Chronic psychological stress has been implicated as a risk factor for Alzheimer’s disease (AD), potentially through cortisol-mediated acceleration of disease progression. However, the molecular pathways underlying this relationship remain poorly understood. Epigenetic regulation of the glucocorticoid and mineralocorticoid receptor genes (NR3C1 and NR3C2), which encode receptors for cortisol, may play an important role, but has not been examined in relation to AD progression. Therefore, this study investigated associations between DNA methylation of NR3C1/NR3C2 and AD-related phenotypes, including cognition, brain amyloid-β (Aβ) burden, and regional brain volumes. These associations were examined in two independent cohorts of cognitively unimpaired individuals with accumulating brain Aβ (n = 89–298 across outcomes) using linear regression and meta-analyses. The study also explored whether DNA methylation within NR3C1 and NR3C2 interacted with depression symptoms to influence relationships with AD-related phenotypes. While only nominal associations were observed in direct analyses, stronger associations emerged in interaction with depressive symptoms. Interaction analyses showed that relationships between DNA methylation and AD-related phenotypes (cognition, hippocampal volume and ventricular expansion) differed depending on the presence of depression symptoms. Consistent patterns across cohorts were observed, with associations primarily evident among individuals with clinically relevant depressive symptoms. One site (NR3C1 cg24052866) was associated with cognitive decline, one (NR3C1 cg08845721) with cross-sectional hippocampal volume, and eight (NR3C1 cg21979215, cg16594263; NR3C2 cg27460943, cg17253842, cg04867484, cg10993059, cg25672354, cg27234800) with ventricular expansion. These exploratory findings suggest epigenetic variation within cortisol receptor genes may influence AD-related neurodegeneration in a depression-dependent manner.

Chronic traumatic encephalopathy (CTE) is a neurodegenerative disease characterized by tau deposition in the depths of the sulci associated with exposure to repetitive head impacts (RHI). It is a post-mortem diagnosis. We previously reported a frontotemporal predominant tau 18F-MK6240 PET pattern resembling the distribution of CTE in a retired Australian Rules Football player in the context of a moderate amyloid-beta plaque burden.1 This study aimed to investigate the utility of 18F-MK6240 PET as a potential biomarker for CTE in contact sports players with exposure to RHI (sRHI). sRHI (n = 33) and age-matched healthy controls (HC) (n = 32) completed amyloid (18F-NAV4694) and tau (18F-MK6240) PET scans. Amyloid PET was quantified in Centiloids. 18F-MK240 standardized uptake value ratios (SUVRs) were generated for the dorsolateral prefrontal cortex and composite regions of interest (ROI) (frontal; mesial temporal; temporoparietal). For sRHI, the primary contact sport was Australian Rules Football (n = 17), boxing/kickboxing/martial arts (n = 11), rugby (n = 4) and soccer (n = 1), with 36.4% participating at a professional level. sRHI had a mean age of 54.2 (±9.2) (vs HC 53.0±9.5, p = 0.61), and 94% were male (vs HC 78%, p = 0.08). sRHI did not differ from HC in years of education (p = 0.46) but had more impaired MMSE (28.1±1.9 vs 29.3±0.8, p = 0.006, d = -0.80) and Clinical Dementia Rating scores (0.21±0.3 vs 0±0, p<0.001, d = 1.25). sRHI and HC did not differ in mean Centiloid values (2.9±8.4 vs 3.0±8.2). sRHI and HC did not differ in 18F-MK6240 SUVR in the regions examined, and no differences were observed between professional and amateur sRHI. Contact sports players with exposure to repetitive head impacts did not differ from age-matched healthy controls on 18F-MK6240 SUVR in frontal, mesial temporal, and temporoparietal brain regions. Study limitations include the small sample size, heterogeneity in sports type and highest level of participation, and participants with relatively mild cognitive and functional impairments. Additionally, while 18F-MK6240 has high affinity for 3R/4R tau in Alzheimer’s disease, its affinity in CTE, particularly important at early stages, remains unclear. References: 1 Krishnadas N et al. Case report: 18F-MK6240 tau positron emission tomography pattern resembling chronic traumatic encephalopathy in a retired Australian Rules Football player. https://doi.org/10.3389/fneu.2020.598980.

