Authors: Marcus Free MD, Rouzbeh Motiei-Langroudi MD, Kelly Daly RDN, and Don Juravin (Don Karl Juravin).
Why do we need BMI
True and personal ideal healthy weight mark is important for optimum health, longevity and happiness. Our most advanced research proves that the body mass (the average weight for every cubic inch of the body) difference between a white woman aged 75 and a black man aged 25 can be as high as about 15% which has not been taken into account for the last 177 years in the current (old) BMI. In other words, the outdated old BMI unfairly represents blacks, and especially black men aged 18 to 30.
The new BMI yields more reliable results as it takes race, gender, and age into account. Therefore, the public will find the weight loss goals based on new BMI more obtainable and better adhere to and pursue their diet.
New BMI calculation synopsis
The new BMI formulation considers variations in body density due to race, gender, and age.
Black: (Weight (kg) / Height (m)2) X
White: (Weight (kg) / Height (m)2)
Body Composition Variations
The new calculations are based on the results of many researches regarding variations in body composition in different races, genders, and ages. The highlights from the researches are reviewed here. Based on these, a mathematical average has been calculated for each factor and the change in body density due to changes in body composition has been deduced.
Body Composition Variations Based on Gender
- Females have between 9% to 12% more body fat than males of the same age and BMI. A 20-year-old man and woman of 23 kg/m2 BMI have 13.3% and 26.0% of body weight as fat, respectively (Gallagher 1996).
- Females have 10.4% higher fat than men with the same BMI during the ages of 17 to 65 years-old (Jackson 2002).
- Males have higher height, weight, lean body mass, and waist-to-hip ratio (Mridha 2010).
- Females have a higher body fat percentage and fat mass throughout their entire lifespan (Mridha 2010, Blaak 2001, Gallagher 1996).
Body Composition Variations Based on Ethnicity
- Body component densities are:
- water = 0.9937 g/cm3, protein = 1.34 g/cm3, mineral = 3.038 g/cm3, fat = 0.9 g/cm3 (Wagner 2000)
- Fat-free body composition and body composition in whites:
- water = 73.8%, protein = 19.4%, and mineral = 6.8% (Wagner 2000)
- water = 56.9%, protein = 14.9%, mineral = 5.2% and fat = 23% (Juravin/Motiei 2016)
- Body composition in blacks:
- water = 57.2% to 56.9%, protein = 15.7% to 16.4%, mineral = 5.6% to 6.2% and fat = 20.2% to 21.8% (Juravin/Motiei 2016)
- Fat composition in Asians:
- fat = 24.2% (Juravin/Motiei 2016)
Blacks vs. whites
- Black and white females differ in body composition. Errors in fat estimates occur when ethnicity is not accounted for in body composition models (Ortiz 1992)
- Blacks have 5.2% and 10.7% more protein and minerals, respectively, and 12% less fat compared to whites with the same BMI and age range (Alois 1997)
- Musculoskeletal mass represents 28% to 38% of total body weight in black females and 26% to 32% in white females (Ortiz 1992)
- Body mineral and protein weights are higher in blacks than whites (11.1% to 28.5% in women and 7.8% to 25.0% in men for mineral, 8% in women and 22.2% in men for protein), while body water is similar (Wagner 2000)
- Fat-free body component density is 0.52% higher in blacks than whites (1.1057 vs. 1.1 g/cm3) (Wagner 2000)
- Black males have a 15% higher bone mineral content and 8% higher bone mineral density than white males (Barondess 1997)
- Black males have 9.8% to 14.7% higher bone mass than white males (Barondess 1997).
- Black females have a 13.8% higher bone mineral density than white females (Ortiz 1992).
- Black females have higher skeletal muscle (14.6%) and bone mineral density (16.2%) in comparison to white females (Ortiz 1992).
- Body fat is 4.9% lower in black girls compared to whites (Meyer 2011).
- Black 7 to 10-year-old girls have greater bone mineral density and lower visceral adipose tissue than white girls (Yanovski 1996).
- Black women have 23% less visceral fat tissue than white women (Conway 1995).
Asians vs. whites
- Asians have 5% higher body fat than whites and Caucasians with the same BMI and age (28.1% vs. 23.0%, respectively) (Wulan 2012, Wulan 2010).
- Chinese have a higher body fat than whites with the same BMI and age (23.7% vs. 22.4%, respectively) (Wang 2011).
- Japanese have a higher body fat percentage compared to BMI-matched Australian Caucasians (Kagawa 2006).
- Asian Indians have a higher body fat percentage compared to BMI-matched whites (Rush 2009).
- Body fat percentage is higher in South Asians (Stone 2008).
Hispanics vs. whites
- Hispanics have higher body fat compared with whites with the same BMI (27.3% vs 23.7%, respectively) (Carpenter 2013).
