Chapter 2

Chapter 2

What is Obesity? Definitions Matter

Robert J. Kuczmarski

25

Introduction

The term obesity can conjure various mental images that may have subjective or objective connotations, depending upon the knowledge, experience, or background one may have. For trained professionals in the medical, public health, and science fields, whether clinical researchers or health care providers, obesity may indicate a condition that not only can be objectively measured but may also immediately bring to mind a heightened awareness of increased risk for associated adverse health outcomes. To others, obesity may bring to the surface social prejudices, biases, or perceptions that often result in preconceived notions of what the obese person’s demeanor, tendencies, or abilities might be. This may be reflected for example, in job hiring and promo- tion practices affecting obese adults at the worksite or in social acceptance or discrimination among obese children on the playground. In such cases, the definition of obesity is often loosely based on a visual impression or a precon- ceived mental attitude about the phenotypic expression or cosmetic appear- ance of a body size that is considered large.

The term overweight is often used interchangeably with obesity, although it sometimes may be construed as a somewhat diluted and therefore less omi- nous degree of obesity, which also may to some extent reduce the related per- ceptions, whether they be objectively medically based or subjectively socially based. There are a variety of other lay and professional terms that may be encountered such as full-figured, heavy, or thick but these are either not in gen- eral use or have limited practical utility for most scientific, clinical, or public health applications.

The term obesity, strictly speaking, means excess adipose tissue or excess body fat beyond a threshold for what is considered a norm or a reference value.

More specifically, to quote Villareal et al. (2005), “Obesity is defined as an

unhealthy excess of body fat, which increases the risk of medical illness and

premature mortality.” Although it is possible to have excess adiposity in the

presence of reduced muscle mass (sarcopenic obesity) and a concomitant nor-

mal weight, in the general population, people who are obese are almost always

overweight. The converse, however, does not follow this general pattern; all

overweight people are not obese. There may be situations where the amount

of body fat is normal, but the amount of muscle mass is large, rendering the classification as overweight but not over-fat.

As will be discussed, the amount of total body fat is important although fat distribution is also highly correlated with adverse health outcomes. So the con- cept of regional adiposity is one that may come into play when defining obe- sity, where the specific focus is on the excess deposition of intra-abdominal or visceral adipose tissue which can be sub-divided further into omental, mesenteric, and retroperitoneal fat (Shen et al., 2003). It is possible that in the future, with advancements in imaging capabilities or even with the use of biomarkers, regional obesity may be more highly specified beyond visceral obesity. For example, “omental obesity” conceivably could be a term used more in the future since fatty acids, cytokines, and hormones from omental adipocytes entering the portal circulation may increase risk for glucose intol- erance, type 2 diabetes, and other abnormalities (Haslam & James, 2005).

Abdominal adiposity is emerging as an active area of research investigation notably among some Asian populations where the outward appearance of obe- sity may not be apparent, but the internal metabolic disturbances are manifest- ed through the metabolic syndrome and perhaps other chronic conditions and diseases (Steering Committee of the Western Pacific Region of the World Health Organization, International Association for the Study of Obesity, International Obesity Task Force, 2000).

There are various applications and settings in which screening and identifi- cation of overweight or obesity are useful. For epidemiological purposes, it is useful to have measures that will permit the assessment of incidence, preva- lence, and trends over time. Obesity influences numerous risk factors and disease conditions. Therefore, analytically it is useful to incorporate either continuous or categorical indicators of obesity in their role as correlates, medi- ators, moderators, confounders, or covariates in analyses that evaluate or con- trol for obesity. In clinical research and office practice, indicators of obesity are used to monitor individuals and track progress of obesity prevention or treatment interventions. Assessment of abdominal obesity is recommended for routine clinical screening although such measurements are not made as rou- tinely as they could be (NHLBI Obesity Education Initiative Expert Panel on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults, 1998).

Measurements

For screening, identification, and classification of obesity in clinical settings or in epidemiological surveys or surveillance programs, tools and procedures that are simple, quick, inexpensive, valid (accurate), and reliable (repro- ducible) continue to be highly regarded. Body weight is a measure that meets these criteria. However, because there is variation across individuals in length (for infants) or standing height it is desirable to consider weight adjusted for height. Therefore, weight and height should typically be measured and then used in combination to interpret weight status adjusted for height, in compar- ison with established references or standards.

Weight-height indices have been in use for a long time. The Quetelet Index

(weight/height

; kg/m

) was first proposed in 1869 by Adolphe Quetelet

(Weigley, 1989). Over time the Quetelet index has continued to be used, but as proposed by Keys et al. (1972) the Quetelet Index has come to be more com- monly known and referred to as the Body Mass Index or BMI. The BMI is now the most commonly used index of weight adjusted for height to define over- weight and obesity. There is an earlier body of literature that examined the ori- gin, characteristics, performance, and comparisons of various weight-height indices in more detail (Florey, 1970; Lee et al., 1981; Norgan & Ferro-Luzzi, 1982; Garrow, 1983; Garrow & Webster, 1985). The overall goal when using weight-height indices to define obesity is to assess body weight, adjusting for height, such that the index yields a measure of weight relatively independent of height. This concept led previous investigators to examine wt/ht

where the value of the exponent p yielded an index that would maximize the correlation with adiposity, given the assumption that adiposity is independent of height (Benn, 1971). However, BMI assessed as weight divided by the square of height, and on the metric scale, has proven to be the most generally useful.

The only direct way to measure total body fat is through chemical analysis of cadavers. All other methods currently available are considered indirect measures. BMI is often referred to as an obesity index because it is moderately to highly correlated with other quantitative estimates of percent body fat. The correlations range from r ⫽ 0.6 to 0.8, depending on age group, gender, and method used to quantify body fat (Roche, 1996). Using dual emission x-ray absorptiometry (DXA) in adolescents for example, Steinberger et al. (2005) reported a correlation between DXA and BMI of r ⫽ 0.85 for percent body fat and r ⫽ 0.95 for fat mass. Consistent with the findings of other investigators and in other age groups, these associations were stronger in heavier persons.

For the majority of people in the general population, higher BMI values will be indicative of higher levels of body fatness. However, there may be excep- tions in certain subgroups of the population where higher body mass values are attributable to excess lean mass (muscle) instead of fat, such as in body builders, professional athletes, or military personnel, resulting in an erroneous overestimate of body fatness. Higher BMI values associated with higher lean mass may also apply to certain ethnic groups such as Pacific Islanders, who may have higher than average BMI levels but also higher levels of muscularity for a given BMI level in comparison to other populations (Rush et al., 2004).

Similarly, in the elderly who lose muscle mass with advancing age or in persons of any age who lose lean mass as a result of disease or infirmity, BMI may underestimate body fat. It is therefore recommended that users be cog- nizant of situations or individuals for whom BMI may not be a valid indicator of total body fatness.

There are other methods that can be used either in place of the BMI

approach or in addition to it for the purpose of verification of relative fat-

ness. These range from the simple, lower risk, lower burden, and less expen-

sive subcutaneous skinfold measurements through bioelectrical impedance

analysis (BIA) and ultrasound, to hydrodensitometry, DXA, magnetic reso-

nance imaging, and computerized tomography (CT) scans (Roche,

Heymsfield, & Lohman, 1996) (Table 2.1). These approaches are based on

prediction equations for estimating total body fatness indirectly or may only

focus on regional estimates of fatness, and rise in complexity of measure-

ment, cost, risk, expense, and general predictive value compared to the

simpler weight and height measures that constitute the BMI. The scientific

literature is replete with prediction equations for skinfold measurements and the various other procedures such as ultrasound and BIA, indicating the lim- itation that prediction equations tend to be specific to the samples of people on which they were developed and often can not be generalized to other groups.

Adults

Interpretation of Measures based on Weight and Height

In research and clinical settings, overweight is defined as weight that exceeds an established criterion threshold value. The threshold may be a statistical cut- off point based either on observed population distributions of measured weight, or on the level of weight beyond which morbidity, other health risk factors, or mortality outcomes increase.

The Metropolitan Life Insurance Company (MLIC) weight-for-height tables were widely used in the U.S. over a 25-year period from approxi- mately 1960 to 1985 in the assessment of weight status for adult men and women. The tables were derived from distributions of weight-for-height associated with minimal mortality among a large group of persons in the United States and Canada at the time they purchased life insurance policies during the period 1935 to 1972. The MLIC tables began to be phased out in the mid-1980s when gender-specific BMI criteria derived from the second National Health and Nutrition Examination Survey (NHANES II) conducted from 1976 to 1980 became available. For approximately the next 15 years from 1985 to 2000 in the U.S., overweight in adults was Table 2.1 Brief descriptions of various methods to estimate body fat.

Skinfolds Skinfold calipers measure a double fold of skin and subcutaneous adipose tissue at selected sites on the body to estimate percent body fat from prediction equations Ultrasound Ultrasound uses high frequency sound waves to image the

thickness of fat tissue at selected sites on the body to estimate percent body fat from prediction equations BIA Bioelectrical impedance analysis measures total body water,

from which lean body mass, total body fat, and percent body fat can be estimated from prediction equations Hydrodensitometry Underwater weighing estimates body density from which

percent fat and percent body fat can be estimated from prediction equations

DXA Dual energy x-ray absorptiometry uses low energy x-rays to estimate fat content in bone-free lean tissue

MRI Magnetic resonance imaging images and estimates subcutaneous and visceral adipose tissue at cross sectional sites on the body

CT Computerized tomography uses high energy x-rays to image cross-sectional areas of adipose tissue, muscle, and bone.

Source: Roche, Heymsfield, & Lohman (Eds.) (1996). Human Body Composition. Champaign, Il:

Human Kinetics.

defined as a BMI ⱖ27.8 for men and ⱖ27.3 for women ages 20 years and older. These BMI cutoff points represented the gender-specific 85th percentile values of the BMI distribution for persons aged 20–29 years in NHANES II. The rationale for selecting this age group as the reference population was based on the concept that young adults are relatively lean and the increase in body weight that usually occurs with age is due almost entirely to accretion of excess body fat (Van Itallie, 1985). The limitations of the MLIC height-weight tables and a chronology of the multiple crite- ria and definitions that led to the current BMI-based classifications of healthy weight, overweight, and obesity have been described in some detail (Kuczmarski & Flegal, 2000).

