Characteristics of Gut Microbiota and Its Relationship With Serum Sex Hormones in Non-obese Polycystic Ovary Syndrome Patients With Insulin Resistance

doi:10.21203/rs.3.rs-1304673/v1

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Characteristics of Gut Microbiota and Its Relationship With Serum Sex Hormones in Non-obese Polycystic Ovary Syndrome Patients With Insulin Resistance

https://doi.org/10.21203/rs.3.rs-1304673/v1

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Background: Insulin resistance (IR) is a common symptom in obese polycystic ovary syndrome (PCOS) patients, as well as in non-obese PCOS patients. Gut microbiota is closely related to IR. However, the relationship between gut microbiota and IR remains unclear in non-obese PCOS patients.

Objective: To analyze the composition of the gut microbiota population in non-obese PCOS-IR patients compared to PCOS alone and healthy women.

Methods: In the present study, fecal samples from non-obese PCOS patients with and without IR and healthy women were analyzed by 16S rRNA gene sequencing. Moreover, serum hormone levels were detected on the third day of menstruation, and the correlation between gut microbiota and serum hormone was analyzed using R language.

Results: We found that the gut microbiota character differed in each group, and the gut microbiota abundance of PCOS-IR group was remarkably reduced.The abundance of Ruminococcaceae_UCG-004 and Acetanaerobacterium in the PCOS-IR group was significantly lower than that of the PCOS-NIR group. Furthermore, the comparison between PCOS-IR and control group revealed that g__Fusobacterium and g__Faecalibaculum were the unique genus of the PCOS-IR group. Moreover, the relationship between gut microbiota, IR, and serum sex hormones was determined. In PCOS with IR group, the relationship between gut microbiota and androgen was more significant. In PCOS without IR group, the relationship between gut microbiota and androgen was no significant. Its mechanism may be related to the different abundance in enzymes (futalosine hydrolase, arogenate dehydrogenase (NADP(+), and L-lysine 6-transaminase) and metabolic pathways (isoquinoline alkaloid biosynthesis, styrene degradation, and atrazine degradation), but it needs to be further explored in subsequent experiments.

Conclusion: The gut microbiota composition of non-obese PCOS patients with insulin resistance is different from that of PCOS alone and healthy women. The difference is correlated with the clinical characteristics of PCOS, with regards to insulin resistance and sex hormone. The abnormality of gut microbiota may lead to the occurrence and development of PCOS-IR. All in all, this study not only provided supplements for the existing studies, but also revealed the potential pathogenic bacteria genus, thus providing a new idea for the treatment of PCOS with IR.

polycystic ovary syndrome

gut microbiota

insulin resistance

serum sex hormones

Polycystic ovary syndrome (PCOS) is a common gynecologic endocrine disease; its incidence was 6%–10% in women and 9%–18% in childbearing age women [1,2]. PCOS is characterized by hyperandrogenemia, continuous anovulation, polycystic ovarian changes, and IR [3]. IR is closely related to PCOS, and 50%–70% of PCOS patients experience IR [4]. IR can cause metabolic abnormalities and increase the risk of type 2 diabetes, metabolic syndrome [5], and the risk of endometrial cancer. In addition, IR will aggravate the symptoms of hyperandrogenemia, thus promoting the development of PCOS [3]. Moreover, the incidence of ovulation disorders, abortion and preeclampsia in PCOS with IR patients is significantly higher than that in normal women [6,7]. Therefore, IR should be studied for preventing the long-term complications, improving the hormone level, and restoring the reproductive function of PCOS patients.

Gut microbiota is an essential microbial organ, which affects the metabolism and immune function and plays a very important role in endogenous secretion metabolic diseases. The incidence of PCOS is closely related to gut microbiota. Notably, the gut microbiota disorder is also an early factor of IR [8-10]. Therefore, gut microbiota plays a crucial role in the occurrence and development of PCOS with IR. The preliminary study of our research group showed that traditional Chinese medicine treatment could improve the IR of patients, and the abundance of [Eubacterium]_rectale_group, Escherichia-Shigella, Fusicatenibacter, and Megamonas also changed significantly [11]. Therefore, the gut microbiota is closely related to PCOS-IR. In addition, our research showed that Lachnoclostridium, Fusobacterium, Coprococcus_2, and Tyzzerela4 are the characteristic genus of the gut microbiota of obese PCOS patients [12]. Lactococcus is the characteristic genus of the gut microbiota of non-obese PCOS patients [13]. However, the gut microbiota characteristics of non-obese PCOS patients with IR have not been studied.

