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EPIDEMIOLOGY

Fitness Change Effects on Midlife Metabolic Outcomes

CHOW, LISA1; EBERLY, LYNN E.1; AUSTIN, ERIN1; CARNETHON, MERCEDES2; BOUCHARD, CLAUDE3; STERNFELD, BARBARA4; ZHU, NA1; SIDNEY, STEVE4; SCHREINER, PAMELA1

Author Information
Medicine & Science in Sports & Exercise: May 2015 - Volume 47 - Issue 5 - p 967-973
doi: 10.1249/MSS.0000000000000481
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Abstract

Americans are becoming increasingly obese and sedentary. In 2011, the majority of Americans reported either no or low physical activity (10). Consequently, there is growing interest in the role of physical activity and fitness in reducing the morbidity and mortality associated with the obesity epidemic.

Physical activity has an established dose–response relation with fitness (11). Moderate to high fitness level, as measured by cardiorespiratory fitness (CRF) from exercise testing, has many established benefits. In several prospective studies, higher baseline CRF was associated with lower risk of future CHD (2), metabolic syndrome (8), diabetes (29) and total mortality (2,4). The implications of fitness, as described by a single measurement of CRF (2,4,29), have been subsequently complemented by studies examining the effects of CRF change. Compared with peers with longitudinal CRF decrease, individuals with longitudinal CRF improvement or maintenance over time had lower risk of hypertension (26), metabolic syndrome (8,26), hyperlipidemia (26), diabetes (9), cardiovascular mortality (3), and total mortality (3). Several factors, however, limit our understanding of CRF change and health outcomes. Most (3,26) but not all (9) of the current literature has focused on a predominantly white male population, reducing generalizability. The time interval for measuring CRF change has varied greatly, typically spanning 5–7 yr (3,8,26). Although it had been previously shown in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort that participants who developed diabetes over 20 yr had larger declines in fitness after 20 yr than those who did not develop diabetes (9), whether certain subpopulations, as described by body mass index (BMI) or insulin resistance (IR), might be particularly affected remains unknown.

Because improving fitness on a population level is challenging and expensive, identification of populations at high risk for metabolic complications from either low baseline or decreased fitness over time would enhance the impact of fitness interventions. Antecedent obesity (21) and IR (13) increase the risk for adverse cardiometabolic outcomes, yet the health outcomes in common subgroups such as the “metabolically obese, normal weight” (34) (∼28% of the US population age 20 yr and older [40]) or “obese but metabolically healthy” (30) (8% of the US population age 20 yr and older [40]) remain less well described. Fitness may contribute to the “obese, metabolically healthy” phenotype (30); however, the relative contribution of fitness change remains less understood.

The CARDIA cohort presents a unique opportunity to address this knowledge gap. CARDIA is a 25-yr biracial prospective cohort study that has collected both fitness and cardiometabolic data over 25 yr by standardized protocols with stringent quality control. Previously in CARDIA, it has been shown that young adults with elevated metabolic risk (as defined from a composite of blood pressure, glucose, insulin, HDL cholesterol (HDL-C), triglycerides (TG), and waist girth) had lower baseline CRF compared with young adults with normal metabolic risk; however, the rate of CRF decline after 20 yr was similar between the two groups (7). As this earlier CARDIA study did not describe the relation between change in long-term fitness and change in cardiometabolic risk factors (7), and another earlier CARDIA study did not subdivide by BMI and IR status (9), we will extend these previous findings by specifically examining fitness change from year 0 (Y0) to Y20 and outcome change from Y0 to Y25 to see whether the association between young adult BMI/IR status and middle-age metabolic con sequences may be influenced by interim fitness change while controlling for baseline fitness. We hypothesize that interim fitness change alters the association between young adult BMI/IR status and middle-age cardiometabolic outcomes, with participants who maintained or increased fitness having more favorable alterations in cardiometabolic measures than participants with decreased fitness, independent of baseline fitness.

METHODS

Participants.

The CARDIA study is a prospective, multicenter, cohort study designed to investigate trends and determinants of CHD risk factors in young adults. Black and white women and men (age 18–30 yr at Y0) were recruited and examined in 1985–1986 from four US communities (Birmingham, AL; Chicago, IL; Minneapolis, MN; and Oakland, CA) and balanced on age, race, sex, and educational attainment as previously described (18). A total of 5115 participants were enrolled at baseline (Y0) with follow-up examinations at 2, 5, 7, 10, 15, 20, and 25 yr after baseline, with retention of 91%, 86%, 81%, 79%, 74%, 72%, and 72% of the surviving cohort, respectively. All participants provided written informed consent. The study was approved by the institutional review boards from each participating institution.

