AETIONOMY, a Cross-Sectional Study Aimed at validating a new taxonomy of Neurodegenerative Diseases: Study design and subject characteristics

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Abstract

Abstract Background Although advances in the understanding of neurodegenerative diseases (NDDs) have led to improvements in classification and diagnosis and most importantly to new therapies, the unmet medical needs remain significant due to high treatment failure rates. The AETIONOMY project funded by the Innovative Medicine Initiative (IMI) aims at using multi-OMICs and bioinformatics to identify new classifications for NDDs based on common molecular pathophysiological mechanisms in view of improving the availability of personalised treatments. Objectives The purpose of the AETIONOMY cross-sectional study is to validate novel patient classification criteria provided by these tools. Methods This was a European multi centre, cross-sectional, clinical study conducted at 6 sites in 3 countries. Standardised clinical data, biosamples from peripheral blood, cerebrospinal fluid, skin biopsies, and data from a multi-OMICs approach were collected in patients suffering from Alzheimer’s and Parkinson’s disease, as well as healthy controls. Results From September 2015 to December 2017 a total of 421 participants were recruited including 95 Healthy Controls. Nearly 1,500 biological samples were collected. The study achieved its objective with respect to Parkinson’s disease (PD) recruitment, however it was unable to recruit many new Alzheimer Disease (AD) patients. Overall, data from 413 evaluable subjects (405 PD and 8 AD) are available for analysis. PD patients and controls were well matched with respect to age (mean 63.4 years), however, close gender matching was not achieved. Approximately half of all PD patients and one At-Risk subject were taking dopamine agonists; rates of Levodopa usage were slightly higher (∼60%). Median MDS-UPDRS Part III Scores (OFF state) ranged from 45 (SD 18) in those with Genetic PD to 2 (SD 3) in Healthy Controls. The standardised methodologies applied resulted in a high-quality database with very few missing data. Conclusion This is one of the collaborative multi-OMICs studies in individuals suffering from PD and AD involving a control group. It is expected that the integration of data will provide new biomarker-led descriptions of clusters of patient subgroups.

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