Physical health composite and risk of cancer mortality in the REasons for Geographic and Racial Differences in Stroke Study

Journal Pre-proof Physical health composite and risk of cancer mortality in the REasons for Geographic and Racial Differences in Stroke Study

Justin Xavier Moore, Stephen J. Carter, Victoria Williams, Saira Khan, Marquita W. Lewis-Thames, Keon Gilbert, George Howard PII:

S0091-7435(20)30013-X

DOI:

https://doi.org/10.1016/j.ypmed.2020.105989

Reference:

YPMED 105989

To appear in:

Preventive Medicine

Received date:

28 January 2019

Revised date:

19 December 2019

Accepted date:

12 January 2020

Please cite this article as: J.X. Moore, S.J. Carter, V. Williams, et al., Physical health composite and risk of cancer mortality in the REasons for Geographic and Racial Differences in Stroke Study, Preventive Medicine(2018), https://doi.org/10.1016/ j.ypmed.2020.105989

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© 2018 Published by Elsevier.

Journal Pre-proof Physical Health Composite and Risk of Cancer Mortality in the REasons for Geographic and Racial Differences in Stroke Study Running title: Physical Health Composite and Risk of Cancer Mortality Justin Xavier Moore, PhD (1,2), Stephen J. Carter, PhD, (3,4,5), Victoria Williams, MPH (4,6), Saira Khan, PhD (2), Marquita W. Lewis-Thames, PhD (2), Keon Gilbert, DrPH (7), George Howard, DrPH (8)

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Abstract Word Count: 244 Manuscript Word Count: 3475

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(1) Division of Epidemiology, Department of Population Health Sciences, Augusta University, Augusta, Georgia, USA (2) Division of Public Health Sciences, Department of Surgery, Washington University in Saint Louis School of Medicine, St Louis, MO (3) School of Public Health, Department of Kinesiology, Indiana University, Bloomington, IN (4) Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL (5) Department of Nutrition Sciences, University of Alabama at Birmingham, Birmingham, AL (6) Department of Health Behavior, University of Alabama at Birmingham, Birmingham, AL (7) Department of Behavioral Science and Health Education, Saint Louis University, St. Louis, MO (8) Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL

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There are no conflicts to declare Send correspondence to:

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Justin Xavier Moore, PhD Division of Epidemiology, Department of Population Health Sciences, Augusta University 1120 15th Street AE-1037, Augusta, Georgia 30912 Email: [email protected] Phone: 706-721-5757 Fax: 706-721-6294 KEYWORDS: Physical activity; rate-pressure product; cancer; mortality

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Journal Pre-proof ABSTRACT It is unclear how resting myocardial workload, as indexed by baseline measures of rate-pressure product (RPP) and physical activity (PA), is associated with the overall risk of cancer mortality. We performed prospective analyses among 28,810 men and women from the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort. We used a novel physical health

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(PH) composite index and categorized participants into one of four groups based on combinations from self-reported PA and RPP: 1) No PA and High RPP; 2) No PA and Low RPP; 3)

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Yes PA and High RPP; and 4) Yes PA and Low RPP. We examined the association between

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baseline PH composite and cancer mortality adjusted for potential confounders using Cox

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regression. A total of 1191 cancer deaths were observed over the 10-year observation period,

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with the majority being lung (26.87%) and gastrointestinal (21.49%) cancers. Even after controlling for sociodemographics, health behaviors, baseline comorbidity score, and

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medications, participants with No PA and High RPP had 71% greater risk of cancer mortality

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when compared to participants with PA and Low RPP (adjusted HR: 1.71, 95% CI: 1.42 – 2.06).

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These associations persisted after examining BMI, smoking, income, and gender as effect modifiers and all-cause mortality as a competing risk. Poorer physical health composite, including the novel RPP metric, was associated with a nearly 2-fold long-term risk of cancer mortality. The physical health composite has important public health implications as it provides a measure of risk beyond traditional measure of obesity and physical activity.

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Journal Pre-proof INTRODUCTION Cancer is a leading cause of morbidity and death in United States (US) resulting in over 580,000 annual deaths (1, 2). However, in recent decades, earlier detection and advances in targeted therapies have contributed to five-year cancer survival rates approaching 70% (3, 4). Still, due in part to societal modernization, characterized by technological advancements including

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accessibility to calorie-rich diets and sedentary behaviors, there is a growing segment of the population living with cardiovascular chronic diseases (5-9). Furthermore, as more than one in

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three US adults live with obesity, obesity and excess body weight are responsible for nearly 9%

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of incident cancers and 6.5% of all cancer deaths (10-14). Despite the known importance of

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physical activity it is estimated that 44.6% of Americans, and 40% of cancer survivors do not

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participate in leisure-time physical activity (15-18). Nevertheless, adequate physical activity (i.e., 150 minutes·week-1) represents a key modifiable lifestyle factor that may mitigate the risk

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of cancer mortality through multiple divergent and overlapping pathways.

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Increased physical health and activity are inversely associated with all-cause mortality, cancer incidence, and cancer survival (19-25). For example, in a large meta-analysis among more than one million US and European participants, those in the highest percentiles of leisure-time physical activity were at 27%, 13%, and 7% reduced risk of lung, colon, and breast cancers, respectively, even after accounting for body mass index (24). While insufficient physical activity has been linked with cardio-metabolic disease and overall mortality, it is unclear whether resting myocardial workload is associated with an elevated overall risk of cancer mortality. Though studies have examined the association between physical activity and cancer survival,

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Journal Pre-proof researchers are often reliant on self reported physical activity (24, 25) and/or usage of accelerometers (26) that may have limited objectivity. Thus, we propose using the product of resting heart rate and resting systolic blood pressure to quantify rate-pressure product (RPP), an objective non-invasive index of myocardial oxygen demand (27), as a predictor of cancer mortality. To our knowledge, this work will be the first to examine the association between

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baseline assessment of RPP and cancer mortality.

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Few studies have focused on the association between baseline health, using cardiovascular

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health parameters like RPP, and physical activity with long-term risk of cancer mortality among

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a nationally representative cohort of community-dwelling participants. Given that health status is multi-dimensional, influenced by diet, physical activity, cardiovascular determinants, and

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psychological factors, we sought to incorporate self-reported physical activity in combination

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with objectively measured RPP. Therefore, we aimed to investigate whether a lower physical

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health composite at baseline, as evidenced by low self-reported PA and higher RPP, is

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associated with greater risk of cancer mortality.

