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Bernard L, Chen J, Kim H, Wong KE, Steffen LM, Yu B, Boerwinkle E, Levey AS, Grams ME, Rhee EP, Rebholz CM. Serum Metabolomic Markers of Protein-Rich Foods and Incident CKD: Results From the Atherosclerosis Risk in Communities Study. Kidney Med 2024; 6:100793. [PMID: 38495599 PMCID: PMC10940775 DOI: 10.1016/j.xkme.2024.100793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024] Open
Abstract
Rationale & Objective While urine excretion of nitrogen estimates the total protein intake, biomarkers of specific dietary protein sources have been sparsely studied. Using untargeted metabolomics, this study aimed to identify serum metabolomic markers of 6 protein-rich foods and to examine whether dietary protein-related metabolites are associated with incident chronic kidney disease (CKD). Study Design Prospective cohort study. Setting & Participants A total of 3,726 participants from the Atherosclerosis Risk in Communities study without CKD at baseline. Exposures Dietary intake of 6 protein-rich foods (fish, nuts, legumes, red and processed meat, eggs, and poultry), serum metabolites. Outcomes Incident CKD (estimated glomerular filtration rate < 60 mL/min/1.73 m2 with ≥25% estimated glomerular filtration rate decline relative to visit 1, hospitalization or death related to CKD, or end-stage kidney disease). Analytical Approach Multivariable linear regression models estimated cross-sectional associations between protein-rich foods and serum metabolites. C statistics assessed the ability of the metabolites to improve the discrimination of highest versus lower 3 quartiles of intake of protein-rich foods beyond covariates (demographics, clinical factors, health behaviors, and the intake of nonprotein food groups). Cox regression models identified prospective associations between protein-related metabolites and incident CKD. Results Thirty significant associations were identified between protein-rich foods and serum metabolites (fish, n = 8; nuts, n = 5; legumes, n = 0; red and processed meat, n = 5; eggs, n = 3; and poultry, n = 9). Metabolites collectively and significantly improved the discrimination of high intake of protein-rich foods compared with covariates alone (difference in C statistics = 0.033, 0.051, 0.003, 0.024, and 0.025 for fish, nuts, red and processed meat, eggs, and poultry-related metabolites, respectively; P < 1.00 × 10-16 for all). Dietary intake of fish was positively associated with 1-docosahexaenoylglycerophosphocholine (22:6n3), which was inversely associated with incident CKD (HR, 0.82; 95% CI, 0.75-0.89; P = 7.81 × 10-6). Limitations Residual confounding and sample-storage duration. Conclusions We identified candidate biomarkers of fish, nuts, red and processed meat, eggs, and poultry. A fish-related metabolite, 1-docosahexaenoylglycerophosphocholine (22:6n3), was associated with a lower risk of CKD.
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Affiliation(s)
- Lauren Bernard
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Jingsha Chen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Hyunju Kim
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Kari E. Wong
- Metabolon, Research Triangle Park, Morrisville, NC
| | - Lyn M. Steffen
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN
| | - Bing Yu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas Health Science Center at Houston, Houston, TX
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas Health Science Center at Houston, Houston, TX
| | | | - Morgan E. Grams
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- Division of Precision of Medicine, Department of Medicine, New York University Grossman School of Medicine, New York, NY
| | - Eugene P. Rhee
- Nephrology Division and Endocrine Unit, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Casey M. Rebholz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
- Division of Nephrology, Department of Medicine, Johns Hopkins University, Baltimore, MD
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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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3
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Han Y, Jung KJ, Kim U, Jeon CI, Lee K, Jee SH. Non-invasive biomarkers for early diagnosis of pancreatic cancer risk: metabolite genomewide association study based on the KCPS-II cohort. J Transl Med 2023; 21:878. [PMID: 38049855 PMCID: PMC10694897 DOI: 10.1186/s12967-023-04670-x] [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: 08/15/2023] [Accepted: 10/27/2023] [Indexed: 12/06/2023] Open
Abstract
BACKGROUND Pancreatic cancer is a lethal disease with a high mortality rate. The difficulty of early diagnosis is one of its primary causes. Therefore, we aimed to discover non-invasive biomarkers that facilitate the early diagnosis of pancreatic cancer risk. METHODS The study subjects were randomly selected from the Korean Cancer Prevention Study-II and matched by age, sex, and blood collection point [pancreatic cancer incidence (n = 128) vs. control (n = 256)]. The baseline serum samples were analyzed by non-targeted metabolomics, and XGBoost was used to select significant metabolites related to pancreatic cancer incidence. Genomewide association study for the selected metabolites discovered valuable single nucleotide polymorphisms (SNPs). Moderation and mediation analysis were conducted to explore the variables related to pancreatic cancer risk. RESULTS Eleven discriminant metabolites were selected by applying a cut-off of 4.0 in XGBoost. Five SNP presented significance in metabolite-GWAS (p ≤ 5 × 10-6) and logistic regression analysis. Among them, the pair metabolite of rs2370981, rs55870181, and rs72805402 displayed a different network pattern with clinical/biochemical indicators on comparison with allelic carrier and non-carrier. In addition, we demonstrated the indirect effect of rs59519100 on pancreatic cancer risk mediated by γ-glutamyl tyrosine, which affects the smoking status. The predictive ability for pancreatic cancer on the model using five SNPs and four pair metabolites with the conventional risk factors was the highest (AUC: 0.738 [0.661-0.815]). CONCLUSIONS Signatures involving metabolites and SNPs discovered in the present research may be closely associated with the pathogenesis of pancreatic cancer and for use as predictive biomarkers allowing early pancreatic cancer diagnosis and therapy.
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Affiliation(s)
- Youngmin Han
- Institute for Health Promotion, Graduate School of Public Health, Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, Republic of Korea
| | - Keum Ji Jung
- Institute for Health Promotion, Graduate School of Public Health, Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, Republic of Korea
| | - Unchong Kim
- Institute for Health Promotion, Graduate School of Public Health, Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, Republic of Korea
| | - Chan Il Jeon
- Institute for Health Promotion, Graduate School of Public Health, Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, Republic of Korea
| | - Kwangbae Lee
- Korea Medical Institute, Seoul, Republic of Korea
| | - Sun Ha Jee
- Institute for Health Promotion, Graduate School of Public Health, Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722, Republic of Korea.
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Landberg R, Karra P, Hoobler R, Loftfield E, Huybrechts I, Rattner JI, Noerman S, Claeys L, Neveu V, Vidkjaer NH, Savolainen O, Playdon MC, Scalbert A. Dietary biomarkers-an update on their validity and applicability in epidemiological studies. Nutr Rev 2023:nuad119. [PMID: 37791499 DOI: 10.1093/nutrit/nuad119] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/05/2023] Open
Abstract
The aim of this literature review was to identify and provide a summary update on the validity and applicability of the most promising dietary biomarkers reflecting the intake of important foods in the Western diet for application in epidemiological studies. Many dietary biomarker candidates, reflecting intake of common foods and their specific constituents, have been discovered from intervention and observational studies in humans, but few have been validated. The literature search was targeted for biomarker candidates previously reported to reflect intakes of specific food groups or components that are of major importance in health and disease. Their validity was evaluated according to 8 predefined validation criteria and adapted to epidemiological studies; we summarized the findings and listed the most promising food intake biomarkers based on the evaluation. Biomarker candidates for alcohol, cereals, coffee, dairy, fats and oils, fruits, legumes, meat, seafood, sugar, tea, and vegetables were identified. Top candidates for all categories are specific to certain foods, have defined parent compounds, and their concentrations are unaffected by nonfood determinants. The correlations of candidate dietary biomarkers with habitual food intake were moderate to strong and their reproducibility over time ranged from low to high. For many biomarker candidates, critical information regarding dose response, correlation with habitual food intake, and reproducibility over time is yet unknown. The nutritional epidemiology field will benefit from the development of novel methods to combine single biomarkers to generate biomarker panels in combination with self-reported data. The most promising dietary biomarker candidates that reflect commonly consumed foods and food components for application in epidemiological studies were identified, and research required for their full validation was summarized.
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Affiliation(s)
- Rikard Landberg
- Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Prasoona Karra
- Department of Nutrition and Integrative Physiology, University of Utah, Salt Lake City, UT, USA
- Cancer Control and Population Sciences Program, Huntsman Cancer Institute, University of Utah Salt Lake City, UT, USA
| | - Rachel Hoobler
- Department of Nutrition and Integrative Physiology, University of Utah, Salt Lake City, UT, USA
- Cancer Control and Population Sciences Program, Huntsman Cancer Institute, University of Utah Salt Lake City, UT, USA
| | - Erikka Loftfield
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Inge Huybrechts
- International Agency for Research on Cancer, Nutrition and Metabolism Branch, Lyon, France
| | - Jodi I Rattner
- International Agency for Research on Cancer, Nutrition and Metabolism Branch, Lyon, France
| | - Stefania Noerman
- Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Liesel Claeys
- International Agency for Research on Cancer, Molecular Mechanisms and Biomarkers Group, Lyon, France
| | - Vanessa Neveu
- International Agency for Research on Cancer, Nutrition and Metabolism Branch, Lyon, France
| | - Nanna Hjort Vidkjaer
- Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Otto Savolainen
- Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Mary C Playdon
- Department of Nutrition and Integrative Physiology, University of Utah, Salt Lake City, UT, USA
- Cancer Control and Population Sciences Program, Huntsman Cancer Institute, University of Utah Salt Lake City, UT, USA
| | - Augustin Scalbert
- International Agency for Research on Cancer, Nutrition and Metabolism Branch, Lyon, France
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5
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Bernard L, Chen J, Kim H, Huang Z, Bazzano L, Qi L, He J, Rao VS, Potts KS, Kelly TN, Wong KE, Steffen LM, Yu B, Rhee EP, Rebholz CM. Serum Metabolomic Markers of Dairy Consumption: Results from the Atherosclerosis Risk in Communities Study and the Bogalusa Heart Study. J Nutr 2023; 153:2994-3002. [PMID: 37541543 PMCID: PMC10613758 DOI: 10.1016/j.tjnut.2023.08.001] [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: 03/23/2023] [Revised: 07/14/2023] [Accepted: 08/01/2023] [Indexed: 08/06/2023] Open
Abstract
BACKGROUND Dairy consumption is related to chronic disease risk; however, the measurement of dairy consumption has largely relied upon self-report. Untargeted metabolomics allows for the identification of objective markers of dietary intake. OBJECTIVES We aimed to identify associations between dietary dairy intake (total dairy, low-fat dairy, and high-fat dairy) and serum metabolites in 2 independent study populations of United States adults. METHODS Dietary intake was assessed with food frequency questionnaires. Multivariable linear regression models were used to estimate cross-sectional associations between dietary intake of dairy and 360 serum metabolites analyzed in 2 subgroups of the Atherosclerosis Risk in Communities study (ARIC; n = 3776). Results from the 2 subgroups were meta-analyzed using fixed effects meta-analysis. Significant meta-analyzed associations in the ARIC study were then tested in the Bogalusa Heart Study (BHS; n = 785). RESULTS In the ARIC study and BHS, the mean age was 54 and 48 years, 61% and 29% were Black, and the mean dairy intake was 1.7 and 1.3 servings/day, respectively. Twenty-nine significant associations between dietary intake of dairy and serum metabolites were identified in the ARIC study (total dairy, n = 14; low-fat dairy, n = 10; high-fat dairy, n = 5). Three associations were also significant in BHS: myristate (14:0) was associated with high-fat dairy, and pantothenate was associated with total dairy and low-fat dairy, but 23 of the 27 associations significant in the ARIC study and tested in BHS were not associated with dairy in BHS. CONCLUSIONS We identified metabolomic associations with dietary intake of dairy, including 3 associations found in 2 independent cohort studies. These results suggest that myristate (14:0) and pantothenate (vitamin B5) are candidate biomarkers of dairy consumption.
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Affiliation(s)
- Lauren Bernard
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Jingsha Chen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Hyunju Kim
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Zhijie Huang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States
| | - Lydia Bazzano
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States
| | - Lu Qi
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States
| | - Varun S Rao
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States
| | - Kaitlin S Potts
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States; Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, United States
| | - Tanika N Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States; Division of Nephrology, Department of Medicine, University of Illinois Chicago, Chicago, IL, United States
| | - Kari E Wong
- Metabolon, Research Triangle Park, Morrisville, NC, United States
| | - Lyn M Steffen
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN, United States
| | - Bing Yu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, United States
| | - Eugene P Rhee
- Division of Nephrology and Endocrine Unit, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Casey M Rebholz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States; Division of Nephrology, Department of Medicine, Johns Hopkins University, Baltimore, MD, United States.
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6
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Kozlowska L, Jagiello K, Ciura K, Sosnowska A, Zwiech R, Zbrog Z, Wasowicz W, Gromadzinska J. The Effects of Two Kinds of Dietary Interventions on Serum Metabolic Profiles in Haemodialysis Patients. Biomolecules 2023; 13:biom13050854. [PMID: 37238723 DOI: 10.3390/biom13050854] [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: 03/19/2023] [Revised: 05/15/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023] Open
Abstract
The goal of this study was to evaluate the effects of two kinds of 24-week dietary interventions in haemodialysis patients, a traditional nutritional intervention without a meal before dialysis (HG1) and implementation of a nutritional intervention with a meal served just before dialysis (HG2), in terms of analysing the differences in the serum metabolic profiles and finding biomarkers of dietary efficacy. These studies were performed in two homogenous groups of patients (n = 35 in both groups). Among the metabolites with the highest statistical significance between HG1 and HG2 after the end of the study, 21 substances were putatively annotated, which had potential significance in both of the most relevant metabolic pathways and those related to diet. After the 24 weeks of the dietary intervention, the main differences between the metabolomic profiles in the HG2 vs. HG1 groups were related to the higher signal intensities from amino acid metabolites: indole-3-carboxaldehyde, 5-(hydroxymethyl-2-furoyl)glycine, homocitrulline, 4-(glutamylamino)butanoate, tryptophol, gamma-glutamylthreonine, and isovalerylglycine. These metabolites are intermediates in the metabolic pathways of the necessary amino acids (Trp, Tyr, Phe, Leu, Ile, Val, Liz, and amino acids of the urea cycle) and are also diet-related intermediates (4-guanidinobutanoic acid, indole-3-carboxyaldehyde, homocitrulline, and isovalerylglycine).
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Affiliation(s)
- Lucyna Kozlowska
- Laboratory of Human Metabolism Research, Warsaw University of Life Sciences, 02-776 Warsaw, Poland
| | - Karolina Jagiello
- Department of Environmental Chemistry and Radiochemistry, Faculty of Chemistry, University of Gdansk, 80-308 Gdansk, Poland
- QSAR Lab Ltd., 80-172 Gdansk, Poland
| | - Krzesimir Ciura
- QSAR Lab Ltd., 80-172 Gdansk, Poland
- Department of Physical Chemistry, Medical University of Gdansk, 80-416 Gdansk, Poland
| | | | - Rafal Zwiech
- Dialysis Department, Norbert Barlicki Memorial Teaching Hospital No. 1, 90-001 Lodz, Poland
| | | | - Wojciech Wasowicz
- Department of Environmental and Biological Monitoring, Nofer Institute of Occupational Medicine, 91-348 Lodz, Poland
| | - Jolanta Gromadzinska
- Department of Environmental and Biological Monitoring, Nofer Institute of Occupational Medicine, 91-348 Lodz, Poland
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7
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Cohen CC, Huneault H, Accardi CJ, Jones DP, Liu K, Maner-Smith KM, Song M, Welsh JA, Ugalde-Nicalo PA, Schwimmer JB, Vos MB. Metabolome × Microbiome Changes Associated with a Diet-Induced Reduction in Hepatic Fat among Adolescent Boys. Metabolites 2023; 13:401. [PMID: 36984841 PMCID: PMC10053986 DOI: 10.3390/metabo13030401] [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: 02/07/2023] [Revised: 03/02/2023] [Accepted: 03/04/2023] [Indexed: 03/30/2023] Open
Abstract
Dietary sugar reduction is one therapeutic strategy for improving nonalcoholic fatty liver disease (NAFLD), and the underlying mechanisms for this effect warrant further investigation. Here, we employed metabolomics and metagenomics to examine systemic biological adaptations associated with dietary sugar restriction and (subsequent) hepatic fat reductions in youth with NAFLD. Data/samples were from a randomized controlled trial in adolescent boys (11-16 years, mean ± SD: 13.0 ± 1.9 years) with biopsy-proven NAFLD who were either provided a low free-sugar diet (LFSD) (n = 20) or consumed their usual diet (n = 20) for 8 weeks. Plasma metabolomics was performed on samples from all 40 participants by coupling hydrophilic interaction liquid chromatography (HILIC) and C18 chromatography with mass spectrometry. In a sub-sample (n = 8 LFSD group and n = 10 usual diet group), 16S ribosomal RNA (rRNA) sequencing was performed on stool to examine changes in microbial composition/diversity. The diet treatment was associated with differential expression of 419 HILIC and 205 C18 metabolite features (p < 0.05), which were enriched in amino acid pathways, including methionine/cysteine and serine/glycine/alanine metabolism (p < 0.05), and lipid pathways, including omega-3 and linoleate metabolism (p < 0.05). Quantified metabolites that were differentially changed in the LFSD group, compared to usual diet group, and representative of these enriched metabolic pathways included increased serine (p = 0.001), glycine (p = 0.004), 2-aminobutyric acid (p = 0.012), and 3-hydroxybutyric acid (p = 0.005), and decreased linolenic acid (p = 0.006). Microbiome changes included an increase in richness at the phylum level and changes in a few genera within Firmicutes. In conclusion, the LFSD treatment, compared to usual diet, was associated with metabolome and microbiome changes that may reflect biological mechanisms linking dietary sugar restriction to a therapeutic decrease in hepatic fat. Studies are needed to validate our findings and test the utility of these "omics" changes as response biomarkers.
