Why the study?
Diagnostic medical sonography was investigated as an approach to assess abdominal adiposity longitudinally and determine risk for metabolic conditions.
Does diagnostic medical sonography (DMS) correlate with BMI, WC, and DXA in assessing abdominal adiposity in overweight and obese cancer survivors?
Does diagnostic medical sonography (DMS) correlate with BMI, WC, and DXA in assessing abdominal adiposity in overweight and obese cancer survivors?
Diagnostic medical sonography is a viable indirect imaging tool that correlates with established measures for assessing abdominal adiposity and metabolic syndrome risk.
Answers are grounded in this paper and cite their sources.
Sonographic abdominal adiposity measures track changes over time and correlate with DXA; leaves open whether DMS should supplement BMI/WC for metabolic risk screening.
Obesity has been linked to several chronic health conditions, including increased risk for cardiovascular disease, type 2 diabetes, some cancers, and metabolic syndrome. Understanding the trends in metabolic syndrome and examining those who are at an increased risk for metabolic conditions is paramount. Currently body mass index (BMI), waist circumference (WC), and dual-energy X-ray absorptiometry (DXA) are believed to be appropriate screening measures for assessing body composition and metabolic syndrome risk. This approach uses diagnostic medical sonography (DMS) as a tool for assessing abdominal adiposity in hopes of this methodology being adopted to help determine an individual’s risk for metabolic conditions. A convenience sample of overweight and obese cancer survivors provided several anthropometric measures as well as imaging data to determine if sonographic measures of abdominal fat could be assessed longitudinally. These measures were then compared to corresponding measures of BMI, WC, and DXA (android percent body fat, subscores). These measures demonstrated moderately positive association and were statistically significant. DMS is an indirect imaging tool that could be used in correlation with other measures to help determine an individual’s risk for metabolic syndrome.
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Stigall et al. (2019) studied this question.
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