Obesity is a major risk factor for multiple common chronic diseases. The prevalence in European countries is high and a significant public health concern. This thesis aims to explore the obesity landscape in the Cooperative Health Research in South Tyrol (CHRIS) study. The first step was to characterise the obese CHRIS population, taking into account the established body mass index (BMI) classification from the World Health Organization (WHO) and looking at metabolically healthy and unhealthy obesity. We investigated the familial aggregation of these traits. We identified several families with significant familial aggregation and observed varying degrees of overlap for these traits in different families. The focus was then on implementing and applying a Genome-Wide Polygenic Score for obese participants. These scores were computed for individuals based on the presence of different genetic variants weighted according to their measured effects in genome-wide association studies (GWAS). We then paid attention to the targeted metabolomics data of the CHRIS study, to identify different serum metabolites associated with metabolically healthy/unhealthy obesity, using logistic regression and random forest methods to explore metabolic signatures to distinguish obesity into metabolically healthy and metabolically unhealthy obesity. Several biomarkers were shown to be related to obesity, many of which confirmed by existing evidence (such as BCAAs, tyrosine, and lysophosphatidylcholines).