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Ma W, Wang Y, Nguyen LH, Mehta RS, Ha J, Bhosle A, Mclver LJ, Song M, Clish CB, Strate LL, Huttenhower C, Chan AT. Gut microbiome composition and metabolic activity in women with diverticulitis. Nat Commun 2024; 15:3612. [PMID: 38684664 PMCID: PMC11059386 DOI: 10.1038/s41467-024-47859-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 04/11/2024] [Indexed: 05/02/2024] Open
Abstract
The etiopathogenesis of diverticulitis, among the most common gastrointestinal diagnoses, remains largely unknown. By leveraging stool collected within a large prospective cohort, we performed shotgun metagenomic sequencing and untargeted metabolomics profiling among 121 women diagnosed with diverticulitis requiring antibiotics or hospitalizations (cases), matched to 121 women without diverticulitis (controls) according to age and race. Overall microbial community structure and metabolomic profiles differed in diverticulitis cases compared to controls, including enrichment of pro-inflammatory Ruminococcus gnavus, 1,7-dimethyluric acid, and histidine-related metabolites, and depletion of butyrate-producing bacteria and anti-inflammatory ceramides. Through integrated multi-omic analysis, we detected covarying microbial and metabolic features, such as Bilophila wadsworthia and bile acids, specific to diverticulitis. Additionally, we observed that microbial composition modulated the protective association between a prudent fiber-rich diet and diverticulitis. Our findings offer insights into the perturbations in inflammation-related microbial and metabolic signatures associated with diverticulitis, supporting the potential of microbial-based diagnostics and therapeutic targets.
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Affiliation(s)
- Wenjie Ma
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Yiqing Wang
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Long H Nguyen
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Raaj S Mehta
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jane Ha
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Amrisha Bhosle
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Lauren J Mclver
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Mingyang Song
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Lisa L Strate
- Division of Gastroenterology, University of Washington School of Medicine, Seattle, WA, USA
| | - Curtis Huttenhower
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Andrew T Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Cancer Center, Massachusetts General Hospital, Boston, MA, USA.
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
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Playdon MC, Tinker LF, Prentice RL, Loftfield E, Hayden KM, Van Horn L, Sampson JN, Stolzenberg-Solomon R, Lampe JW, Neuhouser ML, Moore SC. Measuring diet by metabolomics: a 14-d controlled feeding study of weighed food intake. Am J Clin Nutr 2024; 119:511-526. [PMID: 38212160 PMCID: PMC10884612 DOI: 10.1016/j.ajcnut.2023.10.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 09/12/2023] [Accepted: 10/11/2023] [Indexed: 01/13/2024] Open
Abstract
BACKGROUND Metabolomics has the potential to enhance dietary assessment by revealing objective measures of many aspects of human food intake. Although metabolomics studies indicate that hundreds of metabolites are associated with dietary intake, correlations have been modest (e.g., r < 0.50), and few have been evaluated in controlled feeding studies. OBJECTIVES The aim of this study was to evaluate associations between metabolites and weighed food and beverage intake in a controlled feeding study of habitual diet. METHODS Healthy postmenopausal females from the Women's Health Initiative (N = 153) were provided with a customized 2-wk controlled diet designed to emulate their usual diet. Metabolites were measured by liquid chromatography tandem mass spectrometry in end-of-study 24-h urine and fasting serum samples (1293 urine metabolites; 1113 serum metabolites). We calculated partial Pearson correlations between these metabolites and intake of 65 food groups, beverages, and supplements during the feeding study. The threshold for significance was Bonferroni-adjusted to account for multiple testing (5.94 × 10-07 for urine metabolites; 6.91 × 10-07 for serum metabolites). RESULTS Significant diet-metabolite correlations were identified for 23 distinct foods, beverages, and supplements (171 distinct metabolites). Among foods, strong metabolite correlations (r ≥ 0.60) were evident for citrus (highest r = 0.80), dairy (r = 0.65), and broccoli (r = 0.63). Among beverages and supplements, strong correlations were evident for coffee (r = 0.86), alcohol (r = 0.69), multivitamins (r = 0.69), and vitamin E supplements (r = 0.65). Moderate correlations (r = 0.50-0.60) were also observed for avocado, fish, garlic, grains, onion, poultry, and black tea. Correlations were specific; each metabolite correlated with one food, beverage, or supplement, except for metabolites correlated with juice or multivitamins. CONCLUSIONS Metabolite levels had moderate to strong correlations with weighed intake of habitually consumed foods, beverages, and supplements. These findings exceed in magnitude those previously observed in population studies and exemplify the strong potential of metabolomics to contribute to nutrition research. The Women's Health Initiative is registered at clinicaltrials.gov as NCT00000611.
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Affiliation(s)
- Mary C Playdon
- Department of Nutrition and Integrative Physiology, University of Utah, Salt Lake City, UT; Department of Population Health Sciences, University of Utah, Salt Lake City, UT; Cancer Control and Population Sciences Division, Huntsman Cancer Institute, Salt Lake City, UT; Division of Cancer Epidemiology and Genetics, National Cancer institute, Rockville, MD
| | - Lesley F Tinker
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center and University of Washington, Seattle, WA
| | - Ross L Prentice
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center and University of Washington, Seattle, WA
| | - Erikka Loftfield
- Division of Cancer Epidemiology and Genetics, National Cancer institute, Rockville, MD
| | - Kathleen M Hayden
- School of Medicine, Wake Forest Baptist Medical Center, Winston-Salem, NC
| | - Linda Van Horn
- Feinberg School of Medicine, Northwestern University, Chicago IL
| | - Joshua N Sampson
- Division of Cancer Epidemiology and Genetics, National Cancer institute, Rockville, MD
| | | | - Johanna W Lampe
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center and University of Washington, Seattle, WA
| | - Marian L Neuhouser
- Cancer Prevention Program, Division of Public Health Sciences, Fred Hutchinson Cancer Center and University of Washington, Seattle, WA
| | - Steven C Moore
- Division of Cancer Epidemiology and Genetics, National Cancer institute, Rockville, MD.
