Decoding the Diabetic Transition: A Lipidomic Approach for Biomarker Discovery in an Indian Cohort

Type 2 Diabetes Mellitus (T2DM) is a significant public health burden in India, where the disease presents with unique clinical characteristics, yet data from this high-risk population remain scarce. This study aimed to conduct a lipidomic analysis to identify candidate lipid species associated with different glycemic stages using a refined untargeted lipidomics approach. A cross-sectional study of 95 individuals from Mumbai was stratified into healthy, prediabetic, newly diagnosed diabetic, and advanced diabetic groups based on glycated hemoglobin (HbA1c) levels. An untargeted ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) approach was applied to pooled serum samples to construct a comprehensive lipid library and enable comparative profiling. A stringent filtering pipeline was implemented to ensure robust lipid identification. Our analysis identified a core lipidome of 69 species shared across all cohorts, alongside an increase in lipidomic diversity in diabetic states, with the advanced diabetic group exhibiting the highest number of unique species. Ceramides (Cer) were detected across the glycemic spectrum and exhibited a distinct remodelling pattern. Partial least squares discriminant analysis (PLS-DA) identified 20 candidate lipid species with variable importance in projection (VIP) scores > 1.0, prominently including Cer, phosphatidylinositol (PI), and hexosylceramides (HexCer). Additional lipid classes of interest included sphingomyelins (SM), triacylglycerols (TG), acylcarnitines (ACar), and fatty acids (FA). These hypothesis-generating findings provide a prioritized framework for future large-scale quantitative validation studies aimed at early prediction and risk stratification of T2DM in the Indian population.