Abstract
Alzheimer’s disease is a progressive neurodegenerative disease which is characterised by the increased deposition and spread of extracellular β-amyloid plaques and intracellular hyperphospho-rylated tau as neurofibrillary tangles. This is thought to be driven by the sustained activation of brain microglia and astrocytes. In this review, we will provide an overview of the current understanding of the pathogenesis of Alzheimer’s disease, the role of inflammation and associated factors in disease progression as well as current treatments including those in late-stage clinical trials. We will also discuss how machine learning has been previously used to create Alzheimer’s disease risk metrics and the potential for blood-based inflammatory factors to be used to create an artificial intelligence-based Alzheimer’s disease early warning system. The development of an Alzheimer’s disease-based early warning system would enable the improved use of existing and future disease-modifying agents and thereby help to slow or halt disease progression.