Skip to main content
SpringerLink
Log in

MTNR1B genotype and effects of carbohydrate quantity and dietary glycaemic index on glycaemic response to an oral glucose load: the OmniCarb trial

  • Article
  • Published:
Diabetologia Aims and scope Submit manuscript

Abstract

Aims/hypothesis

A type 2 diabetes-risk-increasing variant, MTNR1B (melatonin receptor 1B) rs10830963, regulates the circadian function and may influence the variability in metabolic responses to dietary carbohydrates. We investigated whether the effects of carbohydrate quantity and dietary glycaemic index (GI) on glycaemic response during OGTTs varied by the risk G allele of MTNR1B-rs10830963.

Methods

This study included participants (n=150) of a randomised crossover-controlled feeding trial of four diets with high/low GI levels and high/low carbohydrate content for 5 weeks. The MTNR1B-rs10830963 (C/G) variant was genotyped. Glucose response during 2 h OGTT was measured at baseline and the end of each diet intervention.

Results

Among the four study diets, carrying the risk G allele (CG/GG vs CC genotype) of MTNR1B-rs10830963 was associated with the largest AUC of glucose during 2 h OGTT after consuming a high-carbohydrate/high-GI diet (β 134.32 [SE 45.69] mmol/l × min; p=0.004). The risk G-allele carriers showed greater increment of glucose during 0–60 min (β 1.26 [0.47] mmol/l; p=0.008) or 0–90 min (β 1.10 [0.50] mmol/l; p=0.028) after the high-carbohydrate/high-GI diet intervention, but not after consuming the other three diets. At high carbohydrate content, reducing GI levels decreased 60 min post-OGTT glucose (mean –0.67 [95% CI: –1.18, –0.17] mmol/l) and the increment of glucose during 0–60 min (mean –1.00 [95% CI: –1.67, –0.33] mmol/l) and 0–90 min, particularly in the risk G-allele carriers (pinteraction <0.05 for all).

Conclusions/interpretation

Our study shows that carrying the risk G allele of MTNR1B-rs10830963 is associated with greater glycaemic responses after consuming a diet with high carbohydrates and high GI levels. Reducing GI in a high-carbohydrate diet may decrease post-OGTT glucose concentrations among the risk G-allele carriers.

Graphical Abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Abbreviations

DI:

Disposition index

GEE:

Generalised estimating equation

GI:

Glycaemic index

IGI:

Insulinogenic index

MAF:

Minor allele frequency

MIS:

Michigan Imputation Server

QC:

Quality control

References

  1. Garaulet M, Qian J, Florez JC, Arendt J, Saxena R, Scheer F (2020) Melatonin effects on glucose metabolism: time to unlock the controversy. Trends Endocrinol Metab 31(3):192–204. https://doi.org/10.1016/j.tem.2019.11.011

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Zhu H, Zhao ZJ, Liu HY et al (2023) The melatonin receptor 1B gene links circadian rhythms and type 2 diabetes mellitus: an evolutionary story. Ann Med 55(1):1262–1286. https://doi.org/10.1080/07853890.2023.2191218

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Lane JM, Chang AM, Bjonnes AC et al (2016) Impact of common diabetes risk variant in MTNR1B on sleep, circadian, and melatonin physiology. Diabetes 65(6):1741–1751. https://doi.org/10.2337/db15-0999

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Vujkovic M, Keaton JM, Lynch JA et al (2020) Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nat Genet 52(7):680–691. https://doi.org/10.1038/s41588-020-0637-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Mahajan A, Spracklen CN, Zhang W et al (2022) Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat Genet 54(5):560–572. https://doi.org/10.1038/s41588-022-01058-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Tuomi T, Nagorny CLF, Singh P et al (2016) Increased melatonin signaling is a risk factor for type 2 diabetes. Cell Metab 23(6):1067–1077. https://doi.org/10.1016/j.cmet.2016.04.009

    Article  CAS  PubMed  Google Scholar 

  7. Gaulton KJ, Ferreira T, Lee Y et al (2015) Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat Genet 47(12):1415–1425. https://doi.org/10.1038/ng.3437

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Lyssenko V, Nagorny CL, Erdos MR et al (2009) Common variant in MTNR1B associated with increased risk of type 2 diabetes and impaired early insulin secretion. Nat Genet 41(1):82–88. https://doi.org/10.1038/ng.288

    Article  CAS  PubMed  Google Scholar 

  9. Sparsø T, Bonnefond A, Andersson E et al (2009) G-allele of intronic rs10830963 in MTNR1B confers increased risk of impaired fasting glycemia and type 2 diabetes through an impaired glucose-stimulated insulin release: studies involving 19,605 Europeans. Diabetes 58(6):1450–1456. https://doi.org/10.2337/db08-1660

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Langenberg C, Pascoe L, Mari A et al (2009) Common genetic variation in the melatonin receptor 1B gene (MTNR1B) is associated with decreased early-phase insulin response. Diabetologia 52(8):1537–1542. https://doi.org/10.1007/s00125-009-1392-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Stancáková A, Kuulasmaa T, Paananen J et al (2009) Association of 18 confirmed susceptibility loci for type 2 diabetes with indices of insulin release, proinsulin conversion, and insulin sensitivity in 5,327 nondiabetic Finnish men. Diabetes 58(9):2129–2136. https://doi.org/10.2337/db09-0117

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Florez JC, Jablonski KA, McAteer JB et al (2012) Effects of genetic variants previously associated with fasting glucose and insulin in the Diabetes Prevention Program. PLoS One 7(9):e44424. https://doi.org/10.1371/journal.pone.0044424

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  13. Jonsson A, Ladenvall C, Ahluwalia TS et al (2013) Effects of common genetic variants associated with type 2 diabetes and glycemic traits on α- and β-cell function and insulin action in humans. Diabetes 62(8):2978–2983. https://doi.org/10.2337/db12-1627

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Dimas AS, Lagou V, Barker A et al (2014) Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity. Diabetes 63(6):2158–2171. https://doi.org/10.2337/db13-0949

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Wood AR, Jonsson A, Jackson AU et al (2017) A genome-wide association study of IVGTT-based measures of first-phase insulin secretion refines the underlying physiology of type 2 diabetes variants. Diabetes 66(8):2296–2309. https://doi.org/10.2337/db16-1452

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Haljas K, Hakaste L, Lahti J et al (2019) The associations of daylight and melatonin receptor 1B gene rs10830963 variant with glycemic traits: the prospective PPP-Botnia study. Ann Med 51(1):58–67. https://doi.org/10.1080/07853890.2018.1564357

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Vejrazkova D, Vankova M, Vcelak J et al (2022) The rs10830963 polymorphism of the MTNR1B gene: association with abnormal glucose, insulin and C-peptide kinetics. Front Endocrinol (Lausanne) 13:868364. https://doi.org/10.3389/fendo.2022.868364

    Article  PubMed  Google Scholar 

  18. Dashti HS, Follis JL, Smith CE et al (2015) Gene-environment interactions of circadian-related genes for cardiometabolic traits. Diabetes Care 38(8):1456–1466. https://doi.org/10.2337/dc14-2709

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Garaulet M, Lopez-Minguez J, Dashti HS et al (2022) Interplay of dinner timing and MTNR1B type 2 diabetes risk variant on glucose tolerance and insulin secretion: a randomized crossover trial. Diabetes Care 45(3):512–519. https://doi.org/10.2337/dc21-1314

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Goni L, Sun D, Heianza Y et al (2019) A circadian rhythm-related MTNR1B genetic variant modulates the effect of weight-loss diets on changes in adiposity and body composition: the POUNDS lost trial. Eur J Nutr 58(4):1381–1389. https://doi.org/10.1007/s00394-018-1660-y

    Article  CAS  PubMed  Google Scholar 

  21. Mirzaei K, Xu M, Qi Q et al (2014) Variants in glucose- and circadian rhythm-related genes affect the response of energy expenditure to weight-loss diets: the POUNDS LOST Trial. Am J Clin Nutr 99(2):392–399. https://doi.org/10.3945/ajcn.113.072066

    Article  CAS  PubMed  Google Scholar 

  22. de Luis DA, Izaola O, Primo D, Aller R (2018) Association of the rs10830963 polymorphism in melatonin receptor type 1B (MTNR1B) with metabolic response after weight loss secondary to a hypocaloric diet based in Mediterranean style. Clin Nutr 37(5):1563–1568. https://doi.org/10.1016/j.clnu.2017.08.015

    Article  CAS  PubMed  Google Scholar 

  23. Lopez-Minguez J, Saxena R, Bandín C, Scheer FA, Garaulet M (2018) Late dinner impairs glucose tolerance in MTNR1B risk allele carriers: a randomized, cross-over study. Clin Nutr 37(4):1133–1140. https://doi.org/10.1016/j.clnu.2017.04.003

    Article  CAS  PubMed  Google Scholar 

  24. Pasmans K, Meex RCR, van Loon LJC, Blaak EE (2022) Nutritional strategies to attenuate postprandial glycemic response. Obes Rev 23(9):e13486. https://doi.org/10.1111/obr.13486

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Hosseini F, Jayedi A, Khan TA, Shab-Bidar S (2022) Dietary carbohydrate and the risk of type 2 diabetes: an updated systematic review and dose-response meta-analysis of prospective cohort studies. Sci Rep 12(1):2491. https://doi.org/10.1038/s41598-022-06212-9

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  26. Greenwood DC, Threapleton DE, Evans CE et al (2013) Glycemic index, glycemic load, carbohydrates, and type 2 diabetes: systematic review and dose-response meta-analysis of prospective studies. Diabetes Care 36(12):4166–4171. https://doi.org/10.2337/dc13-0325

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Hardy DS, Garvin JT, Xu H (2020) Carbohydrate quality, glycemic index, glycemic load and cardiometabolic risks in the US, Europe and Asia: a dose-response meta-analysis. Nutr Metab Cardiovasc Dis 30(6):853–871. https://doi.org/10.1016/j.numecd.2019.12.050

    Article  CAS  PubMed  Google Scholar 

  28. Sacks FM, Carey VJ, Anderson CA et al (2014) Effects of high vs low glycemic index of dietary carbohydrate on cardiovascular disease risk factors and insulin sensitivity: the OmniCarb randomized clinical trial. JAMA 312(23):2531–2541. https://doi.org/10.1001/jama.2014.16658

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Zeevi D, Korem T, Zmora N et al (2015) Personalized nutrition by prediction of glycemic responses. Cell 163(5):1079–1094. https://doi.org/10.1016/j.cell.2015.11.001

    Article  CAS  PubMed  Google Scholar 

  30. Berry SE, Valdes AM, Drew DA et al (2020) Human postprandial responses to food and potential for precision nutrition. Nat Med 26(6):964–973. https://doi.org/10.1038/s41591-020-0934-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Juraschek SP, Miller ER 3rd, Appel LJ, Christenson RH, Sacks FM, Selvin E (2017) Effects of dietary carbohydrate on 1,5-anhydroglucitol in a population without diabetes: results from the OmniCarb trial. Diabet Med 34(10):1407–1413. https://doi.org/10.1111/dme.13391

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Matsuda M, DeFronzo RA (1999) Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care 22(9):1462–1470. https://doi.org/10.2337/diacare.22.9.1462

    Article  CAS  PubMed  Google Scholar 

  33. Abdul-Ghani MA, Williams K, DeFronzo RA, Stern M (2007) What is the best predictor of future type 2 diabetes? Diabetes Care 30(6):1544–1548. https://doi.org/10.2337/dc06-1331

    Article  PubMed  Google Scholar 

  34. ElSayed NA, Aleppo G, Aroda VR et al (2023) 2. Classification and diagnosis of diabetes: standards of care in diabetes-2023. Diabetes Care 46(Suppl 1):S19–S40. https://doi.org/10.2337/dc23-S002

    Article  CAS  PubMed  Google Scholar 

  35. Ramos E, Chen G, Shriner D et al (2011) Replication of genome-wide association studies (GWAS) loci for fasting plasma glucose in African-Americans. Diabetologia 54(4):783–788. https://doi.org/10.1007/s00125-010-2002-7

    Article  CAS  PubMed  Google Scholar 

  36. Takeuchi F, Katsuya T, Chakrewarthy S et al (2010) Common variants at the GCK, GCKR, G6PC2-ABCB11 and MTNR1B loci are associated with fasting glucose in two Asian populations. Diabetologia 53(2):299–308. https://doi.org/10.1007/s00125-009-1595-1

    Article  CAS  PubMed  Google Scholar 

  37. de Luis DA, Izaola O, Primo D, Aller R (2020) A circadian rhythm-related MTNR1B genetic variant (rs10830963) modulate body weight change and insulin resistance after 9 months of a high protein/low carbohydrate vs a standard hypocaloric diet. J Diabetes Complications 34(4):107534. https://doi.org/10.1016/j.jdiacomp.2020.107534

    Article  PubMed  Google Scholar 

  38. de Luis DA, Izaola O, Primo D, Aller R (2020) Dietary-fat effect of the rs10830963 polymorphism in MTNR1B on insulin resistance in response to 3 months weight-loss diets. Endocrinol Diabetes Nutr (Engl Ed) 67(1):43–52. https://doi.org/10.1016/j.endinu.2019.02.007

    Article  PubMed  Google Scholar 

  39. Li-Gao R, Hughes DA, van Klinken JB et al (2021) Genetic studies of metabolomics change after a liquid meal illuminate novel pathways for glucose and lipid metabolism. Diabetes 70(12):2932–2946. https://doi.org/10.2337/db21-0397

    Article  CAS  PubMed  Google Scholar 

  40. Tabák AG, Jokela M, Akbaraly TN, Brunner EJ, Kivimäki M, Witte DR (2009) Trajectories of glycaemia, insulin sensitivity, and insulin secretion before diagnosis of type 2 diabetes: an analysis from the Whitehall II study. Lancet 373(9682):2215–2221. https://doi.org/10.1016/s0140-6736(09)60619-x

    Article  PubMed  PubMed Central  Google Scholar 

  41. Færch K, Witte DR, Tabák AG et al (2013) Trajectories of cardiometabolic risk factors before diagnosis of three subtypes of type 2 diabetes: a post-hoc analysis of the longitudinal Whitehall II cohort study. Lancet Diabetes Endocrinol 1(1):43–51. https://doi.org/10.1016/s2213-8587(13)70008-1

    Article  PubMed  Google Scholar 

  42. Hulman A, Simmons RK, Brunner EJ et al (2017) Trajectories of glycaemia, insulin sensitivity and insulin secretion in South Asian and white individuals before diagnosis of type 2 diabetes: a longitudinal analysis from the Whitehall II cohort study. Diabetologia 60(7):1252–1260. https://doi.org/10.1007/s00125-017-4275-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Walford GA, Green T, Neale B et al (2012) Common genetic variants differentially influence the transition from clinically defined states of fasting glucose metabolism. Diabetologia 55(2):331–339. https://doi.org/10.1007/s00125-011-2353-8

    Article  CAS  PubMed  Google Scholar 

  44. Wellek S, Blettner M (2012) On the proper use of the crossover design in clinical trials: part 18 of a series on evaluation of scientific publications. Dtsch Arztebl Int 109(15):276–281. https://doi.org/10.3238/arztebl.2012.0276

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, USA

    Yoriko Heianza, Tao Zhou, Xuan Wang & Lu Qi

  2. Department of Epidemiology, School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China

    Tao Zhou

  3. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Jeremy D. Furtado, Frank M. Sacks & Lu Qi

  4. Biogen Epidemiology, Cambridge, MA, USA

    Jeremy D. Furtado

  5. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    Lawrence J. Appel

  6. Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA

    Lawrence J. Appel

Authors
  1. Yoriko Heianza
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tao Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jeremy D. Furtado
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lawrence J. Appel
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Frank M. Sacks
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lu Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Yoriko Heianza or Lu Qi.

Ethics declarations

Acknowledgements

We appreciate all the participants and researchers of the OmniCarb trial for their contributions. We also thank the Translational Genomics Core of Partners HealthCare Personalized Medicine team (Cambridge, MA, USA) for their contributions in genotyping and imputation.

Data availability

Data described in the manuscript, code book and analytic code will be available from the corresponding author upon reasonable request. All data supporting the findings of this study are available within the paper and its ESM.

Funding

The OmniCarb trial was supported by an investigator-initiated grant from the National Heart, Lung, and Blood Institute (R01HL084568). The study is supported by the National Institute of Diabetes and Digestive and Kidney Diseases (DK091718, DK100383, DK115679), and National Institute of General Medical Sciences (2P20GM109036-06A1, Sub-Project ID 7233), and the Tulane Research Centers of Excellence Awards. The sponsors had no role in the design or conduct of the present study.

Authors’ relationships and activities

JDF was employed at the Harvard T.H. Chan School of Public Health when the study was conducted and is now an employee of and stockholder in Biogen. All other authors declare that there are no relationships or activities that might bias, or be perceived to bias, their contribution to this manuscript.

Contribution statement

YH contributed to the study concept, statistical analysis and interpretation of data, drafting and revising the manuscript, and study supervision. TZ and XW contributed to the analysis and interpretation of data, and drafting and revising the manuscript. JDF contributed to measurements, interpretation of data and revising the manuscript. LJA and FMS contributed to the trial design, acquisition of data, interpretation of data, revising the manuscript and funding. LQ contributed to the study concept, data acquisition, analysis and interpretation, drafting and revising the manuscript, funding and study supervision. All authors contributed to the manuscript and approved the final version and have access to all data in the present study. The corresponding authors YH and LQ take responsibility for the integrity of the data of the work as a whole.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 438 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Heianza, Y., Zhou, T., Wang, X. et al. MTNR1B genotype and effects of carbohydrate quantity and dietary glycaemic index on glycaemic response to an oral glucose load: the OmniCarb trial. Diabetologia 67, 506–515 (2024). https://doi.org/10.1007/s00125-023-06056-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00125-023-06056-6

Keywords

Search

Navigation