Comparison of obesity based on various obesity indices in metabolic dysfunction-associated fatty liver disease (MAFLD) subjects: A comparative analysis of anthropometric and bioimpedance indices

Widya Inarah Nadhilah Hamid; Andi Makbul Aman; Haerani Rasyid; Syakib Bakri; Nu'man As Daud; Arifin Seweng

doi:10.23750/abm.v96i3.16578

Comparison of obesity based on various obesity indices in metabolic dysfunction-associated fatty liver disease (MAFLD) subjects: A comparative analysis of anthropometric and bioimpedance indices

Authors

Widya Inarah Nadhilah Hamid Department of Internal Medicine, Faculty of Medicine, Hasanuddin University, Makassar, Indonesia https://orcid.org/0009-0004-7004-3097
Andi Makbul Aman Department of Internal Medicine, Faculty of Medicine, Hasanuddin University, Makassar, Indonesia https://orcid.org/0000-0002-1310-9721
Haerani Rasyid Department of Internal Medicine, Faculty of Medicine, Hasanuddin University, Makassar, Indonesia https://orcid.org/0000-0001-7404-2973
Syakib Bakri Department of Internal Medicine, Faculty of Medicine, Hasanuddin University, Makassar, Indonesia https://orcid.org/0000-0002-6615-5166
Nu'man As Daud Department of Internal Medicine, Faculty of Medicine, Hasanuddin University and Dr. Wahidin Sudirohusodo General Hospital, Makassar, Indonesia https://orcid.org/0000-0003-0931-0550
Arifin Seweng Department of Public Health and Community Medicine, Faculty of Medicine, Hasanuddin University, Makassar, Indonesia https://orcid.org/0000-0003-0853-7809

Keywords:

MAFLD, obesity, body mass index, waist circumference, bioelectrical impedance analysis

Abstract

Background and aim: Metabolic dysfunction-associated fatty liver disease (MAFLD) is a terminology that describes the condition of fatty liver accompanied by metabolic disorders. Previous studies have shown obesity is associated with MAFLD, but fatty liver can also be observed in non-obese individuals. The obesity measurement index can reflect obesity levels and can be used as a screening tool for metabolic diseases such as MAFLD. In addition to Body Mass Index (BMI), there are several other measurement indices, such as Waist Circumference (WC), Waist-to-Height Ratio (WHtR), as well as Body Fat Percentage (BF%) and Fat Mass Index (FMI). This study aims to see the comparison of obesity prevalence in MAFLD patients based on various measurement indices.

Methods: This study was conducted at Wahidin Sudirohusodo Hospital in Makassar, Indonesia, using an observational study with a cross-sectional design. The various obesity indices were performed once the patient was newly diagnosed with MAFLD. Data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 25.0.

Results: This study consisted of 44 males (57.9%) and 32 females (42.1%), with an average age 41.8 years. The prevalence of obesity based on various indices in MAFLD subjects was as follows: WHtR (100%), LP (88.2%), FMI (86.8%), BF% (86.8%), and BMI (80.3%).

Conclusions: The prevalence of obesity in MAFLD subjects was highest when measured by the WHtR index, followed by WC, FMI, BF%, and lowest when measured by BMI.

References

1. Eslam M, Newsome PN, Sarin SK, et al. A new definition for metabolic dysfunction-associated fatty liver disease: an international expert consensus statement. J Hepatol. 2020;73(1):202–9. doi: 10.1016/j.jhep.2020.03.039

2. Kuchay MS, Choudhary NS, Mishra SK. Pathophysiological mechanisms underlying MAFLD. Diabetes Metab Syndr. 2020;14(6):1875–87. doi: 10.1016/j.dsx.2020.09.026

3. Roeb E. Excess body weight and metabolic (dysfunction)-associated fatty liver disease (MAFLD). Visc Med. 2021;37(4):273–80. doi: 10.1159/000515445

4. Eigentler T, Lomberg D, Machann J, Stefan N. Lipodystrophic nonalcoholic fatty liver disease induced by immune checkpoint blockade. Ann Intern Med. 2020;172(12):836–7. doi: 10.7326/L19-0635

5. Golia N, Krishan K, Kashyap JR. Assessment of obesity by using various anthropometric measurements among patients with coronary heart disease residing in North India. Cureus. 2020;12(5):5–11. doi: 10.7759/cureus.7948

