Gut microbiome signatures during acute infection are associated with long COVID.

Sleep health notes

Sleep health notes will be added by the Sleepatch editorial team.

中文解读

中文解读待补充：本站会优先为失眠研究、睡眠质量改善、昼夜节律等高价值睡眠研究添加中文说明。

Original abstract

<h4>Background</h4>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 because of the heterogeneous nature of the disease.<h4>Objectives</h4>To determine whether the gut microbiome composition in the acute phase of SARS-CoV-2 infection predicts subsequent LC and to investigate the role of microbiome signatures in disease subphenotypes.<h4>Design</h4>We conducted a longitudinal cohort study involving 799 outpatient participants tested for SARS-CoV-2 due to similar symptom presentation, including 380 SARS-CoV-2 positive and 419 negative individuals. Stool samples were collected at two time points for metagenomic sequencing. Logistic regression with L1 regularization was employed to predict LC based on the microbiome and clinical metadata.<h4>Results</h4>The individuals who developed LC harbored a distinct gut microbiome during acute infection compared to those who recovered fully and uninfected controls with similar symptomatology. However, the temporal changes in the gut microbiome between the acute (0-1 month) and post-acute (1-2 months) phases were similar across the three cohorts. Using machine learning, we showed that the gut microbiome carried a modest signal for subsequent LC, but model performance was insufficient for clinical prediction, likely reflecting the heterogeneous nature of LC. Finally, we identified four LC symptom clusters, with gastrointestinal and fatigue-only groups strongly linked to gut microbiome alterations.<h4>Conclusion</h4>The gut microbiome can potentially offer solutions for understanding the heterogeneous nature of LC. Larger cohorts and phenotype-aware computational algorithms may help overcome current model performance limitations and support the development of targeted diagnostic and therapeutic strategies.

Links and sources

• Official / arXiv page

Comments