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The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 60:1518-1524 (2005)
© 2005 The Gerontological Society of America

Reference Body Composition in Adult Rhesus Monkeys: Glucoregulatory and Anthropometric Indices

Aarthi Raman1, Ricki J. Colman2, Yu Cheng1, Joseph W. Kemnitz2,3, Scott T. Baum2, Richard Weindruch2,4,5 and Dale A. Schoeller1,

1 Department of Nutritional Sciences, 2 Wisconsin National Primate Research Center, 3 Department of Physiology, 4 Department of Medicine, and 5 Veterans Administration Hospital, Geriatric Research, Education and Clinical Center, University of Wisconsin–Madison.

Address correspondence to Dale A. Schoeller, PhD, UW-Madison, Department of Nutritional Sciences, 1415 Linden Drive, Madison, WI 53706. E-mail: dschoell{at}nutrisci.wisc.edu


   Abstract
Top
 Abstract
Subjects
 Methods
 Results
 Discussion
 References
 
Rhesus monkeys have been used as models to study obesity and disease. The aim of this study was to define body mass indices for underweight and obesity in rhesus monkeys. Longitudinal data collected over 8–14 years from 40 male and 26 female rhesus monkeys were analyzed. Body weight, insulin sensitivity index, and disposition index were regressed against percent body fat (%BF). A minimal %BF beyond which further loss of body weight resulted in loss of lean mass was determined to be 11.5% in older males, 8% in adult females, and 9% in younger adult males. Insulin sensitivity index and disposition index reached minimum values at 23% fat in older males, 18% in adult females, and 21% in younger adult males, indicating obesity. The estimated reference range for %BF was 9%–23% in male and 8%–18% in female monkeys, corresponding to body mass indices of 32–44 kg/m2 for male and 27–35 kg/m2 for female monkeys.

ADVANCEMENTS in gerontological research have been promoted through the use of numerous animal models to identify possible mechanisms of aging and age-related diseases. Research using nonhuman primates has provided some valuable information for elucidating the nature and causes of aging processes observed in humans as well as evaluating potential interventions. Because rhesus monkeys can develop diet-dependent obesity and diabetes, they have been highly useful models for discovering anti-obesity and antidiabetic treatments.

Monkeys of both sexes with excess body weight (BW) due to increased fat mass have been shown to have fasting hyperinsulinemia (1), elevated insulin response to intravenous glucose or marginally impaired glucose tolerance (2), and elevated fasting serum triglycerides (3). These glucoregulatory and liporegulatory abnormalities are similar to those of obese humans; nevertheless, there are no uniform definitions for overweight and obesity in rhesus monkeys. Similarly, large losses of lean body mass can have deleterious consequences such as damage to organs and disturbances in cardiac function due to attrition in the myocardial mass (4); however, there are also no uniform definitions of underweight in rhesus monkeys. This creates an ambiguity in the interpretation of results based on the non-uniform definition of underweight, overweight, or obese animals when used as models for human disease.

In humans, obesity is generally defined as a body mass index (BMI) > 30 kg/m2 based on the morbidity risks of cardiovascular diseases, hypertension, diabetes, and associated symptoms (5–7), and underweight is defined as BMI < 19 kg/m2 (8). In contrast, obesity and underweight in rhesus monkeys has been characterized using morphometric parameters such as BW, BMI, and abdominal circumference (AC), which are reliable predictors of body fat (3,9). For example, obesity in rhesus monkeys has been characterized using a BW greater than 2 standard deviations (SD) above the mean for their sex (3) and break-points for percentage body fat (%BF), such as 25% BF (10) and 30% BF (11).

The most important variable that addresses the majority of the gluco- and liporegulatory abnormalities in an individual is body fat mass (3). Hyperinsulinemia, hypertriglyceridemia, decreased glucose clearance rate, and glucose disposal can be seen with elevated %BF (12). Too low a %BF, however, may also be detrimental. Higher all-cause mortality rates have been observed in individuals with low BMI (13,14). The increase in mortality rate due to lower BMI is not fully understood, but factors such as osteoporosis-induced fractures (15), decreased vitamin A status [leading to decreased survival rates for acute illnesses (16)], and deficient levels of body fat (16) have been suggested as possible mechanisms. In addition, a systematic analysis of the composition of weight loss has shown that mortality decreases when the weight loss is due to loss of fat, but increases when it is due to loss of fat-free mass (FFM) (17). Unfortunately, most of these definitions were not based on systematic analysis of any metabolic parameters or variables in rhesus monkeys in a manner similar to that which has been used to define underweight, overweight, and obesity in humans making it difficult to compare outcomes between rhesus monkeys and humans.

