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de la O V, Fernández-Cruz E, Valdés A, Cifuentes A, Walton J, Martínez JA. Exhaustive Search of Dietary Intake Biomarkers as Objective Tools for Personalized Nutrimetabolomics and Precision Nutrition Implementation. Nutr Rev 2024:nuae133. [PMID: 39331531 DOI: 10.1093/nutrit/nuae133] [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] [Indexed: 09/29/2024] Open
Abstract
OBJECTIVE To conduct an exhaustive scoping search of existing literature, incorporating diverse bibliographic sources to elucidate the relationships between metabolite biomarkers in human fluids and dietary intake. BACKGROUND The search for biomarkers linked to specific dietary food intake holds immense significance for precision health and nutrition research. Using objective methods to track food consumption through metabolites offers a more accurate way to provide dietary advice and prescriptions on healthy dietary patterns by healthcare professionals. An extensive investigation was conducted on biomarkers associated with the consumption of several food groups and consumption patterns. Evidence is integrated from observational studies, systematic reviews, and meta-analyses to achieve precision nutrition and metabolism personalization. METHODS Tailored search strategies were applied across databases and gray literature, yielding 158 primary research articles that met strict inclusion criteria. The collected data underwent rigorous analysis using STATA and Python tools. Biomarker-food associations were categorized into 5 groups: cereals and grains, dairy products, protein-rich foods, plant-based foods, and a miscellaneous group. Specific cutoff points (≥3 or ≥4 bibliographic appearances) were established to identify reliable biomarkers indicative of dietary consumption. RESULTS Key metabolites in plasma, serum, and urine revealed intake from different food groups. For cereals and grains, 3-(3,5-dihydroxyphenyl) propanoic acid glucuronide and 3,5-dihydroxybenzoic acid were significant. Omega-3 fatty acids and specific amino acids showcased dairy and protein foods consumption. Nuts and seafood were linked to hypaphorine and trimethylamine N-oxide. The miscellaneous group featured compounds like theobromine, 7-methylxanthine, caffeine, quinic acid, paraxanthine, and theophylline associated with coffee intake. CONCLUSIONS Data collected from this research demonstrate potential for incorporating precision nutrition into clinical settings and nutritional advice based on accurate estimation of food intake. By customizing dietary recommendations based on individualized metabolic profiles, this approach could significantly improve personalized food consumption health prescriptions and support integrating multiple nutritional data.This article is part of a Nutrition Reviews special collection on Precision Nutrition.
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Affiliation(s)
- Victor de la O
- Nutrition Precision and Cardiometabolic Health Program of IMDEA-Food Institute (Madrid Institute for Advances Studies), 28040, Madrid, Spain
- Faculty of Health Sciences, International University of La Rioja, 26006, Logroño, Spain
| | - Edwin Fernández-Cruz
- Nutrition Precision and Cardiometabolic Health Program of IMDEA-Food Institute (Madrid Institute for Advances Studies), 28040, Madrid, Spain
- Faculty of Health Sciences, International University of La Rioja, 26006, Logroño, Spain
| | - Alberto Valdés
- Foodomics Lab, Institute of Food Science Research, Spanish National Research Council, 28049, Madrid, Spain
| | - Alejandro Cifuentes
- Foodomics Lab, Institute of Food Science Research, Spanish National Research Council, 28049, Madrid, Spain
| | - Janette Walton
- Department of Biological Sciences, Munster Technological University, Cork, Republic of Ireland
| | - J Alfredo Martínez
- Nutrition Precision and Cardiometabolic Health Program of IMDEA-Food Institute (Madrid Institute for Advances Studies), 28040, Madrid, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y la Nutrición, Instituto de Salud Carlos III, 28049, Madrid, Spain
- Department of Medicine and Endocrinology, Campus of Soria, University of Valladolid, Valladolid, Spain
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Cifuentes M, Vahid F, Devaux Y, Bohn T. Biomarkers of food intake and their relevance to metabolic syndrome. Food Funct 2024; 15:7271-7304. [PMID: 38904169 DOI: 10.1039/d4fo00721b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
Metabolic syndrome (MetS) constitutes a prevalent risk factor associated with non communicable diseases such as cardiovascular disease and type 2 diabetes. A major factor impacting the etiology of MetS is diet. Dietary patterns and several individual food constituents have been related to the risk of developing MetS or have been proposed as adjuvant treatment. However, traditional methods of dietary assessment such as 24 h recalls rely greatly on intensive user-interaction and are subject to bias. Hence, more objective methods are required for unbiased dietary assessment and efficient prevention. While it is accepted that some dietary-derived constituents in blood plasma are indicators for certain dietary patterns, these may be too unstable (such as vitamin C as a marker for fruits/vegetables) or too broad (e.g. polyphenols for plant-based diets) or reflect too short-term intake only to allow for strong associations with prolonged intake of individual food groups. In the present manuscript, commonly employed biomarkers of intake including those related to specific food items (e.g. genistein for soybean or astaxanthin and EPA for fish intake) and novel emerging ones (e.g. stable isotopes for meat intake or microRNA for plant foods) are emphasized and their suitability as biomarker for food intake discussed. Promising alternatives to plasma measures (e.g. ethyl glucuronide in hair for ethanol intake) are also emphasized. As many biomarkers (i.e. secondary plant metabolites) are not limited to dietary assessment but are also capable of regulating e.g. anti-inflammatory and antioxidant pathways, special attention will be given to biomarkers presenting a double function to assess both dietary patterns and MetS risk.
