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1 Unit of Nutrition and
Preventive Medicine, To determine
whether the age-related reduction in basal metabolic rate (BMR) is
explained by a quantitative and/or qualitative change in the
components of lean tissue, we conducted a cross-sectional study in
groups of young (n = 38, 18-35
yr) and older (n = 24, 50-77 yr)
healthy individuals. BMR was measured by indirect calorimetry. Body
composition was obtained by using dual-energy X-ray absorptiometry (DEXA), which permitted four compartments to be quantified [bone mineral mass, fat mass (FM), appendicular lean tissue mass
(ALTM), and nonappendicular lean
tissue mass (NALTM)].
Absolute BMR and ALTM were lower,
whereas FM was significantly higher in the older, compared with young,
subjects. BMR, adjusted for differences in FM,
ALTM, and
NALTM, was significantly lower in
the older subjects by 644 kJ/day. In separate regression analyses of
BMR on body compartments, older subjects had significantly lower
regression coefficients for ALTM
and NALTM, compared with young
subjects. Hence, the age-related decline in BMR is partly explained by
a reduction in the quantity, as well as the metabolic activity, of
DEXA-derived lean tissue components.
basal metabolic rate; body composition; dual-energy X-ray
absorptiometry; physical activity level; elderly humans
A REDUCTION in the basal metabolic rate (BMR) with
advancing age has been observed in a number of studies. Some
researchers have attributed the decrease in BMR associated with aging
to a change in body composition, i.e., an increase in body fat mass (FM) and a decrease in fat-free mass (FFM) (9, 16). Shock et al. (36)
related changes in resting metabolic rate (RMR) to alterations of the
water content of the body. They demonstrated a reduction in total body
water (TBW) and intracellular water but no change in the extracellular
water content in older individuals. This was interpreted to be a loss
of functioning cells with increasing age. However, when the RMR was
adjusted for the differences in TBW or intracellular water, there was
no significant difference between young and older subjects. Calloway
and Zanni (3) observed a 13% reduction in RMR in six healthy older men
(63-77 yr) compared with younger men; however, RMR adjusted for
total body K+, was similar between
the two groups. Still, in other studies, in which changes in body
composition were taken into account, older individuals had a
significantly lower BMR compared with young subjects (11, 18, 24,
43). This would indicate that a quantitative reduction in
FFM alone cannot explain the lower BMR observed in older subjects. The
work of Tzankoff and Norris (42) showed that after 45 yr of age there
was a progressive reduction of the RMR, which was related to a
concomitant reduction in skeletal muscle mass (measured by 24-h urinary
creatinine excretion). They concluded that an age-related decrease in
skeletal muscle accounted for all the reduction in RMR seen with
increasing age. Recent data confirm the significant contribution of
skeletal muscle to variance in BMR (52).
FFM is not a single homogenous mass but is composed of tissues and
organs that differ in mass and rate of metabolic activity (5). For
example, in relative terms, bone mineral is energetically inert and
skeletal muscle has a low metabolic rate in the resting state, whereas
organ and visceral tissues have a relatively high metabolic rate in the
resting state (5, 8). A reduction in skeletal muscle with no change in
organ tissue mass would result in a higher proportion of organ tissue
within FFM. For the same amount of FFM, one could then expect older
subjects to have a higher BMR, in comparison to young subjects. Elia
(5) has suggested that such changes in the composition of FFM may
contribute to the abnormally high energy expenditure that has been
observed in certain disease states. However, the observation of a
higher BMR adjusted for FFM, in older individuals, has never been made. Instead, most studies have demonstrated either a similar or lower BMR
after adjusting for differences in FFM between older and young subjects
(11, 18, 24, 43). If one accepted the above alteration in the
composition of FFM per se, then the only way the BMR of older
individuals could be similar or lower than in younger subjects would be
for the metabolic activity of the components of fat-free tissue to be
reduced. Theoretically, similar arguments have been proposed to explain
the lower BMR and whole body protein turnover of chronically
energy-deficient individuals in whom skeletal muscle mass, but not
nonskeletal muscle mass, was significantly reduced (39, 40). Therefore,
we hypothesized that the BMR of healthy older individuals would be
lower than that of young subjects, even after adjusting for differences
in the mass of components of fat-free tissue.
Most previous studies on BMR had employed a two-compartment model of
body composition (11, 18, 24, 42, 43). We, however, were interested in
partitioning the body into components with distinctly different
metabolic rates. Hence, we used a model incorporating four tissue
compartments: fat, bone mineral, appendicular lean tissue mass
(ALTM), and nonappendicular lean
tissue mass (NALTM). With the
exception of a small amount of skin, connective tissue, etc., the
fat-free soft tissue of the appendages is almost entirely skeletal
muscle (14). Wang et al. (44) have shown that
ALTM accounts for almost 80% of
the total skeletal muscle mass in young males. Based on these
observations, we validated dual-energy X-ray absorptiometry
(DEXA)-derived total skeletal muscle mass to that estimated from 72-h
urinary creatinine excretion while the subjects were on a meat-free
diet, by using p-aminobenzoic acid to
check for completeness of urine collection (38). The mean difference of
Experimental Design
ABSTRACT
Top
Abstract
Introduction
Methods
Results
Discussion
References
INTRODUCTION
Top
Abstract
Introduction
Methods
Results
Discussion
References
0.26 ± 0.88 (SE) kg was not significant and unrelated to the
mean of the two measurements (1, 38). Whereas this proximate
relationship between ALTM and
total skeletal muscle mass holds true for young men, there remains the
possibility that it may differ in women or older age groups (38, 44).
Hence, NALTM would include, in
addition to organs/viscera, a small, perhaps variable, portion of
skeletal muscle. We have made no attempt to separate out this skeletal
muscle component from NALTM.
