The NHANES used a stratified, multistage and random sampling design. It is a nationally representative and cross-sectional survey conducted on the United States (U.S.) civilian, non-institutionalized resident population. The present study was based on data collected between 2013 and 2014 by the NHANES survey (Fig. 1). A total of 9970 participants were included, with AAC data available for 3140. Participants with missing data for the novel anthropometric indices (a body shape index [ABSI] and body roundness index [BRI]) were excluded (n = 63). Also excluded were participants with missing covariate data (n = 2015), leaving a final total of 1062 participants in the study. An informed consent form was provided to all participants in the NHANES study. All protocols were approved by the National Center for Health Statistics Research Ethics Review Board [19]. The NHANES website provides more information about the survey design, methods, population, and data (https://www.cdc.gov/nchs/nhanes/about_nhanes.htm).

Fig. 1.

NHANES participants in the current study. Abbreviations: AAC, abdominal aortic calcification; ABSI, a body shape index; BRI, body roundness index; NHANES, National Health and Nutrition Examination Surveys.

Basic anthropometric measurements were made by experienced examiners. These used standardized techniques and equipment and included body height (BH), weight and WC. Participants were measured barefoot and in light clothing, with BH and weight being reported to the nearest 0.1 cm and 0.1 kg, respectively. With the participant standing, a flexible anthropometric tape was used to measure the WC midway between the lowest rib and the iliac crest. The following formulae were used to calculate ABSI and BRI [20]:

$$\mathrm{ABSI}=\mathrm{WC}(\mathrm{m})/{\mathrm{BMI}}^{2/3}\left(\mathrm{kg}/{\mathrm{m}}^2\right)\times {\mathrm{BH}}^{1/2}(\mathrm{m})$$

$$\mathrm{BRI}=364.2-365.5\times \sqrt{1-{\left(\frac{\frac{WC(m)}{2\pi }}{0.5\times BH(m)}\right)}^2}$$

To obtain and quantify AAC, trained and certified radiology professional technologists performed the dual-energy X-ray absorptiometry (Densitometer Discovery A, Hologic, Marlborough, MA, USA). And, Kauppila score systems were conducted on the lumbar spine (vertebrae L1–L4) [21, 22]. The higher the AAC score, the more severe the calcification of the abdominal aorta. A range of 0 to 24 was used for the Kauppila score in this study. An AAC score equal to 0 had no calcification, an AAC score greater than 0 and less than or equal to 6 had mild-moderate calcification, and an AAC score greater than 6 had severe calcification [23, 24, 25].

The following covariates were included in this study: sex, age, education level, family poverty income ratio (PIR), race/ethnicity, marital status, drinking status, smoking status, complications from DM, and hypertension, complications from congestive heart failure (CHF), angina pectoris, stroke, coronary heart disease (CHD), and heart attack, BMI, systolic blood pressure (SBP), WC, dietary phosphorus intake, diastolic blood pressure (DBP), dietary calcium intake, mean energy intake, fasting blood glucose (FBG), total bilirubin, glycohemoglobin (HbA1c), hemoglobin (Hb), fast insulin, alkaline phosphatase, serum phosphorus and calcium, high-density lipoprotein-cholesterol (HDL-C), uric acid (UA), total cholesterol (TC), serum creatinine (Scr), triglyceride (TG), estimated glomerular filtration rate (eGFR), and blood urea nitrogen (BUN).

All analyses were performed using R software (version 4.2.0, R Foundation for Statistical Computing, Vienna, Austria) and SPSS software (version 22.0, IBM SPSS statistics, Chicago, IL, USA). A p-value of 0.05 was regarded as statistically significant. ABSI, BRI and BMI were divided into quartiles, with the lowest quartile group (Q1 group) serving as the reference group. Data for continuous variables are presented as the mean and standard deviation (SD), and data for categorical variables as numbers (%). Differences between groups were calculated using weighted T-tests for continuous variables and weighted chi-square tests for categorical variables. The NHANES sample weights were taken into account when calculating all estimates. Multivariate logistic regression analysis was performed to investigate links between ABSI, and BRI and prevalence of AAC and severe AAC (SAAC). Model 1 was adjusted for sex and age. Model 2 was adjusted for the model 1 variables plus drink status, smoke status, education level, family PIR, race or ethnicity, marital status, and complications from DM, and hypertension. Finally, model 3 was adjusted for all variables in Table 1.

