healthy donors, U.S. healthy donors, tuberculosis patients, scrub typhus patients, and leptospirosis patients were included in the previous study [15]. We also determined that $5.1\%$ of nonmelioidosis patients had previous infections with B. pseudomallei. Serological tests detected IgG antibodies against Hcp1 and OPS in $83.3\%$ and $100\%$ of samples tested, respectively, from nonmelioidosis patients with previous infections. The patients were infected 60 days to 20 months prior to this study. The IgG antibodies against Hcp1 and OPS in melioidosis patients were found 3 to 155 days of duration of symptoms before admission [16]. The detectable level of IgG antibodies benefits early detection of Hcp1-ICT in melioidosis patients with <7 days post-symptom onset, as we found the positivity rate was $68.8\%$.
Blood cultures were positive in approximately $50\%$ of melioidosis patients [32], and time to result for B. pseudomallei report in culture could be up to 4 days [1]. However, among patients with initially negative cultures, $31\%$ of patients with a positive Hcp1-ICT subsequently had culture-confirmed B. pseudomallei infections. Since the Hcp1-ICT has a turnaround time of 15 min, it may be a useful tool in prompting clinicians to consider melioidosis as a diagnosis among patients with unidentified infections [17].
The evaluation of the different diagnostic tests in this study showed that the Hcp1-ICT is a promising test for detection of melioidosis, since it demonstrated high sensitivity, high specificity, and a short turnaround time. Using the Hcp1-ICT is rapid and simple. It does not require specific training or equipment. However, the limitations of the Hcp1-ICT are that it cannot distinguish between IgG antibody responses to previous and current infections. Hcp1-ICT can be improved by combining with TTS1 real-time PCR for antigen and antibody detection. Based on our findings, the combination of the Hcp1-ICT and TTS1 real-time PCR could be a rapid diagnostic test for early diagnosis in clinical settings and for surveillance of melioidosis and may also facilitate the identification of initially occult melioidosis.
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|
---
title: Risk investigation of in-stent restenosis after initial implantation of intracoronary
drug-eluting stent in patients with coronary heart disease
authors:
- Hongfei Xi
- Jiasi Liu
- Tao Xu
- Zhe Li
- Xuanting Mou
- Yu Jin
- Shudong Xia
journal: Frontiers in Cardiovascular Medicine
year: 2023
pmcid: PMC10035367
doi: 10.3389/fcvm.2023.1117915
license: CC BY 4.0
---
# Risk investigation of in-stent restenosis after initial implantation of intracoronary drug-eluting stent in patients with coronary heart disease
## Abstract
### Objective
To analyze the risk factors of in-stent restenosis (ISR) after the first implantation of drug-eluting stent (DES) patients with coronary heart disease (CHD) and to establish a nomogram model to predict the risk of ISR.
### Methods
This study retrospectively analyzed the clinical data of patients with CHD who underwent DES treatment for the first time at the Fourth Affiliated Hospital of Zhejiang University School of Medicine from January 2016 to June 2020. Patients were divided into an ISR group and a non-ISR (N-ISR) group according to the results of coronary angiography. The least absolute shrinkage and selection operator (LASSO) regression analysis was performed on the clinical variables to screen out the characteristic variables. Then we constructed the nomogram prediction model using conditional multivariate logistic regression analysis combined with the clinical variables selected in the LASSO regression analysis. Finally, the decision curve analysis, clinical impact curve, area under the receiver operating characteristic curve, and calibration curve were used to evaluate the nomogram prediction model's clinical applicability, validity, discrimination, and consistency. And we double-validate the prediction model using ten-fold cross-validation and bootstrap validation.
### Results
In this study, hypertension, HbA1c, mean stent diameter, total stent length, thyroxine, and fibrinogen were all predictive factors for ISR. We successfully constructed a nomogram prediction model using these variables to quantify the risk of ISR. The AUC value of the nomogram prediction model was 0.806 ($95\%$CI: 0.739–0.873), indicating that the model had a good discriminative ability for ISR. The high quality of the calibration curve of the model demonstrated the strong consistency of the model. Moreover, the DCA and CIC curve showed the model's high clinical applicability and effectiveness.
### Conclusions
Hypertension, HbA1c, mean stent diameter, total stent length, thyroxine, and fibrinogen are important predictors for ISR. The nomogram prediction model can better identify the high-risk population of ISR and provide practical decision-making information for the follow-up intervention in the high-risk population.
## Introduction
CHD is a chronic disease that seriously affects human life and health. Its morbidity and mortality are increasing every year [1]. Percutaneous coronary intervention (PCI) is an essential treatment method for coronary artery disease [2].
ISR refers to the gradual restenosis of primary coronary artery lesions after stent implantation, defined as the diameter of coronary luminal stenosis ≥$50\%$ throughout the stent and/or within the proximal and distal 5 mm segments of the stent [3]. And ISR usually develops six months after PCI and is characterized by recurrent angina pectoris, but myocardial infarction can also occur. In recent years, with the gradual replacement of bare metal stents (BMS) by DES, the incidence of ISR has been significantly reduced [4]. However, ISR after DES implantation, with an incidence of $3\%$–$20\%$ [5], is still one of the main reasons affecting the long-term efficacy of PCI, and there is no standardized treatment strategy [6]. Thus, the prevention and treatment of ISR remain a challenging problem in the cardiovascular field.
The exact mechanism of ISR is still not fully understood [4]. Therefore, early detection and control of related risk factors may be an important means to reduce the incidence of ISR and improve patients' prognosis and quality of life. There still needs to be more suitable prediction models to predict ISR. However, several previous studies have analyzed potential predictors associated with high-risk ISR and developed predictive models based on them [5, 7, 8]. Some limitations suppress their clinical application, such as poor stability and weak discriminative power of prediction models, and poor generalizability of predictors. In addition, we can find that most of the studies focus on the comprehensive search for predictors of ISR and do not fully consider the ease of use for clinicians [9, 10]. Therefore, there is an urgent need to establish a reliable, accurate, and easy-to-use ISR prediction model for clinicians and patients to accurately predict the occurrence of ISR and make clinical decisions for primary prevention.
## Methods
This study retrospectively analyzed the clinical data of patients with CHD who received DES treatment for the first time in the Fourth Affiliated Hospital of Zhejiang University School of Medicine from January 1, 2016, to January 1, 2020. LASSO regression and multivariate logistic stepwise regression analyses were used to determine the risk factors of ISR. Based on these predictors, we constructed a nomogram prediction model to evaluate the risk of ISR in different patients quantitatively.
## Study population
We included 414 patients with CHD who were first treated with DES in our hospital between January 1, 2016, and June 1, 2020. The institutional review boards of the Fourth Affiliated Hospital of Zhejiang University School of Medicine approved this study.
The inclusion criteria were as follows: (i) patients with acute coronary syndromes; (ii) patients who received DES for the first time in our hospital; (iii) DES was performed according to the Chinese Guidelines for Percutaneous Coronary Intervention [2016]; (iv) the presence of ISR was determined by coronary angiography during follow-up; (v) relevant demographic characteristics, laboratory and imaging data can be obtained from the hospital information system. And the exclusion criteria include (i) patients who received bare metal stent implantation; (ii) patients who underwent PCI outside the hospital during the follow-up period; (iii) patients who underwent coronary artery bypass grafting during the follow-up period; (iv) patients with previous heart failure, cardiomyopathy, or myocarditis; (v) patients with severe hepatic or renal insufficiency; (vi) patients with advanced malignant tumors.
## Data collection
All clinical data came from the information system of the Fourth Affiliated Hospital of Zhejiang University School of Medicine. The clinical data included demographic information, past medical history, medication history, coronary angiography characteristics, and laboratory and imaging findings at the first PCI. These data are the results of perioperative and follow-up examinations. All patients received a standardized PCI strategy. Moreover, they were treated with oral aspirin (100 mg/d) in combination with clopidogrel (75 mg/d) or ticagrelor (90 mg twice daily) for one year after emergency PCI and elective PCI. Patients typically have repeat coronary angiography approximately one year after PCI to determine whether ISR has occurred. And patients with postoperative chest tightness and chest pain may be considered for coronary angiography earlier. As confirmed by coronary angiography, ISR was defined as ≥$50\%$ luminal stenosis over the entire length of the stent and/or the 5-mm segment proximal and distal to the stent.
The median follow-up in this study was 386 days. During the perioperative period, a total of eight of the 414 patients developed arrhythmias (four sinus bradycardia, three second-degree atrioventricular block, and one incidental ventricular extrasystole), two had experienced mitral papillary muscle dysfunction, two had acute heart failure, and two had suffered from the postinfarction syndrome. All of these patients improved significantly after standardized treatment.
## Statistical analysis
We used R 4.2.2 and SPSS 26.0 for data processing, curve drawing, and model building. Continuous variables were expressed as the mean ± standard deviation or median (p25, p75) according to normality, and categorical variables were expressed as the counts (%). LASSO regression analysis determined the best variable among the clinical variables. Then we constructed the nomogram prediction model using conditional multivariate logistic regression analysis based on the clinical variables selected in the LASSO regression analysis. To demonstrate the prediction accuracy of this nomogram model, we calculated the area under the curve (AUC) of the receiver operating characteristic (ROC) to evaluate its discriminant ability. *In* general, the diagnostic accuracy of AUC ≥ 0.6 can be considered and accepted [11]. The calibration plots were used to compare the actual predictive power of the model with the ideal predictive power, and a small difference indicates good consistency of the prediction model. Moreover, we can also assess the clinical applicability and validity of the model using the decision curve analysis (DCA) and clinical impact curve (CIC), respectively. Figure 1 shows the flow chart of our study.
**Figure 1:** *Flowchart of this study.*
## Baseline characteristics
As shown in Table 1, 414 patients were enrolled in this study, of which 57 developed ISR after DES, and the incidence of ISR was $13.8\%$ ($\frac{57}{414}$). We divided the 414 patients into the N-ISR group (317 patients) and the ISR group (57 patients). By comparison, it can be found that the significant differences in hypertension, diabetes mellitus, hyperuricemia, carotid plaque, mean stent diameter, number of stenosed vessels, stent number, total stent length, indirect bilirubin (IBIL), total bilirubin (TBIL), HbA1c, fibrinogen, and thyroxine between the two groups.
**Table 1**
| Unnamed: 0 | N-ISR (357) | ISR (57) | P-value |
| --- | --- | --- | --- |
| Clinical Information | Clinical Information | Clinical Information | Clinical Information |
| Gender | | | 0.833 |
| Male | 243 (68.1%) | 38 (66.77%) | |
| Female | 114 (31.9%) | 19 (33.3%) | |
| Smoking | 123 (34.5%) | 24 (42.1%) | 0.262 |
| Drinking | 72 (20.2%) | 11 (19.3%) | 0.879 |
| Age (years old) | 64.00 (55.00,71.00) | 67.00 (61.00,73.00) | 0.159 |
| BMI (kg/m2) | 24.54 (22.43,26.61) | 24.39 (22.05,26.49) | 0.609 |
| Past Medical History | Past Medical History | Past Medical History | Past Medical History |
| Hypertension | 215 (60.2%) | 43 (75.4%) | 0.028 |
| Diabetes mellitus | 87 (24.4%) | 22 (38.6%) | 0.024 |
| Hyperuricemia | 27 (7.6%) | 10 (17.5%) | 0.014 |
| Stroke | 27 (7.6%) | 8 (14.0%) | 0.103 |
| Respiratory diseases | 46 (12.9%) | 8 (14.0%) | 0.811 |
| Carotid plaque | 155 (43.4%) | 33 (57.9%) | 0.041 |
| Arrhythmia | 37 (10.4%) | 8 (14.0%) | 0.408 |
| Medication History | Medication History | Medication History | Medication History |
| ACEI/ARB | 161 (45.1%) | 28 (49.1%) | 0.571 |
| β-blocker | 187 (52.4%) | 28 (49.1%) | 0.648 |
| Coronary Angiography | Coronary Angiography | Coronary Angiography | Coronary Angiography |
| Number of stenosed vessel | 2.00 (1.00,2.00) | 2.00 (1.00,3.00) | 0.008 |
| Stent number | 2.00 (1.00,2.50) | 3.00 (2.00,4.50) | <0.001 |
| Total stent length (mm) | 41.00 (24.00,64.00) | 70.00 (48.50,106.50) | <0.001 |
| Mean stent diameter | | | 0.015 |
| ≤2.83 mm | 128 (35.9%) | 30 (52.6%) | |
| >2.83 mm | 229 (64.1%) | 27 (47.4%) | |
| Laboratory Findings | Laboratory Findings | Laboratory Findings | Laboratory Findings |
| WBC (×109/L) | 6.70 (5.65,8.20) | 6.10 (5.55,7.80) | 0.227 |
| RBC (×1012/L) | 4.20 ± 0.53 | 4.08 ± 0.63 | 0.125 |
| Hb (g/L) | 131.00 (120.00,141.00) | 127.00 (113.50,137.00) | 0.051 |
| HCT (%) | 38.30 (35.20,41.05) | 37.50 (32.95,40.45) | 0.153 |
| PLT (×109/L) | 184.00 (154.00,221.00) | 191.00 (152.50,235.00) | 0.483 |
| N (×109/L) | 4.50 (3.60,5.70) | 4.20 (3.50,5.35) | 0.094 |
| L (×109/L) | 1.40 (1.10,1.80) | 1.50 (1.00,1.80) | 0.863 |
| AST (U/L) | 23.00 (19.00,29.00) | 22.00 (18.50,28.00) | 0.459 |
| ALT (U/L) | 20.00 (15.00,29.00) | 19.00 (13.00,25.50) | 0.290 |
| Total cholesterol (mmol/L) | 3.59 (3.03,4.45) | 3.81 (2.94,5.12) | 0.511 |
| Triglyceride (mmol/L) | 1.37 (0.99,1.91) | 1.39 (0.88,2.12) | 0.932 |
| HLD (mmol/L) | 1.07 (0.92,1.25) | 1.10 (0.88,1.37) | 0.564 |
| LDL (mmol/L) | 1.83 (1.43,2.47) | 1.84 (1.40,2.65) | 0.682 |
| Lp-a (g/L) | 1.18 (1.06,1.31) | 1.17 (1.01,1.35) | 0.932 |
| Lp-b (g/L) | 0.64 (0.53,0.83) | 0.69 (0.56,0.85) | 0.282 |
| Cystatin C (mg/L) | 0.99 (0.86,1.13) | 1.02 (0.89,1.21) | 0.217 |
| Creatinine (µmol/L) | 73.00 (62.00,86.00) | 73.00 (62.00,88.50) | 0.641 |
| Uric Acid (µmol/L) | 327.00 (269.50,383.50) | 324.00 (280.00,390.00) | 0.485 |
| GFR (ml/min·1.73 m2) | 92.00 (80.00,106.00) | 88.00 (75.00,105.00) | 0.348 |
| Homocysteine (µmol/L) | 12.90 (10.70,16.30) | 14.30 (10.30,18.20) | 0.260 |
| TBIL (µmol/L) | 11.30 (8.80,14.85) | 10.20 (7.35,13.40) | 0.022 |
| DBIL (µmol/L) | 3.60 (2.70,5.00) | 3.20 (2.50,4.50) | 0.237 |
| IBIL (µmol/L) | 7.70 (5.90,9.95) | 6.90 (4.70,8.45) | 0.009 |
| Albumin (g/L) | 39.10 (36.90,40.80) | 39.10 (35.65,41.75) | 0.604 |
| CRP (mg/L) | 1.20 (0.50,3.45) | 1.60 (0.65,5.20) | 0.286 |
| FBG (mmol/L | 4.88 (4.49,5.69) | 4.85 (4.43,6.13) | 0.640 |
| HbA1c (%) | 6.00 (5.70,6.70) | 6.40 (5.85,7.76) | 0.001 |
| INR | 0.98 (0.94,1.04) | 1.00 (0.96,1.07) | 0.055 |
| APTT (s) | 26.90 (25.05,28.50) | 27.60 (25.90,29.30) | 0.059 |
| Fibrinogen (g/L) | 2.69 (2.33,3.12) | 3.10 (2.44,3.84) | 0.001 |
| D-dimer (mg/L) | 0.25 (0.18,0.41) | 0.26 (0.16,0.51) | 0.950 |
| TSH (mIU/L) | 1.63 (1.06,2.52) | 1.79 (1.15,2.58) | 0.670 |
| Thyroxine (nmol/L) | 93.80 (83.87,106.79) | 104.83 (87.46,120.39) | 0.003 |
| Imaging Findings | Imaging Findings | Imaging Findings | Imaging Findings |
| LVEF (%) | 65.10 (61.00,68.70) | 66.00 (59.85,69.90) | 0.665 |
| LAD (mm) | 33.00 (30.00,36.00) | 32.00 (29.00,36.00) | 0.342 |
| LVDs (mm) | 30.00(28.00,33.10) | 29.50(27.90,33.00) | 0.443 |
| LVDd (mm) | 47.35 ± 4.93 | 47.36 ± 4.81 | 0.982 |
## Correlation between variables
We performed multivariate correlation analysis for variables that were statistically significant in the baseline data, and the results are shown in Figure 2, in which we can observe a certain correlation between variables and ISR as well as between different variables (blue represents positive correlation, red represents negative correlation, and white represents no correlation). Interestingly, we noted that IBIL and mean stent diameter, unlike other clinical parameters, were negatively correlated with the occurrence of ISR.
**Figure 2:** *Correlation analysis between variables.*
## LASSO regression analysis
LASSO regression could impose a regression penalty on all variable coefficients such that relatively unimportant independent variable coefficients become 0 and are thus excluded from the model. The main difference between LASSO regression and traditional stepwise regression is that it can process all independent variables simultaneously instead of stepwise, and this improvement greatly increases the stability of the modeling. Due to the large number of research variables included in this study and the correlation between different variables, We used LASSO regression to screen the variables to prevent the model from overfitting and to select the characteristic variables that predict the risk of ISR. In this study, the lambda-min was taken as the optimal value of the model to screen the best variable, and we counted the variables with non-zero regression coefficients. The results of LASSO regression analysis showed that hypertension, diabetes mellitus, hyperuricemia, carotid plaque, mean stent diameter, number of stenosed vessels, total stent length, IBIL, HbA1c, fibrinogen, and thyroxine were predictive factors for ISR in patients with CHD after DES (Figure 3).
**Figure 3:** *LASSO regression analysis of clinical variables.*
## Multivariate logistic regression analysis
Furthermore, we included hypertension, diabetes mellitus, hyperuricemia, carotid plaque, mean stent diameter, number of stenosed vessels, total stent length, IBIL, HbA1c, fibrinogen, and thyroxine in multivariate forward stepwise logistic regression analysis. The results showed that mean stent diameter (OR = 0.481, $95\%$CI = 0.255–0.909, $$P \leq 0.024$$), total stent length (OR = 1.017, $95\%$CI = 1.010–1.025, $P \leq 0.001$), HbA1c (OR = 1.436, $95\%$CI = 1.094–1.884, $$P \leq 0.009$$), fibrinogen (OR = 1.712, $95\%$CI = 1.287–2.278, $P \leq 0.001$) and thyroxine (OR = 1.020, $95\%$CI = 1.006–1.035, $$P \leq 0.005$$) were all independent risk factors affecting the occurrence of ISR in patients with CHD after DES (Table 2). Although hypertension was not statistically significant ($$P \leq 0.051$$) in the multivariate logistic regression analysis, we still included hypertension obtained by LASSO regression analysis in the model considering the clinical practicability of LASSO regression analysis, the two-sided statistical significance level and the conclusions of previous studies (12–14).
**Table 2**
| Predictors | β value | OR | 95%CI | P-value |
| --- | --- | --- | --- | --- |
| Hypertension | 0.709 | 2.032 | 0.996–4.145 | 0.051 |
| Mean stent diameter | −0.732 | 0.481 | 0.255–0.909 | 0.024 |
| Total stent length | 0.017 | 1.017 | 1.010–1.025 | <0.001 |
| HbA1c | 0.362 | 1.436 | 1.094–1.884 | 0.009 |
| Fibrinogen | 0.538 | 1.712 | 1.287–2.278 | <0.001 |
| Thyroxine | 0.020 | 1.02 | 1.006–1.035 | 0.005 |
And the ROC curves of each variable were plotted separately. As shown in Figure 4, the total stent length had the largest AUC value for predicting the risk of ISR, with an AUC value of 0.723 ($95\%$CI = 0.651–0.795), followed by 0.636 ($95\%$CI = 0.551–0.722) for fibrinogen.
**Figure 4:** *ROC curve and the diagnostic performance of individual predictors.*
## Interaction analysis
When the influence of a predictor on the dependent variable varies with the levels of other predictors, it suggests the presence of an interaction effect between predictors. The presence of interaction effects indicates that the effects of multiple factors examined simultaneously are not independent. It measures the degree to which the effects of different levels of one factor depend on the levels of another or multiple factors. Many studies have neglected the potential impact of interaction effects on outcome events [5, 7, 8, 15]. In this study, interaction analysis of the selected predictors was performed based on the results of multivariate logistic stepwise regression analysis. Table 3 shows no interaction effect between different variables on the occurrence of ISR after DES ($P \leq 0.05$). Therefore, we excluded the potential influence of interaction effects between variables on the model, which further improved the model's reliability.
**Table 3**
| Predictors | Hypertension | Total stent length | Mean stent diameter | HbA1c | Fibrinogen | Thyroxine |
| --- | --- | --- | --- | --- | --- | --- |
| Hypertension | | 0.0651 | 0.7428 | 0.8024 | 0.7499 | 0.4833 |
| Total stent length | 0.0651 | | 0.7711 | 0.2529 | 0.3627 | 0.855 |
| Mean stent diameter | 0.7428 | 0.7711 | | 0.8537 | 0.1053 | 0.7557 |
| HbA1c | 0.8024 | 0.2529 | 0.8537 | | 0.3636 | 0.1053 |
| Fibrinogen | 0.7499 | 0.3627 | 0.1053 | 0.3636 | | 0.2831 |
| Thyroxine | 0.4833 | 0.855 | 0.7557 | 0.1053 | 0.2831 | |
## Construction of nomogram prediction model
We used these six variables as predictors to establish a nomogram prediction model for ISR after DES (Figure 5). The interpretation method of the nomogram was as follows: a vertical line was drawn on the horizontal axis of each predictor, corresponding to a specific score on the horizontal axis of “Points”; the scores corresponding to the six predictive factors were added to obtain the total score, and the value on the horizontal axis of “Prob of ISR” corresponding to the total score was the risk prediction value of the patient.
**Figure 5:** *Nomogram to predict the probability of ISR.*
## Evaluation and validation of the nomogram model
This study evaluated the nomogram prediction model for ISR in terms of the model's discrimination, calibration, validity, and clinical practicality. The AUC value of the nomogram was 0.806 ($95\%$CI: 0.739–0.873), which was larger than the AUC value of any single predictor in Figure 4 for predicting ISR, indicating that the discrimination power of the nomogram model was good (Figure 6A).
**Figure 6:** *Evaluation of the nomogram prediction model. (A) ROC curves of the nomogram. (B) Calibration plots of the nomogram. (C) CIC of the nomogram. (D) DCA of the nomogram.*
We used calibration plots to assess the consistency of the model, where the abscissa represents the predicted risk of ISR, and the ordinate represents the actual risk of ISR. In this study, the high-quality calibration plots showed that the nomogram prediction model has strong consistency compared to the ideal model, indicating that there is no significant deviation between the predicted probability and the actual probability (Figure 6B).
The nomogram prediction model was analyzed using a clinical impact curve (CIC). The horizontal axis is the threshold probability, and the vertical axis is the number of people. The red curve represents the number of people predicted to be at high risk at different threshold probabilities, and the blue curve represents the number of people predicted to be at high risk by the model and the actual outcome events occurring at different threshold probabilities. When the threshold probability is greater than $50\%$, the population at high risk for ISR identified by this prediction model is highly consistent with the actual population with ISR, confirming the high clinical efficiency of this prediction model (Figure 6A).
Moreover, with the occurrence of ISR as the state variable and the risk prediction value as the test variable, the DCA curve suggested that the level of net clinical benefit to patients was high and that this model had good clinical applicability (Figure 6B).
The conventional method of dividing the entire sample into training sets and validation sets may lead to the accidental selection or omission of some variables due to the uncertainty of the random grouping, which would ultimately affect the stability and authenticity of the model. This effect is pronounced in the data sets with small sample sizes, and many researchers have ignored this point. We adopted the method of bootstrap verification combined with 10-fold cross-validation. In the process of multiple sampling, the model could be repeatedly verified, and the results could better prove the stability and reliability of the model. Bootstrap verification is performed by randomly drawing samples from the original dataset with the same number of samples as the size of the original queue. In our study, the cohort obtained by bootstrap sampling also included 414 patients, with each having the same probability of being sampled. Each bootstrap sampling result typically includes at least about two-thirds of the patients in the original cohort. This process is repeated 1,000 times to generate 1,000 model performance metrics. The corrected C index was calculated to be 0.804 ($95\%$CI: 0.802–0.806). The 10-fold cross-validation method randomly divides the original data into ten groups, alternately uses nine groups to build the model and the remaining one group of data to validate the model, and then calculates the average of the ten results. We repeated this procedure 1,000 times and calculated a corrected C-index of 0.787 ($95\%$CI: 0.780–0.795).
## The predictive value of thyroxine for ISR
We constructed a nested model without thyroxine. Analysis of variance was used to compare the goodness of fit between the nomogram prediction model and the nested model. The results showed that the fitting effect of the two models was significantly different ($F = 9.179$, $$P \leq 0.003$$), and the predictive effect of the nested model without thyroxine was weaker than that of the nomogram prediction model. We then calculated the AIC values for the two models separately. It was found that the AIC value of the nested model without thyroxine was 231.62, while the AIC value of the nomogram prediction model was 224.39, indicating that the model with thyroxine was better than that without thyroxine.
The net reclassification index (NRI) is an indicator to compare the predictive ability of two models. Compared to AUC and other indicators, NRI has higher sensitivity and better clinical interpretation. When NRI > 0, the new model is better at predicting the event than the old model. When NRI < 0, it indicates that the predictive power of the new model is decreasing. In this study, less than $30\%$ of the predicted risk of the model was considered a low-risk group, and more than $70\%$ was considered a high-risk group. We calculated the NRI values for the nomogram prediction model and the nested mode, and the results showed that NRI = 0.134 ($95\%$CI: 0.0031–0.2649, $$P \leq 0.044$$), indicating that the predictive ability of the nomogram model including thyroxine was improved compared with the nested model, and the proportion of correct classification was increased by $13.4\%$.
Our previous study has found that thyroxine could be used as an independent predictor of ISR after PCI (OR = 1.020, $95\%$CI: 1.006–1.035, $$P \leq 0.005$$). The ROC curve also showed that thyroxine had a good discriminative ability (AUC = 0.621). Combined with ANOVA, as well as AIC and NRI values, it is further confirmed that thyroxine has a predictive potential for the occurrence of ISR after PCI, which provides us with a new perspective in future studies of ISR.
## Discussion
PCI is an essential method for the treatment of coronary heart disease. Compared to BMS, the use of DES technology has dramatically improved the efficacy and safety of PCI. However, ISR and the demand for target lesion revascularization still occur at a rate of $1\%$–$2\%$ per year, making the prevention and treatment of ISR a complex problem in the cardiovascular field [16]. Compared with PCI for new lesions, PCI for ISR has accounted for approximately $10\%$ of all PCI in the United States over the past decade and has been associated with a higher risk of major adverse cardiac events [17, 18].
The exact mechanism of ISR formation remains unclear. Therefore, early identification and control of ISR-related risk factors may be an important method to reduce the incidence of ISR. Establishing an effective prediction model may provide a helpful reference for the prevention of ISR. Nomogram is currently widely used in the medical field to establish predictive models. By integrating various prognostic events and crucial variables, the nomogram can generate individual probabilities of clinical events, which satisfies our desire for clinical models and promotes the progress of personalized medicine. Compared with traditional prediction models, such as heat maps or scoring systems, the advantages of nomograms are that they are easy to use and understand. There is no need to convert continuous variables into categorical variables, and the length of the lines in the nomogram can judge the relative importance of predictor variables. With a user-friendly digital interface for rapid calculation, the nomogram model improves predictive accuracy and is easier to use than traditional models, which helps us make clinical decisions promptly and effectively [19].
In this study, we retrospectively analyzed the clinical characteristics of 414 CHD patients who underwent first-time PCI with DES and investigated the incidence of ISR in these patients and the predictors of ISR. Firstly, we found that the incidence of restenosis after DES was $13.8\%$. Secondly, hypertension, mean stent diameter, total stent length, HbA1c, fibrinogen, and thyroxine were independent predictors of ISR. And the nomogram prediction model, including these predictors, has a good predictive value for the occurrence of ISR. Finally, we further explored the predictive value of thyroxine in ISR after PCI.
The effect of hypertension on ISR has been confirmed in many studies. The primary mechanism is that hypertension increases the impact of blood flow on the vessels, causing damage to vascular endothelial cells, resulting in the formation of atherosclerotic plaques, and ultimately increasing the risk of ISR [20]. Zhao et al. reviewed comprehensive data on 398 patients with CHD undergoing percutaneous coronary intervention plus SES. They found that hypertension can be used as an independent predictor of increased risk of ISR [21]. The findings of Sajadian et al. also suggest that hypertension and diabetes are the most probable factors affecting ISR [13]. HbA1c reflects glycemic control over the last 2 to 3 months. Several studies have shown that elevated HbA1c level is an independent predictor of poor prognosis after PCI [22]. Karadeniz et al. showed that a higher HbA1c level was associated with a higher incidence of ISR in diabetic patients with STEMI undergoing primary PCI [23]. We speculate that the possible mechanism of ISR caused by HbA1c is that the continuous increase of HbA1c increases the viscosity of red blood cells. High blood viscosity may cause damage to vascular endothelial cells, enhance the release of endothelin and reduce the release of nitric oxide and prostacyclin, thereby impairing vasomotor function. The continuous increase in HbA1c may also lead to the aggravation of protein glycosylation and oxidation, and advanced glycation end-products may promote the development of atherosclerosis. Our study confirms that elevated HbA1c level is an independent predictor of ISR after PCI and supports previous studies' conclusion that poor glycemic control in diabetes is the main cause of ISR after PCI [24, 25].
Previous studies have suggested that stent length and diameter appear to be important factors in the occurrence of ISR. Dietz et al. found a significant reduction in the occurrence of ISR in patients with a mean stent length of approximately 9 mm compared to patients with a mean stent length of approximately 16 mm [26]. The study by Nita et al. discovered that patients with ISR had smaller stent diameters and longer stent lengths than controls [27]. Moreover, Zhou et al. found that a smaller minimum stent diameter was associated with the incidence of ISR within three years after DES implantation in elderly ACS patients [8]. Our study also confirmed these views. We believe that PCI is an invasive procedure with certain mechanical stimulation, which may cause vascular endothelial cell damage, increase the risk of thrombosis, stimulate the proliferation and migration of smooth muscle cells, and lead to luminal proliferative stenosis. Therefore, the longer the stent length, the more severe the vascular endothelial damage. In clinical practice, we should choose the appropriate stent size for patients with CHD to reduce the occurrence of ISR.
Fibrinogen is the precursor of fibrin, which is involved in the process of inflammation and thrombosis. Elevated fibrinogen levels are a recognized risk factor for adverse cardiovascular events in patients with coronary heart disease [28, 29]. Many studies have shown that fibrinogen levels are associated with the occurrence of ISR after PCI (30–32). Our study also confirmed that fibrinogen is an independent predictor of ISR. Chai et al. suggested that fibrinogen and its metabolites can stimulate endothelial cell degeneration, increase the release of endothelial cell-derived growth factors, lead to endothelial dysfunction, and stimulate the growth of smooth muscle cells, ultimately leading to the occurrence of ISR [32].
There is currently a great deal of controversy regarding the effect of thyroid hormones on CHD. Most studies believe that high levels of thyroid hormones could increase the risk of CHD and its complications. In contrast, some studies believe that low levels of thyroid hormones might be considered a risk factor for CHD [33]. Even some studies reported no significant correlation between them [34]. Bano et al. conducted a large prospective cohort study, the Rotterdam Cohort Study, to investigate the relationship between thyroid function and atherosclerosis. They found that FT4 levels were associated with coronary artery calcification and positively and linearly associated with atherosclerotic cardiovascular disease (ASCVD) events and mortality. They suggested that FT4 should be considered a new predictor of ASCVD risk [35]. Jung and Cols also reported that serum FT4 levels in 192 patients with stable angina pectoris were higher even within the normal reference range and significantly correlated with the presence and severity of coronary artery disease [36]. Studies have shown that thyroid hormones may directly or indirectly affect the formation of atherosclerosis by altering vascular tone, regulating macrophage function, promoting angiogenesis, and regulating vascular smooth muscle cell proliferation. Attabak Toofani Milani et al. demonstrated a potent effect of thyroid hormones on gene and protein expression levels of major mediators of pro-inflammatory, angiogenic, and endothelial dysfunction associated with the development of atherosclerosis [37].
However, there are few studies on the effect of high levels of thyroxine on ISR after PCI. Only Canpolat et al. found that high preoperative serum FT4 level was an effective independent predictor of BMS restenosis in patients with stable and unstable angina pectoris. They suggested that serum FT4 increases the risk of ISR by enhancing the activity of the renin-angiotensin system and the proliferation of vascular smooth muscle cells [38]. Therefore, this study is the first to identify thyroxine as a significant predictor of ISR after drug-eluting stent implantation. Moreover, we further confirmed the high predictive value of thyroxine for ISR by a series of other means.
In summary, we have developed an ISR prediction nomogram model based on hypertension, mean stent diameter, total stent length, HbA1c, fibrinogen, and thyroxine, which was evaluated and validated to help clinicians identify high-risk ISR patients and optimize treatment strategies, thereby improving the prognosis of these patients.
## Limitation
Inevitably, there are some limitations to this study. ( i) *This is* a retrospective study, and the data of this study are only from the Fourth Affiliated Hospital of Zhejiang University School of Medicine, with small sample size and single sample source, which may affect the accuracy of the study results. Therefore, the conclusions still need to be verified by further prospective multicenter cohort studies with large sample sizes. ( ii) Only internal validation was performed in this study, which made the extrapolation of the nomogram prediction model still unknown. For external validation, it is still necessary to select patients with coronary heart disease after DES from other medical centers. ( iii) We have confirmed that thyroxine can be used as an important predictor of ISR, but further clarification of the effect of thyroxine on ISR under different conditions, such as hyperthyroidism, hypothyroidism, and normal levels, will help us to further formulate precise interventions. ( iv) We analyzed the impact of stent characteristics on ISR while ignoring the impact of the characteristics of the culprit blood vessels on ISR. This will be the focus of our subsequent study.
## Conclusion
Hypertension, mean stent diameter, total stent length, HbA1c, fibrinogen, and thyroxine are important predictors of ISR. We developed and validated a personalized nomogram model to predict the risk of ISR after PCI in patients with CHD. It is based on these six common and easily accessible clinical indicators, providing clinicians with a simple and practical assessment tool. Moreover, this nomogram model has good accuracy, which can better identify the high-risk population of ISR and provide practical decision-making information for the follow-up intervention of the high-risk population.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by The institutional review boards at the Fourth Affiliated Hospital of Zhejiang University School of Medicine. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
HX: experimental design, data collection and analysis, and manuscript writing. JL: data analysis and manuscript writing. SX: experimental design and guidance. TX, ZL, XM and YJ: data collection. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Circulating IgA Antibodies Against Fusobacterium nucleatum Amyloid Adhesin
FadA are a Potential Biomarker for Colorectal Neoplasia
authors:
- Jung Eun Baik
- Li Li
- Manish A. Shah
- Daniel E. Freedberg
- Zhezhen Jin
- Timothy C. Wang
- Yiping W. Han
journal: Cancer Research Communications
year: 2022
pmcid: PMC10035380
doi: 10.1158/2767-9764.CRC-22-0248
license: CC BY 4.0
---
# Circulating IgA Antibodies Against Fusobacterium nucleatum Amyloid Adhesin FadA are a Potential Biomarker for Colorectal Neoplasia
## Abstract
Fusobacterium nucleatum (Fn) is a gram-negative oral anaerobe and prevalent in colorectal cancer. Fn encodes a unique amyloid-like adhesin, FadA complex (FadAc), consisting of intact pre-FadA and cleaved mature FadA, to promote colorectal cancer tumorigenesis. We aimed to evaluate circulating anti-FadAc antibody levels as a biomarker for colorectal cancer. Circulating anti-FadAc IgA and IgG levels were measured by ELISA in two study populations. In study 1, plasma samples from patients with colorectal cancer ($$n = 25$$) and matched healthy controls ($$n = 25$$) were obtained from University Hospitals Cleveland Medical Center. Plasma levels of anti-FadAc IgA were significantly increased in patients with colorectal cancer (mean ± SD: 1.48 ± 1.07 μg/mL) compared with matched healthy controls (0.71 ± 0.36 μg/mL; $$P \leq 0.001$$). The increase was significant in both early (stages I and II) and advanced (stages III and IV) colorectal cancer. In study 2, sera from patients with colorectal cancer ($$n = 50$$) and patients with advanced colorectal adenomas ($$n = 50$$) were obtained from the Weill Cornell Medical Center biobank. Anti-FadAc antibody titers were stratified according to the tumor stage and location. Similar as study 1, serum levels of anti-FadAc IgA were significantly increased in patients with colorectal cancer (2.06 ± 1.47 μg/mL) compared with patients with colorectal adenomas (1.49 ± 0.99 μg/mL; $$P \leq 0.025$$). Significant increase was limited to proximal cancers, but not distal tumors. Anti-FadAc IgG was not increased in either study population, suggesting that Fn likely translocates through the gastrointestinal tract and interact with colonic mucosa. Anti-FadAc IgA, but not IgG, is a potential biomarker for early detection of colorectal neoplasia, especially for proximal tumors.
### Significance:
Fn, an oral anaerobe highly prevalent in colorectal cancer, secretes the amyloid-like FadAc to promote colorectal cancer tumorigenesis. We report that circulating levels of anti-FadAc IgA, but not IgG, are increased in patients with both early and advanced colorectal cancer compared with the healthy controls, and especially in those with proximal colorectal cancer. Anti-FadAc IgA may be developed into a serological biomarker for early detection of colorectal cancer.
## Introduction
Colorectal cancer is the third-most common cancer worldwide and the fourth leading cause of cancer death [1]. *Many* genetic and environmental risk factors have been identified for colorectal cancer such as genomic instability, gene mutations, Western diet, lifestyle, and obesity [2, 3]. In addition, accumulating studies have now recognized dysbiosis of gut microbiome as a risk factor in the development and progression of colorectal cancer (4–6). Gut microbiome contributes to colorectal carcinogenesis through several mechanisms: (i) by directly inducing carcinogenesis using their virulence factors or metabolites (7–11), (ii) by altering host immune-surveillance systems (12–15), and (iii) by affecting the efficacy of anticancer therapies such as chemotherapy and immunotherapy (16–19).
Among the cancer-associated bacteria, *Fusobacterium nucleatum* (Fn), a gram-negative oral commensal anaerobe, is considered a key pathogen implicated in colorectal cancer [20, 21]. The Fn level is not only significantly higher in tumor tissues compared with the normal controls (22–24), it is also highly correlated with lower survival rate [25, 26], advanced cancer stages (27–29), proximal location (6, 30–32), metastasis [33, 34], and recurrence [16, 35]. In addition, increased level of Fn has been reported to be associated with microsatellite instability, CpG island methylator phenotype, and oncogenic mutations [26, 36].
FadA is a unique adhesin highly conserved among pathogenic Fusobacterium species and a major virulence factor responsible for bacterial adhesion, induction of inflammation, and colorectal cancer cell proliferation [7, 11]. The fadA gene levels are 10- to 100-fold higher in colorectal tissues of patients with adenomas or colorectal cancer compared with the normal subjects [7]. It is also significantly increased in the fecal microbiome in patients with colorectal cancer compared with the controls [21]. FadA consists of two forms, the intact pre-FadA and mature FadA without signal peptide, both of which constitute the active and amyloid-like FadA complex (FadAc; ref. 37). The fadA mRNA is constitutively expressed [38]; however, amyloid FadAc is only produced under stress and disease conditions, serving as a molecular switch to convert Fn from a benign commensal to a pathogen [39]. Amyloid FadAc binds to extracellular domain of E-cadherin leading to the proliferation of cancer cells via activation of Annexin A1 and Wnt/β-catenin signaling [7, 11].
Early diagnosis and treatment of colorectal cancer is the most important determining factor for prognosis and patient survival. Although there are several commercially available serologic biomarkers for colorectal cancer such as carcinoembryonic antigen (CEA), alpha fetoprotein (AFP), and cancer antigen (CA) 19-9 (40–42), it has become clear that these conventional biomarkers have low sensitivity and specificity and are not suited for early diagnosis and predicting prognosis of colorectal cancer (43–45). Because of the high correlation between Fn and colorectal cancer progression, there have been attempts to use Fn as a biomarker. In this study, we investigate the levels of circulating anti-FadA antibodies in the plasma and serum from two U.S. cohorts to evaluate its applicability as a novel colorectal cancer biomarker.
## Sample Description
Samples from two existing colorectal cancer sources were used following appropriate ethical guidelines. Study 1 samples were randomly selected from the Kentucky Colon Cancer Genetic Epidemiology Study, a population-base case–control study based on the Kentucky Cancer Registry (KCR; refs. 46, 47). Recruitment of participants was conducted between April 2003 and December 2010. A total of 1,040 incident colon cancer cases and 1,750 population-based controls completed the study with the collection of comprehensive lifestyle and epidemiological data, pathology information, and fasting blood samples. Cases were defined as individuals diagnosed with histopathologically confirmed incident primary colon cancer (excluding patients with rectal or syndromic cancers) who were invited to participate in the study within 3 months of KCR registration. Cases were eligible to participate if they: (i) had a nonrecurrent diagnosis; (ii) had no known family history or diagnosis of familial adenomatous polyposis (FAP), hereditary nonpolyposis colorectal cancer (HNPCC), Peutz-Jeghers, or Cowden disease; (iii) had no known diagnosis of inflammatory bowel disease such as Crohn disease or ulcerative colitis; (iv) were at least 21 years of age at the time of diagnosis; (v) had contact information listed in the KCR database; (vi) were willing to complete two questionnaires. The majority of participants completed data collection within 12 months (median of 5 months) of their colon cancer diagnosis.
Random digit dialing and friend referrals were utilized to recruit controls representative of the general Kentucky population. Controls consisted of frequency-matched individuals who have never been diagnosed with any cancer except nonmelanoma skin cancer and are over the age of 30, preferably ≥ 50 years old. For cases and controls, self-reported inflammatory bowel disease (e.g., Crohn disease or ulcerative colitis), family history of FAP, and HNPCC were excluded in the recruitment. The response rate for cases and controls who answered the phone and allowed eligibility determination was $70.8\%$ and $66.7\%$, respectively. The study was approved by the Institutional Review Boards of the University of Kentucky (Lexington, Kentucky) and University of Virginia (Charlottesville, VA). All participants provided written informed consent.
Eligible cases and controls donated one blood sample and answered self-administered questionnaires. A two-step approach was used to collect blood samples and lifestyle risk factor data. First, a prepacked phlebotomy kit with detailed written instructions for blood sample collection and written consent forms was sent to each case subject. Participants were instructed to go to their physician offices or adjacent medical facilities for blood draw after overnight fasting. The samples were collected in purple-top (K3EDTA) blood collection tubes and shipped overnight on frozen ice pack. Upon receipt, the blood tubes were spun for 15 minutes at 600 × g and aliquots of plasma and concentrated buffy coat were prepared and frozen at −80°C. Second, a self-administered lifestyle risk factor questionnaire developed by the NCI Colon Cancer Familial Cancer Registry was sent to each participant to collect detailed information on demographics and lifestyle risk factors. The parent Kentucky Colon Cancer Genetic Epidemiology *Study is* not a matched (or paired) case–control study. Deidentified plasma samples from 25 randomly selected patients with colorectal cancer and 25 controls matched by gender and age (±1 year) were tested. The patient characteristics are summarized in Table 1.
Study 2 included randomly selected patients with advanced adenoma or adenocarcinoma who underwent surgical treatment at NYP/Weill Cornell Medical Center from 2013 to 2015. All patients who were seen in the colorectal group were offered participation in the clinical trial. Tissues were collected among those patients who provided consent, and tissue acquisition was feasible. An advanced adenoma was defined as a polyp that was greater than 3 cm in size, and was unable to be removed endoscopically. Deidentified serum samples from 50 patients with adenoma and 50 patients with adenocarcinoma were tested. Tumor stages were classified according to the Union for International Cancer Control classification. The patient characteristics are summarized in Table 1. For tumor locations in study 2, “proximal” include samples labeled as cecum, ascending, ascending/transverse, right colon, or right/cecum, while “distal” include samples labeled as sigmoid, descending, sigmoid/rectum, left colon, rectosigmoid junction, rectum. Samples from patients with mixed proximal and distal tumors were excluded. No tumor location was available for study 1. The study was approved by the Institutional Review Boards of the Weill Cornell Medical Center. All participants provided written informed consent.
**TABLE 1**
| Study 1 (Kentucky) | Study 1 (Kentucky).1 | Normal (N = 25) | CRC (N = 25) | P |
| --- | --- | --- | --- | --- |
| Gender | MaleFemale | 12 (48%)13 (52%) | 12 (48%)13 (52%) | 1.0 |
| Age | Mean (yrs)No. <65 yrsNo. >65 yrs | 59.7 ± 9.916 (64%)9 (36%) | 59.6 ± 9.216 (64%)9 (36%) | 0.9531.0 |
| Tumor stage | I+IIIII+IV | —— | 11 (44%)14 (56%) | |
| Study 2 (NYP/Cornell) | Study 2 (NYP/Cornell) | Adenomas (N = 50) | CRC (N = 50) | P |
| Gender | MaleFemale | 31 (62%)19 (38%) | 18 (36%)32 (64%) | 0.009 |
| Age | Mean (yrs)No. <65No. >65 | 64.9 ± 12.626 (52%)24 (48%) | 67.6 ± 12.423 (46%)27 (54%) | 0.2910.548 |
| Tumor stage | I+IIIII+IV | —— | 30 (60%)19 (38%) | |
| Tumor locationa | ProximalDistalOther | 17 (34%)11 (22%)22 (44%) | 23 (46%)22 (44%)5 (10%) | <0.001 |
## Purification of FadAc
The recombinant FadAc protein was prepared from *Escherichia coli* (E. coli) BL21 (DE3) carrying pYWH471-6 as described previously [37]. E. coli BL21 (DE3) carrying pYWH471-6 was grown in Luria-Bertani broth containing 100 μg/mL ampicillin to mid-log phase followed by incubation with 0.1 mmol/L Isopropyl β-D-1-thiogalactopyranoside (Sigma-Aldrich) for 2.5 hours at 37°C to induce expression of the recombinant FadAc protein. The bacteria were harvested by centrifugation at 8,000 g for 5 minutes, the pellet (∼6 g) was incubated with 50 mL of buffer A (50 mmol/L NaH2PO4, 300 mmol/L NaCl, 8 mol/L Urea, pH 8.0) at 4°C overnight. After removing cells, debris, and insoluble material by centrifugation, the clear lysate was incubated with 5 mL TALON Metal Affinity Resins (Clontech Laboratories, Inc.) for 4 hours at 4°C. The mixture was transferred to a glass chromatography column (3 × 15 cm) and unbound materials were washed out with 150 mL of buffer A. The column was then eluted with 30 mL of buffer B (50 mmol/L NaH2PO4, 300 mmol/L NaCl, 8 mol/L Urea, pH 5.0) and the elute was serially collected in 3 mL aliquots. The column fraction containing recombinant FadAc were pooled and dialyzed against PBS (pH 7.2) in a dialysis tubing with MWCO of 6–8 kDa (Spectra/Por1; Spectrum Laboratories, Inc.). The concentration of purified recombinant FadA was measured by BCA protein assay kit (Thermo Fisher Scientific).
## Preparation of Monoclonal Anti-FadA IgG
The mouse monoclonal (mAb) anti-FadA IgG (5G113G8) was prepared at the Hybridoma Core (Lerner Research Institute) described previously [37] and was used as a standard for determining the concentrations of anti-FadAc IgA and IgG in the samples. This approach is feasible because FadA is highly conserved in Fn [48].
## ELISA
The levels of anti-FadAc IgA and IgG were determined by indirect ELISA. The 96-well ELISA plates were coated with 100 μL of purified recombinant FadAc (2 μg/mL) in 0.2 mol/L carbonate/bicarbonate buffer (pH 9.4) and incubated at 4°C overnight. After blocking, the wells were incubated with 100 μL of blood samples serially diluted in blocking buffer for 1 hour at room temperature. To obtain a standard curve, the wells were incubated with 100 μL of purified mouse mAb 5G113G8 at increasing concentrations. Each well was then incubated with 100 μL of goat anti-human IgA-HRP (1:6,000 dilution; PA1-74395, Thermo Fisher Scientific), goat-anti-human IgG-HRP (1:10,000 dilution; A2290, Sigma-Aldrich), or goat anti-mouse IgG-HRP (1:12,000 dilution; 62-6520, Thermo Fisher Scientific) for 1 hour at room temperature, followed by incubation with 100 μL substrate solution (1-Step Ultra TMB-ELISA; Thermo Fisher Scientific) for 30 minutes. The reaction was terminated with 2 mol/L H2SO4 and the absorbance at 405 nm was measured using an automated microplate reader. The levels of anti-FadAc IgA and IgG were determined by testing serial dilutions of the plasma or serum. The values that fell into the linear range of the standard curves generated using mouse mAb 5G113G8 were used to calculate the antibody concentrations. Wells with secondary antibodies alone were used to determine background values. All measurements were performed in duplicate. All assays were performed by one single individual. Intraplate and interplate coefficient of variation (CV) in study 1 and study 2 were as follow: study 1 intraplate CV = 8.2 ± $6.12\%$, interplate CV = 9.5 ± $0.6\%$; study 2 intraplate CV = 9.7 ± $6.7\%$, interplate CV = 10.8 ± $1.7\%$.
## Statistical Analysis
Categorical variables were presented as numbers and percentages and were compared using χ2 test or Fisher exact test between two groups. Continuous variables were expressed as mean ± SD and were compared using two-tailed Student t test between two groups. The optimal cutoffs of plasma and serum levels of anti-FadAc IgA and IgG were identified with ROC curve analysis using Youden index criterion with the requirement that sensitivity and specificity were at least 0.50. To examine the effect of colorectal cancer status in relation to the levels of anti-FadAc IgA and IgG, a multivariable linear regression model was used to adjust for confounding factor(s) (e.g., gender) with P value <0.1 in univariable analysis, with the status being coded as binary dummy variables. Study 1 was matched for sex and age, but not study 2. Therefore, the adjustment of gender and age was only considered for study 2. Tumor stage and location were classified into two groups. Analysis with nonparametric methods Wilcoxon rank-sum test and Kruskal–Wallis test yielded similar results. The results with P value <0.05 were considered significant. The analysis was carried out with SAS software version 9.4 (SAS Institute Inc.) and figures were generated using Excel. Additional analyses are available in Supplementary Appendix.
## Data Availability Statement
Data are available upon request.
## Plasma Levels of Anti-FadAc IgA, but not IgG, are Significantly Increased in Patients with Colorectal Cancer Compared with Healthy Controls
In study 1, the plasma levels of anti-FadAc IgA were significantly increased in patients with colorectal cancer compared with healthy subjects (1.48 ± 1.07 μg/mL vs. 0.71 ± 0.36 μg/mL, $$P \leq 0.001$$; Fig. 1A). The Youden index criterion in the ROC curve analysis yielded the cutoff of 0.81 μg/mL for anti-FadAc IgA levels, with $76\%$ sensitivity and $76\%$ specificity. No difference in anti-FadAc IgG was detected between the two groups (1.20 ± 1.26 μg/mL vs. 1.33 ± 1.07 μg/mL, $$P \leq 0.71$$; Fig. 1B). Furthermore, when the IgA titers were stratified by cancer stage, significant increase was detected in both early (stages I and II) and advanced (stages III and IV) colorectal cancer, compared with the normal controls (Fig. 1C). No tumor location was available for study 1.
**FIGURE 1:** *Comparison of anti-FadAc IgA or anti-FadAc IgG levels in plasma from normal subjects and patients with colorectal cancer in study 1. Anti-FadAc IgA (A) and anti-FadAc IgG (B) levels in plasma samples from 25 normal subjects (N) and 25 patients with colorectal cancer (Ca). C, Plasma anti-FadAc IgA levels in normal subject (N), patient with early stages of colorectal cancer (I+II), and patient with advanced stages of colorectal cancer (III+IV). Each symbol represents one human subject. Horizontal lines indicate median values, boxes show the 25th–75th percentiles, and whiskers show the minimal and maximal individual values. **, P < 0.01; ***, P < 0.001.*
## Serum Levels of Anti-FadAc IgA, But not IgG, are Significantly Increased in Patients with Colorectal Cancer Compared with Advanced Precancerous Polyps
Similarly, serum anti-FadAc IgA titers from study 2 were also found to be significantly increased in patients with colorectal cancer compared with those with advanced adenomas (2.06 ± 1.47 μg/mL vs. 1.49 ± 0.99 μg/mL, $$P \leq 0.025$$; Fig. 2A). There was a significant gender difference between the adenoma and colorectal cancer groups in study 2 (Table 1). After adjusting for gender, anti-FadAc IgA levels remain significantly higher in colorectal cancer compared with the advanced adenoma group (estimate = 0.592 μg/mL, SE = 0.262, $$P \leq 0.026$$). No difference was detected in anti-FadAc IgG levels (4.87 ± 4.46 μg/mL vs. 7.04 ± 10.7 μg/mL, $$P \leq 0.190$$; Fig. 2B).
**FIGURE 2:** *Comparison of anti-FadAc IgA or anti-FadAc IgG levels in serum from patients with advanced adenomas and colorectal cancer in study 2. Anti-FadAc IgA (A) and anti-FadAc IgG (B) levels in serum samples were obtained from 50 patients each with advanced adenomas (Ade) or colorectal cancer (Ca). C, Serum anti-FadAc IgA levels in patient with advanced adenomas (Ade), early stages of colorectal cancer (I+II), and advanced stages of colorectal cancer (III+IV). D, Serum anti-FadAc IgA levels in patients with different tumor stages [advanced adenoma (Ade), early colorectal cancer (I+II), advanced colorectal cancer (III+IV)] and tumor locations. Tumor locations were categorized into proximal (cecum, ascending, right colon) and distal (sigmoid, rectum, descending, left colon, rectosigmoid junction). Patients with tumors of unclear location or in both proximal and distal colons were excluded. Each symbol represents one human subject. Horizontal lines indicate median values, boxes show the 25th–75th percentiles, and whiskers show the minimal and maximal individual values. *, P < 0.05; **, P < 0.01; ***, P < 0.001.*
When the results were stratified with tumor stage and location, the anti-FadAc IgA levels were only increased in advanced colorectal cancer compared to the advanced adenoma group (Fig. 2C). However, for patients with proximal tumors, the IgA levels were significantly increased in both early and advanced stages of colorectal cancer compared with the advanced adenoma group (Fig. 2D).
## Discussion
In this investigation, we found that levels of circulating anti-FadAc IgA were significantly increased in patients with colorectal cancer compared with healthy controls or to patient with advanced adenomas. The anti-FadAc IgA levels were elevated in both early and advanced colorectal cancer suggesting that it may be useful for not only advanced-stage but also early-stage diagnosis. This is consistent with our previous finding that the fadA genes levels are increased stepwise from normal to adenomas, and from adenomas to carcinomas [7]. The adenoma group in study 2 exhibited higher titers than the normal subjects of cohort 1. However, because different specimens (plasma vs. serum) were used for these two studies, the results are not directly comparable. The elevated anti-FadAc IgA is closely associated with proximal tumors, consistent with previous reports of enriched Fn in proximal colorectal cancer [30, 31]. Interestingly, we did not observe changes in IgG levels. These findings suggest that Fn likely translocates through the digestive tract inducing mucosal immune responses.
Serologic diagnosis is desirable due to the noninvasive nature, as well as easy attainment of the specimens. Previous studies reported colorectal cancer diagnosis using serum CEA, AFP, and CA19-9, achieving sensitivities of $80.43\%$, $73.91\%$, and $69.57\%$, and specificities of $75.00\%$, $69.44\%$, and $61.11\%$, respectively [41]. Using anti-FadAc IgA, we achieved sensitivity and specificity both at $76\%$, demonstrating its superiority over AFP and CA 19-9 as potential markers. Using whole bacteria Fn as antigen, a previous study tested anti-Fn IgA in colorectal cancer, with a low sensitivity of $36.43\%$. Even when combined with CEA, the sensitivity was still only $53.10\%$ [49]. This may be due to the presence of common bacterial components conserved among most bacterial species such as outer membrane proteins and lipopolysaccharides making it difficult to predict colorectal cancer. Given that FadA is uniquely conserved in Fn, anti-FadAc antibody is a more specific biomarker. Furthermore, we used the colorectal cancer–promoting form of FadA, that is, the amyloid FadAc [39], thus achieving significantly improved sensitivity. We used the Youden index with the requirement of both sensitivity and specificity greater than 0.5 for the selection of the cut-off points. By doing so, it guarantees the cutoff yields a better approach than a random decision with a fair coin toss for potential clinical diagnosis. We anticipate that combining circulating anti-FadAc IgA with additional biomarkers may further improve the diagnostic sensitivity and specificity.
Our study had a few limitations. First, our sample size was limited, especially for those with clearly defined tumor locations. Future studies with larger sample sizes and external validations are warranted to confirm the findings, and to compare with the above-mentioned serologic parameters to stratify to additional patient and tumor characteristics, such as smoking, tumor histology, and molecular types. Second, as a proof-of-concept study, we performed retrospective analysis using samples available to us. As a result, different specimens were used in the two studies, one with plasma and the other with serum, rendering the results from the two studies incomparable. There was no adenoma group in study 1 and no healthy controls in study 2; therefore, we do not know whether anti-FadAc IgA can also be used for early detection of advanced precancerous adenomas. Third, given the retrospective nature of study, we cannot dismiss the possibility of reverse causality. As such, caution must be exercised for causal interpretation of our results. Prospective cohort study is warranted.
In summary, our study sheds novel light on using specific bacterial virulence factors as biomarkers for detection of colorectal neoplasia. Anti-FadAc IgA is a potential novel biomarker for colorectal cancer, especially for the tumors located in the proximal colon.
## Authors’ Disclosures
Y.W. Han reports a patent to detect serum anti-FadA antibodies and related diagnostic methods, U.S. Patent No. 11, 506, 661. No disclosures were reported by the other authors.
## Authors’ Contributions
J.E. Baik: Data curation, writing-original draft. L. Li: Resources, writing-review and editing. M.A. Shah: Resources, writing-review and editing. D.E. Freedberg: Writing-review and editing. Z. Jin: Formal analysis, writing-original draft. T.C. Wang: Resources, writing-review and editing. Y.W. Han: Conceptualization, supervision, funding acquisition, writing-original draft, writing-review and editing.
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|
---
title: 'Diagnostic Accuracy of Blood-based Biomarkers for Pancreatic Cancer: A Systematic
Review and Meta-analysis'
authors:
- Laura E. Kane
- Gregory S. Mellotte
- Eimear Mylod
- Rebecca M. O'Brien
- Fiona O'Connell
- Croí E. Buckley
- Jennifer Arlow
- Khanh Nguyen
- David Mockler
- Aidan D. Meade
- Barbara M. Ryan
- Stephen G. Maher
journal: Cancer Research Communications
year: 2022
pmcid: PMC10035398
doi: 10.1158/2767-9764.CRC-22-0190
license: CC BY 4.0
---
# Diagnostic Accuracy of Blood-based Biomarkers for Pancreatic Cancer: A Systematic Review and Meta-analysis
## Abstract
Pancreatic ductal adenocarcinoma (PDAC) has a 5-year survival rate below $5\%$. Carbohydrate antigen 19-9 (CA19-9) is the most commonly used blood-based biomarker for PDAC in current clinical practice, despite having been shown repeatedly to be inaccurate and have poor diagnostic performance. This review aims to assess the reported diagnostic accuracy of all blood-based biomarkers investigated to date in PDAC, by directly comparing individual biomarkers and multi-biomarker panels, both containing CA19-9 and not (novel). A systematic review was conducted in accordance with PRISMA standards in July 2020. Individualized search strategies for three academic databases identified 5,885 studies between the years 1973 and 2020. After two rounds of screening, 250 studies were included. Data were extracted and assessed for bias. A multivariate three-level meta-analysis with subgroup moderators was run in R using AUC values as effect size. On the basis of this model, the pooled AUC value for all multi-biomarker panels (AUC = 0.898; $95\%$ confidence interval (CI): 0.88–0.91) was significantly higher than all single biomarkers (AUC = 0.803; $95\%$ CI: 0.78–0.83; $P \leq 0.0001$). The pooled AUC value for CA19-9 alone was significantly lower compared with the multi-biomarker panels containing CA19-9 ($P \leq 0.0001$). For the novel biomarkers, the pooled AUC for single biomarkers was also significantly lower compared with multi-biomarker panels ($P \leq 0.0001$). Novel biomarkers that have been repeatedly examined across the literature, such as TIMP-1, CEA, and CA125, are highlighted as promising. These results suggest that CA19-9 may be best used as an addition to a panel of biomarkers rather than alone, and that multi-biomarker panels generate the most robust results in blood-based PDAC diagnosis.
### Significance:
In a systematic review and three-level multivariate meta-analysis, it is shown for the first time that blood-based multi-biomarker panels for the diagnosis of PDAC exhibit superior performance in comparison with single biomarkers. CA19-9 is demonstrated to have limited utility alone, and to perform poorly in patient control cohorts of both healthy and benign individuals. Multi-biomarker panels containing CA19-9 produce the best diagnostic performance overall.
## Introduction
The search for robust biological markers for disease diagnosis and treatment has been a consistent objective within modern health care over the last few decades [1, 2]. In many instances, the use of multiple biological markers is almost intuitive when proceeding with a patient diagnosis. Symptoms, for example, are biological markers of illness used to identify the ailment at hand. As with symptoms, patients will rarely experience just one, and health care professionals will generally use the presence or absence of several symptoms to make a diagnosis [3]. In this way, we have used multiple biomarkers (multi-biomarker panels) for the diagnosis of disease intuitively for thousands of years. Modern medicine often overlooks the utility to be gained from the use of multi-biomarker panels, searching for single proteins or miRNAs that are dysregulated in disease and can be used for a more streamlined diagnosis. However, recent trends in the literature favor multi-biomarker panels over single biomarkers, as single biomarkers alone fail to encompass the biological complexity of disease and therefore lack the comprehensive robustness required for a confident diagnosis [2, 4, 5]. This can be seen clearly in the variable diagnostic performance of certain single markers in patients with underlying conditions, and as such has caused a trend toward the use of “mixed” control cohorts, where both healthy volunteers and patients with benign conditions comprise the control cohort. In this way, the control cohort is arguably more clinically relevant, as it better represents the patient population in question.
Pancreatic cancer has one of poorest prognoses of any cancer, with a 5-year survival rate of below $5\%$ [6]. Late-stage diagnoses contribute hugely to the poor survival rates of this cancer, as the symptoms associated with pancreatic cancer can be vague in nature [7]. As such, earlier diagnosis is the key to improving patient prognosis in this cancer. Pancreatic ductal adenocarcinoma (PDAC) is the most common subtype of pancreatic cancer and represents approximately $85\%$ of all patients with pancreatic cancer [8]. Currently, the only FDA-approved biomarker for PDAC diagnosis is the blood-based biomarker Carbohydrate antigen 19-9 (CA19-9; ref. 5). CA19-9 is a type of antigen released by pancreatic cancer cells, and is therefore detected at higher levels in the blood of patients with PDAC [9]. However, reported sensitivity and specificity values for CA19-9 vary greatly from study to study. While sensitivity values are generally high, being reported at roughly $80\%$, the specificity of this biomarker has been shown to be variable, resulting in many false positives [10]. Serum CA19-9 has been shown to be elevated in some benign conditions, such as pancreatitis, and also in other gastrointestinal malignancies contributing to the limitations of the biomarker [11]. Furthermore, the ability to express CA19-9 at all is dependent on a patient's Lewis blood group (Le), with $5\%$–$7\%$ of the population belonging to the Le(a−b−) group and consequently unable to express CA19-9 at any level [12]. Therefore, even though CA19-9 is widely used in current clinical practice, the results alone cannot be used to diagnose PDAC, and must always be interpreted within the clinical context of imaging and/or histopathology [13]. As such, current methods of diagnosing patients with pancreatic cancer at an early stage rely heavily on the presentation of symptoms, as there is no effective method for screening patients for the presence or absence of pancreatic cancer. A recent review from our group has highlighted the importance of novel biomarker research in pancreatic patients, and argues that the integration of multiple biomarkers to form a multi-biomarker panel could be the vital next step for the identification of more robust biomarkers [5]. Following on from this, we have conducted a systematic review and meta-analysis of biomarker efficacy to truly evaluate whether more really is better in the context of biomarkers.
In this systematic review, we evaluated blood-based biomarkers for the diagnosis of PDAC. The primary aim of this review is to compare the efficacy of single biomarkers and multi-biomarker panels in the context of PDAC diagnosis to determine which biomarker type performs better. The secondary objective is to examine the current clinical standard, CA19-9, and its performance comparatively to novel biomarkers for PDAC diagnosis. The final objective of this review is to highlight promising novel biomarkers that have been examined repeatedly in the literature and may provide direction for future biomarker studies.
## Materials and Methods
A systematic review of blood-based biomarkers for the diagnosis of PDAC was conducted in accordance with PRISMA standards (Supplementary Material S1 and S2; ref. 14). The review was registered with PROSPERO prior to data extraction (CRD42020207241).
## Search Strategy and Inclusion Criteria
Academic databases MEDLINE, Web of Science, and EMBASE were searched using individualized search strategies containing both medical subject headings and text words (Supplementary Material S3). Publications were limited to those written in English, conducted in human participants and published on or before July 20th, 2020. No limit was placed on the date of publication prior to the date of the literature search. Studies that were identified were exported to Endnote X9 and subsequently imported to www.covidence.org for review, where covidence analytics removed any duplicates (Supplementary Material S2).
Human studies reporting on blood-based single biomarkers or multi-biomarker panels for the diagnosis of PDAC were included. As PDAC is considered to be synonymous with pancreatic cancer, where a study did not specify the subtype of pancreatic cancer, it was assumed that this study was referring to PDAC and it was therefore included in the review. Only studies examining primary PDAC in patients of any stage, with or without metastasis, were included. Both PDAC and control cohorts must have had a minimum of 15 patients to be included in the study. This was to ensure sufficient statistical power in each study according to power calculations [15]. Studies reporting on image-based diagnostic methods, such as endoscopic ultrasounds or CT scans, or pancreatic cyst fluid and/or pancreatic tissue-based biomarkers were excluded as they were not deemed directly comparable with blood-based biomarkers. Studies reporting on biomarkers of any omic compartment, for example, proteomics, transcriptomics, or genomics, were included. Multi-biomarker panels consisting of biomarkers from different omic compartments were also included. Studies where the patient data were obtained from an online database were excluded to avoid examining biomarkers that were assessed in the same patient cohorts. Where all patients in a study, PDAC and controls, had preexisting conditions, this study was excluded. Only studies which reported a significant result were included to ensure the results did not skew the downstream analyses. A full list of inclusion and exclusion criteria is given in Supplementary Material S4.
Title and abstract screening was conducted independently by two randomly assigned reviewers, and included studies were then subject to full-text screening in the same manner. Any disagreements were discussed and settled by two senior reviewers (L.E. Kane and G.S. Mellotte). Where the full-text of an article could not be located, corresponding authors were contacted to request access to the article. Reasons for exclusion at the full-text stage are given in Supplementary Material S2.
## Data Extraction and Risk of Bias Assessment
An extraction template in Excel was piloted by two reviewers for a small subset of papers before being finalized. Reviewers extracted data into their own preoptimized template in Excel, with data compilation being carried out once all studies had been extracted. Data extracted included information such as study details (title, corresponding author name and email address, country of study, dates conducted); biomarker details (biomarker name(s), biological properties, detection platform); patient cohort details (number patients per cohort, sex and age breakdown, condition); and reported statistics (analysis performed, P value, sensitivity, specificity, AUC). A complete list of extracted data fields is included in Supplementary Material S5. Data were extracted such that each row represented an individual biomarker or multi-biomarker panel having been assessed in one set of patient cohorts. Where a biomarker or multi-biomarker panel was assessed in multiple cohorts, for example, training and validation cohorts, these data were extracted into separate rows. As a result, larger studies which examined multiple biomarkers in multiple patient cohorts, represent more rows of data than smaller studies.
All included studies were assessed for quality and risk of bias (RoB) using the QUADAS-2 tool. The QUADAS-2 tool provides an assessment for the level of bias an individual study will introduce into the systematic review based on the nature of its design. The selected questions from the four main domains of the tool were amended to align with the review, as per the QUADAS-2 guidelines. This assessment was carried out by reviewers in tandem with the data extraction. Responses were given as either “yes,” “no”, or “unclear” and domains were subsequently scored as “high,” “low”, or “unclear” RoB. Selected questions for the RoB assessment are included in Supplementary Material S6.
Given the high volume of included papers, studies were only extracted and RoB assessed by a single reviewer. To assess the accuracy of this process a random selection of 25 papers, to represent $10\%$ of the total number of studies included, was generated using R. These papers were extracted and RoB assessed by a second reviewer, with both data extractions and RoB assessments subsequently checked for mistakes and/or missing information by a senior reviewer (L.E. Kane or G.S. Mellotte). Extraction accuracy was then calculated for each paper using the total number of correct datapoints as a percentage of the total number of datapoints. If there were disagreements between both senior reviewers with regards to the eligibility of a study, or a discrepancy of greater than $10\%$ for the accuracy of the RoB or data extraction, a third reviewer was consulted (S.G. Maher) to settle disputes. An Excel file with all extracted biomarker and cohort details are given in Supplementary Material S7.
## Statistical Analysis
Data filtering and clean-up was conducted in Microsoft Excel. To calculate uniform $95\%$ confidence intervals (CI) for reported sensitivity and specificity values, 2 × 2 contingency tables were constructed using the extracted values for sensitivity, specificity, number of patients with PDAC, and number of control patients. Both two-level and three-level meta-analyses were run on the data to identify the model of best fit. The three-level model had significantly lower Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values, and was therefore deemed to be the most appropriate model ($P \leq 0.0001$). A multivariate three-level meta-analysis with subgroup moderators was run in R (v 1.3.959) with the “metaphor” package (v. 3.0-2) using reported AUC values as effect size [16, 17]. AUC values <0.5 were removed from the analysis as they are regarded as diagnostically useless. Studies where PDAC was not specified ($$n = 25$$) were excluded from the primary meta-analysis. A secondary meta-analysis, including all 250 studies, was also conducted. Figures were created in GraphPad Prism v9.2.0 and Microsoft PowerPoint v2108.
## Patient and Public Involvement
Given the nature of this systematic review and meta-analysis, it was not possible to involve patients or the public in this research.
## Data Availability
The data generated in this study are available in Supplementary Material S7. The methodology utilized to conduct this review has been registered with, and can be viewed on, PROSPERO (CRD42020207241).
## Identification of Relevant Studies
After removing duplicates, 5,885 studies were identified by our literature search as potential candidates for inclusion in this review. After two stages of screening by reviewers, 250 papers were included in this review (Supplementary Material S2). Most records excluded at the full-text stage were omitted due to having cohorts of less than 15 patients, no accessible full-text, or being a conference proceeding and therefore not a peer-reviewed full-text paper.
## Accuracy and RoB Assessment
Summarized results for RoB and quality assessment as conducted through the use of the QUADAS-2 tool are shown in Figure 1. Concerns regarding index test applicability were generally low once a blood-based biomarker was being assessed for PDAC diagnosis as per the inclusion criteria of the review. Patient selection was frequently high risk ($68\%$) as control cohorts were often not clinically relevant, that is, contained healthy or benign patients only. RoB was low for reference standards ($38.8\%$) and index tests ($23.2\%$) when studies were blinded to the results, and unclear ($31.6\%$ and $20\%$, respectively) when no details were given. Concerns about the applicability of the index test were low in most cases ($98.8\%$) as most biomarkers were for the diagnosis of PDAC.
**FIGURE 1:** *Summary of results for the QUADAS-2 RoB and study quality assessment.*
The accuracy of data extraction and RoB assessments were independently assessed by senior reviewers before proceeding to the data analysis stage. Data extraction was shown to have a mean accuracy of $91.47\%$ ($95\%$ CI: 91.42–91.52), and QUADAS-2 RoB assessment was shown to have a mean accuracy of $92.63\%$ ($95\%$ CI: 92.59–92.67).
## Summary of Extracted Studies
The data extracted from the 250 papers included in the study are broadly summarized in Table 1. As stated previously, CA19-9 is the current FDA-approved biomarker for PDAC diagnosis. However, only $51.6\%$ of papers included this biomarker in their study, while $96\%$ of papers evaluated novel biomarkers. A total of 2,077 rows of data were extracted, each representing an individual biomarker entry, with 982 distinct biomarkers included in the analysis. All studies examined blood-based biomarkers in either plasma, serum, or whole blood, with the majority of entries ($67.9\%$) being investigated in serum. While $79.9\%$ of studies recruited patients prospectively, $12.5\%$ retrospectively examined clinical data of patients with PDAC from a hospital database within a certain time period (mean = 3.7 years, range = 14–0.6 years), and the remaining $7.6\%$ were unclear about the recruitment process of patients. Study size varied greatly between papers, with PDAC cohorts ranging from 15 to 809 patients, and control cohorts ranging from 15 to 898 patients. Blinding across studies was shown to be poor, with only $35.6\%$ of entries examined under blinded conditions, and $15.6\%$ unclear on whether the study was blinded or not. As PDAC is synonymous with pancreatic cancer, studies that did not include a subtype of pancreatic cancer were assumed to be PDAC. Importantly, for 183 biomarker entries ($8.8\%$) there was no specific subtype of pancreatic cancer given. PDAC diagnosis by use of a given reference standard (e.g., histology, cytology) was reported in most cases ($55.8\%$). However, $44.2\%$ of biomarker entries reported no reference standard for PDAC diagnosis. Furthermore, $29.4\%$ provided no sex breakdown, $37.2\%$ gave no indication of the age demographics, and $31.3\%$ of entries had no information regarding the stage of patients with PDAC. Similarly, a substantial number of entries had no information on patient sex ($32.2\%$) or age ($42.5\%$) for their control cohorts. Qualitative assessments of biomarker efficacy were provided for most entries ($71.2\%$); however, 598 entries ($28.9\%$) contained only a P value and did not provide any sensitivity, specificity or AUC values. More than $40\%$ of biomarkers had no AUC value and were therefore not included in the meta-analysis.
**TABLE 1**
| Summary of papers | Summary of papers.1 | Summary of papers.2 | Summary of papers.3 |
| --- | --- | --- | --- |
| Total extracted a | 250 | PDAC cohort details | |
| Examining >1 biomarker | 196 (78.4%) | Mean PDAC cohort size (range) | 60.28 (15–809) |
| Examining CA19 - 9 biomarkers | 129 (51.6%) | PDAC reference standard | 1,158 (55.8%) |
| Examining novel biomarkers | 240 (96%) | No PDAC reference standard | 919 (44.2%) |
| | | No PDAC stage details | 650 (31.3%) |
| Summary of unique biomarker entries | | No sex breakdown | 611 (29.4%) |
| Total number of biomarker entries b | 2077 | No age mean/median/range | 773 (37.2%) |
| Novel (Single/Multi) | 1,467 (1,228/239) | Control cohort details | |
| CA19-9 (Single/Multi) | 610 (293/317) | PDAC vs. healthyc | 1,084 (52.2%) |
| Number of unique biomarkers | 982 | Mean cohort size (range) | 50.7 (15–898) |
| Novel (Single/Multi) | 815 (675/140) | No sex breakdown | 356 (32.8%) |
| CA19–9 (Single/Multi) | 167 (1/166) | No age mean/median/range | 460 (42.4%) |
| Fluid type | | PDAC vs. benignd | 867 (41.7%) |
| Serum | 1,411 (67.9%) | Mean cohort size (range) | 42.47 (15–786) |
| Plasma | 576 (27.7%) | No sex breakdown | 281 (32.4%) |
| Whole Blood | 88 (4.2%) | No age mean/median/range | 393 (45.3&) |
| Serum and Plasma | 2 (0.09%) | PDAC vs. mixede | 126 (6.1%) |
| Study design | | Mean cohort size (range) | 71.78 (33–199) |
| Prospective | 1,659 (79.9%) | No sex breakdown | 31 (24.6%) |
| Retrospective | 259 (12.5%) | No age mean/median/range | 30 (23.8%) |
| Unclear | 159 (7.6%) | Total number of entries with: | |
| Cancer type | | No sex breakdown | 668 (32.2%) |
| PDAC specified | 1,894 (91.2%) | No age mean/median/range | 883 (42.5%) |
| PC unspecified | 183 (8.8%) | Statistical analyses | |
| Cohort blinding | | Qualitative assessment | 1,479 (71.2%) |
| Blinded | 740 (35.6%) | P-value alone | 598 (28.9%) |
| Unblinded | 1,012 (48.7%) | AUC | 1,206 (58.1%) |
| Unclear | 325 (15.6%) | Sensitivity/Specificity | 900 (43.3%) |
## Meta-analysis: Full Dataset
On the basis of the multivariate three-level meta-analysis with subgroup moderators, the pooled AUC value for all multi-biomarker panels (AUC = 0.898; $95\%$ CI: 0.88–0.91) was significantly higher compared with single biomarkers (AUC = 0.803; $95\%$ CI:0.78–0.83; $P \leq 0.0001$; Fig. 2). Overall, multi-biomarker panels show improved sensitivity and specificity compared with single biomarkers (Fig. 3A). To further interrogate these data, biomarkers were subdivided into two groups: those including the current standard biomarker for pancreatic patients, CA19-9, and those without (herein known as novel biomarkers). The pooled AUC value for CA19-9–containing biomarkers (AUC = 0.881; $95\%$ CI: 0.87–0.89) was significantly higher compared with novel biomarkers (AUC = 0.797; $95\%$ CI: 78–81; $P \leq 0.0001$). The sensitivity and specificity values for CA19-9 biomarkers appear improved compared with novel biomarkers (Fig. 3B). A second meta-analysis was also conducted, including all studies, even those for which PDAC is not specified. There was no notable difference between the results of these two meta-analyses (Supplemen-tary Material S8A).
**FIGURE 2:** *Summary of multivariate three-level meta-analysis with subgroup moderators. Number of biomarker entries for each subgroup are given. The forest plot shows the pooled AUC value and 95% CIs for each biomarker subgroup from the multivariate three-level meta-analysis. Subgroups directly compared are separated by a dotted line. Symbols represent the whole dataset (★), CA19-9 subgroup (■) and novel subgroup (●). Colors represent biomarker type: all types (black), multi-biomarker panels (red), and single biomarkers (blue). Significantly higher AUC values are denoted using asterisks. ***, P < 0.0001.* **FIGURE 3:** *Comparison of single biomarkers and multi-biomarker panels overall and subdivided by biomarker group. Representative ROC plot showing the extracted sensitivity and 1-specificity for all biomarkers. Symbol colors represent biomarker type (A) and biomarker group (B), while symbol size represents the number of patients in the PDAC cohort for the given statistics.*
## Meta-analysis: CA19-9 and Novel Biomarker Subgroups
Examining the CA19-9 and novel subgroups independently, the pooled AUC value for CA19-9 alone (AUC = 0.85; $95\%$ CI: 0.83–0.87) was significantly lower compared with the multi-biomarker panels containing CA19-9 (AUC = 0.914; $95\%$ CI: 0.90–0.93; $P \leq 0.0001$; Fig. 2). Multi-biomarker panels containing CA19-9 have improved sensitivity and specificity when compared with CA19-9 alone (Fig. 4A). There is a large amount of variation in the reported sensitivity and specificity values between studies for the current standard biomarker, CA19-9, with some papers reporting values that fall below the random classifier line on the ROC plot. The estimated between-study variance in the model was I2Level 3 = $64.49\%$, and the within-study variance was I2Level 2 = $35.51\%$. We noted that this variation in CA19-9 was not a result of platform-to-platform discrepancies in CA19-9 detection throughout the studies examined in this review, as both immunoassays and mass-spectrometry-based detection of CA19-9 showed high variation within their respective platforms (Supplementary Material S8B). As such, the differences observed are more likely to be a result of the patient populations examined rather than the platforms used. For the novel biomarkers, the pooled AUC for single biomarkers (AUC = 0.783; $95\%$ CI: 0.74–0.83) was also significantly lower compared to novel multi-biomarker panels (AUC = 0.865; $95\%$ CI: 0.84–0.89; $P \leq 0.0001$). Novel multi-biomarker panels show improved sensitivity and specificity values over single biomarkers alone (Fig. 4B and D). Furthermore, there is less variation in the sensitivity and specificity values for multi-biomarker panels containing CA19-9 than CA19-9 alone, with a smaller interquartile range and higher mean and median values being shown for both test statistics (Fig. 4C).
**FIGURE 4:** *Comparison of single biomarkers with multi-biomarker panels for both CA19-9 and novel cohorts. Representative ROC plot showing the extracted sensitivity and 1-specificity for all biomarkers containing CA19-9 (A) and all novel biomarkers (B). Symbol colors represent biomarker type while symbol size represents the number of patients in the PDAC cohort for the given statistics. Box and whisker plot showing the extracted sensitivity and specificity of all biomarkers containing CA19-9 (C) and all novel biomarkers (D). Individual datapoints are represented by the colored dots over the plot. Whiskers show the maximum and minimum value, boxes show the 25th and 75th percentiles, and the line within the box indicates the median.*
## Meta-analysis: CA19-9 and Novel Biomarkers in Different Patient Cohort Subgroups
To further evaluate the efficacy of each biomarker and/or panel, results were subdivided on the basis of the patient cohorts involved as follows: PDAC versus healthy, PDAC versus benign, and PDAC versus mixed (healthy and benign; Fig. 5). Multi-biomarker panels demonstrate improved sensitivity and specificity when compared with single biomarkers across all patient cohorts. On the basis of the meta-analysis, biomarker robustness was also influenced by the patient cohort examined, with CA19-9–containing biomarkers performing best in all cohorts compared with novel biomarkers: PDAC versus healthy (AUC = 0.909; $95\%$ CI: 0.88–0.94), PDAC versus benign (AUC = 0.853; $95\%$ CI: 0.84–0.87), and PDAC versus mixed (AUC = 0.863; $95\%$ CI: 0.82–0.91; $P \leq 0.0001$; Fig. 2). Furthermore, CA19-9 biomarkers examined in PDAC versus healthy cohorts have improved AUC values compared with those examined in PDAC versus mixed cohorts ($P \leq 0.0001$).
**FIGURE 5:** *Comparison of CA19-9 and novel biomarkers subdivided by patient cohorts. Representative ROC plots showing the extracted sensitivity and 1-specificity for all biomarkers. Comparison of CA19-9 alone (blue) and multi-biomarker panels (red) for PDAC versus healthy (A), PDAC versus benign (B), and PDAC versus mixed patient (C) cohorts. Comparison of novel single biomarkers (blue) and novel multi-biomarker panels (red) for PDAC versus healthy (D), PDAC versus benign (E), and PDAC versus mixed patient (F) cohorts. Symbol size presents the number of patients in the PDAC cohort for the given statistics.*
## Biomarker Efficacy of Different “omics” Compartments
Proteomic biomarkers are the most frequently evaluated blood-based biomarkers for pancreatic cancer diagnosis (Supplementary Material S8C). Proteomic biomarkers represent $77.9\%$ of novel single biomarkers and are present in $50.3\%$ of novel multi-biomarker panels examined. Of the panels where some other biomarker(s) was combined with the current standard, CA19-9, $76.4\%$ opted to add another protein biomarker. Given the lack of diversity in omic compartments between studies, no distinct difference can be observed between the sensitivity and specificity values for biomarkers of different omic compartments.
Given the high prevalence of proteomic-orientated studies, biomarkers were pooled into categories that represent generalized cell compartments as follows: Genomics & Transcriptomics & Epigenomics; Metabolomics & Proteomics; and Single-cell omics & Immunomics. After pooling biomarkers into these groups, there was still no visible difference in sensitivity or specificity values between the different omic compartments (Supplementary Material S8D).
## Biomarkers in the 90th Percentile for Sensitivity and Specificity
Biomarkers in the 90th percentile of all entries for which there are sensitivity and specificity values, represented by a sensitivity value equal to or above 0.95 and a specificity value equal to or above 0.979, are summarized in Figure 6 (18–28). A total of 15 biomarkers comprise the 90th percentile, with seven of those being multi-biomarker panels. Most of these biomarkers were proteomic ($$n = 6$$) or transcriptomic ($$n = 5$$), with just one representing more than one omic compartment (proteomics and metabolomics). These biomarkers were identified from just 11 studies spanning 27 years (1993–2020), with four studies reporting on two biomarkers each. Nine of the top 15 biomarkers reported perfect sensitivity (1.00) and specificity (1.00), with a $95\%$ CI of ± 0. Nine biomarkers are reported from studies that were not blinded, while just four biomarkers out of the 15 biomarkers were examined in a blinded study design. The most common patient cohorts for biomarker assessment were PDAC versus healthy ($$n = 11$$), with none of the top 15 biomarkers having been examined in the more clinically relevant PDAC versus mixed cohort. This is reflected in the RoB assessments for these studies, where several have high levels of bias for patient selection and index test (Supplementary Material S8E). Only two biomarkers contained the current clinical standard biomarker, CA19-9, with 13 of 15 biomarkers being novel biomarkers. Furthermore, both biomarkers in the CA19-9 subgroup were multi-biomarker panels, with no reported sensitivity or specificity values for CA19-9 alone across all 250 included papers being within the 90th percentile of examined biomarkers.
**FIGURE 6:** *Details of the biomarkers in the 90th percentile for sensitivity and specificity. Details of the 15 biomarkers that are in the 90th percentile of all biomarkers for both sensitivity (≥0.95) and specificity (≥0.979). Forest plots of sensitivity and specificity values with 95% CIs for each biomarker are shown. 1CA19-9, DUPAN-2, TPA, elastase-1, lipase, amylase, gamma-glutamyl transpeptidase, alkaline phosphatase, and lactate dehydrogenase. 2CA19-9, docosahexanoic acid, lysoPC(14:0), and histidinyl-lysine. 38562.3m/z, 8684.4m/z, 8765.1m/z, 9423.5m/z, 13761.5m/z, 14145.2m/z, and 17250.8m/z. 47,775 Da, 8,567 Da, 5,362 Da, and 5,344 Da. 5FGA, KRT19, HIST1H2BK, ITIH2, MARCH2, CLDN1, MAL2, and TIMP1. 6cfDNA KRAS mutations at PDAC hotspot codons (12, 13, 61). 7cfDNA KRAS mutations at any screened codons reported in any cancer sites.*
## Most Frequently Examined Novel Biomarkers Across Included Studies
Novel biomarkers that have been examined most frequently across the studies included in this review are shown in Table 2. A total of 13 novel biomarkers were examined in more than one study and had a minimum of 20 unique entries. Tissue inhibitor matrix metalloproteinase 1 (TIMP-1) is the most examined biomarker, appearing 79 times in 10 studies, with CEA being a close second with 73 unique appearances in 34 studies. MiR-21 had the lowest number of unique appearances at 21, though these spanned across 10 studies. Of these 13 novel biomarkers, 10 were proteomic and three were transcriptomic in nature, with all 13 having been examined both alone and as part of a panel. The mean sensitivity of all novel biomarkers is higher when examined as part of a multi-biomarker panel. This holds true for mean specificity in most biomarkers also, with just CA125 and thrombospondin-2 (THBS2) showing improved mean specificity alone compared with when part of a panel. Mean AUC values are higher when examined as part of a panel for all biomarkers except Mucin 5AC (MUC5AC), with superior mean AUC values for this biomarker being found when examined alone. Albumin (ALB) had the highest mean sensitivity values both alone and as part of a panel, with LRG1 having the lowest in both cases. THBS1 had the highest mean specificity both alone and as part of a panel, with LRG1 again performing the worst in both categories. MUC5AC had the highest mean AUC value alone; however, it had the lowest mean AUC value as part of a panel. Conversely, ALB has the highest mean AUC value as part of a panel, and the lowest mean AUC value when examined alone. TIMP-1 is the only of these biomarkers that also appears among the biomarkers in the 90th percentile in Figure 6, where it appears as part of an 8-biomarker panel.
**TABLE 2**
| Novel biomarker | Number of papers | Omic compartment | Number of unique appearances | Number of unique appearances.1 | Sensitivity range (mean) | Specificity range (mean) | AUC range (mean) |
| --- | --- | --- | --- | --- | --- | --- | --- |
| TIMP-1 | 10.0 | Transcriptomics | 79.0 | Alone: 30 | 0.1–0.5 (0.28) | 0.09–0.96 (0.4) | 0.61–0.95 (0.78) |
| | | | | Part of panel: 49 | 0.36–1 (0.89) | 0.22–1 (0.81) | 0.83–0.99 (0.94) |
| CEA | 34.0 | Proteomics | 73.0 | Alone: 42 | 0.06–0.8 (0.41) | 0.17–0.99 (0.68) | 0.53–0.82 (0.67) |
| | | | | Part of panel: 31 | 0.16–0.98 (0.77) | 0.67–1 (0.9) | 0.78–0.99 (0.92) |
| CA242 | 5.0 | Proteomics | 45.0 | Alone: 19 | 0.31–0.83 (0.62) | 0.51–1 (0.78) | 0.62–0.89 (0.75) |
| | | | | Part of panel: 26 | 0.57–0.97 (0.77) | 0.75–1 (0.92) | 0.8–0.98 (0.9) |
| CA125 | 11.0 | Proteomics | 38.0 | Alone: 20 | 0.07–0.89 (0.42) | 0.69–1 (0.93) | 0.57–0.79 (0.7) |
| | | | | Part of panel: 18 | 0.79–0.99 (0.96) | 0.69–096 (0.89) | 0.8–0.93 (0.87) |
| miR-483 | 2.0 | Transcriptomics | 37.0 | Alone: 8 | | | 0.7–0.75 (0.73) |
| | | | | Part of panel: 29 | | | 0.63–0.99 (0.82) |
| MUC5AC | 3.0 | Proteomics | 31.0 | Alone: 27 | 0.65–0.95 (0.78) | 0.7–0.9 (0.77) | 0.68–0.94 (0.82) |
| | | | | Part of panel: 4 | | | 0.69–0.93 (0.81) |
| THBS2 | 5.0 | Proteomics | 28.0 | Alone: 15 | 0.07–0.52 (0.19) | 0.974–1 (0.99) | 0.61–0.89 (0.79) |
| | | | | Part of panel: 13 | 0.62–0.9 (0.75) | 0.9–1 (0.95) | 0.76–0.98 (0.9) |
| IL-8 | 7.0 | Proteomics | 24.0 | Alone: 6 | 0.15–0.72 (0.33) | 0.72–0.95 (0.83) | 0.6–0.71 (0.65) |
| | | | | Part of panel: 18 | 0.75–0.99 (0.87) | 0.58–1 (0.85) | 0.81–1 (0.92) |
| CRP | 5.0 | Proteomics | 24.0 | Alone: 7 | 0.2–0.77 (0.48) | 0.55–0.93 (0.71) | |
| | | | | Part of panel: 17 | 0.83–0.99 (0.96) | 0.9–1 (0.91) | 0.91–0.98 (0.96) |
| ALB | 4.0 | Proteomics | 24.0 | Alone: 4 | 0.79–0.79 (0.79) | | 0.18–0.87 (0.4) |
| | | | | Part of panel: 20 | 0.92–0.99 (0.97) | 0.9–1 (0.91) | 0.95–0.98 (0.97) |
| LAMC2 | 2.0 | Proteomics | 23.0 | Alone: 11 | | | 0.65–0.87 (0.81) |
| | | | | Part of panel: 12 | | | 0.8–0.96 (0.88) |
| LRG1 | 3.0 | Proteomics | 22.0 | Alone: 10 | 0.5–0.46 (0.17) | 0.1–0.37 (0.19) | 0.64–0.94 (0.77) |
| | | | | Part of panel: 12 | 0.36–0.92 (0.72) | 0.22–0.83 (0.56) | 0.82–0.96 (0.89) |
| miR-21 | 10.0 | Transcriptomics | 21.0 | Alone: 17 | | | 0.49–0.99 (0.7) |
| | | | | Part of panel: 4 | 0.85–0.9 (0.87) | 0.85–0.87 (0.86) | 0.82–0.95 (0.88) |
## Discussion
Currently, there is no biomarker that can effectively and consistently discriminate patients with PDAC from those without. The aim of this systematic review and meta-analysis was to examine the performance of all published blood-based biomarkers used for the diagnosis of PDAC. Specifically, we examined papers that evaluated some blood-based biomarker(s) for the diagnosis of PDAC, with no limit placed on the publication date of the paper or the “omic” compartment of the biomarker(s) assessed. We evaluated whether single biomarkers or multi-biomarker panels generally have the best efficacy for PDAC diagnosis by performing a multivariate three-level meta-analysis using AUC values as effect sizes, and by comparing sensitivity and specificity values.
## Multi-biomarker Panels—The Better Choice
Overall, multi-biomarker panels are significantly more robust than single biomarkers alone, and in the context of PDAC, this holds true for both CA19-9 and novel biomarker subgroups, as well as across different patient control cohorts. This review shows extensive evidence, both graphically and statistically, that panels of more than one biomarker tend to perform better than single biomarkers alone for the diagnosis of PDAC. Furthermore, it was evident when eliminating confounding variables by subdividing the data into different groups, using variables such as biomarker type (CA19-9 or novel) or patient control cohort examined (healthy, benign, or mixed), that this result is robust and prevails throughout multiple subgroup analyses. Importantly, the inclusion of studies that do not specify PDAC did not greatly alter the results of the meta-analysis, suggesting that the cohorts within these studies are similar to those included in the original analysis. While there were many single biomarkers reported in the included studies with impressive efficacy, the results of the meta-analysis indicate that on the whole, multi-biomarker panels produce the most robust diagnostic performance. This information is crucial for future studies, as it suggests that researchers should focus their efforts on the identification of multiple biomarkers, rather than attempting to isolate one single biomarker. It is important to note also, that the creation of a multi-biomarker panel is not as straightforward as it may seem, and when dealing with multiple levels of patient data and consequently different cutoffs for individual biomarkers, care must be taken to ensure the desired sensitivity and specificity of the panel as a whole. Integration of these data will allow researchers the flexibility to tailor their panel to certain conditions, and determine individual cutoffs based on the needs of the test [5]. Computational approaches, such as machine learning, have shown utility in this context and could provide future research with a more streamlined approach to multi-biomarker panel generation [29]. One caveat to this, however, is the integration of multi-omic data, which would require cautious consideration of the various unit measurements involved and, in each case, careful control of confounding variables and potential artefacts of experimental design [30]. In any case, whether multi-omic or single-omic, the thoughtful generation of highly sensitive and specific multi-biomarker panels should arguably be the primary aim of future studies hoping to identify novel diagnostic biomarkers for PDAC.
## CA19-9 and its Role as the Current Clinical Standard Biomarker
As CA19-9 is regarded as the current standard biomarker for pancreatic cancer diagnosis, the data were separated into two groups, those including CA19-9 and those without (novel biomarkers), to evaluate the performance of CA19-9 across all studies. For both subgroups of data, multi-biomarkers were shown to perform significantly better than single biomarkers alone. Indeed, we found that while CA19-9 is the “gold standard” for pancreatic diagnosis, the addition of some other biomarker to create a multi-biomarker panel with CA19-9 resulted in improved biomarker efficacy compared with CA19-9 alone. Furthermore, there was a substantial amount of variation between studies in the reported sensitivity and specificity values of CA19-9, and this is in keeping with current literature (31–34).
The results of the meta-analysis showed that the addition of CA19-9 to a multi-biomarker panel provided a clear improvement over novel biomarker panels that did not contain CA19-9. In addition, CA19-9 alone appears to have consistently outperformed novel single biomarkers. While CA19-9 may be the most commonly used biomarker for diagnosis of PDAC in patients with pancreatic cancer, elevated expression has been shown in various benign conditions such as pancreatitis, which contributes to its nonspecificity for PDAC [34]. Given the similarities that are often observed between benign pancreatic patient blood and pancreatic cancer patient blood, it follows therefore that a biomarker may have a diminished ability to distinguish benign cohorts from those with cancer when compared with healthy cohorts. To ensure the differences being observed were not a result of the patient cohorts being evaluated by different studies, we separated the data further based on the patient cohorts distinguished from PDAC: PDAC versus healthy, PDAC versus benign, and PDAC versus mixed. When separated based on patient control cohorts, the improved ability of CA19-9 biomarkers over novel biomarkers to diagnose PDAC is clear.
It is also demonstrated here that the efficacy of a biomarker or biomarker panel to diagnose PDAC, whether including CA19-9 or a novel biomarker, is dependent on the reference cohort in question. Both CA19-9 and novel biomarkers exhibit improved ability to distinguish patients with PDAC from healthy controls when compared with both benign and mixed cohorts, with CA19-9 multi-biomarker panels producing the best diagnostic performance across all cohorts. There is considerable variation across pooled AUC values for CA19-9 biomarkers and novel biomarkers in the mixed patient cohort setting, demonstrating the increased difficulty faced in this control cohort. As a result of the recognized poor specificity of CA19-9, the lack of a standardized detection method [10], and the variation in cutoff levels being used for PDAC diagnosis across the studies (35–37), in clinical practice CA19-9 is rarely relied upon for diagnostic purposes, despite being FDA approved for this indication. More often it is used to support a diagnosis, based on appropriate imaging and/ or biopsy, or in staging with Immuno-PET imaging [38], as a biomarker of recurrence [9, 39], or as a biomarker of tumor resectability [40, 41]. In fact, the results of this meta-analysis provide a strong argument in favor of the inclusion of CA19-9 when evaluating a new biomarker panel, while exercising caution given the variation in results obtained across different studies, and the reduced potential of CA19-9 in certain benign conditions. The role of CA19-9 in pancreatic cancer diagnosis, therefore, seems reliant on the identification of a robust multi-biomarker panel that can adequately control for the inherent defects of the biomarker.
## Multi-omics in Biomarker Identification
While it is evident from the results of this review that multi-biomarker panels are the most robust biomarker type, it remains to be seen which biological factors produce the most robust biomarkers. A major limitation of this review results from the lack of diversity seen in the “omic” compartments (genomics, proteomics, etc.) of biomarkers. Indeed, proteomic biomarkers make up the vast majority of biomarkers evaluated across all studies in this review, making comparisons between proteomic biomarkers and other omic compartments difficult. We can see, however, that the combination of different omic compartments with CA19-9 (proteomics) did result in high sensitivity and specificity values, though the number of studies examining multi-omic biomarker panels is too low to see any distinct difference. By examining the biomarkers that fall into the 90th percentile for both sensitivity and specificity, it is evident that while proteomic biomarkers may represent the majority, they do not solely comprise the top biomarkers. Indeed, nearly as many of these “top” biomarkers were transcriptomic in nature, highlighting the importance of examining different biological compartments for the discovery of robust biomarkers. Furthermore, while instances where multiple omic compartments were integrated to form a panel were uncommon among the papers included in this review, one multi-omic biomarker panel was among the 90th percentile biomarkers. A 4-biomarker panel containing CA19-9 and three metabolites was among those with the highest sensitivity and specificity, demonstrating the potential for such multi-omic biomarker panels in this context. While it is not within the scope of this review to evaluate whether multi-omic panels produce better results than single-omic panels, current trends in biomarker discovery are leaning toward multi-omic data integration [5, 42, 43]. The evaluation of multiple biological compartments to give a comprehensive overview of disease, and subsequently the generation of a robust panel that encompasses the complexity of that disease is an appealing concept that has much potential in this context and requires further elucidation.
## Promising Novel Biomarkers for Pancreatic Cancer Diagnosis
Systematic reviews are uniquely poised to identify trends in the literature that may otherwise go unnoticed. Importantly, this systematic review allowed for the identification of 13 novel biomarkers that have been repeatedly examined as blood-based biomarkers for PDAC diagnosis across multiple studies, and show promise both alone and as part of a multi-biomarker panel. While again, the majority of these biomarkers are proteins, the transcriptomic biomarker TIMP-1 emerged as the most frequently assessed novel biomarker. Though it showed poor mean sensitivity and specificity alone, TIMP-1 performed well as part of a panel with improved mean sensitivity, specificity, and AUC values. This is further evident from its appearance among the 90th percentile biomarkers, where it achieved high sensitivity and specificity as part of an 8-biomarker panel of extracellular vesicle long RNAs. Because of its association with cell survival, cell growth, and tumorigenesis, the TIMP-1 protein has been investigated as a potential biomarker, both alone and as part of a panel, in several other cancer types such as gastric [44, 45], colorectal (46–48), and breast [49, 50]. This association, however, could be the reason for TIMP-1s poor utility alone in PDAC diagnosis. Indeed, TIMP-1 protein performance as a blood-based biomarker in PDAC has been shown to be impaired in patients with jaundice [51], though not in patients with chronic pancreatitis [52, 53]. Furthermore, TIMP-1 expression is known to be increased in patients with, and at an increased risk of developing, type 2 diabetes [54, 55], as well as in obese patients [56]. This evidence suggests that the utility of TIMP-1 in PDAC diagnosis is promising, and may lie in its addition to a biomarker panel rather than its use alone due to its impairment in patients with several benign conditions. Interestingly, TIMP-1 has been examined alongside another promising novel biomarker, inflammatory protein leucine-rich-alpha-2-glycoprotein 1 (LRG1). LRG1 has been shown to promote angiogenesis and regulate tumorigenesis, and is a promising biomarker candidate for several other cancer types [57]. Indeed, a plasma-based panel of TIMP-1, LRG1, and CA19-9 discriminated PDAC from healthy controls with improved accuracy compared with CA19-9 alone [58]. LRG1 has also been evaluated in plasma alongside TTR and CA19-9, where this panel exceeded the accuracy of CA19-9 alone by over $10\%$ in its ability to discriminate PDAC from benign controls and other cancers [59]. While LRG1 shows promise as part of a panel for PDAC diagnosis, it has poor mean sensitivity and specificity alone, and there is a lack of research into its performance in control cohorts with various benign conditions.
Conversely, cancer antigen 125 (CA125), carcinoembryonic antigen (CEA), and carbohydrate antigen 242 (CA242) have been evaluated extensively in pancreatic cancer and as such there is a plethora of research on these biomarkers. CEA is an established and widely used tumor biomarker that is known to be increased in several cancers such as colorectal [60], breast [61], and lung [62]. While CEA levels are currently measured for PDAC diagnosis in some clinical settings, it is not FDA approved for PDAC diagnosis and its utility and accuracy remains limited, with a 2018 systematic review and meta-analysis reporting CEA to be inferior to CA19-9 [63]. CEA levels are also known to be elevated in patients with chronic pancreatitis, with serum CEA being unable to distinguish patients with PDAC from those with chronic pancreatitis [64]. Indeed, in this review, we show that CEA alone exhibits poor diagnostic performance across included studies, with improved results being obtained when CEA is examined as part of a panel. CA125 is a known biomarker for ovarian cancer, which has been shown to have superior performance to CEA for PDAC diagnosis [65, 66]. It has also produced higher mean sensitivity, specificity, and AUC values than CEA across the studies included here. Furthermore, a 2017 systematic review and meta-analysis showed that a CA125-based diagnostic panel for PDAC was superior to CA125 or CA19-9 alone [67]. Similar to CEA, CA242 has also been extensively evaluated for PDAC diagnosis, with serum CA242 levels having been shown to positively correlate with CA19-9 levels [68]. CA242 has also been demonstrated to have better diagnostic performance than CEA, with mean sensitivity, specificity and AUC values in this study being higher for CA242 than CEA [69]. Unfortunately, CA242 is known to be elevated in the blood of patients with type 2 diabetes, and as such, has limited utility alone for PDAC diagnosis, despite exhibiting higher specificity than CA19-9, CEA, and CA125 [65, 70]. While none of these biomarkers have stood out on their own as having utility across all patient cohorts, they are frequently examined as part of biomarker panels with other novel biomarkers. Laminin subunit gamma-2 (LAMC2), for example, is a promising new biomarker which was examined in a large-scale study of over 400 patients across three continents, where it was elevated in pancreatic cancer serum compared with controls and demonstrated a sensitivity that was comparable with CA19-9 [71, 72]. Furthermore, a serum-based panel of LAMC2 with both CA19-9 and CA125 has been shown to produce accurate discrimination of PDAC from benign controls [33]. These studies provide promising results for LAMC2 as a potential diagnostic biomarker both alone and as part of a panel, though the breadth of research is limited at this time, with no results for mean sensitivity or specificity being obtained in this review. Another promising biomarker that has produced results similar to CA19-9 is MUC5AC. Serum MUC5AC levels have been shown to be increased in patients with PDAC compared with both benign and chronic pancreatitis cohorts, with MUC5AC performing on par with CA19-9, though again, the combination of the two produced the best results [73, 74]. Interestingly, the measurement of the CA19-9 antigen on circulating MUC5AC proteins showed promise in a study comprising over 500 patients from three different institutions, where both the sensitivity and specificity of the biomarker were improved by this method compared with measuring just CA19-9 alone [75]. While the initial research on MUC5AC shows favorable results, there are few papers examining the capability of MUC5AC as part a multi-biomarker panel.
The addition of CA19-9 to some novel biomarker is a trend across most studies aimed at identifying diagnostic biomarkers for PDAC, with results generally reporting an improved result from the panel compared to individual biomarkers alone. Studies examining the potential of ALB in this setting are no different, with the vast majority of entries for ALB in this review originating from multi-biomarker panels. Indeed, a 5-biomarker panel containing ALB and CA19-9 produced improved diagnostic capabilities compared with CA19-9 alone [76]. Similarly, the combination of ALB with CA19-9 and IGF-1 also performed better than CA19-9 at distinguishing PDAC from chronic pancreatitis [64]. Interleukin-8 (IL-8), a proinflammatory cytokine, has also shown limited utility alone but appears to achieve reasonable results when included in a panel. Serum IL-8 has been shown to be higher in patients with pancreatic cancer than controls; however, the mean AUC value for IL-8 alone is poor and improved when included in a panel with other biomarkers (77–79). This is also the case for THBS2, which produces modest discrimination alone, and is significantly improved when examined alongside CA19-9 [80, 81]. Conversely, c-reactive protein (CRP), a biomarker of inflammation, has shown limited utility as part of a panel, where the panel showed no improvement with the addition of CRP [64]. A 2020 study also showed a significant difference in CRP levels between PDAC and normal controls; however, after running extensive statistical tests on all candidate biomarkers it was not included in the final panel of six biomarkers [82]. While CRP is increased in patients with PDAC compared with controls, it is also elevated in patients with moderate and severe pancreatitis [83]. Moreover, as CRP is derived from the liver, it is substantially influenced by the presence of jaundice making it unreliable in patients with this comorbidity [64]. Interestingly, several studied have examined these more “unreliable” candidates together, and obtained promising results. A 4-biomarker panel with CA19-9, CRP, and IL-8 demonstrated good discrimination of PDAC from controls [84]. While a 2014 study showed that a panel consisting of ALB, CA19-9, CRP, and IL-8 had the highest diagnostic value for distinguishing PDAC from controls, with this panel proving to be effective in identifying other cancers, such as breast, cervical, colorectal, prostate, and lung [84]. These studies highlight the utility of all of these biomarkers together, rather than independently.
Finally, two miRNA emerged as the most frequently examined across the studies included in this review, miR-21 and miR-483. MiR-21 levels in the circulation have been shown to be higher in PDAC compared with healthy controls, and are also associated with advanced stage, metastasis, and shorter survival [85, 86]. However, miR-21 shows poor discriminatory ability between IPMN and PDAC, suggesting the involvement of miR-21 in an early step of pancreatic tumorigenesis [85]. Indeed, the ability of miR-21 to distinguish PDAC from controls was overshadowed by several other miRNA in a 2019 study, such as miR-33a and miR-320a, which outperformed miR-21 in combination, thus excluding miR-21 from the final panel [87]. Overexpression of miR-483 is also thought to be an early event in PDAC progression, having been shown to be present in premalignant pancreatic cystic lesions and early-stage disease [88]. A 2016 large-scale miRNA study with over 400 patients with PDAC showed that serum miR-483 expression was significantly increased in patient with PDAC compared with both benign and healthy controls together [89]. Unfortunately, there is a lack a research into the diagnostic potential of these individual miRNAs, as most studies focus on the large-scale screening of miRNAs and utilize complex modeling to narrow down their validation cohort to the most statistically relevant biomarkers.
On closer examination of the literature around these frequently examined biomarkers, it is clear that no one biomarker produces highly accurate diagnostic results alone. Indeed, the evidence would suggest that the primary utility of all of these biomarkers can be found in their use within multi-biomarker panels. While individually each of these biomarkers has their limitations, it is evident that when put together they can account for the weaknesses of the others to improve the end results. Furthermore, the addition of CA19-9 stands out as a clear prerequisite for the design of future multi-biomarker panels. These novel candidates provide a glimpse into the promising future of PDAC diagnostic biomarker discovery, though they remain to be examined within cohorts of patients with various underlying conditions and comorbidities that may influence their performance. Importantly, while blood-based biomarkers in the PDAC setting are likely to be used primarily as companion diagnostics, several of these biomarkers may also prove useful in the risk stratification of pancreatic patients with underlying conditions, given their dysregulation across certain control cohorts as outlined in this study.
## The State of Current Pancreatic Cancer Research
This review highlights the variability in data quality and study design across pancreatic cancer research. Here, we have interrogated studies which employ biomarkers for the diagnosis of PDAC, identifying many studies that fail to provide sufficient information regarding their patient cohorts, their experimental design or their index test of interest. A substantial number of papers fail to report on the subtype of pancreatic cancer examined, simply conflating all subtypes as pancreatic cancer. For the purposes of the meta-analysis, papers that do not specify PDAC as the subtype of interest were excluded so as to reduce confounding variables. However, this lack of detail is a major flaw within many pancreatic cancer studies, where the specific subtype examined should always be clearly indicated.
Furthermore, almost half of the included biomarker entries did not have information regarding the reference standard used to diagnose patients with PDAC. In these cases, it was unclear whether all patients in this cohort had been diagnosed using the same reference standard or not, resulting in high levels of bias amongst these papers. A third of the biomarkers examined had no details attributed to them regarding the stage details of the PDAC cohort, with reporting of sex and age breakdowns in this cohort also poor. Control cohorts had similar issues, with high numbers of biomarkers also lacking sex and age information.
Unfortunately, the number of studies examining arguably the most clinically relevant control cohort (mixed) is extremely low compared with healthy alone and benign alone. While this review has identified many studies evaluating various types of biomarkers for PDAC diagnosis, the lack of studies conducted in clinically relevant cohorts may be the reason for the unfortunate lack of biomarkers currently in clinical use. Blinding of studies was also extremely poor, with very few opting to adopt this strategy for biomarker identification. This has further contributed to the high levels of bias observed across the studies included in this review.
Finally, evaluation of biomarker efficacy was extremely flawed in some cases, with a substantial number of biomarkers being attributed only with a P value and no qualitative assessment (e.g., AUC or sensitivity) of the biomarker. Overall, huge flaws exist in current pancreatic cancer research in the context of identification of biomarkers for PDAC diagnosis. High levels of bias can be seen in many studies, with missing or unclear information regarding key study design points further compounding these issues. These are major flaws which recur again and again in the literature and could be contributing to the lack of repeated examination of high performing biomarkers in follow-up studies and could subsequently be responsible for the poor progress seen in this field in recent years.
## Limitations of This Systematic Review and Meta-analysis
As modern vernaculars regard pancreatic cancer and its PDAC subtype to be synonymous, any paper that did not specify an alternative subtype of pancreatic cancer was included in the extraction stage of this study and assumed to be PDAC. While a small minority of the total included studies make up this population, it is important to note that the inclusion of these data may not be appropriate in some cases as PDAC may not have been the subtype of pancreatic cancer examined. CA19-9 is highlighted in this review as the current FDA-approved biomarker for PDAC diagnosis; however, CA19-9 cut-off values were not standardized across all studies included in the review. As such, all CA19-9 entries may not have resulted from the same cut-off value and it may not be appropriate to compare them directly, as changes in CA19-9 cut-off values have been demonstrated to improve biomarker robustness [33]. A major caveat of this review, which results from the nature of the data extraction, is that certain biomarkers or biomarker panels may arise several times from a single study, having been examined in multiple patient cohorts within that study, for example, in the context of model training and validation. Unfortunately, as in many studies, there can be overlap between the patients recruited for the training and validation cohorts, resulting in repeated sampling from the same patients. The within-study variance has been controlled for in the multivariate meta-analysis; however, repeated sampling from the same patients was not accounted for and may introduce a level of bias toward some biomarkers. Furthermore, in many instances, studies have opted to evaluate several single biomarkers and subsequently combine these biomarkers to form a multi-biomarker panel. Some multi-biomarker panels have also been examined in some studies both alone and with the addition of CA19-9 to the panel. Possible bias due to repeated entries is an important limitation of this study, which could not be avoided due to the nature of current research papers and study designs. Importantly, while the QUADAS-2 tool that was used to assess study quality and RoB has been used previously for similar systematic reviews of diagnostic biomarkers [90], there may be other forms of bias introduced by these studies that were not accounted for in this assessment.
## Conclusions
In summary, blood-based multi-biomarker panels for the diagnosis of PDAC exhibit superior performance in comparison with single biomarkers, in both CA19-9–containing biomarkers and novel biomarkers, and across all patient control cohorts. CA19-9 shows little utility alone, as it is less effective in mixed control cohorts, though when used in combination with a panel of multiple biomarkers these CA19-9–containing panels produce a better diagnostic performance than novel multi-biomarker panels. These results suggest that future biomarker studies for PDAC diagnosis should focus on the identification of a multi-biomarker panel which includes CA19-9, while drawing from the pool of promising novel biomarkers that have been identified and examined across several different studies. This will allow for better use of the breadth of knowledge that has been accumulated over decades of research and save valuable time and resources as studies steer away from large-scale fishing expeditions, and move toward more focused and specialized research with appropriate blinding and comprehensive experimental design.
## Authors’ Disclosures
A.D. Meade reports grants from Science Foundation Ireland (Grant 13/RC/2106_P2) during the conduct of the study. No disclosures were reported by the other authors.
## Authors’ Contributions
L.E. Kane: Conceptualization, resources, data curation, software, formal analysis, validation, investigation, visualization, methodology, writing-original draft, writing-review and editing. G.S. Mellotte: Conceptualization, resources, data curation, validation, investigation, writing-review and editing. E. Mylod: Resources, data curation, validation, writing-review and editing. R.M. O'Brien: Resources, data curation, validation, writing-review and editing. F. O'Connell: Resources, data curation, validation, writing-review and editing. C.E. Buckley: Resources, data curation, writing-review and editing. J. Arlow: Resources, data curation, validation, writing-review and editing. K. Nguyen: Software, formal analysis, writing-review and editing. D. Mockler: Resources, software, methodology, writing-review and editing. A.D. Meade: Conceptualization, supervision, methodology, writing-review and editing. B.M. Ryan: Conceptualization, resources, supervision, funding acquisition, methodology, writing-original draft, project administration, writing-review and editing. S.G. Maher: Conceptualization, resources, data curation, software, formal analysis, supervision, funding acquisition, investigation, visualization, methodology, writing-original draft, project administration, writing-review and editing.
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|
---
title: Tumor Cell–Autonomous SHP2 Contributes to Immune Suppression in Metastatic
Breast Cancer
authors:
- Hao Chen
- Gregory M. Cresswell
- Sarah Libring
- Mitchell G. Ayers
- Jinmin Miao
- Zhong-Yin Zhang
- Luis Solorio
- Timothy L. Ratliff
- Michael K. Wendt
journal: Cancer Research Communications
year: 2022
pmcid: PMC10035406
doi: 10.1158/2767-9764.CRC-22-0117
license: CC BY 4.0
---
# Tumor Cell–Autonomous SHP2 Contributes to Immune Suppression in Metastatic Breast Cancer
## Abstract
SH2 containing protein tyrosine phosphatase-2 (SHP2) is recognized as a druggable oncogenic phosphatase that is expressed in both tumor cells and immune cells. How tumor cell–autonomous SHP2 contributes to an immunosuppressive tumor microenvironment (TME) and therapeutic failure of immune checkpoint blockades in metastatic breast cancer (MBC) is not fully understood. Herein, we utilized systemic SHP2 inhibition and inducible genetic depletion of SHP2 to investigate immune reprogramming during SHP2 targeting. Pharmacologic inhibition of SHP2 sensitized MBC cells growing in the lung to α-programmed death ligand 1 (α-PD-L1) antibody treatment via relieving T-cell exhaustion induced by checkpoint blockade. Tumor cell–specific depletion of SHP2 similarly reduced pulmonary metastasis and also relieved exhaustion markers on CD8+ and CD4+ cells. Both systemic SHP2 inhibition and tumor cell–autonomous SHP2 depletion reduced tumor-infiltrated CD4+ T cells and M2-polarized tumor-associated macrophages. Analysis of TCGA datasets revealed that phosphorylation of SHP2 is important for immune-cell infiltration, T-cell activation and antigen presentation. To investigate this mechanistically, we conducted in vitro T-cell killing assays, which demonstrated that pretreatment of tumor cells with FGF2 and PDGF reduced the cytotoxicity of CD8+ T cells in a SHP2-dependent manner. Both growth factor receptor signaling and three-dimensional culture conditions transcriptionally induced PD-L1 via SHP2. Finally, SHP2 inhibition reduced MAPK signaling and enhanced STAT1 signaling, preventing growth factor–mediated suppression of MHC class I. Overall, our findings support the conclusion that tumor cell–autonomous SHP2 is a key signaling node utilized by MBC cells to engage immune-suppressive mechanisms in response to diverse signaling inputs from TME.
### Significance:
Findings present inhibition of SHP2 as a therapeutic option to limit breast cancer metastasis by promoting antitumor immunity.
## Introduction
Metastatic breast cancer (MBC) is the most advanced stage of breast cancer (stage IV) with lower 5-year survival rates and higher treatment costs than localized disease [1, 2]. Cases of MBC are also estimated to increase $54.8\%$ by the end of this decade compared with 2015 [3]. Hence, developing novel therapeutic strategies to treat MBC is of immediate clinical importance. Immune checkpoint blockade (ICB) is an important therapeutic in MBC with more than 200 active clinical trials focusing primarily on blockade of programmed cell death protein 1/programmed death-ligand 1 (PD-1/PD-L1) axis [4, 5]. However, response to ICB can be difficult, which has led to the recent approval withdrawal of atezolizumab for the treatment of MBC [6, 7]. Pembrolizumab has been recently approved in combination with chemotherapy for treatment of early-stage triple-negative breast cancer, but response of this treatment in the metastatic setting is difficult to predict [8, 9].
The tumor microenvironment (TME) is composed of numerous immune cell populations such as CD8+ cytotoxic T cells and M2-polarized tumor-associated macrophages [10]. These cells have diverse functions that can be modulated in response to different signaling inputs [11]. This immune diversity can limit the therapeutic potential of ICB and other targeted therapies in the metastatic setting. Thus, validation of multifunctional therapeutic targets that have the potential to influence the heterogeneous cell populations in the metastatic TME may hold the key to successful application of ICB.
SH2 containing protein tyrosine phosphatase-2 (SHP2) is a promising candidate for a multifunctional therapeutic target as it is a druggable oncogenic phosphatase expressed in both tumor cells and immune cells (12–14). In tumor cells, multiple studies, from our lab and others, have revealed that SHP2 is a key shared node regulating multiple growth factor and survival pathways (15–20). In T cells, SHP2 interacts with immune checkpoints, including PD-1, and inhibits CD28 signaling to induce suppression of T cells (21–25). In addition to lymphocytes, myeloid-specific deletion of SHP2 also suppresses tumor growth in vivo [26]. Before achieving an active state, a structural alternation is required for SHP2 to release its PTP catalytic domain from auto-inhibitory interaction with its N-SH2 domain [10, 27, 28]. Hence, SHP2 can be pharmacologically inhibited by allosteric binders, including SHP099 and TNO155, which stabilize SHP2 in its inactive form [29, 30]. Systemic administration of these SHP2 inhibitors showed promising antitumor effects, and some active clinical trials with SHP2 inhibitors have recently emerged (31–34).
Herein we sought to address the hypothesis that tumor cell–autonomous SHP2 contributes to an immune suppressive TME through its regulation of receptor tyrosine kinase (RTK) and extracellular matrix (ECM) signaling. Using a doxycycline-inducible approach, we demonstrate that MBC-cell specific depletion of SHP2 reduces pulmonary metastasis. Mechanistically, inhibition of SHP2 in MBC cells biases upstream signaling toward STAT1 signaling, leading to enhanced expression of MHC class I. Overall, our studies further expand the notion of SHP2 inhibition as a promising strategy to combine with ICB to treat MBC.
## Cell Lines and Cell Culture
The growth conditions of the cell lines in this study are described in Supplementary Table S1. The 4TO7 and D2.A1 cells were obtained from Fred Miller lab at Wayne State University (Detroit, MI). The construction of bioluminescent 4T1 and D2.A1 cells was previously described [35, 36]. The other cell lines were purchased from ATCC. All cell lines were authenticated via the IDEXX IMPACT III CellCheck. All cell lines are regularly tested for Mycoplasma contamination by PCR.
## Animal Care, Dosing, and Depletion Experiments
All in vivo studies were performed in 4-to 6-week old, female BALB/cJ mice purchased from Jackson Laboratories. For the combination study in D2.A1 model, 1 × 106 cells were injected via the lateral tail vein. The SHP099 was administered via oral gavage, and the α-PD-L1 antibodies were administered via intraperitoneal injection at the indicated concentrations and frequencies. The mice were sacrificed at the end of study, and the tumor-bearing lungs were fixed by $10\%$ formaldehyde overnight. Paraffin tissue sectioning and hematoxylin and eosin (H&E) staining were executed by AML Laboratories, Inc. In the 4T1 spontaneous metastasis model, the 4T1 cells bearing doxycycline-inducible depletion of SHP2 were constructed, sorted and verified as previously described [19]. Then, 5 × 104 cells were engrafted onto the mammary fat pads via an intraductal injection. Doxycycline was administrated in drinking water at 2 mg/mL and refreshed every fourth day following the surgical removal of primary tumors. Reagent manufactures and gavage formulations are listed in Supplementary Table S2. Metastasis in both models was monitored using bioluminescent imaging after intraperitoneal injection of luciferin (GoldBio) using an AMI HT (Spectral Instruments). All in vivo studies were performed under IACUC approval from Purdue University (West Lafayette, IN). No randomization or blinding was done.
## Pulmonary Tumor, Spleen Isolation/Digestion, and Flow Cytometry
Tumor bearing lungs were harvested, imaged, weighed, and dissociated with Mouse Tumor Dissociation Kit (Miltenyi Biotec) and GentleMACS Dissociator (Miltenyi Biotec) immediately after sacrificing the mice. The spleens were harvested, weighted, and mechanically disrupted by grinding. The cell suspension was filtered through 70-μm sterile cell strainers and treated with ACK buffer to lyse red blood cells. The single-cell suspension was incubated with TruStain FcX (BioLegend) at 1:50 and Zombie violet (BioLegend) at 1:100. The single-cell suspension from pulmonary tumors was separated into two tubes and subsequently stained with panels of lymphoid antibodies and panels of myeloid antibodies at 1:200 per antibody, respectively. The single-cell suspension from the spleens was subsequently stained with panels of lymphoid antibodies only. Considering the influence of GFP induction with doxycycline induction, the antibody panels were different for the two models. The antibodies for the D2.A1 model and 4T1 model were listed in Supplementary Table S3. The stained cells were fixed with $10\%$ formaldehyde. Within 1 week of staining, flow cytometry was performed using the Fortessa LSR flow cytometry cell analyzer (BD Biosciences). The results were analyzed in a closed-label manner with FlowJo (10.0.7) software.
## Clinical Dataset Analysis and Code Availability
Reverse-phase protein array (RPPA) dataset, mRNA dataset, and clinical outcomes dataset of patients with breast cancer in TCGA were achieved from Firebrowse (http://firebrowse.org/) hosted by Broad Institute by selecting the cohort as “Breast Invasive Carcinoma (BRCA)” on the left panel, and clicking “Reverse Phase Protein Array”, “mRNA” and “Clinical” bars on the right panel. The primary files “RPPA_AnnotateWithGene (MD5)” for RPPA data, “mRNA_Preprocess_Median (MD5)” for mRNA data and “Merge_Clinical (MD5)” for clinical outcomes were downloaded as txt file, and stored locally as raw files named “RPPA_raw.csv”, “mRNA_raw.csv” and “Clinical_raw.csv”.
Immune scores and stromal scores were achieved from an online tool provided by MD Andersen Cancer Center (https://bioinformatics.mdanderson.org/estimate/disease.html) by selecting the “Disease Type” as “Breast Cancer” and the “Platform Type” as “RNA-Seq-v2”. The immune scores and stroma scores here were calculated by ESTIMATE (Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data) at backend of the tool [37]. The file was downloaded as txt file, and stored locally as a raw file named “immune score_raw.csv”.
These four locally stored files were the inputs of the downstream analyses described in the Supplementary Data. The scores of CD4+ T cells, regulatory T cells (Tregs), M1 macrophages and M2 macrophages were estimated with R package Immundeconv [38]. The CD8 T-cell–specific gene expression was estimated with the Impute Cell Expression function of CIBERSORTx running in group model, in which the cell-specific gene expression was impute with a built-in signature matrix file LM22 and merged into 10 major cell subsets including CD8 T cell [39, 40]. *The* gene set enrichment analysis (GSEA) and result visualization were performed with GSEA 4.1.0 developed by UC San Diego and Broad Institute [41, 42]. The other analyses and result visualizations were performed with the original codes executed with Python 3.8.5 on Anaconda 3 and R 4.0.2 on R studio. The original codes are available on GitHub (https://github.com/benchlover/SHP2_immunology).
## Incucyte-Based T-Cell Killing Assays
To induce antitumor immunity, 1 × 105 4T07 cells were engrafted into the mammary fat pads of BALB/cJ mice via an intraductal injection [43]. The enlarged spleens of tumor-bearing mice were harvested 3 weeks postinjection, and mechanically disrupted by grinding. CD8+ cells were isolated from the splenocytes using EasySep Mouse CD8+ T Cell Isolation Kit (STEMCELL Technologies Inc.) following the manufacturer's instructions. The D2.A1 cells were pretreated with growth factors and inhibitors listed in Supplementary Table S4 for 24 hours. The growth factors and inhibitors were washed-off before adding CD8+ cells. The ratio of tumor cells to T cells was 1:10. The coculture system was stained with Incucyte Cytotox Dye for Counting Dead Cells (Essen BioScience), and monitored using the Incucyte S3 (Essen BioScience).
## Flow Cytometry for MBC Cells In Vitro
The tumor cells were treated with growth factors and inhibitors listed in Supplementary Table S5 for 24 hours. The cells were harvested and stained with antibodies at 1:200 per antibody listed in Supplementary Table S6 for 45 minutes at 4°C in the dark. The stained cells were washed with PBS once and fixed by $10\%$ formaldehyde. Flow cytometry was performed using Guava EasyCyte System (Millipore). The results were analyzed with FlowJo (7.6.1) software.
## Immunoblotting
Immunoblotting was performed as previously described [19]. Briefly, the treated cells were harvested and lysed with modified RIPA lysis buffer [43]. The concentration of lysates was determined by Pierce BCA Protein Assay Kit (Thermo Scientific). After SDS-PAGE and transfer, the polyvinylidene difluoride membranes (Millipore) were incubated with primary and secondary antibodies listed in Supplementary Table S7. Results were collected using the ChemiDoc Gel Imaging System (Bio-Rad) and LI-COR imaging (LI-COR Biosciences).
## RNA Isolation and Quantitative Real-time PCR Analysis
The process was performed as previously described [44]. Briefly, total RNA from treated tumor cells was isolated with the EZNA total RNA kit (Omega BioTek). Then, the cDNA was synthesized with the Verso cDNA Synthesis Kit (Thermo Scientific) following the manufacturer's instructions. Quantitative real-time PCR systems were prepared with SYBR Green Master Mix (Thermo Scientific) and amplified with CFX Connect real-time PCR detection system (Bio-Rad). The primer set (forward 5′-CTCGCCTGCAGATAGTTCCC-3′, reverse 5′-GGGAATCTGCACTCCATCGT-3′) was used to detect mouse PD-L1. The primer set (forward 5′-CAACTTTGGCATTGTGGAAGGGCTC-3′, reverse 5′-GCAGGGATGATGTTCTGGGCAGC-3′) was used to detect mouse GAPDH. The results were normalized to GAPDH.
## Statistical Analysis
A Student t test was used for comparing differences between two groups of measurements in analyses of immune composition and in vitro assays. A Mann–Whitney U test was used for comparing differences between two patient groups with differential CD4+ T-cell, Tregs, M1 macrophages, and M2 macrophages’ infiltration; while a Student t test was used for the comparison in analyses of other clinical datasets. Group measurements of in vivo assays were compared with a Mann–Whitney nonparametric test. Error bars show the SEM. No exclusion criteria were used in these studies. All statistical tests were appropriate in which the groups are assumed with similar variance.
## Data Availability
The data analyzed in this study were obtained from Firebrowse at http://firebrowse.org/ and ESTIMATE online tool at https://bioinformatics.mdanderson.org/estimate/disease.html.
## Pharmacologic Inhibition of SHP2 Inhibits MBC
We previously reported that depletion of fibroblast growth factor receptor 1 (FGFR1) increases infiltration of CD8+ lymphocytes into pulmonary tumors [43]. SHP2 is a key node in FGFR1 and other RTK signaling. Therefore, we hypothesized that systemic SHP2 inhibition may also reprogram the TME of pulmonary metastases. To address this hypothesis, D2.A1 cells, a murine model of FGFR1-amplified MBC, were inoculated into mice via the lateral tail vein. Eight days after the tail vein injection, pulmonary tumor–bearing mice were treated with SHP099 and/or α-PD-L1 antibodies (Fig. 1A). As determined by bioluminescent imaging and wet pulmonary weights, the 12-day treatment course of α-PD-L1 antibody did not significantly inhibit the pulmonary tumor growth of D2.A1 cells. In contrast, the growth of D2.A1 tumors in the lungs was significantly reduced by SHP099 alone and when combined with α-PD-L1 antibodies (Fig. 1B–E; Supplementary Fig. S1A and S1B). We did not observe significant weight loss of the mice or a significant change in spleen weight with any of the therapies (Supplementary Fig. S1C and S1D). These data suggest that inhibition of SHP2 can effectively inhibit the pulmonary growth of a syngeneic MBC model that is resistant to ICB.
**FIGURE 1:** *Pharmacologic inhibition of SHP2 inhibits MBC. A, Schematic of the study combining SHP099 with PD-L1 antibodies to treat mice bearing D2.A1 pulmonary tumors. Elements in the scheme were created using BioRender. B, Representative bioluminescent images of pulmonary D2.A1 growth at day 8 and day 20 postinjection. C, Bioluminescent values from pulmonary regions of interest (ROI) quantified as the ratio of day 20 to day 8 postinjection (**, P < 0.01, n = 5 mice per group). D, Plots comparing the wet lung weights of the mice at day 22 postinjection (**, P < 0.01, n = 5 mice per group). E, Representative H&E staining of lung histologic sections at day 22 postinjection.*
## Pharmacologic Inhibition of SHP2 Relieves T-Cell Exhaustion and Reprograms the Tumor–Immune Microenvironment
To identify how the TME and peripheral immune composition are affected by systemic SHP2 inhibition, we collected the pulmonary tumors and spleens of pulmonary tumor–bearing mice after 14 days of treatment. As shown in Fig. 1E, pulmonary metastasis was reduced with SHP099 treatment to such an extent that precluded accurate IHC analyses. Therefore, tissues were dissociated to single cells and analyzed by flow cytometry with desired gating strategies (Supplementary Fig. S2). Flow cytometry revealed that the percentage of CD4+ cells within the CD45+ splenic population significantly decreased upon combination of SHP099 and α-PD-L1 (Fig. 2A, left; Supplementary Fig. S3A). In contrast, the percentage of CD8+ cells increased (Supplementary Fig. S3B). Similar results in the percentage of CD4+ population were observed in the tumor-infiltrating lymphocytes from pulmonary tumors, but there was no difference in the percentage of CD8+ population (Fig. 2A, right; Supplementary Fig. S3C and S3D). To further investigate the status of these T cells, we focused on the exhaustion markers, lymphocyte activating protein 3 (LAG3) and T-cell immunoglobulin domain and mucin domain 3 (TIM3). The percentage of TIM3+LAG3+ in CD4+ T cells was increased by α-PD-L1, and this exhaustion was significantly abolished in the spleen and pulmonary tumor when SHP099 was added in the combination (Fig. 2B; Supplementary Fig. S4A). In spleens, the percentage of TIM3+LAG3+ in CD4+ T cells was also significantly reduced with combination of SHP099 and α-PD-L1 antibody compared with the control group (Fig. 2B, left; Supplementary Fig. S4A, left). Similar results were also observed in the percentage of TIM3+ in CD4+ T cells in spleens and pulmonary tumors (Supplementary Fig. S4B and S4C). Similarly, the percentage of exhausted CD8+ T cells defined as TIM3+LAG3+ in pulmonary tumors was significantly increased by α-PD-L1 antibody, which was significantly abolished by the addition of SHP099 (Fig. 2C; Supplementary Fig. S4D and S4E). The results were confirmed with the percentage of LAG3+, TIM3+, and PD-1+ cells in CD8+ T cells from pulmonary tumors (Supplementary Fig. S4F and S4G). Taken together, these data suggest that SHP099 not only adjusts T-cell composition but also relieves the T-cell exhaustion induced by ICB.
**FIGURE 2:** *Pharmacologic inhibition of SHP2 relieves T-cell exhaustion and reprograms the tumor–immune microenvironment. A, Quantification of CD4+ population as a frequency of CD45+ cells in isolated spleens (left) and lung tissues (right) of each group. B, Quantification of TIM3+LAG3+ population as a frequency of CD45+CD4+ cells in isolated spleens (left) and lung tissues (right) of each group. C, Quantification of TIM3+LAG3+ population as a frequency of CD45+CD8+ cells (left) and CD45+CD8+PD-1+ cells (right) of in isolated lung tissues of each group. D, Quantification of F4/80+ population as a frequency of CD45+CD11b+ cells in isolated lung tissues of each group. E, Plots comparing the ratio of CD86+ and CD206+ in F4/80+ population as M1/M2 in isolated lung tissues of each group. F, Quantification of PD-L1+ population as a frequency of CD45− cells in isolated lung tissues of each group. In all panels. *, P < 0.05; **, P < 0.01; ***, P < 0.001; n = 3.*
Next, we focused on characterization of myeloid cells from the pulmonary tumors to decipher the impact SHP099 has on tumor associated macrophages (TAMs) [10]. The percentage of CD11b+ monocytes was induced by α-PD-L1, which was significantly reduced by addition of SHP099 (Supplementary Fig. S5A). The percentage of TAMs (F$\frac{4}{80}$+ in CD11b+ monocytes) was significantly reduced with SHP099 and the combination therapy as compared with the control (Fig. 2D; Supplementary Fig. S5B). The percentage of M1-polarized macrophages (CD86+ in F$\frac{4}{80}$+CD11b+CD45+ cells) was significantly reduced by α-PD-L1 antibody, which was rescued by SHP099; while the percentage of M2-polarized macrophages (CD206+ in F$\frac{4}{80}$+CD11b+CD45+ cells) was significantly reduced by SHP099 and combination therapy (Supplementary Fig. S5C and S5D). Hence, the ratio of M1/M2 macrophages increased with SHP099 and combination therapy (Fig. 2E). These data demonstrated that systemic SHP2 inhibition reduced total TAMs and shifted the remaining population toward the tumor-suppressive M1 phenotype. To confirm that the α-PD-L1 antibody was on-target, we focused on the PD-L1 levels in the tumor cells. The percentage of PD-L1+ cells in CD45− population was significantly reduced by all the treatments, and the reduction was enhanced with combination therapy (Fig. 2F; Supplementary Fig. S5E).
## Depletion of Tumor Cell–Autonomous SHP2 Reduces Pulmonary Metastasis, Alters Immune Profiles, and Prevents T-Cell Exhaustion
We next sought to evaluate the specific contribution of tumor cell–autonomous SHP2 to MBC pulmonary metastasis and immune composition. To this end, we utilized doxycycline-inducible depletion of SHP2 in the 4T1 orthotopic model of MBC [19]. This model of spontaneous metastasis nicely recapitulates MBC disease progression as primary tumors are grown, removed, and tracked for metastasis using bioluminescence [45]. Using doxycycline inducible depletion, we were able to specifically deplete SHP2 in disseminated tumor cells only after removal of the primary tumor (Fig. 3A; ref. 46). As expected, we did not observe changes in primary tumor growth (Fig. 3B). In contrast, the 14-day administration of doxycycline to induce SHP2 depletion significantly reduced pulmonary metastases as determined by bioluminescent imaging (Fig. 3C and D; Supplementary Fig. S6A). No significant weight loss was observed with doxycycline (Supplementary Fig. S6B). The efficiency of doxycycline administration to induce shRNA expression was verified by measuring GFP signal from the lungs as eGFP and shRNA are under the control of the same tet-responsive element. The GFP signal observed was consistent with the differential efficiency of the shRNA constructs targeting PTPN11 (Supplementary Fig. S6C). The reduction of pulmonary metastases was confirmed by decreases in pulmonary wet weights and ex vivo bioluminescent imaging of the lungs upon necropsy (Supplementary Fig. S6D and S6E).
**FIGURE 3:** *Depletion of tumor cell–autonomous SHP2 reduces pulmonary metastasis, alters immune profiles, and prevents T-cell exhaustion. A, Schematic of the study using doxycycline-inducible depletion of SHP2 in 4T1 cells. Elements in the scheme were created using BioRender. BALB/c mice (n = 7 mice per group for shScramble and shPTPN11 146, n = 6 mice per group for shPTPN11 369) were orthotopically engrafted with 4T1 cells (5 × 104) via intraductal injection. Primary tumors were surgically removed 2 weeks following the injection. Doxycycline was administrated in drinking water at 2 mg/mL 3 days following the removal of primary tumors. B, Plots comparing the primary tumor volume at day 14 postinjection. NS, no significance. C, Representative bioluminescent images of 4T1 pulmonary metastasis at day 17 and day 31 postinjection. D, Bioluminescent values from pulmonary ROI quantified as the ratio of day 31 to day 17 postinjection (*, P < 0.05; **, P < 0.01).*
To elucidate changes in the TME following tumor cell–specific depletion of SHP2, the pulmonary tumors and spleens of the mice were collected after 17 days of doxycycline administration. Cells were analyzed by flow cytometry using the desired gating strategies (Supplementary Fig. S7). Similar to our approach in Fig. 2, we characterized T-cell composition, T-cell exhaustion, and TAM composition. Upon SHP2 depletion, the percentage of CD4+ cells within the CD45+ population of the spleen significantly decreased and the percentage of CD8+ cells significantly increased, which was observed in pulmonary tumors as well (Supplementary Fig. S8). Hence, the ratio of CD4+/CD8+ T cells decreased significantly with depletion of SHP2 (Fig. 4A). The percentage of exhausted CD4+ T cells, described as TIM3+LAG3+, was reduced in spleens and pulmonary tumors by depletion of tumor cell–autonomous SHP2 (Fig. 4B and C; Supplementary Fig. S9A and S9B). The results were confirmed with the percentage of LAG3+ and TIM3+ in CD4+ T cells from spleens (Supplementary Fig. S9C and S9D). In addition, the percentage of TIM3+ in CD8+ T cells was significantly reduced by SHP2 depletion in spleen and pulmonary tumor (Fig. 4D; Supplementary Fig. S10A). The percentage of exhausted CD8+ T cells described as TIM3+LAG3+ was also reduced with SHP2 depletion (Fig. 4E and F; Supplementary Fig. S10B and S10D). The reduction of exhausted CD8+ T cells described as LAG3+ was also observed in spleen (Supplementary Fig. S10E). We next focused on TAM composition. There was reduction of CD11b+ monocytes, but no change in the percentage of F$\frac{4}{80}$+ TAMs with tumor cell–autonomous SHP2 depletion (Fig. 4G; Supplementary Fig. S11A and S11B). The percentage of M1-polarized macrophages increased, and the percentage of M2-polarized macrophages decreased, which led to significant elevation of M1/M2 ratio upon SHP2 depletion (Fig. 4H; Supplementary Fig. S11C and S11D).
**FIGURE 4:** *Depletion of tumor cell–autonomous SHP2 relieves immune suppression. A, Plots comparing the ratio of the frequency of CD4+ and CD8+ as CD4/CD8 in isolated spleen (left) and lung tissues (right) of each group. B, Quantification of TIM3+LAG3+ population as a frequency of CD45+CD4+ cells in isolated spleens of each group. C, Quantification of TIM3+LAG3+ population as a frequency of CD45+CD4+PD-1+ cells in isolated lung tissues of each group. D, Quantification of TIM3+ population as a frequency of CD45+CD8+ cells in isolated spleens (left) and lung tissues (right) of each group. E, Quantification of TIM3+LAG3+ population as a frequency of CD45+CD8+ cells in isolated spleens of each group. F, Quantification of TIM3+LAG3+ population as a frequency of CD45+CD8+PD-1+ cells in isolated lung tissues of each group. G, Quantification of F4/80+ population as a frequency of CD45+CD11b+ cells in isolated lung tissues of each group. H, Plots comparing the ratio of CD86+ and CD206+ in F4/80+ population as M1/M2 in isolated lung tissues of each group. In all panels. NS, no significance. *, P < 0.05; **, P < 0.01; ***, P < 0.001; n = 4 for shScramble and shPTPN11 146, n = 5 for shPTPN11 369 in pulmonary tumor panels, n = 4 for each group in spleen panels.*
Taken together, these data suggest that, similar to systemic inhibition of SHP2, targeted depletion of the SHP2 in pulmonary metastases leads to alterations of both the peripheral and tumor-infiltrating immune components.
## Phosphorylation of SHP2 Predicts Immune Profiles in Patients with MBC
To find clinical evidence to correlate SHP2 with immune profiles in MBC patients, we analyzed the BRCA cohort of TCGA datasets. We have previously demonstrated that phosphorylation of SHP2 at Y542 is associated with decreased patient survival in this cohort [19]. Here we found that patients with higher phosphorylation of SHP2 at Y542 had significant lower immune scores, indicating reduced immune cell infiltration in tumors (Fig. 5A). In contrast to Y542 phosphorylation, differential expression of total levels of SHP2 was not predictive of immune scores, but did correlate with a reduced stromal score in these patients (Fig. 5B). Consistent with our animal data, patients with higher phosphorylation levels of SHP2 had higher CD4+ T-cell and lower M1 Macrophages infiltration predicted by Immundeconv (Fig. 5C and D). M2 macrophage infiltration also increased in patients with higher phosphorylation levels of SHP2, while no difference was observed in Treg infiltration (Supplementary Fig. S12A and S12B). Next, we examined differential expression of specific immune-related markers that correlated with differential phosphorylation of SHP2. We found that the key genes in T-cell composition, T-cell activation and antigen presentation, including PRF1, CD8B, GZMB, LCK, IFNG, and HLA-DOB, were significantly associated with the phosphorylation of SHP2 at Y542, but not total expression levels of SHP2 (Fig. 5E and F). As immune-related markers might not be exclusively expressed in T cells, we imputed the CD8 T cell–specific gene expression and further confirmed elevated levels of T-cell exhaustion markers, TIM3 (HAVCR2) and PD-1 (CD247), in patients with higher phosphorylation levels of SHP2 (Supplementary Fig. S12C). Using single-sample GSEA (ssGSEA), we examined enriched KEGG and GO pathways in patients with differential levels of SHP2 phosphorylation. Using this approach, we found that pathways of activated T-cell proliferation and antigen processing & presentation were significantly enriched in patients with lower phosphorylation of SHP2 (Fig. 5G). The enrichment of these two pathways was confirmed with GSEA (Fig. 5H and I). These results further strengthen the notion that SHP2 activation via phosphorylation at Y542 contributes the weaker immune profiles in patients with MBC.
**FIGURE 5:** *Phosphorylation of SHP2 predicts immune profiles in patients with MBC. A, B, Violin and box plots comparing the differential immune scores and stroma scores in patients grouped by phosphorylation levels of SHP2 at Y542 (A) or expression levels of SHP2 (B). C, D, Violin and box plots comparing the differential phosphorylation levels of SHP2 at Y542 and expression levels of SHP2 in patients grouped by CD4+ T-cell infiltration (C) and M1 Macrophage infiltration (D) levels. Heatmaps comparing the differential gene expression in patients grouped by phosphorylation levels of SHP2 at Y542 (E) or expression levels of SHP2 (F). *, P < 0.05; **, P < 0.01; ***, P < 0.001. G, Volcano plots demonstrating pathways of differential ssGSEA scores in patients grouped by phosphorylation levels of SHP2 at Y542 with statistical significance. Specific pathways of interest are annotated. GSEA plots, Enrichment scores and P values of the key pathways from GO (H) and KEGG (I) enriched in patients with lower phosphorylation levels of SHP2 at Y542.*
## SHP2 Facilitates Growth Factor–Induced Resistance to T-Cell Cytotoxicity via Regulation of PD-L1
To investigate how SHP2 signaling in tumor cells contributes to T-cell exhaustion, we utilized a T-cell cytotoxicity assay. In this assay, CD8+ T cells were isolated from the spleens of tumor-bearing mice and cocultured with MBC cells (Fig. 6A). After coculture with T cells, we could readily observe tumor cell cytotoxicity, a result that could be enhanced by addition of SHP099 and TNO155 treatments (Supplementary Fig. S13A and S13B). Given that RTK signaling is one of the signaling inputs that is dependent on SHP2, we treated the D2.A1 cells with FGF2 or PDGF before coculturing with T cells [19]. Pretreatment with these growth factors significantly reduced T cell–mediated cytotoxicity (Fig. 6B and C). To verify the role of tumor cell–autonomous SHP2 in this immune protection, D2.A1 cells were treated with these two growth factors and TNO155. TNO155 rescued T-cell cytotoxicity in both cases (Fig. 6D and E). The ability of SHP2 inhibition in tumor cells to prevent the ability of growth factors to protect tumor cells from T cell–mediated killing was confirmed by doxycycline-inducible depletion of SHP2 (Supplementary Fig. S13C and S13D). Moreover, similar results were achieved with an α-PD-L1 antibody (Supplementary Fig. S13E and S13F). As shown in Fig. 2F, PD-L1 in tumor cells was also significantly reduced with systemic SHP2 inhibition in vivo. We further hypothesized that the SHP2 might regulate PD-L1 expression levels downstream of RTK signaling. Flow cytometry revealed that FGF2 and PDGF significantly induced PD-L1 levels in D2.A1 cells (Supplementary Fig. S14A and S14B). Quantitative PCR demonstrated that the induction of PD-L1 by growth factors was regulated transcriptionally (Supplementary Fig. S14C). Treatments of 11a-1, SHP099, TNO155, PP2 (Src inhibitor), and trametinib (MEK inhibitor) abolished the induction of PD-L1 by PDGF (Fig. 6F and G; Supplementary Fig. S15A and S15B). In BT549 cells, PD-L1 could be significantly induced by FGF2 and EGF (Supplementary Fig. S15C and S15D). The ability of SHP2 inhibition to reduce growth factor–induced PD-L1 was also confirmed in BT549 cells with treatments of TNO155 (Supplementary Fig. S15E and S15F). In addition to RTK signaling, ECM signaling is another signaling input of SHP2 and phosphorylation of SHP2 at Y542 is elevated in MBC cells under 3D culture environment with fibronectin-coated tessellated scaffolds [19, 47]. Flow cytometry demonstrated that PD-L1 in D2.A1 cells was significantly elevated when cultured on fibronectin-coated scaffolds compared with tissue culture polystyrene (2D culture; Supplementary Fig. S15G). Similar results were observed with laminin-coated scaffolds and other MBC cell lines (Supplementary Fig. S15G). The elevation of PD-L1 with fibronectin-coated scaffolds could be significantly abolished by TNO155 and PF271 (FAK inhibitor) transcriptionally, but not trametinib (Fig. 6H and I; Supplementary Fig. S15H). The involvement of SHP2 in the regulation of PD-L1 was confirmed with MBC cells with doxycycline-inducible depletion of SHP2 (Supplementary Fig. S15I). These findings indicate that upregulation of PD-L1 expression by both growth factor–mediated RTKs signaling and 3D culture environment with ECM signaling in MBC cells is mediated through SHP2.
**FIGURE 6:** *SHP2 facilitates growth factor–induced resistance to T-cell cytotoxicity via regulation of PD-L1. A, Schematic of T-cell cytotoxicity assays. Elements in the scheme were created using BioRender. CD8+ T cells are isolated from the spleens of tumor-bearing mice. The MBC cells are treated with growth factors and inhibitors, and cocultured with CD8+ T cells. The dead cells are quantified by with Incucyte imaging. B, Representative images of the MBC cells treated with FGF2 (20 ng/mL) and PDGF (100 ng/mL) at 1 hour following coculturing with T cells. The MBC cells without T cells served as background. C, Bar graph comparing the percentage of dead cell counts of FGF2 and PDGF groups to the no stimulation (NS) group. *, P < 0.05; n = 4 individual repeats. D, Representative images of the MBC cells treated with FGF2/PDGF and TNO155 (5 μmol/L) at 1 hour following coculture with T cells. E, Bar graph comparing the percentage of dead cell counts with different treatments. *, P < 0.05; ***, P < 0.001, n = 9. Histogram of cell surface PD-L1 using flow cytometry (F) and bar graph (G) comparing fold changes of PD-L1 MFIs in D2.A1 cells induced by PDGF and treated with different inhibitors. *, P < 0.05; **, P < 0.01; ***, P < 0.001; n = 3. H, Histogram of cell surface PD-L1 using flow cytometry in D2.A1 cells induced by 3D culture on a fibronectin-coated scaffold and treated with the indicated inhibitors. I, Bar graph comparing fold change of PD-L1 mRNA of D2.A1 cells cultured and treated as in H. **, P < 0.01; n = 3.*
## SHP2 Regulates the Expression of MHC Class via a Balance Between MAPK and STAT1 Signaling in MBC Cells
Besides the activated T-cell proliferation, we found that antigen processing and presentation was significantly more enriched in patients with lower phosphorylation of SHP2 (Fig. 5G–I). Moreover, we found IFNG was significantly reduced in the patients with higher levels of SHP2 phosphorylation (Fig. 5E). Hence, we focused on the ability of SHP2 to regulate expression of MHC class I, which is critical for antigen presentation and mediated by IFN-γ [48, 49]. Flow cytometry demonstrated that FGF2 and PDGF significantly limited that ability of IFNγ to induce expression of MHC class I (Fig 7A and B). Importantly, this effect was prevented upon treatment with TNO155 (Fig. 7A and B). Similar results were observed in BT549 cells with FGF2, EGF, and TNO155 (Supplementary Fig. S16A and S16B). Immunoblotting showed that TNO155 prevented phosphorylation of ERK$\frac{1}{2}$ induced by FGF2 and PDGF, and augmented the ability of IFNγ to induce STAT1 phosphorylation (Fig. 7C). Similarly, trametinib, but not alpelisib (PI3K inhibitor), also rescued IFNγ induced MHC class I in the presence of FGF2 and PDGF (Fig. 7D). Although PD-L1 is also regulated by IFNγ, the levels of PD-L1 were not significantly influenced by TNO155 or alpelisib, and even significantly induced by trametinib, under growth factors plus IFNγ (Supplementary Fig. S16C). The ability of SHP2 inhibition to rescue MHC class I expression under growth factor–stimulated conditions was further demonstrated by TNO155 enhancing IFNγ-induced T-cell cytotoxicity under PDGF-stimulated conditions (Supplementary Fig. S16D and S16E). These data suggest that SHP2 acts as a key node that regulates the balance between MAPK and STAT1 signaling, the targeting of which is capable of enhancing antigen presentation and increasing antitumor immunity.
**FIGURE 7:** *SHP2 regulates the expression of MHC class I via a balance between MAPK and STAT1 signaling in MBC cells. A, Histogram of cell surface analysis of H-2 in D2.A1 cells treated with different growth factors, mouse IFNγ (200 ng/mL) and TNO155 (5 μmol/L). B, Bar graph comparing fold change of H-2 MFIs induced by different growth factors, IFNγ and TNO155 compared with DMSO+ IFNγ. NS, not significant; **, P < 0.01; ***, P < 0.001; n = 3. C, Immunoblotting showing differential STAT1 and ERK1/2 phosphorylation in D2.A1 cells treated with different growth factors, IFNγ, and TNO155. D, Bar graph comparing fold change of H-2 MFI induced by different growth factors and IFNγ compared with IFNγ alone with different inhibitors in D2.A1 cells. NS, not significant; **, P < 0.01; ***, P < 0.001; n = 3.*
## Discussion
Therapeutic benefits of ICB are limited for metastatic breast cancer, but novel targeted therapies to combine with ICB and enhance efficacy are emerging. Beyond tumor cells, the TME is a dynamic community composed of immune cells with diverse functions in response to a variety of stimuli. In the presence of ICBs, these complicated signaling pathways are engaged both in the tumor cells and immune cells to shift the balance between immunogenicity and immunosuppression in the TME. Recent findings have started to illustrate the potential benefits of combining SHP2 inhibitors with ICB, but the mechanisms by which targeting SHP2 enhances the effects of ICB, especially in a tumor cell autonomous manner, are yet to be fully elucidated [32, 50, 51]. Herein, we demonstrate that tumor cell–autonomous SHP2 facilitates MBC metastasis and resistance to ICB via creating an immunosuppressive TME. Our working model is supported by separate studies illustrating that systemic targeting of SHP2 promotes antitumor immunity via mechanisms that go beyond direct effects on immune cells (52–55).
Our current study did not elucidate a combinatorial effect in terms of tumor growth between SHP099 and α-PD-L1, which might require further dosage optimization and timing, but we did observe the combination group achieved faster regression in pulmonary tumor burden, which could be a benefit from combination therapy [50, 51, 54]. This adjuvant treatment approach allowed us to evaluate the role of SHP2 specifically in the progression of established metastatic tumors, as we initiated SHP2 inhibitor treatments or doxycycline induced depletion of SHP2 only after tail vein injection or primary tumor removal and once metastases were seeded. In addition, several studies indicate that LAG3 and TIM3 are key T-cell exhaustion markers, which contribute to the lack of an antitumor immune response upon ICB therapy (56–62). Consistent with these reports, α-PD-L1 antibody treatment alone did not significantly reduce the pulmonary growth of the syngeneic D2.A1 model of MBC, and resulted in high level expression of TIM3 and LAG3 in T-cells from tumor-bearing mice. Importantly, we demonstrate that T-cell exhaustion was abolished upon treatment with SHP099. The pattern of T-cell exhaustion in the TME upon α-PD-L1 antibody treatments is also supported by previous reports. For instance, both LAG3 and TIM3 were significantly upregulated upon α-PD-L1 antibody treatments in T cells from pulmonary tumors. In contrast, α-PD-L1 induction of these exhaustion markers did not elevate in CD4+ T cells from spleens, demonstrating that reprogramming tumor-infiltrating lymphocytes (TIL) is the key to improved therapeutic outcomes.
