Biological variation (BV) refers to changes in the levels of biochemical constituents in blood or other biological fluids, which reflect the body's regulation through homeostatic processes (1). In an equilibrium state, most measurands exhibit random variations around a homeostatic set point, while others may also be influenced by factors such as different life stages or predictable cyclic variation (2). Moreover, BV has two main components: Coefficient of intra-individual BV (CV_I) and coefficient of interindividual BV, also called group coefficient BV (CV_G); the former refers to the random fluctuation around the homeostatic set point of an analyte within an individual, while the latter represents the variation between the homeostatic set points of an analyte among different individuals (3).

Intra-individual variation can occur in either cyclical or random patterns. Cyclical variation occurs predictably (e.g., variations in diurnal cortisol levels, monthly hormonal changes during the female reproductive cycle and seasonal fluctuations in vitamin D levels). Conversely, random variation is unpredictable and occurs naturally around a subject's homeostatic set point (4).

Currently, BV data are estimated by repeatedly measuring biomarkers or analytes in a healthy population under normal physiological conditions (5). Population-based studies have revealed that healthy individuals' BV estimates remain constant (6). The impact of a specific disease on the BV of an analyte can vary, and it may or may not remain unchanged. Therefore, although a disease may alter the homeostatic set point for an analyte, the intra-individual variability may not necessarily change (4).

Data on BV are also available for populations with different diagnoses, including diabetes or chronic kidney disease, as individuals may be stable despite their pathological condition (7). Research has also revealed that pregnancy and prolonged high-intensity physical exercise can influence individuals' BV (8,9). Moreover, these data are extensively utilized in laboratory medicine for various purposes. These include establishing analytical performance specifications for measurement systems (3,10), calculating the reference change value (RCV) to assess the significance of changes between consecutive measurements, using the individuality index (II) to evaluate the utility of population-based reference intervals and determining personalized reference intervals (2,10). In general, utilizing BV estimates in an individual's test results can provide evidence of pathological conditions or response to therapy (11).

According to Fraser (2001) (4), once the pre-analytical phase is properly managed, the total variation of laboratory results, also known as the total coefficient of variation (CV_T), can be calculated by adding the analytical coefficient of variation (CV_A) and the CV_I. The CV_A can be determined by analyzing control samples with known results (12,13).

At present, the most comprehensive BV database for a wide range of analytes only provides the CV_I and CV_G for plasma (4.5 and 5.8%, respectively) and serum glucose (5.6 and 7.5%, respectively) (14). However, this database does not include the intra- and interindividual BV of capillary blood glucose.

Blood glucose levels can be measured in plasma, serum, whole blood, capillary blood and more recently in interstitial fluid, using laboratory equipment and portable meters called glucometers (15-17).

Plasma glucose testing is used as the gold standard for screening diabetes; however, numerous challenges exist with regard to its widespread use, including low availability in low-resource settings, where capillary glucose testing is suggested as an alternative screening method (18).

Glucometers were first designed to analyze blood samples from capillary sources and are capable of reporting blood glucose levels within seconds (19). These devices are used for monitoring blood glucose (MBG) and can be used by patients and healthcare teams (20).

MBG is recommended for insulin-treated individuals with diabetes in any age group. MBG brings great benefits, by reducing the risk of acute complications, such as ketoacidosis and hypoglycemia, and by allowing the patient to understand the determinants of their blood glucose levels by correlating real-time glycemic results with food intake or physical activity, for example (21,22).

How often an individual with diabetes should check their glucose levels each day can vary from 1 to 10 times, depending on their type of diabetes, treatment plan and individual needs. In hospitalized diabetic patients who can eat, point-of-care (POC) MBG should be performed prior to meals, while in those not eating, MBG is recommended every 4-6 h. POC MBG occurring at a more frequent interval, ranging from every 30 min to every 2 h, is the required standard for safe intravenous insulin therapy (23).

