Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Body composition, energy expenditure and physical activity

Association between fat mass, adipose tissue, fat fraction per adipose tissue, and metabolic risks: a cross-sectional study in normal, overweight, and obese adults

Abstract

Background/objectives

We investigated whether fat mass (FM) and total adipose tissue (TAT) can be used interchangeably and FM per TAT adds to metabolic risk assessment.

Subjects/methods

Cross-sectional data were assessed in 377 adults (aged 18–60 years; 51.2% women). FM was measured by either 4-compartment (4C) model or quantitative magnetic resonance (QMR); total-, subcutaneous- and visceral adipose tissue (TAT, SAT, VAT), and liver fat by whole-body MRI; leptin, insulin, homeostasis model assessment of insulin resistance (HOMA-IR), C-reactive protein (CRP), and triglycerides; resting energy expenditure and respiratory quotient by indirect calorimetry were determined. Correlations and stepwise multivariate regression analyses were performed.

Results

FM4C and FMQMR were associated with TAT (r4C = 0.96, rQMR = 0.99) with a mean FM per TAT of 0.85 and 1.01, respectively. Regardless of adiposity, there was a considerable inter-individual variance of FM/TAT-ratio (FM4C/TAT-ratio: 0.77–0.94; FMQMR/TAT-ratio: 0.89–1.10). Both, FM4C and TAT were associated with metabolic risks. Further, FM4C/TAT-ratio was positively related to leptin but inversely with CRP. There was no association between FM4C/TAT-ratio and VAT/SAT or liver fat. FM4C/TAT-ratio added to the variance of leptin and CRP.

Conclusions

Independent of FM or TAT, FM4C/TAT-ratio adds to metabolic risk assessment. Therefore, the interchangeable use of FM and TAT to assess metabolic risks is questionable as both parameters may complement each other.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Lee SY, Gallagher D. Assessment methods in human body composition. Curr Opin Clin Nutr Metab Care. 2008;11:566–72.

    Article  Google Scholar 

  2. Bosy-Westphal A, Müller MJ. Assessment of fat and lean mass by quantitative magnetic resonance: a future technology of body composition research? Curr Opin Clin Nutr Metab Care. 2015;18:446–51.

    Article  Google Scholar 

  3. Müller MJ, Braun W, Pourhassan M, Geisler C, Bosy-Westphal A. Application of standards and models in body composition analysis. Proc Nutr Soc. 2016;75:181–7.

    Article  Google Scholar 

  4. Ali O, Cerjak D, Kent JW, James R, Blangero J, Zhang Y. Obesity, central adiposity and cardiometabolic risk factors in children and adolescents: a family-based study. Pediatr Obes. 2014;9:e58–62.

    Article  Google Scholar 

  5. Müller MJ, Lagerpusch M, Enderle J, Schautz B, Heller M, Bosy-Westphal A. Beyond the body mass index: tracking body composition in the pathogenesis of obesity and the metabolic syndrome. Obes Rev. 2012;13:6–13.

    Article  Google Scholar 

  6. Kim SK, Kim HJ, Hur KY, Choi SH, Ahn CW, Lim SK, et al. Visceral fat thickness measured by ultrasonography can estimate not only visceral obesity but also risks of cardiovascular and metabolic diseases. Am J Clin Nutr. 2004;79:593–9.

    Article  CAS  Google Scholar 

  7. King RJ, Ajjan RA. Vascular risk in obesity: Facts, misconceptions and the unknown. Diab Vasc Dis Res. 2017;14:2–13.

    Article  Google Scholar 

  8. Report of the task group on reference man ICRP Publication 23 (1975). Ann ICRP. 1980;4:III.

  9. Sohlstrom A, Wahlund LO, Forsum E. Adipose tissue distribution as assessed by magnetic resonance imaging and total body fat by magnetic resonance imaging, underwater weighing, and body-water dilution in healthy women. Am J Clin Nutr. 1993;58:830–8.

