Sun, H. et al. IDF diabetes atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res. Clin. Pract. 183, 109119 (2022).
Google Scholar
American Diabetes Association Professional Practice Committee. 9. Pharmacologic approaches to glycemic treatment: standards of care in diabetes-2024. Diabetes Care 47, S158–S178 (2024).
Google Scholar
Ma, H. et al. Efficacy and safety of GLP-1 receptor agonists versus SGLT-2 inhibitors in overweight/obese patients with or without diabetes mellitus: a systematic review and network meta-analysis. BMJ Open 13, e061807 (2023).
Google Scholar
Dawed, A. Y. et al. Pharmacogenomics of GLP-1 receptor agonists: a genome-wide analysis of observational data and large randomised controlled trials. Lancet Diabetes Endocrinol. 11, 33–41 (2023).
Google Scholar
Jones, A. G. et al. Markers of beta-cell failure predict poor glycemic response to GLP-1 receptor agonist therapy in type 2 diabetes. Diabetes Care 39, 250–257 (2016).
Google Scholar
Young, K. G. et al. Treatment effect heterogeneity following type 2 diabetes treatment with GLP1-receptor agonists and SGLT2-inhibitors: a systematic review. Commun. Med. 3, 131 (2023).
Google Scholar
Belthangady, C. et al. Causal deep learning reveals the comparative effectiveness of antihyperglycemic treatments in poorly controlled diabetes. Nat. Commun. 13, 6921 (2022).
Google Scholar
Sheng, B. et al. Artificial intelligence for diabetes care: current and future prospects. Lancet Diabetes Endocrinol. 12, 569–595 (2024).
Google Scholar
Cardoso, P. et al. Phenotype-based targeted treatment of SGLT2 inhibitors and GLP-1 receptor agonists in type 2 diabetes. Diabetologia 67, 822–836 (2024).
Google Scholar
Dwibedi, C. et al. Randomized open-label trial of semaglutide and dapagliflozin in patients with type 2 diabetes of different pathophysiology. Nat. Metab. 6, 50–60 (2024).
Google Scholar
Kwan, A. Y. M. et al. HbA1c reduction in dulaglutide-treated patients irrespective of duration of diabetes, microvascular disease, and BMI: a post hoc analysis from the REWIND trial. Diabetes Care 45, 547–554 (2022).
Google Scholar
Garber, A. et al. Liraglutide, a once-daily human glucagon-like peptide 1 analogue, provides sustained improvements in glycaemic control and weight for 2 years as monotherapy compared with glimepiride in patients with type 2 diabetes. Diabetes Obes. Metab. 13, 348–356 (2011).
Google Scholar
Schernthaner, G. et al. Canagliflozin compared with sitagliptin for patients with type 2 diabetes who do not have adequate glycemic control with metformin plus sulfonylurea: a 52-week randomized trial. Diabetes Care 36, 2508–2515 (2013).
Google Scholar
Bethel, M. A. et al. HbA(1c) change and diabetic retinopathy during GLP-1 receptor agonist cardiovascular outcome trials: a meta-analysis and meta-regression. Diabetes Care 44, 290–296 (2021).
Google Scholar
Li, L. et al. The ChinaMAP reference panel for the accurate genotype imputation in Chinese populations. Cell Res. 31, 1308–1310 (2021).
Google Scholar
Zhang, Y., Wang, Y., Ning, G., He, P. & Wang, W. Protecting older people: a high priority during the COVID-19 pandemic. Lancet 400, 729–730 (2022).
Google Scholar
Zhang, Y., Wang, W. & Ning, G. Metabolic management center: an innovation project for the management of metabolic diseases and complications in China. J. Diabetes 11, 11–13 (2019).
Google Scholar
Nadimi-Shahraki, M. H., Mohammadi, S., Zamani, H., Gandomi, M. & Gandomi, A. H. A hybrid imputation method for multi-pattern missing data: a case study on type II diabetes diagnosis. Electronics 10, https://doi.org/10.3390/electronics10243167 (2021).
Dennis, J. M. et al. Development of a treatment selection algorithm for SGLT2 and DPP-4 inhibitor therapies in people with type 2 diabetes: a retrospective cohort study. Lancet Digit. Health 4, e873–e883 (2022).
Google Scholar
Nair, A. T. N. et al. Heterogeneity in phenotype, disease progression and drug response in type 2 diabetes. Nat. Med. 28, 982–988 (2022).
Google Scholar
Mariam, A. et al. A type 2 diabetes subtype responsive to ACCORD intensive glycemia treatment. Diabetes Care 44, 1410–1418 (2021).
Google Scholar
Ahlqvist, E. et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 6, 361–369 (2018).
Google Scholar
Dennis, J. M., Shields, B. M., Henley, W. E., Jones, A. G. & Hattersley, A. T. Clusters provide a better holistic view of type 2 diabetes than simple clinical features – Authors’ reply. Lancet Diabetes Endocrinol. 7, 669 (2019).