Although IR is more common in obese PCOS patients, non-obese PCOS patients are also accompanied IR. The relationship between IR and gut microbiota in non-obese PCOS patients remains unclear, and the relationship between sex hormones and gut microbiota remains to be explored. Exploring the relationship between gut microbiota and non-obese PCOS-IR can reveal the pathogenesis of PCOS and provides new ideas for probiotic drug research and development. Association analysis between gut microbiota and hormone can explain the change of PCOS sex hormone and IR. In the present study, 16S rRNA sequencing and R language were used to explore the gut microbiota characteristic in non-obese PCOS patients with IR and the correlation between gut microbiota and hormone levels. The results aim to determine the characteristic gut microbiota in non-obese PCOS-IR patients and provide a supplement for the current research on the gut microbiota of PCOS.

2.1. Analysis of OTU level difference of gut microbiota

Based on PLS-DA analysis, we found significant differences in gut microbiota among the three groups (Fig. 1a). OTU level difference analysis results showed that PCOS-IR group rank-bank curve was steeper than the control group, revealing that the abundance and diversity of gut microbiota decreased (Fig. 1b). No significant difference was observed between the PCOS-NIR and control group (Fig. 1c). In addition, in comparison with the control group, the sobs index of PCOS-IR group was significantly lower (P < 0.05), and the ace index of PCOS-IR group was lower without statistical difference (P>0.05), thus proving the above results. No significant differences were observed in the sobs and ace index between the PCOS-NIR and control group (P>0.05, Figs. 1d, e).

To provide reference for the microbial diagnosis of PCOS, we used ROC curve to evaluate the accuracy of the model. The larger the area under the curve, the higher the diagnostic accuracy. Species with the top 3 OTU level abundance were selected for the analysis, and we found that the AUC value of the PCOS-IR and control group was 0.66 (Fig. 2a), and the AUC value of PCOS-NIR and control group was 0.66 (Fig. 2b). AUC was greater than 0.65, indicating that this model has a potential value for clinical diagnosis, but this result needs to be verified by expanding the sample capacity.

2.2. Genus level analysis of gut microbiota

We found 168 same genera in three groups, five characteristic genus in PCOS-IR group, six characteristic genus in PCOS-NIR group, and 26 characteristic genus in control group (Fig. 3a). Further, we analyzed the proportion of gut microbiota in three groups. The top five bacteria in the PCOS-IR group were Bacteroides (26.02%), Faecalibacterium (13.15%), Prevoteila_9 (9.55%), Phascolarctobacterium (4.08%), and Lachnoclostridium (2.94%, Fig. 3b). The top five bacteria in the PCOS-NIR group were Bacteroides (22.53%), Faecalibacterium (14.52%), Prevoteila_9 (7.72%), Blautia (4.26%), and Phascolarctobacterium (3.67%, Fig. 3c). The top five bacteria in the control group were Bacteroides (20.83%), Faecalibacterium (15.12%), Prevoteila_9 (11.98%), Agathobacter (3.29%), and [Eubacterium] _ellgens_group (2.68%, Fig. 3d). The proportion of bacteria in each group differ. Further, we obtained the bacteria with statistical difference among the three groups. The bacteria with statistical difference were Ruminococcaceae_UCG-014, Lachnospiraceae_ND3007_group, norank_f_Ruminococcaceae, norank_o__Mollicutes_RF39, and Coprococcus_1 (P<0.05, Fig. 3e). We also compared the genus level of gut microbiota in the PCOS-IR and PCOS-NIR group. The results showed that the abundance of Ruminococcaceae_UCG-004 and Acetanaerobacterium in the PCOS-IR group was significantly lower than that of the PCOS-NIR group (P<0.05, Fig. 3f).

Furthermore, Lefse analysis was performed on the gut microbiota of the three groups. The comparison between PCOS-IR and control group revealed that g__Fusobacterium and g__Faecalibaculum were the unique genus of the PCOS-IR group (Figs. 4a, b). Comparison between the PCOS-NIR and control group showed that g__Tyzzerella, g__norank_o__Gastranaerophilales, g__unclassified_p__Firmicutes, g__Morganella, g__Phocea, g__Faecalibaculum, and g__unclassified_f__Desulfovibrionaceae were the unique genus of the PCOS-NIR group (Figs. 4c, d). In conclusion, differences were observed in the genus level abundance among the three groups.

2.3. Analysis of gut microbiota function and enzyme abundance

We used PICRUSt to analyze the KEGG pathway abundance of gut microbiota. The abundance of each pathway can be calculated according to the gut microbiota. KEGG pathway included organismal systems, metabolism, genetic information processing, environmental information processing, and cellular processes. In these pathways, no significant difference was observed in the functional abundance among the three groups (Figs. 5a–e). Considering that IR is closely related to metabolism, we further analyzed the attached pathway of metabolism. The results showed the functional abundance of isoquinoline alkaloid biosynthesis, styrene degradation, and atrazine degradation with statistical difference (P<0.05, Figs. 5f, g).

