Analytical Performance Specifications for Lipoprotein(a), Apolipoprotein B-100, and Apolipoprotein A-I Using the Biological Variation Model in the EuBIVAS Population

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 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.

Developing an SI-traceable Lp(a) reference measurement system: a pilgrimage to selective and accurate apo(a) quantification

In the past decade a remarkable rebirth of serum/plasma lipoprotein(a) (Lp(a)) as an independent risk factor of cardiovascular disease (CVD) occurred. Updated evidence for a causal continuous association in different ethnic groups between Lp(a) concentrations and cardiovascular outcomes has been published in the latest European Atherosclerosis Society (EAS) Lp(a) consensus statement. Interest in measuring Lp(a) at least once in a person's lifetime moreover originates from the development of promising new Lp(a) lowering drugs. Accurate and clinically effective Lp(a) tests are of key importance for the timely detection of high-risk individuals and for future evaluation of the therapeutic effects of Lp(a) lowering medication. To this end, it is necessary to improve the performance and standardization of existing Lp(a) tests, as is also noted in the Lp(a) consensus statement. Consequently, a state-of-the-art internationally endorsed reference measurement system (RMS) must be in place that allows for performance evaluation of Lp(a) field tests in order to certify their validity and accuracy. An ELISA-based RMS from Northwest Lipid Research Laboratory (University of Washington, Seattle, USA) has been available since the 1990s. A next-generation apo(a)/Lp(a) RMS is now being developed by a working group from the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC). The envisioned apo(a) RMS is based on the direct measurement of selected proteotypic fragments generated after proteolytic digestion using quantitative protein mass spectrometry (MS). The choice for an MS-based RMS enables selective measurement of the proteotypic peptides and is by design apo(a) isoform insensitive. Clearly, the equimolar conversion of apo(a) into the surrogate peptide measurands is required to obtain accurate Lp(a) results. The completeness of proteolysis under reaction conditions from the candidate reference measurement procedure (RMP) has been demonstrated for the quantifying apo(a) peptides. Currently, the candidate apo(a) RMP is endorsed by the IFCC and recommendations for suitable secondary reference materials have been made in a recent commutability study paper. Ongoing efforts toward a complete apo(a) RMS that is listed by the Joint Committee on Traceability in Laboratory Medicine (JCTLM) are focused on the peptide-based calibration and the establishment of a network of calibration laboratories running the apo(a) RMS in a harmonized way. Once completed, it will be the holy grail for evaluation and certification of Lp(a) field methods.

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: 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.

Biological variation of cardiovascular biochemical markers in patients with Type 2 Diabetes Mellitus

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a well-established risk factor for cardiovascular diseases. We aimed to identify the biological variation of ten cardiovascular biochemical markers in T2DM patients to aid in their interpretation.

METHODS

Blood samples for evaluating ten biomarkers were collected biweekly from 23 T2DM patients (10 men, 13 women) for three months. The analytical variability and variations of within-subject (CV_I) and between-subject (CV_G) levels were calculated, as well as the analytical performance specifications, reference change value (RCV), and index of individuality (II).

RESULTS

The levels of total cholesterol (CHOL), apolipoprotein A (apoA), homocysteine (HCY), high-sensitivity troponin T (hsTnT) and N-terminal pro-brain natriuretic peptide (NT-proBNP) differed between males and females (P < 0.05). The CV_Is or CV_Gs of the biomakers were higher than those of healthy participants in Westgard online database, except for hsTnT. Triglyceride (TG), lipoprotein (a) [Lp(a)] and NT-proBNP had relatively high CV_I, CV_G and RCV, whereas CHOL, high-density lipoprotein cholesterol (HDL-C), apoA and HCY showed low variation. Moreover, the II of HDL-C, LP(a), apoA, HCY and hsTnT was <0.6 and other biochemical markers was between 0.6 and 1.4.

CONCLUSION

The cardiovascular biochemical markers in T2DM patients showed higher CV_I or CV_G, except for hsTnT. ApoA had the lowest CV_I and CV_G values. Population-based reference intervals should be used with caution in clinical decision-making for T2DM patients.

