Background: Obesity, resulting from complex interactions between genetic and non-genetic factors, is one the most pressing health challenges in our society. Current treatments for losing weight based mainly on diet and exercise are mostly unsuccessful in the long term. Therefore, as an alternative to the current strategy of one-size-fits-all, a more individualized approach is proposed in which genotype data are used to personalize treatment and to optimize the results.
Objective: To inform about the state of the art research related to the influence of genetic variation in the modulation of the association between diet on obesity and weight-related measures.
Results: Most of the published research use observational studies to identify gene by diet interactions modulating obesity risk. Far fewer are randomized clinical intervention trials assessing short-term weight-loss or its long-term maintenance in relation to specific genotypes. The results of the studies undertaken to date show significant progress in identifying polymorphisms in genes related to obesity, the greatest body of literature being reported for the FTO gene. The results on gene-diet interactions in determining obesity phenotypes are very heterogeneous, with few exceptions such as the APOA2 locus with saturated fat and BMI. An important recommendation is to standardize the methodology for undertaking these studies. Furthermore, such lack of replication suggests undetected higher-level interactions and experimental caveats. One of the potential interactive factors is chronobiology. It has been shown that genetic variation in Clock-related genes is associated with obesity and with the response to dietary interventions aimed to lose weight. Moving forward, the integration of different high-throughput omic techniques (i.e., genomics, epigenomics, and metabolomics) will provide the mechanistic basis to well-validated gene-diet interactions and add credibility to this area of nutrition research.
Conclusions: Despite substantial progress, the current evidence level of applying genotype data to obesity treatment is in its early stages. Nevertheless, prospects are promising.
19 May 2018 - 22 May 2018