{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T22:54:18Z","timestamp":1793746458253,"version":"4.6.1"},"reference-count":41,"publisher":"Elsevier BV","issue":"3","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T00:00:00Z","timestamp":1747785600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["cell.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Molecular Therapy Nucleic Acids"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1016\/j.omtn.2025.102576","type":"journal-article","created":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T02:47:23Z","timestamp":1748314043000},"page":"102576","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":6,"title":["DRPM: An advanced predictive model for early diabetes detection and risk stratification"],"prefix":"10.1016","volume":"36","author":[{"given":"Fulei","family":"Nie","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoming","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6857-7696","authenticated-orcid":false,"given":"Wei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.omtn.2025.102576_bib1","series-title":"IDF Diabetes Atlas. Idf Diabetes Atlas","author":"Magliano","year":"2021"},{"key":"10.1016\/j.omtn.2025.102576_bib2","doi-asserted-by":"crossref","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. Clin. Pract."},{"key":"10.1016\/j.omtn.2025.102576_bib3","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/S0140-6736(23)01301-6","article-title":"Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021","volume":"402","year":"2023","journal-title":"Lancet"},{"key":"10.1016\/j.omtn.2025.102576_bib4","doi-asserted-by":"crossref","DOI":"10.1016\/j.diabres.2020.108086","article-title":"Mortality attributable to diabetes in 20-79 years old adults, 2019 estimates: Results from the International Diabetes Federation Diabetes Atlas, 9(th) edition","volume":"162","author":"Saeedi","year":"2020","journal-title":"Diabetes Res. Clin. Pract."},{"key":"10.1016\/j.omtn.2025.102576_bib5","doi-asserted-by":"crossref","first-page":"2163","DOI":"10.1007\/s00125-010-1826-5","article-title":"Combined effects of 17 common genetic variants on type 2 diabetes risk in a Han Chinese population","volume":"53","author":"Qi","year":"2010","journal-title":"Diabetologia (Berl.)"},{"key":"10.1016\/j.omtn.2025.102576_bib6","doi-asserted-by":"crossref","first-page":"121","DOI":"10.2337\/dc10-1265","article-title":"Genetic risk reclassification for type 2 diabetes by age below or above 50 years using 40 type 2 diabetes risk single nucleotide polymorphisms","volume":"34","author":"de Miguel-Yanes","year":"2011","journal-title":"Diabetes Care"},{"key":"10.1016\/j.omtn.2025.102576_bib7","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1111\/j.1464-5491.2011.03438.x","article-title":"Analysis of 32 common susceptibility genetic variants and their combined effect in predicting risk of Type 2 diabetes and related traits in Indians","volume":"29","author":"Janipalli","year":"2012","journal-title":"Diabet. Med."},{"key":"10.1016\/j.omtn.2025.102576_bib8","doi-asserted-by":"crossref","first-page":"E1667","DOI":"10.1210\/jc.2013-1642","article-title":"Assessing the clinical utility of a genetic risk score constructed using 49 susceptibility alleles for type 2 diabetes in a Japanese population","volume":"98","author":"Imamura","year":"2013","journal-title":"J. Clin. Endocrinol. Metab."},{"key":"10.1016\/j.omtn.2025.102576_bib9","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1093\/aje\/kwt123","article-title":"Predicting risk of type 2 diabetes mellitus with genetic risk models on the basis of established genome-wide association markers: a systematic review","volume":"178","author":"Bao","year":"2013","journal-title":"Am. J. Epidemiol."},{"key":"10.1016\/j.omtn.2025.102576_bib10","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pgen.1000337","article-title":"Interpretation of genetic association studies: markers with replicated highly significant odds ratios may be poor classifiers","volume":"5","author":"Jakobsdottir","year":"2009","journal-title":"PLoS Genet."},{"key":"10.1016\/j.omtn.2025.102576_bib11","doi-asserted-by":"crossref","first-page":"305","DOI":"10.3892\/etm.2022.11234","article-title":"Application of machine learning tools: Potential and useful approach for the prediction of type 2 diabetes mellitus based on the gut microbiome profile","volume":"23","author":"Ge","year":"2022","journal-title":"Exp. Ther. Med."