Comparative Analyses Between Vulnerability Biomarkers of Aging and Health Biomarkers in Middle-Aged and Older Female Adults
Bibliographic record
Abstract
Biological aging is normally associated with greater physiological changes which predispose individuals to adverse outcomes. In this way, the evaluation of vulnerability biomarkers and their relationships with other health biomarkers could contribute to the promotion of interventions and the improvement of older adults’ quality of life. Thereby, this study aimed to compare vulnerability biomarkers (Growth Differentiation Factor 15 (GDF-15), General Functional Fitness Index (GFFI), and frailty phenotype) and their influence on health markers (blood biochemistry, body composition, and hemodynamic variables) in middle-aged and older female adults. Methods: A cross-sectional observational study was conducted with community-dwelling females aged 54–84 with at least 6 months of experience with physical training. The participants were categorized based on functional fitness, frailty phenotype, and GDF-15 quartiles. The General Functional Fitness Index (GFFI) was assessed using the AAHPERD test battery, while frailty phenotype was determined using Fried’s criteria. GDF-15 levels were measured through ELISA. Results: A higher training status (TS) showed better functional fitness and favorable biochemical profiles, including lower total cholesterol (p = 0.006, η2p = 0.253), LDL cholesterol (p = 0.001, η2p = 0.346), triglycerides (p = 0.048, η2p = 0.195), and systolic blood pressure (p = 0.001, η2p = 0.333). Individuals classified as robust (non-frail) had better physical performance and lower total cholesterol (p = 0.002, η2p = 0.306) and LDL cholesterol (p = 0.014, η2p = 0.216) compared to those classified as frail and pre-frail. The GDF-15 quartile did not present differences in health markers between groups. Conclusions: These findings suggest that GFFI may be considered a health biomarker for middle-aged and female older adults while highlighting the need for further research on the role of biomarkers of vulnerability and healthy aging.
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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) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".