Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-05T04:47:54.314Z Has data issue: false hasContentIssue false

The impact of genetic polymorphisms on weight regain after successful weight loss

Published online by Cambridge University Press:  05 June 2020

Chanisa Thonusin
Affiliation:
Cardiac Electrophysiology Research and Training Center, Faculty of Medicine, Chiang Mai University, Chiang Mai50200, Thailand Cardiac Electrophysiology Unit, Department of Physiology, Faculty of Medicine, Chiang Mai University, Chiang Mai50200, Thailand Center of Excellence in Cardiac Electrophysiology Research, Chiang Mai University, Chiang Mai50200, Thailand
Krekwit Shinlapawittayatorn
Affiliation:
Cardiac Electrophysiology Research and Training Center, Faculty of Medicine, Chiang Mai University, Chiang Mai50200, Thailand Cardiac Electrophysiology Unit, Department of Physiology, Faculty of Medicine, Chiang Mai University, Chiang Mai50200, Thailand Center of Excellence in Cardiac Electrophysiology Research, Chiang Mai University, Chiang Mai50200, Thailand
Siriporn C. Chattipakorn
Affiliation:
Cardiac Electrophysiology Research and Training Center, Faculty of Medicine, Chiang Mai University, Chiang Mai50200, Thailand Center of Excellence in Cardiac Electrophysiology Research, Chiang Mai University, Chiang Mai50200, Thailand Department of Oral Biology and Diagnostic Sciences, Faculty of Dentistry, Chiang Mai University, Chiang Mai50200, Thailand
Nipon Chattipakorn*
Affiliation:
Cardiac Electrophysiology Research and Training Center, Faculty of Medicine, Chiang Mai University, Chiang Mai50200, Thailand Cardiac Electrophysiology Unit, Department of Physiology, Faculty of Medicine, Chiang Mai University, Chiang Mai50200, Thailand Center of Excellence in Cardiac Electrophysiology Research, Chiang Mai University, Chiang Mai50200, Thailand
*
*Corresponding author: Nipon Chattipakorn, fax +66-53-935368, email [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Obesity is associated with an increased risk of various diseases and mortality. Although nearly 50 % of adults have been reported trying to lose weight, the prevalence of obesity has increased. One factor that hinders weight loss-induced decrease in obesity prevalence is weight regain. Although behavioural, psychological and physiological factors associated with weight regain have been reviewed, the information regarding the relationship between weight regain and genetics has not been previously summarised. In this paper, we comprehensively review the association between genetic polymorphisms and weight regain in adults and children with obesity after weight loss. Based on this information, identification of genetic polymorphism in patients who undergo weight loss intervention might be used to estimate their risks of weight regain. Additionally, the genetic-based risk estimation may be used as a guide for physicians and dietitians to provide each of their patients with the most appropriate strategies for weight loss and weight maintenance.

Type
Full Papers
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of The Nutrition Society

Obesity is associated with an increased risk of numerous chronic diseases. These include the metabolic syndrome, CHD, obstructive sleep apnoea, cancer, depression and infertility(Reference Hruby and Hu1). Although nearly 50 % of adults worldwide were reported to be trying to lose weight(Reference Santos, Sniehotta and Marques2), obesity prevalence has doubled in seventy-three countries since 1980(Reference Friedrich3) and adult obesity exceeded 50 % in several countries(Reference Hruby and Hu1). Moreover, the increase in obesity prevalence resulted in a 28·3 % increase in the rate of obesity-related mortality and a 35·8 % increase in the rate of disability-adjusted life years from 1990 to 2015(Reference Friedrich3), which led to a substantial economic burden conferred by obesity in both developed and developing countries(Reference Friedrich3,Reference Tremmel, Gerdtham and Nilsson4) . One factor that hinders weight loss-induced decrease in obesity prevalence is weight regain. In fact, about half of the weight lost is regained in the first year following weight loss and the weight regain continues thereafter(Reference Byrne, Cooper and Fairburn5,Reference Wadden, Sternberg and Letizia6) . In the 3- to 5-year period following weight reduction, many subjects (about 85 %) have returned to, or exceeded, their initial weight(Reference Byrne, Cooper and Fairburn5,Reference Wadden, Sternberg and Letizia6) . Factors that are associated with weight regain have been reviewed in many previous articles. These include large initial weight loss(Reference Marinilli Pinto, Gorin and Raynor7,Reference Garcia Ulen, Huizinga and Beech8) , high levels of orexigenic peptides after weight loss(Reference Garcia Ulen, Huizinga and Beech8,Reference Wing, Papandonatos and Fava9) , increased consumption of high glycaemic index food and beverages(Reference Bosy-Westphal and Muller10,Reference Phelan, Wing and Loria11) , low levels of exercise(Reference Garcia Ulen, Huizinga and Beech8,Reference Wing, Papandonatos and Fava9) , sedentary activities including excessive television watching(Reference Garcia Ulen, Huizinga and Beech8,Reference Raynor, Phelan and Hill12) , infrequent self-weighing(Reference Wing, Papandonatos and Fava9), internal disinhibition of eating(Reference Garcia Ulen, Huizinga and Beech8,Reference Wing, Papandonatos and Fava9) and dichotomous thinking(Reference Byrne, Cooper and Fairburn5,Reference Garcia Ulen, Huizinga and Beech8) . Indeed, it is widely accepted that weight regain is multifactorial involving physiological, psychological and behavioural factors. However, the information regarding the association between weight regain and genetics, which play an important role in obesity(Reference Elks, den Hoed and Zhao13,Reference Herrera and Lindgren14) , has not been previously summarised.

Heritability estimates of BMI and/or the development of obesity are between 40 and 80 %(Reference Elks, den Hoed and Zhao13,Reference Herrera and Lindgren14) , and therefore, genetic polymorphisms have been believed to be one of the key determinants of weight regain in individuals with obesity following successful weight loss. Genetic polymorphism is defined as the inheritance of a trait controlled by a single genetic locus with two alleles, in which the least common allele has a frequency of about 1 % or greater(Reference Ismail and Essawi15). In this article, we have reviewed genetic polymorphisms associated with weight regain in adults and children with obesity after successful weight loss.

Search strategy and selection criteria

A literature search was performed using PubMed from database inception to April 2020. Only articles written in English are included. The search terms included weight regain, gene and obese. Only relevant articles pertaining to the topic of interest are included. Finally, we identified thirty-one polymorphisms from twenty genes that are associated with weight regain and categorised them depending on their functions, including: (1) nutrient metabolism, (2) food intake and energy expenditure, (3) adipocyte differentiation and (4) inflammation, extracellular matrix and bone metabolism.

Nutrient metabolism-related genes and their association with weight regain

It has been shown that nutrient-related metabolite levels in plasma differ between lean subjects and individuals with obesity. These include branched-chain amino acids, aromatic amino acids, NEFA and acylcarnitine intermediates of fatty acid oxidation and branched-chain amino acid catabolism(Reference Newgard, An and Bain16). Additionally, a study into skeletal muscle revealed lower levels of citric acid cycle metabolites, end products of nutrient metabolism, in rats with obesity compared with their lean counterparts(Reference Koves, Ussher and Noland17). Moreover, they found impaired switching from fat to carbohydrate utilisation during the fasted-to-fed transition in the skeletal muscle of rats with obesity(Reference Koves, Ussher and Noland17). Those results suggest that obesity is associated with alteration in nutrient metabolism; therefore, it is not surprising that the polymorphisms of several nutrient metabolism-related genes are also associated with weight regain. These include perilipin (PLIN), brain-derived neurotrophic factor (BDNF), leptin, PPARγ2, protein phosphatase, Mg2+/Mn2+ dependent 1K (PPM1K), β-2 adrenergic receptor (ADRB2), IL-6 and glucocorticoid receptor (GRL). A comprehensive summary of the reports describing these genes is shown in Table 1.

