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      Comprehensive Metabolomic Profiling and Incident Cardiovascular Disease: A Systematic Review

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          Abstract

          Background

          Metabolomics is a promising tool of cardiovascular biomarker discovery. We systematically reviewed the literature on comprehensive metabolomic profiling in association with incident cardiovascular disease ( CVD).

          Methods and Results

          We searched MEDLINE and EMBASE from inception to January 2016. Studies were eligible if they pertained to adult humans; followed an agnostic and/or comprehensive approach; used serum or plasma (not urine or other biospecimens); conducted metabolite profiling at baseline in the context of examining prospective disease; and included myocardial infarction, stroke, and/or CVD death in the CVD outcome definition. We identified 12 original articles (9 cohort and 3 nested case‐control studies); participant numbers ranged from 67 to 7256. Mass spectrometry was the predominant analytical method. The number and chemical diversity of metabolites were very heterogeneous, ranging from 31 to >10 000 features. Four studies used untargeted profiling. Different types of metabolites were associated with CVD risk: acylcarnitines, dicarboxylacylcarnitines, and several amino acids and lipid classes. Only tiny improvements in CVD prediction beyond traditional risk factors were observed using these metabolites (C index improvement ranged from 0.006 to 0.05).

          Conclusions

          There are a limited number of longitudinal studies assessing associations between comprehensive metabolomic profiles and CVD risk. Quantitatively synthesizing the literature is challenging because of the widely varying analytical tools and the diversity of methodological and statistical approaches. Although some results are promising, more research is needed, notably standardization of metabolomic techniques and statistical approaches. Replication and combinations of novel and holistic methodological approaches would move the field toward the realization of its promise.

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          Most cited references23

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          Metabolomics in Prediabetes and Diabetes: A Systematic Review and Meta-analysis

          OBJECTIVE To conduct a systematic review of cross-sectional and prospective human studies evaluating metabolite markers identified using high-throughput metabolomics techniques on prediabetes and type 2 diabetes. RESEARCH DESIGN AND METHODS We searched MEDLINE and EMBASE databases through August 2015. We conducted a qualitative review of cross-sectional and prospective studies. Additionally, meta-analyses of metabolite markers, with data estimates from at least three prospective studies, and type 2 diabetes risk were conducted, and multivariable-adjusted relative risks of type 2 diabetes were calculated per study-specific SD difference in a given metabolite. RESULTS We identified 27 cross-sectional and 19 prospective publications reporting associations of metabolites and prediabetes and/or type 2 diabetes. Carbohydrate (glucose and fructose), lipid (phospholipids, sphingomyelins, and triglycerides), and amino acid (branched-chain amino acids, aromatic amino acids, glycine, and glutamine) metabolites were higher in individuals with type 2 diabetes compared with control subjects. Prospective studies provided evidence that blood concentrations of several metabolites, including hexoses, branched-chain amino acids, aromatic amino acids, phospholipids, and triglycerides, were associated with the incidence of prediabetes and type 2 diabetes. We meta-analyzed results from eight prospective studies that reported risk estimates for metabolites and type 2 diabetes, including 8,000 individuals of whom 1,940 had type 2 diabetes. We found 36% higher risk of type 2 diabetes per study-specific SD difference for isoleucine (pooled relative risk 1.36 [1.24–1.48]; I 2 = 9.5%), 36% for leucine (1.36 [1.17–1.58]; I 2 = 37.4%), 35% for valine (1.35 [1.19–1.53]; I 2 = 45.8%), 36% for tyrosine (1.36 [1.19–1.55]; I 2 = 51.6%), and 26% for phenylalanine (1.26 [1.10–1.44]; I 2 = 56%). Glycine and glutamine were inversely associated with type 2 diabetes risk (0.89 [0.81–0.96] and 0.85 [0.82–0.89], respectively; both I 2 = 0.0%). CONCLUSIONS In studies using high-throughput metabolomics, several blood amino acids appear to be consistently associated with the risk of developing type 2 diabetes.
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            Early Metabolic Markers of the Development of Dysglycemia and Type 2 Diabetes and Their Physiological Significance

