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Are standardized conditions needed for correct CGM data interpretation in subjects at early stages of glucose intolerance?

Diabetology & Metabolic Syndrome volume 17, Article number: 29 (2025) Cite this article

Abstract

Aim

The present study comparatively evaluated glucose variability (GV) parameters derived from both continuous glucose monitoring (CGM) performed under standard conditions for a 24-h period and under usual everyday conditions for a 14-day period in a high-risk population without diabetes.

Methods and results

Seventy five subjects: 14 with normal glucose tolerance (NGT; mean age 43.6 ± 10.7 years; BMI 30.5 ± 6.9 kg/m2), 19 with high 1-h postload glucose > 8.6 mmol/l (1hrOGTT; mean age 45.6 ± 8.9 years; BMI 33.7 ± 6.9 kg/m2), and 42 with isolated impaired glucose tolerance (iIGT; mean age 47.6 ± 11.8 years; BMI 31.0 ± 6.5 kg/m2), were enrolled. An OGTT was performed. CGM was performed with blinded FreeStyleLibrePro for 24 h under standard conditions and for the rest of the 14-day period under usual everyday conditions. GV parameters derived from both periods were compared. There was a significant increase in GV with worsening of glucose tolerance from NGT, to 1hrOGTT and iIGT, independently of the conditions. Our findings showed moderate to strong correlations among GV indices between the studied periods in the cohort and in the 1hrOGTT and iIGT groups. However, a significant difference was found in some of the GV parameters between the analyzed periods.

Conclusion

The trend in GV is independent of the conditions, under which CGM is performed, in subjects at early stages of glucose intolerance. Although its measurements to some extend differ in standard and everyday conditions, there is no need of standardized conditions for correct interpretation of GV indices in this population.

Introduction

The introduction and subsequent widespread use of sensors for continuous glucose monitoring (CGM) in subjects with overt diabetes mellitus reveals the dynamic properties of glucose profiles by capturing components, which are invisible with the traditional self-monitoring of capillary glucose [1]. CGM sensors represent an important technological development helping the clinician to better manage each individual based on the different degrees and patterns of the glycemic excursions representing intra- and inter-day glucose variability (GV). This component of the overall assessment of glycemia is of great importance due to its impact on the risk of developing diabetes chronic complications, which has already been proven [2, 3].

Beyond the established place of CGM sensors in quantifying the quality of glycemic control, which affects treatment approach in known diabetes mellitus, their role in relation to identifying subjects at risk for type 2 diabetes mellitus (T2DM) and understanding the pathophysiology of prediabetes is now under the spotlight of increased scientific interest. There is accumulating data for a progressively increasing GV in the trajectory moving from normal glucose tolerance (NGT) to different categories of prediabetes [4,5,6]. Such a progressive change in GV in these early stages of dysglycaemia suggests that GV indices extracted from CGM could be used to detect impairments in glucose homeostasis much earlier than the standard functional tests used for the diagnosis and classification of prediabetes and T2DM. CGM is still not approved as a diagnostic tool, but the question that arises in this case is whether we need standardized conditions under which to perform CGM records for correct data interpretation.

To address this question, in the present study we performed CGM with a blinded professional sensor for 24-h period under standardized conditions with prespecified dietary regimen and physical activity and also for the rest of the 14-day period under usual everyday lifestyle in a high-risk population without diabetes. Thereafter GV indices from both periods were calculated based on CGM sensor readings and comparatively evaluated in the investigated groups according to glucose tolerance—(1) with NGT, (2) with high 1-h glucose during OGTT > 8.6 mmol/l (1hrOGTT), and 3) with isolated impaired glucose tolerance (iIGT).

Material and methods

Participants

We recruited, from the beginning of 2020 till the end of 2021, 75 consecutive subjects amongst those referred to the outpatient Clinic of Division of Diabetology, Department of Endocrinology, Medical University of Sofia because of increased risk of T2DM.

As exclusion criteria were adopted: known diagnosis of type 1 or type 2 diabetes mellitus, using glucose-lowering or obesity drugs, using medications known to affect glucose homeostasis, the presence of serious comorbidities such as kidney, liver or cardiovascular disease, as well as recent acute illness. All subjects were provided with detailed information about aims, methods and risks of participating in the current study and provided written informed consent for participation in accordance with the Helsinki Declaration and the rules of Good Clinical Practice before the recruitment. The study was approved by the Ethics Committee of the Medical University—Sofia.

On the first day subjects showed up at our Diabetes Clinic at 8 a.m., after an overnight fasting for at least 12 h, for collection of medical history and anthropometric measurements. Height (m) and weight (kg) were measured for BMI calculation. Subjects then underwent a standard 75-g oral glucose tolerance test (OGTT). Venous blood samples were taken at – 20, – 10, 0, 10, 20, 30, 45, 60, 90, and 120 min for measurement of plasma glucose (hexokinase method).

On the second day all participants underwent mixed meal tolerance test and after that subjects were instructed to follow a pre-defined dietary plan (already published) [7] and to have no more than 30 min of moderate physical activity, ensuing a 24-h period under standardized conditions.

Upon completion of the OGTT, all subjects were given a professional blinded glucose monitoring system (FreeStyle Libre Pro, Abbot GmbH & KG), which were placed on the back of the upper arm, and data recording was initiated immediately after the OGTT, including the 24-h standardized period on the second day and continued for the entire recording period of the sensor for 14 days under everyday usual conditions. The FreeStyle Libre Pro sensor, applied in this study, uses a very thin filament inserted just under the skin and records interstitial glucose levels every 15 min. There is no need of calibration and the mean absolute relative difference (MARD) of the device is less than 11%. Professional blinded CGM, which allows only retrospective data analysis, was supplied so that there was no subjectivity in CGM readings, i.e. the patients might change their diet reflecting glucose measurements they saw in real-time CGM [8].

Calculation

The representativeness of the sample size was confirmed by the calculation based on the SD of CONGA1 as reported by Chen et al. [9], according to which a total number of subjects to be recruited was 62.

Based on the OGTT, the study population was divided into 3 groups: 1. normal glucose tolerance (NGT) defined as fasting blood glucose < 5.6 mmol/l, 1 h blood glucose during OGTT < 8.6 mmol/l and 2 h blood glucose during OGTT < 7.8 mmol/l; 2. isolated 1hrOGTT defined as fasting blood glucose < 5.6 mmol/l, 1 h blood glucose during OGTT > 8.6 mmol/l and 2 h blood glucose during OGTT < 7.8 mmol/l, and 3. isolated impaired glucose tolerance (iIGT), including subjects with fasting blood glucose < 5.6 mmol/l, and 2 h blood glucose during OGTT ≥ 7.8 and < 11.1 mmol/l [10].

Areas under the curve (AUC) after OGTT for glucose was calculated by the trapezoidal rule. Several different parameters, exploring GV, were calculated during the 24-h period under standardized conditions and the rest of the recorded period under real life everyday conditions: Mean ± SD Glucose (mmol/l), Coefficient of Variation (CV), Continuous overall net glycemic action 1 (CONGA1) [11], Mean Absolute Glucose (MAG) [12], M-value [13], the low blood glucose index (LBGI) [14], the high blood glucose index (HBGI) [14], the lability index (L-index) [15], the Mean Amplitude of Glycemic Excursions (MAGE) [16], the Glycemic Risk Assessment in Diabetes Equation (GRADE) [17], and the J-index [18]. All the calculations of the GV indices were made using the EasyGV, version 9.0.R2 available online (https://innovation.ox.ac.uk/licence-details/glycaemic-variability-calculator-easygv/).

Statistical analyses

Statistical analyses were performed with the SPSS version 23.0 software (IBM Corporation, Chicago, IL). Data normal distribution was evaluated by Kolmogorov–Smirnov test and variables with skewed distribution underwent a log-transformation. Differences across groups were tested using descriptive analysis and a single-factor dispersion analysis of variance (ANOVA) with Post Hoc multiple comparisons using Bonferroni test. Differences in GV parameters between the two analyzed periods on CGM in the whole cohort and in the three groups were tested using Wilcoxon signed-rank test for skewed distributed parameters and paired-samples t-test for normal distributed parameters. Pearson Correlation was performed between GV variables for the two evaluated periods with normal distribution or after logarithmic transformation. All variables are shown as mean ± standard deviation or median and interquartile range, depending on their distribution. A p-value (two tailed) of < 0.05 was considered statistically significant.

Results

The main characteristics of the studied groups are presented on Table 1. There was no statistically significant difference in age, BMI, glycated hemoglobin and mean glucose among the groups. As expected, a progressive rise in the AUC-glucose during OGTT was established with the progression of glucose intolerance from NGT, to 1hrOGTT and iIGT.

