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Background <p>Diagnosing acute kidney injury (AKI) in preterm neonates remains difficult, and its underlying causes are not well understood. This study aimed to compare urinary metabolite profiles in preterm neonates with and without AKI.</p> Methods <p>We conducted a prospective observational study of neonates born at &lt; 32&#xa0;weeks’ gestation. AKI was staged using the modified neonatal definition. Urine samples were collected every six hours using cotton in diapers. Metabolomic profiling was performed via liquid chromatography mass spectrometry (LC–MS). Data were acquired in polarity switching mode and processed using Compound Discoverer 3.3.</p> Results <p>Among 32 enrolled neonates (median birth weight 1.25&#xa0;kg, gestational age 29&#xa0;weeks), five developed AKI. A total of 987 urine samples were collected, with 522 age- and sex-matched samples from 16 individuals included in the final analysis. Principal component (PC) analysis revealed that PC1 and PC2 accounted for 15.82% and 11.61% of sample variance, respectively. Comparison of samples from neonates with and without AKI identified significantly elevated levels of furosemide, acesulfame, kynurenic acid, and hexaethylene glycol in the AKI group (<i>p</i> &lt; 0.05, log2 fold change &gt; 1) and with significant differences between metabolites within biochemical pathways including branch chain fatty acid oxidation, tyrosine metabolism, and carnitine synthesis. Longitudinal analysis also revealed metabolite changes preceding AKI onset, with distinct profiles compared to neonates who did not develop AKI.</p> Conclusions <p>Preterm neonates with AKI exhibit distinct urinary metabolomic profiles compared to those without AKI. These findings suggest potential for metabolomic signatures to aid in early AKI detection, phenotype classification, and identification of therapeutic targets. Further research is needed to refine compound identification and timing of metabolite changes.</p> Graphical abstract <p></p>

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Discovery urinary metabolomics of preterm neonatal acute kidney injury

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月5日

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  • Paige E. Condit,
  • Nicole M. Nightingale,
  • Benton J. Anderson,
  • Ian J. Miller,
  • Joshua J. Coon,
  • Katherine A. Overmyer,
  • Matthew W. Harer

摘要

Background

Diagnosing acute kidney injury (AKI) in preterm neonates remains difficult, and its underlying causes are not well understood. This study aimed to compare urinary metabolite profiles in preterm neonates with and without AKI.

Methods

We conducted a prospective observational study of neonates born at < 32 weeks’ gestation. AKI was staged using the modified neonatal definition. Urine samples were collected every six hours using cotton in diapers. Metabolomic profiling was performed via liquid chromatography mass spectrometry (LC–MS). Data were acquired in polarity switching mode and processed using Compound Discoverer 3.3.

Results

Among 32 enrolled neonates (median birth weight 1.25 kg, gestational age 29 weeks), five developed AKI. A total of 987 urine samples were collected, with 522 age- and sex-matched samples from 16 individuals included in the final analysis. Principal component (PC) analysis revealed that PC1 and PC2 accounted for 15.82% and 11.61% of sample variance, respectively. Comparison of samples from neonates with and without AKI identified significantly elevated levels of furosemide, acesulfame, kynurenic acid, and hexaethylene glycol in the AKI group (p < 0.05, log2 fold change > 1) and with significant differences between metabolites within biochemical pathways including branch chain fatty acid oxidation, tyrosine metabolism, and carnitine synthesis. Longitudinal analysis also revealed metabolite changes preceding AKI onset, with distinct profiles compared to neonates who did not develop AKI.

Conclusions

Preterm neonates with AKI exhibit distinct urinary metabolomic profiles compared to those without AKI. These findings suggest potential for metabolomic signatures to aid in early AKI detection, phenotype classification, and identification of therapeutic targets. Further research is needed to refine compound identification and timing of metabolite changes.

Graphical abstract