All-cause mortality risk among active and inactive adults matched for cardiorespiratory fitness
Bibliographic record
Abstract
A high level of cardiorespiratory fitness (CRF) is associated with low all-cause and disease-specific mortality independent of traditional risk factors,1,2 where physical activity is the primary modifiable determinant of CRF. However, CRF is also influenced by non-modifiable, intrinsic factors (e.g., genetics, environment).3 Whether the mortality risk associated with high CRF achieved by becoming physically active differs from that of high CRF achieved without physical activity is unknown. Recent evidence has shown that in people with the same CRF, being physically active does not lower mortality risk, suggesting that intrinsic factors drive the association between CRF and risk.4 However, in that analysis a single measure of physical activity and CRF was used, which does not take into account the extent to which intrinsic factors and physical activity each contribute to CRF and its association with risk. Resolving this limitation requires serial measures to determine whether all-cause mortality risk differs between people who achieve high CRF by becoming active (CRF-A) compared with those who have a high CRF without physical activity (inactive; CRF-I). We used data from the Aerobics Center Longitudinal Study to determine whether CRF-A is associated with a lower risk than CRF-I in a large cohort of men and women with two measures of CRF and physical activity. The Aerobics Center Longitudinal Study is a prospective observational study of participants in the USA.5 Among 29,221 eligible participants, we excluded 22,997 who did not have baseline and follow-up measurements of physical activity and CRF, reported myocardial infarction, stroke, cancer, high blood pressure, had <1 year of mortality follow-up, or <5 months between their first and last examinations and individuals with body mass index (BMI) <18.5 kg/m2. To match CRF-A and CRF-I groups on CRF, age, and BMI, an additional 4,196 individuals were excluded. Our final sample consisted of 2,028 men and women. The study was reviewed and approved annually by the Cooper Institute Institutional Review Board. All participants gave written informed consent for the examinations and follow-up.5 Participants were followed for mortality to date of death or December 31, 2003. Physical activity was assessed by a questionnaire based on self-reported leisure time physical activity performed 3 months prior to the examinations at baseline and follow-up. Participants were classified into one of two physical activity categories at each time point: inactive (reported no physical activity on a weekly basis, n=1014) or active (reported participating in sporting or leisure time physical activity and/or walking, jogging or running on a weekly basis; n=1014).6 CRF was measured at baseline and follow-up by a maximal treadmill test using the modified Balke protocol. CRF in metabolic equivalents (METs) was calculated using the final treadmill speed and grade achieved in the test inputted into the American College of Sports Medicine formula: [3.5+(0.1×speed)+(1.8×speed×grade)]/3.5.7 Participants categorized as CRF-I self-reported that they were inactive at baseline and follow-up. Participants categorized as CRF-A were inactive at baseline, became active and improved their CRF. CRF-A and CRF-I values at follow-up were matched one-to-one on (±tolerance range): METs (±0.5), age (±2.0) and body mass index (±2.0) using a case-control matching method. Cox proportional hazard models were used to estimate the hazard ratios for all-cause mortality. Follow-up CRF was treated as a continuous variable with hazard ratios calculated per 1 MET difference, and was also treated as a categorical variable where hazard ratios for CRF-A were calculated using CRF-I as the referent category. Adjustments to the analysis were made for potential confounding factors, including systolic blood pressure, examination year, sex, family history of cardiovascular disease (CVD), smoking status, alcohol intake, abnormal electrocardiogram, diabetes mellitus, and hypercholesterolemia. Statistical significance was set at p≤0.05. At baseline, participants in the CRF-I group were younger, less likely to be women, had a lower body mass index, a higher CRF, and lower systolic blood pressure than those in the CRF-A group. At follow-up, participants in the CRF-I group had higher weight and total cholesterol levels and were more likely to be smokers (p<0.05). The mean±SD time from baseline to follow-up was 15.2±8.4 years. There were 209 deaths in total from all causes: 109 deaths in the CRF-I group and 100 deaths in the CRF-A group. For individuals in the CRF-I group at follow-up (n=1014) there was a 35% lower mortality risk for every 1 MET increase in CRF after adjusting for sex and examination year (Table 1, model 1; p<0.001). The hazard ratios were not materially different after further adjusting