Nutrition 31 (2015) 556–559
Contents lists available at ScienceDirect
Nutrition journal homepage: www.nutritionjrnl.com
Applied nutritional investigation
Energy expenditure in women with breast cancer Carolina Pereira Zuconi M.Sc. a, Ana Lıgia Ceolin Alves M.Sc. a, Maria Isabel Toulson Davisson Correia M.D., Ph.D. b, * a
Food Science Postgraduate Program, Pharmacy School, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil Alfa Institute of Gastroenterology, Hospital das Clınicas, Medical School, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 February 2014 Accepted 1 May 2014
Objective: To assess the energy expenditure of women with breast cancer and the effectiveness of available predictive equations (PEs) for the estimation of energy requirements in these subjects. Methods: Women with breast cancer and healthy women controls underwent indirect calorimetry and nutritional assessment. The estimation of energy requirements included PEs (Harris-Benedict [HB], corrected by injury and activity factors), the Mifflin St. Jeor, and the quick formula of 25 kcal/ kg of body weight (BW). Statistical analyses, including Student’s t test, a paired t test, Bland-Altman analysis, and backward multivariate linear regression, were performed using the SPSS 17.0 software. Statistical significance was set at P < 0.05. Results: Seventeen women with breast cancer and 19 healthy women were evaluated. Analysis of nutritional status revealed 64.7% of the patients were overweight/obese, and 88.2% had an excess of body fat mass. The resting energy expenditure (REE) of the breast cancer patients was similar to that of the healthy women, even after adjustment for fat free mass (FFM) (P < 0.05). The resting and total energy requirements estimated by the predictive equations widely varied, and the quick formula was the most accurate at determining total energy needs. Conclusions: The REE of women with breast cancer was similar to that of healthy women. The energy requirements of these patients may be calculated based on the quick formula of 25 kcal/kg of BW. Nonetheless, this estimation should be used cautiously as it results in wide variations when used alone. Ó 2015 Elsevier Inc. All rights reserved.
Keywords: Energy expenditure Predictive equations Breast cancer
Introduction Obesity and breast cancer represent two common worldwide diseases. Both are present with increasing prevalence, and each independently has a profound effect on public health. There is also a well-established relationship between the two diseases, with most large epidemiologic studies showing an increased risk of developing breast cancer in postmenopausal overweight or obese women [1–3]. Excess adiposity can result in insulin resistance, chronic inflammation and higher levels of circulating estradiol, which may be associated with the risk of developing complications and the recurrence of breast cancer, especially in premenopausal women [4–6]. Furthermore, overweightness and/or obesity have important adverse consequences during both the pre- and postmenopausal periods [7,8]. Because obesity is a modifiable risk factor, adiposity reduction may positively affect patient outcome. Two possible ways of achieving this are through behavioral changes related to diet and * Corresponding author. Tel.: þ55 31 9168 8239; fax: þ55 31 3261 3226. E-mail address: [email protected] (M. I. Toulson Davisson Correia). 0899-9007/$ - see front matter Ó 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.nut.2014.05.009
physical activity, such as reducing calorie intake and/or increasing energy expenditure [9], which contribute to adipose control. Therefore, the correct determination of the patient’s energy needs is key to weight management, which is an important strategy in the treatment and recovery of these patients [10]. This study aimed to evaluate the resting energy expenditure of women with breast cancer and the effectiveness of available predictive equations for the estimation of the resting and total energy requirements of these subjects. Materials and methods This was a cross-sectional study performed in the Hospital of Clinics, Medical School, Universidade Federal de Minas Gerais, Minas Gerais, Brazil, between October 2011 and October 2012. This study was reviewed and approved by the institution’s ethics committee (ETIC 0601.0.203.000-0). All subjects provided informed consent. Subjects Women newly diagnosed with breast cancer, who were between the ages of 18 and 75 y old, were invited to participate in this study. None of the patients had received previous chemotherapy, radiotherapy or endocrine therapy. Patients who had undergone surgery within 3 wk before the study were excluded. The
C. P. Zuconi et al. / Nutrition 31 (2015) 556–559
557
