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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.

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.

Elevated body mass index (BMI) is heritable and associated with many health conditions that impact morbidity and mortality. The study of the genetic association of BMI across a broad range of common disease conditions offers the opportunity to extend current knowledge regarding the breadth and depth of adiposity-related diseases. We identify 906 (364 novel) and 41 (6 novel) genome-wide significant loci for BMI among participants of European (N~1.1 million) and African (N~100,000) ancestry, respectively. Using a BMI genetic risk score including 2446 variants, 316 diagnoses are associated in the Million Veteran Program, with 96.5% showing increased risk. A co-morbidity network analysis reveals seven disease communities containing multiple interconnected diseases associated with BMI as well as extensive connections across communities. Mendelian randomization analysis confirms numerous phenotypes across a breadth of organ systems, including conditions of the circulatory (heart failure, ischemic heart disease, atrial fibrillation), genitourinary (chronic renal failure), respiratory (respiratory failure, asthma), musculoskeletal and dermatologic systems that are deeply interconnected within and across the disease communities. This work shows that the complex genetic architecture of BMI associates with a broad range of major health conditions, supporting the need for comprehensive approaches to prevent and treat obesity. Elevated body mass index is heritable and associated with many health conditions that impact morbidity and mortality. Here, the authors identify greater than 900 genetic loci for body mass index (BMI) and find over 300 diagnoses associated with increasing BMI.

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.

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.In this Review, Loos and Yeo summarize our current understanding of the genetic underpinnings of monogenic and polygenic obesity. They highlight the commonalities revealed by recent studies and discuss the implications for treatment and prediction of obesity risk.

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.

BackgroundChild eating behaviors are highly heterogeneous and their longitudinal impact on childhood weight is unclear. The objective of this study was to characterize eating behaviors during the first 10 years of life and evaluate associations with BMI at age 11 years.MethodData were parental reports of eating behaviors from 15 months to age 10 years (n = 12,048) and standardized body mass index (zBMI) at age 11 years (n = 4884) from the Avon Longitudinal Study of Parents and Children. Latent class growth analysis was used to derive latent classes of over-, under-, and fussy-eating. Linear regression models for zBMI at 11 years on each set of classes were fitted to assess associations with eating behavior trajectories.ResultsWe identified four classes of overeating; “low stable” (70%), “low transient” (15%), “late increasing” (11%), and “early increasing” (6%). The “early increasing” class was associated with higher zBMI (boys: β = 0.83, 95% CI: 0.65, 1.02; girls: β = 1.1; 0.92, 1.28) compared with “low stable.” Six classes were found for undereating; “low stable” (25%), “low transient” (37%), “low decreasing” (21%), “high transient” (11%), “high decreasing” (4%), and “high stable” (2%). The latter was associated with lower zBMI (boys: β = −0.79; −1.15, −0.42; girls: β = −0.76; −1.06, −0.45). Six classes were found for fussy eating; “low stable” (23%), “low transient” (15%), “low increasing” (28%), “high decreasing” (14%), “low increasing” (13%), and “high stable” (8%). The “high stable” class was associated with lower zBMI (boys: β = −0.49; −0.68–0.30; girls: β = −0.35; −0.52, −0.18).ConclusionsEarly increasing overeating during childhood is associated with higher zBMI at age 11. High persistent levels of undereating and fussy eating are associated with lower zBMI. Longitudinal trajectories of eating behaviors may help identify children potentially at risk of adverse weight outcomes.

Quantitative changes in leptin concentration lead to alterations in food intake and body weight, but the regulatory mechanisms that control leptin gene expression are poorly understood. Here we report that fat-specific and quantitative leptin expression is controlled by redundant cis elements and trans factors interacting with the proximal promoter together with a long noncoding RNA (lncOb). Diet-induced obese mice lacking lncOb show increased fat mass with reduced plasma leptin levels and lose weight after leptin treatment, whereas control mice do not. Consistent with this finding, large-scale genetic studies of humans reveal a significant association of single-nucleotide polymorphisms (SNPs) in the region of human lncOb with lower plasma leptin levels and obesity. These results show that reduced leptin gene expression can lead to a hypoleptinemic, leptin-responsive form of obesity and provide a framework for elucidating the pathogenic mechanism in the subset of obese patients with low endogenous leptin levels. Defects in the regulation of leptin gene expression can lead to a hypoleptinemic, leptin-responsive form of obesity.

In a multi-stage analysis of 52,436 individuals aged 17-90 across diverse cohorts and biobanks, we train, test, and evaluate a polygenic risk score (PRS) for hypertension risk and progression. The PRS is trained using genome-wide association studies (GWAS) for systolic, diastolic blood pressure, and hypertension, respectively. For each trait, PRS is selected by optimizing the coefficient of variation (CV) across estimated effect sizes from multiple potential PRS using the same GWAS, after which the 3 trait-specific PRSs are combined via an unweighted sum called “PRSsum”, forming the HTN-PRS. The HTN-PRS is associated with both prevalent and incident hypertension at 4-6 years of follow up. This association is further confirmed in age-stratified analysis. In an independent biobank of 40,201 individuals, the HTN-PRS is confirmed to be predictive of increased risk for coronary artery disease, ischemic stroke, type 2 diabetes, and chronic kidney disease. Polygenic risk scores have potential to predict an individual’s risk of disease based on genetic markers. Here, the authors develop a polygenic risk score for hypertension and test it in a multi-ethnic cohort, finding that the score is associated with higher likelihood of hypertension development 4-6 years later.

Polygenic risk scores (PRS) summarize genetic liability to a disease at the individual level, and the aim is to use them as biomarkers of disease and poor outcomes in real-world clinical practice. To date, few studies have assessed the prognostic value of PRS relative to standards of care. Schizophrenia (SCZ), the archetypal psychotic illness, is an ideal test case for this because the predictive power of the SCZ PRS exceeds that of most other common diseases. Here, we analyzed clinical and genetic data from two multi-ethnic cohorts totaling 8,541 adults with SCZ and related psychotic disorders, to assess whether the SCZ PRS improves the prediction of poor outcomes relative to clinical features captured in a standard psychiatric interview. For all outcomes investigated, the SCZ PRS did not improve the performance of predictive models, an observation that was generally robust to divergent case ascertainment strategies and the ancestral background of the study participants. The inclusion of polygenic risk scores does not improve the performance of standard-of-care predictive models of disease outcomes in patients with psychosis.