The association between cardiorespiratory fitness and cardiovascular risk may be modulated by known cardiovascular risk factors

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The Association between Cardiorespiratory Fitness and Cardiovascular RiskMay be Modulated by Known Cardiovascular Risk Factors

Aharon Erez, Shaye Kivity, Anat Berkovitch, Assi Milwidsky, RobertKlempfner, Shlomo Segev, Ilan Goldenberg, Yechezkel Sidi, Elad Maor

PII: S0002-8703(15)00174-XDOI: doi: 10.1016/j.ahj.2015.02.023Reference: YMHJ 4848

To appear in: American Heart Journal

Received date: 9 July 2014Accepted date: 24 February 2015

Please cite this article as: Erez Aharon, Kivity Shaye, Berkovitch Anat, MilwidskyAssi, Klempfner Robert, Segev Shlomo, Goldenberg Ilan, Sidi Yechezkel, Maor Elad,The Association between Cardiorespiratory Fitness and Cardiovascular Risk May beModulated by Known Cardiovascular Risk Factors, American Heart Journal (2015), doi:10.1016/j.ahj.2015.02.023

This is a PDF file of an unedited manuscript that has been accepted for publication.As a service to our customers we are providing this early version of the manuscript.The manuscript will undergo copyediting, typesetting, and review of the resulting proofbefore it is published in its final form. Please note that during the production processerrors may be discovered which could affect the content, and all legal disclaimers thatapply to the journal pertain.

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The Association between Cardiorespiratory Fitness and Cardiovascular Risk May

be Modulated by Known Cardiovascular Risk Factors

Aharon Erez 1, Shaye Kivity 2, Anat Berkovitch 1,Assi Milwidsky 1, Robert Klempfner 1, Shlomo

Segev3, Ilan Goldenberg 1 4, Yechezkel Sidi 1 4, Elad Maor 1

Running title: fitness as a predictor for cardiovascular risk

Word count: 4,962

Corresponding author:

Aharon Erez MD

Heart Institute , Sheba Medical Center

Tel Hashomer 52 621

Ramat Gan , Israel

Tel. +972-3-5303068

Fax: +972-3-5305905

E-mail: [email protected]

1 Leviev Heart Center, Chaim Sheba Medical Center

2 Department of internal medicine C, Chaim Sheba Medical Center

3 Institute for Medical Screening, Chaim Sheba Medical Center

4 Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel

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Abstract:

Background: We aimed to evaluate whether reduced cardiovascular fitness has a direct or indirect

effect for the development of cardiovascular disease (CVD). Methods: We investigated 15,595 men and

women who were annually screened in a tertiary medical center. All subjects were free of ischemic

heart disease, and had completed maximal exercise stress test (EST) according to the Bruce protocol at

their first visit. Fitness was categorized into age- and sex-specific quintiles (Q) according to Bruce

protocol treadmill time with Q1 as lowest fitness. Subjects were categorized at baseline into three

groups: low fitness (Q1), moderate fitness (Q2-4) and high fitness (Q5). The primary endpoint of the

current analysis was the development of a first cardiovascular event during follow-up. Results: Mean

age of study patients was 48 ± 10 years and 73% were men. A total of 679 events occurred during

92,092 person-years of follow up. Kaplan-Meier survival analysis showed that the cumulative probability

of cardiovascular events at 6 years was significantly higher among subjects with low fitness (P < 0.001).

Low fitness was associated with known cardiovascular risk factors, including hypercholesterolemia

(OR=1.58, CI: 1.31-1.89), diabetes mellitus (OR=2.32, CI: 1.58-3.41) and obesity (OR=10.46, CI 8.43-

12.98). The effect of low fitness on cardiovascular events was no longer significant when including

diabetes mellitus, hypercholesterolemia and obesity as mediators (HR=0.99, CI: 0.82–1.19) Conclusions:

The association between cardiovascular fitness and adverse cardiovascular outcomes may be modulated

through traditional cardiovascular risk factors. These findings need to be further validated in prospective

clinical trials.

Key words: fitness; cardiovascular; risk.

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Introduction

Regular physical activity is associated with reduced cardiovascular morbidity and mortality 1 .

