Association between circulating metabolites and endometriosis: a bidirectional two-sample Mendelian randomization study

Endometriosis (EM) is a chronic gynecological disorder marked by the presence of endometrial-like tissue outside the uterus. This condition is associated with a range of symptoms including pelvic discomfort, dysmenorrhea, and infertility [1]. Globally, approximately 10% of women of re - productive age are affected, which translates to an estimated 190 million individuals [2]. The disease significantly impacts the quality of life and often requires long-term medical man- agement. The health burden of EM encompasses chronic pain and substantial lifetime costs, estimated at $27,855 per patient per year. This results in annual healthcare ex - penses on EM of approximately $22 billion in the United States and £12.5 billion in the United Kingdom, accounting for treatment, lost work, and related healthcare costs [3]. Therefore, advancing our understanding and treatment of EM are paramount priorities for women's health. The etiolo- gy of EM is complex and encompasses multiple contributing factors, including retrograde menstruation, immune system dysfunction, benign metastasis, coelomic metaplasia, hor - monal imbalances, participation of stem cells, modifications in epigenetic regulation, and various environmental factors [4-6]. Furthermore, another study highlighted a strong link between dietary patterns and the risk of endometrioma de - velopment, demonstrating that lower calcium intake is sig - nificantly associated with an increased risk of endometrioma [7]. Currently, no singular pathophysiological or molecular framework exists that sufficiently elucidates every instance of this disorder. Recent studies have suggested that metabolic alterations play a role in the pathogenesis of EM. Women with EM have demonstrated alterations in lipid, glucose, and amino acid metabolism that may facilitate disease progression by affect- ing the endocrine environment, altering immune responses, and promoting lesion growth [8,9]. Extensive research has consistently demonstrated a strong correlation between the development and advancement of EM and abnormalities in lipid metabolism, as evidenced by significant differences in lipid profiles between women with EM and healthy controls

Endometriosis (EM) is a chronic gynecological condition of unclear etiology, with evidence suggesting a link between metabolite levels and EM risk. A two-sample Mendelian randomization (MR) approach was used to explore the associ- ation between 233 metabolites and EM.

Using publicly available genetic data, we conducted a bidirectional two-sample MR analysis to assess the associations between metabolites and EM. Sensitivity analyses were performed to test robustness and pleiotropy, with Bonferroni correction applied for significance.

MR analysis suggested that genetically elevated diacylglycerol levels were significantly associated with increased EM risk (odds ratio [OR], 1.225; P=1.16×10 -7 ), corresponding to a 22.5% increase in risk per standard deviation increase in genetically predicted diacylglycerol levels, and remained significant after Bonferroni correction. Nominally signifi- cant associations were observed for several other metabolites; lower ratios of 3-hydroxybutyrate and saturated fatty acids to total fatty acids and of total cholesterol to total lipids in very low-density lipoproteins were associated with a higher EM risk (OR, 0.863; P =0.015; OR, 0.865; P=0.030; OR, 0.855; P=1.51×10 -4 ). Reverse MR analysis showed that increased levels of conjugated linoleic acid (CLA) and tyrosine and the CLA to total fatty acid ratio exhibited nominal associations with EM (OR, 1.026; P=0.043; OR, 1.036; P=3.33×10 -4 ; OR, 1.026; P=0.045). No significant heterogeneity or pleiotropy was observed.

This study provides evidence of an association between specific metabolites, especially diacylglycerol, and EM risk, en- hancing our understanding of the metabolic profile associated with EM.

Endometriosis; Mendelian randomization; Plasma metabolites; Genome-wide association study www.ogscience.org214 Vol. 69, No. 3, 2026 [8,10-13]. Previous research has shown that individuals with EM exhibit statistically significant differences in altered amino acid levels within the tissue (eutopic endometrium), serum, follicular fluid, urine, and endometrial fluid compared to healthy controls. These findings are crucial for understanding various aspects of disease progression. Altered amino acid levels may elucidate the mechanisms of tissue injury repair in EM and the heightened energy demands of proliferative endometrial cells [8]. Glucose metabolism is significantly affected in patients with EM. Similar to tumor cells, ectopic endometrial stromal cells exhibit the Warburg effect, which is characterized by an increase in lactate production and heightened consumption of glucose [14]. Elevated levels of aerobic glycolysis and histone lactylation enhance cell pro - liferation and migration, thereby contributing to the patho - physiology of EM [9]. Furthermore, studies indicate that glycolysis and lactate accumulation profoundly influence the regulation of the immunomicroenvironment, with lactate acting as a crucial factor that drives M2 macrophage polar - ization, thereby promoting the invasion of endometriotic stromal cells both in vitro and in vivo [15]. Thus, there is a strong correlation between metabolic abnormalities and EM. A thorough investigation of the roles of relevant metabolites in EM will enhance our understanding of the underlying pathophysiological mechanisms of this complex condition and facilitate the development of novel diagnostic and thera- peutic strategies. Traditional observational studies frequently encounter chal- lenges, such as confounding factors and reverse causation, which can impede the ability to draw definitive conclusions regarding causal relationships. Mendelian randomization (MR) is a robust alternative that employs genetic variants as instru- mental variables to infer causality. This approach mitigates the issues of confounding and reverse causation, yielding more reliable estimates of causal effects. The objective of this study was to clarify the association between circulating metabolites and EM using MR analysis. This study used data from a comprehensive collection of 233 circulating metabo - lites derived from a genome-wide association study (GWAS) repository to examine the possible associations between EM and these metabolites. Furthermore, we sought to detect cir- culating metabolites that may serve as important biomarkers for the early detection of EM and contribute to the develop - ment of effective diagnostic and therapeutic strategies.

