Clinical Chemistry 51:7 1110 –1115 (2005)
Molecular Diagnostics and Genetics
The CAG Repeat Polymorphism in the Androgen Receptor Gene Is Associated with HDLCholesterol but Not with Coronary Atherosclerosis or Myocardial Infarction Martin Hersberger,1* Jo¨rg Muntwyler,2 Harald Funke,3,4 Jacqueline Marti-Jaun,1 Helmut Schulte,3 Gerd Assmann,3 Thomas F. Lu¨scher,2 and Arnold von Eckardstein1
Background: Age-adjusted morbidity and mortality rates from coronary heart disease (CHD) are higher in men than in women. Androgens are suspected to be responsible for the male disadvantage. The genomic effect of androgens is mediated by the androgen receptor (AR), which has a polymorphic CAG repeat in exon 1. The number of repeats is inversely related to the transcriptional activity of the AR on target genes. Methods: We investigated the association of this CAG repeat polymorphism with CHD and myocardial infarction (MI) in 2 independent case– control studies involving 544 Caucasian men. Results: The number of CAG repeats in the AR gene correlated significantly with HDL-cholesterol (HDL-C) in controls (r ⴝ 0.21; P ⴝ 0.015). This effect was independent of triglycerides, body mass index, alcohol intake, smoking, and age in a multiple regression model (R2 ⴝ 50%). Despite decreased HDL-C, lower CAG repeat numbers were not associated with increased risk for CHD (odds ratio ⴝ 0.82; 95% confidence interval, 0.50 –1.36; P ⴝ 0.44) or MI in carriers of AR genes with lower CAG repeat numbers (odds ratio ⴝ 0.72; 95% confidence interval, 0.37–1.39; P ⴝ 0.33). Conclusions: Shorter, more androgenic AR alleles with fewer CAG repeats are associated with lower HDL-C,
1 Institute of Clinical Chemistry and 2 Cardiovascular Center, Division of Cardiology, University Hospital, Zurich, Switzerland. 3 Institute of Atherosclerosis Research, University of Muenster, Muenster, Germany. 4 Molecular Haemostaseology, Friedrich Schiller University, Jena, Germany. * Address correspondence to this author at: Institute of Clinical Chemistry, University Hospital Zurich, Raemistrasse 100, CH-8091 Zurich, Switzerland. Fax 41-1-255-4590; e-mail
[email protected]. Received February 4, 2005; accepted April 8, 2005. Previously published online at DOI: 10.1373/clinchem.2005.049262
but not with an increased risk for CHD or MI, which argues against a detrimental androgen effect on cardiovascular risk under physiologic conditions. © 2005 American Association for Clinical Chemistry
Coronary heart disease (CHD)5 is a leading cause of death and disability in both men and women, but age-adjusted morbidity and mortality rates from CHD are 2.5- to 4.5-fold higher in men than in women (1 ). Androgens, or the lack of estrogens, are traditionally held responsible for the male gender disadvantage, although this paradigm is not well supported by data. Several cross-sectional and case– control studies have associated hypoandrogenemia with increased risk for coronary artery disease in men [reviewed in Refs. (2, 3 )]. In addition, none of the prospective studies found statistically significant associations between physiologic concentrations of serum testosterone and the occurrence of CHD events (3 ). It is important to emphasize, however, that low testosterone is confounded by the presence of chronic disease, including heart failure, obesity, and insulin resistance, which all contribute to cardiovascular morbidity and mortality. Endogenous testosterone is therefore of only minor help in elucidating the pathogenetic contribution of androgens to atherosclerosis. In the majority of experiments with castrated male animals, testosterone substitution inhibited the development or progression of atherosclerosis (2, 3 ). In LDLreceptor– deficient mice, however, this antiatherogenic effect of testosterone was found to depend on its aromatization into estradiol (4 ). This aromatization of testosterone and the correlation of serum testosterone with estro-
5 Nonstandard abbreviations: CHD, coronary heart disease; AR, androgen receptor; HDL-C, HDL-cholesterol; MI, myocardial infarction; CRP, complement-reactive protein; BMI, body mass index; and LDL-C, LDL-cholesterol.
