Abstract
Obesity is a complex multifaceted disease resulting from interactions between genetics and lifestyle. The proportion of phenotypic variance ascribed to genetic variance is 0.4 to 0.7 for obesity and recent years have seen considerable success in identifying disease-susceptibility variants. Although with the advent of genome-wide association studies the list of genetic variants predisposing to obesity has significantly increased the identified variants only explain a fraction of disease heritability. Studies of gene–environment interactions can provide more insight into the biological mechanisms involved in obesity despite the challenges associated with such designs. Epigenetic changes that affect gene function without DNA sequence modifications may be a key factor explaining interindividual differences in obesity, with both genetic and environmental factors influencing the epigenome. Disentangling the relative contributions of genetic, environmental and epigenetic marks to the establishment of obesity is a major challenge given the complex interplay between these determinants.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Introduction
Obesity was once considered a problem of economically developed countries, but the number of overweight and obese people is now dramatically increasing in low- and middle-income countries at a rate never seen before [1]. If recent trends continue unabated, by 2030, the absolute numbers could rise to a total of 2.16 billion overweight and 1.12 billion obese individuals, or 38 % and 20 % of the world’s adult population, respectively [2].
As the fundamental cause of obesity and overweight (defined by anthropometric measures: body mass index [BMI], waist circumference [WC] and/or waist-to-hip ratio [WHR]) is an energy imbalance between calories consumed on the one hand and calories expended on the other hand, increases in rates of obesity must reflect a state of positive energy balance, which is very likely a result of the profound changes in society and in behavioral patterns of populations over recent decades. Indeed, it is widely accepted that multiple factors contribute to this epidemic, including economic growth, modernization, urbanization and, most importantly, changes in our lifestyle, as eating habits have shifted to greater consumption of energy-dense foods that are high in fats and sugars, while at the same time, physical activity has decreased [1, 3]. Although a healthy lifestyle could be the apparent remedy for obesity, its implementation in the general population has proven difficult so far. Given the fact that people respond differently to the “obesogenic” environment owing to genetic predisposition, understanding the causes and pathophysiology of obesity is very important for prevention and therapy. Even in the presence of a strong “obesogenic” environment, hereditary factors remain key contributors to the disease etiology. Ethnic/racial differences in obesity even in comparable environments [4, 5] indicate that obesity is most likely the result of a complex interplay between multiple genetic, behavioral, social and environmental factors that affect energy balance and, thus, body weight regulation [6–9].
Over the past two decades, numerous strategies have been employed for the identification of genetic determinants of obesity, including studies of severe forms of obesity, genome-wide linkage studies, candidate gene analyses and genome-wide association studies (GWAS). Since 2005, the novel GWAS approach has led to breakthrough progress in our understanding of the genetic determinants of common obesity. Almost 50 loci have been identified and are collectively reported in the National Human Genome Research Institute GWAS catalogue (http://www.genome.gov/gwastudies/) [10]. Among those GWAS findings, the first obesity susceptibility locus identified was the FTO gene, which has the largest effect on obesity risk to date; each additional risk allele in FTO was shown to be associated with a 1- to 1.5-kg increase in body weight and a 20 % to 30 % increase in obesity risk [11, 12]. Since it is widely assumed that gene–environment interaction (GEI) must have an effect on adiposity, several epidemiologic studies have explored the relationship between lifestyle and obesity susceptibility genes, reporting significant interactive effects. Despite discrepancies in the reported results, some new insights into the role of gene–lifestyle interaction in obesity have been obtained. In the current review, we evaluate the recent successes in the examination of GEI in obesity and describe the main findings. We then examine the machinery that underlies GEI in obesity, focusing on epigenetics and particularly DNA methylation as a mechanism for these interactions. Finally, we discuss the challenges of the existing and emerging approaches in studying GEI.
Genetic Determinants of Obesity
Until recently, progress in finding obesity-susceptibility genes was rather slow. Numerous groups have been involved in research related to the genetics of common obesity, with a major focus on candidate gene studies. Those genes were selected based on their known functional role in physiologic pathways (e.g. regulating body weight or energy metabolism) [13]. Between 1996 and 2005, the Obesity Gene Map (http://obesitygene.pbrc.edu/) extensively evaluated all published results, including monogenic forms of obesity, transgenic and knockout animal models, quantitative trait loci from animal cross-breeding experiments, linkages from genome scans and candidate gene association studies [14]. In the last update of the Obesity Gene Map published in 2006, 127 candidate genes were reported, of which less than 20 % were replicated by 5 or more studies. Such a high level of non-replication was the result of many limitations of the candidate gene study approach, such as small sample size and, thus, insufficient statistical power to detect small effects, as well as lack of type 1 error control, among others [15•].