METHODS Study Population We performed prospective analyses using data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) cohort. The REGARDS cohort consists of 30,239 participants aged ≥ 45 years at baseline and participant demographics were 55% female and 59% white race. Participants were recruited from January 2003 through October 2007, and baseline health

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Journal Pre-proof information, collection of physiologic, blood, and urine sample were collected during in-home visitations. Participants were interviewed every six months for information regarding hospitalizations. An underlying aim of the REGARDS prospective cohort was to ascertain cardiovascular disease risk in “community-dwelling” participants, those living in their own homes and communities. Due to this focus on community-dwelling participants, REGARDS

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investigators excluded participants receiving treatment for cancer within past two years of baseline study entrance. Further information related to REGARDS study methods are described

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in detail elsewhere (28).

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Primary Exposure of Interest

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Our primary exposure of interest was physical health (PH) composite, a measure derived from a combination of two cardiovascular and physical activity related variables: 1) the baseline rate-

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pressure product (RPP) and 2) baseline self-reported physical activity (PA). The rate-pressure

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product offers a non-invasive indication of myocardial workload and is the product between

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resting heart rate and resting systolic blood pressure (27). RPP is considered a very reliable measurement of myocardial oxygen demand, where lower values of RPP represent better cardiovascular health (i.e., less cardio-metabolic strain) (27). We dichotomized the continuous variable of RPP into high and low groups based on the median of the population distribution. The median (Q1-Q3) was 8280.00 (7200.00 – 9600.00) where the units are in beats per minute – mmHg. We additionally dichotomized REGARDS existing categorical variable for self-reported exercise activity into 1) no physical activity (i.e., no exercise) and 2) physically active (i.e., exercised 1 to 3 times per week and exercised 4 or more times per week). Physical activity was

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Journal Pre-proof derived from the REGARDS question pertaining “how many times per week do you engage in intense physical activity, enough to work up a sweat?”

We combined RPP and PA to make the PH composite variable which had four groups: 1) No PA and High RPP; 2) No PA and Low RPP; 3) Yes PA and High RPP; and 4) Yes PA and Low RPP

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(referent group).

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Primary Outcome of Interest

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The primary outcome of interest in this study was cancer mortality. Mortality status was

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ascertained from bi-annual telephone follow-up through participant proxies, linkages with the

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Social Security Death Index (SSDI), and the National Death Index (NDI) (29). Two trained clinician reviewers independently evaluated death cases, and a REGARDS committee decided

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disagreements (29). Adjudicators utilized proxy interviews, death certificates, and if available,

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medical records from hospitalizations occurring within 30 days of the participant’s death to

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determine the cause of death. Initial review of cancer deaths was excellent in both proxy reports (kappa = 0.87) and death certificates (kappa = 0.77) (29). Follow-up data for this analysis was available through December 31, 2012.

Covariates of Interest In these analyses we included participant baseline demographics, health behaviors, chronic medical conditions, and chronic medication use as potential covariates of interests. Baseline demographic variables used in the analysis included self-reported age, gender, race, education,

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Journal Pre-proof and household income. Health behaviors included self-reported tobacco smoking status, alcohol use, and television (TV) video hours watched daily per week. Baseline chronic medical conditions included self-reported history of atrial fibrillation, body mass index, cancer survivorship status, chronic lung disease, coronary artery disease, diabetes, deep vein thrombosis, dyslipidemia, myocardial infarction, peripheral artery disease, and stroke. We

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identified cancer survivors as participants with self-reported cancer survivorship during baseline interview using the following baseline questionnaire: “Have you ever been diagnosed with

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cancer?” We additionally created an individual level comorbidity score based on the sum of

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total number of baseline medical conditions. Those with missing information for individual

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medical conditions were recorded as having no presence of the medical condition. We have

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Ethics and Consent Statement

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provided detailed information regarding participant characteristics in Supplemental Table 1.

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The Institutional Review Board at University of Alabama at Birmingham approved this study.

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We obtained informed consent from all participants of the study during baseline visit.

Statistical Analysis We compared baseline participant characteristics between physical health composite groups using Chi-Square, ANOVA, and Wilcoxon rank-sum tests as appropriate. We examined the survival function for cancer mortality by physical health composite groups using the KaplanMeier method. We performed Cox Proportional Hazards models to estimate the risk of cancer mortality comparing baseline physical health composite groups. We censored REGARDS

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Journal Pre-proof participants at the time of death, or end of follow-up (December 31, 2012). We sequentially adjusted models for covariates considered potential confounders and statistically significant in bivariate analysis. Our models were adjusted for 1) age and 2) race, gender, education, income, TV video hours watched daily per week, BMI, smoking status, alcohol use, baseline comorbidity score, and baseline chronic medications. We performed analysis comparing PA vs. No PA and

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High vs. Low RPP, and within these models we included mutual adjustments for RPP and PA, respectively, to examine independent effects of exposures. We examined BMI categories as a

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potential effect modifier on the association between physical health and cancer mortality by

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stratifying analysis by BMI category and examining the multiplicative interaction between

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physical health and BMI category, due to prior observations between obesity and cancer

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mortality (30).

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Self-reported measures of physical activity increase in sensitivity as participants’ socioeconomic

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levels increase (31). We examined the risk of cancer mortality on the association with physical

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activity stratified by income levels. We examined smoking status as another possible effect modifier on the association between PH composite and cancer mortality. There are differences in cancer mortality attributed to sex, as sex is a biological factor when examining associations with cancer (i.e., breast and cervical cancers) (32). Thus, we performed analysis examining whether sex modified the differences in physical health composite and risk of cancer mortality. We dichotomized both self-reported physical activity and RPP in main analyses, thus we additionally performed Cox proportional hazard models examining the association between the component groups for physical health (i.e., physical activity vs. no physical activity; and High

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Journal Pre-proof RPP vs. Low RPP) and risk of cancer mortality. We examined the independent effects of selfreported physical activity (using the level categorical variable: 1) 4+ times per week, 2) 1 to 3 times per week, and 3) none) and RPP tertiles on cancer mortality.

We used the Fine & Gray method to examine all-cause mortality as a competing risk for cancer

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mortality (33). We presented the estimates as hazard ratios (HRs), associated 95% confidence intervals (CIs), subdistribution hazard ratios (SHR) and associated 95% CIs. We used SAS version

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9.4 and Stata version 13 for all analyses.

