• 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • 2020-03
  • 2020-07
  • 2020-08
  • Elsevier Inc All rights reserved br Introduction


    © 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 EPZ-6438 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.
    Corresponding author at: Department of Epidemiology, University of Kentucky, 111 Washington Ave, Lexington, KY 40508, USA.
    E-mail address: [email protected] (T. Akinyemiju).
    been shown to increase the risk of cancer mortality by about 10% [9]. Furthermore, obesity is associated with adipose tissue dysfunction and chronic low-grade inflammation that leads to worse prognosis in cancer patients. However, there is also evidence of adipose tissue inflammation and pro-tumorigenic consequences in some lean individuals [10]. Metabolism-related biomarkers [11–13] such as adiponectin, resistin, and lipoprotein (a) (Lp(a)), and inflammation-related cytokines [5,6] such as interleukin (IL)-6, IL-8, IL-10 and C-reactive protein (CRP) have also been shown to reflect a fertile, pro-tumorigenic inflammatory mi-croenvironment that promotes tumor initiation, angiogenesis, and me-tastasis [14].
    Recent studies, including by our group, suggests that metabolic health status may be more clinically and epidemiologically important for cancer risk and mortality than obesity alone [15,16]. Increased risk of cancer mortality has been observed in normal BMI individuals with
    metabolic dysregulation, an association not consistently observed among obese individuals who are metabolically healthy- a phenome-non termed ‘metabolic healthy obesity’ [15]. While the prevalence of obesity has increased significantly among US adults [17], it is important to better delineate the role of obesity, metabolic dysregulation and chronic inflammation in cancer risk. This helps to improve risk predic-tion and stratification, target appropriate clinical and interventions strategies based on precise biomarkers, and reduce reliance on the crude measure of BMI as a predictor of cancer mortality risk. The aim of the present study was to investigate the role of pre-diagnostic meta-bolic and inflammatory biomarkers in the risk of cancer mortality by obesity status, and to assess whether racial disparities exist in this asso-ciation given the higher risk of cancer mortality, obesity, and associated conditions among Blacks.
    2. Material and methods
    2.1. Study participants
    Data for this study was obtained from the Reasons for Geographic and Racial Disparities in Stroke (REGARDS) study. REGARDS is a pro-spective cohort study of Black and White participants recruited nation-ally in the United States between 2003 and 2007, with oversampling of Blacks and residents of the Stroke Belt (South Carolina, North Carolina, Tennessee, Georgia, Louisiana, Arkansas, Mississippi, and Alabama). De-tailed data on demographics, health behaviors, and history of comorbid conditions were collected at baseline using a computer-assisted tele-phone interview. Blood sample collection after 10–12 hour of overnight fasting, echocardiography, and physical measurements including height and weight were conducted during initial in-home visits by trained staff following informed consent. Overall, 30,239 participants aged ≥45 years at baseline, 55% female, 42% Black, and 50% from the Stroke Belt region were recruited. The REGARDS study is ongoing and it is described in de-tail elsewhere [18,19]. In the present analysis, 1822 individuals who were cancer-free at baseline and selected into a sub-cohort with avail-able inflammatory and metabolic biomarker data were included. The sub-cohort was selected as a stratified random sample defined by equal distribution by race (Black/White) and sex, age (20% from each 10-year interval from 45 to 64, 25% from each 10-year interval from 65 to 84, and 10% from those over 84 years old), and region (50% from Stroke Belt) using study weights created based on the inverse probabil-ity of being selected. REGARDS participants were followed-up every 6 months for deaths, hospitalizations ormedical events, and cause of death was ascertained using death certificates, medical records, and/or interviewed proxies. All participating institutional review boards ap-proved the REGARDS study.
    2.2. Exposure variables
    Main exposure variables of interest in this study were inflammatory biomarkers – IL-6, IL-8, IL-10, and CRP, and metabolic biomarkers – adiponectin, resistin, and Lp(a). Biomarkers were analyzed from blood samples that were collected during the baseline in-home visit, centri-fuged, separated and shipped overnight on gel ice packs to the central laboratory (Laboratory for Clinical Biochemistry Research at the Univer-sity of Vermont). Ultra-sensitive ELISA (Quantikine HS Human IL-6 Immunoassay, R&D Systems, Minneapolis, MN) was used to measure IL-6 (minimum detectable dose/sensitivity = 0.031 pg/ml and inter-assay coefficient of variation = 6.3%) and no significant cross-reactivity was observed. The Human Serum Adipokine Panel B LINCOplex Kit (Linco Research, Inc., St. Charles, MO) was used to mea-sure IL-8 (sensitivity = 0.20 pg/ml, intra-assay coefficient of variation ranged from 1.4% to 7.9%, and inter-assay-coefficient of variation was b21%). Milliplex MAP Human Cardiovascular Disease Panel 3 (Millipore Corporation, Billerica, MA) run as a singleplex assay was used to mea-sure IL-10 (sensitivity = 0.30 pg/ml, average analytical coefficient of