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Yoo T, Joo SK, Kim HJ, Kim HY, Sim H, Lee J, Kim HH, Jung S, Lee Y, Jamialahmadi O, Romeo S, Jeong WI, Hwang GS, Kang KW, Kim JW, Kim W, Choi M. Disease-specific eQTL screening reveals an anti-fibrotic effect of AGXT2 in non-alcoholic fatty liver disease. J Hepatol 2021; 75:514-523. [PMID: 33892010 DOI: 10.1016/j.jhep.2021.04.011] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 03/22/2021] [Accepted: 04/07/2021] [Indexed: 12/19/2022]
Abstract
BACKGROUND & AIMS Non-alcoholic fatty liver disease (NAFLD) poses an increasing clinical burden. Genome-wide association studies have revealed a limited contribution of genomic variants to the disease, requiring alternative but robust approaches to identify disease-associated variants and genes. We carried out a disease-specific expression quantitative trait loci (eQTL) screen to identify novel genetic factors that specifically act on NAFLD progression on the basis of genotype. METHODS We recruited 125 Korean patients (83 with biopsy-proven NAFLD and 42 without NAFLD) and performed eQTL analyses using 21,272 transcripts and 3,234,941 genotyped and imputed single nucleotide polymorphisms. We then selected eQTLs that were detected only in the NAFLD group, but not in the control group (i.e., NAFLD-eQTLs). An additional cohort of 162 Korean individuals with NAFLD was used for replication. The function of the selected eQTL toward NAFLD development was validated using HepG2, primary hepatocytes and NAFLD mouse models. RESULTS The NAFLD-specific eQTL screening yielded 242 loci. Among them, AGXT2, encoding alanine-glyoxylate aminotransferase 2, displayed decreased expression in patients with NAFLD homozygous for the non-reference allele of rs2291702, compared to no-NAFLD individuals with the same genotype (p = 4.79 × 10-6). This change was replicated in an additional 162 individuals, yielding a combined p value of 8.05 × 10-8 from a total of 245 patients with NAFLD and 42 controls. Knockdown of AGXT2 induced palmitate-overloaded hepatocyte death by increasing endoplasmic reticulum stress, and exacerbated NAFLD diet-induced liver fibrosis in mice, while overexpression of AGXT2 attenuated liver fibrosis and steatosis. CONCLUSIONS We identified a new molecular role for AGXT2 in NAFLD. Our overall approach will serve as an efficient tool for uncovering novel genetic factors that contribute to liver steatosis and fibrosis in patients with NAFLD. LAY SUMMARY Elucidating causal genes for non-alcoholic fatty liver disease (NAFLD) has been challenging due to limited tissue availability and the polygenic nature of the disease. Using liver and blood samples from 125 Korean individuals (83 with NAFLD and 42 without NAFLD), we devised a new analytic method to identify causal genes. Among the candidates, we found that AGXT2-rs2291702 protects against liver fibrosis in a genotype-dependent manner with the potential for therapeutic interventions. Our approach enables the discovery of causal genes that act on the basis of genotype.
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Affiliation(s)
- Taekyeong Yoo
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sae Kyung Joo
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, Republic of Korea
| | - Hyo Jung Kim
- Department of Biochemistry and Molecular Biology, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hyun Young Kim
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea
| | - Hyungtai Sim
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jieun Lee
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, Republic of Korea
| | - Hee-Hoon Kim
- Laboratory of Liver Research, Graduate School of Medical Science and Engineering, KAIST, Daejeon, Republic of Korea
| | - Sunhee Jung
- Integrated Metabolomics Research Group, Western Seoul Center, Korea Basic Science Institute, Seoul, Republic of Korea
| | - Youngha Lee
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Oveis Jamialahmadi
- Salhgrenska Academy, Institute of Medicine, Department of Molecular and Clinical Medicine, University of Gothenburg, Sweden
| | - Stefano Romeo
- Salhgrenska Academy, Institute of Medicine, Department of Molecular and Clinical Medicine, University of Gothenburg, Sweden; Sahlgrenska University Hospital, Cardiology Department, Sweden; Department of Medical and Clinical Science, Clinical Nutrition Unit, University Magna Graecia, Catanzaro, Italy
| | - Won-Il Jeong
- Laboratory of Liver Research, Graduate School of Medical Science and Engineering, KAIST, Daejeon, Republic of Korea
| | - Geum-Sook Hwang
- Integrated Metabolomics Research Group, Western Seoul Center, Korea Basic Science Institute, Seoul, Republic of Korea; Department of Chemistry & Nanoscience, Ewha Womans University, Seoul, Republic of Korea
| | - Keon Wook Kang
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea
| | - Jae Woo Kim
- Department of Biochemistry and Molecular Biology, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Won Kim
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, Republic of Korea.
| | - Murim Choi
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea.
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A hierarchical approach to removal of unwanted variation for large-scale metabolomics data. Nat Commun 2021; 12:4992. [PMID: 34404777 PMCID: PMC8371158 DOI: 10.1038/s41467-021-25210-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 07/23/2021] [Indexed: 01/13/2023] Open
Abstract
Liquid chromatography-mass spectrometry-based metabolomics studies are increasingly applied to large population cohorts, which run for several weeks or even years in data acquisition. This inevitably introduces unwanted intra- and inter-batch variations over time that can overshadow true biological signals and thus hinder potential biological discoveries. To date, normalisation approaches have struggled to mitigate the variability introduced by technical factors whilst preserving biological variance, especially for protracted acquisitions. Here, we propose a study design framework with an arrangement for embedding biological sample replicates to quantify variance within and between batches and a workflow that uses these replicates to remove unwanted variation in a hierarchical manner (hRUV). We use this design to produce a dataset of more than 1000 human plasma samples run over an extended period of time. We demonstrate significant improvement of hRUV over existing methods in preserving biological signals whilst removing unwanted variation for large scale metabolomics studies. Our tools not only provide a strategy for large scale data normalisation, but also provides guidance on the design strategy for large omics studies. Mass spectrometry-based metabolomics is a powerful method for profiling large clinical cohorts but batch variations can obscure biologically meaningful differences. Here, the authors develop a computational workflow that removes unwanted data variation while preserving biologically relevant information.
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Nayor M, Shah SH, Murthy V, Shah RV. Molecular Aspects of Lifestyle and Environmental Effects in Patients With Diabetes: JACC Focus Seminar. J Am Coll Cardiol 2021; 78:481-495. [PMID: 34325838 DOI: 10.1016/j.jacc.2021.02.070] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 01/07/2021] [Accepted: 02/01/2021] [Indexed: 01/04/2023]
Abstract
Diabetes is characterized as an integrated condition of dysregulated metabolism across multiple tissues, with well-established consequences on the cardiovascular system. Recent advances in precision phenotyping in biofluids and tissues in large human observational and interventional studies have afforded a unique opportunity to translate seminal findings in models and cellular systems to patients at risk for diabetes and its complications. Specifically, techniques to assay metabolites, proteins, and transcripts, alongside more recent assessment of the gut microbiome, underscore the complexity of diabetes in patients, suggesting avenues for precision phenotyping of risk, response to intervention, and potentially novel therapies. In addition, the influence of external factors and inputs (eg, activity, diet, medical therapies) on each domain of molecular characterization has gained prominence toward better understanding their role in prevention. Here, the authors provide a broad overview of the role of several of these molecular domains in human translational investigation in diabetes.
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Affiliation(s)
- Matthew Nayor
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA. https://twitter.com/MattNayor
| | - Svati H Shah
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, North Carolina, USA; Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA. https://twitter.com/SvatiShah
| | - Venkatesh Murthy
- Division of Cardiovascular Medicine, Department of Medicine, University of Michigan, Ann Arbor, Michigan, USA; Frankel Cardiovascular Center, University of Michigan, Ann Arbor, Michigan, USA. https://twitter.com/venkmurthy
| | - Ravi V Shah
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
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Barton M, Cardillo C. Exercise is medicine: key to cardiovascular disease and diabetes prevention. Cardiovasc Res 2021; 117:360-363. [PMID: 32702117 DOI: 10.1093/cvr/cvaa226] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Affiliation(s)
- Matthias Barton
- University of Zürich, Molecular Internal Medicine, Y44 G22, Winterthurerstrasse 190, 8057 Zürich, Switzerland.,Andreas Grüntzig Foundation, Zürich, Switzerland
| | - Carmine Cardillo
- Internal Medicine, Policlinico A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Largo Vito 1, 00168 Roma, Italy.,Internal Medicine, Università Cattolica del Sacro Cuore, Largo Vito 1, 00168 Roma, Italy
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Zeleznik OA, Balasubramanian R, Zhao Y, Frueh L, Jeanfavre S, Avila-Pacheco J, Clish CB, Tworoger SS, Eliassen AH. Circulating amino acids and amino acid-related metabolites and risk of breast cancer among predominantly premenopausal women. NPJ Breast Cancer 2021; 7:54. [PMID: 34006878 PMCID: PMC8131633 DOI: 10.1038/s41523-021-00262-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 04/15/2021] [Indexed: 02/03/2023] Open
Abstract
Known modifiable risk factors account for a small fraction of premenopausal breast cancers. We investigated associations between pre-diagnostic circulating amino acid and amino acid-related metabolites (N = 207) and risk of breast cancer among predominantly premenopausal women of the Nurses' Health Study II using conditional logistic regression (1057 cases, 1057 controls) and multivariable analyses evaluating all metabolites jointly. Eleven metabolites were associated with breast cancer risk (q-value < 0.2). Seven metabolites remained associated after adjustment for established risk factors (p-value < 0.05) and were selected by at least one multivariable modeling approach: higher levels of 2-aminohippuric acid, kynurenic acid, piperine (all three with q-value < 0.2), DMGV and phenylacetylglutamine were associated with lower breast cancer risk (e.g., piperine: ORadjusted (95%CI) = 0.84 (0.77-0.92)) while higher levels of creatine and C40:7 phosphatidylethanolamine (PE) plasmalogen were associated with increased breast cancer risk (e.g., C40:7 PE plasmalogen: ORadjusted (95%CI) = 1.11 (1.01-1.22)). Five amino acids and amino acid-related metabolites (2-aminohippuric acid, DMGV, kynurenic acid, phenylacetylglutamine, and piperine) were inversely associated, while one amino acid and a phospholipid (creatine and C40:7 PE plasmalogen) were positively associated with breast cancer risk among predominately premenopausal women, independent of established breast cancer risk factors.
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Affiliation(s)
- Oana A Zeleznik
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Raji Balasubramanian
- Department of Biostatistics & Epidemiology, University of Massachusetts - Amherst, Amherst, MA, USA
| | - Yibai Zhao
- Department of Biostatistics & Epidemiology, University of Massachusetts - Amherst, Amherst, MA, USA
| | - Lisa Frueh
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Sarah Jeanfavre
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Julian Avila-Pacheco
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Clary B Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Shelley S Tworoger
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA
| | - A Heather Eliassen
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Vernon ST, Tang O, Kim T, Chan AS, Kott KA, Park J, Hansen T, Koay YC, Grieve SM, O’Sullivan JF, Yang JY, Figtree GA. Metabolic Signatures in Coronary Artery Disease: Results from the BioHEART-CT Study. Cells 2021; 10:980. [PMID: 33922315 PMCID: PMC8145337 DOI: 10.3390/cells10050980] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 04/18/2021] [Accepted: 04/20/2021] [Indexed: 01/06/2023] Open
Abstract
Despite effective prevention programs targeting cardiovascular risk factors, coronary artery disease (CAD) remains the leading cause of death. Novel biomarkers are needed for improved risk stratification and primary prevention. To assess for independent associations between plasma metabolites and specific CAD plaque phenotypes we performed liquid chromatography mass-spectrometry on plasma from 1002 patients in the BioHEART-CT study. Four metabolites were examined as candidate biomarkers. Dimethylguanidino valerate (DMGV) was associated with presence and amount of CAD (OR) 1.41 (95% Confidence Interval [CI] 1.12-1.79, p = 0.004), calcified plaque, and obstructive CAD (p < 0.05 for both). The association with amount of plaque remained after adjustment for traditional risk factors, ß-coefficient 0.17 (95% CI 0.02-0.32, p = 0.026). Glutamate was associated with the presence of non-calcified plaque, OR 1.48 (95% CI 1.09-2.01, p = 0.011). Phenylalanine was associated with amount of CAD, ß-coefficient 0.33 (95% CI 0.04-0.62, p = 0.025), amount of calcified plaque, (ß-coefficient 0.88, 95% CI 0.23-1.53, p = 0.008), and obstructive CAD, OR 1.84 (95% CI 1.01-3.31, p = 0.046). Trimethylamine N-oxide was negatively associated non-calcified plaque OR 0.72 (95% CI 0.53-0.97, p = 0.029) and the association remained when adjusted for traditional risk factors. In targeted metabolomic analyses including 53 known metabolites and controlling for a 5% false discovery rate, DMGV was strongly associated with the presence of calcified plaque, OR 1.59 (95% CI 1.26-2.01, p = 0.006), obstructive CAD, OR 2.33 (95% CI 1.59-3.43, p = 0.0009), and amount of CAD, ß-coefficient 0.3 (95% CI 0.14-0.45, p = 0.014). In multivariate analyses the lipid and nucleotide metabolic pathways were both associated with the presence of CAD, after adjustment for traditional risk factors. We report novel associations between CAD plaque phenotypes and four metabolites previously associated with CAD. We also identified two metabolic pathways strongly associated with CAD, independent of traditional risk factors. These pathways warrant further investigation at both a biomarker and mechanistic level.
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Affiliation(s)
- Stephen T. Vernon
- Cardiothoracic and Vascular Health, Kolling Institute, Northern Sydney Local Health District, Sydney, NSW 2065, Australia; (S.T.V.); (O.T.); (K.A.K.); (J.P.); (T.H.)
- Department of Cardiology, Royal North Shore Hospital, Sydney, NSW 2065, Australia
- Northern Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia
| | - Owen Tang
- Cardiothoracic and Vascular Health, Kolling Institute, Northern Sydney Local Health District, Sydney, NSW 2065, Australia; (S.T.V.); (O.T.); (K.A.K.); (J.P.); (T.H.)
- Northern Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia
- Charles Perkins Centre, University of Sydney, Sydney, NSW 2006, Australia; (T.K.); (A.S.C.); (Y.C.K.); (J.F.O.); (J.Y.Y.)
| | - Taiyun Kim
- Charles Perkins Centre, University of Sydney, Sydney, NSW 2006, Australia; (T.K.); (A.S.C.); (Y.C.K.); (J.F.O.); (J.Y.Y.)
- School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia
- Computational Systems Biology Group, Children’s Medical Research Institute, Westmead, NSW 2145, Australia
| | - Adam S. Chan
- Charles Perkins Centre, University of Sydney, Sydney, NSW 2006, Australia; (T.K.); (A.S.C.); (Y.C.K.); (J.F.O.); (J.Y.Y.)
- School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia
| | - Katharine A. Kott
- Cardiothoracic and Vascular Health, Kolling Institute, Northern Sydney Local Health District, Sydney, NSW 2065, Australia; (S.T.V.); (O.T.); (K.A.K.); (J.P.); (T.H.)
- Department of Cardiology, Royal North Shore Hospital, Sydney, NSW 2065, Australia
- Northern Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia
| | - John Park
- Cardiothoracic and Vascular Health, Kolling Institute, Northern Sydney Local Health District, Sydney, NSW 2065, Australia; (S.T.V.); (O.T.); (K.A.K.); (J.P.); (T.H.)
| | - Thomas Hansen
- Cardiothoracic and Vascular Health, Kolling Institute, Northern Sydney Local Health District, Sydney, NSW 2065, Australia; (S.T.V.); (O.T.); (K.A.K.); (J.P.); (T.H.)
- Northern Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia
| | - Yen C. Koay
- Charles Perkins Centre, University of Sydney, Sydney, NSW 2006, Australia; (T.K.); (A.S.C.); (Y.C.K.); (J.F.O.); (J.Y.Y.)
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia
- Heart Research Institute, The University of Sydney, Sydney, NSW 2042, Australia
| | - Stuart M. Grieve
- Imaging and Phenotyping Laboratory, Charles Perkins Centre, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia;
- Department of Radiology, Royal Prince Alfred Hospital, Sydney, NSW 2050, Australia
| | - John F. O’Sullivan
- Charles Perkins Centre, University of Sydney, Sydney, NSW 2006, Australia; (T.K.); (A.S.C.); (Y.C.K.); (J.F.O.); (J.Y.Y.)
- Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia
- Heart Research Institute, The University of Sydney, Sydney, NSW 2042, Australia
| | - Jean Y. Yang
- Charles Perkins Centre, University of Sydney, Sydney, NSW 2006, Australia; (T.K.); (A.S.C.); (Y.C.K.); (J.F.O.); (J.Y.Y.)
- School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia
| | - Gemma A. Figtree
- Cardiothoracic and Vascular Health, Kolling Institute, Northern Sydney Local Health District, Sydney, NSW 2065, Australia; (S.T.V.); (O.T.); (K.A.K.); (J.P.); (T.H.)
- Department of Cardiology, Royal North Shore Hospital, Sydney, NSW 2065, Australia
- Northern Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia
- Charles Perkins Centre, University of Sydney, Sydney, NSW 2006, Australia; (T.K.); (A.S.C.); (Y.C.K.); (J.F.O.); (J.Y.Y.)
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Koch M, Acharjee A, Ament Z, Schleicher R, Bevers M, Stapleton C, Patel A, Kimberly WT. Machine Learning-Driven Metabolomic Evaluation of Cerebrospinal Fluid: Insights Into Poor Outcomes After Aneurysmal Subarachnoid Hemorrhage. Neurosurgery 2021; 88:1003-1011. [PMID: 33469656 PMCID: PMC8046589 DOI: 10.1093/neuros/nyaa557] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Accepted: 11/04/2020] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Aneurysmal subarachnoid hemorrhage (aSAH) is associated with a high mortality and poor neurologic outcomes. The biologic underpinnings of the morbidity and mortality associated with aSAH remain poorly understood. OBJECTIVE To ascertain potential insights into pathological mechanisms of injury after aSAH using an approach of metabolomics coupled with machine learning methods. METHODS Using cerebrospinal fluid (CSF) samples from 81 aSAH enrolled in a retrospective cohort biorepository, samples collected during the peak of delayed cerebral ischemia were analyzed using liquid chromatography-tandem mass spectrometry. A total of 138 metabolites were measured and quantified in each sample. Data were analyzed using elastic net (EN) machine learning and orthogonal partial least squares-discriminant analysis (OPLS-DA) to identify the leading CSF metabolites associated with poor outcome, as determined by the modified Rankin Scale (mRS) at discharge and at 90 d. Repeated measures analysis determined the effect size for each metabolite on poor outcome. RESULTS EN machine learning and OPLS-DA analysis identified 8 and 10 metabolites, respectively, that predicted poor mRS (mRS 3-6) at discharge and at 90 d. Of these candidates, symmetric dimethylarginine (SDMA), dimethylguanidine valeric acid (DMGV), and ornithine were consistent markers, with an association with poor mRS at discharge (P = .0005, .002, and .0001, respectively) and at 90 d (P = .0036, .0001, and .004, respectively). SDMA also demonstrated a significantly elevated CSF concentration compared with nonaneurysmal subarachnoid hemorrhage controls (P = .0087). CONCLUSION SDMA, DMGV, and ornithine are vasoactive molecules linked to the nitric oxide pathway that predicts poor outcome after severe aSAH. Further study of dimethylarginine metabolites in brain injury after aSAH is warranted.