Table 1. SNP or the restriction fragment length polymorphisms (RFLP) of nutrient metabolism-related genes that are associated with weight regain

+, Positively associated with; –, negatively associated with; ADRB2, β-2 adrenergic receptor; BDNF, brain-derived neurotrophic factor; GRL, glucocorticoid receptor; IGT, impaired glucose tolerance; PLIN, perilipin; PPM1K, protein phosphatase, Mg2+/Mn2+ dependent 1K; T2DM, type 2 diabetes.

PLIN coats intracellular lipid droplets and regulates adipocyte lipolysis(Reference Qi, Corella and Sorli18). Soenen and colleagues identified the linkage equilibria between PLIN1 and PLIN4 and between PLIN5 and PLIN7 (Reference Soenen, Mariman and Vogels19). They also revealed that males carrying the C-allele of PLIN1 plus the A-allele of PLIN4 had a lower BMI and body fat at baseline, weight loss phase and weight maintenance phase, whereas females carrying these alleles had larger weight loss and fat loss at both weight loss and weight maintenance periods(Reference Soenen, Mariman and Vogels19). In the case of PLIN6, males with the T/T genotype had a lower BMI and body fat at all time points(Reference Soenen, Mariman and Vogels19). In females, those who carried the G allele of PLIN5 plus the A/A genotype of PLIN7 showed greater reduction on fat mass after weight loss and during weight maintenance(Reference Soenen, Mariman and Vogels19). These findings indicate an association between PLIN gene polymorphisms and risks of weight and fat mass regain in a sex-specific manner. These sex-specific effects of PLIN on obesity risk suggest a particular characteristic of PLIN in obesity-related conditions. A further in-depth study regarding the association between PLIN and sex hormones on obesity and weight regain could be useful in the identification of molecular mechanisms responsible for this association.

BDNF influences various cell types involved in glucose metabolism. These include increased insulin production from pancreatic β-cells, decreased glucose production in hepatocytes and increased insulin sensitivity in skeletal muscle(Reference Marosi and Mattson20). Deletions or mutations of the BDNF gene result in a phenotype of obesity(Reference Xu and Xie21,Reference An Juan, Liao and Kinney Clint22) . Considering the effect on weight regain, it was observed that the G allele of the rs6265 SNP of the BDNF gene was positively associated with weight regain after successful weight loss using lifestyle intervention at two to four and a half years of follow-up(Reference Delahanty, Pan and Jablonski23,Reference McCaffery, Papandonatos and Huggins24) . However, there was no effect of that SNP on weight regain in subjects who underwent bariatric surgery at 6 years of follow-up(Reference Sarzynski, Jacobson and Rankinen25). These findings suggest that: (1) bariatric surgery overwhelms the effect of this polymorphism on weight regain and (2) the effects of this polymorphism on weight regain are not persistent. A shorter-term study in the bariatric surgery model and a longer-term study in the lifestyle intervention model are required to confirm these hypotheses.

Leptin also plays a critical role in glucose and lipid metabolism. Leptin has been shown to decrease glucose production and fat accumulation in the liver and to increase glucose uptake and fatty acid oxidation in skeletal muscle, in addition to increasing glucose uptake into brown adipocytes(Reference Fernandez-Formoso, Perez-Sieira and Gonzalez-Touceda26,Reference Li and Li27) . The G allele of rs4731426 SNP was found to be positively associated with weight regain, while the C allele of rs2071045 SNP was negatively associated with weight regain(Reference Erez, Tirosh and Rudich28). Additionally, a multivariate parsimonious model including rs4731426, rs2071045 and rs3828942 SNP showed a significant association with weight regain, and a model that included rs4731426, rs2071045, rs3828942, rs11763517 and 11760956 SNP also showed a significant association(Reference Erez, Tirosh and Rudich28).

PPARγ2 regulates glucose metabolism by increasing transcription of GLUT2 and glucokinase genes, leading to increased glycolysis in the liver and increased insulin secretion by pancreatic β-cells, that is, PPARγ2 improves insulin sensitivity(Reference Kim and Ahn29). In addition, PPARγ2 is responsible for lipid metabolism by increasing fatty acid oxidation in skeletal muscle(Reference Marion-Letellier, Savoye and Ghosh30). Previous studies investigated the effect of PPARγ2 polymorphism on weight regain by identifying a correlation between rs1801282 SNP of the PPARγ2 gene and weight regain. However, their results were inconsistent. In fact, a study in postmenopausal females demonstrated that the G (Ala) allele of rs1801282 showed a positive correlation with weight regain after diet-induced weight loss(Reference Nicklas, van Rossum and Berman31), whereas the other two studies in both males and females discovered a negative correlation(Reference Delahanty, Pan and Jablonski23,Reference Goyenechea, Dolores Parra and Alfredo Martinez32) . Another study found that the weight regain group had more homozygous Pro alleles (C/C genotype) than the weight maintenance group did, but the relationship of the gene with weight regain was not significant after multiple corrections (body weight, BMI and waist circumference), and also there was only one subject who carried the G/G genotype in that study(Reference Vogels, Mariman and Bouwman33). Six years after bariatric surgery, there was no effect of that SNP on weight regain as well(Reference Sarzynski, Jacobson and Rankinen25). Because there was no relationship between PPARγ2 rs1801282 and weight regain in adults who underwent bariatric surgery at the 6-year follow-up but the relationship was established in other studies using lifestyle intervention-induced weight reduction at earlier stages of follow-up, these findings suggest that: (1) bariatric surgery overcomes the effect of PPARγ2 rs1801282 on weight regain, and (2) the effect of PPARγ2 rs1801282 on weight regain is inconsistent. As in the case of BDNF rs6265, both a shorter-duration study in the bariatric surgery model and a longer-duration study in lifestyle intervention model are necessary. Also of note, the association between weight regain and PPARγ2 in postmenopausal females was different from other populations. PPARγ and oestrogen receptors have been shown to interact with each other in terms of vascular function and the treatment of cancer(Reference Tiyerili, Muller and Fung34Reference Chu, van Hasselt and Vlantis36). Therefore, oestrogen receptors may also mediate the effect of PPARγ2 on weight regain, which may be responsible for differences in effect between pre- and postmenopausal females. Further investigation is required to clarify the relationships between PPARγ2, oestrogen receptor and weight regain.

The PPM1K gene encodes for the mitochondrial branched-chain α-ketoacid dehydrogenase phosphatase, which catalyses oxidative decarboxylation of branched-chain α-ketoacids from branched-chain amino acids(Reference Wynn, Li and Brautigam37). At 24 months after the intervention, the C allele of the rs1440581 SNP of the PPM1K gene was related to smaller weight reduction in subjects with obesity who received a high-fat diet (40 % of fat), whereas no significant genetic effect was observed in a low-fat diet group (20 % of fat)(Reference Xu, Qi and Liang38). These findings suggested that there was a gene-diet interaction on weight maintenance, and the C allele of the rs1440581 was positively associated with the risk of weight regain following high-fat diet intervention.

ADRB2 plays important roles in lipid and glucose metabolism by modulating lipolysis(Reference Schiffelers, Saris and Boomsma39) and insulin secretion(Reference Lacey, Cable and James40). Masuo and colleagues investigated the effects of the rs1042713 SNP of the ADRB2 gene and demonstrated that males with rebound weight gain had a significantly higher frequency of the Gly16 (G) allele compared with those who could maintain their weight loss(Reference Masuo, Katsuya and Kawaguchi41).