Instead, we have worked on the assumption that
ALTM and
NALTM are largely representative
of total skeletal muscle mass and organ/visceral tissue mass,
respectively, in the groups studied. The data of Svendsen et al. (41)
support such an assumption, since DEXA-derived trunk LTM (analogous to
NALTM in this study) was a better
predictor of resting energy expenditure than was peripheral LTM
(analogous to ALTM).
METHODS
Top
Abstract
Introduction
Methods
Results
Discussion
References
Subjects
Subjects from two age groups were recruited by advertisement in the local media and by personal approach. All were resident in Melbourne, Australia. The subjects in the first group were aged between 18 and 35 yr, whereas in the second group participants were over 50 yr of age. Inclusion criteria were as follows: 1) absence of clinical signs or symptoms of chronic disease; 2) weight stability (±2 kg for preceding 12 mo); 3) not on any medication that could affect metabolic rate or body composition; 4) body mass index in the range of 20-30 kg/m2; and 5) resting diastolic blood pressure <90 mmHg. All subjects gave written informed consent to participate in the study. Deakin University Ethics Committee approved the experimental protocol, and all measurements were made at the clinical rooms of the Toorak campus of Deakin University.Anthropometry
Standing height was measured with a stadiometer fixed to the wall and recorded to the nearest 0.1 cm. Body weight, recorded to the nearest 100 g on a beam balance, was measured immediately after voiding, with subjects wearing light indoor clothing and no shoes. Mid arm, waist, and hip circumferences were measured as described by Callaway et al. (2).Body Composition by DEXA
System preparation. Quality assurance tests, as described by the manufacturer (Lunar, Madison, WI), were performed each morning to check the operation of the DEXA system before the subjects were scanned. No scans were performed until a valid quality assurance test was run.Subject preparation. Subjects were questioned about recent fractures, metal implants, and treatments that could affect bone density. Subjects had not ingested, or had been injected with, any radionuclide or radiopaque agents in the previous 3-5 days. All young female subjects were offered a spot pregnancy test to ensure that they were not pregnant. The subjects were requested to remove any material that could attenuate the X-ray beam. Such materials included jewelry, watches, and clothing with zippers, snap buckles, and buttons. They were asked to wear a cotton examination gown and remove their shoes for the duration of the scan. They were then asked to lie supine on the scan table as required by the manufacturer. Subjects were asked not to move after they had been correctly positioned on the table. Ankles and knees were strapped firmly together. This served the dual purpose of greater comfort and immobilization of the legs during the scan.
Scan details. A typical scan lasted 10 min, and the subject received 0.02 mrem of radiation. The maximum scan time was determined by the total area to be scanned (depending on the scan length and width values). Typical scan times were, however, shorter, as the auto width and auto length functions were employed, which adjusted the scan width and length on locating the subject's bone mass. All subjects were scanned by using the fast 150-µA mode, with a voltage of 76 kV being applied to the X-ray tube.
Analysis of total body DEXA scans. Lunar software (Version 1.3) was used to calculate total and regional body composition. The regions of interest were indicated on screen by analysis markers. The analysis program identified four major anatomical regions: head, arms, legs, and trunk (which included ribs, pelvis, thoracic spine, and lumbar spine). The extended research mode of the analysis program was used to determine bone mineral mass (BMM), LTM, and FM, in grams, for each anatomic region and the body as a whole. ALTM was obtained from the sum of the LTM in the four limbs; NALTM was obtained by subtracting ALTM from LTM. DEXA FFM (FFMDEXA) was obtained by subtracting FM from body weight.
DEXA scans were performed on five subjects on five separate occasions over a period of 2 wk to assess intraindividual variability. The coefficient of variation (CV) of FM was 2.2%, that of LTM was 1.7%, and of BMM was 1.0%; values that are comparable to those from the literature (21). The CVs of estimates of ALTM and NALTM were 2.5 and 2.7%, respectively, values that are comparable to those from the literature (10, 11, 23).
Estimation of TBW
TBW was measured by 2H2O dilution in a subset of subjects. A 10-ml fasting blood sample was taken from the subject immediately before and 2 h after the oral administration of 10 g of 2H2O (99.8%, Australian Nuclear Science and Technology Organisation, Lucas Heights, NSW, Australia). Blood samples were centrifuged at 4°C for 10 min, and plasma was separated and frozen at20°C until
nuclear magnetic resonance (NMR) analysis was performed. 2H2O
concentrations were determined as described by Khaled et al. (17).
NMR analysis. Plasma samples were thawed and vortexed. An external standard of 1% deuterated trifluoroacetic acid in benzene (Sigma Chemical, St. Louis, MO) was sealed in a 5-mm NMR tube and placed coaxially into a 10-mm NMR tube containing ~3 ml of plasma. 2H2O spectra for each plasma sample were obtained by using a JEOL JNM GX270 NMR spectrometer (JEOL, Tokyo, Japan). An observing frequency of 41.47 MHz was used with a recycle time of 1.238 s. Two thousand and forty eight sample points were determined across a 900-Hz observation range. The digital resolution was 0.88 Hz. The instrument was shimmed on 2H2O and then run unlocked, without spinning. Each sample was scanned 128 times to obtain a good signal-to-noise ratio. Results from the NMR analysis were then transferred to an IBM-compatible computer. The spectra were analyzed by using the NUTS software package (Acorn NMR, CA), and peaks for the external standard and plasma sample were obtained following Fourier transformation and phasing. The peak areas of the external standard (which was arbitrarily assigned a peak area of 1) and of the plasma sample were integrated by using the NUTS software package. The peak area ratio (PAR) of the plasma sample to the external standard was then obtained. PAR was also obtained by using standard solutions of known 2H2O enrichment (ranging from 0.01 to 0.05 g/100 ml deionized distilled water), together with a distilled water blank. The relationship of the PAR to 2H2O enrichment of the standard solutions was then obtained by regression analysis. The 2H2O enrichment of the plasma samples, collected before and 2 h after the dose of 2H2O, was then estimated by using this regression equation. The change in 2H2O enrichment was determined by subtracting the predose enrichment from the 2-h postdose enrichment.
