Lean body mass and risk of type 2 diabetes - a Danish cohort study

  • Christine Friis BakerEmail author
  • Kim Overvad
  • Christina Catherine Dahm
Research article



Excess body fat is a commonly known risk factor for type 2 diabetes. However, whether lean body mass, or fat free mass, could have a protective effect against type 2 diabetes, remains unclear. The aim of this study was to explore the association between lean body mass, fat mass and type 2 diabetes.


This study used data from the Danish Diet, Cancer and Health cohort of 37,053 men and women, aged 50–64 years at baseline (1993–1997). The exposure was measurements of body composition using bioelectrical impedance analysis. Incident diabetes during follow-up was determined through linkage to the Danish National Diabetes Register. Cox proportional hazards regression analysis was used to estimate HR and 95%CI for the association between lean body mass and incident type 2 diabetes, with and without adjustment for fat mass. A sensitivity analysis was performed, excluding cases of incident type 2 diabetes within the first 2 years of follow-up.


When adjusted for fat mass, the main analysis showed non-linear inverse association between lean body mass and risk of diabetes for men, but not for women. However, the sensitivity analysis found no association for either men or women.


Lean body mass was not associated with incident type 2 diabetes when excluding cases that may have been subclinical at baseline. The results imply that public health should focus on reduction of fat mass for diabetes prevention.


Type 2 diabetes Lean body mass Fat mass Body composition 


Compliance with ethical standards

Conflict of interest

The authors declared that they have no conflict of interest.


