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Association Between Baseline Circulating Cytokines and Cancer Mortality in the REGARDS Cohort

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Association Between Baseline Circulating Cytokines and Cancer Mortality in the REGARDS Cohort

Novelty and Impact: In this prospective cohort, higher baseline circulating IL-6 and IL-10 were associated with significantly increased risk of cancer mortality and the association varied by race.



Objective: to examine the associations between baseline biomarkers of chronic inflammation, measured by circulating cytokines, and the risk of cancer death during follow-up.

Methods: Data from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) was used to investigate the associations between IL-8, IL-6, IL-10 and CRP with risk of cancer mortality. Analysis was conducted using Cox proportional hazard regression models adjusted for baseline covariates to compute hazard ratios and 95% confidence intervals estimated by robust sandwich methods.

Results: Among 1,856 participants representing a total of 26,897 REGARDS participants, each log-transformed unit increase in IL-8 was associated with more than two-fold increased risk of cancer mortality (HR: 2.37, 95% CI: 1.14-4.91), while IL-10 was associated with 77% increased risk of cancer mortality (HR: 1.77, 95% CI: 1.26-2.48). In race-stratified analysis, each log transformed unit increase in IL-6 was associated with nearly 4-fold increased risk of cancer mortality among Blacks (HR: 3.88, 95%: 1.17-12.88) and 5-fold increased risk of cancer mortality among Whites (HR: 5.25, 95%: 1.24-22.31). There was no statistically significant association betweenCRP and cancer mortality in overall or race-stratified models.

Conclusion: If replicated in larger, racially diverse prospective cohorts, these results suggest that cancer patients may benefit from clinical strategies to regulate immune suppressing cytokines.

Keywords: chronic inflammation, cytokines, cancer mortality



The link between chronic inflammation and cancer development and progression is well known, and while inflammation-associated cell proliferation alone does not cause cancer, sustained cell proliferation in a highly enriched inflammatory environment promotes tumorigenesis1. Early in tumorigenesis, inflammatory cells act as powerful tumor promoters, and provide an attractive environment for tumor growth, genomic instability and angiogenesis, while later in the tumorigenic process, chemokines and cytokines promote tumor spread and metastasis. Epidemiologic studies reveal that long-term use of aspirin and other nonsteroidal anti-inflammatory drugs is associated with up to 40-50% reduced risk of colon cancers2, 45% reduced risk of pre-menopausal breast cancer3, and reduced risk of lung, esophagus and stomach cancers 4. Other studies have shown that higher systemic inflammation significantly increases risk of cancer-related mortality5-7. The molecular mechanisms explaining the link between chronic inflammation and cancer risk have also been the subject of much research, however questions remain regarding the potential role of specific cytokines in cancer mortality as prognostic biomarkers, and whether racial differences in cancer mortality may be explained by the distribution of inflammatory cytokines.

Cancer mortality rates vary significantly by race among Black and White adults in the U.S 8. Documented risk and prognostic factors that partly explain this disparity include diet, physical inactivity, smoking and alcohol use 9-12. Obesity is also a well-established predictor of cancer risk and mortality 13, with racial differences that mirror those observed for cancer mortality. Recent studies, including by our group14, have questioned the role of obesity as a cause of cancer mortality, and suggest that obesity-associated chronic inflammation may be an important etiologic link associated with cancer mortality 15, 16. Inflammation-related cytokines such as IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, TNF-alpha and IFN-gamma promote tumor initiation, angiogenesis and metastasis 15, 16. Since these cytokines are easily measured from blood samples, if baseline circulating cytokines are found to be independently associated with cancer mortality during follow-up, they may be useful as prognostic biomarkers for cancer. In addition, if racial differences are observed in the association between the circulating cytokines and cancer mortality, this may further improve understanding of the biological basis of racial disparities in cancer mortality. Here, we examined whether baseline circulating cytokines increase the risk of cancer mortality among REGARDS participants overall and stratified by race, after adjusting for baseline demographics, comorbidities and other risk factors.





Study Participants: Data for this study were obtained from the REGARDS (REason for Geographic and Racial Differences in Stroke) cohort study. REGARDS is one of the largest ongoing national longitudinal cohorts of community-dwelling adults in the United States 17. The REGARDS cohort included 30,239 participants aged ≥ 45 years at baseline; 45% were male, 41% were Black, and 69% were >60 years old who were recruited between January 2003 and October 2007. Data on  demographics, health behaviors, chronic medical conditions, physical status, diet, and medications were collected at baseline 17. Participants were contacted by telephone every 6-months to identify medical events or hospitalizations experienced since the prior contact. Baseline biomarker measurements were available on 1856 sub-cohort participants of the REGARDS study. The cohort random sample was selected to ensure sufficient representation of high-risk groups. The REGARDS cohort was explained in detail elsewhere18-20.

Main Exposure Variables: The exposure variables in this study were inflammatory biomarkers measured at baseline. The biomarkers including interleukin (IL)-6, IL-8, IL-10, and C-reactive protein were measured at the central laboratory for the REGARDS study at University of Vermont, Burlington, VT. The laboratory analytical coefficient of variation (CV) for IL-6 was 6.3%. IL-8 was measured by the Human Serum Adipokine Panel B LINCOplex Kit (Linco Research, Inc.; St. Charles, MO): Intra- and inter-assay CVs ranged from 1.4-7.9% and < 21%, respectively. IL-10 was measured using the Milliplex MAP Human Cardiovascular Disease (CVD) Panel 3 (Millipore Corporation; Billerica, MA) run as a single-plex assay. The average analytical CV was 8.09%. CRP was measured in Special plasma in batches during enrollment utilizing a validated high-sensitivity particle-enhanced immunonepholometric assay on the BNII nephelometer (N High Sensitivity CRP, Dade Behring Inc, Deerfield, IL). Intra-assay CV’s ranged from 2.3 – 4.4% and inter-assay CV’s ranged from 2.1 – 5.7%.

