Faculty Bibliography
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136
Altered bile acid profile in mild cognitive impairment and Alzheimer's disease: Relationship to neuroimaging and CSF biomarkers
INTRODUCTION:Bile acids (BAs) are the end products of cholesterol metabolism produced by human and gut microbiome co-metabolism. Recent evidence suggests gut microbiota influence pathological features of Alzheimer's disease (AD) including neuroinflammation and amyloid-β deposition. METHOD:F]FDG PET). RESULTS:("A") and three with CSF p-tau181 ("T") (corrected P < .05). Furthermore, three, twelve, and fourteen BA signatures were associated with CSF t-tau, glucose metabolism, and atrophy ("N"), respectively (corrected P < .05). DISCUSSION:This is the first study to show serum-based BA metabolites are associated with "A/T/N" AD biomarkers, providing further support for a role of BA pathways in AD pathophysiology. Prospective clinical observations and validation in model systems are needed to assess causality and specific mechanisms underlying this association.
PMCID:6454538
PMID: 30337152
ISSN: 1552-5279
CID: 5134352
Translating Alzheimer's disease-associated polymorphisms into functional candidates: a survey of IGAP genes and SNPs
The International Genomics of Alzheimer's Project (IGAP) is a consortium for characterizing the genetic landscape of Alzheimer's disease (AD). The identified and/or confirmed 19 single-nucleotide polymorphisms (SNPs) associated with AD are located on non-coding DNA regions, and their functional impacts on AD are as yet poorly understood. We evaluated the roles of the IGAP SNPs by integrating data from many resources, based on whether the IGAP SNP was (1) a proxy for a coding SNP or (2) associated with altered mRNA transcript levels. For (1), we confirmed that 12 AD-associated coding common SNPs and five nonsynonymous rare variants are in linkage disequilibrium with the IGAP SNPs. For (2), the IGAP SNPs in CELF1 and MS4A6A were associated with expression of their neighboring genes, MYBPC3 and MS4A6A, respectively, in blood. The IGAP SNP in DSG2 was an expression quantitative trait loci (eQTL) for DLGAP1 and NETO1 in the human frontal cortex. The IGAP SNPs in ABCA7, CD2AP, and CD33 each acted as eQTL for AD-associated genes in brain. Our approach for identifying proxies and examining eQTL highlighted potentially impactful, novel gene regulatory phenomena pertinent to the AD phenotype.
PMCID:6331247
PMID: 30448613
ISSN: 1558-1497
CID: 5134382
Robust Motion Regression of Resting-State Data Using a Convolutional Neural Network Model
Resting-state functional magnetic resonance imaging (rs-fMRI) based on the blood-oxygen-level-dependent (BOLD) signal has been widely used in healthy individuals and patients to investigate brain functions when the subjects are in a resting or task-negative state. Head motion considerably confounds the interpretation of rs-fMRI data. Nuisance regression is commonly used to reduce motion-related artifacts with six motion parameters estimated from rigid-body realignment as regressors. To further compensate for the effect of head movement, the first-order temporal derivatives of motion parameters and squared motion parameters were proposed previously as possible motion regressors. However, these additional regressors may not be sufficient to model the impact of head motion because of the complexity of motion artifacts. In addition, while using more motion-related regressors could explain more variance in the data, the neural signal may also be removed with increasing number of motion regressors. To better model how in-scanner motion affects rs-fMRI data, a robust and automated convolutional neural network (CNN) model is developed in this study to obtain optimal motion regressors. The CNN network consists of two temporal convolutional layers and the output from the network are the derived motion regressors used in the following nuisance regression. The temporal convolutional layer in the network can non-parametrically model the prolonged effect of head motion. The set of regressors derived from the neural network is compared with the same number of regressors used in a traditional nuisance regression approach. It is demonstrated that the CNN-derived regressors can more effectively reduce motion-related artifacts.
