The increasing burden of obesity worldwide and its effect on cardiovascular disease (CVD) risk is an opportunity for evaluation of preventive approaches. Both obesity and CVD have a genetic background and polymorphisms within genes which enhance expression of variant proteins that influence CVD in obesity. Genome‐based prediction may therefore be a feasible strategy, but the identification of genetically driven risk factors for CVD manifesting as clinically recognized phenotypes is a major challenge. Clusters of such risk factors include hyperglycaemia, hypertension, ectopic liver fat, and inflammation. All involve multiple genetic pathways having complex interactions with variable environmental influences. The factors that make significant contributions to CVD risk include altered carbohydrate homeostasis, ectopic deposition of fat in muscle and liver, and inflammation, with contributions from the gut microbiome. A futuristic model depends on harnessing the predictive power of plausible genetic variants, phenotype reversibility, and effective therapeutic choices based on genotype–phenotype interactions. Inverting disease phenotypes into ideal cardiovascular health metrics could improve genetic and epigenetic assessment, and form the basis of a future model for risk detection and early intervention.

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Context Multiple observational studies have reported an inverse relationship between 25-hydroxyvitamin D concentrations (25(OH)D) and type 2 diabetes (T2D). However, the results of short- and long-term interventional trials concerning the relationship between 25(OH)D and T2D risk have been inconsistent. Objectives and methods To evaluate the causal role of reduced blood 25(OH)D in T2D, here we have performed a bidirectional Mendelian randomization study using 59,890 individuals (5,862 T2D cases and 54,028 controls) from European and Asian Indian ancestries. We used six known SNPs, including three T2D SNPs and three vitamin D pathway SNPs, as a genetic instrument to evaluate the causality and direction of the association between T2D and circulating 25(OH)D concentration. Results Results of the combined meta-analysis of eight participating studies showed that a composite score of three T2D SNPs would significantly increase T2D risk by an odds ratio (OR) of 1.24, p = 1.82 × 10–32; Z score 11.86, which, however, had no significant association with 25(OH)D status (Beta -0.02nmol/L ± SE 0.01nmol/L; p = 0.83; Z score -0.21). Likewise, the genetically instrumented composite score of 25(OH)D lowering alleles significantly decreased 25(OH)D concentrations (-2.1nmol/L ± SE 0.1nmol/L, p = 7.92 × 10–78; Z score -18.68) but was not associated with increased risk for T2D (OR 1.00, p = 0.12; Z score 1.54). However, using 25(OH)D synthesis SNP (DHCR7; rs12785878) as an individual genetic instrument, a per allele reduction of 25(OH)D concentration (-4.2nmol/L ± SE 0.3nmol/L) was predicted to increase T2D risk by 5%, p = 0.004; Z score 2.84. This effect, however, was not seen in other 25(OH)D SNPs (GC rs2282679, CYP2R1 rs12794714) when used as an individual instrument. Conclusion Our new data on this bidirectional Mendelian randomization study suggests that genetically instrumented T2D risk does not cause changes in 25(OH)D levels. However, genetically regulated 25(OH)D deficiency due to vitamin D synthesis gene (DHCR7) may influence the risk of T2D.

Dyslipidemia is a well-established risk factor for cardiovascular diseases. Although, advances in genome-wide technologies have enabled the discovery of hundreds of genes associated with blood lipid phenotypes, most of the heritability remains unexplained. Here we performed targeted resequencing of 13 bona fide candidate genes of dyslipidemia to identify the underlying biological functions. We sequenced 940 Sikh subjects with extreme serum levels of hypertriglyceridemia (HTG) and 2,355 subjects were used for replication studies; all 3,295 participants were part of the Asian Indians Diabetic Heart Study. Gene-centric analysis revealed burden of variants for increasing HTG risk in GCKR (p = 2.1x10 -5 ), LPL (p = 1.6x10 -3 ) and MLXIPL (p = 1.6x10 -2 ) genes. Of these, three missense and damaging variants within GCKR were further examined for functional consequences in vivo using a transgenic zebrafish model. All three mutations were South Asian population-specific and were largely absent in other multiethnic populations of Exome Aggregation Consortium. We built different transgenic models of human GCKR with and without mutations and analyzed the effects of dietary changes in vivo . Despite the short-term of feeding, profound phenotypic changes were apparent in hepatocyte histology and fat deposition associated with increased expression of GCKR in response to a high fat diet (HFD). Liver histology of the GCKR mut showed severe fatty metamorphosis which correlated with ~7 fold increase in the mRNA expression in the GCKR mut fish even in the absence of a high fat diet. These findings suggest that functionally disruptive GCKR variants not only increase the risk of HTG but may enhance ectopic lipid/fat storage defects in absence of obesity and HFD. To our knowledge, this is the first transgenic zebrafish model of a putative human disease gene built to accurately assess the influence of genetic changes and their phenotypic consequences in vivo .

Diversity in drug response is attributed to both genetic and non-genetic factors. However, there is paucity of pharmacogenetics information across ethnically and genetically diverse populations of India. Here, we have analyzed 21 SNPs from 12 pharmacogenomics genes in Punjabi Sikhs of Indian origin (N = 1,616), as part of the Sikh Diabetes Study (SDS). We compared the allele frequency of poor metabolism (PM) phenotype among Sikhs across other major global populations from the Exome Aggregation Consortium and 1000 Genomes. The PM phenotype of CYP1A2*1 F for slow metabolism of caffeine and carcinogens was significantly higher in Indians (SDS 42%, GIH [Gujarati] 51%, SAS [Pakistani] 45%) compared to Europeans 29% (pgenotype = 5.3E-05). Similarly, South Asians had a significantly higher frequency of CYP2C9*3 (12% SDS, 13% GIH, 11% SAS) vs. 7% in Europeans (pgenotype = <1.0E-05) and ‘T’ allele of CYP4F2 (36%) SDS, (43%) GIH, 40% (SAS) vs. (29%) in Europeans (pgenotype = <1.0E-05); both associated with a higher risk of bleeding with warfarin. All South Asians –the Sikhs (0.36), GIH (0.34), and SAS (0.36) had a higher frequency of the NAT2*6 allele (linked with slow acetylation of isoniazid) compared to Europeans (0.29). Additionally, the prevalence of the low activity ‘C’ allele of MTHFR (rs1801131) was highest in Sikhs compared to all other ethnic groups [SDS (44%), GIH (39%), SAS (42%) and European (32%) (pgenotype = <1.0E-05)]. SNPs in MTHFR affect metabolism of statins, 5-fluorouracil and methotrexate-based cancer drugs. These findings underscore the need for evaluation of other endogamous ethnic groups of India and beyond for establishing a global benchmark for pre-emptive genotyping in drug metabolizing genes before beginning therapeutic intervention.