Following the publication in 1998 of a National Institutes of Health (NIH) report on clinical guidelines for the identification, evaluation, and treatment of overweight and obesity (NHLBI, 1998), from the late 1990s to the present time for all adults, weight categories and corresponding risk have been defined as: healthy weight, BMI ⫽ 18.5–24.9 (normal risk); overweight, BMI ⫽ 25.0–29.9 (increased risk); obesity BMI ⱖ30.0 (high to extremely high risk), with subclasses of obesity consistent with those recommended by the World Health Organization (WHO) for international applications and comparisons (class I, BMI 30–34.9; class II, BMI 35.0–39.9; class III, BMI ⱖ40.0) as indi- cated in Table 2.2. The World Health Organization (WHO, 1998) classified weight by BMI as follows: normal weight, BMI ⫽ 18.5–24.9 (average risk);

overweight, BMI ⱖ25.0; pre-obese, BMI ⫽ 25.0–29.9 (increased risk); and class 1, 2, and 3 obesity: BMI ⫽ 30.0–34.9 (moderate risk); BMI ⫽ 35.0–39.9 (severe risk); and BMI ⱖ40.0 (very severe risk), respectively. These cutoff points are useful for screening purposes but regardless of which cutoff points are used, when making clinical evaluations for individuals, abdominal circumference, age, ethnicity, gender, clinical history, and presence of other related risk factors should be considered (Bray, 1998a; WHO Expert Consultation, 2004).

Table 2.2 Classification of overweight and obesity by BMI, waist circumference, and associated disease risk

Disease risk

Men ⱕ ⱕ40 in (ⱕ ⱕ102 cm) ⬎ ⬎40 in (⬎ ⬎102 cm) BMI (kg/m

) Obesity class Women ⱕ35 in (ⱕ88 cm) ⬎35 in (⬎88 cm)

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 ⱖ40.0 III Extremely high Extremely high

Source: NHLBI Obesity Education Initiative Expert Panel on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults (2000).

*

Relative to normal weight and waist circumference, disease risk for type 2 diabetes, hypertension, and CVD.

†

Increased waist circumference can also be a marker for increased risk even in persons of normal weight.

Body Fat Ranges

With regard to body fat levels to distinguish normal from abnormal or obese, there is no single cutoff value for percent fat. Instead, body fatness expressed as total body fat as a percentage of body weight is described within ranges.

Furthermore, there are gender differences whereby normal body fat can range from approximately 12 to 20% for males and 20 to 30% for females.

Understanding that fatness occurs across a range in the population, body fat levels greater than approximately 25% for males or 33% for females generally may be used to indicate obesity (Bray, 1998b).

Gallagher et al. (2000) subsequently confirmed these estimated fat levels in white, African American, and Asian subjects in a study designed to develop percentage body fat ranges that correspond to widely accepted BMI guidelines.

Using the criterion of obesity at a BMI ⱖ30.0, the corresponding predicted percentage body fat by age group for white and African American males was 25% at 20–29 years, 28% at 40–59 years, and 30% at 60–79 years. Among women, the percentage body fat corresponding to obesity was 39% at 20–29 years, 40% at 40–59 years, and 42% at 60–79 years. At any BMI level, Asians had a significantly higher percentage body fat than white or African American men and women. As will be discussed, this finding of relatively higher body fat in Asian Americans has implications for the use of cutoff points used to define overweight and obesity.

Regional Obesity

BMI remains the most widely used indicator to define overweight and obesity, based on total body weight adjusted for height as an indictor of total body fatness. However, it is well established that beyond the amount of total adipos- ity, the distribution of fat on the body is strongly related to health outcomes as well. The distribution of stored fat can be broadly characterized as fat stored peripherally on the extremities (arms and legs) and fat stored centrally on the torso of the body. In the 1940s Jean Vague characterized general fat patterns based on the typical fat distribution observed in men and women. Men tend to have more fat in the abdominal region and this was described as the android or male pattern. In contrast, women tend to have more fat stored in the hip/buttocks region and this was termed the gynoid or female pattern. Vague (1956) subsequently noted increased risk for adverse health outcomes with the male fat distribution pattern. In the 1980s the scientific literature identifying increased risk with abdominal fatness and a host of adverse health outcomes expanded tremendously, further establishing the importance of defining regional obesity (Seidell, Deurenberg, & Hautvast, 1987; Seidell, 1992;

Emery et al., 1993).

