Predicting 10-Year Risk of End-Organ Complications of Type 2 Diabetes With and Without Metabolic Surgery: A Machine Learning Approach
Open accessAbstract
OBJECTIVE: To construct and internally validate prediction models to estimate the risk of long-term end-organ complications and mortality in patients with type 2 diabetes and obesity that can be used to inform treatment decisions for patients and practitioners who are considering metabolic surgery. RESEARCH DESIGN AND METHODS: A total of 2,287 patients with type 2 diabetes who underwent metabolic surgery between 1998 and 2017 in the Cleveland Clinic Health System were propensity-matched 1:5 to 11,435 nonsurgical patients with BMI ≥30 kg/m2 and type 2 diabetes who received usual care with follow-up through December 2018. Multivariable time-to-event regression and random forest machine learning models were built and internally validated using fivefold cross-validation to predict the 10-year risk for four outcomes of interest. The prediction models were programmed to construct user-friendly web-based and smartphone applications of Individualized Diabetes Complications (IDC) Risk Scores for clinical use. RESULTS: The prediction tools demonstrated the following discrimination ability based on the area under the receiver operating characteristic curve (1 = perfect discrimination and 0.5 = chance) at 10 years in the surgical and nonsurgical groups, respectively: all-cause mortality (0.79 and 0.81), coronary artery events (0.66 and 0.67), heart failure (0.73 and 0.75), and nephropathy (0.73 and 0.76). When a patient’s data are entered into the IDC application, it estimates the individualized 10-year morbidity and mortality risks with and without undergoing metabolic surgery. CONCLUSIONS: The IDC Risk Scores can provide personalized evidence-based risk information for patients with type 2 diabetes and obesity about future cardiovascular outcomes and mortality with and without metabolic surgery based on their current status of obesity, diabetes, and related cardiometabolic conditions.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Aminian et al. (2020) found that cardiovascular events are among the most frequent complications in patients with type 2 diabetes and obesity.
“They found that cardiovascular events are among the most frequent complications in patients with type 2 diabetes and obesity.”
From Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature (2022) · cites this paper as Aminian et al. (2020)
The Introduction (page unavailable) identifies cardiovascular outcomes as important complications in patients with type 2 diabetes and obesity, and its baseline-characteristics table reports coronary artery disease and heart failure in roughly 10% of the study groups. The paper does not establish that cardiovascular events, as a group, are among the most frequent complications; that frequency ranking goes beyond what these passages show. (LLM, conf=0.80)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Levey, A. S., Bosch, J. P., Lewis, J. B., Greene, T., Rogers, N., Roth, D., & for the Modification of Diet in Renal Disease Study Group* (1999) · Annals of Internal Medicine · doi:10.7326/0003-4819-130-6-199903160-00002 · Cited by 15,079 · see citing works
Effects of Intensive Glucose Lowering in Type 2 Diabetes
(2008) · New England Journal of Medicine · doi:10.1056/nejmoa0802743 · Cited by 7,888 · see citing works
Multiple imputation by chained equations: what is it and how does it work?
Azur, M. J., Stuart, E. A., Frangakis, C., & Leaf, P. J. (2011) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.329 · Cited by 3,542 · see citing works
Bariatric Surgery versus Intensive Medical Therapy for Diabetes — 5-Year Outcomes
Schauer, P. R., Bhatt, D. L., Kirwan, J. P., Wolski, K., Aminian, A., Brethauer, S. A., Navaneethan, S. D., Singh, R. P., Pothier, C. E., Nissen, S. E., et al. (2017) · New England Journal of Medicine · doi:10.1056/nejmoa1600869 · Cited by 2,934 · see citing works
