Showing papers by "Shannon L. Risacher published in 2023"
TL;DR: The most significant signal was at rs2113389 (p-value=1.37x10-8), which explained 4.3% of the variation in cortical tau, while APOE4 rs429358 accounted for 3.6%.
Abstract: Background: Determining the genetic architecture of Alzheimers disease (AD) pathologies can enhance mechanistic understanding and inform precision medicine strategies. A genome-wide association study (GWAS) of cortical tau quantified by positron emission tomography (PET) was performed. Method: Participants included 3,136 non-Hispanic White older adults from 12 independent studies (n=1,449 discovery sample; n=1,687 replication sample) spanning preclinical and clinical stages of AD. Genetic variants associated with cortical tau measured using [18F]flortaucipir or [18F]MK-6240 PET were assessed including relevant covariates. Voxel-wise analysis was used to map the topographic distribution of identified associations. Supporting evidence for the identified SNP from gene expression, methylation quantitative trait loci (QTL), and AD mouse data were evaluated. Findings: Two novel SNPs at the CYP1B1-RMDN2 (Cytochrome P450 Family 1 Subfamily B Member 1 and Regulator of Microtubule Dynamics 2) locus were associated with tau deposition. The most significant signal was at rs2113389 (p-value=1.37x10-8), which explained 4.3% of the variation in cortical tau, while APOE4 rs429358 accounted for 3.6%. The minor allele of rs2113389 (T; MAF=0.146) was associated with higher tau and faster cognitive decline. Additive effects, but no interactions, were observed between rs2113389 and diagnosis, APOE4, and A{beta} positivity. Voxel-wise analysis revealed higher tau in AD-related regions in rs2113389 T-allele carriers. CYP1B1 was upregulated in the temporal cortex in AD. The rs2113389 T-allele was associated with higher temporal cortex CYP1B1 expression and methylation levels. Mouse model studies provided additional functional evidence for a relationship between CYP1B1 and tau deposition but not A{beta}. Interpretation: The minor allele of rs2113389 may be a risk variant for tau and faster cognitive decline in AD. Further investigation of CYP1B1 and RMDN2 is warranted and may provide insight into the genetic basis of cerebral tau and novel pathways for therapeutic development in AD. Studies of multiethnic populations are also needed.
1 citations
TL;DR: The authors examined Black and White prospective participants' views of barriers to and facilitators of participation in Alzheimer's disease (AD) biomarker research, and found that participants were more likely to support participation in research.
TL;DR: In this article , the authors implemented a novel simulative deep learning model to chromosome 19 genetic data from the Alzheimer's Disease Neuroimaging Initiative and the Imaging and Genetic Biomarkers of Alzheimer's disease datasets, and the model successfully estimated the contribution of AD-risk SNPs that account for AD progression at individual level.
Abstract: BACKGROUND
Identifying genetic patterns that contribute to Alzheimer's disease (AD) is important not only for pre-symptomatic risk assessment but also for building personalized therapeutic strategies.
METHODS
We implemented a novel simulative deep learning model to chromosome 19 genetic data from the Alzheimer's Disease Neuroimaging Initiative and the Imaging and Genetic Biomarkers of Alzheimer's Disease datasets. The model quantified the contribution of each single nucleotide polymorphism (SNP) and their epistatic impact on the likelihood of AD using the occlusion method. The top 35 AD-risk SNPs in chromosome 19 were identified, and their ability to predict the rate of AD progression was analyzed.
RESULTS
Rs561311966 (APOC1) and rs2229918 (ERCC1/CD3EAP) were recognized as the most powerful factors influencing AD risk. The top 35 chromosome 19 AD-risk SNPs were significant predictors of AD progression.
DISCUSSION
The model successfully estimated the contribution of AD-risk SNPs that account for AD progression at the individual level. This can help in building preventive precision medicine.
Journal Article•
TL;DR: In this paper , the authors employ an automated machine learning (AutoML) pipeline, STREAMLINE, to conduct the diagnosis binary classification and perform permutation-based feature importance analysis with thirteen machine learning models.
