Investigating the Metabolomic Pathways in Female Reproductive Endocrine Disorders: A Mendelian Randomization Study

Investigating the Metabolomic Pathways in Female Reproductive Endocrine Disorders: A Mendelian Randomization Study | 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 Investigating the Metabolomic Pathways in Female Reproductive Endocrine Disorders: A Mendelian Randomization Study Fei-fan Lu, Zheng Wang, Qian-qian Yang, Feng-shang Yan, Chang Xu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4207495/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Reproductive endocrine disorders (RED) including polycystic ovary syndrome (PCOS), endometriosis (EMs), and female infertility (FI), significantly affect women's health globally, with varying prevalence across different regions. These conditions can be addressed through medication, surgical interventions, and lifestyle modifications. However, the limited understanding of RED's etiology and the substantial economic burden of its treatment highlight the importance of investigating its pathogenesis. Metabolites play a critical role in metabolic processes and are potentially linked to the development of RED. Despite existing studies suggesting correlations between metabolites and RED, conclusive evidence remains scarce, primarily due to the observational nature of these studies, which are prone to confounding factors. Methods This study utilized Mendelian Randomization (MR) to explore the causal relationship between metabolites and RED, leveraging genetic variants associated with metabolite levels as instrumental variables to minimize confounding and reverse causality. Data were obtained from the Metabolomics GWAS Server and the IEU OpenGWAS project. Instrumental variables were selected based on their association with the human gut microbiota composition, and the GWAS summary statistics for metabolites, PCOS, EMs, and FI were analyzed. The MR-Egger regression and random-effects inverse-variance weighted (IVW) methods were employed to validate the causal relationship. Cochran's Q test was employed to evaluate heterogeneity, sensitivity analysis was performed using leave-one-out analysis, and for pleiotropy analysis, the intercept term of MR-Egger's method was investigated. Results The MR analysis revealed significant associations between various metabolites and RED conditions. For instance, a positive association was found between 1-palmitoylglycerophosphocholine and PCOS, while a negative association was noted between phenylacetate and FI. The study identified several metabolites associated with an increased risk and others with protective effects against PCOS, EMs, and FI. These findings highlight the complex interplay between metabolites and RED, suggesting potential pathways through which these conditions could be influenced or treated. Conclusion This MR study provides valuable insights into the causal relationship between metabolites and female reproductive endocrine disorders, suggesting that metabolic alterations play a significant role in the pathogenesis of PCOS, EMs, and FI, and offering a foundation for future research and therapeutic development. Mendelian randomization metabolites reproductive endocrine disorders polycystic ovary syndrome endometriosis female infertility Genome Wide Association Study instrumental variables Figures Figure 1 Figure 2 Figure 3 Background Reproductive endocrine disorders (RED) affecting females, including polycystic ovary syndrome (PCOS), endometriosis (EMs), and female infertility (FI), have been a major health issue for women in their reproductive years [ 1 ]. Research studies reveal that the global prevalence of PCOS varies between 4% and 21%. In China, the reported incidence of PCOS among women aged 19–45 years is 5.6%[ 2 ]. EMs affects approximately 10% of women of reproductive age worldwide, affecting 190 million people [ 3 ]. While in the United States, approximately 10–15% of individuals are affected by FI, with around 30% of cases having an unknown etiology [ 4 ]. RED can be treated using different methods, including medication, surgical intervention, and lifestyle adjustments. The major goal of medical care is to regulate the menstrual cycle, hormone secretion, and metabolism. On the other hand, surgical intervention generally aims to remove lesions, control pain, and assist with reproductive technologies. Lifestyle alterations prioritize the management of diet, exercise routines, and behavioral adjustments. Because the understanding of the etiology of RED is limited, the annual global expenditure on its treatment remains enormous, which places a significant burden on the global economy [ 3 , 5 , 6 ]. Researching the pathogenesis of RED will not only reduce the worldwide economic burden but also aid in the advancement of more efficient treatments [ 1 , 7 , 8 ]. The development of the RED complex usually arises from an unhealthy lifestyle, genetic and hormonal abnormalities, inflammation, and metabolic disturbances [ 7 – 10 ]. Metabolites are pivotal in metabolic processes since they either produce or consume resources, hence ensuring the proper functioning of physiological processes. For example, disturbances in sugar metabolism might result in the onset of chronic illnesses [ 11 ]. The findings of various studies have indicated that the processes of lipid metabolism, glucose metabolism, and sphingolipid metabolism may be intricately linked to the pathogenesis of RED [ 12 – 14 ]. Patients with PCOS exhibit a notable prevalence of common metabolic dysfunction [ 13 ]. A robust association exists between inflammation in the FI signal and elevated androgen metabolism [ 12 , 15 ]. Supplementing vitamin C and E has been proposed as a possible treatment for reducing painful symptoms in patients with Ems [ 16 ]. Hence, metabolic alterations are closely connected to the development of RED. Nevertheless, the existing clinical evidence establishing the correlation between metabolites and RED is restricted and mostly obtained from observational studies, which may be influenced by confounding factors. Therefore, gaining a deeper understanding of the connection between metabolites and RED, as well as the probable mechanisms involved, is of great therapeutic importance for accurately assessing risk and improving treatment approaches. Mendelian randomization (MR) is a commonly employed method for inferring causality. It utilizes genetic variants that are linked to the exposure of interest as instrumental variables (IVs) to establish the causal impact of the exposure on the outcome. This approach is effective in isolating the effect of exposures from potential confounding factors, such as environmental influences and lifestyle choices [ 17 – 21 ]. It can reduce the impact of reverse causality to ensure maximum validity [ 22 , 23 ]. The swift advancement of Genome Wide Association Study (GWAS) data additionally establishes a strong basis for the execution of MR Research. The relationship between metabolomics and MR research, particularly in the context of elucidating the causal pathways in human diseases, exemplifies a compelling application of this methodology [ 24 , 25 ]. Metabolomics, the comprehensive study of small molecule metabolites within biological systems, offers a dynamic and sensitive reflection of both genetic and environmental influences on an organism's state. When integrated with MR, metabolomics can provide powerful insights into the causal relationships between specific metabolic profiles and disease outcomes. At present, there is a lack of research that uses the MR approach to thoroughly examine the cause-and-effect relationship between metabolites and the start of RED. This study aimed to examine the causal relationship between metabolites and PCOS, Ems, and FI using MR analysis. The findings could offer significant insights into the underlying biological mechanisms, potentially guiding targeted therapeutic strategies and preventive measures. Methods Study design In our study on the link between plasma metabolites and the development of PCOS, EMs and FI levels, we propose three key hypotheses. First, we expect a correlation between certain genotypes and plasma metabolite levels, suggesting a genetic influence on these metabolites that could affect disease risk. Second, these genotypes should not be influenced by confounding factors, ensuring that any observed relationship with metabolite levels is likely due to a genuine biological mechanism. Third, we assume that these genotypes directly impact the metabolites in question, thereby affecting the risk or progression of PCOS, EMs, and FI. This framework aims to clarify how genetic variations contribute to the observed outcomes, using Mendelian randomization to explore potential causal connections. Moreover, it employs sensitivity testing to examine the robustness of the findings. Figure 1 depicts the process of Mendelian randomization including metabolites that are linked to PCOS, EMs, and FI. Data sources The metabolite GWAS data was obtained from the Metabolomics GWAS Server website ( https://metabolomips.org/gwas/ ), while the genetic variance data related to PCOS, EMs, and FI was obtained from the IEU OpenGWAS project website ( https://gwas.mrcieu.ac.uk/ ) (Table 1 ). IVs were chosen as single-nucleotide polymorphisms (SNPs) that are linked to the composition of the plasma metabolites. Due to the fact that the data utilized in this study were collected from published studies or public databases, ethics approval was not necessary. The GWAS summary statistics for the metabolites were acquired from the Metabolomics GWAS Server, which may be accessed at https://metabolomips.org/gwas/ . This study involves genome-wide association scans combined with high-throughput metabolic monitoring to get unique insights into the impact of genetic variation on metabolism and complex diseases. The researchers have conducted a thorough investigation into the genetic factors that affect human metabolism. They studied a total of 7,824 adult individuals from two European population studies. The study identified significant associations between 145 metabolic loci and more than 1400 metabolites in human blood, revealing their biochemical connectivity[ 26 ]. The GWAS summary datasets for PCOS were obtained from the OpenGWAS database. The GWAS ID is finn-b-E4_POCS, with a total of 118,870 female participants, consisting of 642 cases and 118,228 controls. The total count of SNPs is 16,379,676. The GWAS data for individuals with endometriosis were obtained from a recent genome-wide association study. The selected trait for analysis was endometriosis, and the discovery sample consisted of 4,511 cases of European ancestry and 227,260 controls of European ancestry [ 27 ]. The GWAS summary datasets for FI were obtained from the OpenGWAS database. The GWAS ID is finn-b-N14_FEMALEINFERT, with a total of 75,470 female participants, consisting of 6,481 cases and 68,969 controls. The total count of SNPs is 16,377,038. Table 1 Detailed Information of Datasets Data source Phenotype Sample size Cases Population Adjustment Exposure The Metabolomics GWAS Server Metabolites 7824 - European - Outcomes finn-b-E4_POCS Polycystic Ovarian Syndrome - 642 European Males and Females ebi-a-GCST90018839 Endometriosis 231771 4511 European - finn-b-N14_FEMALEINFERT Female Infertility - 6481 European Males and Females Selection of IVs The following criteria were employed in this study for the purpose of screening instrumental variables [ 28 ]. Typically, this is accomplished by utilizing data obtained from extensive GWAS. The selected SNPs should possess a strong and firmly proven correlation with the exposure. The IVs demonstrate a robust association with exposure, as evidenced by a significant criteria of P < 1× 10 − 5 (correlation hypothesis) [ 29 ]. It is necessary to measure the degree of correlation between each SNP and the exposure. This entails utilizing statistical analysis to quantify the magnitude of the impact of each SNP on the exposure, often quantified as the beta coefficient in a regression model [ 30 ]. It's crucial to ensure that the SNPs used as IVs affect the outcome only through the exposure and not through other pathways. This is known as the assumption of no pleiotropy [ 31 ]. The SNPs selected by the MR Method adhere to the genetic principle of random parental allele assignment to offspring, thereby minimizing the influence of environmental and acquired factors. Consequently, it can be theoretically assumed that instrumental variables are independent of social, economic, and cultural factors. SNPs in close proximity on the genome can be in linkage disequilibrium, meaning they are often inherited together. It's important to ensure that the selected SNPs are independent of each other, or to adjust the analysis for LD [ 32 ]. The selected SNPs should collectively explain a significant portion of the variance in the exposure. Weak instruments (those that explain only a small fraction of the variance) can lead to biased MR estimates. Moreover, an F statistic greater than 10 is employed as a criterion for assessing weak instrumental variables [ 33 ]. The screening condition for selecting meaningful SNPs from the aggregated GWAS data of metabolites were set as P < 1×10 − 5. The coefficient of linkage disequilibrium is r2 < 0.001 and the width of the linkage disequilibrium region was 10000kb. These were applied to ensure the independence of each SNP and eliminate the impact of linkage imbalance on the results [ 29 , 30 , 32 ]. The PhenoScanner ( http://www.phenoscanner.medschl.cam.ac.uk/ ) was utilized to account for confounding factors and assess the outcome associated with each SNP. From the aforementioned screened SNPs, those extracted from the aggregated GWAS data of PCOS, EMs, and FI had a minimum r2 value greater than 0.8. This summarizes the information contained in this dataset. Verification of Causality The MR-Egger regression and random-effects inverse-variance weighted (IVW) methods were employed to validate the causal relationship between exposure (Metabolites) and outcomes (PCOS, EMs, and FI), using SNPs as instrumental variables. IVW, which is widely recognized as the primary outcome measure, was utilized [ 34 ]. In this approach, each