Abstract
Background:
Physical activity and obesity are well-established factors of colorectal cancer (CRC) risk and prognosis. Here, we investigate associations of individual and combined physical activity and BMI groups with pro-inflammatory biomarkers in CRC patients.
Methods:
Self-reported physical activity levels were classified as ‘active’ (≥8.75 MET-hrs/wk) vs. ‘inactive’ (<8.75 MET-hrs/wk) in n=579 stage I-IV CRC patients enrolled in the ColoCare Study. BMI [normal weight (≥18.5-<25kg/m2), overweight (≥25-<30kg/m2), and obese (≥30kg/m2)] was abstracted from medical records. Patients were classified into four combinations of physical activity levels and BMI. Biomarkers (CRP, SAA, IL-6, IL-8, and TNF-α) in pre-surgery serum samples were measured using Meso-Scale-Discovery platform. Regression models were used to compute relative percent differences in biomarker levels by physical activity and BMI groups.
Results:
‘Inactive’ patients had non-statistically significant higher IL-6 levels compared to ‘active’ patients (+36%, p=0.10). ‘Obese’ patients had 88% and 17% higher CRP and TNF-α levels compared to ‘normal weight’ patients (p=0.03 and 0.02, respectively). Highest CRP levels were observed among ‘overweight or obese/inactive’ compared to ‘normal weight/active’ patients (p=0.03).
Conclusion:
We provide evidence of associations between individual and combined physical activity and BMI groups with pro-inflammatory biomarkers. While BMI was identified as the key driver of inflammation, biomarker levels were higher among ‘inactive’ patients across BMI groups.
Impact:
This is the largest study in CRC patients investigating associations of energy balance components with inflammatory biomarkers. Our results suggest that physical activity may reduce obesity-induced inflammation in CRC patients and support the design of randomized controlled trials testing this hypothesis.
INTRODUCTION
Energy balance components, including physical activity and BMI, are established risk and predictive factors of colorectal cancer (1-4). The mechanistic underpinnings of the energy balance-colorectal cancer link are complex and have yet to be fully elucidated. A thorough understanding of the underlying molecular pathways may provide the opportunity to identify targets to intervene and improve colorectal cancer prevention, treatment, and survivorship.
Various biological mechanisms have been investigated to understand the energy balance-cancer link (e.g., immune function, oxidative stress, inflammation, angiogenesis, growth factors, and the gut microbiome) (3-5). As one of the important hallmarks of cancer (6), systemic inflammation has been identified as a potential key player (3). Adipose tissue in obese individuals leads to systemic low-grade inflammation through the secretion of pro-inflammatory cytokines by hypertrophic adipocytes (3,7-10). Visceral adipose tissue depots are considered the main source of obesity-induced pro-inflammatory processes (9). Physical activity, on the other hand, has been associated with reduced systemic inflammation (11-22). Exercise accompanied with weight loss leads to greater reductions in systemic inflammation; yet, exercise without weight loss also improves pro-inflammatory biomarker levels such as CRP (14,18,23). Previous studies have been limited to healthy individuals, smaller panels of biomarkers, or were conducted in restricted study populations (e.g., female, breast cancer) and results may not be directly generalizable to other types of cancer (11-22).
Another emerging question in the energy balance-cancer link is whether or not physical activity can counteract the adverse metabolic profiles commonly observed among obese individuals. “Metabolically healthy obese” patients are obese individuals do not present the phenotype characterized by obesity, including systemic inflammation and insulin resistance (24-26). No standardized definition of a “metabolically healthy obese” phenotype exists (24,25). Components of metabolic syndrome (blood glucose levels, HDL cholesterol levels, triglycerides, etc.) with and without inflammation are considered important factors differentiating metabolically healthy and unhealthy obese. Some data suggest that “metabolically healthy obese” individuals are generally younger and more active (24,25). That raises the question of whether or not physical activity can offset the deleterious effects of obesity-induced inflammation. This question has never been studied in a cancer population, where inflammation is a key player of disease development and progression.
The objective of this study is to assess associations of individual and combined physical activity and BMI groups with pro-inflammatory biomarkers (CRP, SAA, IL-6, IL-8, TNF-α) in colorectal cancer patients.
