{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T12:52:58Z","timestamp":1787835178140,"version":"build-2784847793"},"reference-count":111,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T00:00:00Z","timestamp":1772668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Type 2 Diabetes Mellitus (T2DM) is a rising global health concern, heavily influenced by modifiable lifestyle and psychosocial factors. However, most predictive tools focus on biomedical markers and rely on real-time data from wearables or electronic health records, limiting their scalability in resource-constrained settings. This study presents a novel digital twin (DT) framework that uses retrospective lifestyle, behavioral, and psychosocial data to forecast T2DM onset and simulate the estimated effects of preventive interventions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data were drawn from 19,774 participants in the UK Biobank cohort, followed for up to 17\u2009years. A penalized Cox proportional hazards model was employed to estimate individual time-to-event risk trajectories based on 90 candidate predictors. Predictors were selected through univariate screening, multicollinearity assessment, and variance filtering, yielding a final model with 14 significant variables. Causal inference techniques, including directed acyclic graphs (DAGs) and counterfactual simulations, were used to explore intervention effects on disease progression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The model demonstrated strong predictive performance (C-index\n                      <jats:inline-formula>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"IM1\">\n                          <mml:mo>=<\/mml:mo>\n                        <\/mml:math>\n                      <\/jats:inline-formula>\n                      0.90, SD\n                      <jats:inline-formula>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"IM2\">\n                          <mml:mo>=<\/mml:mo>\n                        <\/mml:math>\n                      <\/jats:inline-formula>\n                      0.004). Psychosocial stressors such as loneliness, insomnia, and poor mental health emerged as strong independent predictors and were associated with estimated increases in absolute T2DM risk of approximately 35 percentage points individually and nearly 78 percentage points when combined, under the modeled assumptions. These effects were partly reinforced through diet, with high intake of processed meat, salt, and sugary cereals acting as risk amplifiers within the modeled causal pathways. Cheese intake was protective overall, but its estimated benefit was attenuated under psychosocial stress, where reduced consumption produced a small, directionally harmful mediation effect. Counterfactual simulations suggested that improvements in psychosocial conditions could reduce estimated T2DM risk by approximately 11.6 percentage points within the modeled cohort, with protective dietary patterns such as cheese consumption re-emerging as psychosocial stress was alleviated. The model also revealed pronounced ethnic disparities, with South Asian, African, and Caribbean participants exhibiting significantly higher estimated risk than White counterparts within this cohort. These findings highlight the potential of integrated, stress-informed prevention strategies that address both psychosocial and dietary pathways.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study introduces a transparent, simulation-enabled DT framework for estimating T2DM risk and exploring behavioral intervention scenarios without reliance on real-time data streams. It enables interpretable, personalized prevention planning and supports exploration of scalable deployment in public health, particularly in underserved or low-infrastructure environments. The integration of psychosocial and lifestyle data represents an important step toward more equitable and behaviorally informed digital health solutions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdgth.2026.1710829","type":"journal-article","created":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T06:54:39Z","timestamp":1772693679000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["A digital twin framework for predicting and simulating type 2 diabetes onset using retrospective lifestyle data"],"prefix":"10.3389","volume":"8","author":[{"given":"Mahreen","family":"Kiran","sequence":"first","affiliation":[{"name":"Faculty of Health, Medicine and Social Care, Anglia Ruskin University","place":["Chelmsford, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Xie","sequence":"additional","affiliation":[{"name":"Faculty of Business and Management, Cranfield