locus's inverse of variance (R2) served as the weight for estimating the causal effect based on multiple SNPs as instrumental variables. These weighted causal effect estimates were then summed to obtain the final estimate using IVW method. MR-Egger method essentially incorporates a weaker assumption (InSIDE) within IVW framework to perform causal effect estimation while introducing a regression intercept term to detect and correct bias caused by pleiotropic effects of instrumental variables in order to estimate the causal relationship between exposure and outcome [ 35 ]. When there is horizontal pleiotropy present, MR-Egger results can be referenced. Additionally, random-effects inverse-variance weighting method was applied for analyzing the causal relationship between variables with MR-Egger regression serving as a supplementary analysis technique [ 36 ]. All aforementioned methods were implemented using TwoSample MR Package in R 4.1.0 software at a significance level of α = 0.05. The SNP annotation was performed using online tools available at https://biit.cs.ut.ee/gprofiler/snpense . SNPense tool facilitated the mapping of human SNP rs-codes to gene names, providing chromosomal coordinates and predicted variant effects. Mapping was restricted to variants that overlapped with protein-coding Ensembl genes[ 37 ]. All essential data were retrieved from Ensembl variation. Heterogeneity and horizontal pleiotropy The heterogeneity was assessed using Cochran's Q test, with results considered heterogeneous if P < 0.05. I² (I-squared) was also used to measure heterogeneity, indicating the proportion of variation due to heterogeneity in total variation [ 31 ]. The range for I² is from 0–100%, with larger values indicating higher levels of heterogeneity. The formula for calculating I = 2(Q - Q_df)/Q. For pleiotropy analysis, the intercept term of MR-Egger's method was utilized, while leave-one-out analysis was employed for sensitivity analysis. IVs were deemed non-pleiotropic when the intercept term of the MR-Egger regression model equaled zero (P > 0.05)[ 38 ]. To conduct leave-one-out analysis on IVs, each SNP was gradually removed and the remaining SNPs were re-analyzed to observe their individual effects [ 39 ]. Results IVs screening After undergoing multiple screenings, the dataset of PCOS, EMs, and FI associated with metabolites ultimately comprised 13,615 SNPs. Table 2 presents the basic information of select SNPs, while the remaining SNPs exhibit similar characteristics. The F-statistic for each individual SNP ranged from 17.64 to 1294.286 (mean: 26.468), suggesting that weak instrumental variables were unlikely to significantly impact the causal association. See the Supplementary Table 1 for more details. Table 2 MR Results of Causal Links Data source Classification Classification Nsnp Methods OR (95%CI) P-value PCOS Amino acid phenylacetate 8 IVW 4.439(1.070-18.417) 0.0401 Carbohydrate glucose 33 0.058(0.005–0.678) 0.0232 Carbohydrate glycerate 11 17.181(1.015-290.712) 0.0488 Carbohydrate mannitol 12 0.388(0.207–0.729) 0.0032 Lipid 1-oleoylglycerophosphocholine 12 16.147(1.372–190.080) 0.0270 Lipid epiandrosterone sulfate 11 2.938(1.348–6.406) 0.0067 Lipid X-12990 11 4.260(1.074–16.901) 0.0393 Nucleotide uridine 18 0.032(0.001–0.961) 0.0474 Peptide gamma-glutamylisoleucine* 22 0.202(0.041–0.986) 0.0480 Peptide pyroglutamylglycine 3 5.367(1.392–20.685) 0.0146 EMs Amino acid creatinine 22 IVW 4.425(1.281–15.287) 0.0187 Amino acid leucine 149 0.497(0.266–0.931) 0.0289 Amino acid X-04499 20 0.379(0.188–0.764) 0.0066 Carbohydrate mannose 12 1.905(1.020–3.558) 0.0430 Energy acetylphosphate 15 0.162(0.036–0.724) 0.0171 Lipid 1-eicosatrienoylglycerophosphocholine* 14 1.898(1.110–3.243) 0.0191 Lipid 1-linoleoylglycerol (1-monolinolein) 13 0.686(0.482–0.975) 0.0356 Lipid 1-palmitoylglycerophosphoethanolamine 25 0.635(0.434–0.930) 0.0197 Lipid 15-methylpalmitate (isobar with 2-methylpalmitate) 14 2.151(1.042–4.439) 0.0383 Lipid 3-hydroxybutyrate (BHBA) 9 0.829(0.695–0.988) 0.0365 Lipid 4-androsten-3beta,17beta-diol disulfate 1* 17 1.363(1.102–1.685) 0.0042 Lipid epiandrosterone sulfate 10 0.681(0.472–0.982) 0.0398 Lipid X-11445 13 1.328(1.002–1.760) 0.0487 Nucleotide X-11422 5 5.070(1.574–16.330) 0.0065 Peptide leucylleucine 9 0.542(0.335–0.877) 0.0126 FI Amino acid isovalerylcarnitine 19 IVW 0.574(0.394–0.836) 0.0038 Amino acid phenylacetate 8 0.580(0.344–0.978) 0.0408 Carbohydrate glucose 33 0.402(0.187–0.862) 0.0193 Lipid 1-arachidonoylglycerophosphoinositol* 12 2.416(1.274–4.582) 0.0069 Lipid 1-palmitoylglycerophosphocholine 28 3.215(1.295–7.979) 0.0118 Lipid 2-hydroxyglutarate 11 0.525(0.291–0.945) 0.0317 Lipid 2-oleoylglycerophosphocholine* 17 2.738(1.323–5.670) 0.0067 Lipid docosapentaenoate (n3 DPA; 22:5n3) 7 0.400(0.210–0.761) 0.0053 Lipid hexadecanedioate 19 1.431(1.007–2.033) 0.0454 Lipid margarate (17:0) 6 0.321(0.122–0.841) 0.0207 Nucleotide 7-methylguanine 8 2.079(1.129–3.828) 0.0187 Peptide ADpSGEGDFXAEGGGVR* 5 2.077(1.301–3.315) 0.0022 Peptide DSGEGDFXAEGGGVR* 11 1.628(1.127–2.353) 0.0095 Peptide glycylvaline 6 1.619(1.067–2.456) 0.0236 MR: Mendelian Randomization, PCOS: polycystic ovary syndrome, EMs: endometriosis, FI: female infertility, IVW: inversed-variance weighted, OR: odds ratio, CI: confidence interval The analysis of causal relationship We conducted a comprehensive MR analysis to assess the causal relationships between each screened metabolite and conditions with PCOS, EMs, and FI (Fig. 2 ). The IVW analysis results demonstrated that substances with confidence intervals that do not cross the line of no effect and have a p-value less than 0.05 are considered to have a statistically significant association with the outcome. In PCOS (Fig. 3 A), the analysis revealed significant associations across a range of metabolite classes, including amino acids, carbohydrates, lipids, nucleotides, and peptides. Phenylacetate, an amino acid, exhibited a notable association with an increased risk of PCOS (OR = 4.439, 95% CI: 1.070-18.417, p = 0.0401). In the carbohydrate category, glucose showed a protective effect (OR = 0.058, 95% CI: 0.005–0.678, p = 0.0232), while glycerate and mannitol were linked to varying effects on PCOS risk. Lipid metabolites, particularly 1-oleoylglycerophosphocholine and epiandrosterone sulfate, were significantly associated with PCOS, underscoring their potential roles in its pathogenesis. Additionally, nucleotide uridine and peptides like pyroglutamylglycine also showed significant associations, suggesting their involvement in PCOS development. For Endometriosis (Fig. 3 B), our findings indicated significant associations with amino acids such as creatinine and leucine, pointing to a potential influence on EMs risk (OR = 4.425, 95% CI: 1.281–15.287, p = 0.0187 for creatinine). Carbohydrates like mannose and energy metabolites such as acetylphosphate also demonstrated significant links to EMs. Among lipids, several metabolites including 1-eicosatrienoylglycerophosphocholine and 4-androsten-3beta,17beta-diol disulfate 1 showed associations with EMs risk, highlighting the complexity of lipid metabolism in EMs etiology. The analysis further identified significant associations with other lipid metabolites, emphasizing their potential regulatory roles in EMs. In the context of FI (Fig. 3 C ) , isovalerylcarnitine, an amino acid, was inversely associated with FI (OR = 0.574, 95% CI: 0.394–0.836, p = 0.0038), suggesting a protective role against insulin resistance. Carbohydrates and lipids also displayed significant associations with FI levels. Specifically, glucose showed an inverse relationship, while various lipid metabolites such as 1-arachidonoylglycerophosphoinositol and docosapentaenoate were linked to FI alterations, indicating their importance in insulin metabolism and sensitivity. Additionally, nucleotides and peptides demonstrated significant associations, further underscoring the multifaceted nature of genetic influences on FI levels. We summarized a comprehensive information of the metabolite classifications (classified as amino acids, carbohydrates, lipids, nucleotides, and peptides) associated with three diseases, as well as the number of SNPs and their causality (Table 3 ). Table 3 Evaluation of Heterogeneity and Pleiotropy Outcomes Classification Metabolites Heterogeneity Horizontal Pleiotropy I 2 (%) Cochran's Q P-value Egger intercept SE P-value PCOS Amino acid phenylacetate 13 8.0772 0.3258 0.0241 0.0562 0.6826 Carbohydrate glucose 11 36.1044 0.2826 0.0260 0.0271 0.3449 Carbohydrate glycerate 0 7.7892 0.6494 -0.0670 0.0638 0.3206 Carbohydrate mannitol 0 8.1490 0.6999 -0.0945 0.0628 0.1634 Lipid 1-oleoylglycerophosphocholine 7 11.8449 0.3754 0.0737 0.0470 0.1477 Lipid epiandrosterone sulfate 25 13.3746 0.2035 0.0335 0.0436 0.4625 Lipid X-12990 0 7.8790 0.6407 -0.0140 0.0709 0.8479 Nucleotide uridine 0 14.9945 0.5959 0.0195 0.0584 0.7431 Peptide gamma-glutamylisoleucine* 0 18.5263 0.6155 -0.0287 0.0345 0.4160 Peptide pyroglutamylglycine 0 0.9812 0.6122 0.1157 0.1383 0.5563 EMs Amino acid creatinine 15 24.6618 0.2621 -0.0053 0.0123 0.6733 Amino acid leucine 9 163.4983 0.1815 0.0050 0.0036 0.1682 Amino acid X-04499 6 20.2751 0.3782 0.0188 0.0211 0.3838 Carbohydrate mannose 0 5.5446 0.9020 0.0197 0.0144 0.2024 Energy acetylphosphate 18 17.1042 0.2507 0.0058 0.0314 0.8567 Lipid 1-eicosatrienoylglycerophosphocholine* 17 15.7089 0.2652 -0.0284 0.0178 0.1360 Lipid 1-linoleoylglycerol (1-monolinolein) 21 15.2416 0.2285 0.0357 0.0184 0.0781 Lipid 1-palmitoylglycerophosphoethanolamine 0 17.0084 0.8483 0.0052 0.0068 0.4529 Lipid 15-methylpalmitate (isobar with 2-methylpalmitate) 21 16.3602 0.2302 0.0118 0.0140 0.4175 Lipid 3-hydroxybutyrate (BHBA) 15 9.4153 0.3085 -0.0293 0.0130 0.0585 Lipid 4-androsten-3beta,17beta-diol disulfate 1* 0 8.2044 0.9425 -0.0020 0.0101 0.8475 Lipid epiandrosterone sulfate 0 4.1942 0.8982 0.0016 0.0380 0.9668 Lipid X-11445 35 18.5627 0.0996 0.0079 0.0307 0.8013 Nucleotide X-11422 0 0.4459 0.9785 0.0016 0.0354 0.9658 Peptide leucylleucine 0 1.9940 0.9812 -0.0076 0.0201 0.7172 FI Amino acid isovalerylcarnitine 5 18.8542 0.4009 0.0005 0.0084 0.9512 Amino acid phenylacetate 29 9.8058 0.1998 0.0024 0.0208 0.9122 Carbohydrate glucose 0 25.5290 0.7841 0.0004 0.0084 0.9602 Lipid 1-arachidonoylglycerophosphoinositol* 0 10.7537 0.4641 -0.0144 0.0140 0.3279 Lipid 1-palmitoylglycerophosphocholine 0 24.6588 0.5936 0.0149 0.0192 0.4440 Lipid 2-hydroxyglutarate 0 7.8895 0.6396 -0.0163 0.0151 0.3083 Lipid 2-oleoylglycerophosphocholine* 0 15.3490 0.4992 0.0311 0.0193 0.1269 Lipid docosapentaenoate (n3 DPA; 22:5n3) 0 2.4955 0.8690 -0.0034 0.0326 0.9204 Lipid hexadecanedioate 27 24.7590 0.1317 -0.0025 0.0129 0.8468 Lipid margarate (17:0) 0 3.9381 0.5584 0.0052 0.0440 0.9108 Nucleotide 7-methylguanine 0 6.5614 0.4759 -0.0228 0.0196 0.2897 Peptide ADpSGEGDFXAEGGGVR* 9 4.3992 0.3547 0.0106 0.0317 0.7596 Peptide DSGEGDFXAEGGGVR* 0 9.7625 0.4616 0.0535 0.0295 0.1025 Peptide glycylvaline 0 4.4344 0.4887 0.0322 0.0518 0.5683 MR: Mendelian Randomization, PCOS: polycystic ovary syndrome, EMs: endometriosis, FI: female infertility The detailed list provides supplementary information for SNP annotation in metabolites of imparity, and it uncovers genetic loci where metabolites exert an impact on PCOS, EMs, and FI ( Supplementary Table 2 ). Sensitivity Testing In our study employing MR to assess the impact of specific metabolites on the risk of PCOS, EMs, and alterations in FI levels, we evaluated outcomes across various metabolite classes. The results are organized by disease condition, including assessments of heterogeneity and horizontal pleiotropy, which are crucial for understanding the robustness and specificity of the associations identified. For PCOS, heterogeneity, as measured by I 2 (%) and Cochran's Q, varied across metabolites, with phenylacetate (amino acid) showing relatively low heterogeneity (I 2 = 13%) but a significant horizontal pleiotropy p-value (Egger intercept p-value = 0.6826). Other notable findings include glucose (carbohydrate) with an I 2 of 11% and no significant pleiotropy (Egger intercept p-value = 0.3449), and 1-oleoylglycerophosphocholine (lipid) with I 2 = 7% and a marginally non-significant pleiotropy p-value (Egger intercept p-value = 0.1477). These results suggest a varied landscape of genetic instruments' effects on PCOS risk, with some metabolites showing more stable and specific associations than others. In EMs, creatinine (amino acid) exhibited moderate heterogeneity (I 2 = 15%) and no significant pleiotropy (Egger intercept p-value = 0.6733). A high level of heterogeneity was noted for leucine (amino acid) with an I 2 of 9% and a borderline significant pleiotropy (Egger intercept p-value = 0.1682), suggesting the need for cautious interpretation. The lipid metabolite 1-linoleoylglycerol (1-monolinolein) showed a notable I 2 of 21% and a pleiotropy p-value nearing significance (Egger intercept p-value = 0.0781), indicating potential pleiotropic effects influencing its association with EMs. For alterations in FI, isovalerylcarnitine (amino acid) had a low heterogeneity (I 2 = 5%) and showed no evidence of significant horizontal pleiotropy (Egger intercept p-value = 0.9512), suggesting a more direct association with FI. Conversely, phenylacetate (amino acid) and glucose (carbohydrate) displayed higher heterogeneity but also no significant pleiotropy, highlighting the complexity of these associations. We summarized the analysis of MR heterogeneity and directional pleiotropy in metabolites and outcome variables associated with three diseases ( Table 4 ). The identified SNPs did not exhibit a statistically significant impact on the estimates of causal association. Discussion This study represents a pioneering endeavor to unveil the intricate connections between metabolites and REDs from a metabolomics perspective. By employing MR analysis, we have identified a combined total of 39 metabolites (10 for PCOS, 15 for Ems, and 14 for FI) across various classes that are significantly associated with conditions. Through the process of gene annotation, it has been shown that there are potentially 442 different types of genes that could be linked to female REDs ( Supplementary Table 2 ). This work is the first to use MR analysis to establish