    Article  CAS  Google Scholar 

  10. Wang Z, Zhu S, Wang J, Pierson RNJR, Heymsfield SB. Whole-body skeletal muscle mass: development and validation of total-body potassium prediction models. Am J Clin Nutr. 2003;77:76–82.

    Article  CAS  Google Scholar 

  11. Woodard HQ, White DR. The composition of body tissues. Br J Radiol. 1986;59:1209–18.

    Article  CAS  Google Scholar 

  12. Choe SS, Huh JY, Hwang IJ, Kim JI, Kim JB. Adipose tissue remodeling: its role in energy metabolism and metabolic disorders. Front Endocrinol. 2016;7:30.

    Article  Google Scholar 

  13. Spalding KL, Arner E, Westermark PO, Bernard S, Buchholz BA, Bergmann O, et al. Dynamics of fat cell turnover in humans. Nature. 2008;453:783–7.

    Article  CAS  Google Scholar 

  14. Jernas M, Palming J, Sjoholm K, Jennische E, Svensson P-A, Gabrielsson BG, et al. Separation of human adipocytes by size: hypertrophic fat cells display distinct gene expression. FASEB J. 2006;20:1540–2.

    Article  CAS  Google Scholar 

  15. Yuan M, Konstantopoulos N, Lee J, Hansen L, Li ZW, Karin M, et al. Reversal of obesity- and diet-induced insulin resistance with salicylates or targeted disruption of Ikkbeta. Science. 2001;293:1673–7.

    Article  CAS  Google Scholar 

  16. Hirosumi J, Tuncman G, Chang L, Gorgun CZ, Uysal KT, Maeda K, et al. A central role for JNK in obesity and insulin resistance. Nature. 2002;420:333–6.

    Article  CAS  Google Scholar 

  17. Pourhassan M, Schautz B, Braun W, Gluer C-C, Bosy-Westphal A, Muller MJ. Impact of body-composition methodology on the composition of weight loss and weight gain. Eur J Clin Nutr. 2013;67:446–54.

    Article  CAS  Google Scholar 

  18. Korth O, Bosy-Westphal A, Zschoche P, Glüer CC, Heller M, Müller MJ. Influence of methods used in body composition analysis on the prediction of resting energy expenditure. Eur J Clin Nutr. 2007;61:582–9.

    Article  CAS  Google Scholar 

  19. Schoeller DA. Hydrometry. In: Roche AF, Heymsfield SB, Lohman TG, editors. Human body composition. Campaign, IL: Human Kinetics; 1996. pp. 25–43.

  20. Bosy-Westphal A, Schautz B, Later W, Kehayias JJ, Gallagher D, Müller MJ. What makes a BIA equation unique? Validity of eight-electrode multifrequency BIA to estimate body composition in a healthy adult population. Eur J Clin Nutr. 2013;67:S14–21.

    Article  Google Scholar 

  21. Fuller NJ, Jebb SA, Laskey MA, Coward WA, Elia M. Four-component model for the assessment of body composition in humans: Comparison with alternative methods, and evaluation of the density and hydration of fat-free mass. Clin Sci. 1992;82:687–93.

    Article  CAS  Google Scholar 

  22. Siervo M, Prado CM, Mire E, Broyles S, Wells JCK, Heymsfield S, et al. Body composition indices of a load-capacity model: gender- and BMI-specific reference curves. Public Health Nutr. 2015;18:1245–54.

    Article  Google Scholar 

  23. Müller MJ, Bosy-Westphal A, Lagerpusch M, Heymsfield SB. Use of balance methods for assessment of short-term changes in body composition. Obesity. 2012;20:701–7.

    Article  Google Scholar 

  24. Müller MJ, Enderle J, Pourhassan M, Braun W, Eggeling B, Lagerpusch M, et al. Metabolic adaptation to caloric restriction and subsequent refeeding: the Minnesota starvation experiment revisited. Am J Clin Nutr. 2015;102:807–19.

