Bicycle riders have a reduced rate of early death and cardiovascular disease

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People who walk, cycle and travel by train to work are at reduced risk of early death or illness compared with those who commute by car.

These are the findings of a study of over 300,000 commuters in England and Wales, by researchers from Imperial College London and the University of Cambridge.

The researchers say the findings suggest increased walking and cycling post-lockdown may reduce deaths from heart disease and cancer.

The study, published in The Lancet Planetary Health, used Census data to track the same people for up to 25 years, between 1991-2016.

It found that, compared with those who drove, those who cycled to work had a 20 per cent reduced rate of early death, 24 per cent reduced rate of death from cardiovascular disease (which includes heart attack and stroke) during the study period, a 16 per cent reduced rate of death from cancer, and an 11 per cent reduced rate of a cancer diagnosis.

Walking to work was associated with a 7 per cent reduced rate in cancer diagnosis, compared to driving.

The team explain that associations between walking and other outcomes, such as rates of death from cancer and heart disease, were less certain.

One potential reason for this is people who walk to work are, on average, in less affluent occupations than people who drive to work, and more likely to have underlying health conditions which could not be fully accounted for.

The paper also revealed that compared with those who drove to work, rail commuters had a 10 per cent reduced rate of early death, a 20 per cent reduced rate of death from cardiovascular disease, and a 12 per cent reduced rate of cancer diagnosis.

This is likely due to them walking or cycling to transit points, although rail commuters also tend to be more affluent and less likely to have other underlying conditions, say the team.

Dr. Richard Patterson from the MRC Epidemiology Unit at the University of Cambridge who led the research said: “As large numbers of people begin to return to work as the COVID-19 lockdown eases, it is a good time for everyone to rethink their transport choices.

With severe and prolonged limits in public transport capacity likely, switching to private car use would be disastrous for our health and the environment.

Encouraging more people to walk and cycle will help limit the longer-term consequences of the pandemic.”

The study also assessed whether the benefits of each mode of travel differed between occupational groups and found that potential health benefits were similar across these groups.

The team used data from the UK Office for National Statistics Longitudinal Study of England and Wales, a dataset that links data from several sources including the Census of England and Wales, and registrations of death and cancer diagnoses.

The data revealed overall 66 per cent of people drove to work, 19 per cent used public transport, 12 per cent walked, and 3 per cent cycled. Men were more likely than women to drive or cycle to work, but were less likely to use public transport or walk.

Dr. Anthony Laverty, senior author from the School of Public Health at Imperial College London explained: “It’s great to see that the government is providing additional investment to encourage more walking and cycling during the post-lockdown period. While not everyone is able to walk or cycle to work, the government can support people to ensure that beneficial shifts in travel behaviour are sustained in the longer term. Additional benefits include better air quality which has improved during lockdown and reduced carbon emissions which is crucial to address the climate emergency.”

The team add that the benefits of cycling and walking are well-documented, but use of Census data in this new study allowed large numbers of people to be followed up for a longer time.

They explain that these analyses were unable to account for differences in participants’ dietary intakes, smoking, other physical activity or underlying health conditions. However, they add these findings are compatible with evidence from other studies.


Strong evidence supports an inverse dose-response relationship between physical activity levels and mortality.1–3 Current guidelines recommend at least 500–1000 metabolic equivalent task (MET)-minutes per week of moderate-to-vigorous physical activity.4–6 Unfortunately, studies have shown that less than half of adults achieve this level of physical activity, while one-third do no physical activity at all.7,8

The existing evidence on the dose-response relationship is mostly based on healthy people.9–16 While individuals with cardiovascular disease (CVD) are at higher risk for mortality and morbidity, they also tend to have sedentary lifestyles and are less physically active than are those without CVD.17 

Interventions with comprehensive cardiovascular rehabilitation programmes have been shown to reduce cardiac mortality in patients with coronary heart disease.18 While physical activity is generally recommended for secondary CVD prevention by contemporary guidelines, there is still a paucity of data regarding the relationship between physical activity and mortality, specifically among patients with pre-existing CVD.19–21 

In addition, no previous studies have compared the beneficial effect of physical activity between primary vs. secondary CVD prevention.

