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PLoS One. 2014 Jun 27;9(6):e101219. doi: 10.1371/journal.pone.0101219. eCollection 2014.

Predictive properties of plasma amino acid profile for cardiovascular disease in patients with type 2 diabetes.

Author information

1
Department of Medicine, Shiga University of Medical Science, Otsu, Shiga, Japan.
2
Institute for Innovation, Ajinomoto Co., Inc., Kawasaki, Kanagawa, Japan.
3
Division of Diabetology & Endocrinology, Kanazawa Medical University, Kahoku-Gun, Ishikawa, Japan.
4
Division of Metabolism and Biosystemic Science, Department of Medicine, Asahikawa Medical University, Asahikawa, Hokkaido, Japan.

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.

PMID:
24971671
PMCID:
PMC4074128
DOI:
10.1371/journal.pone.0101219
[Indexed for MEDLINE]
Free PMC Article

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