Abstract
Introduction
Continuous glucose monitoring (CGM) devices allow for 24-h real-time measurement of interstitial glucose levels and have changed the interaction between people with diabetes and their health care providers. The large amount of data generated by CGM can be analyzed and evaluated using a set of standardized parameters, collectively named glucometrics. This review aims to provide a summary of the existing evidence on the use of glucometrics data and its impact on clinical practice based on published studies involving adults and children with type 1 diabetes (T1D) in Spain.
Methods
The PubMed and MEDES (Spanish Medical literature) databases were searched covering the years 2018–2022 and including clinical and observational studies, consensus guidelines, and meta-analyses on CGM and glucometrics conducted in Spain.
Results
A total of 16 observational studies were found on the use of CGM in Spain, which have shown that cases of severe hypoglycemia in children with T1D were greatly reduced after the introduction of CGM, resulting in a significant reduction in costs. Real-world data from Spain shows that CGM is associated with improved glycemic markers (increased time in range, reduced time below and above range, and glycemic variability), and that there is a relationship between glycemic variability and hypoglycemia. Also, CGM and analysis of glucometrics proved highly useful during the COVID-19 pandemic. New glucometrics, such as the glycemic risk index, or new mathematical approaches to the analysis of CGM-derived glucose data, such as “glucodensities,” could help patients to achieve better glycemic control in the future.
Conclusion
By using glucometrics in clinical practice, clinicians can better assess glycemic control and a patient's individual response to treatment.
Plain Language Summary
Continuous glucose monitoring (CGM) devices are used to monitor glucose levels in real time over 24 h. This has changed the way people with diabetes and their health care providers interact. These devices produce a large amount of data that can be analyzed and evaluated using standardized parameters called glucometrics, which include the time a patient’s glucose is in range, below range, and above range, and how much it varies over 24 h. Clinicians can use these data to better assess glycemic control and a patient's individual response to treatment. In this article, we summarize evidence from published studies involving adults and children with type 1 diabetes in Spain to look at how the use of these data has affected clinical practice. Studies have shown that cases of severe low blood glucose in children with diabetes were greatly reduced after the introduction of CGM, resulting in a significant reduction in costs. Data from clinical practice in Spain show that CGM is associated with improved blood glucose markers. Many studies analyzed these data during the COVID-19 pandemic and showed that CGM and analysis of glucometrics were highly useful during this time. New glucometrics and approaches to the analysis of data from CGM could help patients achieve better blood glucose control.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Continuous glucose monitoring (CGM) devices are increasingly used by people with type 1 diabetes to monitor glucose levels in real time. |
Glucometrics are a set of standardized parameters used to evaluate data generated by CGM, helping clinicians to better assess glycemic control and an patient's individual response to treatment. |
The use of glucometrics information and its impact on clinical practice in the treatment or adults and children with type 1 diabetes in Spain has been reviewed in this article. |
In Spain, the analysis of glucometrics was highly useful during the COVID-19 pandemic. |
New glucometrics are being developed that could be helpful in the future to achieve better glycemic control. |
Introduction
The defining characteristic of both type 1 diabetes (T1D) and type 2 diabetes (T2D) is abnormally elevated levels of blood glucose (hyperglycemia), and glycemic control has traditionally been assessed by periodically measuring glycated hemoglobin (HbA1c) in blood. However, this approach has limitations when describing and evaluating the true state and evolution of diabetes. Firstly, although glycemic variation is one of the major components of dysglycemia in people with diabetes, periodic HbA1c measurements do not capture actual glycemic fluctuations [1], and episodes of hypo- or hyperglycemia can be undetected. Secondly, caution should be used when evaluating HbA1c levels, because interactions with hemoglobinopathies, anemias, pregnancy, chronic kidney disease, and liver disease have been described [2, 3]. Thirdly, although HbA1c remains the gold standard parameter associated with diabetes-related microvascular complications, the assumed strong relationship between HbA1c and chronic diabetes complications includes an important interindividual variation [4]. For these reasons, recent international consensus has recommended the use of other clinically meaningful outcomes beyond HbA1c as the standard of individual glycemic status [5, 6].
In recent years, due to the development of technologies for continuous glucose monitoring (CGM) devices, which allow for 24-h real-time measurement of interstitial glucose levels, the true picture of individual glycemic control has greatly expanded. A timeline of the development of CGM systems is shown in Fig. 1 [7]. CGM has allowed the creation of new metrics (collectively named “glucometrics”) for the evaluation of glycemic control. From the wealth of data generated by CGM systems, an international consensus panel concluded that these glucometrics would be highly valuable in routine clinical care [8]. Additionally, CGM technology could be the best method to recognize the differences and benefits of pharmacological diabetes treatments, such as new insulins [9, 10]. As CGM-derived metrics improve the precision and granularity of glucose measurements compared with HbA1c, clinicians can have a better understanding of glycemic management and response to treatments in each patient.
CGM is expanding rapidly among patients with diabetes due to convenience of use and increased reimbursement by national health systems [11]. In Spain, some devices are currently reimbursed by the National Health System (NHS), enabling the expansion of its use [12] (Fig. 1). In September 2018, flash glucose monitoring (FGM) was financed by the NHS for people aged ≤ 18 years with T1D [13]. In April 2019, funding of FGM was expanded to adults with T1D and, subsequently, in November 2020, to insulin-dependent patients with diabetes types other than T1D or T2D. Finally, in August 2021, the NHS expanded funding for real-time CGM (rtCGM) to adult patients with T1D at risk of severe hypoglycemia, who undergo intensive insulin therapy (multiple daily doses or with an insulin pump), who require at least six fingersticks per day for self-monitoring of blood glucose, and who show motivation to maintain good adherence to the device [14]. Regarding T2D, since April 2022, the Spanish NHS expanded funding for CGM (no specification) to patients diagnosed with T2D who undergo intensive insulin therapy (multiple daily doses or insulin pump), and require at least six fingersticks a day for self-monitoring of blood glucose. The main objective of this review is to provide a clinical expert summary and opinion of the existing evidence on the use and impact of the new glucometrics in clinical practice in Spain and discuss future perspectives.
