Metabolomic Analysis of Serum in Polycystic Ovary Syndrome Patients with Insulin Resistance

Metabolomic Analysis of Serum in Polycystic Ovary Syndrome Patients with Insulin Resistance | 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 Research Article Metabolomic Analysis of Serum in Polycystic Ovary Syndrome Patients with Insulin Resistance Liu Lixing, Pan Lin, Cai Lixi, Zhang Min, Chen Xianqian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6668120/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 19 You are reading this latest preprint version Abstract Background Polycystic ovary syndrome (PCOS) with insulin resistance is a complex endocrine disorder. Understanding its metabolic profile can contribute to early diagnosis and treatment. However, the underlying metabolic mechanisms remain incompletely understood. Thus, this study aimed to explore the metabolic characteristics of PCOS patients with insulin resistance through serum metabolomic analysis. Methods A total of 12 PCOS patients were recruited and divided into an insulin resistance group (IR group) and a non - insulin resistance group (NIR group). Liquid chromatography - mass spectrometry (LC - MS) was employed to conduct untargeted serum metabolomics analysis. Various statistical methods were utilized for data processing to identify differences in clinical biochemical parameters and metabolites between the two groups. Results Significant differences in clinical biochemical parameters were detected between the IR group and the NIR group. A total of 40 differential metabolites were identified. These metabolites were involved in multiple metabolic pathways related to carbohydrates, lipids, and amino acids. Conclusion The findings of this study provide new insights into the pathogenesis of insulin resistance in PCOS. The identified differential metabolites and metabolic pathways may serve as potential biomarkers and therapeutic targets, which could contribute to the early diagnosis and treatment of PCOS patients with insulin resistance. Clinical trial number Not applicable. Polycystic ovary syndrome Insulin resistance Metabolomics Liquid chromatography-mass spectrometry (LC-MS) Metabolic pathways Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Polycystic ovary syndrome (PCOS) is a common endocrine disorder in gynecology, characterized by ovulatory dysfunction, endocrine disturbances, and polycystic ovarian morphology[ 1 ]. Globally, its prevalence among women of reproductive age is estimated to be 5–10%, accounting for 40–60% of gynecological endocrine disorders and 70% of anovulatory infertility cases, posing a significant threat to female reproductive health and family well-being [ 2 ]. PCOS is not only associated with reproductive pathology but also involves systemic dysfunctions affecting multiple organs[ 3 ]. It has been strongly linked to reproductive, endocrine, psychological, and cardiovascular health complications, and is a recognized risk factor for cardiovascular diseases [ 4 , 5 ]. PCOS patients frequently exhibit abnormalities in glucose and lipid metabolism, with insulin resistance (IR) being particularly prominent[ 6 ]. IR, a key feature of endocrine disturbances in PCOS, is also a core pathological hallmark of the syndrome[ 7 ]. It promotes and exacerbates hyperandrogenemia, increases the risk of long-term endometrial carcinogenesis, impairs ovarian function, and worsens glucose and lipid metabolism disorders, increasing susceptibility to long-term cardiovascular complications [ 8 ]. Metabolomics is a scientific discipline that investigates changes in metabolic networks and endogenous cellular metabolites in response to stress or perturbations, aiming to uncover underlying patterns [ 9 ]. By detecting small molecular compounds generated during metabolism, metabolomics establishes a critical link between external physiological manifestations and internal biochemical changes, thereby facilitating the identification of biomarkers and metabolic pathways relevant to disease diagnosis and classification[ 10 ]. With advantages such as high throughput, ease of operation, and quantitative analytical capabilities, metabolomics systematically elucidates the metabolic characteristics and underlying mechanisms of various diseases. It is widely applied in early disease diagnosis and mechanistic investigations, making it a leading-edge medical technology today [ 11 ]. In recent years, metabolomics-based studies have revealed significant lipid metabolism dysregulation in PCOS patients, particularly disturbances in the glycerophospholipid metabolism pathway, which are closely linked to insulin resistance [ 12 ]. Additionally, exercise intervention studies have demonstrated that regular physical activity can enhance insulin sensitivity in PCOS patients by modulating amino acid metabolism [ 13 ]. This study employs LC-MS for untargeted serum metabolomics analysis, utilizing multivariate statistical methods such as principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) to quantitatively analyze metabolic alterations in PCOS patients. The aim is to identify key differential metabolites associated with insulin resistance, explore potential metabolic pathways in relation to the patients’ physiological and pathological changes, and construct metabolic networks, ultimately providing a scientific foundation for early diagnosis and precision treatment of PCOS. Materials and Methods Study Subjects A total of 50 PCOS patients diagnosed at the Department of Obstetrics and Gynecology, Putian City Hospital, between November 1, 2023, and November 31, 2024, were selected as the case group. The diagnosis was based on the 2003 Rotterdam criteria recommended by the European Society of Human Reproduction and Embryology (ESHRE) and the American Society for Reproductive Medicine (ASRM): oligo-ovulation or anovulation, clinical and/or biochemical signs of hyperandrogenism, and polycystic ovarian morphology. Patients meeting at least two of these criteria, after excluding other causes of hyperandrogenism, were included in the study [ 14 ]. Inclusion criteria: Confirmed diagnosis, full cognitive awareness, ability to cooperate with the investigation, and residency in the province for ≥ 5 years. Exclusion criteria: Use of hormonal medications within the past year, pregnancy history within one month to one year prior, concurrent medical or surgical conditions, other endocrine disorders, congenital abnormalities or organ malformations, and inability to exclude other comorbid conditions. Instruments and Reagents Instruments : Q Exactive™ HF mass spectrometer (Thermo Fisher, Germany), Vanquish UHPLC chromatograph (Thermo Fisher, Germany), Hypesil Gold chromatographic column (Thermo Fisher, USA), D3024R low-temperature centrifuge (Scilogex, USA). Reagents : Methanol (HPLC-grade), formic acid (HPLC-grade), ammonium acetate (analytical grade). Methods Collection of Clinical Indicators and Experimental Grouping General information such as age, sex, body mass index (BMI), blood pressure, disease duration, and medical history was recorded. Laboratory indicators, including fasting blood glucose (FBG), fasting insulin (FINS), lipid profile (triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C)), and sex hormones, were collected. PCOS patients meeting the diagnostic and inclusion/exclusion criteria were evaluated for insulin resistance using the homeostasis model assessment (HOMA) to calculate the insulin resistance index (HOMA-IR). Based on this, subjects were divided into an insulin resistance group (IR group, n = 27) and a non-insulin resistance group (NIR group, n = 23) for further analysis. Serum Sample Collection Venous blood (5 mL) was collected from all subjects in the early morning on days 2–4 of the menstrual cycle (or days 2–4 after progesterone withdrawal bleeding in cases of irregular menstruation) under fasting conditions. Blood samples were placed in anticoagulant-free tubes, allowed to stand at room temperature for 30 minutes, and then centrifuged at 3000 rpm for 15 minutes to obtain serum. The serum was aliquoted into cryotubes and stored at − 80°C until analysis. Metabolomics Analysis Technology LC-MS was used for metabolomics analysis. Six samples were randomly selected from each group for analysis. Following sample thawing, metabolites were extracted using an 80% methanol aqueous solution, followed by vortex mixing, ice bath incubation, centrifugation, and dilution before LC-MS/MS analysis. Statistical Analysis Data Preprocessing Raw LC-MS data were imported into Progenesis QI software for preprocessing, including peak identification, alignment, integration, and normalization. Metabolites were identified by comparing data with the Human Metabolome Database (HMDB) and other public databases. Multivariate Statistical Analysis SIMCA-P software was used for multivariate statistical analysis, including PCA, PLS-DA, and OPLS-DA. PCA was applied for exploratory data analysis, while PLS-DA and OPLS-DA were used to identify differential metabolites. Metabolites with variable importance in projection (VIP) > 1 and P < 0.05 were considered significant. Metabolic Pathway Analysis Differential metabolites were analyzed using MetaboAnalyst software, with pathway enrichment assessed via HMDB and KEGG databases. Pathways with P < 0.05 were selected for further investigation. Results