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Association between MC4R rs17782313 genotype and obesity: A meta-analysis
Journal Pre-proofs Review Association between MC4R rs17782313 genotype and obesity: a meta-analysis Keping Yu, Li Li, Lan Zhang, Li Guo, Chengjian Wang PII: DOI: Reference:
S0378-1119(20)30041-X https://doi.org/10.1016/j.gene.2020.144372 GENE 144372
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Gene Gene
Received Date: Revised Date: Accepted Date:
4 November 2019 8 January 2020 13 January 2020
Please cite this article as: K. Yu, L. Li, L. Zhang, L. Guo, C. Wang, Association between MC4R rs17782313 genotype and obesity: a meta-analysis, Gene Gene (2020), doi: https://doi.org/10.1016/j.gene.2020.144372
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Title page
Association between MC4R rs17782313 genotype and obesity: a meta-analysis Keping Yu, Li Li, Lan Zhang, Li Guo, Chengjian Wang* Department of Endocrinology, Chongqing General Hospital, Chongqing 400013, P.R. China
Corresponding Author: Chengjian Wang, Department of Endocrinology, Chongqing General Hospital, No.312 Zhongshan First Road, Yuzhong District, Chongqing 400013, P.R. China. Tel: +86 023 63532630 E-mail: [email protected]
ABSTRACT Background: Obesity is a huge burden of the world. It is commonly recognized that dietary structure and physical inactivity is essential in the progress of obesity. However, some individuals still face the trouble of obese even though they live a healthy life. Except for the combination of diseases, the operation of both lifestyle and genetic features contributes to obesity. Melanocortin-4-receptor (MC4R) gene is one of the known hereditary factors of obesity. rs17782313, a single nucleotide variant in MC4R gene, has been reported unclear results in whether it plays a role in obesity. This metaanalysis is to estimate the association between MC4R rs17782313 genotype and obesity. Method: A systematic literature retrieval was conducted in four databases: PubMed, Embase, Web of Science and Cochrane Library with specific search strategy. Select qualified studies to identify relevant studies. Odds ratios (ORs) with 95% confidence intervals (CI), P value and I2 value were used to assess the strength of the association in meta-analysis and adjusted with ethnicity, quality and single nucleotide polymorphism (SNP) testing method. Result: 6 eligible studies involving 3133 obese cases and 3123 normal-weight participants were selected from 378 articles. Allele B of MC4R rs17782313 present a statistically significant association with obesity under allele contrast model (OR=1.325, 95%CI: 1.219-1.439), dominant model (OR=1.320, 95%CI: 1.184-1.472), recessive model (OR=1.690, 95%CI: 1.420-2.011) and homozygous type of co-dominant model (OR=1.925, 95%CI: 1.590-2.330), respectively, and P<0.05. Conclusion: Mutated MC4R rs17782313 is associated with higher risk of obesity. People with homozygous mutant genotype of MC4R rs17782313 would be more likely to suffer from obesity, while heterozygous mutant genotype needs further studies to clarify. Key words: obesity, rs17782313, MC4R, meta-analysis
List of Abbreviations Melanocortin-4-receptor (MC4R) Odds ratios (ORs) Confidence intervals (CI) Single nucleotide polymorphism (SNP) Genome-wide association study (GWAS) Newcastle-Ottawa Scale (NOS) Hardy-Weinberg equilibrium (HWE) Single nucleotide polymorphism (SNP) Leptin receptors (LepR) Proopiomelanocortin (POMC) α-melanocyte stimulating hormone (α-MSH) Paraventricular hypothalamic nucleus (PVN) Agouti-related peptide (AgRP) Neuropeptide Y (NPY)
1. Introduction Obesity, as an unhealthy physiological condition for all ages, is abnormally tremendous fat accumulation that may impair health. Its morbidity has reached epidemic level in recent. 13% of adults, over 650 million, and more than 380 million juveniles were obese in 2016 [1]. Based on statistic from 200 countries, the global prevalence of obesity of male climbed from 3.2% in 1975 to 10.8% in 2014 [2]. Concurrently, this data raised
from 6.4% to 14.9% in women [2]. Till 2016, more than one-third (34.9%) American adults are obese [3]. It has been listed as risk factor, equal to alcohol and tobacco, for uncommunicable diseases by WHO and GHO. Obesity brings an increased incidence of type II diabetes, cardiovascular diseases, asthma, chronic back pain and et al. [4].The operation of both lifestyle and genetic factors contributes to obesity. A literature about monozygotic and dizygotic twins presents that the heritability of obesity is among 20%80% [5]. Gene decides the susceptibility of obesity, but environment, diet and exercise decide the expression of DNA [6]. Individuals with the same energy absorption and consumption, would be in different body weight state due to the difference of gene. Gene could influence the inclination of nutrition intake and food choose habit [7]. Thus, it is essential to find out how gene preforms, impacts and manages body weight. Melanocortin-4-receptor (MC4R) gene has been found that it is related with human body weight from 1998 [8]. Since that, scientists try to find out the approach it uses and the function of different mutations for decades. MC4R with 996 base pairs locating at chromosome 18q21.3 is a seven-transmembrane receptor, mainly distributing in the hypothalamus. It involves in the process of appetite regulation [9, 10]. The mutation of rs17782313 on MC4R gene is identified by Genome-wide association study (GWAS). Variants of MC4R-rs17782313 in obesity cohort had been noticed [11]. MC4R is an important receptor functioning the food intake program. Ranadive et al. [12] mentioned that the mutation of MC4R gene could change the ligand-binding ability between MC4R and α-melanocyte stimulating hormone (α-MSH) or/and agouti-related peptide (AgRP), furtherly influence the entire energy intake. Till now, numbers of studies with various designs have been done to exam the relationship between MC4R-rs17782313 and obesity [13-15]. Nevertheless, they all have apparent short comes. The original experiments have limitation of region and race, while other reviews and meta-analyses do not differ overweight and obesity. They can only represent the one region or country. This article searches studies published in English through internet worldwide. It aims to analyze and clarify the association
between MC4R- rs17782313 gene and obesity. 2. Method 2.1 Search strategy To identify available studies, a systematic literature search was operated in 4 medicial and biologic databases: PubMed, Embase, Web of Science and Cochrane Library. The key terms retrieved are “Obesity”, “Appetite Depressants”, “Body Weight”, “Diet, Reducing”, “Skinfold Thickness”, “Lipectomy”, “Anti-Obesity Agents”, “Bariatrics”, “MC4R” and “rs17782313”. Two authors (KY and LL) implemented this work till April 2019. References in these studies were also reviewed to identify additional studies. The last search was updated on 2019.4.24. Totally 378 relevant studies were identified with the