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Affiliation(s)
- Matthew Koch
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts
| | - Animesh Acharjee
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology and NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospital Birmingham, Birmingham, United Kingdom
| | - Zsuzsanna Ament
- Division of Neurocritical Care and Center for Genomic Medicine, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Riana Schleicher
- Division of Neurocritical Care and Center for Genomic Medicine, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Matthew Bevers
- Divisions of Stroke, Cerebrovascular and Critical Care Neurology, Brigham and Women's Hospital, Boston, Massachusetts
| | | | - Aman Patel
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts
| | - W Taylor Kimberly
- Division of Neurocritical Care and Center for Genomic Medicine, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
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Rebholz CM, Gao Y, Talegawkar S, Tucker KL, Colantonio LD, Muntner P, Ngo D, Chen ZZ, Cruz D, Katz D, Tahir UA, Clish C, Gerszten RE, Wilson JG. Metabolomic Markers of Southern Dietary Patterns in the Jackson Heart Study. Mol Nutr Food Res 2021; 65:e2000796. [PMID: 33629508 PMCID: PMC8192080 DOI: 10.1002/mnfr.202000796] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 01/07/2021] [Indexed: 02/02/2023]
Abstract
SCOPE New biomarkers are needed that are representative of dietary intake. METHODS AND RESULTS We assess metabolites associated with Southern dietary patterns in 1401 Jackson Heart Study participants. Three dietary patterns are empirically derived using principal component analysis: meat and fast food, fish and vegetables, and starchy foods. We randomly select two subsets of the study population: two-third sample for discovery (n = 934) and one-third sample for replication (n = 467). Among the 327 metabolites analyzed, 14 are significantly associated with the meat and fast food dietary pattern, four are significantly associated with the fish and vegetables dietary pattern, and none are associated with the starchy foods dietary pattern in the discovery sample. In the replication sample, nine remain associated with the meat and fast food dietary pattern [indole-3-propanoic acid, C24:0 lysophosphatidylcholine (LPC), N-methyl proline, proline betaine, C34:2 phosphatidylethanolamine (PE) plasmalogen, C36:5 PE plasmalogen, C38:5 PE plasmalogen, cotinine, hydroxyproline] and three remain associated with the fish and vegetables dietary pattern [1,7-dimethyluric acid, C22:6 lysophosphatidylethanolamine, docosahexaenoic acid (DHA)]. CONCLUSION Twelve metabolites are discovered and replicated in association with dietary patterns detected in a Southern U.S. African-American population, which could be useful as biomarkers of Southern dietary patterns.
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Affiliation(s)
- Casey M. Rebholz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Yan Gao
- The Jackson Heart Study, University of Mississippi Medical Center, Jackson, Mississippi
| | - Sameera Talegawkar
- Department of Exercise and Nutrition Sciences, Milken Institute School of Public Health, George Washington University, Washington, DC
| | - Katherine L. Tucker
- Department of Biomedical and Nutritional Sciences, University of Massachusetts Lowell, Lowell, Massachusetts
| | - Lisandro D. Colantonio
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Alabama
| | - Paul Muntner
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Alabama
| | - Debby Ngo
- Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Zsu Zsu Chen
- Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Daniel Cruz
- Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Daniel Katz
- Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Usman A. Tahir
- Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | | | - Robert E. Gerszten
- Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - James G. Wilson
- Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
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Metabolomics reveals the impact of Type 2 diabetes on local muscle and vascular responses to ischemic stress. Clin Sci (Lond) 2021; 134:2369-2379. [PMID: 32880388 DOI: 10.1042/cs20191227] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 08/24/2020] [Accepted: 09/03/2020] [Indexed: 02/06/2023]
Abstract
OBJECTIVE Type 2 diabetes mellitus (T2DM) reduces exercise capacity, but the mechanisms are incompletely understood. We probed the impact of ischemic stress on skeletal muscle metabolite signatures and T2DM-related vascular dysfunction. METHODS we recruited 38 subjects (18 healthy, 20 T2DM), placed an antecubital intravenous catheter, and performed ipsilateral brachial artery reactivity testing. Blood samples for plasma metabolite profiling were obtained at baseline and immediately upon cuff release after 5 min of ischemia. Brachial artery diameter was measured at baseline and 1 min after cuff release. RESULTS as expected, flow-mediated vasodilation was attenuated in subjects with T2DM (P<0.01). We confirmed known T2DM-associated baseline differences in plasma metabolites, including homocysteine, dimethylguanidino valeric acid and β-alanine (all P<0.05). Ischemia-induced metabolite changes that differed between groups included 5-hydroxyindoleacetic acid (healthy: -27%; DM +14%), orotic acid (healthy: +5%; DM -7%), trimethylamine-N-oxide (healthy: -51%; DM +0.2%), and glyoxylic acid (healthy: +19%; DM -6%) (all P<0.05). Levels of serine, betaine, β-aminoisobutyric acid and anthranilic acid were associated with vessel diameter at baseline, but only in T2DM (all P<0.05). Metabolite responses to ischemia were significantly associated with vasodilation extent, but primarily observed in T2DM, and included enrichment in phospholipid metabolism (P<0.05). CONCLUSIONS our study highlights impairments in muscle and vascular signaling at rest and during ischemic stress in T2DM. While metabolites change in both healthy and T2DM subjects in response to ischemia, the relationship between muscle metabolism and vascular function is modified in T2DM, suggesting that dysregulated muscle metabolism in T2DM may have direct effects on vascular function.
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Gesper M, Nonnast ABH, Kumowski N, Stoehr R, Schuett K, Marx N, Kappel BA. Gut-Derived Metabolite Indole-3-Propionic Acid Modulates Mitochondrial Function in Cardiomyocytes and Alters Cardiac Function. Front Med (Lausanne) 2021; 8:648259. [PMID: 33829028 PMCID: PMC8019752 DOI: 10.3389/fmed.2021.648259] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 02/19/2021] [Indexed: 12/11/2022] Open
Abstract
Background: The gut microbiome has been linked to the onset of cardiometabolic diseases, in part facilitated through gut microbiota-dependent metabolites such as trimethylamine-N-oxide. However, molecular pathways associated to heart failure mediated by microbial metabolites remain largely elusive. Mitochondria play a pivotal role in cellular energy metabolism and mitochondrial dysfunction has been associated to heart failure pathogenesis. Aim of the current study was to evaluate the impact of gut-derived metabolites on mitochondrial function in cardiomyocytes via an in vitro screening approach. Methods: Based on a systematic Medline research, 25 microbial metabolites were identified and screened for their metabolic impact with a focus on mitochondrial respiration in HL-1 cardiomyocytes. Oxygen consumption rate in response to different modulators of the respiratory chain were measured by a live-cell metabolic assay platform. For one of the identified metabolites, indole-3-propionic acid, studies on specific mitochondrial complexes, cytochrome c, fatty acid oxidation, mitochondrial membrane potential, and reactive oxygen species production were performed. Mitochondrial function in response to this metabolite was further tested in human hepatic and endothelial cells. Additionally, the effect of indole-3-propionic acid on cardiac function was studied in isolated perfused hearts of C57BL/6J mice. Results: Among the metabolites examined, microbial tryptophan derivative indole-3-propionic acid could be identified as a modulator of mitochondrial function in cardiomyocytes. While acute treatment induced enhancement of maximal mitochondrial respiration (+21.5 ± 7.8%, p < 0.05), chronic exposure led to mitochondrial dysfunction (-18.9 ± 9.1%; p < 0.001) in cardiomyocytes. The latter effect of indole-3-propionic acids could also be observed in human hepatic and endothelial cells. In isolated perfused mouse hearts, indole-3-propionic acid was dose-dependently able to improve cardiac contractility from +26.8 ± 11.6% (p < 0.05) at 1 μM up to +93.6 ± 14.4% (p < 0.001) at 100 μM. Our mechanistic studies on indole-3-propionic acids suggest potential involvement of fatty acid oxidation in HL-1 cardiomyocytes. Conclusion: Our data indicate a direct impact of microbial metabolites on cardiac physiology. Gut-derived metabolite indole-3-propionic acid was identified as mitochondrial modulator in cardiomyocytes and altered cardiac function in an ex vivo mouse model.
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Affiliation(s)
- Maren Gesper
- Department of Internal Medicine 1, University Hospital Aachen, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany
| | - Alena B H Nonnast
- Department of Internal Medicine 1, University Hospital Aachen, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany
| | - Nina Kumowski
- Department of Internal Medicine 1, University Hospital Aachen, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany
| | - Robert Stoehr
- Department of Internal Medicine 1, University Hospital Aachen, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany
| | - Katharina Schuett
- Department of Internal Medicine 1, University Hospital Aachen, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany
| | - Nikolaus Marx
- Department of Internal Medicine 1, University Hospital Aachen, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany
| | - Ben A Kappel
- Department of Internal Medicine 1, University Hospital Aachen, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen University, Aachen, Germany
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Drouin-Chartier JP, Hernández-Alonso P, Guasch-Ferré M, Ruiz-Canela M, Li J, Wittenbecher C, Razquin C, Toledo E, Dennis C, Corella D, Estruch R, Fitó M, Eliassen AH, Tobias DK, Ascherio A, Mucci LA, Rexrode KM, Karlson EW, Costenbader KH, Fuchs CS, Liang L, Clish CB, Martínez-González MA, Salas-Salvadó J, Hu FB. Dairy consumption, plasma metabolites, and risk of type 2 diabetes. Am J Clin Nutr 2021; 114:163-174. [PMID: 33742198 PMCID: PMC8246603 DOI: 10.1093/ajcn/nqab047] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 02/08/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Epidemiologic studies have reported a modest inverse association between dairy consumption and the risk of type 2 diabetes (T2D). Whether plasma metabolite profiles associated with dairy consumption reflect this relationship remains unknown. OBJECTIVES We aimed to identify the plasma metabolites associated with total and specific dairy consumption, and to evaluate the association between the identified multi-metabolite profiles and T2D. METHODS The discovery population included 1833 participants from the Prevención con Dieta Mediterránea (PREDIMED) trial. The confirmatory cohorts included 1522 PREDIMED participants at year 1 of the trial and 4932 participants from the Nurses' Health Studies (NHS), Nurses' Health Study II (NHSII), and Health Professionals Follow-Up Study US-based cohorts. Dairy consumption was assessed using validated FFQs. Plasma metabolites (n = 385) were profiled using LC-MS. We identified the dairy-related metabolite profiles using elastic net regularized regressions with a 10-fold cross-validation procedure. We evaluated the associations between the metabolite profiles and incident T2D in the discovery and the confirmatory cohorts. RESULTS Total dairy intake was associated with 38 metabolites. C14:0 sphingomyelin (positive coefficient), C34:0 phosphatidylethanolamine (positive coefficient), and γ-butyrobetaine (negative coefficient) were associated in a directionally similar fashion with total and specific (milk, yogurt, cheese) dairy consumption. The Pearson correlation coefficients between self-reported total dairy intake and predicted total dairy intake based on the corresponding multi-metabolite profile were 0.37 (95% CI, 0.33-0.40) in the discovery cohort and 0.16 (95% CI, 0.13-0.19) in the US confirmatory cohort. After adjusting for T2D risk factors, a higher total dairy intake-related metabolite profile score was associated with a lower T2D risk [HR per 1 SD; discovery cohort: 0.76 (95% CI, 0.63-0.90); US confirmatory cohort: 0.88 (95% CI, 0.78-0.99)]. CONCLUSIONS Total dairy intake was associated with 38 metabolites, including 3 consistently associated with dairy subtypes (C14:0 sphingomyelin, C34:0 phosphatidylethanolamine, γ-butyrobetaine). A score based on the 38 identified metabolites showed an inverse association with T2D risk in Spanish and US populations.
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Affiliation(s)
| | - Pablo Hernández-Alonso
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Hospital Universitari San Joan de Reus, Reus, Spain,Institut d'Investigació Pere Virgili (IISPV), Reus, Spain,Consorcio Centro de Investigación Biomedica en Red Fisiopatologia de la Obesidad y Nutricion, Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Unidad de Gestión Clínica de Endocrinología y Nutrición del Hospital Virgen de la Victoria, Instituto de Investigación Biomédica de Málaga (IBIMA), Málaga, Spain
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Miguel Ruiz-Canela
- Consorcio Centro de Investigación Biomedica en Red Fisiopatologia de la Obesidad y Nutricion, Instituto de Salud Carlos III (ISCIII), Madrid, Spain,University of Navarra, Department of Preventive Medicine and Public Health, Pamplona, Spain
| | - Jun Li
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Clemens Wittenbecher
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Cristina Razquin
- Consorcio Centro de Investigación Biomedica en Red Fisiopatologia de la Obesidad y Nutricion, Instituto de Salud Carlos III (ISCIII), Madrid, Spain,University of Navarra, Department of Preventive Medicine and Public Health, Pamplona, Spain
| | - Estefanía Toledo
- Consorcio Centro de Investigación Biomedica en Red Fisiopatologia de la Obesidad y Nutricion, Instituto de Salud Carlos III (ISCIII), Madrid, Spain,University of Navarra, Department of Preventive Medicine and Public Health, Pamplona, Spain
| | - Courtney Dennis
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Dolores Corella
- Consorcio Centro de Investigación Biomedica en Red Fisiopatologia de la Obesidad y Nutricion, Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Ramon Estruch
- Consorcio Centro de Investigación Biomedica en Red Fisiopatologia de la Obesidad y Nutricion, Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Internal Medicine, Department of Endocrinology and Nutrition Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Montserrat Fitó
- Consorcio Centro de Investigación Biomedica en Red Fisiopatologia de la Obesidad y Nutricion, Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Cardiovascular and Nutrition Research Group, Institut de Recerca Hospital del Mar, Barcelona, Spain
| | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Deirdre K Tobias
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA,Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alberto Ascherio
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Lorelei A Mucci
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Kathryn M Rexrode
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA,Division of Women`s Health, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Elizabeth W Karlson
- Division of Rheumatology, Inflammation and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Karen H Costenbader
- Division of Rheumatology, Inflammation and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Charles S Fuchs
- Yale Cancer Center, Yale University School of Medicine, New Haven, CT, USA
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Clary B Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Miguel A Martínez-González
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA,Consorcio Centro de Investigación Biomedica en Red Fisiopatologia de la Obesidad y Nutricion, Instituto de Salud Carlos III (ISCIII), Madrid, Spain,University of Navarra, Department of Preventive Medicine and Public Health, Pamplona, Spain
| | - Jordi Salas-Salvadó
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Hospital Universitari San Joan de Reus, Reus, Spain,Institut d'Investigació Pere Virgili (IISPV), Reus, Spain,Consorcio Centro de Investigación Biomedica en Red Fisiopatologia de la Obesidad y Nutricion, Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Xuan C, Li H, Tian QW, Guo JJ, He GW, Lun LM, Wang Q. Quantitative Assessment of Serum Amino Acids and Association with Early-Onset Coronary Artery Disease. Clin Interv Aging 2021; 16:465-474. [PMID: 33758500 PMCID: PMC7979345 DOI: 10.2147/cia.s298743] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 02/25/2021] [Indexed: 12/11/2022] Open
Abstract
Background Amino acids play essential roles in protein construction and metabolism. Our study aims to provide a profile of amino acid changes in the serum of patients with early-onset coronary artery disease (EOCAD) and identify potential disease biomarkers. Methods Ultra-performance liquid chromatography-multiple reaction monitoring-multistage/mass spectrometry (UPLC-MRM-MS/MS) was used to determine the amino acid profile of patients with EOCAD in sample pools. In the validation stage, the serum levels of candidate amino acids of interest are determined for each sample. Results A total of 128 EOCAD patients and 64 healthy controls were included in the study. Eight serum amino acids associated with disease state were identified. Compared with the control group, serum levels of seven amino acids (L-Arginine, L-Methionine, L-Tyrosine, L-Serine, L-Aspartic acid, L-Phenylalanine, and L-Glutamic acid) increased and one (4-Hydroxyproline) decreased in the patient group. Results from the validation stage demonstrate that serum levels of 4-Hydroxyproline were significantly lower in myocardial infarction (MI) patients (9.889 ± 3.635 μg/mL) than those in the controls (16.433 ± 4.562 μmol/L, p < 0.001). Elevated serum 4-Hydroxyproline levels were shown to be an independent protective factor for MI (OR = 0.863, 95% CI: 0.822–0.901). The significant negative correlation was seen between serum 4-Hydroxyproline levels and cardiac troponin I (r = −0.667) in MI patients. Conclusion We have provided a serum amino acid profile for EOCAD patients and screened eight disease state-related amino acids, and we have also shown that 4-Hydroxyproline is a promising target for further biomarker studies in early-onset MI.
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Affiliation(s)
- Chao Xuan
- Department of Clinical Laboratory, The Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China
| | - Hui Li
- Department of Clinical Laboratory, The Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China
| | - Qing-Wu Tian
- Department of Clinical Laboratory, The Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China
| | - Jun-Jie Guo
- Department of Cardiology, The Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China
| | - Guo-Wei He
- Center for Basic Medical Research & Department of Cardiovascular Surgery, TEDA International Cardiovascular Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, People's Republic of China.,Department of Surgery, Oregon Health and Science University, Portland, OR, USA
| | - Li-Min Lun
- Department of Clinical Laboratory, The Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China
| | - Qing Wang
- Department of Clinical Laboratory, The Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China
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63
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Ngo D, Benson MD, Long JZ, Chen ZZ, Wang R, Nath AK, Keyes MJ, Shen D, Sinha S, Kuhn E, Morningstar JE, Shi X, Peterson BD, Chan C, Katz DH, Tahir UA, Farrell LA, Melander O, Mosley JD, Carr SA, Vasan RS, Larson MG, Smith JG, Wang TJ, Yang Q, Gerszten RE. Proteomic profiling reveals biomarkers and pathways in type 2 diabetes risk. JCI Insight 2021; 6:144392. [PMID: 33591955 PMCID: PMC8021115 DOI: 10.1172/jci.insight.144392] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 01/28/2021] [Indexed: 01/14/2023] Open
Abstract
Recent advances in proteomic technologies have made high-throughput profiling of low-abundance proteins in large epidemiological cohorts increasingly feasible. We investigated whether aptamer-based proteomic profiling could identify biomarkers associated with future development of type 2 diabetes (T2DM) beyond known risk factors. We identified dozens of markers with highly significant associations with future T2DM across 2 large longitudinal cohorts (n = 2839) followed for up to 16 years. We leveraged proteomic, metabolomic, genetic, and clinical data from humans to nominate 1 specific candidate to test for potential causal relationships in model systems. Our studies identified functional effects of aminoacylase 1 (ACY1), a top protein association with future T2DM risk, on amino acid metabolism and insulin homeostasis in vitro and in vivo. Furthermore, a loss-of-function variant associated with circulating levels of the biomarker WAP, Kazal, immunoglobulin, Kunitz, and NTR domain-containing protein 2 (WFIKKN2) was, in turn, associated with fasting glucose, hemoglobin A1c, and HOMA-IR measurements in humans. In addition to identifying potentially novel disease markers and pathways in T2DM, we provide publicly available data to be leveraged for insights about gene function and disease pathogenesis in the context of human metabolism.
