1 Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD.
2 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
3 Graduate Studies Program in Epidemiology, School of Medicine, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.
Received for publication December 19, 2003; accepted for publication March 24, 2004.
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ABSTRACT |
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androgens; hormone replacement therapy; insulin resistance; postmenopause; testosterone
Abbreviations: Abbreviations: ARIC, Atherosclerosis Risk in Communities; CI, confidence interval; FAI, free androgen index; OR, odds ratio; SHBG, sex hormone-binding globulin.
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INTRODUCTION |
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Free testosterone has been found to be positively correlated with insulin resistance (13, 14) as well as with components of the metabolic syndrome, including fasting plasma glucose (1416), adiposity (14, 15), fasting and postchallenge insulin levels (14, 17, 18), insulin-to-glucose ratio (19), and blood pressure (14, 20, 21). In addition, previous studies have shown that postmenopausal women with type 2 diabetes have higher free testosterone levels than women without diabetes (15, 17, 20). Both low levels of sex hormone-binding globulin (SHBG), a marker of hyperandrogenicity (22, 23), and high levels of free testosterone (14) have been shown to predict incident type 2 diabetes in women, highlighting the relation between androgens and insulin sensitivity.
These data suggest a range of testosterone optimal for vascular health and body composition, with higher levels potentially compromising insulin sensitivity. As testosterone replacement in postmenopausal women is investigated further (24), it will be important to understand the metabolic consequences of treatment. Previous studies have not determined which components of the metabolic syndrome are most strongly related to testosterone levels in postmenopausal women, because many have had small sample sizes (13, 17, 20, 21) or lacked data on certain components of the metabolic syndrome (1416).
With this information in mind, we investigated the association of total and free testosterone with the metabolic syndrome in postmenopausal women who were not taking hormone replacement therapy. We analyzed cross-sectionally a previously drawn prevalent case-control sample of the Atherosclerosis Risk in Communities (ARIC) Study (181 cases of significant carotid atherosclerosis and 181 controls with minimal carotid atherosclerosis).
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MATERIALS AND METHODS |
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Participants in our study were previously selected for a case-control study examining the association between endogenous postmenopausal sex hormones and carotid atherosclerosis in women who were not current or ever users of hormone replacement therapy (2). In that previous study, carotid atherosclerosis was assessed by using B-mode ultrasound measurements of the average carotid artery intimal-medial thickness from visits 1 and 2, conducted 3 years apart (2). Cases were defined as postmenopausal women with no history of hormone replacement therapy whose average for all carotid intimal-medial thickness measurements at each of six sites visualized for visits 1 and 2 was equal to or above the 95th percentile. Controls were postmenopausal women with no history of hormone replacement therapy, frequency matched to cases on 5-year age groups and ARIC center, whose intimal-medial thickness was below the 75th percentile at each of six sites visualized (2).
A woman was considered postmenopausal if she had not menstruated in the last 2 years. Postmenopausal women were further classified as having undergone surgical menopause if they had had a bilateral oophorectomy. Natural menopause also included nonmenstruating women 55 years of age or older who had had a hysterectomy and had at least one intact ovary (27).
Measurements
Androgen exposure was assessed by measuring levels of total testosterone and SHBG at Yerkes Laboratory (Assay Services Laboratory, Yerkes Regional Primate Research Center of Emory University, Atlanta, Georgia) in blood collected during visit 2, because visit 1 sera were not available. Serum testosterone was measured by radioimmunoassay using a Diagnostics Products Corporation kit (Los Angeles, California). Serum SHBG was measured by radioimmunoassay using a Diagnostic Systems Laboratories, Inc. assay kit (Webster, Texas). The intraassay coefficient of variation for each hormone was less than 10 percent. The interassay coefficients of variation were 9 percent for total testosterone and 18.5 percent for SHBG. The lower limits of detection for total testosterone and SHBG were 5 ng/dl and 5 nmol/liter, respectively. We calculated the free androgen index (FAI), a marker of free testosterone, as the total testosterone/SHBG ratio (28). All assays were performed in the same batch for cases and controls.
Covariates
A physical examination was conducted on each participant at visit 1 to ascertain cardiovascular conditions and measure risk factors (25). Venipunctures were performed in the morning after participants had fasted for 12 hours. After standardized processing at the clinical site, samples were aliquoted into 2-ml tubes, frozen at 70°C, and shipped on dry ice to the appropriate ARIC Central Laboratory. Insulin was measured by radioimmunoassay (125-I Insulin 100 test kit; Cambridge Medical Diagnostics, Billerica, Massachusetts). Total triglycerides were measured by using enzymatic methods (26), and high density lipoprotein cholesterol was measured with dextran and magnesium precipitation (26).
