Resting energy expenditure

EXG-09441; No of Pages 4 Experimental Gerontology xxx (2014) xxx–xxx

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Resting energy expenditure (REE) in an old-old population: implications for metabolic stress Michaela Noreik a,⁎, Mareike Maurmann a, Veronika Meier a, Ingrid Becker b, Gabriele Röhrig a, Maria Cristina Polidori a,1, Ralf-Joachim Schulz a,1 a b

University of Cologne, Medical Faculty, Geriatrics Department, Cologne, Germany Institute of Medical Statistics, Informatics and Epidemiology, University of Cologne, Cologne, Germany

a r t i c l e

i n f o

Article history: Received 13 February 2014 Received in revised form 7 May 2014 Accepted 10 June 2014 Available online xxxx Section Editor: A. Simm Keywords: Resting energy expenditure Overweight Obesity Indirect calorimetry Aged

a b s t r a c t The basis of nutritional therapy and thus an adequate nutrient intake is the assessment of energy need. On the other end, the assessment of individual energy requirements based on the gold standard, indirect calorimetry, is associated with feasibility difficulties in geriatric settings. To identify the most accurate predictive equations for resting energy expenditure (REE) in older subjects with overweight, 17 predictive equations were compared to indirect calorimetry measurement in a study population of 20 obese older subjects (mean BMI 33.7 ± 4.5 kg/m2; mean age 79.8 ± 8.1 years; gender 5 males and 15 females) and 20 age-matched controls with a normal body weight (mean BMI 24.9 ± 2.5 kg/m2; mean age 82.1 ± 6.6 years; gender 9 males and 11 females). The comparison led to two significant observations: the predictive equations used led to a much better estimation of the REE in the control group than in the obese older subjects. In addition, the most accurate equation for estimating the REE in the obese older subjects has been shown to be that by Lührmann et al. Further studies are needed to assess the feasibility of using this equation in a routine geriatric setting. © 2014 Published by Elsevier Inc.

The prevalence of overweight and obesity is high and increasing in most industrialized countries and is reaching alarming dimensions (Vainio et al., 2002). Any weight-reduction program is aimed to establish reachable weight loss and dietary intake. While the latter applies to the general overweight population, weight control is also fundamental in advanced age to prevent severe malnutrition and related metabolic stress (generally referred to as the physiological consequence of injurious effects in the organism including those related to age-related changes like sarcopenia) (Kreymann et al., 2009). This requires knowledge of individual energy requirements and relies on accurate methods of assessment. Resting energy expenditure (REE) is the amount of calories needed to maintain the body at rest. REE typically comprises 60–75% of total daily caloric expenditure. In order to measure REE, a standardized protocol has to be followed and various conditions need to be strictly observed. The method usually used for measurement of REE is indirect calorimetry (Müller et al., 1992). In this method, oxygen and carbon dioxide concentrations in the expired air are measured, and energy expenditure is calculated with the help of equations on the basis of oxygen consumption and carbon dioxide production. As the gold standard for REE measurement, indirect calorimetry is hardly feasible ⁎ Corresponding author at: University of Cologne, Medical Faculty, Geriatrics Department, Kerpener Str. 62, 50937 Cologne, Germany. E-mail address: [email protected] (M. Noreik). 1 Equal supervisors.

