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The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 59:B424-B432 (2004)
© 2004 The Gerontological Society of America

Age-Related Changes in Fasting Plasma Cortisol in Rhesus Monkeys: Implications of Individual Differences for Pathological Consequences

Joseph M. Erwin1,2,, Xenia T. Tigno1, Georgielle Gerzanich1 and Barbara C. Hansen1

1 Obesity and Diabetes Research Center, University of Maryland School of Medicine, Baltimore.
2 Biomedical Sciences and Pathobiology Department, Virginia-Maryland Regional College of Veterinary Medicine, Virginia Polytechnic Institute and State University, Blacksburg.

Address correspondence to Joseph M. Erwin, PhD, Obesity and Diabetes Research Center, University of Maryland School of Medicine at Baltimore, 10 South Pine St., MSTF Rm. 6-00, Baltimore, MD 21201-1192. E-mail: jerwin{at}agingapes.org


   Abstract
Top
 Abstract
Methods
 Results and Discussion
 References
 
Elevated cortisol may damage receptor neurons involved in responses to stress, leading to progressive metabolic dysregulation and age-related increases in cortisol; however, documentation of rising cortisol with age in humans has been inconsistent. Here we report fasting cortisol values from rhesus monkeys maintained for obesity, diabetes, and aging research. A modest correlation (r =.20) between age and cortisol was found for 138 rhesus monkeys (aged 4–40 years) and (r =.16) for 30 males for whom at least 10 years of longitudinal data were available. Subgroups of ad libitum-fed and weight-stabilized animals also exhibited significant positive relationships between age and cortisol (r =.14–.37). Individual regression analyses revealed both significant increases (r =.29–.85) and decreases (r = –.47 to –.66) in cortisol relative to age. Unexpectedly, significant age-related increases occurred in 77% of healthy primates, but only 33% of diabetic primates, while significant declines occurred only in diabetics.

IMPAIRED endocrine functioning has been implicated in the etiology of several age-related disorders and as a cause of aging; in fact, decreased ability to maintain homeostasis is widely recognized as a defining characteristic of aging (1). The possible role of stress and associated activation of the hypothalamic-pituitary-adrenal (HPA) axis, including chronic elevation of glucocorticoids, has been especially emphasized as a fundamental cause of aging and age-related disorders (2–4). The glucocorticoids (cortisol in humans and other primates) are a basic component of HPA axis activation in response to stress, and are part of the essential adaptive response that prepares the body to fight or flee in response to dangerous situations (5–6). Glucocorticoids also play important roles in motivation, memory, and mood, and many aspects of peripheral physiology, including energy metabolism, immunity, and maturation (7).

Much of the research regarding glucocorticoids and aging has focused on the brain, because the governing homeostatic mechanisms of the HPA axis are located in the hypothalamus, hippocampus, and pituitary, and because some of the conditions of most obvious concern with regard to aging are the degenerative brain disorders that result in memory disruption, depression, and cognitive decline (8–10). Brain regions involved in HPA axis regulation are known to be affected in the degenerative and dysfunctional disorders of senescence (11), and the damaging consequences of chronic HPA axis activation may be very widespread (12,13). Cell death or dysfunction in the neuroendocrine substrate of the brain might disrupt the entire energy metabolism mechanism, triggering what has come to be known as "Metabolic Syndrome X," a cluster of conditions that includes adult-onset diabetes, insulin resistance, hypertension, dyslipidemia, osteoporosis, atherosclerosis, and possibly depression and dementia (7,13,14).

Despite the obvious importance of understanding the role of the physiological stress response for the prevention and management of healthy aging, a clear and consistent relationship between cortisol levels and age has not been fully established for humans or nonhuman primates (10,15–19), even though some evidence of a direct relationship has accumulated (20–23). Such a relationship has been widely predicted, proposed, and even presumed to exist, but cortisol values are notoriously variable within and between individuals. Apparently, individual differences in cortisol values can sometimes overwhelm and mask differences due to age (24). This makes comparisons between groups of older versus younger individuals particularly problematic. It is difficult to match the older and younger groups in ways that ensure that age is the variable of primary significance (23,24). This suggests the need for longitudinal studies in which cortisol values for individuals can be compared at various ages, so that each individual acts as its own comparison control. Studies employing repeated measures of cortisol for individuals have been remarkably rare, but the longitudinal research that has been done has been supportive of this approach (24,25).