University","place":["Cranfield, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Graham","family":"Ball","sequence":"additional","affiliation":[{"name":"Faculty of Health, Medicine and Social Care, Anglia Ruskin University","place":["Chelmsford, United Kingdom"]},{"name":"Intelligent Omics Ltd.","place":["Nottingham, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rudolph","family":"Schutte","sequence":"additional","affiliation":[{"name":"Faculty of Health, Medicine and Social Care, Anglia Ruskin University","place":["Chelmsford, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nasreen","family":"Anjum","sequence":"additional","affiliation":[{"name":"School of Computing, University of Portsmouth","place":["Portsmouth, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Barbara","family":"Pierscionek","sequence":"additional","affiliation":[{"name":"Faculty of Health, Medicine and Social Care, Anglia Ruskin University","place":["Chelmsford, United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,5]]},"reference":[{"key":"B1","year":""},{"key":"B2","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1038\/498255a","article-title":"The big challenges of big data","volume":"498","author":"Marx","year":"2013","journal-title":"Nature"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2350-9-59","article-title":"Impact of nine common type 2 diabetes risk polymorphisms in Asian Indian Sikhs: PPARG2 (Pro12Ala), IGF2BP2, TCF7L2 and FTO variants confer a significant risk","volume":"9","author":"Sanghera","year":"2008","journal-title":"BMC Med Genet"},{"key":"B4","doi-asserted-by":"publisher","first-page":"100864","DOI":"10.1016\/j.mgene.2021.100864","article-title":"Genetic polymorphisms in TCF7L2 and PPARG genes and susceptibility to type 2 diabetes mellitus","volume":"28","author":"Verma","year":"2021","journal-title":"Meta Gene"},{"key":"B5","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1038\/nrendo.2017.64","article-title":"Type 2 diabetes mellitus and psychological stress\u2014a modifiable risk factor","volume":"13","author":"Hackett","year":"2017","journal-title":"Nat Rev Endocrinol"},{"key":"B6","doi-asserted-by":"publisher","first-page":"1590","DOI":"10.2337\/diacare.24.9.1590","article-title":"Smoking as a modifiable risk factor for type 2 diabetes in middle-aged men","volume":"24","author":"Wannamethee","year":"2001","journal-title":"Diabetes Care"},{"key":"B7","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1007\/s11892-001-0030-x","article-title":"Obesity and sedentary lifestyle: modifiable risk factors for prevention of type 2 diabetes","volume":"1","author":"Mayer-Davis","year":"2001","journal-title":"Curr Diab Rep"},{"key":"B8","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.jare.2023.05.008","article-title":"Insights into modifiable risk factors of erectile dysfunction, a wide-angled mendelian randomization study","volume":"58","author":"Xiong","year":"2024","journal-title":"J Adv Res"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1915","DOI":"10.1111\/acer.15426","article-title":"Alcohol and arterial stiffness in middle-aged and older adults: Cross-sectional evidence from the UK Biobank study","volume":"48","author":"Schutte","year":"2024","journal-title":"Alcohol: Clin Exp Res"},{"key":"B10","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.jsmc.2007.03.004","article-title":"Impact of sleep and sleep loss on glucose homeostasis and appetite regulation","volume":"2","author":"Knutson","year":"2007","journal-title":"Sleep Med Clin"},{"key":"B11","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1146\/annurev-publhealth-052020-110732","article-title":"Social connection as a public health issue: the evidence and a systemic framework for prioritizing the \u201csocial\u201d in social determinants of health","volume":"43","author":"Holt-Lunstad","year":"2022","journal-title":"Annu Rev Public Health"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2458-13-828","article-title":"Associations between psychological stress, eating, physical activity, sedentary behaviours and body weight among women: a longitudinal study","volume":"13","author":"Mouchacca","year":"2013","journal-title":"BMC Public Health"},{"key":"B13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/02701367.2016.1268519","article-title":"Physical inactivity, obesity, and type 2 diabetes: an evolutionary perspective","volume":"88","author":"Eaton","year":"2017","journal-title":"Res Q Exerc Sport"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1049\/htl2.12039","article-title":"Diabetes prediction using machine learning and explainable ai techniques","volume":"10","author":"Tasin","year":"2023","journal-title":"Healthc Technol