Adiposity, metabolites, and colorectal cancer risk: Mendelian randomization study
Bibliographic record
Abstract
Abstract Importance Evidence on adiposity altering colorectal cancer (CRC) risk differently among men and women, and on metabolic alterations mediating effects of adiposity on CRC, is unclear. Objective To examine sex- and site-specific associations of adiposity with CRC risk, and whether adiposity-associated metabolites explain associations of adiposity with CRC. Design Two-sample Mendelian randomization (MR) study. Setting Genetic variants from expanded genome-wide association studies of body mass index (BMI) and waist-to-hip ratio (WHR, unadjusted for BMI; N=806,810), and 123 metabolites (mostly lipoprotein subclass-specific lipids) from targeted nuclear magnetic resonance metabolomics (N=24,925), were used as instruments. Sex-combined and sex-specific MR was conducted for BMI and WHR with CRC risk; sex-combined MR was conducted for BMI and WHR with metabolites, for metabolites with CRC, and for BMI and WHR with CRC adjusted for metabolite classes. Participants 58,221 cases and 67,694 controls (Genetics and Epidemiology of Colorectal Cancer Consortium; Colorectal Cancer Transdisciplinary Study; Colon Cancer Family Registry). Main outcome measures Incident CRC (overall and site-specific). Results Among men, higher BMI (per 4.2 kg/m 2 ) was associated with 1.23 (95%-confidence interval (CI)=1.08, 1.38) times higher CRC odds (inverse-variance-weighted (IVW) model); among women, higher BMI (per 5.2 kg/m 2 ) was associated with 1.09 (95%-CI=0.97, 1.22) times higher CRC odds. Higher WHR was more strongly associated with CRC risk among women (IVW-OR=1.25, 95%-CI=1.08, 1.43 per 0.07-ratio) than men (IVW-OR=1.05, 95%-CI=0.81, 1.36 per 0.07-ratio). BMI or WHR was associated with 104 metabolites (false-discovery-rate-corrected P≤0.05) including low-density lipoprotein (LDL) cholesterol, but these metabolites were generally unassociated with CRC in directions consistent with mediation of adiposity-CRC relations. In multivariable MR, associations of BMI and WHR with CRC were not attenuated following adjustment for representative metabolite classes – e.g. the univariable IVW-OR of BMI for CRC was 1.12 (95%-CI=1.00, 1.26), and 1.11 (95%-CI=0.99, 1.26) adjusting for LDL lipids. Conclusions and relevance Our results suggest that higher BMI more greatly raises CRC risk among men, whereas higher WHR more greatly raises CRC risk among women. Adiposity was associated with numerous metabolic alterations, but none of these alterations explained associations between adiposity and CRC. More detailed metabolomic measures are likely needed to clarify mechanistic pathways.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".