Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Prevention of Non Communicable Diseases

The relative contributions of soft tissue mass components as risk or protective factors of non-alcoholic fatty liver disease in children

European Journal of Clinical Nutrition volume 77, pages 1167–1172 (2023)Cite this article

Subjects

Abstract

Background/Objective

Several body components are known to be associated with non-alcoholic fatty liver disease (NAFLD) in children. However, the relative contributions of soft tissue mass components as risk or protective factors of NAFLD are largely unknown because measurements of these components are often highly correlated. Therefore, we aimed to estimate levels of association between soft tissue mass components and NAFLD.

Subjects/Methods

We collected the medical records of 555 Chinese children (aged 3–18 years). Five mutually exclusive and exhaustive components of soft tissue mass were measured using dual energy X-ray absorptiometry. NAFLD was diagnosed with abdominal B-ultrasound scan. We fit Dirichlet regression and multivariate linear regression models wherein age and NAFLD were used as predictors of the proportional measurements of soft tissue mass components.

Results

The proportion of android fat was significantly higher in children with NAFLD than in those without NAFLD (ratio of proportions ranged from 1.18 to 1.30), whereas proportions of trunk lean and limb lean were significantly lower (ratio of proportions ranged from 0.87 to 0.92 for trunk lean and from 0.82 to 0.91 for limb lean). The proportion of gynoid fat was slightly higher in boys with NAFLD than in those without NAFLD (ratio = 1.05), but this proportion was not significantly higher in girls. The association between the proportion of android fat and NAFLD appeared to be somewhat greater than the associations between proportions of trunk lean or limb lean components and NAFLD.

Conclusion

Our findings suggest that lowering fat mass and increasing lean mass can both be used to combat NAFLD in children and that more studies are needed to determine the association between gynoid fat and NAFLD.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Pairwise scatterplot for proportions of soft tissue mass components.
Fig. 2: Ratios for soft tissue mass composition of children with/without NAFLD using Dirichlet regression.
Fig. 3: Ratios for soft tissue mass composition of children with/without NAFLD using multivariate linear regression.

Similar content being viewed by others

Data availability

De-identified cross-sectional data used in the analysis can be made available after authors' review of request for data.

References

  1. Anderson EL, Howe LD, Jones HE, Higgins JP, Lawlor DA, Fraser A. The Prevalence of Non-Alcoholic Fatty Liver Disease in Children and Adolescents: A Systematic Review and Meta-Analysis. PLoS One. 2015;10:e0140908.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Stefan N, Häring HU, Cusi K. Non-alcoholic fatty liver disease: causes, diagnosis, cardiometabolic consequences, and treatment strategies. Lancet Diabetes Endocrinol. 2019;7:313–24.

    Article  PubMed  Google Scholar 

  3. Newton KP, Hou J, Crimmins NA, Lavine JE, Barlow SE, Xanthakos SA, et al. Prevalence of Prediabetes and Type 2 Diabetes in Children With Nonalcoholic Fatty Liver Disease. JAMA Pediatr. 2016;170:e161971.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Cohen CC, Perng W, Sundaram SS, Scherzinger A, Shankar K, Dabelea D. Hepatic Fat in Early Childhood Is Independently Associated With Estimated Insulin Resistance: The Healthy Start Study. J Clin Endocrinol Metab. 2021;106:3140–50.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Maffeis C, Morandi A. Body composition and insulin resistance in children. Eur J Clin Nutr. 2018;72:1239–45.

    Article  PubMed  Google Scholar 

  6. de Onis M, Onyango AW, Borghi E, Siyam A, Nishida C, Siekmann J. Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ. 2007;85:660–7.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Leskinen T, Eloranta AM, Tompuri T, Saari A, Ollila H, Mäkelä J, et al. Changes in body composition by age and obesity status in preschool-aged children: the STEPS study. Eur J Clin Nutr. 2021;75:57–65.

    Article  CAS  PubMed  Google Scholar 

  8. Wells JC, Williams JE, Chomtho S, Darch T, Grijalva-Eternod C, Kennedy K, et al. Body-composition reference data for simple and reference techniques and a 4-component model: a new UK reference child. Am J Clin Nutr. 2012;96:1316–26.