Besides T-cell exhaustion, the amount of T-cell infiltration and composition of TAMs within tumors are critical factors for the response to ICB (63–65). We observed that M1-polarized macrophages were significantly reduced by α-PD-L1. Our observation that SHP099 reduced TAMs and increased the M1/M2 ratio is supported by recent studies and suggests modulation of this myeloid compartment as a major contributing factor to the efficacy of SHP2-targeted therapies [32, 54, 66]. Finally, we observed that SHP099 resulted in reduced numbers of CD4+ T cells in pulmonary tumors, but no increase in CD8+ infiltration was observed. In contrast, combination of SHP099 and α-PD-L1 led to a dramatic increase in splenic CD8+ T cells. Overall, these data suggest that increasing cytotoxic T-cell infiltration into metastatic tumors remains a challenge for immune therapy that is not overcome by SHP2 targeting.
As SHP2 is expressed in both tumor cells and immune cells, we sought to investigate how tumor cell–autonomous SHP2 contributes to immune escape by MBC. Indeed, previous reports suggest SHP2 in T cells is dispensable for their function and that the tumor-facilitating role of SHP2 lies in myeloid cells and tumor -associated endothelial cells [25, 26, 67]. Using the doxycycline-inducible system we previously established, we demonstrate that depletion of SHP2 specifically in MBC cells reduces pulmonary metastasis [19, 46, 68]. With depletion of SHP2 in tumor cells, reduction of CD4+ and induction of CD8+ T cells were observed in both pulmonary tumors and spleens, and the T-cell exhaustion markers were also reduced, matching the effects of systemic SHP2 inhibition. The enhancement of T-cell cytotoxicity by SHP2 inhibition in MBC cells was also confirmed with in vitro T-cell cytotoxicity assays. The M1/M2 ratio of TAMs was also modulated in similar fashion as compared with systemic inhibitors, but total TAM populations were not significantly changed. These findings are consistent with the notion that SHP2 function in tumor cells can influence the lymphoid, but not the myeloid components of the metastatic TME [69]. Overall, our doxycycline-inducible depletion of SHP2 in MBC cells, allowed the first investigation into role of tumor cell–autonomous SHP2 specifically within the metastatic TME.
The clinical significance of phosphorylation of SHP2 has been supported by multiple studies, including those herein, where we demonstrate that phosphorylation of SHP2 at Y542 is a promising marker to predict immune profiles in patients with MBC [19, 70, 71]. These findings further solidify the correlation between phosphorylation of SHP2 and immune response in patients with MBC. We did observe changes in CD4 T-cell infiltration upon SHP2 targeting. A relationship between SHP2 phosphorylation and Treg was not supported by clinical datasets. However, further characterization of CD4 phenotypes in tumor-bearing and tumor-naïve mice could yield insight into the impact of SHP2 inhibition on immune exhaustion. Mechanistically, we demonstrated that tumor cell–autonomous SHP2 regulates CD8+ T-cell cytotoxicity downstream of multiple growth factors via regulation of PD-L1 and MHC class I. Our studies also identify the ability of 3D culture environment with ECM signaling to promote PD-L1 via SHP2. Studies to determining the mechanistic details behind this event are ongoing, but our studies herein strongly suggest that the ability of SHP2 to balance STAT1 and MAPK signaling always for its regulation of PD-L1 expression at several levels [72].
In summary, we show that tumor cell–autonomous SHP2 is a key signaling node by which MBC cells induce immune suppression from a variety of signaling inputs within the TME (Fig. 8). We establish phosphorylation of SHP2 at Y542 as a predictive marker of immune profiling in patients with MBC. Our studies also provide further mechanistic insights into clinical approaches pursuing combination strategies using SHP2 inhibition with ICB (NCT04000529).
**FIGURE 8:** *Tumor cell–autonomous SHP2 is a key signaling node in response to dynamic TME to induce immune suppression via regulating PD-L1 and MHC class I. SHP2 contributes to various downstream signaling pathways including PD-L1 and MHC class I to facilitate immune suppression in response to a varieties of additional signaling inputs in TME, such as growth factor receptor signaling. Figure was created using BioRender.*
## Authors’ Disclosures
Z.-Y. Zhang reports grants from National Institutes of Health during the conduct of the study; personal fees from Tyligand Bioscience outside the submitted work. No other disclosures were reported.
## Authors’ Contributions
H. Chen: Conceptualization, resources, data curation, software, formal analysis, investigation, visualization, methodology, writing-original draft, project administration. G.M. Creswell: Conceptualization, resources, data curation, formal analysis, methodology. S. Libring: Conceptualization, resources, data curation, formal analysis, methodology, writing-review and editing. M.G. Ayers: Conceptualization, resources, data curation, formal analysis, methodology, writing-review and editing. J. Miao: Resources, data curation, methodology. Z.-Y. Zhang: Conceptualization, resources, formal analysis, supervision, funding acquisition, validation, project administration, writing-review and editing. L. Solorio: Conceptualization, resources, formal analysis, supervision, funding acquisition, project administration, writing-review and editing. T.L. Ratliff: Conceptualization, resources, formal analysis, supervision, funding acquisition, project administration, writing-review and editing. M.K. Wendt: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, methodology, writing-original draft, project administration, writing-review and editing.
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|
---
title: Correlation Analysis of Neutrophil/Albumin Ratio and Leukocyte Count/Albumin
Ratio with Ischemic Stroke Severity
authors:
- Sanying Mao
- Yuanhong Hu
- Xingwu Zheng
- Chengmin Yang
- Meiling Yang
- Xianghong Li
- Jingwei Shang
- Koji Abe
journal: Cardiology and cardiovascular medicine
year: 2023
pmcid: PMC10035411
doi: 10.26502/fccm.92920305
license: CC BY 4.0
---
# Correlation Analysis of Neutrophil/Albumin Ratio and Leukocyte Count/Albumin Ratio with Ischemic Stroke Severity
## Abstract
Ischemic stroke (IS) is a common neurological disease in the elderly, but the relationship between neutrophil/albumin ratio (NAR) and leukocyte count/albumin ratio (LAR) and the severity of neurological function injury and early neurological deterioration (END) occurrence remain elusive in acute IS. A total of 299 patients with acute IS and 56 healthy controls were enrolled. According to the NIHSS score at admission, the disease group was divided into three groups (mild, moderate and severe IS), and the differences in five indexes NAR, LAR, neutrophil count, leukocyte count and albumin among the four groups were analyzed. Furthermore, explore the correlation between the above indicators and the severity of IS and END occurrence. The results showed that higher NAR, LAR, neutrophil count, leukocyte count levels and lower albumin levels were associated with acute IS, and the levels of NAR and LAR increased gradually in three groups of IS. NAR and LAR were positively and albumin was negatively correlated with the severity of IS. Meanwhile, NAR and LAR showed a good predictive value in identifying patients with END after acute IS. NAR and LAR may be predictors of the severity of IS and END occurrence after acute IS.
## Introduction
Ischemic stroke (IS) is a common neurological disease in the middle-aged and elderly population with high morbidity, mortality, and disability rate [1]. In China, IS has an annual death rate of about 1.6 million and a mortality rate of about $\frac{157}{100}$,000, which is higher than that of cardiovascular disease and has become one of the main causes of death and disability in adults [2]. Rapid assessment of the severity of IS in clinical treatment and individualized treatment may play an important role in preventing the deterioration of the patien's condition, reducing complications, and reducing the incidence of mortality and disability. Atherosclerosis is a risk factor for IS, the early formation of atherosclerosis is associated with the accumulation of leukocytes and proinflammatory cytokines. So, atherosclerosis development is accompanied by an inflammatory reaction, which accelerates thrombosis formation and promotes the occurrence of IS and myocardial infarction [3,4]. Serum leukocyte detection is a common, inexpensive and simple indicator of inflammation in clinical work, including neutrophils, monocytes and other sub-cells. The mechanisms of leukocyte sub-cells in the process of acute IS are different. Among them, neutrophils converge in the ischemic penumbra area and release proteolytic enzymes, which damage the blood-brain barrier and promote the accumulation of inflammatory factors in the ischemic area, aggravating brain tissue damage [5,6]. Albumin plays important role in maintaining colloid osmotic pressure balance and influencing microvascular integrity and inflammatory pathways, including neutrophil adhesion [7]. Some studies have shown that the neurological impairment in IS patients may be related to the decreased protection of albumin on ischemic brain tissue [8], and the serum albumin level has a predictive role in the prognosis of acute IS [9]. The neutrophil/albumin ratio (NAR) is a comprehensive inflammation biomarker, which has been used in many clinical studies. For example, in the association study between NAR and delayed cerebral ischemia (DCI) in the early stage of aneurysmal subarachnoid hemorrhage (aSAH), NAR was found to be positively correlated with the severity of subarachnoid hemorrhage, which can be used as a new predictive biomarker for DCI after aSAH [10]. NAR is a potential prognostic biomarker for mortality in patients with cardiogenic shock (CS), and its predictive value is more sensitive than neutrophil percentage or serum albumin level alone [11]. At present, there is no study on the correlation between NAR and the severity of acute IS and early neurological deterioration (END) occurrence after acute IS. Therefore, we performed a retrospective cohort study to analyze the expressive changes of five accessible and inexpensive indexes NAR, leukocyte count/albumin ratio (LAR), albumin, neutrophil count and leukocyte count in patients with acute IS. To determine the relationship between the five indexes and the severity of neurological function injury and END occurrence within acute IS, to better timely treatment of IS and reduce mortality and improve the prognosis.
## Study Population
This was a retrospective, observational study. 299 consecutive patients with acute IS were enrolled from November 2016 to October 2021 at the Affiliated Hospital of Guilin Medical College. 56 age- and sex-matched individuals with no neurological or psychiatric diseases found in medical examinations were included as normal controls. This study was approved by the Ethics Review Committee of Affiliated Hospital of Guilin Medical College and consent was obtained from all participants prior to enrollment. The inclusion criteria were as follows: [1] hospital admission within 48 hours of first stroke onset; [2] symptoms consistent with the 2014 Chinese Acute Ischemic Stroke Diagnostic Criteria with the responsible lesions identified via DWI [12]; [3] the age of onset is more than 18 years old; [4] National Institutes of Health Stroke Scale (NIHSS) score [12] measured within 24 hours after admission; [5] detection of blood biochemical indicators within 24 hours of admission; [6] the score of WORSEN was calculated according to Miyamoto et al [13], and [7] early neurological deterioration (END) was defined as an increase in two or more NIHSS points, an increment of at least one point in motor power, or description of fluctuating of clinical symptoms in medical reports during the first 7 days after admission [14]. The exclusion criteria were as follows: [1] hemorrhagic stroke, transient ischemic attack (TIA), multiple sclerosis (MS), intracranial infection or other diseases; [2] patients who accepted intravenous thrombolysis and(or) mechanical thrombectomy; [3] previous history of IS, TIA, cerebral hemorrhage, serious infection, major surgery, or more severe trauma; [4] severe heart, liver, kidney, lung, digestive tract and other important organ damage, blood, immune diseases and tumors. These patients take many types of medications, which may have adverse factors affecting the inflammatory response; [5] history of major trauma, surgery, blood transfusion, blood donation or immunization in the past six months; [6] pregnant women, dementia, persons with neurological disabilities, and persons with severe psychological disorders; [7] Treatment with hormone drugs, immunosuppressive drugs, or nonsteroidal anti-inflammatory drugs before or after the onset. The drugs have an impact on the immune response and inflammatory response in the body; [8] pneumonia, bloodstream infection, urinary infection, infectious diarrhea, catheter-related infection, and/or sinusitis; [9] early discharge and/or incomplete clinical data.
## Risk Factor Assessments
The related risk factors considered in the present investigation were summarized in Table 1, hypertension: an arterial systolic blood pressure ≥140 mmHg and/or arterial diastolic blood pressure ≥ 90mmHg without the use of blood pressure-lowering drugs measured twice by continuous monitoring of arterial blood pressure [15]. A previous diagnosis of hypertension and continued use of the relevant drugs to control blood pressure were also used to define hypertension; diabetes: random venous blood glucose > 11.1 mmol/L, fasting blood glucose > 7.0 mmol/L, or OGTT 2-h blood glucose > 11.1 mmol/L and accompanying symptoms of diabetes [16]. A previous diagnosis of diabetes and the current use of blood glucose control drugs were also used to define diabetes; dyslipidemia: [1] total cholesterol (TC) ≥ 6.22 mmo1/L, [2] triglyceride (TG) ≥ 2.26 mmo1/L, or [3] low-density lipoprotein (LDL-C) ≥ 4.14 mmo1/L [17]; smoking: [1] previous or current regular smoking habit and smoking > 10 cigarettes/day and [2] smoking duration > 1 year or smoking cessation < 10 years [18], and drinking: daily consumption of alcohol with an average alcohol intake > 50 g/day [19].
## Laboratory Measurements
The clinical information on the normal controls and the patients is summarized in Table 2. According to the NIHSS score, the observation group was divided into a mild IS group (1≤NIHSS score≤4), moderate IS group (5≤NIHSS score≤15), severe IS group (16≤NIHSS score). Fasting blood samples were collected within 24 hours after admission and were measured at the Affiliated Hospital of Guilin Medical College by using an automated analytical platform (Beckman Coulter AU5800: Beckman Coulter Inc. Brea, CA, USA). HDL-C, LDL-C, TC, TG and albumin were measured using blood samples drawn at approximately 7 a.m. after an overnight fast. White blood cell count, neutrophil count and lymphocyte count in EDTA-anticoagulated whole-blood samples from venipuncture were determined with automated particle counters within the first 24 h after admission. NAR and LAR were calculated as the ratio of neutrophil count to albumin and leukocyte count to albumin.
## Statistical Analysis
Statistical analyses were performed using standard statistical software (SPSS 22.0, IBM Corp., Armonk, NY, USA). The measurement data were represented by (x ± s). Logistic regression analysis was performed to evaluate the related risk factors on IS, adjusting for baseline variables when a $p \leq 0.1$ was found in the univariate analysis. One-way ANOVA and Kruskal-Wallis tests were performed to analyze the differences among four groups followed by the Kolmogorov-Smirnov test. Pearson's correlation analysis was performed to analyze the *Correlation analysis* between the levels of the neutrophil count, albumin, NAR, leukocyte count and LAR and the NIHSS score. We calculated the sensitivity and specificity of different levels of NIHSS and WORSEN scores, NAR and LAR for the prediction of END by using receiver operating characteristic (ROC) curves. Differences with a probability value of $p \leq 0.05$ were considered statistically significant.
## Logistic Analysis of the related Risk Factors of Acute IS
299 acute IS patients and 56 normal controls were included in this study. The results of the logistic analysis showed that gender ($p \leq 0.05$, OR=0.334), smoking ($p \leq 0.01$, OR=0.161), drinking ($p \leq 0.01$, OR=11.653), diabetes mellitus (DM, $p \leq 0.01$, OR=0.099), hypertension ($p \leq 0.01$, OR=0.051), and coronary heart disease (CHD, $p \leq 0.05$, OR=10.360) were all related to the onset of IS (Table 1).
## Laboratory Indicators Analysis
IS patients were evaluated according to NIHSS score, there were 100 cases in the mild IS group (1 point≤NIHSS score≤4 points), 81 cases in the moderate IS group (5 points≤NIHSS score≤15 points), and 23 cases in the severe IS group (16 points≤NIHSS score). The results of laboratory indicators were presented in Table 2. Compared with the normal controls, the levels of the neutrophil count, neutrophil ratio, leukocyte count and lymphocyte count were significantly higher, meanwhile, albumin levels showed significantly lower in the three IS groups, but no significant differences were found among them. The levels of NAR, LAR, NIHSS score and WORSEN score were remarkably higher than in the normal controls, meanwhile, with a significant difference among the three IS groups. About the results of serum lipid examination, only the level of HDL-C was remarkably lower in the three IS groups than in the normal controls.
## Correlation Analysis between the Levels of the NAR, LAR, Albumin, Neutrophil Count and Leukocyte Count and the NIHSS Score
The correlation analysis showed that the five indexes were significantly correlated with the NIHSS score (Figure 1), the levels of neutrophil count ($R = 0.483$, $p \leq 0.0001$), NAR ($R = 0.498$, $p \leq 0.0001$), leukocyte count ($R = 0.413$, $p \leq 0.0001$) and LAR ($R = 0.438$, $p \leq 0.0001$) were positively correlated with the NIHSS score. On the other hand, the level of albumin was negatively correlated with the NIHSS score ($R = 0.291$, $p \leq 0.0001$).
## Predictive Value of WORSEN score, NIHSS score, NAR and LAR for Early Neurological Deterioration (END) using ROC
In Figure 2, the area under the ROC curve (AUC) of the WORSEN score for the prediction of END was 0.95 ($95\%$CI 0.91-0.98). The AUC of the NIHSS score for the prediction of END was 0.85 ($95\%$CI 0.76-0.94). Meanwhile, The AUC of the NAR and LAR for the prediction of END was 0.71 ($95\%$CI 0.58-0.85) and 0.71 ($95\%$CI 0.56-0.85), respectively.
## Discussion
Many clinical studies have focused on how to find effective, easily available and inexpensive blood-based biomarkers for the early prediction of IS severity. In this study, we examined differences in the expression levels of several plasma biomarkers in the mild, moderate and severe IS groups, and found the expressive differences are indicative of the diverse pathological mechanisms underlying IS. Our results showed that gender, smoking, drinking, DM, hypertension, and CHD were related to the onset of IS (Table 1). The results of laboratory indicators showed that the levels of the NAR, LAR, neutrophil count, leukocyte count, NIHSS score and WORSEN score were remarkably higher, and albumin remarkably lower in the three IS groups. Meanwhile, NAR, LAR, NIHSS score and WORSEN score showed significant differences among the three IS groups (Table 2). Moreover, the correlation analysis showed that the five indexes (NAR, LAR, albumin, neutrophil count and leukocyte count) were significantly correlated with the NIHSS score (Figure 1). WORSEN score, NIHSS score, NAR and LAR showed a good predictive value in identifying patients with END after acute IS (Figure 2). After IS, the levels of serum leukocytes and neutrophils increased (Table 2), because within 1-6 hours of the onset of acute IS, a large number of leukocytes, main neutrophils, adhere to the post-capillary venules and capillary walls of ischemic tissue. At the same time, oxidative damage and proteolysis of vascular endothelial cells promote the aggregation of leukocytes and red blood cells around ischemic foci, and aggravate the microcirculation disorder and blood hypercoagulability, and further reduce cerebral blood flow [20,21]. Then, over the next 6 to 24 hours, these neutrophils migrate from the damaged vessel wall to the ischemic cortical area. Infiltrating leukocytes and resident brain cells, including neurons and glial cells, release proinflammatory mediators such as cytokines, chemokines, and oxygen-nitrogen free radicals, exacerbating the evolution of brain tissue damage and leading to increased mortality [22-24]. Some studies have found that leukocyte count can be used as independent predictors of IS [25]. Our results further suggested that the level increases of NAR and LAR were associated with different severity of IS (Table 2, Figure 1). Serum albumin offers neuroprotective effects through antagonizing thrombosis, stagnation and leukocyte adhesion within the postcapillary microcirculation in the early reperfusion phase of stroke [26]. Some studies reported that the level of serum albumin is closely related to the occurrence and development of IS [27, 28]. Our result also showed that the albumin levels were significantly lower in the three IS groups, especially in the severe IS group (Table 2, Figure 1). Which suggested that the serum albumin level changes may be used to assess the severity of acute IS in elderly patients. Moreover, albumin treatment has been found to improve neurological function in rats with focal IS and clinical IS patients [29,30]. In acute IS patients, END not only has a high incidence of 5-$40\%$ [31], but is also associated with poor prognosis [32]. Our results showed that the initial NIHSS score and WORSEN score had good predictive values for END (Figure 2), which were consistent with previous studies [14]. We also found that LAR and NAR had good predictive values for END (Figure 2). Thus, inflammation may be one of the risks of END in patients with acute IS.
## Limitations
This study was subject to several limitations. First, this study had a retrospective case-control design and was performed within a small area of China. Second, relatively few patients and controls were included, especially in the severe IS groups, so the validity of our results remains to be tested. Finally, no equal numbers of males and females were achieved in the study.
## Conclusions
In summary, the present study is the first to analyze the changes of NAR, LAR, albumin, neutrophil count and leukocyte count levels in patients with acute IS, and found that the NAR and LAR levels were correlated with the severity of IS. Attention should be paid to the condition changes of IS hospitalized patients with high NAR, LAR, neutrophil count and leukocyte count levels but low albumin level, hoping that the prognosis can be improved by early active and effective treatment. Effective blood markers and NIHSS score and WORSEN score for IS may occur during the END provides the clinical basis, which helps to improve the treatment of acute IS clinical decision. This study further explored the early assessment of the severity of IS and END occurrence, and provided a clinical reference for large-scale marker screening and combination.
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|
---
title: Genome-wide identification of bHLH transcription factors and their response
to salt stress in Cyclocarya paliurus
authors:
- Zijie Zhang
- Jie Fang
- Lei Zhang
- Huiyin Jin
- Shengzuo Fang
journal: Frontiers in Plant Science
year: 2023
pmcid: PMC10035414
doi: 10.3389/fpls.2023.1117246
license: CC BY 4.0
---
# Genome-wide identification of bHLH transcription factors and their response to salt stress in Cyclocarya paliurus
## Abstract
As a highly valued and multiple function tree species, the leaves of *Cyclocarya paliurus* are enriched in diverse bioactive substances with healthy function. To meet the requirement for its leaf production and medical use, the land with salt stress would be a potential resource for developing C. paliurus plantations due to the limitation of land resources in China. The basic helix-loop-helix (bHLH) transcription factor protein family, the second largest protein family in plants, has been found to play essential roles in the response to multiple abiotic stresses, especially salt stress. However, the bHLH gene family in C.paliurus has not been investigated. In this study, 159 CpbHLH genes were successfully identified from the whole-genome sequence data, and were classified into 26 subfamilies. Meanwhile, the 159 members were also analyzed from the aspects of protein sequences alignment, evolution, motif prediction, promoter cis-acting elements analysis and DNA binding ability. Based on transcriptome profiling under a hydroponic experiment with four salt concentrations ($0\%$, $0.15\%$, $0.3\%$, and $0.45\%$ NaCl), 9 significantly up- or down-regulated genes were screened, while 3 genes associated with salt response were selected in term of the GO annotation results. Totally 12 candidate genes were selected in response to salt stress. Moreover, based on expression analysis of the 12 candidate genes sampled from a pot experiment with three salt concentrations ($0\%$, $0.2\%$ and $0.4\%$ NaCl), CpbHLH$\frac{36}{68}$/146 were further verified to be involved in the regulation of salt tolerance genes, which is also confirmed by protein interaction network analysis. This study was the first analysis of the transcription factor family at the genome-wide level of C. paliurus, and our findings would not only provide insight into the function of the CpbHLH gene family members involved in salt stress but also drive progress in genetic improvement for the salt tolerance of C. paliurus.
## Introduction
Plants are constantly challenged by the environmental stresses, and it is estimated that up to $70\%$ of plants can be affected by diverse abiotic stresses from which they cannot escape (Mantri et al., 2012a; Chen et al., 2021). Salinity stress, which affects 8.31 billion hm2 of land, is one of the major abiotic stresses to impair plant growth (Li et al., 2020a; Zhang et al., 2020a; Liao et al., 2022). In order to maintain normal growth and survival, plants turned on or suppressed many genes by transcription factors (TFs) to regulate physiological and biochemical processes in response to changes in the external environment (Agarwal et al., 2006; Bhatnagar-Mathur et al., 2008). It has been noted that six major families of transcription factors (TFs) have vital regulatory functions in plant resistance to various abiotic stresses, including MYBs, basic helix-loop-helix (bHLHs), ethylene responsive element binding factor (ERFs), dehydration responsive element-binding (DREBs), WRKYs and basic region/leucine zipper motif members (bZIPs) (Kavas et al., 2016; Mao et al., 2017; Zhang et al., 2022a). As reported, the bHLH TFs are widespread in all eukaryotes and own the second largest number of TF families in plants (Pires and Dolan, 2010; Feller et al., 2011), while the bHLH members possess highly conserved bHLH domain constituted by two functionally diverse regions with approximately 60 amino acids (Toledo-Ortiz et al., 2003). The basic region, containing approximately 10-17 amino acids and a binding site to bind the specific E-box (CANNTG) DNA sequence, is located at the N-terminus. Inversely, at the C-terminus, the helix-loop-helix (HLH) region, consisting of roughly 40 amino acids and acting as a dimerization domain, is responsible for facilitating the dimerization between proteins (Atchley et al., 1999). On account of diverse binding elements, bHLH transcription factors in animals were organized into six groups (Wang et al., 2018b), whereas the classification of plant bHLH proteins has not been determined though 15-32 groups were suggested according to current studies (Pires and Dolan, 2010).
Over the years, many plant bHLH proteins have been identified and characterized. For example, there are 162 bHLH genes in *Arabidopsis thaliana* (Toledo-Ortiz et al., 2003), 188 in apple (Malus × domestica) (Mao et al., 2017), and 113 in strawberry (Fragaria × ananassa) (Zhao et al., 2018), 115 in spine grapes (Vitis davidii)(Li et al., 2021) and 206 in sweet osmanthus (Osmanthus fragrans) (Li et al., 2020b). Furthermore, some studies on the role of bHLH proteins revealed that the plant bHLH family participated in numerous processes including anthocyanin biosynthesis (Hou et al., 2017; Lim et al., 2017), growth and development (Sorensen et al., 2003; Carretero-Paulet et al., 2010) and response to stress (Babitha et al., 2013; Ji et al., 2016; Sum et al., 2021). Among the functions, regulating the stress tolerance by binding to the promoters of downstream genes has been well characterized in bHLH proteins (Cui et al., 2016). For instance, MdbHLH104 was recognized to response to iron deficiency stress in apple by immediately binding to the P3 cis-acting element of the MdAHA8 promoter (Zhao et al., 2016), while ICE1 (AtbHLH116), could increase the cold tolerance in A. thaliana by activating expression the cold-responsive (COR) genes (Chinnusamy et al., 2003). Besides, gene AtbHLH92 has been shown to have function in responses to osmotic stresses of plants (Jiang et al., 2009) and AtbHLH17 (AtAIB), a nuclear-localized bHLH-type protein, could confer the drought tolerance of transgenic plants via regulating of ABA signaling (Li et al., 2007). More interesting is some bHLH TFs could play essential role in the regulation of multiple abiotic stresses signaling simultaneously. For instance, SlICE1a (a tomato bHLH transcription factor) could enhance the resistance of cold, osmotic and salt stresses (Feng et al., 2013), while overexpressed TabHLH39 in A. thaliana could increase freezing, salt, and drought tolerance (Zhai et al., 2016). It was also reported that ATNIG1 regulates downstream gene expression by specifically binding to E-box motifs (CANNTG) of salt stress-related gene promoters, thereby enhanced plant tolerance to salt stress (Kim and Kim, 2006).
Wheel wingnut (Cyclocarya paliurus), a multiple-fuction tree species, belongs to Juglandaceae family (Fang, 2022). Although now naturally distributed in sub-tropical mountain areas of China, Cyclocarya has a long fossil record of fruits in North America, Europe and eastern Asia, while went extinct in North America and Europe during the Cenozoic (Manchester et al., 2009; Wu et al., 2017). The leaves of C. paliurus has been used as tea, traditional food and medicine for thousands of years in China (Fang et al., 2006), and the leaves have been listed as new food raw material by National Health and Family Planning Commission of China since 2013 (Qin et al., 2021). Many studies have demonstrated that the extractives from C. paliurus leaves possess antioxidant activities, antiproliferative activities and antidiabetic activities (Kurihara et al., 2003; Yao et al., 2015; Zhai et al., 2018; Zhou et al., 2021), and some products derived from the leaves have been developed and put into the market. However, at present, the resources of C. paliurus are mainly distributed in natural forests whereas its plantations can only be established at the sites where the soil is relatively deep and loose, well-drained and moist fertile (Fang et al., 2011; Fang, 2022), resulting in that the amount of its leaves cannot meet the market demand (Qin et al., 2021). Therefore, a feasible option is to develop C. paliurus plantation with oriented cultivation on potential land resources such as coastal saline areas due to the limitation of land resources in China in order to meet the requirement for its leaf production and medical use. Our previous studies found that R2R3-MYB transcription factor family affected salt tolerance of C. paliurus (Zhang et al., 2022b), while some bHLH proteins could regulate the accumulation of flavonoid compounds under salt stress by promoting the expression of genes encoding related enzymes in C. paliurus (Zhang et al., 2021), which provide some evidences for the crucial role of TFs in plant resistance to salt stress. However, so far, no TF family has been systematically identified in the whole genome of C. paliurus. The recent release of high quality whole-genome sequence data of C. paliurus gives us the opportunity to investigate the bHLH gene family and to identify salt-responsive members. In this study, 159 bHLH transcription factors in C. paliurus were analyzed comprehensively and systematically, and some key bHLH genes associated with salt tolerance were identified. Results from this study would not only provide insight into the function of the CpbHLH gene family members involved in salt stress, but also drive progress in genetic improvement for the salt tolerance of C. paliurus to develop C. paliurus plantation in the coastal saline areas of south-east China.
## Identification and sequence analysis of CpbHLH genes
The whole genome data of C. paliurus were available from the Genome Sequence Archive (GSA) database (https://ngdc.cncb.ac.cn/gsa) provided by our research group. The Hidden Markov Model (HMM) profile of the HLH domain (PF00010) was obtained from the Pfam database (version 30.0) (Finn et al., 2014), and was used as a query to search for all protein sequences with default E-values in the whole genome and to identify genes with specific conserved domains by HMMER software (version 3.3; http://hmmer.org/) (Johnson et al., 2010). All screened sequences were aligned and checked with the online tools Batch CD-search (https://www.ncbi.nlm.nih.gov/Structure/bwrpsb/bwrpsb.cgi) (Marchler-Bauer and Bryant, 2004), Pfam, and SMART (http://smart.embl-heidelberg.de) (Letunic et al., 2021) to verify the existence of the conserved bHLH domain. The ExPASy software (https://web.expasy.org/protparam/) was used to obtain basic physical and chemical characteristics of these bHLH genes respectively.
Based on the Genome-wide data of C. paliurus, a total of 174 supposed CpbHLH proteins were discovered by using the HMMER software with default parameters. Subsequently, SMART and CD-Search were performed to confirm the existence of the conserved bHLH domain. After removing redundant sequences, 159 bHLH protein sequences of C. paliurus with typical complete bHLH domain were obtained and they were named CpbHLH1 to CpbHLH159 according to their location on chromosomes (Figure 2; Supplementary Table S2). Sequence analysis showed that the average length of the CpbHLH proteins was 354 amino acids. The relative molecular weight (Nw) ranged from 10454.72 Da (CpbHLH39) to 175494.7 Da (CpbHLH86), whereas the isoelectric point (pI) ranged from 4.65 (CpbHLH48) to 9.66 (CpbHLH66) (Supplementary Table S2).
**Figure 2:** *Chromosomal locations of the CpbHLH genes. The 159 CpbHLH genes were distributed on 21 pseudo-chromosomes of C. paliurus based on their physical positions.*
## Phylogenetic analysis, multiple alignment analysis and chromosomal locations
The A. thaliana MYB sequences data were download from PlantTFDB database (http://planttfdb.cbi.pku.edu.cn/index). The construction of a phylogenetic tree consisted of proteins from A. thaliana and C. paliurus was performed with MEGA X (version 6.0) (Kumar et al., 2018) software using the neighbor-joining (NJ) method with 1000 bootstrap replicates. Multiple sequence alignment (MSA) of C. paliurus and A. thaliana bHLH proteins was performed using ClustalX 2.11 software (Thompson et al., 1997), and Weblogo3 (http://weblogo.threeplusone.com/create.cgi), while Jalview software (http://www.jalview.org/) was used to visualize and analyze the sequences of conserved domains in CpbHLH proteins. The GFF3 (Generic Feature Format Version 3) file, containing the positional and gene structure information of genes on the chromosomes, was obtained from whole genome data of C. paliurus. The TBtools software (version 1.098774) (Chen et al., 2020) was adopted to map the CpbHLH genes onto specific chromosomes.
## Gene structure, conserved motif, and promoter analysis
The exon/intron structures of CpbHLH genes were visualized by TBtools software (version 1.098774) (Chen et al., 2018), whereas fifteen conserved motifs were obtained using the online software MEME (http://MEME-suite.org/) (upper limit of the recognition motif was 20, minimum motif width was 6, and maximum motif width was 50, zoops) (Bailey et al., 2009). The online tool PLACE (Higo et al., 1999) was used to analyze the cis-acting elements of CpbHLH genes.
## RNA-seq data analysis, GO annotation and prediction of the protein interaction network
Raw data were obtained via RNA sequencing of leaves treated with different salt concentration in hydroponic experiment (Zhang et al., 2021). CpbHLHs with reads per kilobase of transcript per million mapped reads or fragments per kilobase of transcript per million mapped reads (RPKM and FPKM, respectively) > 1 were collected for further analyses of all of the transcriptome data. TBtools was performed to generate the heatmap (Chen et al., 2020). Gene ontology (GO) analysis was carried out by the Blast2GO program (Conesa et al., 2005), with selecting the NCBI database as the reference database. The results were divided into three categories, namely molecular function, biological process, and cellular component. The NCBI database (https://www.ncbi.nlm.nih.gov/) were used to search the functions of AtbHLHs, which were predicted to be orthologous genes of CpbHLHs. STRING (https://string-db.org/) (Szklarczyk et al., 2019) was performed to predict the functional interaction network of candidate genes with option value>0.7.
## Plant materials and stress treatments
The experiment was carried out at Baima Experimental Base of Nanjing Forestry University (31°35′ N, 119°09′ E). C. paliurus seeds were collected from Jinzhongshan county (24° 58′ N latitude, 110° 09′ E longitude), Guangxi province, China, in October 2018. After treated by exogenous GA3 (gibberellin A3) and stratification method (Fang et al., 2006), the germinated seeds were sown in nonwoven containers (10.0 cm height, 8.0 cm diameter) in April 2019.
Hydroponic experiment: After three months, uniform size seedlings (height: 40 ± 2.79 cm) were selected and transplanted to polypropylene containers (50L) with $\frac{1}{2}$-strength Hoagland’s nutrient solution (pH 6.0 ± 0.2). Two weeks after hydroponic transplanting, four salt concentration ($0\%$, $0.15\%$, $0.3\%$, and $0.45\%$ NaCl) regimes were implemented in completely randomized design with three biological replicates for each treatment. The detailed information has been described in our previous study (Zhang et al., 2022a).
Pot experiment: After one-year growth in the nonwoven containers, the seedlings were transplanted into the big nonwoven containers (25 cm height, 20 cm diameter) and cut into 3-5 cm height in early spring in 2020. In February 2022, saplings with similar size were selected and all their stems were cut to 120 cm height, whereas in early April 2022, the selected saplings were transplanted from the nonwoven containers into plastic pots (26 cm height, 26 cm top diameter and 20 cm bottom diameter) containing peat: substrates of perlite: rotten bird dung: soil =5: 2:2:1 (v/v/v/v). The plastic pots were placed in plastic trays to prevent NaCl leaching. The substrate was a loam with pH 6.4, and the contents of total N, total P, and total K in the soil were 79.7, 66.5, 2.40, and 9.7 g kg−1, respectively.
Salt treatments were conducted in early May 2022, and a completely randomized design was adopted with three replications per treatment and six plants per replication. Based on previous research (Zhang et al., 2022b), three levels of NaCl concentration were set up: CK (control, distilled water), T1 ($0.2\%$ NaCl) and T2 ($0.4\%$ NaCl). 1L solution were gradually add to the soil every three days (Chen et al., 2021), and electrical conductivity in the substrate was also monitored to keep the soil salt concentration relatively stable. Six complete and mature leaves were respectively collected from the upper, middle and lower positions of each sampled tree at the 45 days after the treatments (obvious differences were observed) (Figure 1) and were immediately frozen in liquid nitrogen and stored at −80°C until needed for further analysis.
**Figure 1:** *Phenotypes of C. paliurus seedlings at the sampling time under various salt treatments of the pot experiment.*
## RNA extraction and real-time quantitative RT-PCR analysis
Plant materials were ground under RNase-free conditions. Trizol reagent kit (Invitrogen, Carlsbad, CA, USA) was used to extract RNA from 9 samples of the 3 treatments (CK, $0.2\%$ NaCl and $0.4\%$ NaCl); subsequently, MonScript RTIII All-in-One Mix with dsDNase kits (Monad, Nanjing, China) was used to acquire cDNA, following the manufacturer’s instructions. The qRTPCRs were performed on BiosystemsTM 7500 Real-Time PCR Systems (Monad, China). Primer Premier 6.0 (Premier Biosoft International, Palo Alto CA, USA) was used to design qRT-PCR primers for 12 genes (Supplementary Table S1). SYBR Premix Ex Taq kit (Takara Biotechnology, Dalian, China) was applied to conduct qRT-PCR analysis. The cDNA diluted 20 times and an 18sRNA gene (Chen et al., 2019) were selected as the template and the internal standard, respectively. PCR reaction conditions were as 95 °C for 3 min; denaturation 5 s at 95 °C; 60 °C for 30 s; 40 cycles. Three technical and three biological replicates were used for each sample. After reaction, the relative expression levels of target gene and internal reference gene were calculated with the 2−ΔΔCT method (Penfield, 2001).
## Statistical analysis
One-way analysis of variance (ANOVA) was conducted to identify significant differences in the related gene expression among the treatments, followed by Duncan’s test for multiple comparisons. All statistical analyses were performed using IBM SPSS Statistics Version 22 software package (SPSS Inc., IBM Company Headquarters, Chicago, IL, USA). Data were presented as means ± standard deviation (SD).
## Conserved residues and DNA-binding ability prediction of the CpbHLH genes
To gain in-depth knowledge of the function of CpbHLH family, the bHLH domains of the CpbHLH proteins were searched and the presence of the conserved amino acid residues were analyzed based on multiple sequence alignment. The alignment results (Figure 3) showed that the CpbHLH domains were composed of four conserved regions, namely one basic region, two helix regions and a loop region. Consistent with previous studies (Heim et al., 2003), the conservation of basic region and helix region is higher than that of the loop region. The bHLH domains of C. paliurus were made up of 79 amino acid residues, of which 24 were highly conserved (> $50\%$ consensus ratio) and 8 were extremely conservative (> $75\%$ consensus ratio). Among the 24 highly conserved amino acid residues, six conserved residues were found in the basic region (His-9, Ala-12, Glu-13, Arg-14, Arg-16, Arg-17), seven conserved residues were found in the first helix region (Ile-20, Asn-21, Arg-23, Leu-27, Leu-30, Val-31, Pro-32), one conserved residues were found in the loop region (Asp-64), and ten conserved residues were found in the second helix region (Lys-65, Ala-66, Ser-67, Leu-69, Ala-72, Ile-73, Tyr-75, Val-76, Lys-77, Leu-79).
**Figure 3:** *Multiple sequence alignments of the bHLH domains in CpbHLH proteins. (A) Visualization of conserved amino acids of bHLH domains of CpbHLH proteins. Amino acids with a conserved degree of more than 50 and their conserved degree were labeled using red and black colors for easy recognition which had no special meaning. (B) Multiple sequence alignments of the bHLH domains of 159 CpbHLH proteins, using the Clustal color scheme.*
It is generally believed that the basic region performs DNA binding functions, and is critical for the bHLH family to achieve its biological function (Carretero-Paulet et al., 2010). Therefore, the DNA-binding ability of the 159 CpbHLH proteins were predicted based on the conserved amino acid residues in the basic region (Supplementary Table S3). The remaining 159 CpbHLH members were classified into three categories: G-box (His/Lys-9, Glu-13 and Arg-17), E-box (Glu-13 and Arg-16) and non-E-box (Glu-13 and Arg-16 do not appear together) in accordance with the classification method reported previously (Katiyar et al., 2012). The predicted results revealed there were 93 G-box-binding proteins, 43 non-G-box-binding proteins and 23 non-E-box-binding proteins in 159 CpbHLHs (Supplementary Table S3).
## Phylogenetic analysis and classification of the CpbHLH genes
In order to explore the evolutionary relationship among the CpbHLH members, the 159 CpbHLH proteins were aligned with 140 bHLH proteins from Arabidopsis, afterwards the phylogenetic tree was constructed using total 299 bHLH proteins based on the alignment (Figure 4). In accordance with the classification of bHLH proteins from Arabidopsis and other plants (Heim et al., 2003; Li et al., 2020b; Li et al., 2021), 299 bHLH protein sequences were classified into 26 subfamilies, and were named from Ia to XV on the basis of the nomenclature of AtbHLHs proposed by Heim et al. ( Heim et al., 2003). Figure 4 showed that the XII subfamily was the largest (contained 35 CpbHLH proteins), while the smallest subfamily (VI) contained only one CpbHLH protein. According to results from Heim et al. ( Heim et al., 2003), CpbHLH proteins in the same subfamily would have similar functions, consequently, the clustering results of phylogenetic tree could contribute to predict the function of CpbHLH proteins.
**Figure 4:** *Phylogenetic tree and classification of bHLH subfamily proteins in A. thaliana and C. paliurus. The number of bHLH proteins of A. thaliana and C. paliurus is 140 and 159, respectively. The red dots represent boot values—the larger the dot, the larger the bootstrap value. Roman numerals line up with the bHLH subfamily.*
## Gene structure and conserved motif analysis of CpbHLH genes
Diversity of exon-intron structures, which could cause divergences in coding regions, is significant to the evolution of multiple gene families (Xu et al., 2012b). Hence, the gene structural characteristics of the CpbHLH family were investigated. The number of exons in the 159 CpbHLH genes varied from 1 to 13 (Figure 5C). In addition, 20 ($12.6\%$) genes were intronless and distributed across subfamilies IIId, IIIe, VIIIa, VIIIb and VIIIc[2], while 13 ($8.2\%$) genes contained one intron, and certainly the remaining genes had two or more introns. The 159 genes in different families varied widely in structure, including the number and relative location of introns and exons (Figure 5C). On the contrary, the intron/exon patterns of genes in the same subfamily had highly similarity, such as in subfamilies Ib[1] (five three-exon genes), Ib[2] (five three-exon genes), III(d+e) (nine one-exon genes), IVc (eight five-exon genes), and VIIIb (seven one-exon genes) (Figure 5C).
**Figure 5:** *Analysis of conserved motifs and gene structure for 159 CpbHLH proteins. (A) Phylogenetic tree. (B) Distribution of conserved motifs. Twenty motifs were represented by twenty kinds of colored blocks. The position of each block represents the location of the motif. (C) organization of gene structure. The length of the gray line represents the length of a sequence relative to that of all the other sequences.*
It is generally accepted that motifs figure prominently in interaction and signal transduction between different modules of the gene transcription process (Toledo-Ortiz et al., 2003). To further understand the evolutionary relationships among these CpbHLH proteins, the conserved motifs were analyzed by using MEME. Twenty motifs were identified and their sequences and length were counted (Figure 5B, Supplementary Table S4). In addition, eight of twenty motifs were annotated by Pfam and CD-search (Supplementary Table S4). Obviously, the composition patterns tended to be consistent with the results from our phylogenetic tree and gene structures, being resemble among genes within the same group, but varying greatly between groups (Figure 5). The number of motifs in 159 CpbHLHs ranged from one (CpbHLH66) to nine (CpbHLH50). All 159 CpbHLH genes contained motif 1 and motif 2, except CpbHLH66, only containing motif 1 (Figure 5B). Interestingly, some conserved motifs were nested in specific groups. For example, motif 13 only existed in group Ia, motif 16 in group VIIIb, motif 18 in group XII, and motif 19 in group IX respectively (Figure 5B). This phenomenon might be the reason why functions for CpbHLH proteins tend to be specific to a particular group.
## GO annotation and cis-element analyses of the CpbHLHs
The highly differentiated sequences outside the conserved bHLH domain suggest that CpbHLH proteins may have a variety of biological functions. GO annotation of these 159 proteins was performed to understand the biological processes associated with CpbHLH genes. The results are shown in (Figure 6; Supplementary Table S5). The identified CpbHLH proteins were classified into three main Gene ontology (GO) terms, which were CC (cellular component), MF (molecular function), and BP (biological process). Within MF category, the majority of CpbHLH proteins were annotated for “molecular function” ($\frac{139}{159}$), “nucleic acid binding” and “DNA binding”, respectively. These functions were closely related to the primary roles that TFs have. As for CC category, most of the CpbHLH proteins were assigned to cellular components and the nucleus ($\frac{139}{159}$). However, there were also a small number of CpbHLH proteins distributed in cytoplasm ($\frac{8}{159}$), organelle part ($\frac{7}{159}$), cytosol ($\frac{4}{159}$), symplast (CpbHLH$\frac{37}{117}$/132) and chloroplast (CpbHLH$\frac{68}{109}$) (Figure 6; Supplementary Table S5). Furthermore, the BP aspect showed that CpbHLH proteins participated in various biological processes. Proteins annotated to be related to multiple biosynthetic and metabolic possessed the largest number of CpbHLHs ($\frac{141}{159}$). Besides, CpbHLH proteins may function in regulating biological processed, such as regulation of cellular process ($\frac{111}{159}$), transcription ($\frac{109}{159}$), DNA-templated ($\frac{109}{159}$) and gene expression ($\frac{109}{159}$). The BP analysis also showed that many CpbHLHs could respond to stimuli ($\frac{46}{159}$), including different types of biotic and abiotic stressors, while CpbHLH$\frac{38}{68}$/109 were predicted to be involved in respond to salt stress (Figure 6; Supplementary Table S5).
**Figure 6:** *Gene ontology (GO) distribution of CpbHLH proteins. GO annotation using a cut-off value of p ≤ 0.05 showed that GO items including molecular function (MF), biological process (BP), and cellular component (CC), while predominant GO items was selected to visualize the result.*
Conserved motifs located in gene promoter regions are recognition and binding sites for proteins. In this study, a large number of cis-regulatory elements (CREs) of CpbHLH genes were identified, and they were classified into three main categories (plant growth and development, phytohormone responsive, as well as abiotic and biotic stresses) according to their roles (Figure 7). Our result showed that CAT-box [105] and O2-site [86], which were involved in the meristem expression and zein metabolism regulation respectively were most frequently found motifs related to plant growth and development. On the contrary, the number of HD-Zip 1 (the differentiation of the palisade mesophyll cells), AACA-motif (involved in endosperm-specific negative expression) and MSA-like (cell cycle regulation) elements were 8, 3 and 2 respectively. Additionally, RY-element (seed-specific regulation) and GCN4_motif (endosperm expression) were also identified in the promoters of the CpbHLH genes (Figure 7). The most common elements in phytohormone responsive category were ABRE (the abscisic acid-responsive element), CGTCA-motif and TGACG-motif (elements involved in MeJA responsiveness) and the TCA element (SA-responsive element) (Figure 7). In the last category, a lot of important CREs related to plant abiotic stress were detected. Most abundant of these were the ABRE (drought response element), ARE (anaerobic induced response element), MBS (drought induced response element) and LTR (low temperature response element). Other stress response CREs, such as GC-motif (anoxic specific inducibility element), TC-rich (defense and stress response element) and ERE elements (oxidative stress responsive elements were also identified (Figure 7).
**Figure 7:** *Cis-regulatory elements in the promoter region of CpbHLH genes. The figure represents the number of each type of motifs identified in the promoter sequence of CpbHLH genes.*
## Expression profiles of CpbHLH genes in salt stress under hydroponic experiment
Analysis of gene expression profiles is an effective way to determine gene functions. Hence, the leaves of C. paliurus treated with different salt concentrations ($0\%$, $0.15\%$, $0.3\%$, and $0.45\%$ NaCl) for 30 days in hydroponic experiment were sequenced and analyzed (Zhang et al., 2021). The raw sequencing data were submitted to the NCBI BioProject database under project number PRJNA700136. The RPKM (Reads Per Kilobase per Million mapped reads) values of 159 CpbHLH genes were obtained from the transcriptome data to estimate the expression levels of bHLH family members. However, CpbHLH$\frac{119}{121}$/$\frac{138}{151}$ were not analyzed because of the absence or low level of expression in the transcriptome data. Figure 8 showed that 155 of these genes were expressed in all concentrations of NaCl treatments with different expression patterns, providing evidence that CpbHLH genes are significantly affected by salt stress.
**Figure 8:** *Clustering expression analysis of 159 CpbHLH genes in salt stress based on hydroponic experiments. The CK, LS, MS and HS represent the NaCl concentrations of 0%, 0.15%, 0.3% and 0.45% respectively. The transcript abundance level was normalized and hierarchically clustered by using the log 2 (FPKM + 1) comparison among genes of different treatments. The expression value is presented on the color scale, with red representing high expression and blue representing low expression. A1-A8 represent different clusters. In order to distinguish A1-A8 clusters more intuitively, lines of different colours were used in the right.*
Based on the similarity of expression patterns, the 155 CpbHLH genes were clustered into 8 clusters, named A1-A8 (Figure 8). *The* genes in cluster A1 were mainly expressed in the middle ($0.30\%$ NaCl) or high ($0.45\%$ NaCl) salinity condition and did not change significantly under low ($0.15\%$ NaCl) salinity condition. In contrast, CpbHLHs in cluster A6, A7 and A8 was strongly and preferentially expressed under low salt concentrations and down-regulated under high salt concentration. In cluster A2, the expression of CpbHLH genes did not change significantly under low and middle salt stress, but reached its highest value at high salinity treatment. However, expressions of most genes in cluster A4 varied with salt concentration treatments, and expression of these genes were all down-regulated under salt treatments and reached its lowest value at $0.45\%$ NaCl treatment. However, very low expression levels of these genes in cluster A3 and A5 were observed at middle and high salt concentrations, respectively (Figure 8). In particular, among these 155 genes, the expression of some genes were strongly induced or inhibited under salt stress. For example, compared with the CK, the expressions of CpbHLH$\frac{36}{74}$/75 in cluster A4 were down regulated by nearly folds of 3 in the low salinity treatment ($0.15\%$ NaCl), especially CpbHLH74 down regulated by nearly folds of 9 in the high salinity treatment ($0.45\%$ NaCl). Similarly, seven differentially expressed genes (DEGs) (CpbHLH$\frac{68}{69}$/$\frac{71}{108}$/$\frac{146}{152}$/158) were identified in the A5, A6 and A7, indicating a response to salt stress (Figure 8).
## Expression analysis of candidate genes in response to salt in pot experiment
Combining the results from both GO annotation and expression profiles analysis in hydroponic experiment, twelve salt-induced candidate genes (CpbHLH$\frac{36}{38}$/$\frac{68}{69}$/$\frac{71}{74}$/$\frac{75}{108}$/$\frac{109}{146}$/$\frac{152}{158}$) were selected for further qRT-PCR analysis using templates from pot experiment with three salt concentrations ($0\%$ NaCl, $0.2\%$ NaCl and $0.4\%$ NaCl) (Figure 9). Notably, eight candidate genes (CpbHLH$\frac{36}{68}$/$\frac{71}{75}$/$\frac{109}{146}$/$\frac{152}{158}$) were up regulated or decreased dramatically under different salt treatments, indicating that the expression of these genes was significantly induced or inhibited under salt stress (Figure 9). Among the eight genes, four genes (CpbHLH$\frac{36}{146}$/$\frac{152}{158}$) were down-regulated under salt stress, with three of these genes (CpbHLH$\frac{146}{152}$/158) being lowest expressed at $0.4\%$ NaCl and one gene (CpbHLH36) being lowest expressed at $0.2\%$ NaCl. On the contrary, three genes (CpbHLH$\frac{68}{71}$/109) were significantly induced by salt stress (Figure 9). In particular, three genes (CpbHLH$\frac{36}{68}$/146) responded strongly to salt treatments. Compared to the control, the variation trend of their expression in the pot experiment was highly consistent with that in the hydroponic experiment (Figure 8; Figure 9), indicating their vital functions in response to salt stress. For example, the expression level of CpbHLH36 in both experiments was strongly inhibited under salt stress, whereas the inhibition degree was greater in low salt concentration than in high salt concentration.
**Figure 9:** *Expression profiles of the 12 candidate CpbHLH genes responding to salt stress treatments in pot experiment. The standard errors from three biological and three technical replications are presented as error bars. Following analysis of variance, significant differences identified by Duncan’s test (p < 0.05), using SPSS v.22, are represented by different letters.*
## Interaction network prediction of candidate genes
It was reported that bHLH proteins exert regulatory effects by forming homodimers or heterodimers between bHLH proteins or between bHLH and non-bHLH proteins (Herold et al., 2002; Hernandez et al., 2007). Thus, the interaction network of three candidate genes was predicted by STRING (Figure 10), based on the CpbHLH homologous genes in A. thaliana. The investigation of CpbHLH146 (MYC2 ortholog) showed that it was involved in light, abscisic acid (ABA), and jasmonic acid (JA) signaling pathways and controlled additively subsets of JA-dependent responses with MYC3 and MYC4 (Figure 10B, Supplementary Table S6). Among the proteins interacting with MYC2, those related to JA signaling pathway accounted for the majority, including JAZ1, JAZ3, JAZ5, JAZ8, JAZ10, JAZ12 and TILY7. Besides, PFT1 was determined as phytochrome and flowering time regulatory protein and the EIN3 probablely acted as a positive regulator in the ethylene response pathway (Figure 10A, Supplementary Table S6). The predicted network for CpbHLH36 (NIG ortholog) showed that it plays central roles in regulating various proteins, and coincidently several of which were also involved in the jasmonic acid signaling pathway (JAZ1 and JAZ10) (Figure 10A, Supplementary Table S6). Other proteins, GSTU1 and GSTU2, could be involved in the conjugation of reduced glutathione to a wide number of exogenous and endogenous hydrophobic electrophiles and have a detoxification role against certain herbicides, whereas bHLH11 and TRFL8 both function in DNA binding (Figure 10A, Supplementary Table S6). Finally, the results of predicted network (Figure 10C, Supplementary Table S6) also indicated that CpbHLH68 (ortholog of bHLH106) has crucial roles in DNA binding, whose function is the same as most of the proteins that interact with it. In addition, several interacting genes possibly regulate light responses, for example CRY1 and CPY2 are cryptochromes, and UVR2 and UVR3 involved in repair of UV radiation-induced DNA damage. However, PRMT4B has been identified as a positive regulator of oxidative stress tolerance that promotes the expression of antioxidant enzymes such as APX1 and GPX1 (Figure 10A, Supplementary Table S6). Overall, the results of the protein interaction network analysis indicated that the three candidate genes interact with proteins of various functions, making them crucial players in regulating plant growth and stress responses.
**Figure 10:** *Interaction network analysis for CpbHLH36
(A), CpbHLH146
(B) and CpbHLH68
(C). The predicted results are based on the orthologous gene in Arabidopsis. CpbHLH genes are shown in brackets.*
## Systematic and comprehensive genome-wide detection of CpbHLHs in C paliurus
Based on the whole genome of C. paliurus, 159 bHLH genes were systematically identified in the present study (Supplementary Table S2). The number of CpbHLH genes was the same as that identified in tomato (Sun et al., 2015), but smaller than that in Arabidopsis (162 genes) (Toledo-Ortiz et al., 2003) and apple (175 genes) (Yang et al., 2017), whereas greater than that in grape (94 genes) (Wang et al., 2018a), strawberry (113 genes) (Zhao et al., 2018) and jujube (92 genes) (Li et al., 2019). Overall, 159 CpbHLH proteins were further categorized into 26 subfamilies (Figure 4), according to the phylogenetic tree with the nomenclature protocol of bHLH proteins in C. paliurus and Arabidopsis (Heim et al., 2003), in agreement with results from previous studies (Pires and Dolan, 2010; Sun et al., 2015; Chu et al., 2018). However, the CpbHLHs were distributed almost evenly across 20 subfamilies, similar to *Camellia sinensis* (Sun et al., 2015) and O. fragrans (Li et al., 2020b). Moreover, our result indicated that no CpbHLHs were found in subfamily X, whereas the most CpbHLH members were detected in subfamily XII (Figure 4), with the number of members in this family increasing from 17 in Arabidopsis to 22 in C. paliurus. Differences in the numbers of bHLH genes among plant species may be due to gene replication events or genome size or gene loss during evolution (Flagel and Wendel, 2009; Li et al., 2020b).
Based on the analysis of the conserved motif and intron/exon (Figure 5B, C), the results showed that CpbHLHs in the same subfamily of the phylogenetic tree were similar in genetic and motif structures, further confirming the accuracy of subgroup classification of phylogenetic tree (Figure 4; Figure 5). Totally twenty motifs were identified in 159 CpbHLH proteins (Figure 5B). However, among them, motifs 1 and 2 existed in almost every CpbHLH protein and represented main components of the bHLH domain with high capability of conserved DNA binding, suggesting that the two motifs had very important implications about the functioning of bHLH genes (Zhang et al., 2020b). Nonetheless, the remaining 18 conserved non-bHLH domains can also feature separately in CpbHLHs in their respective subfamilies, similar to the other plant species (Chu et al., 2018; Li et al., 2020b). For example, most bHLH genes of subfamily III(d+e) in Panax ginseng (Chu et al., 2018) contained MYC-N structures (bHLH-MYC_N domain, Pfam: PF14215), which have been proved functioning in regulating the biosynthesis of phenylpropane. In this study, all CpbHLHs of III(d+e) also contained MYC-N structures (motif 5, 8, 10) (Figure 5B; Supplementary Table S4), implying that CpbHLHs of the same subgroup may have the similar roles. It was reported that gain/loss of exons and introns may result in the functional diversification of gene families (Xu et al., 2012a), whereas introns are related to gene evolution, and especieally the genes with few or no introns are more highly expressed in plants (Chung et al., 2006; Ren et al., 2006). In the present study, the intron-less CpbHLHs were distributed across subfamilies III (d+e) and VIIIb (Figure 5C), in accordance with the phenomenon in P. ginseng (Chu et al., 2018), apple (Yang et al., 2017) and Osmanthus (Li et al., 2020b), suggesting CpbHLHs of these subgroups could facilitates rapid and timely response to various stresses (Jeffares et al., 2008).
## Functional prediction and identification of salt tolerance genes of CpbHLHs
Transcriptional regulation is a basic process of gene regulation in response to stress signals and a mass of TFs are involved in regulating plant responses to a given stress (Riechmann et al., 2000). The results of GO annotation in this study showed the functions of the CpbHLH genes are diverse (Figure 6; Supplementary Table S5), supporting that the bHLH TFs plays a crucial role in regulating plant growth, development and stress response (Shen et al., 2021). Several lines of evidence showed that salt stress had adverse effects on photosynthesis and the accumulation of secondary metabolites in C. paliurus (Zhang et al., 2021; Zhang et al., 2022a). Therefore, the detection of salt stress response genes from CpbHLHs will be helpful to achieve salt-tolerant breeding of C. paliurus.
The transcriptome sequencing analysis of salt treatments in the hydroponics provided specific expression data for the CpbHLHs, which makes it possible to further study the function of these genes. The RPKM values from our hydroponics showed that a large number of CpbHLH genes were induced/repressed under NaCl stress (Figure 8). According to the RPKM data, ten significantly differentially expressed genes (CpbHLH$\frac{36}{68}$/$\frac{69}{71}$/$\frac{74}{75}$/$\frac{108}{146}$/$\frac{152}{158}$) were predicted to function in responding to salt stress (Figure 8). Moreover, the molecular function annotations of 159 CpbHLHs indicated that three genes (CpbHLH$\frac{38}{68}$/109) strongly responded to salt stress (Supplementary Table S5). Thus, the 12 genes mentioned above were predicted to be candidate genes in response to salt stress and were selected for further qRT-PCR analysis, using salt-treated templates collected from our pot experiment. The qRT-PCR results showed that the expression of three genes (CpbHLH$\frac{36}{68}$/146) strongly responded to the salt treatments (Figure 9), and the variation trend of their expression levels was highly similar in the two salt stress experiments (Figure 8; Figure 9), indicating that these genes were specific for the regulation of salt tolerance in C. paliurus.
Phylogenetic analysis can be used to derive orthogonal relationships based on sequence similarity and protein structure, while the most closely related bHLH genes in the phylogenetic tree may share a similar function (Wang et al., 2021). The existed research indicated that AtbHLH106 could enhance salt tolerance of plant by directly interacting with the G-box of salt tolerant genes (Ahmad et al., 2015), whereas the CpbHLH68 was clustered in the same clade that possess high bootstrap value with AtbHLH106 (Figure 4), suggesting CpbHLH68 may be involved in response to salt stress. In addition, DNA sequences are decisive factors of the binding specificity between transcription factors and their genomic targets (Gordân et al., 2013), and our results from the DNA-binding ability of 159 CpbHLHs showed that CpbHLH68 was G-box-binding protein (Supplementary Table S3), which further suggests that CpbHLH68, similar to AtbHLH106, may respond to salt stress by binding to G-box of target genes. Moreover, AtbHLH6 (ATMYC2) has been reported to exhibit a significant response to salt and drought stresses (Abe et al., 1997; Aleman et al., 2016), while AtNIG1 (a salt stress-responsive gene) was the first known TF participating in salt stress signal by binding calcium ions and bound to the E-box sequence (CANNTG) (Kim and Kim, 2006). Our study showed that CpbHLH36 and CpbHLH146 were clustered in the same clade with AtbHLH6(MYC2) and AtbHLH28(AtNIG1) (Figure 4), suggesting that CpbHLH36 and CpbHLH146 are also E-box proteins (Supplementary Table S3), and very likely to be involved in the regulation of salt stress signaling pathways.
*In* general, the function of a given gene can be inferred from its homologous genes (Yue et al., 2016; Qu et al., 2022). Therefore, Arabidopsis orthologs were used to predict the regulatory network of these three candidate genes (CpbHLH$\frac{36}{68}$/146) in this study. Some previous researches showed that AtMYC2 was involved in the regulation of ABA-inducible genes under drought stress conditions (Gordân et al., 2013) and could provide a possible mechanistic link between ABA signaling and JA signaling (Abe et al., 1997; Zhang et al., 2021). The predicted interaction genes of CpbHLH146 (MYC2 ortholog) were mainly involved in the regulation of JA signaling (Figure 10A, Supplementary Table S6). The interaction of plant hormone ABA and JA played a major role in abiotic stress tolerance (Xiong et al., 2002; Zhang et al., 2012b) and ABA-dependent pathways the was one of important abiotic stress response signaling transduction pathways (Zhang et al., 2012a). The promoter region of most ABA regulatory genes contains many ABA responsive elements (Leonhardt et al., 2004; Yamaguchi-Shinozaki and Shinozaki, 2005; Fujita et al., 2011). In this study, a high occurrence of ABRE (ABA-responsive element) and CGTCA-motif (MeJA-responsive element) cis-acting elements was detected in the promoters of CpbHLH146 (Figure 7). Thus, it can be inferred that this gene may have an important role in regulating stress resistance by regulating the expression of key genes in the ABA signaling pathway. Furthermore, most interaction genes of CpbHLH36 (AtNIG1 ortholog) and CpbHLH68 (bHLH106 ortholog) were mainly involved in DNA binding (Figure 10A, Supplementary Table S6), which further supports our hypothesis that these two genes regulate plant salt stress mainly via recognizing G-box of target genes. Moreover, CpbHLH36 (AtNIG1 ortholog) was also interacted with some JA signaling pathway proteins (Figure 10A, Supplementary Table S6), and it was in the same cluster of the phylogenetic tree with CpbHLH146 (MYC2 ortholog) (Figure 4). Besides, the similar expression trend of CpbHLH36 was observed between pot experiment and hydroponic experiment, the same as to CpbHLH146 (Figure 8; Figure 9). Therefore, it could be concluded that there is an indirect interaction between CpbHLH36 and CpbHLH146 at the protein level and these two genes coordinately control the expression of downstream genes, whereas the plant salt tolerance may depend upon the co-expression of these two genes.
In short, combined with the above results, CpbHLH$\frac{36}{68}$/146 could be the key putative candidates in response to salt stress in C. paliurus. However, characterizations of these three genes involved in the regulation of salt tolerance varied. CpbHLH$\frac{36}{68}$/146 are all G-box proteins, and may respond to salt stress by binding to G-box of target genes. Secondly, CpbHLH36 may participate in salt stress signal by binding calcium ions and regulating the expression of key genes in the JA signaling pathway. Thirdly, CpbHLH146 was very likely to be involved in the regulation of salt stress in ABA signaling pathways. Moreover, it is noted that there exists an indirect interaction between CpbHLH36 and CpbHLH146 at the protein level, thus we guess the salt tolerance of C. paliurus may depend upon the co-expression of these two genes.
In conclusion, it is the first report to identify the TF family based on the whole genome of C. paliurus. A total of 159 CpbHLH genes were detected and divided into 26 subfamilies, according to their evolutionary characteristics. In addition to investigating their structures and DNA-binding abilities, expression analysis from both the pot and hydroponic experiments and the regulatory network were also performed to determine which genes are most active for salt stress responses in this species. A total of 12 candidate genes were selected in response to salt stress, whereas the 3 genes (CpbHLH$\frac{36}{68}$/146) were further verified to be involved in regulating the salt tolerance of C. paliurus based on a pot experiment and protein interaction network analysis. Our findings would not only provide a basis for further understanding regulatory mechanisms of bHLH proteins TFs, but also drive progress in genetic improvement for the salt tolerance of C. paliurus.
## Data availability statement
The whole genome sequencing raw data including Illumina short reads, PacBio long reads, Hi-C interaction reads, and transcriptome data have been submitted to the Genome Sequence Archive at the National Genomics Data Center (NGDC), Beijing Institute of Genomics (BIG), Chinese Academy of Sciences (CAS) / China National Center for Bioinformation (CNCB) (GSA: CRA004671 and BioProject: PRJCA005987), and are publicly accessible at https://ngdc.cncb.ac.cn/gsa/.
## Author contributions
ZZ: Conceptualization, writing-original draft, visualization, data analysis, bioinformatics analysis. JF: Participated in the pot experiment. SF: Methodology, writing-review & editing, funding acquisition. LZ: Participated in the hydroponic experiment HJ: Participated in the pot experiment. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1117246/full#supplementary-material
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|
---
title: Metabolomic responses to the mechanical wounding of Catharanthus roseus’ upper
leaves
authors:
- Qi Chen
- Yan Jin
- Xiaorui Guo
- Mingyuan Xu
- Guanyun Wei
- Xueyan Lu
- Zhonghua Tang
journal: PeerJ
year: 2023
pmcid: PMC10035419
doi: 10.7717/peerj.14539
license: CC BY 4.0
---
# Metabolomic responses to the mechanical wounding of Catharanthus roseus’ upper leaves
## Abstract
### Purpose
Plant secondary metabolites are used to treat various human diseases. However, it is difficult to produce a large number of specific metabolites, which largely limits their medicinal applications. Many methods, such as drought and nutrient application, have been used to induce the biosynthetic production of secondary metabolites. Among these secondary metabolite-inducing methods, mechanical wounding maintains the composition of secondary metabolites with little potential risk. However, the effects of mechanical stress have not been fully investigated, and thus this method remains widely unused.
### Methods
In this study, we used metabolomics to investigate the metabolites produced in the upper and lower leaves of *Catharanthus roseus* in response to mechanical wounding.
### Results
In the upper leaves, 13 different secondary metabolites (three terpenoid indole alkaloids and 10 phenolic compounds) were screened using an orthogonal partial least squares discriminant analysis (OPLS-DA) score plot. The mechanical wounding of different plant parts affected the production of secondary metabolites. Specifically, when lower leaves were mechanically wounded, the upper leaves became a strong source of resources. Conversely, when upper leaves were injured, the upper leaves themselves became a resource sink. Changes in the source-sink relationship reflected a new balance between resource tradeoff and the upregulation or downregulation of certain metabolic pathways.
### Conclusion
Our findings suggest that mechanical wounding to specific plant parts is a novel approach to increase the biosynthetic production of specific secondary metabolites. These results indicate the need for a reevaluation of production practices for secondary metabolites from select commercial plants.
## Introduction
Plant secondary metabolites represent important evolutionary adaptations and play an essential role in the response to environmental stress (Liu & Hao, 2018). These metabolites are also widely used to treat human diseases (Bills & Gloer, 2016). Most secondary metabolites can scavenge reactive oxygen species (ROS) and are anti-inflammatory, antibacterial, and antiviral (Lian et al., 2021). Among these metabolites, phenolic compounds (PCs) have particularly great medicinal value. Quercetin, for instance, plays an important antitumor role by reducing the drug resistance of tumor cells and inducing the apoptosis of tumor cells (Hashemzaei et al., 2017). Naringin inhibits the apoptosis of vascular endothelial cells and promotes intraosseous angiogenesis (Shangguan et al., 2017). Plasma-conjugated metabolites of orally administered water-dispersible hesperetin improve vasodilation in endothelial cells (Zhang et al., 2020). Other secondary metabolites such as terpenoid indole alkaloids (TIAs) have also been widely used in medicine. Catharanthus roseus (L.) G. Don (C. roseus) is an important medicinal plant in the investigation of TIAs. Vinblastine, an efficient inhibitor of microtubule polymerization, has been used to treat human neoplasms (Lee et al., 2016). Another TIA serpentine can effectively block the transmission of adrenergic nerve impulses, resulting in vasodilation, lower blood pressure, and a slower heart rate (Mukherjee et al., 2019). It has also been indicated that secondary metabolites can reduce blood fat, delay senility, and improve immunity (Mukherjee et al., 2019).
The synthesis of many secondary metabolites is regulated under stress conditions such as UV-B, wounding, drought, metal toxicity, and nutrient deprivation (Takshak and Agrawal, 2019; Blum, 2017; Erika, Jorge & Daniel, 2018). Mechanical wounding from rain, hail, wind, and herbivores are the most common types of damage that plants face. This can lead to nutrient loss in the damaged tissues, which increases the risk of pathogenic invasions. However, this crisis also results in the massive accumulation of secondary metabolites (Sun et al., 2020). Mechanical wounding is an ideal way to induce the production of secondary metabolites because it reduces the risk of other stresses that pollute secondary metabolites. Nevertheless, the changes involved in secondary metabolites under mechanical wounding stress have been rarely reported (Ibanez et al., 2019).
Metabolomics is a powerful approach used for discovering considerably different metabolites and a useful tool for pinpointing endpoint metabolic effects from external stimuli. It has been widely used for exploring the contents of metabolites, identifying key metabolites, and deciphering central metabolic pathways in plants (Jiang et al., 2019; Li et al., 2016). For example, GC-MS technology has been adopted to compare the medicinal activities of different tissues of *Zingiber mioga* and Zinger roscoe, suggesting that various structural parts of plants have different dietary usages (Soo et al., 2015). Wang et al. [ 2016] discovered that polyphenol accumulation and stress resistance preparation in cacao seed (Theobroma cacao) ripening occurred via the interplay of primary and secondary metabolites at the system level. C. roseus, originating from the coast of the Mediterranean, India, and tropical areas in America, has been extensively studied for its highly economic and pharmaceutical value (Liu et al., 2017; Moon, Mistry & Kim, 2017). The main metabolite TIAs of C. roseus are widely used to treat human diseases. It is considered a remarkable manufacturer of secondary metabolites, and more than 130 kinds of TIAs from the plant have already been described (Pham et al., 2020). Its biosynthesis pathway starts from the coupling of tryptamine and secologanin, then strictosidine is formed by strictosidine synthase (Pham et al., 2020). In the next steps, tabersonine and vindoline are synthesized separately by strictosidine β-glucosidase and deacetylvindoline acetyl CoA acetyltransferase (Singh et al., 2020). Finally, vindoline couples with catharanthine to form valuable vinblastine catalyzed by Peroxidase 1 (Singh et al., 2020). Many of these TIAs are natural anticancer agents, including loganin, catharanthine, serpentine, vindoline, vinblastine, and vincristine. Vinblastine is an efficient inhibitor of microtubule polymerization that is used to treat certain cancers such as Hodgkin’s disease, malignant lymphoma, and a wide variety of other human neoplasms (Mondal et al, 2019). In this study, metabolomics was used to investigate different responses to wounding and the time specificity of secondary metabolites in upper leaves. Our research provides basic data for the response of secondary metabolites to mechanical wounding.
## Plant materials and treatment
C. roseus seeds were sown in pots and grown in a climate chamber (S10H, Conviron, Winnipeg, Canada) with 14 h light (28 °C, irradiation of 450 µmol m−2 s−1) and 10 h dark (25 °C, without irradiation) regime at a humidity of $60\%$. C. roseus seeds were cultivated with hydroponics and irrigated with $\frac{1}{2}$ strength Hoagland’s solution (pH 5.9−6.0). After 80 d, when seven to eight true leaves had developed, plants were randomly assigned to three groups with four plants in each group. They were subjected to either a sham procedure (the control group, CK), mechanical wounding in the upper leaf (the wounded upper leaf group, WUL), or mechanical wounding in the lower leaf (the wounded lower leaf group, WLL). Mechanical wounding was completed with the trim of $\frac{1}{2}$ leaves. Different parts of C. roseus (upper leaf, middle leaf, lower leaf, stem, and root) were collected (Fig. 1). According to the experimental results, the detected parts of plants for the next stage of the experiment were obtained. Each group were detected at 0 h, 1 h, 3 h, and 5 h after wounding. Experiments were conducted for four replicates.
**Figure 1:** *Leaf, stem, and root anatomy of Catharanthus roseus.*
## Metabolite profiling
C. roseus samples were analyzed as previously described (Chen et al., 2017). Briefly, 60 ± 5 mg of plant tissue was gathered, mixed with 360 µL cold methanol and 40 µL 0.3 mg/mL 2-chlorophenylalanine, homogenized (Tissuelyser-192, Shanghai, China), and sonicated for 30 min, then 10,000 g for 10 min at 4 °C. For ultrasonication, 200 µL chloroform and 400 µL water were added to the sample. Samples were methoxyaminated and silylated after dying. After derivatization, samples were analyzed on the GC-MS (Agilent Corporation, Santa Clara, CA, USA). A nonpolar DB-5 capillary column was used for separation. The temperature program was 50−125 °C for 8 min, raised to 125−170 °C for 15 min, raised to 170−210 °C for 4 min, raised to 210−270 °C for 10 min, raised to 270−305 °C for 5 min, and maintained at 305 °C for 5 min. Injection and ion source temperatures were set at 260 °C and 230 °C, respectively. Electron impact ionization (−70 eV) proceeded at full scan mode (m/z 30 −600). The acquisition speed was 20 spectra/s, and mass spectrum (MS) data were analyzed by Chroma TOF software. The data set was normalized using the sum intensities of the peaks in each sample.
For the analyses of secondary metabolites, 1.0 g fresh tissues were mixed with 20 mL analytical grade absolute methanol and exposed to low-frequency ultrasonication (250 W, 40 kHz) for 40 min. After centrifugation at 7,104 g for 10 min, alkaloids were determined using HPLC-MS (Ultra-performance LC, Waters, Milford, MA, USA; MS, AB SCIEX, Framingham, MA, USA) with ACQUITY UPLC BEH C18 Column (1.7 µm, 2.1 mm ×50 mm). TIA content was measured based on the previously described method (Chen et al., 2017). Chromatographic analysis was performed on ACQUITY UPLC BEH C18 Column (1.7 µm, 2.1 mm × 50 mm). The standard solvent system was CH3CN/H2O and 0.05 mol/L ammonium acetate. Retention times were 3.49 min (serpentine), 2.91 min (tabersonine), 3.37 min (vindoline), 3.32 min (vinblastine), 2.81 min (catharanthine), and 0.76 min (loganin). Injection volume was 10 mL and flow rate was one mL/min. After 20 mL methanol extraction, targeted analysis of phenolic metabolites (PCs) was performed using a Waters ACQUITY UPLC system (Waters, Milford, MA, USA) coupled to a quadrupole time-of-flight (QTOF) mass spectrometer (XEVO G2 QTOF, Waters, Milford, MA, USA). The optimized chromatographic conditions were: A%, $0.05\%$ formic acid water; B%, $0.05\%$ formic acid acetonitrile; 120–1,200 m/z; positive ion scanning mode; and leucine enkephalin.
## Statistical analysis
Normalized data were imported into SIMCA-P software (version 13.0, http://www.umetrics.com/simca). A supervised orthogonal partial least squares discriminant analysis (OPLS-DA) was used to identify differential metabolites among the CK, WUL, and WLL groups. Metabolites with both multivariate and univariate statistical significances (VIP >1.0 and p-value <0.05) were extracted by OPLS-DA analysis. Differential metabolites were annotated using the KEGG database (http://www.kegg.jp/kegg/pathway.html) and MBRole 2.0 (http://csbg.cnb.csic.es/mbrole2/). The score of principal component “Q” (Q) was calculated using SPSS software (version 21.0; Chicago, IL, USA). Histograms and pathway maps were generated using GraphPad Prism (version 6.0; GraphPad Software Inc., La Jolla, CA, USA) and Visor (Microsoft, Redmond, WA, USA).
## Screening of plant detection tissues
After screening, we calculated the Q values of secondary metabolites TIAs and PCs sampled from different parts of C. roseus. Stems and roots had robustly lower Q values than leaves. Upper, middle, and lower leaves also showed different Q values with upper leaves obtaining the most intense responses (Fig. 2). Therefore, upper leaves of C. roseus were collected for subsequent metabolomics experiments.
**Figure 2:** *Q values of secondary metabolites in upper leaf, middle leaf, lower leaf, stem, root of Catharanthus roseus.*
## Primary metabolites
To compare the metabolic variations across the CK, WUL, and WLL groups, OPLS-DA was applied. The CK, WUL, and WLL groups were separated by PC1 ($38.1\%$) and PC2 ($9.9\%$) (Fig. 3A). Eleven significantly different metabolites (glucosamine, galactose, xyolopyranose, tagatose, fructofuranose, gentiobiose, fructose, galactitol, octadecanoic acid, hexadecanoic acid, and succinate) were screened from a total of 133 compounds by VIP >1 and a p-value <0.05 criteria (Table 1). These metabolites presented substantial differences in energy supply between the control and wounded groups. Our results suggest that TCA members and sugars significantly increased with mechanical wounding (Figs. 3B and 3C). On the other hand, lipid content was not robustly altered (Fig. 3D). These findings also indicate that TCA members and sugars were in great demand by plants under stress.
**Figure 3:** *Changes primary metabolites.(A) OPLS-DA score plot of primary metabolisms. (B) Relative content of TCA component. (C) Relative content of sugar. (D) Relative content of lipid. CK, control group; WUL group, wounded upper leaf group; WLL group, wounded lower leaf group; WUL group-wul, wounded part in wounded upper leaf group; WUL group-uwul, unwounded part in wounded upper leaf group.* TABLE_PLACEHOLDER:Table 1
## Metabolic fingerprint of TIAs and PCs
The dynamic changes of secondary metabolites in the upper leaves of the control and wounded groups at 0 h, 1 h, 3 h, and 5 h after mechanical wounding were further investigated in detail. TIAs and PCs were separated from the control and wounded groups by OPLS-DA analysis (Fig. 4). The WUL and WLL groups were separated by $21.6\%$ of PC2 in TIAs and $21.7\%$ of PC1 in PCs (Fig. 4). Three different TIAs (vinblastine, loganin, and serpentine) and 10 different PCs (hesperetin, petunidin, daidzenin, chlorogenic acid, syringic acid, hesperidin, naringin, 3-4-hydroxybenzoic acid, and apigenin) were obtained using VIP values (VIP >1) and p-values (p-value <0.05) (Tables 2 and 3). The abundance of these 13 different secondary metabolites was significantly altered at different time points (Fig. 5). The metabolic fingerprint showed that mechanical wounding changed metabolic direction in a tissue-specific and time-dependent manner. Synthetic raw materials and energy from shikimic acid flowed into alkaloid and phenol metabolism, respectively (Fig. 5). Upstream precursor metabolites such as loganin, for instance, responded at 1 h with the most intense response in wounded leaves. Vinblastine, a downstream final product, was substantially reduced in the wounded group. Conversely, serpentine showed a continuously increasing trend, especially in the WLL group.
**Figure 4:** *OPLS-DA score plot of secondary metabolites.(A) OPLS-DA score plot of TIAs. (B) OPLS-DA score plot of PCs. CK, control group; WUL group, wounded upper leaf group; WLL group, wounded lower leaf group.* TABLE_PLACEHOLDER:Table 2 TABLE_PLACEHOLDER:Table 3 **Figure 5:** *The network of altered metabolites after mechanical stress.WUL group-wul, wounded part of wounded upper leaf group; WUL group-uwul, unwounded part of wounded upper leaf group; WLL group-ul, the upper leaf of wounded lower leaf group.*
The PC results showed that the relative contents of chlorogenic acid, petunidin, and daidzein remarkably decreased, suggesting that the synthesis of PCs was affected by mechanical wounding. Myricetin, apigenin, hesperidin, and hesperetin responded to mechanical wounding in the WLL group 3 h after wounding, indicating that these metabolites might participate in common defense reactions. In the WUL group, C6C3C6-structure PCs naringin and hesperidin responded rapidly to wounding. C6C1-structure PCs exhibited a different response to mechanical wounding. 3–4-hydroxybenzoic acid expression elevated hundreds of times at 1 h after wounding and continued to increase at later time points. By comparison, the activation of syringic acid was the opposite in wounded and unwounded leaves in the WUL group, indicating that syringic acid works differently.
## Discussion
Secondary metabolites not only play a key role in protecting plants against stress but are also integral in natural medicine (Rumzum et al., 2020). Many secondary metabolites that are used in healthcare are often in short supply (Bills & Gloer, 2016). Mechanical wounding can stimulate secondary metabolite accumulation and reduce potential hazards from other interference factors (Savatin et al., 2014). The types of secondary metabolites, as well as their abundance, can be affected by the specific part of plant leaf (i.e., upper or lower) and the location of the mechanical wound. In this study, we employed high throughput methods to explore the response mechanisms of the various parts of C. roseus.
Primary metabolites of leaves responded to the mechanical wound. After mechanical wounding, 11 significantly different metabolites, including sugars, lipids, and TCA components, were screened from a total of 133 identified compounds. Among them, some metabolites demonstrated a clear link between mechanical stress and remodeling of the central metabolism. They increased the basic demands of the upper leaves in the wounded group. The function of sugars in this process was mainly to provide substrates for energy production and biosynthesis of secondary metabolites to increase plant resistance. It could also be used as a carbon intermediate in the metabolic cycle.
Besides primary metabolites, the contents of secondary metabolites TIAs, and PCs were also determined. TIAs and PCs are two branches of secondary metabolites that respond to mechanical stress according to their coordination or competition in trade-offs and distribution. In our study, three significantly different metabolites were obtained from the TIA pathway within the different leaf position treatments. Relative to the control group, the content of loganin and serpentine significantly increased following mechanical treatment. Serpentine was generally distributed in the root of C. roseus but largely accumulated in upper leaves after mechanical stress. The loganin-serpentine pathway was found to be stimulated by mechanical wounding, indicating that mechanical stress can improve the yield of some TIAs. Serpentine can combine with norepinephrine to reduce blood pressure (Francisco et al., 2013). Loganin is widely used in clinical medicine as an anticancer drug (Chen et al., 2022). Interestingly, the content of vinblastine also significantly change. They are the end product of the TIAs metabolic pathway. Vinblastine could effectively treat cancer. It was found could management of advanced angiosarcoma by the synergistic combination of Propranolol based metronomic chemotherapy (Pasquier et al., 2016). It is meaningful to affect the content of vinblastine by regulating TIAs metabolism. Ten PCs showed differential responses to mechanical wounding. These metabolites were classified as flavonoids that are usually associated with plant defense. In our study, 3,4-hydroxybenzoic acid, syringic acid, apigenin, and myricetin were significantly accumulated after mechanical treatment. Among these, 3,4-hydroxybenzoic acid was markedly responsive to the mechanical stress at 5 h across all treatment groups. 3,4-hydroxybenzoic acid belongs to C6C1-structure metabolites, and plays an important role in protecting nerves (Ju et al., 2015). Syringic acid with C6C3-structure metabolites can achieve sedative or anesthetic effects through central inhibition (Ogut, Armagan & Gül, 2022), and was massively accumulated after stimulation in the WUL treatment group. Our results showed that PCs with C6C3C6-structures were actively distributed and responded to mechanical stress. Apigenin and myricetin with C6C3C6-structure metabolites also have important medical value and were stimulated in the WLL group for 1 h and 3 h, respectively. Apigenin can inhibit the activity of carcinogens (Pang et al., 2021). Myricetin has a hypoglycemic effect (Negri et al., 2022). When accumulated, these metabolites will benefit plant defense.
After mechanical wounding, plants invest their resources to maximize their fitness (Impa et al., 2019). As shown here, to handle mechanical wounding, the metabolites of C. roseus were relocated in leaves and a new balance was established (Fig. 5). We propose that this relocation is the result of a source–sink relationship and is not correlated to organ biomass. In the WUL group, wounded upper leaves become a stronger sink relative to upper leaves and required more of an investment from the source. This would cause more response metabolites to be produced. Our results has shown that more sugars, TIAs, and parts of PCs were significantly accumulated in wounded upper leaves in the WUL group. By contrast, in the WLL group, upper leaves become a source and provided support for wounded lower leaves. However, the upper leaves were not fully mature and could not produce more comprehensive response metabolites. This led to more TCA compounds and fewer secondary metabolites being produced. The source–sink model provides a mechanism to regulate the resource distribution and effectively alter plant response patterns. This change directly led to a significant increase in the WLL group and a slow response to mechanical pressure. Therefore, more secondary metabolites were obtained without external influence.
## Conclusion
Changes in the source–sink relationship indicate that local responses in upper leaves have different effects among the CK, WUL, and WLL groups. Upper leaves of C. roseus in the WUL group required more substrates and energy to stimulate the production of secondary metabolites for defense. Conversely, in the WLL group, most secondary metabolites in the upper leaves were transported to the damaged region. These different response strategies resulted in discrepant synthetic pathways and secondary metabolite accumulation. Therefore, an observed increase in TIAs and/or PCs in response to stress may encourage a reevaluation of the commercial plant production practice to increase the yield of specific secondary metabolites for their usage in healthcare. In our study, the synthesis of TIAs stays at the upstream product and the content of our expected end products was low. In the future, we will pay more attention to the method of producing TIA end products.
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|
---
title: Advanced polymeric metal/metal oxide bionanocomposite using seaweed Laurencia
dendroidea extract for antiprotozoal, anticancer, and photocatalytic applications
authors:
- Musarat Amina
- Nawal M. Al Musayeib
- Seham Alterary
- Maha F. El-Tohamy
- Samira A. Alhwaiti
journal: PeerJ
year: 2023
pmcid: PMC10035428
doi: 10.7717/peerj.15004
license: CC BY 4.0
---
# Advanced polymeric metal/metal oxide bionanocomposite using seaweed Laurencia dendroidea extract for antiprotozoal, anticancer, and photocatalytic applications
## Abstract
### Background
Biosynthesized nanoparticles are gaining popularity due to their distinctive biological applications as well as bioactive secondary metabolites from natural products that contribute in green synthesis.
### Methodology
This study reports a facile, ecofriendly, reliable, and cost-effective synthesis of silver nanoparticles (AgNPs), copper oxide nanoparticles (CuONPs), and polymeric PVP-silver-copper oxide nanocomposite using ethanol extract of seaweed *Laurencia dendroidea* and were evaluated for antiprotozoal, anticancer and photocatalytic potential. The nanostructures of the AgNPs, CuONPs, and polymeric PVP-Ag-CuO nanocomposite were confirmed by different spectroscopic and microscopic procedures.
### Results
The UV-vis spectrum displayed distinct absorption peaks at 440, 350, and 470 nm for AgNPs, CuONPs, and polymeric Ag-CuO nanocomposite, respectively. The average particles size of the formed AgNPs, CuONPs, and Ag-CuO nanocomposite was 25, 28, and 30 nm, respectively with zeta potential values −31.7 ± 0.6 mV, −17.6 ± 4.2 mV, and −22.9 ± 4.45 mV. The microscopic investigation of biosynthesized nanomaterials revealed a spherical morphological shape with average crystallite sizes of 17.56 nm (AgNPs), 18.21 nm (CuONPs), and 25.46 nm (PVP-Ag-CuO nanocomposite). The antiprotozoal potential of green synthesized nanomaterials was examined against *Leishmania amazonensis* and Trypanosoma cruzi parasites. The polymeric PVP-Ag-CuO nanocomposite exerted the highest antiprotozoal effect with IC50 values of 17.32 ± 1.5 and 17.48 ± 4.2 µM, in contrast to AgNPs and CuONPs. The anticancer potential of AgNPs, CuONPs, and polymeric PVP-Ag-CuO nanocomposite against HepG2 cancer cell lines revealed that all the nanomaterials were effective and the highest anticancer potential was displayed by PVP-Ag-CuO nanocomposite with IC50 values 91.34 µg mL−1 at 200 µg mL−1 concentration. Additionally, PVP-Ag-CuO nanocomposite showed strong photocatalytic effect.
### Conclusion
Overall, this study suggested that the biogenic synthesized nanomaterials AgNPs, CuONPs, and polymeric PVP-Ag-CuO nanocomposite using ethanol extract of seaweed L. dendroidea possesses promising antiprotozoal anticancer and photocatalytic effect and could be further exploited for the development of antiprotozoal and anticancer therapeutics agents.
## Introduction
Nanotechnology is primarily concerned with the shape, controlled dispersion of nanoparticles with small size (1–100 nm), chemical composition, synthesis, and their utilization for human benefit (Caputo et al., 2021; Aflori, 2021). Nanotechnology has tremendous potential to create diagnostic solutions, cure, and prevention of ailments at the cellular level, and its application in the medical industry is known as nanomedicine. Nanomedicine encompasses a number of diverse areas, including regenerative medicine, drug delivery systems, and diagnostics and treatment (Mahmoudi, 2021). Metallic nanoparticles play a crucial role in the pharmaceutical and medical sciences.
Among various metal nanoparticles, biogenic silver and copper oxide nanoparticles are synthesized as multifunctional therapeutic materials that provide the advantage of biomedical and pharmaceutical applications with very low systemic toxicity. Silver nanoparticles (AgNPs) have advanced physicochemical properties such as chemical stability, non-linear optical behavior, enhanced thermal and electrical conductivity, high surface Raman scattering, and various biomedical potential (Lee & Jun, 2019). Silver nanoparticles (AgNPs) have been reported to have an enormous number of applications, particularly in biomedicine due to their broad range of biological potential including antibacterial, antiviral, antiprotozoal, antifungal, and anticancer properties (Jain et al., 2021; Sofi et al., 2021). Besides silver nanoparticles, versatile features of copper oxide (CuO) nanoparticles have attained much attention in recent times due to their diverse applications in many scientific fields including heterogeneous catalysts, solar cells, gas sensors, lithium-ion batteries, and antibacterial agents (Grigore et al., 2016). CuO nanoparticles (CuONPs) are the most stable, and robust with a longer shelf-life period in contrast to organic components, and potential antimicrobials (Zheng et al., 2021). Biogenic copper oxide nanoparticles (CuONPs) have been revealed to exhibit various biological properties including antibacterial, antifungal, antioxidant, and anticancer (Amin et al., 2021; Mani et al., 2021). In spite of numerous multifarious applications of monometallic nanoparticles, advancement in the formation of bimetallic hybrid nanoparticles has also been boosted. The combination of metals added an advantage to the combined properties of individual metals. The combined metals contribute to the area of catalysis, reactivity as well as elevated biological properties which offers numerous benefits over their individual metallic counterparts (Sharma et al., 2019).
Despite various conventional approaches for the preparation of nanomaterials, scientists have shifted their focus to the biogenic pathways due to their environmentally benign approach (Aboyewa et al., 2021). The formation of nanostructures by this route offers tremendous advantages including, less-toxic, economical, and biocompatible compared to physical and chemical procedures. The advantages of green or biological synthesis include the ability to readily scale up to large-scale synthesis and the ability to produce nanoparticles in the right sizes and shapes with improved stability (Ahmed et al., 2022; Restrepo & Villa, 2021). Green synthesis reduces the usage of potentially dangerous industrial chemicals while producing nano-products in a single step (Sagandykova, Szumski & Buszewski, 2021). The use of natural resources (plants, diatoms, bacteria, fungi, Yeast, and marine resources) is gaining enormous attention in scientific research because these natural products serve as reducing, capping, and stabilizing agents for the formation of nanoparticles (Kharissova et al., 2019). The type of natural resource extract used to play important role in the size and shape of nanoparticles as a different natural resource has different mounts of reducing content (Makarov et al., 2014). The extracts of natural products are mainly contained abundant bioactive molecules such as phenolics, steroids, terpenoids, tannins, flavonoids, alkaloids, proteins, sugars, and enzymes (Nyamai et al., 2016).
The enormous biodiversity present in marine ecosystems offers a promising resource of novel bioactive compounds with potential human utility (Ameen, AlNadhari & Al-Homaidan, 2021). Some of these microorganisms can survive in harsh marine environments, resulting in complex components with unique biological features that can be utilized in various industrial and biotechnological applications (Menaa et al., 2020). Marine-based bioactive components can be derived from diverse sources, including microorganisms, marine plants, micro and macroalgae, and sponges, all of these contain their own set of a unique set of biomolecules (Malve, 2016). Macroalgae also known as seaweed, represent $23.4\%$ of the tonnage and $9.7\%$ of the value of the global aquaculture (marine, freshwater, and brackish water) production, analyzed at 59.4 million tones and $ 70.3 billion in 2004 (Ghosh, Banerjee & Mitra, 2012). Seaweeds are commonly susceptible to microbial colonization and produce a huge number of secondary metabolites to protect themselves against herbivory and biofouling (Mrid et al., 2021). Seaweed contains various inorganic and organic compounds, including terpenoids, carotenoids, sterols, chlorophylls, xanthophylls, phycobilins, tocopherol, polysaccharides, polyunsaturated fatty acids, vitamins and phycocyanins (Das et al., 2011). These organic constituents serve as phytochemist-attractants, bioprotectant, biostimulant, and microbial nutrient sources as well as mediate in competitive interactions for space in benthic habitats, acting as allelochemicals (Mrid et al., 2021). They are used as food, feed, fodder, and fertilizer, and many bioactive components produced by macroalgae are known to have potential beneficial use in healthcare (Kadam & Prabhasankar, 2010; Smit, 2004). Seaweed species of the genus Laurencia have attracted substantial attention from scientists for the untapped diversity of secondary metabolites, particularly terpenes and acerogenins (Cikos et al., 2021). The pharmacological properties of these constituents include potential antibiotic, antiviral, antileishmanial, antimalarial, antitrypanosomal, anti-carcinoma, anticancer, anti-inflammatory, and anti-diabetic activities (Minamida et al., 2021; Cikos et al., 2021). Laurencia dendroidea is a red seaweed species widely distributed in the Atlantic Ocean, native to the Brazilian coast. It is found at a 3 m depth from the intertidal to the subtidal zone. An erect violet-greenish or brown-purple-colored thalli forming 4–20 cm dense tufts (Cassano et al., 2012). The macroalgal genus Laurencia has been investigated chemically and pharmacologically since 1960, but has become the subject of great interest, as evidenced by the recent discovery of new phytoconstituents, mainly halogenated constituents (Vairappan et al., 2001). Polyphenols, terpenes, and halogenated compounds are the main components of L. dendroidea and are reported to possess promising biological properties (Gonçalves et al., 2020; Barcellos et al., 2018). A major halogenated sesquiterpene [-]-elatol produced by L. dendroidea exhibited strong biocidal and anti-epibiotic effects (De Oliveira et al., 2015). It can be utilized for the preparation of antifouling paints and the development of antimicrobials (Da Gama et al., 2002). A recent study revealed the [-]-elatol concentration variability in the intra and interpopulation levels in L. dendroidea, indicating that this variability could be due to environmental factors such as salinity, and temperature (De Oliveira et al., 2015). Various biosynthetic nanomaterials such as silver, gold, zinc oxide, and Ag-ZnO composite using seaweed *Codium capitatum* P.C., Fucus gardeneri (Kannan et al., 2013; Princy & Gopinath, 2021), Turbinaria conoides (Rajeshkumar et al., 2013), seaweeds of gulf of Mannar (Nagarajan & Arumugam Kuppusamy, 2013) and *Padina gymnospora* seaweed extract (Rajaboopathi & Thambidurai, 2018) and all these nanoparticles have shown interesting biological properties. There are only few studies reported in the literature for the synthesis of Ag and Au nanoparticles using the marine alga *Laurencia catarinensis* (Abdel-Raouf et al., 2017), red algae *Laurencia aldingensis* and Laurenciella sp. ( Vieira et al., 2016). To the best of our knowledge, no study has been reported for biogenic synthesis of nanomaterials using L. dendroidea till date.
Keeping into consideration the chemical profile and biological properties L. dendroidea, the present study focused on the green synthesis of Ag, CuO, and PVP-Ag–CuO NCS using seaweed L. dendroidea extract and evaluating their antiprotozoal, anticancer, and photocatalytic activities.
Herein, a facile, environmentally benign, cost-effective, and easy approach was used to prepare Ag, CuO, and polymeric PVP-Ag–CuO nanoparticles using seaweed L. dendroidea extract. The as-prepared nanoparticles (AgNPs, CuONPs, and PVP-Ag–CuONPs) were characterized by different spectroscopic including analytical such as ultraviolet–visible (UV-vis), fourier transform infrared (FTIR), and X-ray diffraction (XRD) as well as microscopic such as scanning electron microscope (SEM), Energy dispersive X-ray (ESI), and transmission electron microscope (TEM) methods. In addition, the prepared nanomaterials were evaluated for antiprotozoal activity against L. amazonensis and T. cruzi parasites. The anticancer potential of nanomaterials was determined against HepG2 cancer cell lines. Also, the photocatalytic effect of biosynthesized was tested towards methylene blue dye.
## Chemical and reagents
Methanol ($98.2\%$), ethanol ($95.0\%$), dimethyl sulfoxide (DMSO), silver nitrate (AgNO3, ≥ $99\%$), copper (II) acetate monohydrate (Cu(CO2CH3)2 H2O, ≥ $99\%$), sodium hydroxide (NaOH, $97\%$), glucose (≥ $99.5\%$), polyvinylpyrrolidone (PVP), hemin (≥ $90.0\%$), fetal bovine serum (FBS), ampicillin ($96.0\%$), streptomycin, resazurin, glucantine (≥ $98.0\%$), benznida-zole ($97.0\%$), 3-(4,5-dimethylthiazol-2-yl)-2, 5-diphenyl tetrazoliumbromide (MTT), tri-buffer, 2-nitrobenzoic acid (DTNB, $95.0\%$), thiobarbituric acid (TBA, ≥ $98.0\%$) used for this study were purchased from Sigma-Aldrich (Hamburg, Germany).
## Antiprotozoal strains and cancer cell line
Antiprotozoal parasites, *Leishmania amazonensis* (MNYC/BZ/62/M384), and anti-Trypanosoma cruzi (RA) were obtained from the microbiology Department, King Saud University, Saudi Arabia. Human liver cancer cell lines HepG2 (ATCC-HB-8065) were procured from the American Type Culture Collection (ATCC®, Manassas, VA, USA).
## Biomass material
The red macroalga *Laurencia dendroidea* biomass was collected at 1–2 m depth from the coast of Jeddah, Saudi Arabia, in September 2020. The collected biomass was washed with seawater to clean from necrotic and epiphytes parts at the sampling station and shifted to the laboratory under cold conditions. The sample biomass was then rinsed with sterile water, followed by ethanol ($75\%$) to it free from contaminating materials and associated microflora. The collection of microalgae was supported by identification authorization LD 33241–1(Microbiology department, KSU, Riyadh, Saudi Arabia).
## Preparation of biomass extract
The dried powder of macroalga L. dendroidea (100 g) was subjected to Soxhlet extraction using $95\%$ of ethanol (3 × 500 mL). All the collected extracts were combined, filtered through Whatman filter paper No 1, and concentrated at 50 °C under reduced pressure on a rotavapor. A green-colored residue (12.3 g) was obtained and stored in the refrigerator until further use.
## Preparation of Ag, CuO, and polymeric PVP-Ag–CuO NCS
The L. dendroidea ethanolic extract (2 g) was dissolved in 100 mL Millipore water and stirred for 2 h under sonication. The 10 mL of dissolved material was treated with 0.5 g silver nitrate under continuous stirring for ∼2 h at 80−85 °C. The instant color change from greenish-yellow to dark brown of the reaction mixture occurred, afterward, no color transformation was observed till the reaction ended. The reaction mixture was then cooled down and centrifuged at 10,000 for 30 min. Consequently, the resulting product was washed thoroughly several times using Millipore water. Finally, the obtained black precipitate was collected and dried at 80 °C for 12 h in a domestic oven. The reduced Ag+ ions were spectrophotometrically monitored between 190–800 nm. Whereas the CuONPs was prepared by dissolving 5.0 g of Cu (CO2CH3)2 H2O in 10 mL of distilled water under constant magnetic stirring at ambient temperature for 10 min. Afterward, 2.0 g of L. dendroidea extract dissolving in 10 mL deionized water was added dropwise to the copper acetate solution under constant stirring at 60 °C for 4 h. A blackish precipitate appeared after the complete addition of L. dendroidea extract which was heated to evaporate the excess water for a further 30 min. After the complete removal of water, the sample calcined at 400 °C for 4 h in a furnace oven resulting in the formation of CuONPs. The pre-calcined CuONPs were cooled at ambient temperature prior to exploration. However, PVP-Ag–CuO NCS was prepared with copper acetate, silver nitrate, and L. dendroidea extract in the presence of a polymeric matrix. Briefly, 0.2 mol L−1 of copper acetate was dissolved in 10 mL of $1.0\%$ PVP (w/v) dissolved in deionized water and 0.42 g of silver nitrate (0.025 M) was added slowly to the solution under magnetic stirring for 15 min at ambient temperature. 2 g of L. dendroidea extract dissolved in 10 mL deionized water was added to the above reaction mixture with constant stirring for 20 min and instant color change to dark black suggested the formation of PVP-Ag–CuO NCS. PVP serve as a surface stabilizer, nanoparticle dispersant, reducing agent and growth modifier in the formation of nanomaterial. Afterward, the solution mixture was heated for 15 min at 80 °C and the final resulting product was washed three times with ethanol followed by deionized water. The formed PVP-Ag–CuO NCS was allowed to dry in vacuum desecrator (Scheme 1). Previous studies have shown that nanoparticles such as AgNPs, CuONPs and bionanocomposite were also prepared by using plant extract, sponges, bacteria, proteins, and polysaccharides. A tabulated comparison of biosynthetic method, source used and their application were illustrated in Table 1.
## Characterization
UV–Visible DRS (UV-2600 Shimadzu, Kyoto, Japan) was applied to determine the surface plasmon resonance (SPR) and optical properties of AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS. The structural information and crystalline phase of pre-synthesized nanoparticles and nanocomposite were obtained with Cu Kα (1.5405 Å) in 40 mA and 30 kV using a ProPowder X’Celerator diffractometer PANalytical X’Pert (Malvern, UK). The functional moieties and bonds in pre-synthesized nanomaterials were verified by FT-IR in 4000–400 cm−1 on Perkin Elmer (Llantrisant, United Kingdom). SEM (CarlZeiss, Oberkochen, Germany), TEM (Titan, Corston Bath, United Kingdom), and EDX (ThermoFisher Scientific, Waltham, MA, USA) were applied to measure the surface morphology, nano-sizes, and elemental composition of prepared nanoparticles and nanocomposite.
## Antiprotozoal activity
The antiprotozoal activity of AgNPs, CuONPs, and PVP-Ag–CuO NCS was evaluated by anti-Leishmania and anti-Trypanosoma activity assays by obeying the described methods of (Aguilera et al., 2019; Aguilera et al., 2019). L. amazonensis, promastigotes were cultured in an axenic medium containing BHI-Tryptose provided with 3.0 × 10−4 g/mL glucose, 2 × 10−5 mg mL−1 hemin, $10\%$ of fetal bovine serum (FBS), 1.3 × 10−4 g mL−1 ampicillin, and 2.0 × 10−4 g mL−1 streptomycin at 28 °C (Faral-Tello et al., 2020). 96-well plates were loaded with 2 × 106 promastigotes/well for the assay. The test samples (AgNPs, CuONPs, and PVP-Ag–CuO NCS) were dissolved in dimethyl sulfoxide (DMSO). 200 µL of each sample of different concentrations (25−0.05 µg mL−1) were added separately to individual well-plate and incubated at 28 °C for 48 h. Afterward, 20 µL of a resazurin solution (2 µg mL−1 prepared in PBS, pH 7.4) was added to each well plate. The quantification of oxidation–reduction of the reaction mixture was noted at 570 and 600 nm. The efficacy of pre-synthesized nanoparticles and nanocomposite was measured by determining the IC50 values using OriginLab 8.5® sigmoidal regressions. Glucantime was used as the reference standard. Each applied concentration was examined in triplicates and each anti-proliferative study was performed in duplicate. However, epimastigotes were cultured in an axenic medium (BHI-Tryptose), for the anti-T. cruzi potential (in vitro). 5–7 days old cell cultures were loaded in a freshly prepared culture medium to obtain 2 × 106 cells/mL initial concentration. Each day absorbance of the cell culture was recorded at 600 nm. On the 5th day, different doses (25−0.05 µg mL−1) of the test sample dissolved in $0.4\%$ (v/v) DMSO were inoculated onto the medium from the stock solution. Medium containing $0.4\%$ DMSO v/v was used to cultivate the control parasites and benznidazole was applied as a positive control. The measured absorbance on the 5th day was compared with the control and IC50 values were estimated for each test sample using OriginLab 8.5® sigmoidal regressions. Each applied concentration was examined in triplicates and each anti-proliferative study was performed in duplicate.
**Scheme 1:** *Schematic synthetic mechanism of formation of polymeric Ag-CuO NCS.* TABLE_PLACEHOLDER:Table 1
## Anticancer activity
The anticancer activity of L. dendroidea extract, AgNPs, CuONPs, and PVP-Ag–CuO NCS was examined on the HepG2 cancer cell lines using cell viability and MTT assays. The effect of pre-synthesized nanomaterials on the viability of HepG2 cancerous cells was carried out by obeying the MTT procedure. In 24-well microtiter plates the HepG2 cells with (1.0 × 104 cells/well) density were loaded in a 1.0 mL culture medium. About 50–200 µg mL−1 dilution of pre-synthesized nanomaterials was added to each microtiter plate and incubated at 37 °C for 48 h in a humidified incubator. Afterward, 200 µL of MTT reagent prepared in phosphate-buffered saline (PBS, 5 mg mL−1, pH 7.4) was added to each well plate and placed undisturbed for 2 h at room temperature. Finally, the reaction mixture solution was treated with 200 µL of DMSO and subjected to spun (1800 × g) at 4 °C for 5 min. The absorbance was noted at 540 nm on an Elx-800 microplate reader (Eid & Hawash, 2021). The effect of AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS on the growth inhibition was calculated as percentage cell viability in contrast with DMSO-treated cells as control. The values of test samples were used to subtract the absorbance number of media-containing wells. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}\begin{eqnarray*}\text{% Cell Viability}=({A}_{\mathrm{s}}-{A}_{\mathrm{b}})/({A}_{\mathrm{c}}-{A}_{\mathrm{b}})\times 100 \end{eqnarray*}\end{document}% Cell Viability=As−Ab/Ac−Ab×100 Where As, Ab, and Ac represent the absorbance’s of test samples, blank, and control, respectively.
## Estimation of protein (Bradford assay)
The protein concentration was evaluated using the Bradford assay in the cell pellets obtained from AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS-treated HepG2 cancer cells (Afsar et al., 2016).
## GSH levels determination in HepG2 cancer cell line
The nanomaterials-treated HepG2 cancerous cells were centrifuged at 9,000 rpm for 10 min until the cellular protein was precipitated out. Subsequently, 0.4 mol L−1 of tris buffer (pH 8.9) was added to the supernatant, followed by the addition of DTNB reagent, and incubated at for 10 min under constant shaking. The change of color was noticed and the intensity of color was recorded at 412 nm (Okuno et al., 2003).
## Lipid peroxidation estimation in HepG2 cancer cells
Lipid peroxidation (LPO) quantitation was performed using TBA reactive substances (TBARS) assay using a TBARS kit. Nanomaterials-treated HepG2 cancerous cells were centrifuged at 9,000 rpm for 10 min, sonicated to make a uniform solution, and centrifuged again under similar conditions. After centrifugation, the supernatant was collected, and about 500 µL of supernatant was reacted with one mL of TBA and incubated at 100 °C for 15 min in a water bath. The reaction mixture solution was cooled and then centrifuged for 2 min at 13,000 × g. A lysate supernatant (500 µL) was separated and at 550 nm absorbance of the fluorescent adduct was recorded. TBARS were represented as minimum detectable activity (MDA) equivalents (Rao et al., 2019).
## Photocatalytic effect
The photocatalytic effect of AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS was evaluated by the degradation of methylene Blue (MB) as a reference pollutant. A Xe lamp with 300W in combination with a UV cutoff filter was applied as a source of light to obtain visible light of λmax ≥ 400 nm wavelength, and illumination intensity of 80 mW cm−2. A 0.031 mM solution of MB dye was prepared by dissolving 0.01 g of MB dye in 100 mL of deionized water and stored under a dark condition at room temperature as a stock solution. Afterwards, each test sample (0.1 g) was dispersed in separate customized reactor in 100 mL of 0.031 mM of MB solution. Each obtained sample suspension solution was kept in the dark under a constant magnetic stirring for 30 min to achieve an adsorption–desorption equilibrium between photocatalyst and MB as well as good dispersion. The sample suspensions were exposed to sunlight. After every 5 min interval in the course of visible light exposure, around 10 mL of the reaction mixture was taken out and subjected to centrifugation to eliminate the trace solid particles. Finally, the visible absorption spectra were measured to study the photocatalytic effect of each test sample.
## Characterization of synthesized nanoparticles
The formation of biogenic synthesized AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS was confirmed using UV-Vis spectrophotometric measurement at absorption wavelength in the range of 200–600 nm. The colloidal suspensions of the pre-synthesized nanomaterials displayed three absorption maxima at 440, 330, and 470 nm, for AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS, respectively (Fig. 1). The optical characteristic of these nanomaterials in the visible region was due to the absorption from the collective oscillation of electrons as a result of the electric field of electromagnetic radiation light (Kumar et al., 2012). Tauc’s plot was used to calculate the bandgap using Eg = h υ = hc/λ equation, where h, c, and λ represent Planck’s constant, the velocity of light, and the wavelength, respectively. The estimated bandgaps were found to be 4.51 eV, 2.40 eV, and 1.56 eV, for AgNPs, CuONPs, and PVP-Ag–CuO NCS, respectively (Figs. 2A–2C). The difference in bandgap energy between AgNPs and polymeric PVP-Ag–CuO NCS was 2.95 eV, while the bandgap energy difference between CuONPs and polymeric PVP-Ag–CuO NCS was 0.84 eV, attributed to redshift. The decrease in bandgap energy in polymeric PVP-Ag–CuO NCS enhances the electrons active in oxidation. Moreover, the surface plasma resonance increases the radiation penetration ability, generates the scattering probability, and provides the reduction form to the surface sites to the whole movement on the surface of CuO surface, and their interaction may elevate the process. These processes involve the whole generation and separation of electrons on the surface, improving the process of oxidation.
**Figure 1:** *UV-Vis spectra of CuONPs (350 nm), AgNPs (440 nm), and polymeric PVP-Ag/CuO nanocomposite (470 nm).* **Figure 2:** *Bandgap energy of (A) AgNPs, (B) CuONPs, and (C) PVP-Ag/CuO nanocomposite.*
The FT-IR spectra of pre-synthesized AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS were recorded. The FT-IR of AgNPs showed significant absorption bands at 3405, 3011, 2952, 2853, 1740, 1462, and 1068 cm−1. The absorption bands at 3405 and 3011 cm−1 can be assigned for O-H and N-H stretching vibration which indicates the presence of some polyphenolic constituents in the L. dendroidea extract attached to the surface of AgNPs. The bands appeared at 2952 and 2853 cm−1 indicated the presence of C-H alkane and aldehyde stretching vibration groups, respectively. The three bands observed at 1740, 1462, and 1068 cm−1 can be assigned to C =O, a strong stretching ester group, medium C-H bending methylene group, and strong C-O stretching primary alcohol group, respectively (Fig. 3B). The presence of amine, carbonyl, and alcoholic groups revealed the capping effect of the phytochemicals present in L. dendroidea extract on the synthesized AgNPs (Nguyen et al., 2019). However, the FT-IR spectra of CuONPs and polymeric PVP-Ag–CuO NCS were studied and compared. On investigating the spectra of CuONPs and polymeric PVP-Ag–CuO NCS, it was noticed that CuONPs exhibited strong and sharp absorption bands at 400 to 700 cm−1. These peaks correspond to undoped Cu-O vibrational mode with significant bands at 519, 593, and 623 cm−1 (Rashad et al., 2015; Sharma et al., 2015). Furthermore, it was noticed that the spectrum of CuONPs showed two broad bands at 3307 and 1379 cm−1 corresponding to O-H (intermolecular stretching alcohol group) and (C-H bending of dimethyl group), respectively. Whereas, the FT-IR spectrum of polymeric nanocomposite displayed less intense and shifted bands between 500 and 700 cm−1 representing the Cu-O with major bands located at 502, 575, and 669 cm−1. Also, two broad bands at 2326 and 1753 cm−1 were observed which correspond to C =O and O = C = O stretching vibration of anhydride and carbon dioxide, respectively. A similar band noticed at 2326 cm−1 in the CuONPs spectrum was less in its intensity indicating the difference in reduction and capping process in the CuONPs and polymeric PVP-Ag–CuO NCS samples (Figs. 3C and 3D). However, the FT-IR spectrum of seaweed extract displayed different absorption bands at 3410, 2921, 2848, 1620, 1382, 1095, 986, and 610 cm−1 corresponding to strong O-H stretching of alcohol, weak O-H starching of alcohol, C-H stretching of alkane, C =C stretching of cyclic alkene, medium C-N stretching of amine, strong C =C starching of monosubstituted alkene, strong halo compound stretching vibration, respectively (Fig. 3A). The spectrum of seaweed extracts clearly showed the absence of Ag and CuO peaks in the extract. Thus, the main responsible components for the reduction, capping and stability of AgNPs, CuONPs, and Ag–CuO NCS were sesquiterpenes, phenolics, acids, proteins, and polysaccharides present in the extract of L. dendroidea seaweed (Al-Massarani, 2014).
**Figure 3:** *FT-IR spectra of (A) AgNPs, (B) CuONPs, (C) polymeric PVP-Ag/CuO nanocomposite, and (d) seaweed extract measured at 4000–400 cm−1.*
The XRD estimation of AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS was established by applying Cu Kα radiation whose peaks were compared with the Rietveld profile fitting procedure. The XRD pattern of L. dendroidea extract-mediated AgNPs showed a similar peak profile to typical obtained for reported AgNPs suggesting a high purity of the pre-synthesized nanoparticles. The 2θ values at 38.2° [1 1 1], 46.3° [2 0 0], 64.6° [2 2 0], 77.6° [3 1 1], and 80.4° [2 2 2] were recorded which is indicative of a face-centered cubic crystalline nature of AgNPs (Fig. 4A) and consistent with the database (JCPDS No. 04-0783) (Lanje, Sharma & Pode, 2010). While as, diffraction peaks 2 θ values at 32.5° [1 1 1], 35.7° [2 0 0], 46.7° [2 0 2], and 66.7° [1 1 3] were recorded for the plane orientation of monoclinic CuO structure (Fig. 4B) and matched with standard database (JCPDS80-1268) (Skawky & El-Tohamy, 2021). The purity of pre-synthesized CuONPs was confirmed by the absence of secondary Cu2O or Cu4O3 phases. However, the analysis of polymeric PVP-Ag–CuO NCS exhibited four distinctive peaks for the AgNPs at 2 θ values of 37.89° [1 1 1], 44.04° [2 0 0], 64.21° [2 0 2], and 76.78° [3 1 1] indicating a crystalline cubic structure, in agreement with the findings of other reported studies (Elango et al., 2018). Thus, the crystalline nature and the production of AgNPs as a result of the green biosynthesis technique were confirmed. As a significant peak for Ag was observed in polymeric PVP-Ag–CuO NCS, it was illustrated that Cu sites were interstitially substituted by Ag without creating any other defects (Fig. 4C). Moreover, the Scherer equation was used to calculate the average size of AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS by obeying $D = 0.9$ λ/βCosθ where D, λ, β, and θ represent crystallite size, wavelength, and the half-width of diffraction peak, and the diffraction angle of the highest peak, respectively (Khan et al., 2020). The average crystallite size obtained for AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS was found to be 17.56 nm, 18.21 nm, and 25. 46 nm, respectively.
**Figure 4:** *XRD patterns of (A) AgNPs, (B) CuONPs, and (C) polymeric PVP-Ag/CuO nanocomposite.*
The stability of nanostructures is crucial for various applications and can be measured by zeta potential (Lunardi et al., 2021). The nanostructures stability is the liquid surface charge of nanomaterials in the solution and is assumed stable when the values of zeta potential are higher than 30 mV or less than −30 mV (Narain, 2020). The surface charge of nano-substances was evaluated using deionized water as dispersant (cP: 0.8872, RI: 1.330, and ɛ: 78.5). The net charge of AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS was observed negative, indicating that the nanostructures were attained by capping with organic constituent present in L. dendroidea extract (Figs. 5C–5C). However, zeta potential values obtained for AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS were −31.7 ± 0.6 mV, −17.6 ± 4.2 mV, and −22.9 ± 4.45 mV, respectively, suggesting the physical stability of obtained nanostructures by the reduction with L. dendroidea extract. While the CuONPs reached the delicate dispersion threshold, the polymeric PVP-Ag–CuO NCS, besides the fact, they are considered a similar category, were closer to displaying medium stability levels. Thus, the stability of polymeric PVP-Ag–CuO NCS was found to be enhanced with AgNPs by improving the physical state of CuONPs in the solution.
**Figure 5:** *Zeta potential of (A) AgNPs, (B) CuONPs, and (C) polymeric PVP-Ag/CuO nanocomposite.*
The size, surface morphology, and elemental presence in the biosynthesized AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS were visualized by SEM coupled with EDX. Figure 6A depicted the size of AgNPs in the 19–30 nm range with a 25 nm average size. The images of SEM showed that most of the biosynthesized AgNPs were spherical in shape. Whereas, the SEM images of the pre-synthesized CuONPs revealed a quasi-spherical shape in a 10–40 nm particle size range with a 28 nm average size (Fig. 6B). However, in biosynthesized polymeric PVP-Ag–CuO NCS, surface of CuO was clustered by AgNPs, size, and shape were changed to the lattice arrangement of the nanocomposite. Thus, polymeric PVP-Ag–CuO NCS were found spherical in shape with 20–35 nm range and average size 30 nm (Fig. 6C). The orientation of Ag+ ions with the surface of copper oxide (Cu2+ and O2−), the modification in the size of polymeric PVP-Ag–CuO NCS took place via lattice oxygen vacancy occupied by Ag+ and Cu2+ ions onto the surface (Rehman et al., 2021). The elemental composition of AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS measured by EDX was shown in Figs. 6D, 6E and 6F. Figure 6D showed the presence of Ag with percentage weights of $98.89\%$ and atomic percentage $92.79\%$. Figure 6E, displayed the presence of Cu and O with percentages weight $73.67\%$ and $26.33\%$, atomic percentage $41.33\%$ and $58.67\%$, respectively. However, the polymeric PVP-Ag–CuO NCS spectrum showed the presence of Ag, Cu, and O elements with weight percentages of $4.54\%$, $68.23\%$, and $27.25\%$, atomic percentages $1.27\%$, $39.70\%$, and $59.32\%$ (Fig. 6F), respectively. The low Ag content was reassured by the values obtained in the EDX spectrum.
**Figure 6:** *(A–C) SEM spectra and (D–F) EDX AgNPs, CuONPs, and polymeric PVP-Ag/CuO nanocomposite.*
The spherical shape of AgNPs and polymeric PVP-Ag–CuO NCS as well as the quasi-spherical shape of CuONPs has been visualized in TEM images (Figs. 7A–7C). The average particles size for AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS were observed at 25 nm, 30 nm, and 32 nm, respectively, which were in agreement with the SEM and XRD analysis. In this study, L. dendroidea extract was responsible for the production and growth of Ag/CuO NCS. The potential of Ag+ ions is strongly involved than Cu2+ ions, whereas the surface morphology of Ag/CuO NCS depicted the formation of Ag cluster onto CuO surface. The measurements of lattice fringes showed 0.23 nm of [1 1 1] and 0.26 nm of [2 0 0] plane, for Ag and CuO, respectively. The decoration of Ag phase on the surface of CuO was due to their atomic orientation relationship and the lattice fringes values were found almost similar in Ag and CuO (Fig. 7C). The components of plant extract were reduced and may be combined with O ions of Cu2O nucleate. The Ag cluster was oriented towards CuO surface (Peng et al., 2012). It explores the cubic form that was modified strongly to spherical shape and the polycrystalline nature of pre-synthesized polymeric PVP-Ag–CuO NCS was confirmed by the SAED pattern. The atomic arrangement of biosynthesized AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS was evaluated by EDX mapping analysis (Figs. 7D–7F). Figure 7D showed the mapping of AgNPs, where Ag ions are spread over the O, while the mapping images of CuONPs showed mutual spreading of copper and oxygen (Figs. 7E and 7F). However, Ag/CuO NCS mapping spectrum exhibited the content of Cu was higher than Ag and O (Figs. 7G, 7H and 7I). Furthermore, the decoration of Ag with Cu and O atoms were noticed in the mapping analysis of polymeric PVP-Ag–CuO NCS.
**Figure 7:** *(A–C) TEM images and (D–I) elemental mapping of AgNPs, CuONPs, and polymeric PVP-Ag/CuO nanocomposite.*
## Antiprotozoal potential
The in vitro antiprotozoal efficacy of AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS were tested against L. amazonensis promastigotes and T. cruzi epimastigotes (Fig. 8). The polymeric PVP-Ag–CuO NCS was found to be the most active followed by CuONPs towards T. cruzi epimastigotes with an IC50 value of 17.32 ± 1.0 and 12.36 ± 1.7 µg mL−1, respectively at 4.2 µgmL−1 concentration. Whereas, AgNPs expressed moderate effect towards T. cruzi epimastigotes with IC50 value greater than 25 µg mL−1. Benznidazole has been used as positive control with IC50 of 7.28 ± 0.9 µg mL−1. However, polymeric PVP-Ag–CuO NCS and AgNPs were most active on L. amazonensis promastigotes with IC50 values of 17.48 ± 0.9 (3.9 µg mL−1) and 18.75 ± 0.1 (8.0 µg mL−1), respectively. The CuONPs were found less active with IC50 >25. The enhanced antiprotozoal properties of the bionanocomposite can be attributed to the combined effect of constituents of L. dendroidea extract, AgNPs, CuONPs, and PVP medium. These active bionanocomposite could find application in the area of food agriculture to attain safe and high-quality products (Alarfaj et al., 2021). The amplified effect of bionanocomposite compared to AgNPs and CuONPs toward the tested parasites indicated that the bionanocomposite possesses a combined effect of AgNPs, CuONPs in combination with phytoconstituents present in the extract of L. dendroidea. The incorporation of Ag–CuO bionanocomposite in the PVP polymeric medium with active phytoconstituents of L. dendroidea has alleviated the antiprotozoal effect of the bionanocomposite. The surface of bionanocomposite decorated with Ag and CuONPs can easily enter the target organelles through the L. amazonensis, and T. cruzi, leading to disruption of membranes and release of cytoplasm as well as contents of the cell. Subsequently, results in the destruction of parasites. The antiprotozoal activity of the nanoparticles as well as bionanocomposite depends upon various factors such as absorption rate, metabolite release, metabolic processes, and dispersion in the cell (Diez-Pascual & Diez-Vicente, 2014). The nanostructure binds to the microorganism cell, adheres to the surface via active. If nanomaterial attaches to or interacts with proteins, a crucial biomolecule in the cell that serves a variety of functions, including the formation of cell components (wall, membrane, nucleic acids, and ribosome) or inhibit protein synthesis inside the cell will stop all protein-related functions, which will lead to cell death (Seillier et al., 2012).
**Figure 8:** *Morphological changes in (A) T. cruzi, (B) L. amazonesis after treatment of L. dendroidea, AgNPs, CuONPs, and polymeric PVP-Ag/CuO nanocomposite, (C) effect of polymeric AgNPs, CuONPs, and polymeric PVP-Ag/CuO nanocomposite (25 µM).*
The nanostructure binds with the cell wall of a microorganism, enters into the cell membrane and then disrupts and destroys the membrane (proteins, DNA, and enzymes) (Das et al., 2011). The nanoparticles and nanocomposite generate ROS (hydroxyl radicals OH, hydrogen peroxide H2O2, and superoxide ions O2) inside the cell that causes oxidative stress, which disrupts metabolic processes and causes cell death (He et al., 2011). These free radicals also interact with the biomolecules, disintegrating plasma membrane and leading to lipid oxidation. The nanoparticles and bionanocomposite showed potential antiprotozoal activity in contrast to nanomaterials synthesized chemically. Hence, the adoption of a green approach to prepare nanoparticles and their composites is more plausible for biomedical applications. Further studies and needed to confirm this hypothesis and to establish such targets.
## Growth inhibition and cell viability of HepG2 cancer cells
The anticancer potential of L. dendroidea extract, AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS was examined by MTT assay against HepG2 cancer cells. The antiproliferative potential of L. dendroidea extract, AgNPs, CuONPs, and Ag–CuO NCS was evaluated by MTT assay towards HepG2 cancerous cells. It was noticed that admiration of L. dendroidea extract, AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS at varying concentrations (50–200 µg mL−1 for 24 and 48 h) to HepG2 cancer cells resulted in cell growth inhibition in a time and concentration-dependent manner. The results revealed that HepG2 cells respond to L. dendroidea extract, AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS treatment in 48 h, according to time course analysis. However, the polymeric PVP-Ag–CuO NCS showed strong growth inhibition of HepG2 cells followed by AgNPs and CuONPs. The IC50 values of polymeric PVP-Ag–CuO NCS, AgNPs, and CuONPs treated HepG2 were 38.12 ± 0.13, 41.25 ± 0.25, and 52.14 ± 0.25 µg mL−1 at (200 µg mL−1). However, the L. dendroidea extract showed less effect with IC50 value 58.29 ± 0.58 µg mL−1 at the same concentration. The obtained data revealed that all the prepared nanomaterials showed significant potential for inhibiting HepG2 cell proliferation. However, polymeric Ag–CuO NCS displayed strong inhibition effects in contrast to AgNPs and CuONPs (Fig. 9A). The changes in the cell cycle regulation in tumor patterns can cause cellular proliferation. The rapid proliferation that resulted in the series and extension of tissue accumulation can be used to identify a significant basic origin of cancer succession (Ayyildiz et al., 2021; Kubczak, Szustka & Rogalinska, 2021). The MTT assay results indicated that AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS were effective against HepG2 cancer cells. The growth of cells in HepG2 cells was transformed by AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS. While the administration of polymeric PVP-Ag–CuO NCS was found most effective in the arrest of cellular proliferation in a dose-dependent manner, amplification of cell hammering viability was experimental with magnification in dose concentration.
**Figure 9:** *Anticancer effects against HepG2 cell lines.(A) Cytotoxicity assessment by MTT assay. (B) Effect on lipid peroxidation. (C) Percent change in glutathione levels and (D) morphological changes in pretreated HepG2 cells with AgNPs, CuONPs, and polymeric PVP.*
## Effect of nanomaterials treatment on lipid peroxidation in HepG2 cancer cells
Lipid peroxidation is triggered by reactive oxygen species (ROS) and plays a crucial role in cell death, including autophagy and apoptosis. This vital and well-preserved process is based on the production of excessive ROS, which causes bio-membrane damage, promotes lipid peroxidation chain events, and ultimately leads to cell death. The results of the present study revealed that AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS exerted a potent dose-dependent effect on lipid peroxidation in HepG2 cancer cells. Among the three nanomaterials, a noticeable elevation of lipid peroxidation with the rise in a dose of polymeric PVP-Ag–CuO NCS followed by CuONPs and AgNPs treatment (100 and 200 µg mL−1) was observed in HepG2 cancer cells (Fig. 9B).
## Depletion of intracellular glutathione (GSH) of HepG2 cancerous cells
GSH redox is an important component in a variety of biological activities, including regulation of cell proliferation and apoptosis, control of numerous signal transduction pathways, and gene initiation at the transcription level. The results of the current study showed that the treatment AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS potentially depleted the levels of GSH in HepG2 cancer cells, which suggested that all the three pre-synthesized nanomaterials severed as potent anticancer agents against cancer cells. At 200 µg mL−1, the statistically potential depletion of $58.37\%$, $53.77\%$, and $47.57\%$ in the GSH levels was observed for polymeric PVP-Ag–CuO NCS, AgNPs, and CuONPs treated HepG2 cancer cells, respectively. While at 100 µg mL−1, $38.76\%$, $34.17\%$, and $20.86\%$ decrease in GSH levels was noticed after the treatment of polymeric PVP-Ag–CuO NCS, AgNPs, and CuONPs in HepG2 cancer cells, respectively (Fig. 9C). Redox state (one of the main components being GSH) is a key factor of metastatic aggressiveness and chemotherapeutic susceptibility. The decrease in GSH/glutathione disulfide (GSSG) ratio or GSH deficit, results in an elevated susceptibility to the oxidative stress related to cancer growth, and increased GSH intensities which amplify the antioxidant ability and resistance to the oxidative stress as pragmatic in many types of cancerous cells. GSH is a well-known oxidative stress marker and an important ROS scavenger. The results obtained in this study support earlier studies by reducing intracellular GSH levels in HepG2 cancer cells after the treatment with polymeric PVP-Ag–CuO NCS, CuONPs, and AgNPs. Thus, posing cells become more vulnerable to ROS production, resulting in apoptosis (Fig. 9D). The overall results give a preliminary confirmation view that cellular GSH expression in cancerous cells may be a target for therapeutic operations (Niu et al., 2021). The outcomes support that AgNPs, CuONPs, and polymeric Ag–CuO NCS may contribute as therapeutic anticancer agents. However, the Ag–CuO NCS exhibit results compared to AgNPs and CuONPs in a dose-dependent manner. The potent cytotoxic effect of polymeric Ag–CuO NCS is the result of combined active physiochemical interaction of silver (Ag+) ions, copper oxide (Cu2+) ions, bioactive constituents of L. dendroidea extract with the functional groups of intracellular proteins, nitrogen bases and phosphate groups in DNA (Jadhav et al., 2018). An earlier study reported by Sriram et al. reported that the nanomaterials possessing anticancer potentials are known for their significant capability to lower the activities of abnormally shown by signaling proteins, including Akt and Ras, DNA- or protein-based vaccines towards specific tumor markers, cytokine-based therapies, and tyrosine kinase inhibitors which display a consistent antitumor activity (Sriram et al., 2010). In this study, the significant anticancer potential was observed and the pre-synthesized, AgNPs, CuONPs, and polymeric Ag–CuO NCS induce a dose dependent inhibition effect against HepG2 liver cancer. Although, some of the approved reported chemotherapeutic agents can cause side effects and are expensive. Therefore, there is an urgent need to develop alternative medicines against this deadly disease. The biosynthesized AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS could fulfill the need for new therapeutic treatment after clinical exploration. The previous studies have addressed the promising cytotoxicity of green synthesized AgNPs against HepG2 liver cancer cell line with IC50 values in the range of 5–50 µg mL−1 (Al-Khedhairy & Wahab, 2022; Ananthi & Iswarya, 2019). Whereas, the green synthesized CuONPs showed moderate anticancer potential with IC50 values in the range of 5–15 µg mL−1 against a similar cell line (Abudayyak, Guzel & Ozhan, 2020). However, there is no study reported for Ag–CuONPs polymeric bionanocomposite using L. dendroidea extract. Our study showed that all three formed nanomaterials exhibit good anticancer potential with IC50 values of 41.25, 52.14, and 38.12 for AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS, respectively. The excellent anticancer activity of polymeric PVP-Ag–CuO NCS in contrast to AgNPs and CuONPs, could be attributed to the synergetic effect components of L. dendroidea extract with Ag, CuO, and Ag–CuO blend.
## Photocatalytic activity
The photocatalytic potential of AgNPs, CuONPs, and polymeric Ag–CuO NCS was evaluated by the photocatalytic decomposition of MB under visible light. The results revealed that the photocatalytic effect of AgNPs was closely dependent on irradiation time. As depicted in Fig. 10, the absorption spectra at varied time gaps of an aqueous solution of MB in the presence of undoped and doped CuONPs with varied concentrations of AgNPs ($2\%$–$12\%$) and the characteristic absorption peak (λmax) for MB at 662 nm was recorded. The rate of degradation of MB dye was very fast in the initial step and then the rate started slowing down until no degradation was noticed after 60 min. As evident from the literature, doping with AgNPs reduces the bandgap of CuONPs. The doping slows down the rapid recombination of photoinduced electron–hole pairs in the produced samples, which enhances the photocatalytic efficiency of the polymeric PVP-Ag–CuO NCS. It is evident from the spectra that Ag–CuO bionanocomposite exhibit better degradation performance in comparison to CuONPs and AgNPs. The availability of photo-induced charge carriers for the photocatalytic degradation of MB dye may also be improved by the greater BET surface area of Ag–CuONPs (24.20 m2 g−1) compared to AgNPs (14.68 m2g−1) and CuONPs (12.80 m2 g−1) (Liu et al., 2020; Meng et al., 2020; Li, Chen & Li, 2019). The photoluminescence (PL) spectra demonstrate that the Ag ($4\%$) concentration is sufficient for the efficient separation and transportation of electron–hole pairs. The obtained results showed that the doping of Ag increases the p-type conductivity and $4\%$ is the optimum concentration for doping. Thus, increase in Ag+ ion concentration of more than $4\%$ resulted in the decrease photocatalytic effect and impact the production of less active polymeric Ag–CuO NCS photocatalyst. The incorporation of Ag into the host CuO lattice causes stress in the crystal structure due to the greater ionic radius of Ag+ than Cu2+. After 60 min irradiation, the maximum rate of MB degradation was $65\%$ over polymeric PVP-Ag–CuO NCS (Fig. 10).
**Figure 10:** *Photocatalytic degradation of MB dye using different concentrations of Ag–CuO bionanocomposite (2%–12%).*
## Conclusion
The green ecofriendly biogenic synthesis of AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS was performed by using macroalga L. dendroidea. The formed nanomaterials were characterized and confirmed by different analytical procedures such as UV-vis, FTIR, XRD, zeta potential, SEM combined with EXD and TEM. The pre-synthesized AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS were found to be spherical in shape. The SEM and TEM images of formed nanomaterials were spherical in shape with a little agglomeration. The EDX spectrum of polymeric PVP-Ag–CuO NCS strongly represents silver and copper oxide metals present in the polymeric PVP-Ag–CuO NCS matrix. Furthermore, antiprotozoal and anticancer evaluation of nanomaterials revealed the polymeric PVP-Ag–CuO NCS expressed excellent antiprotozoal and anticancer activities when compared to AgNPs and CuONPs The polymeric PVP-Ag–CuO NCS exhibited strong antiprotozoal effect against both the T.cruzi and L. amazonensis parasites with IC50 values of 17.32 ± 1.0 and 17.48 ± 0.9 µg mL−1, at 4.2 and 3.9 µg mL−1, respectively. These nanomaterials (AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS) were found to be effective towards HepG2 cancerous cells as well as protozoans. However, the highest anticancer activity was exerted by PVP-Ag–CuO NCS with IC50 values 38.12 ± 0.13 µg mL−1 at 200 µg mL−1 concentrations. Additionally, the nanomaterials (AgNPs, CuONPs, and polymeric PVP-Ag–CuO NCS) exhibited promising photocatalytic properties with high effect displayed by bionanocomposite. Thus, the outcomes of this study suggested that the pre-synthesized nanomaterials can be further explored for various biomedical applications.
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|
---
title: The WAVE2/miR-29/Integrin-β1 Oncogenic Signaling Axis Promotes Tumor Growth
and Metastasis in Triple-negative Breast Cancer
authors:
- Priyanka S. Rana
- Wei Wang
- Vesna Markovic
- Justin Szpendyk
- Ernest Ricky Chan
- Khalid Sossey-Alaoui
journal: Cancer Research Communications
year: 2023
pmcid: PMC10035451
doi: 10.1158/2767-9764.CRC-22-0249
license: CC BY 4.0
---
# The WAVE2/miR-29/Integrin-β1 Oncogenic Signaling Axis Promotes Tumor Growth and Metastasis in Triple-negative Breast Cancer
## Abstract
Breast cancer is the most frequently diagnosed malignancy in women and the major cause of death because of its invasion, metastasis, and resistance to therapies capabilities. The most aggressive subtype of breast cancer is triple-negative breast cancer (TNBC) due to invasive and metastatic properties along with early age of diagnosis and poor prognosis. TNBC tumors do not express estrogen, progesterone, and HER2 receptors, which limits their treatment with targeted therapies. Cancer invasiveness and metastasis are known to be promoted by increased cell motility and upregulation of the WAVE proteins. While the contribution of WAVE2 to cancer progression is well documented, the WAVE2-mediated regulation of TNBC oncogenic properties is still under investigated, as does the molecular mechanisms by which WAVE2 regulates such oncogenic pathways. In this study, we show that WAVE2 plays a significant role in TNBC development, progression, and metastasis, through the regulation of miR-29 expression, which in turn targets Integrin-β1 (ITGB1) and its downstream oncogenic activities. Conversely, we found WAVE2 expression to be regulated by miR-29 in a negative regulatory feedback loop. Reexpression of exogenous WAVE2 in the WAVE2-deficient TNBC cells resulted in reactivation of ITGB1 expression and activity, further confirming the specificity of WAVE2 in regulating Integrin-β1. Together, our data identify a novel WAVE2/miR-29/ITGB1 signaling axis, which is essential for the regulation of the invasion-metastasis cascade in TNBC. Our findings offer new therapeutic strategies for the treatment of TNBC by targeting WAVE2 and/or its downstream effectors.
### Significance:
Identification of a novel WAVE2/miR-29/ITGB1 signaling axis may provide new insights on how WAVE2 regulates the invasion-metastasis cascade of TNBC tumors through the modulation of ITGB1 and miR-29.
## Introduction
Breast cancer is the most common cause of cancer in women in United States accounting for $31\%$ of all estimated new cancer cases and $15\%$ of all cancer-related fatalities [1]. Among its variants, triple-negative breast cancer (TNBC) is considered the most aggressive disease that affects many women, due to its early invasive and metastatic properties and absence of targeted therapies that are FDA approved (2–4). Because TNBC tumors do not express cell surface receptors estrogen receptor (ER), progesterone receptor (PR), and Her2 which can be targeted with hormonal and antibody treatments, patient with TNBC are left with limited treatment options in the form of cytotoxic chemotherapy with dismal response and rapid recurrence due to the acquisition of resistance. Several oncogenes orchestrate a myriad of signaling reactions that contribute toward tumor development, invasion, and metastasis [5]. Among the WAVE family of proteins [6, 7], WAVE2 is known to be associated with pathogenesis of several cancers and recently has been a topic of great interest in cancer invasion and metastasis [8]. Owing to its critical role in actin cytoskeleton remodeling, WAVE2 mediates cell motility, migration, and cancer invasion (9–13), which are among the critical properties that promote metastasis when abnormally upregulated in several malignancies [14]. Although, several studies and clinical data revealed that WAVE2 is enhanced in different cancers, the molecular mechanisms by which it exerts its oncogenic properties in specific cancer types still need to be investigated. With this research, we have shown how WAVE2 is involved in the invasion-metastatic cascade of breast cancer, both in vitro and in vivo.
Cancer cell invasion, progression, and metastasis are driven by interactions between tumor cells and their microenvironment [15, 16]. The extracellular matrix (ECM) is a major structural component of tumor microenvironment. ECM is a noncellular component of tissue that is composed of cross-linked macromolecules such as collagens, proteoglycans, and glycoprotein that form a dynamic scaffold and provide physical and chemical stimuli to mediate cancer progression and metastasis [17]. ECM macromolecules also serve as ligands for cell surface integrins [18], which upon activation, trigger a signaling cascade that regulate a myriad of activities ranging from cell adhesion, spreading, as well as cell migration and invasion [19]. While the literature is filled with studies on the regulatory mechanisms of integrins activity, the role of WAVE2 as a potential regulator of integrins has not been reported before, and, as such, is the focus of this study.
Research in the last decade identified the role of epigenetics, such as DNA methylation, histone modifications, abnormal expression of certain noncoding RNAs, and chromatin remodeling, as potential drivers of breast cancer development and progression [20]. miRNAs are a type of conserved noncoding RNAs that are known for modulating the expression of several genes that regulate diverse and complex cellular pathways such as cell development, growth, proliferation, and motility and, thus possess the potential to also regulate the major hallmarks of cancer by modulating several oncogenes [21, 22]. miRNAs have also been established as either oncogenes or tumor suppressors to regulate tumor progression and metastasis [23]. The miR-29 family of miRNAs consists of miR-29a, miR-29b, and miR-29c that regulate several biological processes such as intracellular signaling [24], epigenetic modifications [25], cell proliferation [26], and cell motility [27]. In this study, we report novel findings that describe how a negative regulatory feedback loop between WAVE2 and miR-29 regulates Integrin-β1 (ITGB1) expression and activity, and in the run regulates major hallmarks of TNBC tumors. Our data show that loss of WAVE2 expression results in increased miR-29 levels, which, in turn inhibits ITGB1 expression through binding to its 3′-UTR (untranslated region). Conversely, overexpression of miR-29 suppresses the expression of WAVE2 by also targeting its 3′-UTR. This WAVE2/miR-29/ITGB1 signaling axis is critical for the regulation of tumor growth and metastasis of TNBC tumors. We further identified a potential mechanism whereby WAVE2 activates miR-29, possibly through the regulation of DGCR8, a major mediator of miRNA biogenesis, which may explain, in part, the negative feedback loop between WAVE2 and miR-29. Importantly, our in vitro and mouse preclinical findings are supported by human clinical data where increased expression of WAVE2 and ITGB1, and decreased expression levels of miR-29 and DGCR8 correlate with poor disease outcome in human patients with breast cancer tumors. Together, our data identified a novel WAVE2/miR-29/ITGB1 signaling axis that regulates the invasion-metastasis cascade in breast cancer. Thus, successful characterization of the WAVE2 signaling pathway in cancer cell invasion and metastasis could serve as a novel approach for developing new therapeutic strategies for targeting TNBCs.
## Cell Culture
TNBC 4T1, MDA-MB-231, and MDA-MB-468 cell lines and HEK 293 cells were obtained from ATCC. All the cell lines were maintained according to the manufacturer's protocols. No authentication was performed in the lab, because we relied on the manufacture's quality control statement. Cell lines were routinely (on an average of 9 to 12 months) tested for Mycoplasma contamination. Cells were cultured at early passages (no more than 10), and were passaged in culture no more than five times before a new vial is being thawed. Generation of WAVE2- and ITGB1-deficient cells was used through electroporation of breast cancer cells with single-guide RNA (sgRNA)/Cas9 mixes (Synthego), according to the manufacturer's instructions. For each human or mouse gene, a pool of three verified sgRNAs was used (Synthego). Scrambled sgRNAs (Synthego) were used a negative control. Efficient and stable gene knockout (KO) was verified by Western blot (WB) analysis. In some instances when the knockout efficiency is less than $80\%$, a second round of sgRNA delivery is performed. Generation of breast cancer cells overexpressing miR-29 cells were generated by nucleofection delivery of miR-29 expressing vector or its empty control vector, and stable clones were selected for by adding G418 (2 mg/mL) to the complete culture medium for 2 weeks as described previously in ref. 28. The HA-tagged WAVE2-expressing vector (pCS-HA-WAVE2) was kind gift from Dr. Alexis Gautreau (Institut Curie, Paris, France). Transient transfection of W2-KO-MDA-MB-231 cells with HA-WAVE2 and of HEK293 cell with dual luciferase pmiR-Glo plasmid and miR-29–expressing plasmids were performed using Lipofectamine 3000 reagent according to the manufacturer's instructions and as described previously in ref. 29. All the cell lines were grown in complete culture medium: DMEM culture medium supplemented with $10\%$ FBS and $5\%$ Antibiotics (Penn/Strep). Transfected cell lines were selected in complete culture medium with selection antibiotic.
## Antibodies
The following antibodies were used: rabbit antibodies against WAVE2 (1:1,000, Cell Signaling Technology, catalog no. 3659), Integrin β1/ CD29 (GeneTex, catalog no. GTX128839), and DGCR8 (Abcam, catalog no. Ab191875); mouse antibodies against β-actin (1:5,000, Sigma), WAVE1 (1:200, Santa Cruz Biotechnology, catalog no. Sc-136120), and WAVE2 (1:1,000, Santa Cruz Biotechnology, catalog no. Sc-373889). Rabbit anti FAK (1:1,000, Invitrogen, catalog no. UB281522), rabbit anti-phospho-FAK (1:1,000, Abcam, catalog no. ab81298), rabbit anti Src (1:1,000, Cell Signaling Technology, catalog no. 2109S), rabbit anti phospho-Src (1:1,000, Cell Signaling Technology, catalog no. 6943S), goat horseradish peroxidase–conjugated anti-mouse IgG and goat horseradish peroxidase–conjugated anti-rabbit IgG were from Bio-Rad (1:2,000). ECL reagent was from Thermo Fisher Scientific. For immunoblotting, primary antibodies were made in $5\%$ BSA and secondary antibodies were made in $5\%$ non-fat dry milk (NFDM).
## Western Blotting
Immunoblotting analyses were performed according to standard protocols and as described previously in ref. 30. ChemiDoc MP Imaging system (Bio-Rad) was used for image acquisition of developed gels. Band intensity of proteins targeted were quantitatively analyzed by ImageJ software according to the parameters described in ImageJ user guide (http://rsbweb.nih.gov/ij/docs/guige/146.html, accessed on July 2, 2021).
## Flow Cytometry Analyses
MDA-MB-231 and their derivatives W2KO, miR-29–expressing, and ITGB1KO cells were detached by trypsinization and washed with FACS staining buffer (PBS with $5\%$ BSA). Cells at a density of 5 × 105/mL were stained with conjugated PE Mouse Anti-Human CD-29 antibody (BD Pharmingen, catalog no. 555443) for 30 minutes followed by washing and resuspending the cells in 250 μL FACS staining buffer. Anti-Integrin β1 Antibody, activated clone HUTS-4 (Sigma, catalog no. MAB2079Z) was used to detect the activation of ITGB1. As a positive control for integrin activation, 0.5 mmol/L MnCl2 was used to treat the cells for 10 minutes at 37°C. Processed cells were then run through FACSAria and all the data were analyzed using FlowJo software. Data were corrected for any nonspecific signals and the resultant fluorescence intensities were plotted graphically.
## Semiquantitative and qRT-PCR
TRIzol reagent (Invitrogen) was used to extract total RNA from cancer cell lines according to the manufacturer's instructions. qRT-PCR was performed, as described previously in ref. 30. qPCR Primer Assays for miR-29 were obtained from Qiagen. U6 was used a negative control.
## RNA Sequencing and Biostatistical Analyses
Control and WAVE2-KO MDA-MB-231 and MD-MB468 cells were grown to near confluency ($80\%$ to $90\%$) in 10-cm tissue culture dishes in DMEM supplemented with $10\%$ FBS. Early passages were used for RNA extraction and subsequent RNA sequencing (RNA-seq) analyses as described previously [28].
## TruSeq Total RNA Stranded Library Preparation and Sequencing
RNA quality for each sample assessed using Agilent's Bioanalyzer 2100 RNA Nano 6000 reagent kit, before library preparations. RNA integrity number value higher than 8, indicating minimal sample degradation, was required. Illumina TruSeq Total RNA Stranded Library Preparation Kit was used to generate a range of total RNA from 100 ng to 1 μg. Library preparation was performed according to the manufacturer's instructions. The final cDNA library size distribution was checked using a Bioanalyzer DNA High Sensitivity assay, followed by analysis Qubit to determine the concentration of each sample. Samples are then pooled and the accurate concentration for the pool is determined using the Kapa Library Concentration Kit.
## Sequencing
Illumina HiSeq 2500 set in Rapid Run mode was used for sequencing of up to 12 RNA samples. We used the paired end 100 cycle kit (meaning that 100 cycles will be used for the forward and reverse directions so a total of 200 cycles is used) to generate approximately 30–40 million reads per sample, in biological replicates (three per condition) to be able to yield statistically significant results. Sequencing results are delivered as FASTQ files which can then be aligned and analyzed using bioinformatics.
## RNA-seq Analysis Methods
Sequencing reads generated from the Illumina platform were assessed for quality using FastQC. The reads were trimmed for adapter sequences using TrimGalore. For RNA-seq, reads that passed quality control were then aligned to the human reference genome (GRCh38) using the STAR aligner41. The alignment for the sequences was guided using the GENCODE annotation for GRCh38. The aligned reads were then analyzed for differential expression using cufflinks42, a RNA-seq analysis package which reports the fragments per kilobase of exon per million fragments mapped for each gene. The 12 samples were analyzed in four groups of three (WT, W3, K2, and DKO) and differential expression analysis was performed in a pairwise manner. *Differential* genes were identified using a significance cutoff of FDR < 0.05. *These* genes were then subjected to gene set enrichment analysis (Broad Institute) to determine any relevant processes that may be differentially overrepresented for the conditions tested.
## Cell Adhesion and Spreading Assays
For adhesion and spreading assays, 5 × 105 cells were seeded on round 1.5 mm coverslips (Electron Microscopy Sciences) precoated with 10 mg/mL laminin (Sigma), 10 mg/mL fibronectin (Sigma), or five times diluted growth factor–reduced Matrigel (Corning). Cells were allowed to adhere for 30 minutes before fixation or spread overnight on these polymers. A total of $4\%$ paraformaldehyde (PFA) dissolved in PBS was used to fix the cells for 20 minutes at room temperature followed by 3X wash and permeabilization with $0.5\%$ Triton-X for 10 minutes at room temperature. The spread cells were then washed and probed with phalloidin-643 (1:30, Invitrogen, catalog no. A30107) for 60 minutes and mounted on slides using the ProLong Gold antifade reagent with DAPI (Invitrogen, catalog no. P36931). For adhesion assay, fixed and permeabilized cells were directly mounted on slides using the mounting medium. Cell spreading was assessed by collecting several images of cells and quantified by marking an area around the cells in ImageJ and comparing the surface area between different groups of cells. For adhesion assay, attached cells stained with DAPI were imaged and several fields were used for counting attached cells using ImageJ.
## Plasmid Construction, Site-directed Mutagenesis, and 3′-UTR Dual Luciferase Reporter Assays
The nucleotide sequence flanking the miR-29 seed sequence in the 3′-UTR of WAVE2 and ITGB1 from human genomic DNA was amplified by PCR, then subcloned into the pmirGlo vector (Promega) downstream of the firefly luciferase, as described previously in refs. 29, 31. The correct sequence and orientation of all the inserts was verified by sequencing. *To* generate mutations in the seed sequence, the QuickChange site-directed mutagenesis kit was used where the seed sequence recognized by miR-29 in ITGB1 and WAVE2 was scrambled. The mutated sequence was also verified by sequencing. pmirGlo reporter plasmids were transfected with Lipofectamine-3000 (Invitrogen) in HEK-293 cells as described previously. Cells were collected after 48 hours for assay using the dual luciferase reporter assay system (Promega).
## In Vitro Tumorsphere Growth and Invasion Assays
Assays for three-dimensional (3D) tumorsphere growth and invasion were performed as described previously [32]. For 3D single-tumorsphere formation, MDA-MB-231 cells and their derivative W2KO cells were seeded on 96-well ultralow attachment (ULA) plate at a density of 1.0 × 103 cells per well and centrifuged for 10 minutes at 125 × g at room temperature. The cells were the imaged every 2–3 days for 11 days on a Leica CMi1 microscope to monitor the growth of 3D-tumorsphere formation. For the invasion assays, the cells were supplemented with 90 μL of Matrigel (dissolved at 1:1 in complete medium) on top at the end of day 3 and the plate was imaged every 48 hours. to monitor the invasive potential of the spheroids. For 3D-multiple tumorsphere formation assay, cells were plated in a 6-well dish at a density of 2 × 103/well precoated with polyhema. The dish was imaged every 48 hours for 10 days with a Leica DMi1 microscope to monitor the cell's potential to form multiple tumorsphere in 3D.
## Animal Experiments
MDA-MB-231, and their derivatives W2KO, miR-29–overexpressing, ITGB1KO cells were implanted at a density of 106 cells/injection into the mammary fat pads in both sides of female NSG mice. Mice were monitored for tumor growth twice for 8 weeks and tumor volume was measured with digital Vernier calipers. For lung colonization assay, cells at a density of 100,000 suspended in 100 μL sterile PBS were injected via 28-guage needle into the tail veins of 6 to 8 weeks old female NSG mice. Mice were sacrificed 5 weeks later, and the recovered lungs were imaged under dissecting microscope. Lung metastasis nodules were counted for the images and results were plotted as average number of metastatic foci per lobe. Similar experiments were carried out the 4T1 cells and their W2KO derivatives (100,000 cells/injection in the mammary fat pads and 50,000 cells/injection in tail vein of Balb/C mice).
## Immunofluorescence
For immunofluorescence (IF), cells were processed as described previously [33]. In brief, cells were fixed with $4\%$ PFA in PBS for 20 minutes at room temperature followed by three PBS washes, permeabilization with $0.5\%$ Triton-X for 10 minutes at room temperature and three PBS washes. Cells were then blocked with $5\%$ donkey serum (dissolved in PBS) for 60 minutes and probed with primary antibodies diluted in $5\%$ donkey serum overnight at 4°C. The following day cells were washed with PBS and probed with a secondary antibody for 1–2 hours at room temperature, washed and mounted on slides using the ProLong Gold antifade reagent with DAPI (Invitrogen, catalog no. P36931). The following antibodies were used for IF: Rabbit anti-WAVE2 (Cell Signaling Technology, catalog no. 3659) and mouse anti-Rabbit anti-ITGB1/CD29 (GeneTex, catalog no. GTX128839).
## Oligonucleotide Sequences
Sequences of the oligonucleotide primers used for genomic PCR, RT-PCR, those used to amplify WAVE2 and ITGB1 3′-UTR harboring miR-29 seed sequences, as well as the primers used for mutagenesis were from IDT and are listed in Supplementary Table S1.