Anthropometry was performed in the fasting state with the urinary bladder empty. Height (without shoes) was measured by using a wall-mounted ruler. Weight was measured by using a balance scale (Detecto, model #437; Dynamic Scales, Terre Haute, Indiana), which was zeroed daily. Waist circumference was measured at the level of the umbilicus (26).
Three measurements were taken with a random-zero sphygmomanometer. The mean of the second and third measurements was used to characterize blood pressure at the visit (26).
Definition of the metabolic syndrome
A woman was considered to have the metabolic syndrome if she met at least three of the following criteria: waist circumference 35 inches (88.9 cm), triglycerides
150 mg/dl, high density lipoprotein cholesterol <40 mg/dl, blood pressure >130/80 mmHg, fasting insulin
100 pmol/liter, or impaired glucose homeostasis (fasting glucose
110 mg/dl or diagnosed diabetes) (29, 30). Thirty women had not fasted, and their glucose and insulin criteria were coded as absent. Hyperinsulinemia was included in the definition of the metabolic syndrome because expert panels recognize it as a characteristic of the syndrome (31) and it has been shown to have a role in regulating androgen levels in premenopausal women (32, 33). The fasting insulin criterion is based on a previous analysis of the metabolic syndrome in the ARIC Study in which a fasting insulin level of 100 pmol/liter was found to represent the 80th percentile of the cohort distribution among persons without diabetes (30). Subjects were classified as having diabetes mellitus if they met any of the following criteria: fasting serum glucose levels
126 mg/dl, nonfasting serum glucose levels
200 mg/dl, current self-reported use of medications prescribed to treat diabetes (e.g., insulin or sulfonylureas), or a positive response to the question, "Has a doctor ever told you that you had diabetes?"
Analysis
In univariate analysis, median concentrations of total testosterone and the FAI were compared between women with and without the metabolic syndrome as defined above by using the Wilcoxon rank-sum two-sample test because hormone levels were not distributed normally. A two-tailed p value of 0.05 was used to determine the level of significance. For the purpose of multivariate analysis, hormone levels were divided into quartiles. Odds ratios and 95 percent confidence intervals were calculated from multiple logistic regression models to determine the odds of having the metabolic syndrome according to any definition versus not having the metabolic syndrome, per quartile of total testosterone and FAI. Quartile 1 was used as the reference. In the base model, we adjusted for age, ARIC center, and race. These analyses were performed separately for cases with significant carotid atherosclerosis and controls with minimal carotid atherosclerosis; however, because there was no statistically significant interaction of the relation between androgens and the metabolic syndrome according to the presence of significant versus minimal carotid atherosclerosis, the two groups were combined for the remaining multivariate analyses. These analyses were also conducted by excluding women with diabetes.
To explore which components of the metabolic syndrome were most strongly associated with testosterone levels, separate linear regression models were constructed for each of the 20 possible combinations of three metabolic syndrome components as the independent variable and FAI, expressed in continuous form, as the dependent variable. The linear regression equations took the following form: E(Y) = ß0 + ß1x1 + [ß2x2 + ... ß4x4] + [ß5x5 + ... ß8x8] , where E(Y) = mean FAI, ß0 = intercept, and ß1 = coefficient for the difference in FAI for women with a given metabolic syndrome component grouping compared with those without that grouping. If the metabolic syndrome grouping was present, x1 = 1; if the metabolic syndrome grouping was absent, x1 = 0. The 20 possible combinations of three metabolic syndrome components from a field of six (hyperinsulinemia, hyperglycemia, hypertension, hypertriglyceridemia, low high density lipoprotein cholesterol concentration, elevated waist circumference) gave rise to 20 distinct models and 20 corresponding ß1 coefficients (30).
In this equation, ß2ß4 are coefficients for the three other individual metabolic syndrome components not included in the definition of the syndrome modeled as categorical covariates. For example, if a woman was defined as having the metabolic syndrome based on the presence of hyperinsulinemia, hyperglycemia, and hypertension, the metabolic syndrome covariates adjusted for in that model were low high density lipoprotein cholesterol, hypertriglyceridemia, and elevated waist circumference.
ß5ß8 represent coefficients for demographic covariates (age, race, ARIC center) and the presence of significant versus minimal carotid atherosclerosis. These covariates were the same in all multivariate models.