Traumatic brain injury (TBI) is common among military veterans and has been associated with an increased risk of dementia. It is unclear if this is due to increased risk for Alzheimer's disease (AD) or other mechanisms. This case control study sought evidence for AD, as defined by the 2018 National Institute on Aging - Alzheimer's Association (NIA-AA) research framework, by measuring tau, beta-amyloid, and glucose metabolism using positron emission tomography (PET) in veterans with service-related TBI. Seventy male Vietnam war veterans-40 with TBI (age 68.0 +/- 2.5 years) and 30 controls (age 70.1 +/- 5.3 years)-with no prior diagnosis of dementia or mild cognitive impairment underwent beta-amyloid (F-18-Florbetaben), tau (F-18-Flortaucipir), and fluorodeoxyglucose (F-18-FDG) PET. The TBI cohort included 15 participants with mild, 16 with moderate, and nine with severe injury. beta-Amyloid level was calculated using the Centiloid (CL) method and tau was measured by standardized uptake value ratios (SUVRs) using the cerebellar cortex as reference region. Analyses were adjusted for age and APOE-e4. The findings were validated in an independent cohort from the Department of Defense-Alzheimer's Disease Neuroimaging Initiative (DOD ADNI) study. There were no significant nor trending differences in beta-amyloid or tau levels or F-18-FDG uptake between the TBI and control groups before and after controlling for covariates. The beta-amyloid and tau findings were replicated in the DOD ADNI validation cohort and persisted when the Australian Imaging Biomarkers and Lifestyle study of aging-Veterans study (AIBL-VETS) and DOD ADNI cohorts were combined (114 TBI vs. 87 controls in total). In conclusion, no increase in the later life accumulation of the neuropathological markers of AD in veterans with a remote history of TBI was identified.

Objective Chronic traumatic encephalopathy (CTE) is a post-mortem diagnosis. We previously reported a frontotemporal predominant tau 18F-MK6240 PET pattern resembling the distribution of CTE in a retired Australian Rules Football player, in the context of a moderate amyloid-β plaque burden.1 This study investigated 18F-MK6240 as a potential CTE biomarker in contact sports players with exposure to repetitive head impacts (sRHI). Methods 33 sRHI and 32 age-matched healthy controls (HC) completed amyloid and tau (18F-MK6240) PET scans. Amyloid PET was quantified in Centiloids. 18F-MK240 standardized uptake value ratios (SUVRs) were generated for the dorsolateral prefrontal cortex and composite regions of interest (ROI) (frontal; mesial-temporal; temporoparietal). Results For sRHI, the primary contact sport was Australian Rules Football (n=17), boxing/kickboxing/martial arts (n=11), rugby (n=4) and soccer (n=1); 36.4% played professionally. sRHI had a mean age of 54.2 (±9.2) (vs HC 53.0±9.5, p=0.61). sRHI did not differ from HC in years of education (p=0.46) but had more impaired MMSE (28.1±1.9 vs 29.3±0.8, p=0.006, d=-0.80) and Clinical Dementia Rating scores (0.21±0.3 vs 0±0, p<0.001, d=1.25). sRHI and HC did not differ in mean Centiloids or 18F-MK6240 SUVRs across all ROIs. Conclusions Contact sports players with exposure to repetitive head impacts did not differ from controls in terms of brain tau burden. Limitations: sample size, heterogeneity in sports type and highest level of participation, and participants with relatively mild cognitive and functional impairments. Additionally, while 18F-MK6240 has high affinity for 3R/4R tau in Alzheimer's disease, its affinity in CTE, particularly important at early stages, remains unclear. Reference Krishnadas N, et al. Case report: 18F-MK6240 tau positron emission tomography pattern resembling chronic traumatic encephalopathy in a retired Australian Rules Football player. doi.org/10.3389/fneur.2020.598980.