Abdominal circumference measures are only a surrogate for abdominal adi-

posity and the more influential visceral fat mass. It appears that the intra-

abdominal or visceral fat is the component generally found to be more highly

associated with metabolic abnormalities in contrast to subcutaneous abdomi-

nal fat. However, the contribution of subcutaneous fat patterning (upper-

versus lower-body or truncal/torso versus peripheral/limbs) to the metabolic

disturbances associated with upper-body obesity remains uncertain. Variations

in findings may be linked with different methodologies to measure fat com-

partments, including the instrumentation and the site of the fat depot, as well

as the outcome variable. For example, in a study of obese women, elevated triglyceride levels were most highly associated with increased visceral fat measured by DXA, whereas insulin resistance was related to increased truncal fat mass measured by skinfold measurements (Albu, Kovera, &

Johnson, 2000).

Measurement of the internal visceral fat store is best accomplished with more sophisticated procedures such as magnetic resonance imaging or com- puterized tomography, although these procedures are more complex and expensive than simpler anthropometric procedures such as circumference measurements. The waist-to-hip circumference ratio (WHR) was used for a period of time and showed increased cardiovascular and type 2 diabetes risk, when defined at a WHR ⬎0.90 cm for men and a WHR ⬎0.80 for women (Albu et al., 1997). It was later shown that a measure of abdominal circumfer- ence alone was more predictive of increased risk and could be used as a sur- rogate marker of visceral adiposity in lieu of WHR (Despres et al., 1989; Lean, Han, & Morrison, 1995). A case-control study of persons with type 2 diabetes and matched controls found that among various measures of central obesity, waist circumference was the strongest predictor of risk (Mamtani & Kulkarni, 2005). Another case-control study of participants from 52 countries showed a higher correlation for waist-to-hip ratio with myocardial infarction risk than did either BMI or waist circumference alone (Yusuf et al., 2005). These inves- tigators suggested that loss of muscle mass in the hip area with decreased physical activity or overall weight reduction may increase risk, although this remains a plausible but insufficiently documented hypothesis. Abdominal circumference is a diagnostic screening tool included as part of a small con- stellation of measures for assessing the metabolic syndrome (Klein et al., 2004). The International Diabetes Federation (2005) has proposed the use of ethnic-specific waist circumference measures when screening for the metabolic syndrome.

Because abdominal circumference provides an independent prediction of risk beyond that of BMI, in adults with a BMI ⬍35.0 it is a recommended measurement to define abdominal obesity. In persons with a BMI ⬎35, abdominal circumference measurements and BMI are highly correlated and predictive of increased risk for adverse health outcomes associated with obe- sity. In obese African American men and women with BMI ranging from 30 to 50 kg/m

, abdominal circumference was reported to predict obesity related co-morbidity risk beyond that obtained by BMI alone (Hope et al., 2005).

This study and others suggest that abdominal circumference is an important

clinical screening tool in the assessment of obesity health risk. Based on the

waist circumference measurement, high-risk abdominal obesity in U.S. adults

is defined as a value ⬎102 cm (40 in.) for men and ⬎88 cm (35 in.) for

women (NHLBI, 1998). To identify individuals in need of treatment, meas-

urement of abdominal circumference can be an important screening tool for

those at a healthy weight (BMI ⬍25.0) and with a large abdominal circum-

ference, although relatively few persons meet this criterion. Among over-

weight (BMI 25.0–29.9) and obese (BMI ⱖ30.0) adults, waist circumference

appears to add little information beyond BMI alone for identifying individu-

als in need of weight-loss treatment (Kiernan & Winkleby, 2000) although

with increasing BMI and abdominal circumference values, disease risk also

increases (Table 2.2).

Defining Obesity Across Racial/Ethnic Groups and in Older Men and Women

There are important racial/ethnic differences that influence both the relative utility of abdominal circumference or BMI as indicators of health risk as well as the definition of obesity using these measures. Studies comparing the asso- ciation of BMI with percent body fat in Hispanics have been limited. In an investigation of Hispanic Americans, African Americans, and non-Hispanic white Americans, Fernandez et al. (2003) reported no differences in the relation between BMI and percent fat in men from the three ethnic groups, or between black and white women. However, at a BMI ⬍30, Hispanic-American women had more body fat than women of European or African descent, suggesting a potential underestimation in the relative degree of fatness among Hispanic- American women compared to women from the other major race/ethnic groups. However, whereas definitions of obesity based on BMI and abdominal circumference work quite well for white and African American groups, and perhaps generally for most other ethnic groups, substantial evidence suggests that persons of Asian descent may require a different definition of obesity.

Characterizations of ethnic differences in obesity prevalence are influenced by these definitional issues, as discussed in chapter 3.

There is evidence that for Asians, obesity should be defined at a considerably lower BMI cutoff point and abdominal circumference may be an equally or more effective indicator of disease risk than BMI (Klatsky & Armstrong, 1991;

Fujimoto et al., 1991). Although there are exceptions for some Asian subgroups, in general, at the same age, gender, and BMI level, percentage body fat is higher in Asians than in white persons, or in other words, at a given level of body fat, Asians have lower BMI levels than white persons. The Examination Committee of Criteria for Obesity Disease in Japan of the Japan Society for the Study of Obesity (2002) concluded that at initial screening of Japanese adults, obesity should be defined as a BMI ⱖ25.0; visceral fat obesity should be defined as a BMI ⱖ25.0 and a waist circumference ⱖ85 cm in men or ⱖ90 cm in women.

