For millions of women, a hidden condition often goes undiagnosed for years. Endometriosis affects an estimated 1 in 10 women of reproductive age, yet diagnosis remains a long and arduous journey.
Endometriosis, where tissue similar to the uterine lining grows outside the uterus, affects an estimated 1 in 10 women of reproductive age. Yet, the path to diagnosis remains a long and arduous journey, often delayed by 7 to 12 years from the onset of symptoms 1 . This diagnostic labyrinth not only prolongs suffering from chronic pain and infertility but also imposes a significant socio-economic burden. The gold-standard for diagnosis—laparoscopic surgery—is invasive and carries inherent risks. This reality has fueled an urgent scientific quest for a better solution: discovering reliable biomarkers for early, non-invasive detection 1 3 5 .
Current diagnosis requires invasive laparoscopic surgery, creating barriers to early detection and treatment.
Researchers are developing non-invasive tests using biomarkers from blood, menstrual fluid, and other sources.
Endometriosis is not a disorder with a single cause but a complex condition influenced by genetics, inflammation, and metabolism. Consequently, researchers are investigating a wide array of biomarkers, each reflecting a different biological dimension of the disease.
A strong hereditary component underscores the role of genetics in endometriosis. Genome-wide association studies (GWAS) have identified multiple risk loci in genes such as WNT4, VEZT, and GREB1 3 .
Furthermore, epigenetic modifications, like specific DNA methylation patterns and dysregulated microRNA expression, have emerged as crucial contributors to the disease's development and progression.
Heritable DNA Methylation microRNAEndometriosis is recognized as a chronic inflammatory state. The condition is marked by increased levels of cytokines and chemokines—signaling proteins that drive inflammation 3 6 .
Key players include:
Hormonal dysregulation is a hallmark of endometriosis. Beyond classic imbalances in estrogen and progesterone, researchers are focusing on:
Metabolomics, the large-scale study of small molecules, can capture the functional end-products of biological processes. Research has identified distinct lipid and amino acid profiles in the plasma and peritoneal fluid of women with endometriosis 4 8 .
These metabolic "signatures" hold immense promise as sensitive diagnostic tools, especially when combined with other data types.
While many studies focus on a single type of biomarker, the most promising advances come from integrating them. A 2025 multicenter study exemplifies this powerful "multi-omics" approach, aiming to build a superior diagnostic model by combining metabolomic and proteomic data 4 .
Researchers collected plasma and peritoneal fluid from women undergoing laparoscopic surgery.
Using mass spectrometry, the team analyzed the samples to quantify the levels of 188 metabolites.
The metabolomic data was combined with pre-existing proteomic data from the same samples.
Using chemometric analyses, researchers built a classification model to distinguish endometriosis patients from controls.
The key finding was that the combined multi-omics model drastically outperformed models based on either metabolomic or proteomic data alone.
| Biomarker Source | Model Type | Sensitivity | Specificity |
|---|---|---|---|
| Plasma | Multi-omics (Metabolomic + Proteomic) | 0.98 | 0.86 |
| Plasma | Metabolomic-only | 0.82 | 0.77 |
| Peritoneal Fluid | Multi-omics (Metabolomic + Proteomic) | 0.92 | 0.82 |
| Peritoneal Fluid | Metabolomic-only | 0.85 | 0.79 |
Perhaps one of the most innovative approaches comes from researchers developing an at-home diagnostic device that uses menstrual blood—a biological resource often disregarded as medical waste 7 .
This proof-of-concept device was designed to detect HMGB1, a protein implicated in the onset and progression of endometriosis, which is found at significantly higher levels in the menstrual blood of affected individuals.
The results were striking—the device detected HMGB1 with 500% greater sensitivity than existing lab-based methods, capable of identifying even low concentrations critical for catching early-stage or asymptomatic cases.
| Component | Function | Innovation |
|---|---|---|
| Borophene Nanosheets | Sensor platform | Highly biocompatible with uniform surface |
| Anti-HMGB1 Antibodies | Capture HMGB1 proteins | Precisely bound for accurate detection |
| Lateral Flow Strip | Physical test strip | Easy visual readout (similar to pregnancy test) |
The search for endometriosis biomarkers relies on a sophisticated arsenal of laboratory tools and technologies.
| Tool / Reagent | Function | Application in Research |
|---|---|---|
| Mass Spectrometry | Precisely identifies and quantifies molecules based on their mass-to-charge ratio | Used for metabolomic and proteomic profiling of plasma, peritoneal fluid, and tissue samples 4 |
| Protein Microarrays | Simultaneously measures the presence and quantity of thousands of proteins or autoantibodies | Employed to identify autoantibody signatures associated with different stages of endometriosis 4 |
| AbsoluteIDQ® p180 Kit | A standardized kit for targeted metabolomic analysis | Allows researchers to consistently measure 188 metabolites across different patient cohorts 4 |
| GWAS & eQTL Datasets | Large-scale genetic data linking genetic variations to gene expression and disease | Used to identify hereditary risk factors and prioritize candidate genes like ADK, EHHADH, and CNOT7 9 |
| Machine Learning Algorithms | Computational models that find complex patterns in large, multidimensional datasets | Used to integrate multi-omics data and select the most predictive biomarker panels for diagnosis 3 9 |
The journey to revolutionize endometriosis diagnosis is converging on a single, powerful principle: integration. The future lies not in a single "magic bullet" biomarker but in multi-marker panels that capture the hormonal, inflammatory, genetic, and metabolic essence of the disease 1 3 4 .
Combining biomarkers from different biological pathways for comprehensive diagnosis.
Developing sophisticated algorithms to analyze complex biomarker profiles.
Providing personalized, precise risk assessments based on individual biomarker profiles.
References will be added here in the final publication.