a causal link between metabolites and RED. These findings not only enrich our understanding of the metabolic underpinnings of RED but also pave the way for novel diagnostic and therapeutic approaches. Many recent studies have focused on the significance of metabolites in female reproductive endocrine diseases. Li et al. discovered that mono-(2-ethylhexyl) phthalate (MEHP), a byproduct of DEHP, can disrupt ovarian function by causing irregularities in the 17β-hydroxysteroid dehydrogenase (17β-HSD) signaling pathway. This study sought to validate this idea in living organisms by conducting experiments on adult female Wistar rats[ 40 ]. Li et al conducted a study on the developmental abnormalities of the ovary in quail caused by di-(2-ethylhexyl) phthalate (DEHP) and its metabolite MEHP. They emphasized the receptor-mediated signaling pathway through which these metabolites hinder estradiol production and disrupt the hypothalamic-pituitary-ovarian axis[ 41 ]. Additionally, Charifson et al. suggested that PCOS could be attributed to a mismatch between genetic variations that developed in physically active subsistence settings and the sedentary industrialized surroundings of modern times[ 42 ]. In their study, Chu et al. examined the impact of continuous exposure to light on the disruption of circadian rhythm and its implications on reproductive, metabolic, and gut microbiome abnormalities resembling those seen in PCOS in a rat model. The study highlights the significance of circadian rhythm in the development of PCOS[ 43 ]. In addition, Mukhopadhyay et al. conducted a review on the relationship between bisphenol A (BPA) and the risk of PCOS. Their focus was on how BPA can change the expression of genes that play a role in regulating hormones, which are often linked to the symptoms of PCOS[ 44 ]. Ding et al. emphasized the importance of comprehending the impacts of endocrine disrupting chemicals (EDCs) on female reproductive health, particularly in relation to ovarian aging[ 45 ]. To summarize, the data indicates that metabolites, namely those originating from phthalates and BPA, have a substantial impact on female reproductive endocrine disorders such as PCOS. Gaining a comprehensive understanding of the processes by which these metabolites interfere with endocrine pathways and affect ovarian function is essential for the development of successful treatments and interventions for these disorders[ 46 ]. Additional investigation is necessary to enhance our comprehension of the intricate problems related to female reproductive health, including the impacts of EDCs, circadian rhythm disruption, and toxicological data from biomonitoring investigations[ 45 , 47 ]. From the perspective of pathophysiological mechanisms, the growth of endometriotic deposits is dependent on the presence of estradiol, which is supplied by both systemic hormones and enhanced expression of aromatase and steroidogenic acute regulatory protein locally. Additionally, the expression of 17β-hydroxysteroid dehydrogenase 2 is reduced by endometriotic lesions, further contributing to the proliferation of these deposits[ 48 ]. Furthermore, lesions exhibit heightened expression of estrogen receptor β[ 49 ]. Moreover, the inhibition of progesterone receptor B in both normal and abnormal endometrial tissue is intensified in abnormal endometrial stromal cells due to epigenetic differential methylation of PR-B, HOX, and GATA family transcription-factor genes[ 50 ]. This leads to disrupted progesterone signaling, commonly referred to as "progesterone resistance"[ 51 ]. PCOS is associated with adrenocortical steroidogenic dysfunction, as evidenced by research[ 52 ] Around one third of women with PCOS show elevated levels of dehydroepiandrosterone sulfate, which is an androgen metabolite or prohormone primarily produced by the adrenal cortex. Insulin resistance and compensatory hyperinsulinemia are crucial factors in the development of PCOS. Excessive insulin, in combination with LH, enhances the production of androgens by ovarian theca cells[ 53 ]. Additionally, it reduces the production of sex hormone-binding globulin in the liver, together with the excess of androgens[ 54 ]. The cause of the reduced insulin sensitivity in PCOS is not yet fully understood. However, it is believed that the different genetic and epigenetic abnormalities contribute to impairments in the production and function of the main glucose transporter in cells, known as glucose transporter 4 (GLUT4), as well as impairments in the disposal of glucose through insulin. Patients with PCOS also exhibit abnormalities in insulin-mediated lipolysis. Furthermore, the level of insulin resistance in PCOS is exacerbated by a condition of persistent mild inflammation, partly caused by aberrant production and function of adipocytokines. On the other hand, there is stronger evidence indicating that women with PCOS have adipose tissue that shows different defects that promote an inflammatory or insulin resistant condition. These defects include dysfunction in adipocytokines, dysregulation of free fatty acid metabolism, and abnormalities in epigenetics that affect GLUT4 function[ 52 ]. Further investigation is required to rule out or confirm clinical evaluation using more targeted hormonal testing. Thyroid problems, hyperprolactinemia, and nonclassic adrenal hyperplasia are the main conditions to consider. These can be ruled out by measuring thyroid-stimulating hormone, prolactin, and 17-hydroxyprogesterone, respectively. Nonclassic adrenal hyperplasia caused by abnormalities in CYP21A2 affects a percentage ranging from 1 to 10% of women with excessive hair growth, depending on their ethnic background. This condition is the most prevalent autosomal-recessive ailment in the human population. Nonclassic adrenal hyperplasia caused by abnormalities in CYP21A2 affects a percentage ranging from 1 to 10% of women with excessive hair growth, depending on their ethnic background. This condition is the most prevalent autosomal-recessive ailment in the human population. Additional evidence indicates that promptly identifying the condition and administering corticosteroid treatment may enhance the chances of achieving a successful reproductive outcome[ 55 ]. Therefore, it is recommended to evaluate any woman exhibiting signs, symptoms, or complaints of hyperandrogenism, regardless of severity, for nonclassic adrenal hyperplasia. Practitioners must be aware that it is not feasible to clinically identify or even make an educated guess about the diagnosis of nonclassic adrenal hyperplasia. It is absolutely necessary to evaluate the levels of 17-hydroxyprogesterone[ 56 ]. There are multiple benefits to our study. As far as we know, this is the initial MR investigation conducted to assess the causal connection between the metabolites and REDs. Undoubtedly, our current focus is on conducting exploratory research with the specific aim of enhancing our ability to support future metabolomics studies. This research provides valuable insights and guidance for our future endeavors. Metabolomics is a scientific discipline that seeks to understand how living systems react to genetic alterations, environmental shifts, or disease conditions by examining the entire collection of metabolites within a specific biological setting. This method offers a precise representation of the biochemical processes occurring within the system during the moment of sample collection. Thus, we anticipate that our study can offer guidance for the analysis of metabolites and genomes, enabling researchers to focus on fundamental experiments accordingly. But there also are certain constraints that need to be addressed. First, identifying genetic variations that are strongly linked to specific metabolites and can serve as instrumental factors can be a complex task. Metabolites can be altered by a combination of genetic factors and environmental conditions. These genetic variants account for only a minor fraction of the variability in the metabolites, which may result in a slight instrument bias and diminish the statistical power of the study. The chosen genetic variants may impact the likelihood of female reproductive endocrine disorders through biological pathways that are not connected to the hypothesis, so contravening the third postulate of MR. Pleiotropy may introduce a bias in the estimated causal effect. Second, the study sample consists of individuals with varied genetic backgrounds. Failing to effectively adjust for population stratification could result in false relationships. This poses a significant challenge when the correlation between genetic variations and metabolites differs among different populations. The genetic variations utilized are predominantly derived from specific groups with European heritage, hence the findings may not be generalizable to individuals with different genetic backgrounds. Therefore, the study's capacity to be applied to a wider population may be restricted. Third, if there is a potential for bidirectional causation between specific metabolites and female reproductive endocrine problems, relying solely on MR may not provide an accurate determination of the actual causal relationship. Furthermore, inaccuracies in detecting metabolite levels may introduce bias in the assessment of causal links, particularly if the measurements of metabolites are inaccurate or affected by batch effects. If individuals in the sample are chosen or omitted based on their disease status or other specific criteria, the study results may not be applicable to the wider population. Subsequent metabolomics investigations should include provisions to tackle these constraints. This pioneering study marks a significant advancement in understanding the complex interplay between metabolites and RED from a metabolomics standpoint. Through meticulous Mendelian Randomization analysis, we've unveiled specific associations across various metabolite classes—including amino acids, carbohydrates, lipids, nucleotides, and peptides—with conditions like PCOS, EMs, and FI. These insights not only deepen our comprehension of RED's metabolic foundations but also herald new avenues for diagnosis and treatment. The identification of metabolites as potential biomarkers offers promising pathways for early detection and risk assessment, enabling more precise and personalized management strategies. Furthermore, elucidating these causal relationships opens up possibilities for targeted interventions aimed at correcting metabolic imbalances, thereby offering hope for improved outcomes. The diversity of implicated metabolites underscores the necessity for a personalized medicine approach, tailoring treatments to individual metabolic dysregulations to optimize care. Consequently, this research not only contributes significantly to our understanding of reproductive health but also paves the way for transformative developments in the diagnosis, management, and treatment of reproductive endocrine disorders, spotlighting the critical role of metabolomics in advancing reproductive medicine. Conclusion Our work employed a multivariate two-sample MR analysis using publicly accessible GWAS meta-analysis data to examine the causal connection between metabolites and several RED, such as PCOS, EMs and FI. Ultimately, we conducted a thorough assessment of the potential correlation between the metabolites and REDs. These metabolites and genes could potentially serve as biomarkers and offer valuable insights for future studies on treatment. Declarations Acknowledgments We want to acknowledge the participants and investigators of FinnGen, the UK Biobank study for sharing the genetic data, and we express our sincere gratitude to all the diligent researchers, collaborators, and participants who contributed to the GWAS studies. Authors’ contributions FL: Conceptualization, Methodology, Software, Validation, Formal analysis, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization. ZW: Conceptualization, Methodology, Software, Validation, Formal analysis, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization. QY: Software, Validation, Formal analysis, Data Curation. FY: Methodology, Software, Validation, Formal analysis. CX: Methodology, Software, Validation, Data Curation. MW: Software, Validation. ZX: Formal analysis, Data Curation. RG: Conceptualization, Writing - Review & Editing, Supervision, Project administration. SC：Conceptualization, Writing - Review & Editing, Supervision, Project administration. Funding Rui Guan was supported partially by the Foundation of Changhai Hospital (No. 2020YXK024). Availability of data and materials All used in the present study are publicly available. The original repositories of these data are described in the “Methods” section. Ethics approval and consent to participate All the data used in this study were acquired from publicly available genomewide association study summary statistics. No new data was collected, and no new ethical approval was needed. Consent for publication Not applicable. Competing interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References Liang Y, Zeng W, Hou T, Yang H, Wu B, Pan R, Huang L. Gut microbiome and reproductive endocrine diseases: a Mendelian randomization study. Front Endocrinol 2023, 14. 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Endocrinology. 2007;148(8):3814–26. Azziz R, Carmina E, Chen Z, Dunaif A, Laven JS, Legro RS, Lizneva D, Natterson-Horowtiz B, Teede HJ, Yildiz BO. Polycystic ovary syndrome. Nat Rev Dis Primers. 2016;2:16057. Nestler JE, Powers LP, Matt DW, Steingold KA, Plymate SR, Rittmaster RS, Clore JN, Blackard WG. A direct effect of hyperinsulinemia on serum sex hormone-binding globulin levels in obese women with the polycystic ovary syndrome. J Clin Endocrinol Metab. 1991;72(1):83–9. Nestler JE, Jakubowicz DJ, de Vargas AF, Brik C, Quintero N, Medina F. Insulin stimulates testosterone biosynthesis by human thecal cells from women with polycystic ovary syndrome by activating its own receptor and using inositolglycan mediators as the signal transduction system. J Clin Endocrinol Metab. 1998;83(6):2001–5. Moran C, Azziz R, Weintrob N, Witchel SF, Rohmer V, Dewailly D, Marcondes JA, Pugeat M, Speiser PW, Pignatelli D, et al. Reproductive outcome of women with 21-hydroxylase-deficient nonclassic adrenal hyperplasia. J Clin Endocrinol Metab. 