## Statistical Analysis
All experiments were performed in triplicates and were analyzed using the Student t test. Error bars represent SEM. The two-tailed significance levels for equal means and equal variances were assumed for the two populations and results were considered significant at $P \leq 0.05.$
## Study Approval
All studies involving animals were performed under the protocols approved by Institutional Animal Care and Use Committee. The animal studies were conducted in accordance with the guidelines and regulations that were approved by MetroHealth Medical Center, Case Western Reserve University (Cleveland, OH), and NIH. For all the following animal experiments 6 to 8 weeks old female NSG or Balb/C mice from Jackson Laboratory were used. Experimental mice were routinely observed twice a week according to the protocol.
## Data Availability Statement
Data were generated by the authors and included in the article. The data generated in this study are available within the article and its Supplementary Data.
## WAVE2 is Highly Expressed in Basal Subtype of Breast Cancer Tumors and is Associated with Poor Survival and Worst Patient Outcomes
Our previous studies have extensively reported on the role of WAVE3 in mediating tumor progression and metastasis in breast cancer (reviewed in refs. 6 and 34). The role of WAVE2, a close relative of WAVE3, in the pathogenesis of breast cancer, has, however, not been investigated. To initiate this study, we began by assessing WAVE2 protein expression levels in a series of breast cancer cell lines representing different breast cancer subtypes (Fig. 1A). While we found WAVE2 to be expressed in every cell line, including mouse mammary epithelial cells, WAVE2 expression levels were significantly higher in cell lines of aggressive basal subtype as compared with their less aggressive counterparts (Fig. 1A). To confirm this observation, we interrogated the cancer datasets from the cBioPortal and found breast cancer among the pan-cancer cohort where WAVE2 is predominantly highly expressed (Fig. 1B). Further interrogation of The Cancer Genome Atlas (TCGA) PanCancer breast cancer cohort, which contains clinical information on more than 1,000 patients with breast cancer, showed WAVE2 mRNA expression levels to be significantly ($P \leq 0.01$) higher in the basal (TNBC) breast cancer subtype, as compared with their Her2+ and luminal counterparts or to normal breast tissue (Fig. 1C; Supplementary Fig. S1A and S1B). WAVE2 protein expression levels were also found to be significantly ($P \leq 0.05$) highly expressed in human tumors of TNBC subtypes, when compared with their Her2+ and luminal counterparts (Fig. 1D). Accordingly, a significant positive correlation was observed between mRNA and protein levels of WAVE2 in TCGA PanCancer breast cancer cohort (Fig. 1E). In addition, interrogation of the Protein Atlas database (https://www.proteinatlas.org/ENSG00000158195-WASF2/pathology/breast±cancer), that contains WAVE2 IHC data on human breast cancer tumors, also showed a significant increase of WAVE2 staining in cancer cells, compared with the stroma (Fig. 1F). Next, we interrogated the breast cancer Kaplan Meier (KM) plotter (https://kmplot.com/analysis) cohort, which contains clinical information on approximately 5,000 patients with breast cancer, and found a very significant ($P \leq 1$e−16) correlation between elevated WAVE2 mRNA (Fig. 1G) or protein (Fig. 1H) expression and reduced survival probability. In this cohort, patients with breast cancer with high WAVE2 expression levels in their tumors had worst clinical outcomes when compared with patients with low WAVE2 levels, and patients with high tumor WAVE2 mRNA levels have an average reduced survival of 36 months (Fig. 1G), while those with high tumor WAVE2 protein levels have an average reduced survival of 52 months (Fig. 1H) when compared with patients with low tumor WAVE2. This inverse correlation remains significant when accounting for only ER− (Supplementary Fig. S1C) or ER−/PR− (Supplementary Fig. S1D). Thus, these findings support the hypothesis of WAVE2 as a promoter of breast cancer aggressiveness and warrant further investigations.
**FIGURE 1:** *WAVE2 is highly expressed in basal subtype of breast cancer tumors and is associated with poor survival and worst patient outcomes. A, Representative WB analysis of protein lysates from different breast cancer cell lines probed with the indicated antibodies. β-Actin was used for loading control. B, mRNA expression levels of WAVE2 in different cancer datasets derived from the cBioPortal platform. Data shown are representative of three replicates. C, Quantification of WAVE2 mRNA expression levels based on log2 RSEM values batch normalized from Illumina HiSeq RNA-seq data by breast cancer subtype in the breast cancer BRCA patient data from TCGA PanCancer Atlas. Expression levels of WAVE2 are significantly higher (**, P < 0.01, Wilcoxon) in the basal (TNBC) subtype when compared with other breast cancer subtypes. D, Quantification of WAVE2 protein expression levels by breast cancer subtype in the breast cancer BRCA patient data from TCGA PanCancer Atlas. Expression levels of WAVE2 are significantly higher (**, P < 0.05, Wilcoxon) in the basal (TNBC) subtype when compared with other breast cancer subtypes. E, Correlation between WAVE2 mRNA and protein levels in tumors of patients with breast cancer from TCGA PanCancer Atlas dataset. F, Representative IHC staining pictograms of human TNBC tumors stained with anti-WAVE2 antibody from the Protein Atlas Database. KM plot correlating survival of patients with breast cancer with WAVE2 mRNA (G) and protein (H) expression levels. High WAVE2 expression levels correlate with poor survival probability in patients with breast cancer (P < 1e−16 for mRNA and P = 0.042 for protein). Number of patients at risk and median survival in the low and high WAVE2 cohorts are also shown.*
## Loss of WAVE2 Inhibits the Oncogenic Behavior of TNBC Cell Lines In Vitro
To enable a comprehensive characterization of phenotypic consequences of targeting WAVE2 signaling in vitro, we used nucleofection and CRISPR/Cas9 to generate WAVE2-KO in human MDA-MB-231 and MDA-MB-468 and murine 4T1 TNBC cells (Fig. 2A), by delivering a pool of three verified WAVE2-sgRNAs (Supplementary Table S1) in complex with Cas9 protein into these TNBC lines. *We* generated pools of cell populations of WAVE2-KO for MDA-MB-231, MDA-MB-468, and 4T1 cells. Nontargeting sgRNAs served as controls (CTRL). This approach resulted in almost complete loss of WAVE2 expression (W2KO) in all three cell lines, without affecting expression of WAVE1 and WAVE3 (Fig. 2A). Loss of WAVE2 did not affect cell proliferation in two-dimensional (2D) culture (Fig. 2B). Next, cell migration was assessed in the control (CTRL) MDA-MB-231 cells and their W2KO derivatives using the 2D wound healing assay, and the extent of wound closure was compared between the two groups after 20 hours. Approximately $90\%$ of the wound was closed in the control group (CTRL) whereas only $50\%$ of wound closure was achieved in the W2KO cells (Fig. 2C and D). We also used colony formation assay, which is a hallmark of transformed cells, to assess oncogenic potential, and found that the loss of WAVE2 significantly ($P \leq 0.01$) inhibits colony formation of all three TNBC cell lines: MDA-MB-231, MDA-MB-468, and 4T1 (Fig. 2E and F). In addition, we used 3D single and multiple tumorsphere growth (Fig. 2G and H), as well as tumorsphere-invasion assays (Fig. 2I; Supplementary Fig. S2) to assess the effect of loss of WAVE2 on cancer cell growth and invasion in 3D conditions. We found loss of WAVE2 in MDA-MB-231 cells significantly ($P \leq 0.01$) inhibited the growth and number of tumorspheres (Fig. 2G and H) as compared with the control cells. Moreover, the loss of WAVE2 expression significantly ($P \leq 0.001$) inhibited the Matrigel invasion of MDA-MB-231 cells (Fig. 2I; Supplementary Fig. S2). Thus, our in vitro findings confirm the involvement of WAVE2 in the activation of the oncogenic activities of TNBC cell lines.
**FIGURE 2:** *Loss of WAVE2 inhibits the oncogenic behavior of TNBC cell lines in vitro. A, Representative WB analysis of protein lysates from MDA-MB-231, MDA-MB-468, and 4T1 breast cancer cell lines and their WAVE2-KO derivatives probed with the indicated antibodies. β-Actin was used as loading control. The numbers under the WB bands represent the fold change of the signal with respect to the CTRL band after normalization to the β-Actin signal. B, Cell proliferation of MDA-MB-231 cells (CTRL) and their WAVE2-deficient derivatives (W2KO) over 3 days. C, Representative micrographs of wound healing assays of confluent cell cultures of MDA-MB-231 (CTRL) cells, their WAVE2-deficient derivatives (W2-KO) that were induced to migrate into scratch wounds in confluent monolayers over 20 hours. Scale bar: 500 μm. D, Quantification of the remaining open wound (open area) at 20 hours from 12 different wounds was measured and plotted as the percentage of the wound at time zero for CTRL cells. E, Representative images of colony formation of the indicated CTRL and W2-KO TNBC cell lines. F, Quantification of the number of colonies. G, Representative micrographs of tumorspheres from CTRL and W2-KO MDA-MB-231. Scale bar: 250 μm for 5x and 100 μm for 10x. Tumorspheres were grown in a 96-well ULA plates and Matrigel (2.5 v/v) was added to the tumorsphere cultures at day 3 and images were captured using Incucyte for 14 days. H, Quantification of the number of tumorspheres. I, Quantification of the number of invading microspheres. Data are the means ± SD (n = 3; ** and ***, P < 0.01; Student t test). Data shown are representative of three replicates.*
## Loss of WAVE2 Inhibits Tumor Growth and Metastasis In Vivo
To determine the effects of the loss of WAVE2 on tumor growth in vivo, mammary fat pads of NSG mice were inoculated with control or W2KO MDA-MB-231 cells and tumor growth was assessed over 8 weeks. We found that the loss of WAVE2 significantly ($P \leq 0.001$) inhibited the growth of primary tumors, as determined by tumor volume (Fig. 3A; Supplementary Fig. S3A) and tumor weight (Fig. 3B). Similarly, the loss of WAVE2 in the 4T1 cells also significantly ($P \leq 0.01$) delayed tumor growth in Balb/C mice (Fig. 3C; Supplementary Fig. S3B). These resulted were replicated with MDA-MB-468, another TNBC cell line (Supplementary Fig. 4). Control and W2KO MDA-MB-231 and 4T1 cells were also injected in the lateral tail veins of NSG and Balb/C mice, respectively to assess for colonization (metastasis) to other organs. Loss of WAVE2 expression resulted in a significant reduction in the number of lung metastases in mice injected with WAVE2-deficient MDA-MB-231 cells (Fig. 3D and E) and 4T1 (Fig. 3F and G). Metastatic foci were also observed in the liver from mice injected with CTRL MDA-MB-231 cells, but not with their W2KO derivatives (Fig. 3D). Therefore, our in vivo findings confirm that the loss of WAVE2 inhibits the rate of primary tumor growth and metastasis in both human and mouse models for TNBC.
**FIGURE 3:** *Loss of WAVE2 inhibits tumor growth and metastasis in vivo. A, Quantification of volume of tumors derived from implantation of CTRL or WAVE2-KO MDA-MB-231 cells into the mammary fat pads of NSG mice. The inserts show WBs confirming W2-KO in MDA-MB-231 cells before mice injections, as well images of the resulting tumors from each group. B, Quantification of tumor weights from the experiment described in A. C, Quantification of volume of tumors derived from implantation of CTRL or WAVE2-KO 4T1 cells into the mammary fat pads of Balb/C mice. The inserts show WBs confirming W2-KO in 4T1 cells before mice injections, as well images of the resulting tumors from each group. D, Images of lungs (top) and livers (bottom) from NSG mice injected via the lateral tail veins with CTRL or W2KO MDA-MB-231 cells. E, Quantification of lung metastasis foci from the corresponding experiment. F, Images of lungs from Balb/C mice injected via the lateral tail veins with CTRL or W2KO 4T1 cells. G, Quantification of lung metastasis foci from the corresponding experiment. ** and ***, P < 0.01; Student t test.* **FIGURE 4:** *Loss of WAVE2 inhibits ITGB1 expression and activity. A, Pathway analysis of the RNA-seq generated from CTRL and W2-KO MDA-MB-231 cells. Each dot represents a signaling pathway. The red dots represent the pathways that are significantly (P < 0.05) differentially regulated between the CTRL and the W2-KO groups. The black blots represent nonsignificantly differentially regulated pathways. The ECM receptor interaction pathway is shown with a yellow dot (P = 2.197e−5). B, Volcano plot from the RNA-seq analysis of the differentially expressed genes between CTRL and W2-KO MDA-MB-231 cells. The X-axis represents the log2 fold change in expression levels and the Y-axis shows the P values. Red dots: upregulated genes; Blue dots: downregulated genes; Black dots: no significant change. The dots of the genes of interest are labeled. C, Bars and values representation of the log2 fold change in expression levels of WAVE2, ITGB1, and ITGA6 derived from the RNA-seq data. D, Correlation of expression levels between WAVE2 and ITGB1 in human breast cancer specimens from the cBioPortal breast cancer datasets (P < 0.01). E, Representative WB analysis of protein lysates from CTRL and W2-KO MDA-MB-231and 4T1 breast cancer cell lines probed with the indicated antibodies. β-Actin was used for loading control. The numbers under the WB bands represent the fold change of the signal with respect to the CTRL band after normalization to the β-Actin signal. F, Confocal microscopy images of IF staining of CTRL, W2-KO, and ITGB1-KO MDA-MB-231 cells that were stained for WAVE2 (red) or ITGB1 (green). Nuclei were counterstained with DAPI. Scale bar: 50 μm. Representative histograms of flow cytometry analyses for cell surface expression of ITGB1 (G) and activity: HUTS4 binding (H) of CTRL, W2-KO and ITGB1-KO MDA-MB-231 cells. A shift to the right indicates increased expression or activity. Data shown are representative of three replicates.*
## Loss of WAVE2 Inhibits ITGB1 Expression Which Negatively Affects Interaction of Cancer Cells with the ECM
To investigate the molecular mechanisms underlying the role of WAVE2 in mediating TNBC tumor growth and metastasis, we generated RNA-seq from control and W2KO MDA-MB-231 (Supplementary Data S1) and MDA-MB-468 cells (Supplementary Data S2). Pathway analysis of the RNA-seq data identified the ECM interaction pathway among the most affected pathway as the result of loss of WAVE2. ( Fig. 4A; Supplementary Fig. S5). Subsequent analyses revealed ITGB1 and ITGA6 integrins, which are part of the ECM interaction pathway, to be significantly inhibited in the W2KO cells (Fig. 4B and C). Interrogation of the cBioPortal breast cancer datasets confirmed the positive correlation in expression levels between WAVE2 and ITGB1 in human breast cancer specimens (Fig. 4D). Expression levels of ITGB1, similar to those of WAVE2, were also found to be elevated in the more aggressive basal breast cancer cell lines (Supplementary Fig. S6A). We also confirmed inhibition of expression of ITGB1 in the W2KO MDA-MB-231 and 4T1 cells at the protein levels by immunoblotting (Fig. 4E), by IF (Fig. 4F), and by flow cytometry analyses to show loss of cell surface expression (Fig. 4G) and activity (HUTS4 binding assay) of ITGB1 (Fig. 4H). In all cases, we used ITGB1KO as a control.
By interacting with ECM, integrins are critical regulators of cell adhesion and spreading, which are major hallmarks of cancer cell migration and invasion [14]. Accordingly, we assessed the effect of loss of WAVE2 on ITGB1–mediated regulation of cell adhesion and spreading. Loss of WAVE2 (W2KO) inhibited the ability of MDA-MB-231 cells to adhere to fibronectin-coated culture plates to levels close to those achieved by ITGB1KO (Fig. 5A, top and 5B). Of note, ITGB1 is a major receptor of the ECM fibronectin. W2KO also inhibited adhesion to the more generic ECM mix (Matrigel), albeit to levels that are less pronounced to those achieved by ITGB1KO (Fig. 5A, bottom and 5C). Loss of WAVE2 expression also inhibited cell spreading of MDA-MB-231 cells on fibronectin (Fig. 5D, top and 5E), Matrigel (Fig. 5D, middle and 5G), and laminin (Fig. 5D, bottom and 5H). Inhibition of cell spreading was almost similar between W2KO and ITGB1KO with all three ECM substrata. Thus, we show that loss of WAVE2 expression, by inhibiting ITGB1 expression, negatively affects integrin-mediated regulation of major cell–ECM interactions, as determined by loss of cell adhesion and spreading to ECM. To further confirm that the observed phenotypes (cell adhesion and spreading) were indeed the result of inhibition of ITGB1, we assessed for the Src and FAK signaling activities, which are established down effectors of ITGB1, and found inhibition of ITGB1 both in ITGB1KO or W2KO cells to inhibit phosphorylation of both Src and FAK (Supplementary Fig. S6B and S6D), therefore confirming our findings.
**FIGURE 5:** *Loss of WAVE2 inhibits the ITGB1-mediated interaction of cancer cells with the extracellular matrix. Representative microscopic images of nuclei of CTRL, W2-KO, and ITGB1-KO MDA-MB-231 cells seeded on fibronectin- (top) or Matrigel-coated coverslips (bottom), and allowed to adhere for 30 minutes. Scale bar: 250 μm. Quantification of adhered cells on fibronectin (B) and Matrigel (C). Each dot corresponds to the nucleus of an adherent cell. D, Confocal microscopy images of IF staining of CTRL, W2-KO and ITGB1-KO MDA-MB-231 cells seeded on fibronectin- (top), Matrigel- (middle), or Laminin-coated coverslips (bottom), and allowed to spread overnight and stained for actin (green). Nuclei were counterstained with DAPI. Scale bar: 50 μm. Quantification of cell adhesion by means of cell surface area on fibronectin (E), Matrigel (F), and laminin (G). Data are the means ± SD (n = 3; **, P < 0.01; Student t test). Data shown are representative of three replicates.*
## The WAVE2 Modulation of Integrin Activities is Mediated Through the Regulation of miR-29 microRNA
To further investigate the mechanisms whereby WAVE2 regulates ITGB1 and its downstream signaling, we went back to our RNA-seq data and found expression levels of both miR-29a, as well as miR-29b to be significantly ($P \leq 0.001$) higher in the W2KO MDA-MB-231 cells compared with their control counterparts (Fig. 6A and B). We independently confirmed this finding by qRT-PCR (Fig. 6C). miRNAs are small single-stranded noncoding RNAs that function in posttranslational regulation of gene expression by directly binding to the 3′-UTR of the target gene. Having hypothesized that miR-29 might regulate expression of ITGB1, we surveyed the 3′-UTR of ITGB1 and found a conserved target site of miR-29 (Fig. 6D; Supplementary Fig. S7A and S7B). Indeed, overexpression of miR-29 in MDA-MB-231 cells inhibited expression of ITGB1 (Fig. 6E), further supporting our hypothesis. Overexpression of both miR-29a and miR-29b were confirmed by qRT-PCR (Fig. 6F). To further demonstrate that the posttranscriptional repression of the ITGB1 transcript is caused by direct binding of miR-29 to its target seed sequence within the 3′-UTR of ITGB1, we used the Firefly-Renilla dual luciferase reporter gene assay. We subcloned a part of ITGB1 3′-UTR that contains the miR-29 binding sequence in the pmirGlo vector, and luciferase activity was measured in HEK293 cells transfected with the pmirGlo-ITGB1-3′-UTR along with miR-29–expresing plasmid or its empty control vector. Overexpression of miR-29 in HEK cells transfected with the pmirGlo-ITGB1-3′-UTR resulted in approximately $60\%$ reduction of luciferase activity, compared with cell transfected with empty vector (Fig. 6G). As an independent approach to confirm that miR-29 specifically targets and binds to its seed sequence in the 3′-UTR of ITGB1 to reduce its expression, we scrambled the seed sequence of miR-29 target in the 3′-UTR of ITGB1. This manipulation abrogated the effect of exogenous miR-29 in HEK cells (Fig. 6G). Overexpression of miR-29 in MDA-MB-231 cells also inhibited cell surface (Fig. 6H) and activation (Fig. 6I) levels of ITGB1, as assessed by FACS and HUTS-4 binding assays. Thus, we confirmed that expression of ITGB1 is regulated by miR-29 downstream of WAVE2.
**FIGURE 6:** *The WAVE2 modulation of integrin activities is mediated through the regulation of miR-29 miRNA. A, Volcano plot from the RNA-seq analysis of the differentially expressed genes between CTRL and W2-KO MDA-MB-231 cells. The X-axis represents the log2 fold change in expression levels and the Y-axis shows the P values. Red dots: upregulated genes; Blue dots: downregulated genes; Black dots: no significant change. The dots of the genes of interest are labeled. B, Bars and values representation of the log2 fold change in expression levels of miR-29a and miR-29b derived from the RNA-seq data. C, Quantification of miR-29a (left) and miR-29b (right) expression levels from CTRL and W2KO MDA-MB-231 cells using qRT-PCR. Values are plotted as fold change to the CTRL cells, after normalization to U6 expression levels. D, Nucleotide sequence and location of the seed sequence of miR-29 in the 3′-UTR of ITGB1 mRNA. The sequence alignment with the miR-29 sequence is also shown. E, Representative WB analysis of protein lysates from CTRL and miR-29–expressing MDA-MB-231 probed with anti ITGB1 antibody. β-Actin was used for loading control. F, Quantification of miR-29a (left) and miR-29b (right) expression levels from CTRL, W2KO, or miR-29–expressing MDA-MB-231 cells using qRT-PCR. Values are plotted as fold change to the CTRL cells, after normalization to U6 expression levels. G, Quantification of luciferase activity of ITGB1–3′-UTR. Firefly luciferase reporter plasmid pmirGlo, empty (EV), containing the 3′-UTR of ITGB1 with wild-type (miR-29) or scrambled miR-29 seed sequence (SCRAM) was transiently transfected into HEK293 along with a miR-29–expressing vector. Luciferase activities were measured after 48 hours and plotted after being normalized Renilla luciferase. Representative histograms of flow cytometry analyses for cell surface expression of ITGB1 (H) and activity: HUTS4 binding (I) of CTRL, W2-KO, and ITGB1-KO or miR-29–expressing MDA-MB-231 cells. Shift to the right indicates increased expression or activity. Data are the means ± SD (n = 3; **, P < 0.01; Student t test). Data shown are representative of three replicates.*
To further assess the effect of the miR-29–mediated downregulation of ITGB1 we subjected MDA-MB-231 CTRL cells and their W2KO, miR-29–overexpressing and ITGB1KO derivatives to cell adhesion and spreading assays, and found that the miR-29–expressing cells show reduced cell adhesion on Fibronectin (Fig. 7A) and Matrigel (Fig. 7B), and reduced cell spreading on Fibronectin (Fig. 7C), Matrigel (Fig. 7D), and Laminin (Fig. 7E). ITGB1KO cells served as a negative control in these experiments. Next, to investigate the effect of miR-29 overexpression on tumor growth and metastasis, we injected mammary fat pads of 6–8 weeks old NSG mice with MDA-MB-231 CTRL cells or their W2KO, miR-29–overexpressing or ITGB1KO derivatives, and tumor growth was assessed over 8 weeks. We found overexpression of miR-29 to significantly inhibit tumor growth in the same manner that W2KO or ITGB1KO did (Fig. 7F and G). Lung metastasis was also inhibited as a result of miR-29 overexpression. ( Fig. 7H and I). Together, these finding confirm the hypothesis that miR-29, when upregulated in the W2KO cells, downregulates ITGB1 by directly binding to its 3′-UTR, which in turn causes the cancer cells to lose their oncogenic properties in vitro and their tumor growth and metastasis promoting potentials in vivo.
**FIGURE 7:** *The WAVE2-mediated modulation of miR-29 expression regulates of ITGB1 oncogenic activities. Quantification of cell adhesion of CTRL, W2KO, ITGB1-KO, or mi-R29–expressing MDA-MB-231 cells on fibronectin (A) and Matrigel (B). Quantification of cell adhesion of CTRL, W2KO, ITGB1-KO, or miR-29–expressing MDA-MB-231 cells on fibronectin (C), Matrigel (D), and laminin (E). Data are the means ± SD (n = 3; **, P < 0.01; Student t test). Data shown are representative of three replicates. F, Quantification of volume of tumors derived from implantation of CTRL, WAVE2-KO, ITGB1-KO, or miR-29–expressing MDA-MB-231 cells into the mammary fat pads of NSG mice. The inserts show WBs confirming ITGB1-KO in MDA-MB-231 cells before mice injections, as well images of the resulting tumors from each group. G, Quantification of tumor weights. H, Quantification of lung metastatic foci. I, Images of lungs from the corresponding experiment. Arrow heads point metastatic foci that are abundant in the lungs of CTRL mice compared their W2KO, miR-29, and ITGB1KO counterparts. **, ***, P < 0.01; Student t test.*
## The miR-29 Targets WAVE2 in a Negative Feedback Loop to Modulate Expression and Activity of ITGB1
The data presented in Fig. 6 show that loss of WAVE2 expression results in increased expression of miR-29, which in turn inhibits ITGB1 expression and its downstream signaling. Interestingly, a survey of the WAVE2 3′-UTR identified a conserved target site for miR-29 (Fig. 8A; Supplementary Fig. S8A and S8B). We confirmed the specific binding of miR-29 to its target in the WAVE2-3′-UTR using Firefly-Renilla dual luciferase reporter gene assay; miR-29 inhibited luciferase activity in the presence of wildtype WAVE2-3′-UTR, but not in the presence of the mutated (SCRAM) miR-29 target site (Fig. 8B). These new findings identified a possible negative feedback loop between WAVE2 and miR-29, that coalesces to the regulation of ITGB1. Interrogation of the breast cancer KM plotter datasets showed that, in concordance with WAVE2 (Fig. 1G), increased levels of ITGB1 correlate with decreased survival probability of patients with breast cancer (Fig. 8C and D), while increased expression levels of miR-29a (Fig. 8D and E) and miR-29b (Fig. 8D and F) have the opposite effect. Together, our findings support the interrelationship between WAVE2 and miR-29 in the regulation of the ITGB1–mediated promotion of breast cancer tumor growth and metastasis, through the negative regulatory feedback loop between WAVE2 and miR-29.
**FIGURE 8:** *The miR-29 targets WAVE2 in a negative feedback loop to modulate expression and activity of ITGB1. A, Nucleotide sequence and location of the seed sequence of miR-29 in the 3′-UTR of WAVE2 mRNA. The sequence alignment with the miR-29 sequence is also shown. B, Quantification of luciferase activity of W2–3′-UTR. Firefly luciferase reporter plasmid pmirGlo, empty (EV), containing the 3′-UTR of WAVE2 with wild-type (miR-29) or scrambled miR-29 seed sequence (SCRAM) was transiently transfected into HEK293 along with a miR-29–expressing vector. Luciferase activities were measured after 48 hours and plotted after being normalized Renilla luciferase. **, P < 0.01; Student t test. Data shown are representative of three replicates. KM plot correlating survival of 4.929 patients with breast cancer with mRNA expression levels of ITGB1 (C), miR-29a (D), and miR-29b (E). Low expression levels of miR-29a or miR-29b correlate with poor survival probability in patients with breast cancer. Number of patients at risk and median survival in the low and high WAVE2 cohorts are shown in F.*
## Reexpression of Exogenous WAVE2 in the WAVE2-deficient TNBC Cells Restores ITGB1 Expression and Activity
To confirm the specificity of loss of WAVE2 on ITGB1 expression and activity, as well as the downstream phenotypic and signaling affects, we used an HA-tagged WAVE2-expressing plasmid to restore WAVE2 expression. First, we transiently expressed HEK-293 cells with either the empty vector or the HA-WAVE2–expressing vector, and confirmed expression of exogenous WAVE2 by WB analyses using anti-HA antibody to detected HA-WAVE2 fusion exogenous WAVE2, and anti-WAVE2 antibody to detect both endogenous and exogenous WAVE2 (Supplementary Fig. S9). Next, we overexpressed HA-WAVE2 in W2-KO-MDA231 cells and showed an increase in ITGB1 signal compared with the W2-KO cells, as well as a slight increase, compared with the control MDA-231 cells (Fig. 9A). We also used flow cytometry analyses to confirm that overexpression of WAVE2 in the W2-deficient MDA-MB-231 cells also resulted in increased ITGB1 cell surface expression (CD29; Fig. 9B) and activity (HUTS4; Fig. 9C). Reexpression of WAVE2 in the W2-deficient MDA-MB-231 cells also restored the ability of these cells to adhere to Fibronectin (Fig. 9D and E) and to Matrigel (Fig. 9D and F). Cell spreading was also restored on the same ECM substrata (Fig. 9G and H, for Fibronectin and Fig. 9G and I, for Matrigel), as a result of rescue of WAVE2 expression. Finally, to further confirm that the observed phenotypes (cell adhesion and spreading) were indeed the result of the rescue of ITGB1 activity, we assessed for the downstream effectors of ITGB1, Src and FAK, and found reexpression of WAVE2 restored phosphorylation of both Src and FAK (Fig. 9J), therefore confirming the specificity of WAVE2 in regulating ITGB1 expression and activity.
**FIGURE 9:** *Reexpression of Exogenous WAVE2 in the WAVE2-deficient TNBC cells restores ITGB1 expression and activity. A, Representative WB analysis of protein lysates from CTRL, W2-KO, and W2-KO MDA-MB-231 overexpressing HA-tagged WAVE2 probed with the indicated antibodies. β-Actin was used for loading control. Representative histograms of flow cytometry analyses for cell surface expression of ITGB1 (B) and activity: HUTS4 binding (C) of CTRL MDA-MB-231, W2-KO, and W2-KO cells overexpressing HA-tagged WAVE2. Shift to the right indicates increased expression or activity. Data are the means ± SD (n = 3; **, P < 0.01; Student t test). Data shown are representative of three replicates. D, Representative confocal microscopy images of nuclei of CTRL, W2-KO, and W2-KO MDA-MB-231 overexpressing HA-tagged WAVE2 seeded on fibronectin- (top) or Matrigel-coated coverslips (bottom), and allowed to adhere for 30 minutes. Scale bar: 250 μm. E and F, Quantification of adhered cells on fibronectin (B) and Matrigel (C). Each dot corresponds to the nucleus of an adherent cell. G, Confocal microscopy images of IF staining of CTRL, W2-KO, and W2-KO MDA-MB-231 overexpressing HA-tagged WAVE2 seeded on fibronectin (top) or Matrigel (bottom), and allowed to spread overnight and stained for actin (green). Nuclei were counterstained with DAPI. Scale bar: 50 μm. Quantification of cell adhesion by means of cell surface area on fibronectin (H), and Matrigel (I). Data are the means ± SD (n = 3; **, P < 0.01; Student t test). Data shown are representative of three replicates. J, Representative WB analysis of protein lysates from W2-KO and W2-KO MDA-MB-MB-231 overexpressing HA-tagged WAVE2 probed with the indicated antibodies. β-Actin was used for loading control. K, A diagram depicting the negative feedback signaling loop between WAVE2 and miR-29b, and the consequences of its regulatory effect on ITGB1 on the invasion-metastasis cascade of breast cancer tumors.*
## Discussion
TNBC is one of the most challenging subtypes of breast cancers to treat and has accounted for poor survival rates in diagnosed patients due to its invasive nature. A myriad of signaling reactions have been investigated that are responsible for tumor growth, invasiveness, and metastasis. Several oncogenes have been documented to orchestrate these signaling pathways to promote cancer. Among them a member of Wiskott–Aldrich syndrome protein family, WAVE2, has recently drawn attention toward its role in cancer progression and metastasis. However, the extent to which WAVE2 regulates tumorigenesis and metastasis and the molecular mechanisms by which it subserves such roles remain largely unknown. Because metastasis is the major cause of death in patients with breast cancer, it becomes critical to investigate the molecular mechanisms whereby certain factors such as actin cytoskeleton remodeling proteins (WAVE2), regulators of cell adhesion and spreading (integrins), and regulators of global posttranscriptional repression (miRNAs), and how interrelationships between these molecular mechanisms impact cancer progression and metastasis, which may provide novel therapeutic alternatives and opportunities to treat breast cancer. In the current study, we used a combination of global transcriptomics (RNA-seq), bioinformatics, genetic manipulation of gene expression, different biochemical and cell imaging analyses in vitro, in addition to in vivo mouse models for TNBC, as well as interrogation of public human breast cancer datasets, to investigate the potential role of WAVE2 and its downstream effectors (miR-29 and ITGB1) in TNBC tumor progression and metastasis. As such, we identified a novel WAVE2/miR-29/ITGB1 signaling axis (Fig. 9K) that regulates TNBC tumor growth and metastasis. More importantly, we identified a negative regulatory feedback loop between WAVE2 and miR-29, where inhibition of WAVE2 expression activates miR-29. In turn, miR-29 feeds back and targets WAVE2 to repress its expression. In addition to WAVE2, miR-29 targets and represses expression of ITGB1. In fact, analysis or our RNA-seq data showed that loss of WAVE2 expression results in the repression of not only ITGB1, but several other integrins, including ITGA6, ITGAV, ITGA2, ITGB4, and ITGB5 (Supplementary Data S1 and S2; Supplementary Fig. S10). This study focused mainly on ITGB1, given its established role in the regulation of several major hallmarks of cancer [35]. Our future studies will investigate the novel role of miR-29 as a global regulator of integrins in cancer.
Our data established a negative feedback loop between WAVE2 and miR-29. While the miR-29–mediated regulation of WAVE2 was determined to be through the binding of miR-29 to its target site in the WAVE2 3′-UTR, the mechanism whereby WAVE2 regulates miR-29 was not definitely established. However, while our data explain how miR-29 might regulate WAVE2 (posttranslational regulation), it does not explain how WAVE2 regulates miR-29 expression. A potential mechanism was revealed from our RNA-seq data, where we found loss of WAVE2 expression results in increased expression levels of DGCR8 mRNA (Supplementary Fig. S11A and S11B). DGCR8, also known as PASHA, is an important component of the miRNA biogenesis machinery, where DGCR8, along with DROSHA, are required for the processing of pri-miRNA to the pre-miRNA form before it is exported from nucleus to the cytoplasm to be incorporated in the RISC complex. WB analyses confirmed increased expression levels of DGCR8 protein in both the W2KO and the miR-29–overexpressing MDA-MB-231 cells (Supplementary Fig. S11C and S11D). On the other hand, Supplementary Fig. S11E shows an inverse correlation in expression levels between WAVE2 and DGCR8 in breast cancer cell lines, while interrogation of the KP plotter breast cancer datasets confirmed the positive correlation between increased DGCR8 expression levels and survival probability of human patients with breast cancer tumors (Supplementary Fig. S11F). Therefore, we identified one plausible mechanism, whereby WAVE2 may regulate DGCR8 expression: downregulation of WAVE2 resulted in increased expression of DGCR8, which in turn increase the availability of pre-miR-29 miRNA pool to be exported to the cytoplasm and further processed by DICER, before it is incorporated in the RISC complex. Given the role of DGCR8 as a global regulator of maturation of miRNAs inside the nucleus, we are cognizant that the WAVE2-mediated stabilization of DGCR8 may not be the only mechanism whereby miR-29 expression is regulated in our system, and, therefore, more in-depth investigation is required. WAVE2 is known to play a critical role in cytoskeletal actin reorganization [11]. The spatial reorganization of actin cytoskeleton is crucial for maintaining cellular morphology and functions [12]. Cytoskeletal actin dynamics mediate the regulation of transcriptional machinery in the nucleus which is crucial in RNA processing [36, 37]. Any fluctuations in WAVE2 expression levels may cause changes in actin cytoskeleton reorganization, which could in turn affect the global transcriptomic machinery. This could provide another way to explain how changes in WAVE2 expression affect the observed changes in miR-29 expression. Another mechanism whereby WAVE2 may regulate DGCR8 expression could be through its translocation to the nucleus, where it may bind to DCGR8 and stabilize it, or through its inclusion in the transcription machinery by acting as a transcription factor or biding and activating DCGR8-specific transcription factors. To support this theory, we show localization of WAVE2 in the nucleus via IF (Supplementary Fig. S12A) and WB (Supplementary Fig. S12B). All these plausible mechanisms need, however, to be investigated.
All three WAVE isoforms (1, 2, and 3) have been heavily investigated for their original description as major regulators of actin cytoskeleton [38]. For the past 20 years, our group has been investigating the role of WAVE3 in tumor progression and metastasis [6, 34], and have clearly shown that neither WAVE1 nor WAVE2 can compensate for loss of WAVE3. In this study, we show again that loss of WAVE2 in breast cancer cell lines and tumors can not be compensated for by neither WAVE1 nor WAVE3, even though their expression levels remain unchanged in the WAVE2-KO cells. Loss of WAVE2 had a dramatic effect on lamellipodia at the leading edge of MDA-MB-231 cells (Supplementary Fig. S13), in a similar manner that did loss of WAVE3 [39]. However, the signaling mechanisms by which they regulate actin cytoskeleton dynamics must be specific to each isoform, because loss of one isoform cannot be compensated for by a different isoform. The literature also provides evidence in support of the specific functions of the different WAVE isoforms [13, 40]; global deletion of either WAVE1 [41] or WAVE2 [42] in mice results in embryonic lethality or premature death just a few days after birth, which support the notion that the function of any given WAVE isoform cannot be compensated for by the other isoforms even when they are coexpressed, further supporting the temporal and special specificity of function of each WAVE.
Overall, our findings suggest that combining miR-29 protagonists, combined with inhibition of WAVE2 and/or integrins may provide synergistic benefits in mitigating the invasive and metastatic properties of TNBC tumors. Future studies will explore the utility of combined miRNA therapy targeted against WAVE2 as a better therapeutic alternative for chemotherapies for the treatment of TNBC tumors.
## Authors’ Disclosures
No disclosures were reported.
## Authors’ Contributions
P.S. Rana: Data curation, software, formal analysis, validation, investigation, writing-original draft. W. Wang: Data curation, software, validation, investigation, methodology. V. Markovic: Data curation, investigation, methodology. J. Szpendyk: Validation, methodology. E.R. Chan: Data curation, formal analysis, validation, visualization. K. Sossey-Alaoui: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, investigation, methodology, project administration, writing-review and editing.
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|
---
title: 'Grain, Gluten, and Dietary Fiber Intake Influence Gut Microbial Diversity:
Data from the Food and Microbiome Longitudinal Investigation'
authors:
- Caroline Y. Um
- Brandilyn A. Peters
- Hee Sun Choi
- Paul Oberstein
- Dia B. Beggs
- Mykhaylo Usyk
- Feng Wu
- Richard B. Hayes
- Susan M. Gapstur
- Marjorie L. McCullough
- Jiyoung Ahn
journal: Cancer Research Communications
year: 2023
pmcid: PMC10035461
doi: 10.1158/2767-9764.CRC-22-0154
license: CC BY 4.0
---
# Grain, Gluten, and Dietary Fiber Intake Influence Gut Microbial Diversity: Data from the Food and Microbiome Longitudinal Investigation
## Abstract
Although short-term feeding studies demonstrated effects of grains, fiber, and gluten on gut microbiome composition, the impact of habitual intake of these dietary factors is poorly understood. We examined whether habitual intakes of whole and refined grains, fiber, and gluten are associated with gut microbiota in a cross-sectional study. This study included 779 participants from the multi-ethnic Food and Microbiome Longitudinal Investigation study. Bacterial 16SV4 rRNA gene from baseline stool was amplified and sequenced using Illumina MiSeq. Read clustering and taxonomic assignment was performed using QIIME2. Usual dietary intake was assessed by a 137-item food frequency questionnaire. Association of diet with gut microbiota was assessed with respect to overall composition and specific taxon abundances. Whole grain intake was associated with overall composition, as measured by the Jensen–Shannon divergence (multivariable-adjusted Ptrend for quartiles = 0.03). The highest intake quartile was associated with higher abundance of Bacteroides plebeius, Faecalibacterium prausnitzii, Blautia producta, and Erysipelotrichaceae and lower abundance of Bacteroides uniformis. These bacteria also varied by dietary fiber intake. Higher refined grain and gluten intake was associated with lower Shannon diversity (Ptrend < 0.05). These findings suggest that whole grain and dietary fiber are associated with overall gut microbiome structure, largely fiber-fermenting microbiota. Higher refined grain and gluten intakes may be associated with lower microbial diversity.
### Significance:
Regular consumption of whole grains and dietary fiber was associated with greater abundance of gut bacteria that may lower risk of colorectal cancer. Further research on the association of refined grains and gluten with gut microbial composition is needed to understand their roles in health and disease.
## Introduction
Whole grains are associated with lower risk of chronic diseases [1], including cancer and total cancer mortality, while refined grains do not have similar beneficial associations with cancer risk [2]. Much of the evidence on cancer has focused on whole grains and colorectal cancer, and the World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) has concluded that there is “probable” evidence that whole grains reduce colorectal cancer risk [3]. As a result, the U.S. Dietary Guidelines to promote health and prevent chronic diseases recommend a healthy dietary pattern that includes grains, at least half of which are whole grains [4]. Research on components of overall dietary patterns, such as of grains, helps to build evidence on the building blocks of healthy dietary patterns and inform research and public health messages on healthy diet.
The beneficial association between whole grains and colorectal cancer risk may be at least partly attributable to fermentation of these foods by short-chain fatty acid (SCFA)-producing gut microbes. Whole grains, unlike refined grains, are rich in fermentable dietary fiber and consequently have a “prebiotic” effect on gut microbes to produce SCFAs, such as butyrate, which are suggested to facilitate growth and differentiation of normal colonocytes while inhibiting tumor cell growth [5]. Thus, diets rich in whole rather than refined grains are hypothesized to promote growth of SCFA-producing microbes while decreasing abundance of proinflammatory species [6, 7].
Gluten is another component of both whole and refined grains that may also alter gut microbiome composition. In the human gastrointestinal tract, gluten, composed of grain storage proteins, is cleaved into proline- and glutamine-rich peptides by proteolytic gut microbiota [8]. Incomplete digestion of gluten can lead to immune responses that are characteristic of celiac disease, wheat allergy, and non-celiac gluten sensitivity. Although the role of gut microbiota is unclear in these conditions, differences in microbial composition have been observed in patients with celiac disease when compared with healthy populations [9], suggesting that certain microbes are needed in the digestion of gluten-containing foods.
Although strong evidence exists on the beneficial effect of whole grain foods on colorectal cancer risk, randomized controlled trials of whole grain foods and the gut microbiome have shown inconsistent findings [10]. In addition, limited evidence from dietary intervention studies of gluten suggests that gluten-free or low-gluten diets alter gut microbial composition in healthy populations by decreasing several beneficial species associated with carbohydrate metabolism (11–13), but the evidence is largely based on small, short-term trials, similar to whole grain feeding studies (14–20). Little is known regarding the impact of habitual intake of these dietary factors on gut microbiota, and given that interest in gluten has steadily grown in recent years with increasing adherence to gluten-free diets [21], understanding the association of gluten-containing foods and gut microbiota is also of public interest.
To contribute to the limited evidence on gluten, grains, and gut microbiota, we examined self-reported habitual consumption of whole and refined grains, and their major components dietary fiber and gluten, with gut microbial composition, among participants of the Food and Microbiome Longitudinal Investigation (FAMiLI; ref. 22).
## Study Population
The FAMiLI is an ongoing prospective cohort study of racially and ethnically diverse residents in New York City and surrounding geographic areas. Briefly, men and women ages 40 years or older who were not currently pregnant or on long-term antibiotic therapy were invited to enroll through clinic, community, and web-based recruitment. Participants with recent antibiotic use were eligible if the last dose was at least 2 weeks prior to the enrollment date. To date, over 9,500 participants have enrolled, with the goal of 15,000, and continued recruitment and prospective follow-up for disease outcomes are planned. At baseline, participants completed a questionnaire containing demographic, lifestyle, and dietary questions, and submitted self-collected oral and stool samples. The study was approved by the New York University Langone Health Institutional Review Board (#s12-00855), and all participants provided written informed consent. For the current analysis, participants who completed both demographic and dietary questionnaires and provided a stool sample were included, as published elsewhere [22]. In the first phase, 1,000 participants were recruited and of those, 873 stool samples were selected for microbiome sequencing and included in this analysis.
## Dietary Assessment
Diet was assessed using a 137-item food frequency questionnaire (FFQ), known as the Dietary Questionnaire (DQX; ref. 23). The design of the DQX was based on two previously validated FFQs [24, 25] and is similar to the Diet History Questionnaire (DHQ), which was validated previously (26–29). The DQX was translated into Korean and Spanish for the FAMiLI participants. One additional page of culturally relevant foods was included with the DQX for Korean and Chinese participants, but because this page has not been validated, these food items were not included in the current study.
Study participants reported their frequency of consumption for 18 grain-containing items on the FFQ by selecting one of the 10 listed frequencies (never; less than once per month; one time per month; two to three times per month; one time per week; two times per week; three to four times per week; five to six times per week; one time per day; or two or more times per day) and one of the three listed serving sizes (small, medium, or large). The food items were categorized as whole or refined grains and as high- or low-gluten grain foods. Grain-based foods were classified as whole or refined based on the definition that whole grains retain $100\%$ of the original kernel [30] and on extensive literature, including the WCRF/AICR systematic literature review on whole grains and colorectal cancer risk [3, 31]. Whole grain foods in this analysis included high fiber cold cereals, dark wheat or rye breads, brown or wild rice, and other whole grains. Refined grain foods included cooked cereals or grits, other cold cereals, pancakes/waffles, white bread, corn bread, biscuits/muffins, white rice, pasta, pizza, and crackers. Five additional FFQ items were queried regarding refined grain-based sweets/dessert products; these items were not included in the primary analyses because they are frequently high in sugar and/or saturated fat, which may independently influence gut microbiota, but were included in sensitivity analyses.
To calculate the gluten content of whole and refined grain products, the amount of protein contained in wheat, barley, and rye food items was estimated from the USDA food composition database, FoodData Central [32]. Then, the protein content was multiplied by a conversion factor of 0.75, based on the Osborne plant protein classification system [33]. Previous estimates of the gluten fraction of proteins using this system applied conversion factors of 0.75 or 0.80 (34–36). Gluten estimation using the Osborne system has shown an acceptable correlation ($r = 0.70$; $95\%$ confidence interval, 0.35–0.88) with gluten measurement using ELISAs [37].
The intakes of whole and refined grain, gluten, dietary fiber, and dietary fiber from grain-based foods were energy adjusted using the nutrient density method and then analyzed as continuous variables and sex-specific quartiles. For this analysis, participants who were missing ≥60 FFQ items, which also accounted for those missing >$50\%$ of the grain items ($$n = 48$$), and those who reported implausible total energy intake in the lowest or highest $1\%$ (≤430 or ≥9,243 kcal/day for men and ≤412 or ≥6,703 kcal/day for women; $$n = 16$$) were excluded from this analysis. After exclusion of 64 subjects, 809 of 873 ($93\%$) subjects remained.
## Gut Microbiome Assessment
Stool samples were self-collected by study participants upon enrollment using RNAlater collection kits. Participants were instructed to immediately ship the sample after collection, and average shipping time from the participant to the laboratory was 3–4 days. Because the samples were collected using RNAlater, shipments did not require cold or other special packaging. Samples were frozen immediately upon receipt until sequencing was performed. 16SV4 rRNA gene sequencing was conducted as described previously [22]. DNA was extracted using the Mo Bio PowerSoil DNA isolation kit. The V4 region of the 16S rRNA gene was PCR amplified with the 515F/806R primer pair, which included sequencer adapter sequences used in the Illumina flowcell and sample-specific barcodes [38, 39]. PCR products were quantified using PicoGreen (Invitrogen) and a plate reader (Infinite 200 PRO, Tecan). Sample PCR products were then pooled in equimolar amounts, purified using AMPure XP Beads (Beckman Coulter), and then quantified using a fluorometer (Qubit, Invitrogen). Amplicons were sequenced on a 151 bp × 12 bp × 151 bp MiSeq run [39].
Sequence reads were processed using QIIME2 [40]. Briefly, sequence reads were demultiplexed and paired-end reads were joined, followed by quality filtering as described previously [41]. The Deblur workflow was applied, which uses sequence error profiles to obtain putative error-free sequences, referred to as “sub” operational taxonomic units (s-OTU; ref. 42). s-OTUs were assigned taxonomy using a naïve Bayes classifier pretrained on the Greengenes [43] 13_8 $99\%$ OTUs, where the sequences have been trimmed to only include 250 bases from the 16S V4 region, bound by the 515F/806R primer pair. A phylogenetic tree was constructed via sequence alignment with MAFFT [44], filtering the alignment, and applying the FastTree algorithm [45] to generate the phylogenetic tree. The number of observed s-OTUs and Shannon diversity index were calculated in 100 iterations at 50 different rarefied sequencing depths (from 15 to 5,000 sequence reads per sample) and averaged for each subject at each depth [22].
In addition to the previously mentioned dietary exclusions, subjects for whom sequencing failed ($$n = 9$$) and subjects with sequencing depths <250 sequence reads per sample after the Deblur workflow ($$n = 21$$) were excluded, as described previously [22]. After all exclusions, the final analytic cohort included 779 participants (291 men and 488 women). The data generated in this study are available upon request from the corresponding author.
## Statistical Analysis
The associations of sex-specific quartiles of energy-adjusted whole and refined grain, gluten, and dietary and grain fiber intake with within-subject microbial diversity (alpha-diversity) were evaluated using the number of observed s-OTUs (richness) and the Shannon diversity index. The dietary exposures were modeled using multivariable-adjusted linear regression models with richness and the Shannon index as outcomes, and adjusted for age, sex, race/ethnicity, body mass index (BMI; categories of 18–<25 kg/m2, 25–<30 kg/m2, 30–<35 kg/m2, ≥35 kg/m2), smoking status (ever/never), alcohol intake (g/day), and total energy intake (kcal/day, continuous). We also tested whole grain models additionally adjusted for refined grains and vice versa, and whole and refined grain and gluten models additionally adjusted for dietary fiber and fruit and vegetable (servings/day) intake. Trend tests across quartiles of intake were calculated using the median quartile values, modeled as a continuous term. The association of quartiles of whole and refined grain, gluten, and dietary and grain fiber intake with overall gut microbiota composition was assessed using Jensen–Shannon divergence (JSD). Permutational multivariate analysis of variance was performed using R software (version 3.6.1; R Development Core Team, 2020), R package “vegan” [46], and adonis function to assess this association [47], and models were adjusted for the same covariates mentioned previously.
The analysis of composition of microbiomes (ANCOM) method [48] was used to identify s-OTUs and higher level taxa that were differentially abundant between the highest and lowest intakes of the various dietary exposures, adjusting for the previously mentioned covariates. For this analysis, taxa at the detection level ≥0.80 were considered significant.
In sensitivity analyses, analyses were repeated stratified by racial/ethnic group and after excluding participants who self-reported type 2 diabetes ($$n = 130$$) or inflammatory bowel disease (Crohn’s disease or ulcerative colitis; $$n = 21$$) at enrollment. Grain-based sweets and desserts were also included in the estimation of refined grain and gluten intake. Analyses were performed using R version 3.6.1 (R Core Team, 2020).
## Data Availability
The 16S rRNA sequencing data that support the findings of this study have been deposited in the Sequence Read Archive (PRJNA559143), along with demographic metadata, to be released upon publication. Additional data on the study participants are available from the corresponding author upon reasonable request.
## Participant Characteristics
In this study population, $36.7\%$ of participants were White, $10.4\%$ were Black, $15.0\%$ were Hispanic, $30.2\%$ were Korean, $3.1\%$ were other Asian, and $1.0\%$ were other race/ethnicities. Participants with the highest intakes of whole grain were older, had a lower BMI, and had higher total energy, dietary fiber, and fruit and vegetable intakes but lower calories from fat and intakes of alcohol, processed meat, and refined grains (Table 1). In addition, greater whole grain consumers were more likely to be Korean, more highly educated, regular aspirin users, and physically active. Conversely, greater refined grain consumers were generally younger, heavier, less physically active, and had lower intakes of dietary fiber and fruits and vegetables.
**TABLE 1**
| Unnamed: 0 | Whole grain quartilesa,b | Whole grain quartilesa,b.1 | Whole grain quartilesa,b.2 | Whole grain quartilesa,b.3 | Refined grain quartilesa,b | Refined grain quartilesa,b.1 | Refined grain quartilesa,b.2 | Refined grain quartilesa,b.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Q1 | Q2 | Q3 | Q4 | Q1 | Q2 | Q3 | Q4 |
| | N = 196 | N = 195 | N = 193 | N = 196 | N = 195 | N = 196 | N = 193 | N = 196 |
| Range: Males | 0–8.0 | >8.0–20.2 | >20.2–46.5 | >46.5–299 | 0–47.7 | >47.7–80.5 | >80.5–120 | >120–343 |
| Females | 0–10.3 | >10.3–27.5 | >27.5–63.8 | >63.8–448 | 0–41.9 | >41.9–74.4 | >74.4–119 | >119–370 |
| Age (years) | 57.6 (11.2) | 57.2 (10.5) | 60.2 (11.4) | 62.8 (11.0) | 64.5 (10.9) | 59.7 (10.7) | 58.2 (10.2) | 55.4 (11.1) |
| Female, % | 62.6 | 62.6 | 62.9 | 62.6 | 62.6 | 62.6 | 62.9 | 62.6 |
| BMI (kg/m2) | 29.1 (7.0) | 28.5 (6.3) | 27.6 (5.9) | 24.4 (4.9) | 25.9 (5.3) | 27.5 (6.8) | 28.4 (6.3) | 27.9 (6.5) |
| Race/ethnicity, % | Race/ethnicity, % | Race/ethnicity, % | Race/ethnicity, % | Race/ethnicity, % | Race/ethnicity, % | Race/ethnicity, % | Race/ethnicity, % | Race/ethnicity, % |
| White | 37.9 | 52.3 | 48.5 | 13.8 | 26.7 | 43.1 | 44.8 | 37.9 |
| Black | 15.4 | 12.3 | 9.8 | 5.6 | 5.1 | 14.9 | 12.9 | 10.3 |
| Hispanic | 28.2 | 18.5 | 13.4 | 2.1 | 7.7 | 11.3 | 19.6 | 23.6 |
| Korean | 14.4 | 11.3 | 23.7 | 75.9 | 59.0 | 25.6 | 19.6 | 21.0 |
| Asian, other than Korean | 3.6 | 3.6 | 3.6 | 2.1 | 1.0 | 4.1 | 2.1 | 5.6 |
| Otherc | 0.5 | 2.1 | 1.0 | 0.5 | 0.5 | 1.0 | 1.0 | 1.5 |
| College graduate or higher, % | 44.1 | 59.5 | 64.9 | 53.3 | 49.7 | 61.5 | 58.8 | 51.8 |
| Physical activity, % | Physical activity, % | Physical activity, % | Physical activity, % | Physical activity, % | Physical activity, % | Physical activity, % | Physical activity, % | Physical activity, % |
| | 21.0 | 13.3 | 7.7 | 11.8 | 9.2 | 10.8 | 13.4 | 20.5 |
| <4 hours/week | 56.4 | 60.0 | 54.1 | 49.2 | 44.1 | 61.0 | 62.4 | 52.3 |
| 4 or more hours/week | 22.6 | 26.7 | 38.1 | 39.0 | 46.7 | 28.2 | 24.2 | 27.2 |
| Ever smoker, % | 33.3 | 41.0 | 31.4 | 24.6 | 30.8 | 36.9 | 30.9 | 31.8 |
| Alcohol consumption (g/day) | 8.0 (22.2) | 14.2 (31.9) | 8.0 (22.5) | 4.0 (15.6) | 9.4 (29.5) | 9.6 (23.7) | 9.6 (24.2) | 5.6 (16.8) |
| Family history of cancer, % | 55.9 | 55.9 | 53.1 | 39.0 | 45.6 | 57.9 | 53.1 | 47.2 |
| Regular aspirin use, % | 32.3 | 33.3 | 33.5 | 41.5 | 34.9 | 34.4 | 36.6 | 34.9 |
| History of ulcerative colitis, % | 3.1 | 1.0 | 2.1 | 2.6 | 1.5 | 2.6 | 2.6 | 2.1 |
| History of Crohn’s disease, % | 0.5 | 1.0 | 0.5 | 0.0 | 0.0 | 1.0 | 0.5 | 0.5 |
| Total energy (kcal/day) | 1,889 (1,047) | 2,015 (1,212) | 2,052 (1,050) | 2,122 (1,036) | 2,106 (1,017) | 1,905 (935) | 2,059 (1,064) | 2,007 (1,306) |
| Total fat (% kcal/day) | 31.2 (8.2) | 30.9 (6.6) | 29.4 (5.4) | 25.8 (4.6) | 28.0 (7.7) | 29.9 (7.0) | 30.2 (6.2) | 29.2 (5.6) |
| Total carbohydrates (% kcal/day) | 51.7 (11.2) | 50.4 (9.4) | 54.1 (8.1) | 58.3 (7.0) | 54.3 (11.2) | 53.0 (10.3) | 52.6 (8.3) | 54.5 (7.9) |
| Red meat (servings/day) | 0.6 (0.7) | 0.7 (0.8) | 0.6 (0.6) | 0.5 (0.7) | 0.6 (0.8) | 0.5 (0.5) | 0.6 (0.7) | 0.6 (0.9) |
| Processed meat (servings/day) | 0.7 (1.0) | 0.6 (0.8) | 0.4 (0.7) | 0.2 (0.5) | 0.4 (0.8) | 0.5 (0.8) | 0.6 (0.8) | 0.5 (0.8) |
| Fruits and vegetables (servings/day) | 5.9 (4.6) | 7.2 (4.5) | 8.1 (4.7) | 9.6 (5.3) | 9.8 (5.9) | 7.2 (4.4) | 7.4 (4.5) | 6.4 (4.2) |
| Total dairy (servings/day) | 2.1 (2.2) | 2.2 (2.0) | 2.0 (2.0) | 1.3 (1.5) | 1.7 (1.9) | 2.0 (1.9) | 2.2 (2.2) | 1.7 (1.8) |
| Whole grains (energy-adj; g/day) | 3.5 (3.1) | 16.4 (5.0) | 38.1 (10.2) | 126.6 (74.9) | 79.0 (88.5) | 41.3 (51.6) | 33.6 (38.4) | 30.7 (39.3) |
| Refined grains (energy-adj; g/day) | 105.2 (76.3) | 98.0 (58.8) | 92.9 (58.7) | 62.8 (55.7) | 23.1 (13.9) | 60.9 (9.8) | 95.2 (12.0) | 179.7 (54.6) |
| Gluten (energy-adj; g/day), | 2.1 (1.4) | 2.7 (1.3) | 3.2 (1.8) | 2.8 (2.1) | 1.6 (1.5) | 2.3 (1.2) | 3.0 (1.3) | 3.9 (1.9) |
| Dietary fiber (energy-adj; g/day) | 18.0 (10.2) | 22.9 (13.2) | 28.1 (15.8) | 35.1 (17.8) | 31.6 (18.3) | 23.2 (13.0) | 25.2 (14.0) | 24.1 (16.3) |
## Whole Grain and Fiber Intake
Greater whole grain consumption was not associated with the number of observed s-OTUs (richness; Ptrend = 0.96) or the Shannon diversity index (Ptrend = 0.79; Fig. 1A; alpha-diversity) after adjustment for age, sex, race/ethnicity, BMI, smoking status, alcohol intake, and total energy intake. However, higher whole grain intake was associated with overall gut microbiome composition, as measured by JSD (Fig. 1B; beta-diversity, Q4 vs. Q1 R2 = $0.23\%$, $$P \leq 0.01$$; R2 for trend = $0.22\%$, Ptrend = 0.02), with adjustment for age, sex, and race/ethnicity. The amount of variation remained consistent after further adjustment for BMI, smoking status, alcohol intake, and total energy intake (Q4 vs. Q1 R2 = $0.23\%$, $$P \leq 0.01$$; R2 for trend = $0.22\%$, Ptrend = 0.02; Fig. 1C). We identified bacterial taxa associated with greater whole grain consumption (Fig. 1D): s-OTUs of Bacteroides plebeius, Faecalibacterium prausnitzii, and *Blautia producta* were enriched, while B. uniformis was depleted. Additional significantly differentially abundant s-OTUs belonged to the following higher-order groups: Erysipelotrichaceae and Roseburia were enriched at detection levels of 0.90 and 0.80, respectively, Oscillospira and Rikenellaceae were depleted, and Lachnospira, Ruminococcus, and Ruminococcaceae were both enriched and depleted. Dietary fiber, a major whole grain nutrient, was positively correlated with whole grain intake ($r = 0.42$) and was similarly associated with beta-diversity (JSD, Q4 vs. Q1 R2 = $0.21\%$, $$P \leq 0.02$$; R2 for trend = $0.20\%$, Ptrend = 0.03) but not with alpha-diversity (Ptrend for richness = 0.30, Ptrend for Shannon diversity index = 0.38; Supplementary Fig. S1). Abundant taxa associated with greater dietary fiber intake (Supplementary Fig. S1C) were similar to those associated with whole grains (Fig. 1E). When whole grain models were additionally adjusted for dietary fiber or fruit and vegetable intake, the results for alpha-diversity (Ptrend for richness = 0.59 and 0.84, respectively; Ptrend for Shannon diversity index = 0.43 and 0.60, respectively) and for differentially abundant taxa did not materially change; however, whole grain intake was no longer significantly associated with microbial community diversity as measured by JSD after adjustment for dietary fiber or fruit and vegetable intake (R2 for trend = $0.19\%$ and $0.15\%$, respectively; Ptrend = 0.06 and 0.21, respectively). Grain fiber was similarly positively correlated with whole grain intake ($r = 0.48$), but higher intake was not significantly associated with alpha- (Ptrend for richness = 0.54, Ptrend for Shannon diversity index = 0.53) or beta-diversity (R2 for trend = $0.15\%$, Ptrend = 0.19) or any specific microbial taxa.
**FIGURE 1:** *Gut microbiome alpha- and beta-diversity according to energy-adjusted quartiles of whole grain intake in the FAMiLI. A, Boxplot of Shannon diversity index by whole grain quartiles. B, Principal coordinate analysis of the JSD by whole grain quartiles. C, Bar plot illustrating the R2 for model covariates derived from JSD. D, Volcano plots of differentially abundant s-OTUs as detected by ANCOM (model adjusted for age, sex, race, BMI, smoking status, alcohol intake, and total energy) between quartile 4 (Q4) and quartile 1 (Q1) of whole grain intake. The x-axis represents the difference in mean centered log ratio (clr)-transformed abundance between Q4 and Q1, and the y-axis represents the ANCOM W Statistic. s-OTU points are colored according to level of ANCOM significance, with 0.90 being the highest level and grey points indicating s-OTUs that were not significant. E, Cladograms of phylum through species level taxa; color represents clr mean difference between Q4 and Q1 of whole grain intake.*
## Refined Grain and Gluten Intake
Refined grain consumers were less likely to consume whole grains in their diet (r = −0.27). Refined grain consumption was associated with a lower number of observed s-OTUs (richness, Ptrend = 0.04) and lower Shannon diversity index (Ptrend = 0.03; Fig. 2A), but not related to differences in overall microbiome composition (Ptrend = 0.05; Fig. 2B). Greater refined grain consumption was not significantly associated with any specific taxa (Fig. 2C). Most refined grain food items contributed to gluten intake. Thus, refined grain was moderately positively correlated with gluten intake ($r = 0.46$) and weakly negatively correlated with dietary fiber (r = −0.18). Higher consumption of gluten was similarly associated with lower Shannon diversity (Ptrend = 0.03), not associated with beta-diversity (JSD Ptrend = 0.19), and not associated with specific gut microbial taxa, other than depletion of s-OTUs of *Blautia obeum* (Fig. 3). Refined grain consumption was associated with lower alpha-diversity after additional adjustment for energy-adjusted dietary fiber and fruit and vegetable intake, although P for trend values were no longer statistically significant in most models—(Ptrend for richness = 0.09 and 0.06, respectively; Ptrend for Shannon diversity index = 0.07 and 0.04, respectively), but there remained no differences in overall microbial composition (R2 for trend = $0.17\%$ and $0.18\%$, respectively; Ptrend = 0.11 and 0.08, respectively) or any specific taxa. Additional adjustment for energy-adjusted dietary fiber and fruit and vegetable intake in gluten models did not materially change the results for alpha- (Ptrend for richness = 0.08 and 0.02, respectively; Ptrend for Shannon diversity index = 0.04 and 0.01, respectively) or beta-diversity (R2 for trend = $0.16\%$ and $0.15\%$, respectively; Ptrend = 0.17 and 0.20, respectively) or for taxonomic abundance.
**FIGURE 2:** *Gut microbiome alpha- and beta-diversity according to energy-adjusted quartiles of refined grain intake in the FAMiLI. A, Boxplot of Shannon diversity index by refined grain quartiles. B, Principal coordinate analysis of the JSD by refined grain quartiles. C, Volcano plots of differentially abundant s-OTUs as detected by ANCOM (model adjusted for age, sex, race, BMI, smoking status, alcohol intake, and total energy) between quartile 4 (Q4) and quartile 1 (Q1) of refined grain intake. The x-axis represents the difference in mean centered log ratio (clr)-transformed abundance between Q4 and Q1, and the y-axis represents the ANCOM W Statistic. s-OTU points are colored according to level of ANCOM significance, with 0.90 being the highest level and gray points indicating s-OTUs that were not significant.* **FIGURE 3:** *Gut microbiome alpha- and beta-diversity according to energy-adjusted quartiles of gluten intake in the FAMiLI. A, Boxplot of Shannon diversity index by gluten quartiles. B, Principal coordinate analysis of the JSD by gluten quartiles. C, Volcano plots of differentially abundant s-OTUs as detected by ANCOM (model adjusted for age, sex, race, BMI, smoking status, alcohol intake, and total energy) between quartile 4 (Q4) and quartile 1 (Q1) of gluten intake. The x-axis represents the difference in mean centered log ratio (clr)-transformed abundance between Q4 and Q1, and the y-axis represents the ANCOM W Statistic. s-OTU points are colored according to level of ANCOM significance, with 0.90 being the highest level and gray points indicating s-OTUs that were not significant.*
## Sensitivity Analyses
In sensitivity analyses, associations by racial/ethnic group did not significantly differ, but our limited sample size severely limited our ability to examine differences (Supplementary Fig. S2). Similarly, findings remained largely unchanged when we excluded participants who reported a history of type 2 diabetes (energy-adjusted whole grains: Ptrend for richness = 0.84 and Ptrend for Shannon diversity index = 0.96; JSD R2 for trend = $0.26\%$ and Ptrend = 0.02) or inflammatory bowel disease (energy-adjusted whole grains: Ptrend for richness = 0.92 and Ptrend for Shannon diversity index = 0.76; JSD R2 for trend = $0.21\%$ and Ptrend = 0.02), because grain-based, gluten-containing foods may be avoided with these conditions (Supplementary Figs. S3 and S4). When we included grain-based sweets and dessert items with refined grain (Ptrend for richness = 0.04 and Ptrend for Shannon diversity index = 0.02; JSD R2 for trend = $0.20\%$ and Ptrend = 0.03) and gluten intakes (Ptrend for richness = 0.07 and Ptrend for Shannon diversity index = 0.03; JSD R2 for trend = $0.15\%$ and Ptrend = 0.18), findings also remained unchanged (Supplementary Figs. S5 and S6).
## Discussion
In this study, we found that whole grain intake influences the overall gut microbial composition, which was largely explained by the higher abundance of F. prausnitzii, B. plebeius, and Erysipelotrichaceae and lower abundance of B. uniformis, Oscillospira, and Rikenellaceae. These bacteria were also associated with higher dietary fiber intakes. We further found that refined grain and gluten consumers have lower Shannon diversity but do not have any significant variation in specific bacterial taxa, even with the inclusion of grain-based sweets and desserts.
In contrast to refined grains, whole grains are a rich source of dietary fiber, nutrients, and various bioactive compounds, such as phytochemicals. These compounds may reduce risk of colorectal cancer and other cancers through various antiproliferative and inhibitory effects against cancer cells [49] that may be at least partially modulated by gut microbiota [50]. Through gut microbial fermentation of dietary fibers, the production of the SCFA butyrate is thought to suppress colonic inflammation and carcinogenesis by protecting DNA damage in colonocytes induced by oxidative stress [51], facilitating normal colonocyte growth [52], facilitating assembly of tight junction proteins to maintain the intestinal barrier [53], and inhibiting tumor cell growth [54]. However, unlike whole grains, grain fiber was not associated with alpha- or beta-diversity or abundant taxa in this study, which raises the question whether other components of whole grains beyond dietary fiber may be associated with gut microbiota. Whole grains are more complex than refined grains and contain additional nutrients, including unsaturated fatty acids and various phytochemicals, which have unknown associations with the gut microbiome. Future studies that utilize whole genome shotgun sequencing techniques and examine the fecal metabolome will help elucidate food-microbiome relationships.
We found that greater whole grain intake was associated with increased beta-diversity and increased abundance of various SCFA-producing species, including s-OTUs from F. prausnitzii and family Erysipelotrichaceae. These findings provide further evidence that whole grain consumption is associated with lower colorectal cancer risk due to an associated healthier gut microbial phenotype. Previous intervention trials of whole grains have yielded mixed results related to F. prausnitzii (14–20), which may be partly due to differences in study type (crossover vs. parallel dietary interventions), study length, type of whole grain or dietary fiber, dose, and baseline gut microbiota of different study populations (e.g., healthy vs. obese; ref. 55). Evidence from animal models also suggest that high-fat or Western dietary patterns, which are low in whole grains, may increase abundance of Erysipelotrichaceae [56], but additional studies report higher [57, 58] and lower (59–61) abundances of Erysipelotrichaceae in inflammatory bowel disease and colorectal cancer. This suggests that differences may exist between species. We found that s-OTUs of Erysipelotrichaceae were still enriched with greater whole grain intake after exclusion of participants with self-reported inflammatory bowel disease. Additional studies that utilize whole genome shotgun sequencing are warranted to clarify the relationship between whole grains, SCFA-producing species, and risk of inflammatory bowel disease and colorectal cancer.
Conversely, greater whole grain consumption was associated with lower abundance of s-OTUs of B. uniformis and family Rikenellaceae from phylum Bacteroidetes. Although these propionate-producing microbes have not been associated with whole grain or dietary fiber consumption, evidence suggests potential associations with animal protein and dietary fat consumption (62–64). In our study, participants with the highest whole grain intakes consumed less total fat and processed meat and more fruits and vegetables, suggesting that B. uniformis and Rikenellaceae may be associated with dietary patterns that are less healthy and commonly low in whole grains and dietary fiber [65].
Higher whole grain intake was associated with higher abundance of s-OTUs of B. plebeius, also from phylum Bacteroidetes, but the significance of this finding is uncertain. Although B. plebeius was previously reported to have decreased among participants who consumed a 12-week high-fiber rye bread intervention [66], it was also identified to contain an enzyme capable of digesting nori seaweed among native Japanese populations [67]. This evidence may at least partially explain our finding because participants with the highest whole grain intake were more likely to be of Korean descent; however, seaweed-containing food items were not included on the original FFQ and therefore, were unable to be assessed.
Current evidence on the relationship between gluten and gut microbiota in healthy adult populations is limited. To our knowledge, three dietary intervention trials of gluten-free [11, 12] or low-gluten [13] diets were conducted in adult non-celiac disease populations. Among 10 Spanish adults ages 23–40 years maintained on a gluten-free diet for 1 month, statistically significant decreases in Bifidobacterium and Lactobacillus and increases in *Escherichia coli* and Enterobacteriaceae compared with baseline were observed [11]. A second trial of 21 Dutch men and women ages 16–61 years reported alterations in eight taxa, with the greatest decrease in family Veillonellaceae, after following a 4-week gluten-free diet [12]. A third crossover trial of 54 Danish adults ages 22–65 years reported that an 8-week low-gluten diet altered abundance of 14 bacterial species in comparison with a high-gluten diet of equal dietary fiber content, including decreased abundance of four Bifidobacterium species [13]. Given that Bifidobacterium and Veillonellaceae species mediate or are directly involved in SCFA production, respectively [68, 69], this evidence suggests that gluten-free dietary patterns may cause undesirable shifts in gut microbial composition. We did not observe similar findings as previous studies, but we did observe a depletion of s-OTUs of B. obeum with gluten intake. This, along with our finding that s-OTUs of B. producta were enriched with higher whole grain intake, may reflect the association of *Blautia genus* with visceral fat accumulation and obesity [70]. This finding suggests that greater consumption of whole grains, rather than refined grains, which was correlated with gluten intake, may be associated with enrichment of Blautia species. However, additional studies are needed to understand the significance of different Blautia species and to understand whether gluten has an undesirable effect on gut microbial composition in healthy populations.
Additional components of diet, beyond whole grains and dietary fiber, may influence gut microbial composition, such as dietary fat [71] and fruits and vegetables [72], which correlated with whole grain consumption in this population. Other dietary components, such as artificial sweeteners [73], were not assessed in this study. High whole grain consumption may also be indicative of healthier dietary patterns. Dietary fiber is a major component of healthy dietary patterns, and intake was associated with similar microbial taxa as whole grain intake in this study. Grain fiber was not associated with these taxa, suggesting that whole grain components, other than fiber, may influence gut microbiota. Adjustment for fruit and vegetable intake in models of whole grain and dietary fiber did not change associations with microbial taxa, suggesting that fruit and vegetable intake was not responsible for the gut microbial composition associations observed. Similarly, the addition of grain-based sweets and desserts in refined grain and gluten models did not change associations, suggesting that these foods, which are often low in dietary fiber and high in sugar and/or saturated fat, were also not associated with gut microbial composition. Additional studies are needed to elucidate the associations between individual foods and nutrients, as well as overall dietary patterns, on the gut microbiome.
Strengths of this study include a large study population with collection of stool samples and the assessment of habitual, rather than shorter-term, dietary intakes using the FFQ. This allowed us to examine a wide range of whole and refined grain, dietary fiber, and gluten intakes in relation to gut microbial composition. A limitation of this study was the limited sample size when stratifying by race/ethnicity, likely hindering our ability to observe significant differences between groups. Furthermore, Chinese- and Korean-specific grain foods were not available for analysis, which may have contributed to measurement error of the exposures. Because the DQX and DHQ have not been validated in Chinese-American or Korean-American populations [27], we acknowledge that consumption of certain cultural foods may have been underestimated. There is very limited data on dietary patterns of the Korean American population, but commonly consumed Korean foods include kimchi, rice, garlic, green onions, Korean soups and stews, and various Korean condiments [74]. Although none of these foods are considered whole grains, kimchi and other vegetables may contribute to vegetable and dietary fiber intake. Thus, underestimation of vegetable dietary fiber intake by the DQX may have contributed to residual confounding. Similarly, refined grain consumption may have been underestimated among Korean American participants, which may have contributed to measurement error and attenuation of the refined grain results. However, rice consumption in the native Korean diet has steadily declined [75] so it is unclear whether or to what degree rice consumption was underestimated by the DQX in this study. Acculturation based on length of residence in the United States may also influence dietary patterns of Korean Americans to be more “Americanized” versus “Korean,” but this information was unavailable in this study. Additional studies are needed to validate the DQX and DHQ among Korean American and other Asian American subgroups. Additional limitations of this study include the cross-sectional design, which did not allow us to examine within-person changes in grain and gluten intakes, gut microbiota, and other lifestyle factors over time, as well as any potential health-related consequences of long-term intake of grain- and gluten-containing foods and related changes in the gut microbiome. With continued enrollment, future analyses in the FAMiLI cohort will have greater statistical power to investigate associations by racial/ethnic group and will be able to examine longitudinal changes in dietary intakes and gut microbiome as repeated stool samples and dietary intakes are collected over time.
In summary, our findings from this cross-sectional study of U.S. adults suggest that higher refined grain and gluten consumption may be associated with lower gut microbial alpha-diversity, and that higher whole grain consumption may be associated with altered gut microbial composition. Although these findings suggest that whole grains, independent of their grain fiber and gluten content, may contribute to a healthier gut microbial profile, additional studies are needed to confirm our findings and provide additional evidence as to how these dietary exposures influence gut microbiota and subsequent risk for chronic diseases, including colorectal cancer.
## Authors’ Disclosures
R.B. Hayes reports grants from NCI during the conduct of the study. No disclosures were reported by the other authors.
## Authors’ Contributions
C.Y. Um: Conceptualization, formal analysis, methodology, writing-original draft, writing-review and editing. B.A. Peters: Formal analysis, methodology, writing-review and editing. H.S. Choi: Resources, investigation. P. Oberstein: Resources, investigation. D.B. Beggs: Resources, investigation. M. Usyk: Formal analysis, writing-review and editing. F. Wu: Resources, investigation. R.B. Hayes: Project administration, writing-review and editing. S.M. Gapstur: Conceptualization, writing-review and editing. M.L. McCullough: Conceptualization, supervision, writing-review and editing. J. Ahn: Resources, supervision, project administration, writing-review and editing.
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|
---
title: Interviews with Indigenous Māori with type 1 diabetes using open-source automated
insulin delivery in the CREATE randomised trial
authors:
- Mercedes Burnside
- Tracy Haitana
- Hamish Crocket
- Dana Lewis
- Renee Meier
- Olivia Sanders
- Craig Jefferies
- Ann Faherty
- Ryan Paul
- Claire Lever
- Sarah Price
- Carla Frewen
- Shirley Jones
- Tim Gunn
- Benjamin J. Wheeler
- Suzanne Pitama
- Martin de Bock
- Cameron Lacey
journal: Journal of Diabetes and Metabolic Disorders
year: 2023
pmcid: PMC10035484
doi: 10.1007/s40200-023-01215-3
license: CC BY 4.0
---
# Interviews with Indigenous Māori with type 1 diabetes using open-source automated insulin delivery in the CREATE randomised trial
## Abstract
### Purpose
Open-source automated insulin delivery (AID) is used by thousands of people with type 1 diabetes (T1D), but has unknown generalisability to marginalised ethnic groups. This study explored experiences of Indigenous Māori participants in the CREATE trial with use of an open-source AID system to identify enablers/barriers to health equity.
### Methods
The CREATE randomised trial compared open-source AID (OpenAPS algorithm on an Android phone with a Bluetooth-connected pump) to sensor-augmented pump therapy. Kaupapa Māori Research methodology was used in this sub-study. Ten semi-structured interviews with Māori participants (5 children, 5 adults) and whānau (extended family) were completed. Interviews were recorded and transcribed, and data were analysed thematically. NVivo was used for descriptive and pattern coding.
### Results
Enablers/barriers to equity aligned with four themes: access (to diabetes technologies), training/support, operation (of open-source AID), and outcomes. Participants described a sense of empowerment, and improved quality of life, wellbeing, and glycaemia. Parents felt reassured by the system’s ability to control glucose, and children were granted greater independence. Participants were able to use the open-source AID system with ease to suit whānau needs, and technical problems were manageable with healthcare professional support. All participants identified structures in the health system precluding equitable utilisation of diabetes technologies for Māori.
### Conclusion
Māori experienced open-source AID positively, and aspired to use this therapy; however, structural and socio-economic barriers to equity were identified. This research proposes strength-based solutions which should be considered in the redesign of diabetes services to improve health outcomes for Māori with T1D.
Trial Registration: The CREATE trial, encompassing this qualitative sub-study, was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12620000034932p) on the 20th January 2020.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s40200-023-01215-3.
## Introduction
Health inequities are “differences which are unnecessary and avoidable, but in addition are considered unfair and unjust” [1]. Health inequities based on ethnicity are well reported worldwide [2], and arise from societal structures which restrict access to the social determinants of health [3]. In Aotearoa/New Zealand (referred as New Zealand herein), health inequities between Māori (Indigenous Peoples) and New Zealand Europeans (NZE) are the most compelling [4, 5]; life expectancy for NZE is 8 to 9 years longer, NZE are burdened with a lower prevalence of certain diseases [1, 6], and NZE have greater access to quality healthcare despite lower health needs [7]. The domineering research narrative for Māori health disadvantage has purported that Māori are the loci of negative health outcomes due to inferior genetics, intellect, behaviour, or aptitude [1, 8]. This type of colonial framing, or ‘deficit theory’, shrouds NZE privilege and masks the accrued preferential benefit from the design and continued control of Western health paradigms. However, Māori have resisted this cultural-deficit narrative and continue to advance research in Māori health [9].
Type 1 diabetes (T1D) is an exemplar of a health condition whereby marginalised ethnic groups are over-represented in poorer health outcomes worldwide [10]. While NZE account for $75.8\%$ of people with T1D (followed by Māori ($10.1\%$), Asian ($6.5\%$), Pacific ($4.2\%$), and people of other ethnicities ($1.4\%$) [11]), evidence supports a growing burden in marginalised ethnic groups [12]. Further, Māori with T1D are at greater risk of developing long-term complications with non-optimal glycaemia compared to NZE irrespective of socioeconomic status [13]. Access to publicly funded insulin pump therapy also favours NZE [11, 14], and NZE are less likely to have insulin pump therapy withdrawn [15] due to access criteria. Insulin pumps are publicly funded in New Zealand, but only those with a glycated haemoglobin (HbA1c) between 65 and 90 mmol/mol ($8.1\%$—$10.4\%$) are eligible [16]. These criteria systematically disadvantage Māori who do not qualify for the technology based on HbA1c. Continuous glucose monitoring (CGM), another important diabetes technology proven to improve glycaemia [17], is not funded in New Zealand, either publicly or through health insurance. Burnside et al. [ 18] found CGM use is highest amongst NZE, and universal access to CGM is one way to reduce inequities in glycaemic outcomes between ethnic groups.
Automated insulin delivery (AID) systems, comprising a control algorithm, insulin pump, and CGM, consistently improve glycaemia and reduce management burden for people with T1D [19]. Despite several commercial systems now being available, access varies markedly depending on regional regulatory approval and funding, and insurance and reimbursement policies. A community movement has emerged in T1D which aims to reduce inequities in access to AID through user-centred innovation. A do-it-yourself (DIY) AID system was developed by people with diabetes and shared freely as an open-source system before commercial systems became available [20]. The founders freely shared the algorithm, named OpenAPS, as an open-source system, and it continued to evolve with additional community input. Open-source AID has been repeatedly studied in real-world and retrospective or prospective settings [21]. However, previous AID literature is limited by lack of reported patient demographic characteristics including ethnicity. Huyett et al. found that only six of 99 commercial AID studies reported on ethnicity [22] making it difficult to ascertain the range of people using an AID system and hence its wider applicability.
Before AID can be considered a way to address health inequity, it is necessary to investigate how marginalised ethnic groups experience this therapy. Therefore, the aim of this qualitative study was to explore the experiences of Indigenous Māori participants with use of an open-source AID system to identify cultural, structural, socioeconomic, and clinical enablers/barriers to health equity. Further, this study proposes solutions to identified barriers that are informed by the experiences of Māori and their whānau (extended family).
## Research approach and paradigm
Kaupapa Māori Research (KMR) methodologies informed this qualitative work [23] (Supplemental Table 1). The KMR framework was developed “by Māori, for Māori” [8], and hence is distinctive to New Zealand. This research paradigm is informed by a Māori world view where being Māori is normal, and there is Māori control over the design, data collection and analysis, and interpretation of findings. Pervasive health inequities for Māori with T1D provide a strong rationale for privileging the voices of Māori through the application of KMR methods.
## Context
This qualitative study was conducted as part of a wider research project called the CREATE (Community deRivEd AutomaTEd insulin delivery) randomised controlled trial. The CREATE trial was an open-labelled, randomised (1:1), parallel-group, 24-week superiority trial evaluating safety and efficacy of an open-source AID system (OpenAPS algorithm [24] in a modified version of the AndroidAPS application on an Android phone, pre-production DANA-i insulin pump, and Dexcom G6 CGM) in 97 children and adults (7 – 70 years) with T1D. A 24-week continuation phase followed to assess long-term outcomes. The CREATE trial was conducted across four New Zealand sites, and staff provided $\frac{24}{7}$ clinical and technical support to participants. A detailed description of the trial protocol [25] and results from the trial have been published elsewhere [26].
## Sample
The CREATE trial prioritised recruitment of Māori participants to ensure population representation [11]. Fourteen of the 97 ($14.4\%$) participants randomised in the CREATE trial self-identified as Māori. All 14 Māori participants were invited to be interviewed.
## Ethics
The CREATE trial, encompassing this qualitative study, is registered with the Australian New Zealand Clinical Trials Registry (ACTRN12620000034932p) and was approved by the Southern Health and Disability Ethics Committee (20/STH/1). Written informed consent (or assent from minors aged < 16) was obtained from all participants and parents/guardians of minors prior to participation. This research was conducted in accordance with Health Research Council of New Zealand guidelines for Māori research [27] and integrates KMR methodologies to ensure the research is responsive to Māori.
## Procedure
Semi-structured interviews utilised an interview schedule with key discussion points rather than specific questions. This approach suited the exploratory nature of the research aim and aligned with KMR methodology [23] by positioning participants as experts and exploring topics of salience to them. The interview schedule was informed by; a literature review (conducted by MB for doctoral thesis), input from clinical and Indigenous health colleagues, as well as input from a qualitative researcher with expertise on open-source AID. Topics were designed to seek out cultural, structural, socioeconomic, and clinical enablers and barriers to equity for Māori participants with T1D (Table 1). Table 1Interview schedule (key topics)Pre-trial experiencesDiagnosisOngoing managementAccessing diabetes technology and researchExperiences using the open-source AID systemNavigating the AID systemEase of use of the various functions able to be accessed by participantsChallenges/rewardsInfluences impacting on use/or understanding of the systemTechnical aspects- managing hardware components, troubleshootingClinical impactHow it influenced diabetes self-managementDay to day glucose levelsGlycaemic outcomesImpact on daily life/whānau lifeQuality of lifeWellbeing for person with T1D and whānau (physical, emotional, mental, spiritual, relationships with others)Views on training/educational resources/ongoing supportAID training and ongoing learning after AID initiationDifferent forms of support – healthcare professional, online peer support, training materials (written guides, ‘how to’ videos)
## Data collection and processing
Interviews took place within six weeks of participants completing 24 weeks of open-source AID use, with the exception of one child participant interviewed after 12 weeks of use due to withdrawal from the CREATE trial. This allowed time for the technology to become entrenched in daily whānau living. All ten interviews were facilitated by MB, a Māori researcher and clinical expert on the CREATE trial study team, between June 2021 and April 2022. Participants were interviewed with their whānau at a time and place they chose. All interviews were intended to be face to face; however, seven interviews were conducted via Zoom due to COVID-19 restrictions in New Zealand at the time. Interviews, ranging from 30 – 50 min, were audio-recorded and transcribed verbatim. Transcripts were anonymised assigning a number to each participant (adult 1–5, child 1–5, and parent 1–5).
## Data analysis
Data were analysed by MB, TH, and CL using an inductive thematic approach; the most frequently chosen analytical method among qualitative KMR literature at the time [28]. MB transcribed the first two interviews and the remaining eight transcripts were transcribed by an independent transcriber. Participants were given the opportunity to review transcripts and make changes prior to analysis. MB read all transcripts repeatedly to become familiar with the data. Data were coded using NVivo (QRS International Pty Ltd, 2014). Descriptive codes were developed inductively; MB undertook initial open coding, and this was followed by a process of pattern coding [29]. The resultant coding framework was reviewed by TH and CL, Māori health researchers, and MB completed a second cycle of pattern coding to ensure there was a distinct Kaupapa Māori orientation that considered the role of systemic factors in participants’ experiences of T1D care (Supplemental Fig. 1). Following an iterative process of discussion and review of coding by MB, TH, and CL, consensus on themes and subthemes were reached. Measures to enhance trustworthiness and credibility of the analysis included member checking and audit trail. No new insights emerged in the coding of the final interview, suggestive of data saturation.
## Data display
A summary of descriptive subthemes evident within the themes of Access, Training/Support, Operation, and *Outcomes is* provided. For each descriptive subtheme, a precis of participants’ comments, including critiques about the health system, is reported and supporting quotations are presented in Table 2. Table 3 presents barriers to equity synthesised from participants’ critique, along with some proposed solutions taken directly from transcripts or inferred from identified barriers. Table 2Participant quotationsTheme and SubthemesParticipant quotationAccessProcurement (|)Parent #4: “At that time pumps weren’t that popular, but the District Health Board (Health Service) had them. I had this big speech ready to ask for one because you had to prove [to the healthcare professional] that you could handle it before you got one. ”Funding (|)Parent #1: “Dexcom was just too expensive for us all the time, and we would get really angry if she knocked it out, and it’s not fair that she gets in trouble for accidents. ”Complexity (|)Parent #2: “*The idea* of learning all the stuff that comes with a pump was just too much at that point, and then once we got it we were really glad we did because it was lifechanging. ”Training/SupportHealthcare professional (*)Parent #4: “I’m a kinaesthetic learner, so I found the Healthcare professional ringing and talking me through it was a lot easier than the video or written guides. ”Educational material (*)Parent #1: “It was good to know that they [educational materials] were there. II did read them before we went in and having prior knowledge was helpful. ”Online peer support (|)Adult #4: “With Tribe, I’m relying on people I don’t know, I don’t know their background, and I don’t know their training. So, I didn’t have the time or energy to engage with the Tribe community. ”OperationTrust (*|)Adult #3: “I did have a little bit of anxiety for the first 24 to 48 h. I was obsessed with looking at my phone and worried that it wasn’t going to do the right thing. Then I realised that it was actually doing a better job than what I do. ”Adapting (*|)Adult #2: “It might take a bit of learning to get used to the system, but once you’ve learned, day to day it’s just a lot easier. ”Technology (*|)Adult #5: “It’s [loss of Bluetooth connection between devices] a lot for me, it would go at least once a day if not more. ”OutcomesGlycaemia (*)Adult #1: “It was never an achievable goal to have good blood sugar. I tried, but was never good enough and I was told “this is a bad number.” The biggest thing for me was achieving the best HbA1c I have ever had. ”Wellbeing (*)Parent #5: “The huge benefit is being able to get up every morning and her glucose is five [mmol/L], and then looking back and seeing that she’s been doing this all night. Just a huge relief. ”Empowerment (*)Adult #4: “Because it was immediate feedback I could see if it was working and it felt good.”(*) denotes a potential enabler(|) denotes a potential barrierLanguage used is framed to reflect the mana (prestige) of the Māori participants/whānauTable 3Barriers to equity and solutions by theme/subthemeAccessa. Equitable procurement of insulin pumps and CGM devices for Māori will be achieved by:• Forsaking public funding access criteria to insulin pumps to ensure insulin pump therapy is an option available to all Māori• Including diabetes technology advocacy as a service specification for diabetes centres nationwide• Providing diabetes clinical teams nationally with appropriate training on diabetes technologies and resourcing to offer such therapies to Māori following a diagnosis of T1D• Including diabetes clinical teams in targeted interventions to improve their provision of effective health literacy information about diabetes technologies to all Māori/whānau to improve accessb. Costs of CGM for Māori/whānau will be addressed by:• Making publicly-funded CGM universal to Māori following a diagnosis of T1D• Prioritising universal funding of rt-CGM, over is-CGM, to address inequities in diabetes health outcomes, and allow Māori to access and benefit from AID systemsc. Clinical teams will improve perceived acceptability of diabetes technologies to promote uptake by:• Exploring Māori/whānau concerns related to the use of diabetes technologies, and partnering with Māori/whānau to address such concerns• Providing training and support tailored to the needs of Māori/whānau. This includes flexible appointment scheduling, home based training/support with whānau, access to a range of training materials to suit different learning styles, and access to $\frac{24}{7}$ clinical and technical support using communication mediums suited to the Māori person/whānauOperationa. Clinical teams will help Māori/whānau foster trust in AID systems through:• The provision of training on how the control algorithm operates and makes decisions. Māori/whānau will be informed of certain algorithm operations known to raise doubt (such as the inclusion of rescue carbohydrate in future bolus recommendations). For Māori/whānau considering an open-source AID system, clinical teams will provide reassurance that new additions to the portfolio of pumps that can be used in open-source AID are not susceptible to ‘hacking,’ and that to date there are no reported cases of intentional harm or personal data breaches• Supporting Māori/whānau to optimise core user-specific settings so they are able to benefit the most from AID. This may require an additional level of clinical support during the first weeks of AID useb. Clinical teams will support Māori/whānau affected by T1D to adapt to the AID treatment paradigm by:• Identifying specific functions of standard pump therapy (including the context) the person/whānau found useful, and imparting knowledge on how to replicate outcome(s) using the AID system• Recognising that Māori/whānau may require greater clinical (to optimising settings) and technical (to troubleshoot technical issues) assistance while they learn to navigate the AID system. Therefore, additional contacts with clinical staff, that are motivated by Māori/whānau needs, are available• Publishing and sharing the positive narratives of Māori/whānau who have previously learned to use AIDc. Māori/whānau affected by T1D will be supported by clinical teams to manage the technical aspects of AID through:• Quality in-person training on AID that is responsive/customised to the needs of Māori/whānau. This includes flexible scheduling of the training session(s) (date, time, location, duration e.g. over one or two days), and an optional probationary period where Māori/whānau have the opportunity to use the new hardware components before initiating automation of insulin delivery• Ensuring teams are resourced to work with the Māori/whānau ongoing, providing wraparound support for the integration of technology into other competing priorities and restrictions. This may involve outreach/in-home supports• Recognition that technical issues frequently occur outside of working hours, and $\frac{24}{7}$ technical support from staff will be readily available to Māori/whānau by phone• The availability and provision of a range of training materials (video demonstrations, printed and digital written guides) – these are not intended to replace support by clinical staff
## Results
The study sample comprised 5 adult participants (23 – 47 years), 5 child participants (10 – 16 years), and 5 whānau members from 4 child participants. Four of the 14 Māori participants in the CREATE trial who did not respond to invitations to be interviewed for reasons unknown to the researchers. Participant socio-demographic characteristics are presented in Table 4. Four themes were identified describing enablers and barriers to equitable utilisation of diabetes technologies for Māori participants. Three subthemes within each theme emerged from the analysis (Fig. 1).Table 4Socio-demographic characteristics of $$n = 10$$ Māori children and adults with T1D within the CREATE trialAge groupN = 10- Child5 ($50\%$)- Adult5 ($50\%$)Median age, (IQR) – yr19.5 [12, 36]Gender – no. (%)- Female7 ($70\%$)Highest qualification (parent of children) – no. (%)- Unknown- None- School (1–4)- Dip. or Cert. ( 5–6)- Graduate [7]- Postgraduate (8–10)2 ($20\%$)2 ($20\%$)1 ($10\%$)1 ($10\%$)3 ($30\%$)1 ($10\%$)Income (household) – no. (%) a- Unknown- < $70,000- $70,001—$100,000- $100,001—$150,000- $150,001 or more1 ($10\%$)5 ($50\%$)1 ($10\%$)2 ($20\%$)1 ($10\%$)New Zealand Deprivation Index (quintile) – no. (%) b- 1- 2- 3- 4- 52 ($20\%$)2 ($20\%$)2 ($20\%$)2 ($20\%$)2 ($20\%$)Study Site – no. (%) c- Christchurch- Dunedin- Hamilton- Auckland2 ($20\%$)3 ($30\%$)3 ($30\%$)2 ($20\%$)Initial Randomisation- Open-source AID- Sensor augmented pump therapy4 ($40\%$)6 ($60\%$)Time in range at baseline (%), mean (SD)d$53\%$ [17]Time in range at study end (%), mean (SD)d$60\%$ [11]*Socio-demographic characteristics of whānau/parents not capturedaAnnual household income in New Zealand dollarsbThe New Zealand deprivation index is an area-based measure of socioeconomic deprivation in New Zealand where quintile 5 represents the $20\%$ most deprived areas in New ZealandCGeographic location of participants residence. Christchurch and Dunedin are in the South Island and Hamilton and Auckland, the North Island of New ZealanddPercentage time with sensor glucose level in target range (3.9-10 mmol/L [70-180 mg/dL])Fig. 1Themes and subthemes informed by participants’ comments about diabetes technologies, represented as potential enablers (*) or barriers (|)
## Access
The Access theme identified participants’ tribulations accessing diabetes technologies outside of funded trials, including (publicly funded) insulin pumps and (non-funded) CGM devices; both essential hardware components of AID systems. Three subthemes were extrapolated from the Access theme including participant critiques about: Procurement of technology; lack of technology Funding; and perceived Complexity.
## Procurement
Procurement was defined by participants’ critique of ascertaining and sourcing diabetes technologies outside of the study. Participants commonly learned about diabetes technologies online, and due to technologies only becoming available, funded or unfunded, years after other OECD countries, many reported challenges importing CGM from overseas. One parent noted the present-day challenge of sourcing hardware to build an open-source AID system. Participants did not have difficulty qualifying for a publicly funded insulin pump, but most were mindful of having to meet criteria to retain them, and two participants reported significant worry arising from the threat of having to surrender their pumps. Participants provided examples where healthcare professionals functioned as gatekeepers to technology by not advertising available technologies, or by having to prove their ability and lobby to a healthcare professional to gain access.
## Funding
Funding was reported within participants’ critique of the lack of funded CGM in New Zealand, and the high cost to self-fund it outside of the trial. Young adults often did not explore CGM as a therapy due to cost prohibition. Participants from the parent cohort described extreme measures to fund CGM for their children including various fundraising endeavours, Givealittle (online platform for crowdfunding), and sponsorship. They also described purchasing CGM devices whenever their financial situation would allow. Prior to the trial it was common for whānau to ration the use of CGM, for example prioritising use to gauge the effect of changes to insulin delivery settings. Further, participants from the parent cohort reported cost-related stress, then guilt for scolding their child if a CGM sensor was accidentally displaced.
## Complexity
Complexity was explained by participants’ critique of learning how to use insulin pumps and CGM in their technology experiences previous to the trial. In some cases, participants delayed uptake of insulin pump therapy by years due to perceived complexity and stress associated with learning the technology. Participants noted the need to learn to count the carbohydrate content of foods as a consideration. Two adult participants were reluctant to adopt a pump due to concerns with being attached to a device constantly. Some participants had trialled then abandoned an open-source device that converts intermittently scanned CGM (isCGM) to real-time CGM (rtCGM) due to a lack of support to troubleshoot technical issues. Time, or lack of, was also raised as a barrier to learning diabetes technologies if perceived to be complex.
Despite the barriers to access reported above, all participants described overwhelmingly positive experiences with insulin pumps and CGM, going as far to pronounce the technologies as life changing. Key benefits pertained to greater convenience and reduced user burden, tolerability (especially for children), and freedom. Access to a healthcare professional who advocated diabetes technologies strongly influenced technology utilisation by participants prior to the trial. Child #3: “I got a pump when I was diagnosed because she [healthcare professional] got me onto one straight away.”
## Training/Support
The Training/Support theme captured participants’ views on open-source AID training and support within the CREATE trial, applicable to the training/support likely to be necessary in the real-world for AID. From the analytical process it was apparent that even within this small cohort, participant needs differed – some found a single training day ample and gained little from the run-in period (4-week period using the study devices without AID). Comparatively, some participants left the initial training session feeling overwhelmed and said the run-in period alleviated some of the stress of new diabetes devices.
Three subthemes were identified including participant sentiments about: the importance of training/support by a Healthcare Professional; the appeal of having access to Educational Materials; and the limited usefulness within the trial of Online Peer Support.
## Healthcare Professional
Healthcare Professional was captured through participants’ compelling sentiments that access to a healthcare professional for initial training and ongoing technical/clinical support is a requisite for successful adoption of AID. Participants mainly sought the support of their healthcare professional while learning to navigate the system and troubleshoot technical issues. Contacts via text message, email, or phone call were common, and in-person support was rarely required. Participants preferred this form of support because it was convenient and available $\frac{24}{7}$, they trusted and understood the advice, and valued having another person to share their concerns and support decision making.
## Educational Materials
All participants in the CREATE trial were provided with written guides on AID, and ‘how to’ video demonstrations (all in English). The subtheme of Educational Materials involved participants’ reflections about the usefulness of these materials. Opinions on usefulness varied, but most commented that it was reassuring knowing they were readily available to them, and some found reviewing the materials prior to the initial training helpful. Participants liked that they were provided with printed and digital copies of written guides.
## Online Peer Support
Participants in the CREATE trial were invited to join a closed online community (Tribe Technologies Inc.) for peer support to simulate the community support that is used by real-world open-source AID users. This subtheme involved participant critiques of the online community within the trial. Participants did not find the online community useful in the context of this trial for reasons including: the community lacked momentum and information; some technical issues warranted urgent intervention; many did not engage in social media; and they instead preferred approaching their healthcare professional who was familiar, trusted, approachable, and able to give immediate advice.
## Operation
The Operation theme documented participants’ experiences operating the open-source AID system in daily whānau life. Three subthemes were identified from the Operation theme including participant critiques about: lack of Trust in the system; Adapting to a new treatment paradigm; and managing Technology.
## Trust
Trust included participants’ critique of the open-source system automating insulin delivery. Adult and parent participants described feeling ambivalent about relinquishing control, reporting an initial probationary period (days) when they scrutinised the application’s graphs for reassurance that its decisions were safe and correct. Some admitted to overriding the system if they had any doubts about its functioning, and noted, in hindsight, that this was counterproductive. Participants also described developing trust in the system as a result of seeing the algorithm making logical decisions and responding to glucose excursions quicker than they could. Some instances of mistrust were seeded by a lack of understanding of how the algorithm made treatment decisions, for example, the inclusion of rescue carbohydrates (announced to the system) in future insulin bolus recommendations. One adult participant expressed concern that the open-source system could be vulnerable to hacking. This concern was not shared by other participants, including those with prior experience with open-source innovations.
## Adapting
Adapting comprised participants’ critique of adapting to a new treatment paradigm. Participants reported using their ‘usual diabetes care’ for several years and described challenges (taking time, effort, trial and error) adapting to new ways of thinking and performing diabetes self-care. Many described a struggle to replicate aspects of their usual care and one participant found her low carbohydrate ketogenic diet led to ketosis with AID. Some reported it took time to tweak core insulin settings, and some even described manipulating the system for preferred insulin delivery, for example, announcing additional carbohydrates to liberate the system (this was reported by two adults with highest total daily dose of insulin). One adolescent participant found the change of paradigm overwhelming, preferring to administer insulin through her insulin pump instead of the AID application, and withdrew after three months of open-source AID use. Despite the challenges voiced, participants had an overwhelming consensus was that it is well worth the initial effort to learn to use the open-source AID system.
## Technology
Technology took account of participants’ critique of maintaining hardware components of the AID system, and troubleshooting technical issues. Participants perceived building an open-source AID system from scratch to be technically challenging, noting the provision of a pre-built system as a benefit of the trial. Increased technical troubleshooting (of hardware and connectivity issues rather than AID troubleshooting) was raised ubiquitously. Whilst technical issues were described as stressful, most participants regarded the degree of troubleshooting to be manageable with healthcare professional support. Participants described mild burden associated with maintaining the hardware, for example, charging the Smartphone daily, and child participants found it especially difficult to carry the Smartphone constantly.
Importantly, operational difficulties conveyed by participants mainly related to technical aspects, and use of the application interface on the Smartphone itself was described as easy to use, even by the youngest participant aged 10. Participants enjoyed the information presented on application home-screen (glucose level and future glucose prediction lines), and accessing a wide array of settings and features which allowed for pin point management. Unanticipated benefits included the ease of adjusting insulin delivery settings and discrete insulin bolus administration through the Smartphone. Adult #2: “I found it really intuitive, I really liked the display.”
## Outcomes
The Outcomes theme identified narrated benefits of AID. Three subthemes were ascertained from the Outcomes theme including participant sentiments about: improved Glycaemia; improved Wellbeing; and a sense of Empowerment.
## Glycaemia
Glycaemia acknowledged participants’ sentiments about improved blood glucose outcomes. Many participants reported meaningful reductions in HbA1c (especially participants with the highest HbA1c prior to AID). Others described being able to maintain a low HbA1c with less effort. Participants found AID reduced hypo/hyperglycaemia and glucose fluctuations by providing levels of responsiveness beyond their own capabilities, especially when they were distracted by other responsibilities during the day. Every participant mentioned the positive effect of AID on nocturnal glucose levels, which would invariably also translate to a better day. AID was also reported to aid optimal glucose levels during exercise.
## Wellbeing
Wellbeing contained participants’ sentiments about the holistic benefits of open-source AID use. Participants articulated that the system reduced the burden of diabetes self-management allowing them to experience life more normally. Child participants liked that they no longer needed to finger prick, adolescents could get away with missed meal bolus administration, and adult participants liked that the system did the thinking for them. AID improved mood and cognition, and reduced worry, including the daily fear of dying from T1D for one participant. Participants also reported improved sleep and energy. For participants who were parents, CGM alarms and the ability to follow glucose levels remotely ameliorated the need to constantly monitor their child, and children were granted independence. AID enabled prompt management of glucose excursions, and consequently parents experienced less guilt.
## Empowerment
Empowerment encompassed participants’ sentiments about playing a greater role in the management of their diabetes. The open-source AID system endowed participants with real-time glucose data and additional diabetes data which empowered them to think critically about their diabetes self-care and collaborate more with this system than previous therapies. Participants relished self-governance; adjusting user-specific settings and observing the outcomes.
## Discussion
This study employed KMR methodology to cast a health equity lens on diabetes technology utilisation by Māori with T1D in New Zealand. Participants identified enablers and barriers to equity which aligned with the four key themes of access, training/support, operation, and outcomes. Participants described holistic benefits of open-source AID, and their aspirations to continue using the AID system beyond the trial. Although participants experienced an open-source AID system in this study, they may also experience commercially available systems positively since these alternatives have commercial technical support. Similar to a previous study on healthcare professionals’ experiences within the study [30], participants highlighted basic device functionality troubleshooting, rather than AID specifics, as the biggest source of troubleshooting. Participants’ expert critiques identified structures within the New Zealand health system precluding equal access to diabetes technologies outside of the trial; namely insulin pump access criteria and healthcare professionals influencing technology advocacy and support. The later may explicate disparities in technology utilisation by geographic location. Compounding socioeconomic barriers to CGM access appeared to provide an even longer path of resistance to health equity for Māori.
Participants flourished when afforded the tools to manage T1D, and in recognition of this we propose strength-based solutions to structural factors restricting access to diabetes technologies for Māori, including funding and quality of clinical care. Insulin pumps, CGM, and AID systems should be publicly funded for Māori with T1D – effectively, ameliorating biases forced on healthcare professionals. Importantly, New Zealand should learn from the publicly funded insulin pump example, which illustrates that access criteria can amplify inequities [11, 14]. Consistent with other studies evaluating the experiences of Māori in the New Zealand health system [3132], this study acknowledges the influence clinical teams have on health equity. Accordingly, diabetes clinical teams should be adequately resourced (in knowledge, cultural competency, and time) to support Māori to adopt and maintain emerging technologies. These recommendations align with other research addressing health equity for Māori with type 2 diabetes (T2D); Mana Tū, a whānau ora (family health) approach to T2D, similarly addresses individual, whānau, service, and system factors restricting health equity [33].
Existing literature has identified profound disparities in diabetes health outcomes, including access to diabetes technologies, for marginalised ethnic groups [34]. Further, research has determined that disparities in technology utilisation by ethnicity are not entirely encapsulated by socio-economic deprivation [11, 15]. However, to the best of our knowledge, this research is the first effort to understand the barriers to equity in diabetes technology utilisation in New Zealand. Other strengths of this study include the KMR design which privileges the expertise of Māori participants to identify solutions to barriers to health equity in T1D. Consequently, these findings are unique to Indigenous Māori and they may not be transferable to other contexts. Recruitment of Māori from the CREATE trial may have limited participation to those with greater access to the determinants of Indigenous health since they had to be using an insulin pump to access the CREATE trial. Similarly, their prior experiences with CGM may have influenced their response to adopting AID technology, compared to those who were novice CGM users. Findings may be limited further by lack of interview data from four of the 14 Māori participants in the CREATE trial who did not respond to invitations to be interviewed for reasons unknown to the researchers. Despite this, the sample of ten participants with additional insights from whānau provided the researchers with a great appreciation for the structures challenging equity for Māori with T1D.
In conclusion, use of an open-source AID system in the CREATE trial improved quality of life, wellbeing, and glycaemia for Māori with T1D. However, structural and socio-economic barriers preclude equitable utilisation of diabetes technologies for Māori outside of funded clinical trials. This research proposes solutions to the barriers to equity which should be considered in the strength-based redesign of diabetes health services to improve service provision for Māori with T1D.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (PDF 220 KB)
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|
---
title: Extracellular vesicles derived from CD4+ T cells carry DGKK to promote sepsis-induced
lung injury by regulating oxidative stress and inflammation
authors:
- Guo-wei Tu
- Yi Zhang
- Jie-fei Ma
- Jun-yi Hou
- Guang-wei Hao
- Ying Su
- Jing-chao Luo
- Lulu Sheng
- Zhe Luo
journal: Cellular & Molecular Biology Letters
year: 2023
pmcid: PMC10035494
doi: 10.1186/s11658-023-00435-y
license: CC BY 4.0
---
# Extracellular vesicles derived from CD4+ T cells carry DGKK to promote sepsis-induced lung injury by regulating oxidative stress and inflammation
## Abstract
### Background
Sepsis is an abnormal immune response after infection, wherein the lung is the most susceptible organ to fail, leading to acute lung injury. To overcome the limitations of current therapeutic strategies and develop more specific treatment, the inflammatory process, in which T cell-derived extracellular vesicles (EVs) play a central role, should be explored deeply.
### Methods
Liquid chromatography–tandem mass spectrometry was performed for serum EV protein profiling. The serum diacylglycerol kinase kappa (DGKK) and endotoxin contents of patients with sepsis-induced lung injury were measured. Apoptosis, oxidative stress, and inflammation in A549 cells, bronchoalveolar lavage fluid, and lung tissues of mice were measured by flow cytometry, biochemical analysis, enzyme-linked immunosorbent assay, quantitative real-time polymerase chain reaction, and western blot.
### Results
DGKK, the key regulator of the diacylglycerol (DAG)/protein kinase C (PKC) pathway, exhibited elevated expression in serum EVs of patients with sepsis-induced lung injury and showed strong correlation with sepsis severity and disease progression. DGKK was expressed in CD4+ T cells under regulation of the NF-κB pathway and delivered by EVs to target cells, including alveolar epithelial cells. EVs produced by CD4+ T lymphocytes exerted toxic effects on A549 cells to induce apoptotic cell death, oxidative cell damage, and inflammation. In mice with sepsis induced by cecal ligation and puncture, EVs derived from CD4+ T cells also promoted tissue damage, oxidative stress, and inflammation in the lungs. These toxic effects of T cell-derived EVs were attenuated by the inhibition of PKC and NOX4, the downstream effectors of DGKK and DAG.
### Conclusions
This approach established the mechanism that T-cell-derived EVs carrying DGKK triggered alveolar epithelial cell apoptosis, oxidative stress, inflammation, and tissue damage in sepsis-induced lung injury through the DAG/PKC/NOX4 pathway. Thus, T-cell-derived EVs and the elevated distribution of DGKK should be further investigated to develop therapeutic strategies for sepsis-induced lung injury.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s11658-023-00435-y.
## Background
Sepsis is an abnormal immune response after infection, wherein the lung is the most susceptible organ to fail, leading to acute lung injury (ALI) and progression of acute respiratory distress syndrome (ARDS) [1]. Sepsis-induced ALI is characterized by the enhanced permeability of cells in pulmonary capillary and alveolar epithelia, which might be caused by stepwise inflammation in airspaces and lung parenchyma [1]. Patients with ARDS often present with large amounts of proinflammatory and neutrophil chemotactic cytokines in bronchoalveolar lavage fluid (BALF) [2]. Together with these cytokines, various kinds of immune cells, particularly alveolar macrophages, neutrophils, regulatory T cells, and Th17 cells, are involved in the pathogenesis of ARDS [2].
Oxidative stress is frequently associated with ALI/ARDS [3]. Reactive oxygen species (ROS) are elevated under ALI/ARDS and accelerate tissue damage through multiple mechanisms, such as DNA damage, peroxidation of lipid molecules, oxidation of proteins that changes protein activity, and activation of transcription factors (including inflammatory NF-κB) that trigger the expression of proinflammatory genes [4]. The normal lung tissue produces various types of endogenous antioxidants, including superoxide dismutase (SOD). However, these antioxidants are not sufficient to protect the lungs from prolonged oxidative stress in ALI/ARDS [4].
Extracellular vesicles (EVs) originate from endosomes, with diameters ranging between 30 and 150 nm; they are frequently detected in urine, blood, and cerebrospinal fluid [5]. EVs carry cargo containing multiple cellular components (DNA, lipids, proteins), transposable elements, and RNA (coding and noncoding) [6]. They play an indispensable role in chronic inflammatory lung diseases, including chronic obstructive pulmonary disease (COPD) [7] and asthma [8, 9]. In the human body, several types of cells secrete EVs, including T lymphocytes [10, 11]. T-lymphocyte-derived EVs impair the function of salivary gland epithelial cells by inhibiting Ca2+ flux, cAMP production, and protein secretion [12] and trigger β cell apoptosis [13]. EVs derived from CD4+ T cells increase the antitumor response of CD8+ T cells by enhancing their proliferation and activity [14], induce NOX4-dependent oxidative stress in cardiac microvascular endothelial cells [15], and promote the proliferation, migration, and differentiation of cardiac fibroblasts to improve cardiac remodeling following myocardial infarction [16]. CD4+ T cells are important lymphocytes that play crucial roles in modulating immune responses during sepsis and lung injury. However, the roles of CD4+ T-cell-derived EVs in sepsis-induced lung injury remain unknown.
Diacylglycerol kinase (DGK) phosphorylates diacylglycerol (DAG) to produce phosphatidic acid (PA). DAG and PA play essential roles as critical secondary messengers in cell signaling. DAG activates the Rho/protein kinase C (PKC) pathways, including cPKC, nPKC, and aPKC pathways, whereas PA controls the Raf and mTOR pathways [17]. In mammals, DGK contains ten isozymes (α–κ) and regulates various important functions in cellular biochemistry and physiology [18]. DGK alpha regulates the metastasis of non-small lung cancer [19] and airway contraction in asthma pathogenesis [20]. DGK zeta supports inflammatory reaction and hyperresponsiveness in allergic airways [21]. DGK kappa (DGKK) participates in fragile X syndrome [22], and its nucleotide variants are associated with hypospadias [23]. Although DAG, PKC, and NOX4 are involved in sepsis or ALI [24, 25], the roles of DGK in sepsis-induced lung injury remain unknown.
To elucidate the role of EVs in sepsis-induced lung injury, the present study analyzed the serum EV protein profile of patients with sepsis-induced lung injury. DGKK, the key regulator of the DAG/PKC pathway, exhibited elevated expression in serum EVs of patients and showed strong correlation with sepsis severity and progression. EVs derived from lipopolysaccharide (LPS)-treated CD4+ T cells carrying DGKK induced oxidative stress and inflammation in alveolar epithelial A549 cells and sepsis-induced mice through PKC and NOX4, the downstream effectors of DGKK and DAG. Therefore, this approach established the mechanism that T-cell-derived EVs exerted toxic effects in sepsis-induced lung injury through the DGKK/DAG/PKC/NOX4 pathway. Thus, T-cell-derived EVs and the elevated distribution of DGKK should be further investigated to develop therapeutic strategies for sepsis-induced lung injury.
## Criteria for subject selection and collection of clinical samples
Forty patients with sepsis with lung injury, as defined by the criteria of the North American European Consensus Conference, were enrolled in this study [26]. The exclusion criteria were as follows: [1] patients who had cancer or hematological malignancy, [2] cases that were complicated with autoimmune disease, and [3] patients who were pregnant or breastfeeding. In addition, 20 healthy subjects who had no abnormalities in medical examination were included as healthy controls. The exclusion criteria for healthy controls were similar to those of patients with sepsis. Patients with sepsis with lung injury were paired with the healthy control group in terms of gender, age, and other systemic diseases. The morning fasting venous blood of patients with sepsis was collected within 24 h after admission and centrifuged at 3000 rpm for 15 min. The supernatant was frozen in a refrigerator at −80 °C to avoid repeated freezing and thawing. Then, it was uniformly subjected to EV extraction. The protocols of the present study were approved by the hospital’s ethics committee. All participants provided informed consent to be involved in this study.
## Cell culture and treatment
A549 cells (ATCC CCL-185, USA) were cultured in Dulbecco’s modified *Eagle medium* (DMEM) with $10\%$ fetal bovine serum (FBS; Gibco, USA) and penicillin–streptomycin mixture (100 × dilution; Solarbio Science & Technology, China) in $5\%$ CO2 at 37 °C. Peripheral CD4+ T cells isolated from healthy control subjects were purified using a CD4+ T cell Isolation Kit (Miltenyi, China) and activated in vitro with 2 μg/mL plate‐bound anti‐CD3/CD28 antibodies (eBioscience, San Diego, CA, USA). The isolated CD4+ T cells (1.5 × 106 cells/mL) were cultivated in a 24‐well plate and stimulated with 2 μg/mL plate‐bound anti‐CD3/CD28 antibodies at 37 °C and $5\%$ CO2 for 24 h in FBS‐free RPMI-1640. Then, they were treated with or without 10 μg/mL LPS for 24 h, and the conditioned medium was collected for EV isolation. A549 cells cultured in FBS‐free DMEM were treated with 100 μg/mL EVs isolated from the above-mentioned CD4+ T cells, followed by 2 nM LXS-196 (Selleck Chemicals LLC, Houston, TX, USA) or 5 μM GLX351322 (MedChemExpress, Monmouth Junction, NJ, USA) for 24 h.
## Isolation and identification of EVs
The EVs derived from the above-mentioned CD4+ T cells and serum were isolated and purified in accordance with a previously described method with some modifications [27]. Briefly, the serum samples, which were thawed in a water bath at 25 °C and placed on ice, and conditioned medium of CD4+ T cells with or without LPS treatment, were harvested and centrifuged at 4 °C at 2000 × g for 10 min. Then, the supernatant was taken. Centrifugation was performed at 10,000 × g at 4 °C for 30 min, and the supernatant was taken. The samples were transferred to an ultra-high-speed centrifuge tube (Backman Avanti J-30i, Shanghai, China) at 4 °C and centrifuged at 110,000 × g for 75 min. Then, the supernatant was discarded. The precipitates were suspended in 1 mL of 1 × phosphate-buffered saline (PBS), diluted with 1 × PBS, and filtered with 0.22 μm membrane after suspension. Next, the samples were transferred to an ultra-high-speed centrifuge tube at 4 °C and centrifuged at 110,000 × g for 75 min, and the supernatant was discarded. The precipitation was resuspended with the corresponding 1 × PBS, separated, and stored at −80 °C. The protein content of the concentrated EVs was determined using a BCA protein assay kit. The protein abundance of CD9 (Abcam, Waltham, MA, USA; ab236630), CD81 (Abcam; ab109201), TSG101 (Abcam; ab133586), and GM130 (Abcam; ab52649) was determined by western blot.
## Liquid chromatography–tandem mass spectrometry (LC–MS/MS)
The serum EVs of three patients with sepsis-induced lung injury and three healthy control subjects were selected for LC–MS/MS analysis. The samples were separated by the Easy-nLC system (with an nL flow rate) and analyzed with the Q Exactive Plus MS system [28]. The proteins were recognized and quantified on the basis of the Uniprot_HomoSapiens_20367_20200226 database. MaxQuant software (1.5.5.1) was employed for database searching, and the LFQ algorithm was used for quantitatively analyzing the peptides identified [29].
## Transmission electron microscopy (TEM)
The purified EVs were fixed with $4\%$ paraformaldehyde (Electron Microscopy Science, USA) in PBS for 20 min at room temperature. Then, the fixed EVs were loaded on the carbon-coated grid and fixed with $4\%$ paraformaldehyde for 30 s. The grid was examined using a TEM (JEM-1400plus, Japan) according to the manufacturer’s instructions.
## Nanoparticle tracking analysis (NTA)
Exosomes were diluted to achieve 140–200 particles per frame. ZetaView inspection instrument (Particle Metrix, Meerbusch, Germany) was used to determine the size and number of exosome particles. After loading the exosome samples into the sample chamber, the manufacturer’s settings for nanospheres were set, and the data were captured and calculated by the NTA software (ZetaView 8.04.02).
## EV uptake
A549 cells were used to specifically label EVs with PKH67 Green Fluorescent Probe (Sigma–Aldrich, USA). The signal was visualized as the EVs were taken up by A549 cells. Briefly, the EVs were initially diluted with the kit’s component Diluent C and then carefully incubated with PKH67 Green Fluorescent Probe at 25 °C for 5 min. Next, sterile FBS was added, and the mixture was allowed to sit for 1 min for sufficient staining. Then, the mixture was transferred, washed with basal medium, and centrifuged at the highest speed at 4 °C for about 75 min. The labeled EVs remained at the bottom of the tubes after discarding the upper layer supernatant. Then, they were resuspended by basal medium. Approximately 1 μg of labeled EVs was taken and added into A549 cells on a round-shaped coverslip in a 24-well plate. Then, they were incubated in standard condition ($5\%$ CO2, 37 °C) for 16 h or overnight. Afterward, the cells were washed and fixed with $4\%$ paraformaldehyde for 10 min. The slides were then mounted with antifluorescence quenching agent together with DAPI.
## Animals and study design
Wild-type, male, C57BL/6 mice aged 6–8 weeks were obtained from Sippr-BK Laboratory Animal, Shanghai, China. All animal experiments were conducted according to the rules approved by the hospital’s ethics committee. The model with sepsis-induced lung injury (model group) was developed by performing cecal ligation and puncture (CLP). The animals were anesthetized using $1\%$ pentobarbital sodium through intraperitoneal injection. Then, a midline incision (4 mm) was made to expose the cecum, which was later sutured with 3–0 silk suture, followed by a double “through and through” perforation with a 20-gauge needle about 5 mm from the ligature. The injured cecum was then repositioned and sterilized. After these careful operations for inducing injury, all animals were resuscitated. All animals were subcutaneously injected with $0.9\%$ NaCl for resuscitation.
The mice were randomized into the CLP, CLP + SE (EVs isolated from healthy subjects), CLP + SSE group (EVs isolated from patients with sepsis-induced lung injury), CLP + TE (EVs isolated from CD4+ T cells isolated from healthy subjects), CLP + LTE (EVs isolated from CD4+ T cells isolated from healthy subjects treated with 10 μg/mL LPS), CLP + LTE + vehicle, CLP + LTE + LXS-196, CLP + LTE + GLX351322, CLP + shNC, and CLP + shDgkk groups. To examine the influence of EVs in lung injury and the survival of septic mice, 200 μL of EVs was intravenously injected into mice via the tail vein at 4 h after CLP surgery in accordance with previously described methods with some modifications [30, 31]. To evaluate the function of PKC/NOX4 on EV function, the PKC inhibitor LXS-196 (5 mg/kg/day; Selleck Chemicals, USA) and NOX4 inhibitor GLX351322 (5 mg/kg/day; Medchemexpress, USA) were injected intraperitoneally into mice at 1 h after CLP surgery. Dgkk silenced mice were established by intravenously injecting 1 × 108 pfu/mL of shRNA-*Dgkk adenovirus* (the dose preoptimized before experiments) via the tail vein at 1 h after CLP. The mice were euthanized 24 h later. BALF was collected, and lung tissue was harvested and fixed in $4\%$ paraformaldehyde for 24 h for hematoxylin and eosin (H and E) staining. The severity of histological injury in different groups was assessed by using a scoring system as previously described [32].
## Adenovirus production
A recombinant pShuttle-H1 adenovirus vector, containing shRNA-Dgkk targeting the mouse *Dgkk* gene (shDgkk), and control pShuttle-H1 adenovirus, containing nonspecific shRNA sequence (shNC), were constructed by Novobio Biotech (Shanghai, China). The sequence of shRNA is as follows: shDgkk, 5′-GGA ATG CAC TAC TGG TAT T-3′. Scramble shRNA (shNC, 5′-GAG CAT GTA GCA CTA TGT T-3′) was used as negative control. The packaging, purification, and titration of the above-mentioned adenovirus were carefully performed [33].
## Cell transfection
siRNAs targeting human NOX4 were synthesized by GenePharma Corporation (Shanghai, China) and transfected into A549 cells. The sequences of siRNA are as follows: siNOX4-1 5′-GGG CUA GGA UUG UGU CUA ATT-3′, siNOX4-2 5′- CAG UGA AGA CUU UGU UGA ATT-3′, and siNOX4-3 5′-GCA AGA CCU GGU CAG UAU ATT-3′. Scramble siRNA (siNC 5′-UUC UCC GAA CGU GUC ACG UTT-3′) was used as negative control.
## Enzyme-linked immunosorbent assay (ELISA)
About 100 μL of EVs was resuspended in radioimmunoprecipitation assay (RIPA) buffer and added with 100 μL of protease inhibitor mix (freshly added before use). The DGKK levels in serum EVs were measured using the DGKK ELISA Kit (4A Biotech Co. Ltd., Beijing, China). The levels of tumor necrosis factor (TNF)-α, interleukin (IL)-6, and IL-1β in serum, BALF, or A549 cells were measured using an assay kit (Nanjing Jiancheng, China) following the manufacturer’s protocols. DAG levels and PKC activity in mouse lung tissues or A549 cells were measured using the Diacylglycerol Assay Kit (Abcam, USA) and PKC Kinase Activity Kit (Enzo Life Sciences, USA), respectively.
## Measurement of organ injury markers
Plasma levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), and lactate dehydrogenase (LDH) were measured using commercial assay kits (Pointe Scientific, Lincoln Park, MI, USA) according to the manufacturer’s instructions.
## Measurement of ROS, malondialdehyde (MDA), SOD, glutathione peroxidase (GPX), and endotoxin
The levels of MDA, ROS, and SOD in human serum samples, mouse lung tissues, or A549 cells were measured using an assay kit (Nanjing Jiancheng, China) following the manufacturer’s protocols. GPX levels in human serum samples, mouse lung tissues, or A549 cells were measured using the Micro GPX Kit (Beijing Solarbio, China). Endotoxin levels in human serum samples were measured using the ToxinSensor LAL Endotoxin Kit (GenScript, NJ, USA).
## Apoptosis analysis using flow cytometry
A549 cells were seeded at a density of 3 × 105 cells per well. Next, they were added with 5 μL of Annexin V-FITC solution and incubated at 4 °C for 15 min. Then, they were added with 5 μL of propidium iodide (PI) and incubated for 15 min. Cell apoptosis was examined on an Accuri C6 flow cytometer (BD Biosciences, USA).
## Detection of intracellular ROS levels
ROS production in A549 cells was detected by measuring 2′,7′-dichlorofluorescein diacetate (DCFH-DA) with a flow cytometer following the instructions of the Reactive Oxygen Species Assay Kit (Beyotime Biotech, China).
## Quantitative real-time polymerase chain reaction (RT-qPCR)
Total messenger RNA was extracted from A549 and CD4+ T cells using TRIzol reagent (Thermo Fisher, USA). mRNA was then reverse-transcribed to produce cDNA using the RevertAid™ cDNA Synthesis Kit (Thermo Fisher, USA). RT-qPCR was conducted using SYBR Mix (Thermo Fisher, USA). The primers for PCR were designed by Invitrogen software, and their sequences are listed in Table 1. RT-qPCR was conducted on the 7300 Real-Time PCR System (Applied Biosystems, USA). GAPDH was used as internal control. Table 1Primer sequences used in the studyGeneForward/reverseSequence (5′–3′)DGKKForwardAAGAAACAGTCAGGGTCAACReverseAGGATGGAATGGTGCTAATGNOX1ForwardATAGCAGAAGCCGACAGGReverseCCACCAATGCCGTGAATCNOX2ForwardTAAGATAGCGGTTGATGGGReverseCAGATTGGTGGCGTTATTGNOX3ForwardTTGGCGTGTTCTTCTGTGReverseTCCTGGTGGAGTTCTTTGNOX4ForwardGACTTGGCTTTGGATTTCTGReverseTCTGAGGGATGACTTATGACNOX5ForwardTGCACTGGGCAAGAATGACReverseAGCAGCCACTTTCTGGAACDUOX1ForwardAAGTCTCGCCTTATGTTCReverseATCTTCCCATGTCAGTTCDUOX2ForwardGAACATCGCTGTGTATGAGTGReverseTTCTCCCGAATCCAGTAGTTGIL6ForwardGCACCTCAGATTGTTGTTGReverseAGTGTCCTAACGCTCATACIL1BForwardATCAGCCAGGACAGTCAGReverseGAAGCGGTTGCTCATCAGTNFForwardGGTATGAGCCCATCTATCTGReverseAGGGCAATGATCCCAAAGGAPDHForwardGGAGCGAGATCCCTCCAAAATReverseGGCTGTTGTCATACTTCTCATGGDgkkForwardGGAATTACTGCAACGCTCTTACForwardAACCAAAGATTGCCACAACCIl6ForwardTGGAGCCCACCAAGAACGATAGReverseTGTCACCAGCATCAGTCCCAAGIl1bForwardGCATCCAGCTTCAAATCTCReverseACACCAGCAGGTTATCATCTnfForwardGTGCTCAGAGCTTTCAACReverseACTCTCCCTTTGCAGAACGapdhForwardCTGCCCAGAACATCATCCReverseCTCAGATGCCTGCTTCAC
## Western blot analysis
Lysate of A549 and CD4+ T cells was quickly generated in precooled RIPA buffer. NE-PER Extraction Reagents (Thermo Fisher Scientific) were used to prepare the cytosolic and nuclear fractions according to the protocol provided by the manufacturer along with the reagent. The protein concentration was measured using the BCA kit (Thermo Fisher Scientific) according to the manufacturer’s protocols. The protein extract was resolved in 10–$15\%$ SDS-PAGE gel and transferred and electroblotted onto nitrocellulose membrane (Millipore). The membrane was briefly washed and blocked through incubation with $5\%$ non-fat milk. Next, it was added with primary antibodies against DGKK (Abcam; ab103681), TLR4 (Abcam; ab13867), NOX4 (Abcam; ab154244), NF-κBp65 (Cell Signaling Technology, Danvers, MA, USA; #8242), H3 (Cell Signaling Technology; #4499), or GAPDH (Cell Signaling #5174) at 4 °C for 16 h. The secondary antibodies conjugated with horseradish peroxidase (Cell Signaling Technology) were used for chemiluminescent signal determination. Immunoreactive signals were revealed by using the ECL chromogenic substrate kit (Bio-Rad, Hercules, USA).
## Luciferase reporter assay
The DGKK promoter reporter plasmid, with either wild-type or mutant sequence, was constructed by cloning the PCR-amplified promoter cDNA into the pGL3-Enhancer firefly luciferase reporter plasmid. CD4+ T cells treated with 10 μg/mL LPS and the NF-κB inhibitor QNZ (10 nM; Santa Cruz Biotech, USA) were seeded in flat-bottom 24-well plates and transfected transiently with reporter plasmid with DGKK promoter (each for 40 ng, wild type or mutant). To normalize the reporter read-out with the transfection efficiency for each individual sample, the cells were co-transfected with pRL-TK (50 ng), so that the renilla luciferase was encoded. After 48 h, these cells were harvested and lysed. A Dual-Luciferase Reporter Assay system (Promega, USA) was employed to determine the luciferase activity.
## Chromatin immunoprecipitation (ChIP)
ChIP analysis was carried out as previously reported [34]. Briefly, cells were fixed in $1\%$ formaldehyde, and a Bioruptor Sonicator (Diagenode; five cycles of 3 s on/3 s off) was used to fragment the DNA into sizes ranging between 200 and 1000 base pairs. The extracts were immunoprecipitated with protein A/G beads and incubated with antibodies against anti-NF-κB antibody (Cell Signaling Technology; #8242) and normal rabbit IgG (Proteintech Group, Inc, Rosemont, IL, USA; 30000-0-AP). The immunoprecipitated DNA fragment was then purified and validated using PCR analysis.
## Statistical analysis
Data are reported as mean ± standard deviation (SD). Statistical analysis was performed using GraphPad Prism 8.4.2 software (San Diego, CA, USA). Two-tailed unpaired Student’s t-test was conducted to compare two groups. ANOVA, combined with Dunnett’s multiple comparisons test, was conducted to compare among multiple groups. The receiver operating characteristic (ROC) curves of each parameter and the combination of serum exosomal DGKK were used to evaluate the performance in differentiating between patients with sepsis-induced lung injury and healthy control subjects. The area under the curve (AUC) was calculated. Kaplan–Meier and Cox’s regression models were used to assess overall survival, and the differences were analyzed by a log-rank test. Statistical significance was considered at $P \leq 0.05.$
## Discovery of the differential distribution of DGKK content in serum EVs of patients with sepsis-induced lung injury
Suspecting altered EVs as a driving factor of oxidative and inflammatory damage in sepsis-induced lung injury, we examined the protein components of EVs from the serum of patients with sepsis-induced lung injury and control subjects. The extraction was confirmed by TEM (Additional file 1: Fig. S1A). Western blot demonstrated that the EVs were positive for CD9, CD81, and TSG101 and negative for Golgi membrane bound protein GM130 (Additional file 1: Fig. S1B). NTA showed the size distribution of EVs (Additional file 1: Fig. S1C). The concentration of serum EVs from healthy controls and septic patients was 3.3 × 1011 particles/mL and 3.9 × 1011 particles/mL, respectively (Additional file 1: Fig. S1C). LC–MS/MS was performed for serum EV protein profiling. The separation of tests for control subjects and septic patients was confirmed by partial least square discriminant analysis (PLS-DA), where a cumulative variance of $99.5\%$ was shown (component 1 explaining $95.53\%$ and component 2 explaining $3.98\%$ of the variance) (Fig. 1A). We screened the raw data to select those with at least two nonzero values in three repeat experiments for further analysis. Within the 61 proteins identified as differentially distributed between EVs, Gene Ontology (GO) analysis of the molecular function found that hydrolase activity was the most dominant term (Fig. 1B). Similarly, GO analysis of the cellular component found that membrane bound organelle was the most dominant term (Fig. 1C), and GO analysis of the biological process found that several metabolic processes were the major terms (Fig. 1D). KEGG analysis for these 61 proteins revealed several immune-related functions as the mainly enriched terms (Fig. 1E). Among them, proteins showing fold change > 2 (up- or downregulated), and a t-test P-value < 0.05, were defined as differentially expressed proteins (DEPs) (Additional file 2: Table S1).Fig. 1Discovery of DEPs in serum EVs from patients with sepsis-induced lung injury. A PLS-DA of protein profiles of EVs isolated from the serum of patients with sepsis-induced lung injury ($$n = 3$$) and healthy control subjects ($$n = 3$$). GO analysis of 61 DEPs in the aspects of (B) molecular function, (C) cellular component, and (D) biological process. E KEGG enrichment analysis of the 61 DEPs. F Isolation of serum EVs and ELISA detection of higher DGKK in patients with sepsis-induced lung injury ($$n = 40$$) compared with healthy control subjects ($$n = 20$$). G Higher endotoxin content in serum of patients with sepsis-induced lung injury ($$n = 40$$) compared with healthy control subjects ($$n = 20$$). H Pearson correlation analysis showed positive correlation between serum endotoxin content and exosomal DGKK level in patients with sepsis-induced lung injury ($$n = 40$$). I ROC curve analysis indicating that exosomal DGKK content could be used to differentiate patients with sepsis-induced lung injury from healthy control subjects. *** $P \leq 0.001$ versus control Among the 12 proteins that were upregulated in EVs from patients with sepsis-induced lung injury, DGKK was the most significant one. As the DGK family governs a wide range of pathological processes, including oxidative stress and immune response, and DGKK is a critical member of this family, our further approach will be focused on this protein. Elevated level of DGKK in serum EVs from patients with sepsis-induced lung injury was confirmed by ELISA (Fig. 1F). Moreover, the endotoxin content in these patient samples was significantly increased (Fig. 1G). Interestingly, the level of exosomal DGKK and content of serum endotoxin exhibited strong positive correlation shown by Pearson correlation analysis (Fig. 1H). Furthermore, the ROC curves of serum exosomal DGKK were used to differentiate patients with sepsis-induced lung injury from healthy control subjects. In distinguishing patients with sepsis-induced lung injury from healthy control subjects, serum exosomal DGKK demonstrated high diagnostic performance (AUC of 0.8769) (Fig. 1I). The association of exosomal DGKK with a variety of clinicopathological features was also identified (Table 2). Thus, serum exosomal DGKK facilitates tissue damage in the progression of sepsis-induced lung injury. Table 2Relationship between DGKK expression and clinicopathological features of patients with sepsis-induced lung injuryControl subjectsSepsisP valueDGKK lowDGKK highAgeMale, n (%)BMI (kg/m2)White cell count (× 109/L)C-reactive protein (mg/L)IL-6 (pg/mL)TNF-α (pg/mL)SOFA scoreHistory of hypertension, n (%)History of hyperlipidemia, n (%)History of diabetes, n (%)History of CKD, n (%)History of CCVD, n (%)History of asthma, n (%)History of COPD, n (%)57.9 ± 8.413 [65]25.5 ± 3.05.3 ± 1.01.5 ± 0.716.0 (10.3–16.3)40.8 (28.6–49.9)2 (1–3)11 [55]12 [60]8 [40]9 [45]12 [60]12 [60]10 [50]59.9 ± 11.88 [40]25.3 ± 3.112.7 ± 0.4138.5 ± 48.741.9 (18.7–55.6)118.0 (90.2–127.2)4 (3–5)8 [40]8 [40]13 [65]13 [65]7 [35]8 [40]6 [30]65.7 ± 10.314 [70]23.4 ± 2.414.5 ± 1.13169.8 ± 40.655.9 (37.3–77.2)126.4 (103.4–149.0)7 (5–8)14 [70]13 [65]7 [35]8 [40]14 [70]14 [70]16 [80]0.0600.1190.054 < 0.001 < 0.001 < 0.001 < 0.001 < 0.0010.1620.2430.1250.2470.0720.1500.006Results expressed as mean ± SD or median (interquartile range)CKD chronic kidney disease, CCVD cardiovascular and cerebrovascular diseases, COPD chronic obstructive pulmonary disease, SOFA sequential organ failure assessment
## DGKK was expressed in CD4+ T cells through the activation of the NF-κB pathway
Examining the expression of DGKK in various tissues and cell types from a public database (http://www.proteinatlas.org/), we found that DGKK is mainly expressed in T lymphocytes. The isolated CD4+ T cells from healthy control subjects exhibited elevated expression of DGKK in cell lysates under LPS challenge at those mRNA (Additional file 1: Fig. S2A) and protein (Additional file 1: Fig. S2B) levels. Treatment with NF-κB inhibitor QNZ strongly suppressed the activation of the NF-κB pathway in CD4+ T cells, as indicated by the abundance of TLR4 and cytoplasmic/nuclear distribution of NF-κB p65 (Additional file 1: Fig. S2C). With this treatment, LPS no longer enabled DGKK expression (Additional file 1: Fig. S2D). This was further supported by luciferase reporter assay to determine the activation of promoter elements of DGKK gene, which showed that LPS treatment dramatically induced reporter expression, but QNZ treatment, or use of a mutant promoter element, restrained this increase (Additional file 1: Fig. S2E). The NF-κB binding site in the DGKK promoter was predicted using JASPAR [35] (Additional file 1: Fig. S2F). ChIP–qPCR analysis based on the predicted binding site showed that p65 binding was enhanced by LPS treatment and that QNZ limited this increase (Additional file 1: Fig. S2G).
## EVs derived from the serum of patients with sepsis-induced lung injury and LPS-treated CD4+ T cells promoted sepsis-induced lung injury in mice
How CD4+ T cells affect immune response and the whole process of pathogenesis in sepsis-induced lung injury remains to be elucidated. The present study is focused on exosomal function, so EVs were extracted from the serum of patients with sepsis-induced lung injury (and control subjects) or cultured primary human CD4+ T cells treated with LPS. The quality of EVs extracted from CD4+ T cells, with or without LPS treatment, was examined by TEM (Additional file 1: Fig. S3A) and western blot of exosomal markers (Additional file 1: Fig. S3B). NTA showed the size distribution of EVs (Additional file 1: Fig. S3C). The concentration of EVs derived from CD4+ T cells with or without LPS treatment was 5.3 × 109 particles/mL and 1.6 × 109 particles/mL, respectively (Additional file 1: Fig. S3C). Sepsis was induced by CLP in mice, where the pathological examination by H and E staining showed substantial morphological changes, including edema, hemorrhage, alveolar collapse, and inflammatory cell infiltrations, compared with the control group (Fig. 2A, B). With injection of the above-mentioned EVs, the progression of lung injury was enhanced by EVs from patients with sepsis-induced lung injury and from LPS-treated CD4+ T cells compared with EVs from control subjects and from untreated CD4+ T cells (Fig. 2A, B). Plasma levels of ALT, AST, and LDH were significantly elevated in the CLP group compared with the control group (Fig. 2C–E). Moreover, EVs from patients with sepsis-induced lung injury and LPS-treated CD4+ T cells further increased the levels of ALT, AST, and LDH in CLP mice compared with EVs from control subjects and from untreated CD4+ T cells (Fig. 2C–E). ROS measurement of lung tissue showed that EVs from patients with sepsis-induced lung injury and from LPS-treated CD4+ T cells further elevated ROS levels in CLP mice compared with EVs from control subjects and from untreated CD4+ T cells (Fig. 2F). A similar trend was also observed when the MDA content, a marker of ROS damage, was monitored (Fig. 2G). Consistently, the antioxidant activity, indicated by SOD (Fig. 2H) and GPX activities (Fig. 2I), showed further reduction in CLP model mice treated with EVs from patients with sepsis-induced lung injury and from LPS-treated CD4+ T cells compared with EVs from control subjects and from untreated CD4+ T cells. Similar to oxidative stress markers, the inflammatory cytokines (TNF-α, IL-1β, and IL-6) showed further elevation in CLP model mice treated with EVs from patients and LPS-treated T cells compared with EVs from control subjects and control T cells. RT-qPCR and ELISA were used to measure inflammatory cytokines in lung tissues (Fig. 2J) and BALF (Fig. 2K), respectively. Consistent with these observations, treatment with EVs from patients and LPS-treated T cells, compared with EVs from control subjects and control T cells, further shortened the life of CLP model mice (Fig. 2L). Moreover, the lung injury, oxidative stress, and inflammation induced by EVs isolated from the above-mentioned serum and CD4+ T cells were comparable with those in CLP-treated mice (Additional file 1: Fig. S4A–G). Taken together, these data suggest that EVs derived from the serum of patients with sepsis-induced lung injury and LPS-treated CD4+ T cells promoted sepsis-induced lung injury. Fig. 2Toxic effects of CD4+ T cell-derived EVs on oxidative stress and inflammation in CLP-induced lung injury in mice. CLP model mice were treated with EVs isolated from the serum of patients with sepsis-induced lung injury (SSE) or of healthy subjects (SE), or CD4+ T cells isolated from healthy subjects treated with (LTE) or without (TE) 10 μg/mL LPS. A H and E staining (scale bar, 100 μm). B Severity of histological injury. Plasma levels of (C) ALT, (D) AST, (E) LDH, and (F) ROS level, G MDA content, H SOD activity, (I) GPX activity and (J) mRNA expression of TNF-α, IL-1β, and IL-6 in lung tissues were measured. K The BALF content of TNF-α, IL-1β, and IL-6 in mice was measured by ELISA. L The survival rate of mice was monitored within 5 days, showing shortened survival with EV treatment. Data presented as mean ± SD. *** $P \leq 0.001$ versus control. # $P \leq 0.05$, ##$P \leq 0.01$, ###$P \leq 0.001$ versus CLP + SE. ΔP < 0.05, ΔΔΔP < 0.001 versus CLP + TE
## CD4+ T cells secreted EVs to induce A549 cell apoptosis and oxidative stress in vitro
In light of the findings in CLP model mice, we further examined the effects of CD4+ T cell-secreted EVs on cultured lung epithelial cells. A549 cells, the cell line of adenocarcinomic human alveolar basal epithelial cells, were cultured and treated with EVs derived from CD4+ T cells with or without LPS. Laser scanning confocal microscope analysis of EV uptake is shown in Additional file 1: Fig. S3D. Flow cytometric analysis with Annexin V-FITC/PI double staining indicated that EVs from LPS-treated T cells, but not from control T cells, dramatically promoted cell apoptosis (Fig. 3A). The ROS levels in these cells detected by DCFH-DA staining with flow cytometric analysis revealed that EVs from LPS-treated T cells, but not from control T cells, dramatically exaggerated ROS production (Fig. 3B). MDA content was also elevated in A549 cells treated with EVs from LPS-treated T cells, but not from control T cells (Fig. 3C). The activities of SOD (Fig. 3D) and GPX (Fig. 3E) were reduced in A549 cells treated with EVs from LPS-treated T cells, but not from control T cells. Measured by RT-qPCR analysis for total mRNA (Fig. 3F) and ELISA for cell culture medium (Fig. 3G), the expression and secretion of inflammatory cytokines (TNF-α, IL-1β, and IL-6) were elevated in A549 cells treated with EVs from LPS-treated T cells, but not from control T cells. Therefore, LPS-treated CD4+ T-cell-secreted EVs promoted the apoptosis of alveolar epithelial cells, accompanied with the elevation of ROS and inflammatory reaction, which could be critical factors to drive lung injury. Fig. 3Toxic effects of CD4+ T-cell-derived EVs on oxidative stress and inflammation on apoptosis, oxidative stress, and inflammation in A549 cells. A549 cells were treated with EVs isolated from CD4+ T cells isolated from healthy subjects treated with (LTE) or without (TE) 10 μg/mL LPS. A Flow cytometric analysis with Annexin V-FITC/PI double staining indicated elevation of cell apoptosis by LTE treatment. Representative plot images from flow cytometry and statistical analysis are shown. B ROS production measured by DCFH-DA staining with flow cytometric analysis showed higher ROS in cells treated with LTE. Representative images from flow cytometry and statistical analysis are shown. C Higher MDA content in cells treated with LTE. D SOD and (E) GPX activities were reduced in cells treated with LTE. Elevated (F) mRNA expression and (G) secretion of TNF-α, IL-1β, and IL-6 in cells treated with LTE. Data presented as mean ± SD. *** $P \leq 0.001$ versus control
## PKC/NOX4 pathway mediated the toxic effects of CD4+ T-cell-derived EVs on cultured alveolar epithelial cells
Encouraged by the findings of elevated distribution of CD4+ T-cell-expressed DGKK in serum EVs from patients with sepsis-induced lung injury, the downstream pathway of DGK was examined for the involvement in the toxic effects of LPS-treated CD4+ T-cell-derived EVs on cultured A549 cells. DGK phosphorylates DAG to generate PA, which could activate the PKC pathway. Consistently, the stimulation of EVs isolated from LPS-treated and control CD4+ T cells to A549 cells strongly enhanced DAG content (Additional file 1: Fig. S5A), accompanied with the activation of PKC (Additional file 1: Fig. S5B). As PKC phosphorylates p40phox to activate NADPH oxidase (NOX), the expression of NOX enzymes (NOX1-5 and DUOX$\frac{1}{2}$) was examined. The results showed that only NOX4 exhibited induction by EVs from LPS-treated CD4+ T cells (Additional file 1: Fig. S5C). The upregulation of NOX4 was also detected by western blot (Additional file 1: Fig. S5D), suggesting that NOX4 may be involved in the toxic effects of EVs derived from CD4+ T cells treated with LPS.
To determine the role of the PKC pathway in the toxic effects of LPS-treated CD4+ T-cell-derived EVs, the A549 cells were treated with the selective PKC inhibitor LXS-196 (Darovasertib) or a novel NOX4 inhibitor GLX351322, together with the EVs. Flow cytometric analysis with Annexin V-FITC/PI double staining indicated the dramatic induction of apoptosis by those EVs, but LXS-196 and GLX351322 both attenuated the apoptosis induction (Fig. 4A, B). NOX4 could be specifically depleted through siRNA transfection (Additional file 1: Fig. S5E, F), and this knockdown of NOX4 also weakened the toxic effects of LPS-treated CD4+ T-cell-derived EVs (Fig. 4A, B). In parallel, the elevated ROS levels (Fig. 4C, D) and MDA content (Fig. 4E) in EV-treated A549 cells were also reduced by LXS-196, GLX351322, and siRNA of NOX4. The reduction of SOD (Fig. 4F) and PGX activities (Fig. 4G) in EV-treated A549 cells was restored by LXS-196, GLX351322, and NOX4 RNAi. As LXS-196, GLX351322, and NOX4 RNAi all worked downstream of DAG, the elevation of DAG content in EV-treated A549 cells was not affected by LXS-196, GLX351322, or NOX4 RNAi (Fig. 4H). LXS-196 efficiently suppressed the elevation of PKC activity in EV-treated A549 cells (Fig. 4I). The expression (Fig. 4J) and secretion (Fig. 4K) of inflammatory cytokines (TNF-α, IL-1β, and IL-6) were examined. The results showed that LXS-196, GLX351322, and NOX4 RNAi exerted inhibitory effects on the upregulation of these cytokines in EV-treated A549 cells. Together, the PKC/NOX4 pathway acts downstream of CD4+ T-cell-derived EVs in cultured alveolar epithelial cells to induce oxidative stress, inflammatory response, and cell apoptosis. Fig. 4Effects of T cell EVs on apoptosis, oxidative stress, and inflammation in A549 cells were attenuated by PKC/NOX4 inhibition. A549 cells were stimulated with EVs isolated from CD4+ T cells isolated from healthy subjects treated with 10 μg/mL LPS (LTE) in the absence or presence of LXS-196, GLX351322, or NOX4 siRNA (taking siRNA to nonspecific sequence as control). A, B Flow cytometric analysis with Annexin V-FITC/PI double staining indicated that the elevation of cell apoptosis by LTE treatment was restored by LXS-196, GLX351322, or NOX4 RNAi. Representative plot images from flow cytometry (A) and statistical analysis (B) are shown. C, D DCFH-DA staining with flow cytometric analysis indicated that the elevation of ROS by LTE treatment was restored by LXS-196, GLX351322, or NOX4 RNAi. Representative images from flow cytometry (C) and statistical analysis (D) are shown. E The elevation of MDA content by LTE treatment was restored by LXS-196, GLX351322, or NOX4 RNAi. The reduction of SOD (F) and GPX activities (G) by LTE treatment was restored by LXS-196, GLX351322, or NOX4 RNAi. H The elevation of DAG content by LTE treatment was not affected by LXS-196, GLX351322, or NOX4 RNAi. I The elevation of PKC activity by LTE treatment was restored by LXS-196, but was not affected by GLX351322 or NOX4 RNAi. The increase of (J) mRNA expression and (K) secretion of TNF-α, IL-1β, and IL-6 by LTE treatment was restored by LXS-196, GLX351322, or NOX4 RNAi. Data presented as mean ± SD. *** $P \leq 0.001$ versus control. ### $P \leq 0.001$ versus LTE + vehicle. ΔΔΔP < 0.001 versus LTE + siNC
## PKC/NOX4 pathway mediated the toxic effects of CD4+ T-cell-derived EVs in mice with sepsis-induced lung injury
To further elucidate the action of the PKC/NOX4 pathway downstream of LPS-treated CD4+ T-cell-derived EVs in sepsis-induced lung injury, we directed our attention to CLP-induced sepsis in mice. The toxic effects of EVs were confirmed by H and E staining of lung tissue in CLP model mice, and the protective effects of LXS-196 and GLX351322 were observed (Fig. 5A, B. The increasing plasma levels of ALT, AST, and LDH by treatment of EVs in CLP model mice were weakened by LXS-196 and GLX351322 (Fig. 5C–E). The increasing ROS levels (Fig. 5F) and MDA content (Fig. 5G) by treatment of EVs in CLP model mice were weakened by LXS-196 and GLX351322. Correspondingly, the activities of SOD (Fig. 5H) and GPX (Fig. 5I) were further reduced by treatment of EVs in CLP model mice, which were restored by LXS-196 and GLX351322. Neither LXS-196 nor GLX351322 had an effect on DAG content in CLP model mice with treatment of EVs (Fig. 5J). Only LXS-196 exerted inhibitory effect on PKC activity in CLP model mice with treatment of EVs (Fig. 5K). The induction of NOX4 expression in the lungs by CLP and treatment of EVs, and the inhibitory effects of LXS-196 and GLX351322 were demonstrated by western blot (Fig. 5L). After examining the mRNA expression (Fig. 5M) and BALF content (Fig. 5N) of inflammatory cytokines (TNF-α, IL-1β, and IL-6), LXS-196 and GLX351322 showed inhibitory effects on the upregulation of these cytokines in EV-treated CLP model mice. Consistently, the shortened survival in CLP model mice, and CLP model mice treated with EVs, was rescued by LXS-196 and GLX351322 (Fig. 5O).Fig. 5Effects of T-cell EVs on tissue damage, oxidative stress, and inflammation in mice with CLP-induced lung injury were attenuated by PKC/NOX4 inhibition. Mice with CLP were stimulated with EVs isolated from CD4+ T cells isolated from healthy subjects treated with 10 μg/mL LPS (LTE), in the absence or presence of LXS-196 or GLX351322. A H and E staining indicated that the severe lung injury by LTE treatment in CLP model mice was restored by LXS-196 or GLX351322 (scale bar, 100 μm). B The severity of histological injury and plasma levels of (C) ALT, (D) AST, and (E) LDH by LTE treatment in CLP model mice was restored by LXS-196 or GLX351322. The elevation of (F) ROS levels and (G) MDA content by LTE treatment in CLP model mice was restored by LXS-196 or GLX351322. The reduction of (H) SOD and (I) GPX activities by LTE treatment in CLP model mice was restored by LXS-196 and GLX351322. J The elevation of DAG content by LTE treatment in CLP model mice was not affected by LXS-196 or GLX351322. K The elevation of PKC activity by LTE treatment in CLP model mice was restored by LXS-196, but not GLX351322. L The elevation of NOX4 protein level by LTE treatment in CLP model mice was restored by LXS-196 and GLX351322, as shown by western blot. M The elevation of mRNA expression of TNF-α, IL-1β, and IL-6 by LTE treatment in CLP model mice was restored by LXS-196 or GLX351322. N The elevation of BALF content of TNF-α, IL-1β, and IL-6 by LTE treatment in CLP model mice was restored by LXS-196 or GLX351322. O The shortened survival in CLP model mice treated with LTE was rescued by LXS-196 and GLX351322. Data presented as mean ± SD. *** $P \leq 0.001$ versus control. # $P \leq 0.05$, ##$P \leq 0.01$, ###$P \leq 0.001$ versus CLP. ΔΔΔP < 0.001 versus CLP + LTE + vehicle The toxic effects of EVs, confirmed by H and E staining for lung tissue in CLP model mice, were also ameliorated by injection of virus expressing shRNA control and shRNA targeting DGKK (Fig. 6A–D). The increasing plasma levels of ALT, AST, and LDH in CLP model mice were decreased by DGKK RNAi (Fig. 6E–G). The increase of ROS levels (Fig. 6H) and MDA content (Fig. 6I) in CLP model mice were decreased by DGKK RNAi. Correspondingly, the reduced activity of SOD (Fig. 6J) and GPX (Fig. 6K) was restored by DGKK RNAi. As expected, DGKK RNAi efficiently inhibited the elevation of DAG content (Fig. 6L) and PKC activity (Fig. 6M) in the lungs of CLP model mice. The increased BALF content of inflammatory cytokines (TNF-α, IL-1β, and IL-6) in CLP model mice was also decreased by DGKK RNAi (Fig. 6N). The shortened survival in CLP model mice was restored by DGKK RNAi (Fig. 6O). Therefore, CD4+ T-cell-derived EVs might place unique demands on the DGK/DAG/PKC/NOX4 pathway to promote sepsis-induced lung injury in mice, including oxidative stress and inflammation. Fig. 6DGKK knockdown attenuated tissue damage, oxidative stress, and inflammation in mice with CLP-induced lung injury. Mice with CLP were injected with virus carrying plasmid expressing shRNA control (shNC) and shRNA targeting DGKK (shDgkk). The elevation of Dgkk abundance at the (A) mRNA and (B) protein levels in CLP model mice was restored by Dgkk RNAi. C H and E staining indicated that the severe lung injury in CLP model mice was restored by Dgkk RNAi (scale bar, 100 μm). D The severity of histological injury and plasma levels of (E) ALT, (F) AST, and (G) LDH in CLP model mice was restored by Dgkk RNAi. The elevation of (H) ROS levels and (I) MDA content in CLP model mice was restored by Dgkk RNAi. The reduction of (J) SOD and (K) GPX activities in CLP model mice was restored by Dgkk RNAi. The elevation of (L) DAG content and (M) PKC activity in CLP model mice was restored by Dgkk RNAi. N The elevation of BALF content of TNF-α, IL-1β, and IL-6 in CLP model mice was restored by Dgkk RNAi. O The shortened survival in CLP model mice was restored by Dgkk RNAi. Data presented as mean ± SD. *** $P \leq 0.001$ versus control. ## $P \leq 0.01$, ###$P \leq 0.001$ versus CLP + shNC
## Discussion
In lung tissue affected by ALI, many sources generate ROS, including itinerant and resident leukocytes, parenchymal cells, oxidant-generating enzymes in the blood, and inhaled high-oxygen gases from mechanical ventilation. ROS facilitate tissue damage in ALI with prolonged inflammatory response [4]. However, the upstream regulators of inflammation and oxidative stress supporting the pathogenesis of sepsis-induced ALI are not systematically studied. The present approach expanded this knowledge by elucidating the DGKK/PKC/NOX4 pathway. As DGKK was delivered to target tissue by T-cell-derived EVs in patients with ALI, this study further underscored the importance of EVs in orchestrating pathogenesis of ALI/ARDS.