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gen in men (5 ) further confound any investigation of the influence of androgens on atherosclerosis. These inconsistent data preclude estimating the anti- or proatherogenic potential of androgens. Genetic analysis of the androgen receptor (AR) presents one possibility, however, for distinguishing the androgenic from the estrogenic effect of testosterone and investigating the role of androgens in atherosclerosis. AR, which is a nuclear transcription factor, mediates the genomic effects of testosterone. A variable number of CAG repeats in exon 1 of the AR gene on the X chromosome encode for a variable number of glutamine residues in the amino-terminal domain of the receptor and are inversely correlated with the transcriptional activity on testosterone target genes (6 ). Abnormal expansion of the CAG repeats beyond 36 leads to Kennedy disease, which is accompanied by signs of hypoandrogenism (6 ). In the physiologic range of 9 to 35, the number of CAG repeats was shown to be inversely associated in some studies with the risk of prostate cancer, benign prostatic hyperplasia, sperm production, and bone density, as well as positively associated with endothelial function and HDL-cholesterol (HDL-C), body fat mass, and serum insulin and leptin [reviewed in Ref. (6 )]. In this study, we explored the association of the CAG repeat polymorphism with angiographically assessed CHD and myocardial infarction (MI) to better estimate the physiologic contribution of androgens to CHD in men.
Materials and Methods A total of 344 men in Zurich, Switzerland and 200 men in Muenster, Germany volunteered to participate in the study. We obtained written informed consent from all participants, and the local ethics committees approved the study. The Swiss sample consisted of 205 consecutive Caucasian patients with angiographically documented CHD and ⬎50% stenosis in at least 1 coronary artery (Table 1). The control group, consisting of 139 Caucasians with no history of CHD, stroke, or peripheral vascular disease, was recruited from the general population. An-
giographically negative individuals were also included in the control group. None of the patients or controls was being treated with androgens or estrogens, whereas 81% of the individuals and 8% of the controls were being treated with cholesterol-lowering regimens. The German sample included 100 Caucasian patients, all from a cardiologic rehabilitation clinic, who previously experienced an MI, as well as 100 age- and sex-matched Caucasian controls from the Protective Cardiovascular Muenster study (7 ). Risk factors and use of medication were assessed by questionnaire. Clinical chemistry analysis was done on a Roche-Hitachi Modular Clinical Chemistry analyzer, with commercial tests from Roche Diagnostics. DNA was extracted with the QIAamp® DNA Mini Blood Kit (Qiagen). Primers, used as 10 M solutions in water, were designed with Oligo 4.0 software (MedProbe) and purchased from Microsynth. The CAG repeat polymorphism in the AR gene was analyzed on an ABI 310 genetic analyzer (Applied Biosystems) with fluorescently labeled oligonucleotides and an internal standard. To determine the CAG repeat numbers, a 12.5-L PCR reaction was performed with 8.9 L of water, 1.25 L of buffer 1 (1.5 mM MgCl2), 0.1 L of Gold Taq (5 U/L), 0.25 L of deoxynucleotide mixture (10 mM), 0.25 L of JOE-labeled primer ARU165 (5⬘-CGTGCGCGAAGTGATCCAGA-3⬘), 0.25 L of primer ARL348 (5⬘-CTTGGGGAGAACCATCCTCA-3⬘), and 1.5 L of genomic DNA (⬃50 ng/L). The reaction used the following cycling conditions: 10 min at 94 °C; 38 cycles of 94 °C for 30 s, 60 °C for 30 s, and 72 °C for 45 s; and a final extension of 7 min at 72 °C. Allele assignment was done on an ABI 310 genetic analyzer with ABI Genotyper software after addition of the internal standard Genescan 400HD to the probe (all from Applied Biosystems). Each run incorporated probes from previously sequenced DNAs, including alleles with 8, 19, 27, and 30 CAG repeats as positive controls. The data were analyzed with SAS 8.1 statistical software (SAS Institute). The variables creatinine, triglycer-
Table 1. Characteristics of controls and cases (Swiss study). Age,a years BMI,a kg/m2 Hypertension, % Diabetes, % Current smokers, % Ever smoked, % Alcohol consumption,a drinks/week Creatinine,a mol/L CRP,a mg/L Cholesterol,a mmol/L HDL-C,a mmol/L LDL-C,a mmol/L Triglycerides,a mmol/L a
Median (interquartile range).
