Elinor L. Sullivan, Frank H. Koegler and Judy L. Cameron
Individual differences in physical activity are closely associated with changes in body weight in adult female rhesus monkeys (Macaca mulatta)
Am J Physiol Regul Integr Comp Physiol 291:R633-R642, 2006. First published 13 April 2006; doi:10.1152/ajpregu.00069.2006 You might find this additional info useful. This article cites 112 articles, 44 of which can be accessed free at: http://ajpregu.physiology.org/content/291/3/R633.full.html#ref-list-1 This article has been cited by 2 other HighWire hosted articles A rapidly occurring compensatory decrease in physical activity counteracts diet-induced weight loss in female monkeys Elinor L. Sullivan and Judy L. Cameron Am J Physiol Regul Integr Comp Physiol, April , 2010; 298 (4): R1068-R1074. [Abstract] [Full Text] [PDF]
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Physical activity of adult female rhesus monkeys (Macaca mulatta) across the menstrual cycle Nathan A. Hunnell, Nathan J. Rockcastle, Kristen N. McCormick, Laurel K. Sinko, Elinor L. Sullivan and Judy L. Cameron Am J Physiol Endocrinol Metab, June 1, 2007; 292 (6): E1520-E1525. [Abstract] [Full Text] [PDF] Updated information and services including high resolution figures, can be found at: http://ajpregu.physiology.org/content/291/3/R633.full.html Additional material and information about American Journal of Physiology - Regulatory, Integrative and Comparative Physiology can be found at: http://www.the-aps.org/publications/ajpregu
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Am J Physiol Regul Integr Comp Physiol 291: R633R642, 2006. First published April 13, 2006; doi:10.1152/ajpregu.00069.2006.
Elinor L. Sullivan,1,2 Frank H. Koegler,2 and Judy L. Cameron1,2,3,4
Department of Physiology and Pharmacology, Oregon Health and Science University, Portland; Oregon National Primate Research Center, Beaverton, Oregon; 3Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania; and 4Departments of Behavioral Neuroscience and Obstetrics and Gynecology, Oregon Health and Science University, Portland, Oregon
Submitted 25 January 2006; accepted in nal form 31 March 2006
Sullivan, Elinor L., Frank H. Koegler, and Judy L. Cameron. Individual differences in physical activity are closely associated with changes in body weight in adult female rhesus monkeys (Macaca mulatta). Am J Physiol Regul Integr Comp Physiol 291: R633R642, 2006. First published April 13, 2006; doi:10.1152/ajpregu.00069.2006.The increased prevalence of overweight adults has serious health consequences. Epidemiological studies suggest an association between low activity and being overweight; however, few studies have objectively measured activity during a period of weight gain, so it is unknown whether low activity is a cause or consequence of being overweight. To determine whether individual differences in adult weight gain are linked to an individuals activity level, we measured activity, via accelerometry, over a prolonged period (9 mo) in 18 adult female rhesus monkeys. Weight, food intake, metabolic rate, and activity were rst monitored over a 3-mo period. During this period, there was mild but signicant weight gain (5.5 0.88%; t 6.3, df 17, P 0.0001), whereas caloric intake and activity remained stable. Metabolic rate increased, as expected, with weight gain. Activity level correlated with weight gain (r 0.52, P 0.04), and the most active monkeys gained less weight than the least active monkeys (t 2.74, df 8, P 0.03). Moreover, there was an eightfold difference in activity between the most and least active monkeys, and initial activity of each monkey was highly correlated with their activity after 9 mo (r 0.85, P 0.0001). In contrast, food intake did not correlate with weight gain, and there was no difference in weight gain between monkeys with the highest vs. lowest caloric intake, total metabolic rate, or basal metabolic rate. We conclude that physical activity is a particularly important factor contributing to weight change in adulthood and that there are large, but stable, differences in physical activity among individuals. exercise; obesity; weight gain; energy balance

MATERIALS AND METHODS

Animals Eighteen adult female rhesus monkeys (Macaca mulatta) yr of age, weighing between 4.7 and 11.1 kg, and living in individual stainless steel cages (27 or 27 in.) in a temperature-controlled room (24 2C) with lights on for 12 h per day (0700 1900) were studied. Approximately 1 yr before initiation of this study, the monkeys had been ovariectomized and maintained on a high-fat diet (35% fat) to approximate the conditions experienced
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Address for reprint requests and other correspondence: J. L. Cameron, 505 NW 185th Ave., Beaverton, OR 97006 (e-mail: cameronj@ohsu.edu). http://www.ajpregu.org
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PHYSICAL ACTIVITY LINKED TO WEIGHT CHANGE