IL-6 affects glucose and lipid metabolism through various mechanisms, including decreased glucose uptake into adipocytes, decreased glycogen synthesis in the liver and increased lipolysis, resulting in obesity and insulin resistance(Reference Eder, Baffy and Falus42,Reference El-Kadre and Tinoco43) . A previous study revealed that the C allele of the rs1800795 (–174 G > C) SNP of the IL-6 gene protected against weight regain(Reference Goyenechea, Dolores Parra and Alfredo Martinez32). Moreover, the conjoined presence of the C allele of the rs1800795 and the G (Ala) allele of the rs1801282 SNP of the PPARγ2 gene further improved the ability for weight maintenance(Reference Goyenechea, Dolores Parra and Alfredo Martinez32).

GRL is a transcription factor that binds glucocorticoids and regulates the transcription of specific genes(Reference Wang, Gray and Kuo44). These GRL primary target genes further initiate the physiological and pathological responses of glucocorticoids, which include the homoeostasis involved in lipid metabolism(Reference Wang, Gray and Kuo44). A previous study showed that the G/G genotype of the Bcl1 (rs41423247) RFLP of the GRL gene was independently associated with successful weight maintenance(Reference Vogels, Mariman and Bouwman33).

In summary, evidence indicates that the polymorphisms of several genes related to lipid, glucose and branched-chain amino acid metabolism (PLIN, BDNF, leptin, PPARγ2, PPM1K, ADRB2, IL-6 and GRL) are associated with weight regain.

Food intake and energy expenditure-related genes and their association with weight regain

Obesity is the result of an imbalance between food intake and energy expenditure(Reference Reed, Chaput and Tremblay45). For weight maintenance following weight loss, energy intake must match energy expenditure as well(Reference Galgani and Ravussin46). Unfortunately, weight reduction leads to decreased energy needs, but to an augmented drive to eat, which favours weight regain(Reference Reed, Chaput and Tremblay45). Previous studies have demonstrated that there were relationships between weight regain and polymorphisms of various genes that are related to food intake and energy expenditure, including BDNF, leptin, fat mass and obesity-associated protein (FTO), dopamine receptor D2 (DRD2), K channel tetramerisation domain containing 15 (KCTD15), transmembrane protein 18 (TMEM18), ADRB2, IL-6, neuronal growth regulator 1 (NEGR1) and mitochondrial translational initiation factor 3 (MTIF3). A comprehensive summary of those reports is shown in Table 2.

Table 2. SNP or the restriction fragment length polymorphisms (RFLP) of food intake and energy expenditure-related genes that are associated with weight regain

+, Positively associated with; –, negatively associated with; ADRB2, β-2 adrenergic receptor; BDNF, brain-derived neurotrophic factor; DRD2, dopamine receptor D2; FTO, fat mass and obesity-associated protein; IGT, impaired glucose tolerance; KCTD15, K channel tetramerisation domain containing 15; MTIF3, mitochondrial translational initiation factor 3; NEGR1, neuronal growth regulator 1; SC, subcutaneous; T2DM, type 2 diabetes mellitus; TMEM18, transmembrane protein 18.

BDNF suppresses food intake by acting on neurons in the hypothalamus(Reference Urabe, Kojima and Chan47) and increases energy expenditure by increasing adaptive thermogenesis via the activation of a hypothalamic-adipocyte axis(Reference Cao, Choi and Liu48). Deletions or mutations of the BDNF gene lead to impaired thermogenesis and clinical manifestations of hyperphagia and obesity(Reference Xu and Xie21,Reference An Juan, Liao and Kinney Clint22) . An association between the G allele of the rs6265 SNP of the BDNF gene and weight regain was previously described in ‘Nutrient metabolism-related genes and their association with weight regain’.

Similarly to BDNF, leptin acts on neurons in the hypothalamus and promotes satiety(Reference Fernandez-Formoso, Perez-Sieira and Gonzalez-Touceda26,Reference Li and Li27) . Several SNP of the leptin gene were previously found to be correlated with weight regain, including rs4731426, rs2071045, rs3828942, rs11763517 and rs11760956 SNP, as previously mentioned in ‘Nutrient metabolism-related genes and their association with weight regain’.

Previous studies showed that polymorphisms of the FTO gene were associated with increased food intake(Reference Speakman49,Reference Dang, Samanez-Larkin and Smith50) and obesity(Reference Speakman49). The SNP of the FTO gene that was commonly investigated regarding weight regain was rs9939609, but the results were inconsistent. Specifically, three previous studies, which include a study in children, showed that the A allele of that SNP was positively associated with weight regain(Reference Rodrigues, Resende and Durso51Reference Reinehr, Wolters and Roth53). On the other hand, a study in postmenopausal females and another two studies in subjects with impaired glucose tolerance and type 2 diabetes found there was no relationship of that SNP with weight regain(Reference Delahanty, Pan and Jablonski23,Reference McCaffery, Papandonatos and Huggins24,Reference Rauhio, Uusi-Rasi and Nikkari54) . The A allele of another SNP of the FTO gene, rs3751812, was also positively associated with weight regain in individuals who received diabetic support and education, but not in those who received the intensive lifestyle intervention(Reference McCaffery, Papandonatos and Huggins24). All of these findings suggest that the impact of FTO rs9939609 on weight regain seems to have disappeared in postmenopausal females. There is evidence that oestrogen regulates FTO via the PI3K/AKT, MAPK and mTOR (mammalian target of rapamycin) signalling pathways, resulting in endometrial cancer cell proliferation(Reference Zhu, Shen and Gao55,Reference Zhang, Zhou and Lai56) . Thus, it is possible that oestrogen regulates the effect of FTO on weight regain also and therefore oestrogen deficiency hinders the association between FTO and weight regain in postmenopausal females. A future study is needed to prove this possibility. Interestingly, studies in individuals with impaired glucose tolerance and type 2 diabetes demonstrated no correlation between FTO rs9939609 and weight regain following weight loss. However, a correlation was exhibited in studies in general populations and even in children, indicating the overwhelming effect of impaired glucose tolerance and type 2 diabetes on FTO-associated weight regain. Furthermore, FTO rs9939609 was reportedly associated with insulin resistance and the risk of type 2 diabetes(Reference Takeuchi, Yamamoto and Katsuya57,Reference De Luis, Aller and Izaola58) . Nevertheless, further studies are required to identify the molecular mechanisms responsible for the relationships between FTO, insulin resistance and weight regain.

DRD2 is known to be responsible for the circuit of food reward(Reference Baik59). Genetic variations of the DRD2 gene were previously found to be related to emotional eating(Reference Yeh, Trang and Henning60,Reference Obregon, Valladares and Goldfield61) . It was shown that young adults (21–40 years old) carrying the A1 allele of the rs1800497 (TaqIA) SNP of the DRD2 gene had greater weight regain during the weight maintenance phase, while there was no effect of that SNP on weight regain among older adults (41–60 years old)(Reference Winkler, Woehning and Schultz62). Particularly, age modulates the effect of the DRD2 rs1800497 on weight regain. This might be due to age-induced improvement of response-inhibition capacities, alterations in several hormones and involvement of other acquired factors which are a result of the ageing process(Reference Winkler, Woehning and Schultz62). However, the reduction in D2 receptor binding and endogenous dopamine with age(Reference Backman, Ginovart and Dixon63,Reference Reeves, Bench and Howard64) may be another possible explanation.

