Mol Biol Rep (2012) 39:1739–1744 DOI 10.1007/s11033-011-0914-z
Implication of genetic variants near TMEM18, BCDIN3D/FAIM2, and MC4R with coronary artery disease and obesity in Chinese: a angiography-based study Hao Huang • Zhi Zeng • Li Zhang • Rui Liu • Xian Li • Ou Qiang • Yucheng Chen
Received: 27 October 2010 / Accepted: 18 May 2011 / Published online: 28 May 2011 Ó Springer Science+Business Media B.V. 2011
Abstract Coronary artery disease (CAD) is multifactorial disease which occurs as a result of the interaction of genetic and environmental factors. Obesity is an independent risk factor for cardiovascular disease. Recent genomewide association studies have identified several genes associated with obesity in Europeans. We wondered whether these genetic variants were associated with CAD. Three single nucleotide polymorphisms (SNPs) rs7561317 near TMEM18, rs7138803 near BCDIN3D/FAIM2 and rs12970134 near MC4R were examined in 930 Han Chinese subjects based on coronary angiography, using polymerase chain reaction (PCR) followed by restriction fragment length polymorphism (RFLP) analysis. There were no significant differences in genotypes and allele distributions of three SNPs between CAD and CAD-free groups. The AA genotype of SNP rs12970134 near MC4R was associated to obesity both in CAD group and CAD-free group in Han Chinese population (P \ 0.001, OR = 2.96, 95% CI 2.01–3.73; and P = 0.003, OR = 2.59, 95% CI 1.86–3.19, respectively). Our observations suggest that the polymorphism rs12970134 near MC4R may be associated to the risk of obesity in Han Chinese population.
H. Huang Z. Zeng L. Zhang Y. Chen (&) Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, People’s Republic of China e-mail:
[email protected] R. Liu X. Li O. Qiang Division of Peptides Related with Human Disease, State Key Laboratory of Biotherapy, Sichuan University, Chengdu 610041, Sichuan, People’s Republic of China
Keywords Coronary artery disease Obesity Polymorphism Genetic Coronary angiography
Introduction Coronary artery disease (CAD) is a major cause of death and disability worldwide, and remains a major burden on health care in both developed and developing countries [1]. It is multifactorial disease which occurs as a result of the interaction of genetic and environmental factors [2, 3]. Several risk factors for CAD have been revealed, such as age, sex, hypertension, smoking, diabetes, obesity, hyperlipidemia and family history [4]. Family history of CAD can be reflected as familial clustering of CAD and/or increased probability of the disease among patient’s relatives according to their degree of relatedness. According to these observations genetic susceptibility to CAD is one of the most crucial non-modifiable risk factors in development of the CAD [5, 6]. Obesity is an independent risk factor for cardiovascular disease [7, 8]. Recent genome-wide association studies (GWAS) have identified several loci associated with obesity in the Caucasian populations, including NEGR1, SEC16B/ RASAL2, TMEM18, SFRS10/ETV5/DGKG, LGR4/LIN7C/ BDNF, BCDIN3D/FAIM2, SH2B1/ATP2A1, RPGRIP1L/ FTO, MC4R, CHST8/KCTD15, GNPDA2, MTCH2 [9–11]. Several studies replicated the association between MC4R gene and obesity [12–14]. Hotta et al. [15] found that TMEM18, FAIM2 and MC4R gene also conferred susceptibility to obesity in Japanese. Li et al. [16] reported that FAIM2 and MC4R gene was associated to risk of obesity. To consider obesity as a risk factor of CAD, we wondered whether these genetic variants were also associated with CAD in Chinese population.
