A genetic-driven approach defining two obesity profiles that convey highly concordant and discordant diabetogenic effects
A team of researchers led by Daniel E. Coral and Paul W. Franks at LUDC have used human genetics to do a phenome-wide analysis of the degree of genetic dissimilarity between obesity and type 2 diabetes. Together with colleagues from Dundee and Oxford universities in the UK and the Vanderbilt Genetics Institute in the US, the team recently published their findings in the journal Nature Metabolism.
Obesity and type 2 diabetes are directly linked, usually leading to simultaneously elevated risk. But although the two conditions often occur together, their relationship is complicated and not fully understood. While more than eight out of ten people with type 2 diabetes also have obesity, one to three out of ten people with obesity appear metabolically healthy. And on the other hand, metabolic abnormalities occur in around three out of ten normal-weight individuals, and when type 2 diabetes occurs in people with normal weight, mortality rates are higher than for people who are overweight or obese. These deviations from the norm are referred to as ‘discordant diabesity’, whereas ‘concordant diabesity’ represents the more common scenario where higher risk of obesity and type 2 diabetes coincide.
In the study "A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes", Coral and colleagues wanted to examine the distinct characteristics of genetically driven discordant diabesity in comparison to concordant diabesity. As no cohorts currently include all the genetic and phenotypic data required for this analysis, the authors used a variety of machine-learning methods to analyse publicly available information from several cohorts to examine variations in phenotypic characteristics.
Targets for precision medicine
The team annotated and compared association signals for the discordant and concordant profiles phenome wide. They found that discordant diabesity differs from concordant diabesity in terms of cardiovascular mortality, fat distribution, liver metabolism, blood pressure, specific lipid fractions, and blood levels of proteins involved in extracellular matrix remodelling. In the gut microbiota, there were marginal differences in the abundance of Bacteroidetes and Firmicutes bacteria between the discordant and concordant profiles. Studies have shown that an imbalance in the ratio of these two bacterium phyla can contribute to the development of diabesity. Further analyses revealed prominent causal roles for waist-to-hip ratio, blood pressure, and cholesterol content of HDL particles in the development of diabetes in cases of obesity. Finally, the authors looked at gene expression data and prioritised 17 genes from the discordant phenotype that convey protection against type 2 diabetes in cases of obesity. These genes may thus represent logical targets for precision medicine approaches.
First author Daniel Coral MD MPH, from the Genetic and Molecular Epidemiology (GAME) Unit at LUDC, says about this work:
“We need to move fast towards a more systemic and integrative view of obesity. This is needed not only because of its rapidly increasing prevalence, but also because of its vast complexity and heterogeneity. By exploring the interplay between genetics, proteomics, lipids, the gut microbiome, and gene expression, we hope to have shed some light on the multi-faceted nature of obesity and contributed towards more precise interventions.”
This work shows that this type of genetic-driven approach bring value to the field in terms of understanding why obesity often, but not always, causes diabetes. The differences in clinical features, blood proteins, lipid subfractions, and gene expression suggest that the discordant phenotype in obesity may be related to the regulation of multiple processes in the body.
More studies are needed on this topic, as they may have important implications for the development of targeted interventions to prevent or manage risk in obesity. A stratified medicine approach could be applied not only in screening and in targeted drug treatments, but also in clinical trials to investigate if people of different phenotypes respond differently to life-style interventions or surgery.
Link to the article in Nature Metabolism
Daniel Coral, doctoral student at the Genetic and molecular epidemiology unit at Lund University
daniel [dot] coral [at] med [dot] lu [dot] se
Daniel Coral's research profile at Lund University's research portal