KCTD15 plays an important role in the regulation of eating behaviour and reward(Reference Gamero-Villarroel, González and Rodríguez-López65). A previous study observed treatment-specific effects of the KCTD15 rs29941 SNP on weight regain(Reference Delahanty, Pan and Jablonski23). Specifically, the G allele of that SNP was positively correlated with weight regain in subjects who underwent an intensive lifestyle modification programme(Reference Delahanty, Pan and Jablonski23).

A study in TMEM18-deficient mice found that the loss of TMEM18 expression resulted in increased appetite(Reference Larder, Sim and Gulati66). Similarly to KCTD15, a treatment-specific effect of the TMEM18 rs6548238 SNP on weight regain was exhibited(Reference Delahanty, Pan and Jablonski23). Indeed, the C allele of that SNP was positively associated with weight regain in subjects who underwent an intensive lifestyle modification programme(Reference Delahanty, Pan and Jablonski23).

Not only does ADRB2 modulate lipolysis, but it also regulates energy expenditure via the thermogenic effects of catecholamines(Reference Leite, Silva and Jesus67,Reference Szendrei, Gonzalez-Lamuno and Amigo68) . A study in Japanese males revealed that the Gly16 (G) allele of the rs1042713 SNP of the ADRB2 gene was positively related to weight regain(Reference Masuo, Katsuya and Kawaguchi41).

IL-6 impacts regulation of energy expenditure through several mechanisms, including actions in the hypothalamic-pituitary-adrenal axis, and activation of the sympathetic nervous system(Reference Papanicolaou, Petrides and Tsigos69,Reference Torpy, Papanicolaou and Lotsikas70) . A previous study demonstrated an association between the C allele of the rs1800795 (–174 G > C) SNP of the IL-6 gene and weight regain(Reference Goyenechea, Dolores Parra and Alfredo Martinez32), as previously described in ‘Nutrient metabolism-related genes and their association with weight regain’.

Previous studies suggested that the expression of NEGR1 in the hypothalamus was related to food intake(Reference Speakman71,Reference Boender, van Gestel and Garner72) . Delahanty and colleagues demonstrated that the A allele of the rs2815752 SNP of the NEGR1 gene was negatively associated with weight regain irrespective of treatment arms(Reference Delahanty, Pan and Jablonski23).

MTIF3 promotes the formation of the initiation complex on the mitochondrial 55S ribosome, which is essential for energy balance in the mitochondria(Reference Behrouz, Vilarino-Guell and Heckman73). This gene has also been identified as an obesity susceptibility gene due to its relationship with BMI(Reference Speliotes, Willer and Berndt74,Reference Conn, Vaughan and Garver75) . Papandonatos and colleagues showed that the G allele of the MTIF3 rs1885988 was associated with greater weight loss following lifestyle intervention over 4 years of follow-up(Reference Papandonatos, Pan and Pajewski76). In other words, their result implies that G allele of the MTIF3 rs1885988 is related to lower-risk of weight regain.

In summary, evidence indicates that the polymorphisms of the genes that regulate satiety, food reward, eating behaviour, as well as energy expenditure (BDNF, leptin, FTO, DRD2, KCTD15, TMEM18, ADRB2, IL-6, NEGR1 and MTIF3) are associated with the ability of long-term weight maintenance.

Adipocyte differentiation-related genes and their association with weight regain

Adipocytes are a key regulator of body weight, and obesity is a condition characterised by excess adipose tissue(Reference Stephens77). The generation of new adipocytes consists of two steps: the proliferation of preadipocytes and the differentiation from preadipocytes to adipocytes (adipocyte differentiation)(Reference MacLean, Higgins and Giles78). Taken together, adipocyte differentiation is a process that determines body weight and obesity(Reference Auwerx, Martin and Guerre-Millo79). The polymorphisms of adipocyte differentiation-related genes that are associated with weight regain were previously evidenced, including BDNF, PPARγ2, TMEM18 and NEGR1. A comprehensive summary of these relevant reports is shown in Table 3.

Table 3. SNP or the restriction fragment length polymorphisms (RFLP) of adipocyte differentiation-related genes that are associated with weight regain

+, Positively associated with; –, negatively associated with; BDNF, brain-derived neurotrophic factor; IGT, impaired glucose tolerance; NEGR1, neuronal growth regulator 1; T2DM, type 2 diabetes mellitus; TMEM18, transmembrane protein 18.

A previous study demonstrated that BDNF gene expression was down-regulated during adipocyte differentiation and knockdown of BDNF inhibited adipocyte differentiation(Reference Bernhard, Landgraf and Kloting80). There was an association between the G allele of the rs6265 SNP of the BDNF gene and weight regain as previously described in ‘Nutrient metabolism-related genes and their association with weight regain’.

It is widely known that PPARγ2 is a master regulator of adipocyte differentiation(Reference Marion-Letellier, Savoye and Ghosh30,Reference Poulos, Dodson and Culver81) . The effect of PPARγ2 polymorphism on weight regain was focused on the rs1801282 SNP, but the results were inconsistent, as previously mentioned in ‘Nutrient metabolism-related genes and their association with weight regain’.

Knockdown of TMEM18 inhibited adipocyte differentiation(Reference Bernhard, Landgraf and Kloting80). Delahanty and colleagues demonstrated that there was a treatment-specific effect of the TMEM18 rs6548238 SNP on weight regain. In other words, the C allele of that SNP showed a positive correlation with weight regain in individuals who underwent an intensive programme of lifestyle modification(Reference Delahanty, Pan and Jablonski23).

Unlike BDNF, NEGR1 gene expression was up-regulated during adipocyte differentiation, yet knockdown of NEGR1 did also inhibit adipocyte differentiation(Reference Bernhard, Landgraf and Kloting80). As previously mentioned, a negative correlation between the A allele of the rs2815752 SNP of the NEGR1 gene and weight regain was observed regardless of treatment arms(Reference Delahanty, Pan and Jablonski23).

In summary, evidence indicates that there is a relationship between adipocyte differentiation and weight regain, which is mediated by the polymorphisms of adipocyte differentiation-related genes (BDNF, PPARγ2, TMEM18 and NEGR1).

Inflammation, extracellular matrix and bone metabolism-related genes and their association with weight regain

Obesity is associated with chronic inflammation owing to NEFA-induced activation of inflammatory pathways(Reference Ellulu, Patimah and Khaza’ai82,Reference Stepien, Stepien and Wlazel83) . This inflammatory response leads to increased extracellular matrix synthesis but reduced extracellular matrix degradation, resulting in increased deposition and remodelling of extracellular matrix, especially in adipocytes(Reference Lin, Chun and Kang84,Reference Williams, Kang and Wasserman85) . Bone metabolism is also correlated with body fat and lean mass(Reference Gomez-Ambrosi, Rodriguez and Catalan86). In fact, bone has recently been determined as an endocrine organ that affects body weight regulation via the actions of bone-derived factors such as osteocalcin and osteopontin(Reference Gomez-Ambrosi, Rodriguez and Catalan86). The associations between weight regain and polymorphisms of inflammation, extracellular matrix and bone metabolism-related genes include IL-6, periostin (POSTN), collagen type XXIII α1 chain (COL23A1), fibulin 5 (FBLN5), laminin subunit β1 (LAMB1), fibronectin 1 (FN1) and TNF receptor superfamily member 11a (TNFRSF11A). These reports are comprehensively summarised in Table 4.

