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Keywords:

  • body mass index;
  • waist circumference;
  • coronary risk factor

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Objectives: To examine the power of the combined measurements of body mass index (BMI) and waist circumference (WC) for the prediction of abnormality in coronary heart disease risk factors and to determine whether the additional measurement of WC is predictive in older men and women.

Research Methods and Procedures: 1190 men and 751 women of the Baltimore Longitudinal Study of Aging were dichotomized into younger (<65 years) and older (65+ years) age groups. Coronary risk factors in the realms of glucose/insulin metabolism, blood pressure, and plasma lipids were assessed. The relationship of BMI and WC, singly and combined, to 10 risk factors for coronary heart disease was examined.

Results: In younger and older men and women, BMI and WC are highly correlated (0.84 to 0.88). BMI and WC are also significantly correlated to all 10 coronary risk factors in younger men and women and to 8 of the 10 in the older men and women. Both partial correlation and logistic regression analyses revealed a modest but significant improvement in the prediction of coronary risk in younger men and women by WC after controlling for the level of BMI. There was no improvement in the older subjects.

Discussion: WC adds only modestly to the prediction of coronary risk in younger subjects once BMI is known, and adds nothing to the production of risk in older subjects.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

In 1998, both the World Health Organization (WHO) (1) and the National Heart, Lung, and Blood Institute (NHLBI) (2) issued comprehensive recommendations for classifying abnormalities in body weight and body fat distribution. Both reports recommended body mass index (BMI) and waist circumference (WC) as measures of obesity and fat distribution. Both reports summarized the evidence relating these measures to health risk. The WHO report did not attempt to quantify risk for simultaneously measured BMI and WC. The NHLBI report does consider the impact of the two variables combined. In table ES-4 of that report, it is indicated that WC does not modify the risk classification in subjects who are underweight or normal weight and that WC does not modify the classification of risk in subjects in the higher obesity categories of BMI, that is, in those with BMI of 35 kg/m2 or greater. However the NHLBI table does indicate a higher degree of risk if critical WC levels are exceeded in men and women whose BMI lies in the overweight zone (25.0 to 29.9 kg/m2) and in the obesity class I zone (30.0 to 34.9 kg/m2). However, this increased risk is characterized only qualitatively. Thus, if the WC exceeds the critical cut-points of 102 cm for men or 88 cm for women, then the risk in overweight individuals is described as “high,” in contrast to “increased” in individuals below those WC cut-points. Similarly, in obesity class I individuals, a “high” WC confers a “very high” risk compared with a high risk if the WC is not increased. These are obviously qualitative descriptors; uncertainty exists about the exact increase in risk conferred by a large waist.

The WHO report presents a more complex WC classification than does the NHLBI report in that two cut-points are indicated for men (94 and 102 cm) and two for women (80 and 88 cm). Thus, individuals fall into one of only two zones by the NHLBI classification but into one of three zones by the WHO report.

A natural question arises: is either set of standards applicable to older individuals? Zamboni et al. (3) indicated that the amount of visceral adipose tissue related with WC was significantly higher in older than in younger individuals. Also, Borkan et al. (4) showed that the intra-abdominal fat area measured by computed tomography was greater in older than in younger men, although weight was 8.2 kg greater in younger than in older men. Molarius and Seidell (5) emphasized the need to examine the possibility that there could be age-related differences in the contribution of the pattern of fat distribution to risk factors. However, the WHO and NHLBI reports barely address the question of the applicability of BMI and WC standards for older persons.

We (6) (7) recently reported that at least with respect to the effects of BMI and WC on the traditional coronary heart disease risk factors, these anthropometric variables, considered independently of each other, continue to be associated with harmful effects, even in men and women over 65 years old, although at a lower level of significance than in younger individuals.

The other question that remains is whether measurement of WC gives additional information to the estimation of coronary risk that is predicted by BMI alone in younger and older men and women.

In this report, we have tested the hypothesis that although BMI and WC are highly correlated with each other, the quantification of risk is improved if both variables are considered together. Furthermore, we hypothesized that this conclusion would also apply to older individuals, albeit at a weaker level than in younger individuals.

