Faculty Bibliography
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132
18F-florbetapir Positron Emission Tomography-determined Cerebral beta-Amyloid Deposition and Neurocognitive Performance after Cardiac Surgery
BACKGROUND:Amyloid deposition is a potential contributor to postoperative cognitive dysfunction. The authors hypothesized that 6-week global cortical amyloid burden, determined by F-florbetapir positron emission tomography, would be greater in those patients manifesting cognitive dysfunction at 6 weeks postoperatively. METHODS:Amyloid deposition was evaluated in cardiac surgical patients at 6 weeks (n = 40) and 1 yr (n = 12); neurocognitive function was assessed at baseline (n = 40), 6 weeks (n = 37), 1 yr (n = 13), and 3 yr (n = 9). The association of 6-week amyloid deposition with cognitive dysfunction was assessed by multivariable regression, accounting for age, years of education, and baseline cognition. Differences between the surgical cohort with cognitive deficit and the Alzheimer's Disease Neuroimaging Initiative cohorts (normal and early/late mild cognitive impairment) was assessed, adjusting for age, education, and apolipoprotein E4 genotype. RESULTS:The authors found that 6-week abnormal global cortical amyloid deposition was not associated with cognitive dysfunction (13 of 37, 35%) at 6 weeks postoperatively (median standard uptake value ratio [interquartile range]: cognitive dysfunction 0.92 [0.89 to 1.07] vs. 0.98 [0.93 to 1.05]; P = 0.455). In post hoc analyses, global cortical amyloid was also not associated with cognitive dysfunction at 1 or 3 yr postoperatively. Amyloid deposition at 6 weeks in the surgical cohort was not different from that in normal Alzheimer's Disease Neuroimaging Initiative subjects, but increased over 1 yr in many areas at a rate greater than in controls. CONCLUSIONS:In this study, postoperative cognitive dysfunction was not associated with 6-week cortical amyloid deposition. The relationship between cognitive dysfunction and regional amyloid burden and the rate of postoperative amyloid deposition merit further investigation.
PMCID:5849499
PMID: 29389750
ISSN: 1528-1175
CID: 2994312
Biomarker pattern of ARIA-E participants in phase 3 randomized clinical trials with bapineuzumab
OBJECTIVE:To evaluate whether amyloid-related imaging abnormalities with edema/effusion (ARIA-E) observed in bapineuzumab clinical trials was associated with specific biomarker patterns. METHODS:Bapineuzumab, an anti-β-amyloid monoclonal antibody, was evaluated in patients with mild to moderate Alzheimer disease. Amyloid PET imaging, CSF biomarkers, or volumetric MRI (vMRI) were assessed. RESULTS:. CONCLUSIONS:Baseline biomarkers largely do not predict risk for developing ARIA-E. ARIA-E was associated with significant longitudinal changes in several biomarkers, with larger reductions in amyloid PET and CSF p-tau and t-tau concentrations, and paradoxically greater hippocampal volume reduction and ventricular enlargement, suggesting that ARIA-E in bapineuzumab-treated cases may be related to increased Aβ efflux from the brain and affecting downstream pathogenic processes.
PMID: 29429971
ISSN: 1526-632x
CID: 3256922
Proceedings of the National Academy of Sciences of the United States of America (PNAS). 2018:115(7):1481-1486.DOI: 10.1073/pnas.1719747115
Statistical tests and identifiability conditions for pooling and analyzing multisite datasets
When sample sizes are small, the ability to identify weak (but scientifically interesting) associations between a set of predictors and a response may be enhanced by pooling existing datasets. However, variations in acquisition methods and the distribution of participants or observations between datasets, especially due to the distributional shifts in some predictors, may obfuscate real effects when datasets are combined. We present a rigorous statistical treatment of this problem and identify conditions where we can correct the distributional shift. We also provide an algorithm for the situation where the correction is identifiable. We analyze various properties of the framework for testing model fit, constructing confidence intervals, and evaluating consistency characteristics. Our technical development is motivated by Alzheimer's disease (AD) studies, and we present empirical results showing that our framework enables harmonizing of protein biomarkers, even when the assays across sites differ. Our contribution may, in part, mitigate a bottleneck that researchers face in clinical research when pooling smaller sized datasets and may offer benefits when the subjects of interest are difficult to recruit or when resources prohibit large single-site studies.
