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

Prediction of fat mass from anthropometry at ages 7 to 9 years in Samoans: a cross-sectional study in the Ola Tuputupua’e cohort

European Journal of Clinical Nutrition volume 77, pages 495–502 (2023)Cite this article

Subjects

Abstract

Background/objective

With increasing obesity prevalence in children globally, accurate and practical methods for quantifying body fat are critical for effective monitoring and prevention, particularly in high-risk settings. No population is at higher risk of obesity than Pacific Islanders, including children living in the independent nation of Samoa. We developed and validated sex-specific prediction models for fat mass in Samoan children.

Subjects/methods

Dual X-ray absorptiometry (DXA) assessments of fat mass and weight, height, circumferences, and skinfolds were obtained from 356 children aged 7–9 years old in the Ola Tuputupua’e “Growing Up” study. Sex-specific models were developed from a randomly selected model development sample (n = 118 females, n = 120 males) using generalized linear regressions. In a validation sample (n = 59 females; n = 59 males), Lin’s concordance and Bland-Altman limits-of-agreement (LoA) of DXA-derived and predicted fat mass from this study and other published models were examined to assess precision and accuracy.

Results

Models to predict fat mass in kilograms were: e^[(−0.0034355 * Age8 − 0.0059041 * Age9 + 1.660441 * ln (Weight (kg))−0.0087281 * Height (cm) + 0.1393258 * ln[Suprailiac (mm)] − 2.661793)] for females and e^[−0.0409724 * Age8 − 0.0549923 * Age9 + 336.8575 * [Weight (kg)]−2 − 22.34261 * ln (Weight (kg)) [Weight (kg)]−1 + 0.0108696 * Abdominal (cm) + 6.811015 * Subscapular (mm)−2 − 8.642559 * ln (Subscapular (mm)) Subscapular (mm)−2 − 1.663095 * Tricep (mm)−1 + 3.849035]for males, where Age8 = Age9 = 0 for children at age 7 years, Age8 = 1 and Age9 = 0 at 8 years, Age8 = 0 and Age9 = 1 at 9 years. Models showed high predictive ability, with substantial concordance (ρC > 0.96), and agreement between DXA-derived and model-predicted fat mass (LoA female = −0.235, 95% CI:−2.924–2.453; male = −0.202, 95% CI:−1.977–1.572). Only one of four existing models, developed in a non-Samoan sample, accurately predicted fat mass among Samoan children.

Conclusions

We developed models that predicted fat mass in Samoans aged 7–9 years old with greater precision and accuracy than the majority of existing models that were tested. Monitoring adiposity in children with these models may inform future obesity prevention and interventions.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: Sex-specific associations of anthropometric characteristics with fat mass derived from dual-energy X-ray absorptiometry (DXA) in Samoan children at ages 7 to 9 years (N = 356).
Fig. 2: Concordance and 95% limits of agreement between the observed fat mass using dual-energy X-ray absorptiometry and model predicted fat mass in the validation sample set of Samoan children (n = 59 females, n = 59 males).

Similar content being viewed by others

Data availability

Data are available upon reasonable request to the corresponding author.

References

  1. World Bank Group. Non-Communicable Disease (NCD) Roadmap Report (English) Washington, DC: World Bank Group; 2014. http://documents.worldbank.org/curated/en/534551468332387599/Non-Communicable-Disease-NCD-Roadmap-Report. Accessed 25 Mar 2018.

  2. NCD Risk Factor Collaboration (NCD RisC). Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet. 2017;390:2627–42.

    Article  PubMed Central  Google Scholar 

  3. Simmonds M, Llewellyn A, Owen CG, Woolacott N. Predicting adult obesity from childhood obesity: a systematic review and meta‐analysis. Obes Rev. 2016;17:95–107.

    Article  CAS  PubMed  Google Scholar 

  4. Borga M, West J, Bell JD, Harvey NC, Romu T, Heymsfield SB, et al. Advanced body composition assessment: from body mass index to body composition profiling. J Investig Med. 2018;66:1–9.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Fields DA, Goran MI. Body composition techniques and the four-compartment model in children. J Appl Physiol. 2000;89:613–20.

    Article  CAS  PubMed  Google Scholar 

  6. Tyrrell V, Richards G, Hofman P, Gillies G, Robinson E, Cutfield W. Obesity in Auckland school children: a comparison of the body mass index and percentage body fat as the diagnostic criterion. Int J Obes. 2001;25:164–9.

