Systematic review of the biological variation data for diabetes related analytes

Within-subject biological variation estimated using real-world data strategies (RWD): a systematic review

Biological variation (BV) is defined as the variation in the concentration of a measurand around the homeostatic set point. This is a concept introduced by Fraser and Harris in the second part of the twentieth century. BV is divided into two different estimates: within-subject BV (CVI) and between-subject BV (CVG). Biological variation studies of biomarkers have been gaining importance in recent years due to the potential practical application of these estimates. The main applications of BV in the clinical laboratory include: the establishment of Analytical Performance Specifications (APS), estimation of the individual's homeostatic set point (HSP), calculation of Reference Change Value (RCV), estimation of individuality index calculation (II), and establishment of personalized reference intervals (prRI). The classic models for obtaining BV estimates have been the most used to date. In these studies, a target population ("normal" population), a sampling frequency and time, and a number of samples per individual, among other factors, are defined. The Biological Variation Data Critical Appraisal Checklist (BIVAC) established by the Task Group-Biological Variation Database (TG-BVD) of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) represents a guide for the evaluation and performance of these direct studies. These methods have limitations because they are laborious, expensive, invasive, and are based on an ideal population. In recent years, models have been proposed to obtain BV estimates based on the Real-World Data (RWD) strategy. In this case, we move from a model with a low number of individuals (direct methods) to a population model using the data stored in the Laboratory Information System (LIS). RWD methods are presented as an alternative to overcome the limitations of direct methods. Currently, there is little scientific evidence on the application of RWD models since only five papers have been published. In these papers, three different working algorithms are proposed (Loh et al., Jones et al., and Marques-Garcia et al.). These algorithms are divided into three fundamental stages for their development: patient data and study design, database(s) cleaning, and statistical strategies for obtaining BV estimates. When working with large amounts of data, RWD methods allow us to subdivide the population and thus obtain estimates into subgroups, what would be more difficult using direct methods. Of the three algorithms proposed, the algorithm developed in the Spanish multicenter project BiVaBiDa is the most complete, as it overcomes the limitations of the other two, including the possibility of calculating the confidence interval of the BV estimate. RWD methods also have limitations such as the anonymization of data and the standardization of electronic medical records, as well as the statistical complexity associated with data analysis. It is necessary to continue working on the development of RWD algorithms that allow us to obtain BV estimates that, which are as robust as possible.

Biological Variation of Erythrocyte Total, Metal-Free, and Zinc Protoporphyrin IX in Patients with Erythropoietic Protoporphyria and Healthy Subjects: Implications for Clinical Interpretation and Monitoring

BACKGROUND

There is a lack of robust biological variation (BV) data for porphyria-related biomarkers. Our study aimed to estimate BV of erythrocyte total, metal-free, and zinc protoporphyrin IX in patients with erythropoietic protoporphyria (EPP) and healthy subjects and to explore the clinical implications of these data.

METHODS

Fourteen patients with EPP and 15 healthy subjects were sampled quarterly for 2 years, and erythrocyte protoporphyrin analyses were performed in duplicate in all samples. A Bayesian method was used to estimate the within-subject (CVI) and personal (CVP(i)) BV.

RESULTS

Based on clinical and laboratory assessments, EPP patients were stable during the study, with only 2 data points excluded. CVI in the EPP cohort was estimated as 9.8% (95% credible interval 8.5%-11.5%) for erythrocyte total protoporphyrin, 10.5% (9.0%-12.3%) for metal-free protoporphyrin, and 5.9% (4.3%-8.0%) for zinc protoporphyrin. Baseline metal-free protoporphyrin ranged from 6.9 to 139.8 µmol/L, but the CVP(i)s derived for each patient were similar (20th and 80th percentile of predicted distribution 9.5%-11.5%), and data were homogeneously distributed. Metal-free protoporphyrin was not measurable in the healthy cohort. Data for zinc protoporphyrin were heterogeneously distributed in both study cohorts.

