1. Were the models adjusted for potentially confounding factors? If not, how did they address the potential bias?
2.If you would re-design the study, how would you conduct the study (briefly identify the alternative study design, statistical models, data, or/and covariables)
3.. If the study is for hypothesis testing, what were the authors’ hypotheses?
4. Study design? (Experimental (RCT)? Quasi-experimental? Observational (case-control, cohort longitudinal, cross-sectional…etc)?)
5. What types of statistical models/regressions are used? (Linear regression? Logistics? Etc…)
6. Describe the data type briefly (Claims? Registry? Clinical records? Survey? Etc…)
Association of Coffee Drinking With Mortality by Genetic Variation in Caffeine Metabolism Findings From the UK Biobank Erikka Loftfield, PhD; Marilyn C. Cornelis, PhD; Neil Caporaso, MD; Kai Yu, PhD; Rashmi Sinha, PhD; Neal Freedman, PhD
IMPORTANCE Prospective cohorts in North America, Europe, and Asia show consistent inverse associations between coffee drinking and mortality, including deaths from cardiovascular disease and some cancers. However, concerns about coffee, particularly among people with common genetic polymorphisms affecting caffeine metabolism and among those drinking more than 5 cups per day, remain.
OBJECTIVE To evaluate associations of coffee drinking with mortality by genetic caffeine metabolism score.
DESIGN, SETTING, AND PARTICIPANTS The UK Biobank is a population-based study that invited approximately 9.2 million individuals from across the United Kingdom to participate. We used baseline demographic, lifestyle, and genetic data form the UK Biobank cohort, with follow-up beginning in 2006 and ending in 2016, to estimate hazard ratios (HRs) for coffee intake and mortality, using multivariable-adjusted Cox proportional hazards models. We investigated potential effect modification by caffeine metabolism, defined by a genetic score of previously identified polymorphisms in AHR, CYP1A2, CYP2A6, and POR that have an effect on caffeine metabolism. Of the 502 641 participants who consented with baseline data, we included those who were not pregnant and had complete data on coffee intake and smoking status (n = 498 134).
EXPOSURES Total, ground, instant, and decaffeinated coffee intake.
MAIN OUTCOMES AND MEASURES All-cause and cause-specific mortality. RESULTS The mean age of the participants was 57 years (range, 38-73 years); 271 019 (54%) were female, and 387 494 (78%) were coffee drinkers. Over 10 years of follow-up, 14 225 deaths occurred. Coffee drinking was inversely associated with all-cause mortality. Using non-coffee drinkers as the reference group, HRs for drinking less than 1, 1, 2 to 3, 4 to 5, 6 to 7, and 8 or more cups per day were 0.94 (95% CI, 0.88-1.01), 0.92 (95% CI, 0.87-0.97), 0.88 (95% CI, 0.84-0.93), 0.88 (95% CI, 0.83-0.93), 0.84 (95% CI, 0.77-0.92), and 0.86 (95% CI, 0.77-0.95), respectively. Similar associations were observed for instant, ground, and decaffeinated coffee, across common causes of death, and regardless of genetic caffeine metabolism score. For example, the HRs for 6 or more cups per day ranged from 0.70 (95% CI, 0.53-0.94) to 0.92 (95% CI, 0.78-1.10), with no evidence of effect modification across strata of caffeine metabolism score (P = .17 for heterogeneity).
CONCLUSIONS AND RELEVANCE Coffee drinking was inversely associated with mortality, including among those drinking 8 or more cups per day and those with genetic polymorphisms indicating slower or faster caffeine metabolism. These findings suggest the importance of noncaffeine constituents in the coffee-mortality association and provide further reassurance that coffee drinking can be a part of a healthy diet.
JAMA Intern Med. 2018;178(8):1086-1097. doi:10.1001/jamainternmed.2018.2425 Published online July 2, 2018.
