Dong Hang1,2, Oana A Zeleznik3, Xiaosheng He4,5, Marta Guasch-Ferre2,3, Xia Jiang6, Jun Li2, Liming Liang7,8, A Heather Eliassen3,7, Clary B Clish9, Andrew T Chan3,4,9, Zhibin Hu1, Hongbing Shen1, Kathryn M Wilson3,7, Lorelei A Mucci7, Qi Sun2,3, Frank B Hu2,3,7, Walter C Willett2,3,7, Edward L Giovannucci2,3,7, Mingyang Song10,4,7. 1. Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China. 2. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA. 3. Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA. 4. Clinical and Translational Epidemiology Unit and Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA. 5. Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China. 6. Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA. 7. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA. 8. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA. 9. Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA. 10. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA mingyangsong@mail.harvard.edu.
Abstract
OBJECTIVE: Coffee may protect against multiple chronic diseases, particularly type 2 diabetes, but the mechanisms remain unclear. RESEARCH DESIGN AND METHODS: Leveraging dietary and metabolomic data in two large cohorts of women (the Nurses' Health Study [NHS] and NHSII), we identified and validated plasma metabolites associated with coffee intake in 1,595 women. We then evaluated the prospective association of coffee-related metabolites with diabetes risk and the added predictivity of these metabolites for diabetes in two nested case-control studies (n = 457 case and 1,371 control subjects). RESULTS: Of 461 metabolites, 34 were identified and validated to be associated with total coffee intake, including 13 positive associations (primarily trigonelline, polyphenol metabolites, and caffeine metabolites) and 21 inverse associations (primarily triacylglycerols [TAGs] and diacylglycerols [DAGs]). These associations were generally consistent for caffeinated and decaffeinated coffee, except for caffeine and its metabolites that were only associated with caffeinated coffee intake. The three cholesteryl esters positively associated with coffee intake showed inverse associations with diabetes risk, whereas the 12 metabolites negatively associated with coffee (5 DAGs and 7 TAGs) showed positive associations with diabetes. Adding the 15 diabetes-associated metabolites to a classical risk factor-based prediction model increased the C-statistic from 0.79 (95% CI 0.76, 0.83) to 0.83 (95% CI 0.80, 0.86) (P < 0.001). Similar improvement was observed in the validation set. CONCLUSIONS: Coffee consumption is associated with widespread metabolic changes, among which lipid metabolites may be critical for the antidiabetes benefit of coffee. Coffee-related metabolites might help improve prediction of diabetes, but further validation studies are needed.
OBJECTIVE: Coffee may protect against multiple chronic diseases, particularly type 2 diabetes, but the mechanisms remain unclear. RESEARCH DESIGN AND METHODS: Leveraging dietary and metabolomic data in two large cohorts of women (the Nurses' Health Study [NHS] and NHSII), we identified and validated plasma metabolites associated with coffee intake in 1,595 women. We then evaluated the prospective association of coffee-related metabolites with diabetes risk and the added predictivity of these metabolites for diabetes in two nested case-control studies (n = 457 case and 1,371 control subjects). RESULTS: Of 461 metabolites, 34 were identified and validated to be associated with total coffee intake, including 13 positive associations (primarily trigonelline, polyphenol metabolites, and caffeine metabolites) and 21 inverse associations (primarily triacylglycerols [TAGs] and diacylglycerols [DAGs]). These associations were generally consistent for caffeinated and decaffeinated coffee, except for caffeine and its metabolites that were only associated with caffeinated coffee intake. The three cholesteryl esters positively associated with coffee intake showed inverse associations with diabetes risk, whereas the 12 metabolites negatively associated with coffee (5 DAGs and 7 TAGs) showed positive associations with diabetes. Adding the 15 diabetes-associated metabolites to a classical risk factor-based prediction model increased the C-statistic from 0.79 (95% CI 0.76, 0.83) to 0.83 (95% CI 0.80, 0.86) (P < 0.001). Similar improvement was observed in the validation set. CONCLUSIONS: Coffee consumption is associated with widespread metabolic changes, among which lipid metabolites may be critical for the antidiabetes benefit of coffee. Coffee-related metabolites might help improve prediction of diabetes, but further validation studies are needed.
