Predictive properties of plasma amino acid profile for cardiovascular disease in patients with type 2 diabetes
Prevention of cardiovascular disease (CVD) is an important therapeutic object of diabetes care. This study assessed whether an index based on plasma free amino acid (PFAA) profiles could predict the onset of CVD in diabetic patients. The baseline concentrations of 31 PFAAs were measured with high-pe...
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| Veröffentlicht in: | PloS one 2014-06, Vol.9 (6), p.e101219-e101219 |
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| creator | Kume, Shinji Araki, Shin-ichi Ono, Nobukazu Shinhara, Atsuko Muramatsu, Takahiko Araki, Hisazumi Isshiki, Keiji Nakamura, Kazuki Miyano, Hiroshi Koya, Daisuke Haneda, Masakazu Ugi, Satoshi Kawai, Hiromichi Kashiwagi, Atsunori Uzu, Takashi Maegawa, Hiroshi |
| description | Prevention of cardiovascular disease (CVD) is an important therapeutic object of diabetes care. This study assessed whether an index based on plasma free amino acid (PFAA) profiles could predict the onset of CVD in diabetic patients. The baseline concentrations of 31 PFAAs were measured with high-performance liquid chromatography-electrospray ionization-mass spectrometry in 385 Japanese patients with type 2 diabetes registered in 2001 for our prospective observational follow-up study. During 10 years of follow-up, 63 patients developed cardiovascular composite endpoints (myocardial infarction, angina pectoris, worsening of heart failure and stroke). Using the PFAA profiles and clinical information, an index (CVD-AI) consisting of six amino acids to predict the onset of any endpoints was retrospectively constructed. CVD-AI levels were significantly higher in patients who did than did not develop CVD. The area under the receiver-operator characteristic curve of CVD-AI (0.72 [95% confidence interval (CI): 0.64-0.79]) showed equal or slightly better discriminatory capacity than urinary albumin excretion rate (0.69 [95% CI: 0.62-0.77]) on predicting endpoints. A multivariate Cox proportional hazards regression analysis showed that the high level of CVD-AI was identified as an independent risk factor for CVD (adjusted hazard ratio: 2.86 [95% CI: 1.57-5.19]). This predictive effect of CVD-AI was observed even in patients with normoalbuminuria, as well as those with albuminuria. In conclusion, these results suggest that CVD-AI based on PFAA profiles is useful for identifying diabetic patients at risk for CVD regardless of the degree of albuminuria, or for improving the discriminative capability by combining it with albuminuria. |
| doi_str_mv | 10.1371/journal.pone.0101219 |
| format | Article |
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This study assessed whether an index based on plasma free amino acid (PFAA) profiles could predict the onset of CVD in diabetic patients. The baseline concentrations of 31 PFAAs were measured with high-performance liquid chromatography-electrospray ionization-mass spectrometry in 385 Japanese patients with type 2 diabetes registered in 2001 for our prospective observational follow-up study. During 10 years of follow-up, 63 patients developed cardiovascular composite endpoints (myocardial infarction, angina pectoris, worsening of heart failure and stroke). Using the PFAA profiles and clinical information, an index (CVD-AI) consisting of six amino acids to predict the onset of any endpoints was retrospectively constructed. CVD-AI levels were significantly higher in patients who did than did not develop CVD. The area under the receiver-operator characteristic curve of CVD-AI (0.72 [95% confidence interval (CI): 0.64-0.79]) showed equal or slightly better discriminatory capacity than urinary albumin excretion rate (0.69 [95% CI: 0.62-0.77]) on predicting endpoints. A multivariate Cox proportional hazards regression analysis showed that the high level of CVD-AI was identified as an independent risk factor for CVD (adjusted hazard ratio: 2.86 [95% CI: 1.57-5.19]). This predictive effect of CVD-AI was observed even in patients with normoalbuminuria, as well as those with albuminuria. In conclusion, these results suggest that CVD-AI based on PFAA profiles is useful for identifying diabetic patients at risk for CVD regardless of the degree of albuminuria, or for improving the discriminative capability by combining it with albuminuria.