    Article  CAS  Google Scholar 

  7. Wong MC, Ng BK, Kennedy SF, Hwaung P, Liu EY, Kelly NN, et al. Children and adolescents’ anthropometrics body composition from 3‐D optical surface scans. Obesity. 2019;27:1738–49.

    Article  PubMed  Google Scholar 

  8. Rush EC, Puniani K, Valencia M, Davies P, Plank L. Estimation of body fatness from body mass index and bioelectrical impedance: comparison of New Zealand European, Maori and Pacific Island children. Eur J Clin Nutr. 2003;57:1394–401.

    Article  CAS  PubMed  Google Scholar 

  9. NCD RisC. Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight. Elife 2021;10:e60060.

    Article  Google Scholar 

  10. Slaughter MH, Lohman TG, Boileau R, Horswill C, Stillman R, Van Loan M, et al. Skinfold equations for estimation of body fatness in children and youth. Hum Biol. 1988;60:709–23.

  11. Goran MI, Driscoll P, Johnson R, Nagy TR, Hunter G. Cross-calibration of body-composition techniques against dual-energy X-ray absorptiometry in young children. Am J Clin Nutr. 1996;63:299–305.

    Article  CAS  PubMed  Google Scholar 

  12. Dezenberg CV, Nagy TR, Gower BA, Johnson R, Goran MI. Predicting body composition from anthropometry in pre-adolescent children. Int J Obes. 1999;23:253–9.

    Article  CAS  Google Scholar 

  13. Hudda MT, Fewtrell MS, Haroun D, Lum S, Williams JE, Wells JC, et al. Development and validation of a prediction model for fat mass in children and adolescents: meta-analysis using individual participant data. BMJ. 2019;366:l4293.

  14. Duncan JS, Duncan EK, Schofield G. Ethnic-specific body mass index cut-off points for overweight and obesity in girls. NZ Med J. 2010;123:22–9.

    Google Scholar 

  15. Thompson AA, Duckham RL, Desai MM, Choy CC, Sherar LB, Naseri T, et al. Sex differences in the associations of physical activity and macronutrient intake with child body composition: A cross‐sectional study of 3‐to 7‐year‐olds in Samoa. Pediatr Obes. 2020;15:e12603.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Samoa Bureau of Statistics. Samoa Demographic and Health Survey. Apia, Samoa: Samoa Bureau of Statistics, Government of Samoa, 2014.

  17. The World Bank. Samoa: Databank Washington, DC: The World Bank Group; 2022. https://data.worldbank.org/country/samoa.

  18. Central Intelligence Agency. The World FactBook: Samoa. https://www.cia.gov/the-world-factbook/countries/samoa/. Accessed 6 May 2022.

  19. Troubat N, Faaloa E, Aliyeva R. The State of Food Security and Nutrition in Samoa, based on the analysis of the 2018 Household Income and Expenditure Survey. Apia, Samoa, FAO 2020.

  20. Choy CC, Desai MM, Park JJ, Frame EA, Thompson AA, Naseri T, et al. Child, maternal and household-level correlates of nutritional status: a cross-sectional study among young Samoan children. Public health Nutr. 2017;20:1235–47.

    Article  PubMed  Google Scholar 

  21. Cameron N, Schell L. Human growth and development. Academic Press, 2021.

  22. Rush E, Tautolo el S, Paterson J, Obolonkin V. Pacific Islands Families Study: signs of puberty are associated with physical growth at ages 9 and 11 years. NZ Med J. 2015;128:24–33.

    Google Scholar 

  23. Cole TJ, Pan H, Butler GE. A mixed effects model to estimate timing and intensity of pubertal growth from height and secondary sexual characteristics. Ann Hum Biol. 2014;41:76–83.

    Article  CAS  PubMed  Google Scholar 

  24. Roubenoff R, Kehayias JJ, Dawson-Hughes B, Heymsfield SB. Use of dual-energy x-ray absorptiometry in body-composition studies: not yet a “gold standard”. Am J Clin Nutr. 1993;58:589–91.

    Article  CAS  PubMed  Google Scholar 

  25. Onis MD, 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 

  26. World Health Organization (WHO). Waist circumference and waist-hip ratio: report of a WHO expert consultation, Geneva, 8–11 December 2008. 2011.

