Current Diabetes Reports

, 18:85 | Cite as

Genetics of Severe Obesity

  • Una Fairbrother
  • Elliot Kidd
  • Tanya Malagamuwa
  • Andrew WalleyEmail author
Open Access
Genetics (AP Morris, Section Editor)
Part of the following topical collections:
  1. Topical Collection on Genetics


Purpose of Review

This review aims to present current information on genes underlying severe obesity, with the main emphasis on the three genes LEP, LEPR and MC4R.

Recent Findings

There is a substantial amount of evidence that variants in at least ten different genes are the cause of severe monogenic obesity. The majority of these are involved in the leptin-melanocortin signalling pathway. Due to the frequency of some of the identified variants, it is clear that monogenic variants also make a significant contribution to common obesity.


The artificial distinction between rare monogenic obesity and common polygenic obesity is now obsolete with the identification of MC4R variants of strong effect in the general population.


Obesity Leptin Melanocortin-4 receptor GWAS BMI 


Obesity is both very common, with a prevalence of 12% globally, and accompanied by high rates of serious, life-threatening, complications such as type 2 diabetes, cardiovascular disease and cancer [1]. Its underlying causes are complex and have proven relatively difficult to elucidate [2•]. A person with a body mass index (BMI) of 30 kg/m2, or more, is defined as obese, and severe obesity has been strictly defined as a BMI within the range of 35–39.9 kg/m2 [3]. However, severe obesity is frequently defined with the broader meaning of having a BMI of greater than 35 kg/m2, that is it includes obesity classes II, III and IV (see Table 1). BMI has been used to assess obesity rates in populations in relation to health with some considerable success [4], and prevalence is high in many countries, such as the USA and UK (see Table 1). The economic cost of obesity to national health systems and the wider society is a considerable burden on wealthy economies [5], particularly severe obesity [6] and an alarming prospect for such emerging large economies as China where obesity rates are rising fast [7].
Table 1

Body mass index (BMI) ranges, corresponding descriptions and rates of each category in the USA and UK for adults of both sexes in the latest year for which data was available*

BMI range (kg/m2)



UK %

< 18.5





Normal weight








Obesity class I (obese)




Obesity class II (severely obese)

> 40

Obesity class III (morbidly obese

> 50

Obesity class III (super obese)

Table created using data from WHO Global Health Observatory data;

While it has been evident for many years that there is a strong genetic component affecting obesity, with twin studies providing reproducible heritability values as high as 0.77 across different regions of the world and at different ages [8•], there has been relatively little investigation of severe obesity specifically. This is almost certainly due to the considerable overlap between rare monogenic obesity and common polygenic obesity at this part of the distribution of BMI within the population. However, at least one study has attempted to investigate genetic effects in severe obesity [9]. This used a definition of morbid obesity of 45.5 kg over the ideal weight of an individual, something that would actually place many people in the category of severely obese based on current definitions (see Table 1). Family members of probands were found to be eight times more likely to be severely obese than the general population. They also demonstrated that families with at least one severely obese parent were 2.6 times more likely to have one or more severely obese adult offspring compared to the general population. This suggested that genetic effects were important in this sub-group of the obese but could not eliminate the effects of shared environment.

With a role for genetics well established in obesity, the focus for this review is on severe obesity as a distinct disease with a specific genetic cause rather than syndromic or common polygenic obesity. The overlap of rare monogenic disease with common obesity will also be discussed.

Monogenic Severe Obesity

This review will concentrate on the original obesity gene, leptin (LEP) and its receptor (LEPR), together with a gene that has been identified as having variants of strong effect giving rise to both monogenic obesity and obesity in the general population, the melanocortin-4 receptor (MC4R). Additional selected genes reported to be associated with severe obesity are listed in Table 2, to provide a flavour of the current state of the art without attempting to be comprehensive.
Table 2

Selected genes with variants contributing to severe monogenic obesity with recent reviews

Gene symbol

Full name



Associated traits

Recent reviews


Adenylate cyclase 3


Severe-early onset

Type 2 diabetes

Tian et al. 2018 [10]


Brain-derived neurotrophic factor


Severe-early onset

Hyperphagia, severe obesity, hyperactivity and impaired cognitive function.

Han et al. 2016 [11]


Kinase suppressor of ras 2



Hyperphagia (in childhood), insulin resistance and reduced basal metabolic rate.

Frodyma et al. 2017 [12]




Severe, early onset

Hyperphagia, hypogonadotropic hypogonadism. Some evidence for neuroendocrine/metabolic and immune dysfunction

Wasim et al 2016; Yeo 2017 [13, 14]


Leptin receptor


Severe, early onset

Hyperphagia, hypogonadotropic hypogonadism. Some evidence for neuroendocrine/metabolic and immune dysfunction

Wasim et al 2016; Yeo 2017 [13, 14]


Melanocortin 4 receptor


Severe, early onset

Increased linear growth and final height, fasting hyperinsulinemia and incompletely suppressed growth hormone secretion.

Krashes et al 2016; Yeo 2017; Novoselova et al 2018 [14, 15, 16]


Proprotein convertase subtilisin/kexin type 1


Severe – occurring in childhood

Hyperphagia, impaired glucose homeostasis, decreased linear growth, hypothyroidism, hypocortisolism and hypogonadotropic hypogonadism

Ramos-Molina et al 2016; Stijnen et al 2016 [17, 18]




Severe, from the first months

Adrenocorticotropic hormone (ACTH) deficiency, red hair and pale skin

Anderson et al 2016; Rubinstein and Low 2017 [19, 20]

Early work with mouse models implicated leptin and its cognate receptor as implicated in monogenic obesity [21, 22]. Rare human cases where leptin, or the leptin receptor, is entirely ablated have subsequently been reported and uniformly exhibit severe obesity [23, 24, 25].


Leptin is a hormone secreted by adipose tissue [26] and leptin levels are directly related to adiposity in humans [27]. It is constitutively expressed, and in conditions of prolonged caloric deficit, fat stores and leptin production will decrease [28]. It is a cytokine (or adipokine) essential for regulation of energy balance through feeding behaviour and energy expenditure [21]. Leptin is anorexigenic and seems likely to be our main adiposity indicator and signal of nutritional status: plasma levels are highly correlated to adipocyte number and fat mass [29]. Leptin levels are also strongly correlated with insulin resistance independently of fat volume; thus, hyperleptinaemia can be considered an independent factor in obesity (see [30] for review).

