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

A standard to report biological variation data studies - based on an expert opinion

There is a need for standards for generation and reporting of Biological Variation (BV) reference data. The absence of standards affects the quality and transportability of BV data, compromising important clinical applications. To address this issue, international expert groups under the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) have developed an online resource (https://tinyurl.com/bvmindmap) in the form of an interactive mind map that serves as a guideline for researchers planning, performing and reporting BV studies. The mind map addresses study design, data analysis, and reporting criteria, providing embedded links to relevant references and resources. It also incorporates a checklist approach, identifying a Minimum Data Set (MDS) to enable the transportability of BV data and incorporates the Biological Variation Data Critical Appraisal Checklist (BIVAC) to assess study quality. The mind map is open to access and is disseminated through the EFLM BV Database website, promoting accessibility and compliance to a reporting standard, thereby providing a tool to be used to ensure data quality, consistency, and comparability of BV data. Thus, comparable to the STARD initiative for diagnostic accuracy studies, the mind map introduces a Standard for Reporting Biological Variation Data Studies (STARBIV), which can enhance the reporting quality of BV studies, foster user confidence, provide better decision support, and be used as a tool for critical appraisal. Ongoing refinement is expected to adapt to emerging methodologies, ensuring a positive trajectory toward improving the validity and applicability of BV data in clinical practice.

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.

The influence of sampling time on indirect reference limits, decision limits, and the estimation of biological variation of random plasma glucose concentrations

Objectives

Plasma glucose concentrations exhibit a pronounced daytime-dependent variation. The oscillations responsible for this are currently not considered in the determination of reference limits (RL) and decision limits.

Methods

We characterized the daily variation inherent in large-scale laboratory data from two different university hospitals (site 1 n=513,682, site 2 n=204,001). Continuous and distinct RL for daytime and night were estimated. Diurnal characteristics of glucose concentrations were further investigated by quantile regression analyses introducing age and cosinor-functions as predictors in the model.

Results

Diurnal variations expressed as amplitude/Midline Estimating Statistic of Rhythm (MESOR) ratio, averaged 7.7% (range 5.9–9.3%). The amplitude of glucose levels decreased with increasing concentrations. Between 06:00 and 10:00 h an average decrease of 4% has to be considered. Nocturnal glucose samples accounted for only 5% of the total amount but contributed to 19.5% of all findings over 11.1 mmol/L. Partitioning of RL between day and night is merely justified for the upper reference limit. The nocturnal upper RLs for both genders differed from those obtained during the day by 11.0 and 10.6% at site 1 and by 7.6 and 7.5% at site 2.

Conclusions

We conclude that indirect approaches to estimate upper RL of random plasma glucose concentrations require stratification concerning the time of sample collection.

Models to estimate biological variation components and interpretation of serial results: strengths and limitations

Biological variation (BV) has multiple applications in a variety of fields of clinical laboratory. The use of BV in statistical modeling is twofold. On the one hand, some models are used for the generation of BV estimates (within- and between-subject variability). Other models are built based on BV in combination with other factors to establish ranges of normality that will help the clinician interpret serial results for the same subject. There are two types of statistical models for the calculation of BV estimates: A. Direct methods, prospective studies designed to calculate BV estimates; i. Classic model: developed by Harris and Fraser, revised by the Working Group on Biological Variation of the European Federation of Laboratory Medicine. ii. Mixed-effect models. iii. Bayesian model. B. Indirect methods, retrospective studies to derive BV estimates from large databases of results. Big data. Understanding the characteristics of these models is crucial as they determine their applicability in different settings and populations. Models for defining ranges that help in the interpretation of individual serial results include: A. Reference change value and B. Bayesian data network. In summary, this review provides an overview of the models used to define BV components and others for the follow-up of patients. These models should be exploited in the future to personalize and improve the information provided by the clinical laboratory and get the best of the resources available.