Abstract: Amyloid imaging has been widely used in Alzheimer's disease (AD) diagnosis and biomarker discovery through detecting the regional amyloid plaque density. It is essential to be normalized by a reference region to reduce noise and artifacts. To explore an optimal normalization strategy, we employ an automated machine learning (AutoML) pipeline, STREAMLINE, to conduct the AD diagnosis binary classification and perform permutation-based feature importance analysis with thirteen machine learning models. In this work, we perform a comparative study to evaluate the prediction performance and biomarker discovery capability of three amyloid imaging measures, including one original measure and two normalized measures using two reference regions (i.e., the whole cerebellum and the composite reference region). Our AutoML results indicate that the composite reference region normalization dataset yields a higher balanced accuracy, and identifies more AD-related regions based on the fractioned feature importance ranking.
TL;DR: A review of the state of the field of neuroimaging measures as biomarkers for detection and diagnosis of both slowly progressing and rapidly progressing neurodegenerative diseases, specifically Alzheimer disease, vascular cognitive impairment, dementia with Lewy bodies or Parkinson disease dementia, frontotemporal lobar degeneration spectrum disorders, and prion-related diseases is presented in this paper .
Abstract: ABSTRACT OBJECTIVE Neurodegenerative diseases are significant health concerns with regard to morbidity and social and economic hardship around the world. This review describes the state of the field of neuroimaging measures as biomarkers for detection and diagnosis of both slowly progressing and rapidly progressing neurodegenerative diseases, specifically Alzheimer disease, vascular cognitive impairment, dementia with Lewy bodies or Parkinson disease dementia, frontotemporal lobar degeneration spectrum disorders, and prion-related diseases. It briefly discusses findings in these diseases in studies using MRI and metabolic and molecular-based imaging (eg, positron emission tomography [PET] and single-photon emission computerized tomography [SPECT]). LATEST DEVELOPMENTS Neuroimaging studies with MRI and PET have demonstrated differential patterns of brain atrophy and hypometabolism in different neurodegenerative disorders, which can be useful in differential diagnoses. Advanced MRI sequences, such as diffusion-based imaging, and functional MRI (fMRI) provide important information about underlying biological changes in dementia and new directions for development of novel measures for future clinical use. Finally, advancements in molecular imaging allow clinicians and researchers to visualize dementia-related proteinopathies and neurotransmitter levels. ESSENTIAL POINTS Diagnosis of neurodegenerative diseases is primarily based on symptomatology, although the development of in vivo neuroimaging and fluid biomarkers is changing the scope of clinical diagnosis, as well as the research into these devastating diseases. This article will help inform the reader about the current state of neuroimaging in neurodegenerative diseases, as well as how these tools might be used for differential diagnoses.
TL;DR: This paper found that white matter microstructure may be abnormal along the Alzheimer's disease (AD) continuum, which may indicate that the disease may be triggered by abnormal white matter structures.
TL;DR: In this paper , the association of lipidome profiles with central Alzheimer's disease (AD) biomarkers, including amyloid/tau/neurodegeneration (A/T/N), can provide a holistic view between the lipidome and AD.
Abstract: Investigating the association of lipidome profiles with central Alzheimer's disease (AD) biomarkers, including amyloid/tau/neurodegeneration (A/T/N), can provide a holistic view between the lipidome and AD. We performed cross-sectional and longitudinal association analysis of serum lipidome profiles with AD biomarkers in the Alzheimer's Disease Neuroimaging Initiative cohort (N=1,395). We identified lipid species, classes, and network modules that were significantly associated with cross-sectional and longitudinal changes of A/T/N biomarkers for AD. Notably, we identified the lysoalkylphosphatidylcholine (LPC(O)) as associated with "A/N" biomarkers at baseline at lipid species, class, and module levels. Also, GM3 ganglioside showed significant association with baseline levels and longitudinal changes of the "N" biomarkers at species and class levels. Our study of circulating lipids and central AD biomarkers enabled identification of lipids that play potential roles in the cascade of AD pathogenesis. Our results suggest dysregulation of lipid metabolic pathways as precursors to AD development and progression.