RESULTS

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Cohort Characteristics

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There were a total of 28,810 studied participants and we present a flowchart (Figure 1)

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depicting the exclusion criteria. Of the participants, 10,142 (35.2%) were categorized as Physical Activity (PA) – Low RPP (theoretically the most fit group), 8,765 (30.42%) with PA – High RPP,

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4,243 (14.73%) with No PA – Low RPP, and 5,660 (19.65%) with No PA – High RPP (theoretically the least fit group). At baseline, PA – Low RPP participants were younger, more likely to have White race, male sex, and have higher education and income when compared with all other participants (Table 1; p value <0.01). Compared to all other participants, the PA – Low RPP participants were less likely to be current tobacco users, watched less hours of TV/video daily, had less total number of baseline comorbidities, had lower BMI, higher use of chronic aspirin use, and less chronic steroid use (Table 1; p value <0.01). The means, standard deviations, median and interquartile ranges for each health composite group, self-report physical activity 9

Journal Pre-proof group (dichotomous and three level categories), and RPP group (dichotomous and three level categories) are in Supplemental Table 2.

Physical Health Composite and Cancer Mortality There were a total of 1191 (4.13%) cancer deaths with the majority being attributed to primary

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cancers of the lung (N = 320, 26.87%), gastrointestinal system (N = 256, 21.49%), and hematologic system (N = 124, 10.41%; Figure 1). Participants with No PA – High RPP had highest

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risk of cancer mortality when compared to participants with PA – Low RPP (Figure 2: Log-rank

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Chi Square = 88.70, p value <0.01). Compared with PA-Low RPP participants, those with PA-High

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RPP (adjusted HR: 1.28, 95% CI: 1.08 – 1.51), No PA – Low RPP (adjusted HR: 1.19, 95% CI: 0.95

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– 1.47), and No PA – High RPP (adjusted HR: 1.71, 95% CI: 1.42 – 2.06) had at up to a 71% increased risk of cancer mortality after controlling for sociodemographics, health behaviors,

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baseline comorbidity score (including cancer survivorship status), and medications (Table 2).

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When examining the independent effect of self-report physical activity, participants with no

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physical activity (adjusted HR: 1.27, 95% CI: 1.11 – 1.46) had nearly 30% increased risk of cancer mortality. When examining RPP alone, those with high RPP values (RPP ≥ 8280) were at a 34% (adjusted HR: 1.34, 95% CI: 1.17 – 1.54) increased risk of cancer mortality when compared to participants with low RPP (RPP ≤ 8277.50). BMI categories modified the effect of PH composite on cancer mortality (Table 3; p value for interaction = 0.01). Participants with no PA – High RPP were at nearly a 2-fold increased risk of cancer mortality compared to participants with PA – Low RPP, among both normal weight (adjusted HR: 1.96, 95% CI: 1.45 – 2.65) and those with obese BMI status (adjusted HR: 1.88, 95% CI: 1.32 – 2.69). Contrastingly, no PA – High RPP had a

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Journal Pre-proof non-significant association with cancer mortality among those with overweight (adjusted HR: 1.34, 95% CI: 0.96 – 1.85) BMI, an effect which attenuated after controlling for potential confounders (crude HR: 1.50, 95% CI: 1.20 – 1.88, data not shown).

Secondary Analyses

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Income modified the effects of PH composite on cancer mortality (See Supplemental Table 3), as stronger effects were observed among participants within the wealthiest income category.

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Furthermore, among high income (≥$75,000) participants with no PA – High RPP had 2-fold

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increased risk (HR: 2.18, 95% CI: 1.16 – 4.07) of cancer mortality when compared to participants

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with PA – Low RPP; an association mainly driven by differences in physical activity between high

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income (HR: 1.66, 95% CI: 1.06 – 2.59) and low income (HR: 1.07, 95% CI: 0.82 – 1.40). When examining the independent effects of self-reported physical activity (three level categorical

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variable) on cancer mortality, participants reporting no PA had a 24% higher risk of cancer

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mortality (Supplemental Table 4) when compared to those physically active 4 or more times a

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week. Similarly we observed a dose-response effect with higher levels of RPP and cancer mortality; higher RPP was associated with up to a 35% higher risk of cancer mortality. When examining the effect modification of smoking status among past and never smokers, those participants with No PA – High RPP were at nearly two-fold increased risk of cancer mortality when compared to participants with PA – Low RPP (Supplemental Table 5). There were no significant multiplicative interaction in the effects of sex with physical health composite (p valueinteraction = 0.73), self-reported physical activity p valueinteraction = 0.56), or rate-pressure p valueinteraction = 0.48) product on risk of cancer mortality (Supplemental Table 6). When

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Journal Pre-proof considering all-cause mortality as a competing risk, participants with No PA – High RPP were at an increased risk for cancer mortality compared to participants with PA – Low RPP

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(Supplemental Table 7).

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Journal Pre-proof DISCUSSION We utilized a novel approach by operationalizing a “physical health composite” as noted by the combination of baseline rate-pressure product (RPP) and self-reported measures of physical activity. We observed that participants with a poorer baseline PH composite had nearly a twofold increased risk of dying from a cancer-related cause over the 10-year observation period.

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Additionally, we observed that components of PH composite were independently associated with cancer mortality; that is baseline self-reported physical activity and RPP were associated

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with a 27% and 34% increased risk for cancer mortality, respectively.

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It is empirically accepted that greater adherence to physical activity and favorable

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cardiovascular health are associated with reductions in risk of coronary heart disease, metabolic disorders, and cancer (19, 20, 22, 23, 34-38). However, to our knowledge, no

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previous work has examined the utility of resting RPP or in combination of self-reported

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physical activity as a predictive tool for cancer mortality. We illustrate the importance of overall

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physiologic and cardiovascular health, and to date, our work is first to determine whether the novel PH composite metric is associated with cancer mortality. Our results are supported by evidence from prior studies linking physical activity and health with cancer incidence, progression, and/or survival (19, 20, 22-24, 34-36). For example, in a meta-analysis among more than 1.4 million adults from prospective cohorts, Moore et al (2016) reported that higher levels of leisure-time physical activity (defined as >90% percentile of MET-hours per week) were at reduced risks for 13 cancers including; lung (HR: 0.73, 95% CI: 0.70 – 0.76), colon (HR: 0.87, 95% CI: 0.80 – 0.94), rectal (HR: 0.88, 95% CI: 0.81 – 0.96), and breast (HR: 0.93, 95% CI: 0.90 –

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Journal Pre-proof 0.96), all of which are predominant cancers within the REGARDS cohort (24). In a pooled analysis of more than 600,000 participants, Arem et al (2016) reported that when compared to those physically inactive (no leisiure-time physical activity), higher physical activity was associated with up to a 31 reduced risk of cancer mortality(25). While we similarly used selfreported measures of physical activity in the current study, we have additionally examined a

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physiologic measure that was independently associated with cancer mortality risk. Moreover, the results from our study bring forward much needed evidence that further generalizes the

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benefits of physical health in relation to cancer mortality.

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In this study, poorer PH composite was associated with greater cancer mortality risk (nearly 2-

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fold in the most unfit group) among high-income ($70,000) individuals, but not as strongly

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related when limited to low-income (≤$20,000) participants. It is plausible that when limited to the low-income population, the effect of physical health on cancer mortality is confounded by

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other factors such as dietary patterns or built and social environment (i.e., access to healthy

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foods, gymnasiums, and safe streets to walk). This finding highlights the importance of social determinants in influencing the health of individuals and populations (39). Low-income individuals are more likely to live in economically disadvantaged communities considering neighborhood selection is based primarily on social, economic, and lifestyle circumstances (40). Fleisch Marcus et al. (2017) reported that neighborhood poverty was associated with higher risk of cancer mortality among 16,044 individuals tracked over 17-23 year follow-up (41). There is plausible association between physical activity and the neighborhood built environment (4244). Limited access to physical activity resources, unsafe communities, the absence of

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Journal Pre-proof sidewalks, and unpleasant scenery are barriers to physical activity in economically disadvantaged neighborhoods (40, 45-47). Achieving the Healthy People 2020 objective of enhancing availability and access to physical activity opportunities will decrease physical activity disparities that contribute to cancer mortality.

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BMI status may play an integral role on the effects of composite physical health on cancer mortality. Individuals categorized within the “no physical activity – high RPP” were at the

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highest risk of cancer mortality even when examining across BMI categories, though there was

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a slightly weaker effect observed among those with overweight BMI; an effect that may have

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been explained by differences in RPP by BMI categories. However, the risk of cancer mortality

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was nearly 2-fold among both obese and normal weight participants, with normal weight participants being a group often ignored in studies of cardiovascular health. These findings may

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be used to identify normal weight individuals at risk for cancer mortality and could be

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potentially targeted with interventions. Our observations suggest that not only is weight

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management important, but resting heart rate and blood pressure are important risk factors for long-term cancer mortality. These results are consistent with prior research showing an inverse relationship between cardiovascular health, physical activity and cancer mortality (48-50). For example, a cohort of 5,876 men experienced a 26% and 46% reduction in cancer mortality with moderate to high cardiorespiratory fitness, respectively (48).

A variety of biological mechanisms may be involved in mediating physical activity and cancer mortality (48, 51, 52). Although research regarding biological pathways mediating physical

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Journal Pre-proof activity and cancer mortality has not been well established, studies suggest physical activity effects several stages of carcinogenesis (48, 53-58). In regards to lung and bronchus cancers, murine and population based studies report that physical activity is associated with reduction in chemical carcinogens (56), lower tumor cell retention (57), and greater macrophage function (58). For colon cancer, it is hypothesized that physical activity decreases travel time through the

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gastrointestinal tract and lowers acid concentration of fecal bile (59). For female breast cancer, endometrial cancer, and ovarian cancer, it is hypothesized that physical activity decreases

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cancer mortality through regulating sex steroid hormones (51, 59). Other potential protective

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mechanisms of physical activity and low rate-pressure product on cancer mortality may include

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reducing oxidative stress, decreasing central adiposity and obesity, optimizing DNA repair

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capacity, heightening immune response, and controlling growth factor production and activation (48, 51). Individuals with greater daily physical activity coupled with increased

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cardiovascular health, as indexed by the myocardial oxygen demand, may be at reduced risk of

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cancer through the synergistic and/or concurrent effects of reduced tumor growth and cancer

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cell replication, and blockages of cancer cell initiation (48, 51).

There are a few limitations that should be considered. First, we examined subjective (selfreported) measures of physical activity during baseline interviews, and as a result our exposure of interest may be subject to non-differential misclassification attributed to errors in reporting. However, our utilization of objectively measured heart rate and systolic blood pressure to quantify myocardial workload (RPP) balances our results and gives us confidence that our effect measures between PH composite and cancer mortality are reliable. Secondly, there is possibility

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Journal Pre-proof for other causes to contribute to the “observed” effect measures. Conversely, even after we accounted for all-cause mortality as a competing risk of cancer mortality we still observed similar associations. Thirdly, the REGARDS cohort was not designed to ascertain cancer incidence and deaths and it is possible that we under-detected cancer deaths within our study population. Further, because cancer mortality is function of both incidence and survival, the

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observed risks could be explained by differences in physical health at varying stages of disease progression (i.e., incidence, survival, or both). However, given our retrospective collection of

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cancer mortality we were unable to disentangle cancer mortality into incidence and survival.

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We analyzed personal health (i.e., RPP, self-reported physical activity, BMI, and comorbidities)

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at baseline and it is possible participants experienced life-course physical health changes due to

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interventions. We studied a large cohort of community dwelling US adults, thus we make the assumption that all participants were healthy and living typical lifestyles, making our results

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generalizable to the larger black and white US adult population. In addition, we were not able

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to account for variation in the type of physical activity as our variable focused on whether a

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participant had worked out hard enough to “sweat”.

In conclusion, the baseline physical health composite, including the novel RPP metric, is associated with long-term risk of cancer mortality among community-dwelling adults. Moreover, RPP can be easily calculated from routine health measures (heart rate and blood pressure), is non-invasive, and does not further burden the tasks currently practiced by healthcare professionals. This measure of health/fitness represents a potential modifiable

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Journal Pre-proof lifestyle factor that can be aimed at mitigating the overall burden of cancer mortality among US

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adults.