We analysed a population-based cohort in this study to elucidate how the benefit of physical activity interacts with the presence of CVD. We specifically sought (i) to evaluate the levels of physical activity among participants with and without CVD, (ii) to identify the relationship between physical activity and mortality among patients with CVD, and (iii) to compare the associations between the primary and secondary prevention groups.

Results

Baseline characteristics

The baseline characteristics of a total of 441 798 study subjects are shown in Table 1. The median age of the participants was 59.5 years, 53.5% were men, 38.9% had hypertension, 12.6% had diabetes, and 16.9% were current smokers. The means of body mass index, blood pressure, total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, and fasting blood glucose were 24.0 kg/m2, 125.5/77.6 mmHg, 199.6 mg/dL, 55.0 mg/dL, 118.3 mg/dL, and 101.6 mg/dL, respectively.

Table 1 – Baseline characteristics of the study population

VariablesTotal (n = 441 798)Cardiovascular disease (n = 131 558)No cardiovascular disease (n = 310 240)
Age (years) 59.5 ± 9.1 63.8 ± 9.4 57.8 ± 8.3 
Male sex 236 517/441 798 (53.5%) 66 030/131 558 (50.2%) 170 487/310 240 (55.0%) 
Income levels    
 Fifth quintile (highest) 155 879/441 798 (35.3%) 47 355/131 558 (36.0%) 108 524/310 240 (35.0%) 
 Fourth quintile 92 249/441 798 (20.9%) 27 459/131 558 (20.9%) 64 790/310 240 (21.0%) 
 Third quintile 70 602/441 798 (16.0%) 20 410/131 558 (15.5%) 50 192/310 240 (16.2%) 
 Second quintile 58 818/441 798 (13.3%) 16 529/131 558 (12.6%) 42 289/310 240 (13.6%) 
 First quintile (lowest) 61 998/441 798 (14.0%) 18 743/131 558 (14.2%) 43 255/310 240 (13.9%) 
 Covered by medical aid 2252/441 798 (0.5%) 1062/131 558 (0.8%) 1190/310 240 (0.4%) 
Anthropometric measurements    
 Body mass index (kg/m224.0 ± 3.0 (441 570) 24.4 ± 3.1 (131 444) 23.9 ± 2.9 (310 126) 
 Systolic blood pressure (mmHg) 125.5 ± 15.4 (441 680) 127.5 ± 15.5 (131 501) 124.7 ± 15.2 (310 179) 
 Diastolic blood pressure (mmHg) 77.6 ± 10.0 (441 679) 77.9 ± 10.0 (131 502) 77.4 ± 10.0 (310 177) 
Baseline risk factors    
 Hypertension 171 658/441 798 (38.9%) 90 139/131 558 (68.5%) 81 519/310 240 (26.3%) 
 Diabetes mellitus 55 748/441 798 (12.6%) 28 027/131 558 (21.3%) 27 721/310 240 (8.9%) 
 Dyslipidaemia 182 133/441 798 (41.2%) 86 661/131 558 (65.9%) 95 472/310 240 (30.8%) 
 Current smoking 74 605/441 798 (16.9%) 17 137/131 558 (13.0%) 57 468/310 240 (18.5%) 
 Renal disease 6141/441 798 (1.4%) 3993/131 558 (3.0%) 2148/310 240 (0.7%) 
 End stage renal disease 214/441 798 (0.05%) 176/131 558 (0.13%) 38/310 240 (0.01%) 
 Liver disease 6675/441 798 (1.5%) 3152/131 558 (2.4%) 3523/310 240 (1.1%) 
 Any malignancy 31 386/441 798 (7.1%) 14 274/131 558 (10.8%) 17 112/310 240 (5.5%) 
Pre-existing cardiovascular disease    
 Ischaemic heart disease  96 250/131 558 (73.2%)  
 Prior myocardial infarction  13 445/131 558 (10.2%)  
 Prior stroke  45 252/131 558 (34.4%)  
 Heart failure  36 308/131 558 (27.6%)  
Pre-existing non-cardiovascular disease    
 Peptic ulcer disease 237 844/441 798 (53.8%) 89 243/131 558 (67.8%) 148 601/310 240 (47.9%) 
 Rheumatic disease 49 990/441 798 (11.3%) 21 567/131 558 (16.4%) 28 423/310 240 (9.2%) 
 Diabetes with chronic complication 42 002/441 798 (9.5%) 24 603/131 558 (18.7%) 17 399/310 240 (5.6%) 
 Hemiplegia or paraplegia 5465/441 798 (1.2%) 4 586/131 558 (3.5%) 879/310 240 (0.3%) 
 Renal disease 6141/441 798 (1.4%) 4 009/131 558 (3.0%) 2 132/310 240 (0.7%) 
 Mild liver disease 168 082/441 798 (38.0%) 68 590/131 558 (52.1%) 99 492/310 240 (32.1%) 
 Moderate or severe liver disease 6675/441 798 (1.5%) 3 167/131 558 (2.4%) 3 508/310 240 (1.1%) 
 Any malignancy 31 386/441 798 (7.1%) 14 310/131 558 (10.9%) 17 076/310 240 (5.5%) 
 Metastatic solid tumour 4339/441 798 (1.0%) 1 941/131 558 (1.5%) 2 398/310 240 (0.8%) 
 Dementia 10 181/441 798 (2.3%) 7 680/131 558 (5.8%) 2 501/310 240 (0.8%) 
 HIV/AIDS 1/441 798 (0.0%) 1/131 558 (0.0%) 0/310 240 (0.0%) 
Medication use    
 Aspirin 52 788/441 798 (11.9%) 35 399/131 558 (26.8%) 17 389/310 240 (5.6%) 
 Antihypertensive agents 119 687/441 798 (27.1%) 64 615/131 558 (48.9%) 55 072/310 240 (17.8%) 
 Statin 38 698/441 798 (8.8%) 24 954/131 558 (18.9%) 13 744/310 240 (4.4%) 
Laboratory findings    
 Total cholesterol (mg/dL) 199.6 ± 38.0 (441 649) 194.5 ± 39.5 (131 500) 201.8 ± 37.2 (310 149) 
 Triglyceride (mg/dL) 140.0 ± 90.9 (441 288) 142.7 ± 89.1 (131 365) 138.9 ± 91.7 (309 923) 
 HDL cholesterol (mg/dL) 55.0 ± 28.8 (441 651) 53.9 ± 29.5 (131 504) 55.5 ± 28.5 (310 147) 
 LDL cholesterol (mg/dL) 118.3 ± 37.8 (439 080) 113.8 ± 38.6 (130 678) 120.2 ± 37.2 (308 402) 
 Fasting blood glucose (mg/dL) 101.6 ± 26.0 (441 694) 104.4 ± 28.6 (131 517) 100.4 ± 24.7 (310 177) 
 Serum creatinine (mg/dL) 1.1 ± 1.3 (441 667) 1.1 ± 1.2 (131 509) 1.1 ± 1.3 (310 158) 

