Australian Imaging , Biomarkers And Lifestyle Research Articles

AlzoSure® Predict, a simple minimally‐invasive blood test to predict the early onset of Alzheimer’s disease before the manifestation of clinical symptoms

AbstractBackgroundOngoing research seeks to identify blood‐based biomarkers able to predict the onset and progression of Alzheimer’s disease (AD). AlzoSure® Predict is a rapid, minimally‐invasive blood test that predicts the early onset of AD up to, at least, 6 years in advance of clinical symptoms.MethodsThe AlzoSure® Predict test, an IP‐LC‐MS/MS based method, was performed to quantify the AZ284® peptide of an unfolded variant of the p53 protein (U‐p53AZ). The data were compared with the neuropsychological diagnosis in a longitudinal cohort of 479 subjects between 60 and 85 years old from the Australian Imaging, Biomarkers, and Lifestyle (AIBL) study. The cohort consists of 237 cognitive normal, including both subjects with no and subjective memory complaints (NMC/SMC) and 98 mild cognitive impairment (MCI), 141 AD, and 3 other dementia patients that were followed‐up every 18 months.ResultsThe data confirms the strong diagnostic value of U‐p53AZ with a sensitivity of 95% for AD, discriminating it from NMC and SMC subjects with an AUC of 99.8% (95% CI: 99.6‐100.0%) and a specificity of 99.6%, and from MCI patients at the early symptomatic stage with an AUC of 95.7% (95% CI: 92.8‐98.5%) and a specificity of 91.8%.In the prognostic setting, U‐p53AZ showed a significantly better performance (P<0.0001) with an AUC of 99.0% (95%CI: 98.2‐99.8%) compared to amyloid b‐PET (Ab‐PET) with an AUC of 83.5% (95% CI: 79.5‐87.5%). Due to unavailability of data, Ab‐PET performance was determined on a subset of 422 subjects. In a multifactorial Cox proportional hazards model, U‐p53AZ proved to be the major contributor and and the best performing, independent predictor for cognitive decline to neuropsychological AD over time. In this model with normalized continuous risk factors, U‐p53AZ was the most significant contributor (P<.0001) with a hazards ratio of 2.92, compared to Aβ‐PET (P<.0155), APOE ε4 (P<.2323), and age (P<.0106).ConclusionThe present study confirms the diagnostic performance of U‐p53AZ for AD and expands its potential into the prognostic field. Additional studies are going to be performed based on both ADRC and ADNI samples to further prove the prognostic power and the utility of the AlzoSure® Predict test.

Suboptimal sleep efficiency and duration predicts rate of accumulation of Aβ‐ Amyloid in cognitively normal older adults

AbstractBackgroundThere is increasing evidence of a bidirectional relationship between suboptimal sleep and brain Aβ‐amyloid (Aβ) accumulation. Suboptimal sleep is suggested to both result from and contribute to the accumulation of brain Aβ. This study tested whether aspects of self‐reported sleep quality predict the longitudinal accumulation of brain Aβ.MethodLinear mixed effect model analyses were conducted on 192 cognitively normal older adults (M = 73.8 years, SD = 5.8, 51.6% female). These individuals, drawn from the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study of Ageing, had self‐reported sleep characteristics assessed at baseline and positron emission tomography‐determined brain Aβ burden measured over a minimum of three AIBL timepoints (range 33.3‐73.9 months). Analyses included random slopes and intercepts, interaction for apolipoprotein E (APOE) ε4 allele status and time, adjusting for sex and baseline age.ResultSleep duration of <5 hours and sleep efficiency of <65% both significantly forecast the longitudinal trajectory of Aβ accumulation in the whole cohort (sleep duration <5 hrs β = 4.70 ± 1.58, p = 0.003; sleep efficiency <65% β = 2.94 ± 0.91, p = 0.001) and in APOE ε4 non‐carriers (sleep duration <5 hrs β = 4.70 ± 1.53, p = 0.003; sleep efficiency <65% β = 2.90 ± 0.87, p = 0.001) but not in APOE ε4 carriers.ConclusionThese findings indicate a role for self‐reported suboptimal sleep efficiency and duration in the accumulation of Alzheimer's disease neuropathology in cognitively normal individuals. Individuals with suboptimal sleep may benefit from an intervention to improve these sleep parameters to lessen future Alzheimer's risk. Our findings also suggest the potential utility of self‐report screening for sleep efficiency and duration as a predictor of risk of Aβ accumulation.

