For people with diabetes (type 1 and type 2), with either continuous subcutaneous insulin infusion or intensified insulin therapy, the regular self-monitoring of blood glucose (SMBG) is recommended to make adequate therapy decisions (1). For the last 25 years, SMBG systems were (and still are) primarily used by patients. However, minimally invasive continuous glucose monitoring (CGM) systems are nowadays available and used by an increasing number of people with diabetes. The currently available minimally invasive CGM systems measure glucose concentrations continuously in the interstitial fluid (ISF) of the subcutaneous fatty tissue, but the use of this measuring method is also a general limitation of CGM. To achieve optimal metrological traceability, a direct measurement of glucose in ISF with a reference measurement procedure would be essential. The frequent collection of ISF in sufficiently large volumes over short time periods is, however, currently not feasible. For this reason, no internationally accepted reference measurement procedure is currently available for glucose in ISF. As an alternative to harvesting ISF, samples from capillary blood collected by patients or venous blood in studies is frequently used for calibration of CGM systems.

Physiological differences between the venous, capillary and ISF compartments lead to a time delay, and to different glucose concentrations in these compartments, especially after carbohydrate intake (2). The algorithms of current CGM systems do not only intend to compensate for the time delay between changes in blood glucose and in tissue glucose, but also to calculate a glucose value estimating the capillary or venous blood glucose value (“hybrid-glucose” value). Due to the aforementioned factors, however, these estimates are not directly comparable to capillary, venous or interstitial glucose concentrations.

The compartment, whose glucose concentrations CGM systems are intended to report, is currently not clearly defined and might substantially differ between devices due to the compartment used for calibration. For this reason, a traceability chain of CGM systems has to be established. However, manufacturers of CGM systems don’t provide detailed information about the traceability chain and the measurement uncertainty of their systems. As a result, the obtained glucose values can currently differ between systems and cannot be adequately traced to higher-order standards or methods.

The Mean absolute relative difference (MARD) is a widely used parameter in CGM studies and predominantly describes the accuracy of CGM systems. However, MARD is dependent on many influencing factors and statistical methods, like study design, glucose concentration, glucose rate of change and the number and distribution of paired comparison measurements (3-6). Therefore, suitable and internationally recognized metrics and corresponding acceptance criteria have to be identified for the assessment of analytical performance of CGM systems. Nevertheless, these metrics should not only focus on analytical performance, but also reflect clinical relevance of glucose values obtained by CGM. Since CGM also plays a role in hospitals (e.g. intensive care unit), criteria for the use in clinical settings need to be established as well.

The IFCC Working Group on Continuous Glucose Monitoring aims at establishing a traceability chain for minimally invasive CGM systems, as well as international accepted procedures and metrics for the assessment of their analytical performance ( 


  1. The Diabetes Control and Complications Trial Research Group. The relationship of glycemic exposure (HbA1c) to the risk of development and progression of retinopathy in the diabetes control and complications trial. Diabetes. 1995;44(8):968-83.

  2. Lee I, Lunt H, Chan H, Heenan H, Berkeley J, Frampton CM. Postprandial capillary-venous glucose gradient in Type 1 diabetes: magnitude and clinical associations in a real world setting. Diabet Med. 2016;33(7):998-1003. doi: 10.1111/dme.13025. PubMed PMID: 26536491; PubMed Central PMCID: PMCPMC5064751.

  3. Reiterer F, Polterauer P, Schoemaker M, Schmelzeisen-Redecker G, Freckmann G, Heinemann L, et al. Significance and Reliability of MARD for the Accuracy of CGM Systems. J Diabetes Sci Technol. 2017;11(1):59-67. Epub 2016/08/28. doi: 10.1177/1932296816662047. PubMed PMID: 27566735; PubMed Central PMCID: PMCPMC5375072. Freckmann G, Link M, Pleus S, Westhoff A, Kamecke U, Haug C. Measurement Performance of Two Continuous Tissue Glucose Monitoring Systems Intended for Replacement of Blood Glucose Monitoring. Diabetes Technol Ther. 2018;20(8):541-9. Epub 2018/08/02. doi: 10.1089/dia.2018.0105. PubMed PMID: 30067410; PubMed Central PMCID: PMCPMC6080122.

  4. Heinemann L, Schoemaker M, Schmelzeisen-Redecker G, Hinzmann R, Kassab A, Freckmann G, et al. Benefits and Limitations of MARD as a Performance Parameter for Continuous Glucose Monitoring in the Interstitial Space. J Diabetes Sci Technol. 2020;14(1):135-50. doi: 10.1177/1932296819855670. PubMed PMID: 31216870; PubMed Central PMCID: PMCPMC7189145.

  5. Fokkert M, van Dijk P, Edens M, Díez Hernández A, Slingerland R, Gans R, et al. Performance of the Eversense versus the Free Style Libre Flash glucose monitor during exercise and normal daily activities in subjects with type 1 diabetes mellitus. 2020;(2052-4897 (Electronic)).