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Lee DH, Jin Q, Shi N, Wang F, Bever AM, Liang L, Hu FB, Song M, Zeleznik OA, Zhang X, Joshi A, Wu K, Jeon JY, Meyerhardt JA, Chan AT, Eliassen AH, Clish C, Clinton SK, Giovannucci EL, Li J, Tabung FK. The metabolic potential of inflammatory and insulinaemic dietary patterns and risk of type 2 diabetes. Diabetologia 2024; 67:88-101. [PMID: 37816982 DOI: 10.1007/s00125-023-06021-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 08/31/2023] [Indexed: 10/12/2023]
Abstract
AIMS/HYPOTHESIS Diets with higher inflammatory and insulinaemic potential have been associated with an increased risk of type 2 diabetes. However, it remains unknown whether plasma metabolomic profiles related to proinflammatory/hyperinsulinaemic diets and to inflammatory/insulin biomarkers are associated with type 2 diabetes risk. METHODS We analysed 6840 participants from the Nurses' Health Study and Health Professionals Follow-up Study to identify the plasma metabolome related to empirical dietary inflammatory pattern (EDIP), empirical dietary index for hyperinsulinemia (EDIH), four circulating inflammatory biomarkers and C-peptide. Dietary intakes were assessed using validated food frequency questionnaires. Plasma metabolomic profiling was conducted by LC-MS/MS. Metabolomic signatures were derived using elastic net regression. Multivariable Cox regression was used to examine associations of the metabolomic profiles with type 2 diabetes risk. RESULTS We identified 27 metabolites commonly associated with both EDIP and inflammatory biomarker z score and 21 commonly associated with both EDIH and C-peptide. Higher metabolomic dietary inflammatory potential (MDIP), reflecting higher metabolic potential of both an inflammatory dietary pattern and circulating inflammatory biomarkers, was associated with higher type 2 diabetes risk. The HR comparing highest vs lowest quartiles of MDIP was 3.26 (95% CI 2.39, 4.44). We observed a strong positive association with type 2 diabetes risk for the metabolomic signature associated with EDIP-only (HR 3.75; 95% CI 2.71, 5.17) or inflammatory biomarkers-only (HR 4.07; 95% CI 2.91, 5.69). In addition, higher metabolomic dietary index for hyperinsulinaemia (MDIH), reflecting higher metabolic potential of both an insulinaemic dietary pattern and circulating C-peptide, was associated with greater type 2 diabetes risk (HR 3.00; 95% CI 2.22, 4.06); further associations with type 2 diabetes were HR 2.79 (95% CI 2.07, 3.76) for EDIH-only signature and HR 3.89 (95% CI 2.82, 5.35) for C-peptide-only signature. The diet scores were significantly associated with risk, although adjustment for the corresponding metabolomic signature scores attenuated the associations with type 2 diabetes, these remained significant. CONCLUSIONS/INTERPRETATION The metabolomic signatures reflecting proinflammatory or hyperinsulinaemic diets and related biomarkers were positively associated with type 2 diabetes risk, supporting that these dietary patterns may influence type 2 diabetes risk via the regulation of metabolism.
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Affiliation(s)
- Dong Hoon Lee
- Department of Sport Industry Studies, Yonsei University, Seoul, Republic of Korea
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Qi Jin
- Department of Exercise and Nutrition Sciences, Moyes College of Education, Weber State University, Ogden, UT, USA
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
- Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH, USA
| | - Ni Shi
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
- Division of Medical Oncology, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Fenglei Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alaina M Bever
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA, USA
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Mingyang Song
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Xuehong Zhang
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Amit Joshi
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Kana Wu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Justin Y Jeon
- Department of Sport Industry Studies, Yonsei University, Seoul, Republic of Korea
- Cancer Prevention Center, Yonsei Cancer Center, Seoul, Republic of Korea
| | - Jeffrey A Meyerhardt
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Andrew T Chan
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - A Heather Eliassen
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Clary Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Steven K Clinton
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
- Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH, USA
- Division of Medical Oncology, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Edward L Giovannucci
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jun Li
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Fred K Tabung
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
- Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH, USA.
- Division of Medical Oncology, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, USA.
- Division of Epidemiology, College of Public Health, The Ohio State University, Columbus, OH, USA.
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4
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Jin Q, Shi N, Lee DH, Rexrode KM, Manson JE, Balasubramanian R, Zhang X, Neuhouser ML, Lopez-Pentecost M, Thomson CA, Zick SM, Felix AS, Stover DG, Sardesai SD, Esnakula A, Mo X, Clinton SK, Tabung FK. Hyperinsulinemic and Pro-Inflammatory Dietary Patterns and Metabolomic Profiles Are Associated with Increased Risk of Total and Site-Specific Cancers among Postmenopausal Women. Cancers (Basel) 2023; 15:1756. [PMID: 36980642 PMCID: PMC10046106 DOI: 10.3390/cancers15061756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/02/2023] [Accepted: 03/08/2023] [Indexed: 03/15/2023] Open
Abstract
We evaluated associations of the Empirical Dietary Index for Hyperinsulinemia (EDIH), Empirical Dietary Inflammatory Pattern (EDIP) and Healthy Eating Index (HEI2015) and their metabolomics profiles with the risk of total and site-specific cancers. We used baseline food frequency questionnaires to calculate dietary scores among 112,468 postmenopausal women in the Women's Health Initiative. We used multivariable-adjusted Cox regression to estimate hazard ratios (HR) and 95% confidence intervals for cancer risk estimation. Metabolomic profile scores were derived using elastic-net regression with leave-one-out cross validation. In over 17.8 years, 18,768 incident invasive cancers were adjudicated. Higher EDIH and EDIP scores were associated with greater total cancer risk, and higher HEI-2015 with lower risk: HRQ5vsQ1(95% CI): EDIH, 1.10 (1.04-1.15); EDIP, 1.08 (1.02-1.15); HEI-2015, 0.93 (0.89-0.98). The multivariable-adjusted incidence rate difference(Q5vsQ1) for total cancer was: +52 (EDIH), +41 (EDIP) and -49 (HEI-2015) per 100,000 person years. All three indices were associated with colorectal cancer, and EDIH and EDIP with endometrial and breast cancer risk. EDIH was further associated with luminal-B, ER-negative and triple negative breast cancer subtypes. Dietary patterns contributing to hyperinsulinemia and inflammation were associated with greater cancer risk, and higher overall dietary quality, with lower risk. The findings warrant the testing of these dietary patterns in clinical trials for cancer prevention among postmenopausal women.
