A PANEL of ten plasma protein biomarkers has been identified and validated, showing exceptional accuracy in diagnosing endometriosis. These findings highlight the potential for a non-invasive diagnostic tool that could transform current clinical practice.
Endometriosis is a complex condition that significantly impacts the lives of affected individuals, with diagnosis typically delayed by an average of seven years and often requiring invasive laparoscopy. This delay emphasises the urgent need for a simple, accurate, and non-invasive diagnostic method. Using advanced proteomic techniques, this study sought to identify plasma protein biomarkers that could reliably diagnose endometriosis across various stages of the disease.
The study involved 805 participants from two independent clinical populations, all of whom underwent laparoscopy to confirm their endometriosis status or exclude it in symptomatic controls. Researchers conducted a proteomics discovery experiment to identify candidate biomarkers, which were validated using targeted mass spectrometry. Plasma samples from 464 individuals with endometriosis, 153 general population controls, and 132 symptomatic controls were analysed. Three diagnostic models were developed: Model 1 (general population controls vs all endometriosis cases), Model 2 (symptomatic controls vs moderate to severe cases), and Model 3 (symptomatic controls vs severe cases).
Model 3 exhibited the highest predictive accuracy, achieving an area under the curve (AUC) of 0.997 (95% CI 0.994–1.000). It also distinguished symptomatic controls from early-stage endometriosis with AUCs of ≥0.85 for stages I to III. Model 1 demonstrated strong predictive performance with an AUC of 0.993 (95% CI 0.988–0.998), while Model 2 achieved an AUC of 0.729 (95% CI 0.676–0.783).
The results suggest that this biomarker panel could serve as a groundbreaking diagnostic tool, reducing delays in diagnosis and improving patient outcomes. Further research is required to validate these findings in diverse populations and refine the technology for clinical application. Early implementation of this tool could substantially alleviate the diagnostic burden on patients and clinicians, paving the way for earlier interventions and better disease management.
Abigail Craig, EMJ
Reference
Schoeman EM et al. Identification of plasma protein biomarkers for endometriosis and the development of statistical models for disease diagnosis. Hum Reprod. 2024. DOI: 10.1093/humrep/deae278.
