The Association of Fitness With Reduced Cardiometabolic Risk Among Smokers Darla E. Kendzor, PhD, Carrie E. Finley, MS, Carolyn E. Barlow, MS, Tiffany A. Whitehurst, MPH, Michael S. Businelle, PhD, Bijal A. Balasubramanian, PhD, Nina B. Radford, MD, Kerem Shuval, PhD Introduction: Despite the health benefits associated with smoking cessation, continued smoking and relapse following cessation are common. Physical activity is associated with reduced risk of cardiovascular disease in general, though less is known about how cardiorespiratory fitness may influence cardiometabolic risk among smokers. Strategies are needed to protect against the health consequences of smoking among those unwilling or unable to quit smoking. The purpose of this study is to determine whether greater cardiorespiratory fitness is associated with reduced metabolic risk among smokers. Methods: The prospective influence of estimated cardiorespiratory fitness (i.e., maximal METs) on the development of metabolic syndrome and its components were examined among adult smokers (N¼1,249) who completed at least two preventive medical visits at the Cooper Clinic (Dallas TX) between 1979 and 2011. Statistical analyses were completed in 2013 and 2014. Results: The rate and risk for metabolic syndrome, as well as abnormal fasting glucose and highdensity lipoprotein cholesterol levels declined linearly with increases in cardiorespiratory fitness (all po0.05). Smokers in the moderate and high fitness categories had significantly reduced risk of developing metabolic syndrome and elevated fasting glucose relative to smokers in the lowest fitness category. In addition, smokers in the high fitness category were less likely to develop abnormal highdensity lipoprotein cholesterol levels. Conclusions: Moderate to high cardiorespiratory fitness among smokers is associated with a reduced likelihood of developing certain cardiovascular disease risk factors and metabolic syndrome. (Am J Prev Med 2015;48(5):561–569) & 2015 American Journal of Preventive Medicine
Introduction
S
moking is estimated to cause 443,000 deaths annually in the U.S., primarily because of cancer, cardiovascular diseases, and respiratory diseases.1 Although there is widespread knowledge of the negative health impact, an estimated 18.1% of U.S. adults currently smoke.2 Despite dramatic health benefits associated with smoking cessation,3 continued smoking and relapse following a cessation attempt are common.4 From the University of Texas Health Science Center (Kendzor, Barlow, Whitehurst, Businelle, Balasubramanian), School of Public Health; the University of Texas Southwestern Medical Center (Kendzor, Businelle, Balasubramanian), Harold C. Simmons Cancer Center, Population Science and Cancer Control Program; The Cooper Institute (Finley, Barlow); the Cooper Clinic (Radford), Dallas, Texas; and the Intramural Research Department (Shuval), The American Cancer Society, Atlanta, Georgia Address correspondence to: Darla E. Kendzor, PhD, the University of Texas Health Science Center, School of Public Health, 6011 Harry Hines Blvd., Dallas TX 75390. E-mail: [email protected]. 0749-3797/$36.00 http://dx.doi.org/10.1016/j.amepre.2014.12.004
& 2015 American Journal of Preventive Medicine
Thus, strategies are needed to protect against the health consequences of smoking among those who are unwilling or unable to quit. Moderate to high cardiorespiratory fitness (CRF) is associated with reduced risk of cardiovascular disease and some cancers in the general population.5–7 Promotion of physical activity as a means to increase or maintain CRF levels specifically among smokers may be an effective strategy to reduce the negative health impact of smoking. Although greater CRF is associated with reduced risk of mortality as well as the development of tobacco-related cancers, cardiometabolic risk factors, and cardiovascular disease,8,9–14 very little is known about how CRF might influence cardiometabolic risk among smokers. Plausibly, greater CRF and physical activity may protect against the onset of tobacco-related disease among smokers through several pathways, including increased pulmonary function and peripheral blood flow, reduced arterial stiffness, and decreased inflammation.15–18 Hence, the purpose of the current study was to determine whether
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greater CRF was prospectively associated with reduced risk for metabolic syndrome and its components specifically among smokers in the Cooper Center Longitudinal Study (CCLS). Metabolic syndrome has been defined as the clustering of at least three of the following metabolic risk factors: elevated waist circumference, elevated triglycerides, low high-density lipoprotein (HDL) cholesterol, elevated blood pressure, and elevated fasting glucose.19 Further, it was hypothesized that participants with greater CRF would be less likely to develop (1) elevated fasting glucose; (2) elevated waist circumference; (3) elevated blood pressure; (4) elevated triglycerides; (5) low HDL cholesterol; and (6) metabolic syndrome. Finally, the joint effect of smoking level and CRF on the development of metabolic syndrome was examined.
Methods Study Population The effect of CRF on metabolic syndrome and its components was prospectively evaluated among adult CCLS participants who reported current smoking. The CCLS is an observational database of patient visits to the Cooper Clinic (Dallas TX). Patients are primarily non-Hispanic white, college-educated, and are generally healthy and self-referred or referred by their employer for preventive care (for additional information, see previous studies6,7,10,20) and seen by the physicians at the Cooper Clinic at variable intervals. The CCLS is an updated continuation of the previously described Aerobics Center Longitudinal Study6 that includes additional clinical variables and mortality surveillance through 2010. The CCLS is approved annually by The Cooper Institute IRB, and the present study received exempt status from the Committee for the Protection of Human Subjects at the University of Texas Health Science Center. The influence of baseline CRF on the incidence of metabolic syndrome and its components (i.e., abnormal glucose, waist circumference, blood pressure, triglycerides, and HDL levels) was examined among individuals who reported current smoking at baseline. The CCLS sample consisted of 43,877 men and women who had at least two preventive medicine visits beginning in 1970 at the Cooper Clinic. Of those, 40,057 did not report that they were currently smoking at their baseline visit; 989 did not complete a maximal exercise test or had an abnormal exercise electrocardiogram (ECG); and 965 had missing values pertaining to metabolic syndrome (and components) at baseline, leaving a possible sample of 1,866 participants. Of these, 617 individuals were excluded because they reported a personal history of myocardial infarction, cancer, or stroke (n¼25); they exhibited three or more components of metabolic syndrome at baseline (n¼438); they had o1 year of follow-up (n¼93); or they were missing a component of metabolic syndrome at follow-up (n¼61). As a result, 1,249 participants were included in the primary analytic sample where metabolic syndrome was the outcome. Excluded participants (n¼617) differed significantly from those who were included in the following ways (all po0.01): Excluded participants were older (45.2 vs 42.1 years); more likely to be male
(92.7% vs 84.9%); less fit (10.4 vs 11.5 METs); and heavier smokers (26.3% vs 18.5% reported smoking 420 cigarettes per day). Excluded participants also had greater BMI (28.1 vs 25.2); waist circumference (98.0 vs 88.3 cm); fasting glucose (103.7 vs 96.7 mg/ dL); total cholesterol (214.1 vs 203.4 mg/dL); triglycerides (200.5 vs 113.5 mg/dL); and systolic/diastolic blood pressure (122.4/82.8 vs 115.3/78 mmHg) as well as lower HDL cholesterol (40.7 vs 50.4 mg/dL). Included and excluded participants did not differ in their alcohol consumption. The primary analytic sample size was further reduced when components of metabolic syndrome were examined as outcomes, as those who already exhibited the risk factor at baseline were excluded. Thus, sample sizes ranged from 862 to 1,162 depending on the specific outcome. Supplementary analyses that were conducted to determine whether the effects of CRF on metabolic syndrome varied by smoking level included a subsample of participants who provided their daily smoking rate at baseline and indicated a daily smoking rate of at least one cigarette per day (n¼808; i.e., those who reported smoking zero cigarettes per day or had missing daily smoking rate were excluded).