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Affiliation(s)
- Qi Jin
- Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH 43210, USA
| | - Ni Shi
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Dong Hoon Lee
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Sport Industry Studies, Yonsei University, Seoul 03722, Republic of Korea
| | - Kathryn M. Rexrode
- Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - JoAnn E. Manson
- Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Raji Balasubramanian
- Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts at Amherst, Amherst, MA 01003, USA
| | - Xuehong Zhang
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Marian L. Neuhouser
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Melissa Lopez-Pentecost
- Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Cynthia A. Thomson
- Mel & Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ 85721, USA
| | - Suzanna M. Zick
- Department of Family Medicine, Michigan Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ashley S. Felix
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
- Division of Epidemiology, College of Public Health, The Ohio State University, Columbus, OH 43210, USA
| | - Daniel G. Stover
- Division of Medical Oncology, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Sagar D. Sardesai
- Division of Medical Oncology, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Ashwini Esnakula
- Department of Pathology, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Xiaokui Mo
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Steven K. Clinton
- Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH 43210, USA
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
- Division of Medical Oncology, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Fred K. Tabung
- Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH 43210, USA
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
- Division of Epidemiology, College of Public Health, The Ohio State University, Columbus, OH 43210, USA
- Division of Medical Oncology, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
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5
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Single and Joined Behaviour of Circulating Biomarkers and Metabolic Parameters in High-Fit and Low-Fit Healthy Females. Int J Mol Sci 2023; 24:ijms24044202. [PMID: 36835625 PMCID: PMC9960642 DOI: 10.3390/ijms24044202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 01/13/2023] [Accepted: 02/09/2023] [Indexed: 02/22/2023] Open
Abstract
Biomarkers are important in the assessment of health and disease, but are poorly studied in still healthy individuals with a (potential) different risk for metabolic disease. This study investigated, first, how single biomarkers and metabolic parameters, functional biomarker and metabolic parameter categories, and total biomarker and metabolic parameter profiles behave in young healthy female adults of different aerobic fitness and, second, how these biomarkers and metabolic parameters are affected by recent exercise in these healthy individuals. A total of 102 biomarkers and metabolic parameters were analysed in serum or plasma samples from 30 young, healthy, female adults divided into a high-fit (V̇O2peak ≥ 47 mL/kg/min, N = 15) and a low-fit (V̇O2peak ≤ 37 mL/kg/min, N = 15) group, at baseline and overnight after a single bout of exercise (60 min, 70% V̇O2peak). Our results show that total biomarker and metabolic parameter profiles were similar between high-fit and low-fit females. Recent exercise significantly affected several single biomarkers and metabolic parameters, mostly related to inflammation and lipid metabolism. Furthermore, functional biomarker and metabolic parameter categories corresponded to biomarker and metabolic parameter clusters generated via hierarchical clustering models. In conclusion, this study provides insight into the single and joined behavior of circulating biomarkers and metabolic parameters in healthy females, and identified functional biomarker and metabolic parameter categories that may be used for the characterisation of human health physiology.
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6
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Lopez-Pentecost M, Garcia DO, Sun X, Thomson CA, Chow HHS, Martinez JA. Differences in Metabolomic Profiles by Birthplace in Mexican-Origin Hispanic Men Who Participated in a Weight Loss Lifestyle Intervention. Am J Mens Health 2023; 17:15579883231153018. [PMID: 36842961 PMCID: PMC9972066 DOI: 10.1177/15579883231153018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 12/16/2022] [Accepted: 01/09/2023] [Indexed: 02/28/2023] Open
Abstract
Birthplace, as a proxy for environmental exposures (e.g., diet), may influence metabolomic profiles and influence risk of cancer. This secondary analysis investigated metabolomic profile differences between foreign and U.S.-born Mexican-origin (MO) Hispanic men to shed light on potential mechanisms through which foreign- and U.S.-born individuals experience differences in cancer risk and risk factors. Plasma samples from MO Hispanic men (N = 42) who participated in a previous lifestyle intervention were collected pre-and post-intervention. Metabolomic profiles were characterized from samples using ultra performance liquid chromatography-quadrupole time of flight mass spectrometry (UPLC-QTOF). Models were visualized using supervised orthogonal projections to latent structures-discriminant analysis (OPLS-DA). Progenesis QI was used for peak integration and metabolite identification. Plasma metabolomic profiles differed between foreign- and U.S.-born pre-intervention (R2 = .65) and post-intervention (R2 = .62). Metabolomic profiles differed pre- versus post-intervention (R2 = .35 and R2 = .65) for the foreign- and U.S.-born group, respectively. Both endogenous metabolites and dietary components characterized differences between foreign- and U.S.-born participants pre- and post-intervention. Plasma metabolomic profiles from MO Hispanic men differed by birthplace. These results advance our understanding of relevant exposures that may affect cancer risk among MO Hispanic men born abroad or in the United States.
