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The Journal of Clinical Endocrinology & Metabolism Vol. 90, No. 2 849-854
Copyright © 2005 by The Endocrine Society

Cardiovascular Fitness and Exercise as Determinants of Insulin Resistance in Postpubertal Adolescent Females

J. Z. Kasa-Vubu, C. C. Lee, A. Rosenthal, K. Singer and J. B. Halter

Departments of Pediatrics (J.Z.K.-V., A.R., K.S.) and Internal Medicine (C.C.L., J.B.H.), University of Michigan, Ann Arbor, Michigan 48019

Address all correspondence and requests for reprints to: Josephine Z. Kasa-Vubu, M.D, M.S., Department of Pediatrics, University of Michigan Medical Center, Ann Arbor, Michigan 48019-0718. E-mail: jzkv{at}umich.edu.


   Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
In obese adolescents, body mass index (BMI) is a poor predictor of insulin resistance, and the potential role of diminished physical activity has not been quantitated. We measured possible determinants of sensitivity to insulin in 53 adolescent females with a BMI between the 10th and the 95th percentile. We hypothesized that across weight and fitness spectra, relative fat mass, rather than BMI, and cardiovascular fitness would be predictors of insulin resistance.

We measured body composition by total-body dual x-ray absorptiometry. Self-reported weekly frequency of aerobic exercise for 1 h (RDE) was recorded, and maximal oxygen consumption (VO2max) was measured. Insulin sensitivity was estimated by homeostasis model assessment index (HOMAIR) derived from fasting glucose and insulin concentrations.

BMI was not related to HOMAIR (P = 0.20), RDE showed a marginal relationship (P = 0.049), whereas percent body fat and VO2max were significantly related to HOMAIR (P = 0.01 and 0.0008, respectively). In a multiple regression model, VO2max was a more critical determinant of insulin resistance than percent body fat (P = 0.03 vs. P = 0.67) or RDE (P = 0.01 vs. 0.51).

For prevention strategies in youth, physical inactivity may represent a greater metabolic risk than obesity alone.


   Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results
 Discussion
 References
 
THE OBESITY EPIDEMIC (1) and recent reports about the increased prevalence of type 2 diabetes in youth (2) bring to the forefront the need for prevention. The most frequently identified risk factors for the onset of type 2 diabetes in youth are puberty, obesity, and evidence of insulin resistance (3). A sedentary lifestyle as well as the heavy consumption of high-energy foods and beverages (4) are thought to largely contribute to the obesity epidemic now reaching children of all ages (3). The relative contributions of diet and exercise as risk factors for obesity and type 2 diabetes are still open for debate (3, 5), while the time dedicated to physical activity in school physical education programs is decreasing (6). Trends of relative inactivity increase markedly in girls as they mature through puberty (7). The substantial decrease in physical activity observed in multiethnic cohorts of adolescent girls is associated with a steady increase of body mass index (BMI) (7).

Although insulin resistance is an important risk factor for diabetes, little information is available about insulin resistance in adolescent females (8). In adults, obesity is a strong determinant of insulin resistance (9, 10), and risk assessment is often based on body weight (3). In contrast to adults, recent studies in very obese children and adolescents (11, 12) suggest that an elevated BMI by itself does not appear to predict the degree of insulin resistance. However, the relationship of insulin resistance to BMI in adolescents across the BMI percentiles is unknown. Identifying risk factors for decreased sensitivity to insulin in youth would facilitate the development of preventative strategies against the onset of type 2 diabetes at younger ages (13).

Several methods can be used for the determination of insulin resistance. The homeostasis model assessment index for insulin resistance (HOMAIR) has been used as a surrogate measure for insulin resistance and has been validated in adolescents (11, 12). We recruited healthy young women in the immediate postpubertal period. We tested the hypothesis that BMI, relative fat mass, and cardiovascular fitness would be predictors of insulin resistance in adolescent women across the weight and fitness spectra.


   Subjects and Methods
Top
 Abstract
 Introduction
 Subjects and Methods
Results
 Discussion
 References
 
Subjects

The protocol was approved by the Institutional Review Board of the University of Michigan Health System. Fifty-three healthy adolescent and young women ages 16–21 yr with a BMI between the 10th and the 95th percentile for age (CDC Growth Charts from the National Center for Health Statistics; http://www.cdc.gov/nchs/data/nhanes/growthcharts/set3/chart%2016.pdf) were studied a minimum of 2 yr after the age at menarche. Participants were recruited from local university campuses and high schools and were enrolled after signing an Institutional Review Board-approved consent form. An additional signature was required from one parent or legal guardian for all subjects less than 18 yr of age. All participants were healthy, had completed pubertal maturation, and were taking no medications. Adolescents who were using a hormonal method of birth control or had a history of hirsutism were excluded. TSH was normal in all subjects. Pregnancy was excluded by serum measurement of human chorionic gonadotropin. All participants were instructed to contact the Principal Investigator at the start of the menstrual cycle and were admitted to the General Clinical Research Center (GCRC) in the follicular phase of the cycle, which was confirmed by the determination of serum progesterone.