Cancer Mortality Outcome: The primary outcome of the study was any cancer related death. Cancer mortality was ascertained using death certificates, medical records, interviewed proxies, linkages with the Social Security Death Index (SSDI) as well as the National Death Index (NDI). Date of death was based on data from death certificates, SSDI, and/or NDI. A committee of experts adjudicated the cause of death using all available information as recommended by national guidelines 21. The secondary outcome in this study was obesity-related cancer deaths defined as cancers of the breast, colorectum, kidney, pancreas, gastro-intestinal, endometrium, and esophagus 22.  Follow-up time for each participant was calculated from the enrollment date through date of cancer death, death, or last telephone follow-up through December 31, 2012.

Participant Characteristics: adjusted baseline covariates included age, race, gender, income, education, smoking, alcohol use, exercise activity, aspirin, statins and comorbidity score.  We created baseline comorbidity score from sum of the number of comorbidities including atrial fibrillation, chronic lung disease, chronic kidney disease, coronary artery disease, deep vein thrombosis, diabetes, dyslipidemia, hypertension, myocardial infarction, obesity, peripheral artery disease, and stroke.


Statistical Analysis: Statistical weights were applied to participants included in the sub-cohort based on age, sex, and race stratum from the REGARDS cohort participants. We examined the distribution of inflammatory biomarkers, and performed analysis by tertiles and log-transformed values of each inflammatory biomarker due to non-normal distributions (Table 1). We compared baseline participant characteristics by tertiles of each biomarker using Chi-square and Kruskal-Wallis tests as appropriate. We used Cox proportional hazards regression with robust sandwich estimation method to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between inflammatory biomarkers and cancer mortality, accounting for study population weight using models sequentially adjusted for baseline covariates. In model 1, we adjusted for age, gender, education, income, and cancer type (site). In model 2, we further adjusted for race.  To examine race as an effect modifier, we evaluated the interaction between each biomarker and race, and conducted race-stratified analysis. Participants were censored at of time of death, loss to follow-up, or the end of cancer mortality ascertainment (December 31, 2012), whichever happened first.  We used SAS version 9.4 for all statistical analysis. P values ≤0.05 were considered statistically significant.

Ethics Statement: The institutional review boards of all participating institutions approved this study.



Study Characteristics: A total of 1856 participants represented an estimated 26,897 REGARDS participants during the study period. A total of 86 cancer deaths in this sub-cohort represented approximately 1340 (5%) cancer deaths among REGARDS participants from 2003 through 2012. (Table 2) The most common cancer types were lung (n = 25 (29%), weighted n = 459 (34%), gastro-intestinal (n = 19 (22%), weighted n = 233 (17%), hematological (n = 10 (11%), weighted n = 137 (10%), and genitourinary cancers (n = 8 (9%), weighted n = 169 (13%). Of the 1340 cancer deaths, there were an estimated 428 (32%) obesity-related cancer deaths. The mean follow-up time was 6.5 (SD=2.2) years.

Bivariate Analysis: When comparing the first and third tertiles (Table 3), higher baseline IL-6 was associated with older age, African-American race (51% vs. 35%, p <0.01), lower education (18% vs. 7%, p <0.01), lower income (22% vs. 11%, p<0.01), and greater proportion of current smokers (23% vs. 11%, p <0.01). Participants with higher IL-6 also had lower heavy alcohol consumption (3% vs. 5%, p<0.01), were more likely to have coronary artery, deep vein thrombosis, diabetes, dyslipidemia, hypertension, myocardial infarction, and stroke. Higher baseline IL-8 was associated with older age, lower education (18% vs. 14%, p<0.01), current smoking (17% vs. 11%, p<0.01) and greater statin use (39% vs. 31%, p<0.01). They were also more likely to have atrial fibrillation, coronary artery disease, diabetes, dyslipidemia, hypertension, myocardial infarction, and stroke.  Higher baseline IL-10 was associated with male gender (50% vs. 42%, p<0.01), lower income (18% vs. 15%, p<0.01), and more aspirin (49% vs. 41%, p <0.01) and statin use (37% vs. 31). They were also more likely to have coronary artery disease, diabetes, dyslipidemia, hypertension, and myocardial infarction. Higher baseline CRP was also associated with African-American race (51% vs. 33%, p<0.01) and lower income (21% vs. 11%, p<0.01). Participants with higher CRP were more likely to have diabetes, dyslipidemia, and hypertension.

Risk of Cancer Death: In crude models, higher IL-6 levels were associated with more than a 3-fold increased risk of cancer mortality (adjusted HR: 3.15, 95% CI: 2.11-4.72 for log transformed levels; 3rd vs. 1st tertile HR: 4.41, 95% CI: 2.45-7.95) (Table 4). After adjustment for age, gender, education, income, cancer site, and race, the HR was 6.66 (95% CI: 2.70-16.43) for the 3rd vs. 1st tertile). After further adjustment for exercise, BMI, smoking, alcohol, and comorbidity score, the HR (95% CI) was 12.97 (95% CI :3.46-48.63) for the 3rd vs. 1st tertile, respectively. Similarly, higher level of IL-8 was associated with a 2-fold increased risk of cancer mortality after adjustment for age, gender, education, income, cancer site, and race (adjusted HRs 2.16, 95% CI: 1.05-4.43 for log-transformed levels; and 2.63, 95% CI: 1.13-6.10 for 3rd vs. 1st tertile). After further adjustment for exercise, BMI, smoking, alcohol, and comorbidity score, the HR was 2.37 (95% CI: 1.14-4.91) but the estimates by tertile were no longer statistically significant. The corresponding adjusted HRs for log transformed levels and tertiles of IL-10 were 1.77 (95% CI: 1.26-2.48) and 3.06 (95% CI: 1.35-6.89).  CRP was not associated with cancer mortality in any of the models.

