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Strength Fitness and Body Weight Status on Markers of Cardiometabolic Health


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Medicine & Science in Sports & Exercise: June 2015 - Volume 47 - Issue 6 - p 1211-1218
doi: 10.1249/MSS.0000000000000526


Obesity is associated with increased risk of type 2 diabetes (T2D), cardiovascular disease (CVD), metabolic syndrome, fatty liver disease and certain forms of cancer, as well as reduced quality of life and increased mortality (8). At present, more than one-third of U.S. adults remain obese (21). The causes of obesity are complex, and current intervention programs tend to focus on weight loss as a primary means of ameliorating obesity and its comorbidities (37). However, numerous lifestyle intervention studies have suggested improvements in indices of cardiovascular and metabolic health independent of weight loss or obesity reversal, which questions the notion that body weight is the cause for increased risk of morbidity and mortality. Weight loss as the primary determinant of successful risk-factor modification is not well supported from either a biological or a behavioral perspective (33). For example, weight loss–focused interventions face a high rate of recidivism (3), mediated by a myriad of causes for weight loss variability and weight regain (32).

Although it is recognized that higher levels of adiposity are correlated with increased mortality, higher levels of fitness attenuate this association. Specifically, cardiorespiratory fit and obese (by body composition) individuals have approximately a 50% lower mortality risk compared to lean and unfit individuals (17). McAuley et al. (18) noted that among men with high cardiorespiratory fitness, across BMI, waist circumference, and percent body fat categories, there were no significant differences in CVD and all-cause mortality risk, and Lee et al. (17) noted greater improvements in mortality risk with increased fitness compared to changes in body composition. Recent estimates suggest that overweight and class I obese individuals exhibit similar rates of mortality, compared with normal-weight subjects (8). Thus, the relationship between BMI and mortality is complex and may be influenced by other lifestyle factors, including fitness/training status.

Resistance training (RT) has gained more attention recently for its capacity to increase lean body mass and improve body composition and glucose tolerance (27). Performing RT and increasing muscular strength has been demonstrated to lower the risk for metabolic syndrome (13), CVD (36), and overall mortality (34). Ortega et al. (23) recently noted that, during a 24-yr follow-up, males 16–19 yr old with initially high levels of hand grip and knee-extension strength had a 20%–35% lower risk of mortality due to CVD, independent of BMI.

Because young individuals are at low risk of mortality, phenotypes associated with disease risk are used as surrogates of health. Thus, the present cross-sectional study was designed to investigate whether overweight/class I obese individuals exhibiting high muscular strength display cardiovascular/metabolic phenotypes similar to overweight/class I obese, untrained individuals or normal-weight individuals with high strength fitness. We recruited 90 young adult men separated into one of three phenotypes characterized by BMI (classified as a categorical variable to compare with existing guidelines) and strength fitness status: normal-weight strength-trained (NT), overweight strength-trained (OT), and overweight untrained (OU). We measured cardiometabolic health phenotypes, including central and brachial blood pressures, indices of arterial stiffness, serum lipids, inflammatory and metabolic markers, and steroid hormones. We hypothesized that: 1) the strength-trained groups, NT and OT, would display better metabolic and cardiovascular phenotypes compared to the OU group; and 2) the strength-trained groups with similar strength fitness levels would exhibit similar metabolic and cardiovascular phenotypes, irrespective of lower body weight and total and trunk fat mass in the NT group.


Study participants.

In this cross-sectional study, 90 young adult men, ages 18–30 yr completed informed consent, were enrolled and categorized into three phenotypes based on training status from screening questions and body mass index (BMI). Subjects were eligible for inclusion in strength-trained groups if they performed a minimum of 4 d·wk−1 of structured RT. OU participants participated in only light physical activity two times or less per week and were not in any structured exercise program. Group classifications were as follows: normal-weight trained (NT, n = 30, ≥4 d·wk−1 RT, BMI < 25 kg·m−2), overweight trained (OT, n = 30, ≥4 d·wk−1 RT, BMI > 27 kg·m−2), and overweight untrained (OU, n = 30, no structured exercise program, BMI > 27 kg·m−2). Any participants with overt chronic disease symptoms, as indicated by screening and health history assessment, were excluded from the study. Potential participants were excluded if they had documented CVD, cardiac arrhythmia, or EKG not allowing arterial stiffness indices assessment; history of tobacco use or medications that influence cardiovascular function, body composition, or insulin indices in the pervious 6 months. All of the study protocols were approved by the University of California, Los Angeles (UCLA) Institutional Review Board and were performed according to the Declaration of Helsinki.

