@phdthesis{Wittenbecher2017,
  author    = {Wittenbecher, Clemens},
  title     = {Linking whole-grain bread, coffee, and red meat to the risk of type 2 diabetes},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-404592},
  school      = {Universit{\"a}t Potsdam},
  pages     = {XII, 194, ii},
  year      = {2017},
  abstract  = {Background: Consumption of whole-grain, coffee, and red meat were consistently related to the risk of developing type 2 diabetes in prospective cohort studies, but potentially underlying biological mechanisms are not well understood. Metabolomics profiles were shown to be sensitive to these dietary exposures, and at the same time to be informative with respect to the risk of type 2 diabetes. Moreover, graphical network-models were demonstrated to reflect the biological processes underlying high-dimensional metabolomics profiles. Aim: The aim of this study was to infer hypotheses on the biological mechanisms that link consumption of whole-grain bread, coffee, and red meat, respectively, to the risk of developing type 2 diabetes. More specifically, it was aimed to consider network models of amino acid and lipid profiles as potential mediators of these risk-relations. Study population: Analyses were conducted in the prospective EPIC-Potsdam cohort (n = 27,548), applying a nested case-cohort design (n = 2731, including 692 incident diabetes cases). Habitual diet was assessed with validated semiquantitative food-frequency questionnaires. Concentrations of 126 metabolites (acylcarnitines, phosphatidylcholines, sphingomyelins, amino acids) were determined in baseline-serum samples. Incident type 2 diabetes cases were assed and validated in an active follow-up procedure. The median follow-up time was 6.6 years. Analytical design: The methodological approach was conceptually based on counterfactual causal inference theory. Observations on the network-encoded conditional independence structure restricted the space of possible causal explanations of observed metabolomics-data patterns. Given basic directionality assumptions (diet affects metabolism; metabolism affects future diabetes incidence), adjustment for a subset of direct neighbours was sufficient to consistently estimate network-independent direct effects. Further model-specification, however, was limited due to missing directionality information on the links between metabolites. Therefore, a multi-model approach was applied to infer the bounds of possible direct effects. All metabolite-exposure links and metabolite-outcome links, respectively, were classified into one of three categories: direct effect, ambiguous (some models indicated an effect others not), and no-effect. Cross-sectional and longitudinal relations were evaluated in multivariable-adjusted linear regression and Cox proportional hazard regression models, respectively. Models were comprehensively adjusted for age, sex, body mass index, prevalence of hypertension, dietary and lifestyle factors, and medication. Results: Consumption of whole-grain bread was related to lower levels of several lipid metabolites with saturated and monounsaturated fatty acids. Coffee was related to lower aromatic and branched-chain amino acids, and had potential effects on the fatty acid profile within lipid classes. Red meat was linked to lower glycine levels and was related to higher circulating concentrations of branched-chain amino acids. In addition, potential marked effects of red meat consumption on the fatty acid composition within the investigated lipid classes were identified. Moreover, potential beneficial and adverse direct effects of metabolites on type 2 diabetes risk were detected. Aromatic amino acids and lipid metabolites with even-chain saturated (C14-C18) and with specific polyunsaturated fatty acids had adverse effects on type 2 diabetes risk. Glycine, glutamine, and lipid metabolites with monounsaturated fatty acids and with other species of polyunsaturated fatty acids were classified as having direct beneficial effects on type 2 diabetes risk. Potential mediators of the diet-diabetes links were identified by graphically overlaying this information in network models. Mediation analyses revealed that effects on lipid metabolites could potentially explain about one fourth of the whole-grain bread effect on type 2 diabetes risk; and that effects of coffee and red meat consumption on amino acid and lipid profiles could potentially explain about two thirds of the altered type 2 diabetes risk linked to these dietary exposures. Conclusion: An algorithm was developed that is capable to integrate single external variables (continuous exposures, survival time) and high-dimensional metabolomics-data in a joint graphical model. Application to the EPIC-Potsdam cohort study revealed that the observed conditional independence patterns were consistent with the a priori mediation hypothesis: Early effects on lipid and amino acid metabolism had the potential to explain large parts of the link between three of the most widely discussed diabetes-related dietary exposures and the risk of developing type 2 diabetes.},
  language  = {en}
}
@phdthesis{Penisson2010,
  author    = {P{\´e}nisson, Sophie},
  title     = {Conditional limit theorems for multitype branching processes and illustration in epidemiological risk analysis},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-45307},
  school      = {Universit{\"a}t Potsdam},
  year      = {2010},
  abstract  = {This thesis is concerned with the issue of extinction of populations composed of different types of individuals, and their behavior before extinction and in case of a very late extinction. We approach this question firstly from a strictly probabilistic viewpoint, and secondly from the standpoint of risk analysis related to the extinction of a particular model of population dynamics. In this context we propose several statistical tools. The population size is modeled by a branching process, which is either a continuous-time multitype Bienaym{\´e}-Galton-Watson process (BGWc), or its continuous-state counterpart, the multitype Feller diffusion process. We are interested in different kinds of conditioning on non-extinction, and in the associated equilibrium states. These ways of conditioning have been widely studied in the monotype case. However the literature on multitype processes is much less extensive, and there is no systematic work establishing connections between the results for BGWc processes and those for Feller diffusion processes. In the first part of this thesis, we investigate the behavior of the population before its extinction by conditioning the associated branching process X_t on non-extinction (X_t≠0), or more generally on non-extinction in a near future 0≤θ<∞ (X_{t+θ}≠0), and by letting t tend to infinity. We prove the result, new in the multitype framework and for θ>0, that this limit exists and is non-degenerate. This reflects a stationary behavior for the dynamics of the population conditioned on non-extinction, and provides a generalization of the so-called Yaglom limit, corresponding to the case θ=0. In a second step we study the behavior of the population in case of a very late extinction, obtained as the limit when θ tends to infinity of the process conditioned by X_{t+θ}≠0. The resulting conditioned process is a known object in the monotype case (sometimes referred to as Q-process), and has also been studied when X_t is a multitype Feller diffusion process. We investigate the not yet considered case where X_t is a multitype BGWc process and prove the existence of the associated Q-process. In addition, we examine its properties, including the asymptotic ones, and propose several interpretations of the process. Finally, we are interested in interchanging the limits in t and θ, as well as in the not yet studied commutativity of these limits with respect to the high-density-type relationship between BGWc processes and Feller processes. We prove an original and exhaustive list of all possible exchanges of limit (long-time limit in t, increasing delay of extinction θ, diffusion limit). The second part of this work is devoted to the risk analysis related both to the extinction of a population and to its very late extinction. We consider a branching population model (arising notably in the epidemiological context) for which a parameter related to the first moments of the offspring distribution is unknown. We build several estimators adapted to different stages of evolution of the population (phase growth, decay phase, and decay phase when extinction is expected very late), and prove moreover their asymptotic properties (consistency, normality). In particular, we build a least squares estimator adapted to the Q-process, allowing a prediction of the population development in the case of a very late extinction. This would correspond to the best or to the worst-case scenario, depending on whether the population is threatened or invasive. These tools enable us to study the extinction phase of the Bovine Spongiform Encephalopathy epidemic in Great Britain, for which we estimate the infection parameter corresponding to a possible source of horizontal infection persisting after the removal in 1988 of the major route of infection (meat and bone meal). This allows us to predict the evolution of the spread of the disease, including the year of extinction, the number of future cases and the number of infected animals. In particular, we produce a very fine analysis of the evolution of the epidemic in the unlikely event of a very late extinction.},
  language  = {en}
}