, Volume 62, Issue 5, pp 789–799 | Cite as

Lean mass, grip strength and risk of type 2 diabetes: a bi-directional Mendelian randomisation study

  • Chris Ho Ching Yeung
  • Shiu Lun Au Yeung
  • Shirley Siu Ming Fong
  • C. Mary SchoolingEmail author



Muscle mass and strength may protect against type 2 diabetes as a sink for glucose disposal. In randomised controlled trials, resistance training improves glucose metabolism in people with the metabolic syndrome. Whether increasing muscle mass and strength protects against diabetes in the general population is unknown. We assessed the effect of markers of muscle mass and strength on diabetes and glycaemic traits using bi-directional Mendelian randomisation.


Inverse variance weighting estimates were obtained by applying genetic variants that predict male lean mass, female lean mass and grip strength, obtained from the UK Biobank GWAS, to the largest available case–control study of diabetes (DIAbetes Genetics Replication And Meta-analysis [DIAGRAM]; n = 74,124 cases and 824,006 controls) and to a study of glycaemic traits (Meta-Analyses of Glucose and Insulin-related traits Consortium [MAGIC]). Conversely, we also applied genetic variants that predict diabetes, HbA1c, fasting glucose, fasting insulin and HOMA-B to UK Biobank summary statistics for genetic association with lean mass and grip strength. As sensitivity analyses we used weighted median, Mendelian randomisation (MR)-Egger and Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) and removed pleiotropic SNPs.


Grip strength was not significantly associated with diabetes using inverse variance weighting (OR 0.72 per SD increase in grip strength, 95% CI 0.51, 1.01, p = 0.06) and including pleiotropic SNPs but was significantly associated with diabetes using MR-PRESSO (OR 0.77, 95% CI 0.62, 0.95, p = 0.02) after removing pleiotropic SNPs. Female lean mass was significantly associated with diabetes (OR 0.91, 95% CI 0.84, 0.99, p = 0.02) while male lean mass was not significant but directionally similar (OR 0.94, 95% CI 0.88, 1.01, p = 0.09). Conversely, diabetes was inversely and significantly associated with male lean mass (β −0.02 SD change in lean mass, 95% CI −0.04, −0.00, p = 0.04) and grip strength (β −0.01, 95% CI −0.02, −0.00, p = 0.01).


Increased muscle mass and strength may be related to lower diabetes risk. Diabetes may also be associated with grip strength and lean mass. Muscle strength could warrant further investigation as a possible target of intervention for diabetes prevention.


Body composition Diabetes mellitus Grip strength Hand strength Lean mass Mendelian randomisation Muscle Type 2 diabetes 



DIAbetes Genetics Replication And Meta-analysis


Genome-wide association study


Instrument Strength Independent of Direct Effect


Meta-Analyses of Glucose and Insulin-related traits Consortium


Mendelian randomisation-Egger


Mendelian Randomization Pleiotropy RESidual Sum and Outlier



Data on diabetes have been contributed by DIAGRAM investigators and have been downloaded from Data on glycaemic traits have been contributed by MAGIC investigators and have been downloaded from

Contribution statement

CHCY conducted the literature review and the analysis and drafted the manuscript. SLAY, CMS and SSMF conceptualised ideas and designed the study. SLAY and CMS directed the analytical strategy and supervised the study from conception to completion. SLAY, CMS and SSMF revised drafts of the manuscript. All the authors contributed to the interpretation of the data, critically revising the paper and approval of the final version. CHCY is the guarantor of this work.


This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Duality of interest

The authors declare that there is no duality of interest associated with this manuscript.

Supplementary material

125_2019_4826_MOESM1_ESM.pdf (2.9 mb)
ESM (PDF 2932 kb)


