Florey Department of Neuroscience and Mental Health - Research Publications

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

Gardener, SL ; Rainey‐Smith, SR ; Villemagne, VLL ; Fripp, J ; Dore, V ; Bourgeat, P ; Taddei, K ; Masters, CL ; Maruff, PT ; Rowe, CC ; Ames, D ; Martins, RN (Wiley, 2021-12)

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 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. Method The 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. Result Higher 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. Conclusion Our 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.
Differential associations of modifiable and non‐modifiable dementia risk factors with memory decline and hippocampal volume loss in Aβ‐ and Aβ+ cognitively normal older adults

Rosenich, E ; Pase, MP ; Yassi, N ; Fripp, J ; Laws, SM ; Fowler, CJ ; Rowe, CC ; Masters, CL ; Maruff, PT ; Lim, YY ; Group, AIBLR (Wiley, 2021-12)

Abstract Background The extent to which modifiable and non‐modifiable risk factors contribute to cognitive decline in older people remains unclear. We sought to determine the association of modifiable and non‐modifiable components included in the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) score with memory decline and brain volume loss in cognitively normal (CN) older adults, taking Aβ status into account. Method AIBL study participants (age range: 60‐90) who completed ≥2 neuropsychological assessments and an Aβ PET scan (N=626) were included in this study. We computed the standard CAIDE (age, sex, APOE ε4 status, education, hypertension, body mass index, hypercholesterinemia, physical inactivity), and a modifiable CAIDE (modCAIDE; education, modifiable vascular risk factors) for each participant. Aβ+ was classified using a Centiloid ≥25. Linear mixed models assessed interactions between each CAIDE score on episodic memory (EM) and hippocampal volume (HV) over time in Aβ‐ and Aβ+ CNs. Non‐modifiable variables from the standard CAIDE (age, sex, ε4) were included as separate predictors in all modCAIDE models to assess differential associations. Result We observed a significant standard CAIDE x time interaction on EM decline in Aβ+ (β=‐0.08(0.04); p=0.02) and Aβ‐ participants (β=‐4.07(1.13); p<0.001), and a significant standard CAIDE x time interaction on HV loss in Aβ+ participants only (β=‐0.06(0.02); p=0.003). When modifiable and non‐modifiable CAIDE components were considered separately, we observed a significant ε4 x time interaction only for EM decline (β=‐0.32(0.07); p<0.001) and HV loss (β=‐0.13(0.04); p<0.001) in Aβ+ participants, but no significant modCAIDE x time interaction (both p’s>0.29). In Aβ‐ participants, we observed a significant modCAIDE x time interaction on memory decline (β=‐0.04(0.02); p=0.02), but no significant ε4 x time interaction (β=‐0.07(0.04); p=0.11). No significant ε4 x time or modCAIDE x time interactions were observed for HV loss in Aβ‐ participants. Conclusion Our results are consistent with previous studies showing that ε4 is associated with an increased rate of EM decline and HV loss in Aβ+ CNs. In Aβ‐ CNs, lower prevalence of modifiable cardiovascular risk factors was associated with better EM performance over time, suggesting interventions to reduce modifiable risk factors could be beneficial in this group.
How lifestyle shapes the brain: Associations between physical activity, sleep, beta‐amyloid and cognitive function in older adults

Sewell, KR ; Rainey‐Smith, SR ; Villemagne, VLL ; Peiffer, JJ ; Sohrabi, HR ; Taddei, K ; Ames, D ; Maruff, PT ; Laws, SM ; Masters, CL ; Rowe, CC ; Martins, RN ; Erickson, KI ; Brown, BM (Wiley, 2021-12)

Abstract Background Lifestyle 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. Method Data 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. Result Physical 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). Conclusion Physical 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.
Empirically derived composite cognitive test scores to predict preclinical and clinical stages of Alzheimer’s disease

Shishegar, R ; Chai, TY ; Cox, T ; Lamb, F ; Robertson, JS ; Laws, SM ; Porter, T ; Fripp, J ; Doecke, JD ; Tosun‐Turgut, D ; Maruff, PT ; Savage, G ; Rowe, CC ; Masters, CL ; Weiner, MW ; Villemagne, VLL ; Burnham, SC (Wiley, 2021-12)

