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Monash University Department of Medicine, Dandenong Hospital, Melbourne, Victoria 3175, Australia
Address all correspondence and requests for reprints to: Dr. Helena Teede, Department of Vascular Science and Medicine, Dandenong Hospital, David Street, Dandenong Victoria 3175, Australia. E-mail: helena.teede{at}med.monash.edu.au.
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Abstract |
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Objective: The aim of this study was to clarify whether overweight women with PCOS have an increased prevalence of cardiovascular risk factors and early CVD, compared with age- and body mass index-matched controls, to determine the contribution of PCOS per se to CVD status.
Design and Patients: This was a case control study of 100 overweight women with PCOS and 20 subjects of similar body mass index and age.
Main Outcome Measures: Noninvasive markers of early CVD [carotid intimal media thickness, pulse wave velocity (PWV), and brachial arterial flow-mediated vasodilation] were measured. Metabolic parameters studied included insulin, glucose, C-reactive protein, lipids, and androgens.
Results: Subjects with PCOS had elevated testosterone (2.5 ± 0.2 vs. 1.3 ± 0.1 nmol/liter), dehydroepiandrosterone sulfate (4.9 ± 0.3 vs. 3.6 ± 0.4 mmol/liter), fasting insulin (19.6 ± 1.4 vs. 6.8 ± 0.8 µU/ml), and homeostasis model assessment of IR (4.1 ± 0.3 vs. 1.3 ± 0.2), compared with controls. In addition, those with PCOS had elevated cholesterol (5.1 ± 0.1 vs. 4.6 ± 0.2 mmol/liter) and triglycerides (1.4 ± 0.1 vs. 0.9 ± 0.1 mmol/liter), whereas there were no differences in either C-reactive protein or 24-h ambulatory blood pressure parameters. Subjects with PCOS also had increased arterial stiffness (PWV, 7.4 ± 0.1 vs. 6.6 ± 0.2 m/sec) and endothelial dysfunction (flow-mediated vasodilation, 9.8 ± 0.4 vs. 13.3 ± 0.9), compared with controls. There was no difference in mean intimal media thickness between the groups. Stepwise regression in PCOS subjects showed that IR and lipids were independent predictors of PWV.
Conclusion: Overweight women with PCOS have increased cardiovascular risk factors and evidence of early CVD, compared with weight-matched controls, potentially related to IR.
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Introduction |
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Despite the adverse CV risk profile observed in PCOS, evidence from limited long-term outcome studies has failed to consistently demonstrate increased CV mortality, compared with control populations (2, 3). The inconclusive results from these studies on CV end points are not surprising because data are retrospective, hampered by selection bias, long follow-up periods, poor documentation of clinical CV events, and a relatively young age of women at follow-up.
In the absence of adequate outcome studies, surrogate markers of early CV disease have been examined to determine whether women with PCOS have evidence of subclinical CV disease, compared with controls. Carotid intima-medial thickness (IMT), an established marker for early structural atherosclerotic disease, is predictive of future CV events (4). Pulse wave velocity (PWV), a marker of arterial stiffness, has been shown to be predictive of CV mortality in chronic renal failure (5) and in those with essential hypertension (6, 7). Brachial artery flow-mediated vasodilation (FMD), a marker of endothelial function, is influenced by a variety of CV risk factors (8, 9, 10, 11) and is predictive of future CV events. Together these measurements provide a comprehensive noninvasive assessment of arterial structure and function and an assessment of early CV disease. There are limited small studies that have examined these markers in PCOS populations, but results have been inconsistent. Two studies of carotid IMT have demonstrated an increase in carotid disease only in older women with PCOS (12, 13), whereas a recent study has suggested early CV disease in women aged younger than 35 yr (14). Previous noninvasive studies of endothelial function have also produced conflicting results (15, 16, 17), whereas to date there has been only one small study noting an increase in PWV in women with PCOS, compared with controls (18).
In this setting, the aim of this study was to clarify whether overweight women with PCOS have an increased prevalence of CV risk factors, compared with a population with similar age and body mass index (BMI), to determine the contribution of PCOS per se to CV risk factors. Second, we aimed to determine whether overweight women with PCOS have evidence of subclinical CV disease assessed using well-validated markers of arterial structure (carotid IMT) and function (PWV and brachial artery FMD).
