Decoding Precision Aging: The Intersection of Cognitive Decline, Frailty, and Hormonal Biomarkers
Bibliographic record
Abstract
INTRODUCTION: Cognitive frailty, characterized by the coexistence of cognitive impairment and physical frailty, is a significant predictor of cognitive decline. However, few studies integrate both cognitive and physical assessments alongside hormonal markers, such as cortisol, that may influence frailty and cognitive function. To address this gap, our study combines noninvasive physical, cognitive, and cortisol markers to assess frailty in aging adults. METHODS: Data were collected from four sites as part of the Healthy Minds for Life (HML) longitudinal cohort, a project within the Precision Aging Network. Baseline data included cognitive evaluation using the Montreal Cognitive Assessment (MoCA); frailty assessment using a validated 20-s elbow flexion-extension test analyzed by AI under single-task (ST) and dual-task (DT) conditions; cortisol measurement in eccrine sweat samples via direct analysis in real-time mass spectrometry (DART-MS); and demographic information. RESULTS: Of 202 participants completing all assessments, 60 were identified with mild cognitive impairment (MCI). The dual-task frailty index (FI) derived from the 20-s test significantly differentiated individuals with MCI from cognitively robust participants and correlated strongly with MoCA scores (p = 0.015). The dual-task FI showed superior model fit compared to the single-task FI when predicting cognitive function. A significant correlation between the dual-task FI and cortisol by age interaction was observed (p = 0.0042) highlighting the potential impact of cortisol to moderate the relationship between frailty and age in an otherwise healthy aging population. By contrast, no significant correlation was found between dual-task FI and aging outside of the presence of cortisol (p = 0.116) in this study. CONCLUSIONS: This study highlights practical and efficient methods for assessing frailty emphasizing the value of DT testing and cortisol measures in identifying individuals at higher risk for cognitive and physical decline. The findings underscore the importance of integrating hormonal markers with cognitive and physical assessments to enhance risk stratification and intervention planning in aging populations.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".