Metabolic modulation predicts heart failure tests performance
The metabolic changes that accompany changes in Cardiopulmonary testing (CPET) and heart failure biomarkers (HFbio) are not well known. We undertook metabolomic and lipidomic phenotyping of a cohort of heart failure (HF) patients and utilized Multiple Regression Analysis (MRA) to identify associatio...
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| Veröffentlicht in: | PloS one 2019-06, Vol.14 (6), p.e0218153-e0218153 |
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| creator | Contaifer, Jr, Daniel Buckley, Leo F Wohlford, George Kumar, Naren G Morriss, Joshua M Ranasinghe, Asanga D Carbone, Salvatore Canada, Justin M Trankle, Cory Abbate, Antonio Van Tassell, Benjamin W Wijesinghe, Dayanjan S |
| description | The metabolic changes that accompany changes in Cardiopulmonary testing (CPET) and heart failure biomarkers (HFbio) are not well known. We undertook metabolomic and lipidomic phenotyping of a cohort of heart failure (HF) patients and utilized Multiple Regression Analysis (MRA) to identify associations to CPET and HFBio test performance (peak oxygen consumption (Peak VO2), oxygen uptake efficiency slope (OUES), exercise duration, and minute ventilation-carbon dioxide production slope (VE/VCO2 slope), as well as the established HF biomarkers of inflammation C-reactive protein (CRP), beta-galactoside-binding protein (galectin-3), and N-terminal prohormone of brain natriuretic peptide (NT-proBNP)). A cohort of 49 patients with a left ventricular ejection fraction < 50%, predominantly males African American, presenting a high frequency of diabetes, hyperlipidemia, and hypertension were used in the study. MRA revealed that metabolic models for VE/VCO2 and Peak VO2 were the most fitted models, and the highest predictors' coefficients were from Acylcarnitine C18:2, palmitic acid, citric acid, asparagine, and 3-hydroxybutiric acid. Metabolic Pathway Analysis (MetPA) used predictors to identify the most relevant metabolic pathways associated to the study, aminoacyl-tRNA and amino acid biosynthesis, amino acid metabolism, nitrogen metabolism, pantothenate and CoA biosynthesis, sphingolipid and glycerolipid metabolism, fatty acid biosynthesis, glutathione metabolism, and pentose phosphate pathway (PPP). Metabolite Set Enrichment Analysis (MSEA) found associations of our findings with pre-existing biological knowledge from studies of human plasma metabolism as brain dysfunction and enzyme deficiencies associated with lactic acidosis. Our results indicate a profile of oxidative stress, lactic acidosis, and metabolic syndrome coupled with mitochondria dysfunction in patients with HF tests poor performance. The insights resulting from this study coincides with what has previously been discussed in existing literature thereby supporting the validity of our findings while at the same time characterizing the metabolic underpinning of CPET and HFBio. |
| doi_str_mv | 10.1371/journal.pone.0218153 |
| format | Article |
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We undertook metabolomic and lipidomic phenotyping of a cohort of heart failure (HF) patients and utilized Multiple Regression Analysis (MRA) to identify associations to CPET and HFBio test performance (peak oxygen consumption (Peak VO2), oxygen uptake efficiency slope (OUES), exercise duration, and minute ventilation-carbon dioxide production slope (VE/VCO2 slope), as well as the established HF biomarkers of inflammation C-reactive protein (CRP), beta-galactoside-binding protein (galectin-3), and N-terminal prohormone of brain natriuretic peptide (NT-proBNP)). A cohort of 49 patients with a left ventricular ejection fraction < 50%, predominantly males African American, presenting a high frequency of diabetes, hyperlipidemia, and hypertension were used in the study. MRA revealed that metabolic models for VE/VCO2 and Peak VO2 were the most fitted models, and the highest predictors' coefficients were from Acylcarnitine C18:2, palmitic acid, citric acid, asparagine, and 3-hydroxybutiric acid. Metabolic Pathway Analysis (MetPA) used predictors to identify the most relevant metabolic pathways associated to the study, aminoacyl-tRNA and amino acid biosynthesis, amino acid metabolism, nitrogen metabolism, pantothenate and CoA biosynthesis, sphingolipid and glycerolipid metabolism, fatty acid biosynthesis, glutathione metabolism, and pentose phosphate pathway (PPP). Metabolite Set Enrichment Analysis (MSEA) found associations of our findings with pre-existing biological knowledge from studies of human plasma metabolism as brain dysfunction and enzyme deficiencies associated with lactic acidosis. Our results indicate a profile of oxidative stress, lactic acidosis, and metabolic syndrome coupled with mitochondria dysfunction in patients with HF tests poor performance. The insights resulting from this study coincides with what has previously been discussed in existing literature thereby supporting the validity of our findings while at the same time characterizing the metabolic underpinning of CPET and HFBio.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0218153</identifier><identifier>PMID: 31220103</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Acidosis ; African Americans ; Amino acids ; Analysis ; Asparagine ; Bioindicators ; Biological markers ; Biology and Life Sciences ; Biomarkers ; Biomarkers - blood ; Biosynthesis ; Blood plasma ; Brain ; Brain natriuretic peptide ; Brain research ; C-reactive protein ; Carbon dioxide ; Cardiovascular disease ; Citric acid ; Cohort Studies ; Congestive heart failure ; Diabetes mellitus ; Diagnosis ; Enzymes ; Exercise Test ; Fatty acids ; Female ; Galectin-3 ; Glutathione ; Heart ; Heart failure ; Heart Failure - blood ; Heart Failure - physiopathology ; Humans ; Hyperlipidemia ; Hypertension ; Inflammation ; Kinases ; Lactic acidosis ; Lipid metabolism ; Lipids ; Male ; Males ; Medicine and Health Sciences ; Metabolic disorders ; Metabolic pathways ; Metabolic syndrome ; Metabolism ; Metabolites ; Metabolome ; Metabolomics ; Middle Aged ; Mitochondria ; Monosaccharides ; Mortality ; Multiple regression analysis ; Natriuretic Peptide, Brain - blood ; Natriuretic peptides ; Nitrogen ; Nitrogen metabolism ; Organic acids ; Oxidative stress ; Oxygen ; Oxygen Consumption ; Oxygen uptake ; Palmitic acid ; Pentose ; Pentose phosphate pathway ; Peptides ; Phenotypes ; Phenotyping ; Phosphates ; Prevalence studies (Epidemiology) ; Protein binding ; Proteins ; Regression Analysis ; RNA ; Saturated fatty acids ; Slopes ; Steroids (Organic compounds) ; Transfer RNA ; tRNA ; Ventilation ; Ventricle</subject><ispartof>PloS one, 2019-06, Vol.14 (6), p.e0218153-e0218153</ispartof><rights>COPYRIGHT 2019 Public Library of Science</rights><rights>2019 Contaifer Jr et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2019 Contaifer Jr et al 2019 Contaifer Jr et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-64b3fa19945b57479d57a8a6c41ad85da88fc60250e2b3de4bc8f89e1eccdfd43</citedby><cites>FETCH-LOGICAL-c692t-64b3fa19945b57479d57a8a6c41ad85da88fc60250e2b3de4bc8f89e1eccdfd43</cites><orcidid>0000-0002-2124-5109</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6586291/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6586291/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2095,2914,23846,27903,27904,53770,53772,79347,79348</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31220103$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Passino, Claudio</contributor><creatorcontrib>Contaifer, Jr, Daniel</creatorcontrib><creatorcontrib>Buckley, Leo F</creatorcontrib><creatorcontrib>Wohlford, George</creatorcontrib><creatorcontrib>Kumar, Naren G</creatorcontrib><creatorcontrib>Morriss, Joshua M</creatorcontrib><creatorcontrib>Ranasinghe, Asanga D</creatorcontrib><creatorcontrib>Carbone, Salvatore</creatorcontrib><creatorcontrib>Canada, Justin M</creatorcontrib><creatorcontrib>Trankle, Cory</creatorcontrib><creatorcontrib>Abbate, Antonio</creatorcontrib><creatorcontrib>Van Tassell, Benjamin W</creatorcontrib><creatorcontrib>Wijesinghe, Dayanjan S</creatorcontrib><title>Metabolic modulation predicts heart failure tests performance</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>The metabolic changes that accompany changes in Cardiopulmonary testing (CPET) and heart failure biomarkers (HFbio) are not well known. We undertook metabolomic and lipidomic phenotyping of a cohort of heart failure (HF) patients and utilized Multiple Regression Analysis (MRA) to identify associations to CPET and HFBio test performance (peak oxygen consumption (Peak VO2), oxygen uptake efficiency slope (OUES), exercise duration, and minute ventilation-carbon dioxide production slope (VE/VCO2 slope), as well as the established HF biomarkers of inflammation C-reactive protein (CRP), beta-galactoside-binding protein (galectin-3), and N-terminal prohormone of brain natriuretic peptide (NT-proBNP)). A cohort of 49 patients with a left ventricular ejection fraction < 50%, predominantly males African American, presenting a high frequency of diabetes, hyperlipidemia, and hypertension were used in the study. MRA revealed that metabolic models for VE/VCO2 and Peak VO2 were the most fitted models, and the highest predictors' coefficients were from Acylcarnitine C18:2, palmitic acid, citric