Further, we analyzed the enzyme abundance of gut microbiota among the three groups. The results showed that the top 10 enzymes with significantly difference in abundance were futalosine hydrolase (P<0.01), arogenate dehydrogenase (NADP(+)), (P<0.01), L-lysine 6-transaminase (P<0.05), homoserine O-acetyltransferase (P<0.05), aldehyde dehydrogenase (FAD-independent, P<0.05), kdo(2)-lipid IV(A) lauroyltransferase (P<0.05), scyllo-inositol 2-dehydrogenase (NADP(+), P<0.05), serralysin (P<0.05), arabinose-5-phosphate isomerase (P<0.05), and 3-aminobutyryl-CoA ammonia-lyase(P<0.05, Fig. 6).

2.4. Correlation analysis between gut microbiota and serum sex hormones

After clarifying the level and function differences of gut microbiota in the three groups, we further explored the relationship between gut microbiota and sex hormones. We first compared the hormone levels of the three groups, luteinizing hormone (LH) was significantly higher in the PCOS-IR and PCOS-NIR groups than in the control group (P<0.001, P<0.01), and LH/follicle-stimulating hormone (FSH) was significantly higher (P<0.001, P<0.0001). In addition, FINS and HOMA-IR significantly increased in the PCOS-IR group compared with PCOS-NIR group (P<0.0001, P<0.0001). No significant differences in FSH, estradiol (E2), dehydroepiandrosterone (DHEA), prolactin (PRL), testosterone (T), and FBS were observed among the three groups (P>0.05, Figs. 7a–j). Although T and DHEA in PCOS group had no significant difference compared with the control group, they were higher than the clinical androgen threshold in normal women, and this result is consistent with the biochemical hyper androgen manifestations of PCOS [15].

Based on correlation analysis, the LH level was negatively correlated with Blautia and Agathobacter abundance (P<0.05), LH/FSH was positively correlated with norank_f__Prevotellaceae abundance (P<0.05), T level was negatively correlated with norank_f__Prevotellaceae and Roseburia abundance (P<0.01), DHEA level was positively correlated with Ruminococcus]_torques_group, norank_f__Lachnospiraceae and Parabacteroides abundance (P<0.05), and HOMA-IR was positively correlated with Anaerostipes and norank_f__Lachnospiraceae abundance (P<0.05) in PCOS-IR patients (Fig. 8a). FINS level was positively correlated with Faecalibacterium abundance (P<0.001), HOMA-IR was positively correlated with Faecalibacterium abundance (P<0.01) and negatively correlated with Klebsiella, Lachnospira, and Prevotella_2 abundance (P<0.01, P<0.05), and LH/FSH was positively correlated with Bacteroides and Lachnoclostridium abundance (P<0.05) in PCOS-NIR patients (Fig. 8b).

In this study, we analyzed the characteristics of gut microbiota at different levels in the PCOS-IR, PCOS-NIR, and control group and explored the correlation among gut microbiota, IR, and sex hormones. It provides supplements for the current PCOS gut microbiota research.

Our results showed no significant difference between the PCOS-NIR and control group at the OTU level, while the diversity and abundance of gut microbiota in PCOS-IR group were significantly lower than the control group, suggesting that IR may be closely related to gut microbiota. Pre-diabetes and type 2 diabetes patients have abnormal gut microbiota [14,15], which support our study. In addition, the ROC area under the curve of PCOS-IR and control group, PCOS-NIR, and control group were all greater than 0.65, suggesting that the gut microbiota may have potential value as the discrimination of PCOS subtypes.

Further, the bacterial genera with statistical difference among the three groups were Ruminococcaceae_UCG-014, Lachnospiraceae_ND3007_group, norank_f_Ruminococcaceae. Liu R et al. found that the abundance of Ruminococcaceae in PCOS patients remarkably decreased [16]. Kreete Lüll et al. found that Ruminococcaceae_UCG-002 in PCOS patients was significantly different from normal women [17]. Moreover, the abundance of Ruminococcaceae_UCG-003 in rats significantly increased after TCM treatment [18]. However, Ruminococcaceae_UCG-014 has not been reported. In addition, differences between Lachnospiraceae UCG-008 and Lachnospiraceae_NC2004_group were also revealed at the animal level [19,20]. Therefore, a certain correlation exists between the current research and our results, and the latter can be used as a supplement to study the gut microbiota of PCOS.

