Heritability of and Mortality Prediction With a Longevity Phenotype: The Healthy Aging Index
Bibliographic record
Abstract
BACKGROUND: Longevity-associated genes may modulate risk for age-related diseases and survival. The Healthy Aging Index (HAI) may be a subphenotype of longevity, which can be constructed in many studies for genetic analysis. We investigated the HAI's association with survival in the Cardiovascular Health Study and heritability in the Long Life Family Study. METHODS: The HAI includes systolic blood pressure, pulmonary vital capacity, creatinine, fasting glucose, and Modified Mini-Mental Status Examination score, each scored 0, 1, or 2 using approximate tertiles and summed from 0 (healthy) to 10 (unhealthy). In Cardiovascular Health Study, the association with mortality and accuracy predicting death were determined with Cox proportional hazards analysis and c-statistics, respectively. In Long Life Family Study, heritability was determined with a variance component-based family analysis using a polygenic model. RESULTS: Cardiovascular Health Study participants with unhealthier index scores (7-10) had 2.62-fold (95% confidence interval: 2.22, 3.10) greater mortality than participants with healthier scores (0-2). The HAI alone predicted death moderately well (c-statistic = 0.643, 95% confidence interval: 0.626, 0.661, p < .0001) and slightly worse than age alone (c-statistic = 0.700, 95% confidence interval: 0.684, 0.717, p < .0001; p < .0001 for comparison of c-statistics). Prediction increased significantly with adjustment for demographics, health behaviors, and clinical comorbidities (c-statistic = 0.780, 95% confidence interval: 0.765, 0.794, p < .0001). In Long Life Family Study, the heritability of the HAI was 0.295 (p < .0001) overall, 0.387 (p < .0001) in probands, and 0.238 (p = .0004) in offspring. CONCLUSION: The HAI should be investigated further as a candidate phenotype for uncovering longevity-associated genes in humans.
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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 | 0.001 |
| 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".