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## Background

Recent studies have demonstrated that weight and BMI are not adequate measurements of underlying changes in fat-free mass and body fat mass that can occur during menopause, aging, or general illness. The BMI only reflects body mass, and excess body weight may be made up of adipose tissue or lean muscle.

The body fat percentage is a measure of fitness level, since it is the only body measurement which directly calculates a person's relative body composition without regard to height or weight.

The body fat percentage is the total mass of fat divided by total body mass, multiplied by 100. Total body fat consists of essential body fat (helps maintain critical pathways necessary for life) plus storage body fat. This program compares the output of several equations that are used to estimate the body fat percentage instead of direct measurement.

Age: years

Height:

Weight:

Gender:

## Deurenberg formula

Body fat % = (1.20 * BMI) + (0.23 * Age) - (10.8 * gender) - 5.4

Deurenberg P, Weststrate JA, Seidell JC: Body mass index as a measure of body fatness: age- and sex-specific prediction formulas. Br J Nutr. 1991, 65 (2): 105-114. 10.1079/BJN19910073. https://dx.doi.org/10.1079/BJN19910073

## Deurenberg formula 2

Body fat % = (1.29 * BMI) + (0.20 * Age) - (11.4 * gender) - 8.0

Deurenberg, P., Yap, M. and van Staveren, W.A. (1998) Body mass index and percent body fat. A meta analysis among different ethnic groups. International Journal of Obesity, 22, 1164-1171. doi:https://dx.doi.org/10.1038/sj.ijo.0800741

## Gallagher formula

Body fat % = (1.46 * BMI) + (0.14 * Age) - (11.6 * gender) - 10

Gallagher, D., Visser, M., Sepulveda, D., et al. (1996) How useful is body mass index for comparison of body fatness across age, sex and ethnic groups. American Journal of Epidemiology, 143, 228-239.

## Jackson-Pollock formula

Body fat %= (1.61 * BMI) + (0.13 * Age) - (12.1 * gender) - 13.9

Jackson, A.S., Pollock, M.L. and Ward, A. (1980) Gener-alized equations for predicting body density of women. Medicine & Science in Sports & Exercise, 12, 175-182. doi:10.1249/00005768-198023000-00009.

Jackson, A.S. (1984) Research design and analysis of data procedures for predicting body density. Medicine & Science in Sports & Exercise, 16, 616-620.

## Jackson AS formula

Body fat % = (1.39 * BMI) + (0.16 * Age) - (10.34 * gender) - 9

Jackson, A.S., Stanforth, P.R. and Gagnon, J. (2002) The effect of sex, age and race on estimating percentage body fat from body mass index: The heritage family study. In-ternational Journal of Obesity and Related Metabolic Disorders, 26, 789-96.

## Mittal, R. , Goyal, M. , Dasude, R. , Quazi, S. and Basak, A. (2011) Measuring obesity: results are poles apart obtained by BMI and bio-electrical impedance analysis. Journal of Biomedical Science and Engineering, 4, 677-683. doi: 10.4236/jbise.2011.411084.

• Objective: To analyse the use of BMI and bioelectrical impedance analysis (BIA) in assessment of adiposity among young and elderly population. Materials and methods: Age, height, weight and percent body fat (PBF) of 101 young and 276 elder subjects were recorded. PBF was measured directly by BIA instrument (PBFb) and also calculated from BMI (PBFf). The classification of subjects into underweight, normal, overweight and obese was based on the age- and sex-specific BMI cutoff values and PBFb following standard guidelines.
• Results: The calculated mean BMI values of young and old age groups were statistically the same. PBF was significantly high in elder subjects. There was no statistical difference in mean PBFb and PBFf in young subjects but the difference was significant in eldery subjects. The PBFf values were highly correlated (r: 0.92 to 0.96) with PBFb values in young age groups unlike elder groups of both males and females. PBFb based categorization of subjects’ presented totally different scenario com-pared to results obtained by BMI analysis to assess adiposity.
• Conclusion: The cases such as increasing fatness with aging even when BMI remains constant, the causes of country or ethnic differences in BMI analysis, poor correlation in PBFb and PBFf values in elder age group emphasize on the limitations of BMI based analysis. PBFb within limitations seems to be an improved phenotypic characteristic over BMI.
• Significance of PBF over BMI
BMI is a surrogate of body fat. The consequences lead to mortality and morbidity are due to access accumulation of fat. Unexpectedly, the PBF values of young and old age group were significantly different, either measured by BIA instrument or calculated by different formulas.
• Studies indicate that relative fatness in adults increases with age. Although the mechanisms behind this observation are not fully understood, an important and as yet unanswered question is whether the greater fatness with older age, even after BMI is same as of young population, poses additional health risks. Experts has recommend to measure adiposity in combination of BIA and with other risk factors of morbidity and mortal-ity; rather than relying only on BMI cut-points. However, our results shows that increased PBF and its consequences cannot be predicted by BMI analysis in elder group of both, males and females.

