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ORIGINAL RESEARCH

Data-Driven Identification of Long-Term Glycemia Clusters and Their Individualized Predictors in Finnish Patients with Type 2 Diabetes

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Pages 13-29 | Received 01 Jul 2022, Accepted 14 Dec 2022, Published online: 05 Jan 2023

References

  • International Diabetes Federation. IDF diabetes atlas. International Diabetes Federation; 2021. Availabe from: www.diabetesatlas.org. Accessed December 20, 2022.
  • American Diabetes Association. Economic cost of diabetes in the U.S. in 2017. Diabetes Care. 2018;41:917–928. doi:10.2337/dci18-0007
  • Colayco DC, Niu F, McCombs JS, Cheetham TC. A1C and cardiovascular outcomes in type 2 diabetes: a nested case-control study. Diabetes Care. 2011;34(1):77–83. doi:10.2337/dc10-1318
  • Zoungas S, Patel A, Chalmers J, et al. Severe hypoglycemia and risks of vascular events and death. N Engl J Med. 2010;363(15):1410–1418. doi:10.1056/NEJMoa1003795
  • Selvin E, Marinopoulos S, Berkenblit G, et al. Meta-analysis: glycosylated hemoglobin and cardiovascular disease in diabetes mellitus. Ann Intern Med. 2004;141:421–431. doi:10.7326/0003-4819-141-6-200409210-00007
  • Stratton IM, Adler AI, Neil HA, et al. Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ. 2000;321:405–412. doi:10.1136/bmj.321.7258.405
  • UKPDS Group. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet. 1998;352:837–853. doi:10.1016/S0140-6736(98)07019-6
  • Genuth S, Clinical I-BF. Implications of the ACCORD Trial. J Clin Endocrinol Metab. 2012;97(1):41–48. doi:10.1210/jc.2011-1679
  • Type 2 diabetes. Current care guidelines. working group set up by the Finnish medical society duodecim, the Finnish society of internal medicine and the medical advisory board of the Finnish diabetes society. Helsinki: The Finnish Medical Society Duodecim; 2020. Available from: https://www.kaypahoito.fi/hoi50056#K1. Accessed April 22, 2022.
  • Arnett DK, Blumenthal RS, Albert MA, et al. 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: a report of the American college of cardiology/American Heart Association Task Force on clinical practice guidelines. Circulation. 2019;140:e596–e646. doi:10.1161/CIR.0000000000000678
  • Cosentino F, Grant PJ, Aboyans V, et al. ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD. Eur Heart J. 2019;2020(41):255–323. doi:10.1093/eurheartj/ehz486
  • Buse JB, Wexler DJ, Tsapas A, et al. update to: management of hyperglycaemia in type 2 diabetes, 2018. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetologia. 2019;2020(63):221–228. doi:10.1007/s00125-019-05039-w
  • Ren X, Wang Z, Guo C. Long-term glycemic variability and risk of stroke in patients with diabetes: a meta-analysis. Diabetol Metab Syndr. 2022;14(1):6. doi:10.1186/s13098-021-00770-0
  • Cavalot F. Do data in the literature indicate that glycaemic variability is a clinical problem? Glycaemic variability and vascular complications of diabetes. Diabetes Obes Metab. 2013;15(Suppl. 2):3–8. doi:10.1111/dom.12140
  • Su G, Mi S, Tao H, et al. Association of glycemic variability and the presence and severity of coronary artery disease in patients with type 2 diabetes. Cardiovasc Diabetol. 2011;10:19. doi:10.1186/1475-2840-10-19
  • Wikström K, Lamidi M-L, Rautiainen P, Tirkkonen H, Kivinen P, Laatikainen T. The effect of the integration of health services on health care usage among patients with type 2 diabetes in North Karelia, Finland. BMC Health Serv Res. 2021;21(1):65. doi:10.1186/s12913-021-06059-2
  • WHO Collaborating Centre for Drug Statistics Methodology. ATC classification index with DDDs, 2019. Oslo, Norway; 2018. Available from: http://www.whocc.no/atc_ddd_index/. Accessed November 19, 2021.
  • Pikkujämsä S. Diabeteslaaturekisteripilotti – minimitietosisältöön tarvittava tietopohja. Rekisterin käynnistysvaihe 10.12.2019. (Minimum data content of the Finnish diabetes quality register). National Institute for Health and Welfare (THL). Available from: https://thl.fi/documents/2616650/4353715/12_2019_Minimitietosisa%CC%88lto%CC%88_alkuvaihe_lopullinen.pdf/8a9d733a-5019-4930-638f-7f374883bb01?t=1580828269938. Accessed June 22, 2022.