A review of the literature was conducted to appraise key evidence, based on database searches from PubMed and MEDES (Spanish Medical literature). The search covered the years 2018–2022 and included studies involving patients with T1D or T2D written in English or Spanish, with a focus on articles using new glucometrics in the context of real-time continuous glucose monitoring or flash glucose monitoring devices conducted in Spain. The selected articles included clinical and observational studies, consensus guidelines, and meta-analyses. It did not include data from pre-clinical studies or case studies. Based on the analysis of the literature review, the panel of specialists developed this expert clinical evidence-based narrative review on the use of new glucometrics in Spain. This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors.
CGM-Derived Glucose Monitoring Metrics: Definition and Guidelines
In 2019, an international consensus report, endorsed by several national diabetes societies, confirmed the relevance and importance of the use of the new glucometrics generated by CGM [8]. These glucometrics complement HbA1c for clinical management because they add a comprehensive description of blood glucose dynamics and provide more detailed information [15]. A list of canonical glucometrics, as defined in the consensus report of 2019, is shown in Table 1 [8]. For most patients with T1D or T2D, a time in range (TIR; 70–180 mg/dL, or 3.9–10 mmol/L) > 70% is recommended, with time above range (TAR; > 180 mg/dL or 10 mmol/L) < 25%, and time below range (TBR; < 70 mg/dL or < 3.9 mmol/L) < 4%. However, targets should be individualized and adjusted to personal needs and circumstances [8, 16]. In addition to TIR, TAR, and TBR, it is recommended to consider a measure of glycemic variability, such as the coefficient of variation (CV), which is the standard deviation (SD) of the glucose readings divided by the arithmetic mean glucose [8].
TIR strongly correlates inversely with metrics showing hyperglycemia (Spearman correlation > 0.90), but the correlation was moderate with HbA1c (Spearman correlation 0.6–0.7) [17]. This suggests that patients with a specific HbA1c level can have a wide range of TIR. In a study of 545 patients with T1D, it was found that, over a period of 6 months, a TIR 70–180 of 70% and 50% corresponded on average with an HbA1c of approximately 7% and 8%, respectively. The correlation is inverse, with each increase in TIR of 10% corresponding on average to a decrease in HbA1c of 0.6% [17]. Similar results were found by another clinical study [18], and by a real-world study [19]. In 2018, the Spanish Society of Diabetes published a guideline which highlighted the scientific evidence supporting the benefits of FGM devices in both T1D and T2D, especially in reducing the time in hypoglycemia and increasing TIR [20]. A recent expert consensus has highlighted the need for improved CGM reporting [21].
The glucose management indicator (GMI) is a new glucometric that can be used to calculate an estimated HbA1c [22, 23]. It is calculated exclusively from CGM data and uses the same scale (% or mmol/mol) as HbA1c, but is based on short-term average glucose values, rather than long-term glucose exposure. CGM provides insights on the risk of hypoglycemia and daily fluctuations of glucose, but GMI use in clinical practice is still under investigation, as variations between GMI and measured HbA1c can be observed in a large percentage of individuals [23]. In this regard, a recent study found that GMI could significantly differ with respect to HbA1c in more than a third of children/adolescents with T1D, and that this discrepancy should be taken into careful consideration when the two indices are used in clinical practice [24]. Similarly, these discrepancies have been observed in patients with diabetes and chronic kidney disease [25].
CGM-Derived Glucometrics and Diabetes Complications
The use of TIR and the new glucometrics as an acceptable endpoint measure in clinical trials has been validated in several studies of patients with T1D and T2D, especially in the study of long-term diabetes complications [26,27,28,29,30,31]. For example, a recent study showed that patients’ differences in TIR can be linked to all-cause death and cardiovascular disease (CVD)-related death in patients with T2D, suggesting that TIR is also a valid marker of long-term adverse clinical outcomes [30]. In this study, the patients were classified into four groups according to TIR: > 85%, 71–85%, 51–70%, and < 50%. After a follow-up of 6.9 years, the hazard ratios associated with these levels of TIR were 1.00 (reference), 1.23, 1.30, and 1.83, respectively, for all-cause mortality (p value for trend < 0.001) and 1.00, 1.35, 1.47, and 1.85, respectively, for CVD mortality (p value for trend = 0.015) [30]. The strong inverse relationship between TIR and CV risk in patients with T2D suggest that increasing TIR could be associated with a reduction of long-term complications.
Also, lower TIR has been associated with the presence of composite microvascular complications (neuropathy, retinopathy, or nephropathy) and with hospitalization for hypoglycemia or ketoacidosis in a study of 515 patients with T1D [32]. However, in this study TIR, SD, and CV were not associated with macrovascular complications. Other studies have also highlighted the use of the new glucometrics in diabetic peripheral neuropathy in patients with T2D [33, 34], diabetes retinopathy [35], cardiovascular autonomic neuropathy [28], albuminuria [29], and other diabetes-related complications [31]. In a study of patients with T1D, an increase in TIR by using a sensor-augmented insulin pump over a period of 1 year induced a significant decrease in albuminuria [27].
Use of CGM-Derived Glucometrics in Routine Practice in Spain
The observational studies analyzing the new glucometrics conducted in Spain in recent years are shown in Table 2. All the studies identified were of patients with T1D, who currently are the main users of CGM devices.
