Bidirectional causal relationships between plasma proteins, neuroimaging metrics and risk of Alzheimer's disease - 11/06/26

Doi : 10.1016/j.tjpad.2026.100619 
Xu Xu a, b, 1, Lintong Li c, d, 1, Wei Huang a, b, Ying Yang e, Xu Li f, Pei Wang g, Mengmeng Zhao h, Huiliang Zhang i, 2, , Chaoming Yuan a, b, 2,
a Department of Neurology, The Third People's Hospital of Yunnan Province, China 
b Department of Neurology, The Second Affiliated Hospital of Dali University, China 
c Department of Endocrinology, The Third People's Hospital of Yunnan Province, China 
d Department of Endocrinology, The Second Affiliated Hospital of Dali University, China 
e Department of Neurology, Yiliu Subdistrict Community Health Service Center of Guandu District, Kunming City, China 
f Department of Neurology, Shalatuo Township Health Center, Yuanyang City, China 
g Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA 
h Department of Neurology, Zhengzhou Emergency Medical Rescue Center, Zhengzhou, China 
i Department of Infectious Diseases, Tongji Hospital, Tongji Medical College and State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Disease, Huazhong University of Science and Technology, Wuhan 430000, China 

Corresponding author at: The Second Affiliated Hospital of Dali University, Dali 671000, China. The Second Affiliated Hospital of Dali University Dali 671000 ∗∗ Corresponding author.

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Abstract

Background

Changes in neuroimaging metrics are among the first detectable pathophysiological alterations in Alzheimer's disease (AD). Proteins are closely linked to fluctuations in neuroimaging metrics. Therefore, the analysis of the proteomic signature associated with neuroimaging metrics holds significant promise for uncovering therapeutic targets that contribute to AD.

Methods

GWAS data concerning the Brain Imaging Data Structure (BIDs). The AD cohort comprised a total of 401,661 individuals diagnosed with AD, alongside 10,520 control participants. For a bidirectional MR analysis involving neuroimaging metrics, proteomics, and AD, the methods utilized included inverse variance weighted (IVW), MR Egger, weighted median, weighted mode, and the Wald ratio approaches.

Results

We identified 12 neuroimaging metrics that demonstrate significant relevance to AD (thickness of the left total hemisphere, volume of the right thalamus, and et al.). These metrics are structural magnetic resonance imaging (MRI) biomarkers that remain stable throughout the entire course of AD, from the preclinical stage through mild cognitive impairment (MCI) to dementia. Additionally, we found a substantial number of 1633 proteins that also show a noteworthy causal relationship with AD. Functional enrichment analysis indicated that these proteins were predominantly focused within various pathways linked to AD, encompassing those involved in the synaptic vesicle cycle, synaptic membranes, neurotransmitter release, and the activity of GABA receptors. In addition, our research indicates that the significant relationships observed between the identified proteins and AD are influenced by neuroimaging metrics. Notably, we found that these neuroimaging metrics play a crucial role in mediating a substantial 67% of the inverse relationship that exists between PTPRC and the phenotypic characteristics associated with AD.

Conclusions

This study successfully establishes a connection between proteomic and neuroimaging metrics, as well as the AD that influence them. By creating this relationship, the research offers important information that aids in comprehending the intricate mechanisms involved in AD.

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Keywords : Alzheimer's disease, Neuroimaging metrics, Proteomic, Mendelian randomization


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© 2026  The Author(s). Publicado por Elsevier Masson SAS. Todos los derechos reservados.
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Vol 13 - N° 8

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