Nicolaas P. Pronk, Shuaijie Wang, Richard Bergenstal, Anders Carlson, Colin Woodard, Ross Arena
{"title":"预测美国县级糖尿病患病率的复杂、多层次生态：一项横断面人工智能分析。","authors":"Nicolaas P. Pronk, Shuaijie Wang, Richard Bergenstal, Anders Carlson, Colin Woodard, Ross Arena","doi":"10.1002/dmrr.70184","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Diabetes and its risk factors are embedded in a complex multilevel ecology. Upstream factors (i.e., ‘forcing factors’ that refer to fundamental population-based social, economic, and political structures shaping health outcomes long before disease develops) and downstream risk factors (i.e., the individual-level characteristics and outcomes that result from upstream forcing factors) are to be considered when predicting diabetes. This study aims to predict diabetes prevalence at the United States' (US) county level using analytical methods that account for the contextual complexity of diabetes.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>US county-level datasets incorporating 27 predictor variables were analysed cross-sectionally. A Light Gradient Boosting Machine (LightGBM) model was trained to predict county-level diabetes prevalence, after which model performance and feature importance were evaluated.</p>\n </section>\n \n <section>\n \n <h3> Findings</h3>\n \n <p>The final model retained 17 features and explained 95% (<i>R</i><sup>2</sup> = 0.952) of the variance in county-level diabetes prevalence. Physical inactivity, racial and ethnic minority status, frequent physical distress, and obesity showed the highest Shapley (SHAP) importance values, which exceeded all remaining SHAP values.</p>\n </section>\n \n <section>\n \n <h3> Interpretation</h3>\n \n <p>This study delineates the most important predictors of diabetes prevalence based on a complex multilevel ecology. Efforts to reduce diabetes prevalence should address both upstream and downstream risk factors emphasising physical activity, obesity, equity, and cultural considerations.</p>\n </section>\n </div>","PeriodicalId":11335,"journal":{"name":"Diabetes/Metabolism Research and Reviews","volume":"42 5","pages":""},"PeriodicalIF":8.3000,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/dmrr.70184","citationCount":"0","resultStr":"{\"title\":\"Predicting the Complex, Multilevel Ecology of US County-Level Diabetes Prevalence: A Cross-Sectional, Artificial Intelligence Analysis\",\"authors\":\"Nicolaas P. 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Predicting the Complex, Multilevel Ecology of US County-Level Diabetes Prevalence: A Cross-Sectional, Artificial Intelligence Analysis
Background
Diabetes and its risk factors are embedded in a complex multilevel ecology. Upstream factors (i.e., ‘forcing factors’ that refer to fundamental population-based social, economic, and political structures shaping health outcomes long before disease develops) and downstream risk factors (i.e., the individual-level characteristics and outcomes that result from upstream forcing factors) are to be considered when predicting diabetes. This study aims to predict diabetes prevalence at the United States' (US) county level using analytical methods that account for the contextual complexity of diabetes.
Methods
US county-level datasets incorporating 27 predictor variables were analysed cross-sectionally. A Light Gradient Boosting Machine (LightGBM) model was trained to predict county-level diabetes prevalence, after which model performance and feature importance were evaluated.
Findings
The final model retained 17 features and explained 95% (R2 = 0.952) of the variance in county-level diabetes prevalence. Physical inactivity, racial and ethnic minority status, frequent physical distress, and obesity showed the highest Shapley (SHAP) importance values, which exceeded all remaining SHAP values.
Interpretation
This study delineates the most important predictors of diabetes prevalence based on a complex multilevel ecology. Efforts to reduce diabetes prevalence should address both upstream and downstream risk factors emphasising physical activity, obesity, equity, and cultural considerations.
期刊介绍：
Diabetes/Metabolism Research and Reviews is a premier endocrinology and metabolism journal esteemed by clinicians and researchers alike. Encompassing a wide spectrum of topics including diabetes, endocrinology, metabolism, and obesity, the journal eagerly accepts submissions ranging from clinical studies to basic and translational research, as well as reviews exploring historical progress, controversial issues, and prominent opinions in the field. Join us in advancing knowledge and understanding in the realm of diabetes and metabolism.
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