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 (CV_I) 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 (CV_G) 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.

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.

Apolipoproteins and liver parameters optimize cardiovascular disease risk-stratification in nonalcoholic fatty liver disease

BACKGROUND

Advanced Non-alcoholic fatty liver disease (NAFLD) is associated with increased risk of cardiovascular disease (CVD).

AIM

We determine whether combinations of ultrasound graphic steatosis grades, fibrosis scores and apolipoprotein levels add value to CVD risk prediction in NAFLD patients.

METHODS

The retrospective cohort study enrolled 10,453 individuals (3519 NAFLD; 6934 non NAFLD) from 2004 to 2018. Hepatic ultrasound measurements, lipid and apolipoprotein profiles, Fibrosis-4 and the NAFLD fibrosis scores (NFS) were assessed. The primary outcome included both clinical and subclinical CVD.

RESULTS

During 116-month follow-up period, there were 957 clinical and 752 subclinical CVD events. NAFLD patients had a higher incidence of CVD than non NAFLD patients as the steatosis degree, NFS, and FIB4 scores increased (25.1% vs 11.9%, Log Rank: p < 0.001). For the lipid and apolipoprotein profiles excluding triglyceride or ApoE, subjects with varied steatosis severity in the upper two tertiles had different risk of CVD (p for interaction < 0.001). A nomogram model combination of Framingham Risk Score (FRS), NFS and apolipoprotein profiles presented a higher AUC than FRS in a time-dependent ROC curve (0.816 vs 0.752, p < 0.001).

CONCLUSION

The novel risk score considering ultrasonography-defined steatosis grades, non-invasive liver fibrosis scores and apolipoprotein profiles accurately predicted the 10-year risk of CVD.

Lipoprotein(a): Pathophysiology, measurement, indication and treatment in cardiovascular disease. A consensus statement from the Nouvelle Société Francophone d'Athérosclérose (NSFA)

Lipoprotein(a) is an apolipoprotein B100-containing low-density lipoprotein-like particle that is rich in cholesterol, and is associated with a second major protein, apolipoprotein(a). Apolipoprotein(a) possesses structural similarity to plasminogen but lacks fibrinolytic activity. As a consequence of its composite structure, lipoprotein(a) may: (1) elicit a prothrombotic/antifibrinolytic action favouring clot stability; and (2) enhance atherosclerosis progression via its propensity for retention in the arterial intima, with deposition of its cholesterol load at sites of plaque formation. Equally, lipoprotein(a) may induce inflammation and calcification in the aortic leaflet valve interstitium, leading to calcific aortic valve stenosis. Experimental, epidemiological and genetic evidence support the contention that elevated concentrations of lipoprotein(a) are causally related to atherothrombotic risk and equally to calcific aortic valve stenosis. The plasma concentration of lipoprotein(a) is principally determined by genetic factors, is not influenced by dietary habits, remains essentially constant over the lifetime of a given individual and is the most powerful variable for prediction of lipoprotein(a)-associated cardiovascular risk. However, major interindividual variations (up to 1000-fold) are characteristic of lipoprotein(a) concentrations. In this context, lipoprotein(a) assays, although currently insufficiently standardized, are of considerable interest, not only in stratifying cardiovascular risk, but equally in the clinical follow-up of patients treated with novel lipid-lowering therapies targeted at lipoprotein(a) (e.g. antiapolipoprotein(a) antisense oligonucleotides and small interfering ribonucleic acids) that markedly reduce circulating lipoprotein(a) concentrations. We recommend that lipoprotein(a) be measured once in subjects at high cardiovascular risk with premature coronary heart disease, in familial hypercholesterolaemia, in those with a family history of coronary heart disease and in those with recurrent coronary heart disease despite lipid-lowering treatment. Because of its clinical relevance, the cost of lipoprotein(a) testing should be covered by social security and health authorities.