  27. Callaway C, Chumlea W, Bouchard C, Himes J, Lohman T, Martin A, et al. Circumferences anthropometric standardization reference manual. Human Kinetics Books: Champaign, IL, 1988, p. 39–54.

  28. Almeida SM, Furtado JM, Mascarenhas P, Ferraz ME, Silva LR, Ferreira JC, et al. Anthropometric predictors of body fat in a large population of 9‐year‐old school‐aged children. Obes Sci Pract. 2016;2:272–81.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Royston P, Sauerbrei W. Multivariable model-building: a pragmatic approach to regression analysis based on fractional polynomials for modelling continuous variables. John Wiley & Sons; 2008.

  30. Royston P, Altman DG. Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling. J R Stat Soc: Ser C (Appl Stat). 1994;43:429–53.

    Google Scholar 

  31. Steichen TJ, Cox NJ. A note on the concordance correlation coefficient. Stata J. 2002;2:183–9.

    Article  Google Scholar 

  32. Lawrence I, Lin K. A concordance correlation coefficient to evaluate reproducibility. Biometrics. 1989;45:255–68.

  33. Bland JM, Altman D. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;327:307–10.

    Article  Google Scholar 

  34. McBride G. A proposal for strength-of-agreement criteria for Lin’s concordance correlation coefficient. NIWA client report: HAM2005-062 2005;62.

  35. Amrhein V, Greenland S, McShane B. Scientists rise up against statistical significance. Nature Publishing Group, 2019.

  36. Smith G. Multiple regression. In: Smith G (ed). Essential statistics, regression, and econometrics. Academic Press: Boston, 2015, p. 297–331.

  37. Popkin BM, Gordon-Larsen P. The nutrition transition: worldwide obesity dynamics and their determinants. Int J Obes Relat Metab Disord : J Int Assoc Study Obes. 2004;28:S2–9.

    Article  Google Scholar 

  38. Popkin BM, Adair LS, Ng SW. Global nutrition transition and the pandemic of obesity in developing countries. Nutr Rev. 2012;70:3–21.

    Article  PubMed  Google Scholar 

  39. 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 

  40. Ellis K. Measuring body fatness in children and young adults: comparison of bioelectric impedance analysis, total body electrical conductivity, and dual-energy X-ray absorptiometry. Int J Obes Relat Metab Disord: J Int Assoc Study Obes. 1996;20:866–73.

    CAS  Google Scholar 

  41. Cole TJ, Fewtrell MS, Prentice A. The fallacy of using percentage body fat as a measure of adiposity. Am J Clin Nutr. 2008;87:1959–1959.

    Article  CAS  PubMed  Google Scholar 

  42. Cameron N, Griffiths PL, Wright MM, Blencowe C, Davis NC, Pettifor JM, et al. Regression equations to estimate percentage body fat in African prepubertal children aged 9 y. Am J Clin Nutr. 2004;80:70–5.

    Article  CAS  PubMed  Google Scholar 

  43. L’Abée C, Visser GH, Liem ET, Kok DE, Sauer PJ, Stolk RP. Comparison of methods to assess body fat in non-obese six to seven-year-old children. Clin Nutr. 2010;29:317–22.

    Article  PubMed  Google Scholar 

  44. Hudda MT, Wells JC, Adair LS, Alvero-Cruz JR, Ashby-Thompson MN, Ballesteros-Vásquez MN, et al. External validation of a prediction model for estimating fat mass in children and adolescents in 19 countries: individual participant data meta-analysis. BMJ. 2022;378:e071185.

  45. Stomfai S, Ahrens W, Bammann K, Kovacs E, Mårild S, Michels N, et al. Intra-and inter-observer reliability in anthropometric measurements in children. Int J Obes. 2011;35:S45–51.

    Article  Google Scholar 

  46. Pacific Community (SPC). Pacific Noncommunicable Disease Summit: Translating global and regional commitments into local action (20–22 June 2016, Tonga) summit report. SPC: Noumea, New Caledonia, 2016.

