OBJECTIVES: Because reference intervals (RIs) for biochemistry analytes matched to the Russian population are not well defined, we joined the global study on reference values (RVs) coordinated by the IFCC Committee on Reference Intervals and Decision Limits (C-RIDL). METHODS: According to the C-RIDL harmonized protocol, 793 healthy volunteers were recruited in Saint-Petersburg, Moscow, and Yekaterinburg. Serum samples were tested for 34 biochemistry analytes. Sources of variation of RVs were explored using multiple regression analysis. The need for partitioning RVs by sex and age were judged using standard deviation ratio based on ANOVA. Latent abnormal values exclusion (LAVE) method was applied to reduce the influence of individuals with metabolic syndrome and/or inappropriate sampling conditions. RIs were computed by the parametric method. RESULTS: No appreciable between-city differences were observed. Partition of RVs by sex was required for 17 analytes. Age-related changes in RVs were observed in many analytes, especially in females. The trend was exaggerated in nutritional and inflammatory markers that were closely associated with body mass index (BMI), because BMI increases prominently with age. Therefore, for those analytes, volunteers with BMI > 28 kg/m2 were excluded in determining RIs for age-specific RIs. The LAVE method was effective in lowering the upper limits of the RIs for nutritional and inflammatory markers. CONCLUSION: RIs matched to the Russian population were established for 34 biochemical analytes using up-to-date methods in detailed consideration of sources of variation of RVs. The majority of Russian RIs are similar to those of Caucasian populations among the participating countries.
OBJECTIVES: Because reference intervals (RIs) for biochemistry analytes matched to the Russian population are not well defined, we joined the global study on reference values (RVs) coordinated by the IFCC Committee on Reference Intervals and Decision Limits (C-RIDL). METHODS: According to the C-RIDL harmonized protocol, 793 healthy volunteers were recruited in Saint-Petersburg, Moscow, and Yekaterinburg. Serum samples were tested for 34 biochemistry analytes. Sources of variation of RVs were explored using multiple regression analysis. The need for partitioning RVs by sex and age were judged using standard deviation ratio based on ANOVA. Latent abnormal values exclusion (LAVE) method was applied to reduce the influence of individuals with metabolic syndrome and/or inappropriate sampling conditions. RIs were computed by the parametric method. RESULTS: No appreciable between-city differences were observed. Partition of RVs by sex was required for 17 analytes. Age-related changes in RVs were observed in many analytes, especially in females. The trend was exaggerated in nutritional and inflammatory markers that were closely associated with body mass index (BMI), because BMI increases prominently with age. Therefore, for those analytes, volunteers with BMI > 28 kg/m2 were excluded in determining RIs for age-specific RIs. The LAVE method was effective in lowering the upper limits of the RIs for nutritional and inflammatory markers. CONCLUSION: RIs matched to the Russian population were established for 34 biochemical analytes using up-to-date methods in detailed consideration of sources of variation of RVs. The majority of Russian RIs are similar to those of Caucasian populations among the participating countries.