The prevalence of obesity has tripled over the past four decades, imposing an enormous burden on people's health. Polygenic (or common) obesity and rare, severe, early-onset monogenic obesity are ...often polarized as distinct diseases. However, gene discovery studies for both forms of obesity show that they have shared genetic and biological underpinnings, pointing to a key role for the brain in the control of body weight. Genome-wide association studies (GWAS) with increasing sample sizes and advances in sequencing technology are the main drivers behind a recent flurry of new discoveries. However, it is the post-GWAS, cross-disciplinary collaborations, which combine new omics technologies and analytical approaches, that have started to facilitate translation of genetic loci into meaningful biology and new avenues for treatment.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Over the past 15 years, genome-wide association studies (GWASs) have generated a wealth of new information. Larger samples sizes, refined phenotypes and higher-resolution genome-screens will continue ...to drive gene discovery in years ahead. Meanwhile, GWAS loci are increasingly translated into new biology and opportunities for clinical care.
We have previously shown that multiple genetic loci identified by genome-wide association studies (GWAS) increase the susceptibility to obesity in a cumulative manner. It is, however, not known ...whether and to what extent this genetic susceptibility may be attenuated by a physically active lifestyle. We aimed to assess the influence of a physically active lifestyle on the genetic predisposition to obesity in a large population-based study.
We genotyped 12 SNPs in obesity-susceptibility loci in a population-based sample of 20,430 individuals (aged 39-79 y) from the European Prospective Investigation of Cancer (EPIC)-Norfolk cohort with an average follow-up period of 3.6 y. A genetic predisposition score was calculated for each individual by adding the body mass index (BMI)-increasing alleles across the 12 SNPs. Physical activity was assessed using a self-administered questionnaire. Linear and logistic regression models were used to examine main effects of the genetic predisposition score and its interaction with physical activity on BMI/obesity risk and BMI change over time, assuming an additive effect for each additional BMI-increasing allele carried. Each additional BMI-increasing allele was associated with 0.154 (standard error SE 0.012) kg/m(2) (p = 6.73 x 10(-37)) increase in BMI (equivalent to 445 g in body weight for a person 1.70 m tall). This association was significantly (p(interaction) = 0.005) more pronounced in inactive people (0.205 SE 0.024 kg/m(2) p = 3.62 x 10(-18); 592 g in weight) than in active people (0.131 SE 0.014 kg/m(2) p = 7.97 x 10(-21); 379 g in weight). Similarly, each additional BMI-increasing allele increased the risk of obesity 1.116-fold (95% confidence interval CI 1.093-1.139, p = 3.37 x 10(-26)) in the whole population, but significantly (p(interaction) = 0.015) more in inactive individuals (odds ratio OR = 1.158 95% CI 1.118-1.199; p = 1.93 x 10(-16)) than in active individuals (OR = 1.095 (95% CI 1.068-1.123; p = 1.15 x 10(-12)). Consistent with the cross-sectional observations, physical activity modified the association between the genetic predisposition score and change in BMI during follow-up (p(interaction) = 0.028).
Our study shows that living a physically active lifestyle is associated with a 40% reduction in the genetic predisposition to common obesity, as estimated by the number of risk alleles carried for any of the 12 recently GWAS-identified loci. Please see later in the article for the Editors' Summary.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
IMPORTANCE: Population-based assessment of disease risk associated with gene variants informs clinical decisions and risk stratification approaches. OBJECTIVE: To evaluate the population-based ...disease risk of clinical variants in known disease predisposition genes. DESIGN, SETTING, AND PARTICIPANTS: This cohort study included 72 434 individuals with 37 780 clinical variants who were enrolled in the BioMe Biobank from 2007 onwards with follow-up until December 2020 and the UK Biobank from 2006 to 2010 with follow-up until June 2020. Participants had linked exome and electronic health record data, were older than 20 years, and were of diverse ancestral backgrounds. EXPOSURES: Variants previously reported as pathogenic or predicted to cause a loss of protein function by bioinformatic algorithms (pathogenic/loss-of-function variants). MAIN OUTCOMES AND MEASURES: The primary outcome was the disease risk associated with clinical variants. The risk difference (RD) between the prevalence of disease in individuals with a variant allele (penetrance) vs in individuals with a normal allele was measured. RESULTS: Among 72 434 study participants, 43 395 were from the UK Biobank (mean SD age, 57 8.0 years; 24 065 55% women; 2948 7% non-European) and 29 039 were from the BioMe Biobank (mean SD age, 56 16 years; 17 355 60% women; 19 663 68% non-European). Of 5360 pathogenic/loss-of-function variants, 4795 (89%) were associated with an RD less than or equal to 0.05. Mean penetrance was 6.9% (95% CI, 6.0%-7.8%) for pathogenic variants and 0.85% (95% CI, 0.76%-0.95%) for benign variants reported in ClinVar (difference, 6.0 95% CI, 5.6-6.4 percentage points), with a median of 0% for both groups due to large numbers of nonpenetrant variants. Penetrance of pathogenic/loss-of-function variants for late-onset diseases was modified by age: mean penetrance was 10.3% (95% CI, 9.0%-11.6%) in individuals 70 years or older and 8.5% (95% CI, 7.9%-9.1%) in individuals 20 years or older (difference, 1.8 95% CI, 0.40-3.3 percentage points). Penetrance of pathogenic/loss-of-function variants was heterogeneous even in known disease predisposition genes, including BRCA1 (mean range, 38% 0%-100%), BRCA2 (mean range, 38% 0%-100%), and PALB2 (mean range, 26% 0%-100%). CONCLUSIONS AND RELEVANCE: In 2 large biobank cohorts, the estimated penetrance of pathogenic/loss-of-function variants was variable but generally low. Further research of population-based penetrance is needed to refine variant interpretation and clinical evaluation of individuals with these variant alleles.