Body Composition Variations: Age
- Age influences body fat percentage. Older persons have a higher body fat percentage compared with younger persons with comparable BMIs (Gallagher 1996).
- The increase in body fat percent relation is as follows:
- Black women: 0.072 per year
- White women: 0.096 per year
- Black men: 0.105 per year
- White men: 0.177 per year
- Blacks (men and women): 0.088 per year
- Whites (men and women): 0.133 per year
- Men: 1.0% to 1.1 % per 10 years
- Women: 0.7% to 1.0 % per 10 years
Body density variation by age and gender
- Age and gender both influence body density (Durnin and Womersley 1974). In the below equations, M and F are body densities for males and females, respectively, while L represents height in m:
- < 17 year-old: M = 1.1533 – (0.0643 x L); F = 1.1369 – (0.0598 x L)
- 17 to 19 year-old: M = 1.1620 – (0.0630 x L); F = 1.1549 – (0.0678 x L)
- 20 to 29 year-old: M = 1.1631 – (0.0632 x L); F = 1.1599 – (0.0717 x L)
- 30 to 39 year-old: M = 1.1422 – (0.0544 x L); F = 1.1423 – (0.0632 x L)
- 40 to 49 year-old: 1.1620 – (0.0700 x L); F = 1.1333 – (0.0612 X L)
- > 50 year-old: M = 1.1715 – (0.0779 X L); F = 1.1339 – (0.0645 X L)
- For instance, body densities in a 1.8 m individual would be:
- The Durnin and Womersley formula does not consider racial differences and therefore, is not accurate in blacks and asians (Davidson 2011).
BMI Thresholds Based On Ethnicity
- Asians in general have higher health risks than Caucasians for the same BMIs and the limits of overweight and obesity should be set lower for Asian adults (Henderson 2005).
- Asians, Africans, and Latinos are more likely than whites to have greater body fat and central fat for the same BMI (Henderson 2005, Popkin 2002).
- Different thresholds are proposed for overweightness and obesity in different races (Henderson 2005):
|Asians||18.5 to 22.9||23 to 24.5||25 and higher|
|Latino males with 1.6 m and more height || ||25 to 26.9||27 and more|
|Latino males with below 1.6 m height || ||23 to 24.9||25 and more|
|Latino females with 1.5 m and more height|| ||25 to 26.9||27 and more|
|Latino females with below 1.5 m height || ||23 to 24.9||25 and more|
|Whites||18.5 to 24.9||25 to 29.9||30 and more|
- A BMI of 25 should be considered as the cut-off point for obesity in south Asians (Bodicoat 2014).
New BMI Calculation
Based on the reviewed researches, the variation in body density/mass based on race, gender, and age is calculated and summarized in the table.
New body density table (Gender, race, age)
|% Body Density||White||Black||Asian|
|Average annual body density decrease (ABDD)||0.07952||0.05964||0.07041|
|Average annual body density decrease (ABDD)||0.14662||0.08698||0.08284|
Explanations for the table:
- Fat starts accumulating around the age of 30. In the table, it has been considered a minimum at 30.
- For simplification, white women are considered the reference (value 100), and all other percentages should be compared to it.
New BMI Limitations
- The new BMI calculations are based on the results obtained by other research, not morphologic and body component measurements in a series of individuals. Moreover, different reference studies have used different methods to evaluate body composition. Therefore, pooling their data bares statistical and mathematical inaccuracies.
- The new BMI unifies all Asians in one group. However, Asians are a diverse group of people with possibly different body morphometry and composition.
- New BMI still does not take into account different body composition based on physical activity level (i.e. athletes vs. physically inactive individuals).
- New BMI is a recent proposal. The clinical significance and its superiority over old BMI should be confirmed in further studies.
- Considering all these, the new BMI should be internally and externally validated in a series of individuals first.
Old BMI was introduced in 1832 by the Belgian mathematician Adolphe Quetelet (Eknoyan 2008).
The body mass index (BMI) is a value for weight analysis that quantifies the amount of tissue mass (muscle, fat, and bone) in an individual. Commonly accepted BMI ranges are:
- Underweight: BMI < 18.5
- Normal weight: BMI 18.5 to 25
- Overweight: BMI 25 to 30
- Obese: BMI > 30
Old BMI metric calculation
BMI = Weight (kg) / Height (m)2
Old BMI U.S. Imperial calculation
BMI = (Weight (lbs) x 703) / (Height (Inches)2)
Limitations of the Old BMI
- The recommended ranges for weight analysis vary from country to country, making global surveys problematic.
- Different ethnicities have different body builds, for example, an Asian is generally a smaller build than an African or Aboriginal. The BMI calculation does not take ethnicity into consideration thus providing inaccurate results.
