THE BASELINE GUT MICROBIOME OFFERS INSIGHTS INTO PREDICTING AND UNDERSTANDING THE HETEROGENEOUS NATURE OF LONG COVID

Background: Long COVID (LC) affects approximately 10% of SARS-CoV-2 positive patients, causing significant workforce loss and an annual economic cost of $743 billion in the United States. Previous studies have suggested an association between long COVID and alterations in the gut microbiome. This study aimed to ascertain whether the baseline microbiome at the time of diagnosis can predict the occurrence of LC and/or its sub phenotypes.
Methods: In a prospective cohort study, we examined 400 SARS-CoV-2 positive and 419 negative individuals who provided stool samples at the initial diagnosis. Long COVID was defined as symptoms persisting for four weeks or more after the initial diagnosis. Stool samples were sequenced using Illumina NovaSeq, (~8 million reads/sample). Taxonomy was assigned using Kraken2, statistical analysis was done using standard tools in R, and predictive modeling was done using XGBoost in Python.
Results: Among the 400 SARS-CoV-2 positive participants, 20 with inadequate follow-up were excluded, while 82 developed LC. Baseline characteristics of SARS-CoV-2 positive (LC and no LC) and negative participants showed variations due to clinical risk factors associated with LC and inherent differences between the groups (Figure 1). We adjusted subsequent analyses to account for these factors. Both groups of SARS-CoV-2 positive patients, harbored a distinct gut microbiome compared to SARS-CoV-2 negative participants (Bray-Curtis-based beta diversity, PERMANOVA, p=0.003, R2=0.004; Figure 2A). However, we observed distinct microbial taxas between LC and no LC groups compared to SARS-CoV-2 negative controls (Figure 2B). The gut microbiome at the time of diagnosis was a predictor of LC (XGboost, AUC=0.73, 95% CI =0.62, 0.81; Figure 2C). When evaluating individual symptoms, LC patients with persistent myalgia (Fischer's exact test, OR=3.57, 95% CI=0.76-15.57, p=0.057) and diarrhea (Fischer's exact test, OR=4.04, 95% CI=0.71-20.88, p=0.062) exhibit the greatest divergence in microbial diversity from no LC patients (Bray-Curtis dissimilarity-based irregularity, >90^th percentile). Reference group analysis similarly showed that the microbiome from patients with diarrhea, myalgia, and anosmia/dysgeusia diverged most from the no LC group (Figure 2D, E). On further analysis using K-means and hierarchical clustering of LC symptoms, we identified five LC subphenotypes and these were associated with distinct microbial signatures (Figure 2F, G). The gastrointestinal and sensory cluster had the most prominent changes with 24 species from Lachnospiraceae family positively correlating with symptoms.
Conclusion: Our study found baseline microbiome to be a predictor of LC, with distinct microbial taxa underlying subphenotypes of LC. These findings offer promising targets for the development of therapeutic interventions for LC.