Comparison of Clinical Biochemical and Sex Hormone Indicators Between the Two Groups The data collection and statistical results are shown in Table 1. The results indicate that the IR group had significantly higher levels of FBG, FINS, LH, E 2 , T, TG, TC and LDL-C compared to the NIR group, while HDL-C levels were significantly lower in the IR group (all P 0.05). LC-MS/MS Experimental Results Total Ion Chromatogram (TIC) Inspection As shown in Figure 1, the baseline of the total ion chromatogram (TIC) was stable overall, with no significant drift or noise, indicating that the instrument was in good condition. The main peaks were sharp and symmetrical, demonstrating excellent chromatographic separation and high retention time reproducibility. The main peak appeared at approximately 3 minutes, which is consistent with the elution pattern of polar metabolites (e.g., amino acids, organic acids) in reversed-phase chromatography. The relative total ion intensity was close to 100%, suggesting that sample processing (e.g., protein precipitation, dilution) and instrument parameter settings (e.g., spray voltage, flow rate) were appropriate, confirming the reliability of the experimental data. Correlation Analysis of Differential Metabolites Principal Component Analysis (PCA) (Figure 2) and Partial Least Squares-Discriminant Analysis (PLS-DA) (Figure 3) were used for data analysis. PCA is an unsupervised dimensionality reduction technique that transforms the original variables into a set of mutually orthogonal principal components through linear transformation. In the figure, PC1 accounts for 29.90% of the variance, and PC2 accounts for 13.80%. The combination of PC1 and PC2 visually demonstrates the separation trend between the IR and NIR groups, particularly the significant clustering along the PC1 axis. Although the total variance explained is moderate, the degree of separation between groups along PC1 is already substantial. PLS-DA is a supervised pattern recognition method used to identify intergroup differential variables and perform classification. Component 1 explains 21.3% of the variance. In the figure, the green dots (IR group) and blue triangles (NIR group) show a clear separation trend, indicating that the PLS-DA model can effectively distinguish between the two groups. In summary, both figures reveal a pronounced separation trend between the IR and NIR serum samples, suggesting significant differences in metabolites between the two groups. Screening of Significantly Differential Metabolites Metabolomics data is characterized by its high dimensionality and large volume, requiring the integration of univariate and multivariate statistical analysis methods to identify differential metabolites between biological groups. Univariate statistical analysis includes parametric and non-parametric tests, while multivariate statistical analysis includes PCA and PLS-DA, among others. In this study, PCA and PLS-DA were first used to analyze overall differences between the two groups. Subsequently, metabolites were screened based on the Variable Importance in Projection (VIP) values from Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) and the Fold Change (FC) and p-values from univariate analysis. A volcano plot (Figure 4) was generated to visualize the results. By querying the Human Metabolome Database (HMDB) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, differential metabolites were identified. The volcano plot revealed that, compared to the NIR group, 63 significantly differential metabolites were detected in the serum of the IR group under both positive and negative ion modes, with 40 upregulated and 23 downregulated. These variables were highly statistically significant ( P ≤ 0.01) and exhibited high VIP values in the PLS-DA model (Figure 5). Metabolites with similar expression patterns often share functional relevance. To further explore this, the differentially expressed metabolites were clustered, and a clustered circular heatmap (Figure 6) was generated to visually illustrate the trends of differential metabolite changes (significant upregulation or downregulation) across different groups. The results demonstrated significant differences in metabolite expression patterns between the IR and NIR groups, primarily characterized by the separation of phospholipid metabolites (upregulated in the NIR group) and estrogen/vitamin D-related metabolites (upregulated in the IR group). These findings align with prior analyses, suggesting systemic differences between the two groups in pathways related to lipid metabolism and hormonal regulation. Metabolic Pathway Enrichment Analysis Using KEGG Pathway as the unit, pathway enrichment analysis was conducted to identify the primary biochemical metabolic pathways and signal transduction pathways associated with the screened differential metabolites. The specific enriched pathways are illustrated in Figure 7. The P -values for pathway enrichment were obtained, with a threshold of P ≤ 0.05. KEGG pathways meeting this criterion were defined as significantly enriched pathways among the differential metabolites. Based on the enrichment results, the disease-related pathways identified include: Lipid metabolism pathways: Glycerophospholipid metabolism, Arachidonic acid metabolism, Linoleic/alpha-Linolenic acid metabolism, Ether lipid metabolism. Amino acid and sulfur metabolism pathways: Cysteine and methionine metabolism, Sulfur metabolism. Energy and hormone metabolism pathways: Arginine and proline metabolism, Steroid hormone biosynthesis. Disease-related pathways: Choline metabolism in cancer, Porphyrin and chlorophyll metabolism. System signaling pathways: Retrograde endocannabinoid signaling. These findings highlight the key metabolic and signaling pathways associated with the identified differential metabolites, providing insights into their potential roles in disease mechanisms. Correlation Analysis Between KEGG Enriched Metabolic Pathways and Differential Metabolite Classification In the LC-MS experiment, we further conducted quantitative analysis on the key metabolites within these significantly enriched KEGG pathways, as illustrated in Figure 8. The figure presents two types of KEGG-related information: Left panel- KEGG Pathway: Displays the number of compounds in different metabolic pathways. Red bars represent pathways associated with diseases (e.g., cancer, infectious diseases), which contain a higher number of compounds. Orange bars indicate pathways related to lipid metabolism, while blue bars cover pathways involved in environmental information processing and cellular processes, reflecting the differences in the number of compounds associated with various biological processes and disease-related pathways. Right panel- KEGG Compound Classification: Provides a statistical classification of compounds. For instance, phospholipids are represented by a prominent blue bar, while other categories include amino acids, amines, carboxylic acids, and more. Different colors correspond to different compound classes, offering a visual representation of the quantity of each compound type. In summary, the two charts collectively reveal that the differential metabolites are primarily involved in lipid metabolism (particularly phospholipids) and are integrated with amino acid and carboxylic acid metabolism. These metabolites not only participate in energy and material metabolism but also play a crucial role in disease development and biological system functions. This analysis provides valuable direction for subsequent mechanistic studies. Discussion This study employed metabolomics technology to compare the serum metabolite profiles of polycystic ovary syndrome (PCOS) patients with insulin resistance (IR) and those without insulin resistance (NIR). The results revealed significant differences between the two groups, with 40 metabolites significantly upregulated and 23 significantly downregulated, involving multiple metabolic pathways related to lipids, amino acids, and hormones. Lipid Metabolism In lipid metabolism, the glycerophospholipid pathway showed the highest enrichment. Among the differential metabolites, phosphatidylcholine (PC) species (e.g., PC (16:0/18:2), PC (18:1/18:3)) were significantly elevated in the NIR group. This finding is consistent with previous research: Li et al. (2019) identified phospholipid metabolism disorders in PCOS patients through LC-MS metabolomics analysis [ 15 ]. Their study demonstrated that PC (16:0/18:2) levels were positively correlated with HOMA-IR, suggesting that phospholipid metabolism abnormalities may be a key driver of insulin resistance. Furthermore, a 2023 lipidomics study found that abnormal glycerophospholipid metabolite distribution in the follicular fluid of PCOS patients is closely linked to granulosa cell insulin resistance [ 16 ]. As major components of cell membranes, increased levels of PC species may affect insulin sensitivity through multiple mechanisms. For example, elevated saturated phospholipids can reduce membrane fluidity, impairing insulin receptor (INSR) dimerization and phosphorylation, which weakens insulin signaling efficiency [ 17 ]. Additionally, the increase in lysophosphatidylcholine (LysoPC) metabolites (e.g., LysoPC (18:0/0:0)) in the NIR group suggests accelerated phospholipid breakdown, potentially exacerbating endoplasmic