Next Article in Journal
Readily Available Index of Insulin Sensitivity Is Associated with Metabolic Dysfunction-Associated Steatotic Liver Disease and Liver Fibrosis in Patients with Type 2 Diabetes
Previous Article in Journal
Senolytic Interventions for Type 2 Diabetes: Current Evidence and Future Directions
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Diabetology
Volume 6
Issue 6
10.3390/diabetology6060049
diabetology-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Glycemia Risk Index: A New Metric to Rule Them All?

by
Gonzalo Diaz Soto
1,*,†,
Paloma Pérez López
1,†,
Pablo Fernández Velasco
1 and
Pilar Bahillo Curieses
2
1
Endocrinology Department, Hospital Clínico Universitario de Valladolid, Universidad de Valladolid, 47002 Valladolid, Spain
2
Paediatrics Department, Hospital Clínico Universitario de Valladolid, Universidad de Valladolid, 47002 Valladolid, Spain
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Diabetology 2025, 6(6), 49; https://doi.org/10.3390/diabetology6060049
Submission received: 12 March 2025 / Revised: 29 April 2025 / Accepted: 27 May 2025 / Published: 3 June 2025
Browse Figures
Versions Notes

Abstract

:
The Glycemia Risk Index (GRI) aims to summarize the overall quality of a patient’s glycemic control in a single number, and it is calculated from the hypo- and hyperglycemia times from continuous glucose monitoring, weighted by coefficients. Despite its recent appearance in 2022, this new parameter has strong international support, with almost half a hundred indexed articles already incorporating this metric into their studies. The following is a breakdown of the main papers that have used GRI, divided according to the type of treatment used, the population studied, the type of diabetes, its association with other parameters, and its relationship with chronic complications and the quality of life of people living with diabetes.

1. Introduction

Diabetes mellitus (DM) is a disease of carbohydrate metabolism that affects 537 million people worldwide, with an increase of 16% (74 million) since estimates made in 2019 [1]. Type 1 diabetes mellitus (T1D) accounts for approximately one in ten cases of diabetes, and it is the most common form of diabetes in children and adolescents, with an incidence of 17.69 cases/100,000 population-years worldwide [2].
The pathophysiology of T1D is based on an autoimmune reaction that destroys pancreatic beta cells (responsible for insulin production), leading to a situation of insulinopenia, with a lifelong need for insulin administration [3]. It is therefore essential to know an individual’s glucose levels as a fundamental pillar for monitoring and adjusting treatment [4]. Traditionally, this measurement has been carried out by digital capillary finger prick, obtaining the capillary glucose measurement at a given time. However, this technique is uncomfortable for the patient and provides only an isolated view at the time it is performed, without allowing us to anticipate changes in glucose over time.
Continuous glucose monitoring (CGM) systems allow the measurement of glucose at the interstitial subcutaneous tissue level 24 h a day. These systems are composed of a sensor, a transmitter, and a receiver, providing continuous information on interstitial glucose levels and their changes by means of trend arrows and alarms, which has allowed people with T1D to dispense with capillary blood glucose monitoring except in some specific cases [4].
These CGM systems are becoming an almost indispensable tool for clinicians to assess the quality of glycemic control [5]. Glycemic control encompasses both the risk of acute hypoglycemia and chronic hyperglycemia, which in turn is associated with long-term complications [6]. To synthesize the amount of data provided by the CGM, a widely used report recommended in the American Diabetes Association medical standards is the Ambulatory Glucose Profile (AGP) [7,8], which provides seven metrics that were endorsed in the Time in Range Consensus developed by Battelino et al. in 2019 [9]. These parameters are as follows:
Percentages of time in
-
Hypoglycemia with very low glucose level, below 54 mg/dL (TBR < 54) (level 2 hypoglycemia).
-
Hypoglycemia with low glucose level, between 54 and 70 mg/dL (TBR 54–70) (level 1 hypoglycemia).
-
Time in range of 70–180 mg/dL (TIR).
-
Hyperglycemia with high glucose level, between 180 and 250 mg/dL (TAR 180–250) (level 1 hyperglycemia).
-
Hyperglycemia with very high glucose level above 250 mg/dL (TAR > 250) (level 2 hyperglycemia).
-
Mean glucose and glucose management indicator (GMI).
-
Coefficient of variation (CV) (standard deviation/mean glucose).
These variables are highly interdependent, which means that by trying to improve one of them, the other measures may improve or worsen, making it difficult to optimize treatments to achieve adequate glycemic control. Furthermore, to interpret a CGM profile, these seven metrics must be processed simultaneously with a 14-day glucose profile, which represents a significant workload for professionals dedicated to the care of patients with diabetes. There is therefore a need for new metrics to synthesize existing data in a quick and simple way.
The American Diabetes Association standards state that the TIR measured by CGM can be used to assess glycemic control [7], with many professionals using this single figure as a guide to the quality of a patient’s glycemic control. However, the use of TIR in this context has been criticized for not being adequately sensitive to hypoglycemia, which means that it alone is not considered a suitable metric. As an alternative to TIR, other metrics have been proposed, including Blood Glucose Risk Index (BGRI), Glycemic Risk Assessment Diabetes Equation (GRADE), Index of Glycemic Control (IGC), and the J-Index (J). However, in 2018 Rodbard [10] attempted to analyze the relationship between these measures and parameters of hypo- and hyperglycemia, concluding that the more easily understandable metrics such as the percentage of TIR, TBR, or TAR had a high interrelationship and appeared to be more informative than these new ‘risk indices’. This was because the new parameters did not adequately reflect the two problems of the diabetic patient (hypo- and hyperglycemia) and, more importantly, did not assess the more extreme values (TAR > 250 or TBR < 54), or were too complex to calculate or interpret.
The development of the Glycemia Risk Index (GRI) in 2022 [10] attempts to address the shortcomings of its predecessor metrics. This new parameter aims to summarize the overall quality of a patient’s glycemic control in a single number. The GRI arises from the analysis of the different scores given by 330 international T1D experts to the CGM data of 225 insulin-treated patients with diabetes.
The analysis showed that the experts’ scores depended mainly on two components: one related to time in hypoglycemia (which was termed the hypoglycemia component, (CHypo) and the other related to time in hyperglycemia (hyperglycemia component, (CHyper), with a higher weighting of hypoglycemia over hyperglycemia and a higher weighting of extreme (both very low and very high) glucose values. The sum of these two components of hypo- and hyperglycemia weighted by coefficients allows calculation of the GRI, which has a high correlation (r = 0.95) with the scores given by experts to the glycemic profiles in the original study [11] (Figure 1). The result is a percentile (Pc), ranging from 0 to 100, with zero being the best and 100 the worst possible glycemic control, since the lower the GRI value, the shorter the different times in hypo- and hyperglycemia, and, therefore, the better controlled a given individual will be.
In addition, the GRI can be classified and graphically represented using the GRI grid (Figure 2), a graph that represents the hypoglycemic component on the horizontal axis (X) and the hyperglycemia component on the vertical axis (Y), divided by percentiles into five zones from the best (Pc: 0–20) to the worst (Pc: 80–100) glycemic control. This allows quick and simple identification of the key points to improve and control the effects on glycemic quality for professionals and their patients.

