Obesity and genetics -
To date, genome-wide association studies have identified more than 30 candidate genes on 12 chromosomes that are associated with body mass index. Genetic changes are unlikely to explain the rapid spread of obesity around the globe.
It takes a long time for new mutations or polymorphisms to spread. So if our genes have stayed largely the same, what has changed over the past 40 years of rising obesity rates? Our environment: the physical, social, political, and economic surroundings that influence how much we eat and how active we are.
Environmental changes that make it easier for people to overeat, and harder for people to get enough physical activity, have played a key role in triggering the recent surge of overweight and obesity.
Work on obesity-related gene-environment interactions is still in its infancy. Rather, it seems that eating a healthy diet and getting enough exercise may counteract some of the gene-related obesity risk. In , for example, Andreasen and colleagues demonstrated that physical activity offsets the effects of one obesity-promoting gene, a common variant of FTO.
The study, conducted in 17, Danes, found that people who carried the obesity-promoting gene, and who were inactive, had higher BMIs than people without the gene variant who were inactive. Having a genetic predisposition to obesity did not seem to matter, however, for people who were active: Their BMIs were no higher or lower than those of people who did not have the obesity gene.
Subsequent work on the relationship between the FTO gene, physical activity, and obesity yielded contradictory results. But once again, being physically active lowered the risk: Active adults who carried the obesity-promoting gene had a 30 percent lower risk of obesity than inactive adults who carried the gene.
Most people probably have some genetic predisposition to obesity, depending on their family history and ethnicity. Moving from genetic predisposition to obesity itself generally requires some change in diet, lifestyle, or other environmental factors. Some of those changes include the following:.
Having a better understanding of the genetic contributions to obesity-especially common obesity-and gene-environment interactions will generate a better understanding of the causal pathways that lead to obesity. Such information could someday yield promising strategies for obesity prevention and treatment.
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Variation in FTO contributes to childhood obesity and severe adult obesity. Nat Genet. 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.
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Gene-environment interaction and obesity. Nutr Rev. Over the past two decades, however, advances in high-throughput genome-wide genotyping and sequencing technologies, combined with a detailed knowledge of the human genetic architecture, have enabled the interrogation of genetic variants across the whole genome for their role in body-weight regulation using a hypothesis-generating approach.
Many of the candidate genes and pathways linked to body-weight regulation were initially identified in mice, such as the obese ob 13 and diabetes db 14 mouse lines, in which severe hyperphagia and obesity spontaneously emerged.
Shortly after the cloning of ob , the db gene was cloned and identified as encoding the leptin receptor LEPR This finding linked the melanocortin pathway to body-weight regulation, thereby unveiling a whole raft of new candidate genes for obesity.
Genes identified for monogenic obesity in a given year are shown on the left. Discoveries made for polygenic obesity are shown on the right, including a cumulative count of newly discovered loci per year and by ancestry. Although candidate gene and genome-wide linkage studies became available in the late s, findings were limited, and these study designs are not as frequently used as genome-wide association studies.
Once the genes for leptin and its receptor were identified, they became candidate genes for human obesity, and in the first humans with congenital leptin deficiency were identified This discovery was rapidly followed by the report of humans with mutations in the gene encoding the leptin receptor LEPR 22 , as well as in genes encoding multiple components of the melanocortin pathway, including PCSK1 ref.
Advances in high-throughput DNA sequencing led to candidate gene screening being replaced by WES, an unbiased approach that allows all coding sequences to be screened for mutations.
However, it rapidly became clear that, whereas candidate gene studies yielded few mutations, WES identified too many potential obesity-associated variants such that the noise often masked the true causative mutations.
However, with improved algorithms to predict the pathogenicity of mutations, as well as a rapidly expanding toolkit of functional assays, it has become easier to filter the likely pathogenic mutations.
Additionally, as monogenic obesity often demonstrates a recessive inheritance pattern 31 , consanguinity in populations has further increased the chance of identifying mutations, owing to greater chances of homozygosity of deleterious mutations The discovery of genes that influence polygenic obesity, which is common in the general population, started off slowly with candidate gene studies and genome-wide linkage studies.
The candidate gene approach was first applied in the mids and aimed to validate genes identified through human and animal models of extreme obesity for a role in common obesity Fig. Common variants in such candidate genes were tested for association with obesity risk, BMI or other body composition traits.
Over the subsequent 15 years, hundreds of genes were studied as candidates, but variants in only six ADRB3 ref.
The genome-wide linkage approach made its entrance into the field towards the end of the s Fig. Genome-wide linkage studies rely on the relatedness of individuals and test whether certain chromosomal regions co-segregate with a disease or trait across generations.
Ultimately, candidate gene and genome-wide linkage studies, constrained by small sample sizes, sparse coverage of genetic variation across the genome and lack of replication, only had a marginal impact on the progression of gene discovery for common obesity outcomes.
However, the pace of gene discovery for common diseases accelerated with the advent of genome-wide association studies GWAS Fig. The first GWAS for obesity traits were published in and identified a cluster of common variants in the first intron of the FTO locus that was convincingly associated with BMI 42 , Many more GWAS followed and, to date, nearly 60 GWAS have identified more than 1, independent loci associated with a range of obesity traits 44 Supplementary Tables 1 , 2.
Ten years and numerous GWAS later, the most recent GWAS for BMI included nearly , individuals, identified more than loci, with MAFs as small as 1.
Large-scale international collaborations have been formed, such as the Genetic Investigation for Anthropometric Traits GIANT consortium , that combine summary statistics of individual GWAS to generate data sets comprising hundreds of thousands of individuals.
Furthermore, many GWAS efforts have maximized sample size by focusing on BMI as the primary obesity outcome, an inexpensive and easy-to-obtain measurement that is readily available in most studies. As such, the vast majority of loci have been identified first in GWAS of BMI, but their effects typically transfer to other overall adiposity outcomes.
Even though BMI is widely used, it is considered a crude proxy of overall adiposity because it does not distinguish between lean and fat mass Therefore, GWAS have been performed for more refined obesity traits, such as body fat percentage 47 , 48 , fat-free mass 49 , imaging-derived adipose tissue 50 , circulating leptin levels 51 and LEPR levels In addition, two GWAS have focused on persistent healthy thinness, assuming that genes that determine resistance to weight gain may also inform obesity prevention and weight loss maintenance 53 , Although GWAS of more refined and alternative obesity outcomes are generally much smaller than those for BMI, the phenotypes are often a more accurate representation of body-weight regulation and, as such, the loci identified tend to more often point to relevant biological pathways that underlie obesity.
