Genetic & epigenetic approach to human obesity.

Obesity is an important clinical and public health challenge, epitomized by excess adipose tissue accumulation resulting from an imbalance in energy intake and energy expenditure. It is a forerunner for a variety of other diseases such as type-2-diabetes (T2D), cardiovascular diseases, some types of cancer, stroke, hyperlipidaemia and can be fatal leading to premature death. Obesity is highly heritable and arises from the interplay of multiple genes and environmental factors. Recent advancements in Genome-wide association studies (GWAS) have shown important steps towards identifying genetic risks and identification of genetic markers for lifestyle diseases, especially for a metabolic disorder like obesity. According to the 12th update of Human Obesity Gene Map there are 253 quantity trait loci (QTL) for obesity related phenotypes from 61 genome wide scan studies. Contribution of genetic propensity of individual ethnic and racial variations in obesity is an active area of research. Further, understanding its complexity as to how these variations could influence ones susceptibility to become or remain obese will lead us to a greater understanding of how obesity occurs and hopefully, how to prevent and treat this condition. In this review, various strategies adapted for such an analysis based on the recent advances in genome wide and functional variations in human obesity are discussed.

An age structured model for obesity prevalence dynamics in populations

Objective. Modeling the correlation of the development of obesity in a population with age and time and predict the dynamics of the correlation of the development of obesity in a population with age and time under different scenarios in Valencia (Spain). Materials and methods. An age structured mathematical model is used to describe the future dynamics of obesity prevalence for different ages in human population with excess weight. Simulation of the model with parameters estimated using the Health Survey of the Region of Valencia 2000 (4.319 interviews) and Health Survey of the Region of Valencia 2005 (4.012 interviews). The model considers only overweight and obese populations since these subpopulations are the most relevant on obesity health concern. Results. The model allows predicting and studying the prevalence of obesity for each age. Results showed an increasing trend of obesity in the following years in well accordance with the trend observed in several countries. Conclusions. Based on the numerical simulations it is possible to conclude that the age structured mathematical model is suitable to forecast the obesity epidemic in each age group in different countries. Additionally, this type of models may be applied to study other characteristics of other populations such animal populations.