Accordingly, both the American Heart Association and the European Society of Cardiology recommend

physical activity for primary prevention of cardiovascular disease. 2,3. Among the mechanisms proposed

to mediate the protective effect of regular physical activity are known cardiovascular risk factors such as

inflammatory/hemostatic factors and blood pressure4.

While physical activity is relatively subjective, cardiorespiratory fitness (CRF) is an objective

parameter that can be assessed by exercise stress testing (EST). There is some evidence to suggest that

being unfit is a stronger risk factor for cardiovascular disease relative to inactivity 5 and that CRF is

negatively associated with coronary heart disease independent of physical activity 6. CRF is also

associated with numerous cardiovascular risk factors reductions such as diabetes mellitus 7,

hypertension 8,9, metabolic risk 10,11. However, data on the association of objectively measured CRF and

cardiovascular morbidity are limited, and only few studies have adequately controlled for confounding

variables 12.

The aims of the current study were to: (1) evaluate the association between CRF and the risk for

developing acute coronary syndrome (ACS) in a large cohort of asymptomatic men and women, (2)

Investigate whether CRF has a direct effect or an indirect effect through mediation by other

cardiovascular risk factors on long-term risk for ACS in a large cohort of middle-aged adult males and

females without known coronary heart disease (CHD).

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Methods

Study population

The study population has been described previously13-15. Briefly, The Institute for Medical

Screening of the Chaim Sheba Medical Center performs approximately 9,000 annual screening

examinations of adult males and females annually. This population is composed mainly of subjects with

private or organizational insurance, and therefore represents a higher-than-average socioeconomic

class. When compared with data available for the general Israeli population, our study population had

lower rates of obesity (body mass index [BMI] ≥ 30 kg/m2) and lower rates of active smoking. All

examinations are being recorded in a dedicated computerized database established in the year 2000,

which is the source of data for the current study. Examinations are performed as part of periodic health

screening of apparently healthy men and women. Each year, all participants answer a standard

questionnaire regarding their demographic characteristics, medical history and health related habits

including a dichotomous evaluation of self-reported physical activity and smoking status. Questioning is

followed by a physical exam performed by one of the physicians at the center and a blood test that are

analyzed at the center. Thereafter, a maximal exercise stress test (EST) according to the Bruce protocol

is performed under the supervision of, and interpreted by a board certified cardiologist. Participants

were required to achieve at least 85% of their age- predicted maximal heart rate (220 - age in years) and

were encouraged to reach their maximal effort. The test was terminated due to exhaustion or due to

angina or other medical reasons. Participants are instructed not to take their medications on the

morning of their annual visit. Subjects who failed to reach 85% of their age- predicted maximal heart

rate were invited to a routine early follow-up.

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The Institutional Review Board of the Sheba medical center approved this study on the basis of

strict maintenance of participants’ anonymity during database analyses. Data from subjects were

recorded anonymously. No individual consent was obtained

Inclusion and exclusion criteria

24,998 subjects were screened from January 2000 until April 2013. 849 subjects had not

performed EST and were not eligible to be included in the analysis. 5,998 subjects were excluded due to

missing follow-up examination. 966 subjects were excluded either because they had a documented or

suspected ischemic heart disease at baseline. 1,590 subjects failed to achieve 85% of their age-predicted

maximal heart rate [220 - age (in years)]. Participants who failed to reach 85% of their age-predicted

maximal heart rate were excluded because they were assumed to have subclinical medical conditions,

and less than near-maximal effort would lead to an underestimate of fitness, which may confound

results.

Definition of Cardiovascular Fitness

CRF was quantified using a maximal exercise test with the Bruce protocol16. Maximal exertion

was determined as achieving ≥85% age-predicted maximal heart rate. Fitness was determined based on

the treadmill test time. Previous studies demonstrated that treadmill test time correlates well (r = 0.92)

with maximal oxygen uptake (VO2 max)17. Age- and gender-specific distributions of treadmill duration

were computed for the following age groups: 40 to 49, 50 to 59, 60 to 79, and >79 years. Each gender-

and age-specific distribution of treadmill test time was divided into fifths of CRF to provide the quintiles

of CRF. These quintiles of fitness measures were then combined into 3, mutually exclusive fitness

groups: low fitness: quintile 1 (N=3,036;19.5%); moderate fitness: quintiles 2 to 4 (N=9,487;60.8%) ; and

high fitness: quintile 5 (N=3,072;19.7%). Treadmill test time was used to calculate metabolic equivalents

(METs) based on well-characterized regression equations 17.