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Journal Pre-proof FINANCIAL SUPPORT AND ACKNOWLEDGEMENTS

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This work was supported by award [grant number R01-NR012726] from the National Institute for Nursing Research, [grant number UL1-RR025777] from the National Center for Research Resources, as well as by grants from the Center for Clinical and Translational Science and the Lister Hill Center for Health Policy of the University of Alabama at Birmingham. The parent REGARDS study was supported by cooperative agreement [grant number U01-NS041588]from the National Institute of Neurological Disorders and Stroke, National Institutes of Health, Department of Health and Human Service. This research was further supported by R01HL80477-12 from the National Heart Lung and Blood Institute, Bethesda, MD. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies. Representatives of the funding agencies have been involved in the review of the manuscript but not directly involved in the collection, management, analysis or interpretation of the data. The authors thank the other investigators, the staff, and the participants of the REGARDS study for their valuable contributions. A full list of participating REGARDS investigators and institutions can be found at http://www.regardsstudy.org and http://www.regardssepsis.org.

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Drs. Moore and Carter received grant support from R25CA47888 from the National Cancer Institute (NCI). Drs. Moore, Khan, and Lewis were supported by the Washington University School of Medicine, Public Health Sciences Division Postdoctoral Training in Cancer Prevention and Control, a training grant from the National Cancer Institute of the National Institutes of Health under award number T32CA190194. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies. The research presented in this paper is that of the authors and does not reflect the official policy of the NIH and NCI.

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REFERENCES 1. Siegel RL, Miller, K.D., & Jemal, A. Cancer Statistics, 2015. CA Cancer J Clin. 2015; 65:5-29. 2. Centers for Disease Control and Prevention: Leading causes of death.; Available from: http://www.cdc.gov/nchs/fastats/leading-causes-of-death.htm.

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3. Gloeckler Ries LA, Reichman, M. E., Lewis, D. R., Hankey, B. F., & Edwards, B. K. Cancer survival and incidence from the Surveillance, Epidemiology, and End Results (SEER) program. The oncologist. 2003; 8:541-52.

-p

ro

4. Welch HG, Schwartz, L. M., & Woloshin, S. Are increasing 5-year survival rates evidence of success against cancer? JAMA. 2000; 283:2975-8.

re

5. Akinyemiju T, Jha M, Moore JX, Pisu M. Disparities in the prevalence of comorbidities among US adults by state Medicaid expansion status. Preventive medicine. 2016; 88:196-202.

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6. Moore JX, Chaudhary N, Akinyemiju T. Metabolic Syndrome Prevalence by Race/Ethnicity and Sex in the United States, National Health and Nutrition Examination Survey, 1988-2012. Preventing chronic disease. 2017; 14:E24.

na

7. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011-2012. Jama. 2014; 311:806-14.

Jo

ur

8. Tsujimoto T, Kajio H, Sugiyama T. Obesity, diabetes, and length of time in the United States: Analysis of National Health and Nutrition Examination Survey 1999 to 2012. Medicine. 2016; 95:e4578. 9. Falkner B, Cossrow ND. Prevalence of metabolic syndrome and obesity-associated hypertension in the racial ethnic minorities of the United States. Current hypertension reports. 2014; 16:449. 10. Gallagher EJ, LeRoith D. Obesity and Diabetes: The Increased Risk of Cancer and Cancer-Related Mortality. Physiological reviews. 2015; 95:727-48. 11. Islami F, Goding Sauer A, Gapstur SM, Jemal A. Proportion of Cancer Cases Attributable to Excess Body Weight by US State, 2011-2015. JAMA oncology. 2018. 12. Islami F, Goding Sauer A, Miller KD, Siegel RL, Fedewa SA, Jacobs EJ, et al. Proportion and number of cancer cases and deaths attributable to potentially modifiable risk factors in the United States. CA: a cancer journal for clinicians. 2018; 68:31-54.

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Journal Pre-proof 13. Sung H, Siegel RL, Torre LA, Pearson-Stuttard J, Islami F, Fedewa SA, et al. Global patterns in excess body weight and the associated cancer burden. CA: a cancer journal for clinicians. 2019; 69:88-112. 14. Flegal KM, Panagiotou OA, Graubard BI. Estimating population attributable fractions to quantify the health burden of obesity. Annals of epidemiology. 2015; 25:201-7. 15. Yang L, Toriola AT. Leisure-time physical activity and circulating 25-hydroxyvitamin D levels in cancer survivors: a cross-sectional analysis using data from the US National Health and Nutrition Examination Survey. BMJ open. 2017; 7:e016064.

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16. Weinsier RL, Hunter GR, Desmond RA, Byrne NM, Zuckerman PA, Darnell BE. Freeliving activity energy expenditure in women successful and unsuccessful at maintaining a normal body weight. The American journal of clinical nutrition. 2002; 75:499-504.

-p

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17. Hunter GR, Fisher G, Neumeier WH, Carter SJ, Plaisance EP. Exercise Training and Energy Expenditure following Weight Loss. Medicine and science in sports and exercise. 2015; 47:1950-7.

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18. Ussery EN, Fulton JE, Galuska DA, Katzmarzyk PT, Carlson SA. Joint Prevalence of Sitting Time and Leisure-Time Physical Activity Among US Adults, 2015-2016. Jama. 2018; 320:2036-8.

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19. Lahart IM, Metsios GS, Nevill AM, Carmichael AR. Physical activity, risk of death and recurrence in breast cancer survivors: A systematic review and meta-analysis of epidemiological studies. Acta oncologica. 2015; 54:635-54.

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20. Garcia DO, Thomson CA. Physical activity and cancer survivorship. Nutrition in clinical practice : official publication of the American Society for Parenteral and Enteral Nutrition. 2014; 29:768-79.

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21. Ibrahim EM, Al-Homaidh A. Physical activity and survival after breast cancer diagnosis: meta-analysis of published studies. Medical oncology. 2011; 28:753-65. 22. Meyerhardt JA, Giovannucci EL, Holmes MD, Chan AT, Chan JA, Colditz GA, et al. Physical activity and survival after colorectal cancer diagnosis. Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 2006; 24:3527-34. 23. Meyerhardt JA, Giovannucci EL, Ogino S, Kirkner GJ, Chan AT, Willett W, et al. Physical activity and male colorectal cancer survival. Archives of internal medicine. 2009; 169:2102-8. 24. Moore SC, Lee IM, Weiderpass E, Campbell PT, Sampson JN, Kitahara CM, et al. Association of Leisure-Time Physical Activity With Risk of 26 Types of Cancer in 1.44 Million Adults. JAMA internal medicine. 2016; 176:816-25.