To further explore the relationship between IR and gut microbiota, we compared the genus level of gut microbiota in the PCOS-IR and PCOS-NIR group. The results showed that the abundance of Ruminococcaceae_UCG-004 and Acetanaerobacterium in the PCOS-IR group was obviously lower than that of the PCOS-NIR group. Bo Zeng et al. also found a significant difference in the abundance of Ruminococcaceae between PCOS-IR and PCOS-NIR [21]. However, the difference in abundance of Acetanaerobacterium has not been reported. In addition, Lefse analysis was performed on the gut microbiota of the three groups. Based on the comparison of the PCOS-IR and control group, g__Fusobacterium and g__Faecalibaculum were the endemic genus in the PCOS-IR group. The abundance of Faecalibacterium prausnitzii and Fusobacterium nucleatum in patients with metabolic syndrome is significantly lower than that of the control group [22]. The relationship between Faecalibacterium and metabolism has been verified in mouse model [23,24]. In addition, the application of traditional Chinese medicine extract can remarkably reduce the abundance of Fusobacterium in type 2 diabetic rats [25]. Our results are in line with the current research, and it can be used as a supplement to the related research on gut microbiota and IR.

Based on the analysis of pathway abundance, we found that isoquinoline alkaloid biosynthesis remarkably differed in the three groups. Isoquinoline alkaloid is a tyrosine derived plant alkaloid with isoquinoline skeleton. Berberine is an alkaloid isoquinoline derivative that has been used as an insulin sensitizer. Clinical studies have shown that berberine can improve IR, reduce serum androgen level, and regulate lipid metabolism and chronic inflammation [26,27]. We hypothesized that the functional turbulence of gut microbiota in patients with PCOS-IR may result in the decrease isoquinoline alkaloid synthesis, further resulting in IR and chronic inflammation. IR can further affect serum androgen level, thus forming a vicious cycle.

Our study also showed that the abundance of styrene degradation remarkably differed in the three groups. The absorption of chemical solvents and active metabolic intermediates can increase oxidative stress and cytokine levels, leading to changes in glucose metabolism and inducing IR [28]. Animal experimental evidence also shows that oxidative stress, inflammatory factors, cell viability, and glucose-metabolism-related genes are sensitive to styrene [29]. The oral administration of monodisperse polystyrene nanoplastics remarkably increases ROS, and the high level of ROS disrupts the PI3K/Akt pathway, leading to IR and high plasma glucose in mice [30]. Similarly, Atrazine is a chemical solvent, and long-term exposure to atrazine can cause IR [31,32]. We hypothesized that the occurrence of PCOS-IR may caused by the dysfunction of gut microbiota, causing the decrease in futalosine hydrolase and arogenate dehydrogenase (NADP (+)) enzyme function. Finally, it causes styrene degradation and atrazine degradation disorders.

Moreover, the abundance of L-lysine 6-transaminase, homoserine O-acetyltransferase, and aldehyde dehydrogenase (FAD-independent) substantially differed. Among these enzymes, aldehyde dehydrogenase (FAD-independent) and IR are related [33,34]. L-lysine 6-transaminasey and homoserine O-acetyltransferase are related to the metabolism of amino acids. Gut microbiota can cause abnormal secretion of bile acids, which may promote the growth of Clostridium. The increase in Clostridium abundance can induce protein decomposition to the amino acids, and the amino acids and IR are correlated [35]. Therefore, gut microbiota also plays an important role between IR and amino acid.

Finally, we found a certain relationship between gut microbiota and hormones through association analysis, and different studies have also confirmed the correlation between gut microbiota and hormones [36-39]. However, whether the imbalance of gut microbiota leads to IR or IR leads to the imbalance of gut microbiota has not been determined. The causal relationship between gut microbiota and serum sex hormones remains unclear. The gut microbiota of the PCOS-IR group was associated with androgen, whereas that of the PCOS-NIR group was not. Therefore, IR can aggravate the hyper androgen state, further promoting the development of PCOS. In conclusion, we analyzed the effect of gut microbiota on IR and sex hormones and found the correlation between gut microbiota, IR and sex hormones, but the causal relationship still needs to be verified by subsequent experiments.

The deficiencies of this study are as follows: (1) The sample size is small, which is related to our strict control of the inclusion criteria and the low patient compliance. However, our study has important supplementary significance to the existing literature, and we will increase the sample size in the following study. (2) The present study found pathogenic bacteria genus that has not been reported. Although it can provide a supplement for the current research, it still needs to be verified by subsequent transplantation of animal fecal bacteria. (3) The results show a relationship among gut microbiota, IR, and sex hormones, but the causal relationship between them still needs to be further explored in vitro and in vivo experiments.

