Personalized Reference Intervals in Laboratory Medicine: A New Model Based on Within-Subject Biological Variation

Exploring Appropriate Reference Intervals and Clinical Decision Limits for Glucose-6-Phosphate Dehydrogenase Activity in Individuals From Guangzhou, China

Background

Quantitative detection of glucose-6-phosphate dehydrogenase (G6PD) is commonly done to screen for G6PD deficiency. However, current reference intervals (RIs) of G6PD are unsuitable for evaluating G6PD-activity levels with local populations or associating G6PD variants with hemolysis risk to aid clinical decision-making. We explored appropriate RIs and clinical decision limits (CDLs) for G6PD activity in individuals from Guangzhou, China.

Methods

We enrolled 5,852 unrelated individuals between 2020 and 2022 and screened their samples in quantitative assays for G6PD activity. We conducted further investigations, including G6PD genotyping, thalassemia genotyping, follow-up analysis, and statistical analysis, for different groups.

Results

In Guangzhou, the RIs for the G6PD activities were 11.20-20.04 U/g Hb in male and 12.29-23.16 U/g Hb in female. The adjusted male median and normal male median (NMM) values were 15.47 U/g Hb and 15.51 U/g Hb, respectively. A threshold of 45% of the NMM could be used as a CDL to estimate the probability of G6PD variants. Our results revealed high hemolysis-risk CDLs (male: <10% of the NMM, female: <30% of the NMM), medium hemolysis-risk CDLs (male: 10%-45% of the NMM, female: 30%-79% of the NMM), and low hemolysis-risk CDLs (male: ≥ 45% of the NMM, female: ≥ 79% of the NMM).

Conclusions

Collectively, our findings contribute to a more accurate evaluation of G6PD-activity levels within the local population and provide valuable insights for clinical decision-making. Specifically, identifying threshold values for G6PD variants and hemolysis risk enables improved prediction and management of G6PD deficiency, ultimately enhancing patient care and treatment outcomes.

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.

Reference Intervals Revisited: A Novel Model for Population-Based Reference Intervals, Using a Small Sample Size and Biological Variation Data

BACKGROUND

Conventional population-based reference intervals (popRIs) are established on the ranking of single measurement results from at least 120 reference individuals. In this study, we aimed to explore a new model for popRIs, utilizing biological variation (BV) data to define the reference interval (RI) limits and compared BV-based popRI from different sample sizes with previously published conventional popRIs from the same population.

METHODS

The model is based on defining the population set point (PSP) from single-measurement results of a group of reference individuals and using the total variation around the PSP, derived from the combination of BV and analytical variation, to define the RI limits. Using data from 143 reference individuals for 48 clinical chemistry and hematology measurands, BV-based popRIs were calculated for different sample sizes (n = 16, n = 30, and n = 120) and considered acceptable if they covered 90% of the population. In addition, simulation studies were performed to estimate the minimum number of required reference individuals.

RESULTS

The median ratio of the BV-based to conventional RI ranges was 0.98. The BV-based popRIs calculated from the different samples were similar, and most met the coverage criterion. For 25 measurands ≤16 reference individuals and for 23 measurands >16 reference individuals were required to estimate the PSP.

CONCLUSIONS

The BV-based popRI model delivered robust RIs for most of the included measurands. This new model requires a smaller group of reference individuals than the conventional popRI model and can be implemented if reliable BV data are available.

Advancing personalized medicine: Integrating statistical algorithms with omics and nano-omics for enhanced diagnostic accuracy and treatment efficacy

Medical laboratory services enable precise measurement of thousands of biomolecules and have become an inseparable part of high-quality healthcare services, exerting a profound influence on global health outcomes. The integration of omics technologies into laboratory medicine has transformed healthcare, enabling personalized treatments and interventions based on individuals' distinct genetic and metabolic profiles. Interpreting laboratory data relies on reliable reference values. Presently, population-derived references are used for individuals, risking misinterpretation due to population heterogeneity, and leading to medical errors. Thus, personalized references are crucial for precise interpretation of individual laboratory results, and the interpretation of omics data should be based on individualized reference values. We reviewed recent advancements in personalized laboratory medicine, focusing on personalized omics, and discussed strategies for implementing personalized statistical approaches in omics technologies to improve global health and concluded that personalized statistical algorithms for interpretation of omics data have great potential to enhance global health. Finally, we demonstrated that the convergence of nanotechnology and omics sciences is transforming personalized laboratory medicine by providing unparalleled diagnostic precision and innovative therapeutic strategies.