For many reasons, such a longitudinal approach could be especially productive in an appropriate animal model that shares complex physiological and psychological characteristics with humans, but for whom environmental and dietary conditions can be consistent and controlled. Nonhuman primates have already contributed substantial information that has illuminated some aspects of the possible relationship between cortisol and aging. A recent study found that older rhesus macaque females exhibited higher basal cortisol levels than their younger counterparts (23). A positive relationship between age and cortisol was also found in wild baboons (22). Other studies found no age-related change in basal cortisol in pigtailed macaques (26) and in rhesus macaques and baboons (19).

Here we report studies of cortisol data from a colony of rhesus monkeys maintained for many years to study aging and spontaneously occurring obesity and diabetes (27). We tested the null hypothesis that no significant change in basal morning fasting cortisol levels accompanied advancing age in rhesus macaques. First we assessed the relationship between cortisol concentrations and age for the entire data set (all values for all animals at all ages). Next, we examined the relationship between cortisol and age for 30 male rhesus macaques for whom at least 10 years of data were available. Then we assessed the age and cortisol relationships for 3 subgroups of the 30 males that were fed ad libitum and were healthy or diabetic. Finally, we assessed cortisol concentrations relative to advancing age for 30 individuals.

We reasoned that some individuals might exhibit significant age-related changes in cortisol levels that could be associated with pathology, without all individuals exhibiting such a relationship. The glucocorticoid cascade hypothesis (4) predicts general increases in cortisol with aging, but proponents have recognized that individual differences are associated with dominance status and other characteristics in primate groups. The point has been aptly made (28) that both cortisol and pathobiological outcomes should be highly variable and that longitudinal study of multiple variables in individuals might be essential to detecting such relationships (24). The glucocorticoid cascade hypothesis (4) would predict that the greatest rate of increase in cortisol would be associated with the greatest risk of pathologic outcomes (29–31).


   METHODS
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 Abstract
 Methods
Results and Discussion
 References
 
Animals
The data reported here are from the animal records database of the Obesity and Diabetes Research Center (ODRC) at the University of Maryland, School of Medicine in Baltimore. This database includes information on cortisol concentrations (2146 values) and other variables regarding 138 rhesus macaque monkeys (Macaca mulatta) (aged 4–40 years) presently or previously living at the Obesity and Diabetes Research Center between 1984 and 2003. The colony has been maintained for the study of aging, spontaneously occurring adult-onset diabetes, obesity, and other age-related disorders. The animals were obtained from sources that supplied actual birth dates or documented year of birth. The animals have been maintained under Institutional Animal Care and Use Committee-approved research and care protocols in accordance with U.S. Public Health Service and National Institutes of Health policies and guidance and in compliance with U.S. Department of Agriculture regulations under the Animal Welfare Act in a facility that is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International. Animals were housed individually with multiple animals per room in which all animals had visual, auditory, and olfactory social access to conspecifics, under conditions that allowed monitoring of dietary intake. Most animals had ad libitum access to food and water, and received a standard defined and complete laboratory primate diet (Purina Monkey Chow; Purina Mills, LLC, St. Louis, MO). Some individuals were placed on caloric restriction to stabilize their weight and prevent them from becoming obese and risking onset of the health disorders that often accompany obesity. The focal animals for the refined analyses were 30 male rhesus monkeys for whom data on at least 10 years of basal fasting morning cortisol were available for analysis. They ranged in age from 5 to 30 years of age when sampled. Of the 30 focal animals, 6 had been maintained on caloric restriction to enforce weight stabilization, 12 had been maintained on ad libitum diets and had not developed diabetes, and 12 had been fed ad libitum and had been diagnosed with adult-onset diabetes. Monkeys in the colony with overt diabetes are managed much the same as are human patients. Their blood glucose is checked and oral agents are used in early stages for moderate control, or, as necessary, a combination of long-acting and short-acting insulin is initiated. Some monkeys have been maintained on insulin therapy for at least 10 years. Of course, many monkeys develop the usual complications of diabetes that occur in humans, with varying degrees of severity.