Search
formula
(((“Obesity”[Mesh])
Depressants[Title/Abstract])
OR
Reducing[Title/Abstract])
OR
Skinfold
Lipectomy[Title/Abstract])
OR
Anti-Obesity
Bariatrics[Title/Abstract])))
Body
AND
Weight[Title/
OR
(((((((Appetite
Abstract])
OR
Diet,
Thickness[Title/Abstract])
OR
Agents[Title/Abstract])
OR
((MC4R[Title/Abstract])
AND
rs17782313[Title/Abstract]). 2.2 Inclusion and Exclusion Criteria Inclusion criteria were established as follows: a) study design was case-control; b) objectives in case group are diagnosed as obesity, with BMI fitting Chinese Standard ≥ 28 kg/m2 in China, and WHO standard ≥ 30kg/m2 in the other countries; c) and normal weight for control group (China: 18.5≤BMI≤23.9, others: WHO standard 18.5≤BMI≤24.9); d) language: English; e) only include the latest research for the same author. The exclusion criteria were used as follows: a) removing duplicates; b) Meta-analysis or review; c) animal research; d) failure to extract valid data; e) BMI was not satisfied. 2.3 Methodological quality appraisal
To identify the quality of studies, each study employed the Chinese version of the modified Newcastle-Ottawa Scale (NOS) to evaluate. This quality appraisal was independently assessed by 2 researchers (KY and LZ). This final scores for studies are decided after discussion. Disagreements were resolved through discussion. The total score of this scale is 10. With the quality score ≤ 5, studies were treated as low-andmoderate quality, while researches were identified as high quality for higher than 5 marks in NOS. 2.4 Data Extraction All probably relevant studies were independently estimated according to the inclusion and exclusion criteria. 2 of the authors (LL and LG) only extracted the data from ruled key characteristics. A third author (CW) determined any divergence between these 2 investigators. The key characteristics extracted from each study are as follow: author, year of publication, country, ethnicity, genotyping methods, number of cases and healthy controls with different genotype. 2.5 Statistical Analysis All statistics were analyzed using STATA statistical software (version 14.0) in this meta-analysis. The assumption of Hardy-Weinberg equilibrium (HWE) was examined in control groups, separately. To assess the strength of relationship between MC4Rrs17782313 and obesity, this meta-analysis calculated odds ratios and 95% confidence intervals (CI). In allele contrast model, odds ratio was employed for gene B versus gene A. As for dominant model, recessive model, co-dominant model, homozygous type and heterozygous type, odds ratios were for genotype BB mixed with AB versus genotype AA, genotype AA mixed with AB versus genotype BB, genotype BB versus genotype AA, genotype AB versus genotype AA, severally. Heterogeneity was tested with Chi-square to analyze the diversity of each model. When heterogeneity statistic I2 <50%, use fixed effect model for calculating, and with the contrast situation, random effect model was chosen. Publication bias with Harbord test and sensitivity analysis were performed for all models. Subgroup analysis executed in
the total five models with ethnicity region, quality and single nucleotide polymorphism (SNP) detection technique, respectively. 3. RESULT 3.1 Literature search A total of 378 articles was browsed from the systematic online search with 182 duplicates. Reviews and meta-analysis materials were excluded at first. A detailed screen with titles, abstracts and full-text eliminated unsatisfied literatures, including 13 files that are not able to download, 7 researches without extract data and a non-English result. Finally, six studies are enrolled from all search result, with 3133 obese cases and 3123 normal weight controls. The whole progress of article sifting is showed as the flow chart Fig 1. 3.2 Hardy-Weinberg equilibrium (HWE) test All 6 studies conform to Hardy-Weinberg equilibrium law. The result of HWE test for each research are as follow: Beckers [16] p=0.348, Huang [17] p=0.129, Srivastava [18] p=0.353, Kochetova [19] p=0.286, Rovite [20] p=0.239 and Cyrus [21] p=0.06. All these P-values are larger than 0.05. All of them do not show a statistically significant difference with the hypothesis of Hardy-Weinberg equilibrium. Combining these three test results, the frequency of these two alleles is stable in inheritance. See Table 1 for detailed information. 3.3 Evaluation of heterogeneity, publication bias & sensitivity analysis Heterogeneity influence the selection of calculation of one model. For allele contrast and homozygous type of co-dominant model, the result of heterogeneity is negative, I2=0.00, fixed effect model is chosen. Although dominant model and recessive model have a difference in heterogeneity, the Chi-square test result I2<50%. The fixed effects model still fits in these two models. In contrast, the heterogeneity of co-dominant model-heterozygous type has a I2 just over 50%. The random effects model applied on
it. Besides, no publication bias shows in this meta-analysis. No model has a P-value of Harbord test measuring this bias less than 0.05, so it states that no significant difference exists between publications. Moreover, after carrying out sensitivity analysis for ensemble odds ratio of each model. The cohort of this study could be viewed. These three indexes proofs that the findings are trustworthy. 3.4 Allele contrast The odds ratio (OR) between B and A is OR=1.310 (1.198-1.433) with P<0.001. Further stratification analyses showed that the OR of Europe is 1.275 (1.112-1.463); Asia is 1.337 (1.189-1.504); high quality is 1.362 (1.171-1.584); low-and-moderate quality is 1.283 (1.148-1.433); TaqMan is 1.252 (1.111-1.411) and PCR is 1.389 (1.2141.589). P-values for all subgroups are ≤0.001. Comparing with wild allele A, gene B augment the chance of obesity for human. Details is in Table 2 and Fig 2. 3.5 Dominant model The overall odds ratio between BB+AB and genotype AA is OR=1.305 (1.159-1.469) with P<0.001. All groups have a significantly proportional result in odds ratio and the strongest link appears in PCR, whose OR=1.563 (1.276-1.915). Specific data for dominant model shows in Table 3 and Fig 3. 3.6 Recessive model Recessive model compares homozygous mutant genotype and A allele carrying genotype. Participants with homozygous mutant genotype has 0.641 (0.367-0.970) times higher chance of obesity than those carrying allele A, with P<0.001. All P-value in this model is less or equal to 0.01. No confidence interval crosses 1 and odds ratios are larger than 1. It has a significantly positive result. Exhaustive details are in Table 4 and Fig 4. 3.7 Co-dominant model-homozygous type
