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Background <p>Meconium serves as a valuable biological matrix for characterizing fetal metabolic signatures throughout gestation. Selective fetal growth restriction (sFGR) is associated with adverse neurological outcomes, potentially mediated by underlying metabolic perturbations. However, the specific relationship between dysregulated meconium metabolome and brain injury in sFGR remains poorly understood.</p> Methods <p>Untargeted metabolomics analysis was performed on meconium samples from sFGR (<i>n</i> = 20) and monochorionic diamniotic twins with birth weight concordance (MCDA-C, <i>n</i> = 13) to quantify metabolic alterations. Neonatal brain injury was assessed via cranial ultrasonography, and long-term neurodevelopmental outcomes were evaluated at 2–3 years of age using the Ages and Stages Questionnaire-third edition subscale. Univariate analysis, partial least-squares discrimination analysis (PLSDA) and pathway analysis were employed to compare the metabolic profiles across different brain injury categories. Machine learning algorithms and receiver operating characteristic (ROC) curve were utilized to identify potential biomarkers associated with neonatal brain injury. Spearman’s-based correlation analysis was employed to correlate metabolites levels with physical development and long-term neurodevelopmental outcomes.</p> Results <p>In our study, PLSDA revealed distinct clustering of meconium metabolites profiles in neonates with severe brain injury compared to those with mild brain injury or normal findings. In all sFGR neonates, logistic regression identified two fatty acid metabolism products, 13, 16-docosadienoic acid and nonadecanoic acid, as notably associated with neonatal severe brain injury. Divergent meconium metabolic signatures associated with severe brain injury were observed between the smaller fetus (sFGR-S) and larger fetus (sFGR-L) in sFGR twins. In particular, nicotinamide, hippuric acid, citramalic acid and succinic acid were closely associated with severe brain injury in sFGR-S. Pathway analysis implicated significant dysregulation of the citrate cycle in this subgroup. For sFGR-L, histidine and trans-4-hydroxyproline emerged as best predictive markers for severe brain injury and showed significant correlations with long-term neurodevelopmental outcomes including gross motor and fine motor.</p> Conclusions <p>Dysregulated fatty acid metabolites in the meconium of sFGR neonates are associated with severe brain injury. Divergent metabolomic profiles between sFGR-S and sFGR-L revealed distinct pathological mechanisms underlying brain injury. These findings provide novel insights into metabolic mechanisms of brain injury in sFGR and offer potential predictive biomarkers for adverse neurological outcomes.</p>

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Meconium metabolomic profiling dysregulation and neonatal brain injury in selective fetal growth restriction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月5日

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  • Jingyu Liu,
  • Nana Huang,
  • Youzhen Zhang,
  • Xiya Sun,
  • Hai Jiang,
  • Yixin Li,
  • Yanrong Sun,
  • Jing Yang,
  • Yangyu Zhao

摘要

Background

Meconium serves as a valuable biological matrix for characterizing fetal metabolic signatures throughout gestation. Selective fetal growth restriction (sFGR) is associated with adverse neurological outcomes, potentially mediated by underlying metabolic perturbations. However, the specific relationship between dysregulated meconium metabolome and brain injury in sFGR remains poorly understood.

Methods

Untargeted metabolomics analysis was performed on meconium samples from sFGR (n = 20) and monochorionic diamniotic twins with birth weight concordance (MCDA-C, n = 13) to quantify metabolic alterations. Neonatal brain injury was assessed via cranial ultrasonography, and long-term neurodevelopmental outcomes were evaluated at 2–3 years of age using the Ages and Stages Questionnaire-third edition subscale. Univariate analysis, partial least-squares discrimination analysis (PLSDA) and pathway analysis were employed to compare the metabolic profiles across different brain injury categories. Machine learning algorithms and receiver operating characteristic (ROC) curve were utilized to identify potential biomarkers associated with neonatal brain injury. Spearman’s-based correlation analysis was employed to correlate metabolites levels with physical development and long-term neurodevelopmental outcomes.

Results

In our study, PLSDA revealed distinct clustering of meconium metabolites profiles in neonates with severe brain injury compared to those with mild brain injury or normal findings. In all sFGR neonates, logistic regression identified two fatty acid metabolism products, 13, 16-docosadienoic acid and nonadecanoic acid, as notably associated with neonatal severe brain injury. Divergent meconium metabolic signatures associated with severe brain injury were observed between the smaller fetus (sFGR-S) and larger fetus (sFGR-L) in sFGR twins. In particular, nicotinamide, hippuric acid, citramalic acid and succinic acid were closely associated with severe brain injury in sFGR-S. Pathway analysis implicated significant dysregulation of the citrate cycle in this subgroup. For sFGR-L, histidine and trans-4-hydroxyproline emerged as best predictive markers for severe brain injury and showed significant correlations with long-term neurodevelopmental outcomes including gross motor and fine motor.

Conclusions

Dysregulated fatty acid metabolites in the meconium of sFGR neonates are associated with severe brain injury. Divergent metabolomic profiles between sFGR-S and sFGR-L revealed distinct pathological mechanisms underlying brain injury. These findings provide novel insights into metabolic mechanisms of brain injury in sFGR and offer potential predictive biomarkers for adverse neurological outcomes.