Biological age as a useful index to predict seventeen-year survival and mortality in Koreans
Bibliographic record
Abstract
BACKGROUND: Many studies have been conducted to quantitatively estimate biological age using measurable biomarkers. Biological age should function as a valid proxy for aging, which is closely related with future work ability, frailty, physical fitness, and/or mortality. A validation study using cohort data found biological age to be a superior index for disease-related mortality than chronological age. The purpose of this study is to demonstrate the validity of biological age as a useful index to predict a person's risk of death in the future. METHODS: The data consists of 13,106 cases of death from 557,940 Koreans at 20-93 years old, surveyed from 1994 to 2011. Biological ages were computed using 15 biomarkers measured in general health check-ups using an algorithm based on principal component analysis. The influence of biological age on future mortality was analyzed using Cox proportional hazards regression considering gender, chronological age, and event type. RESULTS: In the living subjects, the average biological age was almost the same as the average chronological age. In the deceased, the biological age was larger than the chronological age: largest increment of biological age over chronological age was observed when their baseline chronological age was within 50-59 years. The death rate significantly increased as biological age became larger than chronological age (linear trend test, p value < 0.0001). The largest hazard ratio was observed in subjects whose baseline chronological age was within 50-59 years when the cause was death from non-cancerous diseases (HR = 1.30, 95% confidence intervals = 1.26 - 1.34). The survival probability, over the 17 year term of the study, was significantly decreased in the people whose biological age was larger than chronological age (log rank test, p value < 0.001). CONCLUSIONS: Biological age could be used to predict future risk of death, and its effect size varied according to gender, chronological age, and cause of death.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".