Measures Participants were defined as smokers if they answered yes to a survey item inquiring about whether they currently smoke (prior to 1988) or currently use tobacco (beginning in 1988). During all study years, smoking level was measured based on responses to the question If you smoke cigarettes, how many per day? Cigarettes smoked per day was dichotomized into light and heavy smoking (one to ten vs more than ten cigarettes per day21). Note that some participants reported current smoking/tobacco use, but did not report smoking on a daily basis (i.e., 9.5% reported smoking zero cigarettes per day; Table 1). Individuals who endorsed current “tobacco” use in response to the 1988 version of the smoking status question were excluded from the study in cases where use of tobacco products other than cigarettes was endorsed and the individual reported smoking zero cigarettes per day or did not report their smoking level. Clinical examinations at all visits (baseline and follow-up) included measurement of blood pressure, lipids, height, weight (BMI was calculated), and waist circumference (cm). Serum samples were analyzed for lipids using automated bioassays; resting blood pressure was auscultated to the first and fifth Korotkoff sounds while adhering to standard sphygmomanometer protocol. Waist circumference was measured at the level of the umbilicus, and BMI was computed using the standard formula (kg/m2). The presence of metabolic syndrome (primary outcome), was determined at the follow-up clinic visits, and defined as the presence of at least three of the following: (1) elevated waist circumference (men, Z102 cm; women, Z88 cm); (2) elevated triglycerides (Z150 mg/dL); (3) low HDL (men, o40 mg/dL; women, o50 mg/dL); (4) elevated blood pressure (systolic Z130 mmHg or diastolic Z 85 mmHg) or self-reported hypertension; or (5) elevated fasting glucose (Z100 mg/dL) or self-reported diabetes.19 Each component of metabolic syndrome was additionally included in separate analyses as an outcome. Estimated CRF was measured at baseline with a maximal exercise test on a treadmill while adhering to a modified Balke protocol.7 Treadmill speed was set initially at 3.3 miles/hour at a 0% incline, and both were increased gradually over 25 minutes. www.ajpmonline.org
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Table 1. Baseline Characteristics of Smoking Participants by Cardiorespiratory Fitness Level
Years of follow-up, M (SD) Years of age, M (SD)
All (N¼1,249)
Low fitness (n¼356)
Moderate fitness (n¼461)
High fitness (n¼432)
6.3 (5.6)
4.9 (4.7)
6.6 (5.7)
7.0 (6.1)
o0.0001
42.1 (8.5)
42.9 (8.2)
40.9 (8.4)
42.6 (8.7)
0.69
Sex, n (%) Males Females Fitness in METs, M (SD)
0.76 1,060 (84.9)
296 (83.2)
400 (86.8)
189 (15.1)
60 (16.9)
61 (13.2)
11.5 (2.1)
9.3 (1.2)
11.4 (1.0)
364 (84.3) 68 (15.7) 13.4 (1.6)
Alcohol use, n (%)
o0.0001 0.004
Nondrinker
112 (9.0)
45 (12.6)
36 (7.8)
31 (7.2)
17 drinks/wk
459 (36.8)
132 (37.1)
182 (39.5)
145 (33.6)
814 drinks/wk
332 (26.6)
88 (24.7)
124 (26.9)
120 (27.8)
414 drinks/wk
297 (23.8)
73 (20.5)
108 (23.4)
116 (26.9)
49 (3.9)
18 (5.1)
11 (2.4)
20 (4.6)
Missing
p-valuea
Smoking level, n (%)
0.94
0 cigarettes/dayb
119 (9.5)
24 (6.7)
28 (6.1)
67 (15.5)
110 cigarettes/day
363 (29.1)
84 (23.6)
151 (32.8)
128 (29.6)
1120 cigarettes/day
214 (17.1)
76 (21.4)
94 (20.4)
44 (10.2)
420 cigarettes/day
231 (18.5)
127 (35.7)
84 (18.2)
20 (4.6)
Missing
322 (25.8)
45 (12.6)
104 (22.6)
173 (40.1)
Cardiometabolic risk factors, M (SD) BMI
25.2 (3.1)
26.3 (3.4)
25.2 (3.1)
24.4 (2.6)
o0.0001
Waist circumference (cm)
88.3 (10.3)
91.4 (10.3)
88.5 (10.3)
85.4 (9.4)
o0.0001
Fasting glucose (mg/dL)
96.7 (13.9)
97.4 (13.8)
96.2 (16.3)
96.6 (10.8)
0.44
Cholesterol (mg/dL)
203.4 (38.3)
211.0 (40.8)
202.2 (39.2)
198.4 (34.2)
o0.0001
Triglycerides (mg/dL)
113.5 (71.1)
132.4 (84.4)
113.6 (63.0)
97.8 (63.4)
o0.0001
50.4 (13.4)
47.1 (13.3)
49.5 (13.2)
53.9 (13.0)
o0.0001
Resting systolic blood pressure (mmHg)
115.3 (12.0)
115.4 (11.2)
114.4 (12.2)
116.2 (12.3)
0.35
Resting diastolic blood pressure (mmHg)
78.0 (8.9)
79.0 (8.2)
77.7 (8.6)
77.5 (9.6)
0.02
HDL (mg/dL)
o0.0001
Total cardiometabolic risk factors, n (%) 0
376 (30.1)
76 (21.4)
132 (28.6)
168 (38.9)
1
472 (37.8)
124 (34.8)
182 (39.5)
166 (38.4)
2
401 (32.1)
156 (43.8)
147 (31.9)
98 (22.7)
377 (30.2)
109 (30.6)
137 (29.7)
131 (30.3)
0.95
84 (6.7)
34 (9.6)
39 (8.5)
11 (2.6)
o0.0001
333 (26.7)
99 (27.8)
111 (24.1)
123 (28.5)
0.73
Cardiometabolic risk factor s, n (%) Elevated fasting glucose Elevated waist circumference Elevated blood pressure
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Table 1. Baseline Characteristics of Smoking Participants by Cardiorespiratory Fitness Level (continued) All (N¼1,249)
Low fitness (n¼356)
Elevated triglycerides
203 (16.3)
83 (23.3)
79 (17.1)
41 (9.5)
o0.0001
Low HDL cholesterol
277 (22.2)
111 (31.2)
110 (23.9)
56 (13.0)
o0.0001
557 (44.6)
170 (47.8)
195 (42.3)
192 (44.4)
0.41
Family history of CVD, n (%)
Moderate fitness (n¼461)
High fitness (n¼432)
p-valuea
a
p-values for continuous variables are from the Wald trend test; p-values for categorical variables are from the Jonckeheere-Terpstra trend test; boldface indicates statistical significance (po0.05). b Please note that 119 participants reported current smoking at their first visit in combination with a self-reported average smoking rate of 0 cigarettes per day (i.e., consistent with non-daily smoking). CVD, cardiovascular disease; HDL, high-density lipoprotein; MET, metabolic equivalent of task.
After this time period, the grade remained constant while the speed continued to increase by 0.3 miles/hour each minute until volitional exhaustion. Maximal METs (1 MET¼3.5 mL O2 uptake kg body mass1 min1) were estimated from the final treadmill speed and incline.22 Maximal METs have been shown to be highly correlated with maximal oxygen uptake in men (r ¼0.92) and women (r¼0.94).23,24 Thus, maximal METS served as an estimate of CRF in the current study. MET values of the analytic sample were categorized into age- (20–39, 40–49, 50–59, and Z60 years) and sex-specific tertiles of fitness (low, moderate, and high). Covariates included age, gender, BMI, alcohol intake, family history of cardiovascular disease, and the number of baseline metabolic syndrome components (for the main metabolic syndrome analysis). Alcohol intake (drinks/week) was determined based on responses to a survey question about the number of drinks consumed per week of beer (12 ounces), wine (5 ounces), and hard liquor (1.5 ounces). Family history of cardiovascular disease was based on checklist responses indicating that a family member (i.e., mother, father, or brother/sister) had a history of heart attack, coronary bypass, angioplasty, angina, or stroke. In
addition, baseline measurements of fasting glucose (mg/dL); waist circumference (cm); blood pressure (systolic and diastolic; mmHg); triglycerides (mg/dL); and HDL cholesterol (mg/dL) were included in the model with the corresponding outcome (e.g., baseline glucose was included as a covariate in the elevated fasting glucose model).
Statistical Analysis Means and SDs were computed for baseline continuous variables, and proportions were computed for baseline categorical variables. Participants were observed at discrete clinic visits, which occurred at irregular intervals through the end of the observation period. When incident metabolic syndrome or any of its components was diagnosed, the event time was treated as interval censored in the interval between the diagnosis visit and the previous visit. An accelerated failure time model was fit to the interval-censored event times using the maximum likelihood method, assuming a baseline Weibull distribution. This model provides estimates of covariate-adjusted incidence rates for a specific time, as well as
Table 2. Association Between Cardiorespiratory Fitness and the Development of Metabolic Syndrome Among 1,249 Smokers
Total follow-upb (person-years)
Covariate-adjusted rate per 1,000 person-yearsc (95% CI)
Hazard ratio (95% CI)
90
1,748
8.7 (3.3, 22.8)
1.0 (ref)
461
94
3,051
6.3 (2.4, 16.8)
0.73 (0.54, 0.98)
432
56
3,027
4.5 (1.5, 12.3)
0.52 (0.37, 0.74)
—
—
—
—
0.0003
Total sample (N¼1,249)
Incident metabolic syndrome casesa (n¼240)
Low
356
Moderate
Fitness level (baseline)d
High e
Linear trend, p
Note: An accelerated failure time model was used to calculate covariate-adjusted incidence rates, hazard ratios, and 95% CIs. The model was adjusted for age; gender; BMI; alcohol intake; family history of cardiovascular disease (CVD); and number of metabolic syndrome components at baseline. a Incident metabolic syndrome was assessed at follow-up and was defined as having at least three of the following: (1) elevated waist circumference (men, Z102 cm; women, Z88 cm); (2) elevated triglycerides (Z150 mg/dL); (3) low HDL (men, o40 mg/dL; women, o50 mg/dL); (4) elevated blood pressure (systolic Z130 mmHg or diastolic Z85 mmHg) or self-reported hypertension; and (5) elevated fasting glucose (Z100 mg/dL) or selfreported diabetes. b Person-years were calculated as the sum of person-years by fitness category from baseline to final exam date. c Covariate-adjusted incidence rates reflect the estimated rate for a population at the mean level of each continuous covariate (age, BMI) and at the reference level for all nominal covariates (gender [ref¼female]; alcohol intake [ref¼non-drinker]; family history of CVD [ref¼no family history of CVD]; and number of metabolic syndrome components at baseline [ref¼0]). d Fitness was assessed at baseline by a maximal exercise test. e The linear trend reflects the association of fitness level with incident metabolic syndrome.
