Lack of significant association between dyslipidemia and COVID-19 mortality.

Recently, Aung et al. reported that distribution frequency of angiotensin converting enzyme (ACE) insertion/insertion (II) genotype had a significant impact on coronavirus disease 2019 (COVID-19) mortality. Dyslipidemia is one of the most comorbidities among COVID-19 patients, however, the conclusions from published articles on the relationship between dyslipidemia and COVID-19 mortality are still controversial. For instance, several studies found that there was a significant relationship between dyslipidemia and an increased risk for mortality among COVID-19 patients,2, 3, 4 while other studies reported that dyslipidemia was not significantly associated with COVID-19 mortality. , Therefore, there is an urgent need to address the relationship between dyslipidemia and COVID-19 mortality by a quantitative meta-analysis. It has been reported that demographical characteristics (age and gender) and certain comorbidities (diabetes mellitus, cardiovascular disease, hypertension, chronic kidney disease and autoimmune diseases, etc.) are well-known modulators that affect the clinical outcomes of COVID-19 patients,7, 8, 9 suggesting that these factors might modulate the relationship between dyslipidemia and COVID-19 mortality. Thus, in this current meta-analysis, risk factors-adjusted effect estimates rather than crude effect estimates were utilized to calculate the pooled effect sizes.

We did this systematic meta-analysis in accordance with the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). All potentially eligible articles published between January 1, 2020 and February 26, 2021 were identify in the online databases (PubMed, Web of Science and EMBASE) with the following keywords: “SARS-CoV-2” or “severe acute respiratory syndrome coronavirus 2” or “COVID-19” or “coronavirus disease 2019” or “2019-nCoV” or "2019 novel coronavirus" and “dyslipidemia” or “hyperlipidemia” or “low-density lipoprotein” or “high-density lipoprotein” or “triglycerides” or “total cholesterol”. Reference lists of eligible articles were also searched to look for additional studies. The exposure group was defined as COVID-19 patients with dyslipidemia and the control group was defined as COVID-19 patients without dyslipidemia. The outcome of interest was mortality, which was defined as mortality, death, died, non-survivor, fatality or deceased. All peer-reviewed articles published in English language reporting the risk factors-adjusted effect estimate on the relationship between dyslipidemia and COVID-19 mortality were eligibly selected. Accordingly, we excluded preprints, case reports, review papers, corrections, comments, animal studies and in vitro studies, studies reporting crude effect estimate, studies without sufficient data and studies reporting clinical outcomes as severe/critical illness, intensive care unit admission, invasive mechanical ventilation/intubation or composite outcomes rather than mortality. Essential information including first author, number of COVID-19 patients, gender distribution, age (mean and standard deviation (SD) or median and interquartile range (IQR)), study design, region/country, clinical outcomes, adjusted effect estimates and adjusted variables was extracted from each included study (Table 1 ).

Table 1

Characteristics of the included studies.