  1. 1.
    Yoon KH, Lee JH, Kim JW et al (2006) Epidemic obesity and type 2 diabetes in Asia. Lancet (London, England) 368(9548):1681–1688.
  2. 2.
    Wang T, Huang T, Li Y et al (2016) Low birthweight and risk of type 2 diabetes: a Mendelian randomisation study. Diabetologia 59(9):1920–1927.
  3. 3.
    Bann D, Wills A, Cooper R et al (2014) Birth weight and growth from infancy to late adolescence in relation to fat and lean mass in early old age: findings from the MRC National Survey of Health and Development. Int J Obes 38(1):69–75.
  4. 4.
    Smith AG, Muscat GEO (2005) Skeletal muscle and nuclear hormone receptors: implications for cardiovascular and metabolic disease. Int J Biochem Cell Biol 37(10):2047–2063. CrossRefPubMedGoogle Scholar
  5. 5.
    Schooling CM, Jiang C, Zhang W, Lam TH, Cheng KK, Leung GM (2011) Adolescent build and diabetes: the Guangzhou Biobank Cohort Study. Ann Epidemiol 21(1):61–66. CrossRefPubMedGoogle Scholar
  6. 6.
    Strasser B, Siebert U, Schobersberger W (2010) Resistance training in the treatment of the metabolic syndrome: a systematic review and meta-analysis of the effect of resistance training on metabolic clustering in patients with abnormal glucose metabolism. Sports Med (Auckland, NZ) 40(5):397–415. CrossRefGoogle Scholar
  7. 7.
    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(8):1133–1163. CrossRefPubMedGoogle Scholar
  8. 8.
    Zheng J, Baird D, Borges MC et al (2017) Recent developments in Mendelian randomization studies. Curr Epidemiol Rep 4(4):330–345. CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Lawlor DA (2016) Commentary: Two-sample Mendelian randomization: opportunities and challenges. Int J Epidemiol 45(3):908–915. CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Taylor AE, Davies NM, Ware JJ, VanderWeele T, Smith GD, Munafo MR (2014) Mendelian randomization in health research: using appropriate genetic variants and avoiding biased estimates. Econ Hum Biol 13:99–106. CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Xu L, Hao YT (2017) Effect of handgrip on coronary artery disease and myocardial infarction: a Mendelian randomization study. Sci Rep 7(1):954. CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    The Neale Lab (2018) GWAS results. Available from, accessed 28 Oct 2018
  13. 13.
    Sudlow C, Gallacher J, Allen N et al (2015) UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 12(3):e1001779. CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    UK Biobank (2011) Grip strength measurement. Available from, accessed 28 Oct 2018
  15. 15.
    Howrigan D (2017) Details and considerations of the UK Biobank GWAS. Available from, accessed 28 Oct 2018
  16. 16.
    Zillikens MC, Demissie S, Hsu YH et al (2017) Large meta-analysis of genome-wide association studies identifies five loci for lean body mass. Nat Commun 8(1):80. CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Visser M, Fuerst T, Lang T, Salamone L, Harris TB (1999) Validity of fan-beam dual-energy X-ray absorptiometry for measuring fat-free mass and leg muscle mass. Health, Aging, and Body Composition Study—Dual-Energy X-ray Absorptiometry and Body Composition Working Group. J Appl Physiol 87(4):1513–1520.
  18. 18.
    Tikkanen E, Gustafsson S, Amar D et al (2018) Biological insights into muscular strength: genetic findings in the UK Biobank. Sci Rep 8(1):6451. CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Wind AE, Takken T, Helders PJ, Engelbert RH (2010) Is grip strength a predictor for total muscle strength in healthy children, adolescents, and young adults? Eur J Pediatr 169(3):281–287. CrossRefPubMedGoogle Scholar
  20. 20.
    Mahajan A, Taliun D, Thurner M et al (2018) Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat Genet 50(11):1505–1513. CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Wheeler E, Leong A, Liu C-T et al (2017) Impact of common genetic determinants of hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: a transethnic genome-wide meta-analysis. PLoS Med 14(9):e1002383. CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Dupuis J, Langenberg C, Prokopenko I et al (2010) New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet 42(2):105–116. CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Manning AK, Hivert M-F, Scott RA et al (2012) A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat Genet 44(6):659–669. CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC (1985) Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28(7):412–419. CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Scott RA, Lagou V, Welch RP et al (2012) Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat Genet 44(9):991–1005. CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Pierce BL, Ahsan H, VanderWeele TJ (2011) Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int J Epidemiol 40(3):740–752. CrossRefPubMedGoogle Scholar
  27. 27.
