Sustained Physical Activity, Not Weight Loss, Associated With Improved Survival in Coronary Heart Disease
Original Investigation
Central Illustration
Abstract
Background:
Individuals with coronary heart disease (CHD) are recommended to be physically active and to maintain a healthy weight. There is a lack of data on how long-term changes in body mass index (BMI) and physical activity (PA) relate to mortality in this population.
Objectives:
This study sought to determine the associations among changes in BMI, PA, and mortality in individuals with CHD.
Methods:
The authors studied 3,307 individuals (1,038 women) with CHD from the HUNT (Nord-Trøndelag Health Study) with examinations in 1985, 1996, and 2007, followed until the end of 2014. They calculated the hazard ratio (HR) for all-cause and cardiovascular disease (CVD) mortality according to changes in BMI and PA, and estimated using Cox proportional hazards regression models adjusted for age, smoking, blood pressure, diabetes, alcohol, and self-reported health.
Results:
There were 1,493 deaths during 30 years of follow-up (55% from CVD, median 15.7 years). Weight loss, classified as change in BMI <–0.10 kg/m2/year, associated with increased all-cause mortality (adjusted HR: 1.30; 95% confidence interval [CI]: 1.12 to 1.50). Weight gain, classified as change in BMI ≥0.10 kg/m2/year, was not associated with increased mortality (adjusted HR: 0.97; 95% CI: 0.87 to 1.09). Weight loss only associated with increased risk in those who were normal weight at baseline (adjusted HR: 1.38; 95% CI: 1.11 to 1.72). There was a lower risk for all-cause mortality in participants who maintained low PA (adjusted HR: 0.81; 95% CI: 0.67 to 0.97) or high PA (adjusted HR: 0.64; 95% CI: 0.50 to 0.83), compared with participants who were inactive over time. CVD mortality associations were similar as for all-cause mortality.
Conclusions:
The study observed no mortality risk reductions associated with weight loss in individuals with CHD, and reduced mortality risk associated with weight gain in individuals who were normal weight at baseline. Sustained PA, however, was associated with substantial risk reduction.
Introduction
Substantial evidence suggests causality between obesity and development of coronary heart disease (CHD) (1–4). In line with this, guidelines for secondary prevention of CHD from the American Heart Association and the American College of Cardiology Foundation recommend that patients should maintain or achieve a body mass index (BMI) within the normal range (18.5 to 24.9 kg/m2) (5). However, despite the strong association between obesity and development of CHD, results from large meta-analyses indicate that subjects with established CHD who have BMI above the normal range have better prognosis, often termed the “obesity paradox” (6,7). Additionally, a recent study of CHD patients indicated worse prognosis associated with weight fluctuations over time (8). Furthermore, among CHD patients with a high cardiorespiratory fitness (9–11), or who have a high level of physical activity (PA) (12), there is no obesity paradox. Previous studies addressing the obesity paradox have, for the most part, been limited to 1 single measure of body weight or PA at a baseline visit, without fully determining how changes in weight and PA levels associate with survival.
The most studied nonpharmacological therapy in CHD is cardiac rehabilitation. Exercise-based cardiac rehabilitation is associated with a 26% reduction in mortality post–myocardial infarction (MI) (13). However, the patients who participate in such programs are predominately men, are younger, and have less comorbidity than do the patients not referred to cardiac rehabilitation. Furthermore, participation rates are often low, varying from 10% to 60% (14–16). Therefore, there is a paucity of data on the associations between survival and changes in cardiorespiratory fitness or PA, as well as in body composition, in a nonselected population of subjects with established CHD.
In this study, we investigated how long-term changes in BMI and PA associated with all-cause and cardiovascular disease (CVD) mortality in subjects with CHD. We hypothesized that maintaining or achieving a high level of PA would associate with improved survival, and that weight loss would associate with improved survival in overweight and obese subjects with CHD.
