Samantha Burnham

Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer's disease: a prospective cohort study

BACKGROUND Similar to most chronic diseases, Alzheimers disease (AD) develops slowly from a preclinical phase into a fully expressed clinical syndrome. We aimed to use longitudinal data to calculate the rates of amyloid β (Aβ) deposition, cerebral atrophy, and cognitive decline. METHODS In this prospective cohort study, healthy controls, patients with mild cognitive impairment (MCI), and patients with AD were assessed at enrolment and every 18 months. At every visit, participants underwent neuropsychological examination, MRI, and a carbon-11-labelled Pittsburgh compound B ((11)C-PiB) PET scan. We included participants with three or more (11)C-PiB PET follow-up assessments. Aβ burden was expressed as (11)C-PiB standardised uptake value ratio (SUVR) with the cerebellar cortex as reference region. An SUVR of 1·5 was used to discriminate high from low Aβ burdens. The slope of the regression plots over 3-5 years was used to estimate rates of change for Aβ deposition, MRI volumetrics, and cognition. We included those participants with a positive rate of Aβ deposition to calculate the trajectory of each variable over time. FINDINGS 200 participants (145 healthy controls, 36 participants with MCI, and 19 participants with AD) were assessed at enrolment and every 18 months for a mean follow-up of 3·8 (95% CI CI 3·6-3·9) years. At baseline, significantly higher Aβ burdens were noted in patients with AD (2·27, SD 0·43) and those with MCI (1·94, 0·64) than in healthy controls (1·38, 0·39). At follow-up, 163 (82%) of the 200 participants showed positive rates of Aβ accumulation. Aβ deposition was estimated to take 19·2 (95% CI 16·8-22·5) years in an almost linear fashion-with a mean increase of 0·043 (95% CI 0·037-0·049) SUVR per year-to go from the threshold of (11)C-PiB positivity (1·5 SUVR) to the levels observed in AD. It was estimated to take 12·0 (95% CI 10·1-14·9) years from the levels observed in healthy controls with low Aβ deposition (1·2 [SD 0·1] SUVR) to the threshold of (11)C-PiB positivity. As AD progressed, the rate of Aβ deposition slowed towards a plateau. Our projections suggest a prolonged preclinical phase of AD in which Aβ deposition reaches our threshold of positivity at 17·0 (95% CI 14·9-19·9) years, hippocampal atrophy at 4·2 (3·6-5·1) years, and memory impairment at 3·3 (2·5-4·5) years before the onset of dementia (clinical dementia rating score 1). INTERPRETATION Aβ deposition is slow and protracted, likely to extend for more than two decades. Such predictions of the rate of preclinical changes and the onset of the clinical phase of AD will facilitate the design and timing of therapeutic interventions aimed at modifying the course of this illness. FUNDING Science and Industry Endowment Fund (Australia), The Commonwealth Scientific and Industrial Research Organisation (Australia), The National Health and Medical Research Council of Australia Program and Project Grants, the Austin Hospital Medical Research Foundation, Victorian State Government, The Alzheimers Drug Discovery Foundation, and the Alzheimers Association.

Intensity of dementia through latent variable modelling (I-DeLV) in the AIBL cohort

69% of Mild Cognitively Impaired and 96% of AD. Conclusions: The described models provide accurate estimations of NAB based on demographic corrected cognitive test scores. If NAB is a predictor for progression to AD and given such models can accurately predict NAB, it follows that this work may lead to an effective and economical screen for early detection of individuals at risk of developing AD, in-turn providing justification for further confirmatory tests (e.g. PET) or to identify suitable participants for intervention or therapeutic trials.

Physical activity and amyloid-β plasma and brain levels: results from the Australian Imaging, Biomarkers and Lifestyle Study of Ageing