Table 4. SNP or the restriction fragment length polymorphisms (RFLP) of inflammation, extracellular matrix and bone metabolism-related genes that are associated with weight regain

+, Positively associated with; –, negatively associated with; COL23A1, collagen type XXIII α1 chain; FBLN5, fibulin 5; FN1, fibronectin 1; LAMB1, laminin subunit β1; POSTN, periostin; T2DM, type 2 diabetes mellitus; TNFRSF11A, TNF receptor superfamily member 11a.

IL-6 is an inflammatory mediator that is oversecreted in response to excessive macronutrients in adipose tissue(Reference Ellulu, Patimah and Khaza’ai82). A prior study demonstrated an association between the C allele of the rs1800795 (–174 G > C) SNP of the IL-6 gene and weight regain(Reference Goyenechea, Dolores Parra and Alfredo Martinez32), as previously described in ‘Nutrient metabolism-related genes and their association with weight regain’.

Roumans and colleagues investigated the effects of one hundred and twenty-four extracellular matrix-related genes on weight regain, revealing six SNP (males) and one SNP (females) that were significantly associated with the weight maintenance score(Reference Roumans, Vink and Gielen87). Indeed, they found that the risks of weight regain in males were increased in C-allele carriers of three SNP (rs7323378, rs9547947 and rs9315503) of the POSTN gene, A-allele carriers of the COL23A1 rs2672826, A-allele carriers of the FBLN1 rs12589592 and A-allele carriers of the LAMB1 rs2158836(Reference Roumans, Vink and Gielen87). In females, however, the risk of weight regain was higher in A-allele carriers of the FN1 rs17516906(Reference Roumans, Vink and Gielen87). As previously mentioned, obesity is associated with extracellular matrix remodelling in adipocytes(Reference Lin, Chun and Kang84,Reference Williams, Kang and Wasserman85) and adipocyte characteristics differ based on sex(Reference Karastergiou and Fried88). Altogether, these facts support the sex-specific effects of extracellular matrix-related genes on weight regain.

TNFRSF11A, or more commonly known as RANK, involves in a signalling pathway that regulates bone metabolism(Reference Boyce and Xing89). A previous study revealed that the A allele of TNFRSF11A rs17069904 was associated with greater weight loss in subjects who received intensive lifestyle intervention-induced weight reduction at 4 years of follow-up(Reference McCaffery, Papandonatos and Huggins90). Indeed, the result from that study implies that A allele of TNFRSF11A rs17069904 is related to lower risk of weight regain.

In summary, not only inflammation, extracellular matrix and bone metabolism-related genes are associated with obesity but also the polymorphisms of these genes (IL-6, POSTN, COL23A1, FBLN5, LAMB1, FN1 and TNFRSF11A) are also associated with weight regain in individuals with obesity after successful weight loss.

Conclusion

There is a growing body of evidence which suggests that genetic polymorphism is associated with weight regain in both adults and children with obesity. Specifically, these genes are responsible for the pathophysiology of obesity, including nutrient metabolism, regulation of food intake and energy expenditure, adipocyte differentiation, inflammation, structure of the extracellular matrix and bone metabolism as summarised in Fig. 1. Additionally, some of these genes affect weight regain in a distinct manner, such as intervention-specific, age-specific, sex-specific, oestrogen status-specific and insulin sensitivity status-specific.

Fig. 1. Polymorphism of genes that are associated with weight regain after successful weight loss. ADRB2, β-2 adrenergic receptor; BDNF, brain-derived neurotrophic factor; COL23A1, collagen type XXIII α1 chain; DRD2, dopamine receptor D2; FBLN5, fibulin 5; FN1, fibronectin 1; FTO, fat mass and obesity-associated protein; GRL, glucocorticoid receptor; KCTD15, K channel tetramerisation domain containing 15; LAMB1, laminin subunit β1; MTIF3, mitochondrial translational initiation factor 3; NEGR1, neuronal growth regulator 1; PLIN, perilipin; POSTN, periostin; PPM1K, protein phosphatase, Mg2+/Mn2+ dependent 1K; TMEM18, transmembrane protein 18; TNFRSF11A, TNF receptor superfamily member 11a.

Future directions and clinical applications

Since there is evidence that suggested relationships between genetic polymorphisms and weight regain, identification of genetic polymorphisms in patients with obesity who undergo weight loss intervention could provide useful information to estimate their risks of weight regain after successful weight reduction. However, the impact that any one of these genetic polymorphisms have on weight regain is still unclear due to short duration of follow-up in those reports. Because long-term follow-up is required for continued long-term weight loss success, it is essential that further studies with the longer duration of follow-up are needed to confirm the persistence of the impact of those genetic polymorphisms on weight maintenance. Furthermore, future studies in twins and siblings clarifying the contribution of each genetic polymorphism on weight regain are required to confirm a clinical valuable of genetic testing. Moreover, it is still controversial which genetic polymorphisms exhibit the maximum effect size on weight regain due to the difference of the design among studies. Therefore, further studies that directly compare the effect size of those polymorphisms are needed. Although most of the previous studies identified the correlations between genetic polymorphisms and weight regain after adjusting for several confounding factors (age, sex, ethnicity, study site, method of weight loss and baseline anthropometry), behavioural and psychological factors were not included in the multiple regression analyses. Adjusting for the degree of weight reduction, behavioural factors and psychological factors are necessary in future studies since these factors have been shown to be associated with weight regain(Reference Marinilli Pinto, Gorin and Raynor7Reference Raynor, Phelan and Hill12). In addition to the genetic polymorphisms, epigenetics could also play important roles on weight regain. Although the association between weight regain and DNA methylation of some genes, including proopiomelanocortin (POMC), neuropeptide Y (NPY), mal, T cell differentiation protein 2 (MAL2), family with sequence similarity 129, member A (FAM129A), periplakin (PPL), PDZ domain containing ring finger 4 (PDZRN4) and major facilitator superfamily domain containing 3 (MFSD3), has been reported(Reference Nicoletti, Pinhel and Noronha91Reference Bollepalli, Kaye and Heinonen93), future epigenetic studies regarding the relationship between post-transcriptional modification and the impacts of genetic polymorphisms on long-term weight maintenance in different populations are also required.

After further investigation mentioned above, the genetic-based risk estimation might be used as a guide for physicians and dietitians to provide each of their own patients with the most appropriate strategies for weight loss and for long-term weight maintenance, which might result in a decreased prevalence of obesity, decreased risk of obesity-associated diseases and mortality, as well as decreased obesity-related economic burden.

Acknowledgements

This work was supported by Thailand Research Fund grants MRG6280014 (C. T.) and RSA5880015 (K. S.), a Senior Research Scholar grant from the National Research Council of Thailand (S. C. C.), a NSTDA Research Chair grant from the National Science and Technology Development Agency Thailand (N. C.), and the Chiang Mai University Center of Excellence Award (N. C.).

C. T. designed the work, drafted the work and revised the work. K. S. revised the work. S. C. C. designed the work. N. C. designed the work, revised the work, and gave final approval of the version to be published.

The authors declare that there are no conflicts of interest.

References

Hruby, A & Hu, FB (2015) The epidemiology of obesity: a big picture. Pharmacoeconomics 33, 673689.CrossRefGoogle ScholarPubMed
Santos, I, Sniehotta, FF, Marques, MM, et al. (2017) Prevalence of personal weight control attempts in adults: a systematic review and meta-analysis. Obes Rev 18, 3250.CrossRefGoogle ScholarPubMed
Friedrich, MJ (2017) Global obesity epidemic worsening. JAMA 318, 603.CrossRefGoogle ScholarPubMed
Tremmel, M, Gerdtham, UG, Nilsson, PM, et al. (2017) Economic burden of obesity: a systematic literature review. Int J Environ Res Public Health 14, 435.CrossRefGoogle ScholarPubMed
Byrne, SM, Cooper, Z & Fairburn, CG (2004) Psychological predictors of weight regain in obesity. Behav Res Ther 42, 13411356.CrossRefGoogle ScholarPubMed
Wadden, TA, Sternberg, JA, Letizia, KA, et al. (1989) Treatment of obesity by very low calorie diet, behavior therapy, and their combination: a five-year perspective. Int J Obes 13, Suppl. 2, 3946.Google ScholarPubMed
Marinilli Pinto, A, Gorin, AA, Raynor, HA, et al. (2008) Successful weight-loss maintenance in relation to method of weight loss. Obesity 16, 24562461.CrossRefGoogle ScholarPubMed
Garcia Ulen, C, Huizinga, MM, Beech, B, et al. (2008) Weight regain prevention. Clin Diabetes 26, 100113.CrossRefGoogle Scholar
Wing, RR, Papandonatos, G, Fava, JL, et al. (2008) Maintaining large weight losses: the role of behavioral and psychological factors. J Consult Clin Psychol 76, 10151021.CrossRefGoogle ScholarPubMed
Bosy-Westphal, A & Muller, MJ (2015) Impact of carbohydrates on weight regain. Curr Opin Clin Nutr Metab Care 18, 389394.CrossRefGoogle ScholarPubMed
Phelan, S, Wing, RR, Loria, CM, et al. (2010) Prevalence and predictors of weight-loss maintenance in a biracial cohort: results from the coronary artery risk development in young adults study. Am J Prev Med 39, 546554.CrossRefGoogle Scholar
Raynor, DA, Phelan, S, Hill, JO, et al. (2006) Television viewing and long-term weight maintenance: results from the National Weight Control Registry. Obesity 14, 18161824.CrossRefGoogle ScholarPubMed
Elks, CE, den Hoed, M, Zhao, JH, et al. (2012) Variability in the heritability of body mass index: a systematic review and meta-regression. Front Endocrinol 3, 29.CrossRefGoogle ScholarPubMed
Herrera, BM & Lindgren, CM (2010) The genetics of obesity. Curr Diab Rep 10, 498505.CrossRefGoogle ScholarPubMed
Ismail, S & Essawi, M (2012) Genetic polymorphism studies in humans. Middle East J Med Genet 1, 5763.CrossRefGoogle Scholar
Newgard, CB, An, J, Bain, JR, et al. (2009) A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab 9, 311326.CrossRefGoogle ScholarPubMed
Koves, TR, Ussher, JR, Noland, RC, et al. (2008) Mitochondrial overload and incomplete fatty acid oxidation contribute to skeletal muscle insulin resistance. Cell Metab 7, 4556.CrossRefGoogle ScholarPubMed
Qi, L, Corella, D, Sorli, JV, et al. (2004) Genetic variation at the perilipin (PLIN) locus is associated with obesity-related phenotypes in white women. Clin Genet 66, 299310.CrossRefGoogle ScholarPubMed
Soenen, S, Mariman, EC, Vogels, N, et al. (2009) Relationship between perilipin gene polymorphisms and body weight and body composition during weight loss and weight maintenance. Physiol Behav 96, 723728.CrossRefGoogle ScholarPubMed
Marosi, K & Mattson, MP (2014) BDNF mediates adaptive brain and body responses to energetic challenges. Trends Endocrinol Metab 25, 8998.CrossRefGoogle ScholarPubMed
Xu, B & Xie, X (2016) Neurotrophic factor control of satiety and body weight. Nat Rev Neurosci 17, 282.CrossRefGoogle ScholarPubMed
An Juan, J, Liao, G-Y, Kinney Clint, E, et al. (2015) Discrete BDNF neurons in the paraventricular hypothalamus control feeding and energy expenditure. Cell Metab 22, 175188.Google Scholar
Delahanty, LM, Pan, Q, Jablonski, KA, et al. (2012) Genetic predictors of weight loss and weight regain after intensive lifestyle modification, metformin treatment, or standard care in the Diabetes Prevention Program. Diabetes Care 35, 363366.CrossRefGoogle ScholarPubMed
McCaffery, JM, Papandonatos, GD, Huggins, GS, et al. (2013) FTO predicts weight regain in the Look AHEAD clinical trial. Int J Obes (2005) 37, 15451552.CrossRefGoogle ScholarPubMed
Sarzynski, MA, Jacobson, P, Rankinen, T, et al. (2011) Associations of markers in 11 obesity candidate genes with maximal weight loss and weight regain in the SOS bariatric surgery cases. Int J Obes 35, 676683.CrossRefGoogle ScholarPubMed
Fernandez-Formoso, G, Perez-Sieira, S, Gonzalez-Touceda, D, et al. (2015) Leptin, 20 years of searching for glucose homeostasis. Life Sci 1, 49.CrossRefGoogle Scholar
Li, S & Li, X (2016) Leptin in normal physiology and leptin resistance. Sci Bull 61, 14801488.CrossRefGoogle Scholar
Erez, G, Tirosh, A, Rudich, A, et al. (2011) Phenotypic and genetic variation in leptin as determinants of weight regain. Int J Obes 35, 785792.CrossRefGoogle ScholarPubMed
Kim, HI & Ahn, YH (2004) Role of peroxisome proliferator-activated receptor-gamma in the glucose-sensing apparatus of liver and beta-cells. Diabetes 53, Suppl. 1, S60S65.CrossRefGoogle ScholarPubMed
Marion-Letellier, R, Savoye, G & Ghosh, S (2016) Fatty acids, eicosanoids and PPAR gamma. Eur J Pharmacol 785, 4449.CrossRefGoogle ScholarPubMed
Nicklas, BJ, van Rossum, EF, Berman, DM, et al. (2001) Genetic variation in the peroxisome proliferator-activated receptor-gamma2 gene (Pro12Ala) affects metabolic responses to weight loss and subsequent weight regain. Diabetes 50, 21722176.CrossRefGoogle ScholarPubMed
Goyenechea, E, Dolores Parra, M & Alfredo Martinez, J (2006) Weight regain after slimming induced by an energy-restricted diet depends on interleukin-6 and peroxisome-proliferator-activated-receptor-gamma2 gene polymorphisms. Br J Nutr 96, 965972.CrossRefGoogle ScholarPubMed
Vogels, N, Mariman, EC, Bouwman, FG, et al. (2005) Relation of weight maintenance and dietary restraint to peroxisome proliferator-activated receptor gamma2, glucocorticoid receptor, and ciliary neurotrophic factor polymorphisms. Am J Clin Nutr 82, 740746.CrossRefGoogle ScholarPubMed
Tiyerili, V, Muller, CF, Fung, S, et al. (2012) Estrogen improves vascular function via peroxisome-proliferator-activated-receptor-gamma. J Mol Cell Cardiol 53, 268276.CrossRefGoogle ScholarPubMed
Zhang, G, Hou, X & Gao, S (2015) Stimulation of peroxisome proliferator-activated receptor gamma inhibits estrogen receptor alpha transcriptional activity in endometrial carcinoma cells. Oncol Rep 33, 12271234.CrossRefGoogle ScholarPubMed
Chu, R, van Hasselt, A, Vlantis, AC, et al. (2014) The cross-talk between estrogen receptor and peroxisome proliferator-activated receptor gamma in thyroid cancer. Cancer 120, 142153.CrossRefGoogle ScholarPubMed
Wynn, RM, Li, J, Brautigam, CA, et al. (2012) Structural and biochemical characterization of human mitochondrial branched-chain alpha-ketoacid dehydrogenase phosphatase. J Biol Chem 287, 91789192.CrossRefGoogle ScholarPubMed
Xu, M, Qi, Q, Liang, J, et al. (2013) Genetic determinant for amino acid metabolites and changes in body weight and insulin resistance in response to weight-loss diets: the Preventing Overweight Using Novel Dietary Strategies (POUNDS LOST) trial. Circulation 127, 12831289.CrossRefGoogle ScholarPubMed
Schiffelers, SL, Saris, WH, Boomsma, F, et al. (2001) Beta(1)- and beta(2)-Adrenoceptor-mediated thermogenesis and lipid utilization in obese and lean men. J Clin Endocrinol Metab 86, 21912199.Google ScholarPubMed
Lacey, RJ, Cable, HC, James, RF, et al. (1993) Concentration-dependent effects of adrenaline on the profile of insulin secretion from isolated human islets of Langerhans. J Endocrinol 138, 555563.CrossRefGoogle ScholarPubMed
Masuo, K, Katsuya, T, Kawaguchi, H, et al. (2005) Rebound weight gain as associated with high plasma norepinephrine levels that are mediated through polymorphisms in the beta2-adrenoceptor. Am J Hypertens 18, 15081516.CrossRefGoogle ScholarPubMed
Eder, K, Baffy, N, Falus, A, et al. (2009) The major inflammatory mediator interleukin-6 and obesity. Inflamm Res 58, 727736.CrossRefGoogle ScholarPubMed
El-Kadre, LJ & Tinoco, AC (2013) Interleukin-6 and obesity: the crosstalk between intestine, pancreas and liver. Curr Opin Clin Nutr Metab Care 16, 564568.Google ScholarPubMed
Wang, J-C, Gray, NE, Kuo, T, et al. (2012) Regulation of triglyceride metabolism by glucocorticoid receptor. Cell Biosci 2, 1919.CrossRefGoogle ScholarPubMed
Reed, JL, Chaput, JP, Tremblay, A, et al. (2013) The maintenance of energy balance is compromised after weight loss. Can J Diabetes 37, 121127.CrossRefGoogle ScholarPubMed
Galgani, J & Ravussin, E (2008) Energy metabolism, fuel selection and body weight regulation. Int J Obes 32, Suppl. 7, S109S119.CrossRefGoogle ScholarPubMed
Urabe, H, Kojima, H, Chan, L, et al. (2013) Haematopoietic cells produce BDNF and regulate appetite upon migration to the hypothalamus. Nat Commun 4, 1526.CrossRefGoogle ScholarPubMed
Cao, L, Choi, EY, Liu, X, et al. (2011) White to brown fat phenotypic switch induced by genetic and environmental activation of a hypothalamic-adipocyte axis. Cell Metab 14, 324338.CrossRefGoogle Scholar