  1. 1.
    World Health Organisation (WHO). Diabetes [Internet]. Available from: Accessed 9 Sept 2019
  2. 2.
    WHO | Obesity and overweight [Internet]. WHO. [cited 2018 Feb 8]. Available from: Accessed 9 Sept 2019
  3. 3.
    Weisman A, Fazli GS, Johns A, Booth GL. Evolving trends in the epidemiology, risk factors, and prevention of Type 2 Diabetes: a review. Can J Cardiol. 2018;34(5):552–64.CrossRefGoogle Scholar
  4. 4.
    Son JW, Lee SS, Kim SR, Yoo SJ, Cha BY, Son HY, et al. Low muscle mass and risk of type 2 diabetes in middle-aged and older adults: findings from the KoGES. Diabetologia. 2017;60(5):865–72.CrossRefGoogle Scholar
  5. 5.
    Hong S, Chang Y, Jung H-S, Yun KE, Shin H, Ryu S. Relative muscle mass and the risk of incident type 2 diabetes: a cohort study. PLoS ONE. [Internet]. 2017;12(11). Available from: Accessed 9 Sept 2019
  6. 6.
    Tatsukawa Y, Misumi M, Kim YM, Yamada M, Ohishi W, Fujiwara S, et al. Body composition and development of diabetes: a 15-year follow-up study in a Japanese population. Eur J Clin Nutr [Internet]. 2018; Available from:
  7. 7.
    Larsen BA, Wassel CL, Kritchevsky SB, Strotmeyer ES, Criqui MH, Kanaya AM, et al. Association of Muscle Mass, area, and strength with incident diabetes in older adults: the health ABC study. J Clin Endocrinol Metab. 2016;101(4):1847–55.CrossRefGoogle Scholar
  8. 8.
    Li JJ, Wittert GA, Vincent A, Atlantis E, Shi Z, Appleton SL, et al. Muscle grip strength predicts incident type 2 diabetes: population-based cohort study. Metabolism. 2016;65(6):883–92.CrossRefGoogle Scholar
  9. 9.
    Garg SK, Maurer H, Reed K, Selagamsetty R. Diabetes and cancer: two diseases with obesity as a common risk factor. Diabetes Obes Metab. 2013;16(2):97–110.Google Scholar
  10. 10.
    Bellou V, Belbasis L, Tzoulaki I, Evangelou E. Risk factors for type 2 diabetes mellitus: an exposure-wide umbrella review of meta-analyses. Nerurkar PV, editor. PLoS ONE. 2018;13(3):e0194127.CrossRefGoogle Scholar
  11. 11.
    Kjøbsted R, Hingst JR, Fentz J, Foretz M, Sanz M-N, Pehmøller C, et al. AMPK in skeletal muscle function and metabolism. FASEB J. 2018 Apr;32(4):1741–77.CrossRefGoogle Scholar
  12. 12.
    Roberts CK, Hevener AL, Barnard RJ. Metabolic syndrome and insulin resistance: underlying causes and modification by exercise training. Compr Physiol. 2013;3(1):1–58.Google Scholar
  13. 13.
    Tjønneland A, Olsen A, Boll K, Stripp C, Christensen J, Engholm G, et al. Study design, exposure variables, and socioeconomic determinants of participation in diet, cancer and health: a population-based prospective cohort study of 57,053 men and women in Denmark. Scand J Public Health. 2007;35(4):432–41.CrossRefGoogle Scholar
  14. 14.
    Pedersen CB. The Danish civil registration system. Scand J Public Health. 2011;39(7_suppl):22–5.CrossRefGoogle Scholar
  15. 15.
    Gallagher D, Heymsfield SB, Heo M, Jebb SA, Murgatroyd PR, Sakamoto Y. Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr. 2000;72(3):694–701.CrossRefGoogle Scholar
  16. 16.
    Stegger JG, Schmidt EB, Obel T, Berentzen TL, Tjønneland A, Sørensen TIA, et al. Body composition and body fat distribution in relation to later risk of acute myocardial infarction: a Danish follow-up study. Int J Obes. 2011;35(11):1433–41.CrossRefGoogle Scholar
  17. 17.
    Heitmann BL. Prediction of body water and fat in adult Danes from measurement of electrical impedance. A validation study. Int J Obes. 1990;14(9):789–802.Google Scholar
  18. 18.
    Bigaard J, Spanggaard I, Thomsen BL, Overvad K, Tjønneland A. Self-reported and technician-measured waist circumferences differ in middle-aged men and women. J Nutr. 2005;135(9):2263–70.CrossRefGoogle Scholar
  19. 19.
    Carstensen B, Kristensen JK, Marcussen MM, Borch-Johnsen K. The National Diabetes Register. Scand J Public Health. 2011;39(7_suppl):58–61.CrossRefGoogle Scholar
  20. 20.
    Malavolti M, Mussi C, Poli M, Fantuzzi AL, Salvioli G, Battistini N, et al. Cross-calibration of eight-polar bioelectrical impedance analysis versus dual-energy X-ray absorptiometry for the assessment of total and appendicular body composition in healthy subjects aged 21-82 years. Ann Hum Biol. 2003;30(4):380–91.CrossRefGoogle Scholar
  21. 21.
    Achamrah N, Colange G, Delay J, Rimbert A, Folope V, Petit A, Grigioni S, Déchelotte P, Coëffier M Comparison of body composition assessment by DXA and BIA according to the body mass index: a retrospective study on 3655 measures. PLoS One [Internet]. 2018 [cited 2018 Sep 6];13(7). Available from:, e0200465
  22. 22.
    Ling CHY, de Craen AJM, Slagboom PE, Gunn DA, Stokkel MPM, Westendorp RGJ, et al. Accuracy of direct segmental multi-frequency bioimpedance analysis in the assessment of total body and segmental body composition in middle-aged adult population. Clin Nutr. 2011;30(5):610–5.CrossRefGoogle Scholar
  23. 23.
    Moon JR, Stout JR, Smith-Ryan AE, Kendall KL, Fukuda DH, Cramer JT, et al. Tracking fat-free mass changes in elderly men and women using single-frequency bioimpedance and dual-energy X-ray absorptiometry: a four-compartment model comparison. Eur J Clin Nutr. 2013;67(S1):S40–6.CrossRefGoogle Scholar
  24. 24.
    Kalyani RR, Corriere M, Ferrucci L. Age-related and disease-related muscle loss: the effect of diabetes, obesity, and other diseases. Lancet Diabetes Endocrinol. 2014;2(10):819–29.CrossRefGoogle Scholar
  25. 25.
    Park SW, Goodpaster BH, Lee JS, Kuller LH, Boudreau R, de Rekeneire N, et al. Excessive loss of skeletal muscle mass in older adults with type 2 diabetes. Diabetes Care. 2009;32(11):1993–7.CrossRefGoogle Scholar
  26. 26.
    Misra A, Khurana L. Obesity-related non-communicable diseases: south Asians vs white Caucasians. Int J Obes 2005. 2011;35(2):167–87.CrossRefGoogle Scholar
  27. 27.
    Han SJ, Kim HJ, Kim DJ, Lee KW, Cho NH. Incidence and predictors of type 2 diabetes among Koreans: a 12-year follow up of the Korean genome and epidemiology study. Diabetes Res Clin Pract. 2017;123:173–80.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Public HealthAarhus UniversityAarhus CDenmark
  2. 2.Department of CardiologyAalborg University HospitalAalborgDenmark

Personalised recommendations