CONCLUSIONS

The EPP patients had different set points for metal-free protoporphyrin, but the CVP(i) was similar, supporting the use of the same treatment goals when monitoring. This study is the first to use Bayesian analysis to demonstrate that personal BV is similar in patients with stable, chronic disease and different set points.

From population-based to personalized laboratory medicine: continuous monitoring of individual laboratory data with wearable biosensors

Monitoring individuals' laboratory data is essential for assessing their health status, evaluating the effectiveness of treatments, predicting disease prognosis and detecting subclinical conditions. Currently, monitoring is performed intermittently, measuring serum, plasma, whole blood, urine and occasionally other body fluids at predefined time intervals. The ideal monitoring approach entails continuous measurement of concentration and activity of biomolecules in all body fluids, including solid tissues. This can be achieved through the use of biosensors strategically placed at various locations on the human body where measurements are required for monitoring. High-tech wearable biosensors provide an ideal, noninvasive, and esthetically pleasing solution for monitoring individuals' laboratory data. However, despite significant advances in wearable biosensor technology, the measurement capacities and the number of different analytes that are continuously monitored in patients are not yet at the desired level. In this review, we conducted a literature search and examined: (i) an overview of the background of monitoring for personalized laboratory medicine, (ii) the body fluids and analytes used for monitoring individuals, (iii) the different types of biosensors and methods used for measuring the concentration and activity of biomolecules, and (iv) the statistical algorithms used for personalized data analysis and interpretation in monitoring and evaluation.

Are Your Laboratory Data Reproducible? The Critical Role of Imprecision from Replicate Measurements to Clinical Decision-making

Measurement results of biological samples are not perfect and vary because of numerous factors related to the biological samples themselves and the measurement procedures used to analyze them. The imprecision in patients' laboratory data arising from the measurement procedure, known as analytical variation, depends on the conditions under which the data are collected. Additionally, the sample type and sampling time significantly affect patients' laboratory results, particularly in serial measurements using samples collected at different time points. For accurate interpretation of patients' laboratory data, imprecision-both its analytical and biological components-should be properly evaluated and incorporated into data management. With advancements in measurement technologies, analytical imprecision can be minimized to an insignificant level compared to biological imprecision, which is inherent to all biomolecules because of the dynamic nature of metabolism. This review addresses: (i) the theoretical background of variation, (ii) the statistical and metrological evaluation of measurement variation, (iii) the assessment of variation under different conditions in medical laboratories, (iv) the impact of measurement variation on clinical decisions, (v) the influence of biases on measurement variation, and (vi) the variability of analytes in human metabolism. Collectively, both analytical and biological imprecision are inseparable aspects of all measurements in biological samples, with biological imprecision serving as the foundation of personalized laboratory medicine.

Systematic review and meta-analysis of biological variation data of urine albumin, albumin to creatinine ratio and other markers in urine

INTRODUCTION

Significant variations in Biological Variation (BV) estimates have been reported for urine markers. This study aimed to systematically review and critically appraise BV studies for albumin, creatinine, albumin-to-creatinine ratio (ACR), and other urine markers to perform a meta-analysis of eligible studies.

METHODS

Publications were identified through a systematic search and evaluated using the Biological Variation Data Critical Appraisal Checklist (BIVAC). BIVAC-compliant studies (grades A-C; A being fully compliant) conducted in healthy individuals were included in the meta-analysis, providing within-subject (CVI) and between-subject (CVG) BV estimates with 95% confidence intervals for various sample collection types.

RESULTS

Out of 37 studies evaluated, 16 were included (one grade A, three B, twelve C). No eligible publications were identified for meta-analysis of albumin and ACR. Limited data were available for first-morning urine specimens. A CVI between 15% and 30% was found for most measurands in 24-hour urine samples, while CVI estimates for random urine appeared higher.

CONCLUSION

Published BV studies on urine markers utilized different sample collections and reporting units. Most were considered unfit for use or ineligible for meta-analysis. Given the critical role of urine albumin and ACR in chronic kidney disease risk assessment, there is a need for more BIVAC-compliant studies.

