Use of B-mode Ultrasound for Visceral Fat Mass Evaluation: Comparisons with Magnetic Resonance Imaging.

Abe, Takashi; Kawakami, Yasuo; Sugita, Masaaki; Yoshikawa, Kohki; Fukunaga, Tetsuo

doi:10.2114/ahs.14.133

Cited by 53 publications

(51 citation statements)

References 22 publications

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“…Abe et al (1993Abe et al ( , 1995 reported that the correlation between the predicted IFM and VFA at the umbilical level measured by MRI was 0.65 when calculating subcutaneous fat mass from whole body surface and mean subcutaneous thickness of whole body, and 0.75 when calculating subcutaneous fat mass as the sum of segmental subcutaneous fat mass determined from the surface area and mean subcutaneous thickness in six body segments (face and neck, upper arm, forearm, thigh, lower leg and trunk). In this study, a comparable relationship between VFA L4-5 measured by CT and IFM based on DXA (r ¼ 0.62) was obtained when predicting IFM based on DXA using mean of three skinfolds, which closely related with the mean of skinfolds at 14 sites.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies required skinfold measurements at 12-15 sites to predict subcutaneous fat mass (Davies et al, 1986;Hattori et al, 1991;Komiya et al, 1992Komiya et al, , 2000Abe et al, 1993Abe et al, , 1995Abe et al, , 1996a. In predicting segmental body surface and subcutaneous fat mass (Hattori et al, 1991;Abe et al, 1995), Abbreviations: BIA, bioelectrical impedance analysis; DXA, dual-energy X-ray absorptiometry; s.e.e., standard error of estimate; VFA, Visceral Fat Area. This Table shows the results of mean difference and regression analysis between the reference and predicted values when applying each prediction equation obtained in this study to the cross-validation group.…”

Section: Discussionmentioning

confidence: 99%

“…Subcutaneous fat mass and IFM were estimated referring to previous studies used with a Japanese population (Hattori et al, 1991;Abe et al, 1993Abe et al, , 1995Abe et al, , 1996aKomiya et al, 2000). The subcutaneous fat mass was calculated from the following equation:…”

Section: Methodsmentioning

confidence: 99%

“…Total fat mass was estimated by a dualenergy X-ray absorptiometry system (DPX-L; Lunar Radiation Corp., Madison, WI, USA; whole-body scanning, software version 1.3Z). In previous studies (Hattori et al, 1991;Abe et al, 1993Abe et al, , 1995Abe et al, , 1996aKomiya et al, 2000), total fat mass was estimated based on hydrostatic weighing. Although measurement principles differ between DXA and hydrostatic weighing, DXA measurement is commonly used as a reference for body composition assessment in many studies.…”

Section: Measurementmentioning

confidence: 99%

“…This method is considered to be practical and its rational validity can be also recognized. Although whether the predicted IFM completely reflects the actual adipose tissue accumulated around the abdominal viscera has not been sufficiently examined, previous studies reported that the predicted IFM significantly correlated with visceral fat mass (area) measured by CT or MRI (Davies et al, 1986;Abe et al, 1993Abe et al, , 1995Abe et al, , 1996aKomiya et al, 2000). These reports indicate that this predicted IFM closely relates to the internal fat content, and can be used as an important predictor of internal fat content.…”

Section: Introductionmentioning

confidence: 97%

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Prediction of visceral fat area in Japanese adults: proposal of prediction method applicable in a field setting

