Models of Neuroimaging, Biomarkers, and Cognitive in Alzheimer's Disease. Implications for Clinical Trial Design.
Bibliographic record
Abstract
Objectives: Identify a window for early treatment by estimating the time course of early pathophysiological changes in Alzheimer's disease, clarify the relationship between emerging pathology and symptom onset as well as estimate the time to clinically meaningful decline in order to inform clinical trial design.Methods: The participants included in the analyses of the five papers were drawn from four cohorts: ADNI, AIBL, BioFINDER, A4.Repeated measures of longitudinal MRI, PET, CSF and cognitive responses were modeled using (1) mixed-effects regression with a random intercept and slope or (2) generalized least squares.Nonlinearity in longitudinal responses was captured using restricted cubic splines.Clinical trial scenarios were simulated to estimate the power to detect assumed drug effects.Results: Clinical trials in preclinical AD are generally underpowered to detect a plausible treatment effect.Optimal composites to capture decline in the observed preclinical AD population were equal weight composites across all available cognitive and functional measures.Estimates of several major milestone events of AD progression include changes in CSF Aβ42 29 years before Aβpositivity, an increase in regional Aβ PET deposition 15 years before, increases in tau pathology 7-8 years before, and signs of cognitive dysfunction 4-6 years before Aβ-positivity.Cognitively unimpaired Aβ+ participants approach early MCI cognitive performance levels on general cognition six years after baseline.To achieve 80% power to detect a 25% treatment effect, 2,000 participants/group for a 4-year trial and 600 participants/group for a 6-year trial are required.Discussion: Including a large number of components in a cognitive/functional composite endpoint may smooth over aberrations in scores in a particular assessment from visit to visit within a subject, thus lowering the withinsubject variance and improving signal to noise.In later stage preclinical AD, suitable power for a phase III trial can be achieved with considerably lower sample sizes while capturing both cognitive and functional change to demonstrate a clinically meaningful drug effect-both while initiating treatment in subjects who are still cognitively unimpaired.Small but meaningful increases in levels of CSF tau and temporoparietal tau are observed years before the current threshold for Aβ-positivity.In the context of secondary prevention trials, tau levels in these participants would already have been increasing for several years, likely more.These data support the use of primary prevention trials against Aβ where treatment is initiated years before the current threshold for Aβ-positivity.The separation between cognitively unimpaired participants and early MCI was just over one SD on the PACC, suggesting that one point of additional decline in Aβ+ participants compared to Aβ-participants could be taken as an approximate benchmark for clinically meaningful decline.Based on the PACC estimates, a treatment effect of 40%-50% would be required to delay the cognitive decline of a group of Aβ+ participants from reaching the one SD milestone by three years.
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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 | 0.068 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".