Weighting approaches for a genetic risk score and an oxidative stress score for predicting the incidence of obesity

Background: We aimed to predict the incidence of obesity in a Korean population using a genetic risk score (GRS) constructed with obesity-related single nucleotide polymorphisms (SNPs) along with an oxidative stress score (OSS). Methods: A total of 9460 Korean subjects and 356 974 SNPs were included. The GRS was constructed using three significant obesity-related SNP loci, and the OSS was calculated with three reliable oxidative stress biomarkers. Results: The GRS showed a more significant association with increased obesity (OR = 2.879) than did individual SNPs after adjusting for age and sex. Three oxidative stress biomarkers, including malondialdehyde, oxidized low-density lipoprotein, and 8-epi-prostaglandin F_2α, showed significantly high levels in the obese group. The OSS, which was the sum of each oxidative stress biomarker score, showed a markedly high association with the incidence of obesity, with an OR of 3.213. Based on the results of the regression tests and a receiver-operating characteristic (ROC) curve analysis, we found that HOMA-IR, high-sensitivity C-reactive protein (hs-CRP), the GRS, and the OSS were the most relevant factors for the increased risk of obesity and were significantly associated with the incidence of obesity. The area under the ROC curve was improved when the GRS was added to the model (from 74.2% to 75.1%). Conclusions: We first identified that subjects with an obesity GRS and a high OSS might have a higher risk of obesity. Our findings and weighting approaches were effective in predicting the incidence of obesity; furthermore, the GRS is a relevant factor that significantly predicts the risk of obesity.