Our study selected participants from primary, middle and high schools in Jiangsu Province (located between 116°21′E - 121°56′E and 30°45'N - 35°08′N) via stratified cluster sampling, thus ensuring representation across gender and age groups. Weight status was assessed via age- and sex-specific BMI percentiles, and obese children (defined as BMI ≥ 95% for age and sex) were included in further analyses (16).The sample size was determined through a statistical power calculation using the formula for estimating proportions in a population, which is a standard method in clinical and epidemiological research. This formula ensures the sample is large enough to detect a statistically significant effect with a high degree of confidence, while remaining feasible to recruit. The formula used was: n=Z2⋅p(1−p)E2, Where: n = required sample size, Z = Z-score corresponding to the desired confidence level. We set this at 95% (a standard in medical research), corresponding to a Z-score of 1.96. p = estimated proportion (prevalence) of the key characteristic in the population (17). Baseline data from the study population were collected through standardized clinical examinations, including anthropometry, blood sampling, and ultrasound evaluation. All anthropometric measurements and clinical indicators were objectively obtained during these physical examinations conducted in a hospital setting, using calibrated instruments and following strict protocols. Ethical approval for this study was obtained from the institutional review boards of Kunshan Municipal Centers for Disease Control and Prevention (approval number: 2024020), and informed consent was obtained from the participants and their parents or guardians.

We measured several key indicators, including exposure factors such as height, body mass, waist circumference, hip circumference, systolic blood pressure, and diastolic blood pressure, as well as the following outcome factors: cIMT. Height and body mass were measured via a calibrated digital scale, with each child wearing light clothing and without shoes or hats. BMI was calculated as body mass (kg) divided by the square of height (m²). Waist circumference was measured at the midpoint between the lowest point of the costal arch and the upper edge of the iliac crest, using a flexible tape measure horizontally around the body, ensuring that the tape was level and untwisted; the measurement was recorded where the tape intersected with the 0 mark. Hip circumference was measured at the most prominent point of the gluteus maximus at the front and back, with the tape snugly enciring the measurement point without compressing the skin, and the measurement was recorded accordingly. The waist‒to-hip ratio (WHR) was calculated by dividing waist circumference (cm) by hip circumference (cm).

Blood pressure measurements were conducted via an automatic electronic blood pressure monitor, which inflates and deflates automatically. Once the measurement was completed, the monitor displayed the systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse rate on the screen. To ensure accuracy, we took two measurements, each separated by an interval of 30–60 s. If the difference in SBP or DBP readings between these two measurements exceeded 5 mmHg, we performed a third measurement and recorded the average of all three readings.

cIMT was assessed via carotid ultrasound. Grayscale features of the outer membrane region were extracted from the collected carotid ultrasound images, and the intima–media boundary and lumen–intima boundary lines were delineated to calculate the cIMT on the basis of these boundaries. The average cIMT was calculated from three measurements taken from both the left and right carotid arteries.

In our study, the distribution of all continuous variables was first assessed using the Shapiro–Wilk test. Variables that were normally distributed are presented as mean ± standard deviation (SD), while non-normally distributed variables are presented as median (interquartile range, IQR). This approach was applied to understand the baseline characteristics of the population by age stratification (≤12 years, prepubertal; >12 years, postpubertal). The distribution of cIMT across age and sex was analyzed via scatter plots and linear fitting. Bivariate associations between anthropometric parameters and cIMT were assessed via Spearman's rank correlation coefficient. For comparisons of continuous variables that were not normally distributed across three or more groups, the Kruskal–Wallis test was employed. The locally weighted scatterplot smoothing (LOESS) method was used to explore the nonlinear relationships between variables (18), which were visualized via scatter plots and smooth curves. To precisely identify the turning points in the relationships between cardiometabolic indicators (SBP, DBP, BMI, WHR) and cIMT, we employed piecewise regression analysis using the segmented package in R (19). This method iteratively fits two linear regression models to the data on either side of a potential break point and estimates the location of the breakpoint that minimizes the residual sum of squares for the entire model. Multiple linear regression analysis was used to determine independent risk factors affecting cIMT: Model 1 did not adjust for any variables, Model 2 adjusted for core demographic characteristics (age, sex), and Model 3 adjusted for all covariates except the measure itself. To control the Type I error (false positive result) in multiple hypothesis testing, the significance level (α) was set at 0.05 and adjusted using the Bonferroni correction where appropriate for multiple comparisons. All analyses were conducted in R 4.4.1. Statistical significance was defined as a two-tailed P-value < 0.05.