Demura

Sato

2006

Eur J Clin Nutr

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Objective: This study aimed to develop a prediction equation for the visceral fat area at the umbilical level (VFA L4-5 ) in Japanese adults, using internal fat mass (IFM) estimated from a few anthropometric variables. Methods: Subjects were 112 adults aged from 25 to 82 years (body mass index (BMI) ¼ 24.273.1 kg/m 2 , ranged from 15.7 to 31.2 kg/m 2 ). Another 60 adults aged from 21 to 71 years were recruited for the crossvalidation group (BMI ¼ 24.574.0 kg/m 2 , ranged from 17.1 to 34.6 kg/m 2 ). We examined (1) the prediction of IFM based on a small number of skinfold thicknesses; (2) the prediction of VFA L4-5 using IFM and (3) the application of bioelectrical impedance analysis (BIA) measurement. VFA L4-5 was measured by computed tomography (reference value). Total fat mass was measured by dual-energy X-ray absorptiometry (DXA) and single-frequency BIA with 8-point tactile electrodes. Results: Three skinfolds at the abdomen, side chest and suprailiac were selected to estimate IFM. From IFM estimated using these three skinfolds, waist-to-hip ratio (WHR), sex and age, about 75% of the variance of VFA L4-5 could be explained (Eq VFA 1: R ¼ 863, R 2 ¼ 0.745, standard error of estimate (s.e.e.) ¼ 20.483 cm 2 ). When substituting IFM based on BIA measurement (IFM BIA ) into Eq VFA 1, there were no significant mean differences from the reference in both equations, and high correlations were found (r ¼ 0.860, s.e.e. ¼ 20.902 cm 2 ), although a significant mean difference in total fat mass was found between DXA and BIA measurements. The prediction equation using IFM BIA (Eq VFA 2) could have prediction accuracy comparable with that of Eq VFA 1 (Eq VFA 2: R ¼ 879, R 2 ¼ 0.773, s.e.e. ¼ 20.324 cm 2 ). Furthermore, when applying these equations to the crossvalidation group, there were cross-validity in both equations. Conclusion: This study proposed a prediction equation for VFA L4-5 from WHR and IFM based on three skinfolds, and the validity of BIA measurement in Japanese adults. We can propose the procedure for a field setting.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Measurementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

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Prediction of visceral fat area in Japanese adults: proposal of prediction method applicable in a field setting

Demura

Sato

2006

Eur J Clin Nutr

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Emergence of imaging technology beyond the clinical setting: Utilization of mobile health tools for at‐home testing

Starkoff,

Nickerson

2024

Nut in Clin Prac

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Body composition assessment plays a pivotal role in understanding health, disease risk, and treatment efficacy. This narrative review explores two primary aspects: imaging techniques, namely ultrasound (US) and dual‐energy x‐ray absorptiometry (DXA), and the emergence of artificial intelligence (AI) and mobile health apps in telehealth for body composition. Although US is valuable for assessing subcutaneous fat and muscle thickness, DXA accurately quantifies bone mineral content, fat mass, and lean mass. Despite their effectiveness, accessibility and cost remain barriers to widespread adoption. The integration of AI‐powered image analysis may help explain tissue differentiation, whereas mobile health apps offer real‐time metabolic monitoring and personalized feedback. New apps such as MeThreeSixty and Made Health and Fitness offer the advantages of clinic‐based imaging techniques from the comfort of home. These innovations hold the potential for individualizing strategies and interventions, optimizing clinical outcomes, and empowering informed decision‐making for both healthcare professionals and patients/clients. Navigating the intricacies of these emerging tools, critically assessing their validity and reliability, and ensuring inclusivity across diverse populations and conditions will be crucial in harnessing their full potential. By integrating advancements in body composition assessment, healthcare can move beyond the limitations of traditional methods and deliver truly personalized, data‐driven care to optimize well‐being.

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Cardiometabolic Risk and Epicardial Adipose Tissue

Iacobellis

Fricke

2020

Epicardial Adipose Tissue

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Use of B-mode Ultrasound for Visceral Fat Mass Evaluation: Comparisons with Magnetic Resonance Imaging.

Cited by 53 publications

References 22 publications

Prediction of visceral fat area in Japanese adults: proposal of prediction method applicable in a field setting

Prediction of visceral fat area in Japanese adults: proposal of prediction method applicable in a field setting

Emergence of imaging technology beyond the clinical setting: Utilization of mobile health tools for at‐home testing

Cardiometabolic Risk and Epicardial Adipose Tissue

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