Relationships between gut microbiota, plasma metabolites, and metabolic syndrome traits in the METSIM cohort

Elin Org, Yuna Blum, Silva Kasela, Margarete Mehrabian, Johanna Kuusisto, Antti J Kangas, Pasi Soininen, Zeneng Wang, Mika Ala-Korpela, Stanley L Hazen, Markku Laakso, Aldons J Lusis, Elin Org, Yuna Blum, Silva Kasela, Margarete Mehrabian, Johanna Kuusisto, Antti J Kangas, Pasi Soininen, Zeneng Wang, Mika Ala-Korpela, Stanley L Hazen, Markku Laakso, Aldons J Lusis

Abstract

Background: The gut microbiome is a complex and metabolically active community that directly influences host phenotypes. In this study, we profile gut microbiota using 16S rRNA gene sequencing in 531 well-phenotyped Finnish men from the Metabolic Syndrome In Men (METSIM) study.

Results: We investigate gut microbiota relationships with a variety of factors that have an impact on the development of metabolic and cardiovascular traits. We identify novel associations between gut microbiota and fasting serum levels of a number of metabolites, including fatty acids, amino acids, lipids, and glucose. In particular, we detect associations with fasting plasma trimethylamine N-oxide (TMAO) levels, a gut microbiota-dependent metabolite associated with coronary artery disease and stroke. We further investigate the gut microbiota composition and microbiota-metabolite relationships in subjects with different body mass index and individuals with normal or altered oral glucose tolerance. Finally, we perform microbiota co-occurrence network analysis, which shows that certain metabolites strongly correlate with microbial community structure and that some of these correlations are specific for the pre-diabetic state.

Conclusions: Our study identifies novel relationships between the composition of the gut microbiota and circulating metabolites and provides a resource for future studies to understand host-gut microbiota relationships.

Keywords: Host-microbiota interactions; Metabolic traits; Serum metabolites; TMAO; Type 2 diabetes.

Figures

Fig. 1
Fig. 1
Microbial community variation in the METSIM cohort. a Top contributors to community variation as determined by canonical correspondence analysis on unscaled genera abundances, plotted on the first principal component (PC) dimensions (arrows scaled to contribution). b The top seven metabolite contributors to microbiome community variation. c A total of 32 out of 60 factors (60%) explained a total of 13% of variance of the gut microbiome according to Bray–Curtis distance. TG triglycerides, FA fatty acids, DHA docosahexaenoic acid, MUFA monounsaturated fatty acid, PUFA polyunsaturated fatty acid
Fig. 2
Fig. 2
Associations between microbiota taxa and circulating serum metabolites. Serum metabolite concentrations were measured from fasting samples using NMR or LC-MS/MS. They were then associated with microbial taxa following adjustment for age and treatment of the subjects. *Ruminococcus from the family Lachnospiraceae; **Ruminococcus from the family Ruminococcaceae. CI confidence interval, DHA docosahexaenoic acid, MUFA monounsaturated fatty acid, PUFA polyunsaturated fatty acid
Fig. 3
Fig. 3
OTU co-occurrence network and module-trait associations. a OTU co-occurrence network where OTU (nodes) are colored according to the phyla to which they belong. Blue edges correspond to positive correlations and red edges to negative correlations. Any resulting correlations with p value ≥0.01 and abs(r) <0.3 were removed. b OTU co-occurrence network were OTU (nodes) are colored according to WGCNA module colors. c Module–trait associations are shown. Each cell of the matrix contains the correlation between one OTU module and a metabolic trait, and the corresponding p value. The table is color-coded by correlation according to the color legend (red for positive correlations and green for negative correlations). FA fatty acids, DHA docosahexaenoic acid, MUFA monounsaturated fatty acid, PUFA polyunsaturated fatty acid, GlycA glycoprotein acetyls, TMAO trimethyl N-oxide
Fig. 4
Fig. 4
The gut microbiota differences in metabolic phenotypes. Mean proportions of significantly different taxa between individuals with different BMI values (a) and individuals with different 2-h glucose tolerance test (NGT and pre-T2D) (b). Examples of BMI/OGGT metabolite–microbiota interaction, where the abundances of Bacteroidales, Collinsella (c) and Coprobacillus (d) exhibit opposite associations in individuals with low versus high BMI levels or individuals with impaired glucose tolerance test (c). *P ≤ 0.01, **P ≤ 0.001, ***P ≤ 0.0001. BMI body mass index, GT glucose tolerance, NGT normal glucose tolerance, T2D type 2 diabetes

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