Prognosis of Alzheimer's Disease Using Quantitative Mass Spectrometry of Human Blood Plasma Proteins and Machine Learning.

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.

Biochip-based approach for comprehensive pharmacogenetic testing.

OBJECTIVES: Individual sensitivity to many widely used drugs is significantly associated with genetic factors. The purpose of our work was to develop an instrument for simultaneous determination of the most clinically relevant pharmacogenetic markers to allow personalized treatment, mainly in patients with cardiovascular diseases. METHODS: Multiplex one-step polymerase chain reaction (PCR) followed by hybridization on a low-density biochip was applied to interrogate 15 polymorphisms in the following eight genes: VKORC1 -1639 G>A, CYP4F2 1297 G>A, GGCX 2374 C>G, CYP2C9 *2,*3 (430 C>T, 1075 A>C), CYP2D6 *3,*4, *6, *9, *41 (2549delA, 1846 G>A, 1707delT, 2615_2617delAAG, 2988 G>A), CYP2C19 *2,*3,*17 (681 G>A, 636 G>A, -806 C>T), ABCB1 (3435 C>T), SLCO1B1 *5. RESULTS: Two hundred nineteen patients with cardiovascular diseases (CVD) and 48 female patients with estrogen receptor (ER)-positive breast cancer (BC) were genotyped. Of the 219 CVD patients, 203 (92.7%) carried one or more actionable at-risk genotypes based on VKORC1/CYP2C9, CYP2C9, CYP2C19, SLCO1B1, and CYP2D6 genotypes. Among them, 67 patients (30.6%) carried one, 58 patients (26.5%) carried two, 51 patients (23.3%) carried three, 26 patients (11.9%) carried four, and one patient (0.4%) carried five risk actionable genotypes. In the ER-positive BC group 12 patients (25%) were CYP2D6 intermediate or poor metabolizers. CONCLUSIONS: The developed biochip is applicable for rapid and robust genotyping of patients who were taking a wide spectrum of medications to optimize drugs and dosage and avoid adverse drug reactions in cardiology, oncology, psychiatry, rheumatology and gastroenterology.