Relationships Between the Body Mass Index and Body Composition

Transcription

1 Relationships Between the Body Mass Index and Body Composition Rita 1. Wellens*, Alex F. Roche*, Harry J. Khamisrt, Andrew S. Jacksons; Michael L. Pollock, Roger M. Siervogel* Abstract WELLENS, RITA I, ALEX F ROCHE, HARRY J KHAMIS, ANDREW S JACKSON, MICHAEL L POLLOCK AND ROGER M SIERVOGEL. Relationships between the body mass index and body composition. Obes Res. 1996;4: The aims of this study were to evaluate the Body Mass Index (BMI) (welght/stature-i) as a proxy for percent body fat (%BF) and to determine its association with fat-free mass (FFM). Multivariate analysis of variance and partial correlations were used to examine relationships between BMI and %BF and FFM from densitometry for 504 men and 511 women, aged 20 to 45 years. Sensitivity/specificity analyses used cut offs of 28 kg/m 2 in men and 26 kglm 2 in women for BMI, and 25% in men and 33% in women for %BF. Significantly higher associations existed in each gender between BMI and %BF in the upper BMI tertile than in the lower BMI tertiles. In the lower BMI tertiles, correlations between BMI and FFM were approximately twice as large as those between BMI and %BF. The BMI correctly identified about 44% of obese men, and 52% of obese women when obesity was determined from %BF. BMI is an uncertain diagnostic index of obesity. Results of Receiver Operator Characteristic (ROC) analyses using %BF and total body fat, both provided a BMI of 25 kg/m 2 in men and 23 kg/m 2 in women as diagnostic screening cut offs for obesity. Submitted for publication February 23, Accepted for publication in final form July 20, From the *Division of Human Biology, Department of Community Health, Wright State University, School of Medicine, Dayton OH 45387, tstatistical Consulting Center, Wright State University, Dayton, OH 45435, :j:department of Health, Physical Education and Recreation, University of Houston, Houston, TX 77004, and the Center for Exercise Science, Department of Medicine and Exercise Science, University of Florida, Gainesville, FL Reprint requests to Dr. Wellens, Washington State University, Department of Kinesiology and Leisure Studies, Pullman WA Tel: ; Fax Copyright 1996 NAASO. Key words: Body Mass Index, fatness, fat-free mass, body composition Introduction Numerous studies have reported associations of obesity with increased morbidity and mortality (28,35). The Body Mass Index (BMI) is widely used in epidemiological research and, commonly, high BMI values (weightlstature 2; kg/m 2) are interpreted as evidence of overweight or obesity. Somewhat arbitrary BMI cut off levels based on associations with mortality and morbidity in large population surveys have been suggested as guidelines for desirable weights (8). Obesity is defined as an excess of adipose tissue. An evident problem is that the amount of body fat is usually not measured in epidemiological surveys because direct methods such as densitometry are difficult to apply. Unlike for BMI, there is a lack of reports that allow the selection of a "cut off' level for body fatness derived from body density above which morbidity and mortality rates are increased. Since methods for the direct measurement of body fat are time-consuming and expensive, their application is limited to research settings and typically they are applied to. small samples. Weight and stature can be measured easily in large samples with high precision. Validation of BMI values against direct measures is needed, however, to justify the use of the BMI as an index of body composition. The BMI has been criticized because the numerator (weight) does not discriminate between muscle, fat, bone or vital organs and, therefore, an individual with high fat-free mass (FFM) relative to stature might have a high BMI value but not be obese (3). A valid index of body composition that is easily available would be important for health professionals so that persons at risk for developing obesity-related diseases could be successfully screened. The present study was conducted to evaluate BMI as a proxy for body fatness versus direct measurements of percent body fat OBESITY RESEARCH Vol. 4 No.1 Jan

