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Wang N, Ockerman FP, Zhou LY, Grove ML, Alkis T, Barnard J, Bowler RP, Clish CB, Chung S, Drzymalla E, Evans AM, Franceschini N, Gerszten RE, Gillman MG, Hutton SR, Kelly RS, Kooperberg C, Larson MG, Lasky-Su J, Meyers DA, Woodruff PG, Reiner AP, Rich SS, Rotter JI, Silverman EK, Ramachandran VS, Weiss ST, Wong KE, Wood AC, Wu L, Yarden R, Blackwell TW, Smith AV, Chen H, Raffield LM, Yu B. Genetic Architecture and Analysis Practices of Circulating Metabolites in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.23.604849. [PMID: 39211135 PMCID: PMC11361093 DOI: 10.1101/2024.07.23.604849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
Circulating metabolite levels partly reflect the state of human health and diseases, and can be impacted by genetic determinants. Hundreds of loci associated with circulating metabolites have been identified; however, most findings focus on predominantly European ancestry or single study analyses. Leveraging the rich metabolomics resources generated by the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program, we harmonized and accessibly cataloged 1,729 circulating metabolites among 25,058 ancestrally-diverse samples. We provided recommendations for outlier and imputation handling to process metabolite data, as well as a general analytical framework. We further performed a pooled analysis following our practical recommendations and discovered 1,778 independent loci associated with 667 metabolites. Among 108 novel locus - metabolite pairs, we detected not only novel loci within previously implicated metabolite associated genes, but also novel genes (such as GAB3 and VSIG4 located in the X chromosome) that have putative roles in metabolic regulation. In the sex-stratified analysis, we revealed 85 independent locus-metabolite pairs with evidence of sexual dimorphism, including well-known metabolic genes such as FADS2 , D2HGDH , SUGP1 , UTG2B17 , strongly supporting the importance of exploring sex difference in the human metabolome. Taken together, our study depicted the genetic contribution to circulating metabolite levels, providing additional insight into the understanding of human health.
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Wulczyn KE, Shafi T, Anderson A, Rincon-Choles H, Clish CB, Denburg M, Feldman HI, He J, Hsu CY, Kelly T, Kimmel PL, Mehta R, Nelson RG, Ramachandran V, Ricardo A, Shah VO, Srivastava A, Xie D, Rhee EP, Kalim S. Metabolites Associated With Uremic Symptoms in Patients With CKD: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study. Am J Kidney Dis 2024; 84:49-61.e1. [PMID: 38266973 PMCID: PMC11193655 DOI: 10.1053/j.ajkd.2023.11.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 10/30/2023] [Accepted: 11/20/2023] [Indexed: 01/26/2024]
Abstract
RATIONALE & OBJECTIVE The toxins that contribute to uremic symptoms in patients with chronic kidney disease (CKD) are unknown. We sought to apply complementary statistical modeling approaches to data from untargeted plasma metabolomic profiling to identify solutes associated with uremic symptoms in patients with CKD. STUDY DESIGN Cross-sectional. SETTING & PARTICIPANTS 1,761 Chronic Renal Insufficiency Cohort (CRIC) participants with CKD not treated with dialysis. PREDICTORS Measurement of 448 known plasma metabolites. OUTCOMES The uremic symptoms of fatigue, anorexia, pruritus, nausea, paresthesia, and pain were assessed by single items on the Kidney Disease Quality of Life-36 instrument. ANALYTICAL APPROACH Multivariable adjusted linear regression, least absolute shrinkage and selection operator linear regression, and random forest models were used to identify metabolites associated with symptom severity. After adjustment for multiple comparisons, metabolites selected in at least 2 of the 3 modeling approaches were deemed "overall significant." RESULTS Participant mean estimated glomerular filtration rate was 43mL/min/1.73m2, with 44% self-identifying as female and 41% as non-Hispanic Black. The prevalence of uremic symptoms ranged from 22% to 55%. We identified 17 metabolites for which a higher level was associated with greater severity of at least one uremic symptom and 9 metabolites inversely associated with uremic symptom severity. Many of these metabolites exhibited at least a moderate correlation with estimated glomerular filtration rate (Pearson's r≥0.5), and some were also associated with the risk of developing kidney failure or death in multivariable adjusted Cox regression models. LIMITATIONS Lack of a second independent cohort for external validation of our findings. CONCLUSIONS Metabolomic profiling was used to identify multiple solutes associated with uremic symptoms in adults with CKD, but future validation and mechanistic studies are needed. PLAIN-LANGUAGE SUMMARY Individuals living with chronic kidney disease (CKD) often experience symptoms related to CKD, traditionally called uremic symptoms. It is likely that CKD results in alterations in the levels of numerous circulating substances that, in turn, cause uremic symptoms; however, the identity of these solutes is not known. In this study, we used metabolomic profiling in patients with CKD to gain insights into the pathophysiology of uremic symptoms. We identified 26 metabolites whose levels were significantly associated with at least one of the symptoms of fatigue, anorexia, itchiness, nausea, paresthesia, and pain. The results of this study lay the groundwork for future research into the biological causes of symptoms in patients with CKD.
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Affiliation(s)
- Kendra E Wulczyn
- Nephrology Division, Massachusetts General Hospital, Boston, Massachusetts.
| | - Tariq Shafi
- Division of Nephrology, Department of Medicine, Houston Methodist Hospital, Houston, Texas
| | - Amanda Anderson
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA
| | - Hernan Rincon-Choles
- Department of Nephrology, Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, Ohio
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Michelle Denburg
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Division of Pediatric Nephrology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Harold I Feldman
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA
| | - Chi-Yuan Hsu
- Division of Nephrology, University of California, San Francisco, School of Medicine, San Francisco, California; Division of Research, Kaiser Permanente Northern California, Oakland, California
| | - Tanika Kelly
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois at Chicago, Chicago, Illinois
| | - Paul L Kimmel
- Division of Kidney, Urologic, and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, Maryland
| | - Rupal Mehta
- Division of Nephrology, Northwestern University, Chicago, Illinois
| | - Robert G Nelson
- Chronic Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona
| | - Vasan Ramachandran
- Department of Epidemiology and Sections of Preventive Medicine and Epidemiology and Cardiology, Department of Medicine, Boston University School of Public Health, Boston, Massachusetts
| | - Ana Ricardo
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois at Chicago, Chicago, Illinois
| | - Vallabh O Shah
- Department of Internal Medicine and Biochemistry, School of Medicine, University of New Mexico, Albuquerque, New Mexico
| | - Anand Srivastava
- Center for Translational Metabolism and Health, Institute for Public Health and Medicine, Division of Nephrology and Hypertension, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Dawei Xie
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Eugene P Rhee
- Nephrology Division, Massachusetts General Hospital, Boston, Massachusetts; Endocrine Unit, Massachusetts General Hospital, Boston, Massachusetts
| | - Sahir Kalim
- Nephrology Division, Massachusetts General Hospital, Boston, Massachusetts
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3
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Bhave VM, Ament Z, Levy DE, Thorndike AN, Kimberly WT. Workplace food purchases, dietary intake, and gut microbial metabolites in a secondary analysis of the ChooseWell 365 study. Am J Clin Nutr 2024; 119:1504-1513. [PMID: 38677520 PMCID: PMC11196865 DOI: 10.1016/j.ajcnut.2024.04.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/03/2024] [Accepted: 04/22/2024] [Indexed: 04/29/2024] Open
Abstract
BACKGROUND Dietary choices can affect human health through alterations in gut microbial metabolism, and gut microbial metabolites could serve as biomarkers for disease risk conferred by dietary intake. However, self-reported dietary intake may not reflect true intake. OBJECTIVES We identified circulating metabolites, including gut microbiome-related metabolites, associated with adherence to a healthy diet in the ChooseWell 365 study. In this randomized clinical trial, the dietary choices of hospital employees were assessed over 24 mo using not only 24-h dietary recalls but also electronic records of hospital cafeteria purchases. METHODS Plasma metabolites were profiled from 470 participants. Two targeted metabolomics methods were developed and implemented to expand detection coverage for metabolites related to gut microbial activity. Linear regression models were used to associate metabolites with Healthy Purchasing Scores (HPSs) derived from cafeteria purchases and Healthy Eating Index-2015 (HEI-15) scores derived from dietary recalls. RESULTS Fourteen metabolites were concordantly associated with the HPS and HEI-15 scores in multivariable models adjusted for age, gender, and race, including the gut microbiome-related metabolites indole-3-propionic acid (HPS, β: 0.16, 95% CI: 0.07, 0.26, P = 7.32 × 10-4; HEI-15, β: 0.16, 95% CI: 0.07, 0.25, P = 6.79 × 10-4), hippuric acid (HPS, β: 0.11, 95% CI: 0.02, 0.21, P = 1.97 × 10-2; HEI-15, β: 0.10, 95% CI: 0.01, 0.19, P = 3.14 × 10-2), and indoxyl sulfate (HPS, β = -0.13, 95% CI: -0.23, -0.03, P = 8.21 × 10-3; HEI-15, β: -0.12, 95% CI: -0.22, -0.03, P = 8.50 × 10-3). These gut microbial metabolites were associated with the intake of specific food groups, such as whole fruits. These metabolites were also associated with clinical variables, including blood pressure, diabetes or prediabetes, and body mass index. CONCLUSIONS In a secondary analysis of the ChooseWell 365 study, associations between circulating gut microbiome-related metabolites and a healthy diet were confirmed using both objective and subjective measures of consumption. Accurate identification of diet-associated metabolites may help guide dietary or microbiome-based interventions aimed at disease prevention.
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Affiliation(s)
| | - Zsuzsanna Ament
- Harvard Medical School, Boston, MA, United States; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States; Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Douglas E Levy
- Harvard Medical School, Boston, MA, United States; Mongan Institute Health Policy Research Center, Massachusetts General Hospital, Boston, MA, United States
| | - Anne N Thorndike
- Harvard Medical School, Boston, MA, United States; Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - W Taylor Kimberly
- Harvard Medical School, Boston, MA, United States; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States; Department of Neurology, Massachusetts General Hospital, Boston, MA, United States.
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Perry AS, Piaggi P, Huang S, Nayor M, Freedman J, North K, Below J, Clish C, Murthy VL, Krakoff J, Shah RV. Human metabolic chambers reveal a coordinated metabolic-physiologic response to nutrition. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.08.24305087. [PMID: 38645000 PMCID: PMC11030300 DOI: 10.1101/2024.04.08.24305087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
The emerging field of precision nutrition is based on the notion that inter-individual responses across diets of different calorie-macronutrient content may contribute to inter-individual differences in metabolism, adiposity, and weight gain. Free-living diet studies have been traditionally challenged by difficulties in controlling adherence to prescribed calories and macronutrient content and rarely allow a period of metabolic stability prior to metabolic measures (to minimize influences of weight changes). In this context, key physiologic measures central to precision nutrition responses may be most precisely quantified via whole room indirect calorimetry over 24-h, in which precise control of activity and nutrition can be achieved. In addition, these studies represent unique "N of 1" human crossover metabolic-physiologic experiments during which specific molecular pathways central to nutrient metabolism may be discerned. Here, we quantified 263 circulating metabolites during a ≈40-day inpatient admission in which up to 94 participants underwent seven monitored 24-h nutritional interventions of differing macronutrient composition in a whole-room indirect calorimeter to capture precision metabolic responses. Broadly, we observed heterogenous responses in metabolites across dietary chambers, with the exception of carnitines which tracked with 24-h respiratory quotient. We identified excursions in shared metabolic species (e.g., carnitines, glycerophospholipids, amino acids) that mapped onto gold-standard calorimetric measures of substrate oxidation preference and lipid availability. These findings support a coordinated metabolic-physiologic response to nutrition, highlighting the relevance of these controlled settings to uncover biological pathways of energy utilization during precision nutrition studies.
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Yao S, Colangelo LA, Perry AS, Marron MM, Yaffe K, Sedaghat S, Lima JAC, Tian Q, Clish CB, Newman AB, Shah RV, Murthy VL. Implications of metabolism on multi-systems healthy aging across the lifespan. Aging Cell 2024; 23:e14090. [PMID: 38287525 PMCID: PMC11019145 DOI: 10.1111/acel.14090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 12/30/2023] [Accepted: 01/11/2024] [Indexed: 01/31/2024] Open
Abstract
Aging is increasingly thought to involve dysregulation of metabolism in multiple organ systems that culminate in decreased functional capacity and morbidity. Here, we seek to understand complex interactions among metabolism, aging, and systems-wide phenotypes across the lifespan. Among 2469 adults (mean age 74.7 years; 38% Black) in the Health, Aging and Body Composition study we identified metabolic cross-sectionally correlates across 20 multi-dimensional aging-related phenotypes spanning seven domains. We used LASSO-PCA and bioinformatic techniques to summarize metabolome-phenome relationships and derive metabolic scores, which were subsequently linked to healthy aging, mortality, and incident outcomes (cardiovascular disease, disability, dementia, and cancer) over 9 years. To clarify the relationship of metabolism in early adulthood to aging, we tested association of these metabolic scores with aging phenotypes/outcomes in 2320 participants (mean age 32.1, 44% Black) of the Coronary Artery Risk Development in Young Adults (CARDIA) study. We observed significant overlap in metabolic correlates across the seven aging domains, specifying pathways of mitochondrial/cellular energetics, host-commensal metabolism, inflammation, and oxidative stress. Across four metabolic scores (body composition, mental-physical performance, muscle strength, and physical activity), we found strong associations with healthy aging and incident outcomes, robust to adjustment for risk factors. Metabolic scores for participants four decades younger in CARDIA were related to incident cardiovascular, metabolic, and neurocognitive performance, as well as long-term cardiovascular disease and mortality over three decades. Conserved metabolic states are strongly related to domain-specific aging and outcomes over the life-course relevant to energetics, host-commensal interactions, and mechanisms of innate immunity.
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Affiliation(s)
- Shanshan Yao
- University of PittsburgPittsburghPennsylvaniaUSA
| | | | | | | | | | | | | | - Qu Tian
- National Institute of AgingBaltimoreMarylandUSA
| | - Clary B. Clish
- Broad Institute of Harvard and MITCambridgeMassachusettsUSA
| | | | - Ravi V. Shah
- Vanderbilt University Medical CenterNashvilleTennesseeUSA
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Qadri S, Wang X, Tran C, Fitzpatrick M, Bonnitcha P, Sullivan D, Yki-Järvinen H, O'Sullivan JF. The first human normative ranges and biomarker performance of dimethylguanidino valeric acid isoforms in fatty liver disease. Pathology 2024; 56:391-397. [PMID: 38071157 DOI: 10.1016/j.pathol.2023.10.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 10/12/2023] [Accepted: 10/26/2023] [Indexed: 03/12/2024]
Abstract
We have recently determined dimethylguanidino valeric acid (DMGV) to be a novel biomarker of liver injury in non-alcoholic fatty liver disease (NAFLD) and an independent predictor of incident diabetes over a decade in advance. DMGV consists of two stereo-isomers, asymmetric dimethylguanidino valeric acid (ADGV) and symmetric dimethylguanidino valeric acid (SDGV). Here we report, for the first time, the upper limits of normal of both isomers in humans at the accepted 5.56% liver fat threshold for NAFLD, determined using in vivo magnetic resonance spectroscopy. We performed independent and blinded comparative analyses of ADGV and SDGV levels using two different liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods in (A) our laboratory, and (B) the New South Wales Chemical Pathology state laboratory, using unique columns, LC-MS/MS equipment, extraction protocols and normalisation approaches. Despite these differences, each laboratory reported consistent absolute concentrations across a range of liver fat percentages. We next determined the diagnostic performance of SDGV compared to ADGV in a cohort of 268 individuals with liver fat measurements. In derivation-validation analyses we determined rule-in/rule-out thresholds and the concentration of SDGV that provides optimal performance across sensitivity and specificity for the identification of NAFLD. In conclusion, we have herein determined for the first time the true human plasma reference range of both isoforms of an emerging novel biomarker of NAFLD, at the accepted upper normal threshold of liver fat. We have also identified that SDGV is the isoform with the best diagnostic performance and determined the optimal cut-point for its detection of NAFLD.
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Affiliation(s)
- Sami Qadri
- Department of Medicine, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - XiaoSuo Wang
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia; Heart Research Institute, Newtown, NSW, Australia; School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Collin Tran
- School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia; Department of Chemical Pathology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - Michael Fitzpatrick
- Department of Chemical Pathology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - Paul Bonnitcha
- Department of Chemical Pathology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia; NSW Health Pathology, NSW, Australia
| | - David Sullivan
- NSW Health Pathology, NSW, Australia; NHMRC Clinical Trials Centre, Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
| | - Hannele Yki-Järvinen
- Department of Medicine, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - John F O'Sullivan
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia; School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia; Department of Cardiology, Royal Prince Alfred Hospital, Sydney, NSW, Australia.
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Schirmer M, Stražar M, Avila-Pacheco J, Rojas-Tapias DF, Brown EM, Temple E, Deik A, Bullock K, Jeanfavre S, Pierce K, Jin S, Invernizzi R, Pust MM, Costliow Z, Mack DR, Griffiths AM, Walters T, Boyle BM, Kugathasan S, Vlamakis H, Hyams J, Denson L, Clish CB, Xavier RJ. Linking microbial genes to plasma and stool metabolites uncovers host-microbial interactions underlying ulcerative colitis disease course. Cell Host Microbe 2024; 32:209-226.e7. [PMID: 38215740 PMCID: PMC10923022 DOI: 10.1016/j.chom.2023.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 11/08/2023] [Accepted: 12/15/2023] [Indexed: 01/14/2024]
Abstract
Understanding the role of the microbiome in inflammatory diseases requires the identification of microbial effector molecules. We established an approach to link disease-associated microbes to microbial metabolites by integrating paired metagenomics, stool and plasma metabolomics, and culturomics. We identified host-microbial interactions correlated with disease activity, inflammation, and the clinical course of ulcerative colitis (UC) in the Predicting Response to Standardized Colitis Therapy (PROTECT) pediatric inception cohort. In severe disease, metabolite changes included increased dipeptides and tauro-conjugated bile acids (BAs) and decreased amino-acid-conjugated BAs in stool, whereas in plasma polyamines (N-acetylputrescine and N1-acetylspermidine) increased. Using patient samples and Veillonella parvula as a model, we uncovered nitrate- and lactate-dependent metabolic pathways, experimentally linking V. parvula expansion to immunomodulatory tryptophan metabolite production. Additionally, V. parvula metabolizes immunosuppressive thiopurine drugs through xdhA xanthine dehydrogenase, potentially impairing the therapeutic response. Our findings demonstrate that the microbiome contributes to disease-associated metabolite changes, underscoring the importance of these interactions in disease pathology and treatment.
