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7 Publications visible to you, out of a total of 7
Digital-Tier Strategy Improves Newborn Screening for Glutaric Aciduria Type 1

Abstract (Expand)

Glutaric aciduria type 1 (GA1) is a rare inherited metabolic disease increasingly included in newborn screening (NBS) programs worldwide. Because of the broad biochemical spectrum of individuals with …dividuals with GA1 and the lack of reliable second-tier strategies, NBS for GA1 is still confronted with a high rate of false positives. In this study, we aim to increase the specificity of NBS for GA1 and, hence, to reduce the rate of false positives through machine learning methods. Therefore, we studied NBS profiles from 1,025,953 newborns screened between 2014 and 2023 at the Heidelberg NBS Laboratory, Germany. We identified a significant sex difference, resulting in twice as many false-positives male than female newborns. Moreover, the proposed digital-tier strategy based on logistic regression analysis, ridge regression, and support vector machine reduced the false-positive rate by over 90% compared to regular NBS while identifying all confirmed individuals with GA1 correctly. An in-depth analysis of the profiles revealed that in particular false-positive results with high associated follow-up costs could be reduced significantly. In conclusion, understanding the origin of false-positive NBS and implementing a digital-tier strategy to enhance the specificity of GA1 testing may significantly reduce the burden on newborns and their families from false-positive NBS results.

Authors: Elaine Zaunseder, Julian Teinert, Nikolas Boy, Sven F. Garbade, Saskia Haupt, Patrik Feyh, Georg F. Hoffmann, Stefan Kölker, Ulrike Mütze, Vincent Heuveline

Date Published: 1st Dec 2024

Publication Type: Journal

Dynamic whole-body models for infant metabolism

Abstract (Expand)

1 Abstract Comprehensive, sex-specific whole-body models (WBMs) accounting for organ-specific metabolism have been developed to allow for the simulation of adult and infant metabolism. These WBMs are …nfant metabolism. These WBMs are evaluated daily, giving insights into metabolic flux changes that occur in one day of an infant’s or adult’s life. However, for medical applications, such as in metabolic diseases and their treatment, an evaluation and concentration predictions on a shorter time scale would be beneficial. Therefore, we developed a dynamic infant-WBM that couples metabolite dynamics in short time frames through physiology-based pharma-cokinetic models with the existing infant whole-body models. We then tailored the dynamic infant-WBM enabling the prediction of isovalerylcarnitine (C5), a clinical biomarker used for the inherited metabolic disease isovaleric aciduria (IVA). Our results show that, as expected, the predicted C5 concentrations exceeded the newborn screening thresholds during the time (36 - 72 hours) newborn screening blood samples are taken in the IVA models but not in models simulating healthy infants. We also demonstrate how the dynamic infant-WBMs can be used to test the effect changes in dietary intake have on the biomarker. Since the dynamic infant-WBMs were parametrised with literature-derived experimental or estimated values, we show how uncertainty quantification can be applied to quantify the parameter uncertainties. We found that the fractional unbound plasma needed to be estimated correctly, as this parameter strongly impacted C5 concentration predictions of the dynamic infant-WBMs. Overall, the dynamic infant-WBMs hold promise for personalised medicine, as it enables personalised biomarker concentration predictions of healthy and diseased infant metabolism in various time intervals.

Authors: Elaine Zaunseder, Faiz Khan Mohammad, Ulrike Mütze, Stefan Kölker, Vincent Heuveline, Ines Thiele

Date Published: 26th Nov 2024

Publication Type: Journal

Personalized metabolic whole-body models for newborns and infants predict growth and biomarkers of inherited metabolic diseases

Abstract

Not specified

Authors: Elaine Zaunseder, Ulrike Mütze, Jürgen G. Okun, Georg F. Hoffmann, Stefan Kölker, Vincent Heuveline, Ines Thiele

Date Published: 1st Aug 2024

Publication Type: Journal

Deep Learning and Explainable Artificial Intelligence for Improving Specificity and Detecting Metabolic Patterns in Newborn Screening

