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Harada S, Ohmomo H, Matsumoto M, Sata M, Iida M, Hirata A, Miyagawa N, Kuwabara K, Kato S, Toki R, Edagawa S, Sugiyama D, Sato A, Hirayama A, Sugimoto M, Soga T, Tomita M, Shimizu A, Okamura T, Takebayashi T. Metabolomics Profiles Alterations in Cigarette Smokers and Heated Tobacco Product Users. J Epidemiol 2024; 34:403-410. [PMID: 37926518 PMCID: PMC11330708 DOI: 10.2188/jea.je20230170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2023] Open
Abstract
BACKGROUND Heated tobacco products (HTPs) have gained global popularity, but their health risks remain unclear. Therefore, the current study aimed to identify plasma metabolites associated with smoking and HTP use in a large Japanese population to improve health risk assessment. METHODS Metabolomics data from 9,922 baseline participants of the Tsuruoka Metabolomics Cohort Study (TMCS) were analyzed to determine the association between smoking habits and plasma metabolites. Moreover, alterations in smoking-related metabolites among HTP users were examined based on data obtained from 3,334 participants involved from April 2018 to June 2019 in a follow-up survey. RESULTS Our study revealed that cigarette smokers had metabolomics profiles distinct from never smokers, with 22 polar metabolites identified as candidate biomarkers for smoking. These biomarker profiles of HTP users were closer to those of cigarette smokers than those of never smokers. The concentration of glutamate was higher in cigarette smokers, and biomarkers involved in glutamate metabolism were also associated with cigarette smoking and HTP use. Network pathway analysis showed that smoking was associated with the glutamate pathway, which could lead to endothelial dysfunction and atherosclerosis of the vessels. CONCLUSION Our study showed that the glutamate pathway is affected by habitual smoking. These changes in the glutamate pathway may partly explain the mechanism by which cigarette smoking causes cardiovascular disease. HTP use was also associated with glutamate metabolism, indicating that HTP use may contribute to the development of cardiovascular disease through mechanisms similar to those in cigarette use.
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Affiliation(s)
- Sei Harada
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
- Institute for Advanced Biosciences, Keio University
| | - Hideki Ohmomo
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University
| | - Minako Matsumoto
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Mizuki Sata
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Miho Iida
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Aya Hirata
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Naoko Miyagawa
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Kazuyo Kuwabara
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Suzuka Kato
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Ryota Toki
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Shun Edagawa
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Daisuke Sugiyama
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Asako Sato
- Institute for Advanced Biosciences, Keio University
| | | | | | | | | | - Atsushi Shimizu
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University
| | - Tomonori Okamura
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Toru Takebayashi
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
- Institute for Advanced Biosciences, Keio University
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Yang Z, Gao H, Ma J, Liang NA, Liang SP, Huda N, Jiang Y, Thoudam T, Tu W, Su J, Hesler M, Chandler K, Liangpunsakul S. Unique urine and serum metabolomic signature in patients with excessive alcohol use: An exploratory study. ALCOHOL, CLINICAL & EXPERIMENTAL RESEARCH 2024; 48:1519-1528. [PMID: 38951043 DOI: 10.1111/acer.15398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 06/02/2024] [Accepted: 06/04/2024] [Indexed: 07/03/2024]
Abstract
BACKGROUND Excessive alcohol consumption has a multifaceted impact on the body's metabolic pathways and organ systems. The objectives of this study were to characterize global metabolomic changes and identify specific pathways that are altered in individuals with excessive alcohol use. METHODS This exploratory study included 22 healthy controls with no known history of excessive alcohol use and 38 patients identified as using alcohol excessively. A Fibrosis-4 score was used to determine the risk of underlying alcohol-associated liver disease among the excessive drinkers. RESULTS We found significantly altered urinary and serum metabolites among excessive drinkers, affecting various metabolic pathways including the metabolism of lipids, amino acids and peptides, cofactors and vitamins, carbohydrates, and nucleotides. Levels of two steroid hormones-5alpha-androstan-3beta,17beta-diol disulfate and androstenediol (3beta,17beta) disulfate-were significantly higher in both the serum and urine samples of excessive drinkers. These elevated levels may be associated with a higher risk of liver fibrosis in individuals with excessive alcohol use. CONCLUSION Alcohol consumption leads to marked alterations in multiple metabolic pathways, highlighting the systemic impact of alcohol on various tissues and organ systems. These findings provide a foundation for future mechanistic studies aimed at elucidating alcohol-induced changes in these metabolic pathways and their implications.
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Affiliation(s)
- Zhihong Yang
- Division of Gastroenterology and Hepatology, Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Hui Gao
- Division of Gastroenterology and Hepatology, Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Jing Ma
- Division of Gastroenterology and Hepatology, Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | | | | | - Nazmul Huda
- Division of Gastroenterology and Hepatology, Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Yanchao Jiang
- Division of Gastroenterology and Hepatology, Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Themis Thoudam
- Division of Gastroenterology and Hepatology, Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Wanzhu Tu
- Department of Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, Indiana, USA
| | - Jing Su
- Department of Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, Indiana, USA
| | - Maggie Hesler
- Division of Gastroenterology and Hepatology, Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Kristina Chandler
- Division of Gastroenterology and Hepatology, Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Suthat Liangpunsakul
- Division of Gastroenterology and Hepatology, Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Roudebush Veterans Administration Medical Center, Indianapolis, Indiana, USA
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Hirata A, Harada S, Iida M, Kurihara A, Fukai K, Kuwabara K, Kato S, Matsumoto M, Sata M, Miyagawa N, Toki R, Edagawa S, Sugiyama D, Sato A, Hirayama A, Sugimoto M, Soga T, Tomita M, Okamura T, Takebayashi T. Association of Nonalcoholic Fatty Liver Disease with Arterial Stiffness and its Metabolomic Profiling in Japanese Community-Dwellers. J Atheroscler Thromb 2024; 31:1031-1047. [PMID: 38311416 PMCID: PMC11224684 DOI: 10.5551/jat.64616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 11/28/2023] [Indexed: 02/10/2024] Open
Abstract
AIMS Nonalcoholic fatty liver disease (NAFLD) is known to be associated with atherosclerosis. This study focused on upstream changes in the process by which NAFLD leads to atherosclerosis. The study aimed to confirm the association between NAFLD and the cardio-ankle vascular index (CAVI), an indicator of subclinical atherosclerosis, and explore metabolites involved in both by assessing 94 plasma polar metabolites. METHODS A total of 928 Japanese community-dwellers (306 men and 622 women) were included in this study. The association between NAFLD and CAVI was examined using a multivariable regression model adjusted for confounders. Metabolites commonly associated with NAFLD and CAVI were investigated using linear mixed-effects models in which batch numbers of metabolite measurements were used as a random-effects variable, and false discovery rate-adjusted p-values were calculated. To determine the extent to which these metabolites mediated the association between NAFLD and CAVI, mediation analysis was conducted. RESULTS NAFLD was positively associated with CAVI (coefficients [95% Confidence intervals (CI)]=0.23 [0.09-0.37]; p=0.001). A total of 10 metabolites were involved in NAFLD and CAVI, namely, branched-chain amino acids (BCAAs; valine, leucine, and isoleucine), aromatic amino acids (AAAs; tyrosine and tryptophan), alanine, proline, glutamic acid, glycerophosphorylcholine, and 4-methyl-2-oxopentanoate. Mediation analysis showed that BCAAs mediated more than 20% of the total effect in the association between NAFLD and CAVI. CONCLUSIONS NAFLD was associated with a marker of atherosclerosis, and several metabolites related to insulin resistance, including BCAAs and AAAs, could be involved in the process by which NAFLD leads to atherosclerosis.
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Affiliation(s)
- Aya Hirata
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Sei Harada
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
- Institute for Advanced Biosciences, Keio University, Yamagata, Japan
| | - Miho Iida
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Ayako Kurihara
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Kota Fukai
- Department of Preventive Medicine, Tokai University School of Medicine, Kanagawa, 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
| | - Minako Matsumoto
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Mizuki Sata
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Naoko Miyagawa
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Ryota Toki
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Shun Edagawa
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Daisuke Sugiyama
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
- Faculty of Nursing and Medical Care, Keio University, Kanagawa, Japan
| | - Asako Sato
- Institute for Advanced Biosciences, Keio University, Yamagata, Japan
| | - Akiyoshi Hirayama
- Institute for Advanced Biosciences, Keio University, Yamagata, Japan
| | - Masahiro Sugimoto
- Institute for Advanced Biosciences, Keio University, Yamagata, Japan
- Institute of Medical Science, Tokyo Medical University, Tokyo, Japan
| | - Tomoyoshi Soga
- Institute for Advanced Biosciences, Keio University, Yamagata, Japan
- Faculty of Environment and Information Studies, Keio University, Kanagawa, Japan
| | - Masaru Tomita
- Institute for Advanced Biosciences, Keio University, Yamagata, Japan
- Faculty of Environment and Information Studies, Keio University, Kanagawa, Japan
| | - Tomonori Okamura
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Toru Takebayashi
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
- Institute for Advanced Biosciences, Keio University, Yamagata, Japan
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4
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Ishibashi Y, Harada S, Eitaki Y, Kurihara A, Kato S, Kuwabara K, Iida M, Hirata A, Sata M, Matsumoto M, Shibuki T, Okamura T, Sugiyama D, Sato A, Amano K, Hirayama A, Sugimoto M, Soga T, Tomita M, Takebayashi T. A population-based urinary and plasma metabolomics study of environmental exposure to cadmium. Environ Health Prev Med 2024; 29:22. [PMID: 38556356 PMCID: PMC10992994 DOI: 10.1265/ehpm.23-00218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 12/30/2023] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND The application of metabolomics-based profiles in environmental epidemiological studies is a promising approach to refine the process of health risk assessment. We aimed to identify potential metabolomics-based profiles in urine and plasma for the detection of relatively low-level cadmium (Cd) exposure in large population-based studies. METHOD We analyzed 123 urinary metabolites and 94 plasma metabolites detected in fasting urine and plasma samples collected from 1,412 men and 2,022 women involved in the Tsuruoka Metabolomics Cohort Study. Regression analysis was performed for urinary N-acetyl-beta-D-glucosaminidase (NAG), plasma, and urinary metabolites as dependent variables, and urinary Cd (U-Cd, quartile) as an independent variable. The multivariable regression model included age, gender, systolic blood pressure, smoking, rice intake, BMI, glycated hemoglobin, low-density lipoprotein cholesterol, alcohol consumption, physical activity, educational history, dietary energy intake, urinary Na/K ratio, and uric acid. Pathway-network analysis was carried out to visualize the metabolite networks linked to Cd exposure. RESULT Urinary NAG was positively associated with U-Cd, but not at lower concentrations (Q2). Among urinary metabolites in the total population, 45 metabolites showed associations with U-Cd in the unadjusted and adjusted models after adjusting for the multiplicity of comparison with FDR. There were 12 urinary metabolites which showed consistent associations between Cd exposure from Q2 to Q4. Among plasma metabolites, six cations and one anion were positively associated with U-Cd, whereas alanine, creatinine, and isoleucine were negatively associated with U-Cd. Our results were robust by statistical adjustment of various confounders. Pathway-network analysis revealed metabolites and upstream regulator changes associated with mitochondria (ACACB, UCP2, and metabolites related to the TCA cycle). CONCLUSION These results suggested that U-Cd was associated with metabolites related to upstream mitochondrial dysfunction in a dose-dependent manner. Our data will help develop environmental Cd exposure profiles for human populations.
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Affiliation(s)
- Yoshiki Ishibashi
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, 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
| | - Yoko Eitaki
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Ayako Kurihara
- 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
| | - Kazuyo Kuwabara
- 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
| | - Aya Hirata
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Mizuki Sata
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Minako Matsumoto
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Takuma Shibuki
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Tomonori Okamura
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Daisuke Sugiyama
- 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
| | - Asako Sato
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Kaori Amano
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Akiyoshi Hirayama
- 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
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
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5
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Joshi AD, Rahnavard A, Kachroo P, Mendez KM, Lawrence W, Julián-Serrano S, Hua X, Fuller H, Sinnott-Armstrong N, Tabung FK, Shutta KH, Raffield LM, Darst BF. An epidemiological introduction to human metabolomic investigations. Trends Endocrinol Metab 2023; 34:505-525. [PMID: 37468430 PMCID: PMC10527234 DOI: 10.1016/j.tem.2023.06.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 06/17/2023] [Accepted: 06/19/2023] [Indexed: 07/21/2023]
Abstract
Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.
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Affiliation(s)
- Amit D Joshi
- Clinical & Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Priyadarshini Kachroo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Kevin M Mendez
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Wayne Lawrence
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sachelly Julián-Serrano
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA; Department of Public Health, University of Massachusetts Lowell, Lowell, MA, USA
| | - Xinwei Hua
- Clinical & Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA; Department of Cardiology, Peking University Third Hospital, Beijing, China
| | - Harriett Fuller
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Nasa Sinnott-Armstrong
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Fred K Tabung
- The Ohio State University College of Medicine and Comprehensive Cancer Center, Columbus, OH, USA
| | - Katherine H Shutta
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Burcu F Darst
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
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Xu YF, Hao YX, Ma L, Zhang MH, Niu XX, Li Y, Zhang YY, Liu TT, Han M, Yuan XX, Wan G, Xing HC. Difference and clinical value of metabolites in plasma and feces of patients with alcohol-related liver cirrhosis. World J Gastroenterol 2023; 29:3534-3547. [PMID: 37389241 PMCID: PMC10303510 DOI: 10.3748/wjg.v29.i22.3534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 03/15/2023] [Accepted: 05/04/2023] [Indexed: 06/06/2023] Open
Abstract
BACKGROUND Alterations in plasma and intestinal metabolites contribute to the pathogenesis and progression of alcohol-related liver cirrhosis (ALC).
AIM To explore the common and different metabolites in the plasma and feces of patients with ALC and evaluate their clinical implications.
METHODS According to the inclusion and exclusion criteria, 27 patients with ALC and 24 healthy controls (HCs) were selected, and plasma and feces samples were collected. Liver function, blood routine, and other indicators were detected with automatic biochemical and blood routine analyzers. Liquid chromatography-mass spectrometry was used to detect the plasma and feces metabolites of the two groups and the metabolomics of plasma and feces. Also, the correlation between metabolites and clinical features was analyzed.
RESULTS More than 300 common metabolites were identified in the plasma and feces of patients with ALC. Pathway analysis showed that these metabolites are enriched in bile acid and amino acid metabolic pathways. Compared to HCs, patients with ALC had a higher level of glycocholic acid (GCA) and taurocholic acid (TCA) in plasma and a lower level of deoxycholic acid (DCA) in the feces, while L-threonine, L-phenylalanine, and L-tyrosine increased simultaneously in plasma and feces. GCA, TCA, L-methionine, L-phenylalanine, and L-tyrosine in plasma were positively correlated with total bilirubin (TBil), prothrombin time (PT), and maddrey discriminant function score (MDF) and negatively correlated with cholinesterase (CHE) and albumin (ALB). The DCA in feces was negatively correlated with TBil, MDF, and PT and positively correlated with CHE and ALB. Moreover, we established a P/S BA ratio of plasma primary bile acid (GCA and TCA) to fecal secondary bile acid (DCA), which was relevant to TBil, PT, and MDF score.
CONCLUSION The enrichment of GCA, TCA, L-phenylalanine, L-tyrosine, and L-methionine in the plasma of patients with ALC and the reduction of DCA in feces were related to the severity of ALC. These metabolites may be used as indicators to evaluate the progression of alcohol-related liver cirrhosis.
