An untargeted metabolomics study of cardiac pathology damage in rats caused by low selenium diet alone or in combination with T-2 toxin

Lin, Xue; Qiao, Lichun; Liu, Haobiao; Bao, Miaoye; Deng, Huan; Jia, Lianxu; Wen, Xinyue; Deng, Feidan; Wan, Ping; Lyu, Yizhen; Han, Jing

doi:10.1016/j.fct.2024.114759

Cited by 1 publication

(1 citation statement)

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“…It involves systematically analyzing the comprehensive metabolites of biological samples, such as biofluids, cells, tissues, or organs, to elucidate their profile features. The chemo-material and quantitative determination of many metabolites in various biological fluid specimens can provide valuable biochemical information on the state of organisms and the interrelations between the different metabolic processes that define the state [ 10 ]. Recent technological advances have enabled the high-throughput profiling of many metabolites in biological samples, increasing their application to disease-specific biomarker discovery, including cardiovascular diseases [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Exploring Novel Biomarkers for an Acute Coronary Syndrome Diagnosis Utilizing Plasma Metabolomics

Shibata,

Sugimoto,

Watanabe

et al. 2024

IJMS

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Acute coronary syndrome (ACS) is a life-threatening condition that requires a prompt diagnosis and therapeutic intervention. Although serum troponin I and creatinine kinase-MB (CK-MB) are established biomarkers for ACS, reaching diagnostic values for ACS may take several hours. In this study, we attempted to explore novel biomarkers for ACS with higher sensitivity than that of troponin I and CK-MB. The metabolomic profiles of 18 patients with ACS upon hospital arrival and those of the age-matched control (HC) group of 24 healthy volunteers were analyzed using liquid chromatography time-of-flight mass spectrometry. Volcano plots showed 24 metabolites whose concentrations differed significantly between the ACS and HC groups. Using these data, we developed a multiple logistic regression model for the ACS diagnosis, in which lysine, isocitrate, and tryptophan were selected as minimum-independent metabolites. The area under the receiver operating characteristic curve value for discriminating ACS from HC was 1.00 (95% confidence interval [CI]: 1.00–1.00). In contrast, those for troponin I and CK-MB were 0.917 (95% confidence interval [CI]: 0.812–1.00) and 0.988 (95% CI: 0.966–1.00), respectively. This study showed the potential for combining three plasma metabolites to discriminate ACS from HC with a higher sensitivity than troponin I and CK-MB.

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Section: Introductionmentioning

confidence: 99%