Background: 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.Methods & Results: 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 ofPDHBandNPFFgenes leading to increased pyruvate concentration in blood plasma. Moreover, the implementation of¹H- NMR spectral pattern in neural network algorithm shows 97.14% accuracy in the detection of disease progression.Conclusion: 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.

Background: Systems biological approach of molecular connectivity map has reached to a great interest to understand the gene functional similarities between the diseases. In this study, we developed a computational framework to build molecular connectivity maps by integrating mutated and differentially expressed genes of neurological and psychiatric diseases to determine its relationship with aging. Results: The systematic large-scale analyses of 124 human diseases create three classes of molecular connectivity maps. First, molecular interaction of disease protein network generates 3632 proteins with 6172 interactions, which determines the common genes/proteins between diseases. Second, Disease-disease network includes 4845 positively scored disease-disease relationships. The comparison of these disease-disease pairs with Medical Subject Headings (MeSH) classification tree suggests 25% of the disease-disease pairs were in same disease area. The remaining can be a novel disease-disease relationship based on gene/protein similarity. Inclusion of aging genes set showed 79 neurological and 20 psychiatric diseases have the strong association with aging. Third and lastly, a curated disease biomarker network was created by relating the proteins/genes in specific disease contexts, such analysis showed 73 markers for 24 diseases. Further, the overall quality of the results was achieved by a series of statistical methods, to avoid insignificant data in biological networks. Conclusions: This study improves the understanding of the complex interactions that occur between neurological and psychiatric diseases with aging, which lead to determine the diagnostic markers. Also, the disease-disease association results could be helpful to determine the symptom relationships between neurological and psychiatric diseases. Together, our study presents many research opportunities in post-genomic biomarkers development.