The inaccuracy of BMI results for height extremes
- The BMI is calculated by weight and the square of height, while mass increases to the cube of linear dimensions (Taylor 2010). This causes inconsistency in interpreting results in tall and short individuals.
Variation in physical characteristics
- BMI does not include loss of height through aging.
- BMI does not differentiate between muscle mass and fat mass.
- BMI does not provide any information about where the body fat is stored. Thus two people with exactly the same BMI can have very different patterns of body fat distribution and thus very different risk of cardiovascular disease, diabetes and obesity:
- BMI does not consider bone mineral density thereby providing inaccurate results for ethnicities with heavier bone structure, such as African Americans.
- BMI does not consider waist or hip circumference, which is pivotal for identifying fat accumulation around organs and thus disease risk.
- BMI considers only two dimensions (height and weight) as opposed to three dimensions including fat distribution, particularly abdominal fat.
- BMI generally overestimates adiposity on those with more lean body mass (e.g., athletes, some ethnicities, etc.).
- BMI uses the same range for men and women, which adds to the inaccuracy. Women tend to carry additional fat than men.
- Many people defined as overweight and obese according to BMI, such as bodybuilders, athletes and those with a higher muscle mass do not face any meaningful increased risk for complications and early death (Campos 2006).
- Overweight people according to BMI may have a death rate similar to normal-weight people, while underweight and obese people may have a higher death rate than normal weight (Flegal 2005, Prospective Studies Collaboration 2009).
- Normal BMI Individuals with coronary artery disease may be at higher risk of death from cardiovascular disease than overweight people (Romero-Corral 2006).
Inconsistency of results with other classifications
- 50% of men and 62% of women are obese according to body fat defined obesity, while only 21% of men and 31% of women are obese according to BMI, indicating that BMI underestimates the number of obese subjects (Romero-Corral 2008).
Old BMI Old Alternatives
- BMI Prime is the ratio of actual BMI to upper limit BMI (e.g. 25 in the US and 23 in East Asia).
- It classifies individuals as:
- Underweight: < 0.74
- Optimal weight: 0.74 to 1
- Overweight: > 1
- BMI Prime offers 2 advantages over the classic BMI. First, it is more comprehendible as it defines the percentage individuals deviate from their upper weight limits. Second, the upper weight limit can be replaced by revised numbers to cover international variations (Gadzik 2006).
Surface-based body shape index
The Surface-based Body Shape Index (SBSI) is based on the body surface area (BSA), vertical trunk circumference (VTC), waist circumference (WC, in cm) and height (Ht, in m) and calculated as:
- SBSI = (Ht7/4 x WC5/6) / (BSA x VTC)
- BSA can be calculated by:
- BSA = 0.00949 × Weight (in kg)0.441 × Ht0.655
- VTC is calculated by arm circumference (AC, in cm), height (H, in cm), subscapular skinfold (SS, in cm), thigh circumference (TC, in cm), triceps skinfold (TS, in cm), upper arm length (UAL, in cm), waist circumference (WC, cm) and weight (W, kg) as:
- 61.2 + AC x 0.315 + H x 0.409 + SS x 0.237 – TC x 0.089 – TS x 0.12 – UAL x 0.453 + WC x 0.137 + W x 0.37
- SBSI resolves BMI limitations regarding height and body morphology.
- Values > 2.28 are generally associated with more hazards (Rahman 2015).
- The Corpulence Index (CI) is calculated as weight (kg) / Height (cm)3
- CI yields more valid results in height extremes, children and athletes (Santos 2016, Babar 2016a, Babar 2016b, Babar 2015, Davies 1980).
Fat-free mass index
- The fat-free mass index determines fat-free mass (muscle, bone, tissue) mostly benefiting athletic individuals.
- FFMI = (Lean Body Weight (lbs) / 2.2) / ((Height (ft) x 12 + Inches) x 0.0254)2
- Lean Body Weight (lbs) = Total Body Weight (lbs) x (1 – (Body Fat %/ 100))
- Adjusted FFMI = FFMI + (6 x (((Height (ft) x 12 + Inches) x 0.0254) – 1.8))
Waist-to-height and waist-to-hip ratios
- Waist-to-height ratio is calculated as:
- WHtR = Waist Circumference / Height
- Waist-to-hip ratio is calculated as:
- WHR = Waist Circumference / Hip Circumference
- WHtR and WHR do not have BMI limitations regarding age, sex and racial variations (Browning 2010).
- Generally, a WHtR >0.5 signifies an increased health concern with obesity (Browning 2010).