Self-citationCardiovascular effects of intensive lifestyle intervention in type 2 diabetes
The Look AHEAD Research Group (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1212914 · Cited by 2,908 · see citing works
Long-term mortality after gastric bypass surgery
Adams, T. D., Gress, R. E., Smith, S. C., Halverson, R. C., Simper, S. C., Rosamond, W. D., LaMonte, M. J., Stroup, A. M., & Hunt, S. C. (2007) · New England Journal of Medicine · doi:10.1056/nejmoa066603 · Cited by 2,572 · see citing works
Mingrone, G., Panunzi, S., De Gaetano, A., Guidone, C., Iaconelli, A., Nanni, G., Castagneto, M., Bornstein, S., & Rubino, F. (2015) · The Lancet · doi:10.1016/s0140-6736(15)00075-6 · Cited by 1,220 · see citing works
The UKPDS risk engine: a model for the risk of coronary heart disease in Type II diabetes (UKPDS 56)
STEVENS, R. J., KOTHARI, V., ADLER, A. I., & STRATTON, I. M. (2001) · Clinical Science · doi:10.1042/cs20000335 · Cited by 1,084 · see citing works
Sjöström, L., Peltonen, M., Jacobson, P., Ahlin, S., Andersson-Assarsson, J., Anveden, Å., Bouchard, C., Carlsson, B., Karason, K., Lönroth, H., et al. (2014) · JAMA · doi:10.1001/jama.2014.5988 · Cited by 1,054 · see citing works
(2015) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(15)00291-0 · Cited by 1,029 · see citing works
Rubino, F., Nathan, D. M., Eckel, R. H., Schauer, P. R., Alberti, K. G. M. M., Zimmet, P., Prato, S. D., Ji, L., SADIKOT, S. M., Herman, W. H., et al. (2016) · Diabetes Care · doi:10.2337/dc16-0236 · Cited by 991 · see citing works
Self-citationAminian, A., Zajichek, A., Arterburn, D. E., Wolski, K. E., Brethauer, S. A., Schauer, P. R., Kattan, M. W., & Nissen, S. E. (2019) · JAMA · doi:10.1001/jama.2019.14231 · Cited by 505 · see citing works
Self-citationHayes, A. J., Leal, J., Gray, A. M., Holman, R. R., & Clarke, P. M. (2013) · Diabetologia · doi:10.1007/s00125-013-2940-y · Cited by 495 · see citing works
Kothari (2002) · doi:10.1161/01.str.0000020091.07144.c7 · Cited by 420 · see citing works
Changes in Utilization of Bariatric Surgery in the United States From 1993 to 2016
Campos, G. M., Khoraki, J., Browning, M. G., Pessoa, B. M., Mazzini, G. S., & Wolfe, L. G. (2019) · Annals of Surgery · doi:10.1097/sla.0000000000003554 · Cited by 326 · see citing works
Fisher, D. P., Johnson, E., Haneuse, S., Arterburn, D., Coleman, K. J., O’Connor, P. J., O’Brien, R., Bogart, A., Theis, M. K., Anau, J., et al. (2018) · JAMA · doi:10.1001/jama.2018.14619 · Cited by 305 · see citing works
Self-citationSegar, M. W., Vaduganathan, M., Patel, K. V., McGuire, D. K., Butler, J., Fonarow, G. C., Basit, M. A., Kannan, V., Grodin, J. L., Everett, B. M., et al. (2019) · Diabetes Care · doi:10.2337/dc19-0587 · Cited by 301 · see citing works
Ikramuddin, S., Korner, J., Lee, W.-J., Thomas, A. J., Connett, J. E., Bantle, J. P., Leslie, D. B., Wang, Q., Inabnet, W. B., Jeffery, R. W., et al. (2018) · JAMA · doi:10.1001/jama.2017.20813 · Cited by 291 · see citing works
Basu, S., Sussman, J. B., Berkowitz, S. A., Hayward, R. A., & Yudkin, J. S. (2017) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(17)30221-8 · Cited by 206 · see citing works
How safe is metabolic/diabetes surgery?
Aminian, A., Brethauer, S. A., Kirwan, J. P., Kashyap, S. R., Burguera, B. G., & Schauer, P. R. (2014) · Diabetes Obesity and Metabolism · doi:10.1111/dom.12405 · Cited by 192 · see citing works
Self-citationO'Brien, R., Johnson, E., Haneuse, S., Coleman, K. J., O'Connor, P. J., Fisher, D. P., Sidney, S., Bogart, A., Theis, M. K., Anau, J., et al. (2018) · Annals of Internal Medicine · doi:10.7326/m17-2383 · Cited by 161 · see citing works
Self-citationThe index of prediction accuracy: an intuitive measure useful for evaluating risk prediction models
Kattan, M. W., & Gerds, T. A. (2018) · Diagnostic and Prognostic Research · doi:10.1186/s41512-018-0029-2 · Cited by 149 · see citing works
Self-citationCarlsson, L. M. S., Sjöholm, K., Karlsson, C., Jacobson, P., Andersson‐Assarsson, J. C., Svensson, P., Larsson, I., Hjorth, S., Neovius, M., Taube, M., et al. (2017) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(17)30061-x · Cited by 130 · see citing works
