Lack of evidence for obesity paradox in patients with cardiovascular diseases: A UK BioBank cohort study
Bibliographic record
Abstract
Abstract Background Obesity paradox, a phenomenon that obesity seems to be associated with reduced risk of mortality in patients with established cardiovascular disease (CVD), has been controversial. We aimed to use Mendelian randomization to examine the causal relationship between obesity measures and CVD mortality in patients with known CVD in the UK BioBank study cohort. Methods A total of 58,278 participants with CVD were included. Polygenic risk scores (PRSs) for body mass index (BMI), body fat percentage (BF%), and waist to hip ration adjusted for BMI (WHRadjBMI) were used as instrumental variables. The following sensitivity analyses were performed: 1) using a representative variant rs1558902 in the fat mass and obesity associated gene as an instrumental variable, 2) by sex, and 3) by disease type. Results A total of 2203 patients died of CVD causes during a median follow-up period of 8.9 years. BMI in the overweight and class-I obesity range was associated with reduced mortality, with class-II or more severe obesity associated with increased mortality; however, there was a linear trend toward increased mortality with increasing BF% and WHRadjBMI. There was no clear indication that increased obesity-PRSs were associated with reduced risk of CVD mortality among individuals with known CVD. Sensitivity analyses using rs1558902 as an instrumental variable, by sex, and by disease type showed similar results. Conclusion Increased obesity does not show a protective effect in patients with CVD. Previously reported obesity paradox in observational studies may be a result of confounding or other biases, which needs further investigation.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".