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Affiliation(s)
- Melanie Schirmer
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Translational Microbiome Data Integration, School of Life Sciences, Technical University of Munich, 85354 Freising, Germany; ZIEL - Institute for Food & Health, Technical University of Munich, 85354 Freising, Germany.
| | - Martin Stražar
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | | | - Eric M Brown
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology and Department of Molecular Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Emily Temple
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Amy Deik
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kevin Bullock
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Sarah Jeanfavre
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kerry Pierce
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Shen Jin
- Translational Microbiome Data Integration, School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
| | | | - Marie-Madlen Pust
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology and Department of Molecular Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Zach Costliow
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - David R Mack
- Division of Gastroenterology, Hepatology & Nutrition, Children's Hospital of Eastern Ontario and University of Ottawa, Ottawa, ON K1H 8L1, Canada
| | - Anne M Griffiths
- Division of Gastroenterology, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Thomas Walters
- Division of Gastroenterology, Division of Gastroenterology, Hepatology and Nutrition, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Brendan M Boyle
- Division of Pediatric Gastroenterology, Hepatology, and Nutrition, Nationwide Children's Hospital, Columbus, OH 43205, USA
| | - Subra Kugathasan
- Department of Pediatrics, Emory University, Atlanta, GA 30322, USA
| | - Hera Vlamakis
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jeffrey Hyams
- Connecticut Children's Medical Center, Division of Digestive Diseases, Hartford, CT 06106, USA
| | - Lee Denson
- Cincinnati Children's Hospital Medical Center and the University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA
| | - Clary B Clish
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ramnik J Xavier
- The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology and Department of Molecular Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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8
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Margara-Escudero HJ, Paz-Graniel I, García-Gavilán J, Ruiz-Canela M, Sun Q, Clish CB, Toledo E, Corella D, Estruch R, Ros E, Castañer O, Arós F, Fiol M, Guasch-Ferré M, Lapetra J, Razquin C, Dennis C, Deik A, Li J, Gómez-Gracia E, Babio N, Martínez-González MA, Hu FB, Salas-Salvadó J. Plasma metabolite profile of legume consumption and future risk of type 2 diabetes and cardiovascular disease. Cardiovasc Diabetol 2024; 23:38. [PMID: 38245716 PMCID: PMC10800064 DOI: 10.1186/s12933-023-02111-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 12/29/2023] [Indexed: 01/22/2024] Open
Abstract
BACKGROUND Legume consumption has been linked to a reduced risk of type 2 diabetes (T2D) and cardiovascular disease (CVD), while the potential association between plasma metabolites associated with legume consumption and the risk of cardiometabolic diseases has never been explored. Therefore, we aimed to identify a metabolite signature of legume consumption, and subsequently investigate its potential association with the incidence of T2D and CVD. METHODS The current cross-sectional and longitudinal analysis was conducted in 1833 PREDIMED study participants (mean age 67 years, 57.6% women) with available baseline metabolomic data. A subset of these participants with 1-year follow-up metabolomics data (n = 1522) was used for internal validation. Plasma metabolites were assessed through liquid chromatography-tandem mass spectrometry. Cross-sectional associations between 382 different known metabolites and legume consumption were performed using elastic net regression. Associations between the identified metabolite profile and incident T2D and CVD were estimated using multivariable Cox regression models. RESULTS Specific metabolic signatures of legume consumption were identified, these included amino acids, cortisol, and various classes of lipid metabolites including diacylglycerols, triacylglycerols, plasmalogens, sphingomyelins and other metabolites. Among these identified metabolites, 22 were negatively and 18 were positively associated with legume consumption. After adjustment for recognized risk factors and legume consumption, the identified legume metabolite profile was inversely associated with T2D incidence (hazard ratio (HR) per 1 SD: 0.75, 95% CI 0.61-0.94; p = 0.017), but not with CVD incidence risk (1.01, 95% CI 0.86-1.19; p = 0.817) over the follow-up period. CONCLUSIONS This study identified a set of 40 metabolites associated with legume consumption and with a reduced risk of T2D development in a Mediterranean population at high risk of cardiovascular disease. TRIAL REGISTRATION ISRCTN35739639.
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Affiliation(s)
- Hernando J Margara-Escudero
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- Alimentació, Nutrició, Desenvolupament i Salut Mental, Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Indira Paz-Graniel
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- Alimentació, Nutrició, Desenvolupament i Salut Mental, Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Jesús García-Gavilán
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain.
- Alimentació, Nutrició, Desenvolupament i Salut Mental, Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain.
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - Miguel Ruiz-Canela
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Instituto de Investigación Sanitario de Navarra (IdiSNA), Pamplona, Spain
| | - Qi Sun
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Clary B Clish
- The Broad Institute of Harvard and MIT, Boston, MA, 02142, USA
| | - Estefania Toledo
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Instituto de Investigación Sanitario de Navarra (IdiSNA), Pamplona, Spain
- Navarra Institute for Health Research, IdiSNA, Pamplona, Navarre, Spain
| | - Dolores Corella
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Ramón Estruch
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Internal Medicine, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Emilio Ros
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Endocrinology and Nutrition, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Lipid Clinic, Hospital Clínic, Barcelona, Spain
| | - Olga Castañer
- Centro de Investigación Biomédica en Red (CIBERESP) de Epidemiología y Salud Pública, Instituto de Salud Carlos III, Madrid, Spain
- Cardiovascular Risk and Nutrition Research Group, Hospital del Mar Research Institute, Barcelona, Spain
| | - Fernando Arós
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Cardiology, University Hospital of Alava, Vitoria, Spain
| | - Miquel Fiol
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Illes Balears Health Research Institute (IdISBa), Hospital Son Espases, Palma, Spain
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Public Health, Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - José Lapetra
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Family Medicine, Research Unit, Distrito Sanitario Atención Primaria Sevilla, Seville, Spain
| | - Cristina Razquin
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Instituto de Investigación Sanitario de Navarra (IdiSNA), Pamplona, Spain
| | - Courtney Dennis
- The Broad Institute of Harvard and MIT, Boston, MA, 02142, USA
| | - Amy Deik
- The Broad Institute of Harvard and MIT, Boston, MA, 02142, USA
| | - Jun Li
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Enrique Gómez-Gracia
- Preventive Medicine and Public Health Department, School of Medicine, University of Málaga, 29010, Malaga, Spain
- Biomedical Research Institute of Malaga-IBIMA Plataforma BIONAND, 29010, Malaga, Spain
| | - Nancy Babio
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain.
- Alimentació, Nutrició, Desenvolupament i Salut Mental, Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain.
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - Miguel A Martínez-González
- Department of Preventive Medicine and Public Health, University of Navarra, Instituto de Investigación Sanitario de Navarra (IdiSNA), Pamplona, Spain
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jordi Salas-Salvadó
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentació, Nutrició, Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- Alimentació, Nutrició, Desenvolupament i Salut Mental, Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de La Obesidad y La Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
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9
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Lee DH, Jin Q, Shi N, Wang F, Bever AM, Liang L, Hu FB, Song M, Zeleznik OA, Zhang X, Joshi A, Wu K, Jeon JY, Meyerhardt JA, Chan AT, Eliassen AH, Clish C, Clinton SK, Giovannucci EL, Li J, Tabung FK. The metabolic potential of inflammatory and insulinaemic dietary patterns and risk of type 2 diabetes. Diabetologia 2024; 67:88-101. [PMID: 37816982 DOI: 10.1007/s00125-023-06021-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 08/31/2023] [Indexed: 10/12/2023]
Abstract
AIMS/HYPOTHESIS Diets with higher inflammatory and insulinaemic potential have been associated with an increased risk of type 2 diabetes. However, it remains unknown whether plasma metabolomic profiles related to proinflammatory/hyperinsulinaemic diets and to inflammatory/insulin biomarkers are associated with type 2 diabetes risk. METHODS We analysed 6840 participants from the Nurses' Health Study and Health Professionals Follow-up Study to identify the plasma metabolome related to empirical dietary inflammatory pattern (EDIP), empirical dietary index for hyperinsulinemia (EDIH), four circulating inflammatory biomarkers and C-peptide. Dietary intakes were assessed using validated food frequency questionnaires. Plasma metabolomic profiling was conducted by LC-MS/MS. Metabolomic signatures were derived using elastic net regression. Multivariable Cox regression was used to examine associations of the metabolomic profiles with type 2 diabetes risk. RESULTS We identified 27 metabolites commonly associated with both EDIP and inflammatory biomarker z score and 21 commonly associated with both EDIH and C-peptide. Higher metabolomic dietary inflammatory potential (MDIP), reflecting higher metabolic potential of both an inflammatory dietary pattern and circulating inflammatory biomarkers, was associated with higher type 2 diabetes risk. The HR comparing highest vs lowest quartiles of MDIP was 3.26 (95% CI 2.39, 4.44). We observed a strong positive association with type 2 diabetes risk for the metabolomic signature associated with EDIP-only (HR 3.75; 95% CI 2.71, 5.17) or inflammatory biomarkers-only (HR 4.07; 95% CI 2.91, 5.69). In addition, higher metabolomic dietary index for hyperinsulinaemia (MDIH), reflecting higher metabolic potential of both an insulinaemic dietary pattern and circulating C-peptide, was associated with greater type 2 diabetes risk (HR 3.00; 95% CI 2.22, 4.06); further associations with type 2 diabetes were HR 2.79 (95% CI 2.07, 3.76) for EDIH-only signature and HR 3.89 (95% CI 2.82, 5.35) for C-peptide-only signature. The diet scores were significantly associated with risk, although adjustment for the corresponding metabolomic signature scores attenuated the associations with type 2 diabetes, these remained significant. CONCLUSIONS/INTERPRETATION The metabolomic signatures reflecting proinflammatory or hyperinsulinaemic diets and related biomarkers were positively associated with type 2 diabetes risk, supporting that these dietary patterns may influence type 2 diabetes risk via the regulation of metabolism.
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Affiliation(s)
- Dong Hoon Lee
- Department of Sport Industry Studies, Yonsei University, Seoul, Republic of Korea
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Qi Jin
- Department of Exercise and Nutrition Sciences, Moyes College of Education, Weber State University, Ogden, UT, USA
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
- Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH, USA
| | - Ni Shi
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
- Division of Medical Oncology, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Fenglei Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alaina M Bever
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA, USA
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Mingyang Song
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Xuehong Zhang
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Amit Joshi
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Kana Wu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Justin Y Jeon
- Department of Sport Industry Studies, Yonsei University, Seoul, Republic of Korea
- Cancer Prevention Center, Yonsei Cancer Center, Seoul, Republic of Korea
| | - Jeffrey A Meyerhardt
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Andrew T Chan
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - A Heather Eliassen
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Clary Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Steven K Clinton
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
- Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH, USA
- Division of Medical Oncology, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Edward L Giovannucci
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jun Li
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Fred K Tabung
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
- Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH, USA.
- Division of Medical Oncology, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, USA.
- Division of Epidemiology, College of Public Health, The Ohio State University, Columbus, OH, USA.
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10
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Reghupaty SC, Dall NR, Svensson KJ. Hallmarks of the metabolic secretome. Trends Endocrinol Metab 2024; 35:49-61. [PMID: 37845120 PMCID: PMC10841501 DOI: 10.1016/j.tem.2023.09.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 09/24/2023] [Accepted: 09/25/2023] [Indexed: 10/18/2023]
Abstract
The identification of novel secreted factors is advancing at an unprecedented pace. However, there is a critical need to consolidate and integrate this knowledge to provide a framework of their diverse mechanisms, functional significance, and inter-relationships. Complicating this effort are challenges related to nonstandardized methods, discrepancies in sample handling, and inconsistencies in the annotation of unknown molecules. This Review aims to synthesize the rapidly expanding field of the metabolic secretome, encompassing the five major types of secreted factors: proteins, peptides, metabolites, lipids, and extracellular vesicles. By systematically defining the functions and detection of the components within the metabolic secretome, this Review provides a primer into the advances of the field, and how integration of the techniques discussed can provide a deeper understanding of the mechanisms underlying metabolic homeostasis and its disorders.
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Affiliation(s)
- Saranya C Reghupaty
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA, USA; Stanford Cardiovascular Institute, Stanford University School of Medicine, CA, USA
| | - Nicholas R Dall
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA, USA; Stanford Cardiovascular Institute, Stanford University School of Medicine, CA, USA
| | - Katrin J Svensson
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA; Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA, USA; Stanford Cardiovascular Institute, Stanford University School of Medicine, CA, USA.
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11
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García-Gavilán JF, Babio N, Toledo E, Semnani-Azad Z, Razquin C, Dennis C, Deik A, Corella D, Estruch R, Ros E, Fitó M, Arós F, Fiol M, Lapetra J, Lamuela-Raventos R, Clish C, Ruiz-Canela M, Martínez-González MÁ, Hu F, Salas-Salvadó J, Guasch-Ferré M. Olive oil consumption, plasma metabolites, and risk of type 2 diabetes and cardiovascular disease. Cardiovasc Diabetol 2023; 22:340. [PMID: 38093289 PMCID: PMC10720204 DOI: 10.1186/s12933-023-02066-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 11/14/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Olive oil consumption has been inversely associated with the risk of type 2 diabetes (T2D) and cardiovascular disease (CVD). However, the impact of olive oil consumption on plasma metabolites remains poorly understood. This study aims to identify plasma metabolites related to total and specific types of olive oil consumption, and to assess the prospective associations of the identified multi-metabolite profiles with the risk of T2D and CVD. METHODS The discovery population included 1837 participants at high cardiovascular risk from the PREvención con DIeta MEDiterránea (PREDIMED) trial with available metabolomics data at baseline. Olive oil consumption was determined through food-frequency questionnaires (FFQ) and adjusted for total energy. A total of 1522 participants also had available metabolomics data at year 1 and were used as the internal validation sample. Plasma metabolomics analyses were performed using LC-MS. Cross-sectional associations between 385 known candidate metabolites and olive oil consumption were assessed using elastic net regression analysis. A 10-cross-validation (CV) procedure was used, and Pearson correlation coefficients were assessed between metabolite-weighted models and FFQ-derived olive oil consumption in each pair of training-validation data sets within the discovery sample. We further estimated the prospective associations of the identified plasma multi-metabolite profile with incident T2D and CVD using multivariable Cox regression models. RESULTS We identified a metabolomic signature for the consumption of total olive oil (with 74 metabolites), VOO (with 78 metabolites), and COO (with 17 metabolites), including several lipids, acylcarnitines, and amino acids. 10-CV Pearson correlation coefficients between total olive oil consumption derived from FFQs and the multi-metabolite profile were 0.40 (95% CI 0.37, 0.44) and 0.27 (95% CI 0.22, 0.31) for the discovery and validation sample, respectively. We identified several overlapping and distinct metabolites according to the type of olive oil consumed. The baseline metabolite profiles of total and extra virgin olive oil were inversely associated with CVD incidence (HR per 1SD: 0.79; 95% CI 0.67, 0.92 for total olive oil and 0.70; 0.59, 0.83 for extra virgin olive oil) after adjustment for confounders. However, no significant associations were observed between these metabolite profiles and T2D incidence. CONCLUSIONS This study reveals a panel of plasma metabolites linked to the consumption of total and specific types of olive oil. The metabolite profiles of total olive oil consumption and extra virgin olive oil were associated with a decreased risk of incident CVD in a high cardiovascular-risk Mediterranean population, though no associations were observed with T2D incidence. TRIAL REGISTRATION The PREDIMED trial was registered at ISRCTN ( http://www.isrctn.com/ , ISRCTN35739639).
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Affiliation(s)
- Jesús F García-Gavilán
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain.
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain.
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain.
| | - Nancy Babio
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
| | - Estefanía Toledo
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA, Pamplona, Spain
| | - Zhila Semnani-Azad
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Cristina Razquin
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA, Pamplona, Spain
| | | | - Amy Deik
- The Broad Institute of Harvard and MIT, Boston, MA, USA
| | - 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
| | - Ramón Estruch
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Internal Medicine, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
- Institut de Nutrició I Seguretat Alimentària (INSA-UB), Universitat de Barcelona, Barcelona, Spain
| | - Emilio Ros
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Endocrinology and Nutrition, Lipid Clinic, IDIBAPS, 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, Institut de Recerca Hospital del Mar, Barcelona, Spain
| | - Fernando Arós
- Bioaraba Health Research Institute, Osakidetza Basque Health Service, Araba University Hospital, University of the Basque Country UPV/EHU, 01009, Vitoria-Gasteiz, Spain
| | - Miquel Fiol
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Plataforma de Ensayos Clínicos, Instituto de Investigación Sanitaria Illes Balears (IdISBa), Hospital Universitario Son Espases, 07120, Palma, 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, Seville, Spain
| | - Rosa Lamuela-Raventos
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Institut de Nutrició I Seguretat Alimentària (INSA-UB), Universitat de Barcelona, Barcelona, Spain
- Polyphenol Research Group, Departament de Nutrició, Ciències de L'Alimentació I Gastronomia, Universitat de Barcelon (UB), Av. de Joan XXII, 27-31, 08028, Barcelona, Spain
| | - Clary Clish
- The Broad Institute of Harvard and MIT, Boston, MA, USA
| | - Miguel Ruiz-Canela
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA, Pamplona, Spain
| | - Miguel Ángel Martínez-González
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA, Pamplona, Spain
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Frank Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jordi Salas-Salvadó
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Public Health, Section of Epidemiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
- Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Øster Farimagsgade 5, 1014, Copenhagen, Denmark.
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12
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Su X, Cheung CYY, Zhong J, Ru Y, Fong CHY, Lee CH, Liu Y, Cheung CKY, Lam KSL, Xu A, Cai Z. Ten metabolites-based algorithm predicts the future development of type 2 diabetes in Chinese. J Adv Res 2023:S2090-1232(23)00365-X. [PMID: 38030128 DOI: 10.1016/j.jare.2023.11.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 11/10/2023] [Accepted: 11/21/2023] [Indexed: 12/01/2023] Open
Abstract
INTRODUCTION Type 2 diabetes (T2D) is a heterogeneous metabolic disease with large variations in the relative contributions of insulin resistance and β-cell dysfunction across different glucose tolerance subgroups and ethnicities. A more precise yet feasible approach to categorize risk preceding T2D onset is urgently needed. This study aimed to identify potential metabolic biomarkers that could contribute to the development of T2D and investigate whether their impact on T2D is mediated through insulin resistance and β-cell dysfunction. METHODS A non-targeted metabolomic analysis was performed in plasma samples of 196 incident T2D cases and 196 age- and sex-matched non-T2D controls recruited from a long-term prospective Chinese community-based cohort with a follow-up period of ∼ 16 years. RESULTS Metabolic profiles revealed profound perturbation of metabolomes before T2D onset. Overall metabolic shifts were strongly associated with insulin resistance rather than β-cell dysfunction. In addition, 188 out of the 578 annotated metabolites were associated with insulin resistance. Bi-directional mediation analysis revealed putative causal relationships among the metabolites, insulin resistance and T2D risk. We built a machine-learning based prediction model, integrating the conventional clinical risk factors (age, BMI, TyG index and 2hG) and 10 metabolites (acetyl-tryptophan, kynurenine, γ-glutamyl-phenylalanine, DG(18:2/22:6), DG(38:7), LPI(18:2), LPC(P-16:0), LPC(P-18:1), LPC(P-20:0) and LPE(P-20:0)) (AUROC = 0.894, 5.6% improvement comparing to the conventional clinical risk model), that successfully predicts the development of T2D. CONCLUSIONS Our findings support the notion that the metabolic changes resulting from insulin resistance, rather than β-cell dysfunction, are the primary drivers of T2D in Chinese adults. Metabolomes as a valuable phenotype hold potential clinical utility in the prediction of T2D.
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Affiliation(s)
- Xiuli Su
- State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, China
| | - Chloe Y Y Cheung
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Junda Zhong
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Yi Ru
- State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, China
| | - Carol H Y Fong
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Chi-Ho Lee
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Yan Liu
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Cynthia K Y Cheung
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China
| | - Karen S L Lam
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China.
| | - Aimin Xu
- Department of Medicine, The University of Hong Kong, Hong Kong, China; State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China; Department of Pharmacology & Pharmacy, The University of Hong Kong, Hong Kong, China.
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong, China.
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13
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Mi MY, Gajjar P, Walker ME, Miller P, Xanthakis V, Murthy VL, Larson MG, Vasan RS, Shah RV, Lewis GD, Nayor M. Association of healthy dietary patterns and cardiorespiratory fitness in the community. Eur J Prev Cardiol 2023; 30:1450-1461. [PMID: 37164358 PMCID: PMC10562138 DOI: 10.1093/eurjpc/zwad113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 04/04/2023] [Accepted: 04/07/2023] [Indexed: 05/12/2023]
Abstract
AIMS To evaluate the associations of dietary indices and quantitative cardiorespiratory fitness (CRF) measures in a large, community-based sample harnessing metabolomic profiling to interrogate shared biology. METHODS AND RESULTS Framingham Heart Study (FHS) participants underwent maximum effort cardiopulmonary exercise tests for CRF quantification (via peak VO2) and completed semi-quantitative food frequency questionnaires. Dietary quality was assessed by the Alternative Healthy Eating Index (AHEI) and Mediterranean-style Diet Score (MDS), and fasting blood concentrations of 201 metabolites were quantified. In 2380 FHS participants (54 ± 9 years, 54% female, body mass index 28 ± 5 kg/m2), 1 SD higher AHEI and MDS were associated with 5.2% (1.2 mL/kg/min, 95% CI 4.3-6.0%, P < 0.0001) and 4.5% (1.0 mL/kg/min, 95% CI 3.6-5.3%, P < 0.0001) greater peak VO2 in linear models adjusted for age, sex, total daily energy intake, cardiovascular risk factors, and physical activity. In participants with metabolite profiling (N = 1154), 24 metabolites were concordantly associated with both dietary indices and peak VO2 in multivariable-adjusted linear models (FDR < 5%). Metabolites that were associated with lower CRF and poorer dietary quality included C6 and C7 carnitines, C16:0 ceramide, and dimethylguanidino valeric acid, and metabolites that were positively associated with higher CRF and favourable dietary quality included C38:7 phosphatidylcholine plasmalogen and C38:7 and C40:7 phosphatidylethanolamine plasmalogens. CONCLUSION Higher diet quality is associated with greater CRF cross-sectionally in a middle-aged community-dwelling sample, and metabolites highlight potential shared favourable effects on cardiometabolic health.