The Steering Committee of the Western Pacific Region of the World Health Organization (2000) proposed that overweight in adult Asians be defined as BMI ⱖ23.0, obesity as BMI ⱖ25.0, and high risk waist circumference ⱖ 90 cm for men and ⱖ 80 cm for women. Among Chinese adults, a BMI ⱖ24.0 was recommended as the cutoff point for overweight, BMI ⱖ28.0 as the cut- off for obesity, and waist circumference ⱖ 85 cm for men and ⱖ80 cm for women as the cutoff points for central obesity (Bei-Fan & the Cooperative Meta-analysis Group of Working Group on Obesity in China, 2002). Among Asian Indian adults from North India, cardiovascular risk factors are present at even lower cutoff values for waist circumference (78 cm and 72 cm for men and women, respectively) and BMI ( ⬎21 kg/m

) (Misra et al., 2006).

The report of the WHO Expert Consultation (2004) stopped short of selecting cutoff points for overweight and obesity for Asians and instead presented risk categories for determining public health and clinical action based on BMI ranges either for general use or for Asian populations (Table 2.3).

Various factors contributed to this conservative recommendation for retaining

the widely used WHO BMI cutoff points for Asian populations, including the

recognized heterogeneity of Asian populations, differences within Asian

populations in the relationships between BMI and body fat, changes in this

relationship over time, and the lack of sufficient outcome data that were

available to derive cutoff points. These factors suggested it might be premature and inappropriate to recommend new definitions for overweight or obesity, despite the recognition that Asians generally have a higher percentage body fat than white persons of the same age, gender, and BMI, and also have elevated health risk at BMI values below 25 kg/m

. Compared with Caucasians, Japanese have greater abdominal visceral fat relative to abdominal subcuta- neous fat which may partially explain the increased risk for type 2 diabetes and cardiovascular complications among Japanese, even at relatively lower BMI values (Tanaka, Horimai, & Katsukawa, 2003). Clearly there is a need for continuing research on the definition of obesity across various Asian popula- tions. Because inadequate data were available, the WHO panel did not make specific recommendations for Pacific Islanders, although it was noted that Pacific Island populations may have a lower proportion of fat mass to lean mass in comparison with other populations at the same BMI level. This was identified as an area for further research (WHO Expert Consultation, 2004).

In a case-control study of 52 countries representing several major ethnic groups from Asia, Europe, the middle east, Africa, Australia, North America, and South America, Yusuf et al. (2005) examined the association of various indicators of obesity (BMI, waist circumference, WHR) with myocardial infarction (MI) risk in more than 12,000 cases with MI and 14,000 control sub- jects. BMI had the weakest association with MI risk in all ethnic groups and no significant relation is south Asians, Arabs, and mixed-race Africans. Waist circumference was the strongest predictor of MI in Chinese and black Africans, but intermediate in most other ethnic groups. These investigators concluded that WHR had the strongest relation with MI risk worldwide sug- gesting that it is the best predictor of MI in most populations, and a redefini- tion of obesity based on WHR instead of BMI may increase the estimate of MI attributable to obesity in most ethnic groups. The findings of this study raise the question of whether waist circumference or WHR may be superior to BMI in defining obesity, at least with regard to the association of obesity with car- diovascular disease and in particular MI (Kragelund & Omland, 2005).

BMI is a surrogate marker for the relative level of total body fat. Waist circumference and WHR are surrogate markers for the relative amount of visceral fat or the ratio of visceral to gluteal-femoral fat mass. These measures and the associated definitions of total or central adiposity based on these measures are important because they allow estimates of increased risk.

As total body fat increases, BMI becomes a better indicator of total body fat.

In older adults, lean body mass decreases, total body fat increases, and there is a redistribution of body fat to the higher risk intra-abdominal area Table 2.3 BMI classification for public health action.

BMI Risk category

All populations 18.5–27.5 Low to moderate risk

23.0–33.0 Moderate to high risk 27.5–37.5 High to very high risk Many Asian populations 18.5–23.0 Increasing but acceptable risk

23.0–27.5 Increased risk

ⱖ27.5 Higher high risk

Source: WHO Expert Consultation (2004).

(Gallagher, 1996). However, changes in body composition (loss of muscle mass) and height (due to vertebral compression) in older adults can diminish the association between BMI and percentage body fat, such that loss of lean mass can tend to underestimate fatness and loss of height can tend to overes- timate fatness (Villareal et al., 2005). Nevertheless, in older persons Harris et al.

(2000) concluded that BMI and waist circumference were more highly correlated with total fat than with visceral fat, and visceral obesity is not well measured by waist circumference at ages 70–79 years.

Children

From the late 1970s to 2000, weight status of infants and children was most often assessed using age- and gender-specific weight-for-length (ages birth to 2 years) or weight-for-height (ages 2–11.5 years for boys and 2–10.0 years for girls) growth references from the 1977 NCHS pediatric growth charts (Hamill et al., 1977). The weight-for-height growth references were developed for pre-pubescent children and did not extend through the adolescent ages. BMI cutoff points derived from national reference data were available for children and adolescents aged ⱖ2 years, but did not receive widespread use (Must, Dallal, & Dietz, 1991). Others used data for distributions of skinfold measures to define obesity in youths, based on statis- tical cutoff points unrelated to health outcomes (Gortmaker et al., 1987).