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Affiliation(s)
- Melissa Lopez-Pentecost
- Sylvester Comprehensive Cancer Center,
University of Miami Miller School of Medicine, Miami, FL, USA
| | - David O. Garcia
- Department of Health Promotion
Sciences, Mel and Enid Zuckerman College of Public Health, The University of
Arizona, Tucson, AZ, USA
- The University of Arizona Cancer
Center, Tucson, AZ, USA
| | - Xiaoxiao Sun
- Department of Epidemiology and
Biostatistics, Mel and Enid Zuckerman College of Public Health, The University of
Arizona, Tucson, AZ, USA
| | - Cynthia A. Thomson
- Department of Health Promotion
Sciences, Mel and Enid Zuckerman College of Public Health, The University of
Arizona, Tucson, AZ, USA
- The University of Arizona Cancer
Center, Tucson, AZ, USA
| | | | - Jessica A. Martinez
- The University of Arizona Cancer
Center, Tucson, AZ, USA
- Department of Nutritional Sciences,
College of Agriculture and Life Sciences, The University of Arizona, Tucson, AZ,
USA
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7
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Ugai T, Liu L, Tabung FK, Hamada T, Langworthy BW, Akimoto N, Haruki K, Takashima Y, Okadome K, Kawamura H, Zhao M, Kahaki SMM, Glickman JN, Lennerz JK, Zhang X, Chan AT, Fuchs CS, Song M, Wang M, Yu K, Giannakis M, Nowak JA, Meyerhardt JA, Wu K, Ogino S, Giovannucci EL. Prognostic role of inflammatory diets in colorectal cancer overall and in strata of tumor-infiltrating lymphocyte levels. Clin Transl Med 2022; 12:e1114. [PMID: 36437503 PMCID: PMC9702366 DOI: 10.1002/ctm2.1114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 11/03/2022] [Accepted: 11/07/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Certain dietary patterns can elicit systemic and intestinal inflammatory responses, which may influence adaptive anti-tumor immune responses and tumor behavior. We hypothesized that pro-inflammatory diets might be associated with higher colorectal cancer mortality and that the association might be stronger for tumors with lower immune responses. METHODS We calculated an empirical dietary inflammatory pattern (EDIP) score in 2829 patients among 3988 incident rectal and colon carcinoma cases in the Nurses' Health Study and Health Professionals Follow-up Study. Using Cox proportional hazards regression analyses, we examined the prognostic association of EDIP scores and whether it might be modified by histopathologic immune reaction (in 1192 patients with available data). RESULTS Higher EDIP scores after colorectal cancer diagnosis were associated with worse survival, with multivariable-adjusted hazard ratios (HRs) for the highest versus lowest tertile of 1.41 (95% confidence interval [CI]: 1.13-1.77; Ptrend = 0.003) for 5-year colorectal cancer-specific mortality and 1.44 (95% CI, 1.19-1.74; Ptrend = 0.0004) for 5-year all-cause mortality. The association of post-diagnosis EDIP scores with 5-year colorectal cancer-specific mortality differed by degrees of tumor-infiltrating lymphocytes (TIL; Pinteraction = .002) but not by three other lymphocytic reaction patterns. The multivariable-adjusted, 5-year colorectal cancer-specific mortality HRs for the highest versus lowest EDIP tertile were 1.59 (95% CI: 1.01-2.53) in TIL-absent/low cases and 0.48 (95% CI: 0.16-1.48) in TIL-intermediate/high cases. CONCLUSIONS Pro-inflammatory diets after colorectal cancer diagnosis were associated with increased mortality, particularly in patients with absent or low TIL.
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8
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Zhang Z, Tabung FK, Jin Q, Curran G, Irvin VL, Shannon J, Velie EM, Manson JE, Simon MS, Vitolins M, Valencia CI, Snetselaar L, Jindal S, Schedin P. Diet-Driven Inflammation and Insulinemia and Risk of Interval Breast Cancer. Nutr Cancer 2022; 74:3179-3193. [PMID: 35471124 PMCID: PMC9439260 DOI: 10.1080/01635581.2022.2063350] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 03/03/2022] [Accepted: 04/01/2022] [Indexed: 01/10/2023]
Abstract
Interval breast cancers (IBCs) emerge after a non-suspicious mammogram and before the patient's next scheduled screen. Risk factors associated with IBC have not been identified. This study evaluated if the empirical dietary inflammatory pattern (EDIP) or empirical dietary index for hyperinsulinemia (EDIH) scores are associated with IBC compared to screen-detected breast cancer. Data were from women 50-79 years-old in the Women's Health Initiative cohort who completed food frequency questionnaires at baseline (1993-98) and were followed through March 31, 2019 for breast cancer detection. Women were identified as having either IBC diagnosed within 1-year after their last negative screening mammogram (N = 317) or screen-detected breast cancer (N = 1,928). Multivariable-adjusted logistic regression analyses were used to estimate odds ratios for risk of IBC compared to screen-detected cancer in dietary index tertiles. No associations were observed between EDIP or EDIH and IBC. Odds ratios comparing the highest to the lowest dietary index tertile were 1.08; 95%CI, 0.78-1.48 for EDIP and 0.92; 95%CI, 0.67-1.27 for EDIH. The null associations persisted when stratified by BMI categories. Findings suggest that diet-driven inflammation or insulinemia may not be substantially associated with IBC risk among postmenopausal women. Future studies are warranted to identify modifiable factors for IBC prevention.
