Identifying a combination of biomarkers to predict treatment response to nabilone for agitation in Alzheimer’s disease – an exploratory <i>post hoc</i> analysis
Bibliographic record
Abstract
Background To identify if a combination of blood-based biomarkers related to inflammation and oxidative stress predict treatment response to nabilone for Alzheimer’s disease (AD)-associated agitation.Research design and methods Agitation was assessed using the Cohen-Mansfield Agitation Inventory (CMAI). Serum concentrations of 13 markers were quantified. Univariable and multivariable regression were used to determine differences in CMAI change given nabilone and placebo. A model combining biomarkers with clinical predictors was also evaluated.Results Overall, 38 participants enrolled in the original trial (76% male, mean ± SD age 87 ± 10). Nabilone was more efficacious in participants with higher IL-6, higher 8-ISO, higher 24S-OHC, and lower clusterin. Participants in the first tertile (T1) of index scores demonstrated better response to nabilone compared to placebo with a mean difference in CMAI change of −20.6 (95%CI: −30.3, −10.4). During the nabilone phase, 83% of participants in T1 were responders versus 38% in T2 + 3 (Fisher’s p = .01). In the combined model, T1 showed better response to nabilone with a mean difference in CMAI change of −26.4 (95%CI: −34.0, −19.6). The proportion of responders was significantly higher in T1 (91%, n = 11) compared to T2 + 3 (32%, n = 19) (Fisher’s p = .002).Conclusion A combination of biomarkers could help characterize 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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".