Development of diagnostic fragment ion library for glycated peptides of human serum albumin: targeted quantification in prediabetic, diabetic, and microalbuminuria plasma by parallel reaction monitoring, SWATH, and MSE - Korwar, Arvind M.; Vannuruswamy, Garikapati; Jagadeeshaprasad, Mashanipalya G.; Jayaramaiah, Ramesha H.; Bhat, Shweta; Regin, Bhaskaran S.; Ramaswamy, Sureshkumar; Giri, Ashok P.; Mohan, Viswanathan; Balasubramanyam, Muthuswamy; Kulkarni, Mahesh J. (Molecular & Cellular Proteomics, 2015)
Human serum albumin is one of the most abundant plasma proteins that readily undergoes glycation, thus glycated albumin has been suggested as an additional marker for monitoring glycemic status. Hitherto, only Amadori-modified peptides of albumin were quantified. In this study, we report the construction of fragment ion library for Amadori-modified lysine (AML), N(epsilon)-(carboxymethyl) lysine (CML)-, and N(epsilon)-(carboxyethyl) lysine (CEL)-modified peptides of the corresponding synthetically modified albumin using high resolution accurate mass spectrometry (HR/AM). The glycated peptides were manually inspected and validated for their modification. Further, the fragment ion library was used for quantification of glycated peptides of albumin in the context of diabetes. Targeted Sequential Window Acquisition of all THeoretical Mass Spectra (SWATH) analysis in pooled plasma samples of control, prediabetes, diabetes, and microalbuminuria, has led to identification and quantification of 13 glycated peptides comprised of four AML, seven CML, and two CEL modifications, representing nine lysine sites of albumin. Five lysine sites namely K549, K438, K490, K88, and K375, were observed to be highly sensitive for glycation modification as their respective m/z showed maximum fold change and had both AML and CML modifications. Thus, peptides involving these lysine sites could be potential novel markers to assess the degree of glycation in diabetes.

Proteomic insight reveals elevated levels of albumin in circulating immunecomplexes in diabetic plasma - Bhat, Shweta; Jagadeeshaprasad, Mashanipalya G.; Patil, Yugendra R.; Shaikh, Mahemud L.; Regin, Bhaskaran S.; Mohan, Viswanathan; Giri, Ashok P.; Balasubramanyam, Muthuswamy; Boppana, Ramanamurthy; Kulkarni, Mahesh J. (Molecular & Cellular Proteomics, 2016)
A Hyperglycemic condition in diabetes promotes formation of advanced glycation end products, which are known to elicit immune response and form complexes with immunoglobulins called circulating immune complexes. To investigate the involvement of advanced glycation end product (AGE)-modified proteins in the elicitation of an immune response, circulating immune complexes were isolated and proteins associated were identified and characterized. Label-free-based mass spectrometric analysis of circulating immune complexes in clinical plasma of prediabetic, newly diagnosed diabetes, and diabetic microalbuminurea revealed elevated levels of serum albumin in the circulating immune complexes, which were also observed to be AGE modified. Further, to examine the role of glycation, circulating immune complexeswere analyzed in the streptozotocin-induced diabetic mice treated with or without aminoguanidine, a prototype glycation inhibitor. Mass spectrometric analysis of circulating immune complexes showed elevated levels of serum albumin in plasma from diabetic mice over that of control animals. Aminoguanidine-treated diabetic mice displayed decreased AGE modification of plasma albumin, accompanied by a reduced level of albumin in the circulating immune complexes. In addition, elevated levels of proinflammatory cytokines such as IL-1b, IL-2, and TNF-alpha were observed in diabetes, which were reduced with aminoguanidine treatment, suggesting the involvement of glycation in the immune response.

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