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Table 3. Associations Between Cardiorespiratory Fitness and the Development of Cardiometabolic Risk Factors Among Smokers Fasting glucose Z100 mg/dL Total sample (N¼862)
Cases (n¼314)
Total follow-upa (person-years)
Covariate- adjusted rate perb 1,000 person-years (95% CI)
Hazard ratio (95% CI)
Low
242
98
1,037
71.4 (42.6, 119.7)
1.0 (ref)
Moderate
323
121
1,739
52.6 (31.1, 88.7)
0.74 (0.56, 0.97)
297
95
1,739
38.8 (22.3, 67.6)
0.54 (0.40, 0.73)
—
—
—
—
o0.0001
Fitness level (baseline)c
High d
Linear trend, p
Waist circumference 4102 cm (men) or 488 cm (women) Total sample (N¼1,162)
Cases (n¼147)
Total follow-up (person-years)a
Covariate- adjusted rate per 1,000 person-years (95% CI)b
Hazard ratio (95% CI)
Low
322
58
1,568
322.0 (148.6, 697.7)
1.0 (ref)
Moderate
419
50
2,735
272.2 (120.0, 618.1)
0.85 (0.57, 1.25)
High
421
39
2,940
322.9 (132.0, 790.0)
1.00 (0.64, 1.57)
—
—
—
—
0.99
Fitness level (baseline)c
Linear trend, pd
Blood pressure Z130/85 Total sample (N¼909)
Cases (n¼285)
Total follow-up (person-years)a
Covariate- adjusted rate per 1,000 person-years (95% CI)b
Hazard ratio (95% CI)
Low
256
82
1,201
70.2 (41.4, 119.0)
1.0 (ref)
Moderate
347
106
2,067
63.5 (36.9, 109.2)
0.90 (0.67, 1.23)
306
97
1,826
66.0 (38.1, 114.2)
0.94 (0.68, 1.29
—
—
—
—
0.70
Fitness level (baseline)c
High d
Linear trend, p
Triglycerides Z150 mg/dL Total sample (N¼1,038)
Cases (n¼208)
Total follow-up (person-years) a
Covariate- adjusted rate per 1,000 person-years (95% CI) b
Hazard ratio (95% CI)
Low
271
58
1,321
41.4 (21.8, 78.8)
1.0 (ref)
Moderate
378
84
2,404
40.9 (21.1, 79.0)
0.99 (0.70, 1.40)
389
66
2,634
35.9 (18.0, 71.4)
0.87 (0.60, 1.26)
—
—
—
—
0.45
Fitness level (baseline)c
High d
Linear trend, p
HDL cholesterol o40 mg/dL (men) or o50 mg/dL (women) Total sample (N¼968)
Cases (n¼128)
Total follow-up (person-years) a
Covariate- adjusted rate per 1,000 person-years (95% CI) b
Hazard ratio (95% CI)
Low
243
42
1,187
27.8 (13.0, 59.3)
1.0 (ref)
Moderate
350
51
2,286
23.7 (10.7, 52.6)
0.85 (0.56, 1.30)
High
375
35
2,579
16.7 (7.3, 38.5)
0.60 (0.37, 0.97)
—
—
—
—
0.04
Fitness level (baseline)c
Linear trend, pd
Note: An accelerated failure time model was used to calculate covariate-adjusted incidence rates, hazard ratios, and 95% CIs. The models were adjusted for age; gender; BMI; alcohol intake; family history of cardiovascular disease (CVD); and baseline value of the corresponding risk factor
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outcome (e.g., baseline glucose was included as a covariate in the elevated fasting glucose model). Cardiometabolic risk factors assessed at follow-up. Individuals with elevated values on the outcome variables at baseline were excluded from the analysis, thus sample sizes ranged from 862-1,162. a Person-years were calculated as the sum of person-years by fitness category from baseline to final exam date. b Covariate-adjusted incidence rates reflect the estimated rate for a population at the mean level of each continuous covariate (age, BMI, baseline value for the corresponding risk factor outcome [e.g., baseline glucose in the elevated fasting glucose model]) and at the reference level for all nominal covariates (gender [ref¼female]; alcohol intake [ref¼non-drinker]; and family history of CVD [ref¼no family history of CVD]. c Fitness was assessed at baseline by a maximal exercise test. d The linear trend reflects the association of fitness level with each cardiometabolic risk factor. HDL, high-density lipoprotein.
characterization of covariate effects in terms of hazard ratios (HRs). A series of models were evaluated where metabolic syndrome and each metabolic risk component were included as dependent variables (i.e., elevated fasting glucose, elevated waist circumference, elevated blood pressure, elevated triglycerides, and low HDL cholesterol). Baseline fitness level was the independent variable, and covariates were included as described above. Nonproportional hazards were evaluated and accommodated by allowing the logarithm of the Weibull shape parameter to depend linearly on covariates. An additional accelerated failure time model was constructed to assess the joint effects of smoking level (light/ heavy) and fitness. All statistical analyses were programmed in SAS/STAT, version 9.2 and completed in 2013 and 2014.
Results The average age of this predominately male (84.9%) sample was 42.1 years (SD=8.5) at baseline. The average number of study visits completed was 3.48 (SD=2.4; median, 3), with a range of two to 22 visits, and an average follow-up of 6.3 years (SD=5.6) for the metabolic syndrome outcome analysis. Participants’ baseline mean CRF level was 11.5 METs (SD=2.1); 45.8% consumed seven or fewer drinks per week (mean, 10.9 drinks per week; SD=11.5; 3.9% had missing data); and 55.7% smoked 20 or fewer cigarettes a day (25.8% had missing data). Participants, on average, were overweight (mean BMI, 25.2; SD=3.1); normotensive (mean systolic blood pressure, 115.3 mmHg, SD=12.0; mean diastolic blood pressure, 78.0 mmHg, SD=8.9); had normal fasting glucose and triglyceride levels (mean fasting glucose, 96.7 mg/dL, SD=13.9; mean triglycerides, 113.5 mg/dL, SD=71.1), and 69.9% met the criteria for one or two components of metabolic syndrome at baseline. Participant characteristics are presented in Table 1. The associations between CRF at baseline (independent variable); metabolic syndrome at follow-up (primary dependent variable); and each cardiometabolic risk factor at follow-up (dependent variables) appear in Tables 2 and 3. The rate and risk for metabolic syndrome, abnormal HDL, and fasting glucose levels declined linearly with increases in fitness (linear trends, all po0.05). For example, the covariate-adjusted rate per 1,000 personyears for metabolic syndrome was 8.7, 6.3, and 4.5 within the low, moderate, and high fitness strata, respectively (linear trend, p=0.0003). When examining the fitness strata categorically, those in the highest fitness strata had
significantly lower risk for the development of metabolic syndrome, elevated fasting glucose, and abnormal HDL cholesterol relative to smokers with the lowest fitness levels. For example, smokers in the highest fitness strata had a 48% reduced risk of developing metabolic syndrome compared to those in the low fitness strata (HR=0.52, 95% CI=0.37, 0.74). In addition, individuals in the moderate fitness strata experienced a reduced risk of developing metabolic syndrome and elevated fasting glucose relative to smokers in the lowest fitness level. The joint effects of smoking and fitness levels on metabolic syndrome risk (while adjusting for covariates) are presented in Table 4. Compared to heavy smokers in the low fitness strata (reference group), both light and heavy smokers in the high fitness strata experienced significantly reduced risk of developing metabolic syndrome.