Asymmetry-enhanced attention network for Alzheimer’s diagnosis with structural Magnetic Resonance Imaging

Background and objective:With the aging of the global population becoming severe, Alzheimer’s disease (AD) has become one of the world’s most common senile diseases. Many studies have suggested that the brain’s left–right asymmetry is one of the possible diagnostic landmarks for AD. However, most published approaches to classification problems may not adequately explore the asymmetry between the left and right hemispheres. At the same time, the relationship between asymmetry traits and other classifier features remains understudied. Methods:In this paper, we proposed an asymmetry enhanced attention network (ASEAN) for AD diagnosis that effectively combines the anatomical asymmetry characteristics of the brain to enhance the accuracy and stability of classification tasks. First, we proposed a multi-scale asymmetry feature extraction module (MSAF) that can extract the asymmetry features of the brain from various scales. Second, we proposed an asymmetry refinement module (ARM) that considers the dependency between feature maps to suppress the irrelevant regions of the asymmetric feature maps. In addition, a parameter-free attention module was introduced to infer 4D attention weights and improve the network’s representation capability. Results:The proposed method achieved performance improvements on two databases: Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarkers and Lifestyle (AIBL). For the classification tasks on ADNI, the proposed method achieves 92.1% accuracy, 96.2% sensitivity, and 91.3% specificity on the AD vs. CN (Cognitively Normal) task. Compared with state-of-the-art methods, the proposed method could achieve comparable results. Conclusion:The proposed model can extract long-range left–right brain similarity as complementary information and improve the model’s diagnostic performance. A large number of experiments also support the model’s validity. At the same time, this work provides a valuable reference for other neurological diseases, particularly those that exhibit left–right brain asymmetry during development.

The interaction between physical activity and sleep on cognitive function and brain beta-amyloid in older adults

BackgroundLifestyle factors such as physical activity and optimal sleep are associated with better cognition and lower levels of Alzheimer’s disease (AD) biomarkers, including brain beta-amyloid (Aβ) burden. ObjectiveWe utilised cross-sectional data from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study to determine whether self-reported physical activity (measured via the International Physical Activity Questionnaire) moderates the relationship between self-reported sleep (measured via the Pittsburgh Sleep Quality Index), cognition, and brain Aβ. MethodsParticipants were 349 community-dwelling cognitively normal older adults (75.3 ± 5.7 years), all of whom underwent comprehensive cognitive assessment. Data from a subset of participants (n = 201) were used for analyses with brain Aβ burden (measured by positron emission tomography) as the outcome. ResultPhysical activity moderated the relationship between sleep duration and episodic memory (β = −0.10, SE =0.03, p = .005), and sleep efficiency and episodic memory (β = −0.09, SE =0.04, p = .011), such that greater amounts of physical activity mitigated the impact of suboptimal sleep duration and efficiency on episodic memory. Physical activity also moderated the relationship between sleep duration and brain Aβ (β = −0.13, SE =0.06, p = .031), and overall sleep quality and brain Aβ (β = 0.13, SE =0.06, p = .027). ConclusionOur findings suggest that physical activity may play an important role in the relationship between sleep and cognitive function, and brain Aβ.

Assessment of a polygenic hazard score for the onset of pre-clinical Alzheimer’s disease

BackgroundWith a growing number of loci associated with late-onset (sporadic) Alzheimer’s disease (AD), the polygenic contribution to AD is now well established. The development of polygenic risk score approaches have shown promising results for identifying individuals at higher risk of developing AD, thereby facilitating the development of preventative and therapeutic strategies. A polygenic hazard score (PHS) has been proposed to quantify age-specific genetic risk for AD. In this study, we assessed the predictive power and transferability of this PHS in an independent cohort, to support its clinical utility.ResultsUsing genotype and imaging data from 780 individuals enrolled in the Australian Imaging, Biomarkers and Lifestyle (AIBL) study, we investigated associations between the PHS and several AD-related traits, including 1) cross-sectional Aβ-amyloid (Aβ) deposition, 2) longitudinal brain atrophy, 3) longitudinal cognitive decline, 4) age of onset. Except in the cognitive domain, we obtained results that were consistent with previously published findings. The PHS was associated with increased Aβ burden, faster regional brain atrophy and an earlier age of onset.ConclusionOverall, the results support the predictive power of a PHS, however, with only marginal improvement compared to apolipoprotein E alone.