Methods
Study design and participants
To date, the HUNT (Nord-Trøndelag Health Study) has been conducted in 3 waves, with data collection in 1984 to 1986 (HUNT1), in 1995 to 1997 (HUNT2), and in 2006 to 2008 (HUNT3) (17). All inhabitants 20 years of age or older in Nord-Trøndelag County in Norway were invited to participate in the HUNT study. Participants attended a clinical examination and filled out detailed questionnaires about their health and lifestyle. The overall participant rates in the HUNT1, HUNT2, and HUNT3 waves were 88%, 70%, and 56%, respectively. Our analysis included men and women reporting to have CHD, as either angina pectoris (AP) or MI. We included only those with data on PA, BMI, diabetes mellitus, self-reported health, blood pressure, smoking, and alcohol consumption in 2 or 3 HUNT study waves (Figure 1). We excluded participants with a BMI of <18.5 kg/m2 due to limited subject numbers and, hence, statistical power in this group. The study protocol was approved by the Regional Committee for Medical and Health Research Ethics in Central Norway (2014/1493).
Selection of Participants for the Study
The number of participants from the HUNT (Nord-Trøndelag Health) study with self-reported coronary heart disease. To be included in this study, the participants had to have self-reported coronary heart disease at participation in at least 2 HUNT study waves and to have valid data on physical activity, body mass index (BMI), diabetes mellitus, self-reported health, blood pressure, smoking, and alcohol consumption. Participants with a BMI of <18.5 kg/m2 were excluded due to limited number of individuals (n = 90).
Assessment of BMI
Height and weight were measured and BMI calculated as weight in kilograms divided by the square of height in meters. We used the World Health Organization categorization of BMI—normal weight (18.5 to 24.9 kg/m2), overweight (25.0 to 29.9 kg/m2), and obese (≥30.0 kg/m2)—and categorized changes in BMI over time as loss (<–0.10 kg/m2/year), stable (–0.10 to 0.09 kg/m2/year), and gain (≥0.10 kg/m2/year) (18).
Assessment of PA
At each HUNT study wave, the participants answered questions about frequency, duration, and intensity of leisure time PA. We grouped the participants into 3 levels of PA—inactive, low PA, and high PA—based on a previously published index (19). We made these categories to group participants according to the current recommendations on PA to promote health in adults (20). The inactive category comprises those participants who reported no PA, the low category comprises those who reported PA below the recommended level, and the high category is those who reported to fulfill or exceed the recommendations. We then categorized changes in PA into 9 categories (inactive-inactive, inactive-low, inactive-high, low-inactive, low-low, low-high, high-inactive, high-low, and high-high).
Ascertainment of outcomes
The primary outcome was all-cause mortality, with CVD mortality (International Classification of Diseases-Ninth Revision: 390 to 459; International Statistical Classification of Diseases-10th Revision: I00 to I99) as a secondary outcome. Follow-up ended on December 31, 2014. Norwegian physicians and public health officers are directed to report all deaths to the National Cause of Death Registry in Norway, and thus our study had a complete follow-up.
Assessment of covariables
The participants answered detailed questions about various health variables and lifestyle. We adjusted for smoking (current, former, or never), alcohol consumption over a 14-day period (abstainer, 0 drinks, 1 to 4 drinks, or ≥5 drinks), self-reported health status (bad, not so good, good, or very good), diabetes mellitus (yes or no), and hypertension (yes or no), which was defined as systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg or taking blood pressure medications.
Statistical analysis
Baseline characteristics of participants according to BMI categories were compared using linear regression for continuous variables and chi-square tests for categorical variables.
We used Cox proportional hazards models to estimate hazard ratios (HRs) with 95% confidence intervals (CIs), conditioning on sex. In the crude analysis (model 1), we adjusted for attained age as time scale and examination year. In model 2, we additionally adjusted for smoking status, alcohol consumption, hypertension, and self-reported health status. In model 3, we additionally adjusted for change in BMI (in categories), and change in PA (in categories). Results are reported as model 2 adjusted HR: with 95% CI if not otherwise stated. We also performed stratified analyses for changes in BMI according to baseline BMI category, and for changes in PA according to baseline PA. All variables were updated over time in the analyses; therefore, for participants who attended all 3 HUNT study waves, changes in BMI and PA were updated over time.