in most dietetic and geriatric settings, it remains important to use the most accurate predictive equation to determine REE in overweight and obese persons especially in advanced age. Predictive equations have generally been developed in healthy subjects on the basis of regression analysis of body weight, height, sex, and age as independent variables and measured REE by indirect calorimetry as a dependent variable. However, there is a substantial lack of knowledge about the possibility to use predictive equations in older nonobese and obese subjects. To fill this gap of information, we compared REE values measured by indirect calorimetry with 17 predictive equations in 40 older subjects (26 female, 14 males, 81.0 ± 7.4 years old) subdivided in two groups according to BMI (study group, BMI 33.7 ± 4.5 kg/m2 vs. control group, BMI 24.9 ± 2.5 kg/m2) (Table 1). Inclusion criteria were age ≥65 years, BMI ≥29 kg/m2 (intervention group), BMI 21–28.9 kg/m2 (control group), and informed consent. Exclusion criteria were critical illness, cardiac pacemaker, infection/fever, chronic obstructive pulmonary disease (COPD), claustrophobia, strongly fluctuating fasting blood glucose levels (N120–140 mg/dl), edema, MiniMental-State-Examination (MMSE) score of ≤24, and physical activity like physiotherapy for the last 12 h. As gender is considered in all predictive equations except Ireton-Jones et al. (1992) no gender-specific analysis was conducted. The local Ethics Committee approved the study and written informed consent was signed by all study patients. To measure REE indirect calorimetry was performed using a ventilated canopy hood system (Vmax Spectra 29, Sensormedics — Viasys

http://dx.doi.org/10.1016/j.exger.2014.06.009 0531-5565/© 2014 Published by Elsevier Inc.

Please cite this article as: Noreik, M., et al., Resting energy expenditure (REE) in an old-old population: implications for metabolic stress, Exp. Gerontol. (2014), http://dx.doi.org/10.1016/j.exger.2014.06.009

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M. Noreik et al. / Experimental Gerontology xxx (2014) xxx–xxx

Table 1 Overview on the predictive equations for REE including correlation between predictive equations and measured REE in the overweight and normal weight study population. REE predictive equations (year of publication), main variables used

No. of patients (n M, n F), age ± SD

BMI ≥ 29 kg/m2 (n = 20) Mean BMI 33.7 ± 4.5 kg/m2 Mean age 79.8 ± 8.1 years Gender 5 males and 15 females Mean ± SD of difference between measured REE and predictive equation (kcal)

Harris–Benedict equation (1918/1919) Revised Harris–Benedict equation (Roza and Shizgal, 1984) Mifflin et al. (1990)

Owen et al. (1986, 1987) Schofield (1985) N60 years Body weight Schofield (1985) N60 years Body weight, height WHO (1985) N60 years Body weight WHO (1985) N60 years Body weight, height Müller et al. (2004) BMI 25–30 kg/m2

Müller et al. (2004) BMI N 30 kg/m2

Müller et al. (2004) FFM

Müller et al. (2004) BMI 25–30 kg/m FFM

Müller et al. (2004) BMI N 30 kg/m2 FFM

Huang et al. (2004) Body weight and height

Huang et al. (2004) Body weight, height, FFM

Lührmann et al. (2002)

n = 239 (136 M, 103 F) 20–50 years n = 337 (168 M, 169 F) Men 30 ± 14 years, women 40 ± 22 years n = 498 (251 M, 248 F) n = 264 normal weight (129 M, 135 F) n = 234 overweight (122 M, 112 F) 19–78 years, BMI 17–42 n = 104 (60 M, 44 F) 18–82 years, BMI 18–50 n = 7173 n = 4814 N 18 years Mean BMI of all 6 groups included: 21–24 n = 7173 n = 4814 N 18 years Mean BMI of all 6 groups included: 21–24 Based on Schofield (1985) n ≈ 11000 Literature review 0–b 60 years Based on Schofield (1985) n ≈ 11000 Literature review 0–b 60 years n = 2528 (1027 M, 1501 F) Based on n = 1046 (388 M, 658 F) 5–80 years; mean BMI 27 n = 2528 (1027 M, 1501 F) Based on n = 1046 (388 M, 658 F), 5–80 years; mean BMI 27 n = 2528 (1027 M, 1501 F) Based on: n = 1046 (388 M, 658 F) 5–80 years; mean BMI 27 BIA with different equations, as multi-center setup n = 2528 (1027 M, 1501 F) Based on n = 1046 (388 M, 658 F) 5–80 years; mean BMI 27 BIA with different equations, as multi-center setup n = 2528 (1027 M, 1501 F) Based on: n = 1046 (388 M, 658 F) 5–80 years; mean BMI 27 BIA with different equations, as multi-center setup n = 1088 (279 M, 759 F) n = 142 diabetics (61 M, 81 F); mean age: 52 years n = 896 non-diabetics (218 M, 678 F); mean age: 44 years BMI N35 (mean BMI 46) n = 1088 (279 M, 759 F) n = 142 diabetics (61 M, 81 F); mean age: 52 years n = 896 non-diabetics (218 M, 678 F); mean age: 44 years BMI N35 (mean BMI 46) n = 286 (107 M; 179 F) BMI range: 22.7–30.1 60–85 years

−78.3 ± 114.7

ICC between REE by indirect calorimetry and each predictive equation

BMI b 29 kg/m2 (n = 20) Mean BMI 24.9 ± 2.5 kg/m2 Mean age 82.1 ± 6.6 years Gender 9 males and 11 females Mean ± SD of difference between measured REE and predictive equation (kcal)

.821 (p b 0.001) .842 (p b 0.001)

−50.2 ± 94.8

−184.6 ± 131.8

.655 (p b 0.001)

−118.8 ± 87.0

−19.5 ± 157.4

.750 (p b 0.001) .798 (p b 0.001)

−53.9 ± 118.0

−39.5 ± 126.2

−13.5 ± 91.2

102.4 ± 106.5 10.36 ± 119.7

ICC between REE by indirect calorimetry and each predictive equation .857 (p b 0.001) .895 (p b 0.001) .809 (p b 0.001)

. 792 (p b 0.001) .792 (p b 0.001)

−60.3 ± 135.5

.754 (p b 0.001)

39.3 ± 100.9

.851 (p b 0.001)

20.9 ± 119.0

.841 (p b 0.001)

39.5 ± 119.8

.783 (p b 0.001)

−3.4 ± 138.3

.785 (p b 0.001)

68.6 ± 109.4

.786 (p b 0.001)

−14.5 ± 119.8

.867 (p b 0.001)

27.0 ± 101.0

.890 (p b 0.001)

−28.2 ± 123.9

.865 (p b 0.001)

−5.1 ± 109.1

.890 (p b 0.001)

3.3 ± 122.6

.859 (p b 0.001)

50.2 ± 102.7

.866 (p b 0.001)

−36.0 ± 131.9

.801 (p b 0.001)

65.5 ± 99.2

.843 (p b 0.001)

−20.4 ± 123.3

.859 (p b 0.001)

16.5 ± 101.6

.891 (p b 0.001)

−0.7 ± 139.5

.833 (p b 0.001)

80.4 ± 110.6

.843 (p b 0.001)

3.8 ± 129.8

.848 (p b 0.001)

51 ± 108.2

.862 (p b 0.001)

19.4 ± 114.9

.876 (p b 0.001)

25.8 ± 99.4

.883 (p b 0.001)



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Table 1 (continued) REE predictive equations (year of publication), main variables used

Ireton-Jones et al. (1992)

No. of patients (n M, n F), age ± SD

N = retrospective 135 spontaneously breathing patients, validation with 66 patients 15–80 years

BMI ≥ 29 kg/m2 (n = 20) Mean BMI 33.7 ± 4.5 kg/m2 Mean age 79.8 ± 8.1 years Gender 5 males and 15 females Mean ± SD of difference between measured REE and predictive equation (kcal)

ICC between REE by indirect calorimetry and each predictive equation

−214.7 ± 265.4

.596 (p b 0.001)

BMI b 29 kg/m2 (n = 20) Mean BMI 24.9 ± 2.5 kg/m2 Mean age 82.1 ± 6.6 years Gender 9 males and 11 females Mean ± SD of difference between measured REE and predictive equation (kcal) 94.6 ± 138.9

ICC between REE by indirect calorimetry and each predictive equation .777 (p b 0.001)

ICC — intraclass correlation coefficient, SD — standard deviation, n — number of patients, M — male, F — female.

