Intestinal Microbiota, Metabolites and Brain Network Structure Changes in Polycystic Ovary Syndrome patients

Intestinal Microbiota, Metabolites and Brain Network Structure Changes in Polycystic Ovary Syndrome patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Intestinal Microbiota, Metabolites and Brain Network Structure Changes in Polycystic Ovary Syndrome patients Mian Ma, Guangjie Liu, Kaidi Wang, Tianyue Cao, Lulu Shen, Shunyu Hou, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7003646/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting reproductive-aged women. We investigated gut microbiota and brain network alterations in PCOS patients. Methods We enrolled 26 PCOS patients and 20 healthy controls, collecting clinical data, blood, and stool samples. Serum hormones, biochemical markers, inflammatory factors (e.g., IL-6, TNFα), and short-chain fatty acids were analyzed. A subset (12 PCOS, 18 controls) underwent fMRI to assess brain network differences. Results We discovered that the circulating inflammatory factors IL-6 and TNFα in obese PCOS patients significantly increased. Gal-3 increased significantly in PCOS patients. Sutterella is significantly related to a number of clinical indicators in PCOS, which might be implicated in the occurrence and progression of PCOS. Based on LC-MS metabonomic methods, we screened 28 differential metabolites in PCOS and analyzed the main metabolic pathways of these metabolites. By analyzing the fMRI results, we found that the FCD index indicated that the activation of the right precuneus in PCOS patients was higher than that in the healthy controls, and the ALFF index indicated that the activation of the left postcentral gyrus in PCOS patients was higher than that in the healthy controls. The CBF of the healthy controls in the left lingual gyrus and the right Parietal_Inf_R were significantly greater than that of the PCOS patients, and there was a significant difference in the Synchronization index. Conclusions PCOS involves gut microbiota dysbiosis, metabolic disturbances, and altered brain network connectivity, our research offers new insights into its pathogenesis. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Endocrinology Health sciences/Medical research polycystic ovary syndrome gut microbiota clinical parameters gal-3 metabolites brain network structure Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Polycystic ovary syndrome (PCOS) is one of the most prevalent gynecological endocrine disorders and also one of the significant factors leading to infertility in women of reproductive age 1 , 2 . The incidence rate of PCOS among Chinese women is approximately 4.9% − 5.6% 3,4 . Therefore, PCOS has always been the focus of research on female reproductive disorders. The main clinical manifestations of PCOS are infrequent ovulation or anovulation, hyperandrogenemia, as well as endocrine and metabolic disorders such as glucose and lipid metabolism disorders and insulin resistance (IR) 5 , 6 . The etiology of PCOS remains unclear and may be related to factors such as genetics, environment and diet. Currently, the pathogenic role of metabolic factors in PCOS has received increasing attention. PCOS is a high-risk group for metabolic syndrome. Among PCOS patients, more than 60% are obese 7 , and the incidence of PCOS among obese patients is as high as 34.10% 4 . Human metabolism is closely related to the intestinal flora. In recent years, the relationship between Intestinal flora and the pathogenesis of endocrine and metabolic diseases such as obesity, Parkinson, Alzheimer's disease, etc. and IR has become a research hotspot. IR and chronic inflammation are important pathological states of PCOS 8 . Therefore, Intestinal flora may play an important role in PCOS 9 . The intestine is the most extensive digestive organ in the human body. Intestinal microbiota refers to the microbial community throughout the gastrointestinal tract, also known as the intestinal microbiota, which has a complex structure and plays a significant role in maintaining human homeostasis 10 . Anaerobic bacteria are the majority of intestinal bacteria, with Firmicutes and Bacteroides being the dominant bacteria, accounting for more than 90%. These bacteria are colonized in the intestinal tract as early as the baby is born. They are interdependent with the host, providing a nutrient-rich environment and coordinating metabolism, barrier protection and structural functions of the host 11 . Some research discovered that the intestinal flora in PCOS patients differs markedly from that of healthy people 12 , 13 . Some scholars used 16S rRNA amplification sequence to analyze the abundance and composition of intestinal flora in PCOS 14 , 15 . It was observed that the intestinal flora in patients with PCOS exhibited abnormalities, and the number of Bacteroides and Bacillus subtilis increased, α- and β- diversity are reduced, and the performance of obese is more obvious 14 , 15 . It has been reported that dysbacteriosis can improve intestinal mucosal permeability. Impaired intestinal barrier function is related to chronic inflammation, IR and hyperandrogenism, which may be an important factor in inducing PCOS 16 . In addition, recent studies have shown that intestinal flora ferments food or decomposes substances from the host to produce a variety of metabolites, which is the key medium of intestinal flora host crosstalk 17 . Metabolomics has been more and more used to find biomarkers with diagnostic significance. It speculates the metabolic pathway that may change by analyzing and studying the changes of the most downstream metabolites. Some studies have indicated that patients with PCOS have intestinal microflora imbalance and abnormal metabolite composition, such as ceramides, short chain fatty acids (SCFAs), branched chain amino acids (BCAAs), bile acids (BAs), and trimethylamine N-oxide (TMAO) 18 . However, the relevant research is still insufficient, and the potential role and regulatory mechanism of intestinal metabolites on PCOS are still unclear. The mechanism by which dysbiosis of gut microbiota leads to symptoms of PCOS remains largely unexplored, especially in the context of brain function and neural network architecture. The brain, as a central organ that regulates endocrine and metabolic processes, is intricately connected to the intestine through the gut-brain axis 19 . The brain function network involves interaction among various regions of the brain and has a significant impact on emotions, cognition and endocrine functions. Patients with PCOS often have psychological problems such as depression and anxiety, and these psychological states are also closely related to the brain functional network and its connectivity. Studies have shown that PCOS may affect certain areas of the brain and their functional connections. For instance, research has found that the functions of areas such as reward mechanisms and emotion regulation may be abnormal, which is closely related to the emotional distress and cognitive dysfunction of patients with PCOS 20 , 21 . The study focuses on the complex interactions among PCOS, the gut microbiota and the brain functional network, with the main aim of clarifying the mechanisms of the association among PCOS, the gut microbiota and the brain functional network. In this study, the characteristics of PCOS intestinal flora were preliminarily analyzed, Functional magnetic resonance imaging (fMRI) was used to evaluate the brain functional network, and the metabolic profile changes between samples of PCOS group and healthy control group were analyzed using the LC-MS based metabonomic method, to furnish a theoretical basis for future research on the pathogenesis and individualized therapy of PCOS. Materials and methods Study participants The subjects of this study were 26 newly diagnosed polycystic ovary syndrome patients, aged 20–35 years, who visited the gynecological endocrine clinic from July 2021 to November 2021 in Suzhou Hospital affiliated to Nanjing Medical University. The control group included 20 healthy women of childbearing age at the same age. 12 PCOS patients and 18 healthy individuals were randomly selected for functional magnetic resonance imaging. This research received approval from the Ethics Committee of Suzhou Hospital affiliated to Nanjing Medical University, and the ethical review batch number was K-2021-GSKY20210208. Each participant involved in this study signed the informed consent form upon detailed understanding of the purpose, content and risks of the study. PCOS diagnostic criteria: according to the PCOS diagnostic criteria issued in the Rotterdam Conference sponsored by The European Society for Human Reproduction and Embryology (ESHRE) and The American Society for Reproductive Medicine (ASRM) in 2003: (1) rare ovulation or anovulation; (2) Elevated androgen level and (or) clinical manifestations of hyperandrogenism (such as hirsutism, acne, etc.); (3) Polycystic changes in the ovary (≥ 12 follicles with a diameter of about 2-9mm in one or both ovaries, and/or ovarian volume increase ≥ 10 cm 3 ). 2 of the above 3 items are in line. Exclusion criteria: (1) Other diseases, including congenital diseases, resulting in increased androgen levels or disorders of ovulation. Adrenal cortical hyperplasia, androgen secreting tumors, premature ovarian insufficiency, pituitary or hypothalamic amenorrhea, thyroid dyshomeostasis, etc.; (2) those who have used hormone drugs and drugs affecting sugar and lipid metabolism in the past three months, or who have lost weight by any drug method (including appetite inhibitors such as fluorophenylalanine, thyroid hormone, progesterone, laxatives, etc., and various Chinese medicine ingredients weight loss drugs) or surgery; (3) those with diabetes, heart Those with abnormal liver and kidney functions, infectious diseases or other serious organic morbidities, including cancer, myocardial infarction and cerebrovascular accident, have received drug treatment for the following diseases in the past three months. Sampling The height, weight, waist circumference and hip girth of PCOS patients and healthy group were assessed by the same measuring tool. After the participants defecated on an empty stomach in the morning, they kept close fitting clothes and measured their weight. The waist circumference is measured at the middle of the iliac bone and the lowest rib. The horizontal circumference of the most salient part behind the hip is taken as the hip circumference. Waist hip rate (WHR) = waist circumference (cm)/hip circumference (cm), and body mass index (BMI) = weight (kg)/height 2 (m 2 ). All subjects collected 5mL of fasting elbow vein blood from the 3rd to 5th day of menstruation. The blood samples should be left at room temperature for 30 minutes and then centrifuged for 10 minutes. The supernatant was transferred the supernatant into EP tube and immediately placed it in − 80℃ refrigerator for standby. Determination of follicle stimulating hormone (FSH), luteinizing hormone (LH), anti Mü llerian hormone (AMH), adrenocortical hormone (ACTH), testosterone (T), cortisol (CORT), fasting blood glucose (FBG), fasting insulin (FINS). The homeostasis model assessment of insulin resistance (HOMA-IR) = FBG (mM) * FINS (mIU/L)/22.5 was used for insulin resistance index. Galactin-3 (gal-3), lipopolysaccharide (LPS), tumor necrosis factor-a (TNF-a), interleukin-6 (IL-6), short chain fatty acids (SCFAs). Next-generation sequencing We collected about 3–5 g of fresh feces from each participant during the non-menstrual period and immediately frozen them at − 80℃ until use. The QIAamp DNA stone mini kit (Qiagen 51604, Germany) was utilized to extract the total DNA from each stool sample, and the quantification of DNA was conducted using a Nanodrop spectrophotometer, and the DNA extraction quality was detected by 1.2% agarose gel electrophoresis. PCR amplification of V4 region of 16S rDNA was carried out with universal sequencing primers 515F (5'-GTGCCAAGCGCGCCGGTAA-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3'). The amplified products were purified and recovered using VAHTS DNA Clean Beads (N411, Vazyme), and fluorescence quantification was carried out with microplate reader (BioTek, FLx800) and Quant it PicoGreen dsDNA Assay Kit (P7589, Galbibochem). Next, TruSeq Nano DNA LT Library Prep Kit (FC-121-4001, Illumine) was employed to construct the sequencing library, and MiSeq Agent Kit V3 (600 cycles) was used for double ended sequencing in MiSeq PE250 platform of Illumina. Non target metabonomic detection We used LC-MS/MS analyses by an UHPLC system (Vanquish, Thermo Fisher Scientific) with a UPLC BEH Amide column (2.1mm*100mm, 1.7µm) coupled to Q Exactive HFX mass spectrometer (Orbitrap MS, Thermo). The mobile phase was composed of 25 mmol/L ammonium acetate and 25 ammonia hydroxides in water (PH = 9.75) (A) and acetonitrile (B). The QE HFX mass spectrometer was employed due to its ability to acquire MS/MS spectra on information-dependent acquisition (IDA) mode under the control of the acquisition software (Xcalibur, Thermo). In this mode, the acquisition software constantly evaluates the full scan MS spectrum. The ESI source conditions were set as follows: sheath gas flow rate as 30 Arb, Aux gas flow rate as 25 Arb, capillary temperature 350℃, full MS resolution as 60000, MS/MS resolution as 7500, collision energy as 10/30/60 in NCE mode, spray Voltage as 3.6 kV (positive) or -3.2 kV (negative), respectively. fMRI data preprocessing and analysis Resting-state (rs) functional magnetic resonance imaging (fMRI) is used to detect low-frequency fluctuations of blood oxygen level-dependent (BOLD) signals in brain regions. The correlation between temporal BOLD signal fluctuations is usually used to infer functional connectivity. We used the low-frequency fluctuation amplitude (ALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo) of the rs-fMRI blood oxygen level-dependent (BOLD) signal to characterize regional neural activity. Collect the functional and structural image data including the patient group and the control group. Subsequently, the data is preprocessed. Firstly, the first five points are deleted, and then steps such as time layer correction, head movement correction, spatial standardization, de-linear trend, and regression covariates are carried out. The four groups of indicators, namely ALFF, REHO, FCD/DC and Voxel_wise_FC, were calculated respectively. ASL images to calculate cerebral blood flow (CBF) First, quality control was carried out on the data, and some data was removed. 