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:

Physiology

Biological and psychological mediators of the relationships between fat mass, fat-free mass and energy intake

Abstract

Background

While recent studies in humans indicate that fat-free mass (FFM) is closely associated with energy intake (EI) when in energy balance, associations between fat mass (FM) and EI are inconsistent.

Objectives

The present study used a cross-sectional design to examine the indirect and direct effects of FFM, FM and resting metabolic rate (RMR) on EI in individuals at or close to energy balance.

Methods

Data for 242 individuals (114 males; 128 females; BMI = 25.7 ± 4.9 kg/m2) were collated from the non-intervention baseline conditions of five studies employing common measures of body composition (air-displacement plethysmography), RMR (indirect calorimetry) and psychometric measures of eating behaviours (Dutch Eating Behaviour Questionnaire). Daily EI (weighed dietary records) and energy expenditure (flex heart rate) were measured for 6–7 days. Sub-analyses were conducted in 71 individuals who had additional measures of body composition (dual-energy X-ray absorptiometry) and fasting glucose, insulin and leptin.

Results

After adjusting for age, sex and study, linear regression and mediation analyses indicated that the effect of FFM on EI was mediated by RMR (P < 0.05). FM also independently predicted EI, with path analysis indicating a positive indirect association (mediated by RMR; P < 0.05), and a stronger direct negative association (P < 0.05). Leptin, insulin and insulin resistance failed to predict EI, but cognitive restraint was a determinant of EI and partially mediated the association between FM and EI (P < 0.05).

Conclusions

While the association between FFM and EI was mediated by RMR, FM influenced EI via two separate and opposing pathways; an indirect ‘excitatory’ effect (again, mediated by RMR), and a stronger direct ‘inhibitory’ effect. Psychological factors such as cognitive restraint remain robust predictors of EI when considered alongside physiological determinants of EI, and indeed, have the potential to play a mediating role in the overall expression of EI.

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. Weise C, Hohenadel M, Krakoff J, Votruba S. Body composition and energy expenditure predict ad-libitum food and macronutrient intake in humans. Int J Obes. 2013;38:243–51.

    Article  Google Scholar 

  2. Lissner L, Habicht J-P, Strupp BJ, Levitsky D, Haas JD, Roe D. Body composition and energy intake: do overweight women overeat and underreport? Am J Clin Nutr. 1989;49:320–5.

    Article  CAS  Google Scholar 

  3. Hopkins M, Finlayson G, Duarte C, Whybrow S, Horgan GW, Blundell J, et al. Modelling the associations between fat-free mass, resting metabolic rate and energy intake in the context of total energy balance. Int J Obes. 2015;40:312–8.

    Article  Google Scholar 

  4. Piaggi P, Thearle MS, Krakoff J, Votruba SB. Higher daily energy expenditure and respiratory quotient, rather than fat-free mass, independently determine greater ad libitum overeating. J Clin Endocrinol Metab. 2015;100:2015-164.

  5. Caudwell P, Finlayson G, Gibbons C, Hopkins M, King N, Naslund E, et al. Resting metabolic rate is associated with hunger, self-determined meal size, and daily energy intake and may represent a marker for appetite. Am J Clin Nutr. 2013;97:7–14.

    Article  CAS  Google Scholar 

  6. Blundell J, Caudwell P, Gibbons C, Hopkins M, Naslund E, King N, et al. Body composition and appetite: fat-free mass (but not fat-mass or BMI) is positively associated with self-determined meal size and daily energy intake in humans. Brit J Nutr. 2012;107:445–59.

    Article  CAS  Google Scholar 

  7. de Castro JM. Eating behavior: lessons from the real world of humans. Nutrition. 2000;16:800–13.

    Article  Google Scholar 

  8. Cugini P, Salandri A, Cilli M, Ceccotti P, Di Marzo A, Rodio A, et al. Daily hunger sensation and body composition: I. Their relationships in clinically healthy subjects. Eat Weight Disord. 1998;3:168–72.

    Article  CAS  Google Scholar 

  9. Cameron JD, Sigal RJ, Kenny GP, Alberga AS, Prud’Homme D, Phillips P, et al. Body composition and energy intake—skeletal muscle mass is the strongest predictor of food intake in obese adolescents: the HEARTY trial. Appl Physiol Nutr Metab. 2016;41:611–7.

    Article  CAS  Google Scholar 

  10. Schwartz MW, Seeley RJ, Zeltser LM, Drewnowski A, Ravussin E, Redman LM, et al. Obesity pathogenesis: an endocrine society scientific statement. Endocr Rev. 2017;38:267–96.

    Article  Google Scholar 

  11. McNeil J, Lamothe G, Cameron JD, Riou M-È, Cadieux S, Lafrenière J, et al. Investigating predictors of eating: is resting metabolic rate really the strongest proxy of energy intake? Am J Clin Nutr. 2017;106:ajcn153718–1212.

    Article  Google Scholar 

  12. Stubbs RJ, O’Reilly LM, Whybrow S, Fuller Z, Johnstone AM, Livingstone MBE, et al. Measuring the difference between actual and reported food intakes in the context of energy balance under laboratory conditions. Brit J Nutr. 2014;111:2032–43.

    Article  CAS  Google Scholar 

  13. Whybrow S, Stubbs R, Johnstone A, O’reilly L, Fuller Z, Livingstone M, et al. Plausible self-reported dietary intakes in a residential facility are not necessarily reliable. Eur J Clin Nutr. 2016;70:130–5.

    Article  CAS  Google Scholar 

  14. Whybrow S, Harrison CL, Mayer C, Stubbs RJ. Effects of added fruits and vegetables on dietary intakes and body weight in Scottish adults. Brit J Nutr. 2006;95:496–503.

    Article  CAS  Google Scholar 

  15. Whybrow S, Mayer C, Kirk TR, Mazlan N, Stubbs RJ. Effects of two weeks’ mandatory snack consumption on energy intake and energy balance. Obesity. 2007;15:673–85.

    Article  Google Scholar 

  16. Fuller Z, Horgan G, O’reilly L, Ritz P, Milne E, Stubbs R. Comparing different measures of energy expenditure in human subjects resident in a metabolic facility. Eur J Clin Nutr. 2008;62:560–9.

    Article  CAS  Google Scholar 

  17. Johnstone AM, Murison SD, Duncan JS, Rance KA, Speakman JR. Factors influencing variation in basal metabolic rate include fat-free mass, fat mass, age, and circulating thyroxine but not sex, circulating leptin, or triiodothyronine. Am J Clin Nutr. 2005;82:941–8.

    Article  CAS  Google Scholar 

  18. Stubbs R, Sepp A, Hughes D, Johnstone A, King N, Horgan G, et al. The effect of graded levels of exercise on energy intake and balance in free-living women. Int J Obes. 2002;26:866–9.

    Article  CAS  Google Scholar 

  19. Stubbs R, Sepp A, Hughes D, Johnstone A, Horgan G, King N, et al. The effect of graded levels of exercise on energy intake and balance in free-living men, consuming their normal diet. Eur J Clin Nutr. 2002;56:129–40.

    Article  CAS  Google Scholar 

  20. Ceesay SM, Prentice AM, Day KC, Murgatroyd PR, Goldberg GR, Scott W, et al. The use of heart rate monitoring in the estimation of energy expenditure: a validation study using indirect whole-body calorimetry. Brit J Nutr. 1989;61:175–86.

    Article  CAS  Google Scholar 

  21. Elia M, Livesey G. Energy expenditure and fuel selection in biological systems: the theory and practice of calculations based on indirect calorimetry and tracer methods. World Rev Nutr Diet. 1991;70:68–131.

    Article  Google Scholar 

  22. Murgatroyd P, Shetty P, Prentice A. Techniques for the measurement of human energy expenditure: a practical guide. Int J Obes Relat Metab Disord. 1993;17:549–68.

    CAS  PubMed  Google Scholar 

  23. Fields DA, Goran MI, McCrory MA. Body-composition assessment via air-displacement plethysmography in adults and children: a review. Am J Clin Nutr. 2002;75:453–67.

    Article  CAS  Google Scholar 

  24. Ginde SR, Geliebter A, Rubiano F, Silva AM, Wang J, Heshka S, et al. Air displacement plethysmography: validation in overweight and obese subjects. Obesity. 2005;13:1232–7.

    Article  Google Scholar 

  25. Durnin JV, Womersley J. Body fat assessed from total body density and its estimation from skinfold thickness: measurements on 481 men and women aged from 16 to 72 years. Brit J Nutr. 1974;32:77–97.

    Article  CAS  Google Scholar 

  26. Van Strien T, Frijters JE, Bergers G, Defares PB. The Dutch eating behavior questionnaire (DEBQ) for assessment of restrained, emotional, and external eating behavior. Int J Eat Disord. 1986;5:295–315.