by many postmenopausal women in the Western world (112). The high-fat diet was formulated according to the recipe developed by Clarkson and colleagues (94, 112) to study diet-induced atherosclerosis. Monkeys were fed ad libitum with meals provided at 0915 and 1515. All aspects of the study were reviewed and approved by the Oregon National Primate Research Center Animal Care and Use Committee and were performed according to federal guidelines. Experimental Design The goal of this experiment was to determine whether the activity level of an individual is predictive of weight gain over a period of time in adulthood during which food intake is stable and there is slow progressive weight gain. In addition, other parameters known to inuence weight gain, such as food intake and metabolic rate, were measured. The experimental period was 9 mo in duration, during which time the activity level of each monkey was measured continuously using a three-way accelerometer. During the rst 3 mo of the study, the weight of each monkey was measured weekly, food intake was quantied at each meal, and percent body fat was determined at the beginning and end of the study. Metabolic rate was measured over a 4-h period at the beginning of the study and for 24 h at the end of the rst 3 mo. Morning metabolic rate during fasting was compared between the two time points. During the last 6 mo, activity was continuously monitored to allow assessment of the stability of this physiological measure. Experimental Measures Body weight. Body weight was measured weekly at 0800, before the morning meal. Dual-energy X-ray absorptiometry scans. Percent body fat was determined using dual-energy X-ray absorptiometry. Animals were sedated with Telazol (3 mg/kg im; Fort Dodge Animal Health, Fort Dodge, IA) supplemented with ketamine HCl (10 mg/kg im Ketaset; Fort Dodge Animal Health) and were positioned supine on the bed of a Lunar DPX scanner (Lunar, Madison, WI). Total body scans were done in the pediatric medium scan mode with a voltage of 76 kV. Lunar software version 3.4 was used to calculate body composition. Two or three scans at each time period were performed per monkey, and body fat was calculated as a percentage of total body mass. Calorie intake. Each monkey was fed more food than she routinely consumed at each meal to ensure ad libitum food intake. Total food consumption at each meal was recorded daily throughout the study by quantifying the amount of food remaining before the next meal. On 1 day during the study, the total amount of stool excreted in a 24-h period was collected from each monkey by placing a metal pan covered with wire mesh under each monkeys cage for 24 h. The amount of stool was weighed, and a representative sample was collected at 0900 the next morning and immediately frozen at 20C. The caloric content of a sample of stool from the two monkeys that consumed the most calories and the two monkeys that consumed the least number of calories was determined using bomb calorimetry (Kinetica, Franklin, OH) to quantify differences in calories excreted vs. calories absorbed. Metabolic rate. Metabolic rate of each monkey was measured by placing the monkey in a sealed Lexan and stainless steel metabolic chamber (Columbus Instruments, Columbus, OH) and measuring the amount of carbon dioxide produced and oxygen consumed with a computer-controlled indirect open-circuit calorimeter (Oxymax system; Columbus Instruments). The metabolic chamber was approximately the same size as the monkeys home cage (inside dimensions: 24 in.). To prevent social isolation during metabolic testing, we placed two monkeys familiar with the test monkey in cages across from and in clear view of the animal in the metabolic testing chamber at all times. The familiar monkeys were animals that were housed in the same room as the test monkey before and after metabolic chamber test periods. Before each recording session, the

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oxygen and carbon dioxide sensors were calibrated with a standard mixture of gases (20.5% oxygen, 0.5% carbon dioxide, and nitrogen balance). Fresh air was pumped into the chamber (l/min) with an external fresh air pump controlled by a owmeter (Columbus Instruments) and was circulated within the chamber with a 4-in. fan. The ow rate into the chamber was adjusted for each monkey so that the difference in oxygen concentration between the chamber and the room air was 0.2% and the carbon dioxide level in the chamber was 0.6%. The chamber air was sampled at a rate of 0.5 l/min and was circulated over a water-absorbent (Drierite) column before passing through the oxygen and carbon dioxide sensors. The oxygen and carbon dioxide concentrations of the ambient air and chamber air were recorded every 4 min. Oxygen consumption, carbon dioxide production, and total energy expenditure (kcal) were calculated using Oxymax software version 2.3 (Columbus Instruments). The Oxymax system calculated oxygen consumption (VO2) by taking the difference between input oxygen ow and output oxygen ow. Similarly, carbon dioxide production (VCO2) was calculated by taking the difference between output and input carbon dioxide ows. To determine energy expenditure, we calculated the respiratory exchange ratio (RER), the ratio of VCO2 to VO2, and the energy expenditure (EE) using the following equation: EE (3.82 1.23 RER) VO2 0.001. Upon study initiation, monkeys were individually placed in the metabolic chamber at 0900 and remained in the chamber until 1300. The day before testing, the monkey was fed its standard meal, and at 1700, all food was removed from the monkeys cage and the monkey was fasted until completion of metabolic testing. After 3 mo, 24-h metabolic rate of the monkeys was assessed. Monkeys (n 16) were placed in the metabolic chamber at 1000 and remained in the chamber until 0900 the next morning. Before placement in the chamber, monkeys were fed a standard meal at 0915 and were fed a 114 1-g banana at 1515 while in the chamber. Basal metabolic rate was calculated as the average number of