- Academic Editor
†These authors contributed equally.
Background: The relationship between novel anthropometric indices, specifically a body shape index (ABSI) and body roundness index (BRI), with abdominal aortic calcification (AAC) or severe AAC (SAAC) is unclear. The aim of our study was therefore to investigate possible relationships between novel anthropometric indices and prevalence of AAC and SAAC. Methods: We obtained U.S. general population data from the National Health and Nutrition Examination Survey between 2013 and 2014. The study used restricted cubic spline (RCS) analysis, multivariable logistic regression modeling, subgroup analysis, and receiver operating characteristic (ROC) curve assessment. We investigated relationships between ABSI or BRI and AAC and SAAC risk. Associations between ABSI or BRI and the degree of AAC were also evaluated using a generalized additive model. Results: The study cohort was comprised of 1062 individuals. The RCS plots revealed a U-shaped curve associating ABSI with AAC risk. A similar trend emerged for SAAC, where the risk initially increased before subsequently decreasing with rising ABSI levels. Additionally, BRI exhibited a positive correlation with both AAC and SAAC risk. As ABSI and BRI values increased, the degree of AAC also increased. In ROC analysis, ABSI displayed a significantly larger area under the curve compared to BRI. Conclusions: ABSI is associated with AAC prevalence following a U-shaped curve. Additionally, BRI is positively correlated with AAC risk. ABSI demonstrates a superior discriminative ability for AAC compared to BRI. Therefore, maintaining an appropriate ABSI and BRI may reduce the prevalence of AAC.
Vascular calcification refers to the pathological buildup of apatite mineral deposits in the vascular system [1]. This process primarily affects the abdominal aorta, the coronary artery, the femoral artery, the thoracic aorta, and the carotid artery [2]. Notably, the calcification of the abdominal aorta, known as abdominal aortic calcification (AAC) serves as an early indicator of atherosclerosis in this artery [3]. The prevalence and progression of AAC are closely linked to several conventional cardiovascular risk factors, including age, gender, and smoking [4, 5]. Epidemiological studies have highlighted the potential of using spine radiographs to gauge the severity of AAC as an effective approach for evaluating cardiovascular morbidity and mortality [6, 7, 8, 9, 10].
Anthropometric indices serve as the primary diagnostic and screening tools for identifying obesity in individuals. These indices include body weight, waist circumference (WC), and body mass index (BMI). Due to their effectiveness, noninvasiveness, and ease of application, these measures are widely adopted in clinical practice [11]. While BMI is commonly used to evaluate general obesity, it lacks accuracy in assessing body fat distribution and the true obesity status of individuals. Recent studies emphasize the heightened impact of abdominal obesity compared to general obesity. To comprehensively evaluate diverse obesity patterns, a combination of BMI and WC assessment is essential [12]. In recent years, two new anthropometric indices have been developed, namely and body roundness index (BRI) and a body shape index (ABSI) [13, 14]. ABSI is calculated by adjusting WC for height and weight, and thus provides a more precise depiction of central abdominal adiposity [15]. Additionally, BRI can be used to predict both body fat and the percentage of visceral adipose tissue [16]. Previous studies have highlighted the association of ABSI with hypertension, diabetes mellitus (DM), and cardiovascular disease, with stronger links to mortality than either BMI or WC [17]. Moreover, Xu et al. [18] concluded that BRI had superior predictive ability and stronger associations with cumulative cardiometabolic risk factors compared to BMI and WC. Nonetheless, data on the correlation between novel anthropometric indices (ABSI and BRI) and prevalence of AAC remains scarce. Therefore, the aim of this study was to explore the connections between ABSI, BRI, and AAC prevalence in adults using data from the U.S. National Health and Nutrition Examination Survey (NHANES). This should provide valuable clinical insight for preventing and intervening in AAC.
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).