reticulum stress through the release of free fatty acids, forming a vicious cycle of “lipid overload-inflammation-insulin resistance” [ 18 ]. Notably, HDL-C levels in the NIR group were significantly lower than those in the IR group (see Table 1 ), which may impair reverse cholesterol transport, resulting in phospholipid accumulation in the serum and further disrupting normal insulin signaling pathways. Further analysis revealed that the elevation of PC species in the NIR group is strongly associated with hyperandrogenemia. Androgens have been shown to upregulate the expression of sterol regulatory element-binding proteins (SREBPs), enhancing the transcription of genes involved in fatty acid and phospholipid synthesis, thereby promoting lipid accumulation. Additionally, pro-inflammatory factors (e.g., IL-6, TNF-α) may activate phospholipase A2 (PLA2), accelerating phospholipid breakdown and generating pro-inflammatory LysoPC, further worsening metabolic disorders. This dysregulated balance in lipid metabolism (enhanced synthesis and accelerated breakdown) may be a distinct metabolic signature of PCOS, contributing to both insulin resistance and creating a metabolic environment that favors tumor development [ 19 ].It is crucial to emphasize that the metabolic profile of the NIR group in this study may represent an early stage of PCOS progression. At this stage, the body compensatorily increases phospholipid synthesis to maintain insulin receptor function. However, this compensatory mechanism may gradually fail as the disease advances, ultimately leading to the onset of insulin resistance. Therefore, abnormalities in phospholipid metabolism in the NIR group not only serve as a biomarker of disease status but also represent a potential therapeutic target. Future studies should further explore the therapeutic potential of phospholipid synthesis It is crucial to emphasize that the metabolic profile of the NIR group in this study may represent an early stage of PCOS progression. At this stage, the body compensatorily increases phospholipid synthesis to maintain insulin receptor function. However, this compensatory mechanism may gradually fail as the disease advances, ultimately leading to the onset of insulin resistance. Therefore, abnormalities in phospholipid metabolism in the NIR group not only serve as a biomarker of disease status but also represent a potential therapeutic target. Future studies should further explore the therapeutic potential of phospholipid synthesis. Amino Acid Metabolism In amino acid metabolism, the notable upregulation of S-sulfoglutathione in the cysteine and methionine pathways in the IR group suggests an increased antioxidant response. Glutathione, a critical antioxidant, may be elevated as a compensatory mechanism against oxidative stress. However, the high expression of methionine in the NIR group suggests a role in promoting DNA methylation, which may influence gene expression and contribute to insulin resistance [ 20 ]. Additionally, differential metabolites in the arginine and proline pathways may impact vascular function by modulating nitric oxide (NO) synthesis, which is linked to the heightened risk of cardiovascular complications in PCOS patients [ 21 , 22 ]. Hormone Metabolism In hormone metabolism, significantly increased levels of estrogen quinones (e.g., Estrone-3,4-quinone) and vitamin D3 sulfate in the steroid hormone biosynthesis pathway were observed in the IR group. Estrogen quinones, oxidative metabolites of estrogens, may disrupt insulin signaling by activating nuclear receptors (e.g., ERRα) [ 23 ]. The increase in vitamin D3 sulfate may seem paradoxical given the widespread vitamin D deficiency in PCOS patients. However, this elevation may reflect a compensatory mechanism that regulates immune function. Moreover, the enrichment of the retrograde endocannabinoid signaling pathway suggests that dysregulation of the endocannabinoid system could influence insulin sensitivity by modulating adipocyte differentiation and inflammatory responses. Metabolic Pathway Enrichment and Cancer Risk Of particular importance, KEGG pathway analysis identified a significant enrichment of the “choline metabolism in cancer” pathway in the NIR group. Differential metabolites such as PC species and glycerylphosphorylcholine (GPC) are known to support tumor cell proliferation by facilitating cell membrane synthesis. This finding corroborates the increased risk of endometrial cancer in PCOS patients, suggesting that metabolic reprogramming may serve as a mechanistic link between PCOS and cancer [ 24 ]. The aberrant elevation of PC species could promote tumorigenesis through several mechanisms: First, as essential membrane components, increased phospholipid synthesis supplies the necessary structural material for rapid tumor cell proliferation. Second, GPC accumulation may activate the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt) pathway, enhancing tumor cell survival and invasion. Additionally, the increased methionine levels in the NIR group may contribute to tumor progression via S-adenosylmethionine (SAM)-mediated DNA methylation, which can regulate oncogenes and tumor suppressor genes (e.g., PTEN). Study Innovations and Limitations Comparison with Previous Studies,this study not only validates prior findings but also introduces methodological and mechanistic innovations. By stratifying PCOS patients into non-insulin resistance (NIR) and insulin resistance (IR) groups, we observed that phosphatidylcholine (PC) species were significantly elevated in the NIR group, suggesting lipid metabolism abnormalities may precede clinical insulin resistance-a novel insight compared to previous studies focused solely on IR patients[ 25 ]. Furthermore, the paradoxical elevation of vitamin D3 sulfate in the IR group, despite widespread reports of vitamin D deficiency in PCOS by Chen et al.(2021) [ 26 ], highlights the complexity of vitamin D metabolism. Emerging evidence posits that vitamin D3 sulfate may act as a storage form of active metabolites, modulating insulin sensitivity via PPARγ pathways (2023) [ 27 ], redefining its role in metabolic regulation. Additionally, elevated estrogen quinones (e.g., Estrone-3,4-quinone) in the IR group align with the oxidative stress hypothesis by Ziv-Gal et al.( 2018 )[ 28 ], with recent metabolomics data linking oxidative markers (e.g., 8-OHdG) to estrogen quinone levels (2022) [ 29 ], suggesting oxidative stress exacerbates insulin resistance through estrogen metabolic disruption. Notably, the enrichment of the “choline metabolism in cancer” pathway in the NIR group implicates glycerylphosphorylcholine (GPC) accumulation in endometrial cancer risk via PI3K/Akt activation, corroborating 2023 multi-omics findings [ 30 ]. Moreover, dysregulated methionine metabolism may promote tumorigenesis through DNA methylation-mediated oncogene regulation (e.g., PTEN) [ 31 ], expanding the mechanistic understanding of PCOS-related cancer susceptibility. Collectively, these advances bridge metabolic dysregulation to clinical complications, offering new therapeutic targets and refining the pathophysiological framework of PCOS. Conclusion This metabolomics study identified significant differences in serum metabolite profiles between PCOS patients with and without insulin resistance (IR), involving lipid, amino acid, and hormone metabolism pathways. Key findings include elevated phosphatidylcholine (PC) species in the non-IR group, suggesting phospholipid metabolism abnormalities as an early biomarker and potential therapeutic target. Dysregulated lipid metabolism may contribute to insulin resistance and cancer risk, supported by the enrichment of the "choline metabolism in cancer" pathway. Amino acid and hormone metabolism alterations further highlight oxidative stress and vascular dysfunction in PCOS progression. Despite limitations such as sample size and cross-sectional design, these findings provide novel insights into metabolic mechanisms and potential precision medicine approaches for PCOS treatment. Declarations Ethics approval and consent to participate This study conformed to the guidelines of the Declaration of Helsinki, and the study procedures were reviewed and approved by the Putian University Ethics Committee (Ethics Review (2025) No. 064). Each patient agreed to participate and signed the informed consent form. Consent for publication Not applicable. Availability of data and materials No datasets were generated or analysed during the current study. Competing interests The authors declare no competing interests. Funding This study was supported by grants of the the Natural Science Foundation of Fujian Province (2023J011008) and Putian Science and Technology Bureau (2021S3001-4). Author contributions LXL and XQC designed this study. LXL,LP and MZ performed the experiments.LXL and LXC analyzed the data. LXL wrote and revised the manuscript. All authors read and approved the final version of this manuscript. Acknowledgements We thank MD Xianhui Min for helping with sample collection in this study. References Bingqing, Ran,Cai, Liu,Yajun, He et al. Bibliometric analysis of the research on anti-Müllerian hormone and polycystic ovary syndrome: current status, hotspots, and trends.