},{"key":"10.1016\/j.omtn.2025.102576_bib12","doi-asserted-by":"crossref","first-page":"358","DOI":"10.2337\/dc20-1536","article-title":"Interpretable Machine Learning Framework Reveals Robust Gut Microbiome Features Associated With Type 2 Diabetes","volume":"44","author":"Gou","year":"2021","journal-title":"Diabetes Care"},{"key":"10.1016\/j.omtn.2025.102576_bib13","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1007\/s43657-023-00095-0","article-title":"Diets, Gut Microbiota and Metabolites","volume":"3","author":"Liu","year":"2023","journal-title":"Phenomics"},{"key":"10.1016\/j.omtn.2025.102576_bib14","article-title":"A correlation-based feature analysis of physical examination indicators can help predict the overall underlying health status using machine learning","volume":"12","author":"Wang","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.omtn.2025.102576_bib15","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1038\/s42256-024-00974-9","article-title":"A machine learning approach to leveraging electronic health records for enhanced omics analysis","volume":"7","author":"Mataraso","year":"2025","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.omtn.2025.102576_bib16","article-title":"Application of Physical Examination Data on Health Analysis and Intelligent Diagnosis","volume":"2021","author":"Wang","year":"2021","journal-title":"BioMed Res. Int."},{"key":"10.1016\/j.omtn.2025.102576_bib17","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1177\/1460458216663023","article-title":"Screening diabetes mellitus 2 based on electronic health records using temporal features","volume":"24","author":"Pimentel","year":"2018","journal-title":"Health Informatics J."},{"key":"10.1016\/j.omtn.2025.102576_bib18","doi-asserted-by":"crossref","first-page":"3667","DOI":"10.1177\/0962280218810911","article-title":"Predicting diabetes-related hospitalizations based on electronic health records","volume":"28","author":"Brisimi","year":"2019","journal-title":"Stat. Methods Med. Res."},{"key":"10.1016\/j.omtn.2025.102576_bib19","doi-asserted-by":"crossref","first-page":"515","DOI":"10.3389\/fgene.2018.00515","article-title":"Predicting Diabetes Mellitus With Machine Learning Techniques","volume":"9","author":"Zou","year":"2018","journal-title":"Front. Genet."},{"key":"10.1016\/j.omtn.2025.102576_bib20","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.inffus.2021.02.015","article-title":"Risk Prediction of Diabetes: Big data mining with fusion of multifarious physical examination indicators","volume":"75","author":"Yang","year":"2021","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.omtn.2025.102576_bib21","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1038\/s44259-024-00068-x","article-title":"Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance","volume":"3","author":"Cesaro","year":"2025","journal-title":"NPJ Antimicrob. Resist."},{"key":"10.1016\/j.omtn.2025.102576_bib22","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1038\/s10038-024-01231-y","article-title":"Advances in AI and machine learning for predictive medicine","volume":"69","author":"Sharma","year":"2024","journal-title":"J. Hum. Genet."},{"key":"10.1016\/j.omtn.2025.102576_bib23","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1093\/jamia\/ocy068","article-title":"Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review","volume":"25","author":"Xiao","year":"2018","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.omtn.2025.102576_bib24","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.jbi.2018.04.007","article-title":"Benchmarking deep learning models on large healthcare datasets","volume":"83","author":"Purushotham","year":"2018","journal-title":"J. Biomed. Inform."},{"key":"10.1016\/j.omtn.2025.102576_bib25","doi-asserted-by":"crossref","first-page":"4039","DOI":"10.1038\/s41467-023-39631-x","article-title":"Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs","volume":"14","author":"Pyrros","year":"2023","journal-title":"Nat. Commun."},{"key":"10.1016\/j.omtn.2025.102576_bib26","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2019.105055","article-title":"Predicting the onset of type 2 diabetes using wide and deep learning with electronic health records","volume":"182","author":"Nguyen","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.omtn.2025.102576_bib27","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/s41551-021-00745-6","article-title":"Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images","volume":"5","author":"Zhang","year":"2021","journal-title":"Nat. Biomed. Eng."},{"key":"10.1016\/j.omtn.2025.102576_bib28","doi-asserted-by":"crossref","DOI":"10.1111\/acel.13872","article-title":"Explainable machine learning framework to predict personalized physiological aging","volume":"22","author":"Bernard","year":"2023","journal-title":"Aging Cell"},{"key":"10.1016\/j.omtn.2025.102576_bib29","doi-asserted-by":"crossref","first-page":"873","DOI":"10.2337\/dc10-1786","article-title":"Blood pressure and the risk of developing diabetes in african americans and whites: ARIC, CARDIA, and the framingham heart study","volume":"34","author":"Wei","year":"2011","journal-title":"Diabetes Care"},{"key":"10.1016\/j.omtn.2025.102576_bib30","doi-asserted-by":"crossref","DOI":"10.1161\/JAHA.120.017015","article-title":"Degree of Blood Pressure Control and Incident Diabetes Mellitus in Chinese Adults With Hypertension","volume":"9","author":"Zhang","year":"2020","journal-title":"J. Am. Heart Assoc."