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Study end point

At each annual visit, physicians update all clinical diagnosis in the computerized record of each

patient. The primary outcome of the current study was the development of acute coronary syndrome

during follow-up, defined as a composite of nonfatal myocardial infarction, according to the third

universal definition of myocardial infarction18, or percutaneous coronary intervention driven by unstable

angina, whichever came first. Mortality events were available from the Israeli Population Register. The

consistency of the results was assessed for the secondary end point that included a first cardiovascular

event (as defined above) and all-cause mortality.

Statistical analysis

Continuous data were compared with Student t-test and one-way ANOVA. Categorical data

were compared with the use of chi-square test or Fisher exact test. Kaplan Meier survival analysis was

employed to evaluate the univariate association between CRF groups and cardiovascular events.

Prior to examination of a possible mediation effect of cardiovascular risk factors on the

association between CRF and outcomes, we evaluated the main effect of low cardiovascular fitness on

cardiovascular events. A Cox proportional hazard model adjusted for age and gender was further

utilized. The consistency of the effect of fitness on cardiovascular events was assessed with the use of

statistical tests of interactions between fitness category and gender within the Cox models. The same

analysis was conducted on the interaction between fitness and age (as a categorical variable,

categorized by median age).

Cardiovascular risk factors were assessed at baseline examination. Hypertension (>140/90

mmHg), obesity (BMI ≥ 30kg/m2), diabetes mellitus (yes/no), hypercholesterolemia (total

cholesterol>240mg/dl) and Impaired fasting glucose (glucose>100mg/dl and glucose <126mg/dl) were

not included as covariables because such factors could be mediators (in causal pathways)19.

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The analysis followed by a standard procedures for mediation analysis 20, employing three main

steps. In the first step, we ran a series of binary logistic regressions, each adjusting for age and gender,

to examine the association between low fitness and a range of potential mediators. In the second step,

we evaluated the effect of each potential mediator on cardiovascular events by using a series of Cox

proportional hazard model adjusted for age and gender (i.e. without including CRF as a potential

covariate). In the third step, we included the potential mediators and CRF, as predictors of

cardiovascular events, in a multivariate Cox proportional hazard model adjusted for age and gender. This

final step enabled us to examine whether low fitness has a direct or indirect effect on cardiovascular

events. Smoking status can be a confounder and therefore we performed a sensitivity analysis with

smoking and results remained consistent.

Because the odds ratio for obesity among subjects with low fitness was high (>10%), the

adjusted odds ratio would overestimate the magnitude of the actual risk associated with low fitness. For

this reason we corrected the risk estimate according to the method of Zhang 21, and report in Figure 3

the corrected risk ratios. In order to maintain consistency, we report in Figure 3 the corrected risk ratios

for diabetes mellitus and for hypercholesteremia.

To assess the relative contribution of traditional cardiovascular risk factors to the observed

association between low fitness and cardiovascular events, we used an approach similar to the one used

by Birkmeyer 22. The relative attenuation of the hazard ratios for cardiovascular events was computed as

[HRF-HRFM] ÷ [HRF-1]; HRF is the hazard ratio of low fitness for cardiovascular events without

consideration of traditional cardiovascular risk factors; HRFM is the hazard ratio of low fitness for

cardiovascular events after adjustment for the significant mediators found in the previous steps. Both

hazard ratios were adjusted for age and gender.

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Statistical significance was accepted for a 2-sided p<0.05. The statistical analyses were

performed with IBM SPSS version 20.0 (Chicago, Illinois, USA).

No extramural funding was used to support this work. The authors are solely responsible for the

design and conduct of this study, all study analyses and drafting and editing of the paper.

