Heritability of Body Composition Measured by DXA in the Diabetes Heart Study
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Department of Public Health Sciences, Wake Forest University School of Medicine, Medical Center Blvd., Winston-Salem, NC 27157. E-mail: email@example.com
Objective: The purpose of this study was to investigate the heritability of body composition measured by DXA in the Diabetes Heart Study (DHS).
Research Methods and Procedures: Participants were 292 women and 262 men (age, 38 to 86 years; BMI, 17 to 57 kg/m2) from 244 families. There were 492 white and 49 African-American sibling pairs. DXA measurements of percentage fat mass (FM), whole body FM, and lean mass (LM), as well as regional measurements of trunk fat mass (TFM) and appendicular lean mass (ALM), were obtained. Heritability of FM, LM, and BMI were estimated using Sequential Oligogenic Linkage Analysis Routines.
Results: After adjusting for age, gender, ethnicity, and height, the heritability estimates of various compositional attributes were %FM = 0.64, whole body FM = 0.71, TFM = 0.63, whole body LM = 0.60, ALM = 0.66, and BMI = 0.64 (all p < 0.0001). Additional adjustment for diabetes status, smoking, dietary intake, and physical activity resulted in only minor changes in the heritability estimates (ĥ2 = 0.63 to 0.72, all p < 0.0001). Furthermore, heritability of TFM after additional adjustment for whole body FM was significant (ĥ2 = 0.55, p < 0.0001), and heritability of ALM after additional adjustment for whole body LM was also significant (ĥ2 = 0.51, p < 0.0001).
Discussion: These data suggest that FM and LM measured by DXA are highly heritable and can be effectively used in designing linkage studies to locate genes governing body composition. In addition, regional distribution of FM and LM may be genetically determined.
Several studies of related individuals indicate that there is a genetic component fundamental to overall body size, as assessed by body weight and by BMI (1,2). However, adverse health effects of excess fat tissue (adiposity) and/or deficiency of muscle tissue (sarcopenia) have led to a growing interest in determining specific environmental and genetic factors determining variation in amounts of these tissues. In this regard, accurate quantification of the genetic component of body composition is necessary for understanding the independent and/or shared genes contributing to individual variation in relative and absolute amounts of fat mass (FM) and lean mass (LM)1.
While heritability estimates for BMI range from 0.40 to 0.70, few data are available regarding the heritability of specific body compartments. In particular, family studies that used DXA, the current “gold-standard” for assessment of quantities of FM and LM (3), are lacking. DXA provides measurements of both whole body and regional quantities of bone mineral content, FM, and LM using a three-compartment model. Better understanding of the heritability of individual compartments is essential for linkage and association approaches to be used successfully to locate genes affecting adiposity and sarcopenia.
The few studies that report estimates of the heritability of FM indicate only a small genetic component, on the order of 25% (4,5,6). However, the heritability of %FM, ranging from 0.54 to 0.76, was shown to be significant in Pima Indians (7), North American whites (8), and a mixed ethnicity sample of young girls and their parents (6). Only one of these studies (6) used DXA to measure FM. Twin and family studies showed a stronger genetic component for fat-free mass, ranging from 0.56 to 0.80 in various populations (9,10,11). Although many important determinants of body composition including age, sex, ethnicity, height, weight, menopausal and disease [especially type 2 diabetes (DM2)] status, and smoking are well established, only a few of these studies have adjusted the heritability estimates for these covariates. Adjustment for medications and lifestyle factors such as diet and exercise habits has been even less consistent.
The aims of this study were to use DXA measurements in a family study of DM2 to determine the heritability of %FM, whole body FM, and LM, as well as regional measurements of trunk fat mass (TFM) and appendicular lean mass (ALM), and to investigate how the heritability was modified by covariates.
