{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:52:01Z","timestamp":1774630321297,"version":"3.50.1"},"reference-count":23,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:00:00Z","timestamp":1774396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korea Health Industry Development Institute (KHIDI)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t              https:\/\/ror.org\/00fdzyk40"},{"name":"Ministry of Health and Welfare\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t              https:\/\/ror.org\/00vxgjw72","award":["HR20C0026"],"award-info":[{"award-number":["HR20C0026"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Type 2 diabetes (T2D) develops gradually over many years through a prolonged preclinical phase, yet traditional static risk scores often fail to capture these dynamic metabolic trajectories. We propose a longitudinal deep learning framework to predict the 10-year risk of Type 2 diabetes onset defined by comprehensive ADA criteria by modeling the physiological acceleration of routine clinical biomarkers. Methods: Utilizing an 18-year longitudinal dataset from the community-based Korean Genome and Epidemiology Study (KoGES) cohort, we selected N=4354 participants with complete follow-up records, ensuring high data integrity without requiring synthetic data augmentation. We constructed a 3-dimensional tensor of 21 non-invasive clinical variables spanning a 6-year observation window. To resolve the inherent precision-recall trade-offs of individual models, we developed a stacking ensemble that integrates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures via a logistic regression meta-learner. To evaluate the added value of longitudinal modeling, we compared this dynamic framework against a static XGBoost baseline that only saw the most recent data. Results: Evaluated on an independent test set (n=874), the ensemble significantly outperformed baseline models, achieving an overall accuracy of 0.90 (95% CI: 0.88\u20130.92) and an AUROC of 0.94 (95% CI: 0.93\u20130.95). By harmonizing LSTM\u2019s sensitivity and GRU\u2019s precision, the model yielded an exceptional Positive Predictive Value (PPV) of 0.97, a sensitivity of 0.80, and a specificity of 0.98. Conclusions: This framework provides a highly accurate, resource-efficient triage instrument for T2D screening, thereby reducing unnecessary clinical alerts and improving screening efficiency.<\/jats:p>","DOI":"10.3390\/diagnostics16070992","type":"journal-article","created":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T15:45:42Z","timestamp":1774626342000},"page":"992","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting 10-Year Diabetes Risk Through Physiological Acceleration: A Longitudinal Deep Learning Ensemble Approach"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9687-8357","authenticated-orcid":false,"given":"Sangsoo","family":"Kim","sequence":"first","affiliation":[{"name":"Division of Endocrinology and Metabolism, Department of Internal Medicine, Pusan National University Hospital, Busan 49241, Gyeongsangnam-do, Republic of Korea"},{"name":"Biomedical Research Institute, Pusan National University Hospital, Busan 49241, Gyeongsangnam-do, Republic of Korea"},{"name":"Department of Internal Medicine, Pusan National University School of Medicine, Busan 49241, Gyeongsangnam-do, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7114-9466","authenticated-orcid":false,"given":"Seonghee","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Big Data Science, Kumoh National Institute of Technology, Gumi 39177, Gyeongsangbuk-do, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6715-0447","authenticated-orcid":false,"given":"Jinmi","family":"Kim","sequence":"additional","affiliation":[{"name":"Biomedical Research Institute, Pusan National University Hospital, Busan 49241, Gyeongsangnam-do, Republic of Korea"},{"name":"Department of Biostatistics, Clinical Trial Center, Biomedical Research Institute, Pusan National University Hospital, Busan 49241, Gyeongsangnam-do, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3293-8461","authenticated-orcid":false,"given":"Ha Jin","family":"Park","sequence":"additional","affiliation":[{"name":"Division of Endocrinology and Metabolism, Department of Internal Medicine, Pusan National University Hospital, Busan 49241, Gyeongsangnam-do, Republic of Korea"},{"name":"Biomedical Research Institute, Pusan National University Hospital, Busan 49241, Gyeongsangnam-do, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5251-5554","authenticated-orcid":false,"given":"Soree","family":"Ryang","sequence":"additional","affiliation":[{"name":"Division of Endocrinology and Metabolism, Department of Internal Medicine, Pusan National University Hospital, Busan 49241, Gyeongsangnam-do, Republic of Korea"},{"name":"Biomedical Research Institute, Pusan National University Hospital, Busan 49241, Gyeongsangnam-do, Republic of Korea"},{"name":"Department of Internal Medicine, Pusan National University School of Medicine, Busan 49241, Gyeongsangnam-do, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2758-9461","authenticated-orcid":false,"given":"Myungsoo","family":"Im","sequence":"additional","affiliation":[{"name":"Division of Endocrinology and Metabolism, Department of Internal Medicine, Pusan National University Hospital, Busan 49241, Gyeongsangnam-do, Republic of Korea"},{"name":"Biomedical Research Institute, Pusan National University Hospital, Busan 49241, Gyeongsangnam-do, Republic of Korea"},{"name":"Department of Internal Medicine, Pusan National University School of Medicine, Busan 49241, Gyeongsangnam-do, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9289-355X","authenticated-orcid":false,"given":"Doohwa","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Endocrinology and Metabolism, Department of Internal Medicine, Pusan National University Hospital, Busan 49241, Gyeongsangnam-do, Republic of Korea"},{"name":"Biomedical Research Institute, Pusan National University Hospital, Busan 49241, Gyeongsangnam-do, Republic of Korea"},{"name":"Department of Internal Medicine, Pusan National University School of Medicine, Busan 49241, Gyeongsangnam-do, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0754-5172","authenticated-orcid":false,"given":"Kyeongjun","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Big Data Science, Kumoh National Institute of Technology, Gumi 39177, Gyeongsangbuk-do, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109119","DOI":"10.1016\/j.diabres.2021.109119","article-title":"IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045","volume":"183","author":"Sun","year":"2022","journal-title":"Diabetes Res. 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