Abstract
Objective
To determine the association of lean vs fat mass with fitness in healthy, overweight and obese African Americans from families with early-onset coronary disease.
Design
Cross-sectional study.
Setting
Baltimore, Maryland.
Participants
191 healthy, overweight, sedentary African Americans (69% women; aged 44.8 ± 11 years; body mass index 34 ± 5 kg/m2).
Main Outcome Measures
Anthropometrics, smoking, blood pressure, lipids, c-reactive protein, and glucose were assessed with standard methods; body composition was determined by dual energy X-ray absorptiometry; cardiorespiratory fitness was expressed as VO2peak attained during a maximal treadmill test.
Results
In both men and women, greater lean mass was independently associated with higher VO2peak (P<.05) and explained >21% of the variance in VO2peak, adjusted for body mass index, fat mass, important covariables, and nonindependence of families.
Conclusions
In this cross-sectional study, lean mass was the key determinant of cardiorespiratory fitness, independent of sex, age, and magnitude of obesity. These data provide a strong rationale for examining whether interventions that increase lean mass may also improve fitness, even among high-risk overweight and obese African Americans.
Keywords: Physical Fitness, Body Composition, Obesity, African Americans
Introduction
Obesity in adults is thought to be in part responsible for high rates of cardiovascular disease, particularly in populations with a high propensity to obesity and cardiovascular risk factors.1 US African Americans (AA) have a particularly high prevalence of obesity: 38.8% of adult men and 58.6% of adult women.2 However, AA more often demonstrate greater amounts of metabolically active lean mass for any given body mass index compared to other ethnic groups.3,4 Lean mass is a key determinant of energy expenditure,5 an important precursor for higher levels of physical fitness, but AA adults have lower levels of physical fitness across levels of body mass index (BMI) compared with other ethnic groups.6–8
Prior studies, primarily in European Americans, have suggested that higher levels of physical fitness are primarily determined by BMI and/or total body fat, after adjusting for age, sex, and habitual physical activity.9–11 Although obesity-related disease outcomes can be better predicted by a combination of body composition components rather than by BMI alone,12 less is known about the contribution of lean muscle mass relative to fat mass and BMI to physical fitness in apparently healthy overweight and obese AA adults with a high prevalence of cardiovascular risk factors, particularly in individuals from families with early-onset cardiovascular disease. We examined the extent to which lean mass, fat mass, and the ratio of lean mass to fat mass measured by dual energy X-ray absorptiometry (DEXA) contributed to cardiorespiratory fitness by measuring oxygen uptake during maximal treadmill testing in healthy overweight and obese AA from high-risk families.
Methods
This study was approved by the Johns Hopkins Medicine Institutional Review Board. All participants gave written informed consent before screening.
African American participants (N=191) were identified from the GeneSTAR (Genetic Study of Atherosclerosis Risk) cohort. Briefly, index cases with documented early-onset (prior to age 60 years) coronary artery disease (CAD) events (myocardial infarction, acute coronary syndromes, coronary artery bypass surgery, percutaneous coronary angioplasty, or stable angina with ≥50% stenosis in one or more vessels treated medically) were identified during hospitalization for a CAD event. The participants’ apparently healthy siblings and adult offspring, as well as the adult offspring of their siblings and the other parents of the offspring, were screened for CAD risk factors. The index cases were not screened and are not included in the analysis. Family members who were overweight or obese (BMI ≥25) at the time of their screening visit and who were not pregnant were invited to participate in screening.
Demographic information, including ethnicity, was determined by self-report obtained from standard questionnaires. A physical examination was performed and medical history was obtained by a study cardiologist and included current use of medications and/or dietary supplements. Smoking status was self-reported with a standard questionnaire,13 and habitual physical activity was assessed with the Stanford 7-Day Physical Activity Recall;14 kilo-calories expended per week were calculated.
Blood pressure was measured according to JNC VII guidelines.15 Participants were seated quietly for five minutes prior to each of three blood pressure measurements using a Dinamap automated cuff (GE Healthcare, Inc., Waukesha, WI, USA). The average of the three measures characterized resting blood pressure. Hypertension was defined as an average resting blood pressure ≥ 140/90 mm Hg and /or current use of antihypertensive medications.
Blood was collected after participants had fasted for eight hours. Glucose, insulin, high sensitivity C-reactive protein, total and HDL cholesterol and triglycerides were measured in the Core Laboratory at the Johns Hopkins Bayview Clinical Research Unit; LDL cholesterol was calculated using the Friedewald formula.16 The quantitative insulin-sensitivity check index (QUICKI), calculated as the 1/(log10 glucose + log10 insulin), was used as a proxy for insulin sensitivity.17
In a subset (n=181), echocardiography was performed using a Philips Sonos 5500 Phased Array System (Phillips Healthcare, Inc, Andover, MA, USA). Left ventricular (LV) and atrial dimensions were measured using standard techniques; LV mass was calculated using the Penn convention.18 Mitral inflow was also assessed. Diastolic dysfunction was defined according to Canadian guidelines19; participants classified as having mild, moderate or severe dysfunction were combined and compared with participants having no diastolic dysfunction.