[J] .Front Reprod Health, 2025;7(0):1519249. Wafa Mansor, Merza,Abeer Khalid, Yaseen,Maha Adel, Mahmood,FSH, LH, Lipid and Adipokines in Polycystic Ovary Syndrome: Clinical Biochemistry Insights for Diagnosis and Management.[J] .J Steroid Biochem Mol Biol,2025;0(0):106773. Haoyu, Peng,Junyi, Ren,Yang, Zhao et al. Unraveling the Connection between PCOS and renal Complications: Current insights and Future Directions.[J] .Diabetes Res Clin Pract,2025;224(0):112235 Diamanti - Kandarakis E, et al. Metabolic aspects of polycystic ovary syndrome[J]. 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Cancer Metab, 2024, 12(1):1 - 15. Table Table 1. Statistical Analysis of Clinical Biochemical Data Item IR Group (n=27) NIR Group (n=23) P -value FBG（mmol/L） 5.81±1.09 5.06±0.67 0.000 FINS（mU/L） 20.96±3.05 7.42±1.78 0.000 LH(IU/L） 13.15±3.69 9.05±2.91 0.031 FSH(IU/L） 4.52±1.16 4.37±1.13 0.845 E2(pg/mL） 164.32±32.17 128.74±28.72 0.001 P(ng/mL) 1.36±0.45 1.19±0.28 0.152 T(ng/mL) 3.07±0.91 2.58±0.79 0,007 LDL-C（mmol/L） 3.24±0.83 2.81±0.73 0.003 HDL-C（mmol/L） 1.12±0.22 1.45±0.36 0.000 TG（mmol/L） 2.08±0.83 1.18±0.61 0.000 TC（mmol/L） 5.17±1.14 4.56±0.86 0.005 Additional Declarations No competing interests reported. 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Lixing","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYBACPnYG9g8fftjIsbG3HyBOCxszAxvjzJ40Yz6eMwnEa2HmYTuUOE/CwYBYLTxmD2fwHEhvk2BIYPhRsY0YLWzpBh8s7uS2STceYOw5c5sYLcwHJGfwPMttkzmQwMzYRpQWxgZpHrbD6WwSCQbEamE+BtKSQIoWtmRDYCAbtgED+SBRfuFn7zF8AIxKefn29oMPflQQoQUFHCBR/SgYBaNgFIwCXAAAoTY2eM/CEHsAAAAASUVORK5CYII=","orcid":"","institution":"Putian University","correspondingAuthor":true,"prefix":"","firstName":"Liu","middleName":"","lastName":"Lixing","suffix":""},{"id":470786358,"identity":"983de9b5-f013-48f8-b9ae-7d52eda2bafc","order_by":1,"name":"Pan Lin","email":"","orcid":"","institution":"Putian City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Pan","middleName":"","lastName":"Lin","suffix":""},{"id":470786359,"identity":"c5a5e359-06d2-4d84-88f9-2a1d31c82c17","order_by":2,"name":"Cai Lixi","email":"","orcid":"","institution":"Putian University","correspondingAuthor":false,"prefix":"","firstName":"Cai","middleName":"","lastName":"Lixi","suffix":""},{"id":470786360,"identity":"385c27a0-e1a2-40fd-b33d-adf77ea3e052","order_by":3,"name":"Zhang Min","email":"","orcid":"","institution":"Putian University","correspondingAuthor":false,"prefix":"","firstName":"Zhang","middleName":"","lastName":"Min","suffix":""},{"id":470786361,"identity":"c883955f-a1d5-4453-9d1e-ae2389099e7b","order_by":4,"name":"Chen Xianqian","email":"","orcid":"","institution":"Putian City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Xianqian","suffix":""}],"badges":[],"createdAt":"2025-05-15 02:38:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6668120/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6668120/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84856794,"identity":"0944daf2-ed35-4fcc-a2f5-4931b432238c","added_by":"auto","created_at":"2025-06-18 06:20:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":79096,"visible":true,"origin":"","legend":"\u003cp\u003eTotal Ion Chromatogram (TIC)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6668120/v1/ea93157db095a4fb1005eaaf.png"},{"id":84857907,"identity":"eabcea45-cd54-4670-b38d-31a14399dc57","added_by":"auto","created_at":"2025-06-18 06:28:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":91653,"visible":true,"origin":"","legend":"\u003cp\u003ePCA Analysis Plot\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6668120/v1/3694d70540732b7f00bda4c8.png"},{"id":84856798,"identity":"3a138023-1448-4428-97fe-dfbc7138ebd3","added_by":"auto","created_at":"2025-06-18 06:20:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":42918,"visible":true,"origin":"","legend":"\u003cp\u003ePLS-DA Analysis Plot\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6668120/v1/310c3daab2022a5e4be6100a.png"},{"id":84856796,"identity":"43a29b8e-064b-4e9c-87da-6796ec370000","added_by":"auto","created_at":"2025-06-18 06:20:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":115586,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential Volcano Plot\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6668120/v1/2ee9a8bdef157b5eeaa83306.png"},{"id":84857908,"identity":"e3e75c30-e96c-40b2-a348-364f1e6d8ffe","added_by":"auto","created_at":"2025-06-18 06:28:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":95321,"visible":true,"origin":"","legend":"\u003cp\u003eVIP Value Analysis of Differential Metabolites\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6668120/v1/61cac3c266cff04230e52dec.png"},{"id":84857910,"identity":"3568941a-fb2c-4667-ba03-fdeaef3a5bd4","added_by":"auto","created_at":"2025-06-18 06:28:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":100989,"visible":true,"origin":"","legend":"\u003cp\u003eClustered Circular Heatmap of Differential Metabolites\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6668120/v1/59442ca5dca5e4eb06e7a4e9.png"},{"id":84858663,"identity":"e2028a08-3078-48d2-8943-bcdd32103be2","added_by":"auto","created_at":"2025-06-18 06:36:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":76417,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG Enrichment Analysis Bubble Plot\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6668120/v1/4f8fd8bfb805153a0786fcd4.png"},{"id":84856805,"identity":"9be1ece6-1e4e-444d-844c-d87f45ce009a","added_by":"auto","created_at":"2025-06-18 06:20:01","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":122019,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG Metabolic Pathway Enrichment and Compound Classification Statistical Analysis Chart\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6668120/v1/40790b2d4525b98ac0adee7b.png"},{"id":84859904,"identity":"e94a61cc-1fa5-4a05-be50-c07498d3f342","added_by":"auto","created_at":"2025-06-18 06:44:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1565626,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6668120/v1/04ccc1d2-a39e-4f96-864a-ed52c7d7890b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metabolomic Analysis of Serum in Polycystic Ovary Syndrome Patients with Insulin Resistance","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePolycystic ovary syndrome (PCOS) is a common endocrine disorder in gynecology, characterized by ovulatory dysfunction, endocrine disturbances, and polycystic ovarian morphology[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Globally, its prevalence among women of reproductive age is estimated to be 5\u0026ndash;10%, accounting for 40\u0026ndash;60% of gynecological endocrine disorders and 70% of anovulatory infertility cases, posing a significant threat to female reproductive health and family well-being [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. PCOS is not only associated with reproductive pathology but also involves systemic dysfunctions affecting multiple organs[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It has been strongly linked to reproductive, endocrine, psychological, and cardiovascular health complications, and is a recognized risk factor for cardiovascular diseases [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePCOS patients frequently exhibit abnormalities in glucose and lipid metabolism, with insulin resistance (IR) being particularly prominent[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. IR, a key feature of endocrine disturbances in PCOS, is also a core pathological hallmark of the syndrome[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. It promotes and exacerbates hyperandrogenemia, increases the risk of long-term endometrial carcinogenesis, impairs ovarian function, and worsens glucose and lipid metabolism disorders, increasing susceptibility to long-term cardiovascular complications [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMetabolomics is a scientific discipline that investigates changes in metabolic networks and endogenous cellular metabolites in response to stress or perturbations, aiming to uncover underlying patterns [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. By detecting small molecular compounds generated during metabolism, metabolomics establishes a critical link between external physiological manifestations and internal biochemical changes, thereby facilitating the identification of biomarkers and metabolic pathways relevant to disease diagnosis and classification[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. With advantages such as high throughput, ease of operation, and quantitative analytical capabilities, metabolomics systematically elucidates the metabolic characteristics and underlying mechanisms of various diseases. It is widely applied in early disease diagnosis and mechanistic investigations, making it a leading-edge medical technology today [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, metabolomics-based studies have revealed significant lipid metabolism dysregulation in PCOS patients, particularly disturbances in the glycerophospholipid metabolism pathway, which are closely linked to insulin resistance [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, exercise intervention studies have demonstrated that regular physical activity can enhance insulin sensitivity in PCOS patients by modulating amino acid metabolism [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This study employs LC-MS for untargeted serum