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Affiliation(s)
- Catherine C. Cohen
- Department of Pediatrics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Helaina Huneault
- Nutrition & Health Sciences Doctoral Program, Laney Graduate School, Emory University, Atlanta, GA 30322, USA
| | - Carolyn J. Accardi
- Department of Medicine, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Dean P. Jones
- Department of Medicine, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Ken Liu
- Department of Medicine, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Kristal M. Maner-Smith
- Emory Integrated Lipidomics Core, School of Medicine, Emory University, Atlanta, GA 30322, USA
| | - Ming Song
- Department of Medicine, University of Louisville School of Medicine, Louisville, KY 40202, USA
- Hepatobiology and Toxicology Center, University of Louisville School of Medicine, Louisville, KY 40202, USA
| | - Jean A. Welsh
- Nutrition & Health Sciences Doctoral Program, Laney Graduate School, Emory University, Atlanta, GA 30322, USA
- Department of Pediatrics, School of Medicine, Emory University, Atlanta, GA 30322, USA
- Children’s Healthcare of Atlanta, Atlanta, GA 30322, USA
| | - Patricia A. Ugalde-Nicalo
- Department of Gastroenterology, Rady Children’s Hospital San Diego, San Diego, CA 92123, USA
- Department of Pediatrics, School of Medicine, University of California, San Diego, CA 92093, USA
| | - Jeffrey B. Schwimmer
- Department of Gastroenterology, Rady Children’s Hospital San Diego, San Diego, CA 92123, USA
- Department of Pediatrics, School of Medicine, University of California, San Diego, CA 92093, USA
| | - Miriam B. Vos
- Nutrition & Health Sciences Doctoral Program, Laney Graduate School, Emory University, Atlanta, GA 30322, USA
- Department of Pediatrics, School of Medicine, Emory University, Atlanta, GA 30322, USA
- Children’s Healthcare of Atlanta, Atlanta, GA 30322, USA
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8
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Shah RV, Steffen LM, Nayor M, Reis JP, Jacobs DR, Allen NB, Lloyd-Jones D, Meyer K, Cole J, Piaggi P, Vasan RS, Clish CB, Murthy VL. Dietary metabolic signatures and cardiometabolic risk. Eur Heart J 2023; 44:557-569. [PMID: 36424694 PMCID: PMC10169425 DOI: 10.1093/eurheartj/ehac446] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 06/23/2022] [Accepted: 07/28/2022] [Indexed: 11/27/2022] Open
Abstract
AIMS Observational studies of diet in cardiometabolic-cardiovascular disease (CM-CVD) focus on self-reported consumption of food or dietary pattern, with limited information on individual metabolic responses to dietary intake linked to CM-CVD. Here, machine learning approaches were used to identify individual metabolic patterns related to diet and relation to long-term CM-CVD in early adulthood. METHODS AND RESULTS In 2259 White and Black adults (age 32.1 ± 3.6 years, 45% women, 44% Black) in the Coronary Artery Risk Development in Young Adults (CARDIA) study, multivariate models were employed to identify metabolite signatures of food group and composite dietary intake across 17 food groups, 2 nutrient groups, and healthy eating index-2015 (HEI2015) diet quality score. A broad array of metabolites associated with diet were uncovered, reflecting food-related components/catabolites (e.g. fish and long-chain unsaturated triacylglycerols), interactions with host features (microbiome), or pathways broadly implicated in CM-CVD (e.g. ceramide/sphingomyelin lipid metabolism). To integrate diet with metabolism, penalized machine learning models were used to define a metabolite signature linked to a putative CM-CVD-adverse diet (e.g. high in red/processed meat, refined grains), which was subsequently associated with long-term diabetes and CVD risk numerically more strongly than HEI2015 in CARDIA [e.g. diabetes: standardized hazard ratio (HR): 1.62, 95% confidence interval (CI): 1.32-1.97, P < 0.0001; CVD: HR: 1.55, 95% CI: 1.12-2.14, P = 0.008], with associations replicated for diabetes (P < 0.0001) in the Framingham Heart Study. CONCLUSION Metabolic signatures of diet are associated with long-term CM-CVD independent of lifestyle and traditional risk factors. Metabolomics improves precision to identify adverse consequences and pathways of diet-related CM-CVD.
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Affiliation(s)
- Ravi V Shah
- Vanderbilt University Medical Center, Vanderbilt Clinical and Translational Research Center (VTRACC), Nashville, TN, USA
| | - Lyn M Steffen
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN, USA
| | - Matthew Nayor
- Cardiology Division, Boston University School of Medicine, Boston, MA, USA
| | - Jared P Reis
- Epidemiology Branch, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - David R Jacobs
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN, USA
| | - Norrina B Allen
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Donald Lloyd-Jones
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Katie Meyer
- Nutrition Department, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Joanne Cole
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Paolo Piaggi
- Department of Information Engineering, University of Pisa, Pisa, Italy
| | - Ramachandran S Vasan
- Sections of Preventive Medicine and Epidemiology and Cardiovascular Medicine, Department of Medicine, and Department of Epidemiology, Boston University Schools of Medicine and Public Health, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Clary B Clish
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Venkatesh L Murthy
- Department of Medicine and Radiology, University of Michigan, 1338 Cardiovascular Center, Ann Arbor, MI 48109-5873, USA
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9
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Mediterranean diet related metabolite profiles and cognitive performance. Clin Nutr 2023; 42:173-181. [PMID: 36599272 DOI: 10.1016/j.clnu.2022.12.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 12/13/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND & AIMS Evidence suggests that adherence to the Mediterranean diet (MedDiet) affects human metabolism and may contribute to better cognitive performance. However, the underlying mechanisms are not clear. OBJECTIVE We generated a metabolite profile for adherence to MedDiet and evaluated its cross-sectional association with aspects of cognitive performance. METHODS A total of 1250 healthy Greek middle-aged adults from the Epirus Health Study cohort were included in the analysis. Adherence to the MedDiet was assessed using the 14-point Mediterranean Diet Adherence Screener (MEDAS); cognition was measured using the Trail Making Test, the Verbal Fluency test and the Logical Memory test. A targeted metabolite profiling (n = 250 metabolites) approach was applied, using a high-throughput nuclear magnetic resonance platform. We used elastic net regularized regressions, with a 10-fold cross-validation procedure, to identify a metabolite profile for MEDAS. We evaluated the associations of the identified metabolite profile and MEDAS with cognitive tests, using multivariable linear regression models. RESULTS We identified a metabolite profile composed of 42 metabolites, mainly lipoprotein subclasses and fatty acids, significantly correlated with MedDiet adherence (Pearson r = 0.35, P-value = 5.5 × 10-37). After adjusting for known risk factors and accounting for multiple testing, the metabolite profile and MEDAS were not associated with the cognitive tests. CONCLUSIONS A plasma metabolite profile related to better adherence to the MedDiet was not associated with the tested aspects of cognitive performance, in a middle-aged Mediterranean population.
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10
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Kiani AK, Medori MC, Dhuli K, Donato K, Caruso P, Fioretti F, Perrone MA, Ceccarini MR, Manganotti P, Nodari S, Codini M, Beccari T, Bertelli M. Clinical assessment for diet prescription. JOURNAL OF PREVENTIVE MEDICINE AND HYGIENE 2022; 63:E102-E124. [PMID: 36479490 PMCID: PMC9710416 DOI: 10.15167/2421-4248/jpmh2022.63.2s3.2753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Accurate nutritional assessment based on dietary intake, physical activity, genetic makeup, and metabolites is required to prevent from developing and/or to treat people suffering from malnutrition as well as other nutrition related health issues. Nutritional screening ought to be considered as an essential part of clinical assessment for every patient on admission to healthcare setups, as well as on change in clinical conditions. Therefore, a detailed nutritional assessment must be performed every time nutritional imbalances are observed or suspected. In this review we have explored different techniques used for nutritional and physical activity assessment. Dietary Intake (DI) assessment is a multidimensional and complex process. Traditionally, dietary intake is assessed through self-report techniques, but due to limitations like biases, random errors, misestimations, and nutrient databases-linked errors, questions arise about the adequacy of self-reporting dietary intake procedures. Despite the limitations in assessing dietary intake (DI) and physical activity (PA), new methods and improved technologies such as biomarkers analysis, blood tests, genetic assessments, metabolomic analysis, DEXA (Dual-energy X-ray absorptiometry), MRI (Magnetic resonance imaging), and CT (computed tomography) scanning procedures have made much progress in the improvement of these measures. Genes also plays a crucial role in dietary intake and physical activity. Similarly, metabolites are also involved in different nutritional pathways. This is why integrating knowledge about the genetic and metabolic markers along with the latest technologies for dietary intake (DI) and physical activity (PA) assessment holds the key for accurately assessing one's nutritional status and prevent malnutrition and its related complications.
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Affiliation(s)
| | | | | | | | - Paola Caruso
- Clinical Unit of Neurology, Department of Medicine, Surgery and Health Sciences, Cattinara University Hospital ASUGI, University of Trieste, Trieste, Italy
| | - Francesco Fioretti
- Department of Cardiology, University of Brescia and ASST "Spedali Civili" Hospital, Brescia, Italy
| | | | | | - Paolo Manganotti
- Clinical Unit of Neurology, Department of Medicine, Surgery and Health Sciences, Cattinara University Hospital ASUGI, University of Trieste, Trieste, Italy
| | - Savina Nodari
- Department of Cardiology, University of Brescia and ASST "Spedali Civili" Hospital, Brescia, Italy
| | - Michela Codini
- Department of Pharmaceutical Sciences; University of Perugia, Perugia, Italy
| | - Tommaso Beccari
- Department of Pharmaceutical Sciences; University of Perugia, Perugia, Italy
| | - Matteo Bertelli
- MAGI EUREGIO, Bolzano, Italy
- MAGI'S LAB, Rovereto (TN), Italy
- MAGISNAT, Peachtree Corners (GA), USA
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11
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Levy J, Silva AM, De Carli E, Cacau LT, de Alvarenga JFR, Fiamoncini J, Benseñor IM, Lotufo PA, Marchioni DM. Biomarkers of Fruit Intake Using a Targeted Metabolomics Approach: an Observational Cross-Sectional Analysis of the ELSA-Brasil Study. J Nutr 2022; 152:2023-2030. [PMID: 35641174 DOI: 10.1093/jn/nxac115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 03/11/2022] [Accepted: 05/24/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Advances in technology have led to the identification of a greater number of metabolites related to diet. Although fruit intake biomarkers have been reported in some studies, these findings require further replication, considering the relevance of fruits for diet quality and health. OBJECTIVES The aim of this study was to explore the associations of a set of potential urinary biomarkers of diet, assessed using a targeted metabolomics approach, with self-reported fruit intake data in participants of a computer-assisted 24-h dietary recall (GloboDiet software) validation study. METHODS A total of 93 individuals aged 43-72 y, 54% female, participated in this study. The subjects were a subsample of the Longitudinal Study of Adult Health (ELSA-Brasil). A 24-h dietary recall was obtained with the aid of GloboDiet software matching a 24-h urine sample from each participant. Candidate biomarkers were selected in a literature search and identified in urine by LC coupled to high-resolution MS. Spearman correlation analyses were performed between fruit intake and each biomarker. RESULTS Spearman correlation analysis showed that total fruits intake was significantly correlated with citric acid (ρ = 0.213, P = 0.041), ferulic acid sulfate I (ρ = 0.240, P = 0.020), hesperetin glucuronide/homoeriodictyol glucuronide (ρ = 0.303, P = 0.003), hydroxyhippuric acid (ρ = 0.239, P = 0.021), homovanillic alcohol sulfate (ρ = 0.339, P = 0.001), methylgallic acid sulfate (ρ = 0.268, P = 0.009), naringenin glucuronide (NG; ρ = 0.278, P = 0.007), proline betaine (PB; ρ = 0.305, P = 0.003), syringic acid sulfate (ρ = 0.210, P = 0.044), and sinapic acid sulfate (ρ = 0.412, P < 0.001). Among them, 3 have been described in literature as promising biomarkers for intake of total fruit, oranges, and citrus fruit: NG, hesperetin glucuronide, and PB. CONCLUSIONS Associations of total fruits intake with urinary measurements indicate the potential usefulness of dietary biomarkers in the Brazilian population as a complement to self-reported dietary assessments.
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Affiliation(s)
- Jessica Levy
- Department of Nutrition, School of Public Health, University of São Paulo, São Paulo, Brazil
| | - Alexsandro Macedo Silva
- Department of Nutrition, School of Public Health, University of São Paulo, São Paulo, Brazil
| | - Eduardo De Carli
- Department of Nutrition, School of Public Health, University of São Paulo, São Paulo, Brazil
| | - Leandro Teixeira Cacau
- Department of Nutrition, School of Public Health, University of São Paulo, São Paulo, Brazil
| | - José Fernando Rinaldi de Alvarenga
- Food Research Center (FoRC), Department of Food and Experimental Nutrition, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
| | - Jarlei Fiamoncini
- Food Research Center (FoRC), Department of Food and Experimental Nutrition, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
| | - Isabela Martins Benseñor
- Clinical and Epidemiological Research Center, University Hospital, University of São Paulo, São Paulo, Brazil
| | - Paulo Andrade Lotufo
- Clinical and Epidemiological Research Center, University Hospital, University of São Paulo, São Paulo, Brazil
| | - Dirce Maria Marchioni
- Department of Nutrition, School of Public Health, University of São Paulo, São Paulo, Brazil
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12
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Chen GC, Chai JC, Xing J, Moon JY, Shan Z, Yu B, Mossavar-Rahman Y, Sotres-Alvarez D, Li J, Mattei J, Daviglus ML, Perkins DL, Burk RD, Boerwinkle E, Kaplan RC, Hu FB, Qi Q. Healthful eating patterns, serum metabolite profile and risk of diabetes in a population-based prospective study of US Hispanics/Latinos. Diabetologia 2022; 65:1133-1144. [PMID: 35357561 PMCID: PMC9890970 DOI: 10.1007/s00125-022-05690-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 01/25/2022] [Indexed: 02/03/2023]
Abstract
AIMS/HYPOTHESIS We aimed to evaluate associations of multiple recommended dietary patterns (i.e. the alternate Mediterranean diet [aMED], the Healthy Eating Index [HEI]-2015 and the healthful Plant-based Diet Index [hPDI]) with serum metabolite profile, and to examine dietary-pattern-associated metabolites in relation to incident diabetes. METHODS We included 2842 adult participants free from diabetes, CVD and cancer during baseline recruitment of the Hispanic Community Health Study/Study of Latinos. Metabolomics profiling of fasting serum was performed using an untargeted approach. Dietary pattern scores were derived using information collected by two 24 h dietary recalls. Dietary-pattern-associated metabolites were identified using multivariable survey linear regressions and their associations with incident diabetes were assessed using multivariable survey Poisson regressions with adjustment for traditional risk factors. RESULTS We identified eight metabolites (mannose, γ/β-tocopherol, N1-methylinosine, pyrraline and four amino acids) that were inversely associated with all dietary scores. These metabolites were detrimentally associated with various cardiometabolic risk traits, especially insulin resistance. A score comprised of these metabolites was associated with elevated risk of diabetes (RRper SD 1.54 [95% CI 1.29, 1.83]), and this detrimental association appeared to be attenuated or eliminated by having a higher score for aMED (pinteraction = 0.0001), HEI-2015 (pinteraction = 0.020) or hPDI (pinteraction = 0.023). For example, RR (95% CI) of diabetes for each SD increment in the metabolite score was 1.99 (1.44, 2.37), 1.67 (1.17, 2.38) and 1.08 (0.86, 1.34) across the lowest to the highest tertile of aMED score, respectively. CONCLUSIONS/INTERPRETATION Various recommended dietary patterns were inversely related to a group of metabolites that were associated with elevated risk of diabetes. Adhering to a healthful eating pattern may attenuate or eliminate the detrimental association between metabolically unhealthy serum metabolites and risk of diabetes.
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Affiliation(s)
- Guo-Chong Chen
- Department of Nutrition and Food Hygiene, School of Public Health, Soochow University, Suzhou, China
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Jin Choul Chai
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Jiaqian Xing
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Jee-Young Moon
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Zhilei Shan
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Bing Yu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yasmin Mossavar-Rahman
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Daniela Sotres-Alvarez
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jun Li
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Josiemer Mattei
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Martha L Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
| | - David L Perkins
- Department of Medicine, University of Illinois, Chicago, IL, USA
| | - Robert D Burk
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Pediatrics, Department of Microbiology and Immunology, Department of Obstetrics, Gynecology and Women's Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Robert C Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 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, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA.
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
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13
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Tanaka T, Talegawkar SA, Jin Y, Candia J, Tian Q, Moaddel R, Simonsick EM, Ferrucci L. Metabolomic Profile of Different Dietary Patterns and Their Association with Frailty Index in Community-Dwelling Older Men and Women. Nutrients 2022; 14:2237. [PMID: 35684039 PMCID: PMC9182888 DOI: 10.3390/nu14112237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 05/23/2022] [Accepted: 05/25/2022] [Indexed: 11/28/2022] Open
Abstract
Diet quality has been associated with slower rates of aging; however, the mechanisms underlying the role of a healthy diet in aging are not fully understood. To address this question, we aimed to identify plasma metabolomic biomarkers of dietary patterns and explored whether these metabolites mediate the relationship between diet and healthy aging, as assessed by the frailty index (FI) in 806 participants of the Baltimore Longitudinal Study of Aging. Adherence to different dietary patterns was evaluated using the Mediterranean diet score (MDS), Mediterranean-DASH Diet Intervention for Neurodegenerative Delay (MIND) score, and Alternate Healthy Eating Index-2010 (AHEI). Associations between diet, FI, and metabolites were assessed using linear regression models. Higher adherence to these dietary patterns was associated with lower FI. We found 236, 218, and 278 metabolites associated with the MDS, MIND, and AHEI, respectively, with 127 common metabolites, which included lipids, tri/di-glycerides, lyso/phosphatidylcholine, amino acids, bile acids, ceramides, cholesterol esters, fatty acids and acylcarnitines, indoles, and sphingomyelins. Metabolomic signatures of diet explained 28%, 37%, and 38% of the variance of the MDS, MIND, and AHEI, respectively. Signatures of MIND and AHEI mediated 55% and 61% of the association between each dietary pattern with FI, while the mediating effect of MDS signature was not statistically significant. The high number of metabolites associated with the different dietary patterns supports the notion of common mechanisms that underly the relationship between diet and frailty. The identification of multiple metabolite classes suggests that the effect of diet is complex and not mediated by any specific biomarkers. Furthermore, these metabolites may serve as biomarkers for poor diet quality to identify individuals for targeted dietary interventions.
