1.
Advances in Research on Diabetes by Human Nutriomics.
Ren, X, Li, X
International journal of molecular sciences. 2019;(21)
Abstract
The incidence and prevalence of diabetes mellitus (DM) have increased rapidly worldwide over the last two decades. Because the pathogenic factors of DM are heterogeneous, determining clinically effective treatments for DM patients is difficult. Applying various nutrient analyses has yielded new insight and potential treatments for DM patients. In this review, we summarized the omics analysis methods, including nutrigenomics, nutritional-metabolomics, and foodomics. The list of the new targets of SNPs, genes, proteins, and gut microbiota associated with DM has been obtained by the analysis of nutrigenomics and microbiomics within last few years, which provides a reference for the diagnosis of DM. The use of nutrient metabolomics analysis can obtain new targets of amino acids, lipids, and metal elements, which provides a reference for the treatment of DM. Foodomics analysis can provide targeted dietary strategies for DM patients. This review summarizes the DM-associated molecular biomarkers in current applied omics analyses and may provide guidance for diagnosing and treating DM.
2.
1H NMR based pharmacometabolomics analysis of metabolic phenotype on predicting metabolism characteristics of losartan in healthy volunteers.
He, C, Liu, Y, Wang, Y, Tang, J, Tan, Z, Li, X, Chen, Y, Huang, Y, Chen, X, Ouyang, D, et al
Journal of chromatography. B, Analytical technologies in the biomedical and life sciences. 2018;:15-23
Abstract
Inter-individual variability in drug metabolism and disposition is common in both preclinical and clinical researches. Losartan and its active metabolite EXP3174 present a high degree of inter-individual differences in blood concentrations that affect drug efficacy and side effect. Pharmacometabolomics has been increasingly applied on predicting the drug responses by analyzing the differences in metabolic profile. A pre-dose metabolic phenotype was investigated to interpret inter-individual variations in the metabolism characteristics of losartan. 1H Nuclear Magnetic Resonance (NMR) spectroscopy-based metabolic profiles were performed on 36 healthy Chinese male volunteers by measuring their pre-dose plasma samples. After oral administration of losartan, the concentrations of losartan and its bioactive metabolite EXP3174 were monitored by liquid chromatography-mass spectrometry (LC-MS). Orthogonal partial least-squares (O-PLS) model was conducted to select potential biomarkers that substantially contributed to the inter-individual variations in the metabolism features via analyzing the ratio of pharmacokinetics (PK) parameters of its metabolite to parent drug. Potential metabolites such as glycine, phosphorylcholine, choline, creatine, creatinine, lactate, citrate, α-glucose, and lipids showed strong correlations with metabolism features of losartan. In addition, the pathway analysis revealed that baseline lipid metabolism, the glycine, serine and threonine pathway, and glycolysis or gluconeogenesis metabolism pathway were significantly associated with the ratio of PK parameters of EXP3174 to losartan. Step-wise multiple linear regression (MLR) was constructed to investigate the potential roles of the selected biomarkers in predicting individualized metabolism characteristics of losartan. These results showed that the pre-dose individual metabolic traits may be a useful approach for characterizing individual differences in losartan metabolism characteristics and therefore for expediting personalized dose-setting in further clinical studies.