In the past few years, a novel approach (GWAS) that involves scanning of many thousands of samples using the latest advances in genotyping technology (i.e. high-density, genome-wide arrays to assay hundreds of thousands of single nucleotide polymorphisms [SNPs] that capture the majority of common variation in the human genome) have led to breakthrough progress in the identification of obesity-susceptibility genes. To date, large-scale meta-analyses of GWAS for overall and abdominal obesity along with a recent GWAS meta-analysis for percent body fat (%BF) have reported 32 genetic loci associated with BMI, 14 loci related to WHR and 2 loci for %BF (see Day and Loos [15•] for a recent overview of GWAS findings of obesity-related traits). The effect sizes of the 32 established BMI-associated loci ranged from 0.06 to 0.39 kg/m2 (or ~0.17 to ~1.13 kg for an adult of ~170 cm in height) per risk allele, with the FTO gene having the largest effect size [16]. For the 14 novel WHR loci, the effect sizes varied from 0.019 to 0.042 units per risk allele [17], while the risk alleles for the new %BF loci were associated with an increase ranging from 0.14 to 0.33 % in body fat [18]. Remarkably, the combined effect of all obesity-associated variants is very modest and explains less then 2 % of the BMI heritability [16]. Since the heritability of BMI is estimated to be relatively high (between 40 % and 70 % [15•]), the major question is: what accounts for the missing heritability? Among the suggested explanations is that the modifying effects of environmental factors on genetic predisposition to obesity might partially account for the unexplained interindividual variation in BMI [19••].
Lifestyle Risk Factors for Obesity
Many specialists and scientists in the obesity research field agree that the dramatic increase in the prevalence of overweight and obesity over the past few decades is mainly attributable to the modern (Western) lifestyle, which is characterized by an excessive caloric intake and a sedentary lifestyle [1]. On the basis of many observational and epidemiologic studies, we currently know that the major environmental risk factors for obesity are unhealthy dietary habits (e.g. low in vegetables and fruits high in fat), decreased physical activity and alcohol consumption [20–25]. Overall and abdominal obesity show a negative association with such modifiable lifestyle habits as a Mediterranean-type diet, moderate alcohol consumption and daily physical activity [26]. However, all these well-established risk factors for obesity cannot explain a large proportion of the obesity cases, as there is a high interindividual susceptibility to weight gain in a common “obesogenic” environment. Thus, the most accepted point of view is that the modern obesity epidemic occurs due to a complex interplay between multiple genetic, behavioral and environmental factors. Recently, Speakman and colleagues [27•] suggested a new interesting model for body weight regulation to explain the mechanism underlying the current obesity epidemic. Briefly, this model suggests the presence of upper and lower boundaries defining the set points at which physiologic regulation of body weight and/or fatness becomes activated. While the distance between these intervention points is genetically determined, the changes in body weight depend on the prevailing direction of the environmental pressure (e.g. in the presence of an “obesogenic” environment with increased food supply driving up food intake, only some people become obese). The hypothesis provides a compelling explanation for the observed complexity of the obesity problem and integrates data and research from both the behavioral–nutritional and the molecular genetic–physiologic fields [27•].
Studies on GEI in Obesity
It is well recognized that the investigation of GEI in obesity etiology has not been given sufficient attention in genetic studies [19••]. The majority of GWAS, in particular, have not examined GEI, mainly due to lack of data on environmental measurements [28]. GEI refers to the situation in which genotypes only have their effect in the context of an environment and environments have modifying effects that are dependent on genotypes. In other words, in the presence of the “obesogenic” environment, some individuals with a genetic predisposition to develop obesity will be more prone to gain weight compared with individuals with genetic “resistance” to obesity [29]. A growing body of recent evidence supports a significant role of GEI in obesity and related metabolic diseases [30–32, 33••]. To provide an overview on the most current publications in relationship to GEI in obesity, we searched PubMed (February 15, 2012) using a combination of keywords for genetic studies (i.e. gene, genetic variant, polymorphism, SNP), different obesity-related phenotypes (i.e. overweight, obesity, BMI, waist, hip, WHR, fat, adiposity) and environmental factors (i.e. feeding, diet, physical activity, alcohol, smoking, stress). This retrieved 522 papers published since January 1, 2011, of which 29 were selected as the most relevant to the present review.
The selected papers examined 1) candidate genes for obesity known to play a role in the functional pathways related to metabolic regulation and 2) novel obesity-susceptibility loci identified in recent GWAS. Two approaches were used to investigate the relationship between those genes and different lifestyle factors, such as dietary components, eating habits, physical activity, sedentary behavior and psychological stress: observational and intervention studies. The observational studies are relatively easy to perform. As soon as environmental exposures and genotyping information are collected, the GEIs are examined using cross-sectional or case–control designs. However, the major limitation of these designs is their inability to identify the individual and combined effects of the genetic and lifestyle risk factors or, in other words, to answer the question of how genetic predisposition and behavior combine to determine the risk of obesity [34]. Moreover, the observational studies are susceptible to multiple sources of bias (e.g. selection or recall bias) because environmental exposure and the outcome of interest are assessed simultaneously. In contrast, intervention study designs allow minimization of bias and provide direct control of the environmental factors by defining the experimental conditions a priori (e.g. a specific diet or level of physical activity). However, because these studies are usually small and short term, they have low statistical power and are not appropriate for investigating long-term effects [32].
GEI Studies for Candidate Genes
Overall, the investigation of GEI for biological candidate genes has not been very successful and only a few findings for GEI in obesity were replicated in independent studies. This is the result of small effect sizes and very modest levels of significance for the majority of candidate genes proven to be associated with obesity [34]. Furthermore, as interaction effect sizes are likely to be of even smaller magnitude, many published small-scale reports of GEI for obesity were underpowered and, thus, are probably false positive [33••].
