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 and reference change value of the estimated glomerular filtration rate in humans: A systematic review and meta-analysis

Background

Knowledge of the biological variation of serum or plasma creatinine (Cr) and the estimated glomerular filtration rate (eGFR) is important for understanding disease dynamics in Chronic Kidney Disease (CKD). The aim of our study was to determine the magnitude of random fluctuation of eGFR by determining its reference change value (RCV).

Methods

We performed a systematic review and meta-analysis of studies on biological variation of Cr. Relevant studies were identified by systematic literature search on PubMed. Additional studies were retrieved from the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Biological Variation Database. Random-effects meta-analysis was conducted to derive an overall estimate of intra-individual variation of creatinine (CV_ICr). Based on our estimate of CV_ICr and RCV for Cr, the RCV for the eGFR was determined.

Results

Among identified studies, 37 met our inclusion criteria. Meta-analysis of all studies yielded a CV_ICr of 5.2% (95% confidence interval [CI] 4.6–5.8%), however high between-study heterogeneity (I² = 82.3%) was found. Exclusion of outliers led to a significant reduction of heterogeneity while still including 85% of all studies and resulted in a slightly lower CV_ICr of 5.0% (95% CI 4.7–5.4%). Assuming an analytical variation of CV_A 1.1%, we found an overall RCV for eGFR of ±16.5%. After exclusion of outlier studies, we found a minimum conservative RCV for eGFR of ±12.5%.

Conclusion

The RCV of the eGFR represents a valuable tool for clinicians to discern true changes in kidney function from random fluctuation.

The potential role of the eGFR in differentiating between true and pseudohyperkalaemia

BACKGROUND

Differentiating between true and pseudohyperkalaemia is essential for patient management. The common causes of pseudohyperkalaemia include haemolysis, blood cell dyscrasias and EDTA contamination. One approach to differentiate between them is by checking the renal function, as it is believed that true hyperkalaemia is rare with normal function. This is logical, but there is limited published evidence to support it. The aim of this study was to investigate the potential role of the estimated glomerular filtration rate in differentiating true from pseudohyperkalaemia.

METHODS

GP serum potassium results >6.0 mmol/L from 1 January 2017 to 31 December 2017, with a repeat within seven days, were included. Entries were retrospectively classified as true or pseudohyperkalaemia based on the potassium reference change value and reference interval. If the initial sample had a full blood count, it was classified as normal/abnormal to remove blood cell dyscrasias. Different estimated glomerular filtration rate cut-points were used to determine the potential in differentiating true from pseudohyperkalaemia.

RESULTS

A total of 272 patients were included with potassium results >6.0 mmol/L, with 145 classified as pseudohyperkalaemia. At an estimated glomerular filtration rate of 90 ml/min/1.73 m², the negative predictive value was 81% (95% CI: 67-90%); this increased to 86% (95% CI: 66-95%) by removing patients with abnormal full blood counts. When only patients with an initial potassium ≥6.5 mmol/L were included (regardless of full blood count), at an estimated glomerular filtration rate of 90 ml/min/1.73 m², the negative predictive value was 100%. Lower negative predictive values were seen with decreasing estimated glomerular filtration rate cut-points.

CONCLUSION

Normal renal function was not associated with true hyperkalaemia, making the estimated glomerular filtration rate a useful tool in predicting true from pseudohyperkalaemia, especially for potassium results ≥6.5 mmol/L.

The Biological Variation Data Critical Appraisal Checklist: A Standard for Evaluating Studies on Biological Variation

BACKGROUND

Concern has been raised about the quality of available biological variation (BV) estimates and the effect of their application in clinical practice. A European Federation of Clinical Chemistry and Laboratory Medicine Task and Finish Group has addressed this issue. The aim of this report is to (a) describe the Biological Variation Data Critical Appraisal Checklist (BIVAC), which verifies whether publications have included all essential elements that may impact the veracity of associated BV estimates, (b) use the BIVAC to critically appraise existing BV publications on enzymes, lipids, kidney, and diabetes-related measurands, and (c) apply metaanalysis to deliver a global within-subject BV (CV_I) estimate for alanine aminotransferase (ALT).

METHODS

In the BIVAC, publications were rated as A, B, C, or D, indicating descending compliance for 14 BIVAC quality items, focusing on study design, methodology, and statistical handling. A D grade indicated that associated BV estimates should not be applied in clinical practice. Systematic searches were applied to identify BV studies for 28 different measurands.

RESULTS

In total, 128 publications were identified, providing 935 different BV estimates. Nine percent achieved D scores. Outlier analysis and variance homogeneity testing were scored as C in >60% of 847 cases. Metaanalysis delivered a CV_I estimate for ALT of 15.4%.

CONCLUSIONS

Application of BIVAC to BV publications identified deficiencies in required study detail and delivery, especially for statistical analysis. Those deficiencies impact the veracity of BV estimates. BV data from BIVAC-compliant studies can be combined to deliver robust global estimates for safe clinical application.

Biological variations of some analytes in renal posttransplant patients: a different way to assess routine parameters

BACKGROUND

Biological variation (BV) data of analytes have been used to evaluate the significant changes in serial results (reference change value, RCV) of healthy individuals in clinical laboratories. However, BV data of healthy subjects may not be identical to the analytes of patients with ongoing clinical condition. The aim of this study was to calculate intra-(CVw) (coefficient of variation for intra-individual BV) and inter-individual (CVg) BV, index of individuality, and RCV of nine serum analytes of renal posttransplant patients.