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.

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.

Diagnosis Based on Population Data versus Personalized Data: The Evolving Paradigm in Laboratory Medicine

The diagnosis of diseases is a complex process involving the integration of multiple parameters obtained from various sources, including laboratory findings. The interpretation of laboratory data is inherently comparative, necessitating reliable references for accurate assessment. Different types of references, such as reference intervals, decision limits, action limits, and reference change values, are essential tools in the interpretation of laboratory data. Although these references are used to interpret individual laboratory data, they are typically derived from population data, which raises concerns about their reliability and consequently the accuracy of interpretation of individuals' laboratory data. The accuracy of diagnosis is critical to all subsequent steps in medical practice, making the estimate of reliable references a priority. For more precise interpretation, references should ideally be derived from an individual's own data rather than from population averages. This manuscript summarizes the current sources of references used in laboratory data interpretation, examines the references themselves, and discusses the transition from population-based laboratory medicine to personalized laboratory medicine.

Analytical performance of publicly dispensed glucometers in primary health care in a southern Brazilian city

Aims

This study aimed to assess the use of glucometers by patients and the analytical performance of glucometers provided by the primary care services.

Methods

The analytical performance of 48 glucometers Accu-Chek® Active, was assessed through quintuplicate analyses of one Roche and one PNCQ (National Quality Control Program) control sample at different concentrations; 31 were also evaluated by a single proficiency testing sample. The evaluation metrics included imprecision, bias, and total error and were measured according to quality specifications based on biological variation (QSBV). Glucometer users answered a questionnaire regarding their experience.

Results

Among the 48 glucometers evaluated with internal control samples, 17 met precision criteria at both control levels according to QSBV, while 24 met the criteria at only one control level. Of the 31 glucometers further evaluated through proficiency test, 11 met accuracy criteria according to QSBV, and only one device showed an unacceptable result. Out of these 31, only 15 demonstrated a total error within the acceptable maximum limits based on QSBV.

Conclusions

Overall, our findings showed that patients had a good understanding of glucometer usage and suggested that some glucometers should be replaced, as they sometimes failed to meet even the manufacturer's acceptable variation limits, and/or did not meet QSBV.

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.

Bias in Laboratory Medicine: The Dark Side of the Moon

Physicians increasingly use laboratory-produced information for disease diagnosis, patient monitoring, treatment planning, and evaluations of treatment effectiveness. Bias is the systematic deviation of laboratory test results from the actual value, which can cause misdiagnosis or misestimation of disease prognosis and increase healthcare costs. Properly estimating and treating bias can help to reduce laboratory errors, improve patient safety, and considerably reduce healthcare costs. A bias that is statistically and medically significant should be eliminated or corrected. In this review, the theoretical aspects of bias based on metrological, statistical, laboratory, and biological variation principles are discussed. These principles are then applied to laboratory and diagnostic medicine for practical use from clinical perspectives.

Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus

BACKGROUND

Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially.

APPROACH

An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association for Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association.

CONTENT

Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (HbA1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of HbA1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed.

SUMMARY

The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.

Within-subject variation of HbA1c: A systematic review and meta-analysis

BACKGROUND

Glycosylated haemoglobin (HbA1c) measurement is used to diagnose and to guide treatment of diabetes mellitus. Within-subject variability in measured HbA1c affects its clinical utility and interpretation, but no comprehensive systematic review has described within-subject variability.

METHODS

A systematic review and meta-analysis was performed of within-subject variability of HbA1c. Multiple databases were searched from inception to November 2022 for follow-up studies of any design in adults or children, with repeated measures of HbA1c or glycosylated haemoglobin. Title and abstract screening was performed in duplicate, full text screening and data extraction by one reviewer and verified by a second. Risk of bias of included papers was assessed using a modified consensus-based standards for the selection of health measurement Instruments (COSMIN) tool. Intraclass correlation coefficient (ICC) results were pooled with a meta-analysis and coefficient of variation (CV) results were described by median and range.