To determine whether the relation between the metabolic syndrome and FAI was greater than what would be expected from the additive effects of the individual components of the metabolic syndrome, a logistic regression model was constructed in which an interaction term for the three-component metabolic syndrome combination most strongly associated with FAI was entered into the model with the main effects of each syndrome component (hyperinsulinemia, hyperglycemia, hypertension, hypertriglyceridemia, elevated waist circumference, and low high density lipoprotein cholesterol) as well as the second-order interaction terms. This linear regression model took on the following form: E(Y) = ß0 + [ß1x1 + ß2x2 + ß3x3 + ß4x4 + ß5x5 + ß6x6] + ß7x1 x x2 x x3 + [ß8x1 x x2 + ß9x1 x x3 + ß10x2 x x3] + [ß11x11 + ... ß14x14], where Y = mean FAI, ß0 = constant, and ß1ß6 represent coefficients for the main effects of the individual metabolic syndrome components, modeled as binary indicators; x1, x2, x3 represent categorical variables for the three components used to define the metabolic syndrome.
ß7 is the coefficient for the excess FAI for women with a given three-component metabolic syndrome combination compared with those without that combination. If the excess FAI is greater than that predicted by the additive effects of the individual components, ß7 should be statistically significant.
ß8ß10 represent coefficients for the second-order interaction terms for the two-component metabolic syndrome combinations among the three components used to define the presence of the metabolic syndrome.
ß11ß14 are coefficients for demographic covariates (age, race, ARIC center) and the presence of significant versus minimal carotid atherosclerosis.
Statistical analyses were carried out by using SAS version 8.0 statistical software (34).
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RESULTS |
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Because there was not a statistically significant interaction of the relation between androgens and the metabolic syndrome by the presence of minimal versus significant carotid atherosclerosis, the two groups were combined for the remaining analyses (two-sided p for interaction = 0.54 for FAI modeled as a continuous variable). Previous data have shown no significant association between atherosclerosis status and FAI (2).
Relation of metabolic syndrome components to FAI
Table 3 shows the results of multivariate linear regression models used to determine which components of the metabolic syndrome were driving the association with FAI. All models were adjusted for age, race, ARIC center, and the presence of minimal versus significant carotid atherosclerosis. When we examined each of the six components individually, hyperglycemia and hyperinsulinemia were most strongly associated with FAI in both unadjusted models (data not shown) and models adjusted for the other metabolic syndrome components. The FAI was significantly higher in women with hyperglycemia (0.18, 95 percent CI: 0.04, 0.34; two-sided p = 0.023) and hyperinsulinemia (0.38, 95 percent CI: 0.22, 0.54; two-sided p < 0.0001) than in women without these traits following adjustment for each of the other five metabolic syndrome components. The magnitude and significance of these associations were unchanged when the 85 women with diabetes were excluded and when additional adjustment was made for body mass index (data not shown).
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The four combinations associated with the greatest absolute difference in FAI contained both hyperglycemia and hyperinsulinemia. As highlighted in the last four rows of table 3, the absolute difference in FAI due to the presence versus the absence of the metabolic syndrome for these four combinations ranged from 0.41 to 0.54 (all two-sided p < 0.001). Most of this association was driven primarily by the combined presence of hyperinsulinemia and hyperglycemia, irrespective of the third component. In a confirmatory subsidiary analysis in which the metabolic syndrome was defined by the presence of hyperinsulinemia and hyperglycemia only, the absolute difference in FAI for the presence versus absence of the syndrome was 0.52 (95 percent CI: 0.34, 0.70; two-sided p < 0.0001), nearly identical to the difference in FAI associated with the three-component combinations containing hyperinsulinemia. To determine whether there was a synergistic effect of the metabolic syndrome on FAI beyond the additive effects of its individual components, we attempted to assess the interaction between FAI and the three-component combination most strongly associated with FAI (hyperglycemia, hyperinsulinemia, and low high density lipoprotein cholesterol). We did not observe a significant interaction for this combination, although we likely lacked sufficient power to detect an interaction because of the small sample size.
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DISCUSSION |
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Our study has several strengths. First, it is one of the few evaluating the relation between androgens and the metabolic syndrome in postmenopausal women. Second, we were able to examine each of the possible three-component combinations of the metabolic syndrome to determine which were most strongly associated with FAI by using multivariate regression models. Third, with the exception of two studies (21, 35), most have examined the relation between individual metabolic syndrome components and androgens by using less precise correlation analyses. Fourth, compared with some previous studies, ours had a larger sample size from a population-based cohort in which to examine these associations. Except for studies conducted in the Rancho Bernardo cohort (1416), the majority of other studies have had very small sample sizes (17, 20, 21) or were not conducted on a population-based sample (13, 17, 19, 21).