Leptin signals by binding to the leptin receptor (a type I cytokine receptor) in the arcuate nucleus of the hypothalamus, reducing the desire to eat and stimulating thermogenesis [31]. Leptin signalling is via the JAK-STAT pathway [32, 33, 34].

Circulating leptin binds to the soluble form of the leptin receptor, sOB-R [35], and activates Janus kinase (JAK2), which then phosphorylates three tyrosine residues in LEPR, which then induces the phosphorylation of STAT transcription factors STAT5 and STAT3 (see [36] for review). This provides the metabolic link between leptin levels and the many downstream energy homeostatic pathways it regulates, which include growth, caloric expenditure and glycaemic control. In addition to behavioural control regarding food intake and secretion of adrenal corticosteroids, circulating leptin induces its effects via LepRb [37], the long form of its receptor, which is expressed specifically in areas of the brain with a role in feeding and energy expenditure [38]. In normal weight individuals, if leptin is decreased due to reduction in white adipose tissue, such as during prolonged starvation, this induces orexigenic signalling, which results in decreased energy use and disruption of glucose homeostasis (see [38] for review).

The mechanism by which this pathway is dysregulated is complex and has been demonstrated to include factors that have varying levels of influence such as feedback inhibition, inflammatory responses, gliosis and endoplasmic reticulum stress [39]. Circulating leptin levels are increased in obese humans and also in animal models, but the essential feedback mechanism that promotes reduction in feeding behaviour and increased energy expenditure fails [40]. Interestingly, leptin receptors continue to respond to the increased leptin [41]. It appears that an obese end point is reached despite increased leptin-leptin receptor signalling but that continuous high levels of leptin receptor signalling induce leptin resistance and “caps” the amplitude of the signal [39, 42].

There are currently only eight different mutations reported in the LEP gene that are thought to cause severe obesity (see [13] for review). Recent studies, including exome sequencing, have increased the numbers of known variants but LEP mutations remain very rare (see Table 2). Allelic variation has been extensively studied with the view that these may confer greater propensity to obesity. For example, family-based association analysis of a large consanguineous Tunisian family identified the functional variants (H1328084 and A19G) in the 5′ UTR of LEP. These variants have been associated with plasma leptin level as a quantitative trait and are thus implicated in affecting plasma leptin levels [43].

Many candidate gene and genome-wide association studies (GWAS) have been carried out where LEP and LEPR polymorphisms have been investigated for their role in measures of adiposity, obesity and its sequelae [13, 44, 45, 46, 47, 48]. There is a relatively small amount of literature positively implicating LEP polymorphisms in this role and the relationship is dependent on ethnicity and age of subject [44, 49]. LEP G2548A has been associated with obesity and serum leptin levels in Turkish subjects [50] and a study that reports a synergistic effect of LEP and LEPR polymorphisms on BMI, in a Han Chinese population [51]. In a Pakistani population, this polymorphism has also been shown to be associated with obesity in female children < 18 years old. In addition, G-2548A polymorphism showed association with BMI, fasting blood glucose and serum leptin levels in male and female children [52].

It is clear that ascertainment of traits has a profound impact on identification of positive associations. LEP polymorphisms G2548A in a recent meta-analysis involving 1372 obese individuals (BMI > 30 kg/m2) and 1616 controls concluded that there was no association with LEP [49]. However, three studies show a positive association with severe obesity in Taiwanese aboriginals and Caucasians [53, 54, 55].

Leptin Receptor

The leptin receptor gene, LEPR at 1p31, encodes a single membrane spanning receptor of the class I cytokine receptor family [22]. There are six leptin receptor or ObR isoforms produced by alternative splicing [56, 57]. With different isoforms being expressed in different tissues [58], these include one long form (LEPR or ObRb), four short forms (ObRa, ObRc, ObRd and ObRf, with unique C-termini) and one soluble form (ObRe). LEPR, the long form, which is the isoform truncated in obese mice (ob/ob) and known to be important in energy and feeding control, contains three highly conserved tyrosine residues (Y985, Y1077, Y1138) required for efficient leptin signalling [21, 38, 59]. Its major site of expression is the arcuate nucleus neurones, which are known to have a role in energy homeostasis and are reported to be responsive to leptin signalling (see [60] for review). Many of these neurones also express pro-opiomelanocortin (POMC) in response to excitation by leptin, and via central nervous system (CNS) melanocortin receptors (MCRs), play a key role in negatively moderating feeding behaviour and increasing energy expenditure [38, 61].

Truncation of LEPR has been shown to cause morbid monogenic obesity and severe hyperphagia in mice (db/db) and humans [37, 62, 63, 64]. Genome scans and association studies have linked LEPR to measures of adiposity including energy expenditure in: a population with elevated obesity levels, fat mass, skinfold and fat-free mass; BMI trends in childhood; and leptin levels, body composition, insulin dysregulation and glucose metabolism. The leptin receptor also has a role in the hypothalamic-pituitary-gonadal axis, thus influencing onset of puberty [65].

Numerous coding single-nucleotide variants (SNVs) have been identified in the LEPR gene including: Lys109Arg (rs1137100), which lies in the cytokine homology domain (CK); Gln223Arg (rs8179183) in the loop region of the CK domain; and the Lys656Asn (rs8179183), in the fibronectin type III (F3) domain [66, 67]. These domains are common to all the isoforms.

The association of these variants with common severe obesity and obesity-related phenotypes has been reinforced through evidence generated by a multitude of candidate gene studies [44, 48]. Understanding common complex obesity through identification of genes mutated in rare severe obesity must be carefully interpreted in terms of the direction of the effects. In addition, care must be taken in selecting a sample group that is not phenotypically heterogeneous, i.e. overweight, obese and morbidly obese pooled, for example. Indeed, extreme phenotyping is a refinement of this practice and has had some notable successes in identifying genes implicated in quantitative traits, including obesity and obstructive sleep apnoea, for which obesity and leptin levels are significant risk factors [68, 69]. With complex disease, subtle phenotypic heterogeneity may mask underlying contributions from variants of modest effect, especially since the precise phenotypic effect of the variant is unknown and may be different in different ethnic groups. Nevertheless, there is overwhelming evidence that common variants in LEPR are associated with measures of adiposity. This seems unlikely to be a direct effect but is likely to be due to subtle variations in functionality whose effect is amplified in downstream targets [39].

