👤 Dorairaj Prabhakaran

Defining lipid goals solely on low-density lipoprotein-cholesterol (LDL-C) levels in Indian population may cause misclassification due to high prevalence of hypertriglyceridemia and small dense LDL-C particles. International guidelines now recommend Apoliporotein-B (Apo-B) and non-high-density lipoprotein-cholesterol (non-HDL-C) levels as alternative targets. In this study, we used a cross-sectional representative population database to determine Apo-B and non-HDL-C cut-offs corresponding to identified LDL-C targets and compared them to international guidelines. A community-based survey carried out in urban Delhi and adjacent rural Ballabhgarh provided lipid values for 3047 individuals. The Spearman correlation coefficient was used to evaluate the degree of relationship between Apo-B and LDL-C and non-HDL-C. Cut-off values for Apo-B and non-HDL-C were established using receiver operator curve analysis correlating with guideline-recommended LDL-C targets. Spearman's rank correlations between Apo-B and LDL-C (0.82) and non-HDL-C and LDL-C (0.93) were significant (p < 0.05). Proposed corresponding cut-off values for LDL-C of 55, 70,100,130 and 160 mg/dl for Apo-B and non-HDL-C in our population were 75.3, 75.5, 91.3, 107.6, 119.4 mg/dL and 92.5,96.5, 123.5, 154.5, 179.5 mg/dL respectively. However, in those with triglycerides >150 mg/dl the corresponding Apo-B and non-HDL-C values were 85.1, 92.7, 103.5, 117.5 and 135 mg/dL and 124.5, 126.5, 147.5, 167.5 and 190.5 mg/L respectively. Based on this study we provide Apo-B and non-HDL cut-offs corresponding to target LDL-C values in Indian patients with and without high triglycerides. It is noted that in individuals with triglycerides ≥ 150 mg/dl, the Apo-B levels are much higher than the values recommended by guidelines. Show less

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. 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. rs6659428 at LOC400796 | SEC16B (β = 0.26, SE = 0.05), and rs7611535 at DRD3 | LOC645180 (β = 0.18, SE = 0.04) were associated with VAI at suggestive significance value of <8.21 × 10 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. Show less

The association of melanocortin receptor 4 (MC4R) gene with adiposity measures is widely studied in European populations. Only six studies have investigated the role of MC4R gene with adiposity measures among Indian populations. We have evaluated the role of MC4R (rs17782313) gene polymorphism in influencing adiposity measures in India among children and adults. The present population based cross sectional study was conducted among 303 individuals (208 children and 95 adults) of age group 10-30 years, belonging to Rajasthan. Somatometric measurements (standing height, weight, and waist and hip girths) and blood samples were taken after obtaining written informed consent. Genotyping of MC4R rs17782313 single nucleotide polymorphism was done using restriction fragment length polymorphism method for polymerase chain reaction amplified fragments. We examined association between rs17782313 and different adiposity measures (height, weight, BMI, WHR, and waist and hip girths) using linear regression models. The MC4R variant (rs17782313) predicted increased body weight (0.15 kg, S.E ± 0.076, P = 0.043) among children. In combined population, the rs17782313 variant was moderately associated with body weight (0.13 kg, S.E ± 0.070, P = 0.057). This variant was not found to be associated with any other adiposity measure. Further studies are needed to evaluate the association of MC4R variants through sequencing and functional genomics with different adiposity measures in Indian populations for understanding the genetic underpinnings of adiposity in India. Show less

Genome wide association studies (GWAS), mostly in Europeans have identified several common variants as associated with key lipid traits. Replication of these genetic effects in South Asian populations is important since it would suggest wider relevance for these findings. Given the rising prevalence of metabolic disorders and heart disease in the Indian sub-continent, these studies could be of future clinical relevance. We studied seven common variants associated with a variety of lipid traits in previous GWASs. The study sample comprised of 3178 sib-pairs recruited as participants for the Indian Migration Study (IMS). Associations with various lipid parameters and quantitative traits were analyzed using the Fulker genetic association model. We replicated five of the 7 main effect associations with p-values ranging from 0.03 to 1.97x10(-7). We identified particularly strong association signals at rs662799 in APOA5 (beta=0.18 s.d, p=1.97 x 10(-7)), rs10503669 in LPL (beta =-0.18 s.d, p=1.0 x 10(-4)) and rs780094 in GCKR (beta=0.11 s.d, p=0.001) loci in relation to triglycerides. In addition, the GCKR variant was also associated with total cholesterol (beta=0.11 s.d, p=3.9x10(-4)). We also replicated the association of rs562338 in APOB (p=0.03) and rs4775041 in LIPC (p=0.007) with LDL-cholesterol and HDL-cholesterol respectively. We report associations of five loci with various lipid traits with the effect size consistent with the same reported in Europeans. These results indicate an overlap of genetic effects pertaining to lipid traits across the European and Indian populations. Show less

Genome-wide association studies (GWAS) have identified several genetic variants associated with coronary heart disease (CHD), and variations in plasma lipoproteins and blood pressure (BP). Loci corresponding to CDKN2A/CDKN2B/ANRIL, MTHFD1L, CELSR2, PSRC1 and SORT1 genes have been associated with CHD, and TMEM57, DOCK7, CELSR2, APOB, ABCG5, HMGCR, TRIB1, FADS2/S3, LDLR, NCAN and TOMM40-APOE with total cholesterol. Similarly, CELSR2-PSRC1-SORT1, PCSK9, APOB, HMGCR, NCAN-CILP2-PBX4, LDLR, TOMM40-APOE, and APOC1-APOE are associated with variations in low-density lipoprotein cholesterol levels. Altogether, forty, forty three and twenty loci have been associated with high-density lipoprotein cholesterol, triglycerides and BP phenotypes, respectively. Some of these identified loci are common for all the traits, some do not map to functional genes, and some are located in genes that encode for proteins not previously known to be involved in the biological pathway of the trait. GWAS have been successful at identifying new and unexpected genetic loci common to diseases and traits, thus rapidly providing key novel insights into disease biology. Since genotype information is fixed, with minimum biological variability, it is useful in early life risk prediction. However, these variants explain only a small proportion of the observed variance of these traits. Therefore, the utility of genetic determinants in assessing risk at later stages of life has limited immediate clinical impact. The future application of genetic screening will be in identifying risk groups early in life to direct targeted preventive measures. Show less