Obesity and type 2 diabetes are two major public health concerns that affect a significant proportion of the global population. While the causal relationship between these conditions is well established, the specific mechanisms of action and their consequences remain poorly understood. This study uses a genetic-driven approach to define two distinct obesity profiles that convey highly concordant and discordant diabetogenic effects.
By annotating and comparing association signals for these profiles across clinical and molecular phenotypic layers, key differences are identified in a range of traits, including cardiovascular mortality, fat distribution, liver metabolism, blood pressure, lipid fractions, and blood levels of proteins involved in extracellular matrix remodeling.
Despite marginal differences in gut bacteria abundance, instrumental analyses reveal prominent causal roles for waist-to-hip ratio, blood pressure, and cholesterol content of high-density lipoprotein particles in the development of diabetes in obesity.
The study prioritizes 17 genes from the discordant signature that convey protection against type 2 diabetes in obesity, providing potential targets for precision medicine approaches. This research sheds light on the heterogeneity of obesity-related conditions and provides a foundation for developing personalized treatment strategies for individuals with obesity and type 2 diabetes.
As per a study published in Nature, Cardiometabolic diseases, including obesity and type 2 diabetes mellitus (T2D), are the leading cause of death worldwide. Despite their well-established relationship, the interplay between these two conditions remains complex and needs to be fully understood. Moreover, while most people with T2D also have obesity, a significant proportion of people with obesity appear metabolically healthy. Conversely, metabolic abnormalities can occur in normal-weight individuals.
This phenomenon, referred to as “discordant diabesity,” is the focus of a new study that aims to shed light on the independent roles of excess adiposity and T2D in life-threatening diseases. Using a quantitative genetics framework, the study characterizes and compares genetically determined discordant diabesity with its concordant counterpart.
Machine learning methods were utilized to identify traits other than T2D that distinctively characterize these profiles. The study’s findings help elucidate the mechanisms underlying the broader health consequences of excess adiposity and lead to the development of personalized treatment strategies for individuals with discordant diabesity.
New research has highlighted the challenges of disease prevention and management for individuals with obesity and its heterogeneous effects on cardiometabolic health. The study used genetics to differentiate between concordant and discordant diabesity, uncovering different health characteristics beyond diabetes and obesity. The research team identified biomarkers that may aid risk stratification, mechanisms of action, and potential targets for drug development and repurposing.
Mechanisms involved in uncoupling obesity risk from T2D risk, such as adipose distribution, may be crucial to disease risk, including biomarkers of liver failure. Blood pressure may be another significant phenotypic distinction between concordant and discordant profiles, as changes in the vascular bed may precede metabolic perturbations through nutrient and hormonal flux. The study also underscores the importance of tissue pleiotropy and tissue cross-talk in the molecular mechanisms of diabesity discordance.
Although no statistically robust differences were observed in gut microbiota between the two diabesity profiles, nominal differences emerged in taxa belonging to the Bacteroidetes and Firmicutes phyla. The findings highlight the need for more advanced profiling of lipid subfractions to determine risk in people with obesity. The research identified potential therapeutic agents to prevent cardiometabolic complications in obesity and called for deeper characterization of mechanisms to improve obesity stratification.
