Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3165
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 317
Function: require_once
98%
921
2 minutes
20
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.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.3961/jpmph.25.030 | DOI Listing |