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Affiliation(s)
- Debby Ngo
- Cardiovascular Institute
- Division of Pulmonary, Critical Care and Sleep Medicine, and
| | - Mark D. Benson
- Cardiovascular Institute
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center (BIDMC), Boston, Massachusetts, USA
| | - Jonathan Z. Long
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Zsu-Zsu Chen
- Cardiovascular Institute
- Division of Endocrinology, Diabetes and Metabolism, BIDMC, Boston, Massachusetts, USA
| | - Ruiqi Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | | | | | | | | | - Eric Kuhn
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | | | | | | | | | - Daniel H. Katz
- Cardiovascular Institute
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center (BIDMC), Boston, Massachusetts, USA
| | - Usman A. Tahir
- Cardiovascular Institute
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center (BIDMC), Boston, Massachusetts, USA
| | | | - Olle Melander
- Department of Cardiology, Clinical Sciences, Lund University and Skåne University Hospital, Lund, Sweden
| | - Jonathan D. Mosley
- Departments of Medicine and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Steven A. Carr
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Ramachandran S. Vasan
- Department of Medicine, Divisions of Preventive Medicine and Cardiology, Boston University School of Medicine, Boston, Massachusetts, USA
- The National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts, USA
| | - Martin G. Larson
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
- The National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts, USA
| | - J. Gustav Smith
- Department of Cardiology, Clinical Sciences, Lund University and Skåne University Hospital, Lund, Sweden
- Wallenberg Center for Molecular Medicine and Diabetes Center, Lund University, Lund, Sweden
- Department of Cardiology and Wallenberg Laboratory, Gothenburg University and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Thomas J. Wang
- Department of Medicine, University of Texas, Southwestern Medical Center, Dallas, Texas, USA
| | - Qiong Yang
- Division of Endocrinology, Diabetes and Metabolism, BIDMC, Boston, Massachusetts, USA
| | - Robert E. Gerszten
- Cardiovascular Institute
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center (BIDMC), Boston, Massachusetts, USA
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
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Sidhom EH, Kim C, Kost-Alimova M, Ting MT, Keller K, Avila-Pacheco J, Watts AJ, Vernon KA, Marshall JL, Reyes-Bricio E, Racette M, Wieder N, Kleiner G, Grinkevich EJ, Chen F, Weins A, Clish CB, Shaw JL, Quinzii CM, Greka A. Targeting a Braf/Mapk pathway rescues podocyte lipid peroxidation in CoQ-deficiency kidney disease. J Clin Invest 2021; 131:141380. [PMID: 33444290 DOI: 10.1172/jci141380] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 01/06/2021] [Indexed: 12/19/2022] Open
Abstract
Mutations affecting mitochondrial coenzyme Q (CoQ) biosynthesis lead to kidney failure due to selective loss of podocytes, essential cells of the kidney filter. Curiously, neighboring tubular epithelial cells are spared early in disease despite higher mitochondrial content. We sought to illuminate noncanonical, cell-specific roles for CoQ, independently of the electron transport chain (ETC). Here, we demonstrate that CoQ depletion caused by Pdss2 enzyme deficiency in podocytes results in perturbations in polyunsaturated fatty acid (PUFA) metabolism and the Braf/Mapk pathway rather than ETC dysfunction. Single-nucleus RNA-Seq from kidneys of Pdss2kd/kd mice with nephrotic syndrome and global CoQ deficiency identified a podocyte-specific perturbation of the Braf/Mapk pathway. Treatment with GDC-0879, a Braf/Mapk-targeting compound, ameliorated kidney disease in Pdss2kd/kd mice. Mechanistic studies in Pdss2-depleted podocytes revealed a previously unknown perturbation in PUFA metabolism that was confirmed in vivo. Gpx4, an enzyme that protects against PUFA-mediated lipid peroxidation, was elevated in disease and restored after GDC-0879 treatment. We demonstrate broader human disease relevance by uncovering patterns of GPX4 and Braf/Mapk pathway gene expression in tissue from patients with kidney diseases. Our studies reveal ETC-independent roles for CoQ in podocytes and point to Braf/Mapk as a candidate pathway for the treatment of kidney diseases.
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Affiliation(s)
- Eriene-Heidi Sidhom
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Choah Kim
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | | | - May Theng Ting
- Department of Neurology, Columbia University Medical Center, New York, New York, USA
| | - Keith Keller
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Andrew Jb Watts
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Katherine A Vernon
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Jamie L Marshall
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | | | - Matthew Racette
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Nicolas Wieder
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Giulio Kleiner
- Department of Neurology, Columbia University Medical Center, New York, New York, USA
| | | | - Fei Chen
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Astrid Weins
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Jillian L Shaw
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Catarina M Quinzii
- Department of Neurology, Columbia University Medical Center, New York, New York, USA
| | - Anna Greka
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
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Differences in the vascular and metabolic profiles between metabolically healthy and unhealthy obesity. ENDOCRINE AND METABOLIC SCIENCE 2021. [DOI: 10.1016/j.endmts.2020.100077] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
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66
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Zeleznik OA, Clish CB, Kraft P, Avila-Pacheco J, Eliassen AH, Tworoger SS. Circulating Lysophosphatidylcholines, Phosphatidylcholines, Ceramides, and Sphingomyelins and Ovarian Cancer Risk: A 23-Year Prospective Study. J Natl Cancer Inst 2021; 112:628-636. [PMID: 31593240 DOI: 10.1093/jnci/djz195] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Revised: 08/05/2019] [Accepted: 09/19/2019] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Experimental evidence supports a role of lipid dysregulation in ovarian cancer progression. We estimated associations with ovarian cancer risk for circulating levels of four lipid groups, previously hypothesized to be associated with ovarian cancer, measured 3-23 years before diagnosis. METHODS Analyses were conducted among cases (N = 252) and matched controls (N = 252) from the Nurses' Health Studies. We used logistic regression adjusting for risk factors to investigate associations of lysophosphatidylcholines (LPCs), phosphatidylcholines (PCs), ceramides (CERs), and sphingomyelins (SMs) with ovarian cancer risk overall and by histotype. A modified Bonferroni approach (0.05/4 = 0.0125, four lipid groups) and the permutation-based Westfall and Young approach were used to account for testing multiple correlated hypotheses. Odds ratios (ORs; 10th-90th percentile), and 95% confidence intervals of ovarian cancer risk were estimated. All statistical tests were two-sided. RESULTS SM sum was statistically significantly associated with ovarian cancer risk (OR = 1.97, 95% CI = 1.16 to 3.32; P = .01/permutation-adjusted P = .20). C16:0 SM, C18:0 SM, and C16:0 CERs were suggestively associated with risk (OR = 1.95-2.10; P = .004-.01; permutation-adjusted P = .08-.21). SM sum, C16:0 SM, and C16:0 CER had stronger odds ratios among postmenopausal women (OR = 2.16-3.22). Odds ratios were similar for serous/poorly differentiated and endometrioid/clear cell tumors, although C18:1 LPC and LPC to PC ratio were suggestively inversely associated, whereas C18:0 SM was suggestively positively associated with risk of endometrioid/clear cell tumors. No individual metabolites were associated with risk when using the permutation-based approach. CONCLUSIONS Elevated levels of circulating SMs 3-23 years before diagnosis were associated with increased risk of ovarian cancer, regardless of histotype, with stronger associations among postmenopausal women. Further studies are required to validate and understand the role of lipid dysregulation in ovarian carcinogenesis.
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Affiliation(s)
- Oana A Zeleznik
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Clary B Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard, Boston, MA
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Julian Avila-Pacheco
- Broad Institute of Massachusetts Institute of Technology and Harvard, Boston, MA
| | - A Heather Eliassen
- Broad Institute of Massachusetts Institute of Technology and Harvard, Boston, MA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Shelley S Tworoger
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA.,Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL
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Bulló M, Papandreou C, Ruiz-Canela M, Guasch-Ferré M, Li J, Hernández-Alonso P, Toledo E, Liang L, Razquin C, Corella D, Estruch R, Ros E, Fitó M, Arós F, Fiol M, Serra-Majem L, Clish CB, Becerra-Tomás N, Martínez-González MA, Hu FB, Salas-Salvadó J. Plasma Metabolomic Profiles of Glycemic Index, Glycemic Load, and Carbohydrate Quality Index in the PREDIMED Study. J Nutr 2021; 151:50-58. [PMID: 33296468 PMCID: PMC7779218 DOI: 10.1093/jn/nxaa345] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 09/10/2020] [Accepted: 10/07/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND The quality of carbohydrate consumed, assessed by the glycemic index (GI), glycemic load (GL), or carbohydrate quality index (CQI), affects the postprandial glycemic and insulinemic responses, which have been implicated in the etiology of several chronic diseases. However, it is unclear whether plasma metabolites involved in different biological pathways could provide functional insights into the role of carbohydrate quality indices in health. OBJECTIVES We aimed to identify plasma metabolomic profiles associated with dietary GI, GL, and CQI. METHODS The present study is a cross-sectional analysis of 1833 participants with overweight/obesity (mean age = 67 y) from 2 case-cohort studies nested within the PREDIMED (Prevención con Dieta Mediterránea) trial. Data extracted from validated FFQs were used to estimate the GI, GL, and CQI. Plasma concentrations of 385 metabolites were profiled with LC coupled to MS and associations of these metabolites with those indices were assessed with elastic net regression analyses. RESULTS A total of 58, 18, and 57 metabolites were selected for GI, GL, and CQI, respectively. Choline, cotinine, γ-butyrobetaine, and 36:3 phosphatidylserine plasmalogen were positively associated with GI and GL, whereas they were negatively associated with CQI. Fructose-glucose-galactose was negatively and positively associated with GI/GL and CQI, respectively. Consistent associations of 21 metabolites with both GI and CQI were found but in opposite directions. Negative associations of kynurenic acid, 22:1 sphingomyelin, and 38:6 phosphatidylethanolamine, as well as positive associations of 32:1 phosphatidylcholine with GI and GL were also observed. Pearson correlation coefficients between GI, GL, and CQI and the metabolomic profiles were 0.30, 0.22, and 0.27, respectively. CONCLUSIONS The GI, GL, and CQI were associated with specific metabolomic profiles in a Mediterranean population at high cardiovascular disease risk. Our findings may help in understanding the role of dietary carbohydrate indices in the development of cardiometabolic disorders. This trial was registered at isrctn.com as ISRCTN35739639.
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Affiliation(s)
- Mònica Bulló
- Universitat Rovira i Virgili, Biochemistry and Biotechnology Department, Human Nutrition Unit, Reus, Spain
- Pere i Virgili Health Research Institute (IISPV), Reus, Spain
- CIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain
- Nutrition Unit, University Hospital of Sant Joan de Reus, Reus, Spain
| | - Christopher Papandreou
- Universitat Rovira i Virgili, Biochemistry and Biotechnology Department, Human Nutrition Unit, Reus, Spain
- Pere i Virgili Health Research Institute (IISPV), Reus, Spain
- CIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain
- Nutrition Unit, University Hospital of Sant Joan de Reus, Reus, Spain
| | - Miguel Ruiz-Canela
- CIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain
- Navarra Institute for Health Research (IdiSNA), Pamplona, Navarra, Spain
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Jun Li
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Pablo Hernández-Alonso
- Universitat Rovira i Virgili, Biochemistry and Biotechnology Department, Human Nutrition Unit, Reus, Spain
- Pere i Virgili Health Research Institute (IISPV), Reus, Spain
- CIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain
- Nutrition Unit, University Hospital of Sant Joan de Reus, Reus, Spain
- Department of Endocrinology and Nutrition, Virgen de la Victoria University Hospital, University of Malaga (IBIMA), Malaga, Spain
| | - Estefania Toledo
- CIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain
- Navarra Institute for Health Research (IdiSNA), Pamplona, Navarra, Spain
| | - Liming Liang
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
- Department of Statistics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Cristina Razquin
- CIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain
- Navarra Institute for Health Research (IdiSNA), Pamplona, Navarra, Spain
| | - Dolores Corella
- CIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain
- Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Ramon Estruch
- CIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain
- Department of Internal Medicine, Hospital Clínic, University of Barcelona, Barcelona, Spain
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Emilio Ros
- CIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain
- August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
- Lipid Clinic, Department of Endocrinology and Nutrition, Hospital Clínic, University of Barcelona, Barcelona, Spain
| | - Montserrat Fitó
- CIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain
- Cardiovascular and Nutrition Research Group, Hospital del Mar Medical Research Institute, Barcelona, Spain
| | - Fernando Arós
- CIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain
- Department of Cardiology, University Hospital of Alava, Vitoria, Spain
| | - Miquel Fiol
- CIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain
- Institute of Health Sciences (IUNICS), University of Balearic Islands and Hospital Son Espases, Palma de Mallorca, Spain
| | - Lluís Serra-Majem
- CIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain
- Research Institute of Biomedical and Health Sciences (IUIBS), University of Las Palmas de Gran Canaria, Las Palmas, Spain
| | - Clary B Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Nerea Becerra-Tomás
- Universitat Rovira i Virgili, Biochemistry and Biotechnology Department, Human Nutrition Unit, Reus, Spain
- Pere i Virgili Health Research Institute (IISPV), Reus, Spain
- CIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain
- Nutrition Unit, University Hospital of Sant Joan de Reus, Reus, Spain
| | - Miguel A Martínez-González
- CIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain
- Navarra Institute for Health Research (IdiSNA), Pamplona, Navarra, Spain
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Frank B Hu
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
- Channing Division for Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jordi Salas-Salvadó
- Universitat Rovira i Virgili, Biochemistry and Biotechnology Department, Human Nutrition Unit, Reus, Spain
- Pere i Virgili Health Research Institute (IISPV), Reus, Spain
- CIBER Physiopathology of Obesity and Nutrition (CIBERobn), Institute of Health Carlos III, Madrid, Spain
- Nutrition Unit, University Hospital of Sant Joan de Reus, Reus, Spain
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Lind L, Salihovic S, Risérus U, Kullberg J, Johansson L, Ahlström H, Eriksson JW, Oscarsson J. The Plasma Metabolomic Profile is Differently Associated with Liver Fat, Visceral Adipose Tissue, and Pancreatic Fat. J Clin Endocrinol Metab 2021; 106:e118-e129. [PMID: 33123723 PMCID: PMC7765636 DOI: 10.1210/clinem/dgaa693] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 08/21/2020] [Indexed: 12/16/2022]
Abstract
CONTEXT Metabolic differences between ectopic fat depots may provide novel insights to obesity-related diseases. OBJECTIVE To investigate the plasma metabolomic profiles in relation to visceral adipose tissue (VAT) volume and liver and pancreas fat percentages. DESIGN Cross-sectional. SETTING Multicenter at academic research laboratories. PATIENTS Magnetic resonance imaging (MRI) was used to assess VAT volume, the percentage of fat in the liver and pancreas (proton density fat fraction [PDFF]) at baseline in 310 individuals with a body mass index ≥ 25 kg/m2 and with serum triglycerides ≥ 1.7 mmol/l and/or type 2 diabetes screened for inclusion in the 2 effect of omega-3 carboxylic acid on liver fat content studies. INTERVENTION None. MAIN OUTCOME MEASURE Metabolomic profiling with mass spectroscopy enabled the determination of 1063 plasma metabolites. RESULTS Thirty metabolites were associated with VAT volume, 31 with liver PDFF, and 2 with pancreas PDFF when adjusting for age, sex, total body fat mass, and fasting glucose. Liver PDFF and VAT shared 4 metabolites, while the 2 metabolites related to pancreas PDFF were unique. The top metabolites associated with liver PDFF were palmitoyl-palmitoleoyl-GPC (16:0/16:1), dihydrosphingomyelin (d18:0/22:0), and betaine. The addition of these metabolites to the Liver Fat Score improved C-statistics significantly (from 0.776 to 0.861, P = 0.0004), regarding discrimination of liver steatosis. CONCLUSION Liver PDFF and VAT adipose tissue shared several metabolic associations, while those were not shared with pancreatic PDFF, indicating partly distinct metabolic profiles associated with different ectopic fat depots. The addition of 3 metabolites to the Liver Fat Score improved the prediction of liver steatosis.
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Affiliation(s)
- Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- Correspondence and Reprint Requests: Lars Lind, MD, Professor, Department of Medical Sciences, Uppsala University, SE-751 85 Uppsala, Sweden.
| | | | - Ulf Risérus
- Department of Public Health and Caring Sciences, Clinical Nutrition and Metabolism, Uppsala University, Uppsala, Sweden
| | - Joel Kullberg
- Antaros Medical AB, Gothenburg, Sweden
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden
| | | | - Håkan Ahlström
- Antaros Medical AB, Gothenburg, Sweden
- Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden
| | - Jan W Eriksson
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Jan Oscarsson
- BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
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Guo Y, Luo S, Ye Y, Yin S, Fan J, Xia M. Intermittent Fasting Improves Cardiometabolic Risk Factors and Alters Gut Microbiota in Metabolic Syndrome Patients. J Clin Endocrinol Metab 2021; 106:64-79. [PMID: 33017844 DOI: 10.1210/clinem/dgaa644] [Citation(s) in RCA: 86] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 09/12/2020] [Indexed: 02/07/2023]
Abstract
CONTEXT Intermittent fasting (IF) is an effective strategy to improve cardiometabolic health. OBJECTIVE The objective of this work is to examine the effects of IF on cardiometabolic risk factors and the gut microbiota in patients with metabolic syndrome (MS). DESIGN AND SETTING A randomized clinical trial was conducted at a community health service center. PATIENTS Participants included adults with MS, age 30 to 50 years. INTERVENTION Intervention consisted of 8 weeks of "2-day" modified IF. MAIN OUTCOME MEASURE Cardiometabolic risk factors including body composition, oxidative stress, inflammatory cytokines, and endothelial function were assessed at baseline and at 8 weeks. The diversity, composition, and functional pathways of the gut microbiota, as well as circulating gut-derived metabolites, were measured. RESULTS Thirty-nine patients with MS were included: 21 in the IF group and 18 in the control group. On fasting days, participants in the IF group reduced 69% of their calorie intake compared to nonfasting days. The 8-week IF significantly reduced fat mass, ameliorated oxidative stress, modulated inflammatory cytokines, and improved vasodilatory parameters. Furthermore, IF induced significant changes in gut microbiota communities, increased the production of short-chain fatty acids, and decreased the circulating levels of lipopolysaccharides. The gut microbiota alteration attributed to the IF was significantly associated with cardiovascular risk factors and resulted in distinct genetic shifts of carbohydrate metabolism in the gut community. CONCLUSION IF induces a significant alteration of the gut microbial community and functional pathways in a manner closely associated with the mitigation of cardiometabolic risk factors. The study provides potential mechanistic insights into the prevention of adverse outcomes associated with MS.
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Affiliation(s)
- Yi Guo
- Guangdong Provincial Key Laboratory of Food, Nutrition, and Health and Department of Nutrition, School of Public Health, Sun Yat-sen University (Northern Campus), Guangzhou, Guangdong Province, China
| | - Shiyun Luo
- Guangdong Provincial Key Laboratory of Food, Nutrition, and Health and Department of Nutrition, School of Public Health, Sun Yat-sen University (Northern Campus), Guangzhou, Guangdong Province, China
| | - Yongxin Ye
- Guangdong Provincial Key Laboratory of Food, Nutrition, and Health and Department of Nutrition, School of Public Health, Sun Yat-sen University (Northern Campus), Guangzhou, Guangdong Province, China
| | - Songping Yin
- Guangdong Provincial Key Laboratory of Food, Nutrition, and Health and Department of Nutrition, School of Public Health, Sun Yat-sen University (Northern Campus), Guangzhou, Guangdong Province, China
| | - Jiahua Fan
- Guangdong Provincial Key Laboratory of Food, Nutrition, and Health and Department of Nutrition, School of Public Health, Sun Yat-sen University (Northern Campus), Guangzhou, Guangdong Province, China
| | - Min Xia
- Guangdong Provincial Key Laboratory of Food, Nutrition, and Health and Department of Nutrition, School of Public Health, Sun Yat-sen University (Northern Campus), Guangzhou, Guangdong Province, China
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Tahir UA, Katz DH, Zhao T, Ngo D, Cruz DE, Robbins JM, Chen ZZ, Peterson B, Benson MD, Shi X, Dailey L, Andersson C, Vasan RS, Gao Y, Shen C, Correa A, Hall ME, Wang TJ, Clish CB, Wilson JG, Gerszten RE. Metabolomic Profiles and Heart Failure Risk in Black Adults: Insights From the Jackson Heart Study. Circ Heart Fail 2021; 14:e007275. [PMID: 33464957 PMCID: PMC9158510 DOI: 10.1161/circheartfailure.120.007275] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Accepted: 10/20/2020] [Indexed: 12/15/2022]
Abstract
BACKGROUND Heart failure (HF) is a heterogeneous disease characterized by significant metabolic disturbances; however, the breadth of metabolic dysfunction before the onset of overt disease is not well understood. The purpose of this study was to determine the association of circulating metabolites with incident HF to uncover novel metabolic pathways to disease. METHODS We performed targeted plasma metabolomic profiling in a deeply phenotyped group of Black adults from the JHS (Jackson Heart Study; n=2199). We related metabolites associated with incident HF to established etiological mechanisms, including increased left ventricular mass index and incident coronary heart disease. Furthermore, we evaluated differential associations of metabolites with HF with preserved ejection fraction versus HF with reduced ejection fraction. RESULTS Metabolites associated with incident HF included products of posttranscriptional modifications of RNA, as well as polyamine and nitric oxide metabolism. A subset of metabolite-HF associations was independent of well-established HF pathways such as increased left ventricular mass index and incident coronary heart disease and included homoarginine (per 1 SD increase in metabolite level, hazard ratio, 0.77; P=1.2×10-3), diacetylspermine (hazard ratio, 1.34; P=3.4×10-3), and uridine (hazard ratio, 0.79; P, 3×10-4). Furthermore, metabolites involved in pyrimidine metabolism (orotic acid) and collagen turnover (N-methylproline) among others were part of a distinct metabolic signature that differentiated individuals with HF with preserved ejection fraction versus HF with reduced ejection fraction. CONCLUSIONS The integration of clinical phenotyping with plasma metabolomic profiling uncovered novel metabolic processes in nontraditional disease pathways underlying the heterogeneity of HF development in Black adults.
