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Gut microbiota
Figure 2 Group-wise comparison of microbial metagenomic and metabolomic pro fi les. (A) Shannon index of diversity for metabolic pathways from all three groups. Pathway diversity is increased in the athlete group when compared with low-body mass index (BMI) and high-BMI controls. Diversity measures are statistically signi fi cant between low-BMI and athlete groups ( p<0.049), with statistical signi fi cance between all groups (Kruskal-Wallis p<0.05). (B) Principle coordinate analysis of Bray-Curtis compiled distance matrix of all microbial metabolic pathway relative abundances. Groups show signi fi cant variation from one another (Adonis PERMANOVA p<0.05). Cross-validated orthogonal partial least squares regression discriminant analysis (OPLS-DA) of full nuclear magnetic resonance ( 1 H-NMR) spectra from urine (R 2 Y=0.86, Q 2 Y=0.60) (C) and faecal water (R 2 Y=0.86, Q 2 Y=0.52) (D) samples. OPLS-DA displays robust separation between athletes and controls. Models comprise 1 predictive (tcv[1]) and 1 orthogonal (tocv[1]) principal component.
was predominantly correlated with CK, total bilirubin and total energy intake. Of the total number of metabolic pathways with associations to the clinical data from all three groups (10 760; data not shown), relevant pathways related to the production of secondary metabolites, cofactors and short-chain fatty acids (SCFAs) were identi fi ed (eg, biotin biosynthesis and pyruvate fermentation to butanoate). Distinct differences between host and microbial metabolites in athletes and controls A combination of multiplatform metabolic phenotyping and multivariate analysis based on orthogonal partial least squares discriminant analysis (OPLS-DA) was used to compare urine and faecal samples from athletes and controls. The cross-validated (CV) OPLS-DA models show strong differences between athletes and controls in urine samples by proton nuclear magnetic reson- ance ( 1 H-NMR) analysis (R 2 Y=0.86, Q 2 Y=0.60, fi gure 2C), hydrophilic interaction ultra-performance liquid chromatog- raphy mass spectroscopy (HILIC UPLC-MS) positive mode ana- lysis (R 2 Y=0.85, Q 2 Y=0.74, online supplementary fi gure S2A) and reversed-phase UPLC-MS (RP UPLC-MS) in both positive
(34 metabolic categories), highlighting a number of differences ( fi gure 3A and online supplementary table S2). Distinct cluster- ing patterns were observed within each cohort, with the high-BMI control group having the lowest average abundance scores across 31 metabolic pathway categories (the exceptions being vitamin biosynthesis (VB), lipid biosynthesis (LB) and amino acid biosynthesis (AAB) categories). The athlete group had the highest mean abundance across 29 of the 34 metabolic categories (eg, carbohydrate biosynthesis, cofactor biosynthesis and energy metabolism) (see online supplementary table S2). Numerous statistically signi fi cant (p<0.05) associations were identi fi ed between pathway abundances and serum creatine kinase (CK) — an enzymatic marker of muscle activity (IU/L), total bilirubin (IU/L) and dietary macronutrient intake of protein (g/day), fi bre (g/day), carbohydrates (g/day), sugars (g/day), starch (g/day), fat (g/day) and total energy (KJ/day) ( fi gure 3B). Each group was represented by distinct association pro fi les of the correlation between clinical measurements and metagenomic pathways. Dietary factors, sugars and other carbo- hydrates, as well as energy intake, provide the majority of the correlations for the control groups, whereas the athlete group
BartonW, et al . Gut 2017; 0 :1 – 9. doi:10.1136/gutjnl-2016-313627
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