Non-standard pipeline without MRI has replicability in computation of Centiloid scale values for PiB and 18F-labeled amyloid PET tracers

The magnetic resonance imaging (MRI)-less non-standard pipeline for amyloid positron emission tomography (PET) published by Bourgeat et al., in 2018 calculates Centiloid scale values that are highly consistent with those computed using the standard pipeline. The purpose of this study was to demonstrate that the non-standard pipeline can compute Centiloid scale values in high agreement with the standard pipeline when using different datasets of amyloid PET tracers in our local computer environment.PET images of 11C-Pittsburgh compound B (11C-PiB), 18F-florbetapir, 18F-flutemetamol, 18F-florbetaben, and 18F-NAV4694 from the calibration dataset were processed using both the standard and non-standard pipelines, and the computed cortical standardized uptake value ratio (SUVr) value was converted to the Centiloid scale value using the method described by Klunk et al., in 2015. The conversion equations from the SUVr to Centiloid scale values for each tracer were obtained during this process. Using these equations, we compared the Centiloid scale values obtained using the standard and non-standard pipelines using the validation datasets of each tracer from the Japanese Alzheimer’s Disease Neuroimaging Initiative (J-ADNI), Alzheimer’s Disease Neuroimaging Initiative (ADNI), and Australian Imaging Biomarkers and Lifestyle (AIBL).In the calibration datasets, there was high agreement (R2 > 0.97) and slight bias between the Centiloid scale values calculated by the non-standard and standard pipelines for all tracers. Despite relatively little NAV4694 data in the validation datasets, there was high agreement between the Centiloid scale values calculated using the non-standard and standard pipelines for all tracers. The bias for florbetaben and NAV4694 using the non-standard pipeline was 1.6% underestimation and 3.3% overestimation, respectively; these values were smaller than those reported by Bourgeat et al. Analysis of outliers also suggested that the non-standard pipeline might be vulnerable to anatomical anomalies. Given the slight variance of the Centiloid scale in young controls, flutemetamol and NAV4694 might be suitable tracers for the non-standard pipelines.This study demonstrates the replicability of the non-standard pipelines across computing environments, datasets, scanners, and tracers. When MRI is not available, the non-standard pipeline may provide information to aid in visual assessment of amyloid PET.

A Conformational Variant of p53 (U-p53AZ) as Blood-Based Biomarker for the Prediction of the Onset of Symptomatic Alzheimer’s Disease

BackgroundOngoing research seeks to identify blood-based biomarkers able to predict onset and progression of Alzheimer’s disease (AD).ObjectiveThe unfolded conformational variant of p53 (U-p53AZ), previously observed in AD individuals, was evaluated in plasma samples from individuals participating in the Australian Imaging, Biomarkers and Lifestyle (AIBL) cohort for diagnostic and prognostic assessment, validated on a neuropsychological-based diagnosis, over the course of six years.DesignRetrospective Longitudinal Prognostic biomarker study.SettingSingle-center study based on the AIBL cohort.Participants482 participants of the AIBL cohort, aged 60–85 years, without uncontrolled diabetes, vascular disease, severe depression or psychiatric illnesses.MeasurementsThe AlzoSure® Predict test, consisting of immunoprecipitation (IP) followed by liquid chromatography (LC) tandem mass spectrometry (MS/MS), was performed to quantify the AZ 284® peptide as readout of U-p53AZ and compared with an independent neuropsychological diagnosis. The amyloid load via amyloid β-positron emission tomography (Aβ-PET) and supporting clinical information were included where possible.ResultsU-p53AZ diagnostic and prognostic performance was assessed in both time-independent and time-dependent (36, 72 and 90 months following initial sampling) analyses. Prognostic performance of Aβ-PET and survival analyses with different risk factors (gender, Aβ-PET and APOE ε4 allele status) were also performed. U-p53AZ differentiated neuropsychologically graded AD from non-AD samples, and its detection at intermediate/high levels precisely identified present and future symptomatic AD. In both time-independent and time-dependent prognostic analyses U-p53AZ achieved area under the curve (AUC) >98%, significantly higher than Aβ-PET AUCs (between 84% and 93%, P respectively <0.0001 and <0.001). As single factor, U-p53AZ could clearly determine the risk of AD neuropsychological diagnosis over time (low versus intermediate/high U-p53AZ hazard ratio=2.99). Proportional hazards regression analysis identified U-p53AZ levels as a major independent predictor of AD onset.ConclusionsThese findings support use of U-p53AZ as blood-based biomarker predicting whether individuals would reach neuropsychologically-defined AD within six years prior to AD diagnosis. Integration of U-p53AZ in screening processes could support refined participant stratification for interventional studies.

