Hemoglobina glicosilada en población diabética en periodo de pandemia covid-19 en un centro de atención primaria
Resumen
Introducción. La diabetes mellitus tipo 2 (DM2) es una enfermedad metabólica que afecta todas las aristas de la vida individual y familiar de la persona que la padece. La pandemia causada por el virus de SARS-COV-2 ha generado un problema relevante a nivel del sistema de salud, provocando una sobrecarga importante y una complejizarían de los servicios para atender la infección. Lo anterior, ha llevado a que muchas personas pierdan sus controles crónicos y no puedan cuidarse de manera adecuada. Métodos. La prueba de Wilcoxon se utilizó para comparar grupos de pacientes y variables continuas. Se aplicó un modelo de regresión lineal para estudiar la asociación entre la glicemia y la hemoglobina glicosilada. Se consideró un valor p <0.05 para aceptar la hipótesis alternativa de las pruebas estadísticas. Resultados. Se encontraron diferencias significativas entre población con niveles inferiores a 9% de hemoglobina glicosilada y niveles superiores o iguales a 9% en variables como glicemia, colesterol total, colesterol HDL, colesterol LDL y triglicéridos. En el modelo de regresión lineal se reportó R2 (0.61) y (0.22) entre la glicemia y la hemoglobina glicosilada con significancia estadística en todos los niveles (valor p<0.05). Conclusiones. Un mal ajuste de los niveles de HBA1C en población con DM2 podría generar una serie de comorbilidades como dislipidemias, hipertensión, enfermedad cardiovascular o infarto agudo al miocardio producto de la glucotoxicidad y lipotoxicidad.
Descargas
Citas
Ahmad Khan, H. (2007). Clinical significance of HbA1c as a marker of circulating lipids in male and female type 2 diabetic patients. Acta Diabetologica, 44(4), 193–200. https://doi.org/10.1007/s00592-007-0003-x
Andersen, G. E., Christiansen, J. S., Mortensen, H. B., Christiansen, K. M., Pedersen-Bjergaard, L., Kastrup, K. W., & Vestermark, S. (1983). Serum lipids and lipoproteins in 157 insulin dependent diabetic children and adolescents in relation to metabolic regulation, obesity and genetic hyperlipoproteinemia. Acta Paediatrica Scandinavica, 72(3), 361–365. https://doi.org/10.1111/j.1651-2227.1983.tb09729.x
Begum, A., Irfan, S. R., Hoque, M. R., Habib, S. H., Parvin, S., Malek, R., Akhter, S., Sattar, S., & Sarkar, S. (2019). Relationship between HbA1c and Lipid Profile Seen in Bangladeshi Type 2 Diabetes Mellitus Patients Attending BIRDEM Hospital: A Cross-Sectional Study. Mymensingh Medical Journal : MMJ, 28(1), 91–95.
Benoit, S. R., Hora, I., Albright, A. L., & Gregg, E. W. (2019). New directions in incidence and prevalence of diagnosed diabetes in the USA. BMJ Open Diabetes Research & Care, 7(1), e000657. https://doi.org/10.1136/bmjdrc-2019-000657
Botana López, M. A., López Ratón, M., Tomé, M. A., Fernández Mariño, A., Mato Mato, J. A., Rego Iraeta, A., Pérez Fernández, R., & Cadarso Suárez, C. (2012). Relationship between glycated hemoglobin and glucose concentrations in the adult Galician population: selection of optimal glycated hemoglobin cut-off points as a diagnostic tool of diabetes mellitus. Endocrinología y Nutrición, 59(8), 496–504. https://doi.org/10.1016/j.endonu.2012.06.001
Brockmeyer, M., Wies, E., Joerges, J., Sommer, J., Borgmann, S. O., Chernyak, N., Lin, Y., Parco, C., Schulze, V., Heinen, Y., Kelm, M., Icks, A., Perings, S., & Wolff, G. (2022). Knowledge of HbA1c and LDL-C treatment goals, subjective level of disease-related information and information needs in patients with atherosclerotic cardiovascular disease. Clinical Cardiology. https://doi.org/10.1002/clc.23948
Buková, L., Galajda, P., Javorský, M., & Mokáň, M. (2020). Glycated haemoglobin as a marker of elevated LDL and TAG: a cohort study. Vnitrni Lekarstvi, 66(6), 28–34.