There was a self-reported assessment of physical activity as well as an interview-based measure of fitness. To quantify individual levels of fitness, maximal oxygen consumption (VO2max) was determined from a standard exercise tolerance test. All patients were exercised on a treadmill using the Bruce protocol (14). Measurements during the test included continuous 12-lead electrocardiogram monitoring with blood pressure measurements made during the last minute of each stage of exercise. An electrocardiogram was obtained at 2 and 3 min of each stage of exercise.

Respiratory measurements made during testing included minute ventilation, oxygen consumption (VO2), carbon dioxide production (VCO2), and respiratory exchange. These were measured continuously during each test by a Sensor Medic Vmax cardiopulmonary exercise system.

Patients were encouraged to exercise as long as they physically could. Testing was stopped if there were any significant changes or symptoms; otherwise, the patients exercised until they were too fatigued to continue. To assure a maximal test was performed and a true VO2max was obtained, at least two of the following criteria needed to be met: 1) respiratory exchange rate at maximal exercise at least 1.10, 2) achievement of more than 90% of age-predicted maximal heart rate (220 –age), and 3) a plateau in VO2 (increase in VO2 < 2 ml/kg·min) despite increased exercise workload.

We estimated exercise level by asking each participant the number of days per week during which she exercised for at least 1 h of continuous aerobic exercise recorded as reported days of exercise (RDE) per week; this activity self-report provided a qualitative assessment of the participant’s perception of her own level of exercise. The duration of 1 h was chosen for its practicality, given that most aerobic classes or exercise videos are limited to 1 h. The types of physical activity reported included jogging, aerobics, field hockey, volleyball, soccer, in-line skating, skiing, diving, daily biking, resistance training, and exercise videotapes.

Body composition and relative body fat were obtained by dual x-ray absorptiometry (DEXA) using a total-body scanner (model DPX-L, Lunar Radiation Corp., Madison, WI) (15). No blood was drawn during the day or for the 36 h after either the treadmill or DEXA tests. Special effort was made to complete all studies within the span of one menstrual cycle. Outpatient visits for both treadmill and DEXA tests were within10.4 ± 0.1 (SE) and 10.7 ± 0.1 d, respectively, from the determination of insulin resistance by HOMAIR.

To obtain an estimation of daily caloric intake and dietary composition, a prospective 3-d food diary was obtained and followed by an interview with the GCRC dietary staff. Nutrient calculations were performed using the Nutrition Data System for Research software, version 4.03, developed by the Nutrition Coordinating Center, University of Minnesota, Minneapolis, MN, Food and Nutrient Database 31, released November 2000 (16). The participants were not asked to change their dietary habits, and while in the GCRC for 36 h, they were fed a self-selected diet from the hospital menu with standard meal times. After an overnight fast from 2100 h the night before, three blood samples were obtained at 10-min intervals between 0500 and 0800 h for measurements of glucose and insulin. Because IGF-I and SHBG have a potential modulating role on insulin sensitivity (17, 18), their levels were determined on these samples. The testosterone level was determined at the time of admission to the GCRC.

Assays

Serum samples were stored at –80 C until assayed. SHBG and IGF-I concentrations were measured using a solid-phase, two-site chemiluminescent enzyme immunometric assay for use with the Immulite automated analyzer (Immulite Diagnostic Product Corp., Los Angeles, CA). For SHBG, assay sensitivity was 0.2 nmol/liter; intra- and interassay coefficients of variation were 6.5 and 8.7%, respectively. For IGF-I, assay sensitivity was 20 ng/ml; intra- and interassay coefficients of variation were 2.5 and 10.7%, respectively.

Progesterone was measured to confirm the phase of the menstrual cycle, and testosterone was measured for the determination of the free androgen index (FAI). Both steroids’ concentrations were measured using the VITROS progesterone reagent pack and VITROS immunodiagnostic products progesterone calibrators on the VITROS ECi immunodiagnostic system. The assay uses a competitive immunoassay technique. For progesterone, assay sensitivity was 0.079 ng/ml (0.25 nmol/liter); intra- and interassay coefficients of variation were 5.2 and 9.5%, respectively. For testosterone, assay sensitivity was 0.865 ng/dl (0.03 nmol/liter); intra- and interassay coefficients of variation were 2.7 and 5.6%, respectively.