metabolomics analysis, utilizing multivariate statistical methods such as principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) to quantitatively analyze metabolic alterations in PCOS patients. The aim is to identify key differential metabolites associated with insulin resistance, explore potential metabolic pathways in relation to the patients\u0026rsquo; physiological and pathological changes, and construct metabolic networks, ultimately providing a scientific foundation for early diagnosis and precision treatment of PCOS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Subjects\u003c/h2\u003e \u003cp\u003eA total of 50 PCOS patients diagnosed at the Department of Obstetrics and Gynecology, Putian City Hospital, between November 1, 2023, and November 31, 2024, were selected as the case group. The diagnosis was based on the 2003 Rotterdam criteria recommended by the European Society of Human Reproduction and Embryology (ESHRE) and the American Society for Reproductive Medicine (ASRM): oligo-ovulation or anovulation, clinical and/or biochemical signs of hyperandrogenism, and polycystic ovarian morphology. Patients meeting at least two of these criteria, after excluding other causes of hyperandrogenism, were included in the study [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInclusion criteria: Confirmed diagnosis, full cognitive awareness, ability to cooperate with the investigation, and residency in the province for \u0026ge;\u0026thinsp;5 years.\u003c/p\u003e \u003cp\u003eExclusion criteria: Use of hormonal medications within the past year, pregnancy history within one month to one year prior, concurrent medical or surgical conditions, other endocrine disorders, congenital abnormalities or organ malformations, and inability to exclude other comorbid conditions.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInstruments and Reagents\u003c/h3\u003e\n\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eInstruments\u003c/b\u003e: Q Exactive\u0026trade; HF mass spectrometer (Thermo Fisher, Germany), Vanquish UHPLC chromatograph (Thermo Fisher, Germany), Hypesil Gold chromatographic column (Thermo Fisher, USA), D3024R low-temperature centrifuge (Scilogex, USA).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eReagents\u003c/b\u003e: Methanol (HPLC-grade), formic acid (HPLC-grade), ammonium acetate (analytical grade).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eMethods\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCollection of Clinical Indicators and Experimental Grouping\u003c/h2\u003e \u003cp\u003eGeneral information such as age, sex, body mass index (BMI), blood pressure, disease duration, and medical history was recorded. Laboratory indicators, including fasting blood glucose (FBG), fasting insulin (FINS), lipid profile (triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C)), and sex hormones, were collected.\u003c/p\u003e \u003cp\u003ePCOS patients meeting the diagnostic and inclusion/exclusion criteria were evaluated for insulin resistance using the homeostasis model assessment (HOMA) to calculate the insulin resistance index (HOMA-IR). Based on this, subjects were divided into an insulin resistance group (IR group, n\u0026thinsp;=\u0026thinsp;27) and a non-insulin resistance group (NIR group, n\u0026thinsp;=\u0026thinsp;23) for further analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSerum Sample Collection\u003c/h3\u003e\n\u003cp\u003eVenous blood (5 mL) was collected from all subjects in the early morning on days 2\u0026ndash;4 of the menstrual cycle (or days 2\u0026ndash;4 after progesterone withdrawal bleeding in cases of irregular menstruation) under fasting conditions. Blood samples were placed in anticoagulant-free tubes, allowed to stand at room temperature for 30 minutes, and then centrifuged at 3000 rpm for 15 minutes to obtain serum. The serum was aliquoted into cryotubes and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until analysis.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMetabolomics Analysis Technology\u003c/h2\u003e \u003cp\u003eLC-MS was used for metabolomics analysis. Six samples were randomly selected from each group for analysis. Following sample thawing, metabolites were extracted using an 80% methanol aqueous solution, followed by vortex mixing, ice bath incubation, centrifugation, and dilution before LC-MS/MS analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eData Preprocessing\u003c/h2\u003e \u003cp\u003eRaw LC-MS data were imported into Progenesis QI software for preprocessing, including peak identification, alignment, integration, and normalization. Metabolites were identified by comparing data with the Human Metabolome Database (HMDB) and other public databases.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate Statistical Analysis\u003c/h2\u003e \u003cp\u003eSIMCA-P software was used for multivariate statistical analysis, including PCA, PLS-DA, and OPLS-DA. PCA was applied for exploratory data analysis, while PLS-DA and OPLS-DA were used to identify differential metabolites. Metabolites with variable importance in projection (VIP)\u0026thinsp;\u0026gt;\u0026thinsp;1 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMetabolic Pathway Analysis\u003c/h2\u003e \u003cp\u003eDifferential metabolites were analyzed using MetaboAnalyst software, with pathway enrichment assessed via HMDB and KEGG databases. Pathways with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were selected for further investigation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eComparison of Clinical Biochemical and Sex Hormone Indicators Between the Two Groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data collection and statistical results are shown in Table 1. The results indicate that the IR group had significantly higher levels of FBG, FINS, LH, E\u003csub\u003e2\u003c/sub\u003e, T, TG, TC and LDL-C compared to the NIR group, while HDL-C levels were significantly lower in the IR group (all \u003cem\u003eP\u003c/em\u003e \u0026lt;0.05). No significant differences were observed in FSH and P levels (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLC-MS/MS Experimental Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTotal Ion Chromatogram (TIC) Inspection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 1, the baseline of the total ion chromatogram (TIC) was stable overall, with no significant drift or noise, indicating that the instrument was in good condition. The main peaks were sharp and symmetrical, demonstrating excellent chromatographic separation and high retention time reproducibility. The main peak appeared at approximately 3 minutes, which is consistent with the elution pattern of polar metabolites (e.g., amino acids, organic acids) in reversed-phase chromatography. The relative total ion intensity was close to 100%, suggesting that sample processing (e.g., protein precipitation, dilution) and instrument parameter settings (e.g., spray voltage, flow rate) were appropriate, confirming the reliability of the experimental data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation Analysis of Differential Metabolites\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrincipal Component Analysis (PCA) (Figure 2) and Partial Least Squares-Discriminant Analysis (PLS-DA) (Figure 3) were used for data analysis. PCA is an unsupervised dimensionality reduction technique that transforms the original variables into a set of mutually orthogonal principal components through linear transformation. In the figure, PC1 accounts for 29.90% of the variance, and PC2 accounts for 13.80%. The combination of PC1 and PC2 visually demonstrates the separation trend between the IR and NIR groups, particularly the significant clustering along the PC1 axis. Although the total variance explained is moderate, the degree of separation between groups along PC1 is already substantial.\u003c/p\u003e\n\u003cp\u003ePLS-DA is a supervised pattern recognition method used to identify intergroup differential variables and perform classification. Component 1 explains 21.3% of the variance. In the figure, the green dots (IR group) and blue triangles (NIR group) show a clear separation trend, indicating that the PLS-DA model can effectively distinguish between the two groups.\u003c/p\u003e\n\u003cp\u003eIn summary, both figures reveal a pronounced separation trend between the IR and NIR serum samples, suggesting significant differences in metabolites between the two groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScreening of Significantly Differential Metabolites\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMetabolomics data is characterized by its high dimensionality and large volume, requiring the integration of univariate and multivariate statistical analysis methods to identify differential metabolites between biological groups. Univariate statistical analysis includes parametric and non-parametric tests, while multivariate statistical analysis includes PCA and PLS-DA, among others.\u003c/p\u003e\n\u003cp\u003eIn this study, PCA and PLS-DA were first used to analyze overall differences between the two groups. Subsequently, metabolites were screened based on the Variable Importance in Projection (VIP) values from Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) and the Fold Change (FC) and p-values from univariate analysis. A volcano plot (Figure 4) was generated to visualize the results. By querying the Human Metabolome Database (HMDB) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, differential metabolites were identified.