Gender differences in the relationships among obesity, adiponectin and brachial artery distensibility in adolescents and young adults



Obesity-related cardiovascular diseases (CVDs) are a major cause of cardiovascular (CV) mortality. Obesity-related reduction in vascular protective adipose-derived proteins, such as adiponectin (APN), has an important role.


We compared brachial artery distensibility (BrachD) with APN, the level of adiposity and other CV risk factors (CVRFs) in 431 post-pubertal subjects (mean 17.9 years). Gender differences in average values were examined by t-tests. Correlations among BrachD, obesity and other CVRFs were examined. Regression analysis was performed to determine whether APN provided an independent contribution to BrachD, while controlling for obesity and other CVRFs.


Male subjects had lower BrachD (5.72±1.37 vs 6.45±1.60% change per mm Hg, P<0.0001) and lower APN (10.50±4.65 vs 13.20±6.53; all P<0.04) than female subjects. BrachD correlated with APN (r=0.25, P< 0.0001). Both BrachD and APN correlated with measures of body size, including height, weight and body mass index (BMI). Both correlated with higher systolic blood pressure, glucose, insulin and lower high-density lipoprotein cholesterol (all P<0.01). In multivariate analysis, APN, gender, APN*gender and BMI z-score predicted BrachD (r2=0.305). On the basis of gender difference, only BMI z-score was significant for male subjects (r2=0.080), whereas APN and BMI z-score contributed for female subjects (r2=0.242, all P<0.0001).


BrachD is independently influenced by obesity in both male and female subjects. In female subjects, APN exerts an additional independent effect even after adjusting for blood pressure (BP), lipid levels and insulin. Differences in the effect of the APN–adiposity relationship on obesity-related vascular disease may be one reason for gender differences in the development and progression of atherosclerosis.


Atherosclerosis, a major cause of cardiovascular disease (CVD),1 is associated with vascular dysfunction.2 Reproducible noninvasive methods for assessing atherosclerosis-related increase in arterial stiffness include the assessment of brachial artery distensibility (BrachD).3, 4, 5 In adults, BrachD has been associated with congestive heart failure, a definitive adverse CVD outcome.6 In addition, BrachD is reduced in adults with increased coronary artery calcium, a measure of advanced atherosclerosis.7 In healthy young adults3 and adolescents,8 BrachD is reduced in the presence of obesity. Despite these associations, the pathophysiological factors that contribute to compromises in BrachD and lead to the development and progression of atherosclerosis are poorly understood.

Recent work suggests that obesity-related reduction in adiponectin (APN) level may have a role in the development of decreased vascular function9 and atherosclerosis.10 APN is a 244-amino acid protein secreted by adipose tissue that has potent anti-inflammatory effects.11 APN levels are low in subjects with coronary artery disease compared with controls12 and have been associated with multiple cardiovascular risk factors (CVRFs), including obesity.13 Data from animal models show a protective effect of APN on the vascular system by preservation of the capacity of endothelium to release nitric oxide in response to stress14 and inhibition of smooth muscle cell proliferation in the arterial wall.15 These data suggest a potential role of APN in mediating human vascular distensibility.

However, given the strong level of correlation between adiposity (that is, body mass index (BMI)) and APN, the question remains as to whether or not both adiposity and APN independently contribute to the variability in BrachD. To address this issue, we measured BrachD in a large bi-racial population of healthy adolescents and young adults to identify associations between the levels of BrachD, APN and the level of adiposity. As APN levels vary by sex, we also sought to determine whether the relationship differed by gender.

Materials and methods

Study population

The study population consisted of 431 subjects aged 14–21 years (mean age 17.9 years; 43% male, 42% non-Caucasian), who were part of the ongoing Princeton School District study (PSD). PSD was a longitudinal, population-based study of the natural history of obesity, insulin resistance and diabetes in a large urban–suburban school district in Cincinnati.16 To enter PSD, the subjects had to be in the fifth through twelfth grades in 2001, have no known chronic disease and be not taking any medication known to affect carbohydrate metabolism. Pregnant women were excluded from PSD. Data were collected yearly on all participants.