Risk of Obesity-related Cancer Death: In unadjusted models, higher IL-6 (adjusted HR: 3.52, 95% CI: 1.86-6.67 for log transformed levels; 3rd vs. 1st tertile HR: 4.30, 95% CI: 1.67-11.09) and IL-8 (adjusted HR: 1.53, 95% CI: 1.01-2.31) were associated with an increased risk of obesity-related cancer mortality (Table 5). After further adjustment for age, gender, education, income, site, and race, only IL-6 remained associated with obesity-related cancer mortality (adjusted HR: 3.33, 95% CI: 1.61-6.87). Other inflammatory biomarkers were not significantly associated with obesity-related cancer mortality.


Risk of Cancer Death Stratified by Race: Higher IL-6 levels were associated with about a 3-fold increased risk of cancer mortality in crude models among African-Americans and Whites (Table 6). However, in adjusted models, the HR for Whites was 5.25 (95% CI: 1.24-22.31) while for African-Americans it was 3.88 (95% CI: 1.17-12.88).HigherIL-8 was significantly associated with increased risk of cancer mortality among African-Americans (crude HR: 2.00, 95% CI: 1.26-3.16), but not among White participants,although the association for African-Americans was no longer statistically significant in the fully adjusted model.Higher IL-10 was significantly associated with risk of cancer mortality among White participants in adjusted models (HR: 1.94, 95% CI: 1.24-3.04), but not among African-American participants. CRP was not associated with cancer mortality in either racial group.






In this cohort study of Black and White REGARDS participants, high levels of baseline circulating IL-6, IL-8 and IL-10 were associated with higher cancer mortality. Higher baseline levels of IL-6 was associated with nearly 4-fold increased risk of cancer death among African-Americans and 5-fold increased risk of cancer death among Whites. IL-6 was also associated with increased risk of mortality among participants with obesity related cancers. The observed associations were independent of cancer risk factors such as age, higher BMI, smoking, and alcohol intake. This suggests that baseline measures of cytokines such as L-6, IL-8 and IL-10 significantly contribute to increased mortality among cancer patients.


These findings are consistent with other studies that found strong associations between pro-inflammatory cytokines such as IL-6, IL-8 and IL-10 with poor cancer outcomes 23-27. Among studies evaluating cancer mortality outcomes in participants who were cancer free at baseline, IL-6 and IL-8 have been shown to be important predictors. For instance, among rheumatoid arthritis patients, England et al (2016) observed a 2-fold increased risk of overall cancer mortality and a 6-fold increased risk of lung cancer mortality comparing those in the highest quartiles of cytokine scores to the lowest 6. Evidence from other studies also support the role of IL-8 in tumor initiation, proliferation, invasion, metastasis and poor prognosis 30-33. CRP was not significantly associated with cancer mortality in the present study consistent with results of previous studies 38-40.  However, the results from a recent meta-analysis of 6 studies including 55,721 participants with 3180 cancer deaths 41, and a cohort study among Korean participants indicated that high CRP level was associated with increased risk of cancer mortality among men but not women 7 although only three out of the six studies in the meta-analysis found a direct association between CRP levels and cancer mortality.

Other studies have evaluated the role of circulating cytokines in cancer survival among cancer patients. A recent systematic review indicated that IL-6 was inversely correlated with cancer survival in 82 studies out of 101 studies 28. In contrast, a study among breast cancer patients observed that high levels of IL-8 was associated with an accelerated clinical course, a higher tumor load, metastasis and poorer post-relapse survival 29. A meta-analysis of 21 studies also revealed that high IL-10 levels were associated with worse disease-free and overall survival in both solid and hematological cancer patients 34. Other studies show significant correlations between IL-10 and tumor stage, tumor size, lymph node metastasis, lymphovascular invasion and poor tumor cell differentiation 35. However, some studies reported a protective association of IL-10 on cancer survival 36, 37. These conflicting results may be due to pleotropic functions of IL-10 as both a pro- and anti- inflammatory cytokine.  Future studies may be needed to clarify whether there is sufficient evidence consistent with pleotropic functions of IL-10 both causing immune stimulation and immune suppression in cancer, and to better delineate the influence of baseline circulating cytokines on a range of cancer outcomes among healthy versus diseased individuals.

While baseline circulating IL-6 was associated with higher cancer mortality overall and among African-Americans and Whites, the association between IL-10 and higher cancer mortality remained significant only among Whites in our study. The majority of studies evaluating racial differences in this association have focused on survival outcomes among cancer patients. For instance, IL-6 was associated with poorer survival in both Black and White lung cancer patients, while IL-10 and IL-12 were associated with poorer survival only among Black lung cancer patients 42. In a study conducted only among Whites, higher IL-6 and IL-8 were associated with poor lung cancer survival 43, and in another study Black and Asian participants were observed to have increased prevalence of the rs1800975 GG allele SNP in the promoter region of IL-6, and that this SNP was associated with distant metastasis 44. The higher prevalence of the  GG allele of  rs180975 SNP among Blacks was believed to potentially explain the higher rate of breast cancer metastasis and poorer median survival among Blacks 44.

