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.

Plasma p-tau181/Aβ1-42ratio predicts Aβ-PET status and correlates with CSF-p-tau181/Aβ1-42and future cognitive decline

“…Other works, while not using a ratio, have also identified that combination analyses on p-tau, Aβ and clinical features can better distinguish AD from unimpaired populations [36] than analyses of analytes alone.…”

“…The current work, conducted in in a larger cohort and in a different ethnic group, supports the utility of this measurement approach. Other works, while not using a ratio, have also identified that combination analyses on p-tau, Aβ and clinical features can better distinguish AD from unimpaired populations [36] than analyses of analytes alone.…”

Leveraging Technology To Diagnose Alzheimer's Disease: A Meta Analysis

“…This is tabulated in Table 1. Of the 28 studies analyzed over the 10-year period, 0 were from 2012, 3 from 2013, [18-20] 2 were from 2014, [21,22] 0 were from 2015, 1 was from 2016, [23] 2 were from 2017, [24,25] 5 were from 2018, [26-30] 3 were from 2019, [31][32][33] 3 were from 2020, [34][35][36] 7 were from 2021, [37][38][39][40][41][42][43] and 2 were from 2022. [44,45] All involved older adults, 61% were quasi-experimental, 21% were observational, and 14% were true experiments.…”

“…Blood based biomarkers were identi ed in 6/28 (21%) of the studies analyzed. [18,26,32,35,40,43] This intervention is more invasive than imaging and causes more risk. Various forms of telehealth were implemented to diagnose AD in 4/28 (14%) of the studies analyzed.…”

Parallel multistep digital analysis SlipChip demonstrated with the quantification of nucleic acid by digital LAMP-CRISPR

“…12 This digital protein analysis significantly improves the detection sensitivity of target protein biomarkers to the level of attomole (aM), and it has great potential in disease diagnostics, including Alzheimer's disease, cancer, immune system disorders, and ischemic stroke. [13][14][15][16] Digital cell analysis can compartmentalize single cells in individual volumes, and it enables the investigation of the behaviour of single cells and cell heterogeneity. [17][18][19][20] These digital biological analysis methods can open up new frontiers for biological research and provide promising tools for disease diagnostics and monitoring.…”