For this particular analysis, participants were excluded if they had elevated fasting glucose ≥7 mmol·L−1 (n = 31) at baseline. Of the remaining 5084 participants, an additional 2288 participants were excluded if they were missing fitness testing at Y0 (n = 150) or Y20 (n = 2138) or if they had incomplete outcome data at Y20 (n = 23 participants not previously excluded) or Y25 (n = 725 participants not previously excluded). In total, 2048 participants (59% of CARDIA Y25 participants) met the study criteria for the analysis.

Designation of Y0 BMI and IR.

Y0 BMI was categorized as either normal (nBMI <25 kg·m−2) or high (hBMI ≥25 kg·m−2). Y0 IR status was measured by the homeostatic model assessment (HOMA-IR) (5). Participants were considered either insulin sensitive (IS) (HOMA-IR <75th percentile (<1.84)) or insulin resistant (IR) (HOMA-IR ≥75th percentile (≥1.84)), similar to previous studies (1,33). We used Y0 BMI and IR status to classify participants into one of four groups: 1) normal BMI/IS (nBMI/IS), 2) high BMI/IS (hBMI/IS), 3) normal BMI/IR (nBMI/IR), and 4) high BMI/IR (hBMI/IR) (Table 1).

TABLE 1
TABLE 1:
Definition of terms used in this article.

Measurement of CRF.

The CARDIA study measured CRF at Y0 and Y20 by symptom-limited graded treadmill exercise testing using a modified Balke protocol (35). Briefly, the exercise test protocol was designed to assess maximal symptom-limited performance. After baseline measurement of pulse, blood pressure, and EKG, the participant started the protocol. The protocol consisted of nine stages (2 min each, maximum of 18 min in total) of progressively increasing difficulty, with the first six stages generally performed by walking. Stage 1 was 3.0 mph at 2% grade (4.1 METs), progressing to stage 9 at 5.6 mph at 25% grade (19.0 METs). Y0 fitness was classified as either low (<33rd percentile for sex) or average high (≥33rd percentile for sex), similar to previous literature (36) (Table 1). Because we were interested in the effects of long-term fitness change, only fitness data from the Y0 and Y20 time points were used. Fitness change (Y20 treadmill time − Y0 treadmill time) was considered either maintained (increase or decline ≤20th percentile for sex) or decreased (decline >20th percentile for sex) (Table 1). This 20th percentile cutoff was derived from the observation that the mean age-related decline in fitness (16) is approximately 10% (5% per decade) from the age of 30 to 50 yr in men and women (16). In our statistical analysis, we adjusted for baseline fitness by incorporating the Y0 treadmill time as a covariate.

Measurement of cardiometabolic risk factors.

Cardiometabolic risk factors were measured using standardized protocols across field centers and examinations with quality control monitoring (12,18). Briefly, body weight was measured in light clothing to the nearest 0.2 lb using a balance beam scale; waist girth was measured midway between the xiphoid process and the umbilicus (12). Blood pressure was measured three times after a 5-min rest using a Hawksley random zero sphygmomanometer (WA Baum Company, Copiague, NY) on the right arm of the seated participant at Y0 and a standard automated aneroid monitor (OmROn model HEM907XL; Omron Healthcare, Inc., Lake Forest, IL) at Y20. For each visit, the average of the last two measures was used (12). Fasting plasma blood samples were sent to the Northwest Lipid Research Laboratories, University of Washington (Seattle, WA), for lipid determination. Total cholesterol and TG were measured enzymatically (38), HDL-C was determined after dextran sulfate–magnesium chloride precipitation (39), and LDL cholesterol (LDL-C) was calculated using the Friedewald equation (17). Serum glucose concentrations were measured using the hexokinase method at Linco Research Inc. (St. Charles, MO). Serum insulin was measured by immunoassay (12). HbA1c samples were sent to the University of Minnesota (Minneapolis, MN) and were measured using the Tosoh G7 high-performance liquid chromatography instrument (Tosoh Bioscience, Inc.; South San Francisco, CA). Incident diabetes in nonpregnant participants was determined at each follow-up visit (year 7, 10, 15, 20, or 25) if any of the following criteria were met: fasting glucose ≥7 mmol·L−1, use of medications for diabetes treatment, 2-h glucose tolerance test ≥11.1 mmol·L−1 (performed at Y10, Y20, or Y25), or hemoglobin HbA1c ≥6.5% (48 mmol·mol−1) (performed at Y20 or Y25).