acid, asparagine, and 3-hydroxybutiric acid. Metabolic Pathway Analysis (MetPA) used predictors to identify the most relevant metabolic pathways associated to the study, aminoacyl-tRNA and amino acid biosynthesis, amino acid metabolism, nitrogen metabolism, pantothenate and CoA biosynthesis, sphingolipid and glycerolipid metabolism, fatty acid biosynthesis, glutathione metabolism, and pentose phosphate pathway (PPP). Metabolite Set Enrichment Analysis (MSEA) found associations of our findings with pre-existing biological knowledge from studies of human plasma metabolism as brain dysfunction and enzyme deficiencies associated with lactic acidosis. Our results indicate a profile of oxidative stress, lactic acidosis, and metabolic syndrome coupled with mitochondria dysfunction in patients with HF tests poor performance. The insights resulting from this study coincides with what has previously been discussed in existing literature thereby supporting the validity of our findings while at the same time characterizing the metabolic underpinning of CPET and HFBio.</description><subject>Acidosis</subject><subject>African Americans</subject><subject>Amino acids</subject><subject>Analysis</subject><subject>Asparagine</subject><subject>Bioindicators</subject><subject>Biological markers</subject><subject>Biology and Life Sciences</subject><subject>Biomarkers</subject><subject>Biomarkers - blood</subject><subject>Biosynthesis</subject><subject>Blood plasma</subject><subject>Brain</subject><subject>Brain natriuretic peptide</subject><subject>Brain research</subject><subject>C-reactive protein</subject><subject>Carbon dioxide</subject><subject>Cardiovascular disease</subject><subject>Citric acid</subject><subject>Cohort Studies</subject><subject>Congestive heart failure</subject><subject>Diabetes mellitus</subject><subject>Diagnosis</subject><subject>Enzymes</subject><subject>Exercise Test</subject><subject>Fatty acids</subject><subject>Female</subject><subject>Galectin-3</subject><subject>Glutathione</subject><subject>Heart</subject><subject>Heart failure</subject><subject>Heart Failure - blood</subject><subject>Heart Failure - physiopathology</subject><subject>Humans</subject><subject>Hyperlipidemia</subject><subject>Hypertension</subject><subject>Inflammation</subject><subject>Kinases</subject><subject>Lactic acidosis</subject><subject>Lipid metabolism</subject><subject>Lipids</subject><subject>Male</subject><subject>Males</subject><subject>Medicine and Health Sciences</subject><subject>Metabolic disorders</subject><subject>Metabolic pathways</subject><subject>Metabolic syndrome</subject><subject>Metabolism</subject><subject>Metabolites</subject><subject>Metabolome</subject><subject>Metabolomics</subject><subject>Middle Aged</subject><subject>Mitochondria</subject><subject>Monosaccharides</subject><subject>Mortality</subject><subject>Multiple regression analysis</subject><subject>Natriuretic Peptide, Brain - blood</subject><subject>Natriuretic peptides</subject><subject>Nitrogen</subject><subject>Nitrogen metabolism</subject><subject>Organic acids</subject><subject>Oxidative stress</subject><subject>Oxygen</subject><subject>Oxygen Consumption</subject><subject>Oxygen uptake</subject><subject>Palmitic acid</subject><subject>Pentose</subject><subject>Pentose phosphate pathway</subject><subject>Peptides</subject><subject>Phenotypes</subject><subject>Phenotyping</subject><subject>Phosphates</subject><subject>Prevalence studies (Epidemiology)</subject><subject>Protein binding</subject><subject>Proteins</subject><subject>Regression Analysis</subject><subject>RNA</subject><subject>Saturated fatty acids</subject><subject>Slopes</subject><subject>Steroids (Organic compounds)</subject><subject>Transfer RNA</subject><subject>tRNA</subject><subject>Ventilation</subject><subject>Ventricle</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNkl2L1DAUhoso7rr6D0QHBNGLGfPd5EJhWfwYWFnw6zakyelMhkwzJq3ovzez012mshfSi5aT57w57-lbVU8xWmBa4zebOKTOhMUudrBABEvM6b3qFCtK5oIgev_o-6R6lPMGIU6lEA-rE4oJQRjR0-rtZ-hNE4O3s210QzC9j91sl8B52-fZGkzqZ63xYUgw6yGX2g5SG9PWdBYeVw9aEzI8Gd9n1fcP779dfJpfXn1cXpxfzq1QpJ8L1tDWYKUYb3jNauV4baQRlmHjJHdGytYKRDgC0lAHrLGylQowWOtax-hZ9fyguwsx69F51oQwRgURHBdieSBcNBu9S35r0h8djdfXhZhWujjxNoBGhlIMjjVKtoyppmGqllAGZNQZhE3RejfeNjRbcBa6PpkwEZ2edH6tV_GXFlwKovbDvBoFUvw5lKXprc8WQjAdxOF6bi44EQIV9MU_6N3uRmpligHftbHca_ei-pxLJVVdPBRqcQdVHgdbb0tMWl_qk4bXk4bC9PC7X5khZ738-uX_2asfU_blEVsyFPp1jmHYZytPQXYAbYo5J2hvl4yR3qf8Zht6n3I9pry0PTv-QbdNN7GmfwHAeffM</recordid><startdate>20190620</startdate><enddate>20190620</enddate><creator>Contaifer, 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Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Contaifer, Jr, Daniel</au><au>Buckley, Leo