  • Statistics Finland. Paavo (open data by postal code area). Available from: https://pxnet2.stat.fi/PXWeb/pxweb/en/Postinumeroalueittainen_avoin_tieto/. Accessed June 22, 2022.
  • Toivakka M, Pihlapuro A, Tykkyläinen M, Mehtätalo L, Laatikainen T. The usefulness of small-area-based socioeconomic characteristics in assessing the treatment outcomes of type 2 diabetes patients: a register-based mixed-effect study. BMC Public Health. 2018;18(1):1258. doi:10.1186/s12889-018-6165-3
  • Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics. 1982;38:963–974. doi:10.2307/2529876
  • Muthén B, Shedden K. Finite mixture modelling with mixture outcomes using the EM algorithm. Biometrics. 1999;55:463–469. doi:10.1111/j.0006-341X.1999.00463.x
  • Jung T, Wickrama KAS. An introduction to latent class growth analysis and growth mixture modeling. Soc Personal Psychol Compass. 2008;2/1:302–317. doi:10.1111/j.1751-9004.2007.00054.x
  • Nagin DS, Odgers CL. Group-based trajectory modelling in clinical research. Annu Rev Clin Psychol. 2010;6:109–138. doi:10.1146/annurev.clinpsy.121208.131413
  • Nguena Nguefack HL, Pagé MG, Katz J, et al. Trajectory modelling techniques useful to epidemiological research: a comparative narrative review of approaches. Clin Epidemiol. 2020;12:1205–1222. doi:10.2147/CLEP.S265287
  • Muthén LK, Muthén BO. Mplus User’s Guide. 8th ed. Los Angeles, CA: Muthén & Muthén; 1998.
  • Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res. 2011;46(3):399–424. doi:10.1080/00273171.2011.568786
  • Chollet F. Deep Learning with Python. 2nd ed. Shelter Island, NY: Manning; 2021.
  • Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–2830.
  • Fisher RA. The use of multiple measurements in taxonomic problems. Ann Eugen. 1936;7:179–188. doi:10.1111/j.1469-1809.1936.tb02137.x
  • Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst. 2017;30:4768–4777.
  • Luo M, Tan CS, Lim WY, et al. Association of diabetes treatment with long-term glycemic patterns in patients with type 2 diabetes mellitus: a prospective cohort study. Diabetes Metab Res Rev. 2019;35:e3122. doi:10.1002/dmrr.3122
  • Karpati T, Leventer-Roberts M, Feldman B, et al. Patient clusters based on HbA1c trajectories: a step toward individualized medicine in type 2 diabetes. PLoS One. 2018;13:e0207096. doi:10.1371/journal.pone.0207096
  • Luo M, Lim WY, Tan CS, et al. Longitudinal trends in HbA1c and associations with comorbidity and all-cause mortality in Asian patients with type 2 diabetes: a cohort study. Diabetes Res Clin Pract. 2017;133:69–77. doi:10.1016/j.diabres.2017.08.013
  • Mast MR, Walraven I, Hoekstra T, et al. Effectiveness of insulin therapy in people with type 2 diabetes in the Hoorn diabetes care system. Diabet Med. 2016;33(6):794–802. doi:10.1111/dme.13110
  • Ravona-Springer R, Heymann A, Schmeidler J, et al. Trajectories in glycemic control over time are associated with cognitive performance in elderly subjects with type 2 diabetes. PLoS One. 2014;9:e97384. doi:10.1371/journal.pone.0097384
  • Chang HY, Wahlqvist ML, Liu WL, et al. Management trajectories in the type 2 diabetes Integrated Delivery System project in Taiwan: accounting for behavioral therapy, nutrition education and therapeutics. Asia Pac J Clin Nutr. 2014;23(4):592–606. PMID: 25516317. doi:10.6133/apjcn.2014.23.4.06
  • Wang CP, Hazuda HP. Better glycemic control is associated with maintenance of lower-extremity function over time in Mexican American and European American older adults with diabetes. Diabetes Care. 2011;34(2):268–273. doi:10.2337/dc10-1405
  • Hertroijs DFL, Elissen AMJ, Brouwers MCGJ, et al. A risk score including body mass index, glycated haemoglobin and triglycerides predicts future glycaemic control in people with type 2 diabetes. Diabetes Obes Metab. 2018;20(3):681–688. doi:10.1111/dom.13148
  • Wikström K, Lamidi M-L, Rautiainen P, Tirkkonen H, Laatikainen T. Type 2 diabetes medication and HbA1c levels in North Karelia Finland, 2013–2019. Diabet Med. 2022;39. doi:10.1111/dme.14866