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Affiliation(s)
- Toshiko Tanaka
- Longitudinal Studies Section, National Institute on Aging, Baltimore, MD 21224, USA; (J.C.); (Q.T.); (E.M.S.); (L.F.)
| | - Sameera A. Talegawkar
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA; (S.A.T.); (Y.J.)
| | - Yichen Jin
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, The George Washington University, Washington, DC 20052, USA; (S.A.T.); (Y.J.)
| | - Julián Candia
- Longitudinal Studies Section, National Institute on Aging, Baltimore, MD 21224, USA; (J.C.); (Q.T.); (E.M.S.); (L.F.)
| | - Qu Tian
- Longitudinal Studies Section, National Institute on Aging, Baltimore, MD 21224, USA; (J.C.); (Q.T.); (E.M.S.); (L.F.)
| | - Ruin Moaddel
- Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, MD 21224, USA;
| | - Eleanor M. Simonsick
- Longitudinal Studies Section, National Institute on Aging, Baltimore, MD 21224, USA; (J.C.); (Q.T.); (E.M.S.); (L.F.)
| | - Luigi Ferrucci
- Longitudinal Studies Section, National Institute on Aging, Baltimore, MD 21224, USA; (J.C.); (Q.T.); (E.M.S.); (L.F.)
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14
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Tariq A, Chen J, Yu B, Boerwinkle E, Coresh J, Grams ME, Rebholz CM. Metabolomics of Dietary Acid Load and Incident Chronic Kidney Disease. J Ren Nutr 2022; 32:292-300. [PMID: 34294549 PMCID: PMC8766597 DOI: 10.1053/j.jrn.2021.05.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 04/29/2021] [Accepted: 05/15/2021] [Indexed: 12/20/2022] Open
Abstract
OBJECTIVE Blood biomarkers of dietary intake are more objective than self-reported dietary intake. Metabolites associated with dietary acid load were previously identified in 2 chronic kidney disease (CKD) populations. We aimed to extend these findings to a general population, replicating their association with dietary acid load, and investigating whether the individual biomarkers were prospectively associated with incident CKD. METHODS Among 15,792 participants in the Atherosclerosis Risk in Communities cohort followed up from 1987 to 1989 (baseline) to 2019, we evaluated 3,844 black and white men and women with dietary and metabolomic data in cross-sectional and prospective analyses. We hypothesized that a higher dietary acid load (using equations for potential renal acid load and net endogenous acid production) was associated with lower serum levels of 12 previously identified metabolites: indolepropionylglycine, indolepropionate, N-methylproline, N-δ-acetylornithine, threonate, oxalate, chiro-inositol, methyl glucopyranoside, stachydrine, catechol sulfate, hippurate, and tartronate. In addition, we hypothesized that lower serum levels of these 12 metabolites were associated with higher risk of incident CKD. RESULTS Eleven out of 12 metabolites were significantly inversely associated with dietary acid load, after adjusting for demographics, socioeconomic status, health behaviors, health status, and estimated glomerular filtration rate: indolepropionylglycine, indolepropionate, N-methylproline, threonate, oxalate, chiro-inositol, catechol sulfate, hippurate, methyl glucopyranoside (α + β), stachydrine, and tartronate. N-methylproline was inversely associated with incident CKD (hazard ratio: 0.95, 95% confidence interval: 0.91, 0.99, P = .01). The metabolomic biomarkers of dietary acid load significantly improved prediction of elevated dietary acid load estimated using dietary data, beyond covariates (difference in C statistics: 0.021-0.077, P ≤ 1.08 × 10-3). CONCLUSION Inverse associations between candidate biomarkers of dietary acid load were replicated in a general population. N-methylproline, representative of citrus fruit consumption, is a promising marker of dietary acid load and could represent an important pathway between dietary acid load and CKD.
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Affiliation(s)
- Anam Tariq
- Division of Nephrology, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Jingsha Chen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Bing Yu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas Health Science Center at Houston, Houston, Texas
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas Health Science Center at Houston, Houston, Texas
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Morgan E Grams
- Division of Nephrology, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Casey M Rebholz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
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15
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Kim H, Yu B, Li X, Wong KE, Boerwinkle E, Seidelmann SB, Levey AS, Rhee EP, Coresh J, Rebholz CM. Serum metabolomic signatures of plant-based diets and incident chronic kidney disease. Am J Clin Nutr 2022; 116:151-164. [PMID: 35218183 PMCID: PMC9257476 DOI: 10.1093/ajcn/nqac054] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 02/24/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Greater adherence to plant-based diets is associated with a lower risk of incident chronic kidney disease (CKD). Metabolomics can help identify blood biomarkers of plant-based diets and enhance understanding of underlying mechanisms. OBJECTIVES Using untargeted metabolomics, we aimed to identify metabolites associated with 4 plant-based diet indices (PDIs) (overall PDI, provegetarian diet, healthful PDI, and unhealthful PDI) and incident CKD in 2 subgroups within the Atherosclerosis Risk in Communities study. METHODS We calculated 4 PDIs based on participants' responses on an FFQ. We used multivariable linear regression to examine the association between 4 PDIs and 374 individual metabolites, adjusting for confounders. We used Cox proportional hazards regression to evaluate associations between PDI-related metabolites and incident CKD. Estimates were meta-analyzed across 2 subgroups (n1 = 1762; n2 = 1960). We calculated C-statistics to assess whether metabolites improved the prediction of those in the highest quintile compared to the lower 4 quintiles of PDIs, and whether PDI- and CKD-related metabolites predicted incident CKD beyond the CKD prediction model. RESULTS We identified 82 significant PDI-metabolite associations (overall PDI = 27; provegetarian = 17; healthful PDI = 20; unhealthful PDI = 18); 11 metabolites overlapped across the overall PDI, provegetarian diet, and healthful PDI. The addition of metabolites improved prediction of those in the highest quintile as opposed to the lower 4 quintiles of PDIs compared with participant characteristics alone (range of differences in C-statistics = 0.026-0.104; P value ≤ 0.001 for all tests). Six PDI-related metabolites (glycerate, 1,5-anhydroglucitol, γ-glutamylalanine, γ-glutamylglutamate, γ-glutamylleucine, γ-glutamylvaline), involved in glycolysis, gluconeogenesis, pyruvate metabolism, and γ-glutamyl peptide metabolism, were significantly associated with incident CKD and improved prediction of incident CKD beyond the CKD prediction model (difference in C-statistics for 6 metabolites = 0.005; P value = 0.006). CONCLUSIONS In a community-based study of US adults, we identified metabolites that were related to plant-based diets and predicted incident CKD. These metabolites highlight pathways through which plant-based diets are associated with incident CKD.
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Affiliation(s)
- Hyunju Kim
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA,Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
| | - Bing Yu
- Department of Epidemiology, Human Genetics & Environmental Sciences, University of Texas Health Sciences Center at Houston School of Public Health, Houston, TX, USA
| | - Xin Li
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
| | | | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics & Environmental Sciences, University of Texas Health Sciences Center at Houston School of Public Health, Houston, TX, USA
| | - Sara B Seidelmann
- College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Andrew S Levey
- Division of Nephrology, Tufts Medical Center, Boston, MA, USA
| | - Eugene P Rhee
- Nephrology Division and Endocrine Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA,Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA,Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
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16
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Rafiq T, Azab SM, Teo KK, Thabane L, Anand SS, Morrison KM, de Souza RJ, Britz-McKibbin P. Nutritional Metabolomics and the Classification of Dietary Biomarker Candidates: A Critical Review. Adv Nutr 2021; 12:2333-2357. [PMID: 34015815 PMCID: PMC8634495 DOI: 10.1093/advances/nmab054] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 01/20/2021] [Accepted: 04/06/2021] [Indexed: 02/06/2023] Open
Abstract
Recent advances in metabolomics allow for more objective assessment of contemporary food exposures, which have been proposed as an alternative or complement to self-reporting of food intake. However, the quality of evidence supporting the utility of dietary biomarkers as valid measures of habitual intake of foods or complex dietary patterns in diverse populations has not been systematically evaluated. We reviewed nutritional metabolomics studies reporting metabolites associated with specific foods or food groups; evaluated the interstudy repeatability of dietary biomarker candidates; and reported study design, metabolomic approach, analytical technique(s), and type of biofluid analyzed. A comprehensive literature search of 5 databases (PubMed, EMBASE, Web of Science, BIOSIS, and CINAHL) was conducted from inception through December 2020. This review included 244 studies, 169 (69%) of which were interventional studies (9 of these were replicated in free-living participants) and 151 (62%) of which measured the metabolomic profile of serum and/or plasma. Food-based metabolites identified in ≥1 study and/or biofluid were associated with 11 food-specific categories or dietary patterns: 1) fruits; 2) vegetables; 3) high-fiber foods (grain-rich); 4) meats; 5) seafood; 6) pulses, legumes, and nuts; 7) alcohol; 8) caffeinated beverages, teas, and cocoas; 9) dairy and soya; 10) sweet and sugary foods; and 11) complex dietary patterns and other foods. We conclude that 69 metabolites represent good candidate biomarkers of food intake. Quantitative measurement of these metabolites will advance our understanding of the relation between diet and chronic disease risk and support evidence-based dietary guidelines for global health.
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Affiliation(s)
- Talha Rafiq
- Medical Sciences Graduate Program, Faculty of Health Sciences, McMaster University, Hamilton, Canada
- Population Health Research Institute, Hamilton Health Sciences, McMaster University, Hamilton, Canada
| | - Sandi M Azab
- Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Canada
- Department of Pharmacognosy, Alexandria University, Alexandria, Egypt
| | - Koon K Teo
- Population Health Research Institute, Hamilton Health Sciences, McMaster University, Hamilton, Canada
- Department of Health Research Methods, Evidence & Impact, McMaster University, Hamilton, Canada
- Department of Medicine, McMaster University, Hamilton, Canada
| | - Lehana Thabane
- Department of Health Research Methods, Evidence & Impact, McMaster University, Hamilton, Canada
| | - Sonia S Anand
- Population Health Research Institute, Hamilton Health Sciences, McMaster University, Hamilton, Canada
- Department of Health Research Methods, Evidence & Impact, McMaster University, Hamilton, Canada
- Department of Medicine, McMaster University, Hamilton, Canada
| | | | - Russell J de Souza
- Population Health Research Institute, Hamilton Health Sciences, McMaster University, Hamilton, Canada
- Department of Health Research Methods, Evidence & Impact, McMaster University, Hamilton, Canada
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17
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He WJ, Chen J, Razavi AC, Hu EA, Grams ME, Yu B, Parikh CR, Boerwinkle E, Bazzano L, Qi L, Kelly TN, Coresh J, Rebholz CM. Metabolites Associated with Coffee Consumption and Incident Chronic Kidney Disease. Clin J Am Soc Nephrol 2021; 16:1620-1629. [PMID: 34737201 PMCID: PMC8729408 DOI: 10.2215/cjn.05520421] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 08/25/2021] [Indexed: 02/04/2023]
Abstract
BACKGROUND AND OBJECTIVES Moderate coffee consumption has been associated with lower risk of CKD; however, the exact biologic mechanisms underlying this association are unknown. Metabolomic profiling may identify metabolic pathways that explain the association between coffee and CKD. The goal of this study was to identify serum metabolites associated with coffee consumption and examine the association between these coffee-associated metabolites and incident CKD. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS Using multivariable linear regression, we identified coffee-associated metabolites among 372 serum metabolites available in two subsamples of the Atherosclerosis Risk in Communities study (ARIC; n=3811). Fixed effects meta-analysis was used to pool the results from the two ARIC study subsamples. Associations between coffee and metabolites were replicated in the Bogalusa Heart Study (n=1043). Metabolites with significant associations with coffee in both cohorts were then evaluated for their prospective associations with incident CKD in the ARIC study using Cox proportional hazards regression. RESULTS In the ARIC study, mean (SD) age was 54 (6) years, 56% were daily coffee drinkers, and 32% drank >2 cups per day. In the Bogalusa Heart Study, mean (SD) age was 48 (5) years, 57% were daily coffee drinkers, and 38% drank >2 cups per day. In a meta-analysis of two subsamples of the ARIC study, 41 metabolites were associated with coffee consumption, of which 20 metabolites replicated in the Bogalusa Heart Study. Three of these 20 coffee-associated metabolites were associated with incident CKD in the ARIC study. CONCLUSIONS We detected 20 unique serum metabolites associated with coffee consumption in both the ARIC study and the Bogalusa Heart Study, and three of these 20 candidate biomarkers of coffee consumption were associated with incident CKD. One metabolite (glycochenodeoxycholate), a lipid involved in primary bile acid metabolism, may contribute to the favorable kidney health outcomes associated with coffee consumption. Two metabolites (O-methylcatechol sulfate and 3-methyl catechol sulfate), both of which are xenobiotics involved in benzoate metabolism, may represent potential harmful aspects of coffee on kidney health.
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Affiliation(s)
- William J. He
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Jingsha Chen
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Alexander C. Razavi
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana
- Department of Medicine, Tulane University School of Medicine, New Orleans, Louisiana
| | - Emily A. Hu
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Morgan E. Grams
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Division of Nephrology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Bing Yu
- Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas Health School of Public Health, Houston, Texas
| | - Chirag R. Parikh
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Division of Nephrology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas Health School of Public Health, Houston, Texas
| | - Lydia Bazzano
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana
- Department of Medicine, Tulane University School of Medicine, New Orleans, Louisiana
| | - Lu Qi
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana
- Department of Medicine, Tulane University School of Medicine, New Orleans, Louisiana
| | - Tanika N. Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana
| | - Josef Coresh
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Casey M. Rebholz
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Division of Nephrology, Department of Medicine, Johns Hopkins University, Baltimore, Maryland
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18
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A Scoping Review of the Application of Metabolomics in Nutrition Research: The Literature Survey 2000-2019. Nutrients 2021; 13:nu13113760. [PMID: 34836016 PMCID: PMC8623534 DOI: 10.3390/nu13113760] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 10/19/2021] [Accepted: 10/19/2021] [Indexed: 12/29/2022] Open
Abstract
Nutrimetabolomics is an emerging field in nutrition research, and it is expected to play a significant role in deciphering the interaction between diet and health. Through the development of omics technology over the last two decades, the definition of food and nutrition has changed from sources of energy and major/micro-nutrients to an essential exposure factor that determines health risks. Furthermore, this new approach has enabled nutrition research to identify dietary biomarkers and to deepen the understanding of metabolic dynamics and the impacts on health risks. However, so far, candidate markers identified by metabolomics have not been clinically applied and more efforts should be made to validate those. To help nutrition researchers better understand the potential of its application, this scoping review outlined the historical transition, recent focuses, and future prospects of the new realm, based on trends in the number of human research articles from the early stage of 2000 to the present of 2019 by searching the Medical Literature Analysis and Retrieval System Online (MEDLINE). Among them, objective dietary assessment, metabolic profiling, and health risk prediction were positioned as three of the principal applications. The continued growth will enable nutrimetabolomics research to contribute to personalized nutrition in the future.
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19
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Nayor M, Shah SH, Murthy V, Shah RV. Molecular Aspects of Lifestyle and Environmental Effects in Patients With Diabetes: JACC Focus Seminar. J Am Coll Cardiol 2021; 78:481-495. [PMID: 34325838 DOI: 10.1016/j.jacc.2021.02.070] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 01/07/2021] [Accepted: 02/01/2021] [Indexed: 01/04/2023]
Abstract
Diabetes is characterized as an integrated condition of dysregulated metabolism across multiple tissues, with well-established consequences on the cardiovascular system. Recent advances in precision phenotyping in biofluids and tissues in large human observational and interventional studies have afforded a unique opportunity to translate seminal findings in models and cellular systems to patients at risk for diabetes and its complications. Specifically, techniques to assay metabolites, proteins, and transcripts, alongside more recent assessment of the gut microbiome, underscore the complexity of diabetes in patients, suggesting avenues for precision phenotyping of risk, response to intervention, and potentially novel therapies. In addition, the influence of external factors and inputs (eg, activity, diet, medical therapies) on each domain of molecular characterization has gained prominence toward better understanding their role in prevention. Here, the authors provide a broad overview of the role of several of these molecular domains in human translational investigation in diabetes.
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Affiliation(s)
- Matthew Nayor
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA. https://twitter.com/MattNayor
| | - Svati H Shah
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, North Carolina, USA; Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA. https://twitter.com/SvatiShah
| | - Venkatesh Murthy
- Division of Cardiovascular Medicine, Department of Medicine, University of Michigan, Ann Arbor, Michigan, USA; Frankel Cardiovascular Center, University of Michigan, Ann Arbor, Michigan, USA. https://twitter.com/venkmurthy
| | - Ravi V Shah
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
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20
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Moslehi N, Marzbani R, Rezadoost H, Mirmiran P, Ramezani Tehrani F, Azizi F. Serum metabolomics study of the association between dairy intake and the anti-müllerian hormone annual decline rate. Nutr Metab (Lond) 2021; 18:66. [PMID: 34176512 PMCID: PMC8237474 DOI: 10.1186/s12986-021-00591-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 06/12/2021] [Indexed: 02/08/2023] Open
Abstract
Background Dairy intake has been implicated in later ovarian aging but mechanism underlying the association is unknown. This study aimed to investigate (1) associations between dairy intake and metabolites previously shown related to anti-müllerian hormone (AMH) decline rate; (2) mediating roles of these metabolites in the prospective association of total dairy consumption with odds of AMH fast decline rate.
