Coffee consumption and risk of renal cell carcinoma in the PLCO cohort
Principal Investigator
Name
Jongeun Rhee
Degrees
ScD, MS
Institution
NCI
Position Title
Postdoc fellow
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-632
Initial CDAS Request Approval
Jun 9, 2020
Title
Coffee consumption and risk of renal cell carcinoma in the PLCO cohort
Summary
Coffee consumption has been associated with a reduced risk of some cancers, but the evidence for renal cell carcinoma (RCC) is inconclusive. A recent meta-analysis of 22 observational studies (16 case-control and six cohort studies) (1) was null, although in an analysis restricted to cohort studies a weak inverse relationship was observed. A previous investigation in PLCO cohort among participants who completed the DQX questionnaire with follow-up through 2011, observed a non-significant inverse association with RCC (n=318; relative risk 0.84, 95% CI 0.65,1.09 for ≥ 2 cups of coffee intake per day vs non-drinking; P for trend=0.17) (2). We aim to conduct updated analyses of DHQ respondents with the most recent available follow-up data to clarify this relationship, extending follow-up by 9 years and including 496 additional RCC cases.
We will exclude individuals who were diagnosed with cancer (except non-melanoma skin cancer) prior to baseline and with missing information on coffee intake. Usual coffee intake over the prior 12 months from DHQ will be assessed using ten frequency categories, ranging from none to ≥6 cups per day. We will also extract information on whether participants drank caffeinated or decaffeinated coffee and caffeine intake (mg/day) using nutrient database. Follow-up time will be calculated from the date of DHQ completed until the first diagnosis of RCC, death, or the end of follow-up, whichever came first.
We will fit Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for coffee intake (vs non-drinkers) with RCC using years of follow-up as the underlying time metric. In our analysis, we will adjust for sex, race/ethnicity, BMI, marriage status, education, cigarette pack-years, alcohol consumption, self-reported hypertension, and self-reported diabetes. We will also model coffee intake as a continuous variable based on the midpoint of coffee intake categories (≤ 1 cup/day, 1 cup/day, 2-3 cups/day, ≥ 4 cups/day) and calculate a Wald statistic as a test for trend.
In addition, we will conduct stratified analyses by sex, race/ethnicity, smoking, BMI, self-reported history of diabetes, self-reported history of hypertension, alcohol consumption and subgroup analyses by caffeine content (caffeinated or decaffeinated coffee) and RCC subtype (clear cell vs. non-clear cell histology). To identify potential effect modifiers, we will include the interaction terms between coffee intake as a continuous midpoint variable and each level of the stratifying variable in the multivariable adjusted model. Finally, we will conduct sensitivity analyses by repeating analyses for different follow-up periods and restricting to coffee drinkers (using ≤ 1 cup/day as the referent category in the analysis).
References
1. Wijarnpreecha K, Thongprayoon C, Thamcharoen N, Panjawatanan P, Cheungpasitporn W. Association between coffee consumption and risk of renal cell carcinoma: a meta‐analysis. Intern Med J. 2017;47:1422-32.
2. Hashibe M, Galeone C, Buys SS, Gren L, Boffetta P, Zhang Z-F, et al. Coffee, tea, caffeine intake, and the risk of cancer in the PLCO cohort. Br J Cancer. 2015;113:809.
We will exclude individuals who were diagnosed with cancer (except non-melanoma skin cancer) prior to baseline and with missing information on coffee intake. Usual coffee intake over the prior 12 months from DHQ will be assessed using ten frequency categories, ranging from none to ≥6 cups per day. We will also extract information on whether participants drank caffeinated or decaffeinated coffee and caffeine intake (mg/day) using nutrient database. Follow-up time will be calculated from the date of DHQ completed until the first diagnosis of RCC, death, or the end of follow-up, whichever came first.
We will fit Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for coffee intake (vs non-drinkers) with RCC using years of follow-up as the underlying time metric. In our analysis, we will adjust for sex, race/ethnicity, BMI, marriage status, education, cigarette pack-years, alcohol consumption, self-reported hypertension, and self-reported diabetes. We will also model coffee intake as a continuous variable based on the midpoint of coffee intake categories (≤ 1 cup/day, 1 cup/day, 2-3 cups/day, ≥ 4 cups/day) and calculate a Wald statistic as a test for trend.
In addition, we will conduct stratified analyses by sex, race/ethnicity, smoking, BMI, self-reported history of diabetes, self-reported history of hypertension, alcohol consumption and subgroup analyses by caffeine content (caffeinated or decaffeinated coffee) and RCC subtype (clear cell vs. non-clear cell histology). To identify potential effect modifiers, we will include the interaction terms between coffee intake as a continuous midpoint variable and each level of the stratifying variable in the multivariable adjusted model. Finally, we will conduct sensitivity analyses by repeating analyses for different follow-up periods and restricting to coffee drinkers (using ≤ 1 cup/day as the referent category in the analysis).
References
1. Wijarnpreecha K, Thongprayoon C, Thamcharoen N, Panjawatanan P, Cheungpasitporn W. Association between coffee consumption and risk of renal cell carcinoma: a meta‐analysis. Intern Med J. 2017;47:1422-32.
2. Hashibe M, Galeone C, Buys SS, Gren L, Boffetta P, Zhang Z-F, et al. Coffee, tea, caffeine intake, and the risk of cancer in the PLCO cohort. Br J Cancer. 2015;113:809.
Aims
• To investigate an association between coffee consumption and risk of RCC
o To examine whether known risk factors of RCC e.g. sex, race/ethnicity, smoking, BMI, history of diabetes, history of hypertension, alcohol consumption modify an association
o To examine whether an association differs by caffeine content and RCC subtype
Collaborators
Mark P. Purdue, PhD, Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute