Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0030567 (Parkinson's disease)
 63,064 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Electroencephalography (EEG) and simultaneously-recorded electromyography (EMG) data are a means to assess integrity of the functional connection between the cortex and the muscle during movement. EEG-EMG coupling is typically assessed with pair-wise squared coherence, resulting in a small, but statistically-significant coherence between a single EEG and a single EMG channel. However, a means to combine results across subjects is not straightforward with this approach because the exact frequency of maximal EEG-EMG coupling may vary between individuals, and it emphasizes the role of an individual locus in the brain in driving the muscle activity, when interactions between head regions may in fact be more influential on ongoing EMG activity. To deal with these issues, we implemented a multiblock Partial Least Squares (mbPLS) procedure, previously proposed in chemical applications, which incorporates a hierarchical structure into the ordinary two-block PLS often used in neuroimaging studies. In the current implementation, each subject's data features are collected in individual data blocks on a sub-level, while simultaneously aggregating the sub-level information to obtain a super-level group "consensus". We further extended the mbPLS model to include 3-dimensional matrices: time-frequency-EEG channel and a time-frequency-connection utilizing Partial Directed Coherence (PDC). We applied the proposed method to concurrent EEG and EMG data collected from ten normal subjects and nine patients with mild-moderate Parkinson's disease (PD) performing a dynamic motor task-that of sinusoidal squeezing. The results demonstrate that connections between EEG electrodes, rather than activity at individual electrodes, correspond more closely to ongoing EMG activity. In PD subjects, there was enhanced connectivity to and from occipital regions, likely related to the previously-described enhanced use of visual information during motor performance in this group. The proposed mbPLS framework is a promising technique for performing multi-subject, multi-modal data analysis and it allows for robust group inferences even in the face of large inter-subject variability.
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PMID:A multiblock PLS model of cortico-cortical and corticomuscular interactions in Parkinson's disease. 2298 2

Background. Parkinson's disease (PD) remains a clinical diagnosis and biomarkers are needed to detect the disease as early as possible. Genetically determined PD provides an opportunity for studying metabolic differences in connection with disease development. Objectives. To study the levels of intermediary metabolites in cerebrospinal fluid (CSF) from patients with PD, either of sporadic type or in carriers of the LRRK2 p.G2019S mutation. Methods. Results from patients with sporadic PD or LRRK2-PD were compared with asymptomatic LRRK2 mutation carriers and healthy control individuals. CSF was analysed by proton MR spectroscopy ((1)H-MRS) giving reliable results for 16 intermediary metabolites. Partial least squares discriminant analysis (PLS-DA) was applied to study group differences. Results. PLS-DA distinguished PD patients from healthy individuals based on the metabolites identified in CSF, with 2-hydroxybutyrate, glutamine, and dimethyl sulphone largely contributing to the separations. Conclusion. Speculatively, all three metabolites could alter concentration in response to metabolic changes connected with neurodegeneration; glutamine as a means of removing excess nitrogen from brain, dimethyl sulphone as an anti-inflammatory agent, and 2-hydroxybutyrate in connection with altered glutathione metabolism. Potentially, (1)H-MRS is a promising tool for identifying novel biomarkers for PD.
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PMID:Changes to Intermediary Metabolites in Sporadic and LRRK2 Parkinson's Disease Demonstrated by Proton Magnetic Resonance Spectroscopy. 2635 83

Basic fibroblast growth factor (bFGF) has a potential role in the treatment of Parkinson's disease (PD) due to its neurotrophic effect on dopaminergic neurons. To address the metabolic mechanisms of bFGF administration on PD, we have analyzed the metabolic profiles in the striatum of 6-hydroxydopamine (6-OHDA)-induced PD rats after the treatment with bFGF using 1H NMR spectroscopy and partial least squares-discriminant analysis (PLS-DA). In the present study, we found that bFGF treatment can effectively recover PD-induced loss of tyrosine hydroxylase (TH)-positive neurons in the substantia nigra. Metabolomic analyses reveal that PLS-DA failed to discriminate between the control and bFGF groups, indicating that the metabolic difference between these two groups was negligible. However, reliable PLS-DA models can be developed between control and PD groups as well as between PD and bFGF groups, which is attributed to changes in a series of metabolites including GABA, glutamate (Glu), glutamine (Gln), lactate, N-acetylaspartate, creatine, taurine, and myo-inositol. ANOVA results show that the levels of all these metabolites were significantly increased in PD rats relative to normal rats, while PD-induced increase can be significantly reduced to normal levels after bFGF administration. In conclusion, our results suggest that a recovery from PD-induced metabolic disorders may be achieved by bFGF treatment, involving Gln/Glu-GABA cycle, energy metabolism, osmoregulation, and inflammation.
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PMID:NMR-Based Metabolomics Reveal a Recovery from Metabolic Changes in the Striatum of 6-OHDA-Induced Rats Treated with Basic Fibroblast Growth Factor. 2665 45

Primary progressive multiple sclerosis (PPMS) shows a highly variable disease progression with poor prognosis and a characteristic accumulation of disabilities in patients. These hallmarks of PPMS make it difficult to diagnose and currently impossible to efficiently treat. This study aimed to identify plasma metabolite profiles that allow diagnosis of PPMS and its differentiation from the relapsing-remitting subtype (RRMS), primary neurodegenerative disease (Parkinson's disease, PD), and healthy controls (HCs) and that significantly change during the disease course and could serve as surrogate markers of multiple sclerosis (MS)-associated neurodegeneration over time. We applied untargeted high-resolution metabolomics to plasma samples to identify PPMS-specific signatures, validated our findings in independent sex- and age-matched PPMS and HC cohorts and built discriminatory models by partial least square discriminant analysis (PLS-DA). This signature was compared to sex- and age-matched RRMS patients, to patients with PD and HC. Finally, we investigated these metabolites in a longitudinal cohort of PPMS patients over a 24-month period. PLS-DA yielded predictive models for classification along with a set of 20 PPMS-specific informative metabolite markers. These metabolites suggest disease-specific alterations in glycerophospholipid and linoleic acid pathways. Notably, the glycerophospholipid LysoPC(20:0) significantly decreased during the observation period. These findings show potential for diagnosis and disease course monitoring, and might serve as biomarkers to assess treatment efficacy in future clinical trials for neuroprotective MS therapies.
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PMID:Metabolomic Profiles for Primary Progressive Multiple Sclerosis Stratification and Disease Course Monitoring. 2991 33