The inconsistent pathogenesis of endometriosis and adenomyosis: insights from endometrial metabolome and microbiome

Chao Li, Xinxin Xu, Xiaojie Zhao, Bin Du
mSystems · 2025 · vol. 10(5) , pp. e0020225 · doi:10.1128/msystems.00202-25 · PMID:40261026 · W4409669124
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⚙ AI-generated summary by claude@2026-06, 2026-06-08 ⓘ

This multi-omic study found distinct metabolic and microbial signatures in endometrial samples, with shared alterations in linoleic acid and phospholipid metabolism, alongside unique bacterial species, differentiating endometriosis, adenomyosis, and healthy controls.

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Abstract

Endometriosis (EM) and adenomyosis (AM) are interrelated gynecological disorders characterized by the aberrant presence of endometrial tissue and are frequently linked with chronic pelvic pain and infertility, yet their pathogenetic mechanisms remain largely unclear. In this cross-sectional study, we analyzed endometrial samples from 244 participants, split into 91 EM patients, 56 AM patients, and 97 healthy controls (HC). We conducted untargeted liquid chromatography-mass spectrometry (LC-MS) and 5R 16S rRNA sequencing to examine endometrial metabolome and microbiome profiles. Additionally, we integrated transcriptomic analysis using nine transcriptomic data sets to investigate the biological basis of these conditions. Metabolomic profiling and 16S rRNA sequencing revealed distinct metabolic and microbial signatures. Specific pathways, including linoleic acid and glycerophospholipid metabolism, show significant alterations in both conditions. Notably, four metabolites, including phosphatidylcholine 40:8 [PC(40:8)], exhibited marked changes in both EM and AM, suggesting shared pathological features. Furthermore, taxonomic analysis identified unique bacterial species associated with each condition, particularly those belonging to the phylum Proteobacteria, which correlated with altered metabolic signatures. Machine learning models demonstrated high predictive accuracy for differentiating between AM, EM, and HC based on metabolic and microbial signatures. Integrative analysis with transcriptomic data highlighted distinct pathways related to immune response and signaling transduction for each condition. Our study provides fresh insights into the pathogenesis of AM and EM through a multi-omic approach, suggesting potential inconsistencies in the underlying pathogenetic mechanisms. IMPORTANCE: Existing research highlighted a connection between endometriosis (EM) and adenomyosis (AM), underscoring their overlapping symptoms and potential shared pathophysiological mechanisms. Although the role of microbiota in inflammatory conditions has been acknowledged, comprehensive investigations into the endometrial microbiota in cases of EM and AM have been limited. Previous studies identified distinct microbial communities associated with these conditions; however, they were constrained by small sample sizes and a lack of integrated analyses of microbiota and metabolomics. Furthermore, the ongoing debate over whether EM and AM should be classified as separate diseases or related phenotypes emphasizes the necessity for further exploration of their molecular interactions. Our study uncovers distinct microbial and metabolic signatures associated with each condition, revealing both shared and unique pathways that may contribute to their pathogenesis. Furthermore, the integration of transcriptomic data offers valuable insights into the complex interactions underlying these disorders.

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Condition tags

mesh:D004715endometriosisadenomyosischronic_pelvic_paininfertility

MeSH descriptors

Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis Adenomyosis

Citation neighborhood

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

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