Biological variation of morning serum cortisol: Updated estimates from the European biological variation study (EuBIVAS) and meta-analysis

The impact of physiological variations on personalized reference intervals and decision limits: an in-depth analysis

The interpretation of laboratory data is a comparative procedure. Physicians typically need reference values to compare patients' laboratory data for clinical decisions. Therefore, establishing reliable reference data is essential for accurate diagnosis and patient monitoring. Human metabolism is a dynamic process. Various types of systematic and random fluctuations in the concentration/activity of biomolecules are observed in response to internal and external factors. In the human body, several biomolecules are under the influence of physiological rhythms and are therefore subject to ultradian, circadian and infradian fluctuations. In addition, most biomolecules are also characterized by random biological variations, which are referred to as biological fluctuations between subjects and within subjects/individuals. In routine practice, reference intervals based on population data are used, which by nature are not designed to capture physiological rhythms and random biological variations. To ensure safe and appropriate interpretation of patient laboratory data, reference intervals should be personalized and estimated using individual data in accordance with systematic and random variations. In this opinion paper, we outline (i) the main variations that contribute to the generation of personalized reference intervals (prRIs), (ii) the theoretical background of prRIs and (iii) propose new methods on how to harmonize prRIs with the systematic and random variations observed in metabolic activity, based on individuals' demography.

Biological variation in the estimated glomerular filtration rate of healthy individuals within 24 h calculated using 2021CKD-EPI equations

BACKGROUND AND AIMS

Use the MDRD (Modification of Diet in Renal Disease) and 2021 CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equation void of race coefficients (CKD-EPICrea, CKD-EPICys-C, and CKD-EPICrea+Cys-C) to estimate the BV (Biological variation) of eGFR (estimated glomerular filtration rate) within 24 h in a healthy population to help explain future studies using eGFR in the context of a known BV.

METHODS

Blood samples were collected from 30 healthy subjects at six time points within 24 h. Serum creatinine (S-Crea) and serum cystatin C (S-Cys-C) were measured, and the BV of eGFR was calculated. Outlier and variance homogeneity analyses were performed, followed by CV-ANOVA on trend-corrected data.

RESULTS

The eGFR CVI for the four equations (MDRD, CKD-EPICrea, CKD-EPICys-C, and CKD-EPICrea+Cys-C) were 8.39% (7.50-9.51%), 3.90% (3.49-4.42%), 6.58% (5.88-7.46%), and 5.03% (4.50-5.71%), respectively. The corresponding II and RCVpos/neg values were 0.69, 0.48, 0.51, and 0.31, and (29.30%, - 22.66%), (12.69%, - 11.2 6%), (20.97%, - 17.33%), and (15.88%, - 13.70%), respectively; RCVpos /neg of eGFR was highest in the MDRD equation and lowest in the CKD-EPI Crea equation. Additionally, the RCVpos/neg values of the individual was highest in the MDRD equation and lowest in the CKD-EPICrea+Cys-C equation; they are (56.51%, - 36.11%) and (5.01%, - 4.77%), respectively.

CONCLUSIONS

We present data on the 24 h BV eGFR of the 2021 CKD-EPI equations. The presence of BV has impact on the interpretation of GFR results, affecting CKD disease grading. The RCVpos/neg differences were large among the individuals. When using eGFRs based on the MDRD and CKD-EPI equations, it is necessary to combine RCVpos/neg values before interpreting the results.

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.

Comparison of four indirect (data mining) approaches to derive within-subject biological variation

OBJECTIVES

Within-subject biological variation (CV _i ) is a fundamental aspect of laboratory medicine, from interpretation of serial results, partitioning of reference intervals and setting analytical performance specifications. Four indirect (data mining) approaches in determination of CV _i were directly compared.

METHODS

Paired serial laboratory results for 5,000 patients was simulated using four parameters, d the percentage difference in the means between the pathological and non-pathological populations, CV _i the within-subject coefficient of variation for non-pathological values, f the fraction of pathological values, and e the relative increase in CV _i of the pathological distribution. These parameters resulted in a total of 128 permutations. Performance of the Expected Mean Squares method (EMS), the median method, a result ratio method with Tukey's outlier exclusion method and a modified result ratio method with Tukey's outlier exclusion were compared.

RESULTS

Within the 128 permutations examined in this study, the EMS method performed the best with 101/128 permutations falling within ±0.20 fractional error of the 'true' simulated CV _i , followed by the result ratio method with Tukey's exclusion method for 78/128 permutations. The median method grossly under-estimated the CV _i . The modified result ratio with Tukey's rule performed best overall with 114/128 permutations within allowable error.

CONCLUSIONS

This simulation study demonstrates that with careful selection of the statistical approach the influence of outliers from pathological populations can be minimised, and it is possible to recover CV _i values close to the 'true' underlying non-pathological population. This finding provides further evidence for use of routine laboratory databases in derivation of biological variation components.

Problems with estimating reference change values (critical differences)

The concept of reference change values (RCVs) for diagnosis and monitoring of diseases has become well established. Several models habe been developed, e. g. one assuming a normal distribution and another one for a log-normal distribution. RCV values calculated for some measurands with both models are compared with each other and led to similar results. A few examples led to RCV values which are not plausible for diagnostic purposes. Although statistical concepts of RCV values are well established, their clinical relevance remains questionable at least for some measurands. Studies with clinicians are required whether RCVs are of practical usefulness.

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.

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.