Clinical utility of personalized reference intervals for CEA in the early detection of oncologic disease

OBJECTIVES

Personalized reference intervals (prRI) have been proposed as a diagnostic tool for assessing measurands with high individuality. Here, we evaluate clinical performance of prRI using carcinoembryonic antigen (CEA) for cancer detection and compare it with that of reference change values (RCV) and other criteria recommended by clinical guidelines (e.g. 25 % of change between consecutive CEA results (RV25) and the cut-off point of 5 μg/L (CP5)).

METHODS

Clinical and analytical data from 2,638 patients collected over 19 years were retrospectively evaluated. A total 15,485 CEA results were studied. For each patient, we calculated prRI and RCV using computer algorithms based on the combination of different strategies to assess the number of CEA results needed, consideration of one or two limits of reference interval and the intraindividual biological variation estimate (CVI) used: (a) publicly available (CVI-EU), (b) CVI calculated using an indirect method (CVI-NOO) and (c) within-person BV (CVP). For each new result identified falling outside the prRI, exceeding the RCV interval, RV25 or CP5, we searched for records identifying the presence of tumour at 3 and 12 months after the test. The sensitivity, specificity and predictive power of each strategy were calculated.

RESULTS

PrRI approaches derived using CVI-EU, and both limits of reference interval achieve the best sensitivity (87.5 %) and NPV (99.3 %) at 3 and 12 months of all evaluated criteria. Only 3 results per patients are enough to calculate prRIs that reach this diagnostic performance.

CONCLUSIONS

PrRI approaches could be an effective tool to rule out new oncological findings during the active surveillance of patients.

The biological variation of insulin resistance markers: data from the European Biological Variation Study (EuBIVAS)

OBJECTIVES

An insulin resistant state is characteristic of patients with type 2 diabetes, polycystic ovary syndrome, and metabolic syndrome. Identification of insulin resistance (IR) is most readily achievable using formulae combining plasma insulin and glucose results. In this study, we have used data from the European Biological Variation Study (EuBIVAS) to examine the biological variability (BV) of IR using the Homeostasis Model Assessment for Insulin Resistance (HOMA-IR) and the Quantitative Insulin sensitivity Check Index (QUICKI).

METHODS

Ninety EuBIVAS non-diabetic subjects (52F, 38M) from five countries had fasting HOMA-IR and QUICKI calculated from plasma glucose and insulin samples collected concurrently on 10 weekly occasions. The within-subject (CVI) and between-subject (CVG) BV estimates with 95 % CIs were obtained by CV-ANOVA after analysis of trends, variance homogeneity and outlier removal.

RESULTS

The CVI of HOMA-IR was 26.7 % (95 % CI 25.5-28.3), driven largely by variability in plasma insulin and the CVI for QUICKI was 4.1 % (95 % CI 3.9-4.3), reflecting this formula's logarithmic transformation of glucose and insulin values. No differences in values or BV components were observed between subgroups of men or women below and above 50 years.

CONCLUSIONS

The EuBIVAS, by utilising a rigorous experimental protocol, has produced robust BV estimates for two of the most commonly used markers of insulin resistance in non-diabetic subjects. This has shown that HOMA-IR, in particular, is highly variable in the same individual which limits the value of single measurements.

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.

Prediction interval: A powerful statistical tool for monitoring patients and analytical systems

Monitoring is indispensable for assessing disease prognosis and evaluating the effectiveness of treatment strategies, both of which rely on serial measurements of patients' data. It also plays a critical role in maintaining the stability of analytical systems, which is achieved through serial measurements of quality control samples. Accurate monitoring can be achieved through data collection, following a strict preanalytical and analytical protocol, and the application of a suitable statistical method. In a stable process, future observations can be predicted based on historical data collected during periods when the process was deemed reliable. This can be evaluated using the statistical prediction interval. Statistically, prediction interval gives an "interval" based on historical data where future measurement results can be located with a specified probability such as 95%. Prediction interval consists of two primary components: (i) the set point and (ii) the total variation around the set point which determines the upper and lower limits of the interval. Both can be calculated using the repeated measurement results obtained from the process during its steady-state. In this paper, (i) the theoretical bases of prediction intervals were outlined, and (ii) its practical application was explained through examples, aiming to facilitate the implementation of prediction intervals in laboratory medicine routine practice, as a robust tool for monitoring patients' data and analytical systems.