kilocalories expended per hour from 2300 to 0300. This time period was selected because this is when monkeys typically sleep, and this is when heart rate is typically slowest (Cameron J, unpublished observations). In addition, this was the time when monkeys exhibited the lowest number of activity counts in this study. The thermic effect of an isocaloric meal (the banana fed at 1515) was calculated by subtracting basal metabolic rate and activity-associated energy expenditure from total energy expenditure for the 4 h after the banana was consumed. Studies have shown that the majority of energy expended due to meal digestion and processing is within the rst 4 h after a meal is eaten (12, 84, 98). Activity. The naturally occurring activity level of each monkey was assessed using triaxial Actical accelerometers (MiniMitter, Bend, OR). The Actical monitor contains an omnidirectional sensor capable of detecting acceleration in all directions. The sensor integrates the speed and distance of acceleration and produces an electrical current that varies in magnitude depending on a change in acceleration. An increased speed or distance of the acceleration, or a change in direction, produces an increase in electrical current. The activity monitors store this information as activity counts. Each monkey was tted with a loose-tting metal collar (Primate Products, Immokalee, FL) with an activity monitor mounted on it, housed in a snug, protective stainless steel box. The monitor was programmed to store the total number of activity counts per minute. These monitors are capable of storing data for up to 45 days. During the study period, monkeys were sedated with ketamine HCl (mg/kg im Ketaset; Fort Dodge Animal Health), and the data from each activity monitor were downloaded at least every 45 days. After the data were downloaded and saved, the activity monitor was reprogrammed and replaced on the collar. Activity counts recorded from 0700 to 1900 (when lights were on) were considered daytime activity, and activity counts recorded from 1900 to 0700 (when lights were off) were considered nighttime activity. Activity-associated energy expenditure was calculated by determining the energy expended (in kcal) per activity count. This was calculated by measuring total energy

Fig. 1. A: food intake for individual monkeys remained stable. There was a correlation between food intake at study initiation and after 3 mo (r 0.95, P 0.0001). B: however, the quartile of monkeys eating the most food showed the same percent change in body weight as the quartile that ate the least amount of food (t 0.20, df 8, P 0.85).
(t 2.08, df 8, P 0.07; Fig. 2B). Once total energy expenditure was adjusted for lean body mass by regression analysis, there was only a 2.6-fold difference in energy expenditure between individual monkeys. However, adjusted daily energy expenditure did not correlate with weight gain (r 0.04, P 0.89), and there was no difference in weight gain between monkeys in the quartile with the highest adjusted daily energy expenditure compared with the quartile of monkeys with the lowest adjusted daily energy expenditure (t 0.04, df 6.1, P 0.97). The average basal metabolic rate was kcal/day and ranged from 172 to 406 kcal/day. On average, basal metabolic rate accounted for 61% of total daily energy expenditure, ranging from 47 to 83% of total energy expenditure in individual monkeys. Basal metabolic rate did not correlate with weight gain (r 0.08, P 0.75), and weight gain was not different between the quartile of monkeys with the highest basal metabolic rate and the quartile of monkeys with the lowest basal metabolic rate (t 0.40, df 8, P 0.70). In addition, basal metabolic rate adjusted for lean body mass with the use of regression analysis was not correlated with weight gain (r 0.04, P 0.89), and there was no difference in weight gain between the quartile that had the highest adjusted
basal metabolic rate and the quartile with the lowest adjusted basal metabolic rate (t 0.04, df 6.1, P 0.97). The mean thermic effect of a 108-calorie meal was 19.9 3.2 kcal and ranged from 8.5 to 59.3 kcal (a 7-fold difference) between individual monkeys. There was no signicant difference in the weight gain in the monkeys with the highest thermic effect of the meal and the monkeys with the lowest thermic effect of the meal (t 1.81, df 8, P 0.11). There was an eightfold difference in activity between the most active and most sedentary monkey (Fig. 3), with the most sedentary monkey displaying a mean of 92,110 7,873 activity counts per day and the most active monkey displaying 770,446 110,476 activity counts per day. The number of activity counts per day did not change signicantly during the 3-mo period (t 1.15, df 15, P 0.27; Table 1), and each monkeys daily activity level (counts/day) was consistent over time such that the number of activity counts per day initially recorded for each monkey was highly correlated with the number of activity counts per day recorded after 3 mo (r 0.79, P 0.0001; Fig. 4A). There was a signicant correlation between the number of daily activity counts and weight gain such that the most active monkeys gained less weight than the least active monkeys (r 0.52, P 0.04). The quartile of

Fig. 2. A: correlation between daily energy expenditure at study initiation and after 3 mo (r 0.68, P 0.002). B: the change in body weight over 3 mo between the monkeys in the top and bottom quartiles of energy expenditure was not signicantly different (t 2.08, df 8, P 0.07).