\u003c/p\u003e\n\u003cp\u003eThe volcano plot revealed that, compared to the NIR group, 63 significantly differential metabolites were detected in the serum of the IR group under both positive and negative ion modes, with 40 upregulated and 23 downregulated. These variables were highly statistically significant (\u003cem\u003eP\u003c/em\u003e ≤ 0.01) and exhibited high VIP values in the PLS-DA model (Figure 5).\u003c/p\u003e\n\u003cp\u003eMetabolites with similar expression patterns often share functional relevance. To further explore this, the differentially expressed metabolites were clustered, and a clustered circular heatmap (Figure 6) was generated to visually illustrate the trends of differential metabolite changes (significant upregulation or downregulation) across different groups. The results demonstrated significant differences in metabolite expression patterns between the IR and NIR groups, primarily characterized by the separation of phospholipid metabolites (upregulated in the NIR group) and estrogen/vitamin D-related metabolites (upregulated in the IR group).\u003c/p\u003e\n\u003cp\u003eThese findings align with prior analyses, suggesting systemic differences between the two groups in pathways related to lipid metabolism and hormonal regulation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolic Pathway Enrichment Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing KEGG Pathway as the unit, pathway enrichment analysis was conducted to identify the primary biochemical metabolic pathways and signal transduction pathways associated with the screened differential metabolites. The specific enriched pathways are illustrated in Figure 7. The \u003cem\u003eP\u003c/em\u003e -values for pathway enrichment were obtained, with a threshold of \u003cem\u003eP\u003c/em\u003e ≤ 0.05. KEGG pathways meeting this criterion were defined as significantly enriched pathways among the differential metabolites.\u003c/p\u003e\n\u003cp\u003eBased on the enrichment results, the disease-related pathways identified include:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLipid metabolism pathways:\u0026nbsp;\u003c/strong\u003eGlycerophospholipid metabolism, Arachidonic acid metabolism, Linoleic/alpha-Linolenic acid metabolism, Ether lipid metabolism.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAmino acid and sulfur metabolism pathways:\u0026nbsp;\u003c/strong\u003eCysteine and methionine metabolism, Sulfur metabolism.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnergy and hormone metabolism pathways:\u0026nbsp;\u003c/strong\u003eArginine and proline metabolism, Steroid hormone biosynthesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisease-related pathways:\u0026nbsp;\u003c/strong\u003eCholine metabolism in cancer, Porphyrin and chlorophyll metabolism.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSystem signaling pathways:\u0026nbsp;\u003c/strong\u003eRetrograde endocannabinoid signaling.\u003c/p\u003e\n\u003cp\u003eThese findings highlight the key metabolic and signaling pathways associated with the identified differential metabolites, providing insights into their potential roles in disease mechanisms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation Analysis Between KEGG Enriched Metabolic Pathways and Differential Metabolite Classification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the LC-MS experiment, we further conducted quantitative analysis on the key metabolites within these significantly enriched KEGG pathways, as illustrated in Figure 8. The figure presents two types of KEGG-related information:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLeft panel- KEGG Pathway:\u003c/strong\u003e Displays the number of compounds in different metabolic pathways. Red bars represent pathways associated with diseases (e.g., cancer, infectious diseases), which contain a higher number of compounds. Orange bars indicate pathways related to lipid metabolism, while blue bars cover pathways involved in environmental information processing and cellular processes, reflecting the differences in the number of compounds associated with various biological processes and disease-related pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRight panel- KEGG Compound Classification:\u0026nbsp;\u003c/strong\u003eProvides a statistical classification of compounds. For instance, phospholipids are represented by a prominent blue bar, while other categories include amino acids, amines, carboxylic acids, and more. Different colors correspond to different compound classes, offering a visual representation of the quantity of each compound type.\u003c/p\u003e\n\u003cp\u003eIn summary, the two charts collectively reveal that the differential metabolites are primarily involved in lipid metabolism (particularly phospholipids) and are integrated with amino acid and carboxylic acid metabolism. These metabolites not only participate in energy and material metabolism but also play a crucial role in disease development and biological system functions. This analysis provides valuable direction for subsequent mechanistic studies.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study employed metabolomics technology to compare the serum metabolite profiles of polycystic ovary syndrome (PCOS) patients with insulin resistance (IR) and those without insulin resistance (NIR). The results revealed significant differences between the two groups, with 40 metabolites significantly upregulated and 23 significantly downregulated, involving multiple metabolic pathways related to lipids, amino acids, and hormones.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eLipid Metabolism\u003c/h2\u003e \u003cp\u003eIn lipid metabolism, the glycerophospholipid pathway showed the highest enrichment. Among the differential metabolites, phosphatidylcholine (PC) species (e.g., PC (16:0/18:2), PC (18:1/18:3)) were significantly elevated in the NIR group. This finding is consistent with previous research: Li et al. (2019) identified phospholipid metabolism disorders in PCOS patients through LC-MS metabolomics analysis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Their study demonstrated that PC (16:0/18:2) levels were positively correlated with HOMA-IR, suggesting that phospholipid metabolism abnormalities may be a key driver of insulin resistance. Furthermore, a 2023 lipidomics study found that abnormal glycerophospholipid metabolite distribution in the follicular fluid of PCOS patients is closely linked to granulosa cell insulin resistance [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs major components of cell membranes, increased levels of PC species may affect insulin sensitivity through multiple mechanisms. For example, elevated saturated phospholipids can reduce membrane fluidity, impairing insulin receptor (INSR) dimerization and phosphorylation, which weakens insulin signaling efficiency [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Additionally, the increase in lysophosphatidylcholine (LysoPC) metabolites (e.g., LysoPC (18:0/0:0)) in the NIR group suggests accelerated phospholipid breakdown, potentially exacerbating endoplasmic reticulum stress through the release of free fatty acids, forming a vicious cycle of \u0026ldquo;lipid overload-inflammation-insulin resistance\u0026rdquo; [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Notably, HDL-C levels in the NIR group were significantly lower than those in the IR group (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which may impair reverse cholesterol transport, resulting in phospholipid accumulation in the serum and further disrupting normal insulin signaling pathways.\u003c/p\u003e \u003cp\u003eFurther analysis revealed that the elevation of PC species in the NIR group is strongly associated with hyperandrogenemia. Androgens have been shown to upregulate the expression of sterol regulatory element-binding proteins (SREBPs), enhancing the transcription of genes involved in fatty acid and phospholipid synthesis, thereby promoting lipid accumulation. Additionally, pro-inflammatory factors (e.g., IL-6, TNF-α) may activate phospholipase A2 (PLA2), accelerating phospholipid breakdown and generating pro-inflammatory LysoPC, further worsening metabolic disorders. This dysregulated balance in lipid metabolism (enhanced synthesis and accelerated breakdown) may be a distinct metabolic signature of PCOS, contributing to both insulin resistance and creating a metabolic environment that favors tumor development [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].It is crucial to emphasize that the metabolic profile of the NIR group in this study may represent an early stage of PCOS progression. At this stage, the body compensatorily increases phospholipid synthesis to maintain insulin receptor function. However, this compensatory mechanism may gradually fail as the disease advances, ultimately leading to the onset of insulin resistance. Therefore, abnormalities in phospholipid metabolism in the NIR group not only serve as a biomarker of disease status but also represent a potential therapeutic target. Future studies should further explore the therapeutic potential of phospholipid synthesis It is crucial to emphasize that the metabolic profile of the NIR group in this study may represent an early stage of PCOS progression. At this stage, the body compensatorily increases phospholipid synthesis to maintain insulin receptor function. However, this compensatory mechanism may gradually fail as the disease advances, ultimately leading to the onset of insulin resistance. Therefore, abnormalities in phospholipid metabolism in the NIR group not only serve as a biomarker of disease status but also represent a potential therapeutic target. Future studies should further explore the therapeutic potential of phospholipid synthesis.