Predicting Macro- and Microvascular Complications in Type 2 Diabetes
Tanaka, S., Tanaka, S., Iimuro, S., Yamashita, H., Katayama, S., Akanuma, Y., Yamada, N., Araki, A., Ito, H., Sone, H., et al. (2013) · Diabetes Care · doi:10.2337/dc12-0958 · Cited by 128 · see citing works
Predictors of Mortality Over 8 Years in Type 2 Diabetic Patients
McEwen, L. N., Karter, A. J., Waitzfelder, B. E., Crosson, J. C., Marrero, D. G., Mangione, C. M., & Herman, W. H. (2012) · Diabetes Care · doi:10.2337/dc11-2281 · Cited by 121 · see citing works
Basu, S., Sussman, J. B., Berkowitz, S. A., Hayward, R. A., Bertoni, A. G., Correa, A., Mwasongwe, S. E., & Yudkin, J. S. (2017) · Diabetes Care · doi:10.2337/dc17-2002 · Cited by 93 · see citing works
Cosmo, S. D., Copetti, M., Lamacchia, O., Fontana, A., Massa, M., Morini, E., Pacilli, A., Fariello, S., Palena, A. P., Rauseo, A., et al. (2013) · Diabetes Care · doi:10.2337/dc12-1906 · Cited by 60 · see citing works
Predicting 6-Year Mortality Risk in Patients With Type 2 Diabetes
Wells, B. J., Jain, A., Arrigain, S., Yu, C., Rosenkrans, W. A., & Kattan, M. W. (2008) · Diabetes Care · doi:10.2337/dc08-1047 · Cited by 51 · see citing works
Self-citationAminian, A., Aleassa, E. M., Bhatt, D. L., Tu, C., Khorgami, Z., Schauer, P. R., Brethauer, S. A., & Daigle, C. R. (2019) · Diabetes Obesity and Metabolism · doi:10.1111/dom.13765 · Cited by 46 · see citing works
Self-citationRisk Assessment in Patients With Diabetes With the TIMI Risk Score for Atherothrombotic Disease
Bergmark, B. A., Bhatt, D. L., D, E. B. M., Morrow, D. A., Steg, P. G., Gurmu, Y., Cahn, A., Mosenzon, O., Raz, I., Bohula, E. A., et al. (2017) · Diabetes Care · doi:10.2337/dc17-1736 · Cited by 31 · see citing works
Prediction of morbidity and mortality in patients with type 2 diabetes
Wells, B. J., Roth, R., Nowacki, A. S., Arrigain, S., Yu, C., Rosenkrans, W. A., & Kattan, M. W. (2013) · PeerJ · doi:10.7717/peerj.87 · Cited by 21 · see citing works
Self-citationDevelopment and validation of a predictive risk model for all-cause mortality in type 2 diabetes
Robinson, T., Elley, C. R., Kenealy, T. W., & Drury, P. (2015) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2015.02.015 · Cited by 20 · see citing works
Copetti, M., Shah, H., Fontana, A., Scarale, M. G., Menzaghi, C., Cosmo, S. D., Garofolo, M., Sorrentino, M. R., Lamacchia, O., Penno, G., et al. (2019) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2019-00215 · Cited by 20 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Obesity as a multisystem disease: Trends in obesity rates and obesity‐related complications
Sarma, S., Sockalingam, S., & Dash, S. (2021) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14290 · Cited by 461 · see citing works
Ravaut, M., Sadeghi, H., Leung, K. K., Volkovs, M., Kornas, K., Harish, V., Watson, T., Lewis, G. F., Weisman, A., Poutanen, T., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00394-8 · Cited by 121 · see citing works
Campos, G. M., Mazzini, G. S., Altieri, M. S., Docimo, S., DeMaria, E. J., & Rogers, A. M. (2021) · Surgery for Obesity and Related Diseases · doi:10.1016/j.soard.2021.03.007 · Cited by 110 · see citing works
Association is not prediction: A landscape of confused reporting in diabetes – A systematic review
Varga, T. V., Niss, K., Estampador, A. C., Collin, C. B., & Moseley, P. L. (2020) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2020.108497 · Cited by 94 · see citing works
Xie, P., Li, Y., Deng, B., Du, C., Rui, S., Deng, W., Wang, M., Boey, J., Armstrong, D. G., Ma, Y., et al. (2021) · International Wound Journal · doi:10.1111/iwj.13691 · Cited by 89 · see citing works
Machine learning for diabetes clinical decision support: a review
Tuppad, A., & Patil, S. D. (2022) · Advances in Computational Intelligence · doi:10.1007/s43674-022-00034-y · Cited by 71 · see citing works
Prediction of complications of type 2 Diabetes: A Machine learning approach