Outpatient visit procedures.

All testing was carried out during a single visit. Before this visit, participants were reminded by phone and e-mail to abstain from the consumption of food, caffeine, alcohol, and vitamin supplements for at least 12 h before testing. They also did not engage in any moderate to vigorous physical activity within 36 h of the visit. On arrival, participants underwent an ordered set of outpatient procedures. Height and weight (for BMI) and waist circumference were measured twice and averaged. After this, body composition was determined by dual-energy X-ray absorptiometry scan (Hologic QDR4500 Fan Beam X-ray Densitometer; Hologic, Inc., Waltham, MA) before measurements of carotid intima–media thickness (IMT) and arterial stiffness. Participants underwent a blood draw, and serum and plasma samples were stored at −80°C for subsequent analysis. In all participants, vascular assessments were carried out before venipuncture to avoid any sympathetic responses due to discomfort related to the blood draw. Participants also completed an International Physical Activity Questionnaire (self-administered, long form) to quantify the amount of routine physical activity performed in the week before the assessment. Activity-specific (moderate, vigorous, or total activity) scores were calculated as continuous variables of metabolic equivalent task (MET) × minutes per week.

Muscular strength testing.

Maximal strength testing of 1-repetition maximum (1-RM) lifts for barbell bench press, 45° incline leg press, and machine-seated row were carried out as previously described (2). Relative strength was calculated as the sum of 1-RM strength values (kg) divided by the subject’s body weight (kg).

Arterial tonometry and carotid IMT.

Assessment of central systolic (cSBP) and diastolic (cDBP) and peripheral (brachial) systolic (bSBP) and diastolic (bDBP) blood pressures and indices of arterial stiffness (augmentation index [AIx], subendocardial viability ratio [SEVR, the ratio of the pressure–time integral during diastole {diastolic pressure time index} to pressure–time integral during systole {tension time index} and used as an index of cardiac perfusion] and carotid–femoral pulse-wave velocity [cfPWV]) were determined noninvasively with the SphygmoCor system (AtCor Medical, Sydney, Australia) (16) as previously described (1). Carotid IMT was determined as previously described (1). In brief, two-dimensional ultrasound images of the carotid artery were obtained using a 12-MHz linear array transducer, and data were digitally recorded on an external computer for offline analysis. Automated edge detection software (Medical Imaging Applications, Coralville, IA) was used to measure the carotid artery diameter (intima–intima) and carotid IMT of both sides at approximately 1 cm distal to the carotid bulb.

Blood chemistry assays.

Samples were assayed for total cholesterol, HDL, non-HDL cholesterol, and triglycerides (TG) using the Olympus AU400 Chemistry Analyzer (Olympus America, Center Valley, PA). LDL was calculated using the equation of Friedewald et al. (9). Serum glucose was assayed via the hexokinase method (Olympus AU400 Chemistry Immuno Analyzer; Olympus America). Serum insulin was measured by means of a solid-phase enzyme-labeled chemiluminescent immunometric assay (Immulite 2000; Diagnostic Products Corp., Los Angeles, CA). Quantitative insulin-sensitivity check index (QUICKI) was calculated by the formula 1 / (log[fasting insulin {μU·mL−1}] × log[fasting glucose {mg·dL−1}]) and homeostatic model assessment (HOMA) was calculated by (fasting insulin [μU·mL−1] × fasting glucose [mg·dL−1]) / 405.

High-sensitivity C-reactive protein (CRP; Alpco, Salem, NH) and oxidized LDL (oxLDL; Mercodia Laboratories, Uppsala, Sweden) concentrations were determined by enzyme-linked immunosorbent assay (ELISA). Interleukin (IL)-8, tumor necrosis factor-α (TNF-α), vascular endothelial growth factor, matrix metalloproteinase-9, myeloperoxidase (MPO), total plasminogen activator inhibitor-1, soluble E-selectin, soluble intercellular adhesion molecule-1 (sICAM-1), soluble vascular cell adhesion molecule-1 (sVCAM-1), monocyte chemotactic protein-1, leptin, and total amylin were determined using Millipore Multiplex assays (Millipore; Billerica, MA). Adiponectin was determined by ELISA (R&D Systems, Minneapolis, MN). Plasma levels of sex hormone–binding globulin (SHBG), cortisol, and testosterone were measured by an electrochemiluminescent immunoassay on an Elecsys 2010 autoanalyzer (Roche Diagnostics, Indianapolis, IN). Free testosterone was calculated via the method of Sodergard et al. (35). Free androgen index (FAI) was calculated by 100 × (total testosterone / SHBG).

