Double blind clinical trial of lemborexant for AD prevention: Protocol for a randomized controlled clinical trial
Bibliographic record
Abstract
BACKGROUND: Poor sleep is a modifiable factor associated with the development and progression of Alzheimer's Disease (AD). Pharmacological treatments and Cognitive Behavioural Therapy for Insomnia (CBT-I) which promote sleep have the potential to improve AD risk profile. In addition, prior research suggests that Dual Orexin Receptor Antagonists (DORAs), which are prescribed more and more to treat insomnia, could also help to decrease tau phosphorylation and concentrations of beta-amyloid (Aβ) in the cerebrospinal fluid. OBJECTIVE: To study the impact of Lemborexant, a DORA, with or without CBT-I on AD blood biomarkers and on cognitive performance in a population at risk of developing AD dementia. METHOD: We will conduct a double-blind randomized clinical trial in men and women aged 50 to 90 years old with symptoms of insomnia. 220 participants will be screened and randomized into 4 treatment groups over 12 months: Lemborexant, Lemborexant plus CBT-I, placebo, or placebo plus CBT-I. All participants will receive education on sleep hygiene measures. PRIMARY OUTCOMES: Change in (1) plasma p-tau181 and (2) modified Preclinical Alzheimer's Cognitive Composite score from baseline to 12-months. SECONDARY OUTCOMES: Change in (1) plasma p-tau217, (2) CSF Aβ 42/40 and CSF p-tau181, and (3) objective sleep measures measured with electroencephalogram (EEG) recording from baseline to 12-months. ANTICIPATED RESULTS: We anticipate that Lemborexant will be associated with a reduction in plasma p-tau181 over time compared to placebo. Lemborexant will improve sleep and cognitive performance compared to placebo, and the addition of CBT-I will accentuate the beneficial effect on sleep and cognition but not on AD biomarkers. CONCLUSION: This study will grant us an opportunity to deepen our understanding of the therapeutic potential of sleep interventions and the influence of DORAs on AD prevention.
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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 | 0.023 | 0.022 |
| Meta-epidemiology (narrow) | 0.009 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.085 | 0.017 |
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".