3D PASL technology and T1 imaging were adopted, and data processing steps such as image reset, head movement correction, registration, removal of extracranial voxel, filtering and regression noise covariates, smoothing, calculation of CBF and normalization according to the mean of the whole brain were carried out. The patient group and the control group underwent a two-sample t-test. Age and BMI were used as covariates for alphasim mass level correction. Brain Network Rich-club Analysis For the DTI tracking FN brain networks of all subjects, the fiber connections existing in more than 50% of the subjects were retained to construct a binary group average network (that is, the connections existing in more than 50% of the subjects were set to 1 and vice versa was set to 0). Calculate the rich-club coefficient of the average network of the group. As shown in Fig. 10A, k is between 7 and 9, and phi_norm is greater than 1. This indicates that the network has a rich-club structure, and the rich-club connection analysis can be conducted. In this study, hub nodes are defined by node degree. When the node degree value of a certain brain region is greater than the average value plus the standard deviation of all nodes in the network, then this node is a core node. The brain regions of the core nodes are defined as rich nodes, and the remaining nodes are defined as non-rich nodes. Thus, the edges in the network can be divided into three categories, namely the connections between rich nodes, which are called rich connections; The connection between the rich node and the non-rich node is called the feeder connection; The connection between non-rich nodes is called a local connection. Calculate the three connections of all the subjects, conduct the two-sample t-test of the healthy group and the PCOS group, and take age and BMI as covariates. DTI Chart Theory Analysis Use the PANDA ( http://vww.nitrcore/projecls/pandal ) preprocessing software. The DTI images of each subject were subjected to craniotomy, dynamic eddy current correction and adjustment of the diffusion gradient direction. Then, the diffusion tensor parameters such as FA, MD, AD and RD were calculated. When conducting deterministic white matter fiber tracking of diffusion tensor data in the PANDA toolkit, the aal90 template was selected as the brain region template. The complete brain is divided into 90 brain regions. Each brain region serves as a node of the brain network, and the edges of the brain network refer to the white fiber bundles connected between these nodes. This process is to nonlinearly register the individual T1-weighted image onto the standard space through the panda toolkit to obtain the inversely transformed T-1. Then apply T-1 to the selected template to obtain 90 brain regions divided based on the aal90 template; Finally, a brain network with these 90 brain regions as nodes was obtained. When conducting deterministic fiber tracking in PANDA, the FACT algorithm is used. Tracking is stopped when the deflection Angle exceeds 45° or the FA value is less than 0.2. And the number of fibers FN (fiber number) in pairwise brain regions is defined as the edges of the network. To avoid false positive results, we only retain the fiber connections existing in more than 80% of the subjects. Then, we set that the number of fiber bundles between each pair of nodes is greater than or equal to 3, and only then is it considered that there is a connection between them (that is, when FN reaches above 3, it is considered that this edge is effectively existing in this network). The Gretna software is used for the calculation and statistics of graph theory indicators. As mentioned above, set the FN threshold to 3, construct a binary network, and calculate various graph theory indicators. A two-sample t-test was conducted on the graph theory indicators of the two groups, with age and BMI as covariates, and FDR multiple comparison correction was performed on the node indicators. Statistical analysis Clinical data statistical analysis The statistical analysis was conducted using the SPSS 21.0 software package. Kolmogorov Smirnov method was employed to assess normality test. For the measurement data conforming to a normal distribution, we adopt the mean ± standard deviation for representation and employ the t-test. The counting data were described by frequency and rate, and chi square test was used. One-way analysis of variance (ANOVA) followed by post hoc Student-Newman-Keuls test or nonparametric Kruskal-Walli’s test were employed to describe the differences between more than two groups. Statistical tests were based on two-tailed probability and a P-value less than 0.05 was considered as statistically significant. Pearson correlation analysis was conducted for continuous variables, while for categorical variables, Spearman correlation analysis was used . Bioinformatics analysis The raw data of high throughput sequencing undergoes preliminarily screened. Subsequently, for the raw sequences that pass the initial quality screening, they are classified into libraries and samples according to the index and Barcode information, while the barcode sequence is removed. We used QIIME (Version 1.9.1) for sequence denoising and OTU (Operational Taxonomic Units) clustering and obtained the distinct composition of each sample group across different species of taxonomic levels. Based on the variation of OTU in different samples, we evaluated the α Level of diversity. At the OTU level, the distance matrix of each sample was calculated to measure the β diversity. We obtained P value derived from partial least squares-discriminate analysis (PLS-DA) by using the Adonis method for matching. The Linear discriminant analysis Effect Size (LEfSe) was carried out by LEfSe version 1.0, with the parameter setting at 2, to discover gene or functional traits that could best account for variations between the groups. In the sample, we employed PICRUSt2 to infer the constitution of functional genes. Then we analyzed the functional differences among different samples and groups. The association network is established, and the topological index is calculated in an attempt to find the key species according to the composition and distribution of species in each sample. In accordance with the 16S rRNA gene sequencing results, the metabolic function of the sample flora was predicted, identifying the differential pathway, and obtaining the species composition of the specific pathway. We utilized Spearman correlation coefficient to assess the relationship between intestinal microbiota and clinical parameters. Results Clinical characteristics of the particpants The three groups of subjects were all women of childbearing age, and no notable difference in age. The age of the three groups was balanced and matched. The three groups of subjects were investigated before entering the group, and the proportion of daily intake of fat, protein and carbohydrate had no statistically significant difference. Obesity is based on the Asia Pacific regional standard put forward by the International Obesity Task Force of the World Health Organization (WHO) in 2000: people with body mass index (BMI) ≥ 25kg/m2 are obese. The weight of the healthy control group fell within the established normal range. Compared to those in non-obese PCOS group and healthy control group, BMI and WHR in obese PCOS group were significantly higher ( P < 0.05), while no significant difference was found between non obese PCOS group and healthy control group. Compared with the control group, LH, LH/FSH, TT and AMH in PCOS groups increased, irrespective of the presence or absence of obesity ( P < 0.05). The FINS and HOMA-IR of the obese PCOS group were significantly lower than those in non-obese PCOS group ( P < 0.05). The levels of Gal-3 in both obese PCOS group and non-obese PCOS group were significantly higher compard to the healthy control group ( P < 0.05). The levels of IL-6 in the three groups were statistically different ( P < 0.05). Compared with non-obese group and control group, TNF-a in obese PCOS group was significantly higher ( P < 0.05). No significant differences in serum LPS levels were observed among the three groups. There was no significant difference in fecal short chain fatty acid levels among the three groups. (Table 1 ) Table 1 Anthropometric and metabolic characteristics of all particpants PCOS group(n = 26) Control group(n = 20) F/χ 2 P P<0.05 obese(n = 14) non-obese(n = 12) Age (year) 23.50(20.25 ~ 29.25) 27.00(21.00 ~ 29.00) 23.00(23.00 ~ 24.75) 0.405 0.817 BMI(kg/m 2 ) 29.54 ± 4.34 21.22 ± 2.39 20.52 ± 2.66 36.53 0.000 * a, b WHR 0.91 ± 0.12 0.81 ± 0.06 0.79 ± 0.64 8.377 0.001 * a, b FSH (mIU/mL) 7.04 ± 1.61 6.98 ± 1.50 5.78 ± 1.25 1.157 0.202 LH (mIU/mL) 11.55 ± 4.66 12.23 ± 6.98 6.06 ± 1.55 9.574 0.000 * b, c LH / FSH 1.65 ± 0.60 1.79 ± 0.97 1.08 ± 0.29 5.972 0.005 * b, c TT (ng/mL) 0.70 ± 0.24 0.79 ± 0.19 0.54 ± 0.12 8.087 0.001 * b, c AMH (ng/mL) 8.54 ± 4.00 7.45 ± 2.13 4.16 ± 0.57 14.45 0.000 * b, c ACTH(pg/mL) 2352.91 ± 3456.99 2665.85 ± 5112.69 1294.31 ± 2974.34 0.598 0.554 CORT (ng/mL) 401.82 ± 28.04 414.57 ± 48.66 435.28 ± 35.82 2.420 0.142 FBG (mmol/L) 5.09 ± 0.76 5.05 ± 0.27 4.77 ± 0.38 2.016 0.146 FINS (uU/mL) 20.36 ± 18.21 11.89 ± 6.63 8.97 ± 4.21 4.593 0.016 * a, b HOMA-IR 4.49 ± 3.49 2.70 ± 1.56 1.92 ± 0.99 5.828 0.006 * a,b,c gal-3 (ng/mL) 2.89 ± 1.14 2.63 ± 0.58 1.89 ± 0.79 6.125 0.005 * b, c IL-6 (pg/mL) 52.03(25.59 ~ 126.31) 24.21(11.44 ~ 59.86) 7.60(4.65 ~ 10.80) 23.44 0.000 * a,b,c LPS (pg/mL) 0.68(0.49 ~ 0.77) 0.98(0.61 ~ 1.27) 0.85(0.51 ~ 2.53) 3.744 0.154 TNF-a (pg/mL) 68.15 ± 58.84 42.54 ± 45.47 30.78 ± 37.53 4.642 0.013 * a, b acetate (umol/g) 121.41 ± 75.28 131.52 ± 54.18 129.04 ± 86.22 0.066 0.936 propionate (umol/g) 60.55 ± 38.99 58.60 ± 29.92 43.36 ± 24.17 1.597 0.214 butyrate(umol/g) 47.03 ± 25.67 49.93 ± 27.29 39.42 ± 29.85 0.613 0.546 Data are presented as mean ± standard deviation or median(interquartile interval). Structural changes of intestinal flora in patients with PCOS Fecal samples from PCOS group and health control group were collected. Then total bacterial DNA was extracted for sequencing analysis. α diversity reflects the species diversity within the bacterial community. This study adopted 7 α indexes. The two groups in five indicators of diversity measurement have no significant difference. Compared to the control group, the faith_pd index in the PCOS group was elevated. The degree of genetic diversity of bacterial flora in PCOS group was higher ( P = 0.037). The Goods_coverage index in PCOS group was lower than in control group. The proportion of species detected in PCOS group was lower. Combined with the comprehensive analysis of 7 indexes, compared with control group and PCOS group α diversity decreases slightly (or the change is not obvious) (Figure 1 A). β diversity is often used to reflect the similarity of the overall composition of gut flora. According to Bray Curtis distance, the principal coordinate analysis was carried out for the two groups of samples. The outcomes showed that the intestinal flora communities of the two treatment groups had little difference, and the distribution was more coincident. In beta diversity, no significant difference was observed between the two treatment groups according to ANOSIM similarity analysis results (t = 0.05, P = 0.095) (Fig. 1 B). Analysis of intestinal flora composition in patients with PCOS It can be seen from Venn diagram that there are 1821 unique species in the control group (48.91%), 1150 unique species in PCOS (30.89%), and 752 species in both groups (20.2%) (Fig. 2 A). In comparison with the control group, the species distribution of PCOS group was notably different. At the phylum, family and genus levels, the colony composition histogram between groups showed that the two groups had similar colony structures, and there were some differences in the proportion of different colonies. At the phyla level, Firmicutes accounted for the most in both groups, and the proportion of PCOS group increased compared with the healthy control group (Fig. 2 B). At the family level, Bifidobacteriaceae accounted for the most in both groups, but the proportion of PCOS group decreased compared with that of healthy control group, while Ruminococcaceae increased in PCOS group (Fig. 3 A). At the genus level, the proportions of Bifidobacterium were the most abundant in the two groups, while the proportions of PCOS group in the healthy control group decreased (Fig. 3 B). Lefse was used to analyze the difference between the two groups. It was found that in PCOS group, Ruminococcaceae and Fusobacteriaceae of Firmicutes were the most abundant, while in healthy control group, mycobacteria of Actinobacteria were the most abundant (Fig. 4 A). The form of heat map clearly shows the changes of 50 bacterial genera among 46 samples (Fig. 4 B). Changes in intestinal flora function in patients with PCOS The alterations of gut flora function in individuals with PCOS were further investigated by Picrust analysis. Drawing on the KEGG database, from the perspective of all samples, the microbial metabolic pathways involved mainly focus on the major nutrient metabolism related pathways, such as carbohydrate metabolism, metabolism of cofactors, amino acid metabolism and vitamins, polyketides and terpenoids 22 . However, no notable difference in metabolic pathways was found between the two groups. (Fig. 5 A) Correlation analysis between intestinal flora and clinical indexes We conducted Spearman correlation analysis on 19 clinical indicators and 12 bacteria with significant differences between groups, aiming to investigate the relationship between intestinal flora and clinical indicators. The results showed that there were marked differences between intestinal flora and PCOS related clinical indicators. Clostridium was notably and positively correlated with BMI, T, gal-3, IL-6 and prolionate. A notable positive correlation was observed between Sutterella and AMH, ACTH, HOMA-IR, IL-6, TNF α, acetate, propionate and butyrate. Anaerostipes exhibited a positive correlation with LH and LH/FSH. Weissella was negatively associated with BMI, WHR, AMH, gal-3 and IL-6. (Fig. 5 B) Nontargeted metabolomics analysis After LC-MS detection, 7507 peaks were detected in all samples. After data processing, 6058 peaks were retained. The above results were compared with databases and 987 substances were obtained. Based on the principal component differences of metabolites of two treatment groups analyzed by orthogonal projects to late structures discriminant analysis (OPLS-DA), the abscissa indicates the sample differences between groups, and the ordinate represents the sample differences within groups. The figure demonstrated that the difference between the two treatment groups is significant (Fig. 6 A). 