MATERIALS AND METHODS
Study population
The present study is conducted as part of the prospective, multicenter ColoCare Study (ClinicalTrials.gov NCT02328677), an international cohort of newly diagnosed stage I–IV colorectal cancer patients (ICD-10 C18–C20) (27). The ColoCare Study design has previously been described (27,28). Briefly, the ColoCare Study is a multicenter cohort to facilitate transdisciplinary research on colorectal cancer outcomes and prognosis. Patients who meet the following inclusion criteria are approached at the participating recruitment sites about 2-4 weeks before undergoing surgery: patients first diagnosed with colon or rectal cancer (stages I–IV), age >18 year, English (US sites) or German (German site) speaking, and mentally/physically able to consent and participate. Participants were staged according to the American Joint Committee on Cancer (AJCC) system based on histopathologic findings. Overall, the recruitment rate was 70%. Baseline assessments include biospecimen collection, and patient-reported symptoms, health behaviors, and health-related quality of life, assessed by questionnaire. All analyses in this manuscript are based on data collected from n=579 patients enrolled between June 2007 and July 2019 at the ColoCare Study sites at the National Center for Tumor Diseases (NCT) and University of Heidelberg (HD), Heidelberg in Germany and the Huntsman Cancer Institute (HCI), Salt Lake City, Utah and the Fred Hutchinson Cancer Center (FHCC), Seattle, Washington in the USA with available baseline blood samples. The study was approved by the institutional review boards of the respective institutions and were conducted in accordance with the 1964 Helsinki Declaration. All patients provided written informed consent.
Pro-inflammatory biomarkers
Non-fasting blood samples were collected from patients prior to surgery. Serum was extracted within four hours after blood draw and stored in aliquots at −80°C until analysis. 110-500μl of each patient’s serum enrolled at the ColoCare site in Germany and FHCC in the USA were shipped on dry ice to HCI, Salt Lake City, Utah, USA for laboratory analyses. Blood samples used in this study were stored on average for 3-5 years (HCI samples: <1-3 years, HD samples: 2-6 years, and FHCC: 8-14 years). Prior studies reported high stability of the analyzed biomarkers over long-term storage (3-6 years) limiting the possibility of measurement error (29-35). We further conducted sensitivity analysis excluding samples that were stored more than 6 years (FHCC samples) to see whether or not long-term storage influences the observed results. No differences in results were observed.
Serum-based assays for multiplexed C-reactive protein (CRP), serum amyloid A (SAA), interleukin-6 (IL-6), interleukin-8 (IL-8), and tumor necrosis factor α (TNF-α) have previously been established on the Mesoscale Discovery Platform (MSD, Rockville, MD, USA) in the Ulrich laboratory at HCI (27,36-40). The biomarker panel selection was based on; 1) our own research/preliminary data using circulating biomarkers as prognostic markers in colorectal cancer patients (36,39,41), 2) the existing literature (11-18,42-46), and 3) a choice of the most clinically relevant biomarkers (41,47-50).
Blinded patient samples plus three intra-plate and inter-plate quality control samples (QC) were assayed for CRP and SAA using the V-PLEX vascular injury plate 2, and for IL-6, IL-8, and TNF-α using the U-PLEX pro-inflammatory custom plate for samples from the HD cohort and V-PLEX pro-inflammatory custom plate for samples from HCI and FHCC cohort. Assays were conducted on the Sector 2400A (MSD, Rockville, MD, USA).
Blinded serum samples were run at dilutions of 1:1000 (HD samples) and 1:2500 (HCI and FHCC) for the vascular injury panel and at dilutions of 1:2 for custom pro-inflammatory panels. The serum samples had no previous freeze-thaw cycles for the vascular injury panels and one freeze-thaw cycle for the pro-inflammatory panels. Data were analyzed with MSD Discovery Workbench 4.0 software (Meso Scale Diagnostics, Rockville, MD). Drift correction was applied to account for batch effects across sites using the batch with the highest coefficients of variability (CVs) as reference batch. The overall inter- and intra-plate CVs after drift correction were between 7.9-14.6% and 2.1-4.8%, respectively (27,36-40).
Physical activity and body mass index (BMI) assessments
Physical activity assessment:
Recreational physical activity within the past year before diagnosis was assessed using an adapted version of the International Physical Activity Questionnaire (IPAQ) questionnaire (51). Multiple choice questions were used instead of the text entry questions in the original questionnaire version. Patients were asked if they walked for exercise, did moderate or vigorous exercise for at least 10 minutes over the past year (‘no’, ‘yes, less than once a week’, ‘yes, more than once a week’). If they responded ‘yes, more than once a week’, patients were asked about how many days per week (1-2, 3-4, 5-7), and how many minutes per day (10-29, 30-44, 45-59, 60+). In addition, patients were asked about their usual walking pace (casual, moderate, fast). The IPAQ questionnaire captures data on usual recreational physical activity during the preceding year (51). The summation of duration (hours) and frequency (per week) of moderate to vigorous activity can be calculated in metabolic equivalent tasks hours per week (MET-hrs/wk) for each patient to determine the average amount of time per week that the patient spent in moderate to vigorous physical activity. The assignment of MET values follows the most recent Compendium of Physical Activities (52) and the questionnaire has previously been validated in a large international cohort (53). The assessment of moderate to vigorous physical activity using the IPAQ instrument has been validated as compared to objective measurements and other self-reported measurements (53).
