European Biological Variation Study Research Articles

A New Concept for Reference Change Values-Regression to the Population Mean.

Reference change values (RCV) are used to indicate a change in analyte concentration that is unlikely to be due to random variation in the patient or the measurement. Current theory describes RCV relative to a first measurement result (X1). We investigate an alternative view predicting the starting point for RCV calculations from X1 and its location in the reference interval. Data for serum sodium, calcium, and total protein from the European Biological Variation study and from routine clinical collections were analyzed for the effect of the position of X1 within the reference interval on the following result from the same patient. A model to describe the effect was determined, and an equation to predict the RCV for a sample in a population was developed. For all data sets, the midpoints of the RCVs were dependent on the position of X1 in the population. Values for X1 below the population mean were more likely to be followed by a higher result, and X1 results above the mean were more likely to be followed by lower results. A model using population mean, reference interval dispersion, and result diagnostic variation provided a good fit with the data sets, and the derived equation predicted the changes seen. We have demonstrated that the position of X1 within the reference interval creates an asymmetrical RCV. This can be described as a regression to the population mean. Adding this concept to the theory of RCVs will be an important consideration in many cases.

The European biological variation study (EuBIVAS): Biological variation data for testosterone, follicle stimulating hormone, prolactin, luteinizing hormone and dehydroepiandrosterone sulfate in men

BackgroundKnowledge of biological variation (BV) of hormones is essential for interpretation of laboratory tests and for diagnostics of endocrinological and reproductive diseases. There is a lack of robust BV data for many hormones in men. MethodsWe used serum samples collected weekly over 10 weeks from the European Biological Variation Study (EuBIVAS) to determine BV of testosterone, follicle-stimulating hormone (FSH), prolactin, luteinizing hormone (LH) and dehydroepiandrosterone sulfate (DHEA-S) in 38 men. We derived within-subject (CVI) and between-subject (CVG) BV estimates by CV-ANOVA after trend, outlier, and homogeneity analysis and calculated reference change values, index of individuality (II), and analytical performance specifications. ResultsThe CVI estimates were 10 % for testosterone, 8 % for FSH, 13 % for prolactin, 22 % for LH, and 9 % for DHEA-S, respectively. The IIs ranged between 0.14 for FSH to 0.66 for LH, indicating high individuality. ConclusionsIn this study, we have used samples from the highly powered EuBIVAS study to derive BV estimates for testosterone, FSH, prolactin, LH and DHEA-S in men. Our data confirm previously published BV estimates of testosterone, FSH and LH. For prolactin and DHEA-S BV data for men are reported for the first time.

Biological variation estimates of Alzheimer's disease plasma biomarkers in healthy individuals.

Blood biomarkers have proven useful in Alzheimer's disease (AD) research. However, little is known about their biological variation (BV), which improves the interpretation of individual-level data. We measured plasma amyloid beta (Aβ42, Aβ40), phosphorylated tau (p-tau181, p-tau217, p-tau231), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) in plasma samples collected weekly over 10weeks from 20 participants aged 40 to 60 years from the European Biological Variation Study. We estimated within- (CVI ) and between-subject (CVG ) BV, analytical variation, and reference change values (RCV). Biomarkers presented considerable variability in CVI and CVG . Aβ42/Aβ40 had the lowest CVI (≈ 3%) and p-tau181 the highest (≈ 16%), while others ranged from 6% to 10%. Most RCVs ranged from 20% to 30% (decrease) and 25% to 40% (increase). BV estimates for AD plasma biomarkers can potentially refine their clinical and research interpretation. RCVs might be useful for detecting significant changes between serial measurements when monitoring early disease progression or interventions. Highlights Plasma amyloid beta (Aβ42/Aβ40) presents the lowest between- and within-subject biological variation, but also changes the least in Alzheimer's disease (AD) patients versus controls. Plasma phosphorylated tau variants significantly vary in their within-subject biological variation, but their substantial fold-changes in AD likely limits the impact of their variability. Plasma neurofilament light chain and glial fibrillary acidic protein demonstrate high between-subject variation, the impact of which will depend on clinical context. Reference change values can potentially be useful in monitoring early disease progression and the safety/efficacy of interventions on an individual level. Serial sampling revealed that unexpectedly high values in heathy individuals can be observed, which urges caution when interpreting AD plasma biomarkers based on a single test result.

