Keywords
Mellitus
Clasificacion
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Abstract
Background: According to data from the International Diabetes Federation (IDF) in 2021, 3 out of every 4 patients with diabetes live in low- or middle-income countries and the number of individuals is expected to reach 783 million by 2045. Diabetes was the cause of 12.2% of mortality in the world and the countries with the highest number of people with diabetes are China, India and the United States. In South America, Brazil occupies first place, followed by Mexico and Colombia. The integral management of diabetes is recommended in the guidelines and the interdisciplinary approach programs seek adequate control, with a reduction in hypoglycemia, complications and generation of self-care. The classification of diabetes is a fundamental pillar in the therapeutic approach, prognosis and possible complications.
Purpose: A narrative bibliographic review of the Diabetes Classification and its characteristics was carried out.
Methodology: In this research project, a search was carried out for indexed articles from the years 2016 to 2022 in the Pubmed, Embase, Science direct and Scopus search engines.
Results: A total of 45 articles were found and 19 were selected by convenience.
Conclusion: An adequate classification of diabetes improves the prognosis, prevents complications and reduces expenses. The ADA classification and the proposed variables are not sufficient to subclassify T2D phenotypes. The use of more variables in the subclassification will generate a more complex process, but will allow better precision in treatments, prevention of complications and reduction of costs.
References
American Diabetes Association Professional Practice Committee. 2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes-2024. Diabetes Care. 2024;47(supl. 1):S20-42. https://doi.org/10.2337/dc24-S002
Coleman K, Austin BT, Brach C, Wagner EH. Evidence on the chronic care model in the new millennium. Health Aff. 2009;28(1):75-85. https://doi.org/10.1377/hlthaff.28.1.75
Vargas-Uricoechea, Casas-Figueroa LA. Epidemiología de la diabetes mellitus en Sudamérica: la experiencia de Colombia. Clin Invest Arterioscl. 2016;28(5):245-56. https://doi.org/10.1016/j.arteri.2015.12.002
International Diabetes Federation. IDF Diabetes Atlas; 2021. https://diabetesatlas.org/
World Health Organization. 2022 Global Health Indicators. https://www.who.int/data/gho/publications/world-health-statistics
Rodríguez J, Ruiz F, Peñaloza E, Eslava J, Gómez LC, Sánchez H, et al. Encuesta Nacional de Salud 2007: Resultados Nacionales; 1.a edición. Bogotá: Ministerio de la Protección Social; 2009.
Cuenta de Alto Costo. Situación de la enfermedad renal crónica, la hipertensión arterial y diabetes mellitus en Colombia 2019. https://cuentadealtocosto.org/publicaciones/situacion-erc-hip-art-dmell-col/
Chung WK, Erion K, Florez JC, Hattersley AT, Hivert MF, Lee CG, et al. Precision medicine in diabetes: a consensus report from the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care. 2020;43(7):1617-35. https://doi.org/10.2337/dci20-0022
Mahajan A, Taliun D, Thurner M, Robertson NR, Torres JM, Rayner NW, et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat Genet. 2018;50:1505-13. https://doi.org/10.1038/s41588-018-0241-6
Udler MS, McCarthy MI, Florez JC, Mahajan A. Genetic risk scores for diabetes diagnosis and precision medicine. Endocr Rev. 2019;40(6):1500-20. https://doi.org/10.1210/er.2019-00088
Hawa MI, Kolb H, Schloot N, Beyan H, Paschou SA, Buzzetti R, et al. Adult-onset autoimmune diabetes in Europe is prevalent with a broad clinical phenotype: Action LADA 7. Diabetes Care. 2013;36:908-13. https://doi.org/10.2337/dc12-0931
Ziegler AG, Rewers M, Simell O, Lempainen J, Steck A, Winkler C, et al. Seroconversion to multiple islet autoantibodies and risk of progression to diabetes in children. JAMA. 2013;309:2473-9. https://doi.org/10.1001/jama.2013.6285
Barker JM, Barriga KJ, Yu L, Miao D, Erlich HA, Norris JM, et al. Prediction of autoantibody positivity and progression to type 1 diabetes: diabetes autoimmunity study in the young (DAISY). J Clin Endocrinol Metab. 2004;89(8):3896?902. https://doi.org/10.1210/jc.2003-031887
Borg H, Gottsater A, Fernlund P, Sundkvist G. A 12?year prospective study of the relationship between islet antibodies and beta?cell function at and after the diagnosis in patients with adult?onset diabetes. Diabetes. 2002;51(6):1754?62. https://doi.org/10.2337/diabetes.51.6.1754