Almost all GWAS loci for obesity outcomes were first identified in adults. Similarly to gene discovery for other common diseases, the obesity genetics field has suffered from a strong bias in population representation, with the vast majority of GWAS being performed in populations that are exclusively or predominantly of European ancestry.
Nevertheless, some loci have first been discovered in populations of Asian 58 , African 59 , 60 , Hispanic or other ancestry 61 , despite their much smaller sample sizes. Broadly, loci identified in one ancestry demonstrate good transferability that is, directionally consistent associations across other ancestries, even though effect sizes and allele frequencies may differ.
CREBRF has been shown to play a role in cellular energy storage and use, and may be implicated in cellular and organismal adaptation to nutritional stress ADCY3 colocalizes with MC4R at the primary cilia of a subset of hypothalamic neurons that have been implicated in body-weight regulation So far, only a few CNVs have been identified that have a convincing association with BMI, such as the 1p To determine the role of other types of variation in obesity, alternative genome-wide screens have been performed.
For example, the impact of low-frequency and rare protein-coding variants has been tested using exome sequencing and exome array data 76 , 77 , 78 , Nevertheless, even large-scale studies identified only a few robust associations for rare coding variants.
For example, exome-wide screening based on array data from more than , individuals identified p. Tyr35Ter rs in MC4R ; p. ArgGln rs and p. GluGly rs in GIPR , which stimulates insulin secretion and mediates fat deposition 80 ; p.
Arg95Ter rs in GRP , which modulates habenular function that controls addiction vulnerability 81 ; and p. ArgTer rs in PKHD1L1 , which has been involved in cancer development 77 , A recent study that leveraged WES data for more than , individuals identified 16 genes for which the burden of rare nonsynonymous variants was associated with BMI, including five brain-expressed G protein-coupled receptors CALCR , MC4R , GIPR , GPR and GPR75 As obesity is a complex, multifactorial condition, some GWAS have integrated demographic factors such as sex and age 82 and environmental factors such as physical activity 83 , diet 84 or smoking 85 into their analyses.
Despite sample sizes of more than , individuals, these genome-wide gene-by-environment G×E interaction analyses remain challenging and so far only 12 loci have been identified, the effects of which on obesity are attenuated or exacerbated by non-genetic factors.
Nevertheless, the G×E interaction between the FTO locus and a healthy lifestyle has been robustly replicated. The increasing availability of large-scale cohorts and biobanks, such as the UK Biobank , the Million Veterans Project , All of Us , Biobank Japan and 23andMe , combined with ongoing work by the GIANT consortium, will boost sample sizes further to easily exceed 4 million participants in meta-analyses, expediting the discovery of many more obesity-associated loci.
However, translation of GWAS-identified loci into new biological insights remains a major challenge. Despite the difficulties in validating causative mutations and variants, genetic studies into both rare and common obesity over the past two decades have revealed two surprisingly cogent, overarching biological messages: first, the leptin—melanocortin pathway is a key appetitive control circuit 31 , 88 Fig.
Pro-opiomelanocortin POMC -expressing neurons and agouti-related protein AGRP -expressing neurons within the arcuate nucleus of the hypothalamus ARC act to sense circulating leptin LEP levels, which reflect fat mass. These neurons signal to melanocortin 4 receptor MC4R -expressing neurons in the paraventricular nucleus of the hypothalamus PVN , which controls appetite, thus linking long-term energy stores to feeding behaviour.
Binding of class 3 semaphorins SEMA3 to their receptors NRP and PLXNA influences the projection of POMC neurons to the PVN. Binding of brain-derived neurotrophic factor BDNF to its receptor neurotrophic receptor tyrosine kinase 2 NTRK2 is thought to be an effector of leptin-mediated synaptic plasticity of neurons, including those in the ARC and PVN.
The transcription factor SIM1 is crucial for the proper development of the PVN. Leptin is a key hormone secreted by adipocytes, which circulates at levels in proportion to fat mass Leptin also responds to acute changes in energy state, as its levels decrease with food deprivation and are restored during re-feeding.
Administration of leptin to fasted mice abrogates many of the neuroendocrine consequences of starvation, suggesting that the normal biological role of leptin is to initiate the starvation response Leptin signals through the LEPR, which exists in several different isoforms.
However, obesity-related effects of leptin are predominantly mediated by a long isoform that contains an intracellular domain LEPRb , which is expressed in various regions of the CNS Within the arcuate nucleus ARC of the hypothalamus, LEPRb is found on two populations of neurons at the heart of the melanocortin pathway, one of which expresses POMC and the other agouti-related protein AGRP 92 Fig.
POMC is post-translationally processed by prohormone convertases to produce several biologically active moieties, including β-lipotrophin and β-endorphin, and, crucially, the melanocortin peptides adrenocorticotrophin ACTH and α-, β- and γ-melanocyte-stimulating hormone MSH The ARC POMC neurons project to MC4R neurons within the paraventricular nucleus PVN where melanocortin peptides signal to decrease food intake By contrast, AGRP acts as an endogenous antagonist of MC4R to increase food intake 92 , MC3R is another centrally expressed receptor that binds to both melanocortin peptides and AGRP; however, as mice with targeted deletions in the gene are not obese but instead have altered fat to lean mass ratio, MC3R is less likely to be related to food intake and more likely to be involved in nutrient partitioning 95 , We can state with confidence that the fine balance of melanocortinergic agonism and AGRP antagonism of MC4R, in response to peripheral nutritional cues such as leptin, plays a central part in influencing appetitive drive The genetic evidence clearly supports this contention, with mutations in most genes of the melanocortin pathway resulting in hyperphagia and severe obesity in both humans and mice 31 , In fact, the vast majority of single-gene disruptions causing severe early-onset obesity in humans fall within this pathway, including LEPR , POMC , AGRP , MCR4R , PCSK1 ref.