Statistical analyses.

Robust measures of statistical significance were obtained by running a Monte Carlo permutation for each respective test statistic (e.g., F-statistic) 10,000 times from which a test for significance was obtained at an α value of <0.05 using one-way ANOVA. Permutation analyses were used to avoid making any assumptions on the distribution of the data. Post hoc permuted t-tests were used to test significance between groups. To investigate the associations of adiposity and strength with phenotypic variables, exploratory pairwise correlation analyses were performed between outcome variables (total and trunk fat, body fat percentage, and relative strength) and these variables and were corrected using Bonferroni to an α value of ≤0.001. The results were similar for all fat mass variables, and body fat percentage correlations were reported. Statistical analyses were performed with the use of STATA Statistical Software 12 (StataCorp LP, College Station, TX). Data are reported as mean ± SD.


Body composition, strength, and physical activity.

The comparison of body composition, muscular strength, and physical activity is depicted in Table 1. The NT group exhibited lower waist circumference, trunk fat, and total fat mass (Fig. 1A) than the OT and OU groups (all P < 0.001). In addition, the OT and OU groups presented no significant differences in weight or BMI, but the OT group presented lower waist circumference, trunk fat, and total fat mass (Fig. 1A) than the OU group (all P < 0.001). The NT and OT groups exhibited higher 1-RM strength for bench press and seated row than the OU group did (all P < 0.0001). The OT group demonstrated the highest strength in the leg press, whereas the NT and OU groups exhibited no differences. A relative strength score was calculated from chest, leg, and row 1-RM and was found to be higher in the OT and NT groups than in the OU group (P < 0.0001, Fig. 1B), and data were similar when relative strength was determined from lean body mass rather than body weight (data not shown). The International Physical Activity Questionnaire confirmed that the NT and OT groups engaged in more vigorous exercise than the OU group did (P < 0.0001).

Body composition, strength, and physical activity.
Comparison of (A) total fat mass and (B) relative strength calculated as the sum of 1-repetition maximum strength values normalized by body weight, (C, D) brachial and (E, F) central blood pressures, (G) SEVR and (H) PWV across NT, OT, and OU groups. *P < 0.05; **P < 0.01; ***P < 0.001. bDBP, brachial diastolic blood pressure; bSBP, brachial systolic blood pressure; cDBP, central diastolic blood pressure; cSBP, central systolic blood pressure; NT, normal-weight trained; OT, overweight trained; OU, overweight untrained; PWV, pulse-wave velocity; SEVR, subendocardial viability ratio.

Vascular function and structure.

For indices of vascular health, bSBP (Fig. 1C) and bDBP (Fig. 1D) were lower in the NT (P < 0.001) and OT groups (P < 0.05) than in the OU group. Similarly, compared to the OU group, cSBP was significantly lower in the NT (P < 0.001) and OT groups (P < 0.05) (Fig. 1E). cDBP was lower in the NT group than in the OU group (P < 0.001) and trended lower in the OT group than in the OU group (P = 0.05) (Fig. 1F). In addition, SEVR was elevated in both strength-trained groups than in the OU group (NT P < 0.001, OT P < 0.01) (Fig. 1G). The OT group exhibited lower cfPWV (P < 0.01; Fig. 1H) and AIx (P < 0.05; Table 2) than the OU group. Differences in cfPWV and AIx were not seen between OU and NT groups, although there was a trend for the latter (P = 0.08). These differences in vascular function were found independent of any differences in carotid IMT (see Table, Supplemental Digital Content 1, Additional Cardiovascular and Metabolic Markers, For each of these outcomes, no differences were noted between the NT and OT groups.

Cardiovascular and metabolic markers.

Lipids, inflammatory, and atherogenic markers.