2 (%BF) and total body fat (TBF) estimated from densitometry in a large sample of healthy men and women and within tertiles of the BMI distribution. In addition, the utility of BMI in the diagnosis of obesity (high %BF) was evaluated and associations of BMI values with fatfree mass (FFM) were explored. Methods and Procedures Subjects The subjects were enrolled in one of three separate studies. All were white and aged 20 to 45 years. The procedures for each study were approved by the Institutional Review Boards of the respective institutions. The Fels sample consisted of 153 men and 162 women from the Fels Longitudinal Study for whom data from their most recent examinations were used. These Fels subjects were from a wide range of social and economic strata with distributions of socio-economic status similar to those in national surveys (29). The Jackson sample included 282 men and 194 women measured at Wake Forest University, Winston-Salem (NC), the Institute for Aerobics Research, Dallas (TX), or the Mount Sinai Medical Center, Milwaukee (WI). These data have been used previously by Jackson et al. (17-18) in the development of predictive equations for body density based on anthropometry. The Pollock sample provided data for 69 men and 155 women measured at the University of Florida, Gainesville (FL). These data have been used to develop predictive equations for body composition based on ultrasound (1,13). Anthropometry and Densitometry In each study, weight and stature were measured using the techniques described in the Anthropometric Standardization Reference Manual (23). BMI was calculated as weight divided by stature squared (kg/m 2). To avoid the problem of fluid retention before and during menstruation, the subjects were not tested from 5 days before until after their menstrual periods. Percent body fat was estimated from body density with a correction for residual lung volume. For underwater weight, the subjects from the Fels Longitudinal Study sat on a chair suspended from four load cells while subjects from the Jackson and Pollock studies were seated on a chair suspended from a scale. In the Fels Longitudinal Study, the average of the largest three weights in a series of 10 trials - indicative of maximum exhalation was used to calculate body density. The Jackson and Pollock studies repeated underwater weighing six to 10 times until three similar readings, to the nearest 20 g, were obtained. If this was not achieved, the average of the three largest weights was used. The Siri two-component model (34) was applied in each study to calculate FFM from body density (BD). From the Siri model %BF = (4.95 1BD - 4.5) x 100 with total body fat (TBF) and FFM calculated from %BF and weight (W) as follows: TBF (kg) =(%BF x W) 1100and FFM (kg) =W TBF. Statistical Analysis The Statistical Analysis System (SAS) was used for data analysis (33). Multivariate analysis of variance (MANOVA) was applied to examine whether there were significant differences among the data from the three study populations with respect to age, BMI, and body composition variables. Selected percentiles were calculated for the total data set (data from the three studies combined) in order to compare these with percentiles from a nationally representative sample. Partial Spearman correlation analyses (adjusting for age) were conducted on the total data set and on BMI tertiles of the total data set. Multiple comparisons among the ageadjusted partial correlations were conducted across these tertiles within gender using a Bonferroniadjustmentwith a 95% family confidence coefficient for three comparisons. The BMI was evaluated as a diagnostic index for obesity by examining its sensitivity and specificity versus %BF from densitometry (20). These analyses required the selection of cut off levels; values derived from published recommendations were used. Obesity by BMI was defined as a value> 28 kg/m 2 in men and > 26 kg/m 2 in women since long term follow-up studies beginning at about 35 years show increases in mortality when BMI at entry is larger than these values (11,35). The chosen cut off levels for obesity by %BF were> 25 % in men and > 33% in women; these match recent recommendations (2,4). The cut off levels for obesity by TBF were calculated from applying these %BF cut offs to the 85th percentiles for weight for the corresponding age groups from the Second National