The aim of the present study was to explore the hypothesis that local measurements of the common carotid artery are associated with body composition. The measurements used were thickness (cIMT) and stiffness, i.e., arterial distensibility, Young’s elastic modulus, and β stiffness index. The study was carried out in a subsample of healthy young women and men from the Swedish Lifestyle, Biomarkers, and Atherosclerosis (LBA) Study.

A highly specific vessel analysis of the common carotid artery [16] was performed on 220 individuals, randomly selected from the cross sectional LBA study (n = 834) [17]. The selected individuals were included in the subsample (here after called the LBA subsample) based on the Sphygmocor quality criteria and the ultrasound image. The quality criteria (pulse length variation, pulse height variation, shape deviation, and diastolic variation) in the Sphygmocor equipment needed to be fulfilled with as high quality index as possible (maximum 100%, no one had an index below 80%), and the near and far wall boundaries of the carotid artery needed to be clear and visible in the ultrasound image. The LBA subsample is representative of the LBA study with respect to gender distribution and also evenly distributed across the data collection period. The edge wall tracking of ultrasound B-mode recordings was not performed in all individuals in the LBA study due to technical and time limitations. The subsample was aiming to reach a fourth of the total LBA population. The individuals included in the LBA study were self-reported healthy without chronic disease, non-smoking, Swedish, young adults, 18–25 years old. All individuals gave their written consent to participate and the study was approved by the Regional Ethics Committee in Uppsala, Sweden (Dnr: 2014/224).

Height, weight, percentage of body fat, and waist circumference were measured with the subject in a fasting state. Height was measured to the nearest 0.5 cm with a fixed stadiometer. Waist circumference was measured with a measuring tape to the nearest 0.5 cm [18]. Weight was measured and percentage of body fat was calculated using an impedance body composition analyzer (Tanita Europe B.V. Tanita BC-418 MA, Amsterdam, Netherlands). BMI (kg/m²) was calculated and categorized into BMI ≤25 and BMI > 25.

Blood samples were collected with the individuals in a fasting state and venipuncture was performed after a resting period of approximately 20 min. Total cholesterol and glucose (mmol/L) were analyzed on an Ortho Clinical Diagnostics™ (Vitros 5.1TM FS; Clinical Chemistry Instruments, Raritan, NJ, U.S.A). Insulin (mU/L) was analyzed on an Architect i2000SR instrument from Abbott (Abbott Park, IL, USA). Analyses were performed at the accredited clinical chemistry laboratory at Örebro University Hospital. For more details, see the previously published description [17].

Brachial blood pressure was measured with an oscillometric, non-invasive blood pressure method, after 10 min rest in a supine position, using a digital automated device (GE Healthcare, Dinamap V100, Buckinghamshire, UK) as earlier described [17]. Ultrasound measurements of the right common carotid artery were performed using a high-resolution B-mode system, (GE Healthcare, Vivid E9, Chicago, Illinois, US) with a 12 MHz linear array transducer. The subjects were examined in a supine position with their heads slightly extended and turned approximately 45 ^o to the left, according to guidelines [19, 20]. The right carotid artery was scanned with transverse and longitudinal views and a Doppler flow measurement was made to verify the location of the examination. A simultaneous ECG-recording was made during the ultrasound measurements. An automatic analysis of the common carotid artery distention and cIMT was performed with edge wall tracking of ultrasound B-mode recordings, see Fig. 1, using custom build Matlab software developed at Maastricht University Medical Centre (MUMC, Maastricht, The Netherlands) [16]. The software is based on previously published algorithms [21]. Analyses were performed by ultrasound specialist, blinded for the study. To assess the reproducibility of ultrasound edge wall tracking analyses, the coefficient of variation (CV) was calculated as ((standard deviation/mean) × 100). The CV for mean distention and mean cIMT were 6,0% and 6,9% respectively.