Comprehensive analysis of epigenetic clocks reveals associations between disproportionate biological ageing and hippocampal volume

The concept of age acceleration, the difference between biological age and chronological age, is of growing interest, particularly with respect to age-related disorders, such as Alzheimer’s Disease (AD). Whilst studies have reported associations with AD risk and related phenotypes, there remains a lack of consensus on these associations. Here we aimed to comprehensively investigate the relationship between five recognised measures of age acceleration, based on DNA methylation patterns (DNAm age), and cross-sectional and longitudinal cognition and AD-related neuroimaging phenotypes (volumetric MRI and Amyloid-β PET) in the Australian Imaging, Biomarkers and Lifestyle (AIBL) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Significant associations were observed between age acceleration using the Hannum epigenetic clock and cross-sectional hippocampal volume in AIBL and replicated in ADNI. In AIBL, several other findings were observed cross-sectionally, including a significant association between hippocampal volume and the Hannum and Phenoage epigenetic clocks. Further, significant associations were also observed between hippocampal volume and the Zhang and Phenoage epigenetic clocks within Amyloid-β positive individuals. However, these were not validated within the ADNI cohort. No associations between age acceleration and other Alzheimer’s disease-related phenotypes, including measures of cognition or brain Amyloid-β burden, were observed, and there was no association with longitudinal change in any phenotype. This study presents a link between age acceleration, as determined using DNA methylation, and hippocampal volume that was statistically significant across two highly characterised cohorts. The results presented in this study contribute to a growing literature that supports the role of epigenetic modifications in ageing and AD-related phenotypes.

Assessing clinical progression from subjective cognitive decline to mild cognitive impairment with incomplete multi-modal neuroimages.

Accurately assessing clinical progression from subjective cognitive decline (SCD) to mild cognitive impairment (MCI) is crucial for early intervention of pathological cognitive decline. Multi-modal neuroimaging data such as T1-weighted magnetic resonance imaging (MRI) and positron emission tomography (PET), help provide objective and supplementary disease biomarkers for computer-aided diagnosis of MCI. However, there are few studies dedicated to SCD progression prediction since subjects usually lack one or more imaging modalities. Besides, one usually has a limited number (e.g., tens) of SCD subjects, negatively affecting model robustness. To this end, we propose a Joint neuroimage Synthesis and Representation Learning (JSRL) framework for SCD conversion prediction using incomplete multi-modal neuroimages. The JSRL contains two components: 1) a generative adversarial network to synthesize missing images and generate multi-modal features, and 2) a classification network to fuse multi-modal features for SCD conversion prediction. The two components are incorporated into a joint learning framework by sharing the same features, encouraging effective fusion of multi-modal features for accurate prediction. A transfer learning strategy is employed in the proposed framework by leveraging model trained on the Alzheimer's Disease Neuroimaging Initiative (ADNI) with MRI and fluorodeoxyglucose PET from 863 subjects to both the Chinese Longitudinal Aging Study (CLAS) with only MRI from 76 SCD subjects and the Australian Imaging, Biomarkers and Lifestyle (AIBL) with MRI from 235 subjects. Experimental results suggest that the proposed JSRL yields superior performance in SCD and MCI conversion prediction and cross-database neuroimage synthesis, compared with several state-of-the-art methods.

Plasma p-tau181/Aβ1-42 ratio predicts Aβ-PET status and correlates with CSF-p-tau181/Aβ1-42 and future cognitive decline.

