- Research article
- Open Access
- Open Peer Review
Melatonin receptor 1 B polymorphisms associated with the risk of gestational diabetes mellitus
- Jason Y Kim†1,
- Hyun Sub Cheong†2,
- Byung-Lae Park2,
- Sei Hyun Baik3,
- Sunmin Park4,
- Si Won Lee5,
- Min-Hyoung Kim5,
- Jin Hoon Chung5,
- June Seek Choi5,
- Moon-Young Kim5,
- Jae-Hyug Yang5,
- Dong-Hee Cho6,
- Hyoung Doo Shin1, 2Email author and
- Sung-Hoon Kim7Email author
https://doi.org/10.1186/1471-2350-12-82
© Kim et al; licensee BioMed Central Ltd. 2011
- Received: 1 October 2010
- Accepted: 10 June 2011
- Published: 10 June 2011
Abstract
Backgrounds
Two SNPs in melatonin receptor 1B gene, rs10830963 and rs1387153 showed significant associations with fasting plasma glucose levels and the risk of Type 2 Diabetes Mellitus (T2DM) in previous studies. Since T2DM and gestational diabetes mellitus (GDM) share similar characteristics, we suspected that the two genetic polymorphisms in MTNR1B may be associated with GDM, and conducted association studies between the polymorphisms and the disease. Furthermore, we also examined genetic effects of the two polymorphisms with various diabetes-related phenotypes.
Methods
A total of 1,918 subjects (928 GDM patients and 990 controls) were used for the study. Two MTNR1B polymorphisms were genotyped using TaqMan assay. The allele distributions of SNPs were evaluated by x 2 models calculating odds ratios (ORs), 95% confidence intervals (CIs), and corresponding P values. Multiple regressions were used for association analyses of GDM-related traits. Finally, conditional analyses were also performed.
Results
We found significant associations between the two genetic variants and GDM, rs10830963, with a corrected P value of 0.0001, and rs1387153, with the corrected P value of 0.0008. In addition, we also found that the two SNPs were associated with various phenotypes such as homeostasis model assessment of beta-cell function and fasting glucose levels. Further conditional analyses results suggested that rs10830963 might be more likely functional in case/control analysis, although not clear in GDM-related phenotype analyses.
Conclusion
There have been studies that found associations between genetic variants of other genes and GDM, this is the first study that found significant associations between SNPs of MTNR1B and GDM. The genetic effects of two SNPs identified in this study would be helpful in understanding the insight of GDM and other diabetes-related disorders.
Keywords
- Gestational Diabetes Mellitus
- Fast Plasma Glucose
- Korean Population
- Melatonin Receptor
- Glucose Challenge Test
Background
The prevalence of type 2 diabetes mellitus (T2DM) in Korean population has dramatically increased over last decades. Although Asian populations traditionally had a low percentage of T2DM patients, it has increased drastically in recent decades. This is largely due to the fact that Asian countries are adopting western lifestyle and diets. However, a recent discovery of diabetes-susceptible loci on human chromosomes suggest that genetic factors may also play a role in the disease development [1].
Gestational diabetes mellitus (GDM) is a condition in which pregnant women exhibit glucose intolerance in various degrees [2], affecting approximately 2-14% of pregnancies [1, 3, 4]. Women with GDM show similar physiological and genetic characteristics found in diabetes outside of pregnancy, and not surprisingly, women with GDM possess higher risk for developing T2DM when they are not pregnant. Therefore, studying GDM is a good way to study early pathogenesis of diabetes and possibly develop treatment or remedy for the disease [5]. However, while genetic studies on T2DM are very robust [6, 7], there are relatively fewer genetic studies for GDM.
Advance of technology in the genetics field has provided us with a number of useful tools to study human genome. Among them, genome wide association studies (GWAS) are a powerful and useful way to detect genes associated with various diseases, including diabetes. Recently, a couple of studies have revealed that the genetic variants in melatonin receptor 1 B (MTNR1B) gene are associated with T2DM and fasting glucose levels [8, 9]. MTNR1B gene encodes MT2 protein which, along with MT1 protein encoded by MTNR1A, is one of two high-affinity forms of melatonin receptor. This gene product is also an integral membrane protein forming a G-protein coupled 7-transmembrane receptor. Melatonin, also known chemically as N-acetyl-5-methoxytryptamine, is a primary neurohormone secreted by pineal gland. It is mostly found in retina and brain, and its main function is thought to be the regulation of circadian rhythm by translating photoperiodic information from the eyes to the brain. There have been some studies suggesting that insulin level is regulated by circadian clock. Furthermore, T2DM patients have exhibited impaired melatonin secretion and circadian rhythm [10].
