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      Obesity Triggers: Sequencing the Genome vs Sequencing the Environment

      1 , 2 , 3

      Obesity (Silver Spring, Md.)

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          Abstract

          Sequencing the Genome Many syndromic and non-syndromic forms of obesity are the consequence of single (monogenic) rare genetic variants. There are currently about 79 known 1 and 11 forms of non-syndromic obesity 2 . Most non-syndromic obesity is a result of monogenetic variation in genes of the melanocortin pathway and the most commonly affected gene is the melanocortin 4 receptor (MC4R). About 5% of severely obese children and 1% of the obese population across all ages are heterozygous for a functional missense variant in the melanocortin 4 receptor (MC4R) 3,4,5 . The effect size of a disease-causing monogenic variant is generally large, and the affected pathway is typically well-defined, making these forms of obesity more amenable to drug therapy. As a recent example, two individuals with loss-of-function variants in the proopriomelanocortin gene were successful treated with a new MC4R agonist. 6 In contrast to monogenic obesity, common forms of overweight or obesity are unquestionably affected by environmental/cultural/lifestyle determinants. The population based prevalence of obesity varies between counties, has increased worldwide in recent decades 7 , and migrants tend to match the new cultural environment in succeeding generations 8 . Perhaps the most convincing evidence that an obesogenic environment can be created is the higher prevalence of obese dogs of obese compared to lean owners 9 . But family studies have also shown that within any cultural environment, body mass index(BMI), as an estimate of adiposity, is significantly and highly heritable. Early, now classical and seminal studies, indicated significantly greater similarity of BMI between offspring and their biologic as compared to adopted parents 10 . Many investigators of twins and families have reported heritabilities of BMI between ~40–75% 2 . The heritability estimates are usually higher in twin studies for many reasons but these estimates may be more reliable since the possible genetic factors are more carefully controlled for than in family based studies. The heritability estimates are also higher at younger ages and for more extreme obesity 11 . Many common (>5%) and uncommon/rare single nucleotide polymorphisms (SNPs) are significantly associated with BMI based on targeted candidate gene analyses or hypothesis-free genome-wide association studies (GWAS) 2 . As many as 200 SNPs in various GWASs have been associated with obesity in many different, but predominantly, European populations. However, only about 3% of the heritability can explained by SNPs currently known to associate with BMI 12 . In contrast, all ~17million imputed SNPs based on whole-genome sequences in a largely unrelated European population sample can account for ~30% of BMI heritability 13 . This indicates that most, if not all, of the heritability of BMI can be explained by a large number of variants across the genome, especially assuming the heritability estimates may be somewhat inflated 12 . As recently reviewed by Loos and Janssens 14 , these findings indicate that although obesity is generally a polygenic disorder, currently identified BMI SNPs are poor predictors of obesity. A better predictor than SNP genotype is knowledge of an individual’s parents/family obesity status. Therefore, the utility and clinical impact of previous and future larger BMI GWAS studies is not for prediction but for identification of biologic pathways that affect body weight which may serve as potential therapeutic targets for obesity. Sequencing the environment It has long been recognized that obesity is a normal, physiological response to an abnormal, pathological (obesogenic) environment 15 . Getting to a body mass index (BMI) of 30 is very easy when the food environment is full of cheap, highly palatable processed foods and the built environment favors cars over active transport. If our goal is reducing obesity, then the environment should be the predominant focus for research and action because that is where the pathology lies. Part of the reason for the discordance, noted above, between high heritability estimates for obesity and the low explanatory power of SNPs may be that heritability studies do not include the breadth of environmental exposure. 16 Heritability is the proportion of genetic variance divided by the total phenotypic variance and it is often not understood that the estimate is for a specific population, in a specific (or narrow) environment, at a specific point in time, and it assumes that there are no interactions between genes and environments (GxE interactions). 17 Wide variance across environments and over time will increase the denominator, thus reducing the heritability estimates. Differential gene expressions in the same environment (ie GxE interactions) are likely to be common in obesity. Research is exploring new aspects of biology, such as epigenetic, neuroendocrine or gut biota pathways to explain GxE interactions. While this research is uncovering fascinating biological mechanisms, they are best thought of as mediators by which the environment influences the organism rather than being the drivers of the obesity epidemic. 