Type 1 Diabetes in Children and Young Adults
A cross-sectional retrospective study showed that, in children with T1D using CGM, patients who were adequately controlled according to HbA1c levels had lower TIR averages than recommended in current consensus guidelines, highlighting the importance of the new glucometrics in improved glycemic control [36]. Interestingly, a cross-sectional study of Spanish patients with T1D using FGM revealed that children (< 12 years) showed the best metabolic control, with higher TIR, lower HbA1c, lower glycemic variability, lower mean glucose, and higher use of the device, compared with the adult population [37]. Also, a study including 70 pediatric patients with T1D with intensive treatment and FGM emphasized the importance of glycemic variability, as measured by the CV, to individualize TIR targets [38]. This study showed that CV modifies the relationship between the TIR and HbA1c/GMI regardless of age or the type of treatment used.
A recent study revealed a significant decrease in episodes of severe hypoglycemia (0.25 vs. 4.2 episodes of severe hypoglycemia per 100 patients in follow-up/year) after the introduction of FGM in a population of children with T1D [39], resulting in a significant reduction of costs. Further, in a study of a cohort of 145 children and young people with T1D aged < 18 years, initiation of FGM was associated with reductions in HbA1c and reduced time in hypoglycemia [40]. The analysis of change in hypoglycemia based on the number of daily sensor scans showed that there was a significant correlation between daily scan rates and the change in hypoglycemia. For TIR < 70 mg/dL, there was a significant decrease from 5.80 to 3.88% in 6 months with scan rates of > 11 scans/day. However, performing too many scans per day (> 15–20) was found to be detrimental for glycemic control [40].
Type 1 Diabetes in Adults
A study with real-world data from 22,949 patients in Spain showed that FGM devices were associated with improved glycemic markers (increased TIR, reduced TBR, TAR, and glycemic variability) [12]. This study showed that there is a relationship between glycemic variability and hypoglycemia and that the reduction of glycemic variability increases TIR (Fig. 2). Compared with studies from other countries, a greater time in hypoglycemia was observed in the Spanish population in the groups with lower scan rates, and a greater time in hyperglycemia was observed in groups with higher scan rates [12].
Recently, a retrospective study of 252 patients with T1D in the region of Asturias (Spain) concluded that, despite the positive correlation between the CGM new glucometrics derived from FGM and HbA1c, more studies are needed to determine whether these parameters can be used as a substitute for HbA1c in predicting whether complications will develop [41].
A study of 114 patients with T1D starting FGM after multiple daily injections (MDI) showed that, although glycemic parameters improved, the change resulted in a higher degree of satisfaction with the management of their diabetes and a decrease in stress associated with MDI. However, there was a modest decline in quality of life (QoL) [42].
The Impact of CGM-Derived Glucometrics in the Spanish Setting
COVID-19
The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) brought major disruption to the delivery of healthcare across the globe. For chronic disease patients such as those with diabetes, disruption of daily activities could have a major impact on glucose levels. Factors such as diet, psychosocial factors (stress, social relations, working environment) and, most importantly, exercise, were severely disrupted during the pandemic, and could have affected glucose control. Also, access to outpatient clinics was restricted as part of public health measures, limiting access to health care services. A recent study showed that the lockdown changed the characteristics of newly diagnosed children with T1D in Spain [43].
An early study of 307 patients with T1D in Spain that used FGM showed that, despite the extraordinary limitations imposed by the health authorities and the lockdown, their glycemic control improved [44]. Mean (SD) TIR increased from 57.8 (15.8)% to 62.5 (16.1)%, TAR > 180 mg/dL and > 250 mg/dL decreased from 37.3 (1.9)% to 32.0 (17.1)% and from 13.0 (11.3)% to 10.3 (10.6)%, respectively (p < 0.001), and TBR < 70 mg/dL increased from 4.9 (4.0)% to 5.5 (4.4)% (p < 0.001). It has been speculated that perhaps increased time for self-management, and increased focus on routines, were the cause of these improvements [44, 45].
A study of 92 patients with T1D showed that mean (SD) TIR (70–180 mg/dL) improved from 59.3 (16.2)% pre-lockdown to 62.6 (15.2)% during lockdown; TAR > 180 decreased from 34.4 (18.0)% to 30.7 (16.9)%, TAR > 250 decreased from 11.1 (10.6)% to 9.2 (9.7)%, and TBR remained unchanged [45].
Spanish patients with T1D using a sensor-augmented pump also had improvements in glycemic control and TIR, according to one study [46]. A statistically significant improvement in TIR (p < 0.001) was also observed after the lockdown, during the de-escalation period, according to a study of 138 patients with T1D in Spain [47]. This study also showed that continuous subcutaneous insulin infusion (CSII), poor prior glycemic control, and shorter diabetes duration were associated with better glycemic control during the lockdown [47].
Generally, the possibilities opened by telemedicine and telemonitoring have greatly improved care for patients in times of restricted clinical access. This suggests that these technologies must be more widely implemented in patients with diabetes.
Impact on Quality of Life
Publications highlight the relevance of glucometrics for patients in routine practice and suggest an impact on patient-reported outcomes (PROs) such as patient experience and satisfaction, work productivity, and health-related quality of daily life (HRQoL) [48, 49]. However, the impact of CGM-derived glucometrics on HRQoL and other PROs in patients with diabetes has not been studied in Spain. A study carried out in Portugal showed that CGM improved both glycemic control and QoL scores in the long term, and that TIR was a significant predictive factor for disease burden [50].
Questionnaires have been developed and validated to assess the use of CGM in patients with T1D during the adolescent years: the “Benefit of CGM” (BenCGM) and “Burdens of CGM” (BurCGM) [51]. These questionnaires can be used to develop targeted interventions to increase CGM wear and to improve diabetes management. In adults with T1D, some studies are starting to identify gaps in training and potential avenues for enhancing device education and CGM onboarding support [52].
Economic Implications
In Spain, FGM is currently reimbursed for all people with T1D. FGM has been shown to improve glycemic control among patients with T1D, and to reduce hypoglycemia and glycemic variability [12].
Three studies have analyzed the costs of FGM (FreeStyle Libre) versus self-monitoring of blood glucose in adults with T1D and T2D and the pediatric population with T1D in Spain [39, 53, 54]. The study in adult patients with T1D showed that, by avoiding hypoglycemic episodes, the use of FGM could result in significant annual savings for the Spanish NHS [53]. Similarly, the use of FGM in patients with T2D could potentially result in cost savings for the NHS due to the reduction in the number of severe hypoglycemic episodes, estimated at 1220 per 1000 patients/year [54]. In contrast, a study of stable well-controlled, insulin-treated patients with T2D showed that patients with CGM presented with higher numbers of hypoglycemic events than did those who were self-monitoring blood glucose, especially at night [55].
Barriers to CGM Implementation
Generally, the decision to use CGM depends on several aspects that must be considered, including expected improvement in glycemic control, technology burden, patient preferences, and cost. Therefore, currently, the widespread adoption of CGM faces several barriers. Technological literacy barriers may limit usage, especially among older populations or those with limited digital skills. In this regard, the active inclusion of family members and caregivers in sharing the educational process can be critical. Also, it has been suggested that visual or hearing difficulties should be considered when selecting devices, choosing those devices that are suitable and promote the understanding of the information provided by the glucose sensor. In these cases, personalized educational sessions or accessible training resources, such as supplementary material in written, video, or audio format, should be provided to ensure that older adults feel comfortable and competent when using CGM. Other barriers identified for the implementation of CGM include the need for device replacement every 10–14 days, the increase in anxiety when dealing with a large amount of data (in patients and caregivers), and alert fatigue if there is an excess of alerts (which can lead to omission of actions or disconnection) [56]. Regulatory hurdles and reimbursement policies may need to be streamlined to facilitate broader usage. Furthermore, cultural factors and preferences for traditional monitoring methods could also influence acceptance and uptake of CGM technology among Spanish individuals with diabetes. Researchers in Spain are beginning to investigate how to address the barriers to implementation of CGM and digital technology [57]; however, additional studies are needed to evaluate the challenges to wider implementation of CGM in Spain as, to date, there are no studies on patient preferences or potential gaps in training.
Future Perspectives
This review has highlighted the benefits of CGM in people with diabetes in Spain, and some of the barriers still to overcome for its implementation. Although many studies have focused on the use of CGM during the COVID-19 pandemic, much remains to be learned on the use and impact of CGM in children and in the elderly population, or in other populations, such as those patients with insulin-dependent diabetes of a type other than types 1 or 2. The effects of CGM on the quality of life in these populations also remains to be investigated.
The measurement of HbA1c and its clinical significance have been a main goal of therapeutical education for decades, and it is highly internalized in people with diabetes and health care providers. The substitution of this unique value for a group of many values, sometimes with a non-intuitive and very statistical appearance, will not be easy [58]. Also, the use of CGM devices and the new glucometrics as the standard for glycemic control is not without limitations. Additional studies are required to establish individualized targets for glycemic control in some populations. In this regard, it has been suggested that different targets should be considered according to time of diagnosis, age, the presence of comorbidities, and for pregnant women [21]. Ethnic characteristics including lifestyle and eating customs can influence the degree of success of implementation of CGM-derived targets based on the new glucometrics. Several studies have shown that ethnic and country-specific differences exist and should be considered to individualize treatments utilizing new glucometrics [41, 59,60,61].
Finally, the increase in data generated by CGM presents a challenge in terms of the real-world analysis, as well as an opportunity to generate evidence beyond conventional measurements. New glucometrics have been proposed for a better description of CGM-derived glucose information. The glycemia risk index (GRI, or dysglycemia index) has recently been proposed to integrate the clinical importance of hypoglycemia when the glycemic control of a person is described [62]. GRI is a composite metric that reflects in a single parameter the percentage of the time in hyper- and hypoglycemia. Therefore, a low GRI represents a better glycemic control, and the index can help clinicians and researchers determining the glycemic effects of prescribed and investigational treatments [62].
A very recent development in the analysis of CGM-derived glucose data is the “glucodensity,” which is a mathematical approach that characterizes the distribution of glucose levels across a specified time frame, integrating the proportion of time individuals spend at each glucose concentration [63, 64]. This approach may overcome some of the drawbacks of current glucose parameters and increase clinical insights into how glucose metabolism is assessed. The main advantages of the glucodensity approach are, first, that it provides a comprehensive representation of glucose levels across the whole glycemic concentration range in a single variable; second, that it provides information on the frequency of every glucose value rather than a sum of variables informing of times in several predefined ranges; third, that it allows for a comprehensive analysis of the glycemic variability; and fourth, that it has a higher sensitivity than the standard TIR value to predict diabetes biomarkers and glycemic metrics [65].
In conclusion, composite metrics for the analysis of CGM data could be useful for quickly assessing a patient’s glycemic control. It should be considered that, although they can be a first flag for more detailed evaluation of individual aspects of therapy, these metrics have often not been tested for their capacity to predict long-term complications. Finally, their relationship with widely established parameters, and adequate training of physicians, is essential before they can be widely implemented in general practice.
Data Availability and Materials
Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.
References
Beck RW, Connor CG, Mullen DM, Wesley DM, Bergenstal RM. The fallacy of average: how using HbA1c alone to assess glycemic control can be misleading. Diabetes Care. 2017;40:994–9.
