Understanding preclinical dementia : early detection of dementia through cognitive and biological markers

Dementia is becoming a growing healthcare crisis, therefore identifying individuals at risk or in the earliest stages of dementia is essential if prevention or disease modification is to be achieved. The objective of this thesis was to examine cognitive performance and decline during the preclinical phase and explore the ability of cognitive and biological markers to identify those at risk of future dementia. Data from a population-based longitudinal study, SNAC-K, were used to investigate this aim. Study I examined the ability of neuropsychological tests, genetics, and structural MRI volumes to predict dementia six years later. Models were systematically created to identify the best combinations for prediction. A model containing all three modalities: hippocampal volume, a task of category fluency, presence of an APOE ɛ4 allele, white-matter hyperintensities volume, and a task of general knowledge, displayed the most predictive value (AUC=.924; C.I=.883–.965). However, this model did not significantly improve predictive value over one containing only cognitive and genetic markers, suggesting that minor increases in predictivity should be weighed against the costs of additional tests. Study II investigated the benefit of DTI, alongside neuropsychological tests, genetics, and brain volume markers in predicting future dementia. MD values for tracts CHC, CS, FMAJ, and IFOF (AUC=.837– .862) and the FA IFOF latent factor (AUC=.839) were significantly associated with dementia at six years. A final model consisting of a measure of perceptual speed, hippocampal volume, and MD of the FMAJ tract was created with the highest predictive value (AUC=.911). Assessment of microstructural white matter integrity via DTI was associated with future dementia but the additional benefit when combined with other markers was relatively small. Study III narrowed its focus to the ability of cognitive markers alone and the effect of modifying factors (age, sex, education, the presence of an ɛ4 allele, AD–only dementia, and time to diagnosis) on identifying those at risk of dementia. The most predictive model, consisting of category fluency, word recall, and pattern comparison, achieved good prediction values (AUC=.913) for dementia six years later. Tests in the domains of category fluency, episodic memory, and perceptual speed were, in general, good predictors across all subgroups and up to 6 years before a dementia diagnosis. However, cognitive tests became increasingly unreliable at predicting dementia beyond that time. Study IV explored the trajectories of cognitive decline over a 12-year period during the preclinical stage of dementia, before examining the ability of early cognitive decline in identifying those with increased likelihood of future dementia. Persons in the preclinical phase showed increased rate of decline in all cognitive domains compared to those who did not develop dementia (β:-.07 to -.11), this difference was particularly noticeable closer to diagnosis. Those classified as fast decliners for 3 or more cognitive tests demonstrated the highest risk of dementia (HR: 3.38, CI: 1.91-6.01). Although, changes in early rates of decline were small and rates of decline may be more predictive closer to diagnosis. Collectively, these studies confirm a long preclinical period in dementia development, which allows for the use of a wide range of markers (cognitive, genetic, MRI, and DTI) capable of identifying those at high risk of dementia. The ability of these markers to predict future dementia is increased through combining within and between modalities

Blood and cerebrospinal fluid biomarkers for Alzheimer’s disease: from clinical to preclinical cohorts

Dementia is a major contributor to global morbidity, mortality and costs associated with health and social care. Alzheimer’s disease (AD) is a common pathology culminating in dementia, but it has a preclinical phase of one to two decades, with early brain deposition of amyloid and tau, followed by synaptic and neuronal degeneration. Early detection during the preclinical phase of AD might enable disease-modifying therapies to be applied during a window of opportunity in which they would be more likely to work. Currently the main biomarkers of AD pathology are neuroimaging markers, which can be costly, or cerebrospinal fluid markers, which require invasive sampling. Blood biomarkers would be relatively less invasive and could be a more cost-effective means for risk stratification, early detection, monitoring progression and measuring response to treatment. The work described here used sensitive assay technology including the Simoa digital immunoassay platform, in large and well-characterised cohorts, to examine candidate blood biomarkers linked to the core AD pathologies of amyloid, tau and neurodegeneration, as specified by the National Institute on Aging and Alzheimer’s Association 2018 research framework. Firstly, experiments on samples from a cognitive clinic cohort established the stability of the blood biomarkers Aβ40, Aβ42, total tau and neurofilament light chain (NFL – a marker of neurodegeneration) to multiple freeze-thaw cycles, and the optimal blood fraction to use for quantifying each of these biomarkers in onward studies. Secondly, an unique large preclinical cohort with life course data (Insight 46, the neuroscience sub-study of 502 individuals from the MRC National Survey of Health and Development; the 1946 British birth cohort) was used to examine the cross-sectional relationships between these blood biomarkers, neuroimaging biomarkers (18F-florbetapir amyloid PET, whole brain and hippocampal volumes, white matter hyperintensity volume and cortical thickness in an AD signature region) and cognitive performance (PACC: preclinical Alzheimer’s composite and its constituents). Through a collaboration with the University of Gothenburg, a novel liquid chromatography-mass spectrometry (LC-MS) method for quantification of plasma amyloid-β species was compared with the commercial Simoa assays in Insight 46. This was the first direct method comparison study of plasma amyloid-β species for the detection of preclinical cerebral amyloid deposition. It showed that the LC-MS method, when combined with age, sex and APOE #-4 carrier status, was able to distinguish PET amyloid status with an optimal (Youden’s cut point) sensitivity of 85.7% and specificity of 72.7%. The Simoa biomarkers of plasma total tau and serum NFL were confirmed to be potentially useful prognostic markers, as lower AD signature cortical thickness was associated with higher plasma total tau and serum NFL, lower whole brain volume was associated with higher plasma total tau, and higher ventricular volume was associated with higher serum NFL. Lower PACC scores were associated with higher serum NFL and lower scores for a paired associative memory test in particular were associated with higher plasma total tau and serum NFL. Thirdly, through a collaboration with Harvard University and the University of California San Diego, a new N-terminal tau biomarker was developed in CSF and plasma that showed good accuracy in distinguishing individuals with symptomatic CSF-defined AD pathology from healthy controls. Taken together, this work has demonstrated the impact of pre-analytical factors on measurements of AD blood biomarkers, validated these biomarkers as indicators of the core pathologies of AD and helped to develop a new tau blood biomarker in AD

