Allostatic Load as a Biological Substrate to Intrinsic Capacity: A Secondary Analysis of CRELES
Bibliographic record
Abstract
OBJECTIVES: Intrinsic capacity (IC) is one of the latest views of positive aging. In its current status lacks a biological substrate amenable to be intervened. The aim of this study was to determine the association of allostatic load (AL) with IC. DESIGN: We present a cross-sectional analysis of the Costa Rican Longevity and Healthy Aging Study. SETTING: This report is from a representative sample of Costa Rican older adults; one of the countries that integrate the Central America region. PARTICIPANTS: 2,827, 60-year or older community-dwelling individuals. METHODS: An IC index was gathered and validated, including different domains: cognitive, psychological, sensory, vitality and locomotion. AL was integrated with: blood pressure, abdominal obesity, body mass index, HDL-cholesterol, glycosylated hemoglobin, DHEAS, cortisol, epinephrine and norepinephrine. AL was grouped in three categories according to the number of abnormal biomarkers (0-1, 2-3 and ≥4). Chronic diseases, socioeconomic level, sex and age were the adjusting variables. Ordinal logistic regression models were estimated in order to test the strength of the association. RESULTS: From a total sample of 1,888 individuals, 51% (n=962) were women, 36.4% were in the 60-69 age category. The mean score of the IC index was of 6.6 (±2.2). Odds ratio (OR) of the adjusted models were significant for the group of those with 2-3 abnormal biomarkers of AL (OR 0.67, p=0.007) and also for those with ≥4 (OR 0.56, p=0.002), when compared to the reference group of AL (0-1 abnormal biomarkers). CONCLUSIONS AND IMPLICATIONS: AL showed an incremental association with IC, even when adjusted for factors such as socioeconomic status and chronic diseases. Targeting therapeutically AL could potentially improve IC in older adults and therefore decreasing the progression to disability or to overt dependency.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".