2016
DOI: 10.1371/journal.pone.0153461
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Discovery of Metabolic Biomarkers for Duchenne Muscular Dystrophy within a Natural History Study

Abstract: Serum metabolite profiling in Duchenne muscular dystrophy (DMD) may enable discovery of valuable molecular markers for disease progression and treatment response. Serum samples from 51 DMD patients from a natural history study and 22 age-matched healthy volunteers were profiled using liquid chromatography coupled to mass spectrometry (LC-MS) for discovery of novel circulating serum metabolites associated with DMD. Fourteen metabolites were found significantly altered (1% false discovery rate) in their levels b… Show more

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Cited by 29 publications
(41 citation statements)
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“…A subset of these 50 peaks was also selected for validation via MS/MS; the MS/MS spectra for the 11 selected peaks considered in this follow-up experiment are in Supplemental Figure 1. Creatine and creatinine are known to be important in DMD and were among the top peaks in our human serum study [8] . We note that the positive mode peak, with m/z = 357.25 (p = 5.11 x 10 -25 unadjusted), appears to be the same as the top peak in our previous work with DMD patients [8] .…”
Section: Comparison Of Genotypes At Baselinementioning
confidence: 94%
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“…A subset of these 50 peaks was also selected for validation via MS/MS; the MS/MS spectra for the 11 selected peaks considered in this follow-up experiment are in Supplemental Figure 1. Creatine and creatinine are known to be important in DMD and were among the top peaks in our human serum study [8] . We note that the positive mode peak, with m/z = 357.25 (p = 5.11 x 10 -25 unadjusted), appears to be the same as the top peak in our previous work with DMD patients [8] .…”
Section: Comparison Of Genotypes At Baselinementioning
confidence: 94%
“…LC-MS metabolomics samples were preprocessed as previously described [8] . In brief, the XCMS approach [16] , available through the xcms package on Bioconductor [17] was used to detect features and estimate intensities, perform retention time correction, and group peaks from different samples, followed by filling in missing peaks via integrating the signal in the peak region defined by the other samples.…”
Section: Data Processingmentioning
confidence: 99%
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