Google Scholar
Dennis, J. M., Shields, B. M., Henley, W. E., Jones, A. G. & Hattersley, A. T. Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared with models based on simple clinical features: an analysis using clinical trial data. Lancet Diabetes Endocrinol. 7, 442–451 (2019).
Google Scholar
Veelen, A., Erazo-Tapia, E., Oscarsson, J. & Schrauwen, P. Type 2 diabetes subgroups and potential medication strategies in relation to effects on insulin resistance and beta-cell function: a step toward personalised diabetes treatment? Mol. Metab. 46, 101158 (2021).
Google Scholar
Mansour Aly, D. et al. Genome-wide association analyses highlight etiological differences underlying newly defined subtypes of diabetes. Nat. Genet. 53, 1534–1542 (2021).
Google Scholar
Dennis, J. M. et al. A five-drug class model using routinely available clinical features to optimise prescribing in type 2 diabetes: a prediction model development and validation study. Lancet 405, 701–714 (2025).
Google Scholar
Gomez-Peralta, F. et al. Interindividual differences in the clinical effectiveness of liraglutide in type 2 diabetes: a real-world retrospective study conducted in Spain. Diabetes Medicat. 35, 1605–1612 (2018).
Google Scholar
Kim, Y. G., Hahn, S., Oh, T. J., Park, K. S. & Cho, Y. M. Differences in the HbA1c-lowering efficacy of glucagon-like peptide-1 analogues between Asians and non-Asians: a systematic review and meta-analysis. Diabetes Obes. Metab. 16, 900–909 (2014).
Google Scholar
Cho, Y. M. Incretin physiology and pathophysiology from an Asian perspective. J. Diabetes Investig. 6, 495–507 (2015).
Google Scholar
Mathieu, C. et al. Effect of once weekly dulaglutide by baseline beta-cell function in people with type 2 diabetes in the AWARD programme. Diabetes Obes. Metab. 20, 2023–2028 (2018).
Google Scholar
Bonadonna, R. C. et al. Lixisenatide as add-on treatment among patients with different beta-cell function levels as assessed by HOMA-beta index. Diabetes Metab. Res. Rev. 33, https://doi.org/10.1002/dmrr.2897 (2017).
Overgaard, R. V., Hertz, C. L., Ingwersen, S. H., Navarria, A. & Drucker, D. J. Levels of circulating semaglutide determine reductions in HbA1c and body weight in people with type 2 diabetes. Cell Rep. Med. 2, 100387 (2021).
Google Scholar
Yale, J. F. et al. Real-world use of once-weekly semaglutide in patients with type 2 diabetes: pooled analysis of data from four SURE studies by baseline characteristic subgroups. BMJ Open Diabetes Res. Care 10, https://doi.org/10.1136/bmjdrc-2021-002619 (2022).
Chitnis, A. S., Ganz, M. L., Benjamin, N., Langer, J. & Hammer, M. Clinical effectiveness of liraglutide across body mass index in patients with type 2 diabetes in the United States: a retrospective cohort study. Adv. Ther. 31, 986–999 (2014).
Google Scholar
Kodama, K. et al. Ethnic differences in the relationship between insulin sensitivity and insulin response: a systematic review and meta-analysis. Diabetes Care 36, 1789–1796 (2013).
Google Scholar
Degn, K. B. et al. One week’s treatment with the long-acting glucagon-like peptide 1 derivative liraglutide (NN2211) markedly improves 24-h glycemia and alpha- and beta-cell function and reduces endogenous glucose release in patients with type 2 diabetes. Diabetes 53, 1187–1194 (2004).
Google Scholar
Kim, Y. & Babu, A. R. Clinical potential of sodium-glucose cotransporter 2 inhibitors in the management of type 2 diabetes. Diabetes Metab. Syndr. Obes. 5, 313–327 (2012).
Google Scholar
Gilbert, R. E. et al. Impact of age and estimated glomerular filtration rate on the glycemic efficacy and safety of canagliflozin: a pooled analysis of clinical studies. Can. J. Diabetes 40, 247–257 (2016).
Google Scholar
Wanner, C. et al. Empagliflozin and clinical outcomes in patients with type 2 diabetes mellitus, established cardiovascular disease, and chronic kidney disease. Circulation 137, 119–129 (2018).
Google Scholar
Lee, J. Y. et al. Predictors of the therapeutic efficacy and consideration of the best combination therapy of sodium-glucose co-transporter 2 inhibitors. Diabetes Metab. J. 43, 158–173 (2019).
Google Scholar
Baggio, L. L. & Drucker, D. J. Biology of incretins: GLP-1 and GIP. Gastroenterology 132, 2131–2157 (2007).
Google Scholar
Zhou, Y. et al. Use of radiomics based on (18)F-FDG PET/CT and machine learning methods to aid clinical decision-making in the classification of solitary pulmonary lesions: an innovative approach. Eur. J. Nucl. Med. Mol. Imaging 48, 2904–2913 (2021).
Google Scholar