The subjects included in these analyses were a sample of the cohort that participated in PSD in 2004 (the fourth year of data collection). Of the 2501 students screened, 1236 were randomly selected to undergo APN analyses. This cohort did not differ from the overall PSD study population with regard to age, sex, race, adiposity or family history of diabetes.17 Once BrachD testing was added to the screening procedure, it was performed on all subjects. A total of 470 subjects had both APN and BrachD. For this study, only post-pubertal subjects were included to eliminate the difference in plasma APN concentration found between pre- and post-pubertal subjects, yielding a total of 431 subjects.18 Pubertal stage was defined based on the use of sex steroid levels and the age of menarche for girls and axillary hair distribution for boys.19 All participants with a fasting plasma glucose of 100 mg per 100 ml (5.5 mmol l−1) or 2-h post-glucose load plasma glucose concentration of 140 (7.8 mmol l−1) were excluded.

The protocol was reviewed and approved by the Institutional Review Board at Cincinnati Children's Hospital. Written informed consent was obtained from the participant if the subject was >18 years of age or from the parent or guardian if the participant was <18 years of age. Written assent was obtained from all participants >11 years of age but <18 years of age.


After written informed consent was obtained, trained personnel obtained two measures of height using a portable stadiometer (RoadRod model, Quick Medical, North Bend, WA, USA, or Accustat, Genentech, San Francisco, CA, USA). Weight was also measured twice using a digital scale (770, SECA, Hanover, MD, USA). The average of the two measures of height and/or weight was used in the analyses. BMI was calculated as kilograms per meter squared, and BMI percentiles and z-scores were determined using the updated growth charts from the Centers for Disease Control and Prevention.20


Venipuncture was performed after a minimum of 10-h fast. Plasma glucose was measured using a Hitachi model 704 glucose analyzer with intra- and inter-assay coefficients of variation of 1.2 and 1.6% respectively (Roche Hitachi, Indiannapolis, IN, USA).17 Plasma insulin was measured by radioimmunoassay using an anti-insulin serum raised in guinea-pigs, 125I-labeled insulin (Linco, St Louis, MO, USA) and a double-antibody method for separating bound from free tracer. This assay has a sensitivity of 2 pmol and intra- and inter-assay coefficients of variations of 5 and 8%.17 Hyperinsulinemia was designated as a fasting insulin level that was >90th percentile for lean subjects in the study population. Subjects with insulin levels 90th percentile for lean subjects were classified as normal insulinemic. APN levels were determined using a radioimmunoassay kit (Linco Research, St. Charles, MO, USA) that has a detection range from 1 to 200 ng ml−1. It requires a 1:500 dilution of the plasma sample, as plasma APN levels are typically of the range 0.5–25 μg ml−1.21 The intraassay coefficient of variation ranged from 1.8 to 6.2%, depending on the concentration, with an interassay coefficient of variation from 6.9 to 9.3%. Measured APN levels were reconverted into μg ml−1 (actual plasma levels) using the procedures suggested by the kit manufacturer, to correct for dilution. An average of two measurements was used in the analysis. Appropriate negative controls and standard curve samples were used to ensure the accuracy of the assay measurements.

Blood pressure (BP) and brachial artery distensibility

After 5 min of rest, trained personnel obtained three measures using a DynaPulse pathway instrument (PulseMetric, Inc., San Diego, CA, USA). Subject demographics were entered into a personal computer interfaced to the DynaPulse pathway instrument. A BP cuff appropriate for the subject's upper arm size was applied.22 Three automatic BP recordings of systolic, diastolic, mean arterial BP, heart rate and brachial artery pressure curves were obtained. The curves were uploaded to the online automated system for calculation of BrachD using the technique of pulse waveform analysis.3 The DynaPulse pathway instrument derives brachial artery distensibility using the technique of pulse dynamic analysis of arterial pressure signals obtained from a standard cuff sphygmomanometer.4 The pressure waveform is calibrated and incorporated into a physical model of the cardiovascular system, assuming a straight tube brachial artery and T-tube aortic system. Validation studies of this method have been published earlier.4, 5 The correlation between compliance measurements obtained during cardiac catheterization and brachial artery compliance derived with the noninvasive method was high (r=0.83). Clinical reproducibility studies showed that the intraclass correlation coefficient for arterial compliance was 0.72 and other analyses indicated that most of the variability in measurement was due to interindividual variation.3 Although body size is used to estimate baseline brachial artery diameter for calculation of compliance, distensibility is equal to compliance divided by baseline brachial artery diameter. Therefore, body size is present in both the numerator and the denominator of the distensibility equation. This results in calculation of a vascular measure that is independent of body size and baseline brachial artery diameter results.3

Statistical analyses

All analyses were performed using statistical analyses software (version 9.1, SAS Institute, Cary, NC, USA).23 For all analyses, a P-value of 0.05 was considered significant. Average values for demographic, anthropometric, laboratory and hemodynamic variables were obtained for the entire group and also by gender because gender differences in BrachD were noted earlier in adults3 and children.8 Variables were examined for extreme outliers and the shape of each variable's distribution was examined. Variance-stabilizing transformations were applied as needed before additional analyses. The t-test was applied to evaluate mean differences by gender and race. Pearson's correlation coefficients were obtained between BrachD and important continuous covariates, including anthropometrics, blood pressures, heart rate, fasting glucose, insulin and lipids.

