<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bhat, Shweta</style></author><author><style face="normal" font="default" size="100%">Jagadeeshaprasad, Mashanipalya G.</style></author><author><style face="normal" font="default" size="100%">Patil, Yugendra R.</style></author><author><style face="normal" font="default" size="100%">Shaikh, Mahemud L.</style></author><author><style face="normal" font="default" size="100%">Regin, Bhaskaran S.</style></author><author><style face="normal" font="default" size="100%">Mohan, Viswanathan</style></author><author><style face="normal" font="default" size="100%">Giri, Ashok P.</style></author><author><style face="normal" font="default" size="100%">Balasubramanyam, Muthuswamy</style></author><author><style face="normal" font="default" size="100%">Boppana, Ramanamurthy</style></author><author><style face="normal" font="default" size="100%">Kulkarni, Mahesh J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Proteomic insight reveals elevated levels of albumin in circulating immunecomplexes in diabetic plasma</style></title><secondary-title><style face="normal" font="default" size="100%">Molecular &amp; Cellular Proteomics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JUN</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">6</style></number><publisher><style face="normal" font="default" size="100%">AMER SOC BIOCHEMISTRY MOLECULAR BIOLOGY INC</style></publisher><pub-location><style face="normal" font="default" size="100%">9650 ROCKVILLE PIKE, BETHESDA, MD 20814-3996 USA</style></pub-location><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">2011-2020</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3><custom4><style face="normal" font="default" size="100%">5.912</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sharma, Sapna</style></author><author><style face="normal" font="default" size="100%">Subrahmanyam, Yalamanchili Venkata</style></author><author><style face="normal" font="default" size="100%">Gupta, Payal</style></author><author><style face="normal" font="default" size="100%">Vadivel, Sangeetha</style></author><author><style face="normal" font="default" size="100%">Deepa, Mohan</style></author><author><style face="normal" font="default" size="100%">Tandon, Ansh</style></author><author><style face="normal" font="default" size="100%">Sreedevi, Sreekumar</style></author><author><style face="normal" font="default" size="100%">Ram, Uma</style></author><author><style face="normal" font="default" size="100%">Narad, Priyanka</style></author><author><style face="normal" font="default" size="100%">Parmar, Dharmeshkumar</style></author><author><style face="normal" font="default" size="100%">Anjana, Ranjit Mohan</style></author><author><style face="normal" font="default" size="100%">Raghunathan, Anu</style></author><author><style face="normal" font="default" size="100%">Balasubramanyam, Muthuswamy</style></author><author><style face="normal" font="default" size="100%">Mohan, Viswanathan</style></author><author><style face="normal" font="default" size="100%">Sengupta, Abhishek</style></author><author><style face="normal" font="default" size="100%">Adamski, Jerzy</style></author><author><style face="normal" font="default" size="100%">Saravanan, Ponnusamy</style></author><author><style face="normal" font="default" size="100%">Panchagnula, Venkateswarlu</style></author><author><style face="normal" font="default" size="100%">Usharani, Dandamudi</style></author><author><style face="normal" font="default" size="100%">Gokulakrishnan, Kuppan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Precision integrated identification of predictive first-trimester metabolomics signatures for early detection of gestational diabetes mellitus</style></title><secondary-title><style face="normal" font="default" size="100%">Cardiovascular Diabetology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">First trimester</style></keyword><keyword><style  face="normal" font="default" size="100%">Gestational diabetes mellitus</style></keyword><keyword><style  face="normal" font="default" size="100%">Indian women</style></keyword><keyword><style  face="normal" font="default" size="100%">Mass spectrometry</style></keyword><keyword><style  face="normal" font="default" size="100%">Metabolomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Prediction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2025</style></year><pub-dates><date><style  face="normal" font="default" size="100%">NOV </style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">24</style></volume><pages><style face="normal" font="default" size="100%">434</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	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 (&amp;lt; 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.&lt;/p&gt;
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