Mutations in MC4R in particular, are the most common single-gene defect leading to hyperphagia and obesity. Of note, the degree of receptor dysfunction, as measured by in vitro assays, can predict the amount of food eaten at a test meal by an individual harbouring that particular mutation In addition to regulating food intake, it also regulates food preference, with individuals who carry mutations in MC4R showing a preference for food with higher fat content The importance of the melanocortin pathway in regulating feeding behaviour is highlighted by the identification of naturally occurring mutations in pathway genes in a wide range of different species where the appropriate selection pressure has been present Table 1.
Also, certain breeds of pig have been shown to carry MC4R missense mutations that are associated with fatness, growth and food intake traits MC4R mutations even contribute to the adaptation and survival of blind Mexican cavefish to the nutrient-poor conditions of their ecosystem It is now clear that in addition to engaging classical neuropeptide—receptor systems within the brain, leptin also rapidly modifies synaptic connections between neurons , and that this structural plasticity is crucial to its downstream functions.
One of the ways in which this plasticity is thought to be achieved is via brain-derived neurotrophic factor BDNF signalling to its receptor TrkB. BDNF is widely expressed in the CNS where it plays an important part in neuronal development , In the hippocampus, BDNF contributes to synaptic plasticity and long-term potentiation associated with memory and learning However, evidence has emerged that implicates BDNF and TrkB in the regulation of mammalian eating behaviour and energy balance BDNF is downregulated by nutritional deprivation and upregulated by leptin within the ventromedial nucleus VMN of the hypothalamus , although this regulation is probably indirect, as very few VMN BDNF neurons express the LEPR Fig.
Crucially, genetic disruption of BDNF , and TrkB , in both humans and mice results in hyperphagia and severe obesity. Another group of neuronal proteins important in the development of neuronal circuitry and linked to energy balance are the class 3 semaphorins SEMA3A—G.
A study in humans found that 40 rare loss-of-function variants in SEMA3A—G and their receptors PLXNA1—4, NRP1 and NRP2 were significantly enriched in individuals with severe obesity compared with 4, controls However, given that these results are from a single study, more data are required to confirm the exact role of class 3 semaphorins in energy homeostasis.
Unlike candidate gene studies, GWAS make no a priori assumptions about the underlying biology that links genetic variants to a disease of interest. While this agnostic approach allows for new biological insights, the vast majority of GWAS-identified variants map to the non-coding parts of genes or to regions between genes.
As such, they do not directly disrupt the protein-coding regions, but instead overlap with regulatory elements that influence expression of genes in close proximity or even over long distances. However, even if the causative genes are unknown, pathway, tissue and functional enrichment analyses based on the genes located in the GWAS loci can provide insights into potential mechanisms.
Since the very first GWAS for BMI 68 , , such analyses have pointed to the CNS being a key player in body-weight regulation, consistent with insights from human and animal models of extreme obesity. Recent analyses that include the latest BMI-associated loci, combined with updated multi-omics databases and advanced computational tools, have further refined these observations.
In addition to the hypothalamus and pituitary gland which are both known appetite regulation sites , other brain areas have been highlighted, including the hippocampus and the limbic system which are involved in learning, cognition and emotion and the insula and the substantia nigra which are related to addiction and reward 58 , 89 , , The enrichment of immune-related cells such as lymphocytes and B cells and adipose tissue was found to be weaker For example, the FTO locus, which was identified more than a decade ago and harbours six genes, is the most extensively studied GWAS-identified obesity locus Fig.
Early functional follow-up analyses suggested that FTO itself might be responsible, as Fto deficiency in mice results in a lean phenotype, whereas Fto overexpression is associated with increased body weight , Studies in mice have suggested that FTO plays a role in cellular nutrient sensing , Other studies found evidence that FTO influences brain regions that affect appetite, reward processing and incentive motivation by regulating ghrelin levels in humans or by controlling dopaminergic signalling in mice , In addition, variants in the FTO locus were shown to alter a regulatory element that controls the transcription of Rpgrip1l in mice, a ciliary gene located immediately upstream of Fto , , Mice with reduced Rpgrip1l activity exhibit hyperphagic obesity, possibly mediated through diminished leptin signalling , , In recent years, studies in human and animal models have shown that variants in the FTO locus directly interact with the promoter of Irx3 , a gene located 0.
Irx3 -deficient mice were found to exhibit weight loss and increased metabolic rate with browning of white adipose tissue, without changes in physical activity or appetite , Further in-depth functional characterization showed that rs in the FTO locus disrupts a conserved binding motif for the transcriptional repressor ARID5B, which leads to a doubling of IRX3 and IRX5 expression during early adipocyte differentiation The authors argue that increased expression of these genes results in a developmental shift from energy-dissipating beige adipocytes to energy-storing white adipocytes, a fivefold reduction in mitochondrial thermogenesis and increased lipid storage However, given that multiple studies have shown that the FTO locus is robustly associated with food intake, with no evidence to date linking it to changes in energy expenditure, the relevance of this observation to the actual observed human phenotype still needs to be explored A recent study reports that the FTO locus affects gene expression in multiple tissues, including adipose tissue and brain, and, more broadly, that the genetic architecture of disease-associated loci may involve extensive pleiotropy and allelic heterogeneity across tissues FTO contains nine exons depicted by blue rectangles and the body mass index BMI -associated SNP identified in genome-wide association studies depicted by a red × maps to intron 1.
IRX3 and RPGRIP1L have both been proposed to be the causal genes for obesity within the locus and to act on body weight through distinct mechanisms. HFD, high-fat diet. Besides the FTO locus, functional follow-up analyses have been performed for only a few obesity-associated GWAS loci.
For example, early studies identified a cluster of variants just downstream of TMEM18 refs 68 , TMEM18 encodes a poorly characterized transmembrane protein that is highly conserved across species and widely expressed across tissues, including in several regions of the brain , Tmem18 deficiency in mice results in a higher body weight owing to increased food intake, whereas Tmem18 overexpression reduces food intake and limits weight gain A knockdown experiment in Drosophila melanogaster suggests that TMEM18 affects carbohydrate and lipid levels by disrupting insulin and glucagon signalling Two other GWAS loci for which functional analyses have been performed are located just upstream of CADM1 ref.
