Skip to main content

Thank you for visiting 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.

Indirect calorimetry: an indispensable tool to understand and predict obesity



Obesity is a physiological condition of chronic positive energy balance. While the regulation of energy metabolism varies widely among individuals, identifying those who are metabolically prone to weight gain and intervening accordingly is a key challenge for reversing the course of the obesity epidemic. Indirect calorimetry is the most commonly used method to measure energy expenditure in the research setting. By measuring oxygen consumption and carbon dioxide production, indirect calorimetry provides minute-by-minute energy expenditure data that makes it the most valuable tool to distinguish the various components of energy expenditure, that is, sleeping and resting metabolic rate, thermic effect of food and the energy cost of activity. Importantly, such measures also provide information on energy substrate utilization. Here we summarized some of the research that revealed resting metabolic rate, spontaneous physical activity and respiratory quotient as key metabolic predictors of weight gain and obesity. Recent studies using indirect calorimetry in response to mid-term fasting or overfeeding have identified 'thrifty' and 'spendthrift' phenotypes in people who differ in propensity to weight gain. We propose the use of indirect calorimetry data as a basis for personalized interventions that may be efficacious in slowing down the rise of global obesity.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1
Figure 2


  1. 1

    AMAAMA Adopts New Policies on Second Day of Voting at Annual Meeting [press release]. AMA: Chicago, IL, USA, 2013.

  2. 2

    World Health Organization. Global Status Report on Noncommunicable Diseases 2014. World Health Organization: Geneva, Switzerland, 2014.

  3. 3

    Weir JB . New methods for calculating metabolic rate with special reference to protein metabolism. J Physiol 1949; 109: 1–9.

    Article  Google Scholar 

  4. 4

    Lifson N, Little WS, Levitt DG, Henderson RM . D2 18O (deuterium oxide) method for CO2 output in small mammals and economic feasibility in man. J Appl Physiol 1975; 39: 657–664.

    CAS  Article  Google Scholar 

  5. 5

    Schoeller DA, van Santen E . Measurement of energy expenditure in humans by doubly labeled water method. J Appl Physiol 1982; 53: 955–959.

    CAS  Article  Google Scholar 

  6. 6

    Jequier E, Schutz Y . Long-term measurements of energy expenditure in humans using a respiration chamber. Am J Clin Nutr 1983; 38: 989–998.

    CAS  Article  Google Scholar 

  7. 7

    Murgatroyd PR, Davies HL, Prentice AM . Intra-individual variability and measurement noise in estimates of energy expenditure by whole body indirect calorimetry. Br J Nutr 1987; 58: 347–356.

    CAS  Article  Google Scholar 

  8. 8

    Ravussin E, Lillioja S, Anderson TE, Christin L, Bogardus C . Determinants of 24- hour energy expenditure in man. Methods and results using a respiratory chamber. J Clin Invest 1986; 78: 1568–1578.

    CAS  Article  Google Scholar 

  9. 9

    James WP, Trayhurn P . An integrated view of the metabolic and genetic basis for obesity. Lancet 1976; 2: 770–773.

    CAS  Article  Google Scholar 

  10. 10

    Roberts SB, Savage J, Coward WA, Chew B, Lucas A . Energy expenditure and intake in infants born to lean and overweight mothers. N Engl J Med 1988; 318: 461–466.

    CAS  Article  Google Scholar 

  11. 11

    Treuth MS, Butte NF, Sorkin JD . Predictors of body fat gain in nonobese girls with a familial predisposition to obesity. Am J Clin Nutr 2003; 78: 1212–1218.

    CAS  Article  Google Scholar 

  12. 12

    Ravussin E, Lillioja S, Knowler WC, Christin L, Freymond D, Abbott WG et al. Reduced rate of energy expenditure as a risk factor for body-weight gain. N Engl J Med 1988; 318: 467–472.

    CAS  Article  Google Scholar 

  13. 13

    Weinsier RL, Nelson KM, Hensrud DD, Darnell BE, Hunter GR, Schutz Y . Metabolic predictors of obesity. Contribution of resting energy expenditure, thermic effect of food, and fuel utilization to four-year weight gain of post-obese and never-obese women. J Clin Invest 1995; 95: 980–985.

    CAS  Article  Google Scholar 

  14. 14

    Bouchard C, Tremblay A, Nadeau A, Despres JP, Theriault G, Boulay MR et al. Genetic effect in resting and exercise metabolic rates. Metabolism 1989; 38: 364–370.

    CAS  Article  Google Scholar 

  15. 15

    Bogardus C, Lillioja S, Ravussin E, Abbott W, Zawadzki JK, Young A et al. Familial dependence of the resting metabolic rate. N Engl J Med 1986; 315: 96–100.