PMCID:6482337
PMID: 31057348
ISSN: 1662-4548
CID: 5134372
Prediction and Classification of Alzheimer's Disease Based on Combined Features From Apolipoprotein-E Genotype, Cerebrospinal Fluid, MR, and FDG-PET Imaging Biomarkers
Alzheimer's disease (AD), including its mild cognitive impairment (MCI) phase that may or may not progress into the AD, is the most ordinary form of dementia. It is extremely important to correctly identify patients during the MCI stage because this is the phase where AD may or may not develop. Thus, it is crucial to predict outcomes during this phase. Thus far, many researchers have worked on only using a single modality of a biomarker for the diagnosis of AD or MCI. Although recent studies show that a combination of one or more different biomarkers may provide complementary information for the diagnosis, it also increases the classification accuracy distinguishing between different groups. In this paper, we propose a novel machine learning-based framework to discriminate subjects with AD or MCI utilizing a combination of four different biomarkers: fluorodeoxyglucose positron emission tomography (FDG-PET), structural magnetic resonance imaging (sMRI), cerebrospinal fluid (CSF) protein levels, and Apolipoprotein-E (APOE) genotype. The Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset was used in this study. In total, there were 158 subjects for whom all four modalities of biomarker were available. Of the 158 subjects, 38 subjects were in the AD group, 82 subjects were in MCI groups (including 46 in MCIc [MCI converted; conversion to AD within 24 months of time period], and 36 in MCIs [MCI stable; no conversion to AD within 24 months of time period]), and the remaining 38 subjects were in the healthy control (HC) group. For each image, we extracted 246 regions of interest (as features) using the Brainnetome template image and NiftyReg toolbox, and later we combined these features with three CSF and two APOE genotype features obtained from the ADNI website for each subject using early fusion technique. Here, a different kernel-based multiclass support vector machine (SVM) classifier with a grid-search method was applied. Before passing the obtained features to the classifier, we have used truncated singular value decomposition (Truncated SVD) dimensionality reduction technique to reduce high dimensional features into a lower-dimensional feature. As a result, our combined method achieved an area under the receiver operating characteristic (AU-ROC) curve of 98.33, 93.59, 96.83, 94.64, 96.43, and 95.24% for AD vs. HC, MCIs vs. MCIc, AD vs. MCIs, AD vs. MCIc, HC vs. MCIc, and HC vs. MCIs subjects which are high relative to single modality results and other state-of-the-art approaches. Moreover, combined multimodal methods have improved the classification performance over the unimodal classification.
PMCID:6805777
PMID: 31680923
ISSN: 1662-5188
CID: 5865332
The Relationship Between Hippocampal Volumes and Delayed Recall Is Modified by APOE ε4 in Mild Cognitive Impairment
PMCID:6399520
PMID: 30863302
ISSN: 1663-4365
CID: 5134362
Two Year Outcomes, Cognitive and Behavioral Markers of Decline in Healthy, Cognitively Normal Older Persons with Global Deterioration Scale Stage 2 (Subjective Cognitive Decline with Impairment)
BACKGROUND:Little is known with respect to behavioral markers of subjective cognitive decline (SCD), a condition initially described in association with Global Deterioration Scale (GDS) stage 2. OBJECTIVE:Two-year interval behavioral markers were investigated herein. METHODS:Subjects from a published 7-year outcome study of GDS stage 2 subjects were selected. This study had demonstrated a hazard ratio of 4.5 for progression of GDS stage 2, in comparison with GDS stage 1 (no subjective or objective cognitive decline) subjects, after controlling for demographic and temporal variables. Because GDS 2 subjects have previously demonstrated impairment in comparison with healthy persons free of complaints, we herein suggest the terminology "SCD(I)" for these persons. 98 SCD(I) persons, 63 women and 35 men, mean baseline age, 67.12±8.75 years, with a mean educational background of 15.55±2.60 years, and mean baseline MMSE scores of 28.9±1.24 were followed for 2.13±0.30 years. RESULTS:Observed annual decline on the GDS was 6.701% per annum, very close to a 1986 published estimate. At follow up, the MMSE, and 7 of 8 psychometric tests did not decline significantly. Of 21 Hamilton Depression Scale items, 2 improved and the remainder were unchanged. Anxieties declined from multiple perspectives. The Brief Cognitive Rating Scale (BCRS) declined significantly (p < 0.001), with component declines in Remote memory (p < 0.01), and Functioning/self-care (p = 0.01). CONCLUSION/CONCLUSIONS:SCD(I) persons decline at an annual rate of approximately 6.7% /year from several recent studies. The BCRS assessments and the Digit Symbol Substitution Test can be sensitive measures for future studies of progression mitigation.
PMID: 30689585
ISSN: 1875-8908
CID: 3626022
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. 2018:115(8):1697-1706.DOI: 10.1073/pnas.1715554115
Identification of genetic risk factors in the Chinese population implicates a role of immune system in Alzheimer's disease pathogenesis
Alzheimer's disease (AD) is a leading cause of mortality among the elderly. We performed a whole-genome sequencing study of AD in the Chinese population. In addition to the variants identified in or around the APOE locus (sentinel variant rs73052335, P = 1.44 × 10-14), two common variants, GCH1 (rs72713460, P = 4.36 × 10-5) and KCNJ15 (rs928771, P = 3.60 × 10-6), were identified and further verified for their possible risk effects for AD in three small non-Asian AD cohorts. Genotype-phenotype analysis showed that KCNJ15 variant rs928771 affects the onset age of AD, with earlier disease onset in minor allele carriers. In addition, altered expression level of the KCNJ15 transcript can be observed in the blood of AD subjects. Moreover, the risk variants of GCH1 and KCNJ15 are associated with changes in their transcript levels in specific tissues, as well as changes of plasma biomarkers levels in AD subjects. Importantly, network analysis of hippocampus and blood transcriptome datasets suggests that the risk variants in the APOE, GCH1, and KCNJ15 loci might exert their functions through their regulatory effects on immune-related pathways. Taking these data together, we identified common variants of GCH1 and KCNJ15 in the Chinese population that contribute to AD risk. These variants may exert their functional effects through the immune system.
PMID: 29432188
ISSN: 1091-6490
CID: 5864612
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