To date, several studies have shown the association between MTNR1B and T2DM [6, 11], but there has yet to be a study which looked into the association between MTNR1B and GDM. Since GDM shares many clinical features with T2DM, there is a high possibility that MTNR1B is associated with predisposition of GDM. Therefore, we conducted an association study between two polymorphisms of MTNR1B, which were previously associated with T2DM, using 928 GDM patients and 990 controls. In addition, we also examined the possible association between the two SNPs of MTNR1B and clinical phenotypes related to GDM, such as insulin sensitivity and beta-cell function.
Methods
Subjects
Clinical profiles of subjects
Profiles | GDM | Controls | P value |
---|---|---|---|
Number of subjects | 928 | 990 | |
Age(yr) | 33.17 (22-52) | 32.24 (23-44) | <0.0001 |
Mean gestation week (wk) | 26.03 ± 2.69 | 26.12 ± 1.69 | 0.37 |
BMI(kg/m2) | 23.32 ± 4.01 | 21.40 ± 2.93 | <0.0001 |
AUC-G (Area under glucose curve) | 482.46 ± 57.04 | 358.72 ± 39.99 | <0.0001 |
HOMA-B (Homeostatic model assessment, β-cell function) | 208.06 ± 112.75 | 268.30 ± 179.88 | <0.0001 |
HOMA-IR (Homeostatic model assessment, insulin resistance) | 3.07 ± 1.76 | 2.14 ± 1.02 | <0.0001 |
Fasting plasma insulin (pmol/liter) | 13.51 ± 6.62 | 10.82 ± 4.72 | <0.0001 |
Fasting plasma glucose (pmol/liter) | 89.95 ± 13.72 | 79.31 ± 6.11 | <0.0001 |
SNP genotyping
Two polymorphisms of MTNR1B previously reported in a T2DM association study were selected and were genotyped using a TaqMan [15] assay in the Korean population. Genotyping quality control was performed in 10% of the samples by duplicate checking (rate of concordance in duplicates > 99%). The genotyping call success rates were 98.07% and 97.71% for rs1387153 and rs10830963, respectively. The probes used were C_1932612_10 for rs1387153 and C_3256858_10 for rs10830963.
Statistical analyses
The allele distributions of polymorphisms among GDM patients and normal subjects were evaluated by x 2 models calculating odds ratios (ORs), 95% confidence intervals (CIs), and corresponding P values. We used SAS version 9.1 (SAS Inc., Cary, NC) for the calculation. Multiple regressions were used for association analyses of GDM-related phenotypes adjusting for age and body mass index (BMI) as covariates, also using SAS version 9.1. Linkage disequilibrium between the two SNPs were calculated by the Haploview v4.1 software downloaded from the Broad Institute http://www.broadinstitute.org/mpg/haploview[16]. Statistical power of association was calculated by using Power for Genetic Association software [17]. For the calculation, disease prevalence of GDM was estimated to be 3%, based on previous researches [18], with risk allele frequencies of 0.503 and 0.521 for rs1387153 and rs10830963 respectively, and odd ratios of 1.3 and 1.35, also for respective polymorphisms. With these parameters, it was calculated that our sample of 928 cases and 990 controls would have over 90% statistical power with a type I error rate of 0.05. In order to correct the data for multiple testing, Bonferroni correction was applied. Also, we used PHASE software for haplotype inference [19], and inferred haplotypes were analyzed using SAS version 9.1 for the logistic analyses.
Also, we used PHASE software to estimate individual haplotypes and their frequencies, which uses a Bayesian approach. Individuals with phase probabilities less than 97% were excluded in analysis. To analyze the associations of haplotypes, we used Haplo.stats http://mayoresearch.mayo.edu/schaid_lab/software.cfm, which provides several haplotype-specific tests for association, as well as adjustment for non-genetic covariates and computation of simulation P-values. We also conducted conditional analyses with PLINK software http://pngu.mgh.harvard.edu/~purcell/plink/.
