title: Imaging proteomics for diagnosis, monitoring and prediction of Alzheimer's disease.
abstract: Proteomic and imaging markers have been widely studied as potential biomarkers for diagnosis, monitoring and prognosis of Alzheimer's disease. In this study, we used Alzheimer Disease Neuroimaging Initiative dataset and performed parallel independent component analysis on cross sectional and longitudinal proteomic and imaging data in order to identify the best proteomic model for diagnosis, monitoring and prediction of Alzheimer disease (AD). We used plasma proteins measurement and imaging data from AD and healthy controls (HC) at the baseline and 1 year follow-up. Group comparisons at baseline and changes over 1 year were calculated for proteomic and imaging data. The results were fed into parallel independent component analysis in order to identify proteins that were associated with structural brain changes cross sectionally and longitudinally. Regression model was used to find the best model that can discriminate AD from HC, monitor AD and to predict MCI converters from non-converters. We showed that five proteins are associated with structural brain changes in the brain. These proteins could discriminate AD from HC with 57% specificity and 89% sensitivity. Four proteins whose change over 1 year were associated with brain structural changes could discriminate AD from HC with sensitivity of 93%, and specificity of 92%. This model predicted MCI conversion to AD in 2 years with 94% accuracy. This model has the highest accuracy in prediction of MCI conversion to AD within the ADNI-1 dataset. This study shows that combination of selected plasma protein levels and MR imaging is a useful method in identifying potential biomarker.
authors.affiliation: Interdisciplinary Neuroscience Research Program, Tehran University of Medical Sciences, Tehran, 1417614411, Iran.
authors.forename: Arash
authors.initials: A
authors.lastname: Nazeri
authors.affiliation: National Brain Mapping Centre, and Department of Neurology, Shahid Beheshti University of Medical Sciences, Tehran 4739, Iran; Department of Statistics, University of Warwick, Coventry CV4 7AL, UK.
authors.forename: Habib
authors.initials: H
authors.lastname: Ganjgahi
authors.affiliation: Interdisciplinary Neuroscience Research Program, Tehran University of Medical Sciences, Tehran, 1417614411, Iran.
authors.forename: Tina
authors.initials: T
authors.lastname: Roostaei
authors.affiliation: Department of Statistics, University of Warwick, Coventry CV4 7AL, UK.
authors.forename: Thomas
authors.initials: T
authors.lastname: Nichols
authors.affiliation: National Brain Mapping Centre, and Department of Neurology, Shahid Beheshti University of Medical Sciences, Tehran 4739, Iran. Electronic address: mzarei@me.com.
authors.forename: Mojtaba
authors.initials: M
authors.lastname: Zarei
mesh_terms: D000368:Aged; D000544:Alzheimer Disease / Q000097:blood / Q000175:diagnosis* / Q000473:pathology; D015415:Biomarkers; D001921:Brain / Q000473:pathology*; D060825:Cognitive Dysfunction / Q000097:blood / Q000175:diagnosis; D003430:Cross-Sectional Studies; D018450:Disease Progression; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging; D008297:Male; D011379:Prognosis; D040901:Proteomics
chemical_list: D015415:Biomarkers
keywords: Alzheimer's disease; Biomarkers; Magnetic resonance imaging; Parallel ICA; Proteomics; Tensor based morphometry