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Figure 1.

Flowchart enrolment of the study population.

5742 participants completed the online questionnaire. 1115 participants were excluded because of missing data (n = 1065), pregnancy (n = 7), or BMI <18.5 (n = 43). The final group sample consisted of 4627 participants.

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Figure 1 Expand

Table 1.

Characteristics of the total study population and according to cardiovascular disease status.

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Figure 2.

Correlations between ABSI, BMI, BRI, and WC.

A. Correlation between ABSI and BMI. Increase in ABSI and increase in BMI show a poor yet significant correlation (r = 0.087, P<0.001). B. Correlation between ABSI and WC. Increase in ABSI and increase in WC show a significant correlation (r = 0.678, P<0.001). C. Correlation between BRI and BMI. Increase in BRI and increase in BMI show a significant correlation (r = 0.780, P<0.001). D. Correlation between BRI and WC. Increase in BRI and increase in WC show a significant correlation (r = 0.898, P<0.001). E. Correlation between BRI and ABSI. Increase in BRI and increase in ABSI show a significant correlation (r = 0.642, P<0.001).

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Figure 2 Expand

Figure 3.

The discriminatory power of ABSI, BRI, BMI, and WC between subjects with or without CVD or CVD risk factors.

A. Area under the Receiver Operating Characteristic curve of ABSI, BRI, BMI, and WC to identify subjects with cardiovascular diseases. B. Area under the Receiver Operating Characteristic curve of ABSI, BRI, BMI, and WC to identify subjects with cardiovascular disease risk factors.

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Table 2.

Correlations between body size and shape.

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Table 2 Expand

Table 3.

Prevalence of CVD and CVD risk factors in quintiles of ABSI, BRI, BMI, and WC.

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Table 4.

The unadjusted odds of CVD and CVD risk factor prevalence for ABSI, BRI, BMI, and WC.

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Table 5.

The odds of CVD and CVD risk factor prevalence for quintiles of ABSI, BRI, BMI, and WC adjusted for sex, age, and smoking.

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