Predictors of Poor Glycemic Control and Increased Glucose Variability Among Admitted Moderate to Critical COVID-19 Patients with Type 2 Diabetes Mellitus

A Single Center Cross-Sectional Study

Authors

  • John Paul Martin Bagos University of Santo Tomas Hospital, Manila, Philippines https://orcid.org/0000-0002-8665-8377
  • Erick Mendoza University of Santo Tomas Hospital, Manila, Philippines
  • Bien Matawaran University of Santo Tomas Hospital, Manila, Philippines

DOI:

https://doi.org/10.15605/jafes.038.02.07

Keywords:

COVID-19, type 2 Diabetes, hyperglycemia, risk factors

Abstract

Objectives. COVID-19 exacerbates the long-standing, low-grade chronic inflammation observed in diabetes leading to heightened insulin resistance and hyperglycemia. Mortality increases with hyperglycemia and poor glycemic variability, hence, this study aims to identify the predictors associated with poor glycemic control and increased glucose variability among patients with COVID-19 and Type 2 Diabetes Mellitus (T2DM).

Methodology. A retrospective chart review of 109 patients with moderate to severe COVID-19 and T2DM admitted from March 2020 to June 2021 was done. Logistic regression was done to determine predictors for hyperglycemia and poor variability.

Results. Of the 109 patients, 78% had hyperglycemia and poor variability and 22% had no poor outcomes. Chronic kidney disease (eOR 2.83, CI [1.07-7.46], p=0.035) was associated with increased glycemic variability. In contrast, increasing eGFR level (eOR 0.97, CI [0.96-0.99], p=0.004) was associated with less likelihood of increased variability. Hs-CRP (eOR 1.01, CI [1.00-1.01], p=0.011), HbA1c (eOR 1.86, CI [1.23-2.82], p=0.003), severe COVID-19 (eOR 8.91, CI [1.77-44.94], p=0.008) and critical COVID-19 (eOR 4.42, CI [1.65-11.75], p=0.003) were associated with hyperglycemia. Steroid use (eOR 71.17, CI [8.53-593.54],  p<0.001) showed the strongest association with hyperglycemia.

Conclusion. Potential clinical, laboratory and inflammatory profiles were identified as predictors for poor glycemic control and variability outcomes. HbA1c, hs-CRP, and COVID-19 severity are predictors of hyperglycemia. Likewise, chronic kidney disease is a predictor of increased glycemic variability.

Downloads

Download data is not yet available.

Author Biographies

John Paul Martin Bagos, University of Santo Tomas Hospital, Manila, Philippines

Department of Medicine, Section of Endocrinology, Diabetes and Metabolism

Erick Mendoza, University of Santo Tomas Hospital, Manila, Philippines

Consultant, Department of Medicine, Section of Endocrinology, Diabetes and Metabolism

Bien Matawaran, University of Santo Tomas Hospital, Manila, Philippines

Consultant, Department of Medicine, Section of Endocrinology, Diabetes and Metabolism

References

Xu Z, Wang Z, Wang S, et al. The impact of type 2 diabetes and its management on the prognosis of patients with severe COVID-19. J Diabetes. 2020;12(12):909–18. https://pubmed.ncbi.nlm.nih.gov/32638507. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7361557. https://doi.org/10.1111/1753-0407.13084

Cheng Y, Yue L, Wang Z, Zhang J, Xiang G. Hyperglycemia associated with lymphopenia and disease severity of COVID-19 in type 2 diabetes mellitus. J Diabetes Complications. 2021;35(2):107809. https://pubmed.ncbi.nlm.nih.gov/33288414. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7690319. https://doi.org/10.1016/j.jdiacomp.2020.107809.

Sonmez A, Demirci I, Haymana C, et al. Clinical characteristics and outcomes of COVID-19 in patients with type 2 diabetes in Turkey: A nationwide study (TurCoviDia). J Diabetes.2021;13(7):585-95. https://pubmed.ncbi.nlm.nih.gov/33655669. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8013711. https://doi.org/10.1111/1753-0407.13171

Cariou B, Hadjadj S, Wargny M, et al. Phenotypic characteristics and prognosis of inpatients with COVID-19 and diabetes: The CORONADO study. Diabetologia. 2020;63(8):1500-15. https://pubmed.ncbi.nlm.nih.gov/32472191. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256180. https://doi.org/10.1007/s00125-020-05180-x.

Lim S, Bae JH, Kwon HS, Nauck MA. COVID-19 and diabetes mellitus: From pathophysiology to clinical management. Nat Rev Endocrinol. 2021;17(1):11–30. https://pubmed.ncbi.nlm.nih.gov/33188364. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7664589. https://doi.org/10.1038/s41574-020-00435-4.

Zhu L, She ZG, Cheng X, et al. Association of blood glucose control and outcomes in patients with COVID-19 and pre-existing type 2 diabetes. Cell Metab. 2020;31(6):1068-77.e3. https://pubmed.ncbi.nlm.nih.gov/32369736. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7252168. https://doi.org/10.1016/j.cmet.2020.04.021.

Peacock JL, Peacock PJ. Research design. Oxford handbook of medical statistics. United States: Oxford University Press; 2011. https://www.academia.edu/40959618/Oxford_Handbook_of_Medical_Statistics_Janet_L_Peacock.