Health Examination Survey (26). These resulted in > 22.0 kg for men and> 24.8 kg in women. Receiver Operator Characteristic (ROC) curve analysis was applied, as an alternative to using published BMI cut off levels, in order to identify cut off levels of BMI that would lead to a good trade-off between high sensitivity and low specificity or vice versa (16). The ROC curve is a plot of the sensitivity (proportion of true positives) versus l-specificity (proportion of false positives) associated with fatness, as indicated by %BF, for a range of BMI cut off values. The choice of a BMI cut off value is based on a balance between high sensitivity and low specificity. Results Age, weight, stature, BMI, and body composition estimates for the three samples are presented in Table 1. A multivariate analysis of variance (MANOVA) showed some significant (p < 0.001) differences among 36 OBESITY RESEARCH Vol. 4 No.1 Jan. 1996

3 Table 1. Descriptive statistics of age, weight, stature, body mass index (BMI), percent body fat (%BF), total body fat (TBF), and fat-free mass (FFM) by densitometry FELS Sample Jackson Sample Pollock Sample MEN (n= 153) (n = 282) (n = 69) MEAN SD MEAN SD MEAN SD Age (y) Weight (kg) Stature (em) 180.5* BMI (kg/m 2) %BF 20.3* TBF (kg) 16.8* FFM (kg) 62.5* WOMEN (n= 162) (n = 194) (n = 155) MEAN SD MEAN SD MEAN SD Age (y) Weight (kg) 66.3* Stature (em) BMI (kg/m 2) 24.1* %BF 31.4* TBF (kg) 21.8* FFM (kg) 44.6* * differences (p < 0.001) among the 3 groups the three populations (Fels, Jackson, and Pollock) after adjustment for the effects of gender (Table 1). Based on Fisher's LSD multiple comparison procedure (7) at the 0.05 level of significance, several of the variables differed among the three groups, especially for women. Among men, the Fels sample had significantly larger mean values for stature and smaller values for FFM in comparison to the Jackson sample. Among women, the means for weight, BMI, and TBF are higher in the Fels sample than in the Pollock sample, and higher in the Pollock sample than in the Jackson sample; similarly, the mean for FFM is ranked (highest to lowest): Pollock, Fels, and Jackson. The mean stature for Pollock women is higher than for Jackson women. Finally, the mean %BF is higher in Fels women than in either of the other two samples. A comparison of percentiles for weight, stature, and BMI from the pooled sample (Fels, Pollock, and Jackson combined) with percentiles from national data (26) is presented in Table 2. Although the three populations differ with regard to some variables, as indicated in the results from the multivariate analysis of variance, the percentiles from the pooled sample may nevertheless be comparable to national values. There are important advantages to pooling the data, including the enhanced sample size and an increase in the validity of the conclusions. Deviations (in absolute value) between the sample percentiles and the national values for each gender and each of the four samples (Fels, Jackson, Pollock and the pooled sample) were computed for the three variables (weight, stature, and BMI) and averaged for each of five percentile levels (10, 25, 50, 75, and 90). This average absolute deviation was used as a measure of the extent to which the combined sample matched the general U.S. population (the lower the average absolute deviation, the closer the match). While each of the individual samples has the highest mean absolute deviation in at least one instance, the pooled sample never has the highest mean absolute deviation (data not shown). So, while none of the four samples is ideally representative of the general population based on this comparison, the pooled sample appears to be at least as representative as any of the individual samples. This fact, combined with its much larger sample size, makes the pooled sample the best choice for purposes of analysis. Table 3 shows the Spearman rank correlations, OBESITY RESEARCH Vol. 4 No.1 Jan

