Obesity and cancer: Mendelian randomization approach utilizing the FTO genotype
Bibliographic record
Abstract
BACKGROUND: Obesity is a risk factor for several cancers although appears to have an inverse association with cancers strongly related to tobacco. Studying obesity is difficult due to numerous biases and confounding. METHODS: To avoid these biases we used a Mendelian randomization approach incorporating an analysis of variants in the FTO gene that are strongly associated with BMI levels among 7000 subjects from a study of lung, kidney and upper-aerodigestive cancer. RESULTS: The FTO A allele which is linked with increased BMI was associated with a decreased risk of lung cancer (allelic odds ratio (OR) = 0.92, 95% confidence interval (CI) 0.84-1.00). It was also associated with a weak increased risk of kidney cancer, which was more apparent before the age of 50 (OR = 1.44, CI 1.09-1.90). CONCLUSION: Our results highlight the potential for genetic variation to act as an unconfounded marker of environmentally modifiable factors, and offer the potential to obtain estimates of the causal effect of obesity. However, far larger sample sizes than studied here will be required to undertake this with precision.
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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 | 0.033 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".