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Affiliation(s)
- Yi-Fan Xu
- Center of Liver Diseases Division 3, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
| | - Yan-Xu Hao
- Center of Liver Diseases Division 3, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
| | - Lei Ma
- Center of Liver Diseases Division 3, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
| | - Meng-Han Zhang
- Center of Liver Diseases Division 3, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
| | - Xuan-Xuan Niu
- Center of Liver Diseases Division 3, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
| | - Yan Li
- Center of Liver Diseases Division 3, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
| | - Yuan-Yuan Zhang
- Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
- Beijing Institute of Infectious Diseases, Beijing Institute of Infectious Diseases, Beijing 100015, China
- National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
| | - Ting-Ting Liu
- Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
- Beijing Institute of Infectious Diseases, Beijing Institute of Infectious Diseases, Beijing 100015, China
- National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
| | - Ming Han
- Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
- Beijing Institute of Infectious Diseases, Beijing Institute of Infectious Diseases, Beijing 100015, China
- National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
| | - Xiao-Xue Yuan
- Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
- Beijing Institute of Infectious Diseases, Beijing Institute of Infectious Diseases, Beijing 100015, China
- National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
| | - Gang Wan
- Department of Statistic, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
| | - Hui-Chun Xing
- Center of Liver Diseases Division 3, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
- Center of Liver Diseases Division 3, Beijing Ditan Hospital, Peking University Ditan Teaching Hospital, Beijing 100015, China
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7
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Trius-Soler M, Praticò G, Gürdeniz G, Garcia-Aloy M, Canali R, Fausta N, Brouwer-Brolsma EM, Andrés-Lacueva C, Dragsted LO. Biomarkers of moderate alcohol intake and alcoholic beverages: a systematic literature review. GENES & NUTRITION 2023; 18:7. [PMID: 37076809 PMCID: PMC10114415 DOI: 10.1186/s12263-023-00726-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 04/04/2023] [Indexed: 04/21/2023]
Abstract
The predominant source of alcohol in the diet is alcoholic beverages, including beer, wine, spirits and liquors, sweet wine, and ciders. Self-reported alcohol intakes are likely to be influenced by measurement error, thus affecting the accuracy and precision of currently established epidemiological associations between alcohol itself, alcoholic beverage consumption, and health or disease. Therefore, a more objective assessment of alcohol intake would be very valuable, which may be established through biomarkers of food intake (BFIs). Several direct and indirect alcohol intake biomarkers have been proposed in forensic and clinical contexts to assess recent or longer-term intakes. Protocols for performing systematic reviews in this field, as well as for assessing the validity of candidate BFIs, have been developed within the Food Biomarker Alliance (FoodBAll) project. The aim of this systematic review is to list and validate biomarkers of ethanol intake per se excluding markers of abuse, but including biomarkers related to common categories of alcoholic beverages. Validation of the proposed candidate biomarker(s) for alcohol itself and for each alcoholic beverage was done according to the published guideline for biomarker reviews. In conclusion, common biomarkers of alcohol intake, e.g., as ethyl glucuronide, ethyl sulfate, fatty acid ethyl esters, and phosphatidyl ethanol, show considerable inter-individual response, especially at low to moderate intakes, and need further development and improved validation, while BFIs for beer and wine are highly promising and may help in more accurate intake assessments for these specific beverages.
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Affiliation(s)
- Marta Trius-Soler
- Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, 1958, Frederiksberg C, Denmark
- Polyphenol Research Laboratory, Department of Nutrition, Food Sciences and Gastronomy, XIA School of Pharmacy and Food Sciences, University of Barcelona, 08028, Barcelona, Spain
- INSA-UB, Nutrition and Food Safety Research Institute, University of Barcelona, 08921, Santa Coloma de Gramanet, Spain
- Centro de Investigación Biomédica en Red de Fisiopatología de La Obesidad Y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029, Madrid, Spain
| | - Giulia Praticò
- Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, 1958, Frederiksberg C, Denmark
| | - Gözde Gürdeniz
- Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, 1958, Frederiksberg C, Denmark
| | - Mar Garcia-Aloy
- Biomarker & Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences and Gastronomy, Faculty of Pharmacy and Food Sciences, University of Barcelona, 08028, Barcelona, Spain
- Metabolomics Unit, Research and Innovation Centre, Fondazione Edmund Mach, San Michele All'Adige, Italy
| | - Raffaella Canali
- Consiglio Per La Ricerca in Agricoltura E L'analisi Dell'economia Agraria (CREA) Research Centre for Food and Nutrition, Rome, Italy
| | - Natella Fausta
- Consiglio Per La Ricerca in Agricoltura E L'analisi Dell'economia Agraria (CREA) Research Centre for Food and Nutrition, Rome, Italy
| | - Elske M Brouwer-Brolsma
- Division of Human Nutrition and Health, Department Agrotechnology and Food Sciences, Wageningen University and Research, P.O. Box 17, 6700 AA, Wageningen, The Netherlands
| | - Cristina Andrés-Lacueva
- INSA-UB, Nutrition and Food Safety Research Institute, University of Barcelona, 08921, Santa Coloma de Gramanet, Spain
- Biomarker & Nutrimetabolomics Laboratory, Department of Nutrition, Food Sciences and Gastronomy, Faculty of Pharmacy and Food Sciences, University of Barcelona, 08028, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad Y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, 28029, Madrid, Spain
| | - Lars Ove Dragsted
- Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, 1958, Frederiksberg C, Denmark.
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8
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Hirayama A, Ishikawa T, Takahashi H, Yamanaka S, Ikeda S, Hirata A, Harada S, Sugimoto M, Soga T, Tomita M, Takebayashi T. Quality Control of Targeted Plasma Lipids in a Large-Scale Cohort Study Using Liquid Chromatography-Tandem Mass Spectrometry. Metabolites 2023; 13:metabo13040558. [PMID: 37110217 PMCID: PMC10146188 DOI: 10.3390/metabo13040558] [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] [Received: 03/02/2023] [Revised: 04/03/2023] [Accepted: 04/11/2023] [Indexed: 04/29/2023] Open
Abstract
High-throughput metabolomics has enabled the development of large-scale cohort studies. Long-term studies require multiple batch-based measurements, which require sophisticated quality control (QC) to eliminate unexpected bias to obtain biologically meaningful quantified metabolomic profiles. Liquid chromatography-mass spectrometry was used to analyze 10,833 samples in 279 batch measurements. The quantified profile included 147 lipids including acylcarnitine, fatty acids, glucosylceramide, lactosylceramide, lysophosphatidic acid, and progesterone. Each batch included 40 samples, and 5 QC samples were measured for 10 samples of each. The quantified data from the QC samples were used to normalize the quantified profiles of the sample data. The intra- and inter-batch median coefficients of variation (CV) among the 147 lipids were 44.3% and 20.8%, respectively. After normalization, the CV values decreased by 42.0% and 14.7%, respectively. The effect of this normalization on the subsequent analyses was also evaluated. The demonstrated analyses will contribute to obtaining unbiased, quantified data for large-scale metabolomics.
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Affiliation(s)
- Akiyoshi Hirayama
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0052, Yamagata, Japan
- Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa 252-0082, Kanagawa, Japan
- Faculty of Environment and Information Studies, Keio University, Fujisawa 252-0082, Kanagawa, Japan
| | - Takamasa Ishikawa
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0052, Yamagata, Japan
| | - Haruka Takahashi
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0052, Yamagata, Japan
| | - Sanae Yamanaka
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0052, Yamagata, Japan
| | - Satsuki Ikeda
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0052, Yamagata, Japan
| | - Aya Hirata
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Shinjuku 160-8582, Tokyo, Japan
| | - Sei Harada
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0052, Yamagata, Japan
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Shinjuku 160-8582, Tokyo, Japan
| | - Masahiro Sugimoto
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0052, Yamagata, Japan
- Institute of Medical Research, Tokyo Medical University, Shinjuku 160-0022, Tokyo, Japan
| | - Tomoyoshi Soga
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0052, Yamagata, Japan
- Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa 252-0082, Kanagawa, Japan
- Faculty of Environment and Information Studies, Keio University, Fujisawa 252-0082, Kanagawa, Japan
| | - Masaru Tomita
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0052, Yamagata, Japan
- Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa 252-0082, Kanagawa, Japan
- Faculty of Environment and Information Studies, Keio University, Fujisawa 252-0082, Kanagawa, Japan
| | - Toru Takebayashi
- Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0052, Yamagata, Japan
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Shinjuku 160-8582, Tokyo, Japan
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9
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Xu R, He L, Vatsalya V, Ma X, Kim S, Mueller EG, Feng W, McClain CJ, Zhang X. Metabolomics analysis of urine from patients with alcohol-associated liver disease reveals dysregulated caffeine metabolism. Am J Physiol Gastrointest Liver Physiol 2023; 324:G142-G154. [PMID: 36513601 PMCID: PMC9870580 DOI: 10.1152/ajpgi.00228.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 11/28/2022] [Accepted: 12/12/2022] [Indexed: 12/15/2022]
Abstract
Excess alcohol intake causes millions of deaths annually worldwide. Asymptomatic early-stage, alcohol-associated liver disease (ALD) is easily overlooked, and ALD is usually only diagnosed in more advanced stages. We explored the possibility of using polar urine metabolites as biomarkers of ALD for early-stage diagnosis and functional assessment of disease severity by quantifying the abundance of polar metabolites in the urine samples of healthy controls (n = 18), patients with mild or moderate liver injury (n = 21), and patients with severe alcohol-associated hepatitis (n = 25). The polar metabolites in human urine were first analyzed by untargeted metabolomics, showing that 209 urine metabolites are significantly changed in patients, and 17 of these are highly correlated with patients' model for end-stage liver disease (MELD) score. Pathway enrichment analysis reveals that the caffeine metabolic pathway is the most affected in ALD. We then developed a targeted metabolomics method and measured the concentration of caffeine and its metabolites in urine using internal and external standard calibration, respectively. The described method can quantify caffeine and its 14 metabolites in 35 min. The results of targeted metabolomics analysis agree with the results of untargeted metabolomics, showing that 13 caffeine metabolites are significantly decreased in patients. In particular, the concentrations of 1-methylxanthine, paraxanthine, and 5-acetylamino-6-amino-3-methyluracil are markedly decreased with increased disease severity. We suggest that these three metabolites could serve as functional biomarkers for differentiating early-stage ALD from more advanced liver injury.NEW & NOTEWORTHY Our study using both untargeted and targeted metabolomics reveals the caffeine metabolic pathway is dysregulated in ALD. Three caffeine metabolites, 1-methylxanthine, paraxanthine, and 5-acetylamino-6-amino-3-methyluracil, can differentiate the severity of early-stage ALD.
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Affiliation(s)
- Raobo Xu
- Department of Chemistry, University of Louisville, Louisville, Kentucky
- Alcohol Research Center, University of Louisville School of Medicine, Louisville, Kentucky
- Hepatobiology and Toxicology Center of Biomedical Research Excellence, University of Louisville School of Medicine Louisville, Louisville, Kentucky
- Center for Regulatory and Environmental Analytical Metabolomics, University of Louisville, Louisville, Kentucky
| | - Liqing He
- Department of Chemistry, University of Louisville, Louisville, Kentucky
- Alcohol Research Center, University of Louisville School of Medicine, Louisville, Kentucky
- Hepatobiology and Toxicology Center of Biomedical Research Excellence, University of Louisville School of Medicine Louisville, Louisville, Kentucky
- Center for Regulatory and Environmental Analytical Metabolomics, University of Louisville, Louisville, Kentucky
| | - Vatsalya Vatsalya
- Alcohol Research Center, University of Louisville School of Medicine, Louisville, Kentucky
- Department of Medicine, University of Louisville, Louisville, Kentucky
| | - Xipeng Ma
- Department of Chemistry, University of Louisville, Louisville, Kentucky
- Alcohol Research Center, University of Louisville School of Medicine, Louisville, Kentucky
- Hepatobiology and Toxicology Center of Biomedical Research Excellence, University of Louisville School of Medicine Louisville, Louisville, Kentucky
- Center for Regulatory and Environmental Analytical Metabolomics, University of Louisville, Louisville, Kentucky
| | - Seongho Kim
- Department of Oncology, Wayne State University, Detroit, Michigan
- Biostatistics and Bioinformatics Core, Karmanos Cancer Institute, Wayne State University, Detroit, Michigan
| | - Eugene G Mueller
- Department of Chemistry, University of Louisville, Louisville, Kentucky
| | - Wenke Feng
- Alcohol Research Center, University of Louisville School of Medicine, Louisville, Kentucky
- Hepatobiology and Toxicology Center of Biomedical Research Excellence, University of Louisville School of Medicine Louisville, Louisville, Kentucky
- Department of Medicine, University of Louisville, Louisville, Kentucky
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, Kentucky
| | - Craig J McClain
- Alcohol Research Center, University of Louisville School of Medicine, Louisville, Kentucky
- Hepatobiology and Toxicology Center of Biomedical Research Excellence, University of Louisville School of Medicine Louisville, Louisville, Kentucky
- Department of Medicine, University of Louisville, Louisville, Kentucky
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, Kentucky
- Robley Rex Louisville Veterans Affairs Medical Center, Louisville, Kentucky
| | - Xiang Zhang
- Department of Chemistry, University of Louisville, Louisville, Kentucky
- Alcohol Research Center, University of Louisville School of Medicine, Louisville, Kentucky
- Hepatobiology and Toxicology Center of Biomedical Research Excellence, University of Louisville School of Medicine Louisville, Louisville, Kentucky
- Center for Regulatory and Environmental Analytical Metabolomics, University of Louisville, Louisville, Kentucky
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, Kentucky
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10
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Shibuki T, Iida M, Harada S, Kato S, Kuwabara K, Hirata A, Sata M, Matsumoto M, Osawa Y, Okamura T, Sugiyama D, Takebayashi T. The association between sleep parameters and sarcopenia in Japanese community-dwelling older adults. Arch Gerontol Geriatr 2023; 109:104948. [PMID: 36764202 DOI: 10.1016/j.archger.2023.104948] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 01/17/2023] [Accepted: 01/27/2023] [Indexed: 02/01/2023]
Abstract
PURPOSE This study aimed to examine the association between sleep duration and quality and sarcopenia, assessed by factors such as low muscle mass (LMM), low muscle strength (LMS), and low physical performance (LPP) among older community-dwellers in Japan. METHODS In this cross-sectional study, a total of 2,069 (men, 902; women, 1,167) participants aged 65 to 80 years were included. Sarcopenia and each low physical function were defined using the definitions of the Asian Working Groups of Sarcopenia 2019. Sleep duration was stratified into three categories: short sleep (<6 h), normal sleep (6-8 h), and long sleep (>8 h). Sleep quality was classified into two groups based on 8-item Athens Insomnia Scale score: insomnia (≥6), and non-insomnia (<6). We analyzed the association between sleep parameters and sarcopenia, including low physical functions, by logistic regression analysis. RESULTS Compared to normal sleepers, long sleepers had a positive association with sarcopenia (odds ratio [OR] 2.11, 95% confidence interval [CI] 1.25-3.58). In particular, long sleep was strongly associated with LMS (OR 1.77, 95%CI 1.07-2.94) and LPP (OR 1.90, 95%CI 1.25-2.88). On the other hand, poor sleep quality was not associated with sarcopenia in long sleepers, but in normal sleepers. CONCLUSIONS Long sleep was associated with sarcopenia, including LMS and LPP. However, in long sleepers, insomnia was not associated with sarcopenia or any of its components.
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Affiliation(s)
- Takuma Shibuki
- 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
| | - 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
| | - Suzuka Kato
- 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
| | - Aya Hirata
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Mizuki Sata
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Minako Matsumoto
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Yusuke Osawa
- Graduate School of Health Management, Keio University, Fujisawa, Kanagawa, Japan; Sports Medicine Research Center, Keio University, Yokohama, Kanagawa, Japan
| | - Tomonori Okamura
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Daisuke Sugiyama
- 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
| | - Toru Takebayashi
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan; Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata Japan.
