Assessing visceral and subcutaneous adiposity using segmented T2-MRI and multi-frequency segmental bioelectrical impedance: A sex-based comparative study: Assessing visceral adiposity among subjects with obesity

Entesar Dalah; Hayder  Hasan; Mohammed  Madkour; Abdulmunhem Obaideen; Moez Al-Islam Faris

doi:10.23750/abm.v92i3.10060

Assessing visceral and subcutaneous adiposity using segmented T2-MRI and multi-frequency segmental bioelectrical impedance: A sex-based comparative study

Assessing visceral adiposity among subjects with obesity

Authors

Entesar Z Dalah Clinical Support Services and Nursing Sector, Dubai Health Authority, Dubai, UAE, Department of Medical Diagnostic Imaging, College of Health Sciences, University of Sharjah, UAE https://orcid.org/0000-0002-4485-4775
Hayder A. Hasan Department of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah, UAE https://orcid.org/0000-0001-5580-1911
Mohammed I. Madkour Department of Medical Laboratory Sciences, College of Health Sciences, University of Sharjah, Sharjah, UAE https://orcid.org/0000-0002-9130-4928
Abdulmunhem Obaideen Department of Diagnostic Imaging, University Hospital Sharjah, Sharjah, UAE https://orcid.org/0000-0002-6269-0583
Moez Al-Islam E. Faris Department of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah, UAE https://orcid.org/0000-0002-7970-2616

DOI: https://doi.org/10.23750/abm.v92i3.10060

Keywords:

Abdominal visceral adipose tissue, Central obesity, Manual segmentation, Obesity, Semi-automated segmentation, T2 Weighted Magnetic Resonance Imaging

Abstract

Background and Aim: This study aims to quantify abdominal visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) using T2-weighted magnetic resonance imaging (MRI), and assess the extent of its concordance with VAT surface-area measured by a state-of-the-art segmental multi-frequency bioelectrical impedance analysis (BIA) device. A comparison between manual and semi-automated segmentation was conducted. Further, abdominal VAT and SAT sex-based comparison in healthy Arab adults was piloted. Methods: A cross-sectional design was followed to recruit subjects. Abdominal VAT and SAT were determined on T2-weighted MRI manually and semi-automatically. Body composition was assessed using a BIA machine. Statistical differences between the abdominal VAT areas defined by BIA, manual, and semi-automated MRI were compared. Correlation between all methods was assessed, and statistical differences between sex abdominal VAT/SAT defined areas were compared. Results: A total of 165 abdominal T2-weighted MR images taken for 55 overweight/obese adult subjects were analyzed Differences between manual and semi-automated MRI-obtained abdominal VAT and SAT were found statistically significant (P<0.001) for all subjects. Mean abdominal VAT using the BIA technique was found to correlate significantly with manually and semi-automated T2-weighted MRI defined VAT (r=0.7436; P<0.001 and r=0.8275; P<0.001, respectively). Abdominal VAT was significantly (P<0.001) different between male and female subjects accumulating at different abdominal levels. Conclusion: Semi-automatic segmentation showed a stronger significant correlation with BIA compared to manual segmentation, implying a more reliable quantification of abdominal VAT/SAT. Segmental BIA technique may serve as a feasible and convenient assessment tool for the visceral adiposity in obese subjects.

References

Seidell JC, Halberstadt J. The global burden of obesity and the challenges of prevention. Annals of Nutrition and Metabolism. 2015;66:7-12.

Lee S, Kuk JL, Hannon TS, Arslanian SA. Race and gender differences in the relationships between anthropometrics and abdominal fat in youth. Obesity. 2008;16:1066-71.

El-Hajj Chehadeh S, Osman W, Nazar S, Jerman L, Alghafri A, Sajwani A, et al. Implication of genetic variants in overweight and obesity susceptibility among the young Arab population of the United Arab Emirates. Gene. 2020;739:144509.

Addeman BT, Kutty S, Perkins TG, Soliman AS, Wiens CN, McCurdy CM, et al. Validation of volumetric and single‐slice MRI adipose analysis using a novel fully automated segmentation method. Journal of Magnetic Resonance Imaging. 2015;41:233-41.

Hu HH, Nayak KS, Goran MI. Assessment of abdominal adipose tissue and organ fat content by magnetic resonance imaging. obesity reviews. 2011;12:e504-e15.

Hu HH, Chen J, Shen W. Segmentation and quantification of adipose tissue by magnetic resonance imaging. Magnetic Resonance Materials in Physics, Biology, and Medicine. 2016;29:259-76.

Elffers TW, de Mutsert R, Lamb HJ, de Roos A, van Dijk KW, Rosendaal FR, et al. Body fat distribution, in particular visceral fat, is associated with cardiometabolic risk factors in obese women. PloS one. 2017;12.

Sulaiman N, Elbadawi S, Hussein A, Abusnana S, Madani A, Mairghani M, et al. Prevalence of overweight and obesity in United Arab Emirates Expatriates: the UAE national diabetes and lifestyle study. Diabetology & metabolic syndrome. 2017;9:88.