Huang G, Yin M, Xiang Y, Li X, Shen W, Luo S, et al. Persistence of glutamic acid decarboxylase antibody (GADA) is associated with clinical characteristics of latent autoimmune diabetes in adults: a prospective study with 3?year follow?up. Diabetes Metab Res Rev. 2016;32(6):615?22. https://doi.org/10.1002/dmrr.2779
Liu L, Li X, Xiang Y, Huang G, Lin J, Yang L, et al. Latent autoimmune diabetes in adults with low?titer GAD antibodies: similar disease progression with type 2 diabetes: a nationwide, multicenter prospective study (LADA China study 3). Diabetes Care. 2015;38(1):16?21. https://doi.org/10.2337/dc14-1770
Holt RIG, DeVries JH, Hess-Fischl A, Hirsch IB, Kirkman MS, Klupa T, et al. The management of type 1 diabetes in adults. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetología. 2021;64(12):2609-52. https://doi.org/10.1007/s00125-021-05568-3
Yasui J, Kawasaki E, Tanaka S, Awata T, Ikegami H, Imagawa A, et al. Japan Diabetes Society Committee on Type 1 Diabetes Mellitus Research. Clinical and genetic characteristics of non-insulin-requiring glutamic acid decarboxylase (GAD) autoantibody-positive diabetes: a nationwide survey in Japan. PLoS One. 2016;11:e0155643. https://doi.org/10.1371/journal.pone.0155643
Carlsson A, Shepherd M, Ellard S, Weedon M, Lenmark A, Forsander G, et al. Absence of islet autoantibodies and modestly raised glucose values at diabetes diagnosis should lead to testing for MODY: lessons from a 5-year pediatric Swedish national cohort study. Diabetes Care. 2020;43(1):82-9. https://doi.org/10.2337/dc19-0747
Ellard S, Colclough K, Patel KA, Hattersley AT. Prediction algorithms: pitfalls in interpreting genetic variants of autosomal dominant monogenic diabetes. J Clin Invest. 2019;130(1):14-16. https://doi.org/10.1172/JCI133516
Schwartz SS, Epstein S, Corkey BE, Grant SFA, Gavin JR, Aguilar RB. The time is right for a new classification system for diabetes: rationale and implications of the b-cell-centric classification schema. Diabetes Care. 2016;39(2):179-86. https://doi.org/10.2337/dc15-1585
Matta GJ, Ballestas-Alarcón LM, Ramírez-Rincón A. GLP-1 agonists plus SGLT2 inhibitors. Additive cardioprotective effects? Med Int Méx. 2018;34(4):601-13. http://dx.doi.org/10.24245/mim.v34i4.1862
Dennis JM, Shields BM, Henley WE, Jones AG, Hattersley AT. 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. 2019;7(6):442-451. https://doi.org/10.1016/S2213-8587(19)30087-7
Gouda P, Zheng S, Peters T, Fudim M, Randhawa VK, Ezekowitz J, et al. Clinical phenotypes in patients with DT2: characteristics, cardiovascular outcomes and treatment strategies. Curr Heart Fail Rep. 2021;18(5):253-63. https://doi.org/10.1007/s11897-021-00527-w
Sharma A, Ofstad A, Ahmad T, Zinman B, Zwiener I, Fitchett D, et al. Patient phenotypes and SGLT-2 inhibition in type 2 diabetes: insights from the EMPA-REG OUTCOME Trial. JACC: Heart Failure. 2021;9(8):568-77. https://doi.org/10.1016/j.jchf.2021.03.003
Ernande L, Audureau E, Jellis CL, Bergerot C, Henegar C, Sawaki D, et al. Clinical Implications of echocardiographic phenotypes of patients with diabetes mellitus. J Am Coll Cardiol. 2017;70(14):1704-16. https://doi.org/10.1016/j.jacc.2017.07.792
Balasubramanyam A. Defining and classifying new subgroups of Diabetes. Annu Rev Med. 2021;72:63-74. https://doi.org/10.1146/annurev-med-050219-034524
Li L, Cheng WY, Glicksberg BS, Gottesman O, Tamler R, Chen R, et al. Identification of type 2 diabetes subgroups through topological analysis of patient similarity. Sci Transl Med. 2015;7(311): 311ra174. https://doi.org/10.1126/scitranslmed.aaa9364
Stidsen J, Henriksen J, Olsen M, Thomsen RW, Nielsen JS, Rungby J, et al. Pathophysiology?based phenotyping in type 2 diabetes: A clinical classification tool. Diabetes Metab Res Rev. 2018;34(5):e3005. https://doi.org/10.1002/dmrr.3005
Ahlqvist E, Storm P, Käräjämäki A, Martinell M, Dorkhan M, Carlsson A, et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 2018;6(5):361-9. https://doi.org/10.1016/S2213-8587(18)30051-2
Anjana RM, Baskar V, Narayanan Nair AT, Jebarani S, Kalhan MS, Pradeepa R, et al. Novel subgroups of type 2 diabetes and their association with microvascular outcomes in an Asían Indian population: a data-driven cluster analysis: The INSPIRED Study. BMJ Open Diab Res Care. 2020;8(1):e001506. https://doi.org/10.1136/bmjdrc-2020-001506