Controls (n ⴝ 139)
Cases (n ⴝ 205)
P
58 (52–64) 26.5 (24.2–29.1) 27.4 4.4 16.4 55.9 3.5 (1.0–7.5) 96 (89–104) 2 (1–2) 5.9 (5.0–6.4) 1.5 (1.2–1.7) 3.5 (2.8–4.0) 1.5 (1.0–2.2)
62 (56–70) 26.8 (24.8–29.4) 42.7 20.9 17.0 80.1 3.0 (0.5–6.0) 89 (82–100) 2 (1–4) 5.0 (4.2–5.8) 1.2 (1.0–1.4) 3.1 (2.4–3.8) 1.3 (1.0–1.8)
0.0006 0.067 0.0042 ⬍0.0001 1.0 ⬍0.0001 0.021 0.19 0.015 ⬍0.0001 ⬍0.0001 0.0026 0.18
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Table 2. Characteristics of controls and cases (German study). Age,a years BMI,a kg/m2 Hypertension, % Diabetes, % Current smokers, % Ever smoked, % CRP,a mg/L Cholesterol,a mmol/L HDL-C,a mmol/L LDL-C,a mmol/L Triglycerides,a mmol/L a
Controls (n ⴝ 100)
Cases (n ⴝ 100)
P
50 (43–54) 26.0 (24.4–27.7) 19.0 3.0 18.8 62.4 0.3 (0.1–0.7) 5.8 (5.3–6.5) 1.2 (1.1–1.4) 3.9 (3.5–4.4) 1.3 (1.0–1.8)
50 (46–52) 26.6 (25.2–29.2) 40.0 13.0 73.0 95.0 1.2 (0.5–2.7) 6.0 (5.2–6.8) 0.9 (0.8–1.0) 4.3 (3.6–4.9) 1.6 (1.2–2.2)
0.62 0.088 0.002 0.016 ⬍0.0001 ⬍0.0001 ⬍0.0001 0.477 ⬍0.0001 0.007 0.002
Median (interquartile range).
ides, complement-reactive protein (CRP), and alcohol intake, which did not follow a gaussian distribution, were log-transformed before the formal analysis. The univariate association of the CAG repeats with other variables was assessed with the Pearson correlation coefficient. The independent effect of the CAG repeat numbers on HDL-C was tested with multiple regression. Two-way interactions were tested for the CAG repeat numbers with body mass index (BMI), triglycerides, alcohol intake, smoking, and age. Categorical variables between cases and controls were compared with the 2 test, and continuous variables were compared with a 2-sided t-test. The relationships between the number of CAG repeats in the AR gene and CHD and MI were assessed by multivariate logistic regression. Two-way interactions were analyzed for CAG repeat numbers with diabetes, age, smoking, and hypertension. All P values were 2-sided, and P values ⬍0.05 were considered statistically significant.