Participants were weighed wearing no shoes and light indoor clothing on a calibrated balance scale, and height was measured using a stadiometer; BMI was calculated as weight (kg)/height squared (m2). Waist circumference was measured three times in the standing position, midway between the lower rib margin and the iliac crest using a cloth tape; the average of the three measures was used in analysis according to methods defined in the National Health and Nutrition Examination Survey.
Dual-energy x-ray absorptiometry (DEXA) was performed using a GE Medical Systems Lunar Prodigy machine software V13 (GE Healthcare, Inc., Waukesha, WI, USA). Total body fat mass, fat free or lean mass, and bone mass in kilograms were determined, and summed to represent total body mass; the proportions of total mass as fat and lean were calculated as fat mass divided by total mass and lean mass divided by total mass, respectively, and expressed as percentages. Lean mass to fat mass ratio was calculated as lean mass divided by fat mass, hereafter referred to as the lean-fat ratio.
Peak oxygen uptake (VO2peak) was measured during maximal treadmill exercise testing using a CareFusion Vmax229 Metabolic/ECG system (Care-Fusion, Inc., San Diego, Calif, USA). We used a modified Balke protocol where the test began at 3 mph, 0% grade, and increased 2.5% grade every 3 minutes. Participants performed until volitional fatigue. VO2peak was expressed as L/min in order to allow for unbiased examination of body mass and composition associations with cardiorespiratory fitness.
Data were analyzed using SAS v.9.2 (SAS Institute Inc., Cary, NC, USA 2002–2008). As the distributions of our fitness and body composition variables showed minimal overlap by sex, all analyses were performed within sex strata. Categorical variables were analyzed with frequencies, contingency table arrays, chi-square and Fisher's exact tests. Normality was tested using the Kolmogorov-Smirnoff statistic for continuous variables, and non-normal variables were log-transformed and analyzed with analysis of variance (ANOVA). Multivariate regression analyses were performed using the generalized estimating equations (GEE) adjustment to account for intrafamilial correlations. Variables demonstrating statistical significance (P<.05) in bivariate associations with fitness were selected for inclusion in the multivariate regression models and retained only if they were not collinear and explained >1% of the variance or were deemed important covariates (age, current smoking, BMI category, and habitual physical activity). Heritabilities were estimated using SAGE v. 6.3 (Statistical Analysis for Genetic Epidemiology, Case Western Reserve University, Cleveland, Ohio, USA, 2012) to assist in determining the extent to which the variables of interest may or may not have a genetic component.
Results
Sample characteristics for the 191 participants (60 males) by sex-specific quartiles of VO2peak are shown in Tables 1a and 1b. Participants came from 98 families with an average of 1.95 ± 1.3 members per family (range 1 to 8). Participants were obese on average, with moderately high levels of cardiovascular risk factors. Decreasing levels of BMI, lean mass and fat mass, were all significantly associated with incremental quartiles of VO2peak, for both men and women. Increasing levels of LV mass were significantly associated with incremental quartiles of VO2peak in men alone, while decreasing levels of age were significantly associated with incremental quartiles of VO2peak in women alone.
Table 1a.