A huge difference presents between two types of co-dominant model. The homozygous utilizing odds ratio valuate between two homozygote, genotype BB and genotype AA, has a corporate odds ratio of 1.864 with 95%CI (1.524-2.280) and P=0.001. The highquality group shares the most powerful relationship among all classifications. Subjects with homozygous mutant genotype are 2.302 times more likely to be at risk for obesity than subjects with wild homozygous genotype in high quality articles, with the 95% CI 1.526-3.473 and P<0.001. Details is in Table 5 and Fig 5. 3.8 Co-dominant model-heterozygous type As for heterozygous type, the overall OR between AB and genotype AA is OR=1.172 (0.973-1.412), whose P=0.094. The confidence interval of odds ratio is cross 1 and Pvalue larger than 0.05. The result of this model is insignificant. In subgroup studies, only high quality, OR=1.215 (1.002-1.474), and PCR, OR=1.464 (1.179-1.818), group have a meaningful result. Though the headline figures for this secondary model is positive, most of them could not support the observed conclusion. More details in Table 5 and Fig 6. 4. Discussion In this meta-analysis, significant results are found for associations of the MC4R rs17782313 with obesity. Four models are used to achieve a more comprehensive and reliable appraisal. Besides the heterozygous type of co-dominant model, all models suggest that MC4R-rs17782313 is an important gene locus effecting the risk of obesity. In allele contrast, allele B is linked with higher risk of obesity, comparing with gene type A. See from models measuring genotype, genotype BB has a strong association with obese, but for genotype AB only high-quality studies and article using PCR to run gene test share significant analysis result. It cannot prove that individuals with genotype AB would expect a higher chance to be obese. Heterogeneity subsists in this metaanalysis. Low-and-moderate quality study is the main source of heterogeneity. This group has a positive result of heterogeneity test for each model. TaqMan technique is
also a common resource, contributing to three models. Both Europe and Asia show heterogeneity for different models. After cross-comparison, it could consider that the location of Europe has more contingency in homozygous genotype, when Asia is heterozygous type. Based on the result of this meta-analysis, if want to clarify the function of genotype AB more high-quality research are needed. Obesity seems to be a health topic studied thoroughly. The truth is it still far from understanding around and still persecute an increasingly cohort. Along with the development of society, technology and globalization, the population suffering from starvation and malnutrition obviously improve. Obesity and overweight become a globally hot topic. According to data from 195 countries gathered by who, the agestandardized estimates prevalence of global obesity for 2016 is 13.1%, rising over 8% in 40 years [22]. The de facto time obesity being concentrated by scientists is over one centenary. Early in 1899, how to treat obesity is discussed by scientists. It is a common knowledge that high calorie diet and inadequate exercise are pivotal for obesity. Changing into a healthy lifestyle seems to be an omnipotent method to treat and cure obesity. However, even well following a scientific guide, people still may fail weight loss. The role of gene is noticed. If what to figure out the relationship between MC4R-rs17782313 and obesity, why choose MC4R and why gene rs17782313 are the first thing need to understand. Ranadive et al. [12] summarized how the Leptin-Melanocortin system, which MC4R runs in, monitor the energy and body weight balance: First, adipocytes secrete leptin. Then, leptin binds to leptin receptors (LepR) on the anorexigenic proopiomelanocortin (POMC)-expressing neurons releasing α-MSH. After that, α-MSH activates MC4R in paraventricular hypothalamic nucleus (PVN) and activated MC4R would relay a satiety signal for brain and body, to stop eating. Another approach leptin used to manage energy intake is through the orexigenic AgRP/neuropeptide Y (NPY)-expressing neurons. When leptin stimulate LepR on AgRP/NPY neurons, the expression of AgRP, which is an antagonist of α-MSH competing MC4R, is inhibited. The bind between
MC4R and AgRP increase appetite, as well as food intake. See from these flow steps, the activation of MC4R could reduce food intake. MC4R could see as the switch of appetite. As mentioned in introduction part, Loos et al. [11] found out that in the genome-wide association scans of MC4R gene, rs17782313 performed the strongest link. Lee et al. [23] used their study proposed that rs17782313 is associated with shorter physical activity time metabolizing energy. MC4R-rs17782313 paly a noteworthy role in decreasing protein intake and replacing into ultra-processed food among Brazil pregnant women [24]. And, in women who had to have bariatric surgery, who carrying MC4R-rs17782313 polymorphism tended a failure in treatment, sharing a higher original BMI [25]. Moreover, the North Indian population also found out a similar conclusion with this article [26]. More importantly, the allocation of mutant rs17782313 is not rare. Allele and genotype frequencies for gene rs17782313 in European is A=76%, B=24%, and wild homozygous genotype=59%, heterozygous mutant genotype=35%, homozygous mutant genotype=6% [11]. MC4R-rs17782313 is a gene that would impact numerous human being. Gene is a complicated and mysterious field. Some genes can bring comprehensive effect with MC4R-rs17782313 for obesity [15]. Because rs17782313 is the only element tested in this study, the density of collaborated genes and alleles are considered the same in both cases and controls. Apart from the normal complete dominance relationship between alleles, incomplete dominance [27] and codominance [28] also often emerges. The relative among heterozygous mutant genotype and wild homozygous genotype is insignificant with only two meaningful subgroups. Face such a result, it may assume that alleles of MC4R-rs17782313 may not completely dominant over each other. It means that the expression of malformed rs17782313 in heterozygote is uncertain or the association is very weak. More high-quality studies and mechanism research are required to help people with mutant allele to manage their body weight wisely.
5. Conclusion The mutant gene MC4R-rs17782313 is a risk factor for obesity. The heterozygote of rs17782313 does not have a significant connection with obesity in this meta-analysis, more high-quality researches are needed to affirm the relationship, whether it is null dominant gene or weak association. Conflict of interest The authors declare that they have no conflict of interest. Funding This research was supported by the project of Chongqing Yuzhong District Science and Technology Committee (No. 2018017). Author Contributions KY and CW designed the study. KY wrote the manuscript and interpreted the data. LL and LG contributed to data collection and collation. KY and LL contributed to literature search, and data analysis. KY and LZ assessed quality appraisal. CW critically reviewed, edited and approved the manuscript. All authors read and approved the final manuscript.
