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

Biological Variation Data in Triathletes for Metabolism and Growth-Related Biomarkers Included in the Athlete Biological Passport

BACKGROUND

When using biological variation (BV) data, BV estimates need to be robust and representative. High-endurance athletes represent a population under special physiological conditions, which could influence BV estimates. Our study aimed to estimate BV in athletes for metabolism and growth-related biomarkers involved in the Athlete Biological Passport (ABP), by 2 different statistical models.

METHODS

Thirty triathletes were sampled monthly for 11 months. The samples were analyzed for human growth hormone (hGH), insulin-like growth factor-1 (IGF-1), insulin-like growth factor binding protein 3 (IGFBP-3), insulin, and N-terminal propeptide of type III procollagen (P-III-NP) by immunoassay. Bayesian and ANOVA methods were applied to estimate within-subject (CVI) and between-subject BV.

RESULTS

CVI estimates ranged from 7.8% for IGFBP-3 to 27.0% for insulin, when derived by the Bayesian method. The 2 models gave similar results, except for P-III-NP. Data were heterogeneously distributed for P-III-NP for the overall population and in females for IGF-1 and IGFBP-3. BV components were not estimated for hGH due to lack of steady state. The index of individuality was below 0.6 for all measurands, except for insulin.

CONCLUSIONS

In an athlete population, to apply a common CVI for insulin would be appropriate, but for IGF-1 and IGFBP-3 gender-specific estimates should be applied. P-III-NP data were heterogeneously distributed and using a mean CVI may not be representative for the population. The high degree of individuality for IGF-1, IGFBP-3, and P-III-NP makes them good candidates to be interpreted through reference change values and the ABP.

Within- and between-subject biological variation estimates for the enumeration of lymphocyte deep immunophenotyping and monocyte subsets

OBJECTIVES

This study aimed to deliver biological variation (BV) estimates for 25 types of lymphocyte subpopulations subjected to deep immunophenotyping (memory T/B cells, regulatory T cells, etc.) and classical, intermediate, and nonclassical monocyte subsets based on the full spectrum flow cytometry (FS-FCM) and a Biological Variation Data Critical Appraisal Checklist (BIVAC) design.

METHODS

Samples were collected biweekly from 60 healthy Chinese adults over 10 consecutive two-week periods. Each sample was measured in duplicate within a single run for lymphocyte deep immunophenotyping and monocyte subset determination using FS-FCM, including the percentage (%) and absolute count (cells/μL). After trend adjustment, a Bayesian model was applied to deliver the within-subject BV (CVI) and between-subject BV (CVG) estimates with 95 % credibility intervals.

RESULTS

Enumeration (% and cells/μL) for 25 types of lymphocyte deep immunophenotyping and three types of monocyte subset percentages showed considerable variability in terms of CVI and CVG. CVI ranged from 4.23 to 47.47 %. Additionally, CVG ranged between 10.32 and 101.30 %, except for CD4+ effector memory T cells re-expressing CD45RA. No significant differences were found between males and females for CVI and CVG estimates. Nevertheless, the CVGs of PD-1+ T cells (%) may be higher in females than males. Based on the desired analytical performance specification, the maximum allowable imprecision immune parameter was the CD8+PD-1+ T cell (cells/μL), with 23.7 %.

CONCLUSIONS

This is the first study delivering BV estimates for 25 types of lymphocyte subpopulations subjected to deep immunophenotyping, along with classical, intermediate, and nonclassical monocyte subsets, using FS-FCM and adhering to the BIVAC design.

Biological variation of inflammatory and iron metabolism markers in high-endurance recreational athletes; are these markers useful for athlete monitoring?

OBJECTIVES

To deliver biological variation (BV) data for serum hepcidin, soluble transferrin receptor (sTfR), erythropoietin (EPO) and interleukin 6 (IL-6) in a population of well-characterized high-endurance athletes, and to evaluate the potential influence of exercise and health-related factors on the BV.

METHODS

Thirty triathletes (15 females) were sampled monthly (11 months). All samples were analyzed in duplicate and BV estimates were delivered by Bayesian and ANOVA methods. A linear mixed model was applied to study the effect of factors related to exercise, health, and sampling intervals on the BV estimates.

RESULTS

Within-subject BV estimates (CVI) were for hepcidin 51.9 % (95 % credibility interval 46.9-58.1), sTfR 10.3 % (8.8-12) and EPO 27.3 % (24.8-30.3). The mean concentrations were significantly different between sex, but CVI estimates were similar and not influenced by exercise, health-related factors, or sampling intervals. The data were homogeneously distributed for EPO but not for hepcidin or sTfR. IL-6 results were mostly below the limit of detection. Factors related to exercise, health, and sampling intervals did not influence the BV estimates.

CONCLUSIONS

This study provides, for the first time, BV data for EPO, derived from a cohort of well-characterized endurance athletes and indicates that EPO is a good candidate for athlete follow-up. The application of the Bayesian method to deliver BV data illustrates that for hepcidin and sTfR, BV data are heterogeneously distributed and using a mean BV estimate may not be appropriate when using BV data for laboratory and clinical applications.

