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.