Melanocortin-4 Receptor

The MC4R protein is a membrane-bound G-protein-coupled receptor found in several brain regions, including the paraventricular nucleus (PVN) in the hypothalamus (see [70] for review). Stimulation of LEPR on POMC neurons causes them to release α-melanocyte-stimulating hormone (α-MSH) (see [38] for review), which binds to the MC4R protein. This results in the exocytosis of brain-derived neurotrophic factor (BDNF) and neurotrophic tyrosine kinase receptor 2 (NTRK2) (see [71] for review), which are both anorexigenic signals. It should be noted that very recent studies have reported that leptin only directly regulates Agouti-related protein (AGRP) neurons [72, 73]. With many functional relationships regulating the orexigenic-anorexigenic signal balance, it is not surprising that variation in the genes involved in the melanocortin-leptin pathway can give rise to severe obesity [70, 74].

MC4R Gene Sequence Variation

MC4R-deficient patients are affected by hyperphagia and, consequently, a higher caloric intake [75, 76]. MC4R variation was recognised relatively early as a monogenic cause of severe obesity, accounting for as much as 6% of people with early onset obesity [77]. Most people affected are heterozygous, demonstrating autosomal dominant inheritance [78, 79]. Homozygous cases have also been reported and they display a more severe form of obesity [77, 80].

Currently, there are 376 SNVs and 189 copy number variants reported in the MC4R gene region. A total of 182 of the SNVs are missense, 10 are nonsense and 12 are frameshift variants. Of these SNVs, 69 are predicted pathogenic, or likely pathogenic. Only two SNVs have been identified with a minor allele frequency (MAF) > 1% in the 1000 genomes (1000G) or the ExAC reference population datasets: namely rs34114122 (1000G MAF = 6.0%, ExAC MAF = not available) and rs2229616 (1000G MAF = 1.6% and ExAC MAF 1.7%) (data from NCBI Variation Viewer, 27th June 2018) [81, 82].

Clinical Phenotype

Obese patients with mutant MC4R genes are very similar to other obese patients with no identified MC4R mutation. They share a similar mean BMI with other obese patients, as well as maximum BMI reached during adult life and minimal BMI reached during caloric restriction [80]. No difference in food intake, incidence of diabetes and glucose intolerance has been observed between the two groups. Fasting glucose, triglyceride levels and mean leptin levels were also the same. However, when looking at childhood obesity, MC4R mutant carriers have a higher percentage body fat composition of 67.0 vs 45.5% in other obese children.

When comparing BMI standard deviation, individuals with heterozygous mutations had a score of 2.79 ± 1.61 (mean ± SD) while homozygotes for MC4R mutations had a score of 4.81 ± 1.63 (mean ± SD). During the first 5 years of life, mutant MC4R carriers show a higher standard deviation for height, a higher body fat percentage of 42.9% compared to 15–25% normal body fat percentage range and a higher fat-free mass than homozygous wild-type subjects, suggesting that MC4R deficiency is characterised by increased fat and lean mass. Therefore, children with MC4R deficiency were taller and more obese than their peers. The study also proved that these children have a higher bone mineral density which corresponds with previous studies [77].

As these patients grow older, hyperphagia decreases and their metabolic rate becomes similar to healthy individuals. Adult MC4R mutation carriers do not have an increased risk of diabetes and the hyperinsulinemia seen in children decreases to normal levels after the age of 10. These individuals also show normal endocrine function. There is no strong evidence that binge eating is a phenotype of MC4R deficiency as originally suggested [83, 84, 85].

Bariatric surgery is currently the only successful option in treating obesity, but when bariatric surgery has been performed on homozygous mutant MC4R patients, it had no impact on long-term weight loss [86, 87].

GWAS for Severe Obesity

The identification of genes underlying common obesity has been predominantly focused on the phenotype of BMI. This was due to three reasons: the presence of this phenotype in many cohorts recruited for other reasons, the clear relationship with definitions of obesity in populations and the fact it is a simple to measure quantitative trait. However, the first association reported with common obesity originated from a type 2 diabetes study. Variants in the FTO gene were associated with type 2 diabetes at genome-wide significance, but this association was not significant when BMI was taken into account [88]. This has led to very many GWAS and meta-analyses for BMI conducted with hundreds of thousands of subjects (e.g. [89, 90]). The main problem with the focus on BMI is that the associations detected are to variants that contribute to the distribution of BMI within a cohort, rather than the variants that associate with obesity specifically.

There are fewer GWAS that have investigated associations with severe obesity and those that have are relatively small and under-powered, with the exception of the most recent meta-analysis (see below). One of the earliest GWAS used both adults with morbid obesity and obese children, arguing that early-onset obesity was likely to be predominantly genetic. Associations were reported with the two genes already identified in the general population, FTO and MC4R, but associations were also reported with SNVs in the NPC1 gene and near to the MAF and PTER genes [91]. Recently, it has been reported that rare variants in the NPC1 gene are enriched in young severely obese Chinese subjects and that heterozygosity for these variants also leads to increased BMI compared to age-matched controls [92]. The variant near to the MAF gene is midway between the MAF gene and the MAFTRR gene (MAF transcriptional regulator RNA), a long non-coding RNA that regulates MAF expression, suggesting that the association may be with a causal variant that affects transcription of MAF, or another, as yet undiscovered, target of MAFTRR. The variant reported near to the PTER gene is in fact closer to several non-coding RNA genes, including the long non-coding RNA RP11-461K13.1 and the U6 small nuclear pseudogene RNU6-1075P, again suggesting the possibility that the association is in fact to a variant that affects expression of gene targets of these non-coding RNAs.

A stepwise analytical approach was used in another study where an initial small GWAS was carried out in 164 morbidly obese and 163 always-lean adults, followed by taking the positive associations in two further stages of 700 SNVs in 460/247 cases and controls and then 23 SNVs in 4214 obese versus 5417 lean or population-based controls [93]. The initial GWAS demonstrated nominal association (p < 0.05) with variants in brain-derived neurotrophic factor (BDNF) and MC4R, but not in FTO. Variants in the genes KCNMA1 (potassium calcium-activated channel subfamily M alpha 1) and BDNF were reported to be associated with obesity at genome-wide significance. Notably, the SNV in KCNMA1, rs2116830, was not associated with BMI in the population-based controls and the KCNMA1 transcript was over-expressed in adipose tissue in obese adults. This suggests the possibility that KCNMA1 is purely associated with obesity rather than BMI, but given the relatively small numbers of controls, this result might simply reflect a lack of statistical power.

