Malondialdehyde, 8-iso-prostaglandin F2α, and lipid profile alterations in COVID-19 patients: insights from South-Western Nigeria

Abstract

Background

Oxidative stress and lipid metabolism disruptions drive COVID-19 severity, but data from African populations, particularly Nigeria, are limited. This study explored malondialdehyde (MDA), 8-iso-prostaglandin F2α (8-iso-PGF2α), and lipid profile changes in COVID-19 patients in South-Western Nigeria, emphasizing co-infections.

Methods

This cross-sectional study included 60 COVID-19 patients, with and without comorbidities, and 50 healthy controls at three Lagos isolation centers from July to December 2021. Disease severity (mild, moderate, severe) was assessed using clinical, oxygen saturation, and radiologic criteria. Serum MDA and lipid profiles were measured by spectrophotometry, and 8-iso-PGF2α by enzyme-linked immunosorbent assay. ANOVA with Tukey post-hoc tests analyzed normally distributed variables, and Kruskal–Wallis tests evaluated skewed variables. Age-adjusted comparisons used ANCOVA, with log-transformation for non-normal data. Spearman’s correlation assessed relationships between oxidative stress and lipid parameters, with Bonferroni correction for multiple comparisons.

Results

COVID-19 patients showed elevated MDA, 8-iso-PGF2α, triglycerides, and TG/HDL-C ratios (p ≤ 0.001), and reduced total cholesterol, LDL-C, and HDL-C (p ≤ 0.001) compared to controls. Oxidative stress markers correlated with lipid parameters, suggesting cardiovascular risk. Severe cases and malaria co-infected patients had more pronounced changes, though small subgroup sizes limited findings.

Conclusion

COVID-19 in Nigerian patients shows elevated MDA, 8-iso-PGF₂α, and dyslipidaemia (higher triglycerides, TG/HDL-C ratio, lower HDL-cholesterol), worsened by severity and malaria co-infection. These suggest biomarkers for risk stratification and therapy. The cross-sectional design limits causality, requiring larger, prospective studies for metabolic monitoring in co-endemic settings.

Clinical trial number

Not applicable.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-026-12622-1.

Introduction

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), extends beyond respiratory involvement to systemic metabolic and inflammatory disturbances [1, 2]. Evidence suggests that oxidative stress and lipid metabolism dysregulation play critical roles in disease progression, contributing to endothelial dysfunction, heightened inflammation, and multiorgan complications [3, 4]. While these biochemical alterations have been extensively studied in Asian and Western populations, data from African populations, particularly Nigeria, remain limited. Given the potential influence of genetic, environmental, and nutritional factors on oxidative stress and lipid metabolism, understanding these alterations in African COVID-19 patients is crucial.

Lipid peroxidation, driven by excessive reactive oxygen species (ROS), produces oxidative stress biomarkers such as malondialdehyde (MDA) and 8-iso-prostaglandin F2α (8-iso-PGF2α), which indicate cellular membrane damage [5, 6]. These markers are associated with cardiovascular and inflammatory disorders frequently observed in COVID-19 patients [7]. Although most studies report elevated MDA and 8-iso-PGF2α in COVID-19 [8, 9], some findings suggest unexpectedly lower MDA levels in infected individuals compared to controls [10], an observation that remains unexplained and could possibly be due to methodological variations, differences in disease severity, or antioxidant defense mechanisms [7, 10]. Additionally, co-infections, particularly malaria, appear to exacerbate oxidative stress, as indicated by significantly higher 8-iso-PGF2α levels in patients with both malaria and COVID-19 [9, 11]. Given the high malaria burden in Nigeria, the interaction between these infections warrants further investigation.

Lipid metabolism abnormalities have also been linked to COVID-19 severity, likely due to the inflammatory response disrupting lipid biosynthesis [12]. Earlier studies have reported lower levels of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) in severe cases, with even lower levels in non-survivors [13–15]. In contrast, triglyceride (TG) levels show inconsistent associations with disease severity, possibly due to variations in metabolic status and comorbidities [16]. Although lipid profiles improve post-recovery, long-term dyslipidemia has been reported in some cases, suggesting a lasting impact of COVID-19 on lipid metabolism [15, 17]. Additionally, inverse correlations between inflammatory markers such as C-reactive protein (CRP) and lipid parameters underscore the role of systemic inflammation in these alterations [18].