Since January 1, 2011, a few studies have reported GEI consistent with those from previous studies (Table 1 provides a summary of the most relevant GEI studies in obesity published during the past year) [30–32]. For example, variants in the β2-adrenergic receptor (ADRB2)- rs1042714 (Gln27Glu) and rs1042713 (Arg16Gly)- were associated with higher risk of obesity among the individuals with unhealthy lifestyles (i.e. smoking and reduced physically activity) [35, 36] and were shown to have a moderating effect on diet-induced changes on body weight and body composition [37]. Significant genotype–dietary fat interactions for obesity traits have also been reported for the apolipoprotein genes that regulate lipid metabolism (APOA1, APOA2, APOA5, APOB) [38–40], confirming previously observed GEI: the APOA2–saturated fat interaction on body weight and the protective effect of the APO5-1131 C minor allele on obesity in individuals on high-fat diets [39–41]. In addition, an association between APOE genotypes and increased BMI and WC dependent on psychological stress was reported in Danish men [42]. Also, the peroxisome proliferator-activated receptor-γ (PPARγ) gene, which has been extensively studied for GEI related to obesity and type 2 diabetes [32, 43], was reported to have a diet-related effect on risk to obesity with the Pro12 allele being associated with increased adiposity in a high-fat diet group [44]. In addition, the lactase (LCT) gene was shown to be associated with risk to obesity only in individuals who had high milk consumption [45]. Three additional observational studies investigated multiple candidate genes from metabolically relevant pathways: a few positive associations between genes, dietary components and obesity were observed (Table 1) [46–48].
In the past year, several intervention studies reported GEIs for variants in the leptin (LEP) [49] and the perilipin (PLIN1) genes [50] being associated with difference in weight loss in responses to calorie-restricted diets. In contrast, no evidence was found for the effect of variation in the melanocortin-3 receptor (MC3R) gene on weight loss after a 10-week dietary intervention with hypo-energetic diets in obese Europeans (n = 760) [51].
GEI Studies for GWAS Genes
The investigation of GEI for obesity-susceptibility loci identified in recent GWAS is thought to be a more useful strategy than the candidate gene approach. The power to detect GEIs for GWAS loci proven to be robustly associated with obesity is very likely to be higher because of the gene’s strengthened causal inference for an interaction [33••]. Among the recent publications examining GEIs of GWAS loci (Table 2), the majority of observational studies evaluated whether dietary components and physical activity interact with variation in the FTO gene for their effect on obesity [52–56]. While three studies observed that the effect of the FTO risk alleles for obesity was modulated by energy intake or physical activity [52–54], one study with a sample size of more than 6,000 individuals found no evidence for this GEI [55]. The issue was clarified by an impressively large meta-analysis that included data from 45 studies involving 218,166 adults and 9 studies comprising 19,268 children and adolescents [57••]. This meta-analysis confirmed that the minor allele of the FTO rs9939609 variant increases the risk of obesity in adults and showed that this risk was reduced among physically active individuals by 27 %. This interaction was more pronounced in North American than in European individuals. The investigators highlighted that their finding is highly relevant for public health implications at the population level (i.e. the individuals with a high genetic susceptibility to obesity can reduce their risk by living a physically healthy lifestyle) [57••]. In addition, a novel finding of the breastfeeding protective effect on the relationship between FTO variants and adiposity indices in Greek children from the ages of three upward has been published [58]. A further three papers reported the effect of lifestyle modifications on the relationship between several GWAS genes and obesity-related traits in observational [59] and intervention studies (Table 2) [60, 61].
Among the recently published papers, two clinical trials reported GEI in response to weight loss interventions. Qi and colleagues [62•] found a novel association between the variant in the insulin receptor substrate 1 (IRS1) gene and response to a weight loss diet: 738 overweight adults (61 % were women) were randomly assigned to 1 of 4 diets varying in macronutrient contents for 2 years. Participants with the IRS1 rs2943641 CC genotype had greater weight loss and improvement of insulin resistance than those without this genotype in response to a high-carbohydrate/low-fat diet. Interestingly, the variant rs2943650 (r 2 = 1.00 with rs2943641) near IRS1 has been reported in a recent GWAS for percentage body fat with the fat percentage–decreasing allele being associated with (counterintuitively) higher levels of insulin resistance [18]. Another study, a randomized controlled trial in overweight and obese adults (n = 3,234), investigated the effect of 16 novel GWAS obesity-susceptibility variants on weight loss during a 2-year intervention program. The researchers reported gene–lifestyle interactions for short-term and long-term weight loss [63•]. Altogether, these novel findings provide supportive information for the development of effective dietary intervention strategies based on genetic background.
So far, only one study has examined whether the genetic predisposition to obesity risk assessed by a genetic risk score (GRS) was modified by lifestyle factors. A large-scale population-based study (n = 20,430) investigated the effect of a GRS calculated by summing 12 BMI-increasing alleles across the 12 genetic variants and its interaction with physical activity on obesity risk. The researchers found that the genetic risk of obesity was attenuated by 40 % in physically active individuals compared with physically inactive individuals [64••]. These results provide further evidence that particular individuals who are genetically predisposed to obesity would benefit more from elevated physical activity levels than individuals who are genetically protected. Importantly, these findings also indicate that GEIs might contribute to the unexplained variance in obesity traits and suggest that future GWAS of obesity-related traits studying, for example, physically inactive individuals may discover new obesity-susceptibility loci because the effect sizes of genetic variants may be more pronounced and, thus, more easily identified. To our knowledge, numerous consortia-based meta-analyses are ongoing in which this innovative genome–environment-wide association approach is deployed, but so far, no results of these studies have been published.