A GWAS for severe early-onset obesity in a total of 2480 children reported four loci, namely leptin receptor (LEPR), protein kinase C eta (PRKCH), phosphofurin acidic cluster sorting protein 1 (PACS1) and rhabdomyosarcoma 2-associated transcript (RMST) [94]. Association of the previously reported 43-kb deletion near to NEGR1 was also reported but this was determined to be due to linkage disequilibrium with an 8-kb deletion the other side of the NEGR1 locus. Pathway analysis was reported to suggest enrichment of g protein coupled receptors involved in the neuronal regulation of energy homeostasis. It is notable that similar to the earlier GWAS, association was seen to a locus containing a long non-coding RNA, RMST, suggesting that non-coding RNA loci deserve more attention as containing possible obesity-causing variants.

More recently, a very large meta-analysis of the data available to the GIANT consortium generated seven new loci associated with BMI, two of which were associated with class II severe obesity [95••]. The study analysed 15,334 cases and 97,858 controls for the specific class II analysis and identified two novel loci, as well as previously reported associations with MC4R and KCNMA1. The new loci were HS6ST3 (heparan sulfate 6-O-sulfotransferase 3) and ZZZ3 (zinc finger ZZ-type containing 3). Neither of the new genes identified have any reported functional relationship with obesity or BMI.

Study sizes continue to grow in order to increase statistical power, in response to the challenge of detecting loci of small effect, with MAF values < 0.05. Recently, in a study analysing data from > 700,000 subjects on an exome array, a further eight novel gene loci have been associated with BMI, implicating novel candidate pathways involved in obesity and related phenotypes [96•]. The strength of this study is that the cohort is over twice the size of most other GWAS studies to date. Interestingly, these data suggest that MC4R and KSR2 were identified as having a role in common, complex obesity. These genes have previously been identified in much smaller studies, where subjects’ obese phenotype was severe and early onset [69, 89, 97, 98].

Two MCR4 SNVs were used in the analysis that were array-wide significant at p < 2 × 10−7: a missense mutation (Asp37Val, rs12447325) and a nonsense mutation (Tyr35Ter, rs13447324), both with a MAF of 0.01%, thus present in 1/5000 subjects. The Tyr35Ter mutation which leads to MC4R deficiency and hyperphagia [75, 76, 78] results in approximately 7 kg of extra weight for a person 1.7 m in height and gave the largest effect size in the whole study. Interestingly, MC4R Ille251Leu (rs52820871) is reported in the literature as ameliorating obesity [99]. The KSR2 variant identified in this study (Arg554Gln, rs56214831) increases body weight by 0.74 kg/allele and is known to be associated with hyperphagia-induced obesity, low basal metabolic rate and insulin resistance in mice and humans [69, 100, 101, 102].

Effect sizes of rare variants are generally higher than common SNVs positively identified in GWAS and this is the case for MC4R and KSR2, but penetrance is low and the 14 SNVs, identified in this large exome array study [96•], combined represent < 0.1% of BMI variation. Of the new loci identified, EPAC1 (RAPGEF3) is known to play a role in energy homeostasis, diabetes and obesity propensity and regulates insulin and leptin signalling [103, 104, 105]. Interestingly, knockdown models of adipose specific epac were lethal in Drosophila, which was not the case for the eight other newly identified loci investigated [96•].


Over 20 years since the report of the first gene involved in obesity, we now have a far better understanding of the genetics of severe obesity. We have identified a range of genes responsible for severe monogenic obesity and we now know that some of these are frequent enough to be significant causes of obesity in the general population. The boundary between rare monogenic obesity and common polygenic obesity is now becoming blurred. This is not unexpected but it has not really become evident until recently with the technological advances in genotyping and sequencing that now allow us to characterise all variants across the whole genome in large numbers of cases and controls.


Compliance with Ethical Standards

Conflict of Interest

Una Fairbrother, Elliot Kidd, Tanya Malagamuwa and Andrew Walley declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.


Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 1.
    GBD 2015 Obesity Collaborators, Afshin A, Forouzanfar MH, et al. Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med. 2017;377:13–27.CrossRefGoogle Scholar
  2. 2.
    • Ghosh S, Bouchard C. Convergence between biological, behavioural and genetic determinants of obesity. Nat Rev Genet. 2017;18:731–48. Excellent review of all the contributing factors to obesity. CrossRefPubMedGoogle Scholar
  3. 3.
    WHO (2013) Fact sheet No 311 Obesity and overweight
  4. 4.
    Corbin LJ, Timpson NJ. Body mass index: has epidemiology started to break down causal contributions to health and disease? Obesity. 2016;24:1630–8.CrossRefPubMedGoogle Scholar
  5. 5.
    Specchia ML, Veneziano MA, Cadeddu C, Ferriero AM, Mancuso A, Ianuale C, et al. Economic impact of adult obesity on health systems: a systematic review. Eur J Pub Health. 2015;25:255–62.CrossRefGoogle Scholar
  6. 6.
    Grieve E, Fenwick E, Yang H-C, Lean M. The disproportionate economic burden associated with severe and complicated obesity: a systematic review. Obes Rev. 2013;14:883–94.CrossRefPubMedGoogle Scholar
  7. 7.
    He Y, Pan A, Wang Y, et al. Prevalence of overweight and obesity in 15.8 million men aged 15–49 years in rural China from 2010 to 2014. Sci Report. 2017;7:5012.CrossRefGoogle Scholar
  8. 8.
    • Silventoinen K, Jelenkovic A, Sund R, et al. Differences in genetic and environmental variation in adult BMI by sex, age, time period, and region: an individual-based pooled analysis of 40 twin cohorts. Am J Clin Nutr. 2017;106:457–66. Largest current study estimating the heritability of obesity in twins. CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Adams TD, Hunt SC, Mason LA, Ramirez ME, Fisher AG, Williams RR. Familial aggregation of morbid obesity. Obes Res. 1993;1:261–70.CrossRefPubMedGoogle Scholar
  10. 10.
    Tian Y, Peng B, Fu X. New ADCY3 variants dance in obesity etiology. Trends Endocrinol Metab. 2018;29:361–3. Scholar
  11. 11.
    Han JC (2016) Rare syndromes and common variants of the brain-derived neurotrophic factor gene in human obesity. In: Prog. Mol. Biol. Transl. Sci. pp 75–95.Google Scholar
  12. 12.
    Frodyma D, Neilsen B, Costanzo-Garvey D, Fisher K, Lewis R. Coordinating ERK signaling via the molecular scaffold kinase suppressor of Ras. F1000Research. 2017;6:1621.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Wasim M, Awan FR, Najam SS, Khan AR, Khan HN. Role of leptin deficiency, inefficiency, and leptin receptors in obesity. Biochem Genet. 2016;54:565–72.CrossRefPubMedGoogle Scholar
  14. 14.
    Yeo GSH. Genetics of obesity: can an old dog teach us new tricks? Diabetologia. 2017;60:778–83.CrossRefPubMedGoogle Scholar
  15. 15.
    Krashes MJ, Lowell BB, Garfield AS. Melanocortin-4 receptor–regulated energy homeostasis. Nat Neurosci. 2016;19:206–19.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Novoselova TV, Chan LF, Clark AJL. Pathophysiology of melanocortin receptors and their accessory proteins. Best Pract Res Clin Endocrinol Metab. 2018;32:93–106.CrossRefPubMedGoogle Scholar
  17. 17.
    Ramos-Molina B, Martin MG, Lindberg I (2016) PCSK1 variants and human obesity. In: Prog. Mol. Biol. Transl. Sci. pp 47–74.Google Scholar
  18. 18.
    Stijnen P, Ramos-Molina B, O’Rahilly S, Creemers JWM. PCSK1 mutations and human Endocrinopathies: from obesity to gastrointestinal disorders. Endocr Rev. 2016;37:347–71.CrossRefPubMedGoogle Scholar
  19. 19.
    Anderson EJP, Çakir I, Carrington SJ, Cone RD, Ghamari-Langroudi M, Gillyard T, et al. 60 YEARS OF POMC: regulation of feeding and energy homeostasis by α-MSH. J Mol Endocrinol. 2016;56:T157–74.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Rubinstein M, Low MJ. Molecular and functional genetics of the proopiomelanocortin gene, food intake regulation and obesity. FEBS Lett. 2017;591:2593–606.CrossRefPubMedGoogle Scholar
  21. 21.
    Zhang Y, Proenca R, Maffei M, Barone M, Leopold L, Friedman JM. Positional cloning of the mouse obese gene and its human homologue. Nature. 1994;372:425–32.CrossRefPubMedGoogle Scholar
  22. 22.
    Tartaglia LA, Dembski M, Weng X, Deng N, Culpepper J, Devos R, et al. Identification and expression cloning of a leptin receptor, OB-R. Cell. 1995;83:1263–71.CrossRefPubMedGoogle Scholar
  23. 23.
    Farooqi IS, Jebb SA, Langmack G, Lawrence E, Cheetham CH, Prentice AM, et al. Effects of recombinant leptin therapy in a child with congenital leptin deficiency. N Engl J Med. 1999;341:879–84.CrossRefPubMedGoogle Scholar
  24. 24.
    Huvenne H, Le Beyec J, Pépin D, et al. Seven novel deleterious LEPR mutations found in early-onset obesity: a ΔExon6-8 shared by subjects from Reunion Island, France, suggests a founder effect. J Clin Endocrinol Metab. 2015;100:E757–66.CrossRefPubMedGoogle Scholar
  25. 25.
    Saeed S, Bonnefond A, Manzoor J, Shabir F, Ayesha H, Philippe J, et al. Genetic variants in LEP , LEPR , and MC4R explain 30% of severe obesity in children from a consanguineous population. Obesity. 2015;23:1687–95.CrossRefPubMedGoogle Scholar
  26. 26.
    Masuzaki H, Ogawa Y, Isse N, Satoh N, Okazaki T, Shigemoto M, et al. Human obese gene expression. Adipocyte-specific expression and regional differences in the adipose tissue. Diabetes. 1995;44:855–8.CrossRefPubMedGoogle Scholar
  27. 27.
    Considine RV, Sinha MK, Heiman ML, Kriauciunas A, Stephens TW, Nyce MR, et al. Serum immunoreactive-leptin concentrations in normal-weight and obese humans. N Engl J Med. 1996;334:292–5.CrossRefPubMedGoogle Scholar
  28. 28.
    Miyawaki T, Masuzaki H, Ogawa Y, Hosoda K, Nishimura H, Azuma N, et al. Clinical implications of leptin and its potential humoral regulators in long-term low-calorie diet therapy for obese humans. Eur J Clin Nutr. 2002;56:593–600.CrossRefPubMedGoogle Scholar
  29. 29.
    Friedman JM, Halaas JL. Leptin and the regulation of body weight in mammals. Nature. 1998;395:763–70.CrossRefPubMedGoogle Scholar
  30. 30.