When focused only on obesity related cancers, IL-6 was associated with more than 3-fold increased risk of cancer mortality while IL-8 and IL-10 were no longer associated with increased cancer mortality. The link between obesity and cancer risk and mortality is thought to operate through chronic inflammation initiated by production of cytokines including IL-6 and adipokines by the altered adipose tissue 45, 46.  The biological mechanisms through which specific cytokines are associated with cancer mortality has been widely investigated. Th2 cytokines such as IL-6 and IL-10 enhance motility and survival of highly tumorigenic cancer stem cells and thus metastasis47, while IL-8  is involved in the regulation of angiogenesis, cancer cell growth and survival, tumor cell motion, leukocyte infiltration, and modification of immune responses 48.  IL-6 and IL-10 are believed to influence tumor progression by  activating Janus Kinase (JAK) causing the phosphorylation of signal transducer and activation of transcription 3 (STAT3), initiation and transcription of STAT3 target genes including cyclin D1, Bcl-xL, c-myc, Mcl1 and vascular endothelial growth factor (VEGF) 49. As an immune-suppressive cytokine, IL-10 promotes tumor cell proliferation and metastasis 34, 50. Activated by cytokines including IL-6, STAT3 is involved in tumor initiation, differentiation, suppression of apoptosis, metastasis and ultimately mortality in cancer patients 51-53. Our study shows that circulating cytokine levels measured at baseline up to 10 years prior to cancer development may provide a useful biomarker for identifying which patients may benefit from more intensive cancer prevention strategies, including prevention of obesity-associated inflammation through lifestyle and clinical approaches. It may also contribute to improved risk-stratification by identifying which patients, once diagnosed, are at higher risk of mortality and require more intensive treatment and follow-up. Improved understanding of the specific molecular subtypes of cancers driving these associations may help inform clinical studies of targeted immunotherapies focusing on IL-6, IL-8 and IL-10 that may counteract their detrimental role on cancer mortality.

The strength of this study include a prospective longitudinal that minimized potential reverse causality where diagnosis of cancer could influence biomarker levels. While we could not rule out the possibility that some latent cancers might be present at baseline, to participate in the REGARDS study, participants had to be free of cancer at baseline, so the impact is likely to be small.  The availability of data on both Blacks and Whites enabled the stratified analysis by race. The limitation of our study include potential over-representation of highly fatal cancers such as lung, pancreatic and ovarian cancers, and under-representation of more indolent breast and prostate cancers that need long follow-up duration;  the limited number of cancer deaths that precluded analysis on site-specific mortality.

We were also underpowered to detect associations between cytokines and obesity-related cancer mortality in race-stratified analyses.

In conclusion, baseline circulating cytokines including IL-6, IL-8 and IL-10 were associated with increased risk of cancer mortality. This information may be useful for cancer prevention, risk stratification and improved clinical therapies such as immunotherapy that may improve cancer prognosis.



1. Coussens LM, Werb Z. Inflammation and cancer. Nature 2002;420: 860-7.

2. Rostom A, Dube C, Lewin G, Tsertsvadze A, Barrowman N, Code C, Sampson M, Moher D, Force USPST. Nonsteroidal anti-inflammatory drugs and cyclooxygenase-2 inhibitors for primary prevention of colorectal cancer: a systematic review prepared for the U.S. Preventive Services Task Force. Ann Intern Med 2007;146: 376-89.

3. Kim S, Shore DL, Wilson LE, Sanniez EI, Kim JH, Taylor JA, Sandler DP. Lifetime use of nonsteroidal anti-inflammatory drugs and breast cancer risk: results from a prospective study of women with a sister with breast cancer. BMC Cancer 2015;15: 960.

4. Gonzalez-Perez A, Garcia Rodriguez LA, Lopez-Ridaura R. Effects of non-steroidal anti-inflammatory drugs on cancer sites other than the colon and rectum: a meta-analysis. BMC Cancer 2003;3: 28.

5. Lee DY, Rhee EJ, Chang Y, Sohn CI, Shin HC, Ryu S, Lee WY. Impact of systemic inflammation on the relationship between insulin resistance and all-cause and cancer-related mortality. Metabolism 2017.

6. England BR, Sokolove J, Robinson WH, Thiele GM, Ganti AK, Sayles H, Michaud K, Caplan L, Davis LA, Cannon GW, Sauer B, Singh N, et al. Associations of Circulating Cytokines and Chemokines With Cancer Mortality in Men With Rheumatoid Arthritis. Arthritis & Rheumatology 2016;68: 2394-402.

7. Ko YJ, Kwon YM, Kim KH, Choi HC, Chun SH, Yoon HJ, Goh E, Cho B, Park M. High-sensitivity C-reactive protein levels and cancer mortality. Cancer Epidemiol Biomarkers Prev 2012;21: 2076-86.

8. Silber JH, Rosenbaum PR, Clark AS, Giantonio BJ, Ross RN, Teng Y, Wang M, Niknam BA, Ludwig JM, Wang W, Even-Shoshan O, Fox KR. Characteristics associated with differences in survival among black and white women with breast cancer. Jama 2013;310: 389-97.

9. Yang M, Kenfield SA, Van Blarigan EL, Batista JL, Sesso HD, Ma J, Stampfer MJ, Chavarro JE. Dietary patterns after prostate cancer diagnosis in relation to disease-specific and total mortality. Cancer Prev Res (Phila) 2015;8: 545-51.

10. Orlich MJ, Singh PN, Sabate J, Fan J, Sveen L, Bennett H, Knutsen SF, Beeson WL, Jaceldo-Siegl K, Butler TL, Herring RP, Fraser GE. Vegetarian dietary patterns and the risk of colorectal cancers. JAMA Intern Med 2015;175: 767-76.

11. Yang B, Jacobs EJ, Gapstur SM, Stevens V, Campbell PT. Active smoking and mortality among colorectal cancer survivors: the Cancer Prevention Study II nutrition cohort. J Clin Oncol 2015;33: 885-93.

12. Ferrari P, Licaj I, Muller DC, Kragh Andersen P, Johansson M, Boeing H, Weiderpass E, Dossus L, Dartois L, Fagherazzi G, Bradbury KE, Khaw KT, et al. Lifetime alcohol use and overall and cause-specific mortality in the European Prospective Investigation into Cancer and nutrition (EPIC) study. BMJ Open 2014;4: e005245.

