Serum metabolomics analysis reveals increased lipid catabolism in mildly hyperbilirubinemic Gilbert's syndrome individuals.

BACKGROUND: The protective role of mildly elevated bilirubin against CVD and diabetes mellitus type 2 (DMT2) is associated with a favorable lipid phenotype. As the mechanistic understanding of this protection in humans remains elusive, we aimed to assess the metabolomics profile of mildly hyperbilirubinemic (Gilbert's syndrome; GS) individuals especially targeting lipid catabolism. METHODS AND RESULTS: Using NMR serum metabolomics of 56 GS individuals and 56 age and gender-matched healthy controls, GS individuals demonstrated significantly greater concentrations of acetylcarnitine (+20%, p < 0.001) and the ketone bodies, 3-hydroxybutyric acid (+132%, p < 0.001), acetoacetic acid (+95%, p < 0.001) and acetone (+46%, p < 0.001). Metabolites associated with an increased mitochondrial lipid metabolism such as citrate (+15%, p < 0.001), anaplerotic amino acid intermediates and creatinine were significantly greater and creatine significantly reduced in GS individuals. Stimulators of lipid catabolism including AMPK (+59%, p < 0.001), pPPARα (+24%, p < 0.001) and T3 (+9%, p = 0.009) supported the metabolomics data while concomitantly blood glucose and insulin (-33%, p = 0.002) levels were significantly reduced. We further showed that the increased lipid catabolism partially mediates the favorable lipid phenotype (lower triglycerides) of GS individuals. Increased trimethylamine (+35%, p < 0.001) indicated changes in trimethylamine metabolism, an emerging predictor of metabolic health. CONCLUSION: We showed an enhanced lipid catabolism in mildly hyperbilirubinemic individuals, novel evidence as to why these individuals are leaner and protected against chronic metabolic diseases emphasizing bilirubin to be a promising future target in obese and dyslipidemia patients.

Improved Automated Quantification Algorithm (AQuA) and Its Application to NMR-Based Metabolomics of EDTA-Containing Plasma.

We have recently presented an Automated Quantification Algorithm (AQuA) and demonstrated its utility for rapid and accurate absolute metabolite quantification in 1H NMR spectra in which positions and line widths of signals were predicted from a constant metabolite spectral library. The AQuA quantifies based on one preselected signal per metabolite and employs library spectra to model interferences from other metabolite signals. However, for some types of spectra, the interspectral deviations of signal positions and line widths can be pronounced; hence, interferences cannot be modeled using a constant spectral library. We here address this issue and present an improved AQuA that handles interspectral deviations. The improved AQuA monitors and characterizes the appearance of specific signals in each spectrum and automatically adjusts the spectral library to model interferences accordingly. The performance of the improved AQuA was tested on a large data set from plasma samples collected using ethylenediaminetetraacetic acid (EDTA) as an anticoagulant (n = 772). These spectra provided a suitable test system for the improved AQuA since EDTA signals (i) vary in intensity, position, and line width between spectra and (ii) interfere with many signals from plasma metabolites targeted for quantification (n = 54). Without the improvement, ca. 20 out of the 54 metabolites would have been overestimated. This included acetylcarnitine and ornithine, which are considered particularly difficult to quantify with 1H NMR in EDTA-containing plasma. Furthermore, the improved AQuA performed rapidly (<10 s for all spectra). We believe that the improved AQuA provides a basis for automated quantification in other data sets where specific signals show interspectral deviations.