Given the significance of capillary blood glucose monitoring (22), it is crucial to ascertain the intra- and interindividual BV data for this parameter, which is the primary objective of the present study.

A review of the four databases produced 461 articles for consideration. The articles were selected based on specific inclusion and exclusion criteria, which eliminated 419 studies after scrutinizing their titles, and with 10 articles dismissed due to duplication. The abstracts of the remaining 32 articles were then read. A further 21 articles were excluded for failing to meet the inclusion criteria. In the end, 11 articles were selected for a full reading, of which only 4 were included in the final review (7,26-28). Fig. 2 presents the article selection process structured according to the PRISMA protocol.

Out of the 11 articles selected for full reading, six were excluded as they solely focused on evaluating the performance and accuracy of glucometers, while 1 article was excluded as one of its objectives was to calculate glycemic variability after consuming certain foods. These studies used data on BV and CV_I from other articles but did not perform an analysis of them.

Table I summarizes the information obtained from the four reviewed articles (7,26-28). All articles had as one of their objectives the investigation of BV or the assessment of glycemic variability, which indirectly provides data on BV. However, various methodologies were employed to determine BV or glycemic variability, directly impacting the results. The articles under review had a range of 3 to 11 authors and were published in English between 2010 and 2019.

Among the four studies, only one aimed specifically to assess individuals' between-visit BV of capillary blood glucose levels (7). This was the study that most complied with the BIVAC criteria, and reported the CV_I values of 4.5% for healthy individuals and 31.1% for diabetic patients (7) (Table II). On the other hand, all four articles presented the CV_I of fasting capillary blood glucose. However, as the studies were conducted on different populations, and the analyses were performed differently among the four articles, it was not possible to estimate an average CV_I.

As demonstrated in Table II, three articles analyzed capillary blood glucose during fasting (7,26,27), while one examined capillary blood glucose throughout the day or after food intake (28). Table III evaluates the four studies based on the 14 BIVAC quality criteria.

The primary objective of the present systematic review was to comprehensively analyze the current state of knowledge regarding the BV of capillary glycemia, as measured by glucometers. The analysis of the present study aimed to establish specifications for analytical quality, II and RCV. Specific protocols were employed to guarantee the reliability of the BV studies (29,30).

Mu et al (26) conducted a study where fasting capillary blood glucose was measured daily for three months. However, the study lacked standardization in terms of fasting time and the food consumed in the previous meal. Additionally, the present study did not provide data on the CV_A of the glucometer used, indicating that it was not considered when calculating the CV_I.

Carlsen et al (7) estimated the BV of capillary blood plasma glucose and venous hemoglobin A1C in healthy individuals and those with diabetes, as presented in Table II. Weekly collections were performed over 10 weeks, with participants required to fast overnight before each collection. However, the specific fasting time was not disclosed. Duplicate samples were analyzed to assess the CV_A, and two-level control samples were utilized to monitor the analytical accuracy of the equipment. The CV_I and CV_G results obtained for plasma glycemia from capillary blood were included in the mean estimate published by the European Federation of Clinical Chemistry and Laboratory Medicine (2023) (14), although they do not represent the capillary glycemia normally analyzed by a glucometer.

The study by Allsop et al (27) implemented a standardized 12-h overnight fast, with each participant recording the food consumed the night before the first collection, which they were then instructed to repeat before the second collection. Nevertheless, the participants did not standardize food intake or quantity consumed. In terms of sample collection, the study only conducted two collections with a 1-week interval between them. According to current literature protocols, this number is considered low, and a minimum of five collections is recommended (31). Although a glucometer was employed, the capillary blood sample was placed in a heparinized tube and mixed with hemolysis solution prior to glucose analysis. This sample treatment does not reflect the routine use of glucometers for capillary blood glucose analysis. Furthermore, the method did not consider the analytical variation when calculating VB.

