Abstract
Objective
To evaluate the association of body adiposity index (BAI) with all-cause and cardiovascular disease (CVD) mortality risk.
Design and Methods
The current analysis comprised 19 756 adult men who enrolled in the Aerobics Centre Longitudinal Study and completed a baseline examination during 1988-2002. All-cause and CVD mortality was registered till December 31, 2003.
Results
During an average follow-up of 8.3 years (163 844 man-years), 353 deaths occurred (101 CVD deaths). Age- and examination year-adjusted hazard ratios (HRs) and 95% confidence intervals (95% CIs) for all-cause mortality risk were higher for men with high values of BMI (HR = 1.63, 95% CI = 1.19–2.23), waist circumference (1.55, 1.22-1.96) and percentage of body fat (%BF) (1.36, 1.04-1.31), but not for men with high values of BAI (1.28, 0.98-1.66). The HRs for CVD mortality risks were higher for men with high values in all adiposity measures (HRs ranged from 1.73 to 2.06). Most of these associations, however, became nonsignificant after adjusting for multiple confounders including cardiorespiratory fitness.
Conclusion
BAI is not a better predictor of all-cause and CVD mortality risk than BMI, waist circumference or %BF.
Keywords: Adiposity, body mass index, mortality, adults
INTRODUCTION
The prevalence of obesity among adults has reached epidemic levels worldwide.(1, 2) It is well known that overweight and obesity are major risk factors which can lead to further morbidity and mortality.(3-5)
Body mass index (BMI) and waist circumference are strong predictors of obesity-related morbidity and mortality.(6-8) Despite their limitations, both are commonly used as adiposity measures in large epidemiological studies where the use of more accurate methods such as dual-energy X-ray absorptiometry (DXA), hydrostatic weighing, bioelectrical impedance or even skinfold thickness are limited due to its complexity and/or cost.(9, 10)
Recently, Bergman et al.(11) proposed the body adiposity index (BAI) as a new method intended to substitute BMI as an estimate of percentage body fat (%BF) without requiring a measure of body weight. Several validation studies have analyzed the correlation between BAI and %BF estimated by accurate gold standard methods such as DXA,(11-17) magnetic resonance imaging,(18) bioelectrical impedance,(19, 20) and computed tomography.(13) Moreover, other studies have examined the associations between BAI and traditional and novel cardiovascular disease (CVD) risk factors.(13, 16-18, 20-23)
To our knowledge, the association between BAI and mortality has not been investigated. Such a study is needed to determine the potential use of BAI as a mortality risk predictor in large epidemiological studies. Therefore, the purpose of this study was to evaluate the association of BAI with all-cause and CVD mortality risk. In addition, we analyzed the cross-sectional associations between BAI and traditional CVD risk factors in a sample of men participating in the Aerobics Center Longitudinal Study (ACLS).
METHODS AND PROCEDURES
Subjects
Data for this report were from the ACLS, a prospective epidemiological study of individuals who received extensive preventive medical examinations at the Cooper Clinic in Dallas, Texas. Details of the study design and the characteristics of the cohort have been reported previously.(24) Study participants were referred by their employers or physicians or were self-referred. They were mainly Caucasian, relatively well-educated, and from middle to upper socioeconomic strata. After receiving complete information about the aims and methods of the study, all participants gave written informed consent for the examinations and follow-up. The study protocol was reviewed and approved annually by the Cooper Institute’s Institutional Review Board.
For the present analysis we included all men who received at least 1 medical examination between 1977 and 2002, and with valid data for hip circumference. For men attending more than one examination we used the first examination (baseline). Among 23 126 men aged ≥20 years at baseline, we excluded 299 with a history of myocardial infarction or stroke; 1 265 reporting cancer; 28 with BMI <18.5 kg/m2; and 631 not reaching 85% of their age-predicted maximal hart rate (220 minus age in years) on a maximal treadmill exercise test. In addition, 1 147 men with less than one year of mortality follow up were excluded to minimize potential bias due to serious underlying illness on mortality. The final sample comprised 19 756 men for analyses of all-cause and CVD mortality.
Clinical Examination
The clinical examination procedures are described in detail elsewhere.(24, 25) Briefly, weight, height, waist and hip circumferences were measured with standard clinical scale, stadiometer and plastic tape according to ACLS standard procedures. BMI was computed as weight in kilograms divided by height in meters squared (kg/m2), and BAI was calculated according to Bergman et al.’s equation ((hip circumference in cm/height in meters1.5)-18).(11) %BF was assessed using hydrostatic weighing, the sum of seven skinfold measures, or both methods, following standardized protocols.(26) Detailed description of our hydrodensitometry procedures have been published elsewhere.(27) Men were classified according to adiposity categories using standard clinical definitions for BMI (18.5 to 24.9 as normal weight; 25 to 29.9 as overweight; ≥30 as obese) and waist circumference (≥102 cm as central obese).(28) Since there is not a specific agreement about obesity cut-off points for BAI and %BF, specific tertiles from this population were used.