13. Kolb R, Sutterwala FS, Zhang W. Obesity and cancer: inflammation bridges the two. Curr Opin Pharmacol 2016;29: 77-89.

14. Akinyemiju T, Moore JX, Pisu M, Judd SE, Goodman M, Shikany JM, Howard VJ, Safford M, Gilchrist SC. A Prospective Study of Obesity, Metabolic Health, and Cancer Mortality. Obesity 2018;26: 193-201.

15. Trabert B, Eldridge RC, Pfeiffer RM, Shiels MS, Kemp TJ, Guillemette C, Hartge P, Sherman ME, Brinton LA, Black A, Chaturvedi AK, Hildesheim A, et al. Pre-diagnostic circulating inflammation markers and endometrial cancer risk in the Prostate, Lung, Colorectal and Ovarian Cancer (PLCO) Screening Trial. Int J Cancer 2016.

16. Epplein M, Xiang YB, Cai Q, Peek RM, Jr., Li H, Correa P, Gao J, Wu J, Michel A, Pawlita M, Zheng W, Shu XO. Circulating cytokines and gastric cancer risk. Cancer Causes Control 2013;24: 2245-50.

17. Howard VJ, Cushman M, Pulley L, Gomez CR, Go RC, Prineas RJ, Graham A, Moy CS, Howard G. The reasons for geographic and racial differences in stroke study: objectives and design. Neuroepidemiology 2005;25: 135-43.

18. Cushman M, Judd, S.E., Howard, V.J., Kissela, B., Gutierrez, O.M., Jenny, N.S., Ahmed, A., Thacker, E.L., & Zakai, N.A. N-terminal pro-B-type natriuretic peptide and stroke risk: the reasons for geographic and racial differences in stroke cohort. Stroke 2014;45: 1646-50.

19. Panwar B, Jenny, N.S., Howard, V.J., Wadley, V.G., Mutner, P., Kissela, B.M., Judd, S.E., & Gutierrez, O.R. Fibroblast growth factor 23 and risk of incident stroke in community-living adults. Stroke 2015;46: 322-8.

20. Zakai NA, Judd, S.E., Alexander, K., McClure, L.A., Kissela, B.M., Howard, G., & Cushman, M. ABO blood type and stroke risk: the reasons for geographic and racial differences in stroke study. Journal of Thrombosis and Haemostasis 2014;12: 564-70.

21. Safford MM, Brown TM, Muntner PM, Durant RW, Glasser S, Halanych JH, Shikany JM, Prineas RJ, Samdarshi T, Bittner VA, Lewis CE, Gamboa C, et al. Association of race and sex with risk of incident acute coronary heart disease events. Jama 2012;308: 1768-74.

22. Ligibel JA, Alfano CM, Courneya KS, Demark-Wahnefried W, Burger RA, Chlebowski RT, Fabian CJ, Gucalp A, Hershman DL, Hudson MM, Jones LW, Kakarala M, et al. American Society of Clinical Oncology position statement on obesity and cancer. J Clin Oncol 2014;32: 3568-74.

23. Grivennikov SI, Karin M. Inflammatory cytokines in cancer: tumour necrosis factor and interleukin 6 take the stage. Annals of the Rheumatic Diseases 2011;70: i104-i8.

24. Studebaker AW, Storci G, Werbeck JL, Sansone P, Sasser AK, Tavolari S, Huang T, Chan MW, Marini FC, Rosol TJ, Bonafe M, Hall BM. Fibroblasts isolated from common sites of breast cancer metastasis enhance cancer cell growth rates and invasiveness in an interleukin-6-dependent manner. Cancer research 2008;68: 9087-95.

25. Necula LG, Chivu-Economescu M, Stanciulescu EL, Bleotu C, Dima SO, Alexiu I, Dumitru A, Constantinescu G, Popescu I, Diaconu CC. IL-6 and IL-11 as markers for tumor aggressiveness and prognosis in gastric adenocarcinoma patients without mutations in Gp130 subunits. Journal of gastrointestinal and liver diseases : JGLD 2012;21: 23-9.

26. Lippitz BE, Harris RA. Cytokine patterns in cancer patients: A review of the correlation between interleukin 6 and prognosis. Oncoimmunology 2016;5: e1093722.

27. Benoy IH, Salgado R, Van Dam P, Geboers K, Van Marck E, Scharpé S, Vermeulen PB, Dirix LY. Increased Serum Interleukin-8 in Patients with Early and Metastatic Breast Cancer Correlates with Early Dissemination and Survival. Clinical Cancer Research 2004;10: 7157-62.

28. Singh JK, Simões BM, Howell SJ, Farnie G, Clarke RB. Recent advances reveal IL-8 signaling as a potential key to targeting breast cancer stem cells. Breast Cancer Research : BCR 2013;15: 210-.

29. Chen Y, Shi M, Yu G-Z, Qin X-R, Jin G, Chen P, Zhu M-H. Interleukin-8, a promising predictor for prognosis of pancreatic cancer. World Journal of Gastroenterology : WJG 2012;18: 1123-9.

30. Chen ZY, He WZ, Peng LX, Jia WH, Guo RP, Xia LP, Qian CN. A prognostic classifier consisting of 17 circulating cytokines is a novel predictor of overall survival for metastatic colorectal cancer patients. Int J Cancer 2015;136: 584-92.

31. Milovanovic J, Todorovic-Rakovic N, Abu Rabi Z. The prognostic role of interleukin-8 (IL-8) and matrix metalloproteinases -2 and -9 in lymph node-negative untreated breast cancer patients. Journal of BUON : official journal of the Balkan Union of Oncology 2013;18: 866-73.

32. Zhao S, Wu D, Wu P, Wang Z, Huang J. Serum IL-10 Predicts Worse Outcome in Cancer Patients: A Meta-Analysis. PLOS ONE 2015;10: e0139598.