Body Mass Index (BMI):
BMI (kg/m2) at baseline (pre-surgery) was abstracted from patient medical charts. We conducted a blinded review of a subset of charts (10%) across sites to ensure quality of the data abstraction. For the purposes of this study, underweight patients (BMI ≤18.5 kg/m2) were excluded from the analyses due to small sample size in this subgroup (n=10) and to minimize confounding by patients with poor prognosis (54,55).
Statistical analyses
Exposure categorization:
Moderate activity was defined as 3.5 to 6 MET-hrs and vigorous activities as ≥6 MET-hrs (56). Thus, 8.75 MET-hrs/wk would be the threshold to meet the guidelines of at least 150 minutes (=2.5 hrs) per week of moderate to vigorous activity as recommended for cancer survivors (57,58). Accordingly, patients were categorized into either ‘inactive’ (<8.75 MET-hrs/wk) or ‘active’ (≥8.75 MET-hrs/wk) categories to test the effect of physical activity levels below and above the guidelines. For sensitivity analyses, patients were grouped into 4 physical activity groups to investigate a dose-response: <75 mins/wk, 75-150 mins/wk, 150-300 mins/wk, and >300 mins/wk. Patients were categorized as being ‘normal weight’ (BMI: ≥18.5 and <25 kg/m2), ‘overweight (BMI: ≥25 and <30 kg/m2), or obese’ (BMI: ≥25 kg/m2) (59). To ensure sufficient sample sizes in each group, patients were categorized into “normal weight” and “overweight/obese” for analyses that tested combinations with physical activity groups. Combining physical activity and BMI information, patients were further categorized into: 1) ‘normal weight/active’ (≥18.5 and <25 kg/m2, ≥8.75 MET-hrs/wk), 2) ‘normal weight/inactive’ (≥18.5 and <25 kg/m2, <8.75 MET-hrs/wk), 3) ‘overweight or obese/active’ (≥25 kg/m2, ≥8.75 MET-hrs/wk), and 4) ‘overweight or obese/inactive’ (≥25 kg/m2, <8.75 MET-hrs/wk) groups at baseline.
Biomarker analyses:
Biomarker levels below and above the detection limit were replaced with the mean of the lowest and highest quintile, respectively. Log-2 transformation was applied to adjust for heteroscedasticity in biomarker levels.
Mean values and standard deviations for continuous variables, and frequencies and percentages for categorical variables were computed to describe patient characteristics. Multivariate linear regression models were used to assess associations of continuous values of physical activity levels and BMI, as well as combined physical activity and BMI groups with biomarker levels. Stratified analyses were conducted to identify effect modification by sex (female/male), regular aspirin and/or NSAID drug use (yes/no, at least 1 time/week during the preceding month), and tumor site (colon/rectum). If no effect modification by these factors was observed, they were considered as potential confounding factors. Models were adjusted for potential confounders, including sex (female/male), age, race (White, non-White), stage at diagnosis (I, II, III, IV - before receipt of any neoadjuvant treatment), cancer site (colon/rectum), neoadjuvant treatment (yes/no), smoking (never, former, current), regular aspirin and/or NSAID drug use (yes/no, at least 1 time/week during the preceding month), biomarker measurement batch, and study site (HCI, FHCC, HD). Correlations between independent variables were computed to detect multicollinearity. Robustness of the model and confounding effects of relevant factors were assessed using standard methods including 10% rule and likelihood ratio test. The final models included age, sex, stage at diagnosis, and neoadjuvant treatment. False Discovery Rate correction was used to account for multiple testing (60). The percent relative difference (RD) was calculated using the β-coefficient derived from the regression models as follows: (exp(β-coefficient)-1)*100. Sensitivity analyses were performed excluding patients 1) who received neo-adjuvant chemotherapy (n=185), 2) who were diagnosed with stage IV colorectal cancer (n=114), and 3) who took aspirin or NSAIDs 24 hours prior to the blood draw. All statistical analyses were performed in SAS (version 9.4).