Research Methods and Procedures

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Subjects

The subjects included 1190 men with initial ages ranging from 19 to 100 years and 751 women with initial ages ranging from 17 to 92 years, participants in the Baltimore Longitudinal Study of Aging (BLSA) at the Gerontology Research Center. They were self-recruited, community-dwelling volunteers, generally highly educated, middle and upper-middle class men and women primarily in executive, managerial, and academic professions. The measurements were taken at the Gerontology Research Center, where the subjects spend 2.5 days in a general clinical research center type of facility approximately every 2 years. They undergo a battery of clinical, physiological, and psychological tests on each visit. Details of the population have been described elsewhere (8). The BLSA is an open-panel study and new subjects are enrolled each year. Although this report is a cross-sectional analysis, each subject may have multiple visits from which to choose anthropometric and risk factor values. Depending on the subject's test schedule, not all variables were assessed on each visit. Data included in this report were those obtained on the earliest visit on which the anthropometric and risk factor variables were available. The period over which each variable was collected varies: anthropometric measurements were made from 1958 to 1998; glucose measurements from 1977 to 1998; insulin measurements from 1977 to 1995; lipid measurements from 1983 to 1998; and blood pressure measurements from 1959 to 1998 for men and from 1977 to 1998 for women. Therefore, in these analyses, one subject may contribute cross-sectional data from different visits (i.e., anthropometry and blood pressure from 1975, anthropometry and glucose from 1977, and anthropometry and lipids from 1983). Correlations between the anthropometric and other risk factor variables were carried out on variables measured on the same visit. To examine the applicability of the recently recommended WC and BMI standards to older men and women, the population was dichotomized at age 65 years into younger and older age groups. Table 1 shows some descriptive statistics of the population.

Table 1.  Characteristics of subjects
 MenWomen
 Younger (age < 65 years)Older (age ≥ 65 years)Younger (age < 65 years)Older (age ≥ 65 years)
 nMean (SE)nMean (SE)nMean (SE)nMean (SE)
  • *

    p < 0.05.

  • p < 0.01, significantly different from young, same sex.

  • Antilog of the log fasting insulin and HOMAIR: the SEs are reported as the antilog of the log mean plus and minus the log SE.

Height (cm)802178.0 (0.24)388173.6 (0.32)533164.7 (0.27)218158.7 (0.43)
Weight (kg)80280.4 (0.41)38874.7 (0.49)53361.5 (0.50)21862.5 (0.73)
BMI (kg/m2)80225.4 (0.11)38824.8 (0.14)*53323.6 (0.18)21824.8 (0.27)
WC (cm)80288.9 (0.34)38891.2 (0.43)53375.3 (0.41)21879.7 (0.64)
Systolic blood pressure (mm Hg)755123.5 (0.59)353139.6 (1.10)386116.2 (0.84)159139.6 (1.80)
Diastolic blood pressure (mm Hg)75579.9 (0.37)35280.9 (0.56)38675.6 (0.50)15977.9 (0.85)
Fasting glucose (mg/dL)61298.8 (0.43)368103.9 (1.08)53193.1 (0.36)21598.0 (0.73)
2-hour glucose (mg/dL)578123.8 (1.59)358153.0 (2.63)505113.1 (1.39)205143.3 (3.04)
Fasting insulin (μU/mL)4988.0 (7.9 to 8.2)2968.3 (8.1 to 8.6)4397.2 (7.0 to 7.3)1547.1 (6.8 to 7.5)
HOMAIR4981.9 (1.9 to 2.0)2962.0 (2.0 to 2.1)4391.6 (1.6 to 1.7)1541.7 (1.6 to 1.8)
Total cholesterol (mg/dL)479212.3 (1.61)340216.8 (1.98)456202.3 (1.91)218228.5 (2.56)
Triglyceride (mg/dL)473127.3 (4.33)337112.5 (3.49)*44782.5 (2.38)220106.6 (3.92)
HDL cholesterol (mg/dL)47642.2 (0.48)33644.3 (0.66) 0.0645452.3 (0.60)22056.2 (0.94)
LDL cholesterol (mg/dL)475119.9 (1.4)336126.1 (1.80)*454108.6 (1.67)220127.3 (2.38)

All subjects provided informed consent according to guidelines approved by the Johns Hopkins Bayview Medical Center Institutional Review Board.