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Affiliation(s)
- Michael Y Mi
- Division of Cardiovascular Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Priya Gajjar
- Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Suite L-516, Boston, MA 02118, USA
| | - Maura E Walker
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Suite L-516, Boston, MA 02118, USA
- Department of Health Sciences, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA, USA
| | - Patricia Miller
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Vanessa Xanthakis
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Suite L-516, Boston, MA 02118, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Framingham Heart Study, Framingham, 73 Mt. Wayte Avenue, Framingham, MA 01702, USA
| | - Venkatesh L Murthy
- Division of Cardiovascular Medicine, Department of Medicine, and Frankel Cardiovascular Center, University of Michigan, Ann Arbor, MI, USA
| | - Martin G Larson
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Framingham Heart Study, Framingham, 73 Mt. Wayte Avenue, Framingham, MA 01702, USA
| | - Ramachandran S Vasan
- Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Suite L-516, Boston, MA 02118, USA
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Suite L-516, Boston, MA 02118, USA
- Framingham Heart Study, Framingham, 73 Mt. Wayte Avenue, Framingham, MA 01702, USA
- University of Texas School of Public Health San Antonio, and Departments of Medicine and Population Health Sciences, University of Texas Health Science Center, San Antonio, TX, USA
| | - Ravi V Shah
- Vanderbilt Translational and Clinical Research Center, Cardiology Division, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Gregory D Lewis
- Cardiology Division and Pulmonary Critical Care Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Matthew Nayor
- Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Suite L-516, Boston, MA 02118, USA
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Suite L-516, Boston, MA 02118, USA
- Framingham Heart Study, Framingham, 73 Mt. Wayte Avenue, Framingham, MA 01702, USA
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14
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Rios S, García-Gavilán JF, Babio N, Paz-Graniel I, Ruiz-Canela M, Liang L, Clish CB, Toledo E, Corella D, Estruch R, Ros E, Fitó M, Arós F, Fiol M, Guasch-Ferré M, Santos-Lozano JM, Li J, Razquin C, Martínez-González MÁ, Hu FB, Salas-Salvadó J. Plasma metabolite profiles associated with the World Cancer Research Fund/American Institute for Cancer Research lifestyle score and future risk of cardiovascular disease and type 2 diabetes. Cardiovasc Diabetol 2023; 22:252. [PMID: 37716984 PMCID: PMC10505328 DOI: 10.1186/s12933-023-01912-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 07/01/2023] [Indexed: 09/18/2023] Open
Abstract
BACKGROUND A healthy lifestyle (HL) has been inversely related to type 2 diabetes (T2D) and cardiovascular disease (CVD). However, few studies have identified a metabolite profile associated with HL. The present study aims to identify a metabolite profile of a HL score and assess its association with the incidence of T2D and CVD in individuals at high cardiovascular risk. METHODS In a subset of 1833 participants (age 55-80y) of the PREDIMED study, we estimated adherence to a HL using a composite score based on the 2018 Word Cancer Research Fund/American Institute for Cancer Research recommendations. Plasma metabolites were analyzed using LC-MS/MS methods at baseline (discovery sample) and 1-year of follow-up (validation sample). Cross-sectional associations between 385 known metabolites and the HL score were assessed using elastic net regression. A 10-cross-validation procedure was used, and correlation coefficients or AUC were assessed between the identified metabolite profiles and the self-reported HL score. We estimated the associations between the identified metabolite profiles and T2D and CVD using multivariable Cox regression models. RESULTS The metabolite profiles that identified HL as a dichotomous or continuous variable included 24 and 58 metabolites, respectively. These are amino acids or derivatives, lipids, and energy intermediates or xenobiotic compounds. After adjustment for potential confounders, baseline metabolite profiles were associated with a lower risk of T2D (hazard ratio [HR] and 95% confidence interval (CI): 0.54, 0.38-0.77 for dichotomous HL, and 0.22, 0.11-0.43 for continuous HL). Similar results were observed with CVD (HR, 95% CI: 0.59, 0.42-0.83 for dichotomous HF and HR, 95%CI: 0.58, 0.31-1.07 for continuous HL). The reduction in the risk of T2D and CVD was maintained or attenuated, respectively, for the 1-year metabolomic profile. CONCLUSIONS In an elderly population at high risk of CVD, a set of metabolites was selected as potential metabolites associated with the HL pattern predicting the risk of T2D and, to a lesser extent, CVD. These results support previous findings that some of these metabolites are inversely associated with the risk of T2D and CVD. TRIAL REGISTRATION The PREDIMED trial was registered at ISRCTN ( http://www.isrctn.com/ , ISRCTN35739639).
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Affiliation(s)
- Santiago Rios
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Jesús F García-Gavilán
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain.
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain.
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain.
| | - Nancy Babio
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Indira Paz-Graniel
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain
| | - Miguel Ruiz-Canela
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA, Pamplona, Spain
| | - 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 MIT and Harvard, Cambridge, MA, USA
| | - Estefania Toledo
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA, Pamplona, Spain
| | - 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
| | - Ramón Estruch
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Internal Medicine, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clínic, Barcelona, Spain
| | - Emilio Ros
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Endocrinology and Nutrition, Lipid Clinic, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), Hospital Clínic, Barcelona, Spain
| | - Montserrat Fitó
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Unit of Cardiovascular Risk and Nutrition, Institut Hospital del Mar de Investigaciones Médicas Municipal d'Investigació Médica (IMIM), Barcelona, Spain
| | - Fernando Arós
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Cardiology, Hospital Universitario de Álava, Vitoria, Spain
| | - Miquel Fiol
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Health Research Institute of the Balearic Islands (IdISBa), Hospital Son Espases, Palma de Mallorca, Spain
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Public Health and Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - José M Santos-Lozano
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Research Unit, Department of Family Medicine, Distrito Sanitario Atención Primaria Sevilla, Sevilla, Spain
| | - Jun Li
- Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA, Pamplona, Spain
- Health Research Institute of the Balearic Islands (IdISBa), Hospital Son Espases, Palma de Mallorca, Spain
| | - Cristina Razquin
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA, Pamplona, Spain
| | - Miguel Ángel Martínez-González
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA, Pamplona, Spain
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Frank B Hu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jordi Salas-Salvadó
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Alimentaciò, Nutrició Desenvolupament i Salut Mental ANUT-DSM, Reus, Spain.
- CIBER de Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid, Spain.
- Institut d'Investigació Sanitària Pere Virgili (IISPV), Reus, Spain.
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15
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Joshi AD, Rahnavard A, Kachroo P, Mendez KM, Lawrence W, Julián-Serrano S, Hua X, Fuller H, Sinnott-Armstrong N, Tabung FK, Shutta KH, Raffield LM, Darst BF. An epidemiological introduction to human metabolomic investigations. Trends Endocrinol Metab 2023; 34:505-525. [PMID: 37468430 PMCID: PMC10527234 DOI: 10.1016/j.tem.2023.06.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 06/17/2023] [Accepted: 06/19/2023] [Indexed: 07/21/2023]
Abstract
Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.
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Affiliation(s)
- Amit D Joshi
- Clinical & Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Ali Rahnavard
- Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Priyadarshini Kachroo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Kevin M Mendez
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Wayne Lawrence
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sachelly Julián-Serrano
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA; Department of Public Health, University of Massachusetts Lowell, Lowell, MA, USA
| | - Xinwei Hua
- Clinical & Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA; Department of Cardiology, Peking University Third Hospital, Beijing, China
| | - Harriett Fuller
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Nasa Sinnott-Armstrong
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Fred K Tabung
- The Ohio State University College of Medicine and Comprehensive Cancer Center, Columbus, OH, USA
| | - Katherine H Shutta
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Burcu F Darst
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
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16
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Vieira JPP, Ottosson F, Jujic A, Denisov V, Magnusson M, Melander O, Duarte JMN. Metabolite Profiling in a Diet-Induced Obesity Mouse Model and Individuals with Diabetes: A Combined Mass Spectrometry and Proton Nuclear Magnetic Resonance Spectroscopy Study. Metabolites 2023; 13:874. [PMID: 37512581 PMCID: PMC10385288 DOI: 10.3390/metabo13070874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 07/19/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023] Open
Abstract
Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy techniques have been used extensively for metabolite profiling. Although combining these two analytical modalities has the potential of enhancing metabolite coverage, such studies are sparse. In this study we test the hypothesis that combining the metabolic information obtained using liquid chromatography (LC) MS and 1H NMR spectroscopy improves the discrimination of metabolic disease development. We induced metabolic syndrome in male mice using a high-fat diet (HFD) exposure and performed LC-MS and NMR spectroscopy on plasma samples collected after 1 and 8 weeks of dietary intervention. In an orthogonal projection to latent structures (OPLS) analysis, we observed that combining MS and NMR was stronger than each analytical method alone at determining effects of both HFD feeding and time-on-diet. We then tested our metabolomics approach on plasma from 56 individuals from the Malmö Diet and Cancer Study (MDCS) cohort. All metabolic pathways impacted by HFD feeding in mice were confirmed to be affected by diabetes in the MDCS cohort, and most prominent HFD-induced metabolite concentration changes in mice were also associated with metabolic syndrome parameters in humans. The main drivers of metabolic disease discrimination emanating from the present study included plasma levels of xanthine, hippurate, 2-hydroxyisovalerate, S-adenosylhomocysteine and dimethylguanidino valeric acid. In conclusion, our combined NMR-MS approach provided a snapshot of metabolic imbalances in humans and a mouse model, which was improved over employment of each analytical method alone.
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Affiliation(s)
- João P P Vieira
- Department of Experimental Medical Science, Faculty of Medicine, Lund University, 22184 Lund, Sweden
- Wallenberg Centre for Molecular Medicine, Lund University, 22100 Lund, Sweden
| | - Filip Ottosson
- Department of Clinical Sciences-Malmö, Faculty of Medicine, Lund University, 20502 Malmö, Sweden
| | - Amra Jujic
- Wallenberg Centre for Molecular Medicine, Lund University, 22100 Lund, Sweden
- Department of Clinical Sciences-Malmö, Faculty of Medicine, Lund University, 20502 Malmö, Sweden
- Department of Cardiology, Skåne University Hospital, 21428 Malmö, Sweden
| | - Vladimir Denisov
- Biomedical Engineering Division, Department of Clinical Sciences-Lund, Faculty of Medicine, Lund University, 22100 Lund, Sweden
| | - Martin Magnusson
- Wallenberg Centre for Molecular Medicine, Lund University, 22100 Lund, Sweden
- Department of Clinical Sciences-Malmö, Faculty of Medicine, Lund University, 20502 Malmö, Sweden
- Department of Cardiology, Skåne University Hospital, 21428 Malmö, Sweden
- Hypertension in Africa Research Team, North-West University, Potchefstroom 2520, South Africa
| | - Olle Melander
- Department of Clinical Sciences-Malmö, Faculty of Medicine, Lund University, 20502 Malmö, Sweden
| | - João M N Duarte
- Department of Experimental Medical Science, Faculty of Medicine, Lund University, 22184 Lund, Sweden
- Wallenberg Centre for Molecular Medicine, Lund University, 22100 Lund, Sweden
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17
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Mittendorfer B, Kayser BD, Yoshino M, Yoshino J, Watrous JD, Jain M, Eagon JC, Patterson BW, Klein S. Heterogeneity in the effect of marked weight loss on metabolic function in women with obesity. JCI Insight 2023; 8:e169541. [PMID: 37159276 PMCID: PMC10371235 DOI: 10.1172/jci.insight.169541] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 05/03/2023] [Indexed: 05/10/2023] Open
Abstract
BACKGROUNDThere is considerable heterogeneity in the effect of weight loss on metabolic function in people with obesity.METHODSWe evaluated muscle and liver insulin sensitivity, body composition, and circulating factors associated with insulin action before and after approximately 20% weight loss in women identified as "Responders" (n = 11) or "Non-responders" (n = 11), defined as the top (>75% increase) and bottom (<5% increase) quartiles of the weight loss-induced increase in glucose disposal rate (GDR) during a hyperinsulinemic-euglycemic clamp procedure, among 43 women with obesity (BMI: 44.1 ± 7.9 kg/m2).RESULTSAt baseline, GDR, which provides an index of muscle insulin sensitivity, and the hepatic insulin sensitivity index were more than 50% lower in Responders than Non-responders, but both increased much more after weight loss in Responders than Non-responders, which eliminated the differences between groups. Weight loss also caused greater decreases in intrahepatic triglyceride content and plasma adiponectin and PAI-1 concentrations in Responders than Non-responders and greater insulin-mediated suppression of plasma free fatty acids, branched-chain amino acids, and C3/C5 acylcarnitines in Non-responders than Responders, so that differences between groups at baseline were no longer present after weight loss. The effect of weight loss on total body fat mass, intra-abdominal adipose tissue volume, adipocyte size, and circulating inflammatory markers were not different between groups.CONCLUSIONThe results from our study demonstrate that the heterogeneity in the effects of marked weight loss on muscle and hepatic insulin sensitivity in people with obesity is determined by baseline insulin action, and reaches a ceiling when "normal" insulin action is achieved.TRIAL REGISTRATIONNCT00981500, NCT01299519, NCT02207777.FUNDINGNIH grants P30 DK056341, P30 DK020579, P30 DK052574, UL1 TR002345, and T32 HL13035, the American Diabetes Association (1-18-ICTS-119), the Longer Life Foundation (2019-011), and the Atkins Philanthropic Trust.
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Affiliation(s)
- Bettina Mittendorfer
- Center for Human Nutrition, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Brandon D. Kayser
- Center for Human Nutrition, Washington University School of Medicine, St. Louis, Missouri, USA
- Genentech, South San Francisco, California, USA
| | - Mihoko Yoshino
- Center for Human Nutrition, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Jun Yoshino
- Center for Human Nutrition, Washington University School of Medicine, St. Louis, Missouri, USA
| | | | - Mohit Jain
- Department of Medicine, UCSD, La Jolla, California, USA
| | - J. Christopher Eagon
- Center for Human Nutrition, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Bruce W. Patterson
- Center for Human Nutrition, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Samuel Klein
- Center for Human Nutrition, Washington University School of Medicine, St. Louis, Missouri, USA
- Sansum Diabetes Research Institute, Santa Barbara, California, USA
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18
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Lee DH, Jin Q, Shi N, Wang F, Bever AM, Li J, Liang L, Hu FB, Song M, Zeleznik OA, Zhang X, Joshi A, Wu K, Jeon JY, Meyerhardt JA, Chan AT, Eliassen AH, Clish CB, Clinton SK, Giovannucci EL, Tabung FK. Dietary Inflammatory and Insulinemic Potentials, Plasma Metabolome and Risk of Colorectal Cancer. Metabolites 2023; 13:744. [PMID: 37367904 PMCID: PMC10304271 DOI: 10.3390/metabo13060744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 06/02/2023] [Accepted: 06/05/2023] [Indexed: 06/28/2023] Open
Abstract
The inflammatory and insulinemic potentials of diets have been associated with colorectal cancer risk. However, it is unknown whether the plasma metabolite profiles related to inflammatory diets, or to insulinemic diets, underlie this association. The aim of this study was to evaluate the association between metabolomic profile scores related to the food-based empirical dietary inflammatory patterns (EDIP), the empirical dietary index for hyperinsulinemia (EDIH), and plasma inflammation (CRP, IL-6, TNFα-R2, adiponectin) and insulin (C-peptide) biomarkers, and colorectal cancer risk. Elastic net regression was used to derive three metabolomic profile scores for each dietary pattern among 6840 participants from the Nurses' Health Study and Health Professionals Follow-up Study, and associations with CRC risk were examined using multivariable-adjusted logistic regression, in a case-control study of 524 matched pairs nested in both cohorts. Among 186 known metabolites, 27 were significantly associated with both the EDIP and inflammatory biomarkers, and 21 were significantly associated with both the EDIH and C-peptide. In men, odds ratios (ORs) of colorectal cancer, per 1 standard deviation (SD) increment in metabolomic score, were 1.91 (1.31-2.78) for the common EDIP and inflammatory-biomarker metabolome, 1.12 (0.78-1.60) for EDIP-only metabolome, and 1.65 (1.16-2.36) for the inflammatory-biomarkers-only metabolome. However, no association was found for EDIH-only, C-peptide-only, and the common metabolomic signatures in men. Moreover, the metabolomic signatures were not associated with colorectal cancer risk among women. Metabolomic profiles reflecting pro-inflammatory diets and inflammation biomarkers were associated with colorectal cancer risk in men, while no association was found in women. Larger studies are needed to confirm our findings.
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Affiliation(s)
- Dong Hoon Lee
- Department of Sport Industry Studies, Yonsei University, Seoul 03722, Republic of Korea; (D.H.L.)
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Qi Jin
- Department of Exercise and Nutrition Sciences, Moyes College of Education, Weber State University, Ogden, UT 84408, USA
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
- Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH 43210, USA
| | - Ni Shi
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
- Division of Medical Oncology, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Fenglei Wang
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Alaina M. Bever
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA 02115, USA
| | - Jun Li
- 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
| | - Liming Liang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, 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 of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Mingyang Song
- 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
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
- Division of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Oana A. Zeleznik
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Xuehong Zhang
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Amit Joshi
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
- Division of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Kana Wu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Justin Y. Jeon
- Department of Sport Industry Studies, Yonsei University, Seoul 03722, Republic of Korea; (D.H.L.)
- Cancer Prevention Center, Yonsei Cancer Center, Seoul 03722, Republic of Korea
| | - Jeffrey A. Meyerhardt
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Andrew T. Chan
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
- Division of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - A. Heather Eliassen
- 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 of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Clary B. Clish
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Steven K. Clinton
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
- Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH 43210, USA
- Division of Medical Oncology, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Edward L. Giovannucci
- 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
| | - Fred K. Tabung
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
- Interdisciplinary Ph.D. Program in Nutrition, The Ohio State University, Columbus, OH 43210, USA
- Division of Medical Oncology, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH 43210, USA
- Division of Epidemiology, College of Public Health, The Ohio State University, Columbus, OH 43220, USA
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19
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Zeleznik OA, Kang JH, Lasky-Su J, Eliassen AH, Frueh L, Clish CB, Rosner BA, Elze T, Hysi P, Khawaja A, Wiggs JL, Pasquale LR. Plasma metabolite profile for primary open-angle glaucoma in three US cohorts and the UK Biobank. Nat Commun 2023; 14:2860. [PMID: 37208353 PMCID: PMC10199010 DOI: 10.1038/s41467-023-38466-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 05/04/2023] [Indexed: 05/21/2023] Open
Abstract
Glaucoma is a progressive optic neuropathy and a leading cause of irreversible blindness worldwide. Primary open-angle glaucoma is the most common form, and yet the etiology of this multifactorial disease is poorly understood. We aimed to identify plasma metabolites associated with the risk of developing POAG in a case-control study (599 cases and 599 matched controls) nested within the Nurses' Health Studies, and Health Professionals' Follow-Up Study. Plasma metabolites were measured with LC-MS/MS at the Broad Institute (Cambridge, MA, USA); 369 metabolites from 18 metabolite classes passed quality control analyses. For comparison, in a cross-sectional study in the UK Biobank, 168 metabolites were measured in plasma samples from 2,238 prevalent glaucoma cases and 44,723 controls using NMR spectroscopy (Nightingale, Finland; version 2020). Here we show higher levels of diglycerides and triglycerides are adversely associated with glaucoma in all four cohorts, suggesting that they play an important role in glaucoma pathogenesis.
