{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:43:51Z","timestamp":1785545031365,"version":"3.56.0"},"reference-count":78,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Gestational diabetes mellitus (GDM) significantly increases the risk of developing type 2 diabetes mellitus (T2DM) post partum, with up to half of affected women progressing within a decade. Early identification of high-risk individuals is critical for implementing preventive interventions. Artificial intelligence (AI) offers enhanced predictive capabilities that can substantially enhance the prevention of postpartum diabetes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This systematic review and meta-analysis aimed to evaluate the performance of AI models in predicting the progression from GDM to T2DM or prediabetes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 7 databases (MEDLINE, Embase, Scopus, Web of Science, IEEE Xplore, ACM Digital Library, and Google Scholar) were systematically searched from inception through September 12, 2025, supplemented by backward and forward reference screening and biweekly alerts to capture newly published studies. This review included peer-reviewed English-language studies that applied AI algorithms to predict T2DM or prediabetes among women with previous GDM. Eligible studies focused on human participants; reported performance metrics (eg, accuracy, sensitivity, and specificity); and excluded non-AI models, animal studies, reviews, protocols, abstracts, and non-English publications. Moreover, 2 reviewers independently conducted study selection, data extraction, and risk of bias assessment using the PROBAST (Prediction Model Risk of Bias Assessment Tool)+AI tool. Pooled estimates were computed using random-effects meta-analysis models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In total, 10 studies met the inclusion criteria, of which 8 were eligible for meta-analysis. The reviewed studies spanned from 2011 to 2025 and were conducted across 7 countries, predominantly in the United States (3\/10, 30%). Most publications were journal articles (9\/10, 90%), and retrospective designs (6\/10, 60%) were slightly more common than prospective designs (4\/10, 40%). AI models demonstrated high predictive performance for T2DM, with pooled accuracy of 0.85 (95% CI 0.79\u20100.90; prediction interval [PI] 0.64\u20100.98), sensitivity of 0.89 (95% CI 0.81\u20100.95; PI 0.63\u20101.00), specificity of 0.88 (95% CI 0.81\u20100.93; PI 0.67\u20100.99),\n                      <jats:italic>F<\/jats:italic>\n                      <jats:sub>1<\/jats:sub>\n                      -score of 0.80 (95% CI 0.75\u20100.85; PI 0.68\u20100.93), and area under the curve of 0.86 (95% CI 0.77\u20100.91; PI 0.54\u20100.97). However, AI performance for prediabetes prediction was modest (area under the curve=0.69, 95% CI 0.60\u20100.77). Subgroup analyses showed that random forest, decision tree, logistic regression, and na\u00efve Bayes models performed comparably. Fasting plasma glucose and BMI were the most identified significant predictors in the included studies.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AI models show potential in predicting T2DM after GDM. However, evidence remains limited by small sample sizes, high heterogeneity, lack of external validation, and high risk of bias. Our findings have important implications for digital health, supporting the integration of AI-driven risk prediction into electronic health record systems and postpartum care pathways to enable early identification, targeted prevention, and improved long-term outcomes. Future research should use large, diverse cohorts, integrate multidimensional data, adopt standardized reporting frameworks, and encourage open-access data sharing.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/87882","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T19:53:57Z","timestamp":1783626837000},"page":"e87882-e87882","source":"Crossref","is-referenced-by-count":1,"title":["Performance of AI in Predicting the Progression of Gestational Diabetes to Type 2 Diabetes: Systematic Review and Meta-Analysis"],"prefix":"10.2196","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7695-4626","authenticated-orcid":false,"given":"Alaa","family":"Abd-alrazaq","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4316-8861","authenticated-orcid":false,"given":"Shahira Padinharepattel","family":"Mohamed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5691-7120","authenticated-orcid":false,"given":"Mohannad","family":"Alajlani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9384-7053","authenticated-orcid":false,"given":"Aliya","family":"Tabassum","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8965-104X","authenticated-orcid":false,"given":"Jos\u00e9 