Doctoral Thesis
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Aiming at the goal of individualized medicine, this dissertation develops a generic methodology to individualize risk factors and phenotypes via metabolomic data from the urine. As metabolomic data can be seen as a holistic representation of the metabolism of an organism at certain time point, metabolomic data contain not only information about current life-style factors like diet and smoking but also about latent genetic traits. Utilizing this integrative attribute, the dissertation delivers a metric for biological age (the metabolic age score) which was shown to be informative beyond chronological age in three independent samples. It was associated with a broad range of age-related comorbidities in two large population-based cohorts, predicted independently of classical risk factors mortality and, moreover, it predicted weight loss subsequently to bariatric surgery in a small sample of heavily obese individuals.
Subsequently to this work, the dissertation built a definitional framework justifying the procedure underlying the metabolic age score, delivering a general framework for the construction of individualized phenotypes and thereby an operationalization of individualization in statistical terms. Conceptualizing individualization of the process of differentiation of individuals showing the same phenotype despite different underlying biological traits, it was shown formally that the prediction error of a statistical model approximating a phenotype is always informative about the underlying biology beyond the phenotype if the predictors fulfill certain statistical requirements. Thus, the prediction error facilitates the meaningful differentiation of individuals showing the same phenotype. The definitional framework presented here is not restricted to any kind of data and is therefore applicable to a broad range of medical research questions.
However, when utilizing metabolomic data, technical factors, data-preprocessing, pre-analytic features introduce unwanted variance into the statistical modeling. Thus, it is unclear whether predictive models like the metabolic age score are stable enough for clinical application. The third part of this doctoral thesis provided two statistical criteria to decide which normalization method to remove the dilution variance from urinary metabolome data performs best in terms of erroneous variance introduced by the different methods, aiding the minimization of biological irrelevant variance in metabolomic analyses.
In conclusion, this doctoral thesis developed a general, applicable, definitional framework for the construction of individualized phenotypes and demonstrated the value of the methodology for clinical phenotypes on metabolomic data, improving on the way the statistical treatment of urinary data regarding the dilution correction.