In 2000, with the release of age- and gender-specific BMI-for-age growth charts for ages 2 to 20 years (Kuczmarski et al., 2000), it became possible to use smoothed percentile curves and z-scores based on reference data for U.S. children to assess and track children who are at-risk-for-overweight (BMI 85th to 95th percentile) or overweight (BMI ⱖ95th percentile), as defined earlier by an expert committee (Himes & Dietz, 1994). Below two years of age, the weight-for-length growth charts are available and may be used in analogous fashion, although per- centile cutoff criteria to identify overweight in this age range have not received widespread endorsement. In clinical assessment, a transition from the weight-for- length to the BMI-for-age charts from 24 to 36 months of age becomes possible when children can stand unassisted and adequately follow directions to assume the correct posture for a height measurement. Even though BMI growth charts identify overweight better than height and weight charts, most pediatricians still do not report using this tool regularly (Perrin, Flower, & Ammerman, 2004).

BMI increases rapidly from birth to approximately 8 months of age, then

decreases until approximately age 6 years when it reaches its nadir before

rebounding or increasing once again (Rolland-Cachera, et al. 1984). This has been

termed the adiposity rebound because it is believed to be the point when body fat-

ness begins to increase after reaching a minimum. When the BMI curve reaches

its nadir at younger ages, the likelihood that the level of adiposity will be higher in

adolescence and early adulthood is greater (Rolland-Cachera, 1993; Siervogel

et al., 1991; Whitaker et al., 1998). Children at higher percentiles for BMI tend to

achieve adiposity rebound at younger ages. These children have a greater likeli-

hood to track at a higher BMI percentile with increasing age. There is a need for

further research to determine the predictive value of adiposity rebound as a meas-

ure to define current overweight status or as an indicator of future obesity, although

it appears to have utility as a potential predictor of future risk. One limitation is that

assessment of adiposity rebound requires accurate serial measurements at frequent

intervals. On a related theme, there is evidence to suggest that rapid weight gain from birth to two years and through 11 years of age, as indicated by progressive increase in BMI z-score, is more predictive of subsequent coronary risk than is obesity defined at any particular age (Barker et al., 2005).

Percentiles are the most commonly used clinical indicator to assess the size and growth patterns of individual children in the United States. Percentiles rank the position of an individual by indicating what percent of the reference population the individual would equal or exceed. The 2000 CDC growth charts are age and gender specific and include nine major percentile curves rang- ing from the 5th to 95th or the 3rd to 97th percentiles (Figures 2.1 and 2.2).

As an example, a girl age 10 years with a BMI of 21.0 kg/m

would be at the

Figure 2.1 2000 CDC body mass index-for-age growth chart.

Source: http://www.cdc.gov/growthcharts/ (Last accessed April 11, 2006).

90th percentile, indicating that 90% of girls in the reference population would have a BMI value equal to or lower than 21.0 and 10% of girls this age in the reference population would have a higher BMI value.

The 2000 CDC growth charts were constructed using pooled data from the National Health and Nutrition Examination Surveys from 1963–1994. However, in the construction of these charts, population increases in body weight that occurred in recent years led to a decision to exclude survey data from the 1988–1994 time period for weight and BMI data for ages 6 years and older. In brief, this decision was made because inclusion of the 1988–1994 survey data Figure 2.2 2000 CDC stature-for-age and weight-for-age growth chart.

Source: http://www.cdc.gov/growthcharts/ (Last accessed April 11, 2006).

resulted in an upward shift of the higher 85th and 95th percentile curves which are used as cutoff criteria to identify risk-of overweight and overweight.

Although an increase in overweight happened because of environmental influ- ences, that is, shifts in dietary intake and physical activity patterns, it is not what is believed to be biologically or medically desirable. Because these references are based on data from an earlier time period, when the 95th percentile for BMI is used to calculate overweight prevalence, it is possible to obtain estimates that exceed 5% over the threshold. More thorough discussions are presented else- where (Kuczmarski et al., 2000; Ogden et al., 2002). A major use of the BMI growth charts is as a clinical screening tool, although they are not intended to be the sole diagnostic criterion for overweight. When used properly, plotting points over time on BMI growth charts can monitor BMI trends and identify individu- als with unusual growth patterns. Large deviations exemplified by data points that cross over two or more of the major percentile lines on growth charts, known as decanalization, can be utilized to identify children who are potentially on a growth trajectory that might require increased attention and possibly interven- tion (Kuczmarski, Kuczmarski, & Roche, 2002).