The BMI-increasing alleles at each locus are associated with increased expression of CADM1 and CADM2 in the hypothalamus , Deficiency of either Cadm1 or Cadm2 in mice results in a lower body weight and increased insulin sensitivity, glucose tolerance and energy expenditure without any change in food intake , Conversely, increased neuronal expression of either Cadm1 or Cadm2 is associated with elevated body weight , Furthermore, CADM1 is expressed in POMC neurons and Cadm1 deficiency leads to an increase in the number of excitatory synapses, suggestive of an increased synaptic plasticity Cadm2 -deficient mice exhibit increased locomotor activity and higher core body temperature Another GWAS locus, just upstream of NEGR1 , harbours two deletions associated with increased obesity risk 68 , , These deletions do not overlap with the coding sequence of NEGR1 , but encompass a conserved transcription factor-binding site for NKX6.
Loss of binding of NKX6. Similar to CADM1 and CADM2, NEGR1 is a cell-adhesion molecule of the immunoglobulin superfamily that is expressed in several regions of the brain and has been shown to have a role in brain connectivity 69 , , a process believed to be important in obesity NEGR1 deficiency in mice was shown to result in lower body weight, mainly due to reduced lean mass, mediated by lower food intake However, two other functional studies, one in mice and one in rats, found that knockdown of Negr1 expression resulted in the opposite phenotype — increased body weight and food intake , While NEGR1 deficiency in mice was found to impair core behaviours, so far, findings and proposed mechanisms are not fully aligned 69 , , , Taken together, functional follow-up analyses for these loci are slowly expanding our understanding of the pathophysiology that drives weight gain.
However, many more obesity-associated loci are waiting to be translated into new biological insights. A major hurdle in translating GWAS loci into plausible candidate genes and appropriate paradigms for functional research is the annotation of the associated variants in a locus.
Defining the regulatory function of the non-coding variants, identifying their putative effector transcripts and determining their tissues of action remains an ongoing challenge. The advent of high-throughput genome-scale technologies for mapping regulatory elements, combined with comprehensive multi-omics databases, advanced computational tools and the latest genetic engineering and molecular phenotyping approaches, is poised to speed up the translation of GWAS loci into meaningful biology Gene discovery is often dichotomized by allele frequency and disease prevalence; that is, mutations are sought for monogenic forms of obesity and common variants for polygenic obesity Fig.
However, it is increasingly recognized that monogenic and polygenic forms of obesity are not discrete entities. Instead, they lie on a spectrum and share — at least in part — the same biology. As GWAS have continued to discover more obesity-associated loci, an increasing number of these loci harbour genes that were first identified for extreme and early-onset obesity in humans or animal models, including MC4R , , BDNF , SH2B1 refs 68 , , POMC 70 , LEP 51 , , LEPR 52 , , NPY , SIM1 ref.
In fact, most of these genes encode components of the leptin—melanocortin and BDNF—TrkB signalling pathways Table 1. Thus, whereas genetic disruption of components of these pathways results in severe obesity, genetic variants in or near these same genes that have more subtle effects on their expression will influence where an individual might sit in the normal distribution of BMI.
Although most genes have been first identified for extreme forms of obesity, a locus harbouring ADCY3 was first identified in GWAS for common obesity 77 , and ADCY3 was subsequently confirmed as having a role in extreme obesity 63 , ADCY3 encodes an adenylate cyclase that catalyses the synthesis of cAMP, an important second messenger in signalling pathways.
There is some evidence that ADCY3 adenylate cyclase colocalizes with MC4R at the primary cilia of PVN neurons 67 and that cilia are required specifically on MC4R-expressing neurons for the control of energy homeostasis In mice, disruption of Adcy3 or Mc4r in the cilia of these neurons impairs melanocortin signalling, resulting in hyperphagia and obesity As more GWAS loci are reported, we expect that findings across different lines of obesity research will continue to converge, providing accumulating evidence for new biology.
Genetic insights from gene discovery efforts are increasingly being used in the context of precision medicine in ways that directly affect health. When a disease is caused by a single mutation and the environmental contribution is limited, as is the case for some forms of extreme and early-onset obesity, a genetic test can be instrumental in correctly diagnosing patients.
Such a genetic diagnosis can lessen the feelings of guilt and blame for the patient, and alleviate social stigma and discrimination. Importantly, a genetic diagnosis can inform disease prognosis and, in some cases, it will determine treatment. To date, there are two treatments for obesity that are tailored to patient genotype.
The prototype of genotype-informed treatment for obesity is the administration of recombinant human leptin in patients who are leptin-deficient owing to mutations in the LEP gene , Although congenital leptin deficiency is exceptionally rare only 63 cases have been reported to date 28 , leptin replacement therapy has been remarkably beneficial for these patients by substantially reducing food intake, body weight and fat mass, and normalizing endocrine function , It has literally transformed their lives.
The second genotype-informed treatment for obesity is setmelanotide, a selective MC4R agonist that was recently approved by the FDA for rare monogenic obesity conditions including LEPR, PCSK1 and POMC deficiency Setmelanotide acts as a substitute for the absent MSH in patients with POMC deficiency owing to mutations in POMC or PCSK1 , and in patients with LEPR deficiency owing to mutations in LEPR , which is essential for POMC function , , Daily subcutaneous injection of setmelanotide results in substantial weight loss and in reduction of hunger , , After a 1-year treatment with setmelanotide in phase III trials, patients with POMC deficiency lost on average Weight loss in patients with LEPR deficiency was less pronounced; on average, they lost The difference in weight loss between the two patient groups may be because POMC deficiency directly affects the production of MC4R ligands α-MSH and β-MSH , whereas LEPR deficiency affects signalling upstream of POMC As such, setmelanotide may be able to completely restore MC4R signalling in POMC deficiency, but only partially in LEPR deficiency.
The reasons for the discrepancy between weight loss and reduction in hunger remain to be studied in greater depth. Although 12, carriers represent only a fraction 0.
In patients without genetic defects, neither setmelanotide nor leptin administration have, to date, demonstrated a substantial effect on weight loss , These two genotype-informed treatments show how insight into the underlying biological mechanisms can guide the development of molecules and medications that restore impaired pathways, at least in monogenic forms of obesity caused by deficiency of one protein.
Nevertheless, there remain substantial obstacles in the transition from conventional to precision medicine for monogenic obesity, which would require the adoption of systematic WES for individuals suspected to be carriers of deleterious mutations, and eventually even standardized screening at birth.