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FIGURE LEGENDS
Fig 1. Flowchart of the study selection process. Fig 2. Forest plots of the association between MC4R rs17782313 genotype and obesity in allelic contrast model. A: overall results; B: ethnicities; C: quality of the articles; D: genotyping methods. Fig 3. Forest plots of the association between MC4R rs17782313 genotype and obesity in dominant model. A: overall results; B: ethnicities; C: quality of the articles; D: genotyping methods. Fig 4. Forest plots of the association between MC4R rs17782313 genotype and obesity in recessive model. A: overall results; B: ethnicities; C: quality of the articles; D: genotyping methods. Fig 5. Forest plots of the association between MC4R rs17782313 genotype and obesity in co-dominant model-homozygous type. A: overall results; B: ethnicities; C: quality of the articles; D: genotyping methods. Fig 6. Forest plots of the association between MC4R rs17782313 genotype and obesity in co-dominant model-heterozygous type. A: overall results; B: ethnicities; C: quality of the articles; D: genotyping methods.
Table 1 Basic characteristics of included studies
Author Year
Case_ Case_ Case_ Con_ Con_ Con_ HWE AA AB BB AA AB BB (p)
Becker 2011 564 s
403
82
192
109
11
Huang 2011 150
275
135
436
554
210 0.139
94
55
149
120
31
119
30
200
118
12
Rovite 2014 241
119
17
253
109
17
Cyrus 2018 63
55
18
60
33
11
Srivast 2014 151 ava Kochet 2014 191 ova
0.348
Country
Ethnicit Qualit SNP Score y y
Belgium
Europe
TaqM an
6
HQ
China
Asia
PCR
4
LMQ
4
LMQ
7
HQ
4
LMQ
8
HQ
TaqM an Republic of TaqM 0.286 Europe Bashkortostan an TaqM 0.239 Latvia Europe an 0.353
0.06
Indian
Saudi Arabia
Asia
Asia
PCR
HQ: High quality, LMQ: Low-and-moderate quality. AA presents wild homozygous genotype AB presents heterozygous mutant genotype BB presents homozygous mutant genotype
Table 2 Allele contrast model for MC4R rs17782313 & Obesity Characteristic
OR (95%CI)
P
I2
Overall
1.310 (1.198-1.433)
<0.001
0.0
T=-1.02
0.367
B vs A Publication bias Sensitivity analysis
1.310 (1.198-1.433)
Ethnicity
Quality
Europe
1.275 (1.112-1.463)
0.001
3.2
Asia
1.337 (1.189-1.504)
<0.001
0.0
OR (95%CI)
P
I2
High quality
1.362 (1.171-1.584)
<0.001
0.0
Low-and-moderate quality
1.283 (1.148-1.433)
<0.001
35.2
TaqMan
1.252 (1.111-1.411)
<0.001
0.0
PCR
1.389 (1.214-1.589)
<0.001
0.0
Characteristic
Genotyping methods
Table 3 Dominant model for MC4R rs17782313 & Obesity Characteristic
OR (95%CI)
P
I2
1.305 (1.159-1.469)
<0.001
33.8
T=-0.70
0.522
BB+AB vs AA Overall Publication bias sensitivity analysis
1.305 (1.159-1.469)
Ethnicity Europe
1.248 (1.058-1.472)
0.009
0.0
Asia
1.368 (1.153-1.622)
<0.001
66.6
High quality
1.334 (1.109-1.604)
0.002
0.0
Low-and-moderate quality
1.285 (1.101-1.499)
0.001
69.3
TaqMan
1.185 (1.023-1.371)
0.023
0.0
PCR
1.563 (1.276-1.915)
<0.001
0.0
Quality
Genotyping methods
Table 4 Recessive model for MC4R rs17782313 & Obesity Characteristic BB vs AA+AB Overall Publication bias Sensitivity analysis
OR (95%CI)
P
I2
1.641 (1.367-1.970)
<0.001
17.7
T=0.11
0.917
1.641 (1.367-1.970)
Ethnicity Europe
1.888 (1.292-2.758)
0.001
54.3
Asia
1.564 (1.269-1.927)
<0.001
0.0
High quality
2.096 (1.402-3.133)
<0.001
0.0
Low-and-moderate quality
1.522 (1.238-1.870)
<0.001
18.6
TaqMan
1.910 (1.420-2.568)
<0.001
31.9
PCR
1.477 (1.169-1.867)
0.001
0.0
Quality
Genotyping methods
Table 5 Co-dominant model for MC4R rs17782313 & Obesity
Characteristic
OR (95%CI)
P
I2
1.864 (1.524-2.280)
<0.001
0.0
T=-0.68
0.531
Homozygous: BB vs AA Overall Publication bias Sensitivity analysis
1.864 (1.524-2.280)
Ethnicity Europe
1.991 (1.357-2.923)
<0.001
54.4
Asia
1.812 (1.430-2.297)
<0.001
0.0
High quality
2.302 (1.526-3.473)
<0.001
0.0
Low-and-moderate quality
1.724 (1.366-2.175)
<0.001
11.8
TaqMan
1.902(1.405-2.575)
<0.001
33.0
PCR
1.832 (1.399-2.399)
<0.001
0.0
1.172 (0.973-1.412)
0.094
50.6
T=-0.54
0.619
Quality
Genotyping methods
Heterozygous: AB vs AA Overall Publication bias Sensitivity analysis Ethnicity
1.172 (0.973-1.412)
Europe
1.164 (0.980-1.383)
0.084
0.0
Asia
1.192 (0.760-1.871)
0.445
78.4
High quality
1.215 (1.002-1.474)
0.048
0.0
Low-and-moderate quality
1.103 (0.778-1.573)
0.575
76.1
TaqMan
1.062 (0.871-1.296)
0.551
38.4
PCR
1.464 (1.179-1.818)
0.001
0.0
Quality
Genotyping methods
Highlights 1. This meta-analysis estimated the association between MC4R rs17782313 genotype and obesity. 2. The original experiments have limitation of region and race, while other reviews and meta-analyses do not differ overweight and obesity. 3. Mutated MC4R rs17782313 is associated with higher risk of obesity.