From the above findings it becomes clear that the relationships between %BF, gluco- and liporegulatory parameters, and composition of weight loss should be considered to better define obesity and underweight in rhesus monkeys. We, therefore, investigated whether various parameters of the metabolic syndrome are associated with %BF and indicate a %BF at which an adult monkey can be defined as overweight and/or obese. Also, we investigated the relationship between BW and %BF to identify the point at which additional loss of weight causes increasing loss of FFM. This %BF point can indicate the minimum %BF an animal should have and hence define underweight. Because not all primate research institutions may have ready access to body fat measuring equipment, reference ranges of BMI will be ascertained using the highly correlated relationship of %BF and BMI. AC is highly correlated with visceral adiposity in humans as well as nonhuman primates (9,18). Studies done on humans have shown that increased abdominal obesity is associated with increased risk of type 2 diabetes, cardiovascular diseases, hypertension, and hypercholesterolemia (19,20). Abdominal adiposity is also associated with hyperinsulinemia, higher plasma glucose and insulin levels, and eventually glucose intolerance which will be reflected in the insulin sensitivity. Similar to those of BMI, reference ranges of AC will be ascertained using the highly correlated relationship of %BF and AC.


   SUBJECTS
Top
 Abstract
 Subjects
Methods
 Results
 Discussion
 References
 
Longitudinal data from 40 male and 26 female rhesus monkeys which are part of Wisconsin National Primate Research Center (WNPRC) were used in this analysis. These monkeys are part of an ongoing dietary restriction and aging study the protocol for which was reviewed and approved by the Institutional Animal Care and Use Committee of the Graduate School at the University of Wisconsin (21). Data consisted of 639 values from 66 animals (34 calorie restricted and 32 control) over a span of 14 years in older animals and 8 years in adult animals. Monkeys were caged individually in standard stainless steel cages with food containers attached to the cages and provision for drinking water in each cage. The cages had inside dimensions of 89 cm width, 86 cm depth, and 86 cm height. Room temperature was maintained at 21°C, and the animals were maintained on a 12-h light/dark cycle with lights on between 6 AM and 6 PM. Animals were fed a semipurified diet (Teklad, Madison, WI) containing 15% lactalbumin, 10% corn oil, and ~65% carbohydrate. Additional details about the study have been published elsewhere (21,22).


   METHODS
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 Abstract
 Subjects
 Methods
Results
 Discussion
 References
 
Body Composition
Whole body composition was measured semiannually using dual energy x-ray absorptiometry (DXA, Model DPX-L; GE/Lunar Corp., Madison, WI). Briefly, animals were sedated with a mixture of ketamine–HCl (10 mg/kg BW, IM) and xylazine (0.6 mg/kg BW, IM) for additional muscular relaxation (18).


Formula

BMI of rhesus monkeys was calculated by dividing BW by the square of the crown-rump length (CRL) of the animal. Crown-rump length was measured with the monkey supine on a calibrated rule with a fixed headrest.


Formula

AC was measured with a non-elastic tape measure to the nearest 0.1 cm when the animal was in lateral recumbency (9,18).

Glucose and Insulin Analysis
Glucose and insulin concentrations were measured annually in all the monkeys using frequently sampled intravenous glucose tolerance tests (FSIGT), the methods of which are detailed elsewhere (23). Briefly, a central venous catheter is positioned for administration of the glucose (300 mg/kg BW) and for blood sample collection. To augment insulin response to the bolus of glucose, animals were dosed with tolbutamide (5 mg/kg) after the first-phase insulin response. Plasma samples collected over a period of 180 minutes were used for measurement of glucose and insulin levels. Plasma glucose concentrations were measured using the glucose oxidase method (Model 23A; YSI, Yellow Springs, OH). Plasma insulin was measured by double antibody radioimmunoassay (Linco Research, St. Louis, MO).