RESULTS

Of 2675 studies identified, 111 met the inclusion criteria. Twenty-five studies reported variability data in healthy patients, 19 in patients with type 1 diabetes and 59 in patients with type 2 diabetes. Median within-subject coefficient of variation (CV) was 0.070 (IQR 0.034 to .09). For healthy subjects the median CV for HbA1c % was 0.017 (IQR 0.013 to 0.022), for patients with type 1 diabetes 0.084 (IQR 0.067 to 0.89) and for type 2 diabetes 0.083 (IQR 0.06 to 0.10). CV increased with mean population HbA1c.

LIMITATIONS

Assessment of variability was not the main aim of many of the included studies and some relevant papers may have been missed. Many included papers had few participants or few repeated measurements.

CONCLUSIONS

Within-subject variability of HbA1c is higher for patients with than without diabetes and increases with mean population HbA1c. This may confound observed relationships between HbA1c variability and health outcomes. Because of its importance in clinical decision-making there is a need for better estimates and understanding of factors associated with of HbA1c variability.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus

BACKGROUND

Numerous laboratory tests are used in the diagnosis and management of diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially.

APPROACH

An expert committee compiled evidence-based recommendations for laboratory analysis in screening, diagnosis, or monitoring of diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association of Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association.

CONTENT

Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (Hb A1c) in the blood. Glycemic control is monitored by the people with diabetes measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring (CGM) devices and also by laboratory analysis of Hb A1c. The potential roles of noninvasive glucose monitoring, genetic testing, and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed.

SUMMARY

The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.

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.

Proteomics profiles of blood glucose-related proteins involved in a Chinese longevity cohort

BACKGROUND

High blood glucose level is one of the main characteristics of diabetes mellitus. Based on previous studies, it is speculated longevity families may have certain advantages in blood glucose regulation. However, limited information on these items has been reported. The purpose of this study was to profile differences of plasma proteomics between longevity subjects (with normal fructosamine (FUN) level) and non-longevity area participants (with exceeding standard FUN level).

METHODS

In this study, a TMT-based proteomics analysis was used to profile differences of plasma proteomics between longevity subjects (with normal FUN level) and non-longevity area participants (with exceeding standard FUN level). Results were validated by Luminex detection.

RESULTS

A total of 155 differentially expressed proteins (DEPs) were identified between these two groups. The DEPs related to blood glucose regulation were mainly involved in glycolysis/gluconeogenesis, pyruvate metabolism and propanoate metabolism, and most of the DEPs were contained in carbohydrate metabolism, PI3K-Akt pathway, glucagon signaling pathway and inflammatory response. Validation by Luminex detection confirmed that CD163 was down-regulated, and SPARC, PARK 7 and IGFBP-1 were up-regulated in longevity participants.

CONCLUSIONS

This study not only highlighted carbohydrate metabolism, PI3K-Akt pathway, glucagon signaling pathway and inflammatory response may play important roles in blood glucose regulation, but also indicated that YWHAZ, YWHAB, YWHAG, YWHAE, CALM3, CRP, SAA2, PARK 7, IGFBP1 and VNN1 may serve as potential biomarkers for predicting abnormal blood glucose levels.

Biological variation estimates obtained from Chinese subjects for 32 biochemical measurands in serum

OBJECTIVES

The European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) have established a program of work to make available, and to enable delivery of well characterized data describing the biological variation (BV) of clinically important measurands. Guided by the EFLM work the study presented here delivers BV estimates obtained from Chinese subjects for 32 measurands in serum.

METHODS

Samples were drawn from 48 healthy volunteers (26 males, 22 females; age range, 21-45 years) for 5 consecutive weeks at Chinese laboratory. Sera were stored at -80 °C before triplicate analysis of all samples on a Cobas 8000 modular analyzer series. Outlier and homogeneity analyses were performed, followed by CV-ANOVA, to determine BV estimates with confidence intervals.

