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Alzheimer's Research & Therapy
Published in: Alzheimer's Research & Therapy 1/2017

Open Access 01-12-2017 | Research

Unbiased estimates of cerebrospinal fluid β-amyloid 1–42 cutoffs in a large memory clinic population

Authors: Daniela Bertens, Betty M. Tijms, Philip Scheltens, Charlotte E. Teunissen, Pieter Jelle Visser

Published in: Alzheimer's Research & Therapy | Issue 1/2017

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Abstract

Background

We sought to define a cutoff for β-amyloid 1–42 in cerebrospinal fluid (CSF), a key marker for Alzheimer’s disease (AD), with data-driven Gaussian mixture modeling in a memory clinic population.

Methods

We performed a combined cross-sectional and prospective cohort study. We selected 2462 subjects with subjective cognitive decline, mild cognitive impairment, AD-type dementia, and dementia other than AD from the Amsterdam Dementia Cohort. We defined CSF β-amyloid 1–42 cutoffs by data-driven Gaussian mixture modeling in the total population and in subgroups based on clinical diagnosis, age, and apolipoprotein E (APOE) genotype. We investigated whether abnormal β-amyloid 1–42 as defined by the data-driven cutoff could better predict progression to AD-type dementia than abnormal β-amyloid 1–42 defined by a clinical diagnosis-based cutoff using Cox proportional hazards regression.

Results

In the total group of patients, we found a cutoff for abnormal CSF β-amyloid 1–42 of 680 pg/ml (95% CI 660–705 pg/ml). Similar cutoffs were found within diagnostic and APOE genotype subgroups. The cutoff was higher in elderly subjects than in younger subjects. The data-driven cutoff was higher than our clinical diagnosis-based cutoff and had a better predictive accuracy for progression to AD-type dementia in nondemented subjects (HR 7.6 versus 5.2, p < 0.01).

Conclusions

Mixture modeling is a robust method to determine cutoffs for CSF β-amyloid 1–42. It might better capture biological changes that are related to AD than cutoffs based on clinical diagnosis.
Appendix
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Metadata
Title
Unbiased estimates of cerebrospinal fluid β-amyloid 1–42 cutoffs in a large memory clinic population
Authors
Daniela Bertens
Betty M. Tijms
Philip Scheltens
Charlotte E. Teunissen
Pieter Jelle Visser
Publication date
01-12-2017
Publisher
BioMed Central
Published in
Alzheimer's Research & Therapy / Issue 1/2017
Electronic ISSN: 1758-9193
DOI
https://doi.org/10.1186/s13195-016-0233-7

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