Manuel","family":"Ord\u00f3\u00f1ez-Mena","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9524-8575","authenticated-orcid":false,"given":"Shehel","family":"Yoosuf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7644-8604","authenticated-orcid":false,"given":"Mais","family":"Alkhateeb","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4025-5767","authenticated-orcid":false,"given":"Arfan","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3096-2641","authenticated-orcid":false,"given":"Mohammed","family":"Bashir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9466-2475","authenticated-orcid":false,"given":"Junaid","family":"Qadir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3822-3798","authenticated-orcid":false,"given":"Ali","family":"AlSanousi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5762-4186","authenticated-orcid":false,"given":"Javaid","family":"Sheikh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,7,9]]},"reference":[{"key":"R1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.m1361","article-title":"Progression to type 2 diabetes in women with a known history of gestational diabetes: systematic review and meta-analysis","volume":"369","author":"Vounzoulaki","journal-title":"BMJ"},{"key":"R2","doi-asserted-by":"publisher","DOI":"10.2337\/dc07-s206","article-title":"Increasing prevalence of gestational diabetes mellitus: a public health perspective","volume":"30 Suppl 2","author":"Ferrara","journal-title":"Diabetes Care"},{"issue":"10","key":"R3","doi-asserted-by":"publisher","first-page":"1862","DOI":"10.2337\/diacare.25.10.1862","article-title":"Gestational diabetes and the incidence of type 2 diabetes: a systematic review","volume":"25","author":"Kim","journal-title":"Diabetes Care"},{"issue":"1","key":"R4","doi-asserted-by":"publisher","DOI":"10.1007\/s11892-015-0699-x","article-title":"Prevalence of gestational diabetes and risk of progression to type 2 diabetes: a global perspective","volume":"16","author":"Zhu","journal-title":"Curr Diab Rep"},{"key":"R5","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/3076463","article-title":"Incidence rate of type 2 diabetes mellitus after gestational diabetes mellitus: a systematic review and meta-analysis of 170,139 women","volume":"2020","author":"Li","journal-title":"J Diabetes Res"},{"issue":"4","key":"R6","doi-asserted-by":"publisher","first-page":"864","DOI":"10.2337\/dc21-1430","article-title":"Persistence of risk for type 2 diabetes after gestational diabetes mellitus","volume":"45","author":"Diaz-Santana","journal-title":"Diabetes Care"},{"key":"R7","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2024.102605","article-title":"Integrated care of diabetes during pregnancy: a Qatari nationwide cohort","volume":"72","author":"Bashir","journal-title":"EClinicalMedicine"},{"issue":"8","key":"R8","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0201247","article-title":"Prevalence of newly detected diabetes in pregnancy in Qatar, using universal screening","volume":"13","author":"Bashir","journal-title":"PLoS ONE"},{"issue":"11","key":"R9","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1038\/s41574-021-00555-5","article-title":"Update on the pathogenesis and treatment of skeletal fragility in type 2 diabetes mellitus","volume":"17","author":"Khosla","journal-title":"Nat Rev Endocrinol"},{"issue":"12","key":"R10","doi-asserted-by":"publisher","first-page":"2298","DOI":"10.2337\/dc19-0587","article-title":"Machine learning to predict the risk of incident heart failure hospitalization among patients with diabetes: the WATCH-DM risk score","volume":"42","author":"Segar","journal-title":"Diabetes Care"},{"issue":"1","key":"R11","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1097\/AOG.0000000000002687","article-title":"Postpartum testing to detect persistent dysglycemia in women with gestational diabetes mellitus","volume":"132","author":"Carson","journal-title":"Obstetrics & Gynecology"},{"issue":"12","key":"R12","doi-asserted-by":"publisher","first-page":"1484","DOI":"10.1111\/j.1471-0528.2011.03083.x","article-title":"Missed opportunities for type 2 diabetes testing following gestational diabetes: a population-based cohort study","volume":"118","author":"Shah","journal-title":"BJOG"},{"issue":"1","key":"R13","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1097\/AOG.0b013e3181fe424b","article-title":"Gaps in diabetes