RESULTS

The within-subject biological variation (CVI) estimates for 30 of the 32 measurands studied, were lower than listed on the EFLM database; the exceptions were alanine aminotransferase (ALT), lipoprotein (a) (LP(a)). Most of the between-subject biological variation (CVG) estimates were lower than the EFLM database entries.

CONCLUSIONS

This study delivers BV data for a Chinese population to supplement the EFLM BV database. Population differences may have an impact on applications of BV Data.

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.

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.

Setting analytical performance specifications using HbA1c as a model measurand

Analytical performance specifications (APS) for measurands describe the minimum analytical quality requirements for their measurement. These APS are used to monitor and contain the systematic (trueness/bias) and random errors (precision/imprecision) of a laboratory measurement to ensure the results are "fit for purpose" in informing clinical decisions about managing a patient's health condition. In this review, we highlighted the wide variation in the setting of APS, using different levels of evidence, as recommended by the Milan Consensus, and approaches. The setting of a priori defined outcome-based APS for HbA1c remains challenging. Promising indirect alternatives seek to link the clinical utility of HbA1c and APS by defining statistical confidence for interpreting the laboratory values, or through simulation of clinical performance at varying levels of analytical performance. APS defined based on biological variation estimates in healthy individuals using the current formulae are unachievable by nearly all routine laboratory methods for HbA1c testing. On the other hand, the APS employed in external quality assurance programs have been progressively tightened, and greatly facilitate the improved quality of HbA1c testing. Laboratories should select the APS that fits their intended clinical use and should document the data and rationale underpinning those selections. Where possible common APS should be adopted across a region or country to facilitate the movement of patients and patient data across health care facilities.

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.

Biological variation of serum neurofilament light chain

OBJECTIVES

The neurofilament light chain (NfL) has emerged as a versatile biomarker for CNS-diseases and is approaching clinical use. The observed changes in NfL levels are frequently of limited magnitude and in order to make clinical decisions based on NfL measurements, it is essential that biological variation is not confused with clinically relevant changes. The present study was designed to evaluate the biological variation of serum NfL.

METHODS

Apparently healthy individuals (n=33) were submitted to blood draws for three days in a row. On the second day, blood draws were performed every third hour for 12 h. NfL was quantified in serum using the Simoa™ HD-1 platform. The within-subject variation (CV_I) and between-subject variation (CV_G) were calculated using linear mixed-effects models.

RESULTS

The overall median value of NfL was 6.3 pg/mL (range 2.1-19.1). The CV_I was 3.1% and the CV_G was 35.6%. An increase in two serial measurements had to exceed 24.3% to be classified as significant at the 95% confidence level. Serum NfL levels remained stable during the day (p=0.40), whereas a minute variation (6.0-6.6 pg/mL) was observed from day-to-day (p=0.02).

CONCLUSIONS

Serum NfL is subject to tight homeostatic regulation with none or neglectable semidiurnal and day-to-day variation, but considerable between-subject variation exists. This emphasizes serum NfL as a well-suited biomarker for disease monitoring, but warrants caution when interpreting NfL levels in relation to reference intervals in a diagnosis setting. Furthermore, NfL's tight regulation requires that the analytical variation is kept at a minimum.

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

BACKGROUND

The concept of personalized medicine has received widespread attention in the last decade. However, personalized medicine depends on correct diagnosis and monitoring of patients, for which personalized reference intervals for laboratory tests may be beneficial. In this study, we propose a simple model to generate personalized reference intervals based on historical, previously analyzed results, and data on analytical and within-subject biological variation.

METHODS

A model using estimates of analytical and within-subject biological variation and previous test results was developed. We modeled the effect of adding an increasing number of measurement results on the estimation of the personal reference interval. We then used laboratory test results from 784 adult patients (>18 years) considered to be in a steady-state condition to calculate personalized reference intervals for 27 commonly requested clinical chemistry and hematology measurands.

RESULTS

Increasing the number of measurements had little impact on the total variation around the true homeostatic set point and using ≥3 previous measurement results delivered robust personalized reference intervals. The personalized reference intervals of the study participants were different from one another and, as expected, located within the common reference interval. However, in general they made up only a small proportion of the population-based reference interval.