Results

The study population comprised of 15,595 individuals, with mean age of 48±10 and 11,416

(73%) men. Our study cohort has relative similar characteristics as the entire population that has

completed at least one screening examination with EST (Supplementary Table 1). A comparison between

study cohort and all excluded subjects is shown in Supplementary Table 2.

Baseline Characteristics of Subjects According to CRF Level

Baseline characteristics according to baseline fitness are presented in Table 1. Overall, the study

population demonstrates modest burden of traditional cardiovascular risk factors: there were 363 (2%)

diabetic subjects, 2,862 (18%) hypertensive subjects, 2,410 (17%) active smokers, 8,881 (58%)

overweight subjects, 5,297 (34%) subjects with low HDL. Higher CRF levels were correlated with lower

burden of traditional risk factors (Figure 1, Table1). Moreover, with the exception of smoking status, the

presence of each risk factor was lower with a higher CRF level.

Outcome Events by CRF Level

During 92,092 person-years of follow up (median follow-up: 5.5 years, interquartile range: 2.4

years to 9.7 years) there were 679 incident cardiovascular events. The incidence-rate per 1,000 person

years was 8.6 in the low fitness group, 7.5 in the moderate fitness group and 5.7 in the high fitness

group. Kaplan Meier survival analysis showed that the cumulative probability of cardiovascular events at

6 years was 4.8% for patients with low CRF, 4.2% for patients with moderate CRF and 3.4% for patients

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with high CRF (Log rank p = 0.006 for the overall difference during follow-up; Figure 2). Consistent with

these findings, multivariate Cox proportional hazards regression modeling showed that low CRF was

associated with a 30% (p=0.04) higher risk for adverse cardiovascular outcomes after adjustment for age

and gender (Table 2; first row). Moderate CRF versus high CRF was not significantly associated with

higher risk for adverse cardiovascular outcomes after adjustment for age and gender (HR=1.22, C.I 0.98-

1.52; p-val=0.076). There was no significant interaction between gender and CRF to cardiovascular

events, (p-value for gender by CRF=0.29, 0.81, 0.41 for high, moderate and low CRF, respectively). Also

there was no significant interaction between age and CRF to cardiovascular events (p-values for age by

CRF= 0.09 , 0.07 ,0.49 for high, moderate and low CRF, respectively)

Test of Mediation

Subsequent results in Table 2 provide the three steps in the test for mediation,20 with results

presented as odds ratios or hazard ratios with lower and upper limits. Step A established that low CRF is

associated with increased traditional cardiovascular risk factors, whereas step B showed that the

potential mediators (impaired fasting glucose, hypercholesterolemia, DM, hypertension and obesity)

were all associated with increased risk for cardiovascular events, without including fitness as a potential

covariate. In step C, the potential mediators were included in the base model with low fitness. This

model showed that CRF is indirectly associated with cardiovascular outcomes, and that

hypercholesterolemia,DM and obesity were significant mediators for the effect of low fitness on

increased cardiovascular events during six years of follow up. Consistent results were obtained when

only the significant mediators were included in the base model (Figure 3).

The adjusted hazard ratios of low fitness as compared with high fitness decreased from 1.3 to

1.2 after adjustment for obesity, hypercholesterolemia and DM. Thus, obesity, hypercholesterolemia

and DM accounted for 33 percent of the apparent difference in cardiovascular events between low and

high fitness ([1.3-1.2] ÷ [1.3-1]) 22.

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The results of a model treating fitness as continuous instead of a categorical variable was also

consistent showing significant reduction of cardiovascular events for every 1-MET's increment after

adjusting for age and gender (HR=0.97, p=0.014), but not after adding obesity, hypercholesterolemia

and DM to the model (HR=0.98, p=0.08). Consistent results were obtained when the secondary end

point of a first cardiovascular event or all-cause mortality was assessed (HR for low vs. high fitness=1.25

[1.01–1.55], P=0.04 in age and gender adjusted model; However, after adding obesity,

hypercholesterolemia and DM to the model HR=1.15 [0.92-1.44], P=0.21).