Insulin Sensitivity Index
Glucose and insulin data were analyzed using the minimal model method (24). This model yields a measure of insulin sensitivity reflecting the ability of insulin to augment the effect of hyperglycemia in promoting glucose uptake and inhibiting hepatic glucose output by insulin (24,25). Basal insulin (Ib) and glucose (Gb) levels, glucose disappearance rate (KG), first-phase (acute) insulin response (AIR), second-phase insulin response, and tolbutamide-induced insulin response are calculated by this model and are then used to deduce the insulin sensitivity index (SI). Disposition index (DI) was calculated as the product of first-phase AIR and SI, and indicated the compensatory adaptation to insulin resistance which is a measure of ß-cell function.

Cholesterol and Triglycerides
Fasting triglycerides (TGb) were measured using the enzymatic colorimetric method with glycerol oxidase and 4-aminophenazone (COBAS INTEGRA; Roche Diagnostics, Indianapolis, IN) with a between-day coefficient of variation (CV) of 1.9%. Fasting total cholesterol levels were measured using the enzymatic colorimetric method with cholesterol esterase and 4-aminoantipyrine at an absorbance of 512 nm (COBAS INTEGRA; Roche Diagnostics) with a between-day CV of 1.9%.

Statistical Analysis
Because age had a significant univariate relationship with %BF (2% increase with age; p <.0001), monkeys were categorized based on sex and age range. The males were divided into younger adult males (AM; mean current age: 18.5 ± 3 years; range: 15–22 years) and older males (OM; mean current age: 23.2 ± 2 years; range: 22–28 years); the adult females (AF; mean current age: 19.5 ± 2 years; range: 17–23 years) were similar in age to AM. Animals were studied based on the groups of the main study of calorie restriction. Data consisted of 639 values from 66 animals over a span of 14 years in OM [(n = 24; N = 297), n = number of animals; N = number of values from ‘n’ animals] and 8 years in AF (n = 26; N = 219) and AM (n = 14; N = 123). Data are presented as mean ± SD with a significance level of p <.05.

To define the %BF values that correspond to obese, we regressed %BF onto each of SI, DI, Ib, TGb, and cholesterol levels and sought to identify a break-point in the curvilinear relationships. Similarly, the minimum %BF was ascertained by regressing %BF onto BW and trying to identify the break-point in the curvilinear relationship. Break-point analysis was performed using the statistical software ‘R’ (version 2.0.0; Free Software Foundation, GNU project), which identified the point at which the relationships between variables became insignificant (i.e., slope not different from zero).


   RESULTS
Top
 Abstract
 Subjects
 Methods
 Results
Discussion
 References
 
The data used for this analysis are from an ongoing study, and each animal has multiple representations in this data set with the OM measured for 14 years and the AF and AM measured for 8 years. The characteristics of the animals are summarized in Table 1. Within males, the OM and AM differed in KG, Gb, DI, plasma cholesterol, and TGb. Female monkeys were significantly different from males (OM and AM; p <.0001) in their BW, BF (kg), BMI, AC, and SI. Basal glucose concentrations (Gb) were significantly higher in the AM compared to the OM and AF (p <.0007), but basal insulin levels were not different among the three groups. TGb was lower in AF than in AM but was not different from the OM monkeys, whereas cholesterol levels were higher in AM than in OM. These findings prompted us to stratify the analysis according to age range and gender for the analyses that follow.


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  		Table 1. Group Characteristics (Mean ± SD).

 
Break-points in the relationships between %BF and SI and DI were at a point of change in slope between the dependent and independent variables. When %BF was regressed with SI, an exponential relationship was observed in male (OM, %BF = 28.216 * e(–0.091 * SI), p <.001; AM, %BF = 23.694 * e(–0.08 * SI), p <.0001) and female monkeys (%BF = 26.12 * e(–0.067 * SI), p <.001) (Figure 1). Using the R software, the break-points for maximum attainable %BF before its SI becomes minimal were 23.2% in OM, 20.8% in AM, and 17.5% in AF monkeys. DI also showed an exponential relationship with %BF in male (AM, %BF = 26.67 * e(–0.001 * DI), p <.001; OM, %BF = 26.025 * e(–0.001 * DI), p <.001) and female (%BF = 22.819 * e(–0.0004 * DI), p <.001) monkeys. Accordingly, the %BF break-points for DI were 23.2% in OM, 22% in AM, and 16.4% in AF monkeys. In a regression plot of %BF against SI, AM and OM showed a similar increase in SI with decreasing %BF compared to AF. The mean difference in %BF among all three groups was significantly different at any given SI (OM and AM = 3.5%, AF and AM = 2.1%, and OM and AF = 1.5%; p <.05). However, for a given %BF, females had higher absolute SI values than males. At a mean value of 5.7 SI units, the average %BF was 20% in OM and 18% in AM versus 21.3% in the AF monkeys. Using the interaction between SI and group, this difference proved to be significant (p <.0001). However, there was no significant effect of age on the relationship of SI and %BF (interaction of SI x Age) when animals in individual groups were analyzed. Also, when the males were grouped together there was no significant interaction between SI and age on %BF indicating that age in this group of animals does not affect the relationship between SI and %BF.