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Affiliation(s)
- Usman A. Tahir
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
| | - Daniel H. Katz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
| | - Tianyi Zhao
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
| | - Debby Ngo
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
| | - Daniel E. Cruz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
| | - Jeremy M. Robbins
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
| | - Zsu-Zsu Chen
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
| | - Bennet Peterson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
| | - Mark D. Benson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
| | - Xu Shi
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
| | - Lucas Dailey
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA
| | | | - Ramachandran S. Vasan
- Framingham Heart Study, Framingham, MA
- Section of Preventive Medicine and Epidemiology and Cardiovascular medicine, Departments of Medicine and Epidemiology, Boston University Schools of Medicine and Public Health, Boston, MA
| | - Yan Gao
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS
| | - Changyu Shen
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS
| | - Michael E. Hall
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS
| | - Thomas J. Wang
- Department of Medicine, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Clary B. Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA
| | - James G. Wilson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
| | - Robert E. Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
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71
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Guasch-Ferré M, Hernández-Alonso P, Drouin-Chartier JP, Ruiz-Canela M, Razquin C, Toledo E, Li J, Dennis C, Wittenbecher C, Corella D, Estruch R, Fitó M, Ros E, Babio N, Bhupathiraju SN, Clish CB, Liang L, Martínez-González MA, Hu FB, Salas-Salvadó J. Walnut Consumption, Plasma Metabolomics, and Risk of Type 2 Diabetes and Cardiovascular Disease. J Nutr 2020; 151:303-311. [PMID: 33382410 PMCID: PMC7850062 DOI: 10.1093/jn/nxaa374] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 10/07/2020] [Accepted: 10/29/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Walnut consumption is associated with lower risk of type 2 diabetes (T2D) and cardiovascular disease (CVD). However, it is unknown whether plasma metabolites related to walnut consumption are also associated with lower risk of cardiometabolic diseases. OBJECTIVES The study aimed to identify plasma metabolites associated with walnut consumption and evaluate the prospective associations between the identified profile and risk of T2D and CVD. METHODS The discovery population included 1833 participants at high cardiovascular risk from the PREvención con DIeta MEDiterránea (PREDIMED) study with available metabolomics data at baseline. The study population included 57% women (baseline mean BMI (in kg/m2): 29.9; mean age: 67 y). A total of 1522 participants also had available metabolomics data at year 1 and were used as the internal validation population. Plasma metabolomics analyses were performed using LC-MS. Cross-sectional associations between 385 known metabolites and walnut consumption were assessed using elastic net continuous regression analysis. A 10-cross-validation (CV) procedure was used, and Pearson correlation coefficients were assessed between metabolite weighted models and self-reported walnut consumption in each pair of training-validation data sets within the discovery population. We further estimated the prospective associations between the identified metabolite profile and incident T2D and CVD using multivariable Cox regression models. RESULTS A total of 19 metabolites were significantly associated with walnut consumption, including lipids, purines, acylcarnitines, and amino acids. Ten-CV Pearson correlation coefficients between self-reported walnut consumption and the plasma metabolite profile were 0.16 (95% CI: 0.11, 0.20) in the discovery population and 0.15 (95% CI: 0.10, 0.20) in the validation population. The metabolite profile was inversely associated with T2D incidence (HR per 1 SD: 0.83; 95% CI: 0.71, 0.97; P = 0.02). For CVD incidence, the HR per 1-SD was 0.71 (95% CI: 0.60, 0.85; P < 0.001). CONCLUSIONS A metabolite profile including 19 metabolites was associated with walnut consumption and with a lower risk of incident T2D and CVD in a Mediterranean population at high cardiovascular risk.
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Affiliation(s)
| | | | - Jean-Philippe Drouin-Chartier
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA,Centre Nutrition, Santé et Société, Institut sur la Nutrition et les Aliments Fonctionnels, Faculté de Pharmacie, Université Laval, Québec, Canada
| | - Miguel Ruiz-Canela
- Consorcio CIBER, M.P. Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain,Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IDISNA), University of Navarra, Pamplona, Spain
| | - Cristina Razquin
- Consorcio CIBER, M.P. Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain,Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IDISNA), University of Navarra, Pamplona, Spain
| | - Estefanía Toledo
- Consorcio CIBER, M.P. Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain,Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IDISNA), University of Navarra, Pamplona, Spain
| | - Jun Li
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Clemens Wittenbecher
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA,Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany,German Center for Diabetes Research, Neuherberg, Germany
| | - Dolores Corella
- Consorcio CIBER, M.P. Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain,Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Ramon Estruch
- Consorcio CIBER, M.P. Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain,Department of Internal Medicine, Institut d'Investigacions Biomèdiques August Pi Sunyer, Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Montserrat Fitó
- Consorcio CIBER, M.P. Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain,Cardiovascular and Nutrition Research Group, Institut de Recerca Hospital del Mar, Barcelona, Spain
| | - Emilio Ros
- Consorcio CIBER, M.P. Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain,Lipid Clinic, Department of Endocrinology and Nutrition, Agust Pi i Sunyer Biomedical Research Institute (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Nancy Babio
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Hospital Universitari San Joan de Reus, Reus, Spain,Institut d'Investigació Pere Virgili, Reus, Spain,Consorcio CIBER, M.P. Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
| | - Shilpa N Bhupathiraju
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA,Channing Division for Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Clary B Clish
- The Broad Institute of Harvard and MIT, Boston, MA, USA
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Miguel A Martínez-González
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA,Consorcio CIBER, M.P. Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain,Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IDISNA), University of Navarra, Pamplona, Spain
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA,Channing Division for Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jordi Salas-Salvadó
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana, Hospital Universitari San Joan de Reus, Reus, Spain,Institut d'Investigació Pere Virgili, Reus, Spain,Consorcio CIBER, M.P. Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
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72
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Szczuko M, Kikut J, Maciejewska D, Kulpa D, Celewicz Z, Ziętek M. The Associations of SCFA with Anthropometric Parameters and Carbohydrate Metabolism in Pregnant Women. Int J Mol Sci 2020; 21:ijms21239212. [PMID: 33287163 PMCID: PMC7731050 DOI: 10.3390/ijms21239212] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 11/27/2020] [Accepted: 11/30/2020] [Indexed: 12/16/2022] Open
Abstract
Short-chain fatty acids (SCFAs) mediate the transmission of signals between the microbiome and the immune system and are responsible for maintaining balance in the anti-inflammatory reaction. Pregnancy stages alter the gut microbiota community structure, which also synthesizes SCFAs. The study involved 90 pregnant women, divided into two groups: 48 overweight/obese pregnant women (OW) and 42 pregnant women with normal BMI (CG). The blood samples for glucose, insulin, and HBA1c were analyzed as well as stool samples for SCFA isolation (C2:0; C3:0; C4:0i; C4:0n; C5:0i; C5:0n; C6:0i; C6:0n) using gas chromatography. The SCFA profile in the analyzed groups differed significantly. A significant positive correlation between C2:0, C3:0, C4:0n and anthropometric measurements, and between C2:0, C3:0, C4:0n, and C5:0n and parameters of carbohydrate metabolism was found. SCFA levels fluctuate during pregnancy and the course of pregnancy and participate in the change in carbohydrate metabolism as well. The influence of C2:0 during pregnancy on anthropometric parameters was visible in both groups (normal weight and obese). Butyrate and propionate regulate glucose metabolism by stimulating the process of intestinal gluconeogenesis. The level of propionic acid decreases with the course of pregnancy, while its increase is characteristic of obese women, which is associated with many metabolic adaptations. Propionic and linear caproic acid levels can be an important critical point in maintaining lower anthropometric parameters during pregnancy.
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Affiliation(s)
- Małgorzata Szczuko
- Department of Human Nutrition and Metabolomics, Pomeranian Medical University in Szczecin, Broniewskiego 24, 71-460 Szczecin, Poland; (J.K.); (D.M.)
- Correspondence: ; Tel.: +48-91-441-4810; Fax: +48-91-441-4807
| | - Justyna Kikut
- Department of Human Nutrition and Metabolomics, Pomeranian Medical University in Szczecin, Broniewskiego 24, 71-460 Szczecin, Poland; (J.K.); (D.M.)
| | - Dominika Maciejewska
- Department of Human Nutrition and Metabolomics, Pomeranian Medical University in Szczecin, Broniewskiego 24, 71-460 Szczecin, Poland; (J.K.); (D.M.)
| | - Danuta Kulpa
- Department of Genetics, Plant Breeding and Biotechnology, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology, Słowackiego 17, 71-434 Szczecin, Poland;
| | - Zbigniew Celewicz
- Department of Perinatology, Obstetrics and Gynecology, Pomeranian Medical University in Szczecin, Siedlecka 2, 72-010 Police, Poland; (Z.C.); (M.Z.)
| | - Maciej Ziętek
- Department of Perinatology, Obstetrics and Gynecology, Pomeranian Medical University in Szczecin, Siedlecka 2, 72-010 Police, Poland; (Z.C.); (M.Z.)
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73
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Murthy VL, Reis JP, Pico AR, Kitchen R, Lima JAC, Lloyd-Jones D, Allen NB, Carnethon M, Lewis GD, Nayor M, Vasan RS, Freedman JE, Clish CB, Shah RV. Comprehensive Metabolic Phenotyping Refines Cardiovascular Risk in Young Adults. Circulation 2020; 142:2110-2127. [PMID: 33073606 PMCID: PMC7880553 DOI: 10.1161/circulationaha.120.047689] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Accepted: 09/17/2020] [Indexed: 01/19/2023]
Abstract
BACKGROUND Whereas cardiovascular disease (CVD) metrics define risk in individuals >40 years of age, the earliest lesions of CVD appear well before this age. Despite the role of metabolism in CVD antecedents, studies in younger, biracial populations to define precise metabolic risk phenotypes are lacking. METHODS We studied 2330 White and Black young adults (mean age, 32 years; 45% Black) in the CARDIA study (Coronary Artery Risk Development in Young Adults) to identify metabolite profiles associated with an adverse CVD phenome (myocardial structure/function, fitness, vascular calcification), mechanisms, and outcomes over 2 decades. Statistical learning methods (elastic nets/principal components analysis) and Cox regression generated parsimonious, metabolite-based risk scores validated in >1800 individuals in the Framingham Heart Study. RESULTS In the CARDIA study, metabolite profiles quantified in early adulthood were associated with subclinical CVD development over 20 years, specifying known and novel pathways of CVD (eg, transcriptional regulation, brain-derived neurotrophic factor, nitric oxide, renin-angiotensin). We found 2 multiparametric, metabolite-based scores linked independently to vascular and myocardial health, with metabolites included in each score specifying microbial metabolism, hepatic steatosis, oxidative stress, nitric oxide modulation, and collagen metabolism. The metabolite-based vascular scores were lower in men, and myocardial scores were lower in Black participants. Over a nearly 25-year median follow-up in CARDIA, the metabolite-based vascular score (hazard ratio, 0.68 per SD [95% CI, 0.50-0.92]; P=0.01) and myocardial score (hazard ratio, 0.60 per SD [95% CI, 0.45-0.80]; P=0.0005) in the third and fourth decades of life were associated with clinical CVD with a synergistic association with outcome (Pinteraction=0.009). We replicated these findings in 1898 individuals in the Framingham Heart Study over 2 decades, with a similar association with outcome (including interaction), reclassification, and discrimination. In the Framingham Heart Study, the metabolite scores exhibited an age interaction (P=0.0004 for a combined myocardial-vascular score with incident CVD), such that young adults with poorer metabolite-based health scores had highest hazard of future CVD. CONCLUSIONS Metabolic signatures of myocardial and vascular health in young adulthood specify known/novel pathways of metabolic dysfunction relevant to CVD, associated with outcome in 2 independent cohorts. Efforts to include precision measures of metabolic health in risk stratification to interrupt CVD at its earliest stage are warranted.
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Affiliation(s)
| | - Jared P. Reis
- National Heart, Lung, and Blood Institute, Bethesda, MD
| | - Alexander R. Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, University of California at San Francisco, San Francisco, CA
| | - Robert Kitchen
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Joao A. C. Lima
- Cardiology Division, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD
| | | | | | | | - Gregory D. Lewis
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Matthew Nayor
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Ramachandran S. Vasan
- Sections of Preventive Medicine and Epidemiology and Cardiovascular Medicine, Department of Medicine, and Department of Epidemiology, Boston University Schools of Medicine and Public Health, Boston, MA, and the Framingham Heart Study, Framingham, MA
| | - Jane E. Freedman
- Department of Medicine, University of Massachusetts Medical School, Worcester, MA
| | | | - Ravi V. Shah
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
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74
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Nayor M, Shah RV, Miller PE, Blodgett JB, Tanguay M, Pico AR, Murthy VL, Malhotra R, Houstis NE, Deik A, Pierce KA, Bullock K, Dailey L, Velagaleti RS, Moore SA, Ho JE, Baggish AL, Clish CB, Larson MG, Vasan RS, Lewis GD. Metabolic Architecture of Acute Exercise Response in Middle-Aged Adults in the Community. Circulation 2020; 142:1905-1924. [PMID: 32927962 PMCID: PMC8049528 DOI: 10.1161/circulationaha.120.050281] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
BACKGROUND Whereas regular exercise is associated with lower risk of cardiovascular disease and mortality, mechanisms of exercise-mediated health benefits remain less clear. We used metabolite profiling before and after acute exercise to delineate the metabolic architecture of exercise response patterns in humans. METHODS Cardiopulmonary exercise testing and metabolite profiling was performed on Framingham Heart Study participants (age 53±8 years, 63% women) with blood drawn at rest (n=471) and at peak exercise (n=411). RESULTS We observed changes in circulating levels for 502 of 588 measured metabolites from rest to peak exercise (exercise duration 11.9±2.1 minutes) at a 5% false discovery rate. Changes included reductions in metabolites implicated in insulin resistance (glutamate, -29%; P=1.5×10-55; dimethylguanidino valeric acid [DMGV], -18%; P=5.8×10-18) and increases in metabolites associated with lipolysis (1-methylnicotinamide, +33%; P=6.1×10-67), nitric oxide bioavailability (arginine/ornithine + citrulline, +29%; P=2.8×10-169), and adipose browning (12,13-dihydroxy-9Z-octadecenoic acid +26%; P=7.4×10-38), among other pathways relevant to cardiometabolic risk. We assayed 177 metabolites in a separate Framingham Heart Study replication sample (n=783, age 54±8 years, 51% women) and observed concordant changes in 164 metabolites (92.6%) at 5% false discovery rate. Exercise-induced metabolite changes were variably related to the amount of exercise performed (peak workload), sex, and body mass index. There was attenuation of favorable excursions in some metabolites in individuals with higher body mass index and greater excursions in select cardioprotective metabolites in women despite less exercise performed. Distinct preexercise metabolite levels were associated with different physiologic dimensions of fitness (eg, ventilatory efficiency, exercise blood pressure, peak Vo2). We identified 4 metabolite signatures of exercise response patterns that were then analyzed in a separate cohort (Framingham Offspring Study; n=2045, age 55±10 years, 51% women), 2 of which were associated with overall mortality over median follow-up of 23.1 years (P≤0.003 for both). CONCLUSIONS In a large sample of community-dwelling individuals, acute exercise elicits widespread changes in the circulating metabolome. Metabolic changes identify pathways central to cardiometabolic health, cardiovascular disease, and long-term outcome. These findings provide a detailed map of the metabolic response to acute exercise in humans and identify potential mechanisms responsible for the beneficial cardiometabolic effects of exercise for future study.
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Affiliation(s)
- Matthew Nayor
- Cardiology Division and the Simches Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Ravi V. Shah
- Cardiology Division and the Simches Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Patricia E. Miller
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Jasmine B. Blodgett
- Cardiology Division and the Simches Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Melissa Tanguay
- Cardiology Division and the Simches Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Alexander R. Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA
| | - Venkatesh L. Murthy
- Division of Cardiovascular Medicine, Department of Medicine, University of Michigan, Ann Arbor
- Frankel Cardiovascular Center, University of Michigan, Ann Arbor
| | - Rajeev Malhotra
- Cardiology Division and the Simches Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
| | - Nicholas E. Houstis
- Cardiology Division and the Simches Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Amy Deik
- Broad Institute of MIT and Harvard, Cambridge, MA
| | | | | | - Lucas Dailey
- Broad Institute of MIT and Harvard, Cambridge, MA
| | - Raghava S. Velagaleti
- Cardiology Section, Department of Medicine, Boston VA Healthcare System, West Roxbury, MA
| | - Stephanie A. Moore
- Cardiology Section, Department of Medicine, Boston VA Healthcare System, West Roxbury, MA
| | - Jennifer E. Ho
- Cardiology Division and the Simches Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Aaron L. Baggish
- Cardiology Division and the Simches Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | | | - Martin G. Larson
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
- Boston University’s and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA
| | - Ramachandran S. Vasan
- Boston University’s and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA
- Sections of Preventive Medicine and Epidemiology, and Cardiology, Department of Medicine, Boston University School of Medicine, Boston, MA
| | - Gregory D. Lewis
- Cardiology Division and the Simches Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- Pulmonary Critical Care Unit, Massachusetts General Hospital, Boston, MA
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75
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Wali JA, Koay YC, Chami J, Wood C, Corcilius L, Payne RJ, Rodionov RN, Birkenfeld AL, Samocha-Bonet D, Simpson SJ, O'Sullivan JF. Nutritional and metabolic regulation of the metabolite dimethylguanidino valeric acid: an early marker of cardiometabolic disease. Am J Physiol Endocrinol Metab 2020; 319:E509-E518. [PMID: 32663097 PMCID: PMC7509244 DOI: 10.1152/ajpendo.00207.2020] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Dimethylguanidino valeric acid (DMGV) is a marker of fatty liver disease, incident coronary artery disease, cardiovascular mortality, and incident diabetes. Recently, it was reported that circulating DMGV levels correlated positively with consumption of sugary beverages and negatively with intake of fruits and vegetables in three Swedish community-based cohorts. Here, we validate these results in the Framingham Heart Study Third Generation Cohort. Furthermore, in mice, diets rich in sucrose or fat significantly increased plasma DMGV concentrations. DMGV is the product of metabolism of asymmetric dimethylarginine (ADMA) by the hepatic enzyme AGXT2. ADMA can also be metabolized to citrulline by the cytoplasmic enzyme DDAH1. We report that a high-sucrose diet induced conversion of ADMA exclusively into DMGV (supporting the relationship with sugary beverage intake in humans), while a high-fat diet promoted conversion of ADMA to both DMGV and citrulline. On the contrary, replacing dietary native starch with high-fiber-resistant starch increased ADMA concentrations and induced its conversion to citrulline, without altering DMGV concentrations. In a cohort of obese nondiabetic adults, circulating DMGV concentrations increased and ADMA levels decreased in those with either liver or muscle insulin resistance. This was similar to changes in DMGV and ADMA concentrations found in mice fed a high-sucrose diet. Sucrose is a disaccharide of glucose and fructose. Compared with glucose, incubation of hepatocytes with fructose significantly increased DMGV production. Overall, we provide a comprehensive picture of the dietary determinants of DMGV levels and association with insulin resistance.
