Next-Generation Endometriosis Diagnostics Through Comprehensive Multi-Dimensional Analysis

Overview

This study is a multicentric, observational, case-control, non-profit with additional procedures. It aims to deepen the understanding of the chronic gynecological conditions of endometriosis and adenomyosis, which significantly impact women's reproductive health. Its purpose is to improve early diagnosis and personalized treatment of these conditions using a multi-omic approach, that integrates genetic, epigenetic, imaging, and endometrial receptivity data. The goal is also to refine image-based predictions through recent advancements in artificial intelligence and to study uterine extracellular vesicles to assess fertility non-invasively. The study targets patients with endometriosis and/or adenomyosis and involves women seeking fertility treatments at assisted reproduction centers, who will serve as a control population. The study comprises both prospective and retrospective components. The prospective recruitment involves the collection of blood and uterine fluid samples, while the retrospective element utilizes pre-existing biobank samples for comprehensive genetic and epigenetic analysis.

Recent research indicates that epigenetic blood analysis could revolutionize the diagnosis of endometriosis, moreover, strong correlations between endometrial and blood methylation have been reported, suggesting significant diagnostic potential. Preliminary data on Polygenic Risk Scores (PRS) also show promise in identifying genetic profiles associated with disease severity. Advancements in artificial intelligence (AI) offer precise image-based diagnostic predictions, highlighting the transformative potential of integrating AI with genetic analyses. Additionally, our preliminary studies have demonstrated the potential of using gene expression data from uterine fluid extracellular vesicles (UF-EVs) to understand endometrial receptivity, with implications for detecting both endometriosis and adenomyosis. Through this study, the investigators hypothesize that differential methylation profiles, integrated with genetic, epigenetic, and clinical data, can accurately classify endometriosis and adenomyosis cases. Additionally, it's hypothesized that UF-EVs gene expression profiles differ significantly between endometriosis, adenomyosis, and fertile controls, providing critical insights into endometrial receptivity and potential diagnostic markers for these conditions. Primary Objective: To identify specific CpG sites that exhibit differential methylation levels between endometriosis cases and controls. These methylation profiles, combined with polygenic risk scores (PRS) and clinical questionnaire data, will be used to classify cases and controls through machine learning analysis. (Aim 1) In addition to the differential methylation analysis, 'high-resolution SNP genotyping' will be employed. This genotyping will adjust the methylation analysis and aid in deriving polygenic risk scores. Secondary Objectives: To develop and validate a diagnostic model integrating ultrasound imaging with genetic, epigenetic, and clinical data to accurately identify and differentiate adenomyosis as an extension of endometriosis, and to predict pregnancy outcomes in women undergoing IVF. (Aim 2) Tertiary Objectives: To characterize the gene expression profiles of uterine fluid extracellular vesicles (UF-EVs) specific to endometriosis and adenomyosis will be analyzed samples at two critical time points: LH+2 (non-receptive) and LH+7 (receptive). The obtained gene expression data will be compared against previously collected data from fertile and infertile patients. By comparing these profiles, the investigators aim to identify distinct molecular signatures associated with each condition, enhancing our understanding of their impact on fertility and endometrial receptivity. Also, this comparison will allow us to refine diagnostic markers and potentially develop targeted interventions for affected women. (Aim 3) This study is conducted as a multicenter project, involving two Assisted Reproduction Centers, it will include a diverse cohort of 800 women. The study involves 530 participants and three distinct groups: women diagnosed with endometriosis, women diagnosed with both endometriosis and adenomyosis, and a control group of infertile women without these conditions. Each group will participate in the study, it is planned to last 24 months from the onset of recruitment to the final analysis of collected data. An additional group will consist of 300 DNA biobanked samples from women diagnosed with endometriosis.