Outcomes.

The primary outcomes were percentage change (100 × [Y25 − Y0]/Y0) in weight and in waist girth between Y0 and Y25 as well as incident diabetes by Y25. The secondary outcomes were percentage change in cardiometabolic risk factors including mean arterial pressure (MAP) ([2 × DBP + SBP]/3), LDL-C, TG, and HDL-C.

Other measurements.

Covariates were selected as possible confounders in our analysis because of their clinical relevance and association with BMI, IR, or fitness (7,8,16). Relevant covariates were measured at Y0 and included age, sex, race (black vs white), field center, and lifestyle factors (physical activity, smoking, energy intake, alcohol intake, and education level). These covariates were measured by trained and certified staff using standardized protocols across field centers and examinations with quality control monitoring (12,18). Age, race, and sex were confirmed during the clinic visits. Educational attainment was based on self-reported number of years of schooling and the highest degree earned at the last follow-up examination attended. Elapsed time between examinations was calculated using the baseline and follow-up examination dates. Physical activity level (reported as exercise units (EU)) was measured using the CARDIA physical activity history questionnaire, an interviewer-based self-report of duration and intensity of participation in 13 categories of exercise over the previous 12 months (22). For reference, 300 EU approximates 150 min of moderate-intensity activity (3–5 METs) per week or 30 min of moderate-intensity activity 5 d·wk−1 (32). Diet was quantified (including total energy intake) using a semiquantitative, interviewer-administrated, validated diet history food frequency questionnaire (28).

Statistical analysis.

All analyses were conducted by using SAS (version 9.2; SAS institute, Inc., Cary, NC). Baseline characteristics were summarized for participants across the baseline groups using ANOVA (for continuous characteristics) and Fisher’s exact test or Pearson’s chi-squared test as appropriate (for categorical characteristics).

We used two multiple linear regression models (described below) to examine the associations of each of the fitness change and baseline BMI/IR status with percentage change from baseline in cardiometabolic outcomes at Y25. Because there was no overall statistically significant interaction between fitness change and BMI/IR status on any of the continuous cardiometabolic outcomes, the interaction term (fitness change × BMI/IR status) was not included in the final models, and subgroup analysis for this interaction was not reported. Cox proportional hazards regression was used to examine the relations between incident diabetes and exposures. Time to diabetes was approximated as the difference between baseline and the first follow-up visit with a report of diabetes. Because the interaction between fitness change and BMI/IR status was statistically significant for incident diabetes, this interaction term was retained in the two models for this outcome only. The proportional hazards assumption was tested using Schoenfeld residuals (37). Model 1, for both continuous and incident diabetes outcomes, included Y0 BMI/IR status, age, race, sex and field center, fitness change (maintained vs decreased), and Y0 fitness (treadmill duration). Model 2 further adjusted for other relevant covariates measured at Y0: education (highest year of school completed), cigarette smoking (current/former/never), alcohol consumption (mL·d−1), total physical activity intensity score (in EU with >800 EU being very high activity) (22), and energy intake (total kilocalories per day).

RESULTS

Baseline characteristics for participants as separated by young adult BMI, IR status.

The mean age ranged from 23 to 26 yr old at baseline (Y0) across BMI and IR groups. The sex distribution was similar across all four categories (nBMI/IS, hBMI/IS, nBMI/IR, and hBMI/IR). Table 2 reports the baseline values (mean ± SD if continuous variables, percentages if categorical). In the hBMI/IR group, there were fewer participants who had education beyond high school (62%) and who are white (37%). Caloric intake and alcohol use were similar across BMI and IR groups.

TABLE 2
TABLE 2:
Baseline variables by baseline BMI/IR status.

