Author Manuscript Published OnlineFirst on September 23, 2014; DOI: 10.1158/1055-9965.EPI-14-0699-T Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Body mass index, physical activity, and serum markers of inflammation, immunity, and insulin resistance Cari M. Kitahara, Britton Trabert, Hormuzd A. Katki, et al. Cancer Epidemiol Biomarkers Prev Published OnlineFirst September 23, 2014.
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Author Manuscript Published OnlineFirst on September 23, 2014; DOI: 10.1158/1055-9965.EPI-14-0699-T Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Body mass index, physical activity, and serum markers of inflammation, immunity, and insulin resistance Cari M. Kitahara1, Britton Trabert1, Hormuzd A. Katki1, Anil K. Chaturvedi1, Troy J. Kemp2, Ligia A. Pinto2, Steven C. Moore1, Mark P. Purdue1, Nicolas Wentzensen1, Allan Hildesheim1, Meredith S. Shiels1 1
Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA HPV Immunology Laboratory, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA 2
Running title: Body mass index, physical activity, inflammation To whom correspondence should be addressed: Cari M. Kitahara, PhD, MHS Division of Cancer Epidemiology and Genetics 9609 Medical Center Drive, Rm. 7E-566, Bethesda, MD 20892-9774 P: 240-276-7406; E-mail: [email protected] Financial disclosures: This work was supported in part by the Intramural Research Program of the National Cancer Institute, National Institutes of Health. Conflicts of interest: None
1
Downloaded from cebp.aacrjournals.org on October 1, 2014. © 2014 American Association for Cancer Research.
Author Manuscript Published OnlineFirst on September 23, 2014; DOI: 10.1158/1055-9965.EPI-14-0699-T Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
ABSTRACT Background: Epidemiologic studies examining circulating levels of inflammatory markers in relation to obesity and physical inactivity may aid in our understanding of the role of inflammation in obesity-related cancers. However, previous studies on this topic have focused on a limited set of markers. Methods: We evaluated associations between body mass index (BMI) and vigorous physical activity level, based on self-report, and serum levels of 78 inflammation-related markers. Markers were measured using a bead-based multiplex method among 1,703 men and women, ages 55-74 years and with no prior history of cancer at blood draw, selected for case-control studies nested within the Prostate, Lung, Ovarian, and Colorectal Cancer Screening Trial. Analyses were adjusted for age, sex, smoking, case-control study, physical activity, and BMI. Results: Twelve markers were positively associated with BMI after False Discovery Rate (FDR) correction. Odds ratios (ORs) and 95% confidence interval (CIs) for highest versus lowest levels of CCL2/MCP-1, CXCL5/ENA-78, sTNFR-II, CXCL10/IP-10, CXCL6/GCP2, CCL13/MCP-4, amylin, CRP, C-peptide, CCL19/MIP-3b, insulin, and leptin were 1.50 (1.14-1.98), 1.52 (1.122.05), 1.61 (1.17-2.20), 1.69 (1.25-2.28), 1.74 (1.24-2.44), 1.75 (1.22-2.50), 1.91 (1.31-2.78), 2.41 (1.36-4.25), 2.78 (1.83-4.24), 3.30 (2.28-4.78), 4.05 (2.51-6.55), 50.03 (19.87-125.99) per 5-kg/m2, respectively. Only CXCL12/SDF-1a was associated with physical activity (≥3 versus 0.8 in 91% of the markers in the lung and 6
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Author Manuscript Published OnlineFirst on September 23, 2014; DOI: 10.1158/1055-9965.EPI-14-0699-T Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
NHL studies and in 78% of the markers in the ovarian cancer study. Eight markers with >90% of values below the lowest limit of detection (LLOD) were excluded from all analyses, resulting in 78 evaluable markers.
Statistical Analysis As shown in Table 1, the inclusion and matching criteria, as well as the number of markers measured, differed across the nested case-control studies. To combine data from these studies, we accounted for these differences and made our analysis as representative as possible of the entire non-Hispanic white PLCO screening arm from which the case and control participants were sampled (as demonstrated in Table 3) by developing sets of propensity-score adjusted sampling weights for each participant (19, 20). The sampling weights allowed us to include all participants with marker data (including cancer cases) (see Supplementary Methods for details). Because markers were either measured in all three studies, in combinations of two studies, or the lung cancer study only, multi-study weights were used in analyses of these markers. To calculate the multi-study weights, screening arm participants who met the eligibility criteria of the combined dataset (i.e., completed the baseline questionnaire, gave biochemical consent, non-Hispanic white, no personal history of any cancer prior to randomization, and complete smoking history) were included in logistic regression models to estimate the probability of being selected into one of the case-control studies, including terms for age group [
Body mass index, physical activity, and serum markers of inflammation, immunity, and insulin resistance Cari M. Kitahara, Britton Trabert, Hormuzd A. Katki, et al. Cancer Epidemiol Biomarkers Prev Published OnlineFirst September 23, 2014.
