{"paper_id":"f12d82b7-efb3-4bc7-9003-0fff3701da1b","body_text":"Journal of Obstetrics and Gynaecology\nISSN: 0144-3615 (Print) 1364-6893 (Online) Journal homepage: www.tandfonline.com/journals/ijog20\nAssociations between 1400 metabolites and\nsubtypes of endometriosis: a two-sample\nMendelian randomisation study\nFei Yan , Zhouxiang Chen , Lingfeng Wu & Zongju Huang\nTo cite this article: Fei Yan , Zhouxiang Chen , Lingfeng Wu & Zongju Huang (2025)\nAssociations between 1400 metabolites and subtypes of endometriosis: a two-sample\nMendelian randomisation study, Journal of Obstetrics and Gynaecology, 45:1, 2552402, DOI:\n10.1080/01443615.2025.2552402\nTo link to this article: https://doi.org/10.1080/01443615.2025.2552402\n© 2025 The Author(s). Published by Informa\nUK Limited, trading as Taylor & Francis\nGroup\nView supplementary material \nPublished online: 28 Aug 2025.\n Submit your article to this journal \nArticle views: 1340\n View related articles \nView Crossmark data\n Citing articles: 1 View citing articles \nFull Terms & Conditions of access and use can be found at\nhttps://www.tandfonline.com/action/journalInformation?journalCode=ijog20\n\nGynecoloGy\nJournal of obstetrics and GynaecoloGy\n2025, Vol. 45, no . 1, 2552402\nAssociations between 1400  metabolites and subtypes of \nendometriosis: a two-sample Mendelian randomisation study\nFei y ana#, Zhouxiang chen b#, lingfeng Wu a and Zongju Huang a\nadepartment of a cpuncture and Moxibustion, Jiangbei d istrict Hospital of traditional chinese Medicine, chongqing, china; \nbdepartment of r eproductive Medicine, Hezhou People’s Hospital of Guangxi, Hezhou, Guangxi, china\nABSTRACT\nBackground: endometriosis is a chronic inflammatory disease with a prevalence of \napproximately 10% in women of childbearing age. Metabolic pathways have been \ndemonstrated by previous studies to be potential avenues for the development of new \ntherapeutic strategies and may be used for early diagnosis of the disease. This study \naimed to investigate the potential causal relationships between 1400 metabolites and \nvarious endometriosis subtypes using Mendelian randomisation (MR) analysis.\nMethods: Data from a genome-wide association study were analysed. MR analysis was \nperformed using the inverse-variance weighted, MR-e gger, and weighted-median \nmethods, accompanied by heterogeneity testing, sensitivity analysis, and pleiotropy \nanalysis. Metabolic-pathway enrichment analysis was conducted on the preliminarily \nscreened differential metabolites, and colocalisation analysis was subsequently \nperformed for exposure–outcome pairs that remained causally associated after \nmultiple-testing correction.\nResults: After multiple-testing correction, only the glycerol-to-palmitoylcarnitine ( c16) \nratio reduced the risk of stage 1–2 endometriosis ( PFDR = 0.045; odds ratio [ oR], 0.737; \n95% confidence interval [ cI], 0.638–0.852) and pelvic peritoneal endometriosis ( PFDR = \n0.039; oR, 0.721; 95% cI, 0.619–0.841). c olocalisation analysis revealed that they did not \nshare causal variant loci at the genetic level. no reverse causal associations were found \nin the reverse Mendelian analysis. Metabolic pathway enrichment analysis identified \nmajor metabolic pathways, including caffeine metabolism, glutathione metabolism, \narginine biosynthesis, sphingolipid metabolism, pantothenate and c oA biosynthesis, \nplasmalogen synthesis, and biosynthesis of unsaturated fatty acids.\nConclusions: o ur study suggests potential causal relationships between metabolites \nand various endometriosis subtypes from an MR perspective. However, the limited \nnumber of associations that survived multiple-testing correction indicates that these \nfindings are preliminary and require validation in larger cohorts. This exploratory \nanalysis may contribute to advancing future research on metabolomics-based diagnosis, \ntreatment, and prevention of endometriosis.\nPLAIN LANGUAGE SUMMARY\nendometriosis is a condition in which tissues similar to the uterine lining grow outside \nthe uterus. It affects approximately 1 in 10 reproductive-age women and can cause \npain and fertility problems. In this study, we investigated whether certain natural \nsubstances in the blood, called metabolites, play a role in different forms of \nendometriosis. We used genetic data to test over 1,000 metabolites and found that \nonly the glycerol-to-palmitoylcarnitine ratio showed a meaningful link. A higher ratio \nmay reduce the risk of mild or pelvic peritoneal endometriosis. This early finding points \nto a new direction for future research; nevertheless, more studies are required to \nconfirm this and understand how this metabolite balance may be involved in the \ndisease.\n© 2025 t he a uthor(s). Published by i nforma uK limited, trading as taylor & f rancis Group\nCONTACT Zongju Huang hzj9488@163.com d epartment of a cpuncture and Moxibustion, Jiangbei d istrict Hospital of traditional \nchinese Medicine, no.35, no.1 Village, Jianxin east r oad, Jiangbei d istrict, chongqing400021, china\n#f ei yan and Zhouxiang chen contributed equally to this work as co-first authors.\n supplemental data for this article can be accessed online at https://doi.org/10.1080/01443615.2025.2552402.\nhttps://doi.org/10.1080/01443615.2025.2552402\nt his is an o pen a ccess article distributed under the terms of the c reative c ommons a ttribution license ( http://creativecommons.org/licenses/by/4.0/), which \npermits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. t he terms on which this article has been \npublished allow the posting of the a ccepted Manuscript in a repository by the author(s) or with their consent.\nARTICLE HISTORY\nReceived 26 April 2025\nAccepted 19 August 2025\nKEYWORDS\nMetabolites; \nendometriosis; glycerol to \npalmitoylcarnitine ( c16) \nratio; Mendelian \nrandomisation\n\n2 F. yAn eT Al.