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.
authors.affiliation: Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.|Alzheimer Center, Neuroscience Campus Amsterdam, VU University Medical Center, De Boelelaan 1118, 1081, HZ, Amsterdam, The Netherlands.
authors.forename: Daniela
authors.initials: D
authors.lastname: Bertens
authors.affiliation: Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.|Alzheimer Center, Neuroscience Campus Amsterdam, VU University Medical Center, De Boelelaan 1118, 1081, HZ, Amsterdam, The Netherlands.
authors.forename: Betty M
authors.initials: BM
authors.lastname: Tijms
authors.affiliation: Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.|Alzheimer Center, Neuroscience Campus Amsterdam, VU University Medical Center, De Boelelaan 1118, 1081, HZ, Amsterdam, The Netherlands.
authors.forename: Philip
authors.initials: P
authors.lastname: Scheltens
authors.affiliation: Department of Clinical Chemistry, Neurochemistry Lab and Biobank, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Charlotte E
authors.initials: CE
authors.lastname: Teunissen
authors.affiliation: Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands. pj.visser@vumc.nl.|Alzheimer Center, Neuroscience Campus Amsterdam, VU University Medical Center, De Boelelaan 1118, 1081, HZ, Amsterdam, The Netherlands. pj.visser@vumc.nl.|Alzheimer Center, School for Mental Health and Neuroscience (MHeNS), University Medical Centre Maastricht, Maastricht, The Netherlands. pj.visser@vumc.nl.
authors.forename: Pieter Jelle
authors.identifier: http://orcid.org/0000-0001-8008-9727
authors.initials: PJ
authors.lastname: Visser
mesh_terms: D000368:Aged; D016229:Amyloid beta-Peptides / Q000134:cerebrospinal fluid*; D001057:Apolipoproteins E / Q000235:genetics; D015415:Biomarkers / Q000134:cerebrospinal fluid; D003072:Cognition Disorders / Q000134:cerebrospinal fluid* / Q000235:genetics; D003430:Cross-Sectional Studies; D003704:Dementia / Q000134:cerebrospinal fluid* / Q000235:genetics; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008875:Middle Aged; D010446:Peptide Fragments / Q000134:cerebrospinal fluid*; D016016:Proportional Hazards Models; D011446:Prospective Studies
chemical_list: D016229:Amyloid beta-Peptides; D001057:Apolipoproteins E; D015415:Biomarkers; D010446:Peptide Fragments; C075222:amyloid beta-protein (1-42)
keywords: Alzheimer’s disease; Cerebrospinal fluid; Diagnosis; MCI