Resting blood pressure was measured in the seated position by trained technicians using auscultatory methods with mercury sphygmomanometer and was recorded as the first and fifth Korotkoff sounds after ≥5 minutes of sitting quietly. Two readings separated by 2 minutes were averaged. If the first 2 readings differed by >5 mmHg, additional readings were obtained and averaged. Hypertension was defined as systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg, or a history of physician diagnosis. Electrocardiogram (ECG) was measured at rest and with exercise, and abnormal ECG responses included rhythm and conduction disturbances and ischemic ST-T wave abnormalities. Serum samples were analyzed for glucose and total cholesterol using standardized automated bioassays at the Cooper Clinic Laboratory. Diabetes mellitus was defined as fasting plasma glucose concentration of ≥126 mg/dL, a history of physician diagnosis, or insulin use. Hypercholesterolemia was defined as total cholesterol of ≥240 mg/dL or a history of physician diagnosis.
Physical activity habits, cigarette smoking habit, alcohol intake, and parental history of CVD were assessed by self-report on a medical history questionnaire. Physically inactive was defined as reporting no leisure-time physical activity in the 3 months before the examination. Smoking status was classified as current smoker or non-current smoker. Heavy alcohol consumption was established as >14 units/week. One unit of alcohol intake was defined as a bottle or can of beer [355 mL (12 oz)], a glass of wine [148 mL (5 oz)], or 44 mL (1.5 oz) of hard liquor. Parental history of CVD was defined as the occurrence of heart attacks, coronary disease, angioplasty, or stroke before the age of 50 years in either father or mother. Cardiorespiratory fitness (CRF) was quantified as the total time of a symptom-limited maximal treadmill exercise test, using a modified Balke protocol.(29, 30) Total treadmill endurance time (in minutes) on this protocol correlates highly with measured maximal oxygen uptake in men (r=0.92).(31) To standardize exercise performance, we estimated maximal metabolic equivalents (METs; 1 MET = 3.5 mL O2 uptake per kilogram per minute) from the final treadmill speed and grade.(32)
Vital status
All participants were followed for mortality from the baseline examination to the date of death or to December, 31, 2003. Deaths were identified from the National Center for Health Statistics National Death Index and official death certificates from the departments of vital records of the various states. The underlying cause of death was determined by a nosologist according to the International Classification of Diseases, Ninth Edition, with CVD defined as codes 390 to 449.9 before 1999 and Tenth Edition, with CVD defined as codes I00 to I78 during 1999 to 2003. The National Death Index has been shown to be an accurate method of ascertaining deaths in observational studies, with high sensitivity (96%) and specificity (100%).(33)
Statistical analysis
Descriptive analyses summarized baseline characteristics of the participants based on vital status and BAI tertiles. Differences between groups were tested using analysis of the variance (ANOVA) for continuous variables and chi square tests for categorical variables. Partial correlations between body adiposity measures were calculated after controlling for age and examination year. We also examined the association of body adiposity measures with CVD risk factors using linear regression, controlling for age and examination year. Cox proportional hazards regression analysis (timescale: years) was used to estimate mortality rates (deaths per 10 000 man-years of follow-up), hazard ratios (HRs), and associated 95% confidence intervals (95% CIs) for all-cause and CVD mortality, according to adiposity exposure categories from BAI, BMI, waist circumference, and %BF. The lowest adiposity category was used as the reference category. To allow comparisons between adiposity measures, the results were also presented as standardized HRs by transforming each variable to have a mean of zero and a SD of 1. In multivariable analyses, model 1 accounted for age and examination year. Model 2 included physical activity, smoking habit, alcohol intake, abnormal ECG, hypercholesterolemia, diabetes, hypertension and parental history of CVD as additional confounders. Model 3 additionally adjusted for CRF. The proportional hazards assumption was examined by comparing the cumulative hazard plots grouped on exposure; no appreciable violations were noted. All the analyses were performed using PASW statistical package version 18.0 (SPSS Inc, Chicago, IL), considering P<0.05 as being statistically significant.
RESULTS
During an average follow-up of 8.3 years and 163 844 man-years of observation, 353 deaths occurred (101 due to CVD). The characteristics of the study population by vital status and BAI tertiles are shown in Table 1. Decedents presented significantly higher values at baseline for age, %BF, total cholesterol, fasting blood glucose, systolic blood pressure, as well as higher prevalence of abnormal ECG and hypertension. On the other hand, decedents had significantly lower values for CRF. All values and prevalence rates showed significant differences among BAI tertiles, except for the prevalence of heavy drinkers.
Table 1.
Baseline characteristics of participants by vital status and body adiposity index (BAI) tertiles.