EVALUATING THE MICROBIOME TO BOOST RECOVERY FROM STROKE: THE EMBRS STUDY

Accumulating evidence suggests that gut microbes modulate brain plasticity via the bidirectional gut-brain axis and may play a role in stroke rehabilitation. A severely imbalanced microbial community has been shown to occur following stroke, causing a systemic flood of neuro- and immunomodulatory substances due to increased gut permeability and decreased gut motility. Here we measure post-stroke increased gut dysbiosis and how it correlates with gut permeability and subsequent cognitive impairment. We recruited 12 participants with acute stroke, 12 healthy control participants, and 18 participants who had risk factors for stroke, but had not had a stroke. We measured the gut microbiome with whole shotgun sequencing on stool samples. We measured cognitive and emotional health with MRI imaging and the NIH toolbox. We normalized all variables and used linear regression methods to identify gut microbial levels associations with cognitive and emotional assessments. Beta diversity analysis revealed that the bacteria populations of the stroke group were statistically dissimilar from the risk factors and healthy control groups. Relative abundance analysis revealed notable decreases in butyrate-producing microbial taxa. The stroke group had higher levels of the leaky gut marker alpha-1-antitrypsin than the control groups, and roseburia species were negatively correlated with alpha-1-antitrypsin. Several Actinobacteria species were associated with cerebral blood flow and white matter integrity in areas of the brain responsible for language, learning, and memory. Stroke participants scored lower on the picture vocabulary and list sorting tests than those in the control groups. Stroke participants who had higher levels of roseburia performed better on the picture vocabulary task. We found that microbial communities are disrupted in a stroke population. Many of the disrupted bacteria have previously been reported to have correlates to health and disease. This preparatory study will lay the foundation for the development of therapeutics targeting the gut following stroke

Hippocampal volumes are important predictors for memory function in elderly women

<p>Abstract</p> <p>Background</p> <p>Normal aging involves a decline in cognitive function that has been shown to correlate with volumetric change in the hippocampus, and with genetic variability in the APOE-gene. In the present study we utilize 3D MR imaging, genetic analysis and assessment of verbal memory function to investigate relationships between these factors in a sample of 170 healthy volunteers (age range 46–77 years).</p> <p>Methods</p> <p>Brain morphometric analysis was performed with the automated segmentation work-flow implemented in FreeSurfer. Genetic analysis of the APOE genotype was determined with polymerase chain reaction (PCR) on DNA from whole-blood. All individuals were subjected to extensive neuropsychological testing, including the California Verbal Learning Test-II (CVLT). To obtain robust and easily interpretable relationships between explanatory variables and verbal memory function we applied the recent method of conditional inference trees in addition to scatterplot matrices and simple pairwise linear least-squares regression analysis.</p> <p>Results</p> <p>APOE genotype had no significant impact on the CVLT results (scores on long delay free recall, CVLT-LD) or the ICV-normalized hippocampal volumes. Hippocampal volumes were found to decrease with age and a right-larger-than-left hippocampal asymmetry was also found. These findings are in accordance with previous studies. CVLT-LD score was shown to correlate with hippocampal volume. Multivariate conditional inference analysis showed that gender and left hippocampal volume largely dominated predictive values for CVLT-LD scores in our sample. Left hippocampal volume dominated predictive values for females but not for males. APOE genotype did not alter the model significantly, and age was only partly influencing the results.</p> <p>Conclusion</p> <p>Gender and left hippocampal volumes are main predictors for verbal memory function in normal aging. APOE genotype did not affect the results in any part of our analysis.</p

