The Intestinal And Oral Microbiomes Are Robust Predictors Of COVID-19 Severity The Main Predictor Of COVID-19-Related Fatality
Ward, D. V.; Bhattarai, S.; Rojas-Correa, M.; Purkayastha, A.; Holler, D.; Qu, M. D.; Mitchell, W. G.; Yang, J.; Fountain, S.; Zeamer, A.; Forconi, C.; Fujimori, G.; Odwar, B.; Cawley, C.; McCormick, B. A.; Moormann, A.; Wessolossky, M.; Bucci, V.; Maldonado-Contreras, A.
Show abstract
The reason for the striking differences in clinical outcomes of SARS-CoV-2 infected patients is still poorly understood. While most recover, a subset of people become critically ill and succumb to the disease. Thus, identification of biomarkers that can predict the clinical outcomes of COVID-19 disease is key to help prioritize patients needing urgent treatment. Given that an unbalanced gut microbiome is a reflection of poor health, we aim to identify indicator species that could predict COVID-19 disease clinical outcomes. Here, for the first time and with the largest COVID-19 patient cohort reported for microbiome studies, we demonstrated that the intestinal and oral microbiome make-up predicts respectively with 92% and 84% accuracy (Area Under the Curve or AUC) severe COVID-19 respiratory symptoms that lead to death. The accuracy of the microbiome prediction of COVID-19 severity was found to be far superior to that from training similar models using information from comorbidities often adopted to triage patients in the clinic (77% AUC). Additionally, by combining symptoms, comorbidities, and the intestinal microbiota the model reached the highest AUC at 96%. Remarkably the model training on the stool microbiome found enrichment of Enterococcus faecalis, a known pathobiont, as the top predictor of COVID-19 disease severity. Enterococcus faecalis is already easily cultivable in clinical laboratories, as such we urge the medical community to include this bacterium as a robust predictor of COVID-19 severity when assessing risk stratification of patients in the clinic.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Mucosal washes are useful for sampling intestinal mucus-associated microbiota despite low biomass 95%
- Blood-borne immune cells carry low biomass DNA remnants of microbes in patients with colorectal cancer or inflammatory bowel disease 95%
- A novel framework for assessing causal effect of microbiome on health: long-term antibiotic usage as an instrument 95%
Similar papers in this journal
- Dysbiosis and structural disruption of the respiratory microbiota in COVID-19 patients with severe and fatal outcomes 96%
- Antibiotics and the developing intestinal microbiome, metabolome and inflammatory environment: a randomized trial of preterm infants 95%
- Functional profiling of COVID-19 respiratory tract microbiomes 93%
Similar papers in this journal
Similar papers in this journal
- Primary human colonic mucosal barrier crosstalk with super oxygen-sensitive Faecalibacterium prausnitzii in continuous culture 93%
- Multimodal surveillance of SARS-CoV-2 at a university enables development of a robust outbreak response framework 91%
- Heterogeneity in statin responses explained by variation in the human gut microbiome 90%
Similar papers in this journal
- Biomarkers to distinguish bacterial from viral pediatric clinical pneumonia in a malaria endemic setting 94%
- Gut microbiota features on nursing home admission are associated with subsequent acquisition of antibiotic resistant organism colonization 91%
- Megasphaera in the stool microbiota is negatively associated with diarrheal cryptosporidiosis 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.