Development of a Plasma Biomarker Diagnostic Model Incorporating Ultrasensitive Digital Immunoassay as a Screening Strategy for Alzheimer Disease in a Chinese Population

Abstract

Background: The ultrasensitive detection of blood-based biomarkers such as amyloid β (Aβ), tau, and neurofilament light (NFL) has drawn much attention in Alzheimer disease (AD) diagnosis. However, few studies have been conducted in the Chinese population. This study aimed to evaluate the ability of plasma biomarker diagnostic models for AD in the Chinese population based on a novel digital immunoassay technology.

Methods: 159 patients with AD, 148 patients with amnestic mild cognitive impairment (aMCI), and 121 cognitively normal control participants were recruited from 2 cohorts. The concentrations of plasma Aβ42, Aβ40, Aβ42/Aβ40, total tau (t-tau), phosphorylated tau 181 (p-tau 181), and NFL were quantified using an ultrasensitive single molecule array (Simoa) platform. Comprehensive and simplified diagnostic models were established based on the plasma biomarker profile and clinical characteristics.

Results: Among all blood biomarkers, p-tau181 had the greatest potential for identifying patients with cognitive impairment. The simplified diagnostic model, which combined plasma p-tau181, Aβ42, and clinical features, achieved 93.3% area under the curve (AUC), 78.6% sensitivity, and 94.2% specificity for distinguishing AD from control participants, indicating a diagnostic ability approaching that of the comprehensive diagnostic model including 5 plasma biomarkers and clinical characteristics (95.1% AUC, 85.5% sensitivity, 94.2% specificity). Moreover, the simplified model reached 95.9% AUC and 94.0% AUC for early- and late-onset AD/control participants, respectively.

Conclusions: We established AD diagnostic models using plasma biomarkers for Chinese participants. These findings suggest the simplified diagnostic model provides an accessible and practical way for large-scale screening in the clinic and community, especially in developing countries.

Keywords: Alzheimer disease; cross-sectional study; diagnostic models; plasma biomarkers; single molecule array.