We are clearly a long way from such a scenario at present. As more variants are being discovered for common obesity, there is a growing expectation that genetic information will soon be used to identify individuals at risk of obesity.
Genetic susceptibility to complex disease, including obesity, is assessed using a polygenic score PGS. PGSs to assess obesity susceptibility are based on GWAS for BMI PGS BMI , the latest of which includes data on more than 2 million variants and explains 8.
The average BMI of individuals with a high PGS BMI top decile is 2. Despite these strong associations with BMI and obesity, the predictive performance of the PGS BMI is weak, which is unsurprising given its limited explained variance.
For example, using the same PGS BMI and data from the UK Biobank, we estimate that the area under the receiver operating characteristic curve AUC ROC is only 0. This means that the probability that an individual with obesity has a higher PGS BMI than an individual without obesity is 0.
Its sensitivity is 0. Given that the current treatment options for obesity are low risk, or even generally beneficial, the high false-positive rate is less concerning than the low sensitivity, as some at-risk individuals may miss the opportunity for early prevention.
Thus, only four of the 21 individuals who developed obesity were correctly classified by the PGS — a sensitivity of 0. Adapted with permission from ref. Thus, the current PGS BMI has a high rate of misclassification and does not reliably predict who is at risk of developing obesity and who is not.
The predictive ability of PGSs are expected to improve as GWAS increase in sample size and algorithms to calculate the scores become more refined.
Nevertheless, given the importance of socio-demographic, lifestyle and clinical risk factors in the aetiology of obesity, it is unlikely that a PGS BMI will ever be able to accurately predict obesity on its own. Instead, effective prediction models will have to include genetic and non-genetic factors, including a broad spectrum of demographic, environmental, clinical and possibly molecular markers, as well.
What initially began as two apparently distinct approaches, one studying rare Mendelian causes of extreme obesity, and the other exploring complex polygenic influences of population body-weight distribution, have eventually converged on the central role of the brain in regulating body weight.
In particular, both approaches have highlighted the roles of the leptin—melanocortin pathway and TrkB—BDNF signalling. Perhaps it seems obvious now, but it was by no means certain that, just because genetic disruption of a pathway resulted in a severe phenotype, polymorphisms within that same pathway would produce a more subtle and nuanced result.
The GWAS approach is hypothesis-free, with the promise to reveal new genes that point to new biology and pathways.
The translation from variant to function is a well-known challenge , but with increasing availability of new omics data, high-throughput technologies and advanced analytical approaches, there is an unprecedented opportunity to speed up the translation of hundreds of GWAS loci. Sample size remains a major driver for gene discovery.
In an ongoing collaboration that combines data from more than 3 million individuals of diverse ancestry from the GIANT consortium, the UK Biobank and 23andMe, the number of BMI-associated GWAS loci is set to double. Also, a recent WES effort of more than , individuals has demonstrated that rare mutations are discoverable when sample sizes are sufficiently large However, alternative study designs, a focus on more refined phenotypes or a focus on population subgroups that is, more homogeneous groups of individuals with similar outcomes could further add to gene discovery.
Translation of only a few dozen of the GWAS-identified loci could tremendously improve our insights into the biology of obesity and possibly reveal new therapeutic targets. GBD Obesity Collaborators. Health effects of overweight and obesity in countries over 25 years.
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This paper provides direct evidence that melanocortin peptides play a key role in the regulation of energy homeostasis.
Yaswen, L. Obesity in the mouse model of pro-opiomelanocortin deficiency responds to peripheral melanocortin. Challis, B. Mice lacking pro-opiomelanocortin are sensitive to high-fat feeding but respond normally to the acute anorectic effects of peptide-YY Natl Acad. USA , — van der Klaauw, A.
Human semaphorin 3 variants link melanocortin circuit development and energy balance. Cell , — e18 Farooqi, S. Genetics of obesity in humans. Saeed, S. Genetics of obesity in consanguineous populations: toward precision medicine and the discovery of novel obesity genes.
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Diabetes 69 , — Kurokawa, N. The ADRB3 Trp64Arg variant and BMI: a meta-analysis of 44 individuals. Shugart, Y. Two British women studies replicated the association between the Val66Met polymorphism in the brain-derived neurotrophic factor BDNF and BMI.
Benzinou, M. Endocannabinoid receptor 1 gene variations increase risk for obesity and modulate body mass index in European populations. Wang, D. Association of the MC4R VI polymorphism with obesity: a Chinese case—control study and meta-analysis in 55, individuals.
Obesity 18 , — Nead, K. Contribution of common non-synonymous variants in PCSK1 to body mass index variation and risk of obesity: a systematic review and meta-analysis with evidence from up to individuals.
Tonjes, A. Association of Pro12Ala polymorphism in peroxisome proliferator-activated receptor gamma with pre-diabetic phenotypes: meta-analysis of 57 studies on nondiabetic individuals. Diabetes Care 29 , — Rankinen, T. The human obesity gene map: the update. Obesity 14 , — Frayling, T.
A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. This paper describes the first GWAS for type 2 diabetes to identify a locus FTO robustly associated with BMI.
Scuteri, A. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet. Buniello, A. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics Nucleic Acids Res.
Yengo, L. Meta-analysis of genome-wide association studies for height and body mass index in approximately individuals of European ancestry.
Javed, A. Diagnostic performance of body mass index to identify obesity as defined by body adiposity in children and adolescents: a systematic review and meta-analysis. Kilpelainen, T. Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile.
Lu, Y. New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk. Zillikens, M. Large meta-analysis of genome-wide association studies identifies five loci for lean body mass.
Chu, A. Multiethnic genome-wide meta-analysis of ectopic fat depots identifies loci associated with adipocyte development and differentiation. Genome-wide meta-analysis uncovers novel loci influencing circulating leptin levels.
Sun, Q. Genome-wide association study identifies polymorphisms in LEPR as determinants of plasma soluble leptin receptor levels. Riveros-McKay, F. Genetic architecture of human thinness compared to severe obesity.
Orthofer, M. Identification of ALK in thinness. e22 Bradfield, J. A trans-ancestral meta-analysis of genome-wide association studies reveals loci associated with childhood obesity. Felix, J. Genome-wide association analysis identifies three new susceptibility loci for childhood body mass index.
