Evidence map›Paper›PMID 41929952›Full record

ReviewJournal of Alzheimer's disease reports

Network meta-analysis of the efficacy of nine drugs for cognitive function in patients with Alzheimer's disease.

Shanshan Huang, Yunyun Guo

Abstract readReview
In one paragraph

Review in Journal of Alzheimer's disease reports. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Shanshan HuangDepartment of Life Sciences, Imperial College London, London, UK.ORCID https://orcid.org/0009-0008-1738-7615
Yunyun GuoAgeing Epidemiology (AGE) Research Unit, School of Public Health, Imperial College London, London, UK.ORCID https://orcid.org/0009-0009-4572-1139

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Alzheimer's disease (AD) remains a global challenge, and the comparative cognitive efficacy of emerging pharmacotherapies is still unclear. Objective: To compare and rank nine pharmacological agents against placebo in AD with respect to key cognitive outcomes using a network meta-analysis. Methods: We systematically searched randomized controlled trials published up to May 2025 that evaluated aducanumab, lecanemab, donanemab, gosuranemab, semorinemab, tilavonemab, zagotenemab, masupirdine, or sodium oligomannate in AD. The primary outcomes were Clinical Dementia Rating-Sum of Boxes (CDR-SB) and Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-cog); Mini-Mental State Examination (MMSE) was a secondary outcome. Treatments were ranked using the Surface Under the Cumulative Ranking curve (SUCRA). Results: Fifteen randomized trials encompassing 33 treatment arms were included. No drug demonstrated statistically robust superiority over placebo in primary outcomes. Semorinemab and tilavonemab achieved highest SUCRA rankings, but without significant pairwise advantage. For MMSE, aducanumab showed a modest mean difference versus placebo (1.98, 95% CI 0.03-3.93), though evidence of publication bias reduced confidence. Conclusions: Current pharmacological treatments do not consistently outperform placebo in AD. Tau-targeted antibodies (semorinemab, tilavonemab) display modest but non-significant promise, whereas aducanumab's apparent benefit is likely confounded by publication bias. Further large, rigorous randomized controlled trials and improved preclinical models are essential.

Indexed as

Alzheimer's diseasecognitive outcomesmonoclonal antibodiesnetwork meta-analysisplacebo response

Identifiers

PMID41929952
PMCPMC13039041

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.