},{"key":"10.1016\/j.omtn.2025.102576_bib31","doi-asserted-by":"crossref","first-page":"e618","DOI":"10.1016\/S2666-7568(21)00177-X","article-title":"Age-related disparities in diabetes risk attributable to modifiable risk factor profiles in Chinese adults: a nationwide, population-based, cohort study","volume":"2","author":"Wang","year":"2021","journal-title":"Lancet. Healthy Longev."},{"key":"10.1016\/j.omtn.2025.102576_bib32","doi-asserted-by":"crossref","first-page":"3002","DOI":"10.2337\/db16-0418","article-title":"BMI as a Modifiable Risk Factor for Type 2 Diabetes: Refining and Understanding Causal Estimates Using Mendelian Randomization","volume":"65","author":"Corbin","year":"2016","journal-title":"Diabetes"},{"key":"10.1016\/j.omtn.2025.102576_bib33","doi-asserted-by":"crossref","DOI":"10.1016\/j.focus.2024.100215","article-title":"Prevalence of Diabetes by BMI: China Nutrition and Health Surveillance (2015-2017) and U.S. National Health and Nutrition Examination Survey (2015-2018)","volume":"3","author":"Yu","year":"2024","journal-title":"AJPM Focus"},{"key":"10.1016\/j.omtn.2025.102576_bib34","article-title":"Anthropometric and adiposity indicators and risk of type 2 diabetes: systematic review and dose-response meta-analysis of cohort studies","volume":"376","author":"Jayedi","year":"2022","journal-title":"BMJ"},{"key":"10.1016\/j.omtn.2025.102576_bib35","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.3758\/s13423-016-1026-5","article-title":"Model comparison in ANOVA","volume":"23","author":"Rouder","year":"2016","journal-title":"Psychon. Bull. Rev."},{"key":"10.1016\/j.omtn.2025.102576_bib36","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106608","article-title":"Fully connected network with multi-scale dilation convolution module in evaluating atrial septal defect based on MRI segmentation","volume":"215","author":"Chen","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.omtn.2025.102576_bib37","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-021-91797-w","article-title":"XGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes","volume":"11","author":"Noh","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.omtn.2025.102576_bib38","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmedinf.2021.104484","article-title":"Comparison of machine learning and logistic regression models in predicting acute kidney injury: A systematic review and meta-analysis","volume":"151","author":"Song","year":"2021","journal-title":"Int. J. Med. Inform."},{"key":"10.1016\/j.omtn.2025.102576_bib39","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1002\/jcc.24667","article-title":"Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest","volume":"38","author":"Wang","year":"2017","journal-title":"J. Comput. Chem."},{"key":"10.1016\/j.omtn.2025.102576_bib40","series-title":"NIPS'19: Proceedings of the 33rd International Conference on Neural Information Processing Systems","first-page":"8026","article-title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","author":"Paszke","year":"2019"},{"key":"10.1016\/j.omtn.2025.102576_bib41","first-page":"315","article-title":"Deep Sparse Rectifier Neural Networks","volume":"15","author":"Glorot","year":"2011","journal-title":"J. Mach. Learn. Res."}],"container-title":["Molecular Therapy Nucleic Acids"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2162253125001301?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2162253125001301?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T20:36:47Z","timestamp":1777322207000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2162253125001301"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":41,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["S2162253125001301"],"URL":"https:\/\/doi.org\/10.1016\/j.omtn.2025.102576","relation":{},"ISSN":["2162-2531"],"issn-type":[{"value":"2162-2531","type":"print"}],"subject":[],"published":{"date-parts":[[2025,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DRPM: An advanced predictive model for early diabetes detection and risk stratification","name":"articletitle","label":"Article Title"},{"value":"Molecular Therapy Nucleic Acids","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.omtn.2025.102576","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Author(s). Published by Elsevier Inc. on behalf of The American Society of Gene and Cell Therapy.","name":"copyright","label":"Copyright"}],"article-number":"102576"}}