Given the potential impact of lipid peroxidation and dyslipidemia on COVID-19 outcomes, region-specific data are essential. This study compares serum levels of malondialdehyde, 8-iso-prostaglandin F2α, and lipid profiles between COVID-19 patients and healthy controls in South-Western Nigeria, with a particular focus on the influence of co-infections on oxidative stress and lipid metabolism in the patient cohort. By addressing this gap, the findings will provide insights into the metabolic disturbances associated with COVID-19 in African populations and their potential role in disease progression and severity.

Methods

Ethical considerations and informed consent

Ethical approval for this study was obtained from the Institutional Review Board of the Nigerian Institute of Medical Research, Yaba, Lagos (Ref: NIMR-IRB/21/034). Written informed consent was obtained from all participants, all of whom were residents of Lagos State, Nigeria. Participants were informed that their data would be kept confidential and used exclusively for research. The study complied with the ethical principles of the Declaration of Helsinki.

Study design

We conducted a cross-sectional comparative study at three COVID-19 isolation centers in Lagos, Nigeria, from July to December 2021. Patients were classified as having mild, moderate, or severe COVID-19 based on WHO guidelines [19], using symptoms, oxygen saturation (SpO₂), and chest imaging:

Mild

Fever, cough, sore throat, fatigue, myalgia, anosmia, or ageusia; SpO₂ ≥ 94% on room air; normal or minor radiologic changes.

Moderate

Pneumonia with fever, cough, dyspnea; SpO₂ 90–93% on room air; ground-glass opacities or infiltrates; required hospitalization and sometimes oxygen.

Severe

Severe pneumonia with respiratory distress, altered mental status, chest pain, or cyanosis; SpO₂ < 90% on room air; bilateral infiltrates or ARDS-like features; managed in ICU with ventilation or advanced care.

Data were collected via standardized questionnaires (Supplementary Fig S1) and medical records, capturing demographics (age, sex), clinical details (comorbidities, symptoms, severity), and confounders (smoking, medications). Subgroup analyses were conducted for key comorbidities, including malaria, obesity, cardiovascular disease, and diabetes mellitus, prioritised based on their established association with adverse COVID-19 outcomes [20].

Inclusion and exclusion criteria

Eligible participants were adults (≥ 18 years), residents of Lagos for at least six months, with COVID-19 confirmed by RT-PCR or rapid antigen testing. Controls were COVID-19–negative individuals, recruited from patients’ relatives and non-clinical hospital staff, matched to cases by age (± 5 years) and sex.

Exclusion criteria included pregnancy or breastfeeding; severe comorbidities (advanced malignancy, chronic kidney disease with eGFR < 30 mL/min/1.73 m², or NYHA class III–IV heart failure); use of lipid-lowering drugs or antioxidant supplements within three months; psychiatric or cognitive impairment precluding informed consent; acute infections, inflammatory diseases, or major surgery within four weeks; heavy alcohol use (> 14 drinks/week for men, > 7 for women) or recreational drug use; and inability to provide blood samples. Participants with missing biomarker data were excluded from final analyses.

Sample size and power analysis

The sample size was determined pragmatically due to recruitment constraints during the COVID-19 pandemic, with 110 of 150 eligible participants enrolled (100 patients approached, 60 enrolled; 50 controls), reflecting higher availability of confirmed COVID-19 cases. A post-hoc power analysis using G*Power (v3.1) confirmed sufficient power to detect differences in primary outcomes: 80% for low-density lipoprotein cholesterol (LDL-C; Cohen’s d = 0.55), 83% for malondialdehyde (MDA; Cohen’s d = 0.56), and 85% for 8-iso-prostaglandin F2α (8-iso-PGF2α; Cohen’s d = 0.58) at α = 0.05 (Supplementary Table S1).

Blood sample collection and storage

Researchers collected 8 mL of venous blood from COVID-19 patients and controls after an overnight fast of 8–12 h. Approximately 3.5 mL was transferred into a potassium-EDTA (1 mg/mL) sample container, while the remaining blood was placed in a plain vacutainer. Samples were immediately stored in a cold chain and processed within 2 h of collection. They were centrifuged at 5,000 rpm for 5 min, and the plasma and serum were carefully separated from the cells into labeled screw-capped bottles. Serum aliquots were stored at − 20 °C until analysis. All samples were collected between July and December 2021, and laboratory assays for oxidative stress markers and lipid profile were conducted promptly at the end of the study period in December 2021, ensuring no prolonged storage beyond the study timeline. Serum lipids are known to remain stable under these storage conditions, and oxidative stress markers were analyzed using validated protocols applied uniformly to all specimens to ensure internal validity.