1. Study design We conducted bidirectional MR analysis to evaluate the po - tential influence of 233 circulating metabolites on EM risk. Following the framework established by Bowden et al. [16], our analysis was based on three key assumptions: first, the selected genetic instruments (IVs) derived from the datasets were linked to the exposure variable; second, these IVs were not associated with any hidden confounders related to ex - posure; and third, the IVs influenced the outcomes solely via the exposure factor without any alternative pathways. Our research involved human subjects and a reanalysis of exist - ing publicly available data that had already received ethical approval and participant consent, thereby eliminating the need for additional ethical reviews or consent procedures. An overview of the study design and methodological flow is shown in Fig. 1. 2. Data sources The circulating plasma metabolite dataset used in this study was obtained from the GWAS Catalog database (ID: GCST90301941-GCST90302173). This dataset comprises 233 metabolic traits, including 213 lipid and lipoprotein parameters or fatty acids, along with 20 non-lipid traits, in - cluding amino acids, ketone bodies, metabolites related to glycolysis/gluconeogenesis, fluid balance, and inflammation. Following variant filtering and quality control, 13,389,637 imputed autosomal single-nucleotide polymorphisms (SNPs) were included in the meta-analysis involving 136,016 par - ticipants [17]. EM data were obtained from the R10 release dataset of the FinnGen Consortium (https://r10.finngen.fi/), which comprised 16,588 case samples and 111,583 control samples, all of which were of European ancestry [18]. 3. Instrumental variables selection In accordance with methodologies utilized in prior research, a diverse array of IVs was systematically selected for each circulating metabolite and EM condition within the frame - work of our MR analysis [19-22]. We identified SNPs that exhibited statistically significant associations with circulating metabolites, adhering to a genome-wide significance thresh- old (P <1×10 -5 ; r 2 =0.001; genetic distance=10,000 KB). To ensure sufficient SNP availability for sensitivity assessments, relaxed selection criteria were applied to circulating metab - www.ogscience.org 215 Lele Pan, et al. Metabolites&endometriosis MR study olites. For EM analyses, however, stringent parameters were adopted ( P<5×10 -8 ; r 2 =0.001) with preserved 10,000 KB genetic distancing. Linkage disequilibrium pattern characteri- zation le veraged the European reference dataset of the 1,000 Genomes Project using clumping procedures. The variance explanation capacity of the IVs was determined through R 2 computation coupled with rigorous F-statistic filtering (F>10) to ensure adequate instrument strength. 4. Statistical analysis In order to assess the associations between 233 circulating metabolites and EM, we employed the Mendelian Random - ization package version 0.4.3 (Stephen Burgess, Cambridge, UK) to perform various analyses, including inverse-variance weighted (IVW), MR Egger, weighted median, simple mode, and weighted mode approaches [16,23-28]. These analytical methodologies were carefully selected to reduce possible bias and enhance the reliability of our research outcomes. In the MR analysis, the IVW method was predominantly employed because of its capacity to offer a dependable assessment of the exposure-outcome relationship under the condition that the IVs exhibited no pleiotropic effects. Cochran’s Q statistics were used to evaluate heterogeneity among individual SNPs. When no significant heterogeneity was found (P <0.05), a fixed-effects model was used; however, when notable het - erogeneity was present, a random-effects model was used. To mitigate potential pleiotropic bias, we implemented MR- Egger regression to evaluate the systematic bias from pleio - tropic effects through an intercept term analysis. Comple - mentary sensitivity assessments included the MR-pleiotropy residual sum and outlier approach to systematically identify and remove genetic variants that exhibited pleiotropic dis - tortions that might compromise causal estimates. A leave- one-out sensitivity analysis was conducted to assess whether any single SNP introduced bias affecting the overall causal conclusions. Scatter plots showed that no outliers signifi - cantly impacted the findings, and funnel plots confirmed the robustness of the association in the absence of heterogene - ity. All statistical analyses were performed with a two-sided significance threshold of 0.05 and were executed using R software (R Foundation for Statistical Computing, Vienna, Austria). A multiple-testing-adjusted threshold of P<1.07×10 -4 (0.05/466) was established based on the Bonferroni cor - rection, to identify a statistically significant association [29]. Furthermore, metabolites with P <0.05, which exceeded the Bonferroni-corrected threshold, were reported as suggestive No association Confounder Instrumental variables: SNPs Exposure Outcome Reliable association No independent association Selection criteria 1. Circulating metabolites: P<1×10 -5 ; EM: P10 1. MR analysis IVW, weighted median, MR Egger, Simple mode, and Weighted mode 2. Sensitivity analysis 3. Reverse MR analysis 4. Metabolic pathway analysis Fig. 1. The study design and workflow of the present MR study. SNPs, single-nucleotide polymorphisms; EM, endometriosis; MR, Mendelian randomization; IVW, inverse-variance weighted. www.ogscience.org216 Vol. 69, No. 3, 2026 risk predictors for EM. The combination of these complemen- tary approaches led to strong and dependable assessments of the association between circulating metabolites and EM.