Healthcare, California, USA), which was calibrated for volume and with 2 standard gases every day before use. By means of a ventilated canopy hood system heat production of the body is indirectly calculated, using multiple regression, by means of gas exchange measurement (oxygen input and carbon dioxide output). This is based on the assumption that oxygen input is only used for oxidation of nutrients and therefore is proportional to energy expenditure. The end product carbon dioxide is quantitatively measured. Measurements were standardized by internal guidelines. The subjects were in a supine position and awake and had fasted overnight. Patients were picked up at their hospital room and, depending on their mobility, brought to the laboratory by foot or with a wheelchair. The measurement was conducted after a rest of at least 30 min. Oxygen consumption and carbon dioxide production were measured, and energy expenditure was calculated by using the Weir equation (Weir, 1949) without urine-nitrogen: REE (kcal/24 h) = VO2 ∗ 3941 + VCO2 ∗ 1106. The measurements took at least 10 min, and only steady-state periods of measurement were selected according to the procedures for the ventilated hood system. The first 5 min of the measurements was discarded. Height was measured by using a 10 m fiber glass measuring tape (SI Manufacturing 10 m/33 ft. Windup Tape #45200) and bodyweight using a Seca chair scale 959 (Seca, Hamburg, Germany). FFM (fat-free mass) was assessed by using bioelectrical impedance analysis (BIA) (Nutriguard-M, Data Input GmbH, Darmstadt, Germany) with Bianostik AT Double size electrodes at 50 kHz alternating current. Measuring of body composition using BIA is based on the measurement of the resistance of body tissue to a faint alternating current. Impedance (Z), consisting of resistance (R) and reactance (Xc), is measured and used to calculate total body water (TBW), which was used to calculate FFM (FFM = TBW / 0.73). The calculations were conducted automatically by the software NutriPlus (Data Input GmbH, Darmstadt, Germany). In order to select REE predictive equations, PubMed was used for a systematic search for publications on Mesh-derived keys ‘Energy metabolism’, ‘Basal metabolism’, and ‘Indirect calorimetry’ and additional terms (‘predict*’, ‘estimate’, ‘equation*’, and ‘formula’) in every possible combination. Applied limitations were ‘english language’ and ‘humans’. More references were obtained by screening publications cited. Only equations developed in adults were retrieved. Inclusion criteria for REE predictive equations were as follows: equations based on body weight, height, age, sex and/or FFM and fat mass (FM). Exclusion criteria were as follows: critically ill patients, mean BMI b 21, insufficient information, specific ethnic group, impractical or suspect body composition as variable, suspect indirect calorimetry. For each patient REE was predicted for all equations in kilocalories per day and compared with measured REE. The actual body weight or FFM at the time of the indirect calorimetry measurement was used for these calculations. A total of 17 predictive equations were selected. These are presented in Table 1 along with their values and correlation to the REE as measured by indirect calorimetry. Statistical analysis was conducted using SPSS Statistics 22 for Windows (International Business Machines Corporation (IBM), New York, 2011). All parameters assessed were tested for normal distribution