    Article  Google Scholar 

  25. Gallagher D, Thornton JC, He Q, Wang J, Yu W, Bradstreet TE, et al. Quantitative magnetic resonance fat measurements in humans correlate with established methods but are biased. Obesity. 2010;18:2047–54.

    Article  Google Scholar 

  26. Bosy-Westphal A, Booke C-A, Blocker T, Kossel E, Goele K, Later W, et al. Measurement site for waist circumference affects its accuracy as an index of visceral and abdominal subcutaneous fat in a Caucasian population. J Nutr. 2010;140:954–61.

    Article  CAS  Google Scholar 

  27. Schautz B, Later W, Heller M, Muller MJ, Bosy-Westphal A. Total and regional relationship between lean and fat mass with increasing adiposity—impact for the diagnosis of sarcopenic obesity. Eur J Clin Nutr. 2012;66:1356–61.

    Article  CAS  Google Scholar 

  28. Lagerpusch M, Enderle J, Eggeling B, Braun W, Johannsen M, Pape D, et al. Carbohydrate quality and quantity affect glucose and lipid metabolism during weight regain in healthy men. J Nutr. 2013;143:1593–601.

    Article  CAS  Google Scholar 

  29. Ma J. Dixon techniques for water and fat imaging. J Magn Reson Imaging. 2008;28:543–58.

    Article  Google Scholar 

  30. Bosy-Westphal A, Kossel E, Goele K, Later W, Hitze B, Settler U, et al. Contribution of individual organ mass loss to weight loss-associated decline in resting energy expenditure. Am J Clin Nutr. 2009;90:993–1001.

    Article  CAS  Google Scholar 

  31. Bader N, Bosy-Westphal A, Dilba B, Muller MJ. Intra- and interindividual variability of resting energy expenditure in healthy male subjects—biological and methodological variability of resting energy expenditure. Br J Nutr. 2005;94:843–9.

    Article  CAS  Google Scholar 

  32. Ravussin E, Bogardus C. Relationship of genetics, age, and physical fitness to daily energy expenditure and fuel utilization. Am J Clin Nutr. 1989;49:968–75.

    Article  CAS  Google Scholar 

  33. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28:412–9.

    Article  CAS  Google Scholar 

  34. Kutner MH, Nachtsheim CJ, Neter J. Applied linear regression models. 4th ed. Boston, MA: McGraw-Hill/Irwin; 2004.

    Google Scholar 

  35. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B. 1995;57:289–300.

    Google Scholar 

  36. Sims EA. Are there persons who are obese, but metabolically healthy? Metabolism. 2001;50:1499–504.

    Article  CAS  Google Scholar 

  37. Lundgren M, Svensson M, Lindmark S, Renstrom F, Ruge T, Eriksson JW. Fat cell enlargement is an independent marker of insulin resistance and ‘hyperleptinaemia’. Diabetologia. 2007;50:625–33.

    Article  CAS  Google Scholar 

  38. Ryden M, Andersson DP, Bergstrom IB, Arner P. Adipose tissue and metabolic alterations: regional differences in fat cell size and number matter, but differently: a cross-sectional study. J Clin Endocrinol Metab. 2014;99:E1870–6.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by the German Research Foundation (DFG MÜ 714/8-3; DFG BO 3296/1-1), the Federal Ministry of Education and Research (BMBF 01EA1336), OMRON, Kyoto, Japan, and Seca GmbH & Co. KG (BCA-01).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Manfred J. Müller.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hübers, M., Geisler, C., Bosy-Westphal, A. et al. Association between fat mass, adipose tissue, fat fraction per adipose tissue, and metabolic risks: a cross-sectional study in normal, overweight, and obese adults. Eur J Clin Nutr 73, 62–71 (2019). https://doi.org/10.1038/s41430-018-0150-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41430-018-0150-x

This article is cited by

Search

Quick links