Metabolic subtypes and biomarkers in preterm and term neonates via targeted screening

Preterm infants exhibit metabolic immaturity, yet metabolic heterogeneity within this population remains underexplored. We performed targeted metabolomics on dried blood spots from 448 preterm (32-36 weeks) and 351 term neonates (37-40 weeks of gestation) using tandem mass spectrometry. Compared with term infants, preterm neonates showed significantly elevated tyrosine, leucine/isoleucine, arginine, and hydroxyoctadecenoylcarnitine (C18:1-OH), along with reduced glutamate (false discovery rate < 0.05). Multivariate analyses, including principal component analysis and partial least squares-discriminant analysis, identified three distinct metabolic clusters associated with gestational maturity and redox-related pathway signals. Pathway enrichment analysis highlighted disruptions in the urea cycle, ammonia recycling, purine metabolism, and mitochondrial fatty acid oxidation. Notably, C18:1-OH emerged as a key discriminatory metabolite and a potential biomarker of mitochondrial immaturity and altered fatty acid oxidation in preterm neonates. These findings support the presence of metabolically distinct subtypes within preterm infants and suggest that metabolomic profiling may contribute to precision neonatal risk stratification, although longitudinal validation is required.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2026-05-30
DOI
https://doi.org/10.1038/s41598-026-50955-8
Primary Topic
Birth, Development, and Health
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Metabolic subtypes and biomarkers in preterm and term neonates via targeted screening

Reihaneh Mohsenipour, Farzaneh Abbasi, Saeideh Abdolahpour, Maryam Gholami
Scientific Reports
Birth, Development, and Health
article

Metabolic subtypes and biomarkers in preterm and term neonates via targeted screening

Reihaneh Mohsenipour, Farzaneh Abbasi, Saeideh Abdolahpour, Maryam Gholami
article en

Abstract

Preterm infants exhibit metabolic immaturity, yet metabolic heterogeneity within this population remains underexplored. We performed targeted metabolomics on dried blood spots from 448 preterm (32-36 weeks) and 351 term neonates (37-40 weeks of gestation) using tandem mass spectrometry. Compared with term infants, preterm neonates showed significantly elevated tyrosine, leucine/isoleucine, arginine, and hydroxyoctadecenoylcarnitine (C18:1-OH), along with reduced glutamate (false discovery rate < 0.05). Multivariate analyses, including principal component analysis and partial least squares-discriminant analysis, identified three distinct metabolic clusters associated with gestational maturity and redox-related pathway signals. Pathway enrichment analysis highlighted disruptions in the urea cycle, ammonia recycling, purine metabolism, and mitochondrial fatty acid oxidation. Notably, C18:1-OH emerged as a key discriminatory metabolite and a potential biomarker of mitochondrial immaturity and altered fatty acid oxidation in preterm neonates. These findings support the presence of metabolically distinct subtypes within preterm infants and suggest that metabolomic profiling may contribute to precision neonatal risk stratification, although longitudinal validation is required.

Scientific Reports
University of Tehran (IR), Children's Medical Center (IR), Tehran University of Medical Sciences (IR)
Tehran University of Medical Sciences and Health Services
Zero hunger
Openalex Percentile: Top 5%
Birth, Development, and Health
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.