Vogelezang, S. Novel loci for childhood body mass index and shared heritability with adult cardiometabolic traits. Akiyama, M. Genome-wide association study identifies new loci for body mass index in the Japanese population. Ng, M. Discovery and fine-mapping of adiposity loci using high density imputation of genome-wide association studies in individuals of African ancestry: African Ancestry Anthropometry Genetics Consortium.
Gurdasani, D. Uganda genome resource enables insights into population history and genomic discovery in Africa. e36 Wojcik, G.
Genetic analyses of diverse populations improves discovery for complex traits. Martin, A. Clinical use of current polygenic risk scores may exacerbate health disparities. Grarup, N. Loss-of-function variants in ADCY3 increase risk of obesity and type 2 diabetes.
Loss-of-function mutations in ADCY3 cause monogenic severe obesity. Minster, R. A thrifty variant in CREBRF strongly influences body mass index in Samoans. Andersen, M. The derived allele of a novel intergenic variant at chromosome 11 associates with lower body mass index and a favorable metabolic phenotype in Greenlanders.
Siljee, J. Subcellular localization of MC4R with ADCY3 at neuronal primary cilia underlies a common pathway for genetic predisposition to obesity.
Willer, C. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Singh, K. Neural cell adhesion molecule Negr1 deficiency in mouse results in structural brain endophenotypes and behavioral deviations related to psychiatric disorders.
Speliotes, E. Association analyses of , individuals reveal 18 new loci associated with body mass index. Soni, A. GPRC5B a putative glutamate-receptor candidate is negative modulator of insulin secretion. Jarick, I. Novel common copy number variation for early onset extreme obesity on chromosome 11q11 identified by a genome-wide analysis.
Sainsbury, A. Synergistic effects of Y2 and Y4 receptors on adiposity and bone mass revealed in double knockout mice. Cell Biol. Falchi, M. Low copy number of the salivary amylase gene predisposes to obesity. Meisler, M. The remarkable evolutionary history of the human amylase genes.
Oral Biol. Hendricks, A. Rare variant analysis of human and rodent obesity genes in individuals with severe childhood obesity. Turcot, V. Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity.
This large-scale exome-wide discovery study reports the use of array data to identify rare coding variations associated with BMI. Emdin, C. Analysis of predicted loss-of-function variants in UK Biobank identifies variants protective for disease.
Akbari, P. Sequencing of , exomes identifies GPR75 variants associated with protection from obesity. Science , eabf This large-scale exome-wide discovery study reports the use of WES data to identify mutations associated with BMI.
Baggio, L. Biology of incretins: GLP-1 and GIP. Gastroenterology , — Antolin-Fontes, B. The habenular G-protein-coupled receptor regulates synaptic plasticity and nicotine intake.
Winkler, T. The influence of age and sex on genetic associations with adult body size and shape: a large-scale genome-wide interaction study. Graff, M. Genome-wide physical activity interactions in adiposity — a meta-analysis of , adults. Smith, C. Genome-wide interactions with dairy intake for body mass index in adults of European descent.
Food Res. Justice, A. Genome-wide meta-analysis of , adults accounting for smoking behaviour identifies novel loci for obesity traits.
Qi, Q. Fried food consumption, genetic risk, and body mass index: gene—diet interaction analysis in three US cohort studies.
BMJ , g Physical activity attenuates the influence of FTO variants on obesity risk: a meta-analysis of , adults and 19, children. Genetics of obesity: can an old dog teach us new tricks?
Diabetologia 60 , — Locke, A. Genetic studies of body mass index yield new insights for obesity biology. This large-scale GWAS for BMI shows that BMI-associated loci frequently localize in or near genes that act in the brain.
Friedman, J. Leptin and the regulation of body weight in mammals. Ahima, R. Role of leptin in the neuroendocrine response to fasting. Cowley, M. Integration of NPY, AGRP, and melanocortin signals in the hypothalamic paraventricular nucleus: evidence of a cellular basis for the adipostat.
Neuron 24 , — Bertagna, X. Proopiomelanocortin-derived peptides. North Am. Ollmann, M. Antagonism of central melanocortin receptors in vitro and in vivo by agouti-related protein. Butler, A. A unique metabolic syndrome causes obesity in the melanocortin-3 receptor-deficient mouse.
Endocrinology , — Chen, A. Inactivation of the mouse melanocortin-3 receptor results in increased fat mass and reduced lean body mass. Doche, M. Human SH2B1 mutations are associated with maladaptive behaviors and obesity.
Marenne, G. Exome sequencing identifies genes and gene sets contributing to severe childhood obesity, linking PHIP variants to repressed POMC transcription.
Cell Metab. e12 Asai, M. Loss of function of the melanocortin 2 receptor accessory protein 2 is associated with mammalian obesity. Michaud, J. Sim1 haploinsufficiency causes hyperphagia, obesity and reduction of the paraventricular nucleus of the hypothalamus.
LOGIC gfnetics Long-term Effects of Lifestyle Intervention Ane Obesity and Genetic Influence in Children. Genetic change gneetics age- and sex-adjusted body weight between weeks gneetics and 6 and week Matcha green tea for inflammation for carriers of the different Polyphenols and hair health Obeslty the Polyphenols and hair health rs Ars Genetjcsrs Crs D and rs E. A indicates adenosine; C, cytosine; G, guanine; and T, thymine. Parenthetical values indicate the number of children included in the analysis. Mean change in age- and sex-adjusted BMI calculated as weight in kilograms divided by height in meters squared between weeks 0 and 6 and week 0 for carriers of the different genotypes of the SNVs rs A and rs B. eTable 2. Associations Between the 56 SNPs and Changes in Body Weight Adjusted for Age and Sex During the Intervention.There are hundreds of HPV vaccination for prevention that influence fat Obesity and genetics and metabolism.
So, do we have any control andd our bOesity at all? The experts ans in. Scientists have found that an mutations that make an individual feel gwnetics satisfied after a meal may be more common than previously Polyphenols and hair health, leading those who carry geneticss gene variants to eat more genettics or to consume Obesigy calorie-rich foods.
