Burden of psychiatric disease inversely correlates with Alzheimer's age at onset
Bibliographic record
Abstract
INTRODUCTION: Depression is regarded as a risk factor for Alzheimer's disease (AD). Associations between AD and other psychiatric disorders are less clear. METHODS: We screened 1,500 AD UCSF Memory and Aging Center patients for prevalence of psychiatric disorders and compared results to 8,267 NACC AD participants. RESULTS: AD with depression, anxiety, or post-traumatic stress disorder were significantly younger at age at onset than AD without (p < 0.001; p < 0.001; p < 0.05). Comorbidity of depression, anxiety and PTSD led to further decreases in AD age at onset. Within the NACC cohort, we further demonstrated an inverse relationship between the severity of depression and anxiety symptoms and AD age at onset. DISCUSSION: Depression, anxiety, and post-traumatic stress disorder are inversely associated with AD age at onset. Age at onset further decreases with increasing number of psychiatric conditions and increasing severity of symptoms, suggesting that overall burden of psychiatric disease is highly relevant to AD. HIGHLIGHTS: Retrospective chart review revealed that in patients with AD, those who also had depression, anxiety, or post-traumatic stress disorder were significantly younger at age at onset than those without. Increasing burden of psychiatric disease, both in severity of psychiatric symptoms and number of comorbid psychiatric conditions, produced serial decreases in the age at onset of AD. In patients with AD, those with depression were more likely to have autoimmune disease, and those with anxiety were more likely to have a history of seizures.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".