Obesity remains a major health problem, partly due to our limited understanding of this complex disease. Obesity carries with it the risk of many other diseases including type 2 diabetes, cardiovascular disease, hyperlipidemia and some types of cancer. The variability in the disease as well as its related comorbidities makes it a complex, multi-factorial condition that is not easily categorised and treated. ‘Omics technologies and bioinformatics tools allow for the investigation of the complex biology behind obesity. These technologies enable production of complex multivariate datasets that can be investigated using bioinformatics tools to identify patterns in the data as well as associations between different features of the data. However, while advances in ‘omics technologies have allowed production of large amounts of data from biological samples, extraction of useful information from the data remains a huge challenge. Choosing the correct methodology and tools to transform heterogeneous data into biological knowledge is especially difficult when different methods on the same data may yield different results, requiring further statistical or biological validation. This thesis uses existing bioinformatics tools and methods to first combine and analyse transcriptomics and biochemical data and then, separately, metabolomics and biochemical data to gain an understanding of obesity. Body mass index (BMI)-discordant as well as BMI-concordant monozygotic (MZ) twin pairs were used to investigate the molecular effects of obesity by looking at gene expression and metabolite profiles in subcutaneous adipose tissue (SAT) and blood plasma, respectively, to gain biological insights into pathways that are associated with obesity and obesity-related clinical manifestations. The SAT was further interrogated using isolated adipocytes, to examine the transcriptomics patterns in obesity of this specific cell type. Using the blood plasma, metabolites associating with different cardiometabolic risk factors were also identified. Variations in the global profiles were also studied to assess if study participants form different subgroups of obesity according to their gene expression or metabolite profiles. Adiposity and blood biochemistry measure differences between these obesity subgroups were also examined.
|Tilldelningsdatum||8 jun 2018|
|Status||Publicerad - 2018|
|MoE-publikationstyp||G5 Doktorsavhandling (artikel)|
Bibliografisk informationM1 - 101 s. + liitteet
- 3121 Inre medicin
- 3142 Folkhälsovetenskap, miljö och arbetshälsa