Call, J. T., Cortés, P., & Harris, D. M. (2022). A practical review of diabetes mellitus type 2 treatment in primary care. Romanian Journal of Internal Medicine = Revue Roumaine de Medecine Interne, 60(1), 14–23. https://doi.org/10.2478/rjim-2021-0031
Cheng, Y. J., Imperatore, G., Geiss, L. S., Wang, J., Saydah, S. H., Cowie, C. C., & Gregg, E. W. (2013). Secular changes in the age-specific prevalence of diabetes among U.S. adults: 1988-2010. Diabetes Care, 36(9), 2690–2696. https://doi.org/10.2337/dc12-2074
Crespo, R., Alvarez, C., Hernandez, I., & García, C. (2020). A spatially explicit analysis of chronic diseases in small areas: a case study of diabetes in Santiago, Chile. International Journal of Health Geographics, 19(1), 24. https://doi.org/10.1186/s12942-020-00217-1
Erciyas, F., Taneli, F., Arslan, B., & Uslu, Y. (2004). Glycemic control, oxidative stress, and lipid profile in children with type 1 diabetes mellitus. Archives of Medical Research, 35(2), 134–140. https://doi.org/10.1016/j.arcmed.2003.10.002
Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). (2001). JAMA, 285(19), 2486–2497. https://doi.org/10.1001/jama.285.19.2486
Feng, L., Nian, S., Zhao, Y., Bai, X., Luo, F., Luo, X., Xu, W., Ye, D., & Tong, Z. (2018). Higher HbA1c and/or glucose levels alter the association patterns between glycated hemoglobin and fasting glucose levels. Diabetes Research and Clinical Practice, 142, 353–362. https://doi.org/10.1016/j.diabres.2018.06.011
Feng, X., Yang, Y., Zhuang, S., Fang, Y., Dai, Y., Fu, Y., Hu, Q., Yuan, Q., Tang, H., & Tang, L. (2021). Influence of Serum Albumin on HbA1c and HbA1c-Defined Glycemic Status: A Retrospective Study. Frontiers in Medicine, 8, 583093. https://doi.org/10.3389/fmed.2021.583093
Galicia-Garcia, U., Benito-Vicente, A., Jebari, S., Larrea-Sebal, A., Siddiqi, H., Uribe, K. B., Ostolaza, H., & Martín, C. (2020). Pathophysiology of Type 2 Diabetes Mellitus. International Journal of Molecular Sciences, 21(17). https://doi.org/10.3390/ijms21176275
HbA1c as a predictor of diabetes and as an outcome in the diabetes prevention program: a randomized clinical trial. (2015). Diabetes Care, 38(1), 51–58. https://doi.org/10.2337/dc14-0886
Ketema, E. B., & Kibret, K. T. (2015). Correlation of fasting and postprandial plasma glucose with HbA1c in assessing glycemic control; systematic review and meta-analysis. Archives of Public Health = Archives Belges de Sante Publique, 73, 43. https://doi.org/10.1186/s13690-015-0088-6
Khan, H. A., Sobki, S. H., & Khan, S. A. (2007). Association between glycaemic control and serum lipids profile in type 2 diabetic patients: HbA1c predicts dyslipidaemia. Clinical and Experimental Medicine, 7(1), 24–29. https://doi.org/10.1007/s10238-007-0121-3
Khaw, K. T., Wareham, N., Luben, R., Bingham, S., Oakes, S., Welch, A., & Day, N. (2001). Glycated haemoglobin, diabetes, and mortality in men in Norfolk cohort of european prospective investigation of cancer and nutrition (EPIC-Norfolk). BMJ (Clinical Research Ed.), 322(7277), 15–18. https://doi.org/10.1136/bmj.322.7277.15
Kishore, P., Kim, S. H., & Crandall, J. P. (2012). Glycemic control and cardiovascular disease: what’s a doctor to do? Current Diabetes Reports, 12(3), 255–264. https://doi.org/10.1007/s11892-012-0268-5
Lind, M., Odén, A., Fahlén, M., & Eliasson, B. (2009). The true value of HbA1c as a predictor of diabetic complications: simulations of HbA1c variables. PloS One, 4(2), e4412. https://doi.org/10.1371/journal.pone.0004412
Martín-Timón, I., Sevillano-Collantes, C., Segura-Galindo, A., & Del Cañizo-Gómez, F. J. (2014). Type 2 diabetes and cardiovascular disease: Have all risk factors the same strength? World Journal of Diabetes, 5(4), 444–470. https://doi.org/10.4239/wjd.v5.i4.444
Nayak, A. U., Holland, M. R., Macdonald, D. R., Nevill, A., & Singh, B. M. (2011). Evidence for Consistency of the Glycation Gap in Diabetes. Diabetes Care, 34(8), 1712–1716. https://doi.org/10.2337/dc10-1767
Nowakowska, M., Zghebi, S. S., Ashcroft, D. M., Buchan, I., Chew-Graham, C., Holt, T., Mallen, C., Van Marwijk, H., Peek, N., Perera-Salazar, R., Reeves, D., Rutter, M. K., Weng, S. F., Qureshi, N., Mamas, M. A., & Kontopantelis, E. (2019). The comorbidity burden of type 2 diabetes mellitus: patterns, clusters and predictions from a large English primary care cohort. BMC Medicine, 17(1), 145. https://doi.org/10.1186/s12916-019-1373-y
Saeedi, P., Petersohn, I., Salpea, P., Malanda, B., Karuranga, S., Unwin, N., Colagiuri, S., Guariguata, L., Motala, A. A., Ogurtsova, K., Shaw, J. E., Bright, D., & Williams, R. (2019). Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9(th) edition. Diabetes Research and Clinical Practice, 157, 107843. https://doi.org/10.1016/j.diabres.2019.107843
Seidu, S., Hambling, C., Holmes, P., Fernando, K., Campbell, N. S., Davies, S., & Khunti, K. (2022). The impact of the COVID pandemic on primary care diabetes services in the UK: A cross-sectional national survey of views of health professionals delivering diabetes care. Primary Care Diabetes, 16(2), 257–263. https://doi.org/10.1016/j.pcd.2021.12.015
Derechos de autor 2023 Daniel Riveros; Alex Ortiz-Cabezas;Jaume Canela-Soler;Antonio Monleón-Getino;Nicolas Ayala-Aldana
Esta obra está bajo licencia internacional Creative Commons Reconocimiento 4.0.
.jpg)