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11
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Kaspy MS, Semnani-Azad Z, Malik VS, Jenkins DJA, Hanley AJ. Metabolomic profile of combined healthy lifestyle behaviours in humans: A systematic review. Proteomics 2022; 22:e2100388. [PMID: 35816426 DOI: 10.1002/pmic.202100388] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 06/28/2022] [Accepted: 07/08/2022] [Indexed: 11/10/2022]
Abstract
A combination of healthy lifestyle behaviours (i.e. regular physical activity, nutritious diet, no smoking, moderate alcohol, and healthy body mass) has been consistently associated with beneficial health outcomes including reduced risk of cardiometabolic diseases. Metabolomic profiles, characterized by distinct sets of biomarkers, have been described for healthy lifestyle behaviours individually and in combination. However, recent literature calls for systematic evaluation of these heterogenous data to identify potential clinical biomarkers relating to a combined healthy lifestyle. The objective was to systematically review existing literature on the metabolomic profile of combined healthy lifestyle behaviours. MEDLINE, EMBASE and Cochrane databases were searched through March 2022. Studies in humans outlining the metabolomic profile of a combination of two or more healthy lifestyle behaviours were included. Collectively, the metabolomic profile following regular adherence to combined healthy lifestyle behaviours points to a positive association with beneficial fatty acids and phosphocreatine, and inverse associations with triglycerides, trimethylamine N-oxide, and acylcarnitines. The findings suggest that a unique metabolomic profile is associated with combined healthy lifestyle behaviours. Additional research is warranted to further describe this metabolomic profile using targeted and untargeted metabolomic approaches along with uniform definitions of combined healthy lifestyle variables across populations. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Matthew S Kaspy
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Zhila Semnani-Azad
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Vasanti S Malik
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.,Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - David J A Jenkins
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.,Clinical Nutrition and Risk Factor Modification Centre, St. Michael's Hospital, Toronto, Ontario, Canada.,Division of Endocrinology & Metabolism, Department of Medicine, St. Michael's Hospital, Toronto, Ontario, Canada.,Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Anthony J Hanley
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.,Division of Endocrinology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.,Leadership Sinai Centre for Diabetes, Mount Sinai Hospital, Toronto, Ontario, Canada
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12
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Simpson S, Mclellan R, Wellmeyer E, Matalon F, George O. Drugs and Bugs: The Gut-Brain Axis and Substance Use Disorders. J Neuroimmune Pharmacol 2022; 17:33-61. [PMID: 34694571 PMCID: PMC9074906 DOI: 10.1007/s11481-021-10022-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 09/06/2021] [Indexed: 02/07/2023]
Abstract
Substance use disorders (SUDs) represent a significant public health crisis. Worldwide, 5.4% of the global disease burden is attributed to SUDs and alcohol use, and many more use psychoactive substances recreationally. Often associated with comorbidities, SUDs result in changes to both brain function and physiological responses. Mounting evidence calls for a precision approach for the treatment and diagnosis of SUDs, and the gut microbiome is emerging as a contributor to such disorders. Over the last few centuries, modern lifestyles, diets, and medical care have altered the health of the microbes that live in and on our bodies; as we develop, our diets and lifestyle dictate which microbes flourish and which microbes vanish. An increase in antibiotic treatments, with many antibiotic interventions occurring early in life during the microbiome's normal development, transforms developing microbial communities. Links have been made between the microbiome and SUDs, and the microbiome and conditions that are often comorbid with SUDs such as anxiety, depression, pain, and stress. A better understanding of the mechanisms influencing behavioral changes and drug use is critical in developing novel treatments for SUDSs. Targeting the microbiome as a therapeutic and diagnostic tool is a promising avenue of exploration. This review will provide an overview of the role of the gut-brain axis in a wide range of SUDs, discuss host and microbe pathways that mediate changes in the brain's response to drugs, and the microbes and related metabolites that impact behavior and health within the gut-brain axis.
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Affiliation(s)
- Sierra Simpson
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, 92093, US.
| | - Rio Mclellan
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, 92093, US
| | - Emma Wellmeyer
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, 92093, US
| | - Frederic Matalon
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, 92093, US
| | - Olivier George
- Department of Psychiatry, University of California San Diego, La Jolla, San Diego, CA, 92093, US
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13
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Kato S, Harada S, Iida M, Kuwabara K, Sugiyama D, Takeuchi A, Sata M, Matsumoto M, Kurihara A, Hirata A, Okamura T, Takebayashi T. Accumulated unhealthy behaviours and insomnia in Japanese dwellers with and without cardiovascular risk factors: a cross-sectional study. BMJ Open 2022; 12:e052787. [PMID: 35428620 PMCID: PMC9014032 DOI: 10.1136/bmjopen-2021-052787] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
OBJECTIVES To date, the association between accumulated unhealthy behaviours and insomnia in individuals stratified according to the presence or absence of major cardiovascular risk factors is unclear. This study aimed to examine the effect of accumulated unhealthy behaviours on insomnia in Japanese dwellers. DESIGN Cross-sectional study. SETTING Baseline data between April 2012 and March 2015. PARTICIPANTS Our study used cross-sectional data among Japanese aged 35-74 years in a rural community (N=9565), the attendees of annual municipal or work site health check-up programmes. MAIN OUTCOME MEASURES Insomnia was assessed by Athens Insomnia Scale, which was set at 6 points and greater; other scales were given. Participants were categorised into three groups by their number of unhealthy behaviours (no exercise habit, smoking, alcohol drinking, skipping breakfast and obesity): 0-1, 2-3, 4 or more. The association between accumulated unhealthy behaviours and insomnia was estimated by logistic regression analysis. Further analysis was done after stratification of cardiovascular risk factors assessed by anthropometrics and clinical biochemistry measurements. RESULTS The overall prevalence of insomnia was 13.3% for men and 19.3% for women. Men with unhealthy behaviour factors were more likely to have insomnia after adjusting for potential confounders, compared with the least unhealthy group (trend p=0.013). Women with four or more unhealthy behaviour factors were more likely to have insomnia, compared with the lowest groups (OR 1.175, 95% CI 1.077 to 1.282). Insomnia has an association with the unhealthy behaviours among men without cardiovascular risk factors (lowest groups: OR 1.133, 95% CI 1.037 to 1.238, trend p=0.026). Women without hypertension were more likely to have suspected insomnia, compared with the lowest group (OR 1.215, 95% CI 1.101 to 1.341). CONCLUSION The results showed accumulated unhealthy behaviours were associated with increased risk of insomnia in Japanese dwellers. For healthy population without cardiovascular risk factors, unhealthy behaviours should be considered as background conditions for insomnia.
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Affiliation(s)
- Suzuka Kato
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan
| | - Sei Harada
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan
| | - Miho Iida
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan
| | - Kazuyo Kuwabara
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan
| | - Daisuke Sugiyama
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan
| | - Ayano Takeuchi
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan
| | - Mizuki Sata
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan
| | - Minako Matsumoto
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan
| | - Ayako Kurihara
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan
| | - Aya Hirata
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan
| | - Tomonori Okamura
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan
| | - Toru Takebayashi
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan
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14
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Piestansky J, Olesova D, Matuskova M, Cizmarova I, Chalova P, Galba J, Majerova P, Mikus P, Kovac A. Amino acids in inflammatory bowel diseases: Modern diagnostic tools and methodologies. Adv Clin Chem 2022; 107:139-213. [PMID: 35337602 DOI: 10.1016/bs.acc.2021.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Amino acids are crucial building blocks of living organisms. Together with their derivatives, they participate in many intracellular processes to act as hormones, neuromodulators, and neurotransmitters. For several decades amino acids have been studied for their potential as markers of various diseases, including inflammatory bowel diseases. Subsequent improvements in sample pretreatment, separation, and detection methods have enabled the specific and very sensitive determination of these molecules in multicomponent matrices-biological fluids and tissues. The information obtained from targeted amino acid analysis (biomarker-based analytical strategy) can be further used for early diagnostics, to monitor the course of the disease or compliance of the patients. This review will provide an insight into current knowledge about inflammatory bowel diseases, the role of proteinogenic amino acids in intestinal inflammation and modern analytical techniques used in its diagnosis and disease activity monitoring. Current advances in the analysis of amino acids focused on sample pretreatment, separation strategy, or detection methods are highlighted, and their potential in clinical laboratories is discussed. In addition, the latest clinical data obtained from the metabolomic profiling of patients suffering from inflammatory bowel diseases are summarized with a focus on proteinogenic amino acids.
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Affiliation(s)
- Juraj Piestansky
- Department of Pharmaceutical Analysis and Nuclear Pharmacy, Faculty of Pharmacy, Comenius University in Bratislava, Bratislava, Slovakia; Toxicological and Antidoping Center, Faculty of Pharmacy, Comenius University in Bratislava, Bratislava, Slovakia
| | - Dominika Olesova
- Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Michaela Matuskova
- Department of Pharmaceutical Analysis and Nuclear Pharmacy, Faculty of Pharmacy, Comenius University in Bratislava, Bratislava, Slovakia
| | - Ivana Cizmarova
- Department of Pharmaceutical Analysis and Nuclear Pharmacy, Faculty of Pharmacy, Comenius University in Bratislava, Bratislava, Slovakia
| | - Petra Chalova
- Department of Pharmaceutical Analysis and Nuclear Pharmacy, Faculty of Pharmacy, Comenius University in Bratislava, Bratislava, Slovakia
| | - Jaroslav Galba
- Department of Pharmaceutical Analysis and Nuclear Pharmacy, Faculty of Pharmacy, Comenius University in Bratislava, Bratislava, Slovakia
| | - Petra Majerova
- Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Peter Mikus
- Department of Pharmaceutical Analysis and Nuclear Pharmacy, Faculty of Pharmacy, Comenius University in Bratislava, Bratislava, Slovakia; Toxicological and Antidoping Center, Faculty of Pharmacy, Comenius University in Bratislava, Bratislava, Slovakia
| | - Andrej Kovac
- Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia.
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15
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Sasaki M, Miyagawa N, Harada S, Tsubota K, Takebayashi T, Nishiwaki Y, Kawasaki R. Dietary Patterns and Their Associations with Intermediate Age-Related Macular Degeneration in a Japanese Population. J Clin Med 2022; 11:jcm11061617. [PMID: 35329943 PMCID: PMC8955354 DOI: 10.3390/jcm11061617] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 03/09/2022] [Accepted: 03/11/2022] [Indexed: 11/22/2022] Open
Abstract
This population-based cross-sectional study investigated the influence of dietary patterns on age-related macular degeneration (AMD) in a Japanese population. The Tsuruoka Metabolomics Cohort Study enrolled a general population aged 35–74 years from among participants in annual health check-up programs in Tsuruoka City, Japan. Eating habits were assessed using a food frequency questionnaire. Principal component analysis was used to identify dietary patterns among food items. The association between quartiles of scores for each dietary pattern and intermediate AMD was assessed using multivariate logistic regression models. Of 3433 participants, 415 had intermediate AMD. We identified four principal components comprising the Vegetable-rich pattern, Varied staple food pattern, Animal-rich pattern, and Seafood-rich pattern. After adjusting for potential confounders, higher Varied staple food diet scores were associated with a lower prevalence of intermediate AMD (fourth vs. first quartile) (OR, 0.63; 95% confidence interval [CI], 0.46–0.86). A significant trend of decreasing ORs for intermediate AMD associated with increasing Varied staple food diet scores was noted (p for trend = 0.002). There was no significant association between the other dietary patterns and intermediate AMD. In a Japanese population, individuals with a dietary pattern score high in the Varied staple food pattern had a lower prevalence of intermediate AMD.
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Affiliation(s)
- Mariko Sasaki
- Department of Ophthalmology, Keio University School of Medicine, Tokyo 160-8582, Japan;
- Department of Ophthalmology, Tachikawa Hospital, Tokyo 190-8531, Japan
- National Institute of Sensory Organs, National Tokyo Medical Center, Tokyo 152-8902, Japan
- Correspondence:
| | - Naoko Miyagawa
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo 160-8582, Japan; (N.M.); (S.H.); (T.T.)
| | - Sei Harada
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo 160-8582, Japan; (N.M.); (S.H.); (T.T.)
| | - Kazuo Tsubota
- Department of Ophthalmology, Keio University School of Medicine, Tokyo 160-8582, Japan;
| | - Toru Takebayashi
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo 160-8582, Japan; (N.M.); (S.H.); (T.T.)
| | - Yuji Nishiwaki
- Department of Environmental and Occupational Health, Toho University, Tokyo 143-8540, Japan;
| | - Ryo Kawasaki
- Department of Vision Informatics (Topcon), Osaka University Graduate School of Medicine, Osaka 565-0871, Japan;
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Ohmomo H, Harada S, Komaki S, Ono K, Sutoh Y, Otomo R, Umekage S, Hachiya T, Katanoda K, Takebayashi T, Shimizu A. DNA Methylation Abnormalities and Altered Whole Transcriptome Profiles after Switching from Combustible Tobacco Smoking to Heated Tobacco Products. Cancer Epidemiol Biomarkers Prev 2022; 31:269-279. [PMID: 34728466 PMCID: PMC9398167 DOI: 10.1158/1055-9965.epi-21-0444] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 08/29/2021] [Accepted: 10/18/2021] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND The use of heated tobacco products (HTP) has increased exponentially in Japan since 2016; however, their effects on health remain a major concern. METHODS Tsuruoka Metabolome Cohort Study participants (n = 11,002) were grouped on the basis of their smoking habits as never smokers (NS), past smokers (PS), combustible tobacco smokers (CS), and HTP users for <2 years. Peripheral blood mononuclear cells were collected from 52 participants per group matched to HTP users using propensity scores, and DNA and RNA were purified from the samples. DNA methylation (DNAm) analysis of the 17 smoking-associated DNAm biomarker genes (such as AHRR, F2RL3, LRRN3, and GPR15), as well as whole transcriptome analysis, was performed. RESULTS Ten of the 17 genes were significantly hypomethylated in CS and HTP users compared with NS, among which AHRR, F2RL3, and RARA showed intermediate characteristics between CS and NS; nonetheless, AHRR expression was significantly higher in CS than in the other three groups. Conversely, LRRN3 and GPR15 were more hypomethylated in HTP users than in NS, and GPR15 expression was markedly upregulated in all the groups when compared with that in NS. CONCLUSIONS HTP users (switched from CS <2 years) display abnormal DNAm and transcriptome profiles, albeit to a lesser extent than the CS. However, because the molecular genetic effects of long-term HTP use are still unknown, long-term molecular epidemiologic studies are needed. IMPACT This study provides new insights into the molecular genetic effects on DNAm and transcriptome profiles in HTP users who switched from CS.
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Affiliation(s)
- Hideki Ohmomo
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Yahaba, Shiwa, Iwate, Japan
| | - Sei Harada
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan
| | - Shohei Komaki
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Yahaba, Shiwa, Iwate, Japan
| | - Kanako Ono
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Yahaba, Shiwa, Iwate, Japan
| | - Yoichi Sutoh
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Yahaba, Shiwa, Iwate, Japan
| | - Ryo Otomo
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Yahaba, Shiwa, Iwate, Japan
| | - So Umekage
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Yahaba, Shiwa, Iwate, Japan
| | - Tsuyoshi Hachiya
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Yahaba, Shiwa, Iwate, Japan
| | - Kota Katanoda
- Division of Cancer Statistics Integration, National Cancer Center Research Institute, Chuo, Tokyo, Japan
| | - Toru Takebayashi
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Shinjuku-ku, Tokyo, Japan
| | - Atsushi Shimizu
- Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Yahaba, Shiwa, Iwate, Japan.,Corresponding Author: Atsushi Shimizu, Iwate Tohoku Medical Megabank Organization, Iwate Medical University, Iwate 028-3694, Japan. Phone: 81-19-651-5110, ext. 5473; E-mail:
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Wang Z, Zhu X, Ni X, Wen Y, Shang D. Knowledge atlas of the involvement of glutamate and GABA in alcohol use disorder: A bibliometric and scientometric analysis. Front Psychiatry 2022; 13:965142. [PMID: 36032235 PMCID: PMC9411946 DOI: 10.3389/fpsyt.2022.965142] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 07/25/2022] [Indexed: 11/13/2022] Open
Abstract
INTRODUCTION Abnormal neurotransmission of glutamate and γ-aminobutyric acid (GABA) is a key characteristic of alcohol-related disorders. To track research output, we conducted a bibliometric analysis to explore the current status and trends in this field over the past decades. METHODS Studies related to neurotransmitters and alcohol use disorder published in English from 2005-2021 were retrieved from the Web of Science Core Collection and Scopus databases. The R-bibliometrix package was used for a descriptive analysis of the publications. Citespace, WOSviewer, and R-bibliometrix were used to construct networks of countries/institutions/authors based on co-authorship, co-citation analysis of cited references and co-occurrence as well as burst detection of keywords. RESULTS A total of 4,250 unique articles and reviews were included in the final analysis. The annual growth rate of publications was 5.4%. The USA was the most productive country in this field, contributing nearly half of the total documents. The top ten most productive institutions were all located in the USA. The most frequent worldwide collaboration was between the USA and Italy. The most productive and influential institution was the University of California. The author contributing the most productions to this field was Marisa Roberto from the Scripps Research Institute. The top co-cited reference was a review titled "Neurocircuitry of addiction." The top journal in terms of the number of records and citations was Alcoholism: Clinical and Experimental Research. Comprehensive analyses have been conducted over past decades based on co-cited reference analysis, including modulators, transporters, receptor subtypes, and animal models. In recent years, the research frontiers have been shifting to the identification of risk factors/biomarkers, drug development for alcohol use disorder, and mechanisms related to alcoholic and non-alcoholic fatty liver. CONCLUSION Our bibliometric analysis shows that glutamate and GABA continue to be of interest in alcohol use disorder. The focus has evolved from mechanisms and medications related to glutamate and GABA in alcohol use disorder, to novel drug development, risk factor/biomarker identification targeting neurotransmitters, and the mechanisms of related diseases.