Previous studies suggest physical activity improves cognition and lowers Alzheimers disease (AD) risk. However, key AD pathogenic factors that are thought to be influenced by physical activity, particularly plasma amyloid-β (Aβ) and Aβ brain load, have yet to be thoroughly investigated. The objective of this study was to determine if plasma Aβ and amyloid brain deposition are associated with physical activity levels, and whether these associations differed between carriers and non-carriers of the apolipoprotein E (APOE) ɛ4 allele. Five-hundred and forty six cognitively intact participants (aged 60–95 years) from the Australian Imaging, Biomarkers and Lifestyle Study of Ageing (AIBL) were included in these analyses. Habitual physical activity levels were measured using the International Physical Activity Questionnaire (IPAQ). Serum insulin, glucose, cholesterol and plasma Aβ levels were measured in fasting blood samples. A subgroup (n=116) underwent 11C-Pittsburgh compound B (PiB) positron emission tomography (PET) scanning to quantify brain amyloid load. Higher levels of physical activity were associated with higher high density lipoprotein (HDL) (P=0.037), and lower insulin (P<0.001), triglycerides (P=0.019) and Aβ1−42/1−40 ratio (P=0.001). After stratification of the cohort based on APOE ɛ4 allele carriage, it was evident that only non-carriers received the benefit of reduced plasma Aβ from physical activity. Conversely, lower levels of PiB SUVR (standardised uptake value ratio) were observed in higher exercising APOE ɛ4 carriers. Lower plasma Aβ1−42/1−40 and brain amyloid was observed in those reporting higher levels of physical activity, consistent with the hypothesis that physical activity may be involved in the modulation of pathogenic changes associated with AD.

A blood-based predictor for neocortical Aβ burden in Alzheimer’s disease: results from the AIBL study

Dementia is a global epidemic with Alzheimer’s disease (AD) being the leading cause. Early identification of patients at risk of developing AD is now becoming an international priority. Neocortical Aβ (extracellular β-amyloid) burden (NAB), as assessed by positron emission tomography (PET), represents one such marker for early identification. These scans are expensive and are not widely available, thus, there is a need for cheaper and more widely accessible alternatives. Addressing this need, a blood biomarker-based signature having efficacy for the prediction of NAB and which can be easily adapted for population screening is described. Blood data (176 analytes measured in plasma) and Pittsburgh Compound B (PiB)-PET measurements from 273 participants from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study were utilised. Univariate analysis was conducted to assess the difference of plasma measures between high and low NAB groups, and cross-validated machine-learning models were generated for predicting NAB. These models were applied to 817 non-imaged AIBL subjects and 82 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) for validation. Five analytes showed significant difference between subjects with high compared to low NAB. A machine-learning model (based on nine markers) achieved sensitivity and specificity of 80 and 82%, respectively, for predicting NAB. Validation using the ADNI cohort yielded similar results (sensitivity 79% and specificity 76%). These results show that a panel of blood-based biomarkers is able to accurately predict NAB, supporting the hypothesis for a relationship between a blood-based signature and Aβ accumulation, therefore, providing a platform for developing a population-based screen.

Clinical and cognitive trajectories in cognitively healthy elderly individuals with suspected non-Alzheimer's disease pathophysiology (SNAP) or Alzheimer's disease pathology: a longitudinal study