Research Methods and Procedures
Participants included individuals enrolled in the Diabetes Heart Study (DHS). DHS is a family study of siblings concordant for DM2, as well as unaffected family members, designed to locate and identify genes contributing to measures of subclinical cardiovascular disease. Only families with at least two siblings with diabetes were recruited for the study. All DM2-affected participants had diabetes diagnosed after the age of 35 years, in the absence of history of ketoacidosis, and of at least a 3-year duration. Subjects with renal insufficiency (serum creatinine ≥ 1.5 mg/dL or blood urea nitrogen ≥ 35 mg/dL) were excluded. Unaffected siblings, similar in age to siblings with DM2, were also recruited. Subjects were recruited from internal medicine clinics and through community advertising. The study was approved by the Institutional Review Board of the Wake Forest University School of Medicine. All participants gave informed consent.
Data for this study included all DHS participants with complete food frequency and physical activity data. There were a total of 554 participants (292 women and 262 men) from 244 families. Pedigree size ranged from 1 to 10. There were 492 white and 49 African-American sibling pairs, with 267 sibling pairs affected, 59 not affected, and 215 discordant for DM2.
Participant examinations, conducted in the General Clinical Research Center of Wake Forest University, included interviews for medical history and health behaviors, anthropometric measures, and fasting blood draws. Body weight was recorded in lightly clothed, shoeless participants to the nearest 0.1 kg; height was measured to the nearest 0.5 cm using a stadiometer. Laboratory assays included fasting glucose and hemoglobin A1C. Dietary intake was assessed using Block food frequency questionnaire (12), and physical activity was measured using the Paffenbarger physical activity questionnaire administered by trained interviewers (13).
DXA measurements of %FM, whole body FM, and LM, as well as TFM and ALM, were obtained using a fan-beam scanner (Delphi A; Hologic, Waltham, MA). Whole body DXA scans were obtained using manufacturer's recommendations for subject positioning, scan protocols, and scan analysis. All scan printouts were reviewed by an expert reader to ensure proper positioning and analysis. Artifacts were noted and, when possible, excluded from analysis. FM and LM were determined for the entire body and its subregions. ALM was determined by adding LM for right arm, left arm, right leg, and left leg. Coefficients of variation (CVs) were 1.2%, whole body FM; 1.6%, TFM; 0.5%, whole body LM; and 0.8%, ALM.
Spearman's rank correlation coefficients were calculated to estimate the magnitude of the association between continuous demographic covariates and measurements of body composition. Demographic covariates included age, body weight, height, and BMI. Partial correlation coefficients were computed to adjust for potential common effects of age, sex, and ethnicity on diabetes and lifestyle covariates as well as measurements of body composition. Diabetes covariates were duration of diabetes, fasting glucose, and hemoglobin A1C; lifestyle covariates were alcohol intake, dietary intake, and physical activity. Secondary to the correlated data structure inherent in a study using siblings, simple associations based on the correlation coefficient tests were deemed invalid and reevaluated using the generalized estimating equation procedure (14), which accounts for familial correlation through a sandwich estimator of the variance under exchangeable correlation. Associations between categorical covariates and measurements of body composition were also determined using the generalized estimating equation procedure by comparing whether the means for different covariate groups were the same. The categorical covariates included sex, ethnicity, medication use, diabetes status, and smoking. All statistical analyses were considered significant when p < 0.05. SAS software (SAS Institute, Cary, NC) was used for the statistical analyses.
To determine the contribution of genetic factors to body composition, the data in family members were analyzed using the Sequential Oligogenic Linkage Analysis Routines software package (Southwest Foundation for Biomedical Research) (15). Sequential Oligogenic Linkage Analysis Routines perform a variance components analysis of family data where the total phenotypic (e.g., whole body FM) variation is partitioned into genetic and nongenetic sources of variation. To minimize the bias associated with shared environmental factors, the estimates of heritability (ĥ2) were based on all available family data and were controlled for covariates related to body composition. The measurements of body composition were transformed to approximate the distributional assumptions of the analysis if necessary. The significance of the heritability estimates was obtained by likelihood ratio tests, where the likelihood of the model in which heritability was estimated was compared with the likelihood of the model in which the heritability was constrained to zero. Twice the difference in the natural logarithmic likelihoods yielded a test statistic that was asymptotically distributed as a 1/2:1/2 mixture of a χ2 variable with 1 degree of freedom and a point mass at zero (16).
A series of models were developed that incorporated an increasing number of covariates to determine the extent that genetic factors contribute to variation in body composition independently of the measured risk factors. For univariate analysis, each of the following covariates was examined independently: age, sex, ethnicity, height, weight, BMI, menopausal status, diabetes status, duration of diabetes, serum glucose, hemoglobin A1C, smoking, alcohol use, dietary intake, and physical activity. For multivariate analysis, the most important models were as follows. First, we examined the combined effect of age, sex, ethnicity, and height. Second, we added the combined effect of comorbid factors, such as diabetes status. Third, we added the combined effect of lifestyle factors, such as smoking, dietary intake, and physical activity. Fourth, we further adjusted for the use of medications, including insulin, glucocorticoids, thyroid hormone, and estrogen. Fifth, for regional measurements of body composition, TFM, and ALM, we further adjusted for whole body FM and LM.
Table 1 shows the characteristics of the study sample. There were 262 men and 292 women, ranging in age from 38 to 86 years. Most of the women (92%) were postmenopausal. Seventy-nine participants (14%) were African American. One hundred sixteen participants (23%) were being treated with insulin, 63 (14%) with estrogen, 30 (7%) with glucocorticoids, 241 (44%) with statins, 2 (0.4%) with testosterone, and 58 (13%) with thyroid hormone (data not shown). The average dietary intake was 1661 ± 731 kcal/d (SD). The average physical activity level was 567 ± 1018 kcal/wk.
Table 1. . Characteristics of the study sample
|Age (years)||61.8 ± 8.6||61.9 ± 8.8||61.9 ± 8.7||38 to 86|
|Ethnicity (% African Americans)||11.8 (31)||16.4 (48)||14.3 (79)|| |
|Weight (kg)||94.3 ± 16.0||85.6 ± 19.2||89.7 ± 18.3||44.3 to 150.5|
|Height (cm)||175.6 ± 6.9||161.7 ± 5.9||168.3 ± 9.5||122.8 to 195.2|
|Duration of diabetes (years)||11.1 ± 7.6||10.1 ± 7.0||10.6 ± 7.3||1 to 40|
|Diabetes status||86.6 (227)||78.4 (229)||82.3 (456)|| |
|Laboratory|| || || || |
| Fasting glucose (mM)||141.7 ± 58.7||138.5 ± 58.4||140.0 ± 58.5||16 to 423|
| Hemoglobin A1C (%)||7.3 ± 1.6||7.2 ± 1.9||7.3 ± 1.7||4.6 to 21.8|
|Lifestyle|| || || || |
| Smoking current (%)||19.5 (51)||14.4 (42)||16.8 (93)|| |
| Smoking past (%)||60.7 (159)||28.8 (84)||43.9 (243)|| |
| Smoking never (%)||19.9 (52)||56.9 (166)||39.4 (218)|| |
| Alcohol intake (% kcal/d)||1.1 ± 3.0||0.4 ± 2.6||0.7 ± 2.8||0 to 36.6|
| Dietary intake (kcal/d)||1805.3 ± 735.2||1532.2 ± 704.4||1661.4 ± 731.3||501.5 to 4531.3|
| Physical activity (kcal/wk)||696.5 ± 1277.9||450.6 ± 690.1||566.8 ± 1018.1||0 to 10, 272|
Table 2 summarizes the whole body and regional body composition for the study sample. Whole body LM and ALM were lower in women than in men (p < 0.0001). Whole body FM, TFM, %FM, and BMI were higher in women than in men (p < 0.005).
Table 2. . Body composition measurements for the study sample
|%FM (%)||27.55 ± 5.29||41.22 ± 5.29||34.75 ± 8.64||11.17 to 52.31|
|Whole body FM (kg)||26.49 ± 8.44||35.66 ± 11.56||31.32 ± 11.18||7.18 to 67.29|
|TFM (kg)||14.96 ± 5.06||18.34 ± 6.24||16.74 ± 5.95||2.72 to 36.40|
|Whole body LM (kg)||66.25 ± 8.46||49.40 ± 8.04||57.37 ± 11.77||29.59 to 93.93|
|ALM (kg)||28.94 ± 4.31||20.74 ± 4.10||24.61 ± 5.87||11.02 to 40.33|
|BMI (kg/m2)||30.6 ± 5.1||32.7 ± 7.0||31.7 ± 6.3||16.7 to 57.2|
Association of Body Composition with Potential Covariates
Table 3 shows Spearman correlations of various possible covariates with body composition. All measurements of body composition were inversely associated with age except %FM. All measurements were positively associated with body weight. LM was positively associated with height, but FM and BMI were not. All measurements were positively associated with BMI. There were no ethnic differences in any measurements. Those who took insulin medication had higher FM, whole body LM, and BMI compared with those who did not take insulin medication after adjusting for age, sex, and ethnicity (p < 0.05; data not shown).
