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The purpose of this study was to examine sex and race differences in the relationship between anthropometric measurements and adiposity in white and African-American AA adults. General linear models were used to compare relationships between WC or BMI, and adiposity across sex and race, within age groups 18—39 and 40—64 years.

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The sample included 1, adults men: white; AA; women: white, AA. Sex differences, and in some instances race differences, in the relationships between anthropometry and fat-specific depots demonstrate that these characteristics need to be considered when predicting adiposity from WC or BMI. Obesity is defined as an excess of body fat, which can be measured in laboratory settings using methods such as dual-energy X-ray absorptiometry DXAmagnetic resonance imaging, or computed tomography CT. DXA methods can provide measures of general adiposity such as total body fat fat mass FMwhereas magnetic resonance imaging or CT can estimate regional adiposity such as visceral adipose tissue VAT and subcutaneous adipose tissue SAT.

Indirect indicators of body composition, such as BMI and waist circumference WCare commonly used as surrogate measures of adiposity in population studies. These anthropometric measures have been instrumental in monitoring the obesity epidemic 1as well as linking obesity status with an increased risk for cardiovascular disease 23type 2 diabetes 4and mortality 56.

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The utility of BMI and WC in describing obesity status depends on the assumption that anthropometric measures are correlated with more direct measures of adiposity such as FM, SAT, or VAT, or with markers of ectopic fat deposition in skeletal muscle, liver, and other organs. However, several studies have shown that for the same BMI, the amount of body fat, regardless of fat depot, is ificantly influenced by sex 7 — 10 and race 11 — 13although race differences have been somewhat inconsistent 14 In addition, the relationship between WC and depot-specific adiposity also shows differences according to sex 16 — 18 and race 1119 These suggest that simple anthropometric markers may not identify the same level of adiposity across different demographic groups.

However, race and sex differences in the relationship between BMI or WC and adiposity have not necessarily been examined together in a large dataset, across varying levels of obesity, or with direct measures of body fat.

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We recently demonstrated ificant differences between white and African-American AA men and women in measures of overall and depot-specific adiposity The Pennington Center Longitudinal Study PCLS is an ongoing investigation of the effects of obesity and lifestyle factors on the development of chronic diseases such as type 2 diabetes, cardiovascular disease, and cancer.

The sample is composed of volunteers who have participated in nutrition, weight loss and other metabolic intervention and observational studies at the Pennington Biomedical Research Center in Baton Rouge, Louisiana since The current cross-sectional study is limited to adult participants who had DXA and abdominal CT scans between and Unfortunately, there is no information available on specific medication use. Each volunteer provided their written informed consent and all PCLS procedures, including this analysis were approved by the Pennington Biomedical Research Center institutional review board.

Standardized anthropometric measures were obtained on all participants. Height was measured using standard methods with a stadiometer. A second nurse then lowered the slide until it reached the vertex of the skull and recorded the reading from the indicator. Weight was measured in duplicate using a digital scale after all outer clothing, heavy pocket items, and shoes were removed.

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Weight was recorded to the nearest 0. WC was measured, in duplicate, at the midpoint between the inferior border of the ribcage and the superior aspect of the iliac crest using an inelastic measuring tape. FM kg was measured by a whole body DXA scanner. Each DXA used a phantom prior to data collection for calibration and to document stability of measures over time.

Manufacturer calibrations were performed twice a year as recommended. Participants lay in a supine position with arms overhead to obtain a cross-sectional image at the L4—L5 intervertebral space.

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CT scanners were calibrated daily to air. Participant age was computed from birth and observation dates. As an initial analytical step, Pearson correlation coefficients were calculated to assess pair-wise associations between anthropometric and adipose tissue measurements. We also considered the impact of smoking status and menopause status on these relationships.

Covariates in each model for the overall sample included age group 18—39 years; 40—64 yearssmoking, menopause as appropriateanthropometric measure BMI or WCand terms were also included for the interactions between anthropometric measure by age group, anthropometric measure by sex by race, and age group by race by sex. Smoking was not ificant in the majority of the models, and was removed as a covariate in the final models.

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Due to ificant age group interactions in the overall models, we further stratified the analyses by age group 18—39 years and 40—64 years. Descriptive characteristics of the sample are presented in Table 1. In the general linear models for the total sample, ificant interactions were observed between anthropometric measures and age group age group by anthropometric measure was ificant in five of six models; age group by race by sex was ificant in two of the six models.

Due to these ificant age interactions, we stratified the analyses by age group, and we summarized the main and interaction effects for race and sex in the figures. Within the figures, we present statistical ificance P values corresponding to the overall main effects for sex and race as well as ificant interaction effects.

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These sex effects were consistent across the younger and older age groups. ificant interactions were observed in the model for BMI in the younger age group. In the younger age group Figure 2athe difference between AA women and white women becomes less pronounced at higher levels of WC, but the opposite holds true for men. In older adults, the sex difference appears to become attenuated at higher levels of WC Figure 2b.

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For VAT there were ificant main effects for sex in the younger age group, but not in the older age group Figure 3. However, as is evident from ificant WC-by-race interactions, the effect of race becomes more pronounced at higher levels of WC, such that white men and women have higher levels of VAT than AA men and women Figure 3a,b. There was a ificant main effect for sex in the BMI model in the younger age group, and the interaction effects indicate that race and sex differences increased at higher levels of BMI Figure 3c but there were no ificant effects in the older age group Figure 3d.

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In addition, there were ificant sex differences for all fat depots for a given level of BMI or WC. Furthermore, regardless of WC, there is about 3. For the same BMI, other studies were consistent with our findings for sex differences: men have lower FM 7814 and lower SAT 7910when compared to women. research has shown either similar levels of VAT in men and women 18or found racial differences at the same BMI: white men and women have higher VAT than AA men and women 81112202728which may demonstrate a race and sex interaction.

The interaction in the current analysis between anthropometric measure and race was ificant for VAT, and this may explain the variation of sex effects across the range of WC and BMI. A possible explanation for our discrepant findings between the two age groups for VAT may be due to the ificantly older age of women in this sample when compared to men. Aging is associated with increases in VAT 1629 — 31 and WC shows greater increases with age in women compared to men of the same race and similar age These suggest that sex effects of VAT diminish with aging.

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Our found no ificant main effects of race in either age group; however, a of interactions involving race were observed indicating that race differences do exist at certain levels or WC or BMI. However, the PCLS is not a population-based sample, rather the subjects are volunteers who have participated in clinical research studies. These characteristics have known influences on adipose tissue accumulation; however, we were able to control for other behavioral and physiological modifiers such as age, sex, race, smoking status, and menopause.

Finally, this is a cross-sectional study, and the age analyses do not reflect actual changes over time. In some cases these differences were simple main effects, and in other cases they involved complex interactions.

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The importance of these findings implies that specific WC and BMI cut points may not reflect the same level of FM or abdominal obesity between white and AA men and women. Further, ificant interactions indicated that the level of depot-specific adiposity differed across levels of the anthropometric indicator. In most cases, the race or sex differences increased at higher levels of adiposity. Future research needs to identify sensitive and specific BMI and WC thresholds which better delineate obesity-related cardiometabolic risk among different sex and age groups.

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We also acknowledge Julia St Amant for her expert supervision of the CT acquisition and for the analysis of the majority of the CT scans reported herein. Barton, Sr. Endowed Chair in Genetics and Nutrition, and E. Gordon Chair in Diabetes and Metabolism.

Pencarian informasi

National Center for Biotechnology InformationU. Obesity Silver Spring. Author manuscript; available in PMC Mar Sarah M. Camhi1 George A. Bray1 Claude Bouchard1 Frank L. Greenway1 William D. Johnson1 Robert L. Newton1 Eric Ravussin1 Donna H. Ryan1 Steven R.

Smith1 and Peter T. Katzmarzyk 1.

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George A. Frank L. William D. Robert L. Donna H. Steven R. Peter T. Author information Copyright and information Disclaimer. Correspondence: Peter T. Katzmarzyk ude. Copyright notice.

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