NT and OT groups exhibited lower total cholesterol (NT P < 0.01, OT P < 0.05; Fig. 2A), LDL (NT P < 0.001, OT P < 0.05; Fig. 2B), TG (P < 0.001; Fig. 2C), and higher HDL (P < 0.001; Fig. 2D) than the OU group. In addition, both oxLDL (P < 0.001; Fig. 2E) and CRP (P < 0.01; Fig. 2F) were lower in both strength-trained groups than in the OU group. For each of these outcomes, no differences were noted between the NT and OT groups. No across-group differences were found among other atherogenic markers (vascular endothelial growth factor, matrix metalloproteinase-9, total plasminogen activator inhibitor-1, soluble E-selectin, sICAM-1, sVCAM-1, MPO, monocyte chemotactic protein-1, IL-8, and TNF-α), except sVCAM-1 and MPO which were elevated in NT (P < 0.05; see Table, Supplemental Digital Content 1, Additional Cardiovascular and Metabolic Markers,

Comparison of (A–D) lipids, (E) oxLDL, and (F) CRP across NT, OT, and OU groups. *P < 0.05; **P < 0.01; ***P < 0.001. CRP, C-reactive protein; NT, normal-weight trained; OT, overweight trained; OU, overweight untrained; oxLDL, oxidized low-density lipoprotein; TG, triglycerides.

Glucose and insulin, adipokines, and steroid hormones.

Contrary to the other outcomes investigated, fasting glucose (Table 2) and insulin (Fig. 3A) were significantly lower in the NT group than in both OT and OU groups (P < 0.01). QUICKI values were significantly higher in the NT and OT groups than in the OU group (P < 0.05), with no difference between NT and OT groups. Alternatively, HOMA was lower in the NT group than in the OU (P < 0.001) and OT groups (P < 0.01) (Table 2). Both strength-trained groups displayed lower amylin (Fig. 3B) and leptin (Fig. 3C) and higher adiponectin (Fig. 3D) than the OU group (NT P < 0.001, OT P < 0.01). SHBG concentration was also higher in both strength-trained groups than in the OU group (P < 0.001; Fig. 3E), whereas corresponding FAI was lower in the strength-trained groups than in the OU group (P < 0.001). Although testosterone was significantly higher in the NT group than in the OU group (P < 0.001), there was no significant difference between OT and OU groups (Fig. 3F). Free testosterone and cortisol were similar among the three groups (see Table, Supplemental Digital Content 1, Additional Cardiovascular and Metabolic Markers, In addition, all adipokine and steroid hormone concentrations were not significantly different between NT and OT groups.

Comparison of (A) insulin, (B) amylin, (C, D) adipokines, and (E, F) steroid hormones across NT, OT, and OU groups. *P < 0.05; **P < 0.01; ***P < 0.001. NT, normal-weight trained; OT, overweight trained; OU, overweight untrained; SHBG, sex hormone–binding globulin.

Correlation analyses.

Indices of vascular and metabolic health were correlated with relative strength and body fat percentage within the combined cohort to explore the relationship between cardiovascular and metabolic phenotypes that were different between the groups (see Table, Supplemental Digital Content 2, Strength and Body Fat Correlation Analyses, Both body fat percentage and relative strength were correlated with cardiometabolic phenotypes, suggesting that they may both influence the group differences noted.


The relative significance of weight status and fitness status as contributors to metabolic and cardiovascular health and related chronic disease risk is debatable. To provide some understanding into the relative importance of body weight and strength fitness, we assessed a variety of metabolic and cardiovascular risk factors in three groups of young men classified by BMI and RT frequency. Consistent with our hypothesis, the principal findings of this study are that participants with high strength fitness (NT and OT), secondary to regular RT, exhibit similar cardiovascular and metabolic risk factor profiles and generally improved phenotypic profiles compared to OU individuals who did not perform any regular resistance training. These findings indicate that those with higher strength fitness exhibit a reduced cardiovascular and metabolic risk profile, even in overweight or class I obese individuals (by BMI), suggesting that strength fitness may be critical to metabolic and cardiovascular health irrespective of normal-weight or class I obesity weight classification. The OT individuals in the present study were overweight/obese by BMI standards (BMI > 27 kg·m−2) although not by body fat percentage (mean = 18%). Nevertheless, despite greater total and trunk fat mass in the OT group than in the NT group, these groups exhibited similar phenotypes, indicating that differences in adiposity had no effect on phenotypes in these two groups.

