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

Abstract

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-epiprostaglandin 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. K E Y W O R D Sgenetic risk score, Korean population, obesity, oxidative stress score, prediction

Non-alcoholic fatty liver disease and lipotoxicity

“…OxLDL is well known for its key pathogenic role in formation and progression of atherosclerotic plaque by promoting infiltration of macrophages followed by their activation and inflammatory cytokine release [63]. Moreover, oxLDL was found to be linked to cardiovascular disease [64], obesity [65], diabetes mellitus [66] and metabolic syndrome [67]. In the pediatric population oxLDL was found to correlate with waist-hip ratio (WHR) and its concentration was significantly higher in obese/overweight children in comparison to lean patients [68].…”

Early Prediction for Prediabetes and Type 2 Diabetes Using the Genetic Risk Score and Oxidative Stress Score

“…Therefore, in establishing a predictive model for prediabetes and T2DM, the OSS model, whose AUC was 0.680, showed better predictive performance than the BMI model in prediabetes and T2DM. In addition, Park et al [28] have previously proposed an OSS that solely used three intrinsic biomarkers of oxidative stress, namely MDA, ox-LDL, and 8-epi-PGF 2α , and showed that a higher OSS was associated with a higher risk of obesity. Therefore, in this study, the OSS using biomarkers can accurately reflect the internal unmodifiable factors of oxidative stress and can act as an indicator to predict the incidence of prediabetes and T2DM.…”

A Machine Learning Algorithm for Quantitatively Diagnosing Oxidative Stress Risks in Healthy Adult Individuals Based on Health Space Methodology: A Proof-of-Concept Study Using Korean Cross-Sectional Cohort Data

“…Contrary to our result, some studies that explored a global oxidative stress index (Oxidative-INDEX) did not reach beyond the good prediction level. Park et al [50] created an oxidative stress score consisting of MDA, oxidized low-density lipoprotein, and 8-epi-prostaglandin F2α. The AUC value was 0.75 when combined with single nucleotide polymorphisms to predict obesity in the Korean population.…”