Table 1 Characteristics of the subjects, Belo Horizonte, 2013
Age (y) Height (m) Weight (kg) BMI (kg/m2) Body fat (%) Body fat (kg) Fat free mass (%) Fat free mass (kg)
Cancer (n ¼ 17)
Healthy (n ¼ 19)
P value
11.5 6.0 (44.8–91.0) 4.5 5.8 (13.4–41.3) (54.6–75.5) 5.2
50.3 10.9 159.0 5.0 62.0 (56.0–88.0) 25.9 3.4 36.9 4.9 22.9 (16.7–40.6) 76.5 (59.5–83.3) 40.5 3.4
>0.05 0.05 >0.05 >0.05 >0.05 0.05
54.8 1.54 63.5 26.9 37.5 25.3 61.6 39.7
BMI, body mass index The data are expressed as the mean standard error or as the median and range exclusion criteria also included the presence of infection, non-cancer-related inflammatory diseases, uncontrolled thyroid disease, liver, kidney, heart or lung disease, and the presence of edema and ascites. The controls subject were healthy women who were age-matched to the cancer patients (5 y) and recruited from a volunteer sample of individuals from affiliated institutions. The exclusion criteria were a history of cancer, the presence of infection, non-cancer-related inflammatory diseases, uncontrolled thyroid disease, liver, kidney, heart or lung disease, the presence of edema or ascites, smokers, and pregnant or breast-feeding women. A minimum sample size of 17 subjects in each group (cases and controls) was estimated to detect significant differences in resting energy expenditure (REE), assuming a standard deviation of 10%, with the power of the test of 80% and a significance level of 5% (bilateral). The calculation was performed using the statistical software MINITAB Release (Minitab Inc., State College, PA, USA). Nutritional assessment Body weight (BW) and bioelectrical impedance were performed by trained dietitians immediately preceding the indirect calorimetry (IC) measurement. Weight was measured on a mechanical scale (Filizola, S~ ao Paulo, Brazil), with the patient standing in the center of the platform wearing light clothing and no shoes. Height was determined using a stadiometer fixed to the scale, on which the patient stood barefoot, with their back to the scale, standing straight, with eyes facing forward. Weight and height were used to calculate body mass index (BMI) (weight in kilograms/height in meters squared), and BMI was classified according to the WHO’s criteria for adults and the OPAS’s criteria for the elderly [11,12]. Body composition assessment was performed using the four-frequency bioelectrical impedance Quantum X model, and the technique and the results were in accordance with those provided by the manufacturer (RJL Systems, Clinton Township, Michigan, USA). Data were further categorized into two groups: Normal if < 32% and excessive body fat if 32% [13].
Fig. 1. Comparison of resting energy expenditures (REEs) adjusted for fat free mass (FFM) in women with cancer and healthy women, Belo Horizonte, 2013. P > 0.05. REE prediction The REE predictive equations (PEs) used in our study included the HarrisBenedict (HB) [15] and Mifflin-St Jeor (Mifflin) equations [16]. Actual BW was used, except when using the HB equation for patients with a BMI > 30 kg/m2, in which case the adjusted weight was used. Adjusted weight was estimated using the following equation: Adjusted weight ¼ ½ðcurrent BW ideal BWÞ 0:25 þ ideal BW Ideal BW was determined using the Hamwi equation [17]. Total energy expenditure The patients’ total energy expenditure (TEE) was calculated using the measured REE (MREE) from IC multiplied by the activity factor (FA) of 1.3 (functional active patients) [18]. This value was compared with the HB equation, which was multiplied by two factors, the activity factor (AF) and the injury factor (IF) for cancer [18]: TEE ðby HBÞ ¼ REE ðHBÞ AF IF;
where AF ¼ 1:3 and IF ¼ 1:1
Additionally, the energy requirement was estimated using the 25 kcal/kg/d formula [19].
REE measurement
Statistical analysis
REE was measured using a ventilated hood and open-circuit calorimeter (QUARK RMR, Cosmed, Rome, Italy). Before each evaluation, the device was calibrated to 95% O2 and 5% CO2. All subjects underwent the test in the morning after a 12-h overnight fast. The subjects were instructed to avoid any intense physical activity during the 24-h period before REE measurement. Oxygen consumption (VO2) and carbon dioxide production (VCO2) were continuously measured for 25 min. The first 5 min were discarded to ensure adequate acclimation. The subjects were instructed to avoid hyperventilation, fidgeting, or falling asleep during the test. Oxygen consumption and carbon dioxide production were measured at five second intervals, and the mean of the last 20 min was used to calculate the REE, according to Weir’s equation [14], without using the urinary urea nitrogen level: REE ¼ [3.9 (VO2) þ 1.1 (VCO2)] 1.44, where VO2 is the volume of oxygen, and VCO2 is the volume of carbon dioxide.