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Affiliation(s)
- Zhanzhang Wang
- Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiuqing Zhu
- Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Xiaojia Ni
- Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yuguan Wen
- Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Dewei Shang
- Department of Pharmacy, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
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Metabolic profiling of charged metabolites in association with menopausal status in Japanese community-dwelling midlife women: Tsuruoka Metabolomic Cohort Study. Maturitas 2021; 155:54-62. [PMID: 34876249 DOI: 10.1016/j.maturitas.2021.10.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 09/27/2021] [Accepted: 10/07/2021] [Indexed: 11/21/2022]
Abstract
BACKGROUND Emerging evidence has shown that charged metabolites, such as amino acids, may play an important role in the pathogenesis of various metabolic disorders, many of which women in the postmenopausal period are at high risk of developing. This study examined the metabolic profile of middle-aged Japanese women to investigate alterations in charged metabolites induced by menopausal transition. METHODS The participants were 1193 female residents aged 40-60 at the baseline survey of the Tsuruoka Metabolomics Cohort Study. We investigated the cross-sectional association of menopausal status with 94 metabolomic biomarkers assayed in fasting plasma samples via capillary electrophoresis time-of-flight mass spectrometry using linear regression analysis. RESULTS Among the participants, 529 were premenopausal, 132 were in menopausal transition (MT), and 532 were postmenopausal. Significant differences were found in age, blood pressure, glucose and lipid levels, and smoking and drinking habits among the three groups. The concentrations of 5 metabolites in the MT group and 15 metabolites in the postmenopausal group were significantly higher than those in the premenopausal group after adjusting for confounding factors. When classified into pathways, these metabolites were related to the tricarboxylic cycle, urea cycle, and homocysteine metabolism, some of which are linked to arteriosclerosis. CONCLUSION Multiple charged metabolites were associated with women's menopausal status, showing a gradual increase as women shifted from pre-, to peri-, to postmenopause. These findings might reflect the early changes behind the increased risk of dyslipidemia, diabetes, cardiovascular disease, and osteoporosis in later life.
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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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20
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Dawidowska J, Krzyżanowska M, Markuszewski MJ, Kaliszan M. The Application of Metabolomics in Forensic Science with Focus on Forensic Toxicology and Time-of-Death Estimation. Metabolites 2021; 11:metabo11120801. [PMID: 34940558 PMCID: PMC8708813 DOI: 10.3390/metabo11120801] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 11/22/2021] [Accepted: 11/22/2021] [Indexed: 12/21/2022] Open
Abstract
Recently, the diagnostic methods used by scientists in forensic examinations have enormously expanded. Metabolomics provides an important contribution to analytical method development. The main purpose of this review was to investigate and summarize the most recent applications of metabolomics in forensic science. The primary research method was an extensive review of available international literature in PubMed. The keywords “forensic” and “metabolomics” were used as search criteria for the PubMed database scan. Most authors emphasized the analysis of different biological sample types using chromatography methods. The presented review is a summary of recently published implementations of metabolomics in forensic science and types of biological material used and techniques applied. Possible opportunities for valuable metabolomics’ applications are discussed to emphasize the essential necessities resulting in numerous nontargeted metabolomics’ assays.
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Affiliation(s)
- Joanna Dawidowska
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, 80-210 Gdańsk, Poland; (J.D.); (M.J.M.)
- Department of Forensic Medicine, Medical University of Gdańsk, 80-210 Gdańsk, Poland;
| | - Marta Krzyżanowska
- Department of Forensic Medicine, Medical University of Gdańsk, 80-210 Gdańsk, Poland;
| | - Michał Jan Markuszewski
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, 80-210 Gdańsk, Poland; (J.D.); (M.J.M.)
| | - Michał Kaliszan
- Department of Forensic Medicine, Medical University of Gdańsk, 80-210 Gdańsk, Poland;
- Correspondence: ; Tel.: +48-58-3491255
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21
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Gao B, Zhu Y, Gao N, Shen W, Stärkel P, Schnabl B. Integrative Analysis of Metabolome and Microbiome in Patients with Progressive Alcohol-Associated Liver Disease. Metabolites 2021; 11:metabo11110766. [PMID: 34822424 PMCID: PMC8621614 DOI: 10.3390/metabo11110766] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 11/05/2021] [Accepted: 11/07/2021] [Indexed: 12/12/2022] Open
Abstract
Alcohol-associated liver disease is one of the most prevalent diseases around the world, with 10–20% of patients developing progressive liver disease. To identify the complex and correlated nature of metabolic and microbial data types in progressive liver disease, we performed an integrated analysis of the fecal and serum metabolomes with the gut microbiome in a cohort of 38 subjects, including 15 patients with progressive liver disease, 16 patients with non-progressive liver disease, and 7 control subjects. We found that although patients were generally clustered in three groups according to disease status, metabolites showed better separation than microbial species. Furthermore, eight serum metabolites were correlated with two microbial species, among which seven metabolites were decreased in patients with progressive liver disease. Five fecal metabolites were correlated with three microbial species, among which four metabolites were decreased in patients with progressive liver disease. When predicting progressive liver disease from non-progressive liver disease using correlated metabolic and microbial signatures with the random forest model, correlated serum metabolites and microbial species showed great predictive power, with the area under the receiver operating characteristic curve achieving 0.91. The multi-omics signatures identified in this study are helpful for the early identification of patients with progressive alcohol-associated liver disease, which is a key step for therapeutic intervention.
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Affiliation(s)
- Bei Gao
- School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China;
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA;
| | - Yixin Zhu
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA;
| | - Nan Gao
- School of Biological and Pharmaceutical Engineering, Nanjing Tech University, Nanjing 211816, China;
| | - Weishou Shen
- School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China;
- Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Inovation Center of Atmospheric Environment and Equipment Technology, Nanjing 210044, China
| | - Peter Stärkel
- Laboratory of Hepato-Gastroenterology, Institute of Experimental and Clinical Research, Université Catholique de Louvain, 1200 Brussels, Belgium
- Department of Hepato-Gastroenterology, St. Luc University Hospital, Université Catholique de Louvain, 1200 Brussels, Belgium
- Correspondence: (P.S.); (B.S.)
| | - Bernd Schnabl
- Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA;
- Department of Medicine, VA San Diego Healthcare System, San Diego, CA 92161, USA
- Correspondence: (P.S.); (B.S.)
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22
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Loftfield E, Stepien M, Viallon V, Trijsburg L, Rothwell JA, Robinot N, Biessy C, Bergdahl IA, Bodén S, Schulze MB, Bergman M, Weiderpass E, Schmidt JA, Zamora-Ros R, Nøst TH, Sandanger TM, Sonestedt E, Ohlsson B, Katzke V, Kaaks R, Ricceri F, Tjønneland A, Dahm CC, Sánchez MJ, Trichopoulou A, Tumino R, Chirlaque MD, Masala G, Ardanaz E, Vermeulen R, Brennan P, Albanes D, Weinstein SJ, Scalbert A, Freedman ND, Gunter MJ, Jenab M, Sinha R, Keski-Rahkonen P, Ferrari P. Novel Biomarkers of Habitual Alcohol Intake and Associations With Risk of Pancreatic and Liver Cancers and Liver Disease Mortality. J Natl Cancer Inst 2021; 113:1542-1550. [PMID: 34010397 PMCID: PMC8562969 DOI: 10.1093/jnci/djab078] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 02/24/2021] [Accepted: 04/09/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Alcohol is an established risk factor for several cancers, but modest alcohol-cancer associations may be missed because of measurement error in self-reported assessments. Biomarkers of habitual alcohol intake may provide novel insight into the relationship between alcohol and cancer risk. METHODS Untargeted metabolomics was used to identify metabolites correlated with self-reported habitual alcohol intake in a discovery dataset from the European Prospective Investigation into Cancer and Nutrition (EPIC; n = 454). Statistically significant correlations were tested in independent datasets of controls from case-control studies nested within EPIC (n = 280) and the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC; n = 438) study. Conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations of alcohol-associated metabolites and self-reported alcohol intake with risk of pancreatic cancer, hepatocellular carcinoma (HCC), liver cancer, and liver disease mortality in the contributing studies. RESULTS Two metabolites displayed a dose-response association with self-reported alcohol intake: 2-hydroxy-3-methylbutyric acid and an unidentified compound. A 1-SD (log2) increase in levels of 2-hydroxy-3-methylbutyric acid was associated with risk of HCC (OR = 2.54, 95% CI = 1.51 to 4.27) and pancreatic cancer (OR = 1.43, 95% CI = 1.03 to 1.99) in EPIC and liver cancer (OR = 2.00, 95% CI = 1.44 to 2.77) and liver disease mortality (OR = 2.16, 95% CI = 1.63 to 2.86) in ATBC. Conversely, a 1-SD (log2) increase in questionnaire-derived alcohol intake was not associated with HCC or pancreatic cancer in EPIC or liver cancer in ATBC but was associated with liver disease mortality (OR = 2.19, 95% CI = 1.60 to 2.98) in ATBC. CONCLUSIONS 2-hydroxy-3-methylbutyric acid is a candidate biomarker of habitual alcohol intake that may advance the study of alcohol and cancer risk in population-based studies.
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Affiliation(s)
- Erikka Loftfield
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute,National Institutes of Health, Bethesda, MD, USA
| | - Magdalena Stepien
- Nutritional Epidemiology Group, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Vivian Viallon
- Nutritional Methodology and Biostatistics Group, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Laura Trijsburg
- Nutritional Methodology and Biostatistics Group, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Joseph A Rothwell
- Nutritional Epidemiology Group, International Agency for Research on Cancer (IARC-WHO), Lyon, France
- Gustave Roussy, F-94805, Villejuif, France
- Biomarkers Group, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Nivonirina Robinot
- Centre for Epidemiology and Population Health (U1018), Generations and Health team, Faculté de Médecine, Université Paris-Saclay, UVSQ, INSERM, Villejuif, France
| | - Carine Biessy
- Nutritional Methodology and Biostatistics Group, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | | | - Stina Bodén
- Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany
| | - Manuela Bergman
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany
| | | | - Julie A Schmidt
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Raul Zamora-Ros
- Unit of Nutrition and Cancer, Epidemiology Research Program, Catalan Institute of Oncology, Bellvitge Biomedical Research Institute (IDIBELL), Hospitalet de Llobregat, Barcelona, Spain
| | - Therese H Nøst
- Department of Community Medicine, UiT- The Arctic University of Norway, Tromsø, Norway
| | - Torkjel M Sandanger
- Department of Community Medicine, UiT- The Arctic University of Norway, Tromsø, Norway
| | - Emily Sonestedt
- Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden
| | - Bodil Ohlsson
- Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden
| | - Verena Katzke
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Fulvio Ricceri
- Department of Clinical and Biological Sciences, University of Turin, Italy; Unit of Epidemiology, Regional Health Service ASL TO3, Grugliasco, TO, Italy
| | - Anne Tjønneland
- Danish Cancer Society Research Center; University of Copenhagen, Department of Public Health
| | | | - Maria-Jose Sánchez
- Escuela Andaluza de Salud Pública (EASP), Granada, Spain; Instituto de Investigación Biosanitaria ibs. GRANADA, Granada, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Granada, Granada, Spain
| | | | - Rosario Tumino
- Cancer Registry and Histopathology Department, Provincial Health Authority (ASP 7), Ragusa, Italy
| | - María-Dolores Chirlaque
- Department of Epidemiology, Regional Health Council, IMIB-Arrixaca, Murcia University, Murcia, Spain
- CIBER in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Giovanna Masala
- Cancer Risk Factors and Life-Style Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network—ISPRO, Florence, Italy
| | - Eva Ardanaz
- Navarra Public Health Institute, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
- CIBER Epidemiology and Public Health CIBERESP, Madrid, Spain
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Utrecht, the Netherlands
| | - Paul Brennan
- Genetic Epidemiology Group, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Demetrius Albanes
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute,National Institutes of Health, Bethesda, MD, USA
| | - Stephanie J Weinstein
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute,National Institutes of Health, Bethesda, MD, USA
| | - Augustin Scalbert
- Centre for Epidemiology and Population Health (U1018), Generations and Health team, Faculté de Médecine, Université Paris-Saclay, UVSQ, INSERM, Villejuif, France
| | - Neal D Freedman
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute,National Institutes of Health, Bethesda, MD, USA
| | - Marc J Gunter
- Nutritional Epidemiology Group, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Mazda Jenab
- Nutritional Epidemiology Group, International Agency for Research on Cancer (IARC-WHO), Lyon, France
| | - Rashmi Sinha
- Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute,National Institutes of Health, Bethesda, MD, USA
| | - Pekka Keski-Rahkonen
- Centre for Epidemiology and Population Health (U1018), Generations and Health team, Faculté de Médecine, Université Paris-Saclay, UVSQ, INSERM, Villejuif, France
| | - Pietro Ferrari
- Nutritional Methodology and Biostatistics Group, International Agency for Research on Cancer (IARC-WHO), Lyon, France
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Harada S, Sata M, Matsumoto M, Iida M, Takeuchi A, Kato S, Hirata A, Kuwabara K, Shibuki T, Ishibashi Y, Sugiyama D, Okamura T, Takebayashi T. Changes in smoking habits and behaviors following the introduction and spread of heated tobacco products in Japan and its effect on FEV 1 decline: a longitudinal cohort study. J Epidemiol 2021; 32:180-187. [PMID: 34657910 PMCID: PMC8918621 DOI: 10.2188/jea.je20210075] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Background Heated tobacco product (HTP) use in Japan has rapidly increased. Despite this rapid spread, little is known about the health effects of HTP use. We conducted a longitudinal cohort study to investigate the change in smoking habits following the spread of HTP use and its effect on forced expiratory volume in 1 second (FEV1) decline. Methods Participants consisted of a resident population (n = 2,612; mean age, 67.7 years) with FEV1 measurement in 2012–2014 and 2018–2019, and a worksite population (n = 722; mean age 49.3 years) without FEV1 data. Participants were categorized as combustible cigarette-only smokers, HTP-only users, dual users, past smokers, and never smokers. The association between smoking group and the change in smoking consumption over a mean 5.6 years was examined. Differences in annual FEV1 change between smoking groups were examined in the resident population. Results Prevalence of HTP-only and dual users in 2018–2019 was 0.8% and 0.6% in the resident population, and 5.0% and 1.9% in the worksite population, respectively. The overall number of tobacco products smoked/used increased in dual users compared to baseline, but not in others. Annual FEV1 decline in dual users tended to be greater than that in cigarette-only smokers (16; 95% confidence interval, −34 to 2 mL/year after full adjustment). Participants switching to HTP-only use 1.7 years before had a similar FEV1 decline as cigarette-only smokers. Conclusions HTP use, including dual use, is prevalent even in a rural region of Japan. Dual users appear to smoke/use tobacco products more and have a greater FEV1 decline. Tobacco policy should consider dual use as high-risk.
