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Gonzalez Izundegui D, Miller PE, Shah RV, Clish CB, Walker ME, Mitchell GF, Gerszten RE, Larson MG, Vasan RS, Nayor M. Response of circulating metabolites to an oral glucose challenge and risk of cardiovascular disease and mortality in the community. Cardiovasc Diabetol 2022; 21:213. [PMID: 36243866 PMCID: PMC9568897 DOI: 10.1186/s12933-022-01647-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 09/29/2022] [Indexed: 11/17/2022] Open
Abstract
Background New biomarkers to identify cardiovascular disease (CVD) risk earlier in its course are needed to enable targeted approaches for primordial prevention. We evaluated whether intraindividual changes in blood metabolites in response to an oral glucose tolerance test (OGTT) may provide incremental information regarding the risk of future CVD and mortality in the community. Methods An OGTT (75 g glucose) was administered to a subsample of Framingham Heart Study participants free from diabetes (n = 361). Profiling of 211 plasma metabolites was performed from blood samples drawn before and 2 h after OGTT. The log2(post/pre) metabolite levels (Δmetabolites) were related to incident CVD and mortality in Cox regression models adjusted for age, sex, baseline metabolite level, systolic blood pressure, hypertension treatment, body mass index, smoking, and total/high-density lipoprotein cholesterol. Select metabolites were related to subclinical cardiometabolic phenotypes using Spearman correlations adjusted for age, sex, and fasting metabolite level. Results Our sample included 42% women, with a mean age of 56 ± 9 years and a body mass index of 30.2 ± 5.3 kg/m2. The pre- to post-OGTT changes (Δmetabolite) were non-zero for 168 metabolites (at FDR ≤ 5%). A total of 132 CVD events and 144 deaths occurred during median follow-up of 24.9 years. In Cox models adjusted for clinical risk factors, four Δmetabolites were associated with incident CVD (higher glutamate and deoxycholate, lower inosine and lysophosphatidylcholine 18:2) and six Δmetabolites (higher hydroxyphenylacetate, triacylglycerol 56:5, alpha-ketogluturate, and lower phosphatidylcholine 32:0, glucuronate, N-monomethyl-arginine) were associated with death (P < 0.05). Notably, baseline metabolite levels were not associated with either outcome in models excluding Δmetabolites. The Δmetabolites exhibited varying cross-sectional correlation with subclinical risk factors such as visceral adiposity, insulin resistance, and vascular stiffness, but overall relations were modest. Significant Δmetabolites included those with established roles in cardiometabolic disease (e.g., glutamate, alpha-ketoglutarate) and metabolites with less defined roles (e.g., glucuronate, lipid species). Conclusions Dynamic changes in metabolite levels with an OGTT are associated with incident CVD and mortality and have potential relevance for identifying CVD risk earlier in its development and for discovering new potential therapeutic targets. Supplementary Information The online version contains supplementary material available at 10.1186/s12933-022-01647-w.
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Affiliation(s)
| | - Patricia E Miller
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Ravi V Shah
- Vanderbilt Translational and Clinical Research Center, Cardiology Division, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Clary B Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Maura E Walker
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, 72 E Concord Street, Suite L-516, Boston, MA, 02118, USA.,Department of Health Sciences, Program in Nutrition, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA, USA.,Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, USA
| | | | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Martin G Larson
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.,Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, USA
| | - Ramachandran S Vasan
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, 72 E Concord Street, Suite L-516, Boston, MA, 02118, USA.,Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, USA.,Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, 72 E Concord Street, Suite L-516, Boston, MA, 02118, USA.,Department of Epidemiology, Boston University Schools of Medicine and Public Health, Center for Computing and Data Sciences, Boston University, Boston, MA, USA
| | - Matthew Nayor
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, 72 E Concord Street, Suite L-516, Boston, MA, 02118, USA. .,Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, USA. .,Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, 72 E Concord Street, Suite L-516, Boston, MA, 02118, USA.
