Serum metabolomics of Indian women with polycystic ovary syndrome using 1H NMR coupled with a pattern recognition approach.

Polycystic ovary syndrome (PCOS) is one of the most commonly occurring metabolic and endocrinological disorders affecting women of reproductive age. Metabolomics is an emerging field that holds promise in understanding disease pathophysiology. Recently, a few metabolomics based studies have been attempted in PCOS patients; however, none of them have included patients from the Indian population. The main objective of this study was to investigate the serum metabolomic profile of Indian women with PCOS and compare them with controls. Proton nuclear magnetic resonance (1H NMR) was used to first identify the differentially expressed metabolites among women with PCOS from the Eastern region of India during the discovery phase and further validated in a separate cohort of PCOS and control subjects. Multivariate analysis of the binned spectra indicated 16 dysregulated bins in the sera of these women with PCOS. Out of these 16 bins, 13 identified bins corresponded to 12 metabolites including 8 amino acids and 4 energy metabolites. Amongst the amino acids, alanine, valine, leucine and threonine and amongst the energy metabolites, lactate and acetate were observed to be significantly up-regulated in women with PCOS when compared with controls. The remaining 4 amino acids, l-glutamine, proline, glutamate and histidine were down-regulated along with 2 energy metabolites: glucose and 3-hydroxybutyric acid. Our findings showed dysregulations in the expression of different metabolites in the serum of women with PCOS suggesting the involvement of multiple pathways including amino acid metabolism, carbohydrate/lipid metabolism, purine and pyrimidine metabolism and protein synthesis.

Proteomic-driven biomarker discovery in gestational diabetes mellitus: a review.

Gestational diabetes mellitus (GDM) is defined as any degree of glucose intolerance with onset or first recognition during pregnancy and it affects 18% of pregnant women worldwide. GDM is considered a high-risk state which may lead to type II diabetes which is associated with an increase in a number of interrelated adverse perinatal outcomes. Given the fact that the progress of a successful pregnancy is dependent on the intricate communication between several biological molecules, identification of the proteomic profile perturbations in women with GDM is expected to help in understanding the disease pathogenesis and also discovery of clinical biomarker(s). In recent years, both gel-free and gel-based proteomics have been extensively investigated for improving maternal and child health. Although there are several reports integrating various aspects of proteomics in pregnancy related diseases such as preeclampsia, extensive Pubmed search shows no review so far on the application of proteomics in gestational diabetes. In this review, we focus on various high-throughput proteomic technologies for the identification of unique biosignatures and biomarkers responsible for the early prediction of GDM. Further, different analytical strategies and biological samples involved in proteomic analysis of this pregnancy-related disease are discussed.This article is part of a Special Issue entitled: Proteomics in India.