Physiological phenotypes have optimal values relevant to healthy aging: sweet spots deduced from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Previous observations on a group of exceptionally healthy "Super-Seniors" showed a lower variance of multiple physiological measures relevant for health than did a less healthy group of the same age. The finding was interpreted as the healthier individuals having physiological measurement values closer to an optimal level, or "sweet spot." Here, we tested the generalizability of the sweet-spot hypothesis in a larger community sample, comparing differences in the variance between healthier and less healthy groups. We apply this method to the Canadian Longitudinal Study on Aging (CLSA) comprehensive cohort of 30,097 participants aged 45 to 85 years with deep phenotype data. Data from both sexes and four age ranges were analyzed. Five instruments were used to represent different aspects of health, physical, and cognitive functioning. We tested 231 phenotypic measures for lower variance in the most healthy vs. least healthy quartile of each sex and age group, as classified by the five instruments. Segmented regression was used to determine sex-specific optimal values. One hundred forty-two physiological measures (61%) showed lower variance in the healthiest than in the least healthy group, in at least one sex and age group. The difference in variance was most significant for hemoglobin A1c and was also significant for many body composition measurements, but not for bone mineral density. Ninety-four phenotypes showed a nonmonotonic relationship with health, consistent with the idea of a sweet spot; for these, we determined optimal values and 95% confidence intervals that were generally narrower than the ranges of current clinical reference intervals. These findings for sweet spot discovery validate the proposed approach for identifying traits important for healthy aging.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".