PMCID:5816202
PMID: 29386387
ISSN: 1091-6490
CID: 3257392
Dual-Model Radiomic Biomarkers Predict Development of Mild Cognitive Impairment Progression to Alzheimer's Disease
Predicting progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is clinically important. In this study, we propose a dual-model radiomic analysis with multivariate Cox proportional hazards regression models to investigate promising risk factors associated with MCI conversion to AD. T1 structural magnetic resonance imaging (MRI) and 18F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) data, from the AD Neuroimaging Initiative database, were collected from 131 patients with MCI who converted to AD within 3 years and 132 patients with MCI without conversion within 3 years. These subjects were randomly partition into 70% training dataset and 30% test dataset with multiple times. We fused MRI and PET images by wavelet method. In a subset of subjects, a group comparison was performed using a two-sample t-test to determine regions of interest (ROIs) associated with MCI conversion. 172 radiomic features from ROIs for each individual were established using a published radiomics tool. Finally, L1-penalized Cox model was constructed and Harrell's C index (C-index) was used to evaluate prediction accuracy of the model. To evaluate the efficacy of our proposed method, we used a same analysis framework to evaluate MRI and PET data separately. We constructed prognostic Cox models with: clinical data, MRI images, PET images, fused MRI/PET images, and clinical variables and fused MRI/PET images in combination. The experimental results showed that captured ROIs significantly associated with conversion to AD, such as gray matter atrophy in the bilateral hippocampus and hypometabolism in the temporoparietal cortex. Imaging model (MRI/PET/fused) provided significant enhancement in prediction of conversion compared to clinical models, especially the fused-modality Cox model. Moreover, the combination of fused-modality imaging and clinical variables resulted in the greatest accuracy of prediction. The average C-index for the clinical/MRI/PET/fused/combined model in the test dataset was 0.69, 0.73, 0.73 and 0.75, and 0.78, respectively. These results suggested that a combination of radiomic analysis and Cox model analyses could be used successfully in survival analysis and may be powerful tools for personalized precision medicine patients with potential to undergo conversion from MCI to AD.
PMCID:6338093
PMID: 30686995
ISSN: 1662-4548
CID: 5134342
Pattern Discovery in Brain Imaging Genetics via SCCA Modeling with a Generic Non-convex Penalty
Brain imaging genetics intends to uncover associations between genetic markers and neuroimaging quantitative traits. Sparse canonical correlation analysis (SCCA) can discover bi-multivariate associations and select relevant features, and is becoming popular in imaging genetic studies. The L1-norm function is not only convex, but also singular at the origin, which is a necessary condition for sparsity. Thus most SCCA methods impose [Formula: see text]-norm onto the individual feature or the structure level of features to pursuit corresponding sparsity. However, the [Formula: see text]-norm penalty over-penalizes large coefficients and may incurs estimation bias. A number of non-convex penalties are proposed to reduce the estimation bias in regression tasks. But using them in SCCA remains largely unexplored. In this paper, we design a unified non-convex SCCA model, based on seven non-convex functions, for unbiased estimation and stable feature selection simultaneously. We also propose an efficient optimization algorithm. The proposed method obtains both higher correlation coefficients and better canonical loading patterns. Specifically, these SCCA methods with non-convex penalties discover a strong association between the APOE e4 rs429358 SNP and the hippocampus region of the brain. They both are Alzheimer's disease related biomarkers, indicating the potential and power of the non-convex methods in brain imaging genetics.
PMCID:5656688
PMID: 29070790
ISSN: 2045-2322
CID: 3257412
Translational Control of APP Expression for Alzheimer Disease Therapy [Editorial]
ORIGINAL:0012875
ISSN: 2573-6051
CID: 3257472
APOE genotype and Alzheimer's immunotherapy [Editorial]
PMCID:5522246
PMID: 28537920
ISSN: 1949-2553
CID: 2574812
A phase 3 trial of IV immunoglobulin for Alzheimer disease
OBJECTIVE:We tested biweekly infusions of IV immunoglobulin (IVIg) as a possible treatment for mild to moderate Alzheimer disease (AD) dementia. METHODS:In a phase 3, double-blind, placebo-controlled trial, we randomly assigned 390 participants with mild to moderate AD to receive placebo (low-dose albumin) or IVIg (Gammagard Liquid; Baxalta, Bannockburn, IL) administered IV at doses of 0.2 or 0.4 g/kg every 2 weeks for 18 months. The primary cognitive outcome was change from baseline to 18 months on the 11-item cognitive subscale of the Alzheimer's Disease Assessment Scale; the primary functional outcome was 18-month change on the Alzheimer's Disease Cooperative Study-Activities of Daily Living Inventory. Safety and tolerability data, as well as serial MRIs and plasma samples, were collected throughout the study from all enrolled participants. RESULTS:No beneficial effects were observed in the dual primary outcome measures for the 2 IVIg doses tested. Significant decreases in plasma Aβ42 (but not Aβ40) levels were observed in IVIg-treated participants. Analysis of safety data showed no difference between IVIg and placebo in terms of the rate of occurrence of amyloid-related imaging abnormalities (brain edema or microhemorrhage). IVIg-treated participants had more systemic reactions (chills, rashes) but fewer respiratory infections than participants receiving placebo. CONCLUSIONS:Participants with mild to moderate AD showed good tolerability of treatment with low-dose human IVIg for 18 months but did not show beneficial effects on cognition or function relative to participants who received placebo. CLINICALTRIALSGOV IDENTIFIER/UNASSIGNED:NCT00818662. CLASSIFICATION OF EVIDENCE/METHODS:This study provides Class II evidence that IVIg infusions performed every 2 weeks do not improve cognition or function at 18 months in patients with mild to moderate AD.