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0101219</identifier><identifier>PMID: 24971671</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Aged ; Amino acids ; Amino Acids - blood ; Angina ; Angina pectoris ; Biomarkers ; Biomarkers - blood ; Cancer ; Cardiovascular disease ; Cardiovascular diseases ; Cardiovascular Diseases - blood ; Cardiovascular Diseases - etiology ; Care and treatment ; Case-Control Studies ; Cerebral infarction ; Chromatography ; Committees ; Confidence intervals ; Coronary vessels ; Diabetes ; Diabetes mellitus ; Diabetes Mellitus, Type 2 - blood ; Diabetes Mellitus, Type 2 - complications ; Diabetes therapy ; Diabetics ; Disease control ; Ethics ; Excretion ; Female ; Hazard identification ; Health aspects ; Heart attack ; Heart diseases ; High performance liquid chromatography ; Humans ; Infarction ; Ionization ; Liquid chromatography ; Male ; Mass spectrometry ; Mass spectroscopy ; Medicine ; Medicine and Health Sciences ; Metabolism ; Metabolites ; Middle Aged ; Myocardial infarction ; Patients ; Predictive Value of Tests ; Regression analysis ; Risk factors ; Science ; Scientific imaging ; Statistical analysis ; Stroke ; Type 2 diabetes ; Urine</subject><ispartof>PloS one, 2014-06, Vol.9 (6), p.e101219-e101219</ispartof><rights>COPYRIGHT 2014 Public Library of Science</rights><rights>2014 Kume 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>2014 Kume et al 2014 Kume et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-e2b50f76a599c91274ac9265d3482a24d86b181fe3f84d9ed414cc8e1b86ffb83</citedby><cites>FETCH-LOGICAL-c692t-e2b50f76a599c91274ac9265d3482a24d86b181fe3f84d9ed414cc8e1b86ffb83</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4074128/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4074128/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23847,27903,27904,53769,53771,79346,79347</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24971671$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Oresic, Matej</contributor><creatorcontrib>Kume, Shinji</creatorcontrib><creatorcontrib>Araki, Shin-ichi</creatorcontrib><creatorcontrib>Ono, Nobukazu</creatorcontrib><creatorcontrib>Shinhara, Atsuko</creatorcontrib><creatorcontrib>Muramatsu, Takahiko</creatorcontrib><creatorcontrib>Araki, Hisazumi</creatorcontrib><creatorcontrib>Isshiki, Keiji</creatorcontrib><creatorcontrib>Nakamura, Kazuki</creatorcontrib><creatorcontrib>Miyano, Hiroshi</creatorcontrib><creatorcontrib>Koya, Daisuke</creatorcontrib><creatorcontrib>Haneda, Masakazu</creatorcontrib><creatorcontrib>Ugi, Satoshi</creatorcontrib><creatorcontrib>Kawai, Hiromichi</creatorcontrib><creatorcontrib>Kashiwagi, Atsunori</creatorcontrib><creatorcontrib>Uzu, Takashi</creatorcontrib><creatorcontrib>Maegawa, Hiroshi</creatorcontrib><title>Predictive properties of plasma amino acid profile for cardiovascular disease in patients with type 2 diabetes</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Prevention of cardiovascular disease (CVD) is an important therapeutic object of diabetes care. This study assessed whether an index based on plasma free amino acid (PFAA) profiles could predict the onset of CVD in diabetic patients. The baseline concentrations of 31 PFAAs were measured with high-performance liquid chromatography-electrospray ionization-mass spectrometry in 385 Japanese patients with type 2 diabetes registered in 2001 for our prospective observational follow-up study. During 10 years of follow-up, 63 patients developed cardiovascular composite endpoints (myocardial infarction, angina pectoris, worsening of heart failure and stroke). Using the PFAA profiles and clinical information, an index (CVD-AI) consisting of six amino acids to predict the onset of any endpoints was retrospectively constructed. CVD-AI levels were significantly higher in patients who did than did not develop CVD. The area under the receiver-operator characteristic curve of CVD-AI (0.72 [95% confidence interval (CI): 0.64-0.79]) showed equal or slightly better discriminatory capacity than urinary albumin excretion rate (0.69 [95% CI: 0.62-0.77]) on predicting endpoints. A multivariate Cox proportional hazards regression analysis showed that the high level of CVD-AI was identified as an independent risk factor for CVD (adjusted hazard ratio: 2.86 [95% CI: 1.57-5.19]). This predictive effect of CVD-AI was observed even in patients with normoalbuminuria, as well as those with albuminuria. In conclusion, these results suggest that CVD-AI based on PFAA profiles is useful for identifying diabetic patients at risk for CVD regardless of the degree of albuminuria, or for improving the discriminative capability by combining it with albuminuria.</description><subject>Aged</subject><subject>Amino acids</subject><subject>Amino Acids - blood</subject><subject>Angina</subject><subject>Angina pectoris</subject><subject>Biomarkers</subject><subject>Biomarkers - blood</subject><subject>Cancer</subject><subject>Cardiovascular disease</subject><subject>Cardiovascular diseases</subject><subject>Cardiovascular Diseases - blood</subject><subject>Cardiovascular Diseases - etiology</subject><subject>Care and treatment</subject><subject>Case-Control Studies</subject><subject>Cerebral infarction</subject><subject>Chromatography</subject><subject>Committees</subject><subject>Confidence intervals</subject><subject>Coronary vessels</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetes Mellitus, Type 2 - blood</subject><subject>Diabetes Mellitus, Type 2 - complications</subject><subject>Diabetes therapy</subject><subject>Diabetics</subject><subject>Disease control</subject><subject>Ethics</subject><subject>Excretion</subject><subject>Female</subject><subject>Hazard