149 differential metabolites were obtained by T test with P 1, as shown in the figure above (Fig. 6 B). The blue color shows the downregulated metabolites of the control group compared with the PCOS group, while the red color is opposite. The 28 differential metabolites matched to the secondary mass spectrometry were visualized (Fig. 7 A). In the figure, the distinct experimental groups are depicted on the abscissa, whereas the ordinate denotes the different metabolites compared in this group. The relative expression number of metabolites at corresponding positions are represented by the color blocks at different positions: red represents the high expression of the substance in the group, while the low expression of the substance in the group is denoted by blue color. The 28 differential metabolites are predominantly divided into benzenoids, hydrocarbons, organic acids and derivatives, lipoids and lipoid like molecules, organic heterocyclic compounds, others. Chord diagram visualization shows the correlation between metabolites (Fig. 7 B). The node size represents the value, and the stripe represents the correlation index. The outcomes of metabolic pathway analysis are presented in bubble chart (Fig. 7 C). Each bubble embodies a metabolic pathway. The abscissa of the bubble and the bubble size represent the influence factor of the pathway in topology analysis. The size of the bubbles is proportional to the influencing factors. The ordinate of the bubble and the bubble color represent the P value of enrichment analysis (negative natural logarithm, i.e. - ln (p)). A deeper color corresponds to a lower P value, reflecting a greater significance in the degree of enrichment. The figure shows metabolism about linoleic acid, vitamin B6, pyrimidine, arginine and proline are enriched in the control group. fMRI data preprocessing and analysis We conducted functional magnetic resonance imaging (fMRI) on 12 patients with PCOS and 18 healthy controls, collecting functional and structural image data. Two healthy controls and one PCOS patient were removed due to incomplete data. After preprocessing, we calculated metrics such as ALFF, REHO, FCD/DC, and voxel-wise FC (Figs. 8A and B ). We first performed a two-sample t-test, using age and BMI as covariates, to compare differences between the patient and control groups. Both ALFF and REHO did not pass FWE, FDR, GRF, or Alphasim cluster-level corrections. For the FCD metric, PCOS > HC showed significance after Alphasim cluster-level correction, with voxel P < 0.01, cluster P < 0.05, and cluster size = 259 (no result for PCOS < HC). We found that the FCD metric indicated higher activation in the right precuneus (Precuneus_R) in the patient group compared to the healthy control group (Fig. 8C). Without adding covariates, neither REHO nor FCD showed significant results after FWE, FDR, GRF, or Alphasim cluster-level correction. For ALFF, PCOS > HC showed significance after FWE cluster-level correction, with voxel P < 0.01, cluster P < 0.05, and cluster size = 119 (no result for PCOS < HC). The ALFF metric indicated higher activation in the left postcentral gyrus (Postcentral_L) in the PCOS patient group compared to the healthy control group (Fig. 8D). ASL images to calculate cerebral blood flow (CBF) After data quality control, a total of 25 datasets, including 15 healthy controls and 10 PCOS patients, were included in the analysis. A two-sample t-test was conducted between the health control group and the PCOS patient group, with age and BMI as covariates. Alphasim cluster-level correction was applied (voxel P < 0.01, cluster P < 0.05). We found that the healthy control group had significantly higher CBF in the left lingual gyrus (Lingual_L) and right inferior parietal lobule (Parietal_Inf_R) compared to the PCOS patient group( Figure 9A and B ). Brain Network Rich-club Analysis After data quality control, a total of 28 datasets, including 18 healthy controls and 10 PCOS patients, were included in the analysis. As shown in Fig. 10A, the value of k ranged from 7 to 9, and phi_norm was greater than 1, indicating that the network had a rich-club structure, allowing for rich-club connectivity analysis. The node degrees of all nodes in the group average network were calculated, and according to the above definition, the core nodes were identified as the following 10 brain regions in the AAL90 atlas: 2, 43, 44, 50, 51, 67, 68, 73, 74, and 88 (Fig. 10B). These regions had node degrees greater than one standard deviation above the network's average degree (the mean node degree of the group average network was 7.8444, and the standard deviation was 3.1973). The three types of connections were calculated for all participants, and a two-sample t-test was performed between the health control group and the PCOS patient group, with age and BMI as covariates. We found no significant differences between the healthy control group and the PCOS patient group in terms of rich-club connections, feeder connections, and local connections (Figs. 10C, D, and E , P >0.05). DTI Chart Theory Analysis After data quality control, 18 healthy controls and 10 PCOS patients were included in the analysis. The graph theory metrics were calculated and analyzed by Gretna software. As described above, an FN threshold of 3 was set to construct a binary network and various graph theory metrics were computed. A two-sample t-test was performed on the graph theory metrics between the two groups, with age and BMI as covariates. Additionally, node metrics were corrected for multiple comparisons using FDR. None of the node metrics passed the multiple comparison correction. The only metric with significant inter-group differences was the synchronization metric. Specifically, the graph theory synchronization metric showed: p = 3.0534133e-02; T = -2.2987103. Next, an edge analysis of the FN was performed. A two-sample t-test was conducted on the edges between the two groups, and the NBS correction method was applied with edge P < 0.001 and component P < 0.05, using 5000 permutations. However, no results passed the correction (Fig. 11). Discussion Intestinal flora, known as the "hidden organ" of the human body, participates in regulating various biological processes such as energy metabolism and immune inflammatory reaction, and exerts an irreplaceable role in the advancement of metabolic health and diseases. When intestinal flora is in disorder, intestinal permeability increases, endotoxin and proinflammatory cytokines increase, energy intake increases, and insulin resistance is induced 23 . More and more studies show that the alteration of intestinal flora is closely associated with the occurrence and development of metabolic diseases such as obesity and type 2 diabetes 15 , 16 , 24 . More and more researchers are focusing on the latent function of intestinal microflora in PCOS has attracted more and more attention. Potential mechanism between intestinal flora disorder and PCOS: intestinal flora may damage intestinal permeability and intestinal barrier dysfunction 25 . The disordered intestinal flora can lead to the impairment of intestinal mucosal barrier function, the inhibition of expression of intestinal epithelial tight junction protein, the increase of intestinal permeability, and the promotion of the absorption of lipopolysaccharide (LPS) into the blood circulation 16 . LPS, also known as endotoxin, is a major component component that initiates inflammatory reaction. The LPS content in circulation is increased. After binding with the endotoxin receptor CD14, it activates MyD88 and interferon induced by the connector protein containing TIR functional region through Toll like receptor 4 (TLR4) on the immune cell membrane β(TIR-domain-containing adapter-inducing interferon- β, TRIF) signaling pathway. Downstream inflammatory signal transduction starts, causing a series of inflammatory reaction processes, and various inflammatory factors such as IL-6, TNFα increasing 16 . With the increase of the level of insulin, chronic inflammation interferes with the function of insulin receptor and causes insulin resistance, while the increase of the level of insulin in the serum causes the ovary to produce more androgens to interfere with the normal follicular development 7 . This study found that the circulating inflammatory factors IL-6 and TNFα in obese PCOS patients significantly elevated, and a chronic inflammatory state existed. Galectin-3 (Gal-3) in PCOS patients was significantly higher than that in healthy people, while no significant difference was observed in PCOS patients with or without obesity. As the only member of the chimeric galactose election, Gal-3 was first found in fish eggs 26 , and can be detected in various cells, including activated macrophages, eosinophils, neutrophils, mast cells, etc 27 . Gal-3 protein is located in the nucleus, cytoplasm and extracellular membrane, and can shuttle back and forth between the nucleus and cytoplasm in the cell, thus participating in a series of cell processes such as differentiation, proliferation, apoptosis, RNA splicing and so on 28 . Gal-3 plays a crucial role in multiple pathophysiological processes, including cell adhesion and proliferation, apoptosis, inflammation, tumor, etc 29 , 30 . In recent years, the role of Gal-3 in obesity and diabetes related metabolic dysfunction has attracted more and more attention 31 – 33 . It has been found that the serum Gal-3 of obese patients is higher than that of healthy people 34 . The mouse experiment found that visceral adipose tissue may be the main tissue secreting Gal-3 35 . Adipose tissue has numerous inflammatory cells infiltrating, and inflammatory cells express and secrete Gal-3 35 . Gal-3 has the ability to bind to Toll like receptors (TLRs) on the cell surface, activate TLRs, activate downstream signals, activate MAPK signaling pathway and NFκB signal pathway regulates the generation of inflammatory cytokines such as tumor necrosis factor, interleukin and interferon, and participates in inflammation and immune regulation 32 . It is reported that compared to the impaired glucose tolerance and healthy people, the level of Gal-3 in diabetes patients is significantly higher 36 . It is speculated that Gal-3 is related to diabetes. A study found that Gal-3 can directly bind to insulin receptor (IR) and inhibit the downstream signal of IR 37 . We observed a significant elevation of Gal-3 levels in patients with PCOS. We also found that there was no significant relationship with the BMI of PCOS patients. Gal-3 may be involved in the pathogenesis of PCOS and might be served as a marker for early diagnosis of PCOS patients 31 . The relationship between Gal-3 and PCOS still needs a lot of basic and clinical research to further reveal. In terms of microbial diversity, some scholars found that compared with healthy people, α and β diversity were decreased in patients with PCOS 14 , 38 . However, it is also reported that no significant differences were found between PCOS group and control group 39 , 40 . We also found no significant difference. According to LEfSe analysis, in PCOS group, Ruminococcaceae and Faecalibacterium of Firmicutes were most significantly enriched. In comparison with the control group, the proportion of Bifidobacteriaceae and Bifidobacterium in PCOS group decreased. Previous studies have shown that the Firmicutes’ increase is correlated with the increase of BMI 41 , 42 . It is the most obvious enrichment of intestinal flora in obese women and is considered to be involved in the occurrence of obesity 43 , 44 . Bifidobacterium , as a beneficial bacterium in the intestine, can enhance immunity and promote nutrient absorption 45 , 46 . Several studies have found that it is significantly reduced in PCOS patients 4 , 13 , 47 – 49 , which aligns with what we have studied. In addition, our study found that Sutterella was significantly associated with multiple clinical indicators of PCOS patients, including AMH, ACTH, HOMA-IR, IL-6, TNFα, acetate, propionate, butyrate. As a gram-negative bacterium of Proteus, Sutterella is associated with many diseases, such as autism, Down syndrome, inflammatory bowel disease, etc 50 , 51 . It has been reported that Sutterella can affect intestinal permeability, which is related to the typical chronic low-grade inflammation of obese subjects 51 . Sutterella may be closely related to the occurrence and progression of PCOS. So, how does intestinal flora "talk" with the host? During recent years, studies have shown that intestinal flora ferments food or decomposes substances from host to produce a variety of metabolites, which is the key medium of intestinal flora host crosstalk 52 . On the one hand, as a signal molecule, it communicates directly with the parenteral target organs through the circulatory system 18 ; on the other hand, it transmits signals indirectly by regulating the release of intestinal endocrine hormones 17 . Intestinal metabolites, such as bile acids (BAS), to a large extent participate in regulating the integrity of the intestinal barrier, thus helping to maintain the stability of internal environment and dynamic balance 53 . Disorders of intestinal metabolites may lead to increased intestinal permeability and endotoxemia, thus disturbing the endocrine system, immune system, glucose and fat metabolism, insulin signal and intestinal microflora 18 . In addition, the secretion and sensitivity of pancreatic insulin in target organs can be directly regulated by SCFAs, BCAAs through endocrine signals 54 . When circulating via the portal vein system, these metabolites arrive at the liver to regulate fat metabolism and oxidation 55 . In addition, these metabolites also participate in the dynamic balance of neurons by regulating the blood-brain barrier 20 , 56 . In this study, nontargeted metabonomic methods were used to find more specific small molecular biomarkers, obtain more comprehensive metabolite change information, and avoid losing important differential intermediates in metabolic pathways. In this experiment, the metabolic profile changes between PCOS group and healthy control group were analyzed by LC-MS based metabonomic method. Through the quality control experiment, the instrument analysis system with good stability and reliable test evidence are verified. The differential metabolites were screened in PCOS group and healthy control group, and 28 potential biomarkers were obtained. The analysis of KEGG metabolic pathway showed that they were involved in multiple metabolic pathways. These metabolic pathways mainly focus on metabolism about linoleic acid, vitamin B6, pyrimidine, arginine and proline. The brain function network involves interaction among various regions of the brain and has a significant impact on emotions, cognition and endocrine functions. Previous studies have found that polycystic ovary syndrome can cause functional changes in brain regions responsible for visual working memory, such as the right middle and superior frontal gyrus 57 . The changes found in these two frontal lobe regions are also related to specific deficiencies in the attention part of working memory. Similarly, the functional connectivity between the superior frontal gyrus and the middle-superior gyrus was negatively correlated with the LH level and the LH/FSH ratio. Previous studies have found that women with PCOS perform worse in visuospatial working memory 58 . Furthermore, PCOS can also have a negative impact on more general visuospatial abilities 59 and visuospatial learning 60 . A functional magnetic resonance imaging study analyzed the effects of excessive exposure to androgens and subsequent anti-androgen therapy on brain activity during working memory processing 61 . The research found that the activation degree of the right superior parietal lobe and the inferior parietal lobe of POCS was higher. After the treatment, there was no difference in overall brain activity between the two groups, and PCOS demonstrated higher accuracy under memory load conditions in working memory tasks. Therefore, polycystic ovary syndrome may require more neural resources in working memory tasks and have lower executive function efficiency 61 , 62 . Patients with PCOS often have psychological problems such as depression and anxiety, and these psychological states are also closely related to the brain functional network and its connectivity 21 , 63 . Most patients with pcos will experience depression, somatization and bipolar disorder. Emotional disorders can affect cognitive functions, such as sustained attention 64 and working memory 65 . The research found that there were differences in brain network structure between PD patients and the HC group, specifically manifested as changes in synchrony indicators. In the case of adding covariables (age, BMI), the FCD index showed that the activation of the right precuneus (Precuneus_R) in the patient group was higher than that in the health control group (HC) and was corrected for Alphasim mass level. When covariates were not added, the ALFF index showed that the activation of the left posterior central gyrus in the patient group was higher than that in the healthy control group. Through the two-sample t-test, it was found that the CBF of the HC group in the left lingual gyrus and the right inferior parietal lobule was significantly greater than that of the PCOS group. However, none of the node indicators were corrected through multiple comparisons, and neither was the edge analysis corrected. The study preliminarily analyzed the relationship between the intestinal microbiota characteristics of PCOS and fecal metabolites and investigated the relationship between PCOS and brain network structure, providing a theoretical basis for the research