Two-sample bidirectional MR analysis was conducted to investigate the association between circulating metabolites and EM. The IVW method served as the primary analytical framework and was supplemented by the MR-Egger regres - sion, simple mode, weighted mode, and weighted median approaches. 1. Exploration of the association effect of circulating metabolites on EM The results of the MR analysis are summarized in Fig. 2. The IVW method suggested that genetically predicted higher levels of diacylglycerol were associated with an increased risk of EM (odds ratio [OR], 1.225; 95% confidence interva [CI], 1.136-1.321; P =1.16×10 -7 ), corresponding to a 22.5% increase in risk per standard deviation increase in genetically predicted diacylglycerol levels, and this association remained significant after applying a strict Bonferroni correction. Sev - eral other metabolites showed nominally significant associa - tions. Genetically predicted lower levels of 3-hydroxybutyrate (OR, 0.863; 95% CI, 0.767-0.972; P=0.015), a lower ratio of saturated fatty acids to total fatty acids (OR, 0.865; 95% CI, 0.759-0.986; P=0.030), and a lower ratio of total cholesterol to total lipids in very small very low-density lipoproteins (VLDL) (OR, 0.855; 95% CI, 0.789-0.927; P =1.51×10 -4 ) were also associated with a higher EM risk. Furthermore, supplemen - tary methodologies validated our results and demonstrated a consistent direction of the effect (Fig. 2). The stability of the identified association links was confirmed using various alternative methods and sensitivity analyses, as outlined in Supplementary Table 1. Visual representations, including scatter and funnel plots, further confirmed the consistency and reliability of the findings (Supplementary Fig. 1). 2. Exploration of the association effect of EM on circulating metabolites The results of the reverse MR analysis are shown in Fig. 3. Using the IVW method, we found evidence suggesting that a genetic predisposition to EM was associated with several circulating metabolites. Specifically, EM showed nominally significant associations with higher levels of conjugated lin - oleic acid (CLA) (OR, 1.026; 95% CI, 1.000-1.053; P=0.043), ratios of CLA to total fatty acids (OR, 1.026; 95% CI, 1.000- 1.053; P=0.045), and tyrosine levels (OR, 1.036; 95% CI, Fig. 2. Forest plots of MR estimates of genetic associations between circulating metabolites and EM. nsnp, number of single-nucleotide polymorphisms; OR, odds ratio; CI, confidence interval; MR, Mendelian randomization; VLDL, very low-density lipoproteins. Exposure Outcome nsnp Method OR (95%CI) P MR Egger 1.277 (1.101, 1.482) 0.001 Weighted median 1.261 (1.126, 1.413) 5.86E-05 Diacylglycerol levels Endometriosis 68 Inverse variance weighted 1.225 (1.136, 1.321) 1.16E-07 Simple mode 1.292 (1.064, 1.569) 0.011 Weighted mode 1.277 (1.120, 1.457) 5.14E-04 MR Egger 0.880 (0.686, 1.127) 0.318 Weighted median 0.887 (0.748, 1.052) 0.169 3-hydroxybutyrate levels Endometriosis 44 Inverse variance weighted 0.863 (0.767, 0.972) 0.015 Simple mode 0.920 (0.674, 1.256) 0.605 Weighted mode 0.913 (0.728, 1.146) 0.440 MR Egger 0.904 (0.692, 1.182) 0.465 Weighted median 0.878 (0.722, 1.069) 0.196 Ratio of saturated fatty acids to total fatty acids Endometriosis 53 Inverse variance weighted 0.865 (0.759, 0.986) 0.030 Simple mode 0.849 (0.586, 1.230) 0.392 Weighted mode 0.917 (0.719, 1.169) 0.489 MR Egger 0.858 (0.746, 0.988) 0.036 Weighted median 0.823 (0.729, 0.929) 0.001 Total cholesterol to total lipids ratio in very small VLDL Endometriosis 92 Inverse variance weighted 0.855 (0.789, 0.927) 1.15E-04 Simple mode 0.864 (0.660, 1.131) 0.291 Weighted mode 0.835 (0.732, 0.952) 0.008 0.6 0.8 1 1.2 Lower risk Higher risk www.ogscience.org 217 Lele Pan, et al. Metabolites&endometriosis MR study 1.016-1.057; P=3.33×10 -4 ). To strengthen the associations identified in our study, we used multiple supplementary analytical approaches, along with sensitivity analyses (Sup - plementary Table 2). We also generated scatter and funnel plots to illustrate the robustness and validity of the results (Supplementary Fig. 2), further enhancing the credibility of the conclusions.