Data availability statement
The data generated in this study are not publicly available due to information that could compromise patient privacy or consent but may be available upon reasonable request from the ColoCare investigator team (colocarestudy_admin@hci.utah.edu)
RESULTS
Table 1 summarizes demographic and clinical characteristics of the study population. Patients were on average 62 years old and 60% were male. Most patients were diagnosed with stage II (26%) or stage III (37%) colorectal cancer and 51% of tumors were located in the rectum. Patients reported average physical activity levels of 12.5 MET-hrs/wk, but 69% reported to be ‘inactive’. Most patients were ‘overweight’ or ‘obese’ (64%, n=372) and had an average BMI of 27.6 kg/m2. A small proportion of patients were current smokers (13%), while 36% were former and 34% were never smokers. Two thirds of the study population (66%) reported use of aspirin and/or NSAIDs on a regular basis (at least 1 time/week during the preceding month). Distributions within the combined physical activity and BMI groups were as follows: n=102 (17%) ‘normal weight/active’, n=105 (18%) ‘normal weight/inactive’, n=137 (24%) ‘overweight or obese/active’, n=235 (41%) ‘overweight or obese/inactive’. Mean biomarker levels by individual and combined groups of physical activity and BMI are presented in Table 2.
Table 1.
Study population characteristics (n=579) of colorectal cancer patients enrolled in the ColoCare Study at the National Center for Tumor Diseases (NCT) and University of Heidelberg (HD), Heidelberg in Germany and the Huntsman Cancer Institute (HCI), Salt Lake City, Utah and the Fred Hutchinson Cancer Center (FHCC), Seattle, Washington in the USA between June 2007 and July 2019.
| N (%) | |
|---|---|
| Age, mean (SD) | 62 (13) |
| Sex | |
| Female | 230 (40) |
| Male | 349 (60) |
| Race | |
| White | 549 (95) |
| Non-White | 28 (5) |
| Ethnicity | |
| Non-Hispanic | 567 (98) |
| Hispanic | 12 (2) |
| Stage at diagnosis | |
| I | 101 (17) |
| II | 148 (26) |
| III | 214 (37) |
| IV | 114 (20) |
| Tumor site | |
| Colon | 301 (51) |
| Rectum | 289 (49) |
| Neoadjuvant treatment | |
| Yes | 185 (32) |
| No | 394 (68) |
| Physical activity (MET-hrs/wk) | |
| Active (<8.75 MET-hrs/wk) | 239 (41) |
| Inactive (≥8.75 MET-hrs/wk) | 340 (59) |
| Mean (SD) | 12.5 (16.4) |
| BMI (kg/m2) | |
| Normal weight (≥18.5 and <25 kg/m2) | 207 (36) |
| Overweight (≥25 and <30 kg/m2) | 218 (38) |
| Obese (≥30 kg/m2) | 154 (26) |
| Mean (SD) | 27.6 (5.87) |
| Smoking status | |
| Never smoker | 198 (34) |
| Former smoker | 206 (36) |
| Current smoker | 77 (13) |
| Regular NSAID/Aspirin use in the past month | |
| Yes | 237 (66) |
| No | 123 (34) |
Data were missing for n=2 (<1%) on race, n=2 (<1%) on stage at diagnosis, n=1 (<1%) on neoadjuvant treatment, n=98 (17%) on smoking status, n=219 (37%) on regular NSAID/Aspirin use. Abbreviations: BMI: body mass index; SD: standard deviation; kg: kilogram; m: meter; hrs/wk: hours per week
Table 2.
Mean (SD) levels of inflammation-related biomarkers by individual and combined physical activity and BMI groups in colorectal cancer patients enrolled in the ColoCare Study at the National Center for Tumor Diseases (NCT) and University of Heidelberg (HD), Heidelberg in Germany and the Huntsman Cancer Institute (HCI), Salt Lake City, Utah and the Fred Hutchinson Cancer Center (FHCC), Seattle, Washington in the USA between June 2007 and July 2019.