    Article  CAS  PubMed  Google Scholar 

  9. Wright CM, Cole TJ, Fewtrell M, Williams JE, Eaton S, Wells JC. Body composition data show that high BMI centiles overdiagnose obesity in children aged under 6 years. Am J Clin Nutr. 2022;116:122–31.

    Article  PubMed  Google Scholar 

  10. Fan JG, Kim SU, Wong VW. New trends on obesity and NAFLD in Asia. J Hepatol. 2017;67:862–73.

    Article  PubMed  Google Scholar 

  11. Ariya M, Koohpayeh F, Ghaemi A, Osati S, Davoodi SH, Razzaz JM, et al. Assessment of the association between body composition and risk of non-alcoholic fatty liver. PLoS One. 2021;16:e0249223.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Parente EB, Dahlström EH, Harjutsalo V, Inkeri J, Mutter S, Forsblom C, et al. The Relationship Between Body Fat Distribution and Nonalcoholic Fatty Liver in Adults With Type 1 Diabetes. Diabetes Care. 2021;44:1706–13.

    Article  CAS  PubMed  Google Scholar 

  13. Alferink LJM, Trajanoska K, Erler NS, Schoufour JD, de Knegt RJ, Ikram MA, et al. Nonalcoholic Fatty Liver Disease in The Rotterdam Study: About Muscle Mass, Sarcopenia, Fat Mass, and Fat Distribution. J Bone Min Res. 2019;34:1254–63.

    Article  CAS  Google Scholar 

  14. Ramírez-Vélez R, Izquierdo M, Correa-Bautista JE, Correa-Rodríguez M, Schmidt-RioValle J, González-Jiménez E, et al. Liver Fat Content and Body Fat Distribution in Youths with Excess Adiposity. J Clin Med. 2018;7:528.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Kim G, Lee SE, Lee YB, Jun JE, Ahn J, Bae JC, et al. Relationship Between Relative Skeletal Muscle Mass and Nonalcoholic Fatty Liver Disease: A 7-Year Longitudinal Study. Hepatology. 2018;68:1755–68.

    Article  CAS  PubMed  Google Scholar 

  16. Bouchi R, Fukuda T, Takeuchi T, Nakano Y, Murakami M, Minami I, et al. Gender difference in the impact of gynoid and android fat masses on the progression of hepatic steatosis in Japanese patients with type 2 diabetes. BMC Obes. 2017;4:27.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Group of China Obesity Task Force. Soft tissue mass index reference norm for screening overweight and obesity in Chinese children and adolescents. Zhonghua Liu Xing Bing Xue Za Zhi. 2004;25:97–102.

    Google Scholar 

  18. Aucouturier J, Meyer M, Thivel D, Taillardat M, Duché P. Effect of android to gynoid fat ratio on insulin resistance in obese youth. Arch Pediatr Adolesc Med. 2009;163:826–31.

    Article  PubMed  Google Scholar 

  19. Gallagher C, Moschonis G, Lambert KA, Karaglani E, Mavrogianni C, Gavrili S, et al. Sugar-sweetened beverage consumption is associated with visceral fat in children. Br J Nutr. 2021;125:819–27.

    Article  CAS  PubMed  Google Scholar 

  20. Chen Y, He D, Yang T, Zhou H, Xiang S, Shen L, et al. Relationship between body composition indicators and risk of type 2 diabetes mellitus in Chinese adults. BMC Public Health. 2020;20:452.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Moschonis G, Chrousos GP, Lionis C, Mougios V, Manios Y. Association of total body and visceral fat mass with iron deficiency in preadolescents: the Healthy Growth Study. Br J Nutr. 2012;108:710–9.

    Article  CAS  PubMed  Google Scholar 

  22. Cruz ML, Bergman RN, Goran MI. Unique effect of visceral fat on insulin sensitivity in obese Hispanic children with a family history of type 2 diabetes. Diabetes Care. 2002;25:1631–6.