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Journal Pre-proof 25. Arem H, Moore SC, Patel A, Hartge P, Berrington de Gonzalez A, Visvanathan K, et al. Leisure time physical activity and mortality: a detailed pooled analysis of the dose-response relationship. JAMA internal medicine. 2015; 175:959-67. 26. Pedisic Z, Bauman A. Accelerometer-based measures in physical activity surveillance: current practices and issues. British journal of sports medicine. 2015; 49:219-23. 27. Carter SJ, Hunter GR, McAuley E, Courneya KS, Anton PM, Rogers LQ. Lower ratepressure product during submaximal walking: a link to fatigue improvement following a physical activity intervention among breast cancer survivors. Journal of cancer survivorship : research and practice. 2016; 10:927-34.

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28. Howard VJ, Cushman, M., Pulley, L., Gomez, C. R., Go, R. C., Prineas, R. J., Graham, A., Moy C.S., Howard, G. . The Reasons for Geographic and Racial Differences in Stroke Study: Objectives and Design. Neuroepidemiology. 2005; 25:135-43.

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-p

29. Halanych JH, Shuaib F, Parmar G, Tanikella R, Howard VJ, Roth DL, et al. Agreement on cause of death between proxies, death certificates, and clinician adjudicators in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study. Am J Epidemiol. 2011; 173:1319-26.

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30. Akinyemiju T, Moore JX, Pisu M, Judd SE, Goodman M, Shikany JM, et al. A Prospective Study of Obesity, Metabolic Health, and Cancer Mortality. Obesity. 2018; 26:193201.

na

31. Gerrard P. Accuracy of self-reported physical activity as an indicator of cardiovascular fitness depends on education level. Archives of physical medicine and rehabilitation. 2012; 93:1872-4.

ur

32. Clayton JA. Applying the new SABV (sex as a biological variable) policy to research and clinical care. Physiology & behavior. 2018; 187:2-5.

Jo

33. Fine JP, & Gray, R.J. A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association. 1999; 94:496-509. 34. Keum N, Bao Y, Smith-Warner SA, Orav J, Wu K, Fuchs CS, et al. Association of Physical Activity by Type and Intensity With Digestive System Cancer Risk. JAMA oncology. 2016; 2:1146-53. 35. Van Blarigan EL, Meyerhardt JA. Role of physical activity and diet after colorectal cancer diagnosis. Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 2015; 33:1825-34. 36. Campbell PT, Patel AV, Newton CC, Jacobs EJ, Gapstur SM. Associations of recreational physical activity and leisure time spent sitting with colorectal cancer survival. Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 2013; 31:876-85.

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Journal Pre-proof 37. McDonnell MN, Hillier SL, Hooker SP, Le A, Judd SE, Howard VJ. Physical activity frequency and risk of incident stroke in a national US study of blacks and whites. Stroke; a journal of cerebral circulation. 2013; 44:2519-24. 38. O'Neal WT, Qureshi WT, Judd SE, Meschia JF, Howard VJ, Howard G, et al. Heart rate and ischemic stroke: the REasons for Geographic And Racial Differences in Stroke (REGARDS) study. International journal of stroke : official journal of the International Stroke Society. 2015; 10:1229-35. 39. Singh GK, Daus GP, Allender M, Ramey CT, Martin EK, Perry C, et al. Social Determinants of Health in the United States: Addressing Major Health Inequality Trends for the Nation, 1935-2016. Int J MCH AIDS. 2017; 6:139-64.

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40. McCormack GR, Shiell A. In search of causality: a systematic review of the relationship between the built environment and physical activity among adults. The International Journal of Behavioral Nutrition and Physical Activity. 2011; 8:125-.

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41. Fleisch Marcus A, Illescas AH, Hohl BC, Llanos AAM. Relationships between social isolation, neighborhood poverty, and cancer mortality in a population-based study of US adults. PLOS ONE. 2017; 12:e0173370.

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42. Sallis JF, Cerin E, Conway TL, Adams MA, Frank LD, Pratt M, et al. Physical activity in relation to urban environments in 14 cities worldwide: a cross-sectional study. Lancet. 2016; 387:2207-17.

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43. Giles-Corti B, Kelty SF, Zubrick SR, Villanueva KP. Encouraging walking for transport and physical activity in children and adolescents: how important is the built environment? Sports medicine (Auckland, NZ). 2009; 39:995-1009.

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44. Smith M, Hosking J, Woodward A, Witten K, MacMillan A, Field A, et al. Systematic literature review of built environment effects on physical activity and active transport – an update and new findings on health equity. The International Journal of Behavioral Nutrition and Physical Activity. 2017; 14:158. 45. Trost SG, Owen N, Bauman AE, Sallis JF, Brown W. Correlates of adults' participation in physical activity: review and update. Med Sci Sports Exerc. 2002; 34:1996-2001. 46. Frost SS, Goins RT, Hunter RH, Hooker SP, Bryant LL, Kruger J, et al. Effects of the Built Environment on Physical Activity of Adults Living in Rural Settings. American Journal of Health Promotion. 2010; 24:267-83. 47. Choi J, Lee M, Lee J-k, Kang D, Choi J-Y. Correlates associated with participation in physical activity among adults: a systematic review of reviews and update. BMC Public Health. 2017; 17:356. 48. Vainshelboim B, Muller J, Lima RM, Nead KT, Chester C, Chan K, et al. Cardiorespiratory fitness, physical activity and cancer mortality in men. Prev Med. 2017; 100:89-94. 23

Journal Pre-proof 49. Li T, Wei S, Shi Y, Pang S, Qin Q, Yin J, et al. The dose-response effect of physical activity on cancer mortality: findings from 71 prospective cohort studies. Br J Sports Med. 2016; 50:339-45. 50. Schmidt ME, Chang-Claude J, Vrieling A, Seibold P, Heinz J, Obi N, et al. Association of pre-diagnosis physical activity with recurrence and mortality among women with breast cancer. Int J Cancer. 2013; 133:1431-40. 51. Clague J, Bernstein L. Physical activity and cancer. Current oncology reports. 2012; 14:550-8.

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52. Brown JC, Winters-Stone K, Lee A, Schmitz KH. Cancer, physical activity, and exercise. Compr Physiol. 2012; 2:2775-809.

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53. Brown JC, Winters-Stone K, Lee A, Schmitz KH. Cancer, Physical Activity, and Exercise. Comprehensive Physiology. 2012; 2:2775-809.