4 Table 2. Comparisons by gender of selected percentiles of weight, stature and BM! from the combined dataset used in the present study with percentiles from national data Percentiles Source Men Weight Present study NCHSa Stature Present study NCHS BM! Present study NCHS Women Weight Present study NCHS Stature Present study NCHS BM! Present study NCHS a NCHS: National Center for Health Statistics (18) adjusted for age, of BMI with %BF and FFM by BMI tertiles from the three samples combined and for the total data set. In men, the middle tertile BM! limits were 22.8 kg/m 2 (lower bound) and 25.6 kg/m 2 (upper bound); and in women they were 20.4 kg/m 2 (lower bound) and 22.7 kg/m 2 (upper bound). In men, the correlation between %BF and BMI in the upper tertile was significantly higher than the correlation for the lower tertile (p < 0.02), and in women the correlation in the upper tertile was significantly higher than the correlations in the middle and lower tertiles (p < 0.001). The correlation between BM! and FFM was significantly higher in the lower than the middle BMI tertile in men (p < 0.02) and in women (p < ). When BMI was used as a diagnostic index for obesity in the total sample, sensitivity was 43.6% for men and 51.8% for women (Table 4). Specificity (% of true negatives) of BMI was close to 100% in each gender. The positive predictive values were high in men, 67% of those who are diagnosed as obese (using BM!) actually have high %BF; in women, 86% of those who test positive actually have high %BF. The negative predictive values were high in each gender. In men, 85% of those who test negative (using BMI) for high %BF actually do not have high %BF; in women, 88% of those who test negative actually do not have high %BF. Figures 1 and 2 show ROC plots of sensitivity versus one minus specificity (or false positive rate) for men and women respectively, using a large BMI as an indicator of obesity. The BM! cut off level was incremented by steps of 0.5 from 18 to 33 kg/m 2. The curves for both men and women increase, but are concave downward. The choice of the optimum cut off point - where there is a good trade-off between sensitivity and the false positive rate - is necessarily subjective and depends upon the relative importanceattributed to sensitivity and specificity. This choice will be near where the ROC curve "turns the comer" (16). For this study, sensitivity was given more importance than specificity Table 3. Spearman rank correlations adjusted for age, of BMI with percent body fat (%BF) and fatfree mass (FFM) from densitometry by BMI tertiles and for the total group by gender BMI Tertiles Total group %BF Men O.l9 L 0.27 M 0.42 U 0.65 Women 0.19 M 0.20 L 0.69 U 0.79 FFM Men 0.19 M 0.31 U 0.43 L 0.54 Women 0.16 M 0.36 U 0.43 L 0.46 L Lower tertile; M Middle tertile; U Upper tertile Values connected by lines do not differ significantly at a = OBESITY RESEARCH Vol. 4 No.1 Jan. 1996

5 Table 4. Diagnostic value (%) of BMI versus %BF from densitometry by gender using BMI cut offs from the literature and from the ROC analysis applied in the present study Men (BMI > 28 kglm 2) Literature Women (BMI > 26 kglm 2) Men (BMI > 25 kglm 2) Present study Women (BMI > 23 kglm 2) Sensitivity (%) Specificity (%) 43.6± ± ± ±O ± ± ± ± 1.8 since a false positive is not considered as serious as a false negative. Therefore a cut off level toward the upper end of where the curve "turns the corner" was chosen. These levels were 25 kg/m 2 for men (78.6% sensitivity and 69.8% specificity) and 23 kg/m 2 for women (81.5% sensitivity and 84.4% specificity). The cut offs suggested in the literature (28 kg/m 2 for men, 26 kg/m 2 for women) would result in 43.6% sensitivity and 93.5% specificity for men, and 51.8% sensitivity and 97.8% specificity for women. The cut offs derived from the ROC analysis are marked on the figures together with the cut offs obtained from the literature. Because the ROC curve for women extends further into the left upper corner of the plot (i.e., has higher curvature) than that for men, the specificity for women is larger than that for men for a given sensitivity (and conversely, for a given specificity, the sensitivity is larger for women than for men). ROC analysis was also conducted using TBF instead of %BF and these resulted in ROC curves (not shown) with cut offs identical to the ones observed for %BF, namely 25 kg/m 2 for men (88.6% sensitivity and 71% specificity) and 23 kg/m 2 for women (97.2% sensitivity and 81.5% specificity). Based on the ROC analysis, the BMI levels chosen in the present study were 25 kg/m 2 for men and 23 kg/m 2 for women. In the study by Hortobagyi et ai ~ >. -.s; 0.5 '';:::; 'w c: Q) 0.4 CJ) ~ 28 kg/m 2 (8) ~ 25 kg/m 2 (A) Men o.0 -+-r...-t"'t""t""t""t'"t""t""~r-t'"i-rt"'t""t""t""t'"t""t"""i'"t"1-rt"'t""t""t""t'"t""t"""i'"t"1r-r-t"'t""t"t""t'",..,-,...,...,r-rt"" Specificity (%) Figure 1: Receiver Operator Characteristic (ROC) curve for men. Note: (A) BMI cut off level from ROC analysis with sensitivity of 78.6% and one minus specificity of 31.2% (B) BMI cut off level from literature with sensitivity of 43.6% and one minus specificity of 6.5% OBESITY RESEARCH Vol. 4 No.1 Jan