In sepsis-induced lung injury, EVs from various cell types exert broad protective or promoting effects on tissue damage. For example, EVs derived from human mesenchymal stem cells effectively downregulated sepsis-induced glycolysis and inflammation in macrophages, which could attenuate lung damage and improve the survival of septic mice [36]. In particular, adipose-derived MSC-derived EVs could inhibit IL-27 secreted from macrophages, which ameliorates sepsis-induced ALI in model mice [37]. In addition, exosomal miR-30d-5p from polymorphonuclear neutrophils is involved in sepsis-induced ALI by inducing M1 polarization and pyroptosis of macrophages [38]. However, no studies have reported on the functional involvement of EVs secreted from CD4+ T cells.
The EVs produced by activated CD4+ T cells not only express similar proteins as in other kinds of EVs, including membrane-anchored tetraspanins, annexins, and representative luminal proteins, but also express immune-related proteins, including integrins, HLA-I, microglobulin, and TCR/CD3 complex subunits. EVs have been associated with various chronic inflammatory lung diseases. For example, EVs derived from lung tissue carry miR-210 that prevents the expression of Atg7 in target cells to prevent autophagy and stimulate myofibroblast differentiation and fibrosis in COPD [7]. CD36+ EVs accelerate disease progression by activating inflammation through the heterodimerization of TLR$\frac{4}{6}$ in asthma [39]. EVs from BALF of patients with asthma contain functional leukotriene-producing enzymes that cause the secretion of inflammatory cytokines by bronchial epithelial cells [8]. EVs derived from infiltrated and activated neutrophils and eosinophils are also proinflammatory [9]. On the basis of this premise, we hypothesized that T-cell-derived EVs could carry important factors to support sepsis-induced lung injury, particularly to promote inflammation and oxidative stress. Through proteomic profiling, 61 DEPs were found in serum EVs from patients with sepsis-induced lung injury compared with serum EVs from healthy control subjects. Among them, DGKK was further examined.
DGK family kinases, which comprise diverse isozymes (α–κ), take part in the pathogenesis of normal and abnormal biological processes, including immune responses, neuronal network activation, brain disorder, cancer, and type 2 diabetes [18]. DGK phosphorylates DAG to activate PKC pathways, followed by NOX activation. The inhibition of PKC or NOX4 attenuated Pseudomonas aeruginosa-induced lung inflammatory injury by inhibiting ROS production [40] and LPS-induced ALI by inhibiting apoptosis and secretion of proinflammatory cytokines in pneumonia cells [41]. Serum apelin-13 could protect against sepsis-induced ALI by regulating NOX4-dependent ROS [42]. NOX4 (versus other NOX isoforms) was specifically involved in damage of the endothelial cell barrier in the lungs of a mouse model of CLP-induced sepsis [43]. Echoing these previous reports, our investigation demonstrated that NOX4 was specifically regulated by the DAG/PKC axis, and this DAG/PKC/NOX4 signaling played a supportive role in sepsis-induced lung injury, again indicating the therapeutic value of this signaling in the treatment of this disease.
Overall, the present study systematically analyzed the protein profile of serum EVs in patients with sepsis-induced lung injury. DGKK was expressed in CD4+ T cells under regulation of the NF-κB pathway and carried by EVs for delivery to target cells. In the cultured alveolar epithelial A549 cell line and the lungs of a mouse model of CLP-induced sepsis, EVs derived from CD4+ T cells exerted toxic effects through DGKK and its stimulation on the DAG/PKC/NOX4 signaling pathways. As there are still other proteins identified with altered distribution in serum EVs of patients with sepsis-induced lung injury, future studies need to elucidate the involvement of these other proteins in the pathogenesis. Moreover, DGK maintains the balance between DAG and PA. The current study was focused on the signaling pathways downstream of DAG, but the PA-initiated signal might also be involved in lung injury in sepsis, which needs to be further studied. Nevertheless, the findings reported here could still be leveraged to develop treatment strategies for patients with ALI/ARDS.
## Conclusions
Our findings demonstrate that the upregulated levels of DGKK in serum EVs derived from septic patients showed strong correlation with sepsis severity and progression. Mechanistically, EVs derived from LPS-treated CD4+ T cells carrying DGKK induced oxidative stress and inflammation in alveolar epithelial A549 cells and sepsis-induced mice through PKC and NOX4, the downstream effectors of DGKK and DAG (Additional file 1: Fig. S6). This approach established the mechanism that T-cell-derived EVs exerted toxic effects in sepsis-induced lung injury through the DGKK/DAG/PKC/NOX4 pathway.
## Supplementary Information
Additional file 1: Figure S1. Characterization of EVs isolated from the serum of patients with sepsis-induced lung injury and healthy control subjects. A TEM observation (scale bar, 200 nm). B Western blot analysis of EV markers. C Diameter distribution of EVs by NTA. Figure S2. LPS promoted DGKK expression via the NF-κB pathway in human CD4+ T cells. Elevation of DGKK expression at the A mRNA and B protein levels in CD4+ T cells isolated from healthy subjects treated with 10 μg/mL LPS at various treatment durations. C Treatment of QNZ strongly suppressed the upregulation of TLR4 and higher nuclear/cytoplasmic distribution of NF-κB p65 in LPS-treated CD4+ T cells, as shown by western blot. D The elevation of DGKK expression at the mRNA and protein levels in human CD4+ T cells treated with LPS was restored by QNZ. E Luciferase reporter assay showed that the WT promoter of the DGKK gene was activated by LPS treatment, which could be restored by QNZ. However, the mutant promoter of the DGKK gene could not be activated by LPS. F The NF-κB binding site in the DGKK promoter was predicted using JASPAR. G ChIP–qPCR showed that the elevation of NF-κB binding to the DGKK promoter by LPS treatment was restored by QNZ. *** $P \leq 0.001$ versus 0 h or control. ### $P \leq 0.001$ versus LPS Figure S3. Characterization of EVs isolated from cultured human CD4+ T cells treated with or without 10 μg/mL LPS. A TEM observation (scale bar, 200 nm). B Western blot analysis of EV markers and DGKK. C Diameter distribution of EVs by NTA. D Laser scanning confocal microscope analysis of EV uptake by A549 cells (scale bar, 50 μm). Figure S4. Toxic effects of CD4+ T-cell-derived EVs on oxidative stress and inflammation in mice. Mice were treated with CLP, SSE, SE, LTE, or TE. A H and E staining (scale bar, 100 μm). B Severity of histological injury. Plasma levels of C ALT, D AST, E LDH, and F ROS level, G MDA content, H SOD activity, I GPX activity, and I BALF content of TNF-α, IL-1β, and IL-6 in lung tissues of mice. K The survival rate of mice was monitored within 5 days, showing the shortened survival with EV treatment. Data presented as mean ± SD. *** $P \leq 0.001$ versus control. ## $P \leq 0.01$, ###$P \leq 0.001$ versus SE. ΔP < 0.05, ΔΔP < 0.01, ΔΔΔP < 0.001 versus TE Figure S5. Activation of the DAG/PKC/NOX4 signaling pathway by T-cell EVs. A549 cells were treated with EVs isolated from CD4+ T cells isolated from healthy subjects treated with (LTE) or without (TE) 10 μg/mL LPS. A DAG content and B PKC activity were increased by LTE. C The mRNA expression of NOX4 in A549 cells was upregulated by LTE, but not NOX1, NOX2, NOX3, NOX5, DUOX1, and DUOX2. D The protein level of NOX4 in A549 cells was upregulated by LTE. The expression of NOX4 in A549 cells was significantly suppressed by NOX4 siRNA at the E mRNA and F protein levels. Data presented as mean ± SD. *** $P \leq 0.001$ versus control or siNC Figure S6. Schematic representation of the regulation of oxidative stress and inflammation in lung injury by EVs from CD4+ T cells via the DGKK/DAG/PKC/NOX4 pathwayAdditional file 2: Table S1. Differently expressed proteins between patients with sepsis lung injury and healthy subjects by using LC–MS/MS analysis
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|
---
title: Meningoencephalitis associated with GAD65 autoimmunity
authors:
- Zuying Kuang
- José Fidel Baizabal-Carvallo
- Mohammad Mofatteh
- Sifen Xie
- Mengqiu Pan
- Jinlong Ye
- Lihua Zhou
- Shuiquang Yang
- Zhanhang Wang
- Yimin Chen
- Yaqin Li
journal: Frontiers in Immunology
year: 2023
pmcid: PMC10035530
doi: 10.3389/fimmu.2023.1120894
license: CC BY 4.0
---
# Meningoencephalitis associated with GAD65 autoimmunity
## Abstract
### Background
Encephalitis has been recognized in patients with autoimmunity related to the 65-kDa isoform of glutamic acid decarboxylase (GAD65) antibodies; however, patients with meningoencephalitis associated with those antibodies have been rarely identified in the medical literature. We aimed to define the frequency, clinical features, response to therapy, and functional outcomes of patients with meningoencephalitis associated with GAD antibodies.
### Methods
We retrospectively studied consecutive patients attending a tertiary care center for evaluation of an autoimmune neurological disorder from January 2018 to June 2022. The modified Rankin Scale (mRS) was used to assess the functional outcome at the last follow-up.
### Results
We evaluated 482 patients with confirmed autoimmune encephalitis during the study period. Four among the 25 patients with encephalitis related to GAD65 antibodies were identified. One patient was excluded owing to the coexistence of NMDAR antibodies. Three male patients aged 36, 24, and 16 years had an acute ($$n = 1$$) or subacute ($$n = 2$$) onset of confusion, psychosis, cognitive symptoms, seizures, or tremor. No patient had fever or clinical signs of meningeal irritation. Mild pleocytosis (<100 leukocytes/106) was identified in two patients, whereas one patient had normal CSF. Following immunotherapy with corticosteroids ($$n = 3$$) or intravenous immunoglobulin ($$n = 1$$), significant improvement was observed in all three cases, achieving a good outcome (mRS 1) in all cases.
### Conclusion
Meningoencephalitis is an uncommon presentation of GAD65 autoimmunity. Patients present with signs of encephalitis but with meningeal enhancement and have good outcomes.
## Introduction
The knowledge of autoimmunity related to the 65-kDa isoform of glutamic acid decarboxylase (GAD65), the enzyme that converts the excitatory glutamate to inhibitory gamma-aminobutyric acid (GABA), has markedly evolved in recent decades [1]. Cerebellar ataxia, epilepsy, and stiff-person syndrome were clinical syndromes initially linked to the presence of these antibodies (Abs) [2, 3]. However, in these cases, conspicuous brain or spinal cord inflammation is usually absent in neuroimaging studies. More recently, a small proportion of patients with GAD65 autoimmunity have been described with clinical and radiological signs of brain inflammation that may affect the limbic and/or extralimbic cortices [4]. Parenchymal inflammation in such cases is believed to originate from an autoimmune cellular response, rather than a direct effect of GAD65 Abs.
Aseptic meningitis is characterized by the lack of an identifiable bacterial cause in cerebrospinal fluid (CSF) cultures [5]. Viruses are the most common cause of aseptic meningitis [6]. Among them, enteroviruses and varicella zoster virus are the most common [6]. Selected patients with inflammatory or autoimmune diseases, including Still disease, systemic lupus erythematosus (SLE), sarcoidosis, Behçet’s disease, Sjögren’s syndrome, and periodic fever syndrome, have been identified with aseptic meningitis secondary to the underlying systemic disorder (5–10). In contrast, meningeal enhancement has been identified in patients with paraneoplastic or autoimmune encephalitis, specifically anti-N-methyl-d-aspartate receptor (NMDAR) encephalitis [11, 12]. However, neuroimaging findings consistent with meningeal enhancement are rarely seen in patients with GAD65 autoimmunity.
In this study, we aimed to retrospectively characterize the frequency, clinical findings, response to therapy, and functional outcomes in patients with meningoencephalitis associated with GAD Abs. As patients with GAD65 encephalitis frequently present with inflammatory CSF, possibly reflecting meningeal inflammation, but without contrast enhancement on MRI, hence, we defined “meningoencephalitis” by the presence of patchy or diffuse contrast enhancement in the leptomeninges, supporting an inflammatory process disrupting the blood–brain barrier in these sites, with or without clinical or neuroimaging findings of brain parenchyma inflammation.
## Methods
We retrospectively studied consecutive patients referred for evaluation at the Department of Neurology and Immunology of the GuangDong 999 Brain Hospital in Guangdong Province in the People’s Republic of China, a tertiary care center for autoimmune neurological disorders from January 2018 to June 2022. Patients were enrolled if they had clinical and neuroimaging features of acute or subacute encephalitis attributed primarily to anti-GAD65 autoimmunity, owing to the presence of serum anti-GAD65 Abs and/or positive anti-GAD65 Abs in the CSF [13].
## General assessment and follow-up
All patients underwent general and neurological examinations to assess for rheumatological or systemic inflammatory causes of meningitis. History of medication, illicit drug consumption, or poisoning was determined. Blood count, glucose, kidney, and liver function tests were performed to assess for evidence of metabolic derangement. The outcome was defined according to the modified Rankin Scale (mRS) determined at the last date of follow-up. The mRS scores were categorized as follows: 0, no symptoms; 1, total independence despite symptoms; 2, unable to carry all previous activities but look after own affairs; 3, requiring some help but able to walk without assistance; 4, unable to walk without assistance; 5, bedridden; and 6, death. A favorable outcome was defined as an mRS score between 0 and 2, while a poor outcome was defined as an mRS score of 3-5 at the last follow-up.
## Antibody screening
We used commercially available cell-based assay or radioimmunoassay to check for anti-GAD65 Abs. Antibody screening ruled out anti-glial fibrillary acidic protein (GFAP), anti-NMDAR, anti-LGI1, anti-CASPR2, anti-GABAB, anti-GABAA, anti-GlyR, anti-AMPA, anti-DPPX, anti-DRD2, anti-IgLON5, anti-mGlutR1, anti-mGlutR5, anti-MOG, and anti-Neurexin-3.
## Infectious disease screening
All patients underwent extensive diagnostic tests to rule out infectious causes. A lumbar puncture was performed in four patients and CSF was collected. Opening pressure was measured in all cases in the lateral decubitus position, with the legs and neck in a neutral position. Intracranial hypertension was considered in case the opening pressure was above 200 mmH2O, whereas intracranial hypotension was considered in case the opening pressure was below 60 mmH2O [14, 15]. CSF samples were extensively assessed for white blood cells (WBC), proteins, and glucose levels using bacterial cultures and Gram stain. Acid-fast staining and India ink/cryptococcal antigen preparation were done in order to detect tuberculosis or Cryptococcus neoformans, respectively [16]. Polymerase chain reaction was carried out in the CSF to assess for the presence of viral causes of meningitis. Hepatitis B (HBV), hepatitis C (HCV), and human immunodeficiency virus (HIV) type 1 and 2 serology were carried out in these patients.
## Results
Among the 482 patients evaluated with confirmed autoimmune encephalitis during the study period, there were 25 patients with encephalitis associated with positive anti-GAD65 Abs. Four ($16\%$) of such patients had meningeal enhancement identified in the brain MRI. However, one patient, a 9-year-old girl with subacute headache and positive serum GAD65 Abs, was excluded from the study, owing to the presence of anti-NMDAR Abs in the CSF. The remaining three were all male patients, of Chinese origin, and between 16 and 36 years of age (Table 1). Extensive diagnostic studies ruled out metabolic, infectious, or drug-induced meningitis in all cases.
**Table 1**
| Age/sex(Author) | Clinical manifestations | MRI | EEG | Therapy | Response to therapy |
| --- | --- | --- | --- | --- | --- |
| 36/M(This report) | Behavioral changes, confusion, poor sleep, visual hallucinations | Diffuse leptomeningeal enhancement | Slow waves in bilateral frontal lobes and centrotemporal regions | RisperidoneMTP 40 mg/day with progressive dose reduction | Complete recovery (mRS: 0) at 16 months |
| 24/M(This report) | Behavioral changes, severe throbbing headache, delusional thoughts, cognitive disturbances, motor/language perseverance, insomnia | Diffuse leptomeningeal enhancement, thickening of the tentorium | Diffuse slow waves; epileptiform discharges in the bilateral frontal lobes and right temporal lobe | OlanzapineIVIg 20 g/day for 5 daysMTP 40 mg/day with progressive dose reduction | Prominent recovery (mRS: 1) at 12 months |
| 16/M(This report) | Sleepiness, fatigue, insomnia | Scattered line-like enhancement in intracranial sulci | | IVIg 25 g/day for 5 daysMTP 500 mg/day for 5 days, followed by prednisone with progressive dose reduction | Complete recovery (mRS: 0) at 10 months |
| 21/F(Triplett 2018) | Seizures, coma | Leptomeningeal enhancement; bilateral frontal, insular, and temporal hyperintensities | Generalized slowing, left frontal epileptic activity | Antiepileptics; cyclophosphamide, rituximab, IVIg, MTP, mycophenolate mofetil, prednisone | Mild improvement |
| 44/F(Salari 2022) | Cognitive symptoms and mental confusion | Hydrocephalus, meningeal enhancementa | | MTP | Good recovery |
## Case 1
A 36-year-old male patient presented for evaluation of a 3-month history of behavioral changes and poor sleep. There was no history of fever or seizures. The patient presented with episodes of odd behavior, restlessness, irritability, aggressiveness, and agitation. There were sporadic visual hallucinations. Neurological examination was remarkable for fluctuating mental confusion with disorientation and bilateral upper limb resting tremor. There was no focal paralysis. Nuchal rigidity and Kernig’s and Brudzinski’s signs were all negative. Brain MRI showed diffuse leptomeningeal enhancement with scattered non-enhancing hyperintensities in the white matter (Figures 1A, B). EEG showed diffuse slow waves in the bilateral frontal lobes and bilateral centrotemporal regions, mostly on the right side. Lumbar puncture revealed an opening pressure of 150 mmH2O, and the CSF showed elevated WBC, 39 × 106/L (normal ≤5 × 106/L); lymphocytes, $74\%$; proteins, 0.28 g/L (normal: 0.15-0.45 g/L); and glucose, 3.4 mmol/L (range: 2.5-4 mmol/L). The patient had positive GAD65 Abs in the serum 1:10, but these Abs were negative in the CSF. Investigations for infectious causes were all negative. During the evaluation, the patient was diagnosed with type 2 diabetes mellitus with glycosylated hemoglobin (HbA1c) ($8.6\%$) and received treatment with oral repaglinide and metformin. Treatment with progressively higher doses of risperidone did not provide benefit. However, marked clinical improvement was observed following treatment with oral methylprednisolone 40 mg per day for 2 weeks followed by progressively decreasing doses of 4 mg every week. The patient achieved an mRS score of 0 at the 16-month follow-up, after the onset of symptoms.
**Figure 1:** *Contrast -enhanced brain MRIs of (A, B) case 1 show diffuse enhancement of the leptomeninges without abnormalities in the brain parenchyma. (C) Case 2 and (D) case 3 also show conspicuous and scattered enhancement, respectively of the leptomeninges.*
## Case 2
A 24-year-old male patient came for evaluation of sudden onset of abnormal behavior and headache that started a few hours before the presentation. There was no history of a triggering event including poisoning or drug consumption. The patient and family denied fever or seizures. The clinical picture was characterized by severe, generalized, throbbing headache plus abnormal behavior with episodes of irritability, motor and language perseverance, disorientation, poor concentration, altered memory, and judgment. There were delusional thoughts, and the patient complained of being electrocuted, although no historical or clinical evidence of such an event was identified. Ritualistic behavior such as repetitive knocking on the wall was also present. There was evidence of insomnia and moderate anxiety. The neurological examination did not show a focal deficit; meningeal and cerebellar signs were negative. Cranial nerves were normal. Brain MRI showed extensive meningeal enhancement with a thickening of the tentorium more prominent on the right side. Brain parenchyma did not show abnormal hyperintensities or contrast enhancement (Figure 1C). EEG showed diffuse slow waves; epileptiform discharges were identified in the bilateral frontal lobes and right temporal lobe. Lumbar puncture revealed an opening pressure of 120 mmH2O, and cell count, proteins, and glucose levels were all normal in the CSF. Anti-GAD65 Abs were positive in the serum (1:30) and cerebrospinal fluid (1:10). The patient initially received variable doses of oral olanzapine with partial improvement. This was followed by a course of intravenous immunoglobulin (IVIg) 20 g per day for 5 days (total: 100 g) plus oral methylprednisolone 40 mg per day for 2 weeks followed by progressively decreasing doses of 4 mg every week. The patient achieved an mRS of 1 at the 12-month follow-up, after the onset of symptoms with a sporadic headache.
## Case 3
A 16-year-old male patient presented for evaluation of a 20-day history of severe daytime sleepiness. There were no apparent precipitating events, and no recent history of fever, psychosis, mental confusion, behavioral changes, or seizures was recorded. The patient showed an increased propensity to fall asleep, poor responsiveness during episodes of sleeping, generalized fatigue, and night insomnia with sleep fragmentation. The neurological examination was consistent with normal cranial nerves. Muscle strength and tendinous reflexes were also normal. Babinski sign was negative, and there were no signs of meningeal irritation. Brain MRI showed a scattered line-like enhancement in the intracranial sulci. The right tentorium was thickened and showed contrast enhancement (Figure 1D). The lumbar puncture showed an opening pressure of 100 mmH2O, and the CSF showed elevated WBC, 18 × 106/L (normal ≤5 × 106/L); lymphocytes, $70\%$; mildly elevated proteins, 1.14 g/L (normal: 0.15-0.45 g/L); and glucose, 3.2 mmol/L (range: 2.5-4 mmol/L). Anti-GAD65 Abs were positive in the serum (1:10), confirmed with radioimmunoassay with >2,000 U/ml, and in the cerebrospinal fluid (1:10). The patient received treatment with IVIg 25 g/day for 5 days and methylprednisolone 500 mg per day for 5 days followed by oral prednisone with progressively decreasing doses. The patient achieved an mRS score of 0 at the 10-month follow-up, after the onset of symptoms.
## Discussion
In this study, we reported three patients with clinical–radiological findings consistent with meningoencephalitis. All three were male patients, despite the higher frequency of GAD65 autoimmunity in women [4]. Reports of meningeal involvement defined as contrast enhancement in the leptomeninges in patients with GAD65 autoimmunity are very scarce. A 44-year- old female patient has been reported under the diagnosis of “meningoencephalitis” associated with GAD65 Abs, 1 month after receiving remdesivir for COVID-19 infection (Table 1). The CT scan showed hydrocephalus and some meningeal enhancement; however, it is unclear whether the findings were related to COVID-19 infection [17]. On the other hand, Triplett and colleagues reported on a 21-year-old female patient with seizures and progressively decreased level of consciousness with MR showing leptomeningeal enhancement aside from prominent hyperintense lesions involving both frontal lobes [18]. The patient showed clinical worsening despite the use of first-line immunosuppressive drugs but responded to cyclophosphamide and rituximab [18].
The question is whether our patients actually had a neurological disorder secondary to GAD65 Abs, as they had a relatively low titer of Abs. Case 3 had >2,000 U/ml in the serum by RIA. This is consistent with GAD65 Abs titers that are considered high according to Saiz and colleagues [13]. It is possible that GAD65 Abs 1:10 reported in commercially available cell-based assay are consistent with titers over 2,000 U/ml by RIA. Low titers of GAD65 Abs have been detected in up to $1\%$ of the general population and $5\%$ with various neurological disorders [19]; therefore, these Abs may coincide and be confused as the cause of a specific neurological disorder. For example, patients with some forms of viral meningitis (i.e., related to the enterovirus) or rheumatic disorders may have meningeal enhancement with a benign clinical course and response to corticosteroids. The suspected autoimmune basis in our patients is supported by the lack of identification of specific infectious agents, despite extensive evaluation, the absence of recent history of poisoning or drug consumption, and no identification of metabolic or rheumatic diseases in the follow-up. Moreover, there is emerging evidence that low titers of GAD65 Abs may be related to some neurological syndromes, such as cerebellar ataxia [3].
The pathological role of GAD65 Abs has been questioned, mostly as the target antigen is intracellular. In contrast, the role of cellular autoimmunity has been highlighted. There are few pathological reports in patients with GAD65 autoimmunity showing microglial proliferation and mild infiltration of CD8+ cytotoxic T cells in the anterior horn cells of the spinal cord in a patient with stiff-person syndrome [20]. On the other hand, in a pathological study of three cases with limbic encephalitis related to GAD65 encephalitis, parenchymal infiltration of CD3+ T cells was low but higher than that of the controls, with intermediate infiltration of CD8+ T cells between patients with autoimmunity related to onconeural and surface antigen Abs [21]. Apposition of GrB+ lymphocytes to single neurons and CD107a, a lysosomal-associated membrane protein-1, both markers of cytotoxic cell attack, were identified in a single patient with limbic encephalitis associated with GAD Abs [21]. These findings were coupled with the loss of neural tissue but glial preservation, suggesting that a cytotoxic rather than a humoral response underlies the pathogenesis in these patients. Moreover, the ratio of CD8/CD3 in the perivascular space of blood vessels is lower than in the parenchyma, supporting the CD8+ T-cell migration into the brain parenchyma [21]. We speculate that a local cellular inflammatory response in the leptomeninges may occur disrupting the blood–brain barrier leading to contrast enhancement. This is supported by the presence of CD3+ T cells in the leptomeninges in a patient with GAD65 meningoencephalitis [18].
Meningeal enhancement has been found in patients with anti-NMDAR encephalitis, which is the prototype of autoimmune encephalitis. In these patients, an abnormal MRI is observed in less than $50\%$ of cases [11]. However, among patients with abnormal MRIs, medial temporal and frontal hyperintensities in T2W and FLAIR sequences along with leptomeningeal enhancement are the most common findings [11]. Evidence from cytokine dynamics suggests that patients with anti-NMDAR encephalitis have an early chemoattractant immune response despite the paucity of cellular T- and B-cell response identified in the brain parenchyma in most of these patients [22]. Minimal inflammatory infiltrates in the leptomeninges have been identified in patients with anti-NMDAR encephalitis [23]. It is unclear why patients with GAD65 encephalitis uncommonly present with meningeal enhancement. Similar to anti-NMDAR encephalitis, patients with GAD65 encephalitis may show a paucity of inflammatory T-cell infiltrates in the leptomeninges that appears during a specific time window during the evolution; however, further studies are required to confirm these findings.
None of our patients had positive anti-GFAP Abs; patients with autoimmunity associated with these Abs have a median age at onset of 44 years and are most commonly women ($54\%$) [24]. Perivascular radial enhancement perpendicular to the ventricles is one characteristic, and it has been detected in about half of the cases in some series; meningeal enhancement is also a distinctive feature [25]. Patients with anti-GFAP Abs usually have a high number of cells in the CSF (>50 × 106/L), which contrasts with the paucity of inflammatory response found in the CSF of patients with GAD65 autoimmunity. Moreover, a third of cases may have an underlying neoplasm [24], contrasting with the apparent low occurrence of cancer in patients with GAD65 autoimmunity.
The main limitation of our study is the lack of pathological specimens in order to assess the presence of lymphocyte infiltration in the meninges. However, justification of leptomeningeal biopsy may be questionable in a patient with aseptic meningitis showing rapid recovery with anti-inflammatory therapies. In this regard, the identification of CSF cytokines can be helpful in understanding the immunological dynamics of T and B cells, which may yield the type of immunological response in these patients.
## Conclusion
In summary, a clinical picture characterized by the presence of signs of encephalitis combined with meningeal enhancement in the brain MRI but lack of fever and/or signs of meningeal irritation may be observed in patients with otherwise positive GAD65 Abs. These patients showed a rapid response to immunotherapy with a favorable outcome. Further studies should clarify the role of GAD65 autoimmunity in meningeal inflammation.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.
## Ethics statement
The studies involving human participants were reviewed and approved by the Guangdong999 Brain Hospital Institute Review Board. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. Written informed consent was not obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
## Author contributions
YC, JB-C, ZK, ZW, and YL designed the study and drafted the manuscript. All authors reviewed and approved the final manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'A highly predictive cardiac positron
emission tomography (PET) risk score for 90-day and one-year major adverse cardiac
events and revascularization'
authors:
- Raymond O. McCubrey
- Steve M. Mason
- Viet T. Le
- Daniel L. Bride
- Benjamin D. Horne
- Kent G. Meredith
- Nishant K. Sekaran
- Jeffrey L. Anderson
- Kirk U. Knowlton
- David B. Min
- Stacey Knight
journal: Journal of Nuclear Cardiology
year: 2022
pmcid: PMC10035554
doi: 10.1007/s12350-022-03028-y
license: CC BY 4.0
---
# A highly predictive cardiac positron
emission tomography (PET) risk score for 90-day and one-year major adverse cardiac
events and revascularization
## Abstract
### Background
With the increase in cardiac PET/CT availability and utilization, the development of a PET/CT-based major adverse cardiovascular events, including death, myocardial infarction (MI), and revascularization (MACE-Revasc) risk assessment score is needed. Here we develop a highly predictive PET/CT-based risk score for 90-day and one-year MACE-Revasc.
### Methods and results
11,552 patients had a PET/CT from 2015 to 2017 and were studied for the training and development set. PET/CT from 2018 was used to validate the derived scores ($$n = 5049$$). Patients were on average 65 years old, half were male, and a quarter had a prior MI or revascularization. Baseline characteristics and PET/CT results were used to derive the MACE-Revasc risk models, resulting in models with 5 and 8 weighted factors. The PET/CT 90-day MACE-Revasc risk score trended toward outperforming ischemic burden alone [$$P \leq .07$$ with an area under the curve (AUC) 0.85 vs 0.83]. The PET/CT one-year MACE-Revasc score was better than the use of ischemic burden alone ($P \leq .0001$, AUC 0.80 vs 0.76). Both PET/CT MACE-Revasc risk scores outperformed risk prediction by cardiologists.
### Conclusion
The derived PET/CT 90-day and one-year MACE-Revasc risk scores were highly predictive and outperformed ischemic burden and cardiologist assessment. These scores are easy to calculate, lending to straightforward clinical implementation and should be further tested for clinical usefulness.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s12350-022-03028-y.
## Introduction
Annually approximately 3.8 million patients undergo cardiac stress testing in the USA.1 As high as $15\%$ of these patients will have a false-negative result and $2.4\%$ of these misdiagnosed patients have a subsequent major adverse cardiac event (MACE).2 Cardiac PET/CT (positron emission tomography/computed tomography) may reduce these misdiagnoses by providing higher image quality.3–10 While PET has been around for decades, clinical use of cardiac PET/CT has been relatively limited. However, new radiopharmaceuticals and changes in reimbursement have contributed to the recent rapid growth of PET/CT.11,12 Like other imaging modalities, the comprehensive interpretation of PET/CT scans has a steep learning curve, especially in more complex or ambiguous cases.13–16 *There is* also inherent variability in the interpretation of cardiac images and cardiac risk assessment.17–21 However, developing a risk assessment score for MACE and revascularization (MACE-Revasc) using the large amount of data gathered (e.g., ischemic burden and myocardial blood flow) by PET/CT could help minimize the learning curve and reduce inter-operator variability in risk assessment.
The purpose of this study was to develop a PET/CT-based risk assessment score for 90-day and one-year MACE-Revasc outcomes. This was done using data from Intermountain Medical Center, which switched to a PET/CT-centric myocardial perfusion imaging center in 2013 and conducts about 4000-5000 cardiac PET/CT scans annually.3,22 Therefore, for the development of a PET-based risk assessment, we were able to have large training, development, and test data sets containing common, standard clinical PET/CT elements. A key focus in the development of the risk assessment score was to ensure that it was useful clinically. Consequently, the developed numeric score was based on assigned weights for categorical values of common clinical and PET/CT results. We compared the developed risk score to ischemic burden and the interpreting cardiologist assessment of risk as documented in the official clinical interpretation of the studies.
## Methods
This study was approved by the Intermountain Healthcare Institutional Review Board with a waiver of consent. Investigations were performed in accordance with the Declaration of Helsinki.
## Study population
All unique patients that completed a PET/CT study from January 1, 2015 to December 31, 2017 at Intermountain Medical Center were used for developing the risk score ($$n = 11$$,552). Intermountain Medical *Center is* the major referral hospital for Intermountain Healthcare, an integrated healthcare system of 24 hospitals and over 215 clinics. This study was restricted to 2015 and after due to the lack of electronic capture and coding of transient ischemic dilation (TID) before this period. This study population was split 70:30 into training ($$n = 7996$$) and development ($$n = 3526$$) sets.
A separate test set was used to assess the accuracy of the generated PET-based risk score algorithm. This test set contained all patients that had a clinically indicated PET/CT study from January 1, 2018 to December 31, 2018 at Intermountain Medical Center ($$n = 5049$$).
## PET/ CT myocardial perfusion imaging
PET/CT imaging was performed on a Siemens Biograph (LSO crystal, 3-dimensional list modes, with 16 slice CT) camera with rubidium-82 chloride (Rb-82). Weight-based Rb-82 dosage varied from 20 to 40 mCi for both rest and stress images. Rest and stress low-dose CT topograms and attenuation correction images are obtained with all PET scans for anatomic alignment of the PET images and for calibration of PET/CT data, respectively. Pharmacologic stress was achieved in all patients with regadenoson. Both gated rest and stress images were acquired and iteratively reconstructed using the manufacturer-recommended protocol. PET/CT images were analyzed using commercially available software packages (syngo. VIA, Siemens Healthineers, Malvern PA, and Corridor4DM, Invia Medical Imaging Solutions, Ann Arbor MI). A complete description of the PET/CT acquisition parameters is provided in the Supplementary File. The reading and interpretation of the PET/CT studies during the study timeframe were performed by cardiologists who were board certified in nuclear cardiology.
## Clinical and PET imaging data
The prior clinical diagnoses were based on a combination of patient self-report at the time of PET/CT and diagnosis coding in Intermountain Healthcare's electronic medical record. Smoking status was based on patient self-report at the time of PET/CT. As formal coronary artery calcification (CAC) quantified scoring was not done routinely on these patients, the presence or absence of CAC was determined using the cardiologist’s report of CAC present based on the low-dose attenuation correction CT images. The estimation of CAC using low-dose attenuation correction CT has been shown to correlate well with the estimation of CAC scores obtained as part of the standard Agatston score calculation.23 The mean global myocardial coronary flow reserve was used as the overall assessment of myocardial blood flow. TID was determined using syngo.via. Change in ejection fraction (EF) from rest to stress periods was calculated. The ischemic burden was determined using the difference between the summed rest and summed stress scores from 17 segments and then dividing this by 68.24 As part of the electronic generated PET/CT report, the risk of a short-term ischemic event (low, moderate, or high) was recorded by the reading cardiologist. While the cardiologist would make this assessment using the measured PET/CT parameters and clinical characteristics, there was no set algorithm or protocol for determining these risk levels. Therefore, the cardiologist’s risk assessment was subjective.
## Endpoints
Major cardiovascular events of all-cause death, myocardial infarction (MI), and revascularization were studied for both 90-day and one-year periods (MACE-Revasc). All-cause death was determined using hospital discharge status and death certificate records from the state of Utah. The cause of death was not available as part of these reports. Intermountain Healthcare’s hospital diagnoses and elevation in troponin were used to determine subsequent MI. Revascularization (percutaneous coronary intervention and/or coronary artery bypass grafting) was based on hospital procedure billings and coronary catheterization reports. We choose to include revascularization in our MACE-Revasc model despite this outcome being driven by the physician’s interpretation and is not a hard event. We realize the outcome could be biased because of this. However, this bias will be present for the other two risk assessments used for comparisons (i.e., ischemic burden and physician risk prediction). Furthermore, a major purpose of a stress test is to determine those individuals with ischemia and for whom revascularization is needed. Therefore, the inclusion of revascularization in the outcome was deemed to be important. We did, however, conduct a sensitivity analysis in which we examined our derived risk scores prediction power for MACE without revascularization (i.e., death and non-fatal MI).
## Development of risk score
The basic flow of the score development is outlined in Fig. 1. The study population was split 70:30 into a training and development set (dev. set). Continuous factors were categorized using existing and commonly used thresholds for change in EF (< $3\%$, $3\%$-$4\%$, ≥ $5\%$) and ischemic burden (< $5\%$, $5\%$-$10\%$, > $10\%$). The cut-offs for coronary flow reserve (CFR) are not established and have been suggested to be “generally arbitrary and may vary slightly between labs, software used, stressors used, and published studies. ”25 Based on our experience with the use of regadenoson, we chose a cut-off of > 2.3 for normal, 1.5-2.3 as abnormal, and < 1.5 as highly abnormal. Given that regadenoson has been shown to achieve only $80\%$ of dipyridamole stress perfusion,26 these values would correspond to CFR of about > 1.8, 1.8-1.2, and < 1.2 when using dipyridamole. These cut-offs would be very similar to the cut-offs for normal (or mildly abnormal), abnormal, and highly abnormal as suggested by others.25 Based on our prior research experience with TID for regadenoson, the thresholds used for TID were ≤ 1.0 for normal, 1.01-1.10 for abnormal, and ≥ 1.10 for highly abnormal.27Figure 1PET/CT Risk Score Development Diagram While less than $8\%$ of the data were missing for any given factor (see supplementary table 1), we examined the impact of missing values by adding a category of “missing” to the factor. The prediction for the missing values was similar to the reference category for all the factors. This indicated that “missing” would not impact the final scoring system and thus, no imputation of the missing values was done.
Using the training set, we determined the demographic, clinical, and PET result factors that were univariately significantly ($P \leq .05$) associated with the outcomes. For those with a significant association, we checked for multicollinearity and pairwise correlation. A meaningful pairwise correlation was defined as a statistically significant correlation above ≥ 0.30. When detected, the factor with the largest AUC value for the outcome was kept. In the case that a factor was correlated with multiple other factors and did not have the largest AUC for all comparisons, it was eliminated in favor of keeping the model parsimonious (see Supplementary Table S2 for factor selection details). Clinically plausible two-way interactions from the PET/CT results, including ischemic burden by transient ischemic dilation and ischemic burden by ejection fraction, were also considered in the model building process. These uncorrelated factors and interactions were included in a backward selection logistic regression model with selection based on AIC (Akaike Information Criterion). From this analysis, several factors including the interactions were eliminated. Once the final model was determined, we used the beta coefficients for significant factors and the ratios of these to generate the weighted scores for each factor in the final models (see supplementary tables S3a and S3b for the final logistic models and beta coefficients).
To qualify the risk associated with a score, we have determined thresholds for low, moderate, and high risk for the 90-day and one-year MACE-Revasc risk. The threshold between high and moderate risk was set where the specificity of the score is ≥ $90\%$, and the threshold between moderate and low risk was set where the sensitivity is ≥ $90\%$. This approach tends to provide an appropriate distribution in all three categories and has been used previously in setting thresholds for similarly derived risk scores.28 Finally, we determined the Brier score29 for our derived risk scores using the training set and the predictive probability for our derived score values (based on logistic regression). The Brier score ranges from 0 to 1, with 0 indicating a perfect model prediction and 1 indicating no predictive value of the model. Thus, lower Brier scores indicate more accurate predictions.
## Statistical comparisons
Our derived PET/CT risk scores were compared to both the ischemic burden and the cardiologist’s reported risk for the prediction of 90-day MACE-Revasc and one-year MACE-Revasc. Receiver operating characteristic (ROC) curves were generated for the training, development, and test sets, and the areas under the curve (AUC) for the continuous score values compared to the continuous ischemic burden values. The significance of this comparison was done using the DeLong test. Additional continuous comparisons with CRF and summed stress scores were done. Finally, the cardiologist reading the PET/CT-reported risk (low, moderate, and high) at the time of the scan was compared to the derived scores categorized as low, moderate, and high, using the method described above. Net classifications for events and non-events were calculated for this comparison using low/moderate risk compared to high risk. We used bootstrapping to determine the $95\%$ confidence interval for these reclassifications. All models and testing were done using R (version 4.0.3) and the non-standard packages used included pROC (for AUC calculations) and rap (for NRI confidence intervals).
## Study population characteristics and MACE-Revasc outcomes
The patient and clinical characteristics for the three study populations are shown in Table 1. *In* general, the PET/CT patients were on average 65 years old, just over half were male, many had risk factors for coronary artery disease, and over a quarter had a prior history of MI or revascularization. While there existed statistically significant differences between the sets, the largest and clinically significant difference was in the decrease in a history of coronary artery disease for the test set compared to the other two sets ($57\%$ vs $77\%$). The PET/CT results for the three groups are shown in Table 2. For all three sets, about $10\%$ of the patients had an ischemic burden > $10\%$ and about $5\%$ were at high risk based on the cardiologist’s assessment. There was a slight decrease in the percentage of patients without CAC in the test set compared to the other two sets ($25\%$ vs $31\%$).Table 1Patient and clinical characteristics for study population setsTrainingDevelopmentTestP-valuea799635265049Age categories.12 < 50931 ($11.6\%$)403 ($11.4\%$)535 ($10.6\%$) 50–591592 ($19.9\%$)722 ($20.5\%$)955 ($18.9\%$) 60–692431 ($30.4\%$)1013 ($28.7\%$)1593 ($31.6\%$) 70–792036 ($25.5\%$)932 ($26.4\%$)1310 ($25.9\%$) 80+1006 ($12.6\%$)456 ($12.9\%$)656 ($13\%$)Gender.02 Male4452 ($55.7\%$)1906 ($54.1\%$)2884 ($57.1\%$) Female3544 ($44.3\%$)1620 ($45.9\%$)2165 ($42.9\%$)Race.008 White7434 ($93.0\%$)3274 ($92.9\%$)4607 ($91.2\%$) Non-White562 ($7.0\%$)252 ($7.1\%$)442 ($8.8\%$)BMI categories.33 Underweight (< 18.5)86 ($1.1\%$)45 ($1.3\%$)54 ($1.1\%$) Normal (18.5–25)1516 ($19\%$)605 ($17.2\%$)932 ($18.5\%$) Overweight (25–30)2474 ($30.9\%$)1107 ($31.4\%$)1601 ($31.7\%$) Obese (30+)3920 ($49\%$)1769 ($50.2\%$)2462 ($48.8\%$)Patient type <.0001 Emergency or outpatient6279 ($78.5\%$)2795 ($79.3\%$)4265 ($84.5\%$) Inpatient1717 ($21.5\%$)731 ($20.7\%$)784 ($15.5\%$)Smoking history.0001 Never4905 ($61.3\%$)2220 ($63.0\%$)3094 ($61.3\%$) Former2096 ($26.2\%$)907 ($25.7\%$)1304 ($25.8\%$) Current659 ($8.2\%$)230 ($6.5\%$)363 ($7.2\%$) Unknown336 ($4.2\%$)169 ($4.8\%$)288 ($5.7\%$)Diabetes3342 ($41.8\%$)1544 ($43.8\%$)2087 ($41.3\%$).06Hypertension6710 ($83.9\%$)2996 ($85\%$)4145 ($82.1\%$).0010Hyperlipidemia6269 ($78.4\%$)2801 ($79.4\%$)3841 ($76.1\%$).0004Prior MI1390 ($17.4\%$)674 ($19.1\%$)830 ($16.4\%$).01History of CAD6117 ($76.5\%$)2730 ($77.4\%$)2852 ($56.5\%$) <.0001Prior Revascularization2299 ($28.8\%$)1108 ($31.4\%$)1387 ($27.5\%$).0003BMI, body mass index; MI, myocardial infarction; CAD, coronary artery diseaseaP values are based on chi-square (categorical variables) and ANOVA (continuous variables) testsTable 2PET/CT results for the study populationsTrainingDevelopmentTestP-value799635265049CAC <.0001 Absent2535 ($31.7\%$)1080 ($30.6\%$)1279 ($25.3\%$) Present5461 ($68.3\%$)2446 ($69.4\%$)3770 ($74.7\%$)Change in EF <.0001 ≥ $5\%$4699 ($58.8\%$)2114 ($60.0\%$)3290 ($65.2\%$) 3–$4\%$1321 ($16.5\%$)515 ($14.6\%$)752 ($14.9\%$) < $3\%$1976 ($24.7\%$)897 ($25.4\%$)1007 ($19.9\%$)CFR <.0001 > 2.33231 ($40.4\%$)1389 ($39.4\%$)2179 ($43.2\%$) 1.5–2.33338 ($41.7\%$)1515 ($43.0\%$)2120 ($42.0\%$) < 1.51427 ($17.8\%$)622 ($17.6\%$)750 ($14.9\%$)TID <.0001 ≤ 1.04084 ($51.1\%$)1772 ($50.3\%$)2769 ($54.8\%$) 1.0–1.102212 ($27.7\%$)1017 ($28.8\%$)1564 ($31.0\%$) > 1.101700 ($21.3\%$)737 ($20.9\%$)716 ($14.2\%$)Ischemic burden.85 < 56586 ($82.4\%$)2894 ($82.1\%$)4178 ($82.7\%$) 5–10621 ($7.8\%$)287 ($8.1\%$)398 ($7.9\%$) > 10789 ($9.9\%$)345 ($9.8\%$)473 ($9.4\%$)Cardiologist conclusions.39 Low risk6567 ($82.1\%$)2887 ($81.9\%$)4198 ($83.1\%$) Moderate risk982 ($12.3\%$)431 ($12.2\%$)596 ($11.8\%$) High risk447 ($5.6\%$)208 ($5.9\%$)255 ($5.1\%$)CAC, coronary artery calcium; EF, ejection fraction; CFR, coronary flow reserve; TID, transient ischemic dilation The 90-day and one-year MACE-Revasc outcomes for the study populations are shown in Table 3. The rate of 90-day MACE-Revasc was about $6\%$ for the sets, most of this driven by revascularization within 90 days. The one-year MACE-Revasc ranged from $13\%$ to $16\%$, and most of these were revascularizations, followed by myocardial infarctions and deaths. Table 3MACE-Revasc outcomes for the study population setsTrainingDevelopmentTestP-value79963526504990-day outcomes MACE-Revasc510 ($6.4\%$)206 ($5.8\%$)367 ($7.3\%$).02 Death105 ($1.3\%$)45 ($1.3\%$)53 ($1\%$).39 MI154 ($1.9\%$)52 ($1.5\%$)76 ($1.5\%$).10 Revascularization292 ($3.7\%$)127 ($3.6\%$)267 ($5.3\%$) <.0001One-year outcomes MACE-Revasc1262 ($15.8\%$)532 ($15.1\%$)640 ($12.7\%$) <.0001 Death217 ($2.7\%$)77 ($2.2\%$)108 ($2.1\%$).07 MI604 ($7.6\%$)247 ($7\%$)205 ($4.1\%$) <.0001 Revascularization594 ($7.4\%$)278 ($7.9\%$)378 ($7.5\%$).68MI, myocardial infarction; MACE-*Revasc is* a composite of death, MI, and revascularization
## PET/CT 90-day MACE-Revasc risk score
Based on the logistic regression factor selection and modeling, the PET/CT 90-day MACE-Revasc risk score and factor weights are shown in Table 4. No interactions made it into the final score due to simpler models having better AIC during the model selection process. Ischemic burden was the major contributor to the developed PET/CT 90-day MACE-Revasc risk score. Therefore, the overall prediction for the PET/CT 90-day MACE-Revasc risk score was only slightly better than the use of ischemic burden ($$P \leq .01$$ development set/$$P \leq .07$$ test set); the area under the curve for the PET/CT 90-day MACE-Revasc risk score was 0.85 for both the development and test sets compared to 0.82 and 0.83, respectively, for ischemic burden alone (Fig. 2a). The summed stress scores had a similar AUC (test set AUC 0.83) and the CFR had a significantly lower AUC (test set AUC 0.83) compared to the PET/CT 90-day MACE-Revasc risk score (Supplementary Table S4). The Brier score for the PET/CT 90-day MACE-Revasc risk score was 0.14 and 0.15 for the development and the test sets, respectively. Table 4PET/CT Risk Score for 90-Day MACE-RevascFactorScoreIschemic burden > 106Ischemic burden 5–104CAC present3CFR < 1.51TID > 1.11Inpatient1Summed values range from 0 to 12, with values 0-3 = low risk, 4-7 = moderate, 8-12 = high riskCAC, coronary artery calcium; CFR, coronary flow reserve; TID, transient ischemic dilationFigure 290-day (a) and One-Year (b) MACE-Revasc PET/CT Risk Score vs Ischemic Burden Receiver Operating Characteristic Curve (ROC) for the development (dev) and test sets. AUC, area under the curve; ROC, receiver operating characteristic curve; Dev, development The PET/CT 90-day MACE-Revasc risk score values and the percentage of MACE-Revasc events in the test set are shown in Fig. 3. Low risk was classified as a PET/CT 90-day MACE-Revasc risk score < 4, 4-7 as moderate risk, and > 7 as high risk. Using the test data, the rates of MACE-Revasc for these three groups were $1.0\%$, $1.9\%$, and $4.3\%$, respectively. The comparison in prediction for the PET/CT 90-day MACE-Revasc risk score to the cardiologist’s assessment of risk is shown in Fig. 4a. The net-reclassification index, using low/moderate risk versus high risk, was $24\%$ ($95\%$ CI $19\%$, $30\%$) and indicated a significant improvement provided using the derived PET/CT 90-Day MACE-Revasc risk score prediction (Supplementary Table S5). In patients with 90-day events ($$n = 367$$), this reclassification would result in 120 ($33\%$) having a higher PET/CT risk score compared to the cardiologist. However, in patients without events ($$n = 4682$$), the reclassification resulted in the incorrect increase in risk for 278 ($6\%$) compared to the cardiologist. Figure 3PET/CT 90-day MACE-Revasc Risk Score (bars) and Percent of 90-day MACE-Revasc Outcomes (line) in the Test set (a). Also, PET/CT One-Year MACE-Revasc Risk Score (bars) and Percent of One-Year MACE-Revasc Outcomes (line) in the Test set (b)Figure 4Prediction for the PET/CT 90-day MACE-Revasc Risk Score (a) & PET/CT One-Year MACE-Revasc Risk Score (b) to the cardiologist’s assessment of risk
## PET/CT one-year MACE-Revasc risk score
Based on the logistic regression factor selection and modeling, Table 5 shows the PET/CT one-year MACE-Revasc risk score. No interactions made it into the final score due to simpler models having better AIC during the model selection process. Ischemic burden and CAC presence were major contributors to the developed PET/CT one-year MACE-Revasc risk score. Other factors such as coronary flow reserve, smoking, and inpatient status were significant contributors to increased risk and obesity had a protective effect. The overall prediction for the PET/CT one-year MACE-Revasc risk score was better than the use of ischemic burden alone ($P \leq .0001$ dev and $P \leq .0001$ test); the area under the curve for the one-year PET/CT risk score was 0.76 for the development set and 0.80 for the test set, compared to 0.69 and 0.76 for ischemic burden alone, respectively (Fig. 2b). The summed stress scores (test set AUC 0.78) and the CFR (test set AUC 0.69) had significantly lower AUCs compared to the PET/CT one-year MACE-Revasc risk score (Supplementary Table S4). The Brier score for the PET/CT one-year MACE-Revasc score was 0.21 and 0.20 for the development set and the test set, respectively. Table 5PET/CT risk score for one-year MACE-RevascFactorScoreIschemic burden > 106Ischemic burden 5–104CAC present5CFR < 1.53CFR 1.5–2.31TID > 11Current smoker2Inpatient2Diabetic1Obese (30 + BMI)− 2Summed value range from − 2 to 20, with values – 2 to 5 = low risk, 6-10 = moderate, 11-20 = high riskBMI, body mass index; CAC, coronary artery calcium; CFR, coronary flow reserve; TID, transient ischemic dilation The PET/CT one-year MACE-Revasc risk score values and the percentage of MACE-Revasc events in the test set are shown in Fig. 3b. Low risk was classified as a one-year MACE-Revasc score < 6, 6-11 as moderate risk, and ≥ 11 as high risk. Using the test data, the rates of MACE-Revasc for these three groups were $1.9\%$, $4.7\%$, and $6.1\%$, respectively. The comparison in prediction for the PET/CT one-year MACE-Revasc risk score to the cardiologist assessment of risk is shown in Fig. 4b. The net-reclassification index was $20\%$ ($95\%$ CI $16\%$, $25\%$) and indicated a significant improvement provided using the score risk prediction (Supplementary Table S5). In patients with one-year events ($$n = 640$$), this reclassification would result in 181 ($28\%$) having a higher risk score compared to the cardiologist. However, in patients without events ($$n = 4409$$), the reclassification resulted in the incorrect increase in risk for 291 ($7\%$) compared to the cardiologist.
## PET/CT risk scores for prediction of MACE without
revascularization
Using the test set, we applied the PET/CT 90-day and one-year risk scores to predict MACE (death and MI) without revascularization. Compared to the MACE-Revasc AUC, the MACE without revascularization included had decreased AUC values for 90-day and one-year 0.72 and 0.74, respectively (Supplementary Table S6). These AUC values were still larger than those associated with the ischemic burden (AUC 0.67) and CFR (AUC 0.67) for the 90-day MACE and the ischemic burden (AUC 0.65), CFR (AUC 0.69), and summed stress (AUC 0.69) for the one-year MACE (Supplementary Table S6). The PET/CT risk score had a significantly larger AUC ($P \leq .05$) for the 90-day MACE compared to ischemic burden and CFR and for the one-year MACE compared to ischemic burden, summed stress, and CFR.
## Discussion
We have derived a PET/CT 90-day MACE-Revasc risk score and a PET/CT one-year MACE-Revasc risk score that are highly predictive of events. These scores had statistically significant, although moderate, improvement over ischemic burden alone and the cardiologist’s assessment of risk. Both contained less than 10 factors and were based on summing integer values. Thus, these risk scores allow for easy implementation into practice.
Both the PET/CT 90-day and one-year MACE-Revasc risk scores were highly predictive of events with an accuracy measured by the area under the ROC curve of 0.85 and 0.80, respectively. While there have been limited numbers of risk scores built for populations undergoing PET/CT evaluation, our risk scores do perform better or similar than risk scores developed for stress testing patients and patients undergoing evaluation for chest pain. The Duke Treadmill Score has a similar accuracy (AUC 0.85 for 4-year death) to our PET/CT MACE-Revasc risk scores.30 However, the Duke Treadmill Score was created for patients without known coronary artery disease and our score comprises all patients being evaluated for coronary artery disease by PET/CT. In a recent study of risk scores in Emergency Department patients with chest pain, the HEART Score had the highest accuracy (AUC 0.77), followed by the TIMI risk score (0.73), GRACE (0.61), and EDACS (0.63).31 Our PET/CT risk scores appear to have greater predictive ability than these, but further evaluation of our score in different populations is needed.
The assessment of MACE risk from a PET/CT scan has routinely been based on ischemic burden.32 In clinical guidelines, it has been suggested that increases in ischemic burden have led to the re-evaluation of the medical regimen or the interventional plan.33 We did compare our risk score to ischemic burden and found a marginal, statistically significant difference for 90-day and a greater difference for our one-year MACE-Revasc risk assessment. As ischemic burden is the highest weighted factor in our risk scores, a marginal increase in 90-day risk is expected. However, the addition of other factors into our risk scores does improve the prediction, particularly for one-year outcomes. This is particularly evident in in the test set. This set had different clinical characteristics than the development set and while the ischemic burden distribution remained similar, the distributions of other PET results were different. Thus, because our risk score incorporates other factors it outperformed the ischemic burden in the test set.
The second most important factor in our risk scores was CAC, particularly in the one-year risk score, where CAC was almost as predictive as an ischemic burden. CAC has been shown to associate with MACE events.34–36 When added to existing risk scores, including ASCVD, CACS, and MESA, it has also been shown to improve risk prediction for MACE and revascularization.37 Therefore, CAC being the second most important factor in the risk scores is not surprising and does not require additional clinical testing as it is already incorporated into a cardiac PET/CT.
Prior machine learning studies have found that the use of other factors related to functional and perfusion data, besides ischemic burden, from cardiac myocardial perfusion imaging scans, increased predictive accuracy for MACE.38,39 We also found that coronary flow reserve and TID played moderate roles in our risk scores. Coronary flow reserve is a surrogate of fractional flow reserve, which has been found to be helpful for driving intervention decision-making regarding the revascularization of stenotic lesions.40 Thus, adding coronary flow reserve in the model, we believe, increases the effectiveness of predicting the revascularization component of our outcome. In addition, increases in TID ratios have been shown to be associated with increased risk of death41 and revascularization.42 Finally, as we developed these risk scores for all PET patients, the use of a factor to indicate whether the patient was currently an inpatient, improved discrimination for both 90-day and one-year MACE-Revasc outcomes. This is most likely a good factor to indicate overall health status. Similarly, smoking and diabetes were good predictors for poor one-year outcomes. Perhaps counterintuitively, obesity provided some protection for one-year MACE, but this is most likely due to the well-known obesity paradox for cardiovascular diseases. Many studies have shown that increased body mass index puts one at risk for cardiovascular disease, but provides a better prognosis for disease.43 Our risk scores, which combine and weight all these factors, could help cardiologists when assessing risks. We have shown that when compared to a cardiologist’s, our risk scores lead to a net reclassification of nearly a quarter for 90-day and $20\%$ for one-year MACE-Revasc. This was mostly due to upward shifts in risks, which resulted in the risk score predicting more of the events. This is also reflected in the sensitivity of $59\%$ for our 90-day and $48\%$ for one-year events compared to $29\%$ and $21\%$, respectively, for the cardiologist assessment. This increase occurred with little impact on specificity. A major reason for these differences is that the cardiologist assessment is focused more on the risk of an ischemic event and less on all-cause mortality risk, which was included in our outcome. However, as the overall health risk for a patient is important, using these risk scores might flag additional concerns for the cardiologist and perhaps drive additional assessment, treatment, and care.
Risk scores have been used in cardiovascular patient settings to drive better patient outcomes. In a small pilot study of heart failure patients where patients were randomized to have daily prediction scores versus a group given standard treatment, the group with the daily risk scores had a significant decrease in 30-day mortality and an increase in home discharges.44 Similarly, a larger study of heart failure inpatients found that a risk score-guided multidisciplinary team-based care process decreased 30-day readmission and mortality.45 An advantage to the implementation of our risk scores into clinical practices is the simplicity of collection and calculation of the scores. Both scores are based on less than 10 factors, with integer weights that are summed. These scores should take less than a minute to calculate, once the PET/CT has been completed, and could be automated for even greater ease.
There are some limitations to our study. First, the development of the risk score was carried out using data from an observational study. Inherent limitations do exist with the data and the risk scores due to this.46 One of these limitations is the possibility of inaccurate or missed reporting of outcomes. While we did not adjudicate the queried outcomes for this study, we have examined these for prior similar studies and found no systematic bias in the reporting of these in our electronic system.
The outcomes are also limited in that the cause of death was not present in the data. Thus, separating cardiovascular causes of death from other causes was not possible. However, forty to sixty percent of all deaths in prior cardiovascular studies for cohorts with coronary artery disease have been found to be related to cardiovascular diseases.47–49 Since our samples have a large percentage with a coronary artery disease history, it is likely that the majority of deaths were cardiovascular related. Another limitation due to observational data is missing data points in the study. To address this, we carried out two types of risk score development, one was with the missing data removed and the other was with the missing data included. In the latter, missing data were given a category of its own in the dataset, and the results between the scores were similar. Accordingly, the development of the risk score proceeded with removing missing factors. Finally, while the use of the test data allowed for an independent set of data for validation of the developed PET risk score, it was pulled from Intermountain Healthcare and not a separate institution. Therefore, the performance in a different patient population is unknown and deserves further investigation.
## Conclusion
A PET/CT 90-day MACE-Revasc risk score and a PET/CT one-year MACE-Revasc risk score were generated that incorporate routinely collected PET/CT results combined with a minimum number of clinical features for simple calculation. These risk scores provide improved prediction over ischemic burden alone and improve the classification, compared to cardiologists, of high-risk patients. The use of these simple PET/CT MACE-Revasc risk scores in an external patient population should be examined as well as the determination of the value of their use in a clinical setting.
## New knowledge gained
The derived PET/CT MACE-Revasc risk scores outperformed ischemic burden alone and the predicted risk of cardiologists. This finding indicates that the combined use of PET/CT data in an easy to calculate risk score may improve clinical assessment and care.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 39 kb)
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|
---
title: Suppression of neuronal apoptosis and glial activation with modulation of Nrf2/HO-1
and NF-kB signaling by curcumin in streptozotocin-induced diabetic spinal cord central
neuropathy
authors:
- Hassan Reda Hassan Elsayed
- Mohammed R. Rabei
- Mohamed Mahmoud Abdelraheem Elshaer
- Eman Mohamad El Nashar
- Mansour Abdullah Alghamdi
- Zainah Al-Qahtani
- Ahmed Nabawy
journal: Frontiers in Neuroanatomy
year: 2023
pmcid: PMC10035597
doi: 10.3389/fnana.2023.1094301
license: CC BY 4.0
---
# Suppression of neuronal apoptosis and glial activation with modulation of Nrf2/HO-1 and NF-kB signaling by curcumin in streptozotocin-induced diabetic spinal cord central neuropathy
## Abstract
### Introduction
Diabetes is a global disease, commonly complicated by neuropathy. The spinal cord reacts to diabetes by neuronal apoptosis, microglial activation, and astrocytosis, with a disturbance in neuronal and glial Nuclear factor erythroid 2-related factor/Heme oxygenase-1 (Nrf2/HO-1) and Nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB) signaling. Curcumin, a bioactive natural substance, showed neuroprotective role in many diseases. However, its role in the treatment of the diabetic central neuropathy of spinal cord and the underlying mechanisms still need clarification. The present study tried to evaluate the role of curcumin in diabetes-induced central neuropathy of the spinal cord in rats.
### Methods
Twenty rats were divided into three groups; group 1: a negative control group; group 2: received streptozotocin (STZ) to induce type I diabetes, and group 3: received STZ + Curcumin (150 mg/kg/day) for eight weeks. The spinal cords were examined for histopathological changes, and immunohistochemical staining for Glia fibrillary acidic protein (GFAP); an astrocyte marker, Ionized calcium-binding adaptor molecule 1 (Iba1), a microglial marker, neuronal nuclear protein (NeuN); a neuronal marker, caspase-3; an apoptosis marker, Nrf2/HO-1, NF-kB, and oxidative stress markers were assessed.
### Results
Curcumin could improve spinal cord changes, suppress the expression of Iba1, GFAP, caspase-3, and NF-kB, and could increase the expression of NeuN and restore the Nrf2/HO-1 signaling.
### Discussion
Curcumin could suppress diabetic spinal cord central neuropathy, glial activation, and neuronal apoptosis with the regulation of Nrf2/HO-1 and NF-kB signaling.
## 1. Introduction
Today, there are 422 million diabetic patients. World Health Organization expects the number to become 700 million by 2045 (Saeedi et al., 2019). Peripheral, as well as, central neuropathies are common complications of diabetes. Treatment of diabetic neuropathy (DN) aims to provide control for blood glucose, manage pain, and suppress nerve damage. Currently, there is no efficient treatment for DN (Sloan et al., 2021).
Although many studies investigated the effect of diabetes on the peripheral nervous system (PNS) and trials to manage DPN, the diabetic-induced changes in the CNS, e.g., spinal cord, didn’t gain the same interest, and it must be studied to find new and effective treatments. Diabetes has been reported to cause microglial activation (Mandour et al., 2022; Wang et al., 2022), astrocytosis (Deng et al., 2017; Kiguchi et al., 2017), and neuronal apoptosis with up-regulation of BAX and Caspase-3 expressions (Mandour et al., 2022). Furthermore, diabetes causes up-regulation in the pro-inflammatory cytokine; Nuclear factor kappa light chain enhancer of activated B cells (NF-kB) (Mandour et al., 2022).
Nuclear factor erythroid 2-related factor (Nrf2)/Heme oxygenase-1 (HO-1) is a natural antioxidant cytoprotective system and a powerful modulator of longevity. This system can counteract oxidative stress, regulate apoptosis, modulate inflammation, and contribute to angiogenesis. When the cells face oxidative stress, Nrf2 translocates to nucleus, to regulate the genes transcription of anti-oxidant mediators (Lv et al., 2019). Diabetes causes a disturbance in Nrf2/HO-1 system (Pouso-Vazquez et al., 2022). Furthermore, the activation Nrf2 in neurons and/or neuroglia attenuated spinal cord ischemia-reperfusion injury through stimulating neuronal anti-oxidant and anti-apoptotic systems (Wang et al., 2017). Therefore, the oxidative stress and its subsequent neuronal apoptosis can be antagonized by activating Nrf2 pathway in damaged spinal cord.
A question was raised concerning whether modulating the spinal cord diabetic central neuropathy can affect directly the neuropathic pain. Zhang et al. could succeed in relieving diabetic neuropathic pain through reducing the spinal cord microglial activation (Zhang et al., 2018). In addition, Zhong et al. could alleviate diabetic neuropathic pain in rats by inhibiting spinal cord astrocyte activation (Zhong et al., 2018). Furthermore, Shayea et al. could control the diabetic neuropathic pain through the control of astrocyte activation and microglia-mediated inflammation (Shayea et al., 2020). Moreover, Basu et al. reviewed the successful role of modulation of spinal Nrf2/HO-1 system in controlling the peripheral neuropathic pain (Basu et al., 2022).
Curcumin, a primary bioactive substance in turmeric, has shown neuroprotective effects in a variety of diseases. Many studies reported the beneficial effect of Curcumin in diabetic peripheral neuropathy. It could attenuate neuropathic pain by inhibiting oxidative stress through suppression of NADPH oxidase, thus decreasing malondialdehyde (MDA) and increasing superoxide dismutase (SOD) activity (Zhao et al., 2014), through activation of the opioid system causing an antinociceptive effect (Banafshe et al., 2014), and through suppression of tumor necrosis factor (TNF) alpha expression (Daugherty et al., 2018). In addition, nano-curcumin supplementation could reduce depression and anxiety after diabetic neuropathy (Asadi et al., 2020). Furthermore, the action of curcumin in neuropathic pain may involve the pJNK pathway in the astrocytes and neurons of the dorsal root ganglia (DRG) (Park et al., 2021). Moreover, Curcumin could inhibit the apoptosis of Schwann cells (SCs) and could promote nerve growth factor (NGF) expression in sciatic nerves of diabetic peripheral neuropathy (DPN) rat model (Zhang et al., 2022). All the previous studies explored the role of Curcumin in diabetic peripheral neuropathy; however, few of these studies explored its role in diabetic central neuropathy.
Many studies reported the beneficial effect of curcumin on models of spinal cord injury (SCI) (Jin et al., 2021; Kahuripour et al., 2022). In addition, Curcumin could attenuate the hypoxia-induced white matter injury (Daverey and Agrawal, 2020). Furthermore, Curcumin could significantly reduce glial activation with down-regulation of spinal NF-kB and up-regulation of Nrf2 and HO-1 in Paclitaxel-treated rats with suppression of neuronal apoptosis (Yardim et al., 2021). However, the role of curcumin in the management of diabetes-induced spinal cord impairment still requires clarification.
The current study aimed for the first time to explored the role of curcumin against diabetes-induced central neuropathy in spinal cord, microglial activation, astrocytosis, neuronal apoptosis, and its role in the regulation of Nrf2/HO-1 and NF-kB signaling pathways.
## 2.1. Ethical statement
The study was designed following the Animals in Research: Reporting in Vivo Experiments (ARRIVE) standards and meeting the standards of Mansoura University Animal Care and Use Committee (MU-ACUC), Egypt (MED.R.22.09.2).
## 2.2. Animals
Twenty adult male Sprague-Dawley (SD) rats weighing 150–200 grams, 7–8 weeks in age, were used. SD rats were used as they are considered efficient models for studying Type I diabetes-induced spinal cord injury (Inam et al., 2019; Shayea et al., 2020) and males were chosen because they have a greater degree of diabetic neuropathy, as compared to females (Fan et al., 2018). The animals were allowed to acclimate before the start of the experiment. They were kept in stainless steel cages, in firm day/night cycles, under appropriate temperature, and humidity, and in aseptic conditions, with a free source of food and water.
## 2.3. Research design
The twenty animals were divided into three groups; Group 1 (vehicle control group; $$n = 6$$) received only $0.5\%$ carboxymethylcellulose (CMC; the solvent of curcumin) by oral gavage once per day for 8 weeks. Group 2 (Diabetic; $$n = 8$$); after 12 h of fasting overnight, rats received an intra-peritoneal injection of freshly prepared streptozotocin (STZ; Sigma Aldrich, St. Louis, MO, USA), at a dose of 55 mg/kg of body weight to induce Type I Diabetes. STZ was dissolved in 0.1 M citrate buffer (pH = 4.5). Blood glucose was detected using an Accu check blood glucose meter (Roche Diagnostic, Germany) three days after STZ injection. The rats were confirmed diabetic when fasting blood glucose was >250 mg/dl, for two consecutive days. The rats received CMC once per day for 8 weeks after induction of diabetes. Group 3 (Diabetic + curcumin group; $$n = 6$$) received STZ, as mentioned above, and after induction of type I diabetes, the rats received Curcumin (Acros organics product of the US), at a dose of 150 mg/kg/day (Varatharajalu et al., 2016; Zheng et al., 2017; Ghelani et al., 2019) by oral gavage for 8 weeks. Curcumin suspensions in $0.5\%$ carboxymethylcellulose were freshly prepared. Throughout the study, two rats from the diabetic group died. At the end of the study; 8 weeks after diabetes induction, the rats were subjected to sacrification through decapitation. Consequently, the spinal cords were removed, washed in saline, and dried. Figure 1 shows a graphical abstract for the study.
**FIGURE 1:** *A graphical abstract demonstrating the research design.*
## 2.4. Detection of serum blood glucose, and oxidative stress markers (MDA and GSH)
Blood was taken from the hearts of the rats. Serum was separated. Serum glucose was detected using an Accu check blood glucose meter (Roche Diagnostic, Germany). MDA and GSH were measured following the technique of Elsayed et al. ( 2021a,b).
## 2.5. Assessment of histopathological changes and histopathological scoring
The spinal cords of the rats were excised and parts of cervical and lumbar segments were fixed in formaldehyde ($10\%$) and then embedded in paraffin to evaluate the histopathological changes, then 7 μm thick sections were stained with hematoxylin and eosin (H&E) (Suvarna et al., 2018). Using the Olympus Light Microscope and SC100 camera, the dorsal horns of the cervical and lumbar segments were examined, as they are commonly affected by diabetic neuropathy and neuropathic pain. Semiquantitative histopathological scoring for the spinal cord changes was performed. Shrinkage of soma, neurons with piknotic nuclei, axon degeneration, inflammatory cell infiltrate, focal bleeding were evaluated and were graded as follows: 0, less than $5\%$; 1, 5–$33\%$; 2, 34–$66\%$; and 3, over $66\%$.
## 2.6. Immunohistochemical staining
Three μm thick sections of the spinal cord were processed for immunohistochemical staining using the immunoperoxidase method (Elhadidy et al., 2021; Elsayed et al., 2021a,2022). Concisely, the slides were deparaffinized and endogenous peroxidase was blocked. Hydrogen peroxide and $0.3\%$ methanol were added to the spinal cord sections for 10 min at room temperature. To stimulate antigen retrieval, the sections were consequently subjected to heating at 95°C for 10 min in 10 mM citrate buffer and then left for 1 h to cool. The slides were kept with primary antibodies for NeuN; a neuronal marker, Iba1; a microglial marker, GFAP; an astrocyte marker, caspase-3; an apoptosis marker, Nrf2/HO-1, and NF-kB inflammatory and oxidative stress markers, overnight at 4°C. Table 1 presents the details of the antibodies and their dilutions. Consequently, the slides were kept for 30 min with a mouse-rabbit polydetector (BSB 0268, Bioscience). For the reagent (no-primary antibody) control, Phosphate-buffered saline (PBS), was added as a substitute for the primary antibody. Lastly, the slides were washed, then dehydrated, and investigated with a light microscope (Ramos-Vara and Miller, 2014). Dark brown areas on a blue background, demonstrate positive staining. Antigen localization was mainly nuclear for NeuN, mainly cytoplasmic for GFAP, cytoplasmic, and nuclear for caspase-3, Iba1, Nrf2, HO-1, and NF-kB.
**TABLE 1**
| Name | Cat. number | Source and clonality | Dilution |
| --- | --- | --- | --- |
| NeuN | ABclonal A19086 | Rabbit monoclonal | 1/100 |
| Iba1 | ABclonal A19776 | Rabbit monoclonal | 1/100 |
| GFAP | Servicebio GB11096 | Rabbit polyclonal | 1/1000 |
| Caspase-3 | Servicebio GB11532 | Rabbit polyclonal | 1/500 |
| Nrf2 | ABclonal A11159 | Rabbit polyclonal | 1/100 |
| HO-1 | Santa Cruz sc-390991 | Mouse monoclonal | 1/200 |
| NF-kB | ABclonal A19653 | Rabbit monoclonal | 1/100 |
## 2.7. Morphometric analysis
This was performed utilizing the 1.52a version of ImageJ software (Schneider et al., 2012) and Fiji ImageJ software (Schindelin et al., 2012). The number of NeuN, Iba1, GFAP, Caspase-3, Nrf2, HO-1, and NF-kB immunopositive cells/high-power field (x400) was counted in spinal cord sections from rats of all groups.
## 2.8. Statistical analysis of immunohistochemical results
Data were analyzed utilizing IBM-SPSS software. After normality testing, normal quantitative data from the three study groups were compared using one-way ANOVA and Post-hoc Tukey test. The data that are not normally distributed were presented as median and interquartile range and a Kruskal–Wallis H test was used to compare them. The results were considered significant if the p-values < 0.050.
## 3.1. Effect of curcumin treatment on serum blood glucose, MDA, and GSH
Serum glucose, MDA, and GSH revealed significant differences between the studied groups (p: < 0.0005). The diabetic group revealed significantly higher serum glucose, and MDA levels, as well as, a significantly lower level of GSH compared to the control group. These findings were reversed by curcumin administration compared to the diabetic group. On the other hand, serum glucose, MDA, and GSH levels, still revealed a significant difference from the control group (Figure 2).
**FIGURE 2:** *(A) Serum glucose, (B) Serum MDA, and (C) Serum GSH, in the studied groups. Histograms show means ± standard errors (SE). Data are mentioned as mean ± SE, different letters = significant difference. P is significant if < 0.05. MDA, malondialdehyde; GSH, reduced glutathione.*
## 3.2. Effect of curcumin administration on diabetes-induced histopathological alteration and histopathological score in the dorsal horn of the cervical and lumbar segments of the spinal cord
The negative control group (Figures 3A, D) revealed medium-sized basophilic neuronal somas, and myelinated axons with their myelin sheaths, together with few glial cells; microglia, and astrocytes. Blood capillaries appear intervening. The diabetic group (Figures 3B, E) revealed shrunken neuronal somas with surrounding haloes and pyknotic nuclei, degenerated nerve axons, and myelin sheaths, together with multiple microglia, and astrocytes. Focal areas of bleeding are noticed. The Curcumin-treated group (Figures 3C, F), revealed relatively normal neuronal somas, with few shrunken neuronal somas. Relatively normal myelinated axons, and myelin sheaths with few degenerate axons, together with few glial cell nuclei; microglia (yellow arrows) and astrocytes. Few areas of congestion are noticed. The histopathological score showed a statistically significant higher scores in the diabetic group (median value = 9) as compared to the control groups (median value = 1), with a significant reduction in the curcumin-treated group (median values = 3.5) when compared to the diabetic group. Table 2 shows the results of the histopathological score.
**FIGURE 3:** *Impact of curcumin on histopathological changes in the dorsal horn of cervical and lumbar segments in diabetic rats by H & E (×400. Scale bar = 50 μm. The control group (A,D) revealed medium-sized basophilic somas of the sensory neurons (blue arrows) in acidophilic neutropil, myelinated axons with their myelin sheaths (green arrows), together with few glial cells; microglia (yellow arrows) and astrocytes (red arrows). Blood capillaries appear intervening (black arrows). The Diabetic group (B,E) revealed shrinkage of neuronal somas with surrounding haloes and pyknotic nuclei (blue arrows), degenerated axons (green arrows), multiple microglia (yellow arrows), and astrocytes (red arrows). Focal areas of hemorrhage (black arrows) are noticed. The Curcumin-treated group (C,F), revealed relatively normal neuronal somas, with few shrunken somas (blue arrows) in acidophilic neuropil. Relatively normal myelinated nerve axons, with few degenerated axons (green arrows), together with few glial cell nuclei; microglia (yellow arrows) and astrocytes (red arrows). Few areas of congestion (black arrows) are noticed.* TABLE_PLACEHOLDER:TABLE 2
## 3.3. Immunohistochemical results
Immunohistochemical detection of NeuN, Iba1, GFAP, Caspase-3, Nrf2, HO-1, and NF-kB positive cells, of the negative control group showed moderate immunoreactivity for NeuN, Iba1, GFAP, mild expression for Nrf2, and HO-1 with weak expression for Caspase3 and NF-kB. Noticeably, the dorsal horns of the spinal cords of the diabetic group revealed a strong expression for Iba1, GFAP, NF-kB, and Caspase3, with moderate expression for HO-1, and a mild expression for NeuN and Nrf2. In contrast, the diabetic + Curcumin group revealed reversed immunoreactivity with moderate immunoreactivity for NeuN, Iba1, GFAP, and Nrf2, with a weak expression for caspase3, and NF-kB. However, there was a strong expression for HO-1 (Figures 4–8).
**FIGURE 4:** *(A–C) Cervical and (D–F) lumbar. Impact of curcumin on the immunohistochemical expression of NeuN in the dorsal horns of spinal cords of diabetic rats (×400). Scale bar = 50 μm. Histogram shows the impact of Curcumin on the number of NeuN, +ve cells/HPF, in the dorsal horns of spinal cords of diabetic rats. Results are mentioned as mean ± standard error. The results were compared using one-way ANOVA and Post-hoc Tukey test. The results were considered significant if the p-values < 0.050. Different letters mean significant differences. HPF, high power field.* **FIGURE 5:** *(A–C) cervical and (D–F) lumbar. Impact of curcumin on the immunohistochemical expression of Iba1 in the dorsal horns of spinal cords of diabetic rats (×400). Scale bar = 50 μm. Histogram shows the impact of Curcumin on the number of Iba1, +ve cells/HPF, in the dorsal horns of spinal cords of diabetic rats. Results are mentioned as mean ± standard error. The results were compared using one-way ANOVA and Post-hoc Tukey test. The results were considered significant if the p-values < 0.050. Different letters mean significant differences. HPF, high power field.* **FIGURE 6:** *(A–C) cervical and (D–F) lumbar. Impact of curcumin on the immunohistochemical expression of GFAP in the dorsal horns of spinal cords of diabetic rats (×400). Scale bar = 50 μm. Histogram shows the impact of Curcumin on the number of GFAP, +ve cells/HPF, in the dorsal horns of spinal cords of diabetic rats. Results are mentioned as mean ± standard error. The results were compared using one-way ANOVA and Post-hoc Tukey test. The results were considered significant if the p-values < 0.050. Different letters mean significant differences. HPF, high power field.* **FIGURE 7:** *(A–C) NF-kB expression in cervical, (D–F) NF-kB expression in lumbar, (G–I) caspase 3 expression in cervical, and (J–L) caspase 3 expression in lumbar. Impact of curcumin on the immunohistochemical expression of NF-kB and caspase-3 in the dorsal horns of spinal cords of diabetic rats (×400). Scale bar = 50 μm. Histograms shows the impact of Curcumin on the number of NF-kB and caspase-3, +ve cells/HPF, in the dorsal horns of spinal cords of diabetic rats. Results are mentioned as mean ± standard error. The results were compared using one-way ANOVA and Post-hoc Tukey test. The results were considered significant if the p-values < 0.050. Different letters mean significant differences. HPF, high power field.* **FIGURE 8:** *(A–C) Nrf2 expression in cervical, (D–F) Nrf2 expression in lumbar, (G–I) HO-1 expression in cervical, and (J–L) HO-1 expression in lumbar. Impact of curcumin on the immunohistochemical expression of Nrf2 and HO-1 in the dorsal horns of spinal cords of diabetic rats (×400). Scale bar = 50 μm. Histograms shows the impact of Curcumin on the number of Nrf2 and HO-1 + ve cells/HPF, in the dorsal horns of spinal cords of diabetic rats. Results are mentioned as mean ± standard error. The results were compared using one-way ANOVA and Post-hoc Tukey test. The results were considered significant if the p-values < 0.050. Different letters mean significant differences. HPF, high power field.*
## 3.4. Results of morphometric analysis of immunohistochemical results
The number of NeuN, Iba1, GFAP, Caspase-3, Nrf2, HO-1, and NF-kB immunopositive cells, revealed a significant difference ($p \leq 0.0005$) between the studied groups. Tukey post-hoc tests showed a significant increase in the number of Iba1, GFAP, NF-kB, caspase3, and HO-1 positive cells with a significant reduction in the number of NeuN and Nrf2 positive cells in the diabetic group as compared to the negative control group. Furthermore, a significant reduction in the number of Iba1, GFAP, Caspase3, and NF-kB positive cells, as well as a significant increase in the number of NeuN, Nrf2, and HO-1 positive cells, were observed in the diabetic + Curcumin group, when compared to the diabetic group. However, the results of the curcumin-treated group revealed a significant difference compared with the control group (Figures 4–8).
## 4. Discussion
Peripheral, as well as, central neuropathies are common complications of diabetes. Treatment of diabetic neuropathy (DN) aims to provide control for blood glucose, manage pain, and suppress nerve damage. Currently, there is no efficient treatment for DN (Sloan et al., 2021). Curcumin (Turmeric), a primary bioactive substance, derived from Curcuma Longa, has shown neuroprotective effects in many diseases. Many studies reported the beneficial effect of Curcumin on diabetic peripheral neuropathy (Zhang et al., 2022) and spinal cord traumatic injury models (Kahuripour et al., 2022). However, the role of curcumin in the management of diabetes-induced spinal cord impairment requires clarification. The current study explored the role of curcumin against diabetes-induced spinal cord microglial activation, astrocytosis, neuronal apoptosis, and its role in the regulation of the Nrf2/HO-1 and NF-kB signaling pathways.
In the present study, STZ could induce type I diabetes, followed by the development of central neuropathy in spinal cord. Diabetes induced oxidative stress with decreased GSH and increased MDA, consistent with Mandour et al. [ 2022] who reported similar findings. The current study also found that diabetes-induced spinal cord microglial activation, as seen by up-regulation of Iba1 expression, is consistent with the findings of Mandour et al. [ 2022] and Wang et al. [ 2022], as microglia are the main provider of inflammatory cytokines; TNF-α, IL-6, and IL-1β, in response to neuronal degeneration. On the other hand, astrocytes; the star-shaped neuroglia in CNS, perform a nutritional function, preserve the ion balance, regulate the blood flow to the brain and perform a trial to repair, otherwise, scarring of CNS after injury. The present study found that diabetes could induce spinal cord astrocytosis, as manifested by increased expression of GFAP, similar to the finding of Dauch et al. [ 2012], Benitez et al. [ 2015], Deng et al. [ 2017], and Kiguchi et al. [ 2017], however, Wodarski et al. [ 2009] and Shayea et al. [ 2020] found that STZ rats had a reduced number of astrocytes. Interestingly, Tsuda et al. [ 2008] and Zhang et al. [ 2018] found an insignificant change in the number of astrocytes in the diabetic spinal cord. The controversy surrounding these findings may be due to the difference in the model, the dose of STZ, and the duration of diabetes.
NeuN is a neuronal marker with a nuclear expression. In the current study, the number of NeuN positive cells was found to decrease in the spinal cords of STZ rats suggesting a reduced neuronal number. This decrease may be at least in part due to neuronal apoptosis as demonstrated by increased caspase3 expression in the diabetic spinal cord group, similar to the results of Inam et al. [ 2019], Niknia et al. [ 2019], and Mandour et al. [ 2022].
The mechanisms underlying neuronal apoptosis may be the disruption of the Nrf2/HO-1 and NF-kB pathways. It is a cytoprotective system and a powerful modulator of longevity. This pathway can counteract oxidative stress, regulate apoptosis, modulate inflammation, and contribute to angiogenesis. The present study reports down-regulation of Nrf2 with up-regulation in the expression of HO-1, consistent with the findings of Pouso-Vazquez et al. [ 2022] with the induction of NF-kB pro-inflammatory pathway similar to the results of Mandour et al. [ 2022]. However, Castany et al. [ 2016] reported no change in HO-1 in diabetes. NF-kB has been reported to co-localize with GFAP, suggesting the role of astrocytes in the regulation of NF-kB activity (Lee et al., 2011). On the other hand, Nrf2 was found to co-localize with Iba1, GFAP, and NeuN, confirming its role in microglia, astrocytes, and neurons, respectively, after spinal cord trauma (Wang et al., 2017).
Curcumin, a primary bioactive substance in turmeric, has shown neuroprotective effects in a variety of diseases. In the present study, Curcumin was found to protect the spinal cord against diabetes-induced injury. Many studies reported the beneficial effect of Curcumin on diabetic peripheral neuropathy through antioxidant activity (Zhao et al., 2014), activation of the opioid system (Banafshe et al., 2014), suppression of TNF alpha expression (Daugherty et al., 2018), reduction of depression and anxiety (Asadi et al., 2020), modulation of the activity of DRG astrocytes and neurons (Park et al., 2021), inhibition of Schwann cell apoptosis and promoting nerve growth factor (NGF) (Zhang et al., 2022). On the other hand, many studies reported the beneficial role of curcumin in the treatment of injury, induced by spinal cord trauma models (SCI) (Kahuripour et al., 2022).
In the present study, curcumin could suppress diabetes-induced microglial activation, consistent with the results of Wang et al. [ 2014] who found that curcumin could promote spinal cord repair by suppressing microglia, thus inhibiting glial scar formation and the inflammatory response following a traumatic injury to the spinal cord, and also consistent with the results of Sheikholeslami et al. [ 2019], who described the beneficial role of curcumin in antagonizing morphine dependence, by inhibiting the microglial activation and decreasing the inflammatory mediators. In the present study, curcumin could attenuate the astrocytosis induced by diabetes, similar to the findings of Daverey and Agrawal (Daverey and Agrawal, 2020) who reported the role of Curcumin in the down-regulation of the hypoxia-induced astrocytosis as demonstrated by the expression of GFAP, in white matter hypoxic injury (WMI), and similar to the results of Yardim et al. [ 2021], as they described the role of curcumin in reducing astrocytosis in the spinal cord of Paclitaxel-treated rats.
Furthermore, the present study found that curcumin could rescue the spinal cord neurons from diabetes-induced injury as demonstrated through the restoration of the number of NeuN positive cells, consistent with the results of Lin et al. [ 2011], who described the role of curcumin in the restoration of NeuN positive neurons after neuronal loss after traumatic spinal cord injury. The mechanisms underlying this role for curcumin may be its antiapoptotic activity that was demonstrated in this study through decreased caspase-3 activity, similar to the results of Hao et al. [ 2017] and Xi et al. [ 2019] in models of traumatic spinal cord injury, Daverey and Agrawal [2020] in the model of spinal cord white matter hypoxic injury (WMI), and Yardim et al. [ 2021] in the spinal cord of Paclitaxel-treated rats. Moreover, Li et al. [ 2021] reported that Curcumin could promote functional recovery and reduce the number of apoptotic neurons after spinal cord trauma by modulating autophagy.
The anti-apoptotic effect of curcumin, as reported here, may be due to its role in the restoration of the Nrf2/HO-1 and NF-kB pathways. The current study found that curcumin could up-regulate Nrf2 and HO-1 expressions and down-regulate NF-kB expression in the diabetic spinal cord and could increase GSH and decrease MDA levels. Jin et al. [ 2021] reported a similar finding that Curcumin could rescue the spinal cord after traumatic injury by activating Nrf2/HO-1 and scavenging free radicals. Furthermore, Yardim et al. [ 2021] found that Curcumin could significantly up-regulate spinal Nrf2 and HO-1 expressions and reduce the expression of NF-kB, TNF-alpha, IL-6, and iNOS in Paclitaxel-treated rats.
The current study found that Curcumin could exert an antihyperglycemic effect on Diabetic rats, another mechanism that helps the neuroprotective role of Curcumin, however, it did not normalize serum glucose, similar to previous reports by Daugherty et al. [ 2018] and Zhang et al. [ 2022].
## 5. Conclusion
Curcumin could improve spinal cord changes- induced by diabetes. It could suppress microglial activation, astrocytosis, and neuronal apoptosis with the restoration of the normal activity of Nrf2/HO-1 and NF-kB. Curcumin is a promising adjuvant therapy to suppress diabetes-induced spinal cord microglial activation, astrocytosis, and neuronal apoptosis through regulation of the Nrf2/HO-1 and NF-kB signaling pathways.
## 5.1. Study limitations
Sprague Dawley rats were used as they are considered efficient models for studying Type I diabetes-induced spinal cord injury (Inam et al., 2019; Shayea et al., 2020) and males were chosen because they have a greater degree of diabetic neuropathy, as compared to females (Fan et al., 2018). So the controversy surrounding the effect of STZ and/or Curcumin on the spinal cord neurons, glia, Nrf2/HO-1, and NF-kB signaling may be due to the difference in sex or species as well as the difference in STZ and curcumin dosage and/or duration, and even the type of diabetes. Furthermore, it may be due to the different segments of spinal cord or horns examined. To better validate the results, further studies should try several doses of STZ and Curcumin, various animals and species, different sex, different regimens, different segments and horns of spinal cord and even more diabetic models.
## 5.2. Clinical application
The present study recommends the use of Curcumin as an adjuvant to suppress diabetic spinal cord central neuropathy, glial activation, and neuronal apoptosis with the regulation of Nrf2/HO-1 and NF-kB signaling.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The study was designed following the Animals in Research: Reporting In Vivo Experiments (ARRIVE) standards and meeting the standards of Mansoura University Animal Care and Use Committee (MU-ACUC), Egypt (MED.R.22.09.2).
## Author contributions
HE and AN: conceptualization, methodology, validation, investigation, data curation, and writing—original draft preparation. MR and ME: software. HE, AN, MR, and ME: formal analysis and visualization. MR, ME, EE, MA, and ZA-Q: resources. HE, MR, ME, EE, MA, and ZA-Q: writing—review and editing. EE, MA, and ZA-Q: supervision and project administration. All authors have read and agreed to the published version of the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Bamboo shoot dietary fiber alleviates gut microbiota dysbiosis and modulates
liver fatty acid metabolism in mice with high-fat diet-induced obesity
authors:
- Xiaolu Zhou
- Lingjun Ma
- Li Dong
- Daotong Li
- Fang Chen
- Xiaosong Hu
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC10035599
doi: 10.3389/fnut.2023.1161698
license: CC BY 4.0
---
# Bamboo shoot dietary fiber alleviates gut microbiota dysbiosis and modulates liver fatty acid metabolism in mice with high-fat diet-induced obesity
## Abstract
### Introduction
Obesity is a common nutritional disorder characterized by an excessive fat accumulation. In view of the critical role of gut microbiota in the development of obesity and metabolic diseases, novel dietary therapies have been developed to manage obesity by targeting the gut microbiome. In this study, we investigated anti-obesity effects of bamboo shoot dietary fiber (BSDF) and the potential mechanisms.
### Methods
After 12 weeks of intervention with BSDF in high-fat mice, we detected obesity-related phenotypic indicators, and made transcriptomic analysis of liver tissue. Then we analyzed the changes of gut microbiota using 16S rRNA gene sequencing, explored the effect of BSDF on gut microbiota metabolites, and finally verified the importance of gut microbiota through antibiotic animal model.
### Results and discussion
We found that BSDF was effective in reducing lipid accumulation in liver and adipose tissue and alleviating dyslipidemia and insulin resistance. Liver transcriptome analysis results showed that BSDF could improve lipid metabolism and liver injury by modulating peroxisome proliferator-activated receptor (PPAR) and fatty acid metabolic pathways. The 16S rRNA gene sequencing analysis of gut microbiota composition showed that BSDF significantly enriched beneficial bacteria such as Bifidobacterium, Akkermansia, Dubosiella, and Alloprevotella. Analysis of fecal metabolomics and gut microbiota metabolites revealed that BSDF increased the levels of several short-chain fatty acids and enriched bile acids, which may be important for improving lipid metabolism. Notably, the obesity-related metabolic disorders were abrogated after the abrogation of gut microbiota, suggesting that gut microbiota is a key factor in the beneficial effects of BSDF.
### Conclusion
Our study suggests that BSDF as a prebiotic supplement has the potential to improve obesity by improving gut microbiota and modulating host PPAR and fatty acid metabolic pathways.
## 1. Introduction
Obesity has become a global epidemic, whose essence is caused by the imbalance between energy intake and consumption [1], which is closely related to hypertension, hyperlipidemia, type 2 diabetes and alcoholic fatty liver. It is urgent to vigorously develop drugs and methods to treat obesity. However, at present, obesity drugs and surgical treatment methods not only bring huge economic burden, but also some side effects [2, 3]. Therefore, it is particularly important to treat obesity safely and efficiently through scientific and reasonable lifestyle changes (especially dietary patterns).
The development of obesity is associated with a number of factors, of which the gut microbiota is one of the key factors that has received a lot of attention in recent years and is a potential target for the prevention of obesity and related metabolic diseases. And nutrients and active substances in food can effectively regulate the gut microbiota (4–6), among which insoluble dietary fiber has a good effect on regulating the structure and function of the gut microbiota [7], but there is less research on obesity reduction and its mechanism of action [8, 9]. At the same time, the structure and function of different sources of dietary fiber vary greatly, and its effect on obesity and its mechanism of action are still unclear. To explore this mechanism, Qiong bamboo shoots (BS) containing abundant dietary fiber were used as study subjects, which is the young shoots of the Qiongzhuea tumidinoda, and a small to medium-sized bamboo mainly distributed in Sichuan and Yunnan provinces in China. Qiong bamboo shoot dietary fiber (BSDF) is an dietary fiber extracted from the buds of BS, which has a higher yield of insoluble dietary fiber than other plants [10]. Previous studies have shown that dietary fiber, as an important energy source of gut microbiota, has beneficial effects on gut metabolites, and can produce SCFAs and other metabolites under the condition of anaerobic bacteria decomposition [11, 12]. In addition, metabolites of dietary fiber gut, including short-chain fatty acids (SCFAs), are also known to have various beneficial effects on host physiology [13]. Given the benefits of dietary fiber in improving gut homeostasis, we hypothesize that gut microbiota may play an irreplaceable role in the beneficial effects of BSDF on metabolic disorders.
In order to verify our hypothesis, this study first tested the relevant phenotypes (body weight, fat weight, insulin resistance, etc.) after the intervention of BSDF in obesity. The phenotypic changes and molecular mechanisms were preliminarily verified by liver transcriptome and Real-Time Quantitative PCR (RT-qPCR) experiments. Next, focusing on gut microorganisms, we explored the effect of BSDE on gut microbiota through 16S rNA, and determined gut metabolites through fecal metabolomics, so as to further explore the underlying mechanism of BSDF. Finally, we used antibiotics to remove most of the bacteria in mice to verify the necessity of the existence of gut microbiota. With these results, we provide new insights into the mechanisms by which BSDF improves obesity through the gut microbiota and promote a more comprehensive understanding of the relationship between BSDF and the gut microbiota.
## 2.1. Preparation of BSDF
The BSDF extraction refers to the method of Bangoura et al. with appropriate modifications [12]. Freeze-dried powder of Bamboo shoots was provided by Shan Yibao Biotechnology Co., Ltd. (Yunnan Yiliang China) and pulverized into powder (80-mesh). BSDF was extracted by enzymatic hydrolysis. Briefly, distilled water (200 mL) was added to the weighed bamboo shoot freeze-dried powder (5 g), and magnetically stirred for 15 min (Bodajingke Instrument Co., Ltd., Shenzhen, China). The temperature was kept constantly at 40°C. After adjusting the pH to 7.0, 5000 U/mL neutral protease (Solarbio Biotechnology Co., Ltd., Beijing, China) was added, and further stirred for 120 min. Then, the mixture was filtered through a 400-mesh filter cloth, obtained residue was washed three times with distilled water at 40°C. Finally, washed residue was freeze-dried at a vacuum freeze dryer (Vaco 5 ZIRBUS, Beijing Hanmei Biotechnology Co., Ltd., Beijing, China), and sieved (100 mesh). The prepared dietary fiber is sealed and stored at low temperature. The basic nutritional components of BSDF freeze-dried powder were determined before the subsequent experiments (Supplementary Table S1).
## 2.2. Animals and diets
Animal experiments [1]: 5-week-old male C57BL/6J mice (Vital River Laboratory Animal Technology Co., Beijing, China) were purchased and raised in a fixed environment (12 h light/dark cycle, 25 ± 2°C, $55\%$ ± $10\%$ humidity). After an adaptive feeding for 1 week, the mice were randomly divided into three groups: normal control diet (NCD), high-fat diet (HFD), high-fat diet supplemented with $6\%$ BSDF freeze-dried powder (HFD-BSDF) ($$n = 8$$ per group) for 12 weeks. In this study, $6\%$ BSDF was added based on the previous studies (14–16). Body weight and food intake were monitored weekly, and relevant tissues were collected at the end of the experiment. The company “Shuyishuer Biotech Co” provides all diets we need. At the same time, we show the specific compositions and energy densities of diets in Supplementary Table S2.
Animal experiments [2]: Mices were administrated with high-fat diet (A-HFD) and high-fat diet supplemented with $6\%$ BSDF freeze-dried powder (A-HFD-BSDF), respectively. Drinking water for both groups was replaced with sterile water containing a mixture of antibiotics. The antibiotic regulation determined from the previous literature [17], was composed of 125 g/mL ciprofloxine hydrocholoride, 100 μg/mL neomycin, 100 μg/mL metronidazole, 100 μg/mL cefazolin, 100 U/mL penicillin, 50 μg/mL streptomycin, 50 μg/mL vancomycin, and 1 mg/mL bacitracin, and replaced twice a week. Our animal experiments were supported by the Animal Protection Professional Committee of China Agricultural University (AW40601202-4-1).
## 2.3. Glucose tolerance test
The mouse intraperitoneal glucose tolerance test (ipGTT) was performed on the 11th week of the experiment. Mice fasted for 12 h and weighed, was intraperitoneally injected with glucose diluted solution (1.0 g/kg), and its glucose concentration was measured by glucose meter (Accu-chek, Roche, Switzerland).
The serum of mice was centrifuged (4°C, 10 min, 3000 rpm) after standing at 25 °C for 30 min, then determined with a mice insulin enzyme-linked immunosorbent assay (ELISA) kit (Alpco, USA). Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) was calculated from fasting glucose and insulin [6].
## 2.4. Histological analysis
We stained liver tissue with both hematoxylin and eosin (H&E) and Oil Red O, but adipose tissue with H&E only, refers to the method of Ke et al. [ 18]. Finally, the sections were observed and photographed under a high-level microscope.
## 2.5. Biochemical analysis
The concentrations of lipopolysaccharide (LPS) and tumor necrosis factor alpha (TNF-α) in serum were detected with commercial enzyme-linked immunosorbent assay (ELISA) kits (Enzyme Link Biotechnology Co., Ltd., Shanghai, China). Meanwhile, serum total triglycerides (TG), total cholesterol (TC), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), alanine aminotransferase (ALT) and aspartate aminotransferase (AST) was measured by the automatic biochemical analyzer (AU480, Olympus Corporation, Tokyo, Japan), refers to the method of Zhao et al. [ 1].
## 2.6. Gut microbiota analysis
We took 100 mg of mice fecal samples, extracted DNA, sequenced the amplification of the microbial 16S rRNA gene, and processed and analyzed the sequencing data to obtain the corresponding results. And details of the specific method are shown in the Supplementary method S1, refers to the method of previous researches (19–21).
## 2.7. RNA-Seq library preparation and sequencing
Total RNA was extracted from mouse liver tissue using TRIzol® reagent according the manufacturer's instructions (Invitrogen). RNA quality was evaluated by electrophoresis using an Agilent 2100 Bioanalyzer (Agilent Technologies, San Diego, CA, USA). Samples with RNA integrity numbers (RINs) > 9.4 and with $\frac{260}{280}$ nm absorbance ratios from 1.9 to 2.1 were used for the construction of RNA Seq libraries. Libraries were constructed using the TruSeqTM RNA Sample Prep kit (Illumina, San Diego, CA, USA) according to the manufacturer's instructions.
Sequencing of the libraries was performed on an Illumina HiSeq 2000 instrument by Shanghai Majorbio Biopharm Biotechnology (Shanghai, China), and individually assessed for quality using FastQC. Analysis of differential expression was carried out using DESeq2 [22]. Statistical significance was assessed using a negative binomial Wald test, then corrected for multiple hypothesis testing with the Benjamini-Hochberg method. Functional enrichment cluster analysis was performed for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis.
## 2.8. Real-time quantitative PCR
*Target* gene expressions were assessed using RT-qPCR on liver mRNA [6]. The used primers are listed in Supplementary Table S3. The specific experimental steps are written in Supplementary method S2.
## 2.9. Quantification of fecal SCFAs
Determination of SCFAs in feces by gas chromatography-mass spectrometry (GC-MS) [23, 24]. The specific experimental steps are written in Supplementary method S3.
## 2.10. Determination of metabolites in feces
The metabolites in faces were analyzed by LC-MS/MS [25]. And details of the specific method shown in Supplementary method S4.
## 2.11. Statistical analysis
Statistical analyses were performed with the SPSS 25.0 software (SPSS Inc., Chicago, IL, USA). The data follow a normal distribution, and the variances of the groups are similar. Data are presented as Means ± SEM. Significant group differences were determined by one-way ANOVA followed by Duncan's test ($P \leq 0.05$). Differences between two groups were analyzed by an independent sample t-test ($P \leq 0.05$). Graphs were prepared using the Prism 7.0 software (La Jolla, CA, USA).
## 3.1. Alleviation of HFD-induced obesity by BSDF supplementation
Figure 1 showed the changes of related phenotypes after BSDF intervention in obese mice induced by HFD. It is noteworthy that at the beginning of the experiment, there was no significant difference in the initial body weight of NCD, HFD and HFD-BSDF mice. When the intervention time reached 6 weeks, the weight of mice in HFD-BSDF group was significantly lower than that in HFD group ($P \leq 0.05$) (Figure 1A). At the end of the experiment, the total weight gain of the HFD-BSDF was also significantly lower than that of the HFD group, which was consistent with the results of the graph of changes in mice body weight ($P \leq 0.05$) (Figure 1B). The weights of three types of white fat, namely epididymal fat, perirenal fat, and groin fat, were also analyzed and it was found that the HFD significantly increased the weight of all three types of white fat, with a significant decrease after the BSDF intervention ($P \leq 0.05$) (Figure 1C). The trends in energy efficiency in mice were consistent with the trends in body weight, weight gain, and white fat weight ($P \leq 0.05$) (Supplementary Figure S1A). However, there was no significant difference in energy intake between HFD-BSDF and HFD groups ($P \leq 0.05$) (Supplementary Figure S1B). This shows that the effect of BSDF on body weight and fat weight has nothing to do with food intake. In addition, BSDF significantly improved the elevated serum lipid levels TC, TG, HDL-C, LDL-C in high-fat mice ($P \leq 0.05$) (Figure 1D), and significantly reduced liver weight and liver function factors AST and ALT ($P \leq 0.05$) (Figures 1E, F). We then measured the inflammatory factors LPS and TNF-α in mice serum and found that BSDF was effective in improving overall inflammation levels in mice ($P \leq 0.05$) (Figures 1G, H). We further analyzed HE sections of epididymal fat and found that severe hypertrophy of white adipose tissue occurred in HFD mice and that BSDF intervention effectively ameliorated this tissue hypertrophy, which can be confirmed by the measurement of white fat area (Figure 1I, Supplementary Figure S1C). Following the observed reduction in liver weight, we made HE sections and oil-red sections of liver tissue, and the results showed that BSDF significantly improved liver steatosis and lipid droplet aggregation associated with a high-fat diet (Figures 1J, K). These results suggest that BSDF supplementation significantly ameliorated the abnormal weight gain, white fat accumulation and hepatic steatosis and tissue damage in HFD mice.
**Figure 1:** *BSDF alleviation of HFD-induced obesity. (A) Body weight vs. time profiles; (B) weight gain; (C) white fat weight; (D) serum lipid level; (E) liver weight; (F) serum ALT and AST concentrations; (G) serum LPS concentration; (H) serum TNF-α concentration; (I) H&E staining of epididymal fat sections; (J) H&E staining of liver tissue; (K) Oil Red O staining of liver. Data presented as mean ± SEM, n = 8 per group. #p < 0.05, ###p < 0.001, HFD vs. NCD; *p < 0.05, **p < 0.01, HFD–BSDF vs. HFD. a, b, c means in the same bar without a common letter differ at p < 0.05. Epi-WAT, epididymal fat; Per-WAT, perirenal fat; Gro-WAT, groin fat. NCD, normal control diet; HFD, high-fat diet; HFD-BSDF, high-fat diet supplemented with 6% BSDF freeze-dried powder.*
## 3.2. Improvement of insulin resistance and glucose tolerance insulin resistance in HFD mice by BSDF supplementation
The effect of BSDF on glucose metabolism in hyperlipidemic mice was shown in Figure 2. The fasting glucose of NCD mice was 7.59 ± 0.23 mmol/L, and that of HFD mice was 11.88 ± 0.10 mmol/L, which was significantly reduced to 10.49 ± 0.15 mmol/L after BSDF intervention ($P \leq 0.05$) (Figure 2A). Similarly, the fasting insulin of mice after BSDF intervention also decreased from 11.51 ± 0.34mIU/L to 6.46 ± 0.56mIU/L ($P \leq 0.05$) (Figure 2B), which indicates that the glucose and insulin regulation of obese mice is abnormal, and BSDF intervention can significantly alleviate the abnormal glucose metabolism. Glucose tolerance experiments also showed a similar effect, with the highest blood glucose values in the HFD group of mice at 15 min after glucose injection and a slow decline in blood glucose until 120 min later. However, after the BSDF intervention, the peak of blood glucose was delayed until 30 min, and the blood glucose values at 60 min, 90 min and 120 min were significantly lower than the corresponding blood glucose values in the HFD group, indicating that BSDF could effectively improve the glucose tolerance and enhance blood glucose regulation in high-fat mice ($P \leq 0.05$) (Figure 2C). Calculation of the area under the curve in Figure 2C further confirmed these results ($P \leq 0.05$) (Figure 2D), as did the trend in the HOMA-IR index ($P \leq 0.05$) (Figure 2E). These results show that BSDF is more effective in improving insulin resistance and regulating glucose homeostasis induced by high-fat diet.
**Figure 2:** *BSDF improved insulin resistance and glucose tolerance in HFD-fed mice. (A) Serum glucose, (B) serum insulin, (C) time courses of blood glucose levels in the intraperitoneal glucose tolerance test (iPGTT) (D) Area under the curve (AUC) of the blood glucose during ipGTT. (E) homeostatic model assessment for insulin resistance (HOMA-IR) index, and Data presented as mean ± SEM, n = 8. ###p < 0.001, HFD vs. NCD; **p < 0.01, ***p < 0.001, HFD-BSDF vs. HFD. a, b, c means in the same bar without a common letter differ at p < 0.05. NCD, normal control diet; HFD, high-fat diet; HFD-BSDF, high-fat diet supplemented with 6% BSDF freeze-dried powder.*
## 3.3. Effects of BSDF on liver transcriptome in mice with HFD-induced obesity
The liver transcriptome profiles in the NCD, HFD, and HFD-BSDF groups ($$n = 5$$) were compared using RNA-Seq to further assess how BSDF affected the whole-gene expression in HFD-induced obese mice. To recognize the differentially expressed genes (DEGs) among the three groups, we compared their transcriptomic profiles and analyzed the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The comparative analysis showed that BSDF was significantly involved in several metabolic pathways (Padjust < 0.05), including retinol metabolism, peroxisome proliferator-activated receptor (PPAR) signaling pathway, steroid hormone biosynthesis, unsaturated fatty acid biosynthesis and fatty acid degradation (Figure 3A). Among these, the PPAR signaling pathway was the more significant signaling regulatory pathway. Combined with our research objectives and considering that the PPAR signaling pathway does play an important role in the regulation of lipid metabolism, we selected key genes on the PPAR signaling pathway for RT-qPCR experiments to validate and found that BSDF significantly increased the expression of Cpt1b, Ehhadh, Cyp4a14, Cyp4a31, Cyp4a31, Cyp4a12b, and other gene expression ($P \leq 0.05$) (Figure 3B). This is one of the mechanisms by which BSDF exerts its effect on improving obesity.
**Figure 3:** *Effects of BSDF administration on the liver transcriptome in HFD-induced obese mice. (A) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis using DEGs between t the NCD, HFD, and HFD-BSDF groups. n = 5 per group. (B) Relative mRNA expression of important genes on the PPAR signaling pathway in liver. n = 8 per group. Data presented as mean ± SEM, n = 8. a, b, c means in the same bar without a common letter differ at p < 0.05. NCD, normal control diet; HFD, high-fat diet; HFD-BSDF, high-fat diet supplemented with 6% BSDF freeze-dried powder.*
## 3.4. BSDF improvement of gut dysbiosis in obese mice
The gut microbiota disturbance caused by HFD was significantly improved after BSDF supplementation. The diversity index reflects the abundance and diversity of microbial communities, where Ace and Chao are indices of community richness and Shannon and Simpson are indices to evaluate community diversity. Compared to NCD mice, HFD mice showed significantly lower microbiota abundance and higher community diversity, but no significant difference from the HFD group after BSDF intervention (Supplementary Figures S2A–D). Bray-Curtis-based PCoA and NMDS could reflect the overall differences in OTU levels of microbes, and the results showed that the composition of gut microbiota was significantly changed after BSDF intervention (Figures 4A, B).
**Figure 4:** *BSDF attenuated gut microbiota dysbiosis in HFD-fed mice. (A, B) PCoA and NMDS score plot based on Bray–Curtis. (C, D) The abundances of the gut microbiota at the phylum level, and the genus level. n = 6, per group. NCD, normal control diet; HFD, high-fat diet; HFD-BSDF, high-fat diet supplemented with 6% BSDF freeze-dried powder.*
At the phylum level, HFD treatment increased the abundance of Firmicutes and Desulfobacterota, but caused the decrease in Bacteroidota and Actinobacteriota compared with the NCD. However, BSDF supplementation reversed this effect (Figure 4C). At the genus level, the HFD-BSDF diet led to a substantial increase in the Bifidobacterium and Akkermansia along with decreases in the Bilophila, norankf_Ruminococcaceae and unclassified_f_Oscillospiraceae abundances (Figure 4D). Herein, we used the LEfSe method to classify bacterial biomarkers from genus to phylum (Supplementary Figure S3A). The cladogram produced from the LEfSe analysis highlighted the dominant bacteria from the genus to the phylum level in each group. The LEfSe results (LDA > 3.5) indicated that the HFD led to greatly lower Bifidobacterium and Dubosiella levels and higher Bilophila and Colidextribacter abundances than the NCD group. Compared with the HFD group, BSDF supplementation not only reduces these effects, but also significantly increases Akkermansia and Alloprevotella abundances (Supplementary Figure S3B).
## 3.5. Effects of BSDF on fecal metabolomic profile in mice with HFD-induced obesity
BSDF has an ameliorating effect on gut microbiota and exerts an ameliorating effect on obesity closely related to gut microbiota, then we further examined gut microbiota and host metabolites to facilitate a more comprehensive understanding of the mechanism of action of BSDF. We found that the concentrations of acetate and propionate in NCD mice were 516.25 ± 47.23 mg/kg and 146.26 ± 14.05 mg/kg, respectively. The concentrations of acetate and propionate in HFD mice were significantly reduced to 347.36 ± 34.89 mg/kg and 87.38 ± 13.88 mg/kg. After adding BSDF, the concentrations of acetate and propionate increased to 935.32 ± 27.35 mg/kg and 265.32 ± 12.54 mg/kg respectively, and the concentrations of butyrate, valerate and isovalerate were also significantly increased ($P \leq 0.05$) (Figure 5A). This suggests that BSDF supplementation can increase the production of SCFAs, which is beneficial to health.
**Figure 5:** *Effects of BSDF administration on fecal short chain fatty acids (SCFAs) and metabolomics in HFD-induced obese mice. (A) Concentration of SCFAs. n = 8 per group. Principal component analysis (PCA) score plots of fecal metabolomic profiles in all treatment groups. (B) Positive ion, and (C) negative ion. (D) Hierarchical cluster analyses of differential metabolites. Red and blue of increasing intensity indicate up-regulation or down-regulation, respectively. n = 6 per group. NCD, normal control diet; HFD, high-fat diet; HFD-BSDF, high-fat diet supplemented with 6% BSDF freeze-dried powder.*
Principal component analysis (PCA) of the electrospray ionization data in positive and negative ion mode showed the clear difference between the three diet groups (Figures 5B, C), indicating that BSDF has a significant effect on improving the overall metabolism of mice. Compared with the HFD group, BSDF supplementation resulted in significantly reduced abundances of 10-nitrolinoleic acid, l-dopa, 7-sulfocholic acid, 7-ketodeoxycholic acid, and nutriacholic acid, as well as significantly increased levels of tocopheronic acid, 9,10,13-trihydroxystearic acid, 9,10,13-trihydroxy-octadecenoic acid (9,10,13-TriHOME), corchorifatty acid F, undecanedioic acid, undecylenic acid, dihydro-3-coumaric acid, hyodeoxycholic acid, sebacic acid, and 3-hydroxydodecanedioic acid (Figure 5D). Above findings showed that BSDF supplementation can reverse the metabolic dysbiosis caused by HFD feeding.
## 3.6. Effects of BSDF on phenotype and insulin resistance of antibiotic-treated mice
The sterile water containing broad-spectrum antibiotics was provided to A-HFD and A-HFD-BSDF groups for 12 weeks. The antibiotic water eliminated most of the gut microbiota, creating artificially germ-free mice. We found that there was never a significant difference in body weight between the A-HFD and A-HFD-BSDF groups of mice during the experiment ($P \leq 0.05$) (Figure 6A). At the end of the experiment, there was also no difference in the weight gain of the two groups ($P \leq 0.05$) (Figure 6B). There were also no differences in the three white fat weights, liver weight, lipid indices (TC,TG, HDL-C, LDL-C, AST and ALT), or inflammatory factors ($P \leq 0.05$) (Figures 6C–H). In addition, the differences in fasting insulin levels, fasting glucose, AUC, HOMA-IR index, changes in glucose tolerance and other glucose metabolism related indicators are all disappeared ($P \leq 0.05$) (Figures 6I–M). H&E staining of epididymal fat sections and liver and Oil Red O staining of liver further confirmed that there was no significant difference in the degree of white adiposity and hepatic steatosis (Figures 6N–P). This result was also supported by changes in white adipocyte area and feeding efficiency (Supplementary Figures S4A, B). These indirectly suggest that the beneficial effects of BSDF on weight loss are closely related to the gut microbiota.
**Figure 6:** *Effects of BSDF administration on phenotype and insulin resistance in antibiotic-treated mice. (A) Bodyweight time course measurements, (B) Weight gain, (C) White fat weight, (D) Serum lipid profile, (E) Liver weight, (F) Serum ALT and AST concentrations (G) Serum LPS concentrations. (H) Serum TNF-α concentrations. (I) Serum glucose, (J) Serum insulin, (K) Area under the curve (AUC) of the blood glucose during ipGTT. (L) Homeostatic model assessment for insulin resistance (HOMA-IR) index, (M) Time courses of blood glucose levels in the intraperitoneal glucose tolerance test (iPGTT), (N) H&E staining of epididymal fat sections. (O) H&E staining of liver tissue, and (P) Liver Oil Red O staining. Data presented as mean ± SEM, n = 8 per group. A-HFD vs. A-HFD-BSDF at p < 0.05. A-HFD, high-fat diet and antibiotic water; A-HFD-BSDF, high-fat diet supplemented with 6% BSDF freeze-dried powder and antibiotic water.*
## 4. Discussion
This study first assessed the effect of BSDF supplementation on improving obesity and related complications in HFD-fed mice. The results showed that BSDF significantly improved the phenotypic symptoms associated with obesity caused by weight gain, lipid accumulation, insulin resistance and inflammatory response (Figures 1, 2). In order to explain why BS can improve the obesity-related phenotype, we made a transcriptome analysis of mice liver. The results showed that the main metabolic pathway for BSDF to improve obesity is the PPAR signaling pathway, in which the differentially expressed genes were verified, which was consistent with the transcriptome analysis results (Figure 3). The effect of BSDF on gut microbiota was then investigated and BSDF was found to improve the gut micro-environment, promote the increase of beneficial microbiota such as Bifidobacterium and Akkermansia, and inhibit the growth of gut pathogenic microbiota (Figure 4). Next, we analyzed metabolites closely related to gut microbiota, and the results showed that BSDF also promoted the growth of SCFAs, enriched tocopheronic acid, 9,10,13-trihydroxystearic acid, 9,10,13-trihydroxy-octadecenoic acid (9,10,13-TriHOME), and other beneficial metabolites (Figure 5). Finally, to further investigate the role played by gut microbiota in the improvement of obesity by BSDF, this study allowed high-fat mice to drink broad-spectrum antibiotic water and found that there was no difference between BSDF intervention and A-HFD mice in artificially created germ-free mice, indicating the necessity of gut microbiota for the improvement of obesity by BSDF (Figure 6). Thus, it is proposed that the potential mechanism of BSDF to improve obesity is based on the composition of the gut microbiota and metabolic homeostasis.
## 4.1. BSDF and other dietary fiber have similar effects in alleviating obesity phenotype
Our results on the improvement of weight gain, lipid accumulation, elevated blood glucose and insulin resistance caused by obesity with different dietary fibers are consistent with the literature. A long-term study of high-fat induced obesity in C57BL/6J mice showed that the addition of oat insoluble fiber suppressed weight gain and lipid accumulation [26]. Insoluble dietary fiber from enoki mushrooms, carrots and oats had hypoglycaemic and hypolipidaemic effects in both in vitro and in vivo experiments [27]. Our results are consistent with all these studies. Interestingly, the dietary fiber we extracted from ciliated BS was also predominantly insoluble dietary fiber, and it has been reported in the literature that insoluble dietary fiber from Banner sweet dragon bamboo is more advantageous than other common dietary fibers in reducing body weight in obese mice [3], but the experimental period was only 6 weeks, and our experiment extended the experimental period to more comprehensively evaluate the beneficial effects of BSDF.
## 4.2. BSDF alleviates HFD-induced obesity by regulating PPAR signal pathway
In our study, BSDF significantly alleviated HFD-induced hepatic lipid accumulation and abnormal lipid metabolism by enhancing the PPAR/ fatty acid metabolic signaling pathway in the liver. The liver is a major regulatory organ of lipid metabolism, regulating various aspects of lipogenesis, fatty acid oxidation, lipoprotein uptake and secretion, and plays a key role in lipid metabolism (28–30). The transcriptome is also essential for interpreting the functional components of the genome, revealing the molecular composition of cells and tissues, and understanding development and disease (31–33), so we selected liver tissue for RNA-seq transcriptomic analysis to further investigate the potential molecular mechanisms underlying the anti-obesity effects of BSDF. The results revealed that peroxisome proliferator-activated receptor (PPAR) is the main signaling pathway involved in BSDF. Combined with our study objectives and considering that the PPAR signaling pathway does play an important role in the regulation of lipid metabolism, we selected key genes in the PPAR signaling pathway for validation by RT-qPCR experiments. Lipid metabolism in the liver is mainly regulated by the (peroxisome proliferator-activated receptor, PPAR) family [13, 34]. When lipid accumulation occurs in the liver, it activates the PPAR signaling pathway, regulating the high expression of (Cytochrome P450 monoxygenase, CYP4A) enzymes, one of the most sensitive target genes of PPARα, and promoting fat energy expenditure [35]. This may be the molecular mechanism by which BSDF acts.
## 4.3. BSDF has a broader effect on the gut microbiota than other insoluble dietary fibers
Analysis of changes in the gut microbiota and its metabolites [3], which are closely associated with the development of obesity, is important for understanding host metabolism and improving organismal health. Our study found that there is no significant difference between HFD group and HFD-BSDFgroup in diversity index. This suggests that the effect of BSDF on the gut microbiota of high-fat mice is not seen to differ in terms of abundance and diversity, and that there is something more worthy of analysis. At the phylum level, the abundance of Bacteroidota and Actinobacteriota was increased and the abundance of Firmicutes and Desulfobacterota was decreased in the HFD-BSDF group compared to the HFD group (Figure 4C). Several studies have shown that the *Bacteroidota is* less efficient at absorbing energy from food than Firmicutes [36], resulting in reduced calorie absorption and subsequent weight gain [37]. The Actinobacteria (containing beneficial gut microbiota such as Bifidobacterium) has been reported to have a positive effect on host health [38]. And the *Desulfobacterota is* associated with promoting LPS release, exacerbating inflammation, and leading to disturbed energy metabolism [39]. These are consistent with our findings. At the genus level, populations of Bifidobacterium and *Akkermansia muciniphila* were increased after BSDF intervention (Figure 4D). Based on previous research, direct addition of Bifidobacterium and Akkermansia to the diet of C57BL/6J high-fat mice improved glucose tolerance and insulin sensitivity, reduced levels of the inflammatory factor TNF-α in obese mice [40, 41], and promoted acetate production [42]. In addition, *Akkermansia muciniphila* also benefits propionate and butyrate production [43]. These SCFAS are important gut metabolites that are beneficial for organismal health, such as reducing body mass in obese mice, improving disorders of glucose and lipid metabolism as well as fatty liver [44], anti-inflammatory [45]. And in terms of the sources of dietary fiber, our literature research results found that soybean insoluble dietary fiber mainly increased the level of Lachnospirace_Nk4A136_group to improve obesity [8], and insoluble dietary fiber derived from brown seaweed *Laminaria japonica* only increased the level of *Akkermansia muciniphila* [9], while Banna sweet dragon bamboo insoluble dietary fiber reduced the level of Akkermansia levels and increased the levels of Prevotella [3]. This shows that insoluble dietary fiber from different sources also differs in regulating gut microbiota, and even insoluble dietary fiber from different strains of bamboo shoots may have different results, suggesting that the structure of the BSDF may be intrinsic to their different functions.
## 4.4. BSDF promotes the production of metabolites related to the PPAR signaling pathway
Further measurements of SCFAS and other metabolites in feces revealed that BSDF improvement in obesity-promoting beneficial metabolite production was associated with activation of the PPAR signaling pathway. In this study, BSDF intervention significantly increased acetate, propionate, butyrate, isovalerate and valerate in mice feces (Figure 5A). This may be related to the fact that BSDF promoted an increase in beneficial bacteria (Bifidobacterium and Akkermansia) [42, 43]. These SCFAs are the main products of dietary fiber fermentation and are thought to be associated with dietary fiber ameliorating metabolic diseases such as obesity. Notably, studies in the literature have indicated that SCFAs can induce a PPAR-dependent switch from lipid synthesis to utilization [46]. Furthermore, fecal metabolomics results showed that BSDF intervention significantly increased a number of fatty acid (9-nitrooctadecenoic acid, 9,10,13-trihydroxystearic acid, 9,10,13-TriHOME, undecylenic acid, sebacic acid, and 3-hydroxydodecanedioic acid), bile acids (3a,7a,12b-trihydroxy-5b-cholanoic acid, 1b,3a,7a-trihydroxy-5b-cholanoic acid, and 7-sulfocholic acid) and other metabolites (Figure 5D). Some of these metabolites (Undecylenic acid, tocopheronic acid and corchorifatty acid F) have antioxidant activity that correlates with the fact that BSDF can improve inflammation (47–49). There are also some metabolites (sebacic acid and 3-hydroxydodecanedioic acid and 9-nitrooctadecenoic acid) associated with lowering blood glucose (50–52). However, it was brought to our attention that 9,10,13-trihydroxystearic acid (a stearic acid) and 9,10,13-TriHOME (a metabolite of linoleic acid) were effective in lowering cholesterol, an effect associated with modulation of the PPAR signaling pathway. 9,10,13-TriHOME is a downstream metabolite of linoleic acid, which is a natural ligand for PPAR and can directly activate the PPAR signaling pathway [53].
## 4.5. Antibiotic experiments proved the necessity of gut microbiota
Having observed the extensive effects of BSDF on the gut microbiota and metabolites of mice, we further used antibiotic experiments to investigate the important role of the microbiota. The antibiotic experiment is a well-established technique to study the effects of different diets on artificially germ-free mice by creating artificially germ-free mice after they have been orally administered a mixture of broad-spectrum antibiotics to clear most of the flora in the gut [17, 54]. To determine whether the effect of BSDF in improving obesity in high-fat mice is dependent on the gut microbiota, we gave a mixture of broad-spectrum antibiotics orally to mice in the HFD and HFD-BSDF groups and ended the experiment after 12 weeks of feeding. After antibiotic treatment, A-HFD and A-HFD- BSDF mice showed a significant decrease in gut microbiota abundance and no significant differences in body weight, white fat weight, liver and adipose tissue lipid accumulation, or glucose homeostasis. These findings are consistent with recent studies. Oral antibiotics reduced body weight and adiposity index in HFD-fed mice, along with an increase in white fat lipolysis genes and a decrease in liver lipogenesis genes [55]. Kevin et al. [ 54] also demonstrated that gut microbiota is an independent factor affecting insulin clearance in obese mice [54]. After gavage of sterile filtered donor fecal suspension (FVT) from a lean donor in a diet-induced obese mouse model by Rasmussen et al. [ 56] obese mice showed reduced weight gain and normalized plasma glucose tolerance, but the beneficial effects associated with FVT were counteracted if the recipient mice were treated with antibiotics prior to FVT [56]. The above suggests that the obesity ameliorating effect of BSDF disappears after removal of gut microbiota, laterally indicating the need for gut microbiota.
In conclusion, our research shows that BSDF supplementation can improve obesity induced by high-fat diet and its accompanying metabolic changes. This effect is related to regulating gut microbiota and PPAR/fatty acid metabolism signal pathway. Considering the limitations of antibiotic experiments, fecal microbiota transplantation (FMT) experiments are needed to verify these experimental results. The mechanism of BSDF improving lipid metabolism by regulating gut microbiota can be further studied. However, according to our existing research results, we suggest that the development and utilization of dietary resources of BS can be increased to give full play to the potential prebiotic value of BSDF to improve obesity and metabolic diseases.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.
## Ethics statement
The animal study was reviewed and approved by the Animal Protection Professional Committee of China Agricultural University.
## Author contributions
XZ: conceptualization, methodology, investigation, data curation, and writing-original draft. LM, LD, DL, and FC: supervision. XH: writing—review and editing and funding acquisition. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.1161698/full#supplementary-material
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|
---
title: 'Influence of the center of pressure on baropodometric gait pattern variations
in the adult population with flatfoot: A case-control study'
authors:
- Luis Padrón
- Javier Bayod
- Ricardo Becerro-de-Bengoa-Vallejo
- Marta Losa-Iglesias
- Daniel López-López
- Israel Casado-Hernández
journal: Frontiers in Bioengineering and Biotechnology
year: 2023
pmcid: PMC10035659
doi: 10.3389/fbioe.2023.1147616
license: CC BY 4.0
---
# Influence of the center of pressure on baropodometric gait pattern variations in the adult population with flatfoot: A case-control study
## Abstract
Background: Adult flatfoot is considered an alteration in the foot bone structure characterized by a decrease or collapse of the medial arch during static or dynamic balance in the gait pattern. The aim of our research was to analyze the center of pressure differences between the population with adult flatfoot and the population with normal feet.
Methods: A case-control study involving 62 subjects was carried out on 31 adults with bilateral flatfoot and 31 healthy controls. The gait pattern analysis data were collected employing a complete portable baropodometric platform with piezoresistive sensors.
Results: Gait pattern analysis showed statistically significant differences in the cases group, revealing lower levels in the left foot loading response of the stance phase in foot contact time ($$p \leq 0.016$$) and contact foot percentage ($$p \leq 0.019$$).
Conclusion: The adult population with bilateral flatfoot evidenced higher contact time data in the total stance phase compared to the control group, which seems to be linked to the presence of foot deformity in the adult population.
## 1 Introduction
Advances in the quality of life in the adult population have increased life expectancy, and shoe design has evolved during this time (Rao et al., 2015; López-López et al., 2016; Navarro-Flores et al., 2022). Both these factors, which greatly influence foot morphology, have caused an increase in the development of flatfoot in the current population (Saldías et al., 2021). Thus, the flatfoot incidence in the adult population has a developing prevalence of increasing from $26.5\%$ to $29\%$ compared to normal feet (Munro and Steele, 1998; Otsuka et al., 2003).
Adult flatfoot is considered an alteration in the bone foot structure characterized by a decrease or collapse of the medial arch during static or dynamic balance in the gait pattern (Shibuya et al., 2010). Flexible flatfoot is characterized by having a normal arch in non-weight bearing without gait pattern activity or in toe walking and a flattening arch in the static phase. In rigid flatfoot, the arch remains stiff and collapsed with or without weight bearing, and the medial arch is collapsed or shows stiffness in walking (Michaudet et al., 2018). The medial arch is a resistant and elastic link from the synergy of the medial ankle ligaments (deltoid-spring ligament complex), muscle tendons, and plantar fascia (Kitaoka et al., 1997). The spring ligament is the main stabilizer of the medial arch on standing, followed by the deltoid ligament (Brodsky et al., 2009; Orr and Nunley, 2013; Mengiardi et al., 2016; Nery et al., 2018). Furthermore, medial arch stabilization is due to the posterior tibial tendon, which is the main inverter of the midfoot (Mann, 1997).
Posterior tibial tendon dysfunction (PTTD) produces changes in gait patterns resulting in a medial displacement of the center of pressure during the stance phase of gait (Imhauser et al., 2004; Neville et al., 2013; Prachgosin et al., 2015) and a decrease in ankle joint dorsiflexion influenced by decreased soleus muscle activity (Houck et al., 2009; Barn et al., 2013; Lenhart et al., 2014). It can also be argued that PTTD generates changes in the forefoot, increasing abduction and dorsiflexion (Richie, 2007); in the case of the hindfoot, an increase in plantarflexion and eversion is produced in patients with PTTD (Brodsky et al., 2009; Takabayashi et al., 2021).
Nowadays, biomechanical measurement systems of the foot are used to better analyze foot and ankle kinematic gait patterns in every situation (Fritz et al., 2022). For the standing position, the measurement systems commonly used are footprints and radiographs (Lamm et al., 2005; Menz and Munteanu, 2005). Baropodometric platforms measure plantar pressure with the arch index contact force ratio. The plantar pressure measurement and foot structure relationship has been described in previous studies (Teyhen et al., 2009). The arch index has been demonstrated to be an important parameter for studying foot structure and is described as the relation of the midfoot area relative to the total foot area, avoiding the toes (Cavanagh and Rodgers, 1987). Flatfoot measurements are described by an increased arch index, and the arch index contact force ratio is calculated by dividing the contact force on the midfoot area by the total contact force on the total foot area, avoiding the toes (Leung et al., 2004).
The center of pressure (COP) is an important measurement to quantify the force applied to the plantar area of the foot. The COP is commonly known as the gait line during the stance phase and is defined as the spatial distribution of pressure over time represented by a centroid line of each active baropodometric sensor (Cornwall and McPoil, 2000; Landorf and Keenan, 2000). However, the measurement of various features related to stance pattern gait (initial contact phase, forefoot contact phase, and flatfoot phase) and the surface contact foot area (percentage), time foot contact area (milliseconds), and frames foot area (images per second) in people with and without adult flatfoot is unclear.
The aim of our research was to analyze the center of pressure differences between the population with adult flatfoot and the population with normal feet. Our hypothesis was that adults with flatfoot have an increase in the arch index contact, augmenting foot contact regarding normal foot contact without flatfoot subjects and medializing the COP during the stance phase.
## 2.1 Design and sample
A total sample of 62 subjects was analyzed in this case-control study (7 men and 55 women). The mean age was 23.48 years old, and the ages of the recruited subjects were between 19 and 34 years old.
The participants of the study were recruited employing a consecutive non-random design in a human movement laboratory of the *Universidade da* Coruña, in the town of Ferrol (Spain), in the months from May to September 2022 (record number PID2019-108009RB-I00).
Finally, a total of 31 subjects that had developed bilateral flatfoot represented the case group, and the other 31 subjects with healthy common feet were the control group.
For this research the inclusion criteria for the flatfoot group were as follows: 1) to be older than 18 and younger than 64 years old, 2) to be healthy adults without musculoskeletal disorders, foot pain, or significant general health diseases, 3) to be without any lower limb surgery or trauma, 4) to have bilateral flatfoot, 5) to agree to sign the written informed consent form, and 6) to complete all the project stages. The exclusion criteria were as follows: 1) subjects of less than 18 or more than 65 years old, 2) subjects who suffered any relevant foot pain or disturbance, 3) subjects being treated with any medication that could affect the final results, 4) subjects who were pregnant or breastfeeding, 5) subjects who suffered any musculoskeletal disorder or neurological disease, 6) subjects without flatfoot, and 7) subjects that rejected or did not understand the guidelines to take part in the research.
For the control group, the inclusion criteria were as follows: 1) to be older than 18 and younger than 64 years old, 2) to be healthy adults without musculoskeletal disorders, foot pain, or significant general health diseases, 3) to be without any lower limb surgery or trauma, 4) to have bilateral neutral feet, 5) to agree to sign the written informed consent form, and 6) to complete all the project stages. The exclusion criteria were as follows: 1) subjects less than 18 or more than 65 years old, 2) subjects who suffered any relevant foot pain or disturbance, 3) subjects being treated with any medication that could affect the final results, 4) subjects who were pregnant or breastfeeding, 5) subjects who suffered any musculoskeletal disorder or neurological disease, and 6) subjects that rejected or did not understand the guidelines to take part in the research.
## 2.2 Procedure
The study was performed by an expert podiatrist in biomechanical assessment with more than 15 years of experience. At the first visit, subjects were interviewed by the podiatrist, who wrote down the clinical features and global health of the subjects. Then, each subject took off their shoes and socks. Subsequently, the podiatrist checked and recorded anthropometric data, such as height and weight; the body mass index (BMI) was recorded with the subject wearing light clothes and while barefoot and was calculated using Quetelet’s equation for BMI = weight/height2 (Macdonald, 1986).
To determine the subjects with flatfoot, the navicular drop (ND) test was performed. Subjects had to stand barefoot on the floor, and the navicular tuberosity was marked by the podiatrist. Next, the talus was placed in a neutral position, palpating the medial and lateral side of the talar dome of the foot with the thumb over the sinus talus and the index over the anteromedial location of the talar dome. The podiatrist performed slowly inverted and everted movements until the talus was settled in a neutral position and the depressions felt under both fingers were the same. Once the subtalar joint was in a neutral position, the distance between the navicular tuberosity and the floor was measured with a ruler and noted in millimeters. Subsequently, the same procedure was repeated in a weight-bearing stance, measuring once again the navicular tuberosity height. The ND was the difference between the two measurement heights. The procedure was repeated three times on each subject (Spörndly-Nees et al., 2011).
In addition to this measurement, a portable baropodometric platform with resistive sensors was used to analyze the normal foot arch (Neo-Plate, Herbitas, Spain), the software being a validated device for foot diagnosis (Painceira-Villar et al., 2021). This study was carried out following the protocol of Becerro de Bengoa Vallejo et al. for recording findings such as dynamic analysis related to the surface area, average COP, body weight on the lower limbs, and foot arch types of each participant in this project (Becerro de Bengoa Vallejo et al., 2013).
## 2.3 Dynamic baropodometric analysis
A complete portable pressure platform with resistive sensors with dual amplifier was used, and automatic multipoint calibration as required for use by the manufacturer was performed before the start of the investigation. The portable platform measured 40 × 40 cm, with a flat surface thickness of 8 mm and a total weight of 4 kg, and comprised 4,096 resistive sensors. Measurements were made to the nearest 0.01 kPa for each sensor. The vertical force was recorded at a frequency of 100–500 Hz. The platform was linked via an interface unit to a personal laptop including the data collection computer software Neo-Plate, version for Windows (Herbitas, Foios, Valencia, Spain), and was used according to the protocol stated by Becerro de Bengoa Vallejo et al. for recording findings such as dynamic analysis related to the stance pattern gait (ICP, FFCP, and FFP), surface contact foot area (percentage), time foot contact area (milliseconds), and frames foot area (images per second) (Becerro de Bengoa Vallejo et al., 2013).
The COP locus area (% CLA) is defined by the area ratio embraced by the COP path and a line between the start and the end points of the COP path to the foot area. For the research, the frames and percentages in each stance pattern gait were acquired. The initial contact phase (ICP) corresponded to the loading response of the stance phase and began with initial floor contact and continued until the other foot was lifted for the swing. The forefoot contact phase (FFCP) corresponded to the total stance of the stance phase, and the foot was in total contact with the ground; and finally, the flatfoot phase (FFP) corresponded to the final phase of the stance when the toe-off occurred. The dynamic was created for each foot variable by including 1) surface contact foot area (percentage), 2) time foot contact area (milliseconds), and 3) frames foot area (images per second).
## 2.4 Sample size calculation
To determine the sample size, G* Power 3.1.9.3 software (Heinrich-Heine-Universität Düsseldorf, Germany) was used to test the correlation between two paired means regarding correspondence with a Spearman correlation coefficient of 0.40 and a $95\%$ confidence interval (CI) for a two-tailed test, an α error of 0.05, and an estimated analysis power of $80\%$ (error β = $20\%$). For all the analyses, the minimum sample size was 62 participants (31 per group).
## 2.5 Ethical and legal considerations
This study was carried out from May to September 2022 and followed all the criteria of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (Vandenbroucke et al., 2014). The study was accepted by an ethics committee, and all the actions were taken according to the ethical standards for human research presented in the Declaration of Helsinki (Shrestha and Dunn, 2020). In addition, subjects were recruited by a human movement laboratory at the *Universidade da* Coruña, in the town of Ferrol (Spain), and took part in the project with record number PID2019-108009RB-I00, which received approval from the Research Ethics Committee at the University of A Coruña, Spain; file number 2019-0017; date: 6 November 2019.
## 2.6 Statistical analysis
The IBM SPSS Statistics 27.0.01.0 package for windows (Armonk, NY, United States) was applied for the analysis of the outcomes in this research. In all the analyses, significance was established at $p \leq 0.05$ with a $95\%$ confidence interval.
Normality was checked using the Kolmogorov–Smirnov test of the variables studied ($p \leq 0.05$) on the data on static plantar measurements. The results of the independent Student’s t-tests were used to decide if the data were normally distributed, and parametric statistical tests were found to be the most appropriate. The non-parametric Mann–Whitney “U” test was performed to consider contrasts among the two groups with or without adult flatfoot.
The independent variables are shown as mean, ranges of minimum to maximum, and standard deviation values for the descriptive data analysis. Concerning the categorical variables, they are presented as percentages and absolute values. The software Neo-Plate, version for Windows, was used to obtain the stance pattern gait (ICP, FFCP, and FFP) and the surface contact foot area (percentage), time foot contact area (milliseconds), and frames foot area (images per second) that were generated for each foot with or without adult flatfoot.
## 3.1 Sociodemographic data
A total sample of 62 subjects, between 19 and 34 years of age, with a mean age ± SD of 23.48 ± 5.46 years, completed all the research. Most voluntary participants were overweight, BMI of 26.51 ± 5.61 kg/m2, with statistically significant differences ($p \leq 0.001$). The main descriptive characteristics of all the subjects, as well as stratified by groups with or without bilateral adult flatfoot, are described in Table 1.
**TABLE 1**
| Characteristics | Total sample (n = 62) | Case group (n = 31) | Control group (n = 31) | p-value |
| --- | --- | --- | --- | --- |
| Characteristics | Mean ± SD (range) | Mean ± SD (range) | Mean ± SD (range) | p-value |
| Age (years) | 23.48 ± 5.46 (19–34) | 23.87 ± 4.23 (19–34) | 23.10 ± 4.21 (19–34) | 0.097 a |
| Weight (kg) | 71.26 ± 14.58 (48–98) | 77.36 ± 15.10 (56–98) | 65.16 ± 11.28 (48–89) | < 0.001 a |
| Height (cm) | 164.69 ± 7.44 (152–185) | 163.13 ± 6.54 (152–175) | 166.26 ± 8.05 (155–185) | 0.138 a |
| BMI (kg/m2) | 26.51 ± 5.61 (19.00–39.26) | 29.25 ± 6.29 (21.08–39.26) | 23.76 ± 2.97 (19.00–30.48) | < 0.001 a |
| Sex, male/female (%) | 7/55 (11.3/88.7) | 4/27 (12.9/87.41) | 3/28 (9.7/90.3) | 1.000 b |
| Foot size | 38.84 ± 2.15 (36–46) | 38.55 ± 1.71 (36–42) | 39.12 ± 2.51 (36–46) | 0.436 a |
## 3.2 Main outcome measures data
The main findings are described in Table 2. When the gait analysis of adult flatfoot was performed, we observed that left foot minimum frame FFP was lower in the case group than in the control group. There was also a difference between groups in time, percentage, and maximum frame in left foot ICP.
**TABLE 2**
| Characteristics | Total sample (n = 62) | Case group (n = 31) | Control group (n = 31) | p-value |
| --- | --- | --- | --- | --- |
| Characteristics | Mean ± SD (range) | Mean ± SD (range) | Mean ± SD (range) | p-value |
| Left foot FFCP (ms) | 240.27 ± 55.82 (157–414) | 241.29 ± 43.39 (178–306) | 239.26 ± 55.82 (157–414) | 0.582 a |
| Left foot FFCP (%) | 33.06 ± 7.13 (20–48) | 33.03 ± 5.97 (21–43) | 33.10 ± 8.22 (20–48) | 0.955 a |
| Left foot min. frame FFCP | 51.95 ± 8.83 (34–73) | 52.61 ± 8.93 (41–73) | 51.29 ± 8.81 (34–70) | 0.921 a |
| Left foot max. frame FFCP | 76.32 ± 7.53 (58–91) | 77.06 ± 7.41 (68–91) | 75.58 ± 7.70 (58–89) | 0.601 a |
| Left foot FFP (ms) | 426.74 ± 91.57 (275–632) | 445.32 ± 101.56 (305–632) | 408.16 ± 77.60 (275–593) | 0.064 a |
| Left foot FFP (%) | 56.10 ± 8.88 (38–70) | 57.68 ± 9.26 (43–70) | 54.52 ± 8.32 (38–69) | 0.191 a |
| Left foot min. frame FFP | 8.61 ± 3.75 (1–13) | 7.42 ± 4.09 (1–13) | 9.81 ± 2.98 (3–13) | 0.012 a |
| Left foot max. frame FFP | 51.95 ± 8.83 (34–73) | 52.61 ± 8.93 (41–73) | 51.29 ± 8.82 (34–70) | 0.921 a |
| Left foot ICP (ms) | 84.26 ± 37.03 (9–128) | 72.55 ± 40.48 (9–128) | 95.97 ± 29.45 (29–128) | 0.016 a |
| Left foot ICP (%) | 10.84 ± 4.91 (1–18) | 9.29 ± 5.64 (1–18) | 12.39 ± 3.50 (4–18) | 0.019 a |
| Left foot min. frame ICP | 0 ± 0.00 (0–0) | 0 ± 0.00 (0–0) | 0 ± 0.00 (0–0) | 1.000 a |
| Left foot max. frame ICP | 8.71 ± 3.89 (1–15) | 7.42 ± 4.09 (1–13) | 10.00 ± 3.25 (3–15) | 0.009 † |
| Right foot FFCP (ms) | 273.02 ± 151.21 (148–748) | 293.74 ± 192.99 (148–748) | 252.29 ± 91.40 (158–455) | 0.871 a |
| Right foot FFCP (%) | 37.37 ± 19.23 (20–99) | 39.65 ± 24.79 (21–99) | 35.10 ± 11.26 (20–58) | 0.849 a |
| Right foot min. frame FFCP | 48.66 ± 16.30 (1–71) | 47.29 ± 20.18 (1–71) | 50.03 ± 11.36 (30–68) | 0.799 a |
| Right foot max. frame FFCP | 76.39 ± 8.62 (54–91) | 77.10 ± 7.50 (68–89) | 75.68 ± 9.68 (54–91) | 0.827 a |
| Right foot FFP (ms) | 398.15 ± 139.89 (0–602) | 379.06 ± 171.40 (0–602) | 417.23 ± 98.28 (254–561) | 0.719 a |
| Right foot FFP (%) | 52.47 ± 16.88 (0–70) | 49.35 ± 21.02 (0–68) | 55.58 ± 10.84 (34–70) | 0.520 a |
| Right foot min. frame FFP | 8.18 ± 3.56 (1–14) | 8.74 ± 3.29 (1–11) | 7.61 ± 3.78 (1–14) | 0.099 a |
| Right foot max. frame FFP | 48.66 ± 16.30 (1–71) | 47.29 ± 20.18 (1–71) | 50.03 ± 11.36 (30–68) | 0.799 a |
| Right foot ICP (ms) | 80.00 ± 35.23 (9.00–138) | 85.55 ± 32.55 (9.00–108) | 74.45 ± 37.43 (9.00–138) | 0.099 a |
| Right foot ICP (%) | 10.16 ± 4.66 (1–16) | 11.00 ± 4.58 (1–16) | 9.32 ± 4.66 (1–15) | 0.171 a |
| Right foot min. frame ICP | 0 ± 0.00 (0–0) | 0 ± 0.00 (0–0) | 0 ± 0.00 (0–0) | 1.000 a |
| Right foot max. frame ICP | 8.18 ± 3.56 (1–14) | 8.74 ± 3.29 (1–11) | 7.61 ± 3.78 (1–14) | 0.099 a |
There were no statistically significant differences between groups in the gait analysis supported by the lower left and right limbs.
## 4 Discussion
This research is the first to show alterations in gait analysis in the adult population with bilateral flatfoot compared to healthy individuals. These changes in stance pattern gait can be attributed to bilateral foot conditions, which leads to flattening of the medial arch in the foot in weight-bearing and can be the cause of lack of a propulsive walk and alterations in time, percentage, and minimum or maximum frames, in all the phases of the gait.
Thus, the aim of our research was to analyze the center of pressure differences between the population with adult flatfoot and the population with normal feet. This procedure was carried out according to the protocol of previous studies (Becerro de Bengoa Vallejo et al., 2013) for recording time, percentages, and frames from the subjects in this project.
However, the results of our findings showed statistically significant differences in the initial contact phase (ICP) in the left foot between the two groups. The case group decreased the contact area in time and percentage. Anyway, characteristics of adult flatfoot are directly related to collapse of the longitudinal arch, hindfoot valgus, and forefoot abduction (Filardi, 2018). The load bearing distinctive of the ankle and foot complex in the stance phases demands determined muscular loading to bear the longitudinal arch. Tissue suffering may happen in determined foot areas, concretely on the plantar foot, and should exhibit different stiffness degrees (Filardi, 2018). According to our findings, an increase in the foot time and an increase in the contact percentage in both feet in the total contact stance phase were observed in the case group versus the control group.
It is not easy to compare the influence of these outcomes with previous studies due to the discrepancies in exclusion and inclusion criteria of the procedures and methodological differences, as we have not been capable of finding research relating to stance pattern gait (ICP, FFCP, and FFP) and the surface contact foot area (percentage), time foot contact area (milliseconds), and frames foot area (images per second) in adults with or without bilateral flatfoot.
However, based on the findings of the previous investigations carried out on this topic, we found that Fan et al. compared natural gait in subjects with flatfoot and subjects with an increased medial arch and showed that vertical ground reaction force of the plantar brings greater muscle tension to the flat-footed and a smaller rate of change of footprint area recording greater stability to the high-arched. The results of their findings showed an increase in the percentage of stance phase in subjects with flatfoot ($61.034\%$) versus subjects with a high medial arch ($60.784\%$), but they did not differentiate between right and left feet (Fan et al., 2011). In our findings, we found an increase in the case group right foot FFCP (%) ($39.65\%$) versus the control group right foot FFCP (%) ($35.10\%$).
Jankowicz-Szymańska et al. analyzed the foot longitudinal arch height in overweight adults and concluded that a high weight was correlated with a decreased height of the medial arch and an excessive body weight contributed to the progression of flatfoot despite age (Jankowicz-Szymańska et al., 2018). According to our research, the case group’s BMI was 29.25 kg/m2, regardless of age, and all the participants presented flatfoot.
We observe some limitations in our research. The baropodometric platform measurement portable system can only record and identify vertical force at a frequency of 60 Hz. Other frequencies and different forces could be relevant in the capturing and recording of force movement on the foot sole, such as shearing stress and pressure on the feet; these were not represented. Moreover, related biomechanical musculoskeletal lower limb gait pattern data, such as electromyography and kinematics parameters, were not recorded, so it is difficult to establish conclusions about these effects on the flatfoot gait parameters in every stance phase of the gait. However, this novel case-control research provides advantageous knowledge on usual foot diseases to clinicians and researchers about stance phase gait parameters in the adult population with flatfoot deformities. Furthermore, it reveals the significance of continuous investigation related to adult flatfoot and its assessment to improve the diagnosis and outcome of foot health problems and people’s quality of life.
## 5 Conclusion
The findings of this research show alterations in gait analysis in the adult population with bilateral flatfoot compared to healthy individuals. Specifically, the patients with bilateral foot problems evidenced that left foot minimum frame FFP was lower in the case group than in the control group. There was also a difference between groups in time, percentage, and maximum frame in left foot ICP, which seems to be linked with the presence of foot deformity in the adult population.
## Data availability statement
The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by the Research Ethics Committee at the University of A Coruña, Spain, file number 2019-0017; date: 6 November 2019. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
Conceptualization: LP, JB, RB-d-B-V, ML-I, DL-L, and IC-H. Data curation: LP, DL-L, and JB. Formal analysis: LP, JB, RB-d-B-V, ML-I, DL-L, and IC-H. Investigation: LP, JB, RB-d-B-V, ML-I, DL-L, and IC-H. Methodology: LP, JB, RB-d-B-V, ML-I, DL-L, and IC-H. Supervision: LP, JB, RB-d-B-V, ML-I, DL-L, and IC-H. Writing—original draft: LP, JB, RB-d-B-V, ML-I, DL-L, and IC-H. Writing—review and editing: LP, JB, RB-d-B-V, ML-I, DL-L, and IC-H.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: The effect of fibroblast growth factor 21 on a mouse model of bovine viral
diarrhea
authors:
- Dan Zhao
- Yu-Hao Song
- Jin-Ming Song
- Kun Shi
- Jian-Ming Li
- Nai-Chao Diao
- Ying Zong
- Fan-Li Zeng
- Rui Du
journal: Frontiers in Veterinary Science
year: 2023
pmcid: PMC10035660
doi: 10.3389/fvets.2023.1104779
license: CC BY 4.0
---
# The effect of fibroblast growth factor 21 on a mouse model of bovine viral diarrhea
## Abstract
Previously, we researched that bovine viral diarrhea virus (BVDV) induced a very significant increase in fibroblast growth factor 21 (FGF21) expression in mouse liver and that FGF21 was increased in the peripheral blood of BVD cattle and BVD mice. To determine the role of FGF21 in relieving clinical symptoms and inhibiting the intestinal damage caused by BVDV in BVD development in mice, BALB/c mice were intraperitoneally injected with cytopathic biotype (cp) BVDV-LS01 (isolated and identified by our group) to establish a BVD mouse model. The role of FGF21 in the BVD mouse model was investigated by injecting the mice with FGF21. The animals were divided into control, BVDV challenge, BVDV + FGF21, BVDV + FGF21Ab (anti-FGF21 antibody), and BVDV + IgG (immunoglobulin G) groups. The stool consistency, the degree of bloody diarrhea, histopathological changes, inflammatory cell infiltration, weight loss percentage, and detection of BVDV in the feces of the mice were examined, and the pathological changes and inflammatory cytokine expression were analyzed. The results showed that after BVDV challenge, the average BVD mouse model score of the BVDV mice was 11.6 points. In addition to mild diarrhea and tissue damage, BVDV was detected in the stools of 13 BVDV mice. Only two mice in the control group had scores (both, 1 point each). The comprehensive scoring results demonstrated the successful establishment of the BVD mouse model. FGF21 alleviated the clinical symptoms in the BVD mice and significantly improved weight loss. Furthermore, FGF21 inhibited the BVDV-induced leukocyte, platelet, and lymphocyte reduction while inhibiting the expression of BVDV-induced inflammatory factors. In the BVD mice, FGF21 promoted duodenal epithelial cell proliferation, thereby significantly improving the damage to the cells. In conclusion, FGF21 exerted a good therapeutic effect on the BVD mouse model.
## 1. Introduction
Bovine viral diarrhea (BVD) is an acute, highly contagious disease caused by BVD virus (BVDV). BVD is widely prevalent worldwide and causes huge economic losses to the global livestock industry. The main clinical symptoms of BVD are subclinical infection, reproductive failure, respiratory and intestinal diseases due to immunosuppression, thrombocytopenia, and bleeding, and fatal mucosal disease, which is one of the most severe clinical forms of BVDV infection, with mortality rates as high as $100\%$ [1]. In addition to mainly infecting cattle, pigs, sheep, goats, and deer, camels and other wild animals are also susceptible hosts for BVDV, which causes serious harm to them [2, 3]. Along with border disease virus and classical swine fever virus, BVDV is a single-stranded RNA virus that belongs to the family Flaviviridae, genus Pestivirus. BVDV genotypes are classified into BVDV1, BVDV2, and HoBi-like viruses based on differences in their 5′ untranslated region, Npro, or E2 genes (HoBi-like viruses are listed as BVDV3, but the International Committee on Taxonomy of Viruses has not confirmed the BVDV3 viral typing) [4]. Each genotype is further divided into multiple sub-genotypes. At least 21 BVDV1 sub-genotypes (1a−1u), four BVDV2 sub-genotypes (2a−2d), and four HoBi-like sub-genotypes have been identified [3, 5, 6]. The main prevalent BVDV types in China are BVDV2 and BVDV1, of which the main subtypes are BVDV1a and BVDV1b [7]. BVDV is divided into the cytopathic biotype (cp) and the non-cp (ncp) based on whether it can cause cellular pathological changes (8–10).