Results The characteristics of the controls and cases in both cities are shown in Tables 1 and 2. In Swiss controls, there was a significant correlation of CAG repeat numbers with HDL-C (r ⫽ 0.21; P ⫽ 0.015; Fig. 1). We observed no significant correlation between CAG repeat numbers and total cholesterol (r ⫽ 0.1; P ⫽ 0.2), triglycerides (r ⫽ 0.06; P ⫽ 0.5), LDL-cholesterol (LDL-C; r ⫽ 0.02, P ⫽ 0.8); CRP (r ⫽ 0.04; P ⫽ 0.7), or BMI (r ⫽ ⫺0.01; P ⫽ 0.9). In Swiss controls, HDL-C was significantly predicted by the number of CAG repeats in the AR gene, triglyceride concentration, BMI, alcohol intake, smoking, and age in a multiple regression model (Table 3). Higher numbers of CAG repeats and alcohol intake were positively associated with HDL-C, whereas higher triglyceride concentrations and BMI, as well as smoking, were inversely associated with HDL-C. Although there was an interaction of the number of CAG repeats with triglycerides for HDL-C prediction (P ⬍0.001), the other variables showed no interaction with the number of CAG repeats. The interaction revealed that the association of number of CAG repeats in the AR gene with HDL-C was significant
only in controls with triglycerides below the median of 1.5 mmol/L (P ⬍0.001) but not in controls with triglycerides ⬎1.5 mmol/L (P ⫽ 0.86). Exclusion of controls on lipid-lowering regimens did not alter the association of number of CAG repeats with HDL-C. We observed similar associations in the Swiss case group, although 81% of those participants were following cholesterol-lowering regimens with statins. In the Swiss cases, the number of CAG repeats was borderline predictive of HDL-C (P ⫽ 0.09), whereas we found no association for BMI, smoking, and age (Table 3). In the entire study population, independent predictors of HDL-C were the number of CAG repeats in the AR gene (P ⫽ 0.0013), triglycerides, BMI, alcohol intake, smoking, and case status (Table 3). Despite the beneficial effect of longer AR alleles with more CAG repeats on HDL-C, there was no association of CAG repeat number with CHD in the Swiss case– control study (Fig. 2). We compared the distribution of allele carriers between groups with fewer vs more repeats than the median (CAG repeats ⬍22 vs ⱖ22; P ⫽ 0.44) and between quartiles (ⱕ19 repeats vs 20 –21 repeats vs 22–23 repeats vs ⱖ24 repeats; P ⫽ 0.71), using multivariate
Fig. 1. Number of CAG repeats in the AR gene directly correlates with HDL-C in controls. Serum HDL-C of controls (⽧) and cases ( ) derived from the multiple regression model in Table 2 are shown according to CAG repeat number. HDL-C was adjusted for triglycerides, BMI, alcohol intake, smoking, and age and therefore appears higher than the raw data.
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Table 3. Multiple regression model for predicting serum HDL-C concentrations.
Table 4. Logistic regression model for predicting case status (CHD) in the Swiss study.
Predictor
Controls
Cases
Entire sample
AR (CAG repeat number) Triglycerides (mmol/L) BMI (kg/m2) Alcohol consumption (drinks/week) Current smoking Age (years) Case status Overall r2, %
0.028a ⫺0.357b ⫺0.021a 0.233b
0.014 ⫺0.222b ⫺0.006 0.107a
0.021a ⫺0.301b ⫺0.013a 0.168b
⫺0.210a ⫺0.010a
⫺0.116 0.002
49.8b
27.8b
⫺0.152a ⫺0.004 ⫺0.267b 46.1b
a b
P ⬍0.05. P ⬍0.001.
