Volume 60, Issue 4 - Special Issue: Biological variation – eight years after the 1st Strategic Conference of EFLM; Issue Editors: Sverre Sandberg, Anna Carobene and Aasne K. Aarsand

Objectives Kidney markers are some of the most frequently used laboratory tests in patient care, and correct clinical decision making depends upon knowledge and correct application of biological variation (BV) data. The aim of this study was to review available BV data and to provide updated BV estimates for the following kidney markers in serum and plasma; albumin, creatinine, cystatin C, chloride, potassium, sodium and urea. Content Relevant studies were identified from a historical BV database as well as by systematic literature searches. Retrieved publications were appraised by the Biological Variation Data Critical Appraisal Checklist (BIVAC). Meta-analyses of BIVAC compliant studies with similar design were performed to deliver global estimates of within-subject (CV I ) and between-subject (CV G ) BV estimates. Out of the 61 identified papers, three received a BIVAC grade A, four grade B, 48 grade C, five grade D grade and one was not appraised as it did not report numerical BV estimates. Most studies were identified for creatinine (n=48). BV estimates derived from the meta-analysis were in general lower than previously reported estimates for all analytes except urea. For some measurands, BV estimates may be influenced by age or states of health, but further data are required. Summary This review provides updated global BV estimates for kidney related measurands. For all measurands except for urea, these estimates were lower than previously reported. Outlook For the measurands analyzed in this review, there are sufficient well-designed studies available to publish a trustworthy estimate of BV. However, for a number of newly appearing kidney markers no suitable data is available and additional studies are required.

Objectives Testing for thyroid disease constitutes a high proportion of the workloads of clinical laboratories worldwide. The setting of analytical performance specifications (APS) for testing methods and aiding clinical interpretation of test results requires biological variation (BV) data. A critical review of published BV studies of thyroid disease related measurands has therefore been undertaken and meta-analysis applied to deliver robust BV estimates. Methods A systematic literature search was conducted for BV studies of thyroid related analytes. BV data from studies compliant with the Biological Variation Data Critical Appraisal Checklist (BIVAC) were subjected to meta-analysis. Global estimates of within subject variation (CV I ) enabled determination of APS (imprecision and bias), indices of individuality, and indicative estimates of reference change values. Results The systematic review identified 17 relevant BV studies. Only one study (EuBIVAS) achieved a BIVAC grade of A. Methodological and statistical issues were the reason for B and C scores. The meta-analysis derived CV I generally delivered lower APS for imprecision than the mean CV A of the studies included in this systematic review. Conclusions Systematic review and meta-analysis of studies of BV of thyroid disease biomarkers have enabled delivery of well characterized estimates of BV for some, but not all measurands. The newly derived APS for imprecision for both free thyroxine and triiodothyronine may be considered challenging. The high degree of individuality identified for thyroid related measurands reinforces the importance of RCVs. Generation of BV data applicable to multiple scenarios may require definition using “big data” instead of the demanding experimental approach.

Objectives Biological variation data (BV) can be used for different applications, but this depends on the availability of robust and relevant BV data. In this study, we aimed to summarize and appraise BV studies for tumor markers, to examine the influence of study population characteristics and concentrations on BV estimates and to discuss the applicability of BV data for tumor markers in clinical practice. Methods Studies reporting BV data for tumor markers related to gastrointestinal, prostate, breast, ovarian, haematological, lung, and dermatological cancers were identified by a systematic literature search. Relevant studies were evaluated by the Biological Variation Data Critical Appraisal Checklist (BIVAC) and meta-analyses were performed for BIVAC compliant studies to deliver global estimates of within-subject (CV I ) and between-subject (CV G ) BV with 95% CI. Results The systematic review identified 49 studies delivering results for 22 tumor markers; four papers received BIVAC grade A, 3 B, 27 C and 15 D. Out of these, 29 CV I and 29 CV G estimates met the criteria to be included in the meta-analysis. Robust data are lacking to conclude on the relationship between BV and different disease states and tumor marker concentrations. Conclusions This review identifies a lack of high-quality BV studies for many tumor markers and a need for delivery of BIVAC compliant studies, including in different disease states and tumor marker concentrations. As of yet, the state-of-the-art may still be the most appropriate model to establish analytical performance specifications for the majority of tumor markers.