2006;91(9):3451–6. Pall M, Azziz R, Beires J, Pignatelli D. The phenotype of hirsute women: a comparison of polycystic ovary syndrome and 21-hydroxylase-deficient nonclassic adrenal hyperplasia. Fertil Steril. 2010;94(2):684–9. Supplementary Files SupplementaryFigure1.pdf Figure S1 Funnel Plot The funnel plot visualizes the distribution of effect estimates from instrumental variables against their precision, assessing potential publication bias and asymmetry in the MR analysis for metabolic markers related to reproductive disorders. SupplementaryFigure2.pdf Figure S2 Scatter Plot This scatter plot depicts the correlation between the genetic associations of instrumental variables and the risk of reproductive endocrine disorders, providing a snapshot of the causal effects estimated by MR. SupplementaryFigure3.pdf Figure S3 Forest Plot The forest plot aggregates the effect sizes and confidence intervals for each metabolite's impact on reproductive health outcomes, facilitating a meta-analytic view of their significance and heterogeneity. SupplementaryFigure4.pdf Figure S4 Leave-one-out Plot The leave-one-out plot examines the influence of each individual instrumental variable on the overall MR results, ensuring the robustness and reliability of the associations with female infertility, polycystic ovary syndrome, and endometriosis. SupplementaryTable1SNPinformation.csv Table S1 Comprehensive Information of All SNPs Associated with Female Infertility, Polycystic Ovary Syndrome, and Endometriosis SupplementaryTable2SNPannotation.xlsx Table S2 Detailed Annotations for All Single Nucleotide Polymorphisms Employed as Instrumental Variables Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4207495","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291112940,"identity":"33dea472-d578-4873-90c8-76aad5dd44f4","order_by":0,"name":"Fei-fan Lu","email":"","orcid":"","institution":"Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fei-fan","middleName":"","lastName":"Lu","suffix":""},{"id":291112941,"identity":"d2e08ce6-06b5-4ff0-acf8-74a88a04b1f4","order_by":1,"name":"Zheng Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYLACxgYgwd4A5R0gWgsPTCnxWiQSiNRicPzs4Zc/d9jkyUc+f7rpZhuDHN+NBMbPBfi0nMlLs5A8k1ZseDvH7HZuG4Ox5I0EZukZeLSYHcgxMzBsO5y4cXYOG0hL4oYbCWzMPPi0nH9jZpDY9j9x48zjz0Ba6glruZFj/OBg24HE+RIMYIclGBDSYn/jjRljY1ty4gYeoF9yzkkYzjzzsFkanxbJ/hzjjz/b7BLntwMdllNmI893PPngZ3xagIBNAkQaHABzQGxwNOEFzB9ApDxBdaNgFIyCUTBiAQDMYVUPAO7xdwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-8177-0354","institution":"Naval Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Wang","suffix":""},{"id":291112942,"identity":"27e00204-13aa-41bd-beee-b27dad80cbd1","order_by":2,"name":"Qian-qian Yang","email":"","orcid":"","institution":"Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qian-qian","middleName":"","lastName":"Yang","suffix":""},{"id":291112943,"identity":"aabf7a2b-2cc9-43d2-a7ef-d0fee1bcb9c4","order_by":3,"name":"Feng-shang Yan","email":"","orcid":"","institution":"Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Feng-shang","middleName":"","lastName":"Yan","suffix":""},{"id":291112944,"identity":"930b32b4-94e5-4040-a04a-8017dd02b1d9","order_by":4,"name":"Chang Xu","email":"","orcid":"","institution":"Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chang","middleName":"","lastName":"Xu","suffix":""},{"id":291112945,"identity":"6561810f-5e75-4ba2-8973-1cf09d1976a6","order_by":5,"name":"Ming-tang Wang","email":"","orcid":"","institution":"Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ming-tang","middleName":"","lastName":"Wang","suffix":""},{"id":291112946,"identity":"7c647ddc-aeef-496a-ab2a-98d2dc40fef5","order_by":6,"name":"Zhu-jing Xu","email":"","orcid":"","institution":"Jinshan Branch of Shanghai Sixth Peoples Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhu-jing","middleName":"","lastName":"Xu","suffix":""},{"id":291112947,"identity":"11dcb111-7570-4cd1-a6d3-45a9c5f1a8d0","order_by":7,"name":"Sheng-yun Cai","email":"","orcid":"","institution":"Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Sheng-yun","middleName":"","lastName":"Cai","suffix":""},{"id":291112948,"identity":"b24008f9-0708-412f-aed0-b67f1f41266c","order_by":8,"name":"Rui Guan","email":"","orcid":"","institution":"Naval Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Guan","suffix":""}],"badges":[],"createdAt":"2024-04-02 14:47:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4207495/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4207495/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55009765,"identity":"e559fff5-c976-47fe-a552-c91d4e53d1a4","added_by":"auto","created_at":"2024-04-19 19:15:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":138070,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow Chart of the Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure illustrates the Mendelian randomization (MR) framework used to investigate the causal effects of plasma metabolite levels on reproductive endocrine disorders, including polycystic ovary syndrome (PCOS), endometriosis (EMs), and female infertility (FI). Instrumental variables (IVs) are selected based on single nucleotide polymorphisms (SNPs) fulfilling stringent criteria for relevance (P \u0026lt; 1 × 10\u003csup\u003e-5\u003c/sup\u003e), linkage disequilibrium (R\u003csup\u003e2\u003c/sup\u003e \u0026lt; 0.001), and a sizable aggregation window (\u0026gt;10,000 kb). MR analyses utilize inverse variance weighted (IVW) methods and MR-Egger regression for causal inference, with additional sensitivity testing (Cochran’s Q and leave-one-out analysis) to assess the robustness of the findings.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4207495/v1/08019afb712ac8f282457f83.png"},{"id":55009766,"identity":"57eff192-897e-422a-a4f8-e94e8cbea821","added_by":"auto","created_at":"2024-04-19 19:15:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":314556,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest Plots of MR-Estimated Effects of Plasma Metabolites on PCOS, Endometriosis, and Female Infertility Risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure depicts a comprehensive Mendelian randomization (MR) analysis to assess the association between plasma metabolites and the risk of developing common reproductive endocrine disorders: polycystic ovary syndrome (PCOS) in panel A, endometriosis in panel B, and female infertility (FI) in panel C. For each disorder, forest plots illustrate the estimated effects (odds ratios with 95% confidence intervals) of the metabolite levels, with the number of single nucleotide polymorphisms (SNPs) utilized as instrumental variables (nSNP) presented alongside. A significant association is denoted by P values that breach the threshold of statistical significance. The effect size for each metabolite is visually captured through the width of the horizontal lines (confidence intervals).\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4207495/v1/20e22795059e820dbfb8f8cf.png"},{"id":55010449,"identity":"c5f914d2-f896-41a4-b638-7fcd9be07104","added_by":"auto","created_at":"2024-04-19 19:23:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":284792,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCircular Heatmap and Bubble Plot of MR Results for Metabolites in Reproductive Endocrine Disorders\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eis a circular heatmap that encapsulates the MR findings for a spectrum of metabolites, with color intensities reflecting the magnitude of -log10 P values, indicating the strength of the associations. Each segment is annotated with the metabolite's name and the specific condition it's associated with (PCOS for polycystic ovary syndrome, EMs for endometriosis, and FI for female infertility), facilitating a comparative overview of metabolites across different outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B)\u003c/strong\u003e is a bubble plot, showcasing the odds ratios (OR) and the corresponding P values (-log10 scale) for each significant metabolite's influence on the disease states. The size of each bubble represents the number of single nucleotide polymorphisms (nSNP) used as instrumental variables, thus reflecting the genetic weight of the analysis. The color gradient represents the significance levels, with cooler blue tones denoting stronger associations and warmer red tones suggesting weaker statistical significance.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4207495/v1/d396ddbfc851c002796aa435.png"},{"id":56084125,"identity":"75afa839-7734-4233-bb01-231c99384799","added_by":"auto","created_at":"2024-05-08 10:34:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1827052,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4207495/v1/05c41766-97f5-41e2-b075-f2a0c1266ece.pdf"},{"id":55010450,"identity":"d47f9971-3270-4dda-9604-36474410b4e1","added_by":"auto","created_at":"2024-04-19 19:23:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":893266,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1 Funnel Plot\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funnel plot visualizes the distribution of effect estimates from instrumental variables against their precision, assessing potential publication bias and asymmetry in the MR analysis for metabolic markers related to reproductive disorders.\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4207495/v1/c6c4709a7329140db5a636d7.pdf"},{"id":55009768,"identity":"dccdfc58-c327-4f9c-aa6e-daf42662bdd6","added_by":"auto","created_at":"2024-04-19 19:15:54","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1143803,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S2 Scatter Plot\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis scatter plot depicts the correlation between the genetic associations of instrumental variables and the risk of reproductive endocrine disorders, providing a snapshot of the causal effects estimated by MR.\u003c/p\u003e","description":"","filename":"SupplementaryFigure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4207495/v1/c3210bd7e7d93ab53b7f54d2.pdf"},{"id":55009769,"identity":"e86d267b-0a83-427b-a97c-9b45db82fee2","added_by":"auto","created_at":"2024-04-19 19:15:54","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1189214,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S3 Forest Plot\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe forest plot aggregates the effect sizes and confidence intervals for each metabolite's impact on reproductive health outcomes, facilitating a meta-analytic view of their significance and heterogeneity.\u003c/p\u003e","description":"","filename":"SupplementaryFigure3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4207495/v1/56017a4d6cbc890404380404.pdf"},{"id":55009772,"identity":"43e57407-9833-46fb-babc-0124c4c80ca7","added_by":"auto","created_at":"2024-04-19 19:15:54","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1104905,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S4 Leave-one-out Plot\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe leave-one-out plot examines the influence of each individual instrumental variable on the overall MR results, ensuring the robustness and reliability of the associations with female infertility, polycystic ovary syndrome, and endometriosis.\u003c/p\u003e","description":"","filename":"SupplementaryFigure4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4207495/v1/28ddfeec06b98c4255037b79.pdf"},{"id":55009773,"identity":"8a3cf5ae-ce9a-43da-aa75-fb518db4ecfa","added_by":"auto","created_at":"2024-04-19 19:15:54","extension":"csv","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":4471858,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1 Comprehensive Information of All SNPs Associated with Female Infertility, Polycystic Ovary Syndrome, and Endometriosis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"SupplementaryTable1SNPinformation.csv","url":"https://assets-eu.researchsquare.com/files/rs-4207495/v1/c3aea84badb076ba5c6cec21.csv"},{"id":55009770,"identity":"c2a6d796-b144-4d02-ba4b-944df2d6bdc6","added_by":"auto","created_at":"2024-04-19 19:15:54","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":52875,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S2 Detailed Annotations for All Single Nucleotide Polymorphisms Employed as Instrumental Variables\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"SupplementaryTable2SNPannotation.