Plasma Microbiome in COVID-19 Subjects: An Indicator of Gut Barrier Defects and Dysbiosis
Bibliographic record
Abstract
The gut is a well-established route of infection and target for viral damage by SARS-CoV-2. This is supported by the clinical observation that about half of COVID-19 patients exhibit gastrointestinal (GI) complications. We aimed to investigate whether the analysis of plasma could provide insight into gut barrier dysfunction in patients with COVID-19 infection. Plasma samples of COVID-19 patients (n = 146) and healthy individuals (n = 47) were collected during hospitalization and routine visits. Plasma microbiome was analyzed using 16S rRNA sequencing and gut permeability markers including fatty acid binding protein 2 (FABP2), peptidoglycan (PGN), and lipopolysaccharide (LPS) in both patient cohorts. Plasma samples of both cohorts contained predominately Proteobacteria, Firmicutes, Bacteroides, and Actinobacteria. COVID-19 subjects exhibit significant dysbiosis (p = 0.001) of the plasma microbiome with increased abundance of Actinobacteria spp. (p = 0.0332), decreased abundance of Bacteroides spp. (p = 0.0003), and an increased Firmicutes:Bacteroidetes ratio (p = 0.0003) compared to healthy subjects. The concentration of the plasma gut permeability marker FABP2 (p = 0.0013) and the gut microbial antigens PGN (p < 0.0001) and LPS (p = 0.0049) were significantly elevated in COVID-19 patients compared to healthy subjects. These findings support the notion that the intestine may represent a source for bacteremia and contribute to worsening COVID-19 outcomes. Therapies targeting the gut and prevention of gut barrier defects may represent a strategy to improve outcomes in COVID-19 patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".