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Affiliation(s)
- Miguel Cifuentes
- Luxembourg Institute of Health, Department of Precision Health, Strassen, Luxembourg.
- Doctoral School in Science and Engineering, University of Luxembourg, 2, Avenue de l'Université, 4365 Esch-sur-Alzette, Luxembourg
| | - Farhad Vahid
- Luxembourg Institute of Health, Department of Precision Health, Strassen, Luxembourg.
| | - Yvan Devaux
- Luxembourg Institute of Health, Department of Precision Health, Strassen, Luxembourg.
| | - Torsten Bohn
- Luxembourg Institute of Health, Department of Precision Health, Strassen, Luxembourg.
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Trichia E, Koulman A, Stewart ID, Brage S, Griffin SJ, Griffin JL, Khaw K, Langenberg C, Wareham NJ, Imamura F, Forouhi NG. Plasma Metabolites Related to the Consumption of Different Types of Dairy Products and Their Association with New-Onset Type 2 Diabetes: Analyses in the Fenland and EPIC-Norfolk Studies, United Kingdom. Mol Nutr Food Res 2024; 68:e2300154. [PMID: 38054622 PMCID: PMC10909549 DOI: 10.1002/mnfr.202300154] [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: 03/17/2023] [Revised: 07/07/2023] [Indexed: 12/07/2023]
Abstract
SCOPE To identify metabolites associated with habitual dairy consumption and investigate their associations with type 2 diabetes (T2D) risk. METHODS AND RESULTS Metabolomics assays were conducted in the Fenland (n = 10,281) and EPIC-Norfolk (n = 1,440) studies. Using 82 metabolites assessed in both studies, we developed metabolite scores to classify self-reported consumption of milk, yogurt, cheese, butter, and total dairy (Fenland Study-discovery set; n = 6035). Internal and external validity of the scores was evaluated (Fenland-validation set, n = 4246; EPIC-Norfolk, n = 1440). The study assessed associations between each metabolite score and T2D incidence in EPIC-Norfolk (n = 641 cases; 16,350 person-years). The scores classified low and high consumers for all dairy types with internal validity, and milk, butter, and total dairy with external validity. The scores were further associated with lower incident T2D: hazard ratios (95% confidence interval) per standard deviation: milk 0.71 (0.65, 0.77); butter 0.62 (0.57, 0.68); total dairy 0.66 (0.60, 0.72). These associations persisted after adjustment for known dairy-fat biomarkers. CONCLUSION Metabolite scores identified habitual consumers of milk, butter, and total dairy products, and were associated with lower T2D risk. These findings hold promise for identifying objective indicators of the physiological response to dairy consumption.
