Relationship between cortisol level and prevalent/incident cognitive impairment and its moderating factors in older adults
Bibliographic record
Abstract
BACKGROUND: The objectives of this study were to examine the factors modifying the relationship between cortisol level and prevalent/incident cognitive impairment in older adults and to verify whether these relationships were non-linear. METHODS: Data were collected from 1,226 individuals aged 65 and older by two in-home interviews separated by 12 months. Cortisol level was measured using saliva samples taken at the beginning of the baseline interview before cognitive, mental, and physical health evaluations. Prevalent and incident cognitive impairment were defined using the Mini-Mental State Examination scores according to normative data for age, education level, and sex. RESULTS: High morning cortisol level increased the risk of incident cognitive impairment in participants with anxiety or depressive episode while low cortisol level increased the risk in participants without anxiety or depressive episode. In high educated participants, but not in low educated participants, high morning cortisol level was associated with prevalent cognitive impairment and high afternoon cortisol level increased the risk of incident cognitive impairment. The results also suggested that lower morning cortisol values could increase the risk of incident cognitive impairment in individuals with few chronic diseases. A curvilinear relationship was observed between morning cortisol and the probability of incident cognitive impairment, but further analyses suggested that it was likely explained by anxiety and depressive episode. CONCLUSIONS: These results suggest that cognitive impairment in older adults is linked to higher or lower cortisol level depending on characteristics such as anxiety, depressive episode, education level, and physical health.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".