Methods The participants comprised 186 reproductive-aged women randomly selected from the Tehran Lipid and Glucose Study. AMH was measured at baseline (1999–2001) and the 5th follow-up (2014–2017), and dietary data was collected at the second follow-up (2005–2008) using a food frequency questionnaire. Untargeted metabolomics was performed by gas chromatography–mass spectrometry using fasting-serum samples of the second follow-up. We analyzed dairy intake in association with the eight metabolites linked to the higher odds of AMH fast decline rate using linear regression with the Benjamini–Hochberg false discovery correction. Mediatory roles of the metabolites were assessed by bootstrapping. Results Mean age and BMI of the participants at metabolomics assessment were 44.7 ± 5.87 years and 28.8 ± 4.88 kg/m2, respectively. Phosphate, branched-chain amino acids (BCAAs), and proline decreased significantly from the first to the third tertile of total dairy intake. Total dairy as a continuous variable inversely associated with phosphate (beta = −0.166; p value = 0.018), valine (beta = −0.176; p value = 0.016), leucine (beta = −0.226; p value = 0.002), proline (beta = −0.219; p value = 0.003), and urea (beta = −0.156; p = 0.035) after accounting for all potential covariates and correction for multiplicity (q-value < 0.1). Fermented dairy showed similar results, but milk did not associate with any of the metabolites. Simple mediation showed significant indirect effects for phosphate, proline, and BCAAs but not urea. Entering the sum of phosphate, proline, and BCAAs as a mediator, the metabolites' total indirect effects were significant [β = −0.12 (95% CIs − 0.26, − 0.04)]. In contrast, the direct association of total dairy intake with the fast decline in AMH was non-significant [β = −0.28 (95% CIs − 0.67, 0.10)]. Conclusions Total dairy was inversely associated with AMH decline rate-related metabolites. Inverse association of dairy intakes with the odds of AMH fast decline rate was indirectly mediated by lower phosphate, proline, and BCAAs.
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Affiliation(s)
- Nazanin Moslehi
- Nutrition and Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Rezvan Marzbani
- Department of Phytochemistry, Medicinal Plants and Drugs Research Institute, Shahid Beheshti University, Tehran, Iran
| | - Hassan Rezadoost
- Department of Phytochemistry, Medicinal Plants and Drugs Research Institute, Shahid Beheshti University, Tehran, Iran
| | - Parvin Mirmiran
- Nutrition and Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. .,Department of Clinical Nutrition and Dietetics, Faculty of Nutrition and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Fahimeh Ramezani Tehrani
- Reproductive Endocrinology Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Fereidoun Azizi
- Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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21
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Thacker JB, He C, Pennathur S. Quantitative analysis of γ-glutamylisoleucine, γ-glutamylthreonine, and γ-glutamylvaline in HeLa cells using UHPLC-MS/MS. J Sep Sci 2021; 44:2898-2907. [PMID: 34042281 DOI: 10.1002/jssc.202001266] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 05/20/2021] [Accepted: 05/25/2021] [Indexed: 12/30/2022]
Abstract
γ-Glutamylpeptides have been identified as potential biomarkers for a number of diseases including cancer, diabetes, and liver disease. In this study, we developed and validated a novel quantitative analytical strategy for measuring γ-glutamylisoleucine, γ-glutamylthreonine, and γ-glutamylvaline, all of which have been previously reported as potential biomarkers for prostate cancer in HeLa cells using ultra-high-performance liquid chromatography-tandem mass spectrometry. A BEH C18 column was used as the stationary phase. Mobile phase A was 99:1 water:formic acid and mobile phase B was acetonitrile. Chemical isotope labeling using benzoyl chloride was used as the internal standardization strategy. Sample preparation consisted of the addition of water to a frozen cell pellet, sonication, derivatization, centrifugation, and subsequent addition of an internal standard solution. The method was validated for selectivity, accuracy, precision, linearity, and stability. The determined concentrations of γ-glutamylisoleucine, γ-glutamylthreonine, and γ-glutamylvaline in HeLa cells were 1.92 ± 0.06, 10.8 ± 0.4, and 1.96 ± 0.04 pmol/mg protein, respectively. In addition, the qualitative analysis of these analytes in human serum was achieved using a modified sample preparation strategy. To the best of our knowledge, this is the first report of the use of benzoyl chloride for chemical isotope labeling for metabolite quantitation in cells.
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Affiliation(s)
- Jonathan B Thacker
- Division of Nephrology, Department of Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Chenchen He
- Division of Nephrology, Department of Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Subramaniam Pennathur
- Division of Nephrology, Department of Medicine, University of Michigan, Ann Arbor, Michigan, USA.,Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, Michigan, USA
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22
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Identification and Reproducibility of Urinary Metabolomic Biomarkers of Habitual Food Intake in a Cross-Sectional Analysis of the Cancer Prevention Study-3 Diet Assessment Sub-Study. Metabolites 2021; 11:metabo11040248. [PMID: 33920694 PMCID: PMC8072637 DOI: 10.3390/metabo11040248] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 04/06/2021] [Accepted: 04/14/2021] [Indexed: 11/16/2022] Open
Abstract
Previous cross-sectional metabolomics studies have identified many potential dietary biomarkers, mostly in blood. Few studies examined urine samples although urine is preferred for dietary biomarker discovery. Furthermore, little is known regarding the reproducibility of urinary metabolomic biomarkers over time. We aimed to identify urinary metabolomic biomarkers of diet and assess their reproducibility over time. We conducted a metabolomics analysis among 648 racially/ethnically diverse men and women in the Diet Assessment Sub-study of the Cancer Prevention Study-3 cohort to examine the correlation between >100 food groups/items [101 by a food frequency questionnaire (FFQ), and 105 by repeated 24 h diet recalls (24HRs)] and 1391 metabolites measured in 24 h urine sample replicates, six months apart. Diet-metabolite associations were examined by Pearson's partial correlation analysis. Biomarkers were evaluated for prediction accuracy assessed using area under the curve (AUC) calculated from the receiver operating characteristic curve and for reproducibility assessed using intraclass correlation coefficients (ICCs). A total of 1708 diet-metabolite associations were identified after Bonferroni correction for multiple comparisons and restricting correlation coefficients to >0.2 or <-0.2 (1570 associations using the FFQ and 933 using 24HRs), 513 unique metabolites correlated with 79 food groups/items. The median ICCs of the 513 putative biomarkers was 0.53 (interquartile range 0.42-0.62). In this study, with comprehensive dietary data and repeated 24 h urinary metabolic profiles, we identified a large number of diet-metabolite correlations and replicated many found in previous studies. Our findings revealed the promise of urine samples for dietary biomarker discovery in a large cohort study and provide important information on biomarker reproducibility, which could facilitate their utilization in future clinical and epidemiological studies.
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23
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Drouin-Chartier JP, Hernández-Alonso P, Guasch-Ferré M, Ruiz-Canela M, Li J, Wittenbecher C, Razquin C, Toledo E, Dennis C, Corella D, Estruch R, Fitó M, Eliassen AH, Tobias DK, Ascherio A, Mucci LA, Rexrode KM, Karlson EW, Costenbader KH, Fuchs CS, Liang L, Clish CB, Martínez-González MA, Salas-Salvadó J, Hu FB. Dairy consumption, plasma metabolites, and risk of type 2 diabetes. Am J Clin Nutr 2021; 114:163-174. [PMID: 33742198 PMCID: PMC8246603 DOI: 10.1093/ajcn/nqab047] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 02/08/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Epidemiologic studies have reported a modest inverse association between dairy consumption and the risk of type 2 diabetes (T2D). Whether plasma metabolite profiles associated with dairy consumption reflect this relationship remains unknown. OBJECTIVES We aimed to identify the plasma metabolites associated with total and specific dairy consumption, and to evaluate the association between the identified multi-metabolite profiles and T2D. METHODS The discovery population included 1833 participants from the Prevención con Dieta Mediterránea (PREDIMED) trial. The confirmatory cohorts included 1522 PREDIMED participants at year 1 of the trial and 4932 participants from the Nurses' Health Studies (NHS), Nurses' Health Study II (NHSII), and Health Professionals Follow-Up Study US-based cohorts. Dairy consumption was assessed using validated FFQs. Plasma metabolites (n = 385) were profiled using LC-MS. We identified the dairy-related metabolite profiles using elastic net regularized regressions with a 10-fold cross-validation procedure. We evaluated the associations between the metabolite profiles and incident T2D in the discovery and the confirmatory cohorts. RESULTS Total dairy intake was associated with 38 metabolites. C14:0 sphingomyelin (positive coefficient), C34:0 phosphatidylethanolamine (positive coefficient), and γ-butyrobetaine (negative coefficient) were associated in a directionally similar fashion with total and specific (milk, yogurt, cheese) dairy consumption. The Pearson correlation coefficients between self-reported total dairy intake and predicted total dairy intake based on the corresponding multi-metabolite profile were 0.37 (95% CI, 0.33-0.40) in the discovery cohort and 0.16 (95% CI, 0.13-0.19) in the US confirmatory cohort. After adjusting for T2D risk factors, a higher total dairy intake-related metabolite profile score was associated with a lower T2D risk [HR per 1 SD; discovery cohort: 0.76 (95% CI, 0.63-0.90); US confirmatory cohort: 0.88 (95% CI, 0.78-0.99)]. CONCLUSIONS Total dairy intake was associated with 38 metabolites, including 3 consistently associated with dairy subtypes (C14:0 sphingomyelin, C34:0 phosphatidylethanolamine, γ-butyrobetaine). A score based on the 38 identified metabolites showed an inverse association with T2D risk in Spanish and US populations.
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Affiliation(s)
| | - Pablo Hernández-Alonso
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Hospital Universitari San Joan de Reus, Reus, Spain,Institut d'Investigació Pere Virgili (IISPV), Reus, Spain,Consorcio Centro de Investigación Biomedica en Red Fisiopatologia de la Obesidad y Nutricion, Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Unidad de Gestión Clínica de Endocrinología y Nutrición del Hospital Virgen de la Victoria, Instituto de Investigación Biomédica de Málaga (IBIMA), Málaga, Spain
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Miguel Ruiz-Canela
- Consorcio Centro de Investigación Biomedica en Red Fisiopatologia de la Obesidad y Nutricion, Instituto de Salud Carlos III (ISCIII), Madrid, Spain,University of Navarra, Department of Preventive Medicine and Public Health, Pamplona, Spain
| | - 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
| | - Clemens Wittenbecher
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Cristina Razquin
- Consorcio Centro de Investigación Biomedica en Red Fisiopatologia de la Obesidad y Nutricion, Instituto de Salud Carlos III (ISCIII), Madrid, Spain,University of Navarra, Department of Preventive Medicine and Public Health, Pamplona, Spain
| | - Estefanía Toledo
- Consorcio Centro de Investigación Biomedica en Red Fisiopatologia de la Obesidad y Nutricion, Instituto de Salud Carlos III (ISCIII), Madrid, Spain,University of Navarra, Department of Preventive Medicine and Public Health, Pamplona, Spain
| | - Courtney Dennis
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Dolores Corella
- Consorcio Centro de Investigación Biomedica en Red Fisiopatologia de la Obesidad y Nutricion, Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Ramon Estruch
- Consorcio Centro de Investigación Biomedica en Red Fisiopatologia de la Obesidad y Nutricion, Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Internal Medicine, Department of Endocrinology and Nutrition Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Montserrat Fitó
- Consorcio Centro de Investigación Biomedica en Red Fisiopatologia de la Obesidad y Nutricion, Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Cardiovascular and Nutrition Research Group, Institut de Recerca Hospital del Mar, Barcelona, Spain
| | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Deirdre K Tobias
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA,Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alberto Ascherio
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Lorelei A Mucci
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Kathryn M Rexrode
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA,Division of Women`s Health, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Elizabeth W Karlson
- Division of Rheumatology, Inflammation and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Karen H Costenbader
- Division of Rheumatology, Inflammation and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Charles S Fuchs
- Yale Cancer Center, Yale University School of Medicine, New Haven, CT, 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
| | - Clary B Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Miguel A Martínez-González
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA,Consorcio Centro de Investigación Biomedica en Red Fisiopatologia de la Obesidad y Nutricion, Instituto de Salud Carlos III (ISCIII), Madrid, Spain,University of Navarra, Department of Preventive Medicine and Public Health, Pamplona, Spain
| | - Jordi Salas-Salvadó
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Hospital Universitari San Joan de Reus, Reus, Spain,Institut d'Investigació Pere Virgili (IISPV), Reus, Spain,Consorcio Centro de Investigación Biomedica en Red Fisiopatologia de la Obesidad y Nutricion, Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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24
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Lightning TA, Gesteira TF, Mueller JW. Steroid disulfates - Sulfation double trouble. Mol Cell Endocrinol 2021; 524:111161. [PMID: 33453296 DOI: 10.1016/j.mce.2021.111161] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 12/24/2020] [Accepted: 01/05/2021] [Indexed: 02/08/2023]
Abstract
Sulfation pathways have recently come into the focus of biomedical research. For steroid hormones and related compounds, sulfation represents an additional layer of regulation as sulfated steroids are more water-soluble and tend to be biologically less active. For steroid diols, an additional sulfation is possible, carried out by the same sulfotransferases that catalyze the first sulfation step. The steroid disulfates that are formed are the focus of this review. We discuss both their biochemical production as well as their putative biological function. Steroid disulfates have also been linked to various clinical conditions in numerous untargeted metabolomics studies. New analytical techniques exploring the biosynthetic routes of steroid disulfates have led to novel insights, changing our understanding of sulfation in human biology. They promise a bright future for research into sulfation pathways, hopefully too for the diagnosis and treatment of several associated diseases.
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Affiliation(s)
- Thomas Alec Lightning
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
| | - Tarsis F Gesteira
- College of Optometry, University of Houston, Houston, TX, USA; Optimvia, LLC, Batavia, OH, USA
| | - Jonathan Wolf Mueller
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK; Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, Birmingham, UK.
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25
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Shibutami E, Ishii R, Harada S, Kurihara A, Kuwabara K, Kato S, Iida M, Akiyama M, Sugiyama D, Hirayama A, Sato A, Amano K, Sugimoto M, Soga T, Tomita M, Takebayashi T. Charged metabolite biomarkers of food intake assessed via plasma metabolomics in a population-based observational study in Japan. PLoS One 2021; 16:e0246456. [PMID: 33566801 PMCID: PMC7875413 DOI: 10.1371/journal.pone.0246456] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 01/19/2021] [Indexed: 11/18/2022] Open
Abstract
Food intake biomarkers can be critical tools that can be used to objectively assess dietary exposure for both epidemiological and clinical nutrition studies. While an accurate estimation of food intake is essential to unravel associations between the intake and specific health conditions, random and systematic errors affect self-reported assessments. This study aimed to clarify how habitual food intake influences the circulating plasma metabolome in a free-living Japanese regional population and to identify potential food intake biomarkers. To achieve this aim, we conducted a cross-sectional analysis as part of a large cohort study. From a baseline survey of the Tsuruoka Metabolome Cohort Study, 7,012 eligible male and female participants aged 40-69 years were chosen for this study. All data on patients' health status and dietary intake were assessed via a food frequency questionnaire, and plasma samples were obtained during an annual physical examination. Ninety-four charged plasma metabolites were measured using capillary electrophoresis mass spectrometry, by a non-targeted approach. Statistical analysis was performed using partial-least-square regression. A total of 21 plasma metabolites were likely to be associated with long-term food intake of nine food groups. In particular, the influential compounds in each food group were hydroxyproline for meat, trimethylamine-N-oxide for fish, choline for eggs, galactarate for dairy, cystine and betaine for soy products, threonate and galactarate for carotenoid-rich vegetables, proline betaine for fruits, quinate and trigonelline for coffee, and pipecolate for alcohol, and these were considered as prominent food intake markers in Japanese eating habits. A set of circulating plasma metabolites was identified as potential food intake biomarkers in the Japanese community-dwelling population. These results will open the way for the application of new reliable dietary assessment tools not by self-reported measurements but through objective quantification of biofluids.
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Affiliation(s)
- Eriko Shibutami
- Graduate School of Health Management, Keio University, Fujisawa, Kanagawa, Japan
| | - Ryota Ishii
- Biostatistics Unit, Clinical and Translational Research Center, Keio University Hospital, Tokyo, Japan
| | - Sei Harada
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Ayako Kurihara
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Kazuyo Kuwabara
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Suzuka Kato
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Miho Iida
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Miki Akiyama
- Graduate School of Health Management, Keio University, Fujisawa, Kanagawa, Japan
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
- Faculty of Environment and Information Studies, Keio University, Fujisawa, Kanagawa, Japan
| | - Daisuke Sugiyama
- Graduate School of Health Management, Keio University, Fujisawa, Kanagawa, Japan
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
- Faculty of Nursing and Medical Care, Keio University, Fujisawa, Kanagawa, Japan
| | - Akiyoshi Hirayama
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Asako Sato
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Kaori Amano
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Masahiro Sugimoto
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Tomoyoshi Soga
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
- Faculty of Environment and Information Studies, Keio University, Fujisawa, Kanagawa, Japan
| | - Masaru Tomita
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
- Faculty of Environment and Information Studies, Keio University, Fujisawa, Kanagawa, Japan
| | - Toru Takebayashi
- Graduate School of Health Management, Keio University, Fujisawa, Kanagawa, Japan
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
- * E-mail:
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26
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Pyle L, Carreau AM, Rahat H, Garcia-Reyes Y, Bergman BC, Nadeau KJ, Cree-Green M. Fasting plasma metabolomic profiles are altered by three days of standardized diet and restricted physical activity. Metabol Open 2021; 9:100085. [PMID: 33665598 PMCID: PMC7903000 DOI: 10.1016/j.metop.2021.100085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 02/01/2021] [Accepted: 02/03/2021] [Indexed: 11/27/2022] Open
Abstract
Objective Few studies have examined the effects of participants' diet and activity prior to sample collection on metabolomics profiles, and results have been conflicting. We compared the effects of overnight fasting with or without 3 days of standardized diet and restricted physical activity on the human blood metabolome, and examined the effects of these protocols on our ability to detect differences in metabolomics profiles in adolescent girls with obesity and polycystic ovary syndrome (PCOS) vs. sex and BMI-matched controls. Methods This was a cross-sectional study of 16 adolescent girls with obesity and PCOS and 5 sex and BMI-matched controls. Fasting plasma metabolomic profiles were measured twice in each participant: once without preceding restriction of physical activity or control of macronutrient content ("typical fasting visit"), and again after 12 h of monitored inpatient fasting with 3 days of standardized diet and avoidance of vigorous exercise ("controlled fasting visit"). Moderated paired t-tests with FDR correction for multiple testing and multilevel sparse partial least-squares discriminant analysis (sPLS-DA) were used to examine differences between the 2 visits and to compare the PCOS and control groups with the 2 visits combined and again after stratifying by visit. Results Twenty-three known metabolites were significantly different between the controlled fasting and typical fasting visits. Hypoxanthine and glycochenodeoxycholic acid had the largest increases in relative abundance at the controlled fasting visit compared to the typical fasting visit, while oleoyl-glycerol and oleamide had the largest increases in relative abundance at the typical fasting visit compared to the controlled fasting visit. sPLS-DA showed excellent discrimination between the 2 visits; however, when the samples from the 2 visits were combined, differences between the PCOS and control groups could not be detected. After stratifying by visit, discrimination of PCOS status was improved. Conclusions There were differences in fasting metabolomic profiles following typical fasting vs monitored fasting with preceding restriction of physical activity and control of macronutrient content, and combining samples from the two visits obscured differences by PCOS status. In studies performing metabolomics analysis, careful attention should be paid to acute diet and activity history. Depending on the sample size of the study and the expected effect size of the outcomes of interest, control of diet and physical activity beyond typical outpatient fasting may not be required.