In Alzheimer's disease (AD), plasma amyloid beta (Aβ)1-42 and phosphorylated tau (p-tau) predict high amyloid status from Aβ positron emission tomography (PET); however, the extent to which combination of these plasma assays can predict remains unknown. Prototype Simoa assays were used to measure plasma samples from participants who were either cognitively normal (CN) or had mild cognitive impairment (MCI)/AD in the Australian Imaging, Biomarkers and Lifestyle (AIBL) study. The p-tau181/Aβ1-42 ratio showed the best prediction of Aβ-PET across all participants (area under the curve [AUC] = 0.905, 95% confidence interval [CI]: 0.86-0.95) and in CN (AUC = 0.873; 0.80-0.94), and symptomatic (AUC = 0.908; 0.82-1.00) adults. Plasma p-tau181/Aβ1-42 ratio correlated with cerebrospinal fluid (CSF) p-tau181 (Elecsys, Spearman's ρ = 0.74, P < 0.0001) and predicted abnormal CSF Aβ (AUC = 0.816; 0.74-0.89). The p-tau181/Aβ1-42 ratio also predicted future rates of cognitive decline assessed by AIBL Preclinical Alzheimer Cognitive Composite or Clinical Dementia Rating Sum of Boxes (P < 0.0001). Plasma p-tau181/Aβ1-42 ratio predicted both Aβ-PET status and cognitive decline, demonstrating potential as both a diagnostic aid and as a screening and prognostic assay for preclinical AD trials.

Higher coffee consumption is associated with slower cognitive decline and Aβ‐amyloid accumulation over 126 months: Data from the AIBL study

AbstractBackgroundWorldwide, coffee is one of the most popular beverages consumed. Several studies have suggested a protective role of coffee, including reduced risk of Alzheimer’s disease (AD). However, there is limited longitudinal data available in cohorts of older adults reporting associations of coffee intake with cognitive decline, in distinct domains, and investigating the neuropathological mechanisms underpinning these associations.MethodThe aim of the current study was to investigate the relationship between self‐reported baseline coffee intake (mean = 280 ± 323 g/day) and cognitive decline assessed using a comprehensive neuropsychological battery, over 126 months, in 227 cognitively normal individuals from the Australian Imaging, Biomarkers, and Lifestyle (AIBL) study. We also sought to investigate the relationship between coffee intake and cerebral Aβ‐amyloid accumulation and brain volumes in a subset of individuals (n=60; and n=51, respectively) over 126 months.ResultHigher baseline coffee consumption was associated with slower cognitive decline in executive function, attention, and the AIBL Preclinical AD Cognitive Composite (PACC; shown to reliably measure the first signs of cognitive decline in at‐risk cognitively normal populations) over 126 months. Higher baseline coffee consumption was also associated with slower Aβ‐amyloid accumulation over 126 months, and lower risk of transitioning from ‘negative’ Aβ‐amyloid status to ‘moderate’, and ‘very high’ Aβ‐amyloid burden over the same time period. There were no associations between coffee intake and atrophy in total grey matter, white matter, or hippocampal volume.ConclusionOur results further support the hypothesis that coffee intake may be a protective factor against AD, with increased coffee consumption reducing cognitive decline potentially by slowing cerebral Aβ‐amyloid accumulation, and thus attenuating the associated neurotoxicity from Aβ‐amyloid‐mediated oxidative stress and inflammatory processes. Further investigation is required to evaluate how coffee intake could be incorporated as one modifiable lifestyle factor aimed at delaying AD onset.

Classification of AD/MCI/HC based on amyloid‐PET using Random Forest Ensemble

AbstractBackgroundPositron emission topography (PET) and magnetic resonance imaging (MRI) are two common in‐vivo techniques for supporting clinical diagnosis of dementia. The correlated neuropathological changes including amyloid‐beta (Aβ) deposition, and cortical atrophy may have the potential to predict conversion from cognitively normal elderly adults (HC), to mild cognitive impairment (MCI) and eventually Alzheimer’s disease (AD). In order to overcome the complex interactions among these biomarker datasets, random forest (RF) as one of the machine learning algorithms is usually used in data classification. In current study, we aim at evaluating the accuracy of RF model in distinguishing HC, MCI from AD and the importance of various neuropathological features in selection.MethodThree cohorts were included in our study. We recruited 94 AD, 82 MCI and 85 HC from GAAIN (The Global Alzheimer’s Association Interactive Network) database, AIBL (Australian imaging, biomarkers and lifestyle) database, and our memory clinic database. Based on Centiloid pipeline, we processed the co‐registered MRI and PET images of each subject. Additionally, to compare quantitative amyloid load across different tracers, we converted SUVR (Standardized Uptake Value Ratio) values into standard Centiloid units. RF algorithm was performed on Python via scikit‐learn package. Finally, 122 regional volumes, 68 regional cortical thicknesses, 16 small regional amyloid distribution and 1 global amyloid load were input as features.ResultIn Table 1, the AUC value was highest (AUC=0.82) in the classification between HC and AD, intermediate (AUC=0.78) between HC and MCI and lowest (AUC=0.65) between AD and MCI. In each binary classification, sensitivity‐71%, specificity‐85%, accuracy‐78%; sensitivity‐88%, specificity‐76%, accuracy‐81% and sensitivity‐86%, specificity‐44%, accuracy‐66% were achieved in differentiating MCI from HC, AD from HC and AD from MCI respectively. For importance ranking of features within the model, 6 features of regional cortical thickness and 4 features of regional volume were found in the classification between HC and MCI, while all 10 essential features belong to regional Aβ ‐ROI in classification between HC and AD as well as MCI and AD (Table 2).ConclusionRandom forest model using regional volume, regional cortical thickness and amyloid load (in Centiloid unit) had moderate to high accuracy in differentiating AD from HC and MCI.

