Alzheimer disease (AD) is a leading cause of dementia that has gained prominence in our aging society. The multitude of tests for diagnosing AD and the invasiveness of these procedures pose a burden to persons with suspected AD. To this end, blood-based biomarkers would be able to alleviate the inconveniences that hamper an accurate diagnosis. In this study, we developed models to diagnose AD and measure the severity of neurocognitive impairment using blood protein biomarkers. Multiple reaction monitoring-mass spectrometry (MRM-MS), a highly selective and sensitive approach for quantifying targeted proteins in samples, was used to analyze blood samples from 4 AD groups: cognitive normal control (CN), asymptomatic AD (AsymAD), prodromal AD (ProdAD), and AD dementia (ADD). Multimarker models were developed using 11 protein biomarkers and apolipoprotein E (APOE) genotypes for amyloid beta and 13 biomarkers with Korean Mini Mnetal Status Examination (K-MMSE) score for predicting Alzheimer disease progression. AD classification model resulted accuracy of 80.1% (95% CI, 77.4 to 82.9), and AD progression monitoring model showed 81.0% of accuracy (95% CI, 80.8 to 81.2). The model was more accurate in diagnosing AD compared with single APOE genotypes and the K-MMSE score. Our study demonstrates the possibility of predicting AD with high accuracy by blood biomarker analysis as an alternative method of screening for AD.

46 CN, 39 AsymAD, 50 ProdAD, and 50 ADD patients were analyzed by MRM-MS method. 119 proteins were analyzed with SIS peptides spiked-in, and light to heavy peptide peak area ratios were calculated. Peak area ratio values were used to develop models for amyloid beta positivity discrimination and Alzheimer disease progression monitoring. Models were evaluated using nested cross validation.

Total 185 human plasma samples (46 CN, 39 AsymAD, 50 ProdAD, and 50 ADD patients) were analyzed in this study.