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Affiliation(s)
- Sei Harada
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Mizuki Sata
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Minako Matsumoto
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Miho Iida
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Ayano Takeuchi
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Suzuka Kato
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Aya Hirata
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Kazuyo Kuwabara
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Takuma Shibuki
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Yoshiki Ishibashi
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Daisuke Sugiyama
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Tomonori Okamura
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Toru Takebayashi
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
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24
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Pan XF, Yang JJ, Shu XO, Moore SC, Palmer ND, Guasch-Ferré M, Herrington DM, Harada S, Eliassen H, Wang TJ, Gerszten RE, Albanes D, Tzoulaki I, Karaman I, Elliott P, Zhu H, Wagenknecht LE, Zheng W, Cai H, Cai Q, Matthews CE, Menni C, Meyer KA, Lipworth LP, Ose J, Fornage M, Ulrich CM, Yu D. Associations of circulating choline and its related metabolites with cardiometabolic biomarkers: an international pooled analysis. Am J Clin Nutr 2021; 114:893-906. [PMID: 34020444 PMCID: PMC8408854 DOI: 10.1093/ajcn/nqab152] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 04/09/2021] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Choline is an essential nutrient; however, the associations of choline and its related metabolites with cardiometabolic risk remain unclear. OBJECTIVE We examined the associations of circulating choline, betaine, carnitine, and dimethylglycine (DMG) with cardiometabolic biomarkers and their potential dietary and nondietary determinants. METHODS The cross-sectional analyses included 32,853 participants from 17 studies, who were free of cancer, cardiovascular diseases, chronic kidney diseases, and inflammatory bowel disease. In each study, metabolites and biomarkers were log-transformed and standardized by means and SDs, and linear regression coefficients (β) and 95% CIs were estimated with adjustments for potential confounders. Study-specific results were combined by random-effects meta-analyses. A false discovery rate <0.05 was considered significant. RESULTS We observed moderate positive associations of circulating choline, carnitine, and DMG with creatinine [β (95% CI): 0.136 (0.084, 0.188), 0.106 (0.045, 0.168), and 0.128 (0.087, 0.169), respectively, for each SD increase in biomarkers on the log scale], carnitine with triglycerides (β = 0.076; 95% CI: 0.042, 0.109), homocysteine (β = 0.064; 95% CI: 0.033, 0.095), and LDL cholesterol (β = 0.055; 95% CI: 0.013, 0.096), DMG with homocysteine (β = 0.068; 95% CI: 0.023, 0.114), insulin (β = 0.068; 95% CI: 0.043, 0.093), and IL-6 (β = 0.060; 95% CI: 0.027, 0.094), but moderate inverse associations of betaine with triglycerides (β = -0.146; 95% CI: -0.188, -0.104), insulin (β = -0.106; 95% CI: -0.130, -0.082), homocysteine (β = -0.097; 95% CI: -0.149, -0.045), and total cholesterol (β = -0.074; 95% CI: -0.102, -0.047). In the whole pooled population, no dietary factor was associated with circulating choline; red meat intake was associated with circulating carnitine [β = 0.092 (0.042, 0.142) for a 1 serving/d increase], whereas plant protein was associated with circulating betaine [β = 0.249 (0.110, 0.388) for a 5% energy increase]. Demographics, lifestyle, and metabolic disease history showed differential associations with these metabolites. CONCLUSIONS Circulating choline, carnitine, and DMG were associated with unfavorable cardiometabolic risk profiles, whereas circulating betaine was associated with a favorable cardiometabolic risk profile. Future prospective studies are needed to examine the associations of these metabolites with incident cardiovascular events.
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Affiliation(s)
- Xiong-Fei Pan
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jae Jeong Yang
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Steven C Moore
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - David M Herrington
- Section on Cardiology, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Sei Harada
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Heather Eliassen
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Thomas J Wang
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, USA
| | - Robert E Gerszten
- Broad Institute of Harvard and Massachusetts Institute of Technology and Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
- Dementia Research Institute, Imperial College London, London, United Kingdom
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Ibrahim Karaman
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
- Dementia Research Institute, Imperial College London, London, United Kingdom
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
- Dementia Research Institute, Imperial College London, London, United Kingdom
| | - Huilian Zhu
- Department of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Lynne E Wagenknecht
- Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Hui Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Charles E Matthews
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Katie A Meyer
- Department of Nutrition and Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
| | - Loren P Lipworth
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jennifer Ose
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, Salt Lake City, UT, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center, Houston, TX, USA
| | - Cornelia M Ulrich
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, Salt Lake City, UT, USA
| | - Danxia Yu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
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25
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Yang JJ, Shu XO, Herrington DM, Moore SC, Meyer KA, Ose J, Menni C, Palmer ND, Eliassen H, Harada S, Tzoulaki I, Zhu H, Albanes D, Wang TJ, Zheng W, Cai H, Ulrich CM, Guasch-Ferré M, Karaman I, Fornage M, Cai Q, Matthews CE, Wagenknecht LE, Elliott P, Gerszten RE, Yu D. Circulating trimethylamine N-oxide in association with diet and cardiometabolic biomarkers: an international pooled analysis. Am J Clin Nutr 2021; 113:1145-1156. [PMID: 33826706 PMCID: PMC8106754 DOI: 10.1093/ajcn/nqaa430] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 12/16/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Trimethylamine N-oxide (TMAO), a diet-derived, gut microbial-host cometabolite, has been linked to cardiometabolic diseases. However, the relations remain unclear between diet, TMAO, and cardiometabolic health in general populations from different regions and ethnicities. OBJECTIVES To examine associations of circulating TMAO with dietary and cardiometabolic factors in a pooled analysis of 16 population-based studies from the United States, Europe, and Asia. METHODS Included were 32,166 adults (16,269 white, 13,293 Asian, 1247 Hispanic/Latino, 1236 black, and 121 others) without cardiovascular disease, cancer, chronic kidney disease, or inflammatory bowel disease. Linear regression coefficients (β) were computed for standardized TMAO with harmonized variables. Study-specific results were combined by random-effects meta-analysis. A false discovery rate <0.10 was considered significant. RESULTS After adjustment for potential confounders, circulating TMAO was associated with intakes of animal protein and saturated fat (β = 0.124 and 0.058, respectively, for a 5% energy increase) and with shellfish, total fish, eggs, and red meat (β = 0.370, 0.151, 0.081, and 0.056, respectively, for a 1 serving/d increase). Plant protein and nuts showed inverse associations (β = -0.126 for a 5% energy increase from plant protein and -0.123 for a 1 serving/d increase of nuts). Although the animal protein-TMAO association was consistent across populations, fish and shellfish associations were stronger in Asians (β = 0.285 and 0.578), and egg and red meat associations were more prominent in Americans (β = 0.153 and 0.093). Besides, circulating TMAO was positively associated with creatinine (β = 0.131 SD increase in log-TMAO), homocysteine (β = 0.065), insulin (β = 0.048), glycated hemoglobin (β = 0.048), and glucose (β = 0.023), whereas it was inversely associated with HDL cholesterol (β = -0.047) and blood pressure (β = -0.030). Each TMAO-biomarker association remained significant after further adjusting for creatinine and was robust in subgroup/sensitivity analyses. CONCLUSIONS In an international, consortium-based study, animal protein was consistently associated with increased circulating TMAO, whereas TMAO associations with fish, shellfish, eggs, and red meat varied among populations. The adverse associations of TMAO with certain cardiometabolic biomarkers, independent of renal function, warrant further investigation.
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Affiliation(s)
- Jae Jeong Yang
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - David M Herrington
- Section on Cardiology, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Steven C Moore
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Katie A Meyer
- Department of Nutrition and Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
| | - Jennifer Ose
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, Salt Lake City, UT, USA
| | - Cristina Menni
- Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Heather Eliassen
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Sei Harada
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, United Kingdom
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
- Dementia Research Institute, Imperial College London, London, United Kingdom
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Huilian Zhu
- Department of Nutrition, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Thomas J Wang
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Hui Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Cornelia M Ulrich
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
- Huntsman Cancer Institute, Salt Lake City, UT, USA
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Ibrahim Karaman
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, United Kingdom
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
- Dementia Research Institute, Imperial College London, London, United Kingdom
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center, Houston, TX, USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Charles E Matthews
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Lynne E Wagenknecht
- Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, United Kingdom
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
- Dementia Research Institute, Imperial College London, London, United Kingdom
| | - Robert E Gerszten
- Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Danxia Yu
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
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26
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Jobard E, Dossus L, Baglietto L, Fornili M, Lécuyer L, Mancini FR, Gunter MJ, Trédan O, Boutron-Ruault MC, Elena-Herrmann B, Severi G, Rothwell JA. Investigation of circulating metabolites associated with breast cancer risk by untargeted metabolomics: a case-control study nested within the French E3N cohort. Br J Cancer 2021; 124:1734-1743. [PMID: 33723391 PMCID: PMC8110540 DOI: 10.1038/s41416-021-01304-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 02/01/2021] [Accepted: 02/04/2021] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND Perturbations in circulating metabolites prior to a breast cancer diagnosis are not well characterised. We aimed to gain more detailed knowledge to help understand and prevent the disease. METHODS Baseline plasma samples from 791 breast cancer cases and 791 matched controls from the E3N (EPIC-France) cohort were profiled by nuclear magnetic resonance (NMR)-based untargeted metabolomics. Partial least-squares discriminant analysis (PLS-DA) models were built from NMR profiles to predict disease outcome, and odds ratios and false discovery rate (FDR)-adjusted CIs were calculated for 43 identified metabolites by conditional logistic regression. RESULTS Breast cancer onset was predicted in the premenopausal subgroup with modest accuracy (AUC 0.61, 95% CI: 0.49-0.73), and 10 metabolites associated with risk, particularly histidine (OR = 1.70 per SD increase, FDR-adjusted CI 1.19-2.41), N-acetyl glycoproteins (OR = 1.53, FDR-adjusted CI 1.18-1.97), glycerol (OR = 1.55, FDR-adjusted CI 1.11-2.18) and ethanol (OR = 1.44, FDR-adjusted CI 1.05-1.97). No predictive capacity or significant metabolites were found overall or for postmenopausal women. CONCLUSIONS Perturbed metabolism compared to controls was observed in premenopausal but not postmenopausal cases. Histidine and NAC have known involvement in inflammatory pathways, and the robust association of ethanol with risk suggests the involvement of alcohol intake.
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Affiliation(s)
- Elodie Jobard
- Univ Lyon, CNRS, Université Claude Bernard Lyon 1, ENS de Lyon, Institut des Sciences Analytiques, UMR 5280, Villeurbanne, France
- Université de Lyon, Centre Léon Bérard, Département d'Oncologie Médicale, Lyon, France
| | - Laure Dossus
- Nutrition and Metabolism Section, International Agency for Research on Cancer, Lyon, France
| | - Laura Baglietto
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Marco Fornili
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Lucie Lécuyer
- Université Paris-Saclay, UVSQ, Univ. Paris-Sud, Inserm, Gustave Roussy, Exposome and Heredity Team, Centre for Epidemiology and Population Health, Villejuif, France
| | - Francesca Romana Mancini
- Université Paris-Saclay, UVSQ, Univ. Paris-Sud, Inserm, Gustave Roussy, Exposome and Heredity Team, Centre for Epidemiology and Population Health, Villejuif, France
| | - Marc J Gunter
- Nutrition and Metabolism Section, International Agency for Research on Cancer, Lyon, France
| | - Olivier Trédan
- Université de Lyon, Centre Léon Bérard, Département d'Oncologie Médicale, Lyon, France
| | - Marie-Christine Boutron-Ruault
- Université Paris-Saclay, UVSQ, Univ. Paris-Sud, Inserm, Gustave Roussy, Exposome and Heredity Team, Centre for Epidemiology and Population Health, Villejuif, France
| | - Bénédicte Elena-Herrmann
- Univ Lyon, CNRS, Université Claude Bernard Lyon 1, ENS de Lyon, Institut des Sciences Analytiques, UMR 5280, Villeurbanne, France
- Univ Grenoble Alpes, CNRS, INSERM, IAB, Allée des Alpes, Grenoble, France
| | - Gianluca Severi
- Université Paris-Saclay, UVSQ, Univ. Paris-Sud, Inserm, Gustave Roussy, Exposome and Heredity Team, Centre for Epidemiology and Population Health, Villejuif, France
- Department of Statistics, Computer Science and Applications "G. Parenti" (DISIA), University of Florence, Firenze, Italy
| | - Joseph A Rothwell
- Université Paris-Saclay, UVSQ, Univ. Paris-Sud, Inserm, Gustave Roussy, Exposome and Heredity Team, Centre for Epidemiology and Population Health, Villejuif, France.
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27
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Quality Assessment of Untargeted Analytical Data in a Large-Scale Metabolomic Study. J Clin Med 2021; 10:jcm10091826. [PMID: 33922230 PMCID: PMC8122759 DOI: 10.3390/jcm10091826] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 04/12/2021] [Accepted: 04/19/2021] [Indexed: 12/12/2022] Open
Abstract
Large-scale metabolomic studies have become common, and the reliability of the peak data produced by the various instruments is an important issue. However, less attention has been paid to the large number of uncharacterized peaks in untargeted metabolomics data. In this study, we tested various criteria to assess the reliability of 276 and 202 uncharacterized peaks that were detected in a gathered set of 30 plasma and urine quality control samples, respectively, using capillary electrophoresis-time-of-flight mass spectrometry (CE-TOFMS). The linear relationship between the amounts of pooled samples and the corresponding peak areas was one of the criteria used to select reliable peaks. We used samples from approximately 3000 participants in the Tsuruoka Metabolome Cohort Study to investigate patterns of the areas of these uncharacterized peaks among the samples and clustered the peaks by combining the patterns and differences in the migration times. Our assessment pipeline removed substantial numbers of unreliable or redundant peaks and detected 35 and 74 reliable uncharacterized peaks in plasma and urine, respectively, some of which may correspond to metabolites involved in important physiological processes such as disease progression. We propose that our assessment pipeline can be used to help establish large-scale untargeted clinical metabolomic studies.
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28
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Shanmuganathan M, Kroezen Z, Gill B, Azab S, de Souza RJ, Teo KK, Atkinson S, Subbarao P, Desai D, Anand SS, Britz-McKibbin P. The maternal serum metabolome by multisegment injection-capillary electrophoresis-mass spectrometry: a high-throughput platform and standardized data workflow for large-scale epidemiological studies. Nat Protoc 2021; 16:1966-1994. [PMID: 33674789 DOI: 10.1038/s41596-020-00475-0] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 11/24/2020] [Indexed: 01/31/2023]
Abstract
A standardized data workflow is described for large-scale serum metabolomic studies using multisegment injection-capillary electrophoresis-mass spectrometry. Multiplexed separations increase throughput (<4 min/sample) for quantitative determination of 66 polar/ionic metabolites in serum filtrates consistently detected (coefficient of variance (CV) <30%) with high frequency (>75%) from a multi-ethnic cohort of pregnant women (n = 1,004). We outline a validated protocol implemented in four batches over a 7-month period that includes details on preventive maintenance, sample workup, data preprocessing and metabolite authentication. We achieve stringent quality control (QC) and robust batch correction of long-term signal drift with good mutual agreement for a wide range of metabolites, including serum glucose as compared to a clinical chemistry analyzer (mean bias = 11%, n = 668). Control charts for a recovery standard (mean CV = 12%, n = 2,412) and serum metabolites in QC samples (median CV = 13%, n = 202) demonstrate acceptable intermediate precision with a median intraclass coefficient of 0.87. We also report reference intervals for 53 serum metabolites from a diverse population of women in their second trimester of pregnancy.
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Affiliation(s)
- Meera Shanmuganathan
- Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Ontario, Canada
| | - Zachary Kroezen
- Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Ontario, Canada
| | - Biban Gill
- Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Ontario, Canada
| | - Sandi Azab
- Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Ontario, Canada
| | - Russell J de Souza
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada.,Population Health Research Institute, Hamilton Health Sciences, Hamilton, Ontario, Canada
| | - Koon K Teo
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Stephanie Atkinson
- Department of Pediatrics, McMaster University, Hamilton, Ontario, Canada
| | - Padmaja Subbarao
- Department of Pediatrics, University of Toronto, Toronto, Ontario, Canada
| | - Dipika Desai
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Sonia S Anand
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada.,Population Health Research Institute, Hamilton Health Sciences, Hamilton, Ontario, Canada.,Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Philip Britz-McKibbin
- Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Ontario, Canada.
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29
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Ishibashi Y, Harada S, Takeuchi A, Iida M, Kurihara A, Kato S, Kuwabara K, Hirata A, Shibuki T, Okamura T, Sugiyama D, Sato A, Amano K, Hirayama A, Sugimoto M, Soga T, Tomita M, Takebayashi T. Reliability of urinary charged metabolite concentrations in a large-scale cohort study using capillary electrophoresis-mass spectrometry. Sci Rep 2021; 11:7407. [PMID: 33795760 PMCID: PMC8016858 DOI: 10.1038/s41598-021-86600-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 03/17/2021] [Indexed: 12/19/2022] Open
Abstract
Currently, large-scale cohort studies for metabolome analysis have been launched globally. However, only a few studies have evaluated the reliability of urinary metabolome analysis. This study aimed to establish the reliability of urinary metabolomic profiling in cohort studies. In the Tsuruoka Metabolomics Cohort Study, 123 charged metabolites were identified and routinely quantified using capillary electrophoresis-mass spectrometry (CE-MS). We evaluated approximately 750 quality control (QC) samples and 6,720 participants’ spot urine samples. We calculated inter- and intra-batch coefficients of variation in the QC and participant samples and technical intraclass correlation coefficients (ICC). A correlation of metabolite concentrations between spot and 24-h urine samples obtained from 32 sub-cohort participants was also evaluated. The coefficient of variation (CV) was less than 20% for 87 metabolites (70.7%) and 20–30% for 19 metabolites (15.4%) in the QC samples. There was less than 20% inter-batch CV for 106 metabolites (86.2%). Most urinary metabolites would have reliability for measurement. The 96 metabolites (78.0%) was above 0.75 for the estimated ICC, and those might be useful for epidemiological analysis. Among individuals, the Pearson correlation coefficient of 24-h and spot urine was more than 70% for 59 of the 99 metabolites. These results show that the profiling of charged metabolites using CE-MS in morning spot human urine is suitable for epidemiological metabolomics studies.
