Gut microbiota associations with metabolic syndrome and relevance of its study in pediatric subjects

Figures & data

Table 1. Relevant gut microbiota studies conducted in pediatric subjects from different countries to unravel their relationship with metabolic disorders related with MetS

Country and age range	Number of study subjects	Health condition	Applied “omic” technologies	Relevant highest abundant bacterial communities	Reference
Switzerland 8–14 years old	30	Normal weight (NW) Obese (O)	16S rRNA gene metabarcoding Targeted metabolomics	No significant differences could be identified in individual microbial communities of NW and O subjects. Higher Bacteroidetes/Firmicutes ratio in NW compared to O.	^Citation17
Kazakhstan 7–13 years old	175	Normal weight (NW) Overweight (OW) Obese (O)	16S rRNA gene metabarcoding	Significant lower Bacteroidetes/Firmicutes ratio in O subjects compared to NW and OW individuals. Significant lower Bacteroidetes abundance in O subjects compared to NW individuals.	^Citation18
Italy 6–16 years old	78	Normal weight (NW) Obese (O)	16S rRNA gene metabarcoding Targeted metabolomics	Significant higher Firmicutes/Bacteroidetes ratio in O subjects compared to NW individuals. Negatively associated with BMI: Bacteroidetes phylum; Bacteroides vulgatus and B. stercoris Positively associated with BMI: Firmicutes phylum; Faecalibacterium prausnitzii NW: Bacteroides genus; Bacteroides vulgatus O: Firmicutes phylum	^Citation19
Mexico 9–11 years old	36	Undernourished (U) Normal weight (NW) Obese (O)	16S rRNA gene metabarcoding	Significant higher Firmicutes/Bacteroidetes ratio in U subjects compared to NW and O individuals. U: Firmicutes phylum; Lachnospiraceae family; Prevotella genus NW: Bacteroidetes phyum; Bacteroides genus O: Proteobacteria phylum	^Citation20
United States 7–18 years old	267	Underweight (UW) Normal weight (NW) Overweight (OW) Obese (O)	16S rRNA gene metabarcoding	Negatively associated with BMI: Actinobacteria phylum Positively associated with BMI: Proteobacteria phylum UW*: Sutterella, Parabacteroides, Dorea, Serratia, and Pseudomonas genera OW*: Faecalibacterium, Serratia, Ruminococcus, Peptostreptococcaceae, and Coprococcus genera O*: Dialister, Acinetobacter, Bacillus, Bifidobacterium, and Lactobacillus genera *Taking NW as reference	^Citation21
China 9–17 years old	58	Healthy (H) Obese with nonalcoholic liver disease (O-NAFLD) Obese non-NAFLD (O)	Shotgun metagenomics	H: Bacteroidetes phylum; Lactobacillus, Oscillobacter, and Ruminiclostridium genera O-NAFLD*: Proteobacteria phylum; Negativicutes class, Phascolarctobacterium genus, and Phascolarctobacterium succinatuten; $\gamma$-proteobacteria class, Klebsiella and Kluyvera genera, and Klebsiella pneumoniae and Kluyvera ascorbata species *Taking H as reference	^Citation22
Mexico 7–10 years old	27	Normal weight (NW) Obese (O) Obese with MetS (OMS)	16S rRNA gene metabarcoding Metatranscriptomics	NW: Bacteroidetes – Bacteroidia – Bacteroidales communities; Phascolarctobacterium genus O: Porphyromonas and Faecalibacterium genera (i.e. Faecalibacterium prausnitzii); Bifidobacterium adolescents OMS: Firmicutes and Proteobacteria phyla; Colinsella, Catenibacterium and Coprococcus genera; Colinsella aerofaciens	^Citation23
Spain 7–12 years old	39	Healthy (H) Obese with a 12-week training program (Oe) Obese (O)	16S rRNA gene metabarcoding Untargeted metabolomics	H: Actinobacteria phylum; Clostridia and Actinobacteria classes; Clostridium, Bifidobacterium, Coprococcus, Akkermansia, and Streptococcus genera O: Proteobacteria phylum (significantly decreased in Oe individuals); Bacteroidia, $\gamma$-proteobacteria (significantly decreased in Oe individuals), and $\beta$-proteobacteria classes; Bacteroides, Prevotella, Phascolarctobacterium, and Paraprevotella genera *Oe showed to start resembling more to H individuals	^Citation24
China 2–18 years old	51	Normal weight (NW) Obese (O)	16S rRNA gene metabarcoding	NW: Bacteroidetes phylum; Oscillospira, Ruminococcus, Prevotella, Adlercreutzia, Sporobacter, Bifidobacterium, Clostridium, Desulfovibrio, Bilophila, Christensenella, Alistipes, Anaerotruncus, Eubacterium, Holdemania, Oxalobacter, Defluviitalea, and Collinsella genera O: Faecalibacterium, Turicibacter, Campylobacter, Actinobacillus, Aggregatibacter, SMB53, Rothia, Granulicatella, Streptococcus, Veillonella, Megamonas, Fusobacterium, Phascolarctobacterium, Haemophilus, Lachnospira, and Sutterella genera	^Citation25
South Korea 7–18 years old	60	Normal weight (NW) Fat gain group (FGG) Fat loss group (FLG)	16S rRNA gene metabarcoding	NW: Bacteroides, Oscillibacter, and Parabacteroides genera FGG: Actinobacteria phylum; Blautia, Dorea, and Fusicatenibacter genera; Eubacterium hallii FLG: Clostridiales order and Clostridia class; Blautia, Dorea, and Fusicatenibacter genera; Eubacterium hallii	^Citation26

Figure 1. General overview of gut microbiota and its relationship with human metabolism according to recent findings retrieved from conventional techniques and “omic” technologies: A. The predominance of a hypoxic environment due to the presence of strict anaerobic bacteria and SCFA producers has been related with a healthy condition, promoting immune homeostasis, preserving the integrity of the intestinal barrier, and potentially being involved in the well-functioning of key organs and tissues that are relevant in the onset of MetS. B. Loss of the hypoxic environment due to an increased presence of facultative anaerobic bacterial communities and PAMP producers has been related to gut dysbiosis, increased intestinal permeability, and the triggering of proinflammatory activity, potentially causing a negative impact in the functionality of key organs and tissues that are involved in the development of MetS

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