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3
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Hu J, Yao J, Deng S, Balasubramanian R, Jiménez MC, Li J, Guo X, Cruz DE, Gao Y, Huang T, Zeleznik OA, Ngo D, Liu S, Rosal MC, Nassir R, Paynter NP, Albert CM, Tracy RP, Durda P, Liu Y, Taylor KD, Johnson WC, Sun Q, Rimm EB, Eliassen AH, Rich SS, Rotter JI, Gerszten RE, Clish CB, Rexrode KM. Differences in Metabolomic Profiles Between Black and White Women and Risk of Coronary Heart Disease: an Observational Study of Women From Four US Cohorts. Circ Res 2022; 131:601-615. [PMID: 36052690 PMCID: PMC9473718 DOI: 10.1161/circresaha.121.320134] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 08/13/2022] [Accepted: 08/21/2022] [Indexed: 01/26/2023]
Abstract
BACKGROUND Racial differences in metabolomic profiles may reflect underlying differences in social determinants of health by self-reported race and may be related to racial disparities in coronary heart disease (CHD) among women in the United States. However, the magnitude of differences in metabolomic profiles between Black and White women in the United States has not been well-described. It also remains unknown whether such differences are related to differences in CHD risk. METHODS Plasma metabolomic profiles were analyzed using liquid chromatography-tandem mass spectrometry in the WHI-OS (Women's Health Initiative-Observational Study; 138 Black and 696 White women), WHI-HT trials (WHI-Hormone Therapy; 156 Black and 1138 White women), MESA (Multi-Ethnic Study of Atherosclerosis; 114 Black and 219 White women), JHS (Jackson Heart Study; 1465 Black women with 107 incident CHD cases), and NHS (Nurses' Health Study; 2506 White women with 136 incident CHD cases). First, linear regression models were used to estimate associations between self-reported race and 472 metabolites in WHI-OS (discovery); findings were replicated in WHI-HT and validated in MESA. Second, we used elastic net regression to construct a racial difference metabolomic pattern (RDMP) representing differences in the metabolomic patterns between Black and White women in the WHI-OS; the RDMP was validated in the WHI-HT and MESA. Third, using conditional logistic regressions in the WHI (717 CHD cases and 719 matched controls), we examined associations of metabolites with large differences in levels by race and the RDMP with risk of CHD, and the results were replicated in Black women from the JHS and White women from the NHS. RESULTS Of the 472 tested metabolites, levels of 259 (54.9%) metabolites, mostly lipid metabolites and amino acids, significantly differed between Black and White women in both WHI-OS and WHI-HT after adjusting for baseline characteristics, socioeconomic status, lifestyle factors, baseline health conditions, and medication use (false discovery rate <0.05); similar trends were observed in MESA. The RDMP, composed of 152 metabolites, was identified in the WHI-OS and showed significantly different distributions between Black and White women in the WHI-HT and MESA. Higher RDMP quartiles were associated with an increased risk of incident CHD (odds ratio=1.51 [0.97-2.37] for the highest quartile comparing to the lowest; Ptrend=0.02), independent of self-reported race and known CHD risk factors. In race-stratified analyses, the RDMP-CHD associations were more pronounced in White women. Similar patterns were observed in Black women from the JHS and White women from the NHS. CONCLUSIONS Metabolomic profiles significantly and substantially differ between Black and White women and may be associated with CHD risk and racial disparities in US women.
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Affiliation(s)
- Jie Hu
- Division of Women’s Health (J.H., M.C.J., K.M.R.), Harvard Medical School, Boston, MA
- Department of Epidemiology (J.H., M.C.J., J.L., Q.S., E.B.R., A.H.E.), Harvard T.H. Chan School of Public Health, Boston, MA
| | - Jie Yao
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA (J.Y., X.G., K.D.T., J.I.R.)
| | - Shuliang Deng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA (S.D., D.E.C., R.E.G.)
| | - Raji Balasubramanian
- Department of Biostatistics and Epidemiology, University of Massachusetts – Amherst (R.B.)
| | - Monik C. Jiménez
- Division of Women’s Health (J.H., M.C.J., K.M.R.), Harvard Medical School, Boston, MA
- Department of Epidemiology (J.H., M.C.J., J.L., Q.S., E.B.R., A.H.E.), Harvard T.H. Chan School of Public Health, Boston, MA
| | - Jun Li
- Division of Preventive Medicine (J.L., N.P.P.), Harvard Medical School, Boston, MA
- Department of Epidemiology (J.H., M.C.J., J.L., Q.S., E.B.R., A.H.E.), Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Nutrition (J.L., Q.S., E.B.R., A.H.E.), Harvard T.H. Chan School of Public Health, Boston, MA
| | - Xiuqing Guo
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA (J.Y., X.G., K.D.T., J.I.R.)
| | - Daniel E. Cruz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA (S.D., D.E.C., R.E.G.)
| | - Yan Gao
- Department of Medicine, University of Mississippi Medical Center, Jackson (Y.G.)
| | - Tianyi Huang
- Channing Division of Network Medicine, Department of Medicine (T.H., O.A.Z., Q.S., E.B.R., A.H.E.), Harvard Medical School, Boston, MA
| | - Oana A. Zeleznik
- Channing Division of Network Medicine, Department of Medicine (T.H., O.A.Z., Q.S., E.B.R., A.H.E.), Harvard Medical School, Boston, MA
| | - Debby Ngo
- Brigham and Women’s Hospital and Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Beth Israel Deaconess Medical Center (D.N.), Harvard Medical School, Boston, MA
| | - Simin Liu
- Department of Epidemiology, Brown University School of Public Health, Providence, RI (S.L.)
