期刊
ALZHEIMERS RESEARCH & THERAPY
卷 9, 期 -, 页码 -出版社
BIOMED CENTRAL LTD
DOI: 10.1186/s13195-016-0233-7
关键词
Alzheimer's disease; MCI; Cerebrospinal fluid; Diagnosis
资金
- Joint Programme Initiative on Neurodegenerative Disorders and ZonMw (BIOMARKAPD project)
- Innovative Medicines Initiative Joint Undertaking [115372]
- European Union
- European Federation of Pharmaceutical Industries and Associations companies
Background: We sought to define a cutoff for beta-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 beta-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 beta-amyloid 1-42 as defined by the data-driven cutoff could better predict progression to AD-type dementia than abnormal beta-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 beta-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 diagnosisbased 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 beta-amyloid 1-42. It might better capture biological changes that are related to AD than cutoffs based on clinical diagnosis.
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