Laboratory procedures

All laboratory procedures were conducted following strict good laboratory practices. Plasma TC, HDL-C, and TG levels were measured spectrophotometrically using enzymatic colorimetric methods with commercial kits from Randox Laboratories Ltd., Crumlin, United Kingdom. Total cholesterol and HDL-C (after removal of non-HDL lipids with dextran-sulphate-magnesium acetate precipitant) were determined using the method of Allain et al. [21]. Triglycerides were measured following the method of Fossati & Prencipe [22]. Low-density lipoprotein cholesterol (LDL-C) was calculated using the Friedewald formula [23]:

Serum malondialdehyde was measured spectrophotometrically using the thiobarbituric acid (TBA) assay method of Nadigar et al. [24], while serum 8-iso-PGF2α was assessed using a sandwich enzyme-linked immunosorbent assay (ELISA) [25], with kits from Elabscience Biotechnology Limited Company, Wuhan, China. Quality control sera were included in each batch to ensure accuracy and consistency.

Data analysis

Statistical analyses were performed using IBM SPSS Statistics, version 27 (IBM Corp., Armonk, NY, USA). Normality was assessed with the Shapiro–Wilk test (p > 0.05). Normally distributed variables, including lipid profile parameters (total cholesterol, triglycerides), were expressed as means ± standard deviations (SD) and compared between COVID-19 cases and controls using independent t tests. Non-normally distributed variables, such as MDA and 8-iso-PGF2α, were reported as medians with interquartile ranges (IQR) and analyzed with the Mann–Whitney U test.

For comparisons across severity subgroups (controls, mild, moderate, severe), ANOVA with Tukey post-hoc tests was used for normally distributed variables, while the Kruskal–Wallis test was applied for skewed variables. Controls were included in all severity-based analyses. To adjust for age, ANCOVA was performed with age as a covariate. For non-normally distributed markers, log-transformed values were analyzed by ANCOVA, and results are reported as estimated marginal means ± standard errors in Supplementary Table S2.

Correlations between oxidative stress markers and lipid parameters were assessed using Spearman’s rank correlation coefficient. All analyses were two-tailed with statistical significance set at p < 0.05 unless otherwise specified. Bonferroni correction was applied where multiple comparisons were made.

Results

General characteristics of the study population

The study included 60 COVID-19 patients (mean age 43.4 ± 5.2 years) and 50 healthy controls (mean age 40.1 ± 6.3 years), with no significant differences in age or sex distribution (Table 1). Compared with controls, patients showed lower levels of TC, HDL-C, and LDL-C, alongside higher TG concentrations and TG/HDL-C ratios (all p < 0.001). Oxidative stress markers were also elevated in patients, with higher median MDA (2.25 vs. 1.17 µmol/L) and 8-iso-PGF2α (323 vs. 165 pg/mL) values (both p < 0.001).

Table 1.

General characteristics of COVID-19 patients and controls

Variables	Patients (n = 60)	Controls (n = 50)	Test Statistic (value)	p-value
Age (years), Mean ± SD	43.39 ± 5.21	40.08 ± 6.32	t = 1.55	0.126
Sex, n (%)
Male Female	31 (51.7%) 29 (48.3%)	25 (50.0%) 25 (50.0%)	χ² = 0.00	1.000
Total Cholesterol (mmol/L), Mean ± SD	3.54 ± 0.35	4.52 ± 0.62	t = -10.410	< 0.001*
Triglyceride (mmol/L), Mean ± SD	1.76 ± 0.17	0.98 ± 0.44	t = 12.638	< 0.001*
High-density lipoprotein cholesterol (mmol/L), Mean ± SD	1.07 ± 0.14	1.61 ± 0.47	t = -8.533	< 0.001*
Low-density lipoprotein cholesterol (mmol/L), Mean ± SD	1.63 ± 0.99	2.41 ± 1.75	t = -9.298	< 0.001*
Triglyceride/high-density lipoprotein cholesterol, Mean ± SD	1.69 ± 0.38	0.67 ± 0.39	t = 13.804	< 0.001*
Malondialdehyde (µmol/L), Median (IQR)	2.25 (0.46–4.04)	1.17 (0.84–1.50)	U = 231.5	< 0.001*
8-iso-PGF2α (pg/mL), Median (IQR)	323 (78–568)	165 (90–240)	U = 222	< 0.001*

