A HPLC-Q-TOF-MS-based Urinary Metabolomic Approach to Identification of Potential Biomarkers of Metabolic Syndrome
Metabolic syndrome (MetS) is a serious threat to public health worldwide with an increased risk of developing type 2 diabetes, cardiovascular diseases and all-cause morbidity and mortality. In this study, a urinary metabolomic approach was performed on high performance liquid chromatography quadrupo...
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Published in | Journal of Huazhong University of Science and Technology. Medical sciences Vol. 34; no. 2; pp. 276 - 283 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
Heidelberg
Huazhong University of Science and Technology
01.04.2014
Tianjin Entry-Exit Inspection and Quarantine Bureau, Tianjin 300308, China%Department of Occupational and Environmental Health, Tianjin Medical University, Tianjin 300070, China Department of Occupational and Environmental Health, Tianjin Medical University, Tianjin 300070, China Offshore Oil Centers for Disease Control and Prevention, Tianjin 300452, China%Department of Occupational and Environmental Health, Tianjin Medical University, Tianjin 300070, China |
Subjects | |
Online Access | Get full text |
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Summary: | Metabolic syndrome (MetS) is a serious threat to public health worldwide with an increased risk of developing type 2 diabetes, cardiovascular diseases and all-cause morbidity and mortality. In this study, a urinary metabolomic approach was performed on high performance liquid chromatography quadrupole time-of-flight mass spectrometry to discriminate 36 male MetS patients and 36 sex and age matched healthy controls. Pattern recognition analyses (principal component analysis and orthogonal projections to latent structures discriminate analysis) commonly demonstrated the difference between MetS patients and no-MetS subjects. This study found 8 metabolites that showed significant changes in patients with MetS, including branch-chain and aromatic amino acids (leucine, tyrosine, phenylalanine and tryptophan), short-chain acylcanitine (tiglylcamitine), tricarboxylic acid (TCA) cycle intermediate (cis-aconitic acid) and glucuronidated products (cortolone-3-glucuronide and tetrahydroaldoster- one-3-glucuronide). The candidate biomarkers revealed in this study could be useful in providing clues for further research focusing on the in-depth investigation of the cause of and cure for MetS. |
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Bibliography: | Metabolic syndrome (MetS) is a serious threat to public health worldwide with an increased risk of developing type 2 diabetes, cardiovascular diseases and all-cause morbidity and mortality. In this study, a urinary metabolomic approach was performed on high performance liquid chromatography quadrupole time-of-flight mass spectrometry to discriminate 36 male MetS patients and 36 sex and age matched healthy controls. Pattern recognition analyses (principal component analysis and orthogonal projections to latent structures discriminate analysis) commonly demonstrated the difference between MetS patients and no-MetS subjects. This study found 8 metabolites that showed significant changes in patients with MetS, including branch-chain and aromatic amino acids (leucine, tyrosine, phenylalanine and tryptophan), short-chain acylcanitine (tiglylcamitine), tricarboxylic acid (TCA) cycle intermediate (cis-aconitic acid) and glucuronidated products (cortolone-3-glucuronide and tetrahydroaldoster- one-3-glucuronide). The candidate biomarkers revealed in this study could be useful in providing clues for further research focusing on the in-depth investigation of the cause of and cure for MetS. metabolic syndrome; metabolomics; HPLC-Q-TOF-MS; urine; biomarker Zhi-rui YU , Yu NING Hao YU Nai-jun TANG (1Department of Occupational and Environmental Health, Tianjin Medical University, Tianjin 300070, China 2Tianjin Entry-Exit Inspection and Quarantine Bureau, Tianjin 300308, China 3 0ffshore Oil Centers for Disease Control and Prevention, Tianjin 300452, China) 42-1679/R ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1672-0733 1993-1352 |
DOI: | 10.1007/s11596-014-1271-7 |