Table 3. . Correlation between body composition measurements and covariates
|Age||−0.04 (0.4366)||−0.18 (<0.0001)||−0.21 (<0.0001)||−0.21 (<0.0001)||−0.22 (<0.0001)||−0.25 (0.0001)|
|Weight (kg)||0.18 (<0.0001)||0.68 (<0.0001)||0.74 (<0.0001)||0.74 (<0.0001)||0.78 (<0.0001)||0.84 (<0.0001)|
|Height (cm)||−0.62 (<0.0001)||−0.20 (<0.0001)||−0.1 (0.0066)||0.76 (<0.0001)||0.75 (<0.0001)||−0.12 (0.0003)|
|BMI (kg/m2)||0.54 (<0.0001)||0.86 (<0.0001)||0.87 (<0.0001)||0.43 (0.0001)||0.41 (<0.0001)||1.00|
|Duration of diabetes (years)||−0.01 (0.6024)†||0.00 (0.7914)†||0.03 (0.7225)†||−0.02 (0.9266)†||−0.06 (0.2994)†||0.02 (0.7493)†|
|Fasting glucose (mM)||0.07 (0.5270)†||0.14 (0.0815)†||0.21 (0.0033)†||0.16 (0.0044)†||0.09 (0.1076)†||0.14 (0.0341)†|
|Hemoglobin A1C (%)||0.08 (0.3266)†||0.15 (0.0081)†||0.23 (<0.0001)†||0.20 (<0.0001)†||0.05 (0.1380)†||0.18 (0.0029)†|
|Alcohol intake||0.01 (0.1500)†||0.03 (0.1875)†||0.01 (0.0967)†||0.03 (0.4117)†||0.00 (0.5978)†||−0.02 (0.2421)†|
|Dietary intake (kcal/d)||0.06 (0.1716)†||0.07 (0.0546)†||0.06 (0.1467)†||0.09 (0.0200)†||0.01 (0.2507)†||0.08 (0.0251)†|
|Physical activity (kcal/wk)||−0.11 (0.0010)†||−0.11 (0.0006)†||−0.10 (0.0020)†||−0.07 (0.2293)†||0.09 (0.0035)†||−0.10 (0.0762)†|
After adjusting for age, sex, and ethnicity, measurements of body composition were not associated with duration of diabetes. TFM, whole body LM, and BMI were positively associated with fasting glucose. Whole body FM, TFM, whole body LM, and BMI were significantly associated with hemoglobin A1C. Averages of %FM, whole body FM, TFM, whole body LM, and BMI were higher for those who had diabetes than for those who did not have diabetes (p < 0.005; data not shown).
After adjusting for age, sex, and ethnicity, measurements of body composition were not associated with alcohol intake. Whole body LM and BMI were positively associated with dietary intake. Measurements of FM were negatively associated with physical activity, but ALM was positively associated. For the three smoking groups, former smokers had the highest averages of body composition except ALM, current smokers had the lowest values, and nonsmokers had intermediate values (p < 0.005; data not shown).
Heritability of Body Composition
Table 4 shows heritability estimates for body composition. In the unadjusted model, the heritability estimates ranged from 0.48 for %FM to 0.76 for whole body FM. Heritability estimates remained significant in the univariate analyses after adding potential covariates (i.e., age, sex, ethnicity, height, weight, BMI, menopausal status, medication use, diabetes status, duration of diabetes, serum glucose, hemoglobin A1C, smoking, dietary intake, alcohol intake, and physical activity) to the model one at a time (data not shown). The proportions of phenotypic variance caused by sex, height, and weight were higher than those caused by other covariates. Sex adjustment increased heritability estimates for the measurements of LM, whereas height adjustment lowered the heritability estimates for the measurements of FM compared with other covariates.
Table 4. . Heritability estimates for body composition
|None||0.48 (0.11)||0.76 (0.11)||0.69 (0.11)||0.49 (0.10)||0.51 (0.10)||0.69 (0.11)|
|Age, sex, ethnicity||0.64 (0.11)||0.74 (0.11)||0.63 (0.11)||0.73 (0.10)||0.80 (0.10)||0.60 (0.11)|
|Age, sex, ethnicity, height||0.64 (0.11)||0.71 (0.11)||0.63 (0.11)||0.60 (0.11)||0.66 (0.11)||0.64 (0.11)|
|Age, sex, ethnicity, height, diabetes status||0.64 (0.11)||0.73 (0.11)||0.67 (0.11)||0.64 (0.11)||0.68 (0.11)||0.67 (0.11)|
|Age, sex, ethnicity, height, diabetes status, smoking, dietary intake, physical activity||0.64 (0.11)||0.72 (0.10)||0.64 (0.11)||0.63 (0.11)||0.67 (0.11)||0.64 (0.11)|
|Age, sex, ethnicity, height, diabetes status, smoking, dietary intake, physical activity, whole body FM|| || ||0.55 (0.13)|| || || |
|Age, sex, ethnicity, height, diabetes status, smoking, dietary intake, physical activity, whole body LM|| || || || ||0.51 (0.12)|| |
Table 4 also shows heritability estimates for body composition adjusted for covariates. First, adjustment for age, sex, and ethnicity increased the heritability estimate for %FM, whole body LM, and ALM (ĥ2 = 0.64, 0.73, and 0.80, respectively). Second, additional adjustment for height lowered the heritability estimates for whole body LM and ALM (ĥ2 = 0.60 and 0.66, respectively). Third, additional adjustment for diabetes status, smoking, dietary intake, and physical activity resulted in similar heritability estimates for all measurements of body composition (ĥ2 = 0.64, 0.72, 0.64, 0.63, 0.67, and 0.64 for %FM, whole body FM, TFM, whole body LM, ALM, and BMI, respectively). Note that further adjustment for the use of medications, including insulin, glucocorticoids, thyroid hormone, and estrogen, also resulted in similar heritability estimates (ĥ2 = 0.64, 0.71, 0.63, 0.56, 0.64, and 0.65 for %FM, whole body FM, TFM, whole body LM, ALM, and BMI, respectively; data not shown). There were 95 participants without complete medication data, so the sample size for the model including medication adjustment is different from the others. The comparison between the models with and without medication adjustment may not be fair. Fourth, for the two regional measurements of body composition, TFM had heritability of 0.55 with additional adjustment for whole body FM (p < 0.0001), and ALM had heritability of 0.51 with additional adjustment for whole body LM (p < 0.0001).
Heritability of Body Composition by Sex
Table 5 shows heritability estimates for body composition by sex. After adjusting for age, ethnicity, height, diabetes status, smoking, dietary intake, and physical activity, men had heritability estimates ranging from 0.53 to 0.70, and women had heritability estimates ranging from 0.60 to 0.96. Although it seems that women had higher heritability compared with men except for %FM, the differences were not significant when considering the standard errors associated with the point estimates.