screening during pregnancy and postpartum","volume":"117","author":"Blatt","journal-title":"Obstet Gynecol"},{"issue":"3","key":"R14","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1214\/ss\/1009213726","article-title":"Statistical modeling: the two cultures","volume":"16","author":"Breiman","journal-title":"Stat Sci"},{"issue":"5","key":"R15","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.84314","article-title":"Machine learning for predicting the transition from gestational diabetes to type 2 diabetes: a systematic review","volume":"17","author":"Elfadel Magboul","journal-title":"Cureus"},{"issue":"1","key":"R16","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-024-02848-x","article-title":"Predictive value of machine learning for the progression of gestational diabetes mellitus to type 2 diabetes: a systematic review and meta-analysis","volume":"25","author":"Zhao","journal-title":"BMC Med Inform Decis Mak"},{"issue":"8","key":"R17","doi-asserted-by":"publisher","first-page":"1728","DOI":"10.1016\/j.clnu.2024.06.006","article-title":"Development of a risk prediction model for postpartum onset of type 2 diabetes mellitus, following gestational diabetes; the lifestyle InterVention in gestational diabetes (LIVING) study","volume":"43","author":"Belsti","journal-title":"Clin Nutr"},{"issue":"3","key":"R18","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1007\/s00592-015-0814-0","article-title":"Development of a simple tool to predict the risk of postpartum diabetes in women with gestational diabetes mellitus","volume":"53","author":"K\u00f6hler","journal-title":"Acta Diabetol"},{"issue":"7","key":"R19","doi-asserted-by":"publisher","first-page":"1436","DOI":"10.1007\/s00125-015-3587-7","article-title":"The prediction of type 2 diabetes in women with previous gestational diabetes mellitus using lipidomics","volume":"58","author":"Lappas","journal-title":"Diabetologia"},{"issue":"6","key":"R20","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0252501","article-title":"A clinical diabetes risk prediction model for prediabetic women with prior gestational diabetes","volume":"16","author":"Man","journal-title":"PLoS ONE"},{"issue":"1","key":"R21","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1007\/s40846-022-00685-9","article-title":"Prediction of type 2 diabetes mellitus according to glucose metabolism patterns in pregnancy using a novel machine learning algorithm","volume":"42","author":"Houri","journal-title":"J Med Biol Eng"},{"issue":"3","key":"R22","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0264648","article-title":"Machine learning prediction of non-attendance to postpartum glucose screening and subsequent risk of type 2 diabetes following gestational diabetes","volume":"17","author":"Periyathambi","journal-title":"PLoS ONE"},{"issue":"1","key":"R23","doi-asserted-by":"publisher","first-page":"51","DOI":"10.7326\/M18-1376","article-title":"PROBAST: a tool to assess the risk of bias and applicability of prediction model studies","volume":"170","author":"Wolff","journal-title":"Ann Intern Med"},{"key":"R24","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2024-082505","article-title":"PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods","volume":"388","author":"Moons","journal-title":"BMJ"},{"issue":"4","key":"R25","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1001\/jama.2017.19163","article-title":"Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies: the PRISMA-DTA statement","volume":"319","author":"McInnes","journal-title":"JAMA"},{"issue":"1","key":"R26","doi-asserted-by":"publisher","DOI":"10.1186\/s13643-020-01542-z","article-title":"PRISMA-S: an extension to the PRISMA statement for reporting literature searches in systematic reviews","volume":"10","author":"Rethlefsen","journal-title":"Syst Rev"},{"key":"R27","doi-asserted-by":"publisher","DOI":"10.2196\/58187","article-title":"Detection of sleep apnea using wearable AI: systematic review and meta-analysis","volume":"26","author":"Abd-Alrazaq","journal-title":"J Med Internet Res"},{"issue":"7414","key":"R28","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1136\/bmj.327.7414.557","article-title":"Measuring inconsistency in meta-analyses","volume":"327","author":"Higgins","journal-title":"BMJ"},{"issue":"4","key":"R29","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1136\/ebmental-2019-300117","article-title":"How to perform a meta-analysis with R: a practical tutorial","volume":"22","author":"Balduzzi","journal-title":"Evid Based Ment Health"},{"issue":"3","key":"R30","first-page":"40","volume":"7","author":"Schwarzer","journal-title":"R