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Subjects and Methods |
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We recruited 100 overweight (BMI > 27 kg/m2), premenopausal subjects with PCOS and 20 control subjects from community advertisements. PCOS was documented by a history of perimenarchal onset of oligo- or amenorrhea together with clinical manifestations of hyperandrogenism (hirsutism, acne, or both) and elevation of at least one circulating ovarian androgen. Secondary causes of hyperandrogenism such as hyperprolactinemia and thyroid disease were excluded in all subjects. Specific adrenal disorders were excluded clinically and, where indicated, biochemically.
The control subjects all had a regular menstrual cycle (25–35 d) and no clinical or biochemical evidence of hyperandrogenism.
PCOS subjects were primarily recruited for a subsequent interventional study with baseline data collected for the purpose of this comparative study. Subjects with similar age and BMI were recruited as controls.
All subjects were nonsmokers and had been off hormonal or insulin-modifying therapy for at least 3 months before entry to the study. Subjects with diabetes were excluded. Pregnancy tests were negative in all subjects before enrollment in the study.
The Southern Health Research Advisory and Ethics Committee approved the study, and all participants gave written informed consent.
Study design
If considered to be eligible after telephone screening by an experienced endocrinologist (C.M.), participants were assessed with a detailed medical examination and history after an overnight fast. Waist and hip circumferences were measured at the level of the umbilicus and greater trochanter. The waist to hip ratio (WHR) was calculated as the waist circumference divided by the hip circumference. Hirsutism was assessed by one examiner using the modified Ferriman-Gallwey score in which a score greater than 7 indicates hirsutism (19). The menstrual cycle length of control subjects was based on self-reporting. No formal confirmation of ovulatory status was made. In control subjects data were collected during the follicular phase of the menstrual cycle. In those with PCOS, we were unable to collect data at any specific part of the cycle because of the erratic nature of their cycles.
Fasting blood samples were drawn for testing of glucose, insulin, androgens, C-reactive protein (CRP), and lipids. Insulin resistance was assessed using both fasting insulin levels and the homeostasis model assessment (HOMA) calculation: fasting serum insulin (microunits per milliliter) x fasting plasma glucose (millimoles per liter)/22.5 (20).
All arterial parameters were measured by an experienced research assistant. Studies were performed after a 12-h fast; during this period caffeine-containing drinks were avoided. All studies were performed in a darkened, quiet, air-conditioned clinical laboratory after 10 min rest in the supine position. Published repeatability data from our laboratory demonstrates the accuracy and repeatability of the vascular end points (21). After this, a blood pressure monitor was fitted for 24-h ambulatory blood pressure recording.
Arterial parameters
PWV. PWV was determined from recorded pressure waveforms over the aortofemoral arterial segments (21). Pulse transit time was defined as the time between the foot of simultaneously recorded pressure waves, occurring at the end of diastole and the beginning of systole, averaged over 10 cardiac cycles. Velocity was derived from computer-generated pulse transit times and measured distances between the two recording sites, as previously described (21). PWV was calculated based on the formula:
PWV = D/
t (meters per second) where D = distance, t = time interval.
Carotid IMT. This parameter was derived from noninvasive ultrasound of the common carotid arteries, using a high-resolution ultrasound machine (Diasonics DRF-400, Diasonics Pty. Ltd., Sydney, Australia) with 7.5-MHz mechanical sector transducer (7.5-SPC). The IMT was defined as the distance between the blood-intima and media-adventitia boundaries on B-mode imaging (4). The far wall of the right common carotid artery 1 cm proximal to the origin of the bulb was selected because it has been shown to be the most reproducible (22). Three B-mode images were recorded from different angles and then digitized and saved on computer via a customized computer program (A House of Windows, C. Smith, Auckland, New Zealand) as previously described (23). Brachial blood pressure was recorded every 5-min throughout the imaging period using a Dinamap device (CRITIKON, 1846 SX, Tampa, FL). Image analysis was performed using a standardized measurement protocol, using 30 data points per subject, by the same sonographer. Measurements were automatically transferred and saved in a database (Quest for Windows, version 2.1). The results are reported as mean common carotid IMT.