Table 5. . Heritability estimates for body composition by sex
|Men|| || || || || || |
| None||0.72 (<0.0001)||0.71 (<0.0001)||0.71 (<0.0001)||0.63 (0.0004)||0.78 (<0.0001)||0.66 (0.0003)|
| Age, ethnicity, height||0.69 (0.0001)||0.71 (<0.0001)||0.67 (0.0001)||0.49 (0.0053)||0.61 (0.0007)||0.54 (0.0025)|
| Age, ethnicity, height, diabetes status, smoking, dietary intake, physical activity||0.66 (0.0001)||0.70 (0.0001)||0.65 (0.0002)||0.53 (0.0054)||0.68 (0.0004)||0.56 (0.0029)|
|Women|| || || || || || |
| None||0.65 (0.0007)||0.93 (<0.0001)||0.77 (<0.0001)||0.99 (<0.0001)||0.99 (<0.0001)||0.85 (<0.0001)|
| Age, ethnicity, height||0.64 (0.0007)||0.85 (<0.0001)||0.71 (<0.0001)||0.92 (<0.0001)||0.96 (<0.0001)||0.78 (<0.0001)|
| Age, ethnicity, height, diabetes status, smoking, dietary intake, physical activity||0.60 (0.0012)||0.81 (<0.0001)||0.69 (0.0001)||0.96 (<0.0001)||0.94 (<0.0001)||0.72 (<0.0001)|
The heritability of anthropometric measures, including BMI, has been widely discussed (1,2). In contrast, the heritability of body composition determined by DXA has not been well studied. In this paper, we show that %FM, whole body FM, TFM, whole body LM, and ALM were all highly heritable (adjusted ĥ2 = 0.63 to 0.72) after adjusting for age, sex, ethnicity, height, diabetes status, smoking, dietary intake, and physical activity. Thus, these traits can be used effectively in linkage studies designed to locate genes for body composition and regional fat and lean mass distribution. Furthermore, although it is tempting to conclude from the stratified results that women have higher heritability compared with men, the large SEs (∼0.20 for all of the measurements) associated with point estimates do not support this contention.
The heritability estimates for %FM, whole body FM, and TFM (ĥ2 = 0.48, 0.69, and 0.76, respectively) were in the range of commonly reported heritability estimates (from 0.30 to 0.90) (17,18) from prior twin and family studies. Although some studies have used computed tomography or underwater weighing to measure body fat (4,8,17,19,20), only two studies have used whole body FM measured by DXA (5,6), and neither of these studied diabetes-affected individuals. One study in 112 female white twin pairs reported that 65% of variance in FM was attributable to genetic factors using univariate model-fitting analysis (5). The other study in 101 girls and their biological parents reported that 50% of the variance in percentage body fat was accounted for by genetic factors (6).
Our heritability estimates were adjusted for a number of confounding factors explaining 65%, 31%, and 24% of the variance of %FM, whole body FM, and TFM, respectively. These confounding factors are likely a subset of a large number of potentially interrelated factors influencing variation in body composition and accounted for >50% of the non-genetic variation in FM. Importantly, however, in our sample population, genetic factors accounted for >50% of both whole body FM and TFM.
There were large interindividual differences in the distribution or location of body fat at any level of whole body fat. Specific patterning of fat distribution may be genetically influenced independently of whole body FM. We examined this hypothesis by estimating the heritability of TFM after adjustment for whole body FM. The heritability of TFM after this adjustment was 0.55 (p < 0.0001). Thus, we found strongly significant evidence that TFM measured by DXA was independently influenced by genetic factors.
Genetic factors explained about 49% of the total variance for whole body LM and about 51% for ALM. One study in 353 postmenopausal white twin pairs reported a heritability estimate of 0.52 for whole body LM measured by DXA (9), which is consistent with our study. Although our study included both men and women, most affected with diabetes, the genetic components for LM measured by DXA were similar. After adjusting for age, sex, and ethnicity, the heritability estimates for LM increased ∼20%. Because this adjustment reduces the remaining unexplained phenotypic variance, the genetic contribution to LM becomes more apparent. After further adjustment for height, the heritability estimates for LM were ∼0.60. LM is highly associated with height (r = 0.76 and 0.75 for whole body LM and ALM, respectively), which is also under strong genetic control (21). It is possible that the genetic component of LM without further adjusting for height may simply reflect a genetic component of body size. Additional adjustment for diabetes status, smoking, dietary intake, and physical activity resulted in similar heritability estimates (ĥ2 = 0.63 and 0.67 for whole body LM and ALM, respectively). These high estimates suggest that the genetic component for LM is not explained solely by the genetic component of body size and is indeed highly heritable. Note that the confounding factors explained 68% and 66% variance of whole body LM and ALM, respectively (data not shown), and accounted for most of the nongenetic variation of LM. Furthermore, the heritability estimate of ALM after adjusting additionally for whole body LM was 0.51 (p < 0.0001). This suggests that ALM is still heritable even after adjustment for whole body LM.