Results
Nine hundred and twenty eight GDM patients were recruited for the present study, and we also recruited 990 pregnant women with normal glucose tolerance as controls. The clinical profiles of the subjects are summarized in Table 1, with characteristics related with T2DM such as the area under glucose curve (AUC-G), fasting plasma insulin (FPI), and fasting plasma glucose (FPG). We also obtained homeostatic model assessment data for both groups in beta-cell function and insulin resistance (HOMA-B and HOMA-IR, respectively). Most of the phenotypes investigated for the subjects showed significant difference between the GDM patients group and the control group, (Table 1, P value < 0.0001 for all phenotypes except mean gestational week), which was to be expected because the phenotypes that showed the significant differences were associated with the diabetic condition. GDM patients were older, possessed higher BMI than the NGT women and clearly exhibited the clinical characteristics of T2DM, as shown in Table 1; in comparison to NGT women, GDM patients exhibited higher blood glucose levels and lower beta-cell function when insulin resistance was increased.
Allele and genotype distributions of MTNR1B polymorphisms in GDM and control subjects
Loci | Genotype | RAF | N (%) | Referent | Co-dominant | Dominant | Recessive | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
GDM | Controls | GDM | Controls | OR(95%CI) | Pcor. | OR(95%CI) | Pcor. | OR(95%CI) | Pcor. | OR(95%CI) | Pcor. | ||
CC | 235(25.9%) | 313(32.2%) | 1 | ||||||||||
CT | 433(47.6%) | 455(46.8%) | 1.33 | 0.1 | 1.3 | 0.0008 | 1.44 | 0.008 | 1.42 | 0.03 | |||
rs1387153 | 0.503 | 0.444 | (1.06-1.66) | (1.14-1.49) | (1.17-1.78) | (1.14-1.78) | |||||||
TT | 241(26.5%) | 204(21.0%) | 1.29 | 0.001 | |||||||||
(1.13-1.47) | |||||||||||||
CC | 217(23.9%) | 294(30.4%) | 1 | ||||||||||
CG | 435(47.9%) | 469(48.6%) | 1.31 | 0.2 | 1.35 | 0.0001 | 1.46 | 0.006 | 1.54 | 0.001 | |||
rs10830963 | 0.521 | 0.453 | (1.04-1.65) | (1.18-1.54) | (1.18-1.81) | (1.23-1.92) | |||||||
GG | 256(28.2%) | 203(21.0%) | 1.34 | 0.0001 | |||||||||
(1.18-1.53) |
Haplotype association analyses with GDM and GDM-related phenotypes
Haplotype | Frequency | Phenotype | ||||||
---|---|---|---|---|---|---|---|---|
GDM(case/control) | BMI | AUC_G* | HOMA_B* | HOMA_IR* | FPI* | FPG | ||
ht1 (C-C) | 0.452 | 0.0001 | 0.93 | 0.0004 | 0.002 | 0.19 | 0.35 | 0.01 |
ht2 (T-G) | 0.486 | 0.000001 | 0.8 | 0.00007 | 0.002 | 0.08 | 0.23 | 0.002 |
ht3 (C-G) | 0.045 | 0.25 | 0.15 | 0.25 | 0.8 | 0.66 | 0.94 | 0.17 |
ht4 (T-C) | 0.018 | 0.008 | 0.42 | 0.94 | 0.82 | 0.37 | 0.44 | 0.77 |
MTNR1B haplotypes' genotype distribution, means and standard deviations of various phenotypes for GDM
Phenotype | Loci | C/C | C/R | R/R |
---|---|---|---|---|
BMI | ht1 | 518(22.34 ± 3.70) | 904(22.34 ± 3.62) | 454(22.35 ± 3.59) |
ht2 | 584(22.39 ± 3.64) | 896(22.35 ± 3.63) | 396(22.28 ± 3.62) | |
ht3 | 1719(22.31 ± 3.58) | 152(22.72 ± 4.04) | 5(23.54 ± 6.94) | |