Statistical analyses were performed using the SPSS 19.0 (SPSS, Inc., Chicago, IL, USA) and R software (R, Vienna, Austria). P < 0.05 was considered statistically significant. The data are expressed as the mean standard deviation (SD) for the normally distributed variables and as the median and ranges for variables that did not present with a normal distribution. Comparisons between two groups were assessed for continuous variables using the unpaired Student’s t test and the Mann-Whitney test for normal and non-normal distributions, respectively. Multiple linear regression analysis was used to evaluate the determinants of REE in the two groups. Predictor variables included the patient group (breast cancer and healthy control women), age, weight, height, BMI, fat mass (FM) (%), FM (kg), fat free mass (FFM) (%) and FFM (kg). MREE from IC was used as a dependent
Table 2 Resting energy expenditures of women with cancer and healthy women, Belo Horizonte, 2013
MREE MREE/kg BW MREE/kg FFM FFM-adjusted REE
Cancer (n ¼ 17)
Healthy (n ¼ 19)
P value
1,247.0 165.8 19.7 2.2 30.8 (28.7–37.7) 1,224.0 159.7
1,228.2 125.9 18.9 1.7 30.5 (25.9–37.7) 1,246.1 103.6
>0.05 >0.05 >0.05 >0.05
BW, body weight; FFM, fat free mass; MREE, measured resting energy expenditure; REE, kcal The data are expressed as the mean standard error or as the median and range
Table 3 Comparison between the REEs predicted by different equations and the MREE in women with breast cancer (n ¼ 17), Belo Horizonte, 2013 Method
REE (kcal/day)
CI HB Mifflin
1,247.0 1,258.5 1,171.2
Bias* (kcal); limits of agreement (2 SD; kcal)
P valuey
Adequationz
11.5 (343.1; 320.1) 75.8 (175.3; 327.0)
>0.05
Contents lists available at ScienceDirect
Nutrition journal homepage: www.nutritionjrnl.com
Applied nutritional investigation
Energy expenditure in women with breast cancer Carolina Pereira Zuconi M.Sc. a, Ana Lıgia Ceolin Alves M.Sc. a, Maria Isabel Toulson Davisson Correia M.D., Ph.D. b, * a
Food Science Postgraduate Program, Pharmacy School, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil Alfa Institute of Gastroenterology, Hospital das Clınicas, Medical School, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 February 2014 Accepted 1 May 2014
Objective: To assess the energy expenditure of women with breast cancer and the effectiveness of available predictive equations (PEs) for the estimation of energy requirements in these subjects. Methods: Women with breast cancer and healthy women controls underwent indirect calorimetry and nutritional assessment. The estimation of energy requirements included PEs (Harris-Benedict [HB], corrected by injury and activity factors), the Mifflin St. Jeor, and the quick formula of 25 kcal/ kg of body weight (BW). Statistical analyses, including Student’s t test, a paired t test, Bland-Altman analysis, and backward multivariate linear regression, were performed using the SPSS 17.0 software. Statistical significance was set at P < 0.05. Results: Seventeen women with breast cancer and 19 healthy women were evaluated. Analysis of nutritional status revealed 64.7% of the patients were overweight/obese, and 88.2% had an excess of body fat mass. The resting energy expenditure (REE) of the breast cancer patients was similar to that of the healthy women, even after adjustment for fat free mass (FFM) (P < 0.05). The resting and total energy requirements estimated by the predictive equations widely varied, and the quick formula was the most accurate at determining total energy needs. Conclusions: The REE of women with breast cancer was similar to that of healthy women. The energy requirements of these patients may be calculated based on the quick formula of 25 kcal/kg of BW. Nonetheless, this estimation should be used cautiously as it results in wide variations when used alone. Ó 2015 Elsevier Inc. All rights reserved.