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Affiliation(s)
- Zhenzhen Zhang
- Division of Oncological Sciences, Oregon Health & Science University, Portland, Oregon, USA
| | - Fred K. Tabung
- College of Medicine and Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA
| | - Qi Jin
- Interdisciplinary PhD Program in Nutrition, The Ohio State University, Columbus, Ohio, USA
| | - Grace Curran
- Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Veronica L Irvin
- College of Public Health and Human Sciences, Oregon State University, Corvallis, Oregon, USA
| | - Jackilen Shannon
- OHSU-PSU School of Public Health, Oregon Health & Science University, Portland, Oregon, USA
| | - Ellen M. Velie
- Zilber School of Public Health, University of Wisconsin at Milwaukee, Milwaukee, Wisconsin, USA
- Departments of Medicine and Pathology, Medical College of Wisconsin, Wauwatosa, Wisconsin, USA
| | - JoAnn E. Manson
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, and the Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Michael S. Simon
- Karmanos Cancer Institute, Department of Oncology, Wayne State University, Detroit, Michigan, USA
| | - Mara Vitolins
- Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Celina I. Valencia
- Department of Family and Community Medicine, College of Medicine-Tucson, The University of Arizona, Tucson, Arizona, USA
| | - Linda Snetselaar
- College of Public Health, Department of Epidemiology, University of Iowa, Iowa City, Iowa, USA
| | - Sonali Jindal
- Department of Cell, Developmental and Cancer Biology, Oregon Health & Science University, Portland, Oregon, USA
| | - Pepper Schedin
- Department of Cell, Developmental and Cancer Biology, Oregon Health & Science University, Portland, Oregon, USA
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9
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Andraos S, Beck KL, Jones MB, Han TL, Conlon CA, de Seymour JV. Characterizing patterns of dietary exposure using metabolomic profiles of human biospecimens: a systematic review. Nutr Rev 2022; 80:699-708. [PMID: 35024860 DOI: 10.1093/nutrit/nuab103] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
CONTEXT Establishing diet-disease associations requires reliable assessment of dietary intake. With the rapid advancement of metabolomics, its use in identifying objective biomarkers of dietary exposure has substantially increased. OBJECTIVE The aim of our review was to systematically combine all observational studies linking dietary intake patterns with metabolomic profiles of human biospecimens. DATA SOURCES Five databases were searched - MEDLINE, Embase, Scopus, Web of Science, and Cochrane CENTRAL - to March 2020. DATA EXTRACTION Of the 14 328 studies initially screened, 35 observational studies that met the specified inclusion criteria were included. DATA ANALYSIS All reviewed studies indicated that metabolomic measures were significantly correlated with dietary patterns, demonstrating the potential for using objective metabolomic measures to characterize individuals' dietary intake. However, similar dietary patterns did not always result in similar metabolomic profiles across different study populations. CONCLUSION Metabolomic profiles reflect a multitude of factors, including diet, genetic, phenotypic, and environmental influences, thereby providing a more comprehensive picture of the impact of diet on metabolism and health outcomes. Further exploration of dietary patterns and metabolomic profiles across different population groups is warranted.
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Affiliation(s)
- Stephanie Andraos
- S. Andraos, K.L. Beck, C.A. Conlon, and J.V. de Seymour are with the School of Sport, Exercise and Nutrition, College of Health, Massey University, Auckland, New Zealand. M.B. Jones is with the Department of Statistics, Faculty of Science, University of Auckland, Auckland, New Zealand. T.-L. Han is with the Department of Obstetrics and Gynaecology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Kathryn Louise Beck
- S. Andraos, K.L. Beck, C.A. Conlon, and J.V. de Seymour are with the School of Sport, Exercise and Nutrition, College of Health, Massey University, Auckland, New Zealand. M.B. Jones is with the Department of Statistics, Faculty of Science, University of Auckland, Auckland, New Zealand. T.-L. Han is with the Department of Obstetrics and Gynaecology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mary Beatrix Jones
- S. Andraos, K.L. Beck, C.A. Conlon, and J.V. de Seymour are with the School of Sport, Exercise and Nutrition, College of Health, Massey University, Auckland, New Zealand. M.B. Jones is with the Department of Statistics, Faculty of Science, University of Auckland, Auckland, New Zealand. T.-L. Han is with the Department of Obstetrics and Gynaecology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ting-Li Han
- S. Andraos, K.L. Beck, C.A. Conlon, and J.V. de Seymour are with the School of Sport, Exercise and Nutrition, College of Health, Massey University, Auckland, New Zealand. M.B. Jones is with the Department of Statistics, Faculty of Science, University of Auckland, Auckland, New Zealand. T.-L. Han is with the Department of Obstetrics and Gynaecology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Cathryn Anne Conlon
- S. Andraos, K.L. Beck, C.A. Conlon, and J.V. de Seymour are with the School of Sport, Exercise and Nutrition, College of Health, Massey University, Auckland, New Zealand. M.B. Jones is with the Department of Statistics, Faculty of Science, University of Auckland, Auckland, New Zealand. T.-L. Han is with the Department of Obstetrics and Gynaecology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jamie Violet de Seymour
- S. Andraos, K.L. Beck, C.A. Conlon, and J.V. de Seymour are with the School of Sport, Exercise and Nutrition, College of Health, Massey University, Auckland, New Zealand. M.B. Jones is with the Department of Statistics, Faculty of Science, University of Auckland, Auckland, New Zealand. T.-L. Han is with the Department of Obstetrics and Gynaecology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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10
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Shi N, Aroke D, Jin Q, Lee DH, Hussan H, Zhang X, Manson JE, LeBlanc ES, Barac A, Arcan C, Clinton SK, Giovannucci EL, Tabung FK. Proinflammatory and Hyperinsulinemic Dietary Patterns Are Associated With Specific Profiles of Biomarkers Predictive of Chronic Inflammation, Glucose-Insulin Dysregulation, and Dyslipidemia in Postmenopausal Women. Front Nutr 2021; 8:690428. [PMID: 34616762 PMCID: PMC8488136 DOI: 10.3389/fnut.2021.690428] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 08/17/2021] [Indexed: 12/13/2022] Open
Abstract
Background: Dietary patterns promoting hyperinsulinemia and chronic inflammation, including the empirical dietary index for hyperinsulinemia (EDIH) and empirical dietary inflammatory pattern (EDIP), have been shown to strongly influence risk of weight gain, type 2 diabetes, cardiovascular disease, and cancer. EDIH was developed using plasma C-peptide, whereas EDIP was based on plasma C-reactive protein (CRP), interleukin-6, and tumor necrosis factor alpha receptor 2 (TNF-αR2). We investigated whether these dietary patterns were associated with a broader range of relevant biomarkers not previously tested. Methods: In this cross-sectional study, we included 35,360 women aged 50-79 years from the Women's Health Initiative with baseline (1993-1998) fasting blood samples. We calculated EDIH and EDIP scores from baseline food frequency questionnaire data and tested their associations with 40 circulating biomarkers of insulin response/insulin-like growth factor (IGF) system, chronic systemic inflammation, endothelial dysfunction, lipids, and lipid particle size. Multivariable-adjusted linear regression was used to estimate the percent difference in biomarker concentrations per 1 standard deviation increment in dietary index. FDR-adjusted p < 0.05 was considered statistically significant. Results: Empirical dietary index for hyperinsulinemia (EDIH) and empirical dietary inflammatory pattern (EDIP) were significantly associated with altered concentrations of 25 of the 40 biomarkers examined. For EDIH, the percent change in biomarker concentration in the insulin-related biomarkers ranged from +1.3% (glucose) to +8% (homeostatic model assessment for insulin resistance) and -9.7% for IGF-binding protein-1. EDIH impacted inflammation and endothelial dysfunction biomarkers from +1.1% (TNF-αR2) to +7.8% (CRP) and reduced adiponectin by 2.4%; and for lipid biomarkers: +0.3% (total cholesterol) to +3% (triglycerides/total cholesterol ratio) while reducing high-density lipoprotein cholesterol by 2.4%. EDIP showed a similar trend of associations with most biomarkers, although the magnitude of association was slightly weaker for the insulin-related biomarkers and stronger for lipids and lipid particle size. Conclusions: Dietary patterns with high potential to contribute to insulin hypersecretion and to chronic systemic inflammation, based on higher EDIH and EDIP scores, were associated with an unfavorable profile of circulating biomarkers of glucose-insulin dysregulation, chronic systemic inflammation, endothelial dysfunction and dyslipidemia. The broad range of biomarkers further validates EDIH and EDIP as mechanisms-based dietary patterns for use in clinical and population-based studies of metabolic and inflammatory diseases.
