Obesity Is Associated with Attenuated Tissue Immunity in COVID-19
Bibliographic record
Abstract
Abstract Rationale Obesity affects 40% of U.S. adults, is associated with a proinflammatory state, and presents a significant risk factor for the development of severe coronavirus disease (COVID-19). To date, there is limited information on how obesity might affect immune cell responses in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Objectives To determine the impact of obesity on respiratory tract immunity in COVID-19 across the human lifespan. Methods We analyzed single-cell transcriptomes from BAL in three ventilated adult cohorts with (n = 24) or without (n = 9) COVID-19 from nasal immune cells in children with (n = 14) or without (n = 19) COVID-19, and from peripheral blood mononuclear cells in an independent adult COVID-19 cohort (n = 42), comparing obese and nonobese subjects. Measurements and Main Results Surprisingly, we found that obese adult subjects had attenuated lung immune or inflammatory responses in SARS-CoV-2 infection, with decreased expression of IFN-α, IFN-γ, and TNF-α (tumor necrosis factor α) response gene signatures in almost all lung epithelial and immune cell subsets, and lower expression of IFNG and TNF in specific lung immune cells. Peripheral blood immune cells in an independent adult cohort showed a similar but less marked reduction in type-I IFN and IFNγ response genes, as well as decreased serum IFNα, in obese patients with SARS-CoV-2. Nasal immune cells from obese children with COVID-19 also showed reduced enrichment of IFN-α and IFN-γ response genes. Conclusions These findings show blunted tissue immune responses in obese patients with COVID-19, with implications for treatment stratification, supporting the specific application of inhaled recombinant type-I IFNs in this vulnerable subset.
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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 | 0.000 |
| 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 | 0.000 | 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".