Florey Department of Neuroscience and Mental Health - Research Publications
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ItemNo Preview AvailableTackling Dementia Together via The Australian Dementia Network (ADNeT): A Summary of Initiatives, Progress and Plans.(IOS Press, 2023)In 2018, the Australian Dementia Network (ADNeT) was established to bring together Australia's leading dementia researchers, people with living experience and clinicians to transform research and clinical care in the field. To address dementia diagnosis, treatment, and care, ADNeT has established three core initiatives: the Clinical Quality Registry (CQR), Memory Clinics, and Screening for Trials. Collectively, the initiatives have developed an integrated clinical and research community, driving practice excellence in this field, leading to novel innovations in diagnostics, clinical care, professional development, quality and harmonization of healthcare, clinical trials, and translation of research into practice. Australia now has a national Registry for Mild Cognitive Impairment and dementia with 55 participating clinical sites, an extensive map of memory clinic services, national Memory and Cognition Clinic Guidelines and specialized screening for trials sites in five states. This paper provides an overview of ADNeT's achievements to date and future directions. With the increase in dementia cases expected over coming decades, and with recent advances in plasma biomarkers and amyloid lowering therapies, the nationally coordinated initiatives and partnerships ADNeT has established are critical for increased national prevention efforts, co-ordinated implementation of emerging treatments for Alzheimer's disease, innovation of early and accurate diagnosis, driving continuous improvements in clinical care and patient outcome and access to post-diagnostic support and clinical trials. For a heterogenous disorder such as dementia, which is now the second leading cause of death in Australia following cardiovascular disease, the case for adequate investment into research and development has grown even more compelling.
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ItemLower levels of soluble β-amyloid precursor protein, but not β-amyloid, in the frontal cortex in schizophrenia(Elsevier, 2024-01)We identified a sub-group (25%) of people with schizophrenia (muscarinic receptor deficit schizophrenia (MRDS)) that are characterised because of markedly lower levels of cortical muscarinic M1 receptors (CHRM1) compared to most people with the disorder (non-MRDS). Notably, bioinformatic analyses of our cortical gene expression data shows a disturbance in the homeostasis of a biochemical pathway that regulates levels of CHRM1. A step in this pathway is the processing of β-amyloid precursor protein (APP) and therefore we postulated there would be altered levels of APP in the frontal cortex from people with MRDS. Here we measure levels of CHRM1 using [3H]pirenzepine binding, soluble APP (sAPP) using Western blotting and amyloid beta peptides (Aβ1-40 and Aβ1-42) using ELISA in the frontal cortex (Brodmann's area 6: BA 6; MRDS = 14, non-MRDS = 14, controls = 14). We confirmed the MRDS cohort in this study had the expected low levels of [3H]pirenzepine binding. In addition, we showed that people with schizophrenia, independent of their sub-group status, had lower levels of sAPP compared to controls but did not have altered levels of Aβ1-40 or Aβ1-42. In conclusion, whilst changes in sAPP are not restricted to MRDS our data could indicate a role of APP, which is important in axonal and synaptic pruning, in the molecular pathology of the syndrome of schizophrenia.
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ItemNo Preview AvailablePrevalence and Associations of Frailty in Preclinical Alzheimer’s Disease(Wiley, 2022-12)Background The prevalence of frailty in asymptomatic Alzheimer’s disease is not clear. In this cohort screened for a preclinical Alzheimer’s disease clinical trial, we aimed to compare the prevalence of frailty between participants with and without elevated amyloid as determined by PET and determine if frailty influences the relationship between amyloid and cognition. Method Analysis of pre‐randomization data from the Anti‐Amyloid Treatment in Asymptomatic Alzheimer’s (A4) Study, a clinical prevention trial of an anti‐amyloid monoclonal antibody in individuals who were cognitively normal with elevated amyloid burden, to determine a cumulative‐deficits Frailty Index (FI). Logistic regression was used to investigate the difference in the prevalence of frailty, defined as a FI greater than 0.25, according to amyloid status (Aβ+/‐), adjusted for age, gender and education. ANCOVA was used to examine the influence of frailty on the relationship between amyloid status and cognition (Preclinical Alzheimer Cognitive Composite [PACC] score), including an interaction term of frailty and amyloid, adjusted for age, gender and education. Result 4,486 participants were included (mean age 71.3±4.7 years, 1323 participants Aβ+(29.5%), 59.4% female). Adjusting for age, sex and education, Aβ+ participants were 1.48 times more likely to be frail compared to Aβ− (p<0.001). Frail participants had a lower PACC score compared to non‐frail participants (p<0.001). Both frailty and amyloid status were associated with poorer cognition after adjusting for age, sex and education (p<0.001), but frailty did not influence the relationship between cognition and amyloid status. Conclusion There is strong evidence that elevated amyloid burden was associated with an increased risk of frailty and that frailty reduced cognitive performance compared to non‐frail participants in this cohort screened for a preclinical AD trial. These relationships, the potential underlying mechanisms, and whether this may be applicable to longitudinal outcomes, warrant further study.