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54. Batty GD, Shipley MJ, Marmot MG, Davey Smith G. Blood pressure and site-specific cancer mortality: evidence from the original Whitehall study. Br J Cancer. 2003; 89:1243-7.

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55. Sui X, Lee DC, Matthews CE, Adams SA, Hebert JR, Church TS, et al. Influence of cardiorespiratory fitness on lung cancer mortality. Medicine and science in sports and exercise. 2010; 42:872-8.

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56. Kruk J, Czerniak U. Physical activity and its relation to cancer risk: updating the evidence. Asian Pacific journal of cancer prevention : APJCP. 2013; 14:3993-4003.

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57. MacNeil B, Hoffman-Goetz L. Effect of exercise on natural cytotoxicity and pulmonary tumor metastases in mice. Medicine and science in sports and exercise. 1993; 25:922-8.

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58. Rogers CJ, Colbert LH, Greiner JW, Perkins SN, Hursting SD. Physical activity and cancer prevention : pathways and targets for intervention. Sports medicine. 2008; 38:271-96. 59. Anzuini F, Battistella A, Izzotti A. Physical activity and cancer prevention: a review of current evidence and biological mechanisms. Journal of preventive medicine and hygiene. 2011; 52:174-80. 60. Willenbring ML, Massey, S.H., & Gardner, M.B. Helping patients who drink too much: an evidence-based guide for primary care clinicians. . American family physician. 2009; 80:4450.

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Figure 1. REGARDS study breakdown. Figure 1 contains a breakdown of the exclusion criteria among the total 30,239 REGARDS participants recruited at baseline used to attain the analytic population.

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Figure 2: Kaplan-Meier survival plot. Figure 2 contains Kaplan-Meier survival curve for time to cancer death stratified by physical health composite group. PA represents physical activity. RPP represents Rate-pressure product.

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Journal Pre-proof Table 1. Participant characteristics by physical health composite groups, among 28,810 REGARDS participants. No PA – No PA – PA – High RPP Low RPP High RPP (N =5,660) (N = 4,243) (N = 8,765) Agea 66.09 (9.50) 65.61 (9.93) 64.77 (9.17) Black Race (%)b 2825 (49.91) 1628 (38.37) 4148 (47.32) Male Sex (%)b 2020 (35.69) 1615 (38.06) 4120 (47.01) College graduate and above (%)b 1446 (25.55) 1387 (32.69) 2868 (32.72) Income ≥$75,000 (%)b 524 (9.26) 622 (14.66) 1189 (13.57) Current Tobacco Use (%)b 1118 (19.75) 567 (13.36) 1440 (16.43) Heavy Alcohol Use (%)b 218 (3.85) 126 (2.97) 401 (4.58) >4 hrs. TV/Video watched daily (%)b 1885 (33.30) 1151 (27.13) 2056 (23.46) Stroke Belt Residence (%)b 3113 (55.00) 2337 (55.08) 4961 (56.60) Chronic Medical Conditions (%)b Atrial fibrillation 609 (10.76) 430 (10.13) 711 (8.11) Cancer survivor 595 (10.51) 460 (10.84) 865 (9.87) Chronic lung disease 714 (12.61) 393 (9.26) 793 (9.05) Coronary artery disease 1110 (19.61) 853 (20.10) 1469 (16.76) Deep vein thrombosis 350 (6.18) 254 (5.99) 412 (4.70) Diabetes 1813 (32.03) 859 (20.25) 2274 (25.94) Dyslipidemia 3336 (58.94) 2452 (57.79) 5085 (58.01) Myocardial infarction 829 (14.65) 607 (14.31) 1051 (11.99) Peripheral artery disease 181 (3.20) 106 (2.50) 180 (2.05) Stroke 576 (10.18) 294 (6.93) 510 (5.82) Comorbidity scored 1.79 (1.44) 1.58 (1.41) 1.52 (1.30) BMIa 30.99 (7.10) 29.30 (6.34) 29.86 (6.13) Baseline Medication Use (%) b Aspirin 2322 (41.02) 1856 (43.74) 3835 (43.75) Statins 1826 (32.26) 1457 (34.34) 2633 (30.04) Steroids 274 (4.84) 170 (4.01) 279 (3.18) Resting systolic blood pressure (SBP)e 134.00 (20.00) 120.00 (18.00) 133.00 (19.00) Resting heart rate (HR)e 74.00 (14.00) 60.00 (9.00) 73.00 (12.00) Rate-pressure product (RPP)e 9750.00 (1917.50) 7300.00 (1158.00) 9522.00 (1720.00) aPresented as mean (standard deviation) – for normally distributed continuous variables. bN (%) – Presented as count and column percentages. cSignificance determined using ANOVA (normal continuous), Wilcoxon rank sums (non-normal continuous), and chi-square test. dComorbidity score presented as means and standard deviations of the sum total of comorbidities. e Presented as median (IQR) PA – Physical activity RPP – Rate-pressure product, units are bpm-mmHg.

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PA – Low RPP (N =10,142) 63.95 (9.23) 3218 (31.73) 5198 (51.25) 4371 (43.10) 2257 (22.25) 1030 (10.16) 392 (3.87) 1931 (19.04) 5599 (55.21)

p value* <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 0.14

710 (7.00) 951 (9.38) 756 (7.45) 1655 (16.32) 477 (4.70) 1528 (15.07) 5595 (55.17) 1124 (11.08) 163 (1.61) 442 (4.36) 1.32 (1.25) 27.93 (5.25)

<0.01 0.02 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01

4477 (44.14) 3171 (31.27) 287 (2.83) 120.00 (17.00) 59.00 (10.00) 7168 (1240.00)

<0.01 <0.01 <0.01 <0.01 <0.01 <0.01

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Journal Pre-proof Table 2. Hazard ratios (HR) and 95% confidence intervals (CI) for the association between physical health composite groups, components of physical health composite and cancer mortality among 28,810 REGARDS participants with 1191 cancer deaths. Hazard Ratio (95% CI) Mean Survival Timeb (95% CI)

Total Person-Years at Risk

Deaths (%a)

