A combination model of AD biomarkers revealed by machine learning precisely predicts Alzheimer's dementia: China Aging and Neurodegenerative Initiative (CANDI) study.

Abstract

To test the utility of the "A/T/N" system in the Chinese population, we study core Alzheimer's disease (AD) biomarkers in a newly established Chinese cohort. A total of 411 participants were selected, including 96 cognitively normal individuals, 94 patients with mild cognitive impairment (MCI) patients, 173 patients with AD, and 48 patients with non-AD dementia. Fluid biomarkers were measured with single molecule array. Amyloid beta (Aβ) deposition was determined by18 F-Flobetapir positron emission tomography (PET), and brain atrophy was quantified using magnetic resonance imaging (MRI). Aβ42/Aβ40 was decreased, whereas levels of phosphorylated tau (p-tau) were increased in cerebrospinal fluid (CSF) and plasma from patients with AD. CSF Aβ42/Aβ40, CSF p-tau, and plasma p-tau showed a high concordance in discriminating between AD and non-AD dementia or elderly controls. A combination of plasma p-tau,apolipoprotein E (APOE)genotype, and MRI measures accurately predicted amyloid PET status. These results revealed a universal applicability of the "A/T/N" framework in a Chinese population and established an optimal diagnostic model consisting of cost-effective and non-invasive approaches for diagnosing AD.