| Biomarker | BMI groups | ||
|---|---|---|---|
| Normal weight (≥18.5 and <25 kg/m2) N=207 |
Overweight (≥25 and <30 kg/m2) N=218 |
Obese (≥30 kg/m2) N=154 |
|
| CRP (mg/L) | 21.1 (41.6) | 14.5 (32.0) | 18.3 (33.3) |
| SAA (mg/L) | 35.3 (66.8) | 27.0 (60.2) | 39.9 (81.4) |
| IL-6 (pg/ml) | 16.3 (77.6) | 16.6 (60.7) | 12.5 (44.2) |
| IL-8 (pg/ml) | 43.5 (99.2) | 58.3 (186) | 40.7 (115) |
| TNF-α (pg/ml) | 2.87 (1.25) | 3.01 (1.86) | 3.20 (1.24) |
| Physical activity groups | ||
|---|---|---|
| Inactive (<8.75 MET-hrs/wk) N=29 |
Active (≥8.75 MET-hrs/wk) N=40 |
|
| CRP (mg/L) | 17.2 (5.9) | 18.2 (6.0) |
| SAA (mg/L) | 28.7 (56.4) | 36.7 (76.2) |
| IL-6 (pg/ml) | 12.4 (49.1) | 17.6 (72.2) |
| IL-8 (pg/ml) | 44.2 (116) | 51.7 (158) |
| TNF-α (pg/ml) | 2.95 (1.91) | 3.04 (1.14) |
| Physical activity/BMI-based groups | ||||
|---|---|---|---|---|
| Normal weight (≥18.5 and <25 kg/m2) |
Overweight/obese (≥0 kg/m2) |
|||
| Active N=102 |
Inactive N=105 |
Active N=17 |
Inactive N=25 |
|
| CRP (mg/L) | 19.1 (40.1) | 32.2 (4.) | 15.8 (2.4) | 16.3 (2.6) |
| SAA (mg/L) | 30.7 (56.9) | 40.1 (75.7) | 27.2 (56.2) | 35.3 (76.6) |
| IL-6 (pg/ml) | 0.39 (28.6) | 22.7 (104) | 14.5 (59) | 15.2 (51.7) |
| IL-8 (pg/ml) | 47.2 (10) | 40.1 (58.0) | 42.1 (107) | 57.3 (189) |
| TNF-α (pg/ml) | 2.85 (1.26) | 2.88 (1.24) | 3.02 (2.26) | 3.12 (1.08) |
Abbreviations: SD: standard deviation, CRP:C reactive protein, SAA: serum amyloid A, IL-6/-8:interleukin-6/-8, TNF-α: tumor necrosis factor alpha, kg: kilogram, hrs/wk: hours per week, mg/L: milligram per liter, pg/L: picogram per liter
Associations of individual physical activity and BMI groups with levels of inflammation-related biomarkers
All measured biomarker levels were elevated among ‘inactive’ compared to ‘active’ patients (Table 3). Despite not being statistically significant, CRP and IL-6 levels tended to be higher among ‘inactive’ compared to ‘active’ patients [percent relative difference (RD)CRP=+39%, p=0.15; RDIL-6=+36%, p=0.10]. Similar observations were made when using 4 physical activity categories indicating a dose-response relationship between physical activity and biomarker levels (Supplementary Table S1). Stronger associations were observed for CRP among non-regular aspirin/NSAID users (Supplementary Table S2). In contrast, differences in SAA levels were more prominent among regular users. However, effect modification by regular aspirin/NSAID use was not statistically significant. No differences in the results were observed when stratifying by sex and tumor site. Similarly, no meaningful differences in the results were observed when excluding patients who received neoadjuvant treatment or who were diagnosed with stage IV disease.
Table 3.
Multiple linear regression model testing associations between individual physical activity and BMI groups with inflammation-related biomarkers in colorectal cancer patients enrolled in the ColoCare Study at the National Center for Tumor Diseases (NCT) and University of Heidelberg (HD), Heidelberg in Germany and the Huntsman Cancer Institute (HCI), Salt Lake City, Utah and the Fred Hutchinson Cancer Center (FHCC), Seattle, Washington in the USA between June 2007 and July 2019.
| RD (%) | β ± SE | p-value | |
|---|---|---|---|
| Physical activity groups | |||
| Inactive (<8.75-MET-hrs/wk) vs. active (≥8.75-MET-hrs/wk) | |||
| CRP (mg/L) | +39 | 0.33±0.23 | 0.15 |
| SAA (mg/L) | +26 | 0.23±0.22 | 0.30 |
| IL-6 (pg/ml) | +36 | 0.31±0.19 | 0.10 |
| IL-8 (pg/ml) | +8 | 0.08±0.13 | 0.51 |
| TNF-α (pg/ml) | +6 | 0.06±0.05 | 0.21 |
| BMI groups | |||
| Overweight vs. normal weight | |||
| CRP (mg/L) | −4 | −0.04±0.26 | 0.89 |
| SAA (mg/L) | −27 | −0.32±0.25 | 0.20 |
| IL-6 (pg/ml) | −10 | −0.10±0.22 | 0.66 |
| IL-8 (pg/ml) | −1 | −0.01±0.15 | 0.94 |
| TNF-α (pg/ml) | +2 | 0.03±0.06 | 0.59 |
| Obese vs. normal weight | |||
| CRP (mg/L) | +88 | 0.63±0.29 | 0.03 |
| SAA (mg/L) | +15 | 0.14±0.27 | 0.20 |
| IL-6 (pg/ml) | +25 | 0.22±0.25 | 0.38 |
| IL-8 (pg/ml) | 0 | 0.002±0.17 | 0.99 |
| TNF-α (pg/ml) | +17 | 0.16±0.07 | 0.02 |
Note: Biomarker levels were log-2 transformed. Analyses were adjusted for age, sex, stage at diagnosis, and neoadjuvant treatment; Abbreviations: RD: percentage relative difference (exp(β-coefficient)-1)*100); β:beta coefficient, SE: standard error, p: p-value, CRP:C reactive protein, SAA: serum amyloid A, IL-6/-8:interleukin-6/-8, TNF-α: tumor necrosis factor alpha; Bold numbers indicate statistical significance
All biomarker levels were elevated among ‘obese’ compared to ‘normal weight’ patients (Table 3). After adjusting for potential confounders, levels of CRP and TNF-α statistically significantly differed among ‘obese’ compared to ‘normal weight’ patients (RDCRP=+88%, p=0.03; RDTNF-α=+17%, p=0.02). Stronger associations in ‘obese’ patients were observed for CRP among regular aspirin/NSAID users (Supplementary Table S2). In contrast, differences in IL-6 and IL-8 levels in ‘obese’ patients and SAA in ‘overweight’ patients were more prominent among non-regular users. Effect modification by regular aspirin/NSAID use was not statistically significant. No differences in the results were observed when stratifying by sex and tumor site. No differences in the results were observed when excluding patients who received neoadjuvant treatment or who were diagnosed with stage IV disease.