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Affiliation(s)
- Eirini Trichia
- MRC Epidemiology UnitInstitute of Metabolic ScienceUniversity of Cambridge School of Clinical MedicineCambridgeCB2 0SLUK
| | - Albert Koulman
- MRC Epidemiology UnitInstitute of Metabolic ScienceUniversity of Cambridge School of Clinical MedicineCambridgeCB2 0SLUK
| | - Isobel D. Stewart
- MRC Epidemiology UnitInstitute of Metabolic ScienceUniversity of Cambridge School of Clinical MedicineCambridgeCB2 0SLUK
| | - Soren Brage
- MRC Epidemiology UnitInstitute of Metabolic ScienceUniversity of Cambridge School of Clinical MedicineCambridgeCB2 0SLUK
| | - Simon J. Griffin
- MRC Epidemiology UnitInstitute of Metabolic ScienceUniversity of Cambridge School of Clinical MedicineCambridgeCB2 0SLUK
| | | | - Kay‐Tee Khaw
- MRC Epidemiology UnitInstitute of Metabolic ScienceUniversity of Cambridge School of Clinical MedicineCambridgeCB2 0SLUK
| | - Claudia Langenberg
- MRC Epidemiology UnitInstitute of Metabolic ScienceUniversity of Cambridge School of Clinical MedicineCambridgeCB2 0SLUK
| | - Nicholas J. Wareham
- MRC Epidemiology UnitInstitute of Metabolic ScienceUniversity of Cambridge School of Clinical MedicineCambridgeCB2 0SLUK
| | - Fumiaki Imamura
- MRC Epidemiology UnitInstitute of Metabolic ScienceUniversity of Cambridge School of Clinical MedicineCambridgeCB2 0SLUK
| | - Nita G. Forouhi
- MRC Epidemiology UnitInstitute of Metabolic ScienceUniversity of Cambridge School of Clinical MedicineCambridgeCB2 0SLUK
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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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Zhang X, Zheng Y, Liu Z, Su M, Cao W, Zhang H. Review of the applications of metabolomics approaches in dairy science: From factory to human. INT J DAIRY TECHNOL 2023. [DOI: 10.1111/1471-0307.12948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
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Wang J, Lin L, Huang J, Zhang J, Duan J, Guo X, Wu S, Sun Z. Impact of PM 2.5 exposure on plasma metabolome in healthy adults during air pollution waves: A randomized, crossover trial. JOURNAL OF HAZARDOUS MATERIALS 2022; 436:129180. [PMID: 35739713 DOI: 10.1016/j.jhazmat.2022.129180] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 05/08/2022] [Accepted: 05/16/2022] [Indexed: 06/15/2023]
Abstract
Air pollution, especially PM2.5 (particulate matter with an aerodynamic diameter ≤2.5 µm) in China, is severe and related to a variety of diseases while the potential mechanisms have not been clearly clarified yet. This study was conducted using a randomized crossover trial protocol among young and healthy college students. Plasma samples were collected before, during, and post two typical air pollution waves with a washout interval of at least 2 weeks under true and sham air purification treatments, respectively. A total of 144 blood samples from 24 participants were included in the final analysis. Metabolomics analysis for the plasma samples was completed by Ultrahigh Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS). Orthogonal Partial Least Squares Discrimination Analysis (OPLS-DA) and linear mixed-effect models were used to identify the differentially expressed metabolites and their associations with PM2.5 exposure. MetaboAnalyst 5.0 was further used to conduct pathway enrichment analysis and correlation analysis of differentially expressed metabolites. A total of 40 metabolites were identified to be differentially expressed between the true and sham air purification treatments, and eleven metabolites showed consistent significant changes upon outdoor, indoor, and time-weighted personal PM2.5 exposures. Short-term exposure to PM2.5 may cause disturbances in metabolic pathways such as linoleic acid metabolism, arachidonic acid metabolism, and tryptophan metabolism.
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Affiliation(s)
- Jiawei Wang
- Department of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China
| | - Lisen Lin
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, China, Capital Medical University, Beijing, China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, China
| | - Jing Huang
- Department of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China
| | - Jingyi Zhang
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, China, Capital Medical University, Beijing, China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, China
| | - Junchao Duan
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, China, Capital Medical University, Beijing, China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, China
| | - Xinbiao Guo
- Department of Occupational and Environmental Health Sciences, Peking University School of Public Health, Beijing, China
| | - Shaowei Wu
- Department of Occupational and Environmental Health, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China; Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, Shaanxi, China; Key Laboratory of Trace Elements and Endemic Diseases in Ministry of Health, Xi'an, Shaanxi, China.
| | - Zhiwei Sun
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, China, Capital Medical University, Beijing, China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, China.
