Abstract
Body mass index (BMI) has limited accuracy for predicting cardiovascular diseases (CVD) and is not capable of identifying sarcopenic obesity, the combination of sarcopenia (an age-associated decline in muscle mass and physical function) and obesity. To overcome this, the z-score of the log-transformed A Body Shape Index (LBSIZ) was recently introduced as a measure of obesity using waist circumference, height, and weight. We aimed to investigate the association of LBSIZ with sarcopenic obesity and CVD, and propose appropriate cut-off values using the National Health and Nutrition Examination Survey 1999–2016 data. Of 92,062 participants, 40,468 adults (≥20 years) were included. Overall area under curve (AUC) of LBSIZ was 0.735 (95% confidence interval [CI]: 0.716–0.754) for sarcopenic obesity, and 0.695 (95% CI: 0.687–0.703) for CVD. The subgroup analysis of ethnicity/race showed similar results. Waist circumference (WC), BMI, conicity index, body roundness index (BRI), Clinica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE), new BMI, and waist to height ratio (WHtR) showed a negative association with sarcopenic obesity, while LBSIZ and conicity index showed a positive association. The AUC of LBSIZ was significantly higher for sarcopenic obesity than that of conicity index (p < 0.001). The AUC of LBSIZ was significantly higher for CVD than those of parameters including WC, BMI, BRI, CUN-BAE, new BMI, and WHtR (p < 0.001). The AUC for conicity index alone was comparable to that of LBSIZ for CVD. Overall LBSIZ cut-off was 0.35 for both sarcopenic obesity (sensitivity, 65.3%; specificity, 71.5%) and CVD (sensitivity, 63.3%; specificity, 66.6%). These results may be useful not only to identify sarcopenic obesity, but also to conduct CVD risk assessment in the clinical setting.
Highlights
In muscle mass and physical function, and may synergistically worsen the adverse effects of obesity, leading to higher disability, morbidity and mortality[7]
LBSIZ showed a positive association with fat mass index (FMI) and a negative association with appendicular skeletal mass index (ASMI), while all other obesity parameters showed a positive association with both FMI and ASMI (Table 3)
In the subgroup analysis according to ethnicity/race, the area under curve (AUC) for sarcopenic obesity were 0.717, 0.740, and 0.804 for the Hispanic, Non-Hispanic white, and Non-Hispanic black groups, respectively
Summary
In muscle mass and physical function, and may synergistically worsen the adverse effects of obesity, leading to higher disability, morbidity and mortality[7]. Obesity can be assessed by directly measuring body fat via computed tomography (CT), magnetic resonance imaging, DEXA, and positron emission tomography (PET)-CT8. These methods are costly and have limitations that make the use of these modalities for diagnosing obesity in real clinical settings challenging; instead, indirect indices of obesity are used. BMI has limited accuracy for predicting the amount and distribution of body fat and is not capable of identifying sarcopenic obesity[9] It is limited in its ability to clinically predict the risk of chronic diseases such as CVD10–12. We examined its relationship with both sarcopenic obesity and CVD risk, compared to other obesity parameters, and provided appropriate cut-off values to identify individuals at high risk for sarcopenic obesity and CVD
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