\nIntroduction\nendometriosis is characterised by the presence of functional endometrial tissues (including glands and \nstroma) outside the uterine cavity ( olive and Pritts 2001, Burney and Giudice 2012) and affects approxi -\nmately 10% of women of childbearing age (Horne et  al. 2019), accounting for 25–50% of infertility cases \nin women ( c ounseller and c renshaw 1951). The primary sites of endometriosis are the ovaries, pelvic \nperitoneum, and rectovaginal septum, and its common symptoms include dysmenorrhoea, pain during \nintercourse, chronic pelvic pain, and infertility ( c ounseller and c renshaw 1951, Sinaii et  al. 2008). Women \nwith these chronic symptoms often experience emotional distress, reduced quality of life, and elevated \nlevels of stress, anxiety, and depression. Some patients may not exhibit noticeable symptoms, making \nthe diagnosis of endometriosis challenging (Soliman et  al. 2017), which is often only considered during \nfertility evaluations or in the presence of other gynaecological symptoms.\nThe pathogenesis of endometriosis is not fully understood. Beyond its local gynaecological manifesta -\ntions, endometriosis is also recognised as a systemic disease involving complex metabolic dysregulation \n(Angioni et  al. 2021, Adamyan et  al. 2024). Women with endometriosis exhibit distinct metabolomic pro -\nfiles, characterised by significant alterations in oxidative stress pathways, lipid metabolism, and amino \nacid turnover. These disruptions include enhanced reactive oxygen species (R oS) production, dysregu -\nlated phospholipid synthesis and fatty acid oxidation, and altered glutamine and tryptophan metabolism, \nwhich collectively contribute to chronic inflammation and energy metabolism dysfunction (Angioni et  al. \n2021, Adamyan et  al. 2024). These metabolic perturbations highlight the potential of metabolomic bio -\nmarkers as diagnostic and therapeutic targets.\nMetabolomics, an emerging research tool, offers new perspectives for the diagnosis and treatment of \nvarious diseases, with existing studies revealing its tremendous potential in endometriosis research. Dutta \n(Soliman et  al. 2017) utilised nuclear magnetic resonance technology to analyse serum samples from \npatients with endometriosis and successfully identified multiple metabolite changes closely related to the \ndisease state, including alterations in specific amino acid and organic acid levels. Jana et  al. (Jana et  al. \n2013) further explored the roles of blood metabolites in endometriosis. Through a more refined analysis, \nthey discovered significant changes in specific amino and organic acid levels in patients with endome -\ntriosis, deepening our understanding of the value of metabolomics in comprehending the pathophysiol -\nogy of endometriosis. Ghazi (negar et  al. 2016) revealed changes in the levels of metabolites such as \n2-methoxyestradiol in patients with endometriosis through serum metabolomic analysis, thereby offering \nnew insights into the role of hormone metabolism in the pathogenesis of endometriosis. Vouk (negar \net  al. 2016), analysed lipid metabolites in blood and peritoneal fluid and found significant changes in \nlipid metabolism, especially in the phosphatidylcholine and phosphatidylserine levels, revealing the \npotential role of lipid metabolism in the pathophysiology of endometriosis. l ee et  al. (l ee et  al. 2014) \nreported that specific lipid metabolites, particularly glycosphingolipids and lactosylceramides, were ele -\nvated in patients with endometriosis, providing crucial evidence for lipid metabolic changes during \ninflammation and cell proliferation processes. Dutta et  al. (Dutta et  al. 2018) conducted a comprehensive \nanalysis of endometrial tissue and serum samples from patients with endometriosis using an integrated \nmetabolomic approach. Their study not only confirmed previously discovered metabolic abnormalities \nbut also revealed new metabolite changes, offering deeper insights into the pathophysiology of endo -\nmetriosis. These studies indicate that metabolic pathways are potential avenues for the development of \nnew therapeutic strategies (li et  al. 2023, l u et  al. 2023) and may be utilised for the early diagnosis of \ndiseases ( ortiz et  al. 2021). nonetheless, the aforementioned investigations are largely observational, \nemploy modest sample sizes, and evaluate limited subsets of metabolites; consequently, they cannot \nestablish whether the observed metabolic alterations are the causes or consequences of endometriosis. \nA comprehensive causally oriented assessment of the circulating metabolome (> 1,000 metabolites) in \nrelation to disease risk is still lacking.\nMendelian randomisation (MR) studies, which utilise genetic markers as instrumental variables (IVs), \ncan effectively infer the causal relationships between exposure and outcomes (Davey Smith and \nHemani  2014). This research design overcomes the limitations of traditional observational studies that are \nsusceptible to confounding factors and reverse causality, offering validity similar to that of randomised \n\nJouRnAl oF oBSTe TRIcS AnD GynAecoloGy 3\ncontrolled trials, but at a lower cost, serving as foundational research for clinical studies (Burgess and \nThompson 2015).\nIn this study, we employed the two-sample MR approach, selecting genome-wide association study \n(GWAS) data from multiple subtypes of endometriosis, to comprehensively explore the causal relation -\nships between 1400 metabolites and endometriosis. We hypothesised that specific circulating metabo -\nlites exert causal influences, either promotive or protective, on the development of endometriosis. The \nfindings may refine the understanding of disease aetiology and inform early non-invasive diagnostic \nstrategies as well as novel metabolic interventions.