Improving newborn screening accuracy through genome sequencing, targeted metabolomics, and machine learning.

ABSTRACT:

Background

Newborn screening (NBS) enables early detection of metabolic disorders, but current tandem mass spectrometry (MS/MS) methods often lead to false positives and require confirmatory testing, causing diagnostic delays. We evaluated whether integrating genome sequencing, expanded metabolite profiling, and artificial intelligence/machine learning (AI/ML) could improve the accuracy of NBS.

Methods

We analyzed dried blood spots (DBS) from 119 screen-positive cases identified by the California NBS program across four disorders: GA-I, PA/MMA, OTCD, and VLCADD. Genome sequencing was performed to identify variants in condition-related genes using ACMG guidelines, and an AI/ML classifier trained on previously generated metabolomic data was applied to differentiate true and fa

SUBMITTER: Xie Y 

PROVIDER: S-EPMC12628566 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature