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Obesity (Silver Spring). Author manuscript; available in PMC 2017 July 01. Published in final edited form as: Obesity (Silver Spring). 2016 July ; 24(7): 1561–1571. doi:10.1002/oby.21495.

Relationship between body fat and BMI in a US Hispanic population-based cohort study: Results from HCHS/SOL William W. Wong1, Garrett Strizich2, Moonseong Heo2, Steven B. Heymsfield3, John H. Himes4, Cheryl L. Rock5, Marc D. Gellman6, Anna Maria Siega-Riz7, Daniela SotresAlvarez8, Sonia M. Davis8, Elva M. Arredondo9, Linda Van Horn10, Judith Wylie-Rosett2, Lisa Sanchez-Johnsen11, Robert Kaplan2, and Yasmin Mossavar-Rahmani2

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1USDA/ARS

Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX 2Department

of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx,

NY 3Pennington 4University

Biomedical Research Center, Baton Rouge, LA

of Minnesota School of Public Health, Minneapolis, MN

5Department

of Family and Preventive Medicine and Public Health, School of Medicine, UCSD, La

Jolla, CA 6Behavioral

Medicine Research Center, Department of Psychology, University of Miami, Coral

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Gables, FL 7Departments

of Epidemiology and Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC

8Collaborative

Studies Coordinating Center, Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC

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Contact information: William W. Wong, Ph.D., USDA/ARS Children’s Nutrition Research Center, 1100 Bates Street, Houston, TX 77030, Tel: 713-798-7168, Fax: 713-798-7194, [email protected] Author contributions: All authors were involved in the research design of the SOLNAS project. WWW was the principal investigator of the Central DLW Laboratory at the USDA/ARS Children’s Nutrition Research Center, Baylor College of Medicine, Houston, TX that supported the doubly labeled water protocol of the SOLNAS project and performed all the mass spectrometric measurements. WWW wrote the initial draft and had primary responsibility for final content. GS and MH at Albert Einstein College of Medicine extracted the appropriate data from the HCHS/SOL and SOLNAS databases, performed the statistical analysis, and assisted in the initial draft. SBH at Pennington Biomedical Research Center in Baton Rouge, LA is an expert in body composition and was an affiliated investigator at the Bronx site. JHM was the principal investigator of the SOLNAS Nutrition Reading Center at the University of Minnesota. CLR was responsible for developing and supervising the study activities at the San Diego site and for contributing to the analysis and interpretation of the results and for contributing to the manuscript. MDG was the Miami Field Center principal investigator of the SOLNAS project at the University of Miami. AMSR and DSA were responsible for the set up and the management of the SOLNAS database at the University of North Carolina, NC. SMD is a statistician at the University of North Carolina, NC. EMA is a Co-Investigator for HCHS/SOL at San Diego State University at the San Diego site. LVH was the principal investigator of the SOLNAS project at Northwestern University field center in Chicago. JWR was a SOLNAS Co-Investigator at the Bronx site. LSJ was a Co-Investigator at the Chicago field center at the University of Illinois, Chicago. RK is the Principal Investigator of the HCHS/SOL study at the Bronx site. YMR was the principal investigator of the SOLNAS project at Albert Einstein College of Medicine and contributed to data interpretation and drafting of the article. All authors contributed to the critical revisions of the article and read and approved the final version of the manuscript for submission. Disclosure: The authors declare no conflict of interest. The contents of this publication do not necessarily reflect the views or policies of the USDA or the NIH, nor does mention of trade names, commercial products, or organizations imply endorsement. Clinical trial registration number: NCT0206034

Wong et al. 9San

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Diego State University, San Diego, CA

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10Feinberg

School of Medicine, Northwestern University, Chicago, IL

11University

of Illinois at Chicago, Chicago, IL (LSJ)

Abstract Objective—To evaluate the percentage of body fat (%BF)-BMI relationship, identify %BF levels corresponding to adult BMI cut-points, and examine %BF-BMI agreement in a diverse Hispanic/Latino population.

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Methods—%BF by bioelectrical impedance analysis (BIA) was corrected against %BF by 18O dilution in 476 participants of the ancillary Hispanic Community Health/Latinos Studies. Corrected %BF were regressed against 1/BMI in the parent study (n=15,261), fitting models for each age group, by sex and Hispanic/Latino background; predicted %BF was then computed for each BMI cut-point. Results—BIA underestimated %BF by 8.7 ± 0.3% in women and 4.6 ± 0.3% in men (P < 0.0001). The %BF-BMI relationshp was non-linear and linear for 1/BMI. Sex- and age-specific regression parameters between %BF and 1/BMI were consistent across Hispanic/Latino backgrounds (P > 0.05). The precision of the %BF-1/BMI association weakened with increasing age in men but not women. The proportion of participants classified as non-obese by BMI but obese by %BF was generally higher among women and older adults (16.4% in women vs. 12.0% in men aged 50-74 y).

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Conclusions—%BF was linearly related to 1/BMI with consistent relationship across Hispanic/Lation backgrounds. BMI cut-points consistently underestimated the proportion of Hispanics/Latinos with excess adiposity. Keywords Hispanics; body composition; BMI; bioelectrical impedance; isotope

Introduction

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Excess adiposity is an important risk factor for mortality and morbidity from cardiovascular diseases, diabetes mellitus, several cancers, and musculoskeletal disorders. Based on BMI cutoffs, all-cause mortality among 1.46 million white adults has been shown to increase with obesity (1). Several other studies also have demonstrated the significant relationship between obesity and prevalence of type 2 diabetes, gallbladder disease, high blood pressure, high cholesterol levels, asthma and/or arthritis (2, 3). While BMI is the most commonly used clinical measure of body habitus, substantial data show that BMI is not an ideal measure of body fat. A systematic review of 25 studies showed that commonly-used BMI cutoff values had high specificity but low sensitivity because they failed to identify half of the people with excess percentage of body fat (4). Therefore, the relationship between adiposity and risks of metabolic disorders and mortality based on BMI alone might be understated.

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In Third National Health and Nutrition Examination Survey (NHANES 1999-2004) (5), BMI had a non-linear relationship with percentage of body fat (%BF) measured by dualenergy X-ray absorptiometry (DXA) and that the discordance in cutoffs for overweight or obesity between %BF and BMI was substantial depending on sex, age, and race-ethnicity (6). The NHANES data also showed that 1/BMI was linearly related to %BF thus allowing generation of regression equations to predict %BF cutoffs based on BMI classifications. However, there is a dearth of information on the relationship between %BF and BMI among US Hispanics/Latinos and the majority of the relevant national surveys have focused only on Mexican-Americans. The Hispanic Community Health Study/Study of Latinos (HCHS/ SOL) measured %BF of the study participants (n = 16,415) using bioelectrical impedance analysis (BIA). These study participants were self-identified with Central American, Cuban, Dominican, Mexican, Puerto Rican, and South American backgrounds (7). A major goal of our analysis was to examine whether the association of %BF with BMI was consistent across these population subgroups which vary not only in genetic makeup but also in diet, physical activity, and degree of adiposity. The US Hispanic/Latino individuals also represent a large and growing segment of the US population, have higher rates of obesity and diabetes than US non-Hispanic Whites, and health risks vary across national background groups (http://www.cdc.gov/vitalsigns/hispanic-health/). Therefore, it is important to understand factors associated with disease risk in this culturally and biologically distinct segment of the US population. In addition, Study of Latinos: Nutrition & Physical Activity Assessment Study (SOLNAS), an ancillary study to HCHS/SOL, measured %BF (n = 471) using 18O dilution, a reference method for body composition measurement (8, 9). The %BF measured by 18O dilution in the ancillary study allowed us to correct the %BF measured by BIA (%BFTanita) in the parent study as BIA is known to be inaccurate and tends to underestimate body fat (8, 9, 10, 11, 12, 13).

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Our aims are: (1) Describe the relationship between %BF and 1/BMI by sex, age and Hispanics/Latino background in HCHS/SOL; (2) Develop prediction equations for BIAbased %BF cutoffs using the reference 18O dilution method; and (3) Compare the %BF cutoffs with those obtained in the general US adult population, particularly Mexican Americans.

Methods Study design

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The HCHS/SOL is a population-based cohort study of Hispanic/Latino adults in four US urban communities (Bronx, NY; Chicago, IL; Miami, FL; San Diego, CA). Details of the study design and sampling methods of HCHC/SOL have been reported previously (14, 15). A total of 16,415 subjects were enrolled in the HCHS/SOL study between 2008 and 2011 and included individuals of Mexican, Dominican, Central American, Cuban, Puerto Rican, and South American backgrounds. Subjects with missing Hispanic/Latino background or more than one background (n = 590), abnormal or missing BIA measurements (abnormal, n = 203; missing, n = 351), and missing height (n = 10) were excluded, thus yielding 15,261 participants for analysis.

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SOLNAS, an ancillary study of HCHS/SOL, recruited 476 participants within seven months of enrollment in the parent study to receive an assessment of body composition with the 18O dilution method (16, 17). The SOLNAS sample reflected the distribution of sex, age and BMI groups in the parent HCHS/SOL study. Participants with unstable weight (> 2 SD change from parent study baseline to SOLNAS study visit, n = 20) were excluded to avoid possible changes in body composition. For this study we assumed stable body composition between visits since we did not collect BIA measurement at the SOLNAS study visit. Other exclusions include incomplete DLW protocol (n=5), missing BIA measurement (n = 13), abnormal BIA measurement (n = 2), and abnormal %BF in repeatability study and/or inconsistent BMI (n = 2) thus yielding 434 participants for analysis.

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A participant flow chart is shown in Figure 1. The study protocol was approved by the Institutional Review Boards for Human Subject Research at the Coordination Center and at all the field centers. Anthropometry All anthropometric measurements were made according to the procedures and quality control guidelines established in the parent study. All field technicians performing the anthropometric measurements were centrally trained and certified. Standing height was measured with a wall-mounted stadiometer. Body weight was measured with a Tanita Body Composition Analyzer (Model TBF-300A, Tanita Corporation of America, Inc, Arlington Heights, IL 60005, USA). BMI was calculated as weight in kilograms divided by squared of height in meters.

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Body composition by bioelectrical impedance analysis The %BF of the participant was measured to the nearest 0.1% with the Tanita Body Composition Analyzer (%BFTanita) using the same procedure as described under body weight measurement. A minimum of 6 body composition measurements made under observation of an expert trainer per month was required for the technician to maintain certification to perform the BIA procedure. The participants placed their heels on the posterior electrodes and the front part of their feet in contact with the anterior electrodes on the weighing platform. The participants remained motionless until stable weight and %BFTanita readings were displayed on the screen of the analyzer. Body composition by 18O dilution method

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A pre-dose spot urine sample was collected from each participant. Each participant then ingested 1.38 g of 10 atom percent 18O labeled water (Sigma-Aldrich Corp, St Louis, MO, USA) per kilogram body weight. Two more spot urine samples were collected from each participant at 3 and 4 hours post-dose and again on day 12 (18). Participants aged ≥ 60 years provided a blood sample 3 hours and 12 days post-isotope to allow adjustment for agerelated post void urine retention (19). The 18O content of the urine and plasma samples was measured by gas-isotope-ratio mass spectrometry (20). The 18O dilution space (NO) was calculated from the zero-time intercept of the 18O turnover rate using the back extrapolation method. The NO was converted to total body water (TBW) after correcting for the 1%

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overestimation of TBW due to isotope exchange with non-aqueous exchangeable oxygen in the body (21). TBW was then converted to lean body mass (LBM) using a LBM hydration of 73% (8, 16, 22, 23). Body fat was the difference between body weight and LBM. Statistical Methods Age-adjusted descriptive statistics for continuous and categorical variables across Hispanic/ Latino background groups were estimated using survey linear regression adjusting each group to the age distribution of the target population. The %BF based on the 18O dilution method in the SOLNAS ancillary study (n = 434), stratified by sex, was used to generate the correction equations for %BFTanita using a linear regression model: %BF by 18O dilution = β0 + β1 × %BFTanita + ε. The estimated regression coefficients were then applied to the parent study sample to compute the corrected values of %BF.

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The relationship between corrected %BF and BMI was thoroughly explored through scatterplot smoothing and density estimation for probability weighted data. Linear regression models for corrected %BF regressed on 1/BMI were fitted separately stratifying by sex, age-group (18-29, 30-49, 50-76 years) and Hispanic/Latino background to allow the variances of the random terms to be different. Then, the predicted %BF corresponding with commonly-used BMI cutoffs (18.5, 25, 30, 35, 40 kg·m-2) were computed based on these sex-, age-, and background group-specific linear regression models. To test whether the effect of 1/BMI on corrected %BF was the same by Hispanic/Latino background (within each sex and age-group strata), we included the interaction of 1/BMI and Hispanic/Latino in pooled models. Lastly, we computed the percentage of men and women from each agegroup and Hispanic/Latino background group who were below a BMI cutpoint yet the corrected %BF was larger than the model-predicted %BF corresponding to that BMI. Differences in frequency of discordance were evaluated using Wald chi-squared tests.

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All analyses were carried out using sampling weights which account for non-response relative to the sampling frame, and were calibrated by Hispanic/Latino background to the characteristics of each field center’s target population using the 2010 U.S. Census. Weighted analyses were conducted using SUDAAN software release 11.0.1 (RTI International, Research Triangle Park, NC), and accounted for cluster sampling and stratification in the sample selection. Exploratory weighted analyses were conducted using R version 3.11 package “survey”. Un-weighted analyses, including correction of %BF, were conducted using SAS version 9.3 (SAS Institute, Cary, NC).

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Socio-demographic characteristics, %BFTanita, and the Hispanic/Latino background of the SOLNAS participants mirrored that of the parent study (Table 1). Most participants were overweight or obese. The relationship between %BF assessed by the 18O dilution method and the %BFTanita by sex is shown in Figure 2. The %BF correction equations by sex are:

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Intercepts and slopes of correction equations for both men and women were statistically significantly different from zero (P < 0.0001). In general, the BIA method underestimated %BF when compared with the %BF measured by the 18O dilution method, particularly at lower body fat. For example, a 30% body fat measured by BIA represented an underestimation of %BF by 18O dilution of 8.7 ± 0.3 percentage points in women (P < 0.0001) and 4.6 ± 0.3% in men (P < 0.0001).

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Table 2 summarizes age-adjusted anthropometric characteristics of the Hispanic/Latino adults from HCHS/SOL by sex and Hispanic/Latino background. Women were generally overweight or obese with corrected %BF accounting for over 40% of their body weight. Women of South American background had the lowest BMI and %BF when compared with the other groups. The relationship between %BF and BMI by sex is shown in Figure 3 (panels A and B), and as anticipated was not linear. However, the inverse of BMI (1/BMI) linearized the relationship (Figure 3, panels C and D). Similar scatter plots were obtained when the results were stratified by Hispanic/Latino background (data not shown).

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The regression coefficients of the equations to predict %BF from the inverse of BMI stratified by sex, age and Hispanic/Latino background are presented in Table 3. The intercepts of the prediction equations were similar across Hispanic/Latino backgrounds among women (0.65 to 0.69) and men (0.57 to 0.65) of all ages. In women, slopes ranged from 7.07 among the 18-29 year age group to 5.97 among the 50-76 year age group, with little difference in R2 values by age (range: 0.80 to 0.83) and generally consistent results by Hispanic/Latino background. Among men, the intercept of the prediction equations also varied little across Hispanic/Latino background groups. Similar to women, in men the slopes decreased over older age groups (8.52 among the 18-29 year age group to 7.38 among the 50-76 year age group), but unlike in women, the R2 values also decreased with age (0.848 in those 18-29 years old, 0.707 among those 30-49 years old, and 0.576 in those 50-76 years old). The slopes of 1/BMI on corrected %BF were not significantly different (P > 0.05) by Hispanic/Latino background for any sex-age-strata. Using these equations, Table 4 presents the age- and sex-specific predicted %BF values corresponding to standard BMI cut-points for US adults. As anticipated, these newly-derived %BF cut-points were higher among female as opposed to male individuals at each BMI (18.5, 25, 30, 35 and 40), increased over higher age groups, and were similar across Hispanic/Latino backgrounds within sex-age strata. In general, the degree to which the BMI standard cut-points for overweight and obesity underestimated these classifications based on the %BF predicted values and increased with age (Table 5, P for any difference between age groups in men and women < 0.0001). Among individuals below the BMI cutoff for overweight (

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To evaluate the percentage of body fat (%BF)-BMI relationship, identify %BF levels corresponding to adult BMI cut points, and examine %BF-BMI agreemen...
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