F</au><au>Wohlford, George</au><au>Kumar, Naren G</au><au>Morriss, Joshua M</au><au>Ranasinghe, Asanga D</au><au>Carbone, Salvatore</au><au>Canada, Justin M</au><au>Trankle, Cory</au><au>Abbate, Antonio</au><au>Van Tassell, Benjamin W</au><au>Wijesinghe, Dayanjan S</au><au>Passino, Claudio</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Metabolic modulation predicts heart failure tests performance</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2019-06-20</date><risdate>2019</risdate><volume>14</volume><issue>6</issue><spage>e0218153</spage><epage>e0218153</epage><pages>e0218153-e0218153</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>The metabolic changes that accompany changes in Cardiopulmonary testing (CPET) and heart failure biomarkers (HFbio) are not well known. We undertook metabolomic and lipidomic phenotyping of a cohort of heart failure (HF) patients and utilized Multiple Regression Analysis (MRA) to identify associations to CPET and HFBio test performance (peak oxygen consumption (Peak VO2), oxygen uptake efficiency slope (OUES), exercise duration, and minute ventilation-carbon dioxide production slope (VE/VCO2 slope), as well as the established HF biomarkers of inflammation C-reactive protein (CRP), beta-galactoside-binding protein (galectin-3), and N-terminal prohormone of brain natriuretic peptide (NT-proBNP)). A cohort of 49 patients with a left ventricular ejection fraction < 50%, predominantly males African American, presenting a high frequency of diabetes, hyperlipidemia, and hypertension were used in the study. MRA revealed that metabolic models for VE/VCO2 and Peak VO2 were the most fitted models, and the highest predictors' coefficients were from Acylcarnitine C18:2, palmitic acid, citric acid, asparagine, and 3-hydroxybutiric acid. Metabolic Pathway Analysis (MetPA) used predictors to identify the most relevant metabolic pathways associated to the study, aminoacyl-tRNA and amino acid biosynthesis, amino acid metabolism, nitrogen metabolism, pantothenate and CoA biosynthesis, sphingolipid and glycerolipid metabolism, fatty acid biosynthesis, glutathione metabolism, and pentose phosphate pathway (PPP). Metabolite Set Enrichment Analysis (MSEA) found associations of our findings with pre-existing biological knowledge from studies of human plasma metabolism as brain dysfunction and enzyme deficiencies associated with lactic acidosis. Our results indicate a profile of oxidative stress, lactic acidosis, and metabolic syndrome coupled with mitochondria dysfunction in patients with HF tests poor performance. The insights resulting from this study coincides with what has previously been discussed in existing literature thereby supporting the validity of our findings while at the same time characterizing the metabolic underpinning of CPET and HFBio.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>31220103</pmid><doi>10.1371/journal.pone.0218153</doi><tpages>e0218153</tpages><orcidid>https://orcid.org/0000-0002-2124-5109</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2019-06, Vol.14 (6), p.e0218153-e0218153 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_2244362651 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Public Library of Science (PLoS); PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Acidosis African Americans Amino acids Analysis Asparagine Bioindicators Biological markers Biology and Life Sciences Biomarkers Biomarkers - blood Biosynthesis Blood plasma Brain Brain natriuretic peptide Brain research C-reactive protein Carbon dioxide Cardiovascular disease Citric acid Cohort Studies Congestive heart failure Diabetes mellitus Diagnosis Enzymes Exercise Test Fatty acids Female Galectin-3 Glutathione Heart Heart failure Heart Failure - blood Heart Failure - physiopathology Humans Hyperlipidemia Hypertension Inflammation Kinases Lactic acidosis Lipid metabolism Lipids Male Males Medicine and Health Sciences Metabolic disorders Metabolic pathways Metabolic syndrome Metabolism Metabolites Metabolome Metabolomics Middle Aged Mitochondria Monosaccharides Mortality Multiple regression analysis Natriuretic Peptide, Brain - blood Natriuretic peptides Nitrogen Nitrogen metabolism Organic acids Oxidative stress Oxygen Oxygen Consumption Oxygen uptake Palmitic acid Pentose Pentose phosphate pathway Peptides Phenotypes Phenotyping Phosphates Prevalence studies (Epidemiology) Protein binding Proteins Regression Analysis RNA Saturated fatty acids Slopes Steroids (Organic compounds) Transfer RNA tRNA Ventilation Ventricle |
| title | Metabolic modulation predicts heart failure tests performance |
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