Lett"},{"key":"B15","doi-asserted-by":"publisher","first-page":"e1834","DOI":"10.1002\/hsr2.1834","article-title":"Predictive model for early detection of type 2 diabetes using patients\u2019 clinical symptoms, demographic features, and knowledge of diabetes","volume":"7","author":"Ojurongbe","year":"2024","journal-title":"Health Sci Rep"},{"key":"B16","doi-asserted-by":"publisher","first-page":"1557467","DOI":"10.3389\/fdgth.2025.1557467","article-title":"Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis","volume":"7","author":"Kiran","year":"2025","journal-title":"Front Digit Health"},{"key":"B17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.13196\/j.cims.2019.01.002","article-title":"Five-dimension digital twin model and its ten applications","volume":"25","author":"Tao","year":"2019","journal-title":"Comput Integr Manuf Syst"},{"key":"B18","author":"Bagaria","year":""},{"key":"B19","doi-asserted-by":"publisher","first-page":"1485464","DOI":"10.3389\/fendo.2024.1485464","article-title":"Personalized nutrition in type 2 diabetes remission: application of digital twin technology for predictive glycemic control","volume":"15","author":"Shamanna","year":"2024","journal-title":"Front Endocrinol (Lausanne)"},{"key":"B20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-019-0918-5","article-title":"A data-driven approach to predicting diabetes and cardiovascular disease with machine learning","volume":"19","author":"Dinh","year":"2019","journal-title":"BMC Med Inform Decis Mak"},{"key":"B21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11042-022-13087-5","article-title":"Enhancing the prediction of type 2 diabetes mellitus using sparse balanced SVM","volume":"81","author":"Shrestha","year":"2022","journal-title":"Multimed Tools Appl"},{"key":"B22","doi-asserted-by":"publisher","first-page":"1218","DOI":"10.1016\/j.numecd.2022.01.010","article-title":"A deep learning model for screening type 2 diabetes from retinal photographs","volume":"32","author":"Yun","year":"2022","journal-title":"Nutr Metab Cardiovasc Dis"},{"key":"B23","doi-asserted-by":"publisher","first-page":"87168","DOI":"10.1109\/ACCESS.2022.3197164","article-title":"IoT-based COVID-19 diagnosing and monitoring systems: a survey","volume":"10","author":"Anjum","year":"2022","journal-title":"IEEE Access"},{"key":"B24","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/s12559-023-10179-8","article-title":"Interpreting black-box models: a review on explainable artificial intelligence","volume":"16","author":"Hassija","year":"2024","journal-title":"Cognit Comput"},{"key":"B25","doi-asserted-by":"publisher","first-page":"111079","DOI":"10.1016\/j.ress.2025.111079","article-title":"Leveraging digital twin for healthcare emergency management system: recent advances, critical challenges, and future directions","volume":"261","author":"Zheng","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"B26","doi-asserted-by":"publisher","first-page":"4099","DOI":"10.1109\/ACCESS.2023.3349379","article-title":"A systematic literature review of digital twin research for healthcare systems: research trends, gaps, and realization challenges","volume":"12","author":"Xames","year":"2024","journal-title":"IEEE Access"},{"key":"B27","doi-asserted-by":"publisher","first-page":"100","DOI":"10.3390\/computers13040100","article-title":"Digital twin and 3d digital twin: concepts, applications, and challenges in industry 4.0 for digital twin","volume":"13","author":"Hananto","year":"2024","journal-title":"Computers"},{"key":"B28","author":"Biobank","year":""},{"key":"B29","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","article-title":"Regression models and life-tables","volume":"34","author":"Cox","year":"1972","journal-title":"J R Stat Soc Ser B (Methodol)"},{"key":"B30","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1017\/S0266466603004109","article-title":"Causality: models, reasoning, and inference, by Judea Pearl, Cambridge University Press, 2000","volume":"19","author":"Neuberg","year":"2003","journal-title":"Econ Theory"},{"key":"B31","author":"Sharma","year":""},{"key":"B32","author":"Qina","year":""},{"key":"B33","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1038\/s41746-024-01073-0","article-title":"Digital twins for health: a scoping review","volume":"7","author":"Katsoulakis","year":"2024","journal-title":"NPJ Digit Med"},{"key":"B34","doi-asserted-by":"publisher","first-page":"2703","DOI":"10.1007\/s13300-020-00931-w","article-title":"Reducing HbA1c in type 2 diabetes using digital twin technology-enabled precision nutrition: a retrospective analysis","volume":"11","author":"Shamanna","year":"2020","journal-title":"Diabetes