Discussion Study findings indicate that there is an inverse relationship between CRF and cardiometabolic risk among adult smokers. Smokers with the highest level of fitness experienced significantly reduced risk of developing metabolic syndrome as well as elevated fasting blood glucose and abnormal HDL cholesterol relative to smokers with the lowest level of fitness. Smokers categorized as moderately fit experienced reduced risk of developing metabolic syndrome as well as elevated fasting glucose. In addition, light and heavy smokers with the highest level of fitness were less likely to develop metabolic syndrome relative to heavy smokers of low fitness. Findings suggest that moderate to high levels of CRF decrease the impact of smoking on the development of metabolic syndrome and other cardiovascular disease risk factors. Results highlight the potential importance of improving CRF among individuals who are unwilling or unable to quit smoking. Findings extend previous research linking CRF with tobacco-related cancer mortality8,14 and physical activity with reduced risk of lung cancer and cardiovascular mortality.25–27 Although higher CRF has previously been linked with reduced cardiometabolic risk,28,29 the current study provides support for this link specifically among smokers. Mechanisms linking CRF with cardiometabolic risk may include adiposity, insulin sensitivity, inflammation, vascular function, blood pressure, and lipid www.ajpmonline.org
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were determined based on a single baseline assessment. Although fitness could potenIncident metabolic Hazard ratio tially decrease over the study a p -value syndrome cases (95% CI) period, in the majority of Low fitnessb (n¼287) cases (89%) fitness levels either remained the same or Heavy smokers 59 1.0 (ref) — increased in comparison to Light smokers 14 0.67 (0.37, 1.22) 0.19 baseline values. Nonetheless, Moderate fitness (n¼329) future research may focus on the influence of fitness trajecHeavy smokers 40 0.81 (0.53, 1.22) 0.32 tories over time on the develLight smokers 30 0.70 (0.44, 1.11) 0.13 opment of metabolic synHigh fitness (n¼192) drome and other health outcomes. Study participants Heavy smokers 8 0.44 (0.21, 0.93) 0.03 were predominantly white, Light smokers 15 0.54 (0.30, 0.96) 0.04 well educated, and male, Note: An accelerated failure time model was used to calculate hazard ratios and 95% CIs, adjusting for age; which may impact the extersex; BMI; alcohol intake; family history of CVD; and baseline number of metabolic syndrome components. nal validity of this study. Heavy smokers reported smoking 410 cigarettes per day and light smokers reported smoking 1–10 Additionally, as the study cigarettes per day. The reference group is Low Fitness/Heavy Smokers. Boldface indicates statistical significance (po0.05). sample consisted specifically a Incident metabolic syndrome was assessed at follow up and was defined as having at least three of the of smokers without prevalent following: (1) elevated waist circumference (men, Z102 cm; women, Z88 cm); (2) elevated triglycerides (Z150 mg/dL); (3) low HDL (men, o40 mg/dL; women, o50 mg/dL); (4) elevated blood pressure (systolic metabolic syndrome, some of Z130 mmHg or diastolic Z85 mmHg) or self-reported hypertension; and (5) elevated fasting glucose the participants’ health be(Z100 mg/dL) or self-reported diabetes. b haviors and clinical measureFitness was assessed at baseline by a maximal exercise test. ments significantly differed from those excluded from the study. On the other hand, internal validity may be metabolism.29 Plausibly, these mechanisms are similar increased because of the homogeneity across sociodemoamong smokers. Smoking is associated with a systemic graphic variables. Given the small sample size of women inflammatory response and elevated circulating levels of 30 in the study, sex was included as a covariate rather than inflammatory markers. In 2006, deRuiter and Faulk31 using stratification, and as a result, findings may be most ner hypothesized that physical activity may counteract relevant for men. Additionally, smoking level was not some of the negative cardiovascular, pulmonary, and available for all participants owing to missing responses immune system effects of smoking. For example, Park 15 and the exclusion of those who smoked less than one et al. reported that smoking-related arterial stiffening cigarette per day. There were also relatively few smokers was absent among physically active men. in the high fitness category (i.e., 24% [n¼192] of the Individuals in the moderate and high fitness categories analytic sample) in the joint effects analyses of fitness and were more likely to quit smoking by study follow-up than smoking level on cardiometabolic risk. Thus, current individuals of lower fitness (i.e., 33.2%, 42.7%, and 45.1% findings involving smoking levels must be replicated in of the low, moderate, and high fitness groups, respeclarger samples. tively, quit smoking by follow-up). Smoking cessation Those who smoke and are not ready or able to quit may would likely contribute to reduced cardiometabolic risk, benefit from engaging in physical activity in an effort to and may function as one link between fitness and increase CRF. Numerous studies have demonstrated a cardiometabolic risk. However, supplementary analyses positive relationship between the duration and intensity in which smoking cessation was included as a covariate of physical activity and CRF.32,33 Anton and colleagues34 along with all other covariates did not change study reported that individuals who engaged in vigorous findings (results available upon request). walking for at least 75 minutes per week experienced Strengths of this study include the objective measureclinically significant increases in CRF. Thus, there is ment of CRF, the prospective design, and the focus on evidence that achieving or exceeding currently recomsmokers. CRF, as an indicator of habitual physical mended physical activity guidelines5 will increase CRF. activity, allows for easy comparison across studies. The promotion of physical activity with the goal of However, the effects of fitness on cardiometabolic risk Table 4. Joint Effects of Cardiorespiratory Fitness and Smoking Level on the Incidence of Metabolic Syndrome
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increasing CRF may be an effective harm reduction strategy for those who continue to smoke, though fitness is also impacted by genetic factors.35
Conclusions CRF was inversely associated with risk for metabolic syndrome and some cardiometabolic risk factors among smokers. Risk for developing metabolic syndrome and elevated fasting glucose were significantly reduced among those in the moderate and high fitness categories relative to those in the lowest fitness category. Those in the high fitness strata additionally experienced reduced risk for abnormal HDL cholesterol. Smoking cessation remains the ideal option for reducing tobacco-related disease risk, though greater CRF may attenuate some of the negative health effects when cessation is unlikely. Research is needed to confirm study findings and uncover the mechanisms linking CRF with reduced cardiometabolic risk in smokers. Findings highlight the potential importance of increasing CRF via physical activity as a means to reduce cardiometabolic risk among individuals who continue to smoke. We are thankful to Dr. Kenneth H. Cooper MD, MPH for establishing The Cooper Center Longitudinal Study (CCLS), the Cooper Clinic physicians and technicians for data collection, The Cooper Institute staff for data management, and the CCLS study participants. Manuscript preparation was supported, in part, by American Cancer Society grants MRSGT10-104-01-CPHPS (awarded to DEK) and MRSGT-12-114-01CPPB (awarded to MSB). No financial disclosures were reported by the authors of this paper.
References 1. CDC. Smoking-attributable mortality, years of potential life lost, and productivity losses—United States, 2000-2004. MMWR Morb Mortal Wkly Rep. 2008;57(45):1226–1228. 2. Agaku IT, King BA, Dube SR. Current cigarette smoking among adults —United states, 2005-2012. MMWR Morb Mortal Wkly Rep. 2014;63 (2):29–34. 3. Jha P, Ramasundarahettige C, Landsman V, et al. 21st-century hazards of smoking and benefits of cessation in the United States. N Engl J Med. 2013;368(4):341–350. http://dx.doi.org/10.1056/NEJMsa1211128. 4. Caraballo RS, Kruger J, Asman K, et al. Relapse among cigarette smokers: the CARDIA longitudinal study—1985-2011. Addict Behav. 2014;39(1):101–106. http://dx.doi.org/10.1016/j.addbeh.2013. 08.030. 5. USDHHS. Physical Activity Guidelines for Americans. Washington, DC: USDHHS; 2008. 6. Blair SN, Kampert JB, Kohl III, et al. Influences of cardiorespiratory fitness and other precursors on cardiovascular disease and all-cause mortality in men and women. JAMA. 1996;276(3):205–210. http://dx. doi.org/10.1001/jama.1996.03540030039029.