on the pathogenesis of PCOS and individualized treatment. At the same time, this study still has several limitations: the sample size is relatively small, and the intestinal flora research is not grouped again, which may lead to incomplete statistical results; no other humoral metabolites were used for comparison; Metabonomics has limitation that is still in the primary stage in the diagnosis of diseases, and the repeatability and stability of metabolites need to be further discussed. As to how these abnormal metabolites are produced and how they participate in the occurrence and development of PCOS. There is still a lot of research to be done, especially in the identification of different types and specific pathogenesis of PCOS. Future research could consider expanding the sample size, adopting other analytical methods or combining other imaging techniques to gain a more comprehensive understanding of the relationship between the characteristics of brain networks and diseases. In addition, the influence of other factors on brain network connections can also be explored, as well as the correlation between these connection patterns and clinical symptoms or disease prognosis. In conclusion, we found that the circulating inflammatory factors IL-6 and TNFα in obese PCOS patients elevated, and there is a chronic inflammatory state. Gal-3 in PCOS patients was significantly increased and had no significant relationship with the BMI of PCOS patients. Gal-3 might be involved in the pathogenesis of PCOS or could be used as a marker for early diagnosis of PCOS patients. Compared with the control group, the intestinal flora in PCOS group has some changes in composition and function. Sutterella is significantly related to a number of clinical indicators of PCOS patients, suggesting that it might be involved in the occurrence and development of PCOS. In addition, based on the metabonomic method of LC-MS technology, we screened 28 differential metabolites in PCOS group and healthy control group, and analyzed the main metabolic pathways of these metabolites. We found through fMRI that there were differences in brain network structure between PD patients and the HC group, specifically manifested as the synchrony indicators of some brain regions and the changes of CBF. In the future, we will further reveal the role of differential metabolites in the occurrence and development of PCOS through a large number of studies, hoping to provide new creative ideas for the study about the pathogenesis of PCOS. Abbreviations PCOS polycystic ovary syndrome BMI Body Mass Index WHR Waist-to-Hip Ratio FSH Follicle-stimulating hormone LH luteinizing hormone TT Testosterone AMH anti-Müllerian hormone ACTH adrenocorticotropic hormone CORT cortisol FBG fasting blood glucose FINS fasting insulin HOMA-IR the homoeostasis model assessment of insulin resistance LPS lipopolysaccharide. Declarations Competing interests: The authors declare no competing interests. Ethical Approval: This research received approval from the Ethics Committee of Suzhou Hospital affiliated to Nanjing Medical University, and the ethical review batch number was K-2021-GSKY20210208. The study was performed in accordance with the ethical guidelines of the World Medical Association’s Declaration of Helsinki. Reprints and permissions information is available at www.nature.com/reprints . Funding: The authors thank all of the patients and their families. This work was funded by the Suzhou Applied Basic Research (Medical and health) Science and Technology Innovation Guiding Project (NO. SYWD2024130) and Suzhou Association of Traditional Chinese Medicine (NO. SYWD2024295). Author Contribution M. M., X. X., S. H. and H. F. contributed to the study conception and design. Data acquisition was performed by L. S., T. C., K. W.. Quality control of data, data analysis and interpretation were performed by H. F. and M. M.. Statistical analysis was performed by G. L. and H. F.. The frst draf of the manuscript was written by G. L., H. F. and M. M.. The manuscript was critically reviewed by all authors. All authors have reviewed the article. They agree that the article is appropriate. It agrees to be published as such. Data Availability All data generated or analyzed during the current study are available from the corresponding author on reasonable request. References Joham, A. E. et al. Polycystic ovary syndrome. 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Ovarian Res. 7 , 31. 10.1186/1757-2215-7-31 (2014). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 17 Oct, 2025 Reviews received at journal 13 Sep, 2025 Reviews received at journal 08 Sep, 2025 Reviews received at journal 07 Sep, 2025 Reviewers agreed at journal 21 Aug, 2025 Reviewers agreed at journal 20 Aug, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviewers invited by journal 19 Aug, 2025 Editor assigned by journal 31 Jul, 2025 Submission checks completed at journal 21 Jul, 2025 First submitted to journal 21 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7003646","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":503751477,"identity":"2a48e9f9-dbc4-45f4-bb3a-7681b89e6c3f","order_by":0,"name":"Mian Ma","email":"","orcid":"","institution":"The Affiliated Suzhou Hospital of Nanjing Medical University, Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mian","middleName":"","lastName":"Ma","suffix":""},{"id":503751478,"identity":"b2a87280-6c3c-4106-b4f6-b29542d09ae3","order_by":1,"name":"Guangjie Liu","email":"","orcid":"","institution":"The Affiliated Suzhou Hospital of Nanjing Medical University, Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guangjie","middleName":"","lastName":"Liu","suffix":""},{"id":503751479,"identity":"40a70e82-d34b-4a13-a6f0-ed9ee87392a4","order_by":2,"name":"Kaidi Wang","email":"","orcid":"","institution":"The Affiliated Suzhou Hospital of Nanjing Medical University, Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kaidi","middleName":"","lastName":"Wang","suffix":""},{"id":503751480,"identity":"f36e60e6-b325-4282-b5eb-1412eef3e21d","order_by":3,"name":"Tianyue Cao","email":"","orcid":"","institution":"The Affiliated Suzhou Hospital of Nanjing Medical University, Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tianyue","middleName":"","lastName":"Cao","suffix":""},{"id":503751481,"identity":"e97b3155-306b-499a-abd7-8e179844d137","order_by":4,"name":"Lulu Shen","email":"","orcid":"","institution":"The Affiliated Suzhou Hospital of Nanjing Medical University, Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lulu","middleName":"","lastName":"Shen","suffix":""},{"id":503751482,"identity":"65abebb3-c168-40e5-9233-192cf176a396","order_by":5,"name":"Shunyu Hou","email":"","orcid":"","institution":"The Affiliated Suzhou Hospital of Nanjing Medical University, Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shunyu","middleName":"","lastName":"Hou","suffix":""},{"id":503751483,"identity":"30fcd2a1-9c57-4dc2-9add-cac9ae1fb1ee","order_by":6,"name":"Xiaoxue XI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYDACZgaGA2AGe2Pjgw+kaeE53Gw4gzTrJNLbpDmIUWhwnMfwwI+Kw4n9kg8bpBkY7OR0GwhokWxmSzjYcyYtcebsxAbjAoZkY7MDBLTwMzMfOMDbZpO44XZiQ/IMhgOJ2whpYWNmbDj4t00icf/Ngw2HeYjRArLlMNgWCcbGZqK0gPxyWOZMmvGMM4nNjDMMiPCLwfkzxh/fVByW7W8//vzHhwo7OYJa0E0gTfkoGAWjYBSMAhwAAFuXRLutJGmzAAAAAElFTkSuQmCC","orcid":"","institution":"The Affiliated Suzhou Hospital of Nanjing Medical University, Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoxue","middleName":"","lastName":"XI","suffix":""},{"id":503751484,"identity":"01e6885d-4f65-47ae-97e2-9b073bfd2038","order_by":7,"name":"Hongxuan Feng","email":"","orcid":"","institution":"The Afffliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hongxuan","middleName":"","lastName":"Feng","suffix":""}],"badges":[],"createdAt":"2025-06-29 15:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7003646/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7003646/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90305705,"identity":"7d59d78e-4dbe-45b2-91f9-6f1d3fdfc70f","added_by":"auto","created_at":"2025-09-01 09:24:24","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":281421,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7003646/v1/4d2af06707d0a4e80aed5972.jpg"},{"id":90305697,"identity":"f900b834-31cd-4873-bc6f-2f2d5a77070c","added_by":"auto","created_at":"2025-09-01 09:24:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":209271,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7003646/v1/3654aef109ad1b8408ce4b77.jpg"},{"id":90307428,"identity":"17b9028c-27e6-4106-a32d-fa2324ef0730","added_by":"auto","created_at":"2025-09-01 09:32:26","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":434738,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7003646/v1/da081db930572d8532383747.jpg"},{"id":90305690,"identity":"9203fa2a-12e1-43ea-affc-c3df82e5fe41","added_by":"auto","created_at":"2025-09-01 09:24:22","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":869220,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7003646/v1/56788ffab588e6c75b748c67.jpg"},{"id":90305732,"identity":"18b4e25d-016b-4c8a-8146-1f9f64d8d210","added_by":"auto","created_at":"2025-09-01 09:24:27","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":600049,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7003646/v1/361815d0a89f9ec0a1804170.jpg"},{"id":90305726,"identity":"e0247354-3305-4749-8ca2-e4a1c078283b","added_by":"auto","created_at":"2025-09-01 09:24:26","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":316441,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7003646/v1/17ff7eee8c6db4a7cc266e6d.jpg"},{"id":90307426,"identity":"96328e15-cde2-49f5-9d10-8f90c19f01f1","added_by":"auto","created_at":"2025-09-01 09:32:26","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":680968,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7003646/v1/017277ca6f3fed3d1b0219d1.jpg"},{"id":90305722,"identity":"d432185b-2eda-4e5e-ba8c-480e10e80a4c","added_by":"auto","created_at":"2025-09-01 09:24:26","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":240523,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7003646/v1/f26c1d76f808587c78864711.jpg"},{"id":90305702,"identity":"35784caa-635a-4567-9d48-c8871a40b451","added_by":"auto","created_at":"2025-09-01 09:24:24","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":257955,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7003646/v1/2de461768170d057c83b4bf6.jpg"},{"id":90305723,"identity":"fef95fd7-f5b2-4f23-ad68-a18c1af960a3","added_by":"auto","created_at":"2025-09-01 09:24:26","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":197279,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7003646/v1/537dbb1c98cd9225d24e4cef.jpg"},{"id":90305724,"identity":"64846ed1-b282-46ab-967a-65d5847cf65a","added_by":"auto","created_at":"2025-09-01 09:24:26","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":76318,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7003646/v1/87dc3c38576102d62812fc3a.jpg"},{"id":90307437,"identity":"75641bc6-06c4-4c73-8070-8a2e247b52ee","added_by":"auto","created_at":"2025-09-01 09:32:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5388325,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7003646/v1/dbaba299-d752-430c-973b-33fb2cdd18d4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Intestinal Microbiota, Metabolites and Brain Network Structure Changes in Polycystic Ovary Syndrome patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePolycystic ovary syndrome (PCOS) is one of the most prevalent gynecological endocrine disorders and also one of the significant factors leading to infertility in women of reproductive age\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The incidence rate of PCOS among Chinese women is approximately 4.9% \u0026minus;\u0026thinsp;5.6%\u003csup\u003e3,4\u003c/sup\u003e. Therefore, PCOS has always been the focus of research on female reproductive disorders. The main clinical manifestations of PCOS are infrequent ovulation or anovulation, hyperandrogenemia, as well as endocrine and metabolic disorders such as glucose and lipid metabolism disorders and insulin resistance (IR)\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The etiology of PCOS remains unclear and may be related to factors such as genetics, environment and diet. Currently, the pathogenic role of metabolic factors in PCOS has received increasing attention. PCOS is a high-risk group for metabolic syndrome. Among PCOS patients, more than 60% are obese\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, and the incidence of PCOS among obese patients is as high as 34.10%\u003csup\u003e4\u003c/sup\u003e. Human metabolism is closely related to the intestinal flora. In recent years, the relationship between Intestinal flora and the pathogenesis of endocrine and metabolic diseases such as obesity, Parkinson, Alzheimer's disease, etc. and IR has become a research hotspot. IR and chronic inflammation are important pathological states of PCOS\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Therefore, Intestinal flora may play an important role in PCOS\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe intestine is the most extensive digestive organ in the human body. Intestinal microbiota refers to the microbial community throughout the gastrointestinal tract, also known as the intestinal microbiota, which has a complex structure and plays a significant role in maintaining human homeostasis\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Anaerobic bacteria are the majority of intestinal bacteria, with \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e being the dominant bacteria, accounting for more than 90%. These bacteria are colonized in the intestinal tract as early as the baby is born. They are interdependent with the host, providing a nutrient-rich environment and coordinating metabolism, barrier protection and structural functions of the host\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eSome research discovered that the intestinal flora in PCOS patients differs markedly from that of healthy people\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Some scholars used 16S rRNA amplification sequence to analyze the abundance and composition of intestinal flora in PCOS\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. It was observed that the intestinal flora in patients with PCOS exhibited abnormalities, and the number of \u003cem\u003eBacteroides\u003c/em\u003e and \u003cem\u003eBacillus subtilis\u003c/em\u003e increased, α- and β- diversity are reduced, and the performance of obese is more obvious\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. It has been reported that dysbacteriosis can improve intestinal mucosal permeability. Impaired intestinal barrier function is related to chronic inflammation, IR and hyperandrogenism, which may be an important factor in inducing PCOS\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn addition, recent studies have shown that intestinal flora ferments food or decomposes substances from the host to produce a variety of metabolites, which is the key medium of intestinal flora host crosstalk\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Metabolomics has been more and more used to find biomarkers with diagnostic significance. It speculates the metabolic pathway that may change by analyzing and studying the changes of the most downstream metabolites. Some studies have indicated that patients with PCOS have intestinal microflora imbalance and abnormal metabolite composition, such as ceramides, short chain fatty acids (SCFAs), branched chain amino acids (BCAAs), bile acids (BAs), and trimethylamine N-oxide (TMAO)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. However, the relevant research is still insufficient, and the potential role and regulatory mechanism of intestinal metabolites on PCOS are still unclear.