The hBMI/IR participants (n = 305) had the highest mean weight (88.5 ± 16.3 kg), waist girth (91 ± 12 cm), BMI (30.4 ± 5 kg·m−2), MAP (85 ± 9 mm Hg), fasting glucose (4.8 ± 0.4 mmol·L−1), LDL-C (3.13 ± 0.78 mmol·L−1), TG (1.05 ± 0.77 mmol·L−1), and HOMA-IR (2.8 ± 1.1). The hBMI/IR participants had the lowest physical activity (355 ± 263 EU), baseline treadmill exercise time (501 ± 158 s), and HDL-C (1.22 ± 0.28 mmol·L−1). In terms of baseline fitness, the hBMI/IR participants had the highest percentage of low fit participants (71%), followed by hBMI/IS (43%), nBMI/IR (33%), and nBMI/IS (21%).

Across BMI and IR groups, 16%–25% of participants maintained fitness after 20 yr. The hBMI/IS participants had the lowest percentage of maintained fitness (16%) and greatest decline in fitness (−189 ± 121 s) compared with the other groups. The nBMI/IR had the highest percentage of maintained fitness (25%) and least decline in fitness (−154 ± 123 s) compared with the other groups.

Fitness change after 20 yr by baseline BMI/IR status and fitness.

Within the CARDIA cohort, 20.6% maintained fitness after 20 yr. Maintained fitness was less common in participants with average-high baseline fitness (7% (hBMI/IR) to 17% (nBMI/IS and nBMI/IR groups)) than participants with low baseline fitness (25% (hBMI/IS) to 41% (nBMI/IR)). Among participants with average-high baseline fitness, treadmill times generally decreased in those who maintained fitness (−12 ± 62 s (nBMI/IS) to −40 ± 54 s (hBMI/IS)). Among participants with low baseline fitness, treadmill times generally increased in those who maintained fitness (+5 ± 82 s (nBMI/IS) to +17 ± 126 (nBMI/IR)) with the exception of the hBMI/IR group (−21 ± 52 s). Consequently, the number of participants who had average-high baseline fitness and maintained fitness after 20 yr was low across the BMI/IR status groups (nBMI/IS, n = 157; nBMI/IR, n = 25; hBMI/IS, n = 19; hBMI/IR, n = 6).

Effect of fitness change on midlife cardiometabolic outcomes.

Associations between fitness change and any of the continuous cardiometabolic outcomes did not differ by baseline BMI/IR status after controlling for baseline fitness level. Consequently, we present the results from fitness change controlling for BMI/IR (Table 3).

TABLE 3
TABLE 3:
Effects of fitness change on midlife cardiometabolic outcomes.

Participants with maintained fitness gained significantly less weight and waist girth by middle age (Y25) than participants with decreased fitness (P < 0.0001 maintained vs decreased fitness, models 1 and 2). In terms of the lipid profile, participants with maintained fitness had higher HDL (P < 0.0001 maintained vs decreased fitness, models 1 and 2), lower LDL (P = 0.02 for model 1 and P = 0.03 for model 2 maintained vs decreased fitness) and lower TG (P < 0.0001 maintained vs decreased fitness, models 1 and 2) than those with decreased fitness. Maintained fitness was also associated with less increase in blood pressure, as measured by MAP, than decreased fitness (P = 0.001 for model 1 and P= 0.01 for model 2 maintained vs decreased fitness).

Effect of young adult BMI/IR status and fitness change on incident diabetes by middle age.

In the CARDIA cohort, 11.5% of the young adults in CARDIA (n = 236) developed incident diabetes by middle age (Y25). Because a statistically significant interaction was observed between BMI/IR status and incident diabetes by fitness change, we present stratum-specific results for this outcome. The incidence rate was 12.4% in participants with decreased fitness (n = 201 out of 1626) and 8.3% in participants with maintained fitness (n = 35 out of 422) (P = 0.02). In young adults who were IS at baseline, the HR for incident diabetes was higher with decreased fitness given normal BMI (HR, 6.7; 95% CI, 2.1–21.8; P < 0.01 decreased vs maintained) or high BMI (HR, 4.5; 95% CI, 1.4–14.6; P = 0.01 decreased vs maintained). In young adults who were IR at baseline, the HR for incident diabetes was not statistically different between decreased fitness and maintained fitness given normal BMI (HR, 1.4; 95% CI, 0.5–3.5; P = 0.51 decreased vs maintained) and was borderline statistically significant given high BMI (HR, 1.5; 95% CI, 0.9–2.5; P = 0.08 decreased vs maintained). Relative to a referent group of nBMI/IS/maintained fitness (HR = 1), IR participants who maintained fitness were at higher risk for developing incident diabetes if normal BMI (HR, 7.7; 95% CI, 1.9–30.8; P = 0.004 compared with nBMI/IS/maintained fitness) or high BMI (HR, 18.1; 95% CI, 5.4–61.0; P < 0.001; 95% CI, 5.4–61.0, compared with nBMI/IS/maintained fitness).

