{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T07:09:54Z","timestamp":1778310594317,"version":"3.51.4"},"reference-count":48,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Guangdong Philosophy and Social Sciences Planning Project","award":["GD25YSH15"],"award-info":[{"award-number":["GD25YSH15"]}]},{"DOI":"10.13039\/501100003785","name":"Guangdong Medical Research Foundation","doi-asserted-by":"publisher","award":["A2024051"],"award-info":[{"award-number":["A2024051"]}],"id":[{"id":"10.13039\/501100003785","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2022A1515110721"],"award-info":[{"award-number":["2022A1515110721"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Rheumatoid arthritis (RA) and diabetes mellitus (DM) frequently coexist, yet the heterogeneity of RA-DM multimorbidity remains unclear. This study aims to develop an interpretable machine learning framework to reveal the phenotypic subgroups of RA-DM multimorbidity, providing a potential direction for precision public health interventions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Utilizing data from the National Health and Nutrition Examination Survey (1999\u20132018), we developed a Bayesian-optimized eXtreme Gradient Boosting (XGBoost) model to classify RA-DM multimorbidity status and compared with other machine learning models. Shapley Additive Explanations (SHAP) was applied to interpret the optimal model and quantify the contributions of different features. A dual-clustering approach combining Self-Organizing Maps and K-means was used to identify RA-DM phenotypic subgroups with different feature contribution patterns based on SHAP profiles.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The optimized XGBoost model achieved the best classification performance, outperforming other models such as K-nearest neighbors, support vector machine and logistic regression. SHAP analysis identified nine key contributing features (homocysteine, age, glucose, etc), and revealed non-linear interactions among the features. The dual-clustering based on SHAP values identified four distinct RA-DM phenotypes\u2014inflammatory, metabolically protective, age-related and non-obese protective\u2014each exhibiting unique clinical and biochemical patterns.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study established an interpretable machine learning framework for identifying distinct phenotypes of RA-DM multimorbidity. These findings provide a data-driven basis for targeted interventions in precision public health, while offering a transferable paradigm for phenotype discovery in other multimorbid conditions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076261450332","type":"journal-article","created":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T18:43:45Z","timestamp":1778093025000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Phenotype identification and precise intervention for multimorbidity of rheumatoid arthritis and diabetes mellitus using interpretable machine learning"],"prefix":"10.1177","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2004-8645","authenticated-orcid":false,"given":"Zhijun","family":"He","sequence":"first","affiliation":[{"name":"Sun Yat-sen University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4749-1404","authenticated-orcid":false,"given":"Jinglin","family":"Han","sequence":"additional","affiliation":[{"name":"Imperial College London"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4040-3533","authenticated-orcid":false,"given":"Xinzhu","family":"Qiao","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5815-9725","authenticated-orcid":false,"given":"Yiqiang","family":"Zhan","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0160-2981","authenticated-orcid":false,"given":"Mingming","family":"Xu","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2026,5,6]]},"reference":[{"key":"e_1_3_8_2_2","first-page":"591","article-title":"Global epidemiology of rheumatoid arthritis","volume":"18","author":"Finckh A","year":"2022","unstructured":"Finckh A, Gilbert B, Hodkinson B, et al. 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