Author	Country	Cases (n)	Male (%)	Age (years)§	Study design	Adjusted-effect (95% CI)	Adjusted variables	Clinical outcomes
Hashemi et al. (PMID: 32585065)	USA	363	55.4%	63.4 ± 16.5	Retrospective study	OR: 0.91 (0.46–1.81)	Chronic liver disease, age, obesity, male, cardiac diseases, hypertension, diabetes, pulmonary disorders	Death
Pettit et al. (PMID: 32589784)	USA	238	47.5%	58.5 ± 17	Retrospective study	OR: 1.7 (0.4–7.0)	Obesity, age, gender, hypertension, diabetes, pulmonary disease, cardiovascular disease, kidney disease, cancer, stroke, venous thromboembolism	Mortality
Grasselli et al. (PMID: 32667669)	Italy	3988	79.9%	63 (56–69)	Retrospective study	HR: 1.25 (1.02–1.52)	Age, men, respiratory support, hypertension, heart disease, type 2 diabetes, malignancy, chronic obstructive pulmonary disease, angiotensin-converting enzyme inhibitor therapy, angiotensin receptor blocker therapy, statin, diuretic, positive end-expiratory pressure at admission, fraction of inspired oxygen at admission, arterial partial pressure of oxygen/fraction of inspired oxygen at admission	Mortality
Tartof et al. (PMID: 32783686)	USA	6916	45%	49.1 ± 16.6	Retrospective study	RR: 1.47 (1.02–2.11)	Body mass index, age, sex, race/ethnicity, smoking, metastatic tumor/cancer, myocardial infarction, other immune condition, organ transplant, congestive heart failure, peripheral vascular disease, cerebrovascular disease, chronic pulmonary disease, renal disease, hypertension, asthma, diabetes mellitus status, time	Death
Czernichow et al. (PMID: 32815621)	France	5795	65.4%	59±14	Retrospective study	OR: 0.97 (0.74–1.27)	Body mass index, age, diabetes, hypertension, sleep apnea, chronic kidney disease, heart failure, malignancies, history of smoking, sex	Mortality
Nimkar et al. (PMID: 32838205)	USA	327	55.7%	71 (59–82)	Retrospective study	OR: 1.4 (0.8–2.2)	Race, chronic kidney disease in addition to six essential covariates (age, sex, race, hypertension, diabetes, cardiac disease)	Mortality
Giorgi-Rossi et al. (PMID: 32853230)	Italy	2653	50.1%	72±24	Prospective study	HR: 1.4 (0.9–2.2)	Age, sex	Death
Esme et al. (PMID: 32871002)	Turkey	16,942	49%	71.2 ± 8.5	Retrospective study	OR: 0.77 (0.64–0.93)	Gender, hypertension, diabetes mellitus, chronic obstructive pulmonary disease, coronary artery disease, atrial fibrillation, chronic kidney disease, dementia, depression, malnutrition	Mortality
Yan et al. (PMID: 32949175)	China	1103	48.6%	63 (51–71)	Retrospective study	HR: 1.91 (0.46–7.99)	Age, male, diabetes, hypertension, chronic obstructive pulmonary disease, chronic heart diseases, chronic kidney diseases, chronic liver diseases, cerebrovascular diseases, tumor, C-reactive protein, d-dimer	Mortality
Ioannou et al. (PMID: 32965502)	USA	10,131	91%	63.6 ± 16.2	Longitudinal corhot study	HR: 0.96 (0.83–1.11)	All sociodemographic characteristics, comorbid conditions, symptoms	Mortality
Ghany et al. (PMID: 33024960)	USA	400	40%	72±8	Retrospective study	RH: 0.99 (0.99–1.00)	Age, gender, charlson score	Death
Graziani et al. (PMID: 33053774)	Spain	14,339	49%	66±15	Retrospective study	OR: 1.03 (0.81–1.31)	Chronic obstructive pulmonary disease, sex, age, heart failure, high blood pressure, stroke, arrythmia, ischemic heart disease, diabetes, sleep apnea, pulmonary thromboembolism, smoking	Death
An et al. (PMID: 33127965)	Korea	10,237	39.9%	44.97±19.79	Nationwide cohort study	HR: 0.89 (0.66–1.20)	Age, sex, income level, residence, household type, disability, symptom, infection route	Death
Zhang et al. (PMID: 33122929)	China	98	59.2%	63.9 ± 1.4	Retrospective study	OR: 2.94 (1.22–7.12)	Age, gender, lymphocyte count, glycated hemoglobin, hypersensitive C-reactive protein, N-terminal brain natriuretic propeptide, creatinine	Mortality
Shah et al. (PMID: 33169090)	USA	487	56.1%	68±17	Retrospective study	OR: 1.36 (0.83–2.21)	Age, gender, patient admitted from home, hypertension, cardiomyopathy, atrial fibrillation, chronic obstructive pulmonary disease, cerebrovascular accident, diabetes mellitus, acute kidney injury, initial chest x-ray/computed tomography findings, dyspnea in emergency department noted as positive	Mortality