Nonetheless, several limitations should be kept in mind when interpreting our data. First, hormone data were available on only a select group of women with minimal and significant carotid atherosclerosis. Second, free testosterone was not measured directly. Although our estimate of the total testosterone/SHBG ratio is a valid one of free testosterone and androgenicity (28), the correlation between FAI and free testosterone has not been tested as extensively in women as in men (5, 36). Third, we had only one measurement of hormones; however, a single measure is thought to reliably characterize a persons androgen status (14, 37). Finally, the temporal relation between FAI and the metabolic syndrome could not be elucidated given the cross-sectional nature of our analysis.
Previous studies have examined the association between free testosterone and individual components of the metabolic syndrome in postmenopausal women. Two studies conducted in the Rancho Bernardo cohort demonstrated that free testosterone was positively correlated with body mass index (14, 15) and waist girth in postmenopausal women (14). Free testosterone has also been positively correlated with total and low density lipoprotein cholesterol in nondiabetic women (20) and, in multivariate regression analyses, was shown to be an independent predictor of the total cholesterol/high density lipoprotein cholesterol ratio, low density lipoprotein cholesterol/high density lipoprotein cholesterol ratio, and total and low density lipoprotein cholesterol (21). However, these studies did not find significant correlations of free testosterone with triglycerides and high density lipoprotein cholesterol, the two lipids most frequently associated with the metabolic syndrome. Phillips et al. (20) demonstrated a significant association between free testosterone and blood pressure. In our multivariate analyses, we did not find elevated waist circumference, hypertriglyceridemia, low high density lipoprotein cholesterol, or elevated blood pressure to be independently related to FAI after adjusting for other metabolic syndrome components.
In our study, hyperinsulinemia and hyperglycemia were independently associated with higher FAI, and the three-component combinations most strongly associated with FAI contained both of these elements. Metabolic syndrome combinations that met strict National Cholesterol Education Program criteria and did not include hyperinsulinemia were not significantly associated with FAI, highlighting the importance of insulin in this association. This conclusion confirms the findings of previous studies of postmenopausal women; however, to our knowledge, previous studies have not compared multiple metabolic syndrome components to determine which combinations are most strongly associated with hyperandrogenism. In three studies of the Rancho Bernardo cohort, free testosterone was positively associated with fasting plasma glucose (1416). Free testosterone has also been associated with higher fasting and postchallenge insulin levels (14, 17) and higher insulin/glucose ratio (19), indicating that it is associated with greater insulin resistance. One study demonstrated a negative correlation between insulin sensitivity, assessed by the euglycemic-hyperinsulinemic clamp, and FAI in women with both normal and impaired glucose tolerance (13). Oh et al. (14) showed that, after adjusting for age, adiposity, and systolic blood pressure, elevated free testosterone predicted incident type 2 diabetes as well as higher insulin levels and homeostasis model assessment-insulin resistance (HOMA-IR) longitudinally. These findings suggest a role for androgens in the regulation of glucose metabolism and insulin sensitivity.
There are several mechanisms by which androgens and insulin resistance may be related (figure 3). First, research of premenopausal women with polycystic ovary syndrome suggests that hyperinsulinemia leads to increased bioavailable testosterone by stimulating P450c 17 activity in ovarian thecal cells following stimulation of ovarian insulin receptors, suppression of SHBG, and stimulation of luteinizing hormone release from the pituitary gland (38). In obese women with polycystic ovary syndrome, suppression of insulin levels with diazoxide leads to a reduction in serum testosterone (38). Second, abdominal obesity itself might directly lead to hyperandrogenism as a result of conversion of adrenal androstenedione to testosterone via 17 ß-hydroxysteroid oxidoreductase in abdominal adipose tissue (39). Alternatively, hyperandrogenism might result in increased insulin resistance. Androgen administration to healthy women has been shown to reduce insulin sensitivity and impair peripheral glucose utilization, as assessed by the hyperinsulinemic-euglycemic clamp (9, 12, 40). In addition, anti-androgen treatment in women with hyperandrogenism has resulted in partial improvement in insulin sensitivity (41, 42). A study of female rats after oophorectomy suggested another mechanism at the level of the skeletal muscle by which testosterone might impair glucose utilization. In this study, rats treated with testosterone replacement therapy showed reduced glycogen synthase protein expression in skeletal muscle (43).
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ACKNOWLEDGMENTS |
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The authors thank the staff of the ARIC Study for their important contributions.
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NOTES |
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REFERENCES |
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