Sample characteristics by fitness quartile in males (n=60)
| Variable | VO2peak Q1: 1.81–2.54 (n=15) | VO2peak Q2: 2.55–2.919 (n=15) | VO2peak Q3: 2.92–3.25 (n=15) | VO2peak Q4: 3.26–1.714 (n=15) | P |
|---|---|---|---|---|---|
| Age, years | 48.5 (9.33) | 47.8 (9.31) | 46.0 (10.7) | 42.4 (13.2) | .4167 |
| Weight, kga | 94.4 (12.0) | 96.2 (13.4) | 95.5 (14.9) | 118.1 (18.3) | .0002 |
| Waist circumference, cm | 101.0 (11.9) | 104.7 (18.4) | 99.1 (8.3) | 109.7 (12.0) | .1399 |
| Body mass index, kg/m2a | 31.1 (4.40) | 31.3 (3.31) | 30.7 (3.43) | 36.2 (5.04) | .0020 |
| Systolic blood pressure, mm Hga | 127.5 (10.5) | 127.8 (13.2) | 126.6 (15.0) | 127.1 (11.4) | .9882 |
| Diastolic blood pressure, mm Hg | 77.5 (9.91) | 76.5 (8.89) | 73.7 (8.52) | 69.2 (10.0) | .0807 |
| Glucose, mmol/La | 5.49 (.84) | 5.79 (.84) | 5.82 (1.02) | 5.65 (1.17) | .7599 |
| Insulin, pmol/La | 149.0 (61.5) | 178.6 (88.7) | 144.9 (68.5) | 179.9 (111.5) | .7593 |
| QUICKI | .305 (.018) | .298 (.024) | .305 (.020) | .303 (.031) | .8509 |
| hsCRP, mg/La | .314 (.314) | .497 (.398) | .304 (.495) | .468 (.364) | .0507 |
| LDL cholesterol, mmol/La | 3.55 (1.62) | 3.42 (.66) | 2.79 (.83) | 2.82 (.80) | .0589 |
| HDL cholesterol, mmol/La | 1.28 (.55) | 1.23 (.41) | 1.20 (.37) | 1.18 (.31) | .9770 |
| Triglycerides, mmol/La | 1.26 (.67) | 1.14 (.65) | 1.00 (.42) | 1.42 (1.01) | .4841 |
| Stanford physical activity, joules/weeka | 59.5 (2.45) | 61.4 (4.52) | 59.3 (2.92) | 58.6 (1.68) | .1793 |
| Left ventricular mass, g | 154.2 (27.0) | 168.8 (37.4) | 167.5 (28.4) | 192.8 (22.4) | .0067 |
| Proportion of total body mass as lean, %a | 63.9 (4.00) | 64.2 (5.09) | 66.1 (6.12) | 60.5 (6.92) | .0501 |
| Proportion of total body mass as fat, % | 32.6 (4.26) | 31.9 (5.32) | 29.9 (6.41) | 36.0 (7.15) | .0480 |
| Total lean mass, kga | 59.6 (5.67) | 61.0 (5.19) | 62.3 (7.01) | 69.7 (8.40) | .0008 |
| Total fat mass, kga | 30.8 (7.32) | 31.1 (8.52) | 29.0 (9.98) | 42.8 (13.2) | .0058 |
| Lean to fat mass ratioa | 2.01 (.42) | 2.11 (.65) | 2.40 (1.00) | 1.78 (.53) | .0598 |
| Smoking | 40.00 | 33.33 | 20.00 | 33.33 | .7633 |
| Hypertension | 53.33 | 46.67 | 40.00 | 46.67 | .9835 |
| Diabetes | 6.67 | 13.33 | 13.33 | 13.33 | 1.0000 |
| Body mass index classification | |||||
| Overweight | 53.33 | 40.00 | 46.67 | 13.33 | |
| Obese I | 33.33 | 53.33 | 33.33 | 26.67 | |
| Obese II | 6.67 | 6.67 | 20.00 | 33.33 | |
| Extremely obese | 6.67 | 0 | 0 | 26.67 | .0513 |
| Diastolic dysfunction (n = 49) | 54.55 | 66.67 | 40.00 | 45.45 | .5810 |
Data are mean (SD) or %.
P from analysis of log-transformed characteristic.
Table 1b.
Characteristics by fitness quartile in females (n=131)
| Variable | VO2peak Q1: 1.188–1.79 (n=33) | VO2peak Q2: 1.80–2.09 (n=32) | VO2peak Q3: 2.10–2.34 (n=33) | VO2peak Q4: 2.35–3.4 (n=33) | P |
|---|---|---|---|---|---|
| Age, years | 48.4 (10.9) | 44.3 (10.7) | 42.8 (11.6) | 39.7 (11.4) | .0179 |
| Weight, kga | 85.5 (11.9) | 90.9 (11.9) | 97.4 (15.0) | 102.5 (15.5) | <.0001 |
| Waist circumference, cm | 97.4 (10.8) | 97.3 (9.19) | 103.2 (10.3) | 102.3 (13.0) | .0474 |
| Body mass index, kg/m2a | 32.6 (4.18) | 33.8 (4.79) | 35.3 (4.94) | 36.6 (5.25) | .0076 |
| Systolic blood pressure, mm Hga | 120.4 (14.6) | 123.5 (18.1) | 119.5 (12.1) | 123.0 (15.0) | .7182 |
| Diastolic blood pressure, mm Hg | 69.3 (8.43) | 71.9 (11.7) | 69.5 (6.94) | 72.1 (9.11) | .4295 |
