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Background Diabetes is among the most prevalent diseases worldwide, of all the affected individuals a significant proportion of the population remains undiagnosed due to lack of specific symptoms early in this disorder and inadequate diagnostics. Diabetes and its associated sequela, i.e., comorbidity are associated with microvascular and macrovascular complications. As diabetes is characterized by an altered metabolism of key metabolites and regulatory pathways. Metabolic phenotyping can provide us with a better understanding of the unique set of regulatory perturbations that predispose to diabetes and its associated complication/comorbidities. Methodology The present study utilizes the analytical platform NMR spectroscopy coupled with Random Forest statistical analysis to identify the discriminatory metabolites in diabetes (DB = 38) vs. diabetes-related complication (DC = 35) along with the healthy control (HC = 50) subjects. A combined and pairwise analysis was performed to identify the discriminatory metabolites responsible for class separation. The perturbed metabolites were further rigorously validated using t-test, AUROC analysis to examine the statistical significance of the identified metabolites. Results The DB and DC patients were well discriminated from HC. However, 15 metabolites were found to be significantly perturbed in DC patients compared to DB, the identified panel of metabolites are TCA cycle (succinate, citrate), methylamine metabolism (trimethylamine, methylamine, betaine), -intermediates; energy metabolites (glucose, lactate, pyruvate); and amino acids (valine, arginine, glutamate, methionine, proline, and threonine). Conclusion The 1H NMR metabolomics may prove a promising technique to differentiate and predict diabetes and its complication on their onset or progression by determining the altered levels of the metabolites in serum.

Background: Diabetes is the third widespread after heart disease and cancer. We have investigated genetic polymorphisms in cytokine genes viz. IL-4, IL-1Ra, IL-1β, IL-18, IL-6, TNF-α, IL-10 and ADIPOQ. The aim of study was to investigate the haplotypes, gene-gene interactions and their role in determining individual susceptibility to T2DM of family members with diabetic history. Methods: Haplotype analysis of 2 SNPs each in IL-6 and adiponectin genes showing Pairwise Linkage disequilibrium (LD) was done by SHEsis software. Logistic regression was used to study various combinations of gene-gene interactions. Results: The TCGT* set of allele combination appeared to increase the disease risk upto 2 times while TATG* upto 51.4 times when four SNPs are taken together viz. IL-1β-511 C/T, IL-18-607 A/C, ADIPOQ1 +45 G/T and ADIPOQ2 +10211 T/G. Interaction of SNPs in eight genes showed one highly significant combination of alleles, TCGAGCTT* which increased the risk of T2DM upto 7.4 times while CAGAGCGT* allele combination increased the risk upto 4 times. Conclusion: During pedigree analysis in six families with four SNPs, it was interesting to note that susceptible ‘AC’ genotype of IL-18-607 A/C was frequent in diabetic individuals in almost all families. Moreover, when checked for the presence of risk haplotypes it was observed that TCGT* and TATG* sets of allele combinations were present in most of the diabetic individuals. Individuals with certain abnormal biochemical parameters but not yet diagnosed for T2DM carried the risk genotype or haplotype. This suggested that individuals carrying risk genotypes/haplotypes might be susceptible to T2DM and develop the disease in the future.

Background: Diabetes is among the most prevalent diseases worldwide, of all the affected individuals a significant proportion of the population remains undiagnosed because of a lack of specific symptoms early in this disorder and inadequate diagnostics. Diabetes and its associated sequela, i.e., comorbidity are associated with microvascular and macrovascular complications. As diabetes is characterized by an altered metabolism of key metabolites and regulatory pathways. Metabolic phenotyping can provide us with a better understanding of the unique set of regulatory perturbations that predispose to diabetes and its associated comorbidities. Methodology: The present study utilizes the analytical platform NMR spectroscopy coupled with Random Forest statistical analysis to identify the discriminatory metabolites of diabetes (DB) and diabetes-related comorbidity (DC) along with the healthy control (HC) subjects. A combined and pairwise analysis was performed, between the serum samples of HC (n=50), and DB (n=38), and DC (n=35) individuals to identify the discriminatory metabolites responsible for class separation. The perturbed metabolites were further rigorously validated using t-test, AUROC analysis to examine the statistical significance of the identified metabolites. Results: The DB and DC patients were well discriminated from HC. However, 15 metabolites were found to be significantly perturbed in DC patients compared to DB, the identified panel of metabolites are TCA cycle (succinate, citrate), methylamine metabolism (trimethylamine, methylamine, betaine), -intermediates; energy metabolites (glucose, lactate, pyruvate); and amino acids (valine, arginine, glutamate, methionine, proline and threonine). The metabolites were further used to identify the perturbed metabolic pathway and correlation of metabolites in DC patients. Conclusion: The 1H NMR metabolomics may prove a promising technique to differentiate and predict diabetes and its comorbidities on their onset or progression by determining the altered levels of the metabolites in serum.