HDL, high-density lipoprotein; HIV/AIDS, human immunodeficiency virus infection and acquired immune deficiency syndrome; LDL, low-density lipoprotein; MET, metabolic equivalent of task.

Among the study population, 29.8% of participants had CVD (n = 131 558) and the other 70.2% (n = 310 240) had no history of CVD. Ischaemic heart disease accounted for the largest proportion of the participants with CVD, followed by prior stroke, chronic HF, and MI.

Cardiovascular disease was associated with older age and a higher prevalence of comorbidities such as hypertension, diabetes, and hyperlipidaemia. The proportion of participants taking cardiovascular medications including aspirin, antihypertensive agents, and statins was higher in those with CVD.

Physical activity

The range of physical activity level of the study population was 0–2429 MET-min/week (Supplementary material online, Figure S1). Participants with CVD were less physically active than were those without CVD (Table 2 and Supplementary material online, Figure S1). The median physical activity level was 520 and 540 MET-min/week in the secondary and primary prevention groups, respectively. This difference was statistically significant (P < 0.001) although the effect size was small (Cohen’s d =0.016). The proportion of subjects who were totally sedentary was higher in the secondary prevention group than in the primary prevention group (27.2% and 24.4%, respectively, P < 0.001), while 49.5% and 48.2% failed to achieve the recommended physical activity level of ≥500 MET-min/week in each group (P < 0.001).