METHODS

Six serum specimens were obtained in an interval of two months in a one-year period from 70 transplant patients who had been stable for three years. Each time creatinine, uric acid, urea, sodium, potassium, calcium, inorganic phosphate, total protein, and albumin of these patients were analyzed with an integrated clinical chemistry/immunoassay auto-analyzer. ANOVA tests were used to calculate the variations. Results were compared with the data of healthy subjects obtained from BV database.

RESULTS

CVw of all nine analytes of the renal transplant patients were higher than the healthy subjects. RCVs of these analytes were calculated as 14.5% for creatinine, 16.5% for urea, 13.7% for urate, 12.57% for albumin, 8.26% for total protein, 3.25% for sodium, 12.81% for potassium, 5.88% for calcium, and 21.57% for inorganic phosphate.

CONCLUSION

RCV concept for predicting the clinical status in posttransplant population represents an optimization of laboratory reporting and could be a valuable tool for clinical decision.

Biological variation at long-term renal post-transplantation

In laboratory testing the reference change value (RCV) is used to detect changes in a patient's clinical status, even before clinical signs are evident. In a previous study we determined the biological variation (BV) of a number of constituents during early post-transplantation in kidney recipients to calculate useful RCVs for this purpose. RCVs for creatinine and urate were identified as the most suitable and were different from those calculated from the normal population. The aim of the current study was to determine the BV components at long-term following renal transplantation to predict potential crises in transplant recipients who have been stable for a number of years. BV components for creatinine and urate were calculated in a new group of 40 kidney transplanted patients (26 men and 14 women, 29-71 years old) who had been stable for period of 4 to 7 years following transplantation (long-term post-TR group). An average of 8 samples per patient was obtained during a period of 1-2 years. Results were compared with those from our described group of recently transplanted patients (short-term post-TR group). There were no statistically significant differences between the groups with regard to within-subject variation or within-subject plus analytical variation (CVI+A) for any of the constituents studied. Distribution of CVI+A values in long-term post-TR was comparable to that of short-term post-TR values. Independence between creatinine and urate was maintained at long-term. The fact that BV components for creatinine and urate were similar in short- and long-term post-TR and that independence was maintained implies that the short-term post-TR RCV can also be applied in long-term post-TR patients. The RCV for predicting crises in this population represents an optimization of laboratory reporting and could be a valuable tool for clinical decision making.

Estimation and Application of Biological Variation of Urinary δ-Aminolevulinic Acid and Porphobilinogen in Healthy Individuals and in Patients with Acute Intermittent Porphyria

Background: Diagnosis of an attack of acute intermittent porphyria (AIP) is based on the demonstration of increased concentrations of porphobilinogen (PBG) and δ-aminolevulinic acid (ALA) in urine, but many AIP patients also have high baseline concentrations in remission. The aim of this study was to estimate the biological variations of ALA, PBG, and porphyrins in healthy individuals and AIP patients to improve interpretation of test results.

Methods: Fifteen healthy individuals and 15 AIP patients were included, and biological variations were calculated based on urine samples collected weekly for 10 consecutive weeks. For the AIP patients, long-term variations were also estimated based on 7 samples collected through a 2-year period.

Results: The porphyrin variances were inhomogeneously distributed; biological variations of porphyrins were therefore not calculated. The within-subject biological variations of ALA and PBG were 16%–20% in the short-term settings and for PBG, 25% in the long-term setting, giving reference change values of ∼50% and 70%, respectively. The probability of detecting a 100% real change in PBG was 97% in the short-term setting and 80% in the long-term setting.

Conclusions: In an AIP patient, a 2-fold increase in PBG, independent of the baseline concentration, will be detected with a probability >80% and is most likely related to the patient’s disease and not caused only by analytical and biological variation. When PBG is used in the assessment of AIP-related symptoms, both the PBG concentration in remission and the length of time since the previous sample must be considered.

Integration of data derived from biological variation into the quality management system

BACKGROUND

[corrected] Data on within- and between-subject biological variation are available for around 250 analytes commonly used in medical laboratories.

METHODS

Integration of this data into the quality system occurs at all three levels of laboratory activity: (a) Preanalytic process: biological variation provides the basis for selecting the most appropriate specimen for analysis, for defining sample stability and for deciding suitable timing between samplings; (b) analytic process: biological variation-derived goals are fundamental for designing internal quality control procedures, and for evaluating laboratory performance; and (c) postanalytic process: delta checks based on within-subject biological variation values are used for validating results and for interpreting serial results from a patient.

CONCLUSION

The biological variation is a pillar for managing quality in laboratory medicine.

Reference change values and power functions

Repeated samplings and measurements in the monitoring ofpatients to look for changes are common clinical problems. The “reference change value”, calculated as z

Are equally spaced specimen collections necessary to assess biological variation? Evidence from renal transplant recipients

The established method for determining the components of biological variation (BV) requires equispaced time intervals between samplings. In a previous study, we determined BV in renal post transplantation patients, taking advantage of the samples obtained within their clinical treatment protocol (not necessarily equispaced). To confirm the validity of this practice, we sought to determine if the use of varying sampling intervals has an effect on the results obtained in such biological variation studies. The study included two phases: comparison of the results found with identical and non-identical sampling intervals and correlation between the within-subject BV and the length of the sampling interval. There were no differences in within-subject BV between the groups or correlations with sampling intervals for any of the constituents studied. We conclude that samples acquired within established clinical protocols for kidney transplant recipients can be used for estimating BV.