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]:
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
Variables | Overall (n = 1062) | Non-AAC (n = 723) | AAC (n = 339) | p-value | |
Age, years | 58.16 |
55.71 |
64.02 |
||
Sex, % | 0.804 | ||||
Male | 518 (48.8%) | 352 (33.1%) | 166 (15.6%) | ||
Female | 544 (51.2%) | 371 (34.9%) | 173 (16.3%) | ||
Race, % | 0.538 | ||||
Mexican American | 137 (12.9%) | 99 (9.3%) | 38 (3.6%) | ||
Other Hispanic | 89 (8.4%) | 63 (5.9%) | 26 (2.4%) | ||
Non-Hispanic Black | 191 (18.0%) | 139 (13.1%) | 52 (4.9%) | ||
Non-Hispanic White | 519 (48.9%) | 330 (31.1%) | 189 (17.8%) | ||
Other race | 126 (11.9%) | 92 (8.7%) | 34 (3.2%) | ||
Family PIR | 3.22 |
3.33 |
2.97 |
0.017 | |
Marital status, % | 0.004 | ||||
Having a partner | 714 (67.2%) | 510 (48.0%) | 204 (19.2%) | ||
No partner | 265 (25.0%) | 153 (14.4%) | 112 (10.5%) | ||
Unmarried | 83 (7.8%) | 60 (5.6%) | 23 (2.2%) | ||
Education level, % | 0.072 | ||||
Less than high school | 216 (20.3%) | 145 (13.7%) | 71 (6.7%) | ||
High school | 219 (20.6%) | 142 (13.4%) | 77 (7.3%) | ||
More than high school | 627 (59.0%) | 436 (41.1%) | 191 (18.0%) | ||
Hypertension, % | |||||
No | 489 (46.0%) | 380 (35.8%) | 109 (10.3%) | ||
Yes | 573 (54.0%) | 343 (32.3%) | 230 (21.7%) | ||
Diabetes mellitus, % | 0.005 | ||||
No | 800 (75.3%) | 571 (53.8%) | 229 (21.6%) | ||
Yes | 262 (24.7%) | 152 (14.3%) | 110 (10.4%) | ||
Smoker, % | 0.013 | ||||
No | 561 (52.8%) | 413 (38.9%) | 148 (13.9%) | ||
Former | 317 (29.8%) | 188 (17.7%) | 129 (12.1%) | ||
Now | 184 (17.3%) | 122 (11.5%) | 62 (5.8%) | ||
Alcohol user, % | 0.462 | ||||
No | 139 (13.1%) | 97 (9.1%) | 42 (4.0%) | ||
Former | 231 (21.8%) | 145 (13.7%) | 86 (8.1%) | ||
Mild | 417 (39.3%) | 289 (27.2%) | 128 (12.1%) | ||
Moderate | 135 (12.7%) | 100 (9.4%) | 35 (3.3%) | ||
Heavy | 140 (13.2%) | 92 (8.7%) | 48 (4.5%) | ||
CHD, % | 0.010 | ||||
No | 1007 (94.8%) | 699 (65.8%) | 308 (29.0%) | ||
Yes | 55 (5.2%) | 24 (2.3%) | 31 (2.9%) | ||
CHF, % | 0.028 | ||||
No | 1024 (96.4%) | 707 (66.6%) | 317 (29.8%) | ||
Yes | 38 (3.6%) | 16 (1.5%) | 22 (2.1%) | ||
Angina pectoris, % | 0.155 | ||||
No | 1028 (96.8%) | 704 (66.3%) | 324 (30.5%) | ||
