Early recognition of the risk of Alzheimer’s disease (AD) onset is a global challenge that requires the development of reliable and affordable screening methods for wide-scale application. Proteomic studies of blood plasma are of particular relevance; however, the currently proposed differentiating markers are poorly consistent. The targeted quantitative multiple reaction monitoring (MRM) assay of the reported candidate biomarkers (CBs) can contribute to the creation of a consistent marker panel. An MRM-MS analysis of 149 nondepleted EDTA–plasma samples (MHRC, Russia) of patients with AD (n = 47), mild cognitive impairment (MCI, n = 36), vascular dementia (n = 8), frontotemporal dementia (n = 15), and an elderly control group (n = 43) was performed using the BAK 125 kit (MRM Proteomics Inc., Canada). Statistical analysis revealed a significant decrease in the levels of afamin, apolipoprotein E, biotinidase, and serum paraoxonase/arylesterase 1 associated with AD. Different training algorithms for machine learning were performed to identify the protein panels and build corresponding classifiers for the AD prognosis. Machine learning revealed 31 proteins that are important for AD differentiation and mostly include reported earlier CBs. The best-performing classifiers reached 80% accuracy, 79.4% sensitivity and 83.6% specificity and were able to assess the risk of developing AD over the next 3 years for patients with MCI. Overall, this study demonstrates the high potential of the MRM approach combined with machine learning to confirm the significance of previously identified CBs and to propose consistent protein marker panels.

The activity of AChE, BChE and NEP was reduced in the blood serum of patients with a-MCI and, to the greater extent, in patients with AD compared to CG and correlated with the level of cognitive dysfunction evaluated by MMSE, ADAS-cog, and other tests. For the first time, it has been shown that treatment of a-MCI patients with ceraxon (citicolin) results in an increase of the activity of blood serum AChE, BChE and NEP to the values observed in the CG. Thus, the activities of blood serum AChE, BChE and NEP reflect the level of cognitive dysfunction and can be used as prognostic biomarkers of the level of dementia progression in patients with impaired memory.

To evaluate the levels of cytokines (IFNα, IFNγ, IL-2, Il-4, IL-6, IL-8, IL-10, IL-12, IL-15), IL-1β receptor antagonist (IL-1RA), vascular endothelial growth factor (VEGF) and its antagonist, the soluble form of receptor 1 (sVEGFR1) in the blood serum of patients with Alzheimer's disease, with early onset (ADEO) and late onset (ADLO), and in patients with mild cognitive impairment (MCI). Levels of interleukins, IL-1RA, VEGF and sVEGFR1 were measured in 20 patients with AD and 11 patients with MCI using ELISA. These parameters were compared to the severity of cognitive impairment assessed by the performance on neurocognitive tests. The levels of key cytokines (IL-8, TNFα, IL-12), VEGF and sVEGFR1 as well as anti-inflammatory proteins were different in patients with ADEO, ADLO and MCI. These differences suggest the phenotypic and genotypic heterogeneity of the disease that demands further research.

Medical article медицинская статья

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.