CI, confidence interval (95%); 8-iso-PGF2α, 8-iso-prostaglandin F2α; IQR, Interquartile range; n, number of participants; p-value, level of significant; SD, standard variation; t-value, student’s t-test; U-value, Mann-Whitney U test; * values differ significantly between COVID-19 patients and controls (p < 0.05)

Classification by ATP III criteria (Supplementary Table S3) indicated a greater prevalence of hypertriglyceridemia in severe COVID-19 (33.3%) compared with controls (10.0%), while the proportions with low HDL-C or elevated LDL-C were relatively similar across groups.

Oxidative stress markers and lipid levels across COVID-19 severity groups

In COVID-19 patients (mild, n = 22; moderate, n = 23; severe, n = 15) versus controls (n = 50), oxidative stress markers (MDA, 8-iso-PGF2α) and lipid profiles (TG, TG/HDL-C ratio) increased with disease severity, peaking in severe cases (medians: 3.40 µmol/L, 559 pg/mL; means: 1.97 mmol/L, 2.26; p < 0.001). Total cholesterol, HDL-C, and LDL-C decreased, with lowest levels in severe cases (means: 3.08, 0.88, 1.29 mmol/L; p < 0.001). Age-adjusted ANCOVA confirmed these trends (severe cases: TC 3.04, TG 1.93, HDL-C 0.89, LDL-C 1.22 mmol/L, TG/HDL-C 2.23; log-transformed MDA 1.24, 8-iso-PGF2α 6.30; p < 0.001). See Table 2 and Supplementary Table S2 for details.

Table 2.

Biochemical characteristics across COVID-19 severity groups

Variable	Controls (n = 50)	Mild (n = 22)	Moderate (n = 23)	Severe (n = 15)	Test Statistic (value)	p-value
Total Cholesterol (mmol/L), Mean ± SD	4.52a ± 0.62	4.14b ± 0.28	3.62bc ± 0.24	3.08d ± 0.18	F (54.652)	< 0.001*
Triglyceride (mmol/L), Mean ± SD	0.98a ± 0.44	1.61b ± 0.94	1.77bc ± 0.09	1.97c ± 0.12	F (62.699)	< 0.001*
High-density lipoprotein cholesterol (mmol/L), Mean ± SD	1.61a ± 0.47	1.19b ± 0.06	1.09bc ± 0.07	0.88c ± 0.06	F (28.4444)	< 0.001*
Low-density lipoprotein cholesterol (mmol/L), Mean ± SD	2.41a ± 0.55	2.15b ± 0.31	1.73bc ± 0.24	1.29d ± 0.12	F (40.236)	< 0.001*
Triglyceride/high-density lipoprotein cholesterol, Mean ± SD	0.67a ± 0.39	1.36b ± 0.11	1.64c ± 0.14	2.26d ± 0.18	F (147.672)	< 0.001*
Malondialdehyde (µmol/L), Median (IQR)	1.17a (0.94–1.21)	1.30b (1.18–1.36)	2.3c (2.10–2.50)	3.40d (3.06–4.37)	K-W (83.586)	< 0.001*
8-iso-PGF2α (pg/mL), Median (IQR)	165a (145 - 206)	215b (210–226)	343c (310 - 388)	559d (520 - 600)	K-W (84.141)	< 0.001*

Biochemical characteristics across COVID-19 severity groups (controls, n = 50; mild, n = 22; moderate, n = 23; severe, n = 15). Values are reported as mean ± standard deviation for parametric variables or median (interquartile range) for non-parametric variables. One-way ANOVA (parametric) or Kruskal-Wallis tests (non-parametric) were used for group comparisons, with Tukey’s post-hoc tests (p ≤ 0.05) for ANOVA and Mann-Whitney U tests with Bonferroni correction (p ≤ 0.0083) for significant differences. Superscripts (a, b, c, d) within rows indicate significant intergroup differences. *p < 0.05

Oxidative stress markers and lipid levels across COVID-19 patients with and without comorbidities

In the primary case–control analysis, COVID-19 patients without comorbidities showed the smallest increases in oxidative stress markers, TG, and the TG/HDL-C ratio compared with those with underlying conditions (Table 3). In exploratory analyses, patients with malaria co-infection (n = 8) had the highest elevations in oxidative stress markers, TG, and TG/HDL-C ratio, along with the least reduction in total cholesterol (TC), HDL-C, and LDL-C, compared with other comorbidities such as obesity (n = 6). Owing to small subgroup sizes and limited statistical power, these comorbidity-specific results are considered exploratory, and emphasis remains on the overall case–control comparisons. This limitation is further addressed in the Discussion.