Analytical performance specifications for the measurement uncertainty of 24,25-dihydroxyvitamin D examinations.

The exploration of the metabolites in the degradation pathways of vitamin D (VTD) has gained importance in recent years and simultaneous quantitation of twenty-five-hydroxy vitamin D (25(OH)D) mass concentration together with 24,25-dihydroxyvitamin D (24,25(OH)2D) has been proposed as a newer approach to define VTD deficiency. Yet, no data are available on 24,25(OH)2D biological variation (BV). In this study, we evaluated 24,25(OH)2D's BV on the European Biological Variation Study (EuBIVAS) cohort samples to determine if analytical performance specifications (APS) for 24,25(OH)2D could be generated. Six European laboratories recruited 91 healthy participants. 25(OH)D and 24,25(OH)2D concentrations in K3-EDTA plasma were examined weekly for up to 10weeks in duplicate with a validated LC-MS/MS method. The Vitamin D Metabolite Ratio (24,25(OH)2D divided by 25(OH)D×100) was also calculated at each time point. Linear regression of the mean 24,25(OH)2D concentrations at each blood collection showed participants were not in steady state. Variations of 24,25(OH)2D over time were significantly positively associated with the slopes of 25(OH)D concentrations over time and the concentration of 25(OH)D of the participant at inclusion, and negatively associated with body mass index (BMI), but not with age, gender, or location of the participant. The variation of the 24,25(OH)2D concentration in participants over a 10weeks period was 34.6%. Methods that would detect a significant change linked to the natural production of 24,25(OH)2D over this period at p<0.05 would need a relative measurement uncertainty (u%)<14.9% while at p<0.01, relative measurement uncertainty should be <10.5%. We have defined for the first time APS for 24,25(OH)2D examinations. According to the growing interest in this metabolite, several laboratories and manufacturers might aim to develop specific methods for its determination. The results presented in this paper are thus necessary prerequisites for the validation of such methods.

Biological variation: recent development and future challenges.

Biological variation (BV) data have many applications in laboratory medicine. However, these depend on the availability of relevant and robust BV data fit for purpose. BV data can be obtained through different study designs, both by experimental studies and studies utilizing previously analysed routine results derived from laboratory databases. The different BV applications include using BVdata for setting analytical performance specifications, tocalculate reference change values, to define the index ofindividuality and to establish personalized reference intervals. In this review, major achievements in the area of BV from last decade will be presented and discussed. These range from new models and approaches to derive BV data, the delivery of high-quality BV data by the highly powered European Biological Variation Study (EuBIVAS), the Biological Variation Data Critical Appraisal Checklist (BIVAC) and other standards for deriving and reporting BV data, the EFLM Biological Variation Database and new applications of BV data including personalized reference intervals and measurement uncertainty.

Biological variation of Alzheimer’s disease plasma biomarkers in healthy individuals

AbstractBackgroundPlasma biomarkers for Alzheimer’s disease (AD) have demonstrated great performance to identify AD pathology in research cohorts, and recent studies also support their potential to monitor effects of disease‐modifying treatments. To properly interpret these biomarkers in research settings and to inform power calculations of future trials using them as surrogate outcomes, information on their physiological variability over time is needed. We conducted a 10‐week biological variation (BV) study to assess the within‐individual (CVI; CV: coefficient of variation), between‐individual (CVG) and analytical variability (CVA) of blood‐based AD biomarkers. These parameters can provide the reference change value (RCV), which determines how much biomarker values must change to represent a significant abnormality‐related change, i.e. exceeding analytical and biological variation.MethodPlasma samples were collected weekly for 10 weeks from 20 healthy individuals from the European Biological Variation Study (n=20; 50% female, median age 46.4). Biomarkers for tau pathology (p‐tau181), brain amyloidosis (Aβ42, Aβ40, Aβ42/40), glial activation (GFAP) and neurodegeneration (NfL) were quantified in duplicate using the Simoa technology. The CV‐ANOVA statistical method was used to compute BV estimates. To evaluate diurnal rhythm of these biomarkers, we also conducted a study with non‐demented older adults (n=24; 58‐82yo), with hourly blood collections during 26h in strictly‐controlled environmental conditions (NCT02091427). P‐tau217 (Lily) analyses are ongoing for both studies.ResultIndividual‐level (Figure 1) and pooled biomarker levels are shown (Figure 2), as well as BV estimates (Table 1). The 10‐week within‐individual physiological variation (CVI) was lower for Aβ42/40 (4.3%), followed by Aβ42 (6.0%), Aβ40 (6.4%), NfL (7.9%), GFAP (9.3%) and p‐tau181 (15.8%). Between‐individual variation (CVG) was lower for Aβ42/40 (8.8%), followed by Aβ42 (13.3%), Aβ40 (17.0%), p‐tau181 (20.5%), NfL (23.0%) and GFAP (31.5%). Analytical variation was low for all biomarkers (CVA range: 2.5‐6.6%). The RCV was higher for p‐tau181 (45.6%), followed by GFAP (31.7%), NfL (27.6%), Aβ40 (19.0%), Aβ42 (18.8%) and Aβ42/40 (15.6%).ConclusionBlood‐based AD biomarkers demonstrate good week‐to‐week stability, but their CVG and RCV’s can be rather high, with implications both for use in clinical routine (misclassification risk) and for future trial designs (high numbers of participants needed). Results for the diurnal variation study will also be presented.

Evaluation of Measurement Uncertainties of Immunoassays Analytes According to Biological Variation Databases.

Quality control (QC) of analytical measurement is a stage used for the evaluation of whether the measurement method is appropriate to the standards required for patient care. QC encompasses internal (IQC) and external laboratory content (EQC). Measurement uncertainty (MU) is an important issue that expresses each analyte's measurement. Autoanalyzer's, calibrators, and control materials of analytes, reagents, measures contribute to MU. The aim of this study was to calculate the measurement uncertainty of nineteen immunoassay analytes in the biochemistry laboratory and to evaluate the compatibility of these measurements with the analytical quality targets derived from different biological variations. In the study, Cobas 6000 Modular Analysis Series (Roche Diagnostics, USA) autoanalyzer was used. IQC data of the analytes between August 2017 and January 2018 were used together with the monthly EQC data of June 2017 to January 2018. NORDTEST guideline was used to calculate the MU. "Desirable" analytical quality goals of each analyte with Fraser's formula were calculated and evaluated the limits from EuBIVAS (The European Biological Variation Study) and Ricos's biological variation databases. MU of fT4 was seen to be higher than TEa (Total Allowable Analytical Error)Ricos in our study. MU of fT4 was seen to be lower than TEaEuBIVAS (MUfT4: 9.20%, TEaRicos: 8.00%; TEaEuBIVAS: 9.92%). The analytical quality performances of the analytes included in the study turned out to be sufficient in our laboratory.

The European Biological Variation Study (EuBIVAS): Biological Variation Data for Coagulation Markers Estimated by a Bayesian Model.

For biological variation (BV) data to be safely used, data must be reliable and relevant to the population in which they are applied. We used samples from the European Biological Variation Study (EuBIVAS) to determine BV of coagulation markers by a Bayesian model robust to extreme observations and used the derived within-participant BV estimates [CVP(i)] to assess the applicability of the BV estimates in clinical practice. Plasma samples were drawn from 92 healthy individuals for 10 consecutive weeks at 6 European laboratories and analyzed in duplicate for activated partial thromboplastin time (APTT), prothrombin time (PT), fibrinogen, D-dimer, antithrombin (AT), protein C, protein S free, and factor VIII (FVIII). A Bayesian model with Student t likelihoods for samples and replicates was applied to derive CVP(i) and predicted BV estimates with 95% credibility intervals. For all markers except D-dimer, CVP(i) were homogeneously distributed in the overall study population or in subgroups. Mean within-subject estimates (CVI) were <5% for APTT, PT, AT, and protein S free, <10% for protein C and FVIII, and <12% for fibrinogen. For APTT, protein C, and protein S free, estimates were significantly lower in men than in women ≤50 years. For most coagulation markers, a common CVI estimate for men and women is applicable, whereas for APTT, protein C, and protein S free, sex-specific reference change values should be applied. The use of a Bayesian model to deliver individual CVP(i) allows for improved interpretation and application of the data.

