{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:25:57Z","timestamp":1760487957966,"version":"build-2065373602"},"reference-count":27,"publisher":"Korean Society for Preventive Medicine","issue":"5","license":[{"start":{"date-parts":[[2025,5,7]],"date-time":"2025-05-07T00:00:00Z","timestamp":1746576000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["jpmph.org"],"crossmark-restriction":true},"short-container-title":["JPMPH"],"published-print":{"date-parts":[[2025,9,30]]},"abstract":"<jats:p>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 3104 adults aged \u226530 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 hemoglobin A1c 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 (odds ratio [OR], 2.05; 95% confidence interval [CI], 1.68 to 2.50), excessive alcohol consumption (OR, 1.27; 95% CI, 1.00 to 1.62), smoking (OR, 1.35; 95% CI, 1.07 to 1.71), insufficient exercise (OR, 1.26; 95% CI, 1.05 to 1.51), and irregular breakfast consumption (OR, 1.31; 95% CI, 1.08 to 1.59). In sequential PAF estimation, abnormal body weight had the largest contribution (22.2%; 95% CI, 16.2 to 28.2), followed by smoking (6.4%; 95% CI, 1.1 to 11.6), insufficient exercise (5.8%; 95% CI, 1.2 to 10.5), irregular breakfast consumption (4.9%; 95% CI, 0.5 to 9.2), and excessive alcohol consumption (3.6%; 95% CI, 0.1 to 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&gt;20%). Diabetes prevention programs in Korea should prioritize weight management within a comprehensive approach to lifestyle modification.<\/jats:p>","DOI":"10.3961\/jpmph.25.030","type":"journal-article","created":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T03:39:02Z","timestamp":1749785942000},"page":"465-474","update-policy":"https:\/\/doi.org\/10.3961\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Population-attributable Fractions of Lifestyle Factors for Prediabetes in Korea: A Regression-based Analysis of National Survey Data"],"prefix":"10.3961","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2485-1400","authenticated-orcid":true,"given":"Yeon Woo","family":"Oh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0985-0928","authenticated-orcid":true,"given":"Chung 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