4.1. Participants

We recruited 29 patients without obesity suffering from PCOS (BMI< 28 kg/m², WHR≤0.85, 16–35 years old) from the TCM Gynecology Clinic of Changhai Hospital in Shanghai between June 2016 and November 2017. The diagnostic criteria for non-obesity PCOS were based on the 2003 Rotterdam Conference revisions and the criteria set by Li et al. [40], respectively. The exclusion criteria are as follows: history of the use of oral contraceptives, antiandrogens, and insulin sensitizers in the past 3 months before the experiment; being pregnant; presence of other known hyperandrogenemia and ovulation disorders, such as 21-hydroxylase deficiency, congenital adrenal hyperplasia, Cushing’s syndrome, androgen-secreting tumors, thyroid diseases, and hyperprolactinemia; history of mental disorders or organic diseases; history of the use of corticosteroids or sex steroids; history of drug and alcohol abuse in the past 2 years before the experiment; history of the use of antibiotics, probiotics, or prebiotics in the past 3 months before the experiment; and ulcerative colitis, Crohn’s disease, and other diseases that would cause dysbacteriosis. The Chinese Ethics Committee of Registering Clinical Trials approved our study (No. ChiCTRCTEC2016050), and each subject voluntarily signed the informed consent form before the trial.

4.2. Sample collection

The samples were examined on the third day of the menstrual cycle, the levels of sex hormones such as, LH, FSH, E2, T, DHEA, and PRL in the peripheral blood were detected immediately by chemiluminescence by using a UniCel Dxl 800 Access Immunoassay System (Beckman Coulter, Brea, United States) in the laboratory diagnostics department of the hospital. Fasting blood glucose (FBG) and fasting insulin (FINS) were detected in the morning after 8 h of fasting. Homeostatic model assessment for insulin resistance (HOMA-IR) was calculated as HOMA-IR= FBG × FINS/22.5, HOMA-IR≥ 1.66 indicated IR [11]. Fecal samples were collected 3–5 days after menstruation, but 3 days before sampling, the patients received guidance for carbohydrate-based diet (300 g/day). Approximately 10 g of fresh fecal samples was collected from each patient by using a sterile plastic spoon and test tube. Before defecation, the patients needed to clear their urine. After spreading the disposable sterile nursing pad, each patient defecated and used a sterile plastic spoon to collect the central part of the feces and transfer it into a sterile tube. The sterile tube was then refrigerated while waiting for a laboratory personnel to obtain the sample. The samples were transported to the laboratory in an ice box within 2 h from sampling and stored at 80 °C. Subsequently, the structure of the gut microbiota was analyzed by 16S rRNA gene sequencing, and the fecal metabolites were examined using non-targeted metabolomics.

4.3 DNA Extraction and 16S rRNA Gene Amplicon Sequencing

Sample DNA extraction, PCR amplification, Illumina MiSeq sequencing, and post-processing of data were performed as previously described [11-13]. The gut microbiota diversity and changes in the abundance were analyzed using the OTU abundance data, and the community composition of each sample at different classification levels was obtained. The alpha diversity was also assessed using Mothur software (v.1.30.1, https:// mothur.org/), including sobs and ace. By using the R language, we generated a bar map of the community structure at the genus level. The differences in the species between the three groups at the genus level were analyzed via Wilcoxon rank-sum test. Furthermore, the difference of species abundance in the gut microbiota between the three groups was evaluated by linear discriminant analysis (LDA). Finally, we used PICRUST to analyze the abundance of KEGG pathways and enzymes. The correlation between serum hormones and gut microbiota was analyzed by R language (Heatmap package), indicating the correlation between serum hormones and gut microbiota through color gradient in the heat map.

4.4. Data Processing and Statistical Analysis

All statistical data were analyzed using SPSS software (version 21.0). The clinical data with normal distribution were analyzed by paired t-test, while those with normal distribution and homogeneity of variance were analyzed by variance analysis. The data are expressed as mean with standard deviation. We employed Wilcoxon rank-sum test to analyze the quantitative sequencing data with non-normal distribution and Benjamini and Hochberg false discovery rate to check the p values for several times. A double-tailed p < 0.05 indicated significant difference.

