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Lean Body Weight - Fat-Free Mass Multi-Calc

LBW, Fat-free mass, Body fat %, weight-targeting


lean body weight calculator - fat free mass - FFMI


Background


This functional multi-tool provides easy access to body composition calculations such as body mass index, lean body weight, fat free mass, body fat percentage, and calculated weight loss required to meet a target goal from simple inputs. As its name suggests, BMI is only a reflection of body mass. The actual composition of body weight is not taken into account, and as a result excess body weight may be made up of adipose tissue (fat) or lean muscle.

Lean body weight equals body weight minus body fat. This body assessment is especially helpful in pharmaceutical dosing calculations in overweight patients.

Fat free mass includes bone, muscle, vital organs and extracellular fluid, or total body weight minus fat mass. The fat free mass is approximately equal to lean body weight.

Body fat percentage is equal to fat mass divided by total body mass, multiplied by 100. 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 target goal tool can be used to determine target weight goal based on a desired body fat percentage.


Data



Age: years   


Height:     


Weight:


Gender:


Body fat % (optional) %


Body fat % Goal (optional) %




 
 



Background


Boer Equation:


LBM (men) = 0.407 * weight [kg] + 0.267 * height [cm] - 19.2

LBM (women) = 0.252 * weight [kg] + 0.473 * height [cm] - 48.3

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/

 

James Formula:


Women: 1.07 * weight [kg]  - 148 * (weight [kg] /height [cm])2

Men: 1.10 * weight [kg]  - 128 * (weight [kg] /height [cm])2

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.

 

Hume Equation


For men: LBM = (0.32810 * Wt[kg]) + (0.33929 * Ht [cm]) - 29.5336

For women: LBM = (0.29569 * Wt[kg]) + (0.41813 * Ht [cm]) - 43.2933

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.

 


Equations used to estimate body fat percentage




Deurenberg formula


 PBFf1 = (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


 PBFf2 = (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


 PBFf3 = (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


 PBFf4= (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


PBFf5 = (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.





Key References (direct quotes)


Key Reference top of page

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.

  • It should, however, be noted that, with the James formula, estimated LBW reaches a plateau at a BMI of about 37 and 43 kg/m2 in women and men, respectively. Estimated LBW then decreases with increasing BMI and approaches zero in severely obese individuals due to the construction of the formula with a quadratic term for weight-to-height ratio.
  • Instead, contrast material dose based on, for example, the Boer formula (5), with its linear function, may be more appropriate because estimated LBW increases steadily with increasing BMI.
  • Thus, the James formula should not be used as a base for contrast material dose at CT in patients with a high BMI. Instead, formulas like the Boer formula may be more appropriate, though correlation between its estimated LBW and contrast material enhancement must be evaluated-especially in patients with a high BMI.

 


Key Reference top of page

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 bioelectri-cal impedance analysis (BIA) in assessment of adi- posity among young and elderly population. Materi- als 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 follow- ing standard guidelines.
  • Results: The calculated mean BMI values of young and old age groups were statistically the same. PBF was significantly high in elder sub- jects. 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.

 


Key Reference top of page

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

  • Objective: To determine reference values for fat-free mass index (FFMI) and fat mass index (FMI) in a large Caucasian group of apparently healthy subjects, as a function of age and gender and to develop percentile distribution for these two parameters.
  • Results: The median FFMI (18-34 y) were 18.9 kg/m2 in young males and 15.4 kg/m2 in young females. No difference with age in males and a modest increase in females were observed. The median FMI was 4.0 kg/m2 in males and 5.5 kg/m2 in females. From young to elderly age categories, FMI progressively rose by an average of 55% in males and 62% in females, compared to an increase in body mass index (BMI) of 9 and 19% respectively.
  • Conclusions: Reference intervals for FFMI and FMI could be of practical value for the clinical evaluation of a deficit in fat-free mass with or without excess fat mass (sarcopenic obesity) for a given age category, complementing the classical concept of body mass index (BMI) in a more qualitative manner. In contrast to BMI, similar reference ranges seems to be utilizable for FFMI with advancing age, in particular in men.
  • The major shortcoming of the BMI is that the actual composition of body weight is not taken into account: excess body weight may be made up of adipose tissue or conversely muscle hypertrophy, both of which will be judged as ‘excess mass’. On the other hand, a deficit of BMI may be due to a fat-free mass (FFM) deficit (sarcopenia) or a mobilization of adipose tissue or both combined.
  • The partitioning of BMI into FFMI and FMI is obviously not possible without associated measurements of body composition. Note that the original idea of calculating the FFM and fat mass (FM) indexes, in analogy to the BMI, was proposed several years ago. The potential advantage is that only one component of body weight, ie FFM or FM, is related to the height squared. Surprisingly, these indexes have not found a wide application yet, probably because appropriate reference standards have yet to be defined. By determining these indexes, quantification of the amount of excess (or deficit) of FFM, respectively FM, can be calculated for each individual.

