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Updated: Mar 7, 2026
Absolute Quantification of Aβ1-42 in CSF Using a Mass Spectrometric Reference Measurement Procedure
Published on: March 21, 2017
Unbiased estimates of cerebrospinal fluid β-amyloid 1-42 cutoffs in a large memory clinic population
Daniela Bertens1,2, Betty M Tijms1,2, Philip Scheltens1,2
1Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
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.