BACKGROUND Brain amyloid β (Aβ) deposition and neurodegeneration have been documented in about 50-60% of cognitively healthy elderly individuals (aged 60 years or older). The long-term cognitive consequences of the presence of Alzheimers disease pathology and neurodegeneration, and whether they have an independent or synergistic effect on cognition, are unclear. We aimed to characterise the long-term clinical and cognitive trajectories of healthy elderly individuals using a two-marker (Alzheimers disease pathology and neurodegeneration) imaging construct. METHODS Between Nov 3, 2006, and Nov 25, 2014, 573 cognitively healthy individuals in Melbourne and Perth, Australia, (mean age 73·1 years [SD 6·2]; 58% women) were enrolled in the Australian Imaging, Biomarker and Lifestyle (AIBL) study. Alzheimers disease pathology (A) was determined by measuring Aβ deposition by PET, and neurodegeneration (N) was established by measuring hippocampal volume using MRI. Individuals were categorised as A(-)N(-), A(+)N(-), A(+)N(+), or suspected non-Alzheimers disease pathophysiology (A(-)N(+), SNAP). Clinical progression, hippocampal volume, standard neuropsychological tests, and domain-specific and global cognitive composite scores were assessed over 6 years of follow-up. Linear mixed effect models and a Cox proportional hazards model of survival were used to evaluate, compare, and contrast the clinical, cognitive, and volumetric trajectories of patients in the four AN categories. FINDINGS 50 (9%) healthy individuals were classified as A(+)N(+), 87 (15%) as A(+)N(-), 310 (54%) as A(-)N(-), and 126 (22%) as SNAP. APOE ε4 was more frequent in participants in the A(+)N(+) (27; 54%) and A(+)N(-) (42; 48%) groups than in the A(-)N(-) (66; 21%) and SNAP groups (23; 18%). The A(+)N(-) and A(+)N(+) groups had significantly faster cognitive decline than the A(-)N(-) group (0·08 SD per year for AIBL-Preclinical AD Cognitive Composite [PACC]; p<0·0001; and 0·25; p<0·0001; respectively). The A (+)N(+) group also had faster hippocampal atrophy than the A(-)N(-) group (0·04 cm(3) per year; p=0·02). The SNAP group generally did not show significant decline over time compared with the A(-)N(-) group (0·03 SD per year [p=0·19] for AIBL-PACC and a 0·02 cm(3) per year increase [p=0·16] for hippocampal volume), although SNAP was sometimes associated with lower baseline cognitive scores (0·20 SD less than A(-)N(-) for AIBL-PACC). Within the follow-up, 24% (n=12) of individuals in the A(+)N(+) group and 16% (n=14) in the A(+)N(-) group progressed to amnestic mild cognitive impairment or Alzheimers disease, compared with 9% (n=11) in the SNAP group. INTERPRETATION Brain amyloidosis, a surrogate marker of Alzheimers disease pathology, is a risk factor for cognitive decline and for progression from preclinical stages to symptomatic stages of the disease, with neurodegeneration acting as a compounding factor. However, neurodegeneration alone does not confer a significantly different risk of cognitive decline from that in the group with neither brain amyloidosis or neurodegeneration. FUNDING CSIRO Flagship Collaboration Fund and the Science and Industry Endowment Fund (SIEF), National Health and Medical Research Council, the Dementia Collaborative Research Centres programme, McCusker Alzheimers Research Foundation, and Operational Infrastructure Support from the Government of Victoria.

Intense physical activity is associated with cognitive performance in the elderly

Numerous studies have reported positive impacts of physical activity on cognitive function. However, the majority of these studies have utilised physical activity questionnaires or surveys, thus results may have been influenced by reporting biases. Through the objective measurement of routine levels of physical activity via actigraphy, we report a significant association between intensity, but not volume, of physical activity and cognitive functioning. A cohort of 217 participants (aged 60–89 years) wore an actigraphy unit for 7 consecutive days and underwent comprehensive neuropsychological assessment. The cohort was stratified into tertiles based on physical activity intensity. Compared with individuals in the lowest tertile of physical activity intensity, those in the highest tertile scored 9%, 9%, 6% and 21% higher on the digit span, digit symbol, Rey Complex Figure Test (RCFT) copy and Rey Figure Test 30-min recall test, respectively. Statistically, participants in the highest tertile of physical activity intensity performed significantly better on the following cognitive tasks: digit symbol, RCFT copy and verbal fluency test (all P<0.05). The results indicate that intensity rather than quantity of physical activity may be more important in the association between physical activity and cognitive function.

Aβ-amyloid and Tau Imaging in Dementia

The introduction of in vivo imaging of Aβ-amyloid (Αβ) pathology more than a decade ago, transformed the assessment of Alzheimer disease (AD) allowing the evaluation of Aβ deposition over time by providing highly accurate, reliable, and reproducible quantitative statements of regional or global Aβ burden in the brain to the extent that Aβ imaging has already been approved for clinical use and is being used for both patient recruitment and outcome measure in current anti-Αβ therapeutic trials. Aβ imaging studies have deepened our insight into Aβ deposition, showing that Aβ accumulation is a slow and protracted process extending for more than two decades before the onset of the clinical phenotype. Although cross-sectional evaluation of Αβ burden does not strongly correlate with cognitive impairment in AD, Αβ burden does correlate with memory impairment and a higher risk for cognitive decline in the aging population and mild cognitive impairment subjects. These associations suggest that Αβ deposition is not a benign process. The recent addition of selective tau imaging will allow to elucidate if these effects are directly associated with Αβ deposition or if they are mediated, in toto or in parte, by tau as it spreads out of the mesial temporal lobe into neocortical association areas. The combination of Aβ and tau imaging studies would likely help elucidate the relationship or interplay between the two pathologic hallmarks of the disease. Longitudinal observations to assess their potential independent or synergistic, sequential or parallel effects on cognition, disease progression, and other disease-specific biomarkers of neurodegeneration would be required to further clarify the respective role of Αβ and tau deposition play in the course of AD.