There are three major strengths of this study. First, heritability of body composition in elderly populations affected by chronic disorders has not been well studied. Many such populations are being used in genetic epidemiology research related to other disorders. In particular, DM2 is an increasingly prevalent condition that has a broad range of clinical consequences and, as such, is of particular interest to geneticists. Demonstration of substantial heritability of body composition in families with DM2 provides a strong rationale for investigating genetic influences on both FM and LM in diabetics. Note that the ascertainment of DM2 may bias heritability estimates upward because body composition is a risk factor for diabetes. Thus, these estimates may not be directly applicable to the nondiabetic population.
Second, although BMI, body weight, waist circumference, and waist-to-hip ratio are commonly used as measures of adiposity, the use of DXA allows a more accurate measurement of %FM, whole body, and trunk FM. Better characterized phenotypes can improve heritability estimates (6). Furthermore, Faith et al. (22) suggested that there might be a substantial genetic contribution to FM but not BMI. This implies that BMI might be a useful but insufficient measure of FM for mapping genes, and more precise measurements of body composition would be more appropriate. In our study, BMI had a reasonably high heritability estimate (ĥ2 = 0.64), and whole body FM had an even higher heritability estimate (ĥ2 = 0.72), although the difference was not significant based on the overlap confidence interval for heritability. BMI is still a useful and convenient measurement. However, DXA measurements constitute a different phenotype than BMI, and they are also heritable.
Last, despite known determinants of body composition, including age, sex, ethnicity, height, weight, BMI, and menopausal status, few studies have adjusted their calculated heritability estimates for these covariates. Adjustment for medications and lifestyle factors such as smoking, diet, and exercise has been even less consistent. This study provides a rigorous computation of heritability estimates after adjusting for these determinants. Furthermore, inclusion of the determinants in the model reduces the phenotypic variance, thereby increasing our ability to estimate the genetic contribution to the variation in body composition.
One limitation of the heritability estimates reported in our study is that they do not delineate between shared genes and shared environment. When common environment is a potential risk factor, the genetic component may be overestimated by the heritability estimate. Another limitation is that, in using a traditional sibling-pair design, we can only estimate heritability using sibling correlations. Thus, the estimated heritability may include both dominant and epistatic effects, potentially inflating the “true” estimate.
Direct comparison of the heritability estimates between studies is difficult. Different ascertainment schemes, study designs, methods of parameter estimation, and population-specific environmental contributions to the phenotypic variance can affect heritability estimates. Therefore, differing heritability estimates for a phenotype occur even when the genetic variance estimates in the different populations are similar (23). Similar heritability estimates in the different populations do not provide evidence for the same genes in the expression of a trait, nor do dissimilar heritability estimates provide evidence for the exclusion of the same genes in the expression of a trait (24).
We have shown that the heritability estimates of these measurements were not statistically different in men compared with women. We did not have sufficient power, based on the small sample size of African Americans, to perform ethnically stratified analyses. We will be able to address this question in the future with recruitment of additional African-American families.
In summary, we have shown that body composition is highly heritable. Different genes may contribute to the expression of FM and LM. Future linkage and association studies to identify the genetic factors underlying the variation in body composition may ultimately improve strategies for the prevention and treatment of obesity and sarcopenia.
This study was supported by NIH Grants R01 AR48797 (J.J.C.) and R01 HL67348 (D.W.B.) and, in part, by General Clinical Research Center of the Wake Forest University School of Medicine Grant M01 RR07122. The authors acknowledge the cooperation of our participants; the contributions of our study recruiters, Bonnie Dryman, Sue Ann Backus, and Jennie Locklear, as well as DXA technicians; and the helpful comments from Dr. Lynne E. Wagenknecht, which improved the quality of this work.
Nonstandard abbreviations: FM, fat mass; LM, lean mass; DM2, type 2 diabetes; TFM, trunk fat mass; ALM, appendicular lean mass; DHS, Diabetes Heart Study; CV, coefficient of variation; ĥ2, estimates of heritability.