In separate analyses, we excluded deaths occurring during the first 3 years from the last HUNT study wave the participants attended to minimize the chance of bias due to reversed causality. We also additionally adjusted for MI and AP in our sensitivity analyses, as well as including only those who reported to have had an MI. To minimize the competing risk bias, we repeated our analyses using competing risk survival regression models (21). We used Stata software version 13.1 (StataCorp, College Station, Texas) for all analyses, with all tests 2 sided and p values of <0.05 considered significant.
Results
Figure 1 shows the participant flow in the study. Of the 3 307 who participated in 2 or more HUNT study waves, 1493 died during 30 (median 15.7) years of follow-up. Of these, 199 died during the first 3 years of follow-up. Table 1 shows the proportion of participants in each BMI category and selected baseline characteristics at HUNT1 study wave. Characteristics of participants according to changes in BMI are presented in Online Table 1. There were 1,507 (45.6%) participants with AP only, 929 (28.1%) with MI only, and 871 (26.3%) with both AP and MI. Almost one-half of the participants were inactive at baseline, with a higher percentage of inactive and women in the obese category.
| Total (N = 2,821) | Body Mass Index | p Value∗ | |||
|---|---|---|---|---|---|
| 18.5–24.9 kg/m2 (n = 1,000) | 25.0–29.9 kg/m2 (n = 1,352) | ≥30.0 kg/m2 (n = 469) | |||
| Female | 1,035 (36.7) | 345 (34.5) | 440 (32.5) | 250 (53.3) | <0.01 |
| Age, yrs | 68.7 ± 9.6 | 69.9 ± 9.7 | 68.2 ± 9.6 | 67.5 ± 9.1 | <0.01 |
| Weight, kg | 74.9 ± 12.8 | 64.6 ± 8.4 | 77.3 ± 8.6 | 90.0 ± 12.0 | <0.01 |
| Height, cm | 167.6 ± 9.0 | 167.9 ± 8.9 | 168.3 ± 8.8 | 165.0 ± 9.5 | <0.01 |
| Physical activity | |||||
| Inactive | 1,381 (48.9) | 486 (48.6) | 628 (46.5) | 267 (56.9) | |
| Low | 917 (32.5) | 308 (30.8) | 473 (35.0) | 136 (29.0) | |
| Recommended | 341 (12.1) | 116 (11.6) | 177 (13.1) | 48 (10.2) | |
| High | 182 (6.5) | 90 (9.0) | 74 (5.4) | 18 (3.8) | <0.01 |
| Diabetes status | |||||
| Yes | 295 (10.5) | 102 (10.2) | 136 (10.1) | 57 (12.2) | |
| No | 2,526 (89.5) | 898 (89.8) | 1,216 (89.9) | 412 (87.8) | 0.42 |
| Smoking status | |||||
| Never | 1,162 (41.2) | 395 (39.5) | 544 (40.2) | 223 (47.5) | |
| Current | 636 (22.5) | 283 (28.3) | 273 (20.2) | 80 (17.1) | |
| Former | 1,023 (36.3) | 322 (32.2) | 535 (39.6) | 166 (35.4) | <0.01 |
| Alcohol consumption† | |||||
| Abstainer | 597 (21.2) | 218 (21.8) | 276 (20.4) | 103 (21.9) | |
| 0 | 1,527 (54.1) | 550 (55.0) | 720 (53.3) | 257 (54.8) | |
| 1–4 | 531 (18.8) | 175 (17.5) | 267 (19.7) | 89 (19.0) | |
| ≥5 | 166 (5.9) | 57 (5.7) | 89 (6.6) | 20 (4.3) | 0.44 |
| Hypertension status‡ | |||||
| Yes | 2,242 (79.5) | 735 (73.5) | 1,093 (80.8) | 414 (88.3) | |
| No | 579 (20.5) | 265 (26.5) | 259 (19.2) | 55 (11.7) | <0.01 |
| Health status§ | |||||
| Bad | 264 (9.4) | 98 (9.8) | 119 (8.8) | 47 (10.0) | |
| Not so good | 1,814 (64.3) | 628 (62.8) | 864 (63.9) | 322 (68.7) | |
| Good | 711 (25.2) | 260 (26.0) | 352 (26.0) | 99 (21.1) | |
| Very good | 32 (1.1) | 14 (1.4) | 17 (1.3) | 1 (0.2) | 0.10 |
BMI change and all-cause mortality
Compared with having a stable BMI, those who lost weight (BMI <–0.10 kg/m2/year) had a 30% increased all-cause mortality risk (HR: 1.30; 95% CI: 1.12 to 1.50), whereas weight gain (BMI ≥0.10 kg/m2/year) was not significantly associated with mortality risk (Table 2). The association between weight loss and increased risk was still significant when excluding deaths occurring the first 3 years of follow-up, with an HR of 1.26 (95% CI: 1.08 to 1.47) (Online Table 2).
