Summary
Endometriosis and adenomyosis are two gynecological conditions that share many symptoms but differ significantly in pathology. Historically underdiagnosed and frequently misclassified, both conditions cause significant burden to patients and have been poorly understood at a mechanistic level.
This study by applies PrecisionLife's combinatorial analytics platform to UK Biobank and All of Us datasets to identify novel genetic signatures and evaluate the genetic and biological differences between endometriosis and adenomyosis.
The findings of this disease study will further our understanding of the overlapping and unique disease mechanisms driving each condition, with direct implications for personalized diagnosis and treatment.
Key insights
- 171 adenomyosis and 182 endometriosis genetic risk signatures were identified using combinatorial analysis.
- 9 genes were found in both conditions in distinct SNP combinations, suggesting shared but mechanistically different pathways.
- Genes unique to adenomyosis were enriched for pathways involved in cell-cell adhesion, while those shared with endometriosis were linked to lipid metabolism and regulation of phagocytosis.
- Several of these findings have been reproduced in the ancestrally diverse All of Us cohort, increasing translational confidence.
- This analysis provides deeper insight than GWAS, stratifying patients into mechanistically defined subgroups and revealing potential therapeutic targets.

Implications for Healthcare and Diagnostics
This study supports the development of our non-invasive, saliva-based diagnostic tools - Mechanostics® - capable of distinguishing between endometriosis, adenomyosis, and related conditions such as IBS, uterine fibroids and fibromyalgia.
These stratification biomarkers can power more accurate differential triage and diagnosis to inform personalized treatment strategies based on a patient’s specific disease mechanism.
It also supports drug repurposing efforts by identifying patient subgroups with shared mechanistic profiles that can be matched with known therapeutic targets, accelerating clinical validation.
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