Discussion

The present study provides several important insights regarding the association between CRF

and the risk for cardiovascular events in apparently healthy middle aged men and women. We have

shown that 1) Low CRF is associated with a higher burden of traditional cardiovascular risk factors. 2)

The excess risk for cardiovascular events associated with low CRF may be modulated by traditional

cardiovascular risk factors (DM, hypercholesterolemia and obesity; as demonstrated in Figure 3). These

findings suggest that low CRF can be used to identify high risk middle-aged subjects without known CHD

in whom intensive primary prevention strategies, focusing on known cardiovascular risk factors, can be

employed.

Fitness level and cardiovascular risk factors

In our work, low CRF at baseline was correlated with higher burden of traditional cardiovascular

risk factors. Subjects with low fitness were more likely to suffer from hypertension, impaired fasting

glucose, hypercholesterolemia, low HDL and obesity. Our results are concordant with other studies on

CRF and cardiovascular risk factors23,24. In a cross-sectional analysis by LaMonte et al, CRF was favorably

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associated with all CHD risk factors among men and women without coronary heart disease 25. Other

works have shown that the association between fitness to later development of risk factors persist (e.g.

diabetes mellitus 7 , hypertension 8, metabolic risk 10 ). The strong association between CRF and

traditional cardiovascular risk factors supports the hypothesis that CRF does not necessarily have a

direct effect on cardiovascular risk, but rather an indirect effect mediated by other risk factors.

Indirect effect of fitness on cardiovascular risk

While it is a consensus that high CRF is beneficial in terms of reducing cardiovascular risk, it is

not conclusive whether the effect of high CRF on cardiovascular disease is a direct effect or is it an

indirect effect mediated by other factors. In a metanalysis by Kodama et al 12, which included data from

33 studies, participants with low CRF compared with participants with high CRF had an RR for

cardiovascular events of 1.56 (95% CI, 1.39-1.75; P<0.001). However, only seven studies out of 33 had

adjusted the RR for the mediators found in our study (i.e, DM, hypercholesterolemia and obesity). Out

of those seven studies, in two studies by Laukkanen 26,27 only men participated and subjects with

diabetes mellitus were not included. In those studies, the adjustment of RR was not made for BMI and

total cholesterol was not included in the model. In two other studies 28,29 there were inconclusive

results, which are discussed in more detail below. Mora et al30, used heart rate recovery (HRR) in

addition to the levels of exercise capacity to predict cardiovascular death. The outcome of

cardiovascular event used by Mora et al, had included cerebrovascular event as well, which wasn't

included in our study. The remaining two studies have used a different definitions for fitness31,32, which

are less validated as the definition we and the other mentioned studies used for fitness. To our

knowledge, our study is the first to employ mediation analysis in order to shed light on this issue. In a

recent study, by Berry et al. that included 20,642 participants with 133,514 person-years of follow-up,

the association between high CRF and myocardial infarction after multivariate adjustment for age and

other cardiovascular risk factors, was a modest reduction in men (0.91 [0.87–0.95]; P<0.001) and no

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association in women (0.97 [0.83–1.13]; P=0.68) 33.In a work by Skretteberg et al., which included 1,357

healthy men aged 44–69 years followed for up to 28 years, CRF did not influenced the prognostic impact

of HDL on cardiovascular disease34. Our study support and elaborate those findings by showing that in a

relatively healthy middle-aged population, the association between baseline CRF and subsequent

cardiovascular outcome during 6-years of follow-up may be modulated by traditional cardiovascular risk

factors (DM, obesity, and hypercholesterolemia). Other studies have shown mixed results regarding the

association between CRF and nonfatal cardiovascular events in asymptomatic subjects. For example, in a

study by Sui et al., which included 20,728 men and 5,909 women with a mean follow up of 10 years an

association between CRF and nonfatal cardiovascular events remained significant, after adjustment for

cardiovascular risk factors, in men but not in women28. A study by Balady et al. aimed to determine the

usefulness of exercise testing among asymptomatic persons in predicting coronary heart disease events

beyond the Framingham coronary heart disease risk score, found that the hazard ratio for exercise

capacity expressed in METs and adjusted for age was not significant among men, whose 10-year risk

category was < 20% using the Framingham risk score, and in women each 1-MET increment in exercise

capacity didn't confer a significant reduction in CHD risk29.

