title: ApoE4 effects on automated diagnostic classifiers for mild cognitive impairment and Alzheimer's disease.
abstract: Biomarkers are the only feasible way to detect and monitor presymptomatic Alzheimer's disease (AD). No single biomarker can predict future cognitive decline with an acceptable level of accuracy. In addition to designing powerful multimodal diagnostic platforms, a careful investigation of the major sources of disease heterogeneity and their influence on biomarker changes is needed. Here we investigated the accuracy of a novel multimodal biomarker classifier for differentiating cognitively normal (NC), mild cognitive impairment (MCI) and AD subjects with and without stratification by ApoE4 genotype. 111 NC, 182 MCI and 95 AD ADNI participants provided both structural MRI and CSF data at baseline. We used an automated machine-learning classifier to test the ability of hippocampal volume and CSF Aβ, t-tau and p-tau levels, both separately and in combination, to differentiate NC, MCI and AD subjects, and predict conversion. We hypothesized that the combined hippocampal/CSF biomarker classifier model would achieve the highest accuracy in differentiating between the three diagnostic groups and that ApoE4 genotype will affect both diagnostic accuracy and biomarker selection. The combined hippocampal/CSF classifier performed better than hippocampus-only classifier in differentiating NC from MCI and NC from AD. It also outperformed the CSF-only classifier in differentiating NC vs. AD. Our amyloid marker played a role in discriminating NC from MCI or AD but not for MCI vs. AD. Neurodegenerative markers contributed to accurate discrimination of AD from NC and MCI but not NC from MCI. Classifiers predicting MCI conversion performed well only after ApoE4 stratification. Hippocampal volume and sex achieved AUC = 0.68 for predicting conversion in the ApoE4-positive MCI, while CSF p-tau, education and sex achieved AUC = 0.89 for predicting conversion in ApoE4-negative MCI. These observations support the proposed biomarker trajectory in AD, which postulates that amyloid markers become abnormal early in the disease course while markers of neurodegeneration become abnormal later in the disease course and suggests that ApoE4 could be at least partially responsible for some of the observed disease heterogeneity.
authors.affiliation: Department of Neurology, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
authors.forename: Liana G
authors.initials: LG
authors.lastname: Apostolova
authors.affiliation: Department of Neurology, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
authors.forename: Kristy S
authors.initials: KS
authors.lastname: Hwang
authors.affiliation: Imaging genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA.
authors.forename: Omid
authors.initials: O
authors.lastname: Kohannim
authors.affiliation: Department of Neurology, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
authors.forename: David
authors.initials: D
authors.lastname: Avila
authors.affiliation: Department of Medicine Statistics Core, UCLA, Los Angeles, CA, USA.
authors.forename: David
authors.initials: D
authors.lastname: Elashoff
authors.affiliation: Department of Diagnostic Radiology, Mayo Clinic, Rochester, MN, USA.
authors.forename: Clifford R
authors.initials: CR
authors.lastname: Jack
authors.affiliation: Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA, USA.
authors.forename: Leslie
authors.initials: L
authors.lastname: Shaw
authors.affiliation: Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA, USA.
authors.forename: John Q
authors.initials: JQ
authors.lastname: Trojanowski
authors.affiliation: Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA ; Department of Veteran's Affairs Medical Center, San Francisco, CA, USA.
authors.forename: Michael W
authors.initials: MW
authors.lastname: Weiner
authors.affiliation: Imaging genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA.
authors.forename: Paul M
authors.initials: PM
authors.lastname: Thompson
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000544:Alzheimer Disease / Q000134:cerebrospinal fluid / Q000175:diagnosis*; D053327:Apolipoprotein E4 / Q000134:cerebrospinal fluid*; D001284:Atrophy / Q000473:pathology; D015415:Biomarkers / Q000134:cerebrospinal fluid; D060825:Cognitive Dysfunction / Q000134:cerebrospinal fluid / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D003937:Diagnosis, Differential; D005260:Female; D006624:Hippocampus / Q000378:metabolism* / Q000473:pathology*; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D009419:Nerve Tissue Proteins / Q000134:cerebrospinal fluid*; D009929:Organ Size; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014018:Tissue Distribution
chemical_list: D053327:Apolipoprotein E4; D015415:Biomarkers; D009419:Nerve Tissue Proteins
keywords: AD, Alzheimer's disease; ADNI; ADNI, Alzheimer's Disease Neuroimaging Initiative; AUC, area under the curve; Abeta; Alzheimer's disease; ApoE, apolipoprotein E; Aβ, Amyloid beta; Aβ42, Amyloid beta with 42 amino acid residues; CSF, cerebrospinal fluid; Diagnosis; Hippocampus atrophy; ICBM, International Consortium for Brain Mapping; MCI, mild cognitive impairment; MCIc, MCI converters; MCInc, MCI nonconverters; MMSE, Mini-Mental State Examination; NC, normal control; ROC, receiver operating curve; SVM, support vector machine; Tau; p-tau, phosphorylated tau protein; t-tau, total tau protein