Modelling prognostic trajectories in Alzheimer’s disease

Progression to dementia due to Alzheimer’s Disease (AD) is a long and protracted process that involves multiple pathways of disease pathophysiology. Predicting these dynamic changes has major implications for timely and effective clinical management in AD. There are two reasons why at present we lack appropriate tools to make such predictions. First, a key feature of AD is the interactive nature of the relationships between biomarkers, such as accumulation of β-amyloid -a peptide that builds plaques between nerve cells-, tau -a protein found in the axons of nerve cells- and widespread neurodegeneration. Current models fail to capture these relationships because they are unable to successfully reduce the high dimensionality of biomarkers while exploiting informative multivariate relationships. Second, current models focus on simply predicting in a binary manner whether an individual will develop dementia due to AD or not, without informing clinicians about their predicted disease trajectory. This can result in administering inefficient treatment plans and hindering appropriate stratification for clinical trials. In this thesis, we overcome these challenges by using applied machine learning to build predictive models of patient disease trajectories in the earliest stages of AD. Specifically, to exploit the multi-dimensionality of biomarker data, we used a novel feature generation methodology Partial Least Squares regression with recursive feature elimination (PLSr-RFE). This method applies a hybrid-feature selection and feature construction method that captures co-morbidities in cognition and pathophysiology, resulting in an index of Alzheimer’s disease atrophy from structural MRI. We validated our choice of biomarker and the efficacy of our methodology by showing that the learnt pattern of grey matter atrophy is highly predictive of tau accumulation in an independent sample. Next, to go beyond predicting binary outcomes to deriving individualised prognostic scores of cognitive decline due to AD, we used a novel trajectory modelling approach (Generalised Metric Learning Vector Quantization – Scalar projection) that mines multimodal data from large AD research cohorts. Using this approach, we derive individualised prognostic scores of cognitive decline due to AD, revealing interactive cognitive, and biological factors that improve prediction accuracy. Next, we extended our machine learning framework to classify and stage early AD individuals based on future pathological tau accumulation. Our results show that the characteristic spreading pattern of tau in early AD can be predicted by baseline biomarkers, particularly when stratifying groups using multimodal data. Further, we showed that our prognostic index predicts individualised rates of future tau accumulation with high accuracy and regional specificity in an independent sample of cognitively unimpaired individuals. Overall, our work used machine learning to combine continuous information from AD biomarkers predicting pathophysiological changes at different stages in the AD cascade. The approaches presented in this thesis provide an excellent framework to support personalised clinical interventions and guide effective drug discovery trials

Unconventional markers of Alzheimer Disease

Although typically conceptualized as a cortical disease, recent neuropathological and neuroimaging investigations on Alzheimer Disease suggest that other brain structures play an important role in the pathogenesis and progression of this devastating condition. In this thesis, we explored novel markers of Alzheimer Disease beyond the classical cortical pathology measures of amyloid, tau, and neurodegeneration. We focused on the role of white matter abnormalities, assessed with magnetic resonance imaging but also with amyloid positron emission tomography, in predicting early pathologic changes and disease progression, as well as on the added value of cognition to amyloid, tau, and neurodegeneration biomarkers. Overall, we found that these unconventional markers provide useful information to detect the earliest pathological changes of the disease, providing a better understanding of the mechanisms that lead to amyloid deposition and cognitive decline

MRI Measures of Neurodegeneration as Biomarkers of Alzheimer's Disease

Indiana University-Purdue University Indianapolis (IUPUI)Alzheimer’s disease (AD) is the most common age-related neurodegenerative disease. Many researchers believe that an effective AD treatment will prevent the development of disease rather than treat the disease after a diagnosis. Therefore, the development of tools to detect AD-related pathology in early stages is an important goal. In this report, MRI-based markers of neurodegeneration are explored as biomarkers of AD. In the first chapter, the sensitivity of cross-sectional MRI biomarkers to neurodegenerative changes is evaluated in AD patients and in patients with a diagnosis of mild cognitive impairment (MCI), a prodromal stage of AD. The results in Chapter 1 suggest that cross-sectional MRI biomarkers effectively measure neurodegeneration in AD and MCI patients and are sensitive to atrophic changes in patients who convert from MCI to AD up to 1 year before clinical conversion. Chapter 2 investigates longitudinal MRI-based measures of neurodegeneration as biomarkers of AD. In Chapter 2a, measures of brain atrophy rate in a cohort of AD and MCI patients are evaluated; whereas in Chapter 2b, these measures are assessed in a pre-MCI stage, namely older adults with cognitive complaints (CC) but no significant deficits. The results from Chapter 2 suggest that dynamic MRI-based measures of neurodegeneration are sensitive biomarkers for measuring progressive atrophy associated with the development of AD. In the final chapter, a novel biomarker for AD, visual contrast sensitivity, was evaluated. The results demonstrated contrast sensitivity impairments in AD and MCI patients, as well as slightly in CC participants. Impaired contrast sensitivity was also shown to be significantly associated with known markers of AD, including cognitive impairments and temporal lobe atrophy on MRI-based measures. The results of Chapter 3 support contrast sensitivity as a potential novel biomarker for AD and suggest that future studies are warranted. Overall, the results of this report support MRI-based measures of neurodegeneration as effective biomarkers for AD, even in early clinical and preclinical disease stages. Future therapeutic trials may consider utilizing these measures to evaluate potential treatment efficacy and mechanism of action, as well as for sample enrichment with patients most likely to rapidly progress towards AD

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