In a recent pre-print study posted to bioRxiv* a team of researchers investigated the predictive role of gut microbiome composition during acute Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection in the development of Long Coronavirus Disease  (Long COVID) (LC) and its association with clinical variables and symptom clusters.

Machine learning models incorporating microbiome and clinical data were utilized to predict LC and to identify symptom clusters providing valuable insights into the heterogeneity of the condition.

The SARS-CoV-2-negative group was older with higher antibiotic use and vaccination rates. These variables were adjusted for in subsequent analyses.

During acute infection gut microbiome diversity differed significantly between groups. Alpha diversity was lower in SARS-CoV-2-positive participants (LC and non-LC) than in SARS-CoV-2-negative participants.

Beta diversity analyses revealed distinct microbial compositions among the groups with LC patients exhibiting unique microbiome profiles during acute infection.

Specific bacterial taxa including Faecalimonas and Blautia were enriched in LC patients while other taxa were predominant in non-LC and negative participants. These findings indicate that gut microbiome composition during acute infection is a potential predictor for LC.

Temporal analysis of gut microbiome changes between the acute and post-acute phases revealed significant individual variability but no cohort-level differences suggesting that temporal changes do not contribute to LC development.

However machine learning models demonstrated that microbiome data during acute infection when combined with clinical variables predicted LC with high accuracy. Microbial predictors including species from the Lachnospiraceae family significantly influenced model performance.

Symptom analysis revealed that LC encompasses heterogeneous clinical presentations. Fatigue was the most prevalent symptom followed by dyspnea and cough.

Each cluster exhibited unique microbial associations with the gastrointestinal and sensory clusters showing the most pronounced microbial alterations. Notably taxa such as those from Lachnospiraceae and Erysipelotrichaceae families were significantly enriched in this cluster.

Key microbial contributors included species from the Lachnospiraceae family such as Eubacterium and Agathobacter and Prevotella spp. These findings highlight the gut microbiome’s potential as a diagnostic tool for identifying LC risk enabling personalized interventions.

Source: https://www.news-medical.net/news/20241216/Gut-microbiome-during-acute-infection-predicts-risk-of-long-COVID.aspx