Abstract
BackgroundAs Alzheimer’s disease (AD) pathology presents decades before dementia manifests, unbiased biomarker cut-points may more closely reflect presence of pathology than clinically defined cut-points. Currently, unbiased cerebrospinal fluid (CSF) tau cut-points are lacking.MethodsWe investigated CSF t-tau and p-tau cut-points across the clinical spectrum using Gaussian mixture modelling, in two independent cohorts (Amsterdam Dementia Cohort and ADNI).ResultsIndividuals with normal cognition (NC) (total n = 1111), mild cognitive impairment (MCI) (total n = 1213) and Alzheimer’s disease dementia (AD) (total n = 1524) were included. In both cohorts, four CSF t- and p-tau distributions and three corresponding cut-points were identified. Increasingly high tau subgroups were characterized by steeper MMSE decline and higher progression risk to AD (cohort/platform-dependent HR, t-tau 1.9–21.3; p-tau 2.2–9.5).LimitationsThe number of subjects in some subgroups and subanalyses was small, especially in the highest tau subgroup and in tau PET analyses.ConclusionsIn two independent cohorts, t-tau and p-tau levels showed four subgroups. Increasingly high tau subgroups were associated with faster clinical decline, suggesting our approach may aid in more precise prognoses.
Highlights
As Alzheimer’s disease (AD) pathology presents decades before dementia manifests, unbiased biomarker cut-points may more closely reflect presence of pathology than clinically defined cut-points
High tau subgroups were associated with faster clinical decline, suggesting our approach may aid in more precise prognoses
Part of the data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database
Summary
As Alzheimer’s disease (AD) pathology presents decades before dementia manifests, unbiased biomarker cut-points may more closely reflect presence of pathology than clinically defined cut-points. Gaussian mixture modelling provides an approach to determine cut-points independent of clinical information [12] This approach is based on the notion that the distribution of biomarker values in a population is a mixture of values belonging to subpopulations, i.e. normal and affected individuals. Previous studies using this approach have found a bimodal distribution of Aβ42 levels, of which the cut-point (i.e. the intersection of these distributions) was higher than clinically based cutpoints, resulting in more sensitive detection of predementia AD [13,14,15,16]. It remains unclear whether it is possible to detect unbiased cutpoints in t-tau and p-tau levels
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