Nicolucci, A., Romeo, L., Bernardini, M., Vespasiani, M., Rossi, M. C., Petrelli, M., Ceriello, A., Di Bartolo, P., Frontoni, E., & Vespasiani, G. (2022) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2022.110013 · Cited by 67 · see citing works
Fan, Y., Long, E., Cai, L., Cao, Q., Wu, X., & Tong, R. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.665951 · Cited by 58 · see citing works
Artificial Intelligence in Bariatric Surgery: Current Status and Future Perspectives
Bektaş, M., Reiber, B. M. M., Pereira, J. C., Burchell, G. L., & van der Peet, D. L. (2022) · Obesity Surgery · doi:10.1007/s11695-022-06146-1 · Cited by 58 · see citing works
Aminian, A., Zajichek, A., Tu, C., Wolski, K. E., Brethauer, S. A., Schauer, P. R., Kattan, M. W., & Nissen, S. E. (2020) · Annals of Surgery · doi:10.1097/sla.0000000000004369 · Cited by 52 · see citing works
Self-citationArtificial intelligence and diabetes technology: A review
Gautier, T., Ziegler, L. B., Gerber, M. S., Campos-Náñez, E., & Patek, S. D. (2021) · Metabolism · doi:10.1016/j.metabol.2021.154872 · Cited by 55 · see citing works
Current Applications of Artificial Intelligence in Bariatric Surgery
Bellini, V., Valente, M., Turetti, M., RIO, P. D., Saturno, F., Maffezzoni, M., & Bignami, E. G. (2022) · Obesity Surgery · doi:10.1007/s11695-022-06100-1 · Cited by 50 · see citing works
Metabolic surgery: A clinical update
Wilson, R. L., Aminian, A., & Tahrani, A. A. (2021) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14235 · Cited by 42 · see citing works
Self-citationExploring Sodium Glucose Co-Transporter-2 (SGLT2) Inhibitors for Organ Protection in COVID-19
Fernández‐Fernández, B., D’Marco, L., Górriz, J. L., Jacobs-Cachá, C., Kanbay, M., Lima, S. L., Porrini, E., Sarafidis, P. A., Soler, M. J., & Ortíz, A. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9072030 · Cited by 42 · see citing works
Use of Machine Learning Approaches in Clinical Epidemiological Research of Diabetes
Basu, S., Johnson, K., & Berkowitz, S. A. (2020) · Current Diabetes Reports · doi:10.1007/s11892-020-01353-5 · Cited by 37 · see citing works
Pantelis, A. G. (2022) · Metabolites · doi:10.3390/metabo12050458 · Cited by 35 · see citing works
Silva, K. D., Mathews, N., Teede, H. J., Forbes, A. B., Jönsson, D., Demmer, R. T., & Enticott, J. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104305 · Cited by 31 · see citing works
Choi, E. Y., Park, Y. W., Lee, M., Kim, M., Lee, C. S., Ahn, S. S., Kim, J., & Lee, S. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.666495 · Cited by 29 · see citing works
Kanda, E., Suzuki, A., Makino, M., Tsubota, H., Kanemata, S., Shirakawa, K., & Yajima, T. (2022) · Scientific Reports · doi:10.1038/s41598-022-24562-2 · Cited by 29 · see citing works
Success (but Unfinished) Story of Metabolic Surgery
Aminian, A., & Nissen, S. E. (2020) · Diabetes Care · doi:10.2337/dci20-0006 · Cited by 28 · see citing works
Self-citationPrediction Models for Type 2 Diabetes Progression: A Systematic Review
Nazirun, N. N. N., Wahab, A. A., Selamat, A., Fujita, H., Krejcar, O., Kuča, K., & Gan, H. (2024) · IEEE Access · doi:10.1109/access.2024.3432118 · Cited by 27 · see citing works
Gosak, L., Martinović, K., Lorber, M., & Stiglic, G. (2022) · Journal of Nursing Management · doi:10.1111/jonm.13894 · Cited by 24 · see citing works
Pantelis, A. G., Stravodimos, G., & Lapatsanis, D. P. (2021) · Obesity Surgery · doi:10.1007/s11695-021-05548-x · Cited by 24 · see citing works
Identification and Predictors for Cardiovascular Disease Risk Equivalents Among Adults With Diabetes
Zhao, Y., Malik, S., Budoff, M. J., Correa, A., Ashley, K. E., Selvin, E., Watson, K. E., & Wong, N. D. (2021) · Diabetes Care · doi:10.2337/dc21-0431 · Cited by 23 · see citing works
Jing, B., Boscardin, W. J., Deardorff, W. J., Jeon, S. Y., Lee, A. K., Donovan, A. L., & Lee, S. J. (2022) · Medical Care · doi:10.1097/mlr.0000000000001720 · Cited by 22 · see citing works
Cardiovascular risk reduction following metabolic and bariatric surgery.
