Submit Manuscript  

Article Details

Emerging Metabolomics Biomarkers of Polycystic Ovarian Syndrome; Targeting the Master Metabolic Disrupters for Diagnosis and Treatment

[ Vol. 18 , Issue. 3 ]


Maxwell Omabe*, Sunday Elom and Kenneth N. Omabe   Pages 221 - 229 ( 9 )


Background and Objective: Metabolomics is a powerful exploratory tool for discovering new diagnostic molecules or biomarkers due to its ability to highlight several interactions between different biochemical molecules and pathways in composition in health and disease thereby advancing our understanding, to provide evidence based diagnosis and treatment of such a complex disease including polycystic ovarian syndrome (PCOS). The aim of this study was to review available literature on the use of metabolomic approach and to critically evaluate and draw a synthesis to highlight novel biochemical markers for clinical application in PCOS.

Method: Studies that applied metabolomic approach to investigate PCOS and those meeting selection criteria were searched and, critically evaluated.

Result: Here we highlighted the metabolic reactions and perturbation of some metabolic pathways present in patients with polycystic ovarian syndrome and normal subjects that can allow better understanding of the disorder and help developing a new generation diagnostic and treatment algorithm.

Conclusion: A number of disease-related metabolites have been discussed which have extraordinary potential for a clinical utility as diagnostic and treatment monitoring biomarkers.


Metabolomics, biomarkers, PCOS, master metabolic disrupter, lipid profile, gluconeogenesis.


Department of Pathology / Clinical Biochemistry; Oncology Division, Cancer Research Cluster, Cancer Immunotherapy and Vaccine Research Group, University of Saskatchewan, Saskatoon, Department of Medical Biochemistry, Faculty of Basic Medical Sciences, Federal University, Ndufu-Alike, Ikwo, Abakaliki, Department of Molecular Biology, Faculty of Science, Federal University, Ndufu-Alike, Ikwo, Abakaliki

Graphical Abstract:

Read Full-Text article