How lifestyle shapes the brain: Associations between physical activity, sleep, beta‐amyloid and cognitive function in older adults

AbstractBackgroundLifestyle factors such as sleep and physical activity influence risk of cognitive decline and dementia. Higher habitual physical activity and optimal sleep are associated with better cognitive function and lower levels of Alzheimer’s disease biomarkers, including beta‐amyloid (Aß). There is currently a poor understanding of how physical activity may influence the relationship between sleep and cognition, and whether exercise and sleep interact to influence cognition and Aß. Developing this understanding is crucial for creating effective lifestyle interventions for dementia prevention.MethodData from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study were utilised to determine whether self‐reported physical activity moderates the cross‐sectional relationship between self‐reported sleep parameters (duration, efficiency, latency, disturbance, quality), cognitive function (episodic memory, attention and processing speed, executive function), and brain Aß (quantified by amyloid positron emission tomography, using the Centiloid scale). Analyses were adjusted for age, sex, APOE ε4 carriage, mood, premorbid intelligence, and collection point. Participants were 404 community‐dwelling cognitively normal older adults aged 60 and above (75.3 5.7 years). Data from a subset of participants (n = 220, aged 75.2 5.6 years) were used for analyses with AB as the outcome.ResultPhysical activity moderated the relationship between sleep duration and episodic memory (ß = ‐.09, SE = .03, p = .005), and sleep efficiency and episodic memory (ß = ‐.08, SE = .03, p = .016). Physical activity moderated the relationship between sleep duration and A® (ß = ‐.12, SE = .06, p = .036), and sleep quality and Aß (ß = .12, SE = .06, p = .029).ConclusionPhysical activity may play an important role in the relationship between sleep and cognitive function, and sleep and brain Aß. Future longitudinal and intervention studies in this area are crucial for informing interventions for dementia prevention.

Higher Coffee Consumption Is Associated With Slower Cognitive Decline and Less Cerebral Aβ-Amyloid Accumulation Over 126 Months: Data From the Australian Imaging, Biomarkers, and Lifestyle Study.

Background: Worldwide, coffee is one of the most popular beverages consumed. Several studies have suggested a protective role of coffee, including reduced risk of Alzheimer’s disease (AD). However, there is limited longitudinal data from cohorts of older adults reporting associations of coffee intake with cognitive decline, in distinct domains, and investigating the neuropathological mechanisms underpinning any such associations.Methods: The aim of the current study was to investigate the relationship between self-reported habitual coffee intake, and cognitive decline assessed using a comprehensive neuropsychological battery in 227 cognitively normal older adults from the Australian Imaging, Biomarkers, and Lifestyle (AIBL) study, over 126 months. In a subset of individuals, we also investigated the relationship between habitual coffee intake and cerebral Aβ-amyloid accumulation (n = 60) and brain volumes (n = 51) over 126 months.Results: Higher baseline coffee consumption was associated with slower cognitive decline in executive function, attention, and the AIBL Preclinical AD Cognitive Composite (PACC; shown reliably to measure the first signs of cognitive decline in at-risk cognitively normal populations), and lower likelihood of transitioning to mild cognitive impairment or AD status, over 126 months. Higher baseline coffee consumption was also associated with slower Aβ-amyloid accumulation over 126 months, and lower risk of progressing to “moderate,” “high,” or “very high” Aβ-amyloid burden status over the same time-period. There were no associations between coffee intake and atrophy in total gray matter, white matter, or hippocampal volume.Discussion: Our results further support the hypothesis that coffee intake may be a protective factor against AD, with increased coffee consumption potentially reducing cognitive decline by slowing cerebral Aβ-amyloid accumulation, and thus attenuating the associated neurotoxicity from Aβ-amyloid-mediated oxidative stress and inflammatory processes. Further investigation is required to evaluate whether coffee intake could be incorporated as a modifiable lifestyle factor aimed at delaying AD onset.