CONCLUSIONS

Our study shows that, if using results from patients in steady state, only a few previous test results and reliable estimates of within-subject biological variation are required to calculate personalized reference intervals. This may be highly valuable for diagnosing patients as well as for follow-up and treatment.

Evaluación crítica y meta-análisis de estudios de variación biológica para albúmina glicosilada, glucosa y HbA1c

Objetivos

A lo largo de los años se han publicado numerosos artículos sobre variación biológica (VB) de diferente calidad. Los objetivos de este trabajo fueron realizar una revisión sistemática y una evaluación crítica de los estudios de VB para albúmina glicosilada y proporcionar datos actualizados de VB para glucosa y HbA_1c, incluyendo prestigiosos estudios recientemente publicados como el Estudio de Variación Biológica Europea (EuBIVAS).

Métodos

Se hizo una búsqueda bibliográfica sistemática para identificar estudios sobre VB, encontrándose 9 estudios no incluidos en la primera revisión: 4 para albúmina glicosilada, 3 para glucosa y 3 para HbA_1c. Se realizó una evaluación crítica de los estudios relevantes, utilizando la herramienta Biological Variation Data Critical Appraisal Checklist (BIVAC). Se obtuvieron los estimados globales de VB mediante meta-análisis de los estudios que cumplían los requisitos BIVAC, realizados en individuos sanos con estudios de diseño similar.

Resultados

Un estudio recibió el grado A, dos el B y 6 el C. en la mayoría de los casos el grado C se asoció a deficiencias en el análisis estadístico de los datos. Los estimados de VB para albúmina glicosilada fueron: CV_I = 1,4%(1,2–2,1) y CV_G = 5,7%(4,7–10,6); para HbA_1c, CV_I = 1,2%(0,3–2,5), CV_G = 5,4%(3,3–7,3) y para glucosa, CV_I = 5,0%(4,1–12,0), CV_G = 8,1%(2,7–10,8) no difirieron de los estimados globales previamente descritos.

Conclusiones

La evaluación crítica y clasificación de los estudios de VB a tenor de su calidad metodológica, seguido de un meta-análisis, genera estimados de VB robustos y fiables. Este estudio proporciona datos de VB para albúmina glicolisada, glucosa y HbA_1c actualizados y basados en la evidencia científica.

Critical appraisal and meta-analysis of biological variation studies on glycosylated albumin, glucose and HbA1c

OBJECTIVES

Numerous biological variation (BV) studies have been performed over the years, but the quality of these studies vary. The objectives of this study were to perform a systematic review and critical appraisal of BV studies on glycosylated albumin and to deliver updated BV estimates for glucose and HbA_1c, including recently published high-quality studies such as the European Biological Variation study (EuBIVAS).

METHODS

Systematic literature searches were performed to identify BV studies. Nine publications not included in a previous review were identified; four for glycosylated albumin, three for glucose, and three for HbA_1c. Relevant studies were appraised by the Biological Variation Data Critical Appraisal Checklist (BIVAC). Global BV estimates were derived by meta-analysis of BIVAC-compliant studies in healthy subjects with similar study design.

RESULTS

One study received BIVAC grade A, 2B, and 6C. In most cases, the C-grade was associated with deficiencies in statistical analysis. BV estimates for glycosylated albumin were: CV_I=1.4% (1.2-2.1) and CV_G=5.7% (4.7-10.6), whereas estimates for HbA_1c, CV_I=1.2% (0.3-2.5), CV_G=5.4% (3.3-7.3), and glucose, CV_I=5.0% (4.1-12.0), CV_G=8.1% (2.7-10.8) did not differ from previously published global estimates.

CONCLUSIONS

The critical appraisal and rating of BV studies according to their methodological quality, followed by a meta-analysis, generate robust, and reliable BV estimates. This study delivers updated and evidence-based BV estimates for glycosylated albumin, glucose and HbA_1c.