Procedures
Plasma samples were collected on a semiannual cycle to determine the metabolic status of each nonhuman primate represented in this study. Blood draws were done 16 hours post fasting, while the animals were under the anesthetic ketamine hydrochloride (10 mg/kg body weight). All cortisol concentrations were measured by radioimmunoassay at the Washington University School of Medicine Diabetes Research and Training Center, St. Louis, Missouri.

Statistical Analyses
First, a linear regression analysis of age versus cortisol was performed on all raw cortisol data for the entire population, including all values for all animals at all ages. Next, a linear regression was performed on all values for the subset of 30 animals for whom 10 years of longitudinal values were available. Then three linear regression analyses were performed: one for the calorie-restricted/weight-stabilized animals (n = 6) (WS), a second for the ad libitum-fed healthy animals (n = 12) (AL), and a third for those who spontaneously developed adult onset diabetes (n = 12) (DM). Finally, individual linear regression analyses were performed on cortisol and age data for all 30 rhesus males. All data were analyzed (by G.G. in consultation with J.M.E.) using Microsoft Access and Microsoft Excel's (Redland, WA) analysis tool pak.


   RESULTS AND DISCUSSION
Top
 Abstract
 Methods
 Results and Discussion
References
 
For the entire data set, there was a statistically significant positive (direct) correlation between fasting morning basal cortisol concentration and age in rhesus monkeys (r =.20, p <.05) (Figure 1). The initial linear regression analysis included all cortisol values at all ages, for all individuals, living and dead, over a period of more than 20 years, including 2146 cortisol values from 138 individuals. While the relationship was highly significant from the perspective of statistical reliability, the amount of variance in cortisol that was accounted for by age was rather small (r2 =.04, i.e., 4% of the variance) and its biological significance could be questionable.



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  		Figure 1. Fasting morning plasma cortisol values by age are shown for rhesus macaques in the Obesity and Diabetes Research Center. This scatter plot and linear regression includes 2146 values from 138 individuals ranging in age from 4 to 40 years of age. This is the complete data set for the population considered here, including all values for all individuals at all ages. The number of cortisol values and the age range varied substantially across individuals. The correlation (r =.20) is statistically significant (p <.05), but age accounts for only 4% of the variance in cortisol

 
A limitation of this analysis was that the individuals for whom data were included did not contribute equally to the data set. Some individuals contributed many values across many ages; others contributed very few values from a very limited age range. Thus, this data set was not optimal because it was a mixture of cross-sectional and longitudinal data. An ideal data set would have included an equal number of cortisol values annually for each individual across an identical age range. This was not possible, of course, because animals entered the colony at different ages and remained in the colony for differing time spans, usually ending at the time of death due to illness or euthanasia for humane reasons. Cortisol data tend to be highly variable across individuals and environmental contexts, so we were concerned about inter-individual differences in addition to intra-individual variability. We were concerned that some individuals might have contributed too few data points; that is, they might have contributed to variability on the basis of individual differences in cortisol concentrations without contributing enough longitudinal values to allow detection of age effects.

To maximize the prospect of finding robust and real age effects if they existed, we decided to further scrutinize the data by evaluating all individuals for whom at least ten years of data existed. Thirty-one individuals were identified that met the 10-year criterion, including 30 male and 1 female rhesus macaques. We chose to focus attention on the 30 males. For these 30 males, there were 1080 cortisol values. The linear regression analysis for the 30 male rhesus monkeys revealed a correlation of r =.16, which was statistically significant (p <.05) but clearly did not differ from the results of the overall analysis.