Before performing multiple regression analysis, BrachD was adjusted for pulse pressure. Removing the influence of distending pressure on BrachD8 allows for examining the properties of the arterial wall among individuals with different baseline blood pressures. Multiple regression modeling was then performed to determine whether the key independent variable of APN provided a significant contribution to BrachD, after controlling for important covariates. Covariates in the initial model included variables that were correlated with BrachD in univariate analyses. In addition, because gender24 and obesity25 affect plasma APN concentrations, an APN*gender interaction term and an APN*z-score of BMI interaction term were also included. Care was taken to ensure that all assumptions for regression were satisfied, including linearity in the relationship between covariates and BrachD and the homogeneity of variance. Analyses also showed that there were no influential outliers or colinearity among independent variables. For all models, a change in the β-slope coefficient for the key independent variable of APN of >20% was considered as evidence for confounding. Covariates were selected for the initial model if they were significantly correlated with BrachD in bivariate correlation analyses and were not mathematically colinear with other terms in the model (that is, only BMI was included as it is calculated from height and weight, and only pulse pressure was included as it is calculated from systolic and diastolic blood pressure). The significance of each covariate in the initial model was assessed and the nonsignificant terms were removed by backward elimination until all remaining covariates or their interaction effect modifier terms were significant. Regression diagnostics to determine the robustness of the model fit were then performed. The final model was then examined for the significance of the key independent variable.


Gender differences in the cohort

Table 1 lists the average demographic, anthropometric, laboratory and hemodynamic variables stratified by gender. Male subjects were taller, heavier and had higher BP levels, wider pulse pressure and slower heart rate, but there was no BMI difference by gender. Although men were 0.6 years older than women (P<0.02), this difference is not clinically relevant. Men differed from women (all P0.04) by way of lower BrachD (5.72±1.37 vs 6.45±1.60% change per mm Hg, P0.0001), plasma APN (10.50±4.65 vs 13.20±6.53 μg ml−1), plasma insulin and high-density lipoprotein cholesterol, but higher glucose and total cholesterol. Although the normal values for BrachD in youth have not been established, our means are similar to the values obtained in a larger sample published earlier from this cohort (N=582, BrachD=5.8±1.1–7.1±1.2 based on the levels of adiposity and hyperinsulinemia)8 and are also similar to the data obtained in young adults from the Bogalusa Heart Study.26

Table 1 Average results overall and by gender: means and s.d.

Univariate relationships with BrachD and APN

BrachD was associated with plasma APN (correlation coefficient=0.25, P0.0001). There were no systematic differences in the pattern of correlations between the covariates and BrachD or APN. Both were correlated with height, weight, BMI, BMI z-score, systolic blood pressure, mean arterial pressure, pulse pressure, glucose, insulin, high-density lipoprotein cholesterol (all P0.01), and both BrachD and APN differed by race and gender (P< 0.05).

Multivariate models

The initial multiple regression model contained the following covariates: gender, race, glucose, insulin, high-density lipoprotein cholesterol, BMI z-score, APN, APN by gender and APN by BMI interaction terms. Using backward selection, the final model was BrachD=0.12−0.0024 × APN+0.016 × gender−0.019 × BMI z-score+0.0020 × APN by gender interaction term (R2=0.305, P for parameter estimates <0.02 except for gender and APN). Given the significant interaction between APN and gender, multivariate gender- specific models were also evaluated (Figure 1). Interestingly, in men, only BMI z-score was significantly associated with BrachD (BrachD=0.13−0.011 × BMI z-score (R2=0.080)), but in women, both BMI z-score and APN were significantly associated (BrachD=0.16+0.0013 × APN−0.026*BMI z-score (R2=0.242, all P for parameter estimates <0.014)). The overall fit for each model was significant at the P0.0001 level. Substituting HOMA-IR (insulin resistance index determined by homeostatic model assessment) calculated using the method of Sinha and Caprio et al.27 for the fasting glucose and insulin in the models did not alter the result.