news"},{"key":"R31","doi-asserted-by":"crossref","unstructured":"Borenstein M Hedges LV Higgins JPT Rothstein HR . Introduction to Meta\u2010Analysis. John Wiley & Sons; 2009. ISBN: 9780470057247","DOI":"10.1002\/9780470743386"},{"issue":"4","key":"R32","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1214\/aoms\/1177729756","article-title":"Transformations related to the angular and the square root","volume":"21","author":"Freeman","journal-title":"Ann Math Statist"},{"issue":"3","key":"R33","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1002\/jrsm.1348","article-title":"Seriously misleading results using inverse of Freeman-Tukey double arcsine transformation in meta-analysis of single proportions","volume":"10","author":"Schwarzer","journal-title":"Res Synth Methods"},{"issue":"5","key":"R34","doi-asserted-by":"publisher","first-page":"4961","DOI":"10.1007\/s10489-021-02635-5","article-title":"Confidence interval for micro-averaged F1 and macro-averaged F1 scores","volume":"52","author":"Takahashi","journal-title":"Appl Intell"},{"issue":"1","key":"R35","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-025-02592-4","article-title":"Extended sample size calculations for evaluation of prediction models using a threshold for classification","volume":"25","author":"Whittle","journal-title":"BMC Med Res Methodol"},{"key":"R36","unstructured":"Debray T de Jong V . metamisc: Meta-analysis of diagnosis and prognosis research studies. CRAN. URL: https:\/\/cran.r-project.org\/web\/packages\/metamisc\/index.html [Accessed 01-06-2026]"},{"issue":"6","key":"R37","doi-asserted-by":"publisher","first-page":"1689","DOI":"10.1177\/0962280218773520","article-title":"Prediction intervals for random-effects meta-analysis: a confidence distribution approach","volume":"28","author":"Nagashima","journal-title":"Stat Methods Med Res"},{"issue":"4","key":"R38","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1016\/j.jclinepi.2013.11.007","article-title":"Systematic review finds overlapping reviews were not mentioned in every other overview","volume":"67","author":"Pieper","journal-title":"J Clin Epidemiol"},{"issue":"9","key":"R39","doi-asserted-by":"publisher","first-page":"2529","DOI":"10.2337\/db15-1720","article-title":"A predictive metabolic signature for the transition from gestational diabetes mellitus to type 2 diabetes","volume":"65","author":"Allalou","journal-title":"Diabetes"},{"issue":"2","key":"R40","doi-asserted-by":"publisher","DOI":"10.1172\/jci.insight.183213","article-title":"Longitudinal clinical and proteomic diabetes signatures in women with a history of gestational diabetes","volume":"10","author":"Chung","journal-title":"JCI Insight"},{"issue":"789219","key":"R41","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2022.789219","article-title":"Unraveling the factors determining development of type 2 diabetes in women with a history of gestational diabetes mellitus through machine-learning techniques","volume":"13","author":"Ilari","journal-title":"Front Physiol"},{"issue":"7","key":"R42","doi-asserted-by":"publisher","first-page":"1516","DOI":"10.1007\/s00125-021-05429-z","article-title":"Postpartum circulating microRNA enhances prediction of future type 2 diabetes in women with previous gestational diabetes","volume":"64","author":"Joglekar","journal-title":"Diabetologia"},{"issue":"4","key":"R43","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1007\/s00125-018-4800-2","article-title":"The discovery of novel predictive biomarkers and early-stage pathophysiology for the transition from gestational diabetes to type 2 diabetes","volume":"62","author":"Khan","journal-title":"Diabetologia"},{"key":"R44","doi-asserted-by":"crossref","unstructured":"Krishnan DR Menakath GP Radhakrishnan A et al. Evaluation of predisposing factors of diabetes mellitus post gestational diabetes mellitus using machine learning techniques. Presented at: 2019 IEEE Student Conference on Research and Development (SCOReD); Oct 15-17, 2019; Bandar Seri Iskandar, Malaysia. [doi: 10.1109\/SCORED.2019.8896323]","DOI":"10.1109\/SCORED.2019.8896323"},{"issue":"5","key":"R45","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1003112","article-title":"Amino acid and lipid metabolism in post-gestational diabetes and progression to type 2 diabetes: a metabolic profiling study","volume":"17","author":"Lai","journal-title":"PLoS Med"},{"issue":"3","key":"R46","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/s10916-009-9364-8","article-title":"An application of artificial immune recognition system