The physio-affective symptoms of Long COVID are strongly predicted by the severity of the acute infectious phase, and lowered antioxidants, nitric oxide, and alanine transaminase levels.
Maes, M.; Tunvirachaisakul, C.; Semeao, L. d. O.; Michelin, A. P.; Matsumoto, A. K.; Brinholi, F. F.; Barbosa, D. S.; Zhang, Y.; Sodsai, P.; Hirankarn, N.; Almulla, A. F.
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AimsThis study investigated a) whether the affective, chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME), respiratory, and neurological Long COVID (LC) symptom domains are intercorrelated manifestations of an underlying construct, namely the LC phenome; and b) the predictive effects of clinical symptoms and biomarkers of the COVID-19 infectious phase on LC. MethodsThis cohort study included 71 LC patients who had suffered from COVID-19 six months prior. The patients complied with the LC disease criteria set forth by the World Health Organization (WHO). We used the WHO symptom list to score the LC symptoms and assessed SARS-CoV-2 positivity using real-time reverse transcription-polymerase chain reaction (RT-PCR), white blood cell counts, anion gap, alanine transaminase (ALT) levels, paraoxonase 1 (PON1) activity and genotypes, total radical trapping potential of plasma (TRAP), and nitric oxide metabolites (NOx). ResultsAll four clinical LC subdomains are significantly intercorrelated, allowing for the extraction of a single latent construct encompassing affective, CFS/ME, respiratory, and neurological symptoms, labeled the physio-affective phenome of LC. The latter was strongly predicted by dyspnea and productive sputum, RT-PCR SARS-CoV-2 cycle threshold values, lower anion gap, and increased neutrophil percentage during the infectious phase. Other predictive biomarkers were lower ALT, TRAP, and NOx levels, and the PON1 QR192 over dominant gene model (protective). ConclusionsLC may be a clinically homogeneous disease characterized by highly intercorrelated affective, CFS, neurological, and respiratory subdomains. The physio-affective phenome and its domains are strongly predicted by biomarkers of acute infection and associated inflammation and oxidative stress.
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