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Affiliation(s)
- Jibran A Wali
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
- Faculty of Science, School of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia
| | - Yen Chin Koay
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
- Faculty of Medicine and Health, School of Medicine, The University of Sydney, Sydney, New South Wales, Australia
- Heart Research Institute, The University of Sydney, Sydney, New South Wales, Australia
| | - Jason Chami
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
- Faculty of Medicine and Health, School of Medicine, The University of Sydney, Sydney, New South Wales, Australia
- Heart Research Institute, The University of Sydney, Sydney, New South Wales, Australia
| | - Courtney Wood
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
- Faculty of Medicine and Health, School of Medicine, The University of Sydney, Sydney, New South Wales, Australia
- Heart Research Institute, The University of Sydney, Sydney, New South Wales, Australia
| | - Leo Corcilius
- School of Chemistry, The University of Sydney, Sydney, New South Wales, Australia
- Australian Research Council Centre of Excellence for Innovations in Peptide and Protein Science, The University of Sydney, Sydney, New South Wales, Australia
| | - Richard J Payne
- School of Chemistry, The University of Sydney, Sydney, New South Wales, Australia
- Australian Research Council Centre of Excellence for Innovations in Peptide and Protein Science, The University of Sydney, Sydney, New South Wales, Australia
| | - Roman N Rodionov
- University Center for Vascular Medicine and Department of Medicine III-Section Angiology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Andreas L Birkenfeld
- Department of Internal Medicine, Division of Endocrinology, Diabetology, and Nephrology, University Hospital Tübingen, Tübingen, Germany
- Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Centre Munich at the University of Tübingen, Tübingen, Germany
- German Centre for Diabetes Research (DZD), Tübingen, Tübingen, Germany
| | - Dorit Samocha-Bonet
- The Garvan Institute of Medical Research, University of New South Wales, Sydney, New South Wales, Australia
| | - Stephen J Simpson
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
- Faculty of Science, School of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia
| | - John F O'Sullivan
- Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia
- Faculty of Medicine and Health, School of Medicine, The University of Sydney, Sydney, New South Wales, Australia
- Heart Research Institute, The University of Sydney, Sydney, New South Wales, Australia
- Department of Cardiology, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia
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76
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Plasma Metabolites Associate with All-Cause Mortality in Individuals with Type 2 Diabetes. Metabolites 2020; 10:metabo10080315. [PMID: 32751974 PMCID: PMC7464745 DOI: 10.3390/metabo10080315] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 07/21/2020] [Accepted: 07/27/2020] [Indexed: 01/07/2023] Open
Abstract
Alterations in the human metabolome occur years before clinical manifestation of type 2 diabetes (T2DM). By contrast, there is little knowledge of how metabolite alterations in individuals with diabetes relate to risk of diabetes complications and premature mortality. Metabolite profiling was performed using liquid chromatography-mass spectrometry in 743 participants with T2DM from the population-based prospective cohorts The Malmö Diet and Cancer-Cardiovascular Cohort (MDC-CC) and The Malmö Preventive Project (MPP). During follow-up, a total of 175 new-onset cases of cardiovascular disease (CVD) and 298 deaths occurred. Cox regressions were used to relate baseline levels of plasma metabolites to incident CVD and all-cause mortality. A total of 11 metabolites were significantly (false discovery rate (fdr) <0.05) associated with all-cause mortality. Acisoga, acylcarnitine C10:3, dimethylguanidino valerate, homocitrulline, N2,N2-dimethylguanosine, 1-methyladenosine and urobilin were associated with an increased risk, while hippurate, lysine, threonine and tryptophan were associated with a decreased risk. Ten out of 11 metabolites remained significantly associated after adjustments for cardiometabolic risk factors. The associations between metabolite levels and incident CVD were not as strong as for all-cause mortality, although 11 metabolites were nominally significant (p < 0.05). Further examination of the mortality-related metabolites may shed more light on the pathophysiology linking diabetes to premature mortality.
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77
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Robbins JM, Herzig M, Morningstar J, Sarzynski MA, Cruz DE, Wang TJ, Gao Y, Wilson JG, Bouchard C, Rankinen T, Gerszten RE. Association of Dimethylguanidino Valeric Acid With Partial Resistance to Metabolic Health Benefits of Regular Exercise. JAMA Cardiol 2020; 4:636-643. [PMID: 31166569 DOI: 10.1001/jamacardio.2019.1573] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Importance Metabolic responses to exercise training are variable. Metabolite profiling may aid in the clinical assessment of an individual's responsiveness to exercise interventions. Objective To investigate the association between a novel circulating biomarker of hepatic fat, dimethylguanidino valeric acid (DMGV), and metabolic health traits before and after 20 weeks of endurance exercise training. Design, Setting, and Participants This study involved cross-sectional and longitudinal analyses of the Health, Risk Factors, Exercise Training, and Genetics (HERITAGE) Family Study, a 20-week, single-arm endurance exercise clinical trial performed in multiple centers between 1993 and 1997. White participants with sedentary lifestyles who were free of cardiometabolic disease were included. Metabolomic tests were performed using a liquid chromatography, tandem mass spectrometry method on plasma samples collected before and after exercise training in the HERITAGE study. Metabolomics and data analysis were performed from August 2017 to May 2018. Exposures Plasma DMGV levels. Main Outcome and Measures The association between DMGV levels and measures of body composition, plasma lipids, insulin, and glucose homeostasis before and after exercise training. Results Among the 439 participants included in analyses from HERITAGE, the mean (SD) age was 36 (15) years, 228 (51.9%) were female, and the median (interquartile range) body mass index was 25 (22-28). Baseline levels of DMGV were positively associated with body fat percentage, abdominal visceral fat, very low-density lipoprotein cholesterol, and triglycerides, and inversely associated with insulin sensitivity, low-density lipoprotein cholesterol, high-density lipoprotein size, and high-density lipoprotein cholesterol (range of β coefficients, 0.17-0.46 [SEs, 0.026-0.050]; all P < .001, after adjusting for age and sex). After adjusting for age, sex, and baseline traits, baseline DMGV levels were positively associated with changes in small high-density lipoprotein particles (β, 0.14 [95% CI, 0.05-0.23]) and inversely associated with changes in medium and total high-density lipoprotein particles (β, -0.15 [95% CI, -0.24 to -0.05] and -0.19 [95% CI, -0.28 to -0.10], respectively), apolipoprotein A1 (β, -0.14 [95% CI, -0.23 to -0.05]), and insulin sensitivity (β, -0.13; P = 3.0 × 10-3) after exercise training. Conclusions and Relevance Dimethylguanidino valeric acid is an early marker of cardiometabolic dysfunction that is associated with attenuated improvements in lipid traits and insulin sensitivity after exercise training. Levels of DMGV may identify individuals who require additional therapies beyond guideline-directed exercise to improve their metabolic health.
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Affiliation(s)
- Jeremy M Robbins
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts.,Cardiovascular Research Center, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Matthew Herzig
- Cardiovascular Research Center, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Jordan Morningstar
- Cardiovascular Research Center, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Mark A Sarzynski
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia
| | - Daniel E Cruz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts.,Cardiovascular Research Center, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Thomas J Wang
- Division of Cardiovascular Medicine, Vanderbilt University, Nashville, Tennessee
| | - Yan Gao
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson
| | - Claude Bouchard
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana
| | - Tuomo Rankinen
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts.,Cardiovascular Research Center, Beth Israel Deaconess Medical Center, Boston, Massachusetts
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78
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Hernández‐Alonso P, Becerra‐Tomás N, Papandreou C, Bulló M, Guasch‐Ferré M, Toledo E, Ruiz‐Canela M, Clish CB, Corella D, Dennis C, Deik A, Wang DD, Razquin C, Drouin‐Chartier J, Estruch R, Ros E, Fitó M, Arós F, Fiol M, Serra‐Majem L, Liang L, Martínez‐González MA, Hu FB, Salas‐Salvadó J. Plasma Metabolomics Profiles are Associated with the Amount and Source of Protein Intake: A Metabolomics Approach within the PREDIMED Study. Mol Nutr Food Res 2020; 64:e2000178. [PMID: 32378786 DOI: 10.1002/mnfr.202000178] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 04/15/2020] [Indexed: 01/24/2023]
Affiliation(s)
- Pablo Hernández‐Alonso
- Universitat Rovira i VirgiliDepartament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana Hospital Universitari San Joan de Reus Reus 43201 Spain
- Institut d'Investigació Pere Virgili (IISPV) Reus 43003 Spain
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Unidad de Gestión Clínica de Endocrinología y Nutrición del Hospital Virgen de la VictoriaInstituto de Investigación Biomédica de Málaga (IBIMA) Málaga 29010 Spain
| | - Nerea Becerra‐Tomás
- Universitat Rovira i VirgiliDepartament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana Hospital Universitari San Joan de Reus Reus 43201 Spain
- Institut d'Investigació Pere Virgili (IISPV) Reus 43003 Spain
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
| | - Christopher Papandreou
- Universitat Rovira i VirgiliDepartament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana Hospital Universitari San Joan de Reus Reus 43201 Spain
- Institut d'Investigació Pere Virgili (IISPV) Reus 43003 Spain
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
| | - Mònica Bulló
- Universitat Rovira i VirgiliDepartament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana Hospital Universitari San Joan de Reus Reus 43201 Spain
- Institut d'Investigació Pere Virgili (IISPV) Reus 43003 Spain
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
| | - Marta Guasch‐Ferré
- Universitat Rovira i VirgiliDepartament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana Hospital Universitari San Joan de Reus Reus 43201 Spain
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Department of NutritionHarvard T. H. Chan School of Public Health Boston MA 02115 USA
| | - Estefanía Toledo
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- University of NavarraDepartment of Preventive Medicine and Public Health Pamplona 31008 Spain
- Navarra Institute for Health Research (IdisNA) Pamplona Navarra 31008 Spain
| | - Miguel Ruiz‐Canela
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- University of NavarraDepartment of Preventive Medicine and Public Health Pamplona 31008 Spain
- Navarra Institute for Health Research (IdisNA) Pamplona Navarra 31008 Spain
| | - Clary B. Clish
- Broad Institute of MIT and Harvard University Cambridge MA 02142 USA
| | - Dolores Corella
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Department of Preventive MedicineUniversity of Valencia Valencia 46020 Spain
| | - Courtney Dennis
- Broad Institute of MIT and Harvard University Cambridge MA 02142 USA
| | - Amy Deik
- Broad Institute of MIT and Harvard University Cambridge MA 02142 USA
| | - Dong D. Wang
- Department of NutritionHarvard T. H. Chan School of Public Health Boston MA 02115 USA
| | - Cristina Razquin
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- University of NavarraDepartment of Preventive Medicine and Public Health Pamplona 31008 Spain
- Navarra Institute for Health Research (IdisNA) Pamplona Navarra 31008 Spain
| | - Jean‐Philippe Drouin‐Chartier
- Department of NutritionHarvard T. H. Chan School of Public Health Boston MA 02115 USA
- Centre Nutrition, Santé et Société (NUTRISS), Institut sur la Nutrition et les Aliments Fonctionnels (INAF)Université Laval Québec G1V 0A6 Canada
- Faculté de PharmacieUniversité Laval Québec G1V 0A6 Canada
| | - Ramon Estruch
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Department of Internal MedicineDepartment of Endocrinology and Nutrition Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital ClinicUniversity of Barcelona Barcelona 08036 Spain
| | - Emilio Ros
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Lipid Clinic, Department of Endocrinology and Nutrition Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital ClinicUniversity of Barcelona Barcelona 08036 Spain
| | - Montserrat Fitó
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Cardiovascular and Nutrition Research GroupInstitut de Recerca Hospital del Mar Barcelona 08003 Spain
| | - Fernando Arós
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Department of CardiologyUniversity Hospital of Alava Vitoria 01009 Spain
| | - Miquel Fiol
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Institute of Health Sciences IUNICSUniversity of Balearic Islands and Hospital Son Espases Palma de Mallorca 07122 Spain
| | - Lluís Serra‐Majem
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- Research Institute of Biomedical and Health Sciences IUIBSUniversity of Las Palmas de Gran Canaria Las Palmas 35001 Spain
| | - Liming Liang
- Departments of Epidemiology and StatisticsHarvard T. H. Chan School of Public Health Boston MA 02115 USA
| | - Miguel A Martínez‐González
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
- University of NavarraDepartment of Preventive Medicine and Public Health Pamplona 31008 Spain
- Navarra Institute for Health Research (IdisNA) Pamplona Navarra 31008 Spain
- Department of NutritionHarvard T. H. Chan School of Public Health Boston MA 02115 USA
| | - Frank B Hu
- Broad Institute of MIT and Harvard University Cambridge MA 02142 USA
- Departments of Epidemiology and StatisticsHarvard T. H. Chan School of Public Health Boston MA 02115 USA
- Channing Division for Network Medicine, Department of MedicineBrigham and Women's Hospital and Harvard Medical School Boston MA 02115 USA
| | - Jordi Salas‐Salvadó
- Universitat Rovira i VirgiliDepartament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana Hospital Universitari San Joan de Reus Reus 43201 Spain
- Institut d'Investigació Pere Virgili (IISPV) Reus 43003 Spain
- Consorcio CIBER, M. P. Fisiopatología de la Obesidad y Nutrición (CIBERObn)Instituto de Salud Carlos III (ISCIII) Madrid 28029 Spain
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Abstract
The persistent increase in the worldwide burden of type 2 diabetes mellitus (T2D) and the accompanying rise of its complications, including cardiovascular disease, necessitates our understanding of the metabolic disturbances that cause diabetes mellitus. Metabolomics and proteomics, facilitated by recent advances in high-throughput technologies, have given us unprecedented insight into circulating biomarkers of T2D even over a decade before overt disease. These markers may be effective tools for diabetes mellitus screening, diagnosis, and prognosis. As participants of metabolic pathways, metabolite and protein markers may also highlight pathways involved in T2D development. The integration of metabolomics and proteomics with genomics in multiomics strategies provides an analytical method that can begin to decipher causal associations. These methods are not without their limitations; however, with careful study design and sample handling, these methods represent powerful scientific tools that can be leveraged for the study of T2D. In this article, we aim to give a timely overview of circulating metabolomics and proteomics findings with T2D observed in large human population studies to provide the reader with a snapshot into these emerging fields of research.
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Affiliation(s)
- Zsu-Zsu Chen
- Division of Endocrinology, Diabetes, and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
- Cardiovascular Institute, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Robert E. Gerszten
- Cardiovascular Institute, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
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80
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Koay YC, Stanton K, Kienzle V, Li M, Yang J, Celermajer DS, O'Sullivan JF. Effect of chronic exercise in healthy young male adults: a metabolomic analysis. Cardiovasc Res 2020; 117:613-622. [PMID: 32239128 DOI: 10.1093/cvr/cvaa051] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 02/05/2020] [Accepted: 03/01/2020] [Indexed: 12/13/2022] Open
Abstract
AIMS To examine the metabolic adaptation to an 80-day exercise intervention in healthy young male adults where lifestyle factors such as diet, sleep, and physical activities are controlled. METHODS AND RESULTS This study involved cross-sectional analysis before and after an 80-day aerobic and strength exercise intervention in 52 young, adult, male, newly enlisted soldiers in 2015. Plasma metabolomic analyses were performed using liquid chromatography, tandem mass spectrometry. Data analyses were performed between March and August 2019. We analysed changes in metabolomic profiles at the end of an 80-day exercise intervention compared to baseline, and the association of metabolite changes with changes in clinical parameters. Global metabolism was dramatically shifted after the exercise training programme. Fatty acids and ketone body substrates, key fuels used by exercising muscle, were dramatically decreased in plasma in response to increased aerobic fitness. There were highly significant changes across many classes of metabolic substrates including lipids, ketone bodies, arginine metabolites, endocannabinoids, nucleotides, markers of proteolysis, products of fatty acid oxidation, microbiome-derived metabolites, markers of redox stress, and substrates of coagulation. For statistical analyses, a paired t-test was used and Bonferroni-adjusted P-value of <0.0004 was considered to be statistically significant. The metabolite dimethylguanidino valeric acid (DMGV) (recently shown to predict lack of metabolic response to exercise) tracked maladaptive metabolic changes to exercise; those with increases in DMGV levels had increases in several cardiovascular risk factors; changes in DMGV levels were significantly positively correlated with increases in body fat (P = 0.049), total and LDL cholesterol (P = 0.003 and P = 0.007), and systolic blood pressure (P = 0.006). This study was approved by the Departments of Defence and Veterans' Affairs Human Research Ethics Committee and written informed consent was obtained from each subject. CONCLUSION For the first time, the true magnitude and extent of metabolic adaptation to chronic exercise training are revealed in this carefully designed study, which can be leveraged for novel therapeutic strategies in cardiometabolic disease. Extending the recent report of DMGV's predictive utility in sedentary, overweight individuals, we found that it is also a useful marker of poor metabolic response to exercise in young, healthy, fit males.
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Affiliation(s)
- Yen Chin Koay
- Heart Research Institute, Sydney, NSW, Australia
- The University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia
| | - Kelly Stanton
- Heart Research Institute, Sydney, NSW, Australia
- Department of Cardiology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | | | - Mengbo Li
- The University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia
- The University of Sydney, School of Mathematics and Statistics, Sydney, NSW, Australia
| | - Jean Yang
- The University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia
- The University of Sydney, School of Mathematics and Statistics, Sydney, NSW, Australia
| | - David S Celermajer
- Heart Research Institute, Sydney, NSW, Australia
- Department of Cardiology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - John F O'Sullivan
- Heart Research Institute, Sydney, NSW, Australia
- The University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia
- Department of Cardiology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
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81
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Mueller NT, Zhang M, Juraschek SP, Miller ER, Appel LJ. Effects of high-fiber diets enriched with carbohydrate, protein, or unsaturated fat on circulating short chain fatty acids: results from the OmniHeart randomized trial. Am J Clin Nutr 2020; 111:545-554. [PMID: 31927581 PMCID: PMC7049528 DOI: 10.1093/ajcn/nqz322] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 12/03/2019] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Short chain fatty acids (SCFAs; e.g., acetate, propionate, and butyrate) are produced by microbial fermentation of fiber in the colon. Evidence is lacking on how high-fiber diets that differ in macronutrient composition affect circulating SCFAs. OBJECTIVES We aimed to compare the effects of 3 high-fiber isocaloric diets differing in %kcal of carbohydrate, protein, or unsaturated fat on circulating SCFAs. Based on previous literature, we hypothesized that serum acetate, the main SCFA in circulation, increases on all high-fiber diets, but differently by macronutrient composition of the diet. METHODS OmniHeart is a randomized crossover trial of 164 men and women (≥30 y old); 163 participants with SCFA data were included in this analysis. We provided participants 3 isocaloric high-fiber (∼30 g/2100 kcal) diets, each for 6 wk, in random order: a carbohydrate-rich (Carb) diet, a protein-rich (Prot) diet (protein predominantly from plant sources), and an unsaturated fat-rich (Unsat) diet. We used LC-MS to quantify SCFA concentrations in fasting serum, collected at baseline and the end of each diet period. We fitted linear regression models with generalized estimating equations to examine change in ln-transformed SCFAs from baseline to the end of each diet; differences between diets; and associations of changes in SCFAs with cardiometabolic parameters. RESULTS From baseline, serum acetate concentrations were increased by the Prot (β: 0.24; 95% CI: 0.12, 0.35), Unsat (β: 0.21; 95% CI: 0.10, 0.33), and Carb (β: 0.12; 95% CI: 0.01, 0.24) diets; between diets, only Prot compared with Carb was significant (P = 0.02). Propionate was decreased by the Carb (β: -0.10; 95% CI: -0.16, -0.03) and Unsat (β: -0.10; 95% CI: -0.16, -0.04) diets, not the Prot diet; between diet comparisons of Carb vs. Prot (P = 0.006) and Unsat vs. Prot (P = 0.002) were significant. The Prot diet increased butyrate (β: 0.05; 95% CI: 0.00, 0.09) compared with baseline, but not compared with the other diets. Increases in acetate were associated with decreases in insulin and glucose; increases in propionate with increases in leptin, LDL cholesterol, and blood pressure; and increases in butyrate with increases in insulin and glucose, and decreases in HDL cholesterol and ghrelin (Ps < 0.05). CONCLUSIONS Macronutrient composition of high-fiber diets affects circulating SCFAs, which are associated with measures of appetite and cardiometabolic health. This trial was registered at clinicaltrials.gov as NCT00051350.