| Vital status |
BAI (tertiles) |
||||||
|---|---|---|---|---|---|---|---|
| Characteristic | Survivors (n=19403) |
Decedents (n=353) |
P valuea |
Lower (n=6554) |
Middle (n=6590) |
Upper (n=6612) |
P valuea |
| Age (years) | 46.5 (9.5) | 57.4 (12.1) | <0.001 | 45.2 (9.7) | 47.2 (9.6) | 47.6 (9.5) | <0.001 |
| Body mass index (kg/m2) | 27.0 (3.9) | 26.8 (4.0) | 0.241 | 24.1 (2.0) | 26.4 (2.2) | 30.5 (4.0) | <0.001 |
| Waist circumference (cm) | 94.5 (10.8) | 95.6 (11.2) | 0.057 | 87.6 (7.3) | 93.3 (7.7) | 102.6 (11.0) | <0.001 |
| BAI (%) | 24.8 (3.5) | 25.1 (3.4) | 0.151 | 21.5 (1.4) | 24.4 (0.7) | 28.5 (3.0) | <0.001 |
| %BF | 22.1 (6.1) | 22.9 (5.9) | 0.025 | 18.0 (5.0) | 21.7 (4.6) | 26.6 (5.2) | <0.001 |
| Treadmill time (min) | 18.5 (4.8) | 15.9 (5.7) | <0.001 | 21.0 (4.5) | 18.7 (4.3) | 15.6 (4.2) | <0.001 |
| Maximal metabolic equivalents | 11.9 (2.4) | 10.7 (2.7) | <0.001 | 13.2 (2.4) | 12.0 (2.1) | 10.6 (2.0) | <0.001 |
| Total cholesterol (mg/dL) | 204.8 (38.9) | 212.2 (44.5) | <0.001 | 196.5 (37.2) | 206.2 (38.3) | 212.0 (40.1) | <0.001 |
| Fasting blood glucose (mg/dL) | 100.3 (17.4) | 104.9 (26.2) | <0.001 | 97.9 (15.0) | 99.7 (15.5) | 103.6 (21.0) | <0.001 |
| Blood pressure (mmHg) | |||||||
| Systolic | 122.6 (13.4) | 127.3 (15.9) | <0.001 | 120.1 (12.8) | 122.3 (13.0) | 125.5 (13.8) | <0.001 |
| Diastolic | 82.2 (9.5) | 82.7 (10.5) | 0.329 | 79.9 (9.0) | 82.1 (9.1) | 84.7 (9.6) | <0.001 |
| Physically inactive, No (%)b | 4554 (23.5) | 93 (26.3) | 0.207 | 1162 (17.7) | 1448 (22.0) | 2037 (30.8) | <0.001 |
| Current smokers, No (%) | 2672 (13.8) | 48 (13.6) | 0.925 | 818 (12.5) | 892 (13.5) | 1010 (15.3) | <0.001 |
| Heavy drinkers, No (%)c | 2075 (10.7) | 43 (12.2) | 0.371 | 685 (10.5) | 731 (11.1) | 702 (10.6) | 0.467 |
| Baseline medical conditions, No (%) | |||||||
| Abnormal ECGd | 1710 (8.8) | 104 (29.5) | <0.001 | 496 (7.6) | 621 (9.4) | 697 (10.5) | <0.001 |
| Hypercholesterolemiae | 5862 (30.2) | 111 (31.4) | 0.167 | 1502 (22.9) | 2047 (31.1) | 2424 (36.7) | <0.001 |
| Diabetes mellitusf | 1070 (5.5) | 22 (6.2) | 0.559 | 237 (3.6) | 296 (4.5) | 559 (8.5) | <0.001 |
| Hypertensiong | 6222 (32.1) | 159 (45.0) | <0.001 | 1473 (22.5) | 2038 (30.9) | 2870 (43.4) | <0.001 |
| Parental history of CVD, No (%) | 4393 (22.6) | 87 (24.6) | 0.373 | 1322 (20.2) | 1523 (23.1) | 1635 (24.7) | <0.001 |
Values are means (standard deviations) or numbers (percentage). %BF indicates percentage of body fat; CVD indicates cardiovascular disease.
Analysis of the variance (ANOVA) for continuous variables and chi-square tests for categorical variables.
Defined as reporting no physical activity during leisure time in the 3 months before the examination.
Defined as >14 units/week.
Abnormal resting or exercise electrocardiogram.
Defined as total cholesterol ≥240 mg/dL or previous physician diagnosed hypercholesterolemia.
Defined as fasting blood glucose ≥126 mg/dL, previous physician diagnosed diabetes or use of insulin.
Defined as resting blood pressure ≥140/90 mmHg or previous physician diagnosed hypertension.
Partial correlations examining the association among body adiposity measurements after controlling for age and examination year are shown in Table 2. Although all body adiposity measurements were positively correlated (P≤0.001), %BF was more strongly correlated with waist circumference (r=0.77) and BMI (r=0.72) than BAI (r=0.65).
Table 2.
Partial correlations examining the association between body adiposity measurements after controlling for age and examination year.
| BAI | BMI | Waist | %BF | |
|---|---|---|---|---|
| BAI | - | |||
| BMI | 0.818 | - | ||
| Waist | 0.680 | 0.889 | - | |
| %BF | 0.646 | 0.724 | 0.765 | - |
BAI indicates body adiposity index; BMI indicates body mass index; waist indicates waist circumference; %BF indicates percentage of total body fat
All correlations were statistically significant at P ≤ 0.001
According to Table 3, all body adiposity measures were significantly associated with CVD risk factors (all P<0.001). Except for systolic blood pressure, correlations between BAI and CVD risk factors were slightly weaker than for BMI, waist circumference and %BF. Among all the risk factors, CRF showed the strongest association with all adiposity measurements.
Table 3.
