title: An improved model for disease progression in patients from the Alzheimer's disease neuroimaging initiative.
abstract: The objective of this analysis was to develop a semi-mechanistic nonlinear disease progression model using an expanded set of covariates that captures the longitudinal change of Alzheimer's Disease Assessment Scale (ADAS-cog) scores from the Alzheimer's Disease Neuroimaging Initiative study that consisted of 191 Alzheimer disease patients who were followed for 2 years. The model describes the rate of progression and baseline disease severity as a function of influential covariates. The covariates that were tested fell into 4 categories: (1) imaging volumetric measures, (2) serum biomarkers, (3) demographic and genetic factors, and (4) baseline cognitive tests. Covariates found to affect baseline disease status were years since disease onset, hippocampal volume, and ventricular volume. Disease progression rate in the model was influenced by age, total cholesterol, APOE ε4 genotype, Trail Making Test (part B) score, and current levels of impairment as measured by ADAS-cog. Rate of progression was slower for mild and severe Alzheimer patients compared with moderate Alzheimer patients who exhibited faster rates of deterioration. In conclusion, this model describes disease progression in Alzheimer patients using novel covariates that are important for understanding the worsening of ADAS-cog scores over time and may be useful in the future for optimizing study designs through clinical trial simulations.
authors.affiliation: Johnson & Johnson Pharmaceutical Research & Development, Raritan, NJ, USA. msamtani@its.jnj.com
authors.forename: Mahesh N
authors.initials: MN
authors.lastname: Samtani
authors.forename: Michael
authors.initials: M
authors.lastname: Farnum
authors.forename: Victor
authors.initials: V
authors.lastname: Lobanov
authors.forename: Eric
authors.initials: E
authors.lastname: Yang
authors.forename: Nandini
authors.initials: N
authors.lastname: Raghavan
authors.forename: Allitia
authors.initials: A
authors.lastname: Dibernardo
authors.forename: Vaibhav
authors.initials: V
authors.lastname: Narayan
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000097:blood / Q000175:diagnosis* / Q000235:genetics / Q000473:pathology / Q000503:physiopathology / Q000523:psychology; D053327:Apolipoprotein E4 / Q000235:genetics; D015415:Biomarkers / Q000097:blood; D001921:Brain / Q000473:pathology* / Q000503:physiopathology; D002784:Cholesterol / Q000097:blood; D003071:Cognition; D016208:Databases, Factual; D018450:Disease Progression; D005260:Female; D020022:Genetic Predisposition to Disease; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D059906:Neuroimaging; D009483:Neuropsychological Tests; D017711:Nonlinear Dynamics; D010641:Phenotype; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D012720:Severity of Illness Index; D013997:Time Factors
chemical_list: D053327:Apolipoprotein E4; D015415:Biomarkers; D002784:Cholesterol