Ther"},{"key":"B35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-94339-6","article-title":"Retrospective study of glycemic variability, BMI, and blood pressure in diabetes patients in the digital twin precision treatment program","volume":"11","author":"Shamanna","year":"2021","journal-title":"Sci Rep"},{"key":"B36","doi-asserted-by":"publisher","first-page":"25478","DOI":"10.1038\/s41598-024-76584-7","article-title":"One-year outcomes of a digital twin intervention for type 2 diabetes: a retrospective real-world study","volume":"14","author":"Shamanna","year":"2024","journal-title":"Sci Rep"},{"key":"B37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40842-021-00134-7","article-title":"Type 2 diabetes reversal with digital twin technology-enabled precision nutrition and staging of reversal: a retrospective cohort study","volume":"7","author":"Shamanna","year":"2021","journal-title":"Clin Diabetes Endocrinol"},{"key":"B38","doi-asserted-by":"publisher","first-page":"101172","DOI":"10.1016\/j.jacadv.2024.101172","article-title":"Digital twin in managing hypertension among people with type 2 diabetes: 1-year randomized controlled trial","volume":"3","author":"Shamanna","year":"2024","journal-title":"JACC: Adv"},{"key":"B39","author":"Silfvergren","year":""},{"key":"B40","author":"Vaskovsky","year":""},{"key":"B41","doi-asserted-by":"publisher","first-page":"100004","DOI":"10.1016\/j.deman.2021.100004","article-title":"Challenges and perspectives for the future of diabetes epidemiology in the era of digital health and artificial intelligence","volume":"1","author":"Fagherazzi","year":"2021","journal-title":"Diabetes Epidemiol Manag"},{"key":"B42","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1016\/S0140-6736(07)61238-0","article-title":"No health without mental health","volume":"370","author":"Prince","year":"2007","journal-title":"Lancet"},{"key":"B43","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1007\/s11606-013-2616-9","article-title":"Quality of care for patients with multiple chronic conditions: the role of comorbidity interrelatedness","volume":"29","author":"Zulman","year":"2014","journal-title":"J Gen Intern Med"},{"key":"B44","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1146\/annurev.psych.52.1.555","article-title":"Health psychology: psychosocial and biobehavioral aspects of chronic disease management","volume":"52","author":"Schneiderman","year":"2001","journal-title":"Annu Rev Psychol"},{"key":"B45","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/S0140-6736(12)60240-2","article-title":"Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study","volume":"380","author":"Barnett","year":"2012","journal-title":"Lancet"},{"key":"B46","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/S0895-4356(97)00306-5","article-title":"Multimorbidity in general practice: prevalence, incidence, and determinants of co-occurring chronic and recurrent diseases","volume":"51","author":"Van den Akker","year":"1998","journal-title":"J Clin Epidemiol"},{"key":"B47","year":""},{"key":"B48","doi-asserted-by":"publisher","first-page":"1714545","DOI":"10.3389\/fdgth.2025.1714545","article-title":"Type 2 diabetes prediction without labs: a systems-level neural framework for risk and behavioral network reorganization","volume":"7","author":"Kiran","year":"2026","journal-title":"Front Digit Health"},{"key":"B49","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","year":"2012","journal-title":"Lancet"},{"key":"B50","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1186\/s12874-017-0442-1","article-title":"When and how should multiple imputation be used for handling missing data in randomised clinical trials\u2014a practical guide with flowcharts","volume":"17","author":"Jakobsen","year":"2017","journal-title":"BMC Med Res Methodol"},{"key":"B51","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4614-6849-3","volume-title":"Applied Predictive Modeling","author":"Kuhn","year":"2013"},{"key":"B52","doi-asserted-by":"crossref","DOI":"10.1002\/9780470434697","volume-title":"Robust Statistics","author":"Huber","year":"2009"},{"key":"B53","volume-title":"Robust Regression and Outlier Detection","author":"Rousseeuw","year":"2003"},{"key":"B54","first-page":"27","volume-title":"Empirical Model Building and Response Surfaces","author":"Box","year":"1987"},{"key":"B55","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"B56","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: a graphical aid to the interpretation and validation of cluster analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J Comput Appl Math"},{"key":"B57","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1037\/h0071325","article-title":"Analysis of a complex of statistical variables into