Abstract
Niemann-Pick type C1 (NPC1) disease is a rare autosomal recessive, neurodegenerative lysosomal storage disorder, which presents with a range of clinical phenotypes and hence diagnosis remains a challenge. In view of these difficulties, the search for a novel, NPC1-specific biomarker (or set of biomarkers) is a topic of much interest. Here we employed high-resolution 1H nuclear magnetic resonance spectroscopy coupled with advanced multivariate analysis techniques in order to explore and seek differences between blood plasma samples acquired from NPC1 (untreated and miglustat treated), heterozygote, and healthy control subjects. Using this approach, we were able to identify NPC1 disease with 91% accuracy confirming that there are significant differences in the NMR plasma metabolic profiles of NPC1 patients when compared to healthy controls. The discrimination between NPC1 (both miglustat treated and untreated) and healthy controls was dominated by lipoprotein triacylglycerol 1H NMR resonances and isoleucine. Heterozygote plasma samples displayed also increases in the intensities of selected lipoprotein triacylglycerol 1H NMR signals over those of healthy controls. The metabolites identified could represent useful biomarkers in the future and provide valuable insight in to the underlying pathology of NPC1 disease.
Highlights
Niemann-Pick type C (NPC) disease is an autosomal recessive, neurodegenerative lysosomal storage disorder, which presents with a range of clinical phenotypes[1]
To the best of our knowledge, to date no NMR-based investigations of Niemann-Pick type C1 (NPC1) blood plasma have been conducted, we have previously investigated the 1H NMR urinary profiles of NPC1 patients[17]
We elected to explore the metabolomics profiles of blood plasma samples collected from NPC1 disease patients to identify novel potential biomarkers, and to provide further insights in to the underlying pathology of this debilitating condition
Summary
Niemann-Pick type C (NPC) disease is an autosomal recessive, neurodegenerative lysosomal storage disorder, which presents with a range of clinical phenotypes[1]. We elected to explore the metabolomics profiles of blood plasma samples collected from NPC1 disease patients to identify novel potential biomarkers, and to provide further insights in to the underlying pathology of this debilitating condition. The resulting metabolic profiles are extremely information-rich and, using associated multivariate (MV) exploratory data analysis and pattern recognition techniques, can discriminate between disease states without the requirement for direct identification of individual compounds. Such distinctive metabolic patterns, which are representative of the disease can serve as a more powerful diagnostic tool than the measurement of a single biomarker in isolation. The metabolic and potential clinical significance of the results acquired are discussed in detail
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