title: COMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer's disease.
abstract: We present COMPASS, a COmputational Model to Predict the development of Alzheimer's diSease Spectrum, to model Alzheimer's disease (AD) progression. This was the best-performing method in recent crowdsourcing benchmark study, DREAM Alzheimer's Disease Big Data challenge to predict changes in Mini-Mental State Examination (MMSE) scores over 24-months using standardized data. In the present study, we conducted three additional analyses beyond the DREAM challenge question to improve the clinical contribution of our approach, including: (1) adding pre-validated baseline cognitive composite scores of ADNI-MEM and ADNI-EF, (2) identifying subjects with significant declines in MMSE scores, and (3) incorporating SNPs of top 10 genes connected to APOE identified from functional-relationship network. For (1) above, we significantly improved predictive accuracy, especially for the Mild Cognitive Impairment (MCI) group. For (2), we achieved an area under ROC of 0.814 in predicting significant MMSE decline: our model has 100% precision at 5% recall, and 91% accuracy at 10% recall. For (3), "genetic only" model has Pearson's correlation of 0.15 to predict progression in the MCI group. Even though addition of this limited genetic model to COMPASS did not improve prediction of progression of MCI group, the predictive ability of SNP information extended beyond well-known APOE allele.
authors.affiliation: Department of Computational Medicine and Bioinformatics, University of Michigan, USA.
authors.forename: Fan
authors.initials: F
authors.lastname: Zhu
authors.affiliation: Department of Computational Medicine and Bioinformatics, University of Michigan, USA.
authors.forename: Bharat
authors.initials: B
authors.lastname: Panwar
authors.affiliation: Department of Neurology and Michigan Alzheimer's Disease Center, University of Michigan, USA.|Department of Neurology and Layton Aging and Alzheimer's Disease Center, Oregon Health &Science University, USA.
authors.forename: Hiroko H
authors.initials: HH
authors.lastname: Dodge
authors.affiliation: Department of Computational Medicine and Bioinformatics, University of Michigan, USA.
authors.forename: Hongdong
authors.initials: H
authors.lastname: Li
authors.affiliation: Department of Psychiatry, University of Michigan, USA.|Mental Health Service, VA Ann Arbor Healthcare System, USA.
authors.forename: Benjamin M
authors.initials: BM
authors.lastname: Hampstead
authors.affiliation: Department of Neurology and Michigan Alzheimer's Disease Center, University of Michigan, USA.|Neurology Service &Geriatric Research Education and Clinical Centers, VA Ann Arbor Healthcare System, USA.|University of Michigan Udall Center, USA.
authors.forename: Roger L
authors.initials: RL
authors.lastname: Albin
authors.affiliation: Department of Neurology and Michigan Alzheimer's Disease Center, University of Michigan, USA.
authors.forename: Henry L
authors.initials: HL
authors.lastname: Paulson
authors.affiliation: Department of Computational Medicine and Bioinformatics, University of Michigan, USA.|Departments of Internal Medicine and Human Genetics, and School of Public Health, University of Michigan, USA.|Departments of Internal Medicine and of Electrical Engineering and Computer Science, University of Michigan, USA.
authors.forename: Yuanfang
authors.initials: Y
authors.lastname: Guan
mesh_terms: D000544:Alzheimer Disease / Q000473:pathology / Q000503:physiopathology*; D003198:Computer Simulation; D016208:Databases, Factual; D018450:Disease Progression; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological