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Association between resting heart rate and incident diabetes risk: a Mendelian randomization study

  • Tengfei Long
  • Jing Wang
  • Xu Han
  • Fei Wang
  • Hua Hu
  • Caizheng Yu
  • Jing Yuan
  • Ping Yao
  • Sheng Wei
  • Youjie Wang
  • Yuan Liang
  • Xiaoping Miao
  • Xiaomin Zhang
  • Huan Guo
  • Dan Zheng
  • Yuhan Tang
  • Handong Yang
  • Suli Huang
  • Meian HeEmail author
Original Article
  • 36 Downloads

Abstract

Aims

Observational studies indicated that resting heart rate (RHR) was associated with diabetes mellitus (DM) risk; however, it remains unclear whether the association between RHR and DM is causal. We aimed to examine whether there was causal association of RHR with DM risk.

Methods

A prospective study including 16,201 middle-aged and older Chinese (7031 males and 9170 females) derived from the Dongfeng-Tongji cohort was performed. Cox proportional hazard regression models were conducted to estimate the associations between RHR and incident DM risk. In 7481 participants, 65 single nucleotide polymorphisms related to RHR were genotyped. A genetic risk score (GRS) of RHR was calculated based on the RHR-associated variants. The causal associations of RHR with DM risk were investigated by Mendelian randomization analysis.

Results

During a mean (SD) follow-up of 4.5 (0.5) years, 1110 diabetes were identified. Compared with the referential RHR group (≤ 60 beats per minute [bpm]), individuals with RHR > 80 bpm have a higher incident diabetes risk, with a hazard ratio of 1.40 (95% confidence interval [CI], 1.05–1.88). With per SD increase in the weighted genetic risk score, the resting heart rate increased by 0.71 bpm (95% CI 0.49–0.93). By using the GRS to estimate the unconfounded effect, we found that higher resting heart rate did not have a causal effect on diabetes risk (OR 1.00 [95% CI 0.95–1.05]).

Conclusions

The present study supported a positive but not a causal association of RHR with incident diabetes risk. More studies are needed to verify our findings.

Keywords

Diabetes Mendelian randomization analysis Prospective cohort study Resting heart rate 

Notes

Acknowledgements

The authors would like to thank all study subjects for participating in the present DFTJ-cohort study as well as all volunteers for assisting in collecting the samples and data.

Funding

This work was supported by the grant from the National Natural Science Foundation (Grant NSFC-81522040 and 81473051); the Program for HUST Academic Frontier Youth Team, and National Key R&D Program of China (2017YFC0907501).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study has been approved by the Ethics and Human Subject Committee of the School of Public Health, Tongji Medical College, and Dongfeng General Hospital, the Dongfeng Motor Corporation (DMC).

Informed consent

All study participants provided written informed consents.

Supplementary material

592_2019_1344_MOESM1_ESM.docx (56 kb)
Supplementary material 1 (DOCX 55 kb)

References

  1. 1.
    Zhou B, Lu Y, Hajifathalian K, Bentham J, Di Cesare M, Danaei G et al (2016) Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants. Lancet 387:1513–1530.  https://doi.org/10.1016/S0140-6736(16)00618-8 CrossRefGoogle Scholar
  2. 2.
    IDF Diabetes Atlas-eighth edition [Internet]. International Diabetes Federation, Brussels, Belgium. http://www.diabetesatlas.org. Accessed 23 Feb 2017
  3. 3.
    Xu Y, Wang L, He J, Bi Y, Li M, Wang T et al (2013) Prevalence and control of diabetes in Chinese adults. JAMA 310:948–959.  https://doi.org/10.1001/jama.2013.168118 CrossRefGoogle Scholar
  4. 4.
    Grassi G, Vailati S, Bertinieri G, Seravalle G, Stella ML, Dell’Oro R et al (1998) Heart rate as marker of sympathetic activity. J Hypertens 16:1635–1639.  https://doi.org/10.1097/00004872-199816110-00010 CrossRefGoogle Scholar
  5. 5.
    Jamerson KA, Julius S, Gudbrandsson T, Andersson O, Brant DO (1993) Reflex sympathetic activation induces acute insulin resistance in the human forearm. Hypertension 21:618–623.  https://doi.org/10.1161/01.hyp.21.5.618 CrossRefGoogle Scholar
  6. 6.
    Shigetoh Y, Adachi H, Yamagishi S, Enomoto M, Fukami A, Otsuka M et al (2009) Higher heart rate may predispose to obesity and diabetes mellitus: 20-year prospective study in a general population. Am J Hypertens 22:151–155.  https://doi.org/10.1038/ajh.2008.331 CrossRefGoogle Scholar
  7. 7.
    Zhang X, Shu XO, Xiang YB, Yang G, Li H, Cai H et al (2010) Resting heart rate and risk of type 2 diabetes in women. Int J Epidemiol 39:900–906.  https://doi.org/10.1093/ije/dyq068 CrossRefGoogle Scholar
  8. 8.
    Grantham NM, Magliano DJ, Tanamas SK, Soderberg S, Schlaich MP, Shaw JE (2013) Higher heart rate increases risk of diabetes among men: The Australian Diabetes Obesity and Lifestyle (AusDiab) Study. Diabet Med 30:421–427.  https://doi.org/10.1111/dme.12045 CrossRefGoogle Scholar
  9. 9.
    Li YQ, Sun CQ, Li LL, Wang L, Guo YR, You AG et al (2014) Resting heart rate as a marker for identifying the risk of undiagnosed type 2 diabetes mellitus: a cross-sectional survey. BMC Public Health 14:1052.  https://doi.org/10.1186/1471-2458-14-1052 CrossRefGoogle Scholar
  10. 10.
    Aune D, OH B, Vatten LJ (2015) Resting heart rate and the risk of type 2 diabetes: a systematic review and dose-response meta-analysis of cohort studies. Nutr Metab Cardiovasc Dis 25:526–534.  https://doi.org/10.1016/j.numecd.2015.02.008 CrossRefGoogle Scholar
  11. 11.
    Wang L, Cui L, Wang Y, Vaidya A, Chen S, Zhang C et al (2015) Resting heart rate and the risk of developing impaired fasting glucose and diabetes: the Kailuan prospective study. Int J Epidemiol 44:689–699.  https://doi.org/10.1093/ije/dyv079 CrossRefGoogle Scholar
  12. 12.
    Kim DI, Yang HI, Park JH, Lee MK, Kang DW, Chae JS et al (2016) The association between resting heart rate and type 2 diabetes and hypertension in Korean adults. Heart 102:1757–1762.  https://doi.org/10.1136/heartjnl-2015-309119 CrossRefGoogle Scholar
  13. 13.
    Lee DH, de Rezende LFM, Hu FB, Jeon JY, Giovannucci EL (2019) Resting heart rate and risk of type 2 diabetes: a prospective cohort study and meta-analysis. Diabetes/Metab Res Rev 35(2):e3095.  https://doi.org/10.1002/dmrr.3095 CrossRefGoogle Scholar
  14. 14.
    Carnethon MR, Golden SH, Folsom AR, Haskell W, Liao D (2003) Prospective investigation of autonomic nervous system function and the development of type 2 diabetes: the Atherosclerosis Risk In Communities study, 1987–1998. Circulation 107:2190–2195.  https://doi.org/10.1161/01.CIR.0000066324.74807.95 CrossRefGoogle Scholar
  15. 15.
    Carnethon MR, Yan L, Greenland P, Garside DB, Dyer AR, Metzger B et al (2008) Resting heart rate in middle age and diabetes development in older age. Diabetes Care 31:335–339.  https://doi.org/10.2337/dc07-0874 CrossRefGoogle Scholar
  16. 16.
    Smith GD, Ebrahim S (2004) Mendelian randomization: prospects, potentials, and limitations. Int J Epidemiol 33:30–42.  https://doi.org/10.1093/ije/dyh132 CrossRefGoogle Scholar
  17. 17.
    den Hoed M, Eijgelsheim M, Esko T, Brundel BJ, Peal DS, Evans DM et al (2013) Identification of heart rate-associated loci and their effects on cardiac conduction and rhythm disorders. Nat Genet 45:621–631.  https://doi.org/10.1038/ng.2610 CrossRefGoogle Scholar
  18. 18.
    Eppinga RN, Hagemeijer Y, Burgess S, Hinds DA, Stefansson K, Gudbjartsson DF et al (2016) Identification of genomic loci associated with resting heart rate and shared genetic predictors with all-cause mortality. Nat Genet 48:1557–1563.  https://doi.org/10.1038/ng.3708 CrossRefGoogle Scholar
  19. 19.
    Wang F, Zhu J, Yao P, Li X, He M, Liu Y et al (2013) Cohort profile: the Dongfeng–Tongji cohort study of retired workers. Int J Epidemiol 42:731–740.  https://doi.org/10.1093/ije/dys053 CrossRefGoogle Scholar
  20. 20.
    He M, Wu C, Xu J, Guo H, Yang H, Zhang X et al (2014) A genome wide association study of genetic loci that influence tumour biomarkers cancer antigen 19-9, carcinoembryonic antigen and alpha fetoprotein and their associations with cancer risk. Gut 63:143–151.  https://doi.org/10.1136/gutjnl-2012-303434 CrossRefGoogle Scholar
  21. 21.
    He M, Xu M, Zhang B, Liang J, Chen P, Lee JY et al (2015) Meta-analysis of genome-wide association studies of adult height in East Asians identifies 17 novel loci. Hum Mol Genet 24:1791–1800.  https://doi.org/10.1093/hmg/ddu583 CrossRefGoogle Scholar
  22. 22.
    American Diabetes Association (2014) Diagnosis and classification of diabetes mellitus. Diabetes Care 37(Suppl 1):S81–S90.  https://doi.org/10.2337/dc14-S081 CrossRefGoogle Scholar
  23. 23.
    Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G (2008) Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med 27:1133–1163.  https://doi.org/10.1002/sim.3034 CrossRefGoogle Scholar
  24. 24.
    Dai X, Yuan J, Yao P, Yang B, Gui L, Zhang X et al (2013) Association between serum uric acid and the metabolic syndrome among a middle- and old-age Chinese population. Eur J Epidemiol 28:669–676.  https://doi.org/10.1007/s10654-013-9829-4 CrossRefGoogle Scholar
  25. 25.
    Freathy RM, Timpson NJ, Lawlor DA, Pouta A, Ben-Shlomo Y, Ruokonen A et al (2008) Common variation in the FTO gene alters diabetes-related metabolic traits to the extent expected given its effect on BMI. Diabetes 57:1419–1426.  https://doi.org/10.2337/db07-1466 CrossRefGoogle Scholar
  26. 26.
    Didelez V, Sheehan N (2007) Mendelian randomization as an instrumental variable approach to causal inference. Stat Methods Med Res 16:309–330.  https://doi.org/10.1177/0962280206077743 CrossRefGoogle Scholar
  27. 27.
    Cho YS, Chen CH, Hu C, Long J, Ong RT, Sim X et al (2011) Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians. Nat Genet 44:67–72.  https://doi.org/10.1038/ng.1019 CrossRefGoogle Scholar
  28. 28.
    Morris AP, Voight BF, Teslovich TM, Ferreira T, Segre AV, Steinthorsdottir V et al (2012) Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet 44:981–990.  https://doi.org/10.1038/ng.2383 CrossRefGoogle Scholar
  29. 29.
    Zhao W, Rasheed A, Tikkanen E, Lee JJ, Butterworth AS, Howson JMM et al (2017) Identification of new susceptibility loci for type 2 diabetes and shared etiological pathways with coronary heart disease. Nat Genet 49:1450–1457.  https://doi.org/10.1038/ng.3943 CrossRefGoogle Scholar
  30. 30.
    Holmes MV, Asselbergs FW, Palmer TM, Drenos F, Lanktree MB, Nelson CP et al (2015) Mendelian randomization of blood lipids for coronary heart disease. Eur Heart J 36:539–550.  https://doi.org/10.1093/eurheartj/eht571 CrossRefGoogle Scholar
  31. 31.
    Runcie CJ, Reeve W, Reidy J, Dougall JR (1990) A comparison of measurements of blood pressure, heart-rate and oxygenation during inter-hospital transport of the critically ill. Intensive Care Med 16:317–322.  https://doi.org/10.1007/bf01706357 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Italia S.r.l., part of Springer Nature 2019

Authors and Affiliations

  • Tengfei Long
    • 1
  • Jing Wang
    • 1
  • Xu Han
    • 1
  • Fei Wang
    • 1
  • Hua Hu
    • 1
  • Caizheng Yu
    • 1
  • Jing Yuan
    • 1
  • Ping Yao
    • 1
  • Sheng Wei
    • 1
  • Youjie Wang
    • 1
  • Yuan Liang
    • 1
  • Xiaoping Miao
    • 1
  • Xiaomin Zhang
    • 1
  • Huan Guo
    • 1
  • Dan Zheng
    • 1
  • Yuhan Tang
    • 1
  • Handong Yang
    • 2
  • Suli Huang
    • 3
  • Meian He
    • 1
    Email author
  1. 1.Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
  2. 2.Dongfeng Central HospitalDongfeng Motor Corporation and Hubei University of MedicineShiyanChina
  3. 3.Department of Molecular EpidemiologyShenzhen Center for Disease Control and PreventionShenzhenChina

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