Relationship between Serum 4-Hydroxynonenal and Agitation Severity in Moderate-to-severe Alzheimer’s Disease
Bibliographic record
Abstract
Oxidative stress is an established mechanism in the pathogenesis of Alzheimer’s disease, but its relationship with neuropsychiatric symptom (NPS) severity in AD populations is not well understood. Agitation is a prevalent and distressing NPS for which safe and effective treatment options are limited by modest efficacy and high risk of adverse outcomes. This study investigates the relationship between serum 4-hydroxynonenal (4-HNE), a marker of oxidative stress, and agitation severity in 39 individuals with moderate-to-severe Alzheimer's Disease (AD). A cross-sectional design was employed using baseline data from a clinical trial involving participants with AD. Agitation severity was measured using the Cohen-Mansfield Agitation Inventory (CMAI) and the Neuropsychiatric Inventory agitation/aggression cluster scores (NPI-4-A/A). No significant association was found between 4-HNE levels and total CMAI scores. However, a significant relationship was observed between 4-HNE and NPI-4-A/A scores (β = 0.345, p = 0.046), as well as between 4-HNE levels and the disinhibition (β = 0.380, p = 0.034) and aberrant motor behavior domains (β = 0.393, p = 0.021) individually after adjusting for sex, MMSE scores, and number of comorbidities. Overall, these findings suggest a potential link between oxidative stress and specific agitation behaviors in AD, highlighting the need for further research into targeted interventions.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".