Albaugh, V. L., Kindel, T. L., Nissen, S. E., & Aminian, A. (2021) · Surgical Clinics of North America · doi:10.1016/j.suc.2020.12.012 · Cited by 21 · see citing works
Self-citationMachine Learning Models for Prediction of Diabetic Microvascular Complications
Kanbour, S., Harris, C., Lalani, B., Wolf, R. M., Fitipaldi, H., Gomez, M. F., & Mathioudakis, N. N. (2024) · Journal of Diabetes Science and Technology · doi:10.1177/19322968231223726 · Cited by 20 · see citing works
Wang, M., Francis, F., Kunz, H., Zhang, X., Wan, C., Liu, Y., Taylor, P., Wild, S. H., & Wu, H. (2022) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2022.100072 · Cited by 15 · see citing works
Prediction models of diabetes complications: a scoping review
Ndjaboué, R., Ngueta, G., Rochefort-Brihay, C., Delorme, S., Guay, D., Ivers, N. M., Shah, B. R., Straus, S. E., Yu, C. H. Y., Comeau, S., et al. (2022) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2021-217793 · Cited by 13 · see citing works
Obeso-Fernández, J., Millán-Alanís, J. M., Rodríguez-Bautista, M., Medrano-Juárez, S., Oyervides-Fuentes, S., Gonzalez‐Cruz, D., González‐González, J. G., & Rodríguez-Gutiérrez, R. (2023) · Surgery for Obesity and Related Diseases · doi:10.1016/j.soard.2023.02.024 · Cited by 12 · see citing works
Xie, P., Yang, C., Yang, G., Jiang, Y., He, M., Jiang, X., Chen, Y., Deng, L., Wang, M., Armstrong, D. G., et al. (2023) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-023-01020-1 · Cited by 11 · see citing works
Mesquita, F., Bernardino, J., Henriques, J., Raposo, J., Ribeiro, R., & Paredes, S. (2023) · Journal of Diabetes and Metabolic Disorders · doi:10.1007/s40200-023-01357-4 · Cited by 11 · see citing works
Obeso-Fernández, J., Millán-Alanís, J. M., Sáenz-Flores, M., Rodríguez-Bautista, M., Medrano-Juárez, S., Oyervides-Fuentes, S., Gonzalez‐Cruz, D., Manzanares-Gallegos, D. M., González‐González, J. G., & Rodríguez-Gutiérrez, R. (2023) · Surgery for Obesity and Related Diseases · doi:10.1016/j.soard.2023.08.016 · Cited by 9 · see citing works
Trischitta, V., & Copetti, M. (2020) · Diabetes Care · doi:10.2337/dci19-0079 · Cited by 9 · see citing works
Tan, X. L., Wu, J., Ma, X., Kang, S., Yue, X., Rao, Y., Li, Y., Huang, H., Chen, Y., Lyu, W., et al. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.926486 · Cited by 7 · see citing works
Frenken, M., Kemmet, O., Frenken, M., Röhrig, I., Fischer, L., & Hellinger, A. (2022) · Obesity Surgery · doi:10.1007/s11695-022-06223-5 · Cited by 7 · see citing works
Görür, K., Çetin, O., Özer, Z., & Temurtaş, F. (2023) · Results in Engineering · doi:10.1016/j.rineng.2023.101109 · Cited by 7 · see citing works
Artificial Intelligence Algorithm to Screen for Diabetic Neuropathy: A Pilot Study
Sartore, G., Ragazzi, E., Pegoraro, F., Pagno, M. G., Lapolla, A., & Piarulli, F. (2025) · Biomedicines · doi:10.3390/biomedicines13051075 · Cited by 5 · see citing works
Varban, O. A., Bonham, A. J., Carlin, A. M., Ghaferi, A. A., Finks, J. F., & Ehlers, A. P. (2022) · Journal of the American College of Surgeons · doi:10.1097/xcs.0000000000000306 · Cited by 5 · see citing works
Kheniser, K. G., Aminian, A., & Kashyap, S. R. (2020) · Current Diabetes Reports · doi:10.1007/s11892-020-01350-8 · Cited by 5 · see citing works
Self-citationNicolucci, A., Vespasiani, G., Mannino, D., Russo, G. T., Lucisano, G., Rossi, M. C., Ponzani, P., Cosmo, S. D., Cianni, G. D., Lencioni, C., et al. (2025) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2025.112900 · Cited by 4 · see citing works
Kostopoulos, G., Doundoulakis, I., Toulis, K. A., Karagiannis, T., Τσάπας, Α., & Haidich, A. (2023) · Heart · doi:10.1136/heartjnl-2022-322044 · Cited by 4 · see citing works
Rayner, D. G., Shah, D. S., Dai, S.-C., Gou, D., Chen, J., Agarwal, A., Mustafa, R. A., Manja, V., Vandvik, P. O., Agoritsas, T., et al. (2025) · BMJ Medicine · doi:10.1136/bmjmed-2025-001369 · Cited by 4 · see citing works