\nMethods\nStudy design\nA bidirectional two-sample MR analysis was conducted to evaluate the causal relationships between \n1,400 blood metabolite phenotypes and various subtypes of endometriosis. The overall study design and \nparticipant flow are summarised in Figure 1 . The MR analysis was based on three core assumptions \n(Haycock et  al. 2016): (i) the IV is associated with exposure; (ii) the IV is independent of any known or \nunknown confounding factors that mediate exposure and outcome; and (iii) the outcome is related to \nthe genetic instrument solely through the exposure effect.\nEthical considerations\nThis study utilised summary statistics from previously published GWAS that had already received ethical \napproval from their respective institutions and obtained informed consent from the participants. As our \nstudy design was a secondary analysis of publicly available summary data without access to individual-level \ninformation, no additional ethical approval, study registration, or informed consent was required. This \ntype of MR analysis using summary statistics is exempt from registration requirements because it does \nnot involve direct interactions with human subjects. o ur research methods complied with all relevant \ninstitutional and research governance standards for genetic epidemiology studies that utilise public data.\nData source\nThe GWAS data on endometriosis were sourced from a Finnish database ( https://www.finngen.fi/en) com -\nprising eight distinct datasets. This included two datasets based on disease staging: stage 1–2 endome -\ntriosis and stage 3–4 endometriosis, which were categorised according to the American Society for \nReproductive Medicine (ASRM) staging system. Additionally, five datasets were classified according to \nspecific locations (not involving disease staging and infiltration depth): endometriosis of the fallopian \ntube, intestine, ovary, pelvic peritoneum, rectovaginal septum, and vagina. Another dataset on deep \nendometriosis referred to cases in which the infiltration depth into the subperitoneal space was equal to \nor greater than 5 mm, without involving disease staging and location. Detailed information is provided \nin Table 1.\nFigure 1. f lowchart of the study design.\n\n4 F. yAn eT Al.\nBlood metabolite data were obtained from the c anadian l ongitudinal Study on Ageing ( clSA), target -\ning 8,299 unrelated european participants for whole-genome genotyping and assessment of circulating \nplasma metabolites ( chen et  al. 2023). After a series of rigorous quality control steps, including eliminat -\ning data that could introduce systematic errors, mismatches, and background noise, 1,091 metabolites \n(850 known and 241 unknown substances) and 309 metabolite ratios were identified. This study provides \na foundational dataset for GWAS. These data were meticulously recorded on the GWAS catalogue website \n(https://www.ebi.ac.uk/gwas/) (IDs GcST90199621–GcST90201020, detailed in Supplementary Table 1 ).\nIt is important to note that the endometriosis GWAS data were sourced from a Finnish population, \nwhereas the metabolite data were obtained from the c anadian l ongitudinal Study on Ageing ( clSA) \ncohort, which primarily represents the c anadian population. These two cohorts were from different geo -\ngraphic locations; thus, we can confidently assert that there was no overlap between the participants in \nthe two datasets. This ensured the independence of the exposure (metabolite) and outcome (endome -\ntriosis) datasets, which are critical for the validity of the MR analysis.\nIV Selection\nIn selecting the IVs for metabolites, we referred to recent studies ( orrù et  al. 2020, yu et  al. 2021), setting \nthe significance level at 5 × 10−6 . The clumping procedure in PlInK software (version 1.90) was used to \nprune these SnPs (linkage disequilibrium [lD] r 2 threshold < 0.001 within a 10,000 kb distance) (The 1000 \nGenomes Project c onsortium, 2015). To select IVs for various subtypes of endometriosis, we set the sig -\nnificance level from a maximum of 5 × 10−6 to a minimum of 5 × 10−8 , depending on the actual situation \n(detailed in Table 1), using the same clumping procedure used for the metabolites.\nFurthermore, to assess the reliability and effectiveness of each SnP as a genetic IV in MR analysis, we \ncalculated the F-statistic for each SnP using the formula F = R2(n − 2)/(1 − R 2). Here, R 2 represents the \nproportion of the variance in the exposure variable explained by IV, and n is the sample size of the \noriginal GWAS serving as the outcome variable. Specifically, R 2 is calculated using the formula:\n 21\n21 21\n2\n2 2\n× ×− () ×\n× ×− () ×+ ][ × ×− () ×× ()(\nEAF EAF\nEAF EAF EAF EAF N SE\nβ\nββ ))\n\n\n\n \nwhere eAF is the effect allele frequency, β is the SnP effect estimate, and Se(β) is the standard error \nof the SnP effect estimate. An IV with an F-statistic of less than 10 was considered unreliable and \nexcluded from the subsequent MR analysis. These steps will ensure that our study uses high-quality \ngenetic IVs for reliable analysis.\nMR analysis\nThe statistical analysis for this study was performed using R software version 4.2.3, and the involved \nanalytical methods were executed using the ‘MendelianRandomization’ package version 0.9.0. The pri -\nmary method employed was the inverse-variance weighted (IVW) method. Additionally, the MR-e gger \nand weighted-median methods were utilised to enhance the reliability of the results. To mitigate the \nimpact of multiple testing, the P-values of the IVW method were adjusted using false discovery rate \n(FDR) correction. A result was considered positive if the corrected P-value was <0.05, even if the P-values \nTable 1. data sources and demographic profiles.