Machinery That Underlies GEI
Environment has inarguably a large impact on human physiologic functions and health. Despite the recent successes in identifying genetic determinants accounting for obesity, the definition and quantification of GEIs has proven difficult. Environmental exposure to nutritional and other stimuli can alter the expression of a subset of genes through changes in the epigenome [65]. Although little is known about the exact role of the epigenome in the pathophysiology of obesity, epigenetic regulation of gene expression may be a key factor explaining interindividual differences in adiposity-related phenotypes [66] and the study of the epigenome offers hope in understanding the machinery that underlies complex GEIs.
Epigenetics
Epigenetics is loosely defined as the study of heritable changes in gene function without modifications in DNA sequences [67]. Epigenetic changes include DNA methylation, chromatin folding and binding, packaging of DNA around nucleosomes and covalent modifications of the histone proteins that make up the nucleosomes around which the DNA double helix is coiled [68]. The epigenome varies across different cell types and undergoes precise, coordinated changes during a lifetime [69••, 70].
DNA methylation is a well-studied epigenetic modification that involves the addition of a methyl (CH3) group to a cytosine located next to a guanine nucleotide (CpG) in CpG dinucleotide–rich regions [70]. Methylation in promoters-associated CpG islands is associated with a transcriptionally repressed state established by two main mechanisms: inability of transcriptional factors to bind to their cognate sequence due to the presence of a methyl group within the binding site or the attraction of methyl-CpG-binding proteins with repressive properties [71, 72].
Environmental and Genetic Effects on the Epigenome in the Context of Obesity
There is increasing evidence of epigenetic regulation of metabolic diseases further supporting a link between genes and environment through their influences on the epigenome [73]. Periconceptional and gestational periods are particularly sensitive to epigenetic perturbation, with the environment exerting different effects on the placenta and embryo [69••]. In particular, nutrition at different developmental stages can influence the epigenome, potentially contributing to an increased susceptibility to chronic diseases, such as obesity [65, 66]. In mammals, early nutrition and in particular dietary components, such as folate, vitamin B6, vitamin B12, betaine, methionine and choline have been associated with changes in DNA methylation patterns by affecting the one-carbon metabolism that ultimately provides the methyl groups for DNA and histone methylation [69••]. Furthermore, maternal food supplementation with bisphenol A—a DNA hypomethylated compound that can leach from polycarbonate plastics into their contents—has been associated with decreased methylation at the Avy allele (the viable yellow agouti allele is a murine metastable epiallele that is variably expressed due to epigenetic marks established during early development) in the offspring and with obesity in early and later life in mammals [68, 74], whereas supplementation of maternal diet with folic acid or genistein negated the hypomethylating effects of BPA [68]. Maternal exposure to several other chemicals (the so-called obesogens) has been associated with increased BMI in offspring, further suggesting that obesity is being programmed prenatally or in early childhood and disruption of normal epigenetic regulation that alters the expression of key genes in adipogenic pathways is likely to be involved [75]. Nevertheless, our understanding of how environmental influence on epigenetic marks can lead to obesity remains rather rudimentary. The potential interaction of environment with the epigenome mediating the expression of genes associated with increased adiposity has also been suggested [76]. For example, the FTO gene encodes for an enzyme that is able to remove methyl groups from DNA [77], long-term exposure to high-fat diet can decrease the melanocortin-4 receptor (MC4R) gene methylation [78] and high-fat diet–induced obesity can modify leptin methylation patterns [79]. The expression of the PPARγ gene, a key regulator of adipocyte differentiation, was found to be reduced due to DNA methylation of its promoter in adipocytes of visceral adipose tissue in mammals [80]. Several other genes involved in adiposity have promoters that seem to be epigenetic targets in relation to obesity (epi-obesogenic genes) [66]. One of the first genome-wide methylation studies revealed increased methylation levels at one CpG site (UBASH3A gene) and decreased methylation levels at one CpG site (TRIM3 gene) in obese subjects compared with lean controls, providing evidence that obesity is associated with epigenetic changes [81•]. Although collectively, these studies could indicate that epigenetic marks lead to obesity, it is not really clear whether they predict or precede obesity [82••]. The causal relationship between epigenetic marks and obesity has yet to be elucidated and other factors, such as nutrition or physical activity, that correlate with both DNA methylation and increased adiposity should be considered to this end [82••]. Genetic differences between individuals can also influence epigenetic regulation [69••] and genetic variants could account for the locus-specific variance in epigenetic states. In humans, it has been shown that 10 % of common SNPs are located in regions with differences in the propensity for local DNA methylation between the two alleles [83]. With this in mind, it is possible that the interplay of genetics and epigenetics could underlie the establishment of diseases, such as obesity. However, the extent to which DNA sequence determines epigenetic changes at specific loci and subsequently leads to obesity is poorly understood. Evaluating the relative contribution of genetic and environmental factors to the establishment of the epigenome and elucidating the causal relationship between epigenetics and obesity constitute major challenges given the complex interrelationship of those determinants (Fig. 1).
