Discrimination of the Geographical Origin of Beef by <sup>1</sup>H NMR-Based Metabolomics

Jung, Youngae; Lee, Jueun; Kwon, Joseph; Lee, Kwang-Sik; Ryu, Do Hyun; Hwang, Geum‐Sook

doi:10.1021/jf102194t

Cited by 163 publications

(116 citation statements)

References 49 publications

Supporting

Mentioning

110

Contrasting

Unclassified

Order By: Relevance

“…R 2 X and R 2 Y were used to quantify the goodness-of-fit; Q 2 Y was employed to assess the predictability of the model (29). To rule out the non-randomness of separation between groups, a 300-iteration permutation test was performed (30). If the values of Q 2 and R 2 resulting from the original model were higher than the corresponding values from the permutation test, the model was considered valid (29).…”

Section: Methodsmentioning

confidence: 99%

Identification and Validation of Urinary Metabolite Biomarkers for Major Depressive Disorder

Zheng

Wang

Chen

et al. 2013

Molecular & Cellular Proteomics

181

148

View full text Add to dashboard Cite

Major depressive disorder (MDD) is a widespread and debilitating mental disorder. However, there are no biomarkers available to aid in the diagnosis of this disorder. In this study, a nuclear magnetic resonance spectroscopy-based metabonomic approach was employed to profile urine samples from 82 first-episode drug-naïve depressed subjects and 82 healthy controls (the training set) in order to identify urinary metabolite biomarkers for MDD. Then, 44 unselected depressed subjects and 52 healthy controls (the test set) were used to independently validate the diagnostic generalizability of these biomarkers. A panel of five urinary metabolite biomarkers-malonate, formate, N-methylnicotinamide, mhydroxyphenylacetate, and alanine-was identified. This panel was capable of distinguishing depressed subjects from healthy controls with an area under the receiver operating characteristic curve ( Major depressive disorder (MDD)1 is a debilitating mental disorder affecting up to 15% of the general population and accounting for 12.3% of the global burden of disease (1, 2). Currently, the diagnosis of MDD still relies on the subjective identification of symptom clusters rather than empirical laboratory tests. The current diagnostic modality results in a considerable error rate (3), as the clinical presentation of MDD is highly heterogeneous and the current symptombased method is not capable of adequately characterizing this heterogeneity (4). An approach that can be used to circumvent these limitations is to identify disease biomarkers to support objective diagnostic laboratory tests for MDD.Metabonomics, which can measure the small molecules in given biosamples such as plasma and urine without bias (5), has been extensively used to characterize the metabolic changes of diseases and thus facilitate the identification of novel disease-specific signatures as putative biomarkers (6 -10). Nuclear magnetic resonance (NMR) spectroscopy-based metabonomic approaches characterized by sensitive, highthroughput molecular screening have been employed previously in identifying novel biomarkers for a variety of neuropsychiatric disorders, including stroke, bipolar disorder, and schizophrenia (11-13).Specifically with regard to MDD, several animal studies have already characterized the metabolic changes in the blood and urine (14 -19). These studies provide valuable clues as to the pathophysiological mechanism of MDD. However, no study has been designed with the aim of diagnosing this disease. Recently, using an NMR-based metabonomic approach, this research group identified a unique plasma metabolic signature that enables the discrimination of MDD from healthy controls with both high sensitivity and specificity (20). These findings motivated further study on urinary diagnostic metabolite biomarkers for MDD, which would be more valuable from a clinical applicability standpoint, as urine can be more non-invasively collected. Moreover, previous studies have also demonstrated the feasibility of identifying diagnostic metabolite biomarkers of psyc...

show abstract

Section: Methodsmentioning

confidence: 99%

Identification and Validation of Urinary Metabolite Biomarkers for Major Depressive Disorder

Zheng

Wang

Chen

et al. 2013

Molecular & Cellular Proteomics

181

148

View full text Add to dashboard Cite

show abstract

“…To achieve this goal, we suggest directing R&D efforts in three areas. The first is to take advantage of the new analytical techniques such as metabolomics (Aiello, De Luca, Gionfriddo, Naccarato, Napoli, Romano et al, 2011;Jung, Lee, Kwon, Lee, Ryu & Hwang, 2010) or proteomics to better characterise typicity, origin and quality of meat products. The second is to develop scientific and technical tools to better assess sustainability of the beef sector by combining different indices related to the three pillars of sustainability: protection of the environment, social acceptability and economic efficiency.…”

Section: Accepted M Manuscriptmentioning

confidence: 99%

Opportunities for predicting and manipulating beef quality

et al. 2012

View full text Add to dashboard Cite

“…Since the metabolite profiles of certain food resources can differ depending on their geographical origins, metabolomics can be useful for determining their authenticity and traceability [14][15][16]. For example, the discrimination of geographical origins is particularly important for foods such as coffee [17], green tea [18], ginseng [19], grape [20], cheese, milk [21,22] and cattle [23]. Furthermore, monitoring the metabolic changes that are caused by climatic and seasonal factors can be useful for obtaining high-quality food resources [24,25].…”

Section: Metabolomics For Food Resource Productionmentioning

confidence: 99%