Standardized regression coefficients examining the association of body adiposity index (BAI), body mass index (BMI), waist circumference and percentage of body fat with cardiovascular disease risk factors.
| Body Adiposity Index (BAI) | Body Mass Index (BMI) | |||||||
|---|---|---|---|---|---|---|---|---|
|
|
||||||||
| Dependent variable | R2 | β | r | R2 change | R2 | β | r | R2 change |
| Total cholesterol | 0.044 | 0.157 | 0.155 | 0.024 | 0.046 | 0.163 | 0.144 | 0.026 |
| Fasting blood glucose | 0.043 | 0.155 | 0.166 | 0.024 | 0.063 | 0.212 | 0.208 | 0.044 |
| Systolic blood pressure | 0.076 | 0.167 | 0.192 | 0.027 | 0.101 | 0.232 | 0.244 | 0.052 |
| Diastolic blood pressure | 0.069 | 0.207 | 0.226 | 0.042 | 0.105 | 0.284 | 0.299 | 0.078 |
| Cardiorespiratory fitnessa | 0.342 | −0.459 | −0.498 | 0.206 | 0.382 | −0.503 | −0.523 | 0.246 |
|
|
||||||||
| Waist Circumference | Percentage of body fat | |||||||
|
|
||||||||
| Total cholesterol | 0.048 | 0.168 | 0.174 | 0.028 | 0.055 | 0.193 | 0.188 | 0.034 |
| Fasting blood glucose | 0.066 | 0.219 | 0.233 | 0.047 | 0.045 | 0.166 | 0.185 | 0.025 |
| Systolic blood pressure | 0.092 | 0.210 | 0.237 | 0.043 | 0.071 | 0.155 | 0.203 | 0.022 |
| Diastolic blood pressure | 0.102 | 0.277 | 0.292 | 0.175 | 0.080 | 0.239 | 0.264 | 0.053 |
| Cardiorespiratory fitnessa | 0.419 | −0.538 | −0.579 | 0.283 | 0.460 | −0.592 | −0.579 | 0.324 |
All analyses adjusted for age and baseline examination year. β indicates multiple regression coefficients; r indicates partial correlations; R2 and R2 change indicate coefficients of determination
All the associations were significant at P ≤ 0.001
Maximal metabolic equivalents, METs.
Table 4 shows death rates and HRs for all-cause mortality according to 3 different sets of confounders (model 1, model 2 and model 3). In model 1, all-cause mortality risk was higher for BMI-based obese men (HR = 1.63, 95% CI = 1.19 2.23), central obese men (1.55, 1.22-1.96) and those in the upper tertile of %BF (1.36, 1.04-1.31). All adiposity measures showed significantly higher hazard ratios per 1 SD increase. After additional adjustments (model 2 and model 3), the association between adiposity status and risk of all-cause mortality became not significant, except for the central obese participants in model 2 (1.31, 1.02-1.68).
Table 4.
Mortality rates and hazard ratios for all-cause mortality.
| Deaths (n) | Man-years | Mortality rate a |
Hazard ratio (95% CI) |
|||
|---|---|---|---|---|---|---|
| Model 1b | Model 2c | Model 3d | ||||
| BAI | ||||||
| Low | 104 (6554) | 57370 | 19.5 | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) |
| Middle | 125 (6590) | 55521 | 20.4 | 1.05 (0.81 – 1.36) | 0.99 (0.76 – 1.28) | 0.89 (0.68 – 1.16) |
| Upper | 124 (6612) | 50953 | 25.0 | 1.28 (0.98 – 1.66) | 1.12 (0.86 – 1.46) | 0.88 (0.66 – 1.17) |
| Per 1 SD increase | 1.15 (1.04 – 1.29) | 1.09 (0.97 – 1.22) | 0.97 (0.86 – 1.09) | |||
| BMI | ||||||
| 18.5 - 24.9 kg/m2 | 139 (6458) | 59921 | 19.1 | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) |
| 25.0 - 29.9 kg/m2 | 154 (9702) | 78143 | 20.2 | 1.06 (0.84 – 1.34) | 0.97 (0.76 – 1.22) | 0.82 (0.65 – 1.05) |
| ≥ 30.0 kg/m2 | 60 (3596) | 25780 | 31.2 | 1.63 (1.19 – 2.23) | 1.28 (0.93 – 1.78) | 0.90 (0.63 – 1.29) |
| Per 1 SD increase | 1.28 (1.14 – 1.42) | 1.17 (1.04 – 1.32) | 1.02 (0.90 – 1.17) | |||
| Waist circumference | ||||||
| < 102 cm | 257 (15352) | 129175 | 19.3 | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) |
| ≥ 102 cm | 96 (4404) | 34669 | 29.9 | 1.55 (1.22 – 1.96) | 1.31 (1.02 – 1.68) | 1.05 (0.81 – 1.36) |
| Per 1 SD increase | 1.20 (1.08 – 1.34) | 1.10 (0.98 – 1.24) | 0.94 (0.82 – 1.07) | |||
| %BF | ||||||
| Low | 103 (6439) | 59042 | 18.8 | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) |
| Middle | 121 (6462) | 52821 | 22.1 | 1.18 (0.91 – 1.53) | 1.10 (0.85 – 1.44) | 0.92 (0.70 – 1.21) |
| Upper | 129 (6502) | 49074 | 25.5 | 1.36 (1.04 – 1.76) | 1.18 (0.90 – 1.55) | 0.83 (0.62 – 1.12) |
| Per 1 SD increase | 1.18 (1.05 – 1.31) | 1.10 (0.98 – 1.23) | 0.92 (0.81 – 1.06) | |||
BAI indicates body adiposity index; BMI indicates body mass index; SD indicates standard deviation; %BF indicates percentage body fat.
Per 10 000 man-years, adjusted for age and examination year.
