28 A perfect storm: expanded disease definitions in alzheimer’s disease and the new era of disease-modifying drugs in mild cognitive impairment
Bibliographic record
Abstract
Introduction Since the 2011 guidelines were published by the National Institute on Aging and Alzheimer’s Association (NIA-AA), two new categories have been added to the definition of Alzheimer’s Disease (AD), namely pre-symptomatic or pre-dementia and mildly symptomatic or Mild Cognitive Impairment (MCI). In addition, a new diagnostic method using biomarkers has been proposed. These changes in turn have been followed by a new era of disease-modifying treatment. The US Food and Drugs Administration (FDA) published new guidelines in 2018 for developing drugs in early AD which created yet another staging system that mirrors the NIA-AA’s overdefinition. These developments contrast with the recommendations from the Preventive Health Task Forces where in the US, there is insufficient evidence to recommend cognitive impairment screening. In Canada, early screening is not recommended. The new disease-modifying drugs in AD act on the pathophysiology targeting the clearance of amyloid β. There was great anticipation as the last drug approved for dementia was Memantine in 2003. The first disease-modifying drug in AD, Aducanumab, was approved by the FDA in June 2021, followed by Lecanemab in January 2023. These approvals were based on reduction of amyloid β plaques with an indication in MCI and early AD. The significance of these new drugs is their indication in MCI. While the previous drugs provide only symptomatic treatment for dementia, the focus has now shifted to treating the biomarkers earlier in AD, in the pre-dementia stages. Objectives To critically appraise the current disease-modifying drug trials in AD for (i) potential benefits and harms and (ii) risk of overtreatment in MCI. Methods Original trials of Aducanumab (ENGAGE and EMERGE) and Lecanemab (Clarity AD) will be reviewed in terms of the study population, primary endpoints, and adverse events. Results The participants consisted mostly of patients with MCI (Aducanumab ~81% and Lecanemab ~62%), which was also reflected in the mean baseline MMSE scores of 25.5-26.4. The primary endpoint was a change in the score for Clinical Dementia Rating Sum of Boxes (CDR-SB). The ENGAGE trial showed no difference, and the results in EMERGE and Clarity AD were statistically significant but did not meet the cut-off for Minimal Clinically Important Difference (MCID). The recommended MCID with CDR-SB is a change of 1 to 2 points. EMERGE showed a difference of 0.26 points and 0.39 points in the low and high dose groups, respectively. Clarity AD showed a difference of 0.45 points. Among adverse events, the most concerning were Amyloid-Related Imaging Abnormalities called ARIA-E for Edema/Effusion or ARIA-H for cerebral hemorrhages and superficial siderosis. For Aducanumab, approximately 35% of participants experienced ARIA-E or ARIA-H. For Lecanemab, 12.6-17.3% experienced ARIA-E or ARIA-H. Conclusion Early screening of AD in pre-dementia stages risk setting patients up for failure when current disease-modifying drugs for MCI do not improve clinical outcomes but in fact cause significant adverse events. There is a risk of overtreatment in the pre-dementia stages, especially when drug approvals are based on surrogate biomarkers that are yet to be proven conclusively.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".