Fibroblast growth factor 21 (FGF21) is a secreted protein with multiple biological functions. Nobuyuki Itoh's team successfully cloned the FGF21 gene in 2000 [11]. In 2005, Kharitonenkov et al., scientists at the Lilly laboratory in the United States, reported the role of FGF21 in diabetes treatment and described the biological significance of FGF21 protein as a new metabolic regulator [12]. FGF21 contains 210 amino acids, is highly conserved in mammals, and has a molecular weight of −22.3 kDa. Human FGF21 contains 209 amino acids and its coding sequence is very similar to that of mice ($75\%$ homology) [13]. FGF21 is mainly expressed in liver tissue; it does not bind to heparan sulfate protein glycans or has a very low affinity for them, which enables its circulatory transport and endocrine action. Research on FGF21 in animal and human metabolic diseases such as obesity and diabetes are relatively mature [14] and FGF21 is closely related to diseases or pathological processes such as metabolic syndrome and non-alcoholic fatty liver [15, 16]. Recent studies reported that FGF21 expression is significantly increased in diverse inflammatory diseases (17–19). These findings suggested that, as a biomarker, FGF21 is becoming a research hotspot for targeted drugs for various metabolic diseases. Many current studies reported that FGF21 is significantly elevated in the peripheral blood of patients with hepatitis B, hepatitis C, and HIV [20]. FGF21 is closely correlated with disease severity, suggesting that it has an antiviral effect, but its mechanism has not been thoroughly explored.
Previously, we researched that BVDV induced a very significant increase in FGF21 expression in mouse liver and that it played an endocrine role in blood circulation. Furthermore, FGF21 expression was increased in the peripheral blood of BVD cattle and BVD mice. Currently, few studies have reported on FGF21 in viral diseases. Therefore, to determine the role of FGF21 in relieving the clinical symptoms and inhibiting the intestinal damage caused by BVDV in BVD development in mice, we established a BVD mouse model by infecting BALB/c mice with the BVDV-LS01 strain we previously isolated and identified, and injected the BVD mice with recombinant FGF21 protein. The mechanism of FGF21 in the BVD mice was identified based on changes in the clinical symptoms, body weight, histopathology, complete blood count (CBC), and cellular inflammatory factors. This study provided evidence for new research directions regarding the role of FGF21 in viral diseases and its application in BVD adjuvant therapy and prevention and control.
## 2.1. Experimental design and BVD mouse model establishment
This study was conducted after receiving Jilin Agriculture University Institutional Animal Care and Use Committee approval (JLAU08201409). The experimental procedures were performed in compliance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals (NIH Publications No. 8,023).
The model objects were 6–8-week-old male BALB/c mice [animal license number: SCXK (Jing) 2019–0008, purchased from Beijing Huafukang Biotechnology Co., Ltd., Beijing, China] [21]. In total, 75 mice were used (15 mice per group). The mice were randomly divided into five groups. Group A mice were injected with Dulbecco's modified Eagle's medium (DMEM) as the negative control group. Group B–E mice were injected intraperitoneally with 0.3 mL tissue culture fluid [median tissue culture infective dose (TCID50) = $\frac{106.6}{0.1}$ mL containing BVDV-LS01]. Group B (BVDV challenge, BVDV) comprised BVD mice. Group C (BVDV + FGF21) mice were injected intraperitoneally with mouse recombinant FGF21 protein [injection dose, 4 mg/kg/day [22]] donated by the Jilin Institute of Agricultural Science and Technology on day 6 of BVDV challenge. From day 4 of the experiment onwards, group D (BVDV + FGF21Ab) mice were intraperitoneally injected with 5 mg/kg anti-FGF21 antibody once every other day, while group E (BVDV + IgG) mice were intraperitoneally injected with 5 mg/kg immunoglobulin G (IgG) antibody every other day [23].
The clinical status of the mice was observed every day after the challenge. The consistency of the mouse feces, presence of bloody stool, tissue damage, inflammatory cell infiltration, and fecal virus detection were recorded. The observations were scored according to the disease scoring standard, where a comprehensive score ≥ 8 points (including BVDV detection) indicated that the mouse qualified as a BVD mouse model. Tables 1, 2 depict the groupings and disease scoring criteria, respectively. The mice were culled when they exhibited obvious clinical symptoms, and the role of FGF21 in the mice was subsequently analyzed from the clinical symptom, tissue change, and inflammatory factor aspects.
## 2.2. Clinical symptoms and body weight changes in BVD mouse model
The diet, metabolism, and mental state of the mice were observed daily after BVDV challenge, and the mouse body weight changes were recorded. Whether FGF21 played a role in BVD development was evaluated based on the clinical symptoms and body weight changes.
## 2.3. BVDV detection in BVD mouse tissues and organs
The mice were observed daily for clinical symptoms and were killed when they exhibited listlessness, rough coat, decreased diet, shapeless feces, or obvious diarrhea. Subsequently, blood was collected from the eyes, and the kidney, spleen, liver, duodenum, and colon tissues were collected.
The fresh feces (500 mg) was obtained from the mouse rectum and soaked in 200 μL pre-cooled diethylpyrocarbonate (DEPC) water for 15 min to dissolve the virus contained in the feces. The DEPC water was centrifuged at 4°C at 10,000 × g for 5 min and the supernatant was stored for subsequent use.
The tissue samples (100 mg) was ground into powder with liquid nitrogen, and 1 mL pre-cooled saline was added. After repeated freeze-thawing, the sample was centrifuged at 10,000 × g at 4°C for 5 min, and the supernatant was obtained.
Total RNA was extracted from the fecal and tissues samples with an RNA extraction kit (Takara Bio, Shiga, Japan). Complementary DNA (cDNA) was synthesized by reverse transcription and the BVDV content was analyzed by qRT-PCR using a Prime Direct™ Probe RT-qPCR Mix (Takara Bio). The relative BVDV content was calculated in relation to that of the normal control. Table 3 shows the primers used in this study.
**Table 3**
| Primer | Sequence (5′ → 3′) |
| --- | --- |
| BVDV-F | CCATGCCCTTAGTAGGACTAG |
| BVDV-R | CTCCATGTGCCATGTACAGCAG |
| IL6-F | ATGAAGTTCCTCTCTGCAAGAGAC |
| IL6-R | CACTAGGTTTGCCGAGTAGATCTC |
| IL1β-F | TTCATCTTTGAAGAAGAGCCCAT |
| IL1β-R | TCGGAGCCTGTAGTGCAGTT |
| MCP1-F | GGCTCAGCCAGATGCAGT |
| MCP1-R | GAGCTTGGTGACAAAAACTACAG |
| TNFα-F | CACCACCATCAAGGACTCAA |
| TNFα-R | AGGCAACCTGACCACTCTCC |
## 2.4. Histopathological examination of the duodenum
The BVD mouse model exhibited obvious duodenal lesions and relatively severe tissue damage. Therefore, we performed an in-depth study focusing on the duodenal lesions after FGF21 injection. Part of the duodenum was fixed in $4\%$ formaldehyde, paraffin-embedded, and sectioned to 6-μm thickness. The tissue sections were stained with hematoxylin–eosin (H&E) and for E-cadherin for pathological analysis.
## 2.5. CBC testing
The peripheral blood was collected from the inferior vena cava and anticoagulant (trisodium citrate) was added to the collection tube. The platelets, leukocytes, and lymphocytes were analyzed with a CBC analytical instrument.
## 2.6. Analysis of inflammatory cytokines in the blood
Peripheral blood that had been collected in non-anticoagulant-containing blood collection tubes was centrifuged at 1,500 × g for 30 min at 4°C to obtain the plasma. The cytokines IL-6, TNF-α, and MCP1 were measured from the cell-free supernatants using enzyme-linked immunosorbent assay kits (Becton, Dickinson and Company, America).
## 2.7. In vitro culture of duodenal tissue and analysis of inflammatory cytokines
The intestinal contents were cleaned from 2-cm long mouse duodenum samples, the tissue opened longitudinally, soaked in phosphate-buffered saline (PBS, containing streptomycin and penicillin) and washed for 1–2 min, then washed 5–6 times in antibiotic-free RPMI 1640 medium. Subsequently, the tissue was cut into 1-cm2 pieces, then incubated in antibiotic-free RPMI 1640 medium at 37°C for 24 h.
Total RNA from virus-infected duodenum cells was extracted using an RNA extraction kit following the instructions for specific operations. cDNA was synthesized by reverse transcription according to the PrimeScript™ RT Reagent Kit. The reverse transcription condition was 42°C for 1.5 h while the PCR conditions were 94°C for 30 s (one cycle), followed by 95°C for 15 s and 58°C for 1 min for 40 cycles. Blank control wells were set up for each amplification and each experiment was repeated no less than three times. The internal reference gene was β-actin. The target genes were quantified using the relative quantitative analysis method (comparative threshold cycle [2−ΔΔCt] method). Table 3 lists the sequences of the primers used.
## 2.8. Analysis of FGF21 promotion of duodenal epithelial cell proliferation
To verify whether FGF21 was involved in duodenal epithelial cell proliferation and regeneration, the BVDV-infected mice were injected intraperitoneally with 30 mg/kg 5-bromodeoxyuridine (BrdU) at 2 h, 24 h, and 48 h before the end of BVDV infection.
## 2.9. Statistical analysis
The data of each group are expressed as the mean ± SEM. Statistical analysis was performed with one-way analysis of variance using GraphPad Prism 8.0.2 (GraphPad Software Inc., La Jolla, CA, USA). The mean between two groups was compared using the t-test. $P \leq 0.05$, $p \leq 0.01$, and $p \leq 0.001$ indicated a statistically significant difference, highly statistically significant difference, and statistically extremely significant difference, respectively.
## 3.1. FGF21 injection alleviated clinical symptoms in mice after BVDV challenge
Score statistics were performed according to the BVD mouse model scoring criteria. Figure 1 depicts the results. The mean statistical scores of the BVDV and BVDV + IgG groups were 11.4 and 11.3, respectively. On day 3 after challenge, some mice exhibited signs of lethargy, rough coats, mild diarrhea, eating less, and crowding. The autopsy results of these mice demonstrated a few hemorrhagic spots of different sizes in the liver and lung and obviously enlarged spleen, mesenteric lymph nodes, and paraduodenal Peyer patches.
**Figure 1:** *BVD mouse model score statistics.*
The BVDV + FGF21Ab mice exhibited more obvious clinical symptoms, where the average statistical score was 12.1 points. Some mice developed obvious diarrhea and lethargy on day 2 after FGF21Ab injection. The autopsy results of the BVDV + FGF21Ab mice revealed a few hemorrhagic spots in the liver and lung and obviously swollen spleen, mesenteric lymph nodes, and paraduodenal Peyer patches.
The BVDV + FGF21 group had an average statistical score of 6.4 points. The BVDV + FGF21 mice had basically normal food intake, smooth fur, no diarrhea symptoms, no hemorrhagic spots in the liver and lungs, and slight swelling of the spleen.
The average statistical score of the control mice was 0.13 points, and they exhibited no obvious clinical symptoms or pathological changes throughout experiment.
## 3.2. FGF21 injection slowed BVDV challenge-induced weight loss
The mice were weighed and recorded daily; Figure 2 depicts weight changes. Throughout the experiment, there were extremely significant differences in the body weight changes between the control and BVDV groups and the BVDV + IgG and BVDV + FGF21Ab groups ($p \leq 0.001$). The BVDV + FGF21 group recorded significantly different weight gain compared the BVDV and BVDV + IgG groups ($p \leq 0.01$). The results indicated that FGF21 attenuated weight loss in the BVD mice during BVD development.
**Figure 2:** *Mouse body weight changes.*
## 3.3. BVDV detection results in mice after challenge
Figure 3 depicts the BVDV detection results in the mice after challenge. Ten days after BVDV challenge, the BVDV mice exhibited obvious clinical symptoms such as loose stool and diarrhea. BVDV was detected in the mouse liver, lung, spleen, kidney, duodenum, colon, jejunum, ileum, and feces. The BVDV content was highest in the mouse spleen, followed by that in the duodenum, colon, and feces.
**Figure 3:** *BVDV levels in mouse tissues.*
## 3.4. CBC analysis
Figure 4 depicts the leukocyte, platelet, and lymphocyte levels in the mouse blood after challenge. The BVDV, BVDV + IgG, and BVDV + FGF21Ab mice had significantly decreased leukocyte, platelet, and lymphocyte levels ($p \leq 0.001$). The platelet and lymphocyte contents in the BVDV, BVDV + FGF21Ab, and BVDV + IgG groups were not significantly changed, while the leukocyte contents increased significantly after FGF21 injection ($p \leq 0.01$).
**Figure 4:** *Alterations in mouse leukocyte (WBC) (A), platelet (PLT) (B), and lymphocyte (C) levels.*
## 3.5. Pathological changes of the duodenum
Figure 5 depicts the histopathological observations of the H&E-stained mouse duodenal tissue. The control group demonstrated intact duodenal epithelial phenotype, good crypt structure, and no lymphocyte infiltration in the mucosa. The duodenal tissue of the BVDV and BVDV + FGF21Ab mice exhibited edema, local epithelial ulcers, submucosal inflammatory cell infiltration, and duodenal villus epithelium shedding after challenge. However, injection of the FGF21 recombinant protein was followed by subsiding duodenal intestinal wall edema, significantly relieved ulcer, and the epithelium was significantly improved and tended to be complete.
**Figure 5:** *Main pathological changes in the mice (H&E staining, × 100; E-cadherin staining, × 200). (A–D) H&E staining of duodenal epithelial cells in the control (A), BVDV (B), FGF21 (C), and BVDV+FGF21Ab groups (D). (E–H) E-cadherin staining of duodenum in the control (E), BVDV (F), FGF21 (G), and BVDV+FGF21Ab groups (H).*
The changes in the mouse duodenal injury were observed using E-cadherin staining, which demonstrated that E-cadherin expression was reduced. The duodenal villous epithelium of the BVDV and BVDV + FGF21Ab mice was damaged and shed after challenge, and the intestinal epithelium was seriously damaged after FGF21Ab injection. After FGF21 recombinant protein injection, the intestinal tract was obviously improved and tended to be complete.
## 3.6. FGF21 inhibited the expression of BVDV-induced inflammatory factors
Figure 6 depicts the results of FGF21 inhibition of BVDV-induced inflammatory factor expression. After challenge, the peripheral blood of the BVDV, BVDV + FGF21Ab, and BVDV + IgG mice contained significantly increased TNF-α, MCP1 (both, $p \leq 0.01$), and IL-6 levels ($p \leq 0.001$). However, after FGF21 injection, the peripheral blood IL-6 and TNF-α levels were significantly decreased ($p \leq 0.01$ and $p \leq 0.05$, respectively), while MCP1 levels were decreased but not significantly (Figure 6A).
**Figure 6:** *Mouse plasma and duodenum levels of inflammatory cytokines. (A, B) Inflammatory cytokine levels in the plasma (A) and the duodenum (B). (C) Inflammatory cytokine mRNA levels in the duodenum.*
After the duodenal tissue had been cultured in vitro for 24 h, the proinflammatory cytokine content in the culture supernatant was detected by ELISA. The results demonstrated that the BVDV, BVDV + FGF21Ab, and BVDV + IgG mice produced significantly more IL-6, TNF-α, and MCP1 than the control group ($p \leq 0.001$). However, after the FGF21 injection, the BVDV + FGF21 mice had significantly decreased duodenal IL-6 content ($p \leq 0.01$) while the TNF-α and MCP1 contents were not significantly decreased (Figure 6B).
Similar to the results of the peripheral blood and duodenal tissue in vitro culture, the duodenal IL-6, TNF-α, and IL-1β mRNA levels in the BVD, BVDV + FGF21Ab, and BVDV + IgG mice were significantly increased ($p \leq 0.01$), as was MCP1 ($p \leq 0.05$). After the FGF21 injection, the IL-6, TNF-α, IL-1β, and MCP1 mRNA levels were all decreased in the BVDV + FGF21 group, but only the MCP1 decrease was significantly different ($p \leq 0.05$) (Figure 6C).
## 3.7. FGF21 promoted duodenal epithelial cell proliferation
Figure 7 depicts the results of FGF21 promotion of duodenal epithelial cell proliferation. BVD mice injected with FGF21 recombinant protein demonstrated a significantly larger number of BrdU-positive duodenal intestinal epithelial cells than the control mice.
**Figure 7:** *BrdU staining of mouse duodenal epithelial cells. (A–C) BrdU staining results at 2 h (A), 24 h (B), and 48 h (C) in the control group. (D–F) BrdU staining results at 2 h (D), 24 h (E), and 48 h (F) in the BVDV group. (G–I) BrdU staining results at 2 h (G), 24 h (H), and 48 h (I) in the FGF21 group. (J–L) BrdU staining results at 2 h (J), 24 h (K), and 48 h (L) in the BVDV+FGF21Ab group.*
## 4. Discussion
Previously, we determined that FGF21 expression in BVD bovine and BVD mouse peripheral blood exhibited the same upward trend, indicating that BVDV infection in animals stimulates high FGF21 expression. These results were similar to that of Xia et al. regarding FGF21 and ulcerative colitis mice [24]. To determine how FGF21 functions in BVD mice, we performed animal experiments based on a BVD mouse model, where BVD mice were injected intraperitoneally with mouse FGF21Ab, IgG, or recombinant FGF21 protein. The results demonstrated that after BVDV challenge, the BVDV and BVDV + IgG groups essentially exhibited the same clinical symptoms and necropsy changes. The results revealed FGF21 delayed or alleviated the clinical symptoms in the BVD mice and significantly improved weight loss, which provided a characterization basis for the role of FGF21.
Diarrhea is one of the main clinical symptoms of BVD. The main pathological changes are hemorrhage spots of different sizes in the intestinal mucosa; swollen and bleeding mesenteric lymph nodes (25–29); intestinal wall thickening; intestinal lymph node enlargement; necrosis and shedding of mucosal epithelial cells in the small intestine, cecum, and colon; lymphocytic infiltration of the mucosal lamina propria; hyaline degeneration and fibrinoid necrosis of the submucosal arteries; crypt epithelial shedding; and vacuolization [28, 29]. The histopathological observation revealed that the BVD mice had hemorrhage spots in the intestinal mucosa and lymphocyte infiltration in the mucosal lamina propria, especially duodenal epithelial cell necrosis and shedding, which was consistent with the previous study [30]. Gumbiner [31] reported that E-cadherin is a calcium-dependent cell adhesion protein expressed in epithelial cells that is critical for maintaining intestinal epithelial integrity and is indirectly involved in defense against enteropathogens. Stephane et al. [ 32] reported that the loss of E-cadherin expression led to the loss of adherens junctions and desmosomes, thereby resulting in apoptosis and cell shedding. E-cadherin function in the gut has been implicated in pathological processes. The E-cadherin staining results in this study demonstrated that E-cadherin expression was decreased in the duodenal epithelial cells of the BVDV and BVDV + FGF21Ab mice. FGF21 injection promoted duodenal epithelial cell proliferation and significantly improved the damage to the BVD mouse duodenal epithelial cells.
Proinflammatory cytokines such as IL-6, TNF-α, and MCP1 are key in the pathogenesis of intestinal inflammation. Diarrhea caused by intestinal inflammation is one of the main clinical symptoms of BVD. The autopsy results of the BVD mice demonstrated that duodenal inflammation was the most serious symptom. Therefore, the cytokine levels in the peripheral blood and duodenal tissue of the mice in all groups were detected, where IL-6, TNF-α, and MCP1 in the peripheral blood and duodenum of the BVD mice were significantly increased ($p \leq 0.01$). Specifically, IL-6 was extremely significantly increased ($p \leq 0.001$). The IL-6 and TNF-α mRNA levels were highly significantly increased in the duodenum of BVD mice ($p \leq 0.01$). Furthermore, the leukocyte, platelet, and lymphocyte numbers in the peripheral blood of the BVD mice were decreased, and the leukopenia was highly significantly different ($p \leq 0.01$), which indicated that BVDV caused intestinal-related inflammation in the mice, which resulted in epithelial cell damage.
However, after FGF21 injection, the peripheral blood leukocytes of the BVD mice were significantly increased to normal levels. Notably, IL-6 in the peripheral blood and duodenum was highly significantly decreased ($p \leq 0.01$). While TNF-α and MCP1 were not significantly different, MCP1 mRNA levels in the duodenum were significantly different ($p \leq 0.05$). The results proved that FGF21 inhibited the BVDV-induced inflammatory factor expression. This result was similar to that of Singhal et al. [ 33] and Johnson et al. [ 34], who reported that FGF21 might be important in inhibiting and regulating inflammation, respectively.
Our CBC test results were consistent with those of a previous study that reported that different BVDV strains can cause leukocyte, lymphocyte, and platelet reduction in mouse peripheral blood [35]. Generally, virus infections cause leukocyte reduction in the peripheral blood. In our experiment, the leukocyte levels returned to the normal range after FGF21 injection.
Although we only focused on the related inflammatory factors in the duodenum with obvious tissue lesions and analysis of the clinical symptoms, body weight changes, CBC testing, histopathology, and changes of inflammatory factors in the BVD mice, the results proved that FGF21 is involved in the improvement and treatment of BVD occurrence and development in mice. The results provided new ideas for BVD prevention and treatment and further research.
## 5. Conclusions
We successfully established a BVD mouse model. FGF21 injection alleviated the clinical symptoms of BVD in the mice and significantly improved the weight loss in the BVD mice. FGF21 inhibited BVDV and reduced leukocyte, platelet, and lymphocyte levels in the peripheral blood. Furthermore, FGF21 inhibited the expression of BVDV-induced inflammatory factors. Moreover, FGF21 promoted duodenal epithelial cell proliferation and significantly improved the damage to the duodenal epithelial cells in the BVD mice. In conclusion, FGF21 exerted a good therapeutic effect on the BVD mouse model.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.
## Ethics statement
This study was conducted following recommendations of the Jilin Agriculture University Institutional Animal Care and Use Committee (JLAU08201409) and the experimental procedures were performed in compliance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals (NIH Publications No. 8023).
## Author contributions
DZ, Y-HS, and J-MS designed the study. KS, J-ML, N-CD, and YZ assisted with data analysis. DZ, F-LZ, and RD performed animal tests, interpreted the results, and wrote the manuscript. All authors have read and approved the final version of manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
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---
title: MiRNA-Seq reveals key MicroRNAs involved in fat metabolism of sheep liver
authors:
- Xiaojuan Fei
- Meilin Jin
- Zehu Yuan
- Taotao Li
- Zengkui Lu
- Huihua Wang
- Jian Lu
- Kai Quan
- Junxiang Yang
- Maochang He
- Tingpu Wang
- Yuqin Wang
- Caihong Wei
journal: Frontiers in Genetics
year: 2023
pmcid: PMC10035661
doi: 10.3389/fgene.2023.985764
license: CC BY 4.0
---
# MiRNA-Seq reveals key MicroRNAs involved in fat metabolism of sheep liver
## Abstract
There is a genetic difference between Hu sheep (short/fat-tailed sheep) and Tibetan sheep (short/thin-tailed sheep) in tail type, because of fat metabolism. Previous studies have mainly focused directly on sheep tail fat, which is not the main organ of fat metabolism. The function of miRNAs in sheep liver fat metabolism has not been thoroughly elucidated. In this study, miRNA-Seq was used to identify miRNAs in the liver tissue of three Hu sheep (short/fat-tailed sheep) and three Tibetan sheep (short/thin-tailed sheep) to characterize the differences in fat metabolism of sheep. In our study, Hu sheep was in a control group, we identified 11 differentially expressed miRNAs (DE miRNAs), including six up-regulated miRNAs and five down-regulated miRNAs. Miranda and RNAhybrid were used to predict the target genes of DE miRNAs, obtaining 3,404 target genes. A total of 115 and 67 GO terms as well as 54 and 5 KEGG pathways were significantly (padj < 0.05) enriched for predicted 3,109 target genes of up-regulated and 295 target genes of down-regulated miRNAs, respectively. oar-miR-432 was one of the most up-regulated miRNAs between Hu sheep and Tibetan sheep. And SIRT1 is one of the potential target genes of oar-miR-432. Furthermore, functional validation using the dual-luciferase reporter assay indicated that the up-regulated miRNA; oar-miR-432 potentially targeted sirtuin 1 (SIRT1) expression. Then, the oar-miR-432 mimic transfected into preadipocytes resulted in inhibited expression of SIRT1. This is the first time reported that the expression of SIRT1 gene was regulated by oar-miR-432 in fat metabolism of sheep liver. These results could provide a meaningful theoretical basis for studying the fat metabolism of sheep.
## 1 Introduction
MicroRNAs (miRNAs) are a kind of small RNA, whose length is about 22 nt (nucleotide). Previous studies revealed that miRNAs have distinctive biological characteristics in proliferation, differentiation, metabolism, and disease (Lin et al., 2020). In animals and plants, miRNAs are involved in the regulation of post-transcriptional gene expression. miRNAs usually bind to the 3'UTR region of mRNA to inhibit the post-transcriptional translation of target genes and enhance the degradation or repress the translation of mRNAs (Rouleau et al., 2017). In *Chinese indigenous* sheep, sheep can be divided into short/thin-tailed sheep, long/thin-tailed sheep, short/fat-tailed sheep, long/fat-tailed sheep, and fat-buttock sheep, because of the degree of fat deposition along the tail vertebra and the length of the tail vertebra (Lu et al., 2020). Hu sheep (short/fat-tailed sheep) and Tibetan sheep (short/thin-tailed sheep) are two *Chinese indigenous* sheep breeds with different tail types. Tail fat is the main energy source for sheep migration, drought, and food deprivation (Luo et al., 2021). However, studies mainly focus directly on tail fat to study fat metabolism, which is not the main organ of fat metabolism (Zhou et al., 2017; Li et al., 2020). The liver is a primary organ of fat metabolism, fat metabolization in the liver is equally important to its metabolism in fat tissue. Triglyceride is one of the lipids mostly formed in the liver, whose metabolism is mainly controlled through liver parenchyma cells. And the degree of fat deposition in fat tissue depends on the fat flow in the liver for fat synthesis. ( Carotti et al., 2020). There are differences in the liver of sheep with different tail types that can reflect the underlying mechanism of sheep fat metabolism.
With the development of high-throughput sequencing technology, miRNA-Seq has been widely used in the omics analysis of humans (Zheng et al., 2016), mice (Peng et al., 2013), chickens (Sikorska et al., 2021) and cows (Zhang et al., 2019; Chen et al., 2020) species. And researchers showed that miRNA has an important function in fat metabolism (Deng et al., 2020). Many studies have explored the role of miRNA in liver fat metabolism disease models to clarify the process of disease occurrence. In a non-alcoholic fatty liver disease (NAFLD) mouse model, Lin et al. identified that miR-29a not only made body weight gain decrease, but also the subcutaneous, visceral, and intestinal fat accumulation and hepatocellular steatosis (Jeon and Carr., 2020). In the non-alcoholic steatohepatitis (NASH) mouse model, inhibiting the expression of miR-21 decreased liver injury, inflammation, and fibrosis (SOARES et al., 2016). In a high-fat-induced mouse model, miR-378 targeted AMPK to promote the occurrence of liver fibrosis and inflammation (Lin et al., 2019). Meanwhile, researchers have analyzed the expression patterns of miRNA in the liver of pigs (Li et al., 2021) and cows (Liang et al., 2017) across periods. These studies represented a foundation for further understanding the molecular regulatory mechanisms of liver tissue fat metabolism.
Because there is a genetic difference between Hu sheep (short/fat-tailed sheep) and Tibetan sheep (short/thin-tailed sheep) in tail type, comparing their livers’ miRNA features may find miRNAs affecting the fat metabolism of Hu sheep (short/fat-tailed sheep) and Tibetan sheep (short/thin-tailed sheep). Our results could provide a theoretical basis for further study of the fat metabolism between different sheep breeds.
## 2.1 Tissue collection and sequencing
All animal experiments were approved by the Science Research Department of the Institute of Animal Sciences, Chinese Academy of Agriculture Sciences (IAS-CAAS). Ethical approval complied with the Animal Ethics Committee of the IAS-CAAS (No. IAS 2019-49). Samples of liver tissues were collected from three Hu sheep (short/fat-tailed sheep, Yongdeng, Gansu, China) and three Tibetan sheep (short/thin-tailed sheep, Yushu, Qinghai, China). Samples from Hu sheep are named HG1, HG2, and HG3, respectively. Samples from Tibetan sheep are named ZG1, ZG2, and ZG3, respectively. All sheep were males and slaughtered at age 1.5. All samples were frozen in liquid nitrogen in 1.5 mL RNase-free freezing tubes and stored at −80°C for use. Trizol (Invitrogen, Carlsbad, CA, United States) was used to extract total RNA. A NanoDrop2000 spectrophotometer (Thermo Fisher Scientific, Wilmington, MA, United States) was used to quantify RNA purity at 260 and 280 nm. Six libraries were constructed with a commercial sequencing provider: BGI (Mortazavi et al., 2008; Wang et al., 2009). An Agilent 2,100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, United States) was used to examine the integrity of the library. All FASTQ sequencing files have been stored in the Sequence Read Archive (accession numbers PRJNA785102).
## 2.2 Sequence analysis
The cleaning of the raw data was performed based on: 1) poor quality sequencing reads, 2) reads with 5′ adaptors and without 3’ adaptors; 3) reads without insert segments; and 5) reads containing poly A; and 6) reads longer than 18 nucleotides. To ensure that each small RNA had a unique label, according to the order of possible ribosomal RNA, small conditional RNA, small nucleolar RNA, small nuclear RNA (snRNA), and transfer RNA sequences to annotate (Balaskas et al., 2020). The sheep reference genome Oar_v3.1 (https://www.ebi.ac.uk/ena/browser/view/GCA_000298735.1, accessed on 20 February 2021) and miRbase21.0 (http://www.mirbase.org, accessed on 20 February 2021) was used to map clean reads with Bowtie2 (Langmead et al., 2009).
## 2.3 MiRNA identification and differential expression analysis
MiRDeep2 software was used to predict novel miRNAs (Kern et al., 2020). The expression of miRNA was calculated by absolute numbers counting of molecules using unique molecular identifiers (Pflug and Haeseler., 2018). Moreover, the lengths of small RNAs (sRNAs) and the proportion of miRNAs were calculated. The “oar-miR-" and “novel_mir” terms identify known miRNAs and novel miRNAs, respectively. Hu sheep is set as a control, DESeq2 software was used to perform the differential expression analysis, in which the statistical significance was set at a fold discover rate (FDR) adjusted p-value (padj ≤0.05) by Benja-mini-Hochberg and |Log2Foldchange| > 0.5.
## 2.4 Target gene prediction of miRNAs and gene function enrichment analysis
Miranda (John et al., 2004) and RNAhybrid (Lin et al., 2022) were used to find more accurate targets of differentially expressed miRNA (DE miRNA). g: Profiler was used for genes function enrichment analysis, in which the statistical significance was set at a fold discover rate (FDR) adjusted p-value (padj ≤0.05) by Benjamini–Hochberg (Raudvere et al., 2019). There are 3,109 target genes of upregulated and 295 target genes of downregulated DE miRNAs were annotated with Gene Ontology (GO) (http://www.geneontology.org/, accessed on 19 January 2022) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.genome.jp, accessed on 19 January 2022), respectively.
## 2.5 Quantitative real-Time PCR
Steam-loop real-time qPCR was used to validate miRNA sequencing data from seven randomly selected miRNAs (oar-miR-432, novel_mir70, novel_mir21, nov-el_mir64, novel_mir58, oar-miR-19b, and oar-miR-29b). The total RNA of each sample was reversed transcribed with a miRNA 1st Strand cDNA Synthesis Kit. RT-qPCR was performed on a LightCycler® 480II qPCR system using miRNA universal SYBR qPCR Master Mix (Vazyme, Nanjing, China). U6 was used as the reference gene. To detect the expression of SIRT1, HiScript III 1st Strand cDNA Synthesis Kit (+gDNA wiper) and ChamQ universal SYBR qPCR Master Mix (Vazyme, Nanjing, China) were used. And beta-actin was used as the reference gene. The reverse transcription and PCR primer sequences are listed in Supplementary Table S1. The relative expression levels of miRNA and mRNA were calculated using 2−ΔΔCT (Rao et al., 2013).
## 2.6 Dual -Luciferase reporter assay
To verify the target relationship of SIRT1 and oar-miR-432, Xho I and NotI restriction enzyme cutting sites were amplified with the wild-type 3'UTR of the SIRT1. The primers are listed in Supplementary Table S1. The wild-type 3'UTR of the SIRT1 was ligated to vectors and named psiCHECK2-SIRT1-3'UTR-WT.
Using a Site-Directed Mutagenesis Kit (Thermo Fisher Scientific, MA, United States), the mutant-type 3'UTR of SIRT1 was obtained and named psiCHECK2-SIRT1-3'UTR-MT. PsiCHECK2-SIRT1-3'UTR-WT, psiCHECK2-SIRT1-3'UTR-MT, or pure vectors were co-transfected with oar-miR-432 mimics; pure vectors were co-transfected with negative control (NC) or oar-miR-432 mimics into 293T (Pan et al., 2018). After incubation for 6 h, the culture medium was changed. After 48 h of incubation, the relative luciferase activity in the cells was measured using a Dual-Luciferase Reporter Assay System (Promega, Promega, WI, United States). Each treatment was performed 4 times for each group. All plasmid, oar-miR-432 mimics, and negative control were synthesized by GenePharma (Shanghai, China).
## 2.7 Sheep preadipocytes culture and transfection
Sheep preadipocytes were isolated from the tail fat of a 70-day-old Hu sheep fetus by collagenase digestion. Preadipocyte transfection and culture were according to our previous method (Jin et al., 2022). When the cell showed contact inhibition, we collected cells and extracted protein.
## 2.8 Western blot
Proteins from cell were extracted with RIPA buffer and separated on SDS-PAGE gel including $4\%$ concentrated glue and $12\%$ separation gel. After transfer, the PVDF blot membranes were blocked and then probed with rabbit polyclonal antibody against SIRT1 (1: 1,000, Proteintech, Chicago, IL, United States) at 4°C overnight. Alpha-tubulin poly-clonal antibody (1:3,000, Abclonal, Beijing, China) was used as an internal reference. These blots were further conjugated with a goat anti-rabbit IgG secondary antibody (1:1,000, Proteintech, Chicago, IL, United States) labeled with HRP via incubation and revealed with an ECL kit (Engreen, Beijing, China), and exposed to X-ray films. Blot intensity quantification was performed using ImageJ software (1.51j8) (Rha and Gyeol Yoo, 2015).
## 2.9 Statistical analysis
The data were processed by SPSS 20.0 two-tailed Student’s t-test (Singh et al., 2019). All the results are presented as means ± standard deviation. Furthermore, * indicates statistically significant ($p \leq 0.05$). ** indicates statistically significant ($p \leq 0.01$).
## 3.1 Quality control
The results of the miRNA-*Seq data* after quality control are displayed in Table 1. The clean tag count of each sample ranged from 27 to 28 million, and the Q20 of clean tags ranged from $98.20\%$ to $98.50\%$. About $88.63\%$–$92.75\%$ of the clean reads were mapped to the sheep reference genome.
**TABLE 1**
| Sample name | Sequence type | Raw tag count | Clean tag count | Percentage of clean tag (%) | Q20* of clean tag (%) | Percentage of mapped tag (%) |
| --- | --- | --- | --- | --- | --- | --- |
| HG1 (short/fat-tailed sheep) | SE50 | 28376193 | 27508714 | 96.94 | 98.5 | 92.75 |
| HG2 (short/fat-tailed sheep) | SE50 | 28289347 | 27054271 | 95.63 | 98.4 | 91.58 |
| HG3 (short/fat-tailed sheep) | SE50 | 29793809 | 28483305 | 95.6 | 98.4 | 90.48 |
| ZG1 (short/thin-tailed sheep) | SE50 | 30184839 | 28487066 | 94.35 | 98.3 | 88.63 |
| ZG2 (short/thin-tailed sheep) | SE50 | 28886721 | 27154416 | 94.7 | 98.2 | 89.46 |
| ZG3 (short/thin-tailed sheep) | SE50 | 29008123 | 27666601 | 95.38 | 98.5 | 89.77 |
## 3.2 Identification of miRNAs
In this study, 134 known miRNAs and 275 novel miRNAs were identified from HG1; 132 known miRNAs and 291 novel miRNAs were identified from HG2; 137 known miRNAs and 298 novel miRNAs were identified from HG3; 132 known miRNAs and 295 novel miRNAs were identified from ZG1; 133 known miRNAs and 198 novel miRNAs were identified from ZG2; and 129 known miRNAs and 273 novel miRNAs were identified from ZG3 (Supplementary Table S2).
## 3.3 Analysis of differentially expressed miRNAs
We found 379 novel miRNAs and 139 known miRNAs. Hu sheep is set as a control, based on the padj ≤0.05, we detected 11 DE miRNAs in ZG compared with HG (Figure 1 and Supplementary Table S3). There are six upregulated miRNAs, including novel_mir471, oar-miR-432, novel_mir21, novel_mir59, novel_mir394 and, novel_mir70. There are five downregulated miRNAs, including oar-miR-29b, novel_mir58, novel_mir54, oar-miR-19b, and novel_mir64. Three miRNAs were reported that were associated with fat metabolism.
**FIGURE 1:** *The volcano plots of all expressed miRNAs in the livers of Hu sheep (short/fat-tailed sheep) and Tibetan sheep (short/thin-tailed sheep). The x-axis denotes the values of log2 (fold-change), whereas the y-axis denotes the −log10 (padj). The colored dots represent the expressed miRNAs, with blue indicating downregulated miRNAs and red indicating upregulated miRNAs (padj ≤0.05). The black dots indicate that the miRNAs are not statistically significant (padj >0.05).*
## 3.4 DE miRNAs target prediction and functional analysis
Miranda and RNAhybrid software were used to predict the target genes of DE miRNAs, resulting in 3,404 predicted target genes (Supplementary Table S4). GO annotation enrichment was used to describe the functions of the target genes of upregulated and downregulated DE miRNAs. These were involved in cellular components (CCs), molecular function (MF), and biological processes (BP), including animal organ development, intracellular organelle lumen, ATP binding, intracellular vesicles, and kinesin and calcium ion binding (Figures 2A,B and Supplementary Table S5). A total of 115 GO terms were significantly enriched by target genes of the upregulated DE miRNAs, and 54 terms were significantly enriched by target genes of the downregulated DE miRNAs. DE miRNAs were used in a KEGG pathway enrichment analysis. Based on all the target genes of upregulated and downregulated miRNAs, 67 and 5 KEGG pathways were significantly enriched, respectively (Supplementary Table S6). As shown in Figures 2C,D, the ECM–receptor interaction signaling pathway, KEGG root term signaling pathway, transcriptional regulation in the cancer signaling pathway, the focal adhesion signaling pathway, and the breast cancer signaling pathway were simultaneously enriched. Other signaling pathways related to fat metabolism were enriched, including the PI3K-Akt signaling pathway, calcium signaling pathway, AMPK signaling pathway, and MAPK signaling pathway, which are related to fat metabolism.
**FIGURE 2:** *Significantly enriched Gene Ontology and KEGG for the target genes of DE miRNAs. (A) Some GO terms of target genes of upregulated DE miRNAs for BP, CC, and MF in two groups. (B) GO terms of target genes of downregulated DE miRNAs for BP, CC, and MF in two groups. The x-axis displays enrichment, and the y-axis rep-resents the GO terms. The filled colored circles display each statistically significant GO term. The size of the circles represents the gene number. (C) Signal pathway of the target genes of upregulated DE miRNAs in two groups. (D) Some signal pathways of the target genes of upregulated DE miRNAs in two groups. The x-axis displays the enrich-ment factor of the target genes, and the y-axis represents the KEGG pathway. The filled colored circles represent each statistically significant KEGG pathway. The size of the circles represents the number of genes.*
## 3.5 Verified the DE miRNA and the expression of miRNA by RT-qPCR
The RT-qPCR technique was used to validate the sequencing results. Seven miRNAs were randomly selected for RT-qPCR verification. The validation results are displayed in Figure 3A and Supplementary Table S7.
**FIGURE 3:** *The results of RT-qPCR and Western blot. (A) RNA-Seq and RT-qPCR results of seven differentially expressed miRNAs in ZG compared with HG. (B) RT-qPCR results of SIRT1 in HG and ZG. (C) (D) Western blot results of SIRT1 in preadipocytes. NC exhibits negative control.*
## 3.6 Plasmid identification
Eight randomly selected monoclonals and vector universal primers were used to identify the wild-type psiCHECK2 plasmid by polymerase chain reaction (PCR) (Supplementary Figure S1) and sequencing. The sequencing primers are shown in Supplementary Table S1. Site-directed mutation was used to obtain the mutant-type psiCHECK2 plasmid. The sequencing results of wild-type psiCHECK2 plasmid and mutant-type psiCHECK2 are in Supplementary Table S8 and Supplementary Table S9. Eventually, the plasmids were constructed successfully.
## 3.7 Validation of the target relationship between oar-miR-432 and SIRT1
A dual-luciferase reporter assay indicated that oar-miR-432 significantly suppressed the luciferase activities for co-transfection with SIRT1 3'UTR wild-types, although did not affect the mutant types of SIRT1 3'UTR or blank vectors (Figure 4B and Supplementary Table S10). These results initially confirmed the direct interactions between oar-miR-432 and SIRT1.
**FIGURE 4:** *Result of the luciferase reporter assay. (A) Potential binding site between oar-miR-432 and SIRT13'UTR. The underlined sequences represent the mutant sites. (B) WT exhibits the psiCHECK2-SIRT1-3'UTR-WT. MT exhibits psiCHECK2-SIRT1-3'UTR-MT. psiCHECK2 exhibits psiCHECK2 pure vectors. Mimics exhibits oar-miR-432 mimics. NC exhibits negative control. **: indicates statistically significant (p < 0.01).*
## 3.8 Expression of SIRT1 in Liver tissue
The RT-qPCR results showed that the expression trends in oar-miR-432 and SIRT1 were contrasting. oar-miR-432 was highly expressed in the liver tissue of Hu sheep, while the SIRT1 was highly expressed in the liver tissue of Tibetan sheep (Figure 3B, Supplementary Table S7).
## 3.9 Expression of SIRT1 in preadipocytes
Oar-miR-432 mimics and negative control were transfected into preadipocytes. Then we detected the expression of oar-miR-432 and SIRT1. The expression of oar-miR-432 was increased by oar-miR-432 mimics (Jin et al., 2022). The result of the Western blot showed the expression of SIRT1 was inhibited by oar-miR-432 mimics (Figures 3C,D, Supplementary Table S11, Supplementary Figure S2, Supplementary Figure S3).
## 4 Discussion
Thus far, miRNA expression has been studied in the liver tissues of buffalos (Rha and Gyeol Yoo, 2015), dairy cows (Bu et al., 2017), mice (Seclaman et al., 2019), rats (Wang et al., 2017), pigeons (Wang et al., 2020), pigs (Kai et al., 2019), chickens (Xu et al., 2019), and geese (Zheng et al., 2015). RNA-Seq was used to construct 41 pairs of ceRNA networks on liver tissue from three Holstein cows, which provide new insight into resolving bovine lipid metabolism (Liang et al., 2017). In bovine hepatocytes, miR-27a-5p inhibited calcium sensing receptor (CASR) expression, triacylglycerol (TAG) accumulation was significantly suppressed, and low very density lipoprotein (VLDL) secretion was reduced (Yang et al., 2018). established miRNA-mRNA regulatory networks related to lipid deposition and metabolism in the livers of Landrace pigs with the extreme backfat thickness (Kai et al., 2019). RNA-Seq was used to construct miRNA-mRNA networks between Jinhua and Landrace pigs (Huang et al., 2019). These studies provided new insights into the molecular mechanisms to explore fat metabolism in pigs. Also, the study found there was a lncRNA-FNIP2/miR-24-3p/FNIP2 axis, which can regulate lipid metabolism in Sanghuang chicken liver (Guo et al., 2021).
In this study, we used high-throughput sequencing to identify the expression of miRNA in the livers of Hu sheep and Tibetan sheep. This study complements the current understanding of miRNA expression patterns in sheep livers and will help future research on the specific role of miRNA in regulating fat metabolism. In our study, we identified 11 differential miRNAs. miR-432, miR-19b, and miR-29b are associated with fat metabolism, and a previous study showed that miR-432 inhibits milk fat synthesis by targeting stearoyl CoA desaturase (SCD) and LPL in ovine mammary epithelial cells. Additionally, miR-432 inhibits the proliferation of ovine mammary epithelial cells (Hao et al., 2021). Transcriptome analysis revealed that miR-432 was differentially expressed in the backfat of cattle; the protein kinase AMP-activated catalytic subunit alpha $\frac{1}{2}$ (PRKAA$\frac{1}{2}$) and peroxisome proliferator-activated receptor alpha (PPARA) were regulation targets to modulate lipid and fatty acid metabolism (Sun et al., 2014). Interestingly, miR-432 was differentially expressed in tail fat between Hu sheep and Tibetan sheep, which could have an important function in sheep fat metabolism (Fei et al., 2022). In mice SVF cells, miR-19b had an inhibitory effect on the browning process of adipose tissue (Lv et al., 2018). Researchers found that miR-29b can regulate blood sugar in adult mice, representing a target for treating metabolism disease (Hung et al., 2019). Additionally, miR-29b inhibits the differentiation of pig muscle and subcutaneous preadipocytes through targeted regulation complement component 1 (C1q) and TNF-related protein 6 (CTRP6) (Wu et al., 2021). Ma et al. found that lncRNAs, including TCONS_00372,767 and TCONS_00171,926, were related to fat metabolism among Lanzhou fat-tailed sheep, small-tailed Han sheep, and Tibetan sheep, and constructed two co-expression networks of differentially expressed mRNA and lncRNA (Ma et al., 2018). The research conducted by Cheng et al. showed that there were differences in the livers of Mongolian and Lanzhou fat-tailed sheep through RNA-Seq, which provided a reference for researching the sheep genome (Cheng et a., 2016).
Hu sheep set as a control to identify DE miRNAs. The extracellular matrix (ECM)–receptor interaction signaling pathway was significantly enriched by the target genes of upregulated DE miRNAs and downregulated DE miRNAs. The main constituents of the ECM–receptor interaction signaling pathway in adipose tissue include collagen (type I, IV, and VI), fibronectin (FN), laminin (LN1,8), hyaluronan, and proteoglycan (Lee et al., 2013). The functional analysis showed differently expressed genes in the subcutaneous and intramuscular fat of cattle were enriched in ECM–receptor interaction signaling pathway. In the study of San et al., some genes which affected intramuscular fat (IMF) deposition was significantly enriched in the ECM–receptor interaction signaling pathway (San et al., 2021). In our study, the target genes of upregulated DE miRNAs were enriched in the PI3K-Akt signaling pathway, calcium signaling pathway, the AMPK signaling pathway, and MAPK signaling pathway, which are associated with fat metabolism (Fu et al., 2022). In our study, forkhead boxO3 (FoxO3) was enriched in the PI3K/AKT signaling pathway and AMPK signal pathway. In mice fed high-glucose and high-sucrose diets, FoxO3 promoted hepatic triglyceride synthesis and hepatic triglyceride accumulation in the liver by positively regulating the sterol regulatory element binding transcription factor 1 (SREBP1c) (Wang et al., 2019). Additionally, SIRT1 was enriched in the AMPK signal pathway. SIRT1 plays an important biological role in regulating liver lipid metabolism, oxidative stress, and inflammation, and can be used as a therapeutic target for the treatment of alcoholic and non-alcoholic fatty liver diseases (Ding et al., 2017). It has been shown that vitamin D can activate the AMPK/SIRT1 pathway to inhibit the accumulation of fat in C2C12 skeletal muscle cells (Chang and Kim., 2019). miR-29 can regulate SIRT1 to inhibit fat deposits in mouse livers (Kurtz et al., 2015). Additionally, Liang et al. that dietary cholesterol can promote the occurrence of steatohepatitis through the calcium signaling pathway (Liang et al., 2018). In a diabetic mouse model, the ginsenoside metabolite compound K inhibits the activation of the NLR family pyrin domain containing 3 (NLRP3) through the NF-κB/p38 signaling pathway (Song et al., 2018). Previous studies have shown that in human liver fat cells, transforming growth factor-beta 1 (TGF-β1) regulates the platelet-derived growth factor receptor beta (PDGFD-β) subunit to maintain the activation and proliferation of fat cells (Pinzani et al., 1995). In our previous study, these pathways were enriched significantly, including ECM–receptor interaction signaling pathway, PI3K-Akt signaling pathway, calcium signaling pathway, AMPK signaling pathway, and MAPK signaling pathway (Fei et al., 2022). All of the results showed that these pathways could have a vital function in sheep fat metabolism.
In this research, our goal was to preliminarily determine how oar-miR-432 and SIRT1 regulate fat metabolism. In our current study, we use dual-luciferase reporter assays to verify the binding relationship between miR-432 and the target gene SIRT1. The expression of SIRT1 was detected in the liver tissues of Hu sheep and Tibetan sheep. RT-qPCR results showed that the expression of SIRT1 in Tibetan sheep was significantly higher than that in Hu sheep. We transfected oar-miR-432 in preadipocytes, and we found oar-miR-432 can inhibit the expression of SIRT1 at the protein level. This is the first time reported that the expression of SIRT1 gene was regulated by oar-miR-432 in fat metabolism of sheep liver. The regulation of the process leading from mRNA to protein is generally very complex. Studies have shown that gene repression could be changed due to the post-transcriptional regulation of miRNA (Pasquier and Gardès., 2016). Our study showed that oar-miR-432 downregulated the expression of SIRT1 at the transcriptional level in sheep liver tissue. Meanwhile, the result of Western blot showed that oar-miR-432 can downregulated the expression of SIRT1 protein in preadipocytes. Our study indicated that p53 is independent of the oar-miR-432 SIRT1 gene regulation.
## 5 Conclusion
In summary, our results provide a comprehensive expression profile of miRNA in the livers between two different sheep breeds. The DE miRNAs reported in this article may play an important role in sheep fat metabolism. We have verified that oar-miR-432 can target the regulation gene SIRT1 in sheep. This study provides a reference for further research addressing the modulation of fat metabolism in different sheep breeds.
## Data availability statement
The datasets presented in this study can be found in online repositories. The sequencing files have been stored in the Sequence Read Archive (accession numbers PRJNA785102).
## Ethics statement
The animal study was reviewed and approved by Ethical approval was in compliance with the Animal Ethics Committee of the Institute of Animal Sciences, Chinese Academy of Agriculture Sciences(IAS-CAAS).
## Author contributions
Conceptualization, CW and YW; methodology, ZL, ZY, and HW; software, JL, KQ, MH, and ZL; validation, XF, MJ, and TL; formal analysis, JY and TW; writing—original draft preparation, XF; writing—review and editing, ZY.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2023.985764/full#supplementary-material
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|
---
title: 'Correlates of Meal Skipping in Community Dwelling Older Adults: A Cross-Sectional
Study'
authors:
- H. Wild
- D. Gasevic
- R.L. Woods
- J. Ryan
- M. Berk
- R. Wolfe
- J. McNeil
- A.J. Owen
journal: The journal of nutrition, health & aging
year: 2023
pmcid: PMC10035663
doi: 10.1007/s12603-023-1884-2
license: CC BY 4.0
---
# Correlates of Meal Skipping in Community Dwelling Older Adults: A Cross-Sectional Study
## Abstract
In this cross-sectional analysis of 10,071 community dwelling adults aged ≥70 years, we examined factors associated with meal skipping (self-reported) using multivariable logistic regression. Prevalence of meal skipping in this study was $19.5\%$. The adjusted odds (aOR [$95\%$CI]) of meal skipping were lower in those 85+ years (vs. 70-74.9 years, 0.56 [0.45-0.70]), and in those in regional areas (vs. urban area, 0.81 [0.72-0.92]). Higher odds of meal skipping were observed for those living alone (vs. living with someone, 1.84 [1.64-2.05]), current smokers (vs. non-smokers, 2.07 [1.54-2.80]), consumers of high amounts of alcohol (vs. abstainers 1.93 [1.35-2.75]), those with poor oral health (vs. excellent oral health, 1.71 [1.07 −2.73]) diabetes (vs. not 1.26 [1.06-1.50]), or frailty (vs. not, 1.63 [1.09-2.43]). This study identified socio-demographic, social, behavioural and biomedical correlates of meal skipping in later life, which may assist in targeting interventions to address meal skipping.
## Introduction
Nutrition-related diseases are major contributors to disability and lowered quality of life in older age [1], and nutrition survey data consistently suggest that people over the age of 65 years do not meet their daily recommended nutritional or energy requirements [2]. Nutrient and energy deficiency in later life can increase the risk and the severity of age related chronic disease [3].
It is accepted that there is a relationship between the number of meals consumed per day and overall dietary quality in those over the age of 65 years [4, 5]. It is also common for dietary intake to change in quantity and quality in later life, with dietary behaviours such as meal skipping cited as influential in the development of late-life malnutrition [4].
Meal skipping is defined as the omission of one of the traditional daily meals, breakfast, lunch, or dinner [6, 7]. A recent systematic review [8] on the prevalence and correlates of meal skipping in community dwelling older adults, reported variability in the proportion of adults who consumed less than 3 meals daily, ranging from 2.1 to $61\%$. Advancing age, male gender, and social exclusion were reported as significant correlates of meal skipping though the evidence base is limited by non-contemporary and heterogeneous studies [8] Sustaining nutritional status in community dwelling older adults is important to lower the risk of age-related disease, hospitalisation and institutionalisation [9] and to maintain independent living [4]. The current evidence base on food behaviours in older adults is predominantly centred on institutionalised people, with limited research on those living independently in the community [9].
Therefore, the aim of this study is to determine the prevalence and socioecological factors associated with meal skipping in a large cohort of community dwelling adults aged 70 years and over.
## Study population
The ASPirin in Reducing Events in the Elderly (ASPREE) study was a multi-centre randomised placebo-controlled trial of low dose aspirin conducted in Australia and the United States (U.S.) between 2010 and 2017. The Australian ASPREE cohort included 16,703 healthy, community dwelling, participants aged 70 years or above who were, at baseline, free from persistent physical disability, dementia, cardiovascular disease or chronic/serious disease that was likely to be fatal within 5 years [10]. Details of the inclusion and exclusion criteria and methods have been previously described [10, 11]. The ASPREE Longitudinal Study of Older Persons (ALSOP) is a longitudinal cohort study, comprised of 14,892 Australian participants from ASPREE [10]. For this study, participants with missing data on exposure (meal skipping) ($$n = 2$$,466) and independent variables of interest ($$n = 2$$,355) were removed from the primary analysis dataset. ( See Supplementary Figure 1).
## Meal skipping
Self-reported dietary intake, including the frequency of meal skipping each week, was ascertained via the year-3 ALSOP medical questionnaire [10]. Participants were asked “How often do you miss meals?”, and they were able to choose from the following responses: “never/rarely, once a week or less, several times a week, everyday”. In line with the available evidence [6], and due to small distribution of meal skipping across all categories of the meal skipping variable, we created a variable that assessed whether meal skipping was present or not (yes/no). We classified responses “once a week or less”, “several times a week” or “every day” as “yes”, and “never/rarely” as “no”.
## Correlates
The selection of potential correlates was informed by previous research into dietary patterning in older adults [1]. These factor correlates were grouped in the following categories: 1) economic and demographic, 2) health behaviours 3) biomedical, 4) social, 5) psychological. The majority of measures were assessed at 3-year ASPREE/ALSOP follow-up interviews, with the exception of years of education, area-level socioeconomic status using the Index of Relative Social Advantage and Disadvantage (IRSAD) [12] and presence of hypertension, polypharmacy, frailty and diabetes which were taken at baseline in the ASPREE trial. The Centre for Epidemiological Studies Depression (CES-D) scale [13] was used to assess level of depressive symptoms and baseline quality of life was assessed using the SF-12 questionnaire, condensed to physical (PCS) and mental component scores (MCS) with higher PCS and MCS scores indicating higher physical and mental quality of life. The mean PCS and MCS were calculated for the study population, and participants were categorised into above or below the average score [14]. Behavioural factors were assessed via self-reported questionnaires. In the year-3 ASPREE follow up interviews, participants reported their smoking status, daily consumption and daily alcohol intake. The year-3 ALSOP medical questionnaire asked participants to rate their difficulty performing tasks such as reading labels. Biomedical factors were assessed via combination of clinical and self-reported information at baseline (polypharmacy, hypertension, diabetes and frailty) and at year-3 by the ALSOP medical questionnaire (BMI, oral health status, saliva levels and pain frequency). Living status was assessed by the year-3 ALSOP social questionnaire, participants were asked to report who they lived with. ( For additional detail on how correlates were measured please see Supplementary Table 1).
## Statistical analysis
Participant characteristics were presented as counts and percentages based on the meal skipping status. Differences in study characteristics between participants who rarely or never skip meals compared to those who skip meals were assessed using Chi-squared tests and two-way T-tests. The association between socio-ecological factors and meal skipping was assessed using multivariable logistic regression where all potential correlates were mutually adjusted. Odds ratios and $95\%$ confidence intervals (CI) were reported. BMI is a recognised correlate of meal skipping in older adults [4, 15, 16] however, substantial missing data were observed for BMI ($$n = 1$$,194); hence, the variable was removed from the primary analysis. Instead, a multivariable binary logistic regression sensitivity analysis model that included BMI was undertaken ($$n = 8$$,877). Similarly, depression correlates with appetite loss 17 in older age, however, a considerable amount of data was also missing for the depression variable ($$n = 1$$,021), and a further sensitivity analysis included all variables in the primary model and the CESD-10 scores ($$n = 9050$$).
We performed correlation analyses between the hypothesized correlates of meal skipping. The highest correlation coefficient reported was between PCS and pain frequency variables ($r = 0.33$) suggesting no multi-collinearity in the data. All statistical analysis was performed in Stata statistical software version 17.0 (StataCorp LLC, College Station, Texas; www.stata.com) [18].
## Participant characteristics
There were 10,071 participants ($54\%$ were female) included in this study with mean age of 77.9 years at the time of meal skipping assessment. A majority of participants lived in major metropolitan areas, lived with others, had ≤12 years of education, lived in high socioeconomic areas ($29.9\%$) and were non-smokers ($97.9\%$) (Table 1). Participant characteristics were compared between participants with missing data ($$n = 2$$,466) and those without ($$n = 10$$,071) with no substantial differences observed.
## Prevalence of meal skipping
Four in five ($80.5\%$) participants reported never or rarely skipping meals, $14.9\%$ reported skipping meals once a week, $3.8\%$ reporting skipping meals several times per week and less than $1\%$ reporting skipping meals daily. The total prevalence of meal skipping in this study population of adults aged 70 years and over was $19.5\%$.
Compared to people who never or rarely skipped meals, those who reported any meal skipping were younger on average, had more than 12 years of education, reside in inner city locations, and have poorer oral health. People who skipped meals were also more likely to live alone, to consume over 3 alcoholic drinks daily, be current smokers and be diagnosed with diabetes and be classified as frail. ( Table 1)
## Correlates of Meal Skipping
The odds of meal skipping were lower among adults aged 85 years and over (vs. 70-74.9 years OR [$95\%$CI], 0.56 [0.45-0.70]), in women (vs. men 0.84 [0.75-0.94]), in those living in regional and remote areas (vs. those living in major cities, 0.81 [0.72-0.92]) and in those with above average MCS (vs. those with below average MCS 0.76 [0.69-85]). In contrast, odds of any meal skipping were greater among older adults living alone (vs. with others 1.84 [1.64-2.05]), those who smoked (vs. non-smokers 2.07 [1.54-2.80]), those who consumed more than 4 alcoholic drinks a day (vs. those who abstained 1.93 [1.35-2.75]), and individuals reporting more than 12 years of formal education (vs. those reporting 12 years or less of formal education 1.15 [1.04-1.28]). People diagnosed with diabetes had higher odds of meal skipping (vs. those without diabetes 1.26 [1.06-1.50]). Higher odds of meal skipping were also observed among older adults with frailty (vs. those without 1.63 [1.09-2.43]). ( Figure 1 and Supplementary Table 2) With regard to oral health, older adults who reported good/fair oral health or poor oral health had higher odds of meal skipping (vs. those who reported excellent oral health 1.21 [1.10-1.35]; 1.71 [1.07 −2.73], respectively). Odds of meal skipping were also higher among people who reported having difficulty reading food labels (vs. those who did not report difficulty 1.44 [1.12-1.86]). ( Figure 1 and Supplementary Table 2)
## Sensitivity analyses
Results were similar after the additional inclusion of BMI in the regression model. No association was observed between meal skipping and BMI. ( Supplementary Table 3) After inclusion of depressive symptoms (CESD-10 overall score) to the primary model, the results indicated that the odds of meal skipping were $26\%$ (1.26 [1.11-1.43]) greater for those who experienced mild depressive symptoms, and $60\%$ (1.6 [1.36-1.90]) greater for those who reported moderate to severe depressive symptoms compared to those who did not report depressive symptoms (Supplementary Table 3). When compared to the primary analysis, the association between female sex and meal skipping did not maintain its significance with meal skipping in this model (0.99 [0.88-1.11], 0.898). The association between smoking and meal skipping was strengthened by the addition of depression to the multivariate model, with greater odds of meal skipping reported for current smokers (compared to non-smokers 2.28 [1.70-3.65]) when compared with the primary model (2.07 (1.54-2.80), <0.001). ( Supplementary Table 3)
## Discussion
We report a wide-ranging examination on the prevalence and the sociodemographic, behavioural, biomedical, psychological and social factors associated with meal skipping in more than 10,000 community-dwelling individuals aged 70 years and over. Among the strongest correlates, we observed that the oldest age group (85+), women and those with above average mental health component scores, were less likely to skip meals; while those who smoked, consumed more than 4 alcoholic drinks daily, lived alone and reported poor oral health were more likely to skip meals. These results are important, as they point to correlates associated with meal skipping, that if addressed may help reduce nutritional deficiency in later life.
The majority of participants in this cohort rarely or never skipped meals. The observed meal skipping prevalence of $19.5\%$ is consistent with similar research in Korean older adults, which noted that $20.9\%$ of participants indicated some level of meal skipping [5]. It is also similar to recent statistics on breakfast skipping in adults, whereby King et al [19] reported that $17.1\%$ of adults skipped breakfast. However, it differs from the prevalence observed in children ($10\%$) [7], adolescents ($29.9\%$) [20] and younger adults ($10\%$) [21] highlighting the need for age specific research on important dietary behaviours, such as meal skipping.
We observed that adults aged 85 years and over were less likely to skip meals compared to those aged 70 to 74.9 years. While demonstrating a similar pattern of consumption across age groups to that reported in the current literature on meal skipping in older adults [5], our findings were more pronounced.
Our results indicate that women were less likely to skip meals compared to men, a result that is consistent with the current evidence [4, 16]. Previous research [1] notes that nutritional vulnerabilities are increased for men, especially after the death of a spouse, due to a lack of nutritional knowledge and food preparation skills. Interestingly, the results of our sensitivity analysis demonstrated that further adjustment for depressive symptoms weakened the association between sex and meal skipping. This may be due to the increased likelihood of Australian women to report depressive symptoms compared to men [22]; a disparity that is also likely to be exacerbated by generational factors associated with the age of this cohort [22]. Previous research on the ASPREE cohort [23] has demonstrated a higher prevalence of women reporting depressive symptoms (CESD-10 overall score 8 or above) compared to men, consistent with findings international cohorts of older adults [24].
In ASPREE participants, the odds of meal skipping were greater among those who smoked and those who consumed higher levels of alcohol compared to those who did not. Smoking is frequently associated with meal skipping in older adults, 5, 15, 16 as well as in younger adults [6, 21]. Conversely, findings on the association between alcohol consumption and meal skipping are mixed, with studies in older adults demonstrating both positive [16], negative [5] or no [15] association between alcohol consumption and meal skipping.
This study observed that those who reported living alone had higher odds of meal skipping than people who reported living with others. These results are consistent with the current body of evidence which highlights living alone as a common factor associated with meal skipping among older adults [4, 5, 15].
This study also highlighted the influence of oral health on meal skipping. Changes in dietary intake in later life as a result of poor oral health have been reported in the literature with Gu et al. [ 25] noting a shift toward a more refined western diet in those over 80 years with difficulty chewing. When considering oral health within the Australian context, it is important to acknowledge that dental care was not, at the time of this study, provided to adults as part of the publicly funded universal healthcare system. This is likely to have increased the influence of the socioeconomic gradient on oral health outcomes [26]. The ALSOP cohort is skewed toward a higher socio-economic status (based on area levels of advantage and disadvantage) and education level (higher level of tertiary education $23\%$ vs $2.4\%$) compared to the general Australian population over the age of 70 years [10]. As a result, these participants are likely to have increased access to the financial resources required for suitable dental care compared to their counterparts in the general Australian population. As such, the influence of oral health on meal skipping may be more pronounced within the general community than within this cohort. Food insecurity affects 1 in 50 adults older than 65 years in Australia and can impact food intake [27]. Socioeconomic factors have a been shown to have a significant impact on food security status for older Australians [27]. Due to the noted differences in socioeconomic status between the ALSOP cohort and the general community, the influence of food insecurity on meal skipping is likely underrepresented in this study.
The results of our sensitivity analysis highlighted a positive association between severity of depressive symptoms and odds of meal skipping. This is consistent with evidence on meal skipping in older adults [5]. Depression is often characterised by lowered appetite [24], interestingly, the results of this sensitivity analysis demonstrated that depression strengthened the association between smoking and meal skipping, a behaviour that can also influence appetite and food intake [28]. Further research may be required to understand the association between variations in symptoms of depression and meal skipping.
Acknowledging the interrelationship of many of the psychological, social, economic, behavioural and biomedical correlates highlighted in our results is important. It will be essential for future research on meal skipping to further examine the ways in which these factors accumulate across the lifespan, and how their interaction in older age influences health behaviours, and alters the nature and severity of disease risk. The diverse implications of social and psychological factors in older age emphasise the importance of tailored psycho-social services that meet the specific needs of community dwelling older adults. It is likely that many of the protective factors for health and wellbeing in later life are established in younger years, further highlighting the importance of a lifespan focus in health research to identify and manage the socio-ecological factors associated with disease development in later life [8].
The strengths of this research include use of a large, well-characterised sample of older adults, high response rates to questionnaires and strong inclusion and exclusion criteria. 10 There are also several limitations. This is a cross sectional study, so we cannot make any conclusions about the direction of the relationship between the socio-ecological correlates and meal skipping. The use of secondary data also limits the type of information available within the analysis, as such the influence of broader socio-political and environmental correlates on meal skipping could not be assessed. This issue was also noted by Pendergast et al. [ 21] in their study on the socio-ecological correlates of meal skipping in younger adults, and potentially highlights the need for nutritional research to more consistently consider and explore the influence of the socio-political and environmental context on dietary behaviour. The Year 3 ALSOP medical questionnaire did not define meal in its question on meal skipping, as such no detail on which meal was skipped is provided, and differing cultural and social definitions of meal may increase the risk of misunderstanding and misclassification. We condensed the meal skipping variable from four categories (never/rarely, once a week or less, several times a week, everyday) to two (yes/no) and while this mirrored other research on this topic [6], it may have reduced our ability to discuss differing severities of meal skipping. Future research may benefit from more robust data, allowing for a more granular understanding of the differing frequencies of meal skipping, and the specific meals skipped in older adults. The ASPREE/ALSOP cohort may possibly be a healthier and wealthier subset of the community, as a result of the ASPREE inclusion criteria and their interest in participating in a long-term study of healthy ageing [10, 11]. However, it should be noted that by the time the self-reported meal skipping measure was assessed (3 years after enrolment into the study), a number of participants had developed chronic disease [29]. This ‘healthy cohort’ effect, compared to the general population, is a factor to consider in the interpretation of the study results, and we advise caution when generalising these findings to population with a lower area level socioeconomic status. A larger body of longitudinal research on meal skipping across the lifespan may be necessary to better determine the influence of age on eating behaviour including meal frequency. Finally, for some variables year 3 data were not available, so baseline derived variables were utilised. This potentially increases the risk of misclassification of participants who may have potentially being diagnosed with hypertension, diabetes, and frailty post baseline.
## Conclusion
In this study on more than 10,000 adults aged 70 years and over, we observed that the prevalence of meal skipping was $19.5\%$. Numerous factors associated with meal skipping were identified, including living circumstance, sex, alcohol intake and smoking status. To better understand the influence of these factors on dietary behaviours such as meal skipping, it will be important for future research to focus on their influence and interaction across the lifespan, and the multiplicity of risk associated with their interaction, and to what extent these factors operate independently or converge on a common mediator such as depression. The results of these analyses, and the identification of key correlates of meal skipping in older adults will be important to inform further investigation on the impact of meal skipping on health outcomes in this cohort.
## Funding:
The ASPREE study was supported by a grant (UA01AG029824 and U19AG062682) from the National Institute on Aging and the National Cancer Institute at the National Institutes of Health, and by grants (334047 and 1127060) from the Australian National Health and Medical Research Council, by Monash University and the Victorian Cancer Agency. The ALSOP sub-study was supported by Monash University and an unencumbered grant from the Wicking Trust. JR is supported by NHMRC Dementia Research Leader Fellowship [1135727]. MB is supported by NHMRC Senior Principal Research Fellowships 1059660and 1156072. Open Access funding enabled and organized by CAUL and its Member Institutions.
## Declaration of Conflicting Interests:
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. The ASPREE study was supported by a grant (UA01AG029824 and U19AG062682) from the National Institute on Aging and the National Cancer Institute at the National Institutes of Health, and by grants (334047 and 1127060) from the Australian National Health and Medical Research Council, by Monash University and the Victorian Cancer Agency. The ALSOP sub-study was supported by Monash University and an unencumbered grant from the Wicking Trust. JR is supported by NHMRC Dementia Research Leader Fellowship [1135727]. MB is supported by NHMRC Senior Principal Research Fellowships 1059660and 1156072.
## Availability of data and materials:
The datasets generated and/or analysed during the current study are not publicly available due data being part of a large ongoing observational cohort study with a rigorous process to access data. The datasets generated and/or analysed during the current study are available in the ASPREE clinical trial data resource repository, https://aspree.org/aus/researchers/. The ASPREE clinical trial data resource is managed in partnership with the US, in the Australian ASPREE National Coordinating Centre. New ASPREE projects with appropriate scientific merit may be proposed by external researchers, and submitted to ASPREE for consideration. Project proposals requesting access to any aspect of data, samples, or analyses from the ASPREE clinical trial and/or sub-studies must gain the support of the ASPREE Principal Investigators. Applications are submitted via a secure web site, the ASPREE Access Management System (AMS). Applicants can obtain information by contacting aspree.ams@monash.edu.
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|
---
title: Pulsed Electromagnetic Field Therapy and Direct Current Electric Field Modulation
Promote the Migration of Fibroblast-like Synoviocytes to Accelerate Cartilage Repair
In Vitro
authors:
- Neeraj Sakhrani
- Robert M. Stefani
- Stefania Setti
- Ruggero Cadossi
- Gerard A. Ateshian
- Clark T. Hung
journal: Applied sciences (Basel, Switzerland)
year: 2022
pmcid: PMC10035757
doi: 10.3390/app122312406
license: CC BY 4.0
---
# Pulsed Electromagnetic Field Therapy and Direct Current Electric Field Modulation Promote the Migration of Fibroblast-like Synoviocytes to Accelerate Cartilage Repair In Vitro
## Abstract
Articular cartilage injuries are a common source of joint pain and dysfunction. As articular cartilage is avascular, it exhibits a poor intrinsic healing capacity for self-repair. Clinically, osteochondral grafts are used to surgically restore the articular surface following injury. A significant challenge remains with the repair properties at the graft-host tissue interface as proper integration is critical toward restoring normal load distribution across the joint. A key to addressing poor tissue integration may involve optimizing mobilization of fibroblast-like synoviocytes (FLS) that exhibit chondrogenic potential and are derived from the adjacent synovium, the specialized connective tissue membrane that envelops the diarthrodial joint. Synovium-derived cells have been directly implicated in the native repair response of articular cartilage. Electrotherapeutics hold potential as low-cost, low-risk, non-invasive adjunctive therapies for promoting cartilage healing via cell-mediated repair. Pulsed electromagnetic fields (PEMFs) and applied direct current (DC) electric fields (EFs) via galvanotaxis are two potential therapeutic strategies to promote cartilage repair by stimulating the migration of FLS within a wound or defect site. PEMF chambers were calibrated to recapitulate clinical standards (1.5 ± 0.2 mT, 75 Hz, 1.3 ms duration). PEMF stimulation promoted bovine FLS migration using a 2D in vitro scratch assay to assess the rate of wound closure following cruciform injury. Galvanotaxis DC EF stimulation assisted FLS migration within a collagen hydrogel matrix in order to promote cartilage repair. A novel tissue-scale bioreactor capable of applying DC EFs in sterile culture conditions to 3D constructs was designed in order to track the increased recruitment of synovial repair cells via galvanotaxis from intact bovine synovium explants to the site of a cartilage wound injury. PEMF stimulation further modulated FLS migration into the bovine cartilage defect region. Biochemical composition, histological analysis, and gene expression revealed elevated GAG and collagen levels following PEMF treatment, indicative of its pro-anabolic effect. Together, PEMF and galvanotaxis DC EF modulation are electrotherapeutic strategies with complementary repair properties. Both procedures may enable direct migration or selective homing of target cells to defect sites, thus augmenting natural repair processes for improving cartilage repair and healing.
## Introduction
Articular cartilage is a highly specialized connective tissue that overlies the surface of bones in diarthrodial joints [1,2]. It provides a smooth, lubricated surface to minimize friction during articulation and distributes mechanical forces across the joint capsule to protect the underlying subchondral bone [1,3]. While articular cartilage functions to absorb compressive loads, applied mechanical stress contributes to gradual wear, tears, and injuries to the joint [2,4]. However, articular cartilage exhibits a poor intrinsic healing capacity for self-repair, as it is avascular, aneural and alymphatic [1,2,5]. Without targeted therapies and repair treatment strategies, articular cartilage damage can contribute to gradual tissue deterioration, debilitating pain, joint inflammation, and ultimately complete degradation in the pathogenesis of osteoarthritis (OA) [2,6].
The poor regenerative capacity of articular cartilage necessitates effective treatment methods to initiate joint healing and repair. Transplantation of cartilage osteochondral grafts, autologous or allogeneic depending on lesion size, is a common operative treatment to repair cartilage defect areas [7,8]. However, a significant challenge remains with the integrative repair properties at the graft-host tissue interface, as proper surgical implantation and integration to the subchondral bone are critical toward restoring normal load distribution across the joint [9,10]. Osteochondral grafts may also undergo central necrosis and appearance of subchondral cyst-like resorption areas, which may contribute to graft mechanical instability, poor cartilage nutrition, and ultimately graft failure [10]. Several cell-based treatments, including autologous chondrocyte implantation (ACI) and mesenchymal stem cell (MSC) delivery therapies, have emerged as surgical options for cartilage repair [11]. Both procedures seed cells that directly participate in defect healing and regulate cartilage metabolism by producing extracellular matrix (ECM) molecules that are critical for native function, including collagens, proteoglycans, and glycosaminoglycans (GAGs) [12,13]. However, ACI and MSC delivery to defect lesions in the joint possess clinical limitations, including risk of further injury to healthy cartilage during autologous harvest, the need for two-step surgical techniques, cellular immune rejection following implantation, and graft hypertrophy [14,15]. Instead of surgical interventions, natural repair processes via direct migration or selective homing of native target cells to defect sites in the cartilage surface hold potential as alterative treatment strategies [13,16]. Articular cartilage is composed of chondrocytes, embedded within a dense extracellular matrix of collagens and proteoglycans [1,5]. However, native chondrocytes have limited metabolic activity, proliferation, and biosynthesis, contributing to poor healing response following joint injury [1,17]. Therefore, in order to address poor tissue integration and limited chondrocyte migration, fibroblast-like synoviocytes (FLS) that exhibit chondrogenic potential may serve as a cell-based strategy to promote in vitro cartilage repair. FLS are derived from the adjacent synovium, the specialized connective tissue membrane that envelops the diarthrodial joint [18–20]. Synovium-derived cells have been directly implicated in both the native repair response as well as degradation of articular cartilage [21], thus providing a promising target for the development of novel strategies aimed toward preventing structural changes to the joint and treating clinical symptoms, especially in the development of early OA [22]. The proposed studies have been inspired by in vivo lineage tracing experiments in mice where the preponderance of evidence (labeled cells from three independent mouse strains including postnatal tamoxifen induction, proliferative capacity of this cell lineage, no migration of adjacent articular chondrocytes) suggests that cells from the synovium are the source for healing [23,24]. These studies suggest repair cells migrating into the full thickness cartilage defects are primarily derived from the synovium rather than bone marrow or adjacent cartilage [23,24]. Therefore, strategies aimed at targeting synovial cells could offer novel and effective therapies to boost repair and regeneration of joint tissues [23].
The poor intrinsic healing ability of articular cartilage is predominantly attributed to scarce migration of native cells that have the potential to repair the defect site [25]. Electrotherapeutics possess potential as low-cost, low-risk, and non-invasive adjunctive therapies for improving the rate of articular cartilage repair [26,27]. Electrical stimulation of native cells within larger tissue and organ systems is promising as endogenous electric field (EF) gradients have been shown to guide cellular behavior during wound healing [28,29]. Pulsed electromagnetic fields (PEMFs) and applied direct current (DC) EFs via galvanotaxis are two potential electrotherapeutic strategies that have yet to be fully explored in the context of promoting cartilage repair by stimulating the migration of native FLS within the wound or defect site.
PEMF treatment has been clinically used to stimulate bone repair and alleviate joint pain [30]. Studies have shown that PEMF stimulation is an adjuvant, anti-inflammatory therapy and pain management tool that contributes to improvements in joint function as well as pain relief following arthroscopy, including microfracture, chondroabrasion, collagen scaffold seeded with bone marrow–derived cells (BMDCs), matrix-assisted chondrocyte implantation (MACI), osteochondral allograft (OCA) transplantation, and knee arthoplasty [31–34]. However, the use of PEMF treatment for articular cartilage repair has not been thoroughly investigated yet, with limited mechanistic studies characterizing the application of this electrotherapeutic technique in initiating an intrinsic healing response [35,36]. Previous work in our laboratory demonstrated the positive impact of PEMF on articular cartilage defect repair in vivo with engineered cartilage constructs [37]. The current study develops an in vitro model, where experimental boundary conditions are well defined, to gain a better understanding of how PEMFs can be used to directly augment natural cartilage repair strategies.
DC EFs is another electrotherapeutic strategy that may stimulate the intrinsic repair process of synovium and articular cartilage. EFs of strengths from 1 to 10 V/cm have been shown to induce directed movement (galvanotaxis) and shape change (galvanotropism) in several musculoskeletal cell types, including chondrocytes, osteoblasts, and meniscal fibrochondrocytes [38–41]. *Endogenously* generated gradients within this range of EF strengths have also been shown to guide cell migration at the cut surface of wounds [29,42]. Clinically, the placement of electrodes for galvanotaxis may involve slightly invasive interventions compared to PEMF systems. Nonetheless, similar to PEMFs, DC EF galvanotaxis may also serve as a useful tool for promoting articular cartilage repair via directed FLS migration toward the defect region.
FLS migration in 2D is yet to be fully explored in the context of promoting intrinsic cartilage repair under electrotherapeutic stimulation. Study 1 characterized the migration of bovine FLS using an in vitro scratch assay under PEMF exposure to assess the rate of wound closure following cruciform injury. To further elucidate the electrotherapeutic potential for synovial cell-mediated repair, study 2 investigated the effect of EF stimulation via galvanotaxis to direct bovine FLS migration within a collagen hydrogel matrix.
The benefits of electrotherapeutic strategies on cartilage and synovium explants within an in vitro defect-repair model have also not been investigated. Therefore, studies were translated into the three-dimensional tissue environment to track FLS migration from healthy bovine synovium to injured cartilage explants. By developing and validating a novel tissue-scale bioreactor capable of applying DC EFs in sterile culture conditions to 3D constructs, study 3 investigated the recruitment of synovial repair cells via galvanotaxis from intact synovium explants to the site of a cartilage wound injury. Similarly, study 4 characterized the effect of PEMF stimulation on modulating the migration of endogenous and/or exogenous FLS from the bovine synovial membrane into the adjacent cartilage defect.
Limited studies characterizing the functional outcomes and intrinsic biochemical properties of both synovium and cartilage explants under electrotherapeutic stimulation have been performed [37]. Therefore, study 5 assessed the biochemical composition of bovine synovium explants and injured cartilage constructs via DNA, GAG, and collagen content following PEMF treatment. Histological characterization of both explants was evaluated in order to confirm differences observed from the functional biochemical assays. Gene expression of cartilage and synovium ECM constituents, specifically collagens, were assessed in order to characterize the recruitment of chondrocytes and FLS into the bovine cartilage defect region following PEMF stimulation.
PEMF and galvanotaxis DC EF modulation are potential complementary strategies that may enhance FLS migration, providing alternative nonoperative methods to accelerate cartilage repair following injury [43,44]. The purpose of the current study is to investigate the potential of both techniques in initiating an intrinsic healing response by stimulating FLS movement toward injured articular cartilage lesions, which may lead to peripheral cellular integration along the defect site and ultimately repair the joint surface with similar biochemical properties and mechanical characteristics as healthy hyaline cartilage [45].
## PEMF System
Custom PEMF generators were assembled by IGEA Clinical Biophysics (Carpi, Italy). *The* generators were constructed using two Helmholtz coils made of copper wire on opposite sides of a plexiglass chamber (Figure 1A). A signal generator was used to create a uniform magnetic field within the culture area (Figure 1B). The PEMF system was calibrated to clinical standards with a 1.5 ± 0.2 mT peak intensity of magnetic field, 1.3 ms pulse duration, and 75 Hz frequency [37]. The magnetic field intensity within the chamber was determined using a gaussmeter (Walker Scientific, Auburn Hills, MI, USA) with a sensitivity reading of $0.2\%$ and the induced electric field was measured using a coil probe (50 turns). A digital oscilloscope (Le Croy, Chestnut Ridge, NY, USA) was used to characterize the temporal pattern of the signal to ensure that the shape and impulse length of the generated electric field remain constant [37].
## Galvanotaxis Chamber for 2D Culture
The galvanotaxis system (Figure 2A) was constructed following a flow chamber assembly [25]. Silver–silver chloride electrodes were fabricated from silver wire and soaked in a hypochlorite solution (Clorox Bleach) for 1 h. Electrodes were connected to ports on both sides of the galvanotaxis channel using a pair of $2\%$ agarose–PBS bridges. These bridges prevented cell contamination from electrolysis products generated during EF stimulation using a power supply (Keithley Instruments, Cleveland, OH, USA), which delivered a current of 3.3 mA (6 V/cm EF strength) [25]. The conductive media that flowed through the galvanotaxis chamber system was Dulbecco’s Modified Eagle’s Medium (DMEM; Cat. No. 12100046; Thermo Fisher Scientific, Waltham, MA, USA) supplemented with $5\%$ fetal bovine serum (FBS; Cat. No. S11550; R&D Systems, Minneapolis, MN, USA), which had adequate protein concentration for cellular adherence as well as ion concentrations for current flow. Galvanotaxis DC EF stimulation was applied for 3 h at room temperature. The chamber also consisted of a glass panel, allowing for microscopic analysis of cellular migration.
## Galvanotaxis Chamber for 3D Culture
Using the mechanism of the 2D galvanotaxis system (Figure 2A), a novel 3D galvanotaxis chamber (Figure 2B,C) was designed using CAD software (Solidworks, Waltham, MA, USA). The upper and lower compartments of the chamber was separated by an O-ring and secured together with screws, creating a sealed cylindrical channel with a 5 mm diameter and a 1 mm height. This internal chamber accommodated the culture of cylindrical specimens while preventing current leakage. The dimensions and well-defined chamber geometry of the 3D galvanotaxis system allowed for the applied electric field strength (E) and current density (J) to be calculated using Ohm’s Law. The calculated resistance of the chamber was 35.8 kΩ, which was consistent with clinical applications. The anode (+) and cathode (−) were positioned above and below the cylindrical culture region. The total chamber volume was approximately 2.5 mL, allowing sufficient media supplementation for constructs and ion concentration to conduct current.
The galvanotaxis chamber was 3D printed using an Ultimaker S5 (Cura v4.2.1; Utrecht, The Netherlands) with Taulman Nylon 680 filament. Nylon filament was selected due to FDA approval and compatibility with autoclaving and ethanol sterilization. The chamber assembly was tapped and drilled prior to sealing the chamber with a clear acrylic cap for the bottom inlet of the bioreactor system. The external power supply, electrodes, and salt bridges were connected to the 3D galvanotaxis chamber and prepared similar to the 2D chamber system.
## Bovine Synovium and Cartilage Explant Harvest
Fresh synovial explants (Figure 3A) and cartilage plugs (Figure 3B) were harvested from discarded (IACUC-exempt) bovine calf knee joints (2–4 weeks old) [46]. The thin synovial sheath was extracted from the region adjacent to the medial and lateral femoral condyles. Synovium explants from 3 bovine knee joints were combined and cut into consistent ~1 cm × 1 cm pieces. Osteochondral dowel grafts were harvested using a 4 mm diameter dermal trephine biopsy from the femoral condyle of the joint. The acquired osteochondral plugs were cut 2 mm through the depth of the construct, from the articular cartilage surface to the deep cartilaginous zone, removing the subchondral bone layer. Prior to studies, synovium explants and cartilage plugs were cultured for five days in serum-free media consisting of DMEM supplemented with 50 μg/mL L-proline (Cat. No. P5607; Sigma-Aldrich, St. Louis, MO, USA), 100 μg/mL sodium pyruvate (Cat. No. S8636; Sigma-Aldrich, St. Louis, MO, USA), $1\%$ ITSTM+ Premix (contains insulin, transferrin, selenous acid, BSA, and linoleic acid; Cat. No. 354352; Corning, Corning, NY, USA), $1\%$ antibiotic–antimycotic (AA; Cat. No. 15240062; Thermo Fisher Scientific, Waltham, MA, USA), and 50 μg/mL ascorbic acid-2-phosphate (Cat. No. A8960; Sigma-Aldrich, St. Louis, MO, USA).
## Bovine FLS and Chondrocyte Isolation
For 2D studies, bovine synovium and articular cartilage explants were separately digested using collagenase type II (Cat. No. LS004177; Worthington Biochemical Corporation, Lakewood, NJ, USA) with gentle stirring at 37 °C for 4 and 11 h, respectively. Digested synovial fibroblasts and articular chondrocytes were filtered through a sterile 70 μm porous nylon mesh to remove any residual explant specimens. Viable FLS were counted and expanded using α-Minimum Essential Medium (αMEM; Cat. No. 12000022; Thermo Fisher Scientific, Waltham, MA, USA) containing $10\%$ FBS, $1\%$ AA, and 5 ng/mL fibroblast growth factor-2 (FGF-2; Cat. No. PHG0264; Thermo Fisher Scientific, Waltham, MA, USA) [21,25,46,47]. Articular chondrocytes were grown to confluency using DMEM supplemented with $10\%$ FBS, and $1\%$ AA, and 50 μg/mL ascorbic acid-2-phosphate. FLS and chondrocytes were both expanded for two passages to obtain a pure population of cells. MSC markers (with no expression of endothelial cells) were previously confirmed in cells isolated using this procedure [21,25].
## Wound Closure Assay with PEMF Stimulation
Wound closure assay was performed to characterize FLS migration via PEMF stimulation following injury. Bovine FLS were seeded in two separate 12-well plates at 0.1 × 106 cells/well and expanded until confluent using αMEM supplemented with $10\%$ FBS, $1\%$ AA, and 0.5 ng/mL FGF-2. FLS were inflicted with a cruciform wound using a P20 pipette tip. Following injury, one plate was cultured within the PEMF chamber, which was active for 8 h per day, while the other plate received 0 h of electrical stimulation (sham) [37]. Resulting images of the wound were assessed at 0, 12, 24, and 48 h (Olympus IX-70 inverted microscope) following the initial cross-shaped scratch. Images were processed in ImageJ (convert to 8-bit, invert black and white) and percent closure area was quantified for both PEMF treated and sham FLS groups ($$n = 6$$ wells per treatment group).
## FLS Migration in Collagen Hydrogel with DC EF Stimulation
Bovine FLS were encapsulated in a type I collagen hydrogel (2 mg/mL; Cat. No. A1064401; Thermo Fisher Scientific, Waltham, MA, USA) at a seeding density of 105 cells/mL, yielding a thin collagen gel with the exact geometry of the galvanotaxis channel. The concentrated collagen mixture was osmotically balanced using 10X PBS and neutralized with 1 N NaOH before cell integration. Prior to DC EF exposure, the FLS-seeded collagen hydrogel was allowed to solidify directly in the galvanotaxis chamber for 2 h at 37 °C.
Photomicrographs of galvanotaxis DC EF stimulation were acquired at 10 min intervals throughout the 3 h stimulation period. Images were manually analyzed using custom MATLAB code in order to determine the overall and incremental speeds, migration direction, and directed velocity of the FLS-seeded collagen gel. The centroid of each cell was monitored, where the initial starting position was considered the coordinates of the origin [39]. Overall migration speed was derived from the net displacement (i.e., magnitude of the vector starting at the origin and ending at the final cell position) divided by the 3 h stimulation time. Incremental speed was computed for all photomicrographs following each 10 min acquisition interval. Migration direction was determined by the orientation of the net displacement vector, whereby the cathode (−) and anode (+) were positioned at 90° and 270°, respectively. Directed velocity was defined as the speed component directed toward the cathode [25]. Mean migration angle was determined by calculating the average of all unit vectors over the 10 min interval for each cell. A total of 20 cells were analyzed for each DC EF stimulation trial ($$n = 3$$).
## FLS Migration into a Cartilage Defect
To form a cartilage defect, 1 mm cores were removed from each 4 mm bovine cartilage plug using a trephine biopsy. The obtained ~1 cm × 1 cm pieces of bovine synovium were stained using a lipophilic membrane dye (Vybrant DiI; Life Technologies, Carlsbad, CA, USA). Synovium samples were oriented in direct apposition to the cartilage explant (Figure 4), covering the 1 mm defect region, prior to PEMF or DC EF stimulation. The cartilage plug with the overlying synovium sheath was cultured in DMEM supplemented with $5\%$ FBS, $1\%$ AA, and 50 μg/mL ascorbic acid-2-phosphate. Explants were separately placed in the PEMF and galvanotaxis chambers ($$n = 6$$). Control samples were handled similarly, but without the application of electrical stimulation. PEMF chamber was active for 8 h per day and explants were treated for 48 h [37]. The galvanotaxis EF current was applied for 3 h, generating an applied field strength of $E = 6$ V/cm (EF) [25].
Following PEMF and DC EF stimulation, explants were fixed in $4\%$ paraformaldehyde overnight. The synovium was removed from the cartilage construct, leaving behind migrated synovial cells. Cartilage plugs were subsequently stained with DAPI (Sigma-Aldrich, St. Louis, MO, USA) for co-localization of endogenous cartilage cells with the DiI stained synovial cells in the sub-cored defect region. Confocal microscopy (Zeiss, Oberkochen, Germany) was used to visualize synovial cell migration through the depth of the cartilage construct. Z-stacks with a 40 μm step-size were obtained in order to track the migration distance from the articular cartilage surface across the defect site (Figure 4). A 40 μm step-size was selected to ensure that no FLS were missed or double counted during image processing and cell counting analysis. Only FLS that had adhered to the periphery of the cartilage construct were counted for migratory behavior, which was assessed by the co-localization of DAPI and DiI stains along the cartilage surface. FLS were classified as “transferred” if cells were in contact with the topmost layer of the defect site (visible in first z-stack image). FLS were considered “migrated” if cells were visible at any location ≥40 μm though the cartilage depth. The number of migrated cells was normalized to the total FLS count across the entire cartilage construct for both PEMF and galvanotaxis treated specimens. For explants exposed to DC EF stimulation, directed FLS velocity was computed assuming constant cell migration throughout the 3 h stimulation period. Transferred or non-migrating cells that remained on the cartilage surface were considered to have a velocity of 0 μm/h.
## Biochemistry
Following PEMF treatment, bovine synovial and cartilage explants ($$n = 6$$) were frozen at −20 °C and lyophilized overnight. Lyophilized explants were weighed to obtain dry weight measurements prior to tissue digestion. Samples were solubilized by incubating for 16 h at 56 °C in 0.5 mg/mL Proteinase K (Cat. No. 193504; MP Biomedicals, Irvine, CA, USA) and Proteinase K buffer solution containing 50 mM Tris saline, 1 mM EDTA, 1 mM iodoacetamide (Cat. No. 12227–1000; Acros Organics, Geel, Belgium), and 10 mg/mL pepstatin A (Cat. No. BP2671100; Thermo Fisher Scientific, Waltham, MA, USA) [21,47]. DNA content was analyzed using Picogreen (Cat. No. P11496; Thermo Fisher Scientific, Waltham, MA, USA) quantitation assay. GAG levels were measured using a 1,9-dimethylmethylene blue dye-binding assay (Product No. 341088; Sigma-Aldrich, St. Louis, MO, USA) and collagen content was quantified via orthohydroxyproline (OHP) assay with a 1:7.64 OHP-to-collagen mass ratio [47].
## Histological Characterization
PEMF treated bovine synovium and cartilage samples were fixed using $4\%$ paraformaldehyde. Specimens were embedded in paraffin wax and sectioned into 4-μm slices. Deparaffinized sections were stained with hematoxylin and eosin (H&E) to determine FLS and chondrocyte distribution within the 1 mm sub-cored region. Synovium and cartilage explants were also stained with Safranin O to assess GAG content and Picrosirius Red to characterize collagen distribution.
## qPCR Preparation
Total RNA was extracted from bovine cartilage explants following PEMF exposure for 7 days, with parallel untreated controls. Cartilage was homogenized using TRIzol reagent (Cat. No. 15596018; Thermo Fisher Scientific, Waltham, MA, USA) and chloroform was added to extract total RNA followed by vigorous agitation [48]. RNA precipitation was performed using Qiagen miRNeasy columns (Cat. No. 74106; QIAGEN, Hilden, Germany). cDNA was synthesized from RNA using the iScript cDNA Synthesis Kit (Product No. 1708891; Bio-Rad, Hercules, CA, USA) and diluted to 1 ng/μL concentration for PCR. Gene specific master mixes were prepared using the designed primers, and iTaq Universal SYBR Green Supermix (Product No. 1725122; Bio-Rad, Hercules, CA, USA). RT-qPCR was run on a QuantStudio™ 6 Flex Real-Time PCR System (Applied Biosystems, Waltham, MA, USA) [47]. The following primers (Integrated DNA Technologies) were evaluated: aggrecan (ACAN) and collagen types I-V, X-XI alpha 1 chain (COL1, COL2, COL3, COL4, COL5, COL10, COL11) (Table 1). GAPDH was used as the housekeeping gene, and all samples were normalized to day 0 controls using the 2−ΔΔCt method.
## Statistics
Normality and homogeneity of the acquired data were assessed using the Kolmogorov–Smirnov Test and Bartlett’s Test, respectively. Non-normal data was log-transformed to achieve normality for subsequent parametric tests. Data sets were analyzed using one-way ANOVA with Tukey post hoc test (α = 0.05). Statistical analysis was performed in GraphPad Prism 9 (La Jolla, CA, USA). Values presented as mean ± standard deviation.
## Study 1. Assess effect of PEMF on FLS wound closure in a 2D injury model.
A scratch assay using a monolayer of bovine FLS was used to determine if PEMF stimulation could promote cell migration and wound closure in vitro. FLS migration rates were calculated by measuring cell coverage into the defect space at defined time points following initial cruciform injury (Figure 5A–H). Compared to untreated controls, a gradual improvement in cell migration was observed in FLS treated with PEMF stimulation with a significant increase in wound closure percentage at 24 h ($$p \leq 0.0319$$) and 48 h ($$p \leq 0.0007$$) following injury (Figure 5I). At the 24 h timepoint, FLS exposed to PEMF exhibited increased wound closure with a percent difference of $23\%$ compared to controls. The percent difference in wound closure remained consistent at 48 h following injury with a $20\%$ increase in PEMF treated groups.
## Study 2. Evaluate the effect of DC EF stimulation on FLS migration within a collagen hydrogel matrix via galvanotaxis.
Following 3 h of DC EF application, photomicrograph analysis of the FLS-seeded collagen hydrogels revealed a significant increase of $79\%$ in overall migration speed (2.5 μm/h) compared to untreated controls (1.4 μm/h) (Figure 6A; $p \leq 0.001$). A 3.6-fold increase in average incremental migration speed was observed in FLS under DC EF stimulation (6.9 μm/h) compared to controls (1.9 μm/h) following each 10 min acquisition interval (Figure 6B; $p \leq 0.001$). Directed velocity of FLS migration within the hydrogels toward the cathode was significantly higher with galvanotaxis EF stimulation versus controls (1.9 and 0.5 μm/h, respectively) (Figure 6C; $p \leq 0.001$). Monitoring the path of each individual cell revealed that the mean FLS migration angle was 46° for the DC EF exposed hydrogels compared to 156° for the untreated control group (Figure 7).
## Study 3. Apply DC EFs via galvanotaxis to synovium explants in order to evaluate migration of synovial repair cells within a 3D cartilage defect model.
DC EF stimulation via galvanotaxis increased cell recruitment and migration within the bovine cartilage defect (Figure 8C,D) compared to controls (Figure 8A,B). Assessing the depth of the defect region following DC EF exposure, approximately $86\%$ of adherent cells had migrated at least 40 μm in the EF group, compared to $38\%$ in the controls (Figure 8E; $p \leq 0.001$). Additionally, EF stimulation increased the directed velocity of FLS into the defect site (28.2 μm/h) relative to untreated controls (8.9 μm/h) (Figure 8F; $p \leq 0.001$).
Following DC EF exposure, viability of the cartilage construct was also assessed using a live/dead cytotoxicity assay. The assay is based on Calcein AM permeating the cells with intact membranes, producing a green fluorescence (live), while Ethidium Homodimer permeates the nuclei of dead cells to produce a red fluorescence (dead) [49]. Compared to untreated controls from initial harvest (Figure 9A), viability of the cartilage construct remained consistent with minimal dead cells under DC EF exposure (Figure 9B). Dead cells were visible along the circumference of the annulus region where the 1 mm sub-core was removed from the cartilage construct. The ring of dead cells was not as profound in the cartilage explants exposed to EF stimulation compared to control samples.
## Study 4. Characterize the effect of PEMF stimulation on modulating the migration of endogenous and/or exogenous FLS repair cells into a cartilage defect model.
PEMF stimulation for 48 h enhanced the migration of FLS into the cartilage defect region compared to untreated controls across both timepoints (Figure 10A–F). The number of migrated FLS was significantly elevated under PEMF exposure, where $78\%$ of adherent cells had migrated at least 40 μm into the defect area compared to $17\%$ in the sham group (Figure 10G; $p \leq 0.001$).
Histological staining of the synovium on cartilage explant model was performed to further assess cell migration into the defect site. In this case, the 1 mm cores that were removed from each 4 mm bovine cartilage plug were reinserted in order to characterize intrinsic repair of the defect region via PEMF-induced cell migration. A piece of bovine synovium was placed in direct apposition to the cartilage construct and the explants were exposed to PEMF treatment for up to 7 days. Structural morphology of the cartilage plugs and the migration of FLS as well as native chondrocytes were visualized with H&E staining. Compared to controls (Figure 11A–D), PEMF treatment slightly improved cell migration within the defect region of the cartilage construct over time (Figure 11E–G). The defect ring at the sub-cored interface visually showed enhanced cellular migration and repair by day 7 of PEMF stimulation compared to day 0 controls, as the cell-free defect region exhibited gradual closure across each timepoint.
## Study 5. Evaluate the biochemical properties and ECM components of synovium and cartilage explants exposed to PEMF treatment.
In bovine synovium and cartilage explants, no significant changes in DNA levels were observed between PEMF treated groups and controls (Figure 12A,B). Synovium exposed to PEMF exhibited a $37\%$ increase in GAG content compared to untreated day 7 samples (Figure 12C; $$p \leq 0.0406$$). In cartilage constructs, the difference in GAG was slightly more profound for PEMF treated explants with an overall increase of $40\%$ relative to day 7 controls (Figure 12D; $$p \leq 0.0076$$). Compared to explants collected during initial harvest (day 0), PEMF exposed synovium exhibited a $45\%$ increase in GAG levels ($$p \leq 0.0173$$). Both day 7 controls and PEMF treated cartilage explants also had significantly elevated GAG compared to day 0 samples ($$p \leq 0.0101$$ and $p \leq 0.0001$, respectively). Synovium under PEMF treatment also exhibited a $66\%$ increase in collagen content compared to day 7 controls ($$p \leq 0.0103$$), while no significant changes were observed between the same treatment groups for the cartilage explants (Figure 12E,F). Compared to day 0 specimens, a greater than 2-fold increase in collagen levels was observed for both synovium and cartilage tissue under PEMF stimulation ($$p \leq 0.0016$$ and $$p \leq 0.0003$$, respectively). In cartilage explants, an $87\%$ increase in collagen levels were also measured in day 7 controls compared to day 0 samples ($$p \leq 0.0117$$).
Histological staining of PEMF treated synovium and cartilage explants yielded similar results. Similarities in synovial structural morphology between PEMF and control samples were visualized with H&E (Figure 13A,B). Slightly deeper staining of Safranin O was observed in both PEMF treated synovial and cartilage explants compared to untreated controls (Figure 13C,D,G,H). Higher intensity Picrosirius Red staining was also evident in synovium exposed to PEMF stimulation (Figure 13E,F), while no visual differences were observed between the cartilage constructs (Figure 13I,J).
Gene expression of PEMF exposed bovine cartilage explants showed significant differences in both cartilage and synovium ECM markers compared to untreated controls. Following 7 days of PEMF stimulation, cartilage makers of ACAN, COL2, and COL10 were significantly upregulated (Figure 14A; $$p \leq 0.0329$$, $$p \leq 0.0073$$, and $$p \leq 0.0243$$, respectively). Compared to controls, synovium ECM markers including COL1, COL3, and COL4 were also elevated under PEMF exposure (Figure 14B; $$p \leq 0.0151$$, $$p \leq 0.0029$$, and $$p \leq 0.0210$$, respectively).
## Discussion
The presented studies characterized the use of PEMFs and DC EF galvanotaxis to promote the migratory behavior and healing capacity of native bovine FLS. The bovine model is widely used in musculoskeletal research and has yielded significant insights to cartilage biology, synovial activation, and regenerative medicine. Previous studies from our laboratory have demonstrated that bovine synovium (engineered and native) respond similarly to pro-inflammatory cytokines as native human synovium, supporting future efforts to develop more effective strategies for promoting repair and restoring joint health [46].
Electrotherapeutic stimulation is yet to be fully explored in the context of promoting intrinsic cartilage repair via FLS modulation. Given its proximity to the underlying cartilage, synovium-derived cells have been implicated in localized repair of injured lesions along the articular surface [50–52]. Combined with the use of electrotherapeutic strategies, direct homing of resident FLS can further be enhanced, thus expediting the rate of cartilage repair without surgical interventions.
Compared to untreated controls, PEMF exposure enhanced bovine FLS migration during wound closure, suggesting that EF stimulation can promote FLS movement in vitro. While no immediate effects of PEMF were observed directly following injury, sustained improvement in the rate of FLS wound closure was evident by 24 h, contributing to current literature findings that support the use of PEMFs in modulating gradual migration of other cell types including MSCs, chondrocytes, osteoblasts, and meniscal derived cells [37,53–57].
From a 2D environment, the migration of bovine FLS was further investigated within a 3D collagen hydrogel via galvanotaxis. In the collagen substrate, bovine FLS exhibited migration toward the cathode with increased incremental and overall speeds of movement, suggesting that DC EF stimulation can promote the directed migration of FLS under 3D culture conditions. In this study, FLS migration was tracked by identifying the centroid and tracing the path followed by each cell within the collagen hydrogel. However, this method of cell tracking may have underestimated the displacement of the cells in the galvanotaxis chamber. FLS may have formed additional focal matrix adhesions with the surrounding collagen gel, potentially contributing to delayed migration rates [58,59].
Following FLS migration within a 3D collagen hydrogel, a custom galvanotaxis chamber was designed in order to assess cell movement within a biological substrate. EF strengths of magnitudes similar to those used to promote 2D galvanotaxis was applied to track cell migration into the defect of the bovine cartilage explant. FLS migrated into the defect region at an average velocity of 28.2 μm/h with an EF strength of 6 V/cm and a current density equal to 73 mA/cm2. These values are comparable to physiologic conditions exhibited by chondrocytes in vivo, where cells can be exposed to field strengths and current densities with magnitudes of up to 15 V/cm and 100 mA/cm2, respectively [60]. The EF parameters of this study were optimized to preserve FLS viability under the tested conditions while also maximizing DC EF exposure to promote migration, suggesting that novel bioreactors can be designed to investigate 3D tissue specimens and subsequent cell migration analysis under galvanotaxis stimulation.
Following a similar procedure, PEMF contributed to increased FLS migration into the bovine cartilage defect region, suggesting that PEMF exposure can promote intrinsic repair. This finding supports previous studies that have shown PEMF treatment improves cartilage graft growth and healing in both a time- and direction-dependent manner [37]. H&E staining of the defect region reveled improved repair under PEMF following 7 days of EF exposure. Whether the repair process was initiated by FLS from the overlying synovium or native chondrocytes from the articular cartilage surface is yet to be determined. However, recent studies have shown that PEMF treatment can induce MSC differentiation to promote immunomodulation and improve cartilage regeneration in vitro and in vivo [53]. The multi-lineage differentiation capabilities of MSCs combined with PEMF stimulation have been shown to promote osteogenesis and chondrogenesis in joint repair [54,57]. With appropriate stimulation, FLS have also been shown to possess a multi-lineage potential to differentiate into chondrocytes and exhibit characteristics of the native cartilaginous environment [61,62]. The accelerated repair of the bovine cartilage defect region in the current study may be induced by a combination of PEMF stimulated chondrocyte migration and potential chondrogenesis of native FLS into the injured site, contributing to an improved healing response compared to untreated controls. PCR analysis of PEMF treated explants revealed that gene expression of chondrogenic and synovium ECM markers were upregulated compared to no-PEMF controls. These results may reflect the contribution of gene expression from cells recruited to the wound site and those derived from the entirety of the cartilage construct.
PEMF-induced chondrogenesis in situ may further contribute to increased cartilage ECM constituents in injured joints [53]. Biochemistry analysis revealed that DNA levels were consistent between PEMF and control treated specimens, suggesting that EF stimulation did not induce cell damage for both synovium and cartilage explants. Collagen and GAG content differed significantly between PEMF and untreated samples, suggesting that EF stimulated environments may affect cell sensitivity and metabolism [43,63]. While lower GAG and collagen content have been shown to be characteristic features associated with cartilage damage, PEMF treatment contributed to elevated matrix constituents for both bovine synovium and cartilage tissue, indicative of its pro-anabolic effect [34,64–68]. Histological analysis of both explants confirmed the differences observed in GAG and collagen levels under PEMF exposure compared to controls.
Future studies will aim to optimize PEMF and DC EF culture conditions. While the current galvanotaxis set-up did not allow for examination of bi-directional cell migration, the system can be modified by culturing labeled FLS on multiple surfaces of the cartilage hydrogel or explant model to further investigate preferential migration towards the cathode (−). In addition, mechanisms that mediate differences in cell attachment versus interactions between the examined substrates can be distinguished in order to confirm the role of PEMFs and DC EF stimulation in promoting FLS migration. While the present PEMF and galvanotaxis systems were unable to perform real-time cell tracking, both procedures allowed for the maintenance of aseptic conditions, multiple treatments over time, and subsequent cell and tissue analyses.
Overall, both PEMFs and galvanotaxis DC EF treatments can enhance synovial cell-mediated cartilage repair using clinically relevant stimulation parameters. The results suggested that PEMF and galvanotaxis are complementary electrotherapeutic strategies to promote the migration of FLS repair cells within cartilage ECM or toward defect regions along the articular surface, thus initiating an intrinsic healing response. The novel tissue-scale bioreactor was designed to generate consistent DC EFs under sterile culture conditions to investigate the effects of galvanotaxis on synovium and cartilage explants. Similarly, custom PEMF generators were designed to test 3D biological specimens under constant EF exposure in order to examine the metabolic and migratory behavior of FLS within an in vitro cartilage defect model. Together, PEMF and galvanotaxis DC EF stimulation provide electrotherapeutic modalities to translate culture findings to a preclinical system.
## Data Availability Statement:
The datasets analyzed in this study are available from the corresponding authors on reasonable request.
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|
---
title: Cardiovascular health research priorities in the United Arab Emirates
authors:
- Nariman Ghader
- Nabeel Al-Yateem
- Sarah Dalibalta
- Hira Abdul Razzak
- Syed Azizur Rahman
- Fatima Al Matrooshi
- Sara Al Shaya
- Amina Al Marzouqi
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10035787
doi: 10.3389/fpubh.2023.1130716
license: CC BY 4.0
---
# Cardiovascular health research priorities in the United Arab Emirates
## Abstract
### Background
Cardiovascular diseases (CVDs) are a leading cause of morbidity and mortality in the United Arab Emirates (UAE) and have been prioritized for intervention by healthcare authorities and clinicians.
### Aim
To identify clinically relevant research priorities for the treatment and prevention of CVDs in the UAE.
### Methods
This study used the nominal group technique to identify CVD-related research priorities. Participants were 37 experts from UAE hospitals, academic and research institutions, CVD associations, and paramedical organizations.
### Results
Initially, 138 research topics were suggested by participating experts. These topics were then refined to identify the most important research priorities related to CVD prevention and treatment. The top research priority areas were: development of evidence-based, customized algorithms for CVD prevention and in-hospital emergency interventions; the availability, accessibility, and affordability of CVD treatment and rehabilitation; identification of relationships between CVDs, lifestyle factors, and mental health; efficacy and constraints in the management of cardiac emergencies; and epidemiological studies that trace CVD in the UAE.
### Conclusion
The identified research priorities will guide a more informed research program for CVD treatment and prevention in the UAE. Funding opportunities and support for researchers should be prioritized for these identified research areas.
## Introduction
Cardiovascular diseases (CVDs) are a leading cause of death globally [1] and account for $40\%$ of mortality in the United Arab Emirates (UAE) [2, 3]. The recent UAE National Health Survey identified CVDs as a significant health burden in the UAE, with physical inactivity, hypertension, obesity, and tobacco use being the top risk factors for CVD-related death [4]. The UAE Health Vision 2021 focused on addressing CVD risk factors and decreasing mortality [5]. Therefore, CVD-related research has been allocated a large amount of funding.
Given global and local statistics related to CVDs, the UAE developed a national strategic plan to reduce CVD-related mortality with input from local stakeholders. The unpublished document lists 52 initiatives that predominantly revolve around community outreach; this approach aimed to empower the public as a partner in assuming national responsibilities and act as a catalyst to introduce positive health changes to people's lifestyles. However, the government's directives to achieve the ultimate goal of reducing CVD-related mortality and the content of relevant policies and strategic plans still need to be backed up by empirical research. The existence of solid research will guide implementation of the strategy and provide a scientific platform for measuring outcomes and revising plans and interventions [6]. The need for clinically relevant and focused healthcare research that contributes to improved healthcare services has also been recognized internationally [7]. Many international health organizations have worked toward identifying research priorities to inform strategic plans for various disciplines. In 2011, the WHO issued a prioritized research agenda for the prevention and control of non-communicable diseases. This agenda stressed prevention and quality care, which required continuous input from updated research work to generate additional evidence-based knowledge and fill chasms in certain emerging areas [8].
The UAE Ministry of Health and Prevention (MOHAP) implemented an initiative to identify CVD-related research priorities to support the fight against CVDs and help the country update its CVD research agenda consistent with international trends. The majority of health policies and guidelines in the UAE are aligned with major global initiatives, such as the United Nations Sustainable Development Goals [9] and WHO research directives, and the perspectives of professional health organizations such as the World Heart Federation. However, in addition to keeping up with international efforts, it is important to incorporate local needs and factors specific to the UAE population and culture. A strong UAE research program will support a structured and evidence-based approach to respond to this global and national health burden, and help in identifying health research priorities for funding allocation. It will also help prioritize efforts to respond to the most relevant and urgent professional, public, and national needs in relation to CVDs [10] and update current research to match international efforts while considering local needs. The aim of this study, therefore, was to identify clinically relevant research priorities for the treatment and prevention of CVDs in the UAE.
## Study design
The nominal group technique (NGT) was used to achieve the objectives of this study. NGT is a structured group discussion method which allows a set of priorities for action to be developed by consensus. NGT is particularly suited to learning about healthcare problems and can generate important solutions, thereby helping to bridge the gap between research and policymaking [11, 12].
Traditionally, NGT gathers group members physically in one place for a group meeting. However, given restrictions related to the COVID-19 pandemic and resulting increased use and acceptance of remote meeting technologies, it was considered appropriate to replace the physical meeting with online techniques. Therefore, we used a modified NGT process with online meetings and voting technologies. As well as being cost effective, this approach was practical as it allowed the inclusion of experts from diverse settings regardless of distance. Participating in this study was easy for these experts as they did not have to leave their workplace and travel to participate in the meeting. This enhanced attendance, thereby enriching the discussion and improving the study's outcomes.
## Participant recruitment
This study used expert sampling technique to recruit participants; this was consistent with the NGT method, where experts in the relevant area of research are selected to participate and give their expert input. The research team from MOHAP, the leading institution in this study, sent invitations through the research department and proper administrative channels to all local and federal government healthcare institutions, cardiovascular health non-government organizations, research institutions, and universities to nominate CVD experts to participate in the NGT meeting. It was specified in the nomination request that all nominated experts must have worked in the cardiovascular specialty area for at least 5 years in the UAE and have been based in the UAE for an uninterrupted period of at least 5 years.
In total, 33 CVD experts were nominated as representatives from different institutions under the authority of the MOHAP, Department of Health-Abu Dhabi, Abu Dhabi Health Services, Dubai Health Authority, Abu Dhabi Police, Dubai Corporation for Ambulance Services, Emirates Cardiac Society, National Ambulance, American Heart Association, the University of Sharjah, and the American University of Sharjah. In addition, five lay people from participating institutions were invited to represent the public's views. Selected experts from participating institutions received e-invites to participate in the event. The date and time for the meeting were coordinated, and all participants were provided with a link to join the online teleconference from their facility.
## Pre-workshop procedure
The research team held multiple meetings to design the workshop content and logistics. Upon completion of the workshop content, the team conducted a pilot meeting with five invited professionals that were not among the participating institutions' representatives. This offered opportunity for the research team to practice the workshop management process. The interaction of and feedback from pilot participants as well as the overall observations of the researchers were used to refine the workshop plan. Next, the team sent out e-invites with pre-reading materials to all nominated representatives, along with instructions for how to access the online meeting room. Microsoft Teams software was used to conduct the online meetings.
## NGT meeting
The NGT meetings took place in October 2020. In total, 37 attendees and three moderators joined the meeting over 2 days. During the opening session on the first day, the moderator presented the workshop's goals and explained the corresponding exercises. Next, as per the NGT process, the moderator asked participants to complete a 5-minute individual thinking exercise (silent idea generation), after which they were asked to privately post their ideas (bullet points) using an online survey platform (i.e., online round-robin recording of ideas). The silent ideas generation and round-robin recording of ideas phases generated 216 ideas for research topics related to CVD prevention and treatment. In exercise two, which lasted 45 min, each participant was invited to discuss their ideas before all attendees in an idea clarifying or discussion phase. During this discussion, these ideas were processed by research assistants, and repeated or similar ideas were merged. The refining resulted in a list of 138 research ideas related to CVD prevention and treatment. These ideas were then put forward for final ranking and prioritizing by all participants as per the NGT protocol. This step was also performed using the online survey system. All ideas were presented to participants, and they were asked to select the five most important ideas. This ranking resulted in the selection of 29 ideas considered the most important, 27 ideas considered the second most important, 29 ideas considered the third most important, 29 ideas considered the fourth most important, and 31 ranked as the least important.
An additional round of ranking was undertaken to further refine these research priorities and select the most important research areas for CVD-related research in the UAE. This round aimed to identify the top research priorities in treating and preventing CVDs in the UAE. The 29 most important ideas were organized in an electronic survey and posted to participants. Participants were asked to rank this list of research priorities one more time for its importance and select the 5 most important priorities.
## Data analysis
A strength of the NGT approach is that results are produced instantly during the meeting as participants vote and revote on generated ideas. This allowed all workshop participants to contribute to generating a list of research priority areas for CVD prevention and treatment. Data analysis in NGT starts as the group meeting starts, and participants generated ideas are recorded. Within this study, this process was done electronically. The ideas were then placed in front of the group members for a thorough discussion; similar ideas were merged during these discussions. Finally, when the final list of ideas was generated, rounds of voting took place until the majority agreed on the top priority research areas. During the final round of voting, participants were asked to rank each identified priority on a scale from 1-10; the mean score of importance for participants' votes was then calculated for each priority area (i.e., the total sum of ratings divided on the total number of participants).
## Ethics
This study was approved by the MOHAP Research Ethics Committee (approval reference no. MOHAP/DXB-REC/AAA/No$\frac{.40}{2020}$). The research team ensured that all participants had read the study information sheet and provided signed written informed consent. The consent form was emailed to the nominated participants by email. Participants were asked to sign the consent and return back before the meeting time. All data were handled in accordance with the MOHAP data privacy and protection policy and the UAE data and privacy protection law [13] *All data* obtained from participants were kept confidential.
## Participants
In total, 37 participants took part in the two modified NGT meeting sessions. Participants included consultant cardiologists, representatives from the ambulance services and emergency medicine, family medicine, a neurologist, representatives from health services management, representatives from local and international CVD-related associations, and a group of academics, researchers, and community members active in the area of CVD prevention, treatment, and research.
## Priority themes
The identified CVD research priorities were: [1] development of customized algorithms for CVD prevention and in-hospital emergency interventions; [2] availability, accessibility, and affordability of CVD treatment and rehabilitation; [3] relationship between CVD, lifestyle, and mental health; [4] efficacy of and constraints on medical services in the management of cardiac emergencies; and 5) epidemiological studies in the area of CVDs in the UAE. Table 1 summarizes the broad priority areas and their mean score of importance.
**Table 1**
| Unnamed: 0 | Priority | Mean score of importance |
| --- | --- | --- |
| 1.0 | Development of customized algorithms for CVD prevention and in-hospital emergency interventions | 8.34 |
| 2.0 | Availability, accessibility, and affordability of CVD treatment and rehabilitation | 8.21 |
| 3.0 | Relationship between CVD, life style, and mental health | 7.95 |
| 4.0 | Efficacy and constraints to medical services in the management of cardiac emergencies | 7.95 |
| 5.0 | Epidemiological studies | 7.1 |
| 6.0 | Efficacy and barriers to CVD awareness programs | 7.13 |
| 7.0 | Studies on public involvement in fighting against CVD | 7.13 |
| 8.0 | Guidelines for CVD diagnosis and treatment in women and children | 7.08 |
| 9.0 | The genetic and epigenetic links to CVD | 6.47 |
## Discussion
This study identified top-priority research areas as a first step toward tackling CVDs as a national health crisis. The next step should be to design and implement national interventions and strategies based on these priority areas. Five research priority areas were identified that cover CVDs management and prevention. The identified research priorities include the development of customized algorithms for CVD prevention and in-hospital emergency interventions; researching the availability, accessibility, and affordability of CVD treatment and rehabilitation; researching the relationship between CVD, lifestyle, and mental health within the UAE context; researching the efficacy of and constraints on medical services in the management of cardiac emergencies; and finally conducting epidemiological studies in the area of CVDs in the UAE.
## Research area 1: Development of customized algorithms for CVD prevention and in-hospital emergency interventions
Methods of predicting who may develop CVD are challenging, and several algorithms have been developed for population-based estimation of CVD risk. These include the Framingham risk score, Pan-European score, Reynolds risk score, ASSIGN Scottish algorithm, and the QRISK2 UK algorithm [14] A recent UAE study investigated some CVD risk assessment tools in a group of 2,621 participants with no history of CVD [15] Those authors found low-to-moderate overall agreement between the different risk assessment tools, which highlighted the need to improve existing tools or generate new tools based on data from the region. In addition, most current algorithms were developed for population-based predictions, which differ from personalized forecasts. Therefore, novel strategies such as high-throughput derived omics biomarkers and genome-scale metabolic models may be better suited for personalized treatment [16] Moreover, algorithms can significantly enhance the efficacy of hospital emergency systems and accurately predict the severity of a patient's medical condition. Machine-learning algorithms, such as logistic regression, Bayesian networks, and deep learning, have been deployed in medicine to predict patient admission and improve patient triage with relatively high accuracy (70–$90\%$) [17].
## Research area 2: Investigating the availability, accessibility, and affordability of CVD treatment and rehabilitation
There is a paucity of research discussing the accessibility and affordability of CVD treatment in the UAE. However, evidence suggests that total expenditure on health has increased, and that progress has been made in the healthcare system with generally high patient satisfaction. The UAE has state-of-the-art facilities, and spends an estimated 13.6 billion USD on healthcare, although there are variations in access, affordability, and quality across the Emirates [18] The UAE also aimed to implement extensive health system reforms consistent with the country's 2021 vision that all Emiratis and residents have access to comprehensive, world-class facilities for early diagnosis and preventive medicine. Further research in the UAE should investigate the costs and accessibility of CVD treatment and the promotion of resources to support more cost-effective strategies, such as CR.
## Research area 3: Relationship between CVD, lifestyle, and mental health
There is an established relationship between poor lifestyle choices and CVD. These lifestyle factors include bad dietary habits, physical inactivity, adiposity and dyslipidemia, excessive stress, hypertension, diabetes mellitus, and smoking [19] These factors have therefore become targets for the assessment of CVD risk and treatment and monitoring of patients with CVD. Notably, modest adjustments of these lifestyle risk factors can have substantial improvements on cardiovascular risk. In the UAE, these risk factors are prevalent, as the country has some of the highest rates of physical inactivity globally[20] with nearly $58\%$ of the adult population being physically inactive [4]. The prevalence of overweight/obesity is approximately $71\%$ in UAE adults [6] and over $30\%$ in children [21, 22] Statistics also suggest the UAE has the second highest prevalence of diabetes globally ($18.7\%$ of the population are affected) [20] and smoking is also common ($9.1\%$ of the population are smokers) [23]. This further emphasizes the need to allocate more resources to researching lifestyle risk factors for CVD in the UAE, raising population awareness, and establishing effective interventions.
It is also important to note the influence of mental health on CVD risk. Mental distress (e.g., depression and anxiety) can increase the risk for developing or worsening CVD conditions by about $80\%$ [24] It can also contribute to increased blood glucose levels, weight gain, unhealthy lifestyles, and increased blood pressure. Mental health disorders are prevalent in the UAE, with a cross sectional study revealing $57.2\%$ of surveyed participants had suffered from at least one mental disorder, with higher rates in women[25, 26]. The most common disorders were anxiety ($56.4\%$), depression ($31.5\%$), posttraumatic stress disorder ($15.1\%$), and phobic disorders ($10.8\%$). These findings also highlight the need for interventions in investigating mental health disorders in this population, another risk factor for CVD, and the necessity to improve mental health outcomes.
## Research area 4: Understanding the efficacy of and constraints on medical services in the management of cardiac emergencies
Cardiac emergencies are a leading cause of death. Therefore, it is vital to establish proper clinical practice guidelines for the initial evaluation and treatment of patients with symptoms of a cardiac emergency both pre-hospital and in-hospital. There have been major advances worldwide in cardiopulmonary resuscitation (CPR) and defibrillation, which are known to significantly increase survival rates from cardiac arrest when performed early. The UAE National Ambulance Service evaluated the characteristics of out-of-hospital cardiac arrest (OHCA) in a report published in 2019 [27] A total of 715 OHCA cases attended by National Ambulance crew were enrolled in that study. Although cardiac arrest was witnessed in more than half of these cases, only $53.2\%$ of patients received bystander CPR. Moreover, an automated external defibrillator (AED) was only applied in two cases before the arrival of ambulance services. This highlighted a clear gap in the chain of survival with a number of barriers regarding OHCA, such as a lack of knowledge in recognizing cardiac arrest or a lack of confidence in performing CPR. Furthermore, the UAE does not have a Good Samaritan law and bystanders may fear legal action following their interference. There also appears to be low public access to AED. That study highlighted the need to improve public awareness of the symptoms of cardiac arrest, develop training programs on how to perform CPR, and enhance community engagement for better prognosis and survival.
## Research area 5: Performing epidemiological studies pertaining to CVD in this population
To fully understand the scale of the CVD issue in the UAE, it is imperative to collect current epidemiological data on CVD and its risk factors in this population, and implement strategies aimed at reducing its burden. The prevalence of CVD in the UAE is increasing, and it is necessary to target modifiable risk factors such as high cholesterol levels, obesity, physical inactivity, high blood pressure, smoking, and high blood glucose levels in this population [28] In addition to the individual (lifestyle) risk factors, there are also societal risk factors (health systems, prevention and care, medical emergency services) related to CVD that should be addressed. The WHO, Centers for Disease Control and Prevention, American Heart Association, and other stakeholders have called for action to meet the challenges of CVD and outline ways to improve prevention and care [29] The most cost-effective and economically feasible policies with the highest likelihood of success are intervention strategies that initiate interventions at the population level [30]. The lack of reliable data from the UAE population that accurately estimates the full burden of CVD hinders the establishment of nationwide prevention and management strategies. Therefore, there is a need for better health information and epidemiological studies to monitor progress and guide health policy decisions in CVD prevention and management using evidence-based and cost-effective preventive approaches.
## Conclusions and implications of our findings
By bringing together a group of multidisciplinary participants across the spectrum of health governing bodies in the UAE using a virtual platform, the MOHAP has been successful in creating a plan to transform the UAE's public health strategy for CVD prevention and intervention. The recommendations derived from this meeting represent a range of relevant perspectives and provide a framework for the development of national guidance on the prevention of CVD. The enormous burden of CVD in terms of suffering and healthcare costs is escalating, with a clear need for comprehensive action plans. The research themes that emerged from this should guide the research agenda in the country for the coming few years. Research funding should therefore be directed to the priority areas identified, which also can provide a basis for cooperation among research funders and key partners.
## Study limitations
This study has some limitations. First is the fact that this is a national-level study. The importance of the study is therefore limited to the UAE healthcare system context. However, the study could be a landmark for future similar regional studies.
The experts who participated in the study were from local institutions, experts from international organizations such as WHO were not included. The inclusion of international experts could have enriched the study results.
Finally, the generalizability of the study may be limited due to a potential selection bias. The participating experts were from specific institutions accessed and invited by the MOHAP, and from institutions that responded to this invitation. Therefore, the study findings could not be generalized beyond the experts who participated in this study.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by the MOHAP Research Ethics Committee (approval reference no. MOHAP/DXB-REC/AAA/No$\frac{.40}{2020}$). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: Does the implementation of pay-for-performance indicators improve the quality
of healthcare? First results in France
authors:
- Marc-Antoine Sanchez
- Stéphane Sanchez
- Leila Bouazzi
- Louise Peillard
- Aline Ohl-Hurtaud
- Catherine Quantin
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10035788
doi: 10.3389/fpubh.2023.1063806
license: CC BY 4.0
---
# Does the implementation of pay-for-performance indicators improve the quality of healthcare? First results in France
## Abstract
### Background
Pay-for-performance (P4P) models are intended to promote quality of care in both hospitals and primary care settings. They are considered as a means of changing medical practices, particularly in primary care.
### Objectives
The first objective of this study was to assess how performance indicators changed over time, measured through “Remuneration on Public Health Objectives” (ROSP) scores, between 2017 and 2020 in a large French region (Grand Est region), and to compare this evolution in the rural vs. urban areas of the region. The second objective was to focus on the area with the least improvement in ROSP scores and to investigate whether the scores and the available sociodemographic characteristics of the area were associated.
### Methods
First, we measured the evolution over time of P4P indicators (i.e., ROSP scores) obtained from the regional health insurance system, for GP practices in the Grand Est region between 2017 and 2020. We then compared the scores between the Aube Department and the rest of the region (urban areas). To address the second objective, we focused on the area found to have the least improvement in indicators to investigate whether there was a relationship between ROSP score and sociodemographic characteristics.
### Results
More than 40,000 scores were collected. We observed an overall improvement in scores over the study period. The urban area (Grand Est region minus the Aube) scored better than the rural area (Aube) for chronic disease management [median 0.91 (0.84–0.95) vs. 0.90(0.79–0.94), $p \leq 0.001$] and prevention [median 0.36 (0.22–0.45) vs. 0.33 (0.17–0.43), $p \leq 0.001$], but not for efficiency, where the rural area (Aube) performed better [median 0.67(0.56–0.74) vs. 0.69 (0.57–0.75 in the rest of the Grand Est region, $$p \leq 0.004$$]. In the rural area, we found no significant association between ROSP scores and sociodemographic characteristics, except for extreme rurality in some sub-areas.
### Conclusions
At the regional level, the overall improvement in scores observed between 2017 and 2020 suggests that the implementation of ROSP indicators have improved the quality of care, particularly in urban areas. These results also suggest that efforts should be focused on rural areas, which already had the lowest scores at the start of the P4P program.
## 1. Introduction
Pay for performance (P4P) models are used to improve the quality of care through economic incentives that are based on the achievement of quality indicators. These models are now widely used in the form of mixed payments (fee-for-service and contracting) and represent the first step in shifting from fee-for-service to capitation-based models. When primary care is predominantly funded on a fee-for-service basis, introducing a P4P model may help to change practices and promote prevention. Indeed, in the P4P model, payment is based on the number of patients being treated (for example, for chronic conditions) rather than on the number of individual procedures. When payment is on a fee-for-service basis, it can lead to artificial “inflation” of the number of procedures [1]. With fee-for-service models, there is a propensity to give precedence to quantity at the cost of quality of care, contrary to capitation-based models, which favor quality [2].
P4P programs have been part of numerous experiments in both the hospital and ambulatory care sectors. In 2004, the United Kingdom (UK) was one of the first countries to introduce this type of model with the Quality and Outcomes Framework, which was designed to change medical practices through the use of performance indicators. Nevertheless, some evaluation studies indicated that performance indicators may not be directly beneficial in the hospital sector (3–5) or in primary care [6, 7]. Many parameters, such as the type of health insurance system and whether patients are seen in ambulatory versus hospital-based settings, may interfere with the results of these P4P programs. While interpreting and evaluating the effects of financial incentives is not a straightforward task [8], this innovative financing approach is a lever for improving practices in various care settings.
Achieving the objectives set by health authorities can be challenging for healthcare professionals, and physicians in disadvantaged areas often have greater difficulty achieving P4P program goals [9], as has recently been observed in a study from the United States [10]. However, in areas with lower baseline performance indicators, P4P models may be particularly useful since there is room for significant improvement [11].
In France, an experimental measure based on voluntary participation, the Contract for Improvement of Individual Practice (Contrat d'Amélioration des Pratiques Individuelles—CAPI), was launched in 2008 to introduce payment by capitation into the remuneration of general practitioners (GP). In 2011, this measure was extended and became Remuneration based on Public Health Objectives (Rémunération sur Objectifs de Santé Publique—ROSP). ROSP applies to GPs as well as to certain specialists and is regularly updated. Currently, it includes 29 clinical indicators for GPs caring for adult patients. This P4P approach rewards all GPs by providing additional payments based on the level of achievement of ROSP indicators, as assessed by quality indicators. The list of indicators is known, so GPs can consult the expected performance criteria for this additional source of income. However, the implementation of ROSPs has been relatively slow: the first payments to GPs were made in 2013, and the number of indicators was expanded in 2016. The first evaluation of the effects of ROSP on physician remuneration took place in 2018. This system is based on a contract between GPs and the national health insurance system, which sets rates of payment according to the level of achievement of each indicator, measured by the scores obtained (National Health Insurance, 2022. La Rosp du médecin traitant de l'adulte. https://www.ameli.fr/medecin/exercice-liberal/remuneration/remuneration-objectifs/medecin-traitant-adulte=). A previous study reported wide variability in obtained scores, which was attributed to the type of physician and their geographical location [12]. In this regard, remoteness is a known limiting factor for the use of primary care (in general or specialized medicine) (13–17). We hypothesized that this limitation could negatively impact the quality of care and may be reflected by lower ROSP scores.
The first objective of this study was therefore to measure the evolution in performance indicators between 2017 and 2020, as measured by ROSP scores, in a large French region (Grand Est region) and to compare the changes in scores between the different areas of the region (rural and urban areas). The second objective of the study was to focus on the area with the least improvement in ROSP scores to investigate whether there was an association between the scores and available sociodemographic characteristics.
## 2. Methods
We performed a retrospective cohort study using data obtained from the Regional Health Insurance System. These routine reimbursement data include payments to physicians based on ROSP scores. ROSP scores are calculated for each individual GP, and they measure the level of achievement for each indicator. A detailed description of the calculation method is given in the Supplementary Figure 1 and Supplementary Table 1. We constructed our analyses in line with the two objectives. First, we sought to investigate whether there was an improvement over time following the implementation of P4P in the region for which we had data (Grand Est region). Then, if an improvement was observed, we compared the course of ROSP scores between the different areas of this region (rural: Aube department, and urban: the rest of the Grand-Est region). To address the second objective, we then focused on the area with the least improvement in ROSP scores in order to assess whether there was an association between the scores and available sociodemographic characteristics. The characteristics we focused on were: population density, potential local accessibility, and sociodemographic category of the area (i.e., urban with poor access to care, city center, rural and unattractive urban area, or rural area).
## 2.1. Primary outcome
We retrieved ROSP scores from 2017 to 2020 for all GPs who were eligible for performance-based payment in the Grand Est, an administrative region in the east of France. Accounting for almost $8\%$ of the French population (5 million inhabitants), the Grand Est region includes five urban areas with more than 250,000 inhabitants each (i.e., Metz, Mulhouse, Nancy, Reims and Strasbourg). The Aube Department, in contrast, is the most rural of the 10 departments that comprise the Grand Est region. We thus compared the Aube department with the rest of the Grand Est region (excluding the Aube). Apart from the difference in population density, the two areas are very similar, which simplifies the comparison. More specifically, the Aube Department and the Grand Est region as a whole are alike in terms of sociodemographic characteristics such as population change, proportion of vacant housing, proportion of taxed households, and employment rate among 15–64 year olds. However, the share of agriculture is lower in the rest of the Grand Est region (7.4 vs. $15.5\%$ in 2019), and the population density is very different (96.7 inhabitants/km2 in the Grand Est region vs. 51.7 in the Aube department) (INSEE: statistics and studies—Comparator of territory-region of the Grand Est.c2022 Available from: https://www.insee.fr/fr/statistiques/1405599?geo=REG-44+DEP-08+DEP-10+DEP-51+DEP-52+DEP-54+DEP-55+DEP-57+DEP-67+DEP-68+DEP-88). We therefore hypothesized that the comparison of these two areas (i.e., the Aube department vs. the rest of the Grand Est region) would highlight differences in GPs' practices in rural and urban areas.
The measurement of ROSP indicators was an existing metric that concerns all GPs and that could be used in this framework of this study. The ROSP indicators are defined by the national health insurance system, and are applicable to three areas of GPs' clinical practice, namely: monitoring of chronic diseases, prevention measures, and efficiency of care. For the national health insurance system, these ROSP indicators are used to measure the quality of care and medical practices. For the majority of the indicators, the aim is to exceed the threshold value defined for each indicator. There are 29 indicators, for a total of 940 points. Each point has a monetary value of 7 euros. In addition to reaching the target rates set by the health insurance system, GPs must treat a minimum number of patients in order to be eligible for financial rewards via the ROSP system. The ROSP scores in the Aube department and the rest of the Grand Est region were compared overall (Supplementary Table 1).
Concerning the Iatrogenesis and Antibiotic-use indicators, the objectives for GPs involve limitation or reduction, i.e., lower scores are better. For example, for the indicator Percentage of patients aged >75 years old who do not have documented long-term psychiatric disorders and who have ≥2 prescribed psychotropic drugs (excluding anxiolytics) is in the Iatrogenesis category. The intermediate objective was to limit this prescription rate to $10\%$ of patients meeting the definition, with an ultimate target of $3\%$ or fewer. Only four indicators require that each GP connects individually to the health insurance website to declare their activity in view of ROSP indicator calculation (Ameli.fr). For all other indicators, the GP is not required to provide any information. The health insurance system computes the indicators automatically and calculates the total financial reward to be allocated to each physician.
## 2.2. Definitions for classification
For the second part of the study, to take into account potential geographical, social and healthcare differences, we classified the Aube department using three methods: (i) the French Office of National Statistics population density grid classification for municipalities was used to classify municipalities as either “high population density zones” (densely populated and intermediate density), or “low population density zones” (sparsely populated, or very sparsely populated); (ii) the local potential accessibility (LPA) score, which is a measure of the supply of and demand for GPs that takes into account volume of activity, and service use rates differentiated by population age structure. LPA was categorized as “high-accessibility” (if the values were above the median value of the LPA score) or “low-accessibility” (if values were below the median LPA score); and (iii) the Institute for Research and Documentation in the Economics of Health (IRDES) social and health classification (in 6 classes), including supply and demand for healthcare and the attractiveness of the area (details given in Supplementary Table 2).
## 2.3. Statistical analysis
Due to the asymmetric nature of the data collected and the presence of outliers, we used median values for our statistical analyses. Wilcoxon tests were used to compare the three ROSP categories, and the sub-categories for the three classifications described above, in the Grand Est region and Aube department between 2017 and 2020. We also assessed the trends in ROSP scores over the four study years using the Kruskal-Wallis test. A p-value < 0.05 was considered statistically significant. All analyses were performed using SAS software version 9.4 (SAS Institute Inc., Cary, NC).
## 2.4. Ethical considerations
This study was conducted in accordance with national laws regarding epidemiological research and data protection. Since this study was entirely retrospective and observational, and relied solely on anonymous data (no personal data), neither ethical approval nor written consent were required.
## 4. Results
We compared 1,919 ROSP scores from the Aube department to 39,017 ROSP scores from the remainder of the Grand Est region. All of the scores were generated between 2017 and 2020.
Between 2017 and 2020, the results tended to improve throughout the Grand Est region, including in the Aube department (Table 1). There was an improvement in Chronic disease follow-up, except for cardiovascular risk (rate variation: −$1.96\%$ for Aube, −$5.36\%$ for Grand Est). Concerning Prevention, the Cancer indicator decreased between 2017 and 2020 for the Aube Department, but was stable for the Grand Est region. The results for Iatrogenesis and Antibiotic use also improved (indicated by a decreased ROSP score) for the Aube and the Grand Est. For Efficiency, ROSP scores were higher for the Aube compared to Grand Est, and there was a greater increase between 2017 and 2020 for the Aube (rate increase: $27.27\%$ for Aube, $25.45\%$ for Grand Est). ROSP scores for Influenza were null for the Aube and the Grand Est in 2020.
**Table 1**
| Unnamed: 0 | 2017 | 2017.1 | 2018 | 2018.1 | 2019 | 2019.1 | 2020 | 2020.1 | Rate of evolution | Rate of evolution.1 | Unnamed: 11 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Aube | GE a | Aube | GE a | Aube | GE a | Aube | GE a | Aube | GE a | Delta |
| | Median (IQR) | Median (IQR) | Median (IQR) | Median (IQR) | Median (IQR) | Median (IQR) | Median (IQR) | Median (IQR) | | | |
| Chronic disease type | 0.87 (0.71–0.91) | 0.86 (0.80–0.91) | 0.90 (0.83–0.94) | 0.91 (0.86–0.94) | 0.93 (0.81–0.95) | 0.93 (0.89–0.96) | 0.91 (0.78–0.94) | 0.92 (0.87–0.95) | 4.60 | 6.98 | −2.38 |
| Diabetes | 0.56 (0.47–0.67) | 0.57 (0.47–0.67) | 0.64 (0.53–0.72) | 0.62 (0.51–0.71) | 0.64 (0.53–0.71) | 0.64 (0.52–0.72) | 0.62 (0.50–0.70) | 0.62 (0.50–0.71) | 10.71 | 8.77 | 1.94 |
| High blood pressure | 0.90 (0.87–0.92) | 0.89 (0.85–0.91) | 0.94 (0.91–0.96) | 0.94 (0.91–0.96) | 0.96 (0.94–0.98) | 0.96 (0.95–0.98) | 0.94 (0.92–0.96) | 0.95 (0.93–0.97) | 4.44 | 6.74 | −2.3 |
| Cardiovascular risk | 0.51 (0.43–0.60) | 0.56 (0.48–0.65) | 0.53 (0.43–0.61) | 0.57 (0.50–0.66) | 0.51 (0.43–0.60) | 0.55 (0.47–0.64) | 0.50 (0.40–0.59) | 0.53 (0.44–0.62) | −1.96 | −5.36 | 3.4 |
| Prevention | 0.36 (0.20–0.44) | 0.40 (0.25–0.48) | 0.38 (0.20–0.45) | 0.39 (0.25–0.47) | 0.36 (0.20–0.44) | 0.38 (0.24–0.46) | 0.26 (0.12–0.33) | 0.29 (0.15–0.36) | −27.78 | −27.50 | −0.28 |
| Influenza | 0.87 (0.71–0.91) | 0.50 (0.36–0.59) | 0.50 (0.40–0.60) | 0.51 (0.39–0.62) | 0.52 (0.42–0.63) | 0.52 (0.40–0.64) | 0.00 (0.00–0.00) | 0.00 (0.00–0.00) | −100.00 | −100.00 | 0 |
| Cancer | 0.56 (0.47–0.67) | 0.48 (0.33–0.55) | 0.42 (0.29–0.50) | 0.47 (0.33–0.54) | 0.43 (0.29–0.50) | 0.47 (0.33–0.54) | 0.44 (0.31–0.53) | 0.48 (0.33–0.56) | 7.32 | 0.00 | 7.32 |
| Iatrogenesis | 0.90 (0.87–0.92) | 0.12 (0.00–0.17) | 0.12 (0.00–0.17) | 0.12 (0.00–0.17) | 0.12 (0.00–0.16) | 0.11 (0.00–0.16) | 0.11 (0.00–0.16) | 0.11 (0.00–0.16) | −15.38 | −8.33 | −7.05 |
| Antibiotic–use | 0.51 (0.43–0.60) | 0.26 (0.00–0.46) | 0.24 (0.00–0.41) | 0.25 (0.00–0.44) | 0.16 (0.00–0.37) | 0.22 (0.00–0.40) | 0.14 (0.00–0.34) | 0.18 (0.00–0.35) | −41.67 | −30.77 | −10.9 |
| Efficiency | 0.36 (0.20–0.44) | 0.55 (0.50–0.63) | 0.69 (0.63–0.75) | 0.68 (0.63–0.74) | 0.72 (0.67–0.77) | 0.71 (0.65–0.76) | 0.70 (0.65–0.65) | 0.69 (0.64–0.75) | 27.27 | 25.45 | 1.82 |
| Prescription of generics | 0.54 (0.50–0.60) | 0.54 (0.49–0.60) | 0.69 (0.62–0.73) | 0.68 (0.62–0.72) | 0.71 (0.66–0.76) | 0.70 (0.65–0.75) | 0.69 (0.64–0.74) | 0.69 (0.63–0.73) | 27.78 | 27.78 | 0 |
| Prescription of biosimilars | 0.00 (0.00–0.04) | 0.00 (0.00–0.03) | 0.08 (0.02–0.17) | 0.03 (0.00–0.14) | 0.13 (0.06–0.27) | 0.09 (0.00–0.24) | 0.25 (0.13–0.44) | 0.20 (0.04–0.41) | 0.17 | 0.17 | 0 |
| Efficiency of prescriptions | 0.93 (0.80–0.97) | 0.91 (0.81–0.96) | 0.93 (0.85–0.98) | 0.93 (0.85–0.97) | 0.95 (0.85–1.00) | 0.93 (0.86–0.98) | 0.94 (0.86–1.00) | 0.93 (0.86–0.98) | 1.08 | 2.20 | −1.12 |
Overall ROSP scores between 2017 and 2020 were compared between the Aube department and the rest of the Grand Est region (excluding the Aube) (Table 2). For indicators relating to chronic diseases, prevention and efficiency of care, while the results were significantly different, the differences were numerically small. Within each category, there were more marked differences between the Aube and the Grand Est for certain sub-criteria, such as the ROSP indicators for cardiovascular risk (median value Aube = 0.51 vs. Grand Est = 0.56, $p \leq 0.001$), antibiotic prescription (median Aube = 0.19 vs. Grand Est = 0.23, $p \leq 0.001$) and prescription of biosimilars (median Aube = 0.10 vs. median Grand Est = 0.05, $p \leq 0.001$).
**Table 2**
| Unnamed: 0 | Aube department N = 1,919 | Grand Est region N = 39,017 | p-value |
| --- | --- | --- | --- |
| | Median (IQR) | Median (IQR) | p-value |
| Chronic disease | 0.90 (0.79–0.94) | 0.91 (0.84–0.95) | < 0.001 |
| Diabetes | 0.61 (0.50–0.69) | 0.61 (0.50–0.70) | 0.492 |
| High blood pressure | 0.94 (0.91–0.97) | 0.94 (0.91–0.97) | 0.333 |
| Cardiovascular risk | 0.51 (0.43–0.60) | 0.56 (0.47–0.64) | < 0.001 |
| Prevention | 0.33 (0.17–0.43) | 0.36 (0.22–0.45) | < 0.001 |
| Influenza | 0.45 (0.00–0.56) | 0.44 (0.00–0.57) | 0.923 |
| Cancer | 0.43 (0.30–0.50) | 0.48 (0.33–0.55) | < 0.001 |
| Iatrogenesis | 0.12 (0.00–0.17) | 0.11 (0.00–0.17) | < 0.001 |
| Antibiotic–use | 0.19 (0.00–0.39) | 0.23 (0.00–0.41) | < 0.001 |
| Efficiency | 0.69 (0.57–0.75) | 0.67 (0.56–0.74) | 0.004 |
| Prescription of generics | 0.68 (0.55–0.73) | 0.67 (0.56–0.73) | 0.108 |
| Prescription of biosimilars | 0.10 (0.00–0.24) | 0.05 (0.00–0.20) | < 0.001 |
| Efficiency of prescriptions | 0.94 (0.85–1.00) | 0.93 (0.84–0.97) | < 0.001 |
In terms of prevention, cancer prevention was significantly worse in the Aube department, with a difference of 0.05 points (InterQuartile Range (IQR) Aube = 0.43 vs. IQR Grand Est = 0.48, $p \leq 0.001$). On the contrary, this department had a better overall efficiency score (IQR Aube = 0.69 vs. IQR Grand Est = 0.67, $p \leq 0.004$).
Table 3 displays the results according to the population density of the area where the GP's practice was located for GPs in the Aube Department. In terms of chronic disease follow-up, there was no significant difference between high- and low-density areas, except for cardiovascular risk, where low-density zones had better results (median 0.55 vs. 0.50, $p \leq 0.001$). The opposite was observed for prevention: high-density zones achieved better results for iatrogenesis and prescription of antibiotics (median 0.11 vs. 0.14, $p \leq 0.0001$, and 0.16 vs. 0.25, $p \leq 0.0001$, respectively). There was no significant difference in cancer prevention between high- and low- population density areas, but the high-density zones obtained better results for prescription efficiency (median 0.93 vs. 0.94, $p \leq 0.0001$).
**Table 3**
| Unnamed: 0 | Overall (n = 1,919) | High–density population (n = 1,387) | Low–density population (n = 532) | p-value |
| --- | --- | --- | --- | --- |
| | Median (IQR) | Median (IQR) | Median (IQR) | |
| Chronic disease type | 0.90 (0.79–0.94) | 0.90 (0.78–0.94) | 0.91 (0.80–0.94) | 0.95 |
| Diabetes | 0.61 (0.50–0.69) | 0.62 (0.50–0.70) | 0.60 (0.50–0.69) | 0.23 |
| High blood pressure | 0.94 (0.91–0.97) | 0.94 (0.90–0.97) | 0.95 (0.91–0.97) | 0.10 |
| Cardiovascular risk | 0.51 (0.43–0.60) | 0.50 (0.40–0.58) | 0.55 (0.48–0.64) | < 0.0001 |
| Prevention | 0.33 (0.17–0.43) | 0.33 (0.15–0.43) | 0.35 (0.22–0.42) | 0.002 |
| Influenza | 0.45 (0.00–0.56) | 0.45 (0.00–0.57) | 0.46 (0.00–0.55) | 0.83 |
| Cancer | 0.43 (0.30–0.50) | 0.43 (0.22–0.50) | 0.42 (0.35–0.49) | 0.09 |
| Iatrogenesis | 0.12 (0.00–0.17) | 0.11 (0.00–0.16) | 0.14 (0.08–0.17) | < 0.0001 |
| Antibiotic-use | 0.19 (0.00–0.39) | 0.16 (0.00–0.38) | 0.25 (0.00–0.40) | 0.0001 |
| Efficiency | 0.69 (0.57–0.75) | 0.68 (0.56–0.76) | 0.69 (0.58–0.74) | 0.71 |
| Prescription of generics | 0.68 (0.55–0.73) | 0.67 (0.55–0.74) | 0.69 (0.57–0.73) | 0.41 |
| Prescription of biosimilars | 0.10 (0.00–0.24) | 0.10 (0.01–0.24) | 0.09 (0.00–0.23) | 0.56 |
| Efficiency of prescriptions | 0.94 (0.85–1.00) | 0.94 (0.85–1.00) | 0.93 (0.82–0.97) | < 0.0001 |
ROSP scores according to high vs. low potential accessibility in the Aube Department are presented in Table 4. The overall score for chronic disease follow-up was lower in low-accessibility zones than in high-accessibility zones (median 0.90 vs. 0.91, $p \leq 0.02$). Conversely, for cardiovascular risk, low-accessibility zones had higher scores (median 0.53 vs. 0.50, $p \leq 0.0001$).
**Table 4**
| Unnamed: 0 | Overall (n = 1,919) | High-accessibility zones (n = 960) | Low-accessibility zones (n = 959) | p-value |
| --- | --- | --- | --- | --- |
| | Median (IQR) | Median (IQR) | Median (IQR) | |
| Chronic disease | 0.90 (0.79–0.94) | 0.91 (0.80–0.95) | 0.90 (0.78–0.93) | 0.02 |
| Diabetes | 0.61 (0.50–0.69) | 0.61 (0.50–0.69) | 0.61 (0.50–0.70) | 0.59 |
| High blood pressure | 0.94 (0.91–0.97) | 0.94 (0.91–0.97) | 0.94 (0.91–0.96) | 0.09 |
| Cardiovascular risk | 0.51 (0.43–0.60) | 0.50 (0.39–0.56) | 0.53 (0.45–0.62) | < 0.001 |
| Prevention | 0.33 (0.17–0.43) | 0.33 (0.16–0.44) | 0.33 (0.19–0.42) | 0.47 |
| Influenza | 0.45 (0.00–0.56) | 0.47 (0.00–0.59) | 0.44 (0.00–0.55) | 0.03 |
| Cancer | 0.43 (0.30–0.50) | 0.43 (0.23–0.51) | 0.41 (0.32–0.50) | 0.84 |
| Iatrogenesis | 0.12 (0.00–0.17) | 0.10 (0.00–0.15) | 0.13 (0.06–0.17) | < 0.001 |
| Antibiotic-use | 0.19 (0.00–0.39) | 0.12 (0.00–0.36) | 0.24 (0.00–0.40) | < 0.001 |
| Efficiency | 0.69 (0.57–0.75) | 0.68 (0.56–0.79) | 0.69 (0.57–0.74) | 0.59 |
| Prescription of generics | 0.68 (0.55–0.73) | 0.67 (0.55–0.75) | 0.68 (0.56–0.73) | 0.63 |
| Prescription of biosimilars | 0.10 (0.00–0.24) | 0.10 (0.004–0.23) | 0.10(0.002–0.24) | 0.71 |
| Efficiency of prescriptions | 0.94 (0.85–1.00) | 0.95 (0.89–1.00) | 0.93 (0.81–0.97) | < 0.0001 |
Regarding prevention, the high-accessibility zones seemed to perform better for the risk of iatrogenesis and prescription of antibiotics (median 0.13 vs. 0.10, $p \leq 0.0001$ and median 0.24 vs. 0.12, $p \leq 0.0001$, respectively). The high-accessibility zones also had a better score for prescription efficiency (median 0.93 vs. 0.95, $p \leq 0.001$).
Table 5 shows a comparison within the Aube Department according to categories of health and social criteria from the IRDES (French Institute for Research and Documentation in the Economics of Health). When significant differences were observed, lower ROSP scores were associated with the socio-economic category (Rural outskirts with low attractiveness and vulnerable populations) for the following indicators: Chronic Disease (median 0.89 vs. 0.9 (overall), $p \leq 0.001$), Cancer (median 0.38 vs. 0.42 (overall), $$p \leq 0.04$$), and Iatrogenesis [median 0.13 vs. 0.11 (overall), $$p \leq 0.005$$].
