Progress and Prospects in Parkinson's Research/Monitoring Parkinson's Disease/Biomarkers

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BackgroundEdit

The FDA defines a biomarker as, "A characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention". The search for a biomarker is a Holy Grail of Parkinson's Disease research.

Add topics here:

  • Measurements of motor and non-motor symptoms
  • Genetic screening
  • ...

Include information on Tracking Parkinson's project[1] and PPMI

ProteomicsEdit

Background

Proteins are a major component of living organism and the study of their metabolism, structure and functions is called Proteomics. Patterns of protein production can be measured and form a unique fingerprint specific to individual diseases.

2009

Licker et al [2] have evaluated the role of proteomics in PD research.

We propose the view that Parkinson's disease may be an acquired or genetically-determined brain proteinopathy involving an abnormal processing of several, rather than individual neuronal proteins, and discuss some pre-analytical and analytical developments in proteomics that may help in verifying this concept.


2010

Alberio and Fasano [3] have carried out a review of the status of proteomics in relation to PD.

In this review, we focus on cellular and animal models of Parkinson's disease by describing their advantages and limitations as useful tools to identify pathogenetic pathways that deserve further exploitation. In parallel, we discuss how proteomics may provide a potent tool to observe altered pathways in models or altered biomarkers in patients with an unbiased, hypothesis-free approach.

UrateEdit

Background

Uric acid is a product of the metabolic processing of a class of nutrients called purines and in humans it acts as a powerful anti-oxidant and is excreted in urine. In high concentrations it is deposited in the lower joints as a salt called urate,leading to the painful condition known as gout.

2008

Schwarzschild et al [4] tested the hypothesis that serum urate could be used as a predictor of clinical and radiographic progression for Parkinson’s disease. Their conclusions were:-

These findings identify serum urate as the first molecular factor directly linked to the progression of typical PD and suggest that targeting urate or its determinants could be an effective disease-modifying therapy in PD.

2009

Andreadou et al [5] measured serum Uric acid levels in 43 PD patients and 47 healthy volunteers, age and sex-matched. UA levels were correlated with disease duration, severity and treatment. The results were:-

There may be increased consumption of UA as a scavenger in PD, possibly heightened by dopaminergic drug treatment. Given the antioxidant properties of UA, manipulation of its concentrations should be investigated for potential therapeutic strategies of the disease.

Voice AnalysisEdit

Background

PD patients experience changes in the pitch of their voice and this has led to attempts to analyse speech patterns with a view to establishing unique characteristics for the disease.

2012

Tsanas et al [6] tested how accurately speech processing algorithms caould be used to discriminate PD subjects from healthy controls.

In total, we compute 132 dysphonia measures from sustained vowels. Then, we select four parsimonious subsets of these dysphonia measures using four feature selection algorithms, and map these feature subsets to a binary classification response using two statistical classifiers: random forests and support vector machines. We use an existing database consisting of 263 samples from 43 subjects, and demonstrate that these new dysphonia measures can outperform state-of-the-art results, reaching almost 99% overall classification accuracy using only ten dysphonia features. We find that some of the recently proposed dysphonia measures complement existing algorithms in maximizing the ability of the classifiers to discriminate healthy controls from PD subjects. We see these results as an important step toward noninvasive diagnostic decision support in PD.

White Blood CellsEdit

Background

White blood cells or leukocytes are a feature of the immune system. Five different types exist and there are normally approximately 7,000 white blood cells per microlitre of blood. They make up about 1% of the total blood volume in a healthy adult. An increase in the number of leukocytes over the upper limits is called leukocytosis, and a decrease below the lower limit is called leukopenia. The volume, type and physical properties such as conductivity, and granularity can be analysed and form a signature specific to unique disease conditions.

2011

Baskup and Funk [7], two researchers at the Clinical Brain Institute in Tubingen, Germany, have been awarded a patent for a method of predicting susceptibility to PD. The stressed cells in neurodegenerative diseases release signalling substances that are attracted to cells of the immune system. This results in an increase in the level of white blood cells, which is measured by this method.

Further ReadingEdit

Use this table to search for other relevant data which can be added to this page

DATABASES
SEARCH WORDS PUBMED PUBMED CENTRAL GOOGLE SCHOLAR
Parkinson’s proteomics Yes Yes Yes
Parkinson’s urate Yes Yes Yes
Parkinson's voice analysis Yes Yes Yes
Parkinson’s white blood cells Yes Yes Yes

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Urate & Uric Acid,

ReferencesEdit

  1. Parkinson's UK Tracking Parkinson's biomarker project: http://www.parkinsons.org.uk/tracking
  2. Licker, V.; Kövari, E.; Hochstrasser, D. F. and Burkhard, P. R. (2009) Abstract Journal of Proteomics 73 (1) 10-29 Proteomics in human Parkinson's disease research. http://ukpmc.ac.uk/abstract/MED/19632367/reload=0;jsessionid=VZFee9vuH2YIJJPzbUgK.0
  3. Alberio, T. and Fasano, M. (2010) Abstract J. Biotechnol.156 (4) 325 - 337. Proteomics in Parkinson's disease: An unbiased approach towards peripheral biomarkers and new therapies. http://www.ncbi.nlm.nih.gov/pubmed/21925549
  4. Schwarzschild, M.A.; Schwid, S. R.; Marek, K.; Watts A.; Lang, A. E.; Oakes, D.; Shoulson, I.; Ascherio A.; Parkinson Study Group PRECEPT Investigators.; Hyson, C,; Gorbold, E.; Rudolph, A.; Kieburtz K,l. Fahn, S,; Gauger, L.; Goetz, C.; Seibyl, J.; Forrest, M and Ondrasik, J. font color="maroon">(2008) Abstract< Arch. Neurol. 65 (6) 716 – 723. Serum urate as a predictor of clinical and radiographic progression in Parkinson disease http://www.ncbi.nlm.nih.gov/pubmed/18413464
  5. Andreadou, E.; Nikolaou C.; Gournaras, F.; Rentzos, M.; Boufidou, F.’ Tsoutsou, A.; Zournas, C,; Zissimopoulos, V. and Vassilopoulos, D.font color="maroon">(2009) Abstract< Serum uric acid levels in patients with Parkinson's disease: their relationship to treatment and disease duration. http://www.ncbi.nlm.nih.gov/pubmed/19632030
  6. Tsanas, A.; Little, M. A.; McSharry, P. E.; Spielman, J. and Ramig, L.O. (2012) Abstract< IEEE Trans. Biomed. Eng. 59 (5) 1264 - 1271. Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease. http://www.ncbi.nlm.nih.gov/pubmed/22249592
  7. http://www.wipo.int/patentscope/search/en/detail.jsf?docId=WO2011029939&recNum=295&docAn=EP2010063406&queryString=(FP/assay)%20&maxRec=7273
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