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Affiliation(s)
- Oana A Zeleznik
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
| | - Jae H Kang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Lisa Frueh
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Clary B Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Bernard A Rosner
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Tobias Elze
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Ophthalmology, Schepens Research Eye Institute of Massachusetts Eye and Ear, Boston, MA, USA
| | - Pirro Hysi
- Department of Ophthalmology, King's College London, London, UK
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
- St. Thomas' Hospital, London, UK
| | - Anthony Khawaja
- National Institute for Health and Care Research Biomedical Research Centre, Moorfields Eye Hospital, London, UK
- National Institute for Health and Care Research Biomedical Research Centre, Institute of Ophthalmology, University College London, London, UK
| | - Janey L Wiggs
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
| | - Louis R Pasquale
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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20
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Zeleznik OA, Welling DB, Stankovic K, Frueh L, Balasubramanian R, Curhan GC, Curhan SG. Association of Plasma Metabolomic Biomarkers With Persistent Tinnitus: A Population-Based Case-Control Study. JAMA Otolaryngol Head Neck Surg 2023; 149:404-415. [PMID: 36928544 PMCID: PMC10020935 DOI: 10.1001/jamaoto.2023.0052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 01/17/2023] [Indexed: 03/18/2023]
Abstract
Importance Persistent tinnitus is common, disabling, and difficult to treat. Objective To evaluate the association between circulating metabolites and persistent tinnitus. Design, Setting, and Participants This was a population-based case-control study of 6477 women who were participants in the Nurses' Health Study (NHS) and NHS II with metabolomic profiles and tinnitus data. Information on tinnitus onset and frequency was collected on biennial questionnaires (2009-2017). For cases, metabolomic profiles were measured (2015-2021) in blood samples collected after the date of the participant's first report of persistent tinnitus (NHS, 1989-1999 and 2010-2012; NHS II, 1996-1999). Data analyses were performed from January 24, 2022, to January 14, 2023. Exposures In total, 466 plasma metabolites from 488 cases of persistent tinnitus and 5989 controls were profiled using 3 complementary liquid chromatography tandem mass spectrometry approaches. Main Outcomes and Measures Logistic regression was used to estimate odds ratios (ORs) of persistent tinnitus (per 1 SD increase in metabolite values) and 95% CIs for each individual metabolite. Metabolite set enrichment analysis was used to identify metabolite classes enriched for associations with tinnitus. Results Of the 6477 study participants (mean [SD] age, 52 [9] years; 6477 [100%] female; 6121 [95%] White individuals) who were registered nurses, 488 reported experiencing daily persistent (≥5 minutes) tinnitus. Compared with participants with no tinnitus (5989 controls), those with persistent tinnitus were slightly older (53.0 vs 51.8 years) and more likely to be postmenopausal, using oral postmenopausal hormone therapy, and have type 2 diabetes, hypertension, and/or hearing loss at baseline. Compared with controls, homocitrulline (OR, 1.32; (95% CI, 1.16-1.50); C38:6 phosphatidylethanolamine (PE; OR, 1.24; 95% CIs, 1.12-1.38), C52:6 triglyceride (TAG; OR, 1.22; 95% CIs, 1.10-1.36), C36:4 PE (OR, 1.22; 95% CIs, 1.10-1.35), C40:6 PE (OR, 1.22; 95% CIs, 1.09-1.35), and C56:7 TAG (OR, 1.21; 95% CIs, 1.09-1.34) were positively associated, whereas α-keto-β-methylvalerate (OR, 0.68; 95% CIs, 0.56-0.82) and levulinate (OR, 0.60; 95% CIs, 0.46-0.79) were inversely associated with persistent tinnitus. Among metabolite classes, TAGs (normalized enrichment score [NES], 2.68), PEs (NES, 2.48), and diglycerides (NES, 1.65) were positively associated, whereas phosphatidylcholine plasmalogens (NES, -1.91), lysophosphatidylcholines (NES, -2.23), and cholesteryl esters (NES,-2.31) were inversely associated with persistent tinnitus. Conclusions and Relevance This population-based case-control study of metabolomic profiles and tinnitus identified novel plasma metabolites and metabolite classes that were significantly associated with persistent tinnitus, suggesting that metabolomic studies may help improve understanding of tinnitus pathophysiology and identify therapeutic targets for this challenging disorder.
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Affiliation(s)
- Oana A. Zeleznik
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - D. Bradley Welling
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Department of Otolaryngology–Head and Neck Surgery, Massachusetts Eye and Ear, Boston
| | - Konstantina Stankovic
- Department of Otolaryngology–Head and Neck Surgery, Stanford University, Palo Alto, California
| | - Lisa Frueh
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Raji Balasubramanian
- Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst
| | - Gary C. Curhan
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | - Sharon G. Curhan
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
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21
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Mi MY, Whitlock M, Shi X, Farrell LA, Bhambhani VM, Quadir J, Blatnik M, Wald KP, Tierney B, Kim A, Loudon P, Chen ZZ, Correa A, Gao Y, Carson AP, Bertoni AG, Roth Flach RJ, Gerszten RE. Mixed meal tolerance testing highlights in diabetes altered branched-chain ketoacid metabolism and pathways associated with all-cause mortality. Am J Clin Nutr 2023; 117:529-539. [PMID: 36811472 PMCID: PMC10356557 DOI: 10.1016/j.ajcnut.2023.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 12/22/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Elevated BCAA levels are strongly associated with diabetes, but how diabetes affects BCAA, branched-chain ketoacids (BCKAs), and the broader metabolome after a meal is not well known. OBJECTIVE To compare quantitative BCAA and BCKA levels in a multiracial cohort with and without diabetes after a mixed meal tolerance test (MMTT) as well as to explore the kinetics of additional metabolites and their associations with mortality in self-identified African Americans. METHODS We administered an MMTT to 11 participants without obesity or diabetes and 13 participants with diabetes (treated with metformin only) and measured the levels of BCKAs, BCAAs, and 194 other metabolites at 8 time points across 5 h. We used mixed models for repeated measurements to compare between group metabolite differences at each timepoint with adjustment for baseline. We then evaluated the association of top metabolites with different kinetics with all-cause mortality in the Jackson Heart Study (JHS) (N = 2441). RESULTS BCAA levels, after adjustment for baseline, were similar at all timepoints between groups, but adjusted BCKA kinetics were different between groups for α-ketoisocaproate (P = 0.022) and α-ketoisovalerate (P = 0.021), most notably diverging at 120 min post-MMTT. An additional 20 metabolites had significantly different kinetics across timepoints between groups, and 9 of these metabolites-including several acylcarnitines-were significantly associated with mortality in JHS, irrespective of diabetes status. The highest quartile of a composite metabolite risk score was associated with higher mortality (HR:1.57; 1.20, 2.05, P = 0.00094) than the lowest quartile. CONCLUSIONS BCKA levels remained elevated after an MMTT among participants with diabetes, suggesting that BCKA catabolism may be a key dysregulated process in the interaction of BCAA and diabetes. Metabolites with different kinetics after an MMTT may be markers of dysmetabolism and associated with increased mortality in self-identified African Americans.
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Affiliation(s)
- Michael Y Mi
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Cardiovascular Institute, Beth Israel Deaconess Medical Center, Boston, MA, USA.
| | | | - Xu Shi
- Cardiovascular Institute, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Laurie A Farrell
- Cardiovascular Institute, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | | | - Juweria Quadir
- Cardiovascular Institute, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | | | - Kyle P Wald
- Early Clinical Development, Pfizer, Groton, CT, USA
| | | | - Albert Kim
- Internal Medicine Research Unit, Pfizer, Cambridge, MA, USA; Cytel, Cambridge, MA, USA
| | - Peter Loudon
- Early Clinical Development, Pfizer, Cambridge, UK; Tenpoint Therapeutics, Cambridge, UK
| | - Zsu-Zsu Chen
- Division of Endocrinology, Diabetes, and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Adolfo Correa
- Department of Population Health Science, University of Mississippi Medical Center, Jackson, MS, USA
| | - Yan Gao
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Alain G Bertoni
- Department of Epidemiology & Prevention, Wake Forest School of Medicine, Winston Salem, NC, USA
| | | | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Cardiovascular Institute, Beth Israel Deaconess Medical Center, Boston, MA, USA
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22
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Shah RV, Steffen LM, Nayor M, Reis JP, Jacobs DR, Allen NB, Lloyd-Jones D, Meyer K, Cole J, Piaggi P, Vasan RS, Clish CB, Murthy VL. Dietary metabolic signatures and cardiometabolic risk. Eur Heart J 2023; 44:557-569. [PMID: 36424694 PMCID: PMC10169425 DOI: 10.1093/eurheartj/ehac446] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 06/23/2022] [Accepted: 07/28/2022] [Indexed: 11/27/2022] Open
Abstract
AIMS Observational studies of diet in cardiometabolic-cardiovascular disease (CM-CVD) focus on self-reported consumption of food or dietary pattern, with limited information on individual metabolic responses to dietary intake linked to CM-CVD. Here, machine learning approaches were used to identify individual metabolic patterns related to diet and relation to long-term CM-CVD in early adulthood. METHODS AND RESULTS In 2259 White and Black adults (age 32.1 ± 3.6 years, 45% women, 44% Black) in the Coronary Artery Risk Development in Young Adults (CARDIA) study, multivariate models were employed to identify metabolite signatures of food group and composite dietary intake across 17 food groups, 2 nutrient groups, and healthy eating index-2015 (HEI2015) diet quality score. A broad array of metabolites associated with diet were uncovered, reflecting food-related components/catabolites (e.g. fish and long-chain unsaturated triacylglycerols), interactions with host features (microbiome), or pathways broadly implicated in CM-CVD (e.g. ceramide/sphingomyelin lipid metabolism). To integrate diet with metabolism, penalized machine learning models were used to define a metabolite signature linked to a putative CM-CVD-adverse diet (e.g. high in red/processed meat, refined grains), which was subsequently associated with long-term diabetes and CVD risk numerically more strongly than HEI2015 in CARDIA [e.g. diabetes: standardized hazard ratio (HR): 1.62, 95% confidence interval (CI): 1.32-1.97, P < 0.0001; CVD: HR: 1.55, 95% CI: 1.12-2.14, P = 0.008], with associations replicated for diabetes (P < 0.0001) in the Framingham Heart Study. CONCLUSION Metabolic signatures of diet are associated with long-term CM-CVD independent of lifestyle and traditional risk factors. Metabolomics improves precision to identify adverse consequences and pathways of diet-related CM-CVD.
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Affiliation(s)
- Ravi V Shah
- Vanderbilt University Medical Center, Vanderbilt Clinical and Translational Research Center (VTRACC), Nashville, TN, USA
| | - Lyn M Steffen
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN, USA
| | - Matthew Nayor
- Cardiology Division, Boston University School of Medicine, Boston, MA, USA
| | - Jared P Reis
- Epidemiology Branch, National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | - David R Jacobs
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN, USA
| | - Norrina B Allen
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Donald Lloyd-Jones
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Katie Meyer
- Nutrition Department, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Joanne Cole
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Paolo Piaggi
- Department of Information Engineering, University of Pisa, Pisa, Italy
| | - Ramachandran S Vasan
- Sections of Preventive Medicine and Epidemiology and Cardiovascular Medicine, Department of Medicine, and Department of Epidemiology, Boston University Schools of Medicine and Public Health, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Clary B Clish
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Venkatesh L Murthy
- Department of Medicine and Radiology, University of Michigan, 1338 Cardiovascular Center, Ann Arbor, MI 48109-5873, USA
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23
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Mi MY, Gajjar P, Walker ME, Miller P, Xanthakis V, Murthy VL, Larson MG, Vasan RS, Shah RV, Lewis GD, Nayor M. Association of Healthy Dietary Patterns and Cardiorespiratory Fitness in the Community. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.09.23285714. [PMID: 36798343 PMCID: PMC9934801 DOI: 10.1101/2023.02.09.23285714] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Aims To evaluate the associations of dietary indices and quantitative CRF measures in a large, community-based sample harnessing metabolomic profiling to interrogate shared biology. Methods Framingham Heart Study (FHS) participants underwent maximum effort cardiopulmonary exercise tests for CRF quantification (via peak VO 2 ) and completed semi-quantitative FFQs. Dietary quality was assessed by the Alternative Healthy Eating Index (AHEI) and Mediterranean-style Diet Score (MDS), and fasting blood concentrations of 201 metabolites were quantified. Results In 2380 FHS participants (54±9 years, 54% female, BMI 28±5 kg/m 2 ), 1-SD higher AHEI and MDS were associated with 5.1% (1.2 ml/kg/min, p<0.0001) and 4.4% (1.0 ml/kg/min, p<0.0001) greater peak VO 2 in linear models adjusted for age, sex, total energy intake, cardiovascular risk factors, and physical activity. In participants with metabolite profiling (N=1154), 24 metabolites were concordantly associated with both dietary indices and peak VO 2 in multivariable-adjusted linear models (FDR<5%). These metabolites included C6 and C7 carnitines, C16:0 ceramide, and dimethylguanidino valeric acid, which were higher with lower CRF and poorer dietary quality and are known markers of insulin resistance and cardiovascular risk. Conversely, C38:7 phosphatidylcholine plasmalogen and C38:7 and C40:7 phosphatidylethanolamine plasmalogens were associated with higher CRF and favorable dietary quality and may link to lower cardiometabolic risk. Conclusion Higher diet quality is associated with greater CRF cross-sectionally in a middle-aged community-dwelling sample, and metabolites highlight potential shared favorable effects on health.
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24
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García-Gavilán J, Nishi S, Paz-Graniel I, Guasch-Ferré M, Razquin C, Clish CB, Toledo E, Ruiz-Canela M, Corella D, Deik A, Drouin-Chartier JP, Wittenbecher C, Babio N, Estruch R, Ros E, Fitó M, Arós F, Fiol M, Serra-Majem L, Liang L, Martínez-González MA, Hu FB, Salas-Salvadó J. Plasma Metabolite Profiles Associated with the Amount and Source of Meat and Fish Consumption and the Risk of Type 2 Diabetes. Mol Nutr Food Res 2022; 66:e2200145. [PMID: 36214069 PMCID: PMC9722604 DOI: 10.1002/mnfr.202200145] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 09/12/2022] [Indexed: 01/18/2023]
Abstract
SCOPE Consumption of meat has been associated with a higher risk of type 2 diabetes (T2D), but if plasma metabolite profiles associated with these foods reflect this relationship is unknown. The objective is to identify a metabolite signature of consumption of total meat (TM), red meat (RM), processed red meat (PRM), and fish and examine if they are associated with T2D risk. METHODS AND RESULTS The discovery population includes 1833 participants from the PREDIMED trial. The internal validation sample includes 1522 participants with available 1-year follow-up metabolomic data. Associations between metabolites and TM, RM, PRM, and fish are evaluated with elastic net regression. Associations between the profiles and incident T2D are estimated using Cox regressions. The profiles included 72 metabolites for TM, 69 for RM, 74 for PRM, and 66 for fish. After adjusting for T2D risk factors, only profiles of TM (Hazard Ratio (HR): 1.25, 95% CI: 1.06-1.49), RM (HR: 1.27, 95% CI: 1.07-1.52), and PRM (HR: 1.27, 95% CI: 1.07-1.51) are associated with T2D. CONCLUSIONS The consumption of TM, its subtypes, and fish is associated with different metabolites, some of which have been previously associated with T2D. Scores based on the identified metabolites for TM, RM, and PRM show a significant association with T2D risk.
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Affiliation(s)
- Jesús García-Gavilán
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana. Hospital Universitari San Joan de Reus, Reus, Spain,Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain,Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Stephanie Nishi
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana. Hospital Universitari San Joan de Reus, Reus, Spain,Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain,Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Toronto 3D (Diet, Digestive Tract and Disease) Knowledge Synthesis and Clinical Trials Unit, Toronto, ON, Canada,Clinical Nutrition and Risk Factor Modification Centre, St. Michael’s Hospital, Unity Health Toronto, ON, Canada
| | - Indira Paz-Graniel
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana. Hospital Universitari San Joan de Reus, Reus, Spain,Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain,Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Marta Guasch-Ferré
- Department of Nutrition, 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
| | - Cristina Razquin
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IDISNA), University of Navarra, Pamplona, Spain
| | | | - Estefanía Toledo
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IDISNA), University of Navarra, Pamplona, Spain
| | - Miguel Ruiz-Canela
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IDISNA), University of Navarra, Pamplona, Spain
| | - Dolores Corella
- Consorcio 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
| | - Amy Deik
- The Broad Institute of Harvard and MIT, Boston, MA, USA
| | - Jean-Philippe Drouin-Chartier
- Centre Nutrition, Santé et Société, Institut sur la Nutrition et les Aliments Fonctionnels, Faculté de Pharmacie, Université Laval, Québec, Canada
| | - Clemens Wittenbecher
- Toronto 3D (Diet, Digestive Tract and Disease) Knowledge Synthesis and Clinical Trials Unit, Toronto, ON, Canada,Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany,German Center for Diabetes Research, Neuherberg, Germany
| | - 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 Sanitària Pere Virgili (IISPV), Reus, Spain,Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Ramon Estruch
- Consorcio 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, Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Emilio Ros
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Lipid Clinic, Department of Endocrinology and Nutrition, Agust Pi i Sunyer Biomedical Research Institute (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Montserrat Fitó
- Consorcio 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
| | - Fernando Arós
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Cardiology, University Hospital of Alava, Vitoria, Spain
| | - Miquel Fiol
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Institute of Health Sciences IUNICS, University of Balearic Islands and Hospital Son Espases, Palma de Mallorca, Spain
| | - Lluís Serra-Majem
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Research Institute of Biomedical and Health Sciences IUIBS, University of Las Palmas de Gran Canaria, Las Palmas, Spain
| | - Liming Liang
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA,Department of Statistics, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Miguel A Martínez-González
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA,Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IDISNA), University of Navarra, Pamplona, Spain
| | - Frank B Hu
- Department of Nutrition, 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,Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, 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 Sanitària Pere Virgili (IISPV), Reus, Spain,Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
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25
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Chen ZZ, Pacheco JA, Gao Y, Deng S, Peterson B, Shi X, Zheng S, Tahir UA, Katz DH, Cruz DE, Ngo D, Benson MD, Robbins JM, Guo X, del Rocio Sevilla Gonzalez M, Manning A, Correa A, Meigs JB, Taylor KD, Rich SS, Goodarzi MO, Rotter JI, Wilson JG, Clish CB, Gerszten RE. Nontargeted and Targeted Metabolomic Profiling Reveals Novel Metabolite Biomarkers of Incident Diabetes in African Americans. Diabetes 2022; 71:2426-2437. [PMID: 35998269 PMCID: PMC9630088 DOI: 10.2337/db22-0033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 07/26/2022] [Indexed: 01/25/2023]
Abstract
Nontargeted metabolomics methods have increased potential to identify new disease biomarkers, but assessments of the additive information provided in large human cohorts by these less biased techniques are limited. To diversify our knowledge of diabetes-associated metabolites, we leveraged a method that measures 305 targeted or "known" and 2,342 nontargeted or "unknown" compounds in fasting plasma samples from 2,750 participants (315 incident cases) in the Jackson Heart Study (JHS)-a community cohort of self-identified African Americans-who are underrepresented in omics studies. We found 307 unique compounds (82 known) associated with diabetes after adjusting for age and sex at a false discovery rate of <0.05 and 124 compounds (35 known, including 11 not previously associated) after further adjustments for BMI and fasting plasma glucose. Of these, 144 and 68 associations, respectively, replicated in a multiethnic cohort. Among these is an apparently novel isomer of the 1-deoxyceramide Cer(m18:1/24:0) with functional geonomics and high-resolution mass spectrometry. Overall, known and unknown metabolites provided complementary information (median correlation ρ = 0.29), and their inclusion with clinical risk factors improved diabetes prediction modeling. Our findings highlight the importance of including nontargeted metabolomics methods to provide new insights into diabetes development in ethnically diverse cohorts.