| Deaths∗ | Model 1 HR (95% CI) | Model 2 HR (95% CI) | Model 3 HR (95% CI) | |
|---|---|---|---|---|
| All cause | ||||
| Loss | 305 | 1.39 (1.21–1.60) | 1.30 (1.12–1.50) | 1.27 (1.10–1.47) |
| Stable | 586 | Reference | Reference | Reference |
| Gain | 602 | 0.96 (0.85–1.08) | 0.97 (0.87–1.09) | 0.96 (0.85–1.07) |
| CVD | ||||
| Loss | 169 | 1.44 (1.19–1.75) | 1.36 (1.12–1.65) | 1.34 (1.11–1.63) |
| Stable | 317 | Reference | Reference | Reference |
| Gain | 333 | 0.97 (0.83–1.14) | 0.98 (0.83–1.14) | 0.96 (0.82–1.13) |
In stratified analyses, we observed different associations between changes in BMI and mortality risk for the different BMI categories (Figure 2A). In normal-weight subjects, weight loss associated with 38% increased risk (HR: 1.38; 95% CI: 1.11 to 1.72), whereas weight gain was associated with 25% decreased risk (HR: 0.75; 95% CI: 0.56 to 0.99). Adjusting also for changes in PA did not affect estimates (Online Table 3). When excluding deaths occurring the first 3 years of follow-up, weight loss was no longer significantly associated with mortality in normal-weight subjects (HR: 1.25; 95% CI: 0.98 to 1.60) (Online Table 4). In overweight and obese subjects, neither weight loss nor weight gain was associated with mortality risk (Figure 2A), neither when adjusting for changes in PA nor when excluding the first 3 years of follow-up (Online Tables 3 and 4). We observed no material changes to the estimates when we repeated our analyses with adjustments for AP and MI (Online Table 5) or when we included only individuals with an MI (Online Table 6).
Change in BMI and Mortality Risk
(A) All-cause mortality hazard ratio according to change in body mass index (BMI), stratified for baseline BMI. (B) Cardiovascular disease mortality hazard ratio according to change in BMI, stratified for baseline BMI. Boxes represent hazard ratios and vertical lines represent 95% confidence intervals, after adjusting for age, examination year, smoking status, diabetes status, alcohol consumption, hypertension, and health status, and stratifying for sex. Dotted lines represent a hazard ratio of 1.0. Normal weight was defined as a BMI of 18.5 to 24.9 kg/m2, overweight was a BMI of 25.0–29.9 kg/m2, and obese was a BMI of ≥30.0 kg/m2. Loss was defined as <–0.10 kg/m2/year, stable as –0.10 to 0.09 kg/m2/year, and gain as ≥0.10 kg/m2/year.
BMI change and CVD mortality
A total of 819 (55%) deaths were due to CVD, and of these 198 occurred during the first 3 years of follow-up. Weight loss associated with 36% increased CVD mortality (HR: 1.36; 95% CI: 1.12 to 1.65), whereas weight gain did not associate with CVD mortality (Table 2, Online Figure 1). Weight loss was still significantly associated with CVD mortality when adjusting also for changes in PA (Table 2) and when excluding deaths occurring during the first 3 years of follow-up (Online Table 2).