Figure 01
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  		Figure 1. Relationship between % body fat (%BF) and insulin sensitivity index (SI) in old male (OM) (A), adult male (AM) (B), and adult female (AF) (C) monkeys. Symbols represent data from individual animal collected over an 8-year (AF and AM) or 14-year (OM) period. The exponential regression lines of animals which were significant (p <.05) are shown in the insets

 
Besides SI and DI, TGb and cholesterol levels and additional indices of glucoregulation, KG, AIR, Ib, and Gb levels were examined for any associations with %BF (Table 2). Though Gb, Ib, TGb, and cholesterol levels showed similar relationships with %BF, a break-point analysis using these variables did not reach significance due to a high variability in the data. Hence these variables did not contribute to the determination of the maximal body fat level.


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  		Table 2. Correlation of Systemic Metabolic Indices.

 
The lower end of the range for %BF was ascertained using the relationship between BW and %BF. The %BF of male monkeys showed an exponential relationship with their BW (OM, %BF = 2.49 * e(0.168 * BW), p <.001; AM, %BF = 1.03 * e(0.225 * BW), p <.001) and female (%BF = 1.02 * e(–0.335 * BW), p <.001). Percent BF was regressed with BW sequentially to identify the break-point where the relationship indicates most of the weight loss as FFM (Figure 2). The minimum %BF an animal should have before increasing loss of lean body mass occurs was ascertained to be 8.5% in OM, 6% in AM, and 5% in AF monkeys.


Figure 02
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  		Figure 2. Relationship between % body fat (%BF) and body weight (BW) in old male (OM) (A), adult male (AM) (B), and adult female (AF) (C) monkeys. Symbols represent data from individual animal collected over an 8-year (AF and AM) or 14-year (OM) period. The exponential regression lines of animals which were significant (p <.05) are shown in the insets

 
The above break-point analysis does not provide a measure of statistical range around the break-point values. The values, therefore, have limitations. There may be individual variation among animals, and the measurement of %BF may not be exact. In either case, animals at the lower end of the reference body fat spectrum could be at a greater risk for negative health-related outcomes even with slight decreases in body fat. It is therefore prudent to add a safety factor to the low-end break-point. Based on a comparison of %BF measurement between DXA and total body water, we calculated a mean difference of 3% for the determination of %BF and used this as the safety level needed on the lower end of reference %BF. In so doing, the minimal %BF below which animals can be classified as underweight were 11.5% in OM, 9% in AM, and 8% in AF monkeys.

The %BF values can also be translated to BMICRL. Percent BF showed significant correlations with BMI in all three groups of monkeys (%BF = –19.3 + 0.97 * BMI; r2 = 0.7, p <.0001 in OM; %BF = –29.2 + 1.4 * BMI; r2 = 0.8, p <.0001 in AF, and %BF = –30.7 + 1.2 * %BF; r2 = 0.8, p <.0001 in AM; Figure 3) with the mean %BF (mean ± SD) at 21.5 ± 10% in OM, 19.9 ± 11% in AM, and 17.9 ± 9% in AF monkeys. Hence a reference BMI of 32–44 kg/m2 in the AM, 34–44 kg/m2 in OM, and 26–38 kg/m2 in AF monkeys was deduced.