Adjusted for age and examination year.
Adjusted for model 1 plus physical activity (active or inactive), smoking (current smoker or not), alcohol intake (> 14 units/week or not), abnormal electrocardiogram, hypercholesterolemia, hypertension and diabetes (present or not for each), and parental history of CVD.
Adjusted for model 2 plus cardiorespiratory fitness (treadmill test duration in minutes).
Table 5 shows death rates and HRs for CVD mortality. In model 1, CVD mortality risk was higher for men in the highest category of all adiposity measurements (HRs ranged from 1.73 to 2.06). After additional adjustments (model 2 and model 3), the association between adiposity status and risk of CVD mortality became not significant.
Table 5.
Mortality rates and hazard ratios for cardiovascular disease (CVD) mortality.
| Deaths (n) | Man-years | Mortality rate a |
Hazard ratio (95% CI) |
|||
|---|---|---|---|---|---|---|
| Model 1b | Model 2c | Model 3d | ||||
| BAI | ||||||
| Low | 23 (6473) | 56716 | 4.5 | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) |
| Middle | 40 (6505) | 54812 | 6.5 | 1.44 (0.86 – 2.41) | 1.32 (0.79 – 2.22) | 1.17 (0.69 – 1.96) |
| Upper | 38 (6526) | 50262 | 7.9 | 1.73 (1.03 – 2.91) | 1.40 (0.82 – 2.38) | 1.02 (0.59 – 1.78) |
| Per 1 SD increase | 1.20 (0.99 – 1.47) | 1.08 (0.88 – 1.34) | 0.92 (0.73 – 1.15) | |||
| BMI | ||||||
| 18.5 - 24.9 kg/m2 | 34 (6353) | 59045 | 4.5 | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) |
| 25.0 - 29.9 kg/m2 | 50 (9598) | 77294 | 6.6 | 1.48 (0.95 – 2.30) | 1.28 (0.81 – 2.00) | 1.04 (0.65 – 1.64) |
| ≥ 30.0 kg/m2 | 17 (3553) | 25452 | 9.2 | 2.06 (1.13 – 3.77) | 1.42 (0.76 – 2.67) | 0.89 (0.46 – 1.75) |
| Per 1 SD increase | 1.49 (1.22 – 1.81) | 1.29 (1.05 – 1.59) | 1.08 (0.85 – 1.36) | |||
| Waist circumference | ||||||
| < 102 cm | 69 (15164) | 127605 | 5.2 | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) |
| ≥ 102 cm | 32 (4340) | 34185 | 10.1 | 1.94 (1.27 – 2.97) | 1.50 (0.96 – 2.33) | 1.10 (0.69 – 1.76) |
| Per 1 SD increase | 1.42 (1.16 – 1.73) | 1.25 (1.01 – 1.54) | 1.02 (0.80 – 1.29) | |||
| %BF | ||||||
| Low | 26 (6439) | 59051 | 4.9 | 1.00 (Referent) | 1.00 (Referent) | 1.00 (Referent) |
| Middle | 31 (6462) | 52869 | 5.7 | 1.18 (0.70 – 1.99) | 1.04 (0.61 – 1.76) | 0.82 (0.48 – 1.41) |
| Upper | 44 (6502) | 49047 | 8.5 | 1.75 (1.07 – 2.85) | 1.46 (0.88 – 2.42) | 0.91 (0.52 – 1.60) |
| Per 1 SD increase | 1.24 (1.002 – 1.52) | 1.12 (0.90 – 1.39) | 0.87 (0.68 – 1.12) | |||
BAI indicates body adiposity index; BMI indicates body mass index; SD indicates standard deviation; %BF indicates percentage body fat.
Per 10 000 man-years, adjusted for age and examination year.
Adjusted for age and examination year.
Adjusted for model 1 plus physical activity (active or inactive), smoking (current smoker or not), alcohol intake (> 14 units/week or not), abnormal electrocardiogram, hypercholesterolemia, hypertension and diabetes (present or not for each), and parental history of CVD.
Adjusted for model 2 plus cardiorespiratory fitness (treadmill test duration in minutes).
To further evaluate the possible bias due to subclinical disease at baseline, all the analyses were repeated excluding participants with resting or exercise abnormal ECG, and also excluding deaths that occurred during the first 3 years of follow-up. The results did not substantially change (data not shown).
DISCUSSION
The results of the present report suggest that, among men enrolled in the ACLS between 1977 and 2002, BAI is not a better predictor for all-cause and CVD mortality risk than other body adiposity measures such as BMI, waist circumference or %BF. Furthermore, associations between BAI and traditional CVD risk factors (e.g. cholesterol, glucose, blood pressure and CRF) are slightly weaker than for BMI, waist circumference or %BF.
