Selection of amyloid positive pre-symptomatic subjects using automatic analysis of neuropsychological and MRI data for cost effective inclusion procedures in clinical trials

International audienc

Novel verbal fluency scores and structural brain imaging for prediction of cognitive outcome in mild cognitive impairment

AbstractIntroductionThe objective of this study was to assess the utility of novel verbal fluency scores for predicting conversion from mild cognitive impairment (MCI) to clinical Alzheimer's disease (AD).MethodVerbal fluency lists (animals, vegetables, F, A, and S) from 107 MCI patients and 51 cognitively normal controls were transcribed into electronic text files and automatically scored with traditional raw scores and five types of novel scores computed using methods from machine learning and natural language processing. Additional scores were derived from structural MRI scans: region of interest measures of hippocampal and ventricular volumes and gray matter scores derived from performing ICA on measures of cortical thickness. Over 4 years of follow-up, 24 MCI patients converted to AD. Using conversion as the outcome variable, ensemble classifiers were constructed by training classifiers on the individual groups of scores and then entering predictions from the primary classifiers into regularized logistic regression models. Receiver operating characteristic curves were plotted, and the area under the curve (AUC) was measured for classifiers trained with five groups of available variables.ResultsClassifiers trained with novel scores outperformed those trained with raw scores (AUC 0.872 vs 0.735; P < .05 by DeLong test). Addition of structural brain measurements did not improve performance based on novel scores alone.ConclusionThe brevity and cost profile of verbal fluency tasks recommends their use for clinical decision making. The word lists generated are a rich source of information for predicting outcomes in MCI. Further work is needed to assess the utility of verbal fluency for early AD

MRI predictors of amyloid pathology: results from the EMIF-AD Multimodal Biomarker Discovery study

BACKGROUND: With the shift of research focus towards the pre-dementia stage of Alzheimer's disease (AD), there is an urgent need for reliable, non-invasive biomarkers to predict amyloid pathology. The aim of this study was to assess whether easily obtainable measures from structural MRI, combined with demographic data, cognitive data and apolipoprotein E (APOE) ε4 genotype, can be used to predict amyloid pathology using machine-learning classification. METHODS: We examined 810 subjects with structural MRI data and amyloid markers from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery study, including subjects with normal cognition (CN, n = 337, age 66.5 ± 7.2, 50% female, 27% amyloid positive), mild cognitive impairment (MCI, n = 375, age 69.1 ± 7.5, 53% female, 63% amyloid positive) and AD dementia (n = 98, age 67.0 ± 7.7, 48% female, 97% amyloid positive). Structural MRI scans were visually assessed and Freesurfer was used to obtain subcortical volumes, cortical thickness and surface area measures. We first assessed univariate associations between MRI measures and amyloid pathology using mixed models. Next, we developed and tested an automated classifier using demographic, cognitive, MRI and APOE ε4 information to predict amyloid pathology. A support vector machine (SVM) with nested 10-fold cross-validation was applied to identify a set of markers best discriminating between amyloid positive and amyloid negative subjects. RESULTS: In univariate associations, amyloid pathology was associated with lower subcortical volumes and thinner cortex in AD-signature regions in CN and MCI. The multi-variable SVM classifier provided an area under the curve (AUC) of 0.81 ± 0.07 in MCI and an AUC of 0.74 ± 0.08 in CN. In CN, selected features for the classifier included APOE ε4, age, memory scores and several MRI measures such as hippocampus, amygdala and accumbens volumes and cortical thickness in temporal and parahippocampal regions. In MCI, the classifier including demographic and APOE ε4 information did not improve after additionally adding imaging measures. CONCLUSIONS: Amyloid pathology is associated with changes in structural MRI measures in CN and MCI. An automated classifier based on clinical, imaging and APOE ε4 data can identify the presence of amyloid pathology with a moderate level of accuracy. These results could be used in clinical trials to pre-screen subjects for anti-amyloid therapies

Blood-based biomarkers for Alzheimer disease: mapping the road to the clinic.