    Article  PubMed  Google Scholar 

  23. Henriksson P, Cadenas-Sanchez C, Leppänen MH, Delisle Nyström C, Ortega FB, Pomeroy J, et al. Associations of Fat Mass and Fat-Free Mass with Physical Fitness in 4-Year-Old Children: Results from the MINISTOP Trial. Nutrients. 2016;8:473.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Gower BA, Nagy TR, Goran MI. Visceral fat, insulin sensitivity, and lipids in prepubertal children. Diabetes. 1999;48:1515–21.

    Article  CAS  PubMed  Google Scholar 

  25. Wyszyńska J, Matłosz P, Asif M, Szybisty A, Lenik P, Dereń K, et al. Association between objectively measured body composition, sleep parameters and physical activity in preschool children: a cross-sectional study. BMJ Open. 2021;11:e042669.

    Article  PubMed  Google Scholar 

  26. Chen M, Liu J, Ma Y, Li Y, Gao D, Chen L, et al. Association between Body Fat and Elevated Blood Pressure among Children and Adolescents Aged 7-17 Years: Using Dual-Energy X-ray Absorptiometry (DEXA) and Bioelectrical Impedance Analysis (BIA) from a Cross-Sectional Study in China. Int J Environ Res Public Health. 2021;18:9254.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Bagheri M, Ansari S, Sotoudeh G, Mahmoudi M, Speakman JR, Djafarian K. Serum ghrelin levels and gender-related indices of body composition in prepubertal children: a cross-sectional study. Eur J Nutr. 2015;54:283–90.

    Article  CAS  PubMed  Google Scholar 

  28. Jin B, Lin H, Yuan J, Dong G, Huang K, Wu W, et al. Abdominal adiposity and total body fat as predictors of cardiometabolic health in Children and adolescents with obesity. Front Endocrinol. 2020;11:579.

    Article  Google Scholar 

  29. Fu Y, Gou W, Hu W, Mao Y, Tian Y, Liang X, et al. Integration of an interpretable machine learning algorithm to identify early life risk factors of childhood obesity among preterm infants: a prospective birth cohort. BMC Med. 2020;18:184.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Bazzocchi A, Ponti F, Albisinni U, Battista G, Guglielmi G. DXA: Technical aspects and application. Eur J Radio. 2016;85:1481–92.

    Article  Google Scholar 

  31. Khov N, Sharma A, Riley TR. Bedside ultrasound in the diagnosis of nonalcoholic fatty liver disease. World J Gastroenterol. 2014;20:6821–5.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Miyake T, Miyazaki M, Yoshida O, Kanzaki S, Nakaguchi H, Nakamura Y, et al. Relationship between body composition and the histology of non-alcoholic fatty liver disease: a cross-sectional study. BMC Gastroenterol. 2021;21:170.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Fang D, Tang W, Zhao X, Sun H, Gu T, Bi Y. Gender differences in the association of body composition and biopsy-proved nonalcoholic steatohepatitis. Hepatol Int. 2022;16:337–47.

    Article  PubMed  Google Scholar 

  34. Zhou J, Zhou F, Wang W, Zhang XJ, Ji YX, Zhang P, et al. Epidemiological Features of NAFLD From 1999 to 2018 in China. Hepatology. 2020;71:1851–64.

    Article  PubMed  Google Scholar 

  35. Vague J. The degree of masculine differentiation of obesities: a factor determining predisposition to diabetes, atherosclerosis, gout, and uric calculous disease. Am J Clin Nutr. 1956;4:20–34.

    Article  CAS  PubMed  Google Scholar 

  36. Thörne A, Löfgren P, Hoffstedt J. Increased visceral adipocyte lipolysis-a pathogenic role in nonalcoholic fatty liver disease? J Clin Endocrinol Metab. 2010;95:E209–213.

    Article  PubMed  Google Scholar 

  37. Jakobsen MU, Berentzen T, Sørensen TI, Overvad K. Abdominal obesity and fatty liver. Epidemiol Rev. 2007;29:77–87.

    Article  CAS  PubMed  Google Scholar 

  38. Ciardullo S, Oltolini A, Cannistraci R, Muraca E, Perseghin G. Sex-related association of nonalcoholic fatty liver disease and liver fibrosis with body fat distribution in the general US population. Am J Clin Nutr. 2022;115:1528–34.

