Identifying a combination of biomarkers to predict treatment response to nabilone for agitation in Alzheimer’s disease – an exploratory <i>post hoc</i> analysis
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.000 | 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 it