\nexposures or outcome\nsample size \n(cases/controls) a ncestry sex\nPublication \ndate(year)\nsignificance \nlevel\nGWas c atalogue id or \nurl\nendometriosis asrM stages 1,2 5769/205101 european f emale 2022 5e-8 https://www.finngen.fi/fi\nendometriosis asrM stages 3,4 7574/203296 european f emale 2022 5e-8 https://www.finngen.fi/fi\ndeep endometriosis 2856 /203296 european f emale 2022 5e-8 https://www.finngen.fi/fi\nendometriosis of fallopian tube 213/107564 european f emale 2022 5e-6 https://www.finngen.fi/fi\nendometriosis of intestine 436 /107564 european f emale 2022 5e-7 https://www.finngen.fi/fi\nendometriosis of ovary 5867 /107564 european f emale 2022 5e-8 https://www.finngen.fi/fi\nendometriosis of pelvic peritoneum 5628 /107564 european f emale 2022 5e-8 https://www.finngen.fi/fi\nendometriosis of rectovaginal \nseptum and vagina\n2456 /107564 european f emale 2022 5e-8 https://www.finngen.fi/fi\nmetabolites na european f emale and male 2023 5e-6 PubMed id :36635386\n\nJouRnAl oF oBSTe TRIcS AnD GynAecoloGy 5\nfrom the MR-e gger and weighted-median methods were greater than 0.05, provided that the direction \nof the results for all three methods was consistent.\nHeterogeneity, pleiotropy, and sensitivity analysis\nHeterogeneity was tested using the IVW and MR-e gger methods. A P-value less than 0.05 indicated the \npresence of heterogeneity among SnPs, while a P-value greater than 0.05 indicated no heterogeneity. A \nsensitivity analysis was conducted using a leave-one-out approach to explore the impact of individual \nSnPs on causal associations.\nThe horizontal pleiotropy was assessed using the intercept of the MR-e gger method. A significant \nintercept implied the existence of horizontal pleiotropy. In addition, the MR-PReSSo method was \nemployed to exclude potential horizontal pleiotropic outliers that could significantly affect the MR anal -\nysis results. For exposures with horizontal pleiotropy that could not be eliminated, the corresponding MR \nanalysis for that exposure factor was disregarded.\nMetabolic pathway enrichment analysis\nPositive metabolites identified from the IVW analysis without correction for multiple testing were sub -\njected to enrichment analysis using MetaboAnalyst 5.0 ( https://www.metaboanalyst.ca/) to explore asso -\nciated metabolic pathways. The analysis utilised the Small Molecule Pathway Database (SMPDB) and \nKyoto encyclopaedia of Genes and Genomes (KeGG) databases. The enrichment method employed was \nthe Hypergeometric Test, with a significance level for metabolic pathway analysis set at 0.01.\nColocalisation analysis\nTo evaluate whether exposures and outcomes with a causal relationship might share common causal vari -\nants, we conducted Bayesian colocalisation analysis using the ‘coloc’ package in R. The colocalisation region \nwas defined within a 100-kb range up and down from the target SnP (Farh et  al. 2015). Five hypotheses \nwere involved in the colocalisation analysis: (H0) no association with either trait within the selected location; \n(H1) association with trait 1 but not with trait 2 at the chosen locus; (H2) association with trait 2 but not \nwith trait 1 at the selected site; (H3) association with both but with distinct causal variants; and (H4) associ -\nation with both, sharing a common causal variant (Giambartolomei et  al. 2014). The essence of the colocal -\nisation analysis is to test the posterior probabilities of the five hypotheses. o ur primary focus is on the \nposterior probability of H4, where 0 indicates a 0% probability, and 1 indicates a 100% probability. A poste -\nrior probability of H4 greater than 0.8(Giambartolomei et  al. 2014) is considered indicative of colocalisation.\nResults\nExploration of the causal effect of metabolites on endometriosis and its subtypes\nIVW analysis without multiple-testing correction revealed that 59 metabolites were causally associated \nwith stage 1–2 endometriosis, 73 with stage 3–4 endometriosis, 67 with deep endometriosis, 61 with \nfallopian tube endometriosis, 63 with intestinal endometriosis, 70 with ovarian endometriosis, 65 with \npelvic peritoneal endometriosis, and 69 with rectovaginal septum and vaginal endometriosis. \nSupplementary Figure 1 shows the metabolites associated with three or more endometriosis subtypes. \nSupplementary Table 2 provides details regarding the metabolites associated with one to two subtypes \nof endometriosis.\nAfter FDR adjustment, only the glycerol-to-palmitoylcarnitine ( c16) ratio reduced the risk of stage 1–2 \nendometriosis ( PFDR = 0.045; odds ratio [ oR], 0.737; 95% confidence interval [ cI], 0.638–0.852) and pelvic \nperitoneal endometriosis ( PFDR = 0.039; oR, 0.721; 95% cI, 0.619–0.841). Specifically, each one standard \ndeviation (SD) increase in this ratio was associated with an approximately 26% reduction in the odds of \nstage 1–2 disease and a 28% reduction in the odds of pelvic endometriosis. The protective effect of the \n\n6 F. yAn eT Al.\nglycerol-to-palmitoylcarnitine ( c16) ratio against stage 1–2 endometriosis and pelvic peritoneal endome -\ntriosis was supported by both the MR-e gger and weighted-median tests, which provided consistent \neffect directions and significance levels ( Figure 2 ). Additionally, the MR-e gger intercept and MR-PReSSo \nglobal test suggested no horizontal pleiotropy ( Table 2).