Florey Department of Neuroscience and Mental Health - Research Publications

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Tackling Dementia Together via The Australian Dementia Network (ADNeT): A Summary of Initiatives, Progress and Plans.

Naismith, SL ; Michaelian, JC ; Santos, C ; Mehrani, I ; Robertson, J ; Wallis, K ; Lin, X ; Ward, SA ; Martins, R ; Masters, CL ; Breakspear, M ; Ahern, S ; Fripp, J ; Schofield, PR ; Sachdev, PS ; Rowe, CC (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.
Plasma pTau181/Aβ42 identifies cognitive change earlier than CSF pTau181/Ab42

Fowler, C ; Stoops, E ; Rainey‐Smith, S ; Vanmechelen, E ; Vanbrabant, J ; Dewit, N ; Mauroo, K ; Rowe, C ; Fripp, J ; Li, Q ; Bourgeat, P ; Collins, S ; Martins, RN ; Masters, CL ; Maruff, P ; Doecke, JD (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.
Objectively measured physical activity and cognition in cognitively normal older adults: A longitudinal analysis of the Australian Imaging Biomarkers and Lifestyle (AIBL) study

Sewell, KR ; Rainey‐Smith, S ; Villemagne, VL ; Peiffer, JJ ; Sohrabi, HR ; Taddei, K ; Ames, D ; Maruff, P ; Laws, SM ; Masters, CL ; Rowe, C ; Martins, RN ; Erickson, KI ; Brown, BM (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.
Leukocyte Surface Biomarkers Implicate Deficits of Innate Immunity in Late‐onset Alzheimer’s Disease

Li, Y ; Huang, X ; Fowler, C ; Doecke, JD ; Trounson, B ; Pertile, K ; Rumble, R ; Lim, YY ; Maruff, P ; Mintzer, JE ; Dore, V ; Rowe, C ; Fripp, J ; Wiley, JS ; Masters, CL ; Gu, BJ (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.
Plasma glial fibrillary acidic protein is associated with reactive astrogliosis assessed via 18F-SMBT-1 PET

Chatterjee, P ; Dore, V ; Pedrini, S ; Krishnadas, N ; Thota, RN ; Bourgeat, P ; Rainey‐Smith, S ; Burnham, SC ; Fowler, C ; Taddei, K ; Mulligan, RS ; Ames, D ; Masters, CL ; Fripp, J ; Rowe, C ; Martins, RN ; Villemagne, VL (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.
Understanding the impact of PET amyloid cutpoints on prognostic modelling for cognitively normal individuals

Goudey, B ; Fedyashov, V ; Fripp, J ; Rowe, C ; Maruff, P ; Masters, CL (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.
Alzheimer’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

Gillis, C ; Cespedes, MI ; Maserejian, NN ; Dore, V ; Maruff, P ; Fowler, C ; Rainey‐Smith, S ; Villemagne, VL ; Rowe, C ; Martins, RN ; Vacher, M ; Masters, CL ; Doecke, JD (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.
Comparing the longitudinal progression of CSF biomarkers with PET Amyloid biomarkers for Alzheimer’s disease

Cox, T ; Bourgeat, P ; Dore, V ; Doecke, JD ; Fripp, J ; Chatterjee, P ; Schindler, EE ; Benzinger, TLS ; Rowe, C ; Villemagne, VL ; Weiner, MW ; Morris, JC ; Masters, CL (Wiley, 2022-12)

Background Cerebrospinal fluid (CSF) soluble biomarkers are useful at detecting pre‐clinical levels of Alzheimer’s disease (AD) biomarkers of b‐amyloid (Ab) and tau. Disease progression times for participants in longitudinal studies can be estimated for different biomarkers. Utilizing a new technique, this work compared the disease progression times between CSF and PET biomarkers. Methods Four hundred and ten participants from the Alzheimer’s Dementia Onset and Progression in International Cohorts (ADOPIC) including participants form ACS/OASIS, ADNI and AIBL with three or more data points of longitudinal CSF Ab42 and pTau181 (pTau) and Ab PET were selected. PET results were expressed in Centiloid (CL), (299 cognitively unimpaired, 107 mild cognitively impaired, 4 AD dementia; aged 69±9; 216 females (NAIBL=30, NADNI=252, NOASIS=128). Disease trajectory curves for individual biomarkers and the pTau/Ab42 ratio were created by: 1) Fitting a function to the rates of change of the variable of interest versus its mean value), 2) integrating the fit to obtain longitudinal trajectory curves as a function of disease progression time for each of the variables. The participants’ disease progression time along each curve were estimated. Threshold values for Ab PET and pTau/Ab42 ratios were calculated using a gaussian mixture model. Estimates of age of onset were calculated using the progression times. The participants’ disease progression times for each of the different variables were compared using rank correlations. Results Rank correlations for the progression times were: r(Ab42, Ab PET) = 0.75, r(pTau, Ab PET)=0.62, and r(pTau/Ab42, Ab PET)=0.83. The estimated ages at which participants’ reach Ab PET and the pTau/Ab42 ratio thresholds are compared in Fig 1, the average age at which were estimated to reach the threshold values were 55 yr for pTau/Ab42 (threshold of 0.021) and 61 yr for Ab PET (threshold of 22 CL). Conclusions The high correlation between pTau/Ab42 and Ab PET, indicates that pTau/Ab42 captures the progression of AD pathology better than the individual CSF biomarkers. On average participants’ reach abnormal levels of pTau/Ab42 earlier than Ab PET. Further work is required to understand individual variations in progression times.
Lipidomic signatures for APOE genotypes provides new insights about mechanisms of resilience in Alzheimer’s disease