The BAI has recently been proposed by Bergman et al.(11) to provide valid direct estimates of %BF. The mean intra-individual difference between BAI and %BF in our study was 3.5%, 2.7% and 1.9% in lower, middle and upper BAI tertiles, respectively. Moreover, %BF showed a relatively higher correlation with BMI (r=0.72) and waist circumference (r=0.77) than BAI (0.65). Similar results have been found in some BAI validation studies when correlation analyses were sex-stratified, indicating that calculation of BAI would result in less accurate estimates of %BF.(12, 17-19)
Previous studies have showed a strong association of CVD risk factors with traditional body adiposity measures,(4, 6, 34, 35) but the association with BAI seemed to be weaker.(13, 17, 18, 20-23) In fact, only one study performed in 13 women aged 33.6±11.5 years with familial partial lipodystrophy, found BAI to be more strongly correlated with leptin than BMI (r=0.57 and r=0.02, respectively).(15) Our results are consistent with previous findings reporting significant associations between all adiposity measures and CVD risk factors (e.g. cholesterol, glucose, blood pressure, and CRF), and showing slightly weaker associations with BAI. Therefore, BAI seems not to provide a meaningful alternative to traditional adiposity measurements as a CVD risk indicator.
The association of obesity with all-cause and CVD mortality is well established.(7, 8, 36, 37) In our best knowledge, no previous studies have analyzed the ability of BAI to predict all-cause and CVD mortality. According to our results, BAI is not a good predictor of mortality risk. Indeed, only men in the upper BAI tertile showed statistically higher CVD mortality risk after adjusting for age and examination year. However, men in the highest adiposity categories for BMI, waist circumference, and %BF presented statistically higher risks of all-cause and CVD mortality after adjusting for age and examination year. The lack of accuracy of BAI estimating %BF, and the fact that it does not reflect fat distribution, which is known to have a large influence on mortality risk in men (android obesity), could partially explain these differences between mortality and the analyzed body adiposity measurements.
Several studies provide evidence that CRF substantially modifies the association of adiposity measures with mortality,(38) but no previous study using BAI included CRF in their analysis. We analyzed 3 different sets of confounders, and in the fully adjusted model including CRF all the associations between obesity and mortality became not significant.
The results of the present analysis should be interpreted with caution due to several limitations. First, since our study only included Caucasian, well-educated men from middle-to-upper socioeconomic strata, the results may not be extended to other populations. However, the homogeneity of the sample enhances internal validity of our findings because it reduces the likelihood of confounding by these characteristics. Second, %BF was estimated from skinfold measures or hydrostatic weighing, each of which has methodological limitations.(39) Third, as we only compared baseline data for exposures, we do not know whether changes in any of these variables occurred during follow-up and how that might have influenced the results. However, we recently published a report on changes in weight, %BF, and CRF with all-cause and CVD mortality.(40) When each of these exposures were adjust for all other exposures, only changes in CRF were associated with mortality. Fourth, the small number of deaths during the follow-up period restricted the statistical power of the analyses. Finally, we could not take into account dietary factors due to lack of adequate dietary information. Despite of these limitations, the main strengths of this study included the large, well-characterized cohort of men, the prospective design of the study, the use of two different mortality outcomes, the extensive follow-up period, and the extensive baseline examination that reduced the possible bias of subclinical disease.
In conclusion, the findings of the present analysis add information about the newly proposed BAI, suggesting that BAI is not a better predictor of all-cause and CVD mortality risk in men than traditional adiposity measures. Also, our results show that the association of BAI with CVD risk factors is relatively weaker than for BMI, waist circumference, and %BF. Further epidemiological studies examining the utility of BAI in other populations and women are still needed for a better understanding of the validity of this new index.
What is already known about this subject.
Traditional body adiposity measures, such as BMI or waist circumference, are strong predictors of obesity-related morbidity and mortality.
Body adiposity index (BAI) has been recently proposed as a new method intended to estimate percentage of body fat without requiring a measure of body weight.
What this study adds.
This study analyze the potential use of BAI as a mortality risk predictor, showing that BAI was not a better predictor for all-cause and CVD mortality risk than traditional body adiposity measures in a sample of adult men enrolled in the ACLS.
Associations between BAI and traditional CVD risk factors were slightly weaker than for traditional body adiposity measures.
Acknowledgments
The authors thank the Cooper Clinic physicians and technicians for collecting the data, and staff at the Cooper Institute for data entry and data management.
This work was supported by “Conselleria de Educación de la Generalitat Valenciana” [BEST/2012/257]; Spanish Ministry of Education [EX-2010-1008]; National Institutes of Health [AG06945, HL62508, R21DK088195] and in part by an unrestricted research grant from The Coca-Cola Company. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding institutions.
SNB, EGA and DMU were responsible for the design of the study. DCL and XS were responsible for the data extraction, and EGA, DMU and VER for the statistical analysis. All authors checked the analysis and were involved in the drafting and critical revision of the manuscript. All authors approved the final manuscript. SNB is the guarantor.
Footnotes
Conflicts of interest statement
The authors declared no conflict of interest.
Contributor Information
Diego Moliner-Urdiales, Department of Education, University Jaume I, Castellón, Spain..
Enrique G Artero, Area of Physical Education and Sport, University of Almería, Almería, Spain..
Duck-chul Lee, Department of Kinesiology, Iowa State University, Ames, Iowa, USA..
Vanesa España-Romero, MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Hills Road, Cambridge, UK..
Xuemei Sui, Department of Exercise Science, University of South Carolina, Columbia, South Carolina, USA..
Steven N Blair, Departments of Exercise Science and Epidemiology/Biostatistics, University of South Carolina, Columbia, South Carolina, USA..