Liu L, Feng J, Zhang G, Yuan X, Li F, Yang T, et al. Visceral adipose tissue is more strongly associated with insulin resistance than subcutaneous adipose tissue in Chinese subjects with pre-diabetes. Current medical research and opinion. 2018;34:123-9.

Faris MeA-IE, Madkour MI, Obaideen AK, Dalah EZ, Hasan HA, Radwan H, et al. Effect of Ramadan diurnal fasting on visceral adiposity and serum adipokines in overweight and obese individuals. Diabetes Research and Clinical Practice. 2019;153:166-75.

Zuriaga MA, Fuster JJ, Farb MG, MacLauchlan S, Bretón-Romero R, Karki S, et al. Activation of non-canonical WNT signaling in human visceral adipose tissue contributes to local and systemic inflammation. Scientific reports. 2017;7:1-10.

Mtintsilana A, Micklesfield LK, Chorell E, Olsson T, Goedecke JH. Fat redistribution and accumulation of visceral adipose tissue predicts type 2 diabetes risk in middle-aged black South African women: a 13-year longitudinal study. Nutrition & diabetes. 2019;9:1-12.

Holowatyj AN, Haffa M, Lin T, Gigic B, Ose J, Warby C, et al. Crosstalk between visceral adipose and tumor tissue in colorectal cancer patients: Molecular signals driving host-tumor interaction. AACR; 2018.

Borga M. MRI adipose tissue and muscle composition analysis—a review of automation techniques. The British journal of radiology. 2018;91:20180252.

Silver HJ, Welch EB, Avison MJ, Niswender KD. Imaging body composition in obesity and weight loss: challenges and opportunities. Diabetes Metab Syndr Obes. 2010;3:337-47.

Lemos T, Gallagher D. Current body composition measurement techniques. Curr Opin Endocrinol Diabetes Obes. 2017;24:310-4.

Nuttall FQ. Body Mass Index: Obesity, BMI, and Health: A Critical Review. Nutr Today. 2015;50:117-28.

Seimon RV, Wild-Taylor AL, Gibson AA, Harper C, McClintock S, Fernando HA, et al. Less Waste on Waist Measurements: Determination of Optimal Waist Circumference Measurement Site to Predict Visceral Adipose Tissue in Postmenopausal Women with Obesity. Nutrients. 2018;10:239.

Who EC. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet (London, England). 2004;363:157.

Kishida K, Funahashi T, Matsuzawa Y, Shimomura I. Visceral adiposity as a target for the management of the metabolic syndrome. Ann Med. 2012;44:233-41.

Poonawalla AH, Sjoberg BP, Rehm JL, Hernando D, Hines CD, Irarrazaval P, et al. Adipose tissue MRI for quantitative measurement of central obesity. Journal of Magnetic Resonance Imaging. 2013;37:707-16.

Hui SC, Zhang T, Shi L, Wang D, Ip C-B, Chu WC. Automated segmentation of abdominal subcutaneous adipose tissue and visceral adipose tissue in obese adolescents in MRI. Magnetic resonance imaging. 2018;45:97-104.

Shen N, Li X, Zheng S, Zhang L, Fu Y, Liu X, et al. Automated and accurate quantification of subcutaneous and visceral adipose tissue from magnetic resonance imaging-based on machine learning. Magnetic resonance imaging. 2019;64:28-36.

Klopfenstein BJ, Kim M, Krisky C, Szumowski J, Rooney W, Purnell J. Comparison of 3 T MRI and CT for the measurement of visceral and subcutaneous adipose tissue in humans. The British journal of radiology. 2012;85:e826-e30.

Pietiläinen KH, Kaye S, Karmi A, Suojanen L, Rissanen A, Virtanen KA. Agreement of bioelectrical impedance with dual-energy X-ray absorptiometry and MRI to estimate changes in body fat, skeletal muscle, and visceral fat during a 12-month weight loss intervention. British journal of nutrition. 2013;109:1910-6.

Wingo BC, Barry VG, Ellis AC, Gower BA. Comparison of segmental body composition estimated by bioelectrical impedance analysis and dual-energy X-ray absorptiometry. Clinical Nutrition ESPEN. 2018;28:141-7.

Chaudry O, Grimm A, Friedberger A, Kemmler W, Uder M, Jakob F, et al. Magnetic Resonance Imaging and Bioelectrical Impedance Analysis to Assess Visceral and Abdominal Adipose Tissue. Obesity. 2020.

Browning LM, Mugridge O, Chatfield MD, Dixon AK, Aitken SW, Joubert I, et al. Validity of a new abdominal bioelectrical impedance device to measure abdominal and visceral fat: comparison with MRI. Obesity. 2010;18:2385-91.

Armao D, Guyon JP, Firat Z, Brown MA, Semelka RC. Accurate quantification of visceral adipose tissue (VAT) using water‐saturation MRI and computer segmentation: Preliminary results. Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine. 2006;23:736-41.

Zgallai W, Brown T, Murtada A, Ali S, Haji A, Khalil K, et al. The application of deep learning to quantify SAT/VAT in the human abdominal area. 2019 Advances in Science and Engineering Technology International Conferences (ASET): IEEE; 2019. p. 1-5.