Clinical trials of early Alzheimer’s disease (AD) dementia use cognition as a primary outcome and therefore cognitive measures that accurately reflect disease progression and represent multiple cognitive domains are required. Current cognitive endpoints are computed by averaging standardized change from baseline scores (i.e., preclinical Alzheimer cognitive composite (PACC)). Here we compare the PACC to composite scores for which the combination of multiple performance scores has been optimized using machine learning (ML) algorithms in a large, harmonised data set, namely ADOPIC. A dataset harmonised across various cognitive scores (Table 1) was used to construct composite scores using ML-based algorithms: a manifold learning dimension reduction technique (UMAP), principal component analysis (PCA) and Latent variable analysis (LVA). Data from ADNI (n = 1470), AIBL (n = 1105) and OASIS participants (n = 412, Table 2) with ≥3 assessments ≤5 years before clinical progression/last visit were included. Participants were classified clinically as; stable cognitively unimpaired (CU), CU progressing to mild cognitive impairment (MCI) or dementia, stable MCI, MCI progressing to dementia, or AD dementia. For UMAP, all stable participants (including dementia) were used in 4-fold cross-validation training sets. However, for testing UMAP and unsupervised models (PCA and LVA) all clinical groups (except for dementia) were used. The validity of ML-based composites was challenged by modelling mean (SD) change over time in the non-demented clinical groups, using linear mixed model (LMM) analysis. Signal-to-noise ratios (SNRs) were calculated (mean change/SD change) for each composite. The mean change was measured in progressives relative to stable participants of each group. Each ML-based cognitive composite showed sensitivity to cognitive decline in the progressor groups (Figure 1 A). For the MCI progressors, PCA and UMAP composites had significantly higher SNRs than PACC (P<0.01; Figure 1B), however, LVA performance was not significantly better than PACC. For the CU progressor group, SNRs for PCA and LVA did not show significant differences with PACC and UMAP performed significantly worse than PACC (P<0.01). ML-based cognitive composite score computed using PCA provides a practical solution to track cognitive decline with improved performance in tracking cognitive decline in MCI progressors compared to PACC, while being comparable in CUs.

Abstract Background Tau deposition in the mesial temporal lobe (MTL) in the absence of amyloid-β (Aβ−) occurs with aging. The tau PET tracer 18F-MK6240 has low non-specific background binding so is well suited to exploration of early-stage tau deposition. The aim of this study was to investigate the associations between MTL tau, age, hippocampal volume (HV), cognition, and neocortical tau in Aβ− cognitively unimpaired (CU) individuals. Methods One hundred and ninety-nine Aβ− participants (Centiloid < 25) who were CU underwent 18F-MK6240 PET at age 75 ± 5.2 years. Tau standardized uptake value ratio (SUVR) was estimated in mesial temporal (Me), temporoparietal (Te), and rest of the neocortex (R) regions and four Me sub-regions. Tau SUVR were analyzed as continuous variables and compared between high and low MTL SUVR groups. Results In this cohort with a stable clinical classification of CU for a mean of 5.3 years prior to and at the time of tau PET, MTL tau was visually observed in 9% of the participants and was limited to Braak stages I–II. MTL tau was correlated with age (r = 0.24, p < 0.001). Age contributed to the variance in cognitive scores but MTL tau did not. MTL tau was not greater with subjective memory complaint, nor was there a correlation between MTL tau and Aβ Centiloid value, but high tau was associated with smaller HV. Participants with MTL tau had higher tau SUVR in the neocortex but this was driven by the cerebellar reference region and was not present when using white matter normalization. Conclusions In an Aβ− CU cohort, tau tracer binding in the mesial temporal lobe was age-related and associated with smaller hippocampi, but not with subjective or objective cognitive impairment.