Brachial artery FMD. Brachial artery diameter was measured from B-mode ultrasound images captured on a Diasonics DRF-400 machine using a 10-MHz transducer, whereas an electrocardiogram trace was simultaneously recorded. Longitudinal scanning identified the clearest image of the brachial artery above the elbow, with continuous scanning held for 30 sec prior and 4 min after ischemia, induced via a pneumatic tourniquet inflated around the upper arm to 40 mm Hg above systolic pressure for 4 min. Vessel diameter was measured during systole and diastole and averaged over five cardiac cycles. FMD was determined as the percentage change from baseline to 60 sec after ischemia, the point of maximal dilation (21).
Twenty-four-hour ambulatory blood pressure monitoring
Ambulatory blood pressure monitoring was performed using a portable lightweight device (Accutracker, model II; Suntech Medical Instruments, Raleigh, NC). The accuracy of the ambulatory blood pressure monitoring was confirmed in each subject by two simultaneous auscultation and sphygmomanometry; systolic (SBP) and diastolic blood pressure (DBP) readings differed by less than 5 mm Hg. Patients wore the device for 26 h with measurements every 30 min during the day and hourly overnight. Subjects received verbal and written instructions on the monitors and completed a diary to record posture, activity, sleep, medication, and symptoms.
Assays
Dehydroepiandrosterone sulfate (DHEAS) and SHBG were analyzed by immunoassay using Immulite 1000 (EURO/Diagnostics Products Corp. Ltd., Los Angeles, CA) The intra- and interassay coefficients of variation were 7.6 and 8.1% for DHEAS and 4.1 and 5.8% for SHBG. Testosterone was analyzed using a chemiluminescent immunoassay (Beckman Coulter, Fullerton, CA). The intra- and interassay coefficients of variation were 1.67 and 4.78%, respectively. The free androgen index (FAI) was calculated using the equation FAI = (testosterone/SHBG) x 100.
Total cholesterol (TC; coefficient of variation 1.5–3.1%) and triglycerides (coefficient of variation 2.3–5.3%) were measured using enzymatic reagents (DADE Diagnostics, Brisbane, Australia); high-density lipoprotein cholesterol (HDL-C) (coefficient of variation 2.5–2.8%) was measured by homogeneous assay techniques (HDLC-Plus, DADE Diagnostics) adapted to a DADE Dimension RXL chemistry analyzer (DADE Diagnostics). Low-density lipoprotein cholesterol (LDL-C) was calculated using the Friedewald equation [LDL-C = (TC-HDL-C) – (triglycerides/2.2)], adapted to SI units. High-sensitivity CRP (coefficient of variation 1.8–4.3%) was measured using a PETIA technique (flex reagent cartridge dimension). The insulin assay is the AxSYM assay based on the microparticle enzyme immunoassay technology. The sensitivity of the assay is 1.0 µU insulin per milliliter, the cross-reactivity with Proinsulin is 0.016%, and there is no cross-reactivity with C-peptide. Plasma glucose was determined with the glucose oxidase method.
Statistics
Results are expressed as mean ± SE. The differences between groups were assessed by using unpaired t tests. Simple correlations were performed to assess the relationships among vascular, hormonal, and metabolic parameters. Subsequently, where individual correlations achieved statistical significance, variables were entered into a linear regression model. P < 0.05 was considered statistically significant.
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Results |
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). The age and BMI ranges of the PCOS and control subjects were 17.6–46.5 yr and 20–46 yr and 29.4–52.3 kg/m2 and 28.2–47.8 kg/m2, respectively.
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As expected, there was a higher level of testosterone, DHEAS, and FAI as well as a higher Ferriman Gallwey score and longer menstrual cycles in the PCOS group, compared with controls (Table 1
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CV risk factor profile
Subjects with PCOS had elevated fasting insulin levels and HOMA assessment of insulin resistance, compared with control subjects. In addition, there was significantly higher TC and triglycerides in subjects with PCOS (Table 1).
There was no significant difference in the total SBP or DBP loads between the groups from the 24 h ambulatory monitoring. In addition, there was no significant difference between either daytime or nighttime blood pressure parameters between the groups (Table 2).
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CV parameters
Subjects with PCOS had increased arterial stiffness as shown by a 12% increase in mean PWV, compared with controls. This was not explained by any difference in blood pressure at the time of arterial measurements. Mean FMD was significantly reduced in PCOS, compared with controls. There was no significant difference in carotid IMT between the groups (Table 2).
