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Research Article
2 December 2025

Evaluating amyloid-beta as a surrogate endpoint in trials of anti-amyloid-beta drugs in Alzheimer’s disease: a Bayesian meta-analysis

Authors: Sa Ren https://orcid.org/0000-0001-9040-249X [email protected], Janharpreet Singh, Sandro Gsteiger, Christopher Cogley, Ben Reed, Keith R Abrams, Dalia Dawoud, Rhiannon K Owen, Paul Tappenden, Terrence J Quinn, and Sylwia Bujkiewicz https://orcid.org/0000-0002-3003-9403Author Info & Affiliations
Volume 15, Number 1
https://doi.org/10.57264/cer-2025-0095
PDFFormats

Abstract

Aim: The use of amyloid-beta (Aβ) clearance to support regulatory approvals of drugs in Alzheimer’s disease (AD) remains controversial. We evaluate Aβ as a potential trial-level surrogate endpoint for clinical function in AD. Materials & methods: Data on the effectiveness of anti-Aβ monoclonal antibodies (MABs) on Aβ and multiple clinical outcomes were identified from randomized controlled trials through a literature review. A Bayesian bivariate meta-analysis was used to evaluate Aβ as a surrogate endpoint for clinical function across all MABs and for each individual anti-Aβ MAB. The analysis for individual therapies was conducted in subgroups of treatments and by applying Bayesian hierarchical models to borrow information across treatments. Results: We identified 23 randomized controlled trials with 39 treatment contrasts for seven MABs. The surrogate relationship between treatment effects on Aβ and Clinical Dementia Rating-Sum of Boxes (CDR-SOB) across all MABs was strong: with a meaningful slope of 1.41 (0.60, 2.21) and small variance of 0.02 (0.00, 0.05). For individual treatments, the surrogate relationships were suboptimal, displaying large uncertainty. Sharing information across treatments considerably reduced the uncertainty, resulting in moderate surrogate relationships for aducanumab and lecanemab. No meaningful association was detected for other clinical outcomes, including Mini Mental State Examination and Alzheimer’s Disease Assessment Scale-Cognitive Subscale. Conclusion: Although our results from the analysis of data across all MABs suggested that Aβ was a potential surrogate endpoint for CDR-SOB, individually the surrogacy patterns varied across treatments and showed no evidence of association. Bayesian information-sharing revealed moderate surrogate relationship only for aducanumab and lecanemab.

Plain language summary

What is this article about?

Some treatments for Alzheimer’s disease (AD) have been approved by regulators based on their ability to reduce levels of amyloid-beta (Aβ), a protein that builds up in the brains of people with AD. However, it remains unclear whether lowering Aβ actually leads to meaningful benefits for patients. In this study, we investigate whether changes in Aβ levels can reliably predict clinical benefit, using evidence from randomized controlled trials of anti-Aβ drugs.

What were the results?

We found that changes in Aβ may be a good predictor of clinical benefits, measured by a test called Clinical Dementia Rating Scale-Sum of Boxes (CDR-SOB), which assesses thinking ability and daily functioning. This relationship was observed when looking at combined data from all anti-Aβ drugs. However, the association did not hold consistently for individual drugs. Using a statistical method that enables information sharing across drugs, we observed a moderate association between Aβ levels and clinical outcomes for the drugs aducanumab and lecanemab. Our findings also suggest that this relationship may vary between drugs and might not apply to new treatments.

Why is this important?

The results from this study could provide valuable insight for decision-makers, such as NICE in England and Wales. The decision-makers often face limited evidence and have to rely on data from surrogate endpoints to make decisions whether a new treatment is likely to benefit patients and offer good value for money. This research may also contribute to a broader understanding of the surrogacy patterns in AD and inform future trials in this area.

Supplementary Material

File (supplementary materials.docx)

References

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