ht4 | 1788(22.36 ± 3.65) | 86(22.16 ± 3.21) | 2(19.80 ± 1.41) | |
AUC_G | ht1 | 449(438.33 ± 74.35) | 757(429.75 ± 79.48) | 375(420.39 ± 84.28) |
ht2 | 480(420.98 ± 83.23) | 753(430.50 ± 78.91) | 348(441.20 ± 73.83) | |
ht3 | 1446(430.46 ± 80.09) | 130(425.51 ± 72.41) | 5(405.10 ± 75.33) | |
ht4 | 1516(429.95 ± 79.07) | 64(431.35 ± 88.89) | 1(361.50) | |
HOMA_B | ht1 | 509(225.47 ± 126.01) | 898(240.16 ± 144.09) | 448(254.96 ± 200.10) |
ht2 | 577(251.20 ± 186.38) | 889(240.64 ± 143.02) | 389(220.51 ± 128.44) | |
ht3 | 1700(239.45 ± 157.09) | 150(242.64 ± 138.61) | 5(239.14 ± 23.78) | |
ht4 | 1768(239.99 ± 157.73) | 85(232.89 ± 98.82) | 2(280.10 ± 66.75) | |
HOMA_IR | ht1 | 512(2.66 ± 1.60) | 898(2.58 ± 1.45) | 450(2.54 ± 1.50) |
ht2 | 579(2.54 ± 1.61) | 889(2.59 ± 1.34) | 392(2.68 ± 1.68) | |
ht3 | 1705(2.59 ± 1.46) | 150(2.65 ± 1.95) | 5(2.58 ± 1.28) | |
ht4 | 1773(2.60 ± 1.52) | 85(2.42 ± 1.16) | 2(2.00 ± 0.24) | |
FPI | ht1 | 512(12.34 ± 6.48) | 898(12.09 ± 5.64) | 450(12.00 ± 5.71) |
ht2 | 579(11.98 ± 6.12) | 889(12.15 ± 5.31) | 392(12.35 ± 6.78) | |
ht3 | 1705(12.11 ± 5.74) | 150(12.45 ± 7.54) | 5(12.36 ± 4.49) | |
ht4 | 1773(12.17 ± 5.96) | 85(11.51 ± 4.61) | 2(10.50 ± 0.71) | |
FPG | ht1 | 449(86.46 ± 12.66) | 757(85.32 ± 11.89) | 375(84.49 ± 13.30) |
ht2 | 480(84.49 ± 12.83) | 753(85.40 ± 11.82) | 348(86.86 ± 13.23) | |
ht3 | 1446(85.53 ± 12.66) | 130(84.65 ± 10.31) | 5(82.20 ± 9.36) | |
ht4 | 1516(85.44 ± 12.50) | 64(85.64 ± 11.87) | 1(80.00) |
Multiple regression analyses of MTNR1B polymorphisms with diabetes-related phenotypes among all subjects
Phenotype | Loci | C/C | C/R | R/R | Pacor. | Pbcor. | Pccor. |
---|---|---|---|---|---|---|---|
BMI | rs1387153 | 548(22.38 ± 3.65) | 888(22.37 ± 3.65) | 445(22.24 ± 3.56) | 1 | 1 | 1 |
rs10830963 | 511(22.34 ± 3.60) | 904(22.33 ± 3.58) | 459(22.37 ± 3.75) | 1 | 1 | 1 | |
AUC_G* | rs1387153 | 454(419.91 ± 81.85) | 743(431.31 ± 79.96) | 386(439.26 ± 74.28) | 0.0007 | 0.004 | 0.03 |
rs10830963 | 419(421.23 ± 85.41) | 763(429.09 ± 78.39) | 403(439.14 ± 74.58) | 0.002 | 0.04 | 0.03 | |
HOMA_B* | rs1387153 | 541(252.38 ± 190.86) | 882(239.90 ± 143.11) | 437(222.90 ± 125.81) | 0.01 | 0.29 | 0.07 |
rs10830963 | 505(251.36 ± 191.00) | 897(240.78 ± 143.84) | 451(223.74 ± 128.37) | 0.03 | 0.72 | 0.04 | |
HOMA_IR* | rs1387153 | 543(2.53 ± 1.63) | 882(2.59 ± 1.35) | 440(2.64 ± 1.62) | 1 | 1 | 1 |
rs10830963 | 507(2.53 ± 1.47) | 897(2.57 ± 1.43) | 454(2.69 ± 1.65) | 1 | 1 | 1 | |
FPI* | rs1387153 | 543(11.98 ± 6.21) | 882(12.15 ± 5.34) | 440(12.26 ± 6.56) | 1 | 1 | 1 |
rs10830963 | 507(11.91 ± 5.59) | 897(12.07 ± 5.61) | 454(12.44 ± 6.64) | 1 | 1 | 1 | |
FPG | rs1387153 | 454(84.30 ± 12.71) | 743(85.55 ± 12.01) | 386(86.58 ± 12.94) | 0.01 | 0.07 | 0.22 |
rs10830963 | 419(84.63 ± 13.24) | 763(85.13 ± 11.62) | 403(86.71 ± 12.99) | 0.04 | 0.72 | 0.07 |
Conditional association analyses of MTNR1B genetic variants
Phenotype | Loci | P | Conditioned P value by | |
---|---|---|---|---|
rs1387153 | rs10830963 | |||
