Validation of a priori candidate Alzheimer’s disease SNPs with brain amyloid-beta deposition

The accumulation of brain amyloid β (Aβ) is one of the main pathological hallmarks of Alzheimer’s disease (AD). However, the role of brain amyloid deposition in the development of AD and the genetic variants associated with this process remain unclear. In this study, we sought to identify associations between Aβ deposition and an a priori evidence based set of 1610 genetic markers, genotyped from 505 unrelated individuals (258 Aβ+ and 247 Aβ−) enrolled in the Australian Imaging, Biomarker & Lifestyle (AIBL) study. We found statistically significant associations for 6 markers located within intronic regions of 6 genes, including AC103796.1-BDNF, PPP3R1, NGFR, KL, ABCA7 & CALHM1. Although functional studies are required to elucidate the role of these genes in the accumulation of Aβ and their potential implication in AD pathophysiology, our findings are consistent with results obtained in previous GWAS efforts

Discovery of a missense mutation (Q222K) of the APOE gene from the Australian imaging, biomarker and lifestyle study

After age, polymorphisms of the Apolipoprotein E (APOE) gene are the biggest risk factor for the development of Alzheimer\u27s disease (AD). During our investigation to discovery biomarkers in plasma, using 2D gel electrophoresis, we found an individual with and unusual apoE isoelectric point compared to APOE ϵ2, ϵ3, and ϵ4 carriers. Whole exome sequencing of APOE from the donor confirmed a single nucleotide polymorphism (SNP) in exon 4, translating to a rare Q222K missense mutation. The apoE ϵ4 (Q222K) mutation did not form dimers or complexes observed for apoE ϵ2 ϵ3 proteins

Application of the NIA-AA research framework: Towards a biological definition of Alzheimer’s disease using cerebrospinal fluid biomarkers in the AIBL study

BACKGROUND: The National Institute on Aging and Alzheimer’s Association (NIA-AA) have proposed a new Research Framework: Towards a biological definition of Alzheimer’s disease, which uses a three-biomarker construct: Aß-amyloid, tau and neurodegeneration AT(N), to generate a biomarker based definition of Alzheimer’s disease. OBJECTIVES: To stratify AIBL participants using the new NIA-AA Research Framework using cerebrospinal fluid (CSF) biomarkers. To evaluate the clinical and cognitive profiles of the different groups resultant from the AT(N) stratification. To compare the findings to those that result from stratification using two-biomarker construct criteria (AT and/or A(N)). DESIGN: Individuals were classified as being positive or negative for each of the A, T, and (N) categories and then assigned to the appropriate AT(N) combinatorial group: A-T-(N)-; A+T-(N)-; A+T+(N)-; A+T-(N)+; A+T+(N)+; A-T+(N)-; A-T-(N)+; A-T+(N)+. In line with the NIA-AA research framework, these eight AT(N) groups were then collapsed into four main groups of interest (normal AD biomarkers, AD pathologic change, AD and non-AD pathologic change) and the respective clinical and cognitive trajectories over 4.5 years for each group were assessed. In two sensitivity analyses the methods were replicated after assigning individuals to four groups based on being positive or negative for AT biomarkers as well as A(N) biomarkers. SETTING: Two study centers in Melbourne (Victoria) and Perth (Western Australia), Australia recruited MCI individuals and individuals with AD from primary care physicians or tertiary memory disorder clinics. Cognitively healthy, elderly NCs were recruited through advertisement or via spouses of participants in the study. PARTICIPANTS: One-hundred and forty NC, 33 MCI participants, and 27 participants with AD from the AIBL study who had undergone CSF evaluation using Elecsys® assays. INTERVENTION (if any): Not applicable. MEASUREMENTS: Three CSF biomarkers, namely amyloid β1-42, phosphorylated tau181, and total tau, were measured to provide the AT(N) classifications. Clinical and cognitive trajectories were evaluated using the AIBL Preclinical Alzheimer Cognitive Composite (AIBL-PACC), a verbal episodic memory composite, an executive function composite, California Verbal Learning Test – Second Edition; Long-Delay Free Recall, Mini-Mental State Examination, and Clinical Dementia Rating Sum of Boxes scores. RESULTS: Thirty-eight percent of the elderly NCs had no evidence of abnormal AD biomarkers, whereas 33% had biomarker levels consistent with AD or AD pathologic change, and 29% had evidence of non-AD biomarker change. Among NC participants, those with biomarker evidence of AD pathology tended to perform worse on cognitive outcome assessments than other biomarker groups. Approximately three in four participants with MCI or AD had biomarker levels consistent with the research framework’s definition of AD or AD pathologic change. For MCI participants, a decrease in AIBL-PACC scores was observed with increasing abnormal biomarkers; and increased abnormal biomarkers were also associated with increased rates of decline across some cognitive measures. CONCLUSIONS: Increasing biomarker abnormality appears to be associated with worse cognitive trajectories. The implementation of biomarker classifications could help better characterize prognosis in clinical practice and identify those at-risk individuals more likely to clinically progress, for their inclusion in future therapeutic trials

