Effective questionnaire-based prediction models for type 2 diabetes across several ethnicities: a model development and validation study

eClinicalMedicine - Tập 64 - Trang 102235 - 2023
Michail Kokkorakis1,2, Pytrik Folkertsma3,4, Sipko van Dam3,4, Nicole Sirotin5, Shahrad Taheri6,7, Odette Chagoury6,7, Youssef Idaghdour8,9, Robert H. Henning1, José Castela Forte1,3, Christos S. Mantzoros2,10, Dylan H. de Vries3,4, Bruce H.R. Wolffenbuttel4
1Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
2Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
3Ancora Health B.V., Groningen, Netherlands
4Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
5Department of Preventive Medicine, Cleveland Clinic Abu Dhabi, Al Maryah Island, Abu Dhabi, United Arab Emirates
6National Obesity Treatment Centre, Qatar Metabolic Institute, Hamad Medical Corporation, Doha, Qatar
7Department of Medicine, Weill Cornell Medicine, Doha, Qatar
8Program in Biology, Division of Science and Mathematics, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
9Public Health Research Center, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates
10Department of Medicine, Boston VA Healthcare System, Boston, MA, USA

Tài liệu tham khảo

Wild, 2004, Global prevalence of diabetes: estimates for the year 2000 and projections for 2030, Diabetes Care, 27, 1047, 10.2337/diacare.27.5.1047 Kokkorakis, 2023, Milestones in the journey towards addressing obesity; Past trials and triumphs, recent breakthroughs, and an exciting future in the era of emerging effective medical therapies and integration of effective medical therapies with metabolic surgery, Metabolism, 148, 155689, 10.1016/j.metabol.2023.155689 2020, Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019, Lancet, 396, 1204, 10.1016/S0140-6736(20)30925-9 Gujral, 2019, Diabetes in normal-weight individuals: high susceptibility in nonwhite populations, Diabetes Care, 42, 2164, 10.2337/dci19-0046 Goff, 2020, Ethnic distinctions in the pathophysiology of type 2 diabetes: a focus on black African-Caribbean populations, Proc Nutr Soc, 79, 184, 10.1017/S0029665119001034 Banerjee, 2018, Differences in prevalence of diabetes among immigrants to Canada from South Asian countries, Diabet Med, 35, 937, 10.1111/dme.13647 Paul, 2017, Comparison of body mass index at diagnosis of diabetes in a multi-ethnic population: a case-control study with matched non-diabetic controls, Diabetes Obes Metab, 19, 1014, 10.1111/dom.12915 Meo, 2017, Prevalence of type 2 diabetes in the Arab world: impact of GDP and energy consumption, Eur Rev Med Pharmacol Sci, 21, 1303 Sulaiman, 2018, Diabetes risk score in the United Arab Emirates: a screening tool for the early detection of type 2 diabetes mellitus, BMJ Open Diabetes Res Care, 6, 10.1136/bmjdrc-2017-000489 Davies, 2022, Management of hyperglycemia in type 2 diabetes, 2022. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD), Diabetes Care, 45, 2753, 10.2337/dci22-0034 Ng, 2011, The prevalence and trends of overweight, obesity and nutrition-related non-communicable diseases in the Arabian Gulf States, Obes Rev, 12, 1, 10.1111/j.1467-789X.2010.00750.x Lindstrom, 2003, The diabetes risk score: a practical tool to predict type 2 diabetes risk, Diabetes Care, 26, 725, 10.2337/diacare.26.3.725 Chen, 2010, AUSDRISK: an Australian Type 2 Diabetes Risk Assessment Tool based on demographic, lifestyle and simple anthropometric measures, Med J Aust, 192, 197, 10.5694/j.1326-5377.2010.tb03478.x Rigla, 2018, Artificial intelligence methodologies and their application to diabetes, J Diabetes Sci Technol, 12, 303, 10.1177/1932296817710475 Morgenstern, 2020, Predicting population health with machine learning: a scoping review, BMJ Open, 10, 10.1136/bmjopen-2020-037860 Kouvari, 2023, Liver biopsy-based validation, confirmation and comparison of the diagnostic performance of established and novel non-invasive steatotic liver disease indexes: Results from a large multi-center study, Metabolism, 147, 155666, 10.1016/j.metabol.2023.155666 Kouvari, 2023, The first external validation of the Dallas steatosis index in biopsy-proven non-alcoholic fatty liver disease: a multicenter study, Diabetes Res Clin Pract, 203, 110870, 10.1016/j.diabres.2023.110870 Collins, 2012, What makes UK Biobank special?, Lancet, 379, 1173, 10.1016/S0140-6736(12)60404-8 Klijs, 2015, Representativeness of the LifeLines cohort study, PLoS One, 10, 10.1371/journal.pone.0137203 2023 Pereira, 2009, Machine learning classifiers and fMRI: a tutorial overview, Neuroimage, 45, S199, 10.1016/j.neuroimage.2008.11.007 DeLong, 1988, Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach, Biometrics, 44, 837, 10.2307/2531595 Ramos-Louro, 2022, mtDNA haplogroup A enhances the effect of obesity on the risk of knee OA in a Mexican population, Sci Rep, 12, 5173, 10.1038/s41598-022-09265-y Harrell, 2023 Lacy, 2016, Racial differences in the performance of existing risk prediction models for incident type 2 diabetes: the CARDIA study, Diabetes Care, 39, 285, 10.2337/dc15-0509 Wilkinson, 2020, Development and validation of a model for predicting incident type 2 diabetes using quantitative clinical data and a Bayesian logistic model: a nationwide cohort and modeling study, PLoS Med, 17, 10.1371/journal.pmed.1003232 Glumer, 2006, Risk scores for type 2 diabetes can be applied in some populations but not all, Diabetes Care, 29, 410, 10.2337/diacare.29.02.06.dc05-0945 Dugee, 2015, Adapting existing diabetes risk scores for an Asian population: a risk score for detecting undiagnosed diabetes in the Mongolian population, BMC Public Health, 15, 938, 10.1186/s12889-015-2298-9 Rokhman, 2022, Translation and performance of the Finnish Diabetes Risk Score for detecting undiagnosed diabetes and dysglycaemia in the Indonesian population, PLoS One, 17, 10.1371/journal.pone.0269853 Wu, 2019, Development and validation of a non-invasive assessment tool for screening prevalent undiagnosed diabetes in middle-aged and elderly Chinese, Prev Med, 119, 145, 10.1016/j.ypmed.2018.12.025 Smith, 2020, Shielding from covid-19 should be stratified by risk, BMJ, 369, m2063, 10.1136/bmj.m2063 Zhou, 2020, Cost-effectiveness of diabetes prevention interventions targeting high-risk individuals and whole populations: a systematic review, Diabetes Care, 43, 1593, 10.2337/dci20-0018 Mantena, 2021, Improving community health-care screenings with smartphone-based AI technologies, Lancet Digit Health, 3, e280, 10.1016/S2589-7500(21)00054-6 Polyzos, 2021, Diabetes mellitus: 100 years since the discovery of insulin, Metabolism, 118, 10.1016/j.metabol.2021.154737