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  • successful smoking cessation group P br ACCEPTED MANUSCRIPT br Table


    successful smoking cessation group, P<0.001.
    Table 2 Genotypes in the three groups
    Never smoking Failure to quit Successful smoking
    Genotypes group smoking group cessation group P
    Data in parentheses are N (%).
    Contents lists available at ScienceDirect
    Metabolism Clinical and Experimental
    Association between obesity and biomarkers of L-NAME and metabolism with cancer mortality in a prospective cohort study
    Daniel T. Dibaba a,b, Suzanne E. Judd c, Susan C. Gilchrist d, Mary Cushman e, Maria Pisu f, Monika Safford g, Tomi Akinyemiju a,b,h,
    a Department of Epidemiology, University of Kentucky, Lexington, KY, USA
    b Markey Cancer Center, University of Kentucky, Lexington, KY, USA
    c Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
    d Department of Clinical Cancer Prevention and Cardiology, University of Texas MD, Anderson Cancer Center, Houston, TX, USA
    e Department of Medicine, University of Vermont Cancer Center, Larner College of Medicine at the University of Vermont, Burlington, VT, USA
    f Division of Preventive Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
    g Department of Medicine, Weill Cornell Medical College, New York, NY, USA
    h Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA
    Article history:
    Inflammatory cytokines
    Metabolic biomarkers
    Cancer mortality
    Objective: To investigate the association between biomarkers of inflammation and metabolic dysregulation and cancer mortality by obesity status. Methods: Data from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort was used to examine the associations between baseline biomarkers of inflammation (IL-6, IL-8, IL-10, and CRP) and metabo-lism (adiponectin, resisting and lipoprotein (a)) with cancer mortality among 1822 participants cancer-free at baseline. Weighted Cox proportional hazard regression with the robust sandwich method was used to estimate the hazard ratios and 95% confidence intervals (CIs) adjusting for baseline covariates and stratified by BMI (nor-mal, overweight/obese) given the significant interaction between biomarkers and BMI (p b 0.1).
    Results: During a mean follow-up of 8 years, there were statistically significant associations between cancer mor-
    fold (HR: 3.5; 95% CI: 1.5, 8.1) increased risk of cancer mortality among participants with overweight/obesity; however, neither CRP nor resistin was significantly associated with cancer mortality in this group. Conclusions: Higher baseline inflammatory and metabolic biomarkers were associated with significantly in-creased risk of cancer mortality after adjusting for baseline risk factors and the associations varied by BMI. Cancer patients may benefit from interventions that modulate inflammatory and metabolic biomarkers. © 2019 Elsevier Inc. All rights reserved.
    1. Introduction
    Established risk factors for cancer include obesity, physical inactivity, smoking, and alcohol use [1–4]. These risk factors are associated with chronic inflammation and metabolic dysregulation, and growing evi-dence links these mechanisms with development, progression and mor-tality of cancer [5–7]. Obesity, specifically, has long been associated with increased risk of cancer mortality [8], every 5 kg/m2 increase in BMI has
    Abbreviations: BMI, body mass index; IL-6, interleukin-6; IL-8, interleukin-8; IL-10, interleukin-10; CRP, C-reactive protein; Lp(a), lipoprotein (a); REGARDS, Reasons for Geographic and Racial Disparities in Stroke; SSDI, Social Security Death Index; NDI, National Death Index; HR, hazard ratio; CI, confidence interval.