- Age-adjusted critical WHtR levels:
- > 0.5 for under 40 years-old
- 0.5 to 0.6 for 40–50 years-old
- > 0.6 for over 50 years-old
- WHR > 0.9 for males and > 0.85 for females is associated with obesity related complications (World Health Organization Report 2011).
- WHtR is a better predictor of heart attack, stroke or death than BMI (Schneider 2010).
- WHR is a better predictor of ischemic heart disease and its related mortality than BMI (Morkedal 2011).
Body adiposity index
- Body Adiposity Index (BAI) is invented by Nick Trefethen, a Professor of Numerical Analysis at Oxford University’s Mathematical Institute.
- BAI, unlike BMI, does not use weight in the calculation.
- BAI = ((100 x HC) / Ht3/2) – 18
- HC = Hip Circumference (in m)
- Ht = Height (in m)
- Although clinical studies have not shown any proof, it is widely believed that Body Adiposity Index is more accurate than BMI as the resulting figures are approximate.
Body volume index (Bvi)
- Body Volume Index (BVI) looks at the relationship between mass and weight distribution instead of mass and weight alone, resolving BMI limitations due to age, gender, or ethnicity.
- BVI is calculated through BMI, waist circumference, waist-to-hip ratio, and 3D body composition analysis (obtained through specific scanners).
- BVI is a measure of ongoing research with acceptable ranges yet to be defined.
Other BMI modifications
- In the BMI calculation formula, some modifications in height exponent (instead of 2) may result in more reliable values (Diverse Populations Collaborative Group 2005, Levitt 2007):
- 2.6 in children
- 1.92 to 1.96 for male adults
- 1.45 to 1.95 for female adults
Ideal Body Weight
There are a variety of proposed formulas for the calculation of Ideal Body Weight (in kg).
- In all below equations, DHT is inches of height over 5 ft.
- Robinson Formula (Robinson 1983):
- Men: 51.65 + 1.85 x DHt
- Women: 48.67 + 1.65 x DHt
- Miller Formula (Miller 1983, Pai 2000):
- Men: 56.2 + 1.41 x DHt
- Women: 53.1 + 1.36 x DHt
- Hamwi 1964’s Formula (Harvey 2006):
- Men: 48 + 2.7 x DHt
- Women: 45.5 + 2.2 x DHt
- Devine 1974’s Formula (Pai 2000):
- Men: 50.0 + 2.3 x DHt
- Women: 45.5 + 2.3 x DHt
- Break-even calories (BEC) are the number of calories an individual needs daily to neither gain nor lose weight.
- BEC is calculated based on Lean Body Weight (LBW, in kg) and basal metabolic rate (BMR).
- LBW is the body weight without fat and calculated by the total body weight (TBW, in kg) and height (Ht, in m) minus fat with either of these formulas:
- Boer Formula (Boer 1984):
- Men: LBW = 0.407 x TBW + 26.7 x Ht – 19.2
- Women: LBW = 0.252 x TBW + 47.3 x Ht – 48.3
- James Formula (James 1976, Absalom 2009):
- Men: LBW = 1.1 x TBW – 128 x (TBW / 100 x Ht)2
- Women: LBW = 1.07 x TBW – 148 x (TBW / 100 x Ht)2
- Hume Formula (Hume 1966):
- Men: LBW = 0.3281 x TBW + 33.929 x Ht – 29.5336
- Women: LBW = 0.29569 x TBW + 41.813 x Ht – 43.2933
- Peters Formula for children < 15 years-old (Peters 2011):
- LBM (kg) = 0.0817 x TBW (kg)0.6469 x Ht (cm)0.7236
- BEC = (21.6 x LBW + 370) x Activity Level Coefficient
- Activity level coefficient is measured as:
- 1.3: Very light activity, almost sedentary and performing only minor activities with a little walking.
- 1.55: Light activity, including office work and some walking.
- 1.65: Moderate activity, including exercises and sports for 1 to 2 hours per day.
- 1.8: Heavy activity, including heavy manual labor or intensive sports for 2 to 4 hours per day.
- 2: Very heavy, including > 8 hours of moderate and heavy activity and 2 to 4 hours of intensive daily training.
Pregnancy Weight Calculator
Normal weight gain during pregnancy is calculated by (Rasmussen 2009a, Rasmussen 2009b):
- 1.1 lbs to 4.4 lbs (0.5 to 2 kg) during the first 3 months
- During the second and third trimesters, it depends on BMI before pregnancy:
- Underweight (BMI < 18.5): 28 lbs to 40 lbs total or 1 lb to 1.3 lbs per week for a singleton pregnancy
- Normal weight (BMI 18.5 to 24.9): 25 lbs to 35 lbs total or 0.8 lbs to 1 lbs per week for singleton and 37 lbs to 54 lbs total for twin pregnancy
- Overweight (BMI 25 to 29.9): 15 lbs to 25 lbs total or 0.5 lbs to 0.7 lbs per week for singleton and 31 lbs to 50 lbs for twin pregnancy
- Obese (BMI ≥ 30): 11 lbs to 20 lbs total or 0.4 lbs to 0.6 lbs per week for singleton and 25 lbs to 42 lbs for twin pregnancy
Indirect Waist Size Calculation
Waist size is an extremely important measurement when identifying obesity risk as it corresponds directly to visceral or intra-abdominal fat (fat surrounding organs). Waist circumference data can be collected in the following ways:
- Pant size: Client to input data of pant size.
