Expanding Conditions for Newborn Screening-Findings from an Untargeted Metabolomics and Lipidomics Discovery Study.
13 affiliations
- 1Department of Genetics and Molecular Pathology, SA Pathology, Adelaide, SA 5000, Australia.
- 2South Australia Neonatal Screening Centre, SA Pathology at Women's & Children's Hospital, Adelaide, SA 5006, Australia.
- 3School of Medicine, College of Health, Adelaide University, Adelaide, SA 5000, Australia.
- 4Allergy & Clinical Immunology Department, Women's and Children's Hospital Network, Adelaide, SA 5006, Australia.
- 5Immunology Directorate, SA Pathology, Adelaide, SA 5000, Australia.
- 6Adelaide Health Technology Assessment (AHTA), School of Public Health, The University of Adelaide, Adelaide, SA 5000, Australia.
- 7Neurology and Clinical Neurophysiology, Women's and Children's Health Network, Adelaide, SA 5006, Australia.
- 8Haematology and Oncology Clinic, Women's and Children's Hospital, Adelaide, SA 5006, Australia.
- 9Metabolic Clinic, Women's and Children's Hospital, Adelaide, SA 5006, Australia.
- 10Paediatric and Reproductive Genetics Unit, Women's and Children's Hospital, Adelaide, SA 5006, Australia.
- 11Centre for Cancer Biology, an Alliance Between SA Pathology and the University of South Australia, Adelaide, SA 5000, Australia.
- 12Sydney Children's Hospital at Westmead, Sydney, NSW 2145, Australia.
- 13South Australian Health and Medical Research Institute, Adelaide, SA 5000, Australia.
Abstract
Advances in metabolomics and genomics present new opportunities to expand newborn screening. Traditional screening approaches have relied on condition-specific biomarkers; however, untargeted metabolomic profiling may enable broader detection of genetic conditions. In this study, we developed and evaluated a novel metabolomic screening approach based on defining a reference profile representing a healthy newborn population. Retrospective dried blood spot samples from 2948 newborns were analysed using untargeted metabolomics and lipidomics, generating more than 30,000 features. A discovery cohort of over 2000 healthy newborns and newborns with genetic conditions was used to identify 1115 features that best discriminated between groups. These features formed the basis of a screening algorithm, which was subsequently evaluated in an independent validation cohort containing genetic conditions not represented in the discovery cohort. Across the discovery and validation cohorts, 180 cases representing 113 unique genetic conditions were assessed, including metabolic, haematological, neurological, and syndromic conditions. In the independent validation cohort, the algorithm achieved an area under the receiver operating characteristic curve of 0.799, with a specificity of over 99%. These findings demonstrate the potential of untargeted metabolomic profiling to identify newborns at increased risk of genetic disease and support future exploration in a multi-omic newborn screening design.
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BibTeX
@article{ashenden2026expanding,
title = {{Expanding Conditions for Newborn Screening-Findings from an Untargeted Metabolomics and Lipidomics Discovery Study.}},
author = {Alex J Ashenden and Tomas Rozek and Lucy Anastasi and Ayesha Chowdhury and Khoa Lam and Carol Wai-Kwan Siu and Jovanka King and Tracy Merlin and Nicholas Smith and Benjamin Saxon and Drago Bratkovic and Christopher Barnett and Hamish S Scott and Enzo Ranieri and Jennie Louise and Karin S Kassahn},
journal = {International journal of neonatal screening},
year = {2026},
month = sep,
doi = {10.3390/ijns12040077},
url = {https://europepmc.org/article/MED/42924754},
note = {CC BY},
}
RIS
TY - JOUR
TI - Expanding Conditions for Newborn Screening-Findings from an Untargeted Metabolomics and Lipidomics Discovery Study.
AU - Alex J Ashenden
AU - Tomas Rozek
AU - Lucy Anastasi
AU - Ayesha Chowdhury
AU - Khoa Lam
AU - Carol Wai-Kwan Siu
AU - Jovanka King
AU - Tracy Merlin
AU - Nicholas Smith
AU - Benjamin Saxon
AU - Drago Bratkovic
AU - Christopher Barnett
AU - Hamish S Scott
AU - Enzo Ranieri
AU - Jennie Louise
AU - Karin S Kassahn
PY - 2026
DA - 2026/09/24
JO - International journal of neonatal screening
DO - 10.3390/ijns12040077
UR - https://europepmc.org/article/MED/42924754
AB - Advances in metabolomics and genomics present new opportunities to expand newborn screening. Traditional screening approaches have relied on condition-specific biomarkers; however, untargeted metabolomic profiling may enable broader detection of genetic conditions. In this study, we developed and evaluated a novel metabolomic screening approach based on defining a reference profile representing a healthy newborn population. Retrospective dried blood spot samples from 2948 newborns were analysed using untargeted metabolomics and lipidomics, generating more than 30,000 features. A discovery cohort of over 2000 healthy newborns and newborns with genetic conditions was used to identify 1115 features that best discriminated between groups. These features formed the basis of a screening algorithm, which was subsequently evaluated in an independent validation cohort containing genetic conditions not represented in the discovery cohort. Across the discovery and validation cohorts, 180 cases representing 113 unique genetic conditions were assessed, including metabolic, haematological, neurological, and syndromic conditions. In the independent validation cohort, the algorithm achieved an area under the receiver operating characteristic curve of 0.799, with a specificity of over 99%. These findings demonstrate the potential of untargeted metabolomic profiling to identify newborns at increased risk of genetic disease and support future exploration in a multi-omic newborn screening design.
SN - 2409-515X
ER -