Tomasoni et al. (PMID: 33179839)	Italy	692	69.5%	67.4 ± 13.2	Retrospective study	HR: 0.82 (0.47–1.44)	Age, sex, heart failure, hypertension, atrial fibrillation, coronary artery disease, chronic obstructive pulmonary disease, chronic kidney disease, oxygen saturation, arterial partial pressure of oxygen/fraction of inspired oxygen, hemoglobin, lymphocytes count, estimated glomerular filtration rate, C-reactive protein on admission, troponin	Death
Loffi et al. (PMID: 33229434)	Italy	1252	63.7%	64.7 ± 15.5	Retrospective study	HR: 0.94 (0.63–1.41)	Sex, left ventricular ejection fraction < 35%, cerebrovascular disease, atrial fibrillation, diabetes mellitus, hypertension, coronary artery disease, chronic kidney disease, age	Death
Rossi et al. (PMID: 33222020)	Italy	590	67.6%	76.2 (68.2–82.6)	Retrospective study	HR: 1.108 (0.859–1.431)	Age, gender, temperature, arterial partial pressure of oxygen / fraction of inspired oxygen, lactate dehydrogenase, C-reactive protein, white blood cell count, lymphocytes rate, cardiovascular disease, diabetes, atrial fibrillation, chronic obstructive pulmonary disease, chronic kidney disease, stroke, malignancy, 3 or more comorbidities, angiotensin-converting enzyme inhibitor, angiotensin receptor blockers, calcium-channel blockers, alpha blockers, diuretics, beta blockers	Mortality
Rosenthal et al. (PMID: 33301018)	USA	35,302	53.4%	63.6 ± 17.7	Retrospective study	OR: 1.11 (1.03–1.19)	Age, sex, race, payer type, admission point of origin, hospital region, hospital beds, hospital teaching status, statin, vitamin C, zinc, angiotensin-converting enzyme inhibitor, b blocker, calcium channel blocker, hydroxychloroquine and azithromycin use, sepsis, acute kidney failure, hypokalemia, hyperkalemia, hyponatremia, acidosis, acute liver damage, neurological disorder, myocardial infarction, congestive heart failure, cerebrovascular disease, chronic pulmonary disease, dementia, diabetes, any malignant neoplasm, metastatic solid tumor, hemiplegia, acquired immunodeficiency syndrome, hypertension	Mortality
Ozyilmaz et al. (PMID: 33322097)	Turkey	105	72.4%	45 (20–87)	Retrospective study	OR: 4.060 (0.011–1555.792)	Troponin I, C-reactive protein, lymphocyte count, shortness of breath, hypertension, diabetes mellitus, coronary artery disease	Mortality
Lohia et al. (PMID: 33453090)	USA	1871	51.6%	66 (54–75)	Retrospective study	OR: 0.97 (0.76–1.23)	Age, sex, race, smoking, body mass index, insurance and comorbidities which include coronary artery disease, congestive heart failure, chronic obstructive pulmonary disease, asthma, chronic kidney disease, end-stage renal disease on dialysis, any malignancy, any liver disease, history of previous stroke, hypertension, diabetes	Mortality
Gupta et al. (PMID: 33461499)	USA	473	45.5%	70 (61–80)	Retrospective study	OR: 1.28 (0.75–2.19)	Race, age, sex, coronary artery disease, diabetes, hypertension, chronic obstructive pulmonary disease/asthma, autoimmune diseases history of cancer, immunocompromised, congestive heart failure, chronic kidney disease with dialysis, chronic kidney disease without dialysis, end-stage renal disease with dialysis	Mortality
Mayer et al. (PMID: 33496668)	Spain	23,844	42.3%	49.93±19.4	Retrospective study	OR: 1.19 (1.03–1.39)	Age, sex	Death
Muhammad et al. (PMID: 33538998)	USA	200	60.5%	58.9 ± 15.1	Retrospective study	OR: 2.12 (0.94–4.77)	Age, hypertension, coronary artery disease, chronic kidney disease, history of stroke, oxygen saturation, creatinine, blood urea nitrogen, creatine phosphokinase, troponin, procalcitonin, lactic acid, lactate dehydrogenase, C-reactive protein, initial d-dimer, ferritin, highest d-dimer	Mortality
Yoshida et al. (PMID: 33546750)	USA	776	47.3%	60.5 ± 16.1	Retrospective study	OR: 0.95 (0.53–1.71)	Age, sex, hospital site, the charlson comorbidity index	Death
Girardin et al. (PMID: 33550849)	USA	4446	58.1%	62±18	Retrospective study	HR: 0.92 (0.79–1.06)	Age, ethnic minority, male sex, low income, smoking, obesity, chronic obstructive pulmonary disease, asthma, sleep apnea, hypertension, diabetes, peripheral artery disease, coronary artery disease, autoimmune disease, cancer	Mortality
Wargny et al. (PMID: 33599800)	France	2796	63.7%	67.9 ± 13.2	Retrospective study	OR: 1.15 (0.95–1.40)	Age	Death