Biological Variation Estimates for Plasma Copeptin and Clinical Implications

BACKGROUND

Plasma copeptin measurement is useful for the differential diagnoses of polyuria-polydipsia syndrome. It has also been proposed as a prognostic marker for cardiovascular diseases. However, limited information is available about the within- (CVI) and between-subject (CVG) biological variation (BV). This study presents BV estimates for copeptin in healthy individuals.

METHODS

Samples were collected weekly from 41 healthy subjects over 5 weeks and analyzed using the BRAHMS Copeptin proAVP KRYPTOR assay after at least 8 h of food and fluid abstinence. Outlier detection, variance homogeneity, and trend analysis were performed followed by CV-ANOVA for BV and analytical variation (CVA) estimation with 95% confidence intervals. Reference change values (RCVs), index of individuality (II), and analytical performance specification (APS) were also calculated.

RESULTS

The analysis included 178 results from 20 males and 202 values from 21 females. Copeptin concentrations were significantly higher in males than in females (mean 8.5 vs 5.2 pmol/L, P < 0.0001). CVI estimates were 18.0% (95% CI, 15.4%-21.6%) and 19.0% (95% CI, 16.4%-22.6%), for males and females, respectively; RCVs were -35% (decreasing value) and 54% (increasing value). There was marked individuality for copeptin. No result exceeded the diagnostic threshold (>21.4 pmol/L) for arginine vasopressin resistance.

CONCLUSIONS

The availability of BV data allows for refined APS and associated II, and RCVs applicable as aids in the serial monitoring of patients with specific diseases such as heart failure. The BV estimates are only applicable in subjects who abstained from oral intake due to the rapid and marked effects of fluids on copeptin physiology.

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.

The biological variation of serum 1,25-dihydroxyvitamin D and parathyroid hormone, and plasma fibroblast growth factor 23 in healthy individuals

BACKGROUND AND AIMS

Measuring 1,25-dihydroxyvitamin D (1,25(OH)2D), parathyroid hormone 1-84 (PTH 1-84) and intact FGF23 (iFGF23) is crucial for diagnosing a variety of diseases affecting bone and mineral homeostasis. Biological variability (BV) data are important for defining analytical quality specifications (APS), the usefulness of reference intervals, and the significance of variations in serial measurements in the same subject. The aim of this study was to pioneer the provision of BV estimates for 1,25(OH)2D and to improve existing BV estimates for iFGF23 and PTH 1-84.

MATERIALS AND METHODS

Serum and plasma-EDTA samples of sixteen healthy subjects have been collected for seven weeks and measured in duplicate by chemiluminescent immunoassay on the DiaSorin Liaison platform. After variance verification, within-subject (CVI) and between-subject (CVG) BV estimates were assessed by either standard ANOVA, or CV-ANOVA. The APSs were calculated according to the EFLM-BV-model.

RESULTS

We found the following CVI estimates with 95% confidence intervals:1,25(OH)2D, 22.2% (18.9-26.4); iFGF23, 16.1% (13.5-19.5); and PTH 1-84, 17.9% (14.8-21.8). The CVG were: 1,25(OH)2D, 21.2% (14.2-35.1); iFGF23, 21.1% (14.5-35.8); and PTH 1-84, 31.1% (22.1-50.8).

CONCLUSIONS

We report for the first time BV estimates for 1,25(OH)2D and enhance existing data about iFGF23-BV and PTH 1-84-BV through cutting-edge immunometric methods.

Short-term biological variation of serum tryptase

OBJECTIVES

Serum tryptase is a biomarker of mast cell activation. Among others, it is used in the diagnosis of anaphylaxis where a significant increase during the acute phase supports the diagnosis. When evaluating changes in biomarker levels, it is of utmost importance to consider the biological variation of the marker. Therefore, the aim of this study was to evaluate the short-term biological variation of serum tryptase.

METHODS

Blood samples were drawn at 9 AM three days in a row from apparently healthy subjects. On day two, additional blood samples were drawn every third hour for 12 h. The tryptase concentration was measured in serum using a fluoroenzyme immunoassay (ImmunoCAP™, Thermo Fisher Scientific). Linear mixed-effects models were used to calculate components of biological variation.

RESULTS

In 32 subjects, the overall mean concentration of tryptase was 4.0 ng/mL (range, 1.3-8.0 ng/mL). The within-subject variation was 3.7 % (95 % confidence interval (CI) 3.0-4.4 %), the between-subject variation was 31.5 % (95 % CI 23.1-39.8 %), and the analytical variation was 3.4 % (95 % CI 2.9-4.1 %). The reference change value was 13.3 % for an increase in tryptase at a 95 % level of significance. No significant day-to-day variation was observed (p=0.77), while a minute decrease in the serum concentration was observed during the day (p<0.0001).

