Fred K Tabung1, Liming Liang2, Tianyi Huang3, Raji Balasubramanian4, Yibai Zhao4, Paulette D Chandler5, JoAnn E Manson6, Elizabeth M Cespedes Feliciano7, Kathleen M Hayden8, Linda Van Horn9, Clary B Clish10, Edward L Giovannucci11, Kathryn M Rexrode12. 1. Division of Medical Oncology, Department of Internal Medicine, The Ohio State University College of Medicine, 410 W 12th Avenue, Columbus, OH, 43210, USA; The Ohio State University Comprehensive Cancer Center - Arthur G. James Cancer Hospital and Richard J. Solove Institute, 410 W 12th Avenue, Columbus, OH, 43210, USA; Department of Nutrition, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA; Division of Women's Health, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA. Electronic address: fred.tabung@osumc.edu. 2. Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA. 3. Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA, 02115, USA. 4. Department of Biostatistics and Epidemiology, University of Massachusetts-Amherst, 715 N Pleasant Street, Amherst, MA, 01003, USA. 5. Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA; Division of Preventive Medicine, Brigham and Women's Hospital, 900 Commonwealth Avenue, Boston, MA, 02115, USA. 6. Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA; Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA, 02115, USA; Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA; Division of Preventive Medicine, Brigham and Women's Hospital, 900 Commonwealth Avenue, Boston, MA, 02115, USA. 7. Division of Research, Kaiser Permanente Northern California, 2051 Franklin Street, Oakland, CA, 94612, USA. 8. Department of Social Sciences and Health Policy, Wake Forest School of Medicine, 475 Vine Street, Winston-Salem, NC, 27101, USA. 9. Department of Preventive Medicine, Northwestern University, 680 N Lakeshore Dr, Chicago, IL, 60611, USA. 10. Broad Institute of the Massachusetts Institute of Technology and Harvard University, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA. 11. Department of Nutrition, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA; Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, MA, 02115, USA; Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA. 12. Division of Women's Health, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA; Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA; Division of Preventive Medicine, Brigham and Women's Hospital, 900 Commonwealth Avenue, Boston, MA, 02115, USA.
Abstract
BACKGROUND: We previously showed that a food-based empirical dietary inflammatory pattern (EDIP) score is associated with circulating inflammatory biomarkers. Metabolomic profiling of inflammatory diets may therefore provide insights on mechanisms contributing to disease etiology and prognosis. We aimed to elucidate metabolites associated with inflammatory diets among postmenopausal women, utilizing a robust study design that incorporates independent discovery and validation datasets. METHODS: This baseline cross-sectional investigation evaluated associations between continuous EDIP scores calculated from food frequency questionnaires and 448 log-transformed plasma metabolites as outcomes in multivariable-adjusted linear regression analyses. Metabolites were measured with liquid chromatography tandem mass spectroscopy. Metabolite discovery was conducted among 1109 Women's Health Initiative (WHI) Hormone Therapy trial participants and results were replicated in an independent dataset of 810 WHI Observational Study participants. Secondary analyses were stratified by standard body mass index (BMI, kg/m2) categories. In discovery and replication datasets statistical significance was based on false-discovery rate adjusted P < 0.05. RESULTS: After adjusting for energy intake, BMI, physical activity, and other confounding variables, 23 metabolites were significantly associated with EDIP score in the discovery dataset. Of these, the following ten were replicated: trigonelline, caffeine, acethylamino-6-amino-3-methyluracil, 7-methylxanthine, 1,7-dimethyluric acid, 3-methylxanthine, C18:3CE, glycine, associated with lower dietary inflammatory potential; whereas C52:3 triacylglycerol and linoleate associated with higher dietary inflammatory potential. Four of the ten were associated [glycine (inversely), caffeine, 1,7-dimethyluric acid, C52:3 triacylglycerol, (positively)], with C-reactive protein levels. In secondary analyses, associations showed differences by BMI category. Four metabolites, related to coffee/caffeine metabolism were inversely associated among normal weight women, and 83 metabolites associated with EDIP among overweight/obese women, including 40 (48%) that were also associated with C-reactive protein. CONCLUSION: Metabolites associated with coffee/caffeine and lipid metabolism may reflect the inflammatory potential of diet. Potential differences by BMI and the linkage to disease outcomes, require further study.
BACKGROUND: We previously showed that a food-based empirical dietary inflammatory pattern (EDIP) score is associated with circulating inflammatory biomarkers. Metabolomic profiling of inflammatory diets may therefore provide insights on mechanisms contributing to disease etiology and prognosis. We aimed to elucidate metabolites associated with inflammatory diets among postmenopausal women, utilizing a robust study design that incorporates independent discovery and validation datasets. METHODS: This baseline cross-sectional investigation evaluated associations between continuous EDIP scores calculated from food frequency questionnaires and 448 log-transformed plasma metabolites as outcomes in multivariable-adjusted linear regression analyses. Metabolites were measured with liquid chromatography tandem mass spectroscopy. Metabolite discovery was conducted among 1109 Women's Health Initiative (WHI) Hormone Therapy trial participants and results were replicated in an independent dataset of 810 WHI Observational Study participants. Secondary analyses were stratified by standard body mass index (BMI, kg/m2) categories. In discovery and replication datasets statistical significance was based on false-discovery rate adjusted P < 0.05. RESULTS: After adjusting for energy intake, BMI, physical activity, and other confounding variables, 23 metabolites were significantly associated with EDIP score in the discovery dataset. Of these, the following ten were replicated: trigonelline, caffeine, acethylamino-6-amino-3-methyluracil, 7-methylxanthine, 1,7-dimethyluric acid, 3-methylxanthine, C18:3CE, glycine, associated with lower dietary inflammatory potential; whereas C52:3 triacylglycerol and linoleate associated with higher dietary inflammatory potential. Four of the ten were associated [glycine (inversely), caffeine, 1,7-dimethyluric acid, C52:3 triacylglycerol, (positively)], with C-reactive protein levels. In secondary analyses, associations showed differences by BMI category. Four metabolites, related to coffee/caffeine metabolism were inversely associated among normal weight women, and 83 metabolites associated with EDIP among overweight/obesewomen, including 40 (48%) that were also associated with C-reactive protein. CONCLUSION: Metabolites associated with coffee/caffeine and lipid metabolism may reflect the inflammatory potential of diet. Potential differences by BMI and the linkage to disease outcomes, require further study.
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