Association between weight-adjusted waist index and carotid atherosclerosis in low-income population: a cross-sectional study

Carotid intima-media thickness (CIMT) is a well-established marker of atherosclerosis and a predictor of cardiovascular events. The Weight-Adjusted Waist Index (WWI), which accounts for both abdominal obesity and body weight, has been suggested as a potential indicator for cardiovascular risk. This study aimed to investigate the association between WWI and CIMT in a rural Chinese population, with a focus on identifying risk factors that contribute to vascular health. A cross-sectional study was conducted between May and June 2019, involving 2,260 participants aged 50 years and older from a low-income rural area in northern China. Participants were excluded if they had cardiovascular or cerebrovascular diseases or missing data. Data were collected on demographic characteristics, lifestyle factors, clinical history, and laboratory measurements, including fasting plasma glucose (FPG), lipids, and blood pressure. Carotid ultrasound was performed to measure CIMT, and the association between WWI and CIMT was assessed using multivariable linear regression and logistic regression adjusted for established risk factors. Subgroup analyses were also performed to evaluate the relationship between WWI and CIMT across different demographic and clinical groups. The study included 2,260 participants, with a mean age of 62.68 ± 8.51 years, of whom 43.8% were male. A significant positive correlation was found between WWI and mean CIMT (β = 0.009, p = 0.012). Additionally, WWI was significantly associated with CIMT thickening (OR = 1.309, 95% CI: 1.059–1.617, p = 0.013). In exploratory subgroup analyses, the association between WWI and CIMT was statistically significant in participants aged ≥ 60 years, those with hypertension, higher education levels, males, and non-diabetic participants, but no interaction survived correction for multiple comparisons. ROC curve analysis revealed that WWI alone had limited discriminative ability for CIMT thickening (AUC = 0.557, p = 0.012), with an optimal threshold of 10.374. This study demonstrates that WWI is significantly associated with both mean CIMT and CIMT thickening in a rural Chinese population. As a simple, non-invasive and inexpensive anthropometric measure, WWI may serve as a complementary indicator to conventional risk factors for identifying individuals with carotid atherosclerosis, although it is not suitable as a stand-alone screening tool. Given the cross-sectional design, longitudinal studies in diverse populations are needed to confirm these findings.

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Journal
BMC Cardiovascular Disorders
Published
2026-10-30
DOI
https://doi.org/10.1186/s12872-026-06909-z
Primary Topic
Diabetes, Cardiovascular Risks, and Lipoproteins
Type
article
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article

Association between weight-adjusted waist index and carotid atherosclerosis in low-income population: a cross-sectional study

宁宪嘉, Chunsheng Yang, Bingyi Li, Haiying Wang et al.
2 links
BMC Cardiovascular Disorders
Diabetes, Cardiovascular Risks, and Lipoproteins
article

Association between weight-adjusted waist index and carotid atherosclerosis in low-income population: a cross-sectional study

宁宪嘉, Chunsheng Yang, Bingyi Li, Haiying Wang, Lifeng Wang, Juan Hao, Huihui Xue, Yan Li, Jinghua Wang, Chenxi Fan
article en

Abstract

Carotid intima-media thickness (CIMT) is a well-established marker of atherosclerosis and a predictor of cardiovascular events. The Weight-Adjusted Waist Index (WWI), which accounts for both abdominal obesity and body weight, has been suggested as a potential indicator for cardiovascular risk. This study aimed to investigate the association between WWI and CIMT in a rural Chinese population, with a focus on identifying risk factors that contribute to vascular health. A cross-sectional study was conducted between May and June 2019, involving 2,260 participants aged 50 years and older from a low-income rural area in northern China. Participants were excluded if they had cardiovascular or cerebrovascular diseases or missing data. Data were collected on demographic characteristics, lifestyle factors, clinical history, and laboratory measurements, including fasting plasma glucose (FPG), lipids, and blood pressure. Carotid ultrasound was performed to measure CIMT, and the association between WWI and CIMT was assessed using multivariable linear regression and logistic regression adjusted for established risk factors. Subgroup analyses were also performed to evaluate the relationship between WWI and CIMT across different demographic and clinical groups. The study included 2,260 participants, with a mean age of 62.68 ± 8.51 years, of whom 43.8% were male. A significant positive correlation was found between WWI and mean CIMT (β = 0.009, p = 0.012). Additionally, WWI was significantly associated with CIMT thickening (OR = 1.309, 95% CI: 1.059–1.617, p = 0.013). In exploratory subgroup analyses, the association between WWI and CIMT was statistically significant in participants aged ≥ 60 years, those with hypertension, higher education levels, males, and non-diabetic participants, but no interaction survived correction for multiple comparisons. ROC curve analysis revealed that WWI alone had limited discriminative ability for CIMT thickening (AUC = 0.557, p = 0.012), with an optimal threshold of 10.374. This study demonstrates that WWI is significantly associated with both mean CIMT and CIMT thickening in a rural Chinese population. As a simple, non-invasive and inexpensive anthropometric measure, WWI may serve as a complementary indicator to conventional risk factors for identifying individuals with carotid atherosclerosis, although it is not suitable as a stand-alone screening tool. Given the cross-sectional design, longitudinal studies in diverse populations are needed to confirm these findings.

BMC Cardiovascular Disorders
Guangxi Medical University (CN), Tianjin Medical University General Hospital (CN), Tianjin People's Hospital (CN), Tianjin Medical University (CN)
Good health and well-being
Openalex Percentile: Top 11%
Diabetes, Cardiovascular Risks, and Lipoproteins
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