Due to small numbers, non-whites were excluded from these analyses. In addition, subjects who were being treated for disorders, such as diabetes mellitus, hypertension, and hyperlipidemia, which could influence the level of the risk factors, were excluded. Specifically, risk factor measurements were excluded if subjects were taking insulin or oral hypoglycemic agents (fasting plasma glucose and 2-hour glucose values excluded), antihypertensive medication (systolic and diastolic blood pressure excluded), or lipid-lowering medication (total, high-density lipoprotein [HDL], low-density lipoprotein [LDL] cholesterol and triglycerides excluded). Because previous studies of blood pressure in this population have detected a first-visit artifact, the blood pressure measurement on the second visit was used to characterize hypertensive status. Measurements from women who were pregnant at the visit or who had had a baby <1 year before the visit were also excluded from these analyses.

Anthropometry

Height and weight were measured after an overnight fast, with subjects wearing a light-weight hospital gown and no shoes. As an index of obesity, BMI was calculated as weight (in kilograms) divided by square height (in meters).

WC was used as the index of the body fat distribution. The waist was defined as the minimal abdominal circumference between the lower edge of the rib cage and the iliac crests. The circumferences were obtained with a flexible, metal tape measure, while maintaining close contact with skin and without compressing the underlying tissues. Subjects were in a standing position and breathing normally. The same small group of trained personnel made these measurements for the entire study.

Plasma Lipids

After an overnight fast, an antecubital venous blood sample was drawn. The concentrations of plasma triglycerides and total cholesterol were determined by enzymatic method (ABA-200 ATC Biochromatic Analyzer; Abbott Laboratories, Irving, TX). HDL cholesterol was determined by dextran sulfate-magnesium precipitation procedure (9). LDL-cholesterol concentrations were estimated by the Friedewald formula (10).

Glucose Tolerance

An oral glucose tolerance test was performed after an overnight fast. No significant physical activity or smoking was allowed before the glucose testing. Blood was obtained through an antecubital venous catheter. A fasting blood sample was drawn immediately before glucose ingestion and 2 hours after ingestion. The glucose dose for the oral glucose tolerance test was 40 g/m2 of body surface area (11). The average glucose dose was 78 g in men and 68 g in women.

Plasma Insulin and Insulin Resistance

Plasma insulin was measured in duplicate by radioimmunoassay (12). The lower limit of detection of the assay was 2.5 μU/mL; the interassay coefficient of variation on nine separate assays was 11.5%, and the intra-assay coefficient of variation was 6%. The homeostasis model assessment (HOMA) was used to characterize insulin resistance (13). The formula for insulin resistance (HOMAIR) was as follows: HOMAIR = fasting insulin (μU/mL) × fasting glucose (mM)/22.5

Blood Pressure

Blood pressure measurements were obtained by auscultation of the first and fifth Korotkoff sounds using a manual sphygmomanometer appropriately sized to the participant's arm. Each participant's blood pressure was taken in both arms while in a seated position; pressure in the right arm was used in this report. Before 1989, measurements of blood pressure were performed by clinical research fellows. Blood pressures were taken with the subjects well-rested, seated in a chair. A mercury sphygmomanometer was used with the cuff at heart level and with appropriate sized cuff. From 1989 to 1997, clinical screening was performed by nurse practitioners or by physician assistants. The blood pressure measurement technique was the same, except that subjects were seated on an examining table; the back was not supported. Mean levels of blood pressure were higher in the later period, but the differences were small: systolic for men, 3.7 mm Hg; diastolic for men, 2.9 mm Hg; systolic for women, 1.1 mm Hg, and diastolic for women, 1.5 mm Hg.

Cut-Points for Abnormalities of BMI, WC, and Risk Factors

Cut-points for BMI and WC abnormalities were determined according to NHLBI and WHO guidelines. The BMI and WC categories were dichotomized at the lower cut-points due to the small number of subjects in the obese and high WC groups. In addition, the designation of the cut-points that define an abnormal level of the risk factors was decided empirically when clear guidelines from the literature or from practice were not available. Thus, the following definitions were used: BMI, 25 kg/m2; WC, 80 cm for women and 94 cm for men; systolic blood pressure, 140 mm Hg; diastolic blood pressure, 90 mm Hg; fasting glucose, 110 mg/dL; 2-hour glucose, 140 mg/dL; fasting insulin, 11 μU/mL; HOMAIR, 2.6; total cholesterol, 240 mg/dL; triglyceride, 200 mg/dL; HDL cholesterol, 35 mg/dL; and LDL cholesterol, 160 mg/dL.

