Unbiased estimates of cerebrospinal fluid β-amyloid 1–42 cutoffs in a large memory clinic population
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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.
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An unbiased cutoff of less than 0.059 was used to define Aβ positivity based on mixture modeling statistics, which previously has proved to provide robust and accurate thresholds.
“An unbiased cutoff of less than 0.059 was used to define Aβ positivity based on mixture modeling statistics, which previously has proved to provide robust and accurate thresholds.35,36”
From Performance of Fully Automated Plasma Assays as Screening Tests for Alzheimer Disease–Related β-Amyloid Status (2019) · cites this paper as [35]
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Self-citationAdditional file 4 of Exploring the ATN classification system using brain morphology
Heinzinger, N., Maaß, A., Berron, D., Yakupov, R., Peters, O., Fiebach, J. B., Villringer, K., Preis, L., Priller, J., Spruth, E. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26574865
Additional file 5 of Exploring the ATN classification system using brain morphology
Heinzinger, N., Maaß, A., Berron, D., Yakupov, R., Peters, O., Fiebach, J. B., Villringer, K., Preis, L., Priller, J., Spruth, E. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26574868
Additional file 3 of Exploring the ATN classification system using brain morphology
Heinzinger, N., Maass, A., Berron, D., Yakupov, R., Peters, O., Fiebach, J. B., Villringer, K., Preis, L., Priller, J., Spruth, E. J., et al. (2023) · Figshare · doi:10.6084/m9.figshare.26574862
Additional file 6 of Exploring the ATN classification system using brain morphology
Heinzinger, N., Maaß, A., Berron, D., Yakupov, R., Peters, O., Fiebach, J. B., Villringer, K., Preis, L., Priller, J., Spruth, E. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26574871
Additional file 10 of Exploring the ATN classification system using brain morphology
Heinzinger, N., Maass, A., Berron, D., Yakupov, R., Peters, O., Fiebach, J. B., Villringer, K., Preis, L., Priller, J., Spruth, E. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26574883
Additional file 8 of Exploring the ATN classification system using brain morphology
Heinzinger, N., Maaß, A., Berron, D., Yakupov, R., Peters, O., Fiebach, J. B., Villringer, K., Preis, L., Priller, J., Spruth, E. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26574877
Additional file 1 of Exploring the ATN classification system using brain morphology
Heinzinger, N., Maaß, A., Berron, D., Yakupov, R., Peters, O., Fiebach, J. B., Villringer, K., Preis, L., Priller, J., Spruth, E. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26574856
Additional file 2 of Exploring the ATN classification system using brain morphology
Heinzinger, N., Maass, A., Berron, D., Yakupov, R., Peters, O., Fiebach, J. B., Villringer, K., Preis, L., Priller, J., Spruth, E. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26574859
Additional file 9 of Exploring the ATN classification system using brain morphology
Heinzinger, N., Maaß, A., Berron, D., Yakupov, R., Peters, O., Fiebach, J. B., Villringer, K., Preis, L., Priller, J., Spruth, E. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26574880
Svenningsson, A. L., Stomrud, E., Palmqvist, S., Hansson, O., & Ossenkoppele, R. (2022) · Figshare · doi:10.6084/m9.figshare.21269527
Brosseron, F., Maaß, A., Kleineidam, L., Ravichandran, K. A., Kolbe, C.-C., Wolfsgruber, S., Santarelli, F., Häsler, L. M., McManus, R. M., Ising, C., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22602371
Additional file 7 of Exploring the ATN classification system using brain morphology
Heinzinger, N., Maaß, A., Berron, D., Yakupov, R., Peters, O., Fiebach, J. B., Villringer, K., Preis, L., Priller, J., Spruth, E. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26574874
Bettcher, B. M., Ren, S., Wang, Y., Carlson, N. E., Lippitt, W., McConnell, B. V., Wilson, E. N., Shapiro, A. L. B., Coughlan, C. M., Carlisle, T. C., et al. (2026) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2026.05.001
Subjective cognition trajectories, Alzheimer biomarkers, and incident mild cognitive impairment
Kuhn, E., Kleineidam, L., Stark, M., Peters, O., Hellmann‐Regen, J., Preis, L., Gref, D., Priller, J., Spruth, E. J., Gemenetzi, M., et al. (2026) · The Journal of Prevention of Alzheimer s Disease · doi:10.1016/j.tjpad.2026.100609
- Authors
- Bertens, D., Tijms, B. M., Scheltens, P., Teunissen, C. E., & Visser, P. J.
- Affiliations
- Amsterdam Neuroscience (NL)
- Maastricht University Medical Centre (NL)
- Corresponding author
- Visser, Pieter Jelle
- Venue
- Alzheimer's Research & Therapy
- Published
- 2017-02-14
- Volume
- 9
- Issue
- 1
- Pages
- 8-8
- ISSN
- 1758-9193 (ISSN-L)
- PMC
- PMC5307885
- OpenAlex
- W2588515696
- Cited by
- 91
- References
- 34
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 12 · 2023: 12 · 2022: 17 · 2021: 13 · 2020: 7 · 2019: 7 · 2018: 5 · 2017: 3
- FWCI
- 4.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Alzheimer s Research & Therapy (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, cc-by)
- Pure Amsterdam UMC (publishedVersion, other-oa)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Funders
- IMI (115372)
- JPND (BIOMARKAPD)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Cerebrospinal fluid and hydrocephalus
- Keywords
- Alzheimer's disease, diagnostic thresholds, Gaussian mixture model, subjective cognitive decline, mild cognitive impairment, dementia progression, APOE genotype, biomarkers
- MeSH terms
- Aged, Apolipoproteins E, Cognition Disorders, Cross-Sectional Studies, Dementia, Female, Follow-Up Studies, Humans, Male, Middle Aged, Peptide Fragments, Prospective Studies, Biomarkers, Proportional Hazards Models, Amyloid beta-Peptides
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