6 ~ 0.6 ~ 26 kg/m 2 (8) 0->. +-' 'S; 0.5 +: '00 c: Q) 0.4 CJ) Women o.0 -+-r""""'tt""rt""irt"t"t"t"t"t"1r-rt-r-rt'"'t""1'"t""t""""t"t"~rt"t"""""t""t"'1r-r-t-r-rt"t"""r-t'"t""'i""t""'t Specificity (%) Figure 2: Receiver Operator Characteristic (ROC) curve for women. Note: (A) BMI cut off level from ROC analysis with sensitivity of 81.5% and one minus specificity of 15.6% (B) BMI cut off level from literature with sensitivity of 51.8% and one minus specificity of 2.2% (14), BMI levels of 24.5 kg/m 2 for men and 22 kg/m 2 for women were derived from ROC curve analysis. These lower BMI cut off levels coincide with a reversal in sensitivity and specificity values. Table 5 shows that compared to the study by Hortobagyi et ai., sensitivity in the present study represents a 14% decrease in men and a 3% increase in women, and the specificity represents a 48% increase in men and a 21% increase in women. The gender differences are also much smaller in the present study (Table 5). Discussion The age range of the subjects was limited to 20 to 45 years because in this age range the density of FFM is close to 1.1 g/ml (13), which is the assumed value in the Siri two-component model (34). The BMI cut off values were based on the findings from long term follow-up studies showing increases in mortality for BMI values greater than 28 kg/m 2 for men and greater than 26 kg/m 2 for women (35). These values are similar to the BMI levels ( men, ;;::: 27.8 kg/m 2 ; women, ;;::: 27.3 kg/m 2) used to calculate the prevalence of overweight in national U.S. surveys (4). The latter levels are based on the 85th percentile values for men and women aged 20 to 29 years. There is a lack of long-term studies relating %BF to morbidity and mortality. Although the supporting research is limited, cut off points for %BF of > 25% in men and > 33 % in womenhave been suggested(3). Sensitivity and specificity reflect the proportion of true positive and negative test results respectively in homogeneous populations, that is, either those that are truly obese (for sensitivity) or truly not obese (for specificity). As such, these estimates are not influenced by the ratio of obese to non-obese individuals in the study sample (20). Notwithstanding the strong correlations in the present study between %BF and BMI for the total group (0.65 in men and 0.79 in women), our findings using published cut off levels indicate low sensitivities (44% in men and 52% in women) and high specificities (93% in men and 98% in women). This indicates that only about 44% to 52% of the truly obese adults as indicated by %BF from densitometry are identified by BMI as being obese while almost all non-obese adults are classified as such by BMI. 40 OBESITY RESEARCH Vol. 4 No.1 Jan. 1996

7 Table 5. analyses Diagnostic value of the BMI versus %BF from densitometry by gender derived from ROC Gender Study Age range BMlcutoff Sensitivity Specificity (y) level (kg/m 2) (%) (%) Men Present study Hortobagyi et ai. (1994) Women Present study Hortobagyi et ai. (1994) These results are in accordance with those from another study of adults using similar cut off levels for BMI (BMI ~ 27.8 kg/m 2 in men and ~ 27.3 kg/m 2 in women), but lower cut off levels for %BF from densitometry, namely> 20% in men and > 25% in women (36). In the latter study, slightly stronger correlations were reported between %BF and BMI in women (n= 213, r=.84) as compared to men (n=150, r=.70); a similar gender difference was observed in the present study. A study of 1280 men and 365 women, aged 19 to 77 years, reported sensitivity values of 54.5% in men and 26.9% in women and specificity values of 91.8% in men and 98.2% in women (15) when the obesity cut off points for BMI were ~ 28 kg/m 2 in