DISCUSSION

We were interested in whether the association between young adult BMI/IR status and middle-age metabolic consequences may be influenced by interim fitness change. Adjusting for baseline fitness, we found that participants with maintained fitness had more favorable alterations in cardiometabolic measures than participants with decreased fitness. The main findings from this study include the following: 1) A greater percentage of maintained fitness was observed in participants with low baseline fitness than participants with average-high baseline fitness. 2) Maintained fitness was less common in hBMI participants than nBMI participants. 3) Participants with maintained fitness after 20 yr had less increase in weight gain, waist girth, LDL, TG, and blood pressure and greater increase in HDL than participants with decreased fitness. 4) Although maintained fitness reduced the rates of incident diabetes in the IS participants, this was not observed in the IR participants.

Currently, it remains highly debated whether obesity itself or low CRF/inactivity plays a greater role in the adverse metabolic consequences associated with obesity. The benefits of physical activity on improving cardiometabolic risk factors in patients at risk for developing diabetes have been shown by interventional studies (24,31). Subsequent follow-up after these interventions (10–20 yr later) found lower rates of incident diabetes in the intervention group than the placebo group (25,27). The clinical significance of these benefits, however, remains unknown. In the Look AHEAD study, overweight/obese patients with type 2 diabetes (age 45–75 yr; mean age, 58 yr) received intensive lifestyle intervention and were followed clinically (median follow-up: 9.6 yr). Although the intervention improved fitness, reduced weight, reduced waist circumference, and reduced glycated hemoglobin, cardiovascular morbidity, and mortality remained unchanged, and the study was terminated early on the basis of a futility analysis (19). These observations are consistent with our findings that maintained fitness may be less effective in reducing cardiometabolic outcomes in high-risk populations. Our study extends the current literature by examining the long-term effects of fitness change using a younger cohort well balanced by sex and race.

Although the response of fitness to physical activity is generally dose related (11), genetics (6) may influence the magnitude of the response. Because exercise therapy without concomitant dietary therapy produces minimal weight loss (41), much of the metabolic benefits from maintained fitness likely arises from alterations in body composition. Short-term, weight-stable, exercise training reduces hepatic fat and visceral fat (23). Reduction in fat mass is associated with improved insulin sensitivity (14) and incident diabetes (20). Our study suggests that 20 yr of maintained fitness is associated with less adiposity, as approximated by changes in weight and waist girth. However, the beneficial effect of maintained fitness was blunted in the presence of IR, particularly within the context of high BMI. Consequently, in young adults with IR, reduction in weight and adiposity may be insufficient to improve cardiometabolic risk factors, and other mechanisms such as ectopic fat deposition or systemic inflammation may need to be addressed.

This study is clinically relevant for several reasons. Although we advocate that increased fitness is an important and laudable goal, relatively few participants in the CARDIA study increased fitness by 20 yr and the reality is maintained fitness. This study examined the implications of maintained fitness, while adjusting for baseline fitness, and found that young adults who maintained fitness had improved cardiometabolic risk factors by middle age, although the results may be blunted by baseline IR. In addition, our observations complement previous findings that body fat, fitness, and lifestyle in adolescence (age of 13 yr) all independently increase the development of metabolic syndrome by young adulthood (age of 36 yr) (15). Because we found that young adults with IR had less benefit from maintained fitness in reducing incident diabetes, reducing IR before young adulthood may be particularly impactful.