Updated version Supplementary Material Author Manuscript
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Access the most recent version of this article at: doi:10.1158/1055-9965.EPI-14-0699-T Access the most recent supplemental material at: http://cebp.aacrjournals.org/content/suppl/2014/09/24/1055-9965.EPI-14-0699-T.DC1.html Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Sign up to receive free email-alerts related to this article or journal. To order reprints of this article or to subscribe to the journal, contact the AACR Publications Department at [email protected]. To request permission to re-use all or part of this article, contact the AACR Publications Department at [email protected].
Downloaded from cebp.aacrjournals.org on October 1, 2014. © 2014 American Association for Cancer Research.
Author Manuscript Published OnlineFirst on September 23, 2014; DOI: 10.1158/1055-9965.EPI-14-0699-T Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Body mass index, physical activity, and serum markers of inflammation, immunity, and insulin resistance Cari M. Kitahara1, Britton Trabert1, Hormuzd A. Katki1, Anil K. Chaturvedi1, Troy J. Kemp2, Ligia A. Pinto2, Steven C. Moore1, Mark P. Purdue1, Nicolas Wentzensen1, Allan Hildesheim1, Meredith S. Shiels1 1
Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA HPV Immunology Laboratory, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA 2
Running title: Body mass index, physical activity, inflammation To whom correspondence should be addressed: Cari M. Kitahara, PhD, MHS Division of Cancer Epidemiology and Genetics 9609 Medical Center Drive, Rm. 7E-566, Bethesda, MD 20892-9774 P: 240-276-7406; E-mail: [email protected] Financial disclosures: This work was supported in part by the Intramural Research Program of the National Cancer Institute, National Institutes of Health. Conflicts of interest: None
1
Downloaded from cebp.aacrjournals.org on October 1, 2014. © 2014 American Association for Cancer Research.
Author Manuscript Published OnlineFirst on September 23, 2014; DOI: 10.1158/1055-9965.EPI-14-0699-T Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
ABSTRACT Background: Epidemiologic studies examining circulating levels of inflammatory markers in relation to obesity and physical inactivity may aid in our understanding of the role of inflammation in obesity-related cancers. However, previous studies on this topic have focused on a limited set of markers. Methods: We evaluated associations between body mass index (BMI) and vigorous physical activity level, based on self-report, and serum levels of 78 inflammation-related markers. Markers were measured using a bead-based multiplex method among 1,703 men and women, ages 55-74 years and with no prior history of cancer at blood draw, selected for case-control studies nested within the Prostate, Lung, Ovarian, and Colorectal Cancer Screening Trial. Analyses were adjusted for age, sex, smoking, case-control study, physical activity, and BMI. Results: Twelve markers were positively associated with BMI after False Discovery Rate (FDR) correction. Odds ratios (ORs) and 95% confidence interval (CIs) for highest versus lowest levels of CCL2/MCP-1, CXCL5/ENA-78, sTNFR-II, CXCL10/IP-10, CXCL6/GCP2, CCL13/MCP-4, amylin, CRP, C-peptide, CCL19/MIP-3b, insulin, and leptin were 1.50 (1.14-1.98), 1.52 (1.122.05), 1.61 (1.17-2.20), 1.69 (1.25-2.28), 1.74 (1.24-2.44), 1.75 (1.22-2.50), 1.91 (1.31-2.78), 2.41 (1.36-4.25), 2.78 (1.83-4.24), 3.30 (2.28-4.78), 4.05 (2.51-6.55), 50.03 (19.87-125.99) per 5-kg/m2, respectively. Only CXCL12/SDF-1a was associated with physical activity (≥3 versus 0.8 in 91% of the markers in the lung and 6
Downloaded from cebp.aacrjournals.org on October 1, 2014. © 2014 American Association for Cancer Research.
Author Manuscript Published OnlineFirst on September 23, 2014; DOI: 10.1158/1055-9965.EPI-14-0699-T Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
NHL studies and in 78% of the markers in the ovarian cancer study. Eight markers with >90% of values below the lowest limit of detection (LLOD) were excluded from all analyses, resulting in 78 evaluable markers.
Statistical Analysis As shown in Table 1, the inclusion and matching criteria, as well as the number of markers measured, differed across the nested case-control studies. To combine data from these studies, we accounted for these differences and made our analysis as representative as possible of the entire non-Hispanic white PLCO screening arm from which the case and control participants were sampled (as demonstrated in Table 3) by developing sets of propensity-score adjusted sampling weights for each participant (19, 20). The sampling weights allowed us to include all participants with marker data (including cancer cases) (see Supplementary Methods for details). Because markers were either measured in all three studies, in combinations of two studies, or the lung cancer study only, multi-study weights were used in analyses of these markers. To calculate the multi-study weights, screening arm participants who met the eligibility criteria of the combined dataset (i.e., completed the baseline questionnaire, gave biochemical consent, non-Hispanic white, no personal history of any cancer prior to randomization, and complete smoking history) were included in logistic regression models to estimate the probability of being selected into one of the case-control studies, including terms for age group [