33. Wang R, Lu M, Zhang J, Chen S, Luo X, Qin Y, Chen H. Increased IL-10 mRNA expression in tumor-associated macrophage correlated with late stage of lung cancer. Journal of Experimental & Clinical Cancer Research : CR 2011;30: 62-.

34. Wu JM, Bensen-Kennedy D, Miura Y, Thoburn CJ, Armstrong D, Vogelsang GB, Hess AD. The effects of interleukin 10 and interferon gamma cytokine gene polymorphisms on survival after autologous bone marrow transplantation for patients with breast cancer. Biology of blood and marrow transplantation : journal of the American Society for Blood and Marrow Transplantation 2005;11: 455-64.

35. Hayashi T, Yamashita T, Terashima T, Suda T, Okada H, Asahina Y, Hayashi T, Hara Y, Nio K, Sunagozaka H, Takatori H, Arai K, et al. Serum cytokine profiles predict survival benefits in patients with advanced hepatocellular carcinoma treated with sorafenib: a retrospective cohort study. BMC Cancer 2017;17: 870.

36. Ahmadi-Abhari S, Luben RN, Wareham NJ, Khaw K-T. Seventeen year risk of all-cause and cause-specific mortality associated with C-reactive protein, fibrinogen and leukocyte count in men and women: the EPIC-Norfolk study. European Journal of Epidemiology 2013;28: 541-50.

37. Oluleye OW, Folsom AR, Nambi V, Lutsey PL, Ballantyne CM, the Asi. Troponin T, B type natriuretic peptide, C-reactive protein and cause-specific mortality. Annals of epidemiology 2013;23: 66-73.

38. Morrison L, Laukkanen JA, Ronkainen K, Kurl S, Kauhanen J, Toriola AT. Inflammatory biomarker score and cancer: A population-based prospective cohort study. BMC Cancer 2016;16: 80.

39. Li Y, Zhong X, Cheng G, Zhao C, Zhang L, Hong Y, Wan Q, He R, Wang Z. Hs-CRP and all-cause, cardiovascular, and cancer mortality risk: A meta-analysis. Atherosclerosis 2017;259: 75-82.

40. Enewold L, Mechanic LE, Bowman ED, Zheng YL, Yu Z, Trivers G, Alberg AJ, Harris CC. Serum concentrations of cytokines and lung cancer survival in African Americans and Caucasians. Cancer Epidemiol Biomarkers Prev 2009;18: 215-22.

41. Bryant RJ, Wastney ME, Martin BR, Wood O, McCabe GP, Morshidi M, Smith DL, Peacock M, Weaver CM. Racial differences in bone turnover and calcium metabolism in adolescent females. The Journal of Clinical Endocrinology & Metabolism 2003;88: 1043-7.

42. Abana CO, Bingham BS, Cho JH, Graves AJ, Koyama T, Pilarski RT, Chakravarthy AB, Xia F. IL-6 variant is associated with metastasis in breast cancer patients PloS one, vol. 12, 2017:e0181725.

43. Divella R, De Luca R, Abbate I, Naglieri E, Daniele A. Obesity and cancer: the role of adipose tissue and adipo-cytokines-induced chronic inflammation. Journal of Cancer 2016;7: 2346-59.

44. Kim HS, Chen YC, Nor F, Warner KA, Andrews A, Wagner VP, Zhang Z, Zhang Z, Martins MD, Pearson AT, Yoon E, Nor JE. Endothelial-derived interleukin-6 induces cancer stem cell motility by generating a chemotactic gradient towards blood vessels. Oncotarget 2017;8: 100339-52.

45. Todorović-Raković N, Milovanović J. Interleukin-8 in Breast Cancer Progression. Journal of Interferon & Cytokine Research 2013;33: 563-70.

46. Wang SW, Sun YM. The IL-6/JAK/STAT3 pathway: potential therapeutic strategies in treating colorectal cancer (Review). International journal of oncology 2014;44: 1032-40.

47. Wu P, Wu D, Zhao L, Huang L, Shen G, Huang J, Chai Y. Prognostic role of STAT3 in solid tumors: a systematic review and meta-analysis. Oncotarget 2016;7: 19863-83.

48. Furtek SL, Backos DS, Matheson CJ, Reigan P. Strategies and Approaches of Targeting STAT3 for Cancer Treatment. ACS Chemical Biology 2016;11: 308-18.

49. Huang X, Meng B, Iqbal J, Ding BB, Perry AM, Cao W, Smith LM, Bi C, Jiang C, Greiner TC, Weisenburger DD, Rimsza L, et al. Activation of the STAT3 signaling pathway is associated with poor survival in diffuse large B-cell lymphoma treated with R-CHOP. J Clin Oncol 2013;31: 4520-8.

50. National Institute on Alcohol Abuse and Alcoholism. Helping Patients Who Drink Too Much, a Clinician’s Guide, vol. 2012, 2005.