Figure 2B shows the associations between changes in BMI and CVD mortality according to baseline BMI category. We observed increased CVD mortality risk associated with weight loss in normal-weight subjects (HR: 1.47; 95% CI: 1.09 to 1.98) (Online Table 3), but this was no longer significant when we excluded deaths occurring during the first 3 years of follow-up (HR: 1.31; 95% CI: 0.94 to 1.83) (Online Table 4). In participants who were overweight and obese at baseline, there were no significant associations between changes in BMI and CVD mortality risk (Online Table 3). Furthermore, the results of competing risk survival regression were not different from our main results (Online Table 7, Online Figure 1).
Change in PA and all-cause mortality
Those who reported high PA over time had a 36% lower all-cause mortality risk (HR: 0.64; 95% CI: 0.50 to 0.83) compared with those who were inactive over time (Central Illustration). We also observed significantly reduced all-cause mortality risk in those who changed from low PA to inactive (HR: 0.82; 95% CI: 0.70 to 0.96), who maintained low PA over time (HR: 0.81; 95% CI: 0.67 to 0.97), and in those who changed from high PA to low PA (HR: 0.74; 95% CI: 0.60 to 0.92). The estimates were similar when also adjusting for changes in BMI (Online Table 8) and when excluding deaths occurring during the first 3 years of follow-up (Online Table 9). Furthermore, we observed no substantial change in estimates when we included only individuals with an MI (Online Table 6).
Change in Physical Activity Level and Mortality Risk
All-cause mortality hazard ratio (bars) with 95% confidence intervals (vertical lines) according to change in physical activity (PA) level, after adjusting for age, examination year, smoking status, diabetes status, alcohol consumption, hypertension, and health status, and stratifying for sex. Blue bars represent participants who were inactive at baseline, orange bars participants with low physical activity level at baseline, and gray bars participants with high PA level at baseline. The inactive category comprises those who reported no PA, the low category those who reported PA below the recommended level, and the high category those who fulfilled or exceeded the recommendations. The dotted line represents a hazard ratio of 1.0.
Change in PA and CVD mortality
A significantly reduced CVD mortality risk was observed only in those who maintained a high level of PA over time (HR: 0.62; 95% CI: 0.43 to 0.89) and in those who changed from inactive to high PA (HR: 0.68; 95% CI: 0.47 to 0.97) (Online Figure 2). Additionally, adjusting for changes in BMI did not affect the estimates (Online Table 8). When we excluded the first 3 years of follow-up, only maintaining a high level of PA over time was significantly associated with reduced CVD mortality (HR: 0.59; 95% CI: 0.39 to 0.89) (Online Table 9). Again, the results of competing risk survival regression were not different from our main results (Online Table 10).
Discussion
Contrary to our original hypothesis, we found an association between weight loss and increased risk for all-cause mortality, as well as for CVD mortality, in individuals with CHD (Figure 2). When stratifying for BMI, this association was only observed in those who were normal weight at baseline, whereas weight gain was associated with reduced all-cause mortality. Maintaining a high level of PA over 2 or 3 decades was associated with substantial reductions in mortality risk, compared with being inactive over time (Central Illustration).