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Materials and methods Study population The study subjects included 624 CAD patients and 306 control individuals from February 2006 to November 2009. All subjects were unrelated individuals of Han Chinese from the southwest region of China, and had undergone coronary angiography for the evaluation of suspected or established CAD at West China Hospital, Sichuan University, China. The inclusion criteria for the CAD group required angiographic evidence of C50% occlusion at least 1 of the major coronary arteries or previous myocardial infarction (MI). The CAD-free group included the subjects admitted to the hospital for the evaluation of chest pain, whose major coronary arteries had no stenosis, and did not have any vascular disease. The subjects with spastic angina pectoris were excluded. Furthermore, the subjects with hepatic or renal dysfunction, inflammatory, infectious or autoimmune diseases, type-1 diabetes mellitus, familial hypercholesterolemia, or thyroid dysfunction were excluded. This study was approved by the ethics committee of the West China Hospital and written informed consent was obtained from all participants. Blood samples were drawn after fasting overnight before angiograph. Genomic DNA was extracted from blood samples with a salting-out procedure [17]. A complete clinical history was obtained from all subjects, including history of hypertension, CAD and type 2 diabetes mellitus (DM), cigarette smoking, alcohol consumption, weight, height, systolic blood pressure, diastolic blood pressure, blood glucose, total plasma cholesterol (TC), High-density lipoprotein cholesterol (HDL-C), Low-density lipoprotein cholesterol (LDL-C) and triglyceride (TG). Plasma biochemical parameters were assayed using an automatic analyzer (Olympus Au1000, Japan). Low-density lipoprotein (LDL) was measured with direct homogenous enzymatic determination. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. Hypertension was defined as the mean of three independent measures of blood pressure [140/90 mmHg or the use of antihypertensive drugs. DM was defined as a fasting blood glucose level C7.0 mmol/l, or 2 h postprandial plasma glucose level C11.1 mmol/l, or current use of insulin or other hypoglycemic drugs. Obesity was defined as body mass index C27.5 kg/m2 [18].
Mol Biol Rep (2012) 39:1739–1744
CACACTTGCTCACTGTGGACAC-30 and 50 -GGATCT TTGGGAACTTGTAGGTAG-30 . Amplification 133 bp products were digested with five units of AccI at 37°C overnight. For SNP rs7138803, primers 50 -GCATCCT GTTTCCCTTCT-30 and 50 -CCATTCACTACCTGCCT TC-30 were used to amplify 278 bp target region, and PCR products were cut with five units of Eco91I at 37°C overnight. For SNP rs12970134, primers were 50 -GACTC TTACCAAACAAAGCCTG-30 and 50 -TGCTAGGTTGGT CCTGGTTG-30 , and 124 bp products were digested with five units of DdeI at 37°C overnight. The PCR conditions were as follows: initial denaturation at 95°C for 5 min; 35 cycles at 95°C for 30 s, 60°C (rs7561317) or 58°C (rs7138803 and rs12970134) for 30 s, and 72°C for 1 min; and a final extension of 72°C for 10 min. To take quality control into consideration, 10% samples of cases and controls were randomly selected to be tested again by different technicians. Genotypes identified by PCR–RFLP were confirmed by DNA sequencing. Statistical analysis Analyses were performed using SPSS 16.0 (SPSS Inc., Chicago, IL, USA). Hardy–Weinberg analysis was examined by chi-square goodness of fit test. Continuous variables, including age, HDL-C, TC, LDL, TG, and BMI, were performed the Kolmogorov–Smirnov (K–S) normality test before compared with an independent-samples t test. The levels of TG in case and control groups were log transformed to approach a normal distribution. Categorical variables were examined with chisquare test. Potential confounding factors, including age, sex, blood pressure, smoking, HDL-C, TC, LDL-C and TG, were analyzed using multivariate adjusted logistic regression model. The association was expressed as an odds ratio (OR) and 95% confidence intervals (CI). Two-sided probability value \ 0.05 was considered to be significant. In addition, to take the multiple comparisons for three SNPs into account, P values were corrected with the Bonferroni adjustment method, and two-sided probability value \ 0.016 was considered to be significant. The power of this study was assessed using the Quanto software version 1.2 (http://hydra.usc. edu/gxe). According to the minor allele frequency, which ranged from 0.093 for rs7561317 to 0.28 for rs7138803 reported in HapMap of CHB population, the study had more than 80% statistical power assuming a dominant model.