Influence of BDNF Val66Met on the relationship between physical activity and brain volume

Objective: To investigate the association between habitual physical activity levels and brain temporal lobe volumes, and the interaction with the brain-derived neurotrophic factor (BDNF) Val66Met polymorphism. Methods: This study is a cross-sectional analysis of 114 cognitively healthy men and women aged 60 years and older. Brain volumes quantified by MRI were correlated with self-reported physical activity levels. The effect of the interaction between physical activity and the BDNF Val66Met polymorphism on brain structure volumes was assessed. Post hoc analyses were completed to evaluate the influence of the APOE ε4 allele on any found associations. Results: The BDNF Val66Met polymorphism interacted with physical activity to be associated with hippocampal (β = −0.22, p = 0.02) and temporal lobe (β = −0.28, p = 0.003) volumes. In Val/Val homozygotes, higher levels of physical activity were associated with larger hippocampal and temporal lobe volumes, whereas in Met carriers, higher levels of physical activity were associated with smaller temporal lobe volume. Conclusion: The findings from this study support higher physical activity levels in the potential attenuation of age- and disease-related hippocampal and temporal lobe volume loss in Val/Val homozygotes.

En Attendant Centiloid

Aims: Test the robustness of a linear regression transformation of semiquantitative values from different Aβ tracers into a single continuous scale. Study Design: Retrospective analysis. Place and Duration of Study: PET imaging data acquired in Melbourne and Perth, Australia, between August 2006 and May 2014. Methodology: Aβ imaging in 633 participants was performed with four different radiotracers: flutemetamol (n=267), florbetapir (n=195), florbetaben (n=126) and NAV4694 (n=45). SUVR were generated with the methods recommended for each tracer, and classified as high (Aβ+) or low (Aβ-) based on their respective thresholds. Linear regression transformation based on reported head-to-head comparisons of each tracer with PiB was applied to each tracer result. Each tracer native classification was compared with the classification derived from the transformed data into PiB-like SUVR units (or BeCKeT: Before the Centiloid Kernel Transformation) using 1.50 as a cut-off. Results: Misclassification after transformation to PiB-like SUVR compared to native classification was extremely low with only 3/267 (1.1%) of flutemetamol, 1/195 (0.5%) of florbetapir, 1/45 (2.2%) of NAV4694, and 1/126 (0.8%) of florbetaben cases assigned into the wrong category. When misclassification occurred (<1% of all cases) it was restricted to an extremely narrow margin (±0.02 BeCKeT) around the 1.50 BeCKeT threshold. Conclusion: While a definitive transformation into centesimal units is being established, application of linear regression transformations provide an interim, albeit robust, way of converting results from different Aβ imaging tracers into more familiar PiB-like SUVR units.

Plasma Amyloid-β Levels are Significantly Associated with a Transition Toward Alzheimer's Disease as Measured by Cognitive Decline and Change in Neocortical Amyloid Burden

BACKGROUND We evaluated the utility of longitudinal measures of plasma amyloid-β (Aβ) as a means to identify pre-symptomatic cognitive decline in Alzheimers disease (AD) when coupled to neuroimaging and neuropsychological parameters. METHODS Participants from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study were grouped based upon cognitive change and changes in measurable levels of neocortical amyloid over 36 months. Participants were classified as those who transitioned for cognitive decline and change in neocortical amyloid, those healthy controls that did not transition, and stable AD participants over 36 months. RESULTS Comparisons of plasma Aβ levels between the transition and non-transitional groups showed Aβ1-42 and the Aβ1-42/Aβ1-40 ratio were significantly decreased at baseline (p = 0.008 and p = 0.002, respectively) and at 18 months (p = 0.003 and p = 0.004, respectively). Both measures of neocortical amyloid and two previously published composite scores validated the creation of the novel transitional grouping (p < 0.0001). In addition Aβn-42 performed well as a longitudinal prognostic indicator of transition toward cognitive decline, with a significant decrease in the transition group at the 18 month time point (p = 0.01). CONCLUSION We demonstrated that baseline plasma Aβ1-42 and the Aβ1-42/Aβ1-40 ratio were putative biomarkers indicative of cognitive decline and validated this result using 18 month data. We created a novel transitional grouping and validated this measure using published measures of neocortical amyloid and composite memory scores. These findings suggest that longitudinal plasma Aβ could contribute to a pre-symptomatic biomarker panel for AD.