Yes | 34 (3.2%) | 19 (1.8%) | 15 (1.4%) | ||
Heart attack, % | |||||
No | 1006 (94.7%) | 702 (66.1%) | 304 (28.6%) | ||
Yes | 56 (5.3%) | 21 (2.0%) | 35 (3.3%) | ||
Stroke, % | 0.006 | ||||
No | 1013 (95.4%) | 702 (66.1%) | 311 (29.3%) | ||
Yes | 49 (4.6%) | 21 (2.0%) | 28 (2.6%) | ||
Hyperlipidemia, % | 0.066 | ||||
No | 241 (22.7%) | 187 (17.6%) | 54 (5.1%) | ||
Yes | 821 (77.3%) | 536 (50.5%) | 285 (26.8%) | ||
BMI, kg/m |
28.52 |
28.96 |
27.47 |
0.003 | |
Waist circumference, cm | 99.68 |
100.06 |
98.78 |
0.309 | |
SBP, mmHg | 124.37 |
122.61 |
128.56 |
0.001 | |
DBP, mmHg | 69.48 |
70.67 |
66.66 |
||
Mean energy intake (kcal/day) | 2017.08 |
2050.69 |
1936.99 |
0.044 | |
Dietary calcium intake, mg | 928.82 |
953.79 |
869.30 |
0.018 | |
Dietary phosphorus intake, mg | 1355.43 |
1386.50 |
1281.40 |
0.006 | |
Hemoglobin, g/dL | 14.30 |
14.31 |
14.28 |
0.778 | |
Fast glucose, mg/dL | 107.51 |
106.32 |
110.34 |
0.152 | |
Fast insulin, pmol/L | 72.76 |
74.14 |
69.48 |
0.575 | |
HbA1c, % | 5.76 |
5.70 |
5.89 |
0.074 | |
Alkaline phosphatase, U/L | 0.68 |
0.68 |
0.69 |
0.713 | |
Total bilirubin, g/dL | 65.68 |
65.07 |
67.12 |
0.482 | |
Phosphorus, mg/dL | 3.73 |
3.73 |
3.73 |
0.967 | |
TC, mg/dL | 195.93 |
196.23 |
195.21 |
0.735 | |
Calcium, mg/dL | 9.41 |
9.39 |
9.46 |
0.085 | |
HDL-C, mg/dL | 55.62 |
56.01 |
54.68 |
0.262 | |
TG, mg/dL | 124.46 |
124.07 |
125.38 |
0.782 | |
Uric acid, mg/dL | 5.47 |
5.42 |
5.58 |
0.341 | |
eGFR, mL/min/1.73 m |
84.75 |
88.15 |
76.66 |
||
Blood urea nitrogen, mg/dL | 13.96 |
13.44 |
15.18 |
0.001 | |
Serum creatinine, mg/dL | 0.92 |
0.88 |
1.01 |
0.008 | |
ABSI | 0.083 |
0.082 |
0.084 |
||
BRI | 5.42 |
5.36 |
5.44 |
0.007 |
Abbreviations: ABSI, a body shape index; BRI, body roundness index; BMI, body mass index; CHD, coronary heart disease; CHF, congestive heart failure; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; HDL-C, high-density lipoprotein-cholesterol; HbA1c, glycosylated hemoglobin; SBP, systolic blood pressure; TC, total cholesterol; TG, triglycerides; PIR, poverty income ratio; AAC, abdominal aortic calcification.
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.