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Affiliation(s)
- Laura Pyle
- Department of Pediatrics, Division of Pediatric Endocrinology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.,Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, 80045, USA
| | - Anne-Marie Carreau
- Department of Pediatrics, Division of Pediatric Endocrinology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Haseeb Rahat
- Department of Pediatrics, Division of Pediatric Endocrinology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Yesenia Garcia-Reyes
- Department of Pediatrics, Division of Pediatric Endocrinology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Bryan C Bergman
- Department of Medicine, Division of Endocrinology and Metabolism, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Kristen J Nadeau
- Department of Pediatrics, Division of Pediatric Endocrinology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.,Center for Women's Health Research, Aurora, CO, 80045, USA
| | - Melanie Cree-Green
- Department of Pediatrics, Division of Pediatric Endocrinology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.,Center for Women's Health Research, Aurora, CO, 80045, USA
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27
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Kim H, Hu EA, E Wong K, Yu B, Steffen LM, Seidelmann SB, Boerwinkle E, Coresh J, Rebholz CM. Serum Metabolites Associated with Healthy Diets in African Americans and European Americans. J Nutr 2020; 151:40-49. [PMID: 33244610 PMCID: PMC7779213 DOI: 10.1093/jn/nxaa338] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 09/28/2020] [Accepted: 10/05/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND High diet quality is associated with a lower risk of chronic diseases. Metabolomics can be used to identify objective biomarkers of diet quality. OBJECTIVES We used metabolomics to identify serum metabolites associated with 4 diet indices and the components within these indices in 2 samples from African Americans and European Americans. METHODS We studied cross-sectional associations between known metabolites and Healthy Eating Index (HEI)-2015, Alternative Healthy Eating Index (AHEI)-2010, the Dietary Approaches to Stop Hypertension Trial (DASH) diet, alternate Mediterranean diet (aMED), and their components using untargeted metabolomics in 2 samples (n1 = 1,806, n2 = 2,056) of the Atherosclerosis Risk in Communities study (aged 45-64 y at baseline). Dietary intakes were assessed using an FFQ. We used multivariable linear regression models to examine associations between diet indices and serum metabolites in each sample, adjusting for participant characteristics. Metabolites significantly associated with diet indices were meta-analyzed across 2 samples. C-statistics were calculated to examine if these candidate biomarkers improved prediction of individuals in the highest compared with lowest quintile of diet scores beyond participant characteristics. RESULTS Seventeen unique metabolites (HEI: n = 6; AHEI: n = 5; DASH: n = 14; aMED: n = 2) were significantly associated with higher diet scores after Bonferroni correction in sample 1 and sample 2. Six of 17 significant metabolites [glycerate, N-methylproline, stachydrine, threonate, pyridoxate, 3-(4-hydroxyphenyl)lactate)] were associated with ≥1 dietary pattern. Candidate biomarkers of HEI, AHEI, and DASH distinguished individuals with highest compared with lowest quintile of diet scores beyond participant characteristics in samples 1 and 2 (P value for difference in C-statistics <0.02 for all 3 diet indices). Candidate biomarkers of aMED did not improve C-statistics beyond participant characteristics (P value = 0.930). CONCLUSIONS A considerable overlap of metabolites associated with HEI, AHEI, DASH, and aMED reflects the similar food components and similar metabolic pathways involved in the metabolism of healthy diets in African Americans and European Americans.
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Affiliation(s)
- Hyunju Kim
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA,Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
| | - Emily A Hu
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA,Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
| | - Kari E Wong
- Metabolon, Research Triangle Park, Morrisville, NC, USA
| | - Bing Yu
- Department of Epidemiology, Human Genetics & Environmental Sciences, University of Texas Health Sciences Center at Houston School of Public Health, Houston, TX, USA
| | - Lyn M Steffen
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | | | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics & Environmental Sciences, University of Texas Health Sciences Center at Houston School of Public Health, Houston, TX, USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA,Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
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28
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Blood Metabolomic Profiling Confirms and Identifies Biomarkers of Food Intake. Metabolites 2020; 10:metabo10110468. [PMID: 33212857 PMCID: PMC7698441 DOI: 10.3390/metabo10110468] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 11/07/2020] [Accepted: 11/13/2020] [Indexed: 12/14/2022] Open
Abstract
Metabolomics can be a tool to identify dietary biomarkers. However, reported food-metabolite associations have been inconsistent, and there is a need to explore further associations. Our aims were to confirm previously reported food-metabolite associations and to identify novel food-metabolite associations. We conducted a cross-sectional analysis of data from 849 participants (57% men) of the PopGen cohort. Dietary intake was obtained using FFQ and serum metabolites were profiled by an untargeted metabolomics approach. We conducted a systematic literature search to identify previously reported food-metabolite associations and analyzed these associations using linear regression. To identify potential novel food-metabolite associations, datasets were split into training and test datasets and linear regression models were fitted to the training datasets. Significant food-metabolite associations were evaluated in the test datasets. Models were adjusted for covariates. In the literature, we identified 82 food-metabolite associations. Of these, 44 associations were testable in our data and confirmed associations of coffee with 12 metabolites, of fish with five, of chocolate with two, of alcohol with four, and of butter, poultry and wine with one metabolite each. We did not identify novel food-metabolite associations; however, some associations were sex-specific. Potential use of some metabolites as biomarkers should consider sex differences in metabolism.
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29
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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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30
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Wang Y, Hodge RA, Stevens VL, Hartman TJ, McCullough ML. Identification and Reproducibility of Plasma Metabolomic Biomarkers of Habitual Food Intake in a US Diet Validation Study. Metabolites 2020; 10:metabo10100382. [PMID: 32993181 PMCID: PMC7600452 DOI: 10.3390/metabo10100382] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 09/14/2020] [Accepted: 09/22/2020] [Indexed: 12/12/2022] Open
Abstract
Previous metabolomic studies have identified putative blood biomarkers of dietary intake. These biomarkers need to be replicated in other populations and tested for reproducibility over time for the potential use in future epidemiological studies. We conducted a metabolomics analysis among 671 racially/ethnically diverse men and women included in a diet validation study to examine the correlation between >100 food groups/items (101 by a food frequency questionnaire (FFQ), 105 by 24-h diet recalls (24HRs)) with 1141 metabolites measured in fasting plasma sample replicates, six months apart. Diet–metabolite associations were examined by Pearson’s partial correlation analysis. Biomarker reproducibility was assessed using intraclass correlation coefficients (ICCs). A total of 677 diet–metabolite associations were identified after Bonferroni adjustment for multiple comparisons and restricting absolute correlation coefficients to greater than 0.2 (601 associations using the FFQ and 395 using 24HRs). The median ICCs of the 238 putative biomarkers was 0.56 (interquartile range 0.46–0.68). In this study, with repeated FFQs, 24HRs and plasma metabolic profiles, we identified several potentially novel food biomarkers and replicated others found in our previous study. Our findings contribute to the growing literature on food-based biomarkers and provide important information on biomarker reproducibility which could facilitate their utilization in future nutritional epidemiological studies.
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Affiliation(s)
- Ying Wang
- Department of Population Science, American Cancer Society, Atlanta, GA 30303, USA; (R.A.H.); (V.L.S.); (M.L.M.)
- Correspondence: ; Tel.: +1-404-329-4341
| | - Rebecca A. Hodge
- Department of Population Science, American Cancer Society, Atlanta, GA 30303, USA; (R.A.H.); (V.L.S.); (M.L.M.)
| | - Victoria L. Stevens
- Department of Population Science, American Cancer Society, Atlanta, GA 30303, USA; (R.A.H.); (V.L.S.); (M.L.M.)
| | - Terryl J. Hartman
- Department of Epidemiology, Rollins School of Public Health, Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA;
| | - Marjorie L. McCullough
- Department of Population Science, American Cancer Society, Atlanta, GA 30303, USA; (R.A.H.); (V.L.S.); (M.L.M.)
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31
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Bagheri M, Willett W, Townsend MK, Kraft P, Ivey KL, Rimm EB, Wilson KM, Costenbader KH, Karlson EW, Poole EM, Zeleznik OA, Eliassen AH. A lipid-related metabolomic pattern of diet quality. Am J Clin Nutr 2020; 112:1613-1630. [PMID: 32936887 PMCID: PMC7727474 DOI: 10.1093/ajcn/nqaa242] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 08/04/2020] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Adherence to a healthy diet has been associated with reduced risk of chronic diseases. Identifying nutritional biomarkers of diet quality may be complementary to traditional questionnaire-based methods and may provide insights concerning disease mechanisms and prevention. OBJECTIVE To identify metabolites associated with diet quality assessed via the Alternate Healthy Eating Index (AHEI) and its components. METHODS This cross-sectional study used FFQ data and plasma metabolomic profiles, mostly lipid related, from the Nurses' Health Study (NHS, n = 1460) and Health Professionals Follow-up Study (HPFS, n = 1051). Linear regression models assessed associations of the AHEI and its components with individual metabolites. Canonical correspondence analyses (CCAs) investigated overlapping patterns between AHEI components and metabolites. Principal component analysis (PCA) and explanatory factor analysis were used to consolidate correlated metabolites into uncorrelated factors. We used stepwise multivariable regression to create a metabolomic score that is an indicator of diet quality. RESULTS The AHEI was associated with 83 metabolites in the NHS and 96 metabolites in the HPFS after false discovery rate adjustment. Sixty-three of these significant metabolites overlapped between the 2 cohorts. CCA identified "healthy" AHEI components (e.g., nuts, whole grains) and metabolites (n = 27 in the NHS and 33 in the HPFS) and "unhealthy" AHEI components (e.g., red meat, trans fat) and metabolites (n = 56 in the NHS and 63 in the HPFS). PCA-derived factors composed of highly saturated triglycerides, plasmalogens, and acylcarnitines were associated with unhealthy AHEI components while factors composed of highly unsaturated triglycerides were linked to healthy AHEI components. The stepwise regression analysis contributed to a metabolomics score as a predictor of diet quality. CONCLUSION We identified metabolites associated with healthy and unhealthy eating behaviors. The observed associations were largely similar between men and women, suggesting that metabolomics can be a complementary approach to self-reported diet in studies of diet and chronic disease.
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Affiliation(s)
- Minoo Bagheri
- Channing Division of Network Medicine Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Community Nutrition, School of Nutritional Sciences and Dietetic, Tehran University of Medical Sciences, Tehran, Iran
| | - Walter Willett
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Mary K Townsend
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - Peter Kraft
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Kerry L Ivey
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- South Australian Health and Medical Research Institute, Infection and Immunity Theme, Adelaide, South Australia, Australia
| | - Eric B Rimm
- Channing Division of Network Medicine Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
- 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
| | - Kathryn Marie Wilson
- Channing Division of Network Medicine Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
| | - Karen H Costenbader
- Channing Division of Network Medicine Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
| | - Elizabeth W Karlson
- Channing Division of Network Medicine Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
| | - Elizabeth M Poole
- Channing Division of Network Medicine Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
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32
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Garcia-Aloy M, Ulaszewska M, Franceschi P, Estruel-Amades S, Weinert CH, Tor-Roca A, Urpi-Sarda M, Mattivi F, Andres-Lacueva C. Discovery of Intake Biomarkers of Lentils, Chickpeas, and White Beans by Untargeted LC-MS Metabolomics in Serum and Urine. Mol Nutr Food Res 2020; 64:e1901137. [PMID: 32420683 DOI: 10.1002/mnfr.201901137] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 05/05/2020] [Indexed: 11/12/2022]
Abstract
SCOPE To identify reliable biomarkers of food intake (BFIs) of pulses. METHODS AND RESULTS A randomized crossover postprandial intervention study is conducted on 11 volunteers who consumed lentils, chickpeas, and white beans. Urine and serum samples are collected at distinct postprandial time points up to 48 h, and analyzed by LC-HR-MS untargeted metabolomics. Hypaphorine, trigonelline, several small peptides, and polyphenol-derived metabolites prove to be the most discriminating urinary metabolites. Two arginine-related compounds, dopamine sulfate and epicatechin metabolites, with their microbial derivatives, are identified only after intake of lentils, whereas protocatechuic acid is identified only after consumption of chickpeas. Urinary hydroxyjasmonic and hydroxydihydrojasmonic acids, as well as serum pipecolic acid and methylcysteine, are found after white bean consumption. Most of the metabolites identified in the postprandial study are replicated as discriminants in 24 h urine samples, demonstrating that in this case the use of a single, noninvasive sample is suitable for revealing the consumption of pulses. CONCLUSIONS The results of the present untargeted metabolomics work reveals a broad list of metabolites that are candidates for use as biomarkers of pulse intake. Further studies are needed to validate these BFIs and to find the best combinations of them to boost their specificity.
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Affiliation(s)
- Mar Garcia-Aloy
- Biomarkers and Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XaRTA, INSA, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, 08028, Spain.,CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Barcelona, 08028, Spain.,Department of Food Quality and Nutrition, Research and Innovation Center, Fondazione Edmund Mach (FEM), San Michele all'Adige, 38010, Italy
| | - Marynka Ulaszewska
- IRCCS San Raffaele Scientific Institute, Center for Omics Sciences, Proteomics and Metabolomics Facility - ProMeFa, Milan, 20132, Italy.,Department of Food Quality and Nutrition, Research and Innovation Center, Fondazione Edmund Mach (FEM), San Michele all'Adige, 38010, Italy
| | - Pietro Franceschi
- Computational Biology Unit, Research and Innovation Center, Fondazione Edmund Mach, San Michele all'Adige, 38010, Italy
| | - Sheila Estruel-Amades
- Biomarkers and Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XaRTA, INSA, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, 08028, Spain
| | - Christoph H Weinert
- Department of Safety and Quality of Fruit and Vegetables, Max Rubner-Institut, Federal Research Institute of Nutrition and Food, Karlsruhe, 76131, Germany
| | - Alba Tor-Roca
- Biomarkers and Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XaRTA, INSA, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, 08028, Spain.,CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Barcelona, 08028, Spain
| | - Mireia Urpi-Sarda
- Biomarkers and Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XaRTA, INSA, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, 08028, Spain.,CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Barcelona, 08028, Spain
| | - Fulvio Mattivi
- Department of Food Quality and Nutrition, Research and Innovation Center, Fondazione Edmund Mach (FEM), San Michele all'Adige, 38010, Italy.,Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Povo, 38123, Italy
| | - Cristina Andres-Lacueva
- Biomarkers and Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XaRTA, INSA, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, 08028, Spain.,CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Barcelona, 08028, Spain
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Hruby A, Dennis C, Jacques PF. Dairy Intake in 2 American Adult Cohorts Associates with Novel and Known Targeted and Nontargeted Circulating Metabolites. J Nutr 2020; 150:1272-1283. [PMID: 32055836 PMCID: PMC7198289 DOI: 10.1093/jn/nxaa021] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 12/03/2019] [Accepted: 01/24/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The role of dairy in health can be elucidated by investigating circulating metabolites associated with intake. OBJECTIVES We sought to identify metabolites associated with quantity and type of dairy intake in the Framingham Heart Study Offspring and Third Generation (Gen3) cohorts. METHODS Dairy intake (total dairy, milk, cheese, yogurt, and cream/butter) was analyzed in relation to targeted (Offspring, n = 2205, 55.1 ± 9.8 y, 52% female, 217 signals; Gen3, n = 866, 40.5 ± 8.8 y, 54.9% female, 79 signals) and nontargeted metabolites (Gen3, ∼7031 signals) in a 2-step analysis including orthogonal projections to latent structures with discriminant analysis (OPLS-DA) in discovery subsets to identify metabolites distinguishing between high and low intake; and linear regression in confirmation subsets to assess putative associations, subsequently tested in the total samples. Previously reported associations were also investigated. RESULTS OPLS-DA in the Offspring targeted discovery subset resulted in a variable importance in projection (VIP) >1 of 65, 60, 58, 66, and 60 metabolites for total dairy, milk, cream/butter, cheese, and yogurt, respectively, of which 5, 3, 1, 6, and 4 metabolites, respectively, remained after confirmation. In the Gen3 targeted discovery subset, OPLS-DA resulted in a VIP >1 of 17, 15, 13, 7, and 6 metabolites for total dairy, milk, cream/butter, cheese, and yogurt, respectively. In the Gen3 nontargeted discovery subset, OPLS-DA resulted in a VIP >2 of 203, 503, 78, 186, and 206 metabolites, respectively. Combining targeted and nontargeted results in Gen3, significant associations of 7 (6 unannotated), 2, 12 (11 unannotated), 0, and 61 (all unannotated) metabolites, respectively, remained. Candidate identities of unannotated signals included fatty acids and food flavorings. Results supported relations previously reported for C14:0 sphingomyelin, and marginal associations for deoxycholates. CONCLUSIONS Dairy in 2 American adult cohorts associated with numerous circulating metabolites. Reports about diet-metabolite relations and confirmation of previous findings might be limited by specificity of dietary intake and breadth of measured metabolites.