Nielsen LR, Ekbom P, Damm P, Glümer C, Frandsen MM, Jensen DM, et al. HbA1c levels are significantly lower in early and late pregnancy. Diabetes Care. 2004;27:1200–1.
Ford ES, Cowie CC, Li C, Handelsman Y, Bloomgarden ZT. Iron-deficiency anemia, non-iron-deficiency anemia and HbA1c among adults in the US. J Diabetes. 2011;3:67–73.
Segar MW, Patel KV, Vaduganathan M, Caughey MC, Butler J, Fonarow GC, et al. Association of Long-term change and variability in glycemia with risk of incident heart failure among patients with type 2 diabetes: a secondary analysis of the ACCORD trial. Diabetes Care. 2020;43:1920–8.
Agiostratidou G, Anhalt H, Ball D, Blonde L, Gourgari E, Harriman KN, et al. Standardizing clinically meaningful outcome measures beyond HbA1c for type 1 diabetes: a consensus report of the American Association of Clinical Endocrinologists, the American Association of Diabetes Educators, the American Diabetes Association, the Endocrine Society, JDRF International, The Leona M. and Harry B. Helmsley Charitable Trust, the Pediatric Endocrine Society, and the T1D Exchange. Diabetes Care. 2017;40:1622–30.
Danne T, Nimri R, Battelino T, Bergenstal RM, Close KL, DeVries JH, et al. International consensus on use of continuous glucose monitoring. Diabetes Care. 2017;40:1631–40.
Hirsch IB. Introduction history of glucose monitoring. In: Role of continuous glucose monitoring in diabetes treatment arlington (VA): American Diabetes Association; [Internet]. 2018 [cited 2022 Nov 16];1–1. Available from: https://diabetesjournals.org/compendia/article/2018/1/1/144616/Introduction-History-of-Glucose-Monitoring
Battelino T, Danne T, Bergenstal RM, Amiel SA, Beck R, Biester T, et al. Clinical targets for continuous glucose monitoring data interpretation: recommendations from the International Consensus on Time in Range. Diabetes Care. 2019;42:1593–603.
Fox BQ, Benjamin PF, Aqeel A, Fitts E, Flynn S, Levine B, et al. Continuous glucose monitoring use in clinical trials for on-market diabetes drugs. Clin Diabetes. 2021;39:160–6.
Battelino T, Alexander CM, Amiel SA, Arreaza-Rubin G, Beck RW, Bergenstal RM, et al. Continuous glucose monitoring and metrics for clinical trials: an international consensus statement. Lancet Diabetes Endocrinol. 2023;11:42–57.
Maiorino MI, Signoriello S, Maio A, Chiodini P, Bellastella G, Scappaticcio L, et al. Effects of continuous glucose monitoring on metrics of glycemic control in diabetes: a systematic review with meta-analysis of randomized controlled trials. Diabetes Care. 2020;43:1146–56.
Gómez-Peralta F, Dunn T, Landuyt K, Xu Y, Merino-Torres JF. Flash glucose monitoring reduces glycemic variability and hypoglycemia: real-world data from Spain. BMJ Open Diabetes Res Care. 2020;8: e001052.
Gobierno de España. El Sistema Nacional de Salud (SNS) financia los sistemas de monitorización de glucosa mediante sensores (tipo flash) a los menores de 18 años con diabetes tipo 1 (Accessed 12 January 2022) [Internet]. 2018. Available from: https://www.lamoncloa.gob.es/serviciosdeprensa/notasprensa/sanidad/Paginas/2018/190918diabetes.aspx
Gobierno de España. El SNS amplía la financiación de los sistemas de monitorización continua de glucosa en tiempo real. Government of Spain. https://www.lamoncloa.gob.es/serviciosdeprensa/notasprensa/sanidad14/Paginas/2021/270821-glucosa.aspx (Accessed on 12 January 2022). 2021
Holt RIG, DeVries JH, Hess-Fischl A, Hirsch IB, Kirkman MS, Klupa T, et al. The management of type 1 diabetes in adults. a consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetologia. 2021;64:2609–52.
Dovc K, Battelino T. Time in range centered diabetes care. Clin Pediatr Endocrinol. 2021;30:1–10.
Beck RW, Bergenstal RM, Cheng P, Kollman C, Carlson AL, Johnson ML, et al. The relationships between time in range, hyperglycemia metrics, and HbA1c. J Diabetes Sci Technol. 2019;13:614–26.
Fabris C, Heinemann L, Beck R, Cobelli C, Kovatchev B. Estimation of hemoglobin A1c from continuous glucose monitoring data in individuals with type 1 diabetes: is time in range all we need? Diabetes Technol Ther. 2020;22:501–8.
Valenzano M, Cibrario Bertolotti I, Valenzano A, Grassi G. Time in range-A1c hemoglobin relationship in continuous glucose monitoring of type 1 diabetes: a real-world study. BMJ Open Diabetes Res Care. 2021;9: e001045.
Díaz-Soto G, Beato P, Antuña R, Giménez M, Bahillo P, Vázquez F, et al. Documento de consenso SED sobre monitorización a demanda (FLASH) de glucosa. 2018. Available at https://www.sediabetes.org/wp-content/uploads/Documento-de-Consenso-sobre-Monitorizacion-a-Demanda-flash-de-glucosa.pdf.
Bellido V, Aguilera E, Cardona-Hernandez R, Diaz-Soto G, Pérez G, de Villar N, Picón-César MJ, et al. Expert recommendations for using time-in-range and other continuous glucose monitoring metrics to achieve patient-centered glycemic control in people with diabetes. J Diabetes Sci Technol. 2021. https://doi.org/10.1177/19322968221088601.
Bergenstal RM, Beck RW, Close KL, Grunberger G, Sacks DB, Kowalski A, et al. Glucose management indicator (GMI): a new term for estimating A1C from continuous glucose monitoring. Diabetes Care. 2018;41:2275–80.