The glucose cut-points are those that separate the normal from the impaired state (American Diabetes Association and WHO) (14) (15). The fasting insulin and HOMAIR cut-points were selected to be the 25th percentile of our combined total population of men and women. The lipid cut-points were derived from the recommendations of the National Cholesterol Education Program (16).

Combined Effects of the BMI and WC Classifications on the Development of Risk Factor Abnormality

Logistic regression analysis was also used to test the combined effect of the BMI and WC classifications and their interaction on the prevalence of each risk factor abnormality in younger and older men and women (see Statistical Methods). We note again that the lower cut-points of the WHO recommendations were used (80 cm for women and 94 cm for men) because of the very small number of individuals in our population that exceeded the upper WHO cut-points of 94 and 102 cm.

Statistical Analysis

All data were analyzed using Statistical Analysis System, version 6 (SAS, Cary, NC). Standard methods were used to compute means, standard errors of the mean, and Pearson correlation coefficients. For analyzing continuous variables, fasting insulin and HOMAIR were treated as natural log-transformed values, because these two variables did not have a normal distribution; p values < 0.05 were regarded as indicating statistical significance. χ2 analysis was performed for analyzing effects of age in the percentage of subjects with abnormal levels of BMI, WC, and 10 risk factors. To examine the effects of WC after controlling for BMI, the partial correlations between WC and the 10 risk factors were computed. BMI and WC classifications were added to the logistic regression model to test combined effect of the BMI and WC classifications and their interaction on the development of each risk factor abnormality. For each logistic regression, the BMI classification was entered first to the model and the addition of the WC classification was tested using type 1 analysis (PROC GENMOD). The odds ratios of the upper BMI and WC classifications were computed for each risk factor.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Effects of Age

Table 1 shows the characteristics of subjects in this study. Statistically significant age differences were common. WC was higher in older than in younger subjects in both men and women. Older women had a higher BMI than younger women, but older men had a lower BMI than younger men, and body weight was lower in older compared with younger men.

Systolic blood pressure, fasting glucose, 2-hour glucose, and LDL cholesterol levels were higher in older than in younger men and women. Older women had worse (higher) levels of total cholesterol and triglyceride than did younger women, but the HDL cholesterol level was better (higher) in older than in younger women. Triglycerides were lower in older than in younger men.

Characteristics of Risk Factor Status in This Population

Table 2 shows the percentage of subjects with an abnormal level of BMI, WC, or risk factors for each age/sex group. In men, the percentage of subjects with an abnormal WC was significantly higher in older than in younger subjects. In women, older subjects had significantly higher abnormalities of both BMI and WC than did younger subjects. In general, the percentage of subjects with an abnormal level of the risk factors was lower in younger than in older subjects; these differences were statistically significant for systolic blood pressure, diastolic blood pressure, fasting glucose, 2-hour glucose, and total and LDL cholesterol in both men and women.

Table 2.  The percentage of subjects with an abnormal level
 MenWomen
 YoungerOlderYoungerOlder
  • The percentage of subjects with an abnormal level of risk factor statistically significantly greater in older than in younger subjects by χ2 analysis.

  • *

    p < 0.05.

  • p < 0.01.

BMI50.244.627.042.2
WC27.837.628.547.2
Systolic blood pressure17.752.710.649.7
Diastolic blood pressure17.223.6*9.617.6
Fasting glucose9.819.83.49.3
2-hour glucose25.153.613.143.9
Fasting insulin29.533.818.718.8
HOMAIR28.734.815.919.5
Total cholesterol20.026.8*16.735.8
Triglyceride13.18.94.06.8
HDL cholesterol25.024.17.94.5
LDL cholesterol10.114.9*9.317.3

To examine the distribution of subjects within the normal or abnormal categories of BMI and WC, scattergrams for younger and older men and women are shown in Figure 1, A–D. There is a high degree of consistency when subjects are categorized according to the WHO cut-point of 25 kg/m2 for BMI and the cut-point of 80 cm for women and 94 cm for men for WC. Only approximately one-half of the participants are in the completely normal group (younger men, 46%; older men, 47%; younger women, 65%; older women, 50%) and approximately one-third are abnormal for both BMI and WC (younger men, 30%; older men, 32%; younger women, 23%; older women, 33%). Relatively small percentages are in the mixed classifications (normal BMI/abnormal WC and abnormal BMI/normal WC). The percentage of subjects in normal BMI/abnormal WC was higher in older than in younger subjects. In contrast, the percentage of subjects in abnormal BMI/normal WC was lower in older than in younger subjects.

image

Figure 1. Scattergram of the relationship between WC and BMI in younger men (A), older men (B), younger women (C), and older women (D). BMI cut-points of 25 and 30 kg/m2 and WC cut-points of 94 and 102 cm in men (80 and 88 cm in women) are indicated. R values of the regression are shown for each age/sex group.