(Kolmogorov–Smirnov test). The correlation between measured REE and 17 predictive equations was analyzed using intraclass correlation coefficient (ICC) for absolute agreement. Table 1 shows the main observations in this study. There was a significant correlation between REE values as measured by indirect calorimetry and all 17 predictive equations utilized. This is an encouraging finding in light of the logistic and technical difficulties related to the measurement of indirect calorimetry as the current gold standard to measure REE in older subjects. Among the predictive equations tested, the most significant statistical correlations were found in the control group (BMI b 29 kg/m2 ) between measured REE and the revised Harris–Benedict equation (Roza and Shizgal, 1984) (r = 0.895, p b 0.0001), as well as the equations of Müller et al. (2004) for BMI N30 kg/m2 including FFM (r = 0.891, p b0.0001), BMI N 30 kg/2 (r = 0.890, p b 0.0001), and BMI 25–30 kg/m2 (r = 0.890, p b 0.0001) (Table 1). The most accurate equation for estimating REE in the obese older subjects was shown to be that by Lührmann et al. (2002) (r = 0.876, p b 0.0001). Interestingly, based on a comparison of published evidence from the equations developed by Harris and Benedict (1918), WHO (1985), Mifflin et al. (1990), and Owen et al. (1986, 1987), Frankenfield et al. (2005) advised to use the Mifflin equation for overweight and obese subjects. However, this expert panel also acknowledges that there are limited data to support the use of the Mifflin equation in overweight and obese subjects (Frankenfield et al., 2005). Surprisingly, equations that take FFM into account did not result in better estimation of REE. This may be due to an altered composition of the fat-free-mass and changes in fat distribution in older subjects, which may lead to measurement errors with standard body composition assessment methods (Woodrow, 2009). Furthermore, BIA measurement is mainly validated for healthy and younger people. Therefore, predictive equations using FFM should be used carefully in older subjects when measuring FFM by BIA. Another important aspect is that the level of overweight might be an important factor in the accuracy of the predictive equation, but the level of overweight varies among studies. Also in our study, the degree of correlation between measured REE and the most accurate predictive equations varied between groups within a wide range (Table 1). For most equations considered, overweight and obese subjects were included, but their relative contribution to the final equation often remains unclear. Therefore, validation of predictive equations should be performed in specific overweight and obese groups of subjects (de Oliveira et al., 2011). In general, it is advisable that REE prediction equations are applied to any single specific population (Frankenfield, 2013; Weijs, 2008), and this might be particularly true for very old people. The application of an easily accessible REE predictive equation for older subjects not only might have caloric reduction implications especially relevant to weight control strategies (Kempf et al., 2013; Klenk et al., 2014). Accurate REE predictive equations might serve to identify decreased values with increasing age. Thus, the increase of targeted values of energy intake to 1.1–1.3 times of the REE would be facilitated in malnourished patients (Kreymann et al., 2009) at high risk of metabolic stress, mitochondrial


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energy unbalance and oxidative damage. This group could also include patients with overweight or obesity. These results are of practical relevance. To be able to adequately feed older subjects, be it through oral, enteral or parenteral nutrition, the knowledge of energy need is vital. In clinical practice energy need is often estimated using body weight and multiplying it with an average energy need per kilogram bodyweight, e.g. 20 kcal/kg bodyweight. However, due to differences in body composition as well as to increased body fat with decreased muscle mass often found in older subjects, energy need is often over- or underestimated. While indirect calorimetry is still the gold standard to measure REE, it is very cost-intensive and time-consuming, and the use of a predictive equation is a suitable tool to estimate REE. When using predictive equations, it is important to use the most suitable one for the target group. Based on our results, we suggest using the revised Harris–Benedict equation (Roza and Shizgal, 1984) or Müller et al. (2004) for older subjects with a normal body weight and the equation of Lührmann et al. (2002) for obese older subjects. As the equations assume normal hydration status, dry weight should be applied when using predictive equations. After estimating REE the physical activity level (PAL) and possible stress factors should be added to estimate total energy need. Conflict of interest The authors declare no conflict of interest. References de Oliveira, E.P., Orsatti, F.L., Teixeira, O., Maestá, N., Burini, R.C., 2011. Comparison of predictive equations for resting energy expenditure in overweight and obese adults. J. Obes. 2011, 534714. Frankenfield, D.C., 2013. Bias and accuracy of resting metabolic rate equations in nonobese and obese adults. Clin. Nutr. 32, 976–982. Frankenfield, D., Roth-Yousey, L., Compher, C., 2005. Comparison of predictive equations for resting metabolic rate in healthy nonobese and obese adults: a systematic review. J. Am. Diet. Assoc. 105, 775–789. Harris, J.A., Benedict, F.G., 1918. A biometric study of human basal metabolism. Proc. Natl. Acad. Sci. 4 (12), 370–373. Harris, J.A., Benedict, F.G., 1919. A Biometric Study of Basal Metabolism in Man. Carnegie Institute of Washington, Washington, DC, (Carnegie Institute of Washington Publication 279).

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