men and ~ 27 kg/m 2 in women, and the cut off levels for %BF from densitometry were ~ 25% in men and ~ 30% in women. Data from the Rosetta Study using cutpoints for BMI > 27.8 kg/m 2 in men and> 27.3 kg/m 2 in women, and cutpoints for %BF from densitometry similar to the ones used in the present study (> 25% in men and > 30% in women), reported sensitivity values in men of 45% in the younger age group (average age 33.7) and 40% in the middle aged group (average age 54.6) while in women sensitivity was 47% in the younger age group (average age 31.8) and 45% in the middle aged group (average age 55.2) with specificity values in each gender being greater than 93% (2). These three studies are in agreement with the present findings that the BMI is an excellent diagnostic index for classifying the nonobese, but is rather insensitive for classifying the obese. The findings that correlations between %BF and BMI are higher in women, and that sensitivity values in women are generally higher and specificities lower relative to the values found in men, are generally explained as due to the observation that women have a greater fat mass and fat percentage than men for any given BMI (2). When ROC curve analysis was applied to derive a reasonable trade-off between sensitivity and one minus specificity, instead of using recommended cut off levels, substantially lower BMI cut off levels were selected as the best (Table 5). The levels chosen in the present study were 25 kg/m 2 for men and 23 kg/m 2 for women. In the study by Hortobagyi et al. (15) BMI levels of 24.5 kg/m 2 for men and 22 kg/m 2 for women were derived from ROC curve analysis (Table 5). These differences between the findings from the present study and those from the study by Hortobagyi might be due to the fact that the age range in the present study was restricted to ages 20 to 45 years to avoid errors in estimating %BF for older adults due to FFM density values deviating from the assumed 1.1 g/ml, but the age range in the Hortobagyi study was 19 to 77 years. The implication of the greater extension of the ROC curve into the left upper comer of the plot in women, compared to men in the present study, is that identification of high %BF using BMI is more accurate for women than for men (Figures 1 and 2). The correlations in the present study between BMI and %BF by BMI tertiles in the combined samples might help explain the low sensitivity of BMI when using recommended BMI cut offs (5). Our results showed strong correlations (.65 in men and.79 in women) between BMI and %BF for the total group (Table 3) with the highest correlations observed in the upper BMI tertile for each gender (.42 in men and.69 in women). A similar trend was noted in a report of BMI correlations with %BF and TBF from densitometry by BMI quartiles showing the highest correlations in the upper quartiles in men (r=.56 for %BF and r=.76 for TBF) and women (r=.42 for %BF and r=.77 for TBF). versus correlations in the lower quartiles ranging from.07 to.33 for %BF and from.21 to.44 for TBF (15). In summary, these studies document that BMI is highly correlated with %BF only for adults in the upper part of the BMI distribution. The higher correlations of BMI with TBF, compared with %BF, would be expected due to the inclusion of weight in both variables (9,12,15). Similarly, results of the ROC analysis show that sensitivity values are higher in both men (by 10%) and women (by 15%) when using TBF instead of %BF. OBESITY RESEARCH Vol. 4 No.1 Jan

8 Because in the present study, the partial correlations between BMI and %BF tend to be higher in the upper BMI tertiles than in the lower tertiles, one might speculate that the predictive capability of BMI is correspondingly higher. This is certainly true with regard to the proportionate reduction in the %BF variation attributable to BMI and age (coefficientof multiple determination or R2, data not shown); in fact, for men, the R2 in the upper tertile is twice what it is in the lower tertiles, and in women the R2 in the upper tertile is seven times what it is in the lower tertiles. However, the standard error (square root of mean square error) ranges from 4.7 to 5.6% and is approximately the same across the three tertiles in each gender, indicating that the predictive capability would