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Gut microbiota
Figure 3 Group variation of microbial metabolic function and associations between pathways and clinical and dietary variables. (A) Mean relative abundance values of statistically signi fi cant (Kruskal-Wallis p<0.05) metabolic pathways binned according to categories of metabolic function. (B) Number of metabolic pathways signi fi cantly (Benjamini-Hochberg corrected p<0.05) correlated with dietary constituents and blood serum metabolites. BMI, body mass index.
and negative mode analysis (R 2 Y=0.83, Q 2 Y=0.73 and R 2 Y=0.83, Q 2 Y=0.67, online supplementary fi gure S2B,C respectively). Likewise, the CV-OPLS-DA models comparing faecal samples, although weaker than the urine models, reveal signi fi cant differences between athletes and controls by 1 H-NMR analysis (R 2 Y=0.86, Q 2 Y=0.52, fi gure 2D) and HILIC UPLC-MS positive mode analysis (R 2 Y=0.65, Q 2 Y=0.34, online supplementary fi gure S2D). The loadings of the pairwise OPLS-DA models were used to identify metabolites discriminating between the two classes. Athletes ’ 1 H-NMR metabolic phenotypes were characterised by higher levels of trimethylamine- N -oxide (TMAO), L-carnitine, dimethylglycine, O-acetyl carnitine, proline betaine, creatine, acetoacetate, 3-hydroxy-isovaleric acid, acetone, N -methylnicotinate, N -methylnicotinamide, phenylacetylglutamine (PAG) and 3-methylhistidine in urine samples and higher levels of propionate, acetate, butyrate, trimethylamine (TMA), lysine and methylamine in faecal samples, relative to controls. Athletes were further characterised by lower levels of glycerate, allantoin and succinate and lower levels of glycine and tyrosine relative to controls in urine and faecal samples, respectively (see online supplementary table S3). While numerous metabolites discriminated signi fi cantly between athletes and controls with RP UPLC-MS positive (490)
and negative (434) modes for urine, as well as with HILIC UPLC-MS positive mode for urine (196) and faecal water (3), key metabolites were structurally identi fi ed using the strategy described below. UPLC-MS analyses revealed higher urinary excretion of N -formylanthranilic acid, hydantoin-5-propionic acid, 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid (CMPF), CMPF glucuronide, trimetaphosphoric acid, acetyl- carnitine (C2), propionylcarnitine (C3), isobutyrylcarnitine (C4), 2-methylbutyroylcarnitine (C5), hexanoylcarnitine (C6), C9:1-carnitine, L-valine, nicotinuric acid, 4-pyridoxic acid and creatine in athletes relative to controls. Levels of glutamine, 7-methylxanthine, imidazoleacetic acid, isoquinoline/quinolone were lower in athletes ’ urinary samples relative to controls. In addition, 16 unknown glucuronides were lower in the athlete samples (see online supplementary table S4). SCFA levels in faeces measured by targeted gas chromatography – mass spectrometry (GC-MS) showed signi fi cantly higher levels of acetate (p<0.001), propionate (p<0.001), butyrate (p<0.001) and valerate (p=0.011) in athletes relative to controls. Isobutyrate and isovalerate did not differ signi fi cantly between the groups ( fi gure 4B and online supplementary table S5). Furthermore, concentrations of propionate strongly correlated to protein intake, while butyrate was shown to have a strong association with intake of dietary fi bre (see online supplementary table S6).
BartonW, et al . Gut 2017; 0 :1 – 9. doi:10.1136/gutjnl-2016-313627
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