\u003c/p\u003e\u003cp\u003eThe mechanism by which dysbiosis of gut microbiota leads to symptoms of PCOS remains largely unexplored, especially in the context of brain function and neural network architecture. The brain, as a central organ that regulates endocrine and metabolic processes, is intricately connected to the intestine through the gut-brain axis\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The brain function network involves interaction among various regions of the brain and has a significant impact on emotions, cognition and endocrine functions. Patients with PCOS often have psychological problems such as depression and anxiety, and these psychological states are also closely related to the brain functional network and its connectivity. Studies have shown that PCOS may affect certain areas of the brain and their functional connections. For instance, research has found that the functions of areas such as reward mechanisms and emotion regulation may be abnormal, which is closely related to the emotional distress and cognitive dysfunction of patients with PCOS\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe study focuses on the complex interactions among PCOS, the gut microbiota and the brain functional network, with the main aim of clarifying the mechanisms of the association among PCOS, the gut microbiota and the brain functional network. In this study, the characteristics of PCOS intestinal flora were preliminarily analyzed, Functional magnetic resonance imaging (fMRI) was used to evaluate the brain functional network, and the metabolic profile changes between samples of PCOS group and healthy control group were analyzed using the LC-MS based metabonomic method, to furnish a theoretical basis for future research on the pathogenesis and individualized therapy of PCOS.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eStudy participants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe subjects of this study were 26 newly diagnosed polycystic ovary syndrome patients, aged 20\u0026ndash;35 years, who visited the gynecological endocrine clinic from July 2021 to November 2021 in Suzhou Hospital affiliated to Nanjing Medical University. The control group included 20 healthy women of childbearing age at the same age. 12 PCOS patients and 18 healthy individuals were randomly selected for functional magnetic resonance imaging.\u003c/p\u003e\u003cp\u003e This research received approval from the Ethics Committee of Suzhou Hospital affiliated to Nanjing Medical University, and the ethical review batch number was K-2021-GSKY20210208. Each participant involved in this study signed the informed consent form upon detailed understanding of the purpose, content and risks of the study. PCOS diagnostic criteria: according to the PCOS diagnostic criteria issued in the Rotterdam Conference sponsored by The European Society for Human Reproduction and Embryology (ESHRE) and The American Society for Reproductive Medicine (ASRM) in 2003: (1) rare ovulation or anovulation; (2) Elevated androgen level and (or) clinical manifestations of hyperandrogenism (such as hirsutism, acne, etc.); (3) Polycystic changes in the ovary (\u0026ge;\u0026thinsp;12 follicles with a diameter of about 2-9mm in one or both ovaries, and/or ovarian volume increase\u0026thinsp;\u0026ge;\u0026thinsp;10 cm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e). 2 of the above 3 items are in line. Exclusion criteria: (1) Other diseases, including congenital diseases, resulting in increased androgen levels or disorders of ovulation. Adrenal cortical hyperplasia, androgen secreting tumors, premature ovarian insufficiency, pituitary or hypothalamic amenorrhea, thyroid dyshomeostasis, etc.; (2) those who have used hormone drugs and drugs affecting sugar and lipid metabolism in the past three months, or who have lost weight by any drug method (including appetite inhibitors such as fluorophenylalanine, thyroid hormone, progesterone, laxatives, etc., and various Chinese medicine ingredients weight loss drugs) or surgery; (3) those with diabetes, heart Those with abnormal liver and kidney functions, infectious diseases or other serious organic morbidities, including cancer, myocardial infarction and cerebrovascular accident, have received drug treatment for the following diseases in the past three months.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSampling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe height, weight, waist circumference and hip girth of PCOS patients and healthy group were assessed by the same measuring tool. After the participants defecated on an empty stomach in the morning, they kept close fitting clothes and measured their weight. The waist circumference is measured at the middle of the iliac bone and the lowest rib. The horizontal circumference of the most salient part behind the hip is taken as the hip circumference. Waist hip rate (WHR)\u0026thinsp;=\u0026thinsp;waist circumference (cm)/hip circumference (cm), and body mass index (BMI)\u0026thinsp;=\u0026thinsp;weight (kg)/height 2 (m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e). All subjects collected 5mL of fasting elbow vein blood from the 3rd to 5th day of menstruation. The blood samples should be left at room temperature for 30 minutes and then centrifuged for 10 minutes. The supernatant was transferred the supernatant into EP tube and immediately placed it in \u0026minus;\u0026thinsp;80℃ refrigerator for standby. Determination of follicle stimulating hormone (FSH), luteinizing hormone (LH), anti M\u0026uuml; llerian hormone (AMH), adrenocortical hormone (ACTH), testosterone (T), cortisol (CORT), fasting blood glucose (FBG), fasting insulin (FINS). The homeostasis model assessment of insulin resistance (HOMA-IR)\u0026thinsp;=\u0026thinsp;FBG (mM) * FINS (mIU/L)/22.5 was used for insulin resistance index. Galactin-3 (gal-3), lipopolysaccharide (LPS), tumor necrosis factor-a (TNF-a), interleukin-6 (IL-6), short chain fatty acids (SCFAs).\u003c/p\u003e\u003cp\u003e\u003cb\u003eNext-generation sequencing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe collected about 3\u0026ndash;5 g of fresh feces from each participant during the non-menstrual period and immediately frozen them at \u0026minus;\u0026thinsp;80℃ until use. The QIAamp DNA stone mini kit (Qiagen 51604, Germany) was utilized to extract the total DNA from each stool sample, and the quantification of DNA was conducted using a Nanodrop spectrophotometer, and the DNA extraction quality was detected by 1.2% agarose gel electrophoresis. PCR amplification of V4 region of 16S rDNA was carried out with universal sequencing primers 515F (5'-GTGCCAAGCGCGCCGGTAA-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3'). The amplified products were purified and recovered using VAHTS DNA Clean Beads (N411, Vazyme), and fluorescence quantification was carried out with microplate reader (BioTek, FLx800) and Quant it PicoGreen dsDNA Assay Kit (P7589, Galbibochem). Next, TruSeq Nano DNA LT Library Prep Kit (FC-121-4001, Illumine) was employed to construct the sequencing library, and MiSeq Agent Kit V3 (600 cycles) was used for double ended sequencing in MiSeq PE250 platform of Illumina.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNon target metabonomic detection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe used LC-MS/MS analyses by an UHPLC system (Vanquish, Thermo Fisher Scientific) with a UPLC BEH Amide column (2.1mm*100mm, 1.7\u0026micro;m) coupled to Q Exactive HFX mass spectrometer (Orbitrap MS, Thermo). The mobile phase was composed of 25 mmol/L ammonium acetate and 25 ammonia hydroxides in water (PH\u0026thinsp;=\u0026thinsp;9.75) (A) and acetonitrile (B). The QE HFX mass spectrometer was employed due to its ability to acquire MS/MS spectra on information-dependent acquisition (IDA) mode under the control of the acquisition software (Xcalibur, Thermo). In this mode, the acquisition software constantly evaluates the full scan MS spectrum. The ESI source conditions were set as follows: sheath gas flow rate as 30 Arb, Aux gas flow rate as 25 Arb, capillary temperature 350℃, full MS resolution as 60000, MS/MS resolution as 7500, collision energy as 10/30/60 in NCE mode, spray Voltage as 3.6 kV (positive) or -3.2 kV (negative), respectively.\u003c/p\u003e\u003cp\u003e\u003cb\u003efMRI data preprocessing and analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eResting-state (rs) functional magnetic resonance imaging (fMRI) is used to detect low-frequency fluctuations of blood oxygen level-dependent (BOLD) signals in brain regions. The correlation between temporal BOLD signal fluctuations is usually used to infer functional connectivity. We used the low-frequency fluctuation amplitude (ALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo) of the rs-fMRI blood oxygen level-dependent (BOLD) signal to characterize regional neural activity. Collect the functional and structural image data including the patient group and the control group. Subsequently, the data is preprocessed. Firstly, the first five points are deleted, and then steps such as time layer correction, head movement correction, spatial standardization, de-linear trend, and regression covariates are carried out. The four groups of indicators, namely ALFF, REHO, FCD/DC and Voxel_wise_FC, were calculated respectively.\u003c/p\u003e\u003cp\u003e\u003cb\u003eASL images to calculate cerebral blood flow (CBF)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFirst, quality control was carried out on the data, and some data was removed. 3D PASL technology and T1 imaging were adopted, and data processing steps such as image reset, head movement correction, registration, removal of extracranial voxel, filtering and regression noise covariates, smoothing, calculation of CBF and normalization according to the mean of the whole brain were carried out. The patient group and the control group underwent a two-sample t-test. Age and BMI were used as covariates for alphasim mass level correction.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBrain Network Rich-club Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor the DTI tracking FN brain networks of all subjects, the fiber connections existing in more than 50% of the subjects were retained to construct a binary group average network (that is, the connections existing in more than 50% of the subjects were set to 1 and vice versa was set to 0). Calculate the rich-club coefficient of the average network of the group. As shown in Fig.\u0026nbsp;10A, k is between 7 and 9, and phi_norm is greater than 1. This indicates that the network has a rich-club structure, and the rich-club connection analysis can be conducted. In this study, hub nodes are defined by node degree. When the node degree value of a certain brain region is greater than the average value plus the standard deviation of all nodes in the network, then this node is a core node. The brain regions of the core nodes are defined as rich nodes, and the remaining nodes are defined as non-rich nodes. Thus, the edges in the network can be divided into three categories, namely the connections between rich nodes, which are called rich connections; The connection between the rich node and the non-rich node is called the feeder connection; The connection between non-rich nodes is called a local connection. Calculate the three connections of all the subjects, conduct the two-sample t-test of the healthy group and the PCOS group, and take age and BMI as covariates.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDTI Chart Theory Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUse the PANDA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://vww.nitrcore/projecls/pandal\u003c/span\u003e\u003cspan address=\"http://vww.nitrcore/projecls/pandal\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) preprocessing software. The DTI images of each subject were subjected to craniotomy, dynamic eddy current correction and adjustment of the diffusion gradient direction. Then, the diffusion tensor parameters such as FA, MD, AD and RD were calculated. When conducting deterministic white matter fiber tracking of diffusion tensor data in the PANDA toolkit, the aal90 template was selected as the brain region template. The complete brain is divided into 90 brain regions. Each brain region serves as a node of the brain network, and the edges of the brain network refer to the white fiber bundles connected between these nodes. This process is to nonlinearly register the individual T1-weighted image onto the standard space through the panda toolkit to obtain the inversely transformed T-1. Then apply T-1 to the selected template to obtain 90 brain regions divided based on the aal90 template; Finally, a brain network with these 90 brain regions as nodes was obtained. When conducting deterministic fiber tracking in PANDA, the FACT algorithm is used. Tracking is stopped when the deflection Angle exceeds 45\u0026deg; or the FA value is less than 0.2. And the number of fibers FN (fiber number) in pairwise brain regions is defined as the edges of the network. To avoid false positive results, we only retain the fiber connections existing in more than 80% of the subjects. Then, we set that the number of fiber bundles between each pair of nodes is greater than or equal to 3, and only then is it considered that there is a connection between them (that is, when FN reaches above 3, it is considered that this edge is effectively existing in this network). The Gretna software is used for the calculation and statistics of graph theory indicators. As mentioned above, set the FN threshold to 3, construct a binary network, and calculate various graph theory indicators. A two-sample t-test was conducted on the graph theory indicators of the two groups, with age and BMI as covariates, and FDR multiple comparison correction was performed on the node indicators.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003e\u003cb\u003eClinical data statistical analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe statistical analysis was conducted using the SPSS 21.0 software package. Kolmogorov Smirnov method was employed to assess normality test. For the measurement data conforming to a normal distribution, we adopt the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for representation and employ the t-test. The counting data were described by frequency and rate, and chi square test was used. One-way analysis of variance (ANOVA) followed by post hoc Student-Newman-Keuls test or nonparametric Kruskal-Walli\u0026rsquo;s test were employed to describe the differences between more than two groups. Statistical tests were based on two-tailed probability and a P-value less than 0.05 was considered as statistically significant. Pearson correlation analysis was conducted for continuous variables, while for categorical variables, Spearman correlation analysis was used .