Age-Adjusted

Fully Adjustedc

Physical Health Composited Physical Activity – Low RPP (Referent) 322 (3.17) 9.41 (9.39 – 9.43) 68923.05 Physical Activity – High RPP 381 (4.35) 9.29 (9.26 – 9.31) 1.37 (1.18 – 1.59) 1.28 (1.08 – 1.51) 57483.99 No Physical Activity – Low RPP 164 (3.87) 8.81 (8.78 – 8.85) 1.19 (0.98 – 1.43) 1.19 (0.95 – 1.47) 27375.12 No Physical Activity – High RPP 324 (5.72) 9.24 (9.20 – 9.28) 1.87 (1.60 – 2.18) 1.71 (1.42 – 2.06) 34705.04 Self-Report Physical Activity Physical Activity (Referent) 703 (3.72) 9.37 (9.35 – 9.39) 126407.04 No Physical Activity 488 (4.93) 9.30 (9.27 – 9.33) 1.34 (1.19 – 1.50) 1.27 (1.11 – 1.46) 62080.16 p-valuetrende <0.001 <0.001 Rate-Pressure Productf Low (3500.00 – 8277.50) (Referent) 486 (3.38) 9.39 (9.37 – 9.41) 96298.17 High (8280.00 – 23310.00) 705 (4.89) 9.31 (9.29 – 9.34) 1.48 (1.31 – 1.66) 1.34 (1.17 – 1.54) 92189.03 p-valuetrende <0.001 <0.001 aRepresents the proportion among strata with cancer death. bMean survival time in years. cFully adjusted for race, age, gender, education, income, TV hours, BMI, smoking, alcohol use, baseline comorbidity score (including cancer), and medications. Model comparing physical activity with no physical activity additionally adjusted for RPP. Model comparing low RPP with high RPP additionally adjusted for self-reported physical activity. dp-value for multiplicative interaction between self-reported physical activity categories and rate-pressure product (p = 0.34). ep-value for trend determined by Wald chi-square tests for hazard ratios. RPP – Rate-pressure product, units are bpm-mmHg.

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Journal Pre-proof Table 3. Hazard ratios (HR) and 95% confidence intervals (CI) for the association between physical fitness and cancer mortality among 28,810 REGARDS participants, stratified by BMI categorya. Normal Weight (N = 7,230)

Hazard Ratio (95% CI)

(BMI 18.5 – 24.9 kg/m2) Deaths (%b)

Total Person-Years at Risk

Physical Health Composite Physical Activity – Low RPP (Referent)

-

118 (3.87)

20375.51

Physical Activity – High RPP

1.26 (0.94 – 1.68)

114 (6.11)

12050.65

No Physical Activity – Low RPP

0.85 (0.57 – 1.27)

49 (4.37)

7122.00

No Physical Activity – High RPP

1.96 (1.45 – 2.65)

118 (9.86)

6908.15

-

232 (4.72)

32426.16

1.25 (0.99 – 1.58)

167 (7.20)

14030.15

Self-Report Physical Activity Physical Activity (Referent) No Physical Activity Low (3500.00 – 8277.50) (Referent)

-

High (8280.00 – 23310.00)

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Rate-Pressure Product 167 (4.01)

232 (7.57) Overweight (N = 10,590)

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Hazard Ratio (95% CI) Physical Health Composite Physical Activity – High RPP No Physical Activity – Low RPP Self-Report Physical Activity No Physical Activity Rate-Pressure Product Low (3500.00 – 8277.50) (Referent)

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Physical Health Composite

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High (8280.00 – 23310.00)

na

Physical Activity (Referent)

Physical Activity – Low RPP (Referent)

Total Person-Years at Risk

132 (3.13)

29234.66

1.21 (0.92 – 1.59)

138 (4.32)

21152.50

1.49 (1.08 – 2.07)

68 (4.52)

9798.67

1.34 (0.96 – 1.85)

84 (5.01)

10507.01

-

270 (3.64)

50387.16

1.27 (1.01 – 1.61)

152 (4.78)

20305.68

-

200 (3.50)

39033.33

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No Physical Activity – High RPP

18958.80

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Physical Activity – Low RPP (Referent)

(BMI 25.0 – 29.9 kg/m2) Deaths (%b)

27497.51

1.09 (0.87 – 1.35)

Hazard Ratio (95% CI)

222 (4.56) Obese (N = 10,990) (BMI ≥ 30 kg/m2) Deaths (%b)

31659.51

Total Person-Years at Risk

-

72 (2.50)

19312.87

Physical Activity – High RPP

1.44 (1.02 – 2.04)

129 (3.48)

24280.84

No Physical Activity – Low RPP

1.23 (0.80 – 1.91)

47 (2.90)

10454.46

No Physical Activity – High RPP

1.88 (1.32 – 2.69)

122 (4.38)

17289.88

-

201 (3.05)

43593.72

1.28 (1.00 – 1.64)

169 (3.84)

27744.34

-

119 (2.65)

29767.33

1.48 (1.14 – 1.92)

251 (3.87)

41570.72

Self-Report Physical Activity Physical Activity (Referent) No Physical Activity Rate-Pressure Product Low (3500.00 – 8277.50) (Referent) High (8280.00 – 23310.00) ap-value

for multiplicative interaction between BMI categories and physical health composite (p = 0.01), physical activity (p = 0.56), and rate-pressure product (p = 0.01). bRepresents the proportion among strata with cancer death. Models adjusted for race, age, gender, education, income, TV hours, smoking, alcohol use, baseline comorbidity score (including cancer), and medications. Model comparing physical activity with no physical activity additionally adjusted for RPP. Model comparing low RPP with high RPP additionally adjusted for self-reported physical activity. RPP – Rate-pressure product

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Journal Pre-proof CRediT Author Statement

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Justin Xavier Moore: Conceptualization, Methodology, Software, Formal Analysis, Data Curation, Writing – Original Draft, Writing – Review & Editing, Visualization. Stephen J Carter: Conceptualization, Writing – Original Draft, Writing – Review & Editing. Victoria Williams: Conceptualization, Writing – Original Draft, Writing – Review & Editing. Saira Khan: Software, Methodology, Formal Analysis, Writing – Review & Editing. Marquita W. Lewis-Thames: Methodology, Writing – Review & Editing. Keon Gilbert: Writing - Review & Editing. George Howard: Supervision, Funding Acquisition, Data Curation, Investigation.

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Journal Pre-proof Highlights  Physical health composite is combined physical activity and rate-pressure product. Physical health composite is associated with 71% increased cancer mortality risk.

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Among both normal weight and obese participants, this risk was nearly 2-fold.

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Higher RPP is associated with 34% increased risk for cancer mortality.

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No physical activity is associated with 27% increased risk for cancer mortality.

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