In this study, we elucidated the association between 233 ge- netically predicted serum metabolites and EM using genetic variation as an instrumental variable within a two-sample MR framework. Our findings indicate associations between two metabolites (diacylglycerol and 3-hydroxybutyrate) and two metabolite ratios (the ratio of saturated fatty acids to total fatty acids and the total cholesterol to total lipid ratio in very small VLDL) in EM, while also demonstrating that EM influences two metabolites (CLA and tyrosine) and one me - tabolite ratio (the ratio of CLA to total fatty acids). Notably, one of these associations was statistically significant after correcting for multiple tests, suggesting a strong relationship. In this exploratory study, we found that multiple metabolites were associated with EM. This study contributes to the un - derstanding of the metabolic factors involved in the patho - genesis of EM and provides a foundation for future studies and potential therapeutic targets. Alterations in lipid metabolism have been associated with the onset and progression of EM, and previous studies have confirmed the dysregulation of various lipids, including phosphatidylcholines, sphingomyelins, phosphatidyletha - nolamines, and triglycerides [13,30,31]. Diacylglycerol is a significant lipid molecule involved in various cellular signaling pathways, including those regulating inflammation and cell proliferation [32]. Research has demonstrated significant dif- ferences between the endometriotic and endometrial tissues in these patients [33]. However, few studies have examined the relationship between diacylglycerol and EM. The findings of this study demonstrated a significant correlation between higher concentrations of diacylglycerol and elevated suscep - tibility to EM onset. These findings are consistent with those of previous studies. This study also found that decreased ratios of saturated to total fatty acids and total cholesterol to total lipids in very small VLDL were associated with an increased risk of EM. Therefore, our findings indicate a close relationship between lipid metabolism and EM, although fur- ther investigation of the underlying mechanisms is warrant - ed. 3-hydroxybutyrate is one of the primary ketone bodies pro- duced during fatty acid metabolism and serves not only as an intermediate metabolite but also as an important regulatory Fig. 3. Forest plots of MR estimates of genetic associations between EM and circulating metabolites. nsnp, number of single-nucleotide polymorphisms; OR, odds ratio; CI, confidence interval; MR, Mendelian randomization; CLA, conjugated linoleic acid; EM, endometriosis. Exposure Outcome nsnp Method OR (95%CI) P MR Egger 1.076 (1.000, 1.158) 0.060 Weighted median 1.048 (1.008, 1.089) 0.016 Endometriosis Conjugated linoleic acid 26 Inverse variance weighted 1.026 (1.000, 1.053) 0.043 Simple mode 1.068 (0.987, 1.155) 0.113 Weighted mode 1.066 (0.992, 1.146) 0.093 MR Egger 1.073 (0.997, 1.155) 0.069 Weighted median 1.034 (0.995, 1.074) 0.085 Endometriosis Ratio of CLA to total fatty acids 26 Inverse variance weighted 1.026 (1.000, 1.053) 0.045 Simple mode 1.044 (0.961, 1.135) 0.313 Weighted mode 1.045 (0.959, 1.138) 0.317 MR Egger 1.021 (0.963, 1.081) 0.487 Weighted median 1.036 (1.011, 1.062) 0.004 Endometriosis Tyrosine levels 26 Inverse variance weighted 1.036 (1.016, 1.057) 3.33E-04 Simple mode 1.038 (0.998, 1.079) 0.069 Weighted mode 1.034 (1.001, 1.067) 0.051 0.6 0.8 1 1.2 1.4 Lower risk Higher risk www.ogscience.org218 Vol. 69, No. 3, 2026 molecule. Research indicates that 3-hydroxybutyrate plays significant biological roles in the regulation of energy me - tabolism, as well as in antioxidant and anti-inflammatory re - sponses [34]. Furthermore, 3-hydroxybutyrate can influence cellular survival and function by modulating intracellular sig - naling pathways and metabolic processes [34,35]. However, studies on the association between 3-hydroxybutyrate and EM are limited. Angioni et al. [36] analyzed 22 serum sam - ples from patients with symptomatic EM and 10 from those without EM using gas chromatography-mass spectrometer and revealed a significant increase in 3-hydroxybutyric acid levels among the patients with EM. In our MR analysis, a nominally significant association was observed between low- er genetically predicted levels of 3-hydroxybutyrate and an increased risk of EM. This exploratory finding suggests that altered 3-hydroxybutyrate metabolism may be involved in the pathogenesis of EM but requires verification in larger and more comprehensive studies. Our findings offer new insights into and perspectives on this issue. Previous studies have demonstrated that individuals diag - nosed with EM display statistically significant alterations in amino acid levels across various biological matrices, including the eutopic endometrium, serum, follicular fluid, urine, and endometrial fluid, compared to healthy controls [8]. However, the conclusions drawn from these studies are inconsistent. A study conducted by Pocate-Cheriet et al. [37] indicated that the concentrations of amino acids such as tyrosine are lower in women with deep-infiltrating EM than in control partici - pants. In contrast, Li