Ignite Creation Date:
2024-10-26 @ 3:38 PM
Last Modification Date:
2024-10-26 @ 3:38 PM
Study NCT ID:
NCT06572852
Status:
NOT_YET_RECRUITING
Last Update Posted:
None
First Post:
2024-08-23
Brief Title:
Next-Generation Endometriosis Diagnostics Through Comprehensive Multi-Dimensional Analysis
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Next-Generation Endometriosis Diagnostics Through Comprehensive Multi-Dimensional Analysis
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
AdEnd
Brief Summary:
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 womens 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 andor 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
The study targets patients with endometriosis andor 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
Detailed Description:
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 its 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 LH2 non-receptive and LH7 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
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 its 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 LH2 non-receptive and LH7 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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None