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ItemNo Preview AvailableCerebrospinal fluid neurofilament light chain differentiates behavioural variant frontotemporal dementia progressors from ‘phenocopy’ non‐progressors(Wiley, 2022-12)Background Distinguishing behavioural variant frontotemporal dementia (bvFTD) from non‐neurodegenerative ‘non‐progressor’, ‘phenocopy’ mimics of frontal lobe dysfunction, can be one of the most challenging clinical dilemmas. A biomarker of neuronal injury, neurofilament light chain (NfL), could reduce misdiagnosis and delay. Method Cerebrospinal fluid (CSF) NfL, amyloid beta 1‐42 (AB42), total and phosphorylated tau (T‐tau, P‐tau) levels were examined in patients with an initial diagnosis of bvFTD. Based on follow up information, patients were categorised as Progressors. Non‐Progressors were subtyped in to Phenocopy Non‐Progressors (non‐neurological/neurodegenerative final diagnosis), and Static Non‐Progressors (static deficits, not fully explained by non‐neurological/neurodegenerative causes). Result Forty‐three patients were included: 20 Progressors, 23 Non‐Progressors (15 Phenocopy, 8 Static), 20 controls. NfL concentrations were lower in Non‐Progressors (Non‐Progressors Mean, M=554pg/mL, 95%CI:[461, 675], Phenocopy Non‐Progressors M=459pg/mL, 95%CI:[385, 539], Static Non‐Progressors M=730pg/mL, 95%CI:[516, 940]), compared to bvFTD Progressors (M=2397pg/mL, 95%CI:[1607, 3332]). NfL distinguished Progressors from Non‐Progressors with the highest accuracy (area under the curve 0.92, 90%/87% sensitivity/specificity, 86%/91% positive/negative predictive value, 88% accuracy). Static Non‐Progressors tended to have higher T‐tau and P‐tau levels compared to Phenocopy Non‐Progressors. Conclusion This study demonstrated strong diagnostic utility of CSF NfL in distinguishing bvFTD from phenocopy non‐progressor variants, at baseline, with high accuracy, in a real‐world clinical setting. This has important clinical implications to improve outcomes for patients and clinicians facing this challenging clinical dilemma, as well as for healthcare services, and clinical trials. Further research is required to investigate heterogeneity within the non‐progressor group and potential diagnostic algorithms, and prospective studies are underway assessing plasma NfL.