Abstract

Not specified

Authors: Elaine Zaunseder, Ulrike Mütze, Sven F. Garbade, Saskia Haupt, Stefan Kölker, Vincent Heuveline

Date Published: 5th Dec 2023

Publication Type: Journal

Personalised metabolic whole-body models for newborns and infants predict growth and biomarkers of inherited metabolic diseases

Abstract (Expand)

Abstract Extensive whole-body models (WBMs) accounting for organ-specific dynamics have been developed to simulate adult metabolism. However, there is currently a lack of models representing infant …ls representing infant metabolism taking into consideration its special requirements in energy balance, nutrition, and growth. Here, we present a resource of organ-resolved, sex-specific, anatomically accurate models of newborn and infant metabolism, referred to as infant-whole-body models (infant-WBMs), spanning the first 180 days of life. These infant-WBMs were parameterised to represent the distinct metabolic characteristics of newborns and infants accurately. In particular, we adjusted the changes in organ weights, the energy requirements of brain development, heart function, and thermoregulation, as well as dietary requirements and energy requirements for physical activity. Subsequently, we validated the accuracy of the infant-WBMs by showing that the predicted neonatal and infant growth was consistent with the recommended growth by the World Health Organisation. We assessed the infant-WBMs’ reliability and capabilities for personalisation by simulating 10,000 newborn models, personalised with blood concentration measurements from newborn screening and birth weight. Moreover, we demonstrate that the models can accurately predict changes over time in known blood biomarkers in inherited metabolic diseases. By this, the infant-WBM resource can provide valuable insights into infant metabolism on an organ-resolved level and enable a holistic view of the metabolic processes occurring in infants, considering the unique energy and dietary requirements as well as growth patterns specific to this population. As such, the infant-WBM resource holds promise for personalised medicine, as the infant-WBMs could be a first step to digital metabolic twins for newborn and infant metabolism for personalised systematic simulations and treatment planning.

Authors: Elaine Zaunseder, Ulrike Mütze, Jürgen G. Okun, Georg F. Hoffmann, Stefan Kölker, Vincent Heuveline, Ines Thiele

Date Published: 23rd Oct 2023

Publication Type: Journal

Machine Learning Methods Improve Specificity in Newborn Screening for Isovaleric Aciduria

Abstract (Expand)

Isovaleric aciduria (IVA) is a rare disorder of leucine metabolism and part of newborn screening (NBS) programs worldwide. However, NBS for IVA is hampered by, first, the increased birth prevalence due …prevalence due to the identification of individuals with an attenuated disease variant (so-called “mild” IVA) and, second, an increasing number of false positive screening results due to the use of pivmecillinam contained in the medication. Recently, machine learning (ML) methods have been analyzed, analogous to new biomarkers or second-tier methods, in the context of NBS. In this study, we investigated the application of machine learning classification methods to improve IVA classification using an NBS data set containing 2,106,090 newborns screened in Heidelberg, Germany. Therefore, we propose to combine two methods, linear discriminant analysis, and ridge logistic regression as an additional step, a digital-tier, to traditional NBS. Our results show that this reduces the false positive rate by 69.9% from 103 to 31 while maintaining 100% sensitivity in cross-validation. The ML methods were able to classify mild and classic IVA from normal newborns solely based on the NBS data and revealed that besides isovalerylcarnitine (C5), the metabolite concentration of tryptophan (Trp) is important for improved classification. Overall, applying ML methods to improve the specificity of IVA could have a major impact on newborns, as it could reduce the newborns’ and families’ burden of false positives or over-treatment.

Authors: Elaine Zaunseder, Ulrike Mütze, Sven F. Garbade, Saskia Haupt, Patrik Feyh, Georg F. Hoffmann, Vincent Heuveline, Stefan Kölker

Date Published: 1st Feb 2023

Publication Type: Journal

Opportunities and challenges in machine learning‐based newborn screening—A systematic literature review

Abstract

Not specified

Authors: Elaine Zaunseder, Saskia Haupt, Ulrike Mütze, Sven F. Garbade, Stefan Kölker, Vincent Heuveline

Date Published: 1st May 2022

Publication Type: Journal

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