Table 1 Main characteristics of the studied groups according to glucose tolerance—with normal glucose tolerance (NGT), with high 1-h postpoad glucose during OGTT > 8.6 mmol/l (1hrOGTT), and with isolated impaired glucose tolerance (iIGT)
Full size table

GV indices in both periods in the three studied groups are presented on Table 2. Data showed a trend towards higher GV with worsening of glucose intolerance, independently of the conditions; however most difference reached statistical significance just in iIGT group in comparison to NGT, with the exception of CV and HBGI during the 14-day period.

Table 2 Glucose variability (GV) parameters in both evaluated periods—14-day period on CGM under usual everyday conditions and 24-h period on CGM under standard conditions, in the groups according to glucose tolerance- with normal glucose tolerance (NGT), with high 1-h postpoad glucose during OGTT > 8.6 mmol/l (1hrOGTT), and with isolated impaired glucose tolerance (iIGT)
Full size table

Table 3 illustrates the direct comparison between GV indices in both periods in the whole cohort, as well as in the three groups in accordance with glucose tolerance. There was a statistically significant difference in some GV indices between both periods in all groups. However, a moderate to very strong correlation (r = 0.50 to 0.85, p < 0.0001) was found between most GV indices except MAGE in the whole cohort. The same tendency was observed in the glucose intolerance groups as follow: a moderate to strong correlation (r = 0.60 to 0.80, p = 0.009 to < 0.0001) was found between most GV indices except CV in the 1hrOGTT group and a weak to very strong correlation (r = 0.37 to 0.88, p = 0.019 to < 0.0001) was found between most GV indices except MAGE in the iIGT group. There was a correlation just between HBGI, LBGI, J-index and M-Value between both periods in the NGT group (r = 0.59 to 0.69, p = 0.035 to 0.009). The main GV metrics in the whole cohort are represented graphically in box plots in both estimated periods (Fig. 1).

Table 3 Glucose variability (GV) parameters in both estimated periods—14-day period on CGM under usual everyday conditions and 24-h period on CGM under standard conditions, in the studied cohort and in the groups according to glucose tolerance—with normal glucose tolerance (NGT), with high 1-h postpoad glucose during OGTT > 8.6 mmol/l (1hrOGTT), and with isolated impaired glucose tolerance (iIGT)
Full size table
Fig. 1
figure 1

Main glucose variability parameters presented graphically in box plots in both estimated periods—14-day period on CGM under usual everyday conditions and 24-h period on CGM under standard conditions in the studied cohort

Full size image

Table 4 shows a significant correlation between time in tight range (3.9–7.8 mmol/l) for the whole 14-day period with CV in % for both standardized and unstandardized periods.

Table 4 The correlation between the time in tight range (TITR) and coefficient of variation (CV) in both evaluated periods—14-day period on CGM under usual everyday conditions and 24-h period on CGM under standard conditions in the studied cohort
Full size table

Discussion

Recently there has been increasing research interest for the use of CGM systems in subjects at early stages of glucose intolerance. CGM technology has already demonstrated unique capabilities in evaluating diversities in postprandial glycemic responses, including subjects without diabetes mellitus. CGM has been applied as a useful tool for improving diet quality in order to prevent T2DM and its complications. CGM data on postprandial glucose excursions has also been integrated into a machine-learning algorithm aiming to build personalized precision nutrition to prevent prediabetes and overt T2DM [19]. In this line great attention has been paid on the application of CGM sensors as a new diagnostic tool in the high-risk population without diabetes mellitus. It has been shown that detailed glucose profiles, based on CGM data in individuals with prediabetes, differ from glucose profiles in those with normoglycemia [5, 6, 20]. In line with the above, the present study also demonstrated elevated GV indices with the transition from NGT to 1hrOGTT and iIGT and this trend remains the same independently of the conditions under which CGM has been performed—standard or usual. Therefore, it might be speculated that CGM is a potentially helpful tool for stratification and reclassification of subjects with intermediate hyperglycemia, but its implementation in the diagnostic algorithm encounters some unresolved issues.