REFERENCES
- 1.WHO Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organization technical report series. 2000;894:i–xii. 1–253. [PubMed] [Google Scholar]
- 2.Finucane MM, Stevens GA, Cowan MJ, et al. National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9.1 million participants. Lancet. 2011;377:557–67. doi: 10.1016/S0140-6736(10)62037-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Haslam DW, James WP. Obesity. Lancet. 2005;366:1197–209. doi: 10.1016/S0140-6736(05)67483-1. [DOI] [PubMed] [Google Scholar]
- 4.Guh DP, Zhang W, Bansback N, Amarsi Z, Birmingham CL, Anis AH. The incidence of co-morbidities related to obesity and overweight: a systematic review and meta-analysis. BMC public health. 2009;9:88. doi: 10.1186/1471-2458-9-88. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Allison DB, Fontaine KR, Manson JE, Stevens J, VanItallie TB. Annual deaths attributable to obesity in the United States. JAMA : the journal of the American Medical Association. 1999;282:1530–8. doi: 10.1001/jama.282.16.1530. [DOI] [PubMed] [Google Scholar]
- 6.Huxley R, Mendis S, Zheleznyakov E, Reddy S, Chan J. Body mass index, waist circumference and waist:hip ratio as predictors of cardiovascular risk--a review of the literature. European journal of clinical nutrition. 2010;64:16–22. doi: 10.1038/ejcn.2009.68. [DOI] [PubMed] [Google Scholar]
- 7.Berrington de Gonzalez A, Hartge P, Cerhan JR, et al. Body-mass index and mortality among 1.46 million white adults. The New England journal of medicine. 2010;363:2211–9. doi: 10.1056/NEJMoa1000367. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Whitlock G, Lewington S, Sherliker P, et al. Body mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. Lancet. 2009;373:1083–96. doi: 10.1016/S0140-6736(09)60318-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Norgan NG. Laboratory and field measurements of body composition. Public health nutrition. 2005;8:1108–22. doi: 10.1079/phn2005799. [DOI] [PubMed] [Google Scholar]
- 10.Prentice AM, Jebb SA. Beyond body mass index. Obesity reviews : an official journal of the International Association for the Study of Obesity. 2001;2:141–7. doi: 10.1046/j.1467-789x.2001.00031.x. [DOI] [PubMed] [Google Scholar]
- 11.Bergman RN, Stefanovski D, Buchanan TA, et al. A better index of body adiposity. Obesity. 2011;19:1083–9. doi: 10.1038/oby.2011.38. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Freedman DS, Thornton J, Pi-Sunyer FX, et al. The body adiposity index (hip circumference / height(1.5)) is not a more accurate measure of adiposity than is BMI, waist circumference, or hip circumference. Obesity. 2012 doi: 10.1038/oby.2012.81. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Barreira TV, Staiano AE, Harrington DM, et al. Anthropometric correlates of total body fat, abdominal adiposity, and cardiovascular disease risk factors in a biracial sample of men and women. Mayo Clinic proceedings. Mayo Clinic. 2012;87:452–60. doi: 10.1016/j.mayocp.2011.12.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Johnson W, Chumlea WC, Czerwinski SA, Demerath EW. Concordance of the recently published body adiposity index with measured body fat percent in European-American adults. Obesity. 2012;20:900–3. doi: 10.1038/oby.2011.346. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Godoy-Matos AF, Moreira RO, Valerio CM, Mory PB, Moises RS. A new method for body fat evaluation, body adiposity index, is useful in women with familial partial lipodystrophy. Obesity. 2012;20:440–3. doi: 10.1038/oby.2011.343. [DOI] [PubMed] [Google Scholar]
- 16.Elisha B, Rabasa-Lhoret R, Messier V, Abdulnour J, Karelis AD. Relationship between the body adiposity index and cardiometabolic risk factors in obese postmenopausal women. European journal of nutrition. 2012 doi: 10.1007/s00394-011-0296-y. [DOI] [PubMed] [Google Scholar]
- 17.Hung CS, Yang CY, Hsieh HJ, Wei JN, Ma WY, Li HY. BMI Correlates Better to Visceral Fat and Insulin Sensitivity Than BAI. Obesity. 2012;20:1141. doi: 10.1038/oby.2012.86. [DOI] [PubMed] [Google Scholar]
- 18.Schulze MB, Thorand B, Fritsche A, et al. Body adiposity index, body fat content and incidence of type 2 diabetes. Diabetologia. 2012;55:1660–7. doi: 10.1007/s00125-012-2499-z. [DOI] [PubMed] [Google Scholar]
- 19.Lopez AA, Cespedes ML, Vicente T, et al. Body adiposity index utilization in a Spanish Mediterranean population: comparison with the body mass index. PloS one. 2012;7:e35281. doi: 10.1371/journal.pone.0035281. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Marques-Vidal P, Waeber G, Vollenweider P. Another (better) index of adiposity. Obesity. 2012;20:1137–8. doi: 10.1038/oby.2011.363. [DOI] [PubMed] [Google Scholar]
- 21.Martin BJ, Verma S, Charbonneau F, Title LM, Lonn EM, Anderson TJ. The Relationship Between Anthropometric Indexes of Adiposity and Vascular Function in the FATE Cohort. Obesity. 2012 doi: 10.1002/oby.20266. [DOI] [PubMed] [Google Scholar]