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Affiliation(s)
- Adela Hruby
- Nutritional Epidemiology, Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, and Tufts University Friedman School of Nutrition Science and Policy, Boston, MA, USA,Address correspondence to AH (e-mail: )
| | - Courtney Dennis
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Paul F Jacques
- Nutritional Epidemiology, Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, and Tufts University Friedman School of Nutrition Science and Policy, Boston, MA, USA
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34
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Chau YP, Au PCM, Li GHY, Sing CW, Cheng VKF, Tan KCB, Kung AWC, Cheung CL. Serum Metabolome of Coffee Consumption and its Association With Bone Mineral Density: The Hong Kong Osteoporosis Study. J Clin Endocrinol Metab 2020; 105:5637088. [PMID: 31750515 DOI: 10.1210/clinem/dgz210] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 11/20/2019] [Indexed: 01/20/2023]
Abstract
BACKGROUND Inconsistent associations between coffee consumption and bone mineral density (BMD) have been observed in epidemiological studies. Moreover, the relationship of bioactive components in coffee with BMD has not been studied. The aim of the current study is to identify coffee-associated metabolites and evaluate their association with BMD. METHODS Two independent cohorts totaling 564 healthy community-dwelling adults from the Hong Kong Osteoporosis Study (HKOS) who visited in 2001-2010 (N = 329) and 2015-2016 (N = 235) were included. Coffee consumption was self-reported in an food frequency questionnaire. Untargeted metabolomic profiling on fasting serum samples was performed using liquid chromatography-mass spectrometry platforms. BMD at lumbar spine and femoral neck was measured by dual-energy X-ray absorptiometry. Multivariable linear regression and robust regression were used for the association analyses. RESULTS 12 serum metabolites were positively correlated with coffee consumption after Bonferroni correction for multiple testing (P < 4.87 × 10-5), with quinate, 3-hydroxypyridine sulfate, and trigonelline (N'-methylnicotinate) showing the strongest association. Among these metabolites, 11 known metabolites were previously identified to be associated with coffee intake and 6 of them were related to caffeine metabolism. Habitual coffee intake was positively and significantly associated with BMD at the lumbar spine and femoral neck. The metabolite 5-acetylamino-6-formylamino-3-methyluracil (AFMU) (β = 0.012, SE = 0.005; P = 0.013) was significantly associated with BMD at the lumbar spine, whereas 3-hydroxyhippurate (β = 0.007, SE = 0.003, P = 0.027) and trigonelline (β = 0.007, SE = 0.004; P = 0.043) were significantly associated with BMD at the femoral neck. CONCLUSIONS 12 metabolites were significantly associated with coffee intake, including 6 caffeine metabolites. Three of them (AFMU, 3-hydroxyhippurate, and trigonelline) were further associated with BMD. These metabolites could be potential biomarkers of coffee consumption and affect bone health.
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Affiliation(s)
- Yin-Pan Chau
- Department of Pharmacology and Pharmacy, the University of Hong Kong, Pokfulam, Hong Kong, China
| | - Philip C M Au
- Department of Pharmacology and Pharmacy, the University of Hong Kong, Pokfulam, Hong Kong, China
| | - Gloria H Y Li
- Department of Pharmacology and Pharmacy, the University of Hong Kong, Pokfulam, Hong Kong, China
| | - Chor-Wing Sing
- Department of Pharmacology and Pharmacy, the University of Hong Kong, Pokfulam, Hong Kong, China
| | - Vincent K F Cheng
- Department of Pharmacology and Pharmacy, the University of Hong Kong, Pokfulam, Hong Kong, China
| | - Kathryn C B Tan
- Department of Medicine, the University of Hong Kong, Pokfulam, Hong Kong, China
| | - Annie W C Kung
- Department of Medicine, the University of Hong Kong, Pokfulam, Hong Kong, China
| | - Ching-Lung Cheung
- Department of Pharmacology and Pharmacy, the University of Hong Kong, Pokfulam, Hong Kong, China
- Department of Medicine, the University of Hong Kong, Pokfulam, Hong Kong, China
- Centre for Genomic Sciences, LKS Faculty of Medicine, the University of Hong Kong, Pokfulam, Hong Kong, China
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35
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Playdon MC, Joshi AD, Tabung FK, Cheng S, Henglin M, Kim A, Lin T, van Roekel EH, Huang J, Krumsiek J, Wang Y, Mathé E, Temprosa M, Moore S, Chawes B, Eliassen AH, Gsur A, Gunter MJ, Harada S, Langenberg C, Oresic M, Perng W, Seow WJ, Zeleznik OA. Metabolomics Analytics Workflow for Epidemiological Research: Perspectives from the Consortium of Metabolomics Studies (COMETS). Metabolites 2019; 9:E145. [PMID: 31319517 PMCID: PMC6681081 DOI: 10.3390/metabo9070145] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 06/28/2019] [Accepted: 07/04/2019] [Indexed: 12/13/2022] Open
Abstract
The application of metabolomics technology to epidemiological studies is emerging as a new approach to elucidate disease etiology and for biomarker discovery. However, analysis of metabolomics data is complex and there is an urgent need for the standardization of analysis workflow and reporting of study findings. To inform the development of such guidelines, we conducted a survey of 47 cohort representatives from the Consortium of Metabolomics Studies (COMETS) to gain insights into the current strategies and procedures used for analyzing metabolomics data in epidemiological studies worldwide. The results indicated a variety of applied analytical strategies, from biospecimen and data pre-processing and quality control to statistical analysis and reporting of study findings. These strategies included methods commonly used within the metabolomics community and applied in epidemiological research, as well as novel approaches to pre-processing pipelines and data analysis. To help with these discrepancies, we propose use of open-source initiatives such as the online web-based tool COMETS Analytics, which includes helpful tools to guide analytical workflow and the standardized reporting of findings from metabolomics analyses within epidemiological studies. Ultimately, this will improve the quality of statistical analyses, research findings, and study reproducibility.
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Affiliation(s)
- Mary C Playdon
- Department of Nutrition and Integrative Physiology, College of Health, University of Utah, Salt Lake City, UT 84112, USA.
- Division of Cancer Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT 84112, USA.
| | - Amit D Joshi
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA 02114, USA
- Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Fred K Tabung
- Division of Medical Oncology, Department of Internal Medicine, The Ohio State University College of Medicine, Columbus, OH 43210, USA
- The Ohio State University Comprehensive Cancer Center, Arthur G. James Cancer Hospital and Richard J. Solove Research Institute, Columbus, OH 43210, USA
- Division of Epidemiology, The Ohio State University College of Public Health, Columbus, OH 43210, USA
| | - Susan Cheng
- Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Mir Henglin
- Cardiovascular Division, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Andy Kim
- Cardiovascular Division, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Tengda Lin
- Division of Cancer Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT 84112, USA
- Department of Population Health Sciences, School of Medicine, University of Utah, Salt Lake City, UT 84112, USA
| | - Eline H van Roekel
- Department of Epidemiology, GROW School for Oncology and Developmental Biology, Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Jiaqi Huang
- Division of Cancer Epidemiology and Genetics, Metabolic Epidemiology Branch, National Cancer Institute, Rockville, MD 20850, USA
| | - Jan Krumsiek
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10021, USA
| | - Ying Wang
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA 30303, USA
| | - Ewy Mathé
- College of Medicine, Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
| | - Marinella Temprosa
- Department of Epidemiology and Biostatistics, Milken Institute School of Public Health, George Washington University, Washington, DC 20052, USA
| | - Steven Moore
- Division of Cancer Epidemiology and Genetics, Metabolic Epidemiology Branch, National Cancer Institute, Rockville, MD 20850, USA
| | - Bo Chawes
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, 1165 Copenhagen, Denmark
| | - A Heather Eliassen
- Channing Division of Network Medicine, 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
| | - Andrea Gsur
- Institute of Cancer Research, Department of Medicine, Medical University of Vienna, 1090 Vienna, Austria
| | - Marc J Gunter
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, World Health Organization, 69008 Lyon, France
| | - Sei Harada
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo 160-8582, Japan
| | - Claudia Langenberg
- MRC Epidemiology Unit, Public Health, University of Cambridge, Cambridge CB2 1 TN, UK
- The Francis Crick Institute, London NW1 1ST, UK
| | - Matej Oresic
- Turku Centre for Biotechnology, University of Turku, 20500 Turku, Finland
- School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden
| | - Wei Perng
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO 80045, USA
- Life course epidemiology of adiposity and diabetes (LEAD) Center, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Wei Jie Seow
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore 119228, Singapore
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
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Fernandes Silva L, Vangipurapu J, Kuulasmaa T, Laakso M. An intronic variant in the GCKR gene is associated with multiple lipids. Sci Rep 2019; 9:10240. [PMID: 31308433 PMCID: PMC6629684 DOI: 10.1038/s41598-019-46750-3] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 07/02/2019] [Indexed: 12/25/2022] Open
Abstract
Previous studies have shown that an intronic variant rs780094 of the GCKR gene (glucokinase regulatory protein) is significantly associated with several metabolites, but the associations of this genetic variant with different lipids is largely unknown. Therefore, we applied metabolomics approach to measure metabolites in a large Finnish population sample (METSIM study) to investigate their associations with rs780094 of GCKR. We measured metabolites by mass spectrometry from 5,181 participants. P < 5.8 × 10-5 was considered as statistically significant given 857 metabolites included in statistical analyses. We found novel negative associations of the T allele of GCKR rs780094 with serine and threonine, and positive associations with two metabolites of tryptophan, indolelactate and N-acetyltryptophan. Additionally, we found novel significant positive associations of this genetic variant with 12 glycerolipids and 19 glycerophospholipids. Significant negative associations were found for three glycerophospholipids (all plasmalogen-cholines), and two sphingolipids. Significant novel associations were also found with gamma-glutamylthreonine, taurocholenate sulfate, and retinol. Our study adds new information about the pleiotropy of the GCKR gene, and shows the associations of the T allele of GCKR rs780094 with lipids.
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Affiliation(s)
- Lilian Fernandes Silva
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Jagadish Vangipurapu
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Teemu Kuulasmaa
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio, Finland.
- Department of Medicine, Kuopio University Hospital, Kuopio, Finland.
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37
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Serra-Majem L, Román-Viñas B, Sanchez-Villegas A, Guasch-Ferré M, Corella D, La Vecchia C. Benefits of the Mediterranean diet: Epidemiological and molecular aspects. Mol Aspects Med 2019; 67:1-55. [PMID: 31254553 DOI: 10.1016/j.mam.2019.06.001] [Citation(s) in RCA: 106] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 06/03/2019] [Accepted: 06/04/2019] [Indexed: 01/16/2023]
Abstract
More than 50 years after the Seven Countries Study, a large number of epidemiological studies have explored the relationship between the Mediterranean diet (MD) and health, through observational, case-control, some longitudinal and a few experimental studies. The overall results show strong evidence suggesting a protective effect of the MD mainly on the risk of cardiovascular disease (CVD) and certain types of cancer. The beneficial effects have been attributed to the types of food consumed, total dietary pattern, components in the food, cooking techniques, eating behaviors and lifestyle behaviors, among others. The aim of this article is to review and summarize the knowledge derived from the literature focusing on the benefits of the MD on health, including those that have been extensively investigated (CVD, cancer) along with more recent issues such as mental health, immunity, quality of life, etc. The review begins with a brief description of the MD and its components. Then we present a review of studies evaluating metabolic biomarkers and genotypes in relation to the MD. Other sections are dedicated to observation and intervention studies for various pathologies. Finally, some insights into the relationship between the MD and sustainability are explored. In conclusion, the research undertaken on metabolomics approaches has identified potential markers for certain MD components and patterns, but more investigation is needed to obtain valid measures. Further evaluation of gene-MD interactions are also required to better understand the mechanisms by which the MD diet exerts its beneficial effects on health. Observation and intervention studies, particularly PREDIMED, have provided invaluable data on the benefits of the MD for a wide range of chronic diseases. However further research is needed to explore the effects of other lifestyle components associated with Mediterranean populations, its environmental impact, as well as the MD extrapolation to non-Mediterranean contexts.
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Affiliation(s)
- Lluis Serra-Majem
- Research Institute of Biomedical and Health Sciences (IUIBS), University of Las Palmas de Gran Canaria, Las Palmas, Spain; Preventive Medicine Service, Centro Hospitalario Universitario Insular Materno Infantil (CHUIMI), Canarian Health Service, Las Palmas, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain; Nutrition Research Foundation, University of Barcelona Science Park, Barcelona, Spain.
| | - Blanca Román-Viñas
- Nutrition Research Foundation, University of Barcelona Science Park, Barcelona, Spain; School of Health and Sport Sciences (EUSES), Universitat de Girona, Salt, Spain; Department of Physical Activity and Sport Sciences, Blanquerna, Universitat Ramon Llull, Barcelona, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Almudena Sanchez-Villegas
- Research Institute of Biomedical and Health Sciences (IUIBS), University of Las Palmas de Gran Canaria, Las Palmas, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H.Chan School of Public Health, Boston, MA, USA; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Dolores Corella
- Genetic and Molecular Epidemiology Unit. Department of Preventive Medicine. University of Valencia, Valencia, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Carlo La Vecchia
- Department of Clinical Sciences and Community Health, Università degli Studi di Milano, 20133, Milan, Italy
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38
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Yu B, Zanetti KA, Temprosa M, Albanes D, Appel N, Barrera CB, Ben-Shlomo Y, Boerwinkle E, Casas JP, Clish C, Dale C, Dehghan A, Derkach A, Eliassen AH, Elliott P, Fahy E, Gieger C, Gunter MJ, Harada S, Harris T, Herr DR, Herrington D, Hirschhorn JN, Hoover E, Hsing AW, Johansson M, Kelly RS, Khoo CM, Kivimäki M, Kristal BS, Langenberg C, Lasky-Su J, Lawlor DA, Lotta LA, Mangino M, Le Marchand L, Mathé E, Matthews CE, Menni C, Mucci LA, Murphy R, Oresic M, Orwoll E, Ose J, Pereira AC, Playdon MC, Poston L, Price J, Qi Q, Rexrode K, Risch A, Sampson J, Seow WJ, Sesso HD, Shah SH, Shu XO, Smith GCS, Sovio U, Stevens VL, Stolzenberg-Solomon R, Takebayashi T, Tillin T, Travis R, Tzoulaki I, Ulrich CM, Vasan RS, Verma M, Wang Y, Wareham NJ, Wong A, Younes N, Zhao H, Zheng W, Moore SC. The Consortium of Metabolomics Studies (COMETS): Metabolomics in 47 Prospective Cohort Studies. Am J Epidemiol 2019; 188:991-1012. [PMID: 31155658 PMCID: PMC6545286 DOI: 10.1093/aje/kwz028] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 01/29/2019] [Accepted: 01/29/2019] [Indexed: 12/11/2022] Open
Abstract
The Consortium of Metabolomics Studies (COMETS) was established in 2014 to facilitate large-scale collaborative research on the human metabolome and its relationship with disease etiology, diagnosis, and prognosis. COMETS comprises 47 cohorts from Asia, Europe, North America, and South America that together include more than 136,000 participants with blood metabolomics data on samples collected from 1985 to 2017. Metabolomics data were provided by 17 different platforms, with the most frequently used labs being Metabolon, Inc. (14 cohorts), the Broad Institute (15 cohorts), and Nightingale Health (11 cohorts). Participants have been followed for a median of 23 years for health outcomes including death, cancer, cardiovascular disease, diabetes, and others; many of the studies are ongoing. Available exposure-related data include common clinical measurements and behavioral factors, as well as genome-wide genotype data. Two feasibility studies were conducted to evaluate the comparability of metabolomics platforms used by COMETS cohorts. The first study showed that the overlap between any 2 different laboratories ranged from 6 to 121 metabolites at 5 leading laboratories. The second study showed that the median Spearman correlation comparing 111 overlapping metabolites captured by Metabolon and the Broad Institute was 0.79 (interquartile range, 0.56-0.89).