Transgenerational Epigenetic Inheritance
Currently, there is increased evidence that environmentally induced epigenetic changes can also be passed to the next generation via gametes and not only through the placenta in the developing embryo (maternal diet) or through breastfeeding in the infant [84]. Transgenerational epigenetic inheritance is also supported by the fact that some epigenetic marks escape reprogramming—that is, erasure and resetting of the gametic epigenome between generations [73]. This reprogramming escape, in combination with the observation that parental exposure to challenging environments, results in maladaptive responses that can be passed to the next generation renders trasngenerational epigenetic inheritance a mechanism of great interest, especially for obesity. A recent study in mice has examined the effect of a maternal exposure to a high-fat diet on body size, not only in the second generation (F2), but also in the third one (F3) in order to test whether the phenotype is transmitted by a germline-based epigenetic mark. Interestingly, the study has shown that the increased body size and length phenotypes were transmitted to F3 females through the F2 paternal lineage, suggesting that maternal high-fat diet programs a germline-based transgenerational phenotype in male gametes [85]. With this in mind, it is possible that environmentally induced epigenetic changes could in theory explain a significant fraction of the missing heritability for adiposity-related phenotypes by affecting both disease penetrance and heritability [86].
Challenges of GEI Studies
GEI studies can be very helpful in unraveling the biological pathways important for predicting disease risk and possibly in explaining some of the missing heritability through the identification of obesity-susceptibility genes that exert their effects through interaction with environment. Furthermore, GEIs could potentially be used for the identification of environmental factors that affect individuals with specific genotypes [19••]. However, the investigation of such interactions in complex diseases such as obesity remains a challenging task, with the major limitations including sample size/power, measurement of environmental factors, heterogeneity and lack of replication. Typically, thousands of samples are needed in candidate gene–based studies or even more in GWAS, in which very stringent cutoffs of significance are used. Not all reviewed studies have had a sufficient sample size to detect interactions and the lack of control for type 1 error continues to be a concern. In addition to that, accurate measurement of exposures that vary over time or are modifiable by other factors, such as time of exposure, has proven difficult and can create biases in the analysis. Another important issue is the observed heterogeneity in the study design that arises due to differences in the way that examined environmental exposures are assessed across studies and due to the possible study-specific characteristics of exposure [19••]. Recent efforts in the establishment of prospective cohorts (e.g. the National Children’s Study [http://www.nationalchildrensstudy.gov] and the Avon Longitudinal Study of Parents and Children [http://www.bristol.ac.uk/alspac/]) with robust and repeated measurements over time of environmental exposures can help in assessing the role of critical windows of susceptibility that likely correspond to the expression of specific genes. The challenges related to GEI studies were the topic of discussion in a recent workshop at which more than 150 researchers representing a wide range of scientific areas participated. Interesting questions were raised and useful recommendations were given regarding GEI study design for overcoming the aforementioned limitations. The need for integration of environment, genetics and epigenetics in the same study was also emphasized, as this could provide insight into their complex interactive role in the establishment of disease [87••]. In the post-GWAS era, the careful design of epidemiologic studies, accurate measurement of exposures and use of standardized methods across studies should facilitate collaborations, which will increase statistical power for assessing GEIs. With the completion of the Human Epigenome Project (http://www.epigenome.org/) [88], a more comprehensive picture of the genetic factors and epigenetic marks underlying cellular homeostasis will be achieved. Determination of disease-specific epigenetic changes and integration of this information with genetic and known environmental risks to obesity will provide more insights and will be proven valuable in predicting the onset and progress of obesity.
Conclusions
Obesity is a complex disease with multiple environmental and genetic causes. Over recent years, the GWAS experimental design has led to the identification of a number of obesity-susceptibility genes that, however, only explain a small portion of the interindividual variation in adiposity. Identifying the genes that predispose to obesity in combination with specific environmental exposures is very important for better understanding of disease etiology and subsequently for disease treatment and prevention. The investigation of gene–environment interplay can also unravel the pathways involved in obesity and be beneficial for drug development and therapy. To date, studies of GEIs have been facing challenges and, thus, are limited compared with those examining only main genetic or environmental effects. Furthermore, the contribution of the epigenome to the establishment of obesity is largely unknown. Further GEI studies that are carefully designed can extend the list of genetic loci that exert effects in the presence of specific environmental exposures. The next generation of studies incorporating genetic–environment–epigenome information and utilizing new analytical approaches and environmental measurement technologies can improve understanding of the complex causes of obesity.
References
Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance
Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. 2000;894:i–xii, 1–253.
Kelly T, Yang W, Chen CS, et al. Global burden of obesity in 2005 and projections to 2030. Int J Obes (Lond). 2008;32:1431–7.
Erlichman J, Kerbey AL, James WP. Physical activity and its impact on health outcomes. Paper 2: prevention of unhealthy weight gain and obesity by physical activity: an analysis of the evidence. Obes Rev. 2002;3:273–87.
Wang Y, Beydoun MA. The obesity epidemic in the United States–gender, age, socioeconomic, racial/ethnic, and geographic characteristics: a systematic review and meta-regression analysis. Epidemiol Rev. 2007;29:6–28.
Ogden CL, Carroll MD, Curtin LR, et al. Prevalence of overweight and obesity in the United States, 1999–2004. JAMA. 2006;295:1549–55.
Hill JO. Understanding and addressing the epidemic of obesity: an energy balance perspective. Endocr Rev. 2006;27:750–61.
Prentice AM. Early influences on human energy regulation: thrifty genotypes and thrifty phenotypes. Physiol Behav. 2005;86:640–5.
Speakman JR. Obesity: the integrated roles of environment and genetics. J Nutr. 2004;134:2090S–105S.
Ravussin E, Bouchard C. Human genomics and obesity: finding appropriate drug targets. Eur J Pharmacol. 2000;410:131–45.
Hindorff LA, Sethupathy P, Junkins HA, et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA. 2009;106:9362–7.
Scuteri A, Sanna S, Chen WM, et al. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet. 2007;3:e115.