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Drouin-Chartier JP. Plasma Lipidomic Profiles of Dairy Consumption: a New Window on Their Cardiometabolic Effects. Hypertension 2022; 79:1629-1632. [PMID: 35861753 DOI: 10.1161/hypertensionaha.122.19491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Jean-Philippe Drouin-Chartier
- Nutrition, health and society (NUTRISS) Research Center, Institute of Nutrition and Functional Foods (INAF), Laval University, Québec, CA. Faculty of pharmacy, Laval University, Québec, CA
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Yun H, Sun L, Wu Q, Luo Y, Qi Q, Li H, Gu W, Wang J, Ning G, Zeng R, Zong G, Lin X. Lipidomic Signatures of Dairy Consumption and Associated Changes in Blood Pressure and Other Cardiovascular Risk Factors Among Chinese Adults. Hypertension 2022; 79:1617-1628. [PMID: 35469422 DOI: 10.1161/hypertensionaha.122.18981] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Omics data may provide a unique opportunity to discover dairy-related biomarkers and their linked cardiovascular health. METHODS Dairy-related lipidomic signatures were discovered in baseline data from a Chinese cohort study (n=2140) and replicated in another Chinese study (n=212). Dairy intake was estimated by a validated food-frequency questionnaire. Lipidomics was profiled by high-coverage liquid chromatography-tandem mass spectrometry. Associations of dairy-related lipids with 6-year changes in cardiovascular risk factors were examined in the discovery cohort, and their causalities were analyzed by 2-sample Mendelian randomization using available genome-wide summary data. RESULTS Of 350 lipid metabolites, 4 sphingomyelins, namely sphingomyelin (OH) C32:2, sphingomyelin C32:1, sphingomyelin (2OH) C30:2, and sphingomyelin (OH) C38:2, were identified and replicated to be positively associated with total dairy consumption (β=0.130 to 0.148; P<1.43×10-4), but not or weakly with nondairy food items. The score of 4 sphingomyelins showed inverse associations with 6-year changes in systolic (-2.68 [95% CI, -4.92 to -0.43]; P=0.019), diastolic blood pressures (-1.86 [95% CI, -3.12 to -0.61]; P=0.004), and fasting glucose (-0.25 [95% CI, -0.41 to -0.08]; P=0.003). Mendelian randomization analyses further revealed that genetically inferred sphingomyelin (OH) C32:2 was inversely associated with systolic (-0.57 [95% CI, -0.85 to -0.28]; P=9.16×10-5) and diastolic blood pressures (-0.39 [95% CI, -0.59 to -0.20]; P=7.09×10-5). CONCLUSIONS The beneficial effects of dairy products on cardiovascular health might be mediated through specific sphingomyelins among Chinese with overall low dairy consumption.
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Affiliation(s)
- Huan Yun
- Shanghai Institute of Nutrition and Health (H.Y., L.S., Y.L., H.L., G.Z., X.L.), University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Liang Sun
- Shanghai Institute of Nutrition and Health (H.Y., L.S., Y.L., H.L., G.Z., X.L.), University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Qingqing Wu
- CAS Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, China (Q.W., R.Z.)
| | - Yaogan Luo
- Shanghai Institute of Nutrition and Health (H.Y., L.S., Y.L., H.L., G.Z., X.L.), University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY (Q.Q.)
| | - Huaixing Li
- Shanghai Institute of Nutrition and Health (H.Y., L.S., Y.L., H.L., G.Z., X.L.), University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Weiqiong Gu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China (W.G., J.W., G.N.).,Shanghai National Clinical Research Center for metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China (W.G., J.W., G.N.)
| | - Jiqiu Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China (W.G., J.W., G.N.).,Shanghai National Clinical Research Center for metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China (W.G., J.W., G.N.)
| | - Guang Ning
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China (W.G., J.W., G.N.).,Shanghai National Clinical Research Center for metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China (W.G., J.W., G.N.)
| | - Rong Zeng
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study (R.Z., X.L.), University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study (R.Z., X.L.), University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,CAS Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, China (Q.W., R.Z.)
| | - Geng Zong
- Shanghai Institute of Nutrition and Health (H.Y., L.S., Y.L., H.L., G.Z., X.L.), University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Xu Lin
- Shanghai Institute of Nutrition and Health (H.Y., L.S., Y.L., H.L., G.Z., X.L.), University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study (R.Z., X.L.), University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study (R.Z., X.L.), University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
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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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10
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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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11
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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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12
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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: 23] [Impact Index Per Article: 5.8] [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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13
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Schwenke DC. Dietary patterns to promote healthy aging. Curr Opin Lipidol 2020; 31:260-261. [PMID: 32487821 DOI: 10.1097/mol.0000000000000685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Dawn C Schwenke
- Associate Chief of Staff/Research & Development, Research Service, VA Northern California Healthcare System, Mather, California, USA
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