The glucose assay was performed on the Cobas Mira Chemistry Analyzer from Roche Diagnostics Corp (Indianapolis, IN). The reagent used for the analysis was purchased from Diagnostic Chemicals Limited (Oxford, CT). Glucose was measured by the hexokinase enzymatic method. Intra- and interassay coefficients of variation were, respectively, 0.6 and 3.2%. Insulin was measured by double-antibody RIA. Sensitivity of the insulin assay is 2.1 µU/ml (15.1 pmol/liter). Intra- and interassay coefficients of variation were 3.8 and 4.8%, respectively. The cross-reactivity with proinsulin was 71.6%.

Calculations

SHBG levels are inversely related to the level of androgenicity. FAI was calculated with the following formula: 3.467 x total testosterone (ng/dl)/SHBG (nmol/liter) (19). The insulin resistance index was estimated using the HOMAIR: mean fasting insulin (µU/ml) x fasting glucose (mmol/liter)/22.5 (20, 21). As an estimate of fitness, VO2max was calculated by dividing the highest recorded VO2 during exercise with the participant’s total body mass. Percent body fat was calculated from body composition analysis by DEXA.

Data and statistical analysis

The results are expressed as mean and SD. The relationships between the dependent variable insulin resistance and the independent variables BMI, percent body fat, diet, androgenicity, physical activity, and VO2max were analyzed using univariate linear regression. Correlations between all these variables were examined using Pearson correlations. Tests for collinearity were performed to assess for significant multicollinearity. Stepwise multiple regression models were constructed using Mallow’s C(p) criterion for the dependent variable of insulin resistance and the independent variables that were significant by univariate linear regression. Statistical analysis was performed using SAS version 6.12 (SAS Institute, Inc., Cary, NC). A value of P ≤ 0.05 was selected to indicate statistical significance.


   Results
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
Discussion
 References
 
Subjects’ characteristics

Table 1Go describes the participants’ nutritional and hormonal characteristics. Among the 53 patients, 36 were non-Hispanic-Whites, 12 were African-Americans, four were Asian-Americans, and one was Hispanic-White. IGF-I, testosterone, and FAI were comparable to age-specific norms (age-dependent reference ranges from Esoterix, Austin, TX: http://www.esoterix.com/files/expected_values.pdf; and Ref.22). The average BMI was at the 75th percentile for this age group, and the range was 18–30 kg/m2, spanning from underweight to overweight and moderately obese. The range for HOMAIR was 0.8–5.2 with 60% of values above 2.5, a cutoff previously used for insulin resistance in healthy adult males (20).


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  		TABLE 1. Subjects’ hormonal and nutritional characteristics

 
Cardiovascular fitness relative to total body mass was in the normal range for this age group with an average VO2max of 40.5 (6.8) ml/kg·min (14). The average caloric intake was 1799.0 (462.9) calories or 30% greater than the estimated resting energy expenditure of 1350 cal/d for this age group (23) and compatible with mild to moderate physical activity (23). RDE correlated with VO2max (P = 0.0003; r = 0.49).

As expected, BMI was significantly correlated with percent body fat (P = 0.0001; r = 0.85) and total fat mass (P = 0.0001; r = 0.74).

Univariate relationships

The results of the univariate regression for HOMAIR for all the variables analyzed are summarized in Table 2Go. BMI was a poor predictor of HOMAIR (P = 0.20), but percent body fat was significantly linked to HOMAIR (r = 0.34; P = 0.01) (Fig. 1Go) as well as RDE (P = 0.049). As shown in Fig. 2Go, VO2max was also highly associated with HOMAIR (r = 0.45; P = 0.0008), and for every unit increase in VO2max, HOMAIR decreased by 0.07. The estimated daily carbohydrate intake over 3 d, fat intake, and protein intake all had poor predictive value for HOMAIR. Neither IGF-I nor FAI was significantly associated with HOMAIR.


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  		TABLE 2. Univariate regression table for HOMAIR as a dependent variable (n = 53)

 


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  		FIG. 1. Univariate association between HOMAIR and percent body fat. Insulin resistance increases with greater percent body fat (r = 0.34; P = 0.01).

 


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  		FIG. 2. Univariate association between HOMAIR and VO2max. Insulin resistance decreases with increased cardiovascular fitness (r = –0.45; P = 0.0008).