## References

1. Boer P. Estimated lean body mass as an index for normalization of body fluid volumes in humans. Am J Physiol. 1984 Oct;247(4 Pt 2):F632-6. doi: 10.1152/ajprenal.1984.247.4.F632. PMID: 6496691. https://pubmed.ncbi.nlm.nih.gov/6496691/
2. Heitmann BL: Evaluation of body fat estimated from body mass index, skinfolds and impedance. A comparative study. Eur J Clin Nutr. 1990, 44 (11): 831-837.
3. Hume, R (Jul 1966). Prediction of lean body mass from height and weight. Journal of Clinical Pathology. 19 (4): 389-91. doi:10.1136/jcp.19.4.389.
4. Janmahasatian S, Duffull SB, Ash S, Ward LC, Byrne NM, Green B: Quantification of lean bodyweight. Clin Pharmacokinet. 2005, 44 (10): 1051-1065. 10.2165/00003088-200544100-00004.

5. Kim CH, Chung S, Kim H, Park JH, Park SH, Ji JW, Han SW, Lee JC, Kim JH, Park YB, Nam HS, Kim C. Norm references of fat-free mass index and fat mass index and subtypes of obesity based on the combined FFMI-%BF indices in the Korean adults aged 18-89 yr. Obes Res Clin Pract. 2011 Jul-Sep;5(3):e169-266. doi: 10.1016/j.orcp.2011.01.004. PMID: 24331103.

6. Kouri EM, Pope HG Jr, Katz DL, Oliva P. Fat-free mass index in users and nonusers of anabolic-androgenic steroids. Clin J Sport Med. 1995 Oct;5(4):223-8. doi: 10.1097/00042752-199510000-00003. PMID: 7496846. [Normalized FFMI]
7. Kyle UG, Schutz Y, Dupertuis YM, Pichard C. Body composition interpretation. Contributions of the fat-free mass index and the body fat mass index. Nutrition. 2003 Jul-Aug;19(7-8):597-604. doi: 10.1016/s0899-9007(03)00061-3. PMID: 12831945.

8. Lee, K., Lee, S., Kim, S.Y. et al. (2007) Percent body fat cutoff values for classifying overweight and obesity recommended by the International Obesity Task Force (IOTF) in Korean children. Asia Pacific Journal of Clinical Nutrition, 16, 649-655.

9. Mittal, R. , Goyal, M. , Dasude, R. , Quazi, S. and Basak, A. (2011) Measuring obesity: results are poles apart obtained by BMI and bio-electrical impedance analysis. Journal of Biomedical Science and Engineering, 4, 677-683. doi: 10.4236/jbise.2011.411084.

10. Nyman U. James Lean Body Weight Formula Is Not Appropriate for Determining CT Contrast Media Dose in Patients with High Body Mass Index. Radiology. 2016 Mar;278(3):956-7. doi: 10.1148/radiol.2016152031. PMID: 26885737.

11. Schutz, Y., Kyle, U. & Pichard, C.. Fat-free mass index and fat mass index percentiles in Caucasians aged 18-98 y. Int J Obes Relat Metab Disord. 2002; 26(7):953-60. https://doi.org/10.1038/sj.ijo.0802037

12. Yu, S., Visvanathan, T., Field, J. et al. Lean body mass: the development and validation of prediction equations in healthy adults. BMC Pharmacol Toxicol 14, 53 (2013). https://doi.org/10.1186/2050-6511-14-53.

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