Application of machine learning tools and integrated OMICS for screening and diagnosis of inborn errors of metabolism

Introduction Tandem mass spectrometry (TMS) has emerged an important screening tool for various metabolic disorders in newborns. However, there is inherent risk of false positive outcomes. Objective To establish analyte-specific cutoffs in TMS by integrating metabolomics and genomics data to avoid false positivity and false negativity and improve its clinical utility. Methods TMS was performed on 572 healthy and 3000 referred newborns. Urine organic acid analysis identified 23 types of inborn errors in 99 referred newborns. Whole exome sequencing was performed in 30 positive cases. The impact of physiological changes such as age, gender, and birthweight on various analytes was explored in healthy newborns. Machine learning tools were used to integrate demographic data with metabolomics and genomics data to establish disease-specific cut-offs; identify primary and secondary markers; build classification and regression trees (CART) for better differential diagnosis; for pathway modeling. Results This integration helped in differentiating B12 deficiency from methylmalonic acidemia (MMA) and propionic acidemia (Phi coefficient=0.93); differentiating transient tyrosinemia from tyrosinemia type 1 (Phi coefficient=1.00); getting clues about the possible molecular defect in MMA to initiate appropriate intervention (Phi coefficient=1.00); to link pathogenicity scores with metabolomics profile in tyrosinemia (r2=0.92). CART model helped in establishing differential diagnosis of urea cycle disorders (Phi coefficient=1.00). Conclusion Calibrated cut-offs of different analytes in TMS and machine learning-based establishment of disease-specific thresholds of these markers through integrated OMICS have helped in improved differential diagnosis with significant reduction of the false positivity and false negativity rates..

Medienart:

E-Artikel

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

Zur Gesamtaufnahme - volume:19

Enthalten in:

Metabolomics - 19(2023), 5 vom: 03. Mai

Sprache:

Englisch

Beteiligte Personen:

Usha Rani, Ganni [VerfasserIn]
Kadali, Srilatha [VerfasserIn]
Kurma Reddy, Banka [VerfasserIn]
Shaheena, Dudekula [VerfasserIn]
Naushad, Shaik Mohammad [VerfasserIn]

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Volltext [lizenzpflichtig]

BKL:

42.15$jZellbiologie

Themen:

Cut-off values
Inborn errors of metabolism
Integrated OMICS
Machine learning
Newborn screening
Tandem mass spectrometry

Anmerkungen:

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

doi:

10.1007/s11306-023-02013-x

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

OLC2134779187

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