Cellular and molecular biomarkers of long COVID: a scoping review

Background Long-COVID (LC) encompasses diverse symptoms lasting months after the initial SARS-CoV-2 infection. Symptoms can be debilitating and affect the quality of life of individuals with LC and their families. Although the symptoms of LC are well described, the aetiology of LC remains unclear, and consequently, patients may be underdiagnosed. Identification of LC specific biomarkers is therefore paramount for the diagnosis and clinical management of the syndrome. This scoping review describes the molecular and cellular biomarkers that have been identified to date with potential use for diagnosis or prediction of LC.Methods This review was conducted using the Joanna Briggs Institute (JBI) Methodology for Scoping Reviews. A search was executed in the MEDLINE and EMBASE databases, as well as in the grey literature for original studies, published until October 5th, 2022, reporting biomarkers identified in participants with LC symptoms (from all ages, ethnicities, and sex), with a previous infection of SARS-CoV-2. Non-English studies, cross-sectional studies, studies without a control group, and pre-prints were excluded. Two reviewers independently evaluated the studies, extracted population data and associated biomarkers.Findings 23 cohort studies were identified, involving 2163 LC patients [median age 51.8 years, predominantly fe-male sex (61.10%), white (75%), and non-vaccinated (99%)]. A total of 239 candidate biomarkers were identified, consisting mainly of immune cells, immunoglobulins, cytokines, and other plasma proteins. 19 of the 239 candidate biomarkers identified were evaluated by the authors, by means of receiver operating characteristic (ROC) curves. Interpretation Diverse cellular and molecular biomarkers for LC have been proposed. Validation of candidate bio-markers in independent samples should be prioritized. Modest reported performance (particularly in larger studies) suggests LC may encompass many distinct aetiologies, which should be explored e.g., by stratifying by symptom clusters and/or sex.