This study had several strengths. This study focused on a key demographic: young adults (age 18–30 yr) who were prospectively followed into middle age (age 43–55 yr). This study capitalizes on several key features of CARDIA, specifically the prospective measurement of fitness by treadmill testing, balanced cohort in terms of sex and race, outcome measurements using standardized protocols with rigorous quality control, and frequent in-person follow-up visits to ascertain incident diabetes. Certainly, some limitations need to be considered. Given the limitations of an observational study, only association and not causation can be shown. The effects of fitness change on cardiometabolic parameters may vary depending on whether the observed interval is between young adulthood and middle age or between middle age and older age. The categorization of fitness change (≤20th percentile as maintained fitness) is arbitrary but parallels well with observed age-associated decline in fitness (0.5%–1% decline per year) (16). We acknowledge that participants with maintained fitness may be a very heterogenous group, with maintained fitness easier to attain in participants with low baseline fitness than participants with high baseline fitness. However, we adjusted for this difference in baseline fitness by incorporating baseline fitness in both of our models. Although the number of participants who developed incident diabetes was low in certain subgroups of young adult BMI/IR status/fitness change, CARDIA remains as one of the best cohorts available to address this issue given its objective measures of fitness change and standardized long-term follow-up. Whether increased fitness may be more significant than maintained fitness cannot be determined at this time because only a small number of CARDIA participants increased fitness after 20 yr. Lastly, we acknowledge that the risk factors of an overweight individual is likely less adverse than an obese individual, which may be masked by the categorization of the overweight and obese participants into the high BMI group. However, we could not separately analyze the obese young adults given the young age of the CARDIA population at the time of recruitment in 1985–1986 and relatively rare prevalence of obesity at the time of recruitment (n = 598/5115 (11.7% of the total CARDIA cohort), n = 175/2048 (8.5% of the analyzed cohort)).

Generally speaking, young adults with maintained fitness after 20 yr had more favorable middle-age cardiometabolic risk factors than their peers with decreased fitness. However, young adults who were IR were at higher risk for developing incident diabetes than young adults who are IS, even with maintained fitness. We advocate the importance of maintained fitness and, in young adults who are IR, the additional need for reducing IR to improve middle-age cardiometabolic outcomes.

The authors thank Jessica DeWolfe Kornaus, Division of Epidemiology and Community Health, University of Minnesota, for creating the analytic data set for analysis. The authors acknowledge the assistance of Dr. Anne Marie Weber-Main, Department of Medicine, University of Minnesota, for her critical review and editing of the manuscript drafts. The authors acknowledge the assistance of Dr. James Shikany, Department of Medicine, University of Alabama-Birmingham, for his critical review of the manuscript. The authors thank the staff and participants of the CARDIA study for their important contributions.

The Coronary Artery Risk Development in Young Adults Study (CARDIA) is supported by contracts HHSN268201300025C, HHSN268201300026C, HHSN268201300027C, HHSN268201300028C, HHSN268201300029C, and HHSN268200900041C from the National Heart, Lung, and Blood Institute (NHLBI), the Intramural Research Program of the National Institute on Aging (NIA), and an intraagency agreement between NIA and NHLBI (AG0005).

L. C., L. E., E. A., M. C., C. B., B. S., N. Z., S. S., P. S. have received support from the National Institutes of Health either related (M. C., C. B., B. S., N. Z., S. S., P. S.) or unrelated to this work (L. C., L. E., E. A.). B. S. and S. S. are supported by the Permanente Medical Group. C. B. has received consulting fees from Pathway Genomics, Nike, Weight Watchers, Pepsi Co, and grants from the Prince Faisal Bin Fahad International Prize for Arab Sport Development and Research, European Union Grant, previous lecture payments from the University of Nevada, Canadian Society of Exercise Physiology, University of Miami, American College of Medical Genetics, East Carolina University, University of Memphis, University of Georgia, payment for manuscript preparation (Elsevier), Royalties (Informa Healthcare, Human Kinetics), and Travel expenses (Brock University, University of Guelph, The Obesity Society, German Sports Medicine Congress, Norwegian University of Science and Technology Faculty of Medicine, FRQS, The Physiological Society, Society of Behavioral Medicine, Laval University, University of Ottawa, Brazilian Symposium on Genetics and Sports, ISCEMIS, Texas Chapter of American College of Sports Medicine, American Physiological Society, European College of Sport Science, Columbia University, International Society of Nutrigenetics and Nutrigenomics, and American Heart Association). There are no duality of interest reported by any of the authors associated with this manuscript.

L. S. C. and P. S. designed the research. M. C., B. S., S. S., C. B., and P. S. provided the essential data for analysis. L. S. C., E. A., L. E. E., and P. S. analyzed the data. L. S. C., E. A., L. E. E., and P. S. wrote the manuscript. L. S. C., M. C., B. S., L. E. E., E. A., N. Z., S. S., and P. S. contributed to the discussion and reviewed/edited the manuscript. L. S. C. has primary responsibility for the final content. All authors have read and approved the final manuscript.

