Purpose To compare blood biomarkers across SARS-CoV-2 fatigue, non-fatigue, and control groups; assessed longitudinal biomarker changes; and identified genetic associations and biomarkers linked to post-COVID-19 fatigue. Methods This longitudinal observational study followed individuals with confirmed SARS-CoV-2 infection over a one-year period, stratifying them into fatigue ( n = 27) and non-fatigue ( n = 13) groups based on the Fatigue Severity Scale (FSS). These groups were cross-sectionally compared with a non-infected control cohort ( n = 27). Blood samples were collected at four time points to assess cytokines, hematological and biochemical markers, inflammatory indices, and matrix metalloproteinases. Selected functional polymorphisms were genotyped using PCR-RFLP. Group comparisons were conducted using generalized estimating equations, and genetic associations were evaluated using odds ratios and 95% confidence intervals. Results At baseline, fatigued post-COVID participants had higher IL-6 (2.70 vs. 1.68 pg/mL, p < 0.001), IL-8 (6.09 vs. 3.68 pg/mL, p = 0.024), and hs-CRP (6.61 vs. 1.06 mg/L, p < 0.001) than controls. Across follow-up, the fatigued group showed higher IL-6 (mean difference [MD] 1.27 pg/mL, p = 0.009), IL-8 (MD 1.98 pg/mL, p = 0.017), and hs-CRP (MD 4.16 mg/L, p < 0.001) than the non-fatigued group. In exploratory genotype analyses, fatigue was associated with COMT rs4680 (OR 13.85, 95% CI 2.74–69.91), NR3C1 rs41423247 (OR 3.76, 95% CI 1.04–13.69), and MMP9 rs3918242 (OR 0.20, 95% CI 0.06–0.67). Hs-CRP was significantly associated with FSS score (B = 0.12, 95% CI 0.02–0.21; p = 0.014). Conclusion Post-COVID fatigue was associated with persistent inflammation and genetic variation, supporting a multifactorial biological basis and highlighting hs-CRP as a potential marker requiring further validation. Graphical abstract Post-COVID fatigue is linked to persistent inflammation and neuroendocrine dysregulation. Genetic variants (COMT, NR3C1, MMP9) are associated with fatigue, while hs-CRP emerges as the strongest predictor of severity.
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Barrero-Santiago et al. (2026) studied this question.
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