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Affiliation(s)
- Noel T Mueller
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
| | - Mingyu Zhang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Stephen P Juraschek
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Edgar R Miller
- Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
| | - Lawrence J Appel
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
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82
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Ji Y, Azuine RE, Zhang Y, Hou W, Hong X, Wang G, Riley A, Pearson C, Zuckerman B, Wang X. Association of Cord Plasma Biomarkers of In Utero Acetaminophen Exposure With Risk of Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder in Childhood. JAMA Psychiatry 2020; 77:180-189. [PMID: 31664451 PMCID: PMC6822099 DOI: 10.1001/jamapsychiatry.2019.3259] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
IMPORTANCE Prior studies have raised concern about maternal acetaminophen use during pregnancy and increased risk of attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) in their children; however, most studies have relied on maternal self-report. OBJECTIVE To examine the prospective associations between cord plasma acetaminophen metabolites and physician-diagnosed ADHD, ASD, both ADHD and ASD, and developmental disabilities (DDs) in childhood. DESIGN, SETTING, AND PARTICIPANTS This prospective cohort study analyzed 996 mother-infant dyads, a subset of the Boston Birth Cohort, who were enrolled at birth and followed up prospectively at the Boston Medical Center from October 1, 1998, to June 30, 2018. EXPOSURES Three cord acetaminophen metabolites (unchanged acetaminophen, acetaminophen glucuronide, and 3-[N-acetyl-l-cystein-S-yl]-acetaminophen) were measured in archived cord plasma samples collected at birth. MAIN OUTCOMES AND MEASURES Physician-diagnosed ADHD, ASD, and other DDs as documented in the child's medical records. RESULTS Of 996 participants (mean [SD] age, 9.8 [3.9] years; 548 [55.0%] male), the final sample included 257 children (25.8%) with ADHD only, 66 (6.6%) with ASD only, 42 (4.2%) with both ADHD and ASD, 304 (30.5%) with other DDs, and 327 (32.8%) who were neurotypical. Unchanged acetaminophen levels were detectable in all cord plasma samples. Compared with being in the first tertile, being in the second and third tertiles of cord acetaminophen burden was associated with higher odds of ADHD diagnosis (odds ratio [OR] for second tertile, 2.26; 95% CI, 1.40-3.69; OR for third tertile, 2.86; 95% CI, 1.77-4.67) and ASD diagnosis (OR for second tertile, 2.14; 95% CI, 0.93-5.13; OR for third tertile, 3.62; 95% CI, 1.62-8.60). Sensitivity analyses and subgroup analyses found consistent associations between acetaminophen buden and ADHD and acetaminophen burden and ASD across strata of potential confounders, including maternal indication, substance use, preterm birth, and child age and sex, for which point estimates for the ORs vary from 2.3 to 3.5 for ADHD and 1.6 to 4.1 for ASD. CONCLUSIONS AND RELEVANCE Cord biomarkers of fetal exposure to acetaminophen were associated with significantly increased risk of childhood ADHD and ASD in a dose-response fashion. Our findings support previous studies regarding the association between prenatal and perinatal acetaminophen exposure and childhood neurodevelopmental risk and warrant additional investigations.
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Affiliation(s)
- Yuelong Ji
- Center on the Early Life Origins of Disease, Department of Population, Family and Reproductive Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
| | - Romuladus E. Azuine
- Division of Research, Office of Epidemiology and Research, Maternal and Child Health Bureau, Health Resources and Services Administration, US Department of Health and Human Services, Rockville, Maryland
| | - Yan Zhang
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
| | - Wenpin Hou
- Department of Computer Science, Johns Hopkins University Whiting School of Engineering, Baltimore, Maryland
| | - Xiumei Hong
- Center on the Early Life Origins of Disease, Department of Population, Family and Reproductive Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
| | - Guoying Wang
- Center on the Early Life Origins of Disease, Department of Population, Family and Reproductive Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
| | - Anne Riley
- Center on the Early Life Origins of Disease, Department of Population, Family and Reproductive Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland
| | - Colleen Pearson
- Department of Pediatrics, Boston University School of Medicine and Boston Medical Center, Boston, Massachusetts
| | - Barry Zuckerman
- Department of Pediatrics, Boston University School of Medicine and Boston Medical Center, Boston, Massachusetts
| | - Xiaobin Wang
- Center on the Early Life Origins of Disease, Department of Population, Family and Reproductive Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland,Division of General Pediatrics & Adolescent Medicine, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland
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83
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Beyoğlu D, Idle JR. Metabolomic and Lipidomic Biomarkers for Premalignant Liver Disease Diagnosis and Therapy. Metabolites 2020; 10:E50. [PMID: 32012846 PMCID: PMC7074571 DOI: 10.3390/metabo10020050] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 01/24/2020] [Accepted: 01/26/2020] [Indexed: 02/07/2023] Open
Abstract
In recent years, there has been a plethora of attempts to discover biomarkers that are more reliable than α-fetoprotein for the early prediction and prognosis of hepatocellular carcinoma (HCC). Efforts have involved such fields as genomics, transcriptomics, epigenetics, microRNA, exosomes, proteomics, glycoproteomics, and metabolomics. HCC arises against a background of inflammation, steatosis, and cirrhosis, due mainly to hepatic insults caused by alcohol abuse, hepatitis B and C virus infection, adiposity, and diabetes. Metabolomics offers an opportunity, without recourse to liver biopsy, to discover biomarkers for premalignant liver disease, thereby alerting the potential of impending HCC. We have reviewed metabolomic studies in alcoholic liver disease (ALD), cholestasis, fibrosis, cirrhosis, nonalcoholic fatty liver (NAFL), and nonalcoholic steatohepatitis (NASH). Specificity was our major criterion in proposing clinical evaluation of indole-3-lactic acid, phenyllactic acid, N-lauroylglycine, decatrienoate, N-acetyltaurine for ALD, urinary sulfated bile acids for cholestasis, cervonoyl ethanolamide for fibrosis, 16α-hydroxyestrone for cirrhosis, and the pattern of acyl carnitines for NAFL and NASH. These examples derive from a large body of published metabolomic observations in various liver diseases in adults, adolescents, and children, together with animal models. Many other options have been tabulated. Metabolomic biomarkers for premalignant liver disease may help reduce the incidence of HCC.
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Affiliation(s)
| | - Jeffrey R. Idle
- Arthur G. Zupko’s Division of Systems Pharmacology and Pharmacogenomics, Arnold & Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, 75 Dekalb Avenue, Brooklyn, NY 11201, USA;
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84
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Li M, Wang A, Quek LE, Vernon S, Figtree GA, Yang J, O'Sullivan JF. Metabolites downstream of predicted loss-of-function variants inform relationship to disease. Mol Genet Metab 2019; 128:476-482. [PMID: 31679996 DOI: 10.1016/j.ymgme.2019.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 08/26/2019] [Accepted: 10/06/2019] [Indexed: 11/21/2022]
Abstract
A small minority (< 3%) of protein-coding genetic variants are predicted to lead to loss of protein function. However, these predicted loss-of-function (pLOF) variants can provide insight into mode of transcriptional effect. To examine how these changes are propagated to phenotype, we determined associations with downstream metabolites. We performed association analyses of 37 pLOF variants - previously reported to be significantly associated with disease in >400,000 subjects in UK Biobank - with metabolites. We conducted these analyses in three community-based cohorts: the Framingham Heart Study (FHS) Offspring Cohort, FHS Generation 3, and the KORA F4 cohort. We identified 19 new low-frequency or rare (minor allele frequency (MAF) <5%) pLOF variant-metabolite associations, and 12 new common (MAF > 5%) pLOF variant-metabolite associations. Rare pLOF variants in the genes BTN3A2, ENPEP, and GEM that have been associated with blood pressure in UK Biobank, were associated with vasoactive metabolites indoxyl sulfate, asymmetric dimethylarginine (ADMA), and with niacinamide, respectively. A common pLOF variant in gene CCHCR1, associated with asthma in UK Biobank, was associated with histamine and niacinamide in FHS Generation 3, both reported to play a role in this disease. Common variants in olfactory receptor gene OX4C11 that associated with blood pressure in UK Biobank were associated with the nicotine metabolite cotinine, suggesting an interaction between altered olfaction, smoking behaviour, and blood pressure. These findings provide biological validity for pLOF variant-disease associations, and point to the effector roles of common metabolites. Such an approach may provide novel disease markers and therapeutic targets.
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Affiliation(s)
- Mengbo Li
- The University of Sydney, School of Mathematics and Statistics, Sydney, NSW 2006, Australia; The University of Sydney, Charles Perkins Centre, Sydney, NSW 2065, Australia
| | - Andy Wang
- The University of Sydney, School of Mathematics and Statistics, Sydney, NSW 2006, Australia; The University of Sydney, Royal North Shore Hospital, Sydney, NSW 2065, Australia
| | - Lake-Ee Quek
- The University of Sydney, School of Mathematics and Statistics, Sydney, NSW 2006, Australia
| | - Stephen Vernon
- The University of Sydney, Royal North Shore Hospital, Sydney, NSW 2065, Australia; The University of Sydney, Kolling Research Institute, Royal North Shore Hospital, Sydney, NSW 2064, Australia
| | - Gemma A Figtree
- The University of Sydney, Royal North Shore Hospital, Sydney, NSW 2065, Australia; The University of Sydney, Kolling Research Institute, Royal North Shore Hospital, Sydney, NSW 2064, Australia
| | - Jean Yang
- The University of Sydney, School of Mathematics and Statistics, Sydney, NSW 2006, Australia; The University of Sydney, Charles Perkins Centre, Sydney, NSW 2065, Australia
| | - John F O'Sullivan
- The University of Sydney, Charles Perkins Centre, Sydney, NSW 2065, Australia; The University of Sydney, Heart Research Institute, Sydney, NSW 2042, Australia; The University of Sydney, Department of Cardiology, Royal Prince Alfred Hospital, NSW 2050, Australia.
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85
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Wishart DS. Metabolomics for Investigating Physiological and Pathophysiological Processes. Physiol Rev 2019; 99:1819-1875. [PMID: 31434538 DOI: 10.1152/physrev.00035.2018] [Citation(s) in RCA: 495] [Impact Index Per Article: 99.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Metabolomics uses advanced analytical chemistry techniques to enable the high-throughput characterization of metabolites from cells, organs, tissues, or biofluids. The rapid growth in metabolomics is leading to a renewed interest in metabolism and the role that small molecule metabolites play in many biological processes. As a result, traditional views of metabolites as being simply the "bricks and mortar" of cells or just the fuel for cellular energetics are being upended. Indeed, metabolites appear to have much more varied and far more important roles as signaling molecules, immune modulators, endogenous toxins, and environmental sensors. This review explores how metabolomics is yielding important new insights into a number of important biological and physiological processes. In particular, a major focus is on illustrating how metabolomics and discoveries made through metabolomics are improving our understanding of both normal physiology and the pathophysiology of many diseases. These discoveries are yielding new insights into how metabolites influence organ function, immune function, nutrient sensing, and gut physiology. Collectively, this work is leading to a much more unified and system-wide perspective of biology wherein metabolites, proteins, and genes are understood to interact synergistically to modify the actions and functions of organelles, organs, and organisms.
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Affiliation(s)
- David S Wishart
- Departments of Biological Sciences and Computing Science, University of Alberta, Edmonton, Alberta, Canada
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86
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Ottosson F, Smith E, Gallo W, Fernandez C, Melander O. Purine Metabolites and Carnitine Biosynthesis Intermediates Are Biomarkers for Incident Type 2 Diabetes. J Clin Endocrinol Metab 2019; 104:4921-4930. [PMID: 31502646 PMCID: PMC6804288 DOI: 10.1210/jc.2019-00822] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Accepted: 07/18/2019] [Indexed: 02/08/2023]
Abstract
CONTEXT Metabolomics has the potential to generate biomarkers that can facilitate understanding relevant pathways in the pathophysiology of type 2 diabetes (T2DM). METHODS Nontargeted metabolomics was performed, via liquid chromatography-mass spectrometry, in a discovery case-cohort study from the Malmö Preventive Project (MPP), which consisted of 698 metabolically healthy participants, of whom 202 developed T2DM within a follow-up time of 6.3 years. Metabolites that were significantly associated with T2DM were replicated in the population-based Malmö Diet and Cancer-Cardiovascular Cohort (MDC-CC) (N = 3423), of whom 402 participants developed T2DM within a follow-up time of 18.2 years. RESULTS Using nontargeted metabolomics, we observed alterations in nine metabolite classes to be related to incident T2DM, including 11 identified metabolites. N2,N2-dimethylguanosine (DMGU) (OR = 1.94; P = 4.9e-10; 95% CI, 1.57 to 2.39) was the metabolite most strongly associated with an increased risk, and beta-carotene (OR = 0.60; P = 1.8e-4; 95% CI, 0.45 to 0.78) was the metabolite most strongly associated with a decreased risk. Identified T2DM-associated metabolites were replicated in MDC-CC. Four metabolites were significantly associated with incident T2DM in both the MPP and the replication cohort MDC-CC, after adjustments for traditional diabetes risk factors. These included associations between three metabolites, DMGU, 7-methylguanine (7MG), and 3-hydroxytrimethyllysine (HTML), and incident T2DM. CONCLUSIONS We used nontargeted metabolomics in two Swedish prospective cohorts comprising >4000 study participants and identified independent, replicable associations between three metabolites, DMGU, 7MG, and HTML, and future risk of T2DM. These findings warrant additional studies to investigate a potential functional connection between these metabolites and the onset of T2DM.
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Affiliation(s)
- Filip Ottosson
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Correspondence and Reprint Requests: Filip Ottosson, PhD, Lund University, Jan Waldenströms Gata 35, Malmö 21421, Sweden. E-mail:
| | - Einar Smith
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Widet Gallo
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | | | - Olle Melander
- Department of Clinical Sciences, Lund University, Malmö, Sweden
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87
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Ottosson F, Ericson U, Almgren P, Smith E, Brunkwall L, Hellstrand S, Nilsson PM, Orho-Melander M, Fernandez C, Melander O. Dimethylguanidino Valerate: A Lifestyle-Related Metabolite Associated With Future Coronary Artery Disease and Cardiovascular Mortality. J Am Heart Assoc 2019; 8:e012846. [PMID: 31533499 PMCID: PMC6806048 DOI: 10.1161/jaha.119.012846] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background Identification of lifestyle modifiable metabolic pathways related to cardiometabolic disease risk is essential for improvement of primary prevention in susceptible individuals. It was recently shown that plasma dimethylguanidino valerate (DMGV) levels are associated with incident type 2 diabetes mellitus. Our aims were to investigate whether plasma DMGV is related to risk of future coronary artery disease and with cardiovascular mortality and to replicate the association with type 2 diabetes mellitus and pinpoint candidate lifestyle interventions susceptible to modulate DMGV levels. Methods and Results Plasma DMGV levels were measured using liquid chromatography‐mass spectrometry in a total of 5768 participants from the MDC (Malmö Diet and Cancer Study—Cardiovascular Cohort), MPP (Malmö Preventive Project), and MOS (Malmö Offspring Study). Dietary intake assessment was performed in the MOS. Baseline levels of DMGV associated with incident coronary artery disease in both the MDC (hazard ratio=1.29; CI=1.16–1.43; P<0.001) and MPP (odds ratio=1.25; CI=1.08–1.44; P=2.4e‐3). In the MDC, DMGV was associated with cardiovascular mortality and incident coronary artery disease, independently of traditional risk factors. Furthermore, the association between DMGV and incident type 2 diabetes mellitus was replicated in both the MDC (hazard ratio=1.83; CI=1.63–2.05; P<0.001) and MPP (odds ratio=1.65; CI=1.38–1.98; P<0.001). Intake of sugar‐sweetened beverages was associated with increased levels of DMGV, whereas intake of vegetables and level of physical activity was associated with lower DMGV. Conclusions We discovered novel independent associations between plasma DMGV and incident coronary artery disease and cardiovascular mortality, while replicating the previously reported association with incident type 2 diabetes mellitus. Additionally, strong associations with sugar‐sweetened beverages, vegetable intake, and physical activity suggest the potential to modify DMGV levels using lifestyle interventions.
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Affiliation(s)
- Filip Ottosson
- Department of Clinical Sciences Lund University Malmö Sweden
| | - Ulrika Ericson
- Department of Clinical Sciences Lund University Malmö Sweden
| | - Peter Almgren
- Department of Clinical Sciences Lund University Malmö Sweden
| | - Einar Smith
- Department of Clinical Sciences Lund University Malmö Sweden
| | | | | | - Peter M Nilsson
- Department of Clinical Sciences Lund University Malmö Sweden
| | | | | | - Olle Melander
- Department of Clinical Sciences Lund University Malmö Sweden
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88
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The Second Life of Methylarginines as Cardiovascular Targets. Int J Mol Sci 2019; 20:ijms20184592. [PMID: 31533264 PMCID: PMC6769906 DOI: 10.3390/ijms20184592] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Revised: 09/13/2019] [Accepted: 09/15/2019] [Indexed: 02/07/2023] Open
Abstract
Endogenous methylarginines were proposed as cardiovascular risk factors more than two decades ago, however, so far, this knowledge has not led to the development of novel therapeutic approaches. The initial studies were primarily focused on the endogenous inhibitors of nitric oxide synthases asymmetric dimethylarginine (ADMA) and monomethylarginine (MMA) and the main enzyme regulating their clearance dimethylarginine dimethylaminohydrolase 1 (DDAH1). To date, all the screens for DDAH1 activators performed with the purified recombinant DDAH1 enzyme have not yielded any promising hits, which is probably the main reason why interest towards this research field has started to fade. The relative contribution of the second DDAH isoenzyme DDAH2 towards ADMA and MMA clearance is still a matter of controversy. ADMA, MMA and symmetric dimethylarginine (SDMA) are also metabolized by alanine: glyoxylate aminotransferase 2 (AGXT2), however, in addition to methylarginines, this enzyme also has several cardiovascular protective substrates, so the net effect of possible therapeutic targeting of AGXT2 is currently unclear. Recent studies on regulation and functions of the enzymes metabolizing methylarginines have given a second life to this research direction. Our review discusses the latest discoveries and controversies in the field and proposes novel directions for targeting methylarginines in clinical settings.
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89
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Pharmacometabolomics of Bronchodilator Response in Asthma and the Role of Age-Metabolite Interactions. Metabolites 2019; 9:metabo9090179. [PMID: 31500319 PMCID: PMC6780678 DOI: 10.3390/metabo9090179] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 08/29/2019] [Accepted: 09/03/2019] [Indexed: 12/16/2022] Open
Abstract
The role of metabolism in modifying age-related differential responses to asthma medications is insufficiently understood. The objective of this study was to determine the role of the metabolome in modifying the effect of age on bronchodilator response (BDR) in individuals with asthma. We used longitudinal measures of BDR and plasma metabolomic profiling in 565 children with asthma from the Childhood Asthma Management Program (CAMP) to identify age by metabolite interactions on BDR. The mean ages at the three studied time-points across 16 years of follow-up in CAMP were 8.8, 12.8, and 16.8 years; the mean BDRs were 11%, 9% and 8%, respectively. Of 501 identified metabolites, 39 (7.8%) demonstrated a significant interaction with age on BDR (p-value < 0.05). We were able to validate two significant interactions in 320 children with asthma from the Genetics of Asthma in Costa Rica Study; 2-hydroxyglutarate, a compound involved in butanoate metabolism (interaction; CAMP: β = -0.004, p = 1.8 × 10-4; GACRS: β = -0.015, p = 0.018), and a cholesterol ester; CE C18:1 (CAMP: β = 0.005, p = 0.006; GACRS: β = 0.023, p = 0.041) Five additional metabolites had a p-value < 0.1 in GACRS, including Gammaminobutyric acid (GABA), C16:0 CE, C20:4 CE, C18.0 CE and ribothymidine. These findings suggest Cholesterol esters and GABA may modify the estimated effect of age on bronchodilator response.
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90
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A library of human gut bacterial isolates paired with longitudinal multiomics data enables mechanistic microbiome research. Nat Med 2019; 25:1442-1452. [PMID: 31477907 DOI: 10.1038/s41591-019-0559-3] [Citation(s) in RCA: 207] [Impact Index Per Article: 41.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 07/23/2019] [Indexed: 12/18/2022]
Abstract
Our understanding of how the gut microbiome interacts with its human host has been restrained by limited access to longitudinal datasets to examine stability and dynamics, and by having only a few isolates to test mechanistic hypotheses. Here, we present the Broad Institute-OpenBiome Microbiome Library (BIO-ML), a comprehensive collection of 7,758 gut bacterial isolates paired with 3,632 genome sequences and longitudinal multi-omics data. We show that microbial species maintain stable population sizes within and across humans and that commonly used 'omics' survey methods are more reliable when using averages over multiple days of sampling. Variation of gut metabolites within people over time is associated with amino acid levels, and differences across people are associated with differences in bile acids. Finally, we show that genomic diversification can be used to infer eco-evolutionary dynamics and in vivo selection pressures for strains within individuals. The BIO-ML is a unique resource designed to enable hypothesis-driven microbiome research.
