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 F2α, 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.
|Journal||Diabetes/Metabolism Research and Reviews|
|Publication status||Published - 2020 Feb 1|
Bibliographical noteFunding Information:
This research was funded by a grant from the Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea (HI13C0715); by the Basic Science Research Program through the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2017R1C1B2007195) and by the Ministry of Education (NRF-2019R1I1A2A01061731); and by Bio-Synergy Research Project through the NRF by the Korean government (MSIT) (NRF-2012M3A9C4048762). The genotype data were produced using the Korean Chip (K-CHIP) available through the K-CHIP consortium. The K-CHIP was designed by the Center for Genome Science at the Korea National Institute of Health (4845-301 and 3000-3031).
This research was funded by a grant from the Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea (HI13C0715); by the Basic Science Research Program through the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF‐2017R1C1B2007195) and by the Ministry of Education (NRF‐2019R1I1A2A01061731); and by Bio‐Synergy Research Project through the NRF by the Korean government (MSIT) (NRF‐2012M3A9C4048762).
© 2019 John Wiley & Sons, Ltd.
All Science Journal Classification (ASJC) codes
- Internal Medicine
- Endocrinology, Diabetes and Metabolism