identification</subject><subject>Health aspects</subject><subject>Heart attack</subject><subject>Heart diseases</subject><subject>High performance liquid chromatography</subject><subject>Humans</subject><subject>Infarction</subject><subject>Ionization</subject><subject>Liquid chromatography</subject><subject>Male</subject><subject>Mass spectrometry</subject><subject>Mass spectroscopy</subject><subject>Medicine</subject><subject>Medicine and Health Sciences</subject><subject>Metabolism</subject><subject>Metabolites</subject><subject>Middle Aged</subject><subject>Myocardial infarction</subject><subject>Patients</subject><subject>Predictive Value of Tests</subject><subject>Regression analysis</subject><subject>Risk factors</subject><subject>Science</subject><subject>Scientific imaging</subject><subject>Statistical analysis</subject><subject>Stroke</subject><subject>Type 2 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one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kume, Shinji</au><au>Araki, Shin-ichi</au><au>Ono, Nobukazu</au><au>Shinhara, Atsuko</au><au>Muramatsu, Takahiko</au><au>Araki, Hisazumi</au><au>Isshiki, Keiji</au><au>Nakamura, Kazuki</au><au>Miyano, Hiroshi</au><au>Koya, Daisuke</au><au>Haneda, Masakazu</au><au>Ugi, Satoshi</au><au>Kawai, Hiromichi</au><au>Kashiwagi, Atsunori</au><au>Uzu, Takashi</au><au>Maegawa, Hiroshi</au><au>Oresic, Matej</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predictive properties of plasma amino acid profile for cardiovascular disease in patients with type 2 diabetes</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2014-06-27</date><risdate>2014</risdate><volume>9</volume><issue>6</issue><spage>e101219</spage><epage>e101219</epage><pages>e101219-e101219</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Prevention of cardiovascular disease (CVD) is an important therapeutic object of diabetes care. This study assessed whether an index based on plasma free amino acid (PFAA) profiles could predict the onset of CVD in diabetic patients. The baseline concentrations of 31 PFAAs were measured with high-performance liquid chromatography-electrospray ionization-mass spectrometry in 385 Japanese patients with type 2 diabetes registered in 2001 for our prospective observational follow-up study. During 10 years of follow-up, 63 patients developed cardiovascular composite endpoints (myocardial infarction, angina pectoris, worsening of heart failure and stroke). Using the PFAA profiles and clinical information, an index (CVD-AI) consisting of six amino acids to predict the onset of any endpoints was retrospectively constructed. CVD-AI levels were significantly higher in patients who did than did not develop CVD. The area under the receiver-operator characteristic curve of CVD-AI (0.72 [95% confidence interval (CI): 0.64-0.79]) showed equal or slightly better discriminatory capacity than urinary albumin excretion rate (0.69 [95% CI: 0.62-0.77]) on predicting endpoints. A multivariate Cox proportional hazards regression analysis showed that the high level of CVD-AI was identified as an independent risk factor for CVD (adjusted hazard ratio: 2.86 [95% CI: 1.57-5.19]). This predictive effect of CVD-AI was observed even in patients with normoalbuminuria, as well as those with albuminuria. In conclusion, these results suggest that CVD-AI based on PFAA profiles is useful for identifying diabetic patients at risk for CVD regardless of the degree of albuminuria, or for improving the discriminative capability by combining it with albuminuria.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>24971671</pmid><doi>10.1371/journal.pone.0101219</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2014-06, Vol.9 (6), p.e101219-e101219 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_1984976712 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry; Public Library of Science (PLoS) |
| subjects | Aged Amino acids Amino Acids - blood Angina Angina pectoris Biomarkers Biomarkers - blood Cancer Cardiovascular disease Cardiovascular diseases Cardiovascular Diseases - blood Cardiovascular Diseases - etiology Care and treatment Case-Control Studies Cerebral infarction Chromatography Committees Confidence intervals Coronary vessels Diabetes Diabetes mellitus Diabetes Mellitus, Type 2 - blood Diabetes Mellitus, Type 2 - complications Diabetes therapy Diabetics Disease control Ethics Excretion Female Hazard identification Health aspects Heart attack Heart diseases High performance liquid chromatography Humans Infarction Ionization Liquid chromatography Male Mass spectrometry Mass spectroscopy Medicine Medicine and Health Sciences Metabolism Metabolites Middle Aged Myocardial infarction Patients Predictive Value of Tests Regression analysis Risk factors Science Scientific imaging Statistical analysis Stroke Type 2 diabetes Urine |
| title | Predictive properties of plasma amino acid profile for cardiovascular disease in patients with type 2 diabetes |
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