The multicenter European Biological Variation Study (EuBIVAS): a new glance provided by the Principal Component Analysis (PCA), a machine learning unsupervised algorithms, based on the basic metabolic panel linked measurands.

The European Biological Variation Study (EuBIVAS), which includes 91 healthy volunteers from five European countries, estimated high-quality biological variation (BV) data for several measurands. Previous EuBIVAS papers reported no significant differences among laboratories/population; however, they were focused on specific set of measurands, without a comprehensive general look. The aim of this paper is to evaluate the homogeneity of EuBIVAS data considering multivariate information applying the Principal Component Analysis (PCA), a machine learning unsupervised algorithm. The EuBIVAS data for 13 basic metabolic panel linked measurands (glucose, albumin, total protein, electrolytes, urea, total bilirubin, creatinine, phosphatase alkaline, aminotransferases), age, sex, menopause, body mass index (BMI), country, alcohol, smoking habits, and physical activity, have been used to generate three databases developed using the traditional univariate and the multivariate Elliptic Envelope approaches to detect outliers, and different missing-value imputations. Two matrix of data for each database, reporting both mean values, and "within-person BV" (CVP) values for any measurand/subject, were analyzed using PCA. A clear clustering between males and females mean values has been identified, where the menopausal females are closer to the males. Data interpretations for the three databases are similar. No significant differences for both mean and CVPs values, for countries, alcohol, smoking habits, BMI and physical activity, have been found. The absence of meaningful differences among countries confirms the EuBIVAS sample homogeneity and that the obtained data are widely applicable to deliver APS. Our data suggest that the use of PCA and the multivariate approach may be used to detect outliers, although further studies are required.

The European Biological Variation Study (EuBIVAS): a summary report.

Biological variation (BV) data have many important applications in laboratory medicine. Concerns about quality of published BV data led the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) 1st Strategic Conference to indicate need for new studies to generate BV estimates of required quality. In response, the EFLM Working Group on BV delivered themulticenter European Biological Variation Study (EuBIVAS). This review summarises the EuBIVAS and its outcomes. Serum/plasma samples were taken from 91 ostensibly healthy individuals for 10 consecutive weeks at 6 European centres. Analysis was performed by Siemens ADVIA 2400 (clinical chemistry), Cobas Roche 8000, c702 and e801 (proteins and tumor markers/hormones respectively), ACL Top 750 (coagulation parameters), and IDS iSYS or DiaSorin Liaison (bone biomarkers). A strict preanalytical and analytical protocol was applied. To determine BV estimates with 95% CI, CV-ANOVA after analysis of outliers, homogeneity and trend analysis or a Bayesian model was applied. EuBIVAS has so far delivered BV estimates for 80 different measurands. Estimates for 10measurands (non-HDL cholesterol, S100-β protein, neuron-specific enolase, soluble transferrin receptor, intact fibroblast growth-factor-23, uncarboxylated-unphosphorylated matrix-Gla protein, human epididymis protein-4, free, conjugated and %free prostate-specific antigen), prior to EuBIVAS, have not been available. BV data for creatinine and troponin I were obtained using two analytical methods in each case. The EuBIVAS has delivered high-quality BV data for a wide range of measurands. The BV estimates are for many measurands lower than those previously reported, having an impact on the derived analytical performance specifications and reference change values.

Within- and between-subject biological variation data for tumor markers based on the European Biological Variation Study.

Reliable biological variation (BV) data are required for the clinical use of tumor markers in the diagnosis and monitoring of treatment effects in cancer. The European Biological Variation Study (EuBIVAS) was established by the EFLM Biological Variation Working Group to deliver BV data for clinically important measurands. In this study, EuBIVAS-based BV estimates are provided for cancer antigen (CA) 125, CA 15-3, CA 19-9, carcinoembryonic antigen, cytokeratin-19 fragment, alpha-fetoprotein and human epididymis protein 4. Subjects from five European countries were enrolled in the study, and weekly samples were collected from 91 healthy individuals (53 females and 38 males; 21-69 years old) for 10 consecutive weeks. All samples were analyzed in duplicate within a single run. After excluding outliers and homogeneity analysis, the BVs of tumor markers were determined by CV-ANOVA on trend-corrected data, when relevant (Røraas method). Marked individuality was found for all tumor markers. CYFRA 21-1 was the measurand with the highest index of individuality (II) at 0.67, whereas CA 19-9 had the lowest II at 0.07. The CV I s of HE4, CYFRA 21-1, CA 19-9, CA 125 and CA 15-3 of pre- and postmenopausal females were significantly different from each other. This study provides updated BV estimates for several tumor markers, and the findings indicate that marked individuality is characteristic. The use of reference change values should be considered when monitoring treatment of patients by means of tumor markers.

European Biological Variation Study (EuBIVAS): within- and between-subject biological variation estimates for serum thyroid biomarkers based on weekly samplings from 91 healthy participants.

Thyroid biomarkers are fundamental for the diagnosis of thyroid disorders and for the monitoring and treatment of patients with these diseases. The knowledge of biological variation (BV) is important to define analytical performance specifications (APS) and reference change values (RCV). The aim of this study was to deliver BV estimates for thyroid stimulating hormone (TSH), free thyroxine (FT4), free triiodothyronine (FT3), thyroglobulin (TG), and calcitonin (CT). Analyses were performed on serum samples obtained from the European Biological Variation Study population (91 healthy individuals from six European laboratories; 21-69 years) on the Roche Cobas e801 at the San Raffaele Hospital (Milan, Italy). All samples from each individual were evaluated in duplicate within a single run. The BV estimates with 95% CIs were obtained by CV-ANOVA, after analysis of variance homogeneity and outliers. The within-subject (CV I ) BV estimates were for TSH 17.7%, FT3 5.0%, FT4 4.8%, TG 10.3, and CT 13.0%, all significantly lower than those reported in the literature. No significant differences were observed for BV estimates between men and women. The availability of updated, in the case of CT not previously published, BV estimates for thyroid markers based on the large scale EuBIVAS study allows for refined APS and associated RCV applicable in the diagnosis and management of thyroid and related diseases.

European Biological Variation Study (EuBIVAS): within- and between-subject biological variation estimates for serum biointact parathyroid hormone based on weekly samplings from 91 healthy participants.

The European Biological Variation Study (EuBIVAS) was created by the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group on Biological Variation to establish high-quality biological variation (BV) estimates for clinically important measurands. In this study, the aim was to deliver reliable BV estimates for the biointact parathyroid hormone (PTH 1-84). Serum samples were obtained from a population of 91 healthy individuals (38 men, 43 pre-menopausal women, and 10 post-menopausal women; 21-69 years) from 5 European countries, with all samples stored at -80 °C prior to analysis. PTH 1-84 analysis was performed at the San Raffaele Hospital (Milan, Italy) on the Roche Cobas e801. All samples from each individual were analysed in duplicate within a single run. CV-ANOVA was applied, after analysis of variance homogeneity and outliers, to obtain BV estimates for PTH 1-84 with 95% CIs. The within-subject BV [CVI (95% CI)] estimates were significantly different between men and women [13.0% (12.1-14.2%) and 15.2% (14.3-16.3%), respectively], while the between-subject estimates [CVG (95% CI)] were similar (men: 26.8% (21.4-35.1%), pre-menopausal women: 27.8% (22.7-36.1%)], allowing for delivery of updated analytical performance specifications and reference change values. Updated BV estimates for serum PTH 1-84 based on the large-scale EuBIVAS may be beneficial for the diagnosis and management of parathyroid glands and bone turnover pathologies.

