Illinois State University Anthropometry Chapter 5

Illinois State University

AnthropometryAnthropometry

Chapter 5



The measurement of the size and shape of the body.

height, weight, length, breadth, circumference, diameter, and skinfold thickness.



Advantages: Instruments are portable Relatively inexpensive

Disadvantages: Less accurate



Procedures are noninvasive, and training can be provided “on the job” without prerequisite courses.



Methods are applicable to large samples

Can provide national estimates Provides data for the analysis of

secular changes.



Assumption: the tissues included in the

measurement are in a “standard” state, for example, that muscles are relaxed

and that soft tissues are normally hydrated.



If these conditions are not met, the interpretation may be invalid.


Height and Weight Height and Weight MeasuresMeasures


Height and WeightHeight and Weight

Initial attempts to gauge the relationship between body type and health relied on measures of height and weight.

Driven by the life insurance industry.


Body Mass IndexBody Mass Index


Body Mass IndexBody Mass Index

BMI is determined by measuring your weight in kilograms and dividing it by your height in meters2

This allows for comparisons of “stoutness”, not body composition.


BMIBMI

Used to classify individuals at risk for obesity-related diseases, and to monitor changes in body fatness of clinical populations.


BMIBMI

BMI is a significant predictor of cardiovascular diseases and type 2 diabetes.


BMIBMI

BMI is widely used in population-based and prospective studies to identify at-risk individuals.


BMIBMI

However, BMI is limited as an index of obesity (i.e., body fatness) because it does not take into account the composition of an individual’s body weight.


BMIBMI

In addition, factors such as age, ethnicity, body build, and frame size affect the relationship between BMI and %BF.


BMIBMI

Using BMI as an index of obesity may result in misclassifications of underweight, overweight, and obesity.

It is also not a preferred method of assessing fat distribution.


Overweight and Obesity Overweight and Obesity (BMI)(BMI)

III> 40

II35-39.9

I30-34.9Obesity

25-29.9Overweight

18.5-24.9Normal Wt

<18.5Underweight

Obesity ClassBMI (kg/m2)

WHO 1998


Lengths and BreadthsLengths and Breadths



Lengths and breadths are interpreted as skeletal dimensions because they are made between bony landmarks.

Table 5.2, p 71 contains information on commonly measured sites.


Lengths and BreadthsLengths and Breadths

The effects of soft tissues on recorded lengths and breadths can be reduced and made less variable by the use of recommended calipers and the application of firm pressure.


CircumferencesCircumferences



Limb and trunk circumferences are measured with a tape measure while minimal tension is applied so that the soft tissues will not be compressed; therefore enlargement of muscle and SAT due to edema increases the recorded measurements.



Figure 5.1, p. 72 shows the locations of common circumference measures.

Table 5.1, on pp. 69-70 describes how these measures should be taken.



Circumferences of the limbs are difficult to interpret because they include skin, SAT, muscle, bone, blood vessels, nerves, and small amounts of deep adipose tissue (DAT).



It is even harder to interpret trunk circumferences, which include organs in addition to various tissues.



Interpretation of buttocks (hip) circumference is uncertain because it includes large amounts of adipose tissue and muscle and it is affected by pelvic size and shape.



Even standing for 1-2 hrs., or prolonged sitting, causes an accumulation of extracellular fluid in the lower limbs leading to increases in ankle and calf circumferences.




Abdominal circumferences are correlated with body density (r = -0.7), and the correlation of limb circumferences with body density are about -0.4.



The correlation of abdominal and limb circumferences with FFM are about 0.6 in each gender.


Waist to Hip RatioWaist to Hip Ratio


Waist to Hip RatioWaist to Hip Ratio

The WHR is commonly used as an indirect measure of lower and upper body fat distribution.

Figure 5.4, p. 74 (pdf file) illustrates how these measures are made.


WHRWHR

Upper body or central adiposity, measured by the WHR, is moderately related (r = 0.48 to 0.61) to risk factors associated with cardiovascular and metabolic diseases in men and women.


WHRWHR

Young adults with WHR values in excess of 0.94 for men and 0.82 for women are at high risk for adverse health consequences.


WHRWHR

Limitations: In women, it is affected by

menopausal status. Not valid for evaluating fat

distribution in prepubertal children. The accuracy of assessing VAT

decreases with increasing levels of fitness.


WHR LimitationsWHR Limitations

And finally: Hip circumference is influenced by

subcutaneous fat deposition only, whereas waist circumference is affected by both VAT and SAT.

Thus, the WHR may not accurately detect changes in VAT.


WHRWHR

Table 5.4, p. 78 (pdf file) contains norms for waist-to-hip circumference ratios for men and women.


Waist CircumferenceWaist Circumference


Waist CircumferenceWaist Circumference

WC is gaining support as an alternative to WHR for assessing regional adiposity in field and clinical settings.


WCWC

Compared to the WHR, WC provides a more accurate indirect measure of visceral fat and is not greatly influenced by age, gender, standing height, and degree of overall adiposity.


WCWC

WC is highly related (r = 0.76 to 0.88) to MRI and CT measures of intra-abdominal (visceral) fat in men and women, and to cardiovascular risk factors in older (67-78 yrs) women.


WCWC

The National Cholesterol Education Program (2001) recommends using WC cutoff values of > 102 cm (40 in) for men and > 88 cm (34.6 in) for women to evaluate obesity as a risk factor for coronary heart disease and metabolic disease.



Anthropometry, when used in relation to body composition, is based on the assumption that the tissue composition is independent of tissue size.



This assumption may be violated. For example, the fat content of

adipose tissue is positively related to SAT thicknesses within age groups and the fat content becomes larger as SAT thicknesses increase during growth.



The choice of anthropometric measures, and the procedures used, differ for some groups.



