Association of serum HDL-cholesterol and apolipoprotein A1 levels with risk of severe SARS-CoV-2 infection

Abstract

Individuals with features of metabolic syndrome are particularly susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel coronavirus associated with the severe respiratory disease, coronavirus disease 2019 (COVID-19). Despite considerable attention dedicated to COVID-19, the link between metabolic syndrome and SARS-CoV-2 infection remains unclear. Using data from the UK Biobank, we investigated the relationship between severity of COVID-19 and metabolic syndrome-related serum biomarkers measured prior to SARS-CoV-2 infection. Logistic regression analyses were used to test biomarker levels and biomarker-associated genetic variants with SARS-CoV-2-related outcomes. Among SARS-CoV-2-positive cases and negative controls, a 10 mg/dl increase in serum HDL-cholesterol or apolipoprotein A1 levels was associated with ∼10% reduced risk of SARS-CoV-2 infection, after adjustment for age, sex, obesity, hypertension, type 2 diabetes, and coronary artery disease. Evaluation of known genetic variants for HDL-cholesterol revealed that individuals homozygous for apolipoprotein E4 alleles had ∼2- to 3-fold higher risk of SARS-CoV-2 infection or mortality from COVID-19 compared with apolipoprotein E3 homozygotes, even after adjustment for HDL-cholesterol levels. However, cumulative effects of all evaluated HDL-cholesterol-raising alleles and Mendelian randomization analyses did not reveal association of genetically higher HDL-cholesterol levels with decreased risk of SARS-CoV-2 infection. These results implicate serum HDL-cholesterol and apolipoprotein A1 levels measured prior to SAR-CoV-2 exposure as clinical risk factors for severe COVID-19 infection but do not provide evidence that genetically elevated HDL-cholesterol levels are associated with SAR-CoV-2 infection.

Keywords: ApoE4; COVID-19; HDL-cholesterol; Mendelian randomization; SAR-CoV-2; apolipoprotein A1; genetic risk score; genetic variants; metabolic syndrome.

Fig. 1

Overview of clinical and genetic analyses. A study was designed using data made available by the UK Biobank where cases were defined as symptomatic subjects who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in a hospital setting or whose cause of death was attributed to coronavirus disease 2019 (COVID-19) (green box). Unmatched controls were defined as subjects who tested negative for SARS-CoV-2 infection in inpatient or outpatient hospital settings. A subset of these subjects was used for a data set where controls were matched to cases at a ratio of 2:1 based on complete data for age, sex, obesity, hypertension, type 2 diabetes, and coronary artery disease (gray box). Comparisons of differences in nine metabolic disease-related biomarker levels between cases and controls (yellow box) were followed by logistic regression analyses with HDL-cholesterol, ApoA1, and triglyceride levels (pink box). Previously identified genetic variants associated with HDL-cholesterol levels were then evaluated for association with risk of SARS-CoV-2 infection or mortality from COVID-19 using various analytical strategies (blue box).

Fig. 2

Association of serum HDL-cholesterol, apolipoprotein A1 (ApoA1), and triglyceride levels with risk of severe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Increased levels of HDL-cholesterol or ApoA1 were associated with ∼15–20% decreased risk of SARS-CoV-2 infection among unmatched (filled-in symbols) or matched (open symbols) cases and controls, even after adjustment for multiple covariates. Increased triglyceride levels were associated with ∼10–15% increased risk of SARS-CoV-2 infection in only the matched case-control data set. Logistic regression analyses were used for comparisons between cases and unmatched controls with adjustment for age, sex, principal component 1–10, obesity, hypertension, type 2 diabetes, CAD, BMI, education, and smoking. Conditional logistic regression analyses were used for comparisons between cases and matched controls with adjustment for only principal component 1–10, BMI, education, and smoking. Data are shown as odds ratios (ORs) with 95% confidence intervals (95% CIs) for a one unit increase in the interquartile range of each biomarker (provided in Table 2). ∗P < 0.05; ∗∗P < 0.01; and ∗∗∗P < 0.005.