TL;DR: In this paper , the authors examined the association between occupational complexity and cognition in a sample of older adults (N = 355) and found that a standard deviation (SD) increase in complex work with people is associated with a 9% to 12% reduction in the probability of mild cognitive impairment or dementia.
Abstract: Individuals with more complex jobs experience better cognitive function in old age and a lower risk of dementia, yet complexity has multiple dimensions. Drawing on the Social Networks in Alzheimer Disease study, we examine the association between occupational complexity and cognition in a sample of older adults (N = 355). A standard deviation (SD) increase in complex work with people is associated with a 9% to 12% reduction in the probability of mild cognitive impairment or dementia, a 0.14–0.19 SD increase in episodic memory, and a 0.18–0.25 SD increase in brain reserve, defined as the gap (residual) between global cognitive function and magnetic resonance imaging (MRI) indicators of brain atrophy. In contrast, complexity with data or things is rarely associated with cognitive outcomes. We discuss the clinical and methodological implications of these findings, including the need to complement data‐centered activities (e.g., Sudoku puzzles) with person‐centered interventions that increase social complexity.
TL;DR: In this paper , the relationship between CBH WM integrity and cognition or amyloid burden in 505 Korean older adults aged greater than or equal to 55 years, including 276 cognitively normal older adults (CN), 142 mild cognitive impairment (MCI), and 87 AD, recruited as part of the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's disease (KBASE) at Seoul National University.
Abstract: BACKGROUND: White matter (WM) microstructural changes in the hippocampal cingulum bundle (CBH) in Alzheimer's disease (AD) have been described in cohorts of largely European ancestry but are lacking in other populations. METHODS: We assessed the relationship between CBH WM integrity and cognition or amyloid burden in 505 Korean older adults aged greater than or equal to 55 years, including 276 cognitively normal older adults (CN), 142 mild cognitive impairment (MCI), and 87 AD, recruited as part of the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's disease (KBASE) at Seoul National University. RESULTS: Compared to CN, AD and MCI subjects showed decreased WM integrity in the bilateral CBH. Cognition, mood, and higher amyloid burden were also associated with poorer WM integrity in the CBH. CONCLUSION: These findings are consistent with patterns of WM microstructural damage previously reported in non-Hispanic White (NHW) MCI/AD cohorts, reinforcing existing evidence from predominantly NHW cohort studies.
TL;DR: The authors applied a graph neural network to take advantage of this rich prior knowledge together with multi-omics data for identification of system level AD markers, which can suggest a more comprehensive view of biological processes underlying complex diseases such as Alzheimer's disease.
Abstract: The recent multi‐omics analysis explores the integration of multiple biological data types, which can suggest a more comprehensive view of biological processes underlying complex diseases, such as Alzheimer’s disease (AD). Various biological networks have been leveraged as prior knowledge with attempt to discovery of more interpretable multi‐omics markers [1]. We applied a new graph neural network to take advantage of this rich prior knowledge together with multi‐omics data for identification of system‐level AD markers.
TL;DR: In this paper , a black-box approach is proposed to decode the significant biological components contributing to the prediction of Alzheimer's disease, which is the leading cause of brain dementia, along with which substantial failure of organs and mental issues arise.
Abstract: Alzheimer’s disease is the leading cause of brain dementia, along with which substantial failure of organs and mental issues arise. The abundance of AD related data in the current decade has allowed for much advancement in the field using modern machine learning and deep learning techniques to decrypt pathology. Though diagnostic tools have been modeled over such large expanses of data, the black box problem of decoding the significant biological components contributing to the prediction has been overlooked in the research field.
TL;DR: In this paper , the accuracy of a phone-based memory screen at diagnosing mild cognitive impairment was evaluated, which showed strong associations for Alzheimer biomarkers: amyloid and tau deposition, hippocampus atrophy, and temporal atrophy.
