The impact of GDF-15, a biomarker for metformin, on the risk of coronary artery disease, breast and colorectal cancer, and type 2 diabetes and metabolic traits: a Mendelian randomisation study
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Bibliographic record
Abstract
AIMS/HYPOTHESIS: Growth differentiation factor 15 (GDF-15), a suggested biomarker for metformin use, may explain the potential cardioprotective and anti-cancer properties of metformin. We conducted a Mendelian randomisation study to examine the role of GDF-15 in risk of coronary artery disease (CAD) and breast and colorectal cancer. Secondary analyses included examination of the association of GDF-15 with type 2 diabetes, glycaemic traits, BP, lipids and BMI. METHODS: : n = 123,665; fasting glucose: n = 46,186]), BP, breast cancer and colorectal cancer (UK Biobank [n ≤ 401,447]), lipids (GLGC [Global Lipids Genetic Consortium; n ≤ 92,820]) and adiposity (GIANT [Genetic Investigation of ANthropometric Traits Consortium; n = 681,275]). Causal estimates were obtained using inverse variance weighting, taking into account correlations between SNPs. Sensitivity analyses included focusing on the lead SNP (rs888663) and validation for CAD in the UK Biobank and for breast cancer in the Breast Cancer Association Consortium. RESULTS: Using 5 SNPs, increased GDF-15 was associated with lower CAD (OR 0.93 per SD increase, 95% CI 0.87, 0.99) and breast cancer (OR 0.89 per SD increase, 95% CI 0.82, 0.96), with similar results from lead SNP analysis. However, the associations with CAD (OR 0.99 per SD increase, 95% CI 0.93, 1.04) and breast cancer (OR 0.97 per SD increase, 95% CI 0.94, 1.01) in the validation studies were not as apparent. GDF-15 was not associated with type 2 diabetes, glycaemic traits, CAD risk factors or colorectal cancer. CONCLUSIONS/INTERPRETATION: There is no convincing evidence that GDF-15 reduces risk of CAD or breast or colorectal cancer. Whether the observed inverse association of metformin use with cancer risk is via other unexplored mechanistic pathways warrants further investigation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 it