Wang, T ; Huynh, K ; Giles, C ; Lim, WLF ; Duong, T ; Mellett, NA ; Smith, A ; Olshansky, G ; Drew, BG ; Cadby, G ; Melton, PE ; Hung, J ; Beilby, J ; Watts, GF ; Chatterjee, P ; Martins, I ; Laws, SM ; Bush, AI ; Rowe, CC ; Villemagne, VL ; Ames, D ; Masters, CL ; Arnold, M ; Kastenmüller, G ; Nho, K ; Saykin, AJ ; Baillie, R ; Han, X ; Martins, RN ; Moses, E ; Kaddurah‐Daouk, RF ; Meikle, PJ (Wiley, 2021-12)

Background The apolipoprotein E gene (APOE) genotype is the first and strongest genetic risk factor for late‐onset Alzheimer’s disease and has emerged as a novel therapeutic target for AD. The encoded protein (Apolipoprotein E, APOE) is well‐known to be involved in lipoprotein transport and metabolism, but its effect on lipid metabolic pathways and the potential mediating effect of these on disease risk have not been fully defined. Method We performed lipidomic analysis on three independent cohorts (AIBL, n = 693; ADNI, n=207; BHS, n=4,384) and defined the association between APOE polymorphisms (ε4 and ε2) and plasma lipid species. To identify associations independent of lipoprotein metabolism, the analyses was performed with adjustment for clinical lipids (total cholesterol, HDL‐C and triglycerides). Causal mediation analysis was performed to estimate the proportion of risk in the outcome model explained by a direct effect of APOE genotype on prevalent AD — the average direct effect (ADE) — and the proportion that was mediated by lipid species or lipidomic risk models — the average causal mediation effect (ACME). Result We identified multiple associations of species from lipid classes such as ceramide, hexosylceramide, sphingomyelin, plasmalogens, alkyldiacylglycerol and cholesteryl esters with APOE polymorphisms (ε4 and ε2) that were independent of clinical lipoprotein measurements. There were 104 and 237 lipid species associated with APOE ε4 and ε2 respectively which were largely discordant. Of these 116 were also associated with Alzheimer’s disease. Individual lipid species (notably the alkyldiacylglycerol subspecies) or lipidomic risk models of APOE genotypes mediated up to 10% and 30% of APOE ε4 and ε2 treatment effect on AD risks respectively. Conclusion We demonstrate a strong relationship between APOE polymorphisms and peripheral lipid species. Lipids species mediate a proportion of the effects of APOE genotypes in risk of AD, particularly resilience with e2. Our results highlight the involvement of lipids in how APOE e2 mediates its resilience to AD and solidify their involvement with the disease pathway.
Relationship between amyloid and tau levels and its impact on tau spreading

Dore, V ; Krishnadas, N ; Bourgeat, P ; Huang, K ; Li, S ; Burnham, SC ; Masters, CL ; Fripp, J ; Villemagne, VL ; Rowe, CC (Wiley, 2021-12)

Background Previous studies have shown that Aß‐amyloid (Aß) likely promotes tau to spread beyond the medial temporal lobe. However, the Aß levels necessary for tau to spread in the neocortex is still unclear. Method 466 participants underwent tau imaging with [18F]MK6420 and Aß imaging with [18F]NAV4694 (Fig. 1). Aß scans were quantified on the Centiloid (CL) scale with a cut‐off of 25CL for abnormal levels of Aß (A+). Tau scans were quantified in three regions of interest (ROI) (mesial temporal (Me); temporoparietal neocortex (Te); and rest of neocortex (R)) and four mesial temporal region (entorhinal cortex, amygdala, hippocampus and parahippocampus) using the cerebellar cortex as reference region. Regional tau thresholds were established as the 95%ile of the cognitively unimpaired A‐ subjects. The prevalence of abnormal tau levels (T+) along the Centiloid continuum was determined. Result The plots of prevalence of T+ (Fig. 2) show earlier and greater increase along the Centiloid continuum in the medial temporal area compared to neocortex. Prevalence of T+ was low but associated with Aß level between 10‐40 CL reaching 23% in Me, 15% in Te and 11% in R. Between 40‐70 CL, the prevalence of T+ subjects per CL increased four‐fold faster and at 70 CL was 64% in Me, 51% in Te and 37% in R. In cognitively unimpaired (Fig. 3), there were no T+ in R below 50 CL. The highest prevalence of T+ was found in the entorhinal cortex, reaching 40% at 40 CL and 80% at 60 CL. Conclusion Outside the entorhinal cortex, abnormal levels of cortical tau on PET are rarely found with Aß levels below 40 CL. Above 40 CL prevalence of T+ accelerates in all areas. Moderate Aß levels are required before neocortical tau becomes detectable.

Florey Department of Neuroscience and Mental Health - Research Publications

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