Body Composition: Measurement Techniques

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Editor: James M. Rippe
Date: 2012
Encyclopedia of Lifestyle Medicine & Health
Publisher: Sage Publications, Inc.
Document Type: Table; Topic overview
Pages: 6
Content Level: (Level 4)

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Body Composition: Measurement Techniques

Body composition is significantly related to disease risk and physical performance. The health risks of excess adiposity, for example, are well-known, including increased risk of type 2 diabetes, hypertension, dyslipidemia, cardiovascular and coronary artery diseases, and some types of cancer. Low bone mass, leading to osteoporosis, increases risk of fragility fractures, and low muscle mass, termed sarcopenia, is associated with declining muscle strength, impaired physical function, and ultimately loss of independence and lower quality of life.

Numerous methods are available for estimating the components of interest. Although the application of many methods is limited to research and clinical settings, several simple methods are adequate for screening, with follow-up with more sophisticated techniques as needed. Body composition assessment is an important aspect of health appraisal. This entry discusses feasible screening methods and

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dual-energy X-ray absorptiometry (DXA), an accessible and relatively inexpensive laboratory method that is recommended when a more accurate follow-up is needed.

Reference Methods

Laboratory methods provide reference or criterion measures for the derivation and evaluation of field methods and prediction equations. Although they have greater accuracy, all laboratory methods make assumptions and are subject to error. When selecting a screening method or equation, it is important to know how it was derived. Historically, validation of many methods has been done using reference methods based on 2-component (2C) models, such as hydrodensitometry (underwater weighing) and hydrometry (measurement of total body water). When using these methods, it is imperative that the appropriate conversion constants be used. For example, conversion of body water to fat-free mass (FFM) usually assumes a constant hydration of FFM of 73.8%, which is appropriate for adults but too low for children. Similarly, estimation of percent fat from body density depends on using an appropriate FFM density that can be derived from age- and gender-specific values for the major constituents of FFM.

Appropriate values and critiques of various models are available in the literature (see the Further Readings). Methods based on multicomponent models and combined methods (e.g., combining hydrodensitometry with hydrometry) are preferred reference methods, since they make fewer assumptions and are more valid and accurate than methods based on 2C models. DXA, described later, is based on a multicompartment model and has emerged as an accurate, accessible, and relatively low-cost criterion method that can be used in children and adults.

Body Mass Index

The BMI is calculated from a person's weight and height. Because it is based on weight, it does not distinguish excess fat from muscle and bone mass. Thus, BMI may overestimate adiposity in persons with above-average lean mass relative to height (e.g., athletes) while underestimating adiposity in persons with lower lean mass relative to height (e.g., the elderly). Despite these limitations, BMI is the most widely used screening index for obesity. Example calculations and BMI standards are shown in Tables 1 and 2.

In adults, weight categories are based on identical ranges of BMI in men and women of all ages and ethnicities. In children and youth, because of age- and gender-specific changes in fat and FFM with growth, BMI categories are based on age- and

Table 1 Calculation of BMI
Measurement Units Formula and Calculation
Note: 1 in. = 2.54 cm; 1 lb = 0.4536 kg.
Kilograms and meters Formula: weight (kg)/[height (m)]2
With the metric system, the formula for BMI is weight in kilograms divided by height in meters squared.
Example: weight = 68 kg; height = 165 cm (1.65 m)
Calculation: 68/(1.65)2 = 24.98
Pounds and inches Formula: weight (lb)/[height (in.)]2 × 703
Dividing weight in pounds (lb) by height in inches (in.) squared and multiplying by a conversion factor of 703.
Example: weight = 150 lb; height = 5 ft, 5 in. (65 in.)
Calculation: [150/(65)2] × 703 = 24.96

Table 1 Calculation of BMI Table 1 Calculation of BMI

Table 2 Adult weight status by BMI
Adult Weight Status BMI Range (kg/m2)
Underweight Below 18.5
Normal 18.5–24.9
Overweight 25.0–29.9
Obese 30.0 and above

Table 2 Adult weight status by BMI Table 2 Adult weight status by BMI

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sex-specific percentiles. Below age 18 years, BMI categories are as follows: <5th percentile, underweight; BMI between the 85th and 95th percentile, at risk of overweight; and >95th percentile, overweight. Corresponding levels of BMI can be obtained from the year 2000 Centers for Disease Control and Prevention growth charts for BMI.