Keywords: Energy expenditure Predictive equations Breast cancer
Introduction Obesity and breast cancer represent two common worldwide diseases. Both are present with increasing prevalence, and each independently has a profound effect on public health. There is also a well-established relationship between the two diseases, with most large epidemiologic studies showing an increased risk of developing breast cancer in postmenopausal overweight or obese women [1–3]. Excess adiposity can result in insulin resistance, chronic inflammation and higher levels of circulating estradiol, which may be associated with the risk of developing complications and the recurrence of breast cancer, especially in premenopausal women [4–6]. Furthermore, overweightness and/or obesity have important adverse consequences during both the pre- and postmenopausal periods [7,8]. Because obesity is a modifiable risk factor, adiposity reduction may positively affect patient outcome. Two possible ways of achieving this are through behavioral changes related to diet and * Corresponding author. Tel.: þ55 31 9168 8239; fax: þ55 31 3261 3226. E-mail address: [email protected] (M. I. Toulson Davisson Correia). 0899-9007/$ - see front matter Ó 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.nut.2014.05.009
physical activity, such as reducing calorie intake and/or increasing energy expenditure [9], which contribute to adipose control. Therefore, the correct determination of the patient’s energy needs is key to weight management, which is an important strategy in the treatment and recovery of these patients [10]. This study aimed to evaluate the resting energy expenditure of women with breast cancer and the effectiveness of available predictive equations for the estimation of the resting and total energy requirements of these subjects. Materials and methods This was a cross-sectional study performed in the Hospital of Clinics, Medical School, Universidade Federal de Minas Gerais, Minas Gerais, Brazil, between October 2011 and October 2012. This study was reviewed and approved by the institution’s ethics committee (ETIC 0601.0.203.000-0). All subjects provided informed consent. Subjects Women newly diagnosed with breast cancer, who were between the ages of 18 and 75 y old, were invited to participate in this study. None of the patients had received previous chemotherapy, radiotherapy or endocrine therapy. Patients who had undergone surgery within 3 wk before the study were excluded. The
C. P. Zuconi et al. / Nutrition 31 (2015) 556–559
557
Table 1 Characteristics of the subjects, Belo Horizonte, 2013
Age (y) Height (m) Weight (kg) BMI (kg/m2) Body fat (%) Body fat (kg) Fat free mass (%) Fat free mass (kg)
Cancer (n ¼ 17)
Healthy (n ¼ 19)
P value
11.5 6.0 (44.8–91.0) 4.5 5.8 (13.4–41.3) (54.6–75.5) 5.2
50.3 10.9 159.0 5.0 62.0 (56.0–88.0) 25.9 3.4 36.9 4.9 22.9 (16.7–40.6) 76.5 (59.5–83.3) 40.5 3.4
>0.05 0.05 >0.05 >0.05 >0.05 0.05
54.8 1.54 63.5 26.9 37.5 25.3 61.6 39.7
BMI, body mass index The data are expressed as the mean standard error or as the median and range exclusion criteria also included the presence of infection, non-cancer-related inflammatory diseases, uncontrolled thyroid disease, liver, kidney, heart or lung disease, and the presence of edema and ascites. The controls subject were healthy women who were age-matched to the cancer patients (5 y) and recruited from a volunteer sample of individuals from affiliated institutions. The exclusion criteria were a history of cancer, the presence of infection, non-cancer-related inflammatory diseases, uncontrolled thyroid disease, liver, kidney, heart or lung disease, the presence of edema or ascites, smokers, and pregnant or breast-feeding women. A minimum sample size of 17 subjects in each group (cases and controls) was estimated to detect significant differences in resting energy expenditure (REE), assuming a standard deviation of 10%, with the power of the test of 80% and a significance level of 5% (bilateral). The calculation was performed using the statistical software MINITAB Release (Minitab Inc., State College, PA, USA). Nutritional assessment Body weight (BW) and bioelectrical impedance were performed by trained dietitians immediately preceding the indirect calorimetry (IC) measurement. Weight was measured on a mechanical scale (Filizola, S~ ao Paulo, Brazil), with the patient standing in the center of the platform wearing light clothing and no shoes. Height was determined using a stadiometer fixed to the scale, on which the patient stood barefoot, with their back to the scale, standing straight, with eyes facing forward. Weight and height were used to calculate body mass index (BMI) (weight in kilograms/height in meters squared), and BMI was classified according to the WHO’s criteria for adults and the OPAS’s criteria for the elderly [11,12]. Body composition assessment was performed using the four-frequency bioelectrical impedance Quantum X model, and the technique and the results were in accordance with those provided by the manufacturer (RJL Systems, Clinton Township, Michigan, USA). Data were further categorized into two groups: Normal if < 32% and excessive body fat if 32% [13].
