高齢中国人男性における食事パターンと肥満と2型糖尿病リスクの関連：機械学習アプローチ
Haowei Sun1, Lijin Zhu1, Peng Wang1
1College of Physical Education and Health, Heze University, Heze, Shandong, China.
Background:
Type 2 diabetes mellitus (T2DM) is a major global public health issue, with a particularly high prevalence in China, especially among older men. Obesity, dietary habits, and metabolic risk factors are key contributors to the development of T2DM. However, research on the relationship between dietary patterns, obesity, and T2DM in elderly Chinese men remains limited. Objective: This study aims to examine the links between obesity, dietary habits, blood pressure, and the risk of developing T2DM in elderly Chinese men. We utilize unsupervised machine learning methods along with SHAP-based model interpretation to identify significant lifestyle and metabolic factors associated with T2DM risk.
Methods:
A cross-sectional study was conducted with 982 participants aged 60 years and older from community health centers in Heze City, China. Unsupervised machine learning methods (UMAP) were used to identify dietary patterns, and supervised machine learning with SHAP was applied to evaluate the importance of obesity, dietary patterns, and lifestyle factors on T2DM risk. Logistic regression analyses were performed to investigate the associations between obesity, dietary habits, blood pressure, and T2DM risk. Sensitivity analyses were performed to verify the robustness of the findings.
Results:
Four distinct dietary patterns were identified: "high-fiber nutrient-dense," "staple-protein," "seafood-eggs," and "sugary and processed foods." The prevalence of newly diagnosed T2DM in males was 48.37%. Obesity was inversely associated with T2DM risk across all models (odds ratios: 0.272-0.278, all P < 0.05). Compared with the high-fiber nutrient-dense pattern, adherence to the staple-protein, seafood-eggs, and sugary and processed foods patterns was significantly associated with increased obesity and T2DM risk (all P < 0.01). Shapley Additive Explanations (SHAP) analysis highlighted dietary behaviors, total energy intake, and physical activity as major contributors to T2DM prediction. Sensitivity analyses confirmed the robustness of these associations, independent of total caloric intake and BMI.
Conclusion:
In this population of elderly Chinese males, unhealthy dietary patterns are positively associated with obesity and T2DM risk, whereas obesity itself showed an inverse relationship with T2DM. These findings underscore the importance of promoting nutrient-dense diets and targeted lifestyle interventions to reduce T2DM risk in this population.
