Abstract
OBJECTIVE
To investigate whether variation in the melatonin receptor 1B gene (MTNR1B), recently identified as a common genetic determinant of fasting glucose levels in healthy, diabetes free individuals is associated with measures of beta-cell function and whole-body insulin sensitivity.
RESEARCH DESIGN AND METHODS
A total of 1,276 healthy individuals of European ancestry were studied at 19 centres of the RISC study. Whole-body insulin sensitivity (M/I) was assessed by hyperinsulinaemic-euglycemic clamp and indices of beta-cell function were derived from a 75-g oral glucose tolerance test (including 30-min insulin response and glucose sensitivity). We studied rs10830963 in MTNR1B using additive genetic models, adjusting for age, sex, and recruitment centre.
RESULTS
The minor (G) allele of rs10830963 in MTNR1B (frequency 0.30 in HapMap CEU; 0.29 in RISC participants) was associated with higher levels of fasting plasma glucose (standardized beta (95% CI) 0.17 (0.085; 0.25) per G allele; p=5.8×10e-5), consistent with recent observations. In addition, the G-allele was significantly associated with lower early insulin response (−0.19 (−0.28; −0.10); p=1.7×10e-5), as well as with decreased beta-cell glucose sensitivity (−0.11 (−0.20; −0.027); p=0.010). No associations were observed with clamp assessed insulin sensitivity (p=0.15) or different measures of body size (all p-values >0.7).
CONCLUSIONS
Genetic variation in MTNR1B is associated with defective early insulin response and decreased beta-cell glucose sensitivity, which may contribute to the higher glucose levels of non-diabetic individuals carrying the minor G allele of rs10830963 in MTNR1B.
INTRODUCTION
Compared to the recent progress in the discovery of genes for type 2 diabetes, our knowledge about genetic influences on fasting glucose (FG) levels in healthy individuals is limited. Common sequence variants related to the glucokinase (GCK) promoter [1-3], the islet specific glucose-6-phosphatase (G6PC2) [2;3], and the glucokinase regulatory protein (GCKR) [4-6] are the most significant determinants of FG levels identified in recent large scale genome wide association studies (GWAS), yet without demonstrable consistent effects on the risk of type 2 diabetes [7]. Vice versa, none of the established type 2 diabetes genes emerged as convincing loci for FG within the normal range in recent GWAS [2;7]. This suggests that common variants contributing to small physiological variation in FG may be different from those increasing type 2 diabetes susceptibility.
One exception is noteworthy; in a recently published exchange of top fasting glucose hits from four large GWAS consortia, variants in the gene encoding the melatonin receptor 1B (MTNR1B) were not only consistently associated with fasting glucose across all studies totalling 36,610 healthy adults included in the meta-analysis of the MTNR1B region [7], but carriers of the risk allele of the most significant overall signal at rs10830963 (minor G allele; frequency 0.30 in HapMap CEU) were also at increased risk for type 2 diabetes (odds ratio (95% confidence interval 1.09 (1.05; 1.12)) in a separate meta-analysis of case-control studies [7]. The novel link between MTNR1B and type 2 diabetes was confirmed in two other studies, one investigating the same variant [8], and another reporting rs1387153 (r2 with rs10830963 = 0.70) as the most significant SNP in a GWAS of 2,151 French participants [2]. Investigating the mechanisms through which MTNR1B contributes to variation in FG levels within healthy, non-diabetic individuals may help to understand what underlies the MTNR1B related risk of progression to clinical diabetes. Lyssenko et al. reported that the risk genotype was associated with impairment of early insulin response to both oral and intravenous glucose and with faster deterioration of insulin secretion over time [8].
Thus our objective was to study whether the association between MTNR1B and fasting glucose is mediated through reduced pancreatic beta-cell function, insulin secretion or whole-body insulin sensitivity in individuals participating in the RISC (Relationship between Insulin Sensitivity and Cardiovascular Disease) study. [9]
RESEARCH DESIGN AND METHODS
The recruitment methods and inclusion and exclusion criteria of the RISC cohort have been described previously [9]. Briefly, healthy men and women of European ancestry, aged between 30 and 60 years, were recruited from 19 centres in 14 European countries. Individuals with diabetes, hypertension, or dyslipidemia were excluded [9]. Analyses presented in this study are based on 1276 participants who met the eligibility criteria and had complete genotype data. Local ethics committee approval was obtained by each recruitment centre, and written consent was obtained from all participants.
Participants underwent a 75-g oral glucose tolerance test (OGTT) with blood sampling before and at 30, 60, 90, and 120 min after the oral glucose load. On a separate day within 1 month of the OGTT, participants underwent a hyperinsulinaemic-euglycemic clamp as previously reported [9]. To ensure consistency across study centres, the clamp procedure was standardized and each centre underwent prestudy training. Clamp data were then transferred and analyzed at the RISC coordinating centre (Pisa, Italy) and quality assured against preset criteria. These were as follows: clamped glucose levels within 20% of target (fasting glucose concentration) and coefficient of variation (CV) of ≤15%, as well as avoidance of hypoglycemia (glucose <3.5 mmol/l). Insulin sensitivity was assessed as the mean glucose infusion rate over the last 40 min of the clamp, corrected for the mean plasma insulin levels achieved during the same period (M/I). Pancreatic beta-cell function was assessed using the OGTT data. The 30-min insulin response was calculated as the ratio of the insulin concentration increment to the 30-min glucose concentration (30 min insulin – 0 min insulin/ 30 min glucose) [10]. We additionally performed sensitivity analyses using other commonly derived indices of early insulin response, including insulinogenic index (30 min insulin – 0 min insulin/ 30 min glucose – 0 min glucose) and corrected insulin response [11].
Indices of beta-cell function parameters were derived from mathematical analysis of plasma glucose and C-peptide, using C-peptide deconvolution, as previously described in more detail [12].
In addition, detailed anthropometric assessment was performed and fat mass determined as the difference between body weight and fat-free mass determined by bioimpedance (Tanita International Division, Yiewsley, U.K.).
Samples were processed and stored locally before being transferred to the central assay laboratories and analyzed as previously reported [9]. Genomic DNA was extracted using a Nucleon BACC2 kit (Tepnel Life Sciences, Manchester, U.K.). All samples were genotyped at KBiosciences [12]; the call rate was 98% and genotype frequencies were in Hardy-Weinberg equilibrium (p-value=0.33). Five percent of DNA samples underwent duplicate genotyping with 100% success.
We normalized the distributions of OGTT and clamp outcomes using the natural logarithm; these data are presented as median and interquartile range. Linear regression analyses using an additive genetic model were performed to test for associations between rs10830963 and selected phenotypes, adjusting for age, sex, and recruitment centre. Standardised measures were used in regression analyses, these were calculated by subtracting the mean of each outcome from an individual's value and dividing this by the standard deviation, separately by sex and using log transformed measures. This results in a normal distribution for each measure with a mean of 0 and standard deviation of 1 and allows comparing the strength of the genotype effect across several outcomes with different distributions and units. As previously reported [12], the cohort had 80% power at p=0.01 to detect differences of 0.18 SD per allele for a minor allele frequency of 0.30.
RESULTS
The minor (G) allele of rs10830963 was significantly associated with higher fasting glucose levels (standardized beta (95% CI) 0.17 (0.085; 0.25) per G allele; p=5.8×10e-5 (figure 1A)), consistent with recent observations. [7] In addition, we observed significantly higher glucose levels at 30 and 60 minutes (standardized beta (95% CI) 0.15 (0.065; 0.23); p=4.7×10e-4 and 0.13 (0.046; 0.21); p=2.3×10e-3, respectively (figure 1A), but not 90 or 120 minutes during the OGTT (p=0.21 and 0.93, respectively).
Figure 1.
Effect of rs10830963 in MTNR1B on Glucose and Insulin Levels during the OGTT (per allele difference and 95% confidence intervals)
Variation at rs10830963 was not associated with whole body insulin sensitivity (M/I) measured by hyperinsulinaemic-euglycemic clamps (standardized beta (95% CI) 0.064 (−0.024; 0.15); p=0.15 (Figure 2)). In contrast, significant differences in indices of pancreatic beta-cell function were found. Individuals carrying the minor (G) allele had significantly lower 30 min insulin response (standardized beta (95% CI) −0.19 (−0.28; −0.10); p=1.7×10e-5 (figure 2)) as well as beta-cell glucose sensitivity (standardized beta (95% CI) −0.11 (−0.20; −0.027); p=0.010). These associations remained significant after additional adjustment for whole body insulin sensitivity (p=0.0001 and 0.014 respectively). Similar results were obtained in sensitivity analyses using the insulinogenic index (p=6.8×10e-5) or corrected insulin response (p=2.7×10e-3) as alternative measures of early insulin response. Associations with insulin (figure 1B) and C-peptide (data not shown) mirrored those observations, with the only time point during the OGTT at which significant differences were found being at 30 minutes (standardized beta (95% CI) −0.15 (−0.23; −0.061); p=8.0×10e-4 for insulin and −0.089 (−0.17; −0.0044); p=0.039 for C-peptide. No significant associations were observed between variation in MTNR1B and different measures of body size (table 1).
Figure 2.
Effect of rs10830963 in MTNR1B on Early Insulin Response, Beta-Cell Glucose Sensitivity and Whole-Body Insulin Sensitivity (M/I) (per allele difference and 95% confidence intervals)
Table 1.
Relationships between rs10830963 genotypes and key phenotypic traits
| Phenotype | Mean (SD)/ Median (IQR) |
Mean (SD), Median(IQR) by genotype | p-value* | ||
|---|---|---|---|---|---|
| CC (Max N=655) |
GC (Max N=505) |
GG (Max N=114) |
|||
| Sex (% Women) | 55.2 | 58.0 | 54.0 | 43.9 | 0.016 |
| Age (yrs) | 43.9 (8.3) | 44 (8.2) | 44 (8.2) | 43 (9.3) | 0.34 |
| BMI (kg/m2) | 25.7 (4.1) | 25.5 (4.0) | 25.6 (4.1) | 25.8 (4.1) | 0.81 |
| Waist (cm) | 86 (77-96) | 85 (76-95) | 86 (78-96) | 88 (79-96) | 0.93 |
| Fat mass (kg) | 21.2 (8.9) | 21.2 (8.9) | 21.0 (8.9) | 20.2 (9.2) | 0.78 |
| Glucose (mmol/l) | |||||
| OGTT 0 mins (fasting) | 5.1 (4.7-5.4) | 5.0 (4.7-5.3) | 5.1 (4.8-5.5) | 5.2 (4.8-5.5) | 5.8×10−5 |
| OGTT 30 mins | 8.0 (6.9-9.3) | 7.8 (6.7-8.9) | 8.2 (7.1-9.4) | 7.9 (7.0-9.5) | 4.7×10−4 |
| OGTT 60 mins | 7.5 (6.0-9.3) | 7.2 (5.8-8.8) | 7.8 (6.3-9.4) | 7.7 (6.1-9.7) | 2.3×10−3 |
| OGTT 90 mins | 6.2 (5.0-7.5) | 6.1 (5.0-7.3) | 6.4 (5.2-7.7) | 6.2 (5.1-7.6) | 0.21 |
| OGTT 120 mins | 5.6 (4.7-6.7) | 5.6 (4.7-6.6) | 5.6 (4.6-6.6) | 5.5 (4.6-6.9) | 0.93 |
| Insulin (pmol/l) | |||||
| OGTT 0 mins (fasting) | 31.0 (21.0-46.0) | 31.0 (21.0-44.0) | 31.0 (21.0-45.5) | 28.5 (19.0-42.0) | 0.32 |
| OGTT 30 mins | 240 (168-355) | 244 (173-358) | 238 (167-347) | 199 (152-297) | 8.0×10−4 |
| OGTT 60 mins | 269 (178-413) | 265 (172-399) | 266 (177-406) | 244 (166-385) | 0.87 |
| OGTT 90 mins | 200 (127-316) | 196 (122-309) | 210 (136-330) | 197 (123-295) | 0.26 |
| OGTT 120 mins | 153 (89-257) | 147 (89-249) | 151 (85-251) | 159 (73-226) | 0.73 |
| C-Peptide (pmol/l) | |||||
| OGTT 0 mins (fasting) | 540 (410-703) | 528 (399-690) | 562 (420-727) | 525 (391-682) | 0.88 |
| OGTT 30 mins | 1891 (1502-2387) | 1910 (1517-2403) | 1894 (1511-2391) | 1704 (1416-2095) | 0.039 |
| OGTT 60 mins | 2509 (2008-3098) | 2440 (1961-3029) | 2558 (2066-3182) | 2562 (1927-3022) | 0.78 |
| OGTT 90 mins | 2406 (1863-3036) | 2336 (1789-2958) | 2509 (1955-3116) | 2512 (1843-3148) | 0.24 |
| OGTT 120 mins | 2154 (1642-2777) | 2121 (1598-2743) | 2213 (1677-2789) | 2252 (1603-2777) | 0.56 |
| 30 min insulin response (pmol/mmol) | 26.3 (18.5-38.7) | 28.0 (19.3-41.4) | 25.9 (18.4-37.0) | 22.0 (14.9-33.8) | 1.7×10−5 |
| Beta-cell glucose sensitivity (pmol min−1 m−2 mM−1) |
113 (79-158) | 118 (82-169) | 109 (74-150) | 102 (74-148) | 0.010 |
| M/I (μmol min−1 kgffm−1 nM−1) | 128 (92-177) | 128 (90-175) | 128 (92-180) | 135 (99-175) | 0.15 |
Comparisons between genotypes (additive model) are based on linear regression analysis of natural log transformed data (where applicable) and using sex-specific standardised outcomes for OGTT and Clamp measures, adjusting for age, sex and recruitment centre. Sex differences were tested using a chi -squared test.
DISCUSSION
Recent evidence from large-scale meta-analysis of GWA studies showed that variation in the melatonin receptor 1B gene (MTNR1B) is a common genetic determinant of fasting glucose in healthy, diabetes free individuals. We show here that variation in MTNR1B is significantly associated with early insulin response and beta-cell glucose sensitivity, while no effect on whole-body insulin sensitivity was observed.
The minor (G) risk allele of rs10830963 in MTNR1B was associated with lower beta-cell glucose sensitivity and 30 min insulin response before and after accounting for whole-body insulin sensitivity levels (M/I). These findings are in keeping with a primary defect of beta cell function rather than secondary changes in response to altered insulin sensitivity, and support the observations of other studies that reported decreased early insulin response and decreased disposition index in G allele carriers of the same variant [8;13]. Interestingly, we found significant associations of rs10830963 with insulin and C-peptide at 30 minutes during the OGTT, but not at any other time point, again highlighting that the main effect appears to be on early phase insulin response. The emerging evidence strongly suggests that the melatonin system modulates directly the insulin secretory response to glucose. It has been shown that MTNR1B is expressed in human islets, and specifically in both pancreatic beta- and alpha-cells [8;14;15]. Furthermore, MTNR1B gene expression was increased in isolated islets from older (>45yrs age) G allele carriers of rs10830963, and exposure of clonal beta-cells to melatonin decreased the acute insulin secretory response to glucose [8]. It has also been postulated that melatonin might influence insulin secretion through a paracrine effect of glucagon [14]. We found that variation in MTNR1B was not associated with fasting glucagon levels (data not shown), but we did not measure the glucagon response during the OGTT.
Melatonin plays a role in the regulation of the circadian clock, and melatonin and insulin both show marked circadian variability [16;17]. Data from human and rodent studies suggests that disturbances of circadian rhythmicity may affect metabolic control and the risk of diabetes [18;19], and over-expression of melatonin receptors has been observed in islets from patients with type 2 diabetes compared to non-diabetic controls [20]. Taken together, these findings suggest that an effect of MTNR1B on the insulin secretory response to glucose may underlie the reported associations with fasting glucose and the risk of type 2 diabetes and add to the body of evidence linking circadian rhythm and metabolic control and disease.
A key observation of our study is that there was no significant association between the MTNR1B variant and whole-body insulin sensitivity (M/I), despite the fact that this is one of the largest collections of healthy people of European ancestry to be phenotyped using the gold standard hyperinsulinaemic-euglycemic clamp technique. Furthermore, we have replicated with the RISC cohort the observations that a common FTO variant and the Pro12Ala PPARG variant do influence whole-body insulin sensitivity in man [12]. It would suggest that if variation in MTNR1B does affect insulin sensitivity, then it is likely to be functionally weak and of questionable clinical significance. In support of this, Staiger et al. recently reported that none of five tagging SNPs covering all common genetic variation of the MTNR1B locus showed an association with clamp derived insulin sensitivity in a selected group of 513 individuals at increased risk for type 2 diabetes [13].