S0378-1119(20)30041-X https://doi.org/10.1016/j.gene.2020.144372 GENE 144372
To appear in:
Gene Gene
Received Date: Revised Date: Accepted Date:
4 November 2019 8 January 2020 13 January 2020
Please cite this article as: K. Yu, L. Li, L. Zhang, L. Guo, C. Wang, Association between MC4R rs17782313 genotype and obesity: a meta-analysis, Gene Gene (2020), doi: https://doi.org/10.1016/j.gene.2020.144372
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Title page
Association between MC4R rs17782313 genotype and obesity: a meta-analysis Keping Yu, Li Li, Lan Zhang, Li Guo, Chengjian Wang* Department of Endocrinology, Chongqing General Hospital, Chongqing 400013, P.R. China
Corresponding Author: Chengjian Wang, Department of Endocrinology, Chongqing General Hospital, No.312 Zhongshan First Road, Yuzhong District, Chongqing 400013, P.R. China. Tel: +86 023 63532630 E-mail: [email protected]
ABSTRACT Background: Obesity is a huge burden of the world. It is commonly recognized that dietary structure and physical inactivity is essential in the progress of obesity. However, some individuals still face the trouble of obese even though they live a healthy life. Except for the combination of diseases, the operation of both lifestyle and genetic features contributes to obesity. Melanocortin-4-receptor (MC4R) gene is one of the known hereditary factors of obesity. rs17782313, a single nucleotide variant in MC4R gene, has been reported unclear results in whether it plays a role in obesity. This metaanalysis is to estimate the association between MC4R rs17782313 genotype and obesity. Method: A systematic literature retrieval was conducted in four databases: PubMed, Embase, Web of Science and Cochrane Library with specific search strategy. Select qualified studies to identify relevant studies. Odds ratios (ORs) with 95% confidence intervals (CI), P value and I2 value were used to assess the strength of the association in meta-analysis and adjusted with ethnicity, quality and single nucleotide polymorphism (SNP) testing method. Result: 6 eligible studies involving 3133 obese cases and 3123 normal-weight participants were selected from 378 articles. Allele B of MC4R rs17782313 present a statistically significant association with obesity under allele contrast model (OR=1.325, 95%CI: 1.219-1.439), dominant model (OR=1.320, 95%CI: 1.184-1.472), recessive model (OR=1.690, 95%CI: 1.420-2.011) and homozygous type of co-dominant model (OR=1.925, 95%CI: 1.590-2.330), respectively, and P<0.05. Conclusion: Mutated MC4R rs17782313 is associated with higher risk of obesity. People with homozygous mutant genotype of MC4R rs17782313 would be more likely to suffer from obesity, while heterozygous mutant genotype needs further studies to clarify. Key words: obesity, rs17782313, MC4R, meta-analysis
List of Abbreviations Melanocortin-4-receptor (MC4R) Odds ratios (ORs) Confidence intervals (CI) Single nucleotide polymorphism (SNP) Genome-wide association study (GWAS) Newcastle-Ottawa Scale (NOS) Hardy-Weinberg equilibrium (HWE) Single nucleotide polymorphism (SNP) Leptin receptors (LepR) Proopiomelanocortin (POMC) α-melanocyte stimulating hormone (α-MSH) Paraventricular hypothalamic nucleus (PVN) Agouti-related peptide (AgRP) Neuropeptide Y (NPY)
1. Introduction Obesity, as an unhealthy physiological condition for all ages, is abnormally tremendous fat accumulation that may impair health. Its morbidity has reached epidemic level in recent. 13% of adults, over 650 million, and more than 380 million juveniles were obese in 2016 [1]. Based on statistic from 200 countries, the global prevalence of obesity of male climbed from 3.2% in 1975 to 10.8% in 2014 [2]. Concurrently, this data raised
from 6.4% to 14.9% in women [2]. Till 2016, more than one-third (34.9%) American adults are obese [3]. It has been listed as risk factor, equal to alcohol and tobacco, for uncommunicable diseases by WHO and GHO. Obesity brings an increased incidence of type II diabetes, cardiovascular diseases, asthma, chronic back pain and et al. [4].The operation of both lifestyle and genetic factors contributes to obesity. A literature about monozygotic and dizygotic twins presents that the heritability of obesity is among 20%80% [5]. Gene decides the susceptibility of obesity, but environment, diet and exercise decide the expression of DNA [6]. Individuals with the same energy absorption and consumption, would be in different body weight state due to the difference of gene. Gene could influence the inclination of nutrition intake and food choose habit [7]. Thus, it is essential to find out how gene preforms, impacts and manages body weight. Melanocortin-4-receptor (MC4R) gene has been found that it is related with human body weight from 1998 [8]. Since that, scientists try to find out the approach it uses and the function of different mutations for decades. MC4R with 996 base pairs locating at chromosome 18q21.3 is a seven-transmembrane receptor, mainly distributing in the hypothalamus. It involves in the process of appetite regulation [9, 10]. The mutation of rs17782313 on MC4R gene is identified by Genome-wide association study (GWAS). Variants of MC4R-rs17782313 in obesity cohort had been noticed [11]. MC4R is an important receptor functioning the food intake program. Ranadive et al. [12] mentioned that the mutation of MC4R gene could change the ligand-binding ability between MC4R and α-melanocyte stimulating hormone (α-MSH) or/and agouti-related peptide (AgRP), furtherly influence the entire energy intake. Till now, numbers of studies with various designs have been done to exam the relationship between MC4R-rs17782313 and obesity [13-15]. Nevertheless, they all have apparent short comes. The original experiments have limitation of region and race, while other reviews and meta-analyses do not differ overweight and obesity. They can only represent the one region or country. This article searches studies published in English through internet worldwide. It aims to analyze and clarify the association
between MC4R- rs17782313 gene and obesity. 2. Method 2.1 Search strategy To identify available studies, a systematic literature search was operated in 4 medicial and biologic databases: PubMed, Embase, Web of Science and Cochrane Library. The key terms retrieved are “Obesity”, “Appetite Depressants”, “Body Weight”, “Diet, Reducing”, “Skinfold Thickness”, “Lipectomy”, “Anti-Obesity Agents”, “Bariatrics”, “MC4R” and “rs17782313”. Two authors (KY and LL) implemented this work till April 2019. References in these studies were also reviewed to identify additional studies. The last search was updated on 2019.4.24. Totally 378 relevant studies were identified with the
Search
formula
(((“Obesity”[Mesh])
Depressants[Title/Abstract])
OR
Reducing[Title/Abstract])
OR
Skinfold
Lipectomy[Title/Abstract])
OR
Anti-Obesity
Bariatrics[Title/Abstract])))