Abstract: Background: Early detection of dementia has become important for interventions that are developed to slow disease progression. Due to technological advancements, healthcare is trending toward using more telehealth screenings due to the convenience it provides patients. In our research, we evaluate the accuracy of a phone-based memory screen at diagnosing mild cognitive impairment.
Methods: 181 participants from the Indiana Alzheimer’s Disease Research Center (IADRC)were screened using the Memory and Aging Telephone Screen (MATS) and diagnosed as cognitively normal (CN), subjective cognitive decline (SCD), or mild cognitive impairment (MCI). 177 underwent Rey Auditory Verbal Learning Testing (RAVLT); 103 received Aβ PET scans([18F]florbetapir or [18F]florbetaben); 91 had plasma tau levels measured; and 140 received MRI scans (Freesurfer v6). ANCOVAs were used to evaluate differences between diagnostic groups covarying for age, sex, and education. ROC analysis and logistic regressions were used to predict MCI and Aβ positivity. Partial correlations covarying for sex and age (and education for RAVLT) were conducted to evaluate relationships between MATS scores with RAVLT, brain atrophy, pTau level, and amyloid deposition.
Results: MCI patients showed significantly lower MATS scores for immediate (p<0.001) and delayed recall (p<0.001) compared to controls. Scores on the MATS correlated well with clinical based testing (MATS learning vs RAVLT learning: r2= 0.318, p<0.001). MATS scores showed strong associations for Alzheimer biomarkers: amyloid and tau deposition, hippocampus atrophy, and temporal atrophy. The accuracy of MATS to predict MCI was found to be about 75% with cutoffs of ≤16 for learning and ≤4 for delayed recall.
Conclusion and Potential Impact: The findings support that the phone memory screen can be used to detect dementia early in disease progression. By establishing cutoffs for this screening tool, physicians can easily and quickly detect signs of early Alzheimer’s disease, thus allowing for early intervention to slow disease progression.
TL;DR: KBASE2 as discussed by the authors is the second phase of KBASE, launched in collaboration with Indiana University and other institutions, which includes systematic longitudinal collection of comprehensive clinical, cognitive and lifestyle data, multimodal neuroimaging, and bio-specimens for the first six years.
Abstract: KBASE is a prospective cohort study launched at Seoul National University (SNU), South Korea, in 2014 using similar design and methods as the North American Alzheimer’s Disease Neuroimaging Initiative (ADNI). The KBASE cohort consists of well‐characterized participants (420 cognitively normal, 140 mild cognitive impairment, and 90 AD dementia) (Figure 1). It includes systematic longitudinal collection of comprehensive clinical, cognitive and lifestyle data, multimodal neuroimaging, and bio‐specimens for the first six years (“KBASE1”) at a single center. “KBASE2: Korean Brain Aging Study, Longitudinal Endophenotypes and Systems Biology” (NIH Grant U01AG072177) is the second phase of KBASE, launched in collaboration with Indiana University and other institutions. KBASE2 began in 2021, and has similar assessment protocols to the KBASE1. The NIA AD Sequencing Project (ADSP) will perform GWAS and whole genome sequencing (WGS).
TL;DR: MoFNet as discussed by the authors proposes a new interpretable deep neural network model MoFNet to jointly model the prior knowledge of functional interactions and multi-omic data set, which aims to identify a subnetwork of functional interaction predictive of Alzheimer's disease evidenced by multi-omics measures.
