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Metabolic, nutritional and inflammatory characteristics in elderly women with advanced cancer

Journal of Geriatric Oncology, 2, 4, pages 183 - 189



Few studies have focused on the metabolic profiling of patients with advanced cancer and the relationship with nutritional and inflammatory characteristics, which have important diagnostic, treatment and prognostic implications, particularly in the elderly. Our objective was to determine differences in energy expenditure during rest and activity, body composition, nutrition, and inflammatory markers between healthy elderly females and those with advanced cancer.

Materials and Methods

Twenty elderly (74.8 ± 6.7 years) females (9 with solid malignancies, 11 healthy) were evaluated for energy expenditure using indirect calorimetry at rest and throughout a 6-min walk test (6MWT). Body composition (dual-energy x-ray absorptiometry); nutritional intake (3-day 24-h food recall); and markers of nutrition and inflammation (complete blood count, albumin and C-reactive protein) were also measured.


Compared to healthy controls, patients with cancer had similar energy expenditures, but significantly lower (p < 0.05) respiratory quotients at rest. During the 6MWT, the group with cancer walked shorter distances at slower speeds (p < 0.001), consumed less oxygen (p < 0.05), and trended toward an increased oxygen cost while walking. The patients with cancer ingested fewer calories and presented with higher levels of inflammatory markers (p < 0.05). No differences in body composition were observed.


Early signs of cachexia (i.e. reduced caloric intake, inflammation and greater fat metabolism) may be present in older patients with cancer, along with poorer levels of functional capacity, compared to healthy controls. Timely recognition of these signs may allow therapeutic interventions to better prevent or delay nutritional and functional demise in elderly patients with cancer.

Keywords: Advanced solid malignancies, Metabolism, Nutrition, Functional capacity, Resting energy expenditure, Six-minute walk test.

1. Background

Energy expenditure measurements are commonly used to provide a reliable estimate of the metabolic demands at rest and throughout different forms of activity for both healthy individuals and those with chronic disease, such as cancer.1 and 2 In patients with advanced cancer suffering from anorexia–cachexia, it is believed that the resting energy expenditure is frequently elevated, reflecting an increased metabolism of the tumor-bearing host. These cancer-induced metabolic adjustments are particularly seen in certain organs, such as the liver, muscle and adipose tissues.3 and 4 However, not all studies have shown an increase in resting energy expenditure,5, 6, and 7 indicating that other characteristics, such as cancer type, stage of disease, age or gender, could potentially play an important role in determining the metabolic heterogeneity associated with this disease.

Thus far, most studies have focused on the resting metabolic activity of patients with cancer.3, 4, 5, 6, and 7 However, considering the importance of identifying a state of de-conditioning and the benefits of exercise intervention, 8 particular attention should be paid to profiling and characterizing the metabolic requirements of patients with cancer during physical activity. Studies should be designed whereby metabolic data is collected during standardized field activity tests. Furthermore, relevant characteristics, such as cancer type, stage of disease, age and gender, should be controlled for during patient selection and recruitment in order to study a more homogeneous group of patients.

The characterization of metabolic profiles at rest and during activity may lead to a better understanding of the factors contributing to weight loss in advanced cancer. As well, it would result in profiling the physiological, functional, and nutritional changes that occur in patients with advanced cancer, thereby facilitating the development of optimal physical and nutritional programs for patients throughout their disease trajectory, preferably before the cachectic condition manifests itself. Finally, determining differences in the metabolic profile during a typical activity, such as the 6-min walk test, may lead to a better understanding of the energy shifts that can occur with physical activity in those with advanced cancer and provide some insight into the development of cancer-related fatigue.

The aim of our pilot study was to begin to characterize a group of elderly females with advanced stage solid malignancies and to compare them to a healthy group of elderly females with respect to resting energy expenditure, functional capacity, body composition, nutritional/caloric intake and biological markers of inflammation.

2. Methods

2.1. Subjects

Patients with cancer and healthy controls were recruited between July 2007 and February 2009 through advertisements and physician referrals from the Royal Victoria Hospital and the Hepato-Pancreato-Biliary Cancer Clinic of the McGill University Health Centre (MUHC). Subjects were excluded if they presented with unstable psychiatric, cardio-respiratory or metabolic disorders, received chemotherapy or radiotherapy four weeks prior, or had any surgeries six weeks prior to the assessment date. All patients were evaluated at the McGill Nutrition and Performance Laboratory and the McGill Nutrition and Food Science Centre of the MUHC between 8 am and 12 pm. The subjects fasted for 12 h prior to their testing, which took about 2.5 h to complete. Each subject provided written informed consent to participate in this study, which had received prior approval from the Institutional Review Board of the MUHC.

2.2. Blood Parameters

Venous blood samples for complete blood count (CBC), albumin, and C-reactive protein (CRP) were analyzed in the MUHC – Royal Victoria Hospital Biochemistry Laboratory.

2.3. Body Composition

With subjects fasted, barefoot, and wearing light clothing with no metal embellishments, height was measured to the nearest 0.5 cm using a SECA stadiometer and weight was measured to the nearest 0.1 kg using an AmCells electronic scale (Amcells Corp., Vista, CA, USA). Body mass index (BMI) was then calculated (kg/m2). For the determination of total lean tissue (kg), fat tissue (kg) and bone mineral density (g/cm3), a total body dual-energy x-ray absorptiometry (DXA – Lunar Prodigy Advance™, GE Healthcare, Madison, WI, USA) scan was completed and analyzed using Advance's enCORE™ 2006 software (GE Healthcare, Madison, WI, USA). A complete description of our laboratory procedures for the total body DXA scan has been published elsewhere. 9 The percent coefficient of variation (%CV) of this DXA machine was previously determined as 1.56% for fat mass and 0.72% for fat free mass in a population with advanced cancer. 10

2.4. Indirect Calorimetry and Heart Rate

Expired pulmonary gases were collected by the portable COSMED K4b2 (COSMED s.r.l., Rome, Italy) unit. The flow meter was attached to a flexible snug-fitting mask that the participants were required to wear for the 30-min resting energy expenditure (REE) and 6-min walk test (6MWT). During the 30-min REE, the subjects were quiet and resting in a supine position under thermo-neutral environmental conditions. Using the COSMED patented O2 analyzer and an infrared non-dispersive temperature controlled CO2 analyzer, 11 the exchange of pulmonary gases was analyzed breath-by-breath during the REE and 6MWT, and immediately calculated on-line using the COSMED software version 8.0b for Windows (COSMED s.r.l., Rome, Italy). Heart rate (HR) was recorded using the Polar heart rate monitor and the measured oxygen consumption (VO2) and carbon dioxide production (VCO2) values were used in the Weir equation to calculate the measured REE:


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REE was normalized for weight and fat free mass (kcal/kg body weight, kcal/fat free mass), and oxygen uptake was calculated (ml O2/kg/min). At the beginning of each testing day, the O2 and CO2 sensors were calibrated using gases containing 16% O2 and 4% CO2, respectively.

2.5. 6-Min Walk Test

For the 6MWT, 13 the subjects walked a 30-m long course and were encouraged to walk as fast as they could while maintaining a walking pace. If the subjects normally used a cane or walker, they were instructed to use the walking aid during the test. The total distance walked in 6 min was measured to the nearest 0.1 m, and walking speed (m/s) was calculated. Averages were also calculated between the 3rd and 6th minutes of the metabolic steady-state period for oxygen uptake (ml O2/kg/min), oxygen cost (ml O2/kg/m), HR (bpm), and respiratory quotient (RQ = VCO2/VO2). Oxygen cost is determined by dividing the rate of oxygen uptake by the walking speed. 14

2.6. Nutritional Recalls and PG-SGA

Dietary intake of total energy, fat, protein and carbohydrates was recorded using a three-day 24-h food recall (2 week-days and one weekend-day over a period of one week) 15 and analyzed using the Food Processor SQL© Nutrition Analysis software (ESHA Research, Salem, OR). 16 Output for total calories (kcal/kg), carbohydrates (g/kg), fat (kcal/kg), and protein (g) was adjusted according to the subject's mass in kilograms. The total calories were also adjusted as a percentage of the recommended daily intake (%RDI). The RDI was calculated based on age, gender, BMI, and activity level. 17 Eight subjects with cancer and 5 healthy subjects completed this assessment. In addition, the patients with cancer completed the Patient-Generated Subjective Global Assessment (PG-SGA), 18 which is a nutritional assessment tool. A portion of the questionnaire was used to determine subjective levels of weight loss over the previous two weeks, one month and six months, as well as performance status on a scale of 0–3, where 0 indicates no limitations and 3 corresponds to the subject spending most of the day in a bed or a chair.

2.7. Statistical Analysis

The breath-by-breath COSMED data were averaged every 20 s during the final 15 min of REE and throughout the entire 6MWT. Data points were excluded if the respiratory quotient (RQ) was less than 0.65 or greater than 1.10 during the REE and 6MWT, as well as data points that were ± 2 standard deviations (SD) from the mean for VO2 during the REE. Mean differences between groups were compared using the student's t-test (SPSS Inc., version 14.0, Chicago, IL). Values were considered significant at p < 0.05. Results are presented as mean ± SD.

3. Results

3.1. Subjects

A total of 20 females over 65 years of age [11 healthy (76.0 ± 5.5 years) and 9 with advanced solid tumors (73.2 ± 8.0 years)] were recruited. The disease and clinical characteristics of the patients with cancer are reported in Table 1 . All patients had locally advanced or metastatic cancers with a majority of cancers prone to develop the cachexia syndrome. Only one third (3 of 9) presented with weight loss at one and six months prior to assessment and over 50% were already experiencing a significant impairment in their performance status according to the PG-SGA questionnaire. The latter did not appear to be influenced by previous cancer treatments as no patients had received recent radiotherapy or chemotherapy with drugs that are commonly deemed to impair functional status (i.e. oxaliplatin). The patients underwent surgery well before their assessments for this study. Four out of 11 healthy elderly women had one medical diagnosis (osteoporosis, hypertension, osteoarthritis and hyperlipidemia) and three out of the four were taking one prescribed medication. The patients with cancer had on average two diagnoses [hypertension (7), osteoarthritis (2), diabetes type II (2), hypothyroidism (2), stable coronary artery disease (2), asymptomatic chronic obstructive lung disease (1), mild peripheral vascular disease (1), non-active systemic lupus erythomatous (1), and dyspepsia (1)], besides cancer and were receiving, on average, 2.4 prescribed medications, excluding NSAIDS and opioids for pain control (in three patients). For each subject with cancer, all conditions were well controlled, including cancer-related pain and none of the diagnoses limited the subject's ability to complete activities of daily living, otherwise the subject would have been excluded.

Table 1 Characteristics of subjects with cancer (n = 9).

Tumor type (N) Cholangiocarcinoma 3
Colon 3
Liver 1
Pancreatic 1
Tumor stage (N) Locally advanced 2
Metastatic 7
Previous oncologic treatment (N) Chemotherapy 4
Surgery 4
Performance status (PG-SGA) (N) 0 4
1 0
2 2
3 3
Time from diagnosis to assessment (weeks)   9.1 ± 7.0
Weight loss 2 weeks (%) 0.0 ± 0.0
1 month (%) − 0.9 ± 3.7
6 months (%) − 4.8 ± 10.1

PG-SGA — Patient-Generated Subjective Global Assessment, NSCLC — non-small cell lung cancer.

3.2. Body Composition and Blood Parameters

There were no significant differences in the age range, weight, height, and BMI between the group with cancer and the healthy group. The total body DXA scans for bone mineral density, fat mass, and lean mass showed no significant difference for measures of arms, legs, trunk and total body between the group with cancer and the healthy group. Hemoglobin, red blood cells, white blood cells, and lymphocytes were not significantly different between groups. When compared to the healthy group, the patients with cancer had significantly higher levels of neutrophils and CRP, with lower levels of albumin ( Table 2 ).

Table 2 Subject characteristics and blood parameters.

  Subjects with cancer Healthy subjects
N = 9 N = 11
Age (years) 73.2 ± 8.0 76.0 ± 5.5
Height (cm) 155.5 ± 10.6 158.4 ± 4.5
Weight (kg) 63.6 ± 14.5 62.4 ± 5.4
BMI (kg/m2) 26.2 ± 4.5 24.9 ± 2.4
Total fat mass (kg) 23.5 ± 9.6 23.7 ± 3.8
Total lean mass (kg) 37.5 ± 6.6 36.1 ± 2.7
Blood parameters
Hemoglobin (g/L) 124 ± 16 130 ± 8
Red blood cells (1012/L) 4.09 ± 0.56 4.23 ± 0.27
White blood cells (109/L) 7.26 ± 2.49 5.60 ± 1.37
Neutrophils (109/L) 4.99 ± 1.76 a 3.40 ± 1.11
Lymphocytes (109/L) 1.54 ± 0.85 1.56 ± 0.41
Albumin (g/L) 34.8 ± 5.8 a 39.9 ± 3.5
C-reactive protein (mg/L) 27.1 ± 40.5 a 2.1 ± 1.4

a p < 0.05. Values are means ± SD.

BMI: body mass index; SD: standard deviation.

3.3. Resting Energy Expenditure

There were no significant group differences in oxygen uptake or the measured REE (kcal/day; kcal/kg body weight; kcal/fat free mass). However, the RQ was significantly lower for the group with cancer ( Table 3 ). Following visual inspection of the data, one subject with cancer was excluded from the final group analysis due to a consistently low RQ (< 0.65).

Table 3 Metabolic and functional capacity measurements.

  Subjects with cancer Healthy subjects
Resting energy expenditure (REE) N = 8 N = 11
VO2 (ml/min) 168.6 ± 38.2 156.7 ± 35.8
VCO2 (ml/min) 122.7 ± 26.8 126.1 ± 31.1
Oxygen uptake (ml O2/kg/min) 2.67 ± 0.64 2.51 ± 0.53
REE (kcal/day) 1129.7 ± 260.8 1059.9 ± 250.0
REE/body weight (kcal/kg) 17.9 ± 4.4 17.0 ± 3.7
REE/fat free mass (kcal/kg) 31.4 ± 6.8 29.2 ± 5.5
Heart rate (beats/min) 71 ± 11 66 ± 13
Respiratory quotient 0.74 ± 0.02 a 0.81 ± 0.07
6-Min walk test (6MWT) N = 9 N = 11
Distance walked in 6 min (m) 337.2 ± 92.0 b 480.4 ± 49.0
Walking speed (m/s) 0.94 ± 0.26 b 1.33 ± 0.14
6MWT steady-state (min 3 to 6) averages N = 8 N = 11
Oxygen uptake (ml O2/kg/min) 11.7 ± 3.6 a 15.0 ± 2.4
Energy cost (ml O2/kg/m) 0.23 ± 0.06 0.19 ± 0.03
Heart rate (beats/min) 101 ± 8 a 117 ± 14
Respiratory quotient 0.77 ± 0.08 0.83 ± 0.09

a p < 0.05.

b p < 0.001. Values are means ± SD.

VO2: oxygen consumption; VCO2: carbon dioxide production; SD: standard deviation.

3.4. 6-Min Walk Test

All subjects walked for the entire six minutes without stopping. Healthy subjects walked significantly farther (p < 0.001) than the patients with cancer. Two of the patients with cancer used walking aids (simple cane and walker); however, when these 2 patients were removed from the analysis, a re-calculation of the data from the 7 remaining subjects still showed that the patients with cancer walked a significantly less distance than the healthy subjects. During the steady-state period (3rd to 6th minute) of the 6MWT, the group with cancer maintained a significantly slower walking speed and HR, as well as a lower oxygen uptake. However, when comparing the group with cancer and the healthy group (0.23 ± 0.06 vs. 0.19 ± 0.03 ml O2/kg/m; p < 0.063), there was a tendency towards a greater oxygen cost for every meter walked in the cohort with cancer. The RQ was not different between groups ( Table 3 ). Following visual inspection of the data, one subject with cancer was excluded from the final group analysis due to a consistently elevated RQ (> 1.10).

3.5. Nutritional Recalls

The subjects with cancer ingested significantly fewer total calories (kcal/kg, 22.4 ± 5.6 vs. 33.1 ± 2.3; p < 0.01), calories as %RDI (75.4 ± 11.5 vs. 99.8 ± 7.8; p < 0.01), carbohydrates (g/kg, 3.2 ± 1.0 vs. 4.4 ± 0.6; p < 0.05), and protein (g, 65.8 ± 10.9 vs. 88.4 ± 23.7; p < 0.05) than their healthy counterparts.

4. Discussion

Our study demonstrates that elderly females with solid malignancies, as compared to healthy controls, show a different reliance on substrate utilization for energy at rest, as evidenced by the RQ; they walk a shorter distance while consuming less oxygen at a lower HR during a standardized 6MWT. However, they tend to be less economical in their consumption of energy during the 6MWT.

The numerous studies that have measured REE in patients with cancer have demonstrated an inconsistent pattern of results. Some studies have identified an increase in REE,3 and 19 more notably the study by Hyltander et al. 19 that examined 106 participants with varying malignancies. However, others have found no significant difference between the groups with cancer and the control groups.5, 6, 7, and 20 More specifically, the study by Cao et al. 20 looked at 714 participants with newly detected esophageal, gastric, colorectal, pancreatic and non-small cell lung cancer (NSCLC) and found no difference between the groups with cancer for measured REE, as well as no difference compared to controls (n = 642). The differing results in REE could be attributed to the different diagnoses and cancer stage, the age of the patients, and whether the cohort was composed of men and/or women. 21 In order to control for these variations in our study, a homogeneous cohort of patients with cancer was recruited with respect to age, gender, cancer type, and stage, and we found no considerable differences in REE between the group with cancer and the healthy group.

Although the REE is a reliable predictor of overall energy expenditure, it is the RQ value that provides us with an approximation of the substrate mixture utilized for metabolic purposes at rest and during exercise. The RQ is an estimate of specific substrate usage for metabolism, with a value of 1.00 indicating the primary use of carbohydrates and a value of 0.70 indicating predominance towards lipid consumption. 1 In the present study, the subjects with cancer are metabolizing a relatively high percentage of lipids at rest (RQ = 0.74 ± 0.02), whereas the healthy subjects are consuming a mixture of lipids, proteins and carbohydrates (RQ = 0.81 ± 0.07), which is more customary for individuals who are not in caloric deficit. 1

The differences in RQ between the groups may be explained by the following results. First of all, three of the nine patients with cancer had lost between 14.5% and 18.2% of their total body weight in the 6 months prior to their assessment. In a previous study, Drott et al. 22 demonstrate that weight-losing patients with cancer have higher concentrations of fasting plasma glycerol than weight-stable patients with cancer, indicating the possibility for increased lipolysis. The results of Drott et al. 22 would explain our study finding of a lower RQ group mean for the subjects with cancer. Secondly, it has been suggested that in the presence of certain tumors, host tissues may metabolically shift in favor of the utilization of fatty acids as their preferred energy source and shift toward relatively greater rates of oxygen consumption, even in the presence of high plasma glucose concentrations. 23 Finally, the group with cancer had a lower total caloric intake, as well as a lower RQ at rest, than the healthy controls, with no difference in REE between the groups. These results are consistent with the extant literature as Fredrix et al. 6 demonstrate a similar trend in gastric and colorectal patients with cancer. The reduction in caloric intake may lead to increased fat mobilization for energy resulting in a lower RQ at rest. However, even with a higher average use of fat during rest and a lower caloric intake for the group with cancer, body composition remained the same for both groups in terms of total weight, fat mass and lean body mass.

Our findings of elevated CRP and lower albumin levels are classic indices of inflammatory activation in patients with cancer. 24 The probable or expectant increase in local tissue energy requirements imposed by the tumor cells and liver 4 could promote a greater reliance on local fat metabolism, along with alterations in oxygen consumption and carbon dioxide production at the cellular level. Thus, this can be a plausible explanation for the lowered RQ values observed at rest in the patients with cancer.

The 6MWT is a self-paced, sub-maximal exercise test commonly used to determine functional capacity in patients with lung and heart disease, 13 and was found to be a valid and reliable sub-maximal test in geriatric populations. 25 The distance covered during the 6MWT has also been studied in those with cancer. 2 The strength of this test lies in the fact that it is a self-paced physical function test that is correlated with the person's ability to perform activities of daily living. 13 Several factors have been shown to affect the walking distance covered during the 6MWT, including subjects' weight, height, sex, age and chronic conditions.13, 26, and 27