Table 3.

Biochemical characteristics in COVID-19 patients with and without comorbidities

Variable	No Comorbidity (n = 27)	Malaria (n = 8)	Diabetes mellitus (n = 10)	CVD (n = 9)	Obesity (n = 6)	p-value
Total Cholesterol (mmol/L), Mean ± SD	3.91 ± 0.38	3.21 ± 0.37	3.52 ± 0.51	3.92 ± 0.41	3.42 ± 0.41	< 0.001*
Triglyceride (mmol/L), Mean ± SD	1.68 ± 0.13	1.93 ± 0.17	1.83 ± 0.17	1.84 ± 0.16	1.69 ± 0.16	0.001*
High-density lipoprotein cholesterol (mmol/L), Mean ± SD	1.14 ± 0.08	0.93 ± 0.11	1.04 ± 0.15	0.96 ± 0.15	1.15 ± 0.08	< 0.001*
Low-density lipoprotein cholesterol (mmol/L), Mean ± SD	1.95 ± 0.36	1.39 ± 0.28	1.61 ± 0.35	1.98 ± 0.43	1.47 ± 0.22	0.002*
TG/HDL-C, Mean ± SD	1.48 ± 0.18	2.10 ± 0.31	1.81 ± 0.43	1.98 ± 0.43	1.49 ± 0.22	< 0.001*
Malondialdehyde (µmol/L), Median (IQR)	1.40 (1.30–2.10)	4.30 (2.80–4.50)	2.60 (1.80–3.10)	2.62 (1.77–3.10)	1.91 (1.28–2.60)	< 0.001*
8-iso-PGF2α (pg/mL), Median (IQR)	225 (210–325)	589 (434–618)	398 (287–539)	450 (273–538)	272 (210–410)	< 0.001*

CI, confidence interval (95%); CVD, cardiovascular disease; 8-iso-PGF2α, 8-iso-prostaglandin F2α; IQR, Interquartile range; HDL-C, High-density lipoprotein cholesterol; n, number of participants; p-value, level of significant; SD, standard variation; TG, Triglyceride; * values differ significantly among COVID-19 patients with and without comorbidities (p < 0.05)

Correlations between oxidative stress and lipid profiles in COVID-19

Spearman’s rank correlations (Table 4) revealed robust associations between oxidative stress markers (MDA and 8-iso-PGF2α) and lipid levels in COVID-19 cases (n = 60), but not in controls (n = 50). In cases, MDA and 8-iso-PGF2α showed strong positive correlations with triglycerides (rho = 0.811 and 0.794; p < 0.001) and TG/HDL-C ratio (rho = 0.848 and 0.849; p < 0.001), and strong negative correlations with total cholesterol (rho=-0.785 and − 0.826; p < 0.001) and HDL-C (rho=-0.775 and − 0.792; p < 0.001). MDA and 8-iso-PGF2α were also highly correlated (rho = 0.937, p < 0.001). In controls, correlations were generally weak and non-significant (p > 0.05), except for MDA with total cholesterol and HDL-C (rho=-0.319 and − 0.323; p < 0.05), suggesting these relationships are specific to COVID-19.

Table 4.

Spearman’s rank correlations of oxidative stress markers and lipid levels in COVID-19 cases and controls