**Table 5**
| Unnamed: 0 | Overall (n = 1,381) | Class 1 (n = 221)1 | Class 2 (n = 334)2 | Class 4 (n = 96)3 | Class 5 (n = 730)4 | p† |
| --- | --- | --- | --- | --- | --- | --- |
| | Median (IQR b ) | Median (IQR b ) | Median (IQR b ) | Median (IQR b ) | Median (IQR b ) | |
| Chronic disease | 0.90 (0.78–0.94) | 0.89 (0.60–0.93) | 0.89 (0.70–0.93) | 0.91 (0.67–0.97) | 0.91 (0.81–0.96) | 0.0002 |
| Diabetes | 0.60 (0.50–0.67) | 0.59 (0.49–0.67) | 0.61 (0.49–0.68) | 0.60 (0.40–0.67) | 0.61 (0.50–0.68) | 0.41 |
| High blood pressure | 0.94 (0.91–0.97) | 0.94 (0.90–0.96) | 0.94 (0.90–0.96) | 0.95 (0.91–1.00) | 0.94 (0.92–0.97) | 0.03 |
| Cardiovascular risk | 0.50 (0.40–0.59) | 0.46 (0.21–0.54) | 0.52 (0.44–0.64) | 0.50 (0.10–0.59) | 0.50 (0.40–0.57) | < 0.0001 |
| Prevention | 0.33 (0.15–0.43) | 0.32 (0.12–0.41) | 0.32 (0.17–0.42) | 0.27 (0.07–0.40) | 0.33 (0.17–0.45) | 0.15 |
| Influenza | 0.45 (0.00–0.57) | 0.44 (0.00–0.56) | 0.43 (0.00–0.54) | 0.33 (0.00–0.57) | 0.47 (0.00–0.62) | 0.04 |
| Cancer | 0.42 (0.22–0.50) | 0.43 (0.15–0.50) | 0.38 (0.23–0.48) | 0.43 (0.14–0.51) | 0.43 (0.23–0.51) | 0.04 |
| Iatrogenics | 0.11 (0.00–0.16) | 0.11 (0.00–0.15) | 0.13 (0.02–0.17) | 0.07 (0.00–0.17) | 0.10 (0.00–0.16) | 0.005 |
| Antibiotics | 0.15 (0.00–0.38) | 0.14 (0.00–0.35) | 0.21 (0.00–0.39) | 0.12 (0.00–0.33) | 0.12 (0.00–0.39) | 0.08 |
| Efficiency | 0.68 (0.56–0.76) | 0.70 (0.58–0.82) | 0.69 (0.56–0.73) | 0.67 (0.50–0.78) | 0.68 (0.55–0.79) | 0.11 |
| Prescription of generics | 0.67 (0.54–0.74) | 0.69 (0.56–0.77) | 0.68 (0.54–0.71) | 0.67 (0.50–0.76) | 0.67 (0.54–0.75) | 0.07 |
| Prescription of biosimilars | 0.10 (0.01–0.22) | 0.12 (0.03–0.23) | 0.08 (0.01–0.18) | 0.14 (0.08–0.36) | 0.09 (0.00–0.23) | 0.19 |
| Efficiency of prescriptions | 0.94 (0.85–1.00) | 0.94 (0.90–1.00) | 0.92 (0.83–0.97) | 0.91 (0.67–0.95) | 0.95 (0.85–1.00) | < 0.0001 |
## 5. Discussion
In our analysis of the temporal trends in ROSP scores from 2017 to 2020, we observed a gradual improvement each year for both the Aube department and the Grand Est region. This result suggests that the implementation ROSP has a positive impact of on quality of care. The increase was particularly marked for the prescription of biosimilars and generic drugs, which is a successful result in view of current health policies that aim to restrict health expenditures. This finding has also been described in the literature [18]. Our results show that the urban area (Grand Est region) had better scores for chronic disease management and prevention, whereas the rural area (Aube) performed better for efficiency. However, the literature does not always show positive effects for these quality of care incentives. A recent study showed that P4P scores were inconsistently associated with quality improvement, which raises questions about the usefulness of the incentives [19].
In the Aube Department, it is worth underlining that overall ROSP scores were similar regardless of the population density (high-density vs. low-density). This shows that GPs can achieve similar quality of care outcomes within a rural area that is supposedly heterogeneous in terms of population density. However, scores in the Prevention category were worse in low-population-density areas for cancer screening, iatrogenesis and antibiotic use.
Again for the Aube department, the Chronic Disease indicator scored worse in areas with a lower potential accessibility score, although the difference in scores was very small. This result should be weighed against the fact that scores were higher for the Cardiovascular risk subcategory in areas with a low LPA score. Our results therefore only partly corroborate those of the literature, where it has been reported that GP activity differs in the city and in the countryside, with those practicing in rural areas tending to manage more patients with chronic diseases and to perform fewer preventive acts [20]. The IRDES classification provides additional results, showing that urban areas with poor access to care had the lowest cardiovascular scores. However, the most rural areas within the Aube department had lower scores on the Chronic Disease, Cancer, and Iatrogenesis indicators, again highlighting significant differences within our rural study area. The prevention and efficiency scores did not differ according to the IRDES classification.
Our results can be at least partially explained by established biases of P4P programs in private practice. It is known that patients for whom P4P goals are more achievable receive more care [21]. In addition, difficulty accessing specialists, such as cardiologists, may lead primary care physicians to over-medicate patients with certain conditions, and this would indirectly affect the ROSP scores compared to other regions. In this case, the indicators reflect more the difference in patients treated between urban and rural areas than the difference in practices related to the professionals themselves. The poorer results obtained in the areas in the Aube department with low potential accessibility could reflect shorter consultation times due to an increased burden of work for health professionals, especially GPs [22]. The lack of time to explain the reasons for antibiotic abstention and to offer additional follow-up consultations could explain the over-prescription of antibiotics.
Overall, our study provides original results by seeking to compare practices between urban and rural areas and within a rural area based on P4P indicators. This investigation was made possible by access to this novel database. Ultimately, our work could be used to develop specific indicators to monitor the quality of care provided, and to provide insights into how we can best adapt the resources available to health professionals in rural areas.
This study has some limitations. Firstly, there is potential for selection bias because our statistics only include GPs who are registered for the P4P system. Although this represents the majority of physicians, it is important to note that their practices may differ from those of GPs who were not registered. We also know that GP have specific motivations for settling in urban vs. rural areas. While the majority of GPs choose to set up their practice in the region where they did their residency training, the criteria for choosing a more or less urban area are predominantly related to the dynamics of supply of care, demand for care and living conditions in the area (23–25). Furthermore, it is not possible to fully assess the magnitude of the effect of ROSP scores on population health without first considering the case mix. The difficulty of assessing the overall impact is compounded by the frequent changes to the indicators, meaning that any assessment of the data and their relationship to patient health is necessarily limited to a short period of time. However, based on the trends we observed for the criteria studied, we can suggest that this limit seems well under control. We obtained results for only one large French region (Grand Est). However, this region has many points in common with the other French regions in terms of healthcare delivery. The design of the article did not enable direct assessment of the impact of the intervention through a comparison of the “here-vs-elsewhere” type, since we were not comparing two areas (i.e., one receiving the intervention and one not). The comparison of intervention vs. non-intervention areas was not possible because, subsequent to the Ministry of Health decision, P4P was implemented on a national level in a uniform manner. All regions in France implemented P4P at the same time. It was also not possible to conduct a before-and-after evaluation, because the available data did not include information at T0, before the intervention began.
It is common practice to evaluate public health policies with a time lag of several years in order to be more objective about the real impact of reforms, as it always takes time for practices to adapt, especially for GPs. Our study had 6 years of hindsight, which seemed reasonable to us. Our data therefore provide information on the evolution of ROSP scores (P4P indicators) during the implementation of P4P in a large French region, allowing us to judge whether a benefit could be expected from this implementation. We also sought to investigate whether the changes over time were different in rural vs. urban areas, bearing in mind that the entire region started P4P at the same time. It would have been very interesting to be able to compare the 2 areas according to population density, LPA, and IRDES classification. Unfortunately, we were unable to obtain sufficiently exhaustive data for the rest of the Grand Est region to ensure the validity of the comparison. Further studies are therefore needed to expand on this comparison and to be able to conclude on the impact of P4P.
Practitioners do not always see the introduction of performance-based payment as a positive change, which could contribute to weaker-than-expected improvements in efficiency [25]. The implementation of P4P could also lead to over-medicalization as practitioners strive meet the indicator targets [26]. Better compliance would likely require greater participation of the healthcare professionals themselves in the co-definition of these indicators. Finally, we cannot rule out a possible classification bias in the definition of groups for our comparisons, which are ultimately based on geographic criteria. However, our results were consistent across the three classification schemes, suggesting a limited effect of classification. It would have been interesting to obtain data on the medical demographics of the other departments of the region to extend our investigation. Future studies could qualitatively investigate the regional profiles of GPs to better understand whether their characteristics explain some of the differences we observed.
## 6. Conclusions
The overall improvement in scores observed between 2017 and 2020 in the Grand Est region suggests that the implementation of ROSP indicators may be useful for improving quality of care in the medium and long term. However, the comparison of ROSP scores in rural and urban areas revealed certain differences, with urban areas doing better overall. When we focused on the rural area (Aube department), our data showed that the scores varied little according to the density of the sub-areas. However, significant differences were observed for some of the social criteria scores, showing lower ROSP scores for the extreme rurality (“Unattractive rural periphery”) of an area. These results suggest that efforts should be concentrated on rural areas, which already had the lowest scores when P4P was first implemented, and which have seen fewer P4P-related benefits than their more urban neighbors.
## Data availability statement
The data analyzed in this study is subject to the following licenses/restrictions: *These data* are provided by the primary health insurance fund, especially for the purposes of this study. They are therefore not available to the public. Requests to access these datasets should be directed to SS: stephane.sanchez@hcs-sante.fr.
## Author contributions
LP and AO-H were involved in the conception and design of the study. SS and CQ were the coordinator of the study. LP and AO-H were responsible for the data collection. M-AS wrote the first draft. LB was in charge of the analysis. M-AS and CQ were involved in the interpretation and critically reviewed the first draft. All authors approved the final version and accept responsibility for the paper as published.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1063806/full#supplementary-material
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|
---
title: 'Prevalence of depression and its association with health-related quality of
life in people with heart failure in low- and middle-income countries: A systematic
review and meta-analysis'
authors:
- Henok Mulugeta
- Peter M. Sinclair
- Amanda Wilson
journal: PLOS ONE
year: 2023
pmcid: PMC10035817
doi: 10.1371/journal.pone.0283146
license: CC BY 4.0
---
# Prevalence of depression and its association with health-related quality of life in people with heart failure in low- and middle-income countries: A systematic review and meta-analysis
## Abstract
### Introduction
Heart failure is a growing public health concern around the world. People with heart failure have a high symptom burden, such as depression, which affects health-related quality of life (HRQoL). The objective of this systematic review and meta-analysis was to estimate the pooled prevalence of depression and evaluate its association with HRQoL among people with heart failure in low- and middle-income countries (LMICs).
### Methods
This systematic review was conducted in accordance with the JBI methodology. Electronic databases such as MEDLINE, PsycINFO, EMBASE, CINAHL, Web of Science, Scopus and JBI EBP were searched to identify relevant studies published from January 2012 to August 2022. The methodological quality of each article was assessed using relevant JBI critical appraisal instruments. A random-effects model was employed to estimate the pooled prevalence of depression. Heterogeneity across the studies was investigated using Cochrane’s Q test and I2 statistic. The Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines 2020 were followed for reporting the results. All statistical analyses were performed using STATA version 17 software.
### Results
After screening, a total of 21 eligible articles with 5074 participants with heart failure were included in this review. The pooled prevalence of depression among people with heart failure in LMICs was $51.5\%$ ($95\%$ CI = 39.7, $63.3\%$, I2 = $99.00\%$). Subgroup analysis revealed, the highest prevalence in studies whose participants were in-patients, and from the Middle East and North Africa, and studies utilizing Becks Depression Inventory (BDI). Depression was positively associated with HRQoL.
### Conclusion
This review revealed that almost half of all people with heart failure in low- and middle-income countries have comorbid depression. People with heart failure and depressive symptoms had poor HRQoL. Therefore, early screening of depression is critical for improving HRQoL in this population.
Systematic review registration: PROSPERO CRD42022361759.
## Introduction
Cardiovascular diseases (CVD) are the leading cause of mortality globally with an estimated 17.9 million deaths in 2019, accounting for $32\%$ of all deaths [1]. It is predicted that over 23 million people will die annually from CVDs worldwide by 2030 [2]. The burden of CVD is increasing in low- and middle-income countries (LMICs) where $75\%$ of all deaths are related to CVD [3]. This burden can be attributed to a lack of primary health care services to support the early detection and management of cardiovascular risk factors [4].
Heart failure is a major CVD associated with high morbidity and mortality [5]. The global prevalence of heart failure (HF) is increasing due to ageing and population growth, with an estimated 64 million people affected [6, 7]. It is responsible for more than 300,000 global deaths annually [8, 9]. Although there are limited data from population-based studies, the available data from hospital-based studies show that heart failure is increasingly prevalent in low- and middle-income countries (LMICs) [10]. People with heart failure have many debilitating symptoms such as depression and poor health-related quality of life (HRQoL) compared to the general population due to the unpredictable nature of the disease [11, 12].
The psychological impact of HF, such as depression, is increasing significantly and leads to a poor prognosis [13]. People with HF who are depressed have an increased risk of poor HRQoL compared to those without depression [13, 14]. The findings from two recent systematic reviews found the prevalence of any severity of depression in people with heart failure was $42\%$ [15], and the overall HRQoL in these populations was moderate [16]. However, these reviews only included a small number of studies from LMICs. This means there is considerable uncertainty about the prevalence of depression in this region. A systematic review and meta-analysis conducted in China found that $43\%$ of people with heart failure have depressive symptoms [17]. However, this figure does not represent the burden of the problem in LMICs as all data were from China.
While there are many studies on depression and its association with HRQoL among HF patients in LMICs, the results are inconsistent and inconclusive, meaning the current burden of the problem remains unknown in these populations [18]. In this systematic review, we aimed to estimate the regional burden of depression and assess the association between depression and HRQoL in people with HF in LMICs. The findings of this review will provide contemporary evidence with the potential to assist healthcare policymakers and researchers in developing intervention programs and guidelines for improving the management and care of people with heart failure in LMICs.
## Review questions
This review sought to answer the following two questions:
## Participants (population)
This review included studies from LMICs whose participants who are 18 years or older and had a confirmed diagnosis of heart failure.
## Condition
The prevalence of depression and/or association of depression with HRQoL in the participants. For the purpose of this review, heart failure is defined as the inability of the heart to effectively pump blood as evidenced by either signs and symptoms based on *Framingham criteria* or reduced ejection fraction (<$40\%$) [19, 20]. Depression is defined as the persistent feeling of unhappiness and lack of interest in daily activities with symptoms for at least two weeks, based on DSM-5 diagnostic criteria [21]. Health-related quality of life was defined as self-reported physical, mental, emotional, and social health functioning [22].
## Low-and-middle income countries
For the purposes of this review, low to middle income countries were defined using the World Bank atlas method [23] based on the stratification of economies based on gross national income (GNI) per capita. Low-income countries are those with a GNI per capita of $US1,045 or less; lower and upper middle-income economies are those with a GNI per capita between $US1,046 and $US4,095 and $US4,096 and $US12,695 and respectively.
## Outcomes
The primary outcome of this review was the prevalence of depression. The secondary outcome was the association between depression and HRQoL scores measured using a psychometrically validated instrument.
## Types of studies
Observational (cross-sectional, cohort, case-control) studies that reported the prevalence of depression and/or association of depression with HRQoL in people with heart failure.
For the secondary objective of this review, the following inclusion criteria were considered using the PEO (P = Population, E = exposure, O = outcome) model.
## Population
Adults with a confirmed diagnosis of heart failure.
## Exposure of interest
Depression.
## Outcome
HRQoL.
## Design
This systematic literature review has followed methodology guidelines outlined by the Joanna Briggs Institute (JBI) methodology for Systematic Reviews [24] and is reported in line with the PRISMA 2020 guidelines [25]. The protocol for this systematic review was registered in the PROSPERO online database (registration number CRD42022361759) and previously published [26].
## Search strategy
The search strategy aimed to locate both published and unpublished studies. Information sources were electronic databases, conference proceedings, websites, dissertations, and direct contact with the author if required. A preliminary original search of MEDLINE (Ovid) and CINAHL (EBSCO) was undertaken in May 2022 and was updated in August 2022. The last search was done on August 20, 2022. The text words in the titles and abstracts of relevant articles and the index terms used to describe the articles were analysed and used to inform a full search strategy in collaboration with a faculty librarian. The search strategy was developed using the CoCoPop (Co = Condition, Co = Context, Pop = Population) model considering the PEO (P = Population, E = exposure, O = outcome) model for the second research question of this review. The databases searched includes MEDLINE (Ovid), PsycINFO (EBSCOhost), EMBASE (Ovid), CINAHL (EBSCOhost), Web of Science (Direct access), Scopus (Direct access) and JBI EBP database (Ovid). Index terms (subject headings) and keywords used for the search strategy were adapted for each database. The full search strategy for each database is attached in S1 Table. The reference lists of all identified relevant studies and systematic reviews were screened to identify additional studies. A search for unpublished studies was conducted using Google scholar, Mednar, ProQuest and dissertation databases. Articles published in English language from January 2012 to August 2022 were included to establish the most recent estimate.
## Study selection and outcome
Following the search, all identified citations were collated and uploaded into EndNote V20 (Clarivate Analytics, PA, USA). After removing duplicates, two researchers (HM and PS) screened all titles and abstracts from the original search against the predefined inclusion criteria. The full text of selected citations was assessed in detail against the inclusion criteria independently by the reviewers (HM and PS). The reasons for excluding papers were recorded and reported. Any disagreements between the reviewers were resolved through discussion. The search results and the study inclusion process were reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines [25].
## Quality appraisal
Two independent reviewers (HM and PS) critically appraised the eligible studies for methodological quality using a standardised JBI critical appraisal instrument for studies reporting prevalence data [27]. The tool is comprised of 9 items that focus on target population, sample size adequacy, study subject and setting (context), reliability of condition measurement, appropriateness of the statistical test used to analyse the data, and adequacy of the response rate with the option to answer ‘No’, ‘Yes’, or ‘unclear’. Authors of papers were contacted to request missing or additional data for clarification, where required. Following the critical appraisal, the reviewers included or excluded studies based on the overall appraisal quality. A study was excluded if it had more than three ‘No’ or ‘unclear’ quality categories. This threshold criterion is consistent with that used in a similar published systematic review [28]. The quality of eligible articles to assess the association between depression and HRQoL were also appraised using the JBI cross-sectional studies critical appraisal tool for studies reporting association (etiology/risk) [29]. Any disagreements were resolved through discussion. The results of the critical appraisal are reported in narrative and table form (Tables 1 and 5).
**Table 1**
| Included articles | Q1 | Q2 | Q3 | Q4 | Q5 | Q6 | Q7 | Q8 | Q9 | Quality score/9 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Edmealem A. et al. [37] | Y | Y | U | Y | Y | U | Y | Y | Y | 7 |
| DeWolfe A, et al. [38] | Y | Y | Y | Y | Y | Y | Y | Y | U | 7 |
| Okviansanti F, et al. [39] | U | Y | Y | Y | Y | Y | N | Y | Y | 7 |
| Pushkarev GS, et al. [40] | U | Y | Y | Y | Y | Y | U | Y | Y | 7 |
| Fan X, et al. [32] | Y | Y | Y | Y | Y | Y | U | Y | Y | 8 |
| Zahid I, et al. [41] | U | U | Y | Y | Y | Y | U | Y | Y | 7 |
| Yazew KG, et al. [33] | Y | Y | Y | Y | Y | Y | U | Y | Y | 8 |
| AbuRuz ME [34] | U | Y | Y | Y | Y | Y | Y | Y | Y | 8 |
| Pan S, et al. [42] | U | Y | Y | Y | Y | Y | U | Y | Y | 7 |
| Husain MI, et al. [35] | Y | Y | Y | Y | Y | Y | U | Y | Y | 8 |
| Tran NN, et al. [36] | U | Y | Y | Y | Y | Y | Y | Y | Y | 8 |
| Tsabedze N, et al. [43] | U | Y | Y | Y | Y | Y | U | Y | Y | 7 |
| Erceg P, et al. [44] | U | Y | Y | Y | Y | Y | N | Y | Y | 7 |
| Alemoush RA, et al. [31] | Y | Y | Y | Y | Y | Y | Y | Y | Y | 9 |
| Saima D, et al. [45] | U | Y | Y | Y | Y | Y | U | Y | Y | 7 |
| Ghanbari A, et al. [46] | U | Y | Y | Y | Y | Y | U | Y | Y | 7 |
| Zhang X, et al. [47] | U | Y | Y | Y | Y | Y | U | Y | Y | 7 |
| Khan S, et al. [48] | U | Y | Y | Y | Y | Y | N | Y | Y | 7 |
| Molavynejad S, et al. [49] | U | Y | Y | Y | Y | Y | N | Y | Y | 7 |
| Son YJ, et al. [50] | U | Y | Y | Y | Y | Y | U | Y | Y | 7 |
| Son YJ, et al. [51] | U | Y | Y | Y | Y | Y | U | Y | Y | 7 |
## Data extraction
The JBI data extraction tool for prevalence data and association (etiology/risk) studies were used to extract the following information from each included study for each research question: authors, year of publication, country, region, design, population, sample size, sampling methods, outcome measuring tool, prevalence of depression, HRQoL mean score based on exposure (depression), measure of association, and quality appraisal score. When there was missing data, authors were contacted for relevant information. Two reviewers (HM and AW) independently conducted the primary data extraction and cross-checked for inconsistencies. Any disagreements and discrepancies between the reviewers were resolved by discussion.
## Data analysis and synthesis
A narrative presentation of the outcomes including text, table, and figure, were used to discuss the characteristics of the included studies and synthesise the prevalence of depression and its association with HRQoL. A random-effects model with DerSimonian and Laird model was used to estimate the pooled effect size of depression, as recommended by Tufanaru et al. [ 30]. Subgroup analyses were conducted to investigate the variation between different study characteristics, such as region, type of outcome measuring instrument, and type of study population. Heterogeneity was assessed statistically using the standard chi-squared and I-squared tests. The sources of heterogeneity were analysed using subgroup analysis, and meta-regression. The presence of publication bias was assessed visually using a funnel plot, and statistical tests for funnel plot asymmetry was checked by Egger test statistics. A leave-one-out sensitivity analysis was also conducted for assessing the influence of each study on the overall effect size estimate. The pooled effect size was presented using a forest plot. All statistical analysis was performed using STATA Version 17 statistical software.
## Search results
The online electronic search process yielded 4222 articles (4156 from databases and 66 from other sources) of which 1303 were duplicates. After reviewing the title and abstract, we excluded 2844 irrelevant articles. From the remaining 75 articles, 49 were removed after full text assessment. A further five articles were excluded due to poor methodological quality leaving 21 relevant primary research articles eligible for this systematic review (Fig 1).
**Fig 1:** *PRISMA flow diagram of literature identification, study selection and inclusion process.*
## Assessment of methodological quality for prevalence studies
This review included 21 articles with moderate to high methodological quality. One study [31] scored 9 points, five studies [32–36] scored 8 points, and the remaining 15 studies [37–51] scored 7 points in the JBI critical appraisal checklist [27] for studies reporting prevalence data (Table 1).
## Overall study characteristics
Of the 21 studies, seven were conducted in East Asia and Pacific region [32, 36, 39, 42, 47, 50, 51], four in the Middle East and North Africa [31, 34, 46, 49], three in Sub-Saharan Africa [33, 37, 43], three in Europe and Central Asia [38, 40, 44], and four in South Asia [35, 41, 45, 48]. Most studies used a descriptive cross-sectional design ($$n = 17$$, $81\%$) and the remaining ($$n = 4$$, $19\%$) were prospective cohort studies. Many ($57\%$) of the studies were conducted in outpatient population, and most ($33\%$) used consecutive sampling technique. The sample size of the included studies ranged from 43 [37] to 1009 [35]. Included studies assessed the prevalence of depression using nine different psychometrically validated instruments. Five studies [32, 35, 40, 45, 49] used Beck Depression Inventory (BDI), five [33, 38, 39, 41, 50] used Patient Health Questionnaire-9 (PHQ-9), four [31, 34, 47, 48] used Hospital Anxiety and Depression Score (HADS), two [44, 51] Geriatric Depression Scale (GDS), and the remaining five [36, 37, 42, 43, 46] each used Cardiac Depression Scale (CDS), Patient Health Questionnaire-2 (PHQ-2), Hamilton Rating Scale for Depression (24-items) (HAM-D24), Geriatric Depression Scale (GDS), International Statistical Classification of Diseases and Related Health Problems V10 (ICD-10) (Table 2).
**Table 2**
| ID | Author (reference) | Publication year | Country | Region | Study design | Population | Sample size | Sampling method | Outcome measuring tool | Prevalence | Quality score |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | Edmealem A. et al. [37] | 2020 | Ethiopia | Sub-Saharan Africa | Cross-Sectional | Outpatient | 43 | Stratified | PHQ-2 | 11.1 | 7 |
| 2 | DeWolfe A, et al. [38] | 2012 | Georgia | Europe and Central Asia | Prospective cohort | Outpatient | 314 | Unreported | PHQ-9 | 13.0 | 8 |
| 3 | Okviansanti F, et al. [39] | 2020 | Indonesia | East Asia and Pacific | Cross-Sectional | Outpatient | 155 | Consecutive | PHQ-9 | 85.2 | 7 |
| 4 | Pushkarev GS, et al. [40] | 2018 | Russia | Europe and central Asia | Prospective cohort | Outpatient | 260 | Unreported | BDI | 60.0 | 7 |
| 5 | Fan X, et al. [32] | 2015 | China | East Asia and Pacific | Cross-Sectional | Inpatient | 152 | Consecutive | BDI | 44.0 | 8 |
| 6 | Zahid I, et al. [41] | 2018 | Pakistan | South Asia | Cross-Sectional | Outpatient | 170 | Consecutive | PHQ-9 | 60.0 | 7 |
| 7 | Yazew KG, et al. [33] | 2019 | Ethiopia | Sub-Saharan Africa | Cross-Sectional | Outpatient | 422 | Systematic random | PHQ-9 | 51.1 | 8 |
| 8 | AbuRuz ME [34] | 2018 | Jordan | Middle East and North Africa | Cross-Sectional | Outpatient | 200 | Convenient | HADS | 65.0 | 8 |
| 9 | Pan S, et al. [42] | 2016 | China | East Asia and Pacific | Cross-Sectional | Inpatient | 366 | Consecutive | HAM-D24 | 57.4 | 7 |
| 10 | Husain MI, et al. [35] | 2019 | Pakistan | South Asia | Cross-Sectional | Outpatient | 1009 | Unreported | BDI | 66.0 | 8 |
| 11 | Tran NN, et al. [36] | 2022 | Vietnam | East Asia and Pacific | Cross-Sectional | Inpatient | 128 | Convenient | ICD-10 | 46.9 | 8 |
| 12 | Tsabedze N, et al. [43] | 2021 | South Africa | Sub-Saharan Africa | Cross-Sectional | Outpatient | 103 | Consecutive | DASS-21 | 52.4 | 7 |
| 13 | Erceg P, et al. [44] | 2013 | Serbia | Europe and Central Asia | Cross-Sectional | Inpatient | 136 | Consecutive | GDS | 55.9 | 7 |
| 14 | Alemoush RA, et al. [31] | 2021 | Jordan | Middle East and North Africa | Prospective follow up | Outpatient | 127 | Consecutive | HADS | 47.3 | 9 |
| 15 | Dastgeer S, et al. [45] | 2016 | Pakistan | South Asia | Prospective follow up | Inpatient | 400 | Unreported | BDI | 64.7 | 7 |
| 16 | Ghanbari A, et al. [46] | 2015 | Iran | Middle East and North Africa | Cross-Sectional | Inpatient | 239 | Gradual sampling | CDS | 57.7 | 7 |
| 17 | Zhang X, et al. [47] | 2020 | China | East Asia and Pacific | Cross-Sectional | Inpatient | 254 | Convenient | HADS | 18.1 | 7 |
| 18 | Khan S, et al. [48] | 2012 | Pakistan | South Asia | Cross-Sectional | Inpatient | 121 | Consecutive | HADS | 30.0 | 7 |
| 19 | Molavynejad S, et al. [49] | 2019 | Iran | Middle East and North Africa | Cross-Sectional | Inpatient | 151 | Convenient | BDI | 97.0 | 7 |
| 20 | Son YJ, et al. [50] | 2018 | South Korea | East Asia and Pacific | Cross-Sectional | Outpatient | 190 | Convenient | PHQ-9 | 30.0 | 7 |
| 21 | Son YJ, et al. [51] | 2012 | South Korea | East Asia and Pacific | Cross-Sectional | Outpatient | 134 | Unreported | GDS | 67.9 | 7 |
## Prevalence of depression in people with heart failure in LMICs
In total, 21 studies reported the prevalence of depression in people with heart failure in LMICs. The lowest and the highest prevalence of depression were $11.1\%$ [37] and $97.0\%$ [49], respectively (Table 1). The pooled regional prevalence of depression among people with heart failure in LMICs was $52\%$ ($95\%$ CI = 39.73, $63.3\%$, I2 = $99.00\%$). The overall pooled effect size of depression presented using forest plot (Fig 2).
**Fig 2:** *The pooled prevalence of depression in people with heart failure in LMICs.*
## Sub-group analysis
Subgroup analysis was done using region where the studies were conducted, study population and the outcome measuring instrument. The result showed that the highest prevalence was observed among studies conducted in Middle East and North Africa, among inpatients and studies that screened depression using BDI (Table 3).
**Table 3**
| Subgroup | Number of studies | Sample size | Pooled Prevalence | Heterogeneity | Heterogeneity.1 |
| --- | --- | --- | --- | --- | --- |
| Subgroup | Number of studies | Sample size | Pooled Prevalence | I 2 | P-value |
| By region | By region | By region | By region | By region | By region |
| East Asia and pacific | 7 | 1379 | 49.91 | 98.5 | <0.001 |
| South Asia | 4 | 3708 | 55.62 | 95.6 | <0.001 |
| Middle East and North Africa | 4 | 717 | 66.91 | 98.8 | <0.001 |
| Sub -Saharan Africa | 3 | 568 | 38.35 | 96.6 | <0.001 |
| Europe and Central Asia | 3 | 710 | 42.86 | 99.1 | <0.001 |
| Latin America and Caribbean | 0 | … | …. | … | … |
| By Population | By Population | By Population | By Population | By Population | By Population |
| Outpatient | 12 | 3127 | 50.79 | 98.6 | <0.001 |
| Inpatient | 9 | 1947 | 52.47 | 99.2 | <0.001 |
| By outcome measurement tool | By outcome measurement tool | By outcome measurement tool | By outcome measurement tool | By outcome measurement tool | By outcome measurement tool |
| BDI | 5 | 1972 | 66.51 | 99 | <0.001 |
| PHQ-9 | 5 | 1251 | 47.82 | 99.2 | <0.001 |
| HADS | 4 | 702 | 40.04 | 97.8 | <0.001 |
| GDS | 2 | 270 | 61.98 | 76.1 | 0.04 |
| Others (CDS, HAM-D24, PHQ-2, ICD-10, DASS-21) | 5 | 879 | 45.35 | 95 | <0.001 |
## Assessment of heterogeneity
The result of this meta-analysis using the random-effects model revealed a high heterogeneity across the included studies (I2 = $99\%$, $$P \leq 0.001$$). Heterogeneity is inevitable in meta-analysis due to difference in study quality, methodology, sample size and inclusion criteria for participants [52, 53]. Consequently, meta-regression was performed using publication year, sample size and quality score as covariates to find possible sources of heterogeneity among the included studies. The result of the meta-regression analysis showed that no significant linear relationship existed between the outcome (depression) and the covariates. Therefore, none of the three covariates were significantly associated with the presence of heterogeneity (Table 4). The high heterogeneity can be attributed to chance or other factors not included in this review.
**Table 4**
| Heterogeneity source | Coefficients | Standard Error | P-value |
| --- | --- | --- | --- |
| Sample size | 0.02 | 0.03 | 0.49 |
| Publication Year | 1.09 | 1.99 | 0.58 |
| Quality score | -7.53 | 10.9 | 0.49 |
## Assessment of publication bias
Visual inspection of the funnel plot suggested asymmetry, as eight studies lay to the left and 13 to the right of the line (Fig 3). However, this was not statistically significant as evidenced by Egger’s test ($$P \leq 0.81$$), which confirmed the results were not influenced by publication bias. It is worth noting that asymmetry in funnel plots is not always linked to publications bias [54], and that high heterogeneity may explain the visual asymmetry in the funnel plot.
**Fig 3:** *Funnel plot to test the publication bias of the 21 studies.*
## Sensitivity analysis
The result of leave-one-out sensitivity analysis using a random effects model demonstrated that no single study unduly influenced the pooled estimate of depression. For each study, the displayed effect size corresponds with the overall effect size computed from meta-analysis excluding that study (Fig 4).
**Fig 4:** *Result of sensitivity analysis of the 21 studies.*
## The association between depression and HRQoL
Of the 21 eligible studies, only six reported an association between depression and HRQoL using depression as exposure variable and HRQoL as outcome. There were variations in the ways that depression and HRQoL were measured among these studies. For instance, two studies [44, 51] used GDS, two studies [31, 34] used HADS, one study [43] used DASS-21 and one study [38] used PHQ-9 to measure depression. Likewise, four studies [38, 43, 44, 51] used MLHFQ and the other two studies [31, 34] used SF-36 to measure HRQoL. Regarding the critical appraisal, the JBI cross-sectional studies critical appraisal tool for studies reporting association (etiology/risk) was used to assess the quality of each study, and the result showed that all six studies had good methodological quality (Table 5).
**Table 5**
| Included articles | Q1 | Q2 | Q3 | Q4 | Q5 | Q6 | Q7 | Q8 | Quality score/8 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| DeWolfe A, et al. [38] | Y | Y | Y | Y | Y | Y | Y | Y | 8 |
| AbuRuz ME [34] | Y | Y | Y | Y | Y | Y | Y | Y | 8 |
| Tsabedze N, et al. [43] | Y | Y | Y | Y | Y | Y | Y | Y | 8 |
| Erceg P, et al. [44] | Y | Y | Y | Y | Y | Y | Y | Y | 8 |
| Alemoush RA, et al. [31] | Y | Y | Y | Y | Y | Y | Y | Y | 8 |
| Son YJ, et al. [50] | Y | Y | Y | Y | Y | Y | Y | Y | 8 |
Concerning the effect size, five studies [31, 34, 38, 44, 51] used beta(β) as effect size to report the association between depression and HRQoL, while one study [43] used Odds Ratio (OR) to indicate the strength of association, with all six studies reporting a statistically significant association between depression and HRQoL. All included studies evaluating the association between depression as the exposure variable and HRQoL as an outcome are profiled in Table 6.
**Table 6**
| Author [year of publication] | Country | Sample size | Depression measuring tool | HRQoL measuring tool | Outcome (HRQoL score) based on exposure | Outcome (HRQoL score) based on exposure.1 | Type of comparison | Outcome measure with result | Conclusion |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Author [year of publication] | Country | Sample size | Depression measuring tool | HRQoL measuring tool | Depressed | Non-Depressed | Type of comparison | Outcome measure with result | Conclusion |
| Erceg P, et al. [2013] [44] | Serbia | 136 | GDS | MLHFQ | 57.9±17.6 | 40.9±17.1 | Linear regression | β = 0.41, P = 0.001 | 1 unit increase in the depression score was associated with a 0.41 unit increase in MLHFQ QoL score |
| Tsabedze N, et al. [2021] [43] | South Africa | 103 | DASS-21 | MLHFQ | 28 (10–54) | 5(0–17) | Logistic regression | OR = 1.04, P = 0.001 | Depressed people are 1.04 times more likely to have poor HRQoL as compared to non-depressed one |
| Son YJ, et al. [2012] [51] | South Korea | 134 | GDS | MLHFQ | 59.4±9.97 | 45.1±8.8 | Linear regression | β = 0.44, P = 0.001 | 1 unit increase in the depression score was associated with a 0.44 unit increase in MLHFQ QoL score |
| DeWolfe A, et al. [2012] [38] | Georgia | 314 | PHQ-9 | MLHFQ, | 74.9±11.9 | 58.4±13.5 | Linear regression | β = 1.83, P = 0.001 | 1 unit increase in the depression score was associated with a 1.83 unit increase in MLHFQ QoL score |
| Alemoush RA, et al. [2021] [31] | Jordan | 127 | HADS | SF-36 | …… | ……… | Linear regression | β = -0.37, P = 0.001 | 1 unit increase in the depression score was associated with a 0.37 unit decrease in SF-36 QoL score |
| AbuRuz ME [2018] [34] | Jordan | 200 | HADS | SF-36 | …… | ……… | Linear regression | β = -0.32, P = 0.001 | 1 unit increase in the depression score was associated with a 0.32 unit decrease in SF-36 QoL score |
## Discussion
The burden of heart failure has increased over the past decade in LMICs with a significant economic impact and alteration in psychological, physical, and emotional well-being [55]. Evidence regarding the burden of depression and its impact on HRQoL in people with HF from LMICs is limited. This review was conducted to estimate the pooled regional prevalence of depression, and to investigate the association between depression and HRQoL among people with heart failure in LMICs. To our knowledge, this is the first review to estimate the current prevalence of depression in people with HF in LMICs. The result of this review revealed that the pooled regional prevalence of depression in people with heart failure in LMICs was $51.5\%$ ($95\%$ CI = 39.73, $63.30\%$, I2 = $99.00\%$). This reinforces the understanding that depression is a common comorbid condition in people with heart failure and is consistent with the findings of the previous systematic review [56]. Our estimate is higher than the global prevalence of depression in people with heart failure [15]. The higher prevalence in LMICs might be due to variation in the socio-demographic characteristics of the study participants, discrepancy of instruments, sample size, study setting, and level of economic status [35, 57].
The subgroup analysis of this review showed significant variation in the prevalence of depression among different groups. For instance, the highest ($66.9\%$) and the lowest ($38.4\%$) pooled prevalence was observed in studies from the Middle East and North Africa regions and Europe and Central Asia, respectively. This variation might be due to socioeconomical, health care coverage, sample size and methodological differences among the included studies across the regions. In the present review, the prevalence of depression is higher among inpatients than outpatients. A similar finding was observed in the previous systematic review [58]. This might be due to the severity of the disease or the fact that hospitalized patients are more unwell and have more socioeconomic burdens than outpatients. Consistent with the previous systematic review conducted in China [17], the pooled prevalence of depression in the current review was highest ($66.5\%$) when measured using Beck Depression Inventory (BDI). The lowest prevalence of depression ($40.1\%$) was observed when measured using Hospital Anxiety and Depression Score (HADS). This difference could be due to differences in definitions of depression and cut-off points to diagnose depression across the various scales. However, further research would be helpful to investigate the factors that might lead to such differences across the depression measuring scales.
The association of depression with HRQoL has been reported in several recent studies. The results of this systematic review also demonstrated that six studies among the included studies showed a significant association between depression and HRQoL, although there were insufficient data to estimate the pooled effect size. This finding is similar to previous studies conducted in Europe [59–61]. These studies found that people with heart failure who have depressive symptoms had poor quality of life compared to those who did not have depressive symptoms, and this was also correlated with an increased burden of morbidity and mortality due to HF [51]. The findings of this review highlight the need to understand the factors that contribute to the increased incidence of depression in people with heart failure living in LMICs, as well as the factors that contribute to a poorer quality of life. This will enable targeted interventions and support strategies to be designed and evaluated to improve outcomes for this population.
The findings of this meta-analysis have implications for clinical practice. We included articles published between 2012 and 2022. This cut-off date was decided arbitrarily by the authors to estimate the most recent prevalence rate which should have more relevance to current clinical practice. Determining the most recent prevalence of depression provides up-to-date evidence to develop comorbid depression prevention strategies in this group and ultimately improving HRQoL. Determining the effect of depression on HRQoL can help health-care providers prioritize during their routine clinical practice, which will reduce the overall burden of morbidity and mortality. However, some limitations should be considered for future research. First, the interpretation of the results must be taken cautiously as the meta-analysis had statistically significant heterogeneity across the included studies which was not fully explained by the variables examined. Second, the conclusion of positive association between depression and HRQoL, as reported in six studies, should be interpreted cautiously due to our inability to summarise the pooled effect size.
## Conclusion
This systematic review and meta-analysis revealed that one in two people with heart failure in LMICs have comorbid depression. Depression was positively associated with HRQoL in people with heart failure. Early detection and treatment of depression in people with heart failure is highly recommended to reduce its burden in LMICs. Future research should investigate the factors associated with depression and HRQoL in this population.
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|
---
title: A comprehensive study of Ecuadorian adult patients with a mild and moderate
presentation of COViD-19
authors:
- Fabricio González-Andrade
- Yenddy Carrero
journal: PLOS ONE
year: 2023
pmcid: PMC10035819
doi: 10.1371/journal.pone.0283535
license: CC BY 4.0
---
# A comprehensive study of Ecuadorian adult patients with a mild and moderate presentation of COViD-19
## Abstract
### Aim
To characterize non-hospitalized patients with mild and moderate clinical presentation.
### Methods
We performed an epidemiological, observational, descriptive, and cross-sectional study carried out in Ecuador, with 1,447 participants between 18 and 66 years, non-hospitalized, with a molecular RT-PCR test for SARS-CoV2. We analyzed demographic characteristics according to sex, age group, clinical findings, behavior after diagnosis, family and social behavior, sequelae, clinical evolution, type of exposure, and personal history.
### Results
The sample analyzed had a mean age of 37 years ($95\%$ CI 18–66), women 713 individuals ($49.27\%$), men 733 individuals ($50.66\%$). Age group distribution was 18–30 years, 524 individuals ($36.29\%$), 31–45, 538 individuals (37.26), and more of 45 years, 382 individuals ($26.46\%$). 1416 individuals were mestizos ($97.99\%$). According to the province of residence from Pichincha were 1019 patients ($70.52\%$), followed by Imbabura, 93 patients ($6.44\%$), and the others 335 ($23.15\%$) patients come from all over the country. In women, the most common findings were fever >38°C ($54.40\%$), sputum ($27.43\%$) and hypoxia ($16.32\%$); HTN ($5.75\%$) and hypercholesterolemia ($3.69\%$). Men were more prevalent in all other findings. Comorbidities were more prevalent in all those over 45 years of age. COVID-19 antibodies test was positive in 416 patients ($28.85\%$). Neuropsychiatric symptoms such as sleep disorders, generalized anxiety disorder, depressed mood, and chronic fatigue were more prevalent in men than women. Still, generalized anxiety disorder and chronic fatigue were more common in individuals of 31 to 45 years. 868 patients ($60.07\%$) were in contact with a known infected person, 318 patients ($22.02\%$) were health workers, and 782 patients ($57.63\%$) were informed about work exposure. 545 patients ($37.72\%$) were overweight, primarily women 310 ($42.29\%$). 609 patients ($42.65\%$) showed symptoms after the acute period, and 331 individuals ($23.49\%$) reported some sequelae.
### Conclusion
The epidemiological and clinical behavior of hospitalized and critical patients differs greatly from ambulatory or mild or moderate symptoms. It is essential to highlight those non-hospitalized patients constitute the predominant population of patients, hence the importance of adequate management that would directly affect the development of complicated forms and, consequently, the collapse of healthcare centers. It is vitally important to open more investigations that compare hospitalized and outpatient patients to have a clearer picture of the epidemic.
## Introduction
Although it seems that the SARS-CoV-2 pandemic has subsided, many cases persist, particularly those with mild or moderate symptoms that can be confused with other similar and milder pathologies [1]. The pandemic is still not over despite global vaccination and epidemiological control efforts. The symptoms in mild-moderate clinical presentations of COViD-19 vary from severe cases worldwide, and human behavior directly influences the clinical findings. Some studies described atypical presentations, and older adults and people with medical comorbidities may have a late manifestation of fever and respiratory symptoms. In other words, the clinical description is not specific [2]. Indeed, coronaviruses are pathogenic agents that can cause a variety of respiratory, enteric, liver, and neurological symptoms [3]. Infection by these agents, for the most part, can go unnoticed or produce clinical pictures ranging from the common cold to more acute severe pictures such as those produced by SARS and MERS-CoV [4]. In the neo-agent SARS-CoV-2, the clinical presentation varies from mild-moderate to severe cases [5]. The clinical forms of COVID-19 present as mild, moderate, or severe disease. Most patients are asymptomatic carriers who have the potential to be contagious to others they come into close contact with. Others have a mild illness similar to influenza infection that cannot be differentiated from a common upper respiratory tract infection.
In COViD-19 infection, the lung parenchyma is involved, causing a type of atypical pneumonia that hinders adequate ventilation and gas exchange, ending in ventilatory assistance, hospitalization, and ultimately death in many cases. However, the predominant clinical spectrum is characterized by mild-moderate respiratory manifestations in at least $80\%$ of cases. The remainder is distributed in patients with a moderate presentation who merit hospital stays in $15\%$ of cases, and severe pneumonia, in $5\%$ of cases, which deserve management in an intensive care unit (ICU), intubation, parenteral drugs, life support, among others [6]. A clinical presentation by cough, anosmia, ageusia, pharyngeal discomfort, dyspnea of different degrees, and fever and touch to the general state characterizes it. Some research has found a lower incidence of diarrhea, abdominal pain, acute abdomen, various dermatoses, and organ failure in severe cases [7]. However, the determinants in these patients’ clinical evolution and prognosis could relate to the clinical and demographic characteristics such as age, associated comorbidities, immune competence, the therapy used, management in different healthcare centers worldwide, and health conditions of their human behavior [8]. Observations of infected patients noted that those over 65 years and males predominated [9]. However, as the pandemic progresses, selective pressure on virion infectivity has been seen, affecting young people, which suggests that other factors influence, such as immunogenicity [10].
Associated comorbidities are risk factors for the severe clinical presentation of the disease. Pathological histories such as hypertension (HTN), type 2 diabetes mellitus (T2DM), or chronic kidney disease (CKD) are linked to the severe form of COViD-19 [11]. Obesity is another variable in these patients’ slow evolution [12], a risk factor for the most common 20 chronic conditions [13]. A factor correlated with the disease’s severity is psychobiological habits such as tobacco smoking. A group of public health experts established that smokers are more likely to develop severe symptoms in the case of COViD-19 [14]. The heterogeneous behavior of the disease, according to age groups, geographical areas, and possible circulation of different strains, plays a critical role in the study of COViD-19 in Ecuador, coupled with the under-registration of cases, little knowledge of the dynamics of infection and distribution of outbreaks, as insufficient detailed data existent [15–18]. The clinical-epidemiological characteristics are still unknown, especially in symptomatic patients with mild-moderate symptoms, which leads to erroneous diagnoses and treatments [19]. Thus, it is crucial to identify the risk factors and the infection’s clinical characteristics in a well-defined population to establish essential guidelines for the containment of the viral spread. Despite the rapid advances in understanding this new respiratory syndrome, data characterizing its epidemiology, clinical features, and response to treatment are fewer in lower and middle-income countries like Ecuador [20].
This paper aims to characterize non-hospitalized patients with mild and moderate clinical presentation.
## Research design
We performed an epidemiological, observational, and cross-sectional study.
## Settings
We performed this study in Ecuador. The study was conducted during the years 2020 and 2021. The surveys were applied throughout the country randomly, but most of the patients came from Quito, province of Pichincha. Not all patients present the same findings since there was a lot of variability in the results, mainly due to the condition being a new disease.
## Participants
Any resident in Ecuador, alive, with an infection with CoViD19, a positive RT-PCR molecular test, non-hospitalized, with mild-moderate clinical presentation. We randomly selected all patients.
## Study size
We analyzed 1447 participants between 18 and 66 years, according to sex and age groups. We collected the data of all patients with symptoms and an RT-PCR+.
## Definitions
We defined mild illness in individuals with various signs and symptoms, e.g., fever, cough, sore throat, malaise, headache, and muscle pain, without dyspnea or abnormal imaging. We consider moderate disease when individuals have lower respiratory disease, evidenced by clinical evaluation or imaging and oxygen saturation (SaO2) greater than $93\%$ in ambient air at sea level.
## Inclusion criteria
Patients in Ecuador of all ages, both sexes, and any ethnic group, randomly selected, non-hospitalized, with a positive RT-PCR molecular test and active infection.
## Exclusion criteria
Patients hospitalized with a severe or critical clinical presentation; with post-intensive care syndrome (PICS), anyone who survives a critical illness that justifies admission to an ICU is susceptible to developing post-intensive care syndrome, characterized by the appearance or worsening of cognitive, physical or mental health of the patient after discharge. We differentiated between this syndrome caused by COVID-19 or caused by just being in the ICU. Patients with symptoms less than four weeks after discharge from the hospital or leaving isolation or who have received the flu vaccine in the last six months or pneumococcus in the previous five years.
## Variables
We analyzed demographic characteristics according to sex, age group, clinical findings, behavior after diagnosis, family and social behavior, sequelae, clinical evolution, type of exposure, and personal history.
## Data sources
We conducted direct interviews with patients maintaining safety measures, and when not possible (isolated patients), we conducted telephone interviews.
## Bias avoidance
The same person conducted the interviews using a standardized data collection form.
## Statistical methods
We analyzed data with the SPSS© software version 22.0. We used descriptive and inferential statistics. Comparing the differences of variables, we used chi-squared for proportional data and t-test or ANOVA for normally distributed continuous data or their nonparametric tests for skewed data. Logistic regression was used to assess the factors for a binary endpoint. For some analyses, we stratified by age: 18 to 30 years, 30 to 45 years, 45 to 65 years, and ≥65 years, and for other analyses, we had one group > 45 years. A p-value < 0.05 was considered statistically significant.
## Exposure
Mild and moderate COVID-19 infection.
## Ethical issues
All the information obtained was filled out in the data collection sheet in a completely anonymous way. No data was filled that could reveal the patient’s identity directly or indirectly. All patients signed informed Consent. The information obtained was confidential, and all individual data was anonymous. Our research group keeps the data. All methods were carried out under relevant guidelines and regulations.
## Institutional Review Board (IRB)
We received IRB approval from the Ethics Committee on Research in Humans from Carlos Andrade Marín Hospital on July 6th of, 2020, with the code IESS-HCAM-CEISH-2020-200-DF. This research is part of the project entitled “Clinical, neurological, and radiological characterization of adult Ecuadorian patients with SARS-CoV2 infection to establish risk prediction models based on the phenotype”. All patients provided the information voluntarily, signed an Informed Consent, or gave their *Consent via* telephone, in both cases, with the presence of a witness. The information obtained is confidential, and we anonymize all individual data. Our research group keeps the data. All methods followed the relevant Helsinki Declaration, developed by the World Medical Association, outlining the ethical standards for research on human participants. Also, we follow other guidelines and national regulations.
## Results
Table 1 shows the distribution of non-hospitalized patients infected with COVID-19 according to sex, age group, and clinical findings. Table 2 shows the distribution of non-hospitalized patients infected with COVID-19 according to sex, age group, behavior after diagnosis, family and social behavior, sequelae, and clinical evolution. Table 3 shows the distribution of non-hospitalized patients infected with COVID-19 according to sex, age group, type of exposure, and personal history.
The sample analyzed had a mean age of 37 years ($95\%$ CI 18–66), women 713 individuals ($49.27\%$), men 733 individuals ($50.66\%$). Age group distribution was 18–30 years, 524 individuals ($36.29\%$), 31–45, 538 individuals (37.26), and more of 45 years, 382 individuals ($26.46\%$. 1416 individuals were mestizos ($97.99\%$). According to the province of residence from Pichincha were 1019 patients ($70.52\%$), followed by Imbabura, 93 patients ($6.44\%$), and the others 335 ($23.15\%$) patients come from all over the country.
In women, the most common findings were fever >38°C ($54.40\%$), sputum ($27.43\%$) and hypoxia ($16.32\%$); HTN ($5.75\%$) and hypercholesterolemia ($3.69\%$). Men were more prevalent in all other findings. Comorbidities were more prevalent in all those over 45 years of age. COVID-19 antibodies test was positive in 416 patients ($28.85\%$). Neuropsychiatric symptoms such as sleep disorders, generalized anxiety disorder, depressed mood, and chronic fatigue were more prevalent in men than women. Still, generalized anxiety disorder and chronic fatigue were more common in individuals of 31 to 45 years. 868 patients ($60.07\%$) were in contact with a known infected person, 318 patients ($22.02\%$) were health workers, and 782 patients ($57.63\%$) reported work exposure. 545 patients ($37.72\%$) were overweight, primarily women 310 ($42.29\%$). 609 patients ($42.65\%$) showed symptoms after the acute period, and 331 individuals ($23.49\%$) reported some sequelae.
Other interesting findings were that $99.43\%$ [1404] of patients were in home isolation, and the remaining patients did continue working; 41 patients ($2.84\%$) were infected by staying in a foreign country. In 988 patients ($68.37\%$), the diagnosis was performed by a physician, and the remaining patients were diagnosed by a registered nurse, laboratory worker, or self-diagnosed. Sixteen ($1.71\%$) were pregnant, and 123 patients ($8.52\%$) were smokers. Three participants patients died after the interview with the infection.
## Discussion
This observational study reflects the characteristics of the non-hospitalized Ecuadorian population, with mild and moderate clinical presentations. Despite the significant challenges found in the specialized healthcare centers for patients with COVID-19, it is essential to highlight those non-hospitalized patients constitute the predominant population. Hence, properly managing these patients would directly affect the development of complicated forms and, consequently, the collapse of healthcare centers and possible reinfections. This study is of great importance since there are scarce data on the clinical and epidemiological characteristics of COVID-19 patients in Ecuador.
We found that the patients’ mean age of our recruited adults was 47 years. Although demographic changes may be more remarkable, the male sex continues to be the most affected. This predisposition is still not entirely apparent. Indeed, the male sex is mainly affected by having a higher BMI, tending toward the complication of obesity. This study obtained a considerable percentage of the infected healthcare personnel, represented by medical doctors, nurses, and health workers.
Nonetheless, healthcare workers are on the front lines in the fight against the pandemic. One risk factor for increasing the disease’s severity is the patient’s metabolic condition. The results show a small percentage of patients who presented some comorbidity, although it has been described that the most frequent was HTN. Metabolic processes are essential mediators of host defense mechanisms that protect against physiological damage during infections and enable survival.
The most frequent symptoms in this study were headache, fatigue, cough, anosmia, fever, and dyspnea. Likewise, we found significant differences concerning sex and symptoms, such as fatigue, fever >38°C, nausea/vomiting, odynophagia, skin lesions, and blood abnormalities. These findings are similar to scientific literature but with some exceptions. There are more than 50 signs and symptoms at least related directly to the infection.
The diagnostic tests focused on symptomatic patients without identifying asymptomatic or pre-symptomatic patients. These individuals play an essential role in the transmission of the disease. The daily reports on the behavior of COVID-19 issued by the Ecuadorian government do not adequately represent the growth in the number of infected each day, nor the natural behavior of the epidemic, affecting possible control measures. On the other hand, the high percentage of unexposed healthy subjects with a pre-existing immunity suggests that a part of the Ecuadorian population is likely to have SARS-CoV-2 reactive T-cells [21], which suggests that it is likely that a part of the Ecuadorian population has T cells reactive to SARS-CoV-2 without a more exact picture established. Increasing the number of clinical and epidemiological research with the molecular characterization of circulating viral variants and the immune response and certain protective factors related to genetic components in the population would be interesting.
Among the main epidemiological findings, we show that the nutritional status of non-hospitalized patients predominates the normal nutritional status, accompanied by overweight. However, this situation is the opposite and different for hospitalized patients. There is growing evidence that obesity is one of the most common conditions associated with COVID-19, and morbid obesity is significantly associated with the disease’s severe presentation. Obesity, especially abdominal obesity, accompanied by low-grade inflammation, could be modified by amplifying the exacerbated immune response to COVID-19. Obese people also frequently have other cardio-metabolic conditions that increase the risk of SARS-CoV-2 infection. Therefore, we infer that a healthy nutritional status is characteristic of the disease’s mild presentation. Indeed, the genetic condition with a tendency towards obesity represents a greater susceptibility to infection and severe presentation, which influences the presence of other comorbidities.
Social behavior is critical to establish knowledge about the diagnosis of the disease. Voluntary social isolation was the most frequent practice. Despite this, experts in the follow-up of asymptomatic patients with mild and moderate symptoms suggest that symptomatic patients should continue to maintain preventive isolation, with frequent hand washing, the social distancing of at least one meter, and the use of masks have already reduced viral transmission. As social determinants of health measure, high social risk can increase the risk of SARS-CoV-2 infection. In Ecuador, a study showed that high levels of education were related to more virus acknowledgment [22]. However, they were less assertive about the virus’s characteristics and used empirical and unproven treatments. This factor is crucial and directly influences the population’s behavior, impacting the rebound or re-emergence of cases.
One of the clinical-epidemiological characteristics of morbidity and the tendency to complications is the persistence of symptoms. We observed a range of remaining symptoms, including cough, shortness of breath, fever, sore throat, chest pain, palpitations, cognitive deficits, myalgia, neurological symptoms, rash, and diarrhea. Regarding the clinical sequelae, our study observed that about half of the non-hospitalized patients had symptoms after the infection; the most common were fatigue and headache. Many studies discuss long-term sequelae; however, this topic should be studied more deeply. Patients with mild or moderate symptoms remitted the infection in the first week. In contrast, severely ill patients cannot eliminate the virus optimally, leading to the disease’s critical form. The mechanism by which post-infectious symptoms persist remains unknown.
Maintaining strict prevention and control measures is essential—the lack of knowledge of the virus’s biology limits future prevention and vaccination campaigns. Outpatients are essential in the viral spread, considering reported reinfection cases and new circulating viral variants. Indeed, educational health and communication programs should emphasize explaining the essential molecular characteristics of SARS-CoV-2; thus, the population can adhere to the measures they must adopt, the possible complications inherent to the infection, and the control program restrictions needed in favor of collective health.
One last important issue is that tests should always be performed on patients with symptoms such as fever, fatigue, headache, malaria, and COVID-19. In the case of challenges due to the COVID-19 pandemic, a malaria diagnosis should be considered for all fever cases in endemic countries. On the other hand, patients with COVID-19-related symptoms that are negative for malaria must undergo isolation to exclude COVID-19 until the repetition of the virological sample, thus reducing the potential risk of transmission.
This study has some limitations. Few studies talk about confirmed non-hospitalized infected patients with mild-moderate illnesses. Due to the pandemic’s size, we believe the sample size could be more extensive. Furthermore, many asymptomatic patients refuse to recognize the disease or confuse the symptoms, making it challenging to identify potential patients. Additionally, the availability and access to molecular diagnostic tests are still reduced in Ecuador. The future perspectives of this research will be the clinical follow-up of these patients and the follow-up with computed tomography and respiratory function tests. It is possible to generalize this research with a prospective design that includes a larger sample.
## Conclusion
The epidemiological and clinical characteristics of hospitalized and critical patients differ greatly from ambulatory patients or those with mild or moderate symptoms. It is essential to emphasize that non-hospitalized patients constitute the predominant patient population, hence the importance of adequate management that would directly impact the development of complicated forms and, consequently, the collapse of healthcare centers and possible infections. It is essential to carry out more research that includes hospitalized and ambulatory patients to have a clearer picture of the epidemic in the country, which will allow more specific and forceful control measures to be taken to reduce cases and deaths due to COVID-19.
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|
---
title: Estimating pulsatile ocular blood volume from intraocular pressure, ocular
pulse amplitude, and axial length
authors:
- Ryan H. Somogye
- Cynthia J. Roberts
- Eberhard Spoerl
- Karin R. Pillunat
- Lutz E. Pillunat
- Robert H. Small
journal: PLOS ONE
year: 2023
pmcid: PMC10035833
doi: 10.1371/journal.pone.0283387
license: CC BY 4.0
---
# Estimating pulsatile ocular blood volume from intraocular pressure, ocular pulse amplitude, and axial length
## Abstract
The purpose of this study was to develop a method of estimating pulsatile ocular blood volume (POBV) from measurements taken during an ophthalmic exam, including axial length and using a tonometer capable of measuring intraocular pressure (IOP) and ocular pulse amplitude (OPA). Unpublished OPA data from a previous invasive study was used in the derivation, along with central corneal thickness (CCT) and axial length (AL), as well as IOP from the PASCAL dynamic contour tonometer (DCT) and intracameral (ICM) measurements of IOP for 60 cataract patients. Intracameral mean pressure was set to 15, 20, and 35 mmHg (randomized sequence) in the supine position, using a fluid-filled manometer. IOP and OPA measurements were acquired at each manometric setpoint (DCT and ICM simultaneously). In the current study, ocular rigidity (OR) was estimated using a published significant relationship of OR to the natural log of AL in which OR was invasively measured through fluid injection. Friedenwald’s original pressure volume relationship was then used to derive the estimated POBV, delivered to the choroid with each heartbeat as a function of OR, systolic IOP (IOPsys), diastolic IOP (IOPdia), and OPA, according to the derived equation POBV = log (IOPsys/IOPdia) / OR. Linear regression analyses were performed comparing OPA to OR and calculated POBV at each of the three manometric setpoints. POBV was also compared to OPA/IOPdia with all data points combined. Significance threshold was $p \leq 0.05.$ OR estimated from AL showed a significant positive relationship to OPA for both DCT ($p \leq 0.011$) and ICM ($p \leq 0.006$) at all three manometric pressure setpoints, with a greater slope for lower IOP. Calculated POBV also showed a significant positive relationship to OPA ($p \leq 0.001$) at all three setpoints with greater slope at lower IOP, and a significant negative relationship with IOPdia. In the combined analysis, POBV showed a significant positive relationship to OPA/ IOPdia ($p \leq 0.001$) in both ICM and DCT measurements with R2 = 0.9685, and R2 = 0.9589, respectively. POBV provides a straight-forward, clinically applicable method to estimate ocular blood supply noninvasively. Higher IOP in combination with lower OPA results in the lowest values of POBV. The simplified ratio, OPA/ IOPdia, may also provide a useful clinical tool for evaluating changes in ocular blood supply in diseases with a vascular component, such as diabetic retinopathy and normal tension glaucoma. Future studies are warranted.
## Introduction
Ocular blood flow has been identified as a possible contributor to the pathogenesis of glaucoma, age-related macular degeneration (AMD), and ocular ischemic syndrome (OIS) [1]. In glaucoma, low ocular perfusion pressure (OPP, the driving force of ocular blood flow) has been identified as a risk factor [2]. Decreases or defects in choroidal blood flow have been linked by both theories and studies to the presence of AMD [1, 3]. Occlusion of the carotid or ophthalmic arteries result in reduced OPP, thus reduced occur blood flow, and are a leading cause of OIS [4, 5].
It has been shown that pulsatile ocular blood flow (POBF) can be estimated from intraocular pressure (IOP) and ocular pulse amplitude (OPA) if the ocular rigidity (OR), or pressure-volume relationship of the eye is known. This process requires analysis of a time-domain waveform of ocular pressure, usually produced by a pneumatonometer and post-processed numerically or graphically [6, 7]. Historically, the only studies to directly measure OR in an invasive procedure, have been on older subjects who are scheduled for ocular surgery to correct existing pathology, usually cataract surgery [8–11]. OR is measured directly by injecting a small, known amount of saline and measuring the pressure rise to give a subject-specific logarithmic pressure-volume relationship. This means the OR of younger, healthy eyes is not well characterized due to the inherent risks of measuring OR directly on otherwise healthy eyes and could bring into question the accuracy of POBF estimates for those subjects. This study presents a method for estimating subject-specific OR and calculating pulsatile ocular blood volume (POBV) with each cardiac cycle as an analysis tool for a variety of ocular diseases. OR has been shown to be highly correlated to axial length (AL) from an invasive study in cataract patients providing an equation to relate the two measurements [9]. OR estimated from the non-invasive AL measurement can be combined with IOP and OPA (replacing the time-domain waveform with the static components) to allow POBV to be estimated with each heartbeat.
## Methods
Unpublished OPA data originally acquired for a previous study on the accuracy of the PASCAL dynamic contour tonometer (DCT; Ziemer Ophthalmic Systems, Port, Switzerland) were used in the present study [12]. The original study was performed in accordance with the Declaration of Helsinki and was approved by the institutional ethics committee of the Medical Department of the University of Dresden. All patients signed written informed consent before entering the original study. Briefly, data from 60 patients were included in the current study (43 women, 17 men), all of whom underwent a complete ophthalmologic exam including central cornea thickness (CCT) measured by ultrasound pachymetry (Heidelberg Engineering, Heidelberg, Germany), corneal curvature measured by keratometry (Carl Zeiss Meditec, Inc., Dublin, CA), and AL measured by A-scan ultrasonography (Sonomed 2500; Technomed Maastricht, The Netherlands) before scheduled cataract surgery. The study hardware included a DCT tip integrated into the manometric pressure circuit for intracameral (ICM) measurements and a DCT attached to a Perkins handheld tonometer to facilitate transcorneal DCT measurements while the subject was supine. Once prepared for surgery but before the actual procedure, the subject corneas were cannulated and placed at three manometric IOP setpoints (15, 20, and 35mmHg). A stopcock was closed to isolate the tubing from the manometer prior to DCT and ICM measurements that were made simultaneously.
The volume of saline injected through the cannula to achieve the three IOP setpoints was not recorded so OR could not be calculated directly from the intracameral measurements, as in other studies [8, 10]. However, a previous invasive study (with similar subject demographics) directly calculating OR found a strong relationship ($P \leq 0.001$) of AL to the OR coefficient K [9]. Each subject’s OR was calculated using the first-order natural log fit line from Dastiridou et al., given in Eq 1 [9].
Eq 2 is Friedenwald’s original pressure volume relationship [13]. Solving for the volume change gives Eq 3 and inserting the corresponding pressures for a pulsatile IOP allows the POBV, units of μL entering the eye with each heartbeat to be calculated in Eq 4. It should be noted that traditionally IOP is the long-term average of ocular pressure with OPA being a pulsatile component that oscillates above and below IOP. However, DCT reports diastolic IOP so OPA is the pressure increase above the IOP reading [14]. Therefore, in the reported DCT values of the current study, DCT IOP = IOPdia in Eq 4. POBV does not have a time-dependent component and can be directly calculated from IOP, OPA, and OR coefficient KAL (estimated from AL).
## Results
Table 1 shows the ocular pressure measurement descriptive statistics of the original study. Table 2 shows the subject-specific ocular measurements made in the original study during a complete ophthalmic exam prior to the ocular pressure measurements and cataract surgery. The last column represents the estimated OR (K) calculated in the present study. Figs 1 and 2 show significant positive correlations of estimated OR (K) to OPA. Figs 3 and 4 show significant positive correlations of calculated POBV to OPA at all three setpoints, while Figs 5 and 6 show significant negative correlations of POBV and IOP at the two highest setpoints for DCT and all three for ICM. Figs 7 and 8 show significant correlations of POBV to the ratio of OPA / IOP, which is a surrogate factor of POBV if axial length is not available.
**Fig 1:** *Positive correlation of OPA to estimated OR (K) for DCT measurements.* **Fig 2:** *Positive correlation of OPA to estimated OR (K) for ICM measurements.* **Fig 3:** *Positive correlation of POBV to OPA for DCT measurements.* **Fig 4:** *Positive correlation of POBV to OPA for ICM measurements.* **Fig 5:** *Negative correlation of POBV to IOP for DCT measurements.* **Fig 6:** *Negative correlation of POBV to IOP for ICM measurements.* **Fig 7:** *Strong correlation of the Calculated POBV to the Factor OPA/IOP for DCT Measurements.* **Fig 8:** *Strong correlation of the Calculated POBV to the Factor OPA/IOP for ICM Measurements.* TABLE_PLACEHOLDER:Table 1 TABLE_PLACEHOLDER:Table 2
## Discussion
Figs 1 and 2 show positive correlations of OPA to estimated OR for the present data set which is consistent with the literature using invasive methods to measure OR [8–10]. It should be noted that the slope of the regression line increased with the manometric IOP setpoint indicating the positive relationship of OPA to IOP. Figs 3 and 4 indicate positive correlations of POBV to OPA and a negative relationship of POBV to IOP since the slope of the regression line decreases with an increase in IOP setpoint. Figs 5 and 6 also show a significant negative correlation of POBV to IOP, for the two highest setpoints using DCT, but all three setpoints for ICM. This result is consistent with studies measuring POBF in normal subjects versus normal tension glaucoma subjects, which reported a negative correlation of POBF to IOP in both subject groups [15, 16]. Additional studies examining the validity of using time-varying measurements of IOP to calculate POBF show that OPA is directly related to POBF by way of the eye’s pressure-volume curve [6, 7]. This relationship supports findings that invasively increased IOP is positively correlated with OPA and negatively correlated to calculated POBF [8, 17]. Figs 7 and 8 show that POBV is also tightly associated with OPA/IOP, which is an excellent surrogate factor for POBF if axial length is not available. Although OPA is positively correlated with both POBV and IOP, IOP is negatively correlated with POBV, so dividing OPA by IOP has the effect of combining these opposite predictors of ocular blood volume. Therefore, an important contribution of the current work is that high IOP with low OPA is shown to be associated with the lowest values of POBV.
In the present study OR was estimated from AL based on an invasive study on cataract patients that found a strong negative correlation between OR and the natural log of AL [9]. This negative correlation between AL and OR is consistent with other studies, although not all showed significance [8, 10]. This may be due to the application of a linear regression fit line rather than the natural log of AL and/or the particular subject population under study. Ocular volume was found to be highly negatively correlated with OR in enucleated eyes [18]. While ocular volume can be estimated from AL, this doesn’t establish a direct correlation of AL to OR, since the pressure component is missing [19]. The positive correlation of OR to age has been established, meaning a limitation of this study is a lack of data on younger subjects which are often not included in invasive studies of OR since they have otherwise healthy eyes not scheduled for surgery [9, 20].
OIS occurs in patients with partial or total occlusion of the internal carotid artery on the same side as the affected eye. Poor collateral circulation between the internal and external carotid arteries usually accompanies the OIS diagnosis [4, 5]. OIS patients show a reduction of blood flow in the ophthalmic and retrobulbar arteries [21, 22]. In some cases ophthalmic artery flow direction is thought to be reversed, emptying into the lower-resistance intracranial arteries [22, 23]. Since the ophthalmic artery feeds the posterior ciliary arteries, which supply the choroid with blood, any reduction or reversal of ophthalmic artery flow would be expected to impact ocular blood flow substantially [24]. Quantifying ocular blood volume could provide insight into the level of carotid occlusion and overall cardiovascular health.
While the complete pathogenesis of glaucoma is unknown, reduced ocular blood flow has been associated with the progression of glaucoma, as well as age-related macular degeneration [1]. Glaucoma patients and hypertensive subjects have been shown to have significantly reduced POBF versus healthy controls [15, 17, 25–27]. Peripapillary retinal vessel diameter was significantly negatively correlated with the progression of glaucoma, independent of age in an age-matched prospective study of 473 eyes [28, 29]. Reduced peripapillary retinal blood flow and delayed choroidal filling was found in normal tension glaucoma (NTG) patients versus normal subjects, suggesting NTG patients experience reduced choroidal blood flow [30]. Delayed or altered choroidal filling measured by angiography, laser doppler flowmetry, and other techniques has been associated with AMD [31–33].
Sophisticated methods for measuring ocular blood flow have been developed, such as color Doppler imaging, Doppler Fourier domain optical coherence tomography, and laser speckle flowgraphy [34, 35]. However, the more fundamental method of interest is the calculation of POBF from time domain waveform of ocular pressure. This method relies on the eye pressure-volume relationship to translate the change in pressure into a change in volume. The derivative of the volume change represents POBF as a function of time [6]. Development of this method was based on the waveform output of a pneumatonometer. The DCT provides OPA and IOP but does not provide a time-domain waveform of ocular pressure. IOP, OPA, and an estimated OR can be used to estimate the volume of blood entering the eye noninvasively with each cardiac cycle, termed POBV.
Other limitations of this study relate to the data collection strategy of the original study. IOP was set manometrically, making the measurement of a μL-scale volume impossible. If an electronically controlled syringe pump had been utilized, then the actual pressure-volume relationship could have been recorded and compared with the estimated OR. However, this was not the primary goal of the study. Subject blood pressure was also not recorded.
Finally, it should be noted that reduced blood flow in the choroid does not necessarily translate to reduced blow flow in the retina or optic nerve head. Choroidal blood flow is influenced by innervation of the sympathetic and parasympathetic nervous system where retina and optic nerve head blood flow are regulated locally [2, 34, 36]. Both of these mechanisms are downstream of the split between the central retinal artery and the posterior ciliary arteries feeding the choroid suggesting that they may be isolated from each other [24, 36, 37]. For the data set used in this study, bupivacaine was used for a peribulbar block meaning autoregulation was likely impaired.
## Conclusion
Estimation of POBV provides a quick indication of ocular blood volume determined from IOP, OPA, and AL without the need to capture the ocular pressure waveform and compute its derivative. POBV is positively correlated with OPA, but negatively correlated with IOP. Higher IOP with lower OPA results in the lowest values of POBV. The OPA/IOP factor may also provide a useful clinical tool for evaluating changes in ocular blood flow in diseases with a vascular component, such as diabetic retinopathy and normal tension glaucoma. However, the Dynamic Contour *Tonometer is* no longer commercially available. The pneumatonometer (Model 30; Reichert Technologies, Inc) is a tonometer that is currently commercially available which measures and reports OPA, along with IOP. Future studies are warranted.
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---
title: Identification of circRNA-miRNA-mRNA regulatory network and its role in cardiac
hypertrophy
authors:
- Ke Gong
- Kai Yang
- Ting Xie
- Yong Luo
- Hui Guo
- Zhiping Tan
- Jinlan Chen
- Qin Wu
- Yibo Gong
- Luyao Wei
- Jinwen Luo
- Yao Yao
- Yifeng Yang
- Li Xie
journal: PLOS ONE
year: 2023
pmcid: PMC10035836
doi: 10.1371/journal.pone.0279638
license: CC BY 4.0
---
# Identification of circRNA-miRNA-mRNA regulatory network and its role in cardiac hypertrophy
## Abstract
### Background
Hypertrophic cardiomyopathy (HCM) is a grave hazard to human health. Circular RNA (circRNAs) and micro RNA (miRNAs), which are competitive endogenous RNA, have been shown to play a critical role inHCM pathogenicity. However, to a great extent, the biological activities of ceRNA in HCM pathophysiology and prognosis remain to be investigated.
### Materials and methods
By analyzing the expression files in the Gene Expression Comprehensive (GEO) database, differentially expressed (DE) circRNAs, miRNAs, and mRNAs in HCM were identified, and the target molecules of circRNAs and miRNAs were predicted. The intersection of the differentially expressed RNA molecules and the expected target was then calculated, and a ceRNA network was subsequently constructed using RNA molecules. Using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, the potential etiology was elucidated. qPCR was used to validate a portion of the hub gene using Angiotensin II to generate a cell hypertrophy model.
### Results
Three large-scale HCM sample datasets were extracted from the GEO database. After crossing these molecules with their expected targets, the circRNA-miRNA-mRNA network had two DEcircRNAs, two DEmiRNAs, and thirty DEmRNAs, compared to normal tissues. Functional enrichment analysis of GO and KEGG demonstrated that many of the HCM pathways and mechanisms were associated with calcium channel release, which is also the primary focus of future research. The qPCR results revealed that circRNA, miRNA, and mRNA expression levels were different. They may include novel noninvasive indicators for the early screening and prognostic prediction of HCM.
### Conclusion
In this study, we hypothesized a circRNA-miRNA-mRNA regulation network that is closely related to the progression and clinical outcomes of HCM and may contain promising biomarkers and treatment targets for HCM.
## Introduction
Hypertrophic cardiomyopathy (HCM) is one of the most prevalent hereditary heart illnesses, affecting approximately $0.2\%$ of the world’s population (1 in 500) [1]. HCM is characterized by an increase in ventricular wall thickness. Potential HCM causes include over 1,500 mutations in at least 15 genes encoding cardiac sarcomere-associated protein components [2–4]. MYH7 and MYBPC3 code for the myosin heavy chain and myosin binding protein C, respectively, and represent 50–$60\%$ of the HCM gene family [5]. However, there are still a significant number of patients whose hereditary genes have not been identified, highlighting the need for precision therapy, given the HCM genetic diversity [6–8]. The leading causes of death include sudden death, heart failure, and thromboembolism, which account for one percent of the annual global mortality rate [9]. HCM is a common cause of unexpected death in young adults [10]. However, the majority of affected individuals may remain unidentified, and many will not experience a significant reduction in life expectancy or significant symptoms [11].
A difficult aspect of identifying HCM is the lack of association between genotype and phenotype, as members of the same family with the same mutation exhibit distinct symptoms [12]. Given its various clinical presentations, phenotypic heterogeneity, vast number of mutations, and substantial consequences, HCM is considered a highly complex illness [13]. HCM may be caused by a mixture of endogenous gene mutations, exogenous protein-protective mechanisms, and environmental variables [2, 4, 14]. Consequently, it is crucial to identify the epigenetic alterations that may initiate HCM, particularly the recently identified hot spot circular RNA (circRNA) [15].
In recent years, circRNA and microRNAs (miRNAs), which are non-coding RNAs, have received a great deal of interest for their role in a variety of disorders. These RNA molecules modulate gene expression through intricate connections and processes. circRNAs are covalently closed endogenous biomolecules in eukaryotes with tissue- and cell-specific expression patterns. Their synthesis is regulated by unique cis-acting elements and trans-acting factors. Some circRNAs are numerous and are evolutionarily stable. Numerous circRNAs perform vital biological functions by acting as miRNA or protein inhibitors ("sponges"), controlling protein functions, or translating themselves. circRNA is also associated with diabetes, neurological illnesses, cardiovascular diseases, and cancer [16, 17]. Competitive endogenous RNAs (ceRNAs) possess sufficient miRNA response elements to bind to and compete with matching miRNAs, isolating them at the post-translational stage and controlling mRNA production [18]. miRNAs are short noncoding RNA consisting of 20–22 nucleotides. They regulate gene expression by lowering messenger RNA stability in various normal and pathological processes [19, 20]. Consequently, the ceRNA regulatory network, comprising circRNA-miRNA-mRNA, plays an essential role in disease regulation.
In this study, we obtained the expression patterns of mRNA, miRNAs, and circRNAs from the Comprehensive Gene Expression (GEO) database for patients with HCM. Using R software, differentially expressed (DE) mRNA, miRNAs, and circRNAs were identified. Additionally, circRNA-miRNA and miRNA-mRNA prediction targets were identified. a circRNA-miRNA-mRNA network was then constructed by merging them. Popular enrichment analysis techniques, such as protein-protein interaction (PPI), gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, were used to anticipate the potential disease processes and regulatory mechanisms of HCM. This study offers new diagnostic biomarkers, therapeutic targets, and major hints for future research, which may enhance our understanding of the probable molecular processes of HCM. Although hypertrophic cardiomyopathy is mainly caused by genetic factors, the process of myocardial hypertrophy and remodeling is similar to that of myocardial remodeling caused by other factors. Previous studies have used angiotensin II (Ang II) to construct HCM models. Therefore, we also used this drug to build an HCM model to study its regulatory targets. In this study, Ang II was employed to generate a cell hypertrophy model, and a quantitative real-time polymerase chain reaction was used to validate some of the hub genes.
## Expression profile data and quality assessment
First, three microarray expression profile datasets from the National Center for Biotechnology Information were selected. Based on the mRNA expression microarray data of GPL15389, Illumina HumanHT-12 V3.0 expression bead chip, GSE36961 contained 145 samples (106 HCM samples and 39 control samples). Based on the GPL8179 and Illumina Human v2 MicroRNA expression bead chip, the GSE36946 dataset included miRNA expression profiles of 107 HCM patients and 20 controls. The GSE148602 dataset contained the circRNA expression profiles of the cardiac tissues of 15 HCM patients and 7 controls using the GPL28387, Agilent-085202 LC human ceRNA array. GEO2R was used to assess the quality control between HCM and normal samples. GEO2R is an online application for comparing and analyzing differentially expressed genes (DEG) in HCM and normal samples using the limma and GEOquery R libraries of the bioconductor project. GEO2R was solely utilized for quality control, and the DEG derived from its analysis was not implemented. We created a histogram of P-values for each gene. S1 File contains all original data.
## Retrieval and screening of array data of differentially expressed mRNA, miRNA, and circRNA in HCM
Perl and R were used for the data analysis and processing [21, 22]. All source codes are contained in the S2 File. Each dataset was annotated and organized using the Perl software. We screened DEmRNAs, DEmiRNAs, and DEcircRNAs using the GSE36961, GSE36946, and GSE148602 datasets and data molecules. The R software "limma" package was used to compare the HCM samples with normal samples. |log2FC|> 2 and P value0.05 were our cut-off criteria for circRNA data determination. We established cutoff standards for miRNA and mRNA as |log2FC>0.5 and P value0.05. The "gplots" software was utilized to generate the volcanic maps and heat maps of DEmRNA, DEmiRNA, and DEcircRNA. Fig 1 displays a flowchart of the entire process.
**Fig 1:** *Ideas and flow chart of the entire research.*
## Target prediction and crossover constructed by the ceRNA network
In the circBase database, DEcircRNA-specific information can be acquired [23]. RNAhybrid and miRanda were used to predict the combination of circRNAs and miRNAs. Eventually, promising miRNAs were identified after crossing them with DEmiRNAs. mRNA targets were predicted using TargetScan, Starbase, and miRDB databases. When two databases supported mRNA as candidate targets, the final mRNA was obtained by intersecting these mRNA with DEmRNA. Based on the aforementioned findings, a circRNA-miRNA-mRNA network was constructed and visualized using Cytoscape 3.7.2.
## GO and KEGG functional enrichment analysis
To elucidate the pathophysiological process and critical signaling pathways of HCM, the GO and KEGG bioinformatics analysis tools were utilized. The R/Bioconductor program "clusterProfiler" was used to examine GO word and KEGG pathway enrichment. Adjusted $P \leq 0.05$ was considered the statistical threshold.
## Cell culture
All cells were obtained from Professor Yifeng Yang. The American Type Culture Collection (Manassas, VA, USA) was used to obtain human AC16 cardiomyocytes (ATCC, Manassas, VA). The cells were grown at 37°C in a humidified incubator containing $5\%$ CO2, per the ATCC standards. AC16 cells were cultured in a DMEM/F12 (Thermo Fisher Scientific, Waltham, MA, USA) mixture with $10\%$ FBS (Thermo Fisher Scientific, Waltham, MA, USA) and $1\%$ antibiotics (Thermo Fisher Scientific, Waltham, MA, USA). After 48 h of subculturing, the cells were treated for 24 h with Ang II 10–7 M to induce hypertrophy.
## Western blot (WB)
AC16 cells were eliminated from the cell hypertrophy model. The cells were rinsed with cold phosphate-buffered saline and lysed with radioimmunoprecipitation assay lysis buffer (89901; Thermo Fisher Scientific) containing $1\%$ protease and phosphatase inhibitor (78442; Thermo Fisher Scientific). The lysate protein concentration was determined according to the manufacturer’s instructions using a total protein quantification kit (Thermo Fisher Scientific, 23227). Protein samples (20 μg) were separated by sodium lauryl sulfate-polyacrylamide gel electrophoresis at a concentration of $10\%$ and then transferred to a polyvinylidene fluoride membrane. The membrane was sealed with $5\%$ skim milk for 1.5 h before being incubated overnight at 4°C with the primary antibodies MYH7 (Proteintech, 22280-1-AP) and GAPDH (Proteintech, 10494-1-AP). The following day, following three washes with Tris-buffered saline and Tween 20, the membrane was incubated with IgG secondary antibodies (Abcam, ab97051) at 37°C for 1 h, and then washed thrice with TBST. Antibody-binding proteins were detected using an enhanced chemiluminescence reagent (34580; Thermo Fisher Scientific). ImageJ was used to quantitatively assess the grayscale of each band.
## qPCR
The GeneJET RNA Purification Kit (Thermo Fisher Scientific) was used to extract total RNA from these cell lines according to the manufacturer’s instructions. cDNA was synthesized using the RevertAid First Strand cDNA Synthesis Kit (Thermo Fisher Scientific, USA) according to the manufacturer’s instructions. qPCR was conducted using PowerUpTM SYBRTM Green Master Mix (Thermo Fisher Scientific) on a Thermo 9700 rapid real-time PCR machine. The following primers were used: circRNA h-hsa circ 0079270-F,5’-CAGGATGCAGAAGGAGATCACT-3’; circRNA h-hsa circ 0079270-R,5’-GATATCATCATCCATGGTGAGCTT-3’. RiboBio (RiboBio Co. Ltd, Guangzhou, Guangdong, China) Bulge-loopTM miRNA qRT-PCR Primer Set (one RT primer and a pair of qPCR primers for each set) for hsa-miR-34c-5p and hsa-miR-34c-5p was used to determine the miRNA concentration. Primer sequences for other mRNAs are shown in S3 File. The internal controls for circRNA and miRNA were GAPDH and U6, respectively. RNA fold changes were calculated using the 2−ΔΔCT method. S4 File contains all data.
## Statistical analysis
All information is presented as the mean standard deviation. All statistical analyses were performed using GraphPad Prism 8.0 (GraphPad Software, San Diego, CA, USA). The significance of the differences between the two groups was determined using a two-tailed Student’s t-test. SPSS was used to generate the statistical results. In this investigation, P-values <0.05 were statistically significant unless otherwise specified.
## Quality control test results and identify differentially expressed and crossover circRNA, miRNA, and mRNA
The results indicate that there was no significant variation in the distribution of most of the genes between the samples, and the corresponding data are provided (Fig 2). Three GEO datasets (GSE36961, GSE36946, and GSE148602) were obtained from the GEO website. In the circRNA profile data of GSE148602, there were a total of 14 DEcircRNAs between HCM samples and normal controls, and their expression levels were downregulated in HCM cardiac tissues. Using miRanda and RNAhybrid, we predicted that 531 miRNAs could be targets of these 14 circRNAs. For abnormally expressed miRNAs in GSE36946, 28 DEmiRNAs in HCM were analyzed, of which 10 were overexpressed and 18 were downregulated. Two miRNAs were found at the intersection of 531 miRNAs and 28 DEmiRNAs predicted from the miRanda and RNAhybrid databases, respectively. TargetScan, starBase, and miRDB databases identified 584 potential target genes for the two cross miRNAs. In the GSE36961 mRNA expression profile, 892 mRNAs were differentially expressed, with 387 mRNAs being strongly expressed in HCM and 505 mRNAs expressed at lower levels in HCM than in normal tissues. A total of 30 mRNA hybrids were produced. The crossover states of miRNAs and mRNA were illustrated using Venn diagrams, and volcano maps and heatmaps were created for GSE36961, GSE36946, and GSE148601, respectively (Figs 3, 4A and 4B).
**Fig 2:** *Quality control chart.A, GSE148602; B, GSE36946; C, GSE36961.* **Fig 3:** *Heatmaps and volcano maps.A and B, GSE148602; C and D, GSE36946; E and F, GSE36961. The heat map shows high expression in red and low expression in green. The volcano map shows high expression in red and low expression in green.* **Fig 4:** *Venn diagram and ceRNA network.A, Intersection of predicted miRNA and DEmiRNA; B, Intersection of predicted mRNA and DEmRNA; C, ceRNA network. The expression levels of these RNA molecules were indicated by different colors, with blue representing low expression and red representing high expression.*
## Construction of HCM-specific ceRNA network
Based on the two circRNAs (hsa_circ_0079270 and hsa_circ_0044237), two miRNAs (hsa-miR-34c-5p and hsa-miR-346), and 30 mRNAs (HRK, FRMD5, MYH9, SLC9A3R1, KLF4, STK38L, PDGFRB, LDHA, DPYSL4, CSNK1D, RCN1, BCL6, FRMD4A, SEMA4B, OSGIN2, HRASLS, ZNF664, PLA2G15, NFE2L1, EXTL1, PTPRM, PDGFRA, FAM107A, MAP2K1, SLC39A1, SCN2B, CSF1R, KLHL3, PVR, and MYADM), Cytoscape 3.7.2 was used to design a circRNA-miRNA-mRNA regulatory network. Different hues represent the expression levels of these RNA molecules, with blue signifying low expression and red signifying high expression (Fig 4C).
## GO and KEGG function enrichment analysis
GO analysis revealed that the top five enriched mRNAs in the ceRNA network primarily focused on protein tyrosine kinase activity, 1-acyl-2-lysophosphatidylserine acylhydrolase activity, transmembrane receptor protein tyrosine kinase activity, and platelet-derived growth factor binding (Table 1). KEGG pathway analysis involving the ceRNA network revealed that Top7, which was predominantly enriched in target genes, was associated with central carbon metabolism in cancer, gap junction, Melanoma, Glioma, EGFR tyrosine kinase inhibitor resistance, Rap1 signaling pathway, and actin cytoskeleton regulation (Table 2). According to these findings, HCM occurrence and progression may involve various pathways and processes (Fig 5).
**Fig 5:** *GO and KEGG enrichment analysis.A and B, GO enrichment analysis; C and D, KEGG enrichment analysis.* TABLE_PLACEHOLDER:Table 1 TABLE_PLACEHOLDER:Table 2
## Validation of cell hypertrophy model by WB
To validate the successful establishment of a cellular hypertrophy model, the expression of MYH7 protein (a conventional indicator protein of myocardial hypertrophy) in cells was confirmed by WB. The average MYH7 protein expression was 1.73 times higher in cells with hypertrophy than in cells from the control group ($$P \leq 0.0037$$), a statistically significant difference (Fig 6A and 6B and S5 File).
**Fig 6:** *Experiment to verify potential targets.A and B, changes in the expression of MYH7 protein after ANGⅡ treatment. C, The RNA expression level of hsa_circ_0079270 in the cell hypertrophy model. D, the RNA expression of hsa-miR-34c-5p in the cell hypertrophy model. E, the RNA expression of hsa-miR-346 in the cell hypertrophy model. *<0.05, **<0.01, ***<0.001, ****<0.0001.*
## qPCR verification of hub genes
qPCR was employed to examine the expression of three genes (including one circRNA and two miRNAs) in AC16 cell lines. Consistent with evidence from bioinformatics analyses, in cell-cell hypertrophy, the RNA expression of hsa circ 0079270 was much lower than that in NC, with a 0.76-fold average decrease ($$P \leq 0.0047$$) (Fig 6C). Hsa-miR-34c-5p was upregulated in hypertrophic cells, with a 2.28-fold average increase ($$P \leq 0.0020$$); hsa-miR-346 RNA expression was 2.82 times higher than that of NC ($$P \leq 0.0002$$), with statistically significant differences. ( Fig 6D and 6E) The RNA expression levels of MYH9, KLF4, STK38L, PDGFRB, LDHA, CSNK1D, RCN1, FRMD4A, SEMA4B, ZNF664, FAM46A, PLA2G15, EXTL1, PDGFRA, MAP2K1, SLC39A1, KLHL3, and MYADM were statistically different ($P \leq 0.05$) (Fig 7). Hsa circ 0079270 is the result of reverse transcription and partial splicing of the ACTB DNA sequence, joining exons 2 and 5 to form a circular shape (Fig 8).
**Fig 7:** *The RNA expression of mRNAs in the cell hypertrophy model and the modified ceRNA network diagram.A, Expression of mRNA in the cellular hypertrophy model compared to the control group. *<0.05, **<0.01, ***<0.001, ****<0.0001. B, The modified ceRNA network diagram. The results marked with red lines are negative results verified by the experiment, and those not marked are positive results.* **Fig 8:** *Schematic diagram of the transcription process of hsa_circ_0079270.During transcription, ligation is performed from before exon 2 and after exon 5 to form a circular RNA. F represents the forward primer and R represents the reverse primer.*
## Discussion
CircRNAs are single-stranded RNAs with no free ends that form a covalently closed loop. Numerous circRNAs are endogenous, stable, and abundant, with cell type-, tissue-, and developmental-stage-specific expression patterns in eukaryotic cells. In recent years, the rapid development of biochemical techniques and the application of high-throughput sequencing technology have made it feasible to isolate and identify a greater variety of circRNAs. Several studies have demonstrated that circRNAs are associated with numerous physiological and pathological processes [16, 17, 24].
Cardiovascular disease is one of the leading causes of death globally. Recent studies have demonstrated that circRNAs are associated with numerous cardiovascular disorders. The effect of circRNAs on the cardiovascular system is unclear. A deeper understanding of circRNAs will lay the groundwork for diagnostic and therapeutic strategy development for cardiovascular disease [14]. As stable, abundant, and conserved ceRNAs that act as miRNA sponges, circRNAs could be useful markers for diagnosing HCM and evaluating its etiology [25, 26]. Similar findings corroborated the hypothesis that the DE circRNAs found in our investigation may be essential components of the ceRNA network, which modulates essential gene expression in the onset and progression of cardiovascular illnesses, particularly HCM. HCM is one of the most prevalent genetic heart illnesses and is associated with an elevated risk of sudden cardiac death [9]. Significant efforts have been made over the past few decades to understand the molecular mechanism of HCM, with a focus on protein-coding genes, the majority of which are responsible for sarcomere formation [27]. Recently, it has been widely reported that circRNAs are involved in a vast array of biological processes and that their dysregulated expression is associated with several complicated human disease phenotypes, including cardiovascular disorders. These findings suggest that circRNAs may play a role in the progression of HCM and may serve as crucial indicators for diagnosis and treatment targeting [15, 24].
Currently, there is little research on hypertrophy and circRNA. RNA‐*Seq data* also showed significant differences in circRNA expression profiles in dilated cardiomyopathy (DCM) and HCM hearts compared with normal control hearts [28]. In particular, circRNAs produced by CAMK2D genes (in DCM and HCM) and titin genes (in DCM) were reduced. Qi et al. performed circRNA sequencing on HCM patient samples for the first time in a recent study and received extremely valuable data [29]. Using WGCNA, researchers determined that circRNAs hsa circ 0043762, hsa circ 0036248, and circ 0071269 may serve as possible regulators of HCM. Additionally, circRNAs and HCM were the subjects of this study. Several circRNA expression patterns were investigated as possible indicators of HCM by Sonnenschein et al. [ 30]. This study enrolled 64 patients with HCM and 53 healthy controls. The quantitative expression of a collection of circRNAs known to be associated with heart disease was assessed in blood. In another study on animal models of myocardial hypertrophy, 3 mRNA, 4 miRNAs, and 4 circRNAs were found to play an important role in myocardial hypertrophy. Bioinformatics methods were used for the study [31].
In a TAC-induced mouse model of cardiomyopathy induced by pressure overload, high circ Foxo3 expression was found to be involved in the protective mechanism of Ganoderma spore oil against cardiomyopathy [32]. In another study, we found that ganoderma spore oil improved cardiac function and reduced elevated circ‐Foxo3 expression in doxorubicin (Dox) DOX-damaged hearts [33]. Recently, Zeng et al. demonstrated the role of circ‐Amotl1, derived from the angiopoietin-like 1 gene (Amotl1), in DoX-induced animal cardiomyopathy [34]. Intraperitoneal injection of circ‐Amotl1 alleviated the abnormal effects of Dox on the heart, which was characterized by reduced apoptosis, hypertrophy, and fibrosis. These findings highlight the potential of circRNAs as a therapeutic intervention for cardiomyopathy. However, regardless of whether it is a basic experiment or a data analysis, the results of many experiments and analyses may be different or even contradictory. This may be due to the small sample size, different races collected, and the use of different algorithms and detection methods, leading to inconsistent results. However, these study aims to lay the groundwork for research in this area despite the small sample size. With more studies with increased sample sizes, the results of these preliminary studies can provide potential screening targets for future studies. This finding is of great significance for future research on the significance of circRNAs in HCM.
This investigation examined the overlap between HCM-specific DE circRNAs, DE miRNAs, and DE mRNAs in the GEO database and circRNA and miRNA target molecules predicted by related databases. Two circRNAs, two miRNAs, and thirty mRNAs were found to establish a circRNA-miRNA-mRNA regulatory network, which may play a significant role in the progression of HCM. Subsequently, we performed GO and KEGG pathway analyses on the 30 mRNAs to increase our understanding of the crucial pathophysiological mechanisms underlying the onset and progression of HCM. The majority of the two DE circRNAs found in our analysis were novel biomarkers for HCM that require further investigation.
GO enrichment was primarily concentrated on protein tyrosine kinase activity, 1-acyl-2-lysophosphatidylserine acylhydrolase activity, transmembrane receptor protein tyrosine kinase activity, platelet-derived growth factor binding, and phosphatidylserine 1-acylhydrolase activity. These two circRNAs exhibited high levels of calcium release channel activity. Since disturbance of calcium homeostasis is one of the most prevalent causes, mutation-specific alterations in the rate of calcium release in HCM are closely correlated with the disease [35–39]. Additionally, calcium homeostasis abnormalities may worsen diastolic dysfunction, resulting in heart failure and substantial morbidity and mortality [40–42]. Platelet-derived growth factor binding is intimately associated with cardiomyocyte fibrosis and is also one of the mechanisms underlying HCM [43]. Additionally, KEGG research demonstrated that it may be associated with gap junctions, EGFR tyrosine kinase inhibitor resistance, Rap1 signaling pathway, and regulation of the actin cytoskeleton. These mechanisms are similar to the calcium release channel activity described previously [44–48]. This strongly suggests that the circRNAs found in the ceRNA network may play an essential role in the etiology of HCM.
In this study, AC16 cells from the human heart were used to develop an angiotensin II-based model of cardiac hypertrophy. MYH7 was employed as an indicator protein to validate the model’s construction by detecting differences in its expression using WB. The total RNA of the cell hypertrophy model was extracted, and the circRNAs and miRNAs identified by qPCR were confirmed. It has been discovered that there is differential expression between one circRNA, two miRNAs, and eighteen mRNAs. These results demonstrated that these hub genes regulate cardiomyocyte hypertrophy.
Although altered circRNAs, miRNAs, and mRNA have been found and their potential relevance in the pathophysiology of HCM has been investigated, certain limitations must be taken into account when interpreting our results. Due to the lack of accessible data, the strength of the statistical findings may be limited, and the strength of any subtype analysis may be weak. Multiple databases were utilized to anticipate the interactions between circRNAs and miRNAs and between mRNAs and miRNAs to ensure their consistency and reliability. With the introduction of larger sample sizes, improved databases, and improved algorithms, a more comprehensive ceRNA network could be constructed in the future. To verify the qPCR experiments, hsa circ 0044237 generated three pairs of primers; however, the results of these primers were too low to examine. Hence, this circRNA was not present in our analysis results. Additionally, molecular biology techniques, including qPCR, luciferase reporter system, and immunoprecipitation analysis, may help validate our findings and reveal the molecular process behind the ceRNA network in HCM.
## Conclusion
Our study established a network of circRNA-related ceRNAs in HCM. Network-identified circRNAs may be the most important risk factors for HCM etiology. From the standpoint of the circRNA-miRNA-mRNA network, our study provides novel insights into the pathophysiology of HCM.
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|
---
title: 'Risk factors of catheter- associated bloodstream infection: Systematic review
and meta-analysis'
authors:
- Elisabeth Lafuente Cabrero
- Roser Terradas Robledo
- Anna Civit Cuñado
- Diana García Sardelli
- Carlota Hidalgo López
- Dolors Giro Formatger
- Laia Lacueva Perez
- Cristina Esquinas López
- Avelina Tortosa Moreno
journal: PLOS ONE
year: 2023
pmcid: PMC10035840
doi: 10.1371/journal.pone.0282290
license: CC BY 4.0
---
# Risk factors of catheter- associated bloodstream infection: Systematic review and meta-analysis
## Abstract
### Introduction
The prevalence of catheter-associated bloodstream infections (CLABSI) is high and is a severe health problem associated with an increase in mortality and elevated economic costs. There are discrepancies related to the risk factors of CLABSI since the results published are very heterogeneous and there is no synthesis in the description of all the predisposing factors.
### Objective
We aimed to perform a systematic review and meta-analysis to synthesize and establish the risk factors predisposing to CLABSI reported in the literature.
### Method
This is a systematic review of observational studies following the PRISMA recommendations. MEDLINE and CINAHL databases were searched for primary studies from 2007 to 2021. The protocol was registered in PROSPERO CRD42018083564.
### Results
A total of 654 studies were identified, 23 of which were included in this systematic review. The meta-analysis included 17 studies and 9 risk factors were analyzed (total parenteral nutrition (TPN), chemotherapy, monolumen and bilumen catheters, days of catheterization, immunosuppression, kidney disease and diabetes mellitus) due to the homogeneity of their definitions and measurements. The risk factors found to increase the probability of developing CLABSI were TPN, multilumen devices, chemotherapy treatment, immunosuppression and the number of days of catheterization. On the other hand, monolumen devices presented a lower likelihood of triggering this infection.
## 1. Introduction
The use of central venous catheters (CVCs) has increased in current medical practice and is widely used in hospitalized patients [1, 2]. Safe administration of different medications and use by nursing teams is ensured by advances in the technology of these devices and insertion techniques, among others. However, despite the multiple benefits, CVCs are also associated with (central line)-associated bloodstream infections (CLABSI) [3–6].
In the United States 80,000 episodes of CLABSI are diagnosed annually and are associated with increased mortality and elevated economic costs (39,000 US dollars per episode) [7]. Despite including CLABSI in the Bacteremia 0 program and in nosocomial infection surveillance programs in Catalonia (VINCAT) or the Study of the Prevalence of Nosocomial Infections in Spain (EPINE), the rates of CLABSI remain elevated in our country [8]. According to EPINE, $45.80\%$ of nosocomial bacteremias are secondary to a vascular device, with central venous access devices and peripherally inserted central catheters (PICC) being the cause in $34.39\%$ and $11.42\%$ of the cases, respectively [1]. The Spanish Society of Infectious Diseases and Clinical Microbiology (SEIMC) reports that the rates of CLABSI range between $15\%$ and $30\%$ in Spain [3]. Other international studies have reported catheter-associated infection rates of $6.3\%$ to $23\%$ of all nosocomial bacteremias and others describe $15.2\%$ [9, 10]. Moreover, the high prevalence of this complication has led to it becoming one of the major causes of morbidity and mortality in hospitalized patients [5, 11]. According to SEIMC, the direct mortality attributable to bacteremia is between $12\%$ and $25\%$ [3, 12], with a repercussion on the health care system of a mean cost of 18,000 euros per episode, depending on the causative microorganism [13].
In addition to the high rates and severity of outcomes, many studies have described a multitude of risk factors. In 2007, one systematic review studied the risk of CLABSI based on the venous device implanted and the time in place [14]. However, this study did not evaluate other related risk factors that could increase the risk of CLABSI, such as those related to some treatments [4, 5, 15, 16], pathological history [5, 17–21] and clinical status [5, 18, 20, 22]. Thus, the results obtained in the different studies are very heterogeneous, and do not synthesize and identify all the factors that favor the appearance of CLABSI. Therefore, here we provide a systematic review and meta-analysis that synthesizes and establishes the risk factors predisposing central venous catheter-associated bacteremia described in the literature.
## 2.1 Design
In accordance with the prevailing guidelines, our systematic review protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO, registration number CRD42018083564). This systematic review followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [23].
## 2.2 Search strategy
We performed serial literature searches for articles published in MEDLINE (via PubMed) and CINAHL, from 2007 to February 25, 2021, using the following keywords: “CLABSI”, “CRBSI” “Catheter” and “Risk factor”. Boolean operators were used to enhance electronic searches. All human studies published in full-text form were eligible for inclusion, with no language restriction in the searches. Additional studies of interest were identified by hand searches of bibliographies of expert authors (Pittiruti, M and Maki, D) (S1 Text).
## 2.3 Study eligibility and selection criteria
Three authors (EL, AT and CE) independently determined study eligibility. Any difference in opinion regarding eligibility was resolved through consensus.
Studies were included if they: involved participants 18 years of age or older; mentioned the risk factors associated with central venous devices, whether centrally or peripherally inserted (CICC/PICC, respectively); definition of catheter- associated bacteremia according to the criteria of the Centers for Disease Control (CDC)/National Healthcare Safety Network (NHSN); studies published in the last 14 years; and the study design was randomized control trials, cohort or case-control studies. We excluded studies with patients not hospitalized during the whole study.
## 2.4 Definition of variables and outcomes
The primary outcome of this study was the presence of CLABSIs or (central line)-related bloodstream infection (CRBSI) in patients with CICC or PICC.
A CICC was defined as any central venous access device inserted into the internal jugular or subclavian vein. PICCs were defined as catheters inserted in the basilic, axillar, cephalic, or brachial veins of the upper extremities with tips terminating in the cavoatrial junction. CLABSI or CRBSI was defined as the occurrence of bacteremia in patients with PICCs or CICCs according to CDC /NHSN criteria [7]
## 2.5 Data abstraction and validity assessment
Data were extracted from the studies included with use of a standardized template designed by our group. The following information was collected from all studies: study characteristics (author, year of publication, country, study design and patient population), variables related to vascular access/device (vascular access device, CLABSI ratio), variables and potential risk factors evaluated in each study and results of multivariate analysis.
## 2.6 Study selection
All the studies containing abstracts and title were imported to Mendeley (version 1.19.3; Mendeley LDT, m Elsevier, London, United Kingdom). After excluding duplicate papers, three investigators (EL, AT and CE) independently screened the title and the abstract according to the inclusion and exclusion criteria. If the selection of the literature could be determined based on the criteria, the full text was further evaluated. Three investigators (EL, AT and CE) independently assessed the quality of the papers included. The grade of evidence and grade of recommendation were established according to the proposal of the Centre for Evidence-Based Medicine of Oxford [24]
## 2.7 Range of bias among the studies
The three authors (EL, AT and CE) independently evaluated the risk of bias.
To analyze the quality of potentially eligible articles the Strengthening the Reporting of Observational studies in Epidemiology (STROBE) [25] statement for cohort, case and control studies was followed.
## 2.8 Inclusion in the meta-analysis, data extraction and statistical methods
A meta-analysis was performed using the most prevalent risk factors for the presence of CLABSI included in the quantitative review (total parenteral nutrition [TPN], number of lumens, days of catheter placement, chemotherapy, immunosuppression, kidney disease and diabetes).
For the data analysis in the case of days of catheterization, mean values and their standard deviations of each study were extracted and weighted mean differences and $95\%$ confidence intervals (CI) were used. In the case of qualitative factors, odds ratios (OR) and $95\%$ CI were calculated for each study. The Cochrane-Q test was performed to assess the degree of heterogeneity among studies, and the I2 index (Higgins et al. 2003) [26] was used to describe the percentage of variation across studies due to heterogeneity (I2 = $25\%$: low; I2 = $50\%$: moderate; I2 = $75\%$: high heterogeneity). Study-specific estimates were pooled using both the fixed effect model (Mantel–Haenzel–Peto test) and the random effect model (Dersimonian-Laird test). If significant heterogeneity was found, the random effect model results were shown. To the contrary, the fixed-effect model was presented. Forest Plots were created to describe the pooled analysis. Statistical significance was defined as a P value < 0.05. All of the statistical analyses were conducted using R Studio.
## 3.1 Search results
After removal of duplicates, 533 articles were identified by our electronic search. Of these, 417 were excluded on the basis of abstract information, and an additional 93 studies were discarded after full text review. Therefore, 23 studies reporting CLABSI in patients with PICCs or CICCs were included in the present systematic review. ( Fig 1).
**Fig 1:** *Flow diagram of study selection.*
## 3.2 Characteristics of the studies included
Table 1 provides a detailed description of the studies analyzed. The 23 studies included were published between 2007 and 2021. Eight studies were undertaken in the United States, [4, 16,17, 20, 21, 27–29], three in Australia [30–32], two in India [18, 33], two in China [34, 35], and one in each of the following countries: Spain [36], Tunisia [19], Japan [15], France [37], Cyprus [38], Germany [22], Korea [39], and Turkey [40]. Among the studies eligible, 22 ($95.65\%$) were cohort follow-up studies [4, 15, 17–22, 27–40] and 1 was a case-control study ($4.34\%$) [16]. Of the studies included, 9 were performed in the Intensive Care Unit (ICU) ($39.13\%$) [17–19, 27, 28, 30, 31, 33, 36], 10 in conventional hospitalization wards ($43.47\%$) [4, 15, 16, 20, 21, 29, 35, 37, 38, 40] and 4 in the Oncology Department ($17.39\%$) [22, 32, 34, 39].
**Table 1**
| STUDY | COUNTRY | STUDY DESIGN | POPULATION | VASCULAR ACCESS DEVICE | CLABSI RATIO | CLABSI DEFINITON | RISK FACTOR | MULTIVARIATE | LEVEL OF EVIDENCE |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Spelman et al 2017 [30] | Australia | Cohort follow- up | ICU patients | CICC | No information | CDC/NHSN | AgeAny infectious diagnosis.Ventilation in the first 24 h.Policy of mandatory ultrasound guidance to localize CVC.Number of registered nursesTotal hours receiving invasive ventilationTotal hours receiving non-invasive ventilationNumber of full-time specialistsNumber of specialists in sessionTotal number of non-intensive care specialistsAnnual number of patients with known mortality.YearsAPACHE III scoreNumber of invasive ventilations.Number of invasive ventilations.Number of non-invasively ventilated patients. | Patients with known mortality RR 1.11; 95% CI 1.04–1.19) P = 0.002APACHE III (RR 1.03; 95%CIb 1.01–1.06) P = 0.031Total hours receiving invasive ventilation (RR 1.14; 95%CIb 1. 08–1.21) P < 0. 001Total hours with non-invasive ventilation per 100 days in bed (RR 1.01; 95%CIb 1.01–1.02) P <0.001.Number of hours with non-invasive ventilation x 100 days in bed (RR 1.07; 95%CI 1.01–1.13) P = 0.030.Ultrasound guided device placement (RR 0.47; 95%CI 0.34–0.64) P < 0.001Median age (RR 0.94; 95%CI 0.90–0.99) P = 0.02Ventilation in first 24 hours (RR 0.85; 95%CI 0.77–0.94) P = 0.002 | 3a |
| Sarah S. Jackson et al 2017 [17] | Michigan USA | Cohort follow- up | ICU | CICC | 85849/162 | CDC/NHSN | AgeSexRaceICU typeCoagulopathyDementiaUncomplicated diabetesComplicated diabetesDrug abuseParalysisHIVLymphomaMalignancyMetastatic cancerLiver diseaseObesityKidney diseaseWeight loss (malnutrition) | ICU medical/ surgical critical care (HR 1.83; 95%CI 1.04–3.20) P = 0.034Coagulopathy (HR 1.65; 95% CI 1.17–2.30) P = 0.004Paralysis (HR 1.76; 95%CI 1.06–2.93) P = 0.029Kidney disease (HR 1.59; 95%CI 1.13–2.22) P = 0.007Weight loss (HR 1.56; 95%CI 1.12–2.19) P = 0.01Age per 10-year increase (HR 0.88; 95%CI 0.80–0.96) P = 0.006 | 3a |
| Kaur et al 2015 [33] | India | Cohort follow- up | ICU patients | CICC | 90/25 | CDC/NHSN | AgeGenderPrimary clinical diagnosisCatheter insertion siteMultilumen catheterDuration catheterizationLocal sign of inflammationLength of ICU stayDeath or surviveUnderlying comorbidity | Duration of catheterization (OR 9.83; 95%CI 1.21–80.05) P = 0.03Erythema (OR 4.61; 95%CI 1.43–14.78) P = 0.012Length of ICU stay >20 days (OR 4.80; 95%CI 1.69–13.62) P = 0.003 | 3b |
| Matew Lissauer et al 2011 [27] | Maryland USA | Cohort follow- up | ICU | CICCPICC | 961/65 | CDC/NHSN | GenderSource of ICU admissionReadmission to SICU during current hospital admissionPrimary admission serviceCharlson comorbidity indexNational predicted ICUMortalityReopening of recent laparotomy | Emergency surgery (OR 1.92 95%CI 1.02–3.61)National predicted ICU mortality:Quartile3 (OR 39.63; 95%CI 1.22–76.3)Quartile 4 (OR 20.8; 95%CI 2.7–162.3)Reopening of recent laparotomy (OR 2.08; 95%CI 1.10–3.94)Gender male (OR 1.93; 95%CI 1.02–3.68) | 3a |
| Jose Garnacho–Montero 2008 [36] | Spain | Cohort follow- up | ICU | CICCPICC | 1366/66 | CDC/NHSN | AgeAPACHEIIGenderType of patientsComorbiditiesSite of catheter insertionType of catheterCatheter insertionNumber of lumensMaterialAntisepticUse of three–way- stopcockUse to measure to CVPConcomitant InfectionDuration of catheterizationCatheter useCatheter insertionSite of insertionNumber of lumens | Change over the guide (OR 4.59; 95%CI 2.28–9.3) P = 0.0001Duration of catheterization (days) (OR 1.028; 95%CI 1.0009–1.048) P = 0.003Tracheostomy (OR 2.3; 95%CI 1.17–4.54) P = 0.016 | 3a |
| S.W. Wong et al 2016 [31] | Australia | Cohort follow- up | ICU | CICCPICC | 6307/46 | CDC/NHSN | AgeSexApache II/IIIPatient typeAdmission typeAccess siteCatheter typeLumenICU | Double-lumen catheter (OR 2.59; 95%CI 1.16–5.77) P = 0.02Insertion before 2011 (OR 2.20; 95%CI 1.22–3.97) P < .0.01ICU CVC-days > 7 (OR 2.07; 95%CI 1.06–4.04) P = 0.03 | 3a |
| C Pepin et al 2015 [28] | Maryland USA | Cohort follow- up | ICU | CICC | 4011/76 | CDC/NHSN | Chronic disease score, meanCharlson comorbidity index totalCentral-line daysAgeSex | Days with (central line) (OR 1.04; 95%CI 1.03–1.06) P < .0001Beta blocker and diuretic treatment (OR 1.85; 95%CI 1.04–3.29) P = 0.036Kidney disease (OR 1.88; 95%CI 1.16–3.05) P = 0.010Cholesterol lowering agents (OR 0.39; 95%CI 0.17–0.89) P = 0.026Myocardial infarction (OR 0.28; 95%CI 0.10–0.76) P = 0.013 | 3a |
| SB Mishra et al 2016 [18] | India | Cohort follow- up | ICU | CICC | 153/46 | CDC/NHSN | AgeDuration of hospitalizationAPACHE IISOFANumber of days with CVCNumber of blood cultures sentDiabetes mellitusHypertensionCOPDCoronary artery diseaseImmunosuppressionSepsisPneumoniaIntra-abdominal infectionBloodstream infectionMortality | Immunosuppression (OR 10.5; 95%CI 1.58–70.02) P = 0.015Days with (central line) > 10 days (OR 5.52; 95%CI 1.8–16.1) P = 0.002 | 3b |
| Hajjej Z 2013 [19] | Tunisia | Cohort follow- up | ICU | CICC | 482/54 | CDC/NHSN | AgeSexAPACHE IIReason for ICU admissionDays in ICUDays with catheterComorbiditiesMechanical ventilationSepsis at insertionOne or more antibioticCatheter siteParenteral nutritionInsertion contextMortality | Diabetes mellitus (OR 2.43; 95%CI 1.09–5.7) P0.027Duration catheterization (OR 1.95; 95%CI 1.21–2.13) P<0.001Sepsis at insertion (OR 3.80; 95%CI 1.91–7.87) P <0.001≥1 antibiotic before insertion (OR 4.46; 95%CI 2.08–10.1) P<0.001 | 3b |
| Ishizuka et al 2008 [4] | Japan | | Hospitalized patients | PICCCICC | 542/6 | CDC/NHSN | Type of catheterSex (male/female)Trouble with insertionKinds of catheterGroshong catheterArgyle catheter 180Types of disinfectant10% povidone-iodine0.05% chlorhexidineAdministration of TPNAgeTime catheter insertedDurationType of chemotherapy | TPN (OR 12.75; 95%CI 2.48–62.26) P = 0.0023 | 3b |
| Herc et al 2017 [4] | Michigan USA | Cohort follow- up | Hospitalized patients | PICC | 23088/249 | CDC/NHSN | RaceAge groupBMIPathological/surgical historyTPNHemodialysisVenous stasisSmoking statusHistory of CLABSIPharmacotherapyAnalytical countLength of hospital stay prior to PICC placementCVC or PICC in prior 6 monthsPresence of another CVCOperator typeDocumented indication -PICC placementHospital localizationArm selectedVein selectedDevice characteristicsPICC gaugeType PICC | Hematological cancer (HR 3.77; 95%CI 2.75–5.16) P >0.001CLABSI history within 3 months (HR 2.84; 95%CI 1.68–4.80) P >0.001Active cancer with receipt of chemotherapy (HR 2.39; 95%CI 1.59–3.59) P >0.001Multiple vs. single Lumen (HR 2.09; 95%CI 1.49–2.92) P>0.001Presence of another CVC at time of PICC placement (HR 1.98; 95%CI 1.40–2.80) P>0.001Receipt of TPN through the PICC (HR 1.82; 95%CI 1.21–2.73) P >0.001 | 3a |
| Sanjiv M et al 2013 [20] | Michigan USA | Cohort follow- up | Hospitalized patients | PICC | 2193/57 | — | LumenAgeSexDiabetesRheumatologic diseaseImmunosuppressedRecent chemotherapyPICC adjustmentPower PICCPICC lumens | Immunosuppression (OR 2.60; 95%CI 1.45–4.67) P<0.013 PICC lumen compared with 1 lumen (OR 3.26; 95%CI 1.09–9.72) P = 0.02 | 3a |
| Caroline Bouzad et al 2015 [37] | France | Cohort follow- up | Hospitalized patients | PICC | 923/31 | CDC/NHSN | GenderOncology diseaseHematology wardIndication of PICCPlacement chemotherapyAuto/allograftOther contextClamped PICCSenior operationHigh blood pressureNeutropeniaAnti-coagulant therapyHistory of PICC/CVCDwell time C/7hours/ 14 hours/21 hours | Chemotherapy (OR 7.2; 95%CI 1.8–29.6)P = 0.006Auto/allograft (OR 6.0; 95%CI 1.2–29.3) P = 0.02Anti-coagulant therapy (OR 4.1; 95%CI 1.4–12.0)P = 0.01 | 3a-3b |
| Makhawadee Pongruangporn et al 2013 [38] | Cyprus | Cohort follow- up | Hospitalized patients | PICC | 485/162 | CDC/NHSN | DemographicComorbidityPICC descriptionPICC where placedPICC insertion siteVein insertionType of PICC | Congestive heart (OR 2.0; 95%CI 1.26–3.17) P = 0.003Intraabdominal perforation (OR 5.66; 95%CI 1.76–18.19) P = 0.004Clostridium difficile (OR 2.25; 95%CI 1.17–4.33) P = 0.02Recent chemotherapy (OR 3.36; 95%CI 1.15–9.78) P = 0.03Tracheostomy (OR 5.88; 95%CI 2.99–11.55) P<0.001Double lumen (OR 1.89; 95%CI 1.15–3.10) P = 0.01Trilumen (OR 2.87; 95% CI 1.39–5.92) P<0.001Underlying COPD (OR 0.48; 95%CI 0.29–0.78) P = 0.03Admission to surgical (OR 0.43; 95%CI 0.24–0.79) P = 0.006Oncology and orthopedic (OR 0.35; 95%CI 0.13–0.99) P = 0.05 | 3a |
| P Ippolito et al 2015 [21] | New York USA | Cohort follow- up | Hospitalized patients | CICC | 4840/220 | CDC/NHSN | AgeCharlson comorbidityScore Duration of parenteral nutritionDuration of catheterizationSexUnderlying disease,MalignancyDiabetes mellitusHIVKidney diseaseSurgical site infection,TPNHistory of transplantICU stayImmunodeficiencyPneumonia | TPN (OR 4.33; 95%CI 2.50–7.48) P<0.001Kidney disease (OR 2.79; 95%CI 2.00–3.88) P<0.001ICU stay (OR 2.26; 95%CI 1.58–3.23) P<0.001Immunodeficiency (OR 2.26; 95%CI 1.70–3.00) P<0.001Diabetes (OR 0.63; 95%CI 0.45–0.88) P = 0.007 | 3a |
| V Chopra et al 2014 [29] | Michigan USA | Cohort follow- up | Hospitalized patients | CICCPICC | 908/58 | CDC/NHSN | AgeSexAdmitting WardComorbiditiesMarkers of severe illnessPICC characteristicsPrimary indication for PICC -InsertionArm of PICC insertionVein of PICC insertionPICC insertion unit/wardPICC operator/inserterNumber of PICC lumensPICC gauge/thickness (French) | Hospital length of stay (HR 1.02; 95%CI 1.00–1.04) P = 0 .003Intensive care unit status (HR 1.02; 95%CI 1.01–1.02) P<0.0001Number lumen 2 (HR 4.08; 95%CI 1.51–11.02) P = 0.006Number lumen 3 (HR 8.52; 95%CI 2.55–28.49) P = 0.0003 | 3a |
| C Conccanon et al 2014 [16] | New York USA | Case/control | Hospitalized patients | CICCPICC | 207/197 | CDC/NHSN | Multiple CVCSexTPNHemodialysisChemotherapyICU StayLength of stayAgeCharlson comorbidityAPACHE II(Central line)–days | Multiple CVC (OR 3.4; 95%CI 2.2–8.4)TPN (OR 2.2; 95%CI 1.2–4.0)Chemotherapy (OR 8.2; 95%CI 3.4–19.9)Length of stay:11–18 days (OR 5.8; 95%CI 2.8–12.3)19–35 days (OR 6.5; 95%CI 3.0–3.7)>35 days (OR 6.5; 95%CI 3.0–14.0) | 3b |
| Bekçibaçi et al 2019 [40] | Turkey | Follow-up of one cohort | Hospitalized patients | CICC | 310/46 | CDC/NHSN | Advanced ageHemodialysisBlood product infusionTotal parenteral nutritionCatheter types:Double lumenTriple lumenCatheter locationSubclavian veinJugular veinFemoral veinExperience of applierEmergency indication for catheter insertionAsepsis complianceKidney diseaseHematologic problemsMonitoring in ICUDiabetes mellitusCharlson comorbidity index score ≥5Surgical intervention -Antibiotic treatment during catheterizationGlycopeptide use | Advanced age (OR 1.02; 95%CI 1.00–1.04) P = 0.018Duration of catheterization (OR 1.03; 95%CI 1.00–1.06) P = 0.010 | 3a |
| Shenghai Wu et al 2017 [35] | China | Follow-up of one cohort | Hospitalized patients | CICC | 477/38 | CDC/NHSN | SexPrimary diseaseGastric cancerColorectal cancerRectal cancer -Gastrointestinal perforationIntestinal obstructionPeritonitisSurgical procedureDiabetes mellitusCVC days | Surgical procedure (OR 3.96; 95% CI 1.01–15.51)P = 0.05CVC days (OR 1.08; 95% CI 1.04–1.13) P<0.001 | 3b |
| P. Mollee et al 2011 [32] | Australia | Cohort follow- up | Oncology patients | PICCCICC | 1127/129 | CDC/NHSN | GenderNº of prior linesNeutrofilosType lineSide of the insertionLumensInsertion siteDiagnosis of patientsPurpose of lineReason removal | Tunneled (HR 2.78; 95% CI 1.40–5.22) P = 0.0035Non tunneled (HR 8.69; 95% CI 3.52–21.5) P< 0.0001Aggressive hematological (HR 3.07; 95% CI 1.18–8.03) P = 0.022 | 3a |
| Yufang Gao et al 2015 [34] | China | Cohort follow- up | Oncology patients | CICCPICC | 912/94 | CDC/NHSN | GenderAgeUnderlying cancerSeason of catheter placementTumor typePlacement timeInsertion veinInsertion armInsertion unitPICC adjustmentsPICC dislodgmentTip positionFixing methodCatheter brand | Catheter care delay (OR 2.612; 95% CI 1.373–4.969) P = 0.003Summer (OR 4.78; 95% CI 2.681–8.538) P<0.001Tip position located in the lower third of the superior vena cava (OR 0.34; 95% CI 0.202–0.517) P<0.001Statclok fixing (OR 0.55; 95% CI 0.326–0.945) P = 0.03 | 3a |
| Baier et al 2019 [22] | Germany | Follow-up of one cohort | Oncology patients | CICCPICC | 610/111 | CDC/NHSN | Age >50 yearsAcute myeloid leukemiaCardiac disease (comorbidity)Body mass index >30 kg/m2Carbapenem therapyAminoglycoside therapyHematopoietic stem cell transplantationAllogenic hematopoietic stem cell/bone marrow transplantationLeukocytopenia <1,000/μLAnemiaThrombocytopenia>1 CVC insertedCVC insertion for conditioning phaseJugular vein insertion as CVC insertion siteNon Hodgkin LymphomaTransfusion of erythrocytesSubclavian vein as CVC insertion siteLength of CVC use <8 days | Leukocytopenia <1,000/μL (OR 69.77 95% CI 15.76–308.86) P<0.001>1 CVC inserted (OR 7.08; 95% CI 2.95–17) P<0.001Carbapenem therapy inserted (OR 6.02; 95% CI 2.29–15.83) P<0.001Pulmonary diseases (OR 3.17; 95% CI 1.32–7.62) P<0.001Acute myeloid leukemia (OR 2.72; 95% CI 1.43–5.17) P = 0.002CVC insertion for conditioning phase (OR 2.07; 95% CI 1.04–4.1) P = 0.037Transfusion of erythrocytes (OR 0.04; 95% CI 0.02–0.08) P<0.001Glycopeptide therapy (OR 0.10 95%; CI 0.03–0.34) P<0.001Subclavian vein as CVC insertion site (OR 0.32; 95% CI 0.14–0.77) P = 0.010 | 3a |
| Lee et al 2020 [39] | Korea | Follow-up of one cohort | Oncology patients | PICC | 539/25 | CDC/NSHN | Mean ageSexHistory of ICU stayPresence of an additional intravascular deviceHospital length of stay-Intravenous infusionTPNAntibiotic therapyChemotherapyCatheter in place more than 3 weeksSingle lumenDouble lumenRight armLeft armBasilic veinBrachial vein | Antibiotic therapy (HR 2.85; 95% CI 1.082–7.530) P = 0.034Chemotherapy (HR 11.42; 95% CI 2.434–53.594) P = 0.002Lumen (Single/Double) (HR 5.46; 95% CI 1.257–23.773) P = 0.024 | 3a-3b |
All the studies specified the type of catheter used; in 9 the type of venous device used was CICC ($39.13\%$) [17–19, 21, 28, 30, 33, 35, 40], in 5 PICC ($21.73\%$) [4, 20, 37–39], and in 9 studies both types of devices were included ($39.13\%$) [15, 16, 22, 27, 29, 31, 32, 34, 36].
The sample size of the studies evaluated established the catheter as the unit of analysis. In the cohort follow-up studies, the sample size ranged between 115 and 85,849 catheters, except in one study [30], which did not report the number of catheters but described rates of days of catheter placement. The only case-control study evaluated [16] included a sample of 197 cases and 207 controls.
## 3.3 Quality of the studies included
Analysis of the quality of the studies included was performed according to the STROBE statement [25]. The quality of the studies included was 3a and 3b. Eighteen studies obtained a grade 3a recommendation ($78.26\%$) while 6 were 3b ($21.74\%$). Of the latter 6 studies, one had a case-control design [16] and the 5 remaining studies [18, 19, 33, 35, 39] had a reduced sample size and did not achieve sufficient statistical power. Thus, the quality of the studies included in the review was good-regular.
## 3.4.1 Demographic characteristics
Gender was analyzed in 20 articles ($89.95\%$), although male sex was identified as having a greater probability of CLABSI in only 1 study [27] (odds ratio [OR] 1.93; $95\%$ confidence interval [CI] 1.02–3.68). Age was evaluated as a risk factor in 18 studies ($78.26\%$). One study independently related age to the risk of CLABSI (OR 1.02; $95\%$ CI 1.00–1.04) [50]. On the other hand, another study [30] demonstrated that age was a protective factor for CLABSI (relative risk [RR] = 0.94; $95\%$ CI 0.90–0.99).
## 3.4.2 Pharmacotherapy administered
Nine ($39.13\%$) articles included the type of pharmacotherapy administered through both an inserted catheter and other administration routes as a study variable. In regard to the treatment administered through the endovenous device, one study related preventive administration of antibiotics prior to catheter insertion to the appearance of infection (OR 4.46; $95\%$ CI 2.08–10.1) [19]. Another study related the administration of antibiotics through the endovenous device to the risk of infection (hazard ratio [HR] 2.854; $95\%$ CI 1.082–7.530) [39]. Specifically, the administration of other drugs, such as carbapenems, was shown to be a risk factor for CRSBI (OR 6.02; $95\%$ CI 2.29–15.83) [22]. To the contrary, the administration of glycopeptides and blood transfusions reduced the probability of catheter-associated infection (OR 0.10; $95\%$ CI 0.03–0.34) and (OR 0.04; $95\%$CI 0.02–0.08), respectively [22]. The administration of chemotherapy was identified as a risk factor in different studies [4, 16, 37–39] (HR 2.39; $95\%$ CI 1.59–3.59), (OR 7.2; $95\%$ CI 1.8–29.6), (OR 3.36; $95\%$CI 1.15–9.78), (OR 8.2; $95\%$ CI 3.4–19.9), (HR 11.421; $95\%$ CI 2.434–53.594), respectively. Likewise, TPN was also shown to be a factor related to CLABSI in 4 articles [4, 15, 16, 21] (HR 1.82; $95\%$ CI 1.21–2.73), (OR 12.75; $95\%$ CI 2.48–62.26), (OR 4.33; $95\%$ CI 2.50–7.48), (OR 2.2; $95\%$ CI 1.2–4.0), respectively. Other factors related to CLABSI [28,37] were the administration of anticoagulants, beta-blockers and diuretics (OR 4.1; $95\%$ CI 1.4–12.0) and (OR 1.85; $95\%$ CI 1.04–3.29), respectively. Finally, cholesterol-reducing drugs (oral statins) were described as protective factors (OR 0.39; $95\%$ CI 0.17–0.89) [28].
## 3.4.3 Interventions and care in critical patients
One of the studies related ICU stay greater than 20 days as a factor which increased the probability of CLABSI (OR 4.80; $95\%$ CI 1.69–13.62) [33]. Another article described the relation which both invasive mechanical ventilation (IMV) and non-invasive mechanical ventilation have with CLABSI (RR 1.14; $95\%$ CI 1. 08–1.21) and (RR 1.01; $95\%$ CI 1.01–1.02), respectively [30]. However, in the same study, IMV during the first 24 hours reduced the probability of developing CLABSI (RR 0.85; $95\%$ CI 0.77–0.94). Only one study identified tracheostomy as a risk factor for CLABSI (OR 2.3; $95\%$ CI 1.17–4.54) [36]. In the critical surgical setting, two studies reported that emergency surgery by laparotomy increased the probability of presenting CLABSI (OR 1.92; CI $95\%$ 1.02–3.61) [27] and (OR 3.96; $95\%$ CI 1.01–15.51) [35], and reopening was also considered a risk factor (OR 2.08; $95\%$ CI 1.10–3.94) [27].
## 3.4.4 Analytical indicators
Four studies evaluated the presence of immunological factors related to the risk of CLABSI, with two studies [18, 20] identifying immunosuppression as a risk factor (OR 10.5; $95\%$ CI 1.58–70.02) and (OR 2.60; $95\%$ CI 1.45–4.67), respectively. A third study related immunodeficiency to the appearance of CLABSI (OR 2.26; $95\%$ CI 1.70–3.00) [21].
Autologous/allogenic hematopoietic stem cell transplantation showed a relationship with catheter-related infection (OR 6.0; $95\%$ CI 1.2–29.3) [37]. Likewise, leucopenia also demonstrated a relationship with CLABSI (OR 69.77; $95\%$ CI 15.76–308.86) [22].
On the other hand, three studies [4, 33, 38] reported that the presence of some microorganisms in different contexts increased the likelihood of developing CLABSI. Colonization-infection by *Clostridium difficile* (OR 2.25; $95\%$ CI 1.17–4.33) [38], a history of CLABSI during the three months prior to new device placement (HR 2.84; $95\%$ CI 1.68–4.80) [4] and sepsis of the exit-site (OR 4.61; $95\%$ CI 1.43–14.78) and (OR 3.80; $95\%$ CI 1.91–7.87) [19, 33] were independently related to CLABSI.
## 3.4.5 Comorbidities
A higher score in the Acute Physiology and Chronic Health Disease Classification System (APACHE III) scale increased the probability of catheter-related sepsis (RR 1.03; $95\%$ CI 1.01–1.06) [30], and coagulopathy was independently related to the appearance of CLABSI (HR 1.65; $95\%$ CI 1.17–2.30) [17]. In addition, in the latter study other factors related to infection were identified: paralysis of the extremity carrying the device (HR 1.76; $95\%$ CI 1.06–2.93) and weight loss (HR 1.56; $95\%$ CI 1.12–2.19). Acute myocardial infarction was also found to be related to CLABSI (OR 0.28; $95\%$ CI 0.1–0.76) [28].
Kidney disease was independently related to CRSBI in three studies (HR 1.59; $95\%$ CI 1.13–2.22) [17], (OR 1.88; $95\%$ CI 1.16–3.05) [28] and (OR 2.79; $95\%$ CI 2.00–3.88) [21]. Pulmonary disease and acute myeloid leukemia were also related to the appearance of CLABSI in one study (OR 3.17; $95\%$ CI 1.32–7.62) (OR 2.72; $95\%$ CI 1.43–5.17), respectively [22]. In addition, two publications identified the presence of hematologic neoplastic disease as a risk factor (HR 3.07; $95\%$ CI 1.18–8.03) [32] and (HR 3.77; $95\%$ CI 2.75–5.16) [4]. In the case of diabetes, on one hand, in one study it was described as a risk factor (OR 2.43; $95\%$ CI 1.09–5.7) [19] while in another study diabetes had a protector effect (OR 0.63; $95\%$ CI 0.45–0.88) [21].
## 3.4.6 Catheter
With regard to catheter-related variables, one study showed that replacing the catheter through a guideline increased the probability of developing catheter infection (OR 4.59; $95\%$ CI 2.28–9.3) [36]. The number of lumens was also related to the appearance of CLABSI in five studies [4, 20, 29, 38, 39], showing that the greater the number of lumens the greater the likelihood of developing infection (HR 2.09; $95\%$ CI 1.49–2.92), (OR 3.26; $95\%$ CI 1.09–9.72), (OR 2.87; $95\%$ CI 1.39–5.92) (HR 8.52; $95\%$ CI 2.55–28.49), and (HR 5.466; $95\%$ CI 1.257–23.773), respectively. The synchronic presence of other venous devices also influenced the appearance of infection (HR 1.98; $95\%$ CI 1.40–2.80) [4] and (OR 3.4; $95\%$ CI 1.7–6.9) [16], (OR 7.08; $95\%$ CI 2.95–17) [22]. On the other hand, the latter study also demonstrated that insertion into the subclavian vein had a protective effect (OR 0.32; $95\%$ CI 0.14–0.77) [22]. Other protective factors reported included ultrasound-guided insertion (RR 0.47; $95\%$ CI 0.34–0.64) [30], correct positioning of the distal point in the lower third of the superior vena cava (OR 0.34; $95\%$ CI 0.2–0.51) [34] and an adhesive fixation system (OR 0.55; $95\%$ CI 0.32–0.94) [34].
## 3.4.7 Temporality
According to the results of four studies, the duration of device implantation had an impact on the appearance of catheter-related bacteremia, being one of the variables most frequently studied and showing the greatest number of significant results (OR 1.028; $95\%$ CI 1.0009–1.048) [36], (OR 1.04; $95\%$ CI 1.03–1.06) [28], (OR 5.52; $95\%$ CI 1.8–16.1) [18], (OR 1.95; $95\%$ CI 1.21–2.13) [19], (OR 1.08; $95\%$ CI 1.04–1.13) [35], (OR 1.02; $95\%$ CI 1.00–1.04) [40]. Two studies related the length of ICU stay to the appearance of CLABSI, with one showing that a stay longer than 7 days increased the probability of the infection and the second determined that a stay greater than 20 days was a factor related to infection (OR 4.80; $95\%$ CI 1.69–13.62) [33] and (OR 2.07; $95\%$ CI 1.06–4.04) [31].
## 3.4.8 Microbiology
Microbiological results were reported in 14 ($60.8\%$) of the studies included in this systematic review. In 11 studies Gram-positive microorganisms were isolated: in 9 studies [20, 22, 29, 32,34–36, 38, 40] coagulase-negative Staphylococci were described as the most prevalent, with 4 identifying S. epidermidis [22, 32, 34, 38]. In another study, the most prevalent microorganism was S. aureus [33] and lastly, Enterobacter spp. [ 31]. In 4 studies [18, 19, 31, 39] Gram-negative bacilli were described as the most prevalent (Enterobacter spp., Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa). Finally, Candida spp. was also isolated [31, 35, 39]. Some studies were cited twice because both microorganisms were isolated with the same prevalence.
## 3.5 Synthesis of the results
Among the 23 studies included, 17 were included in the meta-analysis [4, 15, 16–21, 28, 29, 33, 35–40]. The reasons for excluding six articles were: 1) the remaining risk factors were not defined or measured in the same way and did not allow for conclusive statistical tests, 2) they had not been analyzed in more than one study, and 3) the results of the studies did not show significance in the analyses performed. A total of 9 risk factors were identified and included in the meta-analysis due to the homogeneity of the definitions and measurements: administration of TPN, single, bilumen, or multilumen catheters (including trilumen, tetralumen and pentalumen catheters in the latter group), days of catheterization, chemotherapy, immunosuppression, kidney disease and diabetes mellitus.
The results showed that patients not receiving TPN had a lower probability of having CLABSI (OR = 0.48; $95\%$ CI: 0.35–0.65, $p \leq 0.001$, heterogeneity I2 = $47\%$) [4, 15, 16, 19, 21, 29, 36, 37, 39, 40] (Fig 2).
**Fig 2:** *Forest plot of total parenteral nutrition (TPN) and CLABSI.*
Likewise, patients who did not undergo chemotherapy presented a lower probability of developing this complication (OR 0.33; $95\%$ CI: 0.20–0.54, $p \leq 0.0001$ heterogeneity I2 = $68\%$) [4, 16, 20, 29, 37–39] (Fig 3).
**Fig 3:** *Forest plot of chemotherapy treatment and CLABSI.*
Absence of immune system compromise secondary to treatment or some type of disease was also related to being a protector factor against CLABSI (OR 0.44; $95\%$ CI: 0.24–0.82, $$p \leq 0.01$$, heterogeneity I2 = $66\%$) [18, 20, 21, 36] (Fig 4).
**Fig 4:** *Forest Plot of immune system ompromiso and CLABSI.*
Being a carrier of a CVC with more than one lumen implied a greater risk of CLABSI (OR = 2.74; $95\%$ CI: 1.84–4.07, $$p \leq 0.02$$, heterogeneity I2 = $60\%$) [4, 20, 29, 36–39] (Fig 5).
**Fig 5:** *Forest Plot of unilumen catheter and CLABSI.*
On the other hand, bilumen devices analyzed in 7 articles [20, 29, 36–40] were not related to the appearance of CLABSI (OR 0.78; $95\%$ CI: 0.51–1.19, $$p \leq 0.25$$, heterogeneity I2 = $67\%$) (Fig 6).
**Fig 6:** *Forest plot of bilumen catheter and CLABSI.*
Lastly, it was observed that not having a multilumen catheter reduced the probability of CLABSI (OR 0.45; $95\%$ CI: 0.37–0.55, $p \leq 0.001$, heterogeneity I2 = $0\%$) [(4, 20, 29, 33, 36–38, 40] (Fig 7).
**Fig 7:** *Forest plot of multilumen catheter and CLABSI.*
With regard to the number of days with a catheter, it was found that patients catheterized for a greater number of days had a higher likelihood of developing CLABSI (OR 6.43; $95\%$ CI: 10.75–2.12, $$p \leq 0.003$$, heterogeneity I2 = $89\%$) [15, 19, 21, 29] (Fig 8).
**Fig 8:** *Forest plot of catheter days and CLABSI.*
Lastly, kidney disease was included in a total of 6 articles [4, 17, 21, 29, 33, 38] and showed no relationship with CLABSI (OR 0.63; $95\%$ CI: 0.35–1.12, $$p \leq 0.12$$, heterogeneity I2 = $90\%$) (Fig 9).
**Fig 9:** *Forest plot of kidney disease and CLABSI.*
Likewise, neither was diabetes related to infection [4,17–21, 29, 33, 36, 38, 40] (OR 1.08; $95\%$ CI: 0.94–1.25, $$p \leq 0.27$$ heterogeneity I2 = $41\%$) (Fig 10).
**Fig 10:** *Forest plot of diabetes mellitus and CLABSI.*
## 3.6.1
The biases of publication and measurement were cited in 1 or the 23 studied included [31]. The variability in the insertion, management and treatment of CRSBI related to the bias of classification was observed in 3 of the 23 studies evaluated [31, 32, 36]. A bias of detection related to the variability in the definition and measurement of CLABSI was observed in 7 studies [4, 17, 19, 22, 32, 33, 38], and selection bias was detected in 12 studies [4, 16, 18–20, 22, 28–30, 34, 38, 39]. A bias of notification due to missing data during the data collection process was recognized in 12 studies [16, 17, 38, 39, 19–22, 28, 29, 36, 37]. Some studies had a reduced sample size implying a low statistical power in the analysis of some of the risk factors [16, 18, 28, 33]. Finally, 4 studies did not report any limitation [15, 27, 35, 40].
## 3.6.2
This review has several limitations which are implicit in the studies included in the meta-analysis. Specifically, there was significant heterogeneity in the general results mainly derived from the data belonging to the risk factors of TPN, unilumen and bilumen catheters, days of catheterization, chemotherapy, kidney disease, diabetes and immunosuppression, which were attributed a high-moderate heterogeneity >$25\%$. This heterogeneity could be related to the clinical diversity, sample size and variability of the results since they are very important variables which could explain the heterogeneity of the data as a whole. However, one of the variables studied presented a low heterogeneity < $25\%$ (multilumen catheter) and, thus, may be attributed to very solid results with excellent homogeneity.
## 4. Discussion
The prevention of CLABSI is problematic, with severe clinical repercussions at an individual and organizational level, since the use of venous devices in the hospital setting is a transversal intervention that affects hospitalized, critical, and oncological patients alike. The different studies published show contradictory results and, therefore, the present review has focused on identifying and synthesizing the variables related to the appearance of CLABSI. The results indicate that TPN, multilumen devices, chemotherapy treatment, immune system compromise and the length of catheterization are risk factors for CLABSI. On the other hand, monolumen devices present a lower probability of triggering this infection.
Multiple studies established TPN as a risk factor of CLABSI. The guidelines of the American Society for Parenteral and Enteral Nutrition (ASPEN) and CDC relate TPN with the risk of CLABSI due to the preference of the microorganisms for dextrose [7, 41]. However, ASPEN related other nutritional factors, such as a deficient nutritional status conditioning immune response to the risk of infection. Along the same line, another study corroborated that a state of malnutrition and hypoalbuminemia was associated with CLABSI (OR 3.13; $95\%$ CI 1.38–5.24, $p \leq 0.05$) [42]. Other studies determined that the risk of CLABSI is dependent on the duration of catheterization and the length of TPN [43, 44]. In addition, it has been shown that manipulation of venous devices and TPN by health care professionals may condition the appearance of CLABSI and should be manipulated with maximum precaution of sterile barriers [7]. Nonetheless, the studies included in this review coincide in establishing TPN as a risk factor, but it should be noted that one study [29] found no association between these two factors, perhaps secondary to the creation of a strategy of bundle manipulation/care/approaches that reduce the appearance of the problem. Therefore, the result of TPN as a risk factor should be interpreted with caution since the factors described could be factors independently related to CLABSI.
Chemotherapy has shown to be an independent factor of CLABSI, but as described in the literature, the cause of this association could be because of the vulnerability of developing any infectious process due to the neutropenia induced by cytostatic drugs [45, 46]. In addition, this study shows that a state of immunosuppression is an independent factor of CLABSI due to immune system dysfunction [13, 47, 48]. However, the studies included in this review did not report whether the cause of the immunosuppression was secondary to a hematological disease, organ transplantation, autoimmune disease or acquired immunodeficiency, and thus, it is not possible to stratify the results based on the causative disease. On the other hand, the results of the meta-analysis identified immunosuppression as an independent risk factor, except in one study due to the reduced sample size [18].
In relation to the number of lumens of the venous devices, multilumen catheters were found to be an individual risk factor of CLABSI. These results coincide with the CDC recommendation (category IB) of implanting devices with the least number of lumens, since the microorganisms reach the catheter through the connections and with these devices the risk is higher due to the greater number of entries [7]. In addition, these devices are susceptible to greater manipulation, hindering adequate disinfection and device maintenance. However, multilumen catheters are indicated in patients with high pharmacologic requirements in whom it is not considered safe to reduce the number of lumens because of the risk of pharmacological interaction [49]. In these cases, the importance of the management and maintenance of these devices is important to note. Along this line, it has been demonstrated that the impregnation of lumens with antimicrobial substances reduces the risk of CLABSI [50].
The present review established that monolumen venous devices are a protective factor; however, a meta-analysis determined that there are no differences when high quality studies with homogeneous samples are analyzed [51]. Therefore, this contradiction among studies could also be related to the quality of management, care and adherence to guidelines by the professionals manipulating these devices [52].
In the case of days of catheterization, the studies included showed elevated heterogeneity in the results. Taking into account that the CDC has established that routine replacement of central devices is not necessary (category IB) [45], it seems that the real reason for the development of infection may be the deterioration and dysfunctionality which venous devices acquire by multiple manipulations over time. Previous studies have shown that the quality of catheter care and management is key in the colonization of these devices [52], with thrombosis and intraluminal and extraluminal fibrin favoring the growth of microorganisms [53].
Infection is the second cause of death in patients with kidney disease receiving hemodialysis therapy [54]. These patients live with precursor risk factors of CLABSI of different causes, such as immune compromise, being carriers of a vascular access for renal replacement therapies, resistance to antibiotics, comorbidities such as diabetes, and colonization by nasal *Staphylococcus aureus* which promote the risk of this infection [55]. However, there are discrepancies among the results obtained in the literature, and our study did not describe any association with catheter-related infection and kidney disease. This may be justified in that the concept of kidney disease is very wide, and all the patients with this disease present very different characteristics which may generate very heterogeneous and inconclusive results. In addition, the CDC states that correct manipulation of a vascular device and correct monitoring by professionals is the main intervention for the prevention of CLABSI [56]. This indicates that depending on the preventive measures applied at an institutional level, having kidney disease is a precursor risk factor for the development of CLABSI.
In our meta-analysis, diabetes was not determined to be an independent risk factor of CLABSI. However, in the literature a relationship has been described between this disease and compromise of immune response [57], which would explain the results of some studies which establish diabetes as a related factor [55]. The discordance of our results with others may be due to the fact that most of the studies included did not take into account the type of diabetes, the complexity of this disease, the treatment or the years of evolution, which could justify the heterogeneity in the results obtained.
In relation to the microbiological results, the most frequent microorganisms isolated were Gram-positive cocci, the most prevalent being coagulase-negative Staphylococci, thereby indicating a possible colonization by skin flora of the patient or secondary to manipulation of the device by different health care professionals. Other series of CLABSI in our setting showed the same trend [58, 59]. However, one study performed in the United States described Enterobacter spp. and Candida spp. as the most prevalent and concluded that more evidence is necessary to establish why the patients are at risk of presenting CLABSI by these microorganisms to thereby develop preventive measures aimed at these microorganisms [60]. Despite the improvements implemented in recent years, the results demonstrate that studies should be focused not only on strategies of insertion but also on the management and maintenance of venous catheters.
The main limitation of this review is the long interval of time in the inclusion of the articles which may increase the heterogeneity of some of the variables (days of catheterization). Another limitation is that the quality of the studies was good-regular, despite not including any randomized study, and this did not allow the establishment of cause-effect relationship. One other limitation is that the quality of the maintenance of venous devices is a very important factor for the appearance of CLABSI, and its evaluation is difficult to measure and may induce overestimation of the effect of other variables of catheter-related infection. Another aspect to take into account is the elevated heterogeneity based on the variable analyzed. In addition, the large number of variables that can be analyzed as potential related factors are always subject to changes, modifications and extensions of risk factors predisposing to CLABSI, since there are other risk factors not considered in the articles included for analysis that may be related to the appearance of CLABSI. Finally, the last limitation is related to the microbiological results since we were unable to synthetize the results reported in these studies because some are described in real numbers while others are indicated in percentages, and some studies report the species and others the genus.
Despite these limitations, this review also has great strengths such as the meta-analysis which provided a synthesis of the results obtained to date and their clinical applicability.
Robust identification of risk factors may be useful for their inclusion in algorithms for deciding the most adequate venous device, in addition to the variables of pharmacotherapy and venous accesses available. It also allows including therapeutic strategies based on rigorous measures of asepsis with the aim of preventing and reducing the incidence of CLABSI, especially in patients with some of the present risk factors.
## 5. Conclusions
The decision to insert a venous device should be made based on individual evaluation of risk factors for the development of CLABSI since this complication can involve very severe clinical repercussions with very elevated health care costs. Well-designed studies with homogeneous patient samples are needed to increase the quality of the results and help evaluate the efficacy of these devices as well as the clinical benefits and profitability of the therapeutic strategies implemented.
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|
---
title: 'Associations between socioeconomic status and screen time among children and
adolescents in China: A cross-sectional study'
authors:
- Youzhi Ke
- Sitong Chen
- Jintao Hong
- Yahan Liang
- Yang Liu
journal: PLOS ONE
year: 2023
pmcid: PMC10035844
doi: 10.1371/journal.pone.0280248
license: CC BY 4.0
---
# Associations between socioeconomic status and screen time among children and adolescents in China: A cross-sectional study
## Abstract
### Background
Socioeconomic status (SES) is an important determinant of screen time (ST) in children and adolescents, however, the association between SES and ST is not fully understood in China. This study aimed to investigate the association between SES and ST (operationalized as meeting the ST guidelines; no more than 2 hours per day) in Chinese children and adolescents.
### Methods
Cross-sectional data of 2,955 Chinese children and adolescents aged 8 to 17($53.4\%$ girls) were used. SES was measured using indicators of parental education and perceived family wealth. ST was assessed with detailed items from the Health Behaviour School-aged Children survey questionnaires. Descriptive statistics and a Chi-square test were used to report the sample characteristics and analyse ST differences across different sociodemographic groups. A binary logistic regression was then applied to analyse the association of SES indicators with ST in children and adolescents.
### Results
Overall, $25.3\%$ of children and adolescents met the ST guidelines. Children and adolescents with higher parental education levels were 1.84 [$95\%$ CI 1.31–2.57; father] and 1.42 [$95\%$ CI 1.02–1.98; mother] times more likely to meet the ST guidelines than those with lower parental education levels. Associations between SES and ST varied across sex and grade groups. Moreover, the associations of SES with ST on weekdays and weekends were different.
### Conclusions
This study demonstrated the association between SES and ST in children and adolescents, highlighting the importance of targeting children and adolescents with low SES levels as an intervention priority. Based on our findings, specific interventions can be tailored to effectively reduce ST. Future studies are encouraged to use longitudinal or interventional designs to further determine the association between SES and ST.
## Introduction
Sedentary behaviour is defined as any waking behaviour characterized by energy of ≤1.5 metabolic equivalents undertaken in a sitting, reclining, or recumbent posture [1]. Screen time (ST), which refers to time spent in watching TV, playing computer games, or playing video games in another way, is an important source of sedentary behaviour, although it does not necessarily reflect total sedentary behaviour time [2]. High levels of ST have become a widespread public health concern [3, 4], as it has been recognized as a health risk factor [5, 6] independent of physical activity (PA) levels [7]. The Canadian 24-Hour Movement Guidelines recommend that children and adolescents limit daily ST no more than 2 hours [8, 9]. Evidence has shown that excessive ST in children and adolescents is associated with various unhealthy behaviours such as irregular sleep [10, 11], eating disorders, poor eating habits [12, 13], as well as physical health outcomes, including obesity, cardiovascular disease, musculoskeletal disease, and higher all-cause mortality [14, 15]. In addition, excessive ST is also strongly associated with a decline in children’s cognitive and social skills [16].
Despite increasing health awareness of ST, high levels of ST remain prevalent in children and adolescents [17, 18]. Guthold et al. compared data from 34 countries, and they found that the percentage of children and adolescents who reported 3 hours or more of ST per day exceeded $30\%$ [19]. In the United States, less than $20\%$ of children and adolescents failed to meet the ST guidelines [20], with ST up to 7.7 hours per day [21]. A similar disappointing situation has been reported in Canada, where children and adolescents spend an average of 8.6 hours per day being sedentary [22]. Children and adolescents also reported an increase in ST in low- and middle-income countries. For example, in China, only $25.5\%$ of children and adolescents meet the ST guidelines [23], with TV viewing time increased from 1 hours per day in 1997 to 1.43 hours per day in 2004 [24], then remaining relatively stable between 2004 and 2011 [25].
ST has become an independent factor negatively affecting the health of children and adolescents [26]. ST habits that develop during childhood and adolescence tend to be maintained in later life, which suggests that ST in early life can predict future ST habits and health outcomes [27, 28]. Given this, ST interventions for children and adolescents should be carried out as early as possible [29]. Researchers studying Chinese population have also found that excessive ST is associated with psychological, emotional, and social problems among children and adolescents [30, 31]. Despite these results, little is known about the association between socioeconomic status (SES) and ST in Chinese children and adolescents.
With the ongoing advancement of technology and rapid changes in lifestyles, ST including watching TV or playing with a mobile phone has become an important part of daily life in young people [32]. Due to the impact of COVID-19, education, both classroom formats and homework have also been changed into online tools, greatly increasing the ST of students [33]. Thus, it is critical to focus efforts on modifiable factors as a means of reducing ST among high-risk groups [34]. ST is affected by multiple factors, among which the influence of SES has received much research attention in recent years [4, 35]. SES, reflects the social class status of individuals, and SES is considered to be an important determinant of health and well-being [36], as it can affect people’s attitudes, experiences, and access to health services [37]. A better understanding of the association between SES and ST can help develop more effective and beneficial strategies to reduce ST. However, the association between SES and ST in children and adolescents has not yet been fully understood, which requires further attention and investigation [27, 38]. A recent systematic analysis found that children and adolescents with lower SES in high-income countries had higher levels of ST compared to those with higher SES, and a similar situation was observed in low- and middle-income countries [38]. Other studies have shown that children and adolescents with lower levels of maternal education levels tend to have more ST than those counterparts with higher levels of maternal education levels [39, 40]. Another study revealed that lower parental education and household income were also associated with higher levels of ST in boys but not in girls [41], while a study from Finland suggested that parental SES was not associated with overall sedentary time. However, it is worth noting that there are some SES differences existing in the proportions of ST and reading time at home [42]. When studying the effects of SES on children’s sedentary behaviour, more attention should be paid to the specific types of sedentary behaviour rather than overall sedentary behaviour. A study from 24 countries in the WHO European region found that low parental education levels and low family perceived wealth were risk factors of watching TV or using electronic devices for at least 2 hours a day, except in Kazakhstan, Kyrgyzstan, Tajikistan, and Turkmenistan [27]. Research on SES and ST in Chinese children is very limited. There is a study from Hong Kong showing that children in lower socioeconomic families were increasingly at risk of sedentary behaviors over the years [43]. Therefore, it is suggested that more studies based on different countries with different social and cultural contexts should be conducted to better understand the association between SES and ST in children and adolescents. However, most previous research focused on people in Western countries, while a few studies were conducted on population in developing countries, particularly in China.
Although results from studies of school-aged children suggested that overall ST is higher during after school periods and on weekends [42], current studies mostly focus on average weekly ST without considering possible differences on weekday and weekend behaviours [44]. It is essential to consider that SES differences in ST occur during weekdays and on weekends in future studies [42]. The aim of this study was to investigate the association between SES and ST in children and adolescents and to evaluate whether the association varied by SES indicators and demographics (e.g., sex).