Table 1. Inflammatory biomarkers distribution by tertiles.
  N 1st Tertile N 2nd Tertile N 3rd Tertile Median (IQR)
IL-6 (pg/mL) 618 0.62-2.51 619 2.51-4.11 619 4.11-12.31 3.21 (2.17-4.84)
IL-8 (pg/mL) 618 0.11-2.15 620 2.16-3.25 618 3.26-281.18 2.63 (1.92-3.69)
IL-10 (pg/mL) 619 0.18-7.42 618 7.44-11.97 619 11.98-3806.61 9.48 (6.51-13.84)
CRP (mg/L) 619 0.15-1.31 618 1.32-3.93 619 3.95-111.00 2.37 (0.99-5.11)
CRP = C reactive protein
Table 2. Cancer types among 86 (1340 weighted) participants with cancer deaths in the REGARDS
  N (%) Weighted N (%)
Total 86 1340
Cancer Type
Lung 25 (29.06) 459 (34.26)
Gastro-intestinal 19 (22.09) 233 (17.42)
Hematological 10 (11.63) 137 (10.25)
Genitourinary 8 (9.30) 169 (12.59)
Prostate 4 (4.65) 79 (5.87)
Gynecologic 2 (2.33) 15 (1.11)
Central nervous system 2 (2.33) 81 (6.06)
Head and neck 2 (2.33) 23 (1.74)
Breast 3 (3.49) 3 (0.22)
Other/Unknown 4 (4.65) 23 (1.73)
Obesity-related cancers 32 (37.21) 428 (31.97)
Table 3. Baseline Characteristics of REGARDS Participants by levels of Inflammatory Biomarkers
  Tertiles of Inflammatory Biomarkers
  IL-6 (pg/ml) IL-8 (pg/ml) IL-10 (pg/ml) CRP (mg/L)
  T1 T3 T1 T3 T1 T3 T1 T3
Participants 618 619 618 618 619 619 619 619
Weighted Participants 10,796 7652 9668 8533 9665 8702 9617 8347
  Presented as Column % or Median (IQR)
Age at baseline, Median (IQR) 65 (56-72) 69 (60-76) 64 (56-72) 70 (62-76) 66 (58-75) 70 (61-76) 68 (59-76) 67 (59-75)
African-American Race, % 35.20 50.95 41.08 43.21 42.98 40.67 32.82 50.94
Male Gender, % 48.25 43.63 44.30 47.53 42.31 50.44 54.72 33.62
Education < High School, % 7.28 18.05 10.12 13.54 14.09 13.05 7.66 16.16
Income <$20,000, % 10.66 22.96 14.19 18.26 15.21 17.89 11.18 21.03
No Exercise Activity, % 22.22 41.80 31.45 38.51 32.94 36.67 28.00 37.62
BMI (kg/m2), Median (IQR) 26.9 (24.2-30.3) 29.1 (25.7-33.9) 28.8 (25.8-33.2) 27.9 (24.3-32.1) 28.4 (24.9-32.4) 28.3 (24.9-32.3) 26.5 (23.6-29.3) 30.1 (26.2-34.9)
Current Smoking Status, % 10.75 22.71 11.14 17.04 15.74 14.17 14.31 14.59
Heavy Alcohol Consumption, % 4.65 2.45 2.46 4.35 5.68 2.82 4.03 4.51
Medication Use, %
NSAIDs – Aspirin 44.06 41.66 41.78 44.78 40.94 48.46 44.79 40.96
Statins 32.21 34.11 31.17 38.70 31.27 36.75 33.39 32.28
Comorbid Conditions, %
Atrial fibrillation 10.50 8.30 6.57 10.67 7.08 9.34 8.70 8.47
Chronic lung disease 6.29 7.93 8.73 8.94 7.21 10.63 8.14 8.79
Coronary artery disease 14.79 20.98 10.38 21.76 13.31 19.86 17.76 19.36
Deep vein thrombosis 4.72 8.29 6.00 4.76 5.48 7.03 4.08 6.85
Diabetes 20.46 29.58 17.53 29.83 20.47 28.56 20.67 25.67
Dyslipidemia 54.64 59.45 54.05 65.14 54.37 64.83 55.72 60.53
Hypertension 46.44 68.44 55.24 62.76 54.23 64.79 47.82 68.48
Myocardial infarction 11.00 17.30 8.51 14.93 10.01 15.09 13.52 14.86
Peripheral artery disease 0.60 2.58 1.47 1.29 2.12 1.84 1.08 2.34
Stroke 2.74 6.38 4.01 9.53 5.19 6.66 6.45 5.22
Comorbidity Score, Mean (SD) 1.78 (1.39) 2.41 (1.49) 1.87 (1.41) 2.39 (1.46) 1.95 (1.46) 2.39 (1.48) 1.91 (1.43) 2.36 (1.47)
% – Denotes weighted column percentages 

IQR – Interquartile range

Comorbidity score is total of comorbidities, presented as mean and standard deviation (SD).

Due to statistical weighting, most p values are significantly <0.01.

Table 4. Hazard ratios (HR) and 95% confidence intervals (CI) of inflammatory biomarkers for cancer deaths (n=86, weighted n = 1340) among all participants
  Log-Transformed T1 T2 T3
IL-6 (pg/mL), N (Cases) 618 (16) 619 (27) 619 (43)
Weighted N (Cases) 10,796 (279) 8449 (352) 7652 (709)
Crude 3.15 (2.11-4.72) Referent 2.29 (1.22-4.31) 4.41 (2.45-7.95)
Model 1 3.91 (2.49-6.15) 0.97 (0.37-2.57) 4.99 (2.30-2.57)
Model 2 4.70 (2.86-7.71) 1.31 (0.47-3.62) 6.66 (2.70-16.43)
Model 3 4.05 (2.25-7.31) 2.55 (0.72-9.05) 12.97 (3.46-48.63)
IL-8 (pg/mL), N (Cases)   618 (20) 620 (27) 618 (39)
Weighted N (Cases)   9668 (233) 8697 (338) 8533 (769)
Crude 1.45 (1.11-1.90) Referent 1.56 (0.85-2.84) 2.21 (1.26-3.87)
Model 1 2.00 (0.92-4.34) 0.92 (0.32-2.62) 2.52 (1.04-6.12)
Model 2 2.16 (1.05-4.43) 0.77 (0.29-2.10) 2.63 (1.13-6.10)
Model 3 2.37 (1.14-4.91) 0.60 (0.23-1.60) 2.21 (0.86-5.71)
IL-10 (pg/mL), N (Cases)   619 (33) 618 (22) 619 (31)
Weighted N (Cases)   9665 (482) 8530 (342) 8702 (516)
Crude 0.96 (0.73-1.26) Referent 0.75 (0.43-1.32) 1.12 (0.67-1.86)
Model 1 1.52 (1.16-2.00) 1.46 (0.56-3.79) 1.90 (0.90-4.02)
Model 2 1.51 (1.15-1.98) 1.14 (0.44-2.93) 1.86 (0.91-3.80)
Model 3 1.77 (1.26-2.48) 1.87 (0.76-4.61) 3.06 (1.35-6.89)
CRP (mg/L), N (Cases)   619 (26) 618 (30) 618 (30)
Weighted N (Cases) 9617 (362) 8933 (499) 8347 (479)
Crude 1.18 (0.97-1.43) Referent 1.28 (0.74-2.20) 1.49 (0.86-2.58)
Model 1 1.10 (0.84-1.44) 1.04 (0.44-2.43) 0.84 (0.38-1.89)
Model 2 1.19 (0.90-1.59) 1.20 (0.50-2.88) 1.13 (0.44-2.94)
Model 3 1.16 (0.82-1.64) 1.41 (0.48-4.17) 1.30 (0.45-3.77)
Model 1: Adjusted for age, gender, education, income, and site 