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Affiliation(s)
- Bing Yu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas
| | - Krista A Zanetti
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, Maryland
| | - Marinella Temprosa
- Department of Epidemiology and Biostatistics Milken Institute School of Public Health, George Washington University, Washington, DC
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland
| | - Nathan Appel
- Information Management Services, Inc., Rockville, Maryland
| | - Clara Barrios Barrera
- Department of Nephrology, Hospital del Mar, Institut Mar d´Investigacions Mediques, Barcelona, Spain
| | - Yoav Ben-Shlomo
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas
| | - Juan P Casas
- Institute of Health Informatics Research, UCL Institute of Health Informatics, University College London, London, United Kingdom
| | - Clary Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts
| | - Caroline Dale
- Institute of Health Informatics Research, UCL Institute of Health Informatics, University College London, London, United Kingdom
| | - Abbas Dehghan
- Medical Research Council–Public Health England Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Andriy Derkach
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland
| | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston Massachusetts
| | - Paul Elliott
- Medical Research Council–Public Health England Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- National Institute for Health Research, Imperial College Biomedical Research Center, London, United Kingdom
- Health Data Research UK Center at Imperial College London, London, United Kingdom
| | - Eoin Fahy
- Department of Bioengineering, School of Engineering, University of California, San Diego, La Jolla, California
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research
| | - Marc J Gunter
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, France
| | - Sei Harada
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan
| | - Tamara Harris
- Laboratory of Epidemiology and Population Science Laboratory
| | - Deron R Herr
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Biology, San Diego State University, San Diego, California
| | - David Herrington
- Department of Internal Medicine, Division of Cardiology, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Joel N Hirschhorn
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts
- Division of Endocrinology, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts
- Department of Genetics, Harvard Medical School, Boston, Massachusetts
| | - Elise Hoover
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, Maryland
| | - Ann W Hsing
- Stanford Prevention Research Center, Stanford Cancer Institute, Stanford, California
| | | | - Rachel S Kelly
- Systems Genetics and Genomics Unit, Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Chin Meng Khoo
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Medicine, National University Health System, Singapore
- Duke–National University of Singapore Graduate Medical School, Singapore
| | - Mika Kivimäki
- Department of Epidemiology and Public Health, University College London, London, United Kingdom
| | - Bruce S Kristal
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
- Division of Sleep Medicine, Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Jessica Lasky-Su
- Channing Laboratory, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Deborah A Lawlor
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, United Kingdom
| | - Luca A Lotta
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Massimo Mangino
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
| | - Loïc Le Marchand
- University of Hawaii Cancer Center, Epidemiology Program, Honolulu, Hawaii
| | - Ewy Mathé
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, Ohio
| | - Charles E Matthews
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
| | - Lorelei A Mucci
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston Massachusetts
| | - Rachel Murphy
- Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Matej Oresic
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, Finland
- School of Medical Sciences, Örebro University, Örebro, Sweden
| | - Eric Orwoll
- Department of Medicine, Oregon Health and Science University, Portland, Oregon
| | - Jennifer Ose
- Division of Cancer Population Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah
| | - Alexandre C Pereira
- Instituto de Pesquisas Rene Rachou, Fundação Oswaldo Cruz, Belo Horizonte, Brazil
| | - Mary C Playdon
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland
- Division of Cancer Population Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
- Department of Nutrition and Integrative Physiology, University of Utah, Salt Lake City, Utah
| | - Lucilla Poston
- Department of Women and Children’s Health, School of Life Course Sciences, Faculty of Life Sciences and Medicine, King’s College London, St. Thomas’ Hospital, London, United Kingdom
| | - Jackie Price
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York
| | - Kathryn Rexrode
- Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
- Division of Women’s Health, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Adam Risch
- Information Management Services, Inc., Rockville, Maryland
| | - Joshua Sampson
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland
| | - Wei Jie Seow
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Howard D Sesso
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston Massachusetts
- Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Svati H Shah
- Department of Medicine, Duke University School of Medicine, Durham, North Carolina
- Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, North Carolina
- Duke Clinical Research Institute, Durham, North Carolina
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Gordon C S Smith
- Department of Obstetrics and Gynaecology, National Institute for Health Research, Cambridge Comprehensive Biomedical Research Center, University of Cambridge, Cambridge, United Kingdom
| | - Ulla Sovio
- Center for Trophoblast Research, Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom
| | - Victoria L Stevens
- Department of Obstetrics and Gynaecology, University of Cambridge, National Institute for Health Research Cambridge Comprehensive Biomedical Research Centre, Cambridge, United Kingdom
| | | | - Toru Takebayashi
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
- Epidemiology Research Program, American Cancer Society, Atlanta, Georgia
| | - Therese Tillin
- Institute of Cardiovascular Sciences, University College London, London, United Kingdom
| | - Ruth Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Ioanna Tzoulaki
- Medical Research Council–Public Health England Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Cornelia M Ulrich
- Division of Cancer Population Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
| | - Ramachandran S Vasan
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
- Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts
- Framingham Heart Study, Framingham, Massachusetts
| | - Mukesh Verma
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, Maryland
| | - Ying Wang
- Department of Obstetrics and Gynaecology, University of Cambridge, National Institute for Health Research Cambridge Comprehensive Biomedical Research Centre, Cambridge, United Kingdom
| | - Nick J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at University College London, London, United Kingdom
| | - Naji Younes
- Department of Epidemiology and Biostatistics Milken Institute School of Public Health, George Washington University, Washington, DC
| | - Hua Zhao
- Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Steven C Moore
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland
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Wei R, Ross AB, Su M, Wang J, Guiraud SP, Draper CF, Beaumont M, Jia W, Martin FP. Metabotypes Related to Meat and Vegetable Intake Reflect Microbial, Lipid and Amino Acid Metabolism in Healthy People. Mol Nutr Food Res 2018; 62:e1800583. [PMID: 30098305 DOI: 10.1002/mnfr.201800583] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Revised: 07/25/2018] [Indexed: 01/05/2023]
Abstract
SCOPE The objective of this study is to develop a new methodology to identify the relationship between dietary patterns and metabolites indicative of food intake and metabolism. METHODS AND RESULTS Plasma and urine samples from healthy Swiss subjects (n = 89) collected over two time points are analyzed for a panel of host-microbial metabolites using GC- and LC-MS. Dietary intake is evaluated using a validated food frequency questionnaire. Dietary pattern clusters and relationships with metabolites are determined using Non-Negative Matrix Factorization (NNMF) and Sparse Generalized Canonical Correlation Analysis (SGCCA). Use of NNMF allows detection of latent diet clusters in this population, which describes a high intake of meat or vegetables. SGCCA associates these clusters to i) diet-host microbial and lipid associated bile acid metabolism, and ii) essential amino acid metabolism. CONCLUSION This novel application of NNMF and SGCCA allows detection of distinct metabotypes for meat and vegetable dietary patterns in a heterogeneous population. As many of the metabolites associated with meat or vegetable intake are the result of host-microbiota interactions, the findings support a role for microbiota mediating the metabolic imprinting of different dietary choices.
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Affiliation(s)
- Runmin Wei
- University of Hawaii Cancer Center (UHCC), Honolulu, HI, 96813, USA.,Molecular Biosciences and Bioengineering, University of Hawaii at Manoa, Honolulu, HI, 96822, USA
| | - Alastair B Ross
- Analytical Science Department, Nestlé Research Center, Lausanne, Switzerland.,Division of Food and Nutrition Science, Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - MingMing Su
- University of Hawaii Cancer Center (UHCC), Honolulu, HI, 96813, USA
| | - Jingye Wang
- University of Hawaii Cancer Center (UHCC), Honolulu, HI, 96813, USA
| | - Seu-Ping Guiraud
- Nutrition and Metabolic health Department, Nestle Institute of Health Sciences (NIHS), Lausanne, Switzerland
| | - Colleen Fogarty Draper
- Nutrition and Metabolic health Department, Nestle Institute of Health Sciences (NIHS), Lausanne, Switzerland
| | - Maurice Beaumont
- Clinical Development Unit, Nestlé Research Center, Lausanne, Switzerland
| | - Wei Jia
- University of Hawaii Cancer Center (UHCC), Honolulu, HI, 96813, USA
| | - Francois-Pierre Martin
- Nutrition and Metabolic health Department, Nestle Institute of Health Sciences (NIHS), Lausanne, Switzerland
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Association of circulating metabolites with healthy diet and risk of cardiovascular disease: analysis of two cohort studies. Sci Rep 2018; 8:8620. [PMID: 29872056 PMCID: PMC5988716 DOI: 10.1038/s41598-018-26441-1] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Accepted: 05/02/2018] [Indexed: 12/15/2022] Open
Abstract
Diet may modify metabolomic profiles towards higher or lower cardiovascular disease (CVD) risk. We aimed to identify metabolite profiles associated with high adherence to dietary recommendations - the Alternative Healthy Eating Index (AHEI) - and the extent to which metabolites associated with AHEI also predict incident CVD. Relations between AHEI score and 80 circulating lipids and metabolites, quantified by nuclear magnetic resonance metabolomics, were examined using linear regression models in the Whitehall II study (n = 4824, 55.9 ± 6.1 years, 28.0% women) and were replicated in the Cardiovascular Risk in Young Finns Study (n = 1716, 37.7 ± 5.0 years, 56.3% women). We used Cox models to study associations between metabolites and incident CVD over the 15.8-year follow-up in the Whitehall II study. After adjustment for confounders, higher AHEI score (indicating healthier diet) was associated with higher degree of unsaturation of fatty acids (FA) and higher ratios of polyunsaturated FA, omega-3 and docosahexaenoic acid relative to total FA in both Whitehall II and Young Finns studies. A concordance of associations of metabolites with higher AHEI score and lower CVD risk was observed in Whitehall II. Adherence to healthy diet seems to be associated with specific FA that reduce risk of CVD.
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Cornelis MC, Erlund I, Michelotti GA, Herder C, Westerhuis JA, Tuomilehto J. Metabolomic response to coffee consumption: application to a three-stage clinical trial. J Intern Med 2018; 283:544-557. [PMID: 29381822 DOI: 10.1111/joim.12737] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Coffee is widely consumed and contains many bioactive compounds, any of which may impact pathways related to disease development. OBJECTIVE To identify individual metabolite changes in response to coffee. METHODS We profiled the metabolome of fasting serum samples collected from a previously reported single-blinded, three-stage clinical trial. Forty-seven habitual coffee consumers refrained from drinking coffee for 1 month, consumed four cups of coffee/day in the second month and eight cups/day in the third month. Samples collected after each coffee stage were subject to nontargeted metabolomic profiling using UPLC-ESI-MS/MS. A total of 733 metabolites were included for univariate and multivariate analyses. RESULTS A total of 115 metabolites were significantly associated with coffee intake (P < 0.05 and Q < 0.05). Eighty-two were of known identity and mapped to one of 33 predefined biological pathways. We observed a significant enrichment of metabolite members of five pathways (P < 0.05): (i) xanthine metabolism: includes caffeine metabolites, (ii) benzoate metabolism: reflects polyphenol metabolite products of gut microbiota metabolism, (iii) steroid: novel but may reflect phytosterol content of coffee, (iv) fatty acid metabolism (acylcholine): novel link to coffee and (v) endocannabinoid: novel link to coffee. CONCLUSIONS The novel metabolites and candidate pathways we have identified may provide new insight into the mechanisms by which coffee may be exerting its health effects.
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Affiliation(s)
- M C Cornelis
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - I Erlund
- Genomics and Biomarkers Unit, National Institute for Health and Welfare, Helsinki, Finland
| | | | - C Herder
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.,German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - J A Westerhuis
- Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.,Centre for Human Metabolomics, Faculty of Natural Sciences, North-West University, Potchefstroom, South Africa
| | - J Tuomilehto
- Dasman Diabetes Institute, Dasman, Kuwait.,Department of Neuroscience and Preventive Medicine, Danube-University Krems, Krems, Austria.,Disease Risk Unit, National Institute for Health and Welfare, Helsinki, Finland.,Saudi Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
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42
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Wang Y, Gapstur SM, Carter BD, Hartman TJ, Stevens VL, Gaudet MM, McCullough ML. Untargeted Metabolomics Identifies Novel Potential Biomarkers of Habitual Food Intake in a Cross-Sectional Study of Postmenopausal Women. J Nutr 2018; 148:932-943. [PMID: 29767735 DOI: 10.1093/jn/nxy027] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 02/01/2018] [Indexed: 12/20/2022] Open
Abstract
Background Recent studies suggest that untargeted metabolomics is a promising tool to identify novel biomarkers of individual foods. However, few large cross-sectional studies with comprehensive data on habitual diet and circulating metabolites have been conducted. Objective We aimed to identify potential food biomarkers and evaluate their predictive accuracy. Methods We conducted a cross-sectional analysis of consumption of 91 food groups or items, assessed by a 152-item food-frequency questionnaire, in relation to 1186 serum metabolites measured by mass spectrometry-based platforms from 1369 nonsmoking postmenopausal women (mean age = 68.3 y). Diet-metabolite associations were selected by Pearson's partial correlation analysis (P < 4.63 × 10-7, |r| > 0.2). The predictive accuracy of the selected food metabolites was evaluated from the area under the curve (AUC) calculated from receiver operating characteristic analysis conducted among women in the top and bottom quintiles of dietary intake. Results We identified 379 diet-metabolite associations. Forty-two food groups or items were correlated with 199 serum metabolites. We replicated 63 metabolites as biomarkers of habitual food intake reported in previous cross-sectional studies. Among those not previously shown to be associated with habitual diet, several are biologically plausible and were reported in acute feeding studies including: banana and dopamine 3-O-sulfate (r = 0.34, AUC = 76%) and dopamine 4-O-sulfate (r = 0.33, AUC = 74%), garlic and alliin (r = 0.24, AUC = 69%), N-acetylalliin (r = 0.27, AUC = 70%), and S-allylcysteine (r = 0.23, AUC = 69). Two unannotated metabolites were the strongest predictors for dark fish (X-02269, r = 0.51, AUC = 94%) and coffee intake (X-21442, r = 0.62, AUC = 98%). Conclusion In this comprehensive, cross-sectional analysis of habitual food intake and serum metabolites among postmenopausal women, we identified several potentially novel food biomarkers and replicated others. Our findings contribute to the limited literature on food-based biomarkers and highlight the significant and promising role that large cohort studies with archived blood samples could play in this field. This study was registered at clinicaltrials.gov as NCT03282812.
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Affiliation(s)
- Ying Wang
- Epidemiology Research Program, American Cancer Society, Atlanta, GA
| | - Susan M Gapstur
- Epidemiology Research Program, American Cancer Society, Atlanta, GA
| | - Brian D Carter
- Epidemiology Research Program, American Cancer Society, Atlanta, GA
| | - Terryl J Hartman
- Department of Epidemiology, Rollins School of Public Health, Winship Cancer Institute, Emory University, Atlanta, GA
| | | | - Mia M Gaudet
- Epidemiology Research Program, American Cancer Society, Atlanta, GA
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Yang SJ, Kwak SY, Jo G, Song TJ, Shin MJ. Serum metabolite profile associated with incident type 2 diabetes in Koreans: findings from the Korean Genome and Epidemiology Study. Sci Rep 2018; 8:8207. [PMID: 29844477 PMCID: PMC5974077 DOI: 10.1038/s41598-018-26320-9] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Accepted: 05/03/2018] [Indexed: 02/07/2023] Open
Abstract
The identification of metabolic alterations in type 2 diabetes (T2D) is useful for elucidating the pathophysiology of the disease and in classifying high-risk individuals. In this study, we prospectively examined the associations between serum metabolites and T2D risk in a Korean community-based cohort (the Ansan-Ansung cohort). Data were obtained from 1,939 participants with available metabolic profiles and without diabetes, cardiovascular disease, or cancer at baseline. The acylcarnitine, amino acid, amine, and phospholipid levels in fasting serum samples were analyzed by targeted metabolomics. During the 8-year follow-up period, we identified 282 cases of incident T2D. Of all metabolites measured, 22 were significantly associated with T2D risk. Specifically, serum levels of alanine, arginine, isoleucine, proline, tyrosine, valine, hexose and five phosphatidylcholine diacyls were positively associated with T2D risk, whereas lyso-phosphatidylcholine acyl C17:0 and C18:2 and other glycerophospholipids were negatively associated with T2D risk. The associated metabolites were further correlated with T2D-relevant risk factors such as insulin resistance and triglyceride indices. In addition, a healthier diet (as measured by the modified recommended food score) was independently associated with T2D risk. Alterations of metabolites such as amino acids and choline-containing phospholipids appear to be associated with T2D risk in Korean adults.
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Affiliation(s)
- Soo Jin Yang
- Department of Food and Nutrition, Seoul Women's University, Seoul, 01797, Republic of Korea
| | - So-Young Kwak
- Department of Public Health Sciences, BK21PLUS Program in Embodiment: Health-Society Interaction, Graduate School, Korea University, Seoul, 02841, Republic of Korea
| | - Garam Jo
- Department of Public Health Sciences, BK21PLUS Program in Embodiment: Health-Society Interaction, Graduate School, Korea University, Seoul, 02841, Republic of Korea
| | - Tae-Jin Song
- Department of Neurology, Ewha Womans University School of Medicine, Seoul, 07985, Republic of Korea
| | - Min-Jeong Shin
- Department of Public Health Sciences, BK21PLUS Program in Embodiment: Health-Society Interaction, Graduate School, Korea University, Seoul, 02841, Republic of Korea.
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Wang DD, Hu FB. Precision nutrition for prevention and management of type 2 diabetes. Lancet Diabetes Endocrinol 2018; 6:416-426. [PMID: 29433995 DOI: 10.1016/s2213-8587(18)30037-8] [Citation(s) in RCA: 129] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Revised: 12/02/2017] [Accepted: 12/11/2017] [Indexed: 02/08/2023]
Abstract
Precision nutrition aims to prevent and manage chronic diseases by tailoring dietary interventions or recommendations to one or a combination of an individual's genetic background, metabolic profile, and environmental exposures. Recent advances in genomics, metabolomics, and gut microbiome technologies have offered opportunities as well as challenges in the use of precision nutrition to prevent and manage type 2 diabetes. Nutrigenomics studies have identified genetic variants that influence intake and metabolism of specific nutrients and predict individuals' variability in response to dietary interventions. Metabolomics has revealed metabolomic fingerprints of food and nutrient consumption and uncovered new metabolic pathways that are potentially modified by diet. Dietary interventions have been successful in altering abundance, composition, and activity of gut microbiota that are relevant for food metabolism and glycaemic control. In addition, mobile apps and wearable devices facilitate real-time assessment of dietary intake and provide feedback which can improve glycaemic control and diabetes management. By integrating these technologies with big data analytics, precision nutrition has the potential to provide personalised nutrition guidance for more effective prevention and management of type 2 diabetes. Despite these technological advances, much research is needed before precision nutrition can be widely used in clinical and public health settings. Currently, the field of precision nutrition faces challenges including a lack of robust and reproducible results, the high cost of omics technologies, and methodological issues in study design as well as high-dimensional data analyses and interpretation. Evidence is needed to support the efficacy, cost-effectiveness, and additional benefits of precision nutrition beyond traditional nutrition intervention approaches. Therefore, we should manage unrealistically high expectations and balance the emerging field of precision nutrition with public health nutrition strategies to improve diet quality and prevent type 2 diabetes and its complications.