Table 2– Leisure-time physical activity of subjects with and without cardiovascular disease

Physical activityTotal (n = 441 798)Cardiovascular disease (n = 131 558)No cardiovascular disease (n = 310 240)P-value
Median (interquartile ranges) 540 (0‒980) 520 (0‒980) 540 (87‒980) <0.001 
Classification    <0.001 
 ≥1500 MET-min/week 8.8% (38 820) 9.1% (11 938) 8.7% (26 882)  
 1000‒1499 MET-min/week 13.3% (58 977) 12.4% (16 373) 13.7% (42 604)  
 500‒999 MET-min/week 29.3% (129 458) 29.0% (38 091) 29.5% (91 367)  
 <500 MET-min/week 23.3% (102 986) 22.4% (29 434) 23.7% (73 552)  
 Totally sedentary 25.3% (111 557) 27.2% (35 722) 24.4% (75 835)  

MET, metabolic equivalent of task.

Supplementary material online, Table S2 compares the differences in the baseline characteristics according to physical activity levels. Advanced age, male sex, and a high income were associated with a higher physical activity level.

Comorbidities such as hypertension, diabetes, and dyslipidaemia were associated with a higher physical activity level, while current smoking was associated with a lower physical activity level.

The pattern was similar when the study subjects were stratified according to the presence of CVD (Supplementary material online, Tables S3 and S4). Cardiovascular medication usage and adherence were higher among those with greater physical activity. Among the secondary prevention group, the presence of HF was associated with lower physical activity levels.

The amount of physical activity of the study population increased during the study duration (Supplementary material online, Figure S2). Moreover, a significant number of study subjects changed their amount of physical activity during the study duration and became either more active or less active (Supplementary material online, Figure S3).

The use of medications also increased over time (Supplementary material online, Table S5). While the use of statins showed a remarkable increase over time, the use of aspirin was unchanged.

Impact of physical activity on mortality

The median follow-up duration was 5.9 years. Figure 1shows the incidence rate of mortality per 1000 person-years stratified by the presence of CVD and the level of physical activity. Overall, CVD was associated with a significantly increased risk of mortality (HR, 2.57; 95% CI, 2.49–2.65; P < 0.001) (Supplementary material online, Figures S4 and S5).

As also shown in Table 3, the unadjusted risk of all-cause mortality showed a J-shaped relationship with the amount of physical activity. Mortality risk was highest with a totally sedentary lifestyle and lowest with a physical activity level of 1000–1499 MET-min/week regardless of CVD. A higher level of physical activity (≥1500 MET-min/week) was associated with a significantly higher risk of mortality than the trough.Figure 1

Incidence rate of mortality per 1000 person-years according to physical activity level stratified by cardiovascular disease. Numbers in the box plot indicate incidence rates per 1000 person-years. Error bars indicate 95% confidence intervals.

Incidence rate of mortality per 1000 person-years according to physical activity level stratified by cardiovascular disease. Numbers in the box plot indicate incidence rates per 1000 person-years. Error bars indicate 95% confidence intervals.

Incidence rate of mortality per 1000 person-years according to physical activity level stratified by cardiovascular disease. Numbers in the box plot indicate incidence rates per 1000 person-years. Error bars indicate 95% confidence intervals.

Table 3 – Leisure-time physical activity and the risk of mortality stratified by the presence of cardiovascular disease

