Background: Obesity is among the leading public health threats globally. Over the last few years, visceral adiposity index (VAI), and body adiposity index (BAI), derived from anthropometric, and biochemical measures, have gained importance as a measure of obesity. However, unlike other common indices like body mass index, and waist circumference, the genetic predisposition of VAI, and BAI under-examined. Methods: 2265 sib-pairs from Indian Migration Study were used for examining the association of genetic variants from the Cardio-Metabochip array with VAI, and BAI. Mixed linear regression models were run, and all inferences were based on the within-sib component of the Fulker's association models. Gene-environment/lifestyle interaction analyses were also undertaken. Results: rs6659428 at LOC400796 | SEC16B (beta = 0.26, SE = 0.05), and rs7611535 at DRD3 | LOC645180 (beta = 0.18, SE = 0.04) were associated with VAI at suggestive significance value of <8.21 x 10(-6). For BAI, rs73300702 at JAZF1-AS1 (beta = 0.27, SE = 0.06), was the top hit at p value < 8.21 x 10(-6). Further, rs6659428 showed marginal effect modification with rural/urban location (beta = 0.26, SE = 0.13, p value = 0.047), and rs73300702 with physical activity (beta = -0.29,SE = 0.14, p value = 0.034). Conclusion: We report three novel genetic loci for VAI, and BAI in Indians that are important indicators of adiposity. These findings need to be replicated and validated with larger samples from different ethnicities. Further, functional studies for understanding the biological mechanisms of these adiposity indices need to be undertaken to understand the underlying pathophysiology.

Background: A heavy burden of cardiometabolic conditions on low- and middle-income countries like India that are rapidly undergoing urbanization remains unaddressed. Indians are known to have high levels of triglycerides and low levels of HDL-C along with moderately higher levels of LDL-C. The genome-wide findings from Western populations need to be validated in an Indian context for a better understanding of the underlying etiology of dyslipidemia in India.Objective: We aim to validate 12 genetic variants associated with lipid levels among rural and urban Indian populations and derive unweighted and weighted genetic risk scores (uGRS and wGRS) for lipid levels among the Indian population.Methods: Assuming an additive model of inheritance, linear regression models adjusted for all the possible covariates were run to examine the association between 12 genetic variants and total cholesterol, triglycerides, HDL-C, LDL-C, and VLDL-C among 2,117 rural and urban Indian participants. The combined effect of validated loci was estimated by allelic risk scores, unweighted and weighted by their effect sizes.Results: The wGRS for triglycerides and VLDL-C was derived based on five associated variants (rs174546 at FADS1, rs17482753 at LPL, rs2293889 at TRPS1, rs4148005 at ABCA8, and rs4420638 at APOC1), which was associated with 36.31 mg/dL of elevated triglyceride and VLDL-C levels (β = 0.95, SE = 0.16, p < 0.001). Similarly, every unit of combined risk score (rs2293889 at TRPS1 and rs4147536 at ADH1B) was associated with 40.62 mg/dL of higher total cholesterol (β = 1.01, SE = 0.23, p < 0.001) and 33.97 mg/dL of higher LDL-C (β = 1.03, SE = 0.19, p < 0.001) based on its wGRS (rs2293889 at TRPS1, rs4147536 at ADH1B, rs4420638 at APOC1, and rs660240 at CELSR2). The wGRS derived from five associated variants (rs174546 at FADS1, rs17482753 at LPL, rs4148005 at ABCA8, rs4420638 at APOC1, and rs7832643 at PLEC) was associated with 10.64 mg/dL of lower HDL-C (β = −0.87, SE = 0.14, p < 0.001).Conclusion: We confirm the role of eight genome-wide association study (GWAS) loci related to different lipid levels in the Indian population and demonstrate the combined effect of variants for lipid traits among Indians by deriving the polygenic risk scores. Similar studies among different populations are required to validate the GWAS loci and effect modification of these loci by lifestyle and environmental factors related to urbanization.

Background More than 250 loci have been identified by genome-wide scans for type 2 diabetes in different populations. South Asians have a very different manifestation of the diseases and hence role of these loci need to be investigated among Indians with huge burden of cardio-metabolic disorders. Thus the present study aims to validate the recently identified GWAS loci in an endogamous caste population in North India. Methods 219 T2D cases and 184 controls were recruited from hospitals and genotyped for 15 GWAS loci of T2D. Regression models adjusted for covariates were run to examine the association for T2D and fasting glucose levels. Results We validated three variants for T2D namely, rs11634397 at ZFAND6 (OR = 3.05, 95%CI = 1.02-9.19, p = 0.047) and rs8042680 at PRC1 (OR = 3.67, 95%CI = 1.13-11.93, p = 0.031) showing higher risk and rs6813195 at TMEM154 (OR = 0.28, 95%CI = 0.09-0.90, p = 0.033) showing protective effect. The combined risk of 9 directionally consistent variants was also found to be significantly associated with T2D (OR = 1.91, 95%CI = 1.18-3.08, p = 0.008). One variant rs10842994 at KLHDC5 was validated for 9.15mg/dl decreased fasting glucose levels (SE = -17.25-1.05, p = 0.027). Conclusion We confirm the role of ZFAND6, PRC1 and TMEM154 in the pathophysiology of type 2 diabetes among Indians. More efforts are needed with larger sample sizes to validate the diabetes GWAS loci in South Asian populations for wider applicability.