Abstract Background Alzheimer’s disease (AD) clinical trials require cognitive test scores that assess change in cognitive function accurately. Here, we propose new composite cognitive test scores to detect earlier stages of AD accurately by using the full neuropsychological testing battery (in ADNI) and a manifold learning dimension reduction technique namely UMAP. Method Data for this study included N=1585 ADNI participants ([492 cognitively normal (CN), 804 mild cognitively impaired (MCI), 289 AD; aged 73.8±7.1; 708 females]; Table 1). Subjects with 3 or more follow‐up sessions were included. Cognitive test scores with more than 60% missing data were excluded. Missing data within included test scores were imputed using the MissForest algorithm. A linear mixed model using all follow‐up data was applied to calculate the random slope (rate of change) and random intercept for each cognitive score and for each subject. The scores and demographic measurements: age, gender, years of education and APOE‐ɛ4 status were used to inform the UMAP. Levels for the output variable were defined as: 1) stable CN, 2) CN who progressed to MCI or probable dementia due to AD, 3) stable MCI, 4) MCI who progressed to dementia AD and 5) dementia due to AD. The model calculated two composite scores. These cognitive stages were predicted using Support Vector Machine (SVM) analysis of both the new composite scores and the traditional clinical rating measures of Clinical Dementia Rating (CDR) and Mini‐Mental State Examination (MMSE). Result Predicting cognitive stages using the proposed composite scores show a highly significant improvement with a 0.981 accuracy and 0.976 reliability (evaluated by Cohen's kappa coefficient), compared to using the combination of CDR and MMSE scores covaried for demographics, which had 0.660 accuracy and 0.567 reliability. Individuals’ clinical and preclinical stages with regards to UMAP two‐dimensional embedding and the clinical rating measures, CDR and MMSE, are presented in Figure 1. Table 2 reports the importance of the test measures on the UMAP components used in AD staging predictions. Conclusion The results here suggest that the proposed empirically derived composite cognitive test scores provides a practical solution to differentiate cognitive stages with a high accuracy and reliability.
Unpacking cognitive composites: A longitudinal analysis

Cox, T ; Shishegar, R ; Lim, YY ; Robertson, J ; Lamb, F ; Laws, SM ; Porter, T ; Fripp, J ; Doecke, JD ; Maruff, PT ; Savage, G ; Rowe, CC ; Masters, CL ; Villemagne, VL ; Burnham, SC (Wiley, 2021-12)

Abstract Background The development of cognitive endpoints that can accurately assess changes in cognition over short time frames is crucial for clinical trials and research of Alzheimer’s disease (AD). Understanding the changing influence of contributing test scores on composites throughout the disease course provides the opportunity to optimise cognitive composite scores for different stages of AD. Method AIBL participants with declining cognitive performance were included in this study N=1275 [688 cognitively unimpaired (CU), 277 mild cognitively impaired (MCI), 310 AD; aged 73±9; 718 females]). Two cognitive composite scores (Episodic Memory (EM) and PACC) and their component test scores (California Verbal Learning Test‐II Delayed Recall (CVLT‐II DR), Logical Memory Delayed Recall (LMII), Rey Complex Figure Test 30 minute delayed recall (RCFT‐DR) and CVLT‐II DR, LMII, Digit Symbol Substitution Test (DS), MMSE, respectively) were evaluated. We first examined the relationship between each of component tests score for each composite. We then compared the extent to which longitudinal trajectories of each component test score and each cognitive composite score differed at each disease stage. Result CVLT‐II DR contributed the most to the EM composite followed by RCFT‐DR and LMII with the influence remaining unchanged across each disease stage. For PACC, CVLT‐II DR contributed the most to the initial decline, with MMSE and LMII contributing similar amounts and DS contributing the least. CVLT‐II DR contributed substantially to changes in PACC earlier in the disease course but MMSE drove the PACC change in later stages of disease. Initially, both composites follow similar longitudinal trajectories. However, the EM composite reaches a floor not observed for the PACC. Conclusion Understanding the temporal contribution of component tests scores on cognitive composites could provide improved cognitive endpoints tailored to use. For instance, MMSE is sensitive to change later in the disease trajectory and therefore should be included in a composite endpoint for trials in prodromal or clinical AD, however is unlikely to have value for preclinical AD trials.

Florey Department of Neuroscience and Mental Health - Research Publications

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