An anemia of Alzheimer\u27s disease

Lower hemoglobin is associated with cognitive impairment and Alzheimer\u27s disease (AD). Since brain iron homeostasis is perturbed in AD, we investigated whether this is peripherally reflected in the hematological and related blood chemistry values from the Australian Imaging Biomarker and Lifestyle (AIBL) study (a community-based, cross-sectional cohort comprising 768 healthy controls (HC), 133 participants with mild cognitive impairment (MCI) and 211 participants with AD). We found that individuals with AD had significantly lower hemoglobin, mean cell hemoglobin concentrations, packed cell volume and higher erythrocyte sedimentation rates (adjusted for age, gender, APOE-ε4 and site). In AD, plasma iron, transferrin, transferrin saturation and red cell folate levels exhibited a significant distortion of their customary relationship to hemoglobin levels. There was a strong association between anemia and AD (adjusted odds ratio (OR)=2.43, confidence interval (CI) (1.31, 4.54)). Moreover, AD emerged as a strong risk factor for anemia on step-down regression, even when controlling for all other available explanations for anemia (adjusted OR=3.41, 95% CI (1.68, 6.92)). These data indicated that AD is complicated by anemia, which may itself contribute to cognitive decline

Transfer learning-trained convolutional neural networks identify novel MRI biomarkers of Alzheimer\u27s disease progression.

Introduction: Genome-wide association studies (GWAS) for late onset Alzheimer\u27s disease (AD) may miss genetic variants relevant for delineating disease stages when using clinically defined case/control as a phenotype due to its loose definition and heterogeneity. Methods: We use a transfer learning technique to train three-dimensional convolutional neural network (CNN) models based on structural magnetic resonance imaging (MRI) from the screening stage in the Alzheimer\u27s Disease Neuroimaging Initiative consortium to derive image features that reflect AD progression. Results: CNN-derived image phenotypes are significantly associated with fasting metabolites related to early lipid metabolic changes as well as insulin resistance and with genetic variants mapped to candidate genes enriched for amyloid beta degradation, tau phosphorylation, calcium ion binding-dependent synaptic loss, Discussion: This is the first attempt to show that non-invasive MRI biomarkers are linked to AD progression characteristics, reinforcing their use in early AD diagnosis and monitoring

Hippocampal representations for deep learning on Alzheimer’s disease

Deep learning offers a powerful approach for analyzing hippocampal changes in Alzheimer’s disease (AD) without relying on handcrafted features. Nevertheless, an input format needs to be selected to pass the image information to the neural network, which has wide ramifications for the analysis, but has not been evaluated yet. We compare five hippocampal representations (and their respective tailored network architectures) that span from raw images to geometric representations like meshes and point clouds. We performed a thorough evaluation for the prediction of AD diagnosis and time-to-dementia prediction with experiments on an independent test dataset. In addition, we evaluated the ease of interpretability for each representation–network pair. Our results show that choosing an appropriate representation of the hippocampus for predicting Alzheimer’s disease with deep learning is crucial, since it impacts performance and ease of interpretation

Comparison and aggregation of event sequences across ten cohorts to describe the consensus biomarker evolution in Alzheimer's disease

BACKGROUND: Previous models of Alzheimer's disease (AD) progression were primarily hypothetical or based on data originating from single cohort studies. However, cohort datasets are subject to specific inclusion and exclusion criteria that influence the signals observed in their collected data. Furthermore, each study measures only a subset of AD-relevant variables. To gain a comprehensive understanding of AD progression, the heterogeneity and robustness of estimated progression patterns must be understood, and complementary information contained in cohort datasets be leveraged. METHODS: We compared ten event-based models that we fit to ten independent AD cohort datasets. Additionally, we designed and applied a novel rank aggregation algorithm that combines partially overlapping, individual event sequences into a meta-sequence containing the complementary information from each cohort. RESULTS: We observed overall consistency across the ten event-based model sequences (average pairwise Kendall's tau correlation coefficient of 0.69 ± 0.28), despite variance in the positioning of mainly imaging variables. The changes described in the aggregated meta-sequence are broadly consistent with the current understanding of AD progression, starting with cerebrospinal fluid amyloid beta, followed by tauopathy, memory impairment, FDG-PET, and ultimately brain deterioration and impairment of visual memory. CONCLUSION: Overall, the event-based models demonstrated similar and robust disease cascades across independent AD cohorts. Aggregation of data-driven results can combine complementary strengths and information of patient-level datasets. Accordingly, the derived meta-sequence draws a more complete picture of AD pathology compared to models relying on single cohorts

A New Classification Network for Diagnosing Alzheimer’s Disease in class-imbalance MRI datasets

Automatic identification of Alzheimer’s Disease (AD) through magnetic resonance imaging (MRI) data can eectively assist to doctors diagnose and treat Alzheimer’s. Current methods improve the accuracy of AD recognition, but they are insufficient to address the challenge of small interclass and large intraclass dierences. Some studies attempt to embed patch-level structure in neural networks which enhance pathologic details, but the enormous size and time complexity render these methods unfavorable. Furthermore, several self-attention mechanisms fail to provide contextual information to represent discriminative regions, which limits the performance of these classifiers. In addition, the current loss function is adversely aected by outliers of class imbalance and may fall into local optimal values. Therefore, we propose a 3D Residual RepVGG Attention network (ResRepANet) stacked with several lightweight blocks to identify the MRI of brain disease, which can also trade o accuracy and flexibility. Specifically, we propose a Non-local Context Spatial Attention block (NCSA) and embed it in our proposed ResRepANet, which aggregates global contextual information in spatial features to improve semantic relevance in discriminative regions. In addition, in order to reduce the influence of outliers, we propose a Gradient Density Multiple-weighting Mechanism (GDMM) to automatically adjust the weights of each MRI image via a normalizing gradient norm. Experiments are conducted on datasets from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarker and Lifestyle Flagship Study of Aging (AIBL). Experiments on both datasets show that the accuracy, sensitivity, specificity, and Area Under the Curve are consistently better than for state-of-the-art methods