Metabolomic Markers in the Pathogenesis, Diagnosis, and Treatment of Female Infertility: Current Approaches

Abstract

Background: Female infertility is a multifactorial condition with significant medical and social implications. Despite advances in the diagnosis and treatment of infertility, reliable biomarkers for early detection and selection of personalized therapy remain limited. Objective: To evaluate the role of metabolomic biomarkers in the pathogenesis, diagnosis, and treatment of female infertility, and to assess the potential for integrating metabolomic analysis into clinical practice to improve reproductive outcomes. Materials and methods : A literature review was conducted using articles published up to May 2025 in PubMed, Google Scholar, and eLibrary databases. The search strategy was developed in accordance with the PRISMA guidelines. A total of 222 records were identified. After the removal of 72 duplicates, 150 records were screened. Following title and abstract screening, 90 publications were excluded, and 60 articles underwent full-text assessment. After excluding 10 publications that did not meet the eligibility criteria, 50 studies were included in the final review. Results: The analysis of the data demonstrated that metabolomic markers enable the identification of metabolic dysfunctions associated with endocrine and physiological disturbances in infertility, allowing for more accurate early detection of pathological processes. The application of metabolomic analysis into comprehensive diagnosis may improve prognostic assessment and support the development of personalized therapeutic approaches. Several metabolites were identified as potential predictive biomarkers of therapeutic response to various treatment modalities, including assisted reproductive technologies. Conclusion: This review demonstrates that a comprehensive approach to the investigation of metabolomic markers provides new opportunities for improving the diagnosis and enhancing the effectiveness of treatment for female infertility. The findings of previous studies highlight the potential of integrating metabolomics into clinical practice to enable personalized fertility management and improve patient outcomes. Further development of metabolomic analysis and standardization of data interpretation methods may significantly accelerate the implementation of these technologies into routine clinical practice.