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. 2025 Jul 30:16:1639057.
doi: 10.3389/fmicb.2025.1639057. eCollection 2025.

Association between neurodevelopmental disorders in congenital heart disease and changes in circulatory metabolites and gut microbiota composition

Jia An #  1 , Qiang Wang #  2 , Zihao Bai #  1 , Siyu Ma  2 , Zhaocong Yang  2 , Di Yu  2 , Xuming Mo  1   2
Affiliations

Association between neurodevelopmental disorders in congenital heart disease and changes in circulatory metabolites and gut microbiota composition

Jia An et al. Front Microbiol. .

Abstract

Background: Neurodevelopmental disorder (ND) has emerged as a critical factor affecting the long-term quality of life among patients with congenital heart disease (CHD). The aim of this study was to provide a multi-omics perspective on the mechanisms of ND.

Methods: We analyzed the serum metabolome and gut microbiome of children with ND and non-ND (NND) in CHD populations.

Results: In this prospective observational study, we identified associations between serum metabolites, gut microbial, and ND. Linolenic acid was most closely related to neurodevelopmental outcomes, showing positive correlations with multiple neurodevelopmental domains. Among the gut microbiota, the Escherichia genus was most strongly associated with neurodevelopmental outcomes, and negative correlations with neurodevelopmental domains.

Conclusion: This multi-omics study reveals significant association between altered serum metabolites, gut microbiota dysbiosis, and neurodevelopmental outcomes in children with CHD. The microbes and metabolites identified here may contribute to addressing the challenge of ND in the CHD population. Based on our findings, therapeutic strategies to reduce the risk of ND could be developed, including targeted manipulation of the gut microbiota and metabolites.

Keywords: congenital heart disease; gut microbiota; infant; metabolites; neurodevelopmental disorders.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Comparison of gut microbiome alterations in neurodevelopment disorder (ND) and non-ND (NND) groups. (A) Boxplots of alpha diversity indices (Wilcoxon test). (B) PCoA score plot based on 16S rRNA sequencing microbial taxonomy data. Each points represent a sample and the colors represent different groups. The difference in microbiome composition were assessed by ANOSIM and Bray–Curtis distance. (C) LEfSe used to identify essential differences in bacterial abundance (phylum to species level) between the ND and NND groups. Only taxa with a significant LDA threshold value >3 are shown. (D) Spearman’s correlation heatmap representing relationships between ASQ-3 scores and the abundance of taxa at phylum level. Distinct colors represent correlation levels; “*” indicates a reliable correlation. (E) At the genus level, the bacteria showing significant differences between the ND and NND groups were screened based on the criteria of fold change >2 and p-value <0.05.
Figure 2
Figure 2
The correlation of genus bacteria and neurodevelopmental outcomes. (A) Heatmap representation of spearman correlation between ASQ-3 scores and dominant bacteria at genus level (top 50). (B) The random forest algorithm calculates the dominant bacterial genera related to neural development outcomes.
Figure 3
Figure 3
Comparison of serum metabolites alterations in neurodevelopment disorder (ND) and non-ND (NND) groups. (A) PERMANOVA analysis of covariates influencing the serum metabolomic profiles of the patients ranked by impact (R2 value). p < 0.05 is considered to have a significant impact. (B) A volcano plot showing significant changes in metabolites between different neurodevelopment outcomes. Blue indicates that the metabolite is higher in the NND group, red indicates that the metabolite is higher in the ND group, and the top five metabolites are labeled. According to the Wilcoxon test, a p < 0.05 indicates that the difference is statistically significant. (C) Comparison of the specific quantities of linoleic acid between the ND group and the NND group. (D) The random forest algorithm calculates the dominant serum metabolites related to neural development outcomes. The “0” represent the NDD group, and the “1” represent the ND group.
Figure 4
Figure 4
Pathway enrichment analysis of significantly changed metabolites with enrichment ratio on the x-axis and bars color-coded by p-value.
Figure 5
Figure 5
Heatmap representation of Spearman correlation between ASQ-3 scores and linoleic acid.
Figure 6
Figure 6
The receiver operating characteristic (ROC) curves for predicting the probability of neurodevelopmental disorders (ND) using bacteria and linoleic acid. (A) Predicting the probability of ND at admission based on bacteria and metabolites. (B) Predicting the probability of ND during the follow-up period based on bacteria and metabolites collected at admission. AUC, area under the curve.
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