Investigating the causal associations of diet and gut microbiome in the development of metabolic disease.

Increased body weight (i.e. obesity) is prevalent not only in the UK but worldwide: 1.9 billion people suffer from overweight and obesity. Obesity often brings along other health conditions like diabetes (422 million people) and heart disease that are responsible for taking the lives of 17.7 million people worldwide every year. Often we think of bad diet and lack of exercise as the main drivers of obesity. But research indicates that other factors like our genes, hormones, immune factors and even the microbes in our gut (i.e. gut microbiome) determine our susceptibility to these conditions by affecting how our body responds to what we eat and how we live. If we can understand how these factors work with each other, we can find ways to prevent and cure obesity and its health consequences. In this project, I will study how our diet and gut microbes are related to obesity, diabetes and heart disease. Using genetic methods applied to population cohorts, I will study if diet can change our gut microbes. I will also look at how our gut microbiome changes with obesity, diabetes and heart disease and finally, I will develop mathematical models to predict whether we can use diet to fix our gut microbes such that we can cure or prevent these conditions. I will also find ways to apply my findings for improving the health of general public at-large.

Obesity and its related conditions are leading public health concerns worldwide. Besides diet and physical activity, gut microbiota is a modifiable factor that can be targeted to prevent and treat these conditions. Indeed, alterations in gut microbiome (i.e. gut dysbiosis) have been reported in obesity and diabetes. Gut microbiome itself is affected by diet, however, the causal role of diet in gut dysbiosis, and in turn, a causal role for gut dysbiosis in the development of metabolic disease remains to be determined. Causality is difficult to assess as observational studies are subject to reverse causation and confounding bias. Mendelian Randomization (MR) is an approach that can assess causality using genetic variants, as they are randomly inherited and remain constant over life. In this project, I will begin by identifying genetic variants associated with dietary variables through genome-wide association studies (GWAS) using the UK Biobank. Next, MetaCardis cohort will be utilized to investigate the relationship between diet and gut microbial profiles during metabolic disease development. Finally, 2 sample MR will be applied using the genetic variants for dietary variables identified in step1 to the metagenomic data in step 2 to investigate whether diet causes metabolic disease via changes in the gut microbiome. These observations will also be utilized for developing health policy-related research questions in order to enhance the translational impact of the proposed work.

The proposed project involves components of nutrition, genomics, gut microbiome and heath policy as they relate to the development and management of obesity, type 2 diabetes and cardiovascular disease. My work, thus, has the potential of having a direct impact on each of the respective research communities. To begin with, my work would lead to the identification of genetic instruments associated with dietary variables that can be utilized by others and us for investigating causal relationships in approaches like Mendelian Randomization. In addition, we have set out to characterize the gut microbiome under novel disease conditions like coronary heart disease including heart failure, tease out mechanistic networks and most importantly identify the dietary factors that are causal in the development of gut dysbiosis associated with obesity, type 2 diabetes and heart disease. Indeed, recognition of these dietary factors can allow for the development of personalized nutrition for targeting gut microbiota both for prevention and treatment. Moreover, the microbial profiles can be utilized for risk prediction in otherwise healthy individuals. Gut microbial profiles in combination with metabolomic data (urinary and serum metabolites data available for MetaCardis cohort) can further be utilized for the development of biomarkers of disease progression. Thus, my observations can have a direct impact on the industry and product development as well as health care and public health bodies. Diet, genetics, gut metabolites are also increasingly becoming part of the 'exposome' (i.e. complex set of biological, environmental and economic factors) that increases one's risk to the development of metabolic disease. Indeed, biological information is already being utilized to develop policy-relevant causal pathways and tools that can be utilized for investigating the relationship among obesogenic environments such as food systems, community environments and socioeconomic status as well as for monitoring and evaluating policy interventions in Horizon 2020 Project like STOP targeting childhood obesity in Europe led by Dr. Franco Sassi. Indeed, the observations out of my project can be utilized to develop such public health policy-relevant research questions and directly affect policy development and community as well as food environment design eventually.