Speakman, JR (2015) The ‘Fat Mass and Obesity Related’ (FTO) gene: mechanisms of impact on obesity and energy balance. Curr Obes Rep 4, 7391.CrossRefGoogle ScholarPubMed
Dang, LC, Samanez-Larkin, GR, Smith, CT, et al. (2018) FTO affects food cravings and interacts with age to influence age-related decline in food cravings. Physiol Behav 192, 188193.CrossRefGoogle ScholarPubMed
Rodrigues, GK, Resende, CM, Durso, DF, et al. (2015) A single FTO gene variant rs9939609 is associated with body weight evolution in a multiethnic extremely obese population that underwent bariatric surgery. Nutrition 31, 13441350.CrossRefGoogle Scholar
Woehning, A, Schultz, JH, Roeder, E, et al. (2013) The A-allele of the common FTO gene variant rs9939609 complicates weight maintenance in severe obese patients. Int J Obes 37, 135139.CrossRefGoogle ScholarPubMed
Reinehr, T, Wolters, B, Roth, CL, et al. (2014) FTO gene: association to weight regain after lifestyle intervention in overweight children. Horm Res Paediatr 81, 391396.CrossRefGoogle ScholarPubMed
Rauhio, A, Uusi-Rasi, K, Nikkari, ST, et al. (2013) Association of the FTO and ADRB2 genes with body composition and fat distribution in obese women. Maturitas 76, 165171.CrossRefGoogle ScholarPubMed
Zhu, Y, Shen, J, Gao, L, et al. (2016) Estrogen promotes fat mass and obesity-associated protein nuclear localization and enhances endometrial cancer cell proliferation via the mTOR signaling pathway. Oncol Rep 35, 23912397.CrossRefGoogle ScholarPubMed
Zhang, Z, Zhou, D, Lai, Y, et al. (2012) Estrogen induces endometrial cancer cell proliferation and invasion by regulating the fat mass and obesity-associated gene via PI3K/AKT and MAPK signaling pathways. Cancer Lett 319, 8997.CrossRefGoogle ScholarPubMed
Takeuchi, F, Yamamoto, K, Katsuya, T, et al. (2011) Association of genetic variants for susceptibility to obesity with type 2 diabetes in Japanese individuals. Diabetologia 54, 13501359.CrossRefGoogle ScholarPubMed
De Luis, DA, Aller, R, Izaola, O, et al. (2016) Association of the rs9939609 gene variant in FTO with insulin resistance, cardiovascular risk factor and serum adipokine levels in obese patients. Nutr Hosp 33, 573.CrossRefGoogle ScholarPubMed
Baik, JH (2013) Dopamine signaling in food addiction: role of dopamine D2 receptors. BMB Rep 46, 519526.CrossRefGoogle ScholarPubMed
Yeh, J, Trang, A, Henning, SM, et al. (2016) Food cravings, food addiction, and a dopamine-resistant (DRD2 A1) receptor polymorphism in Asian American college students. Asia Pac J Clin Nutr 25, 424429.Google Scholar
Obregon, AM, Valladares, M & Goldfield, G (2017) Association of the dopamine D2 receptor rs1800497 polymorphism and eating behavior in Chilean children. Nutrition 35, 139145.CrossRefGoogle ScholarPubMed
Winkler, JK, Woehning, A, Schultz, JH, et al. (2012) TaqIA polymorphism in dopamine D2 receptor gene complicates weight maintenance in younger obese patients. Nutrition 28, 9961001.CrossRefGoogle ScholarPubMed
Backman, L, Ginovart, N, Dixon, RA, et al. (2000) Age-related cognitive deficits mediated by changes in the striatal dopamine system. Am J Psychiatry 157, 635637.CrossRefGoogle ScholarPubMed
Reeves, S, Bench, C & Howard, R (2002) Ageing and the nigrostriatal dopaminergic system. Int J Geriatr Psychiatry 17, 359370.CrossRefGoogle ScholarPubMed
Gamero-Villarroel, C, González, LM, Rodríguez-López, R, et al. (2017) Influence of TFAP2B and KCTD15 genetic variability on personality dimensions in anorexia and bulimia nervosa. Brain Behav 7, e00784.CrossRefGoogle ScholarPubMed
Larder, R, Sim, MFM, Gulati, P, et al. (2017) Obesity-associated gene TMEM18 has a role in the central control of appetite and body weight regulation. Proc Natl Acad Sci U S A 114, 94219426.CrossRefGoogle Scholar
Leite, N, Silva, L, Jesus, Í, et al. (2018) ADRB2 Gln27Glu polymorphism influenced changes in leptin but not body composition or metabolic and other inflammatory parameters after twelve weeks of combined training in overweight adolescents. Motriz: Rio Claro 23, e101789.Google Scholar
Szendrei, B, Gonzalez-Lamuno, D, Amigo, T, et al. (2016) Influence of ADRB2 Gln27Glu and ADRB3 Trp64Arg polymorphisms on body weight and body composition changes after a controlled weight-loss intervention. Appl Physiol Nutr Metab 41, 307314.CrossRefGoogle ScholarPubMed
Papanicolaou, DA, Petrides, JS, Tsigos, C, et al. (1996) Exercise stimulates interleukin-6 secretion: inhibition by glucocorticoids and correlation with catecholamines. Am J Physiol 271, E601E605.Google ScholarPubMed
Torpy, DJ, Papanicolaou, DA, Lotsikas, AJ, et al. (2000) Responses of the sympathetic nervous system and the hypothalamic-pituitary-adrenal axis to interleukin-6: a pilot study in fibromyalgia. Arthritis Rheum 43, 872880.3.0.CO;2-T>CrossRefGoogle ScholarPubMed
Speakman, JR (2013) Functional analysis of seven genes linked to body mass index and adiposity by genome-wide association studies: a review. Hum Hered 75, 5779.CrossRefGoogle ScholarPubMed
Boender, AJ, van Gestel, MA, Garner, KM, et al. (2014) The obesity-associated gene Negr1 regulates aspects of energy balance in rat hypothalamic areas. Physiol Rep 2, e12083.CrossRefGoogle ScholarPubMed
Behrouz, B, Vilarino-Guell, C, Heckman, MG, et al. (2010) Mitochondrial translation initiation factor 3 polymorphism and Parkinson’s disease. Neurosci Lett 486, 228230.CrossRefGoogle ScholarPubMed
Speliotes, EK, Willer, CJ, Berndt, SI, et al. (2010) Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 42, 937948.CrossRefGoogle ScholarPubMed
Conn, CA, Vaughan, RA & Garver, WSJCNR (2013) Nutritional genetics and energy metabolism in human. Obesity 2, 142150.Google Scholar
Papandonatos, GD, Pan, Q, Pajewski, NM, et al. (2015) Genetic predisposition to weight loss and regain with lifestyle intervention: analyses from the diabetes prevention program and the Look AHEAD randomized controlled trials. Diabetes 64, 43124321.CrossRefGoogle Scholar
Stephens, JM (2012) The fat controller: adipocyte development. PLoS Biol 10, e1001436.CrossRefGoogle ScholarPubMed
MacLean, PS, Higgins, JA, Giles, ED, et al. (2015) The role for adipose tissue in weight regain after weight loss. Obes Rev 16, Suppl. 1, 4554.CrossRefGoogle ScholarPubMed
Auwerx, J, Martin, G, Guerre-Millo, M, et al. (1996) Transcription, adipocyte differentiation, and obesity. J Mol Med 74, 347352.CrossRefGoogle Scholar
Bernhard, F, Landgraf, K, Kloting, N, et al. (2013) Functional relevance of genes implicated by obesity genome-wide association study signals for human adipocyte biology. Diabetologia 56, 311322.CrossRefGoogle ScholarPubMed
Poulos, SP, Dodson, MV, Culver, MF, et al. (2016) The increasingly complex regulation of adipocyte differentiation. Exp Biol Med 241, 449456.CrossRefGoogle ScholarPubMed
Ellulu, MS, Patimah, I, Khaza’ai, H, et al. (2017) Obesity and inflammation: the linking mechanism and the complications. Arch Med Sci 13, 851863.CrossRefGoogle ScholarPubMed
Stepien, M, Stepien, A, Wlazel, RN, et al. (2014) Obesity indices and inflammatory markers in obese non-diabetic normo- and hypertensive patients: a comparative pilot study. Lipid Health Dis 13, 29.CrossRefGoogle ScholarPubMed
Lin, , Chun, TH & Kang, L (2016) Adipose extracellular matrix remodelling in obesity and insulin resistance. Biochem Pharmacol 119, 816.CrossRefGoogle ScholarPubMed
Williams, AS, Kang, L & Wasserman, DH (2015) The extracellular matrix and insulin resistance. Trends Endocrinol Metab 26, 357366.CrossRefGoogle ScholarPubMed
Gomez-Ambrosi, J, Rodriguez, A, Catalan, V, et al. (2008) The bone-adipose axis in obesity and weight loss. Obes Surg 18, 11341143.CrossRefGoogle ScholarPubMed
Roumans, NJ, Vink, RG, Gielen, M, et al. (2015) Variation in extracellular matrix genes is associated with weight regain after weight loss in a sex-specific manner. Genes Nutr 10, 56.CrossRefGoogle Scholar
Karastergiou, K & Fried, SK (2017) Cellular mechanisms driving sex differences in adipose tissue biology and body shape in humans and mouse models. Adv Exp Med Biol 1043, 2951.CrossRefGoogle ScholarPubMed
Boyce, BF & Xing, L (2008) Functions of RANKL/RANK/OPG in bone modeling and remodeling. Arch Biochem Biophys 473, 139146.CrossRefGoogle ScholarPubMed
McCaffery, JM, Papandonatos, GD, Huggins, GS, et al. (2013) Human cardiovascular disease IBC chip-wide association with weight loss and weight regain in the look AHEAD trial. Hum Hered 75, 160174.CrossRefGoogle ScholarPubMed
Nicoletti, CF, Pinhel, MS, Noronha, NY, et al. (2020) Association of MFSD3 promoter methylation level and weight regain after gastric bypass: assessment for 3 y after surgery. Nutrition 70, 110499.CrossRefGoogle Scholar
Crujeiras, AB, Campion, J, Diaz-Lagares, A, et al. (2013) Association of weight regain with specific methylation levels in the NPY and POMC promoters in leukocytes of obese men: a translational study. Regul Pept 186, 16.CrossRefGoogle ScholarPubMed
Bollepalli, S, Kaye, S, Heinonen, S, et al. (2018) Subcutaneous adipose tissue gene expression and DNA methylation respond to both short- and long-term weight loss. Int J Obes 42, 412423.CrossRefGoogle Scholar
Figure 0