Model 2: Additionally adjusted for race

Model 3: Additionally adjusted for exercise activity, BMI, smoking status, alcohol use, and comorbidity score

T1: 1st tertile; T2: 2nd tertile; T3: 3rd tertile

Bold indicates significance at 0.05 alpha level.

Table 5. Hazard ratios (HR) and 95% confidence intervals (CI) of obesity-related cancer deaths by baseline inflammatory biomarkers
IL-6 (pg/mL), N (Cases) 1856 (32)
Weighted N (Cases) 26,897 (429)
Crude 3.52 (1.86-6.67)
Model 1 2.87 (1.44-5.69)
Model 2 3.33 (1.61-6.87)
IL-8 (pg/mL), N (Cases) 1854 (32)
Weighted N (Cases) 26,898 (429)
Crude 1.53 (1.01-2.31)
Model 1 1.38 (0.79-2.40)
Model 2 1.38 (0.76-2.50)
IL-10 (pg/mL), N (Cases) 1856 (32)
Weighted N (Cases) 26,897 (429)
Crude 0.78 (0.46-1.35)
Model 1 0.58 (0.28-1.21)
Model 2 0.58 (0.27-1.23)
CRP (mg/L), N (Cases) 1856 (32)
Weighted N (Cases) 26,897 (429)
Crude 0.98 (0.74-1.29)
Model 1 0.97 (0.70-1.35)
Model 2 0.97 (0.69-1.37)
Model 1: Adjusted for age, gender, education, and income 

Model 2: Additionally adjusted for race

Bold indicates significance at 0.05 alpha level

Table 6. Hazard ratios (HR) and 95% confidence intervals (CI) of inflammatory biomarkers for cancer deaths, stratified by race/ethnicity
    African-American participants 

(N =813, weighted N = 11,145)

White participants 

(N =1043, weighted N = 15,752)

  P Log-Transformed T3 Log-Transformed T3
IL-6 (pg/mL), N (Cases) 304 (21) 315 (22)
Weighted N (Cases) 3899 (341)   3753 (368)
Crude   3.14 (1.79-5.52) 6.03 (2.05-17.78) 3.06 (1.72-5.45) 3.90 (1.88-8.08)
Model 1 0.749 4.57 (1.44-14.49) 4.04 (0.49-33.20) 3.34 (1.52-7.35) 3.61 (1.02-12.83)
Model 2 0.041 3.88 (1.17-12.88) 5.66 (0.58-55.52) 5.25 (1.24-22.31)
IL-8 (pg/mL), N (Cases) 300 (23) 318 (16)
Weighted N (Cases) 3687 (450)   4846 (320)
Crude   2.00 (1.26-3.16) 2.45 (1.13-5.30) 1.17 (0.82-1.66) 1.93 (0.85-4.38)
Model 1 0.223 0.99 (0.52-1.91) 3.93 (0.92-16.69) 2.19 (0.69-6.94) 1.83 (0.56-5.95)
Model 2 0.013 2.66 (0.54-13.13) 1.88 (0.19-18.63) 1.26 (0.21-7.72) 1.58 (0.25-9.92)
IL-10 (pg/mL), N (Cases) 281 (16) 338 (15)
Weighted N (Cases) 3539 (244)   5163 (272)
Crude 0.82 (0.53-1.27) 1.18 (0.58-2.39) 1.09 (0.82-1.45) 1.07 (0.52-2.23)
Model 1 0.119 1.08 (0.59-1.99) 0.51 (0.15-1.82) 1.94 (1.24-3.04) 5.29 (1.51-18.56)
Model 2 0.093 1.48 (0.61-3.61) 1.56 (0.23-10.53) 1.97 (0.89-4.34) 6.18 (0.94-40.39)
CRP (mg/L), N (Cases) 325 (18) 294 (12)
Weighted N (Cases)     4252 (300) 4095 (179)
Crude 1.14 (0.85-1.53) 1.50 (0.69-3.27) 1.18 (0.93-1.51) 1.31 (0.59-2.90)
Model 1 0.915 1.06 (0.70-1.60) 0.33 (0.08-1.45) 1.41 (0.79-2.52) 3.43 (0.68-17.22)
Model 2 0.426 0.92 (0.55-1.54) 0.30 (0.03-3.36) 1.59 (0.44-5.74)
Model 1: Adjusted for age, gender, education, income, and site 

Model 2: Additionally adjusted for exercise activity, BMI, smoking status, alcohol use, and comorbidity score

T3: 3rd tertile vs. 1st tertile (referent) association

Bold indicates significance at 0.05 alpha level. P is the p-value for interaction between the log of the biomarker and race.

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