Limitation of the Analysis

The generalizability of our present finding is limited by the fact that the study population

comprised relatively healthy middle-aged adults with a very low prevalence of traditional cardiovascular

risk factors, including 2% diabetes mellitus (n=263), 18% hypertension (n=2,862), only 17% active

smokers (n=2,410) and an overall more fit population. Thus, our data may be applicable only to this

population. Our work might suffer from a detection bias in more fit individuals who may experience

more angina because they are more active, however this bias is inherent to every study aim to examine

the association between fitness and cardiovascular events.

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We didn't have sufficient data on the level of physical activity as well as waist circumference to

include in our analysis. Also, we didn't have specific information regarding muscolo-skeletal disorders,

which can have a moderator effect on the association between fitness and cardiovascular risk.

Moreover, the model of mediation we used cannot handle moderator effects, as compare to other

models of mediation19. However our mediation model is easy to understand and potential moderators

could be regarded as confounders as well. Data on the amount of alcohol consumption is missing,

however lipoproteins, which are included in our model, are suggested to mediate the effect of alcohol

consumption on cardiovascular risk35 . Moreover, none of our patients have reported high alcohol

consumption.

Although the fully adjusted analysis didn't show a significant association between fitness and

cardiovascular event, our meditation analysis accounted for a third of the effect and there was still a

trend to an independent association. Thus, we cannot exclude a possibility that a longer follow-up will

yield an independent association but on the other hand there may remain other mediating effects not

accounted for in our analysis. Since individuals were not randomly assigned, we cannot exclude a

possible effect of residual confounding, or confounding by unmeasured variables, such as the fact that

diabetes may be associated with a lower level of physical activity, resulting in poorer CRF, higher LDL,

lower HDL, and subsequently increased cardiovascular outcomes. Nevertheless, our large sample size,

the relatively long follow-up and the availability of various covariates are major strengths of this study.

Lastly, since in the present study the association of CRF and cardiovascular risk factors were

assessed in cross-sectional analysis, we cannot determine the direction of casualty. Thus, it is possible

that cardiovascular risk factors may result in depressed CRF rather than reduced cardiovascular fitness

leading to increased prevalence of cardiovascular risk factors.

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Conclusion

Performing physical activity and avoiding sedentary behavior is recommended for primary

prevention and is based on strong evidence2,3. While CRF can be used to assess levels of physical activity,

the role of objectively assessing CRF is not clear 36. Low CRF appears to have an indirect effect on the

risk for subsequent cardiovascular events moderated through higher metabolic risk. Our findings

suggest that low CRF can be used to identify high risk middle-aged subjects without known CHD in

whom intensive primary prevention strategies, focusing on known cardiovascular risk factors, can be

employed. Randomized-controlled studies with primary prevention strategies, based on CRF level are

needed to support this conclusion.

Disclosures

None.

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Figure Legends

Figure 1 – Cardiovascular comorbidity according to fitness level. Comorbidity burden is the

number of conditions among the following: smoking, obesity, low HDL, Impaired fasting

glucose, Diabetes mellitus, Hypertension, Hypercholesteremia. Data are presented in box-

plots. The inside each box represents the median, and the lower and upper edges of the boxes

represents the 25th and 75thpercentiles, respectively, and upper and lower lines outside the

boxes represent minimum and maximum values (error bars). A line is drawn between mean

values.

Figure 2 – Kaplan-Meier plot of survival free of cardiovascular events according to

cardiorespiratory fitness (CRF) level.

Figure 3 – Path Diagram for Mediational Model. The solid arrows represent significant indirect

effect, and the dashed arrow represents a non-significant effect after adjustment for the

mediators. The coefficients and 95% confidence intervals are positioned above each arrow;

those on the arrows leading from low fitness to each mediator represent the risk ratio for

having the risk factor. The coefficients from each mediator to cardiovascular events show a

significant increased hazard ratio for cardiovascular events.