Table 1. SNP or the restriction fragment length polymorphisms (RFLP) of nutrient metabolism-related genes that are associated with weight regain

Figure 1

Table 2. SNP or the restriction fragment length polymorphisms (RFLP) of food intake and energy expenditure-related genes that are associated with weight regain

Figure 2

Table 3. SNP or the restriction fragment length polymorphisms (RFLP) of adipocyte differentiation-related genes that are associated with weight regain

Figure 3

Table 4. SNP or the restriction fragment length polymorphisms (RFLP) of inflammation, extracellular matrix and bone metabolism-related genes that are associated with weight regain

Figure 4

Fig. 1. Polymorphism of genes that are associated with weight regain after successful weight loss. ADRB2, β-2 adrenergic receptor; BDNF, brain-derived neurotrophic factor; COL23A1, collagen type XXIII α1 chain; DRD2, dopamine receptor D2; FBLN5, fibulin 5; FN1, fibronectin 1; FTO, fat mass and obesity-associated protein; GRL, glucocorticoid receptor; KCTD15, K channel tetramerisation domain containing 15; LAMB1, laminin subunit β1; MTIF3, mitochondrial translational initiation factor 3; NEGR1, neuronal growth regulator 1; PLIN, perilipin; POSTN, periostin; PPM1K, protein phosphatase, Mg2+/Mn2+ dependent 1K; TMEM18, transmembrane protein 18; TNFRSF11A, TNF receptor superfamily member 11a.