Of the 30 males, 6 had been weight stabilized (WS) through calorie restriction (CR) to prevent obesity and 24 had been fed ad libitum. Half of those fed ad libitum had spontaneously developed adult-onset diabetes. Linear regression analyses were performed on the data from each of these sub-groups, designated as weight stabilized (WS or CR), ad libitum fed (AL), and diabetic (DM). For the CR subgroup, r =.37. For the AL sub-group, r =.14. For the DM subgroup, r =.25.

Individual regression analyses revealed that 21 of the 30 male rhesus monkeys exhibited statistically significant relationships between cortisol concentrations and age. Of these, 18 were direct relationships (positive correlations) and only three were indirect relationships (negative correlations). The significant positive correlations were far more common in the weight stabilized (5/6, 83%) and ad libitum fed (9/12, 75%) animals than in the diabetic animals (4/12, 33%). All three of the significant negative correlations (3/12, 25%) were for animals that had spontaneously developed adult-onset diabetes. Figures 4A, 4B, and 4C show the individual regression plots according to subgroup (CR, AL, and DM, respectively). Figure 5 summarizes these results.



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  		Figure 4. Scatter-grams and regressions for all the individuals in each group from
Figure 3. A, Weight stabilized/calorie restricted (5/6 had significant positive relationships between age and cortisol, with a mean r =.54). Age accounted for an average of 29% of variance in cortisol. (Continued next page)

 


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  		Figure 5. Spontaneously diabetic rhesus macaques exhibited fewer significantly positive and more significantly negative correlations between age and cortisol than did nondiabetic animals that had been fed ad libitum or had been weight-stabilized through calorie restriction to prevent obesity. Contrary to expectation, rises in cortisol are associated with the most-healthy outcomes. NS = not significant

 
Figure 4C, which shows the scatter plots and linear regressions for individuals diagnosed with diabetes, and warrants some additional attention. The time of diagnosis is indicated on the figure by an arrow. The onset of insulin therapy is indicated with a vertical dotted line. It should be noted that the age at diagnosis of six of the individuals whose data are shown in the bottom half of Figure 4C exceeded 20 years of age in every case, and in all these cases, insulin therapy was initiated soon after diagnosis. This pattern contrasts sharply with the data shown in the top half of Figure 4C. Only 1 of the 6 individuals in the top half of Figure 4C was diagnosed after age 20. Thus, the average age of onset for those in the bottom half of the figure was about 23 years, while the average age of onset was about 16 years for the individuals in the top half of the figure. One individual, D-08, developed an illness shortly after progressing to diagnosis of diabetes and lost a substantial amount of weight. After losing weight he no longer exhibited hyperglycemia and his glucose has remained in the normal range. Some individuals were calorie restricted following diagnosis (e.g., T-07, W-04, and I-06) and insulin therapy was substantially delayed or not initiated at all. Yet, all the diabetic individuals who exhibited significantly declining cortisol with age had also declined substantially in weight for years prior to diagnosis—and all required insulin therapy soon after diagnosis with diabetes.

We found in nonhuman primates all three of the fundamental patterns of cortisol relationships to age found for humans by Lupien and colleagues (24), with direct (positive) relationships being twice as common as no relationship, and inverse (negative) relationships being only one-third as common as no relationship, and one-sixth as common as direct relationships. These data clearly indicate that the null hypothesis of no relationship between basal morning cortisol levels and age must be rejected for this data set. At the same time, it is quite clear that basal fasting morning cortisol cannot serve as a "biomarker" of aging in the sense of being able to provide predictable cortisol values for macaques of specific ages.

The results of this retrospective longitudinal analysis of data from the Obesity and Diabetes Research Center at the University of Maryland School of Medicine shows a clear and robust relationship between age and basal morning plasma cortisol levels of fasted male rhesus monkeys. While this pattern was predominant, it was not universal, occurring in 60% of the individuals evaluated. Significant age-related declines in cortisol occurred in 10% of the cases, and all the individuals exhibiting significant declines developed diabetes.