et al. [38] reported that the metabolom- ic profile of the eutopic endometrium in patients with EM is marked by a significant increase in L-tyrosine concentration. Our exploratory reverse MR analysis suggested that a genetic predisposition to EM was associated with higher circulating tyrosine levels. This finding is consistent with the observation by Li et al. [38] of increased L-tyrosine levels in the eutopic endometrium of patients with EM. Although this inverse association does not imply causality, it may reflect metabolic alterations secondary to the disease state or related patho - physiology. Thus, tyrosine metabolism could be an area of interest in EM, and its role warrants further investigation to determine whether it represents a compensatory mechanism, biomarker of disease activity, or contributor to progression. CLA is a polyunsaturated fatty acid that previous studies have identified as playing significant roles in various biolog - ical processes. CLA possesses anti-inflammatory, antitumor, and immunomodulatory activities [39]. Furthermore, research indicates that CLA exerts its anticancer effects through mechanisms such as regulation of cell signaling pathways, inhibition of tumor cell proliferation, and induction of apop - tosis [40]. Research on the relationship between CLA and EM is limited. Our reverse MR analysis generated a novel hypoth- esis by revealing a nominally significant association between a genetic predisposition to EM and elevated levels of CLA (and its ratio to total fatty acids). Given the established roles of lo- cal inflammation and immune dysregulation in EM [5,6], one speculative interpretation is that the body may upregulate CLA as a counter-regulatory response to the inflammatory milieu of EM. Alternatively, this association may highlight a dysregulated metabolic pathway in patients with EM. These findings suggest that CLA is a candidate molecule whose re- lationship with EM warrants further investigation to clarify its specific biological role. This study had some limitations that warrant further in - vestigation. First, a significant limitation of this study was its reliance on publicly available genetic datasets, which may pose constraints regarding the cohort scale, demographic representation, and spectrum of genetic variations associated with circulating metabolites and EM. Second, our study was subject to limitations inherent to metabolomic GWAS data. Metabolite measurements are platform-specific and may af - fect the coverage and comparability of certain metabolites. Additionally, potential heterogeneity in sample collection, processing, and quantification across original studies could influence the precision of our genetic instrument variables and the generalizability of the findings. Third, the metabolite data predominantly originated from European populations, and all EM-related data were derived from individuals of Eu - ropean ancestry. Differences in genetic determinants across ethnic groups (e.g., allele frequencies of genes involved in metabolic pathways), dietary habits, gut microbiome compo- sition, and environmental exposures could contribute to vari- ations in metabolite profiles, thus limiting the generalizability of our findings to different ethnic groups. Future studies incorporating large-scale metabolomic data and EM GWAS from diverse populations are crucial for validating and refin - ing these findings. Fourth, although the study encompassed a relatively broad spectrum of metabolites, the functions and mechanisms of certain metabolites in relation to the disease remain inadequately understood, which limits the interpreta- tion of our results from this MR analysis. Consequently, fur - www.ogscience.org 219 Lele Pan, et al. Metabolites&endometriosis MR study ther validation is necessary by repeating the study in different groups and performing functional investigations to reinforce our findings and clarify the underlying mechanisms. In summary, this study employed bidirectional two-sam - ple MR to generate hypotheses regarding the association between circulating metabolites and EM. While one associ - ation survived strict Bonferroni correction and prioritized a metabolic pathway for future mechanistic investigations, all findings, including those that are suggestive, must be con - sidered hypothesis-generating. They collectively proposed novel metabolic risk factors for EM that require substantial functional validation and replication in larger independent cohorts before clinical translation can be considered. Finally, this study provides a foundation for future research exploring the underlying biology and potential therapeutic targets of EM. Conflict of interest The authors declare no competing financial interests or pro - fessional affiliations that could constitute a conflict of inter - est regarding the content of this work. Ethical approval Given that the GWAS data are publicly available, ethical ap - proval was not deemed necessary. Patient consent The study utilized public GWAS data from FinnGen (Release 10; https://r10.finngen.fi/) and the GWAS Catalog. Ethical approval and informed consent were obtained for all origi - nal studies. Consequently, no additional ethical approval or informed consent was required for this analysis, as the data were fully anonymized. Funding information This work was supported by Guangdong Basic and Applied Basic Research Foundation (Grant Nos. 2022A1515011880, 2023A1515011688), and the President Foundation of Zhu - jiang Hospital, Southern Medical University (Grant No. yzjj 2022ms18) to Ying Ma. This work was also supported by the Clinical Study on the Treatment of Dysmenorrhea with Fire Dragon Cupping Combined with Feng's Endometriosis For - mula (Grant No. 202300067) to Xiaohui Huang. Acknowledgments Supplementary Table 1 and 2 information is available on the link. https://doi.org/10.5468/ogs.25180.

