Precision Symptom Phenotyping Identifies Early Clinical and Proteomic Predictors of Distinct COVID-19 Sequelae

Abstract

BACKGROUND: Post-COVID conditions (PCC) are difficult to characterize, diagnose, predict, and treat due to overlapping symptoms and poorly understood pathology. Identifying inflammatory profiles may improve clinical prognostication and trial endpoints. METHODS: This analysis included 1988 SARS-CoV-2 positive U.S. Military Health System beneficiaries who had quantitative post-COVID symptom scores. Among participants who reported moderate-to-severe symptoms on surveys collected 6 months post-SARS-CoV-2 infection, principal component analysis followed by k-means clustering identified distinct clusters of symptoms. RESULTS: Three symptom-based clusters were identified: a sensory cluster (loss of smell and/or taste), a fatigue/difficulty thinking cluster, and a difficulty breathing/exercise intolerance cluster. Individuals within the sensory cluster were all outpatients during their initial COVID-19 presentation. The difficulty breathing cluster had a higher likelihood of obesity and COVID-19 hospitalization than those with no/mild symptoms at 6 months post-infection. Multinomial regression linked early post-infection D-dimer and IL-1RA elevation to fatigue/difficulty thinking and elevated ICAM-1 concentrations to sensory symptoms. CONCLUSIONS: We identified three distinct symptom-based PCC phenotypes with specific clinical risk factors and early post-infection inflammatory predictors. With further validation and characterization, this framework may allow more precise classification of PCC cases and potentially improve the diagnosis, prognostication, and treatment of PCC.