Critical appraisal and meta-analysis of biological variation studies on glycosylated albumin, glucose and HbA1c.

Numerous biological variation (BV) studies have been performed over the years, but the quality of these studies vary. The objectives of this study were to perform a systematic review and critical appraisal of BV studies on glycosylated albumin and to deliver updated BV estimates for glucose and HbA1c, including recently published high-quality studies such as the European Biological Variation study (EuBIVAS). Systematic literature searches were performed to identify BV studies. Nine publications not included in a previous review were identified; four for glycosylated albumin, three for glucose, and three for HbA1c. Relevant studies were appraised by the Biological Variation Data Critical Appraisal Checklist (BIVAC). Global BV estimates were derived by meta-analysis of BIVAC-compliant studies in healthy subjects with similar study design. One study received BIVAC grade A, 2B, and 6C. In most cases, the C-grade was associated with deficiencies in statistical analysis. BV estimates for glycosylated albumin were: CVI=1.4% (1.2-2.1) and CVG=5.7% (4.7-10.6), whereas estimates for HbA1c, CVI=1.2% (0.3-2.5), CVG=5.4% (3.3-7.3), and glucose, CVI=5.0% (4.1-12.0), CVG=8.1% (2.7-10.8) did not differ from previously published global estimates. The critical appraisal and rating of BV studies according to their methodological quality, followed by a meta-analysis, generate robust, and reliable BV estimates. This study delivers updated and evidence-based BV estimates for glycosylated albumin, glucose and HbA1c.

The European Biological Variation Study (EuBIVAS): weekly biological variation of cardiac troponin I estimated by the use of two different high-sensitivity cardiac troponin I assays.

Background Cardiac troponins (cTn) are specific markers for cardiac damage and acute coronary syndromes. The availability of new high-sensitivity assays allows cTn detection in healthy people, thus permitting the estimation of biological variation (BV) of cTn. The knowledge of BV is important to define analytical performance specifications (APS) and reference change values (RCVs). The aim of this study was to estimate the within- and between-subject weekly BV (CVI, CVG) of cTnI applying two high-sensitivity cTnI assays, using European Biological Variation Study (EuBIVAS) specimens. Methods Thirty-eight men and 53 women underwent weekly fasting blood drawings for 10 consecutive weeks. Duplicate measurements were performed with Singulex Clarity (Singulex, USA) and Siemens Atellica (Siemens Healthineers, Germany). Results cTnI was measurable in 99.4% and 74.3% of the samples with Singulex and Atellica assays, respectively. Concentrations were significantly higher in men than in women with both methods. The CVI estimates with 95% confidence interval (CI) were for Singulex 16.6% (15.6-17.7) and for Atellica 13.8% (12.7-15.0), with the observed difference likely being caused by the different number of measurable samples. No significant CVI differences were observed between men and women. The CVG estimates for women were 40.3% and 36.3%, and for men 65.3% and 36.5% for Singulex and Atellica, respectively. The resulting APS and RCVs were similar for the two methods. Conclusions This is the first study able to estimate cTnI BV for such a large cohort of well-characterized healthy individuals deriving objective APS and RCV values for detecting significant variations in cTnI serial measurements, even within the 99th percentile.

European Biological Variation Study (EuBIVAS): within- and between-subject biological variation estimates of β-isomerized C-terminal telopeptide of type I collagen (β-CTX), N-terminal propeptide of type I collagen (PINP), osteocalcin, intact fibroblast growth factor 23 and uncarboxylated-unphosphorylated matrix-Gla protein-a cooperation between the EFLM Working Group on Biological Variation and the International Osteoporosis Foundation-International Federation of Clinical Chemistry Committee on Bone Metabolism.