The Pulse of AI: Implementation of Artificial Intelligence in Healthcare and its Potential Hazards
Zaidi, S. G. S., Shaikh, A., & Surani, S. (2024) · The Open Respiratory Medicine Journal · doi:10.2174/0118743064289936240115105057 · Cited by 4 · see citing works
Bariatric Surgery - Past and Present
Kanat, B. H., Kutluer, N., & Doğan, S. (2022) · IntechOpen eBooks · doi:10.5772/intechopen.100966 · Cited by 2 · see citing works
Haskins, I. N., Jackson, H. T., Sparks, A. D., Vaziri, K., Tanner, T. N., Kothari, V. M., McBride, C. L., & Farrell, T. M. (2022) · Obesity Surgery · doi:10.1007/s11695-022-06243-1 · Cited by 3 · see citing works
Chang, C., & Wu, K.-H. (2024) · Infection and Drug Resistance · doi:10.2147/idr.s487893 · Cited by 2 · see citing works
Moradi, R., Kashanian, M., Yarigholi, F., Pazouki, A., & Sheikhtaheri, A. (2025) · Surgical Endoscopy · doi:10.1007/s00464-025-11640-5 · Cited by 2 · see citing works
Current and Potential Applications of Artificial Intelligence in Metabolic Bariatric Surgery
Pantelis, A. G. (2022) · IntechOpen eBooks · doi:10.5772/intechopen.106365 · Cited by 2 · see citing works
A scoping review of the predictive models of diabetes complicationsPreprint
Ndjaboué, R., Ngueta, G., Rochefort-Brihay, C., Delorme, S., Guay, D., Ivers, N. M., Shah, B. R., Straus, S. E., Yu, C. H. Y., Comeau, S., et al. (2021) · doi:10.31219/osf.io/pnv25 · Cited by 2 · see citing works
ML based IoT Framework for Diabetes DetectionPreprint
Kumar, U., Kumar, T., Gautam, S., & Pandey, S. C. (2023) · Research Square · doi:10.21203/rs.3.rs-3024165/v1 · Cited by 2 · see citing works
Optical Coherence Tomography Angiography for Biomarker Indices in Diabetes
Choi, E. Y., & Kim, M. (2022) · Biomarkers in disease · doi:10.1007/978-3-031-08014-2_39 · Cited by 1 · see citing works
Borozdina, N. A., Kazakova, E. N., Гладких, И. Н., Лейченко, Е. В., & Дьяченко, И. А. (2024) · Research Results in Pharmacology · doi:10.18413/rrpharmacology.10.455 · Cited by 1 · see citing works
Pescol, F., Bosoni, P., Ghilotti, S., Cata, P. D., Sacchi, L., & Bellazzi, R. (2025) · Journal of Diabetes Science and Technology · doi:10.1177/19322968251384314 · Cited by 1 · see citing works
Cortez-Sandoval, M., Lévano, C. J. E., Álvarez, J. F., López-Leal, J., Valenzuela, L. M., Ato, R. S., Keita, H., Gomez-Lujan, M., Candela, F. M. Q., Prado, J. I. P., et al. (2026) · Diagnostics · doi:10.3390/diagnostics16050765 · Cited by 1 · see citing works
Foula, M. S., Bamalan, O. A., Foula, A. S., Eldamati, A. M., Alratrout, H. A., AlQatari, A. A., Alsaleem, H., Aldabaeab, A. E., Foula, H. S., Zakaria, H. M., et al. (2026) · Obesity Surgery · doi:10.1007/s11695-026-08908-7 · Cited by 1 · see citing works
Künstliche Intelligenz und maschinelles Lernen in der Diabetesforschung
Nguyen, P., Ohnmacht, A. J., Galhoz, A., Büttner, M., Theis, F. J., & Menden, M. P. (2021) · Der Diabetologe · doi:10.1007/s11428-021-00817-w · Cited by 1 · see citing works
Abbott, M. R., Beesley, L. J., Bellile, E. L., Shuman, A. G., Rozek, L. S., & Taylor, J. M. G. (2023) · Cancer Informatics · doi:10.1177/11769351231183847 · Cited by 1 · see citing works
SYSTEMATIC REVIEW OF THE LITERATURE ON RISK AND BENEFIT IN BARIATRIC SURGERY
CORREA, E. L., Nara, E. O. B., C., O., Strobel, R., & Cotian, L. F. P. (2023) · Revista SODEBRAS · doi:10.29367/issn.1809-3957.18.2023.211.133 · Cited by 1 · see citing works
ESTUDO COMPARATIVO DE VÁRIOS MÉTODOS DE PRODUÇÃO DE HIDROGÊNIO BASEADO EM CUSTOS E IMPACTO AMBIENTAL
da Silva, G. H. R., & Nascimento, N. (2023) · Revista SODEBRAS · doi:10.29367/issn.1809-3957.18.2023.211.120 · Cited by 1 · see citing works
Diabetes Prediction Using Machine Learning: A Detailed Insight
Thakur, G. S. M., Dutta, S., & Das, B. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-50993-3_13 · Cited by 1 · see citing works