TBW was calculated as follows.
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where conc is concentration, * indicates value estimated at 4%, and
is correction for mass of water (0.3 kg) administered with
2H2O dose.
FFM was estimated from TBW assuming a hydration of 73.2% (28). FM was calculated from the difference of body weight and FFM and was also expressed as a percentage of body weight.
In five subjects, plasma samples (both predose and 2 h postdose) were analyzed by NMR in triplicate. The PAR obtained from the NUTS software package on each of the three occasions was used to estimate the TBW in each subject. There were no significant differences between the three estimates of TBW in each of the five subjects on an analysis of variance for repeated measures (P = 0.33), with a within-sample CV of 1.96%.
BMR
The BMR was measured by indirect calorimetry using a Deltatrac II metabolic monitor (Datex, Finland), an open-circuit ventilated-canopy measurement system. The measurement was conducted under standardized conditions (29): subjects were lying 1) at complete physical rest, 2) in a thermally neutral environment, 3) 12-14 h after their last meal and a minimum of 8 h of sleep, 4) awake and emotionally undisturbed, and 5) without disease and fever.The Deltatrac was calibrated by using a calibration gas mixture of oxygen (95%) and carbon dioxide (5%) (Quickcal, Datex, Finland) each morning before the BMR measurements. Airflow rates through the canopy (46.5 l/min) were checked by means of ethanol-burning tests, as described by the manufacturer (Datex, Finland), conducted once each month during the 3 mo of data collection. Performance of the Deltatrac monitor was also checked by monitoring the ratio of carbon dioxide produced to oxygen consumed during the ethanol burns. The mean (±SD) ratio for the last 15 min of the tests was 0.66 (±0.02), which was within the manufacturer's recommended range of 0.64-0.69.
In a subgroup of subjects (n = 7), BMR and body weight were measured under similar conditions on three separate occasions, 2-4 wk apart. Intraindividual CV in BMR was 2.9%, whereas that for body weight was 0.9%, similar to other published values (4, 37).
Measurement Protocol
Subjects were transported to the laboratory early in the morning (7-9 AM) after a 12-h fast and a minimum of 8-h sleep while keeping physical activity to a minimum. Subjects were also requested to abstain from any strenuous exercise for 36 h before the BMR measurement. On arriving, subjects were asked to void and then asked to lie down for a mandatory 30-min rest period, during which time the Deltatrac was calibrated. During this period, a fasting blood sample was collected in a subset of subjects (n = 36). This was followed by the oral administration of 10 g of 2H2O. At the end of the mandatory rest period, the canopy of the Deltatrac was placed over the head of the subject. The subject was asked to remain awake and motionless, as much as possible, and the 35-min BMR measurement (as described earlier) commenced. We have shown earlier that this protocol yields a BMR not significantly different from a BMR measured immediately on waking, following an overnight stay in the laboratory (37).After the BMR measurement, subjects were asked to void again, remove all jewelry and metal, and change into hospital gowns to have their body weight, height, and body composition measured. The DEXA scan was then carried out as described previously. A final 10-ml blood sample was collected 2 h after the administration of the 2H2O.
Physical Activity and Total Energy Expenditure
All subjects were asked to maintain a 3-day (nonconsecutive) physical activity diary of days most representative of their habitual activity pattern, including 1 weekend day. The diary consisted of ninety six 15-min blocks for each of the 3 days. Subjects were asked to write down the nature of the activity predominantly performed during each 15-min block. These activities were then coded from one to nine, depending on the type and intensity of activity. Each block was then appropriately coded, and the total number of 15-min blocks at each of the 9 levels of activity was determined. Energy expenditure at each level of activity was obtained by multiplying the BMR (kJ/15 min) by an activity factor obtained from the literature (7), appropriate for the type of activity being performed, and by the number of 15-min blocks for which the activity was performed during the day. The sum of the energy expenditure associated with the nine levels of activity over 24 h gave total energy expenditure (TEE, kJ/day). Physical activity levels (PAL) were calculated by dividing the mean TEE (kJ/day) over 3 days by the BMR (kJ/day) measured in each subject.
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Thyroid Hormones
In a subset of the young (n = 16) and older (n = 20) subjects, a 10-ml fasting blood sample was taken from the antecubital vein. The blood sample was centrifuged at 4,000 rpm for 10 min, and the plasma was separated and stored at20°C. Plasma concentrations of thyroxine
(T4) and
3,3',5-triiodo-L-thyronine
(T3) were measured by
radioimmunoassay using a coated-tube radioimmunoassay kit (Spectria, Orion Diagnostica, Finland) and a LKB Wallac multigamma 1261 gamma counter (Wallac Oy, Turku, Finland). Intra-assay variation was <4%,
whereas interassay variation was <6% for both assays.
Statistics
Data were analyzed by using the SPSS for Windows (Version 6.1, SPSS) statistical software package. All data are presented as means ± SD unless otherwise stated. Anthropometric, body composition, energy expenditure, physical activity, and hormonal data were compared between age and gender groups, by using a two-way ANOVA. Estimates of FFM obtained by DEXA and from TBW were compared by the method suggested by Bland and Altman (1). This method requires the absolute difference between the two measurements in question (FFM estimated from TBW and DEXA, in this case), when made in the same individual, to be small and statistically unrelated to the mean of the two measurements.BMR (dependent variable) was analyzed for differences, between the two age (0 = young, 1 = older subjects) and gender groups (0 = women, 1 = men) studied, by stepwise regression analysis with 1) body weight; 2) FFM and FM (2 compartments); 3) LTM, FM, and BMM (3 compartments); and 4) FM, BMM, NALTM, and ALTM (4 compartments), as independent variables. BMR was also similarly analyzed in each age group separately, and the regression coefficients (slopes) for each corresponding compartment, and constant (intercept) of each body-composition model, were compared between the two age groups studied by testing for equality of slopes and intercepts as described by Kleinbaum et al. (19).
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RESULTS |
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Top
Abstract
Introduction
Methods
Results
Discussion
References
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Of the 113 subjects recruited, 62 met all inclusion criteria. These consisted of 38 young subjects (15 men, 23 women) and 24 older subjects (12 men, 12 women). Of the 38 young subjects, 23 were students (1 man and 22 women), whereas one of the older women was also a student. The young women studied were not on oral contraceptive agents of any kind; however, four of the older women were on hormone-replacement therapy. None of the subjects studied performed any regular weight training or muscle-building exercises.
Anthropometry
The anthropometric characteristics of the volunteers are summarized in Table 1. The older subjects were significantly heavier than the young subjects (P = 0.007), and men were significantly heavier (P < 0.0005) and taller than women (P < 0.0005). The older subjects and men had a significantly higher BMI than did the young subjects (P = 0.012) and women (P = 0.047), respectively. Waist (P < 0.0005) and hip (P = 0.015) circumferences were significantly higher in the older age group. However, waist circumference (P < 0.0005), but not hip, was significantly larger in men. The waist-to-hip circumference ratio was significantly higher in older subjects (P < 0.0005) and men (P < 0.0005), compared with younger subjects and women, respectively. There were no significant age × gender group interactions for any these variables.Body Composition
Body composition from DEXA was measured in all 62 subjects. In a subset of 35 subjects (15 young, 20 older), body composition was also estimated from TBW obtained by 2H2O dilution. A comparison of FFM obtained by DEXA (FFMDEXA, 52.9 ± 10.0 kg) and from TBW (FFMTBW, 53.5 ± 9.8 kg) yielded no significant difference on a paired t-test (P = 0.31). Also, the bias (FFMDEXA FFMTBW; 0.56 ± 3.2 kg) of the
two methods was not significantly correlated
(r = 0.04, P = 0.82) to the mean FFM (53.2 ± 9.8 kg) obtained by the two methods. On a paired
t-test, the sum of DEXA-derived LTM
and BMM compared with our estimate of
FFMDEXA (i.e.,
subtracting DEXA-derived FM from body weight) was not significantly
different in either the young (mean difference ±SD, 0.09 ± 0.49 kg; P = 0.29) or older subjects (0.06 ± 1.48 kg; P = 0.85). An estimate of the "hydration" of FFM was obtained in these subjects
(n = 35) by dividing TBW by
FFMDEXA (49), the 95% confidence
interval (72.5, 75.4%) included the accepted norm of 73.2% (28). A
comparison of hydration thus obtained between the young (75.2 ± 3.9%) and older (73.0 ± 4.5%) subjects and between men (73.5 ± 4.0%) and women (74.8 ± 5.0%), using a two-way ANOVA,
showed no significant difference between either the age
(P = 0.12) or gender
(P = 0.14) groups; nor was there a
significant age × gender group interaction (P = 0.21). These results suggest that
the estimates of FFM (and, by extension, FM) by DEXA are comparable to
those obtained by 2H2O dilution in both the
young and older subjects.
The older subjects had significantly higher FM in absolute terms (P < 0.0005) and also when expressed as a percentage of body weight (P < 0.0005). However, there were no significant differences in FFMDEXA (P = 0.858) or LTM (P = 0.894) between the two age groups. Men had significantly less FM in absolute terms (P = 0.005) and also when expressed as a percentage of body weight (P < 0.0005), compared with women. However, they had significantly more FFMDEXA (P < 0.0005) and LTM (P < 0.0005) than did the women. There was no significant difference in BMM between the young and older subjects (P = 0.602), but men had significantly greater BMM than women (P < 0.0005). There were no significant age × gender group interactions for any of these variables (Table 2).
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ALTM tended to be lower in the older subjects, compared with the younger subjects, but this difference was just short of statistical significance (P = 0.056). However, NALTM was not significantly different between the two groups (P = 0.122). The ratio of ALTM to LTM was significantly lower (P < 0.0005), whereas that of NALTM to LTM was significantly higher (P < 0.0005) in the older subjects compared with the young subjects (Table 2). ALTM (P < 0.0005) and NALTM values (P < 0.0005) were significantly lower in women compared with men, as was the ratio of ALTM to LTM (P = 0.019). However, the ratio of NALTM to LTM was significantly higher in the women compared with the men (P = 0.019). There were no significant age × gender group interactions for any these variables.
Energy Expenditure and PAL
Absolute BMR was significantly lower in older subjects compared with the young subjects (P = 0.006) and in the women compared with the men (P < 0.0005). Fasting respiratory quotients, measured in all 62 subjects, were not significantly different between age (P = 0.186) or gender (P = 0.950) groups. There was no significant age × gender group interaction for either of these variables (Table 2).On stepwise regression analysis, with BMR as the dependent variable and body weight, age, and gender groups as the independent variables, there were significant differences between the two age and gender groups in BMR (Table 3). When body composition variables replaced weight, the significant difference in BMR between the young and older subjects persisted. However, the gender differences were no longer apparent, and BMM was excluded from the equations (Table 3). Regardless of the model of body composition employed, there were significant differences in the adjusted BMR between the two age groups studied, the older group of subjects having a BMR ~600-650 kJ/day or 10% lower than the young group. ALTM accounted for 78.4% of the total variance in BMR when the four-compartment model of body composition was used, whereas NALTM, FM, and age group accounted for 4.0, 4.0, and 1.8%, respectively.
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Age group-specific regression coefficients were obtained for each compartment of each body composition model. The older subjects had significantly lower (P < 0.05) coefficients for FFM, LTM, NALTM, and ALTM for all models of body composition employed (Table 4).
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TEE and PAL estimates were obtained in 21 young and 22 older subjects
studied. TEE (P = 0.435) and PAL
(P = 0.133) were not significantly
different between age groups. However TEE
(P < 0.0005) and PAL
(P = 0.006) were significantly higher
in men compared with women. There were no significant age × gender group interactions for either of these variables. On pooling the
data, for the 43 young and older subjects, PAL was significantly
correlated with FFMDEXA
(r = 0.49;
P = 0.001), LTM
(r = 0.47;
P = 0.001),
ALTM, (r = 0.39;
P = 0.009), and
NALTM
(r = 0.50;
P = 0.001); although it tended to be
inversely correlated with FM, it was just short of statistical
significance (r = 0.30;
P = 0.052) (Table 3). PAL, however,
was not significantly correlated with BMR
(r = 0.27, P = 0.083).
Thyroid Hormones
Mean (±SD) plasma T3 [young (n = 16) 1.91 ± 0.26; older subjects (n = 20) 1.79 ± 0.33 nmol/l] and T4 [young (n = 16) 83.3 ± 11.7; older subjects (n = 20) 84.8 ± 12.2 nmol/l] concentrations were not significantly different between the two age groups (T3, P = 0.244; T4, P = 0.706) or gender groups (T3, P = 0.06; T4, P = 0.208), nor was there a significant age × gender group interaction.| |
DISCUSSION |
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Abstract
Introduction
Methods
Results
Discussion
References
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A low relative metabolic rate is one risk factor for weight gain (31-33). Hence, the demonstration of a relatively low BMR in older subjects is of importance, as it would contribute to the high prevalence of overweight and obesity in this age group. The relationship of BMR to FFM in older individuals is, however, confounded by a change in the composition of FFM per se. Hence, it is important to account for these changes when comparing the BMR of young and older subjects (46). This study attempted such a comparison, with the expectation that, in the older individuals, there would be a reduction in metabolic rate, even after accounting for changes in the mass of components of the fat-free tissue.
DEXA-Derived Body Tissue Compartments
The use of DEXA provided a four-compartment model of body composition that included FM, BMM, ALTM, and NALTM. Fuller et al. (12) concluded that DEXA enabled valid and reproducible estimation of fat, fat-free soft tissue, and bone and limb muscle mass. Additionally, evidence in the literature suggested that DEXA-derived FM is not affected by the hydration status of the individual (10, 13, 26). However, there are concerns that the partitioning by DEXA of soft tissue into FM and LTM may be inaccurate (15). We derived FFMDEXA by subtracting DEXA-derived FM from body weight. In the present study, FFM (and FM) obtained independently from TBW and DEXA was not significantly different in the subset of 35 subjects in whom both these measurements were made. In addition, the hydration coefficient (ratio of TBW to FFMDEXA) was not significantly different between the young and older subjects and was similar to the commonly accepted value of 0.732. Given the excellent precision and accuracy of DEXA-derived measurements of BMM (21, 23), and the validation of the DEXA-derived estimates of FFM and FM, we were confident of the accuracy of our estimates of LTM by DEXA. The partitioning of the limbs from the trunk was carried out by using standard methodology and the software package provided by Lunar. The precision of the technique in our laboratory had a CV of 2.5% for ALTM and 2.7% for NALTM, values that are within the range of those obtained by other investigators in this field (10, 11, 23).BMR and the Composition of FFM
ALTM was the only compartment, both in absolute terms and as a percentage of total LTM, that was lower in the older subjects (Table 3). This resulted in a relatively greater proportion of NALTM within LTM in older individuals. In such a situation for the same FFM, one could expect older individuals to have a higher BMR. Elia (5) has proposed this change in the proportion of metabolically active tissues (e.g., organs) to less active tissues (e.g., muscle) as being one of the several possible causes for the hypermetabolic state observed in some disease conditions. However, a higher BMR adjusted for FFM in older subjects has not been reported in the literature. Most recent studies support the finding of a "similar" (3, 16, 36) or a "lower" BMR in older subjects (11, 18, 24, 43), once data are adjusted for differences in FFM. We argue that the only way BMR adjusted for FFM can be similar or lower in older subjects is for the metabolic rate of the tissues that comprise FFM to also be reduced.When BMR was adjusted for body weight, women had significantly lower BMR than did men, and older individuals had significantly lower BMR than younger groups (Table 3). There was no gender difference in BMR when any model of body composition was employed in the intergroup comparison. However, in all instances, there were significant differences between the age groups studied (Table 3). These results support previous observations of a reduced BMR adjusted for differences in FFM in older, compared with young, subjects (11, 18, 24, 43). On using the four-compartment model for body composition, to adjust for variations in the composition of FFM per se, the BMR of older individuals was still lower by 644 kJ/day. This is indicative of a reduction in the metabolic rate of the tissues, a feature that may predispose to weight gain. Significantly lower regression coefficients for the ALTM and NALTM compartments of FFM were also observed in the older subjects, compared with the young subjects. If one accepts that these compartments are representative of the whole body skeletal muscle and organ/visceral tissue, respectively, then our results suggest that both these tissue compartments are metabolically less active in older individuals.
