Integrative metagenomic and lipidomic analyses reveal alterations in children with obesity and after lifestyle intervention

Abstract

Background: Despite emerging evidence linking alterations in gut microbiota to childhood obesity, the metabolic mechanisms linking gut microbiota to the lipid profile during childhood obesity and weight loss remain poorly understood.

Methodology: In this study, children with obesity were treated with lifestyle weight loss therapy. Metagenomics association studies and serum untargeted lipidomics analyses were performed in children with obesity and healthy controls before and after weight loss.

Main findings: We identified alterations in gut microbiota associated with childhood obesity, as well as variations in circulating metabolite concentrations. Children with obesity showed significant decreases in the levels of s-Rothia_kristinae and s-Enterobacter_roggenkampii, alongsige elevated levels of s-Clostridiales_bacterium_Marseille-P5551. Following weight loss, the levels of s-Streptococcus_infantarius and s-Leuconostoc_citreum increased by factors of 3.354 and 1.505, respectively, in comparison to their pre-weight loss levels. Correlation analyses indicated a significant positive relationship between ChE(2:0) levels and both with s-Lachnospiraceae_bacterium_TF09-5 and fasting glucose levels. CoQ8 levels were significantly negatively correlated with s-Rothia_kristinae and HOMA-IR.

Conclusion: We linked altered gut microbiota and serum lipid levels in children with obesity to clinical indicators, indicating a potential impact on glucose metabolism via lipids. This study contributes to understanding the mechanistic relationship between altered gut microbiota and childhood obesity and weight loss, suggesting gut microbiome as a promising target for intervention.

Clinical trial registration: https://www.chictr.org.cn/showproj.html?proj=178971, ChiCTR2300072179.

Keywords: childhood; lipidomic; metagenomics; obesity; weight loss.

Figure 1

Alterations in clinical parameters in control and children with obesity at before and after weight loss intervention. (A) Workflow of clinical research in this study. (B) Comparison of BMI standard deviation score (BMI-SDS), fasting glucose, HOMA-IR, homeostatic model assessment of insulin resistance (HOMA-IR), alanine aminotransferase (ALT), aspartate aminotransferase (AST), triglyceride (TG), total cholesterol (TC), low-density lipoprotein (LDL) and high-density lipoprotein (HDL) between controls and children with obesity. (C) Comparison of BMI, fasting insulin, fasting glucose, HOMA-IT, TC, TG, HDL, LDL and very low-density lipoprotein (VLDL) in children with obesity at baseline and after weight loss. *p < 0.05, **p < 0.01.

Figure 2

Children obesity and weight loss alter gut microbiota. Taxonomic classification at the phylum (A) and species (B) level of gut microbiota. The differences of gut microbiota among controls and children with obesity before and after weight loss intervention using LEfSe analysis (linear discriminant analysis, LDA threshold is 2.66) (C) and metagenomeSeq (The false discovery rate, FDA was controlled by the Benjamini-Hochberg, adj.p < 0.05) (D). Relative abundances of fecal gut microbiota responsible for differentiation between the two groups (E). The spearman correlation analysis was used to explore the co-occurrence network. The relevant networks with Spearman’s correlation, |rho| > 0.6 and p < 0.05 were shown (F).

Figure 3

Children obesity and weight loss alter gut microbiota function. Function enrichment analysis were performed using CAZyme (A), Kyoto Encyclopedia of Genes and Genomes (KEGG) (B), EggNOG (C), gene Ontology (GO) (D), annotations. LDA threshold was 2.66.

Figure 4

Comparisons of serum lipidomic profiles and associations of representative serum metabolites with clinical indices and gut microbial species in controls and children with obesity. (A) Principal component analysis (PCA) of lipidomic profile in control, children with obesity and after intervention samples. (B) Lipidomic profiles of controls and children with obesity. (C) Relative abundance of representative lipids between controls and children with obesity. Heatmap of Spearman’s correlation coefficient between the significantly different lipids and (D) clinical indices and (E) gut microbiota in controls and children with obesity. (F) Correlation network analysis. *p < 0.05, **p < 0.01.

Figure 5

Comparisons of serum lipidomic profiles and associations of representative serum metabolites with clinical indices and gut microbial species in children with obesity before and after weight loss intervention. (A) Lipidomic profiles of children with obesity before and after weight loss intervention. (B) Relative abundance of representative lipids between children with obesity before and after weight loss intervention. Heatmap of Spearman’s correlation coefficient between the significantly different lipids and (C) clinical indices and (D) gut microbiota in children with obesity before and after weight loss intervention. (E) Correlation network analysis. *p < 0.05, **p < 0.01.