Variable Pair	Group	rho	p-value	N
Malondialdehyde (µmol/L) - Total cholesterol (mmol/L)	Cases Controls	− 0.785 − 0.319	< 0.001** 0.024*	60 50
Malonaldiehyde (µmol/L) -Triglyceride (mmol/L)	Cases Controls	0.811 − 0.253	< 0.001** 0.076	60 50
Malondialdehyde (µmol/L) - HDL- cholesterol (mmol/L)	Cases Controls	− 0.775 − 0.323	< 0.001** 0.022*	60 50
Malondialdehyde (µmol/L) - LDL-cholesterol (mmol/L)	Cases Controls	− 0.699 0.047	< 0.001** 0.748	60 50
Malondialdehyde(µmol/L) – Triglyceride/HDL- cholesterol	Cases Controls	0.848 − 0.085	< 0.001** 0.555	60 50
Malondialdehyde (µmol/L) − 8-iso-PGF2α (pg/mL)	Cases Controls	0.937 − 0.014	< 0.001** 0.920	60 50
8-iso-PGF2α (pg/mL) - Total cholesterol (mmol/L)	Cases Controls	− 0.826 0.098	< 0.001** 0.497	60 50
8-iso-PGF2α (pg/mL) - Triglyceride (mmol/L)	Cases Controls	0.794 0.018	< 0.001** 0.900	60 50
8-iso-PGF2α (pg/mL) - HDL- cholesterol (mmol/L)	Cases Controls	− 0.792 0.053	< 0.001** 0.713	60 50
8-iso-PGF2α (pg/mL) - LDL-cholesterol (mmol/L)	Cases Controls	− 0. 706 − 0.153	< 0.001** 0.290	60 50
8-iso-PGF2α (pg/mL) - Triglyceride/HDL- cholesterol	Cases Controls	0.849 − 0.005	< 0.001** 0.971	60 50

CI, confidence interval (95%); 8-iso-PGF2α, 8-iso-prostaglandin F2α; HDL, High-density lipoprotein; LDL, Low-density lipoprotein; p-value, level of significant; *p < 0.05; rho, Spearman’s rank correlation coefficient; N, sample size. Correlations were computed separately for cases (COVID-19 patients) and controls using Spearman’s rank correlation

Plot showing oxidative stress markers in COVID-19 patients with and without comorbidities

A graded variation in MDA and 8-iso-PGF2α levels was observed among COVID-19 patients with and without comorbidities. Notably, patients co-infected with malaria exhibited the highest increase in MDA (Fig. 1a) and 8-iso-PGF2α levels (Fig. 1b).

Fig. 1b.

Box-plot showing the serum levels of 8-iso-prostaglandin-F2α in COVID-19 patients with and without comorbidities. Error bar denotes 95% CI; CI, confidence interval (95%); CVD, Cardiovascular disease

Fig. 1a.

Box-plot showing the serum levels of malondialdehyde in COVID-19 patients with and without comorbidities. Error bar denotes 95% CI; CI, confidence interval (95%); CVD, Cardiovascular disease

Plot showing correlations of oxidative stress markers with some lipid parameters in COVID-19 patients with and without comorbidities

Malondialdehyde exhibited a negative correlation with total cholesterol (Fig. 2a) in COVID-19 patients, while showing a strong positive correlation with triglycerides (Fig. 2b). A similar pattern was observed for 8-iso-prostaglandin F2α, which negatively correlated with total cholesterol (Fig. 2c) but demonstrated a strong positive correlation with triglycerides (Fig. 2d).

Fig. 2d.

Scatter plot showing the positive correlation between malondialdehyde and triglyceride in COVID-19 patients with and without comorbidities. Error bar denotes 95% CI; CI, confidence interval (95%); CVD, Cardiovascular disease

Fig. 2a.

Scatter plot showing the negative correlation between 8-iso-prostaglandin-F2α and total cholesterol in COVID-19 patients with and without comorbidities. Error bar denotes 95% CI; CI, confidence interval (95%); CVD, Cardiovascular disease

Fig. 2b.

Scatter plot showing the positive correlation between 8-iso-prostaglandin-F2α and triglyceride in COVID-19 patients with and without comorbidities. Error bar denotes 95% CI; CI, confidence interval (95%); CVD, Cardiovascular disease

Fig. 2c.

Scatter plot showing the negative correlation between malondialdehyde and total cholesterol in COVID-19 patients with and without comorbidities. Error bar denotes 95% CI; CI, confidence interval (95%); CVD, Cardiovascular disease

Discussion

Key findings

This study in South-Western Nigeria reveals significant oxidative stress and lipid profile changes in COVID-19 patients compared to controls. Elevated MDA and 8-iso-PGF2α indicate heightened lipid peroxidation, while total cholesterol, LDL-C, and HDL-C were reduced, with increased triglycerides and TG/HDL-C ratios. These alterations were most pronounced in severe cases, persisting after age adjustment. Strong correlations between oxidative stress markers and lipid parameters were observed in cases but not controls, suggesting a disease-specific interaction.