7. Blair SN, Kohl III, Paffenbarger Jr, et al. Physical fitness and all-cause mortality: a prospective study of healthy men and women. JAMA. 1989;262 (17):2395–2401. http://dx.doi.org/10.1001/jama.1989.03430170057028. 8. Sui X, Lee DC, Matthews CE, et al. Influence of cardiorespiratory fitness on lung cancer mortality. Med Sci Sports Exerc. 2010;42(5):872– 878. http://dx.doi.org/10.1249/MSS.0b013e3181c47b65. 9. Shuval K, Finley CE, Chartier KG, Balasubramanian BA, Gabriel KP, Barlow CE. Cardiorespiratory fitness, alcohol intake, and metabolic syndrome incidence in men. Med Sci Sports Exerc. 2012;44(11):2125– 2131. http://dx.doi.org/10.1249/MSS.0b013e3182640c4e. 10. LaMonte MJ, Barlow CE, Jurca R, Kampert JB, Church TS, Blair SN. Cardiorespiratory fitness is inversely associated with the incidence of metabolic syndrome: a prospective study of men and women. Circulation. 2005;112(4):505–512. http://dx.doi.org/10.1161/CIRC ULATIONAHA.104.503805. 11. Peel JB, Sui X, Adams SA, Hibert JR, Hardin JW, Blair SN. A prospective study of cardiorespiratory fitness and breast cancer mortality. Med Sci Sports Exerc. 2009;41(4):742–748. http://dx.doi.org/ 10.1249/MSS.0b013e31818edac7. 12. Farrell SW, Cortese GM, Lamonte MJ, Blair SN. Cardiorespiratory fitness, different measures of adiposity, and cancer mortality in men. Obesity. 2007;15(12):3140–3149. http://dx.doi.org/10.1038/oby.2007.374. 13. Thompson AM, Church TS, Janssen I, Katzmarzyk PT, Earnest CP, Blair SN. Cardiorespiratory fitness as a predictor of cancer mortality among men with pre-diabetes and diabetes. Diabetes Care. 2008;31 (4):764–769. http://dx.doi.org/10.2337/dc07-1648. 14. Do Lee C, Blair SN. Cardiorespiratory fitness and smoking-related and total cancer mortality in men. Med Sci Sports Exerc. 2002;34(5):735– 739. http://dx.doi.org/10.1097/00005768-200205000-00001. 15. Park W, Miyachi M, Tanaka H. Does aerobic exercise mitigate the effects of cigarette smoking on arterial stiffness? J Clin Hypertens (Greenwich). 2014;16(9):640–644. http://dx.doi.org/10.1111/jch.12385. 16. Kuller LH, Ockene J, Meilahn E, Svendsen KH. Relation of forced expiratory volume in one second (FEV1) to lung cancer mortality in the Multiple Risk Factor Intervention Trial (MRFIT). Am J Epidemiol. 1990;132(2):265–274. 17. Anton MM, Cortez-Cooper MY, DeVan AE, Neidre DB, Cook JN, Tanaka H. Cigarette smoking, regular exercise, and peripheral blood flow. Atherosclerosis. 2006;185(1):201–205. http://dx.doi.org/10.1016/j. atherosclerosis.2005.05.034. 18. LaMonte MJ, Ainsworth BE, Durstine JL. Influence of cardiorespiratory fitness on the association between C-reactive protein and metabolic syndrome prevalence in racially diverse women. J Womens Health. 2005;14(3):233–239. http://dx.doi.org/10.1089/jwh.2005.14.233. 19. Grundy SM, Cleeman JI, Daniels SR, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute scientific statement. Circulation. http://dx.doi.org/10.1161/CIRCULATIO 2005;112(17):2735–2752. NAHA.105.169404. 20. DeFina LF, Willis BL, Radford NB, et al. The association between midlife cardiorespiratory fitness levels and later-life dementia: a cohort study. Ann Intern Med. 2013;158(3):162–168. http://dx.doi.org/ 10.7326/0003-4819-158-3-201302050-00005. 21. Husten CG. How should we define light or intermittent smoking? Does it matter? Nicotine Tob Res. 2009;11(2):111–121. http://dx.doi.org/ 10.1093/ntr/ntp010. 22. ACSM. ACSM's Guidelines for Exercise Testing and Prescription. In: Pescatello LS, Arena R, Riebe D, Thompson PD, eds. 9th ed., Philadelphia, PA: Lippincott Williams & Wilkins, 2013:173. 23. Pollock ML, Bohannon RL, Cooper KH, et al. A comparative analysis of four protocols for maximal treadmill stress testing. Am Heart J. 1976;92(1):39–46. http://dx.doi.org/10.1016/S0002-8703(76)80401-2. 24. Pollock ML, Foster C, Schmidt D, Hellman C, Linnerud AC, Ward A. Comparative analysis of physiologic responses to three different maximal graded exercise test protocols in healthy women. Am Heart J. 1982; 103(3):363–373. http://dx.doi.org/10.1016/0002-8703(82)90275-7.
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30. Yanbaeva DG, Dentener MA, Creutzberg EC, Wesseling G. Wouters EFM. Systemic effects of smoking. Chest. 2007;131(5):1557–1566. http: //dx.doi.org/10.1378/chest.06-2179. 31. deRuiter W, Faulkner G. Tobacco harm reduction strategies: the case for physical activity. Nicotine Tob Res. 2006;8(2):157–168. http://dx. doi.org/10.1080/14622200500494823. 32. Kaminsky LA, Arena R, Beckie TM, et al. The importance of cardiorespiratory fitness in the United States: the need for a national registry: a policy statement from the American Heart Association. Circulation. 2013;127 (5):652–662. http://dx.doi.org/10.1161/CIR.0b013e31827ee100. 33. Kulinski JP, Khera A, Ayers CR, et al. Association between cardiorespiratory fitness and accelerometer-derived physical activity and sedentary time in the general population. Mayo Clin Proc. 2014; 89(8):1063–1071. http://dx.doi.org/10.1016/j.mayocp.2014.04.019. 34. Anton SD, Duncan GE, Limacher MC, Martin AD, Perri MG. How much walking is needed to improve cardiorespiratory fitness? An examination of the 2008 physical activity guidelines for Americans. Res Q Exerc Sport. 2011;82(2):365–370. 35. Pérusse L, Rankinen T, Hagberg JM, et al. Advances in exercise, fitness, and performance genomics in 2012. Med Sci Sports Exerc. 2013; 45(5):824–831. http://dx.doi.org/10.1249/MSS.0b013e31828b28a3.
Introduction
S
moking is estimated to cause 443,000 deaths annually in the U.S., primarily because of cancer, cardiovascular diseases, and respiratory diseases.1 Although there is widespread knowledge of the negative health impact, an estimated 18.1% of U.S. adults currently smoke.2 Despite dramatic health benefits associated with smoking cessation,3 continued smoking and relapse following a cessation attempt are common.4 From the University of Texas Health Science Center (Kendzor, Barlow, Whitehurst, Businelle, Balasubramanian), School of Public Health; the University of Texas Southwestern Medical Center (Kendzor, Businelle, Balasubramanian), Harold C. Simmons Cancer Center, Population Science and Cancer Control Program; The Cooper Institute (Finley, Barlow); the Cooper Clinic (Radford), Dallas, Texas; and the Intramural Research Department (Shuval), The American Cancer Society, Atlanta, Georgia Address correspondence to: Darla E. Kendzor, PhD, the University of Texas Health Science Center, School of Public Health, 6011 Harry Hines Blvd., Dallas TX 75390. E-mail: [email protected]. 0749-3797/$36.00 http://dx.doi.org/10.1016/j.amepre.2014.12.004
& 2015 American Journal of Preventive Medicine
Thus, strategies are needed to protect against the health consequences of smoking among those who are unwilling or unable to quit. Moderate to high cardiorespiratory fitness (CRF) is associated with reduced risk of cardiovascular disease and some cancers in the general population.5–7 Promotion of physical activity as a means to increase or maintain CRF levels specifically among smokers may be an effective strategy to reduce the negative health impact of smoking. Although greater CRF is associated with reduced risk of mortality as well as the development of tobacco-related cancers, cardiometabolic risk factors, and cardiovascular disease,8,9–14 very little is known about how CRF might influence cardiometabolic risk among smokers. Plausibly, greater CRF and physical activity may protect against the onset of tobacco-related disease among smokers through several pathways, including increased pulmonary function and peripheral blood flow, reduced arterial stiffness, and decreased inflammation.15–18 Hence, the purpose of the current study was to determine whether
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greater CRF was prospectively associated with reduced risk for metabolic syndrome and its components specifically among smokers in the Cooper Center Longitudinal Study (CCLS). Metabolic syndrome has been defined as the clustering of at least three of the following metabolic risk factors: elevated waist circumference, elevated triglycerides, low high-density lipoprotein (HDL) cholesterol, elevated blood pressure, and elevated fasting glucose.19 Further, it was hypothesized that participants with greater CRF would be less likely to develop (1) elevated fasting glucose; (2) elevated waist circumference; (3) elevated blood pressure; (4) elevated triglycerides; (5) low HDL cholesterol; and (6) metabolic syndrome. Finally, the joint effect of smoking level and CRF on the development of metabolic syndrome was examined.
Methods Study Population The effect of CRF on metabolic syndrome and its components was prospectively evaluated among adult CCLS participants who reported current smoking. The CCLS is an observational database of patient visits to the Cooper Clinic (Dallas TX). Patients are primarily non-Hispanic white, college-educated, and are generally healthy and self-referred or referred by their employer for preventive care (for additional information, see previous studies6,7,10,20) and seen by the physicians at the Cooper Clinic at variable intervals. The CCLS is an updated continuation of the previously described Aerobics Center Longitudinal Study6 that includes additional clinical variables and mortality surveillance through 2010. The CCLS is approved annually by The Cooper Institute IRB, and the present study received exempt status from the Committee for the Protection of Human Subjects at the University of Texas Health Science Center. The influence of baseline CRF on the incidence of metabolic syndrome and its components (i.e., abnormal glucose, waist circumference, blood pressure, triglycerides, and HDL levels) was examined among individuals who reported current smoking at baseline. The CCLS sample consisted of 43,877 men and women who had at least two preventive medicine visits beginning in 1970 at the Cooper Clinic. Of those, 40,057 did not report that they were currently smoking at their baseline visit; 989 did not complete a maximal exercise test or had an abnormal exercise electrocardiogram (ECG); and 965 had missing values pertaining to metabolic syndrome (and components) at baseline, leaving a possible sample of 1,866 participants. Of these, 617 individuals were excluded because they reported a personal history of myocardial infarction, cancer, or stroke (n¼25); they exhibited three or more components of metabolic syndrome at baseline (n¼438); they had o1 year of follow-up (n¼93); or they were missing a component of metabolic syndrome at follow-up (n¼61). As a result, 1,249 participants were included in the primary analytic sample where metabolic syndrome was the outcome. Excluded participants (n¼617) differed significantly from those who were included in the following ways (all po0.01): Excluded participants were older (45.2 vs 42.1 years); more likely to be male
(92.7% vs 84.9%); less fit (10.4 vs 11.5 METs); and heavier smokers (26.3% vs 18.5% reported smoking 420 cigarettes per day). Excluded participants also had greater BMI (28.1 vs 25.2); waist circumference (98.0 vs 88.3 cm); fasting glucose (103.7 vs 96.7 mg/ dL); total cholesterol (214.1 vs 203.4 mg/dL); triglycerides (200.5 vs 113.5 mg/dL); and systolic/diastolic blood pressure (122.4/82.8 vs 115.3/78 mmHg) as well as lower HDL cholesterol (40.7 vs 50.4 mg/dL). Included and excluded participants did not differ in their alcohol consumption. The primary analytic sample size was further reduced when components of metabolic syndrome were examined as outcomes, as those who already exhibited the risk factor at baseline were excluded. Thus, sample sizes ranged from 862 to 1,162 depending on the specific outcome. Supplementary analyses that were conducted to determine whether the effects of CRF on metabolic syndrome varied by smoking level included a subsample of participants who provided their daily smoking rate at baseline and indicated a daily smoking rate of at least one cigarette per day (n¼808; i.e., those who reported smoking zero cigarettes per day or had missing daily smoking rate were excluded).
Measures Participants were defined as smokers if they answered yes to a survey item inquiring about whether they currently smoke (prior to 1988) or currently use tobacco (beginning in 1988). During all study years, smoking level was measured based on responses to the question If you smoke cigarettes, how many per day? Cigarettes smoked per day was dichotomized into light and heavy smoking (one to ten vs more than ten cigarettes per day21). Note that some participants reported current smoking/tobacco use, but did not report smoking on a daily basis (i.e., 9.5% reported smoking zero cigarettes per day; Table 1). Individuals who endorsed current “tobacco” use in response to the 1988 version of the smoking status question were excluded from the study in cases where use of tobacco products other than cigarettes was endorsed and the individual reported smoking zero cigarettes per day or did not report their smoking level. Clinical examinations at all visits (baseline and follow-up) included measurement of blood pressure, lipids, height, weight (BMI was calculated), and waist circumference (cm). Serum samples were analyzed for lipids using automated bioassays; resting blood pressure was auscultated to the first and fifth Korotkoff sounds while adhering to standard sphygmomanometer protocol. Waist circumference was measured at the level of the umbilicus, and BMI was computed using the standard formula (kg/m2). The presence of metabolic syndrome (primary outcome), was determined at the follow-up clinic visits, and defined as the presence of at least three of the following: (1) elevated waist circumference (men, Z102 cm; women, Z88 cm); (2) elevated triglycerides (Z150 mg/dL); (3) low HDL (men, o40 mg/dL; women, o50 mg/dL); (4) elevated blood pressure (systolic Z130 mmHg or diastolic Z 85 mmHg) or self-reported hypertension; or (5) elevated fasting glucose (Z100 mg/dL) or self-reported diabetes.19 Each component of metabolic syndrome was additionally included in separate analyses as an outcome. Estimated CRF was measured at baseline with a maximal exercise test on a treadmill while adhering to a modified Balke protocol.7 Treadmill speed was set initially at 3.3 miles/hour at a 0% incline, and both were increased gradually over 25 minutes. www.ajpmonline.org
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Table 1. Baseline Characteristics of Smoking Participants by Cardiorespiratory Fitness Level
Years of follow-up, M (SD) Years of age, M (SD)
All (N¼1,249)
Low fitness (n¼356)
Moderate fitness (n¼461)
High fitness (n¼432)
6.3 (5.6)
4.9 (4.7)
6.6 (5.7)
7.0 (6.1)
o0.0001
42.1 (8.5)
42.9 (8.2)
40.9 (8.4)
42.6 (8.7)
0.69
Sex, n (%) Males Females Fitness in METs, M (SD)
0.76 1,060 (84.9)
296 (83.2)
400 (86.8)
189 (15.1)
60 (16.9)
61 (13.2)
11.5 (2.1)
9.3 (1.2)
11.4 (1.0)
364 (84.3) 68 (15.7) 13.4 (1.6)
Alcohol use, n (%)
o0.0001 0.004
Nondrinker
112 (9.0)
45 (12.6)
36 (7.8)
31 (7.2)
17 drinks/wk
459 (36.8)
132 (37.1)
182 (39.5)
145 (33.6)
814 drinks/wk
332 (26.6)
88 (24.7)
124 (26.9)
120 (27.8)
414 drinks/wk
297 (23.8)
73 (20.5)
108 (23.4)
116 (26.9)
49 (3.9)
18 (5.1)
11 (2.4)
20 (4.6)
Missing
p-valuea
Smoking level, n (%)
0.94
0 cigarettes/dayb
119 (9.5)
24 (6.7)
28 (6.1)
67 (15.5)
110 cigarettes/day
363 (29.1)
84 (23.6)
151 (32.8)
128 (29.6)
1120 cigarettes/day
214 (17.1)
76 (21.4)
94 (20.4)
44 (10.2)
420 cigarettes/day
231 (18.5)
127 (35.7)
84 (18.2)
20 (4.6)
Missing
322 (25.8)
45 (12.6)
104 (22.6)
173 (40.1)
Cardiometabolic risk factors, M (SD) BMI
25.2 (3.1)
26.3 (3.4)
25.2 (3.1)
24.4 (2.6)
o0.0001
Waist circumference (cm)
88.3 (10.3)
91.4 (10.3)
88.5 (10.3)
85.4 (9.4)
o0.0001
Fasting glucose (mg/dL)
96.7 (13.9)
97.4 (13.8)
96.2 (16.3)
96.6 (10.8)
0.44
Cholesterol (mg/dL)
203.4 (38.3)
211.0 (40.8)
202.2 (39.2)
198.4 (34.2)
o0.0001
Triglycerides (mg/dL)
113.5 (71.1)
132.4 (84.4)
113.6 (63.0)
97.8 (63.4)
o0.0001
50.4 (13.4)
47.1 (13.3)
49.5 (13.2)
53.9 (13.0)
o0.0001
Resting systolic blood pressure (mmHg)
115.3 (12.0)
115.4 (11.2)
114.4 (12.2)
116.2 (12.3)
0.35
Resting diastolic blood pressure (mmHg)
78.0 (8.9)
79.0 (8.2)
77.7 (8.6)
77.5 (9.6)
0.02
HDL (mg/dL)
o0.0001
Total cardiometabolic risk factors, n (%) 0
376 (30.1)
76 (21.4)
132 (28.6)
168 (38.9)
1
472 (37.8)
124 (34.8)
182 (39.5)
166 (38.4)
2
401 (32.1)
156 (43.8)
147 (31.9)
98 (22.7)
377 (30.2)
109 (30.6)
137 (29.7)
131 (30.3)
0.95
84 (6.7)
34 (9.6)
39 (8.5)
11 (2.6)
o0.0001
333 (26.7)
99 (27.8)
111 (24.1)
123 (28.5)
0.73
Cardiometabolic risk factor s, n (%) Elevated fasting glucose Elevated waist circumference Elevated blood pressure
(continued on next page)
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Table 1. Baseline Characteristics of Smoking Participants by Cardiorespiratory Fitness Level (continued) All (N¼1,249)
Low fitness (n¼356)
Elevated triglycerides
203 (16.3)
83 (23.3)
79 (17.1)
41 (9.5)
o0.0001
Low HDL cholesterol
277 (22.2)
111 (31.2)
110 (23.9)
56 (13.0)
o0.0001
557 (44.6)
170 (47.8)
195 (42.3)
192 (44.4)
0.41
Family history of CVD, n (%)
Moderate fitness (n¼461)
High fitness (n¼432)
p-valuea
a
p-values for continuous variables are from the Wald trend test; p-values for categorical variables are from the Jonckeheere-Terpstra trend test; boldface indicates statistical significance (po0.05). b Please note that 119 participants reported current smoking at their first visit in combination with a self-reported average smoking rate of 0 cigarettes per day (i.e., consistent with non-daily smoking). CVD, cardiovascular disease; HDL, high-density lipoprotein; MET, metabolic equivalent of task.