    Article  PubMed  Google Scholar 

  39. Johansen MJ, Vonsild Lund MA, Ängquist L, Fonvig CE, Holm LA, Chabanova E, et al. Possible prediction of obesity-related liver disease in children and adolescents using indices of body composition. Pediatr Obes. 2022;17:e12947.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Yodoshi T, Orkin S, Arce Clachar AC, Bramlage K, Sun Q, Fei L, et al. Muscle Mass Is Linked to Liver Disease Severity in Pediatric Nonalcoholic Fatty Liver Disease. J Pediatr. 2020;223:93–99.e92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Dumuid D, Martín-Fernández JA, Ellul S, Kenett RS, Wake M, Simm P, et al. Analysing body composition as compositional data: An exploration of the relationship between body composition, soft tissue mass and bone strength. Stat Methods Med Res. 2021;30:331–46.

    Article  CAS  PubMed  Google Scholar 

  42. Aitchison J. Principal component analysis of compositional data. Biometrika. 1983;70:57–65.

    Article  Google Scholar 

  43. Aitchison J. The Statistical Analysis of Compositional Data. J R Stat Soc: Ser B. 1982;44:139–60.

    Google Scholar 

  44. Dumuid D, Wake M, Clifford S, Burgner D, Carlin JB, Mensah FK, et al. The Association of the Body Composition of Children with 24-Hour Activity Composition. J Pediatr. 2019;208:43–49.e49.

    Article  PubMed  Google Scholar 

  45. Greenacre M. Compositional Data Analysis. Annu Rev Stat Its Application. 2021;8:271–99.

    Article  Google Scholar 

  46. Coenders G, Pawlowsky-Glahn V. On interpretations of tests and effect sizes in regression models with a compositional predictor. Sort: Stat Oper Res Trans. 2020;44:201–20.

    Google Scholar 

  47. Waal DD, Coetzer R, Merwe SVD. A multivariate gamma distribution applied to compositional data analysis. South Afr Stat J. 2016;50:273–83.

    Google Scholar 

  48. Strauss BJ, Gibson PR, Stroud DB, Borovnicar DJ, Xiong DW, Keogh J. Total body dual X-ray absorptiometry is a good measure of both fat mass and fat-free mass in liver cirrhosis compared to "gold-standard" techniques. Melbourne Liver Group. Ann N. Y Acad Sci. 2000;904:55–62.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank the patients collected in our study for their important contribution.

Funding

This work was supported by the National Key R&D Program of China (grant numbers 2021YFC2701901 and 2016YFC1305300); National Natural Science Foundation of China (grant numbers 81570759 and 81270938); Fundamental Research Funds for the Central Universities (grant number 2017XZZX001-01); Zhejiang Provincial Key Science and Technology Project (grant number 2014C03045-2); Zhejiang Provincial Key Disciplines of Medicine (Innovation Discipline grant number 11-CX24); China Postdoctoral Science Foundation (grant number 2021M692852). The funders had no roles in the study design, data collection and analysis, or the preparation of the manuscript.

Author information

Authors and Affiliations

  1. Department of Endocrinology, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China

    JingNan Chen, BingHan Jin, ZhaoYuan Wu, Robert M. Dorazio & JunFen Fu

  2. Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA

    FengLei Wang

Authors
  1. JingNan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. BingHan Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. FengLei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. ZhaoYuan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Robert M. Dorazio
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. JunFen Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JNC and RMD developed the study concept and design with the support from JFF. JNC, FLW, BHJ, and ZYW were responsible for administrative, technical, or material support and contributed to acquisition and interpretation of data. JNC and RMD analyzed the data. JNC wrote the manuscript. JNC and JFF had primary responsibility for final content. All authors revised the manuscript and approved the final version before submission.

Corresponding author

Correspondence to JunFen Fu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethical approval

Our study was approved by the Medical Ethics Committee of the Children’s Hospital of Zhejiang University School of Medicine (2020-IRB-018).

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, J., Jin, B., Wang, F. et al. The relative contributions of soft tissue mass components as risk or protective factors of non-alcoholic fatty liver disease in children. Eur J Clin Nutr 77, 1167–1172 (2023). https://doi.org/10.1038/s41430-023-01326-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41430-023-01326-3

Search

Advanced search

Quick links