Body
AND
Weight[Title/
OR
(((((((Appetite
Abstract])
OR
Diet,
Thickness[Title/Abstract])
OR
Agents[Title/Abstract])
OR
((MC4R[Title/Abstract])
AND
rs17782313[Title/Abstract]). 2.2 Inclusion and Exclusion Criteria Inclusion criteria were established as follows: a) study design was case-control; b) objectives in case group are diagnosed as obesity, with BMI fitting Chinese Standard ≥ 28 kg/m2 in China, and WHO standard ≥ 30kg/m2 in the other countries; c) and normal weight for control group (China: 18.5≤BMI≤23.9, others: WHO standard 18.5≤BMI≤24.9); d) language: English; e) only include the latest research for the same author. The exclusion criteria were used as follows: a) removing duplicates; b) Meta-analysis or review; c) animal research; d) failure to extract valid data; e) BMI was not satisfied. 2.3 Methodological quality appraisal
To identify the quality of studies, each study employed the Chinese version of the modified Newcastle-Ottawa Scale (NOS) to evaluate. This quality appraisal was independently assessed by 2 researchers (KY and LZ). This final scores for studies are decided after discussion. Disagreements were resolved through discussion. The total score of this scale is 10. With the quality score ≤ 5, studies were treated as low-andmoderate quality, while researches were identified as high quality for higher than 5 marks in NOS. 2.4 Data Extraction All probably relevant studies were independently estimated according to the inclusion and exclusion criteria. 2 of the authors (LL and LG) only extracted the data from ruled key characteristics. A third author (CW) determined any divergence between these 2 investigators. The key characteristics extracted from each study are as follow: author, year of publication, country, ethnicity, genotyping methods, number of cases and healthy controls with different genotype. 2.5 Statistical Analysis All statistics were analyzed using STATA statistical software (version 14.0) in this meta-analysis. The assumption of Hardy-Weinberg equilibrium (HWE) was examined in control groups, separately. To assess the strength of relationship between MC4Rrs17782313 and obesity, this meta-analysis calculated odds ratios and 95% confidence intervals (CI). In allele contrast model, odds ratio was employed for gene B versus gene A. As for dominant model, recessive model, co-dominant model, homozygous type and heterozygous type, odds ratios were for genotype BB mixed with AB versus genotype AA, genotype AA mixed with AB versus genotype BB, genotype BB versus genotype AA, genotype AB versus genotype AA, severally. Heterogeneity was tested with Chi-square to analyze the diversity of each model. When heterogeneity statistic I2 <50%, use fixed effect model for calculating, and with the contrast situation, random effect model was chosen. Publication bias with Harbord test and sensitivity analysis were performed for all models. Subgroup analysis executed in
the total five models with ethnicity region, quality and single nucleotide polymorphism (SNP) detection technique, respectively. 3. RESULT 3.1 Literature search A total of 378 articles was browsed from the systematic online search with 182 duplicates. Reviews and meta-analysis materials were excluded at first. A detailed screen with titles, abstracts and full-text eliminated unsatisfied literatures, including 13 files that are not able to download, 7 researches without extract data and a non-English result. Finally, six studies are enrolled from all search result, with 3133 obese cases and 3123 normal weight controls. The whole progress of article sifting is showed as the flow chart Fig 1. 3.2 Hardy-Weinberg equilibrium (HWE) test All 6 studies conform to Hardy-Weinberg equilibrium law. The result of HWE test for each research are as follow: Beckers [16] p=0.348, Huang [17] p=0.129, Srivastava [18] p=0.353, Kochetova [19] p=0.286, Rovite [20] p=0.239 and Cyrus [21] p=0.06. All these P-values are larger than 0.05. All of them do not show a statistically significant difference with the hypothesis of Hardy-Weinberg equilibrium. Combining these three test results, the frequency of these two alleles is stable in inheritance. See Table 1 for detailed information. 3.3 Evaluation of heterogeneity, publication bias & sensitivity analysis Heterogeneity influence the selection of calculation of one model. For allele contrast and homozygous type of co-dominant model, the result of heterogeneity is negative, I2=0.00, fixed effect model is chosen. Although dominant model and recessive model have a difference in heterogeneity, the Chi-square test result I2<50%. The fixed effects model still fits in these two models. In contrast, the heterogeneity of co-dominant model-heterozygous type has a I2 just over 50%. The random effects model applied on
it. Besides, no publication bias shows in this meta-analysis. No model has a P-value of Harbord test measuring this bias less than 0.05, so it states that no significant difference exists between publications. Moreover, after carrying out sensitivity analysis for ensemble odds ratio of each model. The cohort of this study could be viewed. These three indexes proofs that the findings are trustworthy. 3.4 Allele contrast The odds ratio (OR) between B and A is OR=1.310 (1.198-1.433) with P<0.001. Further stratification analyses showed that the OR of Europe is 1.275 (1.112-1.463); Asia is 1.337 (1.189-1.504); high quality is 1.362 (1.171-1.584); low-and-moderate quality is 1.283 (1.148-1.433); TaqMan is 1.252 (1.111-1.411) and PCR is 1.389 (1.2141.589). P-values for all subgroups are ≤0.001. Comparing with wild allele A, gene B augment the chance of obesity for human. Details is in Table 2 and Fig 2. 3.5 Dominant model The overall odds ratio between BB+AB and genotype AA is OR=1.305 (1.159-1.469) with P<0.001. All groups have a significantly proportional result in odds ratio and the strongest link appears in PCR, whose OR=1.563 (1.276-1.915). Specific data for dominant model shows in Table 3 and Fig 3. 3.6 Recessive model Recessive model compares homozygous mutant genotype and A allele carrying genotype. Participants with homozygous mutant genotype has 0.641 (0.367-0.970) times higher chance of obesity than those carrying allele A, with P<0.001. All P-value in this model is less or equal to 0.01. No confidence interval crosses 1 and odds ratios are larger than 1. It has a significantly positive result. Exhaustive details are in Table 4 and Fig 4. 3.7 Co-dominant model-homozygous type
