Metabolic profiling of Parkinson's disease: evidence of biomarker from gene expression analysis and rapid neural network detection
Identifieur interne : 000836 ( Main/Curation ); précédent : 000835; suivant : 000837Metabolic profiling of Parkinson's disease: evidence of biomarker from gene expression analysis and rapid neural network detection
Auteurs : Shiek Ahmed [Inde] ; Winkins Santosh [Inde] ; Suresh Kumar [Inde] ; Hema Christlet [Inde]Source :
- Journal of Biomedical Science [ 1021-7770 ] ; 2009.
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder. The diagnosis of Parkinsonism is challenging because currently none of the clinical tests have been proven to help in diagnosis. PD may produce characteristic perturbations in the metabolome and such variations can be used as the marker for detection of disease. To test this hypothesis, we used proton NMR and multivariate analysis followed by neural network pattern detection.
1H nuclear magnetic resonance spectroscopy analysis was carried out on plasma samples of 37 healthy controls and 43 drug-naive patients with PD. Focus on 22 targeted metabolites, 17 were decreased and 5 were elevated in PD patients (p < 0.05). Partial least squares discriminant analysis (PLS-DA) showed that pyruvate is the key metabolite, which contributes to the separation of PD from control samples. Furthermore, gene expression analysis shows significant (p < 0.05) change in expression of
The results increase the prospect of a robust molecular definition in detection of PD through the early symptomatic phase of the disease. This is an ultimate opening for therapeutic intervention. If validated in a genuinely prospective fashion in larger samples, the biomarker trajectories described here will go a long way to facilitate the development of useful therapies. Moreover, implementation of neural network will be a breakthrough in clinical screening and rapid detection of PD.
Url:
DOI: 10.1186/1423-0127-16-63
PubMed: 19594911
PubMed Central: 2720938
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PMC:2720938Le document en format XML
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>Parkinson's disease (PD) is a neurodegenerative disorder. The diagnosis of Parkinsonism is challenging because currently none of the clinical tests have been proven to help in diagnosis. PD may produce characteristic perturbations in the metabolome and such variations can be used as the marker for detection of disease. To test this hypothesis, we used proton NMR and multivariate analysis followed by neural network pattern detection.</p>
</sec>
<sec sec-type="methods"><title>Methods & Results</title>
<p><sup>1</sup>
H nuclear magnetic resonance spectroscopy analysis was carried out on plasma samples of 37 healthy controls and 43 drug-naive patients with PD. Focus on 22 targeted metabolites, 17 were decreased and 5 were elevated in PD patients (p < 0.05). Partial least squares discriminant analysis (PLS-DA) showed that pyruvate is the key metabolite, which contributes to the separation of PD from control samples. Furthermore, gene expression analysis shows significant (p < 0.05) change in expression of <italic>PDHB </italic>
and <italic>NPFF </italic>
genes leading to increased pyruvate concentration in blood plasma. Moreover, the implementation of <sup>1</sup>
H- NMR spectral pattern in neural network algorithm shows 97.14% accuracy in the detection of disease progression.</p>
</sec>
<sec><title>Conclusion</title>
<p>The results increase the prospect of a robust molecular definition in detection of PD through the early symptomatic phase of the disease. This is an ultimate opening for therapeutic intervention. If validated in a genuinely prospective fashion in larger samples, the biomarker trajectories described here will go a long way to facilitate the development of useful therapies. Moreover, implementation of neural network will be a breakthrough in clinical screening and rapid detection of PD.</p>
</sec>
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