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Affiliation(s)
- Zsu-Zsu Chen
- Division of Endocrinology, Diabetes, and Metabolism, Beth Israel Deaconess Medical Center, Boston, MA
- Harvard School of Medicine, Boston, MA
| | | | - Yan Gao
- University of Mississippi Medical Center, Jacksonville, MS
| | - Shuliang Deng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Bennet Peterson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Xu Shi
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Shuning Zheng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Usman A. Tahir
- Harvard School of Medicine, Boston, MA
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Daniel H. Katz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Daniel E. Cruz
- Harvard School of Medicine, Boston, MA
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Debby Ngo
- Harvard School of Medicine, Boston, MA
- Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Mark D. Benson
- Harvard School of Medicine, Boston, MA
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Jeremy M. Robbins
- Harvard School of Medicine, Boston, MA
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Magdalena del Rocio Sevilla Gonzalez
- Harvard School of Medicine, Boston, MA
- Broad Institute of MIT and Harvard, Boston, MA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA
| | - Alisa Manning
- Harvard School of Medicine, Boston, MA
- Broad Institute of MIT and Harvard, Boston, MA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA
| | - Adolfo Correa
- University of Mississippi Medical Center, Jacksonville, MS
| | - James B. Meigs
- Harvard School of Medicine, Boston, MA
- Broad Institute of MIT and Harvard, Boston, MA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - Stephen S. Rich
- University of Virginia School of Medicine, Charlottesville, VA
| | - Mark O. Goodarzi
- Division of Endocrinology, Diabetes, and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA
| | - James G. Wilson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | | | - Robert E. Gerszten
- Harvard School of Medicine, Boston, MA
- Broad Institute of MIT and Harvard, Boston, MA
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
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26
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Pan XF, Chen ZZ, Wang TJ, Shu X, Cai H, Cai Q, Clish CB, Shi X, Zheng W, Gerszten RE, Shu XO, Yu D. Plasma metabolomic signatures of obesity and risk of type 2 diabetes. Obesity (Silver Spring) 2022; 30:2294-2306. [PMID: 36161775 PMCID: PMC9633360 DOI: 10.1002/oby.23549] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 06/12/2022] [Accepted: 07/14/2022] [Indexed: 01/21/2023]
Abstract
OBJECTIVE The mechanisms linking obesity to type 2 diabetes (T2D) are not fully understood. This study aimed to identify obesity-related metabolomic signatures (MESs) and evaluated their relationships with incident T2D. METHODS In a nested case-control study of 2076 Chinese adults, 140 plasma metabolites were measured at baseline, linear regression was applied with the least absolute shrinkage and selection operator to identify MESs for BMI and waist circumference (WC), and conditional logistic regression was applied to examine their associations with T2D risk. RESULTS A total of 32 metabolites associated with BMI or WC were identified and validated, among which 14 showed positive associations and 3 showed inverse associations with T2D; 8 and 18 metabolites were selected to build MESs for BMI and WC, respectively. Both MESs showed strong linear associations with T2D: odds ratio (95% CI) comparing extreme quartiles was 4.26 (2.00-9.06) for BMI MES and 9.60 (4.22-21.88) for WC MES (both p-trend < 0.001). The MES-T2D associations were particularly evident among individuals with normal WC: odds ratio (95% CI) reached 6.41 (4.11-9.98) for BMI MES and 10.38 (6.36-16.94) for WC MES. Adding MESs to traditional risk factors and plasma glucose improved C statistics from 0.79 to 0.83 (p < 0.001). CONCLUSIONS Multiple obesity-related metabolites and MESs strongly associated with T2D in Chinese adults were identified.
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Affiliation(s)
- Xiong-Fei Pan
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Zsu-Zsu Chen
- Division of Endocrinology, Diabetes, and Metabolism, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Thomas J. Wang
- Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, USA
| | - Xiang Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hui Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Clary B. Clish
- Metabolomics Platform, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Xu Shi
- Broad Institute of Massachusetts Institute of Technology and Harvard & Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Robert E. Gerszten
- Broad Institute of Massachusetts Institute of Technology and Harvard & Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Danxia Yu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
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27
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Brantley KD, Zeleznik OA, Dickerman BA, Balasubramanian R, Clish CB, Avila-Pacheco J, Rosner B, Tamimi RM, Eliassen AH. A metabolomic analysis of adiposity measures and pre- and postmenopausal breast cancer risk in the Nurses' Health Studies. Br J Cancer 2022; 127:1076-1085. [PMID: 35717425 PMCID: PMC9470549 DOI: 10.1038/s41416-022-01873-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 05/20/2022] [Accepted: 05/27/2022] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Adiposity is consistently positively associated with postmenopausal breast cancer and inversely associated with premenopausal breast cancer risk, though the reasons for this difference remain unclear. METHODS In this nested case-control study of 1649 breast cancer cases and 1649 matched controls from the Nurses' Health Study (NHS) and the NHSII, we selected lipid and polar metabolites correlated with BMI, waist circumference, weight change since age 18, or derived fat mass, and developed a metabolomic score for each measure using LASSO regression. Logistic regression was used to investigate the association between this score and breast cancer risk, adjusted for risk factors and stratified by menopausal status at blood draw and diagnosis. RESULTS Metabolite scores developed among only premenopausal or postmenopausal women were highly correlated with scores developed in all women (r = 0.93-0.96). Higher metabolomic adiposity scores were generally inversely related to breast cancer risk among premenopausal women. Among postmenopausal women, significant positive trends with risk were observed (e.g., metabolomic waist circumference score OR Q4 vs. Q1 = 1.47, 95% CI = 1.03-2.08, P-trend = 0.01). CONCLUSIONS Though the same metabolites represented adiposity in pre- and postmenopausal women, breast cancer risk associations differed suggesting that metabolic dysregulation may have a differential association with pre- vs. postmenopausal breast cancer.
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Affiliation(s)
- Kristen D Brantley
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA.
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Barbra A Dickerman
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Raji Balasubramanian
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA
| | - Clary B Clish
- Metabolomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Bernard Rosner
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Rulla M Tamimi
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - A Heather Eliassen
- Department of Epidemiology, Harvard TH 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
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28
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Yi ZY, Chen L, Wang Y, He D, Zhao D, Zhang SH, Yu R, Huang JH. The potential mechanism of Liu-Wei-Di-Huang Pills in treatment of type 2 diabetic mellitus: from gut microbiota to short-chain fatty acids metabolism. Acta Diabetol 2022; 59:1295-1308. [PMID: 35857109 DOI: 10.1007/s00592-022-01922-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 06/11/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND Type 2 diabetes mellitus (T2DM) has already become a global pandemic. Recently, reports showed its pathogenesis was closely related to a disorder of gut microbiota. In China, the Liu-Wei-Di-Huang Pills (LWDH) have treated T2DM for thousands of years. However, its therapeutic mechanism associated with gut microbiota is worthy of further study. AIMS This study aims to investigate the effects of LWDH on T2DM by regulating gut microbiota and short-chain fatty acids (SCFAs) in Goto-Kakizaki (GK) rats. METHODS T2DM models were successfully established based on GK rats and administrated with LWDH. The changes in fasting blood glucose (FBG), oral glucose tolerance test (OGTT), and serum insulin (INS) were determined, and the immunohistochemical (IHC) method was used to test INS expression in pancreas. The 16S-ribosomal DNA (16S rDNA) sequencing analysis assessed gut microbiota structural changes; a gas chromatography-mass spectrometer (GC-MS)-based metabolomics method was adopted to detect SCFA levels. The pathological morphology of jejunum was detected by hematoxylin-eosin (H&E) staining, and the expression of GPR43, GPR41, GLP-1, and GLP-1R was evaluated by qRT-PCR and ELISA, respectively. RESULTS We observed that GK rats treated with LWDH: (a) has altered the microbial structure and promoted the abundance of bacteria in Firmicutes, including Lactobacillus, Allobaculum, and Ruminococcus_2, (b) increased SCFAs levels involving acetic acid, propionic acid, and butyric acid and (c) alleviated T2DM and jejunum injuries potentially based on SCFAs-GPR43/41-GLP-1 pathway. CONCLUSION LWDH could improve T2DM by regulating gut microbiota and SCFAs, and the therapeutic mechanism might be related to the SCFAs-GPR43/41-GLP-1 pathway.
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Affiliation(s)
- Zi-Yang Yi
- Hunan Academy of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, 410013, Hunan, People's Republic of China
| | - Lin Chen
- Hunan Academy of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, 410013, Hunan, People's Republic of China
| | - Yan Wang
- H. E. J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan
| | - Dan He
- Hunan Academy of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, 410013, Hunan, People's Republic of China
| | - Di Zhao
- Hunan Academy of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, 410013, Hunan, People's Republic of China
| | - Shui-Han Zhang
- Hunan Academy of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, 410013, Hunan, People's Republic of China
| | - Rong Yu
- Hunan Academy of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, 410013, Hunan, People's Republic of China.
- Hunan Key Laboratory of TCM Prescription and Syndromes Translational Medicine, Hunan University of Chinese Medicine, Changsha, 410208, Hunan, People's Republic of China.
| | - Jian-Hua Huang
- Hunan Academy of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, 410013, Hunan, People's Republic of China.
- Hunan Key Laboratory of TCM Prescription and Syndromes Translational Medicine, Hunan University of Chinese Medicine, Changsha, 410208, Hunan, People's Republic of China.
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29
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Shah RV, Miller P, Colangelo LA, Chernofsky A, Houstis NE, Malhotra R, Velagaleti RS, Jacobs DR, Gabriel KP, Reis JP, Lloyd‐Jones DM, Clish CB, Larson MG, Vasan RS, Murthy VL, Lewis GD, Nayor M. Blood-Based Fingerprint of Cardiorespiratory Fitness and Long-Term Health Outcomes in Young Adulthood. J Am Heart Assoc 2022; 11:e026670. [PMID: 36073631 PMCID: PMC9683648 DOI: 10.1161/jaha.122.026670] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 07/25/2022] [Indexed: 11/16/2022]
Abstract
Background Cardiorespiratory fitness is a powerful predictor of health outcomes that is currently underused in primary prevention, especially in young adults. We sought to develop a blood-based biomarker of cardiorespiratory fitness that is easily translatable across populations. Methods and Results Maximal effort cardiopulmonary exercise testing for quantification of cardiorespiratory fitness (by peak oxygen uptake) and profiling of >200 metabolites at rest were performed in the FHS (Framingham Heart Study; 2016-2019). A metabolomic fitness score was derived/validated in the FHS and was associated with long-term outcomes in the younger CARDIA (Coronary Artery Risk Development in Young Adults) study. In the FHS (derivation, N=451; validation, N=914; age 54±8 years, 53% women, body mass index 27.7±5.3 kg/m2), we used LASSO (least absolute shrinkage and selection operator) regression to develop a multimetabolite score to predict peak oxygen uptake (correlation with peak oxygen uptake r=0.77 in derivation, 0.61 in validation; both P<0.0001). In a linear model including clinical risk factors, a ≈1-SD higher metabolomic fitness score had equivalent magnitude of association with peak oxygen uptake as a 9.2-year age increment. In the CARDIA study (N=2300, median follow-up 26.9 years, age 32±4 years, 44% women, 44% Black individuals), a 1-SD higher metabolomic fitness score was associated with a 44% lower risk for mortality (hazard ratio [HR], 0.56 [95% CI, 0.47-0.68]; P<0.0001) and 32% lower risk for cardiovascular disease (HR, 0.68 [95% CI, 0.55-0.84]; P=0.0003) in models adjusted for age, sex, and race, which remained robust with adjustment for clinical risk factors. Conclusions A blood-based biomarker of cardiorespiratory fitness largely independent of traditional risk factors is associated with long-term risk of cardiovascular disease and mortality in young adults.
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Affiliation(s)
- Ravi V. Shah
- Vanderbilt Translational and Clinical Research CenterCardiology DivisionVanderbilt University Medical CenterNashvilleTN
| | - Patricia Miller
- Department of BiostatisticsBoston University School of Public HealthBostonMA
| | - Laura A. Colangelo
- Department of Preventive MedicineFeinberg School of MedicineNorthwestern UniversityChicagoIL
| | - Ariel Chernofsky
- Department of BiostatisticsBoston University School of Public HealthBostonMA
| | - Nicholas E. Houstis
- Cardiology DivisionDepartment of MedicineMassachusetts General HospitalHarvard Medical SchoolBostonMA
| | - Rajeev Malhotra
- Cardiology DivisionDepartment of MedicineMassachusetts General HospitalHarvard Medical SchoolBostonMA
| | | | - David R. Jacobs
- Division of Epidemiology and Community HealthSchool of Public HealthUniversity of MinnesotaMinneapolisMN
| | | | - Jared P. Reis
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood InstituteBethesdaMD
| | - Donald M. Lloyd‐Jones
- Department of Preventive MedicineFeinberg School of MedicineNorthwestern UniversityChicagoIL
- Division of CardiologyDepartment of MedicineNorthwestern University Feinberg School of MedicineChicagoIL
| | | | - Martin G. Larson
- Department of BiostatisticsBoston University School of Public HealthBostonMA
- Boston University’s and National Heart, Lung, and Blood Institute’s Framingham Heart StudyFraminghamMA
| | - Ramachandran S. Vasan
- Boston University’s and National Heart, Lung, and Blood Institute’s Framingham Heart StudyFraminghamMA
- Sections of Cardiovascular Medicine and Preventive Medicine and EpidemiologyDepartment of MedicineBoston University School of MedicineBostonMA
- Department of EpidemiologyBoston University School of Public Health, and the Center for Computing and Data SciencesBoston UniversityBostonMA
| | - Venkatesh L. Murthy
- Department of EpidemiologyBoston University School of Public Health, and the Center for Computing and Data SciencesBoston UniversityBostonMA
- Division of Cardiovascular MedicineDepartment of Medicine, and Frankel Cardiovascular Center University of MichiganAnn ArborMI
| | - Gregory D. Lewis
- Cardiology DivisionDepartment of MedicineMassachusetts General HospitalHarvard Medical SchoolBostonMA
- Pulmonary Critical Care UnitMassachusetts General HospitalBostonMA
| | - Matthew Nayor
- Sections of Cardiovascular Medicine and Preventive Medicine and EpidemiologyDepartment of MedicineBoston University School of MedicineBostonMA
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30
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Liu L, Chen X, Liu L, Qin H. Clostridium butyricum Potentially Improves Immunity and Nutrition through Alteration of the Microbiota and Metabolism of Elderly People with Malnutrition in Long-Term Care. Nutrients 2022; 14:3546. [PMID: 36079806 PMCID: PMC9460359 DOI: 10.3390/nu14173546] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 08/22/2022] [Accepted: 08/24/2022] [Indexed: 11/16/2022] Open
Abstract
Recent research advances examining the gut microbiome and its association with human health have indicated that microbiota-targeted intervention is a promising means for health modulation. In this study, elderly people in long-term care (aged 83.2 ± 5.3 year) with malnutrition (MNA-SF score ≤ 7) were recruited in a community hospital for a 12-week randomized, single-blind clinical trial with Clostridium butyricum. Compared with the basal fluctuations of the control group, an altered gut microbiome was observed in the intervention group, with increased (p < 0.05) Coprobacillus species, Carnobacterium divergens, and Corynebacterium_massiliense, and the promoted growth of the beneficial organisms Akketmanse muciniphila and Alistipes putredinis. A concentrated profile of 14 increased Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologs (KOs) that were enriched in cofactor/vitamin production and carbohydrate metabolism pathways were discovered; the genes were found to be correlated (p < 0.05) with an elevated abundance of plasma metabolites and short-chain fatty acids (SCFAs), unsaturated medium- to long-chain fatty acids (MFA, LFA), carnitines, and amino acids, thus suggesting a coordinated ameliorated metabolism. Proinflammatory factor interferon-gamma (IFN-γ) levels decreased (p < 0.05) throughout the intervention, while the gut barrier tight junction protein, occludin, rose in abundance (p = 0.059), and the sensitive nutrition biomarker prealbumin improved, in contrast to the opposite changes in control. Based on our results obtained during a relatively short intervention time, C. butyricum might have great potential for improving nutrition and immunity in elderly people in long-term care with malnutrition through the alteration of gut microbiota, increasing the abundance of beneficial bacteria and activating the metabolism in SCFA and cofactor/vitamin production, bile acid metabolism, along with efficient energy generation.
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Affiliation(s)
- Lin Liu
- Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China
| | - Xiang Chen
- Realbio Genomics Institute, Shanghai 200123, China
| | - Lu Liu
- Pengpu Community Medical Service Center, Shanghai 200436, China
| | - Huanlong Qin
- Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China
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31
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Tahir UA, Katz DH, Avila-Pachecho J, Bick AG, Pampana A, Robbins JM, Yu Z, Chen ZZ, Benson MD, Cruz DE, Ngo D, Deng S, Shi X, Zheng S, Eisman AS, Farrell L, Hall ME, Correa A, Tracy RP, Durda P, Taylor KD, Liu Y, Johnson WC, Guo X, Yao J, Chen YDI, Manichaikul AW, Ruberg FL, Blaner WS, Jain D, Bouchard C, Sarzynski MA, Rich SS, Rotter JI, Wang TJ, Wilson JG, Clish CB, Natarajan P, Gerszten RE. Whole Genome Association Study of the Plasma Metabolome Identifies Metabolites Linked to Cardiometabolic Disease in Black Individuals. Nat Commun 2022; 13:4923. [PMID: 35995766 PMCID: PMC9395431 DOI: 10.1038/s41467-022-32275-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 07/25/2022] [Indexed: 01/27/2023] Open
Abstract
Integrating genetic information with metabolomics has provided new insights into genes affecting human metabolism. However, gene-metabolite integration has been primarily studied in individuals of European Ancestry, limiting the opportunity to leverage genomic diversity for discovery. In addition, these analyses have principally involved known metabolites, with the majority of the profiled peaks left unannotated. Here, we perform a whole genome association study of 2,291 metabolite peaks (known and unknown features) in 2,466 Black individuals from the Jackson Heart Study. We identify 519 locus-metabolite associations for 427 metabolite peaks and validate our findings in two multi-ethnic cohorts. A significant proportion of these associations are in ancestry specific alleles including findings in APOE, TTR and CD36. We leverage tandem mass spectrometry to annotate unknown metabolites, providing new insight into hereditary diseases including transthyretin amyloidosis and sickle cell disease. Our integrative omics approach leverages genomic diversity to provide novel insights into diverse cardiometabolic diseases.
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Affiliation(s)
- Usman A Tahir
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | - Daniel H Katz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | | | | | - Akhil Pampana
- Broad Institute of Harvard and MIT, Cambridge, MA, US
| | - Jeremy M Robbins
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | - Zhi Yu
- Broad Institute of Harvard and MIT, Cambridge, MA, US
| | - Zsu-Zsu Chen
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | - Mark D Benson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | - Daniel E Cruz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | - Debby Ngo
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | - Shuliang Deng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | - Xu Shi
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | - Shuning Zheng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | - Aaron S Eisman
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | - Laurie Farrell
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | - Michael E Hall
- University of Mississippi Medical Center, Jackson, MS, US
| | - Adolfo Correa
- University of Mississippi Medical Center, Jackson, MS, US
| | - Russell P Tracy
- Department of Pathology Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, US
| | - Peter Durda
- Department of Pathology Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, US
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor UCLA Medical Center, Torrance, CA, US
| | - Yongmei Liu
- Department of Medicine, Division of Cardiology, Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, US
| | - W Craig Johnson
- Department of Biostatistics, University of Washington, Seattle, WA, US
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor UCLA Medical Center, Torrance, CA, US
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor UCLA Medical Center, Torrance, CA, US
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor UCLA Medical Center, Torrance, CA, US
| | - Ani W Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, US
- Division of Biostatistics and Epidemiology, Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, US
| | - Frederick L Ruberg
- Section of Cardiovascular Medicine, Boston University School of Medicine and Boston Medical Center, Boston, MA, US
| | | | - Deepti Jain
- University of Washington, Seattle, Washington, US
| | - Claude Bouchard
- Human Genomic Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, US
| | - Mark A Sarzynski
- Department of Exercise Science, University of South Carolina, Columbia, SC, US
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, US
- Division of Biostatistics and Epidemiology, Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, US
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor UCLA Medical Center, Torrance, CA, US
| | - Thomas J Wang
- Department of Medicine, UT Southwestern Medical Center, Dallas, TX, US
| | - James G Wilson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US
| | - Clary B Clish
- Broad Institute of Harvard and MIT, Cambridge, MA, US
| | - Pradeep Natarajan
- Broad Institute of Harvard and MIT, Cambridge, MA, US
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, US
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, US.
- Broad Institute of Harvard and MIT, Cambridge, MA, US.
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One-Year Changes in Urinary Microbial Phenolic Metabolites and the Risk of Type 2 Diabetes-A Case-Control Study. Antioxidants (Basel) 2022; 11:antiox11081540. [PMID: 36009259 PMCID: PMC9405292 DOI: 10.3390/antiox11081540] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 08/02/2022] [Accepted: 08/02/2022] [Indexed: 11/17/2022] Open
Abstract
The intake of polyphenols has been associated with a risk reduction of type 2 diabetes. Nevertheless, to the best of our knowledge, the molecules that might be metabolically active after ingestion are only starting to be investigated regarding this metabolic disease. To investigate the association between one-year changes in urinary microbial phenolic metabolites (MPM) and the incidence of type 2 diabetes, we performed a case-control study using data and samples of the PREDIMED trial including 46 incident type 2 diabetes cases of 172 randomly selected participants. Eight urinary MPMs were quantified in urine by liquid chromatography coupled to mass spectrometry and used to assess their associations with type 2 diabetes risk by multivariable logistic regression models. Compared to participants in the lowest tertile of one-year changes in hydroxybenzoic acid glucuronide, those in the highest tertile had a significantly lowered probability of developing type 2 diabetes (OR [95% CI], 0.39 [0.23−0.64]; p < 0.001 for trend). However, when additionally adjusting for fasting plasma glucose, the statistical significance was lost. Changes in the dietary pattern can increase the concentrations of this compound, derived from many (poly)phenol-rich foods, and might be changing the gut microbial population as well, promoting the production of the metabolite.
