Fibroblast Growth Factor-23 and Risk of Cardiovascular Diseases: a Mendelian Randomisation study
Bibliographic record
Abstract
Abstract Fibroblast growth factor 23 (FGF-23) is associated with a range of cardiovascular and non-cardiovascular diseases in conventional epidemiological studies, but substantial residual confounding may exist. Mendelian randomisation approaches can help control for such confounding. SCALLOP consortium data on 19,195 participants were used to generate an FGF-23 genetic score. Data from 337,448 UK Biobank participants were used to estimate associations between higher genetically-predicted FGF-23 concentration and the odds of any atherosclerotic cardiovascular disease (n=26,266 events), of any non-atherosclerotic cardiovascular disease (n=12,652), and of non-cardiovascular diseases previously linked to FGF-23. Measurements of carotid intima-media thickness (CIMT) and left ventricular mass (LVM) were available in a subset. Associations with cardiovascular outcomes were also tested in three large case-control consortia: CARDIOGRAMplusC4D (coronary artery disease, n=181,249 cases), MEGASTROKE (stroke, n=34,217), and HERMES (heart failure, n=47,309). We identified 34 independent variants for circulating FGF-23 which formed a validated genetic score. There were no associations between genetically-predicted FGF-23 and any of the cardiovascular or non-cardiovascular outcomes. In UK Biobank, the odds ratio for any atherosclerotic cardiovascular disease per 1-SD higher genetically-predicted logFGF-23 was 1.03 (95% confidence interval [CI] 0.98-1.08), and for any non-atherosclerotic cardiovascular disease was 1.01 (0.94-1.09). The odds ratios in the case-control consortia were 1.00 (0.97-1.03) for coronary artery disease, 1.01 (0.95-1.07) for stroke, and 1.00 (0.95-1.05) for heart failure. In those with imaging, logFGF-23 was not associated with CIMT or LVM index. This suggests that previously reported observational associations of FGF-23 with risk of atherosclerotic and non-atherosclerotic cardiovascular diseases are unlikely to be causal.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".