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Affiliation(s)
- Yoshiki Ishibashi
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, Japan
| | - Sei Harada
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, Japan.,Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Ayano Takeuchi
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, Japan
| | - Miho Iida
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, Japan
| | - Ayako Kurihara
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, Japan
| | - Suzuka Kato
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, Japan
| | - Kazuyo Kuwabara
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, Japan
| | - Aya Hirata
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, Japan
| | - Takuma Shibuki
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, Japan
| | - Tomonori Okamura
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, Japan
| | - Daisuke Sugiyama
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, Japan.,Faculty of Nursing And Medical Care, Keio University, Fujisawa, Kanagawa, Japan
| | - Asako Sato
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Kaori Amano
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Akiyoshi Hirayama
- 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
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, Japan. .,Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan.
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30
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Sata M, Okamura T, Harada S, Sugiyama D, Kuwabara K, Hirata A, Takeuchi A, Iida M, Kato S, Matsumoto M, Kurihara A, Takebayashi T. Association of the Estimated Coronary Artery Incidence Risk According to the Japan Atherosclerosis Society Guidelines 2017 with Cardio-Ankle Vascular Index. J Atheroscler Thromb 2021; 28:1266-1274. [PMID: 33678765 PMCID: PMC8629702 DOI: 10.5551/jat.58719] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Aims:
The categories in the comprehensive lipid and risk management guidelines were proposed by the Japan Atherosclerosis Society (JAS Guidelines 2017), which adopted the estimated 10 year absolute risk of coronary artery disease (CAD) incidence in the Suita score. We examined whether those categories were concordant with the degree of arterial stiffness.
Methods:
In 2014, the cardio-ankle vascular index (CAVI), an arterial stiffness parameter, was measured in 1,972 Japanese participants aged 35–74 years in Tsuruoka City, Yamagata Prefecture, Japan. We examined the mean CAVI and the proportion and odds ratios (ORs) of CAVI ≥ 9.0 on the basis of the following three management classifications using the analysis of variance and logistic regression: “Category I (Low risk),” “Category II (Middle risk),” and “Category III (High risk).”
Results:
The mean CAVI and proportion of CAVI ≥ 9.0 were 8.6 and 34.8% among males and 8.1 and 18.3% among females, respectively. The mean CAVI and proportion of CAVI ≥ 9.0 were associated with an estimated 10 year absolute risk for CAD among males and females, excluding High risk for females. These results were similar to the management classification by the guideline: the multivariable-adjusted ORs (95% confidence intervals) of CAVI ≥ 9.0 among Category II and Category III compared with those among Category I were 2.96 (1.61–5.43) and 7.33 (4.03–13.3) for males and 3.99 (2.55–6.24) and 3.34 (2.16–5.16) for females, respectively.
Conclusions:
The risk stratification, which was proposed in the JAS Guidelines 2017, is concordant with the arterial stiffness parameter.
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Affiliation(s)
- Mizuki Sata
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Tomonori Okamura
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Sei Harada
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Daisuke Sugiyama
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Kazuyo Kuwabara
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Aya Hirata
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Ayano Takeuchi
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Miho Iida
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Suzuka Kato
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Minako Matsumoto
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Ayako Kurihara
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Toru Takebayashi
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
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31
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Imaeda N, Goto C, Sasakabe T, Mikami H, Oze I, Hosono A, Naito M, Miyagawa N, Ozaki E, Ikezaki H, Nanri H, Nakahata NT, Kamano SK, Kuriki K, Yaguchi YT, Kayama T, Kurihara A, Harada S, Wakai K. Reproducibility and validity of food group intake in a short food frequency questionnaire for the middle-aged Japanese population. Environ Health Prev Med 2021; 26:28. [PMID: 33653279 PMCID: PMC7923820 DOI: 10.1186/s12199-021-00951-3] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Accepted: 02/21/2021] [Indexed: 11/10/2022] Open
Abstract
Purpose The purpose of this study was to evaluate the reproducibility and validity of a short food frequency questionnaire (FFQ) for food group intake in Japan, the reproducibility and partial validity of which were previously confirmed for nutrients. Methods A total of 288 middle-aged healthy volunteers from 11 different areas of Japan provided nonconsecutive 3-day weighed dietary records (DRs) at 3-month intervals over four seasons. We evaluated reproducibility based on the first (FFQ1) and second (FFQ2) questionnaires and their validity against the DRs by comparing the intake of 20 food groups. Spearman’s rank correlation coefficients (SRs) were calculated between energy-adjusted intake from the FFQs and that from the DRs. Results The intake of 20 food groups estimated from the two FFQs was mostly equivalent. The median energy-adjusted SRs between the FFQ1 and FFQ2 were 0.61 (range 0.38–0.86) for men and 0.66 (0.45–0.84) for women. For validity, the median de-attenuated SRs between DRs and the FFQ1 were 0.51 (0.17–0.76) for men and 0.47 (0.23–0.77) for women. Compared with the DRs, the proportion of cross-classification into exact plus adjacent quintiles with the FFQ1 ranged from 58 to 86% in men and from 57 to 86% in women. According to the robust Z scores and the Bland–Altman plot graphs, the underestimation errors in the FFQ1 tended to be greater in individuals with high mean levels of consumption for meat for men and for other vegetables for both men and women. Conclusion The FFQ demonstrated high reproducibility and reasonable validity for food group intake. This questionnaire is short and remains appropriate for identifying associations between diet and health/disease among adults in Japan. Supplementary Information The online version contains supplementary material available at 10.1186/s12199-021-00951-3.
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Affiliation(s)
- Nahomi Imaeda
- Department of Nutrition, Faculty of Wellness, Shigakkan University, Obu, Aichi, Japan. .,Department of Public Health, Nagoya City University Graduate School of Medical Sciences, Nagoya, Aichi, Japan.
| | - Chiho Goto
- Department of Public Health, Nagoya City University Graduate School of Medical Sciences, Nagoya, Aichi, Japan.,Department of Health and Nutrition, School of Health and Human Life, Nagoya Bunri University, Nagoya, Aichi, Japan
| | - Tae Sasakabe
- Department of Public Health, Aichi Medical University, Nagakute, Aichi, Japan.,Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Haruo Mikami
- Cancer Prevention Center, Chiba Cancer Center Research Institute, Chiba, Chiba, Japan
| | - Isao Oze
- Division of Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, Aichi, Japan
| | - Akihiro Hosono
- Department of Public Health, Nagoya City University Graduate School of Medical Sciences, Nagoya, Aichi, Japan
| | - Mariko Naito
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.,Department of Oral Epidemiology, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Hiroshima, Japan
| | - Naoko Miyagawa
- Department of Public Health, Shiga University of Medical Science, Otsu, Shiga, Japan.,International Center for Nutrition and Information, National Institutes of Biomedical Innovation, Health and Nutrition, Shinjuku, Tokyo, Japan
| | - Etsuko Ozaki
- Department of Epidemiology for Community Health and Medicine, Kyoto Prefectural University of Medicine, Kyoto, Kyoto, Japan
| | - Hiroaki Ikezaki
- Department of General Internal Medicine, Kyushu University Hospital, Fukuoka, Fukuoka, Japan.,Department of Comprehensive General Internal Medicine, Kyushu University Faculty of Medical Sciences, Fukuoka, Fukuoka, Japan
| | - Hinako Nanri
- Section of Behavioral Physiology, Department of Physical Activity Research, National Institutes of Health and Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition, Shinjuku, Tokyo, Japan
| | - Noriko T Nakahata
- Department of Health and Nutrition, Faculty of Nursing and Nutrition, University of Shimane, Hamada, Shimane, Japan
| | - Sakurako K Kamano
- Department of Preventive Medicine, Tokushima University Graduate School of Biomedical Sciences, Tokushima, Tokushima, Japan
| | - Kiyonori Kuriki
- Laboratory of Public Health, Graduate School of Nutritional and Environmental Sciences, Shizuoka University, Shizuoka, Shizuoka, Japan
| | - Yuri T Yaguchi
- Department of Advanced Cancer Science, Faculty of Medicine, Yamagata University, Yamagata, Yamagata, Japan
| | - Takamasa Kayama
- Department of Advanced Cancer Science, Faculty of Medicine, Yamagata University, Yamagata, Yamagata, Japan
| | - Ayako Kurihara
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Shinjuku, Tokyo, Japan
| | - Sei Harada
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Shinjuku, Tokyo, Japan
| | - Kenji Wakai
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
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32
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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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33
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Shi C, Wang L, Zhou K, Shao M, Lu Y, Wu T. Targeted Metabolomics Identifies Differential Serum and Liver Amino Acids Biomarkers in Rats with Alcoholic Liver Disease. J Nutr Sci Vitaminol (Tokyo) 2021; 66:536-544. [PMID: 33390395 DOI: 10.3177/jnsv.66.536] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
To investigate changes in serum and hepatic levels of amino acids in ALD and to provide novel evidence and approaches for the prevention and treatment of ALD. Twenty specific pathogen-free SD male rats were devided into two groups, ten for the control group, and ten for the model group. Serum biochemical markers, including alanine aminotransferase, aspartate aminotransferase, laminin and hyaluronidase were measured. Histological analysis of liver tissues was performed. Serum and liver amino acids levels were quantitatively determined by ultra-high-performance liquid chromatography-tandem quadrupole mass spectrometry (UPLC-TQMS)-based targeted metabolomics. Compared with the normal group, ALD rats showed an obvious increase in the levels of β-alanine, alanine, serine, ornithine, tyrosine and the tyrosine ratio, while there was a decrease in arginine levels, the BTR ratio and Fischer's ratio in serum. Additionally, ALD rats exhibited a significant increase in the levels of cysteine and putrescine, while there was a decrease in sarcosine, β-alanine, serine, proline, valine, threonine, ornithine, lysine, histidine, tyrosine, symmetric dimethylarginine, methionine, isoleucine and methionine-sulfoxide levels in liver tissues compared with the normal group. The serum and liver amino acids showed significant changes in ALD rats and can be considered as potential specific diagnostic biomarkers for ALD.
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Affiliation(s)
- Chenze Shi
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine
| | - Lei Wang
- Department of Hepatology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine
| | - Kejun Zhou
- Shanghai Key Laboratory of Pediatric Gastroenterology and Nutrition, Shanghai Institute for Pediatric Research
| | - Mingmei Shao
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine
| | - Yifei Lu
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine
| | - Tao Wu
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine
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34
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Fu C, Yang Y, Kumrungsee T, Kimoto A, Izu H, Kato N. Low-Dose Ethanol Has Impacts on Plasma Levels of Metabolites Relating to Chronic Disease Risk in SAMP8 mice. J Nutr Sci Vitaminol (Tokyo) 2021; 66:553-560. [PMID: 33390397 DOI: 10.3177/jnsv.66.553] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
The effects of low-dose alcohol on experimental animals are unclear. This study examined plasma metabolites in senescence-accelerated mice 8 (SAMP8) given low-dose ethanol, and compared them with aging progress and skeletal muscle strength. Male SAMP8 mice (10-wk-old) were given drinking water containing 0% (control), 1%, 2%, or 5% (v/v) ethanol for 14 wk. Compared with the control group, only mice who consumed 1% ethanol experienced a lower senescence score at 18 and 23 wk, as well as an increased limb grip strength at 21 wk. Plasma metabolites of control, 1% and 2% ethanol groups were analyzed by capillary electrophoresis-time-of-flight mass spectrometry (CE-TOF/MS). Among the 7 metabolites affected by ethanol, notewhorthy is the positive association of the ethanol levels in drinking water with the levels of α-ketoglutarate (antioxidant and anti-inflammatory metabolite) and hippurate (antioxidant and microbial co-metabolite) (p<0.05). Intriguingly, the levels of 2-hydroxyisobutyrate (the biomarker of energy metabolism and microbial co-metabolite) were higher in the 1% ethanol group (p<0.05), but not in the 2% ethanol group as compared to the control. Furthermore, the levels of some of the metabolites affected were correlated with some variables in the grading score of senescence and muscle strength. This study provides a novel insight into how low-dose ethanol in SAMP8 mice modulates the levels of circulating metabolites relating to chronic disease risk.
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Affiliation(s)
- Churan Fu
- Graduate School of Integrated Sciences for Life, Hiroshima University
| | - Yongshou Yang
- Graduate School of Integrated Sciences for Life, Hiroshima University
| | | | - Akiko Kimoto
- Faculty of Human Ecology, Yasuda Women's University
| | - Hanae Izu
- Quality and Evaluation Research Division, National Research Institute of Brewing
| | - Norihisa Kato
- Graduate School of Integrated Sciences for Life, Hiroshima University
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35
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Matsumoto M, Harada S, Iida M, Kato S, Sata M, Hirata A, Kuwabara K, Takeuchi A, Sugiyama D, Okamura T, Takebayashi T. Validity Assessment of Self-reported Medication Use for Hypertension, Diabetes, and Dyslipidemia in a Pharmacoepidemiologic Study by Comparison With Health Insurance Claims. J Epidemiol 2020; 31:495-502. [PMID: 33361656 PMCID: PMC8328856 DOI: 10.2188/jea.je20200089] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Background Although self-reported questionnaires are widely employed in epidemiologic studies, their validity has not been sufficiently assessed. The aim of this study was to evaluate the validity of a self-reported questionnaire on medication use by comparison with health insurance claims and to identify individual determinants of discordance in the Tsuruoka Metabolomics Cohort Study. Methods Participants were 2,472 community-dwellers aged 37 to 78 years from the Tsuruoka Metabolomics Cohort Study. Information on lifestyle and medications was collected through a questionnaire. Sensitivity and specificity were determined using health insurance claims from November 2014 to March 2016, which were used as a standard. Potential determinants of discordance were assessed using multivariable logistic regression. Results The self-reported questionnaire on medication use showed high validity. Sensitivity and specificity were 0.95 (95% CI, 0.93–0.96) and 0.97 (95% CI, 0.96–0.98) for antihypertensive medications, 0.94 (95% CI, 0.91–0.97) and 0.98 (95% CI, 0.98–0.99) for diabetes medications, and 0.84 (95% CI, 0.82–0.87) and 0.98 (95% CI, 0.97–0.99) for dyslipidemia medications, respectively. Males without high education and those who currently smoke cigarettes were found to be associated with discordant reporting which affected sensitivity, especially those with medication use for dyslipidemia. Conclusions In this population-based cohort study, we found that the self-reported questionnaire on medication use was a valid measure to capture regular medication users. Sensitivity for dyslipidemia medications was lower than those for the other medications. Type of medication, sex, education years, and smoking status influenced discordance, which affected sensitivity in self-reporting.
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Affiliation(s)
- Minako Matsumoto
- Department of Preventive Medicine and Public Health, School of Medicine, Keio University
| | - Sei Harada
- Department of Preventive Medicine and Public Health, School of Medicine, Keio University
| | - Miho Iida
- Department of Preventive Medicine and Public Health, School of Medicine, Keio University
| | - Suzuka Kato
- Department of Preventive Medicine and Public Health, School of Medicine, Keio University
| | - Mizuki Sata
- Department of Preventive Medicine and Public Health, School of Medicine, Keio University
| | - Aya Hirata
- Department of Preventive Medicine and Public Health, School of Medicine, Keio University
| | - Kazuyo Kuwabara
- Department of Preventive Medicine and Public Health, School of Medicine, Keio University
| | - Ayano Takeuchi
- Department of Preventive Medicine and Public Health, School of Medicine, Keio University
| | - Daisuke Sugiyama
- Department of Preventive Medicine and Public Health, School of Medicine, Keio University
| | - Tomonori Okamura
- Department of Preventive Medicine and Public Health, School of Medicine, Keio University
| | - Toru Takebayashi
- Department of Preventive Medicine and Public Health, School of Medicine, Keio University
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36
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Tang J, Xiong K, Zhang T, Han Han. Application of Metabolomics in Diagnosis and Treatment of Chronic Liver Diseases. Crit Rev Anal Chem 2020; 52:906-916. [PMID: 33146026 DOI: 10.1080/10408347.2020.1842172] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Chronic liver disease represents stepwise destruction of the liver parenchyma after chronic liver injury, which is often difficult to be diagnosed accurately. Thus, the development of specific biomarkers of chronic liver disease is important. Metabolomics is a powerful tool for biomarker exploration, which enables the exploration of disease pathogenesis or drug action mechanisms at the global metabolic level. The metabolomics workflow generally includes collection, preparation, and analysis of samples, and data processing and bioinformatics. A metabolomics study can simultaneously detect the dysfunctions in the glucose, lipid, amino-acid, and nucleotide metabolisms. Hence, it facilitates the obtaining of a better understanding of the pathogenesis of chronic liver disease and its diagnosis. Many effective drugs could reverse the change of comprehensive biochemical phenotypes induced by chronic liver disease. They can even potentially restore the normal metabolic signatures of patients. Increasingly more researchers have begun to apply metabolomics technologies to diagnose chronic liver disease and investigate the mechanism of action of effective drugs or the variations in drug responses. We are convinced that deepening the understanding of the metabolic alterations could extend their use as powerful biomarkers, promoting the more effective clinical diagnosis and treatment of chronic liver disease in the future.