Within-subject (CVI) and between-subject (CVG) biological variation (BV) estimates are essential in determining both analytical performance specifications (APS) and reference change values (RCV). Previously published estimates of BV for bone metabolism biomarkers are generally not compliant with the most up-to-date quality criteria for BV studies. We calculated the BV and RCV for different bone metabolism markers, namely β-isomerized C-terminal telopeptide of type I collagen (β-CTX), N-terminal propeptide of type I collagen (PINP), osteocalcin (OC), intact fibroblast growth factor 23 (iFGF-23), and uncarboxylated-unphosphorylated Matrix-Gla Protein (uCuP-MGP) using samples from the European Biological Variation Study (EuBIVAS). In the EuBIVAS, 91 subjects were recruited from six European laboratories. Fasting blood samples were obtained weekly for ten consecutive weeks. The samples were run in duplicate on IDS iSYS or DiaSorin Liaison instruments. The results were subjected to outlier and variance homogeneity analysis before CV-ANOVA was used to obtain the BV estimates. We found no effect of gender upon the CVI estimates. The following CVI estimates with 95% confidence intervals (95% CI) were obtained: β-CTX 15.1% (14.4-16.0%), PINP 8.8% (8.4-9.3%), OC 8.9% (8.5-9.4%), iFGF23 13.9% (13.2-14.7%), and uCuP-MGP 6.9% (6.1-7.3%). The EuBIVAS has provided updated BV estimates for bone markers, including iFGF23, which have not been previously published, facilitating the improved follow-up of patients being treated for metabolic bone disease.

Analytical Performance Specifications for Lipoprotein(a), Apolipoprotein B-100, and Apolipoprotein A-I Using the Biological Variation Model in the EuBIVAS Population.

With increased interest in lipoprotein(a) (Lp[a]) concentration as a target for risk reduction and growing clinical evidence of its impact on cardiovascular disease (CVD) risk, rigorous analytical performance specifications (APS) and accuracy targets for Lp(a) are required. We investigated the biological variation (BV) of Lp(a), and 2 other major biomarkers of CVD, apolipoprotein A-I (apoA-I) and apolipoprotein B-100 (apoB), in the European Biological Variation Study population. Serum samples were drawn from 91 healthy individuals for 10 consecutive weeks at 6 European laboratories and analyzed in duplicate on a Roche Cobas 8000 c702. Outlier, homogeneity, and trend analysis were performed, followed by CV-ANOVA to determine BV estimates and their 95% CIs. These estimates were used to calculate APS and reference change values. For Lp(a), BV estimates were determined on normalized concentration quintiles. Within-subject BV estimates were significantly different between sexes for Lp(a) and between women aged <50 and >50 years for apoA-I and apoB. Lp(a) APS was constant across concentration quintiles and, overall, lower than APS based on currently published data, whereas results were similar for apoA-I and apoB. Using a fully Biological Variation Data Critical Appraisal Checklist (BIVAC)-compliant protocol, our study data confirm BV estimates of Lp(a) listed in the European Federation of Clinical Chemistry and Laboratory Medicine database and reinforce concerns expressed in recent articles regarding the suitability of older APS recommendations for Lp(a) measurements. Given the heterogeneity of Lp(a), more BIVAC-compliant studies on large numbers of individuals of different ethnic groups would be desirable.

European Biological Variation Study (EuBIVAS): Within- and Between-Subject Biological Variation Data for 15 Frequently Measured Proteins.

