Plausible diagnostic value of urinary isomeric dimethylarginine ratio for diabetic nephropathy - Parmar, Dharmeshkumar; Bhattacharya, Nivedita; Kannan, Shanthini; Vadivel, Sangeetha; Pandey, Gautam Kumar; Ghanate, Avinash; Ragi, Nagarjuna Chary; Prabu, Paramasivam; Pramodkumar, Thyparambil Aravindakshan; Manickam, Nagaraj; Mohan, Viswanathan; Sripadi, Prabhakar; et al. (Scientific Reports, 2020)
Altered circulatory asymmetric and symmetric dimethylarginines have been independently reported in patients with end-stage renal failure suggesting their potential role as mediators and early biomarkers of nephropathy. These alterations can also be reflected in urine. Herein, we aimed to evaluate urinary asymmetric to symmetric dimethylarginine ratio (ASR) for early prediction of diabetic nephropathy (DN). In this cross-sectional study, individuals with impaired glucose tolerance (IGT), newly diagnosed diabetes (NDD), diabetic microalbuminuria (MIC), macroalbuminuria (MAC), and normal glucose tolerance (NGT) were recruited from Dr. Mohans' Diabetes Specialties centre, India. Urinary ASR was measured using a validated high-throughput MALDI-MS/MS method. Significantly lower ASR was observed in MIC (0.909) and MAC (0.741) in comparison to the NGT and NDD groups. On regression models, ASR was associated with MIC [OR: 0.256; 95% CI: 0.158-0.491] and MAC [OR 0.146; 95% CI: 0.071-0.292] controlled for all the available confounding factors. ROC analysis revealed ASR cut-point of 0.95 had C-statistic of 0.691 (95% CI: 0.627-0.755) to discriminate MIC from NDD with 72% sensitivity. Whereas, an ASR cut-point of 0.82 had C-statistic of 0.846 (95% CI: 0.800 - 0.893) had 91% sensitivity for identifying MAC. Our results suggest ASR as a potential early diagnostic biomarker for DN among the Asian Indians.

Circulatory levels of lysophosphatidylcholine species in obese adolescents: Findings from cross-sectional and prospective lipidomics analyses - Sharma, Sapna; Subrahmanyam, Yalamanchili Venkata; Ranjani, Harish; Sidra, Sidra; Parmar, Dharmeshkumar; Vadivel, Sangeetha; Kannan, Shanthini; Grallert, Harald; Usharani, Dandamudi; Anjana, Ranjit Mohan; Balasubramanyam, Muthuswamy; Mohan, Viswanathan; et al. (NUTRITION METABOLISM AND CARDIOVASCULAR DISEASES, 2024)
Keywords: Adolescent obesity; Asian Indians; biomarker; Lipidomics; Lysophosphatidyl- choline

Precision integrated identification of predictive first-trimester metabolomics signatures for early detection of gestational diabetes mellitus - Sharma, Sapna; Subrahmanyam, Yalamanchili Venkata; Gupta, Payal; Vadivel, Sangeetha; Deepa, Mohan; Tandon, Ansh; Sreedevi, Sreekumar; Ram, Uma; Narad, Priyanka; Parmar, Dharmeshkumar; Anjana, Ranjit Mohan; Raghunathan, Anu; et al. (Cardiovascular Diabetology, 2025)
Background and aimGestational diabetes mellitus (GDM), a common pregnancy-related metabolic disorder, often goes undiagnosed until the second trimester, limiting early intervention opportunities. Given the higher prevalence of GDM in India, there is a critical need to investigate metabolomic biomarkers among Asian Indians, who exhibit greater insulin resistance and are predisposed to developing type 2 diabetes at an earlier age. This study aimed to identify early pregnancy metabolomic signatures predictive of GDM. MethodsAmong 2115 pregnant women from the STratification of Risk of Diabetes in Early pregnancy (STRiDE) study, we performed untargeted metabolomic profiling using UPLC-MS/MS at early pregnancy (< 16 weeks) plasma samples from 100 women-comprising 50 with GDM and 50 normal (without GDM) based on oral glucose tolerance test (OGTT) at 24-28 weeks. Statistical and machine learning approaches, including logistic regression and random forest (RF), were applied to identify GDM-associated metabolites and construct predictive models. Pathway enrichment analysis was conducted using KEGG database annotations. ResultsA total of 49 metabolites were significantly associated with GDM, primarily involving lipid classes such as phosphatidylcholines, sphingomyelins, and triacylglycerols. RF analysis identified a panel of eight metabolites that achieved best predictive performance (AUC 0.880; 95% CI: 0.809-0.951) for GDM. When combined with conventional clinical risk factors, the integrated model showed comparable prediction of GDM with AUC 0.88;: 95% CI: 0.810-0.952). Enrichment analysis highlighted dysregulated pathways including glycerophospholipid and sphingolipid metabolism, autophagy, and insulin resistance. ConclusionThis study demonstrates the utility of early-pregnancy metabolomic profiling for predicting GDM in Indian women. The eight-metabolite panel offers a promising tool for early risk stratification of GDM, warranting validation in diverse populations.
Keywords: First trimester; Gestational diabetes mellitus; Indian women; Mass spectrometry; Metabolomics; Prediction