GDM | rs1387153 | 0.00008 | - | 0.007 |
rs10830963 | 0.00001 | 0.22 | - | |
BMI | rs1387153 | 0.66 | - | 0.59 |
rs10830963 | 0.77 | 0.51 | - | |
AUC_G* | rs1387153 | 0.00002 | - | 0.34 |
rs10830963 | 0.00006 | 0.11 | - | |
HOMA_B* | rs1387153 | 0.0004 | - | 0.25 |
rs10830963 | 0.0007 | 0.07 | - | |
HOMA_IR* | rs1387153 | 0.15 | - | 0.28 |
rs10830963 | 0.14 | 0.12 | - | |
FPI* | rs1387153 | 0.58 | - | 0.89 |
rs10830963 | 0.49 | 0.49 | - | |
FPG | rs1387153 | 0.0004 | - | 0.36 |
rs10830963 | 0.001 | 0.85 | - |
Discussion
In previous studies on the rs1387153 and rs10830963, researchers found strong associations between the two SNPs of MTNR1B and T2DM, and also with FPG levels, which is an important phenotype for diabetes. A study in European population, which included French and Danish among other nations, showed that rs1387153 was significantly associated with FPG level (P = 1.3 × 10-7, adjusted genome-wide P = 0.04) and T2DM (OR (95% CI) = 1.15(1.08-1.22), P = 6.3 × 10-5). Another study in European population found significant associations of rs10830963 with FPG (P = 3.2 × 10-50) and T2DM (OR (95% CI) = 1.09(1.05-1.12), P = 3.3 × 10-7). The Two SNPs were closely related with each other, as evidenced by the linkage disequilibrium test (|D'| = 0.89) in our study. Their haplotype analyses results showed that ht1 (C/C) and ht2 (T/G) were mostly tagged by rs10830963 and rs1387153, respectively (>92%). Therefore, ht1/ht2 showed similar associations with each SNP, respectively.
Here, we performed the association studies in Korean pregnant women and we found significant associations between the SNPs and GDM, with enough samples for high statistical power. It is well known that T2DM and GDM are closely related diseases, since they exhibit similar characteristics such as glucose intolerance. However, there had yet to be a study that looked into the association between the polymorphisms of MTNR1B and GDM, and our study confirmed the relations between the two. Also, we carried out regression analyses between the polymorphisms of MTNR1B and various phenotypes including FPG. Although both SNPs showed associations with GDM and FPG, our results suggested that the two genetic variants of MTNR1B were stronger risk factors for GDM in Korean population compared to the previous results for T2DM in European population (OR (95% CI) = 1.44 (1.17-1.78) for rs1387153 in dominant inheritance model and 1.46 (1.18 - 1.81) for rs10830963 in the present study and OR = 1.15 (1.08-1.22) for rs1387153 and OR = 1.09 (1.05-1.12) for rs10830963 in the two previous studies). We suspect that the genetic differences between GDM and T2DM and the population difference between Europeans and Asians could have contributed to this result. Previously, there have been a few cases where a gene associated with T2DM was not associated with GDM at all [20], or showed different effect sizes [21, 22], and our results suggest that MTNR1B affects T2DM and GDM in varying degrees as well.
