Causal associations between body fat accumulation and COVID-19 severity: A Mendelian randomization study
Bibliographic record
Abstract
Abstract Purpose The causal effects of body fat mass and body fat-free mass on coronavirus disease 2019 (COVID-19) severity remain unclear. Here, we used Mendelian randomization (MR) to evaluate the causal relationships between body fat-related traits and COVID-19 severity. Material and Methods We identified single nucleotide polymorphisms associated with body mass index (BMI) and direct measures of body fat (i.e., body fat percentage, body fat mass, and body fat-free mass) in 461,460, 454,633, 454,137, and 454,850 individuals of European ancestry from the UK Biobank, respectively. We then performed two-sample MR to ascertain their effects on severe COVID-19 (cases: 4,792; controls: 1,054,664) from the COVID-19 Host Genetics Initiative. Results We found that an increase in BMI, body fat percentage, and body fat mass by one standard deviation were each associated with severe COVID-19 (odds ratio (OR) BMI = 1.49, 95%CI: 1.19–1.87, P = 5.57×10 −4 ; OR body fat percentage = 1.94, 95%CI: 1.41–2.67, P = 5.07×10 −5 ; and OR body fat mass = 1.61, 95%CI: 1.28–2.04, P = 5.51×10 −5 ). Further, we evaluated independent causal effects of body fat mass and body fat-free mass using multivariable MR and revealed that only body fat mass was independently associated with severe COVID-19 (OR body fat mass = 2.91, 95%CI: 1.71–4.96, P = 8.85×10 −5 and OR body fat-free mass = 1.02, 95%CI: 0.61–1.67, P = 0.945). Conclusions This study demonstrates the causal effects of body fat accumulation on COVID-19 severity and indicates that the biological pathways influencing the relationship between COVID-19 and obesity are likely mediated through body fat mass.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".