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Affiliation(s)
- Jie Tang
- Experiment Center for Teaching and Learning, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Kai Xiong
- Experiment Center for Teaching and Learning, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Tong Zhang
- Experiment Center for Teaching and Learning, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Han Han
- Experiment Center for Teaching and Learning, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Metabolomics Study of Serum from a Chronic Alcohol-Fed Rat Model Following Administration of Defatted Tenebrio molitor Larva Fermentation Extract. Metabolites 2020; 10:metabo10110436. [PMID: 33138187 PMCID: PMC7693418 DOI: 10.3390/metabo10110436] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 10/22/2020] [Accepted: 10/27/2020] [Indexed: 12/11/2022] Open
Abstract
We have previously showed that defatted mealworm fermentation extract (MWF) attenuates alcoholic liver injury by regulating lipid, inflammatory, and antioxidant metabolism in chronic alcohol-fed rats. The current metabolomics study was performed to monitor biochemical events following the administration of MWF (daily for eight weeks) to a rat model of alcoholic liver injury by gas chromatography-tandem mass spectrometry (GC-MS/MS). The levels of 15 amino acids (AAs), 17 organic acids (OAs), and 19 free fatty acids (FFAs) were measured in serum. Analysis of variance (ANOVA), principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA) were used to compare the levels of 51 metabolites in serum. In particular, 3-hydroxybutyric acid (3-HB), pyroglutamic acid (PG), octadecanoic acid, and docosahexaenoic acid (DHA) were evaluated as high variable importance point (VIP) scores and PCA loading scores as determined by PLS-DA and PCA, and these were significantly higher in the MWF and silymarin groups than in the EtOH group. MWF showed a protective effect from alcohol-induced liver damage by elevating hepatic β-oxidation activity, and serum 3-HB levels were significantly higher in the MWF group than in the EtOH control group. Glycine levels were higher in the MWF group than in the EtOH group, and PG levels (related to glutathione production) were also elevated, indicating a reduction in alcohol-related oxidative stress. In addition, MWF is protected from alcohol-induced inflammation and steatosis by increasing serum DHA, palmitic, and octadecanoic acid levels as compared with the EtOH group. These results suggest that MWF might attenuate alcoholic liver disease, due to its anti-inflammatory and antioxidant effects by up-regulating hepatic β-oxidation activity and down-regulating liver FFA uptake.
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38
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CUX2, BRAP and ALDH2 are associated with metabolic traits in people with excessive alcohol consumption. Sci Rep 2020; 10:18118. [PMID: 33093602 PMCID: PMC7583246 DOI: 10.1038/s41598-020-75199-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2019] [Accepted: 10/12/2020] [Indexed: 12/21/2022] Open
Abstract
Molecular mechanisms that prompt or mitigate excessive alcohol consumption could be partly explained by metabolic shifts. This genome-wide association study aims to identify the susceptibility gene loci for excessive alcohol consumption by jointly measuring weekly alcohol consumption and γ-GT levels. We analysed the Taiwan Biobank data of 18,363 Taiwanese people, including 1945 with excessive alcohol use. We found that one or two copies of the G allele in rs671 (ALDH2) increased the risk of excessive alcohol consumption, while one or two copies of the C allele in rs3782886 (BRAP) reduced the risk of excessive alcohol consumption. To minimize the influence of extensive regional linkage disequilibrium, we used the ridge regression. The ridge coefficients of rs7398833, rs671 and rs3782886 were unchanged across different values of the shrinkage parameter. The three variants corresponded to posttranscriptional activity, including cut-like homeobox 2 (a protein coded by CUX2), Glu504Lys of acetaldehyde dehydrogenase 2 (a protein encoded by ALDH2) and Glu4Gly of BRCA1-associated protein (a protein encoded by BRAP). We found that Glu504Lys of ALDH2 and Glu4Gly of BRAP are involved in the negative regulation of excessive alcohol consumption. The mechanism underlying the γ-GT-catalytic metabolic reaction in excessive alcohol consumption is associated with ALDH2, BRAP and CUX2. Further study is needed to clarify the roles of ALDH2, BRAP and CUX2 in the liver–brain endocrine axis connecting metabolic shifts with excessive alcohol consumption.
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Kärkkäinen O, Klåvus A, Voutilainen A, Virtanen J, Lehtonen M, Auriola S, Kauhanen J, Rysä J. Changes in Circulating Metabolome Precede Alcohol-Related Diseases in Middle-Aged Men: A Prospective Population-Based Study With a 30-Year Follow-Up. Alcohol Clin Exp Res 2020; 44:2457-2467. [PMID: 33067815 DOI: 10.1111/acer.14485] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 09/16/2020] [Accepted: 10/12/2020] [Indexed: 12/15/2022]
Abstract
BACKGROUND Heavy alcohol use has been associated with altered circulating metabolome. We investigated whether changes in the circulating metabolome precede incident diagnoses of alcohol-related diseases. METHODS This is a prospective population-based cohort study where the participants were 42- to 60-year-old males at baseline (years 1984 to 1989). Subjects who received a diagnosis for an alcohol-related disease during the follow-up were defined as cases (n = 92, mean follow-up of 13.6 years before diagnosis). Diagnoses were obtained through linkage with national health registries. We used 2 control groups: controls who self-reported similar levels of alcohol use as compared to cases at baseline (alcohol-controls, n = 92), and controls who self-reported only light drinking at baseline (control-controls, n = 90). A nontargeted metabolomics analysis of baseline serum samples was performed. RESULTS There were significant differences between the study groups in the baseline serum levels of 64 metabolites: in amino acids (e.g., glutamine [FDR-corrected q-value = 0.0012]), glycerophospholipids (e.g., lysophosphatidylcholine 16:1 [q = 0.0008]), steroids (e.g., cortisone [q = 0.00001]), and fatty acids (e.g., palmitoleic acid [q = 0.0031]). The main finding was that after controlling for baseline levels of self-reported alcohol use and the biomarker of alcohol use, gamma-glutamyl transferase, and when compared to both alcohol-control and control-control group, the alcohol-case group had lower serum levels of asparagine (Cohen's d = -0.48 [95% CI -0.78 to -0.19] and d = -0.49 [-0.78 to -0.19], respectively) and serotonin (d = -0.45 [-0.74 to -0.15], and d = -0.46 [-0.75 to -0.16], respectively), with no difference between the two control groups (asparagine d = 0.00 [-0.29 to 0.29] and serotonin d = -0.01 [-0.30 to 0.29]). CONCLUSIONS Changes in the circulating metabolome, especially lower serum levels of asparagine and serotonin, are associated with later diagnoses of alcohol-related diseases, even after adjustment for the baseline level of alcohol use.
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Affiliation(s)
- Olli Kärkkäinen
- From the, School of Pharmacy, (OK, ML, SA, JR), University of Eastern Finland, Kuopio, Finland
| | - Anton Klåvus
- Institute of Public Health and Clinical Nutrition, (AK, AV, JV, JK), University of Eastern Finland, Kuopio, Finland
| | - Ari Voutilainen
- Institute of Public Health and Clinical Nutrition, (AK, AV, JV, JK), University of Eastern Finland, Kuopio, Finland
| | - Jyrki Virtanen
- Institute of Public Health and Clinical Nutrition, (AK, AV, JV, JK), University of Eastern Finland, Kuopio, Finland
| | - Marko Lehtonen
- From the, School of Pharmacy, (OK, ML, SA, JR), University of Eastern Finland, Kuopio, Finland
| | - Seppo Auriola
- From the, School of Pharmacy, (OK, ML, SA, JR), University of Eastern Finland, Kuopio, Finland
| | - Jussi Kauhanen
- Institute of Public Health and Clinical Nutrition, (AK, AV, JV, JK), University of Eastern Finland, Kuopio, Finland
| | - Jaana Rysä
- From the, School of Pharmacy, (OK, ML, SA, JR), University of Eastern Finland, Kuopio, Finland
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Sasaki M, Harada S, Tsubota K, Yasukawa T, Takebayashi T, Nishiwaki Y, Kawasaki R. Dietary Saturated Fatty Acid Intake and Early Age-Related Macular Degeneration in a Japanese Population. Invest Ophthalmol Vis Sci 2020; 61:23. [PMID: 32181798 PMCID: PMC7401844 DOI: 10.1167/iovs.61.3.23] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Purpose To assess the association of dietary saturated fatty acid (SFA) intake with the presence of early AMD in a Japanese population. Methods The population-based Tsuruoka Metabolomics Cohort Study enrolled general population individuals aged 35 to 74 years from among participants in annual health check-up programs that included fundus photographs in Tsuruoka, Japan. A total of 4010 individuals participated in the baseline survey. After excluding nonresponders to a dietary survey and participants with suboptimal fundus image quality, 3988 participants (median age, 62.4 years) were included in this cross-sectional analysis. Dietary intake was assessed by a validated food frequency questionnaire. Fatty acids intake was adjusted for total energy intake by the residuals method. The association between fatty acid intake and presence of early AMD was assessed by logistic regression models. Results Median daily SFA intake was 11.3 g (interquartile range, 9.6, 13.0 g). After adjustments for potential confounding factors, participants in the highest quartile of SFA intake were less likely to have early AMD, compared with the lowest quartile (odds ratio, 0.71; 95% confidence interval: 0.52–0.96). A significant trend for decreased risk of early AMD with increasing SFA intake was noted (P = 0.011). There was no significant association between poly-unsaturated fatty acid (PUFA) including n3-PUFA intake and early AMD. Conclusions We found that increased SFA intake was associated with reduced risk of early AMD in a Japanese population with low SFA intake. Adequate fatty acid intake may be required to maintain retinal homeostasis and prevent AMD.
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Song H, Xu T, Feng X, Lai Y, Yang Y, Zheng H, He X, Wei G, Liao W, Liao Y, Zhong L, Bin J. Itaconate prevents abdominal aortic aneurysm formation through inhibiting inflammation via activation of Nrf2. EBioMedicine 2020; 57:102832. [PMID: 32574955 PMCID: PMC7322255 DOI: 10.1016/j.ebiom.2020.102832] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 05/23/2020] [Accepted: 05/27/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Identifying effective drugs to suppress vascular inflammation is a promising strategy to delay the progression of abdominal aortic aneurysm (AAA). Itaconate has a vital role in regulating inflammatory activation in various inflammatory diseases. However, the role of itaconate in the progression of AAA is unknown. In this study, we explored the inhibitory effect of itaconate on AAA formation and its underlying mechanisms. METHODS Quantitative PCR, western blotting and immunohistochemistry were used to determine Irg1 and downstream Nrf2 expression in human and mouse AAA samples. Liquid chromatograph-mass spectrometry (LC-MS) analysis was performed to measure the abundance of itaconate. OI treatment and Irg1 knockdown were performed to study the role of OI in AAA formation. Nrf2 intervention in vivo was performed to detect the critical role of Nrf2 in the beneficial effect of OI on AAA. FINDINGS We found that itaconate suppressed the formation of angiotensin II (Ang II)-induced AAA in apolipoprotein E-deficient (Apoe-/-) mice, while Irg1 deficiency exerted the opposite effect. Mechanistically, itaconate inhibited vascular inflammation by enabling Nrf2 to function as a transcriptional repressor of downstream inflammatory genes via alkylation of Keap1. Moreover, Nrf2 deficiency significantly aggravated inflammatory factor expression and promoted AAA formation. In addition, Keap1 overexpression significantly promoted Ang II-induced AAA formation, which was inhibited by itaconate. INTERPRETATION Itaconate inhibited AAA formation by suppressing vascular inflammation, and therapeutic approaches to increase itaconate are potentially beneficial for preventing AAA formation. FUNDING National Natural Science Foundations of China and Guangzhou regenerative medicine and Health Laboratory of Guangdong.
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Affiliation(s)
- Haoyu Song
- Department of Cardiology, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Avenue, Guangzhou 510515, China; Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou 510005, China
| | - Tong Xu
- Department of Cardiology, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Avenue, Guangzhou 510515, China
| | - Xiaofei Feng
- Department of Cardiology, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Avenue, Guangzhou 510515, China
| | - Yanxian Lai
- Department of Cardiology, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Avenue, Guangzhou 510515, China
| | - Yang Yang
- Department of Cardiology, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Avenue, Guangzhou 510515, China
| | - Hao Zheng
- Department of Cardiology, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Avenue, Guangzhou 510515, China
| | - Xiang He
- Department of Cardiology, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Avenue, Guangzhou 510515, China
| | - Guoquan Wei
- Department of Cardiology, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Avenue, Guangzhou 510515, China
| | - Wangjun Liao
- Department of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - Yulin Liao
- Department of Cardiology, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Avenue, Guangzhou 510515, China
| | - Lintao Zhong
- Department of Cardiology, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Avenue, Guangzhou 510515, China; Zhuhai People's Hospital (Zhuhai hospital affiliated with Jinan University), Zhuhai 519000, China.
| | - Jianping Bin
- Department of Cardiology, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, 1838 North Guangzhou Avenue, Guangzhou 510515, China; Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou 510005, China.
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Hardikar S, Albrechtsen RD, Achaintre D, Lin T, Pauleck S, Playdon M, Holowatyj AN, Gigic B, Schrotz-King P, Boehm J, Habermann N, Brezina S, Gsur A, van Roekel EH, Weijenberg MP, Keski-Rahkonen P, Scalbert A, Ose J, Ulrich CM. Impact of Pre-blood Collection Factors on Plasma Metabolomic Profiles. Metabolites 2020; 10:E213. [PMID: 32455751 PMCID: PMC7281389 DOI: 10.3390/metabo10050213] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 05/14/2020] [Accepted: 05/19/2020] [Indexed: 12/30/2022] Open
Abstract
Demographic, lifestyle and biospecimen-related factors at the time of blood collection can influence metabolite levels in epidemiological studies. Identifying the major influences on metabolite concentrations is critical to designing appropriate sample collection protocols and considering covariate adjustment in metabolomics analyses. We examined the association of age, sex, and other short-term pre-blood collection factors (time of day, season, fasting duration, physical activity, NSAID use, smoking and alcohol consumption in the days prior to collection) with 133 targeted plasma metabolites (acylcarnitines, amino acids, biogenic amines, sphingolipids, glycerophospholipids, and hexoses) among 108 individuals that reported exposures within 48 h before collection. The differences in mean metabolite concentrations were assessed between groups based on pre-collection factors using two-sided t-tests and ANOVA with FDR correction. Percent differences in metabolite concentrations were negligible across season, time of day of collection, fasting status or lifestyle behaviors at the time of collection, including physical activity or the use of tobacco, alcohol or NSAIDs. The metabolites differed in concentration between the age and sex categories for 21.8% and 14.3% metabolites, respectively. In conclusion, extrinsic factors in the short period prior to collection were not meaningfully associated with concentrations of selected endogenous metabolites in a cross-sectional sample, though metabolite concentrations differed by age and sex. Larger studies with more coverage of the human metabolome are warranted.
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Affiliation(s)
- Sheetal Hardikar
- Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT 84112, USA; (R.D.A.); (T.L.); (S.P.); (M.P.); (A.N.H.); (J.B.); (J.O.); (C.M.U.)
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT 84108, USA
- Cancer Prevention, Population Health Sciences, Fred Hutchinson Cancer Research Institute, Seattle, WA 19024, USA
| | - Richard D. Albrechtsen
- Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT 84112, USA; (R.D.A.); (T.L.); (S.P.); (M.P.); (A.N.H.); (J.B.); (J.O.); (C.M.U.)
| | - David Achaintre
- International Agency for Research on Cancer, 69372 Lyon, France; (D.A.); (P.K.-R.); (A.S.)
| | - Tengda Lin
- Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT 84112, USA; (R.D.A.); (T.L.); (S.P.); (M.P.); (A.N.H.); (J.B.); (J.O.); (C.M.U.)