 
Multiple regression models

We used multiple regression analysis to further explore the relative contributions of body composition and fitness to insulin resistance as summarized in Table 3Go. The dependent variable in the model was HOMAIR. The independent variables tested in the model were VO2max and percent body fat and accounted for 21% of the variance. VO2max remained a significant predictor even when accounting for percent body fat, whereas percent body fat did not retain significance. Thus, insulin sensitivity was predicted by the VO2max univariate model, accounting for 20% of the variance in HOMAIR (P = 0.0008).


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  		TABLE 3. Multivariate regression models for HOMAIR as a dependent variable

 
When both measures of physical activity in these young women were simultaneously included in a regression model, RDE was no longer statistically significant, indicating VO2max was a better measure of the exercise-HOMAIR relationship (Table 3Go).

The stepwise regression models were also analyzed with the C(p) statistic (Table 4Go). Among the three predictors that were significant by univariate analyses and included into the model using the C(p) statistic, VO2max remained the best predictor of HOMAIR with the smallest C(p) statistic. Tests for collinearity for VO2max, percent body fat, and RDE yielded variation inflation factors that were all less than 10, ranging from 1.33–2.15. No condition indices were more than 30. Thus, the degree of collinearity did not confound the results from the multiple regression models.


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  		TABLE 4. Stepwise regression model using C(p) statistic for HOMAIR as a dependent variable

 

   Discussion
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
References
 
In adult women, a healthy weight considerably reduces the risk of type 2 diabetes (9). In contrast to older populations, the relationship between obesity and the risk for insulin resistance and type 2 diabetes is not known in younger women. In this multiethnic cohort of postpubertal adolescent females, we found that BMI had poor predictive value for insulin resistance, whereas cardiovascular fitness was a predictor of HOMAIR even when accounting for percent body fat.

In youth, the impact of physical activity has been inferred by indirect methods such as activity reports (4, 24), the documentation of sedentary lifestyle (25, 26), physical activity recall (7), or the participation in organized sports (27). The debate about how much exercise is desirable for health maintenance is still open given that there is significant subject-to-subject variation (28). The recommendations for daily physical activity are estimated at a minimum of 30 min a day but more recent reports have proposed at least 1 h a day for the best metabolic benefits (29, 30). In addition, practical considerations in our study design took into account that most exercise classes at the local gymnasiums or exercise videotapes favored by some participants were available in 1-h modules. Our goal was to measure fitness and compare that assessment to the participants’ own assessment of their physical activity based on self-report, similar to the type of information that would be exchanged in a health maintenance visit.

The fitness range in our participants was similar to those reported in previous studies. The adolescent girls studied by Andersen et al. (31) showed a decrease of VO2max during late puberty as body weight increased. In the more recent study of Ahmad et al. (14), VO2max of adolescent girls showed a modest increase between 12 and 14 yr of age followed by a drop for the 16- to 18-yr group with an average of 40.1 ± 6.1 ml/kg·min. This stood in sharp contrast to boys in both studies, who showed an increase in cardiovascular fitness in late puberty. Taken together with the dramatic decline in physical activity reported by Kimm et al. (7), these reports highlight the vulnerability of adolescent girls whose fitness tends to stagnate in face of increasing weight. The contribution of our study is to show the link between insulin resistance and cardiovascular fitness within the range of VO2max measured in this age group, because those with higher than average fitness had a lower HOMAIR index. Longitudinal studies will help determine whether deliberate strategies to optimize VO2max in adolescent girls can minimize the risk for insulin resistance.

We sought to measure physical activity in the participants by reducing the subjectivity inherent to survey methods (32). By measuring VO2max we were able to quantify physical fitness rather than relying solely on self-reported physical activity (26). Although self-reported exercise had a significant positive relationship with HOMAIR, VO2max was a stronger explanatory variable than RDE, which was not significant when included within the same model as VO2max. Our findings identify relative physical inactivity as an important predictor of insulin resistance in young women. In the same adolescents studied, relative fat mass was a predictor of HOMAIR, but it lost its predictive value if cardiovascular fitness was introduced in the statistical model. Thus, in this population, adiposity does not appear to matter as much as fitness as a predictor of insulin resistance.