Figure 1

Scatter plot of brachial artery distensibility (BrachD) regressed on adiponectin (APN) by gender. The lines represent upper and lower 95th% confidence intervals (CI) for the mean. N=416. All models are significant at P0.0001 and all parameter estimates are significant at P0.014. Men: r2=0.08; BrachD=0.13−0.011 × BMI z-score; women: r2=0.24; BrachD=0.16+0.0013 × APN−0.026 × BMI z-score.


Although earlier studies have shown the importance of adiposity and APN in vascular distensibility, no study has examined the joint effects of these two factors. We have shown that plasma APN concentration and adiposity are both important contributors to BrachD. However, upon further examination, we showed that the relationship between APN and BrachD, independent of adiposity, is only present in female subjects. To the best of our knowledge, the association of APN and BrachD has not been reported earlier in healthy adolescents. In addition, men had lower BrachD than women. Although BrachD correlated with traditional CVRFs in both genders, only adiposity remained in the multivariate models, suggesting that the relationship between other CVRFs and BrachD is secondary to adiposity and APN. These data suggest that the most important contributor to arterial stiffness is adiposity, with APN having an independent role only in female subjects. Therefore, absolute or relative hypoadiponectinemia may be one mechanism that mediates gender differences in CVD event rates.

Reports in adults have documented the adverse impact of obesity on arterial stiffness. Even after adjusting for mean distending pressure, overweight subjects show increased carotid and aortic stiffness.28 In pediatrics, there is a small but growing body of literature relating adiposity to decreased distensibility in the brachial8, 29 and carotid arteries.30 Specifically, Whincup et al.29 reported a strong, graded, inverse relationship between brachial distensibility and DBP, adiposity and fasting insulin in adolescents in the United Kingdom. Our data confirm that adiposity is related to increased arterial stiffness, but also suggests that obesity alone does not explain gender differences in vascular function.

Other investigators have also found gender differences in arterial stiffness. In adults, central arterial stiffness (pulse wave velocity)31 and carotid stiffness (Young's elastic pressure modulus)32 differ significantly by gender. In addition, other studies have shown that the brachial and other arteries also have adversely lower levels of distensibility in men when compared with women3, 33 Few data are available regarding brachial artery distensibility and gender in pediatric subjects. In an earlier study, we found lower BrachD in male compared with female adolescents. Gender remained a significant independent contributor to the variance in BrachD in multivariate models adjusted for BP and glucose.8

Investigators have hypothesized that gender differences in vascular function are entirely due to differences in sex hormones. This hypothesis is supported by the findings of BP-independent improvement in pulse wave velocity with estrogen replacement therapy in post-menopausal women,34 the correlation between changes in serum estrogen compounds and progression of carotid atherosclerosis,35 and improvement in carotid stiffness with administration of estrogen.36 In addition, data in children support the estrogen hypothesis in that gender differences in large artery stiffness occurred only after puberty was complete.37 Our finding of gender differences in the relationship between APN and vascular function in adolescent female subjects suggest that adipocytokines, in addition to sex hormones, may be important in explaining gender differences in vascular function and clinical outcomes.

Both animal and human research provide evidence that APN can affect vascular structure and function. Animal studies have shown that APN penetrates the subendothelial space of the vascular wall after injury11 and decreases the adhesion of moncocytes to the injured vessel.12 APN also suppresses the accumulation of lipid in monocyte-derived macrophages, thus preventing their conversion to lipid-laden foam cells.38 Conversely, hypoadiponectinemia exacerbates neointimal thickening in injured arteries by allowing the proliferation and migration of vascular smooth muscle cells into the intima of vessels.15

In studies of adults, hypoadiponectinemia is associated not only with the presence of advanced coronary atherosclerosis,10, 39, 40 but also with traditional CVRFs, including hypercholesterolemia,17 diabetes41 and hypertension.42 Investigators have hypothesized that the reason that angiotensin II receptor blockers are more effective in treating hypertension than other classes of drugs is because of their ability to increase APN levels.43 Hypoadiponectinemia may also have direct vascular effects by way of causing a decrease in endothelial function, resulting in a decrease in the capacity of the endothelium to release nitric oxide in response to stress.14 Although metabolic derangements, such as carbohydrate intolerance and hyperinsulinemia, are commonly found with low levels of APN, studies found a relationship between endothelial dysfunction and APN to be independent of insulin resistance44 or diabetic status.45 Our observation that APN is a determinant of BrachD in female subjects despite their higher HOMA-IR values supports the conclusion that the APN–arterial stiffness relationship is independent of insulin resistance. In children, hypoadiponectinemia has also been associated with CVRFs.13, 17, 46 Therefore, the well-described association between endothelial dysfunction and both CVRFs and adverse events47 may be mediated in part by low plasma APN concentrations.

