The relationship between food intake and depressive symptoms

Depression is one of the most prevalent diseases and an important risk factor for general public health. In particular is very common in elder persons. Accordin- gly, strategies to prevent depression are needed. A growing body of empirical evidence suggests the key role of diet in the prevention of depression. This fact is the basis of the European MooDFOOD project, which is aimed to study the multifaceted links of food intake with depression. The thesis will be focused on the identification of the impact of depressive symp- toms on the food intake behavior. The main challenge to be addressed is to handle with food intake, which are usually collected in large matrices of nutrien- ts and foods. In the nutritional framework, two approaches are usually adopted: a priori and a posteriori patterns. A priori patterns use expertise knowledge to implement nutritional scores. As opposite, food patterns were derived by the study of the covariance matrix. The main goal of this thesis is to understand how the food intake behavior is affected by the occurrence of depressive symptoms. In particular, two dedicated statistical methodologies are proposed to estimate the covariance/precision ma- trix conditionally to a set of covariates. As first approach a heteroscedastic multivariate regression model is implemented. Dedicated prior distributions were specified to infer in a Bayesian framework. Parametrization of the conditional covariance is implemented to favor the inter- pretation of results. A Metropolis within Gibbs sampling scheme is implemented for posterior computation. Moreover, a correlation adjustment is proposed in or- der to ensure the positive estimates. A second approach based on multiple graphical model is implemented. A high dimensional framework was considered. Accordingly, a sparse joint estimation procedure was adopted to estimate the partial correlation matrix. This approach takes into account of a possible common structure across groups. Moreover, a strategy based on an interpolation method (kriging) is implemented for graphs selection. The proposed statistical methodologies provided comparable results in explai- ning the food intake behavior of a sample of participants to the InCHIANTI study, which is an epidemiological study place in the Chianti area of Tuscany. In particular it was evidenced how diets may changes before to be classified as depressed. In both application, the intake of olive oil resulted central. The heteroscedastic multivariate regression model is useful to provide interpre- table results given its smart parameterization. It is able to have different kind of covariates. However, it could be affected by a risk of over parametrization. Moreover, an intense computational effort is required to obtain posterior di- stribution. The joint sparse estimation of multiple graphical models allows to understand the conditional association between food groups in different data- sets. It provided better results in term of computational cost but is limited to analyze only groups of subjects. Furthermore, even if a bootstrap procedure is implemented to select more clear graphs, interpretation can still remain unclear. In conclusion, even if some issue remains open for both methodologies, these approaches could be introduced in the nutritional framework as alternative way to analyze dietary data.