| Glucose, mmol/La | 5.45 (.746) | 5.45 (1.03) | 5.24 (.673) | 5.23 (.564) | .5616 |
| Insulin, pmol/La | 182.3 (80.8) | 169.7 (82.6) | 170.7 (90.1) | 171.2 (83.1) | .8358 |
| QUICKI | .298 (.017) | .301 (.020) | .303 (.020) | .302 (.019) | .6811 |
| hsCRP, mg/La | .754 (.687) | .873 (1.15) | .659 (.511) | .655 (.807) | .3642 |
| LDL cholesterol, mmol/La | 3.18 (1.17) | 3.04 (.82) | 2.90 (.93) | 3.10 (1.10) | .8242 |
| HDL cholesterol, mmol/La | 1.64 (.450) | 1.56 (.401) | 1.54 (.447) | 1.50 (.384) | .6660 |
| Triglycerides, mmol/La | 1.22 (.716) | 1.02 (.507) | .949 (.388) | .875 (.509) | .0669 |
| Stanford physical activity, joules/weeka | 57.6 (1.19) | 58.7 (5.29) | 58.5 (2.02) | 58.7 (3.15) | .1769 |
| Left ventricular mass, g | 139.9 (23.0) | 141.4 (32.5) | 141.5 (29.5) | 150.9 (31.2) | .4675 |
| Proportion of total body mass as lean, %a | 50.1 (4.51) | 49.7 (4.39) | 48.8 (4.73) | 49.8 (6.21) | .7487 |
| Proportion of total body mass as fat, % | 46.5 (4.60) | 46.8 (4.35) | 47.8 (4.92) | 46.9 (6.40) | .7778 |
| Total lean mass, kga | 42.1 (4.72) | 44.6 (5.58) | 46.8 (7.04) | 50.0 (5.84) | <.0001 |
| Total fat mass, kga | 39.7 (8.64) | 42.4 (8.22) | 46.3 (10.0) | 48.2 (12.1) | .0048 |
| Lean to fat mass ratioa | 1.10 (.203) | 1.08 (.202) | 1.04 (.217) | 1.10 (.315) | .7690 |
| Smoking | 27.27 | 21.88 | 12.12 | 6.06 | .0904 |
| Hypertension | 51.52 | 50.00 | 33.33 | 24.24 | .0657 |
| Diabetes | 9.09 | 9.38 | 0 | 3.03 | .2589 |
| Body mass index classification | |||||
| Overweight | 33.33 | 28.13 | 18.18 | 15.15 | |
| Obese I | 39.39 | 28.13 | 30.30 | 18.18 | |
| Obese II | 18.18 | 34.38 | 30.30 | 30.30 | |
| Extremely obese | 9.09 | 9.38 | 21.21 | 36.36 | .0859 |
| Diastolic dysfunction (n = 106) | 32.00 | 34.62 | 27.59 | 19.23 | .6281 |
Data are mean (SD) or %.
P from analysis of log-transformed characteristic.
In GEE-adjusted multiple regression analyses, lean mass was the strongest independent contributor to levels of physical fitness in both men and women (Tables 2a and 2b), explaining more than 27% of the variance in VO2peak in men and more than 21% of the variance in VO2peak in women, independent of all other variables including BMI category and level of fat mass. Lean-fat mass ratio was a significant predictor of fitness in men but not women (Tables 3a and 3b). Sensitivity analyses exploring any interaction between sex and lean mass, fat mass, or the lean-fat ratio showed no significant interaction or change in results (data not shown). In the subset of participants who had diastolic dysfunction assessed, adding diastolic dysfunction to the models showed no change in results, and diastolic dysfunction was not a significant independent predictor of fitness (data not shown).
Table 2a.
Multivariate linear regression analysis predicting physical fitness in males (n=60)
| Parameter | Beta | SE | P | Partial r2 |
|---|---|---|---|---|
| Age, years | –.0043 | .0022 | .0499 | 1.98 |
| Current smoking | –.1164 | .0534 | .0291 | 3.59 |
| (log) Stanford physical activity, joules/week | .004 | .006 | .5036 | .32 |
| (log) Lean mass, kg | .5219 | .2486 | .0358 | 27.20 |
| (log) Fat mass, kg | –.2065 | .0777 | .0079 | 1.18 |
| Left ventricular mass, g | .0017 | .0008 | .044 | 2.72 |
| Body mass index classification | 2.34 | |||
| Extremely obese | .1725 | .1375 | .2097 | |
| Obese II | .0449 | .0453 | .3219 | |
| Obese I | .1615 | .0935 | .0841 | |
| Overweight | reference |
Table 2b.