Moreover, our results suggest that the two polymorphisms investigated are associated with beta-cell function (Table 5). Association between beta-cell function and MTNR1B was previously reported [18, 23], which shows that we were able to replicate the result in Korean population, strengthening the notion that MTNR1B polymorphisms are related with impaired beta-cell function. Recently, several groups of scientists have studied the association between the gene variants of MTNR1B and glucose tolerances. rs10830963 was found to be associated with FPG and decreased beta-cell function in a group of obese children, which is consistent with our finding [24]. Three independent studies of the MTNR1B genetic variants in Han Chinese subjects also found significant associations for increased FPG, impaired beta-cell function, glycated hemoglobin, and T2DM [18, 25, 26]. Furthermore, a study with European populations also found significant associations between variations of MTNR1B with BMI and FPG, but not with maturity-onset diabetes of the young (MODY) or T2DM [27]. Although some of their results do not agree with each other in the association with T2DM, these findings firmly back up the association between the SNPs in our study and FPG or impaired beta-cell function. On the other hand, we could not find any significant associations between insulin resistance (HOMA-IR) and the two genetic variants. Since our results are backed with high statistical power, this leads us to conclude that two genetic variants of MTNR1B may be associated with the disease by affecting glucose metabolism through impaired insulin secretion, as previously suggested by other studies with KCNQ1 genetic variants [8, 18, 28].
In addition, further conditional analyses results suggested that rs10830963 might be more likely functional in case/control analysis, although not clear in GDM-related phenotype analyses.
Although our results showed the significant associations with GDM and several diabetic characteristics, there are a couple of limitations. First, our study only concentrated on pregnant women among Korean population, so we cannot conclude that the MTNR1B gene variants are associated with FPG or impaired beta-cell function in all Korean population. Also, even though our study strongly suggests that the SNPs may also be associated with T2DM in Korean population, this is not confirmed yet. Any further studies on these two genetic variants in Korean population should concentrate on these parts.
Conclusions
The present study showed that two MTNR1B polymorphisms were associated with increased risk for GDM in Korean female population. Two polymorphisms rs1387153 and rs10830963 also showed significant associations with FPG and beta-cell function, but not with insulin resistance. Further conditional analyses results suggested that rs10830963 might be more likely functional in case/control analysis, although not clear in GDM-related phenotype analyses. The effective sizes found between the two polymorphisms and FPG was stronger compared to previous studies, which is possibly due to the genetic difference between European and Korean populations, or the difference between GDM and T2DM. Based on the current results, we suspect that these two polymorphisms will have significant associations with increased risk of GDM in other populations as well. Also, our discovery would be helpful for understanding of genetic etiology of GDM as well.
Notes
Declarations
Acknowledgements
This study was supported by a grant from the Korean Research Foundation in Korea (R04-2008-000-10078-0), Korea Science and Engineering Foundation (KOSEF) funded by the Korea government (MEST) (No. 2009-0080157), and the Korea Health 21 R&D Project, Ministry of Health & Welfare, Republic of Korea (Grant No. A050463).
Authors’ Affiliations
References
