Adiposity distribution and risks of twelve obesity-related cancers: a Mendelian randomization analysis
Bibliographic record
Abstract
Abstract Background There is convincing evidence that overall adiposity (as measured by body mass index) increases the risks of several cancers. Whether there are similar relationships between these cancers and the distribution of adiposity is unclear. Methods In the absence of well-powered individual studies we utilised two-sample Mendelian randomization (MR) to examine causal relationships of five adiposity distribution traits (abdominal subcutaneous adipose tissue; ASAT, visceral adipose tissue; VAT and gluteofemoral adipose tissue; GFAT, liver fat, and pancreas fat) with the risks of 12 obesity-related cancers (endometrial, ovarian, breast, colorectal, pancreas, multiple myeloma, liver, kidney (renal cell), thyroid, gallbladder, oesophageal adenocarcinoma, and meningioma) and cancer subtypes/subsites. We then used multivariable MR to investigate whether plausible molecular intermediates could potentially be mediating the relationships identified. We used the largest available GWAS from European populations for all traits (sample size across all GWAS ranged from 8,407 to 728,896 (median: 57,249); cancer GWAS ranged from 279 to 133,384 cases (median: 4,532) and 3,456 to 727,247 controls (median: 68,802)). Results We found evidence that higher genetically predicted ASAT increased risks of endometrial cancer (inverse variance-weighted (IVW) odds ratio (IVW OR) per standard deviation (SD) higher genetically predicted ASAT = 1.79, 95% confidence interval (CI) = 1.18 to 2.71), liver cancer (IVW OR per SD higher genetically predicted ASAT = 3.83, 95% CI = 1.39 to 10.53) and oesophageal adenocarcinoma (IVW OR per SD higher genetically predicted ASAT = 2.34, 95% CI = 1.15 to 4.78). Conversely, we found evidence that higher genetically predicted GFAT decreased risks of breast cancer (IVW OR per SD higher genetically predicted GFAT= 0.77, 95% CI = 0.62 to 0.97) and meningioma (IVW OR per SD higher genetically predicted GFAT = 0.53, 95% CI = 0.32 to 0.90). We also found evidence for an effect of higher genetically predicted VAT and liver fat on increased liver cancer risk (IVW ORs per SD higher genetically predicted adiposity trait = 4.29 and 4.09, 95% CIs = 1.41 to 13.07 and 2.29 to 7.28, respectively). Multivariable MR analyses suggested that traits related to insulin signalling, sex hormones, and inflammation may play important roles in mediating the effects of adiposity distribution on obesity-related cancers. Conclusions Our analyses provide novel insights into the variability of the effect of adiposity distribution on cancer risk, with respect to both adiposity trait and cancer type, which would not be possible in a conventional observational analysis given the lack of available samples with all required traits measured. These findings demonstrate that adipose tissue at different anatomical locations may have differential effects on adiposity-related molecular traits. These insights enhance our understanding of the complex relationship between adiposity and cancer risk and highlight the importance of adipose tissue distribution alongside maintaining a healthy weight overall for cancer prevention.
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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 | 0.027 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".