Table 1: Male pant size conversion chart
Table 2: Female pant size conversion chart
- Self-measure: Provide instructions on how to take a professional waist measurement. Provide this as an option for those wanting more accurate results.
Body Fat Calculation Formula
- Men: 495 / (1.0324 – 0.19077 x (LOG(waist – neck)) + 0.15456 x (LOG(height))) – 450
- Women: 495 / (1.29579 – 0.35004 x (LOG(waist + hip – neck)) + 0.221 x (LOG(height))) – 450
- Absalom, A., Mani, V., De Smet, T., et al. (2009). Pharmacokinetic models for propofol – defining and illuminating the devil in the detail. British Journal of Anaesthesiology [online], 103 (1), pp. 26-37. Available from: http://bja.oxfordjournals.org/content/103/1/26.long#ref-10 [Accessed 28.07.2016].
- Aloia, J., Vaswani, A., Ma, R., et al. (1997). Comparison of body composition in black and white premenopausal women. Journal of Laboratory and Clinical Medicine [online], 129 (3), pp. 294-9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/9042814 [Accessed 02.08.2016].
- Babar, S. (2015). Evaluating the Performance of 4 Indices in Determining Adiposity. Clinical Journal of Sports Medicine [online], 25 (2), pp. 183. Available from: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=h87RZRMAAAAJ&citation_for_view=h87RZRMAAAAJ:UeHWp8X0CEIC [Accessed 26.07.2016].
- Babar, S., Messick, B., Burroughs, K. (2016a). Best Measure Of Adiposity: Body Mass Index, Corpulence Index, Or Waist Circumference. Medicine and Science in Sports and Exercise [online], 48 (5), pp. 551. Available from: http://www.ncbi.nlm.nih.gov/pubmed/27360806 [Accessed 27.07.2016].
- Babar, S. (2016b). The Use of Adiposity Indices for Wide Receivers From 2015 NFL Combine. Clinical Journal of Sports Medicine (online), 26 (2), pp. 23. Available from: http://journals.lww.com/cjsportsmed/Fulltext/2016/03000/AMSSM_Research_Abstract_Oral_Poster_Presentations.17.aspx [Accessed 27.07.2016].
- Barondess, D., Nelson, D., Schlaen, S. (1997). Whole Body Bone, Fat, and Lean Mass in Black and White Men. Journal of Bone and Mineral Research [online], 12 (6), pp. 967-71. Available from: http://onlinelibrary.wiley.com/doi/10.1359/jbmr.19126.96.36.1997/full [Accessed 31.07.2016].
- Bodicoat, D., Gray, L., Henson, J., et al. (2014). Body mass index and waist circumference cut-points in multi-ethnic populations from the UK and India: the ADDITION-Leicester, Jaipur heart watch and New Delhi cross-sectional studies. PLoS One [online], 9 (3), pp. e90813. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3944886/ [Accessed 05.03.2017].
- Boer, P. (1984). Estimated lean body mass as an index for normalization of body fluid volumes in humans. American Journal of Physiology [online], 247 (4), pp. 632-6. Available from: http://www.ncbi.nlm.nih.gov/pubmed/?term=Boer+P.+%22Estimated+lean+body+mass+as+an+index+for+normalization+of+body+fluid+volumes+in+man.%22+Am+J+Physiol+1984%3B+247%3A+F632-5 [Accessed 28.07.2016].
- Blaak, E. (2001). Gender differences in fat metabolism. Current Opinion in Clinical Nutrition and Metabolic Care [online], 4 (6), pp. 499-502. Available from: http://www.ncbi.nlm.nih.gov/pubmed/11706283 [Accessed 30.07.2016].
- Browning, L., Hsieh, S., Ashwell, M. (2010). A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0.5 could be a suitable global boundary value. Nutrition Research Reviews [online], 23 (2), pp. 247-69. Available from: http://www.ncbi.nlm.nih.gov/pubmed/?term=A+systematic+review+of+waist-to-height+ratio+as+a+screening+tool+for+the+prediction+of+cardiovascular+disease+and+diabetes%3A+0%C2%B75+could+be+a+suitable+global+boundary+value [Accessed 27.07.2016].
