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Metabolomics. 2019 Jun 10;15(6):90. doi: 10.1007/s11306-019-1554-x.

A metabolomic signature of treated and drug-naïve patients with Parkinson's disease: a pilot study.

Author information

1
Department of Medicine, Surgery and Dentistry, "Scuola Medica Salernitana", Neuroscience Section, University of Salerno, Baronissi, SA, Italy. troisi@theoreosrl.com.
2
Theoreo srl, Via degli Ulivi 3, 84090, Montecorvino Pugliano, SA, Italy. troisi@theoreosrl.com.
3
European Biomedical Research Institute of Salerno (EBRIS), Via S. de Renzi, 3, 84125, Salerno, SA, Italy. troisi@theoreosrl.com.
4
Department of Medicine, Surgery and Dentistry, "Scuola Medica Salernitana", Neuroscience Section, University of Salerno, Baronissi, SA, Italy.
5
Department of Motor Science and Wellness, University Parthenope, Naples, Italy.
6
Institute of Diagnosis and Care (IDC) Hermitage-Capodimonte, Naples, Italy.
7
Department of Medicine and Surgery, Center for Neurodegenerative Diseases (CEMAND), Neuroscience Section, University of Salerno, Fisciano, Italy.
8
Ios & Coleman, Naples, Italy.

Abstract

INTRODUCTION:

About 90% of cases of Parkinson's disease (PD) are idiopathic and attempts to understand pathogenesis typically assume a multifactorial origin. Multifactorial diseases can be studied using metabolomics, since the cellular metabolome reflects the interplay between genes and environment.

OBJECTIVE:

The aim of our case-control study is to compare metabolomic profiles of whole blood obtained from treated PD patients, de-novo PD patients and controls, and to study the perturbations correlated with disease duration, disease stage and motor impairment.

METHODS:

We collected blood samples from 16 drug naïve parkinsonian patients, 84 treated parkinsonian patients, and 42 age matched healthy controls. Metabolomic profiles have been obtained using gas chromatography coupled to mass spectrometry. Multivariate statistical analysis has been performed using supervised models; partial least square discriminant analysis and partial least square regression.

RESULTS:

This approach allowed separation between discrete classes and stratification of treated patients according to continuous variables (disease duration, disease stage, motor score). Analysis of single metabolites and their related metabolic pathways revealed unexpected possible perturbations related to PD and underscored existing mechanisms that correlated with disease onset, stage, duration, motor score and pharmacological treatment.

CONCLUSION:

Metabolomics can be useful in pathogenetic studies and biomarker discovery. The latter needs large-scale validation and comparison with other neurodegenerative conditions.

KEYWORDS:

Gas chromatography–mass spectrometry; Metabolome; Parkinson’s disease

PMID:
31183578
DOI:
10.1007/s11306-019-1554-x

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