Note:

The values are presented as mean ± standard deviation or median (interquartile range, IQR); USA, the United States of America; CI, confidence interval; OR, odds ratio; RR, risk ratio; RH, relative hazard; HR, hazard ratio.

We utilized Stata (version 12.1) for all statistical analyses. The pooled effect estimate and its 95% confidence interval (CI) were computed using a random-effects model. Inter-study heterogeneity was investigated using the cochrane Q test and I statistic, P < 0.1 or I > 50% shows a statistically significant heterogeneity. The statistical stability of the overall effects was assessed using leave-one-out sensitivity analysis. The risk of publication bias was evaluated using Begg's test. Subgroup analyses were carried out by sample size, age, male percentage, study design and effect estimate. Two-tailed P-value < 0.05 was considered statistically significant.

Initial search yielded 2608 articles. After screening eligible articles according to inclusion and exclusion criteria, a total of twenty-seven studies composing of 146,364 cases were enrolled into this quantitative meta-analysis. Among the included studies, twenty-four studies were retrospective, one was prospective, one was longitudinal cohort study and one was nationwide cohort study. The sample sizes across the eligible studies ranged from 98 to 35,302. There were sixteen odds ratio (OR)-reported studies, nine hazard ratio (HR)-reported studies, one risk ratio (RR)-reported study and one relative hazard (RH)-reported study.

The results of our pooled analysis are presented in Fig. 1 A, which indicates that there was no significant relationship between dyslipidemia and COVID-19 morality (pooled effect size = 1.05, 95% CI [0.99–1.12], P = 0.100; I 2 = 52.6%, random-effects model). Sensitivity analysis by deleting each study one by one demonstrated that our results were stable (Fig. 1B). When we limited dyslipidemia to hyperlipidemia, there was no significant relationship between hyperlipidemia and COVID-19 mortality (pooled effect size = 1.03, 95% CI [0.95–1.12]). We still observed no significant relationship between dyslipidemia and COVID-19 mortality in the subgroup analyses by age (pooled effect size = 1.08, 95% CI [0.99–1.18] for < 65 years old and pooled effect size = 1.02, 95% CI [0.93–1.12] for ≥ 65 years old), male percentage (pooled effect size = 1.04, 95% CI [0.96–1.13] for < 55% and pooled effect size = 1.08, 95% CI [0.97–1.20] for ≥ 55%), study design (pooled effect size = 1.06, 95% CI [0.99–1.14] for retrospective study), sample size (pooled effect size = 1.09, 95% CI [0.96–1.24] for < 1500 cases and pooled effect size = 1.04, 95% CI [0.96–1.14] for ≥ 1500 cases), and effect estimates (OR = 1.08, 95% CI [0.98–1.20] and HR = 1.02, 95% CI [0.92–1.13]). Begg's test indicated that there was no obvious publication bias (P = 0.505).

Fig. 1

(A) The forest plots demonstrated that dyslipedemia was not significantly associated with the risk of mortality among patients with coronavirus disease 2019 (COVID-19) on the basis of twenty-seven studies with 146,364 cases; (B) Sensitivity analysis by omitting individual study one by one indicated that the results were stable and robust.

This meta-analysis has several limitations that need to be mentioned: most of the included studies were from USA, which limits its wider applicability of the present findings; the majority of studies were retrospective, thus further well-designed studies with more prospective researches are required to verify our results; although the pooled effect estimate was calculated on the basis of adjusted effects, the adjusted variables are not completely consistent across the included studies; only one included study explicitly states the specific type of dyslipidemia as total cholesterol, additional studies does not explicitly states the specific type of dyslipidemia such as abnormal levels of low-density lipoprotein, high-density lipoprotein, triglycerides and total cholesterol. Further studies should focus on the relationship between specific type of dyslipidemia and COVID-19 mortality when more data are available; the detailed information on medications for patients with pre-existing dyslipidemia is not available presently, thus we could not address the effects of medications on the relationship between dyslipidemia and COVID-19 mortality.

In conclusion, our current study based on adjusted effect sizes demonstrated that dyslipidemia was not significantly associated with COVID-19 mortality. Further well-designed studies with large sample sizes are warranted to confirm our findings.

Declaration of Competing Interest

The authors declare that they have no any potential conflict of interest regarding this submitted manuscript.