Nearly a third of the adult Peppermint oil for nausea in the Genetixs States and Polyphenols and hair health one Strengthen immune function six children and adolescents between the ages of generics and 19 are currently overweightaccording Hydration and sports nutrition the National Health and Nutrition Examination Survey.
For the ggenetics in five American adults who are anf, this excess yenetics boosts the risk of developing many Anc diseases, including Obesty 2 geneyics, high blood pressure, stroke, cardiovascular disease, and certain types of cancer. But what genefics causing this epidemic?
Is it Obesitu or is our weight dictated by the genes we inherit? While the kind WHR and overall health food-intake and the level of physical activity play a major role in growing anc of obese people, science is revealing that, similar to Polyphenols and hair health, between 50 and 80 percent of the variation between body weights can be due Obesity and genetics subtle changes annd some Ovesity.
While single genetic mutations that make obesity inevitable genetice super Ohesity, the hundreds of genetic variations that genrtics exert a tiny effect—making some of us slightly more vulnerable to gain weight—are more common.
When someone inherits several of Obesiy variations Polyphenols and hair health risk of obesity gentics significantly, particularly when gneetics with other genftics factors. That nature influences obesity was geenetics serendipitously in Obesihy, when researchers at The Jackson Laboratory Organic chlorogenic acid Bar Harbor, Maine, noticed that a strain of their lab mice grew abnormally "plump" because they ate a lot of food and seemed to be perpetually hungry.
It geetics 45 years to identify a mutation in a gene—named obesity —caused Obeisty mice to overeat and Organic collagen supplements weight. Obesity and genetics sufficient leptin protein the mice felt hungry, ate, and Obexity fat.
Subsequent studies revealed that leptin gene was just one member of a complex network of genes linked together in the so-called melanocortin aand —which also includes ad control appetite. Fat cells gejetics leptin into the bloodstream, which cues the brain to Body toning for men full and helps burn fat.
Forms of obesity caused by Joint health osteoarthritis Obesity and genetics just one gene, like the one that affected amd mice at Jackson Labs, are estimated gennetics be Obesiyy for gneetics than seven percent of morbid obesity worldwide.
Only about six percent of severely obese children carry defects in known single genes that cause their condition. Such single gene mutations, which become apparent early in life, are very rare, says Manfred Obesityy Müller, a nutritionist at the Onesity University of Kiel in Germany.
For example, only about a dozen cases of genetic leptin Weight loss for beginners and only 88 cases of Obesiy receptor deficiency worldwide have been diagnosed.
More genettics are alternate DNA Obesity and genetics, called polymorphisms, Ogesity lead to different versions of a gene that affect its function slightly. To learn more about the roots of complex traits, like obesity, scientists use Genome-Wide Association Studies GWAS to identify variants of genes linked to a particular disease.
The scientists then search for single 'letter' changes and estimate how likely those variants are associated with obesity. Intrigued by why only some people develop obesity, Christian Dina, a genetic epidemiologist at Nantes University in France, compared the sequences of 2, obese patients with 5, people with healthy weight.
Dina discovered that people with specific variations in a gene called FTO had a 22 percent higher risk of becoming obese.
But figuring out why they raise the risk or how these gene variants function can take many more years of research. For example, studies have shown that a different variant of the FTO gene that affects one in six adult European males can increase their risk of becoming obese by 70 percent.
People with this obesity-risk FTO variant have higher levels of the hunger hormone, ghrelin, circulating in their blood, which makes them feel hungry soon after eating a meal. Brain-imaging studies of people carrying this gene variant also reveal that these individuals respond differently to ghrelin and to pictures of food.
But not all gene variants linked to obesity are bad. A rare gene variant has also been found that can protect against obesity. A study of more thanpeople from Mexico, the U.
That means that the findings may not be relevant to people with different ancestry. Diet and lifestyle are the main drivers in the obesity epidemic, says Dina. Dina's, Yeo's and other's work is revealing that variations in many genes involved in our feeding behavior can frequently be linked with a range of obesity traits, such as BMI, body fat percentage, levels of leptin in blood, etc.
So far, scientists have identified more than 1, gene variants that each explain a very small part of the difference in body weight between people.
Their association with increased risk to gain weight usually manifests later in life resulting from an interaction between the presumed risk genes and lifestyle variables, explains Müller. However, the trends toward increasing obesity worldwide have more to do with lifestyle choices since there are no hints of drastic change in the occurrence of genetic variations across generations.
In fact, studies have shown that consumption of fried food in conjunction with the underlying genetic background plays a big role in developing obesity. While frequent consumption of high-calorie food may cause people with obesity-associated genes to gain weight faster, awareness, prevention, and exercise are very effective in avoiding the obesity.
Copyright © National Geographic Society Copyright © National Geographic Partners, LLC. All rights reserved. Science Mind, Body, Wonder.
How much of a role does genetics play in obesity? A technician displays a used flow cell, which holds DNA for sequencing.
To improve our understanding of the complex role genes play in the development of obesity scientists are comparing the genetic codes of people who have the disease with those who don't.
Obesity originates in the brain That nature influences obesity was discovered serendipitously inwhen researchers at The Jackson Laboratory in Bar Harbor, Maine, noticed that a strain of their lab mice grew abnormally "plump" because they ate a lot of food and seemed to be perpetually hungry.
For some, a silver lining? It is Nantes University, France. Share Tweet Email. Read This Next How to get high on your own hormones—naturally. Can you really sweat out toxins?