Philippine Society for Microbiology and Infectious Diseases, Philippine College of Chest Physicians, Philippine College of Physicians, Philippine Rheumatology Association and Philippine College of Hematology and Transfusion Medicine. Interim guidance on the clinical management of adult patients with suspected or confirmed COVID-19 infection version 3.1; 2020. https://www.psmid.org/wp-content/uploads/2020/07/Final-PCP-PSMID-PCCP-COVID-19-Guidelines-20July2020b.pdf.

Tura A, Farngren J, Schweizer A, Foley JE, Pacini G, Ahrén B. Four-point preprandial self-monitoring of blood glucose for the assessment of glycemic control and variability in patients with type 2 diabetes treated with insulin and vildagliptin. Int J Endocrinol. 201;2015:484231. https://pubmed.ncbi.nlm.nih.gov/26587020. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4637474. https://doi.org/10.1155/2015/484231.

Rodbard D. Interpretation of continuous glucose monitoring data: Glycemic variability and quality of glycemic control. Diabetes Technol Ther. 2009;11(Suppl 1):S55–67. https://pubmed.ncbi.nlm.nih.gov/19469679. https://doi.org/10.1089/dia.2008.0132.

Battelino T, Danne T, Bergensta RM, et al. Clinical targets for continuous glucose monitoring data interpretation: Recommendations from the international consensus on time in range. Diabetes Care. 2019;42(8):1593–603. https://pubmed.ncbi.nlm.nih.gov/31177185. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6973648. https://doi.org/10.2337/dci19-0028.

Cutruzzolà A, Irace C, Parise M, et al. Time spent in target range assessed by self-monitoring blood glucose associates with glycated hemoglobin in insulin treated patients with diabetes. Nutr Metab Cardiovasc Dis. 2020;30(10):1800-5. https://pubmed.ncbi.nlm.nih.gov/32669240. https://doi.org/10.1016/j.numecd.2020.06.009.

Monnier L, Colette C, Wojtusciszyn A, et al. Toward defining the threshold between low and high glucose variability in diabetes. Diabetes Care. 2017;40(7):832-8. https://pubmed.ncbi.nlm.nih.gov/28039172. https://doi.org/10.2337/dc16-1769.

Rajpal A, Rahimi L, Ismail-Beigi F. Factors leading to high morbidity and mortality of COVID-19 in patients with type 2 diabetes. J Diabetes. 2020;12(12):895–908. https://pubmed.ncbi.nlm.nih.gov/32671936. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7405270. https://doi.org/10.1111/1753-0407.13085.

Lontchi-Yimagou E, Sobngwi E, Matsha TE, Kengne AP. Diabetes mellitus and inflammation. Curr Diab Rep. 2013;13(3):435–44. https://pubmed.ncbi.nlm.nih.gov/23494755. https://doi.org/10.1007/s11892-013-0375-y.

Pal R, Bhadada SK. COVID-19 and diabetes mellitus: An unholy interaction of two pandemics. Diabetes Metab Syndr. 2020;14(4):513–7. https://pubmed.ncbi.nlm.nih.gov/32388331. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7202837. https://doi.org/10.1016/j.dsx.2020.04.049.

Nakamura M, Oda S, Sadahiro T, et al. Correlation between high blood IL-6 level, hyperglycemia, and glucose control in septic patients. Crit Care. 2012;6(2):R58. https://pubmed.ncbi.nlm.nih.gov/2249810. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3681387. https://doi.org/10.1186/cc11301.

Liu F, Li L, Xu M, et al. Prognostic value of interleukin-6, C-reactive protein, and procalcitonin in patients with COVID-19. J Clin Virol. 2020;127:104370. https://pubmed.ncbi.nlm.nih.gov/32344321. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7194648 https://doi.org/10.1016/j.jcv.2020.104370.

Deng F, Zhang L, Lyu L, et al. Increased levels of ferritin on admission predicts intensive care unit mortality in patients with COVID-19. Med Clin (Engl Ed).2021;156(7):324–31. https://pubmed.ncbi.nlm.nih.gov/33824908. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8016043. https://doi.org/10.1016/j.medcli.2020.11.030.

Noordam R, Vermond D, Drenth H, et al. High liver enzyme concentrations are associated with higher glycemia, but not with glycemic variability, in individuals without diabetes mellitus. Front Endocrinol (Lausanne). 2017;8:236. https://pubmed.ncbi.nlm.nih.gov/28955304. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5601417. https://doi.org/10.3389/fendo.2017.00236.

Aronoff S, Rosenblatt S, Braithwaite S, Egan JW, Mathisen AL, Schneider RL. Pioglitazone hydrochloride monotherapy improves glycemic control in the treatment of patients with type 2 diabetes: A 6-month randomized placebo-controlled dose-response study. The Pioglitazone 001 Study Group. Diabetes Care. 2000;23(11):1605–11. https://pubmed.ncbi.nlm.nih.gov/11092281. https://doi.org/10.2337/diacare.23.11.1605.

Downloads

Published

2023-06-21

How to Cite

Bagos, J. P. M., Mendoza, E., & Matawaran, B. (2023). Predictors of Poor Glycemic Control and Increased Glucose Variability Among Admitted Moderate to Critical COVID-19 Patients with Type 2 Diabetes Mellitus: A Single Center Cross-Sectional Study. Journal of the ASEAN Federation of Endocrine Societies, 38(2), 57–64. https://doi.org/10.15605/jafes.038.02.07

Issue

Section

Original Articles