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Yang FM, Shen L, Fan DD, Chen KH, Lee J. DMGV Is a Rheostat of T Cell Survival and a Potential Therapeutic for Inflammatory Diseases and Cancers. Front Immunol 2022; 13:918241. [PMID: 35990633 PMCID: PMC9389583 DOI: 10.3389/fimmu.2022.918241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 06/13/2022] [Indexed: 11/13/2022] Open
Abstract
Activated effector T cells (Teff) and/or compromised regulatory T cells (Treg) underlie many chronic inflammatory diseases. We discovered a novel pathway to regulate survival and expansion of Teff without compromising Treg survival and a potential therapeutic to treat these diseases. We found dimethylguanidino valeric acid (DMGV) as a rheostat for Teff survival: while cell-intrinsic DMGV generated by Alanine-Glyoxylate Aminotransferase 2 (AGXT2) is essential for survival and expansion by inducing mitochondrial ROS and regulation of glycolysis, an excessive (or exogenous) DMGV level inhibits activated Teff survival, thereby the AGXT2-DMGV-ROS axis functioning as a switch to turn on and off Teff expansion. DMGV-induced ROS is essential for glycolysis in Teff, and paradoxically DMGV induces ROS only when glycolysis is active. Mechanistically, DMGV rapidly activates mitochondrial calcium uniporter (MCU), causing a surge in mitochondrial Ca2+ without provoking calcium influx to the cytosol. The mitochondrial Ca2+ surge in turn triggers the mitochondrial Na+/Ca2+ exchanger (NCLX) and the subsequent mitochondrial Na+ import induces ROS by uncoupling the Coenzyme Q cycle in Complex III of the electron transport chain. In preclinical studies, DMGV administration significantly diminished the number of inflammatory T cells, effectively suppressing chronic inflammation in mouse models of colitis and rheumatoid arthritis. DMGV also suppressed expansion of cancer cells in vitro and in a mouse T cell leukemic model by the same mechanism. Our data provide a new pathway regulating T cell survival and a novel mode to treat autoimmune diseases and cancers.
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Affiliation(s)
- Fengyuan Mandy Yang
- School of Basic Medical Sciences, Guangdong Provincial Key Laboratory of Allergy & Clinical Immunology, and the State Key Laboratory of Respiratory Diseases, Guangzhou Medical University, Guangzhou, China
| | - Liya Shen
- School of Basic Medical Sciences, Guangdong Provincial Key Laboratory of Allergy & Clinical Immunology, and the State Key Laboratory of Respiratory Diseases, Guangzhou Medical University, Guangzhou, China
| | - Dengxia Denise Fan
- School of Basic Medical Sciences, Guangdong Provincial Key Laboratory of Allergy & Clinical Immunology, and the State Key Laboratory of Respiratory Diseases, Guangzhou Medical University, Guangzhou, China
| | - Kuan-Hung Chen
- Department of Orthopedics, The 1st Affiliated Hospital of Sun Yat-sen University, Guangdong, China
| | - Jongdae Lee
- School of Basic Medical Sciences, Guangdong Provincial Key Laboratory of Allergy & Clinical Immunology, and the State Key Laboratory of Respiratory Diseases, Guangzhou Medical University, Guangzhou, China
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Dennis M, Howpage S, McGill M, Dutta S, Koay Y, Lal LN, Lal S, Wu T, Ugander M, Wang A, Munoz PA, Wong J, Constantino MI, O'Sullivan J, Twigg SM, Puranik R. Myocardial fibrosis in type 2 diabetes is associated with functional and metabolomic parameters. Int J Cardiol 2022; 363:179-184. [PMID: 35724800 DOI: 10.1016/j.ijcard.2022.06.049] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 05/30/2022] [Accepted: 06/15/2022] [Indexed: 12/28/2022]
Affiliation(s)
- Mark Dennis
- Sydney Medical School, University of Sydney, Sydney, Australia; Department of Cardiology, Royal Prince Alfred Hospital, Sydney, Australia; Department of Aged Care, LifeHouse Hospital, Sydney, Australia.
| | - Sashie Howpage
- Sydney Medical School, University of Sydney, Sydney, Australia; Department of Endocrinology, Royal Prince Alfred Hospital, Sydney, Australia
| | - Margaret McGill
- Sydney Medical School, University of Sydney, Sydney, Australia; Department of Endocrinology, Royal Prince Alfred Hospital, Sydney, Australia
| | | | - Yen Koay
- Heart Research Institute, Sydney, Australia
| | - Lisa Nguyen Lal
- Sydney Medical School, University of Sydney, Sydney, Australia
| | - Sean Lal
- Sydney Medical School, University of Sydney, Sydney, Australia; Department of Cardiology, Royal Prince Alfred Hospital, Sydney, Australia
| | - Ted Wu
- Sydney Medical School, University of Sydney, Sydney, Australia; Department of Endocrinology, Royal Prince Alfred Hospital, Sydney, Australia
| | - Martin Ugander
- Sydney Medical School, University of Sydney, Sydney, Australia; Kolling Institute, Royal North Shore Hospital, and Charles Perkins Centre, University of Sydney, Sydney, Australia; Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, Sweden
| | - Alexandra Wang
- Department of Cardiology, Royal Prince Alfred Hospital, Sydney, Australia; University of New South, Wales
| | - Phillip A Munoz
- Department of Respiratory Medicine, Royal Prince Alfred Hospital, Sydney, Australia
| | - Jencia Wong
- Sydney Medical School, University of Sydney, Sydney, Australia; Department of Endocrinology, Royal Prince Alfred Hospital, Sydney, Australia
| | - Maria I Constantino
- Sydney Medical School, University of Sydney, Sydney, Australia; Department of Endocrinology, Royal Prince Alfred Hospital, Sydney, Australia
| | - John O'Sullivan
- Sydney Medical School, University of Sydney, Sydney, Australia; Department of Cardiology, Royal Prince Alfred Hospital, Sydney, Australia; Heart Research Institute, Sydney, Australia
| | - Stephen M Twigg
- Sydney Medical School, University of Sydney, Sydney, Australia; Department of Endocrinology, Royal Prince Alfred Hospital, Sydney, Australia
| | - Rajesh Puranik
- Sydney Medical School, University of Sydney, Sydney, Australia; Department of Cardiology, Royal Prince Alfred Hospital, Sydney, Australia
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Ament Z, Patki A, Chaudhary N, Bhave VM, Garcia Guarniz AL, Gao Y, Gerszten RE, Correa A, Judd SE, Cushman M, Long DL, Irvin MR, Kimberly WT. Nucleosides Associated With Incident Ischemic Stroke in the REGARDS and JHS Cohorts. Neurology 2022; 98:e2097-e2107. [PMID: 35264422 DOI: 10.1212/wnl.0000000000200262] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 02/04/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Both genetic and environmental factors contribute to stroke risk. We sought to identify novel metabolites associated with incident stroke in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort and determine whether they reflected genetic or environmental variation. METHODS This was a stroke case-cohort observational study nested in REGARDS. Cases were defined as incident stroke and metabolomic profiles were compared to a randomly selected control cohort. In baseline plasma samples, 162 metabolites were measured using liquid chromatography-tandem mass spectrometry. Cox proportional hazards models were adjusted for age, sex, race, and age by race in the base model. Fully adjusted models included traditional stroke risk factors. Mediation analyses conducted for these stroke risk factors used the metabolite as mediator. Genome-wide associations with the leading candidate metabolites were calculated using array data. Replication analyses in the Jackson Heart Study (JHS) were conducted using random effects meta-analysis. RESULTS There were 2,043 participants who were followed over an average period of 7.1 years, including 1,075 stroke cases and 968 random controls. Nine metabolites were associated with stroke in the base model, 8 of which were measured and remained significant in meta-analysis with JHS. In the fully adjusted model in REGARDS, guanosine (hazard ratio [HR] 1.34, 95% CI 1.18-1.53; p = 7.26 × 10-6) and pseudouridine (HR 1.28, 95% CI 1.13-1.45; p = 1.03 × 10-4) were associated with incident ischemic stroke following Bonferroni adjustment. Guanosine also partially mediated the relationship between hypertension and stroke (17.6%) and pseudouridine did not mediate any risk factor. Genome-wide association analysis identified loci rs34631560 and rs34631560 associated with pseudouridine, but these did not explain the association of pseudouridine with stroke. DISCUSSION Guanosine and pseudouridine are nucleosides associated with incident ischemic stroke independently of other risk factors. Genetic and mediation analyses suggest that environmental exposures rather than genetic variation link nucleoside levels to stroke risk. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that guanosine and pseudouridine are associated with incident stroke.
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Affiliation(s)
- Zsuzsanna Ament
- From the Center for Genomic Medicine, Harvard Medical School (Z.A., W.T.K.), and Department of Neurology (Z.A., A.-L.G.G., W.T.K.), Massachusetts General Hospital, Boston; Departments of Epidemiology (A.P., N.C., R.M.I.) and Biostatistics (S.E.J., L.L.), School of Public Health, University of Alabama at Birmingham; Harvard Medical School (V.M.B.), Boston, MA; The Jackson Heart Study (Y.G., A.C.), University of Mississippi Medical Center, Jackson; Department of Medicine (R.E.G.), Beth Israel Deaconess Medical Center, Boston, MA; and Department of Medicine (M.C.), Larner College of Medicine at the University of Vermont, Burlington
| | - Amit Patki
- From the Center for Genomic Medicine, Harvard Medical School (Z.A., W.T.K.), and Department of Neurology (Z.A., A.-L.G.G., W.T.K.), Massachusetts General Hospital, Boston; Departments of Epidemiology (A.P., N.C., R.M.I.) and Biostatistics (S.E.J., L.L.), School of Public Health, University of Alabama at Birmingham; Harvard Medical School (V.M.B.), Boston, MA; The Jackson Heart Study (Y.G., A.C.), University of Mississippi Medical Center, Jackson; Department of Medicine (R.E.G.), Beth Israel Deaconess Medical Center, Boston, MA; and Department of Medicine (M.C.), Larner College of Medicine at the University of Vermont, Burlington
| | - Ninad Chaudhary
- From the Center for Genomic Medicine, Harvard Medical School (Z.A., W.T.K.), and Department of Neurology (Z.A., A.-L.G.G., W.T.K.), Massachusetts General Hospital, Boston; Departments of Epidemiology (A.P., N.C., R.M.I.) and Biostatistics (S.E.J., L.L.), School of Public Health, University of Alabama at Birmingham; Harvard Medical School (V.M.B.), Boston, MA; The Jackson Heart Study (Y.G., A.C.), University of Mississippi Medical Center, Jackson; Department of Medicine (R.E.G.), Beth Israel Deaconess Medical Center, Boston, MA; and Department of Medicine (M.C.), Larner College of Medicine at the University of Vermont, Burlington
| | - Varun M Bhave
- From the Center for Genomic Medicine, Harvard Medical School (Z.A., W.T.K.), and Department of Neurology (Z.A., A.-L.G.G., W.T.K.), Massachusetts General Hospital, Boston; Departments of Epidemiology (A.P., N.C., R.M.I.) and Biostatistics (S.E.J., L.L.), School of Public Health, University of Alabama at Birmingham; Harvard Medical School (V.M.B.), Boston, MA; The Jackson Heart Study (Y.G., A.C.), University of Mississippi Medical Center, Jackson; Department of Medicine (R.E.G.), Beth Israel Deaconess Medical Center, Boston, MA; and Department of Medicine (M.C.), Larner College of Medicine at the University of Vermont, Burlington
| | - Ana-Lucia Garcia Guarniz
- From the Center for Genomic Medicine, Harvard Medical School (Z.A., W.T.K.), and Department of Neurology (Z.A., A.-L.G.G., W.T.K.), Massachusetts General Hospital, Boston; Departments of Epidemiology (A.P., N.C., R.M.I.) and Biostatistics (S.E.J., L.L.), School of Public Health, University of Alabama at Birmingham; Harvard Medical School (V.M.B.), Boston, MA; The Jackson Heart Study (Y.G., A.C.), University of Mississippi Medical Center, Jackson; Department of Medicine (R.E.G.), Beth Israel Deaconess Medical Center, Boston, MA; and Department of Medicine (M.C.), Larner College of Medicine at the University of Vermont, Burlington
| | - Yan Gao
- From the Center for Genomic Medicine, Harvard Medical School (Z.A., W.T.K.), and Department of Neurology (Z.A., A.-L.G.G., W.T.K.), Massachusetts General Hospital, Boston; Departments of Epidemiology (A.P., N.C., R.M.I.) and Biostatistics (S.E.J., L.L.), School of Public Health, University of Alabama at Birmingham; Harvard Medical School (V.M.B.), Boston, MA; The Jackson Heart Study (Y.G., A.C.), University of Mississippi Medical Center, Jackson; Department of Medicine (R.E.G.), Beth Israel Deaconess Medical Center, Boston, MA; and Department of Medicine (M.C.), Larner College of Medicine at the University of Vermont, Burlington
| | - Robert E Gerszten
- From the Center for Genomic Medicine, Harvard Medical School (Z.A., W.T.K.), and Department of Neurology (Z.A., A.-L.G.G., W.T.K.), Massachusetts General Hospital, Boston; Departments of Epidemiology (A.P., N.C., R.M.I.) and Biostatistics (S.E.J., L.L.), School of Public Health, University of Alabama at Birmingham; Harvard Medical School (V.M.B.), Boston, MA; The Jackson Heart Study (Y.G., A.C.), University of Mississippi Medical Center, Jackson; Department of Medicine (R.E.G.), Beth Israel Deaconess Medical Center, Boston, MA; and Department of Medicine (M.C.), Larner College of Medicine at the University of Vermont, Burlington
| | - Adolfo Correa
- From the Center for Genomic Medicine, Harvard Medical School (Z.A., W.T.K.), and Department of Neurology (Z.A., A.-L.G.G., W.T.K.), Massachusetts General Hospital, Boston; Departments of Epidemiology (A.P., N.C., R.M.I.) and Biostatistics (S.E.J., L.L.), School of Public Health, University of Alabama at Birmingham; Harvard Medical School (V.M.B.), Boston, MA; The Jackson Heart Study (Y.G., A.C.), University of Mississippi Medical Center, Jackson; Department of Medicine (R.E.G.), Beth Israel Deaconess Medical Center, Boston, MA; and Department of Medicine (M.C.), Larner College of Medicine at the University of Vermont, Burlington
| | - Suzanne E Judd
- From the Center for Genomic Medicine, Harvard Medical School (Z.A., W.T.K.), and Department of Neurology (Z.A., A.-L.G.G., W.T.K.), Massachusetts General Hospital, Boston; Departments of Epidemiology (A.P., N.C., R.M.I.) and Biostatistics (S.E.J., L.L.), School of Public Health, University of Alabama at Birmingham; Harvard Medical School (V.M.B.), Boston, MA; The Jackson Heart Study (Y.G., A.C.), University of Mississippi Medical Center, Jackson; Department of Medicine (R.E.G.), Beth Israel Deaconess Medical Center, Boston, MA; and Department of Medicine (M.C.), Larner College of Medicine at the University of Vermont, Burlington
| | - Mary Cushman
- From the Center for Genomic Medicine, Harvard Medical School (Z.A., W.T.K.), and Department of Neurology (Z.A., A.-L.G.G., W.T.K.), Massachusetts General Hospital, Boston; Departments of Epidemiology (A.P., N.C., R.M.I.) and Biostatistics (S.E.J., L.L.), School of Public Health, University of Alabama at Birmingham; Harvard Medical School (V.M.B.), Boston, MA; The Jackson Heart Study (Y.G., A.C.), University of Mississippi Medical Center, Jackson; Department of Medicine (R.E.G.), Beth Israel Deaconess Medical Center, Boston, MA; and Department of Medicine (M.C.), Larner College of Medicine at the University of Vermont, Burlington
| | - D Leann Long
- From the Center for Genomic Medicine, Harvard Medical School (Z.A., W.T.K.), and Department of Neurology (Z.A., A.-L.G.G., W.T.K.), Massachusetts General Hospital, Boston; Departments of Epidemiology (A.P., N.C., R.M.I.) and Biostatistics (S.E.J., L.L.), School of Public Health, University of Alabama at Birmingham; Harvard Medical School (V.M.B.), Boston, MA; The Jackson Heart Study (Y.G., A.C.), University of Mississippi Medical Center, Jackson; Department of Medicine (R.E.G.), Beth Israel Deaconess Medical Center, Boston, MA; and Department of Medicine (M.C.), Larner College of Medicine at the University of Vermont, Burlington
| | - M Ryan Irvin
- From the Center for Genomic Medicine, Harvard Medical School (Z.A., W.T.K.), and Department of Neurology (Z.A., A.-L.G.G., W.T.K.), Massachusetts General Hospital, Boston; Departments of Epidemiology (A.P., N.C., R.M.I.) and Biostatistics (S.E.J., L.L.), School of Public Health, University of Alabama at Birmingham; Harvard Medical School (V.M.B.), Boston, MA; The Jackson Heart Study (Y.G., A.C.), University of Mississippi Medical Center, Jackson; Department of Medicine (R.E.G.), Beth Israel Deaconess Medical Center, Boston, MA; and Department of Medicine (M.C.), Larner College of Medicine at the University of Vermont, Burlington
| | - W Taylor Kimberly
- From the Center for Genomic Medicine, Harvard Medical School (Z.A., W.T.K.), and Department of Neurology (Z.A., A.-L.G.G., W.T.K.), Massachusetts General Hospital, Boston; Departments of Epidemiology (A.P., N.C., R.M.I.) and Biostatistics (S.E.J., L.L.), School of Public Health, University of Alabama at Birmingham; Harvard Medical School (V.M.B.), Boston, MA; The Jackson Heart Study (Y.G., A.C.), University of Mississippi Medical Center, Jackson; Department of Medicine (R.E.G.), Beth Israel Deaconess Medical Center, Boston, MA; and Department of Medicine (M.C.), Larner College of Medicine at the University of Vermont, Burlington
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SARZYNSKI MARKA, RICE TREVAK, DESPRÉS JEANPIERRE, PÉRUSSE LOUIS, TREMBLAY ANGELO, STANFORTH PHILIPR, TCHERNOF ANDRÉ, BARBER JACOBL, FALCIANI FRANCESCO, CLISH CLARY, ROBBINS JEREMYM, GHOSH SUJOY, GERSZTEN ROBERTE, LEON ARTHURS, SKINNER JAMESS, RAO DC, BOUCHARD CLAUDE. The HERITAGE Family Study: A Review of the Effects of Exercise Training on Cardiometabolic Health, with Insights into Molecular Transducers. Med Sci Sports Exerc 2022; 54:S1-S43. [PMID: 35611651 PMCID: PMC9012529 DOI: 10.1249/mss.0000000000002859] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The aim of the HERITAGE Family Study was to investigate individual differences in response to a standardized endurance exercise program, the role of familial aggregation, and the genetics of response levels of cardiorespiratory fitness and cardiovascular disease and diabetes risk factors. Here we summarize the findings and their potential implications for cardiometabolic health and cardiorespiratory fitness. It begins with overviews of background and planning, recruitment, testing and exercise program protocol, quality control measures, and other relevant organizational issues. A summary of findings is then provided on cardiorespiratory fitness, exercise hemodynamics, insulin and glucose metabolism, lipid and lipoprotein profiles, adiposity and abdominal visceral fat, blood levels of steroids and other hormones, markers of oxidative stress, skeletal muscle morphology and metabolic indicators, and resting metabolic rate. These summaries document the extent of the individual differences in response to a standardized and fully monitored endurance exercise program and document the importance of familial aggregation and heritability level for exercise response traits. Findings from genomic markers, muscle gene expression studies, and proteomic and metabolomics explorations are reviewed, along with lessons learned from a bioinformatics-driven analysis pipeline. The new opportunities being pursued in integrative -omics and physiology have extended considerably the expected life of HERITAGE and are being discussed in relation to the original conceptual model of the study.