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 the multicenter 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 10 measurands (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.

Objectives 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). Methods 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. Results 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. Conclusions 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.

Objectives Trace elements (TrEL) are nutritionally essential components in maintaining health and preventing diseases. There is a lack of reliable biological variation (BV) data for TrELs, required for the diagnosis and monitoring of TrEL disturbances. In this study, we aimed to provide updated within- and between-subject BV estimates for zinc (Zn), copper (Cu) and selenium (Se). Methods Weekly serum samples were drawn from 68 healthy subjects (36 females and 32 males) for 10 weeks and stored at −80 °C prior to analysis. Serum Zn, Cu and Se levels were measured using inductively-coupled plasma mass spectrometry (ICP-MS). Outlier and variance homogeneity analyses were performed followed by CV-ANOVA (Røraas method) to determine BV and analytical variation estimates with 95% CI and the associated reference change values (RCV) for all subjects, males and females. Results Significant differences in mean concentrations between males and females were observed, with absolute and relative (%) differences for Zn at 0.5 μmol/L (3.5%), Cu 2.0 μmol/L (14.1%) and Se 0.06 μmol/L (6.0%). The within-subject BV (CV I [95% CI]) estimates were 8.8% (8.2–9.3), 7.8% (7.3–8.3) and 7.7% (7.2–8.2) for Zn, Cu and Se, respectively. Within-subject biological variation (CV I ) estimates derived for male and female subgroups were similar for all three TrELs. Marked individuality was observed for Cu and Se. Conclusions The data of this study provides updated BV estimates for serum Zn, Cu and Se derived from a stringent protocol and state of the art methodologies. Furthermore, Cu and Se display marked individuality, highlighting that population based reference limits should not be used in the monitoring of patients.

Objectives 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. Methods 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). Results 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. Conclusions 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.

Objectives 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. Methods 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” (CV P ) values for any measurand/subject, were analyzed using PCA. Results 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 CV P s values, for countries, alcohol, smoking habits, BMI and physical activity, have been found. Conclusions 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.

Objectives The neurofilament light chain (NfL) has emerged as a versatile biomarker for CNS-diseases and is approaching clinical use. The observed changes in NfL levels are frequently of limited magnitude and in order to make clinical decisions based on NfL measurements, it is essential that biological variation is not confused with clinically relevant changes. The present study was designed to evaluate the biological variation of serum NfL. Methods Apparently healthy individuals (n=33) were submitted to blood draws for three days in a row. On the second day, blood draws were performed every third hour for 12 h. NfL was quantified in serum using the Simoa™ HD-1 platform. The within-subject variation (CV I ) and between-subject variation (CV G ) were calculated using linear mixed-effects models. Results The overall median value of NfL was 6.3 pg/mL (range 2.1–19.1). The CV I was 3.1% and the CV G was 35.6%. An increase in two serial measurements had to exceed 24.3% to be classified as significant at the 95% confidence level. Serum NfL levels remained stable during the day (p=0.40), whereas a minute variation (6.0–6.6 pg/mL) was observed from day-to-day (p=0.02). Conclusions Serum NfL is subject to tight homeostatic regulation with none or neglectable semidiurnal and day-to-day variation, but considerable between-subject variation exists. This emphasizes serum NfL as a well-suited biomarker for disease monitoring, but warrants caution when interpreting NfL levels in relation to reference intervals in a diagnosis setting. Furthermore, NfL’s tight regulation requires that the analytical variation is kept at a minimum.

