title: Improving CSF biomarker accuracy in predicting prevalent and incident Alzheimer disease.
abstract: OBJECTIVE
  To investigate factors, including cognitive and brain reserve, which may independently predict prevalent and incident dementia of the Alzheimer type (DAT) and to determine whether inclusion of identified factors increases the predictive accuracy of the CSF biomarkers Aβ(42), tau, ptau(181), tau/Aβ(42), and ptau(181)/Aβ(42).
  
  
  METHODS
  Logistic regression identified variables that predicted prevalent DAT when considered together with each CSF biomarker in a cross-sectional sample of 201 participants with normal cognition and 46 with DAT. The area under the receiver operating characteristic curve (AUC) from the resulting model was compared with the AUC generated using the biomarker alone. In a second sample with normal cognition at baseline and longitudinal data available (n = 213), Cox proportional hazards models identified variables that predicted incident DAT together with each biomarker, and the models' concordance probability estimate (CPE), which was compared to the CPE generated using the biomarker alone.
  
  
  RESULTS
  APOE genotype including an ε4 allele, male gender, and smaller normalized whole brain volumes (nWBV) were cross-sectionally associated with DAT when considered together with every biomarker. In the longitudinal sample (mean follow-up = 3.2 years), 14 participants (6.6%) developed DAT. Older age predicted a faster time to DAT in every model, and greater education predicted a slower time in 4 of 5 models. Inclusion of ancillary variables resulted in better cross-sectional prediction of DAT for all biomarkers (p < 0.0021), and better longitudinal prediction for 4 of 5 biomarkers (p < 0.0022).
  
  
  CONCLUSIONS
  The predictive accuracy of CSF biomarkers is improved by including age, education, and nWBV in analyses.
authors.affiliation: Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA. cathyr@wustl.edu
authors.forename: C M
authors.initials: CM
authors.lastname: Roe
authors.forename: A M
authors.initials: AM
authors.lastname: Fagan
authors.forename: M M
authors.initials: MM
authors.lastname: Williams
authors.forename: N
authors.initials: N
authors.lastname: Ghoshal
authors.forename: M
authors.initials: M
authors.lastname: Aeschleman
authors.forename: E A
authors.initials: EA
authors.lastname: Grant
authors.forename: D S
authors.initials: DS
authors.lastname: Marcus
authors.forename: M A
authors.initials: MA
authors.lastname: Mintun
authors.forename: D M
authors.initials: DM
authors.lastname: Holtzman
authors.forename: J C
authors.initials: JC
authors.lastname: Morris
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000134:cerebrospinal fluid* / Q000175:diagnosis / Q000453:epidemiology*; D053327:Apolipoprotein E4 / Q000134:cerebrospinal fluid; D015415:Biomarkers / Q000134:cerebrospinal fluid; D003430:Cross-Sectional Studies; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D015994:Incidence; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D015995:Prevalence; D011446:Prospective Studies; D016875:tau Proteins / Q000134:cerebrospinal fluid
chemical_list: D053327:Apolipoprotein E4; D015415:Biomarkers; D016875:tau Proteins