principal components","volume":"24","author":"Hotelling","year":"1933","journal-title":"J Educ Psychol"},{"key":"B58","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1080\/01621459.1963.10500845","article-title":"Hierarchical grouping to optimize an objective function","volume":"58","author":"Ward Jr","year":"1963","journal-title":"J Am Stat Assoc"},{"key":"B59","doi-asserted-by":"publisher","first-page":"1749","DOI":"10.1214\/aos\/1176324322","article-title":"Methods for the analysis of sampled cohort data in the Cox proportional hazards model","volume":"23","author":"Borgan","year":"1995","journal-title":"Ann Stat"},{"key":"B60","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1089\/sysm.2018.0015","article-title":"Balanced event prediction through sampled survival analysis","volume":"2","author":"Datta","year":"2019","journal-title":"Syst Med"},{"key":"B61","doi-asserted-by":"publisher","first-page":"239","DOI":"10.5324\/nje.v13i2.292","article-title":"A review of cohort sampling designs for Cox\u2019s regression model: Potentials in epidemiology","volume":"13","author":"Borgan","year":"2003","journal-title":"Norsk Epidemiol"},{"key":"B62","volume-title":"Introduction to Linear Regression Analysis","author":"Montgomery","year":"2021"},{"key":"B63","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1093\/biomet\/69.1.239","article-title":"Partial residuals for the proportional hazards regression model","volume":"69","author":"Schoenfeld","year":"1982","journal-title":"Biometrika"},{"key":"B64","author":"Kohavi","year":""},{"key":"B65","doi-asserted-by":"publisher","first-page":"2427","DOI":"10.1002\/sim.4780132307","article-title":"Penalized likelihood in Cox regression","volume":"13","author":"Verweij","year":"1994","journal-title":"Stat Med"},{"key":"B66","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1002\/(SICI)1097-0258(19960229)15:4%3C361::AID-SIM168%3E3.0.CO;2-4","article-title":"Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors","volume":"15","author":"Harrell Jr","year":"1996","journal-title":"Stat Med"},{"key":"B67","first-page":"1","article-title":"Obesity: preventing and managing the global epidemic","volume":"894","author":"Consultation","year":"2000","journal-title":"World Health Organ Tech Rep Ser"},{"key":"B68","doi-asserted-by":"publisher","first-page":"2392","DOI":"10.1056\/NEJMoa1014296","article-title":"Changes in diet and lifestyle and long-term weight gain in women and men","volume":"364","author":"Mozaffarian","year":"2011","journal-title":"N Engl J Med"},{"key":"B69","doi-asserted-by":"publisher","first-page":"1742","DOI":"10.1016\/j.tjnut.2023.03.032","article-title":"Dairy product consumption and incident prediabetes in the Australian diabetes, obesity, and lifestyle study with 12\u2009years of follow-up","volume":"153","author":"Slurink","year":"2023","journal-title":"J Nutr"},{"key":"B70","doi-asserted-by":"publisher","first-page":"81","DOI":"10.3945\/jn.115.220699","article-title":"Total and full-fat, but not low-fat, dairy product intakes are inversely associated with metabolic syndrome in adults","volume":"146","author":"Drehmer","year":"2016","journal-title":"J Nutr"},{"key":"B71","doi-asserted-by":"publisher","first-page":"1699","DOI":"10.2337\/dc09-2302","article-title":"Dietary phylloquinone and menaquinones intake and risk of type 2 diabetes","volume":"33","author":"Beulens","year":"2010","journal-title":"Diabetes Care"},{"key":"B72","doi-asserted-by":"publisher","first-page":"1654","DOI":"10.1210\/jc.2011-0585","article-title":"Obesity and type 2 diabetes: what can be unified and what needs to be individualized?","volume":"96","author":"Eckel","year":"2011","journal-title":"J Clin Endocrinol Metab"},{"key":"B73","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1093\/ije\/dyq175","article-title":"Southall and brent revisited: cohort profile of SABRE, a UK population-based comparison of cardiovascular disease and diabetes in people of European, Indian Asian and African Caribbean origins","volume":"41","author":"Tillin","year":"2012","journal-title":"Int J Epidemiol"},{"key":"B74","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1002\/oby.20997","article-title":"Ethnic differences in associations between fat deposition and incident diabetes and underlying mechanisms: the SABRE study","volume":"23","author":"Eastwood","year":"2015","journal-title":"Obesity"},{"key":"B75","author":"England","year":""},{"key":"B76","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.3390\/nu10091245","article-title":"Dietary interventions for the prevention of type 2 diabetes in high-risk groups: current state of evidence and future research