Aducanumab in Alzheimer’s Disease: A Comparative Study of Its Effects on Dementia and Mild Cognitive Impairment
Bibliographic record
Abstract
Alzheimer's disease (AD) is the leading cause of dementia, characterized by progressive cognitive decline. Cholinesterase inhibitors are commonly used to manage symptoms but have limited efficacy as the disease progresses. Aducanumab, a monoclonal antibody targeting amyloid-β (Aβ) plaques, has emerged as a novel therapeutic approach. Despite its Food and Drug Administration (FDA) approval, its efficacy and safety remain contentious, particularly following the European Medicines Agency's (EMA's) rejection. This systematic review aims to evaluate the efficacy, safety, and clinical outcomes of aducanumab in treating mild AD. Adhering to Preferred Reporting Items for Systematic Reviews (PRISMA) 2020 guidelines, we conducted a comprehensive search of PubMed and Science Direct databases, including randomized controlled trials (RCTs), cohort studies, and case-control studies focusing on aducanumab versus placebo in mild AD. Studies were screened based on predefined inclusion and exclusion criteria, and data were extracted on clinical outcomes, biomarkers, and neuroimaging markers. The risk of bias was assessed using the Cochrane Handbook and Newcastle-Ottawa Scale. Out of 967 identified records, seven studies met the inclusion criteria. Findings indicated a dose-dependent reduction in Aβ plaques with aducanumab, but clinical outcomes varied. High-dose aducanumab (10 mg/kg) demonstrated significant improvements in some studies but not others. Adverse events, notably amyloid-related imaging abnormalities (ARIA), were frequent, especially at higher doses. The studies exhibited heterogeneous treatment effects and underscored the potential of cerebrospinal fluid biomarkers as an alternative to amyloid positron emission tomography (PET) scans. Aducanumab shows promise in reducing Aβ plaques and has potential clinical benefits at high doses; however, its safety profile, particularly concerning ARIA, remains a significant concern. The variability in clinical efficacy highlights the need for further research to optimize dosing regimens and identify patient populations most likely to benefit from treatment. Future studies should focus on refining treatment protocols and exploring alternative biomarkers to improve therapeutic outcomes for AD.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".