Background: In Alzheimer’s disease, heterogeneity has been observed in the postmortem distribution of tau neurofibrillary tangles. Visualizing the topography of tau in vivo may facilitate clinical trials and clinical practice. Objective: This study aimed to investigate whether tau distribution patterns that are limited to mesial temporal lobe (MTL)/limbic regions, and those that spare MTL regions, can be visually identified using 18F-MK6240, and whether these patterns are associated with different demographic and cognitive profiles. Methods: Tau 18F-MK6240 PET images of 151 amyloid-β positive participants with mild cognitive impairment (MCI) and dementia were visually rated as: tau negative, limbic predominant (LP), MTL-sparing, and Typical by two readers. Groups were evaluated for differences in age, APOE ɛ4 carriage, hippocampal volumes, and cognition (MMSE, composite memory and non-memory scores). Voxel-wise contrasts were also performed. Results: Visual rating resulted in 59.6% classified as Typical, 17.9% as MTL-sparing, 9.9% LP, and 12.6% as tau negative. Intra-rater and inter-rater reliability was strong (Cohen’s kappa values of 0.89 and 0.86 respectively). Tracer retention in a “hook”-like distribution on sagittal sequences was observed in the LP and Typical groups. The visually classified MTL-sparing group had lower APOE ɛ4 carriage and relatively preserved hippocampal volumes. Higher MTL tau was associated with greater amnestic cognitive impairment. High cortical tau was associated with greater impairments on non-memory domains of cognition, and individuals with high cortical tau were more likely to have dementia than MCI. Conclusion: Tau distribution patterns can be visually identified using 18F-MK6240 PET and are associated with differences in APOE ɛ4 carriage, hippocampal volumes, and cognition.

Introduction: Neocortical 3R4R (3-repeat/4-repeat) tau aggregates are rarely observed in the absence of amyloid beta (A beta). F-18-M K6240 binds specifically to the 3R4R form of tau that is characteristic of Alzheimer's disease (AD). We report four cases with negative A beta, but positive tau positron emission tomography (PET) findings. Methods: All Australian Imaging, Biomarkers and Lifestyle study of aging (AIBL) study participants with A beta (F-18-NAV4694) and tau (F-18-M K6240) PET scans were included. Centiloid <25 defined negative A beta PET (A beta-). The presence of neocortical tau was defined quantitatively and visually. Results:A beta- PET was observed in 276 participants. Four of these participants (one cognitively unimpaired [CU], two mild cognitive impairment [MCI], one AD) had tau tracer retention in a pattern consistent with Braak tau stages V to VI. Fluid biomarkers supported a diagnosis of AD. In silico analysis of APP, PSEN1, PSEN2, and MAPT genes did not identify relevant functional mutations. Discussion: Discordant cases were infrequent (1.4% of all A beta- participants). In these cases, the A beta PET ligand may not be detecting the A beta that is present.

Background: In Alzheimer’s disease, heterogeneity has been observed in the postmortem distribution of tau neurofibrillary tangles. Visualizing the topography of tau in vivo may facilitate clinical trials and clinical practice. Objective: This study aimed to investigate whether tau distribution patterns that are limited to mesial temporal lobe (MTL)/limbic regions, and those that spare MTL regions, can be visually identified using 18 F-MK6240, and whether these patterns are associated with different demographic and cognitive profiles. Methods: Tau 18 F-MK6240 PET images of 151 amyloid-β positive participants with mild cognitive impairment (MCI) and dementia were visually rated as: tau negative, limbic predominant (LP), MTL-sparing, and Typical by two readers. Groups were evaluated for differences in age, APOE ɛ4 carriage, hippocampal volumes, and cognition (MMSE, composite memory and non-memory scores). Voxel-wise contrasts were also performed. Results: Visual rating resulted in 59.6% classified as Typical, 17.9% as MTL-sparing, 9.9% LP, and 12.6% as tau negative. Intra-rater and inter-rater reliability was strong (Cohen’s kappa values of 0.89 and 0.86 respectively). Tracer retention in a “hook”-like distribution on sagittal sequences was observed in the LP and Typical groups. The visually classified MTL-sparing group had lower APOE ɛ4 carriage and relatively preserved hippocampal volumes. Higher MTL tau was associated with greater amnestic cognitive impairment. High cortical tau was associated with greater impairments on non-memory domains of cognition, and individuals with high cortical tau were more likely to have dementia than MCI. Conclusion: Tau distribution patterns can be visually identified using 18 F-MK6240 PET and are associated with differences in APOE ɛ4 carriage, hippocampal volumes, and cognition. Keywords Alzheimer’s disease , cognition , F-MK6240 , patterns , positron emission tomography , tau