    Article  Google Scholar 

  27. Elia M, Livesey G. Energy expenditure and fuel selection in biological systems: the theory and practice of calculations based on indirect calorimetry and tracer methods. World Rev Nutr Diet. 1992;70:68–131.

    Article  CAS  Google Scholar 

  28. Stubbs R, Hughes D, Johnstone A, Whybrow S, Horgan G, King N, et al. Rate and extent of compensatory changes in energy intake and expenditure in response to altered exercise and diet composition in humans. Am J Physiol Regul Integr Comp Physiol. 2004;286:350–R358.

    Article  Google Scholar 

  29. Spurr G, Prentice A, Murgatroyd P, Goldberg G, Reina J, Christman N. Energy expenditure from minute-by-minute heart-rate recording: comparison with indirect calorimetry. Am J Clin Nutr. 1988;48:552–9.

    Article  CAS  Google Scholar 

  30. Goldberg G, Prentice A, Davies H, Murgatroyd P. Overnight and basal metabolic rates in men and women. Eur J Clin Nutr. 1988;42:137–44.

    CAS  PubMed  Google Scholar 

  31. Wareham NJ, Hennings SJ, Prentice AM, Day NE. Feasibility of heart-rate monitoring to estimate total level and pattern of energy expenditure in a population-based epidemiological study: the Ely Young Cohort Feasibility Study 1994–5. Brit J Nutr. 1997;78:889–900.

    Article  CAS  Google Scholar 

  32. Matthews D, Hosker J, Rudenski A, Naylor B, Treacher D, Turner R. Homeostasis model assessment: insulin resistance and β-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28:412–9.

    Article  CAS  Google Scholar 

  33. Blundell JE, Caudwell P, Gibbons C, Hopkins M, Naslund E, King N, et al. Role of resting metabolic rate and energy expenditure in hunger and appetite control: a new formulation. Dis Models Mech. 2012;5:608–13.

    Article  Google Scholar 

  34. Hair JF, Tatham RL, Anderson RE, Black W. Multivariate data analysis. 6. New Jersey: PearsonPrentice Hall; 2006.

    Google Scholar 

  35. Kline RB. Principles and practice of structural equation modeling. New York: Guilford Press; 2005.

    Google Scholar 

  36. Pedersen BK, Febbraio MA. Muscles, exercise and obesity: skeletal muscle as a secretory organ. Nat Rev Endocrinol. 2012;8:457–65.

    Article  CAS  Google Scholar 

  37. Weise CM, Thiyyagura P, Reiman EM, Chen K, Krakoff J. A potential role for the midbrain in integrating fat‐free mass determined energy needs: An H215O PET study. Hum Brain Mapp. 2015;36:2406–15.

    Article  Google Scholar 

  38. Johnstone A, Rance K, Murison S, Duncan J, Speakman J. Additional anthropometric measures may improve the predictability of basal metabolic rate in adult subjects. Eur J Clin Nutr. 2006;60:1437–44.

    Article  CAS  Google Scholar 

  39. Morton G, Cummings D, Baskin D, Barsh G, Schwartz M. Central nervous system control of food intake and body weight. Nature. 2006;443:289–95.

    Article  CAS  Google Scholar 

  40. Woods SC, Ramsay DS. Food intake, metabolism and homeostasis. Physiol Behav. 2011;104:4–7.

    Article  CAS  Google Scholar 

  41. Schaumberg K, Anderson D, Anderson L, Reilly E, Gorrell S. Dietary restraint: what’s the harm? A review of the relationship between dietary restraint, weight trajectory and the development of eating pathology. Clin Obes. 2016;6:89–100.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors’ responsibilities were as follows: RJS, GWH, AMJ and SW conceived the project; RJS, SW, AMJ and the project team (Leona O’Reilley and Zoe Fuller) conducted the research. CD, GWH, MH and RJS analysed the data and performed the statistical analysis. MH, GF, CG, JB and RJS wrote the initial manuscript, while all authors commented on the manuscript. RJS had primary responsibility for final content. The authors report no personal or financial conflicts of interest. The present study was funded by the Food Standards Agency, UK, and The Scottish Government’s Rural and Environment Science and Analytical Services Division. None of the funding bodies had a role in the design, analysis or writing of this article.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mark Hopkins.

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

Hopkins, M., Finlayson, G., Duarte, C. et al. Biological and psychological mediators of the relationships between fat mass, fat-free mass and energy intake. Int J Obes 43, 233–242 (2019). https://doi.org/10.1038/s41366-018-0059-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41366-018-0059-4

This article is cited by

Search

Quick links