Single nucleotide polymorphisms (SNPs) that cluster in the first intron of fat mass and obesity associated (FTO) gene are associated obesity traits in genome-wide association studies. The minor ...allele increases BMI by 0.39 kg/m(2) (or 1,130 g in body weight) and risk of obesity by 1.20-fold. This association has been confirmed across age groups and populations of diverse ancestry; the largest effect is seen in young adulthood. The effect of FTO SNPs on obesity traits in populations of African and Asian ancestry is similar or somewhat smaller than in European ancestry populations. However, the BMI-increasing allele in FTO is substantially less prevalent in populations with non-European ancestry. FTO SNPs do not influence physical activity levels; yet, in physically active individuals, FTO's effect on obesity susceptibility is attenuated by approximately 30%. Evidence from epidemiological and functional studies suggests that FTO confers an increased risk of obesity by subtly changing food intake and preference. Moreover, emerging data suggest a role for FTO in nutrient sensing, regulation of mRNA translation and general growth. In this Review, we discuss the genetic epidemiology of FTO and discuss how its complex biology might link to the regulation of body weight.
Physical inactivity is an important contributor to non-communicable diseases in countries of high income, and increasingly so in those of low and middle income. Understanding why people are ...physically active or inactive contributes to evidence-based planning of public health interventions, because effective programmes will target factors known to cause inactivity. Research into correlates (factors associated with activity) or determinants (those with a causal relationship) has burgeoned in the past two decades, but has mostly focused on individual-level factors in high-income countries. It has shown that age, sex, health status, self-efficacy, and motivation are associated with physical activity. Ecological models take a broad view of health behaviour causation, with the social and physical environment included as contributors to physical inactivity, particularly those outside the health sector, such as urban planning, transportation systems, and parks and trails. New areas of determinants research have identified genetic factors contributing to the propensity to be physically active, and evolutionary factors and obesity that might predispose to inactivity, and have explored the longitudinal tracking of physical activity throughout life. An understanding of correlates and determinants, especially in countries of low and middle income, could reduce the effect of future epidemics of inactivity and contribute to effective global prevention of non-communicable diseases.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
7.
The genetics of adiposity Loos, Ruth JF
Current opinion in genetics & development,
06/2018, Volume:
50
Journal Article
Peer reviewed
Open access
Genome-wide discovery efforts have identified more than 500 genetic loci associated with adiposity traits. The vast majority of these loci were found through large-scale meta-analyses for body mass ...index (BMI) and waist-to-hip ratio (WHR), and in European ancestry populations. However, alternative approaches, focusing on non-European ancestry populations, more refined adiposity measures, and low-frequency (minor allele frequency (MAF)<5%) coding variants, identified additional novel loci that had not been identified before. Loci associated with overall obesity implicate pathways that act in the brain, whereas loci associated with fat distribution point to pathways involved in adipocyte biology. Pinpointing the causal gene within each locus remains challenging, but is a critical step towards translation of genome-wide association study (GWAS) loci into new biology. Ultimately, new genes may provide pharmacological targets for the development of weight loss drugs.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Coronary artery disease (CAD) has substantial heritability and a polygenic architecture. However, the potential of genomic risk scores to help predict CAD outcomes has not been evaluated ...comprehensively, because available studies have involved limited genomic scope and limited sample sizes.
This study sought to construct a genomic risk score for CAD and to estimate its potential as a screening tool for primary prevention.
Using a meta-analytic approach to combine large-scale, genome-wide, and targeted genetic association data, we developed a new genomic risk score for CAD (metaGRS) consisting of 1.7 million genetic variants. We externally tested metaGRS, both by itself and in combination with available data on conventional risk factors, in 22,242 CAD cases and 460,387 noncases from the UK Biobank.
