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A Body Shape Index (ABSI) and Hip Index (HI) Adjust Waist and Hip Circumferences for Body Mass Index, But Only ABSI
Arturo Corbatón-Anchuelo1,2, Jesse C Krakauer3, Irene Serrano-García2,4
1CB07/08/0030 group, Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain.
Insights
The Hip Index (HI) does not predict cardiovascular risk (CVR) in Spanish Caucasians. However, the A Body Shape Index (ABSI) is a strong linear predictor of CVR, outperforming the waist-to-height ratio (WHtR).
Area of Science:
- Cardiovascular Health
- Anthropometry
- Public Health
Background:
- Cardiovascular risk (CVR) assessment relies on various anthropometric indices (AIs).
- The predictive capabilities of the Hip Index (HI), Waist-to-Height Ratio (WHtR), and A Body Shape Index (ABSI) for CVR require further investigation in specific populations.
- Identifying optimal AIs is crucial for early CVR detection and prevention strategies.
Purpose of the Study:
- To evaluate if the Hip Index (HI) enhances cardiovascular risk (CVR) identification beyond the Waist-to-Height Ratio (WHtR) or A Body Shape Index (ABSI).
- To compare the performance of HI, WHtR, and ABSI in predicting CVR in the Spanish Caucasian population.
- To determine the most effective anthropometric index for assessing CVR in this demographic.
Main Methods:
- A cohort of 3,844 Spanish Caucasians (1754 males) was analyzed.
- Anthropometric indices including HI, ABSI, and WHtR were measured.
- Cardiovascular risk was assessed using Framingham, SCORE, and ACC/AHA risk charts, with performance evaluated via receiver operating characteristic (ROC) curve analysis (AUC) and standardized Z-scores.
Main Results:
- The Waist-to-Height Ratio (WHtR) demonstrated the best performance in identifying higher cardiovascular risk (CVR) in males and females according to Framingham and ACC/AHA charts.
- The A Body Shape Index (ABSI), when standardized, emerged as the superior linear predictor for CVR based on SCORE and ACC/AHA risk assessments, outperforming WHtR.
- The Hip Index (HI) showed no significant association with measures of cardiovascular risk in this population.
Conclusions:
- The Hip Index (HI) is not a reliable predictor of high cardiovascular risk (CVR) in the Spanish Caucasian population.
- The A Body Shape Index (ABSI) is directly and linearly associated with high CVR, exhibiting superior predictive performance compared to WHtR when analyzed as a standardized linear predictor.
- WHtR remains a valuable tool for CVR assessment, particularly in males and females according to specific risk stratification tools.
Abstract:
Our aim in this study was to investigate if Hip index (HI) improves the identification of cardiovascular risk (CVR) beyond that achieved with either the waist-to-height ratio (WHtR) or body mass index (BMI)-adjusted waist circumference (A body shape index [ABSI]) in the Spanish Caucasian population. Three thousand eight hundred forty-four subjects (1754 males, response rate 75.8%) were included. Anthropometric indices (AIs) included were HI, ABSI, and WHtR. CVR was estimated using the Framingham, Systematic COronary Risk Evaluation (SCORE), and American College of Cardiology/American Heart Association (ACC/AHA) charts. Areas under the receiver operating characteristic curve (AUC) were obtained to evaluate the performance of AIs in detecting CVR. We also estimated the AIs' standardized Z-scores and compared them against the CVR. AUC demonstrated that the best AI in males to estimate higher CVR according to Framingham and ACC/AHA charts was WHtR. In females, WHtR also achieved good performance and showed higher prediction capacity than the other AIs. After transforming to Z-scores, ABSI was the best linear predictor for CVR according to SCORE and ACC/AHA, although WHtR also proved to be good. HI did not associate with the measures of CVR. HI does not predict high CVR in the Spanish Caucasian Population. However, ABSI is directly and linearly related to high CVR, with a higher performance than WHtR when standardized and evaluated as a linear predictor.
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