Towards the disease biomarker in an individual patient using statistical health monitoring

• Published in: PloS one Vol. 9; no. 4; p. e92452
• Main Authors: Engel, Jasper, Blanchet, Lionel, Engelke, Udo F H, Wevers, Ron A, Buydens, Lutgarde M C
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.04.2014; Public Library of Science (PLoS)
• Subjects: Abnormalities; Batch processes; Biological markers; Biology and Life Sciences; Biomarkers; Biomarkers - urine; CAD; Child; Child, Preschool; Classification; Computer aided design; Computer and Information Sciences; Data analysis; Data processing; Design of experiments; Diagnosis; Diagnostic systems; Disease; Diseases; Fault diagnosis; Female; Handbooks; Health; Humans; Laboratories; Male; Medical diagnosis; Medical electronics; Medicine; Medicine and Health Sciences; Metabolic disorders; Metabolites; Metabolome; Metabolomics; Monitoring, Physiologic; NMR; Nuclear magnetic resonance; Nutrition research; Patients; Physical Sciences; Physiological aspects; Population (statistical); Practice Patterns, Physicians; Principal Component Analysis; Process control; Process controls; Proton Magnetic Resonance Spectroscopy; Research and Analysis Methods; Spectrum analysis; Statistical analysis; Statistical methods; Statistics as Topic; Studies; Urine - chemistry; Netherlands
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0092452

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.