A multilocus genetic risk score for obesity: Association with BMI and metabolic alterations in a cohort with severe obesity

Abstract

Genome wide association studies have identified numerous single nucleotide polymorphisms (SNPs) associated with obesity, yet effect sizes of individual SNPs are small. Therefore, the aim of our study was to investigate whether a genetic risk score (GRS) comprising risk alleles of SNPs identified in the GIANT consortium meta-analyses shows association with body mass index (BMI) and other BMI related metabolic alterations in a cohort with an extreme phenotype. Genotyping of 93 SNPs was performed in 314 obese individuals (mean BMI 40.5 ± 7.8 kg/m², aged 45 ± 12 years), participating in a standardized weight reduction program, and in 74 lean controls (mean BMI 24.6 ± 3.3 kg/m², aged 41.7 ± 13.4 years). Allele numbers of all 93 SNPs were added to a GRS. Anthropometric parameters, parameters of glucose/insulin and lipid metabolism were assessed standardized after a 12 hours fast. GRS was significantly different between controls and obese individuals (unweighted GRS: 86.6 vs 89.0, P = .002; weighted GRS: 84.9 vs 88.3, P = .005). Furthermore, linear regression analysis showed significant associations of GRS with BMI (P < .0001), weight (P = .0005), waist circumference (P = .0039), fat mass (P < .0001) and epicardial fat thickness (P = .0032), yet with small effect sizes (r² < 0.06). In conclusion, in our study GRS could differentiate between extreme obese and lean individuals, and was associated with BMI and its related traits, yet with small effect sizes.

Deciphering the Interplay between Genetic Risk Scores and Lifestyle Factors on Individual Obesity Predisposition

“…Additionally, BDNF rs6265 and rs925946 have been incorporated into GRS association studies with obesity in European origin [6,19], and BDNF rs6265 among the Pakistani population [20]. TMEM18 rs939583 was included in another GRS associated with BMI in individuals with extreme obesity compared to lean controls [21]. This inclusion of several SNPs in GRSs across diverse ethnic backgrounds highlights the versatility and global relevance of our GRS model in assessing obesity risk.…”