Intricate connections between aging and Alzheimer’s diseases molecular trajectories revealed by plasma proteomics

Abstract

AbstractBackgroundAging is the greatest non‐genetic risk factor for many diseases including Alzheimer’s Disease (AD) and its mechanisms are thus increasingly recognized as potential therapeutic targets.Since the molecular relationships between normal aging and AD are still largely unknown, we took advantage of recent methodological developments to measure more than 7k proteins in plasma to identify common but also different molecular mechanisms involved in aging and AD.MethodRecently, we developed new data‐driven bioinformatics approaches that uncovered marked non‐linear alterations in the human plasma proteome with age (Lehallier et al. Nature Medicine 2019). We applied these machine learning approaches to 271 patients at different stages of AD (61 subjects with Subjective Cognitive Decline: SCD, 106 with Mild Cognitive Impairment: MCI and 104 with AD) and compared these changes to those observed in a healthy aging cohort (370 subjects, 18‐69 years).ResultThe analysis of proteomics trajectories confirmed marked non‐linear alterations in the human plasma proteome with aging and uncovered the complex molecular choreography of AD. By analyzing trajectories of groups of proteins, we identified multiple clusters of proteins that are differentially affected during aging and AD. These clusters were enriched for various biological processes relevant for AD progression such as the complement and coagulation cascades, axon guidance and angiogenesis (q<0.05)ConclusionAltogether, our results reveal the intricate connections between aging and AD at the molecular level. The integrated proteomics trajectories analysis performed in this study allows the identification of new targets possibly playing a key role in AD but also pinpoints when these proteins are deviating from their normal trajectories and thus when they should be targeted by new therapies.