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

REFERENCES

1. Ascaso JF, Pardo S, Real JT, et al. Diagnosing insulin resistance by simple quantitative methods in subjects with normal glucose metabolism. Diabetes Care. 2003; 26 (12): 3320–5.
2. Blair SN, Kampert JB, Kohl HW, et al. Influences of cardiorespiratory fitness and other precursors on cardiovascular disease and all-cause mortality in men and women. JAMA. 1996; 276 (3): 205–10.
3. Blair SN, Kohl HW, Barlow CE, et al. Changes in physical fitness and all-cause mortality. A prospective study of healthy and unhealthy men. JAMA. 1995; 273 (14): 1093–8.
4. Blair SN, Kohl HW, Paffenbarger RS, et al. Physical fitness and all-cause mortality. A prospective study of healthy men and women. JAMA. 1989; 262 (17): 2395–401.
5. Bonora E, Saggiani F, Targher G, et al. Homeostasis model assessment closely mirrors the glucose clamp technique in the assessment of insulin sensitivity—studies in subjects with various degrees of glucose tolerance and insulin sensitivity. Diabetes Care. 2000; 23 (1): 57–63.
6. 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): 252–8.
7. Camhi SM, Katzmarzyk PT, Broyles S, et al. Association of metabolic risk with longitudinal physical activity and fitness: Coronary Artery Risk Development in Young Adults (CARDIA). Metab Syndr Relat Disord. 2013; 11 (3): 195–204.
8. Carnethon MR, Gidding SS, Nehgme R, et al. Cardiorespiratory fitness in young adulthood and the development of cardiovascular disease risk factors. JAMA. 2003; 290 (23): 3092–100.
9. Carnethon MR, Sternfeld B, Schreiner PJ, et al. Association of 20-yr changes in cardiorespiratory fitness with incident type 2 diabetes: the Coronary Artery Risk Development in Young Adults (CARDIA) fitness study. Diabetes Care. 2009; 32 (7): 1284–8.
10. Centers for Disease Control and Prevention. Summary Health Statistics for US Adults: National Health Interview Survey 2011. http://www.cdc.gov/nchs/nhis.htm. Series 10, Number 256, January 2013.
11. Church TS, Earnest CP, Skinner JS, et al. Effects of different doses of physical activity on cardiorespiratory fitness among sedentary, overweight or obese postmenopausal women with elevated blood pressure—a randomized controlled trial. JAMA. 2007; 297 (19): 2081–91.
12. Cutter GR, Burke GL, Dyer AR, et al. Cardiovascular risk factors in young adults. The CARDIA baseline monograph. Control Clin Trials. 1991; 12 (1 Suppl): 1S–77.
13. Defronzo RA, Ferrannini E. Insulin resistance. A multifaceted syndrome responsible for NIDDM, obesity, hypertension, dyslipidemia, and atherosclerotic cardiovascular disease. Diabetes Care. 1991; 14 (3): 173–94.
14. Dube JJ, Amati F, Stefanovic-Racic M, et al. Exercise-induced alterations in intramyocellular lipids and insulin resistance: the athlete’s paradox revisited. Am J Physiol Endocrinol Metab. 2008; 294 (5): E882–8.
15. Ferreira I, Twisk JWR, van Mechelen W, et al. Development of fatness, fitness, and lifestyle from adolescence to the age of 36 years—determinants of the metabolic syndrome in young adults: the Amsterdam growth and health longitudinal study. Arch Intern Med. 2005; 165 (1): 42–8.
16. Fleg JL, Morrell CH, Bos AG, et al. Accelerated longitudinal decline of aerobic capacity in healthy older adults. Circulation. 2005; 112 (5): 674–82.
17. 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.
18. Friedman GD, Cutter GR, Donahue RP, Hughes GH, et al. CARDIA: study design, recruitment, and some characteristics of the examined subjects. J Clin Epidemiol. 1988; 41 (11): 1105–16.
19. Group TLAR. Cardiovascular effects of intensive lifestyle intervention in type 2 diabetes. N Engl J Med. 2013; 369 (2): 145–54.
20. Helmrich SP, Ragland DR, Leung RW, et al. Physical activity and reduced occurrence of non–insulin-dependent diabetes-mellitus. N Engl J Med. 1991; 325 (3): 147–52.
