Metabolomics for Diagnosis and Prognosis of Uterine Diseases? A Systematic Review

Janina Tokarz, Jerzy Adamski, Tea Lanišnik Rižner
Journal of personalized medicine · 2020 · vol. 10(4) , pp. 294 · doi:10.3390/jpm10040294 · PMID:33371433 · W3113933142
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⚙ AI-generated summary by claude@2026-06, 2026-06-07 ⓘ

This systematic review of 44 studies found that metabolomics can contribute to diagnostic and prognostic models for uterine diseases, though most studies are in the discovery phase and require further validation for clinical use.

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Abstract

This systematic review analyses the contribution of metabolomics to the identification of diagnostic and prognostic biomarkers for uterine diseases. These diseases are diagnosed invasively, which entails delayed treatment and a worse clinical outcome. New options for diagnosis and prognosis are needed. PubMed, OVID, and Scopus were searched for research papers on metabolomics in physiological fluids and tissues from patients with uterine diseases. The search identified 484 records. Based on inclusion and exclusion criteria, 44 studies were included into the review. Relevant data were extracted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) checklist and quality was assessed using the QUADOMICS tool. The selected metabolomics studies analysed plasma, serum, urine, peritoneal, endometrial, and cervico-vaginal fluid, ectopic/eutopic endometrium, and cervical tissue. In endometriosis, diagnostic models discriminated patients from healthy and infertile controls. In cervical cancer, diagnostic algorithms discriminated patients from controls, patients with good/bad prognosis, and with/without response to chemotherapy. In endometrial cancer, several models stratified patients from controls and recurrent from non-recurrent patients. Metabolomics is valuable for constructing diagnostic models. However, the majority of studies were in the discovery phase and require additional research to select reliable biomarkers for validation and translation into clinical practice. This review identifies bottlenecks that currently prevent the translation of these findings into clinical practice.

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endometriosis

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