Lee JH, et al. Korean J Fam Med. 2014 Sep;35(5):227-236. http://dx.doi.org/10.4082/kjfm.2014.35.5.227

Previous work on predicting type 2 diabetes by integrating clinical and genetic factors has mostly focused on the Western population. In this study, we use genome-wide polygenic risk score (gPRS) and serum metabolite data for type 2 diabetes risk prediction in the Asian population. Data of 1425 participants from the Korean Genome and Epidemiology Study (KoGES) Ansan-Ansung cohort were used in this study. For gPRS analysis, genotypic and clinical information from KoGES health examinee (n = 58,701) and KoGES cardiovascular disease association (n = 8105) sub-cohorts were included. Linkage disequilibrium analysis identified 239,062 genetic variants that were used to determine the gPRS, while the metabolites were selected using the Boruta algorithm. We used bootstrapped cross-validation to evaluate logistic regression and random forest (RF)-based machine learning models. Finally, associations of gPRS and selected metabolites with the values of homeostatic model assessment of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) were further estimated. During the follow-up period (8.3 ± 2.8 years), 331 participants (23.2%) were diagnosed with type 2 diabetes. The areas under the curves of the RF-based models were 0.844, 0.876, and 0.883 for the model using only demographic and clinical factors, model including the gPRS, and model with both gPRS and metabolites, respectively. Incorporation of additional parameters in the latter two models improved the classification by 11.7% and 4.2% respectively. While gPRS was significantly associated with HOMA-B value, most metabolites had a significant association with HOMA-IR value. Incorporating both gPRS and metabolite data led to enhanced type 2 diabetes risk prediction by capturing distinct etiologies of type 2 diabetes development. An RF-based model using clinical factors, gPRS, and metabolites predicted type 2 diabetes risk more accurately than the logistic regression-based model. This work was supported by the 10.13039/501100003725 National Research Foundation of Korea (NRF) grant funded by the Korean government (MEST) (No. 2019M3E5D1A02070863 and 2022R1C1C1005458). This work was also supported by the 2020 Research Fund (1.200098.01) of UNIST (Ulsan National Institute of Science & Technology)

We aimed to identify the association between obstructive sleep apnea (OSA), insulin resistance (IR), and metabolic syndrome (MetS) in a nationwide population-based sample. A total of 7,900 adults with information on the STOP-Bang score and MetS (3,341 men and 4,469 women) were identified from the dataset of the Korea National Health and Nutrition Examination Survey 2019–2020. The association between OSA, IR, MetS, and its components was estimated using complex sample logistic regression analysis with adjustments for age, body mass index, residence, smoking status, alcohol consumption, household income, education, and the presence of diabetes. Participants with a high OSA score were more likely to have IR (odds ratio [OR] 2.78, 95% confidence interval [CI] 1.96–3.95 in men and OR 2.64, 95% CI 0.55–12.80 in women), MetS (OR 6.05, 95% CI 4.23–8.69 in men and OR 4.20, 95% CI 1.23–15.70 in women), and components of MetS, compared to individuals with a low OSA score. Compared to premenopausal women, postmenopausal women had a more intense association between OSA and IR (OR 1.78, 95% CI 0.13–24.43 for premenopausal women and OR 3.64, 95% CI 0.60–22.28 for postmenopausal women) and MetS (OR 2.58, 95% CI 0.23–29.55 for premenopausal women and OR 5.36, 95% CI 1.42–20.21 for postmenopausal women). OSA was associated with abdominal obesity and hypertension in premenopausal women, while all components of MetS were associated with OSA in postmenopausal women. Further studies are necessary to elucidate the underlying mechanisms of these findings.

Background: The association between the dual use of electronic cigarette (e-cigarette) and conventional cigarettes (c-cigarette) and spirometry-defined chronic obstructive pulmonary disease (COPD) has not been studied thoroughly. Methods: A total of 47,217 participants were identified in the 2013–2018 Korea National Health and Nutrition Examination Survey; of them, 12,919 participants aged ≥ 40 who underwent spirometry and had no missing data were enrolled. Pulmonary function testing, urinary cotinine, and urinary 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) levels were compared between dual users, current smokers, former smokers, and non-users using complex sample linear regression analysis. The odds ratio (OR) for COPD was calculated using a complex sample logistic regression model after adjusting for covariates. Results: Among current e-cigarette users, approximately 85% of the participants used c-cigarette concurrently, and 1.3% of all the participants were dual users (2.3% in males and 0.1% in females). Both dual users and current smokers showed higher levels of urine cotinine and NNAL than non-users and former smokers. The weighted prevalence of COPD was the highest in dual users (13.8% for all participants and 14.1% for males). The multivariate-adjusted OR of COPD for male dual users, compared to non-users, was 3.46 (P_trend < 0.001). The OR for COPD was 3.10 (P_trend < 0.001) in middle-aged (40–64 years) and 3.70 (P_trend < 0.001) in older (≥ 65 years) men. In females, the association was not observed and could not be precisely measured because of the small proportion of the smoking population. Conclusions: Dual use of e-cigarette and c-cigarette is associated with COPD in males.

The association between smoking and metabolic syndrome has not been clarified, especially for women, probably because of the inaccurate self-reported smoking status. This study aimed to investigate the association between cotinine-verified smoking status and metabolic syndrome. A total of 11,559 participants from the Korean National Health and Nutrition Examination Surveys were included in this cross-sectional study. Metabolic syndrome was determined according to revised National Cholesterol Education Program Adult Treatment Panel III criteria. Smokers were distinguished from nonsmokers by a urinary cotinine level above 50 ng/mL. Multivariable adjusted logistic regression analysis was used to evaluate the association between cotinine-verified smoking status and metabolic syndrome. Prevalence of metabolic syndrome was 28.2% in men and 24.6% in women. Self-reported smoking status was much less consistent with cotinine-verified smoking status in women (kappa values=43.0%) compared with men (kappa value=88.6%). Risk of metabolic syndrome was significantly higher in cotinine-verified smokers than in nonsmokers for both men and women. Among the components of metabolic syndrome, smokers had an increased risk of high triglycerides (TGs), low high-density lipoprotein cholesterol, and decreased risk of high blood pressure compared with nonsmokers in men. In women, smokers had a higher risk of abdominal obesity and high TGs compared with nonsmokers. This population-based study showed that smoking was associated with increased risk for metabolic syndrome in men as well as in women and this association is mainly due to the association between smoking and dyslipidemia.