Echocardiographic heart ageing patterns predict cardiovascular and non-cardiovascular events and reflect biological age: the SardiNIA study
Bibliographic record
Abstract
AIMS: Age is a crucial risk factor for cardiovascular (CV) and non-CV diseases. As people age at different rates, the concept of biological age has been introduced as a personalized measure of functional deterioration. Associations of age with echocardiographic quantitative traits were analysed to assess different heart ageing rates and their ability to predict outcomes and reflect biological age. METHODS AND RESULTS: Associations of age with left ventricular mass, geometry, diastolic function, left atrial volume, and aortic root size were measured in 2614 healthy subjects. Based on the 95% two-sided tolerance intervals of each correlation, three discrete ageing trajectories were identified and categorized as 'slow', 'normal', and 'accelerated' heart ageing patterns. The primary endpoint included fatal and non-fatal CV events, and the secondary endpoint was a composite of CV and non-CV events and all-cause death. The phenotypic age of the heart (HeartPhAge) was estimated as a proxy of biological age. The slow ageing pattern was found in 8.7% of healthy participants, the normal pattern in 76.9%, and the accelerated pattern in 14.4%. Kaplan-Meier curves of the heart ageing patterns diverged significantly (P = 0.0001) for both primary and secondary endpoints, with the event rate being lowest in the slow, intermediate in the normal, and highest in the accelerated pattern. In the Cox proportional hazards model, heart ageing patterns predicted both primary (P = 0.01) and secondary (P = 0.03 to <0.0001) endpoints, independent of chronological age and risk factors. Compared with chronological age, HeartPhAge was 9 years younger in slow, 4 years older in accelerated (both P < 0.0001), and overlapping in normal ageing patterns. CONCLUSION: Standard Doppler echocardiography detects slow, normal, and accelerated heart ageing patterns. They predict CV and non-CV events, reflect biological age, and provide a new tool to calibrate prevention timing and intensity.
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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 | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".