Fig. 5. Nighttime activity was signicantly correlated with daytime activity (r 0.57, P 0.02).
Fig. 3. The most sedentary monkey (A) was 8 times less active than the most active monkey (B).
monkeys that were most active gained signicantly less weight during the 3-mo period than the quartile of monkeys that were least active (t 2.7, df 8, P 0.03; Fig. 4B). To follow up this initial nding, we measured activity over an additional 6 mo and found that the number of activity counts per day remained stable (F1,16 1.13, P 0.30) and that the number of activity counts initially recorded for each monkey was highly correlated with the number of activity counts recorded for that monkey after 9 mo (r 0.85, P 0.0001). There was a 10-fold difference in the number of activity counts recorded during the day between individual monkeys, and 96% of total
daily activity occurred during daylight hours. Interestingly, although nighttime activity accounted for only 4% of total daily activity, there also was a 10-fold difference in nighttime activity. Nighttime activity was positively correlated with daytime activity such that the monkeys that were the most active during the day were also the most active at night (r 0.57, P 0.02; Fig. 5). Activity counts correlated strongly with activity-associated energy expenditure (adjusted for body mass) during the time periods from both 0200 to 0300 (r 0.80, P 0.0001; Fig. 6) and 0600 to 0700 (r 0.74, P 0.001; data not shown). The regression equation for calculation of activity-associated energy expenditure (AEE) was similar at both times of day [0200 0300: AEE (number of activity counts 0.000025) 0.71; 0600 0700: AEE (number of activity counts 0.000021) 0.54]. On average, 0.045 0.006 kcal were expended per kilogram of body weight per 1,000 activity counts. Average activity-associated energy expenditure was kcal/day and ranged from 24 to 206 kcal/day. On average, 18 3% of total energy was expended by physical activity, with physical activity accounting for 8 43% of total daily energy expenditure in individual monkeys. The quartile of monkeys that expended the most calories due to activity
Fig. 4. A: there was a signicant correlation between physical activity at study initiation and after 3 mo (r 0.79, P 0.0001). B: the quartile of monkeys that had the lowest physical activity had signicantly greater weight gain than the quartile of monkeys that had the highest physical activity (t 2.7, df 8, P 0.03). *Signicant difference in percent change in body weight between groups.
Fig. 6. Correlation between activity counts and activity-associated energy expenditure measured simultaneously from 0200 to 0300 (r 0.80, P 0.0001).