CONCLUSIONS

Serum tryptase is a tightly regulated biomarker with very low within-subject variation, no significant day-to-day variation, and only minor semidiurnal variation. In contrast, a considerable between-subject variation exists. This establishes serum tryptase as a well-suited biomarker for monitoring.

Biological variation of serum neopterin concentrations in apparently healthy individuals

OBJECTIVES

The aims of this study were to determine the biological variation (BV), reference change value (RCV), index of individuality (II), and quality specifications for serum neopterin concentrations; a measurand provided by clinical laboratories as an indicator of cellular immunity.

METHODS

The study delivered serum samples collected for 10 consecutive weeks from 12 apparently healthy individuals (3 male, 9 female). Serum neopterin concentrations were measured using high-performance liquid chromatography with fluorometric detection. The data analysis was performed using an online statistical tool and addressed published criteria for estimation of biological variation.

RESULTS

The mean neopterin concentration was 5.26 nmol/L. The within-subject biological variation (CVI) with 95 % confidence interval (CI) of neopterin serum concentrations was 11.54 % (9.98-13.59), and the between-subject biological variation (CVG) with 95 % CI was 43.27 % (30.52-73.67). The neopterin asymmetrical RCV was -24.9 %/+33.1 %, and the II was 0.27. The desirable quality specifications for neopterin were <5.77 % for precision, <11.20 % for bias, and <20.72 % for total allowable error (TEa). When analytical variation was used instead of CVI to calculate TEa, the desirable TEa was <18.39.

CONCLUSIONS

This study determined BV data for neopterin, an indicator of cell-mediated immune response. Asymmetric RCV values, of 24.9 % decrease or a 33.1 % increase between consecutive measurements indicate significant change. The II of 0.27 indicates a high degree of individuality, therefore that it is appropriate to consider the use of personal reference data and significance of change rather than the reference interval as points of reference for the evaluation of neopterin serum concentrations.

Personalized laboratory medicine in the digital health era: recent developments and future challenges

Interpretation of laboratory data is a comparative procedure and requires reliable reference data, which are mostly derived from population data but used for individuals in conventional laboratory medicine. Using population data as a "reference" for individuals has generated several problems related to diagnosing, monitoring, and treating single individuals. This issue can be resolved by using data from individuals' repeated samples, as their personal reference, thus needing that laboratory data be personalized. The modern laboratory information system (LIS) can store the results of repeated measurements from millions of individuals. These data can then be analyzed to generate a variety of personalized reference data sets for numerous comparisons. In this manuscript, we redefine the term "personalized laboratory medicine" as the practices based on individual-specific samples and data. These reflect their unique biological characteristics, encompassing omics data, clinical chemistry, endocrinology, hematology, coagulation, and within-person biological variation of all laboratory data. It also includes information about individuals' health behavior, chronotypes, and all statistical algorithms used to make precise decisions. This approach facilitates more accurate diagnosis, monitoring, and treatment of diseases for each individual. Furthermore, we explore recent advancements and future challenges of personalized laboratory medicine in the context of the digital health era.

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.

Sex-related differences in within-subject biological variation estimates for 22 essential and non-essential amino acids

BACKGROUND

Measurement of serum amino acid (AA) concentrations is important in particular for the diagnosis and monitoring of inborn errors of AA metabolism. To ensure optimal clinical interpretation of AAs, reliable biological variation (BV) data are essential. In the present study, we derived BV data for 22 non-essential, conditionally essential, and essential AAs and assessed differences in BV of AAs related to sex.

METHODS

Morning blood samples were drawn from 66 subjects (31 males and 35 females) once a week for 10 consecutive weeks. All samples were analyzed in duplicate using liquid chromatography-tandem mass-spectrometry. The data were assessed for outliers, trends, normality and variance homogeneity analysis prior to estimating within-subject (CVI) and between-subject (CVG) BV.

RESULTS

CVI estimates ranged from 9.0 % for histidine (male) to 33.0 % for taurine (male). CVI estimates in males and females were significantly different for all AAs except for aspartic acid, citrulline and phenylalanine, in most cases higher in females than in males. Apart from for arginine, CVG estimates in males and females were similar.

CONCLUSIONS

In this highly powered BV study, we provide updated BV estimates for 22 AAs and demonstrate that for most AAs, CVI estimates differ between males and females, with implications for interpretation and use of AAs in clinical practice.