Model 1 | Model 2 | Model 3 | ||||
OR (95% CI) | p for trend | OR (95% CI) | p for trend | OR (95% CI) | p for trend | |
ABSI | 0.009 | 0.364 | ||||
Q1 (0.068–0.080) | 1.00 | 1.00 | 1.00 | |||
Q2 (0.087–0.108) | 1.079 (0.704, 1.653) | 0.989 (0.636, 1.539) | 0.968 (0.575, 1.630) | |||
Q3 (0.084–0.086) | 1.549 (1.022, 2.348) * | 1.270 (0.821, 1.964) | 1.092 (0.584, 2.044) | |||
Q4 (0.081–0.083) | 2.096 (1.365, 3.218) *** | 1.698 (1.082, 2.666) | 1.453 (0.625, 3.376) | |||
BRI | 0.392 | 0.020 | 0.398 | |||
Q1 (1.436–4.096) | 1.00 | 1.00 | 1.00 | |||
Q2 (4.097–5.267) | 1.098 (0.741, 1.627) | 1.019 (0.677, 1.535) | 1.196 (0.717, 1.994) | |||
Q3 (5.268–6.642) | 0.920 (0.619, 1.367) | 0.740 (0.484, 1.131) | 1.148 (0.603, 2.187) | |||
Q4 (6.643–13.267) | 0.886 (0.594, 1.321) | 0.636 (0.407, 0.992) * | 1.761 (0.690, 4.492) |
Abbreviations: AAC, abdominal aortic calcification; ABSI, a body shape index;
BRI, body roundness index; CI, confidence interval; OR, odds ratio. *p
Model 1 | Model 2 | Model 3 | ||||
OR (95% CI) | p for trend | OR (95% CI) | p for trend | OR (95% CI) | p for trend | |
ABSI | 0.086 | 0.594 | 0.554 | |||
Q1 (0.068–0.080) | 1.00 | 1.00 | 1.00 | |||
Q2 (0.087–0.108) | 1.495 (0.695, 3.217) | 1.377 (0.620, 3.056) | 1.083 (0.700, 4.649) | |||
Q3 (0.084–0.086) | 2.365 (1.158, 4.834) * | 1.785 (0.839, 3.799) | 2.805 (0.723, 6.007) | |||
Q4 (0.081–0.083) | 1.857 (0.899, 3.834) | 1.325 (0.617, 2.847) | 1.669 (0.417, 6.675) | |||
BRI | 0.470 | 0.030 | 0.146 | |||
Q1 (1.436–4.096) | 1.00 | 1.00 | 1.00 | |||
Q2 (4.097–5.267) | 1.407 (0.773, 2.560) | 1.210 (0.640, 2.286) | 2.029 (0.872, 4.725) | |||
Q3 (5.268–6.642) | 1.090 (0.595, 1.996) | 0.781 (0.403, 1.515) | 2.024 (0.703, 5.827) | |||
Q4 (6.643–13.267) | 0.900 (0.484, 1.674) | 0.547 (0.272, 1.102) | 3.914 (0.876, 17.484) |
Abbreviations: ABSI, a body shape index; BRI, body roundness index; CI,
confidence interval; OR, odds ratio; SAAC, severe abdominal aortic calcification.
*p
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).
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).
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.
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
This is the first study to examine the novel anthropometric indices (ABSI and BRI) in relation to the prevalence of AAC and SAAC among people from the United States. The key findings of this study include the following: (1) the association between ABSI and AAC prevalence exhibited a U-shaped pattern; (2) individuals with higher BRI values were more likely to experience AAC compared to those with lower BRI values; (3) as ABSI, BRI and BMI increase, the degree of AAC also increases; (4) the discriminative ability of ABSI to predict the risk of both AAC and SAAC surpassed that of BRI and BMI. Based on these findings, we propose that ABSI and BRI may play critical roles in the risk management for AAC and SAAC.
ABSI and BRI are novel anthropometric measurements used to gauge abdominal obesity and visceral adiposity [26]. In previous studies, ABSI demonstrated a positive correlation with central obesity and metabolic-related diseases [13, 27]. Notably, Geraci et al. [28] reported that ABSI’s potential as a superior indicator compared to traditional markers (BMI and WC) for predicting carotid atherosclerosis in hypertensive patients. However, ABSI’s predictive value was also found to be similar to conventional risk factors (BMI and WC) for predicting disease risk or death.
Furthermore, two studies conducted on Chinese populations of all ages demonstrated the correlation of ABSI with pre-hypertension or hypertension was no stronger than BMI or WC [29, 30]. Ji et al. [17] reported that elevated ABSI levels correlated with a higher risk of hypertension (13%), DM (35%), cardiovascular disease (21%) and overall mortality (55%). While ABSI outperformed weight, height, and BMI in predicting all-cause mortality, its predictive capability for chronic diseases was comparatively weaker [17]. Moreover, among Chinese community adults, Zhang et al. [31] identified a positive correlation between ABSI and urinary albumin-creatinine ratio. However, Wei Li et al. [32] found the linear positive correlation between ABSI and risk of AAC demonstrated a superior discrimination ability compared to BMI, WC, height, or weight.