- Division of Endocrinology, Department of Medicine, Warren Alpert Medical School of Brown University, Providence, RI (S.L.)
| | - Milagros C. Rosal
- Division of Preventive and Behavioral Medicine, Department of Population and Quantitative Sciences, University of Massachusetts Medical School, Worcester (M.C.R.)
| | - Rami Nassir
- Department of Pathology, School of Medicine, Umm Al-Qura University, Saudi Arabia (R.N.)
| | - Nina P. Paynter
- Division of Preventive Medicine (J.L., N.P.P.), Harvard Medical School, Boston, MA
| | - Christine M. Albert
- Department of Cardiology, Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA (C.M.A.)
| | - Russell P. Tracy
- Department of Pathology and Laboratory Medicine (R.P.T., P.D.), Larner College of Medicine, University of Vermont, Burlington
- Department of Biochemistry (R.P.T.), Larner College of Medicine, University of Vermont, Burlington
| | - Peter Durda
- Department of Pathology and Laboratory Medicine (R.P.T., P.D.), Larner College of Medicine, University of Vermont, Burlington
| | - Yongmei Liu
- Divisions of Cardiology and Neurology, Department of Medicine, Duke University Medical Center, Durham, NC (Y.L.)
| | - Kent D. Taylor
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA (J.Y., X.G., K.D.T., J.I.R.)
| | - W. Craig Johnson
- Department of Biostatistics, University of Washington, Seattle (W.C.J.)
| | - Qi Sun
- Channing Division of Network Medicine, Department of Medicine (T.H., O.A.Z., Q.S., E.B.R., A.H.E.), Harvard Medical School, Boston, MA
- Department of Epidemiology (J.H., M.C.J., J.L., Q.S., E.B.R., A.H.E.), Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Nutrition (J.L., Q.S., E.B.R., A.H.E.), Harvard T.H. Chan School of Public Health, Boston, MA
| | - Eric B. Rimm
- Channing Division of Network Medicine, Department of Medicine (T.H., O.A.Z., Q.S., E.B.R., A.H.E.), Harvard Medical School, Boston, MA
- Department of Epidemiology (J.H., M.C.J., J.L., Q.S., E.B.R., A.H.E.), Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Nutrition (J.L., Q.S., E.B.R., A.H.E.), Harvard T.H. Chan School of Public Health, Boston, MA
| | - A. Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine (T.H., O.A.Z., Q.S., E.B.R., A.H.E.), Harvard Medical School, Boston, MA
- Department of Epidemiology (J.H., M.C.J., J.L., Q.S., E.B.R., A.H.E.), Harvard T.H. Chan School of Public Health, Boston, MA
- Department of Nutrition (J.L., Q.S., E.B.R., A.H.E.), Harvard T.H. Chan School of Public Health, Boston, MA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville (S.S.R.)
| | - Jerome I. Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA (J.Y., X.G., K.D.T., J.I.R.)
| | - Robert E. Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA (S.D., D.E.C., R.E.G.)
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge (R.E.G., C.B.C.)
| | - Clary B. Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge (R.E.G., C.B.C.)
| | - Kathryn M. Rexrode
- Division of Women’s Health (J.H., M.C.J., K.M.R.), Harvard Medical School, Boston, MA
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4
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Costanzo M, Caterino M, Sotgiu G, Ruoppolo M, Franconi F, Campesi I. Sex differences in the human metabolome. Biol Sex Differ 2022; 13:30. [PMID: 35706042 PMCID: PMC9199320 DOI: 10.1186/s13293-022-00440-4] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 06/02/2022] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND The sexual dimorphism represents one of the triggers of the metabolic disparities between the organisms, advising about wild implications in research or diagnostics contexts. Despite the mounting recognition of the importance of sex consideration in the biomedical fields, the identification of male- and female-specific metabolic signatures has not been achieved. MAIN BODY This review pointed the focus on the metabolic differences related to the sex, evidenced by metabolomics studies performed on healthy populations, with the leading aim of understanding how the sex influences the baseline metabolome. The main shared signatures and the apparent dissimilarities between males and females were extracted and highlighted from the metabolome of the most commonly analyzed biological fluids, such as serum, plasma, and urine. Furthermore, the influence of age and the significant interactions between sex and age have been taken into account. CONCLUSIONS The recognition of sex patterns in human metabolomics has been defined in diverse biofluids. The detection of sex- and age-related differences in the metabolome of healthy individuals are helpful for translational applications from the bench to the bedside to set targeted diagnostic and prevention approaches in the context of personalized medicine.
