ArticlePloS one2014
Towards the disease biomarker in an individual patient using statistical health monitoring.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Benchmarking Outlier Detection Methods for Detecting IEM Patients in Untargeted Metabolomics Data.Metabolites · 2023Article
- Improved One-Class Modeling of High-Dimensional Metabolomics Data via Eigenvalue-Shrinkage.Metabolites · 2021Article
- The Uprising of Mitochondrial DNA Biomarker in Cancer.Disease markers · 2021Review
- Critical comparison of methods for fault diagnosis in metabolomics data.Scientific reports · 2019Article
- Automated flow cytometric identification of disease-specific cells by the ECLIPSE algorithm.Scientific reports · 2018Article
- Advances in metabolome information retrieval: turning chemistry into biology. Part II: biological information recovery.Journal of inherited metabolic disease · 2018Review
- Next-generation metabolic screening: targeted and untargeted metabolomics for the diagnosis of inborn errors of metabolism in individual patients.Journal of inherited metabolic disease · 2018Article
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Authors and funding
5 authors.
Funding
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Abstract
In metabolomics, identification of complex diseases is often based on application of (multivariate) statistical techniques to the data. Commonly, each disease requires its own specific diagnostic model, separating healthy and diseased individuals, which is not very practical in a diagnostic setting. Additionally, for orphan diseases such models cannot be constructed due to a lack of available data. An alternative approach adapted from industrial process control is proposed in this study: statistical health monitoring (SHM). In SHM the metabolic profile of an individual is compared to that of healthy people in a multivariate manner. Abnormal metabolite concentrations, or abnormal patterns of concentrations, are indicated by the method. Subsequently, this biomarker can be used for diagnosis. A tremendous advantage here is that only data of healthy people is required to construct the model. The method is applicable in current-population based -clinical practice as well as in personalized health applications. In this study, SHM was successfully applied for diagnosis of several orphan diseases as well as detection of metabotypic abnormalities related to diet and drug intake.
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