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4207495/v1/90a2ed9d491c89fdb2dfd27e.xlsx"}],"financialInterests":"","formattedTitle":"Investigating the Metabolomic Pathways in Female Reproductive Endocrine Disorders: A Mendelian Randomization Study","fulltext":[{"header":"Background","content":"\u003cp\u003eReproductive endocrine disorders (RED) affecting females, including polycystic ovary syndrome (PCOS), endometriosis (EMs), and female infertility (FI), have been a major health issue for women in their reproductive years [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Research studies reveal that the global prevalence of PCOS varies between 4% and 21%. In China, the reported incidence of PCOS among women aged 19\u0026ndash;45 years is 5.6%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. EMs affects approximately 10% of women of reproductive age worldwide, affecting 190\u0026nbsp;million people [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. While in the United States, approximately 10\u0026ndash;15% of individuals are affected by FI, with around 30% of cases having an unknown etiology [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. RED can be treated using different methods, including medication, surgical intervention, and lifestyle adjustments. The major goal of medical care is to regulate the menstrual cycle, hormone secretion, and metabolism. On the other hand, surgical intervention generally aims to remove lesions, control pain, and assist with reproductive technologies. Lifestyle alterations prioritize the management of diet, exercise routines, and behavioral adjustments. Because the understanding of the etiology of RED is limited, the annual global expenditure on its treatment remains enormous, which places a significant burden on the global economy [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Researching the pathogenesis of RED will not only reduce the worldwide economic burden but also aid in the advancement of more efficient treatments [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe development of the RED complex usually arises from an unhealthy lifestyle, genetic and hormonal abnormalities, inflammation, and metabolic disturbances [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Metabolites are pivotal in metabolic processes since they either produce or consume resources, hence ensuring the proper functioning of physiological processes. For example, disturbances in sugar metabolism might result in the onset of chronic illnesses [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The findings of various studies have indicated that the processes of lipid metabolism, glucose metabolism, and sphingolipid metabolism may be intricately linked to the pathogenesis of RED [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Patients with PCOS exhibit a notable prevalence of common metabolic dysfunction [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. A robust association exists between inflammation in the FI signal and elevated androgen metabolism [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Supplementing vitamin C and E has been proposed as a possible treatment for reducing painful symptoms in patients with Ems [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Hence, metabolic alterations are closely connected to the development of RED. Nevertheless, the existing clinical evidence establishing the correlation between metabolites and RED is restricted and mostly obtained from observational studies, which may be influenced by confounding factors. Therefore, gaining a deeper understanding of the connection between metabolites and RED, as well as the probable mechanisms involved, is of great therapeutic importance for accurately assessing risk and improving treatment approaches.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is a commonly employed method for inferring causality. It utilizes genetic variants that are linked to the exposure of interest as instrumental variables (IVs) to establish the causal impact of the exposure on the outcome. This approach is effective in isolating the effect of exposures from potential confounding factors, such as environmental influences and lifestyle choices [\u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. It can reduce the impact of reverse causality to ensure maximum validity [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The swift advancement of Genome Wide Association Study (GWAS) data additionally establishes a strong basis for the execution of MR Research. The relationship between metabolomics and MR research, particularly in the context of elucidating the causal pathways in human diseases, exemplifies a compelling application of this methodology [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Metabolomics, the comprehensive study of small molecule metabolites within biological systems, offers a dynamic and sensitive reflection of both genetic and environmental influences on an organism's state. When integrated with MR, metabolomics can provide powerful insights into the causal relationships between specific metabolic profiles and disease outcomes.\u003c/p\u003e \u003cp\u003eAt present, there is a lack of research that uses the MR approach to thoroughly examine the cause-and-effect relationship between metabolites and the start of RED. This study aimed to examine the causal relationship between metabolites and PCOS, Ems, and FI using MR analysis. The findings could offer significant insights into the underlying biological mechanisms, potentially guiding targeted therapeutic strategies and preventive measures.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eIn our study on the link between plasma metabolites and the development of PCOS, EMs and FI levels, we propose three key hypotheses. First, we expect a correlation between certain genotypes and plasma metabolite levels, suggesting a genetic influence on these metabolites that could affect disease risk. Second, these genotypes should not be influenced by confounding factors, ensuring that any observed relationship with metabolite levels is likely due to a genuine biological mechanism. Third, we assume that these genotypes directly impact the metabolites in question, thereby affecting the risk or progression of PCOS, EMs, and FI. This framework aims to clarify how genetic variations contribute to the observed outcomes, using Mendelian randomization to explore potential causal connections. Moreover, it employs sensitivity testing to examine the robustness of the findings. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e depicts the process of Mendelian randomization including metabolites that are linked to PCOS, EMs, and FI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003eThe metabolite GWAS data was obtained from the Metabolomics GWAS Server website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://metabolomips.org/gwas/\u003c/span\u003e\u003cspan address=\"https://metabolomips.org/gwas/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), while the genetic variance data related to PCOS, EMs, and FI was obtained from the IEU OpenGWAS project website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). IVs were chosen as single-nucleotide polymorphisms (SNPs) that are linked to the composition of the plasma metabolites. Due to the fact that the data utilized in this study were collected from published studies or public databases, ethics approval was not necessary. The GWAS summary statistics for the metabolites were acquired from the Metabolomics GWAS Server, which may be accessed at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://metabolomips.org/gwas/\u003c/span\u003e\u003cspan address=\"https://metabolomips.org/gwas/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. This study involves genome-wide association scans combined with high-throughput metabolic monitoring to get unique insights into the impact of genetic variation on metabolism and complex diseases. The researchers have conducted a thorough investigation into the genetic factors that affect human metabolism. They studied a total of 7,824 adult individuals from two European population studies. The study identified significant associations between 145 metabolic loci and more than 1400 metabolites in human blood, revealing their biochemical connectivity[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The GWAS summary datasets for PCOS were obtained from the OpenGWAS database. The GWAS ID is finn-b-E4_POCS, with a total of 118,870 female participants, consisting of 642 cases and 118,228 controls. The total count of SNPs is 16,379,676. The GWAS data for individuals with endometriosis were obtained from a recent genome-wide association study. The selected trait for analysis was endometriosis, and the discovery sample consisted of 4,511 cases of European ancestry and 227,260 controls of European ancestry [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The GWAS summary datasets for FI were obtained from the OpenGWAS database. The GWAS ID is finn-b-N14_FEMALEINFERT, with a total of 75,470 female participants, consisting of 6,481 cases and 68,969 controls. The total count of SNPs is 16,377,038.\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\u003eDetailed Information of Datasets\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAdjustment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExposure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe Metabolomics GWAS Server\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMetabolites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eOutcomes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efinn-b-E4_POCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePolycystic Ovarian Syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eebi-a-GCST90018839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEndometriosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e231771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efinn-b-N14_FEMALEINFERT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale Infertility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMales and Females\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSelection of IVs\u003c/h2\u003e \u003cp\u003eThe following criteria were employed in this study for the purpose of screening instrumental variables [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Typically, this is accomplished by utilizing data obtained from extensive GWAS. The selected SNPs should possess a strong and firmly proven correlation with the exposure. The IVs demonstrate a robust association with exposure, as evidenced by a significant criteria of P\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;5 (correlation hypothesis) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. It is necessary to measure the degree of correlation between each SNP and the exposure. This entails utilizing statistical analysis to quantify the magnitude of the impact of each SNP on the exposure, often quantified as the beta coefficient in a regression model [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. It's crucial to ensure that the SNPs used as IVs affect the outcome only through the exposure and not through other pathways. This is known as the assumption of no pleiotropy [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The SNPs selected by the MR Method adhere to the genetic principle of random parental allele assignment to offspring, thereby minimizing the influence of environmental and acquired factors. Consequently, it can be theoretically assumed that instrumental variables are independent of social, economic, and cultural factors. SNPs in close proximity on the genome can be in linkage disequilibrium, meaning they are often inherited together. It's important to ensure that the selected SNPs are independent of each other, or to adjust the analysis for LD [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The selected SNPs should collectively explain a significant portion of the variance in the exposure. Weak instruments (those that explain only a small fraction of the variance) can lead to biased MR estimates. Moreover, an F statistic greater than 10 is employed as a criterion for assessing weak instrumental variables [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The screening condition for selecting meaningful SNPs from the aggregated GWAS data of metabolites were set as P\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;5. The coefficient of linkage disequilibrium is r2\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and the width of the linkage disequilibrium region was 10000kb. These were applied to ensure the independence of each SNP and eliminate the impact of linkage imbalance on the results [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The PhenoScanner (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.phenoscanner.medschl.cam.ac.uk/\u003c/span\u003e\u003cspan address=\"http://www.phenoscanner.medschl.cam.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to account for confounding factors and assess the outcome associated with each SNP. From the aforementioned screened SNPs, those extracted from the aggregated GWAS data of PCOS, EMs, and FI had a minimum r2 value greater than 0.8. This summarizes the information contained in this dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eVerification of Causality\u003c/h2\u003e \u003cp\u003eThe MR-Egger regression and random-effects inverse-variance weighted (IVW) methods were employed to validate the causal relationship between exposure (Metabolites) and outcomes (PCOS, EMs, and FI), using SNPs as instrumental variables. IVW, which is widely recognized as the primary outcome measure, was utilized [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In this approach, each locus's inverse of variance (R2) served as the weight for estimating the causal effect based on multiple SNPs as instrumental variables. These weighted causal effect estimates were then summed to obtain the final estimate using IVW method. MR-Egger method essentially incorporates a weaker assumption (InSIDE) within IVW framework to perform causal effect estimation while introducing a regression intercept term to detect and correct bias caused by pleiotropic effects of instrumental variables in order to estimate the causal relationship between exposure and outcome [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. When there is horizontal pleiotropy present, MR-Egger results can be referenced. Additionally, random-effects inverse-variance weighting method was applied for analyzing the causal relationship between variables with MR-Egger regression serving as a supplementary analysis technique [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. All aforementioned methods were implemented using TwoSample MR Package in R 4.1.0 software at a significance level of α\u0026thinsp;=\u0026thinsp;0.05. The SNP annotation was performed using online tools available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biit.cs.ut.ee/gprofiler/snpense\u003c/span\u003e\u003cspan address=\"https://biit.cs.ut.ee/gprofiler/snpense\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. SNPense tool facilitated the mapping of human SNP rs-codes to gene names, providing chromosomal coordinates and predicted variant effects. Mapping was restricted to variants that overlapped with protein-coding Ensembl genes[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. All essential data were retrieved from Ensembl variation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eHeterogeneity and horizontal pleiotropy\u003c/h2\u003e \u003cp\u003eThe heterogeneity was assessed using Cochran's Q test, with results considered heterogeneous if P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. I\u0026sup2; (I-squared) was also used to measure heterogeneity, indicating the proportion of variation due to heterogeneity in total variation [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The range for I\u0026sup2; is from 0\u0026ndash;100%, with larger values indicating higher levels of heterogeneity. The formula for calculating I\u0026thinsp;=\u0026thinsp;2(Q - Q_df)/Q. For pleiotropy analysis, the intercept term of MR-Egger's method was utilized, while leave-one-out analysis was employed for sensitivity analysis. IVs were deemed non-pleiotropic when the intercept term of the MR-Egger regression model equaled zero (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05)[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. To conduct leave-one-out analysis on IVs, each SNP was gradually removed and the remaining SNPs were re-analyzed to observe their individual effects [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eIVs screening\u003c/h2\u003e \u003cp\u003eAfter undergoing multiple screenings, the dataset of PCOS, EMs, and FI associated with metabolites ultimately comprised 13,615 SNPs. