Gut Microbiome Signatures During Acute Infection Predict Long COVID
Isin Yagmur Comba; Ruben T Mars; Lu Yang; Mitchell Dumais; Jun Y Chen; Trena M Van Gorp; Jonathan J. Harrington; Jason Paul Sinnwell; Stephen Johnson; La Rinda Holland; Adam K Khan; Efrem Lim; Christopher Aakre; Arjun Athreya; Georg K Gerber; John C. O'Horo; Konstantinos Lazaridis; Purna C Kashyap
Long COVID (LC), manifests in 10-30% of non-hospitalized individuals post-SARS-CoV-2 infection leading to significant morbidity. The predictive role of gut microbiome composition during acute infection in the development of LC is not well understood, partly due to the heterogeneous nature of disease.
We conducted a longitudinal study of 799 outpatients tested for SARS-CoV-2 (380 positive, 419 negative) and found that individuals who later developed LC harbored distinct gut microbiome compositions during acute infection, compared with both SARS-CoV-positive individuals who did not develop LC and negative controls with similar symptomatology. However, the temporal changes in gut microbiome composition between the infectious (0-1 month) and post-infectious (1-2 months) phases was not different between study groups.
Using machine learning, we showed that microbiome composition alone more accurately predicted LC than clinical variables. Including clinical data only marginally enhanced this prediction, suggesting that microbiome profiles during acute infection may reflect underlying health status and immune responses thus, help predicting individuals at risk for LC. Finally, we identified four LC symptom clusters, with gastrointestinal and fatigue-only groups most strongly linked to gut microbiome alterations.
Link | PDF (Preprint: BioRxiv) [Open Access]
Isin Yagmur Comba; Ruben T Mars; Lu Yang; Mitchell Dumais; Jun Y Chen; Trena M Van Gorp; Jonathan J. Harrington; Jason Paul Sinnwell; Stephen Johnson; La Rinda Holland; Adam K Khan; Efrem Lim; Christopher Aakre; Arjun Athreya; Georg K Gerber; John C. O'Horo; Konstantinos Lazaridis; Purna C Kashyap
Long COVID (LC), manifests in 10-30% of non-hospitalized individuals post-SARS-CoV-2 infection leading to significant morbidity. The predictive role of gut microbiome composition during acute infection in the development of LC is not well understood, partly due to the heterogeneous nature of disease.
We conducted a longitudinal study of 799 outpatients tested for SARS-CoV-2 (380 positive, 419 negative) and found that individuals who later developed LC harbored distinct gut microbiome compositions during acute infection, compared with both SARS-CoV-positive individuals who did not develop LC and negative controls with similar symptomatology. However, the temporal changes in gut microbiome composition between the infectious (0-1 month) and post-infectious (1-2 months) phases was not different between study groups.
Using machine learning, we showed that microbiome composition alone more accurately predicted LC than clinical variables. Including clinical data only marginally enhanced this prediction, suggesting that microbiome profiles during acute infection may reflect underlying health status and immune responses thus, help predicting individuals at risk for LC. Finally, we identified four LC symptom clusters, with gastrointestinal and fatigue-only groups most strongly linked to gut microbiome alterations.
Link | PDF (Preprint: BioRxiv) [Open Access]