\nThe robustness of these causal relationships was validated using multiple analytical approaches. For \nstage 1–2 endometriosis, Supplementary Figure 3A (scatter plot) illustrates the correlation between the \neffects of SnPs on exposure and outcome. Supplementary Figure 3B (funnel plot) presents the heteroge -\nneity and potential outliers among the IVs. Supplementary Figure 3c (forest plot) shows consistent effect \nestimates across individual SnPs. l eave-one-out analysis confirmed that no single SnP disproportionately \ninfluenced the overall causal estimate Supplementary Figure 3D ). Similarly, corresponding analyses of pel -\nvic peritoneal endometriosis ( Supplementary Figure 4A–D ) revealed that all visualisations consistently sup -\nported the protective effect of the glycerol-to-palmitoylcarnitine (c16) ratio in both endometriosis subtypes.\nExploration of the causal effect of endometriosis and its subtypes on metabolites\nReverse Mendelian analysis indicated the causal associations of stage 1–2 endometriosis with 15 metabolites, \nstage 3–4 endometriosis with 32 metabolites, deep endometriosis with 44 metabolites, fallopian tube endome-\ntriosis with 3 metabolites, intestinal endometriosis with 4 metabolites, ovarian endometriosis with 44 metabo-\nlites, pelvic peritoneal endometriosis with 16 metabolites, and rectovaginal septum and vaginal endometriosis \nwith 14 metabolites in the unadjusted IVW results(see Supplementary Table 3 for details). Supplementary \nFigure 2 shows the metabolites associated with three or more endometriosis subtypes. Supplementary Table 4 \nprovides details regarding the metabolites related to one to two subtypes of endometriosis.\nno causal association was found between endometriosis and its subtypes or metabolites after FDR \ncorrection ( PFDR < 0.05).\nResults of metabolic pathway enrichment analysis\nusing the positive metabolites identified from the IVW analysis without correction for multiple testing, \nwe conducted eight metabolic pathway enrichment analyses. With the significance level set at P < 0.01, \nthe results highlighted major metabolic pathways, including caffeine metabolism, glutathione metabo -\nlism, arginine biosynthesis, sphingolipid metabolism, pantothenate and c oA biosynthesis, plasmalogen \nsynthesis, and biosynthesis of unsaturated fatty acids (see Table 3 for details; complete enrichment anal -\nysis results are available in Supplementary Tables 5–12 ).\nFigure 2. t his figure shows the Mendelian randomisation analysis results of the ratio of Glycerol to Palmitoylcarnitine \n(c16) with endometriosis stages 1–2 and pelvic peritoneal endometriosis.\nTable 2. Mr sensitivity analyses of genetically predicted metabolites on endometriosis and its subtypes.\no utcome exposure\nHeterogeneity tests\n  directional horizontal \npleiotropycest\nMethods c ochran’sQ(P)\n  Mr-e gger \nintercept ( P) Ppleiotropy*\nendometriosis stages \n1–2\nGlycerol to palmitoylcarnitine ( c16) ratio Mr e gger, iVW 14.42(0.49),17.01(0.38) −0.04(0.12) 0.43\nendometriosis of \npelvic peritoneum\nGlycerol to palmitoylcarnitine ( c16) ratio Mr e gger, iVW 14.74(0.46),18.22(0.31) −0.04(0.08) 0.38\n*detect by Mr-Presso Global test; Mr-e gger, Mendelian randomisation-e gger; iVW, inverse-variance weighted.\n\nJouRnAl oF oBSTe TRIcS AnD GynAecoloGy 7\nResults of colocalisation analysis\nc olocalisation analyses were conducted separately for the glycerol-to-palmitoylcarnitine ( c16) ratio in stage \n1–2 endometriosis and pelvic peritoneal endometriosis. The posterior probabilities were 0.002 and 0.068, \nrespectively, indicating no support for a shared causal variant between the glycerol-to-palmitoylcarnitine \n(c16) ratio and stage 1–2 endometriosis or pelvic peritoneal endometriosis ( Supplementary Table 13 and \nFigure 3A–B ).\nDiscussion\nIn this study, we analysed the potential causal associations between 1,400 metabolites and endometrio -\nsis and its subtypes using the MR approach. After FDR correction for multiple testing, only the \nglycerol-to-palmitoylcarnitine ( c16) ratio remained statistically significant, showing potential protective \neffects against stage 1–2 endometriosis and pelvic peritoneal endometriosis. Sensitivity and pleiotropy \nanalyses supported the robustness of this single significant finding. Reverse Mendelian analysis did not \nreveal any reverse causal associations.\nMetabolic pathway enrichment analysis based on uncorrected results suggested the potential involve -\nment of pathways, including caffeine metabolism, glutathione metabolism, arginine biosynthesis, sphin -\ngolipid metabolism, pantothenate and c oA biosynthesis, plasmalogen synthesis, and biosynthesis of \nunsaturated fatty acids. However, these pathway results should be interpreted cautiously given that most \nindividual metabolite associations did not survive multiple-testing correction. c olocalisation analysis \nrevealed low posterior probabilities for shared causal variants (H4 < 0.1) between glycerol-to-palmitoylcar -\nnitine ( c16) ratio and endometriosis outcomes, indicating limited evidence for genetic colocalisation.\nThe glycerol-to-palmitoylcarnitine ( c16) ratio represents a meaningful metabolic readout that reflects \nthe balance between lipolysis and fatty acid oxidation, two processes that are critically disrupted in the \npathophysiology of endometriosis. This ratio provides insight into cellular energy metabolism dysfunc -\ntion, which is increasingly recognised as a hallmark of endometriosis progression. Palmitoylcarnitine, as \nan important fatty acid, ( c16) serves as a critical intermediate in mitochondrial fatty acid β-oxidation, \nfacilitating the transport of palmitic acid across the mitochondrial membrane via the carnitine palmito -\nyltransferase ( cPT) system. emerging evidence suggests that endometriotic lesions exhibit significant \nmitochondrial dysfunction, characterised by impaired oxidative phosphorylation, reduced ATP produc -\ntion, and altered fatty acid metabolism (Atkins et  al. 2019) Recent studies have demonstrated that endo -\nmetrium and endometriosis tissue from nonhuman primates with endometriosis showed decreased \nmitochondrial respiration and reduced complex I and II-mediated oxygen consumption rates compared \nto normal endometrium (Atkins et  al. 2019). notably, these studies revealed that carnitine levels signifi -\ncantly decreased in endometriotic tissues, suggesting fundamental disruptions in fatty acid metabolism \npathways.