Abstract: Multi-omic data spanning from genotype, gene expression to protein expression have been increasingly explored to interpret findings from genome wide association studies of Alzheimer’s disease (AD) and to gain more insight of the disease mechanism. However, each -omics data type is usually examined individually and the functional interactions between genetic variations, genes and proteins are only used after discovery to interpret the findings, but not beforehand. In this case, multi-omic findings are likely not functionally related and therefore give rise to challenges in interpretation. To address this problem, we propose a new interpretable deep neural network model MoFNet to jointly model the prior knowledge of functional interactions and multi-omic data set. It aims to identify a subnetwork of functional interactions predictive of AD evidenced by multi-omic measures. Particularly, prior functional interaction network was embedded into the architecture of MoFNet in a way that it resembles the information flow from DNA to gene and protein. The proposed model MoFNet significantly outperformed all other state-of-art classifiers when evaluated using multi-omic data from the ROS/MAP cohort. Instead of individual markers, MoFNet yielded multi-omic sub-networks related to innate immune system, clearance of misfolded proteins, and neurotransmitter release respectively. Around 50% of these findings were replicated in another independent cohort. Our identified gene/proteins are highly related to synaptic vesicle function. Altered regulation or expression of these genes/proteins could cause disruption in neuron-neuron or neuron-glia cross talk and further lead to neuronal and synapse loss in AD. Further investigation of these identified genes/proteins could possibly help decipher the mechanisms underlying synaptic dysfunction in AD, and ultimately inform therapeutic strategies to modify AD progression in the early stage.
TL;DR: In this paper , the authors found that white matter tracts (e.g., cingulum bundle) were more vulnerable to abnormal aging than normal aging, and the free-water metric was the most vulnerable to normal and abnormal aging.
Abstract: INTRODUCTION It is unclear how rates of white matter microstructural decline differ between normal aging and abnormal aging. METHODS Diffusion MRI data from several well-established longitudinal cohorts of aging [Alzheimer’s Neuroimaging Initiative (ADNI), Baltimore Longitudinal Study of Aging (BLSA), Vanderbilt Memory & Aging Project (VMAP)] was free-water corrected and harmonized. This dataset included 1,723 participants (age at baseline: 72.8±8.87 years, 49.5% male) and 4,605 imaging sessions (follow-up time: 2.97±2.09 years, follow-up range: 1–13 years, mean number of visits: 4.42±1.98). Differences in white matter microstructural decline in normal and abnormal agers was assessed. RESULTS While we found global decline in white matter in normal/abnormal aging, we found that several white matter tracts (e.g., cingulum bundle) were vulnerable to abnormal aging. CONCLUSIONS There is a prevalent role of white matter microstructural decline in aging, and future large-scale studies in this area may further refine our understanding of the underlying neurodegenerative processes. HIGHLIGHTS Longitudinal data was free-water corrected and harmonized Global effects of white matter decline were seen in normal and abnormal aging The free-water metric was most vulnerable to abnormal aging Cingulum free-water was the most vulnerable to abnormal aging
TL;DR: In this paper , the core of tau filaments was made of residues K254-F378 of 3R Tau and was indistinguishable from Pick's disease and they concluded that MAPT mutation ∆K281 causes Pick's Disease.
Abstract: Two siblings with deletion mutation ∆K281 in MAPT developed frontotemporal dementia. At autopsy, numerous inclusions of hyperphosphorylated 3R Tau were present in neurons and glial cells of neocortex and some subcortical regions, including hippocampus, caudate/putamen and globus pallidus. The inclusions were argyrophilic with Bodian silver, but not with Gallyas-Braak silver. They were not labelled by an antibody specific for tau phosphorylated at S262 and/or S356. The inclusions were stained by luminescent conjugated oligothiophene HS-84, but not by bTVBT4. Electron cryo-microscopy revealed that the core of tau filaments was made of residues K254-F378 of 3R Tau and was indistinguishable from that of Pick's disease. We conclude that MAPT mutation ∆K281 causes Pick's disease.
TL;DR: In this paper , the authors investigated relationships between functional connectivity components, each derived from a subset of time points based on co-fluctuation of regional signals, and measures of domain-specific neuropsychological functions.