    Bowden J, Del Greco MF, Minelli C, Davey Smith G, Sheehan NA, Thompson JR (2016) Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I 2 statistic. Int J Epidemiol 45(6):1961–1974. CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Burgess S (2014) Sample size and power calculations in Mendelian randomization with a single instrumental variable and a binary outcome. Int J Epidemiol 43(3):922–929. CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Staley JR, Blackshaw J, Kamat MA et al (2016) PhenoScanner: a database of human genotype–phenotype associations. Bioinformatics (Oxford, England) 32(20):3207–3209. CrossRefGoogle Scholar
  30. 30.
    MacArthur J, Bowler E, Cerezo M et al (2017) The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res 45(D1):D896–D901. CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Verbanck M, Chen C-Y, Neale B, Do R (2018) Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 50(5):693–698. CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Brion M-JA, Shakhbazov K, Visscher PM (2013) Calculating statistical power in Mendelian randomization studies. Int J Epidemiol 42(5):1497–1501. CrossRefPubMedGoogle Scholar
  33. 33.
    Robinson C, Tomek S, Schumacker R (2013) Tests of moderation effects: difference in simple slopes versus the interaction term. Multiple Linear Regression Viewpoints 39:16–25Google Scholar
  34. 34.
    Hemani G, Zheng J, Elsworth B et al (2018) The MR-Base platform supports systematic causal inference across the human phenome. eLife 7:e34408. CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    R Core Team (2016) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
  36. 36.
    Bowden J, Davey Smith G, Haycock PC, Burgess S (2016) Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol 40(4):304–314. CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Bowden J, Davey Smith G, Burgess S (2015) Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 44(2):512–525. CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Gomez-Ambrosi J, Silva C, Galofre JC et al (2011) Body adiposity and type 2 diabetes: increased risk with a high body fat percentage even having a normal BMI. Obesity 19(7):1439–1444.
  39. 39.
    Li JJ, Wittert GA, Vincent A et al (2016) Muscle grip strength predicts incident type 2 diabetes: population-based cohort study. Metabolism 65(6):883–892. CrossRefPubMedGoogle Scholar
  40. 40.
    Kalyani RR, Corriere M, Ferrucci L (2014) Age-related and disease-related muscle loss: the effect of diabetes, obesity, and other diseases. Lancet Diabetes Endocrinol 2(10):819–829.
  41. 41.
    Kalyani RR, Saudek CD, Brancati FL, Selvin E (2010) Association of diabetes, comorbidities, and A1C with functional disability in older adults: results from the National Health and Nutrition Examination Survey (NHANES), 1999-2006. Diabetes Care 33(5):1055–1060. CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Holten MK, Zacho M, Gaster M, Juel C et al (2004) Strength training increases insulin-mediated glucose uptake, GLUT4 content, and insulin signaling in skeletal muscle in patients with type 2 diabetes. Diabetes 53(2):294–305. CrossRefPubMedGoogle Scholar
  43. 43.
    Geirsdottir OG, Arnarson A, Briem K, Ramel A, Jonsson PV, Thorsdottir I (2012) Effect of 12-week resistance exercise program on body composition, muscle strength, physical function, and glucose metabolism in healthy, insulin-resistant, and diabetic elderly Icelanders. J Gerontol A Biol Sci Med Sci 67(11):1259–1265. CrossRefPubMedGoogle Scholar
  44. 44.
    Ramachandran A, Wan Ma RC, Snehalatha C (2010) Diabetes in Asia. Lancet 375(9712):408–418. CrossRefPubMedGoogle Scholar
  45. 45.
    Rush EC, Freitas I, Plank LD (2009) Body size, body composition and fat distribution: comparative analysis of European, Maori, Pacific Island and Asian Indian adults. Br J Nutr 102(04):632–641. CrossRefPubMedGoogle Scholar
  46. 46.
    Hou WW, Tse MA, Lam TH, Leung GM, Schooling CM (2015) Adolescent testosterone, muscle mass and glucose metabolism: evidence from the ‘Children of 1997’ birth cohort in Hong Kong. Diabet Med 32(4):505–512. CrossRefPubMedGoogle Scholar
  47. 47.
    Davey Smith G, Lawlor DA, Harbord R, Timpson N, Day I, Ebrahim S (2007) Clustered environments and randomized genes: a fundamental distinction between conventional and genetic epidemiology. PLoS Med 4(12):e352. CrossRefGoogle Scholar
  48. 48.
    Schooling CM (2018) Selection bias in population-representative studies? A commentary on Deaton and Cartwright. Soc Sci Med.
  49. 49.
    Anderson CD, Nalls MA, Biffi A et al (2011) The effect of survival bias on case-control genetic association studies of highly lethal diseases. Circ Cardiovasc Genet 4(2):188–196. CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Chris Ho Ching Yeung
    • 1
  • Shiu Lun Au Yeung
    • 1
  • Shirley Siu Ming Fong
    • 1
  • C. Mary Schooling
    • 1
    • 2
    Email author
  1. 1.School of Public Health, Li Ka Shing Faculty of MedicineThe University of Hong KongHong Kong SARChina
  2. 2.Graduate School of Public Health and Health PolicyCity University of New YorkNew YorkUSA

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