- Campos, P., Saguy, A., Ernsberger, P., et al. (2006). The epidemiology of overweight and obesity: public health crisis or moral panic? International Journal of Epidemiology [online], 35 (1), pp. 55-60. Available from: http://ije.oxfordjournals.org/content/35/1/55.long [Accessed 26.07.2016].
- Carpenter, C., Yan, E., Chen, S., et al. (2013). Body fat and body-mass index among a multiethnic sample of college-age men and women. Journal of Obesity [online], 2013, pp. 790654. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3649342/ [Accessed 02.08.2016].
- Conway, J., Yanovski, S., Avila, N., et al. Visceral adipose tissue differences in black and white women. The American Journal of Clinical Nutrition [online], 61 (4), pp. 765-71. Available from: http://ajcn.nutrition.org/content/61/4/765.short [Accessed 30.07.2016].
- Davidson, L., Wang, J., Thornton, J., et al. (2011). Predicting fat percent by skinfolds in racial groups: Durnin and Womersley revisited. Medicine and Science in Sports and Exercises [online], 43 (3), pp. 542-9. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3308342/ [Accessed 05.03.2017].
- Davies, D. (1980). Size at birth and growth in the first year of life of babies who are overweight and underweight at birth. Proceedings of the Nutrition Society [online], 39 (1), pp. 25–33. Available from: http://www.ncbi.nlm.nih.gov/pubmed/?term=Davies%2C+D.+(1980).+Size+at+birth+and+growth+in+the+first+year+of+life+of+babies+who+are+overweight+and+underweight+at+birth.+Proceedings+of+the+Nutrition+Society [Accessed 27.07.2016].
- Diverse Populations Collaborative Group (2005). Weight-height relationships and body mass index: some observations from the Diverse Populations Collaboration. American Journal of Physical Anthropology [online], 128 (1), pp. 220-9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/?term=Weight-height+relationships+and+body+mass+index%3A+some+observations+from+the+Diverse+Populations+Collaboration.+American+Journal+of+Physical+Anthropology [Accessed 26.07.2016].
- Durnin, J., Womersley, J. (1974). Body fat assessed from total body density and its estimation from skinfold thickness: measurements on 481 men and women aged from 16 to 72 years. British Journal of Nutrition [online], 32 (1), pp. 77-97. Available from: https://www.ncbi.nlm.nih.gov/pubmed/4843734 [Accessed 05.03.2017].
- Eknoyan, G. (2008). Adolphe Quetelet (1796-1874) – the average man and indices of obesity. Nephrology Dialysis Transplantation [online], 23 (1), pp. 47-51. Available from: http://ndt.oxfordjournals.org/content/23/1/47.long [Accessed 29.07.2016].
- Flegal, K., Graubard, B., Williamson, D., et al. (2005). Excess Deaths Associated with Underweight, Overweight, and Obesity. JAMA [online], 293 (15), pp. 1861-7. Available from: http://www.ncbi.nlm.nih.gov/pubmed/15840860 [Accessed 26.07.2016].
- Gadzik, J. (2006). How much should I weigh? Quetelet’s equation, upper weight limits, and BMI prime. Connecticut Medicine [online], 70 (2), pp. 81–8. Available from: http://www.ncbi.nlm.nih.gov/pubmed/?term=Gadzik%2C+James+(2006).+%22%27How+much+should+I+weigh%3F%27+Quetelet%27s+equation%2C+upper+weight+limits%2C+and+BMI+prime%22.+Connecticut+Medicine+70+(2)%3A+81%E2%80%938. [Accessed 27.07.2016].
- 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 [online], 143 (3), pp. 228-39. Available from: http://aje.oxfordjournals.org/content/143/3/228.short [Accessed 30.07.2016].
- Harvey, K. (2006). Methods for determining healthy body weight in end stage renal disease. Journal of Renal Nutrition [online], 16 (3), pp. 269-76. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16825033 [Accessed 27.07.2016].
- Henderson, R. (2005). The bigger the healthier: are the limits of BMI risk changing over time? Economics and Human Biology [online], 3 (3), pp. 339-66. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1414803/ [Accessed 04.03.2017].
- Hume, R. (1966). Prediction of lean body mass from height and weight. Journal of Clinical Pathology [online], 19 (4), pp. 389-91. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC473290/ [Accessed 28.07.2016].
- Jackson, A., Stanforth, P., Gagnon, J., et al. (2002). The effect of sex, age and race on estimating percentage body fat from body mass index: The Heritage Family Study. International Journal of Obesity Related Metabolic Disorders [online], 26 (6), pp. 789-96. Available from: http://www.ncbi.nlm.nih.gov/pubmed/12037649 [Accessed 29.07.2016].