Biological variation estimates for serum neurofilament light chain in healthy subjects

OBJECTIVES

Neurofilament light chain (NfL) is an emerging biomarker of neurodegeneration disorders. Knowledge of the biological variation (BV) can facilitate proper interpretation between serial measurements. Here BV estimates for serum NfL (sNfL) are provided.

METHODS

Serum samples were collected weekly from 24 apparently healthy subjects for 10 consecutive weeks and analyzed in duplicate using the Siemens Healthineers sNfL assay on the Atellica® IM Analyzer. Outlier detection, variance homogeneity analyses, and trend analysis were performed followed by CV-ANOVA to determine BV and analytical variation (CVA) estimates with 95%CI and the associated reference change values (RCV) and analytical performance specifications (APS).

RESULTS

Despite observed differences in sNfL concentrations between males and females, BV estimates remained consistent across genders. Both within-subject BV (CVI) for males (10.7%, 95%CI; 9.2-12.6) and females (9.1%, 95%CI; 7.8-10.9) and between-subject BV (CVG) for males (26.1%, 95%CI; 18.0-45.6) and females (30.2%, 95%CI; 20.9-53.5) were comparable. An index of individuality value of 0.33 highlights significant individuality, indicating the potential efficacy of personalized reference intervals in patient monitoring.

CONCLUSIONS

The established BV estimates for sNfL underscore its potential as a valuable biomarker for monitoring neurodegenerative diseases, offering a foundation for improved decision-making in clinical settings.

Biological variation of plasma 25-Hydroxyvitamin D3, Serum vitamin B12, folate and ferritin in Turkish healthy subject

Biological variation (BV) plays a crucial role in determining analytical performance specifications, assessing serial measurements of individuals, and establishing the use of population-based reference intervals. Our study aimed to calculate the BV and BV-based quality goals of 25-hydroxyvitamin D3 (25-OH D3), ferritin, folate and vitamin B12 tests. We included a total of 22 apparently healthy volunteers (9 women and 13 men) aged 18-55 years in the study that we conducted in Turkey. Blood samples were collected from the participants once a week for five weeks. Serum ferritin, folate and vitamin B12 levels were measured using immunochemical method, while plasma 25-OH D3 levels were determined using the high-performance liquid chromatography method. Analysis of variance (ANOVA) was used to estimate analytical variation(CVA), within-subject BV(CVI) and between-subject BV(CVG). The individuality index (II) and reference change value (RCV) were calculated based on these data. The CVI of 25-OH D3, ferritin, folate, and vitamin B12 were found to be 1.8% (0.6%-2.5%), 16.9% (14.4%-20.2%), 10.7% (9.2%-12.7%), and 8.6% (6.8%-10.5%), respectively. CVG were 44.2% (34.3%-69.9%), 132% (87.7%-238%), 19.4% (14.4%-28.8%), and 39.6% (29.8%-59.0%) for the same biomarkers, while CVA were 3.2% (2.81%-3.71%), 3.5% (3.1%-4.1%), 4.0% (3.5%-4.6%), and 7.5% (6.6%-8.6%), respectively. The II values for 25-OH D3, ferritin, folate, and vitamin B12 were calculated as 0.04, 0.13, 0.55, and 0.22, respectively. The RCV were 10.2%, 47.8%, 31.7%, and 31.6%, respectively. Because the tests analyzed in this study exhibit high individuality, RCV should be preferred rather than population-based reference ranges in clinical interpretation of results.

Reference intervals: past, present, and future

Clinical laboratory test results alone are of little value in diagnosing, treating, and monitoring health conditions; as such, a clinically actionable cutoff or reference interval is required to provide context for result interpretation. Healthcare practitioners base their diagnoses, follow-up treatments, and subsequent testing on these reference points. However, they may not be aware of inherent limitations related to the definition and derivation of reference intervals. Laboratorians are responsible for providing the reference intervals they report with results. Yet, the establishment and verification of reference intervals using conventional direct methods are complicated by resource constraints or unique patient demographics. To facilitate standardized reference interval best practices, multiple global scientific societies are actively drafting guidelines and seeking funding to promote these initiatives. Numerous national and international multicenter collaborations demonstrate the ability to leverage combined resources to conduct large reference interval studies by direct methods. However, not all demographics are equally accessible. Novel indirect methods are attractive solutions that utilize computational methods to define reference distributions and reference intervals from mixed data sets of pathologic and non-pathologic patient test results. In an effort to make reference intervals more accurate and personalized, individual-based reference intervals are shown to be more useful than population-based reference intervals in detecting clinically significant analyte changes in a patient that might otherwise go unrecognized when using wider, population-based reference intervals. Additionally, continuous reference intervals can provide more accurate ranges as compared to age-based partitions for individuals that are near the ends of the age partition. The advantages and disadvantages of different reference interval approaches as well as the advancement of non-conventional reference interval studies are discussed in this review.