6. van der Poorten D, Milner KL, Hui J, et al. Visceral fat: a key mediator of steatohepatitis in metabolic liver disease. Hepatology. 2008;48(2):449–57. doi: 10.1002/hep.22350

7. Perseghin G. Lipids in the wrong place: visceral fat and nonalcoholic steatohepatitis. Diabetes Care. 2011;34(Suppl 2):10–3. doi: 10.2337/dc11-s249

8. Chiu TH, Huang YC, Chiu H, et al. Comparison of various obesity-related indices for identification of metabolic syndrome: a population-based study from Taiwan Biobank. Diagnostics. 2020;10(12). doi: 10.3390/diagnostics10121081

9. Roubenoff R. Applications of bioelectrical impedance analysis for body composition to epidemiologic studies. Am J Clin Nutr. 1996;64(Suppl 3). doi: 10.1093/ajcn/64.3.459S

10. Li YC, Li CI, Lin WY, et al. Percentage of body fat assessment using bioelectrical impedance analysis and dual-energy X-ray absorptiometry in a weight loss program for obese or overweight Chinese adults. PLoS One. 2013;8(4):2–8. doi: 10.1371/journal.pone.0058272

11. Ramírez-Vélez R, Correa-Bautista JE, Sanders-Tordecilla A, et al. Percentage of body fat and fat mass index as a screening tool for metabolic syndrome prediction in Colombian university students. Nutrients. 2017;9(9). doi: 10.3390/nu9091009

12. Zhang S, Wang L, Yu M, Guan W, Yuan J. Fat mass index as a screening tool for the assessment of non-alcoholic fatty liver disease. Sci Rep. 2022;12(1):1–12. doi: 10.1038/s41598-022-23729-1

13. Yuan Q, Wang H, Gao P, et al. Prevalence and risk factors of metabolic-associated fatty liver disease among 73,566 individuals in Beijing, China. Int J Environ Res Public Health. 2022;19(4). doi: 10.3390/ijerph19042096

14. Wang H, Zhang Y, Liu Y, et al. Comparison between traditional and new obesity measurement index for screening metabolic associated fatty liver disease. Front Endocrinol (Lausanne). 2023;14:1–12. doi: 10.3389/fendo.2023.1163682

15. Taheri E, Moslem A, Mousavi-Jarrahi A, et al. Predictors of metabolic-associated fatty liver disease (MAFLD) in adults: a population-based study in Northeastern Iran. Gastroenterol Hepatol Bed Bench. 2021;14(Suppl 1):S102–11. doi: 10.22037/ghfbb.vi.2447

16. Peng H, Pan L, Ran S, et al. Prediction of MAFLD and NAFLD using different screening indexes: a cross-sectional study in U.S. adults. Front Endocrinol (Lausanne). 2023;14:1–12. doi: 10.3389/fendo.2023.1083032

17. Duan SJ, Ren ZY, Zheng T, et al. Atherogenic index of plasma combined with waist circumference and body mass index to predict metabolic-associated fatty liver disease. World J Gastroenterol. 2022;28(36):5364–79. doi: 10.3748/wjg.v28.i36.5364

18. Sheng G, Xie Q, Wang R, Hu C, Zhong M, Zou Y. Waist-to-height ratio and non-alcoholic fatty liver disease in adults. BMC Gastroenterol. 2021;21(1):1–8. doi: 10.1186/s12876-021-01824-3

19. Choi JW, Yoo JJ, Kim SG, Kim YS. Bioelectrical impedance analysis can be an effective tool for screening fatty liver in patients with suspected liver disease. Healthc. 2022;10(11). doi: 10.3390/healthcare10112268

20. Ashwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev. 2012;13(3):275–86. doi: 10.1111/j.1467-789x.2011.00952.x

21. Chiu TH, Huang YC, Chiu H, et al. Comparison of various obesity-related indices for identification of metabolic syndrome: a population-based study from Taiwan Biobank. Diagnostics. 2020;10(12). doi: 10.3390/diagnostics10121081

22. Stranges S, Dorn JM, Muti P, et al. Body fat distribution, relative weight, and liver enzyme levels: a population-based study. Hepatology. 2004;39(3):754–63. doi: 10.1002/hep.20149