    CAS  Article  Google Scholar 

  16. 16

    Ravussin E, Swinburn BA . Metabolic predictors of obesity: cross-sectional versus longitudinal data. Int J Obes Relat Metab Disord 1993; 17 (Suppl 3):S28–S31. discussion S41–S42.

    Google Scholar 

  17. 17

    Verga S, Buscemi S, Caimi G . Resting energy expenditure and body composition in morbidly obese, obese and control subjects. Acta Diabetol 1994; 31: 47–51.

    CAS  Article  Google Scholar 

  18. 18

    Ravussin E, Burnand B, Schutz Y, Jequier E . Twenty-four-hour energy expenditure and resting metabolic rate in obese, moderately obese, and control subjects. Am J Clin Nutr 1982; 35: 566–573.

    CAS  Article  Google Scholar 

  19. 19

    Luke A, Durazo-Arvizu R, Cao G, Adeyemo A, Tayo B, Cooper R . Positive association between resting energy expenditure and weight gain in a lean adult population. Am J Clin Nutr 2006; 83: 1076–1081.

    CAS  Article  Google Scholar 

  20. 20

    Bandini LG, Must A, Phillips SM, Naumova EN, Dietz WH . Relation of body mass index and body fatness to energy expenditure: longitudinal changes from preadolescence through adolescence. Am J Clin Nutr 2004; 80: 1262–1269.

    CAS  Article  Google Scholar 

  21. 21

    Wurmser H, Laessle R, Jacob K, Langhard S, Uhl H, Angst A et al. Resting metabolic rate in preadolescent girls at high risk of obesity. Int J Obes Relat Metab Disord 1998; 22: 793–799.

    CAS  Article  Google Scholar 

  22. 22

    Astrup A, Gotzsche PC, van de Werken K, Ranneries C, Toubro S, Raben A et al. Meta-analysis of resting metabolic rate in formerly obese subjects. Am J Clin Nutr 1999; 69: 1117–1122.

    CAS  Article  Google Scholar 

  23. 23

    Fothergill E, Guo J, Howard L, Kerns JC, Knuth ND, Brychta R et al. Persistent metabolic adaptation 6 years after 'The Biggest Loser' competition. Obesity (Silver Spring, MD) 2016; 24: 1612–1619.

    Article  Google Scholar 

  24. 24

    Liesa M, Shirihai OS . Mitochondrial dynamics in the regulation of nutrient utilization and energy expenditure. Cell Metab 2013; 17: 491–506.

    CAS  Article  Google Scholar 

  25. 25

    Levine JA . Non-exercise activity thermogenesis (NEAT). Nutr Rev 2004; 62 (Part 2), S82–S97.

    Article  Google Scholar 

  26. 26

    Garland Jr T, Schutz H, Chappell MA, Keeney BK, Meek TH, Copes LE et al. The biological control of voluntary exercise, spontaneous physical activity and daily energy expenditure in relation to obesity: human and rodent perspectives. J Exp Biol 2011; 214 (Part 2), 206–229.

    Article  Google Scholar 

  27. 27

    Johannsen DL, Ravussin E . Spontaneous physical activity: relationship between fidgeting and body weight control. Curr Opin Endocrinol Diabetes Obes 2008; 15: 409–415.

    Article  Google Scholar 

  28. 28

    Zurlo F, Ferraro RT, Fontvielle AM, Rising R, Bogardus C, Ravussin E . Spontaneous physical activity and obesity: cross-sectional and longitudinal studies in Pima Indians. Am J Physiol 1992; 263 (Part 1), E296–E300.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29

    Levine JA, Eberhardt NL, Jensen MD . Role of nonexercise activity thermogenesis in resistance to fat gain in humans. Science 1999; 283: 212–214.

    CAS  Article  Google Scholar 

  30. 30

    Snitker S, Tataranni PA, Ravussin E . Spontaneous physical activity in a respiratory chamber is correlated to habitual physical activity. Int J Obes Relat Metab Disord 2001; 25: 1481–1486.

    CAS  Article  Google Scholar 

  31. 31

    Westerterp KR, Kester AD . Physical activity in confined conditions as an indicator of free-living physical activity. Obes Res 2003; 11: 865–868.

    Article  Google Scholar 

  32. 32

    Astrup A, Raben A, Buemann B, Toubro S . Fat metabolism in the predisposition to obesity. Ann NY Acad Sci 1997; 827: 417–430.

    CAS  Article  Google Scholar 

  33. 33

    Matarese LE . Indirect calorimetry: technical aspects. J Am Diet Assoc 1997; 97 (Suppl 2), S154–S160.

    CAS  Article  Google Scholar 

  34. 34

    Zurlo F, Lillioja S, Esposito-Del Puente A, Nyomba BL, Raz I, Saad MF et al. Low ratio of fat to carbohydrate oxidation as predictor of weight gain: study of 24- h RQ. Am J Physiol 1990; 259 (Part 1), E650–E657.