ABSTRACTTau deposition plays a critical role over cognition and neurodegeneration in Alzheimer’s disease (AD). Recent generation tracers have high target to background ratios giving a wide dynamic range that may improve sensitivity for detection of low levels of tau (Pascoal, Shin et al. 2018). Building on previous evidence, this study aims to characterize the effects of tau deposition as assessed by 18F-MK6240, in a large cohort of patients across the AD disease spectrum.A total of 464 participants, enrolled in the AIBL-ADNeT study, underwent 18F-MK6240 tau PET, 18F-NAV4964 Aβ PET, 3D structural MRI (hippocampal and whole-brain cortical volumes) and extensive neuropsychological evaluation. Participants included 266 cognitively unimpaired controls (CU), 112 patients with mild cognitive impairment (MCI), and 86 patients with probable AD dementia. Evaluation included the characterization of the pattern and degree of 18F-MK6240 tracer retention in each clinical group as well as assessment of the relationship between 18F-MK6240 and age, Aβ imaging, brain volumetrics and cognition in each of the clinical groups. Standard uptake value ratios (SUVR) were estimated in four predefined composite regions of interest (ROIs), reflecting the stereotypical progression of tau pathology in the brain: 1. Mesial-temporal (Me), 2. Temporoparietal (Te), 3. Remainder of neocortex (R), 4. A temporal meta-region termed metaT+.18F-MK6240 retention was higher in AD patients compared with all other diagnostic groups, with 18F-MK6240 distinguishing patients with AD from CU individuals, with the highest effect size obtained in the amygdala (Cohen’s d: 2.07), and Me (Cohen’s d: 1.99). When considering Aβ status, 18F-MK6240 not only was able to distinguish between Aβ+ AD patients and Aβ- CU (Cohen’s d: 2.23), but also between Aβ+ and Aβ- CU (Cohen’s d: 1.32). In Aβ- CU, 18F-MK6240 retention in Me showed a slow age-related increase, while 18F-MK6240 retention was higher in younger elderly Aβ+ AD patients compared to their older counterparts. There was a sigmoidal relationship between subthreshold tau and Aβ, providing evidence for a very slow but steady increase in subthreshold tau prior to a fast increase in cortical Aβ. Moreover, a non-linear relationship between Aβ and tau suggest that detectable cortical Aβ precedes detectable cortical tau. While age was the main predictor of cognitive decline in CU, and Aβ and hippocampal volume in MCI, the main predictor of cognitive decline in the AD group was tau. High tau was associated with faster cognitive decline and clinical progression in the CU and MCI groups.This large study provides further evidence that 18F-MK6240 discriminates CU from AD and, most importantly, Aβ+ from Aβ- CU individuals with high effect sizes, suggesting that 18F- MK6240 can detect lower tau levels than earlier tau tracers, crucial for early detection of tau deposition as well as tracking small tau changes over time. In conclusion, identification of regional cortical tau deposition has critical diagnostic and prognostic implications and should become a standard tool to identify individuals at risk, as well as outcome measure, in both anti- Aβ and anti-tau trials.