Current status and challenges in establishing reference intervals based on real-world data

Reference intervals (RIs) are the cornerstone for evaluation of test results in clinical practice and are invaluable in judging patient health and making clinical decisions. Establishing RIs based on clinical laboratory data is a branch of real-world data mining research. Compared to the traditional direct method, this indirect approach is highly practical, widely applicable, and low-cost. Improving the accuracy of RIs requires not only the collection of sufficient data and the use of correct statistical methods, but also proper stratification of heterogeneous subpopulations. This includes the establishment of age-specific RIs and taking into account other characteristics of reference individuals. Although there are many studies on establishing RIs by indirect methods, it is still very difficult for laboratories to select appropriate statistical methods due to the lack of formal guidelines. This review describes the application of real-world data and an approach for establishing indirect reference intervals (iRIs). We summarize the processes for establishing iRIs using real-world data and analyze the principle and applicable scope of the indirect method model in detail. Moreover, we compare different methods for constructing growth curves to establish age-specific RIs, in hopes of providing laboratories with a reference for establishing specific iRIs and giving new insight into clinical laboratory RI research. (201 words).

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.

Biological variation estimates for spot urine analytes and analyte/creatinine ratios in 33 healthy subjects

OBJECTIVES

Urine samples are frequently used in the clinical practice. In our study, we aimed to calculate the biological variations (BV) of analytes and analyte/creatinine ratios measured in spot urine.

METHODS

Second-morning spot urine samples were collected from 33 (16 female, 17 male) healthy volunteers once weekly for 10 weeks and analyzed in the Roche Cobas 6,000 instrument. Statistical analyzes were performed using BioVar, an online BV calculation software. The data were evaluated in terms of normality, outliers, steady state, homogeneity of the data, and BV values were obtained by analysis of variance (ANOVA). A strict protocol was established for within-subject (CVI) and between-subject (CVG) estimates for both genders.

RESULTS

There was a significant difference between female/male CVI estimates of all analytes except potassium, calcium and magnesium. No difference was found in CVG estimates. When the analytes that had a significant difference in CVI estimates in spot urine analytes were compared to creatinine, it was observed that the significant difference between the genders disappeared. There was no significant difference between female/male CVI and CVG estimates in all spot urine analyte/creatinine ratios.

CONCLUSIONS

Since the CVI estimates of analyte/creatinine ratios are lower, it would be more reasonable to use them in result reporting. Reference ranges should be used with caution, since II values of almost all parameters are between 0.6 and 1.4. The CVI detection power of our study is 1, which is the highest value.

Physiological Rhythms and Biological Variation of Biomolecules: The Road to Personalized Laboratory Medicine

The concentration of biomolecules in living systems shows numerous systematic and random variations. Systematic variations can be classified based on the frequency of variations as ultradian (<24 h), circadian (approximately 24 h), and infradian (>24 h), which are partly predictable. Random biological variations are known as between-subject biological variations that are the variations among the set points of an analyte from different individuals and within-subject biological variation, which is the variation of the analyte around individuals’ set points. The random biological variation cannot be predicted but can be estimated using appropriate measurement and statistical procedures. Physiological rhythms and random biological variation of the analytes could be considered the essential elements of predictive, preventive, and particularly personalized laboratory medicine. This systematic review aims to summarize research that have been done about the types of physiological rhythms, biological variations, and their effects on laboratory tests. We have searched the PubMed and Web of Science databases for biological variation and physiological rhythm articles in English without time restrictions with the terms “Biological variation, Within-subject biological variation, Between-subject biological variation, Physiological rhythms, Ultradian rhythms, Circadian rhythm, Infradian rhythms”. It was concluded that, for effective management of predicting, preventing, and personalizing medicine, which is based on the safe and valid interpretation of patients’ laboratory test results, both physiological rhythms and biological variation of the measurands should be considered simultaneously.

Long-Term Within- and Between-Subject Biological Variation Data of Hematological Parameters in Recreational Endurance Athletes

BACKGROUND

Hematological parameters have many applications in athletes, from monitoring health to uncovering blood doping. This study aimed to deliver biological variation (BV) estimates for 9 hematological parameters by a Biological Variation Data Critical Appraisal Checklist (BIVAC) design in a population of recreational endurance athletes and to assess the effect of self-reported exercise and health-related variables on BV.