Multivariate linear regression analysis predicting physical fitness in females (n=131)
| Parameter | Beta | SE | P | Partial r2 |
|---|---|---|---|---|
| Age, years | –.0032 | .0011 | .0042 | 4.47 |
| Current smoking | –.1212 | .0309 | <.0001 | 5.68 |
| (log) Stanford physical activity, joules/week | .0062 | .0043 | .1482 | 1.27 |
| (log) Lean mass, kg | .5312 | .1223 | <.0001 | 21.59 |
| (log) Fat mass, kg | .2175 | .1104 | .0489 | 2.44 |
| Body mass index classification | .36 | |||
| Extremely obese | –.0652 | .0923 | .4795 | |
| Obese II | –.0317 | .0504 | .5291 | |
| Obese I | –.0559 | .0674 | .4070 | |
| Overweight | reference |
Table 3a.
Multivariate linear regression analysis predicting physical fitness in males (n=60)
| Parameter | Beta | SE | P | Partial r2 |
|---|---|---|---|---|
| Age, years | –.0047 | .0022 | .0353 | 5.60 |
| Current smoking | –.1387 | .0518 | .0074 | 4.24 |
| (log) Stanford physical activity, joules/week | .3858 | .4854 | .4267 | .63 |
| (log) Lean to fat mass ratio | .264 | .0861 | .0022 | 5.66 |
| Left ventricular mass, g | .0021 | .0007 | .0054 | 14.67 |
| Body mass index classification | 6.57 | |||
| Extremely obese | .2590 | .1340 | .0533 | |
| Obese II | .0706 | .0492 | .1518 | |
| Obese I | .2297 | .1000 | .0216 | |
| Overweight | reference |
Table 3b.
Multivariate linear regression analysis predicting physical fitness in females (n=131)
| Parameter | Beta | SE | P | Partial r2 |
|---|---|---|---|---|
| Age, years | –.0042 | .0012 | .0005 | 5.29 |
| Current smoking | –.1246 | .0358 | .0005 | 7.61 |
| (log) Stanford physical activity, joules/week | 1.1645 | .3654 | .0014 | 2.66 |
| (log) Lean to fat mass ratio | .1396 | .0844 | .0981 | 1.89 |
| Body mass index classification | 9.70 | |||
| Extremely obese | .2038 | .0596 | .0006 | |
| Obese II | .0495 | .0412 | .2300 | |
| Obese I | .1620 | .0445 | .0003 | |
| Overweight | reference |
Finally, we estimated heritability for VO2peak, lean and fat mass, and lean-fat ratio in the available 121 relative pairs: 19 parent-offspring, 33 sibling, 14 half-sibling, 14 cousin, and 41 avuncular (Table 4). Fitness, the lean-fat ratio and fat mass adjusted for lean mass were highly and significantly heritable in these families.
Table 4.
Age-sex adjusted heritability estimates
| Variable | Heritability Estimate | SE | P |
|---|---|---|---|
| VO2peak, mL/kg/min | .409 | .297 | .08397 |
| (log) VO2peak, L/min | .665 | .312 | .01660 |
| (log) Fat mass, kg | .427 | .277 | .06140 |
| (log) Lean mass, kg | .139 | .232 | .2751 |
| (log) Lean-fat ratio | .792 | .255 | .000973 |
| (log) Fat mass, kg adjusted for (log) lean mass, kg | .783 | .258 | .001190 |
| (log) Lean mass, kg adjusted for (log) fat mass, kg | .302 | .258 | .1205 |
Discussion
Even among high-risk overweight and obese AA, lean mass was the key determinant of cardiorespiratory fitness, independent of sex, age, and the magnitude of obesity. We were able to examine several factors hypothesized to be related to fitness, such as left ventricular mass, diastolic dysfunction, and metabolic factors. Still, no other factor was as strong a predictor of fitness as lean mass in both males and females among our overweight and obese population of sedentary African Americans.
Most prior studies have found that fat mass but not lean mass is an independent predictor of fitness,9,10,20 while few have found that lean mass but not fat mass was an independent predictor of fitness.21 Most studies have looked only at percentage total body fat without even considering lean mass.22 In a few instances, when both lean and fat mass were analyzed together, both were independent determinants of fitness.23 Besides the different ways of using measures of body composition, there has also been considerable variation in assessing fitness. Together, these methodological inconsistencies can produce different associations between body composition and fitness.21 In our study, so that both lean and fat mass could be included in our fully adjusted models, we chose to analyze absolute cardiorespiratory fitness expressed as VO2peak (L/min) rather than the more traditionally used body weight adjusted cardiorespiratory fitness expressed as VO2peak (mL/kg body weight/min or mL/kg fat free mass/min). Analyzing absolute fitness as VO2peak (L/min) should also reduce the inflated variance explained between body composition and VO2peak (mL/kg body weight/min or mL/kg fat free mass/min). This should offer a more precise measurement of the relative contributions of lean and fat mass, each in the presence of the other.