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the basic information of select SNPs, while the remaining SNPs exhibit similar characteristics. The F-statistic for each individual SNP ranged from 17.64 to 1294.286 (mean: 26.468), suggesting that weak instrumental variables were unlikely to significantly impact the causal association. See the \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e for more details.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMR Results of Causal Links\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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\"\u003e \u003cp\u003eData source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClassification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClassification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNsnp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMethods\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e 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colname=\"c3\"\u003e \u003cp\u003eepiandrosterone sulfate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.938(1.348\u0026ndash;6.406)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eX-12990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.260(1.074\u0026ndash;16.901)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0393\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e 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\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eX-04499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.379(0.188\u0026ndash;0.764)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbohydrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emannose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.905(1.020\u0026ndash;3.558)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnergy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eacetylphosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.162(0.036\u0026ndash;0.724)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1-eicosatrienoylglycerophosphocholine*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.898(1.110\u0026ndash;3.243)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1-linoleoylglycerol (1-monolinolein)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.686(0.482\u0026ndash;0.975)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1-palmitoylglycerophosphoethanolamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.635(0.434\u0026ndash;0.930)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15-methylpalmitate (isobar with 2-methylpalmitate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.151(1.042\u0026ndash;4.439)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0383\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3-hydroxybutyrate (BHBA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.829(0.695\u0026ndash;0.988)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4-androsten-3beta,17beta-diol disulfate 1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.363(1.102\u0026ndash;1.685)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eepiandrosterone sulfate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.681(0.472\u0026ndash;0.982)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eX-11445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.328(1.002\u0026ndash;1.760)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0487\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNucleotide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eX-11422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.070(1.574\u0026ndash;16.330)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeptide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eleucylleucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.542(0.335\u0026ndash;0.877)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"13\" rowspan=\"14\"\u003e \u003cp\u003e\u003cb\u003eFI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmino acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eisovalerylcarnitine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"13\" rowspan=\"14\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.574(0.394\u0026ndash;0.836)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmino acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ephenylacetate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.580(0.344\u0026ndash;0.978)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0408\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbohydrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eglucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.402(0.187\u0026ndash;0.862)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1-arachidonoylglycerophosphoinositol*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.416(1.274\u0026ndash;4.582)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1-palmitoylglycerophosphocholine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.215(1.295\u0026ndash;7.979)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2-hydroxyglutarate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.525(0.291\u0026ndash;0.945)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2-oleoylglycerophosphocholine*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.738(1.323\u0026ndash;5.670)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edocosapentaenoate (n3 DPA; 22:5n3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.400(0.210\u0026ndash;0.761)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehexadecanedioate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.431(1.007\u0026ndash;2.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emargarate (17:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.321(0.122\u0026ndash;0.841)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNucleotide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7-methylguanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.079(1.129\u0026ndash;3.828)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeptide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eADpSGEGDFXAEGGGVR*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.077(1.301\u0026ndash;3.315)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeptide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDSGEGDFXAEGGGVR*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.628(1.127\u0026ndash;2.353)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeptide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eglycylvaline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.619(1.067\u0026ndash;2.456)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eMR: Mendelian Randomization, PCOS: polycystic ovary syndrome,\u003c/p\u003e \u003cp\u003eEMs: endometriosis, FI: female infertility, IVW: inversed-variance weighted, OR: odds ratio, CI: confidence interval\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eThe analysis of causal relationship\u003c/h2\u003e \u003cp\u003eWe conducted a comprehensive MR analysis to assess the causal relationships between each screened metabolite and conditions with PCOS, EMs, and FI (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The IVW analysis results demonstrated that substances with confidence intervals that do not cross the line of no effect and have a p-value less than 0.05 are considered to have a statistically significant association with the outcome.\u003c/p\u003e \u003cp\u003eIn PCOS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), the analysis revealed significant associations across a range of metabolite classes, including amino acids, carbohydrates, lipids, nucleotides, and peptides. Phenylacetate, an amino acid, exhibited a notable association with an increased risk of PCOS (OR\u0026thinsp;=\u0026thinsp;4.439, 95% CI: 1.070-18.417, p\u0026thinsp;=\u0026thinsp;0.0401). In the carbohydrate category, glucose showed a protective effect (OR\u0026thinsp;=\u0026thinsp;0.058, 95% CI: 0.005\u0026ndash;0.678, p\u0026thinsp;=\u0026thinsp;0.0232), while glycerate and mannitol were linked to varying effects on PCOS risk. Lipid metabolites, particularly 1-oleoylglycerophosphocholine and epiandrosterone sulfate, were significantly associated with PCOS, underscoring their potential roles in its pathogenesis. Additionally, nucleotide uridine and peptides like pyroglutamylglycine also showed significant associations, suggesting their involvement in PCOS development.\u003c/p\u003e \u003cp\u003eFor Endometriosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), our findings indicated significant associations with amino acids such as creatinine and leucine, pointing to a potential influence on EMs risk (OR\u0026thinsp;=\u0026thinsp;4.425, 95% CI: 1.281\u0026ndash;15.287, p\u0026thinsp;=\u0026thinsp;0.0187 for creatinine). Carbohydrates like mannose and energy metabolites such as acetylphosphate also demonstrated significant links to EMs. Among lipids, several metabolites including 1-eicosatrienoylglycerophosphocholine and 4-androsten-3beta,17beta-diol disulfate 1 showed associations with EMs risk, highlighting the complexity of lipid metabolism in EMs etiology. The analysis further identified significant associations with other lipid metabolites, emphasizing their potential regulatory roles in EMs.\u003c/p\u003e \u003cp\u003eIn the context of FI (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e, isovalerylcarnitine, an amino acid, was inversely associated with FI (OR\u0026thinsp;=\u0026thinsp;0.574, 95% CI: 0.394\u0026ndash;0.836, p\u0026thinsp;=\u0026thinsp;0.0038), suggesting a protective role against insulin resistance. Carbohydrates and lipids also displayed significant associations with FI levels. Specifically, glucose showed an inverse relationship, while various lipid metabolites such as 1-arachidonoylglycerophosphoinositol and docosapentaenoate were linked to FI alterations, indicating their importance in insulin metabolism and sensitivity. Additionally, nucleotides and peptides demonstrated significant associations, further underscoring the multifaceted nature of genetic influences on FI levels.\u003c/p\u003e \u003cp\u003eWe summarized a comprehensive information of the metabolite classifications (classified as amino acids, carbohydrates, lipids, nucleotides, and peptides) associated with three diseases, as well as the number of SNPs and their causality (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEvaluation of Heterogeneity and Pleiotropy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClassification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMetabolites\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e \u003cp\u003eHeterogeneity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eHorizontal Pleiotropy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI\u003csup\u003e2\u003c/sup\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCochran's Q\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEgger intercept\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u003cb\u003ePCOS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmino acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ephenylacetate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.0772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.6826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbohydrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eglucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.1044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.3449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbohydrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eglycerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.7892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.3206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbohydrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emannitol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.1490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.1634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1-oleoylglycerophosphocholine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.8449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.1477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eepiandrosterone sulfate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.3746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.4625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eX-12990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.8790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.8479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNucleotide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003euridine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.9945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeptide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003egamma-glutamylisoleucine*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.5263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.4160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeptide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epyroglutamylglycine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.1383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.5563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"14\" rowspan=\"15\"\u003e \u003cp\u003e\u003cb\u003eEMs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmino acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.6618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.6733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmino acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eleucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e163.4983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.1682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmino acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eX-04499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.2751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.3838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbohydrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emannose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.5446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnergy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eacetylphosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.1042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.8567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1-eicosatrienoylglycerophosphocholine*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.7089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.1360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1-linoleoylglycerol (1-monolinolein)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.2416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1-palmitoylglycerophosphoethanolamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.0084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.4529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15-methylpalmitate (isobar with 2-methylpalmitate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.3602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.4175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3-hydroxybutyrate (BHBA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.4153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.0585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4-androsten-3beta,17beta-diol disulfate 1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.2044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.8475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eepiandrosterone sulfate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.1942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.9668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eX-11445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.5627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.8013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNucleotide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eX-11422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.9658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeptide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eleucylleucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.9940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"13\" rowspan=\"14\"\u003e \u003cp\u003e\u003cb\u003eFI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmino acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eisovalerylcarnitine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.8542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.9512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmino acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ephenylacetate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.8058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.9122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbohydrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eglucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.5290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.9602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1-arachidonoylglycerophosphoinositol*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.7537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.3279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1-palmitoylglycerophosphocholine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.6588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.4440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2-hydroxyglutarate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.8895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.3083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2-oleoylglycerophosphocholine*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.3490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.1269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edocosapentaenoate (n3 DPA; 22:5n3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.9204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehexadecanedioate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.7590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.8468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emargarate (17:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.9381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.9108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNucleotide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7-methylguanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.5614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.2897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeptide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eADpSGEGDFXAEGGGVR*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.3992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeptide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDSGEGDFXAEGGGVR*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.7625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.1025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeptide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eglycylvaline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.4344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.0518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.5683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003eMR: Mendelian Randomization, PCOS: polycystic ovary syndrome, EMs: endometriosis, FI: female infertility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe detailed list provides supplementary information for SNP annotation in metabolites of imparity, and it uncovers genetic loci where metabolites exert an impact on PCOS, EMs, and FI (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity Testing\u003c/h2\u003e \u003cp\u003eIn our study employing MR to assess the impact of specific metabolites on the risk of PCOS, EMs, and alterations in FI levels, we evaluated outcomes across various metabolite classes. The results are organized by disease condition, including assessments of heterogeneity and horizontal pleiotropy, which are crucial for understanding the robustness and specificity of the associations identified.\u003c/p\u003e \u003cp\u003eFor PCOS, heterogeneity, as measured by I\u003csup\u003e2\u003c/sup\u003e(%) and Cochran's Q, varied across metabolites, with phenylacetate (amino acid) showing relatively low heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;13%) but a significant horizontal pleiotropy p-value (Egger intercept p-value\u0026thinsp;=\u0026thinsp;0.6826). Other notable findings include glucose (carbohydrate) with an I\u003csup\u003e2\u003c/sup\u003e of 11% and no significant pleiotropy (Egger intercept p-value\u0026thinsp;=\u0026thinsp;0.3449), and 1-oleoylglycerophosphocholine (lipid) with I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;7% and a marginally non-significant pleiotropy p-value (Egger intercept p-value\u0026thinsp;=\u0026thinsp;0.1477). These results suggest a varied landscape of genetic instruments' effects on PCOS risk, with some metabolites showing more stable and specific associations than others.\u003c/p\u003e \u003cp\u003eIn EMs, creatinine (amino acid) exhibited moderate heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;15%) and no significant pleiotropy (Egger intercept p-value\u0026thinsp;=\u0026thinsp;0.6733). A high level of heterogeneity was noted for leucine (amino acid) with an I\u003csup\u003e2\u003c/sup\u003e of 9% and a borderline significant pleiotropy (Egger intercept p-value\u0026thinsp;=\u0026thinsp;0.1682), suggesting the need for cautious interpretation. The lipid metabolite 1-linoleoylglycerol (1-monolinolein) showed a notable I\u003csup\u003e2\u003c/sup\u003e of 21% and a pleiotropy p-value nearing significance (Egger intercept p-value\u0026thinsp;=\u0026thinsp;0.0781), indicating potential pleiotropic effects influencing its association with EMs.\u003c/p\u003e \u003cp\u003eFor alterations in FI, isovalerylcarnitine (amino acid) had a low heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;5%) and showed no evidence of significant horizontal pleiotropy (Egger intercept p-value\u0026thinsp;=\u0026thinsp;0.9512), suggesting a more direct association with FI. Conversely, phenylacetate (amino acid) and glucose (carbohydrate) displayed higher heterogeneity but also no significant pleiotropy, highlighting the complexity of these associations.\u003c/p\u003e \u003cp\u003eWe summarized the analysis of MR heterogeneity and directional pleiotropy in metabolites and outcome variables associated with three diseases (\u003cb\u003eTable\u0026nbsp;4\u003c/b\u003e). The identified SNPs did not exhibit a statistically significant impact on the estimates of causal association.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study represents a pioneering endeavor to unveil the intricate connections between metabolites and REDs from a metabolomics perspective. By employing MR analysis, we have identified a combined total of 39 metabolites (10 for PCOS, 15 for Ems, and 14 for FI) across various classes that are significantly associated with conditions. Through the process of gene annotation, it has been shown that there are potentially 442 different types of genes that could be linked to female REDs (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). This work is the first to use MR analysis to establish a causal link between metabolites and RED. These findings not only enrich our understanding of the metabolic underpinnings of RED but also pave the way for novel diagnostic and therapeutic approaches.\u003c/p\u003e \u003cp\u003eMany recent studies have focused on the significance of metabolites in female reproductive endocrine diseases. Li et al. discovered that mono-(2-ethylhexyl) phthalate (MEHP), a byproduct of DEHP, can disrupt ovarian function by causing irregularities in the 17β-hydroxysteroid dehydrogenase (17β-HSD) signaling pathway. This study sought to validate this idea in living organisms by conducting experiments on adult female Wistar rats[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Li et al conducted a study on the developmental abnormalities of the ovary in quail caused by di-(2-ethylhexyl) phthalate (DEHP) and its metabolite MEHP. They emphasized the receptor-mediated signaling pathway through which these metabolites hinder estradiol production and disrupt the hypothalamic-pituitary-ovarian axis[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Additionally, Charifson et al. suggested that PCOS could be attributed to a mismatch between genetic variations that developed in physically active subsistence settings and the sedentary industrialized surroundings of modern times[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. In their study, Chu et al. examined the impact of continuous exposure to light on the disruption of circadian rhythm and its implications on reproductive, metabolic, and gut microbiome abnormalities resembling those seen in PCOS in a rat model. The study highlights the significance of circadian rhythm in the development of PCOS[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In addition, Mukhopadhyay et al. conducted a review on the relationship between bisphenol A (BPA) and the risk of PCOS. Their focus was on how BPA can change the expression of genes that play a role in regulating hormones, which are often linked to the symptoms of PCOS[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Ding et al. emphasized the importance of comprehending the impacts of endocrine disrupting chemicals (EDCs) on female reproductive health, particularly in relation to ovarian aging[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. To summarize, the data indicates that metabolites, namely those originating from phthalates and BPA, have a substantial impact on female reproductive endocrine disorders such as PCOS. Gaining a comprehensive understanding of the processes by which these metabolites interfere with endocrine pathways and affect ovarian function is essential for the development of successful treatments and interventions for these disorders[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Additional investigation is necessary to enhance our comprehension of the intricate problems related to female reproductive health, including the impacts of EDCs, circadian rhythm disruption, and toxicological data from biomonitoring investigations[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom the perspective of pathophysiological mechanisms, the growth of endometriotic deposits is dependent on the presence of estradiol, which is supplied by both systemic hormones and enhanced expression of aromatase and steroidogenic acute regulatory protein locally. Additionally, the expression of 17β-hydroxysteroid dehydrogenase 2 is reduced by endometriotic lesions, further contributing to the proliferation of these deposits[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Furthermore, lesions exhibit heightened expression of estrogen receptor β[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Moreover, the inhibition of progesterone receptor B in both normal and abnormal endometrial tissue is intensified in abnormal endometrial stromal cells due to epigenetic differential methylation of PR-B, HOX, and GATA family transcription-factor genes[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. This leads to disrupted progesterone signaling, commonly referred to as \"progesterone resistance\"[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. PCOS