Prognostic significance of HDL-C on long-term mortality in patients with COVID-19 pneumonia in the Turkish population: A potential mechanism for population differences
DOI:
https://doi.org/10.17305/bjbms.2022.7545Keywords:
COVID-19, HDL-C, lipoproteins, personalized medicine, public healthAbstract
Coronavirus disease 2019 (COVID-19) is diagnosed by the evidence of the presence of multiple phenotypes, including thrombosis, inflammation, and alveolar and myocardial damage, which can cause severe illness and mortality. High-density lipoprotein cholesterol (HDL-C) has pleiotropic properties, including anti-inflammatory, anti-infectious, antithrombotic, and endothelial cell protective effects. The aim of this study was to investigate the HDL-C levels and one-year mortality after the first wave of patients with COVID-19 were hospitalized. Data from 101 patients with COVID-19 were collected for this single-center retrospective study. Lipid parameters were collected on the admission. The relationship between lipid parameters and long-term mortality was investigated. The mean age of the non-survivor group (n = 38) was 68.8 ± 14.1 years, and 55% were male. The HDL-C levels were significantly lower in the non-survivors group compared with the survivors (26.9 ± 9.5 vs 36.8 ± 12.8 mg/dl, respectively p < 0.001). Multivariate regression analysis determined that age, C-reactive protein, D-dimer, hypertension, and HDL-C as independent predictors for the development of COVID-19 mortality. HDL-C levels <30.5 mg/dl had 71% sensitivity and 68% specificity to predict one-year mortality after COVID-19. The findings of this study showed that HDL-C is a predictor of one-year mortality in Turkish patients with COVID-19. COVID-19 is associated with decreased lipid levels, and it is an indicator of the inflammatory burden and increased mortality rate. The consequences of long-term metabolic dysregulations in patients that have recovered from COVID-19 still need to be understood.
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Copyright (c) 2022 Ömer Faruk Baycan, Furkan Bölen, Başak Atalay, Mehmet Agirbasli
This work is licensed under a Creative Commons Attribution 4.0 International License.