Adiposity has gradually become a global public threat over the years with drastic increase in the attributable deaths and disability adjusted life years (DALYs). Given an increased metabolic risk among Asians as compared to Europeans for any given body mass index (BMI) and considering the differences in genetic architecture between them, the present review aims to summarize the findings from genome-wide scans for various adiposity indices and related anthropometric measures from Asian populations. The search for related studies, published till February 2022, were made on PubMed and GWAS Catalog using search strategy built with relevant keywords joined by Boolean operators. It was recorded that out of a total of 47 identified studies, maximum studies are from Korean population (n = 14), followed by Chinese (n = 7), and Japanese (n = 6). Nearly 200 loci have been identified for BMI, 660 for height, 16 for weight, 28 for circumferences (waist and hip), 32 for ratios (waist hip ratio [WHR] and thoracic hip ratio [THR]), 5 for body fat, 16 for obesity, and 28 for adiposity-related blood markers among Asians. It was observed that though, most of the loci were unique for each trait, there were 3 loci in common to BMI and WHR. Apart from validation of variants identified in European setting, there were many novel loci discovered in Asian populations. Notably, 125 novel loci form Asian studies have been reported for BMI, 47 for height, 5 for waist circumference, and 2 for adiponectin level to the existing knowledge of the genetic framework of adiposity and related measures. It is necessary to examine more advanced adiposity measures, specifically of relevance to abdominal adiposity, a major risk factor for cardiometabolic disorders among Asians. Moreover, in spite of being one continent, there is diversity among different ethnicities across Asia in terms of lifestyle, climate, geography, genetic structure and consequently the phenotypic manifestations. Hence, it is also important to consider ethnic specific studies for identifying and validating reliable genetic variants of adiposity measures among Asians.

BackgroundDyslipidemia and abnormal glycemic traits are leading causes of morbidity and mortality. Although the association between the two traits is well established, there still exists a gap in the evidence for the direction of causality.ObjectiveThis study aimed to examine the direction of the causal relationship between lipids and glycemic traits in an Indian population using bidirectional Mendelian randomization (BMR).MethodsThe BMR analysis was conducted on 4900 individuals (2450 sib-pairs) from the Indian Migration Study. Instrument variables were generated for each lipid and glycemic trait (fasting insulin, fasting glucose, HOMA-IR, HOMA-β, LDL-cholesterol, HDL-cholesterol, total cholesterol and triglycerides) to examine the causal relationship by applying two-stage least squares (2SLS) regression in both directions.ResultsLipid and glycemic traits were found to be associated observationally, however, results from 2SLS showed that only triglycerides, defined by weighted genetic risk score (wGRS) of 3 SNPs (rs662799 at APOAV, rs780094 at GCKR and rs4420638 at APOE/C1/C4), were observed to be causally effecting 1.15% variation in HOMA-IR (SE = 0.22, P = 0.010), 1.53% in HOMA- β (SE = 0.21, P = 0.001) and 1.18% in fasting insulin (SE = 0.23, P = 0.009). No evidence for a causal effect was observed in the reverse direction or between any other lipid and glycemic traits.ConclusionThe study findings suggest that triglycerides may causally impact various glycemic traits. However, the findings need to be replicated in larger studies.

Introduction Pregnancy is characterised by a high rate of metabolic shifts from early to late phases of gestation in order to meet the raised physiological and metabolic needs. This change in levels of metabolites is influenced by gestational weight gain (GWG), which is an important characteristic of healthy pregnancy. Inadequate/excessive GWG has short-term and long-term implications on maternal and child health. Exploration of gestational metabolism is required for understanding the quantitative changes in metabolite levels during the course of pregnancy. Therefore, our aim is to study trimester-specific variation in levels of metabolites in relation to GWG and its influence on fetal growth and newborn anthropometric traits at birth. Methods and analysis A prospective longitudinal study is planned (start date: February 2018; end date: March 2023) on pregnant women that are being recruited in the first trimester and followed in subsequent trimesters and at the time of delivery (total 3 follow-ups). The study is being conducted in a hospital located in Bikaner district (66% rural population), Rajasthan, India. The estimated sample size is of 1000 mother-offspring pairs. Information on gynaecological and obstetric history, socioeconomic position, diet, physical activity, tobacco and alcohol consumption, depression, anthropometric measurements and blood samples is being collected for metabolic assays in each trimester using standardised methods. Mixed effects regression models will be used to assess the role of gestational weight in influencing metabolite levels in each trimester. The association of maternal levels of metabolites with fetal growth, offspring's weight and body composition at birth will be investigated using regression modelling. Ethics and dissemination The study has been approved by the ethics committees of the Department of Anthropology, University of Delhi and Sardar Patel Medical College, Rajasthan. We are taking written informed consent after discussing the various aspects of the study with the participants in the local language.