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    2020-08-14

    
    variation = 8.09%). CRP was measured in batches utilizing a validated high-sensitivity particle-enhanced immunonephelometric assay on the BN II nephelometer (High Sensitivity CRP, Dade Behring Inc., Deerfield, IL), (sensitivity = 0.16 μg/ml, intra-assay-coefficient of varia-tion ranged from 2.3% to 4.4%, and inter-assay coefficient of variation = 2.1% to 5.7%). Adiponectin and resistin were measured using the Human Serum Adipokine Panel A LINCOplex Kit (Linco Research, Inc., St. Charles, MO) (for adiponectin: sensitivity = 80.3 pg/ml, inter-assay co-efficient of variation ranged from 5.68 to 8.20%, and for resistin: sensi-tivity = 4.5 pg/ml and inter-assay coefficient of variation ranged from 8.04% to 9.42%). Lp(a) was measured with the BN II nephelometer utiliz-ing a particle-enhanced immunonephelometric assay (N Latex Lipoprotein-a, Siemens Healthcare Diagnostics, Deerfield, IL) (sensitiv-ity = 0.002 g/Ll, inter-assay coefficient of variation ranged from 6.10% to 10.28%). For LP(a), no cross-reactivity with apolipoprotein B (b1%) and plasminogen (b5%) was observed.
    Study covariates included baseline demographic variables such as age (continuous), gender (male/female), education (college graduate, some college, high school graduate or less than high school), 3-Deazaneplanocin A (Black/White), and income (≥$75K, $35K–$74K, $20K–$34K, b$20K or refused). Additionally, analyses adjusted for baseline data on exercise (≥4 times/week, 1–3 times/week or none), BMI (kg/m2), smoking (cur-rent, past, or never smoker), alcohol intake (heavy, moderate, or none), comorbidity score (number of comorbidities- score ranging from 0 to 7), regular aspirin use (yes/no), and statin use (yes/no).
    2.4. Cancer mortality
    Cancer mortality was identified through semi-annual telephone follow-up, death information from participants' proxies, linkages with the Social Security Death Index (SSDI) and the National Death Index (NDI). Date of death was confirmed using death certificates, SSDI and/ or NDI. A committee of experts adjudicated the cause of death using all available information as recommended by national guidelines [20]. Follow-up data for this analysis was available through December 31, 2015.
    2.5. Statistical analysis
    Chi-squared tests were used to compare baseline categorical partic-ipants' characteristics by BMI category i.e. obese/overweight (BMI ≥ 25 kg/m2) and normal BMI (BMI = 18.5–24.9 kg/m2), and t-tests were used to compare continuous variables by BMI category. Weighted Cox proportional hazard regression analysis was used to compare the
    risk of cancer mortality by levels of metabolic and inflammatory bio-markers stratified by BMI category in models sequentially adjusted for potential confounding variables. Robust sandwich estimation method was used to estimate the confidence intervals around the hazard ratio. The crude model included each of the main exposure variable (i.e. IL-6, IL-8, IL-10, CRP, adiponectin, leptin, resistin or Lp(a)) and age. Model 1 further adjusted for sex, BMI, education, income, and race. Model 2 (the main analytical model) further adjusted for exercise. Model 3 additionally adjusted for alcohol, smoking, aspirin and statin use, and comorbidity score. Since the continuous exposure variables were not normally distributed, notochord were log-transformed. Statistical
    interactions between log-transformed biomarker levels and BMI were tested using the likelihood ratio test and interaction p-values ≤0.1 were considered statistically significant. In addition, participants were ranked into tertiles according to the levels of biomarkers and the highest tertiles were compared to the lowest for risk of cancer mortality. Associations between the exposure variables and mortality were con-sidered statistically significant if the 95% confidence intervals (95% CIs) do not include the null value (1.0) or if the p-values are ≤0.05,
    and for interaction terms, if the p-values are ≤0.1. The interactions be-tween BMI and each biomarker in the non-stratified crude models were statistically significant at α = 0.1, therefore results from BMI strat-ified models were presented. Participants were censored at the date of death, loss to follow-up or December 31, 2015, whichever happened first. All analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA.)
    3. Results
    At baseline, compared with participants with normal BMI, those with overweight or obesity were younger, more likely to be male and Black, have less than high school education, and higher average comor-bidity score (all p-values b 0.05, Table 1). Participants with normal BMI tended to have higher IL-8 and adiponectin levels, while those with overweight or obesity tended to have higher CRP, IL-6, and Lp(a) (p-value b 0.05). Distribution of IL-10 and resistin values were not signifi-cantly different between BMI categories (Table 2), however a higher proportion of participants with normal BMI were in the highest tertile of IL-6 and CRP compared with participants with overweight/obesity, Fig. 1. Participants with normal BMI also tended to be in the highest tertile of adiponectin compared with participants with overweight/obe-sity, while those with overweight/obesity tended to be in the highest tertile of leptin, Fig. 1.