The main advantage of CGM is its ability to measure interstitial glucose every 5 to 15 min, which reveals a much more detailed glucose profile with every single glucose fluctuation detected, but despite the option for calibration of these devices, there is still a difference of 0.55 to 1.11 mmol/l which might be crucial when glucose concentrations are borderline, such as in prediabetes categories [21, 22]. Another disadvantage is the lag time, especially in periods with rapid changes in glucose levels, such as the postprandial period. Because of inexact relation between interstitial and blood glucose concentrations, CGM still is not approved as a diagnostic tool for prediabetes and overt T2DM [23].

Available data from CGM use in prediabetes has observed an association between intraday glucose variability and the progression of glucose intolerance, expressed by CV in % for glucose [5, 6, 24]. However, in subjects without T2DM, CV failed to distinguish between the different stages of dysglycemia and it remained debatable whether GV indices correspond to the relevant category of prediabetes [5, 25]. This could be explained with the need for standardized conditions for the time of CGM records or just different GV parameters might be more relevant in this high-risk population. The feasibility of using CGM-derived GV indices to distinguish subjects with prediabetes or T2DM from healthy subjects has also been explored by means of a machine-learning approach. CGM data were gathered in the everyday usual lifestyle and about 25 GV parameters were analyzed. The authors reported over 91% accuracy in distinguishing between NGT and prediabetes or T2DM, whereas just 79% accuracy in classifying IGT or T2DM [25].

The current study addresses the question for the need of standardized diet and physical regimen during the time of CGM recording in a high-risk population, to provide readings comparable to standardized functional tests, which to be applied as diagnostic criteria. A comparative evaluation of GV indices based on CGM data performed both under standard conditions or usual everyday lifestyle has been performed in subjects at different stages of glucose intolerance without diabetes. The results showed moderate to strong correlation between GV indices between both periods. This highlights no need of special conditions, under which to perform CGM, in subjects without diabetes mellitus in order to use the data as a diagnostic tool, rather than selecting the most appropriate GV indices for evaluation.

Characterization with reference ranges of CGM parameters has been performed in an epidemiological study in the general population evolving more than 7 000 subjects without diabetes mellitus [26]. Studies like this provide insights into glycemic patterns in healthy subjects with significant clinical implication for detecting early impairments of glucose tolerance, and thus preventing or delaying the diagnosis of diabetes mellitus because they are performed under real-life conditions. The opportunity of obtaining data in real life conditions is a major advantage of CGM. Regardless of the person's individual risk of developing T2DM, the sensor reveals what is the lifestyle of this person and what glucose fluctuations he/she is exposed to on a usual daily basis. Analyzing CGM readings in this direction actually enables us, through CGM data, to assess how personal lifestyle modifies the risk individually.

The selection of the groups was made on the basis of data related to the risk of developing T2DM and cardio-vascular risk. High 1-h plasma glucose during OGTT has emerged as a useful early biomarker of impaired glucose homeostasis with a cut-off value of 8.6 mmol/L [27]. Initially it was reported in the San Antonio Heart Study [28] and then it was confirmed in two longitudinal studies in European population—the Botnia and the Malmö Prevention Project cohorts, which results demonstrated the highest predictive value for the development of T2DM of 1hrOGTT > 8.6 mmol/l [29]. As a natural course of events, the inclusion of this group in the continuum of high-risk population with prediabetes is already a fact [30].

Another unresolved issue regarding CGM use in prediabetes is how to best exploit GV indices in order to distinguish among different categories of prediabetes. There is a big plethora of GV indices which compete for a place in describing mild deteriorations in glucose metabolism. It seems that the selection of GV indices with best predictive value in this high-risk population differ from those, that best characterize GV patterns in people with overt T2DM. Results of the current study recognized CV, SD, GRADE, HBGI, LI, and J-index to differ significantly between iIGT and NGT, with same numerical trend in 1hrOGTT for both estimated periods. These data encourage further evaluation of these parameters in the state of borderline hyperglycemia as a potentially diagnostic markers for prediabetes and T2DM.

Recently, the use of time in tight range (3.9–7.8 mmol/l) has been in the spotlight of research interest as a glycemic control parameter, both in subjects with diabetic mellitus, but also as a reliable marker in subjects with prediabetes and early glycemic alterations. Our data confirmed its correlation with CV and, thus, emphasizing its potential role in the assessment of GV in this high-risk population [31].