METHODS

Samples were drawn from 30 triathletes monthly for 11 months and measured in duplicate for hematological measurands on an Advia 2120 analyzer (Siemens Healthineers). After outlier and homogeneity analysis, within-subject (CVI) and between-subject (CVG) BV estimates were delivered (CV-ANOVA and log-ANOVA, respectively) and a linear mixed model was applied to analyze the effect of exercise and other related variables on the BV estimates.

RESULTS

CVI estimates ranged from 1.3% (95%CI, 1.2-1.4) for mean corpuscular volume to 23.8% (95%CI, 21.6-26.3) for reticulocytes. Sex differences were observed for platelets and OFF-score. The CVI estimates were higher than those reported for the general population based on meta-analysis of eligible studies in the European Biological Variation Database, but 95%CI overlapped, except for reticulocytes, 23.9% (95%CI, 21.6-26.5) and 9.7% (95%CI, 6.4-11.0), respectively. Factors related to exercise and athletes' state of health did not appear to influence the BV estimates.

CONCLUSIONS

This is the first BIVAC-compliant study delivering BV estimates that can be applied to athlete populations performing high-level aerobic exercise. CVI estimates of most parameters were similar to the general population and were not influenced by exercise or athletes' state of health.

LMS-based continuous reference percentiles for 14 laboratory parameters in the CALIPER cohort of healthy children and adolescents

OBJECTIVES

Marked physiological changes in growth and development present challenges in defining pediatric reference intervals for biomarkers of health and disease. Lambda, Mu, and Sigma (LMS)-based statistical modeling provides a continuous normal distribution by negating skewness and variation, and is commonly used to establish growth charts. Such LMS reference curves are suggested to enhance laboratory test result interpretation. The current study establishes LMS-based continuous reference percentiles for 14 biomarkers in the CALIPER cohort of healthy children and adolescents.

METHODS

Data from healthy children and adolescents aged 1-<19 years were used to establish continuous reference percentiles using a novel LMS-based statistical method, including 2.5th, 25th, 50th, 75th, and 97.5th percentiles. The LMS approach applies a Box-Cox data transformation and summarizes continuous distributions by age via three curves: skewness (Lambda), median (Mu), and coefficient of variation (Sigma).

RESULTS

LMS-based percentiles and z-scores were generated for 14 common pediatric biomarkers that demonstrate dynamic concentration patterns with age (e.g., alkaline phosphatase) and/or wherein the magnitude of difference from the population mean may be clinically relevant (e.g., triglycerides). The LMS model captured age- and sex-specific distributions accurately and was not substantially influenced by outlying points.

CONCLUSIONS

This is the first study to establish LMS-based continuous reference percentiles for biochemical markers in a healthy Canadian pediatric population. The current LMS-based approach builds upon previous continuous reference interval models by providing graded percentiles to improve test result interpretation, particularly with repeated measures over time. This method may assist in facilitating a patient-centered approach to laboratory medicine.

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 BV data for setting analytical performance specifications, to calculate reference change values, to define the index of individuality 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.

Personalized reference intervals: from theory to practice

Using laboratory test results for diagnosis and monitoring requires a reliable reference to which the results can be compared. Currently, most reference data is derived from the population, and patients in this context are considered members of a population group rather than individuals. However, such reference data has limitations when used as the reference for an individual. A patient's test results preferably should be compared with their own, individualized reference intervals (RI), i.e. a personalized RI (prRI).The prRI is based on the homeostatic model and can be calculated using an individual's previous test results obtained in a steady-state situation and estimates of analytical (CV_A) and biological variation (BV). BV used to calculate the prRI can be obtained from the population (within-subject biological variation, CV_I) or an individual's own data (within-person biological variation, CV_P). Statistically, the prediction interval provides a useful tool to calculate the interval (i.e. prRI) for future observation based on previous measurements. With the development of information technology, the data of millions of patients is stored and processed in medical laboratories, allowing the implementation of personalized laboratory medicine. PrRI for each individual should be made available as part of the laboratory information system and should be continually updated as new test results become available.In this review, we summarize the limitations of population-based RI for the diagnosis and monitoring of disease, provide an outline of the prRI concept and different approaches to its determination, including statistical considerations for deriving prRI, and discuss aspects which must be further investigated prior to implementation of prRI in clinical practice.

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.

