Extraction of Reduced Infrared Biomarker Signatures for the Stratification of Patients Affected by Parkinson’s Disease: An Untargeted Metabolomic Approach

Abstract

An untargeted Fourier transform infrared (FTIR) metabolomic approach was employed to study metabolic changes and disarrangements, recorded as infrared signatures, in Parkinson’s disease (PD). Herein, the principal aim was to propose an efficient sequential classification strategy based on SELECT-LDA, which enabled optimal stratification of three main categories: PD patients from subjects with Alzheimer’s disease (AD) and healthy controls (HC). Moreover, sub-categories, such as PD at the early stage (PDI) from PD in the advanced stage (PDD), and PDD vs. AD, were stratified. Every classification step with selected wavenumbers achieved 90.11% to 100% correct assignment rates in classification and internal validation. Therefore, selected metabolic signatures from new patients could be used as input features for screening and diagnostic purposes.

Keywords

• untargeted metabolomics
• Parkinson’s disease
• patient stratification
• health and wellbeing monitoring
• metabolic signatures
• FTIR