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Background: Plasma p217+tau has shown high concordance with cerebrospinal fluid (CSF) and positron emission tomography (PET) measures of amyloid- (A ) and tau in Alzheimer’s Disease (AD). However, its association with longitudinal cognition and comparative performance to PET A and tau in predicting cognitive decline are unknown. Objectives: To evaluate whether p217+tau can predict the rate of cognitive decline observed over two-year average follow-up and compare this to prediction based on A (18F-NAV4694) and tau (18F-MK6240) PET. We also explored the sample size required to detect a 30% slowing in cognitive decline in a 2-year trial and selection test cost using p217+tau (pT+) as compared to PET A (A+) and tau (T+) with and without p217+tau pre-screening. Design: A prospective observational cohort study. Setting: Participants of the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) and Australian Dementia Network (ADNeT). Participants: 153 cognitively unimpaired (CU) and 50 cognitively impaired (CI) individuals. Measurements: Baseline p217+tau Simoa assay 18F-MK6240 tau-PET and 18F-NAV4694 A -PET with neuropsychological follow-up (MMSE, CDR-SB, AIBL-PACC) over 2.4 ± 0.8 years. Results: In CI, p217+tau was a significant predictor of change in MMSE ( = −0.55, p < 0.001) and CDR-SB ( =0.61, p < 0.001) with an effect size similar to A Centiloid (MMSE = −0.48, p = 0.002; CDR-SB = 0.43, p = 0.004) and meta-temporal (MetaT) tau SUVR (MMSE: = −0.62, p < 0.001; CDR-SB: = 0.65, p < 0.001). In CU, only MetaT tau SUVR was significantly associated with change in AIBL-PACC ( = −0.22, p = 0.008). Screening pT+ CI participants into a trial could lead to 24% reduction in sample size compared to screening with PET for A+ and 6–13% compared to screening with PET for T+ (different regions). This would translate to an 81–83% biomarker test cost-saving assuming the p217+tau test cost one-fifth of a PET scan. In a trial requiring PET A+ or T+, p217+tau pre-screeni

Accumulation of β-amyloid (Aβ) in the brain is associated with memory decline in healthy individuals as a prelude to Alzheimer’s disease (AD). Genetic factors may moderate this decline. We examined the role of apolipoprotein E (ε4 carrier[ε4+], ε4 non-carrier[ε4−]) and brain-derived neurotrophic factor (BDNFVal/Val, BDNFMet) in the extent to which they moderate Aβ-related memory decline. Healthy adults (n=333, Mage=70 years) enrolled in the Australian Imaging, Biomarkers and Lifestyle study underwent Aβ neuroimaging. Neuropsychological assessments were conducted at baseline, 18-, 36- and 54-month follow-ups. Aβ positron emission tomography neuroimaging was used to classify participants as Aβ− or Aβ+. Relative to Aβ−ε4−, Aβ+ε4+ individuals showed significantly faster rates of cognitive decline over 54 months across all domains (d=0.40–1.22), while Aβ+ε4− individuals showed significantly faster decline only on verbal episodic memory (EM). There were no differences in rates of cognitive change between Aβ−ε4− and Aβ−ε4+ groups. Among Aβ+ individuals, ε4+/BDNFMet participants showed a significantly faster rate of decline on verbal and visual EM, and language over 54 months compared with ε4−/BDNFVal/Val participants (d=0.90–1.02). At least two genetic loci affect the rate of Aβ-related cognitive decline. Aβ+ε4+/BDNFMet individuals can expect to show clinically significant memory impairment after 3 years, whereas Aβ+ε4+/BDNFVal/Val individuals can expect a similar degree of impairment after 10 years. Little decline over 54 months was observed in the Aβ− and Aβ+ ε4− groups, irrespective of BDNF status. These data raise important prognostic issues in managing preclinical AD, and should be considered in designing secondary preventative clinical trials.

Pittsburgh Compound B (PiB) is a C11 PET tracer designed to bind to amyloid plaques, one of the hallmark of Alzheimer's disease. The potential of PiB as an early marker of Alzheimer's disease has lead to an increasing use of PiB and the development of several F18 equivalents. Quantitative analysis of PiB images requires an accurate normalisation, parcellation and estimation of retention in the brain's gray matter. Typically this relies on co-registered MRI to extract the cerebellum, compute the standardized uptake value ratio (SUVR) and provide parcellation and segmentation for quantification of neocortical SUVR. However, not all subjects undergo MRI. In this paper we propose a highly accurate MR-less parcellation, SUVR normalisation and quantification method for PiB images. This involves rigidly registering the raw PiB images to a PiB atlas, computing pair-wise normalised mutual information, and constructing a 2D manifold. Each new scan is mapped on the manifold and its k nearest neighbours are selected as atlases in a segmentation propagation scheme with their associated MRI segmentations and parcellation used as priors to estimate the SUVR normalisation and quantification. Comparison of our MRless approach to an MR-based approach showed a coefficient of correlation of neocortical PiB SUVR of R2=0.94 and an absolute mean error of 5.9%.

Burnham, S. C., Coloma, P. M., Li, Q. X., Collins, S., Savage, G., Laws, S., ... & Rowe, C. C. (2019). Application of the NIA-AA research framework: Towards a biological definition of Alzheimer’s disease using cerebrospinal fluid biomarkers in the AIBL study. The Journal of Prevention of Alzheimer's Disease, 6(4), 248-255. https://doi.org/10.14283/jpad.2019.25

Gardener S.L., Rainey-Smith S.R., Barnes M.B., Sohrabi H.R., Weinborn M., Lim Y.Y., Harrington K., Taddei K., Gu Y., Rembach A., Szoeke C., Ellis K.A., Masters C.L., Macaulay S.L., Rowe C.C., Ames D., Keogh J.B., Scarmeas N., & Martins R.N. (2015). Dietary patterns and cognitive decline in an Australian study of ageing. Molecular Psychiatry. Available here

Cheng, L., Doecke, J., Sharples, R., Villemagne, V., Fowler, C., Rembach, A., Martins, R. N., Rowe, C., Macaulay, S., Masters, C., Hill, A., & AIBL Research Group, T. (2015). Prognostic serum miRNA biomarkers associated with Alzheimer's disease shows concordance with neuropsychological and neuroimaging assessment . Molecular Psychiatry, 20(10), 1188- 1197 Available here