## Study design and sampling procedure
This study conducted a cross-sectional survey of schools in China’s provinces of Jiangsu, Anhui, Zhejiang, and Shanghai from September to December 2019. A multistage sampling approach was used to recruit study respondents from students in primary, junior middle, and high schools. However, because students below third grade were not considered to be able to read the questionnaire, only healthy students of 3rd to 12th grades were included in this study. Exclusion criteria were children and adolescents who were nonverbal or ill and whose first language was not Chinese. In the first step, a total of 34 primary, junior middle, and high schools from Jiangsu, Anhui, Zhejiang, and Shanghai were selected using a convenient sample approach. In the second stage, a random cluster sampling was used to select classes in the target grades within these schools. This study was approved by the Institutional Review Board (IRB) of Shanghai University of Sport (SUS), and because none of the survey items was concerned with any personal or ethical issues, the IRB determined that verbal assent from participation was sufficient. Consequently, the necessity for written consent was waived.
## Participants
Participants were 3,368 students from the selected primary schools (3rd to 6th grades, aged 8 to 11 years old, $$n = 527$$), junior middle schools (7th to 9th grades, 12 to 14 years old, $$n = 1$$,809), and high schools (10th to 12th grades, 15 to 17 years old, $$n = 619$$) schools, with participants ranging in age from 8 to17 years old. The self-reported questionnaire was completed by 2,955 students (response rate = $87.7\%$).
## Procedures
Teachers and principals of the participating schools allowed the research staff to conduct the study. All children and adolescents involved in the study, together with their parents or guardians, were informed that participation was entirely voluntary, that verbal informed consent was obtained from all parents or guardians, and affirmative consent was obtained from all children and adolescents prior to data collection. Trained research assistants pre-arranged the survey in accordance with a standardized administration protocol during regular school hours, and the survey was thus completed on paper in the classroom setting. Students were instructed on how to complete the survey and given ample time to fill in the questionnaire. Data from the survey was then collected and analysed anonymously.
## Measurements
Sociodemographic. Apart from body height and weight, all measures used in this study were based on self-report from the survey questionnaire. Children and adolescents were asked to report their demographic information, including sex (1 = boy, 2 = girl), age, and grade (3rd to 12th grades), and ethnicity (Han or other). Among children under 10 years, questionnaires were completed with the assistance of trained research assistants. Further details on each measure used in this study were provided below.
Screen time. ST was measured by reliable and valid items derived from the Health Behaviour in School-aged Children instrument [45]. TV time was assessed by using the question "How many hours do you usually spend watching television in your free time?", with separations for weekdays and weekends (reliability coefficients of 0.74 and 0.72, respectively). Computer time was assessed by using the question "How many hours do you usually spend using a computer or game console (such as PS, Wii, Xbox, etc.) to surf the Internet or play games in your free time?", again for weekdays and weekends separately (reliability coefficients of 0.54 and 0.69, respectively). Smartphone time was assessed by the question "How many hours do you usually spend using electronic products such as tablets or smartphones to surf the Internet or play games in your free time?", again for weekdays and weekends separately (reliability coefficients of 0.33 and 0.50, respectively). The available responses to each question were "none", "about 0.5 h", "1 h", "2 h", or "3 h or more". Total ST was then calculated by summing up all answers from questions (TV, computer, smartphone time, etc). According to the Canadian 24-Hour Movement Guidelines, meeting the ST guideline requires a total daily ST ≤ 2h per day [8].
Socioeconomic status. Individual SES measures were adopted based on both parental education and a measure of perceived family wealth assessments [46]. Parental education level was determined based on reported data, categorizing parents’ educational experience into seven groups: 1) Below elementary school; 2) Elementary school; 3) Junior middle school; 4) High school or occupational school; 5) College; 6) Undergraduate; and 7) Postgraduate and above. Parental education level were further divided into three categories for analysis: a low education level (below the elementary school, elementary school, and junior middle school), a medium education level (high school or occupational school and college), and a high level of education (undergraduate or postgraduate and above).
The perceived family wealth was assessed by the study participants’ perceptions towards their family’s current SES. This variable was developed from the question "How well off do you think your family is?" with the available response of "very well off", "quite well off", "average", "not very well off", and "not at all well off". In the analysis, perceived family wealth was further divided into three categories: a low economic level (not very well off and not at all well off), a medium economic level (average), and a high economic level (very well off and quite well off).
## Statistical analyses
All the statistical analysis was performed using SPSS 24.0 version. All missing cases and abnormal values were removed. Considering the Chinese educational system, grade groups are divided into primary, junior middle, and high schools. Descriptive statistics were used to report the sample characteristics, with continuous variables expressed as mean ± standard deviation, and categorical variables expressed as numbers (n) or with percentages (%). Between-group differences in categorical demographic variables were tested by using a chi-square test. Binary logistic regression was used to analyse the association between SES indicators and ST, adjusted for sociodemographic factors. All logistic regression analysis results were presented as odds ratios (OR) with a $95\%$ confidence interval (CI). All p ≤ 0.05 were considered to be statistically significant.
## Results
The descriptive characteristics of the analytical sample in this study are shown in Table 1. A total of 2,955 children and adolescents ($53.4\%$ girls) were included in the final analysis, with an average age of 13.36 ± 2.46 years (13.08 ± 2.43 of boys and 13.01 ± 2.47 of girls, $p \leq 0.001$). Participants from primary school, junior middle school, and high school accounted for $17.9\%$, $61.2\%$, and $20.9\%$, respectively. There was a statistically significant sex difference between grade groups ($p \leq 0.001$). The majority of participants were Han Chinese ($96.9\%$), and no significant difference was found between ethnic groups ($p \leq 0.05$). About half of the participants reported that their fathers and mothers had low levels of education ($41.0\%$ and $47.7\%$, $p \leq 0.05$), respectively, and $57.5\%$ of the participants had medium perceived family wealth ($55.2\%$ for boys, and $59.5\%$ for girls, $p \leq 0.05$).
**Table 1**
| Unnamed: 0 | Unnamed: 1 | Unnamed: 2 | Overall (2955) | Boys (1378) | Girls (1577) | P |
| --- | --- | --- | --- | --- | --- | --- |
| Age (years), M±SD | Age (years), M±SD | Age (years), M±SD | 13.36±2.46 | 13.08±2.43 | 13.01±2.47 | <0.001 |
| Grade groups, n (%) | Grade groups, n (%) | Grade groups, n (%) | | | | <0.001 |
| | Primary school | Primary school | 527(17.9) | 269(19.5) | 258(16.4) | |
| | Junior middle school | Junior middle school | 1809(61.2) | 934(67.8) | 875(55.5) | |
| | High school | High school | 619(20.9) | 175(12.7) | 444(28.2) | |
| Ethnicity, n (%) | Ethnicity, n (%) | Ethnicity, n (%) | | | | 0.071 |
| | Han | Han | 2862(96.9) | 1399(97.2) | 1523(96.6) | |
| | Others | Others | 93(3.2) | 39(2.9) | 54(3.4) | |
| SES, n (%) | SES, n (%) | SES, n (%) | | | | |
| | Paternal education level | Paternal education level | | | | 0.345 |
| | | Low | 1211(41.0) | 546(39.6) | 665(42.2) | |
| | | Medium | 1021(34.6) | 483(35.1) | 538(341) | |
| | | High | 723(24.4) | 349(25.3) | 374(23.7) | |
| | Maternal education level | Maternal education level | | | | 0.113 |
| | | Low | 1409(47.7) | 629(45.6) | 780(49.5) | |
| | | Medium | 899(30.4) | 433(31.4) | 466(29.5) | |
| | | High | 647(21.9) | 316(22.9) | 331(21.0) | |
| | Perceived family wealth | Perceived family wealth | | | | 0.002 |
| | | Low | 333(11.3) | 143(10.4) | 190(12.0) | |
| | | Medium | 1699(57.5) | 760(55.2) | 939(59.5) | |
| | | High | 923(31.2) | 475(34.5) | 448(28.4) | |
Table 2 shows the prevalence of ST by sex and grade group. Overall, approximately a quarter ($25.3\%$) of children and adolescents met ST guidelines, with $81.5\%$ and $37.9\%$ meeting ST guidelines on weekdays and weekends, respectively. The percentage of boys meeting ST guidelines was higher than that of girls both in total and on weekends ($25.6\%$ vs $25.1\%$ and $39.0\%$ vs $36.9\%$, respectively). On weekdays, the percentage of girls meeting ST guidelines was higher than that of boys ($84.0\%$ vs $78.7\%$, $p \leq 0.001$). Percentages meeting the ST guidelines across the three grade groups significantly differed (primary school, $34.9\%$; junior middle school, $28.7\%$; high school:$7.3\%$; $p \leq 0.001$).
**Table 2**
| Category | Category.1 | Total ST b | Total ST b.1 | Weekday ST a,b | Weekday ST a,b.1 | Weekend ST b | Weekend ST b.1 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Category | Category | Not Meet (%) | Meet (%) | Not Meet (%) | Meet (%) | Not Meet (%) | Meet (%) |
| Total | Total | 74.7 | 25.3 | 18.5 | 81.5 | 62.1 | 37.9 |
| Sex | Sex | | | | | | |
| | Boys | 74.4 | 25.6 | 21.3 | 78.7 | 61.0 | 39.0 |
| | Girls | 74.9 | 25.1 | 16.0 | 84.0 | 63.1 | 36.9 |
| Grade | Grade | | | | | | |
| | Primary school | 65.1 | 34.9 | 26.6 | 73.4 | 39.5 | 60.5 |
| | Junior middle school | 71.3 | 28.7 | 14.5 | 85.5 | 62.0 | 38.0 |
| | High school | 92.7 | 7.3 | 23.3 | 76.7 | 81.9 | 18.1 |
Associations between SES and the prevalence of meeting the ST guidelines are shown in Fig 1. Participants with medium and high paternal education levels were 1.28 [$95\%$ CI 1.00–1.63] and 1.84 [$95\%$ CI 1.31–2.57] times more likely to spend less than 2 hours a day on watching TV or using electronic devices than those with low paternal education levels, respectively. Participants with high maternal education levels were 1.42 [$95\%$ CI 1.02–1.98] times more likely to meet ST guidelines than participants with low maternal education levels. Similarly, participants whose fathers had medium and high education levels were 1.25 [$95\%$ CI 1.01–1.55] and 2.22 [$95\%$ CI 1.64–3.01] times more likely to meet ST guidelines than participants whose fathers had low education levels on weekends, respectively. Participants whose mothers had medium and high education levels were 1.26 [$95\%$ CI 1.01–1.57] and 1.69 [$95\%$ CI 1.25–2.29] times more likely to meet ST guidelines than participants whose mothers had low education levels on weekends, respectively.
**Fig 1:** *Regression analysis of socioeconomic status and screen time.*
The summary results of OR for participants meeting ST guidelines by sex are shown in Fig 2. Both boys and girls with high paternal education levels were 1.97 [$95\%$ CI 1.21–3.20] and 1.74 [$95\%$ CI 1.09–2.77] times more likely to meet ST guidelines than participants with low paternal education levels, respectively. Girls with high perceived family wealth were 1.73 [$95\%$ CI 1.09–2.75] and 1.95 [$95\%$ CI 1.29–2.95] times more likely to spend no more than 2 hours per day on ST overall and on weekends, respectively. Boys with high perceived family wealth were more likely to spend more than 2 hours on ST per day on weekdays (OR 0.54 ($95\%$ CI 0.31–0.95)). Girls with medium maternal education levels were more likely to spend more than 2 hours per day on ST than girls with low maternal education levels on weekdays (OR 0.57 ($95\%$ CI 0.39–0.84)). Both boys and girls with high paternal education levels were 2.03 [$95\%$ CI 1.31–3.14] and 2.44 [$95\%$ CI 1.60–3.74] times more likely to meet the ST guidelines than participants with low paternal education levels on weekends, respectively. Boys with medium and high maternal education levels were 1.42 [$95\%$ CI 1.03–1.95] and 1.92 [$95\%$ CI 1.24–2.98] times more likely to meet ST guidelines than boys with low maternal education levels, respectively.
**Fig 2:** *Regression analysis of sex differences in socioeconomic status and screen time.*
The summary results of OR for participants meeting the ST guidelines by grade group are shown in Fig 3. Participants from primary school students and junior middle school students with high paternal education levels were more likely to spend no more than 2 hours per day on ST than participants with low paternal education levels on weekdays and weekends. Participants from junior middle school students and high school students with high maternal education levels were more likely to spend no more than 2 hours per day on ST than participants with low maternal education levels on weekends (OR = 1.67,$95\%$CI:1.07–2.58; OR = 5.62,$95\%$CI:2.39–13.22, respectively). Participants from high school students with high paternal education levels were 3.14 [$95\%$ CI 1.04–9.49] times more likely to meet ST guidelines than participants with low paternal education levels.
**Fig 3:** *Regression analysis of grade differences in socioeconomic status and screen time.*
## Discussion
This study investigated the association between SES and ST in Chinese children and adolescents. The main findings of this study are that children and adolescents with low parental education levels spent more ST on weekends than their counterparts with highly educated parents, with the difference being particularly notable for parental education levels. Various SES indicators also show different associations with ST across sex and grade groups of children and adolescents.
The underlying mechanism explaining the association between parental education level and ST may be that parents with higher education levels are more aware of the health impacts of excessive ST and pay more attention to their children’s academic development, thus limiting children’s and adolescent’s time spent in front of screen-based devices and encouraging to participate in physical activity [42]. However, less educated parents probably ignore the health effects of ST, and may thus tend to be less likely to limit their children’s ST [47]. Some research has suggested that parents have important modelling roles to their children, which can directly affect their children’s behaviours, such as preference for ST or participation in physical activity [33, 48]. Parental role models, attitudes, and awareness can thus have an impact on ST in children and adolescents, and this should be considered when aiming to reduce SES differences across ST in children and adolescents.
In addition, parents with higher education levels are more likely to provide more financial supports to help their children participate in physical activity, thereby potentially reducing ST [23, 49], while parents with lower education levels may not be able to provide such supports, making it challenging for such parents to limit children’s ST [42, 50]. Moreover, children with less educated parents may have more time unsupervised at home because of parents’ prolonged working hours, and this could increase their children’s longer exposure to ST [44]. It should be noted that, however, this study showed that paternal education levels appear to have a greater impact on ST in children and adolescents than maternal education levels, suggesting that priority should be given to groups with lower paternal education levels in future interventions.
The increasing use of electronic screen-based devices is another contributory factor to explain the findings. Previous studies have shown that children and adolescents with low SES are more likely to have televisions and video game systems in their bedrooms [34, 35], a practice that is associated with higher ST levels [51, 52]. However, a recent meta-analysis found that the associations between SES and ST in children and adolescents mainly depend on the country context, with SES being inversely related to ST in high-income countries and positively related to it in low- and middle-income countries [38]. This means that different intervention approaches should be formulated according to the specific social and cultural contexts. Based on this, there may be a need to counter-market electronic products such as gaming devices to low socio-economic households, especially for younger children.
This study showed that boys and girls with high levels of paternal education levels have lower ST throughout the whole week. Potential reasons for these findings include the fact that fathers with higher levels of education and social status may be more aware of the health consequences of excessive ST and thus have stricter rules on children’s ST behaviours [42]. Previous studies have suggested that fathers with lower levels of SES have fewer regulations around their children’s television access, as well as watching television more often with their children [34, 53]. Based on this, children may engage in more ST. Future interventions to reduce SES differences in children’s ST may thus need to focus on parental regulations and limits on children’s ST [42, 43].
An interesting finding in this work was that the higher levels of maternal education was associated with less ST among boys while more ST among girls. This result was inconsistent with previous findings [12, 54]. Cultural and lifestyle differences between developed and developing countries may help explain these differences. Girls whose mothers have higher education levels may be encouraged to spend more time on educational ST, such as drawing with electronics, than girls with less educated mothers [55]. Moreover, girls are seen as more vulnerable to exposure to screen-based devices than boys, and mothers are more concerned about girls’ safety, while boys are more likely to be encouraged in sports activities on weekends [56].
Results from previous studies on Chinese adolescent showed that any form of parental support, including verbal encouragement and additional parental presence, linked less time spent on ST [57]. Raising parents’ awareness of ST limit should be a priority to reduce sedentary behaviours in children with lower levels of SES. In addition, this study showed that girls with lower levels of perceived family wealth had more ST. This perhaps because parents with low levels of SES are more concerned about the safety of their neighbourhoods [58, 59], as well as lacking time to supervise their children in neighbourhoods [60] and have fewer opportunities and resources to encourage their children to engage in physical activity [61], leading to indoor screen-based activities [62]. Overall, the current findings suggest that boys with lower levels of parental education are a good target group for ST reduction, while for girls, more attention needs to be paid to the impact of maternal education. This highlights the importance of taking parental SES status into account when implementing interventions for ST reductions in children and adolescents [41], although such information may help to target and design more effective family-based interventions to reduce socioeconomic outcomes for both boys and girls [42].
Different school stages represent different grade groups of study participants, and these different grade groups showed several differences in ST. This research showed that the higher levels of parental education, less ST occurs in primary and junior middle school students, mainly on weekends. For primary school students, only paternal education level showed a positive association with ST. However, in a previous Finnish study, maternal education had no impacts, while highly educated fathers were associated with less ST in children. Yet less educated fathers were not associated with ST in their children [63]. In contrast to the work of Maatta et al. [ 42], the current study suggested that paternal education level potentially had an impact on ST in children and adolescents of different ages, while maternal education level had no significant impact on ST in children and adolescents of younger ages. Thus, that fathers could have a more profound impact on ST in children and adolescents, while mothers are important to affect ST in older children and adolescents. In China, children and adolescents from primary school may find their father’s role to be more important to their learning and health, their mothers are likely to have a greater direct impact on their lives due to different cultural roles. Fathers with higher education levels are more likely to be aware of the harms of ST and thus more likely to encourage their children to participate in physical activity [64]. As children and adolescents from junior middle school develop self-awareness, they are, however, likely to participate in more activities autonomously. Many schools are thus now promoting post-secondary education and training after high school in China, reducing the possibility of ST on weekdays for students. These supportive high schools have a certain relationship with parental education. However, families with high parental education levels are more inclined to make efforts to make their children into better schools [54]. Additionally, participants from high school students with higher SES are more likely to participate in many weekend clubs on weekends, while participants with lower SES have more ST [65]. Affording additional clubs and training is a major challenge for families with low SES, highlighting the need to focus any intervention on families with low-educated fathers, based on their influence on children’s ST on weekends.
## Strengths and limitations
This study includes some strengths, such as a relatively equal distribution of samples across different demographic groups (e.g., sex, grade), a large sample size that can increase the generalizability of findings. However, there are some limitations of this study. As data were collected by a self-reported questionnaire, this may be affected by respondents’ recall bias. The cross-sectional design also cannot draw a cause-and-effect association between SES and ST. Future studies should apply improved methodological approaches to further determine the association between SES and ST.
## Conclusion
The most consistent finding from this study is that children and adolescents with lower SES are more likely to have higher levels of ST in China. It would therefore be worthwhile to develop strategies to reduce ST that focus on these children and adolescents. The findings also illustrate the multidimensionality of the relationship between ST and SES in children and adolescents, including multiple ST measurements and multiple SES indicators. Moreover, taking into account the various contexts emerging over the course of a week, including weekdays and weekends, our findings would deepen understanding of the association between SES and ST in children and adolescents.
This study suggest that work to limit ST is urgently needed among children and adolescents with low SES in China, as this may improve their future health outcomes. Activities on weekends for children and adolescents with low parental education levels should be targeted as a priority.
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|
---
title: The effect of menopause on cardiovascular risk factors according to body mass
index in middle-aged Korean women
authors:
- Do Kyeong Song
- Young Sun Hong
- Yeon-Ah Sung
- Hyejin Lee
journal: PLOS ONE
year: 2023
pmcid: PMC10035845
doi: 10.1371/journal.pone.0283393
license: CC BY 4.0
---
# The effect of menopause on cardiovascular risk factors according to body mass index in middle-aged Korean women
## Abstract
### Background
Menopausal status and obesity are associated with an increased risk for cardiovascular diseases. However, there are few studies on the effect of menopause on cardiovascular risk factors according to the degree of obesity during the menopausal transition. We aimed to evaluate the effect of menopause on cardiovascular risk factors according to body mass index (BMI) in middle-aged Korean women.
### Methods
We analyzed 361 postmenopausal women and 758 premenopausal women (age: 45–55 years) without diabetes mellitus, hypertension, or dyslipidemia, using a cohort database released by the Korean National Health and Nutrition Examination Survey 2016–2018. Subjects were divided into two groups based on BMI. Women who underwent a hysterectomy or were pregnant were excluded from this study. Differences between groups adjusted for age and BMI were assessed.
### Results
Postmenopausal women (52 ± 2 years) were older than premenopausal women (48 ± 2 years), and BMI did not differ between the two groups (22.8 ± 2.9 vs. 23.0 ± 3.1 kg/m2). After adjustment for age and BMI in total and non-obese subjects (not obese subjects), postmenopausal women exhibited higher hemoglobin A1c and total cholesterol levels than premenopausal women. Subgroup analysis for 138 postmenopausal and 138 age- and BMI-matched premenopausal women showed that postmenopausal women had higher total cholesterol levels than premenopausal women with marginal significance (201 ± 25 vs. 196 ± 27 mg/dL).
### Conclusion
Menopausal status was associated with increased glucose and cholesterol levels independent of age and BMI in middle-aged Korean women. Menopausal status showed a significant relationship with increased total cholesterol levels even after adjusting for age and BMI in non-obese women but not obese women. Therefore, intensive monitoring and treating of lipid status is necessary to prevent cardiovascular events during the menopausal transition, especially in non-obese subjects.
## Introduction
Cardiovascular disease is the leading cause of death for women [1]. Menopausal status is associated with an increased risk for cardiovascular diseases mainly due to changes in body fat distribution, glucose metabolism, and serum lipids [2]. Traditionally, menopause is defined as the absence of menstruation with no other cause for 12 consecutive months [3]. The menopausal transition is characterized by ovarian hormone changes, menstrual cycle irregularities, and an increased risk for cardiovascular diseases [4]. Cardiovascular disease incidence rates were higher in postmenopausal women than premenopausal women in each age group among women below 55 years in a cohort of 2873 Framingham women [5]. A rise in coronary heart disease incidence after menopause was also noted in the Framingham study [6].
Previous studies on changes in cardiovascular risk factors among women during the menopausal transition demonstrate inconsistent results. Most epidemiological studies show that changes in lipid metabolism during the menopausal transition are associated with a more atherogenic lipid profile, and menopause is associated with increased total cholesterol and low-density lipoprotein (LDL) cholesterol levels. However, there are variations in the lipid profiles during the menopausal transition between studies [7–10]. A cross-sectional study conducted on Korean women (age: 44–56 years) showed that blood pressure was significantly higher during late (than early) menopausal transition [11]. However, blood pressure depended more on age than menopausal status among middle-aged Korean women after excluding women on medication for hypertension [8]. It is debatable whether menopause increases cardiovascular risk factors independent of aging. Although the risk of cardiovascular disease linearly increased with increased body mass index (BMI) in premenopausal and postmenopausal Korean women, using a nationwide health examination database [12], the effect of BMI on cardiovascular risk factors during the menopausal transition among women in Korea was unclear.
Furthermore, the effect of postmenopausal status on cardiovascular risk factors is known to vary by ethnicity, partly due to different lifestyles and dietary patterns. Ethnic differences in serum reproductive hormone concentrations exist independent of menopausal status. Studies show that Chinese and Japanese women exhibit lower estradiol concentrations during the menopausal transition than Caucasian women [13]. To date, there are few studies on the effect of menopause on cardiovascular risk factors according to the degree of obesity during the menopausal transition among women in Korea. We aimed to evaluate the effect of menopause on cardiovascular risk factors according to BMI in middle-aged Korean women.
## Data source
We used the data from the Korea National Health and Nutrition Examination Survey (KNHANES) from 2016 to 2018. The KNHANES is a national surveillance system assessing the health and nutritional status of non-institutionalized Korean citizens since 1998. This nationally representative cross-sectional survey collects data on socioeconomic status, health-related behaviors, quality of life, healthcare utilization, dietary intake, anthropometric measures, and biochemical profiles using fasting blood serum and urine and clinical profiles for major chronic diseases through a health interview, nutrition survey, and annual health examinations. The health interviews and physical health examinations are conducted by trained medical staff and interviewers [14]. Because KNHANES data comprise nationally representative samples of Korea, including the health interview, physical examination, and nutrition survey, they are valuable resources for evaluating the relationship between risk factors and diseases in Korea.
We did not obtain informed consent from individuals because we did not collect data for the study. The patient records were anonymous before being released by the KNHANES. This study was approved by the Institutional Review Board of Ewha Medical Center. All methods followed the relevant guidelines and regulations.
## Study population & outcome variables
We included women (age: 45–55 years) using a cohort database released by the KNHANES 2016–2018. The study excluded women who underwent a hysterectomy or were pregnant and subjects diagnosed with diabetes, hypertension, or dyslipidemia (based on self-reported questionnaires). Finally, we enrolled 361 postmenopausal and 758 premenopausal women 45–55 years.
The BMI was calculated as body weight in kilograms divided by height in meters squared. Body weight and height were measured during the health examinations. Subjects were divided into two groups according to BMI (non-obese subjects: BMI < 25 kg/m2, obese subjects: BMI ≥ 25 kg/m2), following Asian-specific criteria [15]. Menopausal status was determined based on self-reported questionnaires; we categorized menopause status as pre- and postmenopausal. Subgroup analysis included 138 postmenopausal women and 138 age- and BMI-matched premenopausal women.
Blood pressure was calculated as the mean of two manual sphygmomanometer readings with patients in sitting positions. A blood sample was obtained in the morning after an overnight fast. Total cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides, fasting glucose, and hemoglobin A1c (HbA1c) were measured, and LDL cholesterol was calculated using the Friedewald equation [16].
## Statistical analysis
The Kolmogorov-Smirnov statistic was used to analyze the continuous variables for normality. Quantitative variables were reported as the means ± the standard deviations. The between-group differences were assessed using the Student unpaired t-tests. Differences between groups adjusted for age and BMI were assessed using analysis of covariance. Multiple linear regression analyses were performed to determine the independent association between menopausal status and total cholesterol after controlling for age, BMI, systolic blood pressure, and fasting glucose and confirm the association between menopausal status and HbA1c after controlling for age, BMI, systolic blood pressure, and total cholesterol. Statistical analysis was performed using the SPSS 23.0 software package for Windows (IBM Corporation, Chicago, IL, USA). P values < 0.05 were considered statistically significant.
## Results
Table 1 represents the baseline characteristics of participants by menopausal status. Postmenopausal women were older than premenopausal women ($P \leq 0.05$). The mean age was 48 years for premenopausal women and 52 years for postmenopausal women. BMI did not differ between the two groups. After adjustment for age and BMI, postmenopausal women had higher hemoglobin A1c (HbA1c) and total cholesterol levels than premenopausal women. After adjustment for age and BMI, fasting plasma glucose, HDL cholesterol, triglycerides, LDL cholesterol, systolic blood pressure, and diastolic blood pressure did not differ between postmenopausal and premenopausal women (Table 1).
**Table 1**
| Unnamed: 0 | Total subjects | Total subjects.1 | Total subjects.2 | Total subjects.3 |
| --- | --- | --- | --- | --- |
| | Premenopausal women (n = 758) | Postmenopausal women (n = 361) | P-value | Adjusted P-value* |
| Age (y) | 48 ± 2 | 52 ± 2 | <0.001 | |
| BMI (kg/m2) | 23.0 ± 3.1 | 22.8 ± 2.9 | 0.395 | |
| Fasting glucose (mg/dL) | 93 ± 12 | 94 ± 9 | 0.241 | 0.122 |
| HbA1c (%) | 5.4 ± 0.4 | 5.5 ± 0.3 | <0.001 | 0.002 |
| Total cholesterol (mg/dL) | 191 ± 28 | 201 ± 25 | <0.001 | 0.045 |
| HDL cholesterol (mg/dL) | 55 ± 12 | 56 ± 13 | 0.366 | 0.650 |
| Triglycerides (mg/dL) | 96 ± 56 | 101 ± 48 | 0.190 | 0.499 |
| LDL cholesterol (mg/dL) | 116 ± 25 | 125 ± 22 | <0.001 | 0.087 |
| Systolic BP (mmHg) | 110 ± 11 | 110 ± 12 | 0.209 | 0.185 |
| Diastolic BP (mmHg) | 73 ± 8 | 73 ± 8 | 0.214 | 0.977 |
On dividing the subjects into two groups according to BMI, $33.6\%$ ($$n = 291$$) women were postmenopausal in non-obese subjects, and $27.7\%$ ($$n = 70$$) women were postmenopausal in obese subjects. Postmenopausal women were older than premenopausal women in non-obese and obese groups (all Ps < 0.05). After adjustment for age and BMI, postmenopausal women exhibited higher HbA1c and total cholesterol levels than premenopausal women only in non-obese subjects. Fasting glucose, HDL cholesterol, triglycerides, LDL cholesterol, systolic blood pressure, and diastolic blood pressure did not differ between postmenopausal and premenopausal women in non-obese and obese subjects after adjustment for age and BMI (Table 2).
**Table 2**
| Unnamed: 0 | Non-obese subjects | Non-obese subjects.1 | Non-obese subjects.2 | Non-obese subjects.3 | Obese subjects | Obese subjects.1 | Obese subjects.2 | Obese subjects.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Premenopausal women (n = 575) | Postmenopausal women (n = 291) | P-value | Adjusted P-value* | Premenopausal women (n = 183) | Postmenopausal women (n = 70) | P-value | Adjusted P-value* |
| Age (y) | 48 ± 3 | 52 ± 2 | <0.001 | | 48 ± 2 | 53 ± 2 | <0.001 | |
| BMI (kg/m2) | 21.6 ± 1.9 | 21.7 ± 1.8 | 0.322 | | 27.3 ± 2.1 | 27.3 ± 2.2 | 0.996 | |
| Fasting glucose (mg/dL) | 92 ± 8 | 93 ± 8 | 0.013 | 0.091 | 97 ± 18 | 97 ± 11 | 0.776 | 0.580 |
| HbA1c (%) | 5.4 ± 0.3 | 5.5 ± 0.3 | <0.001 | 0.001 | 5.5 ± 0.5 | 5.6 ± 0.3 | 0.512 | 0.483 |
| Total cholesterol (mg/dL) | 190 ± 27 | 201 ± 25 | <0.001 | 0.032 | 194 ± 28 | 201 ± 24 | 0.045 | 0.966 |
| HDL cholesterol (mg/dL) | 57 ± 12 | 58 ± 12 | 0.506 | 0.185 | 51 ± 11 | 50 ± 11 | 0.848 | 0.064 |
| Triglycerides (mg/dL) | 90 ± 42 | 95 ± 42 | 0.066 | 0.549 | 117 ± 82 | 123 ± 64 | 0.584 | 0.669 |
| LDL cholesterol (mg/dL) | 115 ± 24 | 124 ± 22 | <0.001 | 0.123 | 120 ± 26 | 126 ± 23 | 0.061 | 0.575 |
| Systolic BP (mmHg) | 108 ± 11 | 110 ± 12 | 0.155 | 0.258 | 113 ± 11 | 114 ± 13 | 0.481 | 0.547 |
| Diastolic BP (mmHg) | 72 ± 7 | 73 ± 7 | 0.210 | 0.989 | 75 ± 7 | 76 ± 8 | 0.369 | 0.855 |
The association between menopausal status and total cholesterol according to BMI was similar in the subgroup analysis. Subgroup analysis for 138 postmenopausal and 138 age- and BMI-matched premenopausal women showed that postmenopausal women had higher total cholesterol levels than premenopausal women with marginal significance in total (201 ± 25 mg/dL vs. 196 ± 27 mg/dL, $$P \leq 0.091$$) and non-obese subjects (201 ± 25 mg/dL vs. 194 ± 25 mg/dL, $$P \leq 0.060$$); however, total cholesterol levels did not differ between postmenopausal and premenopausal women in obese subjects (206 ± 23 mg/dL 141 vs. 205 ± 36 mg/dL, $$P \leq 0.944$$). The levels of fasting glucose, HbA1c, HDL cholesterol, triglycerides, LDL cholesterol, systolic blood pressure, and diastolic blood pressure did not differ between postmenopausal and age- and BMI- matched premenopausal women (Table 3).
**Table 3**
| Unnamed: 0 | Total subjects | Total subjects.1 | Total subjects.2 | Non-obese subjects | Non-obese subjects.1 | Non-obese subjects.2 | Obese subjects | Obese subjects.1 | Obese subjects.2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Premenopausal women (n = 138) | Postmenopausal women (n = 138) | P-value | Premenopausal women (n = 117) | Postmenopausal women (n = 117) | P-value | Premenopausal women (n = 21) | Postmenopausal women (n = 21) | P-value |
| Age (y) | 51 ± 2 | 51 ± 2 | 1.000 | 51 ± 2 | 51 ± 2 | 1.000 | 51 ± 2 | 51 ± 2 | 1.000 |
| BMI (kg/m2) | 22.5 ± 2.4 | 22.6 ± 2.4 | 0.792 | 21.7 ± 1.6 | 21.8 ± 1.6 | 0.653 | 26.7 ± 1.8 | 26.7 ± 1.7 | 0.960 |
| Fasting glucose (mg/dL) | 93 ± 9 | 93 ± 7 | 0.710 | 93 ± 9 | 93 ± 7 | 0.836 | 95 ± 8 | 96 ± 7 | 0.602 |
| HbA1c (%) | 5.5 ± 0.3 | 5.5 ± 0.3 | 0.099 | 5.5 ± 0.4 | 5.5 ± 0.3 | 0.152 | 5.4 ± 0.3 | 5.5 ± 0.3 | 0.381 |
| Total cholesterol (mg/dL) | 196 ± 27 | 201 ± 25 | 0.091 | 194 ± 25 | 201 ± 25 | 0.060 | 205 ± 36 | 206 ± 23 | 0.944 |
| HDL cholesterol (mg/dL) | 56 ± 11 | 59 ± 13 | 0.116 | 56 ± 11 | 59 ± 14 | 0.051 | 56 ± 9 | 53 ± 10 | 0.313 |
| Triglycerides (mg/dL) | 93 ± 39 | 99 ± 46 | 0.246 | 90 ± 37 | 96 ± 44 | 0.290 | 109 ± 43 | 118 ± 50 | 0.575 |
| LDL cholesterol (mg/dL) | 122 ± 23 | 123 ± 21 | 0.497 | 120 ± 22 | 122 ± 21 | 0.508 | 127 ± 31 | 129 ± 21 | 0.815 |
| Systolic BP (mmHg) | 110 ± 11 | 108 ± 12 | 0.387 | 109 ± 11 | 109 ± 12 | 0.808 | 114 ± 11 | 108 ± 12 | 0.106 |
| Diastolic BP (mmHg) | 73 ± 7 | 73 ± 8 | 0.527 | 72 ± 7 | 73 ± 7 | 0.318 | 76 ± 5 | 74 ± 9 | 0.490 |
Multiple linear regression analyses showed that menopausal status was an independent determinant of total cholesterol after adjustment for age, BMI, systolic blood pressure, and fasting glucose (β = 4.58, $P \leq 0.05$) in the total number of subjects (Table 4). Menopausal status was also an independent determinant of HbA1c after adjustment for age, BMI, systolic blood pressure, and total cholesterol (β = 0.08, $P \leq 0.01$) in the total number of subjects (Table 5).
## Discussion
Using data from the KNHANES from 2016 to 2018, we demonstrated that menopausal status was associated with increased glucose and cholesterol levels, independent of age and BMI, among middle-aged Korean women. Multiple linear regression analyses showed that menopausal status was an independent determinant of total cholesterol and HbA1c. Menopausal status showed a significant relationship with increased total cholesterol levels, even after adjusting for age and BMI in non-obese women. However, total cholesterol levels did not differ between postmenopausal and premenopausal subjects in obese women.
The effects of menopause on the lipid profiles during the menopausal transition in our study were comparable with the results of previous studies. Numerous epidemiological studies suggest menopause-associated changes in the lipid profile. In the Study of Women’s Health Across the Nation (a longitudinal, community-based, multiethnic population study), total cholesterol and LDL cholesterol levels increased significantly within a year of the final menstrual period in 1,054 women who achieved the final menstrual period by the end of 9 years of follow-up during the menopausal transition [10]. In middle-aged Caucasian women (age: 47–55 years), the menopausal transition was associated with increased total cholesterol, LDL cholesterol, and HDL cholesterol levels, independent of age [17]. In 593 healthy Chinese women 35 to 64 years, late perimenopausal status showed a significant association with an accelerated increase in total cholesterol and triglycerides. However, HDL cholesterol levels did not differ among different menopausal status groups [9]. Of the 1,169 perimenopausal Korean women (age: 40–64 years) from the KNHANES 2005, postmenopausal women exhibited higher total cholesterol and LDL cholesterol levels than premenopausal women. However, there were no significant differences in HDL cholesterol levels between premenopausal and postmenopausal women after excluding subjects on medications for hypercholesterolemia [8]. Consistent with the results of our study, total cholesterol level was higher in postmenopausal women than premenopausal women, mostly in previous studies. Although we could not clarify the mechanism of change in lipid profiles during the menopausal transition in this study, decreasing estrogen levels during the menopausal transition known to affect hepatic lipase and lipoprotein lipase activity [18–20] may have an important role in the lipid metabolism during the menopausal transition. However, HDL and LDL cholesterol results according to the menopausal status were inconsistent between studies. We estimated LDL cholesterol using the Friedewald equation, contrary to the study on Caucasian women [17]. The age range of the participants was narrow in our study compared to previous studies on Chinese [9] or Korean women [8]. The age of the study participants, differences in methods measuring cholesterol levels, or different ethnicities may be responsible for the differences in LDL and HDL cholesterol results in women according to the menopausal status between studies.
The metabolic syndrome is characterized by abdominal adiposity, insulin resistance, and dyslipidemia and is closely associated with cardiovascular diseases [21]. The prevalence of metabolic syndrome increased during the menopausal transition in 949 women without diabetes or the metabolic syndrome at baseline was independent of aging in the Study of Women’s Health Across the Nation [22]. Postmenopausal status was associated with an increased risk of metabolic syndrome after adjusting for age in 2,671 women who did not receive hormone replacement therapy in the KNHANES 2001 [23]. Postmenopausal status was an independent risk factor for metabolic syndrome after adjustment for age and BMI in 1,002 Korean women who participated in annual health examinations [24]. Postmenopausal status was associated with dysglycemia independent of aging in Japanese individuals [25]. A prospective study including 1,303 British women (age: 53 years) showed that HbA1c levels increased across the natural menopause transition after adjustment for BMI [26]. In a cross-sectional study, postmenopausal women showed higher HbA1c levels than premenopausal women after adjustments for age and BMI in Chinese women with BMI < 30 kg/m2 [27]. HbA1c estimates long-term glucose status and predicts cardiovascular disease better than fasting or post-challenge glucose in women without diabetes mellitus [28]. Consistent with the results of previous studies, postmenopausal women had higher HbA1c levels than premenopausal women in our subjects after adjustment for age and BMI; menopausal status was an independent determinant of HbA1c in multiple linear regression analyses. Although we did not estimate abdominal obesity indicators, such as waist circumference or waist circumference/hip ratio, abdominal obesity accompanying menopause may be associated with increased insulin resistance and glucose levels in postmenopausal women [29].
In a study using the database from the KNHANES 2007–2010, waist circumference was significantly associated with systolic blood pressure after adjustment for age and BMI in 1,422 women (age: 45–55 years) during the menopausal transition. Waist circumference was associated with systolic blood pressure in non-obese women but not in obese women in this study group [30]. A cross-sectional study conducted in health-screening centers involving 2,037 Korean women (age: 44–56 years) showed significantly higher systolic blood pressure and diastolic blood pressure values during the late (than early) menopausal transition [11]. Blood pressure depended more on age than the menopausal status among middle-aged Korean women from the KNHANES 2005, after excluding women on medication for hypertension [8]. Although we did not evaluate the blood pressure between late and early menopausal transition or the association between waist circumference and blood pressure among women during the menopausal transition, blood pressure did not differ between postmenopausal and premenopausal women after adjustment for age and BMI in our study, consistent with the result of the previous KNHANES 2005.
The effect of menopause on cardiovascular risk factors differed according to BMI among middle-aged Korean women in our study. Menopausal status was associated with increased total cholesterol levels only in non-obese women. Among 2,659 women followed in the Study of Women’s Health Across the Nation annually for up to 7 years, both LDL cholesterol and total cholesterol peaked in late peri- and early menopause; however, increases in LDL cholesterol and total cholesterol were smallest in the highest baseline weight tertile [31]. A cross-sectional study involving 1,553 Korean women (age: 44–56 years) who underwent a health screening examination showed that an increased prevalence of high non-HDL cholesterol was associated with postmenopausal status and more pronounced in lean women than in overweight or obese women after excluding subjects on lipid-lowering medications or history of hypercholesterolemia [32]. Studies show that the effect of BMI on serum reproductive hormone levels varies by menopausal status. Increasing BMI was associated with decreasing estradiol levels in premenopausal women and increasing estradiol levels in postmenopausal women among 3,257 participants in the Study of Women’s Health Across the Nation [13]. Relatively high estradiol levels in postmenopausal women with higher BMI may affect the cardiovascular risk factors differently when compared to non-obese postmenopausal women. Further studies measuring levels of reproductive hormones concurrently to evaluate the mechanism of the BMI effect on cardiovascular risk factors during the menopausal transition are needed.
This study is the first to evaluate the effect of menopause on cardiovascular risk factors according to BMI in middle-aged Korean women during the menopausal transition. The strengths of our study include a database from a nationally representative survey. We excluded subjects with a history of diabetes, hypertension, or dyslipidemia because underlying diseases may affect cardiovascular risk factors. Furthermore, we used well-matched age and BMI postmenopausal and premenopausal women, which enabled us to perform group comparisons without bias.
There were several limitations in this study. First, because the study was retrospective and observational, we could not identify a cause-and-effect relationship or mechanism underlying the changes in cardiovascular risk factors during the menopausal transition. Although we adjusted for potential confounding factors (age and BMI), residual confounding factors (smoking, alcohol intake, physical activity, dietary habits, or family history of premature cardiovascular diseases) could have influenced the results of our study. The exclusion of subjects with missing data may have introduced a selection bias. We identified the menopausal status based on self-reports, and therefore, misclassification of menopausal status may have occurred. We did not exclude women on hormone replacement (which could affect the lipid parameters). Although BMI is the standard measure to define obesity, it does not represent body fat composition. We did not evaluate body fat distribution known to be affected by menopausal status.
In conclusion, we observed an association between menopausal status and increased total cholesterol levels only in non-obese subjects among middle-aged Korean women. Therefore, intensive monitoring and treating lipid status, including lifestyle modification education, would be needed to prevent cardiovascular events during the menopausal transition, especially in non-obese subjects.
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|
---
title: Amino acid substitutions in human growth hormone affect secondary structure
and receptor binding
authors:
- Andrei Rajkovic
- Sandesh Kanchugal
- Eldar Abdurakhmanov
- Rebecca Howard
- Sebastian Wärmländer
- Joseph Erwin
- Hugo A. Barrera Saldaña
- Astrid Gräslund
- Helena Danielson
- Samuel Coulbourn Flores
journal: PLOS ONE
year: 2023
pmcid: PMC10035860
doi: 10.1371/journal.pone.0282741
license: CC BY 4.0
---
# Amino acid substitutions in human growth hormone affect secondary structure and receptor binding
## Abstract
The interaction between human Growth Hormone (hGH) and hGH Receptor (hGHR) has basic relevance to cancer and growth disorders, and hGH is the scaffold for Pegvisomant, an anti-acromegaly therapeutic. For the latter reason, hGH has been extensively engineered by early workers to improve binding and other properties. We are particularly interested in E174 which belongs to the hGH zinc-binding triad; the substitution E174A is known to significantly increase binding, but to now no explanation has been offered. *We* generated this and several computationally-selected single-residue substitutions at the hGHR-binding site of hGH. We find that, while many successfully slow down dissociation of the hGH-hGHR complex once bound, they also slow down the association of hGH to hGHR. The E174A substitution induces a change in the Circular *Dichroism spectrum* that suggests the appearance of coiled-coiling. Here we show that E174A increases affinity of hGH against hGHR because the off-rate is slowed down more than the on-rate. For E174Y (and certain mutations at other sites) the slowdown in on-rate was greater than that of the off-rate, leading to decreased affinity. The results point to a link between structure, zinc binding, and hGHR-binding affinity in hGH.
## Introduction
Human Growth Hormone (hGH) binds a single hGH Receptor (hGHR) using its Site 1, a large, physicochemically diverse binding region. It then recruits a second hGHR to bind at its lower-affinity Site 2 [1]. The hGHR dimerization initiates signaling through the JAK/STAT pathway. Thus one strategy to disrupt signaling is to prevent dimerization. This in turn can be done by destroying binding at site 2, which is easily effected with mutations such as G120K [2]. It is also useful to simultaneously strengthen binding at site 1 [3], and both were done in Pegvisomant development. As part of that process the interesting substitution E174 was discovered which increases binding but whose mechanism could not be explained by Cunningham & Wells [4]. In this work we measure the kinetics and secondary-structural effects of that mutation, along with that of a different substitution at the same position (E174Y), a negative control (L52F, which is positioned far from position 174, and outside the helices), and the Wild Type (WT, the neutral control).
Pegvisomant is a recombinant hGH (rhGH) which was affinity matured at site 1 using phage display, reaching an affinity 400-fold higher than WT [3]. However, this generated substitutions at 15 amino acid positions in site 1, and other considerations required manually selected reversions and mutagenesis. The G120K mutation was also generated. Lastly, the rhGH was PEGylated to extend serum half-life. These manipulations resulted in significant reduction of affinity compared to WT [5]. As a result, there remains significant potential for rhGH variants to be used as therapeutics and diagnostics for several cancers and growth disorders, hence motivation to understand the biophysics of binding at site 1. We are interested in the possibility of choosing amino acid substitutions at a small number of residue positions, obtaining higher affinity while also working within constraints such as maintaining solubility and enabling modifications such as PEGylation. One could in principle also avoid patented substitutions, though that is not a concern in the case of Pegvisomant.
Cunningham & Wells discovered E174A during alanine scanning—a technique used to map binding interfaces in the absence of structural data. Alanine is smaller than all other canonical amino acids except glycine. Therefore protein-protein interactions (PPIs) are usually weakened if the substitution is at the interface. However E174A is an exception–it increases affinity [4]. E174, along with H18 and H21, is part of a zinc-binding triad [6] (Fig 1). E174 is also packed between two helices and follows the typical heptad repeat [7]. We therefore paid particular attention to E174. How could an amino acid which is part of the zinc binding triad, be mutated (to alanine, no less) and increase affinity?
**Fig 1:** *Conserved position E174 is part of the zinc binding triad and obeys the heptad pattern.Coiled-coils tend to follow the heptad pattern of HxxHCxC (H = hydrophobic, C = charged). (A) Helix 1 appears coiled with its antiparrallel partner helix 4. However for the former no coiled-coil signal was found by COILS, MultiCoil, or other expasy coiled-coil predictors. H18 and H21 are conserved (multiple sequence alignment from PFAM family PF00103, full alignment, profile generated with Skylign, vertical size proportional to information) and part of the zinc binding triad. Thus the triad appears important for coiled-coiling (B) in the hGH monomer. Site 1 residues are shown in iceblue, remaining residues in cyan cartoons. At several positions, we generated mutations with binding kinetics similar to those of WT (green labels). We identified a second cluster of mutations with similar koff, but much slower kon (red labels). Mutants with kinetics that did not fall into either cluster are labelled in yellow. At some positions different substitutions were tested which produced different kinetics (M14E,W, S62Y,W, E174A,W,R). (C) Helix 4 follows the heptad pattern closely, and is predicted to be in a coiled-coil by COILS [7]. E174 is conserved and part of the zinc binding triad.*
More broadly, we are also interested in the general problem of computationally engineering proteins by introducing single-residue-position substitutions, especially to improve binding affinity [8]. Other computational-experimental methods have yielded considerable improvements in affinity, but only because they included an experimental high-throughput screening and affinity maturation stage [9,10]. The required expenditure is out of reach of many labs. The end product typically has substitutions at multiple positions, as occurred in [3], making it difficult to know which substitutions were most important and why. This lack of guiding knowledge is the likely reason some modifications of [5] were detrimental to binding. If one could start with a given scaffold, generate a small number of substitutions at single positions, and obtain improvements in affinity for even a small number of these, this would mean a better ability to 1) fine-tune protein properties without immunological, off-target and other unintended effects, 2) avoid patent-protected modifications, and 3) understand the evolutionary purpose of the native scaffold sequence. Thus in addition to E174A, E174Y, L52F, and WT, we also computationally selected 21 other substitutions at several single-amino-acid positions, including position 174, expressed the corresponding rhGH variants, and measured their effect on rhGH-hGHR binding. This screen did not result in any affinity-increasing substitutions other than E174A, but the results have interesting consequences for protein engineering in general, so they are included here as a secondary outcome of the work.
## Method
We used HomologyScanner [8] with FoldX [11] to identify single-residue substitutions in rhGH which have potential to increase affinity to hGHR. We expressed and purified a number of such variants, and measured their hGHR binding kinetics by Surface Plasmon Resonance (SPR). Lastly, we measured the Circular Dichroism (CD) spectra for WT, E174A, E174Y, and L52F.
## Computational selection of mutations
HomologyScanner is an automated service which computes the FoldX energy for a specified mutation using not one but several PDB structures (here 1A22, 1HWG, 1HWH, and 1AXI); [8] The average over the structures is reported as ΔΔGhomologyScanner. We performed computational saturation mutagenesis over site 1. That is to say, we computed the effect of all 19 possible substitutions, at every position in site 1. Then we sorted by ascending ΔΔGhomologyScanner. No more than one substitution of each physicochemical class (positive, negative, hydrophic, polar) was included in this list. We then selected the 30 mutants with lowest ΔΔGhomologyScanner, for experimental evaluation. The HomologyScanner results are given in Table 1.
**Table 1**
| Substitution | Class | HomologyScanner | SSIPe | Surface Plasmon Resonance | Surface Plasmon Resonance.1 | Surface Plasmon Resonance.2 | Surface Plasmon Resonance.3 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Substitution | Class | ΔΔG [kcal/mol] | ΔΔG [kcal/mol] | kon [1000/Ms] | koff [0.001/s] | KD [nM] | ΔΔG [kcal/mol] |
| E174Y | polar | -0.88 | -0.331 | 8 | 0.03 | 4 | 1.63 |
| E174A | hydrophobic | -0.73 | 0.633 | 900 | 0.09 | 0.1 | -0.38 |
| H18F | hydrophobic | -0.56 | 0.804 | 300 | 0.09 | 0.3 | 0.22 |
| H18D | positive | -0.76 | 2.004 | 500 | 0.1 | 0.3 | 0.22 |
| E32A | hydrophobic | -0.86 | 0.195 | 800 | 0.2 | 0.3 | 0.22 |
| L52F | hydrophobic | -0.54 | 0.707 | 600 | 0.2 | 0.4 | 0.38 |
| Y28R | negative | -0.68 | 1.283 | 400 | 0.2 | 0.6 | 0.60 |
| WT | | | | 2000 | 0.3 | 0.2 | 0.00 |
| E65Y | polar | -0.65 | 0.478 | 700 | 0.3 | 0.4 | 0.38 |
| E174W | hydrophobic | -2.54 | -0.644 | 200 | 0.3 | 1 | 0.87 |
| E174R | negative | -0.75 | 0.84 | 7 | 0.3 | 40 | 2.87 |
| S62Y | polar | -1.25 | -0.17 | 600 | 0.4 | 0.5 | 0.50 |
| Q22H | polar | -0.74 | 0.56 | 50 | 0.4 | 8 | 2.00 |
| S62W | hydrophobic | -1.45 | -0.31 | 10 | 0.4 | 30 | 2.72 |
| M14E | positive | -0.91 | 0.00 | 40 | 0.5 | 10 | 2.12 |
| Q46R | negative | -0.84 | 0.44 | 10 | 0.6 | 40 | 2.87 |
| S51D | positive | -0.52 | 0.87 | 1000 | 0.7 | 0.6 | 0.60 |
| N63I | hydrophobic | -1.12 | 0.32 | 4000 | 1 | 0.3 | 0.22 |
| K41R | negative | -1.32 | 0.26 | 60 | 1 | 20 | 2.50 |
| R178Y | polar | -0.81 | -0.40 | 300 | 6 | 20 | 2.50 |
| R178F | hydrophobic | -1.22 | -0.33 | 300 | 8 | 30 | 2.72 |
| M14W | hydrophobic | -1.11 | 0.12 | 600 | 1000000 | 2 | 1.25 |
| E174D | positive | -1.38 | 0.46 | - | - | - | - |
| E32R | negative | -1.10 | 0.20 | - | - | - | - |
| Q46M | hydrophobic | -1.08 | -0.59 | - | - | - | - |
| E32H | polar | -0.88 | 0.20 | - | - | - | - |
| E65R | negative | -0.83 | 1.46 | - | - | - | - |
| S62K | negative | -0.71 | 1.25 | - | - | - | - |
| G190F | hydrophobic | -0.61 | 0.16 | - | - | - | - |
| H18S | polar | -0.55 | 1.85 | - | - | - | - |
| E65M | hydrophobic | -0.53 | 0.70 | - | - | - | - |
| T60F | hydrophobic | -0.52 | 0.17 | - | - | - | - |
## Expression of hGH variants in E. coli
The selected rhGH variant DNA was generated by gene synthesis and cloned into pET-28b vectors by Genscript. The sequence included a hexahistidine tag at the N-terminus, in order to facilitate purification using a nickel column. Our “WT” rhGH sequence was thus: HHHHHHFPTIPLSRLFDNAMLRAHRLHQLAFDTYQEFEEAYIPKEQKYSFLQNPQTSLCFSESIPTPSNREETQQKSNLELLRISLLLIQSWLEPVQFLRSVFANSLVYGASDSNVYDLLKDLEEKIQTLMGRLEDGSPRTGQIFKQTYSKF|DDALLKNYGLLYCFRKDMDKVETFLRIVQCRSVEGSCG Where the “K” in bold type is the G120K substitution intended to destroy site 2 binding [2], and (for reference) “E” in bold type is E174. The pipe “|” indicates where we deleted the six-residue fragment “DTNSHN,” which is blurred in PDB structures 1A22 and 1BP3, suggesting it is labile. This deletion makes the expressed protein more similar to that used in the modeling. The distance of this fragment from Site 1, plus its position within the longer, surface-exposed, mostly-random-coil residue stretch 129–154 means it is unlikely to affect receptor binding, as indeed is borne out by the agreement between WT and E174A affinities in our work vs. that of others.
In all cases, recombinant protein expression was carried out in XJB BL21(DE3) cells. rhGH was expressed by growing cells in LB until mid-exponential phase and induced with 1 mM IPTG (isopropyl-β-d-thiogalactopyranoside). Protein expression was completed with overnight growth at 16°C for solubility, per [12]. Cells were harvested after two rounds of pelleting at 4,500 × g for 10 min. Lysis of cell pellets and all subsequent purification were carried out at room temperature, unless otherwise noted. Cell pellets were resuspended in 10 mL of lysis buffer (50 mM Tris-HCl [pH 8.0], 100 mM NaCl, Roche Complete protease inhibitor) and lysed by a cell disruptor (Constant Systems Ltd, UK). Lysate clarification was achieved after 45 mins of centrifugation at 16,000 rpm with temperature set to 4°C. Clarified lysate was filtered through a 0.45 μm membrane. Prior to loading the clarified lysate, the gravity column with 1ml of Ni-Sepharose resin was washed with 10 column volumes of ddH2O and equilibrated with 10 column volumes of lysis buffer (10 mM Tris-HCl [pH 7.4], 500 mM NaCl, 5 mM imidazole). Lysate was applied directly to the capped gravity column and set on a shaker at 4°C to gently mix for 40 minutes. After 40 minutes, lysate was eluted and a series of wash steps followed. The column was first washed with 5 column volumes of lysis buffer supplemented with 40 mM imidazole and then washed with 5 column volumes of lysis buffer supplemented with 10mM imidazole. The stepwise gradient was repeated a total of two times. rhGH was eluted into 1 mL fractions using 5 column volumes of 250 mM imidazole in lysis buffer. Protein purity was assessed by Commassie staining and fractions with the lowest impurities were pooled and concentrated to 250 μL using Amicon Ultra 4 mL Centrifugal Filters. A second round of purification was carried out via size exclusion chromatography, using the Agilent infinity 1220 HPLC system. The fractions containing protein were evaluated for purity using SDS gel electrophoresis and Coomassie staining. All fractions containing only rhGH were then pooled together and dialyzed at 4°C in 10 mM HEPES, 150 mM NaCl, 3 mM EDTA, $0.005\%$ Tween 20, pH 7.4. After 24hrs of dialysis, protein was concentrated to 50 μL through the Amicon® Ultra 0.5 mL Centrifugal Filters. The concentration of final growth hormone purified using this method was assessed using a Bradford assay. The purified rhGH was then flash-frozen for storage.
## Interaction analysis using surface plasmon resonance (SPR) biosensor
The experiments were performed using a Biacore 3000 instrument (Cytiva, Uppsala, Sweden) at 25°C. The immobilization of hGHR was carried out by a standard amine coupling procedure on a CM5 biosensor chip (Cytiva, Uppsala, Sweden). hGHR was purchased from AbCam (ab180053) and diluted to 100 μg/ml in sodium acetate buffer, pH 4.5. The CM5 chip surface was activated by an injection of a 1:1 mixture of EDC and NHS for 7 min, at a flow rate of 10 μl/min. hGHR was injected over the activated surface at a flow rate of 2 μl/min until reaching an immobilization level of 1400–2700 RU. Then, the surface was deactivated by the injection of 1 M ethanolamine for 7 min. After immobilization, a concentration series of rhGH, ranging from 0.3 to 10 nM, was injected over the surface, at a flow rate 30 μl/min. An association phase was monitored for 60 sec and a dissociation phase for 240 sec. The surface was regenerated after each cycle by injecting 4.5 M MgCl2 for 2 min. The data was analyzed using Biaevaluation Software, v. 4.1 (Cytiva, Uppsala, Sweden). Sensorgrams were double-referenced by subtracting the signals from a reference surface and the average signals from two blank injections and fitted to a 1:1 Langmuir binding model [13].
## Circular Dichroism (CD) measurements
CD spectroscopy was used in order to identify the impact of each mutation on rhGH structure. 1 mL of purified rhGH samples was dialyzed against 2 L of 20 mM sodium phosphate buffer at pH 7.2 overnight to remove most of the tris buffer and salt used during purification via 2000-fold dilution. This dialyzed sample was dialyzed a second time overnight against and additional 2 L of fresh 20 mM sodium phosphate buffer at pH 7.2 to remove trace amounts of tris buffer and sodium chloride still present in the sample via a second 2000-fold dilution. This reduced the concentrations of both tris and sodium chloride in solution to 2.5 nM and 37.5 nM respectively and therefore minimized their impact on CD measurements.
The dialyzed sample was then loaded into a 10 mm length quartz cuvette. The sample was then loaded into the spectrophotometer (Chirascan). CD spectra between 240–190 nm were collected every two degrees centigrade as the sample was heated from 20–90°C, and then cooled back down to 20°C. The ratio of the measured ellipticity at 222 nm to the ellipticity at 208 nm was measured over a range of temperatures, for the WT and three variants.
The CD measurements feature a clear isodichroic point, for WT, L52F, and E174A/Y. (Fig 2) The salient difference between the four variants is the relative depths of the troughs at 208 and 222 nm (θ222/θ208 ratio), which indicate an effect of E174Y and especially E174A on structure. The differences in θ222/θ208 ratio reduce with temperature, and disappear around 40–45°C. ( Fig 3) L52F is not positioned to affect helix 1 & 4 structure and serves as a control for that purpose.
**Fig 2:** *Circular Dichroism spectra for WT and three rhGH variants, observed at temperatures from 20 to 92C.Note the presence of clear isodichroic points at 201.5 nm (circled), indicating only α-helical and random-coil content (no β sheets), [14] consistent with crystallographic data (note this is clearly below 203 nm). The maximum at 190 nm and minima at 208 and 222 nm likewise indicate alpha helices. Higher ratio of ellipticity at 222 vs 208 nm (θ222/θ208) has been associated with coiled-coil content [15–17]. Dashed vertical lines indicate 203, 209, and 222nm. Dashed horizontal lines indicate the ellipticity at these two wavelengths and 20C –the smaller relative gap for E174A (compared to WT, E174Y, and L52F) is visually apparent. The isodichroic point (zero crossing) indicates the protein is two-state, coil and helix [18]; our isodichroic point is near the 201.5nm reported by [19], rather than the 203-205nm reported elsewhere [18].* **Fig 3:** *Mutations at position E174 affect θ222/θ208.We plotted θ222/θ208, for temperatures from 20 to 92C. Ratios greater than unity typically indicate coiled-coils. θ222/θ208 did not exceed unity for E174A. θ222/θ208 lower than 0.9 indicates helices in isolation [20], and indeed there is no clear coiled-coiling in experimental hGH structures (see PDB ID 1A22). Differences in ratio (comparing WT to E174A and E174Y) are greater at the lower end of the temperature range, presumably due to greater overall order. Linear regressions are shown for all variants (except for L52F, left out for clarity) over the temperature range of 20 to 58C (in which θ222/θ208 varies linearly). Also shown are 95% confidence intervals for the regressions (generated by the seaborn regplot function, in light shading). E174A has clearly higher θ222/θ208 than E174Y and WT. E174Y has somewhat higher θ222/θ208 than WT. L52F is a control, as it is not in a helix and not near the zinc-binding triad; its θ222/θ208 is indistinguishable from that of WT, and is less than that of E174A. E174A / E174Y increase θ222/θ208 ratio, while L52F has no clear effect.*
## Homologyscanner calculations
The homologyScanner results are given in Table 1. As can be seen, the computationally-selected mutations span all four physicochemical classes and are well spread out over site 1 (Fig 1). There was no particular correlation between ΔΔGhomologyScanner and ΔΔGexperimental, see the Discussion. Other than E174A, no mutants had demonstrably higher affinity than WT. However we did identify a group of mutants which were comparable in affinity, some of which (interestingly) had a slower off-rate than WT.
## SSIPe calculations
Would another ΔΔG prediction method have given us better results? After the SPR experiment we retrospectively applied SSIPe, a newer method with good results on a benchmark dataset. SSIPe uses a sequence profile generated using PSI-BLAST, a structural profile generated with iAlign, and a physics-based energy function, EvoEF. [ 21] The results (ΔΔGSSIPe) are given in Table 1.
## Surface Plasmon Resonance (SPR) measurements
The SPR measurements produced clear koff and kon (given in Fig 4 and Table 1). The SPR sensograms are provided in S1 File. Our SPR experiments yielded an unexpected result. As mentioned, we produced 22 GH Variants: the wild-type (WT), positive control (E174A), and 20 novel mutants (including E174Y). In this text all comparisons of kinetic quantities (KD, koff, kon, etc.) are with respect to WT. Recall that the dissociation rate is KD=koffkon.
**Fig 4:** *kon vs koff.Dashed diagonals are iso-affinity contours (at WT and 1nM affinities). Vertical dashed line separates koff slower on left (blue circles) from faster than WT on right (red squares). M14W is off the scale (very fast koff, low affinity). We identified a cluster of higher- affinity mutants (green circles) of which many (including E174A) have slower koff than WT, but also slower kon. The slowest koff belong to substitutions at zinc binding triad positions H18 and E174 (we did not mutate the third position, H21). We also identified a group with moderately faster koff but much slower kon (red squares). Mutants outside these two groups are marked with yellow triangles. “+” markers superimposed on the above indicate an increase in molecular mass, from WT to mutant, while “-” markers indicate a decrease in molecular mass. Substitutions with small mass changes have neither of these markers. Note that the three mass-decreasing mutants (E174A, H18D, E32A) are among the four highest-affinity mutants. N63I, the fourth mutant, also has a (slight) decrease in mass.*
E174A had a much lower koff but also a lower kon−the koff won out leading to overall higher affinity (smaller KD) compared to WT. 13 of the novel mutants had significantly higher KD, and in particular koff faster than WT. Six of the novel mutants (including E174Y) had slower koff and kon, but unlike E174A in these six cases the slowdown in kon won out and the end result was a higher KD. Interestingly, the highest-affinity mutants all decreased molecular mass (Fig 4).
## Discussion
The affinity increase induced by the E174A substitution (at 0.08nM, vs. 0.3nM for WT) was quite surprising at the time of its discovery [4] as alanine scanning usually disrupts rather than increasing binding. This position is in the zinc binding triad, and is highly conserved. In this work we confirm the earlier reported [4] increase in affinity of E174A, but now clarify that it is due to a decrease in koff which more than counters a decrease in kon. The decrease in both quantities (albeit to varying degree), is shared by several other mutations. The mutations E174A/Y increase θ222/θ208 ratio, at least in the absence of zinc. This suggests E174A and to a lesser extent E174Y may be inducing some amount of coiled-coiling.
The CD experiment was done with HPLC-purified rhGH variants–so no ions were present. E174 is part of the zinc-binding triad which by inspection would appear to stabilize the helix 1 –helix 4 interface. So for physiological zinc concentrations the θ222/θ208 ratio may change. Zinc-induced hGH homodimerization also competes with hGH-hGHR binding, and E174A, by affecting zinc binding, may reduce homodimerization to the benefit of hGH-hGHR binding (when zinc is present). In the absence of zinc, E174A could induce coiled-coiling. We speculate that it could do this by encouraging packing, though further experiments are needed to resolve this.
A separate matter is that of M14W, reported in [3], as a mutation that was overrepresented in a phage-display screen, and was included in their 852d multiple-substitution mutant (F10A, M14W, H18D, H21N, K41I, Y42H, L45W, Q46W, F54P, R64K, R167N, D171S, E174S, F176Y, I179T). M14W disappeared without clear explanation in the later B2036 mutant which formed the basis of Pegvisomant. F10A and F176Y also disappeared with little explanation, but did not have favorable HomologyScanner energy. Our results show that removing M14W was the correct decision on the part of [3].
The results also speak to the dearth of good published results for affinity maturation by computational selection of substitutions at a single position, compared to the many successes of combinatorial techniques like phage display [10]. As we have seen the former may change monomer properties, which the latter may compensate with substitutions at other positions. All potentials must balance the stabilizing effect of additional contacts, against the destabilizing effect of increased molecular volume. The latter can be due to entropy, desolvation, and sterics. The steric effect may be particularly important in our case since the mutations are mostly at alpha helical positions, which have little backbone flexibility. In [8], Dataset C, of 11 SKEMPI2 [22] mutations with ΔΔGexperimental < -0.7 kcal/mol, only three increased mass, and included two positions in coils (1KAC, 1JTG) and one in a very short helix on the interface rim (2GYK). Many mass-increasing mutations were selected by our method, while only the mass-decreasing mutations yielded near-WT affinities, thus FoldX appears not to get this balance right in the context of helices in the PPI core [23]. In prior work we found near-WT affinities even with increases in molecular mass [24], but these were on the PPI rim [23], where there is more space available. Thus in future application of this method for that goal, one may consider preferring mass- or volume-decreasing substitutions for any positions in the core and on secondary-structural elements.
As noted in Table 1, there was no significant correlation between ΔΔGhomologyScanner and ΔΔGexperimental. This could be expected because substitutions were selected for low ΔΔGhomologyScanner and so (aside from one -2.54 kcal/mol substitution) values range from -1.45 to -0.52 kcal/mol), over a small number of mutants. HomologyScanner has a reported [8] best-case Root Mean Square Error of 1.1 kcal/mol–about equal to the range of most of our data. SSIPe likewise showed no significant correlation.
## Conclusion
In summary, we have investigated the mechanism of the unexpected increase in hGHR-binding affinity of hGH substitution E174A. We found that it actually decreases the on-rate but overcompensates by decreasing the off-rate to an even greater extent. The CD results indicate the E174A mutation induces coiled-coiling.
All substitutions that yielded affinities comparable to wild type also decreased both on-rate and off-rate, though in cases other than E174A, the net effect was to slightly decrease affinity. Four of the highest-affinity substitutions decreased mass compared to WT, suggesting that there was not enough space or flexibility in these helices to accommodate any increases in side-chain size.
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|
---
title: Changes in gut microbial community upon chronic kidney disease
authors:
- Wu Liu
- Jiaqi Huang
- Tong Liu
- Yutian Hu
- Kaifeng Shi
- Yi Zhou
- Ning Zhang
journal: PLOS ONE
year: 2023
pmcid: PMC10035866
doi: 10.1371/journal.pone.0283389
license: CC BY 4.0
---
# Changes in gut microbial community upon chronic kidney disease
## Abstract
With the increasing incidence and mortality of chronic kidney disease (CKD), targeted therapies for CKD have been explored constantly. The important role of gut microbiota on CKD has been emphasized increasingly, it is necessary to analyze the metabolic mechanism of CKD patients from the perspective of gut microbiota. In this study, bioinformatics was used to analyze the changes of gut microbiota between CKD and healthy control (HC) groups using 315 samples from NCBI database. Diversity analysis showed significant changes in evenness compared to the HC group. PCoA analysis revealed significant differences between the two groups at phylum level. In addition, the F/B ratio was higher in CKD group than in HC group, suggesting the disorder of gut microbiota, imbalance of energy absorption and the occurrence of metabolic syndrome in CKD group. The study found that compared with HC group, the abundance of bacteria associated with impaired kidney was increased in CKD group, such as Ralstonia and Porphyromonas, which were negatively associated with eGFR. PICRUSt2 was used to predict related functions and found that different pathways between the two groups were mainly related to metabolism, involving the metabolism of exogenous and endogenous substances, as well as Glycerophospholipid metabolism, which provided evidence for exploring the relationship between gut microbiota and lipid metabolism. Therefore, in subsequent studies, special attention should be paid to these bacteria and metabolic pathway, and animal experiments and metabolomics studies should be conducted explore the association between bacterial community and CKD, as well as the therapeutic effects of these microbial populations on CKD.
## Introduction
Chronic kidney disease (CKD), refers to renal function or structural abnormalities lasting for at least 3 months, which can be caused by various primary, secondary or hereditary kidney diseases. However, with the insidious onset, majority of patients with CKD are poorly recognized their disease, especially in early stages [1]. With the gradual increase in its incidence, CKD has become a major health concern worldwide [2]. A cross-sectional survey conducted in 2012 demonstrated that the prevalence of CKD in China was $10.8\%$, the mortality caused by CKD has been gradually increasing and it was ranked fourteenth on the list of leading causes of death [2, 3]. CKD causes a huge social burden, not only the influence of renal replacement therapy, but also the increase of global mortality caused by CKD-related cardiovascular events [2, 4]. Therefore, actively controlling factors which can contribute to the exacerbation of CKD is essential to delay the progression of CKD. Many factors had been proven to promote the progression of CKD, such as hypertension, hyperglycemia, poor control of proteinuria, and infection [5]. Apart from these typical disease course influences, the important impact of gut microbiota homeostasis on human health has been widely recognized and studied in recent years [6]. The introduction of concepts such as "Intestinal-renal syndrome" [7] and "The gut-kidney axis" [8] has opened the door to the study of gut microbiota associated with kidney disease. It has been found in several studies that the microecology of the gut microbiota, especially the structural composition and metabolites of the microbes, probably play an important role in CKD [7, 8]. With the gradually aggravated renal function, intestinal urinary toxins increase and accumulate in the blood without being timely cleared by impaired kidneys, thus aggravating the damage of kidney, destroying the intestinal barrier function and homeostasis of gut microbiota [8]. Intestinal dysbacteriosis and impairment of intestinal barrier function are two key links in the interaction between intestinal microecosystem and CKD, and as contributing factors in the progression of CKD to end-stage renal disease (ESRD) have been in focus [9].
Gut microbiome is involved in energy metabolism, contributes to the nitrogen and micronutrient homeostasis, and provides nutritional and protective functions [10, 11]. The gut microbiota can produce short-chain fatty acids (SCFA) by fermenting indigestible dietary complex carbohydrates, which is crucial for the maintenance of colonic homeostasis in healthy populations [12, 13]. However, patients with renal failure have an imbalanced intestinal ecosystem, with increased harmful aerobic bacteria and decreased beneficial anaerobic such as Bifidobacteria and Lactobacillus [14]. Furthermore, urea hydrolysis processes that correspond to the community composition dynamics raise intestinal ammonia and ammonium hydroxide [15]. Ammonia accumulation has been reported to disrupt the intestinal epithelial barrier and function, causing the development of systemic inflammation in CKD patients [10, 15]. All these lead to an increase in intestinal pH, which in turn can cause mucosal irritation and negatively affect the growth of commensal bacteria [16]. The intestinal epithelial barrier function depends on the effects of probiotics and commensal bacteria, and the composition of the gut microbiota in CKD patients has undergone a change from a symbiotic to dysbiotic state [16]. When progressing to ESRD, an increase in bacterial families with urease, uricase, indole and p-cresol-forming enzymes can be observed, and the flora containing enzymes that convert dietary fiber into SCFA are decreasing, aggravating the accumulation of toxins in intestine, severely decreasing the mucosal barrier integrity, disrupting immune tolerance, and promoting endotoxemia and systemic inflammation [10, 15]. Apart from the changes and feedback effects of gut microbiota caused by CKD, drug use and dietary restrictions can also lead to changes in the gut microbiota. CKD patients need to control potassium intake, leading to a lower intake of potassium-rich fruits and vegetables, which may in turn lead to inadequate intake of dietary fibers, the dominant substrate for bacterial fermentation. Insufficient dietary fibers intake then prolonged colonic transit time and inducing an imbalance of glycolytic bacteria and proteolytic bacteria, eventually converting the flora metabolism from glycolytic to protein fermentation pattern [17].
In addition, some gut microbiota metabolites, such as Indoxyl sulfate (IS), p-Cresyl sulfate (pCS) and trimethylamine-nitrogen oxide (TMAO), have been reported to impair endothelial function in CKD patients, and to be involved in the development and progression of CKD and cardiovascular complications by causing inflammation and oxidative stress [18]. The content of IS and pCS in early CKD patients begins to rise [19]. As protein-bound uremic retention solutes, IS and pCS are difficult to be cleared via binding to albumin by hemodialysis (HD) [20]. In addition to being associated with the deterioration of CKD, elevated levels of TMAO also indicate a poor prognosis, the increased cardiovascular risk and premature death in patients with CKD [9]. Intervention of intestinal microecology has a certain effect on delaying the progression of CKD. Enterotoxin sorbents [21], probiotics [22], and fecal flora transplantation [23] can reduce serum toxins and improve the state of microinflammation in patients with CKD. As adjuvants, probiotics and prebiotics have beneficial effects on removing uremic toxins. Several studies have shown that taking probiotics and prebiotics to regulate the intestinal environment and microbiota of CKD patients can reduce blood urea nitrogen levels in CKD stages 3–4 [14], and decline the uremic toxins as well as increase beneficial bacteria counts in dialysis patients [24, 25].
Even though the role of gut microbiota homeostasis of CKD has been recognized to some extent. Due to the lack of relevant studies and samples, the patterns of the gut microbiota composition associated with the course of CKD remain unclear. Investigating the composition characteristics and classification of intestinal communities in CKD patients can provide a theoretical basis for subsequent targeted treatment. Moreover, the discovery of new beneficial bacteria as supplements can improve the intestinal homeostasis of CKD, reduce the accumulation of endotoxin, as well as delay the deterioration of CKD. Changes in dominant flora caused by alterations in bacterial community compositions lead to the difference in microorganism functions. While the correlation between microbiota and pathways needs further study. With the development of technologies, “smart” bacteria may guide the immune system, metabolism pathways, and so on. Comprehensively understanding the functions and pathways of human microbiota to design personal medicine programs according to specific microbiota for CKD patients is also a core issue [26]. To answer these questions, we integrated studies related to the gut microbiota of CKD patients and the corresponding amplicon sequencing data from NCBI database. We analyzed the gut microbiota associated with the course of CKD by comparing the composition patterns and abundance characteristics between CKD patients and healthy individuals. The effect of CKD on the structure of gut microbiota community was explored by amplicon sequences, and the differences in dominant species of gut microbiota with and without CKD were elucidated. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) was used to predict the major functions differences and underlying metabolic process to improve awareness of the structure and characteristics of gut microbiota in patients with CKD, as well as pharmacological interventions to regulate the abundance and diversity of gut microbiota.
## Acquisition of sequence information and Bioinformatics Analysis
A total of 315 gut microbiota samples from patients with CKD and healthy control (HC) were retrieved from SRA database of NCBI (https://www.ncbi.nlm.nih.gov/sra), the schematic representation of bioinformatic analysis was presented in Fig 1. All sequences were from V3-V4 regions of 16S rRNA. Collecting the metadata from relevant literature, and the samples were divided into CKD and HC groups, the detail was in S1 Table. QIIME2 2022.2 [27] was used to perform the bioinformatics of gut microbiota. Raw sequence data from MiniSeq and HiSeq platforms (paired-end) were quality filtered using the Demux plugin and then denoised with DADA2 [28]. The Shannon and ACE indexes of alpha diversity were compared using Welch Two Sample t-test, meantime beta diversity was analyzed via estimating the Principal Coordinate Analysis (PCoA) by using Bray–Curtis dissimilarity matrix. And phylogeny tree construction was using fasttree2 [29]. Using Bayesian SILVA 138 Full Classifier to annotate species [30]. Finally, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of gut microbiota was performed by PICRUSt2 [31]. However, it should be noted that the prediction results of PICRUSt2 were limited. And R 4.0.4 software was used to analyze the data in this study, the ALDEx2 package in R was used to compare the gut microbiota between the two group.
**Fig 1:** *The schematic representation of bioinformatic analysis.*
## Analysis of gut microbiota diversity
After denoising by DADA2, a total of 298 samples were obtained, and more than 4 million sequences were obtained, with an average of 13531 sequences per sample. The alpha diversity of the microbiota could be divided into richness and evenness. The ACE index reflected the richness of samples while the Shannon index considered both richness and evenness. The Welch test was performed on ACE richness index to analyze differences in microbiota richness in different groups, and the following results were obtained (Fig 2A). The Welch Two Sample t-test testing the difference of ACE by group (mean in group CKD = 235.65, mean in group HC = 239.08) suggested that the effect was negative, statistically not significant, and very small (difference = -3.43, $95\%$ CI [-23.16, 16.31], t (293.97) = -0.34, $$p \leq 0.733$$; Cohen’s d = -0.04, $95\%$ CI [-0.27, 0.19]). Therefore, the influence of different groups on microorganisms richness might be limited. Welch test was further performed on Shannon index, and the results showed in Fig 2B presented that the difference of Shannon by group (mean in group CKD = 4.77, mean in group HC = 4.30) suggested that the effect was positive, statistically significant, and medium (difference = 0.47, $95\%$ CI [0.31, 0.64], t (271.82) = 5.73, $p \leq .001$; Cohen’s $d = 0.70$, $95\%$ CI [0.45, 0.94]). These results suggested that CKD significantly improved the diversity of gut microbiota. Since only the Shannon index but not ACE index was significantly changed, we could infer that CKD mainly affected the evenness of the gut microbiota, resulting in a change in the species distribution within community, which might be associated with progressive renal failure. Elevated serum urea caused by renal failure could be converted into ammonia in gastrointestinal tract, contributing to overgrowth of bacterial family with a tendency of urea metabolism and further impairing endothelial function in CKD patients [10, 18].
**Fig 2:** *The Alpha diversity of gut microbiota in different groups.(a) ACE index reflected the richness of two groups, it showed that the influence of different groups on microorganisms richness might be limited (P = 0.733), (b) The results showed that Shannon index between two groups was statistically different (P < 0.001), indicating that the evenness of the gut microbiota was affected by CKD.*
PCoA was run at the phylum level, the abundance of phylum was first calculated, and then log transformed for the calculation of Bray–Curtis dissimilarity matrix (Note for the performance of log transformation, a pseudo 1 was first add to the abundance counts). As shown in Fig 3A, the scree plot analyzed by PCoA analysis indicated that the first two axes represent $56\%$ of the total variance, and the third axis represents $13\%$ of the total variance. As shown in Fig 3B and 3C, the samples were relatively separated at axis 1 of the PCoA, with slight separation at axis 2, but almost no visual separation on axis 3, although axis 3 explained $13.4\%$ variance between the samples. In addition, PCoA plot of the different platforms was shown in S1 Fig.
**Fig 3:** *PCoA analysis in phylum level in different groups.(a) PCoA scree plot showed the first two axes account for 56% of the total variance. (b) PCoA ordination plot of axis 1 and 2, its showed that the samples were relatively separated at axis 1. (c) PCoA ordination plot of axis 1 and 3, it showed that there was no visual separation on axis 3. (d) ANOSIM test exhibited the intergroup differences in two groups were greater than the intragroup group (P = 0.001).*
As shown in Fig 3D, analysis of Similarities (ANOSIM) results presented that intergroup differences in HC and CKD groups were greater than the intragroup difference ($p \leq 0.05$). This demonstrated that sample separation in the PCoA plot was not only visual, but also statistically significant. Further analysis was performed using adonis2 function of the vegan package to perform permutational MANOVA on the data with 999 permutations. The results displayed that the groups of samples were statistically significant, and the null hypothesis of the same groups could be rejected.
Similarly, we also analyzed PCoA at the genus level, as shown in Fig 4a, the scree plot exhibited the first two axes represent $29\%$ of the total variance. The samples were separated at the axis 1 of PCoA, accounting for $16.9\%$ variance. However, Fig 4b was presented that samples in HC group were visibly divided into two parts. This might be related to the data collected from different countries and genders. Differences in diet structure or living habits among healthy people from different regions might have influenced the distribution of gut microbiota. In contrast, CKD samples exhibited tight clustering. We inferred that this may be related to an increase in endotoxin-producing microbiota due to changes in intestinal environment, which transformed the microbiota from a complex, homogeneous and coordinated community to one that simpler but more dominant [32]. Therefore, we also used ANOSIM similarity analysis on the data at genus level. As shown in Fig 4C, the intergroup difference was still statistically significant ($p \leq 0.05$), although R was lower indicating greater intragroup variation in the HC group.
**Fig 4:** *PCoA analysis in genus level in different groups.(a) PCoA scree plot showed the first two axes account for 29% of the total variance. (b) PCoA ordination plot of axis 1 and 2 showed the clustering of samples in different groups. (c) ANOSIM test showed the intergroup difference between CKD and HC groups (P = 0.001).*
## Annotation analysis of gut microbiota species
According to the results of species annotation, the species of each sample were analyzed by the taxonomy of the phylum and genus level. As shown in Fig 5, species differences among samples and the proportions can be understood from the bar chart. The top 10 phyla were Firmicutes, Bacteroidota, Proteobacteria, Actinobacteriota, Fusobacteriota, Verrucomicrobiota, Desulfobacterota, Synergistota, and Patescibacteria. Consistent with the literature [33], Firmicutes, Bacteroidota, Proteobacteria, and Actinobacteriota were the dominant bacterial phyla in the two groups. Among them, the proportions of Firmicutes and Bacteroidota in two groups were shown in Table 1.
**Fig 5:** **Annotation analysis* of gut microbiota species.(a) bar plot of the top 10 phyla with the highest abundance, (b) bar plot of the top 10 genera with the highest abundance, (c) average relative abundance of the top 10 phyla or genera in abundance.* TABLE_PLACEHOLDER:Table 1 In the human gut microbiota, Firmicutes and Bacteroidota were the two most abundant phyla. They played a crucial role in regulating the host inflammation and immune balance [34]. The change of the ratio of Firmicutes and Bacteroidota (F/B) in the microbial community was an important indicator, it reflected the disorder of gut microbiota, and the increase of F/B ratio was positively correlated with intestinal permeability [35, 36]. As shown in Table 1, the F/B ratios in CKD and HC groups were 4.07 and 1.93, respectively, suggesting the dysregulation of gut microbiota in CKD patients. Changes in energy absorption caused by the imbalance of the proportion of these two phyla resulted in metabolic syndrome and exacerbation of CKD [33]. Fig 6 showed that compared with HC group, the abundances of Methanobrevibacter, Ralstonia, Fenollaria, Porphyromonas in CKD group were increased. Inversely, the abundances of Tyzzerella, Fusobacterium, Fusobacteria, Brevibacterium, Nocardiopsis, Providencia, Halomonas, Fermentimonas, Salinimicrobium, Flavobacterium, Acinetobacter, Lachnospiraceae_UCG-004, Succinivibrio, Herbinix, Morganella, Holdemanella were decreased. Besides, most of the bacteria with decreased abundance belonged to Firmicutes, Bacteroidota, Proteobacteria, and Actinobacteriota.
**Fig 6:** *Volcano map of the genus-level microbial communities with different abundance in the CKD group compared with HC group.Most of the bacteria with decreased abundance belonged to Firmicutes, Bacteroidota, Proteobacteria, and Actinobacteriota.*
## Regulatory mechanism prediction of gut microbiota in CKD
In order to explore how gut microbiota regulate CKD, functional composition predictions and annotations were made using PICRUSt2 based on KEGG database. The ALDEx2 package was used to extract different pathways, and a total of 176 pathways were obtained in S2 Table, which were divided into six categories, including Metabolism, Human Diseases, Organismal Systems, Genetic Information Processing, Environmental Information Processing, and Cellular Processes. According to we.eBH <0.05, we got 78 different pathways. The top 10 significantly different pathway between the two groups were displayed in Table 2, which were mainly associated with metabolism. And the abundances of these metabolism pathways in CKD group were decreased. Among them, current studies had known that Chloroalkane and chloroalkene degradation, Glycerophospholipid metabolism were related to the deterioration of renal function [37, 38].
**Table 2**
| ko | Level 1 pathway | Level 3 pathway | We.eBH |
| --- | --- | --- | --- |
| ko05131 | Human Diseases | Shigellosis | 1.76e-12 |
| ko00625 | Metabolism | Chloroalkane and chloroalkene degradation | 9.7e-08 |
| ko00983 | Metabolism | Drug metabolism—other enzymes | 5.3e-07 |
| ko00643 | Metabolism | Styrene degradation | 2.6e-06 |
| ko02010 | Environmental Information Processing | ABC transporters | 3.26e-05 |
| ko00791 | Metabolism | Atrazine degradation | 3.73e-05 |
| ko00960 | Metabolism | Tropane, piperidine and pyridine alkaloid biosynthesis | 3.98e-05 |
| ko00633 | Metabolism | Nitrotoluene degradation | 7.65e-05 |
| ko00361 | Metabolism | Chlorocyclohexane and chlorobenzene degradation | 0.000135 |
| ko00564 | Metabolism | Glycerophospholipid metabolism | 0.000246 |
## Discussion
A study demonstrated that the disturbance of gut microbiota was interrelated with CKD. In the early stage of CKD, both the composition and metabolic activity of gut microbiota began to change, simultaneously, the imbalance of gut microbiota was a risk factor for the CKD progression and various complications [39]. The increase of urea level and the proliferation of urease bacteria in patients with CKD led to an accumulation of ammonium in the gastrointestinal tract, raising the intestinal pH and weakening the junctions of intestinal cells, ultimately altering the permeability of the intestinal mucosa [40]. In the course of CKD, the alteration of intestinal barrier, intestinal permeability, and gut bacterial community contributed to disruption of gut epithelial barrier complexes so that endotoxins and other harmful substances could flow into systemic circulation, inducing the occurrence of systemic inflammation, further promoting the release of pro-inflammatory cytokines and exacerbating CKD [40]. Changes in the composition, abundance and functional gene differences of human intestinal microecology were vital factors that could exacerbate the progression of diseases in the body. Alterations of the gut microbiota at various phases of CKD were also focus of current research. It is important to explore the relationship between gut microbiota and CKD, and investigate the difference of gut microbiota between patients with CKD and healthy people, as well as the different microbiota that could aggravate disease progression. Thereby adjusting the use of probiotics, prebiotics and synbiotics according to the distribution characteristics of floras at different phases has become a crucial measure to delay the course of CKD to ESRD. By comparing the gut microbiota and intestinal metabolites of different stages of CKD patients and healthy participants, Chen et al. verified that in contrast to the healthy group, unique bacterial aggregation was found in the fecal samples of CKD patients at different stages, and the abundance of Streptococcus, Klebsiella pneumonia, and *Haemophilus parainfluenzae* in different stages of CKD patients was relatively high [41]. Moreover, the relative abundance of *Klebsiella pneumonia* could be used as a marker for CKD progression, and the metabolites, such as S-adenosylhomocysteine, L-Carnitine, Propionic acid, and Myristic acid in fecal were gradually increased across the exacerbation of CKD [41]. A Chinese study elucidated the characteristics of gut microbiota in non-dialysis patients with CKD stages 2–5 [42]. Compared with CKD stages 3–5, dominant bacterial phyla in CKD2 were more evenly distributed, and the abundance of Bifidobacteria was high, and the abundances of Faecalibacterium, Escherichia-Shigella, and Ruminococcus were higher than those in CKD stages 2–4, despite the difference was not statistically significant [42]. The level of protein-bound uremic toxin in plasma also increased with the progression of CKD, and the removal of pCS, p-cresyl glucuronide, IS was decreased due to the impaired kidney, resulting in an overall solute removal disorder, although this might be affected by multiple factors, modulating the gut microbiota could still reduce the accumulation of uremic toxins [43]. Besides, changes in gut microbiota caused by different renal replacement therapies have also been expounded. Crespo-Salgado et al. demonstrated that a significant increase in Proteobacteria in patients undergoing peritoneal dialysis (PD) via comparing the gut microbiota of pediatric patients undergoing HD and PD [44]. And compared with healthy participants, the abundance of Enterobacteriaceae in PD patients was increased, which was related to the intestinal absorption of glucose from dialysate, the increase of Enterobacteriaceae might lead to the risk of peritonitis infection in PD patients [44]. As previously stated, as the main source of urinary toxin accumulation in patients with CKD, the study of gut microbiota had been brought into focus.
In this study, we analyzed the gut microbiota of CKD patients and healthy participants by bioinformatics analysis. Higher Shannon index but comparable ACE index were found in CKD group compared to HC group, indicating that CKD process mainly changed evenness but not richness of gut microbiota. These results were consistent with the results from Vaziri et al. [ 10] and Hida et al. [ 45]. According to estimated glomerular filtration rate (eGFR), Kim et al. [ 46] classified CKD patients before dialysis and analyzed the gut microbiota of each subgroup, they indicated that changes in diversity of gut microbiota were associated with uremia progression. Research on the different species between CKD and HC groups could help to delay the progression of renal function and improve the gut-kidney axis via modulating beneficial bacteria in the intestine. An increased F/B ratio has been widely recognized as the marker of intestinal dysbiosis [36]. In this study, the F/B ratio in CKD group was increased compared with HC group, it demonstrated that intestinal microbiome disorder and abnormal energy metabolism occurred in CKD group. Firmicutes and Bacteroidota were involved in the production of SCFA in intestine, fermenting food into butyrate and propionate, and there was a symbiotic relationship between Firmicutes and Bacteroidota that could jointly promote host to absorb or store energy [47]. Firmicutes mainly helped the host to absorb energy from the diet [48]. A growing body of evidence showed that Firmicutes was positively correlated with gut microbiota dysbiosis, and it could convert polysaccharides into SCFA and monosaccharides, resulting in more energy absorption, eventually causing obesity and insulin resistance [49, 50]. Colonized in the human distal intestine, Bacteroides were able to provide nutrients for human host via degrading and fermenting multiple polysaccharides and host-derived glycoconjugates (glycans), maintaining the normal physiological function of the intestine [48, 51]. And the concentrations of SCFA were significantly associated with Bacteroidetes, polysaccharides were fermented in the distal gastrointestinal tract under anaerobic conditions to produce SCFA, providing energy for colonic epithelial cells [18]. With declining kidney function, the accumulated nitrogenous metabolic waste products such as circulating urea and creatinine spread to the gastrointestinal tract, then disrupted microorganisms associated with SCFAs production. Impaired production of SCFAs, especially butyrate, further contributed to a lack of energy sources for colon cells and damage to intestinal integrity [52]. The increase of proteolytic bacteria, which might lead to an increase in serum urea, creatine, and toxic compounds such as indoles, phenols and ammonia, was also associated with disease-propagating and pro-inflammatory pathways [38, 52].
In addition, as previously mentioned, we analyzed the differential bacterial between the two groups and found that consistent with current literature, the relative abundances of Ralstonia and Porphyromonas, which were negatively correlated with eGFR, increased in CKD group [53, 54]. Ralstonia was associated with increased endotoxin levels, ultimately resulting in systemic endotoxins and chronic inflammation [55]. Expression of KIM-1 was significantly increased in kidney organoids treated with Ralstonia, and tubular damage was observed in those treated with *Ralstonia pickettii* [53]. Porphyromonas was a genus of Bacteroidota, among which *Porphyromonas gingivalis* was revealed associated with periodontal disease and CKD [54]. This study also found that the relative abundance of Fenollaria in CKD group was higher than HC group. Most studies were focused on Fenollaria massiliensis, which is close with infection [56]. There was still a lack of studies on Fenollaria and CKD, the correlation between Fenollaria and infection might be related to the systemic inflammatory state of CKD. In addition, the increased abundance of Methanobrevibacter might be associated with its properties. Methanobrevibacter could regulate the specificity of polysaccharide fermentation and affect calories stored in fat, it had gas-producing properties, the increase in relative abundance could lead to severe flatulence [57, 58]. The decreased renal function in CKD patients resulted in reduced excretion of metabolites. Accumulated metabolic wastes in digestive tract and stimulated the intestinal mucosal, eventually causing abdominal distension, nausea, vomiting and other digestive tract symptoms. These symptoms in CKD patients might be related to the increase of Methanobrevibacter. Firmicutes, Bacteroidota, Proteobacteria, and Actinobacteriota account for $98\%$ of the human gut microbiota, and the reduction of these microbiota was crucial for the disturbance of intestinal microecology and the occurrence of diseases [59]. In this study, we found that compared with HC group, most decreased bacteria abundance in CKD group belonged to Firmicutes and Bacteroidota, which were the dominant bacteria producing SCFA (such as butyrate, propionate and acetate). Fusobacterium, one of the decreased bacteria in CKD group, was associated with the production of SCFA, especially butyrate [60]. And studies showed that shifted abundance of Lachnospiraceae family was a strong indication of gut health, Lachnospiraceae might convert the deoxy sugars rhamnose and fucose into propionate and propanol through propanediol pathway [38, 61]. Moreover, we also found the abundance of Holdemanella was reduced, it is contrary to the results that Holdemanella was positive with the progression of CKD pointed out by Lun et al. [ 62]. Therefore, the specific role of Holdemanella still needed to be further explored. However, we must admit the deficiencies. Since the original data of renal function indicators of patients were not accessible, we cannot perform a correlation analysis of differential bacterial and renal function. This study still provided some evidence for the alterations of CKD gut microbiota, while the regulation of these differential bacteria needed to be verified by basic research in the future. Similarly, the relationship between differential bacteria and impaired kidney, and the composition of the microbiota at different stages of CKD should be studied, and understanding the diversity, the alterations of abundance and evenness could provide bases for the subsequent clinical targeted therapies of related bacteria.
According to the functional prediction analysis of the gut microbiota in two groups based on level 1 and level 3, of which, level 1 was most related to metabolism, and level 3 clearly indicated the degradation of various metabolites, such as Styrene, Chloroalkane and chloroalkene, Atrazine, Nitrotoluene, Chlorocyclohexane and chlorobenzene degradation. As a metabolic organ, kidney could excrete exogenous substances, such as Chloroalkane and chloroalkene [37], Nitrotoluene [63], Chlorocyclohexane and chlorobenzen [64]. Liu et al. indicated that Chloroalkane and chloroalkene degradation pathway could predict the morbidity of CKD [37], with the decline of renal function, impaired glomerular filtration led to the accumulation of toxins in metabolite excretion disorders which aggravated the progression of CKD. Besides, Atrazine degradation pathway was mainly used to remove excess or unwanted bacteria in the intestinal tract [65]. With the decrease of eGFR in CKD patients, the floras which could produce uremic toxin precursors such as indoles, p-cresol and trimethylamine (TMA) increased, leading to an increase in IS, pCS, TMAO, and reactive oxygen species (ROS), inducing renal fibrosis [66]. Whether the Atrazine degradation pathway could play a role in eliminating harmful floras in intestine was still worth exploring. In addition, glycerophospholipid was the main component of cell membranes and played an important role in cell signal transmission. Glycerophospholipid metabolism was the most significantly altered pathway in CKD rats, and the increase in glycerophospholipid levels was inversely associated with eGFR [67]. However, it should be noted that PICRUSt2 was used to analyze the pathway inference of the data in this study, and the functional pathway association between gut microbiota and CKD should still be verified by animal experiments or human fecal metabolomics.
## Conclusion
In this study, bioinformatics was used to analyze the gut microbiota of patients in CKD and HC groups, and the species annotation, diversity analysis and functional prediction analysis in two groups were performed. The results showed that the F/B ratio in CKD group was higher than that in HC group, confirming that the gut microbiota in CKD patients was disturbed, and further indicating that gut microbiota was involved in the occurrence and development of CKD. Consistent with disease progression, increased abundances of Ralstonia and Porphyromonas were found in CKD patients, which were considered associated with worse kidney injury and poor eGFR. Therefore, Ralstonia and Porphyromonas may be indicators of the progression of CKD. In addition, 8 of the top10 main differential pathways were concentrated in metabolism, including the metabolism of a variety of endogenous and exogenous substances through the kidney. Correspondingly, many differential bacteria abundances, such as Methanobrevibacter, Fusobacterium and Lachnospiraceae, may affect host metabolism such as lipid metabolism, which is consistent with the concentration of differential metabolic pathways, suggesting a vital link between metabolism and gut microbiota in the pathogenesis of CKD. The above also provided a basis for the follow-up study to explore the connection between gut microbiota and lipid metabolism in CKD. Based on the above, we conclude the following hypothesis, gut microbiota of CKD patients is dislocated, with significant evenness changes, and may affect the excretion of uremic toxins via regulating the metabolism of endogenous and exogenous substances through kidney, thus affecting the progression of CKD. Further animal experiments and human fecal metabolomics studies were needed for verification.
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|
---
title: Glycemic control and diabetes complications among adult type 2 diabetic patients
at public hospitals in Hadiya zone, Southern Ethiopia
authors:
- Abraham Lomboro Dimore
- Zerihun Kura Edosa
- Asmelash Abera Mitiku
journal: PLOS ONE
year: 2023
pmcid: PMC10035868
doi: 10.1371/journal.pone.0282962
license: CC BY 4.0
---
# Glycemic control and diabetes complications among adult type 2 diabetic patients at public hospitals in Hadiya zone, Southern Ethiopia
## Abstract
### Background
Diabetes is one of the biggest worldwide health emergencies of the 21st century. A major goal in the management of diabetes is to prevent diabetic complications that occur as a result of poor glycemic control. Identification of factors contributing to poor glycemic control is key to institute suitable interventions for glycemic control and prevention of chronic complications.
### Methods
A hospital-based cross-sectional study was conducted among 305 adult type 2 diabetic patients at public hospitals in Hadiya zone from March 1–30, 2019. The study participants were selected by systematic sampling technique. Data were collected using a pretested structured questionnaire and patient chart review; anthropometric and blood pressure measurements were taken. Multivariable logistic regression analysis was used to identify factors associated with poor glycemic control. Adjusted odds ratios (AOR) with respective $95\%$ Confidence Interval (CI) and $p \leq 0.05$ were used to set statistically significant variables.
### Results
Out of 305 diabetic patients, 222 ($72.8\%$) were found to have poor glycemic control. Longer duration of diabetes (5–10 years) [AOR = 2.24, $95\%$ CI: 1.17–4.27], lack of regular follow-up [AOR = 2.89, $95\%$ CI: 1.08–7.71], low treatment adherence [AOR = 4.12, $95\%$ CI: 1.20–8.70], use of other alternative treatments [AOR = 3.58, $95\%$ CI: 1.24–10.36], unsatisfactory patient physician relationship [AOR = 2.27, $95\%$ CI: 1.27–4.04], and insufficient physical activity [AOR = 4.14, $95\%$ CI: 2.07–8.28] were found to be independent predictors of poor glycemic control. Diabetes Mellitus (DM) complications were slightly higher among participants with poor glycemic control ($39.2\%$), duration of DM 10 and above years ($41.9\%$), low medication adherence ($48.5\%$), taking oral anti-diabetics ($54.3\%$), and DM patients having unsatisfactory patient provider relationship ($72.4\%$).
### Conclusion
A significant proportion of diabetic patients had poor glycemic control and DM complications. Therefore, appropriate interventions are required to maintain optimal glycemic control and prevent the development of life-threatening complications among DM patients.
## Introduction
Diabetes Mellitus (DM) refers to a group of common metabolic disorders that has a main characteristic feature of hyperglycemia [1]. Globally, an estimated 463 million people ($9.3\%$ of adults, 20–79 years) were living with diabetes in 2019. Its age-standardized prevalence increased by $62\%$ within 10 years; from 285 million in 2009 to 463 million in 2019. The number of people living with diabetes was predicted to rise to $10.2\%$ (578 million) by 2030 and $10.9\%$ (700 million) by 2045 [2]. In 2019, an estimated more than four million adults died of diabetes and its complications ($11.3\%$ of all-cause mortality) [3].
The African region, where diabetes was once rare, has witnessed a surge in the condition. Its prevalence in this region among 20–79 years adults is $4.7\%$. According to global projections for 2030 and 2045, DM prevalence in the region is predicted to rise to $5.1\%$ and $5.2\%$, respectively [2]. In 2019, there were around 366,227 deaths attributed to diabetes, with $73.1\%$ of these deaths occurring in people under the age of 60 years, which was higher in proportion to any other region in the world [3].
In Ethiopia, diabetes prevalence is increasing among the adult population. According to the report from systematic review and meta-analysis the prevalence of DM in Ethiopia was $6.5\%$ [4]. It is becoming a growing public health problem along with other non-communicable diseases in Ethiopia. Furthermore, its prevalence is also reported increasingly across different localities of the country, which is $0.3\%$ for the lowest and $7.0\%$ for the highest prevalence [5].
For successful control of risk resulting from long-term diabetic complications, optimal glycemic control is paramount. Deprived and insufficient glycemic control among patients with type 2 diabetes establishes a main public health problem and the foremost risk for the development of diabetic complications. Uncontrolled diabetes mellitus leads to micro-vascular and macro-vascular complications [1]. Furthermore, these complications due to poorly controlled diabetes are major causes of disability, premature death, and reduced quality of life [6].
Evidence in Ethiopia has reported that with increasing prevalence and related complications, diabetes is becoming a pressing public health problem [5]. Despite this alarming growth in the prevalence of diabetes, little has been studied regarding glycemic control status, related factors and DM complications. There is a gap and little information is available on these conditions in Ethiopia, particularly in this study area. Therefore, this study aimed to assess glycemic control and DM complications among adult type 2 diabetic patients at public hospitals in Hadiya zone, Southern Ethiopia.
## Study design, area, and study period
A facility-based cross-sectional study was conducted at public hospitals in Hadiya zone from March 1, 2019 to March 30, 2019. Hadiya zone is one of the administrative zones in Southern Nations, Nationalities, and Peoples Regional State (SNNPR). The Zone has four public hospitals (one teaching hospital and three primary hospitals). From these public hospitals, two (Nigist Ellen Mohammed Memorial and Shone primary Hospitals) of them provide chronic illness care for diabetic patients and there are 1,241 diabetic patients (56 type 1 and 1,185 type 2 DM). The hospitals do not have the glycated hemoglobin (HbA1c) test, but the fasting blood glucose of patients was measured based on their follow-up appointment.
## Population
All type 2 diabetic patients aged ≥ 18 years old on follow-up at Nigist Ellen Mohammed Memorial and Shone primary hospitals were a source population, and type 2 diabetic patients aged ≥ 18 years old who present during the study period and fulfilled the eligibility criteria were study population. Type 2 diabetic patients on ant-diabetic(s) treatment for at least six months and patients who had at least three consecutive blood glucose measurements in three months were included in this study. Patients with critical illness who were unable to communicate at the time of data collection, patients with hearing problems and previously diagnosed psychiatric illness and pregnant women with diabetes were excluded from the study.
## Sample size and sampling technique
The required sample was calculated using a single population proportion estimation formula considering the following assumptions: $59.2\%$ prevalence of poor glycemic control from the study done in Shanan Gibe Hospital, Southwest Ethiopia [7], $95\%$ confidence level (CI), $5\%$ margin of error and $10\%$ non-response rate. Since the source population was less than 10,000, considering the correction formula, the total calculated sample yielded 311.
A systematic random sampling technique was applied to recruit study participants. The diabetic clinic provides services three days per week and on average 92 type 2 diabetic patients are served per day at Nigist Ellen Mohammed Memorial Hospital. In Shone primary hospital, diabetic patients had two days per month for follow-up and on average 40 patients were served per day. The study participants were allocated for both hospitals by proportional to population size allocation. By dividing the total type 2 DM patients eligible [1,185] by the sample size required [311], which yields a sampling interval of four. Sample recruitment was performed concurrently in both hospitals. The first participant was selected by lottery method. Thus, every fourth patient coming to the clinic for a follow-up service was interviewed until the total sample size reached.
## Data collection procedure
Data were collected by using pretested structured questionnaires to capture information on socio-demographic and economic characteristics; clinical characteristics; knowledge about diabetes, and attitudes toward DM care; and adherence to diabetic self-care activities. A checklist was used to abstract data from the medical records. Sphygmomanometer, weight scale, and stadiometer were used to measure blood pressure, weight, and height, respectively.
## Measurements and operational definition
Fasting blood glucose readings of the last three diabetic clinic visits were obtained from patients’ medical records and computed mean fasting glucose levels. Poor glycemic control was operationally defined if the mean fasting glucose(FBG) level was above 130mg/dL [8].
Adherence to antidiabetic medications was measured by using Morisky Medication Adherence Scale (MMAS 8-item) [9]. The scale contains questions asking the patient to respond "Yes" or "No" to a set of eight questions. A positive response indicated a problem with medication adherence. Therefore, higher scores indicate that a patient has the least adherence to medications. For all questions, responses were coded 1 if patients responded "Yes" otherwise, 0 if not, except one question (Did you take all your medicines yesterday?) that was coded in reverse. The total score was computed and adherence was categorized as high, medium, and low if the participants score was 0, 1–2, and 3–8, respectively.
Patient-provider relationship was measured by using Patient Doctor Relationship Questionnaire (PDRQ_9) consisting of nine questions with a five-point Likert-type scale, where 1 = very inappropriate and 5 = very appropriate [10]. The total score was computed and participants who scored mean and above were considered to have a satisfactory patient-provider relationship.
Knowledge of patients about diabetes was assessed by using eight knowledge questions. Percentage out of total score was computed and participants who answered six ($75\%$) questions out of total knowledge questions correctly were categorized as having good knowledge about diabetes. Attitude of patients towards diabetic care was assessed by using seven questions on a five-point Likert- type scale, where 1 = strongly disagree and 5 = strongly agree. Three items have been negatively worded, which requires reverse coding. Its internal consistency was checked by using reliability statistics with Cronbach’s α = 0.81 during the pretest. The total score was computed and patients were considered as having a positive attitude towards diabetic care if s/he scored mean and above for attitude questions.
Blood pressure was measured after the patient sat and rested for a few minutes with the arm held at a position that was around the heart. Blood pressure was measured twice and recorded from a mean of two measurements as per American Diabetes Association (ADA) recommendations [8]. Study participants whose systolic BP ≥ 140 mmHg and/ or diastolic BP ≥ 90 mmHg or current use of antihypertensive medication irrespective of the current BP were considered as hypertensive.
Anthropometric measurements were measured using standardized techniques and calibrated equipment. The weight of the participants was measured to the nearest 0.1 kg. The scale was placed on a hard surface and the participants were measured by wearing light clothing and bare feet. Height of the participants was measured to the nearest 0.5 cm using a stadiometer. Then, Body Mass Index (BMI) of the participants was calculated as weight in kg divided by height in meters squared and subjects were considered as normal (BMI = 18.5–24.9 kg/m2), overweight (BMI = 25–29 kg/m2) and obese (BMI ≥ 30 kg/m2) [8].
Diabetic self- care activities were assessed by using Summary of Diabetic Self-care Activity measure (SDSCA), which contains 11 items on diet, exercise, self- monitoring of blood glucose, foot care, and cigarette smoking [11]. Exercise was measured based on response to items five and six, then participants who participated in at least 30 minutes of physical activity for 3 or more days or participated in specific exercise session during the last seven days were categorized as having adequate adherence to exercise.
The study participants who used other non-medical treatment options like traditional or herbal medicines and religious healing practices for the treatment of diabetes were considered as having used other alternative treatments. A diabetic patient who visited the diabetic clinic based on appointment regularly within the previous six months was considered to having regular follow-up at the diabetic clinic.
## Data management and quality assurance
The questionnaire and checklist were translated from English language to Amharic and Hadiyissa (local language) and translated back to English language to check its consistency. One-day training was given for data collectors and supervisors on the objectives, process of data collection, and how to take anthropometric measurements. Pretest was done on $5\%$ of the sample size in order to check the clarity and internal consistency of the questionnaire and checklist prior to the actual data collection.
The equipment for measuring weight, height, and blood pressure were calibrated to the standard before measuring each participant. Completeness, accuracy, clarity, and consistency of data were checked daily after data collection time by supervisors. The overall activities were monitored by the principal investigators. Finally, the collected data were entered into a computer using epidata3.1 version software.
## Statistical analysis
The analysis was done in Statistical Package for the Social Science (SPSS) 20 version software. Descriptive statistics including mean (standard deviation), median (inter-quartile range) and range values for continuous variables; and percentage and frequency tables for categorical variables were employed. Normality assumption was checked for continuous variables.
Bivariate analysis was employed to determine the presence of an association between poor glycemic control and each independent variable using binary logistic regression. Variables that were found significant at p-value less than 0.25 in bivariate analysis were selected as candidate variables for multivariable analysis.
Multivariable analysis was carried out to identify independent predictors of poor glycemic control and to control for confounders. Backward stepwise logistic regression was used to determine independent predictors with P-value less than 0.05 with their respective AOR and $95\%$ CI. The model fitness was tested by using Hosmer and Lemeshow goodness of fit test and was declared fit.
## Ethical considerations
The study was approved by Institutional Review Board (IRB) of Institute of Health Sciences at Jimma University; Southwest Ethiopia. Permission to conduct the study was obtained from both hospital administrative offices. We have informed the participants about the objectives of the study, the procedures, and their voluntary participation in the study before conducting the interviews. Then, we obtained informed verbal consent from each participant and it was documented on each participant questionnaire. Data were collected anonymously to ensure confidentiality. Moreover, individual counseling on self-care practices was given for participants with poor glycemic control to maximize the benefits of the study.
## Socio-demographic characteristics of the respondents
A total of three hundred and five type two diabetes patients participated in this study with a response rate of $98\%$. Out of the total participants, 182 ($59.7\%$) were males and the median (IQR) age of the respondents was 44 [19] years, ranging from 19 to 78 years. Nearly half ($47.5\%$) of them were within the age category of 40–60 years. Three fifths ($60\%$) of the respondents were following Protestant religion and four out of five ($83.9\%$) respondents were married. Nearly one third 96 ($31.5\%$) of the respondents attained college and above; 105 ($34.4\%$) were government employees and 212 ($69.5\%$) were urban residents (Table 1).
**Table 1**
| Characteristics(N = 305) | Categories | Number (%) |
| --- | --- | --- |
| Sex | Male | 182(59.7) |
| Sex | Female | 123(40.3) |
| Age Median(IRQ) = 44(19) | < 40 | 111(36.4) |
| Age Median(IRQ) = 44(19) | 40–60 | 145(47.5) |
| Age Median(IRQ) = 44(19) | > 60 | 49(16.1) |
| Religion | Protestant | 183(60) |
| Religion | Orthodox | 79(25.9) |
| Religion | Muslim | 25(8.2) |
| Religion | Catholic | 18(5.9) |
| Marital status | Single | 21(6.9) |
| Marital status | Married | 256(83.9) |
| Marital status | Divorced/widowed | 28(9.2) |
| Educational status | Unable to read and write | 72(23.6) |
| Educational status | Able to read and write | 81(26.6) |
| Educational status | Primary school (1–8 grade) | 27(8.9) |
| Educational status | Secondary school(9–12 grade) | 29(9.5) |
| Educational status | College & above | 96(31.5) |
| Occupational status | Government employee | 105(34.4) |
| Occupational status | Merchant | 71(23.3) |
| Occupational status | Housewife | 59(19.3) |
| Occupational status | Farmer | 52(17.0) |
| Occupational status | Othersa | 18(5.9) |
| Residence | Urban | 212(69.5) |
| Residence | Rural | 93(30.5) |
| Family income | < 3500(ETB) | 97(31.8) |
| Family income | ≥ 3500(ETB) | 208(68.2) |
## Glycemic control status
Fasting blood glucose readings of the last three diabetic clinic visits were obtained from patients’ medical records. Mean fasting blood glucose (FBG) measurements of the last three months were used to determine glycemic control. The mean (SD) FBG level of the participants was 167.49 (58.183) mg/dL. The minimum and maximum FBG measurements were 90 mg/dL and 478 mg/dL, respectively. The prevalence of poor glycemic control was $72.8\%$ ($95\%$ CI: $67.8\%$ -$78.1\%$). The prevalence of poor glycemic control was $72.8\%$ ($95\%$ CI: $67.8\%$ -$78.1\%$). Poor glycemic control was higher among government employees (77 [$73.3\%$]), age category 40–60 years (106 [$73.1\%$]), married (186 [$72.7\%$]), male (127 [$69.8\%$]), urban dwellers (146 [$68.9\%$]), and those who attained college and above educational level (65 [$67.7\%$]) (S1 Table).
## Diabetes complications
Diabetes-related complications were found in 105 ($34.4\%$) of the study participants. The common DM complication among the study participants were retinopathy ($26.7\%$), foot ulcer ($17.1\%$), nephropathy ($14.3\%$), and neuropathy ($10.5\%$) (S2 Table). The prevalence of DM complications was predominant among participants with duration of 10 and above years (44 [$41.9\%$]), low medication adherence (51 [$48.5\%$]), taking oral anti-diabetics (57 [$54.3\%$]), and DM patients having unsatisfactory patient provider relationship (76 [$72.4\%$]) (Table 2). Likewise, prevalence of DM complications was higher among patients with poor glycemic control 87 ($39.2\%$) than those with good glycemic control.
**Table 2**
| Variables | Category | Glycemic control | Glycemic control.1 | DM complications | DM complications.1 |
| --- | --- | --- | --- | --- | --- |
| Variables | Category | Poor N (%) | Good N (%) | Yes N (%) | No N (%) |
| Family history of DM | No | 143 (27.0) | 53 (73.0) | 68 (64.8) | 128 (64.0) |
| Family history of DM | Yes | 79 (72.5) | 30 (27.5) | 37 (35.2) | 72 (36.0) |
| Family support | No | 38 (71.7) | 15 (28.3) | 10 (10.5) | 43 (21.5) |
| Family support | Yes | 184 (73.0) | 68 (27) | 95 (89.5) | 157 (78.5) |
| Duration of diabetes | <5 years | 86 (64.7) | 47 (35.3) | 31 (29.5) | 102 (51.0) |
| Duration of diabetes | 5–10 years | 86 (79.6) | 22 (20.4) | 30 (28.6) | 78 (39.0) |
| Duration of diabetes | ≥ 10 years | 50 (78.1) | 14 (21.9) | 44 (41.9) | 20 (10.0) |
| Co morbidity | No | 153 (70.2) | 65 (29.8) | 52 (49.5) | 166 (83.0) |
| Co morbidity | Yes | 69 (79.3) | 18 (20.7) | 53 (50.5) | 34 (17.0) |
| Type of anti-diabetics | Insulin only | 77 (74.8) | 26 (25.2) | 34 (32.4) | 69 (34.5) |
| Type of anti-diabetics | Oral medication | 130 (72.6) | 49 (27.4) | 57 (54.3) | 122 (61.0) |
| Type of anti-diabetics | Insulin and oral | 15 (65.2) | 8 (34.8) | 14 (13.3) | 9 (4.5) |
| Regular follow up | No | 40 (87.0) | 6 (13.0) | 16 (15.2) | 30 (15.0) |
| Regular follow up | Yes | 182 (70.3) | 77 (29.7) | 89 (84.8) | 170 (85.0) |
| Counseling | No | 72 (75.8) | 23 (24.2) | 34 (32.4) | 61 (30.5) |
| Counseling | Yes | 150 (71.4) | 60 (28.6) | 71 (67.6) | 139 (69.5) |
| use of other alternative treatments | No | 177 (69.4) | 78 (30.6) | 77 (73.3) | 178 (89.0) |
| use of other alternative treatments | Yes | 45 (90.0) | 5 (10.0) | 28 (26.7) | 22 (11.0) |
| Patient provider relationship | Satisfactory | 76 (64.4) | 42 (35.6) | 29 (27.6) | 89 (44.5) |
| Patient provider relationship | Unsatisfactory | 146 (78.1) | 41 (21.9) | 76 (72.4) | 111 (55.5) |
| Body mass index | Normal | 146 (75.3) | 48 (24.7) | 67 (63.8) | 127 (63.5) |
| Body mass index | Overweight | 76 (68.5) | 35 (31.5) | 38 (36.2) | 73 (36.5) |
| Blood pressure | Normal | 145 (69.4) | 64 (30.6) | 52 (49.5) | 157 (78.5) |
| Blood pressure | Hypertensive | 77 (80.2) | 19 (19.8) | 53 (50.5) | 43 (21.5) |
| Medication adherence | High adherence | 88 (62.4) | 53 (37.6) | 32 (30.5) | 109 (54.5) |
| Medication adherence | Moderate adherence | 41 (69.5) | 18 (30.5) | 22 (21.0) | 37 (18.5) |
| Medication adherence | Low adherence | 93 (88.6) | 12 (11.4) | 51 (48.5) | 54 (27) |
## Clinical characteristics of T2 DM patients
The median (IQR) diabetes duration of the participants was 5 [5] years and 133 ($43.6\%$) of the participants had duration of less than five years. Out of total participants, 87 ($28.5\%$) of them had other chronic diseases and 105 ($34.4\%$) of the respondents had diabetes-related complications that were previously diagnosed. Diabetic retinopathy was the most common DM complication, accounting for $73.3\%$ (S2 Table).
Of the total respondents, 50 ($16.4\%$) of them use other alternative treatments for diabetes, of which 44 ($88\%$) use traditional medicine and six ($12\%$) use religious healing practices (S2 Table). Three-fifths ($61.3\%$) of the participants had an unsatisfactory patient-provider relationship and 46 ($15.1\%$) of them did not have regular follow-up at the diabetic clinic within the previous six months. Regarding medication adherence, 105 ($34.4\%$) of the respondents had low adherence (Table 2).
The mean (SD) BMI of the respondents was 24.18 (2.76) Kg/m2 and $36.4\%$ of the respondents had overweight. The mean (SD) systolic and diastolic BP was 131.92 (16.42) and 84.72 (7.76) mmHg, respectively. Out of total participants, about 96 ($31.5\%$) of them were hypertensive (Table 2).
## Knowledge and attitude towards diabetic care
Of the total participants, 139 ($45.6\%$) had poor knowledge about diabetes and the rest had good knowledge. The mean score for attitude is 28.21 (±3.079) with a minimum score of 17 and a maximum score of 35. Nearly half ($48.9\%$) of the respondents had a negative attitude towards diabetic care.
## Factors associated with glycemic control
Bivariate analysis was done to see the association between the independent variables and poor glycemic control. According to bivariate analysis; sex, educational status, marital status, residence, income, duration of diabetes, comorbidity, regular follow-up, use of other alternative treatments, patient-provider relationship, medication adherence, knowledge, attitude, blood pressure, body mass index, and physical activity showed association with poor glycemic control at P-value less than 0.25 (S3 and S4 Tables). These variables were entered into multivariable analysis to determine independent predictors of poor glycemic control.
In multiple logistic regression, a statistically significant difference was found in poor glycemic control due to duration of diabetes, follow-up to DM clinic, medication adherence, using other alternative treatments, unsatisfactory patient-provider relations and insufficient physical activity (Table 3).
**Table 3**
| Variables (n = 305) | Category | Glycemic control, n (%) | Glycemic control, n (%).1 | COR (95% CI) | AOR (95% CI) | P -value |
| --- | --- | --- | --- | --- | --- | --- |
| Variables (n = 305) | Category | Poor (n = 222) | Good (n = 83) | COR (95% CI) | AOR (95% CI) | P -value |
| Sex | male | 127 | 55 | 1 | 1 | |
| Sex | female | 95 | 28 | 1.47(0.87–2.49) | 1.25(0.66–2.38) | 0.491 |
| Marital status | single | 13 | 8 | 1 | 1 | |
| Marital status | Married | 186 | 70 | 1.64(0.65–4.14) | 1.46(0.47–0.52) | 0.512 |
| Marital status | Divorced/widowed | 23 | 5 | 2.83(0.77–10.47) | 0.96(0.18–4.96) | 0.967 |
| Educational status | Unable to read and write | 63 | 9 | 3.34(1.47–7.57) | 0.98(0.33–2.92) | 0.973 |
| Educational status | Able to read and write | 60 | 21 | 1.36(0.71–2.63) | 0.84(0.38–1.81) | 0.649 |
| Educational status | Primary school | 16 | 11 | 0.69(0.29–1.67) | 0.47(0.16–1.32) | 0.153 |
| Educational status | Secondary school | 18 | 11 | 0.78(0.33–1.85) | 0.54(0.21–1.40) | 0.206 |
| Educational status | College and above | 65 | 31 | 1 | 1 | |
| Residence | Urban | 146 | 66 | 1 | 1 | |
| Residence | Rural | 76 | 17 | 2.02(1.11–3.69) | 1.40(0.67–2.93) | 0.367 |
| Family income | < 3500(ETB) | 78 | 19 | 1.83(1.02–3.26) | 1.02(0.42–2.47) | 0.959 |
| Family income | ≥ 3500(ETB) | 144 | 64 | 1 | 1 | |
| Comorbidity | No | 153 | 65 | 1 | 1 | |
| Comorbidity | Yes | 69 | 18 | 1.63(0.90–2.95) | 0.40(0.13–1.25) | 0.115 |
| Duration of diabetes | < 5 years | 86(38.7) | 47(56.6) | 1 | 1 | |
| Duration of diabetes | 5–10 years | 86(38.7) | 22(26.5) | 2.14(1.19–3.85) | 2.24(1.17–4.27) | 0.014* |
| Duration of diabetes | > = 10 years | 50(22.5) | 14(16.9) | 1.95(0.98–3.90) | 1.40(0.64–3.06) | 0.395 |
| Regular follow up | No | 40(18.1) | 6(7.2) | 2.82(1.15–6.93) | 2.89(1.08–7.71) | 0.035* |
| Regular follow up | Yes | 182(81.9) | 77(92.8) | 1 | 1 | |
| Blood pressure | Normal | 145 | 64 | 1 | 1 | |
| Blood pressure | Hypertensive | 77 | 19 | 1.79(1.00–3.20) | 1.31(0.62–2.77) | 0.478 |
| BMI | Normal | 146 | 48 | 1 | 1 | |
| BMI | Overweight | 76 | 35 | 0.71(0.43–1.20) | 0.87(0.48–1.58) | 0.641 |
| Medication adherence | High | 88(39.6) | 53(63.9) | 1 | 1 | |
| Medication adherence | Moderate | 41(18.5) | 18(21.7) | 1.37(0.72–2.63) | 1.57(0.77–3.21) | 0.220 |
| Medication adherence | Low | 93(41.9) | 12(14.5) | 4.67(2.34–9.32) | 4.12(1.20–8.70) | <0.001* |
| Use of other alternative treatments | No | 177(79.7) | 78(94.0) | 1 | 1 | |
| Use of other alternative treatments | Yes | 45(20.3) | 5(6.0) | 3.97(1.52–10.37) | 3.58(1.24–10.36) | 0.018* |
| Patient provider relation | satisfactory | 76(34.2) | 42(50.6) | 1 | 1 | |
| Patient provider relation | Unsatisfactory | 146(65.8) | 41(49.4) | 1.97(1.18–3.28) | 2.27(1.27–4.04) | 0.005* |
| Knowledge about DM | Good | 110 | 56 | 1 | 1 | |
| Knowledge about DM | poor | 112 | 27 | 2.11(1.24–3.59) | 0.98(0.51–1.90) | 0.970 |
| Attitude towards DM care | Positive | 99 | 57 | 1 | | |
| Attitude towards DM care | Negative | 123 | 26 | 2.72(1.60–4.65) | 1.65(0.89–3.08) | 0.114 |
| Physical exercise | Adequate | 28(12.6) | 26(31.3) | 1 | 1 | |
| Physical exercise | Inadequate | 194(87.4) | 57(68.7) | 3.16(1.72–5.82) | 4.14(2.07–8.28) | <0.001* |
## Discussion
It is an established fact that diabetes can cause complications in those patients whose blood glucose level is not controlled [1]. The main goal of diabetes management is to ensure optimal glycemic control to delay and prevent complications. This study assessed the prevalence of poor glycemic control and its associated factors among type two diabetic patients.
The findings of this study showed that nearly three-fourths ($72.8\%$) of diabetic patients in the study area had poor glycemic control. This finding was comparable with earlier studies done in Saudi Arabia ($74.9\%$) [12], Tanzania ($69.7\%$) [13], Dessei, Northeast Ethiopia ($70.8\%$) [14], and Jimma, Southwest Ethiopia ($70.9\%$) [15]. However, it is ahigher prevalence than that of studies which reported $64.9\%$ in Nekemte referral Hospital and $59.2\%$ in Shanan Gibe Hospital, Southwest Ethiopia [7, 16]. The possible reason for this high prevalence of poor glycemic control could be the clinical characteristics of the patients, low medication adherence, and insufficient physical activity of the patients in the current study. This finding was lower than a study done in Tikur Anbesa specialized Hospital (TASH), Ethiopia, which reported $80\%$ [17] of the study participants had poor glycemic control. The possible explanation for this difference could be that patients seeking advanced management were referred to TASH and patients from the whole region of the country were referred to TASH [17]. The results of the current study highlight the need to work more on the optimal management of diabetes, since maintaining the recommended glycemic level is the main therapeutic goal for all patients with diabetes.
The current study showed that longer duration of diabetes is significantly associated with poor glycemic control. This finding is consistent with other similar studies [12, 14, 17–19]. However, this finding is slightly lower in strength of association than the finding from a study done in Shanan Gibe Hospital [7]. The possible reason for this difference could be the majority ($43.6\%$) of the patients in the current study had short duration (less than five years) of diabetes, while in that one $49.4\%$ of the participants had long duration (greater than 10 years) of diabetes. The possible explanation for this finding could be due to progressive impairment of insulin secretion over time because of the failure of β-cells and increased insulin resistance to control blood sugar [20]. Moreover, it might be due to difficulty for the patients to continue monitoring of blood glucose level and adjust with the treatment, exercise and diet [21, 22]. Therefore, measures should be put in place for education for diabetes patients, emphasizing more on self-care activities, especially for patients with long duration of diabetes.
In the current study, a lack of regular follow-up was significantly associated with poor glycemic control. This finding is in agreement with previous studies done in Brazil and Southwest Ethiopia [7, 23]. The possible reason for this finding could be that patients who are not regularly following the diabetic clinic might be noncompliant to diabetic self-care activities and treatment [24, 25]. In addition, those patients who are not regularly following the diabetic clinic might not know their blood sugar level and they might not get counseling about their disease condition. This finding implies that health care providers should give attention to encourage the patients to visit the diabetic clinic regularly.
In this study, poor glycemic control appeared to be greater among patients who had low medication adherence compared with high adherence. This finding is comparable with other studies conducted in Jimma and Gondar hospitals [15, 26]. However, the current finding is higher in strength of association than the finding from a study done in Tripoli, Libya [27]. The reason for this difference might be due to the different measurement scores in these two studies. The possible explanation for this finding is that low adherence to treatment is one of the barriers that prevents many diabetic patients from achieving optimal glycemic levels [28]. Furthermore, it might be due to lack of patients’ knowledge about the importance of treatment adherence, which results in better glycemic control. The finding implies that health facilities should consider developing educational programs that emphasize life-style modification with the importance of adherence to treatment would be of great benefit for optimal glycemic control. Moreover, health care providers should discuss barriers to treatment adherence when counseling patients and solutions should be tailored toward individual needs.
In the present study, use of other alternative treatments (traditional medicine and religious healing practices) is significantly associated with poor glycemic control. Patients who used other alternative treatments were more likely to have poor glycemic control. This finding is supported by a systematic review of literature in Sub-Sahara African countries in which the use of herbal medicines and traditional healers was frequently mentioned, although it is not part of the ADA self-management guidelines [29]. A study in Northern Ethiopia also revealed that the majority ($62\%$) of diabetes patients were herbal medicine users and most ($87.1\%$) of them did not consult their physicians about their herbal medicine use [30]. This finding could be due to the fact that patients who used other alternative treatments might be low medication adherent and this might be leading to poor glycemic control [28]. Thus, health care providers should consult patients regarding use of other alternative treatments and encourage them to adhere to prescribed medication.
Having an unsatisfactory patient-provider relationship was found to be an independent predictor of poor glycemic control among type 2 diabetic patients. The possible reason could be those patients who have a satisfactory patient-physician relationship might be well encouraged to act in accordance with self-care activities. The finding implies that health care providers should pay attention to developing effective patient-provider relation and communication skills when counseling diabetic patients.
The current study also revealed that patients with insufficient physical activity had poor glycemic control, which is consistent with prior studies done in Tripoli, Libya, and Jimma, Southwest Ethiopia [27, 31]. Nevertheless, it is lower than the finding from the study done in Saudi Arabia [12]. The variation could be due to that the previous study measured physical activity at least 30 minutes for three days per week, while the current study measured physical activity by mean score for physical exercise done in the last seven days using the SDSCA tool. The possible explanation for this finding might be due to having inadequate knowledge about the benefits of regular physical exercise and a fear of hypoglycemia. This implies that encouraging diabetic patients to do physical exercise is a crucial part of diabetes education for optimal glycemic control. Furthermore, physical exercise has not only been reported to raise glycemic control, but also to improve a patients insulin sensitivity and to repair some of the damage caused by diabetes-associated complications, such as impaired cardiovascular health, one of the most common complications [32].
Prevalence of diabetic complications in this study was slightly higher among diabetic patients having poor glycemic control (87 [$39.2\%$]) compared to their counterparts. A study done in Gondar Ethiopia, also found diabetic complications were higher among DM patients with poor glycemic control [33]. The commonest diabetic complication identified in this study was retinopathy ($26.7\%$). Likewise, a case-control study conducted in Brazil revealed that retinopathy is predominant among DM patients with poor glycemic control [34]. Moreover, a follow-up study in the USA showed the association between the level of glucose and diabetic retinopathy, which indicated that controlling blood glucose level using rigorous treatment gave rise to delayed slow progression of diabetic retinopathy [35].
The lack of a relationship between educational status and poor glycemic control in this study is not consistent with the findings of previous studies [14, 15, 18, 36], which reported that no formal education was associated with poor glycemic control. The reason for this difference could be that the majority of patients in previous studies had no formal education, while in the current study, the majority ($31.5\%$) of the patients had attained college and above. In addition to this, type of treatment (being on insulin treatment) does not show significant association with poor glycemic, which is not in line with studies done previously elsewhere [17, 26, 27, 37, 38]. This might be due to the majority ($58.7\%$) of the patients in the current study were taking oral anti-diabetics. The other reason could be that type 2 diabetes patients are treated by insulin when their blood glucose level is not controlled by oral anti-diabetics.
## Limitations of the study
The current study has its own limitations that should be acknowledged. The use of mean FBG over HbA1c is one limitation; thus possibly under estimate the prevalence of poor glycemic control. However, an effort was made to overcome this issue by taking the mean average of the last three consecutive visits for FBG measurements. In addition, the incompleteness of the patients’ charts is one of the shortcomings of this study since some items like co-morbidities were abstracted from the patient charts. Furthermore, the subjective nature of self-reported responses for some items might be limited by recall bias, and since the data collectors were health professionals, social desirability bias may also occur for some items.
## Conclusion
The current study revealed that the prevalence of poor glycemic control and diabetic complications is noticeably high among diabetes patients. DM complications were found slightly higher among patients with poor glycemic control. Poor glycemic control showed significant association with longer duration of diabetes, lack of regular follow-up, low adherence to treatment, use of other alternative treatments, unsatisfactory patient-physician relations, and insufficient physical exercise. Therefore, we recommend considering developing educational programs that emphasize the importance of medication adherence. Regular follow-up and physical activity would be of great benefit in poor glycemic control. It is also paramount to enhance effective patient-provider relations and communication skills when counseling diabetic patients. Furthermore, considering consulting the patients regarding the use of other alternative treatments at each visit is also essential.
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|
---
title: 'Pompe Disease: a Clinical, Diagnostic, and Therapeutic Overview'
authors:
- David Stevens
- Shadi Milani-Nejad
- Tahseen Mozaffar
journal: Current treatment options in neurology
year: 2022
pmcid: PMC10035871
doi: 10.1007/s11940-022-00736-1
license: CC BY 4.0
---
# Pompe Disease: a Clinical, Diagnostic, and Therapeutic Overview
## Abstract
### Purpose of Review
This review summarizes the clinical presentation and provides an update on the current strategies for diagnosis of Pompe disease. We will review the available treatment options. We examine newly approved treatments as well as upcoming therapies in this condition. We also provide commentary on the unmet needs in clinical management and research for this disease.
### Recent Findings
In March 2015, Pompe disease was added to the Recommended Uniform Screening Panel (RUSP) and since then a number of states have added Pompe disease to their slate of diseases for their Newborn Screening (NBS) program. Data emerging from these programs is revising our knowledge of incidence of Pompe disease. In 2021, two randomized controlled trials involving new forms of enzyme replacement therapy (ERT) were completed and one new product is already FDA-approved and on the market, whereas the other product will come up for FDA review in the fall. Neither of the new ERT were shown to be superior to the standard of care product, alglucosidase. The long-term effectiveness of these newer forms of ERT is unclear. Newer versions of the ERT are in development in addition to multiple different strategies of gene therapy to deliver GAA, the gene responsible for producing acid alpha-glucosidase, the defective protein in Pompe Disease. Glycogen substrate reduction is also in development in Pompe disease and other glycogen storage disorders.
### Summary
There are significant unmet needs as it relates to clinical care and therapeutics in Pompe disease as well as in research. The currently available treatments lose effectiveness over the long run and do not have penetration into neuronal tissues and inconsistent penetration in certain muscles. More definitive gene therapy and enzyme replacement strategies are currently in development and testing.
## Introduction
Pompe disease, also known as glycogen storage disease type II (GSD II) or acid maltase deficiency (AMD), is a genetic disorder caused by a deficiency of the acid alpha-glucosidase (GAA) enzyme, due to recessive mutations in the GAA gene, which leads to accumulation of lysosomal glycogen [1], diffusely but primarily affecting the skeletal and cardiac muscle tissue. More than 300 different mutations have been described in the GAA gene. The clinical presentation of Pompe disease is a spectrum between the cardiac and skeletal muscle dysfunction and has wide variability. It is divided into two forms which are referred to as infantile onset Pompe disease (IOPD), described by Johannes Pompe in the 1930s and late-onset Pompe disease (LOPD), described by Andrew Engel in 1969 [2, 3]. IOPD has more severe cardiac involvement with hypertrophic cardiomyopathy, hypotonia, and respiratory insufficiency and is often fatal within 1 year of age without treatment. LOPD presents primarily as a muscle disease and has a more insidious course. LOPD can have onset anywhere from infancy to late adulthood. It presents with a pattern of symmetric limb-girdle muscle weakness and LOPD was till recently classified additional under the limb-girdle muscular dystrophy umbrella, with a designation of LGMD2V [4]. However, the revised classification system removed Pompe disease [5•]. It is the severity of the enzyme deficiency that determines which phenotype (IOPD vs. LOPD) will result. Patients with IOPD have a severe or complete GAA deficiency with < $1\%$ residual enzyme activity, whereas LOPD is caused by only a partial deficiency (< $30\%$ residual activity) of GAA [6].
## Epidemiology
The traditional estimate of incidence is around $\frac{1}{40}$,000 overall [7, 8], with about $\frac{3}{4}$ of the cases as LOPD and $\frac{1}{4}$ as IOPD. Incidence can vary widely among different ethnic groups and has historically been based on retrospective data from carrier frequencies. The populations that appear to be at higher risk include people of African American, Taiwanese, Dutch, and Israeli descent. However, now that newborn screening protocols are being put in place, we are getting more definitive incidence frequencies.
Newborn screening (NBS) from California showed a birth prevalence of $\frac{1}{25}$,200 [9•]. NBS in Illinois, Pennsylvania, and Missouri have shown incidences of $\frac{1}{23}$,596, $\frac{1}{16}$,095, and $\frac{1}{10}$,152 respectively [10–12]. Analysis of NBS data in Japan showed an overall incidence of ~ $\frac{1}{37}$,000 from 2013 to 2020 [13]. Studies from Taiwan have shown birth prevalence rates of $\frac{1}{26}$,466 or $\frac{1}{20}$,114 for LOPD and $\frac{1}{67}$,047 for IOPD [14•, 15]. Overall, the incidences from these newer studies for Pompe disease are higher than the previously estimated $\frac{1}{40}$,000 as above. For IOPD specifically, the data has been quite variable. The screening data from Japan, California, Pennsylvania, and Illinois has shown IOPD incidence ranging from around $\frac{1}{200}$,000 to $\frac{1}{300}$,000 [9•, 11–13]. However, the incidence rates seen in Taiwan and Missouri are much higher at $\frac{1}{67}$,047 and $\frac{1}{46}$,700 respectively [12, 15]. Further data collected with newborn screening in different parts of the world will be key in gaining a better understanding of the epidemiology of this disease.
The new data does raise an interesting conundrum. If the incidence is indeed so much higher, it approximates the incidence of relatively more common neuromuscular disorders such as facioscapulohumeral muscular dystrophy (FSHD) (1 in 15,000) [16] and myotonic dystrophy (DM1) (1 in 8000) [17]. The prevalence of Pompe disease in the Neuromuscular Clinics or the Muscular Dystrophy Association (MDA) clinics is nowhere close to those of FSHD or DM1, which begs the question whether these patients with Pompe disease are misdiagnosed as other musculoskeletal disorders or whether not all mutations have the same penetrance and may not manifest disease?
## Pathophysiology
Acid alpha-glucosidase (GAA) facilitates the breakdown of glycogen to glucose within the lysosomes of cells throughout various tissues in the body [6]. With a deficiency of this enzyme, as seen in Pompe disease, there is abnormal accumulation of glycogen and progressive expansion of these glycogen-filled lysosomes. The skeletal and cardiac muscle are the most affected. Primary mechanisms of cellular injury are lysosomal rupture and autophagy. In 1970, Engel reported that autophagic function was abnormal in Pompe patients [3], and more recent mouse models showing accumulation of autophagosomes have supported this idea [6]. Typically, autophagy works in nutrient poor states by recycling intracellular material to supply amino acids for energy production. Additionally, autophagy helps clear out mis-folded proteins and other intracellular debris [6]. This process entails autophagosomes forming and collecting intracellular contents, after which they fuse with lysosomes to degrade the collected material. It is thought that this autophagic buildup may work in conjunction with the expanded lysosomes from glycogen build up, and their rupture spilling the contents into the sarcoplasm, to cause dysfunction and injury to muscle [6].
## Diagnostic evaluation
The diagnosis of Pompe disease is ultimately confirmed with enzyme assays and genetic testing. Clinical history, exam, muscle enzymes, and electromyography (EMG) are the core of the initial workup and are used to help determine which patient should undergo further testing specific to Pompe disease, particularly in the setting of LOPD [1].
A clinical history of a limb-girdle pattern of weakness that is slowly progressive over years is the typical clinical phenotype of LOPD. Respiratory insufficiency is present in the majority of patients [18]. Examination will show a limb-girdle pattern of weakness with most prominent weakness typically affecting thigh adductors [18]. This is often accompanied by postural changes such as lumbar lordosis or camptocormia, with scapular winging. Laboratory workup is expected to show an elevated creatine kinase (CK) level in the majority of patients but can be normal. The CK level will typically not be higher than 2000 U/L, and average around 600–700 U/L [19]. Electromyography will classically show an irritable myopathy affecting the proximal muscles. One key feature often seen in Pompe disease on EMG is myotonic discharges in the paraspinals muscles. The IPANEMA study found that within patients with proximal muscle weakness and elevated CK levels presenting undiagnosed to academic neuromuscular centers, the prevalence of LOPD was $1\%$ [20••].
More definitive and specific testing for Pompe disease consists of muscle biopsy, enzyme assays, and genetic testing. Muscle biopsy shows vacuolated fibers filled with glycogen as can be seen on PAS or acid phosphatase staining [21]. Enzyme assays are able to detect a deficiency of acid alpha-glucosidase, as seen in this condition. This can be done on either muscle tissue or blood spot, leukocytes, or fibroblasts [22•, 23]. Some would consider enzyme deficiency confirmed on two different sample types to be diagnostic even without muscle biopsy or genetic testing. Lastly, genetic testing with sequencing of the GAA gene to detect mutations is another definitive diagnostic step [23]. As mentioned above, over 300 different mutations have been identified in the GAA gene. Specific mutations vary between IOPD and LOPD and across different ethnic groups.
It is common for there to be a 12–13-year delay in diagnosis from onset of symptoms for LOPD due to the rarity of the condition and insidious progression. However, IOPD is diagnosed rapidly with the assistance of newborn screening panels. In 2015, Pompe disease was added to the Recommended Uniform Screening Panel (RUSP), and as of January 2021 there were 23 states screening for Pompe disease. The common method used for newborn screening is to start with a blood spot enzyme assay, followed up by genetic sequencing for confirmation if enzyme levels are reduced. One challenge is that the enzyme assays used cannot reliably differentiate between IOPD and LOPD [15]. Therefore, confirmatory testing with genetic sequencing or other workup (CK, cardiac evaluation) can help determine if a positive blood spot result indicates already symptomatic IOPD necessitating early treatment, or LOPD which may not manifest until years or decades later. If the clinical picture fits IOPD and the assay shows deficiency, treatment will often begin prior to genetic confirmation, which can take weeks to result.
## Diagnostic challenges
A challenging situation arises when LOPD is diagnosed genetically at birth with newborn screening panels before any symptoms are present. This occurs often because LOPD is much more common than IOPD and LOPD accounts for $75\%$ of all cases of Pompe disease diagnosed through NBS. At this time, there is no clear consensus on how to manage these patients and the guidelines, developed primarily by a group of metabolic geneticists without much neurology input [24], are not uniformly enforced. Many of these kids will not manifest any symptoms until their teenage years or much later. It is not known if early treatment can have any prophylactic effect to delay or prevent symptoms, or if we should wait until symptom onset or laboratory abnormalities arise to initiate treatment. Further, insurance companies generally do not reimburse for asymptomatic checks or care, so it is not clear who is supposed to pay for these routine surveillance visits. There is a desperate need for sensitive biomarkers. In addition to serum CK levels and urinary excretion of tetrasaccharides (Hex4), an interesting potential biomarker is MR spectroscopy that can assess glycogen levels in tissue, and has been demonstrated to be useful in quantifying hepatic and muscle glycogen in glycogen storage diseases and other metabolic conditions [25–27]. This or other advanced imaging techniques could serve as non-invasive methods of early disease detection in these asymptomatic LOPD cases to determine when to initiate treatment.
The knowledge of the diagnosis, in individuals without symptoms or functional loss, may cause anxiety or depression and unnecessary modifications to their lifestyles as well as unnecessary treatments. This is an area of Pompe disease that needs much more study. The optimal time to initiate ERT is not settled and adds to the complexity of managing pre-symptomatic or asymptomatic patients.
Another major challenge relates to diagnosis of Pompe disease is GAA pseudodeficiency. There are haplotypes of the GAA gene that cause GAA pseudodeficiency, which shows up as low enzyme activity on assays but does not have any clinical effects or lead to symptomatic disease. Using enzyme assays in isolation can lead to many false-positives in the initial screening steps. Therefore, it is important to follow up with genetic testing in these cases, to detect the pseudodeficiency haplotypes, which presents often as homozygous for the c.[1726A; 2065A] pseudodeficiency allele [28]. This genotype appears to be more common than Pompe disease. In Illinois, the birth prevalence of pseudodeficiency was $\frac{1}{17}$,546, in Missouri it was $\frac{1}{8811}$, in California it was $\frac{1}{22}$,658, and in Pennsylvania it was $\frac{1}{35}$,409 [9•, 10–12]. Other countries have shown even higher rates of pseudodeficiency such as $\frac{1}{1368}$ in Taiwan and $\frac{1}{8747}$ in Japan [13, 15]. The IPANEMA study showed a prevalence of $1\%$ for both LOPD and pseudodeficiency alleles in that population [20••].
## Supportive care/complications
In Pompe disease, in addition to the disease-modifying treatments, overall management of this condition requires symptomatic management and screening for complications, particularly in LOPD, preferably through a multidisciplinary clinic allowing a team of allied health care professionals, including physical therapy, speech therapy, respiratory therapy, dieticians, and genetic counselors, ensuring that all aspects of patient care are being addressed. Additionally, coordination and communication between different medical teams such as neurology, genetics, pulmonology, gastroenterology, and cardiology can be crucial for proper management.
Light exercise or aerobic exercise such as swimming is very beneficial in maintaining mobility and functionality. The goal is to stay active and exercise as able without inducing muscle soreness or prolonged recovery times after activity.
The two most life-threatening complications of LOPD are related to respiratory and cardiac dysfunction. Patients should undergo routine pulmonary function testing to determine their degree of respiratory insufficiency related to diaphragm weakness. When a patient’s forced vital capacity (FVC) approximates $50\%$ predicted, it is advised to initiate non-invasive ventilation (NIV) with a positive airway pressure respiratory assist devices that help support the diaphragm function. Without this supportive treatment, patients continue to have sleep disordered breathing and may begin to retain CO2 chronically, leading to headaches, daytime sleepiness, and lack of energy. There should be a low threshold to order polysomnographic studies to monitor for it. It has been shown that NIV can improve survival and quality of life [29]. Cardiac management is crucial in IOPD, but cardiac dysfunction is less common in LOPD [30]. It is important to screen patients regularly for cardiac hypertrophy or conduction abnormalities.
An understudied area in the management of LOPD is in regard to vascular malformations, which appear to be quite common in this disease, with $60\%$ of LOPD patients showing intracranial arterial abnormalities, such as vertebrobasilar dolichoectasia and unruptured aneurysms [31, 32]. These types of malformations could place patients at risk for strokes, compression, or hemorrhage in severe cases. However, there are no guidelines regarding monitoring for these complications, as well as other complications, in Pompe disease [33].
## Disease-modifying therapies
The treatment of Pompe disease has mainly been targeted at correction of the underlying GAA deficiency. This has included trying to supplement the enzyme in various ways, and gene therapy allowing endogenous production of the GAA enzyme.
## Currently available treatments
Enzyme replacement therapy (ERT) is given as human recombinant GAA (rhGAA) has been used in Pompe disease as early as the 1970s primarily studied initially for IOPD [34, 35]. A randomized controlled trial in 2006 for IOPD ultimately led to FDA approval of rhGAA [36] for all forms of Pompe disease. The study in 2006 was done in IOPD and showed that ERT improved overall survival and ventilator-free survival in patients [37], and suggested earlier intervention provided greater benefit. Significant reduction in left ventricular hypertrophy was seen in all surviving patients in this study as well. Despite these clear benefits seen in early life, long-term follow up of IOPD patients still shows significant morbidity and mortality and requires further study [38]. While ERT has allowed these patients to survive cardiac and respiratory effects into childhood and often achieve independent walking, many of them start to experience decline in skeletal muscle strength years later and develop cardiac arrhythmias even when cardiac hypertrophy has been avoided or reduced with ERT. Additionally, these IOPD that survive into childhood with ERT go on to develop other problems including hearing loss, speech dysfunction, cognitive impairment, and GI as well as respiratory dysfunction [39]. There is an unmet need for management of these patients.
With the publication of the LOTS data in 2010, enzyme replacement therapy with alglucosidase alfa in LOPD was shown to improve or at least slow the decline of ambulation, arm and leg function, and respiratory function [39]. However, the effectiveness seems to wear off after 2–3 years and patients return to their slow decline [40•, 41]. Because of this lack of a sustained response to alglucosidase alfa, more recently two new forms of enzyme replacement therapy were developed and tested in clinical trials to meet the unmet need in Pompe Disease. These new iterations of ERT, avalglucosidase alfa and cipaglucosidase alfa plus miglustat, were compared to alglucosidase alfa (the standard of care) in the COMET and PROPEL trials, both published in December 2021 [42••, 43••]. Avalglucosidase alfa (COMET trial) is a form of rhGAA that is designed with enhanced targeting of mannose-6-phosphate receptors, through chemical conjugation of synthetic linkers, to increase the uptake of rhGAA into cells on the target tissues [42••]. The PROPEL trial examined a two-component therapy that included cipaglucosidase alfa, an rhGAA with enhanced glycosylation for improved cellular uptake, through clonal selection of rhGAA with CHO-cell derived M6P and bis-M6P moieties, and miglustat, a stabilizer of the cipaglucosidase alfa molecule, which prolongs half-life and increases distribution [43••]. Both new forms of ERT were shown to be non-inferior to alglucosidase alfa and did not meet the prespecified criteria for superiority compared to alglucosidase alfa [42••, 43••]. At this point, we do not have the long term data on these new agents to see how they will fare after 2–3 years of treatment (Table 1).
## Treatments under investigation
Newer treatments under development or under investigation are depicted in Table 2 and shown graphically in Fig. 1.
## Enhancements in enzyme replacement strategies
Alternate enzyme replacement strategies have been tried or being developed. A recent trial of a new glycosylation-independent lysosomal targeting (GILT)-tagged ERT that utilized the IGF-II receptors in skeletal muscles to allow entry of the enzyme into the muscles was undertaken but was terminated early due to development of significant symptomatic hypoglycemia [33]. Newer approaches include a combination of gene therapy to target the liver and using monoclonal antibody (to CD63 or ITGA7)-conjugates with the enzyme to allow for targeted entry into skeletal muscles [44•]. Another effort to show improvement in ERT delivery through an antibody-enzyme fusion product showed safety and tolerability, but the program was discontinued due to lack of funding [45•].
## Gene therapy
Gene therapy is a very exciting treatment modality on the horizon for Pompe disease. Through a one-time treatment of the transgene that would then endogenously produce the enzyme, this would obviate the need for chronic ERT therapy biweekly. While majority of the gene therapies use adeno-associated virus vectors (AAV) [46] for a one-time delivery of a non-integrating vector carrying the transgene, one group is proposing use of lentivirus-driven correction of autologous hematopoietic stems cells and reinfusion of cells. *The* gene therapy approaches differ as well with trials using a liver-directed approach vs. a muscle-directed approach.
For liver-directed therapy, treatment would consist of a one-time intravenous (IV) infusion of the AAV-packaged transgene, which would be delivered into the nucleus of liver cells and would begin to produce the therapeutic protein; in this case GAA, in a sustainable fashion. This would create liver depot for GAA production and the secretable GAA released into the bloodstream and available for delivery to skeletal and cardiac muscle tissues. This approach takes advantage of the high tropism of AAV vectors for hepatic cells, requiring lower vector dose. Further, proteins produced in the liver appear to be immunologically privileged. This form of gene therapy is currently under investigation in phase $\frac{1}{2}$a trials (NCT04093349 and NCT03533673). There have been no major safety signals and preclinical findings have shown promising results of reduced glycogen accumulation in skeletal and cardiac tissue and improvement of muscle function [47, 48]. However, with all the AAV approaches, there still remain safety concerns related to capsid-related hepatotoxicity as well as development of neutralizing antibodies to the capsid. Currently, individuals who have pre-existing antibodies to AAV are excluded from participation due to concerns for premature neutralization of the capsid and the transgene.
The muscle approach is another exciting opportunity, either intravenous approach with muscle targeting or direct intramuscular approach. With either path, the GAA transgene would be delivered to muscle cells, and begin to produce a functional GAA enzyme to mitigate lysosomal glycogen accumulation in those cells. An ongoing trial uses a skeletal muscle targeting approach, given as an IV infusion using AAV vector with muscle-specific serotype and promoters (NCT04174105). As only $1\%$ of enzyme produced in the liver actually makes it into the target organs (skeletal and cardiac muscle), muscle-directed AAV therapy resolves this problem. The IV delivery method of muscle-directed therapy would aim to deliver the AAV vector systemically to all muscle tissue, but would require a much higher vector dose, which may increase the likelihood of anti-GAA antibodies developing and interfering with the therapeutic effect as well as hepatotoxicity and cardiomyopathy. Preclinical data for muscle-directed IV therapy has been positive in showing substantial clearance of lysosomal glycogen in skeletal and cardiac muscle tissue in mice [49]. Another approach consists of direct intramuscular (IM) injection to deliver the vector directly to muscle fibers. One advantage of the IM therapy is that certain muscles that are more affected could be targeted, such as the diaphragm, allowing for more flexibility and specificity of treatment. However, this is also a disadvantage in that the positive effects appear to be quite local at the site of injection, which may suggest the need for multiple injections at different sites and potentially a higher risk for antibody development. Preclinical data for this muscle-directed IM therapy has been encouraging in showing success of intralingual and intradiaphragmatic injection in mice [50, 51•]. *Overall* gene therapy treatment options are a very exciting area of on-going research and show great promise for more definitive long-term treatment of Pompe disease.
Another approach being considered is an intrathecal or intraventricular approach to maximize delivery into the CNS, especially for IOPD cases, where the burden of disease, in addition to the cardiac muscles, is maximal in motor neuron cells.
Finally, an antisense approach to improve the IVS splicing in Pompe disease was discussed by the Erasmus group at an international meeting (Nadine van der Beek—personal communication). This offers a promising approach to improve enzyme production through mitigation of the most common genetic abnormality in Caucasian patients with Pompe disease.
## Glycogen reduction strategies
Alternative strategy of substrate (glycogen) reduction is being studied with the aim of reducing the amount of glycogen in cells, either through small molecules, currently in phase 1 in healthy individuals (NCT05249621), or through genetic approaches [52, 53, 54•], thus delaying the onset of symptoms from Pompe disease. This treatment can be used either alone or as an adjunct to the ERT. This approach would also be applicable for other glycogen storage disorders, and may be particularly attractive for delaying disease in at-risk asymptomatic individuals.
## Unmet needs
Unmet needs in clinical care as well as in research are described in Table 1. In addition to the need to improve diagnostic times and diagnosing these patients earlier, before the burden of disease becomes large, and the muscles accrue irreversible damage, there are research unmet needs related to inadequacies of the current available treatments. Additionally, the current outcome measures used in quantifying disease burden, monitoring disease progression, and to quantify treatment-related outcomes are woefully inadequately. Six-minute walk test, traditionally used in this disease unfortunately, is not sensitive enough especially in younger individuals and subject to training effects. Forced vital capacity does not change till much later in the disease and is not a direct measure of diaphragmatic strength. There is a desperate need to develop newer and more sensitive outcome measures and biomarkers in this disease. There has been considerable work that is being done to validate magnetic resonance imaging (MRI) as an outcome measure. There are new strategies to develop MR-spectroscopy as an outcome measure, since it has the potential to assess glycogen burden in muscles non-invasively.