To improve understanding of Alzheimer's disease, large observational studies are needed to increase power for more nuanced analyses. Combining data across existing observational studies represents one solution. However, the disparity of such datasets makes this a non-trivial task. Here, a machine learning approach was applied to impute longitudinal neuropsychological test scores across two observational studies, namely the Australian Imaging, Biomarkers and Lifestyle Study (AIBL) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) providing an overall harmonised dataset. MissForest, a machine learning algorithm, capitalises on the underlying structure and relationships of data to impute test scores not measured in one study aligning it to the other study. Results demonstrated that simulated missing values from one dataset could be accurately imputed, and that imputation of actual missing data in one dataset showed comparable discrimination (p < 0.001) for clinical classification to measured data in the other dataset. Further, the increased power of the overall harmonised dataset was demonstrated by observing a significant association between CVLT-II test scores (imputed for ADNI) with PET Amyloid-β in MCI APOE-ε4 homozygotes in the imputed data (N = 65) but not for the original AIBL dataset (N = 11). These results suggest that MissForest can provide a practical solution for data harmonization using imputation across studies to improve power for more nuanced analyses.

Extracellular amyloid‐β (Aβ) plaques and intracellular tau neurofibrillary tangles characterize Alzheimer’s disease (AD). Combined use of Aβ and tau PET biomarkers demonstrates that high neocortical tau is rarely observed in the absence of high Aβ. We describe the prevalence of participants with high neocortical tau PET retention (visually and quantitatively) with a correspondingly low Aβ PET result and characterize these participants.

Objective: Since 2000, over 350,000 US military personnel have been diagnosed with a traumatic brain injury (TBI) (VA, 2010). Whilst epidemiological studies report up to a fourfold increased risk for dementia associated with brain injury amongst veterans there is limited controlled research into the long-term neuropsychological burden of injury.Main aim: The study aimed to determine whether Australian Vietnam war veterans with service-related TBI were more likely to exhibit cognitive deficits, 30-50 years after injury when compared to healthy veteran controls.Materials and methods: 69 male veterans 60-85 years old, underwent psychiatric and neuropsychological assessment; 40 with a TBI (mean age = 68.0 ± 2.5) and 29 without (mean age = 70.1 ± 5.3). The TBI cohort included 15 mild, 16 moderate and nine severe TBI.Results: After adjustment for identified covariates, veterans with moderate-to-severe TBI performed significantly worse than controls on composite measures of memory and learning (M = -0.55 ± 0.69, t(67) = 2.86, p=0.006, d=0.70) and attention and processing speed (M = -0.71 ± 1.08, t(52) = 2.53, p=0.014, d=0.69). There were no differences in cognitive performance between veterans with mild TBI (mTBI) and controls. Conclusion: Results from this study suggest that amongst ageing veterans, a moderate-to-severe TBI sustained during early adulthood is associated with later-life cognitive deficits in memory and learning, attention and processing speed.

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.

Longitudinal tau PET may prove useful for clinical trials, through its ability to detect patterns and rates of in vivo tau accumulation in ageing and Alzheimer’s disease (AD). Clinical trials are increasingly targeting the preclinical phase of AD. Flortaucipir studies estimate a 3% annual increase in global cortical tau SUVR in amyloid-β positive (Aβ+ve) cognitively impaired (CI) cohorts, whereas either no change, or low rates of increase (0.5%), have been demonstrated in Aβ+ve cognitively unimpaired (CU) cohorts. F-18 MK6240 is a novel tau tracer, with high target to background binding. We aimed to evaluate regional rates of 18 F-MK6240 accumulation in ageing and the AD continuum. We performed PET acquisition 90-100 minutes post-injection of 185MBq (±10%) 18 F-MK6240 at baseline and 12 months for 67 Aβ-ve CU, 20 Aβ+ve CU and 19 Aβ+ve CI participants. SUVR (standardized uptake value ratio) for the entorhinal cortex, amygdala, hippocampus, parahippocampus and composite regions of interest (ROI) (Me, mesial temporal; Te, temporoparietal cortices) were generated using the cerebellar cortex as the reference region. Age did not significantly differ between the groups (mean age 74 ± 4.4 Aβ-ve CU, 76.2 ± 5.7 Aβ+ve CU, 72.5 ± 6.4 Aβ+ve CI). Aβ+ve participants (CU and CI) had higher baseline tau SUVR and higher annual percentage increases in tau SUVR compared to Aβ-ve participants in all regions examined (Table 1) (Figure 1). CU Aβ+ve participants had larger increases in Me vs Te (1.6% vs 0.7%), while CI Aβ+ve participants had larger increases in Te vs Me (4.3% vs 1.9%). Compared to Aβ-ve CU participants, Aβ+ve CU participants had higher increases in the amygdala (2.9% vs 1.8%) and entorhinal cortex (1.9% vs 0.7%). Longitudinal tau imaging using 18 F-MK6240 discriminates between ageing and stages of AD. Rate of accumulation in preclinical AD (Aβ+ve CU) was highest in mesial temporal regions, while in CI individuals, rates were highest in the temporoparietal cortex. The amygdala and entorhinal cortex may be early regions to discriminate tau accumulation between Aβ-ve CU and Aβ+ve groups. However, as the variance is large, the precision of these estimates may be refined with a larger sample size. Recruitment is ongoing.