Genotyping Results Three polymorphisms were genotyped in our case–control population. The polymorphisms rs7561317 near the TMEM18 gene, rs7138803 near the BCDIN3D and FAIM2 genes, rs12970134 near the MC4R gene were genotyped by PCR–RFLP analysis. Primers for SNP rs7561317 were 50 -
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Characteristics of the Subjects The clinical and biological characteristics of the subjects are shown in Table 1. Compared with the CAD-free group,
Mol Biol Rep (2012) 39:1739–1744 Table 1 General characteristics of subjects
CAD coronary artery disease, M male, F female, BMI body mass index, DM diabetes mellitus, TC total cholesterol, HDL-C high density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, TG triglyceride
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Variables
CAD (n = 624)
CAD-free (n = 306)
P
Age (years)
67.0 ± 9.3
61.4 ± 9.7
\0.001
Gender (M/F)
438/186
177/129
\0.001
BMI (kg/m2)
26.2 ± 3.5
25.8 ± 3.2
DM (%)
51.0
16.7
\0.001
Hypertension (%)
61.5
40.2
\0.001
Smoking (%)
40.4
27.5
\0.001
TC (mmol/l)
4.14 ± 1.08
4.11 ± 1.03
0.658
HDL-C (mmol/l)
1.17 ± 0.28
1.30 ± 0.42
\0.001
LDL-C (mmol/l)
2.42 ± 0.89
2.39 ± 0.76
0.682
TG (mmol/l)
1.93 ± 1.05
1.88 ± 1.28
0.327
the CAD patients were older and predominantly male. The CAD group also had lower HDL-C, more smokers and higher rates of hypertension and type 2 diabetes. Because of the use of statin drugs, the levels of TG, TC and LDL were not significantly different between cases and controls. Association with CAD Genotype and allele distribution of three SNPs are shown in Table 2. All three SNPs were consistent with Hardy– Weinberg equilibrium (HWE) in both case and control groups (P [ 0.05). The minor allele frequencies of the polymorphisms were similar to the reported frequencies in Han Chinese population from the International HapMap Project. We found no significant difference in genotypes and allele distributions of three SNPs between CAD and CAD-free groups.
0.078
Association with obesity Genotypes and allele distributions of three SNPs are shown in Tables 3 and 4. All SNPs investigated in each group followed Hardy–Weinberg equilibrium. As shown in Table 3, in CAD patients, compared with wild type carriers, a significant increased obesity risk was associated with variant genotypes of SNPs rs7561317 (AA genotype), rs7138803 (AA genotype) and rs12970134 (AA genotype) (P = 0.038, P = 0.001 and P \ 0.001, respectively). Multivariate adjusted logistic regression analysis with adjustment for age, sex, DM, hypertension, smoking, TC, HDL-C, LDL-C and TG was performed (for rs7561317: adjusted OR = 2.13, 95% CI 1.69–2.67; for rs7138803: adjusted OR = 2.40, 95% CI 1.93–2.97; for rs12970134: adjusted OR = 2.96, 95% CI 2.01–3.73). No significant differences were observed in allele frequencies, expect for
Table 2 Genotypes and allele distributions of SNPs between CAD and CAD-free groups SNP
Nearest gene
Genotype/allele
rs7561317
TMEM18
GG
495 (79.3)
246 (80.4)
GA
123 (19.7)
60 (19.6)
AA
6 (1.0)
rs7138803
CAD-free (%)
0 (0)
P-value
0.916
1.08 (0.98–1.19)
0.085
NA
1113 (89.2)
552 (90.2)
A
135 (10.8)
60 (9.8)
BCDIN3D,
GG
276 (44.2)
120 (39.2)
FAIM2
GA
282 (45.2)
153 (50.0)
0.136 0.560
MC4R
66 (10.6)
33 (10.8)
G
834 (66.8)
393 (64.2)
A
414 (33.2)
219 (35.8)
GG
390 (62.5)
177 (57.8)
GA AA
210 (33.7) 24 (3.8)
117 (38.2) 12 (4.0)
G
990 (79.3)
471 (77.0)
A
258 (20.7)
141 (23.0)
ORa (95% CI) 1.00 (Ref.)