Associations of combined physical activity and BMI groups with levels of inflammation-related biomarkers
We observed differences in biomarker levels when comparing ‘normal weight/active’ patients to other combined physical activity and BMI groups (Table 4). All biomarker levels were elevated among ‘normal weight/inactive’, ‘overweight or obese/active’ and ‘inactive’ compared to ‘normal weight/active’ patients (Table 4). CRP levels were statistically significantly and marginally significantly elevated in ‘overweight or obese/inactive’ and ‘normal weight/inactive’ patients, respectively (RD=+103%, p=0.03; RD=+88%, p=0.09). In addition, ‘normal weight/inactive’ patients had marginally significantly increased levels of SAA (RD=+67%, p=0.15). ‘Overweight or obese/inactive’ patients tended to have increased levels of IL-6 (RD=+48%, p=0.15)
Table 4.
Multiple linear regression model testing associations between combined physical activity and BMI groups with inflammation-related biomarkers in colorectal cancer patients enrolled in the ColoCare Study at the National Center for Tumor Diseases (NCT) and University of Heidelberg (HD), Heidelberg in Germany and the Huntsman Cancer Institute (HCI), Salt Lake City, Utah and the Fred Hutchinson Cancer Center (FHCC), Seattle, Washington in the USA between June 2007 and July 2019.
| Normal weight | ||||||
|---|---|---|---|---|---|---|
| Active | Inactive | |||||
| RD (%) | β ± SE | p-value | RD (%) | β ± SE | p-value | |
| CRP (mg/L) | Reference group | +88 | 0.63±0.37 | 0.09 | ||
| SAA (mg/L) | +67 | 0.51±0.35 | 0.15 | |||
| IL-6 (pg/ml) | +54 | 0.43±0.31 | 0.17 | |||
| IL-8 (pg/ml) | −12 | −0.13±0.21 | 0.52 | |||
| TNF-α (pg/ml) | −1 | −0.008±0.08 | 0.92 | |||
| Overweight/obese | ||||||
| Active | Inactive | |||||
| CRP (mg/L) | +54 | 0.43±0.36 | 0.22 | +103 | 0.71±0.31 | 0.03 |
| SAA (mg/L) | +9 | 0.09±0.34 | 0.80 | +25 | 0.22±0.30 | 0.47 |
| IL-6 (pg/ml) | +12 | 0.11±0.31 | 0.71 | +48 | 0.39±0.27 | 0.15 |
| IL-8 (pg/ml) | −19 | −0.21±0.20 | 0.30 | +1 | 0.01±0.18 | 0.95 |
| TNF-α (pg/ml) | −0 | 0.003±0.08 | 0.96 | +14 | 0.13±0.07 | 0.08 |
Analyses were adjusted for age, sex, stage at diagnosis, and neoadjuvant treatment; Abbreviations: RD: percentage relative difference (exp(β-coefficient)-1)*100); β:beta coefficient, p: p-value, SE: standard error, CRP:C reactive protein, SAA: serum amyloid A, IL-6/-8:interleukin-6/-8, TNF-α: tumor necrosis factor alpha; Bold numbers indicate statistical significance
When comparing biomarker levels by physical activity group among ‘overweight/obese’ patients, elevated levels across the biomarker panel were observed among ‘overweight or obese/inactive’ patients (Table 5). TNF-α levels were statistically significantly higher in ‘overweight or obese/inactive’ patients compared to their ‘active’ counterparts (RD=+16%, p=0.02). Meaningful differences were further observed for CRP, and IL-6 levels (RDCRP=+43%, p=0.17; RDIL-6=+39%, p=0.16).
Table 5.