Examination of their clinical histories revealed that all 3 were diagnosed with diabetes more than 5 years after they entered the colony. All 3 began receiving insulin therapy to reduce excessive glucose about 8 years after entering the colony. Although insulin is directly antagonistic to cortisol, it is clear that the decline in cortisol with age could not have resulted from the insulin injections. More detailed examination of clinical histories and genetic risk factors may reveal the reason for the high initial cortisol values exhibited by these 3 individuals. Clearly, other diabetic individuals did not exhibit this pattern, so the effect was not universal; however, it is tempting to speculate that the elevated initial cortisol values might have been responsible, at least in part, for the progressive glucose dysregulation and insulin insensitivity that eventually emerged in these individuals.

Detection of a direct relationship between rising cortisol levels and aging in the majority of the rhesus monkeys in this study does not imply that such a relationship is typical of normal aging. More detailed analyses might reveal that the pattern typical of healthy aging in monkeys is the maintenance into maturity of the relatively low cortisol levels common in young individuals; however, the results of these analyses suggest exactly the opposite: that significantly rising cortisol levels are associated more with successful aging than with endocrine dysregulation. The results reported here represent a methodological step forward that should facilitate detailed assessment of the factors that contribute to the individual patterns observed.

We are not asserting that previous reports of no relationship between cortisol levels and aging are erroneous, but the results of our initial study illustrate how easy it would have been to conclude from examination of the population data that the magnitude of the relationship between cortisol and age was not biologically important. This leads us to be suspicious of pooled cortisol data that includes cross-sectional evidence, even on many data points from many individuals. Our results also remind us to be cautious about seeking "standard" values to apply generally to a population as reference values or "biomarkers."

Several likely sources of variance may be responsible for finding only slight age effects in populations for which systematic changes occur in individuals. These largely depend on the methods of measuring cortisol. A common method—the one used here—has been basal plasma cortisol from blood drawn quickly and with a minimum of disturbance that could initiate a physiological stress response. The results may vary in proportion to the extent to which these methods lack consistency and in interaction with individual differences in responsiveness to whatever inconsistencies occur.

Cortisol values typically vary dramatically by time of day, exhibiting a pattern of highest elevation between 6:00 AM and 10:00 AM, and a nocturnal nadir between 8:00 PM and 12:00 AM. Consequently, 24-hour urinary cortisol is commonly used to modulate excessive variation. Methodology has been developed for measuring cortisol from feces, and this technique is often used in field studies of nonhuman primates. This method has some advantages in being noninvasive, and it also presents more extended and temporally modulated values. Plasma cortisol seems to be a more direct and biologically relevant measure, however, reflecting as it does the concentrations that actually circulate to the brain and other target tissues rather than the amount of cortisol eliminated from the system as in urinary and fecal cortisol analyses. It must be borne in mind that plasma cortisol is subject to extraneous sources of variation that can mask relevant effects. Some studies have documented age differences in circadian variation of cortisol, with higher nocturnal nadir and/or lower morning cortisol peaks, and in some of these cases, overall differences were not detected. In addition to the very well-established circadian rhythmicity of cortisol and its correlates, seasonal or annual variation has been documented for some primate species. Rhesus macaques exhibit profound reproductive seasonality that carries with it dramatic annual endocrine changes (32–34). These patterns can persist across many years, even in closed captive colonies with standardized light–dark cycles and nutritional consistency. In the colony studied here, such changes could have contributed to variability, but there is no reason to expect that it would have been systematically related to age.

The values reported here are from animals who had been fasted for 16 hours prior to morning sampling. Fasting can double cortisol concentrations, stimulating gluconeogenesis, which raises serum glucose values. Fasting has important central nervous system consequences as well (35,36). The consequences of fasting for diabetic animals differed from those of the others in that fasting glucose concentrations rose significantly with age in all diabetics and none of the other animals.

