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Area of Science:

  • Biochemistry
  • Neonatal Medicine
  • Public Health

Background:

  • Preterm birth is a leading cause of neonatal mortality globally.
  • Accurate postnatal gestational age (GA) estimation is crucial but challenging without prenatal ultrasound.
  • Metabolomics analysis of newborn blood shows potential for GA estimation.

Purpose of the Study:

  • To develop and validate a metabolomics-based algorithm for estimating gestational age (GA) postnatally.
  • To improve the accuracy of GA estimation in newborns, particularly in settings lacking routine prenatal ultrasound.
  • To assess the international applicability and limitations of the developed GA estimation model.

Main Methods:

  • Utilized metabolomic data from routine newborn screening and demographic information.
  • Developed a predictive algorithm correlating circulating analytes with GA.
  • Validated the algorithm using internal, external, and international datasets, comparing results to ultrasound-validated GA.

Main Results:

  • The developed algorithm estimates postnatal GA with an accuracy of approximately one week compared to ultrasound-validated GA.
  • The model's performance was robust across internal, external, and international validation studies.
  • Inclusion of additional analytes improved the model's predictive precision.

Conclusions:

  • Metabolomics offers a promising approach for postnatal GA estimation, crucial for preterm birth assessment.
  • Current limitations include cost-effectiveness and resource accessibility in low-income settings.
  • Future research should focus on improving precision and developing accessible point-of-care tests for resource-limited environments.