Beran, M., Ekstrom, H. L., Crain, A. L., Hooker, S. A., Chumba, L. N., Appana, D., Kromrey, K., Kunisetty, G., McKinney, Z. J., Pronk, N. P., et al. (2025) · Contemporary Clinical Trials · doi:10.1016/j.cct.2025.107830 · Cited by 1 · see citing works
Application of Machine Learning to Predict And Diagnose Diabetes
Li, X. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1824/1/012008
Albaugh, V. L. (2021) · Surgery for Obesity and Related Diseases · doi:10.1016/j.soard.2021.06.012
Hong, N., Park, Y., You, S. C., & Rhee, Y. (2021) · Springer eBooks · doi:10.1007/978-3-030-58080-3_328-1
Hong, N., Park, Y., You, S. C., & Rhee, Y. (2022) · Springer eBooks · doi:10.1007/978-3-030-64573-1_328
Optical Coherence Tomography Angiography for Biomarker Indices in Diabetes
Choi, E. Y., & Kim, M. (2022) · Biomarkers in disease · doi:10.1007/978-3-030-81303-1_39-1
Silva, A. C., Nara, E. O. B., Junior, O. C., Benitez, G. B., Dias, I. C. P., & Piovesan, C. (2023) · Revista SODEBRAS · doi:10.29367/issn.1809-3957.18.2023.211.125
DETERMINATION LI-ION BATTERIES STATE OF CHARGE, AN ANALYSIS OF DIFFERENT METHODS
Ribeiro, A. L. Z., & de Souza, T. M. (2023) · Revista SODEBRAS · doi:10.29367/issn.1809-3957.18.2023.211.88
VISÃO COMPUTACIONAL APLICADA À UM PROTÓTIPO DE MÃO ROBÓTICA UTILIZANDO IO
de Souza, K. M., Maciel, J. S., Bastos, M., & K., A. (2023) · Revista SODEBRAS · doi:10.29367/issn.1809-3957.18.2023.211.21
DO PETRÓLEO AO PLÁSTICO: CONHECENDO PARA PRESERVAR
Nascimento, M. L. S., SOUZA, T., & Nascimento, A. (2023) · Revista SODEBRAS · doi:10.29367/issn.1809-3957.18.2023.211.55
Maluf, T., & Nascimento, N. (2023) · Revista SODEBRAS · doi:10.29367/issn.1809-3957.18.2023.211.112
IMPLEMENTAÇÃO DE SENSOR FLEXÍVEL EM UMA MÃO ROBÓTICA UTILIZANDO IOT
Maciel, J. S., de Souza, K. M., Monteiro, A. S., K., A., & Bastos, M. P. (2023) · Revista SODEBRAS · doi:10.29367/issn.1809-3957.18.2023.211.14
Pedro, B. M. F., da Silva, G. H. R., Nascimento, N., & Nascimento, A. (2023) · Revista SODEBRAS · doi:10.29367/issn.1809-3957.18.2023.211.28
ANÁLISE DO MATERIAL TRITAN PARA FABRICAÇÃO DE PÁS EÓLICAS
Jesus, N. M. R., Souza, R. S. D. D., Castro, T. S., & Souza, T. M. (2023) · Revista SODEBRAS · doi:10.29367/issn.1809-3957.18.2023.211.73
Prado, M. L., Andrade, J. M. M., Meza, G. R., & BeniteZ, G. B. (2023) · Revista SODEBRAS · doi:10.29367/issn.1809-3957.18.2023.211.151
Santos, C. E. M., Nara, E. O. B., C., O., & Szejka, A. L. (2023) · Revista SODEBRAS · doi:10.29367/issn.1809-3957.18.2023.211.142
DESENVOLVIMENTO DE UM PROTÓTIPO DE UM VASO DE PLANTAS INTELIGENTE
Silva, E. T. S., Mota, S. A., de Souza, K. M., Costa, M. V. S., Bastos, M. P., & K., A. (2023) · Revista SODEBRAS · doi:10.29367/issn.1809-3957.18.2023.211.08
RASTREABILIDADE DO ÓLEO VEGETAL RESIDUAL PÓS-CONSUMO DOMÉSTICO
Crotti, B. F., Marmone, G. P., Paiva, J. C. P., & Guedes, C. L. B. (2023) · Revista SODEBRAS · doi:10.29367/issn.1809-3957.18.2023.211.64
ESTUDO DE CASO NO ENSINO MÉDIO: MINERAÇÃO DE DADOS E ANÁLISE DOS SIMULADOS MODELO ENEM
Nascimento, D. D. P., Tsunoda, D. F., & de Menezes, G. G. (2023) · Revista SODEBRAS · doi:10.29367/issn.1809-3957.18.2023.211.42
GERAÇÃO DISTRIBUIDA COMPARTILHADA NO BRASIL: BARREIRAS E INCENTIVOS
Santos, J. R., Paiva, J. H. Z., Guedes, C. L. B., & Paiva, J. C. P. (2023) · Revista SODEBRAS · doi:10.29367/issn.1809-3957.18.2023.211.94
PROJETO DE PRODUÇÃO DA CARROCERIA DE VEÍCULO ELÉTRICO POR MANUFATURA ADITIVA
Neto, J. C., & Souza, T. M. (2023) · Revista SODEBRAS · doi:10.29367/issn.1809-3957.18.2023.211.82
Reis, A. C. C. S., Nordi, M. R., PAIVA, J., & Guedes, C. L. B. (2023) · Revista SODEBRAS · doi:10.29367/issn.1809-3957.18.2023.211.100
Delgado, J. P. M., & Bello, C. A. C. (2023) · Revista SODEBRAS · doi:10.29367/issn.1809-3957.18.2023.211.36
Diabèe type 2 : place de la chirurgie métabolique
Lassen, P. B., Ciangura, C., & Aron‐Wisnewsky, J. (2024) · Pratiques en nutrition · doi:10.1016/j.pranut.2024.08.005