The baseline characteristics for the research participants are presented in Table 1. In this study cohort the incidence of AAC and SAAC was 31.9% and 11.7%, respectively. The participants in this study represent an estimated 44,977,275 individuals in the US population. Participant characteristics were subclassified according to ABSI quartiles (Q1: 0.068–0.080; Q2: 0.081–0.083; Q3: 0.084–0.086; Q4: 0.087–0.108), BRI quartiles (Q1: 1.436–4.096; Q2: 4.097–5.267; Q3: 5.268–6.642; Q4: 6.643–13.267) and BMI quartiles (Q1: 15.400–24.625; Q2: 24.626–27.800; Q3: 27.801–31.300; Q4: 31.301–50.400). Significant differences were found between the Non-AAC and AAC groups in terms of age, family PIR, marital status, complications from hypertension and DM, smoking, the complication of CHF, heart attack, CHD, stroke, BMI, SBP, DBP, dietary phosphorus and calcium intake, mean energy intake, Scr, eGFR, BUN and ABSI.

The relationship between ABSI and BRI with the prevalence of AAC and SAAC was explored using multivariate logistic regression analysis, as presented in Tables 2,3. The restricted cubic spline (RCS) plot revealed a U-shaped association between ABSI and AAC prevalence (p for nonlinearity = 0.013, Fig. 2A). As ABSI increased, the prevalence of AAC decreased significantly. This decline was most pronounced when ABSI reached 0.0805, followed by an upward trend in prevalence.

Fig. 2.

RCS curves showing the relationships between AAC and (A) a body shape index, (B) body roundness index, and (C) body mass index. Abbreviations: AAC, abdominal aortic calcification; RCS, restricted cubic spline.

Similarly, an intriguing pattern emerged for ABSI and BMI concerning SAAC risk, with a trend of initial increase followed by decrease (p for nonlinearity = 0.325, Fig. 3A, and p for nonlinearity = 0.257, Fig. 3C, respectively). However, both BRI and BMI exhibited a positive correlated with AAC risk (p for nonlinearity = 0.659, Fig. 2B, and p for nonlinearity = 0.560, Fig. 2C, respectively). Furthermore, BRI displayed a positive correlation with SAAC risk (p for nonlinearity = 0.550, Fig. 3B).

Fig. 3.

RCS curves showing the relationships between SAAC and (A) a body shape index, (B) body roundness index, and (C) body mass index. Abbreviations: RCS, restricted cubic spline; SAAC, severe abdominal aortic calcification.

Based on the findings from the generalized additive models, a consistent linear positive correlation emerged between ABSI, BRI and BMI. As ABSI, BRI and BMI increased, there was a gradual and corresponding increase in the degree of calcification (Fig. 4A–C, respectively).

Fig. 4.

Associations between the degree of calcification and a body shape index (A), body roundness index (B), and body mass index (C).

To evaluate the capacity of the anthropometric measures (ABSI, BRI, and BMI) to differentiate individuals with AAC from those with SAAC, a receiver operating characteristic (ROC) curve was employed. The ROC curve analysis demonstrated that ABSI outperformed both BRI and BMI in effectively discriminating between AAC (Fig. 5A) and SAAC (Fig. 5B). The optimal threshold values for detection of AAC using ASBI, BRI and BMI were determined to be 0.0836, 3.7696 and 32.10, respectively. Similarly, for identifying SAAC, the optimal cut-off values were 0.0834 for ABSI, 4.1287 for BRI, and 18.90 for BMI.

Fig. 5.

ABSI, BRI, and BMI used to identify individuals with AAC (A) or SAAC (B). Abbreviations: AAC, abdominal aortic calcification; ABSI, a body shape index; AUC, area under the curve; BMI, body mass index; BRI, body roundness index; SAAC, severe abdominal aortic calcification.

Subgroup analysis, stratified by age, sex, hypertension and DM was conducted to investigate the association between ABSI, BRI, and the prevalence of AAC and SAAC (Supplementary Tables 1,2). The U-shaped relationship between ABSI and AAC prevalence identified in individuals 60 years old, males and females, individuals with or without hypertension, and individuals without DM (Supplementary Fig. 1). Furthermore, the stratified analysis revealed a consistent positive linear correlation between BRI and AAC across all age groups, genders, individuals with or without hypertension, and individuals without DM (Supplementary Fig. 2). The scope of subgroup analysis extended to exploring the correlations between ABSI, BRI, and the risk of SAAC (Supplementary Tables 3,4; Supplementary Figs. 3,4).