    Chen W, Balland E, Cowley MA. Hypothalamic insulin resistance in obesity: effects on glucose homeostasis. Neuroendocrinology. 2017;104:364–81.CrossRefPubMedGoogle Scholar
  31. 31.
    Dulloo AG, Stock MJ, Solinas G, Boss O, Montani JP, Seydoux J. Leptin directly stimulates thermogenesis in skeletal muscle. FEBS Lett. 2002;515:109–13.CrossRefPubMedGoogle Scholar
  32. 32.
    Ghilardi N, Ziegler S, Wiestner A, Stoffel R, Heim MH, Skoda RC. Defective STAT signaling by the leptin receptor in diabetic mice. Proc Natl Acad Sci U S A. 1996;93:6231–5.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Kloek C, Haq AK, Dunn SL, Lavery HJ, Banks AS, Myers MG. Regulation of Jak kinases by intracellular leptin receptor sequences. J Biol Chem. 2002;277:41547–55.CrossRefPubMedGoogle Scholar
  34. 34.
    Wunderlich CM, Hövelmeyer N, Wunderlich FT. Mechanisms of chronic JAK-STAT3-SOCS3 signaling in obesity. JAK-STAT. 2013;2:e23878.CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Lammert A, Kiess W, Bottner A, Glasow A, Kratzsch J. Soluble leptin receptor represents the main leptin binding activity in human blood. Biochem Biophys Res Commun. 2001;283:982–8.CrossRefPubMedGoogle Scholar
  36. 36.
    Allison MB, Myers MG. 20 YEARS OF LEPTIN: connecting leptin signaling to biological function. J Endocrinol. 2014;223:T25–35.CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Chen H, Charlat O, Tartaglia LA, Woolf EA, Weng X, Ellis SJ, et al. Evidence that the diabetes gene encodes the leptin receptor: identification of a mutation in the leptin receptor gene in db/db mice. Cell. 1996;84:491–5.CrossRefPubMedGoogle Scholar
  38. 38.
    Flak JN, Myers MG. Minireview: CNS mechanisms of leptin action. Mol Endocrinol. 2016;30:3–12.CrossRefPubMedGoogle Scholar
  39. 39.
    Pan WW, Myers MG. Leptin and the maintenance of elevated body weight. Nat Rev Neurosci. 2018;19:95–105.CrossRefPubMedGoogle Scholar
  40. 40.
    Frederich RC, Hamann A, Anderson S, Löllmann B, Lowell BB, Flier JS. Leptin levels reflect body lipid content in mice: evidence for diet-induced resistance to leptin action. Nat Med. 1995;1:1311–4.CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Münzberg H, Flier JS, Bjørbæk C. Region-specific leptin resistance within the hypothalamus of diet-induced obese mice. Endocrinology. 2004;145:4880–9.CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Knight ZA, Hannan KS, Greenberg ML, Friedman JM. Hyperleptinemia is required for the development of leptin resistance. PLoS One. 2010;5:e11376.CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Fourati M, Mnif M, Kharrat N, Charfi N, Kammoun M, Fendri N, et al. Association between leptin gene polymorphisms and plasma leptin level in three consanguineous families with obesity. Gene. 2013;527:75–81.CrossRefPubMedGoogle Scholar
  44. 44.
    Ghalandari H, Hosseini-Esfahani F, Mirmiran P. The Association of Polymorphisms in leptin/leptin receptor genes and ghrelin/ghrelin receptor genes with overweight/obesity and the related metabolic disturbances: a review. Int J Endocrinol Metab. 2015;13:e19073.CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Wu L, Sun D. Leptin receptor gene polymorphism and the risk of cardiovascular disease: a systemic review and meta-analysis. Int J Environ Res Public Health. 2017;14:375.CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Bell BB, Rahmouni K. Leptin as a mediator of obesity-induced hypertension. Curr Obes Rep. 2016;5:397–404.CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Lin T-C, Huang K-W, Liu C-W, Chang Y-C, Lin W-M, Yang T-Y, et al. Leptin signaling axis specifically associates with clinical prognosis and is multifunctional in regulating cancer progression. Oncotarget. 2018;9:17210–9.PubMedPubMedCentralGoogle Scholar
  48. 48.
    Dubern B, Clement K. Leptin and leptin receptor-related monogenic obesity. Biochimie. 2012;94:2111–5.CrossRefPubMedGoogle Scholar
  49. 49.
    Zhang L, Yuan L-H, Xiao Y, Lu M, Zhang L, Wang Y. Association of Leptin Gene −2548 G/a polymorphism with obesity: a meta-analysis. Ann Nutr Metab. 2014;64:127–36.CrossRefPubMedGoogle Scholar
  50. 50.
    Şahın S, Rüstemoğlu A, Tekcan A, Taşliyurt T, Güven H, Yığıt S. Investigation of associations between obesity and LEP G2548A and LEPR 668A/G polymorphisms in a Turkish population. Dis Markers. 2013;35:673–7.CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Lu J, Zou D, Zheng L, Chen G, Lu J, Feng Z. Synergistic effect of LEP and LEPR gene polymorphism on body mass index in a Chinese population. Obes Res Clin Pract. 2013;7:e445–9.CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Shahid A, Rana S, Mahmood S, Saeed S. Role of leptin G-2548A polymorphism in age- and gender-specific development of obesity. J Biosci. 2015;40:521–30.CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Li WD, Reed DR, Lee JH, Xu W, Kilker RL, Sodam BR, et al. Sequence variants in the 5′ flanking region of the leptin gene are associated with obesity in women. Ann Hum Genet. 1999;63:227–34.CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    Nieters A, Becker N, Linseisen J. Polymorphisms in candidate obesity genes and their interaction with dietary intake of n-6 polyunsaturated fatty acids affect obesity risk in a sub-sample of the EPIC-Heidelberg cohort. Eur J Nutr. 2002;41:210–21.CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Wang T-N, Huang M-C, Chang W-T, Ko AM-S, Tsai E-M, Liu C-S, et al. G-2548A polymorphism of the leptin gene is correlated with extreme obesity in Taiwanese aborigines*. Obesity. 2006;14:183–7.CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Mercer JG, Hoggard N, Williams LM, Lawrence CB, Hannah LT, Trayhurn P. Localization of leptin receptor mRNA and the long form splice variant (Ob-Rb) in mouse hypothalamus and adjacent brain regions by in situ hybridization. FEBS Lett. 1996;387:113–6.CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Fei H, Okano HJ, Li C, Lee GH, Zhao C, Darnell R, et al. Anatomic localization of alternatively spliced leptin receptors (Ob-R) in mouse brain and other tissues. Proc Natl Acad Sci U S A. 1997;94:7001–5.CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Wauman J, Zabeau L, Tavernier J. The leptin receptor complex: heavier than expected? Front Endocrinol (Lausanne). 2017;8:30.Google Scholar