in humans showing that the amount of total energy expenditure due to activity averages 30% (110) and ranges from 21 to 51% (61). Individual differences in the number of calories consumed per day were great and ranged from 411 to 2,210 kcal/day. The number of calories consumed by each monkey remained stable during the experimental period; however, food intake was not predictive of weight gain. This parallels previous studies in humans that failed to nd an association between individual caloric intake and individual weight gain or body fat gain (3, 5, 7, 38, 46, 66, 71, 73, 85, 95, 116). This frequent failure to nd an association between weight gain and caloric intake may reect the large role that individual differences in activity level play in regulating body weight. Interestingly, in our study the number of calories excreted per gram of stool was correlated with energy intake such that the individuals with the highest caloric intake excreted twice as many calories per gram of stool. The fact that individuals absorb fewer calories when they consume more calories is well documented in the animal literature (10, 13, 91) but is not generally considered in human studies. Individual differences in energy absorption may contribute to the lack of association between caloric intake and weight gain. Measurements of energy expenditure in this study were similar to what has been previously reported in rhesus monkeys (9, 55). Daily energy expenditure increased over the period of weight gain and was correlated with change in body weight such that the monkeys that gained the most weight increased their energy expenditure the most. The nding that energy expenditure increases with increased body weight has been documented in studies with humans (4, 16, 36, 78, 88), so it is not surprising that energy expenditure would increase as the volume of metabolically active tissue increases. Although there was a change in energy expenditure, the initial energy expenditure of each individual monkey was correlated with that monkeys nal energy expenditure. However, we found that the weight gain of monkeys with the highest energy expenditure did not differ from the weight gain of the monkeys with the lowest energy expenditure. In addition, when energy expenditure was normalized for lean body mass with the use of regression analysis, there was still no difference in weight gain between the monkeys with the highest adjusted energy expenditure and the lowest adjusted energy expenditure. We note that in no monkey did energy balance (intake minus expenditure) equal zero. Energy that was excreted in the stool and thus not absorbed would account in part for this discrepancy. This has been reported to be 6.3% in rhesus monkeys (56). Energy excreted in urine and in skin cells, hair, and nails also was not accounted for. We determined that the average basal metabolic rate accounted for 61% of total daily energy expenditure and ranged from 47 to 83% of total energy expenditure in individual monkeys. Similarly, the basal metabolic rate of humans accounts for 60% of total energy expenditure (78, 110), ranging from 22 to 83% (8, 61). Basal metabolic rate also did not predict weight gain in this study. In addition, when basal metabolic rate was adjusted for lean body mass with the use of regression analysis, there was still no difference in weight gain between the monkeys with the highest adjusted basal metabolic rate and the monkeys with the lowest adjusted basal metabolic rate. These ndings are supported by previous studies in both

humans and mice showing that individuals with a low metabolic rate are not more susceptible to weight gain than individuals with a high metabolic rate (40, 93). However, there are certain populations in which low metabolic rate predicts weight gain. For example, in Pima Indians, low metabolic rate is a risk factor for weight gain (102). In addition, children with stunted growth because of poor nutrition (37) and children with Down syndrome (68) have a lower basal metabolic rate and are more prone to obesity and weight gain than individuals in a control population. Basal metabolic rate accounts for the largest proportion of energy expenditure; however, the lack of relationship between low basal metabolic rate and weight gain suggests that differences in basal metabolic rate within the normal range are not as likely to underlie weight gain as differences in activity. Ovariectomy (surgical menopause) is associated with changes in energy balance. We have previously shown that ovariectomy is associated with a rapid increase in caloric intake (29%) and weight (3%) in female rhesus monkeys (99). In addition, it is well documented in small animals (mice, rats, cats; Refs. 2, 20, 31, 34) that ovariectomy leads to a 14 21% increase in weight within several weeks of ovariectomy. Also, it is well documented that along with weight gain, an increase in BMI and an increase in adiposity occur during the menopausal transition in women (18, 39, 52, 64, 67, 103, 113). Thus it is important to note that the monkeys in this study were ovariectomized. It is possible that the relationships between energy balance parameters are different in the ovariectomized vs. the ovary-intact state. Thus caution should be used in extending the ndings we report in this study to all weight gain over adulthood. Future studies are needed to objectively measure activity in gonad-intact females and males over periods of adult weight gain. In conclusion, this study shows that physical activity level is the best predictor of weight gain in adulthood in ovariectomized female monkeys consuming a diet typical of that consumed in the Western world. This nding suggests that the best way to prevent weight gain over adulthood is to focus on living an active lifestyle, as opposed to only dieting. However, even though a high percentage of adults in Western countries are overweight and/or obese, there is little evidence that people are routinely opting for a more active lifestyle. More than 60% of Americans do not participate in the recommended amount of physical activity, and 25% are inactive (59, 81). Although physicians routinely advocate that obese patients adopt a more active lifestyle, the results of this study suggest that this strategy deserves greater emphasis.

ACKNOWLEDGMENTS We are grateful to the Division of Animal Resources at the Oregon National Primate Research Center for expert care of the monkeys used in this study and to Darla Kneeland, Diana Takahashi, Lindsay Pranger, and Meghan Martin for technical assistance. GRANTS This work was supported by National Institutes of Health Grants DK55819, HD-18185, and RR-00163. REFERENCES 1. Abbott RA and Davies PS. Habitual physical activity and physical activity intensity: their relation to body composition in 5.0 10.5-y-old children. Eur J Clin Nutr 58: 285291, 2004.
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