logistic regression. We found no association of shorter AR alleles with CHD (Table 4), although HDL-C was strongly associated with case status (P ⬍0.001; data not shown). To the contrary, these shorter and more androgenic AR alleles (⬍22 repeats) tended to be associated with a reduced risk for CHD (Table 4). No interaction of the number of CAG repeats in the AR alleles with other variables was observed. As expected, the traditional risk factors were strongly associated with case status. The German sample was matched for age and cholesterol, whereas all other traditional risk factors were associated with MI as expected (Table 2). Multiple regression analysis for the prediction of HDL-C revealed that only
95% CIa
Variable
Odds ratio
Length of AR gene (CAG repeats ⬍22 vs ⱖ22) Age (years) Diabetesb Hypercholesterolemiab Smokingc Hypertensionb
0.82
0.50–1.36
0.44
1.04 4.16 3.95 3.01 1.42
1.01–1.07 1.62–10.64 2.31–6.74 1.76–5.14 0.84–2.42
0.003 0.003 ⬍0.001 ⬍0.001 0.19
Parameter estimate
P
a
CI, confidence interval. History of diabetes, hypercholesterolemia, or hypertension. c Ever smoked. b
triglycerides were predictive of HDL-C in the German controls, whereas number of CAG repeats, BMI, smoking, and age were not predictive of HDL-C (data not shown). Similar to the Swiss sample, there was no significant association of the AR alleles with MI in the German sample (Table 5). Consistent with the Swiss CHD study, however, in the German cohort the shorter, more androgenic AR alleles (⬍22 CAG repeats) tended to be associated with a reduced risk for MI (Table 5). When we combined the 2 studies, we found that the shorter, more androgenic AR alleles (⬍22 CAG repeats) tended to be associated with reduced risk for coronary atherosclerosis (odds ratio ⫽ 0.79; 95% confidence interval, 0.53–1.18; P ⫽ 0.25). Again, this association did not reach statistical significance. The combined sample had a power of 80% to detect an odds ratio of 0.61 or 1.64.
Fig. 2. No association of number of CAG repeats in the AR gene with atherosclerosis. The distribution of CAG repeats in the AR gene in the case– control studies is shown, with allele frequencies for Swiss controls (n ⫽ 139; f), Swiss cases (n ⫽ 205; u), German controls (n ⫽ 100; ), and German cases (n ⫽ 100; 䡺). The allele frequency (%) is indicated at the top of each column.
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Table 5. Logistic regression model for predicting case status (MI) in the German study. Variable
Length of AR gene (CAG repeats ⬍22 vs ⱖ22) Age (years) Diabetesb Hypercholesterolemiab Smokingc Hypertensionb
Odds ratio
0.72 0.99 3.74 2.70 15.02 2.94
95% CIa
0.37–1.39 0.94–1.05 0.88–14.83 1.38–5.26 5.11–44.16 1.37–6.33
P
0.33 0.99 0.07 ⬍0.01 ⬍0.001 ⬍0.01
a
CI, confidence interval. History of diabetes, hypercholesterolemia, or hypertension. c Ever smoked. b
Discussion The CAG-repeat polymorphism in exon 1 of the AR gene influences the transcriptional activity of this nuclear receptor and, subsequently, the genomic response of tissue to testosterone. In agreement with a previous study (8 ), results from our Swiss population sample showed that the shorter, more androgenic AR alleles with fewer CAG repeats are associated with lower HDL-C than AR alleles with higher repeat numbers. Despite this potentially higher risk profile for atherosclerosis, carriers of short AR alleles had no increased risk for CHD and MI in 2 independent case– control studies involving a total of 544 Caucasian males. In line with the lack of a proatherogenic effect of shorter AR alleles with fewer CAG repeats are the results of studies investigating the association of testosterone with atherosclerosis. The outcomes of 7 prospective cohort or nested case– control studies showed no independent relationship of testosterone with CHD or MI in men (3 ). Furthermore, of the 32 cross-sectional studies that investigated the association of testosterone with atherosclerosis and its clinical cardiac outcomes, 16 showed no relationship between testosterone and case status, whereas 16 found an inverse association of testosterone with cardiac endpoints (2, 3 ). None of these studies indicated a positive relationship between testosterone and CHD, arguing that high testosterone in the physiologic range is not a risk factor for CHD. In contrast, some of the cross-sectional studies showed an association of low testosterone with increased CHD (3, 9 ). These results need careful interpretation because men with low testosterone tend to have a higher BMI; higher fasting and 2-h glucose, fasting and 2-h insulin, total cholesterol, LDL-C, and apolipoprotein B; and lower HDL-C and apolipoprotein A-I (10 ). Even in obese patients and in patients with type 2 diabetes, however, there is evidence for an inverse association of testosterone with the carotid intima media thickness (11, 12 ). These prospective and cross-sectional studies, together with our current genetic study, provide consistent data on the lack of a direct relationship between androgens and CHD in men (3 ). The consistent trend of a negative association between the shorter, more
