Identifying a combination of biomarkers to predict treatment response to nabilone for the treatment of agitation in Alzheimer's disease – a secondary analysis
Bibliographic record
Abstract
BACKGROUND: Agitation is a challenging neuropsychiatric symptom (NPS) of Alzheimer's disease (AD). A crossover trial found that nabilone significantly improved agitation in AD patients over 6 weeks compared to placebo. Here, we aim to identify a combination of biomarkers that could be used to predict treatment response to nabilone for AD-associated agitation. METHODS: Agitation was assessed using the Cohen-Mansfield Agitation Inventory (CMAI). Serum concentrations of 13 markers were measured. Linear regression was used to estimate change in CMAI due to nabilone for the high and low groups of each biomarker. Biomarkers with a difference ≥8.5 points between groups were included in subsequent multivariate models. Index scores representing the difference between expected CMAI change given nabilone and placebo were calculated and divided into quartiles. Mean difference in CMAI change and 95% confidence intervals were estimated via bootstrapping. RESULTS: Four of the 13 biomarkers which met criteria specified above were included in multivariate modeling (n = 67). Nabilone was more efficacious in participants with higher IL-6 (estimated change in CMAI -15.4, standard error (SE) 5.6), higher ISO-8 (-14.4, SE=5.0), higher 24S-OHC (-14.2, SE=4.1), and lower clusterin (-14.6, SE=4.4). Participants in Q1 of index scores demonstrated better response to nabilone with a mean difference in CMAI change of -20.9 (95% CI: -31.8, -9.2), while those in Q2-4 showed no difference between treatments. CONCLUSIONS: Participants with higher levels of inflammation, oxidative stress, and cholesterol metabolite were more likely to benefit from nabilone for agitation in AD. A combination of biomarkers could help in distinguishing responders and non-responders to nabilone.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".