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The thyroid gland is of crucial importance in human metabolism. Its main secretion products, L-thyroxine (T4) and 3,3’,5-triiodo-L-thyronine (T3), are essential for proper development of multiple tissues and organs as well as for their functioning in the adult organism. The secretion of thyroid hormones (TH) is stimulated by thyrotropin (TSH) released from the pituitary gland. This tight connection between both hormones is of crucial importance for the clinical diagnosis of thyroid dysfunction. During the last two decades the concept of TH action developed to increased complexity. However, most of the recent advances in the field of TH research are based either on cell culture, tissue or animal models or stem from studies investigating specific hypotheses in humans. Thus, experimental approaches for the comprehensive, hypothesis-free characterization of metabolic effects of classical and non-classical TH in human are urgently needed. This holds true in particular for the TH derivative 3,5-diiodothyronine (3,5-T2). It was described to alleviate the typical detrimental metabolic consequences of a high-fat diet and even reversed hepatic steatosis. To replicate these experimental findings from rodents in humans, comprehensive data from the population-based Study of Health in Pomerania (SHIP) was analyzed in the present work. Based on a euthyroid, diabetes-free SHIP-subsample (N=761), non-linear associations between the serum concentrations of 3,5-T2 and glucose as well as TSH were detected. In contrast, no significant 3,5-T2 associations with several anthropometric markers or blood lipid parameters were observed, partially questioning the transferability of the beneficial metabolic 3,5-T2 effects reported for pharmacological intervention studies on rodents to humans. Recent advances in technological development now allow for the use of high-throughput spectrometric platforms to characterize the small molecule content (metabolome) of blood and urine samples. The detected metabolome constituents can be associated with any relevant parameters of interest, thereby extending the scope of classical association studies. Therefore, in the second part of the present thesis, the metabolic fingerprints of FT4, TSH as well as the ratio log(TSH)/FT4 as markers of thyroid function were profiled. Strong differences between the metabolic fingerprints of FT4 and TSH were observed, partially alleviated by the log(TSH)/FT4 ratio. These findings not only emphasize the high diagnostic value of the combined evaluation of TSH and FT4 in the assessment of thyroid function but additionally argue for a holistic approach in the diagnosis of thyroid function. More moderate endogenous effects of 3,5-T2 were evaluated by comparing its urinary metabolic fingerprint with that of the classical TH. A number of associations became apparent, indicating a function of endogenous 3,5-T2 in intermediary metabolism. Besides partially confirming associations with respect to the presented findings in animal studies, the strongest 3,5-T2-association was observed with trigonelline, a metabolite described earlier to exhibit similar beneficial effects as 3,5-T2 on glucose metabolism when used as a pharmacological agent in animal studies. An association towards hippurate indicated a partial overlap with the metabolic profile of TSH and hence consolidated results from the first two projects in the sense of a thyromimetic role of 3,5-T2 in the feedback regulation of TH. The diagnosis of thyroid disorders based on the classical markers TSH and FT4 suffers from restricted sensitivity in the subclinical range as both parameters have broad reference ranges in the general population. Therefore, in an approach to detect novel peripheral biomarkers of thyroid function, sixteen healthy young men were challenged with 250 µg of levothyroxine (L-T4) over a period of eight weeks in the fourth project presented here as part of this thesis. Monitoring of the volunteers over a period of sixteen weeks allowed delineation of the metabolic shifts first towards thyrotoxicosis and later in the context of the restoration of euthyroidism. The use of mass spectrometry for the comprehensive characterization of the metabolite as well as the protein content of samples taken at the different time points revealed profound molecular alterations, despite the lack of any clinical symptoms in the volunteers. Molecular signatures of thyrotoxicosis indicated increased energy expenditure, pronounced defense against systemic oxidative stress, a general drop in apolipoproteins, as well as increased abundances of proteins related to the coagulation cascade and the complement system. Good and robust classification of the thyroid state independent of TSH and FT4 was achieved using random forest analysis with a subset of fifteen metabolites and proteins, indicating new options in the individualized diagnosis of thyroid disorders.
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.