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Supplement: Looking Ahead in Honoring the Past |
1 From the Research Institute for Health Fundamentals (YN, YF, and MM), the Institute of Life Sciences (TS, RS, and TK), and the Pharmaceutical Research Laboratories (Q-WZ and MT), Ajinomoto Company Inc, Kanagawa, Japan
ABSTRACT
Background: Few studies exist on the use of metabolic profiling of amino acids to examine underlying physiologic and disease states.
Objective: We aimed to introduce a new method for studying relations among amino acids and to generate a diagnostic index, or amino index, based on amino acid concentrations.
Design: For network analysis, 35 Fischer-344 rats were randomly divided into 7 groups and fed diets containing 5%, 10%, 15%, 20%, 30%, 50%, or 70% protein. Amino acid concentrations in plasma and various organs were used to derive correlation coefficients that were then used to construct correlation networks. To build a diagnostic index for diabetic rats, the plasma amino acid concentrations of diabetic and normal rats were analyzed by using a novel algorithm developed to generate amino acid–based indexes. Plasma amino acid concentrations from human growth hormone transgenic rats and insulin-treated diabetic rats were used to evaluate the index obtained for diabetes. Dimethylnitrosamine-treated Sprague-Dawley rats were used to generate an index for hepatic fibrosis.
Results: The scatter plots of plasma amino acid concentrations showed distinct patterns in different organs that were due to the different protein contents of the diets. Network analysis showed that data-driven networks for blood and tissue could be obtained. We derived a diagnostic index for the discrimination of diabetic rats with both sensitivity and specificity >97% and another surrogate index for liver hydroxyproline with a correlation of r2 = 0.85.
Conclusions: Correlation-based network analysis may help to uncover specific physiologic conditions or states. A novel approach using amino acid molar ratios was shown to generate indexes that can be used to separate animal disease models and monitor the progression of a disease parameter. Some of the methods described here may be applicable to the clinical setting.
Key Words: Aminogram • amino acids • metabolism • metabolomics • metabonomics • network analysis • amino index • diagnosis • correlation • cluster analysis
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