BMR and Physical Activity
There is evidence to suggest that physically active older men show very little change in their BMR or body composition (16). Self-reported PAL suggested that the majority of subjects studied were sedentary to moderately active, as defined by the Food and Agriculture Organization/World Health Organization/United Nations University report (8). There was a significant inverse correlation of PAL with FM and a direct correlation with all lean tissue components in this study. However, mean PAL of the young subjects (1.57 ± 0.14) was not significantly different from that of older subjects (1.64 ± 0.14). In addition, PAL was not a significant predictor of BMR on stepwise regression that included body-composition data. It is, therefore, unlikely that the PAL of these subjects influenced their BMR.Possible Mechanisms Underlying the Reduction in BMR
A lower oxygen consumption and, hence, energy expenditure of some tissues and organs is also supported by the literature. Age-related changes in the oxygen consumption of the heart and hepatomesenteric bed have been documented. Aging is associated with a progressive loss of myocytes (27) and an increase in the rate of degenerative changes, such as lipid deposition, tubular dilation, and lipofuscin deposition (20, 27). In the liver, aging is associated with a decrease in liver volume (7) and a reduction in function, as indicated by a variety of hepatic function tests (7, 22, 35). These structural and functional changes can be expected to affect oxygen utilization and may explain, in part, the lower metabolic activity of the NALTM compartment in the older subjects.There are several other possible mechanisms that may account for an age-related reduction in the metabolic rate. Poehlman et al. (30) examined the possibility that alterations in the Na+-K+ pump would contribute to the age-related decline in RMR. They estimated that a reduction in the Na+-K+ pump activity may account for a 3% reduction in the RMR of older individuals. Whole body protein turnover can account for between 15 and 20% of the BMR (34, 39, 45, 47), and a reduced turnover of protein has been demonstrated in older individuals (48). In addition, data suggest that the contribution of skeletal muscle to whole body protein turnover decreases with age, with a relatively larger contribution from the visceral organs in the elderly (25, 50, 51).
Morgan and York (24) speculated that factors like thyroid hormones, or the tissue sensitivity to them, may be implicated in the age-related decline in BMR. This study had young and older subjects with similar FFM but differing BMR. In the subset of older subjects in whom thyroid hormone concentrations were measured, plasma concentrations of T3 and T4 were normal and not significantly different from those observed in the young subjects. However, the possibility that the tissue responsiveness to these hormones was diminished cannot be ruled out. This may also be the case with the activity of the sympathetic nervous system. Although the available evidence suggests that aging increases the rate of sympathetic firing, at least in some organs, this is not necessarily translated into greater adrenergic responses (6). Factors such as adrenergic-receptor subsensitivity or uncoupling can blunt end-organ responsiveness (6). Because catecholamines stimulate cellular metabolism, it is conceivable that changes in sympathetic nervous system activity may in part explain the reduction in metabolic rate that occurs with aging.
In summary, the BMR of older individuals is significantly lower than that of young subjects, even after accounting for differences in the composition of FFM per se. We have demonstrated significantly lower regression coefficients for both NALTM and ALTM of older subjects. These results are indicative of a reduction in the metabolic rate of LTM and its components, a feature that would contribute to a low relative BMR. It is likely that age-related gains in body weight are related, in part, to such changes.
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ACKNOWLEDGEMENTS |
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We thank Stacey L. Frandsen, Mark Lutschini, Dr. Karen Walker, Connie Karschimkus, Olga Strommer, and Bronwyn Diffey for their assistance with the project; Gail Dyson for assistance with the NMR, and Dr. Sing Kai Lo for assistance with the statistical analysis.
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FOOTNOTES |
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L. S. Piers was the recipient of a postdoctoral research fellowship from Deakin University, Australia.
A poster based on part of the data contained in this paper was presented at the 16th International Congress of Nutrition, Montreal, Canada, July 27-August 1, 1997. Part of this data also appeared in the published abstracts (p. 77; Abstracts PW 14.1).
Address for reprint requests: L. S. Piers, Unit of Nutrition and Preventive Medicine, Dept. of Epidemiology and Preventive Medicine, Monash Univ., Monash Medical Centre, 246 Clayton Road, Clayton, VIC 3168, Australia (E-mail: sunil.piers{at}med.monash.edu.au).
Received 30 December 1997; accepted in final form 13 August 1998.
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Abstract
Introduction
Methods
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Discussion
References
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|---|
1.
Bland, J. M.,
and
D. G. Altman.
Statistical methods for assessing agreement between two methods of clinical measurement.
Lancet
8:
307-310,
1986.
2.
Callaway, C. W.,
W. C. Chumlea,
C. R. Bouchard,
J. H. Himes,
T. G. Lohman,
A. D. Martin,
C. D. Mitchell,
W. H. Mueller,
A. F. Roche,
and
V. D. Seefeldt.
Circumferences.
In: Anthropometric Standardisation Reference Manual, edited by T. G. Lohman,
A. F. Roche,
and R. Martorell. Champaign, IL: Human Kinetics, 1988, p. 39-54.
3.
Calloway, D. H.,
and
E. Zanni.
Energy requirements and energy expenditure of elderly men.
Am. J. Clin. Nutr.
33:
2088-2092,
1980
4.
Durnin, J. V. G. A. Energy requirements:
general principles. Eur. J. Clin.
Nutr. 50, Suppl:
S2-S10, 1996.
5.
Elia, M.
Organ and tissue contribution to metabolic rate.
In: Energy Metabolism: Tissue Determinants and Cellular Corollaries, edited by J. M. Kinney,
and H. N. Tucker. New York: Raven, 1992, p. 61-79.
6.
Esler, M.
The sympathetic nervous system and catecholamine release and plasma clearance in normal blood pressure control, in aging, and in hypertension.
In: Hypertension: Pathophysiology, Diagnosis and Management (2nd ed.), edited by J. H. Laragh,
and B. M. Brener. New York: Raven, 1995, p. 755-773.
7.