In the present study, 16S rRNA sequencing and R language were used to explore the characteristics of gut microbiota and its correlation with serum in non-obese PCOS-IR, PCOS-NIR, and normal women. Results show that the diversity and abundance of gut microbiota in the PCOS-IR group were reduced. In comparison with normal women, g__Fusobacterium and g__Faecalibaculum are endemic genera of the PCOS-IR group, and g__Tyzzerella, g__norank_o__Gastranaerophilales, g__unclassified_p__Firmicutes, g__Morganella, g__Phocea, g__Faecalibaculum, and g__unclassified_f__Desulfovibrionaceae are endemic genera of the PCOS-NIR group. In addition, a relationship was observed among gut microbiota, IR and serum sex hormones. The relationship between gut microbiota disorder and androgen in PCOS-IR group was significant, which may be related to the abundance of enzymes and metabolic pathways. This study not only complements the existing research on the gut microbiota of PCOS, but also reveals the potential pathogenic bacteria, thus providing new information for the treatment of PCOS-IR.

PCOS: Polycystic ovary syndrome; IR: Insulin resistance; P: Progesterone; PRL: Prolactin; T: Testosterone; LH: Luteinizing hormone; FSH: Follicle-stimulating hormone; E2: Oestradiol; DHEA: Dehydroisoandrosterone; FBG: Fasting blood glucose; FINS: fasting insulin; HOMA-IR: Homeostatic model assessment for insulin resistance.

Ethics Approval and Consent to Participate: The experiments were approved by the Chinese Ethics Committee of Registering Clinical Trials (Approval No. ChiCTRCTE2016050). 2016-09-13, retrospectively registered. All subjects gave written, informed consent before inclusion in the study.

Informed Consent Statement: Written informed consent was obtained from each subject.

Conflicts of Interest: The authors declare that they have no conflict of interest.

Author Contributions: D.Z. and L.Z. designed the research. M.C., Z.N., and Y.Y. performed this study. M.C. analyzed the data. M.C., Z.N. and Y.Y. wrote the paper. Z.N., J.Y. and C.Y. was responsible for the critical revision of the paper. M.C., Z.N. and Y.Y. contributed equally to this work. All authors read and approved the final manuscript.

Funding: This research was funded by National Natural Science Foundation of China, grant number 82004408, 81973896, and Shanghai Sailing Program, grant number 20YF1448600.

Institutional Review Board Statement: The experiments were approved by the Chinese Ethics Committee of Registering Clinical Trials (Approval No. ChiCTRCTE2016050).

Data Availability Statement: The data presented in this study are available on request from the corresponding author.

Acknowledgments: Thanks to the First Affiliated Hospital of Naval Military Medical University for their support of this work.