In the study by Colomo et al (28), there was no report on the standardization of food intake before sample collection. Additionally, fasting samples were not collected in the study. Furthermore, the analysis did not include information on the accuracy and precision of the equipment used in the tests (i.e., the CV_A was not provided). Considering that this study was conducted after the publication of BV verification protocols, such as BIVAC, the analysis of glycemic BV should have followed the criteria established by these protocols.

The reviewed articles presented varying CV_I values of capillary blood glucose. This discrepancy could be attributed to differences in verification protocols and the evaluation of different populations, reflecting varying study objectives. Specifically, one study focused on CV_I in healthy individuals (27), while two other studies examined it solely in people with diabetes (26,28). Lastly, one study explored the variation between healthy individuals and those with diabetes (7). Two studies were conducted with children (27,28), while the remaining two involved adult populations (7,26).

The number of data collections varied across studies, and the samples analyzed also differed. Carlsen et al (7) and Allsop et al (27) assessed glycemia in capillary blood plasma. There were also variations in the timing of blood glucose sample collection, with three studies monitoring fasting blood glucose levels and one study evaluating glucose levels before meals, without specifying the fasting duration, and 2 h after meals (7,26-28). In the study conducted by Allsop et al (27), food standardization prior to fasting was performed.

Specifically, in capillary blood glucose testing, the variation in glycemic index from food consumed immediately before sample collection could introduce bias into the results, thereby necessitating standardization (22). The choice of blood glucose measurement equipment also varied across studies, with Carlsen et al (7) and Allsop et al (27) utilizing biochemical analyzers while the others used glucometers. Moreover, concerning BV, the study by Allsop et al (27) solely evaluated CV_G, while the study by Colomo et al (28) did not present either CV_I or CV_G, instead solely calculating the coefficient of variation for capillary blood glucose between fasting and post-meal collections.

The difference in study protocols can be justified by the fact that three of the analyzed articles predate the initiative to harmonize the generation and reporting of BV data with the publication of BIVAC (7,13,25-27), which brings weaknesses and uncertainties in the results found. However, even the subsequent article did not use the BIVAC protocol, demonstrating the need for reliable studies to estimate the BV of capillary blood glucose measured by glucometers (28). Moreover, BIVAC allows for the critically evaluation and classification of BV studies concerning study design, pre-analytical handling, analytical methods and statistical analysis (24). The data from applying BIVAC indicate that studies either omitted or did not address essential details related to BIVAC quality indicators. Currently, BIVAC allows for a retrospective assessment of published studies and serves as a guide for future studies.

The evaluated studies found that Mu et al (26) did not meet BIVAC QI 7, which refers to the steady state of the sample. The research evaluated the results both with and without the use of insulin. Both Mu et al (26) and Colomo et al (28) did not meet QI 6, which requires estimating the analytical variation of the method for estimating BV.

Allsop et al (27) and Colomo et al (28) did not meet QI 14 regarding the presentation of the concentration of the measurand among participants. Corroborating the theory of systematic reviews described by Gurevitch et al (32), the review results allowed the identification of future research priorities that would otherwise not have been noticed. For example, the reviewed studies were conducted primarily in Europe and Asia, suggesting the need to conduct them in other regions to consider each continent's eating habits and lifestyle.

Lastly, considering that the present review did not limit the publication period of the studies, it is understood that these are the current state-of-the-art findings. The reported capillary glycemia BV results were intended to answer the specific questions of their respective studies but cannot be used as a reference for BV and CV_I or CV_G for calculating analytical quality specifications, II or RCV. The present systematic review revealed a lack of specific studies that adhere to standardized criteria to assess the BV of intra-individual and interindividual capillary blood glucose levels as measured by glucometers. Consequently, as reported in the current literature, the existing data on BV cannot be considered reliable for establishing analytical quality specifications, II and reference change values for capillary blood glucose measurement using glucometers. Nevertheless, these findings have significant implications for managing patients who monitor their capillary blood glucose levels, as they can contribute to more effective treatment strategies.