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Affiliation(s)
- Ni Shi
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, United States.,Division of Medical Oncology, Department of Internal Medicine, The Ohio State University, Columbus, OH, United States
| | - Desmond Aroke
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, United States.,Department of Medicine, Rutgers Health, Newark Beth Israel Medical Center, Newark, NJ, United States
| | - Qi Jin
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, United States.,Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH, United States
| | - Dong Hoon Lee
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, United States
| | - Hisham Hussan
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, United States.,Division of Gastroenterology, Hepatology and Nutrition, Department of Internal Medicine, The Ohio State University, Columbus, OH, United States
| | - Xuehong Zhang
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, United States
| | - JoAnn E Manson
- Department of Medicine, Brigham and Women's Hospital, Harvard, Medical School, Boston, MA, United States.,Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, United States
| | - Erin S LeBlanc
- Kaiser Permanente Center for Health Research NW, Portland, OR, United States
| | - Ana Barac
- Medstar Heart and Vascular Institute, Georgetown University, Washington, DC, United States
| | - Chrisa Arcan
- Department of Family, Population, and Preventive Medicine, Nutrition Division, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, United States
| | - Steven K Clinton
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, United States.,Division of Medical Oncology, Department of Internal Medicine, The Ohio State University, Columbus, OH, United States.,Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH, United States
| | - Edward L Giovannucci
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, United States.,Department of Medicine, Brigham and Women's Hospital, Harvard, Medical School, Boston, MA, United States.,Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, United States
| | - Fred K Tabung
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, United States.,Division of Medical Oncology, Department of Internal Medicine, The Ohio State University, Columbus, OH, United States.,Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH, United States.,Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, United States.,Division of Epidemiology, College of Public Health, The Ohio State University, Columbus, OH, United States
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11
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Li SX, Hodge AM, MacInnis RJ, Bassett JK, Ueland PM, Midttun Ø, Ulvik A, Rinaldi S, Meyer K, Navionis AS, Shivappa N, Hébert JR, Flicker L, Severi G, Jayasekara H, English DR, Vineis P, Southey MC, Milne RL, Giles GG, Dugué PA. Inflammation-Related Marker Profiling of Dietary Patterns and All-cause Mortality in the Melbourne Collaborative Cohort Study. J Nutr 2021; 151:2908-2916. [PMID: 34320210 DOI: 10.1093/jn/nxab231] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 05/16/2021] [Accepted: 06/22/2021] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Nutritional epidemiology research using self-reported dietary intake is prone to measurement error. Objective methods are being explored to overcome this limitation. OBJECTIVES We aimed to examine 1) the association between plasma markers related to inflammation and derive marker scores for dietary patterns [Mediterranean dietary score (MDS), energy-adjusted Dietary Inflammatory Index (E-DIITM), Alternative Healthy Eating Index 2010 (AHEI)] and 2) the associations of these marker scores with mortality. METHODS Weighted marker scores were derived from the cross-sectional association between 30 plasma markers and each dietary score (assessed using food-frequency questionnaires) using linear regression for 770 participants in the Melbourne Collaborative Cohort Study (aged 50-82 y). Prospective associations between marker scores and mortality (n = 249 deaths) were assessed using Cox regression (median follow-up: 14.4 y). RESULTS The MDS, E-DII, and AHEI were associated (P < 0.05) with 9, 14, and 11 plasma markers, respectively. Healthier diets (higher MDS and AHEI, and lower anti-inflammatory, E-DII) were associated with lower concentrations of kynurenines, neopterin, IFN-γ, cytokines, and C-reactive protein. Five of 6 markers common to the 3 dietary scores were components of the kynurenine pathway. The 3 dietary-based marker scores were highly correlated (Spearman ρ: -0.74, -0.82, and 0.93). Inverse associations (for 1-SD increment) were observed with all-cause mortality for the MDS marker score (HR: 0.84; 95% CI: 0.72-0.98) and the AHEI marker score (HR: 0.76; 95% CI: 0.66-0.89), whereas a positive association was observed with the E-DII marker score (HR: 1.18; 95% CI: 1.01-1.39). The same magnitude of effect was not observed for the respective dietary patterns. CONCLUSIONS Markers involved in inflammation-related processes are associated with dietary quality, including a substantial overlap between markers associated with the MDS, the E-DII, and the AHEI, especially kynurenines. Unfavorable marker scores, reflecting poorer-quality diets, were associated with increased mortality.