\u003c/p\u003e\u003cp\u003e\u003cb\u003eBioinformatics analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe raw data of high throughput sequencing undergoes preliminarily screened. Subsequently, for the raw sequences that pass the initial quality screening, they are classified into libraries and samples according to the index and Barcode information, while the barcode sequence is removed. We used QIIME (Version 1.9.1) for sequence denoising and OTU (Operational Taxonomic Units) clustering and obtained the distinct composition of each sample group across different species of taxonomic levels. Based on the variation of OTU in different samples, we evaluated the α Level of diversity. At the OTU level, the distance matrix of each sample was calculated to measure the β diversity. We obtained \u003cem\u003eP\u003c/em\u003e value derived from partial least squares-discriminate analysis (PLS-DA) by using the Adonis method for matching. The Linear discriminant analysis Effect Size (LEfSe) was carried out by LEfSe version 1.0, with the parameter setting at 2, to discover gene or functional traits that could best account for variations between the groups. In the sample, we employed PICRUSt2 to infer the constitution of functional genes. Then we analyzed the functional differences among different samples and groups. The association network is established, and the topological index is calculated in an attempt to find the key species according to the composition and distribution of species in each sample. In accordance with the 16S rRNA gene sequencing results, the metabolic function of the sample flora was predicted, identifying the differential pathway, and obtaining the species composition of the specific pathway. We utilized Spearman correlation coefficient to assess the relationship between intestinal microbiota and clinical parameters.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eClinical characteristics of the particpants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe three groups of subjects were all women of childbearing age, and no notable difference in age. The age of the three groups was balanced and matched. The three groups of subjects were investigated before entering the group, and the proportion of daily intake of fat, protein and carbohydrate had no statistically significant difference. Obesity is based on the Asia Pacific regional standard put forward by the International Obesity Task Force of the World Health Organization (WHO) in 2000: people with body mass index (BMI)\u0026thinsp;\u0026ge;\u0026thinsp;25kg/m2 are obese. The weight of the healthy control group fell within the established normal range. Compared to those in non-obese PCOS group and healthy control group, BMI and WHR in obese PCOS group were significantly higher (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while no significant difference was found between non obese PCOS group and healthy control group. Compared with the control group, LH, LH/FSH, TT and AMH in PCOS groups increased, irrespective of the presence or absence of obesity (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The FINS and HOMA-IR of the obese PCOS group were significantly lower than those in non-obese PCOS group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The levels of Gal-3 in both obese PCOS group and non-obese PCOS group were significantly higher compard to the healthy control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The levels of IL-6 in the three groups were statistically different (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Compared with non-obese group and control group, TNF-a in obese PCOS group was significantly higher (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant differences in serum LPS levels were observed among the three groups. There was no significant difference in fecal short chain fatty acid levels among the three groups. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnthropometric and metabolic characteristics of all particpants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003ePCOS group(n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eControl group(n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eF/χ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u0026lt;0.05\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eobese(n\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003enon-obese(n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.50(20.25\u0026thinsp;~\u0026thinsp;29.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.00(21.00\u0026thinsp;~\u0026thinsp;29.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.00(23.00\u0026thinsp;~\u0026thinsp;24.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.405\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29.54\u0026thinsp;\u0026plusmn;\u0026thinsp;4.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21.22\u0026thinsp;\u0026plusmn;\u0026thinsp;2.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e36.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ea, b\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ea, b\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFSH (mIU/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLH (mIU/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.55\u0026thinsp;\u0026plusmn;\u0026thinsp;4.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.23\u0026thinsp;\u0026plusmn;\u0026thinsp;6.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eb, c\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLH / FSH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.005\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eb, c\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTT (ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eb, c\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAMH (ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.54\u0026thinsp;\u0026plusmn;\u0026thinsp;4.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.45\u0026thinsp;\u0026plusmn;\u0026thinsp;2.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eb, c\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eACTH(pg/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2352.91\u0026thinsp;\u0026plusmn;\u0026thinsp;3456.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2665.85\u0026thinsp;\u0026plusmn;\u0026thinsp;5112.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1294.31\u0026thinsp;\u0026plusmn;\u0026thinsp;2974.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.554\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCORT (ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e401.82\u0026thinsp;\u0026plusmn;\u0026thinsp;28.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e414.57\u0026thinsp;\u0026plusmn;\u0026thinsp;48.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e435.28\u0026thinsp;\u0026plusmn;\u0026thinsp;35.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFBG (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFINS (uU/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.36\u0026thinsp;\u0026plusmn;\u0026thinsp;18.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.89\u0026thinsp;\u0026plusmn;\u0026thinsp;6.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.97\u0026thinsp;\u0026plusmn;\u0026thinsp;4.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.016\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ea, b\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHOMA-IR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.49\u0026thinsp;\u0026plusmn;\u0026thinsp;3.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.006\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ea,b,c\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003egal-3 (ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.005\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eb, c\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIL-6 (pg/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52.03(25.59\u0026thinsp;~\u0026thinsp;126.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.21(11.44\u0026thinsp;~\u0026thinsp;59.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.60(4.65\u0026thinsp;~\u0026thinsp;10.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ea,b,c\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLPS (pg/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.68(0.49\u0026thinsp;~\u0026thinsp;0.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.98(0.61\u0026thinsp;~\u0026thinsp;1.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.85(0.51\u0026thinsp;~\u0026thinsp;2.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNF-a (pg/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68.15\u0026thinsp;\u0026plusmn;\u0026thinsp;58.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42.54\u0026thinsp;\u0026plusmn;\u0026thinsp;45.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.78\u0026thinsp;\u0026plusmn;\u0026thinsp;37.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.013\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ea, b\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eacetate (umol/g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e121.41\u0026thinsp;\u0026plusmn;\u0026thinsp;75.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e131.52\u0026thinsp;\u0026plusmn;\u0026thinsp;54.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e129.04\u0026thinsp;\u0026plusmn;\u0026thinsp;86.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epropionate (umol/g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60.55\u0026thinsp;\u0026plusmn;\u0026thinsp;38.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58.60\u0026thinsp;\u0026plusmn;\u0026thinsp;29.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e43.36\u0026thinsp;\u0026plusmn;\u0026thinsp;24.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ebutyrate(umol/g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47.03\u0026thinsp;\u0026plusmn;\u0026thinsp;25.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49.93\u0026thinsp;\u0026plusmn;\u0026thinsp;27.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39.42\u0026thinsp;\u0026plusmn;\u0026thinsp;29.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median(interquartile interval).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eStructural changes of intestinal flora in patients with PCOS\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFecal samples from PCOS group and health control group were collected. Then total bacterial DNA was extracted for sequencing analysis. α diversity reflects the species diversity within the bacterial community. This study adopted 7 α indexes. The two groups in five indicators of diversity measurement have no significant difference. Compared to the control group, the faith_pd index in the PCOS group was elevated. The degree of genetic diversity of bacterial flora in PCOS group was higher (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037). The Goods_coverage index in PCOS group was lower than in control group. The proportion of species detected in PCOS group was lower. Combined with the comprehensive analysis of 7 indexes, compared with control group and PCOS group α diversity decreases slightly (or the change is not obvious) (Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). β diversity is often used to reflect the similarity of the overall composition of gut flora. According to Bray Curtis distance, the principal coordinate analysis was carried out for the two groups of samples. The outcomes showed that the intestinal flora communities of the two treatment groups had little difference, and the distribution was more coincident. In beta diversity, no significant difference was observed between the two treatment groups according to ANOSIM similarity analysis results (t\u0026thinsp;=\u0026thinsp;0.05, P\u0026thinsp;=\u0026thinsp;0.095) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalysis of intestinal flora composition in patients with PCOS\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIt can be seen from Venn diagram that there are 1821 unique species in the control group (48.91%), 1150 unique species in PCOS (30.89%), and 752 species in both groups (20.2%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In comparison with the control group, the species distribution of PCOS group was notably different. At the phylum, family and genus levels, the colony composition histogram between groups showed that the two groups had similar colony structures, and there were some differences in the proportion of different colonies. At the phyla level, \u003cem\u003eFirmicutes\u003c/em\u003e accounted for the most in both groups, and the proportion of PCOS group increased compared with the healthy control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). At the family level, \u003cem\u003eBifidobacteriaceae\u003c/em\u003e accounted for the most in both groups, but the proportion of PCOS group decreased compared with that of healthy control group, while \u003cem\u003eRuminococcaceae\u003c/em\u003e increased in PCOS group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). At the genus level, the proportions of \u003cem\u003eBifidobacterium\u003c/em\u003e were the most abundant in the two groups, while the proportions of PCOS group in the healthy control group decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Lefse was used to analyze the difference between the two groups. It was found that in PCOS group, \u003cem\u003eRuminococcaceae\u003c/em\u003e and \u003cem\u003eFusobacteriaceae\u003c/em\u003e of \u003cem\u003eFirmicutes\u003c/em\u003e were the most abundant, while in healthy control group, mycobacteria of Actinobacteria were the most abundant (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The form of heat map clearly shows the changes of 50 bacterial genera among 46 samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eChanges in intestinal flora function in patients with PCOS\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe alterations of gut flora function in individuals with PCOS were further investigated by Picrust analysis. Drawing on the KEGG database, from the perspective of all samples, the microbial metabolic pathways involved mainly focus on the major nutrient metabolism related pathways, such as carbohydrate metabolism, metabolism of cofactors, amino acid metabolism and vitamins, polyketides and terpenoids\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. However, no notable difference in metabolic pathways was found between the two groups. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCorrelation analysis between intestinal flora and clinical indexes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe conducted Spearman correlation analysis on 19 clinical indicators and 12 bacteria with significant differences between groups, aiming to investigate the relationship between intestinal flora and clinical indicators. The results showed that there were marked differences between intestinal flora and PCOS related clinical indicators. Clostridium was notably and positively correlated with BMI, T, gal-3, IL-6 and prolionate. A notable positive correlation was observed between \u003cem\u003eSutterella\u003c/em\u003e and AMH, ACTH, HOMA-IR, IL-6, TNF α, acetate, propionate and butyrate. \u003cem\u003eAnaerostipes\u003c/em\u003e exhibited a positive correlation with LH and LH/FSH. \u003cem\u003eWeissella\u003c/em\u003e was negatively associated with BMI, WHR, AMH, gal-3 and IL-6. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB)\u003c/p\u003e\u003cp\u003e\u003cb\u003eNontargeted metabolomics analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAfter LC-MS detection, 7507 peaks were detected in all samples. After data processing, 6058 peaks were retained. The above results were compared with databases and 987 substances were obtained. Based on the principal component differences of metabolites of two treatment groups analyzed by orthogonal projects to late structures discriminant analysis (OPLS-DA), the abscissa indicates the sample differences between groups, and the ordinate represents the sample differences within groups. The figure demonstrated that the difference between the two treatment groups is significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). 