COVID-19 and the Genetics of Inflammation
Bibliographic record
Abstract
OBJECTIVE: Interindividual variability in the clinical progression of COVID-19 may be explained by host genetics. Emerging literature supports a potential inherited predisposition to severe forms of COVID-19. Demographic and inflammatory characteristics of COVID-19 suggest that acquired hematologic mutations leading to clonal hematopoiesis (CH) may further increase vulnerability to adverse sequelae. This review summarizes the available literature examining genetic predispositions to severe COVID-19 and describes how these findings could eventually be used to improve its clinical management. DATA SOURCES: A PubMed literature search was performed. STUDY SELECTION: Studies examining the significance of inherited genetic variation or acquired CH mutations in severe COVID-19 were selected for inclusion. DATA EXTRACTION: Relevant genetic association data and aspects of study design were qualitatively assessed and narratively synthesized. DATA SYNTHESIS: Genetic variants affecting inflammatory responses may increase susceptibility to severe COVID-19. Genome-wide association studies and candidate gene approaches have identified a list of inherited mutations, which likely alter cytokine and interferon secretion, and lung-specific mechanisms of immunity in COVID-19. The potential role of CH in COVID-19 is more uncertain at present; however, the available evidence suggests that the various types of acquired mutations and their differential influence on immune cell function must be carefully considered. CONCLUSIONS: The current literature supports the hypothesis that host genetic factors affect vulnerability to severe COVID-19. Further research is required to confirm the full scope of relevant variants and the causal mechanisms underlying these associations. Clinical approaches, which consider the genetic basis of interindividual variability in COVID-19 and potentially other causes of critical illness, could optimize hospital resource allocation, predict responsiveness to treatment, identify more efficacious drug targets, and ultimately improve outcomes.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".