Plasma Protein Panel for Assessing the Risk of Alzheimer’s Disease by MRM-MS Analysis: The Study of Two Independent Clinical Cohorts

Early recognition of a risk of Alzheimer's disease (AD) remains a global challenge, and blood proteomic markers are of particular interest for wide-scale diagnostic use. Quantitative multiple reaction monitoring (MRM) approach demonstrates good reproducibility in the characteristic changes in the levels of reported candidate biomarkers (CBs) in different cohorts in AD. Following up on our previous study, we performed a joint analysis of 331 blood plasma samples from two different clinical cohorts of participants, comprising a total of 95 samples from patients with AD, 136 samples from patients with mild cognitive impairment (MCI), and 100 samples from controls. The obtained results confirm the significance of 37 CBs. A logistic regression-based algorithm was used to build protein classifiers, and a total of 21 important proteins were selected, 13 of which (ORM1, APOA4, LBP, HP, FN1, BCHE, APOE, PZP, A1BG, TF, SERPINA7, TTR, and F12) formed a universal panel that demonstrated strong classification performance in distinguishing AD patients from controls (ROC-AUC = 0.90) and in separating stable and progressing patients with MCI (ROC-AUC = 0.81). Overall, the analysis confirms the high potential of the MRM method for validating CBs in independent cohorts.

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Publication Details

Journal
International Journal of Molecular Sciences
Published
2025-12-19
DOI
https://doi.org/10.3390/ijms27010015
Primary Topic
Advanced Proteomics Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Plasma Protein Panel for Assessing the Risk of Alzheimer’s Disease by MRM-MS Analysis: The Study of Two Independent Clinical Cohorts

Аnna Morozova, Vladimir A. Mitkevich, Alexander Makarov, Polina A. Strelnikova et al.
International Journal of Molecular Sciences
Advanced Proteomics Techniques and Applications
article

Plasma Protein Panel for Assessing the Risk of Alzheimer’s Disease by MRM-MS Analysis: The Study of Two Independent Clinical Cohorts

Аnna Morozova, Vladimir A. Mitkevich, Alexander Makarov, Polina A. Strelnikova, Natalia V. Zakharova, Maria I. Indeykina, А. С. Кононихин, Dmitry Gryadunov, Marina Emelyanova, Е. Н. Николаев, Elena Fedoseeva, И. В. Колыхалов, Gavrilova Si, Ya. B. Fedorova, Alisa Andryushchenko, Anna E. Bugrova, Yulia A. Chaika
article en

Abstract

Early recognition of a risk of Alzheimer's disease (AD) remains a global challenge, and blood proteomic markers are of particular interest for wide-scale diagnostic use. Quantitative multiple reaction monitoring (MRM) approach demonstrates good reproducibility in the characteristic changes in the levels of reported candidate biomarkers (CBs) in different cohorts in AD. Following up on our previous study, we performed a joint analysis of 331 blood plasma samples from two different clinical cohorts of participants, comprising a total of 95 samples from patients with AD, 136 samples from patients with mild cognitive impairment (MCI), and 100 samples from controls. The obtained results confirm the significance of 37 CBs. A logistic regression-based algorithm was used to build protein classifiers, and a total of 21 important proteins were selected, 13 of which (ORM1, APOA4, LBP, HP, FN1, BCHE, APOE, PZP, A1BG, TF, SERPINA7, TTR, and F12) formed a universal panel that demonstrated strong classification performance in distinguishing AD patients from controls (ROC-AUC = 0.90) and in separating stable and progressing patients with MCI (ROC-AUC = 0.81). Overall, the analysis confirms the high potential of the MRM method for validating CBs in independent cohorts.

International Journal of Molecular SciencesVol. 27(1)
Engelhardt Institute of Molecular Biology (RU), Sechenov University (RU), Lomonosov Moscow State University (RU), Russian Medical Academy of Continuous Professional Education (RU), Institute of Biochemical Physics NM Emanuel (RU), Mental Health Research Center of Russian Academy of Medical Sciences (RU)
Skolkovo Institute of Science and Technology
Openalex Percentile: Top 63%
Advanced Proteomics Techniques and Applications
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