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

Author:

Kouřil Štěpán1,de Sousa Julie23,Fačevicová Kamila3,Gardlo Alžběta12,Muehlmann Christoph4,Nordhausen Klaus5ORCID,Friedecký David1ORCID,Adam Tomáš126

Affiliation:

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

2. Laboratory of Metabolomics, Institute of Molecular and Translational Medicine, Palacký University Olomouc, 779 00 Olomouc, Czech Republic

3. Department of Mathematical Analysis and Applications of Mathematics, Faculty of Science, Palacký University Olomouc, 779 00 Olomouc, Czech Republic

4. Institute of Statistics & Mathematical Methods in Economics, Vienna University of Technology, 1040 Vienna, Austria

5. Department of Mathematics and Statistics, University of Jyväskylä, 40014 Jyväskylä, Finland

6. Faculty of Health Care, The Slovak Medical University in Bratislava, 974 05 Banská Bystrica, Slovakia

Abstract

Newborn screening (NBS) of inborn errors of metabolism (IEMs) is based on the reference ranges established on a healthy newborn population using quantile statistics of molar concentrations of biomarkers and their ratios. The aim of this paper is to investigate whether multivariate independent component analysis (ICA) is a useful tool for the analysis of NBS data, and also to address the structure of the calculated ICA scores. NBS data were obtained from a routine NBS program performed between 2013 and 2022. ICA was tested on 10,213/150 free-diseased controls and 77/20 patients (9/3 different IEMs) in the discovery/validation phases, respectively. The same model computed during the discovery phase was used in the validation phase to confirm its validity. The plots of ICA scores were constructed, and the results were evaluated based on 5sd levels. Patient samples from 7/3 different diseases were clearly identified as 5sd-outlying from control groups in both phases of the study. Two IEMs containing only one patient each were separated at the 3sd level in the discovery phase. Moreover, in one latent variable, the effect of neonatal birth weight was evident. The results strongly suggest that ICA, together with an interpretation derived from values of the “average member of the score structure”, is generally applicable and has the potential to be included in the decision process in the NBS program.

Publisher

MDPI AG

Subject

Obstetrics and Gynecology,Immunology and Microbiology (miscellaneous),Pediatrics, Perinatology and Child Health

Reference28 articles.

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3. Clinical validation of cutoff target ranges in newborn screening of metabolic disorders by tandem mass spectrometry: A worldwide collaborative project;McHugh;Genet. Med.,2011

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5. Wilson, J.M.G., and Jungner, G. (1968). Principles and Practice of Screening for Disease, World Health Organization.

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