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ItemNo Preview AvailablePlasma pTau181/Aβ42 identifies cognitive change earlier than CSF pTau181/Ab42(Wiley Open Access, 2022-12)Background Plasma biomarkers now show an accuracy in detecting Amyloid Beta (Aβ) similar to AD biomarkers derived from cerebral spinal fluid (CSF). However, the ability of plasma AD biomarkers, alone or in combination, to predict cognitive decline has not yet been compared to that of CSF AD biomarkers. Method Plasma biomarker data from 233 participants’ first visit in the Australian Imaging, Biomarkers and Lifestyle study (AIBL) was submitted to linear mixed effects models (LME) to quantify the relationship with change in cognition (measured using the AIBL PACC) and in clinical disease stage (CDR SoB) in both PET Aβ‐ (Centiloid value <20CL) and Aβ+ (Centiloid value ≥20CL) participant subgroups. Separate models were used to assess CSF (Elecsys) and plasma (ADx NeuroSciences) data for Aβ42, pTau181 and the pTau181/Aβ42 ratio. Biomarker values were classified into low vs high levels based on ROC‐derived thresholds optimizing separation of PET Aβ status (low vs high at 20 CL). Changes in cognitive and clinical symptoms were then compared between the low/high plasma biomarker groups. Result In Aβ‐ participants, no significant interactions between binary biomarker classification and time were observed for AIBL PACC or CDR SoB, for either CSF or plasma biomarkers. In the Aβ+ participants, interactions between the binary plasma biomarker classification and change in cognition were greater in magnitude that those detected for CSF biomarker classification. For plasma, abnormally high values of both pTau181 and the pTau181/Aβ42 ratio predicted a significant increase over time in CDR SoB (Figure 1H & 1L) and a significant decrease over time in the AIBL PACC score (Figure 1F & 1J), compared the group with low values on the same biomarkers. In cognitively unimpaired Aβ‐ participants, the AIBL PACC score declined in those with abnormally high values of the pTau181 and the pTau181/Aβ42 ratio (Figure 1F & 1J). Conclusion Assays to measure pTau181 and Aβ42 in the plasma possess an accuracy equivalent to those derived from CSF. In particular, abnormally high levels of plasma pTau181 or the ratio of pTau181 to Aβ42 ratio provide a strong prediction of early cognitive changes, even in those with normal PET Aβ status.
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ItemNo Preview AvailableObjectively measured physical activity and cognition in cognitively normal older adults: A longitudinal analysis of the Australian Imaging Biomarkers and Lifestyle (AIBL) study(Wiley Open Access, 2022-12)Background Physical inactivity is one of the greatest modifiable risk factors for dementia and research shows physical activity can delay cognitive decline in older adults. However, much of this research has used subjective physical activity data and a single follow‐up cognitive assessment. Further studies using objectively measured physical activity and comprehensive cognitive data measured at multiple timepoints are required. Methods Participants were 199 community‐dwelling cognitively normal older adults (68.7 5.9 years) from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study. Actigraphy was used to measure physical activity at baseline, yielding measures of intensity (peak counts), total activity (total counts) and energy expenditure (kilocalories; k/cal). Cognitive function was assessed using a cognitive battery administered every 18‐months from baseline (3‐11 years follow‐up), yielding composite scores for episodic memory, executive function, attention and processing speed, and global cognition. Results Higher baseline energy expenditure predicted improvements in episodic memory and maintained global cognition over time (β = 0.011, SE = 0.005, p = 0.031; β = 0.009, SE = 0.004, p = 0.047, respectively). Both physical activity intensity and total activity predicted global cognition, such that those with higher peak and total counts had better cognition over time (β = 0.012, SE = 0.004, p = 0.005; β = 0.012, SE = 0.004, p = 0.005, respectively). Finally, higher total activity predicted improved episodic memory over time (β = 0.011, SE = 0.005, p = .022). Conclusion These results suggest that physical activity is associated with preserved cognitive function over time, and that activity intensity may play an important role. This research further highlights the importance of early intervention to prevent cognitive decline and may aid in informing lifestyle interventions for dementia prevention.
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ItemNo Preview AvailableLeukocyte Surface Biomarkers Implicate Deficits of Innate Immunity in Late‐onset Alzheimer’s Disease(Wiley, 2022-12)Background Alzheimer’s disease (AD) is characterized by amyloid‐β (Aβ) plaques, neurofibrillary tangles, reactive astrogliosis, and microgliosis. Aberrant Aβ accumulation starts 20–30 years before clinical onset, so biomarker test is essential to diagnose people living with early AD. PET imaging and CSF measurements allow the diagnosis of preclinical and prodromal AD in research and clinical trials, but their invasiveness and costliness might limit their application in hospital setting. Therefore, developing non‐invasive population screening tests is necessary for the early diagnosis of AD. Recent genetic findings strongly implicate the role of innate and adaptive immunity in AD and suggest that a systemic failure of cell‐mediated Aβ clearance contributes to AD onset and progression. Our research question was to develop an immune‐related blood‐based biomarker test to facilitate the diagnosis and prognosis of AD. It was hypothesized that the pattern of immune‐related receptors and molecules expressed on peripheral leukocytes could differentiate people living with AD from healthy population. Method We recruited 180 and 200 participants from AIBL in two discovery phases and validated our findings by an independent cohort of 112 participants from AIBL. A total of 34 innate and adaptive immunity‐related leukocyte antigens on peripheral lymphocytes, monocytes, and neutrophils were examined by flow cytometry immunophenotyping. Data was analysed by logistic regression and ROC analyses. Result We identified upregulated CD35, CD59, CD91, RAGE, and Scara‐1 expressions and downregulated CD11c, CD18, CD36, CD163, MerTK, and P2X7 expressions on leukocytes of MCI/AD patients. Significant correlation between them and Aβ burden, episodic memory, and PACC score was observed, such as CD59 and CD91. Pathway analysis revealed upregulation of complement inhibition and downregulation of cargo receptor activity and Aβ clearance in AD. We proposed a marker panel including CD11c, CD59, CD91 and CD163 and this panel predicted patients’ PET Aβ status with AUC of 0.93 (0.88 to 0.97), which was repeated in validation cohort. Regarding adaptive immunity, we did not see significant results. Conclusion Our study suggested deficits in innate immunity in AD, which is consistent with genomic studies. Our proposed leukocyte‐based biomarker panel might be sensitive and practical for AD screening and diagnosis.