Correlation analyses within the PCOS population
Bivariate correlation analysis in women with PCOS demonstrated that insulin resistance, cholesterol, BMI, and blood pressure were related to PWV. As shown in Table 3, insulin resistance (HOMA) and cholesterol made independent contributions to the variance in PWV, whereas BMI and SBP did not contribute significantly in the regression model.
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Discussion |
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There have been many previous studies that have documented abnormalities of insulin metabolism in both lean and overweight women with PCOS (24, 25, 26, 27). In keeping with these results, we demonstrated that overweight/obese women with PCOS have both fasting insulin levels and a HOMA model of insulin resistance almost three times that of controls. Importantly, these findings occurred despite equivalent BMI and WHR and suggest that PCOS per se and not simply body weight is responsible for these differences. This is consistent with previous reports suggesting that whereas obesity is associated with insulin resistance, individual variation in insulin sensitivity exists largely independent of the degree of obesity (28, 29).
Previous studies of obese women with PCOS have shown that hyperinsulinemia directly reduces SHBG (30). This has led to the suggestion that SHBG might be a surrogate marker of insulin resistance in this population (31). Other studies, however, in both PCOS and non-PCOS populations have suggested that BMI and percentage body fat, rather than insulin, are independent predictors of SHBG (32, 33). These latter studies are consistent with our findings that SHBG was not different in BMI-matched populations despite substantial differences in insulin resistance.
There is some controversy as to whether PCOS per se is associated with hypertension. Two previous studies comparing a total of 38 women with PCOS and age- and sex-matched control populations have found no difference in blood pressure, including 24-h ambulatory monitoring (34, 35). In contrast to this, a single study of 36 women with PCOS vs. 55 BMI-matched controls examined both clinic and 24-h ambulatory blood pressure profiles and found that those with PCOS had higher mean ambulatory blood pressures and higher daytime SBP. These differences persisted after adjustment for body fat distribution and insulin resistance (36). Consistent with Sampson et al. (34) and Zimmermann et al. (35), we did not demonstrate any differences between PCOS women and controls in total daytime or nighttime blood pressure parameters assessed using 24-h ambulatory monitoring. Despite the presence of obesity and insulin resistance in the PCOS population, the average awake and asleep blood pressures were 120/72 mm Hg and 105/59 mm Hg, respectively. Our study population was relatively young (mean age 33 yr). With advancing age, it is plausible that blood pressure may increase at an accelerated rate in patients with PCOS, given the stimulatory effects of hyperinsulinemia on the sympathetic nervous system and vascular smooth muscle (37) and the changes we have noted in arterial function.
The pattern of dyslipidemia (increased TC and triglycerides with a trend toward higher LDL-C and lower HDL-C) demonstrated in the current study is consistent with previous literature showing abnormal lipid profiles (similar to the dyslipidemia of type 2 diabetes) in PCOS (38, 39, 40, 41).
Our study is the first to comprehensively noninvasively assess both arterial structure and function in overweight/obese women with PCOS. We have demonstrated that, compared with controls, those with PCOS had increased central PWV, consistent with increased arterial stiffness, and reduced FMD, consistent with endothelial dysfunction. Insulin resistance was significantly different between the two groups and in those with PCOS, insulin resistance (HOMA) was found to be an independent predictor of both PWV and FMD. There has been one small previous study (18) demonstrating increased PWV in 19 subjects with PCOS, compared with controls. The authors also suggested that impaired insulin action in the CV system may have an important pathogenic role (18).
The mechanisms through which hyperinsulinemia increases arterial stiffness are not clear; however, insulin is known to have direct effects promoting vascular smooth muscle hyperplasia and collagen synthesis (42), with both factors leading to increased arterial stiffness. Arterial stiffness was positively correlated with serum cholesterol in the current study, supporting previous literature in non-PCOS populations in whom hypercholesterolemia has been shown to be a significant independent determinant of PWV (43). It is also likely that cholesterol adversely affects arterial function through disruption of arterial elasticity secondary to vessel wall fibrosis, plaque accumulation, and calcification (43).
Previous small studies examining endothelial function in women with PCOS have yielded inconsistent results. Mather et al. (15) found no difference in either endothelium-dependent or -independent vasodilation in 18 women with PCOS, compared with age-matched controls. In contrast to this are three other studies (70 subjects in total) suggesting reduced endothelium-dependent vasodilation in both lean and obese women with PCOS, compared with age- and weight-matched controls (16, 17, 44). In support of these findings, treatment of women with PCOS with the insulin sensitizing peroxisomal proliferator-activated receptor-
agonist troglitazone has been shown to restore endothelium-dependent vasodilation to control levels (45). Our results provide additional evidence of endothelial dysfunction in women with PCOS. Moreover, as with PWV, we found that insulin resistance was a significant predictor of reduced FMD.