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Affiliation(s)
- MARK A. SARZYNSKI
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC
| | - TREVA K. RICE
- Division of Biostatistics, Washington University in St. Louis School of Medicine, St. Louis, MO
| | - JEAN-PIERRE DESPRÉS
- Department of Kinesiology, Faculty of Medicine, Laval University, Quebec, QC, CANADA
- Quebec Heart and Lung Institute Research Center, Laval University, Québec, QC, CANADA
| | - LOUIS PÉRUSSE
- Department of Kinesiology, Faculty of Medicine, Laval University, Quebec, QC, CANADA
- Institute of Nutrition and Functional Foods (INAF), Laval University, Quebec, QC, CANADA
| | - ANGELO TREMBLAY
- Department of Kinesiology, Faculty of Medicine, Laval University, Quebec, QC, CANADA
- Institute of Nutrition and Functional Foods (INAF), Laval University, Quebec, QC, CANADA
| | - PHILIP R. STANFORTH
- Department of Kinesiology and Health Education, University of Texas at Austin, Austin, TX
| | - ANDRÉ TCHERNOF
- Quebec Heart and Lung Institute Research Center, Laval University, Québec, QC, CANADA
- School of Nutrition, Laval University, Quebec, QC, CANADA
| | - JACOB L. BARBER
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC
| | - FRANCESCO FALCIANI
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UNITED KINGDOM
| | - CLARY CLISH
- Metabolomics Platform, Broad Institute and Harvard Medical School, Boston, MA
| | - JEREMY M. ROBBINS
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
- Cardiovascular Research Center, Beth Israel Deaconess Medical Center, Boston, MA
| | - SUJOY GHOSH
- Cardiovascular and Metabolic Disorders Program and Centre for Computational Biology, Duke-National University of Singapore Medical School, SINGAPORE
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA
| | - ROBERT E. GERSZTEN
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
- Cardiovascular Research Center, Beth Israel Deaconess Medical Center, Boston, MA
| | - ARTHUR S. LEON
- School of Kinesiology, University of Minnesota, Minneapolis, MN
| | | | - D. C. RAO
- Division of Biostatistics, Washington University in St. Louis School of Medicine, St. Louis, MO
| | - CLAUDE BOUCHARD
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA
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Ottosson F, Smith E, Ericson U, Brunkwall L, Orho-Melander M, Di Somma S, Antonini P, Nilsson PM, Fernandez C, Melander O. Metabolome-Defined Obesity and the Risk of Future Type 2 Diabetes and Mortality. Diabetes Care 2022; 45:1260-1267. [PMID: 35287165 PMCID: PMC9174969 DOI: 10.2337/dc21-2402] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 02/14/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Obesity is a key risk factor for type 2 diabetes; however, up to 20% of patients are normal weight. Our aim was to identify metabolite patterns reproducibly predictive of BMI and subsequently to test whether lean individuals who carry an obese metabolome are at hidden high risk of obesity-related diseases, such as type 2 diabetes. RESEARCH DESIGN AND METHODS Levels of 108 metabolites were measured in plasma samples of 7,663 individuals from two Swedish and one Italian population-based cohort. Ridge regression was used to predict BMI using the metabolites. Individuals with a predicted BMI either >5 kg/m2 higher (overestimated) or lower (underestimated) than their actual BMI were characterized as outliers and further investigated for obesity-related risk factors and future risk of type 2 diabetes and mortality. RESULTS The metabolome could predict BMI in all cohorts (r2 = 0.48, 0.26, and 0.19). The overestimated group had a BMI similar to individuals correctly predicted as normal weight, had a similar waist circumference, were not more likely to change weight over time, but had a two times higher risk of future type 2 diabetes and an 80% increased risk of all-cause mortality. These associations remained after adjustments for obesity-related risk factors and lifestyle parameters. CONCLUSIONS We found that lean individuals with an obesity-related metabolome have an increased risk for type 2 diabetes and all-cause mortality compared with lean individuals with a healthy metabolome. Metabolomics may be used to identify hidden high-risk individuals to initiate lifestyle and pharmacological interventions.
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Affiliation(s)
- Filip Ottosson
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Einar Smith
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Ulrika Ericson
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | | | | | - Salvatore Di Somma
- Department of Medical-Surgery Sciences and Translational Medicine, University of Rome Sapienza, Rome, Italy
- GREAT Health Sciences, Rome, Italy
| | | | | | | | - Olle Melander
- Department of Clinical Sciences, Lund University, Malmö, Sweden
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Nelson AB, Chow LS, Hughey CC, Crawford PA, Puchalska P. Artifactual FA dimers mimic FAHFA signals in untargeted metabolomics pipelines. J Lipid Res 2022; 63:100201. [PMID: 35315332 PMCID: PMC9034316 DOI: 10.1016/j.jlr.2022.100201] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 03/15/2022] [Accepted: 03/16/2022] [Indexed: 12/01/2022] Open
Abstract
FA esters of hydroxy FAs (FAHFAs) are lipokines with extensive structural and regional isomeric diversity that impact multiple physiological functions, including insulin sensitivity and glucose homeostasis. Because of their low molar abundance, FAHFAs are typically quantified using highly sensitive LC-MS/MS methods. Numerous relevant MS databases house in silico-spectra that allow identification and speciation of FAHFAs. These provisional chemical feature assignments provide a useful starting point but could lead to misidentification. To address this possibility, we analyzed human serum with a commonly applied high-resolution LC-MS untargeted metabolomics platform. We found that many chemical features are putatively assigned to the FAHFA lipid class based on exact mass and fragmentation patterns matching spectral databases. Careful validation using authentic standards revealed that many investigated signals provisionally assigned as FAHFAs are in fact FA dimers formed in the LC-MS pipeline. These isobaric FA dimers differ structurally only by the presence of an olefinic bond. Furthermore, stable isotope-labeled oleic acid spiked into human serum at subphysiological concentrations showed concentration-dependent formation of a diverse repertoire of FA dimers that analytically mimicked FAHFAs. Conversely, validated FAHFA species did not form spontaneously in the LC-MS pipeline. Together, these findings underscore that FAHFAs are endogenous lipid species. However, nonbiological FA dimers forming in the setting of high concentrations of FFAs can be misidentified as FAHFAs. Based on these results, we assembled a FA dimer database to identify nonbiological FA dimers in untargeted metabolomics datasets.
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Affiliation(s)
- Alisa B Nelson
- Division of Molecular Medicine; Department of Medicine, University of Minnesota, Minneapolis, MN, USA; Bioinformatics and Computational Biology Program, University of Minnesota, Minneapolis, MN, USA
| | - Lisa S Chow
- Division of Diabetes, Endocrinology and Metabolism; Department of Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Curtis C Hughey
- Division of Molecular Medicine; Department of Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Peter A Crawford
- Division of Molecular Medicine; Department of Medicine, University of Minnesota, Minneapolis, MN, USA; Bioinformatics and Computational Biology Program, University of Minnesota, Minneapolis, MN, USA; Department of Integrative Biology and Physiology, University of Minnesota, Minneapolis, MN, USA; Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN, USA.
| | - Patrycja Puchalska
- Division of Molecular Medicine; Department of Medicine, University of Minnesota, Minneapolis, MN, USA.
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Intrapersonal Stability of Plasma Metabolomic Profiles over 10 Years among Women. Metabolites 2022; 12:metabo12050372. [PMID: 35629875 PMCID: PMC9147746 DOI: 10.3390/metabo12050372] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 04/01/2022] [Accepted: 04/11/2022] [Indexed: 11/17/2022] Open
Abstract
In epidemiological studies, samples are often collected long before disease onset or outcome assessment. Understanding the long-term stability of biomarkers measured in these samples is crucial. We estimated within-person stability over 10 years of metabolites and metabolite features (n = 5938) in the Nurses’ Health Study (NHS): the primary dataset included 1880 women with 1184 repeated samples donated 10 years apart while the secondary dataset included 1456 women with 488 repeated samples donated 10 years apart. We quantified plasma metabolomics using two liquid chromatography mass spectrometry platforms (lipids and polar metabolites) at the Broad Institute (Cambridge, MA, USA). Intra-class correlations (ICC) were used to estimate long-term (10 years) within-person stability of metabolites and were calculated as the proportion of the total variability (within-person + between-person) attributable to between-person variability. Within-person variability was estimated among participants who donated two blood samples approximately 10 years apart while between-person variability was estimated among all participants. In the primary dataset, the median ICC was 0.43 (1st quartile (Q1): 0.36; 3rd quartile (Q3): 0.50) among known metabolites and 0.41 (Q1: 0.34; Q3: 0.48) among unknown metabolite features. The three most stable metabolites were N6,N6-dimethyllysine (ICC = 0.82), dimethylguanidino valerate (ICC = 0.72), and N-acetylornithine (ICC = 0.72). The three least stable metabolites were palmitoylethanolamide (ICC = 0.05), ectoine (ICC = 0.09), and trimethylamine-N-oxide (ICC = 0.16). Results in the secondary dataset were similar (Spearman correlation = 0.87) to corresponding results in the primary dataset. Within-person stability over 10 years is reasonable for lipid, lipid-related, and polar metabolites, and varies by metabolite class. Additional studies are required to estimate within-person stability over 10 years of other metabolites groups.
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Bonnitcha P, Sullivan D, Fitzpatrick M, Ireland A, Nguyen VL, Koay YC, O'Sullivan J. Design and validation of an LC-MS/MS method for simultaneous quantification of asymmetric dimethylguanidino valeric acid, asymmetric dimethylarginine and symmetric dimethylarginine in human plasma. Pathology 2022; 54:591-598. [DOI: 10.1016/j.pathol.2022.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 01/11/2022] [Accepted: 01/20/2022] [Indexed: 10/18/2022]
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Murthy VL, Nayor M, Carnethon M, Reis JP, Lloyd-Jones D, Allen NB, Kitchen R, Piaggi P, Steffen LM, Vasan RS, Freedman JE, Clish CB, Shah RV. Circulating metabolite profile in young adulthood identifies long-term diabetes susceptibility: the Coronary Artery Risk Development in Young Adults (CARDIA) study. Diabetologia 2022; 65:657-674. [PMID: 35041022 PMCID: PMC8969893 DOI: 10.1007/s00125-021-05641-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 11/10/2021] [Indexed: 12/19/2022]
Abstract
AIMS/HYPOTHESIS The aim of this work was to define metabolic correlates and pathways of diabetes pathogenesis in young adults during a subclinical latent phase of diabetes development. METHODS We studied 2083 young adults of Black and White ethnicity in the prospective observational cohort Coronary Artery Risk Development in Young Adults (CARDIA) study (mean ± SD age 32.1 ± 3.6 years; 43.9% women; 42.7% Black; mean ± SD BMI 25.6 ± 4.9 kg/m2) and 1797 Framingham Heart Study (FHS) participants (mean ± SD age 54.7 ± 9.7 years; 52.1% women; mean ± SD BMI 27.4 ± 4.8 kg/m2), examining the association of comprehensive metabolite profiles with endophenotypes of diabetes susceptibility (adipose and muscle tissue phenotypes and systemic inflammation). Statistical learning techniques and Cox regression were used to identify metabolite signatures of incident diabetes over a median of nearly two decades of follow-up across both cohorts. RESULTS We identified known and novel metabolites associated with endophenotypes that delineate the complex pathophysiological architecture of diabetes, spanning mechanisms of muscle insulin resistance, inflammatory lipid signalling and beta cell metabolism (e.g. bioactive lipids, amino acids and microbe- and diet-derived metabolites). Integrating endophenotypes of diabetes susceptibility with the metabolome generated two multi-parametric metabolite scores, one of which (a proinflammatory adiposity score) was associated with incident diabetes across the life course in participants from both the CARDIA study (young adults; HR in a fully adjusted model 2.10 [95% CI 1.72, 2.55], p<0.0001) and FHS (middle-aged and older adults; HR 1.33 [95% CI 1.14, 1.56], p=0.0004). A metabolite score based on the outcome of diabetes was strongly related to diabetes in CARDIA study participants (fully adjusted HR 3.41 [95% CI 2.85, 4.07], p<0.0001) but not in the older FHS population (HR 1.15 [95% CI 0.99, 1.33], p=0.07). CONCLUSIONS/INTERPRETATION Selected metabolic abnormalities in young adulthood identify individuals with heightened diabetes risk independent of race, sex and traditional diabetes risk factors. These signatures replicate across the life course.
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Affiliation(s)
- Venkatesh L Murthy
- Department of Medicine and Radiology, University of Michigan, Ann Arbor, MI, USA.
| | - Matthew Nayor
- Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | | | - Jared P Reis
- National Heart, Lung, and Blood Institute, Bethesda, MD, USA
| | | | | | - Robert Kitchen
- Simches Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Paolo Piaggi
- Department of Information Engineering, University of Pisa, Pisa, Italy
| | - Lyn M Steffen
- University of Minnesota School of Public Health, Minneapolis, MN, USA
| | - Ramachandran S Vasan
- Section of Preventive Medicine and Epidemiology, Boston University School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Jane E Freedman
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Clary B Clish
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Ravi V Shah
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA.
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Sasamoto N, Zeleznik OA, Vitonis AF, Missmer SA, Laufer MR, Avila-Pacheco J, Clish CB, Terry KL. Presurgical blood metabolites and risk of postsurgical pelvic pain in young patients with endometriosis. Fertil Steril 2022; 117:1235-1245. [PMID: 35367064 PMCID: PMC9149031 DOI: 10.1016/j.fertnstert.2022.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 02/09/2022] [Accepted: 02/09/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVE To identify metabolites in presurgical blood associated with risk of persistent postsurgical pelvic pain 1 year after endometriosis surgery in adolescent and young adult patients. DESIGN Prospective observational study within the Women's Health Study: From Adolescence to Adulthood, a US-based longitudinal cohort of adolescents and women enrolled from 2012-2018. SETTING Two tertiary care hospitals. PATIENT(S) Laparoscopically confirmed endometriosis patients (n = 180) with blood collected before their endometriosis surgery. Of these, 77 patients additionally provided blood samples 5 weeks to 6 months after their surgery. We measured plasma metabolites using liquid chromatography tandem mass spectrometry, and a total of 390 known metabolites were included in our analysis. INTERVENTION(S) None. MAIN OUTCOME MEASURE(S) Persistent postsurgical pelvic pain, defined as severe, life-impacting pelvic pain 1 year after endometriosis surgery. RESULT(S) Most patients (>95%) were at stage I/II of the revised American Society for Reproductive Medicine classification. Their average age at diagnosis was 18.7 years, with 36% reporting persistent postsurgical pelvic pain. Of the 21 metabolites in presurgical blood that were associated with risk of persistent postsurgical pelvic pain, 19 metabolites, which were mainly lipid metabolites, were associated with increased risk. Only 2 metabolites-pregnenolone sulfate (odds ratio = 0.64, 95% confidence interval = 0.44-0.92) and fucose (odds ratio = 0.69, 95% confidence interval = 0.47-0.97)-were associated with decreased risk. Metabolite set enrichment analysis revealed that higher levels of lysophosphatidylethanolamines (false discovery rate = 0.01) and lysophosphatidylcholines (false discovery rate = 0.01) in presurgical blood were associated with increased risk of persistent postsurgical pelvic pain. CONCLUSION(S) Our results suggest that dysregulation of multiple groups of lipid metabolites may play a role in the persistence of pelvic pain postsurgery among young endometriosis patients.
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Affiliation(s)
- Naoko Sasamoto
- Department of Obstetrics and Gynecology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts; Boston Center for Endometriosis, Boston Children's Hospital and Brigham and Women's Hospital, Boston, Massachusetts.
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Allison F Vitonis
- Department of Obstetrics and Gynecology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts; Boston Center for Endometriosis, Boston Children's Hospital and Brigham and Women's Hospital, Boston, Massachusetts
| | - Stacey A Missmer
- Boston Center for Endometriosis, Boston Children's Hospital and Brigham and Women's Hospital, Boston, Massachusetts; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts; Department of Obstetrics, Gynecology, and Reproductive Biology, College of Human Medicine, Michigan State University, Grand Rapids, Michigan
| | - Marc R Laufer
- Department of Obstetrics and Gynecology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts; Boston Center for Endometriosis, Boston Children's Hospital and Brigham and Women's Hospital, Boston, Massachusetts; Division of Gynecology, Department of Surgery, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts
| | | | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Kathryn L Terry
- Department of Obstetrics and Gynecology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts; Boston Center for Endometriosis, Boston Children's Hospital and Brigham and Women's Hospital, Boston, Massachusetts; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
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Nelson AB, Chow LS, Stagg DB, Gillingham JR, Evans MD, Pan M, Hughey CC, Myers CL, Han X, Crawford PA, Puchalska P. Acute aerobic exercise reveals FAHFAs distinguish the metabolomes of overweight and normal weight runners. JCI Insight 2022; 7:158037. [PMID: 35192550 PMCID: PMC9057596 DOI: 10.1172/jci.insight.158037] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 02/18/2022] [Indexed: 11/23/2022] Open
Abstract
Background Responses of the metabolome to acute aerobic exercise may predict maximum oxygen consumption (VO2max) and longer-term outcomes, including the development of diabetes and its complications. Methods Serum samples were collected from overweight/obese trained (OWT) and normal-weight trained (NWT) runners prior to and immediately after a supervised 90-minute treadmill run at 60% VO2max (NWT = 14, OWT = 11) in a cross-sectional study. We applied a liquid chromatography high-resolution–mass spectrometry–based untargeted metabolomics platform to evaluate the effect of acute aerobic exercise on the serum metabolome. Results NWT and OWT metabolic profiles shared increased circulating acylcarnitines and free fatty acids (FFAs) with exercise, while intermediates of adenine metabolism, inosine, and hypoxanthine were strongly correlated with body fat percentage and VO2max. Untargeted metabolomics-guided follow-up quantitative lipidomic analysis revealed that baseline levels of fatty acid esters of hydroxy fatty acids (FAHFAs) were generally diminished in the OWT group. FAHFAs negatively correlated with visceral fat mass and HOMA-IR. Strikingly, a 4-fold decrease in FAHFAs was provoked by acute aerobic running in NWT participants, an effect that negatively correlated with circulating IL-6; these effects were not observed in the OWT group. Machine learning models based on a preexercise metabolite profile that included FAHFAs, FFAs, and adenine intermediates predicted VO2max. Conclusion These findings in overweight human participants and healthy controls indicate that exercise-provoked changes in FAHFAs distinguish normal-weight from overweight participants and could predict VO2max. These results support the notion that FAHFAs could modulate the inflammatory response, fuel utilization, and insulin resistance. Trial registration ClinicalTrials.gov, NCT02150889. Funding NIH DK091538, AG069781, DK098203, TR000114, UL1TR002494.
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Affiliation(s)
- Alisa B Nelson
- Division of Molecular Medicine, Department of Medicine, University of Minnesota, Minneapolis, United States of America
| | - Lisa S Chow
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, University of Minnesota, Minneapolis, United States of America
| | - David B Stagg
- Division of Molecular Medicine, Department of Medicine, University of Minnesota, Minneapolis, United States of America
| | - Jacob R Gillingham
- Division of Molecular Medicine, Department of Medicine, University of Minnesota, Minneapolis, United States of America
| | - Michael D Evans
- Clinical and Translational Science Institute, University of Minnesota, Minneapolis, United States of America
| | - Meixia Pan
- Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, United States of America
| | - Curtis C Hughey
- Division of Molecular Medicine, Department of Medicine, University of Minnesota, Minneapolis, United States of America
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, United States of America
| | - Xianlin Han
- Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, United States of America
| | - Peter A Crawford
- Division of Molecular Medicine, Department of Medicine, University of Minnesota, Minneapolis, United States of America
| | - Patrycja Puchalska
- Division of Molecular Medicine, Department of Medicine, University of Minnesota, Minneapolis, United States of America
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Izundegui DG, Nayor M. Metabolomics of Type 1 and Type 2 Diabetes: Insights into Risk Prediction and Mechanisms. Curr Diab Rep 2022; 22:65-76. [PMID: 35113332 PMCID: PMC8934149 DOI: 10.1007/s11892-022-01449-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/21/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE OF REVIEW Metabolomics enables rapid interrogation of widespread metabolic processes making it well suited for studying diabetes. Here, we review the current status of metabolomic investigation in diabetes, highlighting its applications for improving risk prediction and mechanistic understanding. RECENT FINDINGS Findings of metabolite associations with type 2 diabetes risk have confirmed experimental observations (e.g., branched-chain amino acids) and also pinpointed novel pathways of diabetes risk (e.g., dimethylguanidino valeric acid). In type 1 diabetes, abnormal metabolite patterns are observed prior to the development of autoantibodies and hyperglycemia. Diabetes complications display specific metabolite signatures that are distinct from the metabolic derangements of diabetes and differ across vascular beds. Lastly, metabolites respond acutely to pharmacologic treatment, providing opportunities to understand inter-individual treatment responses. Metabolomic studies have elucidated biological mechanisms underlying diabetes development, complications, and therapeutic response. While not yet ready for clinical translation, metabolomics is a powerful and promising precision medicine tool.