Amount of leisure-time physical activity10-year event rate (%)UnadjustedAge, sex adjustedMultivariable adjusteda
HR (95% CI)P-valueHR (95% CI)P-valueHR (95% CI)P-value
Cardiovascular disease        
 Totally sedentary 18.0 3.81 (3.51–4.13) <0.001 2.18 (2.01–2.36) <0.001 1.87 (1.72–2.04) <0.001 
 <500 MET-min/week 10.6 2.25 (2.06–2.46) <0.001 1.60 (1.47–1.75) <0.001 1.45 (1.32–1.58) <0.001 
 500–999 MET-min/week 10.6 2.25 (2.07–2.45) <0.001 1.47 (1.35–1.60) <0.001 1.37 (1.26–1.50) <0.001 
 1000–1499 MET-min/week 7.4 1.58 (1.42–1.76) <0.001 1.19 (1.07–1.32) 0.002 1.14 (1.02–1.27) 0.018 
 ≥1500 MET-min/week 9.0 1.92 (1.72–2.14) <0.001 1.19 (1.06–1.33) 0.002 1.14 (1.02–1.28) 0.019 
No cardiovascular disease        
 Totally sedentary 6.4 1.35 (1.24–1.46) <0.001 1.41 (1.30–1.53) <0.001 1.27 (1.17–1.39) <0.001 
 <500 MET-min/week 4.0 0.84 (0.77–0.91) <0.001 1.13 (1.04–1.24) 0.006 1.08 (0.99–1.18) 0.099 
 500–999 MET-min/week 4.4 0.93 (0.85–1.01) 0.078 1.06 (0.98–1.16) 0.155 1.02 (0.94–1.11) 0.671 
 1000–1499 MET-min/week 3.8 0.80 (0.72–0.88) <0.001 1.03 (0.93–1.13) 0.613 1.01 (0.91–1.11) 0.913 
 ≥1500 MET-min/week 4.7 Reference — Reference — Reference — 

CI, confidence interval; HR, hazard ratio; MET, metabolic equivalent of task.a

Multivariable-adjusted model was adjusted for age, sex, income levels, residential area (urban or non-urban), body mass index, hypertension, diabetes mellitus, dyslipidaemia, smoke, renal disease, end-stage renal disease, liver disease, malignancy, fasting blood sugar, creatinine, aspirin, statin use, and antihypertensive medication.

Figure 2 shows the multivariable-adjusted risk of mortality (fitted spline curve is shown in the Take home figure). After statistical adjustment, physical activity and mortality exhibited an inverse relationship (Table 3).

The difference in the risk of mortality between a physical activity level of ≥1500 MET-min/week and 1000–1499 MET-min/week mostly disappeared. A 500 MET-min/week increase in physical activity was associated with a 14% and 7% risk reduction in mortality in the secondary and primary prevention groups, respectively (interaction P <0.001).Figure 2

Distribution and adjusted risk of mortality according to physical activity levels stratified by cardiovascular disease. Cox regression analysis with physical activity classified as a categorical variable: 0 (totally sedentary), <500, 500 to 999, 1000 to 1499, and ≥1500 MET-min/week. The statistical models were adjusted for age, sex, income level, body mass index, hypertension, diabetes mellitus, dyslipidaemia, smoking, renal disease, end-stage renal disease, liver disease, any malignancy, fasting blood sugar, and serum creatinine levels. The red and blue lines indicate hazard ratios for subjects with and without CVD, respectively. Bar graph indicates the number of study subjects in each category.

Distribution and adjusted risk of mortality according to physical activity levels stratified by cardiovascular disease. Cox regression analysis with physical activity classified as a categorical variable: 0 (totally sedentary), <500, 500 to 999, 1000 to 1499, and ≥1500 MET-min/week. The statistical models were adjusted for age, sex, income level, body mass index, hypertension, diabetes mellitus, dyslipidaemia, smoking, renal disease, end-stage renal disease, liver disease, any malignancy, fasting blood sugar, and serum creatinine levels. The red and blue lines indicate hazard ratios for subjects with and without CVD, respectively. Bar graph indicates the number of study subjects in each category.

Take home figure

Non-linear relationship between physical activity and mortality risk according to the presence of cardiovascular disease. Restricted cubic spline curves were constructed with regard to physical activity treated as a continuous variable. The red and blue lines and shades indicate adjusted hazard ratio and 95% confidence intervals for subjects with and without cardiovascular disease, respectively.
Non-linear relationship between physical activity and mortality risk according to the presence of cardiovascular disease. Restricted cubic spline curves were constructed with regard to physical activity treated as a continuous variable. The red and blue lines and shades indicate adjusted hazard ratio and 95% confidence intervals for subjects with and without cardiovascular disease, respectively.

The relationship between physical activity and mortality showed different patterns between the primary and secondary prevention groups (Take home figure). Among the healthy subjects without CVD, the slope was the steepest between 0 and 499 MET-min/week, tended to flatten above 500 MET-min/week, and was essentially flat above 1000 MET-min/week.