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91
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Hernández-Alonso P, Papandreou C, Bulló M, Ruiz-Canela M, Dennis C, Deik A, Wang DD, Guasch-Ferré M, Yu E, Toledo E, Razquin C, Corella D, Estruch R, Ros E, Fitó M, Arós F, Fiol M, Serra-Majem L, Liang L, Clish CB, Martínez-González MA, Hu FB, Salas-Salvadó J. Plasma Metabolites Associated with Frequent Red Wine Consumption: A Metabolomics Approach within the PREDIMED Study. Mol Nutr Food Res 2019; 63:e1900140. [PMID: 31291050 PMCID: PMC6771435 DOI: 10.1002/mnfr.201900140] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 05/14/2019] [Indexed: 01/25/2023]
Abstract
SCOPE The relationship between red wine (RW) consumption and metabolism is poorly understood. It is aimed to assess the systemic metabolomic profiles in relation to frequent RW consumption as well as the ability of a set of metabolites to discriminate RW consumers. METHODS AND RESULTS A cross-sectional analysis of 1157 participants is carried out. Subjects are divided as non-RW consumers versus RW consumers (>1 glass per day RW [100 mL per day]). Plasma metabolomics analysis is performed using LC-MS. Associations between 386 identified metabolites and RW consumption are assessed using elastic net regression analysis taking into consideration baseline significant covariates. Ten-cross-validation (CV) is performed and receiver operating characteristic curves are constructed in each of the validation datasets based on weighted models. A subset of 13 metabolites is consistently selected and RW consumers versus nonconsumers are discriminated. Based on the multi-metabolite model weighted with the regression coefficients of metabolites, the area under the curve is 0.83 (95% CI: 0.80-0.86). These metabolites mainly consisted of lipid species, some organic acids, and alkaloids. CONCLUSIONS A multi-metabolite model identified in a Mediterranean population appears useful to discriminate between frequent RW consumers and nonconsumers. Further studies are needed to assess the contribution of these metabolites in health and disease.
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Affiliation(s)
- Pablo Hernández-Alonso
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Sant Joan Hospital, Institut d’Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Christopher Papandreou
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Sant Joan Hospital, Institut d’Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Mònica Bulló
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Sant Joan Hospital, Institut d’Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Miguel Ruiz-Canela
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- University of Navarra, Department of Preventive Medicine and Public Health, Pamplona, Spain
- Navarra Institute for Health Research (IdisNA), Pamplona, Navarra, Spain
| | - Courtney Dennis
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
| | - Amy Deik
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
| | - Dong D. Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Marta Guasch-Ferré
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Sant Joan Hospital, Institut d’Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Edward Yu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Estefanía Toledo
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- University of Navarra, Department of Preventive Medicine and Public Health, Pamplona, Spain
- Navarra Institute for Health Research (IdisNA), Pamplona, Navarra, Spain
| | - Cristina Razquin
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- University of Navarra, Department of Preventive Medicine and Public Health, Pamplona, Spain
- Navarra Institute for Health Research (IdisNA), Pamplona, Navarra, Spain
| | - Dolores Corella
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Ramon Estruch
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Internal Medicine, Department of Endocrinology and Nutrition Institut d’Investigacions Biomèdiques August Pi Sunyer (IDI-BAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Emilio Ros
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Lipid Clinic, Department of Endocrinology and Nutrition Institut d’Investigacions Biomèdiques August Pi Sunyer (IDI-BAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Montserrat Fitó
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Cardiovascular and Nutrition Research Group, Institut de Recerca Hospital del Mar, Barcelona, Spain
| | - Fernando Arós
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Cardiology, University Hospital of Alava, Vitoria, Spain
| | - Miquel Fiol
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Institute of Health Sciences IUNICS, University of Balearic Islands and Hospital Son Espases, Palma de Mallorca, Spain
| | - Lluís Serra-Majem
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Research Institute of Biomedical and Health Sciences IUIBS, University of Las Palmas de Gran Canaria, Las Palmas, Spain
| | - Liming Liang
- Departments of Epidemiology and Statistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Clary B. Clish
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
| | - Miguel A Martínez-González
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- University of Navarra, Department of Preventive Medicine and Public Health, Pamplona, Spain
- Navarra Institute for Health Research (IdisNA), Pamplona, Navarra, Spain
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Frank B Hu
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
- Departments of Epidemiology and Statistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division for Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, MA, USA
| | - Jordi Salas-Salvadó
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Sant Joan Hospital, Institut d’Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
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92
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Barupal DK, Fiehn O. Generating the Blood Exposome Database Using a Comprehensive Text Mining and Database Fusion Approach. ENVIRONMENTAL HEALTH PERSPECTIVES 2019; 127:97008. [PMID: 31557052 PMCID: PMC6794490 DOI: 10.1289/ehp4713] [Citation(s) in RCA: 110] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 09/09/2019] [Accepted: 09/11/2019] [Indexed: 05/18/2023]
Abstract
BACKGROUND Blood chemicals are routinely measured in clinical or preclinical research studies to diagnose diseases, assess risks in epidemiological research, or use metabolomic phenotyping in response to treatments. A vast volume of blood-related literature is available via the PubMed database for data mining. OBJECTIVES We aimed to generate a comprehensive blood exposome database of endogenous and exogenous chemicals associated with the mammalian circulating system through text mining and database fusion. METHODS Using NCBI resources, we retrieved PubMed abstracts, PubChem chemical synonyms, and PMC supplementary tables. We then employed text mining and PubChem crowdsourcing to associate phrases relating to blood with PubChem chemicals. False positives were removed by a phrase pattern and a compound exclusion list. RESULTS A query to identify blood-related publications in the PubMed database yielded 1.1 million papers. Matching a total of 15 million synonyms from 6.5 million relevant PubChem chemicals against all blood-related publications yielded 37,514 chemicals and 851,999 publications records. Mapping PubChem compound identifiers to the PubMed database yielded 49,940 unique chemicals linked to 676,643 papers. Analysis of open-access metabolomics papers related to blood phrases in the PMC database yielded 4,039 unique compounds and 204 papers. Consolidating these three approaches summed up to a total of 41,474 achiral structures that were linked to 65,957 PubChem CIDs and to over 878,966 PubMed articles. We mapped these compounds to 50 databases such as those covering metabolites and pathways, governmental and toxicological databases, pharmacology resources, and bioassay repositories. In comparison, HMDB, the Human Metabolome Database, links 1,075 compounds to blood-related primary publications. CONCLUSION This new Blood Exposome Database can be used for prioritizing chemicals for systematic reviews, developing target assays in exposome research, identifying compounds in untargeted mass spectrometry, and biological interpretation in metabolomics data. The database is available at http://bloodexposome.org. https://doi.org/10.1289/EHP4713.
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Affiliation(s)
- Dinesh Kumar Barupal
- National Institutes of Health (NIH) West Coast Metabolomics Center, Genome Center, University of California, Davis, Davis, California, USA
| | - Oliver Fiehn
- National Institutes of Health (NIH) West Coast Metabolomics Center, Genome Center, University of California, Davis, Davis, California, USA
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93
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Pinget G, Tan J, Janac B, Kaakoush NO, Angelatos AS, O'Sullivan J, Koay YC, Sierro F, Davis J, Divakarla SK, Khanal D, Moore RJ, Stanley D, Chrzanowski W, Macia L. Impact of the Food Additive Titanium Dioxide (E171) on Gut Microbiota-Host Interaction. Front Nutr 2019; 6:57. [PMID: 31165072 PMCID: PMC6534185 DOI: 10.3389/fnut.2019.00057] [Citation(s) in RCA: 77] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 04/12/2019] [Indexed: 12/27/2022] Open
Abstract
The interaction between gut microbiota and host plays a central role in health. Dysbiosis, detrimental changes in gut microbiota and inflammation have been reported in non-communicable diseases. While diet has a profound impact on gut microbiota composition and function, the role of food additives such as titanium dioxide (TiO2), prevalent in processed food, is less established. In this project, we investigated the impact of food grade TiO2 on gut microbiota of mice when orally administered via drinking water. While TiO2 had minimal impact on the composition of the microbiota in the small intestine and colon, we found that TiO2 treatment could alter the release of bacterial metabolites in vivo and affect the spatial distribution of commensal bacteria in vitro by promoting biofilm formation. We also found reduced expression of the colonic mucin 2 gene, a key component of the intestinal mucus layer, and increased expression of the beta defensin gene, indicating that TiO2 significantly impacts gut homeostasis. These changes were associated with colonic inflammation, as shown by decreased crypt length, infiltration of CD8+ T cells, increased macrophages as well as increased expression of inflammatory cytokines. These findings collectively show that TiO2 is not inert, but rather impairs gut homeostasis which may in turn prime the host for disease development.
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Affiliation(s)
- Gabriela Pinget
- The Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia.,Faculty of Medicine and Health, School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia.,Sydney Nano Institute, The University of Sydney, Sydney, NSW, Australia
| | - Jian Tan
- The Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia.,Faculty of Medicine and Health, School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia.,Sydney Nano Institute, The University of Sydney, Sydney, NSW, Australia.,Human Health, Nuclear Science & Technology and Landmark Infrastructure (NSTLI), Australian Nuclear Science and Technology Organisation, Sydney, NSW, Australia
| | - Bartlomiej Janac
- The Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia.,Faculty of Medicine and Health, School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia
| | - Nadeem O Kaakoush
- School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Alexandra Sophie Angelatos
- The Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia.,Faculty of Medicine and Health, School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia
| | - John O'Sullivan
- Department of Cardiology, Charles Perkins Centre, Royal Prince Alfred Hospital, Heart Research Institute, University of Sydney, Sydney, NSW, Australia
| | - Yen Chin Koay
- Department of Cardiology, Charles Perkins Centre, Royal Prince Alfred Hospital, Heart Research Institute, University of Sydney, Sydney, NSW, Australia
| | - Frederic Sierro
- Human Health, Nuclear Science & Technology and Landmark Infrastructure (NSTLI), Australian Nuclear Science and Technology Organisation, Sydney, NSW, Australia
| | - Joel Davis
- Human Health, Nuclear Science & Technology and Landmark Infrastructure (NSTLI), Australian Nuclear Science and Technology Organisation, Sydney, NSW, Australia
| | - Shiva Kamini Divakarla
- Sydney Nano Institute, The University of Sydney, Sydney, NSW, Australia.,Sydney Pharmacy School, The University of Sydney, Sydney, NSW, Australia
| | - Dipesh Khanal
- Sydney Nano Institute, The University of Sydney, Sydney, NSW, Australia.,Sydney Pharmacy School, The University of Sydney, Sydney, NSW, Australia
| | - Robert J Moore
- School of Science, RMIT University, Bundoora, VIC, Australia
| | - Dragana Stanley
- School of Health, Medical and Applied Sciences, Central Queensland University, Rockhampton, QLD, Australia
| | - Wojciech Chrzanowski
- Sydney Nano Institute, The University of Sydney, Sydney, NSW, Australia.,Sydney Pharmacy School, The University of Sydney, Sydney, NSW, Australia
| | - Laurence Macia
- The Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia.,Faculty of Medicine and Health, School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia.,Sydney Nano Institute, The University of Sydney, Sydney, NSW, Australia
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94
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Papandreou C, Hernández-Alonso P, Bulló M, Ruiz-Canela M, Yu E, Guasch-Ferré M, Toledo E, Dennis C, Deik A, Clish C, Razquin C, Corella D, Estruch R, Ros E, Fitó M, Arós F, Fiol M, Lapetra J, Ruano C, Liang L, Martínez-González MA, Hu FB, Salas-Salvadó J. Plasma Metabolites Associated with Coffee Consumption: A Metabolomic Approach within the PREDIMED Study. Nutrients 2019; 11:E1032. [PMID: 31072000 PMCID: PMC6566346 DOI: 10.3390/nu11051032] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 05/02/2019] [Accepted: 05/03/2019] [Indexed: 02/07/2023] Open
Abstract
Few studies have examined the association of a wide range of metabolites with total and subtypes of coffee consumption. The aim of this study was to investigate associations of plasma metabolites with total, caffeinated, and decaffeinated coffee consumption. We also assessed the ability of metabolites to discriminate between coffee consumption categories. This is a cross-sectional analysis of 1664 participants from the PREDIMED study. Metabolites were semiquantitatively profiled using a multiplatform approach. Consumption of total coffee, caffeinated coffee and decaffeinated coffee was assessed by using a validated food frequency questionnaire. We assessed associations between 387 metabolite levels with total, caffeinated, or decaffeinated coffee consumption (≥50 mL coffee/day) using elastic net regression analysis. Ten-fold cross-validation analyses were used to estimate the discriminative accuracy of metabolites for total and subtypes of coffee. We identified different sets of metabolites associated with total coffee, caffeinated and decaffeinated coffee consumption. These metabolites consisted of lipid species (e.g., sphingomyelin, phosphatidylethanolamine, and phosphatidylcholine) or were derived from glycolysis (alpha-glycerophosphate) and polyphenol metabolism (hippurate). Other metabolites included caffeine, 5-acetylamino-6-amino-3-methyluracil, cotinine, kynurenic acid, glycocholate, lactate, and allantoin. The area under the curve (AUC) was 0.60 (95% CI 0.56-0.64), 0.78 (95% CI 0.75-0.81) and 0.52 (95% CI 0.49-0.55), in the multimetabolite model, for total, caffeinated, and decaffeinated coffee consumption, respectively. Our comprehensive metabolic analysis did not result in a new, reliable potential set of metabolites for coffee consumption.
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Affiliation(s)
- Christopher Papandreou
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, 43201 Reus, Spain.
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
| | - Pablo Hernández-Alonso
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, 43201 Reus, Spain.
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
| | - Mònica Bulló
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, 43201 Reus, Spain.
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
| | - Miguel Ruiz-Canela
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- University of Navarra, Department of Preventive Medicine and Public Health, IdiSNA, 31009 Pamplona, Spain.
| | - Edward Yu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
| | - Marta Guasch-Ferré
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, 43201 Reus, Spain.
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
| | - Estefanía Toledo
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- University of Navarra, Department of Preventive Medicine and Public Health, IdiSNA, 31009 Pamplona, Spain.
| | - Courtney Dennis
- Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA.
| | - Amy Deik
- Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA.
| | - Clary Clish
- Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA.
| | - Cristina Razquin
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- University of Navarra, Department of Preventive Medicine and Public Health, IdiSNA, 31009 Pamplona, Spain.
| | - Dolores Corella
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- Department of Preventive Medicine, University of Valencia, 46010 Valencia, Spain.
| | - Ramon Estruch
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- Department of Internal Medicine, Department of Endocrinology and Nutrition Institut d' Investigacions Biomediques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, 08007 Barcelona, Spain.
| | - Emilio Ros
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- Lipid Clinic, Department of Endocrinology and Nutrition Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, 08007 Barcelona, Spain.
| | - Montserrat Fitó
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- Cardiovascular Risk and Nutrition Research Group (CARIN), Hospital del Mar Research Institute (IMIM), 08003 Barcelona, Spain.
| | - Fernando Arós
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- Department of Cardiology, University Hospital of Álava, 01009 Vitoria, Spain.
| | - Miquel Fiol
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- Illes Balears Health Research Institute (IdISBa), Hospital Son Espases, 07120 Palma de Mallorca, Spain.
| | - José Lapetra
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- Department of Family, Research Unit, Distrito Sanitario Atención Primaria Sevilla, 41013 Sevilla, Spain.
| | - Cristina Ruano
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- Department of Clinical Sciences, University of Las Palmas de Gran Canaria, 35001 Las Palmas, Spain.
| | - Liming Liang
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
| | - Miguel A Martínez-González
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
- University of Navarra, Department of Preventive Medicine and Public Health, IdiSNA, 31009 Pamplona, Spain.
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
- Channing Division for Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, MA 02115, USA.
| | - Jordi Salas-Salvadó
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, 43201 Reus, Spain.
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain.
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95
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Guasch-Ferré M, Ruiz-Canela M, Li J, Zheng Y, Bulló M, Wang DD, Toledo E, Clish C, Corella D, Estruch R, Ros E, Fitó M, Arós F, Fiol M, Lapetra J, Serra-Majem L, Liang L, Papandreou C, Dennis C, Martínez-González MA, Hu FB, Salas-Salvadó J. Plasma Acylcarnitines and Risk of Type 2 Diabetes in a Mediterranean Population at High Cardiovascular Risk. J Clin Endocrinol Metab 2019; 104:1508-1519. [PMID: 30423132 PMCID: PMC6435097 DOI: 10.1210/jc.2018-01000] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Accepted: 11/08/2018] [Indexed: 01/14/2023]
Abstract
CONTEXT The potential associations between acylcarnitine profiles and incidence of type 2 diabetes (T2D) and whether acylcarnitines can be used to improve diabetes prediction remain unclear. OBJECTIVE To evaluate the associations between baseline and 1-year changes in acylcarnitines and their diabetes predictive ability beyond traditional risk factors. DESIGN, SETTING, AND PARTICIPANTS We designed a case-cohort study within the PREDIMED Study including all incident cases of T2D (n = 251) and 694 randomly selected participants at baseline (follow-up, 3.8 years). Plasma acylcarnitines were measured using a targeted approach by liquid chromatography-tandem mass spectrometry. We tested the associations between baseline and 1-year changes in individual acylcarnitines and T2D risk using weighted Cox regression models. We used elastic net regressions to select acylcarnitines for T2D prediction and compute a weighted score using a cross-validation approach. RESULTS An acylcarnitine profile, especially including short- and long-chain acylcarnitines, was significantly associated with a higher risk of T2D independent of traditional risk factors. The relative risks of T2D per SD increment of the predictive model scores were 4.03 (95% CI, 3.00 to 5.42; P < 0.001) for the conventional model and 4.85 (95% CI, 3.65 to 6.45; P < 0.001) for the model including acylcarnitines, with a hazard ratio of 1.33 (95% CI, 1.08 to 1.63; P < 0.001) attributed to the acylcarnitines. Including the acylcarnitines into the model did not significantly improve the area under the receiver operator characteristic curve (0.86 to 0.88, P = 0.61). A 1-year increase in C4OH-carnitine was associated with higher risk of T2D [per SD increment, 1.44 (1.03 to 2.01)]. CONCLUSIONS An acylcarnitine profile, mainly including short- and long-chain acylcarnitines, was significantly associated with higher T2D risk in participants at high cardiovascular risk. The inclusion of acylcarnitines into the model did not significantly improve the T2D prediction C-statistics beyond traditional risk factors, including fasting glucose.