\nThe role of altered fatty acid metabolism in endometriosis is further supported by evidence of meta -\nbolic reprogramming in endometriotic lesions. endometriotic cells have been shown to strategically \nreduce energy production to avoid excessive mitochondrial R oS production, leading to a metabolic shift \nfrom oxidative phosphorylation to glycolysis (Kobayashi et  al. 2021). This metabolic conversion may result \nin the accumulation of fatty acid metabolites such as palmitoylcarnitine, reflecting the cells’ inability to \nefficiently utilise fatty acids for energy production. Glycerol, which is released during triglyceride \nTable 3. Metabolic pathway enrichment analysis results ( P < 0.01).\nMetabolic pathway trait database P\nc affeine metabolism endometriosis stages 1–2 sMPdb 0.0073\nGlutathione metabolism endometriosis stages 1–2 KeGG 0.0009\na rginine biosynthesis endometriosis stages 1–2 KeGG 0.0046\ndeep endometriosis KeGG 0.0046\nendometriosis of pelvic peritoneum KeGG 0.0046\nsphingolipid metabolism deep endometriosis KeGG 0.0014\nPantothenate and c oa biosynthesis endometriosis of fallopian tube KeGG 0.0093\nPlasmalogen synthesis endometriosis of intestine sMPdb 0.0062\nbiosynthesis of unsaturated fatty acids endometriosis of intestine KeGG 0.0001\n\n8 F. yAn eT Al.\nbreakdown, serves as both a gluconeogenic substrate and a marker of lipolytic activity. In endometriosis, \ndysregulated lipolysis may reflect metabolic reprogramming associated with chronic inflammation and \ntissue remodelling. Inflammatory cytokines prevalent in endometriosis, such as TnF-α and Il -1β, can sig -\nnificantly alter lipid metabolism and promote lipolysis (Feingold et  al. 1992, Plomgaard et  al. 2008). TnF-α \nstimulates lipolysis by activating hormone-sensitive lipase and adipocyte triglyceride lipase, leading to \nincreased free fatty acid release (Plomgaard et  al. 2008). Therefore, the glycerol-to-palmitoylcarnitine ratio \nFigure 3. a: c o-localization analysis of Glycerol to Palmitoylcarnitine ( c16) ratio and s tage 1–2 endometriosis. b: \nc o-localization analysis of Glycerol to Palmitoylcarnitine ( c16) ratio and Pelvic Peritoneal endometriosis.\n\nJouRnAl oF oBSTe TRIcS AnD GynAecoloGy 9\nmay capture the imbalance between lipid breakdown (reflected by glycerol) and utilisation (reflected by \npalmitoylcarnitine), providing a composite biomarker for metabolic dysfunction.\nThe protective effect of a higher glycerol-to-palmitoylcarnitine ratio suggests that enhanced lipolytic \ncapacity relative to fatty acid oxidation demand may be metabolically beneficial in endometriosis. This find -\ning aligns with the emerging evidence that interventions targeting fatty acid metabolism may have thera -\npeutic potential for endometriosis. Palmitoylethanolamide, a derivative of palmitic acid, has demonstrated \nanti-inflammatory and analgesic effects in endometriosis patients (Indraccolo and Barbieri 2010, Stochino-l oi \net  al. 2019). These studies suggest that modulation of fatty acid metabolic pathways may represent a novel \ntherapeutic approach for the management of endometriosis.\nFurthermore, this ratio may reflect the metabolic flexibility of tissues in adapting to the energy \ndemands of chronic inflammation. Women with higher glycerol-to-palmitoylcarnitine ratio may exhibit \ngreater metabolic resilience, enabling better adaptation to the inflammatory stress characteristic of endo -\nmetriosis and reducing disease susceptibility. While this mechanistic framework provides biological plau -\nsibility for our findings, direct experimental validation of these proposed mechanisms is essential. Future \nstudies should investigate the expression of key enzymes involved in fatty acid metabolism ( cPT1A, \ncPT2, and hormone-sensitive lipase) in endometriotic tissues and explore how alterations in the \nglycerol-to-palmitoylcarnitine ratio correlate with mitochondrial function parameters, inflammatory mark -\ners, and clinical outcomes. Additionally, metabolomic studies of endometriotic tissue samples could pro -\nvide direct evidence for the proposed metabolic dysregulation and validate the systemic metabolic \nchanges reflected in serum measurements.\nPrevious research on the association between metabolites and endometriosis did not involve the \nglycerol-to-palmitoylcarnitine (c16) ratio. However, previous studies have revealed the significant roles of \nmetabolites derived from lipid metabolism, gut microbiota, environmental exposure, and hormonal path -\nways in the development, progression, and symptomatology of endometriosis. Sasamoto et  al. (Sasamoto \net  al. 2022) found that the dysregulation of lipid metabolites in younger patients with endometriosis may \nbe associated with persistent pelvic pain after surgery, highlighting the role of lipid metabolism in pain \npathophysiology. l e et  al. (l e et  al. 2021) focused on the interaction between the gut microbiota and \noestrogen metabolites, noting the association between dysbiosis and changes in urinary oestrogen levels \nin patients and emphasising the influence of the gut microbiota on disease progression. ermolova et  al. \n(eRMoloVA et  al. 2020) discussed the role of cytokines, nitric oxide metabolites, and lipid metabolism at \ndifferent stages of endometriosis and suggested a complex interplay between these factors in the patho -\ngenesis and progression of the disease. li (li et  al. 