[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128326-en":3,"doc-seo-128326-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128326,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Dietary patterns and obesity are associated with type 2 diabetes risk in elderly Chinese men - a machine learning approach","Type 2 diabetes mellitus poses a major public health burden, with especially high prevalence among older men in China. This study investigates how obesity, dietary habits, and blood pressure relate to future T2DM risk in elderly Chinese men. Unsupervised machine learning identifies dietary patterns, while supervised models with SHAP quantify key obesity- and lifestyle-related predictors. Associations are further tested with logistic regression and sensitivity analyses to assess robustness. Results support targeted dietary and lifestyle interventions to reduce T2DM risk.","TYPE Original Research PUBLISHED 26 November 2025 DOI 10. 3389/fnut.2025.1705683  \nOPEN ACCESS  \nEDITED BY  \nMohd Dilshad Ansari,  \nSRM University (Delhi-NCR), India  \nREVIEWED BY  \nShula Shazman,  \nOpen University of Israel, Israel Djeane Debora Onthoni, University of Tartu, Estonia  \n*CORRESPONDENCE  \nLongfei Li  \n [li.longfei.q4@alumni.tohoku.ac.jp](li.longfei.q4@alumni.tohoku.ac.jp)  \nRECEIVED 15 September 2025  \nREVISED 02 November 2025  \nACCEPTED 11 November 2025  \nPUBLISHED 26 November 2025  \nCITATION  \nSun H, Zhu L, Wang P, Yuan K, Nawrin SS, Cui Y and Li L (2025) Dietary patterns and obesity are associated with type 2 diabetes risk in elderly Chinese men: a machine learning approach. Front. Nutr. 12:1705683 .  \ndoi: 10.3389/fnut.2025.1705683  \nCOPYRIGHT  \n© 2025 Sun, Zhu, Wang, Yuan, Nawrin, Cui and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nDietary patterns and obesity are associated with type 2 diabetes risk in elderly Chinese men: a machine learning approach  \nHaowei Sun1 , Lijin Zhu1 , Peng Wang1 , Keqing Yuan2 , Saida Salima Nawrin2 , Yufei Cui2 and Longfei Li1,2*  \n1 College of Physical Education and Health, Heze University, Heze, Shandong, China, 2 Graduate School of Medicine, Tohoku University, Sendai, Japan  \nBackground: 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.  \nMethods: 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.  \nResults: Four distinct dietary patterns were identified: “high-fiber nutrientdense,”“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 \u003C 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 \u003C 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.  \nConclusion: 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 u","cbCaiog8v6o9XjFQ","https://ap.wps.com/l/cbCaiog8v6o9XjFQ","pdf",1578987,5,1,15,"English","en",105,"# Introduction\n## Methods\n## Results\n## Conclusion","[{\"question\":\"What is the main objective of this study?\",\"answer\":\"To examine the links between obesity, dietary habits, blood pressure, and the risk of developing type 2 diabetes in elderly Chinese men.\"},{\"question\":\"How were dietary patterns identified?\",\"answer\":\"Unsupervised machine learning (UMAP) was used to identify distinct dietary patterns among participants.\"},{\"question\":\"Which factors were highlighted as major contributors to T2DM prediction?\",\"answer\":\"SHAP analysis emphasized dietary behaviors, total energy intake, and physical activity as key contributors.\"}]","Dietary patterns and obesity are associated with type 2 diabetes risk in elderly Chinese men - a machine learning approach | PDF",1785946853,38,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"dietary-patterns-and-obesity-are-associated-with-type-2-diabetes-risk-in-elderly-chinese-men-a-machine-learning-approach","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/dietary-patterns-and-obesity-are-associated-with-type-2-diabetes-risk-in-elderly-chinese-men-a-machine-learning-approach/128326/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What is the main objective of this study?","Question",{"text":77,"@type":78},"To examine the links between obesity, dietary habits, blood pressure, and the risk of developing type 2 diabetes in elderly Chinese men.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How were dietary patterns identified?",{"text":82,"@type":78},"Unsupervised machine learning (UMAP) was used to identify distinct dietary patterns among participants.",{"name":84,"@type":75,"acceptedAnswer":85},"Which factors were highlighted as major contributors to T2DM prediction?",{"text":86,"@type":78},"SHAP analysis emphasized dietary behaviors, total energy intake, and physical activity as key contributors.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":20,"slug":139},19,"General","general"]