Systematic review and meta-analysis of within-subject and between-subject biological variation estimates of 20 haematological parameters

Background Interpretation of the complete blood count (CBC) parameters requires reliable biological variation (BV) data. The aims of this study were to appraise the quality of publications reporting BV data for CBC parameters by applying the BV Data Critical Appraisal Checklist (BIVAC) and to deliver global BV estimates based on BIVAC compliant studies. Methods Relevant publications were identified by a systematic literature search and evaluated for their compliance with the 14 BIVAC criteria, scored as A, B, C or D, indicating decreasing compliance. Global CVI and CVG estimates with 95% CI were delivered by a meta-analysis approach using data from BIVAC compliant papers (grades A-C). Results In total, 32 studies were identified; four received a BIVAC grade A, 2 B, 20 C and 6 D. Meta-analysis derived CVI and CVG estimates were generally lower or in line with those published in a historical BV database available online. Except for reticulocytes, CVI estimates of erythrocyte related parameters were below 3%, whereas platelet (except MPV and PDW) and leukocyte related parameters ranged from 5% to 15%. Conclusions A systematic review of CBC parameters has provided updated, global estimates of CVI and CVG that will be included in the newly published European Federation of Clinical Chemistry and Laboratory Medicine BV Database.

European Biological Variation Study (EuBIVAS): within- and between-subject biological variation estimates of β-isomerized C-terminal telopeptide of type I collagen (β-CTX), N-terminal propeptide of type I collagen (PINP), osteocalcin, intact fibroblast growth factor 23 and uncarboxylated-unphosphorylated matrix-Gla protein-a cooperation between the EFLM Working Group on Biological Variation and the International Osteoporosis Foundation-International Federation of Clinical Chemistry Committee on Bone Metabolism

We have calculated the biological variation (BV) of different bone metabolism biomarkers on a large, well-described cohort of subjects. BV is important to calculate reference change value (or least significant change) which allows evaluating if the difference observed between two consecutive measurements in a patient is biologically significant or not.

INTRODUCTION

Within-subject (CV_I) and between-subject (CV_G) biological variation (BV) estimates are essential in determining both analytical performance specifications (APS) and reference change values (RCV). Previously published estimates of BV for bone metabolism biomarkers are generally not compliant with the most up-to-date quality criteria for BV studies. We calculated the BV and RCV for different bone metabolism markers, namely β-isomerized C-terminal telopeptide of type I collagen (β-CTX), N-terminal propeptide of type I collagen (PINP), osteocalcin (OC), intact fibroblast growth factor 23 (iFGF-23), and uncarboxylated-unphosphorylated Matrix-Gla Protein (uCuP-MGP) using samples from the European Biological Variation Study (EuBIVAS).

METHODS

In the EuBIVAS, 91 subjects were recruited from six European laboratories. Fasting blood samples were obtained weekly for ten consecutive weeks. The samples were run in duplicate on IDS iSYS or DiaSorin Liaison instruments. The results were subjected to outlier and variance homogeneity analysis before CV-ANOVA was used to obtain the BV estimates.

RESULTS

We found no effect of gender upon the CV_I estimates. The following CV_I estimates with 95% confidence intervals (95% CI) were obtained: β-CTX 15.1% (14.4-16.0%), PINP 8.8% (8.4-9.3%), OC 8.9% (8.5-9.4%), iFGF23 13.9% (13.2-14.7%), and uCuP-MGP 6.9% (6.1-7.3%).

CONCLUSIONS

The EuBIVAS has provided updated BV estimates for bone markers, including iFGF23, which have not been previously published, facilitating the improved follow-up of patients being treated for metabolic bone disease.

The analytical performance of laboratory plasma glucose and HbA1c measurements are largely acceptable

AIMS

Poor comparability between laboratory results may have a strong impact on clinical decisions. The aim of this study was to assess the quality of glucose and HbA_1c measurements in a large cohort of laboratories in various countries, in order to evaluate whether the current state of these very basic laboratory examinations can be considered to be adequate with respect to the clinical needs in the management of glucose control in diabetic patients.