149 differential metabolites were obtained by T test with \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 and VIP score\u0026gt;1, as shown in the figure above (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The blue color shows the downregulated metabolites of the control group compared with the PCOS group, while the red color is opposite. The 28 differential metabolites matched to the secondary mass spectrometry were visualized (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). In the figure, the distinct experimental groups are depicted on the abscissa, whereas the ordinate denotes the different metabolites compared in this group. The relative expression number of metabolites at corresponding positions are represented by the color blocks at different positions: red represents the high expression of the substance in the group, while the low expression of the substance in the group is denoted by blue color. The 28 differential metabolites are predominantly divided into benzenoids, hydrocarbons, organic acids and derivatives, lipoids and lipoid like molecules, organic heterocyclic compounds, others. Chord diagram visualization shows the correlation between metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). The node size represents the value, and the stripe represents the correlation index. The outcomes of metabolic pathway analysis are presented in bubble chart (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Each bubble embodies a metabolic pathway. The abscissa of the bubble and the bubble size represent the influence factor of the pathway in topology analysis. The size of the bubbles is proportional to the influencing factors. The ordinate of the bubble and the bubble color represent the \u003cem\u003eP\u003c/em\u003e value of enrichment analysis (negative natural logarithm, i.e. - ln (p)). A deeper color corresponds to a lower \u003cem\u003eP\u003c/em\u003e value, reflecting a greater significance in the degree of enrichment. The figure shows metabolism about linoleic acid, vitamin B6, pyrimidine, arginine and proline are enriched in the control group.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003efMRI data preprocessing and analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe conducted functional magnetic resonance imaging (fMRI) on 12 patients with PCOS and 18 healthy controls, collecting functional and structural image data. Two healthy controls and one PCOS patient were removed due to incomplete data. After preprocessing, we calculated metrics such as ALFF, REHO, FCD/DC, and voxel-wise FC (Figs.\u0026nbsp;8A \u003cb\u003eand B\u003c/b\u003e). We first performed a two-sample t-test, using age and BMI as covariates, to compare differences between the patient and control groups. Both ALFF and REHO did not pass FWE, FDR, GRF, or Alphasim cluster-level corrections. For the FCD metric, PCOS\u0026thinsp;\u0026gt;\u0026thinsp;HC showed significance after Alphasim cluster-level correction, with voxel \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, cluster \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and cluster size\u0026thinsp;=\u0026thinsp;259 (no result for PCOS\u0026thinsp;\u0026lt;\u0026thinsp;HC). We found that the FCD metric indicated higher activation in the right precuneus (Precuneus_R) in the patient group compared to the healthy control group (Fig.\u0026nbsp;8C). Without adding covariates, neither REHO nor FCD showed significant results after FWE, FDR, GRF, or Alphasim cluster-level correction. For ALFF, PCOS\u0026thinsp;\u0026gt;\u0026thinsp;HC showed significance after FWE cluster-level correction, with voxel \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, cluster \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and cluster size\u0026thinsp;=\u0026thinsp;119 (no result for PCOS\u0026thinsp;\u0026lt;\u0026thinsp;HC). The ALFF metric indicated higher activation in the left postcentral gyrus (Postcentral_L) in the PCOS patient group compared to the healthy control group (Fig.\u0026nbsp;8D).\u003c/p\u003e\u003cp\u003e\u003cb\u003eASL images to calculate cerebral blood flow (CBF)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAfter data quality control, a total of 25 datasets, including 15 healthy controls and 10 PCOS patients, were included in the analysis. A two-sample t-test was conducted between the health control group and the PCOS patient group, with age and BMI as covariates. Alphasim cluster-level correction was applied (voxel \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, cluster \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). We found that the healthy control group had significantly higher CBF in the left lingual gyrus (Lingual_L) and right inferior parietal lobule (Parietal_Inf_R) compared to the PCOS patient group(\u003cb\u003eFigure 9A and B\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eBrain Network Rich-club Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAfter data quality control, a total of 28 datasets, including 18 healthy controls and 10 PCOS patients, were included in the analysis. As shown in Fig.\u0026nbsp;10A, the value of k ranged from 7 to 9, and phi_norm was greater than 1, indicating that the network had a rich-club structure, allowing for rich-club connectivity analysis. The node degrees of all nodes in the group average network were calculated, and according to the above definition, the core nodes were identified as the following 10 brain regions in the AAL90 atlas: 2, 43, 44, 50, 51, 67, 68, 73, 74, and 88 (Fig.\u0026nbsp;10B). These regions had node degrees greater than one standard deviation above the network's average degree (the mean node degree of the group average network was 7.8444, and the standard deviation was 3.1973). The three types of connections were calculated for all participants, and a two-sample t-test was performed between the health control group and the PCOS patient group, with age and BMI as covariates. We found no significant differences between the healthy control group and the PCOS patient group in terms of rich-club connections, feeder connections, and local connections (Figs.\u0026nbsp;10C, \u003cb\u003eD, and E\u003c/b\u003e, \u003cem\u003eP\u003c/em\u003e\u0026gt;0.05).\u003c/p\u003e\u003cp\u003e\u003cb\u003eDTI Chart Theory Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAfter data quality control, 18 healthy controls and 10 PCOS patients were included in the analysis. The graph theory metrics were calculated and analyzed by Gretna software. As described above, an FN threshold of 3 was set to construct a binary network and various graph theory metrics were computed. A two-sample t-test was performed on the graph theory metrics between the two groups, with age and BMI as covariates. Additionally, node metrics were corrected for multiple comparisons using FDR. None of the node metrics passed the multiple comparison correction. The only metric with significant inter-group differences was the synchronization metric. Specifically, the graph theory synchronization metric showed: p\u0026thinsp;=\u0026thinsp;3.0534133e-02; T = -2.2987103. Next, an edge analysis of the FN was performed. A two-sample t-test was conducted on the edges between the two groups, and the NBS correction method was applied with edge \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and component \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, using 5000 permutations. However, no results passed the correction (Fig.\u0026nbsp;11).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIntestinal flora, known as the \"hidden organ\" of the human body, participates in regulating various biological processes such as energy metabolism and immune inflammatory reaction, and exerts an irreplaceable role in the advancement of metabolic health and diseases. When intestinal flora is in disorder, intestinal permeability increases, endotoxin and proinflammatory cytokines increase, energy intake increases, and insulin resistance is induced\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. More and more studies show that the alteration of intestinal flora is closely associated with the occurrence and development of metabolic diseases such as obesity and type 2 diabetes\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMore and more researchers are focusing on the latent function of intestinal microflora in PCOS has attracted more and more attention. Potential mechanism between intestinal flora disorder and PCOS: intestinal flora may damage intestinal permeability and intestinal barrier dysfunction\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The disordered intestinal flora can lead to the impairment of intestinal mucosal barrier function, the inhibition of expression of intestinal epithelial tight junction protein, the increase of intestinal permeability, and the promotion of the absorption of lipopolysaccharide (LPS) into the blood circulation\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. LPS, also known as endotoxin, is a major component component that initiates inflammatory reaction. The LPS content in circulation is increased. After binding with the endotoxin receptor CD14, it activates MyD88 and interferon induced by the connector protein containing TIR functional region through Toll like receptor 4 (TLR4) on the immune cell membrane β(TIR-domain-containing adapter-inducing interferon- β, TRIF) signaling pathway. Downstream inflammatory signal transduction starts, causing a series of inflammatory reaction processes, and various inflammatory factors such as IL-6, TNFα increasing\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. With the increase of the level of insulin, chronic inflammation interferes with the function of insulin receptor and causes insulin resistance, while the increase of the level of insulin in the serum causes the ovary to produce more androgens to interfere with the normal follicular development\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis study found that the circulating inflammatory factors IL-6 and TNFα in obese PCOS patients significantly elevated, and a chronic inflammatory state existed. Galectin-3 (Gal-3) in PCOS patients was significantly higher than that in healthy people, while no significant difference was observed in PCOS patients with or without obesity.\u003c/p\u003e\u003cp\u003eAs the only member of the chimeric galactose election, Gal-3 was first found in fish eggs \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, and can be detected in various cells, including activated macrophages, eosinophils, neutrophils, mast cells, etc\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Gal-3 protein is located in the nucleus, cytoplasm and extracellular membrane, and can shuttle back and forth between the nucleus and cytoplasm in the cell, thus participating in a series of cell processes such as differentiation, proliferation, apoptosis, RNA splicing and so on\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Gal-3 plays a crucial role in multiple pathophysiological processes, including cell adhesion and proliferation, apoptosis, inflammation, tumor, etc\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In recent years, the role of Gal-3 in obesity and diabetes related metabolic dysfunction has attracted more and more attention\u003csup\u003e\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. It has been found that the serum Gal-3 of obese patients is higher than that of healthy people \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The mouse experiment found that visceral adipose tissue may be the main tissue secreting Gal-3\u003csup\u003e35\u003c/sup\u003e. Adipose tissue has numerous inflammatory cells infiltrating, and inflammatory cells express and secrete Gal-3\u003csup\u003e35\u003c/sup\u003e. Gal-3 has the ability to bind to Toll like receptors (TLRs) on the cell surface, activate TLRs, activate downstream signals, activate MAPK signaling pathway and NFκB signal pathway regulates the generation of inflammatory cytokines such as tumor necrosis factor, interleukin and interferon, and participates in inflammation and immune regulation\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. It is reported that compared to the impaired glucose tolerance and healthy people, the level of Gal-3 in diabetes patients is significantly higher\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. It is speculated that Gal-3 is related to diabetes. A study found that Gal-3 can directly bind to insulin receptor (IR) and inhibit the downstream signal of IR\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWe observed a significant elevation of Gal-3 levels in patients with PCOS. We also found that there was no significant relationship with the BMI of PCOS patients. Gal-3 may be involved in the pathogenesis of PCOS and might be served as a marker for early diagnosis of PCOS patients\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The relationship between Gal-3 and PCOS still needs a lot of basic and clinical research to further reveal.\u003c/p\u003e\u003cp\u003eIn terms of microbial diversity, some scholars found that compared with healthy people, α and β diversity were decreased in patients with PCOS\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. However, it is also reported that no significant differences were found between PCOS group and control group\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. We also found no significant difference. According to LEfSe analysis, in PCOS group, \u003cem\u003eRuminococcaceae\u003c/em\u003e and \u003cem\u003eFaecalibacterium\u003c/em\u003e of \u003cem\u003eFirmicutes\u003c/em\u003e were most significantly enriched. In comparison with the control group, the proportion of \u003cem\u003eBifidobacteriaceae\u003c/em\u003e and \u003cem\u003eBifidobacterium\u003c/em\u003e in PCOS group decreased. Previous studies have shown that the \u003cem\u003eFirmicutes\u0026rsquo; increase\u003c/em\u003e is correlated with the increase of BMI\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. It is the most obvious enrichment of intestinal flora in obese women and is considered to be involved in the occurrence of obesity\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eBifidobacterium\u003c/em\u003e, as a beneficial bacterium in the intestine, can enhance immunity and promote nutrient absorption\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Several studies have found that it is significantly reduced in PCOS patients\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, which aligns with what we have studied.\u003c/p\u003e\u003cp\u003eIn addition, our study found that \u003cem\u003eSutterella\u003c/em\u003e was significantly associated with multiple clinical indicators of PCOS patients, including AMH, ACTH, HOMA-IR, IL-6, TNFα, acetate, propionate, butyrate. As a gram-negative bacterium of Proteus, \u003cem\u003eSutterella\u003c/em\u003e is associated with many diseases, such as autism, Down syndrome, inflammatory bowel disease, etc \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. It has been reported that \u003cem\u003eSutterella\u003c/em\u003e can affect intestinal permeability, which is related to the typical chronic low-grade inflammation of obese subjects\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eSutterella\u003c/em\u003e may be closely related to the occurrence and progression of PCOS.