This is a cross-sectional study conducted on Chinese subjects. From a group of Beijing residents attending an annual physical examination in a period from August to October, 2011, 472 subjects (272 males and 200 females, 42–97 y of age) were recruited randomly. The sera from fasting blood samples obtained from volunteers were isolated and stored at −80°C until being analyzed.

This study has been reviewed and approved by the Ethics Committee of Beijing Hospital, Ministry of Health. All studied individuals had been made aware in writing of the intended use of their blood sample and provided written consent.

The serum BCAAs [valine (Val), isoleucine (Ile) and leucine (Leu)] and AAAs [tyrosine (Tyr) and phenylalanine (Phe)] were measured using our previously reported isotope dilution LC/MS/MS method [8]. Briefly, 0.05 ml aliquots of calibrators or serum samples were mixed with 0.05 ml of the isotopic labeled internal standard solution and the amino acids were extracted with 0.4 ml of acetonitrile containing 0.1% formic acid, and analyzed using LC/MS/MS. The LC separation was performed on an Agilent 1200 series LC system. Two microliter aliquots of the prepared samples were injected onto a Waters Shield C18 column (3.5 µm, 2.1×150 mm) maintained at 20°C and eluted with a mobile phase of 0.01% formic acid in water-acetonitrile (90∶10) at a flow rate of 0.3 ml/min. An API 4000 triple quadrupole mass spectrometer (Sciex Applied Biosystems) was used for the MS/MS detection. The detection was performed with positive electronic spray ionization (ESI) in multiple reaction monitoring (MRM) mode at a source temperature of 700°C and a voltage of 5500 V. The dwell times were 0.08 s for MRM. Nitrogen was used as the curtain, nebulizer and collision gas at pressures of 50, 60 and 70 psi, respectively. Certain ion transitions for the amino acids and their internal standards were monitored following our previously reported method [8]. The calibration curve was generated using a linear regression of the peak area ratios (y) vs. the amino acid concentrations (x) of the calibrators.

Bilateral B-mode carotid artery images for intima-media thickness were acquired at end diastole (defined as the R wave of an electrocardiogram) using a SDU-1200 ultrasound imager (Shimadzu, Japan) with a linear array 5.5/7.5 MHz probe. The posterior wall of the distal common carotid artery, one centimeter below the bifurcation, was assessed by using the automated IMT analysis system. The maximum of the left and right cIMT readings was used for the analyses. The imaging analysis was performed by an experienced technician blinded to the study participants.

A history of diseases and medications and a family history of diseases were recorded using a list of questions. A subject's smoking status was classified as current, former, or never smoker. At the physical examination center, height, weight, waist circumference (WC) and sitting blood pressure were measured. The serum samples were also tested for fasting blood glucose (FBG), total cholesterol (TC), triglycerides (TG), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), apolipoprotein (apo) AI, B, CII, CIII and high sensitive C reactive protein (hs-CRP) using assay kits from Sekisui Medical Technologies (Osaka, Japan) on a Hitachi 7180 chemistry analyzer.

Categorical variables are presented as frequencies and percentages, and continuous variables as means and standard deviations, or medians and interquartile ranges (25th to 75th percentile) for variables with skewed distributions. Increased cIMT was defined as a cIMT of 0.9 mm or higher [11]; hypertension as a systolic blood pressure (SBP) of 140 mm Hg or higher, or a diastolic blood pressure (DBP) of 90 mm Hg or higher or receiving antihypertensive treatment; diabetes mellitus as a FBG of 7.0 mmol/l or higher or receiving diabetes treatment; dyslipidemia as a serum TC>6.21 mmol/L, or LDL-C>4.14 mmol/L, or TG>1.70 mmol/L, or HDL-C<1.04 mmol/L; abdominal obesity as a waist circumference of 90 cm or higher for men and 80 cm or higher for women. The one-way analysis of variance (ANOVA) test was used to evaluate the differences in the parametric samples between the normal and increased cIMT groups, while the nonparametric Mann-Whitney test was used to determine significant differences when the data were not normally distributed. The associations of certain AAs with cIMT and other risk factors were assessed with the use of nonparametric Spearman correlation analyses. Stepwise multiple linear regression analysis was used to test the independent relationships of the measured variables with cIMT; meanwhile, collinearity testing was used to avoid including interdependent model variables. The significance levels for entering and removing an explanatory variable were set at 0.05 and 0.10, respectively. A stepwise logistic regression analysis was used to estimate the association between variables and subclinical atherosclerosis status, i.e., the presence or absence of increased cIMT. Odds ratios and 95% confidence intervals (CIs) for increased cIMT versus normal cIMT were calculated. Data were adjusted for age, sex, BMI, smoking status, presence or absence of diabetes, hypertension and dyslipidemias, family history of CAD, and therapies for diabetes, hypertension and dyslipidemias (hypercholesterolemia). All reported P values were two-tailed, with a P value of 0.05 indicating statistical significance. Analyses were performed with the use of SPSS software, version 16.0 (SPSS Inc.).