A huge difference presents between two types of co-dominant model. The homozygous utilizing odds ratio valuate between two homozygote, genotype BB and genotype AA, has a corporate odds ratio of 1.864 with 95%CI (1.524-2.280) and P=0.001. The highquality group shares the most powerful relationship among all classifications. Subjects with homozygous mutant genotype are 2.302 times more likely to be at risk for obesity than subjects with wild homozygous genotype in high quality articles, with the 95% CI 1.526-3.473 and P<0.001. Details is in Table 5 and Fig 5. 3.8 Co-dominant model-heterozygous type As for heterozygous type, the overall OR between AB and genotype AA is OR=1.172 (0.973-1.412), whose P=0.094. The confidence interval of odds ratio is cross 1 and Pvalue larger than 0.05. The result of this model is insignificant. In subgroup studies, only high quality, OR=1.215 (1.002-1.474), and PCR, OR=1.464 (1.179-1.818), group have a meaningful result. Though the headline figures for this secondary model is positive, most of them could not support the observed conclusion. More details in Table 5 and Fig 6. 4. Discussion In this meta-analysis, significant results are found for associations of the MC4R rs17782313 with obesity. Four models are used to achieve a more comprehensive and reliable appraisal. Besides the heterozygous type of co-dominant model, all models suggest that MC4R-rs17782313 is an important gene locus effecting the risk of obesity. In allele contrast, allele B is linked with higher risk of obesity, comparing with gene type A. See from models measuring genotype, genotype BB has a strong association with obese, but for genotype AB only high-quality studies and article using PCR to run gene test share significant analysis result. It cannot prove that individuals with genotype AB would expect a higher chance to be obese. Heterogeneity subsists in this metaanalysis. Low-and-moderate quality study is the main source of heterogeneity. This group has a positive result of heterogeneity test for each model. TaqMan technique is
also a common resource, contributing to three models. Both Europe and Asia show heterogeneity for different models. After cross-comparison, it could consider that the location of Europe has more contingency in homozygous genotype, when Asia is heterozygous type. Based on the result of this meta-analysis, if want to clarify the function of genotype AB more high-quality research are needed. Obesity seems to be a health topic studied thoroughly. The truth is it still far from understanding around and still persecute an increasingly cohort. Along with the development of society, technology and globalization, the population suffering from starvation and malnutrition obviously improve. Obesity and overweight become a globally hot topic. According to data from 195 countries gathered by who, the agestandardized estimates prevalence of global obesity for 2016 is 13.1%, rising over 8% in 40 years [22]. The de facto time obesity being concentrated by scientists is over one centenary. Early in 1899, how to treat obesity is discussed by scientists. It is a common knowledge that high calorie diet and inadequate exercise are pivotal for obesity. Changing into a healthy lifestyle seems to be an omnipotent method to treat and cure obesity. However, even well following a scientific guide, people still may fail weight loss. The role of gene is noticed. If what to figure out the relationship between MC4R-rs17782313 and obesity, why choose MC4R and why gene rs17782313 are the first thing need to understand. Ranadive et al. [12] summarized how the Leptin-Melanocortin system, which MC4R runs in, monitor the energy and body weight balance: First, adipocytes secrete leptin. Then, leptin binds to leptin receptors (LepR) on the anorexigenic proopiomelanocortin (POMC)-expressing neurons releasing α-MSH. After that, α-MSH activates MC4R in paraventricular hypothalamic nucleus (PVN) and activated MC4R would relay a satiety signal for brain and body, to stop eating. Another approach leptin used to manage energy intake is through the orexigenic AgRP/neuropeptide Y (NPY)-expressing neurons. When leptin stimulate LepR on AgRP/NPY neurons, the expression of AgRP, which is an antagonist of α-MSH competing MC4R, is inhibited. The bind between
MC4R and AgRP increase appetite, as well as food intake. See from these flow steps, the activation of MC4R could reduce food intake. MC4R could see as the switch of appetite. As mentioned in introduction part, Loos et al. [11] found out that in the genome-wide association scans of MC4R gene, rs17782313 performed the strongest link. Lee et al. [23] used their study proposed that rs17782313 is associated with shorter physical activity time metabolizing energy. MC4R-rs17782313 paly a noteworthy role in decreasing protein intake and replacing into ultra-processed food among Brazil pregnant women [24]. And, in women who had to have bariatric surgery, who carrying MC4R-rs17782313 polymorphism tended a failure in treatment, sharing a higher original BMI [25]. Moreover, the North Indian population also found out a similar conclusion with this article [26]. More importantly, the allocation of mutant rs17782313 is not rare. Allele and genotype frequencies for gene rs17782313 in European is A=76%, B=24%, and wild homozygous genotype=59%, heterozygous mutant genotype=35%, homozygous mutant genotype=6% [11]. MC4R-rs17782313 is a gene that would impact numerous human being. Gene is a complicated and mysterious field. Some genes can bring comprehensive effect with MC4R-rs17782313 for obesity [15]. Because rs17782313 is the only element tested in this study, the density of collaborated genes and alleles are considered the same in both cases and controls. Apart from the normal complete dominance relationship between alleles, incomplete dominance [27] and codominance [28] also often emerges. The relative among heterozygous mutant genotype and wild homozygous genotype is insignificant with only two meaningful subgroups. Face such a result, it may assume that alleles of MC4R-rs17782313 may not completely dominant over each other. It means that the expression of malformed rs17782313 in heterozygote is uncertain or the association is very weak. More high-quality studies and mechanism research are required to help people with mutant allele to manage their body weight wisely.
5. Conclusion The mutant gene MC4R-rs17782313 is a risk factor for obesity. The heterozygote of rs17782313 does not have a significant connection with obesity in this meta-analysis, more high-quality researches are needed to affirm the relationship, whether it is null dominant gene or weak association. Conflict of interest The authors declare that they have no conflict of interest. Funding This research was supported by the project of Chongqing Yuzhong District Science and Technology Committee (No. 2018017). Author Contributions KY and CW designed the study. KY wrote the manuscript and interpreted the data. LL and LG contributed to data collection and collation. KY and LL contributed to literature search, and data analysis. KY and LZ assessed quality appraisal. CW critically reviewed, edited and approved the manuscript. All authors read and approved the final manuscript.