Gómez-Peralta F, Choudhary P, Cosson E, Irace C, Rami-Merhar B, Seibold A. Understanding the clinical implications of differences between glucose management indicator and glycated haemoglobin. Diabetes Obes Metab. 2022;24:599–608.
Piona C, Marigliano M, Mozzillo E, Di Candia F, Zanfardino A, Iafusco D, et al. Evaluation of HbA1c and glucose management indicator discordance in a population of children and adolescents with type 1 diabetes. Pediatr Diabetes. 2022;23:84–9.
Oriot P, Viry C, Vandelaer A, Grigioni S, Roy M, Philips JC, et al. discordance between glycated hemoglobin A1c and the glucose management indicator in people with diabetes and chronic kidney disease. J Diabetes Sci Technol. 2022. https://doi.org/10.1177/19322968221092050.
Beck RW, Bergenstal RM, Riddlesworth TD, Kollman C, Li Z, Brown AS, et al. Validation of time in range as an outcome measure for diabetes clinical trials. Diabetes Care. 2019;42:400–5.
Ranjan AG, Rosenlund SV, Hansen TW, Rossing P, Andersen S, Nørgaard K. Improved time in range over 1 year is associated with reduced albuminuria in individuals with sensor-augmented insulin pump-treated type 1 diabetes. Diabetes Care. 2020;43:2882–5.
Guo Q, Zang P, Xu S, Song W, Zhang Z, Liu C, et al. Time in range, as a novel metric of glycemic control, is reversely associated with presence of diabetic cardiovascular autonomic neuropathy independent of HbA1c in Chinese Type 2 diabetes. J Diabetes Res. 2020;2020:5817074.
Yoo JH, Choi MS, Ahn J, Park SW, Kim Y, Hur KY, et al. Association between continuous glucose monitoring-derived time in range, other core metrics, and albuminuria in Type 2 diabetes. Diabetes Technol Ther. 2020;22:768–76.
Lu J, Wang C, Shen Y, Chen L, Zhang L, Cai J, et al. Time in Range in relation to all-cause and cardiovascular mortality in patients with type 2 diabetes: a prospective cohort study. Diabetes Care. 2021;44:549–55.
Kuroda N, Kusunoki Y, Osugi K, Ohigashi M, Azuma D, Ikeda H, et al. Relationships between time in range, glycemic variability including hypoglycemia and types of diabetes therapy in Japanese patients with type 2 diabetes mellitus: Hyogo Diabetes Hypoglycemia Cognition Complications study. J Diabetes Investig. 2021;12:244–53.
El Malahi A, Van Elsen M, Charleer S, Dirinck E, Ledeganck K, Keymeulen B, et al. Relationship between time in range, glycemic variability, HbA1c, and complications in adults with type 1 diabetes mellitus. J Clin Endocrinol Metab. 2022;107:e570–81.
Li F, Zhang Y, Li H, Lu J, Jiang L, Vigersky RA, et al. TIR generated by continuous glucose monitoring is associated with peripheral nerve function in type 2 diabetes. Diabetes Res Clin Pract. 2020;166: 108289.
Mayeda L, Katz R, Ahmad I, Bansal N, Batacchi Z, Hirsch IB, et al. Glucose time in range and peripheral neuropathy in type 2 diabetes mellitus and chronic kidney disease. BMJ Open Diabetes Res Care. 2020;8: e000991.
Lu J, Ma X, Zhou J, Zhang L, Mo Y, Ying L, et al. Association of time in range, as assessed by continuous glucose monitoring, with diabetic retinopathy in type 2 diabetes. Diabetes Care. 2018;41:2370–6.
Porcel-Chacón R, Antúnez-Fernández C, Mora Loro M, Ariza-Jimenez A-B, Tapia Ceballos L, Jimenez Hinojosa JM, et al. Good metabolic control in children with type 1 diabetes mellitus: does glycated hemoglobin correlate with interstitial glucose monitoring using freestyle libre? JCM. 2021;10:4913. https://doi.org/10.3390/jcm10214913.
Bahíllo-Curieses MP, Díaz-Soto G, Vidueira-Martínez AM, Torres-Ballester I, Gómez-Hoyos E, de Luis-Román D. Assessment of metabolic control and use of flash glucose monitoring systems in a cohort of pediatric, adolescents, and adults patients with Type 1 diabetes. Endocrine. 2021;73:47–51.
Díaz-Soto G, Bahíllo-Curieses MP, Jimenez R, et al. Relación entre hemoglobina glucosilada, tiempo en rango y variabilidad glucémica en una cohorte de pacientes pediátricos y adultos con diabetes tipo 1 con monitorización flash de glucosa. Endocrinol, Diabetes y Nutr. 2021;68:465–71. https://doi.org/10.1016/j.endinu.2020.09.008.
Leiva-Gea I, Porcel Chacón R, Ariza Jiménez AB, Mora Loro M, Tapia-Ceballos L, Jiménez-Hinojosa J, et al. Impact on variables of severe hypoglycaemia and healthcare costs of the use of the FreeStyle system in paediatric population with type 1 diabetes mellitus. Endocrinol Diabetes Nutr. 2022;69:561–5. https://doi.org/10.1016/j.endien.2021.10.011.
Leiva-Gea I, Martos-Lirio MF, Gómez-Perea A, Ariza-Jiménez A-B, Tapia-Ceballos L, Jiménez-Hinojosa JM, et al. Metabolic control of the freestyle libre system in the pediatric population with type 1 diabetes dependent on sensor adherence. J Clin Med. 2022;11:286.