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Affiliation(s)
- Sherly X Li
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Allison M Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Robert J MacInnis
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Julie K Bassett
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Per M Ueland
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | | | - Arve Ulvik
- Bevital A/S, Laboratoriebygget, Bergen, Norway
| | - Sabina Rinaldi
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, France
| | - Klaus Meyer
- Bevital A/S, Laboratoriebygget, Bergen, Norway
| | - Anne-Sophie Navionis
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, France
| | - Nitin Shivappa
- Cancer Prevention and Control Program and Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA
- Department of Nutrition, Connecting Health Innovations LLC, Columbia, SC, USA
| | - James R Hébert
- Cancer Prevention and Control Program and Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA
- Department of Nutrition, Connecting Health Innovations LLC, Columbia, SC, USA
| | - Leon Flicker
- WA Centre for Health and Ageing of the University of Western Australia, Crawley, Western Australia, Australia
| | - Gianluca Severi
- Centre de Recherche en Épidémiologie et Santé des Populations (CESP, Inserm U1018), Université Paris-Saclay, UPS, USQ, Gustave Roussy, Villejuif, France
- Human Genetics Foundation (HuGeF), Turin, Italy
| | - Harindra Jayasekara
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Dallas R English
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Paolo Vineis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Pierre-Antoine Dugué
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
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12
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Hang D, Zeleznik OA, He X, Guasch-Ferre M, Jiang X, Li J, Liang L, Eliassen AH, Clish CB, Chan AT, Hu Z, Shen H, Wilson KM, Mucci LA, Sun Q, Hu FB, Willett WC, Giovannucci EL, Song M. Metabolomic Signatures of Long-term Coffee Consumption and Risk of Type 2 Diabetes in Women. Diabetes Care 2020; 43:2588-2596. [PMID: 32788283 PMCID: PMC7510042 DOI: 10.2337/dc20-0800] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 07/12/2020] [Indexed: 02/03/2023]
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.
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Affiliation(s)
- Dong Hang
- 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.,Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Xiaosheng He
- Clinical and Translational Epidemiology Unit and Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA.,Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Marta Guasch-Ferre
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Xia Jiang
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Jun Li
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Clary B Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA
| | - Andrew T Chan
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.,Clinical and Translational Epidemiology Unit and Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA.,Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA
| | - Zhibin Hu
- 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
| | - Hongbing Shen
- 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
| | - Kathryn M Wilson
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Lorelei A Mucci
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Qi Sun
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Walter C Willett
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Edward L Giovannucci
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Mingyang Song
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA .,Clinical and Translational Epidemiology Unit and Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
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13
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Kumar AA, Satheesh G, Vijayakumar G, Chandran M, Prabhu PR, Simon L, Kutty VR, Kartha CC, Jaleel A. Plasma leptin level mirrors metabolome alterations in young adults. Metabolomics 2020; 16:87. [PMID: 32772182 DOI: 10.1007/s11306-020-01708-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 08/03/2020] [Indexed: 12/29/2022]
Abstract
INTRODUCTION Leptin is known to regulate pathways of energy metabolism, reproduction, and control appetite. Whether plasma leptin levels reflect changes in metabolites of these pathways is unknown. OBJECTIVES We aimed to find whether there is an association between leptin levels and levels of metabolites of energy and hormone metabolism. METHODS We performed an untargeted metabolomics analysis of plasma from 110 healthy adults (men: women = 1:1; aged 18-40 years), using liquid chromatography-tandem mass spectrometry. Blood samples were collected from all the study subjects in the fasting state. Clinical features and markers of obesity and Type 2 diabetes mellitus (T2DM) were assessed in all. The association between levels of metabolites and clinical and biochemical parameters was identified using the multivariable-adjusted linear regression model and PLS-DA analysis. RESULTS The leptin level was found to have a significant association with a substantial number of metabolites in women and men. Leptin level was positively associated with glycocholic acid and arachidic acid, metabolites related to energy metabolisms, pregnanediol-3-glucuronide, a metabolite of progesterone metabolism, and quercetin 3'-sulfate, a diet-derived metabolite. Leptin level was negatively associated with ponasteroside A and barringtogenol C levels. Leptin level was positively correlated with adiponectin and negatively with total calorie intake and levels of triglyceride and very-low-density lipoprotein. Leptin levels were associated with lipid and sex hormone metabolism in women, while metabolites involved in amino acid metabolism were correlated to leptin in men. CONCLUSION Our study indicates that leptin level reflects metabolome alterations and hence could be a useful marker to detect early changes in energy and hormone metabolisms.
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Affiliation(s)
- A Aneesh Kumar
- Cardiovascular Diseases & Diabetes Biology, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, 695012, India
- Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Gopika Satheesh
- Cardiovascular Diseases & Diabetes Biology, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, 695012, India
| | - Gadadharan Vijayakumar
- Medical Trust Hospital and Diabetes Care Centre, Kulanada, Pathanamthitta, Kerala, India
| | - Mahesh Chandran
- Mass Spectrometry and Proteomics Core Facility, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, 695012, India
| | - Priya R Prabhu
- Cancer Research Program, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, 695012, India
| | - Leena Simon
- Medical Trust Hospital and Diabetes Care Centre, Kulanada, Pathanamthitta, Kerala, India
| | - Vellappillil Raman Kutty
- Achutha Menon Centre for Health Science Studies, Sree Chitra Tirunal Institute for Medical Sciences & Technology, Thiruvananthapuram, Kerala, 695012, India
| | - Chandrasekharan C Kartha
- Society for Continuing Medical Education & Research, Kerala Institute of Medical Sciences, Thiruvananthapuram, Kerala, 695029, India
| | - Abdul Jaleel
- Cardiovascular Diseases & Diabetes Biology, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, 695012, India.
- Mass Spectrometry and Proteomics Core Facility, Rajiv Gandhi Centre for Biotechnology, Thiruvananthapuram, Kerala, 695012, India.
- Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
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14
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Hernández‐Alonso P, Becerra‐Tomás N, Papandreou C, Bulló M, Guasch‐Ferré M, Toledo E, Ruiz‐Canela M, Clish CB, Corella D, Dennis C, Deik A, Wang DD, Razquin C, Drouin‐Chartier J, Estruch R, Ros E, Fitó M, Arós F, Fiol M, Serra‐Majem L, Liang L, Martínez‐González MA, Hu FB, Salas‐Salvadó J. Plasma Metabolomics Profiles are Associated with the Amount and Source of Protein Intake: A Metabolomics Approach within the PREDIMED Study. Mol Nutr Food Res 2020; 64:e2000178. [PMID: 32378786 DOI: 10.1002/mnfr.202000178] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 04/15/2020] [Indexed: 01/24/2023]
Affiliation(s)
- Pablo Hernández‐Alonso
- Universitat Rovira i VirgiliDepartament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana Hospital Universitari San Joan de Reus Reus 43201 Spain
- Institut d'Investigació Pere Virgili (IISPV) Reus 43003 Spain
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Unidad de Gestión Clínica de Endocrinología y Nutrición del Hospital Virgen de la VictoriaInstituto de Investigación Biomédica de Málaga (IBIMA) Málaga 29010 Spain
| | - Nerea Becerra‐Tomás
- Universitat Rovira i VirgiliDepartament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana Hospital Universitari San Joan de Reus Reus 43201 Spain
- Institut d'Investigació Pere Virgili (IISPV) Reus 43003 Spain
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
| | - Christopher Papandreou
- Universitat Rovira i VirgiliDepartament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana Hospital Universitari San Joan de Reus Reus 43201 Spain
- Institut d'Investigació Pere Virgili (IISPV) Reus 43003 Spain
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
| | - Mònica Bulló
- Universitat Rovira i VirgiliDepartament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana Hospital Universitari San Joan de Reus Reus 43201 Spain
- Institut d'Investigació Pere Virgili (IISPV) Reus 43003 Spain
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
| | - Marta Guasch‐Ferré
- Universitat Rovira i VirgiliDepartament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana Hospital Universitari San Joan de Reus Reus 43201 Spain
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Department of NutritionHarvard T. H. Chan School of Public Health Boston MA 02115 USA
| | - Estefanía Toledo
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- University of NavarraDepartment of Preventive Medicine and Public Health Pamplona 31008 Spain
- Navarra Institute for Health Research (IdisNA) Pamplona Navarra 31008 Spain
| | - Miguel Ruiz‐Canela
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- University of NavarraDepartment of Preventive Medicine and Public Health Pamplona 31008 Spain
- Navarra Institute for Health Research (IdisNA) Pamplona Navarra 31008 Spain
| | - Clary B. Clish
- Broad Institute of MIT and Harvard University Cambridge MA 02142 USA
| | - Dolores Corella
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Department of Preventive MedicineUniversity of Valencia Valencia 46020 Spain
| | - Courtney Dennis
- Broad Institute of MIT and Harvard University Cambridge MA 02142 USA
| | - Amy Deik
- Broad Institute of MIT and Harvard University Cambridge MA 02142 USA
| | - Dong D. Wang
- Department of NutritionHarvard T. H. Chan School of Public Health Boston MA 02115 USA
| | - Cristina Razquin
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- University of NavarraDepartment of Preventive Medicine and Public Health Pamplona 31008 Spain
- Navarra Institute for Health Research (IdisNA) Pamplona Navarra 31008 Spain
| | - Jean‐Philippe Drouin‐Chartier
- Department of NutritionHarvard T. H. Chan School of Public Health Boston MA 02115 USA
- Centre Nutrition, Santé et Société (NUTRISS), Institut sur la Nutrition et les Aliments Fonctionnels (INAF)Université Laval Québec G1V 0A6 Canada
- Faculté de PharmacieUniversité Laval Québec G1V 0A6 Canada
| | - Ramon Estruch
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Department of Internal MedicineDepartment of Endocrinology and Nutrition Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital ClinicUniversity of Barcelona Barcelona 08036 Spain
| | - Emilio Ros
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Lipid Clinic, Department of Endocrinology and Nutrition Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital ClinicUniversity of Barcelona Barcelona 08036 Spain
| | - Montserrat Fitó
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Cardiovascular and Nutrition Research GroupInstitut de Recerca Hospital del Mar Barcelona 08003 Spain
| | - Fernando Arós
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Department of CardiologyUniversity Hospital of Alava Vitoria 01009 Spain
| | - Miquel Fiol
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Institute of Health Sciences IUNICSUniversity of Balearic Islands and Hospital Son Espases Palma de Mallorca 07122 Spain
| | - Lluís Serra‐Majem
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Research Institute of Biomedical and Health Sciences IUIBSUniversity of Las Palmas de Gran Canaria Las Palmas 35001 Spain
| | - Liming Liang
- Departments of Epidemiology and StatisticsHarvard T. H. Chan School of Public Health Boston MA 02115 USA
| | - Miguel A Martínez‐González
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- University of NavarraDepartment of Preventive Medicine and Public Health Pamplona 31008 Spain
- Navarra Institute for Health Research (IdisNA) Pamplona Navarra 31008 Spain
- Department of NutritionHarvard T. H. Chan School of Public Health Boston MA 02115 USA
| | - Frank B Hu
- Broad Institute of MIT and Harvard University Cambridge MA 02142 USA
- Departments of Epidemiology and StatisticsHarvard T. H. Chan School of Public Health Boston MA 02115 USA
- Channing Division for Network Medicine, Department of MedicineBrigham and Women's Hospital and Harvard Medical School Boston MA 02115 USA
| | - Jordi Salas‐Salvadó
- Universitat Rovira i VirgiliDepartament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana Hospital Universitari San Joan de Reus Reus 43201 Spain
- Institut d'Investigació Pere Virgili (IISPV) Reus 43003 Spain
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
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