Barthelmess EK, Naz RK. Polycystic ovary syndrome: current status and future perspective. Front Biosci (Elite Ed). 2014; 6: 104-19.
Forslund M, Landin-Wilhelmsen K, Trimpou P, Schmidt J, Brännström M, Dahlgren E. Type 2 diabetes mellitus in women with polycystic ovary syndrome during a 24-year period: importance of obesity and abdominal fat distribution. Hum Reprod Open. 2020; 2020 :hoz042.
Zeng X, Xie YJ, Liu YT, Long SL, Mo ZC. Polycystic ovarian syndrome: Correlation between hyperandrogenism, insulin resistance and obesity. Clin Chim Acta. 2020; 502: 214-221.
Farrell K, Antoni MH. Insulin resistance, obesity, inflammation, and depression in polycystic ovary syndrome: biobehavioral mechanisms and interventions. Fertil Steril. 2010; 94: 1565-74.
Osibogun O, Ogunmoroti O, Michos ED. Polycystic ovary syndrome and cardiometabolic risk: Opportunities for cardiovascular disease prevention. Trends Cardiovasc Med. 2020; 30: 399-404.
Macut D, Bjekić-Macut J, Rahelić D, Doknić M. Insulin and the polycystic ovary syndrome. Diabetes Res Clin Pract. 2017; 130: 163-170.
Katulski K, Czyzyk A, Podfigurna-Stopa A, Genazzani AR, Meczekalski B. Pregnancy complications in polycystic ovary syndrome patients. Gynecol Endocrinol. 2015; 31: 87-91.
Brun P, Castagliuolo I, Di Leo V, Buda A, Pinzani M, Palù G, Martines D. Increased intestinal permeability in obese mice: new evidence in the pathogenesis of nonalcoholic steatohepatitis. Am J Physiol Gastrointest Liver Physiol. 2007; 292: G518-25.
Burcelin R, Garidou L, Pomié C. Immuno-microbiota cross and talk: the new paradigm of metabolic diseases. Semin Immunol. 2012; 24: 67-74.
Saad MJ, Santos A, Prada PO. Linking Gut Microbiota and Inflammation to Obesity and Insulin Resistance. Physiology (Bethesda). 2016; 31: 283-93.
Ni Z, Cheng W, Ding J, Yao R, Zhang D, Zhai D, Zhou L, Yu C. Impact of Buzhong Yiqi Prescription on the Gut Microbiota of Patients with Obesity Manifesting Polycystic Ovarian Syndrome. Evid Based Complement Alternat Med. 2021; 2021: 6671367.
Zhou L, Ni Z, Yu J, Cheng W, Cai Z, Yu C. Correlation Between Fecal Metabolomics and Gut Microbiota in Obesity and Polycystic Ovary Syndrome. Front Endocrinol (Lausanne). 2020; 11: 628.
Zhou L, Ni Z, Cheng W, Yu J, Sun S, Zhai D, Yu C, Cai Z. Characteristic gut microbiota and predicted metabolic functions in women with PCOS. Endocr Connect. 2020; 9: 63-73.
Rosenfield RL, Ehrmann DA. The Pathogenesis of Polycystic Ovary Syndrome (PCOS): The Hypothesis of PCOS as Functional Ovarian Hyperandrogenism Revisited. Endocr Rev. 2016; 37: 467-520.
Allin KH, Tremaroli V, Caesar R, Jensen BAH, Damgaard MTF, Bahl MI, Licht TR, Hansen TH, Nielsen T, Dantoft TM, Linneberg A, Jørgensen T, Vestergaard H, Kristiansen K, Franks PW; IMI-DIRECT consortium, Hansen T, Bäckhed F, Pedersen O. Aberrant intestinal microbiota in individuals with prediabetes. Diabetologia. 2018; 61: 810-820.
Doumatey AP, Adeyemo A, Zhou J, Lei L, Adebamowo SN, Adebamowo C, Rotimi CN. Gut Microbiome Profiles Are Associated With Type 2 Diabetes in Urban Africans. Front Cell Infect Microbiol. 2020; 10: 63.
Liu R, Zhang C, Shi Y, Zhang F, Li L, Wang X, Ling Y, Fu H, Dong W, Shen J, Reeves A, Greenberg AS, Zhao L, Peng Y, Ding X. Dysbiosis of Gut Microbiota Associated with Clinical Parameters in Polycystic Ovary Syndrome. Front Microbiol. 2017; 8: 324.
Lüll K, Arffman RK, Sola-Leyva A, Molina NM, Aasmets O, Herzig KH, Plaza-Díaz J, Franks S, Morin-Papunen L, Tapanainen JS, Salumets A, Altmäe S, Piltonen TT, Org E. The Gut Microbiome in Polycystic Ovary Syndrome and Its Association with Metabolic Traits. J Clin Endocrinol Metab. 2021; 106: 858-871.
Zhu Y, Li Y, Liu M, Hu X, Zhu H. Guizhi Fuling Wan, Chinese Herbal Medicine, Ameliorates Insulin Sensitivity in PCOS Model Rats With Insulin Resistance via Remodeling Intestinal Homeostasis. Front Endocrinol (Lausanne). 2020; 11: 575.
Shen HR, Xu X, Ye D, Li XL. Berberine Improves the Symptoms of DHEA-Induced PCOS Rats by Regulating Gut Microbiotas and Metabolites. Gynecol Obstet Invest. 2021; 86: 388-397.
Zeng B, Lai Z, Sun L, Zhang Z, Yang J, Li Z, Lin J, Zhang Z. Structural and functional profiles of the gut microbial community in polycystic ovary syndrome with insulin resistance (IR-PCOS): a pilot study. Res Microbiol. 2019; 170: 43-52.
Haro C, Garcia-Carpintero S, Alcala-Diaz JF, Gomez-Delgado F, Delgado-Lista J, Perez-Martinez P, Rangel Zuñiga OA, Quintana-Navarro GM, Landa BB, Clemente JC, Lopez-Miranda J, Camargo A, Perez-Jimenez F. The gut microbial community in metabolic syndrome patients is modified by diet. J Nutr Biochem. 2016; 27: 27-31.