The European Biological Variation Study (EuBIVAS) was established to deliver rigorously determined data for biological variation (BV). Here, EuBIVAS-based BV estimates are provided for α1-acid glycoprotein, α1-antitrypsin, albumin, β2-microglobulin, ceruloplasmin, complement component 3, complement component 4, C-reactive protein (CRP), cystatin C, haptoglobin, IgA, IgG, IgM, soluble transferrin receptor (sTfR), and transferrin (Trf), together with their associated analytical performance specifications (APSs) and reference change values (RCVs). Serum samples from weekly blood samplings of 91 healthy study participants (38 males and 53 females, ages 21-69 years old) over 10 consecutive weeks in 6 European laboratories were stored at -80 °C before duplicate analysis on a Roche Cobas c702. Outlier and variance homogeneity analyses were performed followed by CV-ANOVA on trend-corrected data if relevant, to determine BV and analytical variation estimates with CI and the associated RCV. For the acute phase proteins, several participants experienced mild inflammatory episodes during the study, requiring exclusion of 7% of the 25290 results. Within-subject BV (CVI) estimates for specific proteins obtained in our study were lower than those available in the online 2014 BV database, except for Trf, whereas between-subject BV (CVG) estimates were similar. CVI and CVG estimates for sTfR, which have not previously been published, were 6.0% and 19.1%, respectively. In addition to new BV estimates for sTfR, this EuBIVAS substudy generated more demanding APS for frequently requested plasma specific proteins. APS for CRP should not be calculated from BV data except when CRP is used as a risk factor for cardiovascular disease.

A Bayesian Approach to Biological Variation Analysis.

Biological variation (BV) data have many applications for diagnosing and monitoring disease. The standard statistical approaches for estimating BV are sensitive to "noisy data" and assume homogeneity of within-participant CV. Prior knowledge about BV is mostly ignored. The aims of this study were to develop Bayesian models to calculate BV that (a) are robust to "noisy data," (b) allow heterogeneity in the within-participant CVs, and (c) take advantage of prior knowledge. We explored Bayesian models with different degrees of robustness using adaptive Student t distributions instead of the normal distributions and when the possibility of heterogeneity of the within-participant CV was allowed. Results were compared to more standard approaches using chloride and triglyceride data from the European Biological Variation Study. Using the most robust Bayesian approach on a raw data set gave results comparable to a standard approach with outlier assessments and removal. The posterior distribution of the fitted model gives access to credible intervals for all parameters that can be used to assess reliability. Reliable and relevant priors proved valuable for prediction. The recommended Bayesian approach gives a clear picture of the degree of heterogeneity, and the ability to crudely estimate personal within-participant CVs can be used to explore relevant subgroups. Because BV experiments are expensive and time-consuming, prior knowledge and estimates should be considered of high value and applied accordingly. By including reliable prior knowledge, precise estimates are possible even with small data sets.

The EuBIVAS: Within- and Between-Subject Biological Variation Data for Electrolytes, Lipids, Urea, Uric Acid, Total Protein, Total Bilirubin, Direct Bilirubin, and Glucose.

The European Federation of Clinical Chemistry and Laboratory Medicine European Biological Variation Study (EuBIVAS) has been established to deliver rigorously determined data describing biological variation (BV) of clinically important measurands. Here, EuBIVAS-based BV estimates of serum electrolytes, lipids, urea, uric acid, total protein, total bilirubin, direct bilirubin, and glucose, as well as their associated analytical performance specifications (APSs), are presented. Samples were drawn from 91 healthy individuals (38 male, 53 female; age range, 21-69 years) for 10 consecutive weeks at 6 European laboratories. Samples were stored at -80 °C before duplicate analysis of all samples on an ADVIA 2400 (Siemens Healthineers). Outlier and homogeneity analyses were performed, followed by CV-ANOVA on trend-corrected data, when relevant, to determine BV estimates with CIs. The within-subject BV (CVI) estimates of all measurands, except for urea and LDL cholesterol, were lower than estimates available in an online BV database, with differences being most pronounced for HDL cholesterol, glucose, and direct bilirubin. Significant differences in CVI for men and women/women <50 years of age were evident for uric acid, triglycerides, and urea. The CVA obtained for sodium and magnesium exceeded the EuBIVAS-based APS for imprecision. The EuBIVAS, which is fully compliant with the recently published Biological Variation Data Critical Appraisal Checklist, has produced well-characterized, high-quality BV estimates utilizing a stringent experimental protocol. These new reference data deliver revised and more exacting APS and reference change values for commonly used clinically important measurands, thus having direct relevance to diagnostics manufacturers, service providers, clinical users, and ultimately patients.