Fifteen Years of the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study: Progress and Observations from 2,359 Older Adults Spanning the Spectrum from Cognitive Normality to Alzheimer's Disease.

Background:The Australian Imaging, Biomarkers and Lifestyle (AIBL) Study commenced in 2006 as a prospective study of 1,112 individuals (768 cognitively normal (CN), 133 with mild cognitive impairment (MCI), and 211 with Alzheimer’s disease dementia (AD)) as an ‘Inception cohort’ who underwent detailed ssessments every 18 months. Over the past decade, an additional 1247 subjects have been added as an ‘Enrichment cohort’ (as of 10 April 2019).Objective:Here we provide an overview of these Inception and Enrichment cohorts of more than 8,500 person-years of investigation.Methods:Participants underwent reassessment every 18 months including comprehensive cognitive testing, neuroimaging (magnetic resonance imaging, MRI; positron emission tomography, PET), biofluid biomarkers and lifestyle evaluations.Results:AIBL has made major contributions to the understanding of the natural history of AD, with cognitive and biological definitions of its three major stages: preclinical, prodromal and clinical. Early deployment of Aβ-amyloid and tau molecular PET imaging and the development of more sensitive and specific blood tests have facilitated the assessment of genetic and environmental factors which affect age at onset and rates of progression.Conclusion:This fifteen-year study provides a large database of highly characterized individuals with longitudinal cognitive, imaging and lifestyle data and biofluid collections, to aid in the development of interventions to delay onset, prevent or treat AD. Harmonization with similar large longitudinal cohort studies is underway to further these aims.

Core Alzheimer\u2019s disease cerebrospinal fluid biomarker assays are not affected by aspiration or gravity drip extraction methods

BackgroundCSF biomarkers are well-established for routine clinical use, yet a paucity of comparative assessment exists regarding CSF extraction methods during lumbar puncture. Here, we compare in detail biomarker profiles in CSF extracted using either gravity drip or aspiration.MethodsBiomarkers for β-amyloidopathy (Aβ1–42, Aβ1–40), tauopathy (total tau), or synapse pathology (BACE1, Neurogranin Trunc-p75, α-synuclein) were assessed between gravity or aspiration extraction methods in a sub-population of the Australian Imaging, Biomarkers and Lifestyle (AIBL) study (cognitively normal, N = 36; mild cognitive impairment, N = 8; Alzheimer’s disease, N = 6).ResultsHigh biomarker concordance between extraction methods was seen (concordance correlation > 0.85). Passing Bablock regression defined low beta coefficients indicating high scalability.ConclusionsLevels of these commonly assessed CSF biomarkers are not influenced by extraction method. Results of this study should be incorporated into new consensus guidelines for CSF collection, storage, and analysis of biomarkers.

Androgen receptor CAG repeat length as a moderator of the relationship between free testosterone levels and cognition

Age-related decrease in testosterone levels is a potential risk factor for cognitive decline in older men. However, observational studies and clinical trials have reported inconsistent results on the effects of testosterone on individual cognitive domains. Null findings may be attributed to factors that studies have yet to consider. In particular, individual variations in polyglutamine (CAG) length in the androgen receptor (AR) gene could alter androgenic activity in brain regions associated with cognitive processes including memory and executive functions. However, the role of AR CAG repeat length as a moderator of the relationship between testosterone levels and cognition has not been investigated. Therefore, we aimed to examine the relationship between baseline calculated free testosterone (cFT) levels, change in cFT levels over 18 months and CAG repeat length on cognitive performance in memory, executive function, language, attention and processing speed domains. These relationships were examined in 304 cognitively normal older male participants of the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study of Ageing. In the attention and processing speed domain, a short CAG repeat length appears to exacerbate the effects of low baseline cFT levels that are also lower than expected at follow-up. These results highlight that individual variations in AR CAG repeat length should be considered in future studies and clinical trials that examine the complex relationship between testosterone and cognition.

Relevance of a Truncated PRESENILIN 2 Transcript to Alzheimer's Disease and Neurodegeneration.