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Affiliation(s)
- Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Pere Virgili Health Research Institute, Rovira i Virgili University, Reus, Spain
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Miguel Ruiz-Canela
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- University of Navarra, Department of Preventive Medicine and Public Health, Pamplona, Spain
- Health Research Institute of Navarra, Pamplona, Spain
| | - Jun Li
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Yan Zheng
- State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China
| | - Mònica Bulló
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Pere Virgili Health Research Institute, Rovira i Virgili University, Reus, Spain
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
| | - Dong D Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Estefanía Toledo
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- University of Navarra, Department of Preventive Medicine and Public Health, Pamplona, Spain
- Health Research Institute of Navarra, Pamplona, Spain
| | - Clary Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts
| | - Dolores Corella
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Ramon Estruch
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Internal Medicine, Hospital Clinic, August Pi Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain
| | - Emilio Ros
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Lipid Clinic, Department of Endocrinology and Nutrition, Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Montserrat Fitó
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Cardiovascular and Nutrition Research Group (REGICOR Study Group), Hospital del Mar Research Institute, Barcelona, Spain
| | - Fernando Arós
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Cardiology, Organización Sanitaria Integrada (OSI) ARABA, Universidad del País Vasco/Euskal Herriko Univertsitatea (UPV/EHU), Vitoria-Gasteiz, Spain
| | - Miquel Fiol
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Institute of Health Sciences (Institut Universitari d’Investigació en Ciències de la Salut-IUNICS), University of Balearic Islands and Hospital Son Espases, Palma de Mallorca, Spain
| | - José Lapetra
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Family Medicine, Research Unit, Distrito Sanitario Atención Primaria Sevilla, Sevilla, Spain
| | - Lluís Serra-Majem
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Research Institute of Biomedical and Health Sciences, University of Las Palmas de Gran Canaria and Service of Preventive Medicine, Complejo Hospitalario Universitario Insular Materno Infantil (CHUIMI), Canary Health Service, Las Palmas de Gran Canaria, Spain
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Christopher Papandreou
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Pere Virgili Health Research Institute, Rovira i Virgili University, Reus, Spain
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
| | - Courtney Dennis
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts
| | - Miguel A Martínez-González
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- University of Navarra, Department of Preventive Medicine and Public Health, Pamplona, Spain
- Health Research Institute of Navarra, Pamplona, Spain
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- 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
| | - Jordi Salas-Salvadó
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Pere Virgili Health Research Institute, Rovira i Virgili University, Reus, Spain
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
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96
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Solon-Biet SM, Cogger VC, Pulpitel T, Wahl D, Clark X, Bagley E, Gregoriou GC, Senior AM, Wang QP, Brandon AE, Perks R, O’Sullivan J, Koay YC, Bell-Anderson K, Kebede M, Yau B, Atkinson C, Svineng G, Dodgson T, Wali JA, Piper MDW, Juricic P, Partridge L, Rose AJ, Raubenheimer D, Cooney GJ, Le Couteur DG, Simpson SJ. Branched chain amino acids impact health and lifespan indirectly via amino acid balance and appetite control. Nat Metab 2019; 1:532-545. [PMID: 31656947 PMCID: PMC6814438 DOI: 10.1038/s42255-019-0059-2] [Citation(s) in RCA: 190] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Accepted: 03/22/2019] [Indexed: 12/11/2022]
Abstract
Elevated branched chain amino acids (BCAAs) are associated with obesity and insulin resistance. How long-term dietary BCAAs impact late-life health and lifespan is unknown. Here, we show that when dietary BCAAs are varied against a fixed, isocaloric macronutrient background, long-term exposure to high BCAA diets leads to hyperphagia, obesity and reduced lifespan. These effects are not due to elevated BCAA per se or hepatic mTOR activation, but rather due to a shift in the relative quantity of dietary BCAAs and other AAs, notably tryptophan and threonine. Increasing the ratio of BCAAs to these AAs resulted in hyperphagia and is associated with central serotonin depletion. Preventing hyperphagia by calorie restriction or pair-feeding averts the health costs of a high BCAA diet. Our data highlight a role for amino acid quality in energy balance and show that health costs of chronic high BCAA intakes need not be due to intrinsic toxicity but, rather, a consequence of hyperphagia driven by AA imbalance.
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Affiliation(s)
- Samantha M Solon-Biet
- Charles Perkins Centre, The University of Sydney NSW, Australia
- School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, NSW, Australia
| | - Victoria C Cogger
- Charles Perkins Centre, The University of Sydney NSW, Australia
- Sydney Medical School, Faculty of Health and Medicine, The University of Sydney NSW, Australia
- Ageing and Alzheimers Institute and Centre for Education and Research on Ageing, Concord Hospital, Concord NSW, Australia
- ANZAC Research Institute, The University of Sydney NSW, Australia
| | - Tamara Pulpitel
- Charles Perkins Centre, The University of Sydney NSW, Australia
- Sydney Medical School, Faculty of Health and Medicine, The University of Sydney NSW, Australia
| | - Devin Wahl
- Charles Perkins Centre, The University of Sydney NSW, Australia
- Sydney Medical School, Faculty of Health and Medicine, The University of Sydney NSW, Australia
- Ageing and Alzheimers Institute and Centre for Education and Research on Ageing, Concord Hospital, Concord NSW, Australia
| | - Ximonie Clark
- Charles Perkins Centre, The University of Sydney NSW, Australia
- School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, NSW, Australia
| | - Elena Bagley
- Charles Perkins Centre, The University of Sydney NSW, Australia
- School of Medical Sciences, Faculty of Health and Medicine, The University of Sydney NSW, Australia
| | - Gabrielle C Gregoriou
- Charles Perkins Centre, The University of Sydney NSW, Australia
- School of Medical Sciences, Faculty of Health and Medicine, The University of Sydney NSW, Australia
| | - Alistair M Senior
- Charles Perkins Centre, The University of Sydney NSW, Australia
- School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, NSW, Australia
| | - Qiao-Ping Wang
- Charles Perkins Centre, The University of Sydney NSW, Australia
- School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, NSW, Australia
- School of Pharmaceutical Sciences (Shenzhen), Sun Yat-Sen University, Guangzhou 510275, China
| | - Amanda E Brandon
- Charles Perkins Centre, The University of Sydney NSW, Australia
- Sydney Medical School, Faculty of Health and Medicine, The University of Sydney NSW, Australia
| | - Ruth Perks
- Charles Perkins Centre, The University of Sydney NSW, Australia
| | - John O’Sullivan
- Charles Perkins Centre, The University of Sydney NSW, Australia
- Heart Research Institute, The University of Sydney, NSW, Australia
| | - Yen Chin Koay
- Charles Perkins Centre, The University of Sydney NSW, Australia
- Heart Research Institute, The University of Sydney, NSW, Australia
| | - Kim Bell-Anderson
- Charles Perkins Centre, The University of Sydney NSW, Australia
- School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, NSW, Australia
| | - Melkam Kebede
- Charles Perkins Centre, The University of Sydney NSW, Australia
- School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, NSW, Australia
| | - Belinda Yau
- Charles Perkins Centre, The University of Sydney NSW, Australia
- School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, NSW, Australia
| | - Clare Atkinson
- Charles Perkins Centre, The University of Sydney NSW, Australia
- School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, NSW, Australia
| | | | - Timothy Dodgson
- Charles Perkins Centre, The University of Sydney NSW, Australia
- School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, NSW, Australia
| | - Jibran A Wali
- Charles Perkins Centre, The University of Sydney NSW, Australia
- School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, NSW, Australia
| | | | - Paula Juricic
- Max Planck Institute for Biology of Aging, Cologne, Germany
| | | | - Adam J Rose
- Monash Biomedicine Discovery Institute, Monash University VIC, Australia
| | - David Raubenheimer
- Charles Perkins Centre, The University of Sydney NSW, Australia
- School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, NSW, Australia
| | - Gregory J Cooney
- Charles Perkins Centre, The University of Sydney NSW, Australia
- Sydney Medical School, Faculty of Health and Medicine, The University of Sydney NSW, Australia
| | - David G Le Couteur
- Charles Perkins Centre, The University of Sydney NSW, Australia
- Sydney Medical School, Faculty of Health and Medicine, The University of Sydney NSW, Australia
- Ageing and Alzheimers Institute and Centre for Education and Research on Ageing, Concord Hospital, Concord NSW, Australia
- ANZAC Research Institute, The University of Sydney NSW, Australia
| | - Stephen J Simpson
- Charles Perkins Centre, The University of Sydney NSW, Australia
- School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, NSW, Australia
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97
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Yu E, Ruiz-Canela M, Razquin C, Guasch-Ferré M, Toledo E, Wang DD, Papandreou C, Dennis C, Clish C, Liang L, Bullo M, Corella D, Fitó M, Gutiérrez-Bedmar M, Lapetra J, Estruch R, Ros E, Cofán M, Arós F, Romaguera D, Serra-Majem L, Sorlí JV, Salas-Salvadó J, Hu FB, Martínez-González MA. Changes in arginine are inversely associated with type 2 diabetes: A case-cohort study in the PREDIMED trial. Diabetes Obes Metab 2019; 21:397-401. [PMID: 30146690 PMCID: PMC6329637 DOI: 10.1111/dom.13514] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Revised: 08/16/2018] [Accepted: 08/23/2018] [Indexed: 01/05/2023]
Abstract
The associations between arginine-based metabolites and incident type 2 diabetes (T2D) are unknown. We employed a case-cohort design, nested within the PREDIMED trial, to examine six plasma metabolites (arginine, citrulline, ornithine, asymmetric dimethylarginine [ADMA], symmetric dimethylarginine [SDMA] and N-monomethyl-l-arginine [NMMA]) among 892 individuals (251 cases) for associations with incident T2D and insulin resistance. Weighted Cox models with robust variance were used. The 1-year changes in arginine (adjusted hazard ratio [HR] per SD 0.68, 95% confidence interval [CI] 0.49, 0.95; Q4 vs. Q1 0.46, 95% CI 0.21, 1.04; P trend = 0.02) and arginine/ADMA ratio (adjusted HR per SD 0.73, 95% CI 0.51, 1.04; Q4 vs. Q1 0.52, 95% CI 0.22, 1.25; P trend = 0.04) were associated with a lower risk of T2D. Positive changes of citrulline and ornithine, and negative changes in SDMA and arginine/(ornithine + citrulline) were associated with concurrent 1-year changes in homeostatic model assessment of insulin resistance. Individuals in the low-fat-diet group had a higher risk of T2D for 1-year changes in NMMA than individuals in Mediterranean-diet groups (P interaction = 0.02). We conclude that arginine bioavailability is important in T2D pathophysiology.
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Affiliation(s)
- Edward Yu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Miguel Ruiz-Canela
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain
- Medicina Preventiva y Salud Pública, IdiSNA (Instituto de Investigación Sanitaria de Navarra), Pamplona, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Cristina Razquin
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
| | - Estefania Toledo
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain
- Medicina Preventiva y Salud Pública, IdiSNA (Instituto de Investigación Sanitaria de Navarra), Pamplona, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Dong D Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Christopher Papandreou
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
| | - Courtney Dennis
- Broad Institute of MIT and Harvard University, Cambridge, Massachusetts
| | - Clary Clish
- Broad Institute of MIT and Harvard University, Cambridge, Massachusetts
| | - Liming Liang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Monica Bullo
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
| | - Dolores Corella
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Montserrat Fitó
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | | | - José Lapetra
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Family Medicine, Unit Research, Distrito Sanitario Atención Primaria Sevilla, Sevilla, Spain
| | - Ramón Estruch
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Internal Medicine, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDI- BAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Emilio Ros
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Lipid Clinic, Department of Endocrinology and Nutrition, IDIBAPS, Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Montserrat Cofán
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Lipid Clinic, Department of Endocrinology and Nutrition, IDIBAPS, Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Fernando Arós
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Cardiology, University Hospital of Álava, Vitoria, Spain
| | - Dora Romaguera
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Epidemiología Clínica y Salud Pública, Health Research Institute of Palma (IdISPa), University Hospital Son Espases, Palma de Mallorca, Spain
| | - Lluis Serra-Majem
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Clinical Sciences, Research Institute of Biomedical and Health Sciences, University of Las Palmas de Gran Canaria, Las Palmas, Spain
| | - Jose V Sorlí
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Jordi Salas-Salvadó
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Channing Division for Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Massachusetts
| | - Miguel A Martínez-González
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain
- Medicina Preventiva y Salud Pública, IdiSNA (Instituto de Investigación Sanitaria de Navarra), Pamplona, Spain
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98
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Jiang X, Zeleznik OA, Lindström S, Lasky‐Su J, Hagan K, Clish CB, Eliassen AH, Kraft P, Kabrhel C. Metabolites Associated With the Risk of Incident Venous Thromboembolism: A Metabolomic Analysis. J Am Heart Assoc 2018; 7:e010317. [PMID: 30571496 PMCID: PMC6404443 DOI: 10.1161/jaha.118.010317] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Background Venous thromboembolism ( VTE ) is a complex thrombotic disorder that constitutes a major source of mortality and morbidity. To improve understanding of the cause of VTE , we conducted a metabolomic analysis in a case-control study including 240 incident VTE cases and 6963 controls nested within 3 large prospective population-based cohorts, the Nurses' Health Study, the Nurses' Health Study II , and the Health Professionals Follow-Up Study. Methods and Results For each individual, we measured 211 metabolites and collected detailed information on lifestyle factors. We performed logistic regression and enrichment analysis to identify metabolites and biological categories associated with incident VTE risk, accounting for key confounders, such as age, sex, smoking, alcohol consumption, body mass index, and comorbid diseases (eg, cancers). We performed analyses of all VTEs and separate analyses of pulmonary embolism. Using the basic model controlling for age, sex, and primary disease, we identified 60 nominally significant VTE - or pulmonary embolism-associated metabolites ( P<0.05). These metabolites were enriched for diacylglycerols ( Ppermutation<0.05). However, after controlling for multiple testing, only 1 metabolite (C5 carnitine; odds ratio, 1.25; 95% confidence interval, 1.10-1.41; Pcorrected=0.03) remained significantly associated with VTE . After further adjustment for body mass index, no metabolites were significantly associated with disease after accounting for multiple testing, and no metabolite classes were enriched for nominally significant associations. Conclusions Although our findings suggest that circulating metabolites may influence the risk of incident VTE , the associations we observed were confounded by body mass index. Larger studies involving additional individuals and with broader metabolomics coverage are needed to confirm our findings.
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Affiliation(s)
- Xia Jiang
- Program in Genetic Epidemiology and Statistical GeneticsHarvard T.H. Chan School of Public HealthBostonMA
- Unit of Cardiovascular EpidemiologyInstitute of Environmental MedicineKarolinska InstitutetStockholmSweden
| | - Oana A. Zeleznik
- Channing Division of Network MedicineDepartment of MedicineBrigham and Women's Hospital and Harvard Medical SchoolBostonMA
- Department of MedicineHarvard Medical SchoolBostonMA
| | - Sara Lindström
- EpidemiologyUniversity of WashingtonSeattleWA
- Public Health SciencesFred Hutchinson Cancer Research CenterSeattleWA
| | - Jessica Lasky‐Su
- Channing Division of Network MedicineDepartment of MedicineBrigham and Women's Hospital and Harvard Medical SchoolBostonMA
| | - Kaitlin Hagan
- Channing Division of Network MedicineDepartment of MedicineBrigham and Women's Hospital and Harvard Medical SchoolBostonMA
- Department of MedicineHarvard Medical SchoolBostonMA
| | | | | | - Peter Kraft
- Program in Genetic Epidemiology and Statistical GeneticsHarvard T.H. Chan School of Public HealthBostonMA
| | - Christopher Kabrhel
- Channing Division of Network MedicineDepartment of MedicineBrigham and Women's Hospital and Harvard Medical SchoolBostonMA
- Department of Emergency MedicineCenter for Vascular EmergenciesMassachusetts General HospitalBostonMA
- Department of Emergency MedicineHarvard Medical SchoolBostonMA
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99
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Steinhauser ML, Olenchock BA, O'Keefe J, Lun M, Pierce KA, Lee H, Pantano L, Klibanski A, Shulman GI, Clish CB, Fazeli PK. The circulating metabolome of human starvation. JCI Insight 2018; 3:121434. [PMID: 30135314 DOI: 10.1172/jci.insight.121434] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Accepted: 07/10/2018] [Indexed: 12/17/2022] Open
Abstract
The human adaptive starvation response allows for survival during long-term caloric deprivation. Whether the physiology of starvation is adaptive or maladaptive is context dependent: activation of pathways by caloric restriction may promote longevity, yet in the context of caloric excess, the same pathways may contribute to obesity. Here, we performed plasma metabolite profiling of longitudinally collected samples during a 10-day, 0-calorie fast in humans. We identify classical milestones in adaptive starvation, including the early consumption of gluconeogenic amino acids and the subsequent surge in plasma nonesterified fatty acids that marks the shift from carbohydrate to lipid metabolism, and demonstrate findings, including (a) the preferential release of unsaturated fatty acids and an associated shift in plasma lipid species with high degrees of unsaturation and (b) evidence that acute, starvation-mediated hypoleptinemia may be a driver of the transition from glucose to lipid metabolism in humans.
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Affiliation(s)
- Matthew L Steinhauser
- Department of Medicine, Division of Genetics, and.,Department of Medicine, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA.,Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Benjamin A Olenchock
- Department of Medicine, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA.,Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - John O'Keefe
- Department of Medicine, Division of Genetics, and
| | - Mingyue Lun
- Department of Medicine, Division of Genetics, and
| | - Kerry A Pierce
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Hang Lee
- Harvard Medical School, Boston, Massachusetts, USA.,MGH Biostatistics Center, Boston, Massachusetts, USA
| | - Lorena Pantano
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Anne Klibanski
- Harvard Medical School, Boston, Massachusetts, USA.,Neuroendocrine Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Gerald I Shulman
- Departments of Internal Medicine and Cellular and Molecular Physiology and Howard Hughes Medical Institute, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Pouneh K Fazeli
- Harvard Medical School, Boston, Massachusetts, USA.,Neuroendocrine Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
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100
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Yu E, Papandreou C, Ruiz-Canela M, Guasch-Ferre M, Clish CB, Dennis C, Liang L, Corella D, Fitó M, Razquin C, Lapetra J, Estruch R, Ros E, Cofán M, Arós F, Toledo E, Serra-Majem L, Sorlí JV, Hu FB, Martinez-Gonzalez MA, Salas-Salvado J. Association of Tryptophan Metabolites with Incident Type 2 Diabetes in the PREDIMED Trial: A Case-Cohort Study. Clin Chem 2018; 64:1211-1220. [PMID: 29884676 PMCID: PMC6218929 DOI: 10.1373/clinchem.2018.288720] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Accepted: 05/14/2018] [Indexed: 12/29/2022]
Abstract
BACKGROUND Metabolites of the tryptophan-kynurenine pathway (i.e., tryptophan, kynurenine, kynurenic acid, quinolinic acid, 3-hydroxyanthranilic) may be associated with diabetes development. Using a case-cohort design nested in the Prevención con Dieta Mediterránea (PREDIMED) study, we studied the associations of baseline and 1-year changes of these metabolites with incident type 2 diabetes (T2D). METHODS Plasma metabolite concentrations were quantified via LC-MS for n = 641 in a randomly selected subcohort and 251 incident cases diagnosed during 3.8 years of median follow-up. Weighted Cox models adjusted for age, sex, body mass index, and other T2D risk factors were used. RESULTS Baseline tryptophan was associated with higher risk of incident T2D (hazard ratio = 1.29; 95% CI, 1.04-1.61 per SD). Positive changes in quinolinic acid from baseline to 1 year were associated with a higher risk of T2D (hazard ratio = 1.39; 95% CI, 1.09-1.77 per SD). Baseline tryptophan and kynurenic acid were directly associated with changes in homeostatic model assessment for insulin resistance (HOMA-IR) from baseline to 1 year. Concurrent changes in kynurenine, quinolinic acid, 3-hydroxyanthranilic acid, and kynurenine/tryptophan ratio were associated with baseline-to-1-year changes in HOMA-IR. CONCLUSIONS Baseline tryptophan and 1-year increases in quinolinic acid were positively associated with incident T2D. Baseline and 1-year changes in tryptophan metabolites predicted changes in HOMA-IR. Tryptophan levels may initially increase and then deplete as diabetes progresses in severity.
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Affiliation(s)
- Edward Yu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Christopher Papandreou
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Miguel Ruiz-Canela
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain
- IdiSNA (Instituto de Investigación Sanitària de Navarra), Navarra, Spain
| | - Marta Guasch-Ferre
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Clary B Clish
- Broad Institute of MIT and Harvard University, Cambridge, MA
| | - Courtney Dennis
- Broad Institute of MIT and Harvard University, Cambridge, MA
| | - Liming Liang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Dolores Corella
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Montserrat Fitó
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Cardiovascular and Nutrition Research Group, Institut de Recerca Hospital del Mar, Barcelona, Spain
| | - Cristina Razquin
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain
- IdiSNA (Instituto de Investigación Sanitària de Navarra), Navarra, Spain
| | - José Lapetra
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Family Medicine, Unit Research, Distrito Sanitario Atención Primaria Sevilla, Sevilla, Spain
| | - Ramón Estruch
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Internal Medicine Institut d'Investigacions Biomèdiques August Pi Sunyer (IDI- BAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Emilio Ros
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Lipid Clinic, Department of Endocrinology and Nutrition (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Montserrat Cofán
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Lipid Clinic, Department of Endocrinology and Nutrition (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Fernando Arós
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Cardiology, University Hospital of Álava, Vitoria, Spain
| | - Estefania Toledo
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain
- IdiSNA (Instituto de Investigación Sanitària de Navarra), Navarra, Spain
| | - Lluis Serra-Majem
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Research Institute of Biomedical and Health Sciences, University of Las Palmas de Gran Canaria, Las Palmas, Spain
| | - José V Sorlí
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA
- Channing Division for Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, MA
| | - Miguel A Martinez-Gonzalez
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain
- IdiSNA (Instituto de Investigación Sanitària de Navarra), Navarra, Spain
| | - Jordi Salas-Salvado
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain;
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
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