Pujante Alarcón P, Alonso Felgueroso C, Ares Blanco J, Morales Sánchez P, Lambert Goitia C, Rodríguez Escobedo R, et al. Correlación entre parámetros glucométricos de la monitorización continua flash y la hemoglobina glucosilada. Experiencia en vida real en Asturias. Endocrinol, Diabetes y Nutr. 2022. https://doi.org/10.1016/j.endinu.2021.10.008.
Gómez Hoyos E, Díaz Soto G, Nieto de la Marca MO, Sánchez Ibáñez M, del Amo SS, Torres Torres B, et al. Impacto del inicio de la monitorización flash de glucosa en la calidad de vida y en los parámetros de control glucémico en pacientes adultos con diabetes tipo 1. Endocrinol, Diabetes y Nutr. 2020;2:6–7.
Leiva-Gea I, Antúnez Fernández C, Cardona-Hernandez R, Ferrer Lozano M, Bahíllo-Curieses P, Arroyo-Díez J, et al. Increased presentation of diabetic ketoacidosis and changes in age and month of type 1 diabetes at onset during the COVID-19 pandemic in Spain. JCM. 2022;11:4338. https://doi.org/10.3390/jcm11154338.
Fernández E, Cortazar A, Bellido V. Impact of COVID-19 lockdown on glycemic control in patients with type 1 diabetes. Diabetes Res Clin Pract. 2020;166:108348. https://doi.org/10.1016/j.diabres.2020.108348.
Mesa A, Viñals C, Pueyo I, Roca D, Vidal M, Giménez M, et al. The impact of strict COVID-19 lockdown in Spain on glycemic profiles in patients with type 1 Diabetes prone to hypoglycemia using standalone continuous glucose monitoring. Diabetes Res Clin Pract. 2020;167: 108354.
Viñals C, Mesa A, Roca D, Vidal M, Pueyo I, Conget I, et al. Management of glucose profile throughout strict COVID-19 lockdown by patients with type 1 diabetes prone to hypoglycaemia using sensor-augmented pump. Acta Diabetol. 2021;58:383–8.
Moreno-Domínguez Ó, González-Pérez de Villar N, Barquiel B, Hillman-Gadea N, Gaspar-Lafuente R, Arévalo-Gómez M, et al. Factors related to improvement of glycemic control among adults with type 1 diabetes during lockdown due to COVID-19. Diabetes Technol Ther. 2021;23:399–400.
Ehrmann D, Priesterroth L, Schmitt A, Kulzer B, Hermanns N. Associations of time in range and other continuous glucose monitoring-derived metrics with well-being and patient-reported outcomes: overview and trends. Diabetes Spectr. 2021;34:149–55. https://doi.org/10.2337/ds20-0096.
Raj R, Mishra R, Jha N, Joshi V, Correa R, Kern PA. Time in range, as measured by continuous glucose monitor, as a predictor of microvascular complications in type 2 diabetes: a systematic review. BMJ Open Diab Res Care. 2022;10:e002573. https://doi.org/10.1136/bmjdrc-2021-002573.
Duarte DB, Fonseca L, Santos T, Silva VB, Puga FM, Saraiva M, et al. Impact of intermittently scanned continuous glucose monitoring on quality of life and glycaemic control in persons with type 1 diabetes: A 12-month follow-up study in real life. Diabetes Metab Syndr. 2022;16:102509.
Messer LH, Cook PF, Tanenbaum ML, Hanes S, Driscoll KA, Hood KK. CGM benefits and burdens: two brief measures of continuous glucose monitoring. J Diabetes Sci Technol. 2019;13:1135–41.
Tanenbaum ML, Messer LH, Wu CA, Basina M, Buckingham BA, Hessler D, et al. Help when you need it: perspectives of adults with T1D on the support and training they would have wanted when starting CGM. Diabetes Res Clin Pract. 2021;180:109048.
Oyagüez I, Merino-Torres JF, Brito M, Bellido V, Cardona-Hernandez R, Gomez-Peralta F, et al. Cost analysis of the flash monitoring system (FreeStyle Libre 2) in adults with type 1 diabetes mellitus. BMJ Open Diabetes Res Care. 2020;8:e001330.
Oyagüez I, Gómez-Peralta F, Artola S, Carrasco FJ, Carretero-Gómez J, García-Soidan J, et al. Cost analysis of freestyle Libre® 2 system in type 2 diabetes mellitus population. Diabetes Ther. 2021;12:2329–42. https://doi.org/10.1007/s13300-021-01064-4.
Pazos-Couselo M, García-López JM, González-Rodríguez M, Gude F, Mayán-Santos JM, Rodríguez-Segade S, et al. High incidence of hypoglycemia in stable insulin-treated type 2 diabetes mellitus: continuous glucose monitoring vs. self-monitored blood glucose Observational prospective study. Can J Diabetes. 2015;39:428–33.
Tejera Pérez C Monitorización continua de glucosa en los ancianos con diabetes tipo 2. Diabetes (Sociedad Española de Diabetes). 2024. https://www.revistadiabetes.org/wp-content/uploads/Monitorizacion-continua-de-glucosa-en-los-ancianos-con-diabetes-tipo-2.pdf.
Barajas Galindo DE, Martínez Pillado M, Ballesteros Pomar MD, Said Criado I, Ramos Bachiller B, Regueiro Martínez A, et al. Validation of a questionnaire for the analysis of digital competence in patients with type 1 diabetes mellitus. J Healthc Qual Res. 2022;37:374–81.
Nguyen M, Han J, Spanakis EK, Kovatchev BP, Klonoff DC. A review of continuous glucose monitoring-based composite metrics for glycemic control. Diabetes Technol Ther. 2020;22:613–22.
Urakami T, Yoshida K, Kuwabara R, Mine Y, Aoki M, Suzuki J, et al. Individualization of recommendations from the international consensus on continuous glucose monitoring-derived metrics in Japanese children and adolescents with type 1 diabetes. Endocr J. 2020;67:1055–62.
