Calculation of Biological Variation Components and Reference Change Value for Serum Copper and Zinc

2019 ◽  
Vol 65 (06/2019) ◽  
Author(s):  
Çiğdem Yücel ◽  
Müjgan Ercan ◽  
Turan Turhan ◽  
Ahmet Esendemir ◽  
Mesude Falay ◽  
...  
Keyword(s):  
Serum Copper ◽  
Biological Variation ◽  
Copper And Zinc ◽  
Reference Change Value

scholarly journals Biological variation and reference change value data for serum copper, zinc and selenium in Turkish adult population

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ceylan Bal ◽  
Serpil Erdogan ◽  
Gamze Gök ◽  
Cemil Nural ◽  
Betül Özbek ◽  
...  
Keyword(s):  
Adult Population ◽  
Reference Intervals ◽  
Serum Copper ◽  
Biological Variation ◽  
Serum Samples ◽  
Reference Change Value ◽  
Index Of Individuality ◽  
Copper Zinc ◽  
Clinically Significant ◽  
Analytical Variation

Abstract Objectives Calculation of biological variation (BV) components is very important in evaluating whether a test result is clinically significant. The aim of this study is to analyze BV components for copper, zinc and selenium in a cohort of healthy Turkish participants. Methods A total of 10 serum samples were collected from each of the 15 healthy individuals (nine female, six male), once a week, during 10 weeks. Copper, zinc and selenium levels were analyzed by atomic absorption spectrometer. BV parameters were calculated with the approach suggested by Fraser. Results Analytical variation (CVA), within-subject BV (CVI), between-subject BV (CVG) values were 8.4, 7.1 and 4.3 for copper; 4.2, 9.1 and 13.7 for zinc; 7.6, 2.5 and 6.9 for selenium, respectively. Reference change values (RCV) were 30.46, 27.56 and 22.16% for copper, zinc and selenium, respectively. The index of individuality (II) values were 1.65, 0.66 and 0.36 for copper, zinc and selenium, respectively. Conclusions According to the results of this study, traditional reference intervals can be used for copper but we do not recommend using it for zinc and selenium. We think that it would be more accurate to use RCV value for zinc and selenium in terms of following significant changes in recurrent results of a patient.

Serum copper and zinc levels and copper/zinc ratio in patients with breast cancer

1994 ◽  
Vol 40 (1) ◽  
pp. 31-38 ◽  
Author(s):  
Idris Yücel ◽  
Fikret Arpaci ◽  
Ahmet Özet ◽  
Bülent Döner ◽  
Turan Karayilanoĝlu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Serum Copper ◽  
Copper And Zinc ◽  
Copper Zinc ◽  
Zinc Levels

Annual biological variation and personalized reference intervals of clinical chemistry and hematology analytes

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Shuo Wang ◽  
Min Zhao ◽  
Zihan Su ◽  
Runqing Mu
Keyword(s):  
Clinical Chemistry ◽  
Population Based ◽  
Reference Intervals ◽  
Biological Variation ◽  
Annual Data ◽  
Healthy Individuals ◽  
Reference Change Value ◽  
Index Of Individuality ◽  
Personalized Diagnosis

Abstract Objectives A large number of people undergo annual health checkup but accurate laboratory criterion for evaluating their health status is limited. The present study determined annual biological variation (BV) and derived parameters of common laboratory analytes in order to accurately evaluate the test results of the annual healthcare population. Methods A total of 43 healthy individuals who had regular healthcare once a year for six consecutive years, were enrolled using physical, electrocardiogram, ultrasonography and laboratory. The annual BV data and derived parameters, such as reference change value (RCV) and index of individuality (II) were calculated and compared with weekly data. We used annual BV and homeostatic set point to calculate personalized reference intervals (RIper) which were compared with population-based reference intervals (RIpop). Results We have established the annual within-subject BV (CVI), RCV, II, RIper of 24 commonly used clinical chemistry and hematology analytes for healthy individuals. Among the 18 comparable measurands, CVI estimates of annual data for 11 measurands were significantly higher than the weekly data. Approximately 50% measurands of II were <0.6, the utility of their RIpop were limited. The distribution range of RIper for most measurands only copied small part of RIpop with reference range index for 8 measurands <0.5. Conclusions Compared with weekly BV, for annual healthcare individuals, annual BV and related parameters can provide more accurate evaluation of laboratory results. RIper based on long-term BV data is very valuable for “personalized” diagnosis on annual health assessments.

scholarly journals Serum copper and zinc levels at diagnosis and hepatocellular carcinoma survival in the Guangdong Liver Cancer Cohort

2019 ◽  
Vol 144 (11) ◽  
pp. 2823-2832 ◽  
Author(s):  
Ai‐Ping Fang ◽  
Pei‐Yan Chen ◽  
Xiao‐Yan Wang ◽  
Zhao‐Yan Liu ◽  
Dao‐Ming Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Serum Copper ◽  
Copper And Zinc ◽  
Zinc Levels

Fetal and maternal serum copper and zinc levels in human pregnancy

1982 ◽  
Vol 2 (5) ◽  
pp. 591-602 ◽  
Author(s):  
I.E. Dreosti ◽  
A.J. McMichael ◽  
G.T. Gibson ◽  
R.A. Buckley ◽  
J.M. Hartshorhe ◽  
...  
Keyword(s):  
Serum Copper ◽  
Maternal Serum ◽  
Human Pregnancy ◽  
Copper And Zinc ◽  
Zinc Levels

scholarly journals Biological Variation: The Effect of Different Distributions on Estimated Within-Person Variation and Reference Change Values

2016 ◽  
Vol 62 (5) ◽  
pp. 725-736 ◽  
Author(s):  
Thomas Røraas ◽  
Bård Støve ◽  
Per Hyltoft Petersen ◽  
Sverre Sandberg
Keyword(s):  
Computer Simulations ◽  
Standard Method ◽  
Simulated Data ◽  
Biological Variation ◽  
Nonparametric Method ◽  
Anova Model ◽  
Reference Change Value ◽  
Performance Specifications ◽  
The Impact ◽  
True Values

Abstract BACKGROUND Good estimates of within-person biological variation, CVI, are essential for diagnosing and monitoring patients and for setting analytical performance specifications. The aim of the present study was to use computer simulations to evaluate the impact of various measurement distributions on different methods for estimating CVI and reference change value (RCV). METHOD Data were simulated on the basis of 3 models for distributions of the within-person effect. We evaluated 3 different methods for estimating CVI: standard ANOVA, ln-ANOVA, and CV-ANOVA, and 3 different methods for calculating RCV: classic, ln-RCV, and a nonparametric method. We estimated CVI and RCV with the different methods and compared the results with the true values. RESULTS The performance of the methods varied, depending on both the size of the CVI and the type of distributions. The CV-ANOVA model performed well for the estimation of CVI with all simulated data. The ln-RCV method performed best if data were ln-normal distributed or CVI was less than approximately 12%. The nonparametric RCV method performed well for all simulated data but was less precise. CONCLUSIONS The CV-ANOVA model is recommended for both calculation of CVI and the step-by-step approach of checking for outliers and homogeneity in replicates and samples. The standard method for calculation of RCV should not be used when using CVs.

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