be poor. In fact, the estimated slope (after adjusting for the effects of age) for %BF plus or minus the standard error for the first, second, and third tertiles are, respectively, 0.7 ± 0.35, 1.8 ± 0.53, and 0.9 ± 0.15 for men and l.l± 0.40, 1.6 ± 0.71, and 1.3 ± 0.09 for women. For PPM they are 2.1 ± 0.36, 1.3 ± 0.52, and 0.9±0.22 for men and 2.0 ±0.31,1.2 ±0.57,0.5 ±0.10for women. Further study is needed on the predictive capability of BMI, especially in light of its relationship with %BF and PPM over BMI tertiles as discussed above. Other studies of adults have reported strong correlations between BMI and %BF for groups not selected by BMI (9, 12, 22, 30, 36, 39), but comparison between the present results and those from other studies are complicated by differences in statistical methods. Scatterplots (not shown) of %BF versus BMI and %BF versus PPM showed that the middle tertile corresponded to a short BMI-interval while the lower and upper tertiles corresponded to longer BMI-intervals. This poses a serious problem with the use of the Pearson correlation coefficient as a measure of the general linear relationship since the r-value generally increases in absolute value with the range of the X-variable (in this case, BMI). Consequently, the Pearson correlations for the lower and upper tertiles may be artificially high (or artificially low in the middle tertile) because of the substantially differing widths of the BMI intervals. In fact, comparison of Pearson correlation coefficients would only be appropriate for tertiles from a uniformly distributed random variable. The Spearman rank correlation coefficient (adjusted for age) is a more appropriate measure since it is not influenced by the width of the BMI interval and hence allows a more accurate comparison of correlations. That is, the Spearman coefficient is based on the ranks of the BMI values, and the range of the ranks of BMI values is the same for all three tertiles. The correlation coefficients reported from most other studies are Pearson coefficients and generally not ageadjusted. In the present study, the Pearson correlations (not shown) between BMI and age were.26 in men and.15 in women, and between %BF and age these were.42 in men and.24 in women. The associations between BMI and %BF are strengthened by adjusting for age and gender, but adjustments for frame size do not influence the strengths of these associations (32). Note that the correlations for the total group tend to be higher than those associated with the BMI tertiles (Table 3). This is to be expected since the range associated with the BMI values for the total group is much larger than for the BMI tertiles. For this reason it is not appropriate to compare the total group correlation to the correlations for the BMI tertiles, however, it is appropriate to compare the correlations for the BMI tertilesamong themselves. Few have examined the relationship between persons selected by BMI and by PPM. A recent study of adults, aged 20 to 79 years, reported correlations from two samples between BMI and PPM from densitometry of.57 and.61 in men and of.45 and.59 in women (38). Although these correlations were not age-adjusted, they are similar to the present findings of.54 in men and.46 in women. In men and women with BMI values in the lowest tertile, the age-adjusted correlation of BMI with PPM (r =.43 in both men and women) in the present study was higher than with measures of %BF (r =.19 in men and women). Correlations of about 0.6 have been found between BMI values and PPM from densitometry in adults not selected by BMI (10, 38). Muscle mass is a major component of body weight, especially at low BMI values, and therefore, low BMI values can be used as an indicator of muscle mass. The association of sarcopenia, as indexed by low BMI values, with increased mortality rates has been suggested in a recent review (31). This study was undertaken to examine the association between BMI and the main constituents of body composition (%BF and FPM) in a group of healthy young and middle-aged adults. We will also briefly summarize the contribution that our findings add to the interpretation of the relationships between BMI and morbidity and mortality statistics. The f-shaped or U shaped curves indicative of increased mortality