Test-retest variability of plasma biomarkers in Alzheimer's disease and its effects on clinical prediction models.

Introduction: The effect of random error on the performance of blood-based biomarkers for Alzheimer's disease (AD) must be determined before clinical implementation.

Methods: We measured test-retest variability of plasma amyloid beta (Aβ)42/Aβ40, neurofilament light (NfL), glial fibrillary acidic protein (GFAP), and phosphorylated tau (p-tau)217 and simulated effects of this variability on biomarker performance when predicting either cerebrospinal fluid (CSF) Aβ status or conversion to AD dementia in 399 non-demented participants with cognitive symptoms.

Results: Clinical performance was highest when combining all biomarkers. Among single-biomarkers, p-tau217 performed best. Test-retest variability ranged from 4.1% (Aβ42/Aβ40) to 25% (GFAP). This variability reduced the performance of the biomarkers (≈ΔAUC [area under the curve] -1% to -4%) with the least effects on models with p-tau217. The percent of individuals with unstable predicted outcomes was lowest for the multi-biomarker combination (14%).

Discussion: Clinical prediction models combining plasma biomarkers-particularly p-tau217-exhibit high performance and are less effected by random error. Individuals with unstable predicted outcomes ("gray zone") should be recommended for further tests.