Multiple linear regression model testing associations between joint physical activity and BMI groups (‘overweight or obese/active’ vs. ‘overweight or obese/inactive’) with inflammation-related biomarkers in colorectal cancer patients enrolled in the ColoCare Study at the National Center for Tumor Diseases (NCT) and University of Heidelberg (HD), Heidelberg in Germany and the Huntsman Cancer Institute (HCI), Salt Lake City, Utah and the Fred Hutchinson Cancer Center (FHCC), Seattle, Washington in the USA between June 2007 and July 2019.
| RD (%) | β ± SE | p-value | |
|---|---|---|---|
| ‘Overweight or obese/inactive’ vs. ‘overweight or obese/active’ | |||
| CRP (mg/L) | +43 | 0.36±0.26 | 0.17 |
| SAA (mg/L) | +23 | 0.21±0.27 | 0.43 |
| IL-6 (pg/ml) | +39 | 0.33±0.24 | 0.16 |
| IL-8 (pg/ml) | +26 | 0.23±0.17 | 0.17 |
| TNF-α (pg/ml) | +16 | 0.15±0.06 | 0.02 |
Analyses were adjusted for age, sex, stage at diagnosis, and neoadjuvant treatment; Abbreviations: RD: percentage relative difference (exp(β-coefficient)-1)*100); β: beta coefficient, p:p-value, SE: standard error, CRP: C reactive protein, SAA: serum amyloid A, IL-6/-8:interleukin-6/-8, TNF-α: tumor necrosis factor alpha. Bold numbers indicate statistical significance
There was some evidence that associations for ‘overweight or obese/inactive’ patients were stronger among regular aspirin/NSAID users (Supplementary Tables S3 and S4). However, effect modification was not statistically significant. No differences in the results were observed when either stratifying by sex and tumor site or excluding patients who received neoadjuvant treatment, or who were diagnosed with stage IV disease.
DISCUSSION
In this large international cohort of colorectal cancer patients, we investigated associations of physical activity and BMI with pro-inflammatory biomarkers. We observed that obesity was associated with significantly different levels of CRP and TNF-α levels compared to ‘normal weight’ patients. Moreover, obese patients reporting physical activity levels below the current guidelines (<8.75 MET-hrs/wk) had statistically significantly higher CRP levels and marginally significantly TNF-α levels as compared to their ‘active/normal weight’ counterpart. We further observed some differences in effect sizes by regular aspirin/NSAID use.
Although not statistically significant, we observed that CRP and IL-6 levels were 36% and 39% higher in ‘inactive’ compared to ‘active’ patients. Our data complement previous results from other cross-sectional and randomized controlled trials suggesting an inverse association between physical activity and pro-inflammatory biomarker levels (11-22). Overall, studies observed similar effect sizes indicating reduced levels of CRP by ~30%, and IL-6 ~20% among active compared to inactive individuals (11-20). For example, a recently published randomized controlled trial tested the effect of a 12-week exercise program on changes in biomarker levels among 139 primarily breast and a small proportion (36%) of colorectal cancer patients (11). Brown et al. observed that exercise alone and in combination with metformin reduced systemic levels of CRP and IL-6 by about 30% over the 12-week program period (11). The authors did not observe effect modification by BMI (11). Overall, our results complement existing data in the largest sample of colorectal cancer patients, to date, showing that physical activity may improve systemic inflammatory biomarker profiles in colorectal cancer patients.
Obesity was associated with altered biomarker levels (CRP and TNF-α) in comparison with ‘normal weight’ patients. Obesity has been strongly associated with cancer risk including colorectal cancer (1,3,4,8). Chronic low-grade systemic inflammation caused by hypertrophic adipocytes secreting pro-inflammatory cytokines and adipokines is one of the key hypothesized mechanisms (3,7,9). Cross-sectional studies have demonstrated higher levels of pro-inflammatory biomarkers including CRP and TNF-α in obese individuals including colorectal cancer patients (42-45). In addition, weight-loss interventions reduce pro-inflammatory biomarker levels (e.g., CRP, IL-6) among obese individuals including cancer patients (18,46). We have further previously identified a direct association between visceral adiposity and systemic biomarkers of inflammation and angiogenesis (36). Our study supports the existing body of literature showing BMI-defined obesity is associated with increased pro-inflammatory biomarker levels among colorectal cancer patients, which may lead to worse clinical outcomes.
