Diabetes and elevated plasma glucose in heart valve calcification and disease: the Copenhagen General Population Study
Bibliographic record
Abstract
AIMS: The only treatment available for aortic valve stenosis is valve replacement, which makes it important to identify modifiable risk factors. We tested the hypotheses that diabetes and elevated plasma glucose are associated with aortic and mitral valve calcification and aortic valve stenosis, and that these associations are explained partly by elevated plasma triglycerides, hypertension, and body mass index (BMI). METHODS AND RESULTS: In the Copenhagen General Population Study with 110 291 individuals, we evaluated risk of aortic valve stenosis and mitral valve regurgitation from health registers, and in a subset of 12 006 cardiac CT scanned individuals aortic and mitral valve calcification. Of individuals with cardiac CT, 3018 (25%) had aortic and 1521 (13%) had mitral valve calcification. For individuals with vs. without diabetes, the multi-variable adjusted odds ratios were 1.67 (95%:1.34-2.08) for aortic and 1.89 (1.48-2.40) for mitral valve calcification. The corresponding hazard ratio was 1.71 (1.44-2.03) for aortic valve stenosis.For individuals with glucose ≥6.6 mmol/L (≥118 mg/dL) vs. ≤5.1 mmol/L (≤92 mg/dL), the multi-variable adjusted odds ratios were 1.27 (1.07-1.52) for aortic and 1.44 (1.18-1.77) for mitral valve calcification. The corresponding hazard ratio was 1.33 (1.12-1.57) for aortic valve stenosis.In the relationship between diabetes and aortic valve stenosis, 4.8% (95% CI: 0.5-11%) of the association was explained by plasma triglycerides, 19% (14-28%) by hypertension, and 27% (18-43%) by BMI. CONCLUSION: Diabetes and elevated plasma glucose were associated with risk of aortic and mitral valve calcification and aortic valve stenosis. The risk of aortic valve stenosis was partly explained by elevated plasma triglycerides, hypertension, and BMI.
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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 | 0.001 | 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 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".