Metabolic Polygenic Risk Scores for Prediction of Obesity, Type 2 Diabetes, and Related MorbiditiesPreprint
Kim, M., Chen, Q.-L., Sui, Y., Yang, X., Wang, S.-Q., Weng, L.-C., Cho, S. M. J., Koyama, S., Zhu, X., Yu, K., et al. (2024) · medRxiv · doi:10.1101/2024.11.01.24316620 · Cited by 3 · see citing works
Comparative Outcomes in Metabolic Surgery: Diabetes and Cardiovascular Health
Lee, T. C., Flores, R. D. L., Yonge, J. D., & Stroud, A. (2024) · Springer eBooks · doi:10.1007/978-3-031-72309-4_26
Fair machine learning models for disease prediction: In-depth interviews with key health expertsPreprint
Nghiem, N., & Tiatia, R. (2025) · medRxiv · doi:10.1101/2025.02.04.25321632
Place de la chirurgie bariatrique dans le traitement du diabète de type 2
Lassen, P. B., Ciangura, C., & Aron‐Wisnewsky, J. (2025) · EMC - Endocrinologie - Nutrition · doi:10.1016/s1155-1941(24)45211-6
Albalawi, I. A., & Mirghani, H. O. (2025) · Journal of Research in Medical Sciences · doi:10.4103/jrms.jrms_360_24
Ayoade, O. B., Shahrestani, S. A., & Ruan, C. (2025) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-97352-9_9
Dutta, N., Tessier, K., Langer, J., Harindhanavudhi, T., Sibley, S. D., Billington, C. J., Vock, D. M., Wise, E. S., Bomberg, E. M., & Bramante, C. T. (2025) · Cureus · doi:10.7759/cureus.94070
Xie, P., Yang, C., Yang, G., Jiang, Y., He, M., Jiang, X., Chen, Y., Deng, L., Wang, M., Armstrong, D. G., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26574415
Brar, K., Chattha, I., Gobraeil, S., Mocanu, V., Corcelles, R., Kroh, M., Shin, T. H., Dang, J. T., & Lee, Y. (2026) · Surgery for Obesity and Related Diseases · doi:10.1016/j.soard.2026.05.002
Lee, S., Kim, Y., Yu, M. H., Lee, Y., Lee, B. I., Kang, E. S., Cha, B., Park, S.-H., Park, S., Kim, M., et al. (2026) · Nutrients · doi:10.3390/nu18111696
Bayesian models to forecast complications and their severity in type 2 diabetes
Aljenfawi, H., Mohammed, A., Alfailakawi, M. G., Al‐Mulla, F., & Hasan, A. (2026) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2026.100439
Aleman-Gonzales, L., Choque‐Quispe, B. M., Mamani-Zapana, W. H., Mendoza-Mollocondo, C.-I., Bernedo, E. G. M., Mamani, E. W. C., & Paredes-Quispe, J. R. (2026) · Journal Of Advanced Pharmacy Education And Research · doi:10.51847/oam3w008h7
- Authors
- Aminian, A., Zajichek, A., Arterburn, D. E., Wolski, K. E., Brethauer, S. A., Schauer, P. R., Nissen, S. E., & Kattan, M. W.
- Affiliations
- Cleveland Clinic (US)
- Kaiser Permanente Washington Health Research Institute (US)
- Pennington Biomedical Research Center (US)
- Corresponding author
- Aminian, Ali
- ORCID
- Venue
- Diabetes Care
- Publisher
- American Diabetes Association
- Published
- 2020-02-06
- Volume
- 43
- Issue
- 4
- Pages
- 852-859
- ISSN
- 0149-5992 (ISSN-L)
- 1935-5548
- PMC
- PMC7646205
- OpenAlex
- W3004712038
- Cited by
- 99
- References
- 33
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 10 · 2023: 29 · 2022: 19 · 2021: 17 · 2020: 7
- FWCI
- 16.45
- Type
- journal article
- Language
- en
- Update notices
- correction (2020-06-01)
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Diabetes Care (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Medtronic
- Topics
- Health Sciences › Medicine › Surgery › Bariatric Surgery and Outcomes
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
- Keywords
- type 2 diabetes, metabolic surgery, obesity, end-organ complications, 10-year risk prediction, all-cause mortality, coronary artery disease, heart failure, diabetic nephropathy, risk prediction model, mobile health apps, cardiovascular outcomes
- MeSH terms
- Machine Learning, Adolescent, Adult, Aged, Aged, 80 and over, Computer Simulation, Diabetes Mellitus, Type 2, Female, Follow-Up Studies, Humans, Male, Middle Aged, Prognosis, Retrospective Studies, Time Factors, Disease Progression, Diabetes Complications, Bariatric Surgery, Young Adult, Organs at Risk
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