A study with a large European cohort observed a J-shaped relationship between BMI, WC and overall mortality, whereas higher ABSI was correlated with higher all-cause mortality [33]. Additionally, the study also highlighted that BMI was a more effective predictor of mortality from cardiovascular disease compared to ABSI [33]. These results are not in agreement with the present study. The risk of disease associated with ABSI has not been previously described as a U-shaped curve. Thus, further studies are needed to confirm that the association between AAC risk associated with ABSI follows the pattern observed in our data. Our study is also the first to show an association between BRI and AAC prevalence.
In a previous study, Tian et al. [34] demonstrated that using BRI as a solitary anthropometric measure was more effective in identifying a cluster of cardiometabolic abnormalities in Chinese women. This effectiveness surpassed the discriminative capability of both BMI and the waist-to-height ratio [34]. Zhang et al. [35] found that the risk of hypertension increases with the increase of ABSI and BRI in Chinese individuals. Additoanlly, BRI was found to be superior to ABSI in identifying new-onset hypertension. Work by Wu et al. [36] further supported an independent positive association between BRI and hypertension risk, aligning with our study’s findings. Finally, Zhou et al. [37] demonstrated the relationship between BRI, cardiovascular-related mortality, and overall mortality also followed a U-shaped curve.
While other studies have explored the correlation between BRI and different diseases, to our knowledge BRI has not been compared to other anthropometric measures for its association with AAC. Our study also demonstrated a positive correlation between BMI and AAC prevalence. Bacha reported the process of vascular calcification in obese youths begins in childhood and is primarily driven by obesity [38], aligning with our findings. Yet, Uhlinova et al. [39] found that obesity was not an independent predictor of vascular calcification in patients with chronic kidney disease. Finally, our study revealed that ABSI was a stronger independent predictor of AAC, and SAAC, compared to BRI and BMI.
The findings of this study provide further evidence supporting the idea that a more accurate evaluation of AAC risk may be achieved by assessing ABSI and BRI. Nevertheless, our study has several limitations. Firstly, this study’s cross-sectional design restricts the identification of causal relationships between ABSI, BRI AAC, and SAAC. Secondly, while the multivariate logistic regression models adjusted for significant confounding factors, some potential confounding factors including inflammation indicators and medication use were not considered. Thirdly, ABSI and BRI values were highly concentrated around their means, showing only small variance. This circumstance complicates the identification of crucial values with clinical relevance. Finally, this study solely relies on NHANES data from the 2013–2014 U.S. general population, which could limit the applicability of the results to other ethnic populations and nations.
This study focused on assessing the relationship between ABSI and BRI, and the prevalence of AAC and SAAC. In ABSI, the prevalence of AAC displayed a U-shaped curve, featuring an inflection point at 0.0805, marking the lowest occurrence of AAC. Furthermore, both ABSI and BRI exhibited positively correlations with SAAC risk. A noteworthy finding was that ABSI showcased a stronger discriminative ability in predicting the risk of both AAC and SAAC when compared to BRI and BMI. However, whether ABSI and BRI are suitable for clinical practice requires further studies to determine if our results can be generalized to other ethnic population groups.
The survey data are publicly available on the Internet for data users and researchers throughout the world https://www.cdc.gov/nchs/nhanes/.
CY, YY, and HW designed the research study. FL, WL, and YS performed the research. WH, XL, and XY analyzed the data. All authors contributed to editorial changes in the manuscript. CY, YY, and HW drafted and revised the manuscript. All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.
The NHANES 2013–2014 was approved by the NCHS Research Ethics Review Board (Continuation of Protocol #2013–2014), and each participant signed the written informed consent.
The authors thank the staff and the participants of the NHANES study for their valuable contributions.
This research received no external funding.
The authors declare no conflict of interest.
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