Frayling TM, Timpson NJ, Weedon MN, et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science. 2007;316:889–94.
van Vliet-Ostaptchouk JV, Hofker MH, van der Schouw YT, et al. Genetic variation in the hypothalamic pathways and its role on obesity. Obes Rev. 2009;10:593–609.
Rankinen T, Zuberi A, Chagnon YC, et al. The human obesity gene map: the 2005 update. Obesity (Silver Spring) 2006;14:529–644.
• Day FR, Loos RJ. Developments in obesity genetics in the era of genome-wide association studies. J Nutrigenet Nutrigenomics. 2011;4:222–38. This review provides a comprehensive overview of GWAS findings in obesity..
Speliotes EK, Willer CJ, Berndt SI, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 2010;42:937–48.
Heid IM, Jackson AU, Randall JC, et al. Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat Genet. 2010;42:949–60.
Kilpelainen TO, Zillikens MC, Stancakova A, et al. Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile. Nat Genet. 2011;43:753–60.
•• Thomas D. Gene--environment-wide association studies: emerging approaches. Nat Rev Genet. 2010;11:259–72. This is an interesting review on the available epidemiologic designs and statistical analysis approaches for studying GEIs.
Schroder H, Fito M, Covas MI, et al. Association of fast food consumption with energy intake, diet quality, body mass index and the risk of obesity in a representative Mediterranean population. Br J Nutr. 2007;98:1274–80.
Romaguera D, Norat T, Mouw T, et al. Adherence to the Mediterranean diet is associated with lower abdominal adiposity in European men and women. J Nutr. 2009;139:1728–37.
Leite ML, Nicolosi A. Lifestyle correlates of anthropometric estimates of body adiposity in an Italian middle-aged and elderly population: a covariance analysis. Int J Obes (Lond). 2006;30:926–34.
Knoops KT, de Groot LC, Kromhout D, et al. Mediterranean diet, lifestyle factors, and 10-year mortality in elderly European men and women: the HALE project. JAMA. 2004;292:1433–9.
Koh-Banerjee P, Chu NF, Spiegelman D, et al. Prospective study of the association of changes in dietary intake, physical activity, alcohol consumption, and smoking with 9-y gain in waist circumference among 16 587 US men. Am J Clin Nutr. 2003;78:719–27.
Lahti-Koski M, Pietinen P, Heliovaara M, et al. Associations of body mass index and obesity with physical activity, food choices, alcohol intake, and smoking in the 1982–1997 FINRISK Studies. Am J Clin Nutr. 2002;75:809–17.
Bullo M, Garcia-Aloy M, Martinez-Gonzalez MA, et al. Association between a healthy lifestyle and general obesity and abdominal obesity in an elderly population at high cardiovascular risk. Prev Med. 2011;53:155–61.
• Speakman JR, Levitsky DA, Allison DB, et al. Set points, settling points and some alternative models: theoretical options to understand how genes and environments combine to regulate body adiposity. Dis Model Mech. 2011;4:733–45. In this paper, the authors discuss the fundamental problems in obesity research and suggest alternative models for body weight regulation that could explain the mechanisms underlying the current obesity epidemic.
Stranger BE, Stahl EA, Raj T. Progress and promise of genome-wide association studies for human complex trait genetics. Genetics. 2011;187:367–83.
Bouchard C. Gene-environment interactions in the etiology of obesity: defining the fundamentals. Obesity (Silver Spring). 2008;16 Suppl 3:S5–S10.
Ordovas JM, Shen J. Gene-environment interactions and susceptibility to metabolic syndrome and other chronic diseases. J Periodontol. 2008;79:1508–13.
Andreasen CH, Andersen G. Gene-environment interactions and obesity–further aspects of genomewide association studies. Nutrition. 2009;25:998–1003.
Qi L, Cho YA. Gene-environment interaction and obesity. Nutr Rev. 2008;66:684–94.
•• Franks PW. Gene x environment interactions in type 2 diabetes. Curr Diab Rep. 2011;11:552–61. This paper takes a critical look at the current evidence on GEIs in type 2 diabetes and discusses the practical and methodologic issues related to detection and examination of GEIs in complex disease traits.
Wareham NJ, Young EH, Loos RJ. Epidemiological study designs to investigate gene-behavior interactions in the context of human obesity. Obesity (Silver Spring). 2008;16 Suppl 3:S66–71.
Lee S, Kim CM, Kim HJ, et al. Interactive effects of main genotype, caloric intakes, and smoking status on risk of obesity. Asia Pac J Clin Nutr. 2011;20:563–71.
Lagou V, Liu G, Zhu H, et al. Lifestyle and socioeconomic-status modify the effects of ADRB2 and NOS3 on adiposity in European-American and African-American adolescents. Obesity (Silver Spring). 2011;19:595–603.
Ruiz JR, Larrarte E, Margareto J, et al. Role of beta-adrenergic receptor polymorphisms on body weight and body composition response to energy restriction in obese women: preliminary results. Obesity (Silver Spring). 2011;19:212–5.
Phillips CM, Goumidi L, Bertrais S, et al. Gene-nutrient interactions and gender may modulate the association between ApoA1 and ApoB gene polymorphisms and metabolic syndrome risk. Atherosclerosis. 2011;214:408–14.
Mattei J, Demissie S, Tucker KL, et al. The APOA1/C3/A4/A5 cluster and markers of allostatic load in the Boston Puerto Rican Health Study. Nutr Metab Cardiovasc Dis. 2011;21:862–70.
