Evaluating the effects of coding variation in inflammatory genes in cardio-metabolic disease
Principal Investigator: Satya Dash
Approved Research ID: 48839
Approval date: October 1st 2019
Diabetes and pre-diabetes, which are associated with increased blood glucose (sugar) are very common conditions typically associated with increased body weight. Studies in mice have overwhelmingly shown that increased inflammation, in the setting of weight gain, is a major cause for diabetes and pre-diabetes. Inflammation has also been associated with heart disease. Whether increased inflammation causes or contributes to diabetes/prediabetes in humans has not been conclusively established. A number of treatments which reduce inflammation have been developed and are continuing to be developed, but so fat they have not been shown to be effective treatments for diabetes. Some of these treatments have been shown to lower the rates of heart disease suggesting that inflammation may be an important cause for heart disease. We know that a number of genes affect inflammation. In mice, genetic changes that reduce inflammation protect against diabetes and heart disease, even in the presence of weight gain. The effect of genetic changes that influence inflammation, on diabetes/pre-diabetes in humans is not known. Our aim is to study the effect of changes in genes that we know influence inflammation in the UK Biobank participants. We are interested in this study as details about health conditions and treatments along with genetic information is available for participants. If inflammation is a major cause for diabetes we would expect participants with genetic changes that reduce inflammation to have lower rates of diabetes and pre-diabetes compared to other participants even in the setting of being overweight. If we confirm that this is the case and identify the precise genes that are protective, this may inform development of future treatments for this serious medical condition. As a comparison we will also been studying the effects of these genes on heart disease. We anticipate completing our analysis over the next 3 years.