at the low and high extreme values of body weight or BMI have been well documented (6,25,35). However, recent findings from one follow-up study of 8,828 non-smoking Seventh Day Adventist men showed a lack of excess mortality at low BMI values (no 'J' in the BMI-mortality curve) and did not observe an increase in mortality until a BMI value of 27.5 kg/m 2 or greater was reached (37). Chronic energy deficiency levels have been defined based on low BMI indices varying from 16 to 18.5 kglm 2 (19), but allowance should be made for leg length when using BMI to assess nutritional status due to the contribution of Iong-leggedness to low BMI (27). The measurement of leg length is not likely to be practical for screening. The I-shaped or U-shaped curve 42 OBESITY RESEARCH Vol. 4 No.1 Jan. 1996

9 indicative of increased mortality at low and high BMI values might be explained if BMI is a good index of FFM at low BMI values and of %BF at high BMI as shown by our findings. Data from the prospective Established Populations for Epidemiologic Studies of the Elderly examined the BMI at middle age, at old age, and weight change between age 50 and old age in relation to mortality at old age in 6,387 white men and women (24). The results show that at age 50, relative mortality was significantly higher in both men and women for those situated in the heaviest quintile of the BMI ( ~ 28.4 kg/m 2 in men; ~ 29.2 kg/m 2 in women) while the risk of mortality in old age associated with the BMI showed the opposite trend, namel:r those situated in the lowest BMI quintile «22.3 kg/m in men; < 21.0 kg/m 2 in women) had highest relative mortality. A significantly increased risk of mortality was associated with weight loss. The association of increased weight loss with older age along with the association with poor health, suggest that most of the weight loss resulted from a decline in health and not from positive lifestyle changes (24). If BMI is an index of FFM at low BMI values, the increased mortality at lower BMI ranges may be attributed to increased risk for immunoincompetence and for fractures due to low bone mineral density (31). The observations in this study that the correlations between BMI and %BF were substantially higher in the upper BMI tertile than in the two lower tertiles, and that BMI correlated higher with FFM than with %BF in the lower tertile in each gender, might partly explain why BMI was an insensitive index for the detection of obesity for the entire group. The insensitivity of the BMI has implications for identifying the obese. Health professionals should be cautious when using BMI as an index of obesity, although BMI correlates well with %BF in the adults most likely to be easily identified as obese, namely those in the upper BMI range. Based on the results of the present ROC curve analysis, lower BMI values than those commonly recommended for white adults are suggested, namely 25 kg/m 2 in men and 23 kg/m 2 in women. These lower cut off BMI values (see Figures 1 and 2) would allow for the successful detection of obese adults that would be generally missed by the current recommended standards for overweight (21). Since the correlations between BMI and %BF have been shown to vary by ethnic group, the present recommendations apply only to white adults (39). Acknowledgments We thank the data collection, data analytical and secretarial staff from the Division of Human Biology, as well as Parveen Kapoor from the Statistical Consulting Center, Wright State University and Linda Garzarella from the University of Florida, for their assistance with the many aspects of this study. This work was supported by grant from the National Institutes of Health, Bethesda, MD and by grant MV YI from the American Heart Association, Ohio Affiliate. References 1. Ayers S, Ishida Y, Garzarella L, et al. The development of prediction equations for estimating body composition in males by B-mode ultrasound. Med Sci Sports Exerc. 1994;26;5:S Baumgartner RN, Heymsfield SB, Roche AF. Human body composition and the epidemiology of chronic disease. Obes Res. 1995;3: Behnke AR, Wilmore JH. Evaluation and Regulation ofbody Build and Composition. New Jersey: Prentice Hall Inc.; Bray GA. Fat distribution and body weight. 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