- James, W., Waterlow, J. (1976). Research on obesity: a report of the DHSS/MRC group. HM Stationery Office. Available from: https://scholar.google.com/scholar_lookup?title=Research%20on%20obesity.&author=W%20James&publication_year=1976 [Accessed 28.07.2016].
- Kagawa, M., Kerr, D., Uchida, H., et al. (2006). Differences in the relationship between BMI and percentage body fat between Japanese and Australian-Caucasian young men. British Journal of Nutrition [online], 95 (5), pp. 1002-7. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16611393 [Accessed 02.08.2016].
- Levitt, D., Heymsfield, S., Pierson, R., et al. (2007). Physiological models of body composition and human obesity. Nutrition and Metabolism [online], 6, pp. 7. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2649131/ [Accessed 26.07.2016].
- Meyer, K., Friend, S., Hannan, P., et al. (2011). Ethnic variation in body composition assessment in a sample of adolescent girls. International Journal of Pediatric Obesity [online], 6 (5-6), pp. 481-90. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3174327/ [Accessed 02.08.2016].
- Miller, D., Carlson, J., Lloyd, B., et al. (1983). Determining ideal body-weight (and mass). American Journal of Hospital Pharmacy [online], 40 (10), pp. 1622. Available from: http://www.ajhp.org/content/40/10.toc [Accessed 27.07.2016].
- Mørkedal, B., Romundstad, P., Vatten, L. (2011). Informativeness of indices of blood pressure, obesity and serum lipids in relation to ischaemic heart disease mortality: the HUNT-II study. European Journal of Epidemiology [online], 26 (6), pp. 457-61. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3115050/ [Accessed 26.07.2016].
- Mridha, S. (2010). A comparative study on body composition of male and female national level sub-junior volleyball players. British Journal of Sports Medicine [online], 44 (1), pp. I37-8. Available from: http://bjsm.bmj.com/content/44/Suppl_1/i37.4.abstract [Accessed 30.07.2016].
- Ortiz, O., Russell, M., Daley, T., et al. (1992). Differences in skeletal muscle and bone mineral mass between black and white females and their relevance to estimates of body composition. The American Journal of Clinical Nutrition [online], 55 (1), pp. 8-13. Available from: http://ajcn.nutrition.org/content/55/1/8.short [Accessed 30.07.2016].
- Pai, M., Paloucek, F. (2000). The origin of the “ideal” body weight equations. Annals of Pharmacotherapy [online]. 34 (9), pp. 1066-9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/10981254 [Accessed 27.07.2016].
- Peters, A., Snelling, H., Glass, D., et al. (2011). Estimation of lean body mass in children. British Journal of Anaesthesiology [online], 106 (5), pp. 719-23. Available from: http://bja.oxfordjournals.org/content/106/5/719.long [Accessed 28.07.2016].
- Popkin, B. (2002). An overview on the nutrition transition and its health implications: the Bellagio meeting. Public Health Nutrition [online], 5 (1A), pp. 93-103. Available from: https://www.ncbi.nlm.nih.gov/pubmed/12027297/ [Accessed 04.03.2017].
- Prospective Studies Collaboration. (2009). Body-mass index and cause-specific mortality in 900000 adults: collaborative analyses of 57 prospective studies. Lancet [online], 373 (9669), pp. 1083-96. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2662372/ [Accessed 26.07.2016].
- Rahman, S., Adjeroh, D. Surface-Based Body Shape Index and Its Relationship with All-Cause Mortality. PLoS One [online], 10 (12), pp. 0144639. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4692532/ [Accessed 27.07.2016].
- Rasmussen, K., Yaktine, A. (2009a). Weight Gain During Pregnancy: Reexamining the Guidelines. Washington (DC): National Academies Press (US) [online], pp.1-4. Available from: http://www.ncbi.nlm.nih.gov/books/NBK32813/ [Accessed 28.07.2016].
- Rasmussen, K., Catalano, P., Yaktinec, A. (2009b). New guidelines for weight gain during pregnancy: what obstetrician/gynecologists should know. Current Opinion in Obstetrics and Gynecology [online]. 21 (6), pp. 521-6. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2847829/ [Accessed 28.07.2016].
- Robinson JD, Lupkiewicz SM, Palenik L, et al. (1983). Determination of ideal body weight for drug dosage calculations. American Journal of Hospital Pharmacy [online], 40 (6), pp. 1016-9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/6869387 [Accessed 27.07.2016].