16 The research endeavor to sequence the human genome cost about $3 billion. Imagine if even a fraction of that cost could be invested in ‘sequencing’ human environments – those factors which are external to humans but which powerfully influence human behaviors and health. This would give us powerful comparative data on these key drivers, such as food composition, food prices, food marketing, urban walkability, school food, school physical education and so on. Such data across multiple geographies and over time could be used to explain the wide variance and changing rates of obesity prevalence 16,18 , identify which drivers to prioritize for intervention, identify which countries have best practice policies, and to evaluate the impact of interventions. Measuring progress on government and private sector actions is also of high importance. This vision of sequencing the human environment has been taken up for food environments by an international network of food researchers. INFORMAS (International Network For Food and Obesity/non-communicable disease Research, Monitoring and Action Support 19 ) was launched in 2013 and, to date, 22 countries 20 are using its protocols for measuring various aspects of the food environments and food policies. The first set of international comparisons, due in 2018, will be on government progress on food policies, the composition of processed foods, marketing of unhealthy foods to children, food labelling, and food industry actions. In some countries, such as Chile and Mexico, where major food policies are being implemented, the INFORMAS data will help to evaluate the impact of the policies on food environments. Genetic research is advancing our understanding of biology, but for it to translate into advances in obesity treatment or prevention, certain assumptions need to hold. The first assumption is that we really need to know the biological mechanisms to guide our actions. Knowing the mediating pathways of problems and solutions is helpful but often not essential. Digitalis and aspirin were used for centuries before the mechanisms were understood and Sudden Infant Death Syndrome has been reduced simply by converting the epidemiological relationships with sleeping position and bed-sharing into public health actions, while the mechanisms of action remain unknown. A second assumption is that understanding mechanisms will allow the identification of potential therapeutic targets for pharmacological therapy. To date, drugs have been about as effective in reducing obesity as blocking one road into a city has on reducing downtown traffic. Energy balance and road networks have many interconnecting pathways as an intrinsic feature of their prime purposes which are to maintain energy stores and get people in and out of the city respectively. Single interventions on complex problems are destined to have relatively small, and often temporary, impacts. A third assumption relates to genotyping people to achieve a more accurate risk profile for predicting the development of obesity or tailoring treatment options. This assumes that telling someone that they have 20% greater genetic susceptibility to becoming obese will galvanize them into healthier lifestyles or that they are genetically 20% more likely to respond to a particular diet will improve long term adherence to that diet. Even a small amount of personal or clinical experience with obesity will uncover the fallacy of this personalized medicine assumption. While obesogenic environments are manifold and differ by population, standardized protocols for measuring these environmental factors, at least for food, 19 are available and are being applied, even in low income countries. 20 A population, at the national or sub-national level, which has its own data on its own obesogenic environments and which are benchmarked against environments in other countries is well-armed for better prevention. It can diagnose the problem in terms of priority environments for action, it can use international benchmarks as a catalyst and target for outcomes, and it can readily measure the impact of policies or actions on food environments. Healthier food and activity environments are not only the mainstay of prevention but also help those with obesity to maintain healthier diets and regular physical activity. Conclusions Major advances in research in recent years are rapidly increasing our understanding of the genetic, epigenetic and metabolic pathways to obesity. This is expected to lead to innovative treatment and prevention strategies, including new and better pharmaceuticals. Research on measuring, understanding and influencing environmental determinants of obesity is much more nascent, but it will form the basis for catalyzing public health policy action in the coming decades. For now, we consider that two clear conclusions can be drawn from our current understandings on the interrelationships between genes and environments. The first is that both genetic and environmental influences are powerful and can readily overwhelm the personal willpower needed to maintain a healthy weight throughout life in an obesogenic environment, especially for those people already struggling on low incomes. Also, for those people who have obesity, there are vigorous metabolic responses that counteract weight loss attempts. Exhorting people to be more responsible with their diet and physical activity is, therefore, an unfair and unrealistic approach to reducing obesity. This leads to the second conclusion, which is that the best way to reduce the prevalence of obesity is to prevent it, particularly in children and there are signs in several high income countries that childhood obesity is plateauing or declining.

