Causal relationships between interleukins, interferons and COVID-19 risk: a Mendelian randomization study
Bibliographic record
Abstract
Observational studies have shown close associations between COVID-19 risk and cytokines, especially interleukins (ILs) and interferons (IFNs). However, the causal relationships between ILs, IFNs and COVID-19 were still unclear. To resolve the problem, we conducted a Mendelian randomization analysis between COVID-19 and 47 cytokines, including 35 ILs and 12 IFNs. First, three methods were applied to estimate causal effects by using single nucleotide polymorphisms as instrumental variables (IVs). Subsequently, the MR–Egger method was used to estimate the horizontal pleiotropy of IVs. Finally, sensitivity analyses were applied to assess the robustness of results. As a result, one IFN (IFN-W1) and five ILs (IL-5, IL-6, IL-13, IL-16 and IL-37) were identified to significantly decrease the COVID-19 risk. In contrast, one IFN (IFNG) and five ILs (IL-3, IL-8, IL-27, IL-31 and IL-36β) were found to be significantly associated with an increased risk of COVID-19. In summary, the findings of this study provide insights into potential therapeutic interventions for COVID-19.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".