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FIGURE LEGENDS
Fig 1. Flowchart of the study selection process. Fig 2. Forest plots of the association between MC4R rs17782313 genotype and obesity in allelic contrast model. A: overall results; B: ethnicities; C: quality of the articles; D: genotyping methods. Fig 3. Forest plots of the association between MC4R rs17782313 genotype and obesity in dominant model. A: overall results; B: ethnicities; C: quality of the articles; D: genotyping methods. Fig 4. Forest plots of the association between MC4R rs17782313 genotype and obesity in recessive model. A: overall results; B: ethnicities; C: quality of the articles; D: genotyping methods. Fig 5. Forest plots of the association between MC4R rs17782313 genotype and obesity in co-dominant model-homozygous type. A: overall results; B: ethnicities; C: quality of the articles; D: genotyping methods. Fig 6. Forest plots of the association between MC4R rs17782313 genotype and obesity in co-dominant model-heterozygous type. A: overall results; B: ethnicities; C: quality of the articles; D: genotyping methods.
Table 1 Basic characteristics of included studies
Author Year
Case_ Case_ Case_ Con_ Con_ Con_ HWE AA AB BB AA AB BB (p)
Becker 2011 564 s
403
82
192
109
11
Huang 2011 150
275
135
436
554
210 0.139
94
55
149
120
31
119
30
200
118
12
Rovite 2014 241
119
17
253
109
17
Cyrus 2018 63
55
18
60
33
11
Srivast 2014 151 ava Kochet 2014 191 ova
0.348
Country
Ethnicit Qualit SNP Score y y
Belgium
Europe
TaqM an
6
HQ
China
Asia
PCR
4
LMQ
4
LMQ
7
HQ
4
LMQ
8
HQ
TaqM an Republic of TaqM 0.286 Europe Bashkortostan an TaqM 0.239 Latvia Europe an 0.353
0.06
Indian
Saudi Arabia
Asia
Asia
PCR
HQ: High quality, LMQ: Low-and-moderate quality. AA presents wild homozygous genotype AB presents heterozygous mutant genotype BB presents homozygous mutant genotype
Table 2 Allele contrast model for MC4R rs17782313 & Obesity Characteristic
OR (95%CI)
P
I2
Overall
1.310 (1.198-1.433)
<0.001
0.0
T=-1.02
0.367
B vs A Publication bias Sensitivity analysis
1.310 (1.198-1.433)
Ethnicity
Quality
Europe
1.275 (1.112-1.463)
0.001
3.2
Asia
1.337 (1.189-1.504)
<0.001
0.0
OR (95%CI)
P
I2
High quality
1.362 (1.171-1.584)
<0.001
0.0
Low-and-moderate quality
1.283 (1.148-1.433)
<0.001
35.2
TaqMan
1.252 (1.111-1.411)
<0.001
0.0
PCR
1.389 (1.214-1.589)
<0.001
0.0
Characteristic
Genotyping methods
Table 3 Dominant model for MC4R rs17782313 & Obesity Characteristic
OR (95%CI)
P
I2
1.305 (1.159-1.469)
<0.001
33.8
T=-0.70
0.522
BB+AB vs AA Overall Publication bias sensitivity analysis
1.305 (1.159-1.469)
Ethnicity Europe
1.248 (1.058-1.472)
0.009
0.0
Asia
1.368 (1.153-1.622)
<0.001
66.6
High quality
1.334 (1.109-1.604)
0.002
0.0
Low-and-moderate quality
1.285 (1.101-1.499)
0.001
69.3
TaqMan
1.185 (1.023-1.371)
0.023
0.0
PCR
1.563 (1.276-1.915)
<0.001
0.0
Quality
Genotyping methods
Table 4 Recessive model for MC4R rs17782313 & Obesity Characteristic BB vs AA+AB Overall Publication bias Sensitivity analysis
OR (95%CI)
P
I2
1.641 (1.367-1.970)
<0.001
17.7
T=0.11
0.917
1.641 (1.367-1.970)
Ethnicity Europe
1.888 (1.292-2.758)
0.001
54.3
Asia
1.564 (1.269-1.927)
<0.001
0.0
High quality
2.096 (1.402-3.133)
<0.001
0.0
Low-and-moderate quality
1.522 (1.238-1.870)
<0.001
18.6
TaqMan
1.910 (1.420-2.568)
<0.001
31.9
PCR
1.477 (1.169-1.867)
0.001
0.0
Quality
Genotyping methods
Table 5 Co-dominant model for MC4R rs17782313 & Obesity
Characteristic
OR (95%CI)
P
I2
1.864 (1.524-2.280)
<0.001
0.0
T=-0.68
0.531
Homozygous: BB vs AA Overall Publication bias Sensitivity analysis
1.864 (1.524-2.280)
Ethnicity Europe
1.991 (1.357-2.923)
<0.001
54.4
Asia
1.812 (1.430-2.297)
<0.001
0.0
High quality
2.302 (1.526-3.473)
<0.001
0.0
Low-and-moderate quality
1.724 (1.366-2.175)
<0.001
11.8
TaqMan
1.902(1.405-2.575)
<0.001
33.0
PCR
1.832 (1.399-2.399)
<0.001
0.0
1.172 (0.973-1.412)
0.094
50.6
T=-0.54
0.619
Quality
Genotyping methods
Heterozygous: AB vs AA Overall Publication bias Sensitivity analysis Ethnicity
1.172 (0.973-1.412)
Europe
1.164 (0.980-1.383)
0.084
0.0
Asia
1.192 (0.760-1.871)
0.445
78.4
High quality
1.215 (1.002-1.474)
0.048
0.0
Low-and-moderate quality
1.103 (0.778-1.573)
0.575
76.1
TaqMan
1.062 (0.871-1.296)
0.551
38.4
PCR
1.464 (1.179-1.818)
0.001
0.0
Quality
Genotyping methods
Highlights 1. This meta-analysis estimated the association between MC4R rs17782313 genotype and obesity. 2. The original experiments have limitation of region and race, while other reviews and meta-analyses do not differ overweight and obesity. 3. Mutated MC4R rs17782313 is associated with higher risk of obesity.