The right common carotid artery was also examined with applanation tonometry, SphygmoCor (AtCor Medical Pty Ltd., SphygmoCor, Sydney, Australia), in conjunction with a pulse wave velocity examination. Before the tonometry examination was performed, brachial blood pressure measurements were repeated to ensure a representative and stable blood pressure. The common carotid pulse waves were recorded with the subject in a supine position, in a temperature-controlled room (22-24 °C). At least three measurements were made on each test subject. The carotid blood pressure was obtained by a calibration method using the brachial artery pressure and wave, which is based on the observation, assuming that mean and diastolic blood pressure are constant throughout the large artery tree [22]. The measurement with the highest quality index on the SphygmoCor equipment [23] (pulse length variation, pulse height variation, diastolic variation and shape deviation) was reported for each subject.

Calculations of the local stiffness measurements, arterial distensibility, Young’s elastic modulus, and β stiffness index were made according to the formulas presented in the background [1].

All statistical calculations were made in females and males separately, and performed using IBM SPSS Statistics version 24 for Windows (IBM Corp, Armonk, New York, USA). Normal distribution was checked for all variables with Kolmogorov Smirnov test and Shapiro-Wilk test. Descriptive data are presented as mean and standard deviation. Unpaired Student’s t-test was used to analyze differences between the sexes and to analyze mean differences between the total LBA subsample (n = 220) and the LBA population (n = 834).

Simple linear regression were used to study associations between cIMT and the local stiffness measurements as dependent variables, and body composition measurements and blood pressure as independent variables, respectively. Correction for multiple comparisons were made according to Bonferroni. Based on the number of tests, the Bonferroni correction resulted in the following: Significant level < 0.05 requires p < 0.00104, significant level < 0.01 requires p < 0.00021, and significant level < 0.001 requires p < 0.000021. The local stiffness measure which had the strongest associations to the independent variables mentioned above (i.e. body composition measurements and blood pressure), was further explored in multiple regression analyses to assess the effect of the different body composition measurements on the dependent variable. The included variables, MAP and age, have earlier been described in the literature as determining factors for local stiffness measurements [1].

A regression model validation was made. Exclusion criteria’s were high values on Leverage and Cook’s distance, indicating outliers that could disturb the regression model, and high values on Variance inflation factors (VIF), indicating multicollinearity.

Unpaired Student’s t-test was used to study comparisons of mean values of arterial distensibility between the individuals with BMI ≤ 25 and the individuals with BMI > 25. Furthermore, the LBA subsample was split into the categories high body fat and low body fat by the median (gender specific) and an unpaired Student’s t-test was used to study comparisons of mean values of arterial distensibility between the individuals in the high body fat category and the low body fat category. The LBA subsample was also split into the categories high arterial distensibility and low arterial distensibility by the median (gender specific) and an unpaired Student’s t-test was used to study comparisons of mean values of percentage of body fat between the individuals in the high arterial distensibility category and the low arterial distensibility category.

The LBA subsample (n = 220) did not differ in basic characteristics from the full LBA study population (n = 834) [17]. Basic characteristics and results of body composition measurements, blood pressure, carotid diameter, cIMT, and biomarkers are presented in women and men separately. In most variables, with exceptions for age, BMI, brachial diastolic blood pressure, and insulin there were significant differences between women and men, p < 0.05 (Table 1). There were significant differences between women and men (p < 0.001) in the calculated local stiffness measurements (Table 2). Women had higher arterial distensibility and lower Young’s elastic modulus, and β stiffness index than men, all p < 0.001.

Simple linear regression models (Table 3) demonstrate significant inverse associations between arterial distensibility and BMI in both women and men (p < 0.01 and p < 0.05 in women and men, respectively). Significant inverse associations were also found between arterial distensibility and waist circumference in women (p < 0.05), and between arterial distensibility and percentage of body fat in men (p < 0.05). Among the blood pressure variables, significant inverse associations were seen between arterial distensibility and brachial systolic blood pressure in both women and men (p < 0.001 and p < 0.05 in women and men, respectively). Young’s elastic modulus was positively associated with BMI (p < 0.05) in women, but not in men. β stiffness index was also positively associated with BMI (p < 0.01) in women. No associations was seen between cIMT and the independent variables, (Table 3). Based on the observation that arterial distensibility was the local stiffness measurement that was significantly associated to most body composition measurements, in both women and men, the arterial distensibility was chosen as dependent variable in the multiple regression analyses to explore which of the body composition measurements, BMI, percentage of body fat, and waist circumference that contributed mostly to arterial distensibility.

