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Research

DOES ABDOMINAL SUBCUTANEOUS FAT THICKNESS MEASURED BY ULTRASOUND PREDICT TOTAL FAT MASS IN YOUNG AND MIDDLE-AGED ADULTS?

The 28th Annual Meeting of the European Orthopaedic Research Society (EORS), held online, 17–18 September 2020.



Abstract

Background

Although there are predictive equations that estimate the total fat mass obtained from multiple-site ultrasound (US) measurements, the predictive equation of total fat mass has not been investigated solely from abdominal subcutaneous fat thickness. Therefore, the aims of this study were; (1) to develop regression-based prediction equations based on abdominal subcutaneous fat thickness for predicting fat mass in young- and middle-aged adults, and (2) to investigate the validity of these equations to be developed.

Methods

The study was approved by the Local Research Ethics Committee (Decision number: GO 19/788). Twenty-seven males (30.3 ± 8.7 years) and eighteen females (32.4 ± 9.5 years) were randomly divided into two groups as the model prediction group (19 males and 12 females) and the validation group (8 males and 6 females). Total body fat mass was determined by dual-energy X-ray absorptiometry (DXA). Abdominal subcutaneous fat thickness was measured by US. The predictive equations for total fat mass from US were determined as fat thickness (in mm) × standing height (in m). Statistical analyses were performed using R version 4.0.0. The association between the total fat mass and the abdominal subcutaneous fat thickness was interpreted using the Pearson test. The linear regression analysis was used to predict equations for total body fat mass from the abdominal subcutaneous fat thickness acquired by US. Then these predictive equations were applied to the validation group. The paired t-test was used to examine the difference between the measured and the predicted fat masses, and Lin's concordance correlation coefficient (CCC) was used as a further measure of agreement.

Results

There was a significant positive moderate correlation between the total fat mass and the abdominal subcutaneous fat thickness × height in the model prediction group of males (r = 0.588, p = 0.008), whereas significant positive very strong correlation was observed in the model prediction group of females (r = 0.896, p < 0.001). Predictive equations for DXA-measured total body fat mass from abdominal subcutaneous fat thickness using US were as follows: for males “Fat mass-DXA = 0.276 × (Fat thickness-US × Height) + 17.221” (R2 = 0.35, SEE = 3.6, p = 0.008); for females “Fat mass-DXA = 0.694 x (Fat thickness-US × Height) + 7.085” (R2 = 0.80, SEE = 2.8, p < 0.001). When fat mass prediction equations were applied to the validation groups, measured- and estimated-total fat masses in males and females were found similar (p = 0.9, p = 0.5, respectively). A good level of agreement between measurements in males and females was attained (CCC = 0.84, CCC = 0.76, respectively).

Conclusion

We developed and validated prediction equations that are convenient for determining total fat masses in young- and middle-aged adults using abdominal subcutaneous fat thickness obtained from the US. The abdominal subcutaneous fat thickness obtained from a single region by US might provide a noninvasive quick and easy evaluation not only in clinical settings but also on the field.