The lack of association between BMI and insulin resistance found in our study is consistent with two recent studies with a focus on the relationship between obesity and type 2 diabetes in children and adolescents. In a multiethnic cohort of 167 obese children, Sinha et al. (12) found a high prevalence of impaired glucose tolerance, yet the BMI had a poor predictive value for risk factors associated with type 2 diabetes, possibly because the population studied was already severely and homogeneously obese. Similar findings were recently reported in a larger cohort of obese European adolescents, in which the degree of obesity was not linked to insulin resistance per se (11). However, we cannot exclude an important relationship between BMI and HOMAIR in a larger group of participants and a broader range of BMI including leaner as well as heavier subjects than those studied here.

The norm for insulin resistance as determined by HOMAIR in postpubertal young women has not been defined before (11, 33), and clearly more age- and gender-specific studies are needed. Over 60% of our participants had HOMAIR greater than the normal cutoff of 2.5 used by others (11) to identify impaired insulin sensitivity. Our findings parallel those reported by Invitti et al. (11) in their large cohort of children of both genders across pubertal stages where 64% of obese children had HOMAIR greater than 2.5. However, whether cardiovascular fitness was a predictor of relative sensitivity to insulin in that group was not determined (11).

Despite its ease of determination, HOMAIR does have some limitations in the assessment of responsiveness to insulin. First, it may not account for the effects of oscillatory insulin release, although this pitfall can be partially overcome by repeated sampling as was done in our study (20). Second, HOMAIR does not assess insulin sensitivity with the breadth and accuracy of the steady-state euglycemic clamp (33), which remains the gold standard for this measurement. HOMAIR derives from basal insulin level rather than a quantitative measured response to an insulin challenge as in the euglycemic clamp or response to dynamic changes of endogenous or exogenous insulin as in the frequently sampled iv glucose tolerance test (34). Basal insulin depends on a sensitive insulin assay and may be confounded by the measurement of proinsulin, which could vary among subjects (35). More studies are needed to establish whether the HOMAIR has a predictive value for insulin-stimulated states in adolescent females.

Because overt evidence of androgen excess such as hirsutism was an exclusion criteria, polycystic ovary syndrome was an unlikely contributor to insulin resistance (36) in our population. This was confirmed by the lack of association between FAI and HOMAIR. In an effort to standardize for the enhancing effect of puberty on insulin resistance (33, 37), all participants were recruited after the completion of puberty and a minimum of 2 yr after menarche, a time when participants were expected to be near final height. To further control for insulin resistance associated with puberty, IGF-I was measured and showed no significant relationship with HOMAIR.

A role for the relative contribution of dietary protein, carbohydrate, and fat to insulin sensitivity has been proposed (38, 39). We were not able to detect a relationship between dietary composition and HOMAIR, although the data collection was limited to only 3 d. Long-term dietary studies with a more detailed analysis of dietary fats and high-fiber carbohydrates, which was not done in this study, might be more informative on the potential for relationship of dietary factors to insulin resistance in young women.

Adolescent females show a dramatic decrease of physical activity as they progress through puberty (7), which places them at risk for obesity. There have been mounting reports about the lack of physical activity in children across the United States (6). However, much focus has been placed on the deleterious effects resulting from the frequent consumption of high-energy foods (4, 40). If VO2max is a strong predictor of insulin resistance in adolescent girls, then an effective strategy to reduce metabolic risk may be to reverse the serious trend of decreasing physical activity both in the schools and in the more general extracurricular environment.

Our subjects were in the latter part of adolescence and would for the most part qualify as young adults. More studies are needed in younger pediatric populations. Albeit well characterized, our study sample was small, thus limiting the ability to generalize about the relationships observed. However, our study echoes the recent report that total fat mass reduction does not by itself improve metabolic outcomes (41). As the fight against obesity gains momentum (42, 43) and more radical treatments are being considered for youth (44, 45), it will be important to determine whether they provide long-term protection against type 2 diabetes without a concomitant long-term exercise intervention that is sustained enough to improve cardiovascular fitness.


   Acknowledgments
 
We thank Sheila Gahagan for her assistance in reviewing this manuscript and Terrin Meckmongkol for technical assistance. We are most grateful to Andrzej Galecki for his statistical expertise.


   Footnotes
 
This study was funded by Grant 1K12HD01438-01 BIRCWH from the Office of Women’s Health Research and the National Institute of Child Health (J.Z.K.-V.), a Department of Veterans Affairs’ Career Development Award (to C.C.L.), and GCRC Grant M01-RR-0042.

First Published Online November 30, 2004

Abbreviations: BMI, Body mass index; DEXA, dual x-ray absorptiometry; FAI, free androgen index; GCRC, General Clinical Research Center; RDE, reported days of exercise; VO2max, maximal oxygen consumption.

Received March 11, 2004.

Accepted November 15, 2004.


   References
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 

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