The BMI standard deviation score (z-score) is the deviation of the value for an individual from the mean BMI value of the reference population divided by the standard deviation for the reference population. Z-scores and percentiles are interchangeable. Which one is used is based primarily on convention or preference. In certain population-based applications, such as research settings and surveillance systems, the mean and standard deviation are often calculated for a group of z-scores (WHO Working Group on Infant Growth, 1995). In selected clinical situations where monitoring of growth is an important evalu- ation tool and greater measurement precision is necessary, z-scores or exact percentiles may be preferred (Kuczmarski et al., 2000). Cole et al. (2005) reported that change in BMI units is the preferred measure to quantify short- term changes in adiposity. When ranking children, BMI values, BMI per- centiles, and BMI z-scores have correlation coefficients with percentage body fat that are almost identical (Field et al., 2003).

For children of all ages, The Centers for Disease Control and Prevention has recommended use of the term overweight rather than obesity. Cautious and lim- ited use of the term obesity is urged when only BMI screening data are avail- able, especially in the absence of additional measures of body fat. Because chil- dren are still growing linearly, a child who appears to be overweight at one point in time may subsequently achieve a body weight that is appropriate for height with a BMI in the normal range. There may be social stigma concerns associated with prematurely or inaccurately labeling a child as obese that could lead to unwarranted social discrimination by peers or inappropriate behaviors by the overweight child potentially resulting in exacerbation of the condition.

Nevertheless, “childhood obesity” is the term in common usage in the larger

public health and policy arena, to highlight attention to the problem and avoid

the confusion that might be caused by the difference in the way overweight is

used in children and adults (Committee on Prevention of Obesity in Children

and Youth, 2005). As previously mentioned, the gender specific 2000 CDC

BMI-for age growth charts allow the classification of overweight (BMI ⱖ95th

percentile) and at-risk-of-overweight (BMI ⱖ85th percentile to <95th

percentile). The at-risk-of-overweight category is intended to identify and

clinically monitor youths with BMI values in a range that may subsequently

progress over the threshold to the overweight category. However, readers should be aware that some authors have taken the liberty of publishing reports that define overweight (BMI ⱖ85th to 95th percentile) and obese (>BMI ⱖ95th percentile). Since most obese youths are also overweight, such an approach sometimes dramatically inflates the prevalence of overweight in children and adolescents. This reflects the multiple uses that the CDC BMI growth charts receive in diverse clinical and epidemiological applications.

Definitions Matter

From the perspective of policy development and setting public health priorities, the BMI cutoff points for overweight and obesity are not a trivial issue because of the absolute proportion of the population that may be defined as overweight or obese and also because changes in BMI cutoff points can drastically change prevalence estimates. The impact of changes in BMI cutoff points became evi- dent when the switch was made from use of the NHANES II BMI cutoff values (27.8 for men and 27.3 for women) to a BMI ⱖ25.0 for estimating the prevalence of overweight and obesity in the U.S. The prevalence of overweight among U.S.

adults in NHANES III (1988–1994) was 33.3 and 36.4% for men and women respectively using the NHANES II definition of overweight. For the exact same data set, using the overweight definition of BMI ⱖ25.0, the prevalence of over- weight increased to 59.4 and 50.7% for men and women respectively (Kuczmarski et al., 1997). This was an increase in the national prevalence of over- weight from 1/3 to 1/2 of the population simply by changing the BMI cutoff point at which overweight was defined. Similarly, the prevalence of obesity, previously termed “severe overweight” at a BMI ⱖ31.1 and 32.3 for adult men and women respectively, increased when these cutoff points were shifted downward to a BMI ⱖ30.0 (Kuczmarski & Flegal, 2000). The change in BMI cutoff points used to define overweight was a seemingly minor transition that had a dramatic effect on estimates of the number of overweight people in the U.S. and raised consider- able interest in the topic from various sectors, including the news media.

In the above example, the switch to a lower BMI cutoff value at which over-

weight is defined not only changed the magnitude of overweight prevalence,

but also indicated that men now had a higher prevalence of overweight than

women. This raises the issue of potential misclassification for overweight near

a BMI of 25 and especially among men who may appear to be overweight near

this threshold because of an accumulation of lean body mass. A study from

Australia, where, as in the United States more than half of all adult men are

overweight or obese (BMI ⱖ25.0), reported that a significant proportion of

men did not consider themselves to be overweight, when in fact they had BMI

values ⱖ25.0. At all adult ages, the men in this study indicated their percep-

tion that overweight begins at body weights corresponding to BMI values clos-

er to approximately 26–27 (Crawford & Campbell, 1999). The extent to which

men in the BMI range of approximately 25–27 may be incorrectly classified

as overweight, attributable to more lean body mass, may be an area for further

research. Regardless of which BMI cutoff points are used to determine the

prevalence of overweight, among adults in the U.S., when compared to

NHANES II data, the NHANES III data revealed that the entire BMI distribu-

tion had shifted upward and has become more skewed at higher BMI levels,

indicating that changes in the BMI distribution are most marked at the upper

end of the distribution (Flegal & Troiano, 2000). These elevated obesity patterns have remained through the 2004 NHANES (Ogden et al., 2006).

When considering recommendations for BMI among Asian populations, the aforementioned WHO Expert Panel concluded that the WHO BMI cutoff point classification should be retained to facilitate international classification and comparisons (WHO Expert Consultation, 2004). However, recognizing that health risk increases along the BMI continuum, this Panel also recommended that additional BMI cut-off points for public health action among Asian pop- ulations be considered (23.0, 27.5, 32.5, and 37.5 kg/m

).