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. 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. 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. 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.

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.

Objective To determine the effect of β-amyloid (Aβ) level on progression risk to mild cognitive impairment (MCI) or dementia and longitudinal cognitive change in cognitively normal (CN) older individuals. Methods All CN from the Australian Imaging Biomarkers and Lifestyle study with Aβ PET and ≥3 years follow-up were included (n = 534; age 72 ± 6 years; 27% Aβ positive; follow-up 5.3 ± 1.7 years). Aβ level was divided using the standardized 0–100 Centiloid scale: <15 CL negative, 15–25 CL uncertain, 26–50 CL moderate, 51–100 CL high, >100 CL very high, noting >25 CL approximates a positive scan. Cox proportional hazards analysis and linear mixed effect models were used to assess risk of progression and cognitive decline. Results Aβ levels in 63% were negative, 10% uncertain, 10% moderate, 14% high, and 3% very high. Fifty-seven (11%) progressed to MCI or dementia. Compared to negative Aβ, the hazard ratio for progression for moderate Aβ was 3.2 (95% confidence interval [CI] 1.3–7.6; p < 0.05), for high was 7.0 (95% CI 3.7–13.3; p < 0.001), and for very high was 11.4 (95% CI 5.1–25.8; p < 0.001). Decline in cognitive composite score was minimal in the moderate group (−0.02 SD/year, p = 0.05), while the high and very high declined substantially (high −0.08 SD/year, p < 0.001; very high −0.35 SD/year, p < 0.001). Conclusion The risk of MCI or dementia over 5 years in older CN is related to Aβ level on PET, 5% if negative vs 25% if positive but ranging from 12% if 26–50 CL to 28% if 51–100 CL and 50% if >100 CL. This information may be useful for dementia risk counseling and aid design of preclinical AD trials.

BACKGROUND:Epidemiological studies suggest a relationship between posttraumatic stress disorder (PTSD) and dementia.OBJECTIVE:This study assessed whether Alzheimer's disease (AD) imaging biomarkers were elevated in Vietnam veterans with PTSD.METHODS:The study compared cognition, amyloid-β, tau, regional brain metabolism and volumes, and the effect of APOE in 83 veterans with and without PTSD defined by the Clinician-Administered PTSD Scale.RESULTS:The PTSD group had significantly lower education, predicted premorbid IQ, total intracranial volume, and Montreal Cognitive Assessment score compared with the controls. There was no difference between the two groups in the imaging or genetic biomarkers for AD.CONCLUSION:Our findings do not support an association between AD pathology and PTSD of up to 50 years duration. Measures to assess cognitive reserve, a factor that may delay the onset of dementia, were lower in the PTSD group compared with the controls and this may account for the previously observed higher incidence of dementia with PTSD.

To ensure the generalisability of findings and consider more nuanced hypotheses, larger sample sizes are required. Combining data from different but similar study cohorts is one solution. However, the disparity of these datasets, e.g. using differing tests to assess specific cognitive domains, makes this a non‐trivial task. Here, we propose a harmonisation solution using imputation strategies1,2 for cognitive memory performance in AIBL3 and ADNI4.