Metabolomic Profiling: A Diagnostic Blood Test for Parkinson Disease?
Abstract & Commentary
By Claire Henchcliffe, MD, DPhil Assistant Professor, Department of Neurology and Neuroscience, Weill Medical College, Cornell University Dr. Henchcliffe reports that she is on the speaker's bureau for the following companies: GlaxoSmithKline, Teva, Boehringer Ingelheim, Schwarz Pharma, and Allergan.
Synopsis: Metabolomic profiling is a technique that measures and analyzes a large array of low molecular weight species in any chosen sample. Metabolomic analysis of plasma accurately distinguishes between subjects with Parkinson disease and healthy controls, thus providing a potential disease biomarker.
Source: Bogdanov M, et al. Metabolomic profiling to develop biomarkers for Parkinson's disease. Brain 2008;131(Pt 2):389-396.
The present study used metabolomic profiling to analyze plasma samples obtained from patients with Parkinson disease (PD) (n=66) and control subjects (n=25). High performance liquid chromatography with electrochemical coulometric array detection (LCECA) was chosen as the metabolomic platform to provide quantitative measurements of approximately 2000 small (<1000 daltons) compounds or analytes that comprised the "metabolome" for each subject. Resulting data were then organized into a format for multivariate analysis. Mean age of the PD subjects was 66.0 +/-11.1 years (vs. 61.5 +/- 12.2 years for controls), and they were 59% male (vs. 33% male for controls). Mean disease duration was 7.1 +/- 4.9 years. Of the 66 PD subjects, 15 took no medications, 20 took carbidopa-levodopa alone, 7 took dopamine agonists, and 24 took a combination of carbidopa-levodopa and dopamine agonists. Using a partial least squares discriminant analysis (PLS-DA) of metabolomic profiles demonstrated complete differentiation between unmedicated PD samples (n=15) and control samples (n=25) (p<0.01). From these data, a subset of variables within the metabolomic profile was chosen according to "variable influence on projection" (VIP) (i.e., those analytes with the most significant contribution to differences in metabolomes of PD vs. control subjects). Using this subset of variables, metabolomic analysis completely distinguished PD subjects taking carbidopa-levodopa vs. controls, as well as PD subjects taking carbidopa-levodopa plus dopamine agonists vs. controls (p<0.01). Of note, urate levels were found to be decreased in PD vs. control samples, as expected based on epidemiological studies demonstrating an association of low urate levels with PD risk in large populations. Moreover, targeted testing for markers of oxidative stress demonstrated an increase in mean plasma concentrations of 8-hydroxy-2-deoxyguanosine (8-OHdG).
Commentary
The "omics" approach has been investigated as a means to develop disease biomarkers in neurodegenerative diseases, including Huntington's disease (HD), amyotrophic lateral sclerosis (ALS), and now PD. It allows analysis of any chosen sample (such as plasma or CSF) based on patterns of transcripts (transcriptomics), proteins (proteomics), or small molecules as in this study (metabolomics). Although some of the analytes measured are identified, in this study it is the pattern of analytes (i.e., identified and unidentified) that is useful as a biomarker, rather than a single known compound. The authors convincingly demonstrate that metabolomic analysis of plasma samples can reliably and completely distinguish between subjects with PD vs. controls, and that this is independent of PD medication effects. As such, this holds great promise as a potential diagnostic marker for PD. At present, PD remains a clinical diagnosis. Despite recent advances in PD research, clinical accuracy (even using the most stringent criteria) may be no more than 93%, and underdiagnosis (particularly in early cases) is common. Another important application of this technology will be to identify which metabolites are responsible for contributing to separation of PD vs. control metabolomic profiles. This will not only improve our understanding of PD pathogenesis, but also help identify targets for therapeutic intervention. There is now a critical need to develop biomarkers for PD to improve diagnostic accuracy, to diagnose earlier, to monitor disease progression, and possibly to define "endophenotypes" in this heterogeneous disease. Metabolomic profiling certainly provides promise, and this study is a first step toward developing a diagnostic PD biomarker.
Metabolomic profiling is a technique that measures and analyzes a large array of low molecular weight species in any chosen sample. Metabolomic analysis of plasma accurately distinguishes between subjects with Parkinson disease and healthy controls, thus providing a potential disease biomarker.You have reached your article limit for the month. Subscribe now to access this article plus other member-only content.
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