21. Hubert HB, Feinleib M, McNamara PM, et al. Obesity as an independent risk factor for cardiovascular disease: a 26-year follow-up of participants in the Framingham Heart Study. Circulation. 1983; 67 (5): 968–77.
22. Jacobs DR, Hahn LP, Haskell WL, et al. Validity and reliability of short physical activity history: CARDIA and the Minnesota Heart Health Program. J Cardiopulm Rehabil Prev. 1989; 9 (11): 448–59.
23. Johnson NA, Sachinwalla T, Walton DW, et al. Aerobic exercise training reduces hepatic and visceral lipids in obese individuals without weight loss. Hepatology. 2009; 50 (4): 1105–12.
24. Knowler WC, Barrett-Connor E, Fowler SE, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002; 346 (6): 393–403.
25. Knowler WC, Fowler SE, Hamman RF, et al. 10-year follow-up of diabetes incidence and weight loss in the Diabetes Prevention Program Outcomes Study. Lancet. 2009; 374 (9702): 1677–86.
26. Lee DC, Sui X, Church TS, Lavie CJ, et al. Changes in fitness and fatness on the development of cardiovascular disease risk factors hypertension, metabolic syndrome, and hypercholesterolemia. J Am Coll Cardiol. 2012; 59 (7): 665–72.
27. Li GW, Zhang P, Wang JP, et al. The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study: a 20-year follow-up study. Lancet. 2008; 371 (9626): 1783–9.
28. Liu K, Slattery M, Jacobs D Jr, et al. A study of the reliability and comparative validity of the CARDIA dietary history. Ethn Dis. 1994; 4 (1): 15–27.
29. Lynch J, Helmrich SP, Lakka TA, et al. Moderately intense physical activities and high levels of cardiorespiratory fitness reduce the risk of non–insulin-dependent diabetes mellitus in middle-aged men. Arch Intern Med. 1996; 156 (12): 1307–14.
30. Ortega FB, Lee DC, Katzmarzyk PT, et al. The intriguing metabolically healthy but obese phenotype: cardiovascular prognosis and role of fitness. Eur Heart J. 2013; 34 (5): 389–97.
31. Pan XR, Li GW, Hu YH, et al. Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance—The Da Qing IGT and diabetes study. Diabetes Care. 1997; 20 (4): 537–44.
32. Parker ED, Schmitz KH, Jacobs DR Jr, et al. Physical activity in young adults and incident hypertension over 15 years of follow-up: the CARDIA study. Am J Public Health. 2007; 97 (4): 703–9.
33. Radikova Z, Koska J, Huckova M, et al. Insulin sensitivity indices: a proposal of cut-off points for simple identification of insulin-resistant subjects. Exp Clin Endocrinol Diabetes. 2006; 114 (5): 249–56.
34. Ruderman N, Chisholm D, Pi-Sunyer X, et al. The metabolically obese, normal-weight individual revisited. Diabetes. 1998; 47 (5): 699–713.
35. Sidney S, Haskell WL, Crow R, et al. Symptom-limited graded treadmill exercise testing in young adults in the CARDIA study. Med Sci Sports Exerc. 1992; 24 (2): 177–83.
36. Sui X, Hooker SP, Lee IM, et al. A prospective study of cardiorespiratory fitness and risk of type 2 diabetes in women. Diabetes Care. 2008; 31 (3): 550–5.
37. Therneau T, Grambsch PM. Modeling Survival Data: Extending the Cox Model, New York: Springer; 2000.
38. Warnick GR. Enzymatic methods for quantification of lipoprotein lipids. Methods Enzymol. 1986; 129: 101–23.
39. Warnick GR, Benderson J, Albers JJ. Dextran sulfate-Mg2+ precipitation procedure for quantitation of high-density-lipoprotein cholesterol. Clin Chem. 1982; 28 (6): 1379–88.
40. Wildman RP, Muntner P, Reynolds K, et al. The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering—prevalence and correlates of 2 phenotypes among the US population (NHANES 1999–2004). Arch Intern Med. 2008; 168 (15): 1617–24.
41. Wing RR. Physical activity in the treatment of the adulthood overweight and obesity: current evidence and research issues. Med Sci Sports Exerc. 1999; 31 (11): S547–52.
Keywords:

BODY MASS INDEX; INSULIN RESISTANCE; CARDIOMETABOLIC RISK FACTORS; CARDIORESPIRATORY FITNESS

© 2015 American College of Sports Medicine