            Metabolomic screening of fasting plasma from nondiabetic subjects identified α-hydroxybutyrate (α-HB) and linoleoyl-glycerophosphocholine (L-GPC) as joint markers of insulin resistance (IR) and glucose intolerance. To test the predictivity of α-HB and L-GPC for incident dysglycemia, α-HB and L-GPC measurements were obtained in two observational cohorts, comprising 1,261 nondiabetic participants from the Relationship between Insulin Sensitivity and Cardiovascular Disease (RISC) study and 2,580 from the Botnia Prospective Study, with 3-year and 9.5-year follow-up data, respectively. In both cohorts, α-HB was a positive correlate and L-GPC a negative correlate of insulin sensitivity, with α-HB reciprocally related to indices of β-cell function derived from the oral glucose tolerance test (OGTT). In follow-up, α-HB was a positive predictor (adjusted odds ratios 1.25 [95% CI 1.00–1.60] and 1.26 [1.07–1.48], respectively, for each standard deviation of predictor), and L-GPC was a negative predictor (0.64 [0.48–0.85] and 0.67 [0.54–0.84]) of dysglycemia (RISC) or type 2 diabetes (Botnia), independent of familial diabetes, sex, age, BMI, and fasting glucose. Corresponding areas under the receiver operating characteristic curve were 0.791 (RISC) and 0.783 (Botnia), similar in accuracy when substituting α-HB and L-GPC with 2-h OGTT glucose concentrations. When their activity was examined, α-HB inhibited and L-GPC stimulated glucose-induced insulin release in INS-1e cells. α-HB and L-GPC are independent predictors of worsening glucose tolerance, physiologically consistent with a joint signature of IR and β-cell dysfunction.
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              The Emerging Role of Metabolomics in the Diagnosis and Prognosis of Cardiovascular Disease.

              Perturbations in cardiac energy metabolism are major contributors to a number of cardiovascular pathologies. In addition, comorbidities associated with cardiovascular disease (CVD) can alter systemic and myocardial metabolism, often contributing to the worsening of cardiac function and health outcomes. State-of-the-art metabolomic technologies give us the ability to measure thousands of metabolites in biological fluids or biopsies, providing us with a metabolic fingerprint of individual patients. These metabolic profiles may serve as diagnostic and/or prognostic tools that have the potential to significantly alter the management of CVD. Herein, the authors review how metabolomics can assist in the interpretation of perturbed metabolic processes, and how this has improved our ability to understand the pathology of ischemic heart disease, atherosclerosis, and heart failure. Taken together, the integration of metabolomics with other "omics" platforms will allow us to gain insight into pathophysiological interactions of metabolites, proteins, genes, and disease states, while advancing personalized medicine.
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                Author and article information

                Contributors
                mamartinez@unav.es
                Journal
                J Am Heart Assoc
                J Am Heart Assoc
                10.1002/(ISSN)2047-9980
                JAH3
                ahaoa
                Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
                John Wiley and Sons Inc. (Hoboken )
                2047-9980
                28 September 2017
                October 2017
                : 6
                : 10 ( doiID: 10.1002/jah3.2017.6.issue-10 )
                : e005705
                Affiliations
                [ 1 ] Department of Nutrition Harvard T.H. Chan School of Public Health Boston MA
                [ 2 ] Department of Epidemiology Harvard T.H. Chan School of Public Health Boston MA
                [ 3 ] Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA
                [ 4 ] Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University Boston MA
                [ 5 ] Channing Laboratory Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA
                [ 6 ] Department of Preventive Medicine and Public Health University of Navarra Pamplona Spain
                [ 7 ] IDISNA (Instituto de Investigación Sanitaria de Navarra) Pamplona Spain
                [ 8 ] CIBER Fisiopatología de la Obesidad y Nutrición Instituto de Salud Carlos III Madrid Spain
                [ 9 ] The Broad Institute of MIT and Harvard Cambridge MA
                Author notes
                [*] [* ] Correspondence to: Miguel A. Martínez‐González, MPH, MD, PhD, Department of Preventive Medicine and Public Health, Facultad de Medicina‐Clínica Universidad de Navarra, Irunlarrea 1, 31008 Pamplona, Spain. E‐mail: mamartinez@ 123456unav.es
                [†]

                Dr Ruiz‐Canela and Dr Hruby contributed equally to this work.

                Article
                JAH32400
                10.1161/JAHA.117.005705
                5721826
                28963102
                dfaca1b2-b047-4bd8-bdc1-3e3793299b72
                © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

                This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 26 January 2017
                : 05 June 2017
                Page count
                Figures: 1, Tables: 5, Pages: 22, Words: 12703
                Funding
                Funded by: National Institutes of Health
                Funded by: NHLBI
                Award ID: 1R01HL118264
                Funded by: NIDDK
                Award ID: R01DK 102896
                Funded by: US Department of Agriculture–Agricultural Research Service
                Award ID: 58‐1950‐4‐003
                Categories
                Systematic Review and Meta‐Analysis
                Systematic Review and Meta‐Analysis
                Custom metadata
                2.0
                jah32400
                October 2017
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.2.1 mode:remove_FC converted:24.10.2017

                Cardiovascular Medicine
                epidemiology,metabolomics,myocardial infarction,stroke,clinical studies,mechanisms,metabolism,ischemic stroke

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