{"title":"Population-attributable Fractions of Lifestyle Factors for Prediabetes in Korea: A Regression-based Analysis of National Survey Data.","authors":"Yeon Woo Oh, Chung Mo Nam, Eun-Cheol Park","doi":"10.3961/jpmph.25.030","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>Although lifestyle modification programs are widely implemented for diabetes prevention, the contributions of individual lifestyle factors remain unclear. This study investigated lifestyle risk factors for prediabetes and employed a regression-based approach for estimating their population-attributable fractions (PAFs) using nationally representative data.</p><p><strong>Methods: </strong>We analyzed data from 3,104 adults aged ≥30 years without diabetes from the 2022 Korea National Health and Nutrition Examination Survey. Seven lifestyle factors were assessed: body weight, alcohol consumption, smoking, physical activity, sleep duration, vegetable intake, and breakfast consumption. Prediabetes was defined as fasting blood glucose of 100-125 mg/dL or HbA1c levels of 5.7-6.4%. Complex survey-adjusted logistic regression was used to identify significant lifestyle risk factors, and their PAFs were estimated using a regression-based sequential method.</p><p><strong>Results: </strong>Five lifestyle factors were significantly associated with prediabetes: abnormal body weight (OR: 2.046; 95% CI, 1.676-2.498), excessive alcohol consumption (OR: 1.274; 95% CI, 1.000-1.623), smoking (OR: 1.354; 95% CI, 1.073-1.709), insufficient exercise (OR: 1.259; 95% CI, 1.049-1.512), and irregular breakfast consumption (OR: 1.309; 95% CI, 1.078-1.590). In sequential PAF estimation, abnormal body weight had the largest contribution (22.2%; 95% CI, 16.2-28.2%), followed by smoking (6.4%; 95% CI, 1.1-11.6%), insufficient exercise (5.8%; 95% CI, 1.2-10.5%), irregular breakfast consumption (4.9%; 95% CI, 0.5-9.2%), and excessive alcohol consumption (3.6%; 95% CI, 0.1-7.4%). These results remained consistent in sensitivity analyses including undiagnosed diabetes cases.</p><p><strong>Conclusions: </strong>Abnormal body weight emerged as the largest contributor to prediabetes (PAF>20%). Diabetes prevention programs in South Korea should prioritize weight management within a comprehensive approach to lifestyle modification.</p>","PeriodicalId":520687,"journal":{"name":"Journal of preventive medicine and public health = Yebang Uihakhoe chi","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of preventive medicine and public health = Yebang Uihakhoe chi","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3961/jpmph.25.030","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Objectives: Although lifestyle modification programs are widely implemented for diabetes prevention, the contributions of individual lifestyle factors remain unclear. This study investigated lifestyle risk factors for prediabetes and employed a regression-based approach for estimating their population-attributable fractions (PAFs) using nationally representative data.
Methods: We analyzed data from 3,104 adults aged ≥30 years without diabetes from the 2022 Korea National Health and Nutrition Examination Survey. Seven lifestyle factors were assessed: body weight, alcohol consumption, smoking, physical activity, sleep duration, vegetable intake, and breakfast consumption. Prediabetes was defined as fasting blood glucose of 100-125 mg/dL or HbA1c levels of 5.7-6.4%. Complex survey-adjusted logistic regression was used to identify significant lifestyle risk factors, and their PAFs were estimated using a regression-based sequential method.
Results: Five lifestyle factors were significantly associated with prediabetes: abnormal body weight (OR: 2.046; 95% CI, 1.676-2.498), excessive alcohol consumption (OR: 1.274; 95% CI, 1.000-1.623), smoking (OR: 1.354; 95% CI, 1.073-1.709), insufficient exercise (OR: 1.259; 95% CI, 1.049-1.512), and irregular breakfast consumption (OR: 1.309; 95% CI, 1.078-1.590). In sequential PAF estimation, abnormal body weight had the largest contribution (22.2%; 95% CI, 16.2-28.2%), followed by smoking (6.4%; 95% CI, 1.1-11.6%), insufficient exercise (5.8%; 95% CI, 1.2-10.5%), irregular breakfast consumption (4.9%; 95% CI, 0.5-9.2%), and excessive alcohol consumption (3.6%; 95% CI, 0.1-7.4%). These results remained consistent in sensitivity analyses including undiagnosed diabetes cases.
Conclusions: Abnormal body weight emerged as the largest contributor to prediabetes (PAF>20%). Diabetes prevention programs in South Korea should prioritize weight management within a comprehensive approach to lifestyle modification.