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Affiliation(s)
| | - Matthew Nayor
- Sections of Cardiology and Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, 72 E Concord Street, Suite L-516, Boston, MA, 02118, USA.
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Brantley KD, Zeleznik OA, Rosner B, Tamimi RM, Avila-Pacheco J, Clish CB, Eliassen AH. Plasma Metabolomics and Breast Cancer Risk Over 20 Years of Follow-up Among Postmenopausal Women in the Nurses' Health Study. Cancer Epidemiol Biomarkers Prev 2022; 31:839-850. [DOI: 10.1158/1055-9965.epi-21-1023] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 12/08/2021] [Accepted: 01/10/2022] [Indexed: 12/09/2022] Open
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Li Z, Zhang Y, Hoene M, Fritsche L, Zheng S, Birkenfeld A, Fritsche A, Peter A, Liu X, Zhao X, Zhou L, Luo P, Weigert C, Lin X, Xu G, Lehmann R. Diagnostic Performance of Sex-Specific Modified Metabolite Patterns in Urine for Screening of Prediabetes. Front Endocrinol (Lausanne) 2022; 13:935016. [PMID: 35909528 PMCID: PMC9333093 DOI: 10.3389/fendo.2022.935016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 06/13/2022] [Indexed: 12/03/2022] Open
Abstract
AIMS/HYPOTHESIS Large-scale prediabetes screening is still a challenge since fasting blood glucose and HbA1c as the long-standing, recommended analytes have only moderate diagnostic sensitivity, and the practicability of the oral glucose tolerance test for population-based strategies is limited. To tackle this issue and to identify reliable diagnostic patterns, we developed an innovative metabolomics-based strategy deviating from common concepts by employing urine instead of blood samples, searching for sex-specific biomarkers, and focusing on modified metabolites. METHODS Non-targeted, modification group-assisted metabolomics by liquid chromatography-mass spectrometry (LC-MS) was applied to second morning urine samples of 340 individuals from a prediabetes cohort. Normal (n = 208) and impaired glucose-tolerant (IGT; n = 132) individuals, matched for age and BMI, were randomly divided in discovery and validation cohorts. ReliefF, a feature selection algorithm, was used to extract sex-specific diagnostic patterns of modified metabolites for the detection of IGT. The diagnostic performance was compared with conventional screening parameters fasting plasma glucose (FPG), HbA1c, and fasting insulin. RESULTS Female- and male-specific diagnostic patterns were identified in urine. Only three biomarkers were identical in both. The patterns showed better AUC and diagnostic sensitivity for prediabetes screening of IGT than FPG, HbA1c, insulin, or a combination of FPG and HbA1c. The AUC of the male-specific pattern in the validation cohort was 0.889 with a diagnostic sensitivity of 92.6% and increased to an AUC of 0.977 in combination with HbA1c. In comparison, the AUCs of FPG, HbA1c, and insulin alone reached 0.573, 0.668, and 0.571, respectively. Validation of the diagnostic pattern of female subjects showed an AUC of 0.722, which still exceeded the AUCs of FPG, HbA1c, and insulin (0.595, 0.604, and 0.634, respectively). Modified metabolites in the urinary patterns include advanced glycation end products (pentosidine-glucuronide and glutamyl-lysine-sulfate) and microbiota-associated compounds (indoxyl sulfate and dihydroxyphenyl-gamma-valerolactone-glucuronide). CONCLUSIONS/INTERPRETATION Our results demonstrate that the sex-specific search for diagnostic metabolite biomarkers can be superior to common metabolomics strategies. The diagnostic performance for IGT detection was significantly better than routinely applied blood parameters. Together with recently developed fully automatic LC-MS systems, this opens up future perspectives for the application of sex-specific diagnostic patterns for prediabetes screening in urine.
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Affiliation(s)
- Zaifang Li
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
- University of Chinese Academy of Sciences, Beijing, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
| | - Yanhui Zhang
- School of Computer Science & Technology, Dalian University of Technology, Dalian, China
| | - Miriam Hoene
- Department for Diagnostic Laboratory Medicine, Institute for Clinical Chemistry and Pathobiochemistry, University Hospital Tübingen, Tübingen, Germany
| | - Louise Fritsche
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Zentrum München at the University of Tuebingen, Tuebingen, Germany
- German Center for Diabetes Research (DZD), Tübingen, Germany
| | - Sijia Zheng
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
- University of Chinese Academy of Sciences, Beijing, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
| | - Andreas Birkenfeld
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Zentrum München at the University of Tuebingen, Tuebingen, Germany
- German Center for Diabetes Research (DZD), Tübingen, Germany
- Internal Medicine 4, University Hospital Tuebingen, Tuebingen, Germany
| | - Andreas Fritsche
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Zentrum München at the University of Tuebingen, Tuebingen, Germany
- German Center for Diabetes Research (DZD), Tübingen, Germany
- Internal Medicine 4, University Hospital Tuebingen, Tuebingen, Germany
| | - Andreas Peter
- Department for Diagnostic Laboratory Medicine, Institute for Clinical Chemistry and Pathobiochemistry, University Hospital Tübingen, Tübingen, Germany
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Zentrum München at the University of Tuebingen, Tuebingen, Germany
- German Center for Diabetes Research (DZD), Tübingen, Germany
| | - Xinyu Liu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
- University of Chinese Academy of Sciences, Beijing, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
| | - Xinjie Zhao
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
- University of Chinese Academy of Sciences, Beijing, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
| | - Lina Zhou
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
- University of Chinese Academy of Sciences, Beijing, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
| | - Ping Luo
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
- University of Chinese Academy of Sciences, Beijing, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
| | - Cora Weigert
- Department for Diagnostic Laboratory Medicine, Institute for Clinical Chemistry and Pathobiochemistry, University Hospital Tübingen, Tübingen, Germany
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Zentrum München at the University of Tuebingen, Tuebingen, Germany
- German Center for Diabetes Research (DZD), Tübingen, Germany
| | - Xiaohui Lin
- School of Computer Science & Technology, Dalian University of Technology, Dalian, China
- *Correspondence: Guowang Xu, ; Rainer Lehmann,
| | - Guowang Xu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
- University of Chinese Academy of Sciences, Beijing, China
- Liaoning Province Key Laboratory of Metabolomics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
- *Correspondence: Guowang Xu, ; Rainer Lehmann,
| | - Rainer Lehmann
- Department for Diagnostic Laboratory Medicine, Institute for Clinical Chemistry and Pathobiochemistry, University Hospital Tübingen, Tübingen, Germany
- Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Zentrum München at the University of Tuebingen, Tuebingen, Germany
- German Center for Diabetes Research (DZD), Tübingen, Germany
- *Correspondence: Guowang Xu, ; Rainer Lehmann,
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Corey KE, Pitts R, Lai M, Loureiro J, Masia R, Osganian SA, Gustafson JL, Hutter MM, Gee DW, Meireles OR, Witkowski ER, Richards SM, Jacob J, Finkel N, Ngo D, Wang TJ, Gerszten RE, Ukomadu C, Jennings LL. ADAMTSL2 protein and a soluble biomarker signature identify at-risk non-alcoholic steatohepatitis and fibrosis in adults with NAFLD. J Hepatol 2022; 76:25-33. [PMID: 34600973 PMCID: PMC8688231 DOI: 10.1016/j.jhep.2021.09.026] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 09/14/2021] [Accepted: 09/18/2021] [Indexed: 01/03/2023]
Abstract
BACKGROUND & AIMS Identifying fibrosis in non-alcoholic fatty liver disease (NAFLD) is essential to predict liver-related outcomes and guide treatment decisions. A protein-based signature of fibrosis could serve as a valuable, non-invasive diagnostic tool. This study sought to identify circulating proteins associated with fibrosis in NAFLD. METHODS We used aptamer-based proteomics to measure 4,783 proteins in 2 cohorts (Cohort A and B). Targeted, quantitative assays coupling aptamer-based protein pull down and mass spectrometry (SPMS) validated the profiling results in a bariatric and NAFLD cohort (Cohort C and D, respectively). Generalized linear modeling-logistic regression assessed the ability of candidate proteins to classify fibrosis. RESULTS From the multiplex profiling, 16 proteins differed significantly by fibrosis in cohorts A (n = 62) and B (n = 98). Quantitative and robust SPMS assays were developed for 8 proteins and validated in Cohorts C (n = 71) and D (n = 84). The A disintegrin and metalloproteinase with thrombospondin motifs like 2 (ADAMTSL2) protein accurately distinguished non-alcoholic fatty liver (NAFL)/non-alcoholic steatohepatitis (NASH) with fibrosis stage 0-1 (F0-1) from at-risk NASH with fibrosis stage 2-4, with AUROCs of 0.83 and 0.86 in Cohorts C and D, respectively, and from NASH with significant fibrosis (F2-3), with AUROCs of 0.80 and 0.83 in Cohorts C and D, respectively. An 8-protein panel distinguished NAFL/NASH F0-1 from at-risk NASH (AUROCs 0.90 and 0.87 in Cohort C and D, respectively) and NASH F2-3 (AUROCs 0.89 and 0.83 in Cohorts C and D, respectively). The 8-protein panel and ADAMTSL2 protein had superior performance to the NAFLD fibrosis score and fibrosis-4 score. CONCLUSION The ADAMTSL2 protein and an 8-protein soluble biomarker panel are highly associated with at-risk NASH and significant fibrosis; they exhibited superior diagnostic performance compared to standard of care fibrosis scores. LAY SUMMARY Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of liver disease worldwide. Diagnosing NAFLD and identifying fibrosis (scarring of the liver) currently requires a liver biopsy. Our study identified novel proteins found in the blood which may identify fibrosis without the need for a liver biopsy.
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Affiliation(s)
- Kathleen E. Corey
- Division of Gastroenterology, Massachusetts General Hospital (MGH) and Harvard Medical School (HMS), Boston, MA, USA
| | - Rebecca Pitts
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | - Michelle Lai
- Division of Hepatology, Beth Israel Deaconess Hospital (BIDMC) and HMS, Boston, MA, USA
| | - Joseph Loureiro
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | - Ricard Masia
- Department of Pathology, MGH and HMS, Boston, MA, USA
| | - Stephanie A. Osganian
- Division of Gastroenterology, Massachusetts General Hospital (MGH) and Harvard Medical School (HMS), Boston, MA, USA
| | - Jenna L. Gustafson
- Division of Gastroenterology, Massachusetts General Hospital (MGH) and Harvard Medical School (HMS), Boston, MA, USA
| | | | | | | | | | | | - Jaison Jacob
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | - Nancy Finkel
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | - Debby Ngo
- Department of Pulmonary/Critical Care, Cardiovascular Institute, BIDMC and HMS, Boston, MA, USA
| | - Thomas J Wang
- Department of Cardiology, Vanderbilt University School of Medicine, Nashville, TN USA
| | - Robert E. Gerszten
- Division of Cardiovascular Medicine and Cardiovascular Institute, BIDMC and HMS, Boston, MA, USA
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Bariatric Surgery Improves the Atherogenic Profile of Circulating Methylarginines in Obese Patients: Results from a Pilot Study. Metabolites 2021; 11:metabo11110759. [PMID: 34822417 PMCID: PMC8624057 DOI: 10.3390/metabo11110759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Revised: 10/27/2021] [Accepted: 10/31/2021] [Indexed: 12/04/2022] Open
Abstract
Bariatric surgery improves obesity-related comorbidities. Methylarginines are biomarkers of cardiometabolic risk, liver steatosis, and insulin resistance. Here, we aimed to investigate methylarginines in obese patients undergoing bariatric surgery and compared them to age- and sex-matched healthy subjects. Thirty-one obese patients who underwent bariatric surgery and 31 healthy individuals were used for this retrospective study. The basal serum methylarginine levels were determined in the healthy individuals and the obese patients, before surgery and 6 and 12 months after surgery, by mass spectrometry. Compared with the healthy individuals, the obese patients displayed elevated monomethylarginine (mean change: +95%, p < 0.001), asymmetric-dimethylarginine (+105%, p < 0.001), symmetric-dimethylarginine (+25%, p = 0.003), and dimethylguanidino valerate (+32%, p = 0.008) concentrations. Bariatric surgery durably reduced the body mass index by 28% (12 months, 95%CI: 24–33, p = 0.002) and improved plasma lipids, insulin resistance, and liver function. Bariatric surgery reduced the serum levels of monomethylarginine and asymmetric-dimethylarginine by 12% (95%CI: 6–17) and 36% (95%CI: 27–45) (12 months, p = 0.003), respectively, but not symmetric-dimethylarginine or dimethylguanidino valerate. The monomethylarginine and asymmetric-dimethylarginine concentrations were strongly correlated with markers of dyslipidemia, insulin resistance, and a fatty liver. Serum dimethylguanidino valerate was primarily correlated with glycemia and renal function, whereas serum symmetric-dimethylarginine was almost exclusively associated with renal function. In conclusion, the monomethylarginine and asymmetric-dimethylarginine levels are efficiently decreased by bariatric surgery, leading to a reduced atherogenic profile in obese patients. Methylarginines follow different metabolic patterns, which could help for the stratification of cardiometabolic disorders in obese patients.
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Goldberg RB, Tripputi MT, Boyko EJ, Budoff M, Chen ZZ, Clark JM, Dabelea DM, Edelstein SL, Gerszten RE, Horton E, Mather KJ, Perreault L, Temprosa M, Wallia A, Watson K, Irfan Z. Hepatic Fat in Participants With and Without Incident Diabetes in the Diabetes Prevention Program Outcome Study. J Clin Endocrinol Metab 2021; 106:e4746-e4765. [PMID: 33705543 PMCID: PMC8530730 DOI: 10.1210/clinem/dgab160] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Indexed: 11/19/2022]
Abstract
CONTEXT There is little information about fatty liver in prediabetes as it transitions to early diabetes. OBJECTIVE This study is aimed at evaluating the prevalence and determinants of fatty liver in the Diabetes Prevention Program (DPP). METHODS We measured liver fat as liver attenuation (LA) in Hounsfield units (HU) in 1876 participants at ~14 years following randomization into the DPP, which tested the effects of lifestyle or metformin interventions versus standard care to prevent diabetes. LA was compared among intervention groups and in those with versus without diabetes, and associations with baseline and follow-up measurements of anthropometric and metabolic covariates were assessed. RESULTS There were no differences in liver fat between treatment groups at 14 years of follow-up. Participants with diabetes had lower LA (mean ± SD: 46 ± 16 vs 51 ± 14 HU; P < 0.001) and a greater prevalence of fatty liver (LA < 40 HU) (34% vs 17%; P < 0.001). Severity of metabolic abnormalities at the time of LA evaluation was associated with lower LA categories in a graded manner and more strongly in those with diabetes. Averaged annual fasting insulin (an index of insulin resistance [OR, 95% CI 1.76, 1.41-2.20]) waist circumference (1.63, 1.17-2.26), and triglyceride (1.42, 1.13-1.78), but not glucose, were independently associated with LA < 40 HU prevalence. CONCLUSION Fatty liver is common in the early phases of diabetes development. The association of LA with insulin resistance, waist circumference, and triglyceride levels emphasizes the importance of these markers for hepatic steatosis in this population and that assessment of hepatic fat in early diabetes development is warranted.
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Affiliation(s)
- Ronald B Goldberg
- Diabetes Research Institute, Division of Endocrinology, Diabetes and Metabolism, University of Miami Miller School of Medicine, Miami, FL 33136USA
- Correspondence: Ronald B Goldberg, MD, Diabetes Research Institute, Division of Endocrinology, Diabetes and Metabolism, University of Miami Miller School of Medicine, 1450 Northwest 10th Avenue, Miami, FL 33136 USA.
| | - Mark T Tripputi
- Milken Institute School of Public Health and Health Services, The Biostatistics Center, George Washington University, Rockville, MD 20852USA
| | - Edward J Boyko
- University of Washington, Department of Medicine, Seattle, WA 98108USA
| | - Matthew Budoff
- Los Angeles Biomedical Research Institute, Torrance CA 90502USA
| | - Zsu-Zsu Chen
- Division of Endocrinology, Bone, and Metabolism, Beth Israel Deaconess Medical Center, Harvard, Boston, MA 02215, USA
| | - Jeanne M Clark
- Division of General Internal Medicine, The Johns Hopkins University, Baltimore MD 21287USA
| | - Dana M Dabelea
- Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045USA
| | - Sharon L Edelstein
- Milken Institute School of Public Health and Health Services, The Biostatistics Center, George Washington University, Rockville, MD 20852USA
- Sharon L. Edelstein, ScM, The Biostatistics Center, Milken Institute School of Public Health, The George Washington University, 6110 Executive Blvd., Suite 750, Rockville, MD 20852, USA.
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard, Boston, MA 02215, USA
| | | | | | - Leigh Perreault
- University of Colorado Anschutz Medical Campus, Aurora, CO 80045USA
| | - Marinella Temprosa
- Milken Institute School of Public Health and Health Services, The Biostatistics Center, George Washington University, Rockville, MD 20852USA
| | - Amisha Wallia
- Northwestern University Feinberg School of Medicine, Chicago IL 60610USA
| | - Karol Watson
- David Geffen School of Medicine at UCLA, Los Angeles, CA 90095USA
| | - Zeb Irfan
- J. W. Ruby Memorial Hospital, Morgantown, WV 26505USA
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50
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Zeleznik OA, Balasubramanian R, Ren Y, Tobias DK, Rosner BA, Peng C, Bever AM, Frueh L, Jeanfavre S, Avila-Pacheco J, Clish CB, Mora S, Hu FB, Eliassen AH. Branched-Chain Amino Acids and Risk of Breast Cancer. JNCI Cancer Spectr 2021; 5:pkab059. [PMID: 34585062 PMCID: PMC8460878 DOI: 10.1093/jncics/pkab059] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 03/16/2021] [Accepted: 07/14/2021] [Indexed: 12/12/2022] Open
Abstract
Background Circulating branched-chain amino acid (BCAA) levels reflect metabolic health and dietary intake. However, associations with breast cancer are unclear. Methods We evaluated circulating BCAA levels and breast cancer risk within the Nurses’ Health Study (NHS) and NHSII (1997 cases and 1997 controls). A total of 592 NHS women donated 2 blood samples 10 years apart. We estimated odds ratios (ORs) and 95% confidence intervals (CIs) of breast cancer risk in multivariable logistic regression models. We conducted an external validation in 1765 cases in the Women’s Health Study (WHS). All statistical tests were 2-sided. Results Among NHSII participants (predominantly premenopausal at blood collection), elevated circulating BCAA levels were associated with lower breast cancer risk (eg, isoleucine highest vs lowest quartile, multivariable OR = 0.86, 95% CI = 0.65 to 1.13, Ptrend = .20), with statistically significant linear trends among fasting samples (eg, isoleucine OR = 0.74, 95% CI = 0.53 to 1.05, Ptrend = .05). In contrast, among postmenopausal women, proximate measures (<10 years from blood draw) were associated with increased breast cancer risk (eg, isoleucine OR = 1.63, 95% CI = 1.12 to 2.39, Ptrend = .01), with stronger associations among fasting samples (OR = 1.73, 95% CI = 1.15 to 2.61, Ptrend = .01). Distant measures (10-20 years since blood draw) were not associated with risk. In the WHS, a positive association was observed for distant measures of leucine among postmenopausal women (OR = 1.23, 95% CI = 0.96 to 1.58, Ptrend = .04). Conclusions No statistically significant associations between BCAA levels and breast cancer risk were consistent across NHS and WHS or NHSII and WHS. Elevated circulating BCAA levels were associated with lower breast cancer risk among predominantly premenopausal NHSII women and higher risk among postmenopausal women in NHS but not in the WHS. Additional studies are needed to understand this complex relationship.
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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
- Correspondence to: Oana A. Zeleznik, PhD, Channing Division of Network Medicine, Brigham and Women’s Hospital,181 Longwood Ave, Boston, MA 02115, USA (e-mail: )
| | - Raji Balasubramanian
- Department of Biostatistics and Epidemiology, University of Massachusetts–Amherst, Amherst, MA, USA
| | - Yumeng Ren
- 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
| | - Deirdre K Tobias
- Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Bernard A Rosner
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Cheng Peng
- Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Alaina M Bever
- 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
| | - 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
| | - Samia Mora
- Department of Biostatistics and Epidemiology, University of Massachusetts–Amherst, Amherst, MA, USA
| | - Frank B Hu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, 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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