Download references

Acknowledgements

We are very grateful to the children and families who participate, as well as, our partners in the Samoa Ministry of Health, Bureau of Statistics, Ministry of Women, Community, Social Development, and the OLaGA field team (especially Vaimoana Lupematasila Filipo, Maria Siulepa Arorae, Fa’atali Tafunaina, Folla Unasa, Melania Selu, Lupesina Vesi, and Kima Savusa). Thank you to Dr. Hudda for critical advice, which contributed to the final manuscript. The Ola Tuputupua’e study received financial support from the following sources: Yale University (Faculty Funding, David Dull Internship Fund, Jan A.J. Stolwijk Fellowship Fund, Downs International Health Student Travel Fellowship, Thomas C. Barry Travel Fellowship), US National Institutes of Health (NIH) Minority and Health Disparities International Research Training Program (NIMHD T37MD008655), U.S. Fulbright Graduate Student Research Fellowship, Brown University (International Health Institute, Nora Kahn Piore Award, and Framework in Global Health Program), Brown University Population Studies and Training Center which receives funding from the NIH for training (T32 HD007338) and general support (P2C HD041020), and NIH National Lung, Health, Blood Institute for infrastructure support (R01 HL093093 and HL140570). CCC was supported by Yale-Brown Ivy Plus Exchange Program, Ruth L. Kirschstein Predoctoral Individual National Research Service Award (NIH 1F31HL147414), and the Fogarty Global Health Equity Scholars Program (FIC D43TW010540). WJ acknowledges support from the National Institute for Health Research (NIHR) Leicester Biomedical Research Centre, which is a partnership between University Hospitals of Leicester NHS Trust, Loughborough University, and the University of Leicester.

Author information

Author notes

  1. These authors contributed equally: Stephen T. McGarvey, Nicola L. Hawley.

Authors and Affiliations

  1. Department of Epidemiology, International Health Institute, School of Public Health, Brown University, 121 South Main Street, Providence, RI, 02906, USA

    Courtney C. Choy, Take Naseri, Stephen T. McGarvey & Nicola L. Hawley

  2. Department of Chronic Disease Epidemiology, Yale School of Public Health, 60 College Street, New Haven, CT, 06520, USA

    Courtney C. Choy & Nicola L. Hawley

  3. School of Sport, Exercise, and Health Sciences, Loughborough University, Epinal Way, Loughborough, LE11 3TU, UK

    William Johnson

  4. Institute for Physical Activity and Nutrition (IPAN), Deakin University, 221 Burwood Highway, Burwood, VIC, 3125, Australia

    Rachel L. Duckham

  5. Australian Institute for Musculoskeletal Science (AIMSS), The University of Melbourne and Western Health, 176 Furlong Road, St. Albans, VIC, 3021, Australia

    Rachel L. Duckham

  6. Ministry of Health, Ififi Street, Motootua, Apia, Samoa

    Take Naseri & Christina Soti-Ulberg

  7. Lutia i Puava ae Mapu i Fagalele, Apia, Samoa

    Muagututia S. Reupena

  8. Center for Children’s Environmental Health, Department of Epidemiology, School of Public Health, Brown University, 121 South Main Street, Providence, RI, 02906, USA

    Joseph M. Braun

  9. Department of Anthropology, Brown University, 128 Hope Street, Providence, RI, 02912, USA

    Stephen T. McGarvey

Authors
  1. Courtney C. Choy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. William Johnson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rachel L. Duckham
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Take Naseri
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Christina Soti-Ulberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Muagututia S. Reupena
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Joseph M. Braun
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Stephen T. McGarvey
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Nicola L. Hawley
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

CCC conceptualized this project for her Ph.D. dissertation with WJ, RLD, TN, JMB, STM, and NLH. CCC analyzed the data with statistical support from WJ, interpreted data from dual x-ray absorptiometry scans with RLD, drafted the initial manuscript with guidance from STM and NLH, and revised with WJ, JMB, RLD, CSU, and TN. CCC and NLH designed the study and directed data collection with support and assistance from CSU, TN, MSR, and RLD. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Nicola L. Hawley.

Ethics declarations

Competing interests

The authors declare no competing interests.

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

Choy, C.C., Johnson, W., Duckham, R.L. et al. Prediction of fat mass from anthropometry at ages 7 to 9 years in Samoans: a cross-sectional study in the Ola Tuputupua’e cohort. Eur J Clin Nutr 77, 495–502 (2023). https://doi.org/10.1038/s41430-022-01256-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41430-022-01256-6

This article is cited by

Search

Advanced search

Quick links