Although the obesity paradox in CHD has been described in numerous cohorts over the last decades, there are fewer studies assessing weight change (22–27). In clinical decision making, the fundamental question is not whether being normal weight is beneficial in CHD, but rather whether weight loss is associated with improved prognosis (6). Patients in the clinical setting would like to know whether trying to lose weight is beneficial and worth the effort. Common of previous studies on the prognostic importance of weight change is a relatively short follow-up time, with the longest follow-up period being 7 years (22). Pack et al. (28) performed a systematic review and meta-analysis of the prognostic effects of weight loss in patients with CHD on a composite outcome of all-cause mortality, cardiovascular mortality, and major adverse cardiovascular events, including 35,335 patients. They found a 5% body weight loss over a mean of 3.2 years to be associated with a 30% greater risk of the composite outcome, compared with keeping a stable weight. However, a presumed intentional weight loss (i.e., in the presence of programmed therapeutic lifestyle changes) was associated with a 33% risk reduction. In contrast, observational weight loss, analyzed from 10 cohorts, associated with a 62% increased risk (28). We observed a lower (26%) increased risk of mortality associated with weight loss compared with that meta-analysis (28), likely due to our longer follow-up time. It is, however, difficult to compare data from different cohorts due to large variations in weight loss definitions, weight loss time intervals, adjustments for covariates, population characteristics, and follow-up time. One possible explanation for higher short-term mortality risk associated with weight loss is occult disease. However, when we excluded deaths occurring during the first 3 years of follow-up, the estimated HR did not change substantially, indicating that the association is not merely a result of reversed causality. In our opinion, purposeful weight loss could be beneficial in individuals who are overweight or obese, although there is little data to support this in CHD populations.
Our findings suggest that PA needs to be regular and sustained to confer the largest cardiovascular benefits. However, compared with being inactive over time, all patterns of PA change had lower all-cause mortality risk estimates. Furthermore, we observed greater risk reductions associated with taking up or maintaining high PA, compared with low PA. This was particularly evident for CVD mortality risk, where maintaining a low PA level over time was not associated with reduced risk. Prospective studies of change in PA and mortality in CHD are sparse (29). Wannamethee et al. (29) included older men with and without diagnosed CVD and studied the relations between changes in PA and all-cause mortality and found that maintaining or taking up light or moderate PA reduced mortality over 4 years of follow up. In line with this, men without known CVD who maintain or improve cardiorespiratory fitness (30,31) and healthy men and women who increase their PA level over time (32,33) reduce their risk of all-cause and CVD mortality. Indeed, cardiorespiratory fitness is a strong predictor of mortality, independent from traditional CVD risk factors, in healthy individuals and those with CVD (34). Cardiorespiratory fitness and PA markedly impact the obesity paradox, with no obesity paradox seen in those who are relatively fit or who report high levels of PA (12,35,36). Physical inactivity and low aerobic capacity has largely been overlooked as a risk factor in primary and secondary prevention of CVD (37), and is currently the only major risk factor not routinely assessed in clinical practice (38). The time has come for health care providers to promote PA in their patients with CHD.
This study presents novel data about the importance of changes in body weight and PA on mortality risk in individuals with CHD. We included a relatively large number of participants, both men and women across a wide age range, had a complete mortality follow-up for 30 years, and controlled extensively for potential confounders. Furthermore, given that both changes in PA and in BMI associate with mortality, mutually adjusting for each other strengthen our results.
Study limitations
The CHD diagnosis used as basis for inclusion in our analyses was based on self-report and a validation with hospital records has not been undertaken. The validity of self-reported MI in a Norwegian population has been found to be acceptable, with a sensitivity of 91.1% and a specificity of 99.5% (39). We believe that we can be more certain about the diagnosis in those who reported to have had an MI compared with AP (40). In the sensitivity analysis adjusting for MI and AP, or including individuals with an MI only, we observed no substantial difference in the estimates compared with the main analysis. We used The National Cause of Death Registry in Norway to ascertain cause of death. We did not have the opportunity to quality control death certificates to ensure correct causes of death in this study, and this must be regarded as a limitation in our secondary outcome analysis (CVD mortality). Another limitation to our study is that BMI was used as the only measure of body composition; however, the same obesity paradox has been observed when comparing patients with high and low body fat percent as when comparing high and low BMI (9,23,41). Furthermore, we could not distinguish between intentional and unintentional weight loss. Reversed causality can be a problem in observational studies, especially when studying diseased cohorts. We limited the chance of reversed causality by adjusting for known confounders, such as self-reported health status, smoking, hypertension, and diabetes, and performed secondary analyses where we excluded the deaths occurring during the first 3 years of follow-up, with only a minimal effect on the outcomes. Our study included mostly elderly participants and we are unsure if the results extrapolate to younger populations.