    CAS  Google Scholar 

  35. 35

    Seidell JC, Muller DC, Sorkin JD, Andres R . Fasting respiratory exchange ratio and resting metabolic rate as predictors of weight gain: the Baltimore Longitudinal Study on Aging. Int J Obes Relat Metab Disord 1992; 16: 667–674.

    CAS  Google Scholar 

  36. 36

    Shook RP, Hand GA, Paluch AE, Wang X, Moran R, Hebert JR et al. High respiratory quotient is associated with increases in body weight and fat mass in young adults. Eur J Clin Nutr 2015; 70: 1197–1202.

    Article  Google Scholar 

  37. 37

    Abbott WG, Howard BV, Christin L, Freymond D, Lillioja S, Boyce VL et al. Short-term energy balance: relationship with protein, carbohydrate, and fat balances. Am J Physiol 1988; 255 (Part 1), E332–E337.

    CAS  Google Scholar 

  38. 38

    Toubro S, Sorensen TI, Hindsberger C, Christensen NJ, Astrup A . Twenty-four-hour respiratory quotient: the role of diet and familial resemblance. J Clin Endocrinol Metab 1998; 83: 2758–2764.

    CAS  Google Scholar 

  39. 39

    Weyer C, Vozarova B, Ravussin E, Tataranni PA . Changes in energy metabolism in response to 48 h of overfeeding and fasting in Caucasians and Pima Indians. Int J Obes Relat Metab Disord 2001; 25: 593–600.

    CAS  Article  Google Scholar 

  40. 40

    Reinhardt M, Thearle MS, Ibrahim M, Hohenadel MG, Bogardus C, Krakoff J et al. A human thrifty phenotype associated with less weight loss during caloric restriction. Diabetes 2015; 64: 2859–2867.

    CAS  Article  Google Scholar 

  41. 41

    Reinhardt M, Schlogl M, Bonfiglio S, Votruba SB, Krakoff J, Thearle MS . Lower core body temperature and greater body fat are components of a human thrifty phenotype. Int J Obes (Lond) 2016; 40: 754–760.

    CAS  Article  Google Scholar 

  42. 42

    Keil G, Cummings E, de Magalhaes JP . Being cool: how body temperature influences ageing and longevity. Biogerontology 2015; 16: 383–397.

    CAS  Article  Google Scholar 

  43. 43

    Kelley DE, Mandarino LJ . Fuel selection in human skeletal muscle in insulin resistance: a reexamination. Diabetes 2000; 49: 677–683.

    CAS  Article  Google Scholar 

  44. 44

    Galgani JE, Heilbronn LK, Azuma K, Kelley DE, Albu JB, Pi-Sunyer X et al. Metabolic flexibility in response to glucose is not impaired in people with type 2 diabetes after controlling for glucose disposal rate. Diabetes 2008; 57: 841–845.

    CAS  Article  Google Scholar 

  45. 45

    Galgani JE, Moro C, Ravussin E . Metabolic flexibility and insulin resistance. Am J Physiol Endocrinol Metab 2008; 295: E1009–E1017.

    CAS  Article  Google Scholar 

  46. 46

    Smith SR, de Jonge L, Zachwieja JJ, Roy H, Nguyen T, Rood JC et al. Fat and carbohydrate balances during adaptation to a high-fat. Am J Clin Nutr 2000; 71: 450–457.

    CAS  Article  Google Scholar 

  47. 47

    Thomas DM, Martin CK, Redman LM, Heymsfield SB, Lettieri S, Levine JA et al. Effect of dietary adherence on the body weight plateau: a mathematical model incorporating intermittent compliance with energy intake prescription. Am J Clin Nutr 2014; 100: 787–795.

    CAS  Article  Google Scholar 

  48. 48

    Hall KD, Sacks G, Chandramohan D, Chow CC, Wang YC, Gortmaker SL et al. Quantification of the effect of energy imbalance on bodyweight. Lancet 2011; 378: 826–837.

    Article  Google Scholar 

  49. 49

    Levy DT, Mabry PL, Wang YC, Gortmaker S, Huang TT, Marsh T et al. Simulation models of obesity: a review of the literature and implications for research and policy. Obes Rev 2011; 12: 378–394.

    CAS  Article  Google Scholar 

  50. 50

    Lin BH, Smith TA, Lee JY, Hall KD . Measuring weight outcomes for obesity intervention strategies: the case of a sugar-sweetened beverage tax. Econ Hum Biol 2011; 9: 329–341.

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Y Y Lam.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lam, Y., Ravussin, E. Indirect calorimetry: an indispensable tool to understand and predict obesity. Eur J Clin Nutr 71, 318–322 (2017).

Download citation

Further reading


Quick links