The PRESENILIN genes (PSEN1, PSEN2) encoding for their respective proteins have critical roles in many aspects of Alzheimer's disease (AD) pathogenesis. The PS2V transcript of PSEN2 encodes a truncated protein and is upregulated in AD brains; however, its relevance to AD and disease progression remains to be determined. Assess transcript levels in postmortem AD and non-AD brain tissue and in lymphocytes collected under the Australian Imaging Biomarker and Lifestyle (AIBL) study. Full length PSEN2 and PS2V transcript levels were assessed by quantitative digital PCR in postmortem brain tissue (frontal cortex and hippocampus) from control, AD, frontotemporal dementia (FTD), and Lewy body dementia (LBD). Transcript levels were also assessed in lymphocytes obtained from the Perth subset of the AIBL study (n = 160). Linear regression analysis was used to assess correlations between transcript copy number and brain volume and neocortical amyloid load. PS2V levels increased in AD postmortem brain but PS2V was also present at significant levels in FTD and LBD brains. PS2V transcript was detected in lymphocytes and PS2V/PSEN2 ratios were increased in mild cognitive impairment (p = 0.024) and AD (p = 0.019) groups compared to control group. Increased ratios were significantly correlated with hippocampal volumes only (n = 62, β= -0.269, p = 0.03). Taken together, these results suggest that PS2V may be a marker of overall neurodegeneration.

Visual Memory Deficits in Middle-Aged APOEɛ4 Homozygotes Detected Using Unsupervised Cognitive Assessments.

The apolipoprotein E (APOE) ɛ4 allele is associated with dose-response effects on cognitive dysfunction and dementia risk in older adults. However, its effects on cognition in middle-aged adults remains unclear. We examined effects of ɛ4 heterozygosity and homozygosity on objective and subjective cognition in middle-aged adults enrolled in the Healthy Brain Project (HBP) and in older adults from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study. HBP participants (1,000 non-carriers; 450 ɛ4 heterozygotes; 50 ɛ4 homozygotes) completed unsupervised assessments of the Cogstate Brief Battery (CBB), ratings of subjective cognitive function and provided a saliva sample. AIBL cognitively normal participants (650 non-carriers; 204 ɛ4 heterozygotes; 31 ɛ4 homozygotes) completed in-person assessments of the CBB, ratings of subjective cognitive function and provided a blood sample. Greater memory impairment was observed in middle-aged ɛ4 homozygotes compared with ɛ4 heterozygotes and non-carriers. When data from middle-aged (HBP) and older (AIBL) adults were pooled, the effect of ɛ4 homozygosity and memory impairment increased with age. In both middle-aged and older adults, ɛ4 heterozygotes did not differ from non-carriers on any measure of objective or subjective cognition. Memory impairment in ɛ4 homozygotes is evident in adults aged 50-60 years, and this can be detected through unsupervised cognitive assessments. The effect of ɛ4 homozygosity increases with older age. APOEɛ4 homozygosity has a negative impact on memory as early as midlife, but due to the subtle magnitude of effect, our findings support the necessity of online platforms in large cohorts to assess these complex relationships.

BDNF VAL66MET polymorphism and memory decline across the spectrum of Alzheimer's disease

The brain-derived neurotrophic factor (BDNF) Val66Met (rs6265) polymorphism has been shown to moderate the extent to which memory decline manifests in preclinical Alzheimer's disease (AD). To date, no study has examined the relationship between BDNF and memory in individuals across biologically confirmed AD clinical stages (i.e., Aβ+). We aimed to understand the effect of BDNF on episodic memory decline and clinical disease progression over 126 months in individuals with preclinical, prodromal and clinical AD. Participants enrolled in the Australian Imaging, Biomarkers and Lifestyle (AIBL) study who were Aβ + (according to positron emission tomography), and cognitively normal (CN; n=238), classified as having mild cognitive impairment (MCI; n=80), or AD (n=66) were included in this study. Cognition was evaluated at 18 month intervals using an established episodic memory composite score over 126 months. We observed that in Aβ + CNs, Met66 was associated with greater memory decline with increasing age and were 1.5 times more likely to progress to MCI/AD over 126 months. In Aβ + MCIs, there was no effect of Met66 on memory decline or on disease progression to AD over 126 months. In Aβ + AD, Val66 homozygotes showed greater memory decline, while Met66 carriers performed at a constant and very impaired level. Our current results illustrate the importance of time and disease severity to clinicopathological models of the role of BDNF Val66Met in memory decline and AD clinical progression. Specifically, the effect of BDNF on memory decline is greatest in preclinical AD and reduces as AD clinical disease severity increases.