European Kidney Function Consortium Equation vs. Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) Refit Equations for Estimating Glomerular Filtration Rate: Comparison with CKD-EPI Equations in the Korean Population

The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation is the most commonly used equation for estimated glomerular filtration rate (eGFR). Recently, the European Kidney Function Consortium (EKFC) announced a full-age spectrum equation, and the CKD-EPI announced the CKD-EPI refit equations (CKD-EPI-R). We compared CKD-EPI, EKFC, and CKD-EPI-R equations in a large-scale Korean population and investigated their potential implications for CKD prevalence. In a total of 106,021 individuals who received annual check-ups from 2018 to 2020, we compared the eGFR equations according to the Clinical and Laboratory Standards Institute guidelines. Weighted kappa (κ) agreement was used to compare the potential implications for CKD prevalence across the equations. The median value of eGFR tended to increase in the order of EKFC, CKD-EPI, and CKD-EPI-R equations (92.4 mL/min/1.73 m², 96.0 mL/min/1.73 m², and 100.0 mL/min/1.73 m², respectively). The EKFC and CKD-EPI-R equations showed a very high correlation of eGFR and good agreement for CKD prevalence with CKD-EPI equation (r = 0.98 and 1.00; κ = 0.80 and 0.82, respectively). Compared with the CKD-EPI equation, the EFKC equation overestimated CKD prevalence (3.5%), and the CKD-EPI-R equation underestimated it (1.5%). This is the first study comparing CKD-EPI, EKFC, and CKD-EPI-R equations simultaneously. The EKFC and CKD-EPI-R equations were statistically interchangeable with CKD-EPI equations in this large-scale Korean population. The transition of eGFR equations, however, would lead to sizable changes in the CKD prevalence. To improve kidney health, in-depth discussion considering various clinical aspects is imperative for the transition of eGFR equations.

Individual Reference Intervals for Personalised Interpretation of Clinical and Metabolomics Measurements

The Population Reference Interval (PRI) refers to the range of outcomes that are expected in a healthy population for a clinical or a diagnostic measurement. It is widely used in daily clinical practice and is essential for assisting clinical decision-making in diagnostics and treatment. In this manuscript, we start from the observation that each healthy individual has its own range for a given variable, depending on personal biological traits. This Individual Reference Interval (IRI) can be calculated and be utilised in clinical practice, in combination with the PRI for improved decision making. Nonparametric estimation of IRIs would require quite long time series. To circumvent this problem, we propose methods based on quantile models in combination with penalised parameter estimation methods that allow for information-sharing among the subjects. Our approach considers the calculation of an IRI as a prediction problem rather than an estimation problem. We perform a simulation study designed to benchmark the methods under different assumptions. From the simulation study we conclude that the new methods are robust and provide empirical coverages close to the nominal level. Finally, we evaluate the methods on real-life data consisting of eleven clinical tests and metabolomics measurements from the VITO IAM Frontier study.

Critical review and meta-analysis of biological variation estimates for tumor markers

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.

Personalized reference intervals - statistical approaches and considerations

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.

Long-term within- and between-subject biological variation of 29 routine laboratory measurands in athletes

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.

Biological variation estimates of thyroid related measurands - meta-analysis of BIVAC compliant studies

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.

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

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.

Biological variation of venous acid-base status measurands in athletes

BACKGROUND

Analysis of acid-base status (ABS) is requested in a wide range of clinical scenarios, including in the assessment of athletes' performance and follow up, but there is a lack of high-quality biological variation (BV) data. The aims of this study were to estimate the BV of ABS related parameters in athletes and to evaluate if variables related to exercise may influence the estimates.

MATERIAL AND METHODS

Eleven samples from 30 triathletes were drawn, on a monthly basis. The samples were measured for pH, pCO₂, bicarbonate, base excess, TCO₂, Ca²⁺ and lactate. A CV-ANOVA was performed to calculate within-subject (CV_I) estimates and a linear mixed model was applied to analyze the effect of the folowing variables on the BV; health status, sampling interval, intensity and duration of the exercise.

RESULTS

For all ABS parameters except for lactate, higher CV_I estimates were found in athletes than what have been reported for the general population. No significant effect of the exercise and sampling related variables were observed, except for Ca²⁺.

CONCLUSION

This difference founds in ABS parameters between athletes and the general population could be explained by the physiological stress during exercise. Laboratories attending this population could use these BV estimates to establish quality goals.

Within- and between-subject biological variation data for serum zinc, copper and selenium obtained from 68 apparently healthy Turkish subjects

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.

Biological variation data for kidney function related parameter: serum beta trace protein, creatinine and cystatin C from 22 apparently healthy Turkish subjects

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.

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

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_Ps 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.

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.

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

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.

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

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.