Gut Microbiota Markers in Obese Adolescent and Adult Patients: Age-Dependent Differential Patterns

Federica Del Chierico, Francesca Abbatini, Alessandra Russo, Andrea Quagliariello, Sofia Reddel, Danila Capoccia, Romina Caccamo, Stefano Ginanni Corradini, Valerio Nobili, Francesco De Peppo, Bruno Dallapiccola, Frida Leonetti, Gianfranco Silecchia, Lorenza Putignani, Federica Del Chierico, Francesca Abbatini, Alessandra Russo, Andrea Quagliariello, Sofia Reddel, Danila Capoccia, Romina Caccamo, Stefano Ginanni Corradini, Valerio Nobili, Francesco De Peppo, Bruno Dallapiccola, Frida Leonetti, Gianfranco Silecchia, Lorenza Putignani

Abstract

Obesity levels, especially in children, have dramatically increased over the last few decades. Recently, several studies highlighted the involvement of gut microbiota in the pathophysiology of obesity. We investigated the composition of gut microbiota in obese adolescents and adults compared to age-matched normal weight (NW) volunteers in order to assemble age- and obesity-related microbiota profiles. The composition of gut microbiota was analyzed by 16S rRNA-based metagenomics. Ecological representations of microbial communities were computed, and univariate, multivariate, and correlation analyses performed on bacterial profiles. The prediction of metagenome functional content from 16S rRNA gene surveys was carried out. Ecological analyses revealed a dissimilarity among the subgroups, and resultant microbiota profiles differed between obese adolescents and adults. Using statistical analyses, we assigned, as microbial markers, Faecalibacterium prausnitzii and Actinomyces to the microbiota of obese adolescents, and Parabacteroides, Rikenellaceae, Bacteroides caccae, Barnesiellaceae, and Oscillospira to the microbiota of NW adolescents. The predicted metabolic profiles resulted different in adolescent groups. Particularly, biosynthesis of primary bile acid and steroid acids, metabolism of fructose, mannose, galactose, butanoate, and pentose phosphate and glycolysis/gluconeogenesis were for the majority associated to obese, while biosynthesis and metabolism of glycan, biosynthesis of secondary bile acid, metabolism of steroid hormone and lipoic acid were associated to NW adolescents. Our study revealed unique features of gut microbiota in terms of ecological patterns, microbial composition and metabolism in obese patients. The assignment of novel obesity bacterial markers may open avenues for the development of patient-tailored treatments dependent on age-related microbiota profiles.

Keywords: bacterial markers; dysbiosis; gut microbiota; metabolic pathways; obesity.

Figures

FIGURE 1
FIGURE 1
Principal coordinates analysis (PCoA) plot of obese and CTRL groups (A–C), and of CTRL_ado, CTRL_adult, ob_ado, and ob_adult groups (B–D). The plots show the first two principal coordinates (axes) for PCoA using a weighted (A,B) and unweighted (C,D) UniFrac algorithm.
FIGURE 2
FIGURE 2
Bar chart representing Kruskal–Wallis test results on operational taxonomic units (OTUs) grouped in phyla (A) and in families/species (B) of the CTRL_ado, CTRL_adult, ob_ado, ob_adult groups. Each column in the plot represents a group, and each color in the column represents: (A) the percentage of relative abundance for each OTU; (B) the values of relative abundance for each OTU.
FIGURE 3
FIGURE 3
Canonical discriminant plot. Scatter plot of canonical discriminant analysis (DA) based on univariate ANOVA and Fisher’s coefficient applied to all OTUs of samples belonging to CTRL_ado, CTRL_adult, ob_ado, and ob_adult groups.
FIGURE 4
FIGURE 4
Receiver operating characteristic (ROC) curve plots. The areas under the ROC curves (AUROC) represent the specificity and sensitivity of the seven selected OTUs (AUROC > 0.7) able to discriminate ob_ado (A) and CTRL_ado (B) groups.
FIGURE 5
FIGURE 5
Gut microbiota model in obese and normal weight (NW) controls. Gut microbiota ecological richness (ChaoI index) results are lower in obese patients with respect to NW study participants. The microbiota profile (OTU distribution) is completely different in the four groups. By applying statistical analyses, it has been possible to generate a model able to associate microbial and metabolic markers with obese and NW adolescents.

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