Bosoni P, Calcaterra V, Tibollo V, Malovini A, Zuccotti G, Mameli C, et al. Exploring the inter-subject variability in the relationship between glucose monitoring metrics and glycated hemoglobin for pediatric patients with type 1 diabetes. J Pediatr Endocrinol Metab. 2021;34:619–25.
Yoo JH, Yang SH, Kim G, Kim JH. Glucose management indicator for people with type 1 asian diabetes is different from that of the published equation: differences by glycated hemoglobin distribution. Diabetes Technol Ther. 2021;23:745–52.
Klonoff DC, Wang J, Rodbard D, Kohn MA, Li C, Liepmann D, et al. A glycemia risk index (GRI) of hypoglycemia and hyperglycemia for continuous glucose monitoring validated by clinician ratings. J Diabetes Sci Technol. 2022;17(5):1226–42.
Matabuena M, Petersen A, Vidal JC, Gude F. Glucodensities: a new representation of glucose profiles using distributional data analysis. Stat Methods Med Res. 2021;30:1445–64. https://doi.org/10.1177/0962280221998064.
Matabuena M, Félix P, García-Meixide C, Gude F. Kernel machine learning methods to handle missing responses with complex predictors. application in modelling five-year glucose changes using distributional representations. Comput Methods Programs Biomed. 2022;221:106905.
Gomez-Peralta F, Chico Ballesteros A, Marco Martínez A, Pérez Corral B, Conget Donlo I, Fuentealba Melo P, et al. Insulin glargine 300 U/ml versus insulin degludec 100 U/ml improves nocturnal glycaemic control and variability in type 1 diabetes under routine clinical practice: a glucodensities-based post hoc analysis of the OneCare study. Diabetes Obes Metab. 2024;26:1993–7.
Beato-Víbora PI, Gallego-Gamero F, Ambrojo-López A. Real-world outcomes with different technology modalities in type 1 diabetes. Nutr Metab Cardiovasc Dis. 2021;31:1845–50.
Conget I, Mangas MÁ, Morales C, Caro J, Giménez M, Borrell M, et al. Effectiveness and safety of insulin glargine 300 U/ml in comparison with insulin degludec 100 U/ml evaluated with continuous glucose monitoring in adults with type 1 diabetes and suboptimal glycemic control in routine clinical practice: The OneCARE study. Diabetes Ther. 2021;12:2993–3009.
Moreno-Fernandez J, Gomez FJ, Pinés P, González J, López J, López LM, et al. Continuous subcutaneous insulin infusion in adult type 1 diabetes mellitus patients: results from a public health system. Diabetes Technol Ther. 2019;21:440–7.
Quirós C, Jansà M, Viñals C, Giménez M, Roca D, Escarrabill J, et al. Experiences and real life management of insulin pump therapy in adults with type 1 diabetes. Endocrinol Diabetes Nutr (Engl Ed). 2019;66:117–23.
Viñals C, Quirós C, Giménez M, Conget I. Real-life management and effectiveness of insulin pump with or without continuous glucose monitoring in adults with type 1 diabetes. Diabetes Ther. 2019;10:929–36.
Beato-Víbora PI, Quirós-López C, Lázaro-Martín L, Martín-Frías M, Barrio-Castellanos R, Gil-Poch E, et al. Impact of sensor-augmented pump therapy with predictive low-glucose suspend function on glycemic control and patient satisfaction in adults and children with type 1 diabetes. Diabetes Technol Ther. 2018;20:738–43.
Medical Writing/Editorial Assistance
The authors would like to acknowledge Francisco López de Saro and Sheridan Henness (Rx Communications, Mold, UK) for medical writing assistance with the preparation of this manuscript, funded by Eli Lilly and Company.
Funding
This review was supported by Eli Lilly and Company (medical writing assistance with the preparation of the manuscript). The journal's Rapid Service Fee and Open Access Fee were funded by Eli Lilly and Company.
Author information
Authors and Affiliations
Contributions
Fernando Gómez-Peralta made substantial contributions to the conception and design of the work; the acquisition, analysis, and interpretation of data for the work; and the drafting and critical revision of the article for important intellectual content. Isabel Leiva-Gea made substantial contributions to the conception and design of the work, the interpretation of data for the work, and the drafting and critical revision of the article for important intellectual content. Natalia Duque made substantial contributions to the conception and design of the work, the analysis and interpretation of data for the work, and the drafting and critical revision of the article for important intellectual content. Esther Artime made substantial contributions to the conception and design of the work, the interpretation of data for the work, and critical revision of the article for important intellectual content. Miriam Rubio de Santos made substantial contributions to the conception and design of the work, the analysis and interpretation of data for the work, and the drafting and critical revision of the article for important intellectual content. All authors agree to be accountable for all aspects of the work.
Corresponding author
Ethics declarations
Conflict of Interest
Fernando Gómez-Peralta reports research funds for ISS study from Abbott Diabetes and speaker honoraria from Abbott Diabetes and Eli Lilly and Company. Isabel Leiva-Gea has received support from Medtronic Diabetes for educational events and reports speaker honoraria from Eli Lilly and Company. Natalia Duque, Esther Artime, and Miriam Rubio de Santos are employees and minor shareholders of Eli Lilly and Company.
Ethical Approval
This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/.
About this article
Cite this article
Gómez-Peralta, F., Leiva-Gea, I., Duque, N. et al. Impact of Continuous Glucose Monitoring and its Glucometrics in Clinical Practice in Spain and Future Perspectives: A Narrative Review. Adv Ther 41, 3471–3488 (2024). https://doi.org/10.1007/s12325-024-02943-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12325-024-02943-5