2020) found that exposure to specific pesticide metab -\nolites might increase the risk of endometriosis, indicating the potential role of environmental factors. \nchadchan ( chadchan et  al. 2023) demonstrated the contribution of the gut microbiota and their metab -\nolites to lesion growth, underlining the importance of the gut microbiome in disease progression. \nAdditionally, emond et  al. (emond et  al. 2022) highlighted the significance of the oestradiol metabolic \npathway in ovarian endometriosis and pain-related outcomes. The meta-analysis conducted by c ai et  al. \n(c ai et  al. 2019) indicated a potential association between certain phthalate metabolites (particularly \nMeHHP) and endometriosis, suggesting a link between environmental toxins and the disease. c ollectively, \nthese studies demonstrate the potential roles of metabolites from lipid metabolism, gut microbiota, envi -\nronmental exposure, and hormonal pathways in the development, progression, and symptomatology of \nendometriosis.\nThrough enrichment analysis of the differential metabolites selected via Mendelian analysis, we iden -\ntified several key metabolic pathways. Interactions between these pathways reveal the complex mecha -\nnisms underlying endometriosis, highlighting the significant role of metabolic regulation in disease \nprogression. Sphingolipid metabolism, which regulates fundamental processes, such as cell proliferation, \nmigration, and apoptosis, plays a pivotal role in the development of endometriosis. Variations in the \nactivity of key enzymes such as sphingomyelin synthase 1 (SMS1), sphingomyelin phosphodiesterase 3 \n(SMPD3), and glucosylceramide synthase (GcS) may reflect the involvement of sphingolipid metabolism \nin the pathogenesis of endometriosis, providing opportunities for developing new therapeutic targets \n(l ee et  al. 2014). Research on glutathione metabolism has revealed a complex relationship between the \ngenetic predisposition to detoxification mechanisms and endometriosis. Specifically, studies on genetic \npolymorphisms in n-acetyltransferase 2 (nAT2) and glutathione S-transferases (GST) M1 and T1 have \n\n10 F. yAn eT Al.\nprovided strong evidence for the crucial role of detoxification pathways in disease onset (Baranova, \n1999). Furthermore, analysis of caffeine metabolism highlights the role of lifestyle and dietary factors in \nthe risk and severity of endometriosis. This necessitates further research to explore how caffeine and its \nmetabolites interact with genetic susceptibility through pathways such as oestrogen regulation, thereby \ninfluencing the development of endometriosis. changes in arginine biosynthesis, particularly its upregu -\nlation in the serum of patients with endometriosis, provide insights into metabolic dysregulation associ -\nated with the disease. Broader disruption of amino acid metabolism may be related to immune responses \nand angiogenesis in endometriosis, which merits further exploration (l etsiou et  al. 2017). Studies on fatty \nacid metabolism emphasise the potential role of lipid metabolic pathways in endometriosis, with research \nby Gassler et  al. highlighting the extensive impact of metabolic dysregulation in the disease’s pathology \n(Gassler et  al. 2005). The link between pantothenate (vitamin B5) and c oA biosynthesis and its associa -\ntion with endometriosis have not been exhaustively discussed in the existing literature. c oA, a central \ncofactor in various metabolic pathways, including fatty acid synthesis and oxidation, and the tricarboxylic \nacid (T c A) cycle, plays a key role in energy metabolism regulation, which could be crucial in the patho -\nlogical environment of endometriosis. Disturbances in pantothenate and c oA biosynthesis, leading to \nchanges in c oA levels, can affect the cellular energy balance, thereby contributing to the disease mech -\nanism. For instance, pantothenate kinase, which catalyses the initial step of c oA biosynthesis, is tightly \nregulated and its activity can influence metabolism and cellular homeostasis (l eonardi and Jackowski \n2007). In contrast, plasmalogens, a class of ether phospholipids involved in membrane integrity and sig -\nnalling, have been implicated in oxidative stress and inflammation, which are key components of endo -\nmetriosis. However, direct research linking plasmalogen synthesis with endometriosis is scarce. The \nsignificance of lipid metabolism in the pathophysiology of endometriosis suggests that changes in plas -\nmalogen levels or synthesis may affect endometriosis (Zhang et  al. 2004, l eonardi and Jackowski 2007).\nc olocalisation analysis revealed low posterior probabilities for shared causal variants (H4 < 0.1) between \nglycerol-to-palmitoylcarnitine (c16) ratio and endometriosis outcomes. This finding presents an important \nlimitation to our causal inference, as it suggests several alternative explanations for the observed MR \nassociations.\nFirst, the low probability of colocalisation may indicate the presence of horizontal pleiotropy, \nwhere genetic variants affect both the metabolite ratio and endometriosis through independent bio -\nlogical pathways rather than through a direct causal relationship. Second, it may reflect linkage \neffects, where the IVs are in linkage disequilibrium with the true causal variants affecting either the \nexposure or the outcome, but not necessarily both. Third, limited colocalisation evidence suggests \nthat the genetic architectures underlying metabolite levels and endometriosis risk operate through \ndistinct mechanisms.