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ItemNo Preview AvailablePlasma glial fibrillary acidic protein is associated with reactive astrogliosis assessed via 18F-SMBT-1 PET(Wiley, 2022-12)Background Reactive astrogliosis is an early event along the Alzheimer’s disease (AD) continuum. We have shown that plasma glial fibrillary acidic protein (GFAP), reflecting reactive astrogliosis, is elevated in cognitively unimpaired individuals with preclinical AD (Chatterjee et al., 2021). We reported similar findings using 18F‐SMBT‐1, a PET tracer for monoamine oxidase B (MAO‐B) (Villemagne et al., 2022). To provide further evidence of their relationship with reactive astrogliosis we investigated the association between GFAP and 18F‐SMBT‐1 in the same participants. Method Plasma GFAP, Aβ42 and Aβ40 levels were measured using the Single Molecule Array platform in 71 participants comprising 54 healthy controls (12 Aβ+ and 42 Aβ‐), 11 MCI(3 Aβ+ and 8 Aβ‐) and 6 probable AD(5 Aβ+ and 1 Aβ‐) patients from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing cohort. These participants also underwent 18F‐SMBT‐1 and Aβ PET imaging. Aβ imaging results were expressed in Centiloids (CL; ≥20 CL classified as Aβ+). 18F‐SMBT‐1 Standard Uptake Value Ratio (SUVR) were generated using the subcortical white matter as reference region. Linear regression analyses were carried out using plasma GFAP levels as the dependent variable and regional 18F‐SMBT‐1 SUVR as the independent variable, before and after adjusting for age, sex, soluble Aβ (plasma Aβ1‐42/Aβ1‐40 ratio) and insoluble Aβ (Aβ PET). Result Plasma GFAP was significantly associated with 18F‐SMBT‐1 SUVR in brain regions of early Aβ deposition, such as the supramarginal gyrus (SG, β=.361, p=.002), posterior cingulate (PC, β=.308, p=.009), lateral temporal (LT, β=.299, p=.011), lateral occipital (LO, β=.313, p=.008) before adjusting for any covariates. After adjusting for covariates age, sex and soluble Aβ, GFAP was significantly associated with 18F‐SMBT‐1 PET signal in the SG (β=.333, p<.001), PC (β=.278, p=.005), LT (β=.256, p=.009), LO (β=.296, p=.004) and superior parietal (SP, β=.243, p=.016). On adjusting for age, sex and insoluble Aβ, GFAP was significantly associated with SMBT‐1 PET in the SG (β=.211, p=.037) however only a trend towards significance was observed in the PC (β=.186, p=.052) and LT (β=.171, p=.067) (Figure 1). Conclusion There is an association between plasma GFAP and regional SMBT‐1 PET that is primarily driven by brain Aβ load.