Ahlqvist E, Prasad RB, Groop L. 100 years of insulin: towards improved precision and a new classification of diabetes mellitus. J Endocrinol. 2021;252(3):R59-70. https://doi.org/10.1530/JOE-20-0596
Deutsch AJ, Ahlsqvist E, Udler MS. Phenotypic and genetic classification of diabetes. Diabetologia. 2022;65:1758-69. https://doi.org/10.1007/s00125-022-05769-4
Zaharia OP, Strassburger K, Strom A, Bönhof GJ, Karusheva Y, Antoniou S, et al. Risk of diabetes-associated diseases in subgroups of patients with recent-onset diabetes: a 5-year follow-up study. Lancet Diabetes Endocrinol. 2019;7:684-94. https://doi.org/10.1016/S2213-8587(19)30187-1
Zou X, Zhou X, Zhu Z, Ji L. Novel subgroups of patients with adult-onset diabetes in Chinese and US populations. Lancet Diabetes Endocrinol 2019;7(1):9-11. https://doi.org/10.1016/S2213-8587(18)30316-4
Tanabe H, Saito H, Kudo A, Machii N, Hirai H, Maimaituxun G, et al. Factors associated with risk of diabetic complications in novel cluster-based diabetes subgroups: a Japanese retrospective cohort study. J Clin Med. 2020;9(7):2083. https://doi.org/10.3390/jcm9072083
Bello-Chavolla OY, Bahena-López JP, Vargas-Vázquez A, Antonio-Villa NE, Márquez-Salinas, Fermín-Martínez CA, et al. Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach. BMJ Open Diabetes Res Care. 2020;8(1):e001550. https://doi.org/10.1136/bmjdrc-2020-001550
Tao R, Yu X, Lu J, Shen Y, Lu W, Zhu W, et al. Multinivel clustering approach driven by continuous glucose monitoring data for further classification of type 2 diabetes. BMJ Open Diabetes Res Care. 2021;9:e001869. http://dx.doi.org/10.1136/bmjdrc-2020-001869
Wesolowska-Andersen A, Brorsson CA, Bizzotto R, Mari A, Tura A, Koivula R, et al. Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT STUDY. Cell Rep Med. 2022;3(1): 100477. https://doi.org/10.1016/j.xcrm.2021.100477
Li X, Yang S, Cao C, Yan X, Zheng L, Zheng L, et al. Validation of the Swedish diabetes re-grouping scheme in adult-onset diabetes in China. J Clin Endocrinol Metab. 2020;105(10):dgaa524.
Prasad RB, Ahlqvist E, Groop L 2019 Heterogeneity of diabetes - an Indian perspective. Diabetes and Metabolic Syndrome. 2019;13(5):3065-7. https://doi.org/10.1016/j.dsx.2018.07.001
Xiong XF, Yang Y, Wei L, Xiao Y, Li L, Sun L. Identification of two novel subgroups in patients with diabetes mellitus and their association with clinical outcomes: a two-step cluster analysis. J Diabetes Investig. 2021;12(8):1346-58. https://doi.org/10.1111/jdi.13494
Prasad RB, Asplund O, Shukla SR, Wagh R, Kunte P, Bhat D, et al. Subgroups of young type 2 diabetes in india reveal insulin deficiency as a major driver. Diabetologia. 2022;65(1):65-78. https://doi.org/10.1007/s00125-021-05543-y
Polack F. Resistencia a la insulina: verdades y controversias. Rev Med Clin Las Condes. 2016;27(2):171-8. https://doi.org/10.1016/j.rmclc.2016.04.006
Radziuk J. Homeostastic Model Assessment and Insulin Sensitivity/Resistance. Diabetes. 2014;63(6):1850-4. https://doi.org/10.2337/db14-0116
García García C, Labrac Aranda P, Bordón Poderoso C, Muñoz Hinojosa M, Boxó Cifuentes JR. HOMA como herramienta para la decisión en diabetes. valoración de su aplicación en atención primaria. Med Fam Andal. 2021;22(1).
Mishra R, Hodge KM, Cousminer DL, Leslie RD, Grant SFA. A global perspective of latent autoimmune diabetes in adults. Trends Endocrinol Metab. 2018;29(9):638-50. https://doi.org/10.1016/j.tem.2018.07.001
Jones AG, McDonald TJ, Shields BM, Hagoplan W, Hattersley AT. Latent autoinmmune diabetes of adult (LADA) is likely to represent a mixed population of autoimmune (type 1) and nonautoinmmune (type 2) diabetes. Diabetes Care. 2021;44(6):1243-51. https://doi.org/10.2337/dc20-2834
Banerjee P, Khan NZ, Singh ST, Singh N, Qamar I. Latent autoimmune diabetes in adults: complication, management and treatment modalities. Endocrinol Metab Int J. 2019;7(3):67-72. https://doi.org/10.15406/emij.2019.07.00246
Pigeyre M, Hess S, Gomez MF, Asplund O, Groop L, Paré G, et al. Validation of the classification for type 2 diabetes into five subgroups: a report from the ORIGIN trial. Diabetologia. 2022;65(1):206-215. https://doi.org/10.1007/s00125-021-05567-4
Kahkoska AR, Geybels MS, Klein KR, Kreiner FF, Marx N, Nauck MA, et al. Validation of distinct type 2 diabetes clusters and their association with diabetes complications in the DEVOTE, LEADER and SUSTAIN-6 cardiovascular outcomes trials. Diabetes Obes Metab. 2020;22(9):1537-47. https://doi.org/10.1111/dom.14063
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