PMCID:5409846
PMID: 28381506
ISSN: 1526-632x
CID: 3257462
APOE Genotype Differentially Modulates Effects of Anti-Abeta, Passive Immunization in APP Transgenic Mice
BACKGROUND: APOE genotype is the foremost genetic factor modulating beta-amyloid (Abeta) deposition and risk of sporadic Alzheimer's disease (AD). Here we investigated how APOE genotype influences response to anti-Abeta immunotherapy. METHODS: APPSW/PS1dE9 (APP) transgenic mice with targeted replacement of the murine Apoe gene for human APOE alleles received 10D5 anti-Abeta or TY11-15 isotype control antibodies between the ages of 12 and 15 months. RESULTS: Anti-Abeta immunization decreased both the load of fibrillar plaques and the load of Abeta immunopositive plaques in mice of all APOE backgrounds. Although the relative reduction in parenchymal Abeta plaque load was comparable across all APOE genotypes, APP/epsilon4 mice showed the greatest reduction in the absolute Abeta plaque load values, given their highest baseline. The immunization stimulated phagocytic activation of microglia, which magnitude adjusted for the post-treatment plaque load was the greatest in APP/epsilon4 mice implying association between the epsilon4 allele and impaired Abeta phagocytosis. Perivascular hemosiderin deposits reflecting ensued microhemorrhages were associated with vascular Abeta (VAbeta) and ubiquitously present in control mice of all APOE genotypes, although in APP/epsilon3 mice their incidence was the lowest. Anti-Abeta immunization significantly reduced VAbeta burden but increased the number of hemosiderin deposits across all APOE genotypes with the strongest and the weakest effect in APP/epsilon2 and APP/epsilon3 mice, respectively. CONCLUSIONS: Our studies indicate that APOE genotype differentially modulates microglia activation and Abeta plaque load reduction during anti-Abeta immunotherapy. The APOE epsilon3 allele shows strong protective effect against immunotherapy associated microhemorrhages; while, conversely, the APOE epsilon2 allele increases risk thereof.
PMCID:5282859
PMID: 28143566
ISSN: 1750-1326
CID: 2424252
Adding Recognition Discriminability Index to the Delayed Recall Is Useful to Predict Conversion from Mild Cognitive Impairment to Alzheimer's Disease in the Alzheimer's Disease Neuroimaging Initiative
Background: Ongoing research is focusing on the identification of those individuals with mild cognitive impairment (MCI) who are most likely to convert to Alzheimer's disease (AD). We investigated whether recognition memory tasks in combination with delayed recall measure of episodic memory and CSF biomarkers can predict MCI to AD conversion at 24-month follow-up. Methods: A total of 397 amnestic-MCI subjects from Alzheimer's disease Neuroimaging Initiative were included. Logistic regression modeling was done to assess the predictive value of all RAVLT measures, risk factors such as age, sex, education, APOE genotype, and CSF biomarkers for progression to AD. Estimating adjusted odds ratios was used to determine which variables would produce an optimal predictive model, and whether adding tests of interaction between the RAVLT Delayed Recall and recognition measures (traditional score and d-prime) would improve prediction of the conversion from a-MCI to AD. Results: 112 (28.2%) subjects developed dementia and 285 (71.8%) subjects did not. Of the all included variables, CSF Aβ1-42 levels, RAVLT Delayed Recall, and the combination of RAVLT Delayed Recall and d-prime were predictive of progression to AD (Ç2 = 38.23, df = 14, p < 0.001). Conclusions: The combination of RAVLT Delayed Recall and d-prime measures may be predictor of conversion from MCI to AD in the ADNI cohort, especially in combination with amyloid biomarkers. A predictive model to help identify individuals at-risk for dementia should include not only traditional episodic memory measures (delayed recall or recognition), but also additional variables (d-prime) that allow the homogenization of the assessment procedures in the diagnosis of MCI.
PMCID:5344912
PMID: 28344552
ISSN: 1663-4365
CID: 3257492
