Ischemic heart disease and cardiac arrhythmia are associated with increased cardiac aging
Bibliographic record
Abstract
Abstract Ischemic heart disease (IHD) and cardiac arrhythmia (CA) patients experience alterations in cardiac structure and function which can speed up cardiac aging. Estimating biological heart age using cardiac magnetic resonance (CMR) and electrocardiogram (ECG)-derived phenotypes provides a biomarker for cardiac aging. We investigated the impact of IHD and CA on cardiac aging using biological age estimation biomarkers, and the role of age-related cardiac changes and vascular risk factors (VRF)s using data from United Kingdom Biobank. Cardiac age was estimated in prevalent IHD (n = 2,142) and CA (n = 1,683) subjects using a Bayesian ridge regression model with CMR radiomics and ECG features. Heart age gap (HAG), the difference between predicted and chronological heart age, was calculated. Mediation analysis explored CMR metrics as mediators in the HAG-cardiac disease association. The association of HAG and VRFs in each disease cohort was also analysed. IHD subjects had significantly increasing heart age (HAG: 1.55 years ± 5.66; p < 0.001), as did CA individuals (HAG: 1.57 years ± 5.77; p < 0.001). Conventional CMR metrics describing normal age-related changes partially mediated the effect of disease on HAG. High adiposity contributed most to increasing HAG in IHD, followed by hypertension. Hypertension had the greatest impact on cardiac aging, followed by high cholesterol in CA.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".