for prediction of diabetes following gestational diabetes","volume":"35","author":"Lin","journal-title":"J Med Syst"},{"issue":"10","key":"R47","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2023.107846","article-title":"Prediction of postpartum prediabetes by machine learning methods in women with gestational diabetes mellitus","volume":"26","author":"Parkhi","journal-title":"iScience"},{"issue":"6","key":"R48","doi-asserted-by":"publisher","first-page":"658","DOI":"10.1016\/j.pcd.2025.09.006","article-title":"Predicting the future risk of developing type 2 diabetes in women with a history of gestational diabetes mellitus using machine learning and explainable artificial intelligence","volume":"19","author":"Prashanthan","journal-title":"Prim Care Diabetes"},{"issue":"46","key":"R49","doi-asserted-by":"publisher","first-page":"4831","DOI":"10.1093\/eurheartj\/ehad727","article-title":"Five critical quality criteria for artificial intelligence-based prediction models","volume":"44","author":"van Royen","journal-title":"Eur Heart J"},{"key":"R50","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2019.02.004","article-title":"A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models","volume":"110","author":"Christodoulou","journal-title":"J Clin Epidemiol"},{"key":"R51","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2015.04.005","article-title":"Prediction models need appropriate internal, internal-external, and external validation","volume":"69","author":"Steyerberg","journal-title":"J Clin Epidemiol"},{"issue":"5","key":"R52","doi-asserted-by":"publisher","first-page":"951","DOI":"10.1111\/j.1365-2753.2008.00986.x","article-title":"Interpretation of tests of heterogeneity and bias in meta-analysis","volume":"14","author":"Ioannidis","journal-title":"J Eval Clin Pract"},{"key":"R53","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2016.12.005","article-title":"Machine learning and data mining methods in diabetes research","volume":"15","author":"Kavakiotis","journal-title":"Comput Struct Biotechnol J"},{"issue":"13","key":"R54","doi-asserted-by":"publisher","first-page":"1216","DOI":"10.1056\/NEJMp1606181","article-title":"Predicting the future - big data, machine learning, and clinical medicine","volume":"375","author":"Obermeyer","journal-title":"N Engl J Med"},{"issue":"1","key":"R55","doi-asserted-by":"publisher","DOI":"10.1214\/088342306000000060","article-title":"Classifier technology and the illusion of progress","volume":"21","author":"Hand","journal-title":"Statist Sci"},{"issue":"1","key":"R56","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-7-91","article-title":"Bias in error estimation when using cross-validation for model selection","volume":"7","author":"Varma","journal-title":"BMC Bioinformatics"},{"issue":"12","key":"R57","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbc.2023.105405","article-title":"Beta-cell compensation and gestational diabetes","volume":"299","author":"Usman","journal-title":"J Biol Chem"},{"issue":"4","key":"R58","doi-asserted-by":"publisher","first-page":"773","DOI":"10.2337\/db09-9028","article-title":"From the triumvirate to the ominous octet: a new paradigm for the treatment of type 2 diabetes mellitus","volume":"58","author":"Defronzo","journal-title":"Diabetes"},{"issue":"9833","key":"R59","doi-asserted-by":"publisher","first-page":"2279","DOI":"10.1016\/S0140-6736(12)60283-9","article-title":"Prediabetes: a high-risk state for diabetes development","volume":"379","author":"Tab\u00e1k","journal-title":"The Lancet"},{"issue":"7121","key":"R60","doi-asserted-by":"publisher","first-page":"840","DOI":"10.1038\/nature05482","article-title":"Mechanisms linking obesity to insulin resistance and type 2 diabetes","volume":"444","author":"Kahn","journal-title":"Nature"},{"issue":"12","key":"R61","doi-asserted-by":"publisher","first-page":"1821","DOI":"10.1172\/JCI19451","article-title":"Chronic inflammation in fat plays a crucial role in the development of obesity-related insulin resistance","volume":"112","author":"Xu","journal-title":"J Clin Invest"},{"issue":"6","key":"R62","doi-asserted-by":"publisher","first-page":"1424","DOI":"10.2337\/dc11-0447","article-title":"Obesity and type 2 diabetes: what can be unified and what needs to be individualized?","volume":"34","author":"Eckel","journal-title":"Diabetes Care"},{"issue":"6","key":"R63","doi-asserted-by":"publisher","first-page":"2111","DOI":"10.1172\/JCI57132","article-title":"Inflammatory links between obesity and metabolic disease","volume":"121","author":"Lumeng","journal-title":"J Clin