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Multivariate Independent Component Analysis Identifies Patients in Newborn Screening Equally to Adjusted Reference

Štěpán Kouřil1, Julie de Sousa2,3, Kamila Fačevicová3

  • 1Department of Clinical Biochemistry, University Hospital Olomouc, 779 00 Olomouc, Czech Republic.

International Journal of Neonatal Screening
|October 24, 2023
PubMed
Summary
This summary is machine-generated.

Independent component analysis (ICA) effectively identifies inborn errors of metabolism (IEMs) in newborn screening (NBS) data. This method shows potential for improving diagnostic decision-making in NBS programs.

Keywords:
compositional data analysisinborn errors of metabolismindependent component analysismass spectrometrymultivariate statistical analysisnewborn screening

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Genetics

Background:

  • Newborn screening (NBS) relies on reference ranges from healthy populations to detect inborn errors of metabolism (IEMs).
  • Current methods use quantile statistics of biomarker concentrations and ratios, which may not capture complex metabolic interdependencies.

Purpose of the Study:

  • To evaluate the utility of multivariate independent component analysis (ICA) for analyzing NBS data.
  • To explore the structural characteristics of ICA-derived scores in the context of IEM detection.

Main Methods:

  • Independent component analysis (ICA) was applied to a large dataset (10,213 controls, 77 patients) from a routine NBS program (2013-2022).
  • A discovery/validation phase approach was used, with the same ICA model applied to both phases.
  • Results were analyzed by evaluating ICA scores against standard deviation (sd) levels, identifying outlying patient samples.

Main Results:

  • Patient samples from 7 distinct IEMs were clearly identified as outliers (5sd) from control groups in both discovery and validation phases.
  • Two IEMs with single patients were separated at the 3sd level in the discovery phase.
  • A significant association between neonatal birth weight and one latent variable was observed.

Conclusions:

  • Multivariate ICA is a promising tool for analyzing NBS data and detecting IEMs.
  • ICA has the potential to be integrated into the decision-making process of NBS programs.
  • Interpretation of ICA score structures can aid in identifying metabolic abnormalities.