Evidence mapPaperPMID 40529145Full record

ReviewFrontiers in medicine2025

Integrating genetic and immune profiles for personalized immunotherapy in Alzheimer's disease.

Cong He, Yiwei Shen, Miao Zhang, Xiaoqing Zhou

Abstract readReview
In one paragraph

Review in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Cong He *Second Clinical Medical College, Heilongjiang University of Chinese Medicine, Harbin, China.
Yiwei Shen *Second Clinical Medical College, Heilongjiang University of Chinese Medicine, Harbin, China.
Miao ZhangSecond Clinical Medical College, Heilongjiang University of Chinese Medicine, Harbin, China.
Xiaoqing ZhouShenzhen Hospital of Beijing University of Chinese Medicine (Longgang), Shenzhen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer's disease (AD) is the most frequent cause of dementia worldwide, and it is estimated that the number of patients will increase to 131 million by 2050. Most of the current methods of dealing with AD are designed to alleviate the symptoms, and there is no effective way of stopping the progression of the disease. Personalized immunotherapy has the potential to be highly effective and cut down on side effects because it can be targeted accurately and intervened early. Considering the genetic factors, many studies are increasingly looking at taking the immune status into account. This article further discusses the genetic and immune characteristics of AD, the methods of integrating multiple histological data, the identification of biomarkers, the stratification of patients, the precise treatment plans, and the application and future trends of immunotherapy, giving new directions for the future treatment of AD. In this mini-review, the authors address the critical role that genetic background and immune status play in shaping therapeutic strategies for AD, noting that there is a unique immune response in carriers of the APOEε4 allele compared to non-carriers, and that this difference may affect the course of the disease as well as the efficacy of immunotherapy. The aim of this review is to give an overview of the current understanding of the influence of genetic and immune factors on each other in AD, focusing on the impact of the APOEε4 allele on the immune response and its implications for immunotherapy.

Indexed as

Alzheimer’s diseasebiomarker identificationgenetic-immune integrationpersonalized immunotherapyprecision treatment

Identifiers

PMID40529145
PMCPMC12171180

What Socratic holds

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Registered trials

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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 Socratic graph.