Erikka Loftfield1, Marilyn C Cornelis2, Neil Caporaso3, Kai Yu4, Rashmi Sinha1, Neal Freedman1. 1. Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland. 2. Feinberg School of Medicine, Northwestern University, Chicago, Illinois. 3. Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland. 4. Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland.
Abstract
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.
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.
Authors: Erikka Loftfield; Neal D Freedman; Barry I Graubard; Kristin A Guertin; Amanda Black; Wen-Yi Huang; Fatma M Shebl; Susan T Mayne; Rashmi Sinha Journal: Am J Epidemiol Date: 2015-11-27 Impact factor: 4.897
Authors: Erikka Loftfield; Meredith S Shiels; Barry I Graubard; Hormuzd A Katki; Anil K Chaturvedi; Britton Trabert; Ligia A Pinto; Troy J Kemp; Fatma M Shebl; Susan T Mayne; Nicolas Wentzensen; Mark P Purdue; Allan Hildesheim; Rashmi Sinha; Neal D Freedman Journal: Cancer Epidemiol Biomarkers Prev Date: 2015-05-21 Impact factor: 4.254
Authors: Susan M Gapstur; Rebecca L Anderson; Peter T Campbell; Eric J Jacobs; Terryl J Hartman; Janet S Hildebrand; Ying Wang; Marjorie L McCullough Journal: Cancer Epidemiol Biomarkers Prev Date: 2017-07-27 Impact factor: 4.254
Authors: Marc J Gunter; Neil Murphy; Amanda J Cross; Laure Dossus; Laureen Dartois; Guy Fagherazzi; Rudolf Kaaks; Tilman Kühn; Heiner Boeing; Krasimira Aleksandrova; Anne Tjønneland; Anja Olsen; Kim Overvad; Sofus Christian Larsen; Maria Luisa Redondo Cornejo; Antonio Agudo; María José Sánchez Pérez; Jone M Altzibar; Carmen Navarro; Eva Ardanaz; Kay-Tee Khaw; Adam Butterworth; Kathryn E Bradbury; Antonia Trichopoulou; Pagona Lagiou; Dimitrios Trichopoulos; Domenico Palli; Sara Grioni; Paolo Vineis; Salvatore Panico; Rosario Tumino; Bas Bueno-de-Mesquita; Peter Siersema; Max Leenders; Joline W J Beulens; Cuno U Uiterwaal; Peter Wallström; Lena Maria Nilsson; Rikard Landberg; Elisabete Weiderpass; Guri Skeie; Tonje Braaten; Paul Brennan; Idlir Licaj; David C Muller; Rashmi Sinha; Nick Wareham; Elio Riboli Journal: Ann Intern Med Date: 2017-07-11 Impact factor: 25.391
Authors: Marilyn C Cornelis; Tim Kacprowski; Cristina Menni; Stefan Gustafsson; Edward Pivin; Jerzy Adamski; Anna Artati; Chin B Eap; Georg Ehret; Nele Friedrich; Andrea Ganna; Idris Guessous; Georg Homuth; Lars Lind; Patrik K Magnusson; Massimo Mangino; Nancy L Pedersen; Maik Pietzner; Karsten Suhre; Henry Völzke; Murielle Bochud; Tim D Spector; Hans J Grabe; Erik Ingelsson Journal: Hum Mol Genet Date: 2016-12-15 Impact factor: 6.150
Authors: Cathie Sudlow; John Gallacher; Naomi Allen; Valerie Beral; Paul Burton; John Danesh; Paul Downey; Paul Elliott; Jane Green; Martin Landray; Bette Liu; Paul Matthews; Giok Ong; Jill Pell; Alan Silman; Alan Young; Tim Sprosen; Tim Peakman; Rory Collins Journal: PLoS Med Date: 2015-03-31 Impact factor: 11.069
Authors: Jason Y Y Wong; Bryan A Bassig; Erikka Loftfield; Wei Hu; Neal D Freedman; Bu-Tian Ji; Paul Elliott; Debra T Silverman; Stephen J Chanock; Nathaniel Rothman; Qing Lan Journal: JNCI Cancer Spectr Date: 2019-12-12
Authors: Irma Karabegović; Eliana Portilla-Fernandez; Yang Li; Jiantao Ma; Silvana C E Maas; Daokun Sun; Emily A Hu; Brigitte Kühnel; Yan Zhang; Srikant Ambatipudi; Giovanni Fiorito; Jian Huang; Juan E Castillo-Fernandez; Kerri L Wiggins; Niek de Klein; Sara Grioni; Brenton R Swenson; Silvia Polidoro; Jorien L Treur; Cyrille Cuenin; Pei-Chien Tsai; Ricardo Costeira; Veronique Chajes; Kim Braun; Niek Verweij; Anja Kretschmer; Lude Franke; Joyce B J van Meurs; André G Uitterlinden; Robert J de Knegt; M Arfan Ikram; Abbas Dehghan; Annette Peters; Ben Schöttker; Sina A Gharib; Nona Sotoodehnia; Jordana T Bell; Paul Elliott; Paolo Vineis; Caroline Relton; Zdenko Herceg; Hermann Brenner; Melanie Waldenberger; Casey M Rebholz; Trudy Voortman; Qiuwei Pan; Myriam Fornage; Daniel Levy; Manfred Kayser; Mohsen Ghanbari Journal: Nat Commun Date: 2021-05-14 Impact factor: 14.919
Authors: Jason Y Y Wong; Rena R Jones; Charles Breeze; Batel Blechter; Nathaniel Rothman; Wei Hu; Bu-Tian Ji; Bryan A Bassig; Debra T Silverman; Qing Lan Journal: Environ Int Date: 2021-06-15 Impact factor: 9.621