After this time period, the grade remained constant while the speed continued to increase by 0.3 miles/hour each minute until volitional exhaustion. Maximal METs (1 MET¼3.5 mL O2 uptake kg body mass1 min1) were estimated from the final treadmill speed and incline.22 Maximal METs have been shown to be highly correlated with maximal oxygen uptake in men (r ¼0.92) and women (r¼0.94).23,24 Thus, maximal METS served as an estimate of CRF in the current study. MET values of the analytic sample were categorized into age- (20–39, 40–49, 50–59, and Z60 years) and sex-specific tertiles of fitness (low, moderate, and high). Covariates included age, gender, BMI, alcohol intake, family history of cardiovascular disease, and the number of baseline metabolic syndrome components (for the main metabolic syndrome analysis). Alcohol intake (drinks/week) was determined based on responses to a survey question about the number of drinks consumed per week of beer (12 ounces), wine (5 ounces), and hard liquor (1.5 ounces). Family history of cardiovascular disease was based on checklist responses indicating that a family member (i.e., mother, father, or brother/sister) had a history of heart attack, coronary bypass, angioplasty, angina, or stroke. In
addition, baseline measurements of fasting glucose (mg/dL); waist circumference (cm); blood pressure (systolic and diastolic; mmHg); triglycerides (mg/dL); and HDL cholesterol (mg/dL) were included in the model with the corresponding outcome (e.g., baseline glucose was included as a covariate in the elevated fasting glucose model).
Statistical Analysis Means and SDs were computed for baseline continuous variables, and proportions were computed for baseline categorical variables. Participants were observed at discrete clinic visits, which occurred at irregular intervals through the end of the observation period. When incident metabolic syndrome or any of its components was diagnosed, the event time was treated as interval censored in the interval between the diagnosis visit and the previous visit. An accelerated failure time model was fit to the interval-censored event times using the maximum likelihood method, assuming a baseline Weibull distribution. This model provides estimates of covariate-adjusted incidence rates for a specific time, as well as
Table 2. Association Between Cardiorespiratory Fitness and the Development of Metabolic Syndrome Among 1,249 Smokers
Total follow-upb (person-years)
Covariate-adjusted rate per 1,000 person-yearsc (95% CI)
Hazard ratio (95% CI)
90
1,748
8.7 (3.3, 22.8)
1.0 (ref)
461
94
3,051
6.3 (2.4, 16.8)
0.73 (0.54, 0.98)
432
56
3,027
4.5 (1.5, 12.3)
0.52 (0.37, 0.74)
—
—
—
—
0.0003
Total sample (N¼1,249)
Incident metabolic syndrome casesa (n¼240)
Low
356
Moderate
Fitness level (baseline)d
High e
Linear trend, p
Note: An accelerated failure time model was used to calculate covariate-adjusted incidence rates, hazard ratios, and 95% CIs. The model was adjusted for age; gender; BMI; alcohol intake; family history of cardiovascular disease (CVD); and number of metabolic syndrome components at baseline. a Incident metabolic syndrome was assessed at follow-up and was defined as having at least three of the following: (1) elevated waist circumference (men, Z102 cm; women, Z88 cm); (2) elevated triglycerides (Z150 mg/dL); (3) low HDL (men, o40 mg/dL; women, o50 mg/dL); (4) elevated blood pressure (systolic Z130 mmHg or diastolic Z85 mmHg) or self-reported hypertension; and (5) elevated fasting glucose (Z100 mg/dL) or selfreported diabetes. b Person-years were calculated as the sum of person-years by fitness category from baseline to final exam date. c Covariate-adjusted incidence rates reflect the estimated rate for a population at the mean level of each continuous covariate (age, BMI) and at the reference level for all nominal covariates (gender [ref¼female]; alcohol intake [ref¼non-drinker]; family history of CVD [ref¼no family history of CVD]; and number of metabolic syndrome components at baseline [ref¼0]). d Fitness was assessed at baseline by a maximal exercise test. e The linear trend reflects the association of fitness level with incident metabolic syndrome.
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Table 3. Associations Between Cardiorespiratory Fitness and the Development of Cardiometabolic Risk Factors Among Smokers Fasting glucose Z100 mg/dL Total sample (N¼862)
Cases (n¼314)
Total follow-upa (person-years)
Covariate- adjusted rate perb 1,000 person-years (95% CI)
Hazard ratio (95% CI)
Low
242
98
1,037
71.4 (42.6, 119.7)
1.0 (ref)
Moderate
323
121
1,739
52.6 (31.1, 88.7)
0.74 (0.56, 0.97)
297
95
1,739
38.8 (22.3, 67.6)
0.54 (0.40, 0.73)
—
—
—
—
o0.0001
Fitness level (baseline)c
High d
Linear trend, p
Waist circumference 4102 cm (men) or 488 cm (women) Total sample (N¼1,162)
Cases (n¼147)
Total follow-up (person-years)a
Covariate- adjusted rate per 1,000 person-years (95% CI)b
Hazard ratio (95% CI)
Low
322
58
1,568
322.0 (148.6, 697.7)
1.0 (ref)
Moderate
419
50
2,735
272.2 (120.0, 618.1)
0.85 (0.57, 1.25)
High
421
39
2,940
322.9 (132.0, 790.0)
1.00 (0.64, 1.57)
—
—
—
—
0.99
Fitness level (baseline)c
Linear trend, pd
Blood pressure Z130/85 Total sample (N¼909)
Cases (n¼285)
Total follow-up (person-years)a
Covariate- adjusted rate per 1,000 person-years (95% CI)b
Hazard ratio (95% CI)
Low
256
82
1,201
70.2 (41.4, 119.0)
1.0 (ref)
Moderate
347
106
2,067
63.5 (36.9, 109.2)
0.90 (0.67, 1.23)
306
97
1,826
66.0 (38.1, 114.2)
0.94 (0.68, 1.29
—
—
—
—
0.70
Fitness level (baseline)c
High d
Linear trend, p
Triglycerides Z150 mg/dL Total sample (N¼1,038)
Cases (n¼208)
Total follow-up (person-years) a
Covariate- adjusted rate per 1,000 person-years (95% CI) b
Hazard ratio (95% CI)
Low
271
58
1,321
41.4 (21.8, 78.8)
1.0 (ref)
Moderate
378
84
2,404
40.9 (21.1, 79.0)
0.99 (0.70, 1.40)
389
66
2,634
35.9 (18.0, 71.4)
0.87 (0.60, 1.26)
—
—
—
—
0.45
Fitness level (baseline)c
High d
Linear trend, p
HDL cholesterol o40 mg/dL (men) or o50 mg/dL (women) Total sample (N¼968)
Cases (n¼128)
Total follow-up (person-years) a
Covariate- adjusted rate per 1,000 person-years (95% CI) b
Hazard ratio (95% CI)
Low
243
42
1,187
27.8 (13.0, 59.3)
1.0 (ref)
Moderate
350
51
2,286
23.7 (10.7, 52.6)
0.85 (0.56, 1.30)
High
375
35
2,579
16.7 (7.3, 38.5)
0.60 (0.37, 0.97)
—
—
—
—
0.04
Fitness level (baseline)c
Linear trend, pd
Note: An accelerated failure time model was used to calculate covariate-adjusted incidence rates, hazard ratios, and 95% CIs. The models were adjusted for age; gender; BMI; alcohol intake; family history of cardiovascular disease (CVD); and baseline value of the corresponding risk factor
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outcome (e.g., baseline glucose was included as a covariate in the elevated fasting glucose model). Cardiometabolic risk factors assessed at follow-up. Individuals with elevated values on the outcome variables at baseline were excluded from the analysis, thus sample sizes ranged from 862-1,162. a Person-years were calculated as the sum of person-years by fitness category from baseline to final exam date. b Covariate-adjusted incidence rates reflect the estimated rate for a population at the mean level of each continuous covariate (age, BMI, baseline value for the corresponding risk factor outcome [e.g., baseline glucose in the elevated fasting glucose model]) and at the reference level for all nominal covariates (gender [ref¼female]; alcohol intake [ref¼non-drinker]; and family history of CVD [ref¼no family history of CVD]. c Fitness was assessed at baseline by a maximal exercise test. d The linear trend reflects the association of fitness level with each cardiometabolic risk factor. HDL, high-density lipoprotein.
characterization of covariate effects in terms of hazard ratios (HRs). A series of models were evaluated where metabolic syndrome and each metabolic risk component were included as dependent variables (i.e., elevated fasting glucose, elevated waist circumference, elevated blood pressure, elevated triglycerides, and low HDL cholesterol). Baseline fitness level was the independent variable, and covariates were included as described above. Nonproportional hazards were evaluated and accommodated by allowing the logarithm of the Weibull shape parameter to depend linearly on covariates. An additional accelerated failure time model was constructed to assess the joint effects of smoking level (light/ heavy) and fitness. All statistical analyses were programmed in SAS/STAT, version 9.2 and completed in 2013 and 2014.