Biomarker discovery and development for clinical research, diagnostics and therapy monitoring in clinical trials have advanced rapidly in key areas of medicine - most notably, oncology and cardiovascular diseases - allowing rapid early detection and supporting the evolution of biomarker-guided, precision-medicine-based targeted therapies. In Alzheimer disease (AD), breakthroughs in biomarker identification and validation include cerebrospinal fluid and PET markers of amyloid-β and tau proteins, which are highly accurate in detecting the presence of AD-associated pathophysiological and neuropathological changes. However, the high cost, insufficient accessibility and/or invasiveness of these assays limit their use as viable first-line tools for detecting patterns of pathophysiology. Therefore, a multistage, tiered approach is needed, prioritizing development of an initial screen to exclude from these tests the high numbers of people with cognitive deficits who do not demonstrate evidence of underlying AD pathophysiology. This Review summarizes the efforts of an international working group that aimed to survey the current landscape of blood-based AD biomarkers and outlines operational steps for an effective academic-industry co-development pathway from identification and assay development to validation for clinical use.I recieved an honorarium from Roche Diagnostics for my participation in the advisory panel meeting leading to this pape

Genetic determinants of rates of cognitive decline in preclinical Alzheimer’s Disease

In 2015 the number of people worldwide living with Dementia was 46.8 million, with approximately 50-75% of these cases being clinically defined as Alzheimer’s disease (AD). Despite extensive efforts, clinical trials have so far failed to yield a treatment that successfully addresses the underlying cause of AD. This lack of treatment has been suggested, in part, to be a result of late stage of intervention in current clinical trial design. For this reason, greater focus has been placed on preclinical trials and in turn both the identification of individuals at-risk for AD and, amongst these, those that are expected to decline over the course of a trial. While brain imaging to determine Aβ- amyloid burden has utility in identifying individuals with preclinical AD, further work needs to be conducted to determine what influences rates of change during these early disease stages. Of particular focus is the rate of decline in cognitive performance, as it is the primary outcome measure of efficacy in clinical trials. A number of genetic variants have been associated with cognitive performance, however additional research needs to be conducted to accurately understand the influence that genetic variation has on cognition in preclinical AD. Aims Initially the aim of this thesis was to assess the combined genetic influence of established AD risk genetic variants on preclinical cognitive performance, specifically using AD-risk effect-size weighted polygenic risk scores (PRSs) (Chapter 2). It was then aimed to evaluate the effects on cognitive rates of change in preclinical AD of genes with a priori evidence for association with cognition, both individually (Chapter 3) and then when combined (Chapter 4). The results of the preceding chapters informed the final aim which was to determine a novel method of weighting individual variants in genes associated with AD-risk and/or cognition, for use in a genetic risk score that would improve the prediction of preclinical cognitive rates of change (Chapter 5). Methods All studies presented in this thesis utilised data from the highly characterised Australian Imaging, Biomarkers and Lifestyle Study of Aging (AIBL). The AIBL study is a longitudinal cohort study collecting data at 18-monthly intervals, currently consisting of 7.5 years of follow up. Individuals investigated in this thesis had been Positron Emission Tomography (PET) imaged to determine neocortical amyloid burden. Further, all individuals were classified as Αβhigh or Αβlow based on tracer specific cut offs. In addition, a subset of these samples underwent lumbar puncture for CSF collection at the study baseline, and Aβ42, total-tau and phospho-tau were quantified. Finally, based on the AIBL neuropsychological test battery, three cognitive composites previously developed were calculated for all participants. The cognitive composites investigated were; verbal episodic memory, a statistically driven global cognition composite, and the Pre-Alzheimer’s Cognitive Composite. The AD-risk weighted PRS (Chapter 2) consisted of 22 genetic variants associated with AD classification, and was calculated by weighting individual variants based on their previously published associations with risk for AD. A statistically derived Cognitive Genetic Risk Profile (Cog-GRP), specifically driven by verbal episodic memory, was developed using a decision tree analysis (Chapter 4). Finally, a 27 genetic variant cognition weighted PRS (cwPRS), was developed and tested in a preclinical AD sample (Chapter 5). For the cwPRS, effect sizes for decline in a verbal episodic memory were determined individually for all variants in a reference sample. The resulting effect sizes were then used to calculate the cwPRS for each participant in a test sample (Chapter 5). For both the AD-risk weighted PRS (Chapter 2) and the cwPRS (Chapter 5), PRS calculations were conducted with both the inclusion and exclusion of the major genetic risk factor for, Apolipoprotein E (APOE). In all studies, linear mixed models were used to investigate associations between genetic factors, independent or in combination, and longitudinal rates of cognitive performance. Results In CN older adults the AD-risk weighted PRS, both including and excluding APOE, was positively correlated with brain and blood biomarkers, specifically; brain Aβ burden, CSF total-tau and phospho-tau (Chapter 2). When investigating cognitive performance, specifically in CN Αβhigh participants, significant associations with baseline and longitudinal cognition were only observed in the AD-risk weighted PRS with APOE (Chapter 2). When investigating gene variants previously reported to influence cognition, in CN Αβhigh participants, no independent associations were observed for any variant (Chapter 3). However, in the same sample, after interaction with APOE e4, significant associations were observed for variants in the Kidney Brain Expressed Protein (KIBRA) and Spondin-1 (SPON1) genes (Chapter 3). The combination of variants investigated in Chapter 3, with additional variants, resulted in the development of the Cog-GRP (Chapter 4). The Cog-GRP was able to delineate four groups: APOE ε4+ Risk, APOE ε4+ Resilient, APOE ε4- Risk, APOE ε4- Resilient, with the ε4+ Risk group reporting significantly faster decline in cognition than all other groups (Chapter 4). Finally, a PRS encompassing a combination of AD-risk genes (Chapter 2) and cognitive-risk genes (Chapters 3 and 4), weighted by episodic memory (cwPRS), was reported to be associated with preclinical longitudinal cognitive performance (Chapter 5). Further, these associations were observed irrespective of the presence or absence of APOE in the calculation of the cwPRS (Chapter 5). Conclusions The work presented in this thesis provides an in depth investigation of genetic influences in preclinical AD, particularly on cognitive performance. Importantly, it supports the hypothesis that there is are differences between the genetic architectures of AD-risk and AD progression. The results presented here support the use of combinatory approaches when investigating genetic influence. Finally, reported here is a novel method for PRS weighting, with the ability to predict preclinical cognitive performance in the presence and absence of APOE. Further investigation is required in cohorts with comparable data to the AIBL study, to validate the methods explored in this thesis, allowing for their eventual use in a clinical setting

Diagnostic performance and prediction of clinical progression of plasma phospho-tau181 in the Alzheimer's Disease Neuroimaging Initiative

Whilst cerebrospinal fluid (CSF) and positron emission tomography (PET) biomarkers for amyloid-β (Aβ) and tau pathologies are accurate for the diagnosis of Alzheimer’s disease (AD), their broad implementation in clinical and trial settings are restricted by high cost and limited accessibility. Plasma phosphorylated-tau181 (p-tau181) is a promising blood-based biomarker that is specific for AD, correlates with cerebral Aβ and tau pathology, and predicts future cognitive decline. In this study, we report the performance of p-tau181 in >1000 individuals from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including cognitively unimpaired (CU), mild cognitive impairment (MCI) and AD dementia patients characterized by Aβ PET. We confirmed that plasma p-tau181 is increased at the preclinical stage of Alzheimer and further increases in MCI and AD dementia. Individuals clinically classified as AD dementia but having negative Aβ PET scans show little increase but plasma p-tau181 is increased if CSF Aβ has already changed prior to Aβ PET changes. Despite being a multicenter study, plasma p-tau181 demonstrated high diagnostic accuracy to identify AD dementia (AUC = 85.3%; 95% CI, 81.4–89.2%), as well as to distinguish between Aβ− and Aβ+ individuals along the Alzheimer’s continuum (AUC = 76.9%; 95% CI, 74.0–79.8%). Higher baseline concentrations of plasma p-tau181 accurately predicted future dementia and performed comparably to the baseline prediction of CSF p-tau181. Longitudinal measurements of plasma p-tau181 revealed low intra-individual variability, which could be of potential benefit in disease-modifying trials seeking a measurable response to a therapeutic target. This study adds significant weight to the growing body of evidence in the use of plasma p-tau181 as a non-invasive diagnostic and prognostic tool for AD, regardless of clinical stage, which would be of great benefit in clinical practice and a large cost-saving in clinical trial recruitment

The Alzheimer's Disease Neuroimaging Initiative 3: Continued innovation for clinical trial improvement

INTRODUCTION: The overall goal of the Alzheimer's Disease Neuroimaging Initiative (ADNI) is to validate biomarkers for Alzheimer's disease (AD) clinical trials. ADNI-3, which began on August 1, 2016, is a 5-year renewal of the current ADNI-2 study. METHODS: ADNI-3 will follow current and additional subjects with normal cognition, mild cognitive impairment, and AD using innovative technologies such as tau imaging, magnetic resonance imaging sequences for connectivity analyses, and a highly automated immunoassay platform and mass spectroscopy approach for cerebrospinal fluid biomarker analysis. A Systems Biology/pathway approach will be used to identify genetic factors for subject selection/enrichment. Amyloid positron emission tomography scanning will be standardized using the Centiloid method. The Brain Health Registry will help recruit subjects and monitor subject cognition. RESULTS: Multimodal analyses will provide insight into AD pathophysiology and disease progression. DISCUSSION: ADNI-3 will aim to inform AD treatment trials and facilitate development of AD disease-modifying treatments