Research Article: Leveraging Genetics to Advance Type 2 Diabetes Prevention

Date Published: July 26, 2016

Publisher: Public Library of Science

Author(s): Jose C. Florez

Abstract: In this Perspective, Jose Florez discusses how information from genetics and genomics may be able to contribute to prevention of type 2 diabetes and predicting individual responses to behavioral and other interventions.

Partial Text: In a world of finite resources, it may make sense to prioritize those most likely to benefit. Therefore, great interest has emerged in whether nascent technologies can be used to identify individuals at risk of future type 2 diabetes and/or predisposed to experience a favorable response to preventive strategies.

While the value of genetic testing in type 2 diabetes prediction is limited, it may have a more useful role in the selection of patients more likely to respond to intervention. Though many of these observations require independent replication, the DPP Research Group has shown that variation in the obesity-associated gene MC4R modifies the ability of the lifestyle intervention to induce weight loss [20], the missense type 2 diabetes-associated variant P446L in GCKR modifies the effect of the intensive lifestyle intervention on triglyceride concentrations [21], and a genetic risk score for lipid traits also modifies the response to lifestyle intervention on LDL cholesterol concentrations and small LDL particle number [22]. A combined meta-analysis of the DPP (in prediabetic participants) and the Look AHEAD trial (in participants with established type 2 diabetes) has shown that genotype at the obesity-associated gene MTIF3 also predicts the degree of weight loss after a lifestyle intervention [23].

In summary, while type 2 diabetes represents one of the most serious threats to global public health in the 21st century, strategies exist to stem its spread. To rationalize deployment of diabetes prevention strategies in a cost-efficient manner, it may help to stratify the population into groups at highest risk or most likely to benefit. Genetic prediction does not seem to provide much additional information beyond traditional clinical predictors in identifying those at increased risk, with the potential exception of some variants with strong effects that are more prevalent in specific ethnic groups; thus, if included, genetic predictors should always be considered in conjunction with other markers to obtain an overall estimate of risk. Whether the suggestive evidence that genetic predictors may help stratify response to preventive interventions is eventually translated into clinical practice awaits the completion of well-powered clinical trials.



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