17 research outputs found

    Key Role for the 12-Hydroxy Group in the Negative Ion Fragmentation of Unconjugated C24 Bile Acids

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    Host-gut microbial interactions contribute to human health and disease states and an important manifestation resulting from this cometabolism is a vast diversity of bile acids (BAs). There is increasing interest in using BAs as biomarkers to assess the health status of individuals and, therefore, an increased need for their accurate separation and identification. In this study, the negative ion fragmentation behaviors of C24 BAs were investigated by UPLC-ESI-QTOF-MS. The step-by-step fragmentation analysis revealed a distinct fragmentation mechanism for the unconjugated BAs containing a 12-hydroxyl group. The unconjugated BAs lacking 12-hydroxylation fragmented via dehydration and dehydrogenation. In contrast, the 12-hydroxylated ones, such as deoxycholic acid (DCA) and cholic acid (CA), employed dissociation routes including dehydration, loss of carbon monoxide or carbon dioxide, and dehydrogenation. All fragmentations of the 12-hydroxylated unconjugated BAs, characterized by means of stable isotope labeled standards, were associated with the rotation of the carboxylate side chain and the subsequent rearrangements accompanied by proton transfer between 12-hydroxyl and 24-carboxyl groups. Compared to DCA, CA underwent further cleavages of the steroid skeleton. Accordingly, the effects of stereochemistry on the fragmentation pattern of CA were investigated using its stereoisomers. Based on the knowledge gained from the fragmentation analysis, a novel BA, 3β,7β,12α-trihydroxy-5β-cholanic acid, was identified in the postprandial urine samples of patients with nonalcoholic steatohepatitis. The analyses used in this study may contribute to a better understanding of the chemical diversity of BAs and the molecular basis of human liver diseases that involve BA synthesis, transport, and metabolism

    Tryptophan Predicts the Risk for Future Type 2 Diabetes

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    <div><p>Recently, 5 amino acids were identified and verified as important metabolites highly associated with type 2 diabetes (T2D) development. This report aims to assess the association of tryptophan with the development of T2D and to evaluate its performance with existing amino acid markers. A total of 213 participants selected from a ten-year longitudinal Shanghai Diabetes Study (SHDS) were examined in two ways: 1) 51 subjects who developed diabetes and 162 individuals who remained metabolically healthy in 10 years; 2) the same 51 future diabetes and 23 strictly matched ones selected from the 162 healthy individuals. Baseline fasting serum tryptophan concentrations were quantitatively measured using ultra-performance liquid chromatography triple quadruple mass spectrometry. First, serum tryptophan level was found significantly higher in future T2D and was positively and independently associated with diabetes onset risk. Patients with higher tryptophan level tended to present higher degree of insulin resistance and secretion, triglyceride and blood pressure. Second, the prediction potential of tryptophan is non-inferior to the 5 existing amino acids. The predictive performance of the combined score improved after taking tryptophan into account. Our findings unveiled the potential of tryptophan as a new marker associated with diabetes risk in Chinese populations. The addition of tryptophan provided complementary value to the existing amino acid predictors.</p></div

    a) Baseline tryptophan levels (mean with S.E.) in future NGT (n = 162), matched future NGT (n = 23), and future T2D (n = 51) groups. b) Association of tryptophan with metabolic markers in all participants (n = 213).

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    <p><b>Abbreviations:</b> BMI = body mass index; Glucose0 = fasting plasma glucose; Glucose120 = 2 h plasma glucose; INS0 = fasting insulin; INS120 = 2 h insulin; HDL = high-density lipoprotein; LDL = low-density lipoprotein; SP = systolic blood pressure; DP = diastolic blood pressure; TC = total cholesterol; TG = total triglycerides; HOMA-IR = homeostatic model assessment of insulin resistance (FPG*INS0/22.5); Matsuda index = 10000/(Glucose0×INS0×Glucose120×INS120)<sup>0.5</sup>; HOMA-Beta = homeostatic model assessment of beta-cell function (20*INS0/(Glucose0-3.5); First-phase secretion = 2032+4.681*INS0-135*Glucose120+0.995*INS120+27.99*BMI-269.1*Glucose0; Second-phase secretion = 277+0.8*INS0-42.79*Glucose120+0.321*INS120+5.338*BMI.</p
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