G
AA
rs12970134
CAD (%)
1.00 (Ref.) 0.503
1.10 (0.83–1.47) 1.00 (Ref.) 0.83 (0.71–1.05) 0.91 (0.77–1.13) 1.00 (Ref.)
0.264
0.93 (0.81–1.06) 1.00 (Ref.)
0.162 0.7941
0.77 (0.64–1.02) 0.85 (0.67–1.08) 1.00 (Ref.)
0.243
0.90 (0.75–1.08)
a
The associations were performed by multivariate logistic regression analysis adjusted with covariates (age, gender, BMI, smoking, hypertension, diabetes, TC, TG, HDL-C, and LDL-C) OR odds ratio, 95% CI 95% confidence interval, NA not applicable, Ref. reference category
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Mol Biol Rep (2012) 39:1739–1744
Table 3 Genotypes and allele distributions of SNPs between obesity and non-obesity groups in CAD patients Nearest gene
Genotype/allele
Obesity (%)
Non-obesity (%)
rs7561317
TMEM18
GG
204 (80.3)
291 (78.6)
GA
45 (17.7)
78 (21.1)
0.349
1.02 (0.88–1.18)
AA
5 (2.0)
1 (0.3)
0.038
2.13 (1.69–2.67)
G
453 (89.2)
660 (89.2)
A
55 (10.8)
80 (10.8)
0.993
1.00 (0.72–1.39)
rs7138803
1.00 (Ref.)
1.00 (Ref.)
BCDIN3D,
GG
114 (45.2)
162 (43.5)
FAIM2
GA
96 (38.1)
186 (50.0)
0.077
0.87 (0.41–1.54)
0.001
2.40 (1.93–2.97)
AA
rs12970134
P-value
ORa (95% CI)
SNP
MC4R
42 (16.7)
24 (6.5)
G
324 (64.3)
510 (68.5)
A
180 (35.7)
234 (31.5)
GG GA
138 (54.8) 96 (38.1)
252 (67.7) 114 (30.7)
AA
18 (7.1)
6 (1.6)
G
372 (73.8)
618 (83.1)
A
132 (26.2)
126 (16.9)
1.00 (Ref.)
1.00 (Ref.) 0.117
1.14 (0.97–1.33)
0.013
1.00 (Ref.) 1.07 (0.72–1.57)
\0.001
2.96 (2.01–3.73) 1.00 (Ref.)
\0.001
1.55 (1.25–1.92)
a
The associations were performed by multivariate logistic regression analysis adjusted with covariates (age, gender, smoking, hypertension, diabetes, TC, TG, HDL-C, and LDL-C) OR odds ratio, 95% CI 95% confidence interval, Ref. reference category
SNP rs12970134 (P \ 0.001, OR = 1.55, 95% CI 1.25–1.92). As shown in Table 4, in CAD-free subjects, compared with wild type carriers, a significant increased obesity risk was associated with variant genotype of SNP rs12970134 (AA genotype) (P = 0.003, adjusted OR = 2.59, 95% CI 1.86–3.19). And A allele of rs12970134 was found to be associated with increased obesity risk (P = 0.04, OR = 1.36, 95% CI 1.02–1.81). Association with BMI As shown in Table 5, for SNP rs7138803, compared with G allele carriers, AA genotype was related to higher level of BMI in CAD group (P \ 0.001). For SNP rs12970134, AA genotype was found to be associated with increased level of BMI compared with G allele carriers, no matter in CAD group, obesity group, or control group without CAD or obesity.
