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

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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.

Original languageEnglish
Article number8
JournalAlzheimer's Research and Therapy
Volume9
Issue number1
DOIs
Publication statusPublished - 14 Feb 2017

Cite this

@article{d7afa8912546491c946ccc545e2ea0a1,
title = "Unbiased estimates of cerebrospinal fluid β-amyloid 1-42 cutoffs in a large memory clinic population",
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.",
keywords = "Alzheimer’s disease, Cerebrospinal fluid, Diagnosis, MCI",
author = "Daniela Bertens and Tijms, {Betty M.} and Philip Scheltens and Teunissen, {Charlotte E.} and Visser, {Pieter Jelle}",
year = "2017",
month = "2",
day = "14",
doi = "10.1186/s13195-016-0233-7",
language = "English",
volume = "9",
journal = "Alzheimer's Research & Therapy",
issn = "1758-9193",
publisher = "BioMed Central",
number = "1",

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TY - JOUR

T1 - Unbiased estimates of cerebrospinal fluid β-amyloid 1-42 cutoffs in a large memory clinic population

AU - Bertens, Daniela

AU - Tijms, Betty M.

AU - Scheltens, Philip

AU - Teunissen, Charlotte E.

AU - Visser, Pieter Jelle

PY - 2017/2/14

Y1 - 2017/2/14

N2 - 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.

AB - 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.

KW - Alzheimer’s disease

KW - Cerebrospinal fluid

KW - Diagnosis

KW - MCI

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U2 - 10.1186/s13195-016-0233-7

DO - 10.1186/s13195-016-0233-7

M3 - Article

VL - 9

JO - Alzheimer's Research & Therapy

JF - Alzheimer's Research & Therapy

SN - 1758-9193

IS - 1

M1 - 8

ER -