Sanchez-Moreno C, Ordovas JM, Smith CE, et al. APOA5 gene variation interacts with dietary fat intake to modulate obesity and circulating triglycerides in a Mediterranean population. J Nutr. 2011;141:380–5.
Corella D, Tai ES, Sorli JV, et al. Association between the APOA2 promoter polymorphism and body weight in Mediterranean and Asian populations: replication of a gene-saturated fat interaction. Int J Obes (Lond). 2011;35:666–75.
Iqbal Kring SI, Barefoot J, Brummett BH, et al. Associations between APOE variants and metabolic traits and the impact of psychological stress. PLoS One. 2011;6:e15745.
Palla L, Higgins JP, Wareham NJ, et al. Challenges in the use of literature-based meta-analysis to examine gene-environment interactions. Am J Epidemiol. 2010;171:1225–32.
Dedoussis GV, Manios Y, Kourlaba G, et al. An age-dependent diet-modified effect of the PPARgamma Pro12Ala polymorphism in children. Metabolism. 2011;60:467–73.
Corella D, Arregui M, Coltell O, et al. Association of the LCT-13910 C > T polymorphism with obesity and its modulation by dairy products in a Mediterranean population. Obesity (Silver Spring). 2011;19:1707–14.
Edwards TL, Velez Edwards DR, Villegas R, et al. HTR1B, ADIPOR1, PPARGC1A, and CYP19A1 and obesity in a cohort of Caucasians and African Americans: an evaluation of gene-environment interactions and candidate genes. Am J Epidemiol. 2012;175:11–21.
Jourdan C, Kloiber S, Nieters A, et al. Gene-PUFA interactions and obesity risk. Br J Nutr. 2011;106:1263–72.
Du H, Vimaleswaran KS, Angquist L, et al. Genetic polymorphisms in the hypothalamic pathway in relation to subsequent weight change–the DiOGenes study. PLoS One. 2011;6:e17436.
Erez G, Tirosh A, Rudich A, et al. Phenotypic and genetic variation in leptin as determinants of weight regain. Int J Obes (Lond). 2011;35:785–92.
Ruiz JR, Larrarte E, Margareto J, et al. Preliminary findings on the role of PLIN1 polymorphisms on body composition and energy metabolism response to energy restriction in obese women. Br J Nutr. 2011;106:486–90.
Santos JL, De la Cruz R, Holst C, et al. Allelic variants of melanocortin 3 receptor gene (MC3R) and weight loss in obesity: a randomised trial of hypo-energetic high- versus low-fat diets. PLoS One. 2011;6:e19934.
Ahmad T, Lee IM, Pare G, et al. Lifestyle interaction with fat mass and obesity-associated (FTO) genotype and risk of obesity in apparently healthy U.S. women. Diabetes Care. 2011;34:675–80.
Corella D, Arnett DK, Tucker KL, et al. A high intake of saturated fatty acids strengthens the association between the fat mass and obesity-associated gene and BMI. J Nutr. 2011;141:2219–25.
Demerath EW, Lutsey PL, Monda KL, et al. Interaction of FTO and physical activity level on adiposity in African-American and European-American adults: the ARIC Study. Obesity (Silver Spring). 2011;19:1866–72.
Hubacek JA, Pikhart H, Peasey A, et al. FTO variant, energy intake, physical activity and basal metabolic rate in Caucasians. The HAPIEE study Physiol Res. 2011;60:175–83.
Moleres A, Ochoa MC, Rendo-Urteaga T, et al. Dietary fatty acid distribution modifies obesity risk linked to the rs9939609 polymorphism of the fat mass and obesity-associated gene in a Spanish case–control study of children. Br J Nutr. 2012;107:533–8.
•• Kilpelainen TO, Qi L, Brage S, et al. Physical activity attenuates the influence of FTO variants on obesity risk: a meta-analysis of 218,166 adults and 19,268 children. PLoS Med. 2011;8:e1001116. This paper reports the results of a large-scale meta-analysis investigating the effect of physical activity by FTO interaction, providing the first convincing example of GEIs in obesity.
Dedoussis GV, Yannakoulia M, Timpson NJ, et al. Does a short breastfeeding period protect from FTO-induced adiposity in children? Int J Pediatr Obes. 2011;6:e326–35.
Xi B, Wang C, Wu L, et al. Influence of physical inactivity on associations between single nucleotide polymorphisms and genetic predisposition to childhood obesity. Am J Epidemiol. 2011;173:1256–62.
Scherag A, Kleber M, Boes T, et al. SDCCAG8 obesity alleles and reduced weight loss after a lifestyle intervention in overweight children and adolescents. Obesity (Silver Spring). 2012;20:466–70.
Orkunoglu-Suer FE, Harmon BT, Gordish-Dressman H, et al. MC4R variant is associated with BMI but not response to resistance training in young females. Obesity (Silver Spring). 2011;19:662–6.
• Qi Q, Bray GA, Smith SR, et al. Insulin receptor substrate 1 gene variation modifies insulin resistance response to weight-loss diets in a 2-year randomized trial: the Preventing Overweight Using Novel Dietary Strategies (POUNDS LOST) trial. Circulation. 2011;124:563–71. This study reports the results of a clinical trial and suggests effective dietary interventions based on genetic background.