Discussion Our study showed that polymorphisms rs7561317 and rs7138803 were significantly associated with obesity in CAD patients. And SNP rs12970134 was found to be associated with obesity both in CAD group and CAD-free group in Han Chinese population. The AA genotypes of three SNPs were associated to increased obesity risk.
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However, we failed to demonstrate the association between all tested SNPs and CAD. Metabolic syndrome is associated with an increased risk of type 2 diabetes and CAD [19, 20]. It is recognized as a major CAD risk factor [21–23]. A meta-analysis has indicated that the presence of metabolic syndrome increases the risk of CAD [24]. The recent 3 GWAS identified strong associations with variants near TMEM18, BCDIN3D/ FAIM2 cluster, MC4R and risk of obesity in white population [9–11]. Renstro¨m et al. [12], Zobel et al. [13] and Been et al. [14] replicated the association between MC4R gene and obesity. Hotta et al. [15] found that TMEM18, FAIM2 and MC4R gene also conferred susceptibility to obesity in Japanese. Li et al. [16] reported that FAIM2 and MC4R gene was associated to risk of obesity. There was no relevant information about the association between TMEM18, BCDIN3D/FAIM2 cluster, MC4R and risk of obesity and CAD before. In our study, SNP rs12970134 of MC4R gene was found to be associated with obesity in both CAD and CAD-free groups, and also associated with BMI. The MC4R gene is located on chromosome 18q22 and involved in appetite regulation [25]. SNP rs12970134 is located between 188 and 109 kb downstream of MC4R. It is unclear which variants are causal in the genomic region of MC4R, and also unknown whether the causal variants involve in genomic regulation. We just hypothesized that polymorphism rs12970134 may be in linkage disequilibrium with other causal variant. Pollin et al. [26] had ever found that
Mol Biol Rep (2012) 39:1739–1744
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Table 4 Genotypes and allele distributions of SNPs between obesity and non-obesity groups in CAD-free subjects SNP
Nearest gene
Genotype/allele
rs7561317
TMEM18
GG
87 (80.6)
159 (80.3)
GA
21 (19.4)
39 (19.7)
AA
rs7138803
Non-obesity (%)
P-value
ORa (95% CI)
0.958
0.96 (0.82–1.12)
NA
NA
0.960
0.99 (0.60–1.63) 1.04 (0.91–1.19)
1.00 (Ref.)
0 (0)
0 (0)
G
195 (90.3)
357 (90.2)
A
21 (9.7)
39 (9.8)
BCDIN3D,
GG
39 (36.1)
81 (40.9)
FAIM2
GA
60 (55.6)
93 (47.0)
0.252 0.567
AA
rs12970134
Obesity (%)
MC4R
1.00 (Ref.) 1.00 (Ref.)
9 (8.3)
24 (12.1)
G
138 (63.9)
255 (64.4)
A
78 (36.1)
141 (35.6)
0.901
1.01 (0.81–1.27)
GG GA
57 (52.8) 42 (38.9)
120 (60.6) 75 (37.9)
0.512
1.00 (Ref.) 0.60 (0.32–1.12)
0.003
2.59 (1.86–3.19)
AA
9 (8.3)
3 (1.5)
G
156 (72.2)
315 (79.5)
A
60 (27.8)
81 (20.5)
0.79 (0.22–2.81) 1.00 (Ref.)