is associated with adrenocortical steroidogenic dysfunction, as evidenced by research[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] Around one third of women with PCOS show elevated levels of dehydroepiandrosterone sulfate, which is an androgen metabolite or prohormone primarily produced by the adrenal cortex. Insulin resistance and compensatory hyperinsulinemia are crucial factors in the development of PCOS. Excessive insulin, in combination with LH, enhances the production of androgens by ovarian theca cells[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Additionally, it reduces the production of sex hormone-binding globulin in the liver, together with the excess of androgens[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The cause of the reduced insulin sensitivity in PCOS is not yet fully understood. However, it is believed that the different genetic and epigenetic abnormalities contribute to impairments in the production and function of the main glucose transporter in cells, known as glucose transporter 4 (GLUT4), as well as impairments in the disposal of glucose through insulin. Patients with PCOS also exhibit abnormalities in insulin-mediated lipolysis. Furthermore, the level of insulin resistance in PCOS is exacerbated by a condition of persistent mild inflammation, partly caused by aberrant production and function of adipocytokines. On the other hand, there is stronger evidence indicating that women with PCOS have adipose tissue that shows different defects that promote an inflammatory or insulin resistant condition. These defects include dysfunction in adipocytokines, dysregulation of free fatty acid metabolism, and abnormalities in epigenetics that affect GLUT4 function[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Further investigation is required to rule out or confirm clinical evaluation using more targeted hormonal testing. Thyroid problems, hyperprolactinemia, and nonclassic adrenal hyperplasia are the main conditions to consider. These can be ruled out by measuring thyroid-stimulating hormone, prolactin, and 17-hydroxyprogesterone, respectively. Nonclassic adrenal hyperplasia caused by abnormalities in CYP21A2 affects a percentage ranging from 1 to 10% of women with excessive hair growth, depending on their ethnic background. This condition is the most prevalent autosomal-recessive ailment in the human population. Nonclassic adrenal hyperplasia caused by abnormalities in CYP21A2 affects a percentage ranging from 1 to 10% of women with excessive hair growth, depending on their ethnic background. This condition is the most prevalent autosomal-recessive ailment in the human population. Additional evidence indicates that promptly identifying the condition and administering corticosteroid treatment may enhance the chances of achieving a successful reproductive outcome[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Therefore, it is recommended to evaluate any woman exhibiting signs, symptoms, or complaints of hyperandrogenism, regardless of severity, for nonclassic adrenal hyperplasia. Practitioners must be aware that it is not feasible to clinically identify or even make an educated guess about the diagnosis of nonclassic adrenal hyperplasia. It is absolutely necessary to evaluate the levels of 17-hydroxyprogesterone[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are multiple benefits to our study. As far as we know, this is the initial MR investigation conducted to assess the causal connection between the metabolites and REDs. Undoubtedly, our current focus is on conducting exploratory research with the specific aim of enhancing our ability to support future metabolomics studies. This research provides valuable insights and guidance for our future endeavors. Metabolomics is a scientific discipline that seeks to understand how living systems react to genetic alterations, environmental shifts, or disease conditions by examining the entire collection of metabolites within a specific biological setting. This method offers a precise representation of the biochemical processes occurring within the system during the moment of sample collection. Thus, we anticipate that our study can offer guidance for the analysis of metabolites and genomes, enabling researchers to focus on fundamental experiments accordingly.\u003c/p\u003e \u003cp\u003eBut there also are certain constraints that need to be addressed. First, identifying genetic variations that are strongly linked to specific metabolites and can serve as instrumental factors can be a complex task. Metabolites can be altered by a combination of genetic factors and environmental conditions. These genetic variants account for only a minor fraction of the variability in the metabolites, which may result in a slight instrument bias and diminish the statistical power of the study. The chosen genetic variants may impact the likelihood of female reproductive endocrine disorders through biological pathways that are not connected to the hypothesis, so contravening the third postulate of MR. Pleiotropy may introduce a bias in the estimated causal effect. Second, the study sample consists of individuals with varied genetic backgrounds. Failing to effectively adjust for population stratification could result in false relationships. This poses a significant challenge when the correlation between genetic variations and metabolites differs among different populations. The genetic variations utilized are predominantly derived from specific groups with European heritage, hence the findings may not be generalizable to individuals with different genetic backgrounds. Therefore, the study's capacity to be applied to a wider population may be restricted. Third, if there is a potential for bidirectional causation between specific metabolites and female reproductive endocrine problems, relying solely on MR may not provide an accurate determination of the actual causal relationship. Furthermore, inaccuracies in detecting metabolite levels may introduce bias in the assessment of causal links, particularly if the measurements of metabolites are inaccurate or affected by batch effects. If individuals in the sample are chosen or omitted based on their disease status or other specific criteria, the study results may not be applicable to the wider population. Subsequent metabolomics investigations should include provisions to tackle these constraints.\u003c/p\u003e \u003cp\u003eThis pioneering study marks a significant advancement in understanding the complex interplay between metabolites and RED from a metabolomics standpoint. Through meticulous Mendelian Randomization analysis, we've unveiled specific associations across various metabolite classes\u0026mdash;including amino acids, carbohydrates, lipids, nucleotides, and peptides\u0026mdash;with conditions like PCOS, EMs, and FI. These insights not only deepen our comprehension of RED's metabolic foundations but also herald new avenues for diagnosis and treatment. The identification of metabolites as potential biomarkers offers promising pathways for early detection and risk assessment, enabling more precise and personalized management strategies. Furthermore, elucidating these causal relationships opens up possibilities for targeted interventions aimed at correcting metabolic imbalances, thereby offering hope for improved outcomes. The diversity of implicated metabolites underscores the necessity for a personalized medicine approach, tailoring treatments to individual metabolic dysregulations to optimize care. Consequently, this research not only contributes significantly to our understanding of reproductive health but also paves the way for transformative developments in the diagnosis, management, and treatment of reproductive endocrine disorders, spotlighting the critical role of metabolomics in advancing reproductive medicine.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur work employed a multivariate two-sample MR analysis using publicly accessible GWAS meta-analysis data to examine the causal connection between metabolites and several RED, such as PCOS, EMs and FI. Ultimately, we conducted a thorough assessment of the potential correlation between the metabolites and REDs. These metabolites and genes could potentially serve as biomarkers and offer valuable insights for future studies on treatment.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe want to acknowledge the participants and investigators of FinnGen, the UK Biobank study for sharing the genetic data, and we express our sincere gratitude to all the diligent researchers, collaborators, and participants who contributed to the GWAS studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFL: Conceptualization, Methodology, Software, Validation, Formal analysis, Data Curation, Writing - Original Draft, Writing - Review \u0026amp; Editing, Visualization.\u003c/p\u003e\n\u003cp\u003eZW: Conceptualization, Methodology, Software, Validation, Formal analysis, Data Curation, Writing - Original Draft, Writing - Review \u0026amp; Editing, Visualization.\u003c/p\u003e\n\u003cp\u003eQY: Software, Validation, Formal analysis, Data Curation.\u003c/p\u003e\n\u003cp\u003eFY: Methodology, Software, Validation, Formal analysis.\u003c/p\u003e\n\u003cp\u003eCX: Methodology, Software, Validation, Data Curation.\u003c/p\u003e\n\u003cp\u003eMW: Software, Validation.\u003c/p\u003e\n\u003cp\u003eZX: Formal analysis, Data Curation.\u003c/p\u003e\n\u003cp\u003eRG: Conceptualization, Writing - Review \u0026amp; Editing, Supervision, Project administration.\u003c/p\u003e\n\u003cp\u003eSC：Conceptualization, Writing - Review \u0026amp; Editing, Supervision, Project administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRui Guan was supported partially by the Foundation of Changhai Hospital (No. 2020YXK024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll used in the present study are publicly available. The original repositories of these data are described in the \u0026ldquo;Methods\u0026rdquo; section.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eAll the data used in this study were acquired from publicly available genomewide association study summary statistics. No new data was collected, and no new ethical approval was needed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLiang Y, Zeng W, Hou T, Yang H, Wu B, Pan R, Huang L. 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Fertil Steril. 2010;94(2):684\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mendelian randomization, metabolites, reproductive endocrine disorders, polycystic ovary syndrome, endometriosis, female infertility, Genome Wide Association Study, instrumental variables","lastPublishedDoi":"10.21203/rs.3.rs-4207495/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4207495/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eReproductive endocrine disorders (RED) including polycystic ovary syndrome (PCOS), endometriosis (EMs), and female infertility (FI), significantly affect women's health globally, with varying prevalence across different regions. These conditions can be addressed through medication, surgical interventions, and lifestyle modifications. However, the limited understanding of RED's etiology and the substantial economic burden of its treatment highlight the importance of investigating its pathogenesis. Metabolites play a critical role in metabolic processes and are potentially linked to the development of RED. Despite existing studies suggesting correlations between metabolites and RED, conclusive evidence remains scarce, primarily due to the observational nature of these studies, which are prone to confounding factors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study utilized Mendelian Randomization (MR) to explore the causal relationship between metabolites and RED, leveraging genetic variants associated with metabolite levels as instrumental variables to minimize confounding and reverse causality. Data were obtained from the Metabolomics GWAS Server and the IEU OpenGWAS project. Instrumental variables were selected based on their association with the human gut microbiota composition, and the GWAS summary statistics for metabolites, PCOS, EMs, and FI were analyzed. The MR-Egger regression and random-effects inverse-variance weighted (IVW) methods were employed to validate the causal relationship. Cochran's Q test was employed to evaluate heterogeneity, sensitivity analysis was performed using leave-one-out analysis, and for pleiotropy analysis, the intercept term of MR-Egger's method was investigated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe MR analysis revealed significant associations between various metabolites and RED conditions. For instance, a positive association was found between 1-palmitoylglycerophosphocholine and PCOS, while a negative association was noted between phenylacetate and FI. The study identified several metabolites associated with an increased risk and others with protective effects against PCOS, EMs, and FI. These findings highlight the complex interplay between metabolites and RED, suggesting potential pathways through which these conditions could be influenced or treated.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis MR study provides valuable insights into the causal relationship between metabolites and female reproductive endocrine disorders, suggesting that metabolic alterations play a significant role in the pathogenesis of PCOS, EMs, and FI, and offering a foundation for future research and therapeutic development.\u003c/p\u003e","manuscriptTitle":"Investigating the Metabolomic Pathways in Female Reproductive Endocrine Disorders: A Mendelian Randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-19 19:15:49","doi":"10.21203/rs.3.rs-4207495/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6da560f5-c16c-494f-9929-70a261b568a8","owner":[],"postedDate":"April 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-08T09:56:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-19 19:15:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4207495","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4207495","identity":"rs-4207495","version":["v1"]},"buildId":"B-jG_2CBjPDmsCi4Wdhf-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}