- Cho YM, Kim TH, Lim S, Choi SH, Shin HD, Lee HK, Park KS, Jang HC: Type 2 diabetes-associated genetic variants discovered in the recent genome-wide association studies are related to gestational diabetes mellitus in the Korean population. Diabetologia. 2009, 52: 253-261. 10.1007/s00125-008-1196-4.View ArticlePubMedGoogle Scholar
- Metzger BE: Summary and recommendations of the Third International Workshop-Conference on Gestational Diabetes Mellitus. Diabetes. 1991, 40 (Suppl 2): 197-201.View ArticlePubMedGoogle Scholar
- Amankwah K, Prentice R, Fleury F: The incidence of gestational diabetes. Obstet Gynecol. 1977, 49: 497-498.PubMedGoogle Scholar
- Jovanovic L, Pettitt DJ: Gestational diabetes mellitus. Jama. 2001, 286: 2516-2518. 10.1001/jama.286.20.2516.View ArticlePubMedGoogle Scholar
- Buchanan TA, Xiang AH: Gestational diabetes mellitus. J Clin Invest. 2005, 115: 485-491.View ArticlePubMedPubMed CentralGoogle Scholar
- Evans JC, Frayling TM, Cassell PG, Saker PJ, Hitman GA, Walker M, Levy JC, O'Rahilly S, Rao PV, Bennett AJ, Jones EC, Menzel S, Prestwich P, Simecek N, Wishart M, Dhillon R, Fletcher C, Millward A, Demaine A, Wilkin T, Horikawa Y, Cox NJ, Bell GI, Ellard S, McCarthy MI, Hattersley AT: Studies of association between the gene for calpain-10 and type 2 diabetes mellitus in the United Kingdom. Am J Hum Genet. 2001, 69: 544-552. 10.1086/323315.View ArticlePubMedPubMed CentralGoogle Scholar
- Salonen JT, Uimari P, Aalto JM, Pirskanen M, Kaikkonen J, Todorova B, Hypponen J, Korhonen VP, Asikainen J, Devine C, Tuomainen TP, Luedemann J, Nauck M, Kerner W, Stephens RH, New JP, Ollier WE, Gibson JM, Payton A, Horan MA, Pendleton N, Mahoney W, Meyre D, Delplanque J, Froguel P, Luzzatto O, Yakir B, Darvasi A: Type 2 diabetes whole-genome association study in four populations: the DiaGen consortium. Am J Hum Genet. 2007, 81: 338-345. 10.1086/520599.View ArticlePubMedPubMed CentralGoogle Scholar
- Prokopenko I, Langenberg C, Florez JC, Saxena R, Soranzo N, Thorleifsson G, Loos RJ, Manning AK, Jackson AU, Aulchenko Y, et al: Variants in MTNR1B influence fasting glucose levels. Nat Genet. 2009, 41: 77-81. 10.1038/ng.290.View ArticlePubMedGoogle Scholar
- Bouatia-Naji N, Bonnefond A, Cavalcanti-Proenca C, Sparso T, Holmkvist J, Marchand M, Delplanque J, Lobbens S, Rocheleau G, Durand E, et al: A variant near MTNR1B is associated with increased fasting plasma glucose levels and type 2 diabetes risk. Nat Genet. 2009, 41: 89-94. 10.1038/ng.277.View ArticlePubMedGoogle Scholar
- Peschke E, Frese T, Chankiewitz E, Peschke D, Preiss U, Schneyer U, Spessert R, Muhlbauer E: Diabetic Goto Kakizaki rats as well as type 2 diabetic patients show a decreased diurnal serum melatonin level and an increased pancreatic melatonin-receptor status. J Pineal Res. 2006, 40: 135-143. 10.1111/j.1600-079X.2005.00287.x.View ArticlePubMedGoogle Scholar
- Lee YH, Kang ES, Kim SH, Han SJ, Kim CH, Kim HJ, Ahn CW, Cha BS, Nam M, Nam CM, Lee HC: Association between polymorphisms in SLC30A8, HHEX, CDKN2A/B, IGF2BP2, FTO, WFS1, CDKAL1, KCNQ1 and type 2 diabetes in the Korean population. J Hum Genet. 2008, 53: 991-998. 10.1007/s10038-008-0341-8.View ArticlePubMedGoogle Scholar
- Carpenter MW, Coustan DR: Criteria for screening tests for gestational diabetes. Am J Obstet Gynecol. 1982, 144: 768-773.View ArticlePubMedGoogle Scholar
- Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC: Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985, 28: 412-419. 10.1007/BF00280883.View ArticlePubMedGoogle Scholar
- Krzyzanowska K, Zemany L, Krugluger W, Schernthaner GH, Mittermayer F, Schnack C, Rahman R, Brix J, Kahn BB, Schernthaner G: Serum concentrations of retinol-binding protein 4 in women with and without gestational diabetes. Diabetologia. 2008, 51: 1115-1122. 10.1007/s00125-008-1009-9.View ArticlePubMedPubMed CentralGoogle Scholar
- Livak KJ: Allelic discrimination using fluorogenic probes and the 5' nuclease assay. Genet Anal. 1999, 14: 143-149.View ArticlePubMedGoogle Scholar
