02464nas a2200301   4500008004100000022001400041245009900055210006900154260000800223300000900231490000700240520142600247100002601673700002701699700002201726700002301748700002601771700002101797700002701818700002301845700004101868700002201909700002301931700002301954700002701977700003102004856012702035       2020                        eng d  a2045-232200aPlausible diagnostic value of urinary isomeric dimethylarginine ratio for diabetic nephropathy0 aPlausible diagnostic value of urinary isomeric dimethylarginine   cFEB  a29700 v103 a<p>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.</p>
1 aParmar, Dharmeshkumar1 aBhattacharya, Nivedita1 aKannan, Shanthini1 aVadivel, Sangeetha1 aPandey, Gautam, Kumar1 aGhanate, Avinash1 aRagi, Nagarjuna, Chary1 aPrabu, Paramasivam1 aPramodkumar, Thyparambil, Aravindaks1 aManickam, Nagaraj1 aMohan, Viswanathan1 aSripadi, Prabhakar1 aKuppan, Gokulakrishnan1 aPanchagnula, Venkateswarlu  uhttps://library.ncl.res.in/content/plausible-diagnostic-value-urinary-isomeric-dimethylarginine-ratio-diabetic-nephropathy03451nas a2200433   4500008004100000245014700041210006900188260000900257300000800266490000700274520201300281653002002294653003402314653001702348653002202365653001702387653001502404100001802419700004002437700001702477700002302494700001702517700001702534700002402551700001302575700002002588700002602608700002602634700002102660700003202681700002302713700002302736700001902759700002502778700003102803700002402834700002702858856013202885       2025                        eng d00aPrecision integrated identification of predictive first-trimester metabolomics signatures for early detection of gestational diabetes mellitus0 aPrecision integrated identification of predictive firsttrimester  cNOV   a4340 v243 a<p>
	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 (&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.</p>
10aFirst trimester10aGestational diabetes mellitus10aIndian women10aMass spectrometry10aMetabolomics10aPrediction1 aSharma, Sapna1 aSubrahmanyam, Yalamanchili, Venkata1 aGupta, Payal1 aVadivel, Sangeetha1 aDeepa, Mohan1 aTandon, Ansh1 aSreedevi, Sreekumar1 aRam, Uma1 aNarad, Priyanka1 aParmar, Dharmeshkumar1 aAnjana, Ranjit, Mohan1 aRaghunathan, Anu1 aBalasubramanyam, Muthuswamy1 aMohan, Viswanathan1 aSengupta, Abhishek1 aAdamski, Jerzy1 aSaravanan, Ponnusamy1 aPanchagnula, Venkateswarlu1 aUsharani, Dandamudi1 aGokulakrishnan, Kuppan  uhttps://library.ncl.res.in/content/precision-integrated-identification-predictive-first-trimester-metabolomics-signatures-early