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  • Schwartz AG Cote ML Wenzlaff AS Land S

    2020-08-06

    22. Schwartz AG, Cote ML, Wenzlaff AS, Land S, Amos CI. Racial differences in the
    association between SNPs on 15q25.1, smoking behavior, and risk of non-small cell lung cancer. Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer. 2009;4:1195-1201. 23. Bierut LJ. Nicotine dependence and genetic variation in the nicotinic receptors. Drug and
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    24. Tomaz PRX, Santos JR, Scholz J, et al. Cholinergic receptor nicotinic alpha 5 subunit polymorphisms are associated with smoking cessation success in women. BMC medical genetics. 2018;19:55. 25. Sasaki H, Hikosaka Y, Okuda K, et al. CHRNA5 Gene D398N Polymorphism in Japanese Lung Adenocarcinoma. Journal of Surgical Research. 2010;162:75-78.
    26. Islam MS, Ahmed MU, Sayeed MS, et al. Lung cancer risk in relation to nicotinic Bortezomib (PS-341) receptor, CYP2A6 and CYP1A1 genotypes in the Bangladeshi population. Clinica chimica acta; international journal of clinical chemistry. 2013;416:11-19. 27. Thunnissen FB. Acetylcholine Receptor Pathway and Lung Cancer. Journal of Thoracic Oncology. 2009;4:943-946.
    28. Hopkins RJ, Young RP. Gene by Environment Interaction Linking the Chromosome 15q25 Locus With Cigarette Consumption and Lung Cancer Susceptibility — Are African American Affected Differently? EBioMedicine. 2016;4:13-14.
    29. Niu X, Chen Z, Shen S, et al. Association of the CHRNA3 Locus with Lung Cancer Risk and Prognosis in Chinese Han Population. Journal of Thoracic Oncology. 2010;5:658-666.
    30. Carcereny E, Ramirez JL, Sanchez-Ronco M, et al. Blood-based CHRNA3 single nucleotide polymorphism and outcome in advanced non-small-cell lung cancer patients. Lung Cancer. 2010;68:491-497.
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    Table 1 Characteristics of three groups of lung cancer patients
    Never smoking Failure to quit Successful
    smoking
    Variable group smoking group
    P
    Educational level (n, %)
    Less than high school diploma 1 (1.2)
    Marital status
    SCLC
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    successful smoking cessation group, P<0.001.
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    Table 2 Genotypes in the three groups
    Never smoking Failure to quit Successful smoking
    Genotypes group smoking group cessation group P
    AA
    Data in parentheses are N (%).
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    Contents lists available at ScienceDirect
    Metabolism Clinical and Experimental
    Association between obesity and biomarkers of inflammation 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:
    Keywords:
    Inflammatory cytokines
    Metabolic biomarkers
    Cancer mortality
    Obesity 
    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.