According to the formula for calculating the sample size, p was the prevalence of obesity in our study population. Based on national data indicating a childhood obesity prevalence of approximately 17%–20% (8, 20) (17.06% according to statistics from Kunshan, Jiangsu Province, Supplementary Material 1), we used p = 0.20 (20%) for a conservative estimate. E = desired margin of error (precision). We set this at ±5% (0.05), meaning we can be 95% confident that the true population parameter lies within 5% of our sample estimate. Calculation: n=1.962⋅0.2(1−0.2)0.052=245.86. A total of 245 obese adolescents with a mean age of 12 years were included in this study. Most participants were boys (n = 172, 70.2%), with a mean BMI of 25.5 kg/m², a median SBP of 118.67 mmHg, a median DBP of 68.67 mmHg, and a median WHR of 0.83. Table 1 details the anthropometric characteristics of obese adolescents by sex and age group. The results of the Kruskal‒Wallis test revealed significant differences in related indicators between different sex and age groups.

Figure 1 shows the distribution of cIMT in obese children by age and sex. The mean left IMT was 0.42 mm and ranged from 0.30 to 0.58 mm, whereas the mean right IMT was 0.41 mm and ranged from 0.28 to 0.60 mm. Notably, after the 10‒12 years of age, males presented higher cIMT values than females did, suggesting a possible sex difference in carotid atherosclerosis progression.

Correlation analysis revealed that the waist‒hip ratio, BMI, systolic blood pressure and diastolic blood pressure were positively correlated with cIMT (P < 0.001). The Spearman correlation coefficients are shown in Table 2. The results revealed that increased anthropometric indices were strongly associated with elevated cIMT, suggesting their potential role in the development of early vascular changes.

Nonlinear fitting was performed to analyze the trends between each indicator and cIMT. The piecewise regression analysis identifies exact inflection points under different trends. For SBP, right cIMT remained relatively stable between 95.0 and 115.7 mmHg but showed a steep increase as SBP exceeded 115.7 mmHg; left cIMT remained relatively stable between 93.3 and 131.0 mmHg but showed a steep increase as SBP exceeded 131.0 mmHg (Figure 2). With respect to DBP, a multi-phase association was also observed. Right cIMT decreased as DBP increased from 40.0 to 60.0 mmHg, and then increased markedly above 70.9 mmHg. Left cIMT declined with increasing DBP up to 58.7 mmHg, and increased rapidly above 81.8 mmHg (Figure 3). In terms of BMI, the association with cIMT was complex and side-dependent. Right cIMT decreased gradually until BMI reached 22.3 kg/m², and then rose above 26.0 kg/m². Left cIMT showed a different trajectory, with a increase until 21.0 kg/m², remained relatively stable between 21.0 and 23.0 kg/m², and a sharp rise above 23.0 kg/m² (Figure 4). The relationship between WHR and cIMT also demonstrated lateral differences. Right cIMT remained steadily rising during the increase in WHR. Left cIMT showed a stable phase between WHR 0.78 and 0.84, and a rapid increase above 0.84 (Figure 5).

To comprehensively evaluate the independent effects of each index on cIMT, multivariate logistic regression analysis was performed. The analysis revealed that, after adjusting for other covariates, SBP was the only anthropometric measure among those analyzed that maintained a statistically significant independent association with cIMT (P < 0.05), as presented in Tables 3, 4. This finding suggests that SBP may be a key target for interventions to reduce cardiovascular risk in this population.