Objectives Cardiac myosin-binding protein C (cMyC) is a novel biomarker of myocardial injury, with a promising role in the triage and risk stratification of patients presenting with acute cardiac disease. In this study, we assess the weekly biological variation of cMyC, to examine its potential in monitoring chronic myocardial injury, and to suggest analytical quality specification for routine use of the test in clinical practice. Methods Thirty healthy volunteers were included. Non-fasting samples were obtained once a week for ten consecutive weeks. Samples were tested in duplicate on the Erenna ® platform by EMD Millipore Corporation. Outlying measurements and subjects were identified and excluded systematically, and homogeneity of analytical and within-subject variances was achieved before calculating the biological variability (CV I and CV G ), reference change values (RCV) and index of individuality (II). Results Mean age was 38 (range, 21–64) years, and 16 participants were women (53%). The biological variation, RCV and II with 95% confidence interval (CI) were: CV A (%) 19.5 (17.8–21.6), CV I (%) 17.8 (14.8–21.0), CV G (%) 66.9 (50.4–109.9), RCV (%) 106.7 (96.6–120.1)/−51.6 (−54.6 to −49.1) and II 0.42 (0.29–0.56). There was a trend for women to have lower CV G. The calculated RCVs were comparable between genders. Conclusions cMyC exhibits acceptable RCV and low II suggesting that it could be suitable for disease monitoring, risk stratification and prognostication if measured serially. Analytical quality specifications based on biological variation are similar to those for cardiac troponin and should be achievable at clinically relevant concentrations.

Objectives Biological variation is defined as the variation in analytical concentration between and within individuals, and being aware of this biological variation is important for understanding disease dynamics. The aim of our study is to calculate the within-subject (CV I ) and between-subject (CV G ) biological variations of serum creatinine, cystatin C and beta trace protein (BTP), as well as the reference change value (RCV) and individuality indexes (II), which are used to calculate the glomerular filtration rate while evaluating kidney damage. Methods Blood samples were collected from 22 healthy volunteers for 10 consecutive weeks and stored at −80 °C until the day of analysis. While the analysis for serum creatinine was performed colorimetrically with the kinetic jaffe method, the nephelometric method was employed for cystatin C and BTP measurements. All analyses were carried out in a single session for each test. Results Analytical coefficient of variation (CV A ) for serum creatinine, cystatin C and beta trace protein was 5.56, 3.48 and 5.37%, respectively. CV I and CV G : for serum creatinine: 3.31, 14.50%, respectively, for cystatin C: 3.15, 12.24%, respectively, for BTP: 9.91, 14.36%, respectively. RCV and II were calculated as 17.94%, 0.23 for serum creatinine, 13.01%, 0.26 for cystatin C, 31.24%, 0.69 for BTP, respectively. Conclusions According to the data obtained in our study, serum creatinine and cystatin C show high individuality, therefore we think that the use of RCV instead of reference ranges would be appropriate. Although II is found to be low for BTP, more studies are needed to support this finding.

Objectives Determine variability of serum anti-Müllerian hormone (AMH) levels during ovulatory menstrual cycles between different women (inter-participant), between non-consecutive cycles (inter-cycle) and within a single cycle (intra-cycle) in healthy women. Methods Eligible participants were women aged 18–40 years with regular ovulatory menstrual cycles. Serum samples were collected every second day during two non-consecutive menstrual cycles. AMH levels were measured in triplicate using the Elecsys ® AMH Plus immunoassay (Roche Diagnostics). AMH level variability was evaluated using mixed-effects periodic regression models based on Fourier series. The mesor was calculated to evaluate inter-participant and inter-cycle variability. Inter- and intra-cycle variability was evaluated using peak-to-peak amplitudes. Separation of biological and analytical coefficients of variation (CVs) was determined by analysing two remeasured AMH levels (with and without original AMH levels). Results A total of 47 women were included in the analysis (42 assessed over two cycles; five one cycle only). CV of unexplained biological variability was 9.61%; analytical variability was 3.46%. Inter-participant variability, given by time-series plots of AMH levels, was greater than inter-cycle variability. Between individual participants, both mesor and peak-to-peak amplitudes proved variable. In addition, for each participant, intra-cycle variability was higher than inter-cycle variability. Conclusions Inter-participant and intra-cycle variability of AMH levels were greater than inter-cycle variability. Unexplained biological variability was higher than analytical variability using the Elecsys AMH Plus immunoassay. Understanding variability in AMH levels may aid in understanding differences in availability of antral ovarian follicles during the menstrual cycle, which may be beneficial in designing gonadotropin dosage for assisted reproductive technology.