Comparison with previous studies

Our results align with global reports of redox imbalance in SARS-CoV-2 infection [4, 8, 9] and local studies noting elevated 8-iso-PGF2α and lipid reductions in Nigerian COVID-19 patients [8, 26]. Lipid dysregulation is consistent with inflammation-driven metabolic remodeling reported in other populations [15, 27, 28], reinforcing the systemic impact of COVID-19.

The severity-dependent increase in oxidative stress mirrors mechanisms linked to cytokine storm induced reactive oxygen species and endothelial injury [29–31].

Mechanistic interpretation of lipid alteration

The lipid abnormalities observed in this study appear to reflect inflammation-driven metabolic remodeling during SARS-CoV-2 infection. Cytokine-mediated suppression of hepatic sterol regulatory element-binding proteins (SREBPs) may reduce cholesterol synthesis and disrupt lipoprotein assembly, contributing to the lower total cholesterol, LDL-C, and HDL-C levels [32]. At the same time, inflammatory pathways can impair lipoprotein lipase activity, leading to the accumulation of triglyceride-rich VLDL remnants and the elevated triglycerides and TG/HDL-C ratios seen in our cohort [33]. Although reduced LDL-C and total cholesterol could be interpreted as protective, the concurrent rise in triglycerides suggests a shift toward an atherogenic profile [34]. These contrasting changes underscore the complexity of lipid metabolism in COVID-19 and highlight the need for longitudinal studies to determine their prognostic relevance.

Role of comorbidities

Exploratory analyses showed greater oxidative and lipid disturbances in patients with comorbidities, notably malaria co-infection. Malaria’s hemolysis and inflammation may amplify SARS-CoV-2–induced metabolic changes, potentially via shared pathways like endothelial dysfunction [7, 11, 35]. For example, malaria-driven heme release could exacerbate reactive oxygen species production. However, small subgroup sizes (n = 8 for malaria co-infection) limit these findings’ robustness, making them hypothesis-generating for future co-infection research.

Clinical and public health implications

These findings support integrating metabolic assessments into COVID-19 care, particularly in malaria-endemic settings. Monitoring triglycerides, TG/HDL-C ratios, and oxidative stress markers could identify patients at elevated cardiovascular risk, enabling targeted interventions like lipid management or antioxidant therapy. In resource-limited settings, simplified screening tools may offer cost-effective risk stratification, potentially applicable to other viral infections with metabolic impacts.

Study limitations and future directions

The cross-sectional design prevents causal inferences, and small subgroup sizes weaken comorbidity analyses. Despite adequate post-hoc power for primary outcomes (power = 0.83 for MDA differences), unequal group sizes were a constraint. Incomplete anthropometric data limited exploration of body weight effects, and the lack of functional cardiovascular measures restricted clinical correlations. Future studies with larger, prospective cohorts could address these gaps and assess selection bias in regional recruitment.

Conclusions

This Nigerian cohort demonstrates severity-dependent oxidative stress and lipid disturbances in COVID-19, with strong associations between redox and lipid metabolism. Malaria co-infection may exacerbate these changes, warranting further investigation in co-endemic regions. These findings advocate for metabolic monitoring and co-infection screening in COVID-19.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

MDA

Malondialdehyde

8-iso-PGF2α

8-iso-prostaglandin Factor 2α

TG

Triglycerides

TC

Total cholesterol

LDL-C

Low-density lipoprotein cholesterol

HDL-C

High-density lipoprotein cholesterol

CRP

C-Reactive protein

RT-PCR

Rapid test-polymerase chain reaction

Author contributions

M. O. E conceived the study, interpreted the data, and reviewed the manuscript. D. U. O performed the laboratory procedures, interpreted the data, and reviewed the manuscript. P. C. N performed the statistical analysis, interpreted the data, drafted, and reviewed the manuscript. B. E. O contributed to statistical analysis, data interpretation, and manuscript editing. O. I. O performed laboratory procedures, data interpretation, and manuscript editing. All authors approve the final version for submission.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Data availability

The authors confirm that the data supporting the findings of this study is available within the article and its supplementary information file.

Declarations

Ethical considerations and informed consent

Ethical approval for this study was obtained from the Institutional Review Board of the Nigerian Institute of Medical Research, Yaba, Lagos (Ref: NIMR-IRB/21/034). Written informed consent was obtained from all participants, all of whom were residents of Lagos State, Nigeria. Participants were informed that their data would be kept confidential and used exclusively for research. The study complied with the ethical principles of the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.