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. 2020 Mar 15;8(3):416.
doi: 10.3390/microorganisms8030416.

There is No Distinctive Gut Microbiota Signature in the Metabolic Syndrome: Contribution of Cardiovascular Disease Risk Factors and Associated Medication

Adrián Cortés-Martín  1 , Carlos E Iglesias-Aguirre  1 , Amparo Meoro  2 , María Victoria Selma  1 , Juan Carlos Espín  1
Affiliations

There is No Distinctive Gut Microbiota Signature in the Metabolic Syndrome: Contribution of Cardiovascular Disease Risk Factors and Associated Medication

Adrián Cortés-Martín et al. Microorganisms. .

Abstract

The gut microbiota (GM) has attracted attention as a new target to combat several diseases, including metabolic syndrome (MetS), a pathological condition with many factors (diabetes, obesity, dyslipidemia, hypertension, etc.) that increase cardiovascular disease (CVD) risk. However, the existence of a characteristic taxonomic signature associated with obesity-related metabolic dysfunctions is under debate. To investigate the contribution of the CVD risk factors and(or) their associated drug treatments in the composition and functionality of GM in MetS patients, we compared the GM of obese individuals (n = 69) vs. MetS patients (n = 50), as well as within patients, depending on their treatments. We also explored associations between medication, GM, clinical variables, endotoxemia, and short-chain fatty acids. Poly-drug treatments, conventional in MetS patients, prevented the accurate association between medication and GM profiles. Our results highlight the heterogeneity of taxonomic signatures in MetS patients, which mainly depend on the CVD risk factors. Hypertension and(or) its associated medication was the primary trait involved in the shaping of GM, with an overabundance of lipopolysaccharide-producing microbial groups from the Proteobacteria phylum. In the context of precision medicine, our results highlight that targeting GM to prevent and(or) treat MetS should consider MetS patients more individually, according to their CVD risk factors and associated medication.

Keywords: Gut microbiota; cardiovascular risk; diabetes; drug treatment; dyslipidemia; hypertension; metabolic syndrome; obesity; precision medicine.

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Conflict of interest statement

None of the authors had any conflicts of interest to declare. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
(a) Microbial taxonomic composition in fecal samples from obese (OB) and metabolic syndrome (MetS) participants, showing the mean abundance (%) at phylum, family, and genus levels. # Higher abundance in OB; * higher abundance in MetS. (b) Differently abundant bacterial communities between OB and MetS were identified using linear discriminant analysis (LDA) combined with effect size (LEfSe) algorithm. The taxa shown have a value of LDA score (log10) above 2.0. (c) Cladogram, derived from LEfSe analysis of differential gut microbial taxa, is represented by rings with phyla in the outermost ring and genera in the innermost ring. The nodes indicate the abundance of the microorganism. Red and green nodes represent taxa significantly (p < 0.05) overabundant in OB and MetS, respectively, while yellow nodes indicate taxa that were not differentially abundant (p > 0.05).
Figure 2
Figure 2
Genera significantly (p < 0.05) overabundant in (a) MetS vs. OB, and (b) overabundant in OB vs. MetS. OB, red bars; MetS, green bars. The comparison was performed using the Mann–Whitney Rank Sum Test.
Figure 3
Figure 3
Contribution of drug therapy to the differently abundant genera (p < 0.05) in OB vs. MetS patients. (a) OB vs. AD-consuming MetS patients (MetS-AD); (b) OB vs. LL-consuming MetS patients (MetS-LL); (c) OB vs. HP-consuming MetS patients (MetS-HP); (d) Lactobacillus and Proteobacteria in MetS-AD and MetS-HP patients, respectively, as microbial groups significantly associated with drug treatments (or their corresponding linked CVD risk factor) in the comparison of OB vs. MetS. AD, oral anti-diabetics; LL, lipid-lowering drugs; HP, anti-hypertensive drugs. The comparison was performed using ANOVA on Ranks and Dunn’s test.
Figure 4
Figure 4
Comparison of the Firmicutes to Bacteroidetes ratio (F/B) in OB vs. MetS patients, as well within MetS, depending on the drug therapy. AD, oral anti-diabetics; LL, lipid-lowering drugs; HP, anti-hypertensive drugs. The comparison was performed using ANOVA on Ranks and Dunn’s test.
Figure 5
Figure 5
Differently abundant bacterial groups within MetS patients, depending on their therapy, using LEfSe analysis. (a) AD, oral anti-diabetics; (b) LL, lipid-lowering drugs; (c) HP, anti-hypertensive drugs.
Figure 6
Figure 6
(a) Comparison of the plasma lipopolysaccharide-binding protein (LBP) concentration in OB, all MetS patients, MetS-HP, and MetS non-HP. The comparison was performed using ANOVA on Ranks and Dunn’s test. (b) Genera potentially involved in endotoxemia-related processes that were significantly different in MetS-HP vs. MetS non-HP patients.
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