- 22.Snijder MB, Nicolaou M, van Valkengoed IG, Brewster LM, Stronks K. Newly Proposed Body Adiposity Index (BAI) by Bergman et al. Is Not Strongly Related to Cardiovascular Health Risk. Obesity. 2012;20:1138–9. doi: 10.1038/oby.2011.338. [DOI] [PubMed] [Google Scholar]
- 23.de Lima JG, Nobrega LH, de Souza AB. Body adiposity index indicates only total adiposity, not risk. Obesity. 2012;20:1140. doi: 10.1038/oby.2012.3. [DOI] [PubMed] [Google Scholar]
- 24.Blair SN, Kohl HW, 3rd, Paffenbarger RS, Jr., Clark DG, Cooper KH, Gibbons LW. Physical fitness and all-cause mortality. A prospective study of healthy men and women. JAMA. 1989;262:2395–401. doi: 10.1001/jama.262.17.2395. [DOI] [PubMed] [Google Scholar]
- 25.Sui X, LaMonte MJ, Laditka JN, et al. Cardiorespiratory fitness and adiposity as mortality predictors in older adults. JAMA : the journal of the American Medical Association. 2007;298:2507–16. doi: 10.1001/jama.298.21.2507. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Lee CD, Blair SN, Jackson AS. Cardiorespiratory fitness, body composition, and all-cause and cardiovascular disease mortality in men. The American journal of clinical nutrition. 1999;69:373–80. doi: 10.1093/ajcn/69.3.373. [DOI] [PubMed] [Google Scholar]
- 27.Jackson AS, Pollock ML. Practical Assessment of Body Composition. Physician Sport Medicine. 1985;13:76–90. doi: 10.1080/00913847.1985.11708790. [DOI] [PubMed] [Google Scholar]
- 28.Expert Panel on Detection E, Treatment of High Blood Cholesterol in Adults Executive summary of the third report of the national cholesterol education program (ncep) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel iii) JAMA: The Journal of the American Medical Association. 2001;285:2486–97. doi: 10.1001/jama.285.19.2486. [DOI] [PubMed] [Google Scholar]
- 29.Blair SN, Goodyear NN, Gibbons LW, Cooper KH. Physical fitness and incidence of hypertension in healthy normotensive men and women. JAMA : the journal of the American Medical Association. 1984;252:487–90. [PubMed] [Google Scholar]
- 30.Balke B, Ware RW. An experimental study of physical fitness of Air Force personnel. United States Armed Forces medical journal. 1959;10:675–88. [PubMed] [Google Scholar]
- 31.Pollock ML, Bohannon RL, Cooper KH, et al. A comparative analysis of four protocols for maximal treadmill stress testing. American heart journal. 1976;92:39–46. doi: 10.1016/s0002-8703(76)80401-2. [DOI] [PubMed] [Google Scholar]
- 32.Thompson WR, Gordon NF, Pescatello LS, American College of Sports Medicine . ACSM’s guidelines for exercise testing and prescription. 8th edn Wolters Kluwer Health/Lippincott Williams & Wilkins; Philadelphia: 2010. [Google Scholar]
- 33.Stampfer MJ, Willett WC, Speizer FE, et al. Test of the National Death Index. American journal of epidemiology. 1984;119:837–9. doi: 10.1093/oxfordjournals.aje.a113804. [DOI] [PubMed] [Google Scholar]
- 34.de Koning L, Merchant AT, Pogue J, Anand SS. Waist circumference and waist-to-hip ratio as predictors of cardiovascular events: meta-regression analysis of prospective studies. European heart journal. 2007;28:850–6. doi: 10.1093/eurheartj/ehm026. [DOI] [PubMed] [Google Scholar]
- 35.Gelber RP, Gaziano JM, Orav EJ, Manson JE, Buring JE, Kurth T. Measures of obesity and cardiovascular risk among men and women. Journal of the American College of Cardiology. 2008;52:605–15. doi: 10.1016/j.jacc.2008.03.066. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Flegal KM, Graubard BI, Williamson DF, Gail MH. Cause-specific excess deaths associated with underweight, overweight, and obesity. JAMA : the journal of the American Medical Association. 2007;298:2028–37. doi: 10.1001/jama.298.17.2028. [DOI] [PubMed] [Google Scholar]
- 37.Pischon T, Boeing H, Hoffmann K, et al. General and abdominal adiposity and risk of death in Europe. The New England journal of medicine. 2008;359:2105–20. doi: 10.1056/NEJMoa0801891. [DOI] [PubMed] [Google Scholar]
- 38.Fogelholm M. Physical activity, fitness and fatness: relations to mortality, morbidity and disease risk factors. A systematic review. Obesity reviews : an official journal of the International Association for the Study of Obesity. 2010;11:202–21. doi: 10.1111/j.1467-789X.2009.00653.x. [DOI] [PubMed] [Google Scholar]
- 39.American College of Sports Medicine . ACSM’s guidelines for exercise testing and prescription. 7th edn Lippincott Williams & Wilkins; Philadelphia, Pa.: 2006. [Google Scholar]
- 40.Lee DC, Sui X, Artero EG, et al. Long-term effects of changes in cardiorespiratory fitness and body mass index on all-cause and cardiovascular disease mortality in men: the Aerobics Center Longitudinal Study. Circulation. 2011;124:2483–90. doi: 10.1161/CIRCULATIONAHA.111.038422. [DOI] [PMC free article] [PubMed] [Google Scholar]