Although the mortality risk further reduced with a higher physical activity level, the benefit of increased physical activity was small beyond 500 MET-min/week. Among the participants with CVD, the benefit was also the greatest between 0 and 499 MET-min/week. However, the dose-response relationship extended beyond 500 to 1000 MET-min/week.

The mortality risk of participants with CVD who performed physical activity ≥1000 MET-min/week (1000–1499 and ≥1500 MET-min/week) was significantly lower than that in participants who were free from CVD but were totally sedentary (P = 0.008 and 0.010, respectively).

Supplementary material online, Tables S6 and S7 show the unadjusted and adjusted risk of cardiac and non-cardiac death according to the level of physical activity. The overall pattern was similar to that of all-cause mortality except that CVD was associated with a significantly elevated risk of cardiac death despite a high level of physical activity. The benefit of physical activity in the secondary prevention group was consistent irrespective of the specific type of CVD (Supplementary material online, Table S8).

Sensitivity analysis found no significant interactions between sex and the amount of physical activity both in the CVD and in the no CVD groups (interaction P =0.178 and 0.087, respectively) (Supplementary material online, Figures S6 and S7).

Subjects without CVD were further divided according to the expected 10-year risk of fatal CVD (Figure 3). While the mortality risk was higher with increasing expected cardiovascular risk, the survival benefit of physical activity was also greater among those with higher expected cardiovascular risk. Analysis using pooled cohort equations showed similar patterns (Supplementary material online, Figure S8).Figure 3

Non-linear relationship between physical activity and mortality risk according to the presence of cardiovascular disease and expected 10-year risk of fatal cardiovascular disease. Restricted cubic spline curves were constructed with regard to physical activity treated as a continuous variable. Adjusted hazard ratio and 95% confidence intervals are illustrated. The red, orange, green, and blue lines and shades indicate subjects with cardiovascular disease and subjects without cardiovascular disease who have an estimated 10-year risk of fatal cardiovascular disease ≥10%, 5–10%, and <5%, respectively.
Non-linear relationship between physical activity and mortality risk according to the presence of cardiovascular disease and expected 10-year risk of fatal cardiovascular disease. Restricted cubic spline curves were constructed with regard to physical activity treated as a continuous variable. Adjusted hazard ratio and 95% confidence intervals are illustrated. The red, orange, green, and blue lines and shades indicate subjects with cardiovascular disease and subjects without cardiovascular disease who have an estimated 10-year risk of fatal cardiovascular disease ≥10%, 5–10%, and <5%, respectively.

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12 Howard RA, Leitzmann MF, Linet MS, Freedman DM. Physical activity and breast cancer risk among pre- and postmenopausal women in the U.S. Radiologic Technologists cohort. Cancer Causes Control 2009;20:323–333.

13 Lee IM, Cook NR, Gaziano JM, Gordon D, Ridker PM, Manson JE, Hennekens CH, Buring JE. Vitamin E in the primary prevention of cardiovascular disease and cancer: the Women’s Health Study: a randomized controlled trial. JAMA 2005;294:56–65.

14 Cook NR, Lee IM, Gaziano JM, Gordon D, Ridker PM, Manson JE, Hennekens CH, Buring JE. Low-dose aspirin in the primary prevention of cancer: the Women’s Health Study: a randomized controlled trial. JAMA 2005;294:47–55.

15 Margolis KL, Mucci L, Braaten T, Kumle M, Trolle Lagerros Y, Adami HO, Lund E, Weiderpass E. Physical activity in different periods of life and the risk of breast cancer: the Norwegian-Swedish Women’s Lifestyle and Health cohort study. Cancer Epidemiol Biomarkers Prev 2005;14:27–32.

16 Ridker PM, Cook NR, Lee IM, Gordon D, Gaziano JM, Manson JE, Hennekens CH, Buring JE. A randomized trial of low-dose aspirin in the primary prevention of cardiovascular disease in women. N Engl J Med 2005;352:1293–1304.