needs","volume":"10","author":"Guess","year":"2018","journal-title":"Nutrients"},{"key":"B77","author":"Fayyad","year":""},{"key":"B78","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1080\/17498430.2018.1450055","article-title":"Edward L. Kaplan and the Kaplan\u2013Meier survival curve","volume":"33","author":"Stalpers","year":"2018","journal-title":"BSHM Bull: J Br Soc Hist Math"},{"key":"B79","first-page":"163","article-title":"Evaluation of survival data and two new rank order statistics arising in its consideration","volume":"50","author":"Mantel","year":"1966","journal-title":"Cancer Chemother Rep"},{"key":"B80","author":"Charles","year":""},{"key":"B81","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.gerinurse.2024.08.032","article-title":"Prevalence of sleep disorders in geriatrics: an exploratory study using sleep questionnaires","volume":"60","author":"Fernandes","year":"2024","journal-title":"Geriatr Nurs (Minneap)"},{"key":"B82","doi-asserted-by":"publisher","first-page":"108841","DOI":"10.1016\/j.diabres.2021.108841","article-title":"Perceived built environment and type 2 diabetes incidence: exploring potential mediating pathways through physical and mental health, and behavioural factors in a longitudinal study","volume":"176","author":"Dendup","year":"2021","journal-title":"Diabetes Res Clin Pract"},{"key":"B83","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1378\/chest.09-0360","article-title":"Interactions between obesity and obstructive sleep apnea: implications for treatment","volume":"137","author":"Romero-Corral","year":"2010","journal-title":"Chest"},{"key":"B84","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1097\/ALN.0b013e3181799d45","article-title":"Obesity-induced insulin resistance and hyperglycemia: etiological factors and molecular mechanisms","volume":"109","author":"Martyn","year":"2008","journal-title":"Anesthesiology"},{"key":"B85","doi-asserted-by":"publisher","first-page":"1335","DOI":"10.2337\/dc22-1993","article-title":"Ultra-processed food consumption and risk of type 2 diabetes: three large prospective us cohort studies","volume":"46","author":"Chen","year":"2023","journal-title":"Diabetes Care"},{"key":"B86","doi-asserted-by":"publisher","first-page":"1930","DOI":"10.5114\/aoms\/188068","article-title":"The causal association of cheese intake with type 2 diabetes mellitus: results from a two-sample mendelian randomization study","volume":"20","author":"Zhong","year":"2024","journal-title":"Arch Med Sci"},{"key":"B87","doi-asserted-by":"publisher","first-page":"l145","DOI":"10.1136\/bmj.l1451","article-title":"Ultra-processed food intake and risk of cardiovascular disease: prospective cohort study (NutriNet-Sant\u00e9)","volume":"365","author":"Srour","year":"2019","journal-title":"bmj"},{"key":"B88","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.1111\/dom.14980","article-title":"The role of dietary salt in metabolism and energy balance: Insights beyond cardiovascular disease","volume":"25","author":"Wu","year":"2023","journal-title":"Diabetes Obes Metab"},{"key":"B89","doi-asserted-by":"publisher","first-page":"afae223","DOI":"10.1093\/ageing\/afae223","article-title":"Social isolation, loneliness and low dietary micronutrient intake amongst older people in England","volume":"53","author":"Steptoe","year":"2024","journal-title":"Age Ageing"},{"key":"B90","doi-asserted-by":"publisher","first-page":"771","DOI":"10.3390\/nu16060771","article-title":"Understanding the interplay of dietary intake and eating behavior in type 2 diabetes","volume":"16","author":"Gal","year":"2024","journal-title":"Nutrients"},{"key":"B91","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1111\/nyas.14926","article-title":"Connecting insufficient sleep and insomnia with metabolic dysfunction","volume":"1519","author":"Duan","year":"2023","journal-title":"Ann N Y Acad Sci"},{"key":"B92","doi-asserted-by":"publisher","first-page":"939","DOI":"10.1016\/j.cub.2012.03.038","article-title":"Social jetlag and obesity","volume":"22","author":"Roenneberg","year":"2012","journal-title":"Curr Biol"},{"key":"B93","year":""},{"key":"B94","doi-asserted-by":"publisher","first-page":"1311","DOI":"10.1007\/s00127-019-01758-y","article-title":"Ethnic inequalities in the incidence of diagnosis of severe mental illness in England: a systematic review and new meta-analyses for non-affective and affective psychoses","volume":"54","author":"Halvorsrud","year":"2019","journal-title":"Soc Psychiatry Psychiatr Epidemiol"},{"key":"B95","doi-asserted-by":"publisher","first-page":"E267","DOI":"10.1017\/S0029665124005056","article-title":"Dietary patterns in ethnic minority groups: data analysis of vegetable intake from \u2018understanding society\u2019 (the uk household