Figure 03
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  		Figure 3. Relationship between % body fat and body mass index (BMI) in older male (OM, closed triangles), adult female (AF, open squares), and adult male (AM, plus symbols) monkeys. Percent body fat showed significant correlations with BMI in all three groups of monkeys (% body fat = –19.3 + 0.97 * BMI, r2 = 0.7, p <.0001 in OM; % body fat = –29.2 + 1.4 * BMI, r2 = 0.8, p <.0001 in AF; % body fat = –30.7 + 1.2 * %BF, r2 = 0.8, p <.0001 in AM)

 
Similarly, these break-points can be translated to AC values. Because %BF and AC have a linear relationship (%BF = –11.9 + 0.66 * AC; r2 = 0.6, p <.0001 in OM; %BF = –29.5 + 1.08 * AC; r2 = 0.8, p <.0001 in AF, and %BF = –24.9 + 0.9 * AC; r2 = 0.9, p <.0001 in AM), we estimated a reference AC of 40–54 cm in AM, 35–53 cm in OM, and 35–44 cm in AF monkeys using the reference range of body fat (Figure 4).


Figure 04
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  		Figure 4. Relationship between % body fat and abdominal circumference (AC) in older male (OM, closed triangles), adult female (AF, open squares), and adult male (AM, plus symbols) monkeys. Percent body fat showed significant correlations with AC in all three groups of monkeys (% body fat = –11.9 + 0.66 * AC, r2 = 0.6, p <.0001 in OM; % body fat = –29.5 + 1.08 * AC, r2 = 0.8, p <.0001 in AF; % body fat = –24.9 + 0.9 * AC, r2 = 0.9, p <.0001 in AM)

 

   DISCUSSION
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 Abstract
 Subjects
 Methods
 Results
 Discussion
References
 
Using glucoregulatory indices and changes in body composition, we developed a reference range of %BF a monkey can have before being classified as underweight or overweight and obese. Insulin-sensitivity measures and changes in FFM during weight change have been used to identify the reference range for %BF; hence, these data promise to be a good index to define health using a group of metabolic predictors in young and old rhesus monkeys. This is an effort in classifying rhesus monkeys into underweight, reference, and obese based on their %BF and will make it easier to compare health outcomes with humans.

The linear relationship between BMI and body fat can be used effectively to ascertain the %BF of an individual based on their BMI. However, this relationship needs to be analyzed with caution, because a higher BW can be due to a higher lean body mass, in which instance using BMI may lead to misclassification of the individual as overweight or obese. Nonetheless, BMI has been used effectively to assess obesity and underweight in numerous human studies (26–28).

Overweight and Obesity
The literature is replete with evidence of body fat being strongly associated with most of the glucoregulatory processes in the body. A lower %BF has been associated with better glucose regulation and better insulin sensitivity (29). Significant correlations between basal and stimulated insulin levels with various indices of obesity have been noted in monkeys (2,9) and humans (30,31). Hyperinsulinemia has been shown to occur as one of the initial consequences of increased BW or body fat (32). In fact, this relationship has been best reported in monkeys with body fat greater than 30% of their BW (33,34). Conversely, a reduction in BW or body fat has been shown to decrease insulin dosage or eliminate the need for supplemental insulin in type 2 diabetics (35,36). Hence, we used insulin sensitivity and disposition indices to identify the upper end of reference %BF.

Hypertriglyceridemia and hypercholesterolemia have been observed in obese humans (37) and nonhuman primates with higher %BF (3,9), but we were unable to find a break-point associated with these variables. Perhaps this is because there is a strong genetic component to the elevated plasma triglycerides (32,38). This genetic component may have obscured the break-points, wherein the fasting triglyceride and total cholesterol levels were higher in animals with higher body fat but were highly variable (CV was 0.3 for plasma cholesterol and 1.9 for TGb). Nonetheless, the mean TGb levels in the male and female monkeys with a reference %BF was 1.1 ± 0.9 mmol/L and 0.7 ± 0.4 mmol/L, respectively, compared to 2.7 ± 5 mmol/L and 1.4 ± 0.9 mmol/L in obese animals (p <.001). The levels seen in reference %BF animals were within the ranges defined for humans (39).

Overweight Versus Obese
The relationship between %BF and glucoregulatory indices was exponential and could not be used to differentiate between overweight and obese monkeys. Nonetheless, obesity is associated with hyperinsulinemia, insulin resistance, and glucose intolerance (11). It has been shown that animals with increasing body fat may gradually become diabetic after going through a sequence of events of normoglycemia–normoinsulinemia, normoglycemia–hyperinsulinemia, and hyperglycemia–hyperinsulinemia. With regard to this sequence, there are two diabetic monkeys in the larger study cohort that were not included in this analysis. The %BF of these two animals was 34.2% and 37.6% at the time of diagnosis. In addition, Gresl and colleagues (1) reported one additional animal in the larger study that has since died and was not included in the current data analysis (this animal became diabetic and had a %BF of 36% at the time of diagnosis). In our analysis, we concluded that the maximum body fat a male animal could have before SI became minimal was ~22% of BW. Hence, we conjecture that male animals with %BF between 22% and 36% can be categorized as overweight, above which we see more animals with frank diabetes.