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Affiliation(s)
- Dong D Wang
- Department of Nutrition, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Frank B Hu
- Department of Nutrition, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Epidemiology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Harvard T H Chan School of Public Health, and Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
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Guasch-Ferré M, Bhupathiraju SN, Hu FB. Use of Metabolomics in Improving Assessment of Dietary Intake. Clin Chem 2017; 64:82-98. [PMID: 29038146 DOI: 10.1373/clinchem.2017.272344] [Citation(s) in RCA: 168] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Accepted: 09/07/2017] [Indexed: 01/23/2023]
Abstract
BACKGROUND Nutritional metabolomics is rapidly evolving to integrate nutrition with complex metabolomics data to discover new biomarkers of nutritional exposure and status. CONTENT The purpose of this review is to provide a broad overview of the measurement techniques, study designs, and statistical approaches used in nutrition metabolomics, as well as to describe the current knowledge from epidemiologic studies identifying metabolite profiles associated with the intake of individual nutrients, foods, and dietary patterns. SUMMARY A wide range of technologies, databases, and computational tools are available to integrate nutritional metabolomics with dietary and phenotypic information. Biomarkers identified with the use of high-throughput metabolomics techniques include amino acids, acylcarnitines, carbohydrates, bile acids, purine and pyrimidine metabolites, and lipid classes. The most extensively studied food groups include fruits, vegetables, meat, fish, bread, whole grain cereals, nuts, wine, coffee, tea, cocoa, and chocolate. We identified 16 studies that evaluated metabolite signatures associated with dietary patterns. Dietary patterns examined included vegetarian and lactovegetarian diets, omnivorous diet, Western dietary patterns, prudent dietary patterns, Nordic diet, and Mediterranean diet. Although many metabolite biomarkers of individual foods and dietary patterns have been identified, those biomarkers may not be sensitive or specific to dietary intakes. Some biomarkers represent short-term intakes rather than long-term dietary habits. Nonetheless, nutritional metabolomics holds promise for the development of a robust and unbiased strategy for measuring diet. Still, this technology is intended to be complementary, rather than a replacement, to traditional well-validated dietary assessment methods such as food frequency questionnaires that can measure usual diet, the most relevant exposure in nutritional epidemiologic studies.
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Affiliation(s)
- Marta Guasch-Ferré
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA
| | - Shilpa N Bhupathiraju
- Department of Nutrition, Harvard TH 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 TH 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 TH Chan School of Public Health, Boston, MA
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Playdon MC, Ziegler RG, Sampson JN, Stolzenberg-Solomon R, Thompson HJ, Irwin ML, Mayne ST, Hoover RN, Moore SC. Nutritional metabolomics and breast cancer risk in a prospective study. Am J Clin Nutr 2017; 106:637-649. [PMID: 28659298 PMCID: PMC5525118 DOI: 10.3945/ajcn.116.150912] [Citation(s) in RCA: 107] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Accepted: 05/30/2017] [Indexed: 12/16/2022] Open
Abstract
Background: The epidemiologic evidence for associations between dietary factors and breast cancer is weak and etiologic mechanisms are often unclear. Exploring the role of dietary biomarkers with metabolomics can potentially facilitate objective dietary characterization, mitigate errors related to self-reported diet, agnostically test metabolic pathways, and identify mechanistic mediators.Objective: The aim of this study was to evaluate associations of diet-related metabolites with the risk of breast cancer in the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial.Design: We examined prediagnostic serum concentrations of diet-related metabolites in a nested case-control study in 621 postmenopausal invasive breast cancer cases and 621 matched controls in the multicenter PLCO cohort. We calculated partial Pearson correlations between 617 metabolites and 55 foods, food groups, and vitamin supplements on the basis of the 2015 Dietary Guidelines for Americans and derived from a 137-item self-administered food-frequency questionnaire. Diet-related metabolites (P-correlation < 1.47 × 10-6) were evaluated in breast cancer analyses. ORs for the 90th compared with the 10th percentile were calculated by using conditional logistic regression, with body mass index, physical inactivity, other breast cancer risk factors, and caloric intake controlled for (false discovery rate <0.2).Results: Of 113 diet-related metabolites, 3 were associated with overall breast cancer risk (621 cases): caprate (10:0), a saturated fatty acid (OR: 1.77; 95% CI = 1.28, 2.43); γ-carboxyethyl hydrochroman (γ-CEHC), a vitamin E (γ-tocopherol) derivative (OR: 1.64; 95% CI: 1.18, 2.28); and 4-androsten-3β,17β-diol-monosulfate (1), an androgen (OR: 1.61; 95% CI: 1.20, 2.16). Nineteen metabolites were significantly associated with estrogen receptor (ER)-positive (ER+) breast cancer (418 cases): 12 alcohol-associated metabolites, including 7 androgens and α-hydroxyisovalerate (OR: 2.23; 95% CI: 1.50, 3.32); 3 vitamin E (tocopherol) derivatives (e.g., γ-CEHC; OR: 1.80; 95% CI: 1.20, 2.70); butter-associated caprate (10:0) (OR: 1.81; 95% CI: 1.23, 2.67); and fried food-associated 2-hydroxyoctanoate (OR: 1.46; 95% CI: 1.03, 2.07). No metabolites were significantly associated with ER-negative breast cancer (144 cases).Conclusions: Prediagnostic serum concentrations of metabolites related to alcohol, vitamin E, and animal fats were moderately strongly associated with ER+ breast cancer risk. Our findings show how nutritional metabolomics might identify diet-related exposures that modulate cancer risk. This trial was registered at clinicaltrials.gov as NCT00339495.
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Affiliation(s)
- Mary C Playdon
- Yale School of Public Health, Yale University, New Haven, CT; .,Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
| | - Regina G Ziegler
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
| | - Joshua N Sampson
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
| | | | - Henry J Thompson
- Cancer Prevention Laboratory, Colorado State University, Fort Collins, CO
| | - Melinda L Irwin
- Yale School of Public Health, Yale University, New Haven, CT;,Yale Cancer Center, New Haven, CT; and
| | - Susan T Mayne
- Yale School of Public Health, Yale University, New Haven, CT;,US Food and Drug Administration, College Park, MD
| | - Robert N Hoover
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
| | - Steven C Moore
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
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Eguchi A, Sakurai K, Watanabe M, Mori C. Exploration of potential biomarkers and related biological pathways for PCB exposure in maternal and cord serum: A pilot birth cohort study in Chiba, Japan. ENVIRONMENT INTERNATIONAL 2017; 102:157-164. [PMID: 28262321 DOI: 10.1016/j.envint.2017.02.011] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2016] [Revised: 02/21/2017] [Accepted: 02/23/2017] [Indexed: 05/21/2023]
Abstract
Polychlorinated biphenyls (PCBs) have been associated with adverse human reproductive and fetal developmental measures or outcomes because of their endocrine-disrupting effects; however, the biological mechanisms of adverse effects of PCB exposure in humans are not currently well established. In this study, we aimed to identify the biological pathways and potential biomarkers of PCB exposure in maternal and umbilical cord serum using a hydrophilic interaction chromatography-tandem mass spectrometry (HILIC-MS/MS) metabolomics platform. The median concentration of total PCBs in maternal (n=93) and cord serum (n=93) were 350 and 70pgg-1 wet wt, respectively. PCB levels in maternal and fetal serum from the Chiba Study of Mother and Children's Health (C-MACH) cohort are comparable to those of earlier cohort studies conducted in Japan, the USA, and European countries. We used the random forest model with the metabolome profile to predict exposure levels of PCB (first quartile [Q1] and fourth quartile [Q4]) for pregnant women and fetuses. In the prediction model for classification of Q1 versus Q4 (area-under-curve [AUC]: pregnant women=0.812 and fetuses=0.919), citraconic acid level in maternal serum and ethanolamine, p-hydroxybenzoate, and purine levels in cord serum had >0.70 AUC values. These candidate biomarkers and metabolite included in composited models were related to glutathione and amino acid metabolism in maternal serum and the amino acid metabolism and ubiquinone and other terpenoid-quinone biosynthesis in cord serum (FDR <0.10), indicating disruption of metabolic pathways by PCB exposure in pregnant women and fetuses. These results showed that metabolome analysis might be useful to explore potential biomarkers and related biological pathways for PCB exposure. Thus, more detailed studies are needed to verify sensitivity of the biomarkers and clarify the biochemical changes resulting from PCB exposure.
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Affiliation(s)
- Akifumi Eguchi
- Chiba University, Center for Preventive Medical Sciences, Inage-ku Yayoi-cho 1-33, Chiba, Japan
| | - Kenichi Sakurai
- Chiba University, Center for Preventive Medical Sciences, Inage-ku Yayoi-cho 1-33, Chiba, Japan
| | - Masahiro Watanabe
- Chiba University, Center for Preventive Medical Sciences, Inage-ku Yayoi-cho 1-33, Chiba, Japan
| | - Chisato Mori
- Chiba University, Center for Preventive Medical Sciences, Inage-ku Yayoi-cho 1-33, Chiba, Japan; Chiba University, Department of Bioenvironmental Medicine, Graduate School of Medicine, Chuo-ku Inohana 1-8-1, Chiba, Japan.
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48
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Esko T, Hirschhorn JN, Feldman HA, Hsu YHH, Deik AA, Clish CB, Ebbeling CB, Ludwig DS. Metabolomic profiles as reliable biomarkers of dietary composition. Am J Clin Nutr 2017; 105:547-554. [PMID: 28077380 PMCID: PMC5320413 DOI: 10.3945/ajcn.116.144428] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Accepted: 12/08/2016] [Indexed: 01/01/2023] Open
Abstract
Background: Clinical nutrition research often lacks robust markers of compliance, complicating the interpretation of clinical trials and observational studies of free-living subjects.Objective: We aimed to examine metabolomics profiles in response to 3 diets that differed widely in macronutrient composition during a controlled feeding protocol.Design: Twenty-one adults with a high body mass index (in kg/m2; mean ± SD: 34.4 ± 4.9) were given hypocaloric diets to promote weight loss corresponding to 10-15% of initial body weight. They were then studied during weight stability while consuming 3 test diets, each for a 4-wk period according to a crossover design: low fat (60% carbohydrate, 20% fat, 20% protein), low glycemic index (40% carbohydrate, 40% fat, 20% protein), or very-low carbohydrate (10% carbohydrate, 60% fat, 30% protein). Plasma samples were obtained at baseline and at the end of each 4-wk period in the fasting state for metabolomics analysis by using liquid chromatography-tandem mass spectrometry. Statistical analyses included adjustment for multiple comparisons.Results: Of 333 metabolites, we identified 152 whose concentrations differed for ≥1 diet compared with the others, including diacylglycerols and triacylglycerols, branched-chain amino acids, and markers reflecting metabolic status. Analysis of groups of related metabolites, with the use of either principal components or pathways, revealed coordinated metabolic changes affected by dietary composition, including pathways related to amino acid metabolism. We constructed a classifier using the metabolites that differed between diets and were able to correctly identify the test diet from metabolite profiles in 60 of 63 cases (>95% accuracy). Analyses also suggest differential effects by diet on numerous cardiometabolic disease risk factors.Conclusions: Metabolomic profiling may be used to assess compliance during clinical nutrition trials and the validity of dietary assessment in observational studies. In addition, this methodology may help elucidate mechanistic pathways linking diet to chronic disease risk. This trial was registered at clinicaltrials.gov as NCT00315354.
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Affiliation(s)
- Tõnu Esko
- Center for Basic and Translational Obesity Research,,Estonian Genome Center, University of Tartu, Tartu, Estonia;,Broad Institute of MIT and Harvard, Cambridge, MA; and
| | - Joel N Hirschhorn
- Center for Basic and Translational Obesity Research,,Broad Institute of MIT and Harvard, Cambridge, MA; and
| | | | - Yu-Han H Hsu
- Center for Basic and Translational Obesity Research,,Broad Institute of MIT and Harvard, Cambridge, MA; and
| | - Amy A Deik
- Metabolomics Platform, Broad Institute, Cambridge, MA
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, MA; and,Metabolomics Platform, Broad Institute, Cambridge, MA
| | - Cara B Ebbeling
- New Balance Foundation Obesity Prevention Center, Boston Children’s Hospital, Boston, MA
| | - David S Ludwig
- New Balance Foundation Obesity Prevention Center, Boston Children's Hospital, Boston, MA;
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49
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Playdon MC, Moore SC, Derkach A, Reedy J, Subar AF, Sampson JN, Albanes D, Gu F, Kontto J, Lassale C, Liao LM, Männistö S, Mondul AM, Weinstein SJ, Irwin ML, Mayne ST, Stolzenberg-Solomon R. Identifying biomarkers of dietary patterns by using metabolomics. Am J Clin Nutr 2017; 105:450-465. [PMID: 28031192 PMCID: PMC5267308 DOI: 10.3945/ajcn.116.144501] [Citation(s) in RCA: 153] [Impact Index Per Article: 21.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Accepted: 11/18/2016] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Healthy dietary patterns that conform to national dietary guidelines are related to lower chronic disease incidence and longer life span. However, the precise mechanisms involved are unclear. Identifying biomarkers of dietary patterns may provide tools to validate diet quality measurement and determine underlying metabolic pathways influenced by diet quality. OBJECTIVE The objective of this study was to examine the correlation of 4 diet quality indexes [the Healthy Eating Index (HEI) 2010, the Alternate Mediterranean Diet Score (aMED), the WHO Healthy Diet Indicator (HDI), and the Baltic Sea Diet (BSD)] with serum metabolites. DESIGN We evaluated dietary patterns and metabolites in male Finnish smokers (n = 1336) from 5 nested case-control studies within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study cohort. Participants completed a validated food-frequency questionnaire and provided a fasting serum sample before study randomization (1985-1988). Metabolites were measured with the use of mass spectrometry. We analyzed cross-sectional partial correlations of 1316 metabolites with 4 diet quality indexes, adjusting for age, body mass index, smoking, energy intake, education, and physical activity. We pooled estimates across studies with the use of fixed-effects meta-analysis with Bonferroni correction for multiple comparisons, and conducted metabolic pathway analyses. RESULTS The HEI-2010, aMED, HDI, and BSD were associated with 23, 46, 23, and 33 metabolites, respectively (17, 21, 11, and 10 metabolites, respectively, were chemically identified; r-range: -0.30 to 0.20; P = 6 × 10-15 to 8 × 10-6). Food-based diet indexes (HEI-2010, aMED, and BSD) were associated with metabolites correlated with most components used to score adherence (e.g., fruit, vegetables, whole grains, fish, and unsaturated fat). HDI correlated with metabolites related to polyunsaturated fat and fiber components, but not other macro- or micronutrients (e.g., percentages of protein and cholesterol). The lysolipid and food and plant xenobiotic pathways were most strongly associated with diet quality. CONCLUSIONS Diet quality, measured by healthy diet indexes, is associated with serum metabolites, with the specific metabolite profile of each diet index related to the diet components used to score adherence. This trial was registered at clinicaltrials.gov as NCT00342992.
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Affiliation(s)
- Mary C Playdon
- Yale School of Public Health, Yale University, New Haven, CT;
- Division of Cancer Epidemiology and Genetics and
| | | | | | - Jill Reedy
- Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD
| | - Amy F Subar
- Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD
| | | | | | - Fangyi Gu
- Division of Cancer Epidemiology and Genetics and
| | - Jukka Kontto
- Department of Health, National Institute for Health and Welfare, Helsinki, Finland
| | - Camille Lassale
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Linda M Liao
- Division of Cancer Epidemiology and Genetics and
| | - Satu Männistö
- Department of Health, National Institute for Health and Welfare, Helsinki, Finland
| | - Alison M Mondul
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI
| | | | - Melinda L Irwin
- Yale School of Public Health, Yale University, New Haven, CT
- Yale Cancer Center, New Haven, CT; and
| | - Susan T Mayne
- Yale School of Public Health, Yale University, New Haven, CT
- Food and Drug Administration, College Park, MD
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50
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Miranda AM, Carioca AAF, Steluti J, da Silva IDCG, Fisberg RM, Marchioni DM. The effect of coffee intake on lysophosphatidylcholines: A targeted metabolomic approach. Clin Nutr 2016; 36:1635-1641. [PMID: 28029506 DOI: 10.1016/j.clnu.2016.10.012] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Revised: 10/06/2016] [Accepted: 10/12/2016] [Indexed: 11/16/2022]
Abstract
BACKGROUND & AIM Lysophosphatidylcholines (lysoPC) are known to be a pathological component of oxidized-LDL, and several studies demonstrate its pro-inflammatory properties in vitro. Nevertheless, bioactive compounds found in coffee, such as phenolic acids might inhibit LDL oxidation. The relationship between coffee consumption and lysoPC has not been described previously in humans. The aim of the present study was to assess the association between coffee intake and plasma lysoPC levels in adults. METHODS Data was from the "Health Survey of Sao Paulo (ISA-Capital)", a cross-sectional population-based survey in Sao Paulo, among 169 individuals aged 20 years or older. This population was categorized into three groups: non-coffee consumers (0 mL/day-G1), low coffee consumers (≤100 mL/day-G2), and high coffee consumers (>100 mL/day-G3). Usual coffee intake was estimated by two 24HR and one FFQ, using Multiple Source Method. Quantification of the metabolites was performed by mass spectrometry (FIA-MS/MS and HPLC-MS/MS) and 14 lysoPC species were identified. The association between coffee intake and lysoPC was analyzed by multiple linear regression adjusted for age, sex, household per capita income, smoking, physical activity, body mass index, total energy intake, use of drugs, vegetables and fruit consumption and caffeine intake. RESULTS LysoPC levels were significantly lower in G3 than in G1, for the lysoPC a C16:1 (β = -0.56; p = 0.014), lysoPC a C18:1 (β = -2.57; p = 0.018), and lysoPC a C20:4 (β = -1.14; p = 0.037). In opposition, the ratios of C16:0/C16:1 and C18:0/18:1 was higher in G3 (β = 5.04; p = 0.025 and β = 0.28; p = 0.003, respectively). CONCLUSION LysoPC profile differed according to coffee intake, showing a possible beneficial health effect of this beverage on inflammatory and oxidative processes.
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Affiliation(s)
| | | | - Josiane Steluti
- Department of Nutrition, School of Public Health, University of Sao Paulo, SP, Brazil
| | | | - Regina Mara Fisberg
- Department of Nutrition, School of Public Health, University of Sao Paulo, SP, Brazil
| | - Dirce Maria Marchioni
- Department of Nutrition, School of Public Health, University of Sao Paulo, SP, Brazil.
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