Fabbri, A,
G. Marchesini,
G. Bianchi,
E. Bugianesi,
M. Zoli,
and
E. Pisi.
Kinetics of hepatic amino-nitrogen conversion in ageing man.
Liver
14:
288-294,
1994[Medline].
8.
FAO/WHO/UNU.
Principles for the estimation of energy requirements.
In: Energy and Protein Requirements. Geneva: World Health Organisation, 1985, p. 34-52. (Tech. Rep. Ser. 724)
9.
Flynn, M. A.,
G. B. Nolph,
A. S. Baker,
W. M. Martin,
and
G. Krause.
Total body potassium in aging humans: a longitudinal study.
Am. J. Clin. Nutr.
50:
713-717,
1989
10.
Formica, C.,
M. G. Atkinson,
I. Nyulasi,
J. McKay,
W. Heale,
and
E. Seeman.
Body composition following hemodialysis: studies using dual-energy X-ray absorptiometry and bioelectrical impedance analysis.
Osteoporos. Int.
3:
192-197,
1993[Medline].
11.
Fukagawa, N. K.,
L. G. Bandini,
and
J. B. Young.
Effect of age on body composition and resting metabolic rate.
Am. J. Physiol.
259 (Endocrinol. Metab. 22):
E233-E238,
1990.
12.
Fuller, N. J.,
M. A. Laskey,
and
M. Elia.
Assessment of the composition of major regions by dual-energy X-ray absorptiometry (DEXA), with special reference to limb muscle mass.
Clin. Physiol.
12:
253-266,
1992[Medline].
13.
Going, S. B.,
M. P. Massett,
M. C. Hall,
L. A. Bare,
P. A. Root,
D. P. Williams,
and
T. G. Lohman.
Detection of small changes in body composition by dual-energy X-ray absorptiometry.
Am. J. Clin. Nutr.
57:
845-850,
1993
14.
Heymsfield, S. B.,
R. Smith,
M. Aulet,
B. Bensen,
S. Lichtman,
J. Wang,
and
R. N. Pierson, Jr.
Appendicular skeletal mass: measurements by dual-photon absorptiometry.
Am. J. Clin. Nutr.
52:
214-218,
1990
15.
Jebb, S. A.
Measurement of soft tissue composition by dual energy X-ray absorptiometry.
Br. J. Nutr.
77:
151-163,
1997[Medline].
16.
Keys, A.,
H. L. Taylor,
and
F. Grande.
Basal metabolism and age of adult man.
Metabolism
22:
579-587,
1973[Medline].
17.
Khaled, M. A.,
H. C. Lukaski,
and
C. L. Watkins.
Determination of total body water by deuterium NMR.
Am. J. Clin. Nutr.
45:
1-6,
1987
18.
Klausen, B.,
S. Toubro,
and
A. Astrup.
Age and sex effects on energy expenditure.
Am. J. Clin. Nutr.
65:
895-907,
1996
19.
Kleinbaum, D. G.,
L. L. Kupper,
and
K, and E. Muller.
Applied Regression Analysis and Other Multivariable Methods. Boston, MA: PWS-Kent Publishing, 1988.
20.
Lie, J. T.,
and
P. I. Hammond.
Pathology of the senescent heart: autonomic observations on 237 autopsy studies of patients 90 to 105 years old.
Mayo Clin. Proc.
63:
552-564,
1988[Medline].
21.
Lukaski, H. C.
Soft tissue composition and bone mineral status: evaluation by dual-energy X-ray absorptiometry.
J. Nutr.
123:
438-443,
1993.
22.
Marchesini, G.,
V. Bua,
G. Brunori,
G. Bianchi,
P. Pisi,
A. Fabbri,
M. Zoli,
and
E. Pisi.
Galactose elimination capacity and liver volume in ageing man.
Hepatology
8:
1079-1083,
1988[Medline].
23.
Mazess, R. B.,
H. S. Barden,
J. P. Bisek,
and
J. Hanson.
Dual-energy X-ray absorptiometry for total-body and regional bone-mineral and soft-tissue composition.
Am. J. Clin. Nutr.
51:
1106-1112,
1990
24.
Morgan, J. B.,
and
D. A. York.
Thermic effect of feeding in relation to energy balance in elderly men.
Ann. Nutr. Metab.
27:
71-77,
1983[Medline].
25.
Munro, H. N.,
and
V. R. Young.
Protein metabolism and requirements.
In: Metabolic and Nutritional Disorders in the Elderly, edited by A. N. Exton-Smith,
and F. I. Courd. Bristol, UK: Wright, 1980, p. 13-25.
26.
Nord, R. H.,
and
R. K. Payne.
Dual-energy X-ray absorptiometry vs.underwater weighing comparison of strengths and weaknesses.
Asia Pac. J. Public Health
4:
173-175,
1995.
27.
Olivetti, G.,
M. Melissari,
J. M. Capasso,
and
P. Anversa.
Cardiomyopathy of the ageing human heart: myocyte loss and reactive cellular hypertrophy.
Circ. Res.
68:
1560-1568,
1991
28.
Pace, N.,
and
E. N. Rathburn.
Studies on body composition. III. The body water and chemically combined nitrogen content in relation to fat content.
J. Biol. Chem.
158:
685-691,
1945
29.
Piers, L. S.,
B. Diffey,
M. J. Soares,
S. L. Frandsen,
L. M. McCormack,
M. J. Lutschini,
and
K. O'Dea.
The validity of predicting basal metabolic rates of young Australians.
Eur. J. Clin. Nutr.
51:
333-337,
1997[Medline].
30.
Poehlman, E. T.,
M. J. Toth,
and
G. D. Webb.
Na-K pump activity contributes to the age-related decline in RMR.
J. Clin. Endocrinol. Metab.
76:
1054-1059,
1993[Abstract].
31.
