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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".