Multiple regression analyses, with the variables known to affect arterial stiffness (age and MAP) [1] were performed with arterial distensibility as dependent variable and body composition measurements, BMI, percentage of body fat, and waist circumference as independent variables in separate models. In addition, the total cholesterol, glucose, and insulin, were also included in the models. The standardized β coefficients showed that BMI contributed slightly more to the variation in arterial distensibility than waist circumference and percentage of body fat in women (standardized β for BMI − 0.277, for waist circumference -0.218, and for body fat − 0.213). In men, the standardized β coefficients showed that BMI and percentage of body fat contributed equally to the variation in arterial distensibility (standardized β for BMI − 0.440, for body fat − 0.450, and for waist circumference − 0.375). Age, MAP, and the biomarkers contributed less than the body composition measurements to the variation in arterial distensibility in this age group (Table 4). Combining all three body composition measurements (BMI, percentage of body fat, and waist circumference) in a multiple regression model with arterial distensibility as dependent variable was not possible because of too high VIF values in the model validation, indicating multicollinearity between the independent determinants, as expected.

There were significant differences in mean values of arterial distensibilty between the individuals with BMI ≤ 25 and the individuals with BMI > 25, both in women and men. Women and men with BMI > 25 had significantly lower arterial distensibility than the women and men with BMI ≤ 25 (p < 0.001 in both women and men), see Fig. 2. Furthermore, individuals in the high arterial distensibility group (i.e the individuals with more elastic arteries) had lower percentage of body fat. The individuals in the high body fat group had less elastic arteries (p < 0.05 and p < 0.01 respectively).

A decrease of arterial wall elasticity is a useful and early predictor for vascular disease. However, there is no precise direct method for the determination [24]. A lack of standards and the use of many different measures of local stiffness makes it difficult to compare different study results. Of the three local stiffness measurements that were calculated from the common carotid artery in the present study, arterial distensibility was the measure with the strongest associations with the body composition measurements, BMI, percentage of body fat, and waist circumference, in both women and men. The discrepancy between the three different local stiffness measurements is explained by the variables included in the calculations. When taking into account the intima media thickness that is included in the calculation of Young’s elastic modulus, associations with body composition measurements get weaker (in women) or disappear (in men). This could however be either a power issue because of the lower number of male individuals in the LBA subsample or the fact that the variation of cIMT in this young and healthy cohort was very low.

The guidelines recommend using the local blood pressure instead of the brachial blood pressure because of the pulse amplification between central and peripheral arterial sites, especially in young individuals [10]. In the present study, the local blood pressure in the common carotid artery was used in the calculations of the three local stiffness measurements, in opposite to several other studies using the brachial blood pressure [25‐28]. One study reported that the brachial pulse pressure correlated well with the local pulse pressure both in women and men but the correlation was weakest in the youngest age group (r = 0.57) [28]. Since the study population in the present study is between 18 and 25 years, it is of importance to take into account that the stiffness measurements can be affected by the choice of blood pressure (e.g. brachial or carotid) used in the calculations.

Significant differences were found between women and men in the common carotid artery diameter and cIMT, p < 0.001 and p < 0.05 respectively. Women had smaller carotid diameter (as expected) and thinner cIMT than men. In the Young Finns study, the cIMT differed between women and men in the same age group as the LBA subsample, also showing that women had thinner cIMT than men [29]. The same findings are demonstrated in a healthy sub-population in a multi-center study collecting reference intervals for carotid cIMT measured with echotracking [30].

