Journal
CHEMOSENSORS
Volume 10, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/chemosensors10060229
Keywords
untargeted metabolomics; Parkinson's disease; patient stratification; health and wellbeing monitoring; metabolic signatures; FTIR; chemometrics; classification strategy
Funding
- European Union [801586]
- Ministry of Science and Innovation [CTQ2011-26603]
- Marie Curie Actions (MSCA) [801586] Funding Source: Marie Curie Actions (MSCA)
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An untargeted FTIR metabolomic approach was used to study metabolic changes in Parkinson's disease. A classification strategy based on SELECT-LDA was proposed, achieving high correct assignment rates in distinguishing PD patients from AD patients and healthy controls, as well as stratifying different stages of PD and differentiating between PDD and AD. The selected metabolic signatures could be used for screening and diagnosis.
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.
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