In recent years, increasing evidence of differences across populations in the associations between BMI, adiposity, and fat distribution, and thus differences in the BMI/health risk relationship, has led to calls for population-specific BMI cut-points in adults. While not currently in wide use, revised Asia-Pacific BMI classifications of BMI >23 as overweight and >25 as obese may better reflect the level of obesity that is associated with increased health risks for these populations.

Waist Circumference

Abdominal adiposity is correlated with increased risk of metabolic syndrome, type 2 diabetes mellitus, and heart disease. Waist circumference has long been used as a surrogate of abdominal adiposity and is a simple way to predict health risk due to obesity. Current waist circumference cut-points, from the National Institutes of Health, indicate an increased disease risk when waist measurements are >102 cm (>40 in.) and >88 cm (>35 in.) for men and women, respectively (see Table 3 ).

Limited data suggest that there may be racial/ethnic differences in visceral adipose tissue at a given waist circumference. Altered relationships between anthropometric measures and visceral adipose tissue may have implications for defining metabolic risk in different populations. Different waist circumference cut-points may be necessary to adequately reflect risk in different racial/ethnic groups, although at this time race/ethnicity-specific cut-points are not in use.


Table 3 Waist circumference cut-points endorsed by the U.S. National Institutes of Health
  Normal Increased Risk
Men ≤102 cm (≤40 in.) >102 cm (>40 in.)
Women ≤88 cm (≤35 in.) >88 cm (>35 in.)

Table 3 Waist circumference cut-points endorsed by the U.S. National Institutes of Health Table 3 Waist circumference cut-points endorsed by the U.S. National Institutes of Health

It is important to recognize that various procedures and landmarks have been recommended for measuring waist circumference. For example, waist circumference has been measured midway between the iliac crest and the lowest rib, at the iliac crest, and at the level of the umbilicus. While we endorse the standard procedures outlined in the Anthropometric Reference Standardization Manual from the Arlie conference, when comparing measurements against a reference database (e.g., the National Health and Nutrition Examination Survey), it is essential to match the measurement protocol used in the survey from which the data originate.

BMI and Waist Circumference

Although BMI and waist circumference are independently correlated with adiposity and cardio-metabolic disease risk, the combination of these measures improves the predictive power for disease risk. This approach is particularly useful in persons with BMIs between 18.5 and 29.9; at BMIs ≥35, the addition of waist circumference adds little to the prediction of disease risk. Thus, to provide the most accurate assessment possible, whenever feasible both BMI and waist circumference measurements should be performed (see Table 4 ).


Measurements of skinfold thickness provide direct estimates of subcutaneous fat. When compared against age- and gender-appropriate norms (e.g., the National Health and Nutrition Examination Survey), they provide an indication of fatness relative to population distributions. Moreover, changes in skinfolds directly reflect changes in fatness. Given the differences in fat patterns, it is useful to sample limb and trunk sites to adequately represent fatness. Skinfolds are also useful for estimating whole-body fatness with acceptable accuracy if an appropriate equation is used. Historically, many equations were developed to estimate body density (BD) from skinfolds, since hydodensitometry was a common reference method. Once density is known, percent body fat and FFM are calculated using a 2C equation, such as the oft-used Siri equation:

%fat = (4.95/BD − 4.50) × 100.