Higher muscle strength in both NT and OT subjects compared to OU subjects was accompanied by lower central and peripheral blood pressure, indices of arterial stiffness, plasma lipids, oxLDL, and CRP. Previous studies support the contention that strength fitness may be superior in the prediction of cardiovascular phenotypes. Fahs et al. (6) noted that, after adjusting for body weight, a modest but significant inverse relationship between central PWV and muscular strength was noted. Obesity has been characterized by increased levels of proinflammatory cytokines and atherogenic markers, including oxLDL (39) and CRP (31). In the present study, both of these markers were lower in strength-trained groups than in the OU group, suggesting that muscular fitness is a better predictor of oxidative stress and inflammation than weight status. Consistent with these findings, Kosola et al. (15) demonstrated that the high oxLDL associated with overweight and obese individuals is attenuated in subjects with higher muscular fitness. In addition, RT has been noted to decrease CRP with no change in weight or fat mass (5). Notably, other biomarkers of CVD risk including cell adhesion molecules and markers of endothelial dysfunction were generally not different between the three groups in the present investigation. Data on the ability of RT to alter these markers suggest limited effects. Olson et al. (22) noted no effect of RT on IL-6, sICAM-1, sVCAM-1, or E-selectin, whereas Klimcakova et al. (14) noted no changes in TNF-α, IL-6, or IL-1β after RT.

We also investigated a variety of endocrine markers that affect cardiovascular and metabolic health, including adipokines and steroid hormones, as well as glucose and insulin. Both NT and OT groups exhibited lower leptin levels than the OU group. In addition, we noted higher adiponectin concentrations in both strength-trained groups than in the OU group. Although data are limited with regard to circulating leptin and adiponectin with RT, some RT intervention studies have noted an increase in adiponectin (22) and a decrease in leptin (14) without weight loss. Interestingly, the changes in leptin and adiponectin may be dependent on the intensity of RT (7). Data on amylin with exercise training are scarce; however, we previously noted that a 12-wk RT intervention did not significantly decrease total amylin concentration (2), whereas a short-term diet and exercise intervention decreased amylin in obese children, despite remaining obese after the intervention (12). In addition, SHBG is positively associated with glycemic control (10) and predicts both metabolic syndrome and T2D (4). In the present study, we noted increased SHBG and decreased FAI in both NT and OT subjects compared with the OU group. We recently noted an increase in SHBG and decrease in FAI in young men after a 12-wk RT, albeit in the presence of weight gain (26). Contrary to our other findings, glucose and insulin levels were not different between OT and OU. QUICKI, a surrogate measure of insulin sensitivity, based on fasting glucose and insulin levels, was higher in both strength-trained groups, and HOMA, an index of insulin resistance, was lower in the NT group than in both OT and OU groups. What may account, at least in part, for these findings and a limitation of this study, is that fasting measures and indices based on fasting values are poor surrogates for dynamic changes in glucose and insulin metabolism, which are best measured by an oral glucose tolerance test or other tests for insulin sensitivity (i.e., clamp or intravenous glucose tolerance test). Other RT interventions have demonstrated an improvement in insulin sensitivity independent of weight loss and without concomitant reductions in fasting glucose and insulin (14). In addition, Minges et al. (20) noted that ≥40 min·wk−1 of RT was associated with a 31% decrease in odds of displaying impaired glucose metabolism, after controlling for leisure time physical activity.