Cai W, Xu J, Li G, Liu T, Guo X, Wang H, Luo L. Ethanol extract of propolis prevents high-fat diet-induced insulin resistance and obesity in association with modulation of gut microbiota in mice. Food Res Int. 2020 Apr; 130: 108939.
Bai YF, Wang SW, Wang XX, Weng YY, Fan XY, Sheng H, Zhu XT, Lou LJ, Zhang F. The flavonoid-rich Quzhou Fructus Aurantii extract modulates gut microbiota and prevents obesity in high-fat diet-fed mice. Nutr Diabetes. 2019; 9: 30.
Xu T, Ge Y, Du H, Li Q, Xu X, Yi H, Wu X, Kuang T, Fan G, Zhang Y. Berberis kansuensis extract alleviates type 2 diabetes in rats by regulating gut microbiota composition. J Ethnopharmacol. 2021; 273: 113995.
Zhang SW, Zhou J, Gober HJ, Leung WT, Wang L. Effect and mechanism of berberine against polycystic ovary syndrome. Biomed Pharmacother. 2021; 138: 111468.
Wei W, Zhao H, Wang A, Sui M, Liang K, Deng H, Ma Y, Zhang Y, Zhang H, Guan Y. A clinical study on the short-term effect of berberine in comparison to metformin on the metabolic characteristics of women with polycystic ovary syndrome. Eur J Endocrinol. 2012; 166: 99-105.
Won YL, Ko Y, Heo KH, Ko KS, Lee MY, Kim KW. The Effects of Long-Term, Low-Level Exposure to Monocyclic Aromatic Hydrocarbons on Worker's Insulin Resistance. Saf Health Work. 2011; 2: 365-74.
Niaz K, Hassan FI, Mabqool F, Khan F, Momtaz S, Baeeri M, Navaei-Nigjeh M, Rahimifard M, Abdollahi M. Effect of styrene exposure on plasma parameters, molecular mechanisms and gene expression in rat model islet cells. Environ Toxicol Pharmacol. 2017; 54: 62-73.
Fan X, Wei X, Hu H, Zhang B, Yang D, Du H, Zhu R, Sun X, Oh Y, Gu N. Effects of oral administration of polystyrene nanoplastics on plasma glucose metabolism in mice. Chemosphere. 2022; 288: 132607.
Lim S, Ahn SY, Song IC, Chung MH, Jang HC, Park KS, Lee KU, Pak YK, Lee HK. Chronic exposure to the herbicide, atrazine, causes mitochondrial dysfunction and insulin resistance. PLoS One. 2009; 4: e5186.
Gupta HP, Jha RR, Ahmad H, Patel DK, Ravi Ram K. Xenobiotic mediated diabetogenesis: Developmental exposure to dichlorvos or atrazine leads to type 1 or type 2 diabetes in Drosophila. Free Radic Biol Med. 2019; 141: 461-474.
Hu N, Ren J, Zhang Y. Mitochondrial aldehyde dehydrogenase obliterates insulin resistance-induced cardiac dysfunction through deacetylation of PGC-1α. Oncotarget. 2016; 7: 76398-76414.
Demozay D, Rocchi S, Mas JC, Grillo S, Pirola L, Chavey C, Van Obberghen E. Fatty aldehyde dehydrogenase: potential role in oxidative stress protection and regulation of its gene expression by insulin. J Biol Chem. 2004; 279: 6261-70.
Tremblay F, Krebs M, Dombrowski L, Brehm A, Bernroider E, Roth E, Nowotny P, Waldhäusl W, Marette A, Roden M. Overactivation of S6 kinase 1 as a cause of human insulin resistance during increased amino acid availability. Diabetes. 2005; 54: 2674-84.
Zhang Q, Xiao X, Li M, Yu M, Ping F, Zheng J, Wang T, Wang X. Vildagliptin increases butyrate-producing bacteria in the gut of diabetic rats. PLoS One. 2017; 12 :e0184735.
Pedersen HK, Gudmundsdottir V, Nielsen HB, Hyotylainen T, Nielsen T, Jensen BA, Forslund K, Hildebrand F, Prifti E, Falony G, Le Chatelier E, Levenez F, Doré J, Mattila I, Plichta DR, Pöhö P, Hellgren LI, Arumugam M, Sunagawa S, Vieira-Silva S, Jørgensen T, Holm JB, Trošt K; MetaHIT Consortium, Kristiansen K, Brix S, Raes J, Wang J, Hansen T, Bork P, Brunak S, Oresic M, Ehrlich SD, Pedersen O. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature. 2016; 535: 376-81.
Yurtdaş G, Akdevelioğlu Y. A New Approach to Polycystic Ovary Syndrome: The Gut Microbiota. J Am Coll Nutr. 2020; 39: 371-382.
Shin JH, Park YH, Sim M, Kim SA, Joung H, Shin DM. Serum level of sex steroid hormone is associated with diversity and profiles of human gut microbiome. Res Microbiol. 2019; 170: 192-201.
Li YQ, Hou LH, Gao JJ, Wang YY. Chinese medicine syndrome distribution and study on characteristics of sexual hormones and glucose metabolism of polycystic ovarian syndrome. Chinese Archives of Traditional Chinese Medicine. 2016; 34:579–582.

No competing interests reported.

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Characteristics of Gut Microbiota and Its Relationship With Serum Sex Hormones in Non-obese Polycystic Ovary Syndrome Patients With Insulin Resistance

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