The Dark side of Obesity: Multi-omics analysis of the dysmetabolic morbidities spectrum
Doctoral thesis, 2023

Obesity is one of the most prevalent clinical conditions worldwide and is associated with a wide spectrum of dysmetabolic comorbidities. Complex cardio-metabolic disease cohorts, such as obesity cohorts are characterised by population heterogeneity, multiple underlying diseases status and different comorbidities’ treatment regiments. The systematic collection of multiple types of clinical and biological data from such cohorts and the data-analysis in an integrative manner is a challenging task due to the variables’ dimensionality and the lack of standardised know-how of post-processing.

The main resource of this thesis has been the BARIA cohort, a detailed collection over time of multiple omics and demographic data from participants in bariatric surgery. BARIA datasets included plasma metabolites, RNA from hepatic, jejunal, mesenteric and subcutaneous adipose tissues and gut microbial metagenome, besides biometric data. The work presented in this thesis included the development of a systems biology integrative framework based on BARIA that (i) utilised unsupervised machine learning algorithms, self-organizing maps in particular, and multi-omics integrative frameworks, the DIABLO library, in order to stratify the BARIA heterogeneous obesity cohort and predict the bariatric surgery’s outcome. The thesis covered how BARIA can be the onset for (ii) studying molecular mechanisms related to type 2 diabetes (T2D) and G-protein coupled receptors (GPCRs) and for identifying a minimal set of biomarkers for obesity’s comorbidities such as (iii) non-alcoholic fatty liver disease (NAFL) and (iv) gallstones formation after bariatric surgery.

The results indicated that the metabotypes comprising a bariatric surgery cohort exhibited a concrete metabolic status and different responses over time after the bariatric surgery. It has been demonstrated how obesity and T2D associated metabolites, such as 3-hydroxydecanoate, can increase inflammatory responses via GPCRs molecular activation and signalling. Last but not least, minimal sets of both evasive and non-evasive multi-omic discriminatory biomarkers for obesity’s dysmetabolic morbidities (NAFLD and gallstones after bariatric surgery) were obtained. Taking into consideration all the findings, this thesis presented how data-driven approaches can be used for studying in-depth heterogeneous cohorts, hereby facilitating early diagnosis and enabling potential preventive actions.

systems biology

Obesity

multi-omics integration

biomarkers

gallstones

non-alcoholic fatty liver disease

GPCR receptors

metabotyping

bariatric surgery

self-organizing maps

KE-hallen, Kemigården 4, Chalmers
Opponent: Associate professor Vassily Hatzimanikatis, Ecole polytechnique fédérale de Lausanne Institut des sciences et ingénierie chimiques EPFL SB ISIC LCSB

Author

Dimitra Lappa

Chalmers, Life Sciences, Systems and Synthetic Biology

Lappa D, Meijnikman AS, Krautkramer KA, Olsson LM, Aydin Ö, Van Rijswijk AS, Acherman YIZ, De Brauw ML, Tremaroli V, Olofsson LE, Lundqvist A, Hjorth SA, Ji B, Gerdes VEA, Groen AK, Schwartz TW, Nieuwdorp M, Bäckhed F, Nielsen J. Self-organized metabotyping of obese individuals identifies clusters responding differently to bariatric surgery

Obesity is one of the most rising pandemics worldwide and is directly associated to a wide spectrum of comorbidities. Overweight and obese populations are very diverse and are characterised by multiple underlying diseases and different combinations of medical treatments. Hence, obese individuals are very difficult to analyze systematically. Besides a healthy diet and increased physical exercise, one of the most efficient ways to deal with obesity its’ implications is through weight-loss surgery (bariatric surgery). The BARIA cohort is used as a means to study obesity and associated medical conditions, via the collection of multiple omics and demographic data from participants in bariatric surgery. By employing systems biology methods and machine learning algorithms we were able to distinguish subgroups with specific metabolic profiles from the BARIA cohort and predict their weight-loss response over time. In addition, we could isolate and study molecular mechanisms linking obesity to type 2 diabetes and G-protein coupled receptors. Systems biology methods identified biomarkers for obesity's comorbidities such as non-alcoholic fatty liver disease and gallstones formation after bariatric surgery. Altogether, these data-driven approaches can be used not only in the context of obesity, but as a platform for studying heterogeneous disease cohorts, thus facilitating early diagnostics and enabling potential preventive actions.

Gut microbiome effects on cardiometabolic disease through metabolism-modifying metabolites (Gut-MMM)

Novo Nordisk Foundation (NNF15OC0016798), 2016-07-01 -- 2022-12-31.

Subject Categories

Biological Sciences

Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)

Bioinformatics and Systems Biology

Infrastructure

C3SE (Chalmers Centre for Computational Science and Engineering)

Areas of Advance

Life Science Engineering (2010-2018)

ISBN

978-91-7905-815-9

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5281

Publisher

Chalmers

KE-hallen, Kemigården 4, Chalmers

Opponent: Associate professor Vassily Hatzimanikatis, Ecole polytechnique fédérale de Lausanne Institut des sciences et ingénierie chimiques EPFL SB ISIC LCSB

More information

Latest update

3/22/2023