METHODS

External quality assessment schemes and proficiency testing surveys performed in 2017 in several European and American laboratories were analyzed in order to estimate the percentage of laboratories reaching the desired quality criteria based on the allowable total error in accordance with various international recommendations.

RESULTS

In 2017 more than 95% of laboratories met the allowable total error for measuring HbA_1c, and 92-94% of the studied laboratories met the target for glucose measurement.

CONCLUSIONS

The analytical quality for measuring glycated hemoglobin and glucose at laboratory level is generally acceptable, and accreditation to the ISO 15189:2012 standard is a robust guarantee that the laboratory meets the required criteria of acceptability. Several pre-analytical factors which may explain the discrepancies between the measured HbA_1c and that estimated from other indicators of glucose control have to be taken into account, by focusing more on the pre-analytical than the analytical phase. In the case of glucose, special attention should be paid to the use of the correct anticoagulant, in order to avoid false negative results.

European Biological Variation Study (EuBIVAS): Within- and Between-Subject Biological Variation Data for 15 Frequently Measured Proteins

BACKGROUND

The European Biological Variation Study (EuBIVAS) was established to deliver rigorously determined data for biological variation (BV). Here, EuBIVAS-based BV estimates are provided for α1-acid glycoprotein, α1-antitrypsin, albumin, β2-microglobulin, ceruloplasmin, complement component 3, complement component 4, C-reactive protein (CRP), cystatin C, haptoglobin, IgA, IgG, IgM, soluble transferrin receptor (sTfR), and transferrin (Trf), together with their associated analytical performance specifications (APSs) and reference change values (RCVs).

METHOD

Serum samples from weekly blood samplings of 91 healthy study participants (38 males and 53 females, ages 21–69 years old) over 10 consecutive weeks in 6 European laboratories were stored at −80 °C before duplicate analysis on a Roche Cobas c702. Outlier and variance homogeneity analyses were performed followed by CV-ANOVA on trend-corrected data if relevant, to determine BV and analytical variation estimates with CI and the associated RCV.

RESULTS

For the acute phase proteins, several participants experienced mild inflammatory episodes during the study, requiring exclusion of 7% of the 25290 results. Within-subject BV (CVI) estimates for specific proteins obtained in our study were lower than those available in the online 2014 BV database, except for Trf, whereas between-subject BV (CVG) estimates were similar. CVI and CVG estimates for sTfR, which have not previously been published, were 6.0% and 19.1%, respectively.

CONCLUSIONS

In addition to new BV estimates for sTfR, this EuBIVAS substudy generated more demanding APS for frequently requested plasma specific proteins. APS for CRP should not be calculated from BV data except when CRP is used as a risk factor for cardiovascular disease.

Biological variation data for lipid cardiovascular risk assessment biomarkers. A systematic review applying the biological variation data critical appraisal checklist (BIVAC)

BACKGROUND

Biological variation (BV) data can be used to set analytical performance specifications (APS) for lipid assays. Poor performance will impact upon the efficacy of international guidelines for cardiovascular risk assessment (CVR) and relevant clinical decision limits. This systematic review applies the Biological Variation Data Critical Appraisal Checklist (BIVAC) to published studies of BV of CVR biomarkers enabling metanalysis of the data.

METHODS

Studies of BV of total cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides and apolipoproteins A₁ and B, retrieved using a systematic literature search, were evaluated and graded using the BIVAC. Meta-analysis of CV_I and CV_G estimates were performed utilizing weightings based upon BIVAC grades and the width of the data confidence intervals.

RESULTS

Applying the BIVAC, ten publications were graded as D, 43 as C, 5 as B and 1 as A (fully compliant). A total of 196 CV_I and 87 CV_G estimates were available for the different lipid measurands. The meta-analysis-derived BV data estimates were generally concordant with those in the online 2014 BV database.

CONCLUSIONS

Application of BIVAC identifies BV data suitable for many important applications including setting APS. Additionally, this review identifies a need for new BIVAC compliant studies to deliver BV reference data in different subpopulations.