Abstract: Understanding the interrelationships of brain function as measured by resting-state magnetic resonance imaging and neuropsychological/behavioral measures in Alzheimer's disease is key for advancement of neuroimaging analysis methods in clinical research. The edge time-series framework recently developed in the field of network neuroscience, in combination with other network science methods, allows for investigations of brain-behavior relationships that are not possible with conventional functional connectivity methods. Data from the Indiana Alzheimer's Disease Research Center sample (53 cognitively normal control, 47 subjective cognitive decline, 32 mild cognitive impairment, and 20 Alzheimer's disease participants) were used to investigate relationships between functional connectivity components, each derived from a subset of time points based on co-fluctuation of regional signals, and measures of domain-specific neuropsychological functions. Multiple relationships were identified with the component approach that were not found with conventional functional connectivity. These involved attentional, limbic, frontoparietal, and default mode systems and their interactions, which were shown to couple with cognitive, executive, language, and attention neuropsychological domains. Additionally, overlapping results were obtained with two different statistical strategies (network contingency correlation analysis and network-based statistics correlation). Results demonstrate that connectivity components derived from edge time-series based on co-fluctuation reveal disease-relevant relationships not observed with conventional static functional connectivity.
TL;DR: In this paper , item-level self-report questionnaire data from international aging studies was linked with item response theory (IRT) techniques using a graded response model with a Bayesian estimator, and items that made the greatest contribution to measurement precision (i.e., "top items") assessed general and specific memory problems.
Abstract: OBJECTIVE
Self-perceived cognitive functioning, considered highly relevant in the context of aging and dementia, is assessed in numerous ways-hindering the comparison of findings across studies and settings. Therefore, the present study aimed to link item-level self-report questionnaire data from international aging studies.
METHOD
We harmonized secondary data from 24 studies and 40 different questionnaires with item response theory (IRT) techniques using a graded response model with a Bayesian estimator. We compared item information curves to identify items with high measurement precision at different levels of the self-perceived cognitive functioning latent trait. Data from 53,030 neuropsychologically intact older adults were included, from 13 English language and 11 non-English (or mixed) language studies.
RESULTS
We successfully linked all questionnaires and demonstrated that a single-factor structure was reasonable for the latent trait. Items that made the greatest contribution to measurement precision (i.e., "top items") assessed general and specific memory problems and aspects of executive functioning, attention, language, calculation, and visuospatial skills. These top items originated from distinct questionnaires and varied in format, range, time frames, response options, and whether they captured ability and/or change.
CONCLUSIONS
This was the first study to calibrate self-perceived cognitive functioning data of geographically diverse older adults. The resulting item scores are on the same metric, facilitating joint or pooled analyses across international studies. Results may lead to the development of new self-perceived cognitive functioning questionnaires guided by psychometric properties, content, and other important features of items in our item bank. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
TL;DR: In this article , the authors assessed the relationship between CBF quantified by arterial spin labeled MRI, hypertension, APOE ε4, and tau and amyloid PET in 77 older adults.
TL;DR: In this article , the authors explored a new data-driven prior knowledge that captures the subject-level similarity by fusing multi-modal similarity networks, which was incorporated into the sparse canonical correlation analysis (SCCA) model, which is aimed to identify a small set of brain imaging and genetic markers that explain the similarity matrix supported by both modalities.
Abstract: Introduction Brain imaging genetics aims to explore the genetic architecture underlying brain structure and functions. Recent studies showed that the incorporation of prior knowledge, such as subject diagnosis information and brain regional correlation, can help identify significantly stronger imaging genetic associations. However, sometimes such information may be incomplete or even unavailable. Methods In this study, we explore a new data-driven prior knowledge that captures the subject-level similarity by fusing multi-modal similarity networks. It was incorporated into the sparse canonical correlation analysis (SCCA) model, which is aimed to identify a small set of brain imaging and genetic markers that explain the similarity matrix supported by both modalities. It was applied to amyloid and tau imaging data of the ADNI cohort, respectively. Results Fused similarity matrix across imaging and genetic data was found to improve the association performance better or similarly well as diagnosis information, and therefore would be a potential substitute prior when the diagnosis information is not available (i.e., studies focused on healthy controls). Discussion Our result confirmed the value of all types of prior knowledge in improving association identification. In addition, the fused network representing the subject relationship supported by multi-modal data showed consistently the best or equally best performance compared to the diagnosis network and the co-expression network.