Conclusions
In this large cohort of subjects with CHD, we observed an increased all-cause and cardiovascular mortality in individuals who lost weight, compared with those being weight stable, especially in those who had a normal weight at baseline. Maintaining or taking up PA was associated with substantial reductions in all-cause and CVD mortality risk, with larger reductions seen with high PA levels compared with low levels. Increased attention should be placed on strategies to increase PA in secondary prevention of CHD.
COMPETENCY IN PATIENT CARE AND PROCEDURAL SKILLS: In observational studies, there is no reduction in mortality risk associated with overweight or obesity among individuals with high cardiorespiratory fitness. Sustained PA is associated with reduced mortality risk, in contrast to weight loss, which is associated with increased mortality.
TRANSLATIONAL OUTLOOK: Further studies are needed to determine the impact of deliberate weight loss on patients with various forms of CVD.
1. : "Obesity as compared with physical activity in predicting risk of coronary heart disease in women". Circulation 2006; 113: 499.
2. : "Abdominal and total adiposity and risk of coronary heart disease in men". Int J Obes Relat Metab Disord 2001; 25: 1047.
3. : "Management of cardiovascular diseases in patients with obesity". Nat Rev Cardiol 2018; 15: 45.
4. : "Obesity and cardiovascular disease". Circ Res 2016; 118: 1752.
5. : "AHA/ACCF Secondary Prevention and Risk Reduction Therapy for Patients with Coronary and other Atherosclerotic Vascular Disease: 2011 update: a guideline from the American Heart Association and American College of Cardiology Foundation". J Am Coll Cardiol 2011; 58: 2432.
6. : "Association of bodyweight with total mortality and with cardiovascular events in coronary artery disease: a systematic review of cohort studies". Lancet 2006; 368: 666.
7. : "Association of body mass index with mortality and cardiovascular events for patients with coronary artery disease: a systematic review and meta-analysis". Heart 2015; 101: 1631.
8. : "Body-weight fluctuations and outcomes in coronary disease". N Engl J Med 2017; 376: 1332.
9. : "The obesity paradox, cardiorespiratory fitness, and coronary heart disease". Mayo Clin Proc 2012; 87: 443.
10. : "Combined effect of cardiorespiratory fitness and adiposity on mortality in patients with coronary artery disease". Am Heart J 2011; 161: 590.
11. : "The interaction of cardiorespiratory fitness with obesity and the obesity paradox in cardiovascular disease". Prog Cardiovasc Dis 2017; 60: 30.
12. : "Interaction of physical activity and body mass index on mortality in coronary heart disease: data from the Nord-Trondelag Health study". Am J Med 2017; 130: 949.
13. : "Efficacy of exercise-based cardiac rehabilitation post-myocardial infarction: a systematic review and meta-analysis of randomized controlled trials". Am Heart J 2011; 162: 571.
14. : "Trends and disparities in referral to cardiac rehabilitation after percutaneous coronary intervention". Am Heart J 2011; 161: 544.
15. : "Temporal trends and factors associated with cardiac rehabilitation referral among patients hospitalized with heart failure: findings from Get With The Guidelines-Heart Failure Registry". J Am Coll Cardiol 2015; 66: 917.
16. : "Predictors of cardiac rehabilitation referral in coronary artery disease patients: findings from the American Heart Association's Get With The Guidelines Program". J Am Coll Cardiol 2009; 54: 515.
17. : "Cohort profile: the HUNT study, Norway". Int J Epidemiol 2013; 42: 968.
18. : "Changes of body mass index in relation to mortality: results of a cohort of 42,099 adults". PLoS One 2014; 9:
e84817 .19. : "Current physical activity guidelines for health are insufficient to mitigate long-term weight gain: more data in the fitness versus fatness debate (The HUNT study, Norway)". Br J Sports Med 2014; 48: 1489.
20. : "Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association". Circulation 2007; 116: 1081.
21. Gutierrez R. Competing-risk regression - Stata. Available at: https://www.stata.com/meeting/boston10/boston10_gutierrez.pdf. Accessed November 16, 2017.