  59. 59.
    de Luca C, Kowalski TJ, Zhang Y, Elmquist JK, Lee C, Kilimann MW, et al. Complete rescue of obesity, diabetes, and infertility in db/db mice by neuron-specific LEPR-B transgenes. J Clin Invest. 2005;115:3484–93.CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Nuzzaci D, Laderrière A, Lemoine A, Nédélec E, Pénicaud L, Rigault C, et al. Plasticity of the Melanocortin system: determinants and possible consequences on food intake. Front Endocrinol (Lausanne). 2015;6:143.Google Scholar
  61. 61.
    Waterson MJ, Horvath TL. Neuronal regulation of energy homeostasis: beyond the hypothalamus and feeding. Cell Metab. 2015;22:962–70.CrossRefPubMedGoogle Scholar
  62. 62.
    Clément K, Vaisse C, Lahlou N, Cabrol S, Pelloux V, Cassuto D, et al. A mutation in the human leptin receptor gene causes obesity and pituitary dysfunction. Nature. 1998;392:398–401.CrossRefPubMedGoogle Scholar
  63. 63.
    Farooqi IS, Volders K, Stanhope R, Heuschkel R, White A, Lank E, et al. Hyperphagia and early-onset obesity due to a novel homozygous missense mutation in prohormone convertase 1/3. J Clin Endocrinol Metab. 2007;92:3369–73.CrossRefPubMedGoogle Scholar
  64. 64.
    Chua SC, Chung WK, Wu-Peng XS, Zhang Y, Liu SM, Tartaglia L, et al. Phenotypes of mouse diabetes and rat fatty due to mutations in the OB (leptin) receptor. Science. 1996;271:994–6.CrossRefPubMedGoogle Scholar
  65. 65.
    Quinton ND, Smith RF, Clayton PE, Gill MS, Shalet S, Justice SK, et al. Leptin binding activity changes with age: the link between leptin and puberty. J Clin Endocrinol Metab. 1999;84:2336–41.PubMedGoogle Scholar
  66. 66.
    Considine RV, Considine EL, Williams CJ, Hyde TM, Caro JF. The hypothalamic leptin receptor in humans: identification of incidental sequence polymorphisms and absence of the db/db mouse and fa/fa rat mutations. Diabetes. 1996;45:992–4.CrossRefPubMedGoogle Scholar
  67. 67.
    Matsuda J, Masuzaki H, Miyawaki T, Azuma N, Nishimura H, Nishi S, et al. Human leptin receptor gene in obese Japanese subjects: evidence against either obesity-causing mutations or association of sequence variants with obesity. Diabetologia. 1997;40:1204–10.CrossRefPubMedGoogle Scholar
  68. 68.
    Hinney A, Becker I, Heibült O, Nottebom K, Schmidt A, Ziegler A, et al. Systematic mutation screening of the pro-Opiomelanocortin gene: identification of several genetic variants including three different insertions, one nonsense and two missense point mutations in Probands of different weight extremes. J Clin Endocrinol Metab. 1998;83:3737–41.CrossRefPubMedGoogle Scholar
  69. 69.
    Pearce LR, Atanassova N, Banton MC, Bottomley B, van der Klaauw A, Revelli JP, et al. KSR2 mutations are associated with obesity, insulin resistance, and impaired cellular fuel oxidation. Cell. 2013;155:765–77.CrossRefPubMedPubMedCentralGoogle Scholar
  70. 70.
    Hinney A, Volckmar A-L, Knoll N (2013) Melanocortin-4 receptor in energy homeostasis and obesity pathogenesis. In: Prog. Mol. Biol. Transl. Sci. pp 147–191Google Scholar
  71. 71.
    Ju SH, Cho G-B, Sohn J-W. Understanding melanocortin-4 receptor control of neuronal circuits: toward novel therapeutics for obesity syndrome. Pharmacol Res. 2018;129:10–9.CrossRefPubMedGoogle Scholar
  72. 72.
    Xu J, Bartolome CL, Low CS, Yi X, Chien C-H, Wang P, et al. Genetic identification of leptin neural circuits in energy and glucose homeostases. Nature. 2018;556:505–9.CrossRefPubMedGoogle Scholar
  73. 73.
    Bell BB, Harlan SM, Morgan DA, Guo D-F, Rahmouni K. Differential contribution of POMC and AgRP neurons to the regulation of regional autonomic nerve activity by leptin. Mol Metab. 2018;8:1–12.CrossRefPubMedGoogle Scholar
  74. 74.
    da Fonseca ACP, Mastronardi C, Johar A, Arcos-Burgos M, Paz-Filho G. Genetics of non-syndromic childhood obesity and the use of high-throughput DNA sequencing technologies. J Diabetes Complicat. 2017;31:1549–61.CrossRefPubMedGoogle Scholar
  75. 75.
    Yeo GSH, Farooqi IS, Aminian S, Halsall DJ, Stanhope RG, O’Rahilly S. A frameshift mutation in MC4R associated with dominantly inherited human obesity. Nat Genet. 1998;20:111–2.CrossRefPubMedGoogle Scholar
  76. 76.
    Vaisse C, Clement K, Guy-Grand B, Froguel P. A frameshift mutation in human MC4R is associated with a dominant form of obesity. Nat Genet. 1998;20:113–4.CrossRefPubMedGoogle Scholar
  77. 77.
    Farooqi IS, Keogh JM, Yeo GSH, Lank EJ, Cheetham T, O’Rahilly S. Clinical Spectrum of obesity and mutations in the Melanocortin 4 receptor gene. N Engl J Med. 2003;348:1085–95.CrossRefPubMedGoogle Scholar
  78. 78.
    Lubrano-Berthelier C, Dubern B, Lacorte J-M, Picard F, Shapiro A, Zhang S, et al. Melanocortin 4 receptor mutations in a large cohort of severely obese adults: prevalence, functional classification, genotype-phenotype relationship, and lack of association with binge eating. J Clin Endocrinol Metab. 2006;91:1811–8.CrossRefPubMedGoogle Scholar
  79. 79.
    Paolini B, Maltese PE, Del Ciondolo I, Tavian D, Missaglia S, Ciuoli C, et al. Prevalence of mutations in LEP, LEPR, and MC4R genes in individuals with severe obesity. Genet Mol Res. 2016;15
  80. 80.
    Vaisse C, Clement K, Durand E, Hercberg S, Guy-Grand B, Froguel P. Melanocortin-4 receptor mutations are a frequent and heterogeneous cause of morbid obesity. J Clin Invest. 2000;106:253–62.CrossRefPubMedPubMedCentralGoogle Scholar
  81. 81.
    Gibbs RA, Boerwinkle E, Doddapaneni H, et al. A global reference for human genetic variation. Nature. 2015;526:68–74.CrossRefGoogle Scholar
  82. 82.
    Lek M, Karczewski KJ, Minikel EV, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016;536:285–91.CrossRefPubMedPubMedCentralGoogle Scholar