Cortisol declined significantly with age in 25% of the diabetic animals and in none of the others and increased significantly in only 33% of the diabetic animals. The animals that exhibited differential patterns of fasting cortisol and glucose are candidates for additional detailed studies that may help to advance understanding of relationships between gluconeogenesis, insulin resistance, and adult-onset diabetes.

Longitudinal studies such as the ones that revealed the effects reported here can be powerful in detecting age differences that would otherwise remain obscure. A limitation of longitudinal surveys such as this is the possibility that factors other than age might contribute systematically across time—sometimes called "historical artifacts." These can occur, for example, when methods of measurement or maintenance or laboratory assays have changed across the period of the study. Such problems are exacerbated if all measures begin at the same time and same age for all individuals. While longitudinal studies can seldom avoid potential historical bias completely, the data examined here were inoculated to some extent from such effects because individuals entered the colony from a variety of sources, in different years, and at a wide range of ages. Thus, historical effects would not be systematically related to factors of interest.

Our results are strikingly similar to those with humans in which longitudinal measures of individual patterns of age-related rises in cortisol accurately predicted cognitive decline (8). While our data set does not yet include measures of memory or indicators of mood that might reflect depression or dementia, it does include detailed data on insulin, glucose, lipids, and other measures that can help to characterize health status and changes that may reveal relationships with HPA axis activation and cortisol exposure dosage. These additional data should amplify the value of the National Institute on Aging-supported Obesity, Diabetes, and Aging Animal Resource rhesus colony.

The retrospective analysis of archived data sets from nonhuman primate research colonies and zoological institutions can address many questions not easily amenable to exclusively prospective experimental or epidemiological approaches. When coordinated with detailed postmortem studies involving refined measures such as stereologic quantification of the volumes and abundance of specific cell types (37), along with genomic and proteomic sequences predictive of relative health risk, such resources offer exciting prospects of fulfilling the promises of integrative bioinformatics.



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  		Figure 2. Fasting morning plasma cortisol values by age are shown for adult male rhesus macaques at the Obesity and Diabetes Research Center for whom at least 10 years of data were available. The scatter-gram and linear regression includes 1080 values for 30 individuals ranging in age from 5 to 30 years. The correlation (r =.16) is statistically significant (p <.05) but does not differ from the result for the population as shown in
Figure 1. Age accounts for less than 3% of the variance in cortisol

 


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  		Figure 3. Fasting morning plasma cortisol values are shown for 3 subsets of the animals for which data are included in
Figure 2. The scatter-grams and linear regressions are shown for the following: A, Animals (n = 6) that underwent caloric restriction for weight stabilization and prevention of obesity (r =.37, p <.05). Age accounted for more than 14% of the variance in cortisol. B, Animals (n = 12) that were fed ad libitum but did not develop adult-onset diabetes (r =.14, p <.05). Age accounted for less than 2% of variance in cortisol. C, Animals (n = 12) that were fed ad libitum and spontaneously developed adult-onset diabetes (r =.25, p <.05). Age accounted for more than 6% of the variance in cortisol

 

   Acknowledgments
 
The authors are especially grateful to all the Obesity and Diabetes Research Center staff members who contributed to gathering and entering data for the database, including Theresa Alexander, Holly Jermyn, Michelle Izuka, Wallace Evans, and Karen Brocklehurst. Drs. Noni Bodkin and Heidi Ortmeyer read a draft of the manuscript and contributed ideas and suggestions.

The work reported here was supported by contract AG02100 from the National Institutes of Health to the Obesity and Diabetes Research Center at the University of Maryland at Baltimore, School of Medicine, Dr. Barbara Caleen Hansen, Principal Investigator.


   Footnotes
 
Decision Editor: James R. Smith, PhD

Received September 9, 2003

Accepted February 18, 2004


   References
Top
 Abstract
 Methods
 Results and Discussion
 References
 

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