Similar to our findings, other studies have reported the relationships between plasma APN concentration and arterial function. Multiple adult studies have shown impaired endothelial function associated with hypoadiponectinemia.44, 45 Plasma APN level was also found to be an independent contributor to stiffness of the carotid artery (β-stiffness index) in healthy nondiabetic subjects,48 and of the aorta (pulse wave velocity) in patients with uncomplicated hypertension.9 Similarly, in children, low plasma APN concentrations were associated with increased carotid intima–media thickness,49 a condition associated with increased arterial stiffness.50 In contrast, when Singhal et al.51 examined the relationship among APN and endothelial function and arterial distensibility, no relationship was found. However, this study was performed in a cohort that was predominantly premature, making these data less generalizable to healthy adolescents born at term. Our data relating APN to vascular function in healthy adolescents, independent of other CVRFs such as fasting insulin level, HOMA-IR and lipid levels, suggest that a direct relationship between APN and arterial stiffness does exist.

Gender differences in APN are well documented. APN levels fall with increasing age because of changes in sex hormones and growth factors associated with pubertal development.52 However, the degree of puberty-related decline differs by gender. Therefore, only post-pubertal, but not pre-pubertal girls have higher APN levels than their male counterparts.24 These male/female contrasts may relate to the gender differences in peroxisome proliferator-activated receptor (PPAR) levels documented in animals53, 54 that may occur early in fetal development55 or, as seen in murine studies, estrogen may interact with PPAR-α signaling or directly stimulate PPAR target genes.56 PPAR-γ ligands regulate APN expression in vitro57 and human studies show increases in APN levels in subjects with obesity or type 2 diabetes mellitus with administration of agents such as troglitazone.58 Therefore, one potential mechanism for the gender differences in post-pubertal adolescents seen in our study may result from estrogen-related increase in PPAR activity leading to higher APN levels and higher BrachD in female subjects.

This study has a number of limitations. First, the cross-sectional design in predominantly post-pubertal adolescents does not allow examination of time- and puberty-related changes in APN and how they relate to changes in arterial function. Furthermore, the proportion of the variability in BrachD explained by our model is low, especially in male subjects. This suggests that other environmental factors, perhaps diet and physical activity, which were not measured in this study, may have a role in determining both BrachD and APN. One such factor not measured in our study and known to affect APN levels is body composition.59 Furthermore, genetic influences were not assessed and APN levels are known to be highly heritable.60 Finally, this is a young cohort who would be expected to have less advanced atherosclerosis, and hence the results may not be generalizable to an adult population with more advanced disease. Although this device has only been validated against catheterization data in adults,4 our subjects, with an average age of 18 years, were predominantly adult size, thus reducing concern regarding any systematic differences in the applicability of the original validation results.

In conclusion, this study shows that gender and adiposity are important contributors to BrachD in adolescents, with plasma APN having a strong, independent effect on BrachD in female subjects only. Therefore, the contribution of absolute or relative hypoadiponectinemia needs to be considered as one mechanism that may be responsible for gender differences in CVD event rates. This, in turn, may suggest new strategies for prevention of myocardial infarctions and strokes.

Conflict of interest

The authors declare no conflict of interest.


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We gratefully acknowledge the work of the PSD research team and the administration, staff, teachers, students and parents of the Princeton School District. The authors had full access to the data and take responsibility for its integrity. We have read and have agreed to the paper as written. This work was supported by NIH grants DK59183, 0M01 RR 08084, NHLBI (5K23HL80447) and a Trustee Grant from Cincinnati Children's Hospital.

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Correspondence to E M Urbina.

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Urbina, E., Khoury, P., Martin, L. et al. Gender differences in the relationships among obesity, adiponectin and brachial artery distensibility in adolescents and young adults. Int J Obes 33, 1118–1125 (2009).

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  • elasticity
  • pediatrics
  • sex
  • risk factors
  • brachial artery

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