Invest"},{"key":"R64","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.m441","article-title":"Calculating the sample size required for developing a clinical prediction model","volume":"368","author":"Riley","journal-title":"BMJ"},{"issue":"1","key":"R65","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1093\/ckj\/sfaa188","article-title":"External validation of prognostic models: what, why, how, when and where?","volume":"14","author":"Ramspek","journal-title":"Clin Kidney J"},{"issue":"1","key":"R66","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-022-00614-9","article-title":"Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening","volume":"5","author":"Yang","journal-title":"NPJ Digit Med"},{"key":"R67","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","journal-title":"BMJ"},{"issue":"12","key":"R68","doi-asserted-by":"publisher","first-page":"889","DOI":"10.7326\/M15-0807","article-title":"Lactation and progression to type 2 diabetes mellitus after gestational diabetes mellitus: a prospective cohort study","volume":"163","author":"Gunderson","journal-title":"Ann Intern Med"},{"issue":"7","key":"R69","doi-asserted-by":"publisher","first-page":"1403","DOI":"10.1007\/s00125-016-3927-2","article-title":"Quantification of the type 2 diabetes risk in women with gestational diabetes: a systematic review and meta-analysis of 95,750 women","volume":"59","author":"Rayanagoudar","journal-title":"Diabetologia"},{"key":"R70","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2022.1069625","article-title":"Prolactin in relation to gestational diabetes and metabolic risk in pregnancy and postpartum: a systematic review and meta-analysis","volume":"13","author":"Rassie","journal-title":"Front Endocrinol (Lausanne)"},{"issue":"7","key":"R71","doi-asserted-by":"publisher","first-page":"1250","DOI":"10.2337\/dc16-0043","article-title":"Maternal serum prolactin and prediction of postpartum \u03b2-cell function and risk of prediabetes\/diabetes","volume":"39","author":"Retnakaran","journal-title":"Diabetes Care"},{"issue":"1","key":"R72","doi-asserted-by":"publisher","DOI":"10.1186\/s40168-018-0472-x","article-title":"Gestational diabetes is associated with change in the gut microbiota composition in third trimester of pregnancy and postpartum","volume":"6","author":"Crusell","journal-title":"Microbiome"},{"issue":"188","key":"R73","doi-asserted-by":"publisher","DOI":"10.3389\/fcimb.2020.00188","article-title":"Gut microbiota and gestational diabetes mellitus: a review of host-gut microbiota interactions and their therapeutic potential","volume":"10","author":"Hasain","journal-title":"Front Cell Infect Microbiol"},{"issue":"3","key":"R74","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1016\/j.diabres.2012.04.020","article-title":"Genetic prediction of postpartum diabetes in women with gestational diabetes mellitus","volume":"97","author":"Ekelund","journal-title":"Diabetes Res Clin Pract"},{"issue":"4","key":"R75","doi-asserted-by":"publisher","first-page":"1646","DOI":"10.1210\/jc.2014-3761","article-title":"The effect of lifestyle intervention and metformin on preventing or delaying diabetes among women with and without gestational diabetes: the diabetes prevention program outcomes study 10-year follow-up","volume":"100","author":"Aroda","journal-title":"J Clin Endocrinol Metab"},{"issue":"12","key":"R76","doi-asserted-by":"publisher","first-page":"4774","DOI":"10.1210\/jc.2008-0772","article-title":"Prevention of diabetes in women with a history of gestational diabetes: effects of metformin and lifestyle interventions","volume":"93","author":"Ratner","journal-title":"J Clin Endocrinol Metab"},{"issue":"2","key":"R77","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1136\/bmjinnov-2019-000359","article-title":"Bridging the implementation gap of machine learning in healthcare","volume":"6","author":"Seneviratne","journal-title":"BMJ Innov"},{"key":"R78","unstructured":"Topol E . Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books; 2019. ISBN: 1541644646"}],"container-title":["Journal of Medical Internet Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jmir.org\/2026\/1\/e87882","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T22:25:06Z","timestamp":1785536706000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jmir.org\/2026\/1\/e87882"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,9]]},"references-count":78,"URL":"https:\/\/doi.org\/10.2196\/87882","relation":{},"ISSN":["1438-8871"],"issn-type":[{"value":"1438-8871","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,9]]},"article-number":"v28i10e87882"}}