We found that patients with cancer walked at a lower speed and had lower oxygen uptake as compared to healthy controls. Energy requirements increase whenever walking speed is much slower or faster than a person's self-selected or customary walking speed, 14 which could partially explain the increased oxygen cost of walking for the group with cancer. Furthermore, the association between oxygen intake and walking speed is consistent with the work of Troosters et al., 28 who found that both body weight and walking speed, together, explained 56% of the variance in VO2 during the walking test in elderly patients with pulmonary disease. It is not readily apparent from our data exactly why the group with cancer had an overall slower walking speed than healthy individuals of a similar age and the same gender. Any reference to cancer-induced changes in lean muscle mass as a plausible explanation for the discrepancy in walking speed between groups can be dispelled since our DXA measurements clearly indicated that there were no differences in lean mass, especially in that of the lower limb. However, the quality of the muscle was not measured, and may partially explain the difference in the distance walked. Another plausible explanation not factored into our findings could be related to a possible pre-existing level of cancer-related fatigue. This symptom is a frequently occurring and persistent problem in the population with cancer and it has been linked to both asthenia 29 and muscle mass. 30

Given the small number of patients included in our study, we are restricted from making any generalizations to the population with cancer. Moreover, the findings of this study may not be transferable to males or other age groups. Although it cannot be excluded that some of the lower performances during the 6MWT in the group with cancer could be related, in part, to a higher burden of chronic diseases compared with the healthy control group, importantly, all participants were clinically stable and patients with cancer were asymptomatic from other medical conditions at the time of the assessment as determined by the physicians involved in the study (AV, JAM). Finally, some patients with cancer received anti-cholesterol, anti-diabetic, anti-hypertensive and anti-inflammatory medications, which could dampen the inflammatory response and/or early signs of cachexia. 31 However, the markers of inflammation were still present and significantly different in the population with cancer as compared to the healthy controls, further confirming our hypotheses on the pathophysiologic mechanisms underlying the metabolic and nutritional characteristics in elderly women with cancer.

5. Conclusions

Our study shows that elderly patients with advanced solid malignancies have a greater reliance on fat metabolism at rest and walk shorter distances at a lower relative intensity than their age-matched, healthy controls. Early markers of cachexia (i.e. decreased caloric intake and increased inflammatory response) rather than late changes associated with this syndrome (i.e. muscle wasting) may explain the poorer functional capacity during the 6MWT in the group with cancer. Timely recognition of these signs along with other predisposing factors for cachexia may allow therapeutic interventions to better prevent or delay nutritional and functional demise in elderly patients with cancer.

Future studies should include determining whether shifts in metabolism and changes in energy economy occur in all groups of patients with cancer, and if so, what may be the cause. Looking at specific inflammatory markers and how they correlate with walking distance, speed, and energy economy, may give an indication as to whether the same factors that play a role in the development of fatigue and cancer cachexia, also play a role in the decline of efficiency of an organism during activity.

Disclosures and Conflict of Interest Statements

This work was supported by the Canada Graduate Scholarship-Canadian Institutes for Health Research (CIHR) Master's Award (BT) and the Fonds de la recherche en santé du Québec (FRSQ) Master's Award (BT), as well as the Canadian Foundation for Innovation (CFI)-New Opportunities Award (AV), the CIHR-Operating Grant (AV), the McGill University Health Centre Research Institute-Pilot Project Competition (AV), the FRSQ Chercheur-clinicien Senior Award (JAM) and the Québec Network for Research in Aging (JAM).

Author Contributions

Concept and design: Barbara Trutschnigg, Robert D. Kilgour, JoséA. Morais, Antonio Vigano

Data collection: Barbara Trutschnigg, Enriqueta Lucar, Haneen Molla

Analysis and interpretation of data: Barbara Trutschnigg, Robert D. Kilgour, José A. Morais, Laura Hornby, Antonio Vigano

Manuscript writing and approval: Barbara Trutschnigg, Robert D. Kilgour, José A. Morais, Enriqueta Lucar, Laura Hornby, Haneen Molla, Antonio Vigano.


We are grateful to the 20 women who volunteered and participated in this pilot project. We thank Connie Nardollilo who helped with patient recruitment.


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a McGill Nutrition and Performance Laboratory, 5252 de Maisonneuve W, Suite 105-B, Montreal, QC, Canada H4A 3S5

b Department of Exercise Science, Concordia University, 7141 Sherbrooke W, Montreal, QC, Canada H4B 1R6

c Division of Geriatrics, MUHC-Royal Victoria Hospital, 687 Pine Ave W, H6.61, Montreal, QC, Canada H3A 1A1

lowast Corresponding author at: McGill Nutrition and Performance Laboratory (, 5252 de Maisonneuve, Suite 105-B, Montreal, QC, Canada H4A 3S5. Tel.: + 1 514 934 1934x78716; fax: + 1 514 489 2718.

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