Fat-Free Mass Index (FFMI) - Actual and Estimated
---------------------- Popular Weight Related Calculators ----------------------
Estimation of the total body fat percentage
Lean Body Weight (LBW) Multi-Calc
Weight Loss Target Based on Body Fat Percentage
Fat Free Mass, Lean Body Weight, Body Fat percent Multi-Calc
Fat-Free Mass Index (FFMI) Calculator based on body fat percentage
Adjusted body weight and Ideal body weight calculator
---------------------------------Nutrition Calculators ---------------------------------
Check out the new BMR Multi-calc !!
Harris-Benedict Equation-Most common equation for BMR
Resting Metabolic Rate (RMR) -Mifflin-St Jeor equation
Calculate your estimated calories/day (EER) and BMI
---------------------------------Protein Calculator ---------------------------------
Calculate amount of PROTEIN that should be consumed daily
---------------------------------Fiber Calculators ---------------------------------
Fiber tool for dieters - Total Fiber
How much daily FIBER do I need???
Soluble FIBER calculator - cholesterol lowering benefits
Background
Body composition measurements, and their trends, are important data used by medical professionals to estimate patient health and to monitor changes due to aging or illness.
Body mass index (BMI) is equal to the fat-free mass index (FFMI) plus the fat mass index (FMI).
BMI (kg/m2 )=
FFMI (kg/m2 ) + FMI (kg/m2 )
A major advantage of using the combination of FFMI + FMI instead of BMI is that one can tell whether a change in body weight is due to a change in fat free mass (FFM) or fat mass (FM) or both. For example, during the aging process, BMI might remain constant although FFMI and FMI might reveal body composition changes. Increases in body fat are often of interest as these patients can be at increased risk for health problems.
FFM is related to height and consists of bone, muscle, vital organs and extracellular fluid. This measurement is often very close to lean body mass, which also includes the fatty component of cellular membranes.
Reference ranges for FFMI are not commonly used, but are more accurate than BMI references.
In the results section (after the form data is submitted) you will find
reference ranges based on extrapolated population values.
Data (Inputs)
Height:
Inches
Centimeters
Weight:
Kilograms
Pounds
Gender:
Male
Female
Body fat % (optional)
%
Key References
(direct quotes)
Key Reference
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.
Key Reference
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
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
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.
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.
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]
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.
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.
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.
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.
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
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.