Johannes Hertel
Linking differential equation modeling to population statistics in metabolomics  insights from general population data for statistical analyses and study design of metabolome wide association analyses
Was 


Wann 
28.07.2017 von 12:00 bis 13:00 
Wo  Eckerstraße 1, Raum 404, 4. OG 
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Metabolomics has developed fast in the last decade, presenting promising results, both in terms of improving the understanding of physiological and pathophysiological processes and in terms of predictive and diagnostic models aiming at personalized medicine. However, statistical modeling has been relying almost exclusively on linear models like partial least squares or ordinary least squares regression analyses, despite them being physiologically implausible in a wide range of scenarios. Here, by using data from the large general population cohort Study of Health in Pomerania (SHIP, n=4068), we show that differential equation modeling can be utilized to inform and refine statistical regression models on the population level, describing successfully important features of onetime metabolome measures. As shown, the information derived from differential equation modeling can then be used to modify and optimize several steps of metabolome wide association analyses from data sampling (e.g. which factors should be sampled or controlled) and data preparation (e.g normalization of urine data) to model specification (e.g. correct adjustment for important confounder) and data interpretation (e.g. metabolitephenotype interactions). In conclusion, we demonstrate that metabolome data contain more information than usually extracted and that theoretical modelling via differential equations can be helpful in understanding attributes of onetime metabolomic measurements, paving the way for better applications of metabolomics in the clinical sciences.