We also found no significant associations between MTNR1B and different measures of body size, suggesting that the effects we see on beta-cell function are not influenced by an alteration in adiposity.
As we recently reported, individual type 2 diabetes risk alleles in TCFL72, HHEX/ IDE and CDKAL1 combine in an additive manner to impact upon pancreatic beta-cell function [21]. Beta-cell glucose sensitivity was decreased by 39% in those individuals with five or more risk alleles compared with those individuals with no risk alleles. When we included the MTNR1B risk variant in the analysis we saw a 47% difference (p=1.5×10−7) between the 0 allele and 6+ allele groups. A similar change was noted for the 30 min insulin response. We previously found a 43% decrease between the 0 and 5+ allele group, and when we included the MTNR1B variant this increased to 49% between the 0 and 6+ allele groups.
We conclude that MTNR1B is associated with defective early insulin response and decreased beta-cell glucose sensitivity, both of which may contribute to the higher glucose levels and increased diabetes risk of individuals carrying the minor G allele of rs10830963. In contrast, no association with whole-body insulin sensitivity was observed in this large collection of healthy people of European ancestry.
ACKNOWLEDGMENTS
The RISC Study is supported by European Union grant QLG1-CT-2001-01252 and AstraZeneca. Laura Pascoe is the recipient of a joint BBSRC and Unilever UK Ltd case PhD studentship. Inês Barroso acknowledges funding from The Wellcome Trust grant 077016/Z/05/Z.
Online Appendix
Members of the RISC Consortium
Amsterdam: R.J. Heine, J Dekker, G Nijpels, W Boorsma, A Kok
Athens: A Mitrakou, S Tournis, K Kyriakopoulou, P Thomakos
Belgrade: N Lalic, K Lalic, A Jotic, L Lukic, M Civcic
Dublin: J Nolan, TP Yeow, M Murphy, C DeLong, G Neary, MP Colgan, M Hatunic, P Gaffney, G Boran
Frankfurt: T Konrad, H Böhles, S Fuellert, F Baer, H Zuchhold
Geneva: A Golay, E. Harsch Bobbioni,V. Barthassat, V. Makoundou, TNO Lehmann, T Merminod
Glasgow: JR Petrie (now Dundee), C Perry, F Neary, C MacDougall, K Shields, L Malcolm
Kuopio: M Laakso, U Salmenniemi, A Aura, R Raisanen, U Ruotsalainen, T Sistonen, M Laitinen, H Saloranta
London: SW Coppack, N McIntosh, P Khadobaksh
Lyon: M Laville, F. Bonnet, A Brac de la Perriere, C Louche-Pelissier, C Maitrepierre, J Peyrat, A Serusclat
Madrid: R. Gabriel, EM Sánchez, R. Carraro, A Friera, B. Novella
Malmö: P Nilsson, M Persson, G Östling, O Melander, P Burri
Milan: PM Piatti, LD Monti, E Setola, E Galluccio, F Minicucci, A Colleluori
Newcastle-upon-Tyne: M Walker, IM Ibrahim, M Jayapaul, D Carman, K Short, Y McGrady, D Richardson, L Pascoe, Sheila Patel
Odense: H Beck-Nielsen, P Staehr, K Hojlund, V Vestergaard, C Olsen, L Hansen
Padova: A Mari, G Pacini, C Cavaggion
Paris : B Balkau, L Mhamdi, MT Guillanneuf
Perugia: GB Bolli, F Porcellati, C Fanelli, P Lucidi, F Calcinaro, A Saturni
Pisa: E Ferrannini, A Natali, E Muscelli, S Pinnola, M Kozakova, BD Astiarraga, SA Hills, L Landucci, L Mota, A Gastaldelli, D Ciociaro
Rome: G Mingrone, C Guidone, A Favuzzi. P Di Rocco
Vienna: C Anderwald, M Bischof, M Promintzer, M Krebs, M Mandl, A Hofer, A Luger, W Waldhäusl, M Roden
Footnotes
CONFLICT OF INTEREST
The authors declare that there is no duality of interest associated with this manuscript.
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