Results The average age of this predominately male (84.9%) sample was 42.1 years (SD=8.5) at baseline. The average number of study visits completed was 3.48 (SD=2.4; median, 3), with a range of two to 22 visits, and an average follow-up of 6.3 years (SD=5.6) for the metabolic syndrome outcome analysis. Participants’ baseline mean CRF level was 11.5 METs (SD=2.1); 45.8% consumed seven or fewer drinks per week (mean, 10.9 drinks per week; SD=11.5; 3.9% had missing data); and 55.7% smoked 20 or fewer cigarettes a day (25.8% had missing data). Participants, on average, were overweight (mean BMI, 25.2; SD=3.1); normotensive (mean systolic blood pressure, 115.3 mmHg, SD=12.0; mean diastolic blood pressure, 78.0 mmHg, SD=8.9); had normal fasting glucose and triglyceride levels (mean fasting glucose, 96.7 mg/dL, SD=13.9; mean triglycerides, 113.5 mg/dL, SD=71.1), and 69.9% met the criteria for one or two components of metabolic syndrome at baseline. Participant characteristics are presented in Table 1. The associations between CRF at baseline (independent variable); metabolic syndrome at follow-up (primary dependent variable); and each cardiometabolic risk factor at follow-up (dependent variables) appear in Tables 2 and 3. The rate and risk for metabolic syndrome, abnormal HDL, and fasting glucose levels declined linearly with increases in fitness (linear trends, all po0.05). For example, the covariate-adjusted rate per 1,000 personyears for metabolic syndrome was 8.7, 6.3, and 4.5 within the low, moderate, and high fitness strata, respectively (linear trend, p=0.0003). When examining the fitness strata categorically, those in the highest fitness strata had
significantly lower risk for the development of metabolic syndrome, elevated fasting glucose, and abnormal HDL cholesterol relative to smokers with the lowest fitness levels. For example, smokers in the highest fitness strata had a 48% reduced risk of developing metabolic syndrome compared to those in the low fitness strata (HR=0.52, 95% CI=0.37, 0.74). In addition, individuals in the moderate fitness strata experienced a reduced risk of developing metabolic syndrome and elevated fasting glucose relative to smokers in the lowest fitness level. The joint effects of smoking and fitness levels on metabolic syndrome risk (while adjusting for covariates) are presented in Table 4. Compared to heavy smokers in the low fitness strata (reference group), both light and heavy smokers in the high fitness strata experienced significantly reduced risk of developing metabolic syndrome.
Discussion Study findings indicate that there is an inverse relationship between CRF and cardiometabolic risk among adult smokers. Smokers with the highest level of fitness experienced significantly reduced risk of developing metabolic syndrome as well as elevated fasting blood glucose and abnormal HDL cholesterol relative to smokers with the lowest level of fitness. Smokers categorized as moderately fit experienced reduced risk of developing metabolic syndrome as well as elevated fasting glucose. In addition, light and heavy smokers with the highest level of fitness were less likely to develop metabolic syndrome relative to heavy smokers of low fitness. Findings suggest that moderate to high levels of CRF decrease the impact of smoking on the development of metabolic syndrome and other cardiovascular disease risk factors. Results highlight the potential importance of improving CRF among individuals who are unwilling or unable to quit smoking. Findings extend previous research linking CRF with tobacco-related cancer mortality8,14 and physical activity with reduced risk of lung cancer and cardiovascular mortality.25–27 Although higher CRF has previously been linked with reduced cardiometabolic risk,28,29 the current study provides support for this link specifically among smokers. Mechanisms linking CRF with cardiometabolic risk may include adiposity, insulin sensitivity, inflammation, vascular function, blood pressure, and lipid www.ajpmonline.org
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were determined based on a single baseline assessment. Although fitness could potenIncident metabolic Hazard ratio tially decrease over the study a p -value syndrome cases (95% CI) period, in the majority of Low fitnessb (n¼287) cases (89%) fitness levels either remained the same or Heavy smokers 59 1.0 (ref) — increased in comparison to Light smokers 14 0.67 (0.37, 1.22) 0.19 baseline values. Nonetheless, Moderate fitness (n¼329) future research may focus on the influence of fitness trajecHeavy smokers 40 0.81 (0.53, 1.22) 0.32 tories over time on the develLight smokers 30 0.70 (0.44, 1.11) 0.13 opment of metabolic synHigh fitness (n¼192) drome and other health outcomes. Study participants Heavy smokers 8 0.44 (0.21, 0.93) 0.03 were predominantly white, Light smokers 15 0.54 (0.30, 0.96) 0.04 well educated, and male, Note: An accelerated failure time model was used to calculate hazard ratios and 95% CIs, adjusting for age; which may impact the extersex; BMI; alcohol intake; family history of CVD; and baseline number of metabolic syndrome components. nal validity of this study. Heavy smokers reported smoking 410 cigarettes per day and light smokers reported smoking 1–10 Additionally, as the study cigarettes per day. The reference group is Low Fitness/Heavy Smokers. Boldface indicates statistical significance (po0.05). sample consisted specifically a Incident metabolic syndrome was assessed at follow up and was defined as having at least three of the of smokers without prevalent following: (1) elevated waist circumference (men, Z102 cm; women, Z88 cm); (2) elevated triglycerides (Z150 mg/dL); (3) low HDL (men, o40 mg/dL; women, o50 mg/dL); (4) elevated blood pressure (systolic metabolic syndrome, some of Z130 mmHg or diastolic Z85 mmHg) or self-reported hypertension; and (5) elevated fasting glucose the participants’ health be(Z100 mg/dL) or self-reported diabetes. b haviors and clinical measureFitness was assessed at baseline by a maximal exercise test. ments significantly differed from those excluded from the study. On the other hand, internal validity may be metabolism.29 Plausibly, these mechanisms are similar increased because of the homogeneity across sociodemoamong smokers. Smoking is associated with a systemic graphic variables. Given the small sample size of women inflammatory response and elevated circulating levels of 30 in the study, sex was included as a covariate rather than inflammatory markers. In 2006, deRuiter and Faulk31 using stratification, and as a result, findings may be most ner hypothesized that physical activity may counteract relevant for men. Additionally, smoking level was not some of the negative cardiovascular, pulmonary, and available for all participants owing to missing responses immune system effects of smoking. For example, Park 15 and the exclusion of those who smoked less than one et al. reported that smoking-related arterial stiffening cigarette per day. There were also relatively few smokers was absent among physically active men. in the high fitness category (i.e., 24% [n¼192] of the Individuals in the moderate and high fitness categories analytic sample) in the joint effects analyses of fitness and were more likely to quit smoking by study follow-up than smoking level on cardiometabolic risk. Thus, current individuals of lower fitness (i.e., 33.2%, 42.7%, and 45.1% findings involving smoking levels must be replicated in of the low, moderate, and high fitness groups, respeclarger samples. tively, quit smoking by follow-up). Smoking cessation Those who smoke and are not ready or able to quit may would likely contribute to reduced cardiometabolic risk, benefit from engaging in physical activity in an effort to and may function as one link between fitness and increase CRF. Numerous studies have demonstrated a cardiometabolic risk. However, supplementary analyses positive relationship between the duration and intensity in which smoking cessation was included as a covariate of physical activity and CRF.32,33 Anton and colleagues34 along with all other covariates did not change study reported that individuals who engaged in vigorous findings (results available upon request). walking for at least 75 minutes per week experienced Strengths of this study include the objective measureclinically significant increases in CRF. Thus, there is ment of CRF, the prospective design, and the focus on evidence that achieving or exceeding currently recomsmokers. CRF, as an indicator of habitual physical mended physical activity guidelines5 will increase CRF. activity, allows for easy comparison across studies. The promotion of physical activity with the goal of However, the effects of fitness on cardiometabolic risk Table 4. Joint Effects of Cardiorespiratory Fitness and Smoking Level on the Incidence of Metabolic Syndrome
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increasing CRF may be an effective harm reduction strategy for those who continue to smoke, though fitness is also impacted by genetic factors.35
Conclusions CRF was inversely associated with risk for metabolic syndrome and some cardiometabolic risk factors among smokers. Risk for developing metabolic syndrome and elevated fasting glucose were significantly reduced among those in the moderate and high fitness categories relative to those in the lowest fitness category. Those in the high fitness strata additionally experienced reduced risk for abnormal HDL cholesterol. Smoking cessation remains the ideal option for reducing tobacco-related disease risk, though greater CRF may attenuate some of the negative health effects when cessation is unlikely. Research is needed to confirm study findings and uncover the mechanisms linking CRF with reduced cardiometabolic risk in smokers. Findings highlight the potential importance of increasing CRF via physical activity as a means to reduce cardiometabolic risk among individuals who continue to smoke. We are thankful to Dr. Kenneth H. Cooper MD, MPH for establishing The Cooper Center Longitudinal Study (CCLS), the Cooper Clinic physicians and technicians for data collection, The Cooper Institute staff for data management, and the CCLS study participants. Manuscript preparation was supported, in part, by American Cancer Society grants MRSGT10-104-01-CPHPS (awarded to DEK) and MRSGT-12-114-01CPPB (awarded to MSB). No financial disclosures were reported by the authors of this paper.
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