The ratio of metabolically active lean muscle mass vs fat mass incorporates both fat and lean mass measurements, but varies markedly in different populations.24–26 This heterogeneity could explain our lack of statistically significant results for the lean-fat ratio as compared with lean mass alone. While our study indicates that both lean and fat mass are important determinants of fitness, lean mass appears to be a more important determinant of fitness than fat mass. More importantly, both lean and fat mass are mutable. Ross et al have found that reducing fat mass even without weight loss can improve fitness.27,28 Resistance training alone has been shown to increase lean mass,29 generally while decreasing fat mass. Increasing lean mass could be a more easily achievable goal than reducing fat, which also requires weight loss and dietary restrictions, activities that are particularly resistant to change. Increasing lean mass however, could be done by adding resistance training to a daily regimen, perhaps without as much hardship. Given that weight regain after loss is more likely to be fat than lean mass and that weight loss is notoriously poorly preserved after weight interventions,30 it is possible that interventions that increase lean mass may be more effective long-term at improving cardiorespiratory fitness. Interventions that include resistance training to increase lean mass may be particularly effective, especially among high-risk persons who are overweight and obese. Increasing lean mass while decreasing fat mass will improve the lean-fat ratio, which may be particularly important for African Americans, who have greater lean mass than other ethnic groups.
Peak oxygen consumption is a highly genetically determined trait. Our heritability estimates for VO2peak are similar to those reported by others, where estimates have ranged from 44%–59%.31–33 While our heritability estimates for lean and fat mass were not statistically significant, our heritability estimates for the lean to fat mass ratio and for lean mass adjusted for fat mass were at the high limit of previously reported estimates of lean or fat mass alone, with heritability estimates of fat mass reported from 55%–74% and heritability estimates of lean mass reported from 47–81%.34–36 It remains likely that genetic factors contribute to exercise capacity through both lean and fat mass.
It is important to note that the determinants of fitness differed by sex. Age, smoking status, and habitual physical activity were important for women, while LV mass was important for men. No clear universal biopsychosocial pattern predicting fitness was observed.
Limitations of our study included a smaller sample size for males and the use of peak VO2 rather than true maximal VO2. In addition, the cross-sectional nature of our study precluded examining potential causal determinants of fitness.
In conclusion, we found that lean mass was the most significant predictor of fitness in a high-risk population of overweight and obese African American men and women. These cross-sectional data provide a rationale for examining interventions that include a component such as resistance training to increase lean mass as a more effective strategy for improving cardiorespiratory fitness, especially among high-risk African Americans who are overweight and obese.
We examined the extent to which lean mass, fat mass, and the ratio of lean mass to fat mass measured by dual energy X-ray absorptiometry (DEXA) contributed to cardiorespiratory fitness...
...it is possible that interventions that increase lean mass may be more effective long-term at improving cardiorespiratory fitness.
Acknowledgments
This study was supported by grant # HL089474-01A1 from the National Institutes of Health/National Heart, Lung, and Blood Institute and grant # UL1 RR 025005 from the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. None of the authors have any financial or personal conflicts of interest to disclose which are relevant to this manuscript.
Footnotes
Author Contributions
Design and concept of study: Vaidya, Kral, Stewart, Becker
Acquisition of data: Yanek, Kral, Dobrosielski, Moy, Stewart
Data analysis and interpretation: Yanek, Vaidya, Kral, Dobrosielski, Stewart, Becker
Manuscript draft: Yanek, Kral, Dobrosielski, Moy, Stewart, Becker