To ensure proper utilizations of GV indices, it is essential to assess the reproducibility of these metrics over time. The proper clinical decision making is highly dependent on the consistency of the CGM measurements. However, ensuring the usability and reliability of CGM data as a diagnostic tool is still scarcely investigated. One step in this direction is a recently published study encompassing about 600 subjects, which explored the reproducibility of the CGM readings. The authors found the greatest reproducibility of the CGM metrics in subjects with diabetes mellitus, intermediate place for prediabetes and poorest one in those with NGT, especially younger people, explaining the results with bigger functional adaptation capacity in this population. The main implication of the study is the poor overall inter-day reproducibility of the CGM data, especially in the prediabetes and normoglycemia population [32]. This is to a great extent in line with the correlations we observed between both periods, the strongest been for the group with iIGT, intermediate for 1hrOGTT and the weakest for NGT. This means that the greatest dependence between glucose fluctuations and diet regimen and physical activity is seen in subjects with NGT. One explanation is probably the preserved beta-cell function capacity in NGT and its deterioration with the progression of dysglycaemia in different prediabetes categories, which allows pronounced dynamic complexity of glucose profiles in NGT and to some extent a limited one in prediabetes. Therefore, the main clinical implication should be the need for a longer CGM monitoring period in these non-diabetic populations and it seems imperative to comprehensively investigate aspects of CGM data behavior across different populations. Therefore, standardized conditions might be taken into account when drawing conclusions on the glucose homeostasis in this high-risk population.

On the other hand, better CGM data reproducibility in patients with overt diabetes mellitus is probably based on the clinical recommendations in this case, regardless of the type of the disease, the treatment and glycemic control. All patients with known diabetes mellitus are given similar advices to maintain a healthy eating plan with restriction of carbohydrate intake as well as carrying out a unified physical exercise plan. These recommendations make the glycemic response more predictable and reproducible.

The type of CGM system used is also of particular importance. Real-time CGM sensors provide glucose readings to participants wearing the device in real time settings. It has been reported that wearing real time CGMs with the ability to observe changes in glucose levels has the potential to influence users’ food choices and physical activity. Accordingly, these sensors might have the potential to be used as a behavior modification tool for lifestyle changes in T2DM [32, 33]. Professional CGM systems, used in the present study, refer to devices, which generate data just for retrospective analysis. These devices can be used in the “blinded” mode to capture information about what patients are doing without influencing their behavior.

Limitations

The sample size is representative for the analyses performed but still small for this socially significant disease. Another limitation is the cross-sectional design of the study. However, advantages of the study are the sensor technology used—a professional blinded sensor; the random selection of the subjects enrolled, and the inclusion of people with NGT, as well as the recently proposed category of intermittent glucose—1hrOGTT. Moreover, for correct interpretation of GV metrics, of particular importance is the unification of the studied groups by BMI, which was done in this study, since morbidly obese subjects with or without prediabetes have indistinguishable GV parameters [4], which has been shown even in type 1 diabetes mellitus [34].

Conclusion

The results of the present study demonstrated an increase in glucose fluctuations with worsening of dysglycaemia. The trend in GV indices is independent of the conditions, under which CGM is performed, in the studied high-risk population at early stages of impaired glucose tolerance. Although some of the CGM measurements to some extend differ in standard and everyday conditions, a strong correlation was found between the two studied periods in most of them. Therefore, CGM data under everyday conditions appears to be valuable for unraveling the early changes in the patterns of GV, and there is no need of standardized conditions for correct interpretation of GV indices in this population. Larger studies are needed to confirm the role for CGM readings in the diagnostic algorithm for diabetes mellitus.

Data availability

No datasets were generated or analysed during the current study.

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Funding

The study was funded by National science fund—financial support of basic research projects—2019, contract КП-06 H 33/7 from 13.12.2019; by the European Union-NextGenerationEU, through the National Recovery and Resilience Plan of the Republic of Bulgaria, project № BG-RRP-2.004-0004-C01.

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  1. Department of Endocrinology, Medical University of Sofia, 2 Zdrave Str., 1431, Sofia, Bulgaria

    R. Dimova, N. Chakarova & T. Tankova

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Contributions

R. D. designed the project. R. D. and N. C. collected the data. R. D. analysed the results and wrote the article in consultation with N. C. and T. T. All authors reviewed the manuscript.

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Correspondence to R. Dimova.

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Dimova, R., Chakarova, N. & Tankova, T. Are standardized conditions needed for correct CGM data interpretation in subjects at early stages of glucose intolerance?. Diabetol Metab Syndr 17, 29 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13098-025-01579-x

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13098-025-01579-x

Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

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