Indians undergoing socioeconomic and lifestyle transitions will be maximally affected by epidemic of type 2 diabetes (T2D). We conducted a two-stage genome-wide association study of T2D in 12,535 Indians, a less explored but high-risk group. We identified a new type 2 diabetes–associated locus at 2q21, with the lead signal being rs6723108 (odds ratio 1.31; P = 3.32 × 10−9). Imputation analysis refined the signal to rs998451 (odds ratio 1.56; P = 6.3 × 10−12) within TMEM163 that encodes a probable vesicular transporter in nerve terminals. TMEM163 variants also showed association with decreased fasting plasma insulin and homeostatic model assessment of insulin resistance, indicating a plausible effect through impaired insulin secretion. The 2q21 region also harbors RAB3GAP1 and ACMSD; those are involved in neurologic disorders. Forty-nine of 56 previously reported signals showed consistency in direction with similar effect sizes in Indians and previous studies, and 25 of them were also associated (P < 0.05). Known loci and the newly identified 2q21 locus altogether explained 7.65% variance in the risk of T2D in Indians. Our study suggests that common susceptibility variants for T2D are largely the same across populations, but also reveals a population-specific locus and provides further insights into genetic architecture and etiology of T2D.

In this first report on the association of an IL-10 promoter polymorphism with type 2 diabetes mellitus in a North Indian population, the −592A/C SNP (rs1800872) was genotyped by PCR–RFLP and the IL-10 level measured using ELISA. Although no significant difference was observed in the genotypic frequencies (P = 0.657), diabetes patients carried a significantly higher number of A alleles at the −592 position, 25.6% (P < 0.001, odds ratio 0.887, 95% CI 0.670–1.184). Significant correlations were detected in postprandial glucose levels of CC-genotype patients and controls (P = 0.025), age and waist–hip ratio of CA patients and controls (P ≤ 0.001), and fasting glucose (P = 0.045) and low-density lipoprotein (P = 0.049) in all patients and controls. The serum IL-10 level was significantly higher in patients than in controls (P = 0.033). The polymorphism was significantly associated with disease incidence and its biochemical manifestations.

IntroductionType 2 diabetes mellitus (T2DM) affects huge populations in India and abroad. Genetic polymorphisms (SNPs) in scavenger receptors such as CD36 have been implicated in the pathogenesis of diabetic atherosclerosis and cardiovascular diseases. Since CD36 gene expression contributes to T2DM, we proposed to study the association of two of its polymorphisms.MethodsA population of 400 subjects from North India was analyzed according to clinical parameters. Out of them 150 controls and 250 T2DM patients were genotyped for two SNPs namely rs1761667 (G>A) and rs1527483 (C>T) in the CD36 gene using polymerase chain reaction and restriction fragment length polymorphism (PCR–RFLP) followed by statistical analysis.Results and discussionNo association of rs1527483 (C>T) SNP was observed in T2DM patients. The GA genotype was prevalent in 76.0% diabetic population and a highly significant genotypic association of rs1761667 (G>A) SNP in CD36 gene was observed in them (P=

An effort has been made in this review to outline the origin of diabetes as a serious disease. A large number of studies are going on in order to understand the cause of this disease, both environmental as well as genetic factors have been found to be involved. We have tried to throw some light on inflammation in diabetes with special emphasis on interleukins and adipocytokines. Recently the involvements of cytokines and adipocytokines have been extensively studied and have been found to play an extremely important role in the manifestation of diabetes and its associated complications.

Diabetes mellitus is a combined metabolic disorder which includes hyperglycemia, dyslipidemia, stroke and several other complications. Various groups all over the world are relentlessly working out the possible role of a vast number of genes associated with type 2 diabetes (T2DM). Inflammation is an important outcome of any kind of imbalance in the body and is therefore an indicator of several diseases, including T2DM. Various ethnic populations around the world show different levels of variations in single nucleotide polymorphisms (SNPs). The present review was undertaken to explore the association of cytokine gene polymorphisms with T2DM in populations of different ethnicities. This will lead to the understanding of the role of cytokine genes in T2DM risk and development. Association studies of genotypes of SNPs present in cytokine genes will help to identify risk haplotype(s) for disease susceptibility by developing prognostic markers and alter treatment strategies for T2DM and related complications. This will enable individuals at risk to take prior precautionary measures and avoid or delay the onset of the disease. Future challenges will be to understand the genotypic interactions between SNPs in one cytokine gene or several genes at different loci and study their association with T2DM.