Affective Fluctuations in Depressive and Anxiety Symptomatology Predict Cognitive Performance Decline in Older Adults
Bibliographic record
Abstract
BACKGROUND: Previous research supports that affective symptoms related to depression and anxiety are associated with lower cognitive performance in cognitively unimpaired (CU) older adults. Most studies reporting associations are cross-sectional and have not investigated whether or how affective variability may correlate with cognitive variability or change over time. This study aimed to assess whether intraindividual variability of self-reported affective symptoms is associated with cognitive decline. METHOD: Longitudinal data from 817 predominantly highly-educated, CU middle-age and older adults (age range 53-102 years) were included. Cognition was measured using the Montreal Cognitive Assessment (MoCA). Center for Epidemiologic Studies Depression 10-Item was used to assess depressive symptoms, the Penn State Worry Questionnaire was used to assess anxiety symptoms. Intrasubject standard deviation (ISD) was used to quantify year-to-year variability of the affective measures. RESULT: In primary analyses using independent mixed-effects models adjusted for age, education, and sex, greater variability in depression and anxiety symptomatology was associated with cognitive decline. In exploratory analyses using combined models to assess for additive versus additive and synergistic effects only depressive symptom variability was significantly associated with cognitive decline. The interaction of depressive and anxiety symptom variability was not a significant predictor of cognitive decline. CONCLUSION: In cognitively unimpaired older adults within-subject affective variability is associated with linear decline on cognitive performance over several years. Understanding and accounting for the potential impact of affective fluctuations on cognitive performance may improve trajectory and power estimates in observational and interventional studies, and suggest strategies to mitigate components of cognitive decline.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".