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT 84108, USA
| | - Svenja Pauleck
- Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT 84112, USA; (R.D.A.); (T.L.); (S.P.); (M.P.); (A.N.H.); (J.B.); (J.O.); (C.M.U.)
| | - Mary Playdon
- Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT 84112, USA; (R.D.A.); (T.L.); (S.P.); (M.P.); (A.N.H.); (J.B.); (J.O.); (C.M.U.)
- Department of Nutrition and Integrative Physiology, University of Utah, Salt Lake City, UT 84108, USA
| | - Andreana N. Holowatyj
- Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT 84112, USA; (R.D.A.); (T.L.); (S.P.); (M.P.); (A.N.H.); (J.B.); (J.O.); (C.M.U.)
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT 84108, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt-Ingram Cancer Center, Nashville, TN 37232, USA
| | - Biljana Gigic
- Department of Surgery, University of Heidelberg, 69120 Heidelberg, Germany;
| | - Petra Schrotz-King
- Division of Preventive Oncology, National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (P.S.-K.); (N.H.)
| | - Juergen Boehm
- Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT 84112, USA; (R.D.A.); (T.L.); (S.P.); (M.P.); (A.N.H.); (J.B.); (J.O.); (C.M.U.)
| | - Nina Habermann
- Division of Preventive Oncology, National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (P.S.-K.); (N.H.)
- Genome Biology, European Molecular Biology Laboratory (EMBL), 69117 Heidelberg, Germany
| | - Stefanie Brezina
- Institute of Cancer Research, Department of Medicine I, Medical University of Vienna, 1090 Vienna, Austria; (S.B.); (A.G.)
| | - Andrea Gsur
- Institute of Cancer Research, Department of Medicine I, Medical University of Vienna, 1090 Vienna, Austria; (S.B.); (A.G.)
| | - Eline H. van Roekel
- Department of Epidemiology, GROW School for Oncology and Developmental Biology, Maastricht University, 6211 LK Maastricht, The Netherlands; (E.H.v.R.); (M.P.W.)
| | - Matty P. Weijenberg
- Department of Epidemiology, GROW School for Oncology and Developmental Biology, Maastricht University, 6211 LK Maastricht, The Netherlands; (E.H.v.R.); (M.P.W.)
| | - Pekka Keski-Rahkonen
- International Agency for Research on Cancer, 69372 Lyon, France; (D.A.); (P.K.-R.); (A.S.)
| | - Augustin Scalbert
- International Agency for Research on Cancer, 69372 Lyon, France; (D.A.); (P.K.-R.); (A.S.)
| | - Jennifer Ose
- Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT 84112, USA; (R.D.A.); (T.L.); (S.P.); (M.P.); (A.N.H.); (J.B.); (J.O.); (C.M.U.)
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT 84108, USA
| | - Cornelia M. Ulrich
- Population Sciences, Huntsman Cancer Institute, Salt Lake City, UT 84112, USA; (R.D.A.); (T.L.); (S.P.); (M.P.); (A.N.H.); (J.B.); (J.O.); (C.M.U.)
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT 84108, USA
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Blood Metabolite Signatures of Metabolic Syndrome in Two Cross-Cultural Older Adult Cohorts. Int J Mol Sci 2020; 21:ijms21041324. [PMID: 32079087 PMCID: PMC7072935 DOI: 10.3390/ijms21041324] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 02/11/2020] [Accepted: 02/12/2020] [Indexed: 02/07/2023] Open
Abstract
Metabolic syndrome (MetS) affects an increasing number of older adults worldwide. Cross-cultural comparisons can provide insight into how factors, including genetic, environmental, and lifestyle, may influence MetS prevalence. Metabolomics, which measures the biochemical products of cell processes, can be used to enhance a mechanistic understanding of how biological factors influence metabolic outcomes. In this study we examined associations between serum metabolite concentrations, representing a range of biochemical pathways and metabolic syndrome in two older adult cohorts: The Tsuruoka Metabolomics Cohort Study (TMCS) from Japan (n = 104) and the Baltimore Longitudinal Study of Aging (BLSA) from the United States (n = 146). We used logistic regression to model associations between MetS and metabolite concentrations. We found that metabolites from the phosphatidylcholines-acyl-alkyl, sphingomyelin, and hexose classes were significantly associated with MetS and risk factor outcomes in both cohorts. In BLSA, metabolites across all classes were uniquely associated with all outcomes. In TMCS, metabolites from the amino acid, biogenic amines, and free fatty acid classes were uniquely associated with MetS, and metabolites from the sphingomyelin class were uniquely associated with elevated triglycerides. The metabolites and metabolite classes we identified may be relevant for future studies exploring disease mechanisms and identifying novel precision therapy targets for individualized medicine.
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Cresci GAM, Lampe JW, Gibson G. Targeted Approaches for In Situ Gut Microbiome Manipulation. JPEN J Parenter Enteral Nutr 2020; 44:581-588. [PMID: 32027044 PMCID: PMC9291485 DOI: 10.1002/jpen.1779] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 12/17/2019] [Indexed: 12/16/2022]
Abstract
The 2019 Dudrick Research Symposium, entitled "Targeted Approaches for In Situ Gut Microbiome Manipulation," was held on March 25, 2019, at the American Society for Parenteral and Enteral Nutrition (ASPEN) 2019 Nutrition Science & Practice Conference in Phoenix, AZ. The Dudrick Symposium honors the many pivotal and innovative contributions to the development and advancement of parenteral nutrition (PN) made by Dr Stanley J. Dudrick, physician scientist, academic leader, and a founding member of ASPEN. As the 2018 recipient of the Dudrick award, Dr Gail Cresci organized and chaired the symposium. The symposium addressed the evolving field of nutrition manipulation of the gut microbiome as a means to mitigate disease and support health. Presentations focused on (1) the role of prebiotics as a means to beneficially support gut microbiome composition and function and health; (2) designer synbiotics targeted to support metabolic by-products altered by ethanol exposure and microbial effectors that manipulate host metabolic outcomes; and, lastly, (3) types of intervention designs used to study diet-gut microbiome interactions in humans and a review of findings from recent interventions, which tested the effects of diet on the microbiome and the microbiome's effect on dietary exposures. New molecular techniques and multiomic approaches have improved knowledge of the structure and functional activity of the gut microbiome; however, challenges remain in establishing causal relationships between changes in the gut microbial-community structure and function and health outcomes in humans.
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Affiliation(s)
- Gail A. M. Cresci
- Department of Pediatric GastroenterologyCleveland Clinic Children's HospitalClevelandOhioUSA
- Department of Inflammation and ImmunityLerner Research InstituteCleveland ClinicClevelandOhioUSA
- Center for Human NutritionDigestive Disease InstituteCleveland ClinicClevelandOhioUSA
| | | | - Glenn Gibson
- Department of Food and Nutritional SciencesThe University of ReadingReadingUK
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Alcohol consumption and serum metabolite concentrations in young women. Cancer Causes Control 2019; 31:113-126. [PMID: 31828464 DOI: 10.1007/s10552-019-01256-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Accepted: 12/02/2019] [Indexed: 12/17/2022]
Abstract
PURPOSE Alcohol consumption is an established breast cancer risk factor, though further research is needed to advance our understanding of the mechanism underlying the association. We used global metabolomics profiling to identify serum metabolites and metabolic pathways that could potentially mediate the alcohol-breast cancer association. METHODS A cross-sectional analysis of reported alcohol consumption and serum metabolite concentrations was conducted among 211 healthy women 25-29 years old who participated in the Dietary Intervention Study in Children 2006 Follow-Up Study (DISC06). Alcohol-metabolite associations were evaluated using multivariable linear mixed-effects regression. RESULTS Alcohol was significantly (FDR p < 0.05) associated with several serum metabolites after adjustment for diet composition and other potential confounders. The amino acid sarcosine, the omega-3 fatty acid eicosapentaenoate, and the steroid 4-androsten-3beta,17beta-diol monosulfate were positively associated with alcohol intake, while the gamma-tocopherol metabolite gamma-carboxyethyl hydroxychroman (CEHC) was inversely associated. Positive associations of alcohol with 2-methylcitrate and 4-androsten-3beta,17beta-diol disulfate were borderline significant (FDR p < 0.10). Metabolite set enrichment analysis identified steroids and the glycine pathway as having more members associated with alcohol consumption than expected by chance. CONCLUSIONS Most of the metabolites associated with alcohol in the current analysis participate in pathways hypothesized to mediate the alcohol-breast cancer association including hormonal, one-carbon metabolism, and oxidative stress pathways, but they could also affect risk via alternative pathways. Independent replication of alcohol-metabolite associations and prospective evaluation of confirmed associations with breast cancer risk are needed.
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Voutilainen T, Kärkkäinen O. Changes in the Human Metabolome Associated With Alcohol Use: A Review. Alcohol Alcohol 2019; 54:225-234. [PMID: 31087088 DOI: 10.1093/alcalc/agz030] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Revised: 03/15/2019] [Accepted: 03/20/2019] [Indexed: 12/28/2022] Open
Abstract
AIMS The metabolome refers to the functional status of the cell, organ or the whole body. Metabolomic methods measure the metabolome (metabolite profile) which can be used to examine disease progression and treatment responses. Here, our aim was to review metabolomics studies examining effects of alcohol use in humans. METHODS We performed a literature search using PubMed and Web of Science for reports on changes in the human metabolite profile associated with alcohol use; we found a total of 23 articles published before end of 2018. RESULTS Most studies had investigated plasma, serum or urine samples; only four studies had examined other sample types (liver, faeces and broncho-alveolar lavage fluid). Levels of 51 metabolites were altered in two or more of the reviewed studies. Alcohol use was associated with changes in the levels of lipids and amino acids. In general, levels of fatty acids, phosphatidylcholine diacyls and steroid metabolites tended to increase, whereas those of phosphatidylcholine acyl-alkyls and hydroxysphingomyelins declined. Common alterations in circulatory levels of amino acids included decreased levels of glutamine, and increased levels of tyrosine and alanine. CONCLUSIONS More studies, especially with a longitudinal study design, or using more varied sample materials (e.g. organs or saliva), are needed to clarify alcohol-induced diseases and alterations at a target organ level. Hopefully, this will lead to the discovery of new treatments, improved recognition of individuals at high risk and identification of those subjects who would benefit most from certain treatments.
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Affiliation(s)
- Taija Voutilainen
- School of Pharmacy, University of Eastern Finland, Yliopistonranta 1, Kuopio, Finland
| | - Olli Kärkkäinen
- School of Pharmacy, University of Eastern Finland, Yliopistonranta 1, Kuopio, Finland
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Fukushima K, Harada S, Takeuchi A, Kurihara A, Iida M, Fukai K, Kuwabara K, Kato S, Matsumoto M, Hirata A, Akiyama M, Tomita M, Hirayama A, Sato A, Suzuki C, Sugimoto M, Soga T, Sugiyama D, Okamura T, Takebayashi T. Association between dyslipidemia and plasma levels of branched-chain amino acids in the Japanese population without diabetes mellitus. J Clin Lipidol 2019; 13:932-939.e2. [PMID: 31601483 DOI: 10.1016/j.jacl.2019.09.002] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2019] [Revised: 07/10/2019] [Accepted: 09/04/2019] [Indexed: 12/18/2022]
Abstract
BACKGROUND Branched-chain amino acids (BCAAs) play a key role in energy homeostasis. OBJECTIVE We aimed to investigate the association between plasma BCAA levels and dyslipidemia in the Japanese population without diabetes mellitus. METHODS This cross-sectional study included 4952 participants without diabetes mellitus, enrolled in the Tsuruoka Metabolomic Cohort Study. Plasma BCAA levels were measured by capillary electrophoresis-mass spectrometry. Correlations between lipid and BCAA profiles were evaluated by sex-stratified multiple linear regression analyses, after adjusting for confounders. Logistic regression was used to identify associations between BCAAs and metabolic dyslipidemia (MD) defined as triglyceride levels ≥150 mg/dL, high-density lipoprotein cholesterol levels ≤40 mg/dL for men and ≤50 mg/dL for women, or low-density lipoprotein cholesterol (LDL-C) levels ≥140 mg/dL. RESULTS In both sexes, the levels of individual BCAAs and the total BCAA levels correlated positively with triglyceride levels and negatively with high-density lipoprotein cholesterol levels. Valine, leucine, and total BCAA levels were weakly and positively correlated with LDL-C levels. Increased BCAA levels showed positive associations with MD. However, associations between BCAAs and elevated LDL-C levels were unclear. Furthermore, the associations between BCAA levels and MD regardless of fasting blood sugar (FBS) levels (high or low). Although valine, leucine, and total BCAA levels were weakly associated with elevated LDL-C levels in the high-FBS group, no such association was observed in the low-FBS group. CONCLUSIONS BCAAs might be associated with MD independently of the FBS level and might play an important role in lipid metabolism and dyslipidemia.
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Affiliation(s)
- Keiko Fukushima
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Sinjuku-ku, Tokyo, Japan; Department of Cardiology, Tokyo Women's Medical University, Sinjuku-ku, Tokyo, Japan; Student Health Care Center, Tokyo Women's Medical University, Sinjuku-ku, Tokyo, Japan
| | - Sei Harada
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Sinjuku-ku, Tokyo, Japan; Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Ayano Takeuchi
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Sinjuku-ku, Tokyo, Japan
| | - Ayako Kurihara
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Sinjuku-ku, Tokyo, Japan; Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Miho Iida
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Sinjuku-ku, Tokyo, Japan
| | - Kota Fukai
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Sinjuku-ku, Tokyo, Japan
| | - Kazuyo Kuwabara
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Sinjuku-ku, Tokyo, Japan
| | - Suzuka Kato
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Sinjuku-ku, Tokyo, Japan
| | - Minako Matsumoto
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Sinjuku-ku, Tokyo, Japan
| | - Aya Hirata
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Sinjuku-ku, Tokyo, Japan
| | - Miki Akiyama
- 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
| | - Akiyoshi Hirayama
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Asako Sato
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan
| | - Chizuru Suzuki
- 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
| | - Daisuke Sugiyama
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Sinjuku-ku, Tokyo, Japan
| | - Tomonori Okamura
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Sinjuku-ku, Tokyo, Japan
| | - Toru Takebayashi
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Sinjuku-ku, Tokyo, Japan; Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, Japan; Graduate School of Health Management, Keio University, Fujisawa, Kanagawa, Japan.
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48
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Tillmann T. Atherosclerotic metabolites: basic science is progressing, so we need to think about clinical implications. Eur Heart J 2019; 40:2897-2898. [PMID: 31102399 DOI: 10.1093/eurheartj/ehz252] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
Abstract
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Affiliation(s)
- Taavi Tillmann
- Centre for Non-Communicable Disease, Institute for Global Health, University College London, UK
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49
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Iida M, Harada S, Takebayashi T. Application of Metabolomics to Epidemiological Studies of Atherosclerosis and Cardiovascular Disease. J Atheroscler Thromb 2019; 26:747-757. [PMID: 31378756 PMCID: PMC6753246 DOI: 10.5551/jat.rv17036] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Metabolomics has developed as a powerful tool for investigating the complex pathophysiology underlying atherosclerosis and cardiovascular disease. Many epidemiological studies have applied this technique to accurately and comprehensively assess the effects of environmental factors on health outcomes, which used to be a perpetual challenge. Metabolites are defined as small molecules which are intermediate products of metabolic reactions catalyzed by numerous enzymes occurring within cells. Consequent to both genetic variation and environment, they allow us to explore the gene–environment interactions and to gain a better understanding of multifactorial diseases like cardiovascular disease. This review article highlights the findings of well-known prospective cohort studies around the world that have utilized metabolomics for a wide range of purposes, including biomarker discovery, improving cardiovascular risk prediction and early disease diagnosis, and exploring detailed mechanisms of disease onset and progression. However, technical challenges still exist in applying them clinically. One limitation is due to various analytical platforms that are used based on the judgment of each study; comparative assessments among different platforms need to be conducted in order to correctly interpret and validate each data externally. Secondly, metabolite levels obtained in most high-throughput metabolomics profiling studies are often semiquantitative rather than fully quantitative concentrations, which makes it difficult to compare and combine results among different studies and to determine the levels for practical use. In 2014, the Consortium of Metabolomics Studies was developed, which is expected to take the lead in overcoming these issues.
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Affiliation(s)
- Miho Iida
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Sei Harada
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
| | - Toru Takebayashi
- Department of Preventive Medicine and Public Health, Keio University School of Medicine
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50
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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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