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          Most cited references 10

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          An adoption study of human obesity.

          We examined the contributions of genetic factors and the family environment to human fatness in a sample of 540 adult Danish adoptees who were selected from a population of 3580 and divided into four weight classes: thin, median weight, overweight, and obese. There was a strong relation between the weight class of the adoptees and the body-mass index of their biologic parents - for the mothers, P less than 0.0001; for the fathers, P less than 0.02. There was no relation between the weight class of the adoptees and the body-mass index of their adoptive parents. Cumulative distributions of the body-mass index of parents showed similar results; there was a strong relation between the body-mass index of biologic parents and adoptee weight class and no relation between the index of adoptive parents and adoptee weight class. Furthermore, the relation between biologic parents and adoptees was not confined to the obesity weight class, but was present across the whole range of body fatness - from very thin to very fat. We conclude that genetic influences have an important role in determining human fatness in adults, whereas the family environment alone has no apparent effect.
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            Is Open Access

            Variability in the Heritability of Body Mass Index: A Systematic Review and Meta-Regression

            Evidence for a major role of genetic factors in the determination of body mass index (BMI) comes from studies of related individuals. Despite consistent evidence for a heritable component of BMI, estimates of BMI heritability vary widely between studies and the reasons for this remain unclear. While some variation is natural due to differences between populations and settings, study design factors may also explain some of the heterogeneity. We performed a systematic review that identified 88 independent estimates of BMI heritability from twin studies (total 140,525 twins) and 27 estimates from family studies (42,968 family members). BMI heritability estimates from twin studies ranged from 0.47 to 0.90 (5th/50th/95th centiles: 0.58/0.75/0.87) and were generally higher than those from family studies (range: 0.24–0.81; 5th/50th/95th centiles: 0.25/0.46/0.68). Meta-regression of the results from twin studies showed that BMI heritability estimates were 0.07 (P = 0.001) higher in children than in adults; estimates increased with mean age among childhood studies (+0.012/year, P = 0.002), but decreased with mean age in adult studies (−0.002/year, P = 0.002). Heritability estimates derived from AE twin models (which assume no contribution of shared environment) were 0.12 higher than those from ACE models (P < 0.001), whilst lower estimates were associated with self reported versus DNA-based determination of zygosity (−0.04, P = 0.02), and with self reported versus measured BMI (−0.05, P = 0.03). Although the observed differences in heritability according to aspects of study design are relatively small, together, the above factors explained 47% of the heterogeneity in estimates of BMI heritability from twin studies. In summary, while some variation in BMI heritability is expected due to population-level differences, study design factors explained nearly half the heterogeneity reported in twin studies. The genetic contribution to BMI appears to vary with age and may have a greater influence during childhood than adult life.
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              An "ecological" approach to the obesity pandemic.

               B Swinburn,  G Egger (1997)
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                Author and article information

                Journal
                101264860
                32902
                Obesity (Silver Spring)
                Obesity (Silver Spring)
                Obesity (Silver Spring, Md.)
                1930-7381
                1930-739X
                11 August 2017
                November 2017
                01 November 2018
                : 25
                : 11
                : 1861-1863
                Affiliations
                [1 ]Phoenix Epidemiology and Clinical Research Branch, National Institute and Diabetes and Digestive and Kidney Diseases, National Institutes of Health
                [2 ]School of Population Health, University of Auckland, Auckland, New Zealand
                [3 ]Global Obesity Centre, Deakin University, Victoria, Australia
                Article
                NIHMS899203
                10.1002/oby.21985
                5708890
                29086501

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