The characteristics of the population are shown in Table 1. The most common characteristic was increased cIMT (72% of women, 85% of men). The prevalence of hypertension, diabetes, obesity, increased TG, TC and LDL-C, and decreased HDL-C were 59%, 18%, 12%, 27%, 9%, 7% and15%, respectively. Two percent of the women and 41% of the men were current or former smokers.

The concentrations of each AA showed skewed and leptokurtic distributions. As shown in Table 2, the increased cIMT group had significantly higher levels of Val, Ile, Leu, total BCAAs and Phe than the normal cIMT group, P<0.01. However, there were no significant differences for Tyr in the increased cIMT group compared to the normal cIMT group, though increment levels were observed. The normal and increased cIMT individuals differed significantly in age, BMI, WC, SBP and FBG concentration, P<0.05. Individuals with obesity and diabetes had significantly elevated BCAAs (P<0.01) and Phe levels (P<0.05).

The relationships between certain amino acids and cIMT, and other parameters are shown in Table 3. In the nonparametric Spearman correlation analyses, Val, Ile, Leu, Phe and the total BCAA and AAA concentrations were significantly and positively correlated with cIMT (P<0.05). There was also a strong correlation between each amino acid and BMI (P<0.001) and waist circumference (P<0.001). Furthermore, all BCAA and AAA concentrations were significantly and positively correlated with TG and the apoB/apoAI ratio (P<0.001) and were inversely associated with HDL-C and apoAI (P<0.001). Ile and Leu were negatively correlated with TC (P<0.05). Positive correlations of BCAA and AAA concentrations with hsCRP, apoCII and apoCIII were also found. Serum BCAAs seemed to be negatively correlated with age, especially Leu (P<0.01). Positive correlations between all AAs and DBP (P<0.05) as well as Val and SBP (P<0.05), were observed. Most of BCAAs and AAAs were correlated with FBG and apoB. In addition, cIMT was significantly correlated with age (r = 0.228, P<0.001), BMI (r = 0.125, P<0.01), WC (r = 0.169, P<0.001), SBP (r = 0.218, P<0.001), FBG (r = 0.105, P<0.05), TG (r = 0.100, P<0.05), HDL-C (r = −0.125, P<0.01), apoAI (r = −0.116, P<0.05), and the apoB/apoAI ratio (r = 0.105, P<0.05). The BCAAs and AAAs were also strongly correlated with each other (P<0.001).

For multivariate reevaluation of the univariate correlations, all variables given in Table 3 were entered into a stepwise multiple linear regression analysis as independent variables to identify significant contributors to the distribution of cIMT. The stepwise multiple linear regression analysis revealed that age (β = 0.175, P<0.001), log BCAA (β = 0.147, P<0.001) and SBP (β = 0.141, P = 0.012) were independent factors that correlate with cIMT (adjusted R² = 0.041, P = 0.002) as shown in Table 4.

The results of the logistic regression analysis of the differences between the increased and normal cIMT groups are shown in Table 5. Although the univariate models demonstrated significant differences in several variables between increased cIMT and normal cIMT groups (Table 2), many of them did not appear in the model adjusted for age, sex, BMI, smoking, BCAA quartile, AAA quartile, diabetes mellitus, hypertension, dyslipidemias, family history of CAD, and diabetes, hypertension and cholesterol-lowering therapies. In the logistic regression model, BCAA was the most and sole powerful laboratory factor correlated with increased cIMT (the odds ratio of the fourth quartile compared to the first quartile was 2.679; P = 0.009).