The mechanisms involved in the observed endothelial dysfunction remain unclear; however, insulin resistance is likely to be integrally involved. The association between insulin resistance and endothelial dysfunction has been demonstrated consistently in subjects with type 2 diabetes mellitus, obesity (46), and the metabolic syndrome (47) and in children of parents with type 2 diabetes (48). Thus, at a mechanistic level, endothelial dysfunction appears to occur early in the insulin-resistant state; the progression of insulin resistance to diabetes parallels the progression of endothelial dysfunction to atherosclerosis (49). There are several mechanisms through which insulin resistance can adversely affect the endothelium. Insulin-resistant states are characterized by increased production of free fatty acids and proinflammatory cytokines such as TNF
and leptin, which contribute to endothelial dysfunction. There is also evidence to suggest that insulin resistance induces increased oxidant stress, which may have an important pathogenic role (50). The observed relationship between insulin resistance and endothelial dysfunction in overweight women with PCOS supports the suggestion that hyperinsulinemia per se represents a significant CV risk factor.
In the current study, we did not demonstrate any difference in carotid IMT between the two groups. Two previous studies (total of 110 women) have demonstrated increased age- and BMI-adjusted carotid IMT in women with PCOS aged older than 40 yr, compared with controls (12, 13). The current study population was younger and did not allow for a subgroup analysis in those older than 40 yr. A more recent smaller study of 19 women with PCOS aged younger than 35 yr (mean 29 yr) found that those with PCOS had increased carotid IMT, compared with controls, after adjustment for age, blood pressure, insulin, and lipids (0.54 ± 0.1 vs. 0.40 ± 0.1 mm, P < 0.006) (14). In our study, the average IMT in those with PCOS was 0.55 mm, consistent with that found in the study by Lakhani et al. (14); however, our BMI-matched control population had a higher mean IMT of 0.54 mm. Potentially the more obese control subjects in our study, compared with those of Lakhani et al. (14) (BMI 36.7, compared with 24.2 kg/m2), may have accounted for this difference.
Carotid IMT is a structural marker of arterial disease, which becomes abnormal later in the progression to atherosclerosis than functional markers such as PWV or FMD. It is not surprising that in a relatively young cohort of women, we have demonstrated differences only in early functional markers of arterial disease (PWV and FMD), which occur earlier in the disease progression.
Although our study has demonstrated differences between PCOS and controls, the small number of controls may have limited our ability to detect differences in other parameters such as IMT. The parameters we studied are surrogate markers of early CV disease, which have been shown in other populations to predict future events; however, the results cannot necessarily be extrapolated to clinical CV events. In addition, the HOMA assessment of insulin resistance is not the gold standard for assessment of insulin action but is one of the best methods available for studies involving a large number of subjects. Despite these limitations, our study has demonstrated unequivocal differences between the groups.
Conclusion
Our study adds significantly to the growing body of evidence suggesting that in women with PCOS, CV risk factors such as hyperinsulinemia and dyslipidemia have long-term detrimental effects on the arterial system. Until the results of adequately designed studies based on clinical end points are available, these findings support the theory that women with PCOS are at increased risk of CV disease. Clinicians should be aware of these associations and consider more aggressive screening and management of these CV risk factors. In addition, the impact of medical therapies on long-term CV risk warrants consideration. To this end, interventional studies are required to clearly document the impact of both lifestyle and medical therapies on CV risk factors and surrogate markers of early CV disease in women with PCOS.
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Footnotes |
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Abbreviations: BMI, Body mass index; CRP, C-reactive protein; CV, cardiovascular; DBP, diastolic blood pressure; DHEAS, dehydroepiandrosterone sulfate; FAI, free androgen index; FMD, flow-mediated vasodilation; HOMA, homeostasis model assessment; IMT, intima-medial thickness; PCOS, polycystic ovary syndrome; PWV, pulse wave velocity; SBP, systolic blood pressure; TC, total cholesterol; WHR, waist to hip ratio.
Received January 4, 2005.
Accepted July 7, 2005.
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