1.00 (Ref.) 0.040
1.36 (1.02–1.81)
a
The associations were performed by multivariate logistic regression analysis adjusted with covariates (age, gender, smoking, hypertension, diabetes, TC, TG, HDL-C, and LDL-C) OR odds ratio, 95% CI 95% confidence interval, NA not applicable, Ref. reference category
Table 5 Relation between genotypes of three SNPs and the levels of BMI Group and BMI (kg/m2)
Genotype GG
P-value GA
AA
GG VS GA
GG vs AA
GA vs AA
rs7561317 CAD
23.7 ± 2.1
24.1 ± 2.0
NA
0.140
NA
NA
Obesity
29.2 ± 1.2
29.1 ± 1.0
NA
0.783
NA
NA
Control
24.6 ± 2.6
23.8 ± 2.5
NA
0.071
NA
NA
rs7138803 CAD
23.5 ± 2.4
23.8 ± 1.8
25.2 ± 1.3
0.207
\0.001
\0.001
Obesity Control
29.2 ± 0.9 24.4 ± 1.6
29.0 ± 1.3 24.1 ± 3.3
29.4 ± 0.6 23.7 ± 1.7
0.576 0.530
0.344 0.127
0.075 0.159
23.7 ± 2.1
24.0 ± 2.2
24.8 ± 0.7
0.429
\0.001
\0.001
rs12970134 CAD Obesity
29.2 ± 1.4
29.2 ± 0.9
30.4 ± 0.2
0.921
\0.001
\0.001
Control
24.0 ± 2.7
24.6 ± 2.6
25.7 ± 0.4
0.108
0.003
\0.001
CAD coronary artery disease, BMI body mass index, NA not applicable Control means subjects without CAD or obesity
associated SNP can be in linkage disequilibrium with functional variant over 800 kb away. However, SNPs rs7561317 and rs7138803 were only associated with obesity in CAD patients, but not in CAD-free group. In CAD group, all participants with CAD have an abnormal condition which is different from healthy subjects. CAD and obesity are often coexistent and affect each other. CAD-obesity interaction increased confounding bias when we analyzed the association between genetic variants and obesity. So taking the confounding bias into account, we can only draw a conclusion that SNPs rs7561317 and
rs7138803 may be associated with obesity in CAD group, but not extend to the whole because of negative results in healthy controls. However, our study has several limitations should be also recognized. First, because of the small sample size, the genetic parameter estimates for Han Chinese population may be biased. Second, selected subjects of our study to risk of CAD and obesity do not represent common population, although the angiography-based design allows us to select CAD-free subjects with an objectively defined control status. Allow for CAD and CAD-free groups can’t be
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considered a typical study population for obesity research, we performed subgroup analyses based on subjects with or without CAD. In conclusion, our study indicates that the polymorphism rs12970134 near MC4R is associated to the risk of obesity in Han Chinese population. However, there were no significant differences in genotypes and allele distributions of SNPs rs7561317, rs7138803 and rs12970134 between CAD and CAD-free groups. Furthermore, large sample size studies, especially prospective cohort studies, will be required. Acknowledgments The authors would like to thank Yi Huang, Sheyu Li, and other physicians (Department of Cardiology, West China Hospital, Sichuan University) for blood sample and clinical data collection. This work was supported by a grant from the Applied Basic Research Programs of Science and Technology Commission Foundation of Sichuan Province (07FG002-028).