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

INTRODUCTION

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.

METHODS

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 10 weeks 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).

RESULTS

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).

DISCUSSION

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.

Unfractionated heparin and the activated clotting time in non-cardiac arterial procedures

INTRODUCTION

Unfractionated heparin is administered during non-cardiac arterial procedures (NCAP) to prevent thromboembolic complications. In order to achieve a safe level of anticoagulation, the effect of heparin can be measured. The aim of this review was to provide an overview on what is known about heparin, suggested tests to monitor the effect of heparin, including the activated clotting time (ACT), and the factors that could influence that ACT.

EVIDENCE ACQUISITION

A literature search in PubMed was performed. Articles reporting on heparin, clotting time tests (including thrombin time, activated partial thromboplastin time, anti-activated factor X and ACT), and ACT measurement devices were selected.

EVIDENCE SYNTHESIS

Heparin has a non-predictable effect in the individual patient, which could be measured using the ACT. However, ACT values can be influenced by many factors, such as hemodilution, hypothermia and thrombocytopenia. In addition, a high variation in ACT outcomes is found between measurement devices of different brands. In the sparse literature on the role of ACT during NCAP, no consensus has been reached on optimal target ACT values. An ACT >250 seconds leads to more bleeding complications. Females have a longer ACT after heparin administration, with a higher risk of bleeding complications.

CONCLUSIONS

The effect of heparin is unpredictable. ACT can be used to monitor the effect of heparin and achieve individualized anticoagulation, tailored to the patient and the specifics of the operative procedure. However, the ACT itself can be affected by several factors and caution must be present, as measured ACT values differ between measurement devices.

Personalized and Population-Based Reference Intervals for 48 Common Clinical Chemistry and Hematology Measurands: A Comparative Study

BACKGROUND

Personalized reference intervals (prRIs) have the potential to improve individual patient follow-up as compared to population-based reference intervals (popRI). In this study, we estimated popRI and prRIs for 48 clinical chemistry and hematology measurands using samples from the same reference individuals and explored the effect of using group-based and individually based biological variation (BV) estimates to derive prRIs.

METHODS

143 individuals (median age 28 years) were included in the study and had fasting blood samples collected once. From this population, 41 randomly selected subjects had samples collected weekly for 5 weeks. PopRIs were estimated according to Clinical Laboratory Standards Institute EP28 and within-subject BV (CVI) were estimated by CV-ANOVA. Data were assessed for trends and outliers prior to calculation of individual prRIs, based on estimates of (a) within-person BV (CVP), (b) CVI derived in this study, and (c) publically available CVI estimates.

RESULTS

For most measurands, the individual prRI ranges were smaller than the popRI range, but overall about half the study participants had a prRI wider than the popRI for 5 or more out of 48 measurands. The dispersion of prRIs based on CVP was wider than that of prRIs based on CVI.

CONCLUSION

The prRIs derived in our study varied significantly between different individuals, especially if based on CVP. Our results highlight the limitations of popRIs in interpreting test results of individual patients. If sufficient data from a steady-state situation are available, using prRI based on CVP estimates will provide a RI most specific for an individual patient.

Systematic review and meta-analysis of within-subject and between-subject biological variation data of coagulation and fibrinolytic measurands

OBJECTIVES

The diagnosis and monitoring of bleeding and thrombotic disorders depend on correct haemostatic measurements. The availability of high-quality biological variation (BV) data is important in this context. Many studies have reported BV data for these measurands, but results are varied. The present study aims to deliver global within-subject (CVI) and between-subject (CVG) BV estimates for haemostasis measurands by meta-analyses of eligible studies, by assessment with the Biological Variation Data Critical Appraisal Checklist (BIVAC).

METHODS

Relevant BV studies were graded by the BIVAC. Weighted estimates for CVI and CVG were obtained via meta-analysis of the BV data derived from BIVAC-compliant studies (graded A-C; whereby A represents optimal study design) performed in healthy adults.

RESULTS

In 26 studies BV data were reported for 35 haemostasis measurands. For 9 measurands, only one eligible publication was identified and meta-analysis could not be performed. 74% of the publications were graded as BIVAC C. The CVI and CVG varied extensively between the haemostasis measurands. The highest estimates were observed for PAI-1 antigen (CVI 48.6%; CVG 59.8%) and activity (CVI 34.9%; CVG 90.2%), while the lowest were observed for activated protein C resistance ratio (CVI 1.5%; CVG 4.5%).

CONCLUSIONS

This study provides updated BV estimates of CVI and CVG with 95% confidence intervals for a wide range of haemostasis measurands. These estimates can be used to form the basis for analytical performance specifications for haemostasis tests used in the diagnostic work-up required in bleeding- and thrombosis events and for risk assessment.