longitudinal study)","volume":"83","author":"Demashkieh","year":"2024","journal-title":"Proc Nutr Soc"},{"key":"B96","year":""},{"key":"B97","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/S2213-8587(22)00007-9","article-title":"Body-mass index and risk of obesity-related complex multimorbidity: an individual-level analysis of 120,813 adults from 16 cohort studies","volume":"10","author":"Kivim\u00e4ki","year":"2022","journal-title":"Lancet Diabetes Endocrinol"},{"key":"B98","doi-asserted-by":"publisher","first-page":"548","DOI":"10.1186\/s12877-022-03548-9","article-title":"Multimorbidity and long-term disability and physical functioning decline: a prospective cohort study","volume":"22","author":"Stirland","year":"2022","journal-title":"BMC Geriatr"},{"key":"B99","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1037\/h0037350","article-title":"Estimating causal effects of treatments in randomized and nonrandomized studies","volume":"66","author":"Rubin","year":"1974","journal-title":"J Educ Psychol"},{"key":"B100","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1093\/biomet\/70.1.41","article-title":"The central role of the propensity score in observational studies for causal effects","volume":"70","author":"Rosenbaum","year":"1983","journal-title":"Biometrika"},{"key":"B101","doi-asserted-by":"publisher","first-page":"3083","DOI":"10.1002\/sim.3697","article-title":"Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples","volume":"28","author":"Austin","year":"2009","journal-title":"Stat Med"},{"key":"B102","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1056\/NEJM199801153380307","article-title":"Protective and damaging effects of stress mediators","volume":"338","author":"McEwen","year":"1998","journal-title":"N Engl J Med"},{"key":"B103","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1152\/physrev.00041.2006","article-title":"Physiology and neurobiology of stress and adaptation: central role of the brain","volume":"87","author":"McEwen","year":"2007","journal-title":"Physiol Rev"},{"key":"B104","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1016\/j.mehy.2006.04.008","article-title":"The inflammatory consequences of psychologic stress: relationship to insulin resistance, obesity, atherosclerosis and diabetes mellitus, type II","volume":"67","author":"Black","year":"2006","journal-title":"Med Hypotheses"},{"key":"B105","doi-asserted-by":"publisher","first-page":"346","DOI":"10.3390\/antiox9040346","article-title":"Diet and mental health: review of the recent updates on molecular mechanisms","volume":"9","author":"Godos","year":"2020","journal-title":"Antioxidants"},{"key":"B106","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40337-018-0210-6","article-title":"Emotional eating and weight regulation: a qualitative study of compensatory behaviors and concerns","volume":"6","author":"Frayn","year":"2018","journal-title":"J Eat Disord"},{"key":"B107","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1016\/S0889-1591(03)00048-5","article-title":"The inflammatory response is an integral part of the stress response: implications for atherosclerosis, insulin resistance, type II diabetes and metabolic syndrome X","volume":"17","author":"Black","year":"2003","journal-title":"Brain Behav Immun"},{"key":"B108","year":""},{"key":"B109","doi-asserted-by":"publisher","first-page":"1435","DOI":"10.1016\/S0140-6736(99)01376-8","article-title":"Impact of sleep debt on metabolic and endocrine function","volume":"354","author":"Spiegel","year":"1999","journal-title":"Lancet"},{"key":"B110","doi-asserted-by":"crossref","first-page":"722523","DOI":"10.3389\/fnint.2021.722523","article-title":"Metabolic disturbances induced by sleep restriction as potential triggers for alzheimer\u2019s disease","volume":"15","author":"Garc\u00eda-Aviles","year":"2021","journal-title":"Front Integr Neurosci"},{"key":"B111","doi-asserted-by":"publisher","first-page":"941","DOI":"10.1016\/S0140-6736(17)30003-X","article-title":"Syndemics and the biosocial conception of health","volume":"389","author":"Singer","year":"2017","journal-title":"Lancet"}],"container-title":["Frontiers in Digital Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2026.1710829\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T06:54:42Z","timestamp":1772693682000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2026.1710829\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,5]]},"references-count":111,"alternative-id":["10.3389\/fdgth.2026.1710829"],"URL":"https:\/\/doi.org\/10.3389\/fdgth.2026.1710829","relation":{},"ISSN":["2673-253X"],"issn-type":[{"value":"2673-253X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,5]]},"article-number":"1710829"}}