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Table 4 Disease risk associated with BMI and waist circumference combined
  BMI (kg/m2) Obesity Class Men ≤102 cm (40 in.)
Women ≤88 cm (35 in.)
Men >102 cm (>40 in.)
Women >88 cm (>35 in.)
Source: Scott Going.
Underweight 18.5      
Normal+ 18.5–24.9      
Overweight 25.0–29.9   Increased High
Obesity 30.0–34.9 I High Very high
  35.0–39.9 II Very high Very high
Extreme obesity ≥40 III Extremely high Extremely high

Table 4 Disease risk associated with BMI and waist circumference combined Table 4 Disease risk associated with BMI and waist circumference combined Scott Going.

Other equations based on multicomponent reference methods estimate percent fat directly. Heyward and Wagner (2004) have published a thorough review and recommended equations for different populations. Skinfold equations that include 3 or more skinfold sites are more generalizable than those that use only 1 or 2 sites. When densitometry is the reference method, an equation based on an appropriate reference body must be used to avoid introducing model error that adds to the error associated with the estimation of body density. Reference bodies designed specifically for various segments of the adult population are not well developed, although some are available for different ages and races. Equations based on combined reference methods or a reference method based on a multicomponent model, such as DXA, generally have less model error and provide more valid and accurate estimates of percent fat.

Bioelectrical Impedance Analysis

Bioelectrical impedance analysis (BIA) is a relatively inexpensive, noninvasive, and nonintrusive method for measuring body composition. BIA measures the tissue resistance to a small, safe, high-frequency alternating current. FFM, because of its fluid content and electrolytes, is a good conductor, whereas fat, which is anhydrous, is a poor conductor. Thus, bioresistance can be used to estimate body water and FFM and then derive fat mass and percent fat. Single-frequency and multiple-frequency analyzers are available for measuring bioresistance.

The single-frequency approach has proved useful in healthy, normally hydrated persons but is not suitable for persons with altered hydration status (i.e., dehydration, certain diseases/illnesses) since it assumes normal hydration and does not distinguish between intracellular water (ICW) and extracellular water (ECW). In clinical settings, multifrequency BIA, which is based on resistance measurements over a wide range of frequencies (e.g., 0, 1, 5, 50, 100, 200, and 500 kHz) may give more accurate estimates of body composition since this technique does not require an assumption of normal hydration and unchanging ICW:ECW ratio.

A segmental approach may also have advantages. In the typical whole-body approach, which models the body as a single conductive cylinder with a uniform length (equal to a person's height) and uniform cross-sectional area, arms and legs contribute disproportionately to resistance compared with trunk. In contrast, the segmental BIA approach assumes that the body consists of 5 interconnected cylinders: 2 arms, 2 legs, and the trunk. These segments have individual lengths and cross-sectional areas and are electrically connected in a series configuration (arms connected to trunk and trunk connected to legs), which theoretically would provide accurate estimates of both segmental and whole-body composition. Analyzers that provide segmental measures are becoming available, and although they may provide more accurate estimates of composition, their advantages have yet to be proven.

As with skinfolds, many BIA equations for estimating components of body composition are available,

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and it is important to know how they were developed, and for whom they were meant, and to carefully evaluate the reference method. Heyward and Wagner (2004) provide a careful review and give recommendations for the most useful equations. It is also important to follow recommendations regarding timing of meals, exercise, alcohol and caffeine intakes, and the manufacturer's protocol for obtaining the measurements.

Dual-Energy X-Ray Absorptiometry

DXA has emerged as a reference research and clinical method. Based on a 3-compartment model of composition (mineral, lean soft tissue, and fat), DXA is relatively unaffected by fluctuations in hydration status and thus, under most conditions, is not confounded by failure to meet the assumption of chemical constancy (constant ratios among chemical components) that underlies methods based on the simpler, 2C models. DXA provides regional and whole-body estimates of soft tissue composition (fat and lean) and bone, thus a single whole-body scan provides information related to bone mineral status, adiposity and, indirectly, muscle mass. Current definitions of osteopenia and osteoporosis are based on regional (hip and lumbar spine) DXA scans. Similarly, some sarcopenia definitions are based on regional (arms and legs) scans since appendicular lean soft tissue is a good surrogate for appendicular skeletal muscle mass.