Statistical expertise: Yanek, Vaidya, Kral, Becker
Acquisition of funding: Becker
Administrative: Yanek, Kral, Moy, Stewart, Becker
Supervision: Yanek, Kral, Moy, Stewart, Becker
REFERENCES
- 1.Roger VL, Go AS, Lloyd-Jones DM, et al. Heart disease and stroke statistics–2012 update: a report from the American Heart Association. Circulation. 2012;125(1):e2–e220. doi: 10.1161/CIR.0b013e31823ac046. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among US adults, 1999–2008. JAMA. 2010;303(3):235–241. doi: 10.1001/jama.2009.2014. [DOI] [PubMed] [Google Scholar]
- 3.Li C, Ford ES, Zhao G, Balluz LS, Giles WH. Estimates of body composition with dual-energy X-ray absorptiometry in adults. Am J Clin Nutr. 2009;90(6):1457–1465. doi: 10.3945/ajcn.2009.28141. [DOI] [PubMed] [Google Scholar]
- 4.Borrud LG, Flegal KM, Looker AC, Everhart JE, Harris TB, Shepherd JA. Body composition data for individuals 8 years of age and older: U.S. population, 1999–2004. Vital Health Stat 11. 2010(250):1–87. [PMC free article] [PubMed] [Google Scholar]
- 5.Cunningham JJ. Body composition as a determinant of energy expenditure: a synthetic review and a proposed general prediction equation. Am J Clin Nutr. 1991;54(6):963–969. doi: 10.1093/ajcn/54.6.963. [DOI] [PubMed] [Google Scholar]
- 6.Ceaser TG, Fitzhugh EC, Thompson DL, Bassett DR., Jr. Association of physical activity, fitness, and race: NHANES 1999–2004. Med Sci Sports Exerc. 2013;45(2):286–293. doi: 10.1249/MSS.0b013e318271689e. [DOI] [PubMed] [Google Scholar]
- 7.Duncan GE, Li SM, Zhou XH. Cardiovascular fitness among U.S. adults: NHANES 1999–2000 and 2001–2002. Med Sci Sports Exerc. 2005;37(8):1324–1328. doi: 10.1249/01.mss.0000174893.74326.11. [DOI] [PubMed] [Google Scholar]
- 8.Wang CY, Haskell WL, Farrell SW, et al. Cardiorespiratory fitness levels among US adults 20–49 years of age: findings from the 1999–2004 National Health and Nutrition Examination Survey. Am J Epidemiol. 2010;171(4):426–435. doi: 10.1093/aje/kwp412. [DOI] [PubMed] [Google Scholar]
- 9.Yu R, Yau F, Ho S, Woo J. Cardiorespiratory fitness and its association with body composition and physical activity in Hong Kong Chinese women aged from 55 to 94 years. Maturitas. 2011;69(4):348–353. doi: 10.1016/j.maturitas.2011.05.003. [DOI] [PubMed] [Google Scholar]
- 10.Lakoski SG, Barlow CE, Farrell SW, Berry JD, Morrow JR, Jr, Haskell WL. Impact of body mass index, physical activity, and other clinical factors on cardiorespiratory fitness (from the Cooper Center longitudinal study). Am J Cardiol. 2011;108(1):34–39. doi: 10.1016/j.amjcard.2011.02.338. [DOI] [PubMed] [Google Scholar]
- 11.Brown RV, Kral BG, Yanek LR, et al. Ethnic-specific determinants of exercise capacity in a healthy high-risk population. Med Sci Sports Exerc. 2012;44(6):1150–1156. doi: 10.1249/MSS.0b013e3182456990. [DOI] [PubMed] [Google Scholar]
- 12.Sharp DS, Andrew ME, Burchfiel CM, Violanti JM, Wactawski-Wende J. Body mass index versus dual energy x-ray absorptiometry-derived indexes: predictors of cardiovascular and diabetic disease risk factors. Am J Hum Biol. 2012;24(4):400–405. doi: 10.1002/ajhb.22221. [DOI] [PubMed] [Google Scholar]
- 13.Rose GABH, Gillum RF, Prineas RJ. Cardiovascular Survey Methods. World Health Organization; 1982. [Google Scholar]
- 14.Blair SN, Haskell WL, Ho P, et al. Assessment of habitual physical activity by a seven-day recall in a community survey and controlled experiments. Am J Epidemiol. 1985;122(5):794–804. doi: 10.1093/oxfordjournals.aje.a114163. [DOI] [PubMed] [Google Scholar]
- 15.Chobanian AV, Bakris GL, Black HR, et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003;289(19):2560–2572. doi: 10.1001/jama.289.19.2560. [DOI] [PubMed] [Google Scholar]
- 16.Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18(6):499–502. [PubMed] [Google Scholar]
- 17.Katz A, Nambi SS, Mather K, et al. Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. J Clin Endocrinol Metab. 2000;85(7):2402–2410. doi: 10.1210/jcem.85.7.6661. [DOI] [PubMed] [Google Scholar]
- 18.Devereux RB, Reichek N. Echocardiographic determination of left ventricular mass in man. Anatomic validation of the method. Circulation. 1977;55(4):613–618. doi: 10.1161/01.cir.55.4.613. [DOI] [PubMed] [Google Scholar]