- Romero-Corral, A., Montori, V., Somers, V., et al. (2006). Association of bodyweight with total mortality and with cardiovascular events in coronary artery disease: A systematic review of cohort studies. Lancet [online], 368 (9536), pp. 666-78. Available from: http://www.ncbi.nlm.nih.gov/pubmed/?term=Romero-Corral%2C+A%3B+Montori%2C+V+M%3B+Somers%2C+V+K%3B+Korinek%2C+J%3B+Thomas%2C+R+J%3B+Allison%2C+T+G%3B+Mookadam%2C+F%3B+Lopez-Jimenez%2C+F+(2006).+Association+of+bodyweight+with+total+mortality+and+with+cardiovascular+events+in+coronary+artery+disease%3A+A+systematic+review+of+cohort+studies.+The+Lancet+368 [Accessed 26.07.2016].
- Romero-Corral, A., Somers, V., Sierra-Johnson, J. et al. (2008). Accuracy of body mass index in diagnosing obesity in the adult general population. International Journal of Obesity [online], 32 (6), pp. 959-66. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2877506/ [Accessed 26.07.2016].
- Rush, E., Freitas, I., Plank, L. (2009). Body size, body composition and fat distribution: comparative analysis of European, Maori, Pacific Island and Asian Indian adults. British Journal of Nutrition [online], 102 (4), pp. 632-41. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19203416 [Accessed 02.08.2016].
- Santos, L., Ong, K., Day, F., et al. (2016). Body shape and size in 6-year old children: assessment by three-dimensional photonic scanning. International Journal of Obesity [online]. 40 (6), pp. :1012-7. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4899819/ [Accessed 27.07.2016].
- Schneider, H., Friedrich, N., Klotsche, J., et al. (2010). The Predictive Value of Different Measures of Obesity for Incident Cardiovascular Events and Mortality. Journal of Clinical Endocrinology and Metabolism [online], 95 (4), pp. 1777-85. Available from: http://www.ncbi.nlm.nih.gov/pubmed/?term=Schneider%2C+H.+The+Predictive+Value+of+Different+Measures+of+Obesity+for+Incident+Cardiovascular+Events+and+Mortality.+Journal+of+Clinical+Endocrinology+%26+Metabolism [Accessed 26.07.2016].
- Stone, M., Williams, L., Chatterjee, S., et al. (2008). Ethnic differences in body composition in adolescents. Primary Care Diabetes [online], 2 (1), pp. 55-7. Available from: http://www.ncbi.nlm.nih.gov/pubmed/18684422 [Accessed 02.08.2016].
- Taylor, R. (2010). Use of Body Mass Index For Monitoring Growth and Obesity. Paediatrics and Child Health [online], 15 (5), pp. 258. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2912631/ [Accessed 26.07.2016].
- Wagner, D., Heyward, V. (2000). Measures of body composition in blacks and whites: a comparative review. American Journal of Clinical Nutrition [online], 71 (6), pp. 1392-402. Available from: http://ajcn.nutrition.org/content/71/6/1392.long [Accessed 29.07.2016].
- Wang, D., Li, Y., Lee, S., et al. (2011). Ethnic differences in body composition and obesity related risk factors: study in Chinese and white males living in China. PLoS One [online], 6 (5), pp. e19835. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3098253/ [Accessed 02.08.2016].
- World Health Organization Report. (2011). Waist Circumference and Waist–Hip Ratio: Report of a WHO Expert Consultation. Pp.12-18. Available from: http://apps.who.int/iris/bitstream/10665/44583/1/9789241501491_eng.pdf [Accessed 27.07.2016].
- Wulan, S., Westerterp, K., Plasqui, G. (2012). Dietary and 24-h fat oxidation in Asians and whites who differ in body composition. American Journal of Clinical Nutrition [online]. 95 (6), pp. 1335-41. Available from: http://ajcn.nutrition.org/content/95/6/1335.long [Accessed 02.08.2016].
- Wulan, S., Westerterp, K., Plasqui, G. (2010). Ethnic differences in body composition and the associated metabolic profile: a comparative study between Asians and Caucasians. Maturitas [online], 65 (4), pp. 315-9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20079586 [Accessed 02.08.2016].
- Yanovski, J., Yanovski, Y.., Filmer, K., et al. (1996). Differences in body composition of black and white girls. The American Journal of Clinical Nutrition [online]. 64 (6), pp. 833-9. Available from: http://ajcn.nutrition.org/content/64/6/833.short [Accessed 30.07.2016].
This research was sponsored by GLOBESITY FOUNDATION (nonprofit organization) and managed by Don Juravin. GLOBESITY Bootcamp for the obese is part of GLOBESITY FOUNDATION which helps obese with 70 to 400 lbs excess fat to adopt a healthy lifestyle and thereby achieve a healthy weight.
Tags: BMI, GLOBESITY FOUNDATION, ethnicity, BMI prime, corpulence index, fat-free mass index, body adiposity index, body volume index, calorie break-even