References 1. Mathers CD, Loncar D (2006) Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med 3:e442 2. Castelli WP (1984) The epidemiology of coronary heart disease: the Framingham study. Am J Med 76:4–12 3. Brenn T (1994) Genetic and environmental effects on coronary heart disease risk factors in Northern Norway: the cardiovascular disease study in Finnmark. Ann Hum Genet 58:369–379 4. Assmann G, Cullen P, Schulte H (2002) Simple scoring calculating the risk of acute coronary events based on 10 year followup of the Prospective Cardiovascular Munster (PROCAM) study. Circulation 105:310–315 5. Marenberg ME, Risch N, Berkman LF, Floderus B, de Faire U (1994) Genetic susceptibility to death from coronary heart disease in a study of twins. New Engl J Med 330:1041–1046 6. Chamberlain JC, Galton DJ (1990) Genetic susceptibility to atherosclerosis. Br Med Bull 40:917–940 7. Hubert HB, Feinleib M, McNamara PM, Castelli WP (1983) Obesity as an independent risk factor for cardiovascular disease: a 26-year follow-up of participants in the Framingham Heart Study. Circulation 67:968–977 8. Yusuf S, Hawken S, Ounpuu S et al (2005) Obesity and the risk of myocardial infarction in 27,000 participants from 52 countries: a case-control study. Lancet 366:1640–1649 9. Chambers JC, Elliott P, Zabaneh D, Zhang W, Li Y, Froguel P, Balding D, Scott J, Kooner JS (2008) Common genetic variation near MC4R is associated with waist circumference and insulin resistance. Nat Genet 40:716–718 10. Thorleifsson G, Walters GB, Gudbjartsson DF, Steinthorsdottir V, Sulem P et al (2009) Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat Genet 41:18–24 11. Willer CJ, Speliotes EK, Loos RJ, Li S, Lindgren CM et al (2009) Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet 41:25–34
123
Mol Biol Rep (2012) 39:1739–1744 12. Renstro¨m F, Payne F, Nordstr} om A, Brito EC, Rolandsson O et al (2009) Replication and extension of genome-wide association study results for obesity in 4923 adults from northern Sweden. Hum Mol Genet 18:1489–1496 13. Zobel DP, Andreasen CH, Grarup N, Eiberg H, Sø´rensen TI et al (2009) Variants near MC4R are associated with obesity and influence obesity-related quantitative traits in a population of middle-aged people: studies of 14, 940 Danes. Diabetes 58:757–764 14. Been LF, Nath SK, Ralhan SK, Wander GS, Mehra NK et al (2010) Replication of association between a common variant near melanocortin-4 receptor gene and obesity-related traits in Asian Sikhs. Obesity 18:425–429 15. Hotta K, Nakamura M, Nakamura T, Matsuo T, Nakata Y et al (2009) Association between obesity and polymorphisms in SEC16B, TMEM18, GNPDA2, BDNF, FAIM2 and MC4R in a Japanese population. J Hum Genet 54:727–731 16. Li S, Zhao JH, Luan J, Luben RN, Rodwell SA et al (2010) Cumulative effects and predictive value of common obesitysusceptibility variants identified by genome-wide association studies. Am J Clin Nutr 91:184–190 17. Miller SA, Dykes DD, Polesky HF (1988) A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res 16:1215 18. WHO Expert Consultation (2004) Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 363:157–163 19. Eckel RH, Grundy SM, Zimmet PZ (2005) The metabolic syndrome. Lancet 365:1415–1428 20. Grundy SM (2006) Metabolic syndrome: connecting and reconciling cardiovascular and diabetes worlds. J Am Coll Cardiol 47:1093–1100 21. Alberti KG, Zimmet PZ (1998) Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1. Diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med 15:539–553 22. Expert Panel on Detection, Evaluation, Treatment of High Blood Cholesterol in Adults (2001) Executive summary of the third report of the national cholesterol education program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). JAMA 285: 2486–2497 23. Alberti KG, Zimmet P, Shaw J (2005) The metabolic syndrome–a new worldwide definition. Lancet 366:1059–1062 24. Galassi A, Reynolds K, He J (2006) Metabolic syndrome and risk of cardiovascular disease: a meta-analysis. Am J Med 119: 812–819 25. Kask A, Ra¨go L, Wikberg JE, Schio¨th HB (1998) Evidence for involvement of the melanocortin MC4 receptor in the effects of leptin on food intake and body weight. Eur J Pharmacol 360: 15–19 26. Pollin TI, Damcott CM, Shen H, Ott SH, Shelton J et al (2008) A null mutation in human APOC3 confers a favorable plasma lipid profile and apparent cardioprotection. Science 322:1702–1705