- 19.Yamada H, Goh PP, Sun JP, et al. Prevalence of left ventricular diastolic dysfunction by Doppler echocardiography: clinical application of the Canadian consensus guidelines. J Am Soc Echocardiogr. 2002;15(10 Pt 2):1238–1244. doi: 10.1067/mje.2002.124877. [DOI] [PubMed] [Google Scholar]
- 20.Patkar KU, Joshi AS. Comparison of VO2max in obese and non-obese young Indian population. Indian J Physiol Pharmacol. 2011;55(2):188–192. [PubMed] [Google Scholar]
- 21.Goran M, Fields DA, Hunter GR, Herd SL, Weinsier RL. Total body fat does not influence maximal aerobic capacity. Int J Obes Relat Metab Disord. 2000;24(7):841–848. doi: 10.1038/sj.ijo.0801241. [DOI] [PubMed] [Google Scholar]
- 22.Willig AL, Hunter GR, Casazza K, Heimburger DC, Beasley TM, Fernandez JR. Body fat and racial genetic admixture are associated with aerobic fitness levels in a multiethnic pediatric population. Obesity (Silver Spring) 2011;19(11):2222–2227. doi: 10.1038/oby.2011.109. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Wong SY, Chan FW, Lee CK, et al. Maximum oxygen uptake and body composition of healthy Hong Kong Chinese adult men and women aged 20–64 years. J Sports Sci. 2008;26(3):295–302. doi: 10.1080/02640410701552658. [DOI] [PubMed] [Google Scholar]
- 24.Parise C, Sternfeld B, Samuels S, Tager IB. Brisk walking speed in older adults who walk for exercise. J Am Geriatr Soc. 2004;52(3):411–416. doi: 10.1046/j.0002-8614.2003.52114.x. [DOI] [PubMed] [Google Scholar]
- 25.Eisner MD, Blanc PD, Sidney S, et al. Body composition and functional limitation in COPD. Respir Res. 2007;8:7. doi: 10.1186/1465-9921-8-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Ghosh S, Meister D, Cowen S, Hannan WJ, Ferguson A. Body composition at the bedside. Eur J Gastroenterol Hepatol. 1997;9(8):783–788. doi: 10.1097/00042737-199708000-00009. [DOI] [PubMed] [Google Scholar]
- 27.Ross R, Janssen I, Dawson J, et al. Exercise-induced reduction in obesity and insulin resistance in women: a randomized controlled trial. Obes Res. 2004;12(5):789–798. doi: 10.1038/oby.2004.95. [DOI] [PubMed] [Google Scholar]
- 28.Ross R, Dagnone D, Jones PJ, et al. Reduction in obesity and related comorbid conditions after diet-induced weight loss or exercise-induced weight loss in men. A randomized, controlled trial. Ann Intern Med. 2000;133(2):92–103. doi: 10.7326/0003-4819-133-2-200007180-00008. [DOI] [PubMed] [Google Scholar]
- 29.Peterson MD, Sen A, Gordon PM. Influence of resistance exercise on lean body mass in aging adults: a meta-analysis. Med Sci Sports Exerc. 2011;43(2):249–58. doi: 10.1249/MSS.0b013e3181eb6265. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Beavers KM, Lyles MF, Davis CC, Wang X, Beavers DP, Nicklas BJ. Is lost lean mass from intentional weight loss recovered during weight regain in postmenopausal women? Am J Clin Nutr. 2011;94(3):767–774. doi: 10.3945/ajcn.110.004895. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Gaskill SE, Rice T, Bouchard C, et al. Familial resemblance in ventilatory threshold: the HERITAGE Family Study. Med Sci Sports Exerc. 2001;33(11):1832–1840. doi: 10.1097/00005768-200111000-00006. [DOI] [PubMed] [Google Scholar]
- 32.Bouchard C, Daw EW, Rice T, et al. Familial resemblance for VO2max in the sedentary state: the HERITAGE family study. Med Sci Sports Exerc. 1998;30(2):252–258. doi: 10.1097/00005768-199802000-00013. [DOI] [PubMed] [Google Scholar]
- 33.Perusse L, Gagnon J, Province MA, et al. Familial aggregation of submaximal aerobic performance in the HERITAGE Family study. Med Sci Sports Exerc. 2001;33(4):597–604. doi: 10.1097/00005768-200104000-00014. [DOI] [PubMed] [Google Scholar]
- 34.Hsu FC, Lenchik L, Nicklas BJ, et al. Heritability of body composition measured by DXA in the diabetes heart study. Obes Res. 2005;13(2):312–319. doi: 10.1038/oby.2005.42. [DOI] [PubMed] [Google Scholar]
- 35.Luke A, Guo X, Adeyemo AA, et al. Heritability of obesity–related traits among Nigerians, Jamaicans and US Black people. Int J Obes Relat Metab Disord. 2001;25(7):1034–1041. doi: 10.1038/sj.ijo.0801650. [DOI] [PubMed] [Google Scholar]
- 36.Mustelin L, Latvala A, Pietilainen KH, et al. Associations between sports participation, cardiorespiratory fitness, and adiposity in young adult twins. J Appl Physiol. 2011;110(3):681–686. doi: 10.1152/japplphysiol.00753.2010. [DOI] [PubMed] [Google Scholar]