1. Lee HJ, Yoon SH, Lee JH, Chung YJ, Park SY, Kim SW, et al. Clinical evaluation and management of endometrio - sis: 2024 guideline for Korean patients from the Korean Society of Endometriosis. Obstet Gynecol Sci 2025;68: 43-58. 2. Samare-Najaf M, Razavinasab SA, Samareh A, Jamali N. Omics-based novel strategies in the diagnosis of endo - metriosis. Crit Rev Clin Lab Sci 2024;61:205-25. 3. Horne AW, Missmer SA. Pathophysiology, diagnosis, and management of endometriosis. BMJ 2022;379:e070750. 4. Lamceva J, Uljanovs R, Strumfa I. The main theories on the pathogenesis of endometriosis. Int J Mol Sci 2023; 24:4254. 5. Wang Y, Nicholes K, Shih IM. The origin and pathogene- sis of endometriosis. Annu Rev Pathol 2020;15:71-95. 6. Symons LK, Miller JE, Kay VR, Marks RM, Liblik K, Koti M, et al. The immunopathophysiology of endometriosis. Trends Mol Med 2018;24:748-62. 7. Lee HJ, Noh HK, Kim SC, Joo JK, Suh DS, Kim KH. Di - etary pattern and risk of endometrioma in Korean wom- en: a case-control study. Obstet Gynecol Sci 2021;64:99- 106. 8. Ortiz CN, Torres-Reverón A, Appleyard CB. Metabolo - mics in endometriosis: challenges and perspectives for future studies. Reprod Fertil 2021;2:R35-50. 9. Wen X, Zhang J, Xu Z, Li M, Dong X, Du Y, et al. Highly expressed lncRNA H19 in endometriosis promotes aer - obic glycolysis and histone lactylation. Reproduction 2024;168:e240018. www.ogscience.org220 Vol. 69, No. 3, 2026 10. Li J, Gao Y, Guan L, Zhang H, Sun J, Gong X, et al. Dis - covery of phosphatidic acid, phosphatidylcholine, and phosphatidylserine as biomarkers for early diagnosis of endometriosis. Front Physiol 2018;9:14. 11. Mu F, Rich-Edwards J, Rimm EB, Spiegelman D, Forman JP , Missmer SA. Association between endometriosis and hypercholesterolemia or hypertension. Hypertension 2017;70:59-65. 12. Brinca AT, Peiró AM, Evangelio PM, Eleno I, Oliani AH, Silva V, et al. Follicular fluid and blood monitorization of infertility biomarkers in women with endometriosis. Int J Mol Sci 2024;25:7177. 13. Dutta M, Anitha M, Smith PB, Chiaro CR, Maan M, Chaudhury K, et al. Metabolomics reveals altered lipid meta bolism in a mouse model of endometriosis. J Pro - teome Res 2016;15:2626-33. 14. Young VJ, Brown JK, Maybin J, Saunders PT, Duncan WC, Horne AW. Transforming growth factor- β induced Warburg-like metabolic reprogramming may underpin the development of peritoneal endometriosis. J Clin En - docrinol Metab 2014;99:3450-9. 15. Gou Y, Wang H, Wang T, Wang H, Wang B, Jiao N, et al. Ectopic endometriotic stromal cells-derived lactate in - duces M2 macrophage polarization via Mettl3/Trib1/ERK/ STAT3 signalling pathway in endometriosis. Immunology 2023;168:389-402. 16. Bowden J, Davey Smith G, Burgess S. Mendelian ran - domization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epide- miol 2015;44:512-25. 17. Karjalainen MK, Karthikeyan S, Oliver-Williams C, Sliz E, Allara E, Fung WT, et al. Genome-wide characterization of circulating metabolic biomarkers. Nature 2024;628: 130-8. 18. Kurki MI, Karjalainen J, Palta P , Sipilä TP , Kristiansson K, Donner KM, et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 2023; 613:508-18. 19. Auton A, Brooks LD, Durbin RM, Garrison EP , Kang HM, Korbel JO, et al. A global reference for human genetic variation. Nature 2015;526:68-74. 20. Zhong MM, Xie JH, Feng Y, Zhang SH, Xia JN, Tan L, et al. Causal effects of the gut microbiome on COVID-19 susceptibility and severity: a two-sample Mendelian ran - domization study. Front Immunol 2023;14:1173974. 21. Wu Y, Zheng Z, Visscher PM, Yang J. Quantifying the mapping precision of genome-wide association studies using whole-genome sequencing data. Genome Biol 2017;18:86. 22. Yengo L, Sidorenko J, Kemper KE, Zheng Z, Wood AR, Weedon MN, et al. Meta-analysis of genome-wide association studies for height and body mass index in ~700000 individuals of European ancestry. Hum Mol Genet 2018;27:3641-9. 23. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in mendelian randomization with some invalid instruments using a weighted median esti - mator. Genet Epidemiol 2016;40:304-14. 24. Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol 2017;32:377-89. 25. Slob EAW, Burgess S. A comparison of robust Mendelian ran domization methods using summary data. Genet Ep - idemiol 2020;44:313-29. 26. Verbanck M, Chen CY, Neale B, Do R. Detection of wide spread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between com - plex traits and diseases. Nat Genet 2018;50:693-8. 27. Hartwig FP , Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol 2017;46: 1985-98. 28. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-base platform supports systematic causal inference across the human phenome. Elife 2018; 7:e34408. 29. Curtin F, Schulz P . Multiple correlations and Bonferroni's correction. Biol Psychiatry 1998;44:775-7. 30. Peinado FM, Olivas-Martínez A, Iribarne-Durán LM, Ubiña A, León J, Vela-Soria F, et al. Cell cycle, apoptosis, cell differentiation, and lipid metabolism gene expression in endometriotic tissue and exposure to parabens and benzophenones. Sci Total Environ 2023;879:163014. 31. Lu J, Ling X, Liu L, Jiang A, Ren C, Lu C, et al. Emerging hallmarks of endometriosis metabolism: a promising tar- get for the treatment of endometriosis. Biochim Biophys Acta Mol Cell Res 2023;1870:119381. 32. Cooke M, Kazanietz MG. Overarching roles of diacyl - glycerol signaling in cancer development and antitumor immunity. Sci Signal 2022;15:eabo0264. www.ogscience.org 221 Lele Pan, et al. Metabolites&endometriosis MR study 33. Adamyan LV, Starodubtseva N, Borisova A, Stepanian AA, Chagovets V, Salimova D, et al. Direct mass spec - trometry differentiation of ectopic and eutopic endome- trium in patients with endometriosis. J Minim Invasive Gynecol 2018;25:426-33. 34. Mierziak J, Burgberger M, Wojtasik W. 3-hydroxybutyr - ate as a metabolite and a signal molecule regulating pro- cesses of living organisms. Biomolecules 2021;11:402. 35. Newman JC, Verdin E. β-hydroxybutyrate: a signaling metabolite. Annu Rev Nutr 2017;37:51-76. 36. Angioni S, Congiu F, Vitale SG, D'Alterio MN, Noto A, Monni G, et al. Gas chromatography-mass spectrometry (GC-MS) metabolites analysis in endometriosis patients: a prospective observational translational study. J Clin Med 2023;12:922. 37. Pocate-Cheriet K, Santulli P , Kateb F, Bourdon M, Maignien C, Batteux F, et al. The follicular fluid metabo - lome differs according to the endometriosis phenotype. Reprod Biomed Online 2020;41:1023-37. 38. Li J, Guan L, Zhang H, Gao Y, Sun J, Gong X, et al. En - dometrium metabolomic profiling reveals potential bio - markers for diagnosis of endometriosis at minimal-mild stages. Reprod Biol Endocrinol 2018;16:42. 39. Viladomiu M, Hontecillas R, Bassaganya-Riera J. Modula- tion of inflammation and immunity by dietary conjugat - ed linoleic acid. Eur J Pharmacol 2016;785:87-95. 40. Rakib MA, Lee WS, Kim GS, Han JH, Kim JO, Ha YL. Anti proliferative action of conjugated linoleic acid on human MCF-7 breast cancer cells mediated by enhance- ment of gap junctional intercellular communication through inactivation of NF- κ B. Evid Based Complement Alternat Med 2013;2013:429393. www.ogscience.org 1 Lele Pan, et al. Metabolites&endometriosis MR study Supplementary Table 1. Summary of Mendelian randomization analysis results and metabolite information for the association of circulating metabolites with endometriosis www.ogscience.org2 Vol. 69, No. 3, 2026 Supplementary Fig. 1. (A) Scatter plots of the associations between circulating metabolites and endometriosis using the Approach-1 MR study. The two-sample MR analyses were conducted using the MR-Egger, inverse-variance weighted, weighted median, simple mode, and weighted mode analyses. The estimated MR effect per method is depicted by the slope of each line. (B) Funnel plots of the asso cia- tions between circulating metabolites and endometriosis using the Approach-1 MR study. (C) The leave‐one‐out analysis of the effect of asso ciations between circulating metabolites and endometriosis. SNP , single-nucleotide polymorphism; MR, Mendelian randomization. MR test Inverse variance weighted Weighted median MR Egger Weighted made Simple mode 0.2 0.1 0.0 -0.1 -0.2 0.05 0.10 0.15 0.20 SNP effect on exposure SNP effect on outcome MR test Inverse variance weighted Weighted median MR Egger Weighted made Simple mode 0.1 0.0 -0.1 -0.2 0.05 0.10 0.15 SNP effect on exposure SNP effect on outcome MR test Inverse variance weighted Weighted median MR Egger Weighted made Simple mode 0.2 0.1 0.0 -0.1 -0.2 0.05 0.10 SNP effect on exposure SNP effect on outcome MR test Inverse variance weighted Weighted median MR Egger Weighted made Simple mode 0.2 0.1 0.0 -0.1 -0.2 0.05 0.10 0.15 0.20 SNP effect on exposure SNP effect on outcome A www.ogscience.org 3 Lele Pan, et al. Metabolites&endometriosis MR study Supplementary Fig. 1. (Continued) MR method Inverse variance weighted MR Egger 7.5 5.0 2.5 -1.0 -0.5 0.0 0.5 1.0 βIV 1/SEIV MR method Inverse variance weighted MR Egger 6 5 4 3 2 -1 0 1 βIV 1/SEIV MR method Inverse variance weighted MR Egger 5 4 3 2 1 -1 0 1 βIV 1/SEIV MR method Inverse variance weighted MR Egger 7.5 5.0 2.5 -2 -1 0 1 βIV 1/SEIV B www.ogscience.org4 Vol. 69, No. 3, 2026 Supplementary Fig. 1. (Continued) C rs2645429 rs28809016 rs35726795 rs1961456 rs2575876 rs9271770 rs4930724 rs72836561 rs800544 rs76744666 rs12212146 rs34168560 rs17676564 rs114820087 rs10455872 rs6494437 rs42125 rs12705610 rs781098 rs2414577 rs1800588 rs12972842 rs3734674 rs62001733 rs10147786 rs1168085 rs2207132 rs117300555 rs139915535 rs1398636 rs1154130 rs187429064 rs144018203 rs2419921 rs72690714 rs3201892 rs9471629 rs10203950 rs5765090 rs584007 rs143343360 rs79468765 rs12359135 rs3780546 rs58489806 