\nThese findings urge caution when interpreting the MR results as definitive evidence of causality. While \nthe MR analysis suggests a potential association, the lack of strong colocalisation evidence indicates that \nthis relationship may be more complex than a simple direct causal pathway. Therefore, the observed \nassociation should be considered suggestive rather than confirmatory evidence and requires additional \nvalidation through alternative study designs and experimental approaches.\no ur study employed several MR approaches to systematically evaluate the potential causal rela -\ntionships between 1,400 circulating metabolites and multiple endometriosis subtypes. c oncordant \nresults across IVW, MR-e gger, and weighted-median estimators minimised the bias from horizontal \npleiotropy and other confounders, thereby reinforcing the robustness of our conclusions. c ompared \nwith previous work, we investigated a broader metabolite repertoire and finer disease classification. \nRecent pertinent studies are summarised in Supplementary Table 14 . o f particular interest, the sole \nsignal that remained significant after FDR correction—that is, the glycerol-to-palmitoylcarnitine \n(c16) ratio—can be quantified rapidly through high-throughput lc–MS/MS and therefore has imme -\ndiate translational potential. Pending replication, this ratio could support non-invasive risk stratifi -\ncation before laparoscopy and serve as a pharmacodynamic marker during treatment. Moreover, its \nlinkage to lipid oxidation pathways provides a tentative basis for personalised management of \nendometriosis.\nHowever, this study has several limitations. First, the fact that only the glycerol/c16 ratio remained \nsignificant after multiple-testing correction underscores the exploratory nature of our findings and \n\nJouRnAl oF oBSTe TRIcS AnD GynAecoloGy 11\nsuggests that some initial associations may represent false positives. Second, the low posterior probabil -\nity of genetic colocalisation with endometriosis outcomes (H4 < 0.1) weakens causal inference and raises \nthe possibility that the observed association is driven by horizontal pleiotropy or linkage effects. Third, \nthe analysis was confined to individuals of e uropean ancestry, limiting generalisability. Differences in \nallele frequencies, linkage-disequilibrium patterns, and genetic architecture across populations may have \nmodified effect estimates.\nTo address these gaps, future work should (i) replicate the glycerol/c16 signal across east-Asian, \nAfrican, South-Asian and admixed populations to evaluate its universality or reveal ancestry-specific \neffects; (ii) perform longitudinal metabolomic profiling to track temporal changes in the ratio before \nsymptom onset and throughout therapy; (iii) integrate eQTl, pQTl, organoid and animal-model data in \nmulti-omics mediation and functional studies to elucidate underlying mechanisms and exclude pleiot -\nropy; and (iv) initiate early-phase intervention trials targeting lipid-oxidation pathways, using the glyc -\nerol/c16 ratio as a responsive biomarker to explore novel therapeutic strategies.\nConclusion\no ur study provides preliminary evidence for the potential associations between metabolites and endo -\nmetriosis subtypes through bidirectional MR analysis. However, the limited colocalisation evidence \n(H4 < 0.1) and the fact that only one metabolite ratio remained significant after FDR correction suggest \nthat these relationships may involve complex mechanisms beyond simple causality, including potential \nhorizontal pleiotropy or linkage effects. These findings should be interpreted as hypotheses rather than \nconfirmatory evidence. Future studies with larger sample sizes and experimental validations are essential \nto confirm these preliminary results. This analysis may advance metabolomics-based research on the \ndiagnosis and treatment of endometriosis.\nAuthor contributions\ncRediT: Fei Yan : c onceptualization, Writing – original draft; Zhouxiang Chen : Resources, Software, Supervision, \nValidation, Visualization, Writing – original draft; Lingfeng Wu : Formal analysis, Funding acquisition, Investigation, \nMethodology, Project administration; Zongju Huang : c onceptualization, Writing – original draft. Fei yan and \nZhouxiang chen contributed equally to this work as co-first authors.\nDisclosure statement\nno potential conflict of interest was reported by the authors.\nFunding\nThis study received no external funding or financial support from public, commercial, or not-for-profit funding bodies.\nData availability statement\nThe datasets analysed in this study were obtained from publicly available genome-wide association studies (GWAS), \nas described in detail in the Methods section. Summary-level statistics supporting the findings of this study are \navailable from the corresponding author upon reasonable request. Access to individual-level genetic data is restricted \nin accordance with ethical guidelines to protect participant privacy and confidentiality. This study utilised only sum -\nmary data from previously published studies, all of which had obtained appropriate ethical approvals for data col -\nlection and sharing. o ur research protocol adhered to all applicable ethical standards governing human genetic \nresearch and data sharing, including considerations for informed consent and privacy protection. The specific data \nsources for metabolite and endometriosis phenotypes, along with their accession numbers and uRls, are listed in \nthe Supplementary Materials . Due to the volume of supplementary data involved, some files are not directly down -\nloadable but can be made available upon request from the authors.\n\n12 F. yAn eT Al.\nReferences\nAtkins, H.M., et al. , 2019. endometrium and endometriosis tissue mitochondrial energy metabolism in a nonhuman \nprimate model. Reproductive Biology and Endocrinology: RB&E , 17 (1), 70.\nBaranova, H., et al. , 1999. 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