Sex Hormones and Adrenal Steroids: Biological Variation Estimated Using Direct and Indirect Methods

BACKGROUND

Biological variation (BV) data may be used to develop analytical performance specifications (APS), reference change values (RCV), and support the applicability of population reference intervals. This study estimates within-subject BV (CVI) for several endocrine biomarkers using 3 different methodological approaches.

METHODS

For the direct method, 30 healthy volunteers were sampled weekly for 10 consecutive weeks. Samples were analyzed in duplicate for 17-hydroxyprogesterone (17-OHP), androstenedione, cortisol, cortisone, estradiol, follicle-stimulating hormone (FSH), luteinizing hormone (LH), sex hormone-binding globulin (SHBG), and testosterone. A CV-ANOVA with outlier removal and a Bayesian model were applied to derive the CVI. For estradiol, FSH and LH, only the male subgroup was included. In the indirect method, using the same analytes and groups, pairs of sequential results were extracted from the laboratory information system. The total result variation for individual pairs was determined by identifying a central gaussian distribution in the ratios of the result pairs. The CVI was then estimated by removing the effect of analytical variation.

RESULTS

The estimated CVI from the Bayesian model (μCVP(i)) in the total cohort was: 17-OHP, 23%; androstenedione, 20%; cortisol, 18%; cortisone, 11%; SHBG, 7.4%; testosterone, 16%; and for the sex hormones in men: estradiol, 14%; FSH, 8%; and LH, 26%. CVI-heterogeneity was present for most endocrine markers. Similar CVI data were estimated using the CV-ANOVA and the indirect method.

CONCLUSIONS

Similar CVI data were obtained using 2 different direct and one indirect method. The indirect approach is a low-cost alternative ensuring implementation of CVI data applicable for local conditions.

Within-subject biological variation estimates using an indirect data mining strategy. Spanish multicenter pilot study (BiVaBiDa)

OBJECTIVES

The estimates of biological variation (BV) have traditionally been determined using direct methods, which present limitations. In response to this issue, two papers have been published addressing these limitations by employing indirect methods. Here, we present a new procedure, based on indirect methods that analyses data collected within a multicenter pilot study. Using this method, we obtain CV_I estimates and calculate confidence intervals (CI), using the EFLM-BVD CV_I estimates as gold standard for comparison.

METHODS

Data were collected over a 18-month period for 7 measurands, from 3 Spanish hospitals; inclusion criteria: patients 18-75 years with more than two determinations. For each measurand, four different strategies were carried out based on the coefficient of variation ratio (rCoeV) and based on the use of the bootstrap method (OS1, RS2 and RS3). RS2 and RS3 use symmetry reference change value (RCV) to clean database.

RESULTS

RS2 and RS3 had the best correlation for the CV_I estimates with respect to EFLM-BVD. RS2 used the symmetric RCV value without eliminating outliers, while RS3 combined RCV and outliers. When using the rCoeV and OS1 strategies, an overestimation of the CV_I value was obtained.

CONCLUSIONS

Our study presents a new strategy for obtaining robust CV_I estimates using an indirect method together with the value of symmetric RCV to select the target population. The CV_I estimates obtained show a good correlation with those published in the EFLM-BVD database. Furthermore, our strategy can resolve some of the limitations encountered when using direct methods such as calculating confidence intervals.

Short-term biological variation study of plasma hemophilia and thrombophilia parameters in a population of apparently healthy Caucasian adults

OBJECTIVES

Biological variation (BV) data obtained in a standardized way is valuable to assess the analytical requirements and the utility of a reference interval. Our study aimed to determine the short-term BV of thrombophilia (protein S, protein C, activated protein C resistance (APCR) and factor VIII) and hemophilia (factors VIII, IX and XI) parameters in plasma. Coagulation factors V and XII were also evaluated. Based on the obtained data, we assessed analytical performance specifications for the parameters. Finally, we intended to provide a robust tool for comparison of serial measurements of factors V, VIII, IX and XI.

METHODS

A blood draw was performed weekly in 19 apparently healthy Caucasian adults for five weeks at Saint-Luc University Hospital (Brussels, Belgium). Parameters were measured in duplicate. BV components were calculated with a nested analysis of variance after exclusion of outliers.

RESULTS

The analytical coefficient of variation (CV) varied from 1.5 to 4.6%, the within-subject CV from 1.6 to 8.9% and the between-subject CV from 3.8 to 24.1%. All parameters showed high individuality. For most parameters, the analytical goal was met with our assays. Reference change values (RCV) of -16.7% to +20.0%, -20.7% to +26.0%, -15.3% to +18.1% and -13.1% to +15.1% were obtained for factors V, VIII, IX and XI respectively.

CONCLUSIONS

All studied parameters were highly individualized. The assessment of BV data can guide setting analytical goal specifications. Comparison of serial measurements in the follow-up of patients suffering from hepatic failure or mild hemophilia is facilitated by evaluation of the RCV.

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 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.