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Figure 1

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Figure 2

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Figure 3

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Table 1- Baseline characteristics of study population according to fitness level groups

Fitness

Quintile 1

N=3,204

Fitness

Quintiles 2-4

N=9,319

Fitness

Quintile 5

N=3,072

All

N=15,595

P-

Value

*

Males, % 72 74 73 73 0.34

Smoker, % 15 17 17 17 0.02

Self-reported physically activity,% 45 57 77 59 0.001

Obesity (BMI>30), % 26 13 3 14 0.001

Low High Density Lipoprotein, % 41 35 23 23 0.001

Impaired Fasting Glucose, % 19 15 10 15 0.001

Diabetes Mellitus, % 3 3 1 2 0.001

Hypertension, % 26 18 13 18 0.001

Hypercholesterolemia, % 10 9 7 9 0.001

COPD, % 0.2 0.1 0 0.1 0.02

Treadmill test duration, seconds 388±154 630±91 806±114 615±174 0.001

Metabolic equivalents (METs) 6.6±2.2 10.8±1.6 14.3±1.9 10.6±3.0 0.001

Hemoglobin, mg/dl 14.3±3 14.4±2 14.4±2 14.4±2.3 0.03

Creatinine, mg/dl 1.0±0.2 1.0±0.2 1.1±0.2 1.0±0.2 0.001

GFR<60 mL/min/1.73 m2, % 5 5 6 5 0.149

Age, years 48±11 48±10 47±10 48±10 0.001

Body Mass Index 28±6 26±6 25±43 26±20 0.001

Glucose, mg/dl 95±23 91±18 88±20 91±20 0.001

Data presented as mean±SD or % except as noted. BMI=Body Mass Index; COPD=Chronic Obstructive Pulmonary Disease; GFR= Glomerular filtration rate.

* Possibly due to the large sample of the present study population, several statistically significant differences in the baseline characteristics were observed across the fitness categories (e.g, creatinine).

However, these differences were not clinically meaningful.

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Table 2: Tests of the effects of mediation on primary outcome *:

Effect Value 95% C.I p-

value

Association between low fitness (vs. high fitness) and primary outcome (Hazard Ratio)

1.3 1.01-1.66

0.04

MEDIATION ANALYSIS

Step A: Association between low fitness and cardiovascular risk factors (Odds Ratio)

Impaired Fasting Glucose 2.05 1.76-2.38

0.001

Hypercholesterolemia 1.58 1.31-1.89

0.001

Diabetes mellitus 2.32 1.58-3.41

0.001

Hypertension 2.30 2.00-2.65

0.001

Obesity (BMI≥30kg/m2) 10.46 8.43-12.98

0.001

Step B: Association between cardiovascular risk factors and primary outcome (Hazard Ratio)

Impaired Fasting Glucose 1.19 1.00-1.41

0.05

Hypercholesterolemia 1.44 1.15-1.80

0.001

Diabetes mellitus 1.92 1.38-2.66

0.001

Hypertension 1.18 0.99-1.39

0.05

Obesity (BMI≥30kg/m2) 1.19 1.01-1.41

0.04

Step C: Association of cardiovascular risk factors and low fitness on primary outcome (Hazard Ratio) *

Low fitness (vs. high fitness) 0.99 0.82-1.19

0.92

Impaired Fasting Glucose 1.15 0.97- 0.12

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1.37

Hypercholesterolemia 1.45 1.16-1.82

0.001

Diabetes mellitus 1.85 1.33-2.59

0.001

Hypertension 1.11 0.94-1.32

0.23

Obesity (BMI≥30kg/m2) 1.31 1.07-1.61

0.008

All models are adjusted for age and gender. The three steps in the test for mediation were designed to examine the extent to which the association between fitness and cardiovascular risk reduction (i.e., the Effect indicated in the first row) is mediated by classic cardiovascular risk factors. Step A illustrates that low fitness is associated with an increased probability for cardiovascular risk factors. Step B shows that cardiovascular risk factors are associated with increased risk for cardiovascular events. The hazard ratios in Step C shows that when including such classical cardiovascular risk factors, the effect of low fitness was not significant, suggesting that the protective effect of high fitness is fully explained by the classical cardiovascular risk factors. *We run the model with smoking and COPD and results remained the same.