Authors: William R Wikoff; Andrew T Anfora; Jun Liu; Peter G Schultz; Scott A Lesley; Eric C Peters; Gary Siuzdak Journal: Proc Natl Acad Sci U S A Date: 2009-02-20 Impact factor: 11.205
Authors: Kerstin Kempf; Christian Herder; Iris Erlund; Hubert Kolb; Stephan Martin; Maren Carstensen; Wolfgang Koenig; Jouko Sundvall; Siamak Bidel; Suvi Kuha; Jaakko Tuomilehto Journal: Am J Clin Nutr Date: 2010-02-24 Impact factor: 7.045
Authors: Dong Hang; Ane Sørlie Kværner; Wenjie Ma; Yang Hu; Fred K Tabung; Hongmei Nan; Zhibin Hu; Hongbing Shen; Lorelei A Mucci; Andrew T Chan; Edward L Giovannucci; Mingyang Song Journal: Am J Clin Nutr Date: 2019-03-01 Impact factor: 7.045
Authors: Ying Bao; Monica L Bertoia; Elizabeth B Lenart; Meir J Stampfer; Walter C Willett; Frank E Speizer; Jorge E Chavarro Journal: Am J Public Health Date: 2016-07-26 Impact factor: 9.308
Authors: Barbara E Millen; Steve Abrams; Lucile Adams-Campbell; Cheryl Am Anderson; J Thomas Brenna; Wayne W Campbell; Steven Clinton; Frank Hu; Miriam Nelson; Marian L Neuhouser; Rafael Perez-Escamilla; Anna Maria Siega-Riz; Mary Story; Alice H Lichtenstein Journal: Adv Nutr Date: 2016-05-16 Impact factor: 8.701
Authors: Fred K Tabung; Liming Liang; Tianyi Huang; Raji Balasubramanian; Yibai Zhao; Paulette D Chandler; JoAnn E Manson; Elizabeth M Cespedes Feliciano; Kathleen M Hayden; Linda Van Horn; Clary B Clish; Edward L Giovannucci; Kathryn M Rexrode Journal: Clin Nutr Date: 2019-06-17 Impact factor: 7.324
Authors: Clary B Clish; Shelley S Tworoger; Oana A Zeleznik; A Heather Eliassen; Peter Kraft; Elizabeth M Poole; Bernard A Rosner; Sarah Jeanfavre; Amy A Deik; Kevin Bullock; Daniel S Hitchcock; Julian Avila-Pacheco Journal: Cancer Res Date: 2020-01-22 Impact factor: 12.701
Authors: William J He; Jingsha Chen; Alexander C Razavi; Emily A Hu; Morgan E Grams; Bing Yu; Chirag R Parikh; Eric Boerwinkle; Lydia Bazzano; Lu Qi; Tanika N Kelly; Josef Coresh; Casey M Rebholz Journal: Clin J Am Soc Nephrol Date: 2021-11-04 Impact factor: 8.237
Authors: Dong Hang; Oana A Zeleznik; Jiayi Lu; Amit D Joshi; Kana Wu; Zhibin Hu; Hongbing Shen; Clary B Clish; Liming Liang; A Heather Eliassen; Shuji Ogino; Jeffrey A Meyerhardt; Andrew T Chan; Mingyang Song Journal: Eur J Epidemiol Date: 2022-01-15 Impact factor: 12.434
Authors: Jakub Morze; Clemens Wittenbecher; Lukas Schwingshackl; Anna Danielewicz; Andrzej Rynkiewicz; Frank B Hu; Marta Guasch-Ferré Journal: Diabetes Care Date: 2022-04-01 Impact factor: 17.152
Authors: Jae H Kang; Oana Zeleznik; Lisa Frueh; Jessica Lasky-Su; A Heather Eliassen; Clary Clish; Bernard A Rosner; Louis R Pasquale; Janey L Wiggs Journal: Invest Ophthalmol Vis Sci Date: 2022-08-02 Impact factor: 4.925