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Background <p>Early prediction of Bronchopulmonary dysplasia (BPD) remains challenging. Utilizing the NRN 2019 definition and a standardized feeding protocol, this study aimed to identify gestational age (GA)-specific metabolic signatures to optimize early prediction.</p> Methods <p>This retrospective study enrolled 455 preterm infants (GA &lt; 32 weeks). Dried blood spots collected at ~14 days postnatal age following standardized nutritional management were analyzed for 83 metabolites via LC-MS/MS to construct GA-stratified predictive models.</p> Results <p>Distinct GA-specific metabolic signatures involving specific amino acid and acylcarnitine alterations were identified. Incorporating these biomarkers significantly improved predictive models compared to baseline clinical models: the AUC increased from 0.765 to 0.852 in the GA &lt; 28 weeks subgroup, and from 0.629 to 0.707 in the GA 28–31<sup>+6</sup> weeks subgroup (both <i>P</i> &lt; 0.05).</p> Conclusions <p>Post-standardized feeding metabolic profiling at 2 weeks captures distinct GA-specific alterations. Integrating these biomarkers with NRN 2019 criteria significantly enhances early BPD prediction, offering clinical utility for targeted risk stratification.</p>

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Gestational age-specific metabolic biomarkers for early prediction of bronchopulmonary dysplasia following standardized enteral feeding

  • 期刊论文

  • 发表日期：

    2026年8月3日

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  • Pingjiao Gu,
  • Yang Yang,
  • Jipeng Shi,
  • Weiji Li,
  • Qiaoyi Shao,
  • Yiheng Dai

摘要

Background

Early prediction of Bronchopulmonary dysplasia (BPD) remains challenging. Utilizing the NRN 2019 definition and a standardized feeding protocol, this study aimed to identify gestational age (GA)-specific metabolic signatures to optimize early prediction.

Methods

This retrospective study enrolled 455 preterm infants (GA < 32 weeks). Dried blood spots collected at ~14 days postnatal age following standardized nutritional management were analyzed for 83 metabolites via LC-MS/MS to construct GA-stratified predictive models.

Results

Distinct GA-specific metabolic signatures involving specific amino acid and acylcarnitine alterations were identified. Incorporating these biomarkers significantly improved predictive models compared to baseline clinical models: the AUC increased from 0.765 to 0.852 in the GA < 28 weeks subgroup, and from 0.629 to 0.707 in the GA 28–31+6 weeks subgroup (both P < 0.05).

Conclusions

Post-standardized feeding metabolic profiling at 2 weeks captures distinct GA-specific alterations. Integrating these biomarkers with NRN 2019 criteria significantly enhances early BPD prediction, offering clinical utility for targeted risk stratification.