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Affiliation(s)
- Michele Costanzo
- Department of Molecular Medicine and Medical Biotechnology, School of Medicine, University of Naples Federico II, 80131 Naples, Italy
- CEINGE – Biotecnologie Avanzate s.c.ar.l., 80145 Naples, Italy
| | - Marianna Caterino
- Department of Molecular Medicine and Medical Biotechnology, School of Medicine, University of Naples Federico II, 80131 Naples, Italy
- CEINGE – Biotecnologie Avanzate s.c.ar.l., 80145 Naples, Italy
| | - Giovanni Sotgiu
- Clinical Epidemiology and Medical Statistics Unit, Department of Medical, Surgical and Experimental Sciences, University of Sassari, 07100 Sassari, Italy
| | - Margherita Ruoppolo
- Department of Molecular Medicine and Medical Biotechnology, School of Medicine, University of Naples Federico II, 80131 Naples, Italy
- CEINGE – Biotecnologie Avanzate s.c.ar.l., 80145 Naples, Italy
| | - Flavia Franconi
- Laboratory of Sex-Gender Medicine, National Institute of Biostructures and Biosystems, 07100 Sassari, Italy
| | - Ilaria Campesi
- Laboratory of Sex-Gender Medicine, National Institute of Biostructures and Biosystems, 07100 Sassari, Italy
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
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5
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Zhang M, Buckley JP, Liang L, Hong X, Wang G, Wang MC, Wills-Karp M, Wang X, Mueller NT. A metabolome-wide association study of in utero metal and trace element exposures with cord blood metabolome profile: Findings from the Boston Birth Cohort. ENVIRONMENT INTERNATIONAL 2022; 158:106976. [PMID: 34991243 PMCID: PMC8742133 DOI: 10.1016/j.envint.2021.106976] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 10/18/2021] [Accepted: 11/07/2021] [Indexed: 05/07/2023]
Abstract
BACKGROUND Exposure to metals lead (Pb), mercury (Hg), and cadmium (Cd) and trace elements selenium (Se) and manganese (Mn) has been linked to the developmental origins of cardiometabolic diseases, but the mechanisms are not well-understood. OBJECTIVE Conduct a metabolome-wide association study to understand how in utero exposure to Pb, Hg, Cd, Se, and Mn affects the metabolic programming of fetuses. METHODS We used data from the Boston Birth Cohort, which enrolled mother-child pairs from Boston, MA. We measured metals and trace elements in maternal red blood cells (RBCs) collected 24-72 h after delivery, and metabolites in cord blood collected at birth. We used multivariable linear regression to examine associations of metals and trace elements with metabolites and Bonferroni correction to account for multiple comparisons. We assessed non-linear associations of metals and trace elements with metabolites using restricted cubic spline plots. RESULTS This analysis included 670 mother-child pairs (57% non-Hispanic Black and 24% Hispanic). After Bonferroni correction, there were 25 cord metabolites associated with at least one of the metals or trace elements. Pb was negatively associated with the xenobiotic piperine, Cd was positively associated with xenobiotics cotinine and hydroxycotinine, and Hg was associated with 8 lipid metabolites (in both directions). Se and Mn shared associations with 6 metabolites (in both directions), which mostly included nucleotides and amino acids; Se was additionally associated with 7 metabolites (mostly amino acids, nucleotides, and carnitines) and Mn was additionally associated with C36:4 hydroxy phosphatidylcholine. Restricted cubic spline plots showed that most associations were linear. DISCUSSION Maternal RBC metal and trace element concentrations were associated in a dose-dependent fashion with cord blood metabolites. What remains to be determined is whether these metals- and trace elements-associated changes in cord metabolites can influence a child's risk of cardiometabolic diseases.
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Affiliation(s)
- Mingyu Zhang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
| | - Jessie P Buckley
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 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
| | - Xiumei Hong
- Center on the Early Life Origins of Disease, Department of Population, Family and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Guoying Wang
- Center on the Early Life Origins of Disease, Department of Population, Family and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Mei-Cheng Wang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Marsha Wills-Karp
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Xiaobin Wang
- Center on the Early Life Origins of Disease, Department of Population, Family and Reproductive Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Noel T Mueller
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA.
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