rs1092834 rs998584 rs4130929 rs207078 rs117733303 rs139974673 rs56022644 rs4704834 rs2927323 rs1042034 rs13702 rs1203110 rs10098077 rs17145750 rs57209787 rs1260326 rs10410805 rs964184 rs9686661 rs247616 rs78058190 rs10195252 rs116843064 All 0.0 0.1 0.2 0.3 MR leave‐one‐out sensitivity analysis for 'exposure' on 'outcome' rs2954021 rs150072275 rs62111729 rs36039532 rs2156552 rs1009062 rs61824906 rs117643180 rs114165349 rs516979 rs429358 rs277407 rs76484733 rs1215112 rs117341151 rs1698148 rs28442086 rs147507218 rs249698 rs7424006 rs189288878 rs13284054 rs2760105 rs77807654 rs4762701 rs261342 rs115985916 rs11581618 rs45611540 rs148921528 rs9302635 rs111962457 rs2792759 rs143099396 rs12653566 rs627108 rs12867528 rs1859913. rs1982099 rs11046944 rs6562430 rs7024300 rs4841132 rs28929474 All -0.3 -0.2 -0.1 0.0 MR leave‐one‐out sensitivity analysis for 'exposure' on 'outcome' rs964184 rs67981690 rs4645936 rs149553138 rs2025106 rs191347211 rs12046045 rs9303025 rs6539531 rs188247550 rs72753977 rs 192687038 rs183064231 rs148532534 rs137932018 rs6460047 rs1229984 rs151264254 rs35318168 rs7554117 rs58542926 rs603424 rs35194512 rs7704772 rs952275 rs62039480 rs2294915 rs117525819 rs9266504 rs138050176 rs7528419 rs3750720 rs11580878 rs2329656 rs117965774 rs4970737 rs4976941 rs12639940 rs11920975 rs79236197 rs112505274 rs116479611 rs41278166 rs6467314 rs315432 rs4753170 rs916217 rs4986970 rs2737252 rs11571787 rs1061808 rs429358 rs174574 All -0.3 -0.2 -0.1 0.0 MR leave‐one‐out sensitivity analysis for 'exposure' on 'outcome' rs4149056rs964184rs116843064rs295402rs1260326rs84473rs1265103rs78085190rs112333700rs600626rs1042034rs9687846rs6680258rs113879698rs78867939rs35739466rs139974673rs3767306rs6792725rs7787268rs141501282rs236987rs57457691rs12208947rs7994900rs2161456rs71581921rs588136rs10935980rs17054273rs11207970rs17145739rs12443775rs6905288rs113607731rs58542926rs1229984rs141469619rs9384970rs2642442rs2715634rs34088055rs6445881rs261337rs11587071rs73045691rs7632367rs7830528rs2294915rs12928099rs2460760rs10796449rs890928rs139915535rs17347255rs3011802r6029126rs12022365rs12655753rs11563512rs2169387rs55789316rs140394rs4845597rs78778914rs74741542rs406315rs2737248rs3827743rs8169rs72836561rs62229368rs1051052rs675849rs1801390rs1532085rs794268rs2251319rs75679663rs77303550rs11731799rs75460349rs2620798rs1313996rs11639845rs4846913rs11170413rs6073958rs7412rs183130rs61983884rs174574 All -0.2 -0.1 0.0 MR leave‐one‐out sensitivity analysis for 'exposure' on 'outcome' www.ogscience.org 5 Lele Pan, et al. Metabolites&endometriosis MR study Supplementary Table 2. Summary of Mendelian randomization analysis results and metabolite information for the association of endometriosis with circulating metabolites www.ogscience.org6 Vol. 69, No. 3, 2026 Supplementary Fig. 2. (A) Scatter plots of the associations between endometriosis and circulating metabolites using the Approach-1 MR study. The two-sample MR analyses were conducted using the MR-Egger, inverse-variance weighted, weighted median, simple mode, and weighted mode analyses. The estimated MR effect per method is depicted by the slope of each line. (B) Funnel plots of the asso cia- tions between endometriosis and circulating metabolites using the Approach-1 MR study. (C) The leave‐one‐out analysis of the asso cia- tions between endometriosis and circulating metabolites. SNP , single-nucleotide polymorphism; MR, Mendelian randomization. MR test Inverse variance weighted Weighted median MR Egger Weighted made Simple mode 0.04 0.02 0.00 -0.02 0.1 0.2 0.3 SNP effect on exposure SNP effect on outcome MR test Inverse variance weighted Weighted median MR Egger Weighted made Simple mode 0.04 0.02 0.00 0.1 0.2 0.3 SNP effect on exposure SNP effect on outcome MR test Inverse variance weighted Weighted median MR Egger Weighted made Simple mode 0.02 0.01 0.00 -0.01 0.1 0.2 0.3 SNP effect on exposure SNP effect on outcome MR test Inverse variance weighted MR Egger 25 20 15 10 -0.10 -0.05 0.00 0.05 0.10 βIV 1/SEIV MR test Inverse variance weighted MR Egger 25 20 15 10 -0.10 -0.05 0.00 0.05 0.10 βIV 1/SEIV MR test Inverse variance weighted MR Egger 35 30 25 20 15 0.0 0.1 0.2 βIV 1/SEIV A B C rs4516787 rs4762308 rs34068702 rs13264285 rs11940163 rs10778172 rs66741626 rs9368804 rs727412 rs1209731 rs851984 rs12317475 rs12535837 rs940061 rs34803821 rs2473321 rs635634 rs3803361 rs2779747 rs2800709 rs61778046 rs11776952 rs11031005 rs6546324 rs75969278 rs58415480 All 0.00 0.02 0.04 0.06 MR leave‐one‐out sensitivity analysis for 'exposure' on 'outcome' rs4516787 rs4762308 rs13264285 rs34068702 rs851984 rs9368804 rs11940163 rs727412 rs10778172 rs66741626 rs2800709 rs1209731 rs12317475 rs635634 rs3803361 rs34803821 rs12535837 rs2473321 rs11031005 rs940061 rs11776952 rs61778046 rs2779747 rs6546324 rs75969278 rs58415480 All 0.00 0.02 0.04 0.06 MR leave‐one‐out sensitivity analysis for 'exposure' on 'outcome' rs12317475 rs75969278 rs4762308 rs9368804 rs4516787 rs12535837 rs66741626 rs34068702 rs2779747 rs11940163 rs1209731 rs11776952 rs34803821 rs61778046 rs727412 rs940061 rs10778172 rs11031005 rs2473321 rs58415480 rs3803361 rs6546324 rs2800709 rs13264285 rs851984 rs635634 All 0.00 0.02 0.04 0.06 MR leave‐one‐out sensitivity analysis for 'exposure' on 'outcome'