2015
DOI: 10.1152/ajpendo.00019.2015
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Systemic alterations in the metabolome of diabetic NOD mice delineate increased oxidative stress accompanied by reduced inflammation and hypertriglyceremia

Abstract: Hara M. Systemic alterations in the metabolome of diabetic NOD mice delineate increased oxidative stress accompanied by reduced inflammation and hypertriglyceremia. Am J Physiol Endocrinol Metab 308: E978-E989, 2015. First published April 8, 2015; doi:10.1152/ajpendo.00019.2015.-Nonobese diabetic (NOD) mice are a commonly used model of type 1 diabetes (T1D). However, not all animals will develop overt diabetes despite undergoing similar autoimmune insult. In this study, a comprehensive metabolomic approach, co… Show more

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Cited by 51 publications
(65 citation statements)
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“…Small molecules were semi-quantitatively determined in EDTA plasma collected at baseline and 2 hr post-EVOO ingestion by gas chromatography time-of-flight mass spectrometry as previously described (Fahrmann et al, 2015). Briefly, 30 µL aliquots of plasma, were thawed on ice, extracted with methyl tert-butyl ether, derivatized by methoximation/silylation, and analysed.…”
Section: Methodsmentioning
confidence: 99%
“…Small molecules were semi-quantitatively determined in EDTA plasma collected at baseline and 2 hr post-EVOO ingestion by gas chromatography time-of-flight mass spectrometry as previously described (Fahrmann et al, 2015). Briefly, 30 µL aliquots of plasma, were thawed on ice, extracted with methyl tert-butyl ether, derivatized by methoximation/silylation, and analysed.…”
Section: Methodsmentioning
confidence: 99%
“…MetaMapR leverages the KEGG and PubChem databases to provide methods for integration and visualization of complex metabolomics experiments, even in cases where biochemical domain knowledge or molecular annotations are unknown . For example, MetaMapR has been used to integrate both biochemical reaction information with molecular structural and mass spectral similarity to identify pathway‐independent relationships among metabolites, even in cases where the metabolite annotation or structural identify was unknown . Network calculation is further bolstered through an interface to the Chemical Translation System [http://cts.fiehnlab.ucdavis.edu/], allowing metabolite identifier translation between > 200 common biochemical databases.…”
Section: Pathway Analysis Network Construction Visualization and Bmentioning
confidence: 99%
“…However, one caveat that must always be considered is whether serum or urinary DAS differs between malignant and benign tumors. This is particularly relevant in lung cancer as screening methodologies have been largely hindered by high false positive rates, a consequence of the low prevalence of malignant solitary pulmonary nodules (SPNs) and high incidence of benign SPNs [17]. Thus, a clear delineation between malignant, benign, and control must be demonstrated, a matter being actively addressed.…”
Section: Commentarymentioning
confidence: 99%