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Associations among BMI, WC, and 10 Coronary Risk Factors

The strength of the relationship between WC and BMI in younger and older men and women can also be seen in Figure 1, A-D. The R values ranged from 0.84 to 0.88 in the four age/sex groups.

Both BMI and WC were also highly correlated with the coronary risk factor levels as shown in Table 3. Both BMI and WC were invariably significantly correlated with all risk factors in younger men and women. In the older groups, both BMI and WC were generally significantly strongly correlated with the risk factors, the only exceptions being systolic blood pressure and total cholesterol in men, and total and LDL cholesterol in women. In general, risk factor correlations were stronger for WC than for BMI in the younger men and women (8 of 10 for men and 9 of 10 for women), but there was no consistent advantage for WC or BMI in the older men and women.

Table 3.  Correlations among BMI, WC, and the risk factors
 MenWomen
 YoungerOlderYoungerOlder
 WCBMIWCBMIWCBMIWCBMI
 RRRRRRRR
  • *

    p < 0.05.

  • p < 0.01, significance of R values.

  • Log-transformed values.

Systolic blood pressure0.310.260.020.070.330.310.17*0.14
Diastolic blood pressure0.320.280.270.290.380.370.16*0.17*
Fasting glucose0.370.310.300.280.350.320.360.33
2-hour glucose0.430.350.330.310.330.310.260.25
Fasting insulin0.380.380.330.320.260.260.290.33
HOMAIR0.410.400.360.350.310.300.330.37
Total cholesterol0.290.23−0.06−0.060.280.260.020.04
Triglyceride0.370.330.340.360.390.320.310.26
HDL cholesterol−0.26−0.24−0.31−0.30−0.21−0.18−0.31−0.29
LDL cholesterol0.180.12−0.13*−0.14*0.270.260.010.04

To answer the questions of whether the WC is a significant predictor of the coronary risk factors after BMI is taken into account, we computed partial correlations between the WC and each of the 10 risk factors, controlling for the BMI. As seen in Table 4, in the younger men and women, WC remained a significant predictor, albeit of reduced strength, compared with the R values shown in Table 3. This is not the case for older men and women. Almost all of the significance of WC as a predictor of risk is lost when BMI is factored in, a striking age difference.

Table 4.  Partial correlations between WC and the risk factors after controlling for BMI
 MenWomen
 YoungerOlderYoungerOlder
 RRRR
  • *

    p < 0.05.

  • p < 0.01, significance of R values.

  • Log-transformed values.

Systolic blood pressure0.19−0.070.12*0.10
Diastolic blood pressure0.160.050.14*0.02
Fasting glucose0.200.11*0.16*0.16*
2-hour glucose0.280.13*0.13*0.10
Fasting insulin0.120.110.080.01
HOMAIR0.130.12*0.12*0.03
Total cholesterol0.200.000.11*−0.03
Triglyceride0.170.070.240.17*
HDL cholesterol−0.13−0.11−0.11*−0.13
LDL cholesterol0.16−0.020.10*−0.05

Combined Effects of the BMI and WC Classifications on the Prevalence of Risk Factor Abnormalities: Logistic Regression

The effect of the WC measurement independent of BMI was also analyzed by logistic regression (Table 5). In younger men, the effects of the WC classification, after controlling for the BMI classification, were significant for all risk factors with the exception of total and LDL cholesterol. In younger women, the WC classification influenced diastolic blood pressure, fasting insulin, HOMAIR, triglycerides, and HDL cholesterol. In older subjects, the WC classification influenced only 2-hour glucose and HOMAIR in men, and no effects were found in women. There was no significant interaction term between BMI and WC for most risk factors in younger and older men and women.

Table 5.  Odds ratios for the presence of coronary risk factor abnormalities; higher (abnormal) WC class compared with lower (normal) WC class
 MenWomen
Coronary risk factor abnormalityYoungerOlderYoungerOlder
  • Logistic regressions adjusted for BMI classification.

  • *

    p < 0.05.

