The Impact of Amyloid Burden and <i>APOE</i> on Rates of Cognitive Impairment in Late Life Depression
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment (CI) is a key feature of late life depression (LLD), but the contribution of underlying neurodegenerative pathology remains unclear. OBJECTIVE: To evaluate cognitive dysfunction in LLD relative to a sample of nondepressed (ND) older adults with matched levels of memory impairment and amyloid-β (Aβ) burden. METHODS: Participants included 120 LLD and 240 ND older adults matched on age, education, sex, Mini-Mental State Exam, mild cognitive impairment diagnosis, and PET Aβ burden. RESULTS: LLD showed higher rates of impairment relative to ND with 54.6% of the LLD sample demonstrating impairment in at least one cognitive domain compared to 42.9% of controls (H = 7.13, p = 0.008). LLD had poorer performance and higher rates of impairment on Rey Auditory Verbal Learning Test learning and memory compared to controls. In the overall sample, Aβ positivity was associated with worse performance on Logical Memory I (p = 0.044), Logical Memory II (p = 0.011), and Trail Making Test -B (p = 0.032), and APOEɛ4 genotype was associated with worse performance on Logical Memory I (p = 0.022); these relationships did not differ between LLD and ND. CONCLUSION: LLD showed higher rates of CI driven by focal deficits in verbal learning and memory. Alzheimer's disease (AD) biomarkers were associated with worse performance on timed set-shifting and story learning and memory, and these relationships were not impacted by depression status. These findings suggest that AD may account for a portion of previously reported multi-domain CI in LLD and highlight the potential for AD to confound studies of cognition in LLD.
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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 | 0.001 | 0.000 |
| 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".