Adipose dysfunction and its induced systemic inflammation may not only be found in obese individuals defined based on their BMI. As such, increased physical activity among obese individuals is one hypothesized mechanism underlying the “metabolically healthy obese” phenomenon, which refers to a proportion of obese individuals that do not represent the obesity-characterized phenotype of chronic systemic inflammation and insulin resistance (24,25). In contrast, normal weight individuals with insulin resistance and increased inflammation have also been discovered (61-63). Systemic levels of pro-inflammatory biomarkers, statistically significantly CRP, but also, marginally significantly SAA and IL-6 were increased among ‘inactive’ patients regardless of their BMI. In contrast, biomarker levels except for CRP were similar when comparing ‘normal weight/active’ patients with ‘overweight or obese/active’ patients. Other studies testing associations between physical activity and biomarkers yielded inconclusive results showing no changes or an attenuated association with increasing BMI (11,12). Taken together, we observed associations between combined physical activity and BMI groups and inflammatory biomarkers. Future studies should expand on our results investigating this association beyond BMI assessments using more comprehensive body composition measurements including computed tomography (CT) or dual-energy X-ray (DXA) scans to differentiate and quantity adipose tissue compartments and muscle mass.
This study has several strengths and limitations. To date, this is the largest study investigating systemic inflammation as potential underlying mechanism of the association between energy balance components and colorectal cancer. It is further the first study to test combined associations of physical activity and BMI on systemic inflammation in colorectal cancer patients. Inflammation-related biomarkers were measured following standardized protocols for biospecimen collection, processing, storage, quality controls, and analyses. Overall, baseline characteristics of our study population are consistent with those of cancer registries (64). The slightly higher proportion of rectal cancer cases (49%) as compared to the general cancer population may be a result of recruitment largely at national Comprehensive Cancer Centers and University clinics, which are more likely to conduct complex surgeries. Drift correction was used to account for batch-effects across study sites and False Discovery Rate correction was applied to account for multiple testing at the analyses stage. Environmental factors including anti-inflammatory drug use, smoking, and cancer treatment that may influence biomarker levels were accounted for in statistical analyses. Although the ColoCare Study assesses a multitude of potential confounders, the possibility of residual confounding remains. Physical activity was self-reported, which may have introduced reporting bias. Misclassification resulting from reporting bias would be assumed to be non-differential and, therefore, our results are expected to represent a smaller observed effect size compared to the actual effect size. Blood samples were stored on average for 3-5 years before the biomarker analyses were conducted, which may have introduced measurement errors. As previously noted, prior studies reported robust stability of the analyzed biomarkers over long-term storage (29-35). Longitudinal blood sample collections will be useful in future studies to account for intra-personal variability of the biomarker levels.
Taken together, we provide evidence of associations between individual and combined physical activity and BMI groups with inflammation-related biomarkers in the largest study of colorectal cancer patients to date. We identified pro-inflammatory biomarkers as potential interventional targets to improve colorectal cancer survivorship. Effect sizes by different exercise types and intensities have yet to be clarified. Our study suggests that higher physical activity may be a critical lifestyle change that reduces obesity-induced inflammation among cancer patients. To fully elucidate the question of whether or not physical activity can counteract obesity-induced inflammation, exercise intervention studies in overweight or obese cancer patients are needed.
Supplementary Material
ACKNOWLEDGEMENTS
C.M. Ulrich, E.M. Siegel, J.C. Figueiredo, D. Shibata, C.I. Li, A.T. Toriola, and M. Schneider have been awarded U01 CA206110. C.M. Ulrich has been awarded R01 CA254108 and R01 CA211705, as well as funding from the German Consortium of Translational Cancer Research, (DKTK), German Cancer Research Center, Matthias Lackas Stiftung, ERA-NET, JTC 2012 call on Translational Cancer Research (TRANSCAN), and Federal Ministry of Education and Research (BMBF), Germany, projects 01KT1503 and 01KD2101D.. C.M. Ulrich and C.I. Li have been awarded R01 CA189184 and R01 CA207371. S. Hardikar was awarded K07 CA222060. J.N. Cohan was awarded R03 AG067994. C. Himbert was awarded funding from the Stiftung LebensBlicke and Claussen Simon Stiftung. C.M. Ulrich, C Himbert, J. Ose, and S. Hardikar have been awarded funding from the Huntsman Cancer Foundation and Cancer Control and Population Health Sciences (CCPS) at the University of Utah. This study was in addition supported by KL2TR002539 and funding from the Immunology, Inflammation, and Infectious Disease Initiative at the University of Utah.
Footnotes
Potential competing interests: The authors declare no conflicts of interest. C.M. Ulrich has as cancer director oversight over research funded by several pharmaceutical companies but has not received funding directly herself. W. M. Grady is an advisory board member for Freenome, Guardant Health, and SEngine and consultant for DiaCarta, Nephron, Guidepoint and GLG. He is an investigator in a clinical trial sponsored by Janssen Pharmaceuticals and receives research support from Tempus and LucidDx.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data generated in this study are not publicly available due to information that could compromise patient privacy or consent but may be available upon reasonable request from the ColoCare investigator team (colocarestudy_admin@hci.utah.edu)