Can biomarkers be used to improve diagnosis and prediction of metabolic syndrome in childhood cancer survivors? A systematic review

Childhood cancer survivors (CCS) are at increased risk to develop metabolic syndrome (MetS), diabetes, and cardiovascular disease. Common criteria underestimate adiposity and possibly underdiagnose MetS, particularly after abdominal radiotherapy. A systematic literature review and meta-analysis on the diagnostic and predictive value of nine newer MetS related biomarkers (adiponectin, leptin, uric acid, hsCRP, TNF-alpha, IL-1, IL-6, apolipoprotein B (apoB), and lipoprotein(a) [lp(a)]) in survivors and adult non-cancer survivors was performed by searching PubMed and Embase. Evidence was summarized with GRADE after risk of bias evaluation (QUADAS-2/QUIPS). Eligible studies on promising biomarkers were pooled. We identified 175 general population and five CCS studies. In the general population, valuable predictive biomarkers are uric acid, adiponectin, hsCRP and apoB (high level of evidence), and leptin (moderate level of evidence). Valuable diagnostic biomarkers are hsCRP, adiponectin, uric acid, and leptin (low, low, moderate, and high level of evidence, respectively). Meta-analysis showed OR for hyperuricemia of 2.94 (age-/sex-adjusted), OR per unit uric acid increase of 1.086 (unadjusted), and AUC for hsCRP of 0.71 (unadjusted). Uric acid, adiponectin, hsCRP, leptin, and apoB can be alternative biomarkers in the screening setting for MetS in survivors, to enhance early identification of those at high risk of subsequent complications.

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.

Managing biological variation data: modern approaches for study design and clinical application

For more than one half-century, variability observed in clinical test result measurements has been ascribed to three major independent factors: (i) pre-analytical variation, occurring at sample collection and processing steps; (ii) analytical variation of the test method for which measurements are taken, and; (iii) biological variation (BV). Appreciation of this last source of variability is the major goal of this review article. Several recent advances have been made to generate, collate, and utilize BV data of biomarker tests within the clinical laboratory setting. Consideration of both prospective and retrospective study designs will be addressed. The prospective/direct study design will be described in accordance with recent recommendations discussed in the framework of a newly-developed system of checklist items. Potential value of retrospective/indirect study design, modeled on data mining from cohort studies or pathology laboratory information systems (LIS), offers an alternative approach to obtain BV estimates for clinical biomarkers. Moreover, updates to BV databases have made these data more current and widely accessible. Principal aims of this review are to provide the clinical laboratory scientist with a historical framework of BV concepts, to highlight useful applications of BV data within the clinical laboratory environment, and to discuss key terms and concepts related to statistical treatment of BV data.

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.

Development of an LC-MS/MS Proposed Candidate Reference Method for the Standardization of Analytical Methods to Measure Lipoprotein(a)

BACKGROUND

Use of lipoprotein(a) concentrations for identification of individuals at high risk of cardiovascular diseases is hampered by the size polymorphism of apolipoprotein(a), which strongly impacts immunochemical methods, resulting in discordant values. The availability of a reference method with accurate values expressed in SI units is essential for implementing a strategy for assay standardization.

METHOD

A targeted LC-MS/MS method for the quantification of apolipoprotein(a) was developed based on selected proteotypic peptides quantified by isotope dilution. To achieve accurate measurements, a reference material constituted of a human recombinant apolipoprotein(a) was used for calibration. Its concentration was assigned using an amino acid analysis reference method directly traceable to SI units through an unbroken traceability chain. Digestion time-course, repeatability, intermediate precision, parallelism, and comparability to the designated gold standard method for lipoprotein(a) quantification, a monoclonal antibody-based ELISA, were assessed.

RESULTS

A digestion protocol providing comparable kinetics of digestion was established, robust quantification peptides were selected, and their stability was ascertained. Method intermediate imprecision was below 10% and linearity was validated in the 20-400 nmol/L range. Parallelism of responses and equivalency between the recombinant and endogenous apo(a) were established. Deming regression analysis comparing the results obtained by the LC-MS/MS method and those obtained by the gold standard ELISA yielded y = 0.98*ELISA +3.18 (n = 64).

CONCLUSIONS

Our method for the absolute quantification of lipoprotein(a) in plasma has the required attributes to be proposed as a candidate reference method with the potential to be used for the standardization of lipoprotein(a) assays.