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Approved Research

Integrative analysis of the effect of gene-environment interaction on obesity, diabetes and their complications toward precision medicine

Principal Investigator: Dr Yuta Hiraike
Approved Research ID: 78912
Approval date: February 22nd 2022

Lay summary

Diabetes and obesity now amount to a world-wide epidemic. Poorly controlled diabetes and obesity result in complications including microvascular (nephropathy, retinopathy and neuropathy) and macrovascular (cardiovascular and cerebrovascular diseases) complications, certain types of cancer, and musculoskeletal comorbidities. Obesity and diabetes develop as a result of genetic susceptibility, environmental factors, and interaction of these two factors. While pharmacological treatment for diabetes as well as surgical treatment for morbid obesity have been significantly advanced over the last decade, intervention on environmental factors or lifestyle including proper diet and exercise is obviously an important measure for prevention and treatment of obesity, diabetes and their complications. However, lifestyle modifications possess limited evidence on the effectiveness compared to pharmacological as well as surgical interventions so far, probably due to a limited availability of large-scale, quantitatively-measured dataset on environmental factors and also due to a huge variation of the response to lifestyle modifications among individuals driven by gene-environment interactions.

While gene-environment interaction studies on diabetes and obesity are emerging in the field of metabolic disease research, to what extent we can overcome genetic predisposition to obesity, diabetes and their complications via lifestyle modifications remains largely elusive. To address this question, in Aim 1, we will examine the effect of genetic risk for obesity and diabetes on incidence of their complications such as major adverse cardiovascular events, chronic kidney disease, cancer and overall death. In Aim 2, we will examine whether and to what extent lifestyle factors can ameliorate causal associations between genetic risk of obesity and diabetes and their complications. In Aim 3, we will develop a machine learning-assisted algorithm to determine the best possible lifestyle recommendation for each individual based on their genetic risk, to minimize the burden of complications and overall death.

Through this work, we will establish a robust evidence of the effect of lifestyle modifications especially in the context of gene-environment interactions. Moreover, successful conclusion of this work would result in an establishment of an algorithm toward personalized, genetic risk-based lifestyle recommendations to avoid complications of obesity and diabetes such as major cardiovascular adverse events, chronic kidney disease and cancer as well as to extend healthy lifespan. Our 3-year project would be a milestone for an implementation of precision medicine in the field of metabolic diseases.