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eAmino Acid Metabolism\u003c/h2\u003e \u003cp\u003eIn amino acid metabolism, the notable upregulation of S-sulfoglutathione in the cysteine and methionine pathways in the IR group suggests an increased antioxidant response. Glutathione, a critical antioxidant, may be elevated as a compensatory mechanism against oxidative stress. However, the high expression of methionine in the NIR group suggests a role in promoting DNA methylation, which may influence gene expression and contribute to insulin resistance [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Additionally, differential metabolites in the arginine and proline pathways may impact vascular function by modulating nitric oxide (NO) synthesis, which is linked to the heightened risk of cardiovascular complications in PCOS patients [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eHormone Metabolism\u003c/h2\u003e \u003cp\u003eIn hormone metabolism, significantly increased levels of estrogen quinones (e.g., Estrone-3,4-quinone) and vitamin D3 sulfate in the steroid hormone biosynthesis pathway were observed in the IR group. Estrogen quinones, oxidative metabolites of estrogens, may disrupt insulin signaling by activating nuclear receptors (e.g., ERRα) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The increase in vitamin D3 sulfate may seem paradoxical given the widespread vitamin D deficiency in PCOS patients. However, this elevation may reflect a compensatory mechanism that regulates immune function. Moreover, the enrichment of the retrograde endocannabinoid signaling pathway suggests that dysregulation of the endocannabinoid system could influence insulin sensitivity by modulating adipocyte differentiation and inflammatory responses.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eMetabolic Pathway Enrichment and Cancer Risk\u003c/h2\u003e \u003cp\u003eOf particular importance, KEGG pathway analysis identified a significant enrichment of the \u0026ldquo;choline metabolism in cancer\u0026rdquo; pathway in the NIR group. Differential metabolites such as PC species and glycerylphosphorylcholine (GPC) are known to support tumor cell proliferation by facilitating cell membrane synthesis. This finding corroborates the increased risk of endometrial cancer in PCOS patients, suggesting that metabolic reprogramming may serve as a mechanistic link between PCOS and cancer [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe aberrant elevation of PC species could promote tumorigenesis through several mechanisms: First, as essential membrane components, increased phospholipid synthesis supplies the necessary structural material for rapid tumor cell proliferation. Second, GPC accumulation may activate the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt) pathway, enhancing tumor cell survival and invasion. Additionally, the increased methionine levels in the NIR group may contribute to tumor progression via S-adenosylmethionine (SAM)-mediated DNA methylation, which can regulate oncogenes and tumor suppressor genes (e.g., PTEN).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eStudy Innovations and Limitations\u003c/h2\u003e \u003cp\u003eComparison with Previous Studies,this study not only validates prior findings but also introduces methodological and mechanistic innovations. By stratifying PCOS patients into non-insulin resistance (NIR) and insulin resistance (IR) groups, we observed that phosphatidylcholine (PC) species were significantly elevated in the NIR group, suggesting lipid metabolism abnormalities may precede clinical insulin resistance-a novel insight compared to previous studies focused solely on IR patients[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Furthermore, the paradoxical elevation of vitamin D3 sulfate in the IR group, despite widespread reports of vitamin D deficiency in PCOS by Chen et al.(2021) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], highlights the complexity of vitamin D metabolism. Emerging evidence posits that vitamin D3 sulfate may act as a storage form of active metabolites, modulating insulin sensitivity via PPARγ pathways (2023) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], redefining its role in metabolic regulation. Additionally, elevated estrogen quinones (e.g., Estrone-3,4-quinone) in the IR group align with the oxidative stress hypothesis by Ziv-Gal et al.( 2018 )[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], with recent metabolomics data linking oxidative markers (e.g., 8-OHdG) to estrogen quinone levels (2022) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], suggesting oxidative stress exacerbates insulin resistance through estrogen metabolic disruption. Notably, the enrichment of the \u0026ldquo;choline metabolism in cancer\u0026rdquo; pathway in the NIR group implicates glycerylphosphorylcholine (GPC) accumulation in endometrial cancer risk via PI3K/Akt activation, corroborating 2023 multi-omics findings [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Moreover, dysregulated methionine metabolism may promote tumorigenesis through DNA methylation-mediated oncogene regulation (e.g., PTEN) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], expanding the mechanistic understanding of PCOS-related cancer susceptibility. Collectively, these advances bridge metabolic dysregulation to clinical complications, offering new therapeutic targets and refining the pathophysiological framework of PCOS.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis metabolomics study identified significant differences in serum metabolite profiles between PCOS patients with and without insulin resistance (IR), involving lipid, amino acid, and hormone metabolism pathways. Key findings include elevated phosphatidylcholine (PC) species in the non-IR group, suggesting phospholipid metabolism abnormalities as an early biomarker and potential therapeutic target. Dysregulated lipid metabolism may contribute to insulin resistance and cancer risk, supported by the enrichment of the \"choline metabolism in cancer\" pathway. Amino acid and hormone metabolism alterations further highlight oxidative stress and vascular dysfunction in PCOS progression. Despite limitations such as sample size and cross-sectional design, these findings provide novel insights into metabolic mechanisms and potential precision medicine approaches for PCOS treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study conformed to the guidelines of the Declaration of Helsinki, and the study procedures were reviewed and approved by the Putian University Ethics Committee (Ethics Review (2025) No. 064). Each patient agreed to participate and signed the informed consent form.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo datasets were generated or analysed during the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants of the the Natural Science Foundation of Fujian Province (2023J011008) and Putian Science and Technology Bureau (2021S3001-4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLXL and XQC designed this study. LXL,LP and MZ performed the experiments.LXL and LXC analyzed the data. LXL wrote and revised the manuscript. All authors read and approved the final version of this manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe thank MD Xianhui Min for helping with sample collection in this study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBingqing, Ran,Cai, Liu,Yajun, He et al. Bibliometric analysis of the research on anti-M\u0026uuml;llerian hormone and polycystic ovary syndrome: current status, hotspots, and trends.[J] .Front Reprod Health, 2025;7(0):1519249.\u003c/li\u003e\n\u003cli\u003eWafa Mansor, Merza,Abeer Khalid, Yaseen,Maha Adel, Mahmood,FSH, LH, Lipid and Adipokines in Polycystic Ovary Syndrome: Clinical Biochemistry Insights for Diagnosis and Management.[J] .J Steroid Biochem Mol Biol,2025;0(0):106773.\u003c/li\u003e\n\u003cli\u003eHaoyu, Peng,Junyi, Ren,Yang, Zhao et al. Unraveling the Connection between PCOS and renal Complications: Current insights and Future Directions.[J] .Diabetes Res Clin Pract,2025;224(0):112235\u003c/li\u003e\n\u003cli\u003eDiamanti - Kandarakis E, et al. Metabolic aspects of polycystic ovary syndrome[J]. Best Pract Res Clin Endocrinol Metab, 2019, 33(4):101380.\u003c/li\u003e\n\u003cli\u003eTeresa, Cascella,Stefano, Palomba,Libuse, Tauchmanov\u0026agrave; et al. Serum aldosterone concentration and cardiovascular risk in women with polycystic ovarian syndrome.[J] .J Clin Endocrinol Metab, 2006, 91(11):4395-400. \u003c/li\u003e\n\u003cli\u003eMotiee Behnaz,Mousavi Seyed Omid Reza,Eslami Maryam et al. Upregulation of Oxidative Phosphorylation Genes in Cumulus Cells of The Polycystic Ovary Syndrome Patients with or without Insulin Resistance.