  • p < 0.01, higher WC significantly different from lower WC.

Systolic blood pressure2.81.12.21.9
Diastolic blood pressure2.51.53.5*1.5
Fasting glucose4.41.22.92.3
2-hour glucose1.8*2.01.81.2
Fasting insulin2.01.73.81.0
HOMAIR2.02.0*4.81.1
Total cholesterol1.40.81.40.9
Triglyceride2.91.75.3*2.3
HDL cholesterol1.8*1.25.42.8
LDL cholesterol0.70.82.01.0

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Previous reports on the BLSA population showed that BMI and WC independently were significant predictors of coronary risk factors in the blood pressure, glucose insulin, and plasma lipid domains in younger and older men and women (6) (7). Furthermore, the gradation of BMI into normal, overweight, and obese zones according to NHLBI and WHO recommendations (1) (2) was supported. Also, the sex-specific WHO cut-points for WC that provided three zones (NHLBI standards provided two zones) were also found to be applicable to the risk factors in the four age/sex categories.

Both WC and BMI have been shown to be related to cardiovascular disease risk factors (17) (18) (19) (20) (21). Although the waist-to-hip ratio had been the favored index of an unfavorable pattern of fat distribution, in recent years WC has become the preferred measure of body fat distribution (22) (23) (24). However, the important question of whether assessment of WC adds to the estimation of coronary risk predicted by BMI alone remains. Certainly, if WC is added to BMI for assessment of risk, as Booth et al. (25) found, then the percentage of individuals in a population classifiable as being overweight by BMI criteria alone will be significantly increased. As we have shown (see Figure 1), there are younger and older men and women whose BMI is in the normal zone who fall in the intermediate WC zone (1), while those with BMIs in the 25- to 29-kg/m2 range (now identified as overweight) will fall within all three WC zones. However, the question still remains whether WC gives additional information to the estimation of coronary risk to that predicted by BMI alone in younger and older men and women.

In the present study, this question was examined by two statistical methods: partial correlation, in which WC and BMI and the 10 coronary risk factors were analyzed as continuous variables and logistic regression, in which these same variables were stratified into well-defined categories. As noted in the Research Methods and Procedures section, the lower WHO cut-points for WC were used. Table 6 summarizes these analyses along with the results of the simple bivariate correlation of WC and the 10 risk factors (unadjusted for BMI).

Table 6.  Summary of the statistical significance of the relationship of WC to 10 coronary risk factors by three methods of analysis (see Discussion section)
 MenWomen
 YoungerOlderYoungerOlder
 BivariatePartial correlationLogistic analysisBivariatePartial correlationLogistic analysisBivariatePartial correlationLogistic analysisBivariatePartial correlationLogistic analysis
  • *

    p < 0.05.

  • p < 0.01, significant relationship between WC and each risk factor.

  • Log-transformed values.

Systolic blood pressure   * *  
Diastolic blood pressure  ***  
Fasting glucose* * * 
2-hour glucose***   
Fasting insulin     
HOMAIR***  
Total cholesterol    *    
Triglyceride  ** 
HDL cholesterol*  *  
LDL cholesterol *  *    

To place this study in the context of previous publications, we have summarized the data from 25 studies that, at a minimum, had simultaneous measures of fatness (usually BMI) and of fat distribution (usually anthropometric measurements that included the WC) and also presented correlation coefficients of these measures with one or more of the traditional coronary risk factors (19) (24) (26) (27) (28) (29) (30) (31) (32) (33) (34) (35) (36) (37) (38) (39) (40) (41) (42) (43) (44) (45) (46) (47) (48). We are preparing a detailed analysis of these reports but will limit the present discussion to a summary of that analysis. For clarity of presentation, indices of fatness will be referred to as BMI and indices of fat distribution will be referred to as WC.