While DXA cannot measure visceral (intra-abdominal) fat separately from subcutaneous abdominal fat, some studies have shown that DXA abdominal fat is significantly correlated with metabolic disorders such as dyslipidemia and insulin resistance and thus provides a lower cost alternative to expensive imaging techniques such as magnetic resonance imaging and computed tomography. The low radiation exposure makes DXA feasible in children as well as adults, and cost is relatively low compared with other reference techniques. Precision and accuracy are excellent when standard techniques are followed, making it possible to detect changes in composition over time.

Differences in technology (e.g., pencil beam vs. fan beam machines), differences between manufacturers, and differences among machines from the same manufacturer can introduce errors, and so whenever possible, longitudinal assessments should be done on the same machine or an appropriate correction must be applied. Despite these potential limitations, DXA is sufficiently accurate under most conditions to be considered a reference method and is recommended for follow-up when simpler screening methods suggest that more accurate measures are warranted.

Nobuko Hongu, Lee R. Vinson, and Scott Going

Further Readings

Centers for Disease Control and Prevention. About BMI for Adults. Atlanta, GA: Division of Nutrition, Physical Activity and Obesity, Centers for Disease Control and Prevention; 2009. . Accessed June 11, 2011.

Deurenberg P, Yap M, van Staveren WA. Body mass index and percent body fat: a meta analysis among different ethnic groups. Int J Obes Relat Metab Disord. 1998;22:1164–1171.

Di Iorio A, Abate M, Di Renzo D, et al. Sarcopenia: age-related skeletal muscle changes from determinants to physical disability. Int J Immunopathol Pharmacol. 2006;19:703–719.

Flegal KM, Shepherd JA, Looker AC, et al. Comparisons of percentage body fat, body mass index, waist circumference, and waist-stature ratio in adults. Am J Clin Nutr. 2009;89:500–508.

Heymsfield SB, Lohman TG, Wang ZM, Going SB, eds. Human Body Composition: Methods and Findings. 2nd ed. Champaign, IL: Human Kinetics; 2005.

Houtkooper LB, Lohman TG, Going SB, Howell WH. Why bioelectrical impedance analysis should be used for estimating adiposity. Am J Clin Nutr. 1996;64(3) (suppl):436S–448S.

Kyle UG, Bosaeus I, De Lorenzo AD, et al. Bioelectrical impedance analysis—part I: review of principles and methods. Clin Nutr. 2004;23:1226–1243.

LaMonte MJ, Blair SN. Physical activity, cardiorespiratory fitness, and adiposity: contributions to disease risk. Curr Opin Clin Nutr Metab Care. 2006;9:540–546.

Lohman T, Roche A, Martorell R. Anthropometric Standardization Reference Manual. Champaign, IL: Human Kinetics; 1991.

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Mohan V, Deepa M, Farooq S, Narayan KM, Datta M, Deepa R. Anthropometric cut points for identification of cardiometabolic risk factors in an urban Asian Indian population. Metabolism. 2007;56:961–968.

National Heart Lung and Blood Institute, National Institutes of Health, US Department of Health and Human Services. Guidelines on Overweight and Obesity: Electronic Textbook. . Accessed June 11, 2011.

Thomas BJ, Cornish BH, Pattemore MJ, Jacobs M, Ward LC. A comparison of the whole-body and segmental methodologies of bioimpedance analysis. Acta Diabetol. 2003;40(suppl 1):S236–237.

Thomas BJ, Ward LC, Cornish BH. Bioimpedance spectrometry in the determination of body water compartments: accuracy and clinical significance. Appl Radiat Isot. 1998;49(5–6):447–455.

WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363(9403):157–163.

World Health Organization. BMI Classification. . Accessed June 11, 2011.

Source Citation

Source Citation   

Gale Document Number: GALE|CX1959000056

Disclaimer:   This information is not a tool for self-diagnosis or a substitute for professional care.