Significant differences (p < 0.001) were also found between women and men in the calculated local stiffness measurements in the common carotid artery. Men had lower mean arterial distensibility and higher mean Young’s elastic modulus, and β stiffness index, indicating stiffer arteries than in women. The findings in the present study are in line with results from the Young Finns Study [27, 31]. They found that men had significantly lower carotid artery distensibility and higher pulse wave velocity, indicating stiffer arteries, compared to women in all age groups (30–36 years, 39–45 years, 46–76 years). The findings are also in line with the gender difference in pulse wave velocity, as a measurement of regional stiffness, which was demonstrated earlier in the LBA study, showing that men had higher pulse wave velocity than women [32].

Reference values for carotid distensibility coefficient, as a measurement of local arterial stiffness, were published in 2015 [28], in healthy test subjects, in the ages 15–85 years. The authors calculated carotid distensibility coefficient with artery area instead of diameter and found a negative and non-linear relationship between the carotid distensibility coefficient and age. When looking at the 50th percentile for the test subjects in the age of 20 years, the mean carotid distensibility coefficient was higher in women than in men, which is in line with the findings in the present study. Reference values from a younger age group, 6–18 years, have also been published, showing age- and sex-specific differences in distensibility measurements from the age of 15 years, were girls having higher mean value of carotid distensibility coefficient than boys [25]

Previous data from the LBA study indicates that young, Swedish adults with obesity and low cardiorespiratory fitness have significantly higher cfPWV than non-obese adults with medium or high cardiorespiratory fitness [32]. The present study, with focus specifically on local stiffness in the common carotid artery, shows partly similar results. In the LBA subsample, women and men with BMI ≤ 25 and with lower percentage of body fat, had significantly higher arterial distensibility, indicating more elastic carotid arteries compared to women and men with BMI > 25 and a higher percentage of body fat. There are many instruments for the assessment of percentage of body fat, however there are no well-established cut-off values for the interpretation of the results.

Multiple regression analyses were performed to explore which of the body composition measurements that contributed mostly to the variation in arterial distensibility. The analyses showed that BMI had the highest impact on arterial distensibility in women, and that BMI and percentage of body fat had equal impact on arterial distensibility in men. One explanation to this gender difference could be the different fat distribution in women and men [33]. A man with a high percent of body fat is more likely to have a central/android fat distribution with fat stored preferentially in the abdominal area. In women it is more common with fat distributed to the hips and thighs [33]. Abdominal obesity, often assessed with waist-hip ratio, is associated with increased blood pressure, CVD, and diabetes [13, 34, 35]. This gender difference in fat distribution may explain why percentage of body fat have a greater impact on arterial distensibility in men, than in women.

There are several limitations in the present study, one is the lower number of male individuals. For unknown reasons it was much more difficult to recruit men to the study.

The possible number of edge wall tracking analyses were restricted by limited access to the wall tracking system and quality requirements of the registrations. A greater number of participants in the LBA subsample had been desirable to increase the power in the statistical calculations.

The fact that the individuals included in the study were self-reported healthy is another limitation in the study. We didn’t include individuals with a chronic disease but we accepted for example individuals with obesity, which is a reversible condition and an important risk factor for CVD.

Another limitation that needs to be highlighted is the cross-sectional design in the LBA study that prevents us from drawing conclusions in terms of causality.

One strength in the present study is the age of the population. Healthy young adults are underrepresented in the CVD literature compared to different patient groups. This age group can contribute to the detection of early changes affecting the development of CVD. The young adults in the age of 18–25 years are about to create their own habits and it is of great importance to highlight the benefits of a healthy lifestyle and to detect young adults who need cardiovascular risk follow-up and life-style counseling.

The body composition measurements, BMI, percentage of body fat, and waist circumference are common examinations that are easy to perform. In this age group, the body composition measurements contributed more than age, MAP, and the biomarkers to the variation in arterial distensibility. Our data suggests, that in this age group, BMI is the most simple and still useful tool to identify young adults that need to be further examined for future cardiovascular risk.