  • Calculation of FFM and FM indexes
    The FFM and FM indexes are equivalent concepts to the BMI,
    as shown in the following definition:

    FFMI  (Fat-free mass index) = fat-free mass [kg]/height[m]2
    FMI (Fat mass index) = fat mass [kg] / height[m]2
  • BMI (kg/m2)=  FFMI (kg/m2) + FMI (kg/m2)
  • Expression of FFM:
    An issue which has plagued nutritionists and body composition specialists is the expression of body composition results when inter-individual comparison are made: comparison in absolute value (kg) vs in relative value (ie percentage of body weight) or normalized value for ‘size’ (ie typically height squared in FFMI concept or occasionally adjustment for body surface area). Since FFM is related to height, it seems inappropriate to give, for any individual, a cut-off point of FFM in absolute value (kg) below which FFM is judged as ‘low’. For example, a short individual would be penalized since his absolute FFM is expected to be lower than that of a tall individual. Indeed a healthy and well-nourished young man would have a FFM expressed in absolute terms virtually the same as that of a similarly aged but taller individual suffering from proteinenergy malnutrition.
  • Age related issues:
    When the BMI increases with age, it is expected that an increase fat storage would specifically affect FMI and very little FFMI. The impact of the weight gain in the percentile distribution cannot be assessed without carrying out a prospective study. The fact that we used a standardized BMI range at all ages is evidence that the rise in BMI with age is not characteristic of all populations and is not something desirable.

 

Key Reference top of page

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.

  • We calculated fat-free mass index (FFMI) in a sample of 157 male athletes, comprising 83 users of anabolic-androgenic steroids and 74 nonusers. FFMI is defined by the formula (fat-free body mass in kg) x (height in meters)-2. We then added a slight correction of 6.3 x (1.80 m - height) to normalize these values to the height of a 1.8-m man. The normalized FFMI values of athletes who had not used steroids extended up to a well-defined limit of 25.0. Similarly, a sample of 20 Mr. America winners from the presteroid era (1939-1959), for whom we estimated the normalized FFMI, had a mean FFMI of 25.4. By contrast, the FFMI of many of the steroid users in our sample easily exceeded 25.0, and that of some even exceeded 30. Thus, although these findings must be regarded as preliminary, it appears that FFMI may represent a useful initial measure to screen for possible steroid abuse, especially in athletic, medical, or forensic situations in which individuals may attempt to deny such behavior.

  • Normalized FFMI = FFMI + (6.1 * (1.8 - Height (m) ) )

 

 

Key Reference top of page

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.

Effect of ageing on BMI, FMI, and FFMI
Although BMI is the most commonly used in most of epidemiologic studies due to its simplicity and ease, it is well known that BMI is not sufficient to reflect fatness, and it has many limitations such as ethnicity problem. Asian populations, including the Korean population, reportedly had a higher %BF at a lower BMI compared to Western populations. For the same BMI, %BF of Korean-Asians was 3-5% points higher compared to New York-Caucasians. For the same %BF, BMI of Korean-Asians was 3-4 units lower compared to New York-Caucasians. Eventually obesitycutoff- points and maximal value of BMI in Korean population is lower than Caucasian population. The low BMI at high %BF can be partly explained by differences in body shape.

 

 

Key Reference top of page

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.

  • Effect of Aging on FFMI and BFMI
    Mean FFMI was higher in men and women older than 60 y than in those 18 to 39 y (Table I). The relation between FFMI and age was curvilinear, with the highest predicted values in the age category of 30 to 59 y and lower values noted in younger and older subjects (Fig. 1). Forbes proposed that a 2.3 kg/decade weight gain (or 0.9 kg/m2 BMI increase/decade) is required to counteract the loss of FFM with aging. Our study suggested that mean BMI increases of 1.9 kg/m2 in men and 4.0 kg/m2 in women older than 60 y is sufficient to maintain FFMI at or above the levels of those 18 to 39 y. Thus, small BMI increases would prevent FFMI from decreasing in older subjects. The differences noted between the study by Forbes and the current study are likely due to methodologic differences (n = 75 subjects evaluated longitudinally by Forbes versus n = 5629 subjects evaluated cross-sectionally in our study).
  • Interpretation of FFMI and BFMI
    The purpose of using FFMI and BFMI is to facilitate interpretation of body composition parameters regardless of height. FFMI and BFMI values corresponding to low, normal, high, and very high BMI categories allow the classification of subjects in low, normal, high, and very high FFMI and FMI categories.
  • OBJECTIVE: Low and high body mass index (BMI) values have been shown to increase health risks and mortality and result in variations in fat-free mass (FFM) and body fat mass (BF). Currently, there are no published ranges for a fat-free mass index (FFMI; kg/m2), a body fat mass index (BFMI; kg/m2), and percentage of body fat (%BF). The purpose of this population study was to determine predicted FFMI and BFMI values in subjects with low, normal, overweight, and obese BMI.
  • METHODS: FFM and BF were determined in 2986 healthy white men and 2649 white women, age 15 to 98 y, by a previously validated 50-kHz bioelectrical impedance analysis equation. FFMI, BFMI, and %BF were calculated.
  • RESULTS: FFMI values were 16.7 to 19.8 kg/m2 for men and 14.6 to 16.8 kg/m2 for women within the normal BMI ranges. BFMI values were 1.8 to 5.2 kg/m2 for men and 3.9 to 8.2 kg/m2 for women within the normal BMI ranges. BFMI values were 8.3 and 11.8 kg/m2 in men and women, respectively, for obese BMI (>30 kg/m2). Normal ranges for %BF were 13.4 to 21.7 and 24.6 to 33.2 for men and women, respectively.
  • CONCLUSION BMI alone cannot provide information about the respective contributions of FFM and FM to body weight. This study presented the FFMI, BFMI, and %BF values that correspond to low, normal, overweight, and obese BMIs. FFMI and BFMI can provide meaningful information about body compartments, regardless of height.

 

 

 

 

 


References top of page

  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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