The relationship between body weight, fitness, and cardiometabolic risk is complex. Treatment guidelines for overweight and obese adults encourage weight loss of 10% of initial body weight to improve cardiovascular and metabolic risk factors (37). A growing number of studies indicate that overweight/obese adults can achieve a reduction in obesity-related comorbid conditions with increased physical activity and minimal or no weight loss (24,27). These data, along with the present findings, suggest that a normal body weight status is not a prerequisite in achieving the beneficial effects of regular exercise training and/or lifestyle modifications. Studies that support the concept that fitness status alone can improve metabolic health come from epidemiologic studies on mortality rates and fitness (17,19) as well as intervention studies that note improved risk factors for cardiovascular and metabolic disease by exercise training with minimal or no weight loss or change in body composition. Studies like these corroborate that a significant portion of normal-weight individuals (by BMI) are metabolically unhealthy (40), whereas many obese individuals are not. There is also evidence that performing RT and increasing muscular strength lowers the risk for metabolic syndrome (13), coronary heart disease (36), T2D (11), CVD mortality (23), and overall mortality (34). For example, Tanasescu et al. (36) noted that >30 min·wk−1 of RT decreased coronary heart disease risk by 35% in age-adjusted analysis and by 23% in analysis accounting for other factors such as smoking and dietary factors. Grøntved et al. (11) noted that ≥150 min·wk−1 of RT lowered risk of T2D by 54% in age-adjusted analysis and by 34% after adjusting for aerobic exercise, other moderate activity, and television viewing. Our data suggest that regular RT decreases several risk factors related to CVD, T2D, and metabolic syndrome, even in OT subjects with BMI > 27 kg·m−2 and to a similar degree compared to those NT with lower body weight and total and trunk fat mass. In addition, several randomized controlled exercise trials have noted that exercise intervention is associated with a significant reduction in one or more cardiometabolic risk factors in the absence of weight loss (14), and we previously demonstrated that short-term lifestyle modification could ameliorate metabolic and/or CVD risk factors in men (29,30), women (38), and children (12,25), despite small changes in weight and participants remaining overweight/obese. Furthermore, we recently noted that normal-weight and obese children responded similarly to short-term lifestyle modification despite no weight loss in the normal-weight group and the obese group remaining obese (28). Collectively, this work supports the notion that BMI and body weight are poor indices to predict cardiometabolic risk, and indices of fitness, such as strength fitness, may be better predictors.

Important to this discussion, in the present study, we matched the BMIs of the OT and OU groups. Our use of dual-energy X-ray absorptiometry allowed us to compare differences in body composition and metabolic and cardiovascular phenotypes between two groups of similarly strength-trained participants, NT and OT, and an untrained group, OU. Differences in total and trunk fat mass did not account for differences in phenotypes between NT and OT groups because both exhibited similar phenotypes. However, we cannot attribute the differences between OT and OU solely to training because fat mass also differed between these two groups. Exploratory correlation analyses do suggest that both strength and adiposity are associated with cardiometabolic phenotypes. Nonetheless, matching these groups for fat mass is difficult because of the effects of regular RT on fat mass. However, analyses of subgroups of subjects with equal fat mass from OT and OU indicated trends similar to the full group analyses (data not shown). Furthermore, this study used strict inclusion criteria to investigate young men in a controlled environment and their 1-RM strength assessment confirmed training status. We focused on a wide variety of cardiovascular and metabolic risk factors to better understand the intricate interplay between weight status, strength fitness, and factors contributing to the pathogenesis of cardiovascular and metabolic diseases. A limitation deserving comment is that our study design did not allow us to extend our hypothesis to include normal-weight, untrained subjects, who may exhibit elevated cardiometabolic risk factors compared to the trained groups and similar to the OU group.

Thus, individuals who are overweight/obese class I and strength-trained exhibit cardiovascular and metabolic phenotypes similar to those who are normal-weight and strength-trained, rather than those overweight/obese and untrained, suggesting that adaptations associated with chronic RT are related to improved risk profiles irrespective of body weight classification in normal-weight, overweight, and obese class I individuals. These findings provide preliminary evidence to challenge the existing view of the importance of body weight per se and suggest that strength fitness may be more critical to metabolic and cardiovascular health. These data also indicate that BMI and body weight may be poor surrogates for risk and that other factors contribute to cardiovascular and metabolic health. Furthermore, strength fitness may be an alternate therapeutic target, especially in those unable to normalize body weight. Future studies should include strength fitness when investigating cardiometabolic health and more work is needed to tackle the difficult task of clarifying fitness-versus-fatness relationships.

The authors would like to thank the entire Exercise Physiology and Metabolic Disease Research Laboratory team for their commitment to this study. The authors also thank the Gonda (Goldschmied) Diabetes Center and Elisa Terry, Mick DeLuca, and colleagues at the John Wooden Recreation Center. Furthermore, the authors thank all participants for their time and effort.

This work was supported by the American Heart Association (no. 0765139Y to C.K.R.), the National Heart, Lung, and Blood Institute (P50HL105188 to C.K.R.), the National Institute of Diabetes and Digestive and Kidney Diseases (R01DK090406 to C.K.R.), and the National Center for Advancing Translational Sciences through UCLA CTSI Grant UL1TR000124.

The authors declare no conflicts of interest.

The results of the present study do not constitute endorsement by the American College of Sports Medicine.


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