The findings of Hotta and colleagues (40) can be compared with ours. Hotta and colleagues grouped a cohort of rhesus monkeys according to their fasting plasma insulin and glucose levels using a priori values to characterize the animals as lean (normal) hyperinsulinemic or diabetic (obese). They reported that the "normal weight" monkeys in their study had a mean %BF of 18.1 ± 3.3% and normal fasting plasma glucose and insulin concentrations. The male monkeys in our data set with a "reference" %BF (<22%; mean = 16 ± 4.5%) were also normoglycemic (3.4 ± 0.4 mmol/L) and normoinsulinemic (200 ± 143 pmol/L); these values are comparable to those of the animals of Hotta and colleagues (40) (Table 3). The obese group of Hotta and colleagues had a mean %BF of 32.6 ± 2.7% and were hyperinsulinemic but normoglycemic, thus paralleling our data in the animals above the upper %BF break-point. Among the obese group reported by Hotta and colleagues (40), noninsulin-dependent diabetes mellitus was observed in a group of monkeys with mean %BF 35.1 ± 4%, similar to the three diabetic animals in our larger study. These data support our conjecture on classifying overweight rhesus monkeys as 22–34%BF and obese rhesus monkeys as ≥35%BF, although further evidence is needed because the number of animals on which this is based is still small.


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  		Table 3. Characteristics of Monkeys When Assigned to Underweight, Normal, and Obese Categories (Mean ± SD).

 
Underweight with Minimal Body Fat
On the other end of the spectrum of body fat, we identified a minimal %BF to define reference or normal weight of ~10% in male and 8% in female rhesus monkeys. There are, however, few studies in literature that provide data against which we can compare these values. One study by Altmann and colleagues (41), however, reported that young adult baboons foraging in the wild had %BF as low as 2% (in adult females) and 1% (in adult males). Anthropometric data indicated that, despite their lower %BF, growth among the female baboons continued and that the animals maintained reproductive function. This might indicate that our minimal %BF values were too conservative; however, caution should be maintained when comparing our data to animals under free-living conditions, due to the high fiber content of the diet and the high activity level of the free-living animals compared to our caged animals. Both of these factors could differentially influence the relationship between SI and %BF.

Despite the data on %BF in wild animals, we were concerned about using minimal %BF obtained by break-point analysis to define underweight. This concern stemmed from the rapid weight loss and loss of FFM that was observed when some animals neared but were still above these critical values and the potential for serious negative health outcomes that could accompany the loss in FFM (13,14). This concern was amplified by the knowledge that the measurement of %BF is accompanied by a measurement error, and thus when the break-point values are applied to individual animals, %BF might be overestimated and the animal could be at risk of being underweight despite an apparently normal %BF. Because of these two factors, we added a safety margin of 3% to the %BF in an effort to reduce the risk in individual animals.

Finally, it should be noted that our data were derived from animals that were part of a long-term dietary restriction study. Thus, there was a possibility that the relationships between %BF and the other variables in the diet-restricted animals were a little different from general colony animals. No interactions between dietary treatment and the parameters used for the above analyses were observed, however; thus we conclude that these values to define underweight, overweight, and obesity are reasonable. By using these cut-offs to define nutritional status in rhesus monkeys, comparison of studies between those conducted in rhesus monkeys with those conducted in humans should be easier.


   Acknowledgments
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 Methods
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 Discussion
 References
 
This work was supported by grants P01 AG-11915 (to R. Weindruch) and P51 RR000167 (to the Wisconsin National Primate Research Center, University of Wisconsin, Madison). This research was conducted in part at a facility constructed with support from Research Facilities Improvement Program grant numbers RR15459-01 and RR020141-01.

We gratefully acknowledge the excellent technical assistance provided by J. A. Adriansjach, C. E. Armstrong, and the animal care and veterinary staff of the Wisconsin National Primate Research Center.


   Footnotes
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Decision Editor: James R. Smith, PhD

Received May 18, 2005

Accepted July 7, 2005


   References
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