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 (RI per ) which were compared with population-based reference intervals (RI pop ). Results We have established the annual within-subject BV (CV I ), RCV, II, RI per of 24 commonly used clinical chemistry and hematology analytes for healthy individuals. Among the 18 comparable measurands, CV I 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 RI pop were limited. The distribution range of RI per for most measurands only copied small part of RI pop 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. RI per based on long-term BV data is very valuable for “personalized” diagnosis on annual health assessments.

Objectives Within- and between-subject biological variation (BV) estimates have many applications in laboratory medicine. However, robust high-quality BV estimates are lacking for many populations, such as athletes. This study aimed to deliver BV estimates of 29 routine laboratory measurands derived from a Biological Variation Data Critical Appraisal Checklist compliant design in a population of high-endurance athletes. Methods Eleven samples per subject were drawn from 30 triathletes monthly, during a whole sport season. Serum samples were measured in duplicate for proteins, liver enzymes, lipids and kidney-related measurands on an Advia2400 (Siemens Healthineers). After outlier and homogeneity analysis, within-subject (CV I ) and between-subject (CV G ) biological variation estimates were delivered (CV-ANOVA and log-ANOVA, respectively) and a linear mixed model was applied to analyze the effect of exercise and health related variables. Results Most CV I estimates were similar or only slightly higher in athletes compared to those reported for the general population, whereas two- to three-fold increases were observed for amylase, ALT, AST and ALP. No effect of exercise and health related variables were observed on the CV I estimates. For seven measurands, data were not homogeneously distributed and BV estimates were therefore not reported. Conclusions The observation of higher CV I estimates in athletes than what has been reported for the general population may be related to physiological stress over time caused by the continuous practice of exercise. The BV estimates derived from this study could be applied to athlete populations from disciplines in which they exercise under similar conditions of intensity and duration.

For many measurands, physicians depend on population-based reference intervals (popRI), when assessing laboratory test results. The availability of personalized reference intervals (prRI) may provide a means to improve the interpretation of laboratory test results for an individual. prRI can be calculated using estimates of biological and analytical variation and previous test results obtained in a steady-state situation. In this study, we aim to outline statistical approaches and considerations required when establishing and implementing prRI in clinical practice. Data quality assessment, including analysis for outliers and trends, is required prior to using previous test results to estimate the homeostatic set point. To calculate the prRI limits, two different statistical models based on ‘prediction intervals’ can be applied. The first model utilizes estimates of ‘within-person biological variation’ which are based on an individual’s own data. This model requires a minimum of five previous test results to generate the prRI. The second model is based on estimates of ‘within-subject biological variation’, which represents an average estimate for a population and can be found, for most measurands, in the EFLM Biological Variation Database. This model can be applied also when there are lower numbers of previous test results available. The prRI offers physicians the opportunity to improve interpretation of individuals’ test results, though studies are required to demonstrate if using prRI leads to better clinical outcomes. We recommend that both popRIs and prRIs are included in laboratory reports to aid in evaluating laboratory test results in the follow-up of patients.

Objectives Within-subject biological variation ( CV i ) is a fundamental aspect of laboratory medicine, from interpretation of serial results, partitioning of reference intervals and setting analytical performance specifications. Four indirect (data mining) approaches in determination of CV i were directly compared. Methods Paired serial laboratory results for 5,000 patients was simulated using four parameters, d the percentage difference in the means between the pathological and non-pathological populations, CV i the within-subject coefficient of variation for non-pathological values, f the fraction of pathological values, and e the relative increase in CV i of the pathological distribution. These parameters resulted in a total of 128 permutations. Performance of the Expected Mean Squares method (EMS), the median method, a result ratio method with Tukey’s outlier exclusion method and a modified result ratio method with Tukey’s outlier exclusion were compared. Results Within the 128 permutations examined in this study, the EMS method performed the best with 101/128 permutations falling within ±0.20 fractional error of the ‘true’ simulated CV i , followed by the result ratio method with Tukey’s exclusion method for 78/128 permutations. The median method grossly under-estimated the CV i . The modified result ratio with Tukey’s rule performed best overall with 114/128 permutations within allowable error. Conclusions This simulation study demonstrates that with careful selection of the statistical approach the influence of outliers from pathological populations can be minimised, and it is possible to recover CV i values close to the ‘true’ underlying non-pathological population. This finding provides further evidence for use of routine laboratory databases in derivation of biological variation components.