## Conclusion
Pompe disease is a heterogeneous disorder with bimodal presentation. Enzyme replacement therapy is currently the mainstay of treatment for all forms of Pompe disease but the current therapies have significant unmet needs. ERT improves overall survival, ventilator free survival, and cardiac function in infantile cases, and stabilizes mobility and skeletal and respiratory muscle strengths in adult. However, after 2–3 years, ERT begins to lose effectiveness and patients continue to decline. Two new ERT treatments were showed to be non-inferior to the existing standard of care but could not establish superiority, and it is not clear if these treatments would lose effectiveness in a few years. There are significant unmet needs in terms of lack of guidance on management of LOPD patients diagnosed at birth, potentially long before the disease will manifest any symptoms. Similarly optimal outcome measures to measure clinical phenotype, progress, and treatment outcomes need to be defined. Newer promising treatments with liver-directed and muscle-directed gene therapies are in clinical trials, and these if they are effective, would result provide long-lasting therapy with only a single necessary treatment, creating endogenous production of the GAA enzyme to correct the underlying deficiency and cardiac and skeletal muscle pathology. Additional development include newer enhanced forms of ERT as well as substrate (glycogen) reduction strategies, which are about to enter clinical trials for LOPD. In addition to the current and upcoming therapies, there remains a need for a multidisciplinary approach and more wholistic approach to care of patients with Pompe disease.
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|
---
title: 'Protocol for a randomized controlled trial to assess the effect of Self-Management
for Amputee Rehabilitation using Technology (SMART): An online self-management program
for individuals with lower limb loss'
authors:
- Elham Esfandiari
- WC Miller
- Sheena King
- Michael Payne
- W. Ben Mortenson
- Heather Underwood
- Crystal MacKay
- Maureen C. Ashe
journal: PLOS ONE
year: 2023
pmcid: PMC10035895
doi: 10.1371/journal.pone.0278418
license: CC BY 4.0
---
# Protocol for a randomized controlled trial to assess the effect of Self-Management for Amputee Rehabilitation using Technology (SMART): An online self-management program for individuals with lower limb loss
## Abstract
### Background
Lower limb loss (LLL) is a distressing experience with psychological, physical, and social challenges. Education is needed to enhance the coping skills and confidence of patients to improve LLL outcomes. However, access to rehabilitation services and education is limited outside of urban centers. To address this service gap, we co-created an eHealth platform, called Self-Management for Amputee Rehabilitation using Technology (SMART).
### Objectives
First, we will test the effect of SMART and usual care compared with usual care only on walking capacity and confidence among individuals with LLL. Second, we will describe key implementation factors for program delivery and adoption at the person- and provider-level.
### Methods
This is a Type 1 Effectiveness-Implementation Hybrid Design, mixed-methods, multi-site (British Columbia and Ontario, Canada), parallel, assessor-blinded randomized controlled trial. Participants will include adults with unilateral LLL, during early prosthetic fitting (<2 years after casting for initial prosthesis). Participants in both groups will receive usual care. The experimental group will receive SMART with weekly support sessions from a trained peer mentor for goal setting and action planning for six weeks. Participants will be encouraged to continue using SMART for an additional four weeks. The control group will receive usual care, and weekly social contacts for six weeks. The primary outcome measure is walking capacity operationalized as the performance based Timed Up and Go test. The secondary outcome is walking confidence using the Ambulatory Self-Confidence Questionnaire. Outcome measures will be assessed at baseline, immediately post-intervention, and four weeks follow-up. We will describe key implementation factors (such as, participant experience, intervention adoption, fidelity) throughout the study using questionnaires, semi-structured interviews, and direct observation.
### Results
No participants have been enrolled.
### Conclusions
SMART has the potential to provide knowledge and skill development to augment rehabilitation outcomes for adults with LLL.
### Trial registration
NCT04953364 in Clinical Trial Registry (https://clinicaltrials.gov/).
## 1.1. Background
Limb loss is distressing [1] and associated with considerable psychological, physical and social challenges [2]. Each year, more than 7,300 Canadians are admitted to hospitals nationwide to undergo lower limb loss (LLL) [3]. The majority of LLLs ($86\%$) occur in adults over 50 years of age [3]. The challenges associated with LLL include body image changes, mobility restrictions, pain [4], depression [5], social isolation, and decreased quality of life [6].
Education is an important part of rehabilitation after LLL to support and engage patients in self-management of their health condition [7–9]. Self-management programs with education and supportive interventions may increase people’s coping skills and build confidence to better manage disease-related physical and psychological challenges [7, 8]. Tailored self-management programs, using different feedback and peer support strategies, are reported to be effective at promoting positive health outcomes in other chronic conditions [10, 11]. Additionally, a community-based self-management program for individuals with LLL noted promising improvements in well-being, pain, general self-efficacy [9], and quality of life [12]. However, as rehabilitation services are a primary access-point for education [13, 14], the predominant localization of these services in Canadian urban centers limits their accessibility for some population groups [13, 14].
eHealth–the use of technologies such as computers and smart devices to support health services [15]–is considered an effective approach for delivering self-management programs [10, 15]. For example, cardiac rehabilitation delivered by eHealth may improve physical activity behavior and quality of life [16]. eHealth interventions with interactive features may also increase long-term medication adherence [16]. Further, a LLL physical activity program delivered by video calls increased weekly step count [17].
Based on evidence [18] and theory [19, 20], we co-created [21] an eHealth platform for individuals with LLL called Self-Management for Amputee Rehabilitation using Technology (SMART) [22]. SMART is a web-based app containing educational modules to guide adults with LLL to actively engage in self-management. In a pre-post mixed methods study [22, 23] with 12 individuals with LLL, we assessed the feasibility of SMART delivered with peer-support. This demonstrated SMART was an acceptable intervention, which can support individuals with LLL by providing knowledge and facilitating skill acquisition and development. Participants perceived SMART to be a complementary resource to gain knowledge and encourage them to take actions towards their self-management goals [22]. For this current study, we will conduct an assessor-blinded randomized controlled trial (RCT) and use a mixed methods approach [23] to evaluate the effectiveness of SMART on LLL-relevant outcomes compared to a control group, in older adults (≥50yrs) with unilateral LLL, while documenting implementation factors (Type 1 Effectiveness-Implementation Hybrid Design) [24].
## 1.2. Hypotheses
We hypothesize adults (50 years and older) with LLL who receive SMART and usual care (intervention group), compared with social contact calls and usual care (control group), will have greater walking capacity (Timed-Up and Go test (TUG)) after six weeks.
## 1.3. Trial Design
This is a 1:1 parallel single (assessor)-blinded RCT with a clinical superiority framework [25] using a Type 1 Effectiveness-Implementation Hybrid Design [24].
## 2. Methods
We used the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) to report the study protocol [26]; please see S1 Checklist. Fig 1 shows the SPIRIT schedule of enrollment, interventions, and assessments.
**Fig 1:** *Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) schedule of enrolment, interventions, and assessments.(TUG = Timed Up and Go; ASCQ = Ambulatory Self-Confidence Questionnaire; CES-D = The Center for Epidemiologic Studies Depression Scale; ABIS-R = Revised Amputee Body Image Scale; __ SMFA = Short Musculoskeletal Function Assessment; ABC = Activities-specific Balance Confidence scale; PAI = Physical Activity Identity; EQ-5D-5L = Euro Quality of Life–Five Level Instrument; TGP = Tenacious goal pursuit; AP = Action planning scale; SMASc = Self-Management Assessment Scale; SHRI = Self-Report Habit Index).*
## 2.1. Trial setting
We will recruit from four sites, one in British Columbia and three in Ontario. In British Columbia, we will recruit potential participants with LLL from five health authorities: Vancouver Coastal Health (including Providence Health Care), Vancouver Island Health, Fraser Health, Northern Health, and Interior Health. In Ontario, we will recruit from two health regions: South West (Parkwood Institute) and Central Health (West Park Healthcare Centre, and Sunnybrook Hospital). We chose these sites for three key reasons: 1) access to a large population of individuals with LLL to enhance the recruitment; 2) the characteristics of LLL population from these sites are representative of Canadians with LLL (e.g., primarily older individuals with dysvascular-related amputation [3]) and includes some from rural and remote communities to support generalizability of findings; and 3) similarity of inpatient and outpatient rehabilitation services across the sites and provinces.
## 2.2. Participants
We will invite people who meet the following criteria to join the study: 1) aged 50 years or older; 2) have a unilateral transtibial amputation, knee disarticulation or transfemoral amputation due to diabetes or vascular disease; 3) fitted with their initial prosthesis no longer than two years; 4) self-identify as being able to speak and read English; 5) sufficient cognitive capabilities (Telephone Montreal Cognitive Assessment ≥ 19 [27]); 6) have access to the internet and a computer/smartphone. Individuals will be excluded if they have a substantial health condition (e.g., congestive heart failure, diagnosed dementia), anticipate further surgery (e.g., LLL revision), or are unable to use the computer independently (e.g., using hands for typing).
## 2.3.1. Self-Management for Amputee Rehabilitation using Technology (SMART)
Participants allocated to the intervention group will receive usual care after LLL and access to SMART. We previously provided a detailed description of SMART using the Template for Intervention Description and Replication checklist [22, 28]. Participants will receive a link enabling direct connection with the SMART platform via individualized username and password. SMART link is housed on the affiliated university educational platform, and data will be kept in Canada.
SMART is a web-based eHealth intervention with six interactive, narrated modules (Table 1). There is an additional section, “More resources” which includes additional resources for LLL such as timeline after amputation, insurance, and information to locate a prosthetist. Each module is about 20 to 30 minutes in duration. SMART will be delivered with the support of peer mentors using Brief Action Planning (BAP) [29], based on motivational interviewing [30]. Six peer mentors will be recruited from either British Columbia or Ontario. Peer mentors will be more than 50 years in age and have either transfemoral and transtibial amputations. They will support participants with goal setting and action planning [31]. All peer mentors will take the online BAP training [29]. Peer mentors will provide a 20-minute weekly secure online meeting (via Zoom platform) with SMART group participants, for six weeks [29]. The peer mentors will ask the participants how long they spent on each SMART module and the adverse events in the weekly meeting. All participants in the SMART group will receive online training. The trainer will be a research assistant who will not be involved in data collection. They will arrange the first meeting between the participant and the peer mentor. Participants will be asked to complete one module each week, at their own convenience, over the 6-week intervention period. The SMART platform will be asynchronously monitored through a secure web portal by the trainer, who can observe participants’ progress and provide feedback if required. If there is no online activity in seven consecutive days, the trainer will contact the participant to inquire about the reasons and troubleshoot any problems. After six weeks (end of the intervention), the trainer will ask the participants to use SMART independently and refer back to the information for four more weeks and document their usage.
**Table 1**
| SMART Module | Content |
| --- | --- |
| 1. Setting-up my goals | Overviews of keys for turning intentions into behavior (e.g., goal setting, rating confidence) |
| 2. Understanding my amputation | Levels of amputation, pain management, skin care for residual limb, taking care of diabetic foot |
| 3. Taking care of myself | Well-being or feeling about amputation, body image, relationships, fatigue and energy conservation, diet, and weight control |
| 4. My life at home | Home modifications for a safe environment, daily tasks after an amputation, getting up from the floor if a fall happens, mobility aids and driving |
| 5. My prosthesis | Types and parts of a prosthesis, wearing a prosthesis, sock management, selecting a shoe for prosthesis, cleaning, and maintenance of a prosthesis |
| 6. Getting active | Benefits of getting active and exercise, examples of physical activities, training exercises, tips for walking with a prosthesis |
| More resources | Additional resources for LLL such as timeline after amputation, insurance, and finding a prosthetist. |
## 2.3.2. Control Intervention
Participants randomized to the control arm will receive usual care after LLL, and a weekly social contact calls by a trainer via phone. In this call, the trainer will answer the participants’ questions regarding the study and ask about any adverse events. This weekly contact is intended to control attention bias and interpersonal interactions [32].
## 2.4.1. Sociodemographic and clinical characteristics
Descriptive characteristics will be collected at baseline (T1) including, age, sex, gender, level of education, and marital status. Clinical variables such as presence of comorbidities, level of amputation, cause of amputation, side of amputation, date of amputation, discharge date from hospital, duration of rehabilitation, and dates of temporary and permanent prosthesis receival will also be collected. We will ask the participants to report their visits or calls with outpatient rehabilitation services, such as a physiotherapist, physiatrist or their surgeon.
## 2.4.2. Clinical outcome measures
We will measure the following clinical outcomes to address the two study objectives.
Primary clinical outcome. Timed Up and Go test (TUG) is a global functional measure of mobility. It includes transitions from sit to stand, initiation of gait, acceleration, deceleration and turning, and how individuals with LLL function within their environment [33]. Participants will be asked to set a standard chair (48-cm-high) and mark a 3-meter spot from the front legs of the chair. Participants will be asked to sit on the chair. When instructed, the participant will stand and walk 3 meters, turn around, walk back, and sit back in the chair [33]. The time will be reported in seconds, rounded to the nearest 0.1s. The reliability and validity for remote measuring of TUG using an iPad app have been reported for LLL population [33]. According to the prior feasibility study, conducting TUG remotely was safe [22]. We will first ensure the participants are allowed to walk without clinician supervision; otherwise, we will skip the test. A minimal detectable difference of 1.28 seconds is reported for the TUG for people with LLL [33].
Secondary clinical outcomes. Ambulatory Self-Confidence Questionnaire (ASCQ) assesses self-efficacy in walking [34]. Self-efficacy affects an individuals’ belief in performing an activity, and is a predictor for actual behavior, including walking [19, 35]. The ASCQ includes 22 items; each item is scored from zero (which indicates not at all confident) to 10 (which indicates extremely confident) [34]. A mean score will be calculated for overall ambulatory confidence, with higher scores indicating higher confidence. There is evidence for reliability and validity of the ASCQ for community-dwelling older adults [34]. It has been reported that a change more than 0.23 is indicative of change in ambulatory confidence for older adults [34].
The Center for Epidemiologic Studies Depression Scale (CES-D) is used to assess depressive symptoms over the past week in adults [36–38]. Depression is an important health outcome in adults with LLL. It is associated with pain and dysfunction [39, 40], and could negatively affect the use of prosthesis [41] and quality of life [42]. The CES-D includes 20 items. Each item is scored on a four-point Likert scale from zero (which indicates the least or none of the time) to 3 (which indicates the most or all of the time). Total score ranges from zero to 60, and higher scores mean higher level of distress. There is evidence for reliability [36] and validity of the CES-D for adults with LLL [36] and the general population [43]. A total score of 19 or more indicates substantial symptoms of depression [44, 45].
Revised Amputee Body Image Scale (ABIS-R) assesses feelings about the body experienced after amputation [37, 46]. Poorer perceived body image is associated with depression and lower level of prosthetic satisfaction [47]. The ABIS-R includes 14 items; each item is scored from zero (which indicates never happened) to two (which indicates happening all the time) [37, 46]. The overall score ranges from zero to 28; higher scores show higher body image disturbance. There is evidence for reliability and validity of ABIS-R for adults with LLL [46]. The minimal clinically important difference of 6.3 is reported for ABIS-R [48].
Pain is common in individuals with LLL [4, 49, 50] and can adversely affect quality of life [51]. We will assess the following: residual limb pain (pain in the remaining part of the amputated site); phantom limb pain (in missing part of the limb); and phantom sensation (in missing part of the limb) [52]. The intensity of pain and sensation in the past week will be assessed using a 10-cm visual analogue scale (VAS), ranging from zero (which indicates no pain or sensation) to 10 (which indicates the worst pain or sensation possible). The frequency of pain and sensation in the past week will be measured using the following scale: “none”, “intermittent”, “constant with variation in intensity”, and “constant with little variation in intensity”. There is evidence for validity of VAS to assess pain [53–55]. A $33\%$ decrease in pain indicates a meaningful change [56].
Short Musculoskeletal Function Assessment (SMFA) assesses individuals’ perceived health status [57, 58]. The SMFA includes 46 items in three sections: difficulty with daily activities (items 1 to 25), experiencing problems because of injury (items 26 to 34), and the extent to which the person is bothered by the problems (items 35 to 46). In this study, we will use items 1 to 34, which cover the functional assessment. The last 12 items cover how patients are bothered by their symptoms. Each item is scored on a five-point Likert scale from one (which indicates not at all bothered) to five (which indicates extremely bothered). A percentage score will be calculated: higher percentage shows higher level of dysfunction. There is evidence for validity of the SMFA for individuals with lower limb vascular injury [59]. The minimal clinically important difference of 9.7 is reported for SMFA [57].
Activities-specific Balance Confidence scale (ABC) assesses perceived balance confidence in different ambulatory activities [60]. Fear of falling may cause restriction in participation in daily and social activities for individuals with LLL [61]; therefore, balance confidence is a determinant of quality of life [60]. The ABC includes 16 items; each item is scored from zero (which indicates not at all confident) to 100 (which indicates extremely confident). A percentage score will be calculated; higher scores mean more confident [62]. There is evidence for reliability and validity of the ABC for individuals with LLL [62]. The minimal clinically important difference of $10\%$ is reported for ABC [63].
Physical Activity Identity (PAI) assesses the extent that physical activity is considered as an integral part of the concept of self [64, 65]. The PAI includes 9 items; each item is scored on a seven-point Likert scale from one (which indicates strongly disagree) to seven (which indicates strongly agree). The total score ranges from nine to 63; higher scores mean higher identity [65]. There is evidence for reliability and validity of the PAI for community dwelling adults [64].
Euro Quality of Life–Five Level Instrument (EQ-5D-5L) assesses five dimensions of health-related quality of life: “mobility, self-care, usual activities, pain/discomfort, and anxiety/depression” [66]. Each dimension is scored on a five-severity level: no, slight, moderate, severe, extreme problems/unable. The total score rages from zero (which indicates the worst imaginable health state) to 100 (which indicates the best imaginable health state). There is evidence for validity of EQ-5D-5L for people with LLL [67].
Tenacious goal pursuit (TGP) [68] assesses the tendency to persist and increase effort in pursuing goals facing obstacles. The TGP includes five items [69]. Each item is scored on a five-point Likert scale from one (which indicates “strongly agree”) to five (which indicates “strongly disagree”). The scores are reversed and summed [69]. Total score ranges from five to 25; higher scores mean higher tenacity [70]. There is evidence for validity of TGP for adults mid-to-late life [69].
Action planning scale (AP) assesses whether people had formed a plan which links goal-directed behavior to environmental cues by identifying when, where, and how to act [71]. AP includes five items [71]. Each item is scored on a five-point Likert scale from completely disagree to completely agree [71, 72]. The total score will be calculated by summing each item score [72]. We will assess the AP for “exercising”, “skin monitoring”, and “cleaning the prosthesis”. There is evidence for reliability of the AP for in-patients in rehabilitation facilities [71].
Self-Management Assessment Scale (SMASc) [73] assesses five domains that are important for an effective self-management, including “knowledge, goals for future, daily routines, emotional adjustment, and social support” [73]. SMASc includes 10 items. Each item is scored on a six-item Likert scale from one (Strongly disagree) to six (Totally agree) [73]. Each domain ranges from two to 12. Lower scores show higher needs of support for self-management. The SMASc will be slightly modified to measure self-management based upon the LLL health condition. There is evidence for reliability and validity of SMASc for people with Type II diabetes [74].
Self-Report Habit Index (SRHI) assesses the automaticity of a behavior and the extent to which individuals with LLL have integrated self-management tasks into their self-concept [75, 76]. We will assess the habit formation for “skin monitoring” and “cleaning the prosthesis,” which are important in managing a LLL. The SRHI includes 12 items; each item is scored on a 7-point Likert scale. The total score is calculated as mean score and ranges from one to seven; higher scores mean stronger habit [75]. There is evidence for reliability and validity of the SRHI for community dwelling adults [77].
## 2.4.3. Implementation factors
We will follow the RE-AIM framework [74] to explore implementation factors including, reach (target population), effectiveness (effects of SMART), adoption (by setting), implementation (dose delivered and received, fidelity to the intervention), and maintenance of the behavior (in target population and settings). During the study, the research coordinator will keep detailed logs to monitor fidelity to research protocols. Also, the peer mentor will ask the participants “how long they spent on each module” (dose) and the adverse events, such as falls or pain. We will conduct and audio record 30-minute semi-structured interviews with participants in the SMART group ($$n = 20$$) to explore their experiences using SMART. The interviews will be conducted over the Zoom platform with auto transcription. A research assistant who will not be familiar with the participants will moderate the interviews. The research coordinator will call any participants who drop out of the study to record their reason(s).
## 2.5. Measuring outcomes at follow-up
Data collection will be performed for SMART and control group at baseline (T1), within one week of completing the intervention (T2), and four weeks post-intervention (retention period) (T3). The amount and type of usual care or rehabilitation services participants received throughout the study duration, will be asked at T2 and T3. The one-on-one semi-structured interviews will be conducted at T3 with the SMART group to determine impressions of the protocol and intervention. Fig 2 shows the flow diagram of the study [78]. All data collection will occur online via Zoom utilizing a secure connection.
**Fig 2:** *Flow diagram of study progress in terms of participants enrolment, group allocation, follow-up, and data analysis [78].*
## 2.6. Sample Size
The sample size calculation was based on clinical superiority design [25], in which we hypothesize SMART is more effective than providing written educational information [79]. For the effectiveness portion of the study, we will aim to compare the primary outcome, TUG, using a ratio 1:1 group allocation. In our prior feasibility study, the minimal detectable difference of improvement in TUG after six weeks of using SMART was 2.1 seconds in adults with unilateral LLL (median age = 56 y) [22]. We interpreted this change as meaningful because the findings of the qualitative component of the same study revealed that participants perceived SMART and peer support could improve their mobility confidence and encourage them to take actions towards their goals, which was more walking. Assuming a minimum detectable difference of 2.1 seconds, $80\%$ power, and type-I error probability of 0.05, we need to enroll a minimum of 38 participants per group. In our feasibility study, the retention rate was $100\%$ [22]. However, due to challenges with participant drop out in clinical trials [80], we will account for $15\%$ attrition rate in the sample size calculation. Therefore, we will recruit 86 participants (43 per arm). Sample size was calculated using G*Power [Version 3.1.9.4, Program written by Franz Faul, University Kiel, Germany] [81].
## 2.7. Recruitment
We will invite eligible candidates through physiotherapists, occupational therapists, prosthetists and physiatrists at the associated rehabilitation facilities, and private prosthetic clinics in British Columbia and Ontario. We will also post the study information at the hospitals and prosthetic clinics at the four sites. The electronic version of study information and a recruitment video will be posted on amputation-related web pages, such as Amputee Coalition of Canada, or social media platforms, including Facebook and Twitter. A $25 token of appreciation will be offered to all participants at the end of each evaluation timepoint. According to our feasibility study, the recruitment rate was 1.7 participants per month at British Columbia [22]; therefore, we anticipate it will take approximately 13 months to enroll 86 participants at British Columbia and Ontario sites.
## 2.8. Sequence generation and randomization
We will use a central computerized randomization process with variable block sizes to randomly allocate participants to the intervention (SMART) group or control group using a 1:1 ratio. An independent statistician will provide the randomization list through REDCap (REDCap Software, Vanderbilt University and National Institute of Health, USA). Upon the enrolment of a participant a study ID number will be allocated. The study ID number will be linked to the randomization list.
## 2.9. Allocation concealment
After completing the baseline assessment, the research coordinator will reveal the group allocation of the participant through REDCap randomization list.
## 2.10. Randomization
Interested individuals will contact the research coordinator through phone or email. The research coordinator will screen the participants and send them the consent form for their review. After expressing interest, the research coordinator will send the consent form link to participants’ emails via Qualtrics (Qualtrics Software Company, USA). Upon obtaining consent, the research coordinator will schedule the first online meeting with a blinded assessor to complete the baseline assessment. The research coordinator will log onto the online randomization system to determine the next allocation within 48 hours. The research coordinator will forward the participant’s contact information to the group trainer to schedule the first online meeting.
## 2.11. Blinding
Due to the nature of the SMART, blinding to receipt (or not) of the eHealth intervention is impossible [82]. In this study, we will hire one assessor for all data collection, who will be blinded to group allocation. We will ask participants not to disclose their group allocation during assessments [82]. Furthermore, we will have separate trainers for each group (to minimize trainer bias); and the primary outcome measure, the TUG, is a performance-based measure with standardized instructions.
## 2.12. Adherence
The SMART group trainer will monitor the activity of participants asynchronously. If there is no activity within seven consecutive days, the SMART group trainer will contact the participant. The control group will also receive a weekly contact from the trainer for six weeks. The research coordinator will contact all participants every four weeks until the end of study to remind them of the next assessment session and confirm the schedule. This strategy was found to successfully reduce the loss to follow-up to less than $20\%$ in a previous study [83]. Furthermore, in the prior feasibility study the loss to follow-up was zero [22].
## 2.13. Data management
The research coordinator will be responsible for screening, obtaining consent, scheduling the assessment sessions, obtaining group allocation via the website, and scheduling the training session. The research coordinator will train the assessor and group trainers. The SMART group trainer will schedule the first meeting of the peer mentor and the participant. All data will be collected by a trained assessor who will be blinded to group status. A trained research assistant will be hired to complete the interviews. All quantitative and qualitative data will be password encrypted and stored in a secure server hosted by the primary affiliated university. Two research assistants will be trained to correct the Zoom auto transcription’s inaccuracies. We will also hire two research assistants to code the transcripts of interviews with the SMART group.
## 2.14. Data Analyses
We will assess the distribution of data using the one-sample Kolmogorov-Smirnov test. We will report the descriptive statistics using means and standard deviations (SD) for continuous variables and frequency and percentage for categorical variables.
## 2.14.1. Clinical Outcomes
We will use intention-to-treat analyses [84]. All primary and secondary outcomes will be compared between the SMART and control groups, using analysis of covariance (ANCOVA), controlling for baseline scores. We will report the endpoint and change score using analysis of variance (ANOVA) to compare precision with ANCOVA for sensitivity analyses. We will conduct interim data analyses on the primary outcome when $50\%$ of participants have completed T2. If the results show the SMART or control arm is superior, and sufficient information is available, we will end participant recruitment to minimize burden and cost. We will also disaggregate the data, such as walking capacity and confidence, by sex and gender to explore differences.
## 2.14.2. Study and intervention fidelity, and participant experience
We will analyze transcriptions using conventional content analyses [85] with NVivo (Version 12.6.0.959); in which the coding categories will be derived directly from the transcriptions [85]. Two coders will review the data repeatedly and code them. After completing interviews, the data will be re-coded and similar codes will be grouped into themes. Themes will reflect common meanings in participants’ experiences regarding SMART. We will also use different strategies to ensure trustworthiness of the research. To support confirmability, multiple investigators will be involved in data coding. To promote dependability, the study protocol will be reported in detail [86, 87]. Reflexivity will be facilitated with self-reflection notes after each interview, involving multiple investigators in analysis, and member checking [88]. Interview results will complement the quantitative results to inform intervention fidelity, and provide a more in-depth assessment of benefits of SMART and user acceptability [23].
## 2.15. Monitoring
We will implement several strategies for monitoring. First, based on the preliminary findings of our SMART feasibility study, there was minimal risk to study participants. Only two out of 12 participants reported falls, related to LLL comorbidities such as phantom sensation at night and increasing physical activities [22]. Second, the preliminary findings showed SMART content and study protocol were acceptable [22]. SMART content also includes safety-related instruction such as avoiding extra pressure on residual limb, or tasks that can increase risk of falls. Third, at each session, the peer mentors and the control group trainer will ask the participants if they experienced any adverse events, such as falls, pain, discomfort, and report it to the research team. Fourth, all participants can contact their group trainer if they experience unusual discomfort, pain, or physical symptoms. Finally, if the trainer or the research coordinator notices any issues, they will refer the participants to their family doctor. The participants will be informed that they can discuss issues with care providers, as necessary.
Data and Safety Monitoring Board (DSMB) will review outcome data. The DSMB will provide suggestions regarding safety, SMART benefit, or study protocol modification. The DSMB will include four members who are external to the research team. The members are a biostatistician, a physiatrist, a rehabilitation therapist (occupational or physiotherapist), and an individual with LLL. The DSMB members will meet at least twice a year. Adverse events (e.g., falls, skin breakdown) will be documented by the assessor using a Treatment Protocol Checklist and will be reported to the DSMB as well as the applicable Ethics Review Board.
## 2.16. Ethics and funding
Ethics approval has been obtained from Research Ethics Boards at University of British Columbia [March 7th, 2022; H20-03316-A003], and regional health authorities, Sunnybrook Research Institute [April 28th, 2022], Joint West Park Healthcare Centre-The Salvation Army Toronto Grace Health Centre Research Ethics Board (JREB) [June 28th, 2022; 22-002-WP], and Western Research Ethics Board (REB). Any protocol amendments such as changes to inclusion criteria, outcomes, or analyses, will be submitted to the relevant ethics board(s) as well as the clinical trial registry. All participants will provide their informed consent electronically using an online consent form hosted on a primary affiliated university server. All data will be collected and stored anonymously using a specific participant ID number in Canada. This study has been funded by the Canadian Institutes of Health Research (CIHR) [Grant number: 438258]. We will establish data sharing, where de-identified data will be accessible upon a reasonable request after the findings are published. The results of this study will be submitted to international conferences and peer-reviewed journals for publication. We will disseminate the results electronically, including on the websites of health authorities in British Columbia and Ontario. We will provide a summary of study findings in plain language with our patient partners and share them via newsletters and websites, such as Amputee Coalition of British Columbia and Ontario Association of Amputee Care.
## 3. Results
The ethics at each university has been submitted and under review. Hiring study staff, training the peers, and group trainers at each site are currently underway. No participants have been enrolled.
## 4. Discussion
Based on the results of a previous exploratory study [22], education in rehabilitation services was limited for adults with LLL due to inaccessibility, such as living in remote areas, or due to limited time visiting the clinicians. This reduction in care creates a gap in the consolidation of education and skills to manage LLL-related outcomes which could result in decreased mobility, limited function, and quality of life [89, 90]. SMART has the potential to address this gap and improve LLL outcomes. Our study will provide an understanding about the clinical effectiveness, and key implementation factors of SMART for people with LLL. In sum, it will provide essential information for possible future integration of SMART into clinical practice to augment rehabilitation services, if it is the right fit for older adults with LLL [24].
## 5. Limitations
There are four main limitations in this study. Due to the nature of SMART, as an eHealth intervention, blinding of participants is impossible [82]. However, the outcome assessor will be blinded, and participants will be asked not to disclose their group to the assessor. Second, the participants may be receiving an ongoing rehabilitation treatment, as usual care, which could affect knowledge acquisition. Moreover, the dose and content of usual care may be different among participants. Third, while participants in SMART will be asked to complete one module per week, they may ignore the protocol. Therefore, we cannot report the precise dose of SMART as there is no tracking system for module completion. Fourth, the results may be affected by social desirability bias for self-reported outcomes.
## 6. Conclusions
This study will provide an evaluation of SMART benefits in recovery of individuals with LLL. SMART has the potential to provide accessible, low-cost educational and skill-based content regardless of geographical boundaries, which may augment current rehabilitation resources. The use of eHealth innovations for self-management and education training can promote motivation for people living with a chronic health condition to take actions to reach their goals. The eHealth innovations can be used as a complementary resource in addition to other in-person rehabilitation programs.
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|
---
title: Combining viral genomics and clinical data to assess risk factors for severe
COVID-19 (mortality, ICU admission, or intubation) amongst hospital patients in
a large acute UK NHS hospital Trust
authors:
- Max Foxley-Marrable
- Leon D’Cruz
- Paul Meredith
- Sharon Glaysher
- Angela H. Beckett
- Salman Goudarzi
- Christopher Fearn
- Kate F. Cook
- Katie F. Loveson
- Hannah Dent
- Hannah Paul
- Scott Elliott
- Sarah Wyllie
- Allyson Lloyd
- Kelly Bicknell
- Sally Lumley
- James McNicholas
- David Prytherch
- Andrew Lundgren
- Or Graur
- Anoop J. Chauhan
- Samuel C. Robson
journal: PLOS ONE
year: 2023
pmcid: PMC10035897
doi: 10.1371/journal.pone.0283447
license: CC BY 4.0
---
# Combining viral genomics and clinical data to assess risk factors for severe COVID-19 (mortality, ICU admission, or intubation) amongst hospital patients in a large acute UK NHS hospital Trust
## Abstract
Throughout the COVID-19 pandemic, valuable datasets have been collected on the effects of the virus SARS-CoV-2. In this study, we combined whole genome sequencing data with clinical data (including clinical outcomes, demographics, comorbidity, treatment information) for 929 patient cases seen at a large UK hospital Trust between March 2020 and May 2021. We identified associations between acute physiological status and three measures of disease severity; admission to the intensive care unit (ICU), requirement for intubation, and mortality. Whilst the maximum National Early Warning Score (NEWS2) was moderately associated with severe COVID-19 ($A = 0.48$), the admission NEWS2 was only weakly associated ($A = 0.17$), suggesting it is ineffective as an early predictor of severity. Patient outcome was weakly associated with myriad factors linked to acute physiological status and human genetics, including age, sex and pre-existing conditions. Overall, we found no significant links between viral genomics and severe outcomes, but saw evidence that variant subtype may impact relative risk for certain sub-populations. Specific mutations of SARS-CoV-2 appear to have little impact on overall severity risk in these data, suggesting that emerging SARS-CoV-2 variants do not result in more severe patient outcomes. However, our results show that determining a causal relationship between mutations and severe COVID-19 in the viral genome is challenging. Whilst improved understanding of the evolution of SARS-CoV-2 has been achieved through genomics, few studies on how these evolutionary changes impact on clinical outcomes have been seen due to complexities associated with data linkage. By combining viral genomics with patient records in a large acute UK hospital, this study represents a significant resource for understanding risk factors associated with COVID-19 severity. However, further understanding will likely arise from studies of the role of host genetics on disease progression.
## Introduction
Coronavirus disease 2019 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) pathogen [1], has resulted in arguably the most significant global health crisis in recent history. SARS-CoV-2 was first identified in Wuhan, China in winter 2019 [2] and quickly spread across the globe, being declared a pandemic by the World Health Organisation (WHO) a few months later in March 2020 [3]. At the time of writing, COVID-19 has resulted in over 625 million infections and 6.57 million deaths worldwide [4]. As well as this significant death toll, many survivors have suffered life-altering complications as a result of contracting the disease [5]. The COVID-19 pandemic has also resulted in significant social and economic disruption, including the biggest global recession since the Great Depression [6]. Additionally, healthcare services such as the UK National Health Service (NHS) have been significantly impacted by COVID-19, resulting in staff shortages, long wait times for ambulances [7] and a significant backlog for patients needing elective care [8].
The factors that influence severe cases of COVID-19 are not yet fully understood but have been clearly linked primarily to older age groups (predominantly the over 65s), primarily due to a higher proportion of comorbidities [9]. However, younger patients still experience severe outcomes from COVID-19, albeit more rarely. Severe outcomes include requirements for intubation and mechanical ventilation, admission to intensive care units (ICU), and death. Factors currently associated with an increased risk of severe outcomes include smoking, having a pre-existing condition such as obesity, asthma, cardiovascular disease, and diabetes, or socio-economic factors [9–11]. Children especially are far less likely to become seriously ill from COVID-19 [12,13].
Large-scale SARS-CoV-2 sequencing programs throughout the pandemic have allowed researchers to explore the role of viral genomics and different variants of the virus. However, whilst genomic epidemiology has been used in a number of studies to understand viral transmission in settings such as hospitals [14–17], long-term care facilities [18–22], and army barracks [23,24], well-powered studies of patient outcomes with high numbers of cases currently remain limited [25–31]. One of the largest studies looking at large-scale effects of viral genomics on patient outcomes are papers from the Hospital Onset COVID-19 Infections (HOCI) study in the UK [25,26,28]. In one such study, Stirrup et al. [ 2021] identified a higher hazard ratio of mortality for female patients with the Alpha variant compared to other variants when compared to male patients [26]. More recently, Webster et al. [ 30] identified lower or equivalent risk of severe outcomes for the BA.2 Omicron variant compared to BA.1.
Here we combined data from two resources developed over the course of the pandemic in the city of Portsmouth in the UK; whole-genome sequencing (WGS) of SARS-CoV-2 samples from COVID-19 positive samples collected through the COVID-19 Genomics UK (COG-UK) Consortium by researchers at the University of Portsmouth (UoP), and patient-specific information (e.g. demographics, COVID-19 status, illness severity scores, comorbidities, treatments and outcomes) for all hospital admissions collected by the Portsmouth Academic Consortium For Investigating COVID-19 (PACIFIC-19) team at Portsmouth Hospitals University NHS Trust (PHU). PHU saw a steep rise in COVID-19 cases over the winter of 2020, with 3,272 new hospital cases between September and February, and a peak of 539 positive inpatients, representing a national outlier for infections compared to the average peak of 219 in the South East (https://coronavirus.data.gov.uk/).
The aim of this study was to combine the COG-UK dataset and PACIFIC-19 Clinical Outcomes Research Group (CORG) database to develop a data resource linking clinical disease severity, therapeutic interventions, comorbidities and demographics to SARS-COV-2 genomic lineage data. One such metric, the National Early Warning Score 2 (NEWS2), provides a simple metric for identifying acutely ill patients and those requiring transfer to ICU [32,33]. It is calculated based on 6 physiological parameters recorded at the bedside (respiration rate, oxygen saturation, systolic blood pressure, pulse rate, level of consciousness or new-onset confusion, temperature), each assigned a score of 0–3 by the healthcare team, with a score greater than 7 suggesting a high-risk patient requiring emergency assessment by the critical care team. These data cover COVID-19 infections in the area between March 2020 and May 2021, including the major UK wave of COVID-19 over winter 2020, and were used to explore factors influencing clinical severity of COVID-19 and identify specific mutations or constellations of mutations associated with severe COVID-19. In particular, this time period covers the introduction of the first variant of concern (VOC) Alpha, known also by the Pangolin (https://cov-lineages.org/) lineage name B.1.1.7, allowing us to address whether the emergence of this lineage impacted on the clinical severity of COVID-19.
As global restrictions continue to flex in response to ongoing changes in case-loads, and we learn to live with the SARS-CoV-2 virus as new VOCs develop, it is increasingly important to look back at what we have learned to fully understand the factors associated with poor outcomes from COVID-19. There is significant motivation to further expand our knowledge of potential risk factors for severe COVID-19, especially where such factors may allow medical staff to predict a severe outcome of COVID-19 for early intervention. This study thus provides a significant resource for understanding the role that a variety of clinical factors and viral genomics play in determining patient outcomes.
## Study sites
PHU is one of England’s largest acute hospital trusts, serving the major coastal port city of Portsmouth and surrounding areas on the South Coast of the UK. The primary site for this study was Queen Alexandra Hospital (QAH), a research hospital within PHU with an 800-bed capacity treating >500,000 patients per year.
## Laboratory diagnosis
Quantitative polymerase chain reaction (qPCR) COVID-19 tests for hospital staff, patients, and members of the local community within Portsmouth and surrounding areas were carried out at QAH. Samples were collected from participants using nasopharyngeal swabs and stored and transported in Sigma-Virocult 1 mL Viral Transport Media (VTM) (Medical Wire & Equipment, Corsham, UK).
Multiple clinically validated testing methods were used over the period of the study, following manufacturer’s directions. These approaches include using the Panther system with the Aptima SARS-CoV-2 assay (Hologic, Marlborough, USA). This method involves automated RNA extraction and transcription-mediated amplification, providing a qualitative result to confirm the presence or absence of SARS-CoV-2 by amplifying two conserved regions of the SARS-CoV-2 ORF1ab gene, comparing the fluorescence signal to an internal control.
Additional testing was performed using the Anatolia Geneworks SARS-CoV-2 PCR v2 kit, which has 2 SARS-CoV-2 targets: ORF1ab and E gene alongside an internal control. VTM sample extraction was performed on the QIAsymphony SP/AS extraction system (Qiagen, Hilden, Germany) off-board lysis protocol (PATHOGEN, COMPLEX 200_OBL_V4_DSP) using the QIAsymphony DSP Virus/Pathogen Midi or Mini Kit and reverse transcription (RT) real-time qPCR amplification was performed on the LightCycler 480 II (Roche, Basel, Switzerland).
Additional rapid testing was conducted using the Xpert® Xpress SARS-CoV-2 assay on the GeneXpert (Cepheid, California, USA), a cartridge-based system for rapid detection, extraction and amplification using real-time RT-qPCR to detect 2 targets for SAR-COV-2 in the N2 and E gene regions, alongside internal controls.
## Sampling
All samples, including patients, healthcare workers (HCWs) and community cases tested for COVID-19 at PHU, were made available for viral extraction and whole genome sequencing. Samples from PHU were sequenced alongside samples from a wide range of NHS Trusts across the South Coast of the UK by the University of Portsmouth as part of the COG-UK consortium [34]. Where samples could not be sequenced due to limits in capacity, the COG-UK surveillance sampling strategy was applied to ensure that cases represented a random representation of currently circulating variants. Briefly, samples were selected either due to targeted sequencing priorities, such as HCWs for the SARS-CoV-2 Immunity & Reinfection EvaluatioN (SIREN) study (https://snapsurvey.phe.org.uk/siren/), or were selected randomly from available samples each day up to local capacity.
## Whole genome sequencing
Sequencing was conducted following the ARTIC nCoV-2019 sequencing protocol V.3 (LoCost) [35]. RNA was reverse transcribed and then amplified with amplicon PCR using the ARTIC nCoV-2019 V3 primer panel (Integrated DNA Technologies, Iowa, USA). This primer panel tiles the SARS-CoV-2 genome with 98 pairs of primers, each producing an amplicon of ~500 bp. Odd-numbered primers were pooled separately from even-numbered primers to prevent over-amplification of overlapping amplicon regions.
Nuclease-free water (NFW) was used as a negative control on each sequencing run to assess contamination in the amplification stage. A synthetic SARS-CoV-2 RNA control (Twist Bioscience, San Francisco, CA, USA) was also added to each run as a positive control. To confirm sample quality and assess likely failures or contamination issues, positive and negative controls, along with representative samples from each run, were quantified using the Qubit DNA Assay Kit in a Qubit 2.0 Fluorometer (Life Technologies, California, USA).
The LSK-109 Ligation Sequencing Kit and EXP-NBD196 Native Barcoding Expansion 96 Kit from Oxford Nanopore Technologies (ONT, Oxford, UK) were used to generate libraries for Nanopore sequencing. Libraries were sequenced on R9.4.1 flow cells on a GridION X5 platform (ONT, Oxford, UK) for 24–36 hours (depending on library sample number) to achieve a final coverage of ~100,000 reads per sample. Raw reads were demultiplexed by the MINKnow software on the GridION using Guppy v3.2.10.
Sequencing data were processed using the ARTIC field bioinformatics toolkit v1.2.1 (https://github.com/artic-network/artic-ncov2019). Real-time sequencing performance was monitored using RAMPART (v1.0.6) [36]. Reads were mapped to the SARS-CoV-2 reference genome (Wuhan-Hu-1, GenBank, MN908947.3) using MiniMap2 (v2.17-r941) [37]. Nucleotide variation from the reference sequence was identified using Nanopolish (v0.13.2; https://github.com/jts/nanopolish). SARS-CoV-2 variant type was assigned using Pangolin (https://github.com/cov-lineages/pangolin) with PANGOLearn version 2021-10-18.
## Sample exclusion
If genome sequencing failed (e.g., as a result of the negative control showing evidence of PCR contamination), samples were repeated from scratch. If sufficient RNA was not available, samples were excluded from the study. Samples from PHU were also excluded if the participant involved indicated their retrospective desire to opt out from the study.
For the outcome analysis, further exclusions were also applied to the combined dataset. Samples where the sequence data covered less than $50\%$ of the genome were excluded due to poor resolution of viral variant subclasses. Samples were also excluded for individuals aged less than 16 years old, individuals that were not admitted to the main hospital (e.g., residents of long-term care facilities), and individuals who had not yet completed their hospital stay. In cases where multiple samples were taken from a single individual, the sample with the highest genome coverage was taken forward for further analysis. This is summarised in S1 Fig.
## Clinical outcome data
The PACIFIC-19 team at PHU holds a database of patient-specific information (e.g. demographics, COVID-19 status, illness severity scores, treatments and outcomes) for all hospital admissions, including COVID-19 positive patients, between January 2018 and May 2021. The PACIFIC-19 CORG database contains data collated from the Local Laboratory Information Systems (LIMS) using COGNOS for interrogation to identify all positive samples, and manually from the APEX Pathology LIMS. These data were linked to SARS-CoV-2 genome sequence data using the COG-UK sequencing codes and locally assigned sample source IDs.
## Clinical data analysis
To maximise the number of near-complete entries usable for our analyses, we dropped data columns where $15\%$ or more of the entries contained missing data. Imputation of missing values was not used to avoid significantly biassing the results.
Three main measures of severity as a result of COVID-19 infection were used in this analysis; patient death within 30 days of diagnosis, patient admission to ICU or intubation of the patient. In addition, we took a general measure of case severity based on the occurrence of at least one of these three outcomes.
For pair-wise associations between categorical variables, the association strength was calculated using Cramer’s V score V (with bias correction) [38], based on the χ2 statistic, with statistical significance calculated using the p-value from a χ2 test [39]. For pair-wise associations between continuous variables, the correlation coefficient ρ and p-value from a Spearman’s Rank test were used to determine the association strength and statistical significance respectively. For pair-wise associations between categorical and continuous variables, the association strength was determined using the Correlation Ratio η2 [40].
To ensure no bias as a result of non-normally distributed data, the continuous variable was ranked prior to calculation. Statistical significance was determined using the p-value from a Kruskal–Wallis H test. In each case, the association strength score A was assumed to be negligible if |A| < 0.1, weak if 0.1 ≥ |A| > 0.3, moderate if 0.3 ≥ |A| > 0.5, and strong if |A| ≥ 0.5. Associations were determined to be statistically significant when $p \leq 0.05.$
## Machine learning for the identification of mutations associated with disease severity
Mutation information from sequencing experiments was numerically encoded as follows: 1 = wild-type, 2 = substitution, 3 = insertion, 4 = deletion. These data were linked to clinical data as input for machine learning models to further explore the role of viral mutations of SARS-CoV-2 on severity of disease in COVID-19. We screened nine machine learning models and one deep-learning neural network method to rank and identify mutations with a possible role in determining patient outcomes. Training of models and calculation of accuracy metrics were determined from 6-fold stratified cross-validation screening using Python V3.8.8 with TensorFlow V2. Data were proportioned into an 80:20 train-test split.
A binary-outcome variable for severity was defined based on mortality having occurred following escalation to the ICU. To address the imbalance in these data, with 3.2-fold fewer cases of mortality than survival, Synthetic Minority Oversampling (SMOTE) techniques were implemented. Hyperparameters were tuned for optimal performance using a Grid-search method while implementing 6-fold cross-validation. To get an overall view of the metrics incorporating both classes, precision, recall and F1 statistics were calculated for cases in the test set with outcome = 0 or outcome = 1 separately, with the macro-average scores calculated based on the mean of the two.
The best accuracy combined with minimal loss scores were obtained using the multi-layer perceptron artificial neural network (MLP-ANN), using the sequential API within TensorFlow. The input layer to the MLP-ANN introduces linear weighted input variables to the neurons in the hidden-layers. Dropout regularization was employed to offset the overfitting dilemma typically encountered in machine-learning models [41]. This approximates training of a large number of neural networks with different architectures in parallel, where a number of layers are randomly ignored or dropped out. Model accuracy and loss scores began to plateau by 4,000 epochs, so were run to 10,000 epochs to maximise the accuracy (S2 Fig).
## Ethics statement
This work has been approved by the Health Research Authority (HRA) and Health and Care Research Wales (HCRW) following a favourable opinion from the North West–Haydock Research Ethics Committee on 24th April 2020 (Ref: 20/NW/0217). Participants were offered the opportunity to opt out of having their anonymised data used in this study retrospectively. This work is part of the Sequencing and Tracking of Phylogeny (STOP COVID-19) study, which was posted to ClinicalTrials.gov (Ref: NCT04359849) on 24th April 2020. This work also forms part of the wider COVID-19 Genomics UK (COG-UK) Consortium surveillance study, which was approved by the Public Health England Research Ethics Governance Group and granted ethical approval by the PHE Research Ethics and Governance Group (REGG) on 8th April 2020, (PHE R&D ref: R&D NR0195). The PACIFIC-19 Clinical Outcomes Research Group (CORG) database was approved by the HRA Research Ethics Committee in April 2021 (Ref: 21/SC/0080), with a study extension provided to allow access to the data for the STOP COVID-19 project (IRAS 282394).
## Patient demographics
The primary dataset used in this analysis combines viral genomics with clinical metadata for PHU. Following filtering of cases (see Materials and methods) and merging of the data sets, combined data for 929 individual patients were used for downstream analyses (S1 Fig). A breakdown of these data based on some of the key demographics and clinical factors can be seen in Table 1. Of these 929 cases, 360 ($38.8\%$) showed severe outcomes (ICU admission, intubation or death within 30 days of diagnosis), with 569 ($61.2\%$) showing non-severe outcomes. Looking at the severe outcomes in more detail, 295 ($31.8\%$) patients died, 111 ($11.9\%$) patients were admitted to ICU, and 93 ($10.0\%$) required intubation in ICU. Of those patients on ICU, 46 ($41.4\%$) also died, suggesting that the majority of fatalities (249; $84.4\%$) occurred outside of ICU, with 70 ($23.7\%$) occurring outside of the hospital. However, the majority of these deaths (181; $61.4\%$) occurred in patients aged 80 or above, with only 5 admitted to ICU. *In* general, patients suffering severe outcomes were older, with a median age of 76 (IQR [63,85]), with $52.8\%$ of cases between 70 and 90 years old. The split between male and female cases was relatively even, with 426 ($45.8\%$) female compared with 503 ($54.1\%$) male cases. The majority of all cases were of white ethnic background (701; $75.5\%$), 192 ($20.7\%$) cases were of unstated or unknown ethnic origin and the remaining 36 ($3.8\%$) cases were comprised of non-white ethnic minority groups.
**Table 1**
| Unnamed: 0 | All Cases | Non-Severe | Severe | Fatal | ICU | Intubation |
| --- | --- | --- | --- | --- | --- | --- |
| All Cases | 929 | 569 (61.2%) | 360 (38.8%) | 295 (31.8%) | 111 (11.9%) | 93 (10.0%) |
| Admission Age | 76 [63, 85] | 74 [60, 85] | 80 [68, 86] | 82 [73, 88] | 63 [54, 71] | 64 [56, 71] |
| 0–59 | 192 (20.7%) | 142 (15.3%) | 50 (5.4%) | 16 (1.7%) | 44 (4.7%) | 33 (3.6%) |
| 60–69 | 133 (14.3%) | 83 (8.9%) | 50 (5.4%) | 29 (3.1%) | 37 (4.0%) | 34 (3.7%) |
| 70–79 | 205 (22.1%) | 126 (13.6%) | 79 (8.5%) | 69 (7.4%) | 25 (2.7%) | 21 (2.3%) |
| 80–89 | 285 (30.7%) | 156 (16.8%) | 129 (13.9%) | 129 (13.9%) | 5 (0.5%) | 5 (0.5%) |
| 90–99 | 109 (11.7%) | 60 (6.5%) | 49 (5.3%) | 49 (5.3%) | - | - |
| 100+ | 5 (0.5%) | 2 (0.2%) | 3 (0.3%) | 3 (0.3%) | - | - |
| Length of Stay (days) | 13 [6, 23] | 11 [5, 20] | 16 [8, 26] | 14 [6, 22] | 25 [16, 54] | 25 [16, 54] |
| Time to Discharge or Death from Diagnosis (hours) | 201 [98, 383] | 164 [87, 344] | 245 [123, 502] | 202 [110, 325] | 546 [351, 1033] | 584 [355, 1073] |
| Admission NEWS2 Score | 3 [1, 6] | 3 [1, 5] | 4 [1, 8] | 4 [1, 7] | 7 [4, 10] | 8 [5, 9] |
| Maximum NEWS2 Score | 6 [4, 9] | 5 [3, 7] | 9 [6, 10] | 9 [6, 11] | 8 [7, 10] | 8 [7, 10] |
| Sex | - | - | - | - | - | - |
| Male | 503 (54.1%) | 276 (29.7%) | 227 (24.4%) | 180 (19.4%) | 79 (8.5%) | 64 (6.9%) |
| Female | 426 (45.8%) | 293 (31.5%) | 133 (14.3%) | 115 (12.4%) | 32 (3.4%) | 29 (3.1%) |
| Ethnic Origin | - | - | - | - | - | - |
| Asian | 16 (1.7%) | 8 (0.9%) | 8 (0.9%) | 6 (0.6%) | 7 (0.8%) | 7 (0.8%) |
| Black | 6 (0.6%) | 3 (0.3%) | 3 (0.3%) | 1 (0.1%) | 3 (0.3%) | 1 (0.1%) |
| Mixed | 8 (0.9%) | 4 (0.4%) | 4 (0.4%) | 1 (0.1%) | 4 (0.4%) | 2 (0.2%) |
| Other | 6 (0.6%) | 6 (0.6%) | - | - | - | - |
| Unknown | 192 (20.7%) | 122 (13.1%) | 70 (7.5%) | 59 (6.4%) | 18 (1.9%) | 19 (2.0%) |
| White | 701 (75.5%) | 426 (45.9%) | 275 (29.6%) | 228 (24.5%) | 79 (8.5%) | 64 (6.9%) |
| Lineage | - | - | - | - | - | - |
| Alpha | 404 (43.5%) | 255 (27.4%) | 149 (16.0%) | 116 (12.5%) | 57 (6.1%) | 51 (5.5%) |
| Non-Alpha | 525 (56.5%) | 314 (33.8%) | 211 (22.7%) | 179 (19.3%) | 54 (5.8%) | 42 (4.5%) |
| Patient Type | - | - | - | - | - | - |
| Inpatients | 450 (48.4%) | 290 (31.2%) | 160 (17.2%) | 147 (15.8%) | 18 (1.9%) | 12 (1.3%) |
| Emergency Department | 360 (38.8%) | 238 (25.6%) | 122 (13.1%) | 108 (11.6%) | 29 (3.1%) | 29 (3.1%) |
| Critical Care | 62 (6.7%) | 3 (0.3%) | 59 (6.4%) | 23 (2.5%) | 59 (6.4%) | 49 (5.3%) |
| Acute Medical Unit | 33 (3.6%) | 21 (2.3%) | 12 (1.3%) | 11 (1.2%) | 3 (0.3%) | 2 (0.2%) |
| Outpatients | 15 (1.6%) | 10 (1.1%) | 5 (0.5%) | 4 (0.4%) | 2 (0.2%) | 1 (0.1%) |
| Healthcare Workers | 4 (0.4%) | 4 (0.4%) | 0 (0%) | - | - | - |
| Community Cases | 3 (0.3%) | 2 (0.2%) | 1 (0.1%) | 1 (0.1%) | - | - |
| External PHU Hospital Patients | 1 (0.1%) | 1 (0.1%) | 0 (0%) | - | - | - |
| Long-Term Care Facility Residents | 1 (0.1%) | 0 (0%) | 1 (0.1%) | 1 (0.1%) | - | - |
| Diabetes | - | - | - | - | - | - |
| 0 (No) | 636 (68.5%) | 403 (43.4%) | 233 (25.1%) | 195 (21.0%) | 61 (6.6%) | 52 (5.6%) |
| 1 (Yes) | 293 (31.5%) | 166 (17.9%) | 127 (13.7%) | 100 (10.8%) | 50 (5.4%) | 41 (4.4%) |
| Hypertension | - | - | - | - | - | - |
| 0 (No) | 444 (47.8%) | 293 (31.5%) | 151 (16.3%) | 123 (13.2%) | 45 (4.8%) | 37 (4.0%) |
| 1 (Yes) | 485 (52.2%) | 276 (29.7%) | 209 (22.5%) | 172 (18.5%) | 66 (7.1%) | 56 (6.0%) |
| Renal Disease | - | - | - | - | - | - |
| 0 (No) | 583 (62.8%) | 401 (43.2%) | 182 (19.6%) | 151 (16.3%) | 49 (5.3%) | 42 (4.5%) |
| 1 (Yes) | 346 (37.2%) | 168 (18.1%) | 178 (19.2%) | 144 (15.5%) | 62 (6.7%) | 51 (5.5%) |
| Malignancy | - | - | - | - | - | - |
| 0 (No) | 821 (88.4%) | 513 (55.2%) | 308 (33.2%) | 246 (26.5%) | 104 (11.2%) | 87 (9.4%) |
| 1 (Yes) | 108 (11.6%) | 56 (6.0%) | 52 (5.6%) | 49 (5.3%) | 7 (0.8%) | 6 (0.6%) |
| Heart Disease | - | - | - | - | - | - |
| 0 (No) | 439 (47.3%) | 298 (32.1%) | 141 (15.2%) | 107 (11.5%) | 53 (5.7%) | 44 (4.7%) |
| 1 (Yes) | 490 (52.7%) | 271 (29.2%) | 219 (23.6%) | 198 (20.2%) | 58 (6.2%) | 49 (5.3%) |
| Asthma | - | - | - | - | - | - |
| 0 (No) | 828 (89.1%) | 504 (54.3%) | 324 (34.9%) | 267 (28.s%) | 98 (10.5%) | 80 (8.6%) |
| 1 (Yes) | 101 (10.9%) | 65 (7.0%) | 36 (3.9%) | 28 (3.0%) | 13 (1.4%) | 13 (1.4%) |
| COPD | - | - | - | - | . | . |
| 0 (No) | 760 (81.8%) | 477 (51.3%) | 283 (30.5%) | 225 (24.2%) | 96 (10.3%) | 80 (8.6%) |
| 1 (Yes) | 169 (18.2%) | 92 (9.9%) | 77 (8.3%) | 70 (7.5%) | 15 (1.6%) | 13 (1.4%) |
| Number of Pre-Existing Conditions | - | - | - | - | - | . |
| 0 | 128 (13.8%) | 102 (11.0%) | 26 (2.8%) | 19 (2.0%) | 10 (1.1%) | 6 (0.6%) |
| 1 | 220 (23.7%) | 145 (15.6%) | 75 (8.1%) | 59 (6.4%) | 24 (2.6%) | 22 (2.4%) |
| 2 | 261 (28.1%) | 161 (17.3%) | 100 (10.8%) | 88 (9.5%) | 23 (2.5%) | 22 (2.4%) |
| 3 | 212 (22.8%) | 112 (12.1%) | 100 (10.8%) | 79 (8.5%) | 32 (3.4%) | 24 (2.6%) |
| 4 | 95 (10.2%) | 46 (5.0%) | 49 (5.3%) | 41 (4.4%) | 20 (2.2%) | 17 (1.8%) |
| 5 | 13 (1.4%) | 3 (0.3%) | 10 (1.1%) | 9 (1.0%) | 2 (0.2%) | 2 (0.2%) |
At the time of COVID-19 diagnosis, almost half of all patients were inpatients (450 cases, $48.4\%$), with a large proportion being identified through the Emergency Department (ED; 360 cases, $38.8\%$). A smaller proportion of cases were identified in Critical Care (CC; 62 cases, $6.7\%$) and the Acute Medical Units (AMU; 33 cases, $3.6\%$). The majority of patients suffered from at least one of the comorbidities (801 cases, $86.2\%$) explored in this dataset; diabetes, hypertension, renal disease, malignancy (cancer), heart disease, asthma, or chronic obstructive pulmonary disease (COPD). Hypertension and heart disease were the most common, with 485 ($52.2\%$) and 490 ($52.7\%$) cases respectively, whilst asthma and cancer were rarer, with 101 ($10.9\%$) and 108 ($11.6\%$) cases respectively.
## Associations with disease severity
To understand the factors that most affect disease severity (defined by either admission to ICU, receiving invasive mechanical intubation, or death within 30 days of diagnosis), pairwise statistical association analyses were performed for all experimental variables using either Cramer’s V, Spearman’s Rank or the Correlation Ratio, depending on the data types (see Materials and methods). S3 Fig shows the pairwise association score between all variables in the data set, and Table 2 shows those with a statistically significant (p < = 0.05) and non-negligible (A ≥ 0.1) association with COVID-19 severity. A description of these data points is shown in S1 Table. *In* general, these data show that clinical variables show mild association with outcomes, and demonstrate a lack of individual strong indicators in our dataset that could potentially predict a severe case of COVID-19.
**Table 2**
| Feature | Association Strength Metric | A | p | A.1 | p.1 | A.2 | p.2 | A.3 | p.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Feature | Association Strength Metric | (Combined) | (Combined) | (Fatal) | (Fatal) | (ICU) | (ICU) | (Intubated) | (Intubated) |
| Maximum NEWS2 Score | Correlation Ratio | 0.48 | 5.10E-46 | 0.43 | 3.49E-37 | 0.21 | 2.30E-09 | 0.21 | 2.52E-09 |
| Location Category | Cramer’s V | 0.28 | 4.50E-16 | - | - | 0.66 | 2.39E-89 | 0.59 | 1.77E-71 |
| Ward | Cramer’s V | 0.21 | 7.88E-06 | - | - | 0.49 | 1.63E-43 | 0.43 | 3.31E-31 |
| Number of Pre-Existing Conditions | Correlation Ratio | 0.20 | 6.81E-09 | 0.18 | 9.10E-08 | - | - | - | - |
| Renal Disease Indicator | Cramer’s V | 0.19 | 6.32E-09 | 0.15 | 3.30E-06 | 0.12 | 7.40E-05 | - | - |
| Admission NEWS2 Score | Correlation Ratio | 0.17 | 1.11E-08 | - | - | 0.24 | 1.15E-16 | 0.24 | 5.61E-17 |
| Length of Stay | Correlation Ratio | 0.17 | 1.82E-07 | - | - | 0.30 | 3.51E-19 | 0.28 | 5.26E-16 |
| Swab Location | Cramer’s V | 0.16 | 9.74E-03 | - | - | 0.43 | 9.61E-24 | 0.32 | 3.52E-12 |
| Time to Discharge/Death from Diagnosis | Correlation Ratio | 0.15 | 6.14E-07 | - | - | 0.36 | 5.32E-28 | 0.34 | 1.12E-24 |
| Admission Age | Correlation Ratio | 0.15 | 1.00E-04 | 0.30 | 4.62E-18 | 0.29 | 1.15E-18 | 0.25 | 4.55E-14 |
| Pre-Existing Condition Indicator | Cramer’s V | 0.13 | 2.05E-05 | 0.13 | 4.78E-05 | - | - | - | - |
| Sex | Cramer’s V | 0.13 | 6.05E-05 | - | - | 0.10 | 5.00E-04 | - | - |
| Heart Disease Indicator | Cramer’s V | 0.12 | 3.10E-04 | 0.14 | 2.13E-05 | - | - | - | - |
| Malignancy Indicator | Cramer’s V | - | - | 0.11 | 4.29E-03 | - | - | - | - |
| Admission Specialty | Cramer’s V | - | - | - | - | 0.32 | 6.01E-15 | 0.32 | 2.17E-16 |
| Ethnic Origin | Cramer’s V | - | - | - | - | 0.22 | 1.12E-07 | 0.22 | 4.14E-04 |
| Lineage | Cramer’s V | - | - | - | - | 0.18 | 3.76E-03 | - | - |
| Diabetes Indicator | Cramer’s V | - | - | - | - | 0.11 | 3.93E-03 | - | - |
The maximum NEWS2 score showed the highest association with severe cases of COVID-19 ($A = 0.48$, $$p \leq 5.10$$e-46; Fig 1A), indicating a moderate but statistically significant association. In particular, this metric was more strongly associated with death ($A = 0.43$, $$p \leq 3.49$$e-37) than with ICU admission ($A = 0.21$, $$p \leq 2.30$$e-09) or intubation ($A = 0.21$, $$p \leq 2.52$$e-09). The maximum NEWS2 score shows median scores of 9 (high-risk; IQR [6,10]) for severe cases and 5 (medium-risk; IQR [3,7]) for non-severe cases. The NEWS2 score assigned to a patient on admission is also associated with severity, albeit much more weakly overall ($A = 0.17$, $$p \leq 1.11$$e-08; Fig 1B). Interestingly, whilst associated with ICU admission ($A = 0.24$, $$p \leq 1.15$$e-16) and intubation ($A = 0.24$, $$p \leq 5.61$$e-17), the admission NEWS2 score was not associated with patient death alone. The admission NEWS2 score shows median scores of 4 (IQR [1,8]) for severe cases and 3 (IQR [1,5]) for non-severe cases with a large amount of overlap between the distributions, indicating that the NEWS2 score at admission may not be a strong initial predictor of severe COVID-19.
**Fig 1:** *Association of disease severity with continuous variables.Violin plots comparing the distribution of continuous variables with statistically significant relationships with disease severity between severe cases (death, ICU admission or intubation) and non-severe cases. Variables shown are a) the maximum NEWS2 score, b) the admission NEWS2 score, c) the age at admission and d) the length of stay (days). Association strength A (based on the Correlation Ratio) and p-values p are shown above each panel.*
Moderate to weak associations were also seen with the ward location category ($A = 0.28$, $$p \leq 4.50$$e-16), specific ward ($A = 0.21$, $$p \leq 7.88$$e-06) and the location where the qPCR swab was originally collected ($A = 0.16$, $$p \leq 9.74$$e-03). All three are associated with ICU admission and intubation, but not with patient death as an outcome (Table 2). All three were also associated with admission NEWS2 score (ward location category $A = 0.44$, $$p \leq 1.59$$e-37; specific ward $A = 0.60$, $$p \leq 3.15$$e-49; qPCR swab location $A = 0.56$, $$p \leq 8.61$$e-38) and maximum NEWS2 score (ward location category $A = 016$, $$p \leq 1.27$$e-03; specific ward $A = 0.33$, $$p \leq 1.56$$e-06; qPCR swab location $A = 0.31$, $$p \leq 6.08$$e-03), as well as whether the patient was admitted to ICU (ward location category $A = 0.66$, $$p \leq 2.39$$e-89; specific ward $A = 0.49$, $$p \leq 1.63$$e-43; qPCR swab location $A = 0.43$, $$p \leq 9.61$$e-24). Together, these indicate that patients who suffered from a severe case of COVID-19 were typically acutely unwell in general and therefore more likely to be transferred to ICU.
Pre-existing comorbidities also appeared to play a role in susceptibility for severe COVID-19, with a weak association seen for the number of pre-existing conditions a patient might have ($A = 0.20$, $$p \leq 6.81$$e-09), as well as a weak association to those who have any pre-existing conditions ($A = 0.13$, $$p \leq 2.05$$e-05; Fig 2A). These links were seen with the death outcome, but not with ICU admission nor intubation (Table 2). Specifically, those with renal disease ($A = 0.19$, $$p \leq 6.32$$e-09; Fig 2B) or heart disease ($A = 0.12$, $$p \leq 3.10$$e-04; Fig 2C) showed weak but statistically significant associations with COVID-19 severity, in particular death. These links therefore result in increased odds of having a severe case of COVID-19 (pre-existing condition OR = 2.81, $95\%$ CI [1.79, 4.42]; renal disease OR = 2.33, $95\%$ CI [1.77, 3.07]; heart disease OR = 1.71, $95\%$ CI [1.31, 2.24]).
**Fig 2:** *Association of disease severity with discrete variables.Heatmaps comparing the counts between severe cases (death, ICU admission or intubation) and non-severe cases for a selection of categorical features with statistically significant relationships with disease severity. Variables shown are a) the presence of existing conditions, b) whether the patient suffers from renal disease, c) whether the patient suffers from heart disease and d) sex at birth. Association strength A (based on Cramer’s V), p-values p, and odds ratio between the classes are shown above each panel.*
Demographics such as age ($A = 0.15$, $$p \leq 1.00$$e-04; Fig 1C) and sex ($A = 0.13$, $$p \leq 6.05$$e-05; Fig 2A) of the patient also show statistically significant, albeit weak effects on COVID-19 severity. These data show a median age of 80 (IQR [68,86]) in severe cases compared to 74 (IQR [60,85]) in non-severe cases, and that male patients showed a higher ratio of severe to non-severe cases when compared to female patients (OR = 1.81, $95\%$ CI [1.38, 2.37], $$p \leq 6.05$$e-05; Fig 2D).
In addition, the length of stay ($A = 0.17$, $$p \leq 1.82$$e-07; Fig 1D) and time to discharge or death ($A = 0.15$, $$p \leq 6.14$$e-07) also appear to be statistically significant (albeit weak) factors, with a longer median stay of 16 days (IQR [8,26]) seen in severe cases compared to 11 days (IQR [5,20]) in non-severe cases. In particular, we see a long tail for long stays for severe cases, as a result of patients who contracted severe, but non-fatal, COVID-19 and required a significant amount of recovery time. Interestingly, both metrics were associated with ICU admission and intubation, but not with patient death (Table 2).
## The effect of the Alpha variant on clinical severity of COVID-19
The Alpha variant (B.1.1.7) was imported to PHU during the second wave of COVID-19 cases in the UK, in particular rising in prevalence during the winter period in December 2020. Over the period between September 2020 and June 2021, 1,404 cases were sequenced from PHU, of which 970 (523 patient, 447 HCWs) proved to be of the Alpha lineage. Across the dataset used in this study, spanning cases in PHU from March 2020 until May 2021, $43.5\%$ of COVID-19 cases were cases of the Alpha lineage (Table 1). In contrast, the next most common variant, Pangolin lineage B.1.1, comprised only $6\%$ of cases, making Alpha the most common single variant identified by a significant margin. Across the whole dataset, we found no statistically significant link between lineage and case severity ($A = 0.08$, $$p \leq 0.356$$), and no statistically significant links between COVID-19 severity and whether the case was Alpha lineage or not ($A = 0.00$, $$p \leq 0.435$$).
To further understand the effect of the Alpha lineage on disease severity, we looked at associations with disease severity for Alpha and non-Alpha cases separately (Fig 3). Fig 3A–3F explore how the Alpha variant may have impacted severity outcomes for those with pre-existing conditions, and specifically renal and heart disease, which were identified as being associated with disease severity (Table 2). We see that the Alpha variant had no statistically significant impact on patients with (OR = 0.85, $95\%$ CI [0.64, 1.13], $$p \leq 0.292$$; Fig 3A) or without (OR = 1.47, $95\%$ CI [0.61, 3.51], $$p \leq 0.510$$; Fig 3B) a pre-existing condition; with (OR = 1.02, $95\%$ CI [0.66, 1.57], $$p \leq 1.00$$; Fig 3C) or without (OR = 0.83, $95\%$ CI [0.58, 1.18], $$p \leq 0.348$$; Fig 3D) renal disease; or with (OR = 0.72, $95\%$ CI [0.50, 1.03], $$p \leq 0.089$$; Fig 3E) or without (OR = 1.16, $95\%$ CI [0.78, 1.73], $$p \leq 0.546$$; Fig 3H) heart disease. Finally, we see that whilst the Alpha variant had no statistically significant impact on male patients (OR = 1.17, $95\%$ CI [0.82, 1.68], $$p \leq 0.437$$; Fig 3G), female patients showed a mild but non-significant decrease in the ratio of severe cases with the Alpha variant (OR = 0.65, $95\%$ CI [0.43, 0.99], $$p \leq 0.054$$; Fig 3H).
**Fig 3:** *Effect of the Alpha variant on disease severity.Heatmaps exploring the changes in relative risk of severe outcome (death, ICU admission or intubation) for subpopulations within the data when comparing Alpha cases to non-Alpha cases. The subpopulations shown are based on those identified as being associated with disease severity. They are (in column order): Whether the patient has a pre-existing condition or not (a-b), whether the patient has renal disease or not (c-d), whether the patient has heart disease or not (e-f), and sex at birth (g-h). The odds ratio and p-values p between the classes are shown above each panel.*
Further breaking down the relationship of the Alpha variant with severity for male and female patients, the Alpha variant showed no statistically significant impact on mortality for male patients (OR = 0.94, $95\%$ CI [0.65, 1.37], $$p \leq 0.839$$; Fig 4A) nor for female patients (OR = 0.65, $95\%$ CI [0.42, 1.00], $$p \leq 0.061$$; Fig 4B), although a moderate decrease in odds was seen. Interestingly, the Alpha variant did have an impact on ICU admission (OR = 2.03, $95\%$ CI [1.25, 3.30], $$p \leq 5.50$$e-03; Fig 4C) and whether intubation was required (OR = 2.32, $95\%$ CI [1.36, 3.95], $$p \leq 2.51$$e-03; Fig 4E) for male patients, with almost twice the odds when compared to non-Alpha variants in both cases. However, no statistically significant impact on ICU admission (OR = 0.84, $95\%$ CI [0.41, 1.73], $$p \leq 0.762$$; Fig 4D) nor whether intubation was required (OR = 1.02, $95\%$ CI [0.48, 2.17], $$p \leq 1.00$$; Fig 4F) was seen with female patients.
**Fig 4:** *Effect of the Alpha variant on disease severity for male and female patients.Heatmaps exploring the changes in relative risk of death (a-b), ICU admission (c-d) and intubation (e-f) for male and female patients when comparing Alpha cases to non-Alpha cases. The odds ratio and p-values p between the classes are shown above each panel.*
## SARS-CoV-2 mutations associated with severe COVID-19
As the SARS-CoV-2 virus has mutated over time, a number of key mutations have been identified, particularly in the spike protein of the virus. We used our dataset to identify whether any specific mutations or clusters of mutations could be identified that might be associated with an increased risk of a negative outcome, thus acting as predictors of outcome in future cases. Features from the joint outcomes and mutation dataset that showed statistically significant relationships with single nucleotide polymorphisms (SNPs) and deletions of the SARS-CoV-2 genome are shown in Table 3. We found no statistically significant link between the SNPs and any of our chosen indicators of disease severity (death, ICU admission, or intubation). Interestingly, we found a moderately weak link between the mutations and the NEWS2 score, both at admission ($A = 0.25$, $$p \leq 1.01$$e-15) and the maximum recorded score ($A = 0.23$, $$p \leq 1.27$$e-09). This may indicate that some mutations impact acute physiological status, but not enough to directly result in a severe case of COVID-19. This hypothesis is supported by the weak association between the mutations and the patient’s length of stay ($A = 0.25$, $$p \leq 5.89$$e-16), indicating a weak link between the types of mutations found in patients who experienced symptoms of COVID-19 for longer periods of time. Also interestingly, we see that the mutations were associated with the number of pre-existing conditions the patient has ($A = 0.24$, $$p \leq 3.15$$e-06) as well as whether the patient has cancer ($A = 0.14$, $$p \leq 1.57$$e-13), renal disease ($A = 0.13$, $$p \leq 4.08$$e-11), or COPD ($A = 0.11$, $$p \leq 7.01$$e-08), although these associations are quite weak. We also see weak associations between the mutations and the demographics of the patient, in particular their ethnic origin ($A = 0.15$, $p \leq 1.00$e-300), sex ($A = 0.12$, $$p \leq 7.48$$e-08) and age ($A = 0.10$, $$p \leq 1.94$$e-283). Similar weak associations are also seen with the locations of the patient, particularly the admission speciality ($A = 0.16$, $p \leq 1.00$e-300), the location where the patient was swabbed ($A = 0.14$, $p \leq 1.00$e-300) and the ward the patient was located after admission to the hospital ($A = 0.14$, $p \leq 1.00$e-300), likely arising as a result of nosocomial spread of the virus within wards.
**Table 3**
| Feature | Association Strength Metric | A | p |
| --- | --- | --- | --- |
| Length of Stay | Correlation Ratio | 0.25 | 5.89e-16 |
| Admission NEWS2 Score | Correlation Ratio | 0.25 | 1.01e-15 |
| Number of Pre-Existing Conditions | Correlation Ratio | 0.24 | 3.15e-06 |
| Maximum NEWS2 Score | Correlation Ratio | 0.23 | 1.27e-09 |
| Time to Discharge/Death from Diagnosis | Correlation Ratio | 0.23 | 5.93e-06 |
| Admission Specialty | Correlation Ratio | 0.16 | 0.0 |
| Ethnic Origin | Cramer’s V | 0.15 | 0.0 |
| Malignancy Indicator | Cramer’s V | 0.14 | 1.57e-13 |
| Swab Location | Cramer’s V | 0.14 | 0.0 |
| Ward | Cramer’s V | 0.14 | 0.0 |
| Renal Disease Indicator | Cramer’s V | 0.13 | 4.08e-11 |
| Sex | Cramer’s V | 0.12 | 7.48e-08 |
| COPD Indicator | Cramer’s V | 0.11 | 7.01e-08 |
| Location Category | Cramer’s V | 0.11 | 1.0200000000000001e-35 |
| Admission Age | Cramer’s V | 0.1 | 1.94e-283 |
## Machine learning (ML) and artificial neural network (ANN) analysis of mutations and comorbidity risk-factors associated with disease severity
To further explore the role of viral mutations of SARS-CoV-2 in severity of disease in COVID-19, we utilised machine learning approaches to identify mutations with a possible role in determining patient outcomes. Nine machine learning algorithms and one deep-learning neural network method were tested and ranked according to their accuracy (Fig 5A), with a binary outcome of death (outcome = 1) or no death (outcome = 0) following escalation of care to the ICU. Of these, the XGradient Boosted (XGBoost) and the Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) approaches produced the best results, with the MLP-ANN model resulting in slightly improved accuracy ($76.2\%$; Fig 5B, right) compared to XGBoost ($74.6\%$; Fig 5B, left).
**Fig 5:** *Machine learning and artificial neural network analysis of mutations in the SARS-CoV2 genome isolated from patients with COVID-19.(a) Screening and comparison of nine machine-learning and one deep-learning multi-layer perceptron artificial neural network (MLP-ANN) method showing mean percentage accuracy of prediction of outcome; death (1) or no death (0). Error bars shown represent standard deviation of accuracy run over 200 estimators (machine-learning) or 300 epochs for neural-net analysis. (b) Macro-average (where metrics for outcomes 1 and 0 are averaged) metrics comparison between the XGBoost and the MLP-ANN method. The MLP-ANN gave better macro-average accuracy metrics. (c) Individual metrics shown for outcomes 1 (death) and 0 (no death), compared between XGBoost and MLP-ANN models. The MLP-ANN gave better accuracy metrics and overall Sensitivity metrics. (d) Ranking of SNPs and clinical parameters in order of importance, displayed according to their SHapley Additive exPlanations (SHAP) values in their predictive ability in the respective models. The location of the SNPs in relation to the SARS-CoV2 genome is based on mapping to the reference genome (Wuhan-Hu-1, GenBank, MN908947.3) and shown using colour-coded keys. Also shown are the nucleotide changes noted in relation to each SNP.*
Comparison of macro-average metric scores between the XGBoost and MLP-ANN models are shown in Fig 5B, with the breakdown for the different outcomes shown in Fig 5C. In both models, Precision and Recall were high in discrimination of COVID-19 patients with greater survival probability (low-risk patients) following their admission to intensive care units, but low for discrimination of high-risk patients. The MLP-ANN model showed a higher Recall ($100\%$ vs $92\%$), but lower Precision ($74\%$ vs $78\%$) for identification of low-risk patients compared to the XGBoost model. Whilst potentially unsuitable for identification of high-risk patients, this model may potentially offer an approach for exclusion of low-risk patients, allowing for the remaining cohort to be observed as potentially high risk, with resources prioritised for more intensive clinical surveillance, management, and attention.
The ranking of SNPs, deletions, and clinical criteria in the order of importance to the model (from top to bottom), based on the Shapley additive explanation (SHAP) values, is shown for the XGBoost model (Fig 5D) and the MLP-ANN model (Fig 5E). For mutations identified as the top predictors variables, the numeric ID indicates the nucleotide position of the nucleotide change relative to the reference SARS-CoV-2 genome, and the gene domain in which the mutation is located is highlighted (S4 Fig).
The feature importance of the predictor variables is different for the XGBoost model compared to the MLP-ANN model, with individual genomic features in the XGBoost model typically showing higher mean SHAP values than those in the MLP-ANN model. Another striking difference is in the ranking of clinical variables, which represent the top ranked features in the XGBoost model, but appear less significant than a number of mutations (in particular deletions) in the MLP-ANN model. Renal disease, heart disease and diabetes feature for both models, with risk factors such as history of hypertension, COPD, prior history of malignancy, and asthma prominent in the XGBoost model, but not in the MLP-ANN model.
One notable similarity between the two models, SNP 23403, corresponds to an A->G mutation at site 23,403, resulting in an Aspartic Acid to Glycine amino acid substitution (D614G) within the spike (S) protein domain. This SNP, seen in the top 20 for both models, became rapidly dominant globally due to increased viral fitness and higher viral loads [42–45]. It became fixed in the population after the first wave in the UK, with $0\%$ of cases showing the Glycine residue in January 2020, rapidly increasing to $70\%$ of cases in May 2020 [45]. It is therefore likely that association of this SNP with severity is closely linked with temporal developments of the pandemic, with significant improvements in treatment options and vaccine developments from the second wave onwards.
Whilst the spike protein has been linked with increased viral load and fitness [46], and thus may represent an obvious source for identification of mutations linked with disease severity due to its role in host cell receptor binding, the majority of the identified mutations actually seem to lie in other regions of the viral genome. In particular, for the XGBoost model, the majority of other significantly associated mutations were identified within the ORF1ab gene. These include synonymous SNPs such as SNP 913 (C->T), SNP 3037 (C->T), SNP 14408 (C->T), SNP 16468 (C->T), as well as non-synonymous SNPs such as SNP 23603, an A->T transition resulting in a change from Asparagine to Tyrosine at amino acid (AA) 501 in the spike domain identified in Alpha cases.
Interestingly, whilst no deletions are present in the top ranked features for the XGBoost model, they represent the top three mutations in the MLP-ANN model. All three deletions (SNP 28270, a 1 bp frameshift deletion in the Nucleocapsid (N) domain; SNP 21764, a 6 bp deletion in the spike (S) domain; SNP 11287, a 9 bp deletion in ORF1ab) are specific to the Alpha lineage B.1.1.7, and clearly delineate Alpha from non-Alpha cases. Similarly, the majority of the remaining mutations identified by the MLP-ANN model appear to be highly specific to the Alpha lineage, indicating that this model primarily identifies the presence of the Alpha lineage as being associated with disease severity. As with SNP 23403, this is likely linked to temporal development in treatment options for those with Alpha later in the pandemic compared to cases with severe symptoms in earlier waves.
## Discussion
As the world returns to a more normal state after being plunged into a global pandemic, many questions remain to be answered about COVID-19. In particular, it is still not well understood exactly which factors are most associated with the likelihood of an individual suffering from the most significant negative outcomes, including long-term post-COVID-19 respiratory issues (“long COVID”), requirement for invasive mechanical intubation, admission to ICU, or even death.
In this study, clinical data were linked to viral genomic data from patients seen across an acute NHS Trust on the south coast of the UK. This data resource was used to explore potential links between severe outcomes and viral subtype, patient demographics, and clinical history, to further understand factors that may influence patient responses to the virus. Overall, this study found no strong factors associated with severe cases of COVID-19, instead showing weak influence from myriad factors including age, sex, and existence of pre-existing conditions.
Of course, certain pre-existing conditions are more likely than others to directly influence COVID-19 illness. For example, given that cataracts are typically seen in older individuals, many of those most clinically vulnerable for severe COVID-19 outcomes may suffer from cataracts, with one-fifth of patients awaiting cataract surgery found to be at high risk of severe disease or death from COVID-19 in a 2022 study [47]. However, whilst a serious malady and a leading cause of preventable blindness, suffering from cataracts is itself unlikely to have a significant bearing on the severity of COVID-19 pneumonia. The context of the comorbidity in relation to the subsequent pathology of SARS-CoV-2 pathophysiology is important, since the primary target organs are the lungs, and pathophysiological progression may require mechanical ventilation in areas where high-dependency or intensive care is offered.
One of the frequently observed disease progressions in COVID-19 is the persistence of micro-coagulopathy, where tiny clots systemically occlude capillaries, such as in the glomeruli [48]. Thus, a patient with a pre-existing compromised renal function, or those with pre-existing cardiac dysfunction (especially previous coronary ischaemia) might show poor recovery trajectories in hospital. It is thus clear that certain pre-existing conditions, particularly renal and heart disease, may make an individual more likely to suffer from severe complications with COVID-19 and have been previously identified as risk factors [49]. Indeed, as shown in Table 2, renal disease ($A = 0.15$), heart disease ($A = 0.14$), and cancer ($A = 0.11$) were identified as being significantly associated with the likelihood of death. Such patients should therefore continue to be monitored closely, to observe signs of deterioration.
It is worth noting however, that the absolute increase to risk is low based on our data, and a relatively large proportion of those analysed suffered from heart disease ($52.7\%$), renal disease ($37.2\%$), and cancer ($11.6\%$) (Table 1). Indeed, only $13.8\%$ of patients in our dataset had no pre-existing condition at all, highlighting a significant selection bias in the data. There is also a selection bias with admission age, with $79.3\%$ of patients aged 60 and above and a median age of 76. These biases are likely closely related, since older patients typically experience a higher proportion of comorbidities compared to younger age groups [9]. These selection biases may impact other association scores, potentially resulting in underestimated scores for pairwise associations with admission age and comorbidities.
These data also highlight that acute physiological derangement of the patient is linked to severe COVID-19, indicated by a moderate-strong association between the maximum NEWS2 score and whether the patient died within 30 days of diagnosis ($A = 0.43$), was admitted to ICU ($A = 0.21$), or required invasive mechanical intubation ($A = 0.21$) (Table 2). The NEWS2 score reports on a constellation of dynamically changing (particularly within an acute setting) clinical features, but is a simple to calculate metric to identify and address patient deterioration [32,33], and has been previously identified as a potential screening tool for severe patient outcomes [50–52]. However, a UK multicentre study identified poor to moderate discrimination of medium-term COVID-19 outcomes from NEWS2 scores and age alone, calling into question its use as a screening tool [53]. A common observation with COVID-19 is of mild phenotypes deteriorating towards severe phenotypes (resulting in an increased NEWS2 score) as a result of the respiratory distress caused by COVID-19 pneumonitis. In comparison, the NEWS2 score given to a patient on admission shows no significant association with death, suggesting that it is unlikely to represent a significant predictive factor for COVID-19 severity. A weak association is seen between admission NEWS2 and both ICU admission ($A = 0.24$) and intubation ($A = 0.24$), but given that the NEWS2 score is often a tool used to determine whether a patient has deteriorated sufficiently to require intubation or ICU treatment, this is perhaps unsurprising. Length of stay also showed moderate associations with ICU admission ($A = 0.30$). There are several risk factors which become apparent with an increased length of hospital stay, for instance the likelihood of the patient being on prolonged prescription of several non-routine medications. These include medication for prevention of venous thromboembolism (heparin and other anticoagulants), medications to aid somnolence at night (sleep medication is frequently requested by the elderly while at hospital due to unfamiliar disturbing noises at night in a busy clinical environment), antibiotics, anti-anxiety medication, medication to help bowel movements (due to prolonged bed-rest and immobility), medication to offload water retention from immobility (again from prolonged bed-rest) and pain medication.
Other factors showing moderate association with ICU admission included the location category of their treatment ward ($A = 0.66$), the specific ward number ($A = 0.49$), and the ward in which their COVID-19 test swab was collected ($A = 0.43$). Patients at PHU are triaged and risk-stratified on admission, and the location of the clinical setting that they are initially taken to for treatment would reflect the clinical need for specialist services, equipment or staff-training levels distributed within a particular sector within the hospital. Such a sector is typically populated with a high number of patients needing high-dependency care and treatment. Since aerosolization of the virus is a potential and proven risk, along with the potential for direct transmission from person to person, nosocomial spread within such high-dependency care units results in increased cases within these areas. It is therefore likely that associations of outcomes with location-related data are a result of localized outbreaks, resulting in cases with shared mutation patterns between patients who share similar treatment and comorbidity characteristics. Indeed, nationally over $15\%$ of all cases have been estimated as having been hospital acquired in the first wave in the UK [54], with up to $20\%$ of infections in inpatients and $73\%$ in HCW due to nosocomial transmission [55]. It has been suggested that up to $80\%$ of nosocomial infections were caused by only $20\%$ of patients due to “super-spreader” events [14], with such rapid outbreak dynamics having been previously characterised in at least one outbreak at PHU [56].
One key question to address as new variants of SARS-CoV-2 continue to arise is the effect on severity of the disease as a result of new variants. Whilst the data described here do not span the emergence of variants such as Delta and Omicron, they do represent the emergence and subsequent rapid expansion of the first VOC, Alpha (B.1.1.7). Increased prevalence of Alpha in the local region led to increased transmission of a range of currently circulating variants within the hospital [56]. Interestingly, Table 1 shows that the rate of severe cases amongst Alpha cases ($36.9\%$) was actually slightly lower than amongst non-Alpha cases ($40.2\%$), suggesting that Alpha cases may present a lower risk of severe outcomes in our dataset compared to other variants (Table 1). However, whilst lineage was weakly associated with ICU admission ($A = 0.18$), we otherwise saw no statistically significant links between lineage and death, intubation, nor case severity in general (Table 2). In addition, we identified changes to the odds of severe outcomes for cases of the Alpha VOC compared to other circulating variants for certain sub-populations. In particular, whilst the risk of severe outcomes was significantly higher amongst males compared to females in general (OR = 1.81), which is consistent to previous studies [57–61], the overall risk showed a moderate (although not significant) reduction in cases of the Alpha variant when compared to other cases for females (OR = 0.65) but not males (OR = 1.17). Looking specifically at our three severity indicators identified a mild non-significant decreased risk for mortality amongst females, but in contrast showed a significant increase in risk in males for admission to ICU (OR = 2.03) and intubation (OR = 2.32).
Overall, these results suggest that whilst the Alpha variant had no significant impact on COVID-19 severity overall, specific subgroups of the population may be more or less impacted by specific variants of the COVID-19 virus over others. Differences in the impact of SARS-CoV-2 infections between males and females has been suggested to result from differences in the expression of angiotensin converting enzyme (ACE2) receptors [62]. Indeed, circulating ACE2 levels have been shown to be higher in men, as well as in those with diabetes and pre-existing cardiovascular conditions [63]. The study of Stirrup et al [26], a large-scale multi-centre study in the UK, also found that overall hazard of mortality and ICU admission were not significantly affected in cases of Alpha compared to other lineages, but that sex-specific effects may be present. Interestingly, however, they showed that it was women specifically that showed increased risk of mortality and ICU admission in their cohort. Increased mortality appeared to be specific to those 70 years and above, with a slight decrease seen in 50–69 year olds. One possible explanation for this discrepancy may therefore be in differences in the age profiles of those included in the two studies. Another possible explanation may be that our dataset contains cases from across the entire course of the pandemic, including the first UK wave where risk of severe outcomes was higher as a result of a lack of identified treatment and vaccine options. Indeed, a recent large-scale study of 30 million people in the UK showed that risk of severe COVID-19 outcomes is reduced as a result of ongoing vaccine programs [31]. However, our result remains when focussing only on cases from September 2021, indicating that wave 1 patients do not affect the outcome data. It is worth also noting that whilst the Alpha variant data are largely homogenous, significant heterogeneity exists in the non-Alpha data, with cases coming from 46 distinct lineages in these data. Another difference may be with respect to the population under consideration, since Stirrup et al was a multi-centre study, primarily from hospitals within London (although did include data from the nearby city of Southampton). Both studies however point towards the role of Alpha in disease severity being context specific and mild overall.
Linkage of WGS and clinical data represents a powerful approach for assessment of the effects of Alpha on severity, in comparison to studies which used surrogate measures such as S-gene target failure (SGTF) in qPCR tests to differentiate Alpha from other lineages. Indeed, other studies based on community testing and SGTF have shown conflicting results, with studies showing increased risks of Alpha, but no difference in the effects of Alpha on mortality [64,65] or ICU admission [65] between male and female cases. Thus, the evidence for increased severity of the Alpha variant of concern remains inconclusive [66]. Beyond the role of VOCs in determining disease severity, we sought to identify potential mutations or mutation clusters associated with patients who suffered severe outcomes. Whilst we found no significant link between lineage and overall severity, we did find a weak link between the mutation type and the NEWS2 scores given to the patient at admission ($A = 0.25$) and the maximum score assigned ($A = 0.23$) (Table 3). Whilst this may indicate that there are mutations associated with patient health and physical derangement, it is also possible that such links relate to nosocomial transmission of the disease amongst clinically vulnerable patients, as previously discussed. This is further suggested given that the association is mostly enriched for mutations associated with non-severe outcomes.
To explore this in more detail, we utilised a range of machine learning models with individual mutations encoded alongside other patient factors, to further explore associations with patient mortality. Deep learning models have previously been developed for use in the diagnosis and screening of COVID-19 through interrogation of CT and chest X-ray images [67]. The two models with highest accuracy, XGBoost and MLP-ANN, were compared to identify features most linked with mortality. Renal disease, heart disease and diabetes feature for both models, with risk factors such as history of hypertension, COPD, prior history of malignancy, and asthma prominent in the XGBoost model, but not in the MLP-ANN model. The stochastic nature of algorithms such as XGBoost and MLP-ANN models means a degree of randomness exists, contrasted with deterministic algorithms such as linear regression or logistic regression-based models. Regardless, it is clear that comorbidities are amongst the features most closely associated with disease severity. Whilst the XGBoost model identified comorbidity status and sex as being most predictive of severity (Fig 5D), the MLP-ANN identified a number of deletions as being the features with the most impact (Fig 5E). These deletions were all specific to the Alpha variant B.1.1.7, including the Δ69–70 deletion on the Spike protein responsible for SGTF in qPCR testing for Alpha [68–71].
These deletions are therefore likely identified by the model as surrogates for Alpha vs non-Alpha cases. Whilst this may indicate that Alpha may be associated with mortality, this is not borne out when looking at male and female cases individually (Fig 4). This is therefore likely the result of non-Alpha lineages primarily representing cases from earlier in the pandemic, but may also be linked to selection bias due to Alpha being over-represented in these data. Similarly, the well documented D614G mutation was identified by both models, which was introduced at low levels during the first wave of infections in the UK, but became dominant and fixed in the population in subsequent waves [45]. This mutation is also linked with the temporal nature of the pandemic, with severity often being worse in earlier waves due to the lack of treatment options, reduced testing and interventions, and lack of vaccine program. It is therefore likely that these mutations are highlighting differences between cases early and later in the pandemic, rather than inherently having a functional role in increasing disease severity.
Overall, our analysis indicates that there are no clear strong factors that determine severe outcomes from COVID-19 (mortality, ICU admission or intubation). Whilst we detected a number of significant associations, most were mild and could be explained due to conflation with either general patient health, their location within the hospital, or changes in our treatment capabilities for the disease throughout the pandemic. It has been previously shown that comorbidities such as cancer, renal disease and heart disease are linked to negative outcomes, particularly mortality [49]. Also, whilst it is interesting to note that the NEWS2 score showed significant association with disease outcomes, these are not suitable for prediction of outcomes as discussed above. Similarly, the characteristics of the viral variant at the root of the infection is unlikely to present a suitable predictive tool for determining disease outcomes. Whilst there was some evidence of effects on severity from the Alpha variant compared to other circulating variants, the effect was inconsistent, with both increase and decrease in severity seen, sometimes at odds with previous studies.
Whilst this study focuses on only the Alpha variant, and thus cannot draw conclusions for further VOCs such as Delta and Omicron, these results suggest that within these data the introduction of the Alpha variant did not have a significant impact on severity of the disease. Of course, these data represent only a limited population, with 929 patient samples from a single hospital site. One other key limitation of this study is that the demographics of the patient cohort are skewed for those of the local area, in particular with over $75\%$ of those in the study being of a white background (Table 1). These results may therefore not be generalisable to the population as a whole. However, despite these limitations, our study represents a useful and in-depth interim exploration of the effects on disease severity in response to both clinical measures and viral genomics. Recently, a large-scale analysis of over 1 million patients in England showed lower or similar risks of death, hospital admission and hospital attendance between the BA.1 and BA.2 Omicron variants [30], matching our observation that emerging SARS-CoV-2 variants do not result in more severe outcomes for patients.
Since our data indicate that virus genomics have limited impact on disease severity, it is likely that understanding of those most susceptible to severe outcomes when infected by SARS-CoV-2 (beyond clinically vulnerable individuals) will come from studies such as the GenOMICC study in the UK (https://genomicc.org/about/), which aim to understand the interaction between virus and host, and explore genetic factors in humans that dictate disease outcomes. Indeed, multiple studies have already been conducted identifying potential susceptibility loci in the human genome that may put patients at increased risk of death or other severe outcomes, including mutations in genes linked to immune response, blood clotting and mucus production [72–75]. In particular, a recent study using machine learning approaches such as XGBoost identified variants from whole exome sequencing associated with severe COVID-19 [76]. These data identified associations between age, gender, and 16 variants linked to immune system and inflammatory processes able to predict severe outcomes with high accuracy. Such studies will help to further understand the factors that predispose individuals to severe outcomes from SARS-CoV-2 infection.
As society accustoms itself to a “new normal” way of life, we are learning to live with endemic COVID-19. New variants will continue to emerge, and it is therefore imperative that we learn what we can from existing data. It is particularly important for us to understand how the most severe disease cases arise, in the hope that we may target such cases specifically and early. Studies like this which combine clinical and laboratory data, will thus be essential to that task.
## Conclusion
Whilst many risk factors for severe COVID-19 have been identified, the precise mechanisms resulting in severe outcomes for those infected by SARS-CoV-2 (including admission to ICU, the need for mechanical ventilation, and mortality) remain poorly understood. In this study, we aimed to combine genomic sequencing data of SARS-CoV-2 viral variants with an extensive database of patient records to further understand those factors most associated with severe outcomes. In particular, we were interested to understand the precise role played by mutations in the virus itself, and whether infection with certain variants or viruses with specific mutations might be more likely to cause severe disease. Whilst patient outcome was weakly associated with factors linked with acute physiological status and human genetics, including age, sex and pre-existing conditions, our data suggest that severity risk is not significantly impacted by specific mutations in SARS-CoV-2. It is therefore likely that risk of severe outcomes results from a combination of patient health and innate genetic predisposition. Thus, whilst studies such as ours significantly further our understanding of the pathophysiology of the virus, ongoing studies exploring the role of host genetics on disease progression will continue to disentangle the complex factors that might increase risk to those infected with SARS-CoV-2.
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|
---
title: 'Efficacy of continuous positive airway pressure on TNF-α in obstructive sleep
apnea patients: A meta-analysis'
authors:
- Yong Luo
- Fa-Rong Zhang
- Jun-Lin Wu
- Xi-Jiao Jiang
journal: PLOS ONE
year: 2023
pmcid: PMC10035913
doi: 10.1371/journal.pone.0282172
license: CC BY 4.0
---
# Efficacy of continuous positive airway pressure on TNF-α in obstructive sleep apnea patients: A meta-analysis
## Abstract
### Background
Tumor necrosis factor-α (TNF-α) is an important mediator of the immune response. At present, the improvement of TNF-α after continuous positive airway pressure (CPAP) treatment of obstructive sleep apnea-hypopnea syndrome (OSAHS) is still controversial.
### Methods
We conducted a systematic review of the present evidence based on a meta-analysis to elucidate the effects of TNF-α on OSAHS after CPAP treatment.
### Results
To measure TNF-α, ten studies used enzyme-linked immunosorbent assay (ELISA), and one used radioimmunoassay. The forest plot outcome indicated that CPAP therapy would lower the TNF-α levels in OSAHS patients, with a weighted mean difference (WMD) of 1.08 ($95\%$ CI: 0.62–1.55; $P \leq 0.001$) based on the REM since there is highly significant heterogeneity (I2 = $90\%$) among the studies. Therefore, we used the subgroup and sensitivity analyses to investigate the source of heterogeneity. The findings of the sensitivity analysis revealed that the pooled WMD ranged from 0.91 ($95\%$ CI: 0.52–1.31; $P \leq 0.001$) to 1.18 ($95\%$ CI: 0.74–1.63; $P \leq 0.001$). The findings were not influenced by any single study. Notably, there was homogeneity in the Asia subgroup and publication year: 2019, implying that these subgroups could be the source of heterogeneity.
### Conclusion
Our meta-analysis recommends that CPAP therapy will decrease the TNF-α level in OSAHS patients, but more related research should be conducted.
## Introduction
Obstructive sleep apnea-hypopnea syndrome (OSAHS) is characterized by repeated episodes of partial or complete upper airway obstruction during sleep, resulting in chronic intermittent hypoxemia, excessive daytime sleepiness, irregular snoring at night, and increased nocturia. It is recognized as a new controllable risk factor for cerebrovascular and cardiovascular disease [1, 2]. Numerous studies reported that 3–$9\%$ of females and 10–$17\%$ of males have an apnea–hypopnea index (AHI) ≥ 15/h, which is present in approximately one billion people aged 30 to 69 years globally [3, 4]. Repeated hypoxic events and concomitant sleep disruption cause ventilatory instability, oxidative stress, inflammation, and disruption of vascular function [5]. The gold-standard treatment for OSAHS is a device that provides continuous pressure on the upper airway, called continuous positive airway pressure (CPAP), which can correct intermittent hypoxemia, reduce vascular endothelial dysfunction, and decreases ventilatory responsiveness to hypoxia [6]. Moreover, CPAP has a consistent ancillary effect, which includes intermittent hypercapnia, sleep fragmentation, repetitive increases in negative intrathoracic pressure, sympathetic nerve activity surge, and blood pressure (BP) [7–9].
Tumor necrosis factor-α (TNF-α) is a multidirectional pro-inflammatory cytokine secreted by various cells, including adipocytes, activated monocytes, macrophages, B cells, T cells, and fibroblasts. TNF-α is an important mediator of the immune response. Chronic inflammatory injury involving numerous inflammatory factors such as TNF-α, C-reactive protein, interleukin-6 (IL-6), and interleukin-8 (IL-8) is the leading cause of cardiovascular and cerebrovascular complications [10, 11]. In OSAHS patients, an increase in serum TNF-α affects lipid metabolism and energy consumption, resulting in weight gain and metabolic disorders [12]. Concurrently, TNF-α and other pro-inflammatory factors can also regulate the hypothalamus and hippocampus during sleep, causing structural sleep disturbances [13]. Many studies currently revealed that appropriate CPAP treatment could improve arterial inflammation and metabolic status in OSAHS patients [14]. However, some studies indicated that CPAP treatment had no significant effect on the inflammatory factor TNF-α [15]. The improvement of TNF-α after CPAP treatment of OSAHS is still controversial. Borges et al. [ 15] revealed no significant differences in the levels of oxidative stress and inflammation markers in OSAHS patients after eight weeks of CPAP. While Wang et al. [ 16] demonstrated that the CPAP therapy significantly reduced the incidence of all arrhythmia in OSAHS patients, TNF-α was significantly lower in the CPAP group than in the sham-CPAP group. Despite numerous original studies on CPAP and TNF-α in OSAHS patients, there is a lack of comprehensive scientific evidence. The question in the present analysis is: "Does CPAP treatment reduce TNF-α levels in OSAHS patients?" Therefore, we conducted a meta-analysis-based systematic review of the current evidence to elucidate the effects of TNF-α on OSAHS after CPAP treatment.
## Search strategy
To determine potential applicable original articles, a thorough search in the PubMed, EMBASE, and Web of Science databases up to January 2, 2022, using the following words: "CPAP", "continuous positive airway pressure", "tumor necrosis factor-α", "TNF-α", "OSA", "obstructive sleep apnea-hypopnea syndrome", "obstructive sleep apnea" and "OSAHS". We first screened the article’s title and abstract and reviewed the included research reference lists to find additional relevant articles.
## Inclusion and exclusion standard
The present study had no national limitations. All OSAHS patients received CPAP treatment; plasma TNF-α was measured before and after the CPAP; studies were limited to humans, published in English, and included detailed raw data. Cancers and other illnesses that could be associated with TNF-α were not included. The present study excluded repeated studies, letters, case reports, abstracts, and comments. We extracted the first author’s name and publication year, sample size, country, CPAP time, AHI, BMI, and age from the final included studies. After removing duplicates, two independent reviewers (ZFR and LY) assessed the studies against the inclusion and exclusion criteria. The third author (JJX) resolves conflicts.
## Statistical analysis
The value of the I2 index determines heterogeneity. I2 values of 75–$100\%$, 50–$75\%$, 25–$50\%$, and < $25\%$ indicated high, medium, and low heterogeneity and homogeneity. If the I2 value is > $50\%$, the random effect model (REM) is used; otherwise, the fixed effect model (FEM) is used. The weighted mean difference (WMD) of TNF-α levels was calculated for each study. The sensitivity analysis was repeated several times to evaluate the impact of each study in the analysis, each time excluding a different individual study. By excluding different individual studies each time, the sensitivity analysis was repeated to evaluate the impact of each study in the analysis.
## Article features
Sixty-three articles were identified as relevant. The removal of repeated studies reduced the number of studies to thirty-seven. The remaining thirty-seven studies were screened, resulting in the exclusion of seventeen studies. After reviewing the full text of the twelve studies, nine articles were excluded. Finally, eleven articles were included in the meta-analysis [15, 17–26]. Fig 1 depicts the literature retrieval procedure. Meanwhile, Table 1 presents the study data. Three studies came from Asia, five from Europe, and three from America. All the articles had NOS scores of five or above, indicating their high quality. Moreover, ten articles were observational studies, while one was a randomized controlled trial [18]. To measure TNF-α, ten studies used enzyme-linked immunosorbent assay (ELISA), and one used radioimmunoassay.
**Fig 1:** *Selection process for studies included in the meta-analysis.* TABLE_PLACEHOLDER:Table 1
## Pooled analysis
The forest plot outcome indicated that CPAP therapy would lower the TNF-α levels in OSAHS patients, with a WMD of 1.08 ($95\%$ CI: 0.62–1.55; $P \leq 0.001$) based on the REM since there is highly significant heterogeneity (I2 = $90\%$) among the studies Fig 2. Therefore, we used the subgroup and sensitivity analyses to investigate the source of heterogeneity. The findings of the sensitivity analysis revealed that the pooled WMD ranged from 0.91 ($95\%$ CI: 0.52–1.31; $P \leq 0.001$) to 1.18 ($95\%$ CI: 0.74–1.63; $P \leq 0.001$). The findings were not influenced by any single study.
**Fig 2:** *The forest plot outcome indicated that the CPAP therapy will not change the TNF-α level in OSAHS patients.*
## Subgroup analysis
Change in TNF-α levels in OSAHS patients was also investigated using subgroup analysis. Patient number and CPAP duration time were also significant in the region subgroup, and the complete information is presented in Table 2. Notably, there was homogeneity in the Asia subgroup and publication year: 2019, implying that these subgroups could be the source of heterogeneity. Begg’s test ($$P \leq 0.075$$) and Egger’s test ($$P \leq 0.154$$) indicated a significant negative publication bias.
**Table 2**
| Subgroup | Studies included (N) | Heterogeneity | Heterogeneity.1 | Pooled WMD | Pooled P value |
| --- | --- | --- | --- | --- | --- |
| Subgroup | Studies included (N) | I2 (%) | P value | Pooled WMD | Pooled P value |
| Asia | 3 | 1 | 0.36 | 1.02 (0.76–1.28) | <0.001 |
| Europe | 5 | 96 | <0.001 | 1.19 (0.32–2.06) | 0.006 |
| America | 3 | 83 | 0.003 | 0.92 (-0.12–2.04) | 0.08 |
| CPAP>3 months | 6 | 76 | 0.0007 | 1.11 (0.69–1.53) | <0.001 |
| CPAP<3 months | 5 | 94 | <0.001 | 1.05 (0.10–2.01) | 0.03 |
| Number<15 | 2 | 27 | 0.24 | 1.06 (0.21–1.91) | 0.01 |
| Publication year: 2019 | 2 | 0 | 0.49 | 0.21 (-0.02–0.45) | 0.08 |
## Discussion
Our meta-analysis suggests that CPAP therapy reduces TNF-α levels in OSAHS patients. Simultaneously, the present study illustrates high heterogeneity. In addition, subgroup analysis was performed to investigate the differences between TNF-α level and OSAHS. Furthermore, sensitivity analysis revealed that the overall results remained unchanged when any single study was excluded or REM was converted to FEM. Therefore, we believe the data obtained from the present study are reliable.
Long-term CPAP therapy decreases circulating levels of TNF-αin OSA patients [23], which is consistent with our findings, as CPAP therapy lowers TNF-α levels in OSAHS patients. Neutrophils and monocytes secreted cytokines and proteins stimulated by TNF-α during nocturnal hypoxia. Various studies in animal models of OSAHS have indicated that TNF-α can stimulate cells to produce IL-1 and IL-17, which ultimately leads to the recruitment of neutrophils and plays an important role in the deterioration of OSA at night [27]. Similarly, Minoguchi et al. [ 20] described that deterioration of sleep quality due to repeated apnea-associated hypoxia is associated with increased TNF-α production in OSA patients. In addition, NF-κB signaling is important in stimulate TNF-α in OSA [20]. Studies indicated that TLR2/TLR4 activation had been associated with the release of TNF-αfrom monocytes in OSA patients through NF-κB signaling [21]. Moreover, body mass index (BMI) and AHI have been independently associated with systemic TNF-α production [12]. BMI was the strongest predictor of TNF-α production by monocytes, indicating that adipocytes and monocytes primarily produce TNF-α in response to hypoxia [20]. Due to insufficient data, we were unable to investigate the role of BMI in TNF-α levels in OSA patients in the present study. Because sympathetic nervous activation causes 2-adrenergic receptor-mediated leukocytosis and CPAP reduces catecholamine levels and sympathetic nerve activation, the monocytes gained before and after CPAP may represent distinct populations, illustrating the decrease in TNF-α production. Meanwhile, AHI is a predictor of inflammation in OSA patients. A significant correlation was found between AHI changes and spontaneous TNF-α production by monocytes [20]. However, due to the lack of data in the present study, we cannot investigate the role of AHI in TNF-α levels in OSA patients. So far, no meta-analysis has explored the change of TNF-α in OSA patients to strengthen the evidence. Therefore, our meta-analysis indicated that CPAP therapy reduces the TNF-α levels in OSAHS patients, which may contribute to better clinical management of OSA patients.
OSA is a risk factor for obesity and cardiovascular disease. The cytokine TNF-α gene is also linked to OSAHS susceptibility; the frequency of the ’-308A’ allele in the TNF-α gene was significantly higher in obese patients with OSA compared to obese subjects without OSA [28]. In addition, the present study found that inhibiting TNF-α activity was associated with a significant reduction in objective sleepiness in obese OSA patients. This effect is approximately three times greater than the effect of CPAP ventilation on objective sleepiness in OSA patients, indicating that pro-inflammatory cytokines promote OSA pathogenesis [29]. Therefore, early use of CPAP can reduce the level of inflammation in the OSA patient, alleviating drowsiness and lowering the morbidity and mortality from cardiovascular diseases [30]. In the present study, we also found a significant result in the subgroup analysis, implying that changes in clinical characteristics will affect changes in TNF-α levels in OSAHS patients, but further investigation is required. While we could not investigate the role of BMI and AHI in the impact of TNF-α in OSAHS patients due to a lack of data, additional correlated research on BMI and AHI stratification should be conducted.
So far, this is the first meta-analysis to determine whether OSAHS patients are related to TNF-α and CPAP treatment. Simultaneously, our study also has some limitations. First, we lack information on BMI, AHI, age, gender, and CPAP time due to a lack of data. Second, the results may be biased due to the differences in TNF-α susceptibility and measurement methods. Finally, primary prevention necessitates a large number of subjects and a more extended follow-up period, which can cause information deviation and affect the accuracy of the results.
## Conclusion
Our meta-analysis recommends that CPAP therapy decrease the TNF-α level in OSAHS patients, but more related research should be conducted.
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|
---
title: Comparison of alcohol consumption and tobacco use among Korean adolescents
before and during the COVID-19 pandemic
authors:
- Wonseok Jeong
journal: PLOS ONE
year: 2023
pmcid: PMC10035916
doi: 10.1371/journal.pone.0283462
license: CC BY 4.0
---
# Comparison of alcohol consumption and tobacco use among Korean adolescents before and during the COVID-19 pandemic
## Abstract
### Background
The COVID-19 pandemic has brought significant changes worldwide, and due to the strict “Social Distancing Plan” including school closures, Korean adolescents have experienced unprecedented changes in their lives. Considering the peer effect on adolescents’ health behavior impacted due to the changes brought about by the pandemic, it would be interesting to explore differences in substance use in Korean adolescents. This study examines how these risk behaviors among Korean adolescents have changed before and during the COVID-19 pandemic.
### Methods
Korea Youth Risk Behavior Web-based Survey of 87,532 adolescents was used to collect the data for the period 2019, 2020, and 2021. The KYRBWS is conducted by a national institution which uses a stratified two-stage cluster sampling, and the data is statistically reliable and representative of the population. The Cochran-Armitage and Chi-squared test for linear and non-linear time trends, respectively, were calculated to assess the difference across the period [2019, 2020, 2021]. Also, the odds ratios (ORs) with $95\%$ CIs for current smoking status and current alcohol use status among 2020 and 2021 participants were compared with those of the 2019 participants using multiple logistic regression analysis.
### Results
The degree of current smoking status was lower in 2020 and 2021 participants than in the 2019 participants (2020: OR = 0.66, $95\%$ CI = 0.61–0.71; 2021: OR = 0.66, $95\%$ CI = 0.61–0.71). On the same token, current alcohol use status was also lower in the participants during the pandemic than those before the pandemic (2020: OR = 0.70, $95\%$ CI = 0.66–0.73; 2021: OR = 0.70, $95\%$ CI = 0.66–0.73).
### Conclusion
This study found that alcohol and tobacco use were reduced among Korean adolescents during the COVID-19 pandemic. Despite the decrease, future research on the potential effects of the COVID-19 pandemic on adolescents is warranted.
## Introduction
COVID-19 was first recognized in December 2019 in Wuhan, China, and due to its high contagiousness, more than 218,000 infected patients and 8,900 deaths had been reported by 18 March 2020, and the virus had reached 173 countries [1]. A total of 9786 confirmed cases of COVID-19 were reported in South Korea by 31 March 2020 [2]. Subsequently, on 11 March 2020, the World Health Organization announced that COVID-19 was a global pandemic, and to control the outbreak and minimize its spread, the South Korean government enforced a strict “Social Distancing Plan” which includes contact tracing, quarantine, social distancing, and school closures.
We know that adolescence is often associated with an increased need for social connection and peer acceptance, and a heightened sensitivity to peer influence [3]. The presence of friends increases the likelihood that adolescents will take certain risks; having friends who smoke or drink is one of the biggest predictors of adolescent engagement in these behaviors [4]. Furthermore, according to the previous study, peer differentiation strongly influences psychosocial maturity of adolescents, which can also eventually lead to problematic behaviors [5]. These factors refer that social distancing rules and the school closures due to the COVID-19 pandemic may have especially affected the risk and health behaviors of young people, more than those of the other age groups.
Furthermore, risk behaviors among adolescents are extremely maleficent since such behaviors often extend into adulthood, rendering them vulnerable in adulthood to preventable morbidities and mortalities [4]. For instance, adolescents who drink alcohol are more likely to have alcohol-related disorders, mental health problems, and chronic diseases in adulthood, while smoking in adolescents is positively associated with nicotine-dependent, cancer, and cardiovascular diseases in adulthood [6].
As such, the Korean government has enforced “stay-at-home” orders due to the COVID-19 outbreak, when the peers highly influence youths’ risk behaviors. The behavior changes among adolescents are likely to happen and thus, youths’ risk behaviors require special attention. Prior studies on adults or focused on different aspects of adolescents, have not explored the differential patterns of substance use among Korean adolescents due to the pandemic. This led us to compare the alcohol consumption and tobacco use before and during COVID-19 pandemic among South Korean adolescents.
## Data and study participants
The Korea Youth Risk Behavior Web-based Survey (KYRBWS) was used to collect information on the health-risk behaviors in Korean students (from seventh to the twelfth grades) for the period 2019, 2020, and 2021. The KYRBWS is conducted by a national institution, the Korea Centers for Disease Control and Prevention (KCDC), based on stratified two-stage random cluster sampling. 400 middle schools and 400 high schools are randomly selected within 131 districts in the first stage. In the second sampling stage, one class is selected from each grade within each chosen school [7]. Lastly, every student in the selected classes is surveyed except for school dropouts, those with special needs, and those who have difficulty in reading comprehension. Also, the written informed consent was obtained from each students’ parents for the survey [8]. The survey data is statistically reliable and representative of the Korean adolescents. The KYRBWS was approved by the KCDC Institutional Review Board (2014-06EXP-02-P-A) in 2014. From 2015, the ethics approval for the KYRBWS was waived by the KCDC Institutional Review Board under the Bioethics & Safety Act and opened to the public. As the data is publicly available, no approval related to the ethics of research was needed. All data is available from https://www.kdca.go.kr/yhs/home.jsp with the permission of the reasonable request.
Of the 167,099 individuals who participated in the surveys, those aged >19 years ($$n = 166$$,590) were excluded for the analysis. Nobody was excluded from the dependent variables, alcohol use status and smoking status, due to the missing data. Next, 87,532 participants remained due to the missing data from the variables ‘educational level of father and mother.’ There was no missing data in other covariates such as economic level, subjective health status, stress level, and BMI, leading the remaining 87,532 as the final study sample size.
## Dependent variables
Current smoking status and current alcohol use status were included as the main dependent variables in this study. Participants who smoked more than one conventional cigarette within a month was categorized as ‘Yes’ and those who have never smoked or have not smoked a single conventional cigarette within a month were categorized as ‘No’ in the variable, current smoking status. Similarly, participants who drank at least one glass of any type of alcohol within a month were categorized as ‘Yes’ while those who have never drunk or have not drunk a single glass of alcohol within a month were categorized as ‘No’ in the dependent variable, current alcohol use status. All answers were based on self-reported measures.
## Primary comparison variable
The primary independent variable was the COVID-19 pandemic. Since the first patient with COVID-19 in South Korea was diagnosed on February 19, 2020, the study compared alcohol consumption and tobacco use between 2019 (before), 2020 and 2021 (during) participants [9].
## Demographic and socioeconomic variables
The demographic characteristics included in the study were participants’ age (14−16 (Middle School), 17−19 (High School)) and gender. Socioeconomic factors included participants’ scholastic performance (low, middle, and high), subjective economic level (low, middle, and high) and education level of parents (middle school or less, high school, and college or over). Each participant’s academic performance was self-reported and evaluated through one question: “In the past 12 months, how has your average academic performance been? [ 10]”
## Health-related variables
The health-related characteristics included the participants’ self-reported health status (high, middle, and low), stress level (high, middle, and low) and body mass index (normal, and overweight & obese). Participants with > = 23 BMI were categorized as overweight & obese [11].
## Statistical analysis
Cochran-Armitage tests were used to test linear time-trend estimates, while Chi-squared tests were conducted to assess the association of demographic and socioeconomic characteristics of the study population with their substance use [12, 13]. Multiple logistic regression analyses were performed to compare the alcohol consumption and tobacco use before and after the COVID-19 pandemic after accounting for potential confounding variables, including demographic, socioeconomic, and health-related characteristics. Results are reported as odds ratios (OR) with a $95\%$ confidence interval (CI). Differences were considered statistically significant with a p-value of <0.05. All data analyses were conducted using SAS 9.4 software (version 9.4; SAS Institute Inc., Cary, NC, USA).
## Results
A total of 167,099 participants were included. After all exclusions, data from 87,532 participants were analyzed. Table 1 presents the general characteristics of the study population; 27,205 youths in 2019 were compared with 30,366 youths in 2020 and 29,961 youths in 2021. In 2019, $5.6\%$ and $14.2\%$ of the participants smoked conventional cigarettes and consumed alcohol, respectively. In 2020, the rates have decreased by $3.8\%$ and $10.2\%$, and it continued to reduce until $3.6\%$ and $10.0\%$ in 2021. The 2019 participants had a lower severe stress level ($39.9\%$) than that of 2020 ($33.6\%$) and 2021 ($38.9\%$) participants. Also, 2019 youths had the lowest overweight and obese rate of $14.7\%$ compared to $15.9\%$ and $16.7\%$ from 2020 and 2021 participants.
**Table 1**
| Variables | Variables.1 | Total | Total.1 | Participants, n (%) | Participants, n (%).1 | Participants, n (%).2 | Participants, n (%).3 | Participants, n (%).4 | Participants, n (%).5 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Variables | Variables | Total | Total | 2019.0 | 2019 | 2020.0 | 2020 | 2021 | 2021 |
| Variables | Variables | 87532 | 87532 | 27205.0 | 27205 | 30366.0 | 30366 | 29961 | 29961 |
| Level of School | Level of School | | | | | | | | |
| Middle school | Middle school | 46359 | (53.0) | 13979.0 | (51.4) | 15900.0 | (52.4) | 16480 | (55.0) |
| High school | High school | 41173 | (47.0) | 13226.0 | (48.6) | 14466.0 | (47.6) | 13481 | (45.0) |
| Sex | | | | | | | | | |
| Male | Male | 40404 | (46.2) | 12409.0 | (45.6) | 14165.0 | (46.6) | 13830 | (46.2) |
| Female | Female | 47128 | (53.8) | 14796.0 | (54.4) | 16201.0 | (53.4) | 16131 | (53.8) |
| Scholastic performance | Scholastic performance | Scholastic performance | Scholastic performance | | | | | | |
| High | High | 37722 | (43.1) | 11908.0 | (43.8) | 12831.0 | (42.3) | 12983 | (43.3) |
| Middle | Middle | 43428 | (49.6) | 13321.0 | (49.0) | 15253.0 | (50.2) | 14854 | (49.6) |
| Low | Low | 6382 | (7.3) | 1976.0 | (7.3) | 2282.0 | (7.5) | 2124 | (7.1) |
| Economic level of family | Economic level of family | Economic level of family | Economic level of family | | | | | | |
| High | High | 38428 | (43.9) | 11902.0 | (43.7) | 13224.0 | (43.5) | 13302 | (44.4) |
| Middle | Middle | 47927 | (54.8) | 14912.0 | (54.8) | 16705.0 | (55.0) | 16310 | (54.4) |
| Low | Low | 1177 | (1.3) | 391.0 | (1.4) | 437.0 | (1.4) | 349 | (1.2) |
| Educational level of father | Educational level of father | Educational level of father | Educational level of father | | | | | | |
| College or over | College or over | 61739 | (70.5) | 18783.0 | (69.0) | 21293.0 | (70.1) | 21663 | (72.3) |
| High school | High school | 24323 | (27.8) | 7929.0 | (29.1) | 8551.0 | (28.2) | 7843 | (26.2) |
| Middle school or less | Middle school or less | 1470 | (1.7) | 493.0 | (1.8) | 522.0 | (1.7) | 455 | (1.5) |
| Educational level of mother | Educational level of mother | Educational level of mother | Educational level of mother | | | | | | |
| College or over | College or over | 58818 | (67.2) | 17810.0 | (65.5) | 20242.0 | (66.7) | 20766 | (69.3) |
| High school | High school | 27589 | (31.5) | 9000.0 | (33.1) | 9744.0 | (32.1) | 8845 | (29.5) |
| Middle school or less | Middle school or less | 1125 | (1.3) | 395.0 | (1.5) | 380.0 | (1.3) | 350 | (1.2) |
| Subjective health status | Subjective health status | Subjective health status | Subjective health status | | | | | | |
| High | High | 60468 | (69.1) | 19233.0 | (70.7) | 21537.0 | (70.9) | 19698 | (65.7) |
| Middle | Middle | 20044 | (22.9) | 5903.0 | (21.7) | 6586.0 | (21.7) | 7555 | (25.2) |
| Low | Low | 7020 | (8.0) | 2069.0 | (7.6) | 2243.0 | (7.4) | 2708 | (9.0) |
| Stress level | Stress level | | | | | | | | |
| High | High | 17227 | (19.7) | 5107.0 | (18.8) | 6536.0 | (21.5) | 5584 | (18.6) |
| Middle | Middle | 37574 | (42.9) | 11234.0 | (41.3) | 13626.0 | (44.9) | 12714 | (42.4) |
| Low | Low | 32731 | (37.4) | 10864.0 | (39.9) | 10204.0 | (33.6) | 11663 | (38.9) |
| BMI | | | | | | | | | |
| Normal | Normal | 62589 | (71.5) | 19875.0 | (73.1) | 21534.0 | (70.9) | 21180 | (70.7) |
| Overweight & Obese | Overweight & Obese | 24943 | (28.5) | 7330.0 | (26.9) | 8832.0 | (29.1) | 8781 | (29.3) |
| Current smoking status | Current smoking status | Current smoking status | Current smoking status | | | | | | |
| Yes | Yes | 3768 | (4.3) | 1529.0 | (5.6) | 1146.0 | (3.8) | 1093 | (3.6) |
| No | No | 83764 | (95.7) | 25676.0 | (94.4) | 29220.0 | (96.2) | 28868 | (96.4) |
| Current alcohol use status | Current alcohol use status | Current alcohol use status | Current alcohol use status | | | | | | |
| Yes | Yes | 9988 | (11.4) | 3876.0 | (14.2) | 3109.0 | (10.2) | 3003 | (10.0) |
| No | No | 77544 | (88.6) | 23329.0 | (85.8) | 27257.0 | 27257 | (89.8) | (90.0) |
Tables 2 and 3 present the trends in the proportion of Korean adolescents smoking cigarettes and taking alcohol respectively by their demographic, socioeconomic, and health-related variables. Considerable decreases in the levels of conducting both risk behaviors were observed during the pandemic among the majority of the adolescents categorized by their level of school, sex, scholastic performance, subjective health status, stress level, and BMI. Adolescents with low economic level of family and low educational level of parents did not present statistically significant differences in current smoking rates over the pandemic. Similarly, adolescents with low educational level of parents showed no indication of significant differences in the use of alcohol before and during the pandemic.
Table 4 shows the association between two risk behaviors and COVID-19 pandemic among Korean adolescents. The results are adjusted for age, sex, economic level, educational level of father and mother, scholastic performance, subjective health status, stress level and BMI. Participants in 2020 and 20201 were estimated with lower odd ratios of current smoking status compared to the participants in 2019. These results were statistically significant (2020: OR = 0.66, $95\%$ CI = 0.61–0.71; 2021: OR = 0.66, $95\%$ CI = 0.61–0.71). Also, 2020 and 2021 participants presented with lower odd ratios of current alcohol use status compared to the participants in 2019. These results were statistically significant (2020: OR = 0.70, $95\%$ CI = 0.66–0.73; 2021: OR = 0.70, $95\%$ CI = 0.66–0.73).
**Table 4**
| Variables | 2020 | 2020.1 | 2020.2 | 2020.3 | 2021 | 2021.1 | 2021.2 | 2021.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Variables | Adjusted OR a | 95% CI | 95% CI | 95% CI | Adjusted OR a | 95% CI | 95% CI | 95% CI |
| Current smoking status | Current smoking status | | | | | | | |
| No | 1.00 | | | | 1.00 | | | |
| Yes | 0.66* | (0.61 | – | 0.71) | 0.66* | (0.61 | – | 0.71) |
| Current alcohol use status | Current alcohol use status | | | | | | | |
| No | 1.00 | | | | 1.00 | | | |
| Yes | 0.70* | (0.66 | – | 0.73) | 0.70* | (0.66 | – | 0.73) |
Table 5 shows the results of a subgroup analysis between two risk behaviors and COVID-19 pandemic, focusing on sex and scholastic performance. Both male and female groups demonstrated lower odds of smoking cigarettes and taking alcohol during COVID-19. In addition, according to the scholastic performance, all groups (low, middle, and high) illustrated lower odds of smoking cigarettes and taking alcohol in the 2020 and 2021 participants than in the 2019 participants.
**Table 5**
| Variables | 2020 | 2020.1 | 2020.2 | 2020.3 | 2021 | 2021.1 | 2021.2 | 2021.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Variables | Adjusted OR a | 95% CI | 95% CI | 95% CI | Adjusted OR a | 95% CI | 95% CI | 95% CI |
| Sex | Sex | | | | | | | |
| Men (n = 40,404) | Men (n = 40,404) | | | | | | | |
| Current smoking status | Current smoking status | | | | | | | |
| No | 1.00 | | | | 1.00 | | | |
| Yes | 0.63* | (0.57 | – | 0.70) | 0.61* | (0.55 | – | 0.68) |
| Current alcohol use status | Current alcohol use status | | | | | | | |
| No | 1.00 | | | | 1.00 | | | |
| Yes | 0.68* | (0.64 | – | 0.73) | 0.69* | (0.65 | – | 0.75) |
| Women (n = 47,128) | Women (n = 47,128) | | | | | | | |
| Current smoking status | Current smoking status | | | | | | | |
| No | 1.00 | | | | 1.00 | | | |
| Yes | 0.71* | (0.62 | – | 0.82) | 0.76* | (0.66 | – | 0.87) |
| Current alcohol use status | Current alcohol use status | | | | | | | |
| No | 1.00 | | | | 1.00 | | | |
| Yes | 0.71* | (0.66 | – | 0.76) | 0.70* | (0.66 | – | 0.76) |
| Scholastic performance | Scholastic performance | | | | | | | |
| High (n = 37,722) | High (n = 37,722) | | | | | | | |
| Current smoking status | Current smoking status | | | | | | | |
| No | 1.00 | | | | 1.00 | | | |
| Yes | 0.57* | (0.49 | – | 0.68) | 0.56* | (0.47 | – | 0.66) |
| Current alcohol use status | Current alcohol use status | | | | | | | |
| No | 1.00 | | | | 1.00 | | | |
| Yes | 0.72* | (0.65 | – | 0.78) | 0.74* | (0.68 | – | 0.81) |
| Middle (n = 43,428) | Middle (n = 43,428) | | | | | | | |
| Current smoking status | Current smoking status | | | | | | | |
| No | 1.00 | | | | 1.00 | | | |
| Yes | 0.66* | (0.59 | – | 0.73) | 0.69* | (0.62 | – | 0.77) |
| Current alcohol use status | Current alcohol use status | | | | | | | |
| No | 1.00 | | | | 1.00 | | | |
| Yes | 0.66* | (0.62 | – | 0.71) | 0.67* | (0.63 | – | 0.72) |
| Low (n = 6,382) | Low (n = 6,382) | | | | | | | |
| Current smoking status | Current smoking status | | | | | | | |
| No | 1.00 | | | | 1.00 | | | |
| Yes | 0.79* | (0.66 | – | 0.94) | 0.73* | (0.61 | – | 0.88) |
| Current alcohol use status | Current alcohol use status | | | | | | | |
| No | 1.00 | | | | 1.00 | | | |
| Yes | 0.80* | (0.69 | – | 0.92) | 0.70* | (0.60 | – | 0.81) |
## Discussion
There is little empirical evidence on the changes of alcohol and tobacco consumption during the COVID-19 pandemic of Korean teenagers. This study describes the connections between COVID-19 pandemic and its subsequent “stay-at-home” orders and changes in substance uses of Korean adolescents, using demographic, socioeconomic, and health-related variables gained from the 2019, 2020, and 2021 KYRBWS data. There were positive associations with COVID-19 and a statistically significant decreased substance use; adolescents smoked less cigarettes and drank less alcohol compared to how they did before the pandemic. The lower odds of alcohol consumption and cigarettes use were consistent in all subgroups by both sex and scholastic performance.
Social distancing plan and its subsequent school closure due to the COVID-19 outbreak seem to be the reasons for the reduction in the substance uses of teenagers. Adolescence is associated with an increased need for social connection and therefore, presence of friends increases the likelihood of risk behaviors including smoking cigarettes and taking alcohol [3, 4]. Reduced peer pressure and importance of peer relationships from the school closure could have stopped youths from conducting unnecessary risk behaviors. According to the subgroup analysis results of risk behaviors and COVID-19 pandemic in Table 3, such peer pressure must have affected adolescents equally regardless of sex and educational performances. In addition, reduced stress due to the stay-at-home orders might also have reduced the substance uses of adolescents. According to the previous study, school closures can alleviate stress in youths by reducing academic burdens and school bullying, while there was a significant association between high perceived stress and cigarette use [14–16]. On the same token, stressful school environment due to South Korea’s educational fever can also place students at risk of alcohol [17]. Lastly, increased family warmth and connectedness due to the social distancing plan might have served as a protective factor against many of the risky behaviors engaged in by adolescents [18]. For example, the prior study presents that in comparison with children who spent more time with their parents, those who spent less time were more inclined to start smoking and try alcohol [19]. Considering that $21.4\%$ of school-aged children found the parent-children discussions to be more satisfying during the school closures, the argument is pretty valid [20].
Yet, the current study’s limitations should be noted. First, the data in this study are based on self-reported measures, and health status measurements might be subject to recall bias. Therefore, caution should be taken when interpreting these results. Also, due to this study’s cross-sectional design, cause, and effect, as well as the direction of the relationships observed, could not be determined. Third, cultural aspects could have influenced the impact of the COVID-19 pandemic on smoking and alcohol use status of Korean adolescents. Lastly, adolescents were considered as current smokers and drinkers if they have smoked or drank at least once within the past thirty days. The exact number of cigarettes smoked, and amount of alcohol consumed were not involved in the investigation.
Despite these limitations, our study does possess several strengths. The KYRBWS is conducted by a national institution based on random cluster sampling, and therefore, the data gained from it is statistically reliable and representative in comparison to surveys performed by private institutions. Moreover, as this study was conducted for over three years, compared to most recent COVID-19 research which only include one or two years, the representativeness of the sample was improved upon. Lastly, many covariates, including age, sex, economic level, educational levels of parents, scholastic performance, subjective health status, and BMI, were included to reduce the possible confounding effects.
## Conclusions
Along with the severe health consequences from the pandemic, long-term health consequences from the risk behaviors, especially among adolescents, are significant public health concern. This study found that the substance uses (alcohol and tobacco usage) were reduced among Korean adolescents during the COVID-19 pandemic period regardless of sex and scholastic performance. Despite the decrease, however, we must not belittle the prevalence of risk behaviors among adolescents and the potential effects of the COVID-19 pandemic on adolescents require additional follow-up studies.
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|
---
title: 'Effects of acupuncture on age-related macular degeneration: A systematic review
and meta-analysis of randomized controlled trials'
authors:
- Wu Sun
- Yuwei Zhao
- Liang Liao
- Xueyao Wang
- Qiping Wei
- Guojun Chao
- Jian Zhou
journal: PLOS ONE
year: 2023
pmcid: PMC10035922
doi: 10.1371/journal.pone.0283375
license: CC BY 4.0
---
# Effects of acupuncture on age-related macular degeneration: A systematic review and meta-analysis of randomized controlled trials
## Abstract
### Background
In recent years, an increasing number of patients with age-related macular degeneration (AMD) have received acupuncture treatment, but there has been no systematic review to evaluate the effect of acupuncture on patients with AMD.
### Purpose
This meta-analysis aims to review the clinical efficacy of acupuncture in the treatment of AMD.
### Methods
Randomized controlled trials up to September 4, 2022 were searched in the following databases: PubMed, Ovid Medline, Embase, Cochrane Library, The Chinese National Knowledge Infrastructure Database, VIP, Wanfang, and SINOMED. Two reviewers independently performed literature screening and data extraction. RevMan 5.4 was used for the meta-analysis.
### Results
Nine of the 226 articles were finally included. A total of 508 AMD patients (631 eyes) were enrolled, including 360 dry eyes and 271 wet eyes. The results showed that acupuncture alone or as an adjunct therapy improved both the clinical efficacy and best-corrected visual acuity (BCVA) of AMD patients and reduced their central macular thickness. The certainty of the evidence ranged from "low" to "very low".
### Conclusion
There is no high-quality evidence that acupuncture is effective in treating patients with AMD; patients with dry AMD may benefit from acupuncture treatment. Considering the potential of acupuncture treatment for AMD, it is necessary to conduct a rigorously designed randomized controlled trials to verify its efficacy.
## 1. Introduction
Age-related macular degeneration (AMD) is a disease that affects the macular area of the retina and causes progressive damage to central vision [1]. The early stage manifests as drusen and retinal pigment epithelium abnormalities, and the later stage manifests as dry AMD characterized by non-vascular proliferation or wet AMD characterized by choroidal neovascularization (CNV). Later, AMD can cause loss of central visual acuity of the patient, accompanied by severe visual impairment and even blindness [2]. Currently, AMD is the leading cause of irreversible blindness in developed countries [3, 4]. A meta-analysis involving a total of 129,664 participants worldwide showed that the overall global prevalence of early- and late-stage AMD in the adult population was $8.01\%$ and $0.37\%$, respectively, with a total prevalence of $8.69\%$ [5]. Considering that aging is the greatest risk factor for AMD, the number of people suffering from AMD will continue to increase [6]. By 2040, the number of AMD patients is expected to increase to 288 million [5].
The current treatment measures for AMD include anti-vascular endothelial growth factor (VEGF) injection therapy, antioxidant vitamins and minerals, photodynamic therapy, thermal laser photocoagulation surgery, etc [7, 8]. To date, there is no effective treatment for dry AMD patients other than appropriate antioxidant supplementation. Anti-VEGF therapy, the current first-line treatment, may reduce the odds of legal blindness caused by neovascular AMD [9]. However, a long-term follow-up study of patients initially receiving regular anti-VEGF agents showed that among patients who were followed up for more than 7 years, two-thirds lost most of their gains in visual acuity (VA) [10]. Currently, an increasing number of other treatment measures are causing concern. Relevant studies have shown that acupuncture may become an effective method for the treatment of AMD, which can improve the eye symptoms and visual acuity of patients [11, 12]. There are the following hypotheses about the mechanism of acupuncture treatment for AMD. Previous research revealed that acupuncture has obvious specificity for the macular area [13]. Acupuncture could improve microcirculation in the macular area by expanding the surrounding blood vessels after acting on the peripheral area of the eyes [14–16]. Clinical studies have further confirmed that acupuncture can reduce central macular thickness (CMT) and promote the absorption of fundus exudate [16–18]. In addition, relevant study have also shown that acupuncture may reduce serum VEGF levels in patients with wet AMD [19, 20]. Although many studies have revealed an intervention effect of acupuncture on AMD, there has been no review to scientifically evaluate the efficacy of acupuncture therapy. This review aims to evaluate the efficacy and safety of acupuncture in the treatment of AMD.
## 2.1. Study registration
This meta-analysis was registered with the International Prospective Register of Systematic Reviews (PROSPERO; registration number: CRD42020168611) and strictly adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) [21].
## 2.2.1. Types of studies
Randomized controlled trials (RCTs).
## 2.2.2. Types of participants
Patients diagnosed with AMD by the investigators of the original study. Dioagnostic criteria inlucding at least one of the following: 1) Presence of at least intermediate-size drusen (≥63 μm in diameter); 2) Retinal pigment epithelium (RPE) abnormalities; 3) Presence of any of the following features: geographic atrophy of the RPE, CNV (wet), polypoidal choroidal vasculopathy, reticular pseudodrusen, or retinal angiomatous proliferation [8].
## 2.2.3. Types of interventions
All types of acupuncture were included, including electroacupuncture, warm acupuncture, and scalp acupuncture. Interventions in the control group included sham acupuncture, no treatment, placebo, antioxidants or other treatments. To evaluate the efficacy of acupuncture as an adjunctive therapy, studies that compared acupuncture combined with other therapies with the other therapies were also included.
## 2.2.4. Outcome measures
The primary outcomes were clinical efficacy rates and BCVA. The clinical efficacy rates were defined as the number of patients who showed improvement in VA and the absorption of fundus bleeding and exudate, and the improvements were assessed by clinicians based on the “Criteria of Diagnosis and Therapeutic Effect of Internal Diseases and Syndromes in Traditional Chinese Medicine (TCM)” [22] (S1 File in S1 Appendix). The secondary outcomes included changes in the CMT of the patients and adverse events.
## 2.3. Exclusion criteria
Studies involving any of the following were not included: 1) research that involved a comparison of different acupuncture techniques or a comparison of different acupuncture points; 2) treatments involving laser needle or hydro-acupuncture therapy; 3) duplicate published studies or case reports.
## 2.4. Search strategy
Relevant literature was searched in the following databases: PubMed, Ovid Medline, Embase, Cochrane Library, The Chinese National Knowledge Infrastructure Database, VIP, Wanfang, and SINOMED. The search time was from inception to September 4, 2022. In addition, relevant web pages were also manually searched (www.clinicaltrials.gov; www.clinicaltrialsregister.eu; trialsearch.who.int) for ongoing trials or unpublished clinical trial reports. The specific search strategy can be found in S1 Table in S1 Appendix.
## 2.5. Data extraction
Two reviewers conducted a literature search independently. After screening out the duplicate documents in EndNote software, a preliminary review was carried out by reading the titles and abstracts of the retrieved documents. The literature that satisfied the inclusion and exclusion criteria was read in full to determine its eligibility for further inclusion. Any differences between the two reviewers were resolved through communication and negotiation with an arbiter.
## 2.6. Quality assessment
The methodological quality of the included studies was evaluated according to the Cochrane risk-of-bias tool for randomized trials (RoB 2.0) as follows [23]: randomization process, deviations from the intended interventions, missing outcome data, measurement of the outcome and selection of the reported result. Each item was classified as “low risk of bias”, “some problems”, and “high risk of bias”.
## 2.7. Data analysis
RevMan 5.4 was used for the meta-analysis. Continuous outcome variables were calculated by mean differences (MDs) or standard mean differences (SMDs) with $95\%$ confidence intervals (CIs), and dichotomous outcome variables were calculated by risk ratios (RRs) with $95\%$ CIs. When the heterogeneity of outcome variables was low ($P \leq 0.10$, I2 < $50\%$), the fixed-effect model was used; otherwise, the random-effect model was used. Publication bias assessment based on funnel plots was performed when the number of included studies was greater than 10. Subgroup analysis was performed by intervention type (with or without TCM), AMD type (dry or wet), or intervention course. Sensitivity analyses were performed to observe changes in synthetic results according to the following operations: 1) excluding low-quality studies; 2) excluding studies with small sample size; 3) excluding studies with the largest sample size; 4) excluding studies containing Chinese herbal medicine; 5) or switching between fixed and random effects models.
## 2.8. Quality of evidence
The quality of the pooled evidence for all the outcomes was judged by two independent reviewers according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system [24]. The strength of evidence was graded as “high”, “moderate”, “low” or “very low” based on five assessment items: risk of bias, inconsistency, indirectness, imprecision, and other considerations.
## 3.1. Literature search
A total of 226 articles were included, of which 102 studies were removed due to duplication. After reading the titles and abstracts, 15 articles remained (110 articles were removed, including 68 irrelevant articles, 26 reviews, 15 case reports and case series, and 1 mechanistic study). Of these, 4 included inappropriate comparisons, 1 was a comparison of different acupuncture manipulation techniques, and 1 was a comparison of different acupoints. Finally, nine studies were included [15–18, 25–29] (Fig 1). Excluded studies were listed in S1 Table in S1 Appendix.
**Fig 1:** *Study flow diagram.*
## 3.2. Characteristics of the studies
All 9 RCTs were conducted in China. A total of 508 AMD patients (631 eyes) were enrolled, including 360 eyes with dry AMD and 271 eyes with wet AMD. The study period was from 2011 to 2020, and the patients were over 50 years old. Of the 9 studies, 3 studies consisted of patients with wet AMD [15, 16, 18], 4 studies consisted of patients with dry AMD [25–28], and the remaining 2 studies included patients with dry and wet AMD [17, 29]. Three studies compared acupuncture with antioxidants (vitamin E/C) [18, 25, 29]. Five studies compared acupuncture combined with medication therapy with the same medication therapy, in which all the medications were Chinese herbs [15–17, 26, 27]. One study compared an acupuncture group with a control group, and the control group was only observed regularly without any treatment [28]. All the studies involved true acupuncture with filiform needles. The course of treatment ranged from 20 days to 3 months. In terms of outcome indicators, 7 studies reported clinical effectiveness [15–18, 26, 27, 29], 5 studies reported BCVA [16–18, 25, 27], and 3 studies mentioned CMT [16–18]. Side effects were reported in only 2 studies [17, 28]. While the BCVA included in all studies were tested using international standard visual acuity charts, the recording method used was the five-point recording method or decimal recording method. Tables 1 & 2 list the specific information from the studies included.
## 3.3. Risk of bias assessment
All studies mentioned randomization, of which 4 studies used a random number table [15, 25, 28, 29] and 5 did not mention random methods. None of the studies mentioned allocation concealment. Considering that without sham acupuncture, the patients could not be blinded to the treatment, we think that blinded the acupuncture practitioner is equivalent to blinding. Three studies had a high risk of selective reporting [17, 26, 29]. All studies reported complete results with no significant missing data. Five studies were supported by government funding [15, 16, 18, 25, 28], and the remaining studies did not mention funding information. See Fig 2 for details.
**Fig 2:** *Risk of bias assessment of included studies.A, literature quality evaluation; B, summary of the quality evaluation of the literature.*
## 3.4.1. Clinical efficacy rates
The clinical efficacy rates were evaluated in 7 studies [15–18, 26, 27, 29], and there was no heterogeneity in the outcome (I2 = $0\%$). The fixed-effect model showed that compared with the control, acupuncture significantly improved the clinical efficacy rates of the AMD patients (RR = 1.29, $95\%$ CI: 1.17,1.42) (Fig 3A).
**Fig 3:** *Forest plot of meta-analysis results.(A) Clinical response rates; (B) BCVA; (C) CMT.*
## 3.4.2. BCVA
BCVA was mentioned in 5 studies [16–18, 25, 27], with very high heterogeneity in the results (I2 = $89\%$). The random-effect model indicated that there was a significant difference between the acupuncture group and the control group in terms of BCVA (SMD = 0.95, $95\%$ CI: 0.26,1.64) (Fig 3B).
## 3.4.3 CMT
CMT was mentioned in three studies [16–18], with high heterogeneity in the results (I2 = $67\%$). The random-effect model suggested that the acupuncture group had reduced CMT compared with that in the control group (MD = - 32.74, $95\%$ CI: - 60.96,-4.55). ( Fig 3C).
## 3.4.4 Adverse events
Adverse events were reported in only 2 studies [17, 28]. In one study, 10 of 22 patients in the acupuncture group had bleeding [28], while no side effects occurred in the control group. In another study, 3 patients in the control group and the acupuncture group each had nausea, vomiting, and flushing [17].
## 3.4.5 Sensitivity analysis and subgroup analysis
Sensitivity analysis showed the stability of both Clinical efficacy rates and BCVA outcomes. When one study was removed [16], the difference in CMT between the two groups was no longer statistically significant.
Subgroup analysis showed that heterogeneity in BCVA and CMT outcomes disappeared when limiting the type of AMD to wet AMD, suggesting that heterogeneity was mainly related to the type of AMD. In addition, heterogeneity decreased to varying degrees when restricting the duration of the intervention (duration = 20–50 days) and the type of intervention (intervention includes TCM).
In the subgroup analysis of wet AMD or short intervention course (duration = 20–50 days), we noted changes in BCVA outcomes, with no statistically significant difference between the acupuncture group and the control group. See Tables 3 & 4 for details.
## 4.1. Summary of evidence
The meta-analysis showed that compared with conventional treatment, acupuncture treatment increased the clinical efficacy and visual acuity of patients with AMD. In addition, acupuncture may have had a positive effect on the CMT of AMD patients. However, the certainty of the evidence was low due to concerns about the quality of the included studies.
According to TCM theory, inner eye tissue is closely related to meridians, which are the distribution network of essential substances of qi, blood, and body fluids throughout the body. The macula is yellowish on the unshaded fundus or eyeball, so it is considered to belong to the foot Taiyin Spleen Channel [30]. The spleen has the function of controlling the normal operation of blood in the meridians. Once the spleen-qi movement is not comfortable, it will impair the spleen’s ability to control the blood flow, which will cause the macular blood to flow out of the blood vessels. Of the twelve channels, only the liver channel is directly connected to the eyes. Therefore, the liver channel plays an important role in connecting the eyes with the liver, which can communicate the flow of qi and blood between the two organs. In the TCM system, the liver is the main reservoir of blood and the eyes need to be nourished by blood. In addition, liver qi is also closely related to eye function. Only when the liver-qi is comfortable can the eyes perform optimally [30]. Therefore, Qi and blood are very important for the eyes, and regulating qi and blood has become an important principle in the treatment of AMD in TCM theory.
In TCM theory, acupoints are specific parts of the body surface that reflect the state of human organs and regulate their physiological functions. Through acupuncture to stimulate the acupoint of the meridians, the human qi machinery is regulated, so as to ease the movement of qi and blood of the meridians, so that the eyes can get the nourishment of qi and blood, and finally treat the discomfort of the eyes. In addition, acting on acupoints around the eye is also believed to stimulate the movement of qi and blood in the eye and treat eye diseases. For example, the acupoint around the eyes, Jingming Point (BL1). As the first point of the bladder meridian, BL1 receives the ascending qi and blood from the bladder meridian and supplies it to the eyes. The eye receives the supply of qi and blood and therefore can see clearly. At present, acupuncture treatment of AMD mostly uses a combination of periocular and systemic acupuncture points [31].
Relevant histological studies have found that local acupoints may include high-density nerve endings, nerve and vascular components, and mast cells with sensory stimulation functions [32]. Acupuncture treatment of AMD mostly uses a combination of periocular and systemic acupuncture points. When the points are stimulated by acupuncture, in addition to the local release of biological factors to regulate local effects, acupuncture also transmits somatosensory information to the central nervous system by stimulating the nerves connected to the skin and muscles, thereby regulating the function of the autonomic nervous system [32, 33].
Oxidative stress is known to be one of the important pathogenesis of AMD [34]. The photoreceptor cells and retinal pigment epithelium (RPE) cells in the retina need to be metabolized in a high oxygen environment to fully exert their physiological functions, but at the same time, this will lead to a large accumulation of reactive oxygen species (ROS). In addition, due to the presence of a large number of unsaturated fatty acids and photoreceptor cytochromes, the macular region exhibits characteristics including high oxygen consumption and sensitivity to light radiation, which will further generate ROS [34, 35]. The increased level of ROS not only directly damages the components of RPE cells and photoreceptor cells such as proteins and lipids, thereby impairing their physiological functions, but also stimulates RPE cells to produce VEGF and hypoxia-inducible factor 1 to stimulate CNV generation [34, 36].
Several studies have found that acupuncture has the effect of maintaining redox homeostasis, which is achieved by modulating the imbalance between pro-oxidants and antioxidants [37]. Acupuncture can reduce oxidative stress and injury by inhibiting the production of ROS, reducing the ratio of the redox state of plasma glutathione/oxidized glutathione, and increasing the expression of redox effector [32, 33, 38, 39]. In addition, the regulatory effects of acupuncture on autophagy [40], inflammatory factors [41], and complement levels [42] may also play a role in delaying the progression of AMD.
Some studies have reported positive effects of acupuncture in AMD patients. Krenn et al. found in an observational study that acupuncture may have a positive effect on the vision of AMD patients [11]. Li et al. found that acupuncture improved the visual acuity of patients and reduced the levels of the macular nerve fiber layer, retinal neuroepithelium layer, pigment epithelium, and choroid capillary composite layer. Further, a 3-month follow-up showed that the improvement effect on visual acuity and macular retinal structure was still be maintained [12]. Other studies have also reported positive effects of acupuncture in patients with dry or wet AMD, including improving visual prognosis [43–45], alleviating ocular symptoms (e.g., visual distortion, blurred vision, visual fatigue, shadow occlusion) [43, 44], and enhancing the quality of life of the patients [45]. Similarly, our meta-analysis showed that acupuncture improved clinical efficacy rates and BCVA in AMD patients, and sensitivity analysis showed stability for both outcomes. However, when limiting the duration of intervention to 20–50 days, we did not observe significant improvements in BCVA and CMT in the acupuncture group compared to the control group, suggesting that prolonged intervention may be necessary for the efficacy of acupuncture. In addition, a subgroup analysis of wet AMD patients showed no improvement in BCVA and CMT with acupuncture, despite its ability to improve clinical efficiency in patients with wet AMD. It should be noted that the limitation of the number of studies in the subgroup analysis reduces the certainty of this conclusion. None of the studies mentioned the follow-up of patients’ visual acuity; therefore, we cannot provide evidence to support the effects of acupuncture on the long-term vision prognosis of patients.
Results from a meta-analysis of 3 studies showed that compared with the control, acupuncture reduced the CMT more in the AMD patients. However, the lack of included literature and the high heterogeneity of outcomes greatly limit the certainty of this evidence. CMT is an important indicator of OCT for observing the morphological structure and pathological changes of the macular area. An increase in CMT is related to the deterioration of retinal function and can damage visual acuity in AMD patients [46–49]. Acupuncture can reduce CMT, which may be related to the expansion of the surrounding blood vessels, thereby improving the microcirculation in the macular region [16–18]. This microcirculation can not only improve the nutrient supply to the working cells in the fundus [50] but also promote the absorption of hemorrhage and edema caused by CNV to restore the normal shape and function of the macular area. Only two studies reported side effects [17, 28]. The failure to mention the qualifications of the acupuncturists and the small number of research samples make their finding extremely uncertain. We noted that one of the studies reported 10 cases of bleeding at the site of acupuncture [28]. After further communication with the author, the bleeding occurred when the needle was withdrawn and could be stopped after a cotton swab was pressed against the bleeding site for around 10 seconds. It is necessary to standardize the extraction process of acupuncture needles. In reports of acupuncture treatment of other eye diseases, acupuncture was a relatively safe treatment method [51–53], and its safety in application to AMD treatment still needs further research.
All the included studies were conducted in China, so the global inference is limited. The population involved in this study was over 50 years old; considering that cases of AMD between the ages of 40 and 50 are not uncommon [54], we cannot assume that our results apply to all age groups. In addition, we reviewed the related technologies for acupuncture treatment in AMD. None of the studies provided detailed information on the acupuncture practitioners. Our senior acupuncture review authors (WQP and ZJ) found that most of the studies ($88.9\%$) selected appropriate acupoints because these acupoints were used by professionally trained and experienced clinicians for a long time. Seven studies used a sufficient frequency and treatment time. Nine studies mentioned specific acupuncture techniques, which can ensure the reproducibility of the acupuncture implementation. None of the included studies guaranteed that participants or acupuncturists had been blinded successfully. The quality of evidence was limited by the high risk of co-intervention bias (performance bias), failure to use intentional analysis (attrition bias), and the inconsistency (high heterogeneity; I2 = $89\%$, for BCVA; I2 = $67\%$, for CMT) and imprecision (335 samples for BCVA, 201 samples for CMT) of studies involving BCVA and CMT. Therefore, no evidence was highly certain. The quality of our evidence ranged from “low” to “very low” according to the GRADE evaluation system (S2 Table in S1 Appendix).
## 4.2 Limitations
First, the quality of included studies is of concern, with many studies failing to mention allocation concealment, or blinding of participants or personnel, limiting our understanding of the evidence. Second, although we implemented an adequate and detailed search strategy, the possibility of publication bias cannot be ruled out, which means that some result values may be amplified, especially in the presence of selective reporting bias in some included studies. Also, most studies used clinical efficacy rates as an outcome measure rather than BCVA or other international standard vision outcomes, failing to thoroughly assess the efficacy of acupuncture.
## 5. Conclusion
Limited evidence suggests that patients with AMD may benefit from acupuncture, especially those with dry AMD. Considering the potential of acupuncture treatment, it is necessary to carry out a rigorously designed RCT to verify its efficacy.
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