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Most neurodegenerative disorders are associated with aggregated protein deposits. In the case of Alzheimer disease (AD), extracellular amyloid-β (Aβ) aggregates and intracellular tau neurofibrillary tangles are the two neuropathological hallmarks of the disease. Aβ-PET imaging has already been approved for clinical use and is being used in clinical trials of anti-Aβ therapies both for patient recruitment and as an outcome measure. These studies have shown that Aβ accumulation is a protracted process that can extend for more than 2 decades before the onset of clinical AD. This Review describes how in vivo brain imaging of Aβ pathology has revolutionized the evaluation of patients with clinical AD by providing robust and reproducible statements of global or regional brain Aβ burden and enabling the monitoring of disease progression. The role of selective tau imaging is discussed, focusing on how longitudinal tau and Aβ imaging studies might reveal the various effects (sequential and/or parallel, independent and/or synergistic) of these proteins on progression, cognition and other disease-specific biomarkers of neurodegeneration. Finally, imaging studies are discussed in the context of elucidating the respective roles of Aβ and tau in AD and in advancing our understanding of the relationship and/or interplay between these two proteinopathies.

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Previous studies suggest physical activity improves cognition and lowers Alzheimer's disease (AD) risk. However, key AD pathogenic factors that are thought to be influenced by physical activity, particularly plasma amyloid-β (Aβ) and Aβ brain load, have yet to be thoroughly investigated. The objective of this study was to determine if plasma Aβ and amyloid brain deposition are associated with physical activity levels, and whether these associations differed between carriers and non-carriers of the apolipoprotein E (APOE) ε4 allele. Five-hundred and forty six cognitively intact participants (aged 60-95 years) from the Australian Imaging, Biomarkers and Lifestyle Study of Ageing (AIBL) were included in these analyses. Habitual physical activity levels were measured using the International Physical Activity Questionnaire (IPAQ). Serum insulin, glucose, cholesterol and plasma Aβ levels were measured in fasting blood samples. A subgroup (n=116) underwent (11)C-Pittsburgh compound B (PiB) positron emission tomography (PET) scanning to quantify brain amyloid load. Higher levels of physical activity were associated with higher high density lipoprotein (HDL) (P=0.037), and lower insulin (P<0.001), triglycerides (P=0.019) and Aβ1-42/1-40 ratio (P=0.001). After stratification of the cohort based on APOE ε4 allele carriage, it was evident that only non-carriers received the benefit of reduced plasma Aβ from physical activity. Conversely, lower levels of PiB SUVR (standardised uptake value ratio) were observed in higher exercising APOE ε4 carriers. Lower plasma Aβ1-42/1-40 and brain amyloid was observed in those reporting higher levels of physical activity, consistent with the hypothesis that physical activity may be involved in the modulation of pathogenic changes associated with AD.

Although Aβ did not differ by sex, cognitive decline was greater in females with higher Aβ. Our findings suggest that sex may play a modifying role on risk of Alzheimer's disease-related cognitive decline.

A major barrier to the effective conduct of clinical trials of new drug candidates against Alzheimer's disease (AD) and to identifying patients for receiving future disease-modifying treatments is the limited capacity of the current health system to find and diagnose patients with early AD pathology. This may be related in part to the limited capacity of the current health systems to select those people likely to have AD pathology in order to confirm the diagnosis with available cerebrospinal fluid and imaging biomarkers at memory clinics. In the current narrative review, we summarize the literature on candidate blood tests for AD that could be implemented in primary care settings and used for the effective identification of individuals at increased risk of AD pathology, who could be referred for potential inclusion in clinical trials or future approved treatments following additional testing. We give an updated account of blood-based candidate biomarkers and biomarker panels for AD-related brain changes. Our analysis centres on biomarker candidates that have been replicated in more than one study and discusses the need of further studies to achieve the goal of a primary care-based screening algorithm for AD.

Genetic association studies have identified 44 common genome-wide significant risk loci for late-onset Alzheimer’s disease (LOAD). However, LOAD genetic architecture and prediction are unclear. Here we estimate the optimal P-threshold (Poptimal) of a genetic risk score (GRS) for prediction of LOAD in three independent datasets comprising 676 cases and 35,675 family history proxy cases. We show that the discriminative ability of GRS in LOAD prediction is maximised when selecting a small number of SNPs. Both simulation results and direct estimation indicate that the number of causal common SNPs for LOAD may be less than 100, suggesting LOAD is more oligogenic than polygenic. The best GRS explains approximately 75% of SNP-heritability, and individuals in the top decile of GRS have ten-fold increased odds when compared to those in the bottom decile. In addition, 14 variants are identified that contribute to both LOAD risk and age at onset of LOAD.