17 Vasankari V, Husu P, Vähä-Ypyä H, Suni JH, Tokola K, Borodulin K, Wennman H, Halonen J, Hartikainen J, Sievänen H, Vasankari T. Subjects with cardiovascular disease or high disease risk are more sedentary and less active than their healthy peers. BMJ Open Sport Exerc Med 2018;4:e000363.

18 Anderson L, Oldridge N, Thompson DR, Zwisler AD, Rees K, Martin N, Taylor RS. Exercise-based cardiac rehabilitation for coronary heart disease: cochrane systematic review and meta-analysis. J Am Coll Cardiol 2016;67:1–12.

19 Task Force Members, Montalescot G, Sechtem U, Achenbach S, Andreotti F, Arden C, Budaj A, Bugiardini R, Crea F, Cuisset T, Di Mario C, Ferreira JR, Gersh BJ, Gitt AK, Hulot JS, Marx N, Opie LH, Pfisterer M, Prescott E, Ruschitzka F, Sabate M, Senior R, Taggart DP, van der Wall EE, Vrints CJESC Committee for Practice GuidelinesZamorano JL, Achenbach S, Baumgartner H, Bax JJ, Bueno H, Dean V, Deaton C, Erol C, Fagard R, Ferrari R, Hasdai D, Hoes AW, Kirchhof P, Knuuti J, Kolh P, Lancellotti P, Linhart A, Nihoyannopoulos P, Piepoli MF, Ponikowski P, Sirnes PA, Tamargo JL, Tendera M, Torbicki A, Wijns W, Windecker S, Document R, Knuuti J, Valgimigli M, Bueno H, Claeys MJ, Donner-Banzhoff N, Erol C, Frank H, Funck-Brentano C, Gaemperli O, Gonzalez-Juanatey JR, Hamilos M, Hasdai D, Husted S, James SK, Kervinen K, Kolh P, Kristensen SD, Lancellotti P, Maggioni AP, Piepoli MF, Pries AR, Romeo F, Ryden L, Simoons ML, Sirnes PA, Steg PG, Timmis A, Wijns W, Windecker S, Yildirir A, Zamorano JL. 2013 ESC guidelines on the management of stable coronary artery disease: the Task Force on the management of stable coronary artery disease of the European Society of Cardiology. Eur Heart J 2013;34:2949–3003.

20 Roffi M, Patrono C, Collet JP, Mueller C, Valgimigli M, Andreotti F, Bax JJ, Borger MA, Brotons C, Chew DP, Gencer B, Hasenfuss G, Kjeldsen K, Lancellotti P, Landmesser U, Mehilli J, Mukherjee D, Storey RF, Windecker S; ESC Scientific Document Group. 2015 ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation: task Force for the Management of Acute Coronary Syndromes in Patients Presenting without Persistent ST-Segment Elevation of the European Society of Cardiology (ESC). Eur Heart J 2016;37:267–315.

21 Ibanez B, James S, Agewall S, Antunes MJ, Bucciarelli-Ducci C, Bueno H, Caforio ALP, Crea F, Goudevenos JA, Halvorsen S, Hindricks G, Kastrati A, Lenzen MJ, Prescott E, Roffi M, Valgimigli M, Varenhorst C, Vranckx P, Widimsky P; ESC Scientific Document Group. 2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: the Task Force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European Society of Cardiology (ESC). Eur Heart J 2018;39:119–177.

22 Seong SC, Kim YY, Park SK, Khang YH, Kim HC, Park JH, Kang HJ, Do CH, Song JS, Lee EJ, Ha S, Shin SA, Jeong SL. Cohort profile: the National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS) in Korea. BMJ Open 2017;7:e016640.

23 Goff David C, Lloyd-Jones Donald M, Bennett G, Coady S, D’Agostino Ralph B, Gibbons R, Greenland P, Lackland Daniel T, Levy D, O’Donnell Christopher J, Robinson Jennifer G, Schwartz JS, Shero Susan T, Smith Sidney C, Sorlie P, Stone Neil J, Wilson Peter WF. 2013 ACC/AHA guideline on the assessment of cardiovascular risk. Circulation 2014;129(25_suppl_2):S49–S73.