Five questions were addressed:

  • 1
    . Does fatness or fat distribution pattern correlate more strongly with other risk factors? In all, the data provide 178 correlations among BMI, WC, and other risk factors. The importance of these indices is shown by the fact that r values were statistically significant in 150 of the 178 correlations. However, there was no clear superiority of BMI or WC in the overall strength of the associations.
  • 2
    . Which of the risk factor domains (blood pressure, glucose/insulin, or lipids) are most strongly related to the anthropometric measures? Correlations with total and LDL cholesterol were decidedly the least consistent: 51% of the r values were not statistically significant. In contrast, only 8% of the correlations with blood pressure, 5% to 6% of the glucose and insulin correlations, and 8% of the triglycerides and HDL cholesterol correlations failed to reach statistical significance. Only in the triglycerides/HDL domain was there clear superiority of the WC measurement over BMI. There was no clear superiority of WC or BMI in any of the other domains.
  • 3
    . Is there an age difference in the strengths of associations? The present report is the only study that reports both younger and older men and women in the same population. As we have shown, the correlations are very much stronger in the younger individuals.
  • 4
    . Is there a sex difference in the strengths of associations? There is an interesting sex difference in the strength of the BMI and WC associations with three lipid moieties—triglycerides, HDL, and LDL cholesterol, but this is true only in the older individuals, the correlations in older men being stronger than those in older women. In 19 of the 22 comparisons in the literature, r values were higher in men.
  • 5
    . Does the measurement of the fat distribution pattern add strength to that of BMI alone in the prediction of abnormality in the associated risk factors? This question was approached in the present study and in 9 of the other 25 reports by conducting partial regressions of WC (controlling for BMI) on the coronary risk factors (24) (33) (39) (40) (42) (43) (44) (45) (47). The results of these partial correlations are highly age-dependent. In younger men, 32 of the 37 analyses (86%) and in younger women 12 of the 15 analyses (80%) showed WC to remain a statistically significant correlate. In contrast, only 5 of 20 (25%) and 4 of 17 (24%) of the analyses in the older men and women achieved statistical significance. Thus, it is clear that WC is quite a consistent independent influence on coronary risk factor in younger but not in older men and women.

In summary, WC and BMI independently are significantly related to the risk factors in all four demographic groups in nearly all cases. When BMI is brought into the analysis, WC remains a significant predictor for most of the variables in younger men and women, but significance is almost entirely lost in older men and women. The logistic regression technique confirms this conclusion in that significance of WC is almost entirely lost in older men and, indeed, in older women, no risk factor remains significantly related to WC. Thus, the effects of obesity on coronary risk factors are captured almost entirely by the measurement of BMI in older individuals, but, in younger men and women, WC still adds significantly to the assessment of risk by BMI alone. The explanation for this age difference is not clear. Mykkänen et al. (35) discuss some possibilities extensively. They note that equal BMIs in younger and older adults represent different degrees of fatness; lean body mass, especially, muscle mass, and bone mass decrease with aging, whereas fat mass increases. Thus, equal body weights in young and old individuals represent different degrees of fatness. BMI then could be expected to increase in predictive power with age. Furthermore, the relationship of WC to intra-abdominal fat changes with age. The relative distribution of subcutaneous to intra-abdominal fat probably changes, and abdominal wall laxness may increase with age so that a simple measurement of WC, although still predictive in itself of other risk factors, may not be as reliable a measure in older individuals.

It must be noted that these conclusions are based on studies in a Europid population and comparable studies in other racial/ethnic groups are needed. In addition, a truism of cross-sectional studies in elderly individuals is that only survivors can be evaluated; the truly predictive power of WC needs to be quantified in prospective studies. It also must be emphasized that we used only lower cut-points for BMI (25 kg/m2) and WC (94 cm for men and 80 cm for women) due to the small number of subjects in the obese and high WC groups. Because the variables examined are risk factors for coronary heart disease, direct analyses of the predictive power of WC (with and without BMI) on the development of coronary heart disease would be instructive. In larger populations, it should be possible to define more accurately than we were able to do, the age range at which the predictive power of WC is lost. We empirically chose 65 years old as a definition of an older population, but the effects of WC in, for example, decades of the lifespan, would be important. Finally, the high degree of correlation between BMI and WC in this population introduced the problem of colinearity. Colinearity inflates the estimates of variance in both the continuous and categorical analyses when BMI and WC are entered into the regression models. The four correlation coefficients between BMI and WC in younger and older men and women in our study ranged from 0.84 to 0.88. These values agree closely with those reported in three other studies (49) (50) (51). However, because the correlations between BMI and WC were so similar in younger and older men and women, we believe the conclusion of the relative importance of WC still applies.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

This work was supported by the Intramural Research Program, National Institute on Aging. We thank Research Fellowships for Japanese Biomedical and Behavioral Researchers at the National Institutes of Health funded by the Japan Society for the Promotion of Science. The first two authors are equal contributors.

References

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  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
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