Ravussin, E.,
S. Lillioja,
W. C. Knowler,
L. Christin,
D. Freymond,
W. G. Abbott,
V. Boyce,
B. V. Howard,
and
C. Bogardus.
Reduced rate of energy expenditure as a risk factor for body-weight gain.
N. Engl. J. Med.
318:
467-472,
1988[Abstract].
32.
Ravussin, E.,
and
B. Swinburn.
Energy metabolism.
In: Obesity: Theory and Therapy (2nd ed.), edited by A. J. Stunkard,
and T. A. Wadden. New York: Raven, 1993, p. 97-123.
33.
Ravussin, E.,
and
B. Swinburn.
Metabolic predictors of obesity.
Int. J. Obes.
17:
S28-S31,
1993.
34.
Reeds, P. J.,
and
P. J. Garlick.
Nutrition and protein turnover in man.
In: Advances in Nutrition Research, edited by H. H. Draper. New York: Plenum, 1984, vol. 6, p. 93-138.
35.
Schnegg, M.,
and
B. H. Lauterburg.
Quantitative liver function in the elderly assessed by galactose elimination capacity, aminopyrine demethylation and caffeine clearance.
J. Hepatol.
3:
174-181,
1986.
36.
Shock, N. W.,
D. M. Watkin,
M. J. Yiengst,
A. H. Norris,
G. W. Gaffney,
R. I. Gregerman,
and
J. A. Falzone.
Age differences in the water content of the body as related to basal oxygen consumption in males.
J. Gerontol.
18:
1-8,
1963[Medline].
37.
Soares, M. J.,
L. S. Piers,
L. Kraai,
and
P. S. Shetty.
Day-to-day variations in basal metabolic rates and energy intakes of human subjects.
Eur. J. Clin. Nutr.
43:
465-472,
1989[Medline].
38.
Soares, M. J., L. S. Piers, M. J. Lutschini, S. L. Frandsen, L. M. MacCormack, and K. O'Dea. Appendicular lean tissue mass and 24-h creatinine
excretions for the measurement of skeletal muscle mass in young men and
women. The 16th Int. Congr. Nutrition Montreal Canada
July 27-August 1 1997, p. 79. (Abstracts PW 14.16)
39.
Soares, M. J.,
L. S. Piers,
P. S. Shetty,
A. A. Jackson,
and
J. C. Waterlow.
Whole body protein turnover in chronically undernourished individuals.
Clin. Sci. (Colch.)
86:
441-446,
1994[Medline].
40.
Soares, M. J.,
and
P. S. Shetty.
Basal metabolic rates and metabolic economy in chronic undernutrition.
Eur. J. Clin. Nutr.
45:
363-373,
1991[Medline].
41.
Svendsen, O. L.,
C. Hassager,
and
C. Christiansen.
Impact of regional and total body composition and hormones on resting energy expenditure in overweight postmenopausal women.
Metabolism
42:
1588-1591,
1993[Medline].
42.
Tzankoff, S. P.,
and
A. H. Norris.
Effect of muscle mass decrease on age related BMR changes.
J. Appl. Physiol.
43:
1001-1006,
1977
43.
Visser, M.,
P. Deurenberg,
W. A. van Staveren,
and
J. G. A. J. Hautvast.
Resting metabolic rate and diet-induced thermogenesis in young and elderly subjects: relationship with body composition, fat distribution, and physical activity level.
Am. J. Clin. Nutr.
61:
772-778,
1995
44.
Wang, Z. M.,
M. Visser,
R. Ma,
R. N. Baumgartner,
D. Kotler,
D. Gallagher,
and
S. B. Heymsfield.
Skeletal muscle mass: evaluation of neutron activation and dual-energy X-ray absorptiometry methods.
J. Appl. Physiol.
80:
824-831,
1996
45.
Waterlow, J. C.
Observations on the mechanism of adaptation to low protein intakes.
Lancet
ii:
1091-1097,
1968.
46.
Weinsier, R. L.,
Y. Schutz,
and
D. Bracco.
Reexamination of the relationship of resting metabolic rate to fat-free mass and to metabolically active components of fat-free mass in humans.
Am. J. Clin. Nutr.
55:
790-794,
1992
47.
Welle, S.,
and
K. S. Nair.
Relationship of resting metabolic rate to body composition and protein turnover.
Am. J. Physiol.
258 (Endocrinol. Metab. 21):
E990-E998,
1990
48.
Welle, S.,
C. Thornton,
R. Jozefowicz,
and
M. Statt.
Myofibrillar protein synthesis in young and old men.
Am. J. Physiol.
264 (Endocrinol. Metab. 27):
E693-E698,
1993
49.
Woodrow, G.,
B. Oldroyd,
J. H. Turney,
P. S. W. Davies,
J. M. E. Day,
and
M. A. Smith.
Four-component model of body composition in chronic renal failure comprising dual-energy X-ray absorptiometry and measurement of total body water by deuterium oxide dilution
Clin. Sci.
91:
763-769,
1996[Medline].
50.
Yarasheski, K. E.,
J. J. Zachwieja,
and
D. M. Bier.
Acute effects of resistance exercise on muscle protein synthesis rate in young and elderly men and women.
Am. J. Physiol.
265 (Endocrinol. Metab. 28):
E210-E214,
1993
51.
Young, V. R.
Impact of aging on protein metabolism.
In: Nutritional Intervention in the Aging Process, edited by H. J. Armbrecht,
J. M. Prendergast,
and R. M. Coe. New York: Springer Verlag, 1984, p. 27-47.
52.
Zurlo, F.,
K. Larson,
C. Bogardus,
and
E. Ravussin.
Skeletal muscle metabolism is a major determinant of resting energy expenditure.
J. Clin. Invest.
86:
1423-1427,
1990.
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