- Barrett JC, Fry B, Maller J, Daly MJ: Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005, 21: 263-265. 10.1093/bioinformatics/bth457.View ArticlePubMedGoogle Scholar
- Menashe I, Rosenberg PS, Chen BE: PGA: power calculator for case-control genetic association analyses. BMC Genet. 2008, 9: 36-View ArticlePubMedPubMed CentralGoogle Scholar
- Zhou Q, Zhang K, Li W, Liu JT, Hong J, Qin SW, Ping F, Sun ML, Nie M: Association of KCNQ1 gene polymorphism with gestational diabetes mellitus in a Chinese population. Diabetologia. 2009, 52: 2466-2468. 10.1007/s00125-009-1500-y.View ArticlePubMedGoogle Scholar
- Stephens M, Smith NJ, Donnelly P: A new statistical method for haplotype reconstruction from population data. Am J Hum Genet. 2001, 68: 978-989. 10.1086/319501.View ArticlePubMedPubMed CentralGoogle Scholar
- Shaat N, Lernmark A, Karlsson E, Ivarsson S, Parikh H, Berntorp K, Groop L: A variant in the transcription factor 7-like 2 (TCF7L2) gene is associated with an increased risk of gestational diabetes mellitus. Diabetologia. 2007, 50: 972-979. 10.1007/s00125-007-0623-2.View ArticlePubMedGoogle Scholar
- Grant SF, Thorleifsson G, Reynisdottir I, Benediktsson R, Manolescu A, Sainz J, Helgason A, Stefansson H, Emilsson V, Helgadottir A, Styrkarsdottir U, Magnusson KP, Walters GB, Palsdottir E, Jonsdottir T, Gudmundsdottir T, Gylfason A, Saemundsdottir J, Wilensky RL, Reilly MP, Rader DJ, Bagger Y, Christiansen C, Gudnason V, Sigurdsson G, Thorsteinsdottir U, Gulcher JR, Kong A, Stefansson K: Variant of transcription factor 7-like 2 (TCF7L2) gene confers risk of type 2 diabetes. Nat Genet. 2006, 38: 320-323. 10.1038/ng1732.View ArticlePubMedGoogle Scholar
- Leipold H, Knoefler M, Gruber C, Huber A, Haslinger P, Worda C: Peroxisome proliferator-activated receptor gamma coactivator-1alpha gene variations are not associated with gestational diabetes mellitus. J Soc Gynecol Investig. 2006, 13: 104-107. 10.1016/j.jsgi.2005.12.004.View ArticlePubMedGoogle Scholar
- Staiger H, Machicao F, Schafer SA, Kirchhoff K, Kantartzis K, Guthoff M, Silbernagel G, Stefan N, Haring HU, Fritsche A: Polymorphisms within the novel type 2 diabetes risk locus MTNR1B determine beta-cell function. PLoS One. 2008, 3: e3962-10.1371/journal.pone.0003962.View ArticlePubMedPubMed CentralGoogle Scholar
- Holzapfel C, Siegrist M, Rank M, Langhof H, Grallert H, Baumert J, Irimie C, Klopp N, Wolfarth B, Illig T, Hauner H, Halle M: Association of a MTNR1B gene variant with fasting glucose and HOMA-B in children and adolescents with high BMI-SDS. Eur J Endocrinol. 2010Google Scholar
- Kan MY, Zhou DZ, Zhang D, Zhang Z, Chen Z, Yang YF, Guo XZ, Xu H, He L, Liu Y: Two susceptible diabetogenic variants near/in MTNR1B are associated with fasting plasma glucose in a Han Chinese cohort. Diabet Med. 2010, 27: 598-602. 10.1111/j.1464-5491.2010.02975.x.View ArticlePubMedGoogle Scholar
- Tam CH, Ho JS, Wang Y, Lee HM, Lam VK, Germer S, Martin M, So WY, Ma RC, Chan JC, Ng MC: Common polymorphisms in MTNR1B, G6PC2 and GCK are associated with increased fasting plasma glucose and impaired beta-cell function in Chinese subjects. PLoS One. 2010, 5: e11428-10.1371/journal.pone.0011428.View ArticlePubMedPubMed CentralGoogle Scholar
- Andersson EA, Holst B, Sparso T, Grarup N, Banasik K, Holmkvist J, Jorgensen T, Borch-Johnsen K, Egerod KL, Lauritzen T, Sorensen TI, Bonnefond A, Meyre D, Froguel P, Schwartz TW, Pedersen O, Hansen T: The MTNR1B G24E variant associates with BMI and fasting plasma glucose in the general population in studies of 22,142 Europeans. Diabetes. 2010Google Scholar
- Mussig K, Staiger H, Machicao F, Kirchhoff K, Guthoff M, Schafer SA, Kantartzis K, Silbernagel G, Stefan N, Holst JJ, Gallwitz B, Haring HU, Fritsche A: Association of type 2 diabetes candidate polymorphisms in KCNQ1 with incretin and insulin secretion. Diabetes. 2009, 58: 1715-1720. 10.2337/db08-1589.View ArticlePubMedPubMed CentralGoogle Scholar
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