The hazard ratio (HR) for CAD was 1.71 (95% confidence interval CI: 1.68 to 1.73) per SD increase in metaGRS, an association larger than any other externally tested genetic risk score previously published. The metaGRS stratified individuals into significantly different life course trajectories of CAD risk, with those in the top 20% of metaGRS distribution having an HR of 4.17 (95% CI: 3.97 to 4.38) compared with those in the bottom 20%. The corresponding HR was 2.83 (95% CI: 2.61 to 3.07) among individuals on lipid-lowering or antihypertensive medications. The metaGRS had a higher C-index (C = 0.623; 95% CI: 0.615 to 0.631) for incident CAD than any of 6 conventional factors (smoking, diabetes, hypertension, body mass index, self-reported high cholesterol, and family history). For men in the top 20% of metaGRS with >2 conventional factors, 10% cumulative risk of CAD was reached by 48 years of age.
The genomic score developed and evaluated here substantially advances the concept of using genomic information to stratify individuals with different trajectories of CAD risk and highlights the potential for genomic screening in early life to complement conventional risk prediction.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Melanocortin 4 receptor (MC4R) deficiency, caused by mutations in MC4R, is the most common cause of monogenic forms of obesity. However, these mutations have often been identified in small-scale, ...case-focused studies. Here, we assess the penetrance of previously reported MC4R mutations at a population level. Furthermore, we examine why some carriers of pathogenic mutations remain of normal weight, to gain insight into the mechanisms that control body weight.
We identified 59 known obesity-increasing mutations in MC4R from the Human Gene Mutation Database (HGMD) and Clinvar. We assessed their penetrance and effect on obesity (body mass index BMI ≥ 30 kg/m2) in >450,000 individuals (age 40-69 years) of the UK Biobank, a population-based cohort study. Of these 59 mutations, only 11 had moderate-to-high penetrance and increased the odds of obesity by more than 2-fold. We subsequently focused on these 11 mutations and examined differences between carriers of normal weight and carriers with obesity. Twenty-eight of the 182 carriers of these 11 mutations were of normal weight. Body composition of carriers of normal weight was similar to noncarriers of normal weight, whereas among individuals with obesity, carriers had a somewhat higher BMI than noncarriers (1.44 ± 0.07 standard deviation scores SDSs ± standard error SE versus 1.29 ± 0.001, P = 0.03), because of greater lean mass (1.44 ± 0.09 versus 1.15 ± 0.002, P = 0.002). Carriers of normal weight more often reported that, already at age 10 years, their body size was below average or average (72%) compared with carriers with obesity (48%) (P = 0.01). To assess the polygenic contribution to body weight in carriers of normal weight and carriers with obesity, we calculated a genome-wide polygenic risk score for BMI (PRSBMI). The PRSBMI of carriers of normal weight (PRSBMI = -0.64 ± 0.18) was significantly lower than of carriers with obesity (0.40 ± 0.11; P = 1.7 × 10-6), and tended to be lower than that of noncarriers of normal weight (-0.29 ± 0.003; P = 0.05). Among carriers, those with a low PRSBMI (bottom quartile) have an approximately 5-kg/m2 lower BMI (approximately 14 kg of body weight for a 1.7-m-tall person) than those with a high PRS (top quartile). Because the UK Biobank population is healthier than the general population in the United Kingdom, penetrance may have been somewhat underestimated.
We showed that large-scale data are needed to validate the impact of mutations observed in small-scale and case-focused studies. Furthermore, we observed that despite the key role of MC4R in obesity, the effects of pathogenic MC4R mutations may be countered, at least in part, by a low polygenic risk potentially representing other innate mechanisms implicated in body weight regulation.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
A recent Perspective article described the “carbohydrate-insulin model (CIM)” of obesity, asserting that it “better reflects knowledge on the biology of weight control” as compared with what was ...described as the “dominant energy balance model (EBM),” which fails to consider “biological mechanisms that promote weight gain.” Unfortunately, the Perspective conflated and confused the principle of energy balance, a law of physics that is agnostic as to obesity mechanisms, with the EBM as a theoretical model of obesity that is firmly based on biology. In doing so, the authors presented a false choice between the CIM and a caricature of the EBM that does not reflect modern obesity science. Here, we present a more accurate description of the EBM where the brain is the primary organ responsible for body weight regulation operating mainly below our conscious awareness via complex endocrine, metabolic, and nervous system signals to control food intake in response to the body’s dynamic energy needs as well as environmental influences. We also describe the recent history of the CIM and show how the latest “most comprehensive formulation” abandons a formerly central feature that required fat accumulation in adipose tissue to be the primary driver of positive energy balance. As such, the new CIM can be considered a special case of the more comprehensive EBM but with a narrower focus on diets high in glycemic load as the primary factor responsible for common obesity. We review data from a wide variety of studies that address the validity of each model and demonstrate that the EBM is a more robust theory of obesity than the CIM.
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CMK, GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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