Research Press Release

Neurodegeneration: Biomarkers predict accumulation of amyloid-beta in the brain

Nature

February 1, 2018

Plasma biomarkers that can predict amyloid beta deposition in the brain - the earliest pathological signature of Alzheimer’s disease - are reported online in Nature this week. The findings demonstrate the potential clinical use of plasma biomarkers in predicting brain amyloid-beta status in individuals. However, further validation studies with longitudinal data are needed prior to general clinical application.

At present, levels of amyloid-beta in the brain can only be assessed reliably via PET imaging or by measuring amyloid-beta levels in the cerebrospinal fluid. There is, therefore, an urgent need for a more cost-effective and less-invasive diagnostic tool. Using an approach that combines a technique known as immunoprecipitation with mass spectrometry, Katsuhiko Yanagisawa and colleagues measured the levels of several amyloid-beta-associated peptide fragments in the blood. The authors tested their method using two independent datasets - a discovery dataset from Japan with samples from 121 people and a validation dataset from Australia comprising 252 samples. Both cohorts included cognitively normal individuals, individuals with mild cognitive impairment and patients with Alzheimer’s disease. They show that the ratios of the different amyloid-beta-associated peptide fragments and a composite score can accurately predict the level of amyloid-beta deposition in an individual’s brain.

These plasma biomarkers are minimally invasive and also have cost–benefit and scalability advantages over current techniques, potentially enabling broader clinical access. For example, clinical trials of disease-modifying therapies for Alzheimer’s disease are expected to be most effective when patients are at the earliest stages of the disease; these biomarkers may thus aid the selection of suitable clinical trial participants. In the future, they could potentially be used for population screening to identify at-risk individuals irrespective of socio-economic status, although the authors note that their usefulness as a monitoring tool remains to be evaluated.

DOI:10.1038/nature25456 | Original article

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