Future Type 2 Diabetes Research Articles

The Evil Axis of Obesity, Inflammation and Type-2 Diabetes

Obesity and type 2 diabetes (T2D) are global problems affecting all age groups and have been characterized as lifestyle disorders. Though no study has clearly proved a direct correlation between obesity and T2D, a number of factors are associated with obesity causing insulin resistance and T2D. The factors such as adipokines and various transcription factors help to maintain a proper metabolic state in the body. Deregulation in any of these signalling balances due to obesity may trigger an inflammatory cascade which could lead to the aforesaid problems of insulin resistance and T2D. In this review, we have discussed the factors that probably link inflammation to obesity-induced insulin resistance and subsequently T2D and the possible therapeutic opportunities to decrease health risk of T2D in future.

Integration of metabolomics and transcriptomics data to aid biomarker discovery in type 2 diabetes

Type 2 diabetes (T2D), one of the most common diseases in the western world, is characterized by insulin resistance and impaired beta-cell function but currently it is difficult to determine the precise pathophysiology in individual T2D patients. Non-targeted metabolomics technologies have the potential for providing novel biomarkers of disease and drug efficacy, and are increasingly being incorporated into biomarker exploration studies. Contextualization of metabolomics results is enhanced by integration of study data from other platforms, such as transcriptomics, thus linking known metabolites and genes to relevant biochemical pathways. In the current study, urinary NMR-based metabolomic and liver, adipose, and muscle transcriptomic results from the db/db diabetic mouse model are described. To assist with cross-platform integration, integrative pathway analysis was used. Sixty-six metabolites were identified in urine that discriminate between the diabetic db/db and control db/+ mice. The combined analysis of metabolite and gene expression changes revealed 24 distinct pathways that were altered in the diabetic model. Several of these pathways are related to expected diabetes-related changes including changes in lipid metabolism, gluconeogenesis, mitochondrial dysfunction and oxidative stress, as well as protein and amino acid metabolism. Novel findings were also observed, particularly related to the metabolism of branched chain amino acids (BCAAs), nicotinamide metabolites, and pantothenic acid. In particular, the observed decrease in urinary BCAA catabolites provides direct corroboration of previous reports that have inferred that elevated BCAAs in diabetic patients are caused, in part, by reduced catabolism. In summary, the integration of metabolomics and transcriptomics data via integrative pathway mapping has facilitated the identification and contextualization of biomarkers that, presuming further analytical and biological validation, may be useful in future T2D clinical studies by identifying patient populations that share common disease pathophysiology and therefore may identify those patients that may respond better to a particular class of anti-diabetic drugs.

Mechanisms by which common variants in the TCF7L2 gene increase risk of type 2 diabetes

Genetic variants in the gene encoding for transcription factor-7-like 2 (TCF7L2) have been associated with type 2 diabetes (T2D) and impaired beta cell function, but the mechanisms have remained unknown. We therefore studied prospectively the ability of common variants in TCF7L2 to predict future T2D and explored the mechanisms by which they would do this. Scandinavian subjects followed for up to 22 years were genotyped for 3 SNPs (rs7903146, rs12255372, and rs10885406) in TCF7L2, and a subset of them underwent extensive metabolic studies. Expression of TCF7L2 was related to genotype and metabolic parameters in human islets. The CT/TT genotypes of SNP rs7903146 strongly predicted future T2D in 2 independent cohorts (Swedish and Finnish). The risk T allele was associated with impaired insulin secretion, incretin effects, and enhanced rate of hepatic glucose production. TCF7L2 expression in human islets was increased 5-fold in T2D, particularly in carriers of the TT genotype. Overexpression of TCF7L2 in human islets reduced glucose-stimulated insulin secretion. In conclusion, the increased risk of T2D conferred by variants in TCF7L2 involves the enteroinsular axis, enhanced expression of the gene in islets, and impaired insulin secretion.

Genetic Prediction of Future Type 2 Diabetes

BackgroundType 2 diabetes (T2D) is a multifactorial disease in which environmental triggers interact with genetic variants in the predisposition to the disease. A number of common variants have been associated with T2D but our knowledge of their ability to predict T2D prospectively is limited.Methods and FindingsBy using a Cox proportional hazard model, common variants in the PPARG (P12A), CAPN10 (SNP43 and 44), KCNJ11 (E23K), UCP2 (−866G>A), and IRS1 (G972R) genes were studied for their ability to predict T2D in 2,293 individuals participating in the Botnia study in Finland. After a median follow-up of 6 y, 132 (6%) persons developed T2D. The hazard ratio for risk of developing T2D was 1.7 (95% confidence interval [CI] 1.1–2.7) for the PPARG PP genotype, 1.5 (95% CI 1.0–2.2) for the CAPN10 SNP44 TT genotype, and 2.6 (95% CI 1.5–4.5) for the combination of PPARG and CAPN10 risk genotypes. In individuals with fasting plasma glucose ≥ 5.6 mmol/l and body mass index ≥ 30 kg/m2, the hazard ratio increased to 21.2 (95% CI 8.7–51.4) for the combination of the PPARG PP and CAPN10 SNP43/44 GG/TT genotypes as compared to those with the low-risk genotypes with normal fasting plasma glucose and body mass index < 30 kg/m2.ConclusionWe demonstrate in a large prospective study that variants in the PPARG and CAPN10 genes predict future T2D. Genetic testing might become a future approach to identify individuals at risk of developing T2D.