22. : "The obesity paradox and weight loss". Am J Med 2011; 124: 924.
23. : "The obesity paradox, weight loss, and coronary disease". Am J Med 2009; 122: 1106.
24. : "The effect of weight loss on the outcome after coronary artery bypass grafting in obese patients". Ann Card Anaesth 2012; 15: 190.
25. : "Prognostic importance of weight loss in patients with coronary heart disease regardless of initial body mass index". Eur J Cardiovasc Prev Rehabil 2008; 15: 336.
26. : "Weight-change as a prognostic marker in 12 550 patients following acute myocardial infarction or with stable coronary artery disease". Eur Heart J 2006; 27: 2755.
27. : "Weight change after myocardial infarction–the Enhancing Recovery in Coronary Heart Disease patients (ENRICHD) experience". Am Heart J 2008; 155: 478.
28. : "The prognostic importance of weight loss in coronary artery disease: a systematic review and meta-analysis". Mayo Clin Proc 2014; 89: 1368.
29. : "Changes in physical activity, mortality, and incidence of coronary heart disease in older men". Lancet 1998; 351: 1603.
30. : "Long-term effects of changes in cardiorespiratory fitness and body mass index on all-cause and cardiovascular disease mortality in men: the Aerobics Center Longitudinal Study". Circulation 2011; 124: 2483.
31. : "Changes in physical fitness and all-cause mortality. A prospective study of healthy and unhealthy men". JAMA 1995; 273: 1093.
32. : "Changes in physical activity in leisure time and the risk of myocardial infarction, ischemic heart disease, and all-cause mortality". Eur J Epidemiol 2012; 27: 91.
33. : "Total mortality after changes in leisure time physical activity in 50 year old men: 35 year follow-up of population based cohort". BMJ 2009; 338: b688.
34. : "Exercise capacity and mortality among men referred for exercise testing". N Engl J Med 2002; 346: 793.
35. : "Obesity and prevalence of cardiovascular diseases and prognosis-the obesity paradox updated". Prog Cardiovasc Dis 2016; 58: 537.
36. : "Impact of cardiorespiratory fitness on the obesity paradox in patients with heart failure". Mayo Clin Proc 2013; 88: 251.
37. : "Physical activity and cardiorespiratory fitness as major markers of cardiovascular risk: their independent and interwoven importance to health status". Prog Cardiovasc Dis 2015; 57: 306.
38. : "Importance of assessing cardiorespiratory fitness in clinical practice: a case for fitness as a clinical vital sign: a scientific statement from the American Heart Association". Circulation 2016; 134: e653.
39. : "Validity of self-reported myocardial infarction and stroke in regions with Sami and Norwegian populations: the SAMINOR 1 Survey and the CVDNOR project". BMJ Open 2016; 6:
e012717 .40. : "Validation of self-reported incident cardiovascular disease events in the Greek EPIC cohort study". Italian J Pub Health 2012; 9: e75381.
41. : "Body composition and coronary heart disease mortality–an obesity or a lean paradox?". Mayo Clin Proc 2011; 86: 857.
Abbreviations and Acronyms
| AP | angina pectoris |
| BMI | body mass index |
| CHD | coronary heart disease |
| CI | confidence interval |
| CVD | cardiovascular disease |
| HR | hazard ratio |
| MI | myocardial infarction |
| PA | physical activity |
Footnotes
This study was supported by a grant from the Norwegian Health Association (Dr. Moholdt). The authors were also supported by grants from the K. G. Jebsen Foundation, Norway (Dr. Nauman); and from the Liaison Committee between the Central Norway Regional Health Authority and the Norwegian University of Science and Technology, Trondheim, Norway (Drs. Nauman and Moholdt). The HUNT (Nord-Trøndelag Health) study is a collaboration between the HUNT Research Centre (Faculty of Medicine, Norwegian University of Science and Technology), Nord-Trøndelag county Council, Central Norway Health Authority, and the Norwegian Institute of Public Health. Dr. Lavie is author of the book The Obesity Paradox. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
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