• Delahanty LM, Pan Q, Jablonski KA, et al. Genetic predictors of weight loss and weight regain after intensive lifestyle modification, metformin treatment, or standard care in the diabetes prevention program. Diabetes Care. 2012;35:363–6. This study is another example of gene–lifestyle interactions affecting short-term and long-term weight loss.
•• Li S, Zhao JH, Luan J, et al. Physical activity attenuates the genetic predisposition to obesity in 20,000 men and women from EPIC-Norfolk prospective population study. PLoS Med. 2010;7. This is the first study to examine whether genetic predisposition to obesity risk assessed by a GRS is modified by lifestyle. The results of the analysis suggest that individuals who are genetically predisposed to obesity would benefit more from elevated physical activity levels.
Waterland RA, Jirtle RL. Early nutrition, epigenetic changes at transposons and imprinted genes, and enhanced susceptibility to adult chronic diseases. Nutrition. 2004;20:63–8.
Campion J, Milagro FI, Martinez JA. Individuality and epigenetics in obesity. Obes Rev. 2009;10:383–92.
Bird A. Perceptions of epigenetics. Nature. 2007;447:396–8.
Dolinoy DC, Jirtle RL. Environmental epigenomics in human health and disease. Environ Mol Mutagen. 2008;49:4–8.
•• Feil R, Fraga MF. Epigenetics and the environment: emerging patterns and implications. Nat Rev Genet. 2011;13:97–109. This review provides an up-to-date overview of the mechanisms underlying environmental and genetic effects on the epigenome during the different developmental stages and highlights the importance of experimental model systems in helping us better understand those mechanisms..
Feinberg AP. Epigenetics at the epicenter of modern medicine. JAMA. 2008;299:1345–50.
Bird A. DNA methylation patterns and epigenetic memory. Genes Dev. 2002;16:6–21.
Van den Veyver IB. Genetic effects of methylation diets. Annu Rev Nutr. 2002;22:255–82.
Barres R, Zierath JR. DNA methylation in metabolic disorders. Am J Clin Nutr. 2011;93:897S–900S.
Rubin BS, Murray MK, Damassa DA, et al. Perinatal exposure to low doses of bisphenol a affects body weight, patterns of estrous cyclicity, and plasma LH levels. Environ Health Perspect. 2001;109:675–80.
Janesick A, Blumberg B. Obesogens, stem cells and the developmental programming of obesity. Int J Androl. 2012.
Herrera BM, Keildson S, Lindgren CM. Genetics and epigenetics of obesity. Maturitas. 2011;69:41–9.
Gerken T, Girard CA, Tung YC, et al. The obesity-associated FTO gene encodes a 2-oxoglutarate-dependent nucleic acid demethylase. Science. 2007;318:1469–72.
Widiker S, Karst S, Wagener A, et al. High-fat diet leads to a decreased methylation of the Mc4r gene in the obese BFMI and the lean B6 mouse lines. J Appl Genet. 2010;51:193–7.
Milagro FI, Campion J, Garcia-Diaz DF, et al. High fat diet-induced obesity modifies the methylation pattern of leptin promoter in rats. J Physiol Biochem. 2009;65:1–9.
Fujiki K, Kano F, Shiota K, et al. Expression of the peroxisome proliferator activated receptor gamma gene is repressed by DNA methylation in visceral adipose tissue of mouse models of diabetes. BMC Biol. 2009;7:38.
• Wang X, Zhu H, Snieder H, et al. Obesity related methylation changes in DNA of peripheral blood leukocytes. BMC Med. 2010;8:87. This is one of the first studies showing association between obesity and epigenetic changes in humans genome wide.
•• Franks PW, Ling C. Epigenetics and obesity: the devil is in the details. BMC Med. 2010;8:88. This review provides a comprehensive overview of the epigenetic mechanisms underlying GEIs in the context of obesity.
Hellman A, Chess A. Extensive sequence-influenced DNA methylation polymorphism in the human genome. Epigenetics Chromatin. 2010;3:11.
Daxinger L, Whitelaw E. Understanding transgenerational epigenetic inheritance via the gametes in mammals. Nat Rev Genet. 2012;13:153–62.
Dunn GA, Bale TL. Maternal high-fat diet effects on third-generation female body size via the paternal lineage. Endocrinology. 2011;152:2228–36.
Bell JT, Saffery R. The value of twins in epigenetic epidemiology. Int J Epidemiol. 2012.
•• Bookman EB, McAllister K, Gillanders E, et al. Gene-environment interplay in common complex diseases: forging an integrative model-recommendations from an NIH workshop. Genet Epidemiol. 2011. This is an important paper that discusses approaches for identifying genetic and/or environmental risk factors for complex disease, and formulates requirements and study design for the investigation of GEIs.
Eckhardt F, Beck S, Gut IG, et al. Future potential of the human epigenome project. Expert Rev Mol Diagn. 2004;4:609–18.
Acknowledgments
Dr. van Vliet-Ostaptchouk is supported by a Rubicon grant from the Netherlands Organization for Scientific Research (NWO file no. 825.10.035) and the Netherlands Consortium for Healthy Ageing (NCHA) (NCHA NGI grant no. 050-060-810).
Disclosure
No potential conflicts of interest relevant to this article were reported.
Open Access
This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
About this article
Cite this article
van Vliet-Ostaptchouk, J.V., Snieder, H. & Lagou, V. Gene–Lifestyle Interactions in Obesity. Curr Nutr Rep 1, 184–196 (2012). https://doi.org/10.1007/s13668-012-0022-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13668-012-0022-2