Fig. 1. Comparison of resting energy expenditures (REEs) adjusted for fat free mass (FFM) in women with cancer and healthy women, Belo Horizonte, 2013. P > 0.05. REE prediction The REE predictive equations (PEs) used in our study included the HarrisBenedict (HB) [15] and Mifflin-St Jeor (Mifflin) equations [16]. Actual BW was used, except when using the HB equation for patients with a BMI > 30 kg/m2, in which case the adjusted weight was used. Adjusted weight was estimated using the following equation: Adjusted weight ¼ ½ðcurrent BW ideal BWÞ 0:25 þ ideal BW Ideal BW was determined using the Hamwi equation [17]. Total energy expenditure The patients’ total energy expenditure (TEE) was calculated using the measured REE (MREE) from IC multiplied by the activity factor (FA) of 1.3 (functional active patients) [18]. This value was compared with the HB equation, which was multiplied by two factors, the activity factor (AF) and the injury factor (IF) for cancer [18]: TEE ðby HBÞ ¼ REE ðHBÞ AF IF;
where AF ¼ 1:3 and IF ¼ 1:1
Additionally, the energy requirement was estimated using the 25 kcal/kg/d formula [19].
REE measurement
Statistical analysis
REE was measured using a ventilated hood and open-circuit calorimeter (QUARK RMR, Cosmed, Rome, Italy). Before each evaluation, the device was calibrated to 95% O2 and 5% CO2. All subjects underwent the test in the morning after a 12-h overnight fast. The subjects were instructed to avoid any intense physical activity during the 24-h period before REE measurement. Oxygen consumption (VO2) and carbon dioxide production (VCO2) were continuously measured for 25 min. The first 5 min were discarded to ensure adequate acclimation. The subjects were instructed to avoid hyperventilation, fidgeting, or falling asleep during the test. Oxygen consumption and carbon dioxide production were measured at five second intervals, and the mean of the last 20 min was used to calculate the REE, according to Weir’s equation [14], without using the urinary urea nitrogen level: REE ¼ [3.9 (VO2) þ 1.1 (VCO2)] 1.44, where VO2 is the volume of oxygen, and VCO2 is the volume of carbon dioxide.
Statistical analyses were performed using the SPSS 19.0 (SPSS, Inc., Chicago, IL, USA) and R software (R, Vienna, Austria). P < 0.05 was considered statistically significant. The data are expressed as the mean standard deviation (SD) for the normally distributed variables and as the median and ranges for variables that did not present with a normal distribution. Comparisons between two groups were assessed for continuous variables using the unpaired Student’s t test and the Mann-Whitney test for normal and non-normal distributions, respectively. Multiple linear regression analysis was used to evaluate the determinants of REE in the two groups. Predictor variables included the patient group (breast cancer and healthy control women), age, weight, height, BMI, fat mass (FM) (%), FM (kg), fat free mass (FFM) (%) and FFM (kg). MREE from IC was used as a dependent
Table 2 Resting energy expenditures of women with cancer and healthy women, Belo Horizonte, 2013
MREE MREE/kg BW MREE/kg FFM FFM-adjusted REE
Cancer (n ¼ 17)
Healthy (n ¼ 19)
P value
1,247.0 165.8 19.7 2.2 30.8 (28.7–37.7) 1,224.0 159.7
1,228.2 125.9 18.9 1.7 30.5 (25.9–37.7) 1,246.1 103.6
>0.05 >0.05 >0.05 >0.05
BW, body weight; FFM, fat free mass; MREE, measured resting energy expenditure; REE, kcal The data are expressed as the mean standard error or as the median and range
Table 3 Comparison between the REEs predicted by different equations and the MREE in women with breast cancer (n ¼ 17), Belo Horizonte, 2013 Method
REE (kcal/day)
CI HB Mifflin
1,247.0 1,258.5 1,171.2
Bias* (kcal); limits of agreement (2 SD; kcal)
P valuey
Adequationz
11.5 (343.1; 320.1) 75.8 (175.3; 327.0)
>0.05