For example, the precise measurement of infants and preschool children requires that they be content; one cannot obtain precise measurements of hungry or thirsty children.



Precise refers to repeatability judged from inter- or intraobserver differences, and the term validity refers to comparisons between observed measures and the true values.



Disabled and elderly subjects who cannot stand erect must be measured recumbent to obtain precise and valid data.



The utility and interpretation of anthropometric variables are related to their short-term variations.



There is a loss of stature and an increase in abdominal and calf circumferences with prolonged standing, and there is considerable day-to-day variability of weight due mainly to the intake and elimination of food and water.



These fluctuations in weight reflect alterations in extracellular water but are not otherwise related to changes in body composition.



Because of these short-term fluctuations, it is recommended that relationships between anthropometric variables and body composition be determined from data recorded in the morning from fasting subjects after they have eliminated.



It is also recommended that data not be collected in the week before a menstrual period or during a menstrual period, when there may be an increase in the fluid content of the fat-free mass.



Knee height can be used to predict stature in those who are unable to assume the standard position for the measurement of stature.



Alternatively, arm span, which is also little affected by aging, can be used in place of stature for elderly individuals who are unable to stand.



Abdominal depth is of interest because it is related to the amount of deep abdominal adipose tissue.



In the discussion of the relationships between anthropometric variables and total body composition, some of the anthropometric variables are referred to as “indices” because they can be used to categorize individuals (e.g., lean, obese), but, they do not provide metric values for aspects of body composition.



Some variables that are indices of total body composition are actually measures of regional body composition.



All reported relationships between anthropometric variables and total body composition understate the actual relationships because neither the anthropometric variables nor the body composition variables are measured with exact precision.



The total body composition variables considered here are percent fat (% BF), fat-free mass (FFM), total body muscle, and total body bone mineral (TBBM).



Some prefer body density to % BF as the dependent variable in predictive equations because the relationships with anthropometric values are not affected by the uncertain calculation of % BF or FFM from body density.



After body density has been predicted, a body composition variable is calculated from it and these calculations are based on assumptions that may be inaccurate.


Anthropometry and Fat-Anthropometry and Fat-free Massfree Mass

Stature is not an effective predictor of FFM when used alone.


Anthropometry and Fat-Anthropometry and Fat-free Massfree Mass

Skeletal lengths and breadths have only low correlations with % BF, but breadths have a correlation with FFM of about 0.6 that are reduced to about 0.3 when the effects of stature are removed.



The term RMSE is used instead of the standard error of the estimate to summarize the differences between observed and predicted values.

These terms are mathematically the same.



BMI values are moderately correlated with % BF (r about 0.6 to 0.8), but the RSME of the prediction of % BF from BMI is about 3.5 to 5% BF.



Despite these errors, BMI has high specificity (recognition of true negatives) in screening for high % BF values.



Predictive equations should be applied only after they have been successfully cross-validated for a population similar to the one that will be studied.



While some anthropometric predictive equations have been successfully cross-validated, they may not perform well in all other groups.



There is no advantage in predicting FFM in preference to % BF since a predicted value for either can be used to calculate the other.



Nevertheless, it is logical to predict FFM if BIA, circumferences, breadths, and lengths have been measured and to predict % BF if skinfold thicknesses dominate among the predictor variables.



In judging equations to predict FFM, it should be recalled that the error in FFM values from body density is about 1.9 kg for men and 1.5 kg for women.



The PE of anthropometric equations to predict FFM are about 1.2 kg in boys and 3.0 kg in young adults.



There is a lack of equations to predict total muscle mass because it is difficult to measure the dependent variable.



The only methods for the measurement of total muscle mass in the living are serial whole-body CT or MRI scans, which are not applicable to large samples.



Indices of muscle mass from creatine excretion, labeled creatine, or the potassium/nitrogen ratio are too uncertain to be used as the dependent variable.



Total body bone mineral (TBBM, g) affects body density and, therefore, it is included in four-component models based on density.



TBBM, which is usually measured by DEXA, is the sum of the osseous and non-osseous mineral, but the latter is a small near-constant proportion of the total mineral.



Anthropometric values, in combination with age, gender, and ethnicity, may be useful in predicting TBBM.



During infancy and childhood, TBBM is highly correlated with weight and stature (r = 0.9), but the corresponding correlations in adulthood are only moderate ( r= 0.3 to 0.7).


Frame SizeFrame Size



Skeletal dimensions are used to classify frame size.

The rationale for measuring frame size is that skeletal breadths are important estimators of the bone and muscle components of FFM.



The best estimators of frame size are those that are highly related to FFM (independent of stature) and poorly related to FM.

The wrist, ankle, and elbow breadths are valid measures of frame size.



Table 5.5, p. 80 shows how measures of elbow breadth can be used to estimate frame size.



There are considerable errors in all body composition measures; these errors may be larger in the

obese and they are necessarily larger for

predicted values than for observed values.



Therefore, anthropometry is unlikely to provide accurate measures of changes in body composition.



Efforts to estimate the changes in body composition with weight loss in the obese should be based on equations that use circumferences rather than skinfold thicknesses because the changes in circumferences are larger, with the possible exception of subscapular skinfold thickness.



Adipose tissue distribution will be used in reference to the absolute and relative amounts of adipose tissue in body regions.



The term fat patterns, which is in common use, is misleading since it usually refers to adipose tissue distribution. Fat is a chemical term Adipose tissue contains more than fat

(lipid)



Further research is needed to establish the best anthropometric description of SAT distribution taking into account relationships with risk factors for selected cardiovascular and metabolic diseases and the functional characteristics of adipocytes at different locations.



DAT areas are markedly larger in the elderly than in young adults at the same BMI.

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Illinois State University Anthropometry Chapter 5