Affective Neuropsychiatric Symptoms as Early Signs of Dementia Risk in Older Adults
Bibliographic record
Abstract
BACKGROUND: Affective neuropsychiatric symptoms (aNPS: depression, anxiety, apathy, irritability) have been linked to increased dementia risk. However, less is known whether this association is independent of Alzheimer's disease (AD) pathophysiology. OBJECTIVE: To investigate the contribution of early aNPS to dementia risk in cognitively normal (CN) older adults and mild cognitive impairment (MCI) patients, with and without AD biomarker abnormality. METHODS: Participants included 763 community-dwelling, stroke-free older adults identified as CN and 617 with MCI at baseline, drawn from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Baseline assessments included a neuropsychological battery, the Neuropsychiatric Inventory (NPI), and apolipoprotein E ɛ4 (ApoE4) genotyping. A participant subset completed cerebrospinal fluid (CSF) AD biomarker assessment. Time to progression to dementia was measured based on months at follow-up when an individual was diagnosed with dementia, over the follow-up period of 48 months. RESULTS: Latent class analysis identified 3 subgroups of older adults in CN and MCI, indicated by the baseline profiles of neuropsychiatric symptoms (NPS). Subgroups with higher aNPS were at increased risk of progression to dementia in both CN (HR = 3.65, 95% CI [1.80, 7.40]) and MCI (HR = 1.52, 95% CI [1.16, 2.00]; HR = 1.86 [1.05, 3.30]) groups, adjusting for age, sex, global cognition, and ApoE4, compared with their counterparts with minimal NPS. There was no difference between higher aNPS and minimal NPS subgroups in their CSF AD biomarker profiles. CONCLUSION: Findings suggest that aNPS may represent a neurobiological vulnerability that uniquely contribute to the dementia risk, independent of AD biomarker profiles.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".