  83. 83.
    Branson R, Potoczna N, Kral JG, Lentes K-U, Hoehe MR, Horber FF. Binge eating as a major phenotype of Melanocortin 4 receptor gene mutations. N Engl J Med. 2003;348:1096–103.CrossRefPubMedGoogle Scholar
  84. 84.
    Valette M, Bellisle F, Carette C, Poitou C, Dubern B, Paradis G, et al. Eating behaviour in obese patients with melanocortin-4 receptor mutations: a literature review. Int J Obes. 2013;37:1027–35.CrossRefGoogle Scholar
  85. 85.
    Valette M, Poitou C, Kesse-Guyot E, Bellisle F, Carette C, Le Beyec J, et al. Association between melanocortin-4 receptor mutations and eating behaviors in obese patients: a case–control study. Int J Obes. 2014;38:883–5.CrossRefGoogle Scholar
  86. 86.
    Valette M, Poitou C, Le Beyec J, Bouillot J-L, Clement K, Czernichow S. Melanocortin-4 receptor mutations and polymorphisms do not affect weight loss after bariatric surgery. PLoS One. 2012;7:e48221.CrossRefPubMedPubMedCentralGoogle Scholar
  87. 87.
    Censani M, Conroy R, Deng L, Oberfield SE, McMahon DJ, Zitsman JL, et al. Weight loss after bariatric surgery in morbidly obese adolescents with MC4R mutations. Obesity. 2014;22:225–31.CrossRefPubMedGoogle Scholar
  88. 88.
    Frayling TM, Timpson NJ, Weedon MN, et al (2007) A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science (80- ) 316:889–894.Google Scholar
  89. 89.
    Locke AE, Kahali B, Berndt SI, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518:197–206.CrossRefPubMedPubMedCentralGoogle Scholar
  90. 90.
    Justice AE, Winkler TW, Feitosa MF, Graff M, Fisher VA, Young K, et al. Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits. Nat Commun. 2017;8:14977.CrossRefPubMedPubMedCentralGoogle Scholar
  91. 91.
    Meyre D, Delplanque J, Chevre JC, et al. Genome-wide association study for early-onset and morbid adult obesity identifies three new risk loci in European populations. Nat Genet. 2009;41:157–9.CrossRefPubMedGoogle Scholar
  92. 92.
    Liu R, Zou Y, Hong J, Cao M, Cui B, Zhang H, et al. Rare loss-of-function variants in NPC1 predispose to human obesity. Diabetes. 2017;66:935–47.CrossRefPubMedGoogle Scholar
  93. 93.
    Jiao H, Arner P, Hoffstedt J, Brodin D, Dubern B, Czernichow S, et al. Genome wide association study identifies KCNMA1contributing to human obesity. BMC Med Genet. 2011;4:51.Google Scholar
  94. 94.
    Wheeler E, Huang N, Bochukova EG, Keogh JM, Lindsay S, Garg S, et al. Genome-wide SNP and CNV analysis identifies common and low-frequency variants associated with severe early-onset obesity. Nat Genet. 2013;45:513–7.CrossRefPubMedPubMedCentralGoogle Scholar
  95. 95.
    •• Berndt SI, Gustafsson S, Mägi R, et al. Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture. Nat Genet. 2013;45:501–12. Largest current study of the genetics of severe obesity. CrossRefPubMedPubMedCentralGoogle Scholar
  96. 96.
    • Turcot V, Lu Y, Highland HM, et al. Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity. Nat Genet. 2018;50:26–41. Large study of rare variant contribution to obesity. CrossRefPubMedGoogle Scholar
  97. 97.
    Sina M, Hinney A, Ziegler A, Neupert T, Mayer H, Siegfried W, et al. Phenotypes in three pedigrees with autosomal dominant obesity caused by Haploinsufficiency mutations in the Melanocortin-4 receptor gene. Am J Hum Genet. 1999;65:1501–7.CrossRefPubMedPubMedCentralGoogle Scholar
  98. 98.
    Hinney A, Schmidt A, Nottebom K, Heibült O, Becker I, Ziegler A, et al. Several mutations in the Melanocortin-4 receptor gene including a nonsense and a frameshift mutation associated with dominantly inherited obesity in humans. J Clin Endocrinol Metab. 1999;84:1483–6.CrossRefPubMedGoogle Scholar
  99. 99.
    Stutzmann F, Vatin V, Cauchi S, Morandi A, Jouret B, Landt O, et al. Non-synonymous polymorphisms in melanocortin-4 receptor protect against obesity: the two facets of a Janus obesity gene. Hum Mol Genet. 2007;16:1837–44.CrossRefPubMedGoogle Scholar
  100. 100.
    Brommage R, Desai U, Revelli J-P, Donoviel DB, Fontenot GK, DaCosta CM, et al. High-throughput screening of mouse knockout lines identifies true lean and obese phenotypes. Obesity (Silver Spring). 2008;16:2362–7.CrossRefGoogle Scholar
  101. 101.
    Costanzo-Garvey DL, Pfluger PT, Dougherty MK, Stock JL, Boehm M, Chaika O, et al. KSR2 is an essential regulator of AMP kinase, energy expenditure, and insulin sensitivity. Cell Metab. 2009;10:366–78.CrossRefPubMedPubMedCentralGoogle Scholar
  102. 102.
    Revelli J-P, Smith D, Allen J, Jeter-Jones S, Shadoan MK, Desai U, et al. Profound obesity secondary to Hyperphagia in mice lacking kinase suppressor of Ras 2. Obesity. 2011;19:1010–8.CrossRefPubMedGoogle Scholar
  103. 103.
    Kai AKL, Lam AKM, Chen Y, Tai ACP, Zhang X, Lai AKW, et al. Exchange protein activated by cAMP 1 ( Epac1 )-deficient mice develop β-cell dysfunction and metabolic syndrome. FASEB J. 2013;27:4122–35.CrossRefPubMedGoogle Scholar
  104. 104.
    Hu Y, Robichaux WG, Mei FC, Kim ER, Wang H, Tong Q, et al. Role of exchange protein directly activated by cyclic AMP isoform 1 in energy homeostasis: regulation of leptin expression and secretion in white adipose tissue. Mol Cell Biol. 2016;36:2440–50.CrossRefPubMedPubMedCentralGoogle Scholar
  105. 105.
    Komai AM, Musovic S, Peris E, Alrifaiy A, El Hachmane MF, Johansson M, et al. White adipocyte adiponectin exocytosis is stimulated via β 3 -adrenergic signaling and activation of Epac1: catecholamine resistance in obesity and type 2 diabetes. Diabetes. 2016;65:3301–13.CrossRefPubMedGoogle Scholar

Copyright information

© The Author(s) 2018

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Una Fairbrother
    • 1
  • Elliot Kidd
    • 1
  • Tanya Malagamuwa
    • 2
  • Andrew Walley
    • 2
    Email author
  1. 1.School of Human SciencesLondon Metropolitan UniversityLondonUK
  2. 2.Institute of Medical and Biomedical EducationSt George’s University of LondonLondonUK

Personalised recommendations