Evidence map›Paper›PMID 41639807›Full record

ArticleBMC medicine2026

A neuroimaging biomarker for disease staging in clinically diagnosed Alzheimer's disease.

Zhuangzhuang Li, Shaozhen Yan, Kun Zhao, Dawei Wang, Hongxiang Yao, Bo Zhou, Zhifa Zhang, Pan Wang, Zhengluan Liao, Yan Chen and 4 more

Abstract read
In one paragraph

Article in BMC medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
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

14 authors.

Zhuangzhuang Li *School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China.
Shaozhen Yan *Department of Radiology & Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
Kun ZhaoSchool of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China. kunzhao@bupt.edu.cn.
Dawei WangDepartment of Radiology, Qilu Hospital of Shandong University, Jinan, 250012, China.
Hongxiang YaoDepartment of Radiology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100039, China.
Bo ZhouDepartment of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100039, China.
Zhifa ZhangDepartment of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100039, China.
Pan WangDepartment of Neurology, Tianjin Huanhu Hospital, Tianjin, 300350, China.
Zhengluan LiaoDepartment of Psychiatry, People's Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital, Hangzhou, 310014, China.
Yan ChenDepartment of Psychiatry, People's Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital, Hangzhou, 310014, China.
Xi ZhangDepartment of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100039, China.
Ying HanDepartment of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.
Jie LuDepartment of Radiology & Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
Yong LiuSchool of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China. yongliu@bupt.edu.cn.

Funding

Beijing Municipal Natural Science Foundation No. 7244519BUPT Excellent Ph.D. Students Foundation No. CX2023117National Natural Science Foundation of China No. T2425027, No.62333002
6 · The paper itself

Abstract

backgroundPrecision medicine for Alzheimer's disease (AD) requires the development of a robust management framework grounded in individualized disease staging systems. To date, only a limited number of studies have supplemented the existing AD staging systems.

methodsThis retrospective study included 7491 MRI examinations from five independent cohorts. We used a novel pseudo-healthy synthesis method to capture individualized brain atrophy patterns. An individualized brain atrophy score (BAS) was computed from the 30 regions with the most severe brain atrophy and used to stratify participants into distinct disease stages. The Jenks natural breaks optimization method was used to determine an optimal number of disease stages based on the individual BAS.

resultsBAS exhibited a strong biological basis and revealed a synergistic relationship among biomarker-based staging systems. Four stages were delineated based on the BAS for participants with MCI and clinically diagnosed AD. Stage I showed a slight cognitive decline with only mild hippocampal atrophy evident. Stage II showed mild cognitive decline and mild brain atrophy and shrinkage, extending to the temporal and parietal lobes. Stage III showed moderate cognitive decline and more severe brain atrophy in the temporal lobe, amygdala, hippocampus, parietal lobe, and frontal lobe. Stage IV showed severe mental impairment and diffuse atrophy across the whole brain. The disease stages are associated with dementia severity and abnormalities in AD biomarkers, such as cerebrospinal fluid (CSF) Aβ

conclusionsThe individualized staging system can accurately assess disease severity, enabling risk stratification at ultra-early pathological stages and facilitating precise AD management.

Indexed as

Alzheimer DiseaseBrainNeuroimagingAgedAged, 80 and overAtrophyBiomarkersDisease ProgressionFemaleHumansMagnetic Resonance ImagingMaleRetrospective StudiesSeverity of Illness Indextau ProteinsBiomarkerstau ProteinsAlzheimer’s diseaseBrain atrophy scoreDisease severityIndividualized staging systemRisk stratification

Identifiers

PMID41639807
PMCPMC12973640

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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 OpenQuestion graph.