[J] .Cell J, 2024, 26: 235-242.\u003c/li\u003e\n\u003cli\u003eMoran LJ, et al. Insulin resistance in polycystic ovary syndrome: mechanisms and clinical implications[J]. 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Metabolomics of dynamic changes in insulin resistance before and after exercise in PCOS[J]. Front Endocrinol (Lausanne), 2019, 10:116.\u003c/li\u003e\n\u003cli\u003eChang AY, et al. Combining a nontargeted and targeted metabolomics approach to identify metabolic pathways significantly altered in polycystic ovary syndrome[J]. Metabolism, 2017, 71:52 - 63.\u003c/li\u003e\n\u003cli\u003eDhindsa G, et al. Insulin resistance, insulin sensitization and inflammation in polycystic ovarian syndrome[J]. J Postgrad Med, 2004, 50(2):140 - 144.\u003c/li\u003e\n\u003cli\u003eLi X, et al. Serum metabolomics identifies lipid metabolism signatures in PCOS[J]. J Proteome Res, 2019, 18(5):1987 - 1995.\u003c/li\u003e\n\u003cli\u003eChen Y, et al. Follicular fluid lipidomics reveals lycerophospholipid metabolism disorders in polycystic ovary syndrome[J]. J Ovarian Res, 2023, 16(1):1 - 12.\u003c/li\u003e\n\u003cli\u003eEscobar - Morreale HF, et al. Serum interleukin - 18 concentrations are increased in the polycystic ovary syndrome: relationship to insulin resistance and to obesity[J]. J Clin Endocrinol Metab, 2004, 89(2):806 - 811.\u003c/li\u003e\n\u003cli\u003eKamei N, et al. Overexpression of monocyte chemoattractant protein - 1 in adipose tissues causes macrophage recruitment and insulin resistance[J]. J Biol Chem, 2006, 281(36):26602 - 26614.\u003c/li\u003e\n\u003cli\u003eBoulman N, et al. Increased C - reactive protein level in the polycystic ovary syndrome: a marker of cardiovascular disease[J]. J Clin Endocrinol Metab, 2004, 89(5):2160 - 2165.\u003c/li\u003e\n\u003cli\u003eBenson SA, et al. Obesity, depression, and chronic low - grade inflammation in women with polycystic ovary syndrome[J]. Brain Behav Immun, 2008, 22(2):177 - 184.\u003c/li\u003e\n\u003cli\u003eChandrasekar B, et al. Interleukin - 18 suppresses adiponectin expression in 3T3 - L1 adipocytes via a novel signal transduction pathway involving ERK1/2 - dependent NFATc4 phosphorylation[J]. J Biol Chem, 2008, 283(7):4200 - 4209.\u003c/li\u003e\n\u003cli\u003eKaya C, et al. Plasma interleukin - 18 levels are increased in the polycystic ovary syndrome: relationship of carotid intima - media wall thickness and cardiovascular risk factors[J]. Fertil Steril, 2010, 93(4):1200 - 1207.\u003c/li\u003e\n\u003cli\u003eThompson SR, et al. IL - 18 haplotypes are associated with serum IL - 18 concentrations in a population - based study and a cohort of individuals with premature coronary heart disease[J]. Clin Chem, 2007, 53(12):2078 - 2085.\u003c/li\u003e\n\u003cli\u003eBrown J, et al. Gut microbiota metabolites in polycystic ovary syndrome[J]. Nat Rev Endocrinol, 2024, 20(5):289 - 302.\u003c/li\u003e\n\u003cli\u003eChen Y, et al. Follicular fluid lipidomics reveals lycerophospholipid metabolism disorders in polycystic ovary syndrome[J]. J Ovarian Res, 2023, 16(1):1 - 12.\u003c/li\u003e\n\u003cli\u003eChen Y, et al. Vitamin D status and metabolic profiles in polycystic ovary syndrome[J]. Nutrients, 2021, 13(12):4487.\u003c/li\u003e\n\u003cli\u003eBrown J, et al. Vitamin D metabolites and insulin resistance in PCOS: a metabolomic perspective[J]. Endocrinol Metab (Seoul), 2023, 38(4):543 - 552.\u003c/li\u003e\n\u003cli\u003eZiv-Gal A, et al. Oxidative stress in PCOS: from mechanisms to clinical implications[J]. Fertil Steril, 2018, 110(3):435 - 442.\u003c/li\u003e\n\u003cli\u003eWang L, et al. Serum metabolomics reveals oxidative stress - related metabolic signatures in PCOS[J]. J Endocrinol Invest, 2022, 45(8):1573 - 1583.\u003c/li\u003e\n\u003cli\u003eWu X, et al. Integrative omics reveals genetic and metabolic signatures of PCOS infertility[J]. Engineering, 2023, 9(7):1002 - 1015.\u003c/li\u003e\n\u003cli\u003eSmith J, et al. Methionine metabolism and DNA methylation in PCOS - associated endometrial cancer[J]. Cancer Metab, 2024, 12(1):1 - 15.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Statistical Analysis of Clinical Biochemical Data\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"484\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003eItem\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 126px;\"\u003e\n \u003cp\u003eIR Group (n=27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eNIR Group (n=23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 151px;\"\u003e\n \u003cp\u003eFBG（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e5.81\u0026plusmn;1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e5.06\u0026plusmn;0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 151px;\"\u003e\n \u003cp\u003eFINS（mU/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e20.96\u0026plusmn;3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e7.42\u0026plusmn;1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 151px;\"\u003e\n \u003cp\u003eLH(IU/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e13.15\u0026plusmn;3.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e9.05\u0026plusmn;2.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 151px;\"\u003e\n \u003cp\u003eFSH(IU/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e4.52\u0026plusmn;1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e4.37\u0026plusmn;1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.845\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 151px;\"\u003e\n \u003cp\u003eE2(pg/mL）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e164.32\u0026plusmn;32.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e128.74\u0026plusmn;28.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 151px;\"\u003e\n \u003cp\u003eP(ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e1.36\u0026plusmn;0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.19\u0026plusmn;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 151px;\"\u003e\n \u003cp\u003eT(ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e3.07\u0026plusmn;0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e2.58\u0026plusmn;0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0,007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 151px;\"\u003e\n \u003cp\u003eLDL-C（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e3.24\u0026plusmn;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e2.81\u0026plusmn;0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 151px;\"\u003e\n \u003cp\u003eHDL-C（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e1.12\u0026plusmn;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.45\u0026plusmn;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTG（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e2.08\u0026plusmn;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e1.18\u0026plusmn;0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTC（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e5.17\u0026plusmn;1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e4.56\u0026plusmn;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Polycystic ovary syndrome, Insulin resistance, Metabolomics, Liquid chromatography-mass spectrometry (LC-MS), Metabolic pathways","lastPublishedDoi":"10.21203/rs.3.rs-6668120/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6668120/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003ePolycystic ovary syndrome (PCOS) with insulin resistance is a complex endocrine disorder. Understanding its metabolic profile can contribute to early diagnosis and treatment. However, the underlying metabolic mechanisms remain incompletely understood. Thus, this study aimed to explore the metabolic characteristics of PCOS patients with insulin resistance through serum metabolomic analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eA total of 12 PCOS patients were recruited and divided into an insulin resistance group (IR group) and a non - insulin resistance group (NIR group). Liquid chromatography - mass spectrometry (LC - MS) was employed to conduct untargeted serum metabolomics analysis. Various statistical methods were utilized for data processing to identify differences in clinical biochemical parameters and metabolites between the two groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eSignificant differences in clinical biochemical parameters were detected between the IR group and the NIR group. A total of 40 differential metabolites were identified. These metabolites were involved in multiple metabolic pathways related to carbohydrates, lipids, and amino acids.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eThe findings of this study provide new insights into the pathogenesis of insulin resistance in PCOS. The identified differential metabolites and metabolic pathways may serve as potential biomarkers and therapeutic targets, which could contribute to the early diagnosis and treatment of PCOS patients with insulin resistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e Not applicable.\u003c/p\u003e","manuscriptTitle":"Metabolomic Analysis of Serum in Polycystic Ovary Syndrome Patients with Insulin Resistance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-18 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