RESUMEN
OBJECTIVES: A multicenter study was organized to explore sources of variation (SVs) of reference values (RVs) for 22 major immunochemistry analytes and to determine reference intervals (RIs) for the Russian population. METHODS: According to IFCC Committee on Reference Intervals and Decision Limits (C-RIDL) protocol, 758 healthy volunteers were recruited in St. Petersburg, Moscow, and Yekaterinburg. Serum samples were tested for five tumor markers, 17 hormones and related tests by Beckman Coulter's UniCel DxI 800 immunochemistry analyzer. SVs were explored using multiple regression analysis and ANOVA. Standard deviation ratio (SDR) of 0.4 was used as primary guide for partitioning RIs by gender and age. RESULTS: SDR for between-city difference was <0.4 for all analytes. Secondary exclusion of individuals was done under the following conditions: for female sex-hormones, those with contraceptives (8%); for CA19-9, those supposed to have negative Lewis blood-group (10.5% males and 11.3% females); for insulin, those with BMI≥28 kg/m2 (31%); for the thyroid panel, those with anti-thyroid antibodies (10.3% males; 24.5% females), for CEA those with smoking habit (30% males and 16% females). Gender-specific RIs were required for all analytes except CA19-9, CA15-3, thyroid-related tests, parathyroid hormone, and insulin. Age-specific RIs were required for alpha-fetoprotein, CEA, all sex-hormones for females, FSH and progesterone for both sexes. RIs were generally derived by parametric method after Gaussian transformation using modified Box-Cox formula. Exceptions were growth hormone, estradiol for females in postmenopause, and progesterone for females in premenopause, for which nonparametric method was required due to bimodal distribution and/or insufficient detection limit. CONCLUSION: RIs for major hormones and tumor markers specific for the Russian population were derived based on the up-to-date internationally harmonized protocol by careful consideration of analyte-specific SVs.
Asunto(s)
Biomarcadores de Tumor/metabolismo , Hormonas/metabolismo , Adolescente , Adulto , Anciano , Antígeno CA-19-9/metabolismo , Femenino , Voluntarios Sanos , Humanos , Masculino , Persona de Mediana Edad , Mucina-1/metabolismo , Análisis Multivariante , Valores de Referencia , Análisis de Regresión , Federación de Rusia , Adulto Joven , alfa-Fetoproteínas/metabolismoRESUMEN
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.
Asunto(s)
Biomarcadores/sangre , Índice de Masa Corporal , Pruebas de Química Clínica/normas , Salud Global/normas , Síndrome Metabólico/diagnóstico , Adolescente , Adulto , Femenino , Voluntarios Sanos , Humanos , Masculino , Síndrome Metabólico/sangre , Persona de Mediana Edad , Valores de Referencia , Federación de Rusia , Adulto JovenRESUMEN
OBJECTIVES: The intent of this study, based on a global multicenter study of reference values (RVs) for serum analytes was to explore biological sources of variation (SVs) of the RVs among 12 countries around the world. METHODS: As described in the first part of this paper, RVs of 50 major serum analytes from 13,396 healthy individuals living in 12 countries were obtained. Analyzed in this study were 23 clinical chemistry analytes and 8 analytes measured by immunoturbidimetry. Multiple regression analysis was performed for each gender, country by country, analyte by analyte, by setting four major SVs (age, BMI, and levels of drinking and smoking) as a fixed set of explanatory variables. For analytes with skewed distributions, log-transformation was applied. The association of each source of variation with RVs was expressed as the partial correlation coefficient (rp). RESULTS: Obvious gender and age-related changes in the RVs were observed in many analytes, almost consistently between countries. Compilation of age-related variations of RVs after adjusting for between-country differences revealed peculiar patterns specific to each analyte. Judged fromthe rp, BMI related changes were observed for many nutritional and inflammatory markers in almost all countries. However, the slope of linear regression of BMI vs. RV differed greatly among countries for some analytes. Alcohol and smoking-related changes were observed less conspicuously in a limited number of analytes. CONCLUSION: The features of sex, age, alcohol, and smoking-related changes in RVs of the analytes were largely comparable worldwide. The finding of differences in BMI-related changes among countries in some analytes is quite relevant to understanding ethnic differences in susceptibility to nutritionally related diseases.