androgenic AR allele with fewer CAG repeats and CHD in both studies also raises the question of whether androgens in the physiologic range exert atheroprotective genomic effects in men. Androgens have been shown to exert a variety of adverse and beneficial effects on risk factors of atherogenesis, but dissection of these effects into androgenic and estrogenic effects in the physiologic range is virtually impossible (3, 13 ). For example, estrogens increase HDL-C (13 ), whereas the influence of physiologic testosterone on lipid metabolism is controversial. Most crosssectional studies found a positive correlation (5 ) between serum testosterone and HDL-C. Conversely, suppression of endogenous testosterone production by a long-acting gonadotropin-releasing hormone agonist increased HDL-C in young eugonadal men, whereas supplementation of testosterone in these men led to a dose-dependent decrease in HDL-C (14 ). Even more pronounced is the decrease in HDL-C produced by exogenous treatment with nonaromatizable androgens, suggesting that aromatization of testosterone to estrogens lessens the androgenmediated decrease in HDL-C (15 ). Similarly, our results indicate that shorter, more androgenic AR alleles with fewer CAG repeats are associated with lower HDL-C and argue for an HDL-C–lowering effect of androgens even under physiologic conditions. In epidemiologic studies, low HDL-C is a risk factor for atherosclerosis, and HDL-C was also associated with CHD and MI in our case– control studies. Our data argue, nevertheless, for an atheroprotective effect of the more androgenic, short AR alleles (Tables 3 and 4), although they are associated with lower HDL-C. This outcome may have 2 explanations. One explanation is that any adverse effect of a more androgen-sensitive AR isoform may be balanced by beneficial effects on other risk factors and atherogenic pathways. Consistent with this explanation, a low number of CAG repeats was previously associated with decreases in body fat and plasma insulin, leptin, and triglycerides (16 ). Low CAG repeat numbers, however, have been associated with impaired endothelium-dependent vasoreactivity, which is an indicator of increased cardiovascular risk (17 ). The other explanation is that the mechanism by which testosterone decreases HDL-C may not be detrimental but may even increase reverse cholesterol transport. Testosterone stimulates SR-BI gene expression in both macrophages and the liver, thereby enhancing cholesterol efflux and selective cholesterol uptake, respectively (18 ). Furthermore, administration of supraphysiologic concentrations of testosterone in men increases hepatic lipase (19 ), which leads to lower HDL-C through increased phospholipid hydrolysis on the surface of HDL. This process again facilitates hepatic removal of HDL via receptor-mediated processes. Hence, the testosterone-induced decrease in HDL-C produced by the shorter, more androgenic AR allele with fewer CAG repeats may reflect enhanced reverse cholesterol trans-
Clinical Chemistry 51, No. 7, 2005
port, which inhibits rather than enhances the development of CHD. Our results contrast with a recent study that found a weak association between short AR alleles with fewer CAG repeats and coronary artery stenosis but not with carotid intima thickness (20 ). That study, however, enrolled only a limited number of patients and controls (134 in total). In summary, we show that short, more androgenic AR alleles are associated with lower HDL-C in men without evidence that they confer a higher risk of CHD and MI. These findings argue against a detrimental androgen effect on cardiovascular risk under physiologic conditions.
We thank our coworkers at the Institute of Clinical Chemistry at the University Hospital Zurich for their efforts in efficiently analyzing study samples in addition to their routine work. We are indebted, furthermore, to the Cardiovascular Center Division of Cardiology for collection of patient and control samples. This work was supported by grants from the Forschungskommission of the University of Zurich and the EMDO Stiftung (to M.H.).
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