for changes in systolic blood pressure, smoking, alcohol intake, family history of CVD, diabetes mellitus, total cholesterol level, or abnormal electrocardiogram (Table 1, Model 3; p<0.001). Hazards ratios for all-cause mortality by active or inactive cardiorespiratory fitness at follow-up. CI: confidence interval; CRF: cardiorespiratory fitness; CRF-I: inactive cardiorespiratory fitness; CRF-A: active cardiorespiratory fitness; HR: hazards ratio; METs: metabolic equivalents. Model 1: adjusted for sex and examination year. Model 2: adjusted for model 1 and for follow-up systolic blood pressure, smoking, alcohol intake, and baseline family history of cardiovascular disease. Model 3: adjusted for model 2 and follow-up diabetes mellitus, total cholesterol, and abnormal electrocardiogram status. Hazards ratios for all-cause mortality by active or inactive cardiorespiratory fitness at follow-up. CI: confidence interval; CRF: cardiorespiratory fitness; CRF-I: inactive cardiorespiratory fitness; CRF-A: active cardiorespiratory fitness; HR: hazards ratio; METs: metabolic equivalents. Model 1: adjusted for sex and examination year. Model 2: adjusted for model 1 and for follow-up systolic blood pressure, smoking, alcohol intake, and baseline family history of cardiovascular disease. Model 3: adjusted for model 2 and follow-up diabetes mellitus, total cholesterol, and abnormal electrocardiogram status. For individuals in the CRF-A group at follow-up (n=1014) there was a 50% lower mortality risk for every 1 MET increase in CRF after adjusting for sex and examination year (Table 1, model 1; p<0.001). The hazard ratios were not materially different after further adjusting for common risk factors associated with increased mortality risk (Table 1, model 3; p<0.001). The CRF-A group was not associated with a significantly lower mortality risk compared with the CRF-I group after adjusting for sex and examination year (Table 1, model 1; examination year 0.93 (95% confidence interval 0.71–1.23); p>0.05). Hazard ratios were not materially different after further adjusting for common risk factors associated with increased mortality risk (Table 1, model 3; p>0.05). The primary finding is that having a high CRF, achieved with or without physical activity, substantially lowers mortality risk. The observation that those participants who adopted physical activity achieved the same risk reduction as those with an intrinsically high CRF highlights the unique role of physical activity as the only clinically valid way to improve CRF. These observations underscore the need to incorporate CRF as a vital sign in clinical settings. A novel aspect of our study design was the use of two measures to compare the relative contributions of physical activity and intrinsic factors on the association between CRF and mortality. These observations extend previous findings that a participant’s current CRF status is associated with risk independent of physical activity.4,6,8,9 By using follow-up measures we were able to answer the question of whether it matters how CRF is achieved. Physical activity leads to an improvement in CRF10 and cardiometabolic risk.11 Thus the observation that those who were active did not have significantly lower risk is surprising. However, physical activity was measured using questionnaires, which are susceptible to over-reporting,12 thus the benefit of physical activity on the association between CRF and risk may have been underestimated. Further, we dichotomized our population into inactive and active participants, which may have underestimated the health benefit of higher amounts of physical activity.6 In this analysis, CRF-A was associated with a lower, albeit not significant, mortality risk compared with CRF-I. Thus it is possible that if physical activity had been measured objectively, then the risk association with CRF-A would have been stronger and significantly different from CRF-I. Future studies using objective measures of physical activity are required to determine if this is the case. The strengths of this study include the large sample size encompassing a wide age range, extensive mortality follow-up, and objective measures of CRF. The limitations include the fact that the cohort consists primarily of well-educated white adults from middle to upper socioeconomic strata. However, the homogeneity of our sample population prevents the confounding of education, income and ethnicity.13 LDL, RR, XS, and SB contributed to the conception and design of the work. LDL, XS and SB contributed to the acquisition, analysis, and interpretation of data for the work. LDL drafted the manuscript. RR, XS and SB critically revised the manuscript. All gave final approval and agree to be accountable for all aspects of work ensuring integrity and accuracy. The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Canadian Institutes of Health Research (Grant OHN-63277).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".