\u003c/p\u003e\u003cp\u003eSo, how does intestinal flora \"talk\" with the host? During recent years, studies have shown that intestinal flora ferments food or decomposes substances from host to produce a variety of metabolites, which is the key medium of intestinal flora host crosstalk\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. On the one hand, as a signal molecule, it communicates directly with the parenteral target organs through the circulatory system\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e; on the other hand, it transmits signals indirectly by regulating the release of intestinal endocrine hormones\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Intestinal metabolites, such as bile acids (BAS), to a large extent participate in regulating the integrity of the intestinal barrier, thus helping to maintain the stability of internal environment and dynamic balance \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Disorders of intestinal metabolites may lead to increased intestinal permeability and endotoxemia, thus disturbing the endocrine system, immune system, glucose and fat metabolism, insulin signal and intestinal microflora\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In addition, the secretion and sensitivity of pancreatic insulin in target organs can be directly regulated by SCFAs, BCAAs through endocrine signals\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. When circulating via the portal vein system, these metabolites arrive at the liver to regulate fat metabolism and oxidation\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. In addition, these metabolites also participate in the dynamic balance of neurons by regulating the blood-brain barrier\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn this study, nontargeted metabonomic methods were used to find more specific small molecular biomarkers, obtain more comprehensive metabolite change information, and avoid losing important differential intermediates in metabolic pathways. In this experiment, the metabolic profile changes between PCOS group and healthy control group were analyzed by LC-MS based metabonomic method. Through the quality control experiment, the instrument analysis system with good stability and reliable test evidence are verified. The differential metabolites were screened in PCOS group and healthy control group, and 28 potential biomarkers were obtained. The analysis of KEGG metabolic pathway showed that they were involved in multiple metabolic pathways. These metabolic pathways mainly focus on metabolism about linoleic acid, vitamin B6, pyrimidine, arginine and proline.\u003c/p\u003e\u003cp\u003eThe brain function network involves interaction among various regions of the brain and has a significant impact on emotions, cognition and endocrine functions. Previous studies have found that polycystic ovary syndrome can cause functional changes in brain regions responsible for visual working memory, such as the right middle and superior frontal gyrus \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. The changes found in these two frontal lobe regions are also related to specific deficiencies in the attention part of working memory. Similarly, the functional connectivity between the superior frontal gyrus and the middle-superior gyrus was negatively correlated with the LH level and the LH/FSH ratio. Previous studies have found that women with PCOS perform worse in visuospatial working memory\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Furthermore, PCOS can also have a negative impact on more general visuospatial abilities\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003eand visuospatial learning \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. A functional magnetic resonance imaging study analyzed the effects of excessive exposure to androgens and subsequent anti-androgen therapy on brain activity during working memory processing\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. The research found that the activation degree of the right superior parietal lobe and the inferior parietal lobe of POCS was higher. After the treatment, there was no difference in overall brain activity between the two groups, and PCOS demonstrated higher accuracy under memory load conditions in working memory tasks. Therefore, polycystic ovary syndrome may require more neural resources in working memory tasks and have lower executive function efficiency\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Patients with PCOS often have psychological problems such as depression and anxiety, and these psychological states are also closely related to the brain functional network and its connectivity\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Most patients with pcos will experience depression, somatization and bipolar disorder. Emotional disorders can affect cognitive functions, such as sustained attention\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003eand working memory\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. The research found that there were differences in brain network structure between PD patients and the HC group, specifically manifested as changes in synchrony indicators. In the case of adding covariables (age, BMI), the FCD index showed that the activation of the right precuneus (Precuneus_R) in the patient group was higher than that in the health control group (HC) and was corrected for Alphasim mass level. When covariates were not added, the ALFF index showed that the activation of the left posterior central gyrus in the patient group was higher than that in the healthy control group. Through the two-sample t-test, it was found that the CBF of the HC group in the left lingual gyrus and the right inferior parietal lobule was significantly greater than that of the PCOS group. However, none of the node indicators were corrected through multiple comparisons, and neither was the edge analysis corrected.\u003c/p\u003e\u003cp\u003eThe study preliminarily analyzed the relationship between the intestinal microbiota characteristics of PCOS and fecal metabolites and investigated the relationship between PCOS and brain network structure, providing a theoretical basis for the research on the pathogenesis of PCOS and individualized treatment. At the same time, this study still has several limitations: the sample size is relatively small, and the intestinal flora research is not grouped again, which may lead to incomplete statistical results; no other humoral metabolites were used for comparison; Metabonomics has limitation that is still in the primary stage in the diagnosis of diseases, and the repeatability and stability of metabolites need to be further discussed. As to how these abnormal metabolites are produced and how they participate in the occurrence and development of PCOS. There is still a lot of research to be done, especially in the identification of different types and specific pathogenesis of PCOS. Future research could consider expanding the sample size, adopting other analytical methods or combining other imaging techniques to gain a more comprehensive understanding of the relationship between the characteristics of brain networks and diseases. In addition, the influence of other factors on brain network connections can also be explored, as well as the correlation between these connection patterns and clinical symptoms or disease prognosis.\u003c/p\u003e\u003cp\u003eIn conclusion, we found that the circulating inflammatory factors IL-6 and TNFα in obese PCOS patients elevated, and there is a chronic inflammatory state. Gal-3 in PCOS patients was significantly increased and had no significant relationship with the BMI of PCOS patients. Gal-3 might be involved in the pathogenesis of PCOS or could be used as a marker for early diagnosis of PCOS patients. Compared with the control group, the intestinal flora in PCOS group has some changes in composition and function. \u003cem\u003eSutterella\u003c/em\u003e is significantly related to a number of clinical indicators of PCOS patients, suggesting that it might be involved in the occurrence and development of PCOS. In addition, based on the metabonomic method of LC-MS technology, we screened 28 differential metabolites in PCOS group and healthy control group, and analyzed the main metabolic pathways of these metabolites. We found through fMRI that there were differences in brain network structure between PD patients and the HC group, specifically manifested as the synchrony indicators of some brain regions and the changes of CBF. In the future, we will further reveal the role of differential metabolites in the occurrence and development of PCOS through a large number of studies, hoping to provide new creative ideas for the study about the pathogenesis of PCOS.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCOS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epolycystic ovary syndrome\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody Mass Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWHR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWaist-to-Hip Ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFSH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFollicle-stimulating hormone\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eluteinizing hormone\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTestosterone\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAMH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eanti-M\u0026uuml;llerian hormone\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eACTH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eadrenocorticotropic hormone\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCORT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecortisol\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFBG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003efasting blood glucose\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFINS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003efasting insulin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHOMA-IR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ethe homoeostasis model assessment of insulin resistance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLPS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003elipopolysaccharide.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical Approval:\u003c/strong\u003e\u003cp\u003eThis research received approval from the Ethics Committee of Suzhou Hospital affiliated to Nanjing Medical University, and the ethical review batch number was K-2021-GSKY20210208. The study was performed in accordance with the ethical guidelines of the World Medical Association\u0026rsquo;s Declaration of Helsinki.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eReprints and permissions information\u003c/h2\u003e\u003cp\u003e is available at www.nature.com/reprints\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.nature.com/reprints\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThe authors thank all of the patients and their families. This work was funded by the Suzhou Applied Basic Research (Medical and health) Science and Technology Innovation Guiding Project (NO. SYWD2024130) and Suzhou Association of Traditional Chinese Medicine (NO. SYWD2024295).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM. M., X. X., S. H. and H. F. contributed to the study conception and design. Data acquisition was performed by L. S., T. C., K. W.. Quality control of data, data analysis and interpretation were performed by H. F. and M. M.. Statistical analysis was performed by G. L. and H. F.. The frst draf of the manuscript was written by G. L., H. F. and M. M.. The manuscript was critically reviewed by all authors. All authors have reviewed the article. They agree that the article is appropriate. It agrees to be published as such.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJoham, A. E. et al. 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Ovarian Res.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1757-2215-7-31\u003c/span\u003e\u003cspan address=\"10.1186/1757-2215-7-31\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"polycystic ovary syndrome, gut microbiota, clinical parameters, gal-3, metabolites, brain network structure","lastPublishedDoi":"10.21203/rs.3.rs-7003646/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7003646/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePolycystic ovary syndrome (PCOS) is a common endocrine disorder affecting reproductive-aged women. We investigated gut microbiota and brain network alterations in PCOS patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe enrolled 26 PCOS patients and 20 healthy controls, collecting clinical data, blood, and stool samples. Serum hormones, biochemical markers, inflammatory factors (e.g., IL-6, TNFα), and short-chain fatty acids were analyzed. A subset (12 PCOS, 18 controls) underwent fMRI to assess brain network differences.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe discovered that the circulating inflammatory factors IL-6 and TNFα in obese PCOS patients significantly increased. Gal-3 increased significantly in PCOS patients. \u003cem\u003eSutterella\u003c/em\u003e is significantly related to a number of clinical indicators in PCOS, which might be implicated in the occurrence and progression of PCOS. Based on LC-MS metabonomic methods, we screened 28 differential metabolites in PCOS and analyzed the main metabolic pathways of these metabolites. By analyzing the fMRI results, we found that the FCD index indicated that the activation of the right precuneus in PCOS patients was higher than that in the healthy controls, and the ALFF index indicated that the activation of the left postcentral gyrus in PCOS patients was higher than that in the healthy controls. The CBF of the healthy controls in the left lingual gyrus and the right Parietal_Inf_R were significantly greater than that of the PCOS patients, and there was a significant difference in the Synchronization index.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003ePCOS involves gut microbiota dysbiosis, metabolic disturbances, and altered brain network connectivity, our research offers new insights into its pathogenesis.\u003c/p\u003e","manuscriptTitle":"Intestinal Microbiota, Metabolites and Brain Network Structure Changes in Polycystic Ovary Syndrome patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 09:24:05","doi":"10.21203/rs.3.rs-7003646/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-17T18:26:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-13T11:47:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-08T08:36:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-08T02:22:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277386116294752509329898651268389821546","date":"2025-08-21T14:54:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"83853764508692756625462741362343088502","date":"2025-08-20T04:01:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145069829911499088391309486695047764202","date":"2025-08-20T03:45:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-20T03:20:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-31T10:07:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-21T14:56:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-21T14:51:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"037d46b5-d5ac-4dbf-9327-57e24b93d6cc","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":53519339,"name":"Health sciences/Biomarkers"},{"id":53519340,"name":"Health sciences/Diseases"},{"id":53519341,"name":"Health sciences/Endocrinology"},{"id":53519342,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-03-30T18:38:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-01 09:24:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7003646","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7003646","identity":"rs-7003646","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}