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: Obesity and genetics| Genetic Causes of Obesity: Polygenic, Monogenic, and Syndromic Causes | BDNF regulates eating behavior and locomotor activity in mice. Obestiy YLanktree MB an, Organic collagen supplements Feneticset al. Comments 1. Organic collagen supplements years and Natural fat loss journey GWAS later, the most recent GWAS for BMI included nearlyindividuals, identified more than loci, with MAFs as small as 1. An approach to treating or managing one of these syndromes would typically look very different from other types of obesity. Our data revealed that 3 obesity-associated SNVs rs, rs, and rs were instead associated with greater weight reductions. |
| RACGP - Genetics of obesity | Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark. Effectively, familial studies showed that BMI is highly correlated with parental obesity. Martinez et al. In addition to BMI, responses to overfeeding and underfeeding, energy expenditure, food choices, hunger, and satiation have all been shown to be significantly heritable [ ]. Obesity-associated gene TMEM18 has a role in the central control of appetite and body weight regulation. Meta-analysis of genome-wide association studies for body fat distribution in individuals of European ancestry. The genomics era yielded several advances on the understanding of the genetic susceptibility to obesity. |
| Genes Are Not Destiny | Obesity Prevention Source | Harvard T.H. Chan School of Public Health | Numerous studies of laboratory rodents provide strong evidence that genetics play an important role in obesity. The risk of obesity is determined by not only specific genotypes but also gene-gene interactions. However, there are still challenges associated with detecting gene-gene interactions for obesity. There are also genes that can be protective against obesity. For instance, in GPR75 variants were identified as such alleles in ~, sequenced exomes which may be relevant to e. therapeutic strategies against obesity. The term "non-syndromic obesity" is sometimes used to exclude these conditions. Contents move to sidebar hide. Article Talk. Read Edit View history. Tools Tools. What links here Related changes Upload file Special pages Permanent link Page information Cite this page Get shortened URL Download QR code Wikidata item. Download as PDF Printable version. Relation between obesity and genetic factors. General concepts. Obesity Epidemiology Overweight Underweight Body shape Weight gain Weight loss Gestational weight gain Diet nutrition Weight management Overnutrition Childhood obesity Epidemiology. Medical concepts. Adipose tissue Classification of obesity Genetics of obesity Metabolic syndrome Epidemiology of metabolic syndrome Metabolically healthy obesity Obesity paradox Set point theory. Body adiposity index Body mass index Body fat percentage Body Shape Index Corpulence index Lean body mass Relative Fat Mass Waist—hip ratio Waist-to-height ratio. Related conditions. Obesity-associated morbidity. Arteriosclerosis Atherosclerosis Fatty liver disease GERD Gynecomastia Heart disease Hypertension Obesity and cancer Osteoarthritis Prediabetes Sleep apnea Type 2 diabetes. Management of obesity. Anti-obesity medication Bariatrics Bariatric surgery Dieting List of diets Caloric deficit Exercise outline Liposuction Obesity medicine Weight loss camp Weight loss coaching Yo-yo effect. Social aspects. Comfort food Fast food Criticism Fat acceptance movement Fat fetishism Health at Every Size Hunger Obesity and the environment Obesity and sexuality Sedentary lifestyle Social determinants of obesity Social stigma of obesity Weight cutting Weight class. Is This the Beginning of Narcolepsy in a Five Year Old? European Society of Sleep Technologists. Retrieved April 6, Mar doi : PMID S2CID British Medical Bulletin. Advance articles 1 : — Treatment of the Obese Patient Contemporary Endocrinology. Totowa, NJ: Humana Press. ISBN Retrieved April 5, Br J Anaesth. Epidemiol Rev. The American Journal of Clinical Nutrition. ISSN May CiteSeerX Diabetes Care. Archived from the original PDF on Retrieved Adrienne; Hirschhorn, Joel N. Nature Genetics. PMC January Obesity Silver Spring. Arch Intern Med. June April Carola; Speliotes, Elizabeth K. November October American Journal of Physiology. Endocrinology and Metabolism. Are the obese to blame for their condition? Recent research has revealed that obesity is to a very large extent encoded in our genes. Indeed, studies of identical twins reveal that the heritability of obesity ranges between 70—80 percent, a level that is exceeded only by height and is greater than for many conditions that people accept as having a genetic basis. While there has also been an overall increase in the prevalence of obesity over the last several decades, it is the particular set of weight-regulating genes that a person inherits that determines who is lean and who is obese in America. Could it be then, as Maher implies, that thin people control their urge to eat and the obese do not? For those who believe that being thin is a result of greater self-control, consider the case of a massively obese four-year-old boy in England, who weighed 80 pounds. After consuming a single test meal of 1, calories half the daily intake of an average adult , he asked for more. This boy had a similarly affected eight-year-old cousin who weighed more than pounds. Both children carry a genetic defect causing their obesity that runs in the family. The defective gene encodes the adipocyte hormone leptin, and the children do not produce it. However, when they receive leptin injections, their appetite is reduced to normal, and they lose enormous amounts of weight. The boy in fact is now quite thin. In normal people without leptin mutations, the hormone is secreted by fat cells into the bloodstream and then acts on specialized brain cells that regulate appetite. When the amount of fat increases, leptin production increases, and food intake goes down. When weight is lost, leptin decreases, which then stimulates appetite. This system serves a vital evolutionary function by maintaining optimal levels of adipose tissue, thus providing a source of calories when food is not available, a not uncommon occurrence during human evolution. However, the decreased mobility associated with excess fat can increase the risk posed by predators. The leptin system appears to have evolved to balance the risk of being too thin starvation and the risk of being too obese predation. Indeed, the weight of all mammals is precisely regulated—notwithstanding that only humans have ever expressed a conscious desire to lose weight. Specific genetic differences that predispose to obesity or leanness are then propagated by natural selection depending on whether starvation or predation was the greater risk. The weight of each individual is then stably maintained by the leptin system with remarkable precision. The average person takes in a million or more calories per year, maintaining weight within a narrow range over the course of decades. The body balances calorie consumption with expenditure, and with accuracy greater than Mutations in hormones are rare and there are only a few dozen patients who fail to produce leptin. So, while studies of these individuals establish a role for leptin to control appetite in human, defects in the gene itself are a very infrequent cause of obesity. However, mutations in the neural circuit that is regulated by leptin are more common, including mutations in the receptor for leptin. The leptin receptor is expressed in the hypothalamus, a primitive part of the brain that regulates most basic biologic drives, including the basic drive to eat. In the hypothalamus, there are specialized neurons expressing the leptin receptor that regulate appetite. One type promotes food intake; a second neural population reduces food intake. Leptin acts by inhibiting the one and activating the other. Similar to mutations in the leptin receptor, mutations in other key genes downstream of the hypothalamus also cause human obesity. Recent genetic studies have shown that as many as 10 percent of markedly obese children carry mutations in one or another of these individual genes. Another mistake that Maher and others make is to assume that the drive to eat is the same for all. Leptin regulates the intensity of the feeding drive. In its absence, patients report being unable to control their appetite and eat voraciously. That is how leptin deficient people feel all the time. This sensation appears to be similar for obese patients who lose weight e. |
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