24 Conroy RM, Pyörälä K, Fitzgerald AP, Sans S, Menotti A, De Backer G, De Bacquer D, Ducimetière P, Jousilahti P, Keil U, Njølstad I, Oganov RG, Thomsen T, Tunstall-Pedoe H, Tverdal A, Wedel H, Whincup P, Wilhelmsen L, Graham IM; SCORE project group. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J 2003;24:987–1003.

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27 Myers J, Prakash M, Froelicher V, Do D, Partington S, Atwood JE. Exercise capacity and mortality among men referred for exercise testing. N Engl J Med 2002;346:793–801.

28 Piepoli MF, Davos C, Francis DP, Coats AJ, ExTra MC. Exercise training meta-analysis of trials in patients with chronic heart failure (ExTraMATCH). BMJ 2004;328:711–713.

29 O’Connor CM, Whellan DJ, Lee KL, Keteyian SJ, Cooper LS, Ellis SJ, Leifer ES, Kraus WE, Kitzman DW, Blumenthal JA, Rendall DS, Miller NH, Fleg JL, Schulman KA, McKelvie RS, Zannad F, Pina IL. Efficacy and safety of exercise training in patients with chronic heart failure: HF-ACTION randomized controlled trial. JAMA 2009;301:1439–1450.

30 Fihn SD, Gardin JM, Abrams J, Berra K, Blankenship JC, Dallas AP, Douglas PS, Foody JM, Gerber TC, Hinderliter AL, King SB3rd, Kligfield PD, Krumholz HM, Kwong RY, Lim MJ, Linderbaum JA, Mack MJ, Munger MA, Prager RL, Sabik JF, Shaw LJ, Sikkema JD, Smith CRJr, Smith SCJr, Spertus JA, Williams SV. 2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS Guideline for the diagnosis and management of patients with stable ischemic heart disease: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines, and the American College of Physicians, American Association for Thoracic Surgery, Preventive Cardiovascular Nurses Association, Society for Cardiovascular Angiography and Interventions, and Society of Thoracic Surgeons. J Am Coll Cardiol 2012;60:2564–2603.

31 Haskell WL, Lee IM, Pate RR, Powell KE, Blair SN, Franklin BA, Macera CA, Heath GW, Thompson PD, Bauman A. Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Med Sci Sports Exerc 2007;39:1423–1434.

32 Kraus WE, Houmard JA, Duscha BD, Knetzger KJ, Wharton MB, McCartney JS, Bales CW, Henes S, Samsa GP, Otvos JD, Kulkarni KR, Slentz CA. Effects of the amount and intensity of exercise on plasma lipoproteins. N Engl J Med 2002;347:1483–1492.

33 Strasser B, Siebert U, Schobersberger W. Resistance training in the treatment of the metabolic syndrome: a systematic review and meta-analysis of the effect of resistance training on metabolic clustering in patients with abnormal glucose metabolism. Sports Med 2010;40:397–415.

34 Smith JK, Dykes R, Douglas JE, Krishnaswamy G, Berk S. Long-term exercise and atherogenic activity of blood mononuclear cells in persons at risk of developing ischemic heart disease. JAMA 1999;281:1722–1727.

35 Wang JS, Jen CJ, Chen HI. Effects of exercise training and deconditioning on platelet function in men. Arterioscler Thromb Vasc Biol 1995;15:1668–1674.

36 Gielen S, Adams V, Mobius-Winkler S, Linke A, Erbs S, Yu J, Kempf W, Schubert A, Schuler G, Hambrecht R. Anti-inflammatory effects of exercise training in the skeletal muscle of patients with chronic heart failure. J Am Coll Cardiol 2003;42:861–868.

37 Mozaffarian D, Furberg CD, Psaty BM, Siscovick D. Physical activity and incidence of atrial fibrillation in older adults: the cardiovascular health study. Circulation 2008;118:800–807.

38 Wilson MG, Ellison GM, Cable NT. Basic science behind the cardiovascular benefits of exercise. Br J Sports Med 2016;50:93–99.

39 Imboden MT, Harber MP, Whaley MH, Finch WH, Bishop DL, Kaminsky LA. Cardiorespiratory fitness and mortality in healthy men and women. J Am Coll Cardiol 2018;72:2283–2292.


Provided by Imperial College London

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