BACKGROUND: Redox mechanisms are implicated in the pathogenesis of many diseases and several assays of total antioxidant capacity (TAOC) have been reported. Large epidemiological databases contain information on individual serum antioxidants as well as disease-specific phenotypic data. However, antioxidants work co-operatively in biological systems and it is important to be able to translate individual antioxidant measures into those of global antioxidant defence. Models therefore need developing to quantify contributions made by individual species to global antioxidant defence. OBJECTIVE: To develop a predictive model that translates individual antioxidant concentrations into an index of TAOC, enabling interrogation of epidemiological databases that contain information about individual antioxidants, but not about TAOC. METHODS: Sera from 256 volunteers were simultaneously assayed for key antioxidants and TAOC by enhanced chemiluminescence (TAOC(ECL)). A predictive model was developed for serum TAOC using multiple linear regression analysis. RESULTS: The model explained 86% of TAOC(ECL) variability in serum. The strongest predictor of TAOC(ECL) was uric acid (1 SD increase associated with TAOC(ECL) increase of 103 micromol/l Teq-95% CI: 96.8-109), followed by vitamins A, C, E. CONCLUSIONS: The reported model represents a powerful tool for interrogating databases where individual serum antioxidant concentrations are known, and TAOC measures are required.
BACKGROUND: Redox mechanisms are implicated in the pathogenesis of many diseases and several assays of total antioxidant capacity (TAOC) have been reported. Large epidemiological databases contain information on individual serum antioxidants as well as disease-specific phenotypic data. However, antioxidants work co-operatively in biological systems and it is important to be able to translate individual antioxidant measures into those of global antioxidant defence. Models therefore need developing to quantify contributions made by individual species to global antioxidant defence. OBJECTIVE: To develop a predictive model that translates individual antioxidant concentrations into an index of TAOC, enabling interrogation of epidemiological databases that contain information about individual antioxidants, but not about TAOC. METHODS: Sera from 256 volunteers were simultaneously assayed for key antioxidants and TAOC by enhanced chemiluminescence (TAOC(ECL)). A predictive model was developed for serum TAOC using multiple linear regression analysis. RESULTS: The model explained 86% of TAOC(ECL) variability in serum. The strongest predictor of TAOC(ECL) was uric acid (1 SD increase associated with TAOC(ECL) increase of 103 micromol/l Teq-95% CI: 96.8-109), followed by vitamins A, C, E. CONCLUSIONS: The reported model represents a powerful tool for interrogating databases where individual serum antioxidant concentrations are known, and TAOC measures are required.