At low to moderate BMI values, BMI and fatness may have a low sensitiv- ity although at high values for percent body fat, BMI has high specificity as a screening tool (Roche, 1996). Sensitivity is the extent to which BMI correctly classifies persons who are overweight or obese. Specificity is the extent to which BMI correctly classifies persons who are not overweight or obese. In any event, BMI tends to be predictive of common health risks such as hyper- tension and dyslipidemias and at BMI ⱖ25.0 there is evidence of increased risk for coronary heart disease, type 2-diabetes, and premature mortality (Willett, Dietz, & Colditz, 1999; NHLBI Obesity Education Initiative Expert Panel, 1998). The exact impact of obesity on population mortality remains controversial. Earlier estimates indicated that approximately 300,000 excess deaths annually were associated with obesity (Allison et al., 1999). A subse- quent report that used estimation methods believed to better account for selective confounding and effect modification by age, reported that in 2000 obesity (BMI ⱖ30.0) was associated with approximately 112,000 excess deaths in the U.S., although overweight (BMI 25.0–29.9) was not associated with excess mortality (Flegal et al., 2005). The data for excess deaths were based on BMI definitions of overweight instead of measures of fatness derived from body composition. The associations do not confirm causality, the cross- sectional data do not consider previous health status, and the role of additional confounders was not fully explored. These studies have led others to question how obesity is defined and how obesity statistics are derived and interpreted (Gibbs, 2005). Clearly there are other aspects that must be critically factored in when analyzing obesity data and drawing conclusions.

Nationally representative trend data for prevalence of overweight and obesity based on measured height and weight are available from the National Health Examination Surveys beginning in 1960. BMI trend analyses in which over- weight was defined at a BMI 25.0–29.9 indicate that from 1960 to 2002 over- weight (but not obese) has remained fairly constant at approximately 40% among adult men and approximately 25% among adult women (National Center for Health Statistics, 2005). However, at higher BMI cutoff levels ( ⱖ30.0) increases in the prevalence of obesity become more apparent. At a BMI ⱖ30.0, from 1960 to 2002, the prevalence of obesity nearly tripled from 10.7 to 28.1% among adult men and more than doubled from 15.7 to 34.0% among adult women. Thus, BMI cutoff values to define overweight and obesity can have a considerable impact on identification of these conditions and this applies regardless of whether BMI is treated as a continuous or categorical variable in epidemiological analyses.

Theoretically, it would be possible to set the prevalence of overweight any-

where from zero to 100% simply by shifting the cutoff point in one direction or

the other. The cutoff points in current use are not at all this arbitrary although

there clearly is some rounding to facilitate ease of use. BMI, waist circumference,

or other indicators for the definition of obesity are used because at increasing

values for these measures health risk increases in populations. However, for any given individual the strength of the relationship between measures of obesity and health outcomes can vary considerably. Other considerations may pertain to the quality of measurements. Self-reported values as recorded in various interview surveys for example, are known to over-estimate height and under-estimate weight in some groups. Among persons ages 60 years or older, self-reported height and weight can result in misclassification of overweight status because the person reporting may not have taken into account aging related decreases in height. For persons 70 years and older, BMI calculated from self-reported meas- ures can be one unit lower than when height and weight are measured (Kuczmarski, Kuczmarski, & Najjar, 2001). Comparisons of surveillance and survey data indicate that self-reported weight and height have also been found to yield disproportionate underestimates for the prevalence of overweight and obe- sity across gender, race, age, and education subgroups in the U.S. (Yun et al., 2006). Inconsistent anthropometric measurement procedures can also contribute to misclassification. Standardized procedures are recommended, and both text (Lohman, Roche, & Martorell, 1988) and video (Kuczmarski, 2005) reference procedures can facilitate standardization.

In summary, the BMI-based classification scheme is the most frequently used approach to define overweight and obesity for both clinical and public health pur- poses. However, BMI remains a screening tool and should not be used as the sole clinical diagnostic criterion for the assessment of obesity. BMI is useful for adults because the BMI levels used to define overweight and obesity are associated with increased risk for numerous chronic health conditions. Chronic diseases are com- paratively less common among children and adolescents than among adults so the high risk BMI cutoff points for children are not as well known. Nevertheless, the recommended 85th and 95th age- and gender-specific percentile values in late adolescence appear to be fairly consistent with the adult BMI cutoff values ⱖ25 used to define overweight and ⱖ30 to define obesity (Cole et al., 2000). These adult values are known to be associated with increased health risk. Given that BMI tends to track from late childhood and especially from adolescence into early adulthood and beyond (Whitaker et al., 1997), consistent with current knowledge and technology, BMI remains a most useful tool for screening, identification, and classification of overweight and obesity in clinical and public health applications for most groups. Emerging evidence suggests that selected Asian groups may be a notable exception. BMI is still an extremely useful tool in these groups although the cutoff points may need to be shifted downward and abdominal circumference may hold greater importance in defining increased risk for obesity.

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