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ItemNo Preview AvailableUnderstanding the impact of PET amyloid cutpoints on prognostic modelling for cognitively normal individuals(Wiley, 2022-12)Background Amyloid beta (Aβ), measured using PET imaging, is a key biomarker for Alzheimer’s disease (AD) with the consequences of abnormally high Aβ levels (Aβ+) well‐established from prospective research cohorts. A critical question is whether the prognostic capabilities of Aβ can be improved further, for example by refinement of optimal criteria for abnormality. To date, existing studies have explored such issues using association analyses, which may not reflect performance in prognostic settings due to potential overfitting. Here, the impact of different Aβ cut‐points is determined in a cross‐validation framework, providing performance estimates on data from individuals that were not used for model construction, which better reflects realworld prognostic application. Using data for cognitively normal individuals (CN) from ADNI and AIBL, we estimate time to i) MCI or AD diagnosis and ii) cognitive deficit, defined as MMSE≤26. Method We analyse measurements from 344 and 748 CN from ADNI and AIBL respectively who have available PET Aβ scans. PET Aβ SUVRs were transformed to the centiloid scale (CL). For each task, the Aβ cut‐point is varied from ‐10 to 65CL and Cox models are constructed within 10 repeats of 10‐fold cross‐validation. From the resulting 100 models, performance is quantified as the median concordance index (i.e. Harrell’s C). Result Details of the two cohorts are shown in Table 1. Across both AIBL and ADNI, a PET only model shows robust performance for cut‐points within a wide range (5 and 50CL) for predicting either time to diagnosis cognitive deficit (Figure 1), with performance dropping rapidly outside this range. When additional covariates are included 2, we see maximal performance for lower cutpoints (5‐20CL) for diagnosis in ADNI and cognitive deficit in ADNI, while remaining tasks show improved performance with higher cut‐point ranges (20‐50CL). Trends in cut‐point are consistent regardless of covariates. Leaving Aβ as a continuous variable yields near‐optimal performance across all tasks. Conclusion Our results suggest that within a range (5 and 50CL), prognostic performance is robust to the choice of cut‐point for Aβ, suggesting further refinement of a single cut‐point within this range may not yield substantial improvements for prognostic tasks for CN individuals.
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ItemNo Preview AvailableAlzheimer’s disease specific MRI brain regions are differentially associated with accelerated decline as defined using sigmoidal cognitive turning point methodology in amyloid‐positive AIBL participants(Wiley, 2022-12)Background Variability in cognitive decline among adults with Alzheimer’s disease (AD) is seen across studies. While such variability is often modelled using linear models, in the Australian Imaging, Biomarkers and Lifestyle (AIBL) study, application of a sigmoidal methodology has shown excellent precision in modelling cognitive and biomarker changes. Here we expand these findings by examining associations of brain volumes in AD specific Regions of Interest (ROIs) with accelerated cognitive decline among amyloid‐beta positive (Ab+) AIBL participants. Method Longitudinal cognitive scores for the AIBL PACC, Language, Visuospatial functioning and CDR‐SB were mapped to sigmoidal trajectories, with a threshold defining the inflection point of accelerated cognitive decline. Participants to the left of the threshold were classified as having non‐accelerated decline (non‐accelerators), and participants beyond the threshold were classed as accelerators (Figure 1B). Using these classifications, we investigated differences in 16 ICV corrected ROI (left and right hemispheres pooled) for reductions in brain volume via generalised linear models adjusted for age, gender, and APOE‐e4 status. Three participant subgroups were tested: 1) Ab+/Tau unknown, 2) Ab+/Tau‐ and 3) Ab+/Tau+. Significant t‐values for the summed ROI volumes were mapped on a standard brain mesh for visualisation. Result Of regions tested, two stood out consistently amongst top markers in each of the participant subgroups and cognitive outcomes: 1) supramarginal volume and 2) middle temporal volume (Figure 1C). Largest volume differences between accelerators and non‐accelerators were seen in the Ab+/Tau+ group; whilst smallest p‐values were in the Ab+/Tau unknown group due to a larger sample size (Table 1). Brain mesh visualization showed most of the AD signature ROIs altered in accelerator groups as compared with non‐accelerator groups. Figure 1D shows the AD signature for each cognitive outcome amongst the Ab+/Tau participant group. Top ranked ROI for the left being middle temporal volume (T=7.10, PACC) and supramarginal volume (T=7.10, CDR‐SB). Conclusion Sigmoid analyses of MRI using binary cognitive scores show decreased ROI volumes in AIBL Ab+ participants with accelerated cognitive decline. This effect was mediated by known information on Tauopathy. Whilst effect sizes were high, smaller sample sizes in some groups affected p‐values and should therefore be replicated in larger samples.