2. Methodology

A search was carried out in the source of research Pubmed® for all published articles that used the GRI in their analysis since its appearance in 2022. The terms ‘Glycemia Risk Index’, ‘Glycaemia Risk Index’ and ‘GRI’ were used for the search. The main selection criteria were based on methodological quality, the originality of the proposed approaches, and the robustness of the results obtained. Priority has been given to the inclusion of papers that present significant innovations, conceptual advances, or practical applications of GRI.
The selected articles were grouped into 6 sections according to the type of treatment used, the population studied, the type of diabetes, its association with other parameters, and its relationship with chronic complications and the quality of life of people living with diabetes.

3. GRI Scenarios

Despite its recent appearance in 2022, this new parameter has strong international support, with almost half a hundred indexed articles already incorporating this metric in their studies. The following is a breakdown of the main papers that have used GRI, grouped into 6 sections according to the field of knowledge on which they focused: continuous subcutaneous insulin infusion systems, pediatric population, other types of diabetes, relationship between GRI and other glycemic parameters, and complications of diabetes and quality of life.

3.1. Evidence in T1D with Continuous Subcutaneous Insulin Infusion (CSII)

The first to incorporate GRI (apart from the original work which described this metric) was an article by the French group of Benhamou et al. [12], in which an analysis of three randomized clinical trials with the initiation of the Hybrid Closed-Loop (HCL) Diabeloop Generation 1® system (Grenoble, France) was performed. This study showed an improvement in glycemic control after the initiation of the system of 5.1% for TIR compared to 13.2% for GRI. The reason for this difference, according to the authors, stems from the GRI formula, which takes into account the time spent in both hypoglycemia and hyperglycemia, giving greater weight to hypoglycemia and extreme values. TIR, by contrast, treats all times outside the target range as equally significant. Furthermore, in the study population transitioning to an HCL system, there was a goal of minimizing hypoglycemia, so the GRI may be a more sensitive tool for assessing improvement in glycemic control in these patients.
Other studies have examined the GRI as a new assessment parameter in patients initiating HCL systems. In the work by Lee et al. [13], a post hoc analysis was performed to evaluate the sensitivity of GRI in the assessment of glycemic quality in 61 adults with T1D randomized to 26 weeks of HCL initiation vs. 59 patients in manual mode. This study showed an improvement in GRI with HCL versus manual mode (33.5 [11.7] vs. 56.1 [14.4], respectively), with an increase in GRI greater than TIR (+22.6 vs. +14.8) as well as a significant correlation of GRI with all metrics of hypo- and hyperglycemia, suggesting that GRI may be an appropriate primary endpoint for trials of HCL systems. Good results have also been demonstrated when using GRI as a parameter to assess glycemic control in patients with HCL systems presenting with asymptomatic hypoglycemia [14,15] and in children and adolescents [12,16].

3.2. Evidence in T1D Pediatric Population

At the beginning of 2023, the first work to evaluate GRI in real clinical practice in pediatric and adult populations treated with multiple doses of insulin (MDI) or CSII and intermittent scanning (is-) CGM was published [17], since both the original article in which Klonoff et al. described the GRI [11] and Benhamou et al. [12] used data collected from some clinical trials in adults with the Dexcom G4 and G6® CGM systems (Dexcom Inc. San Diego, CA, USA). The aim of this article was to evaluate the GRI as a new metric of glycemic control in adult and pediatric patients with T1D in real clinical practice. For this purpose, a cross-sectional study of 202 patients (137 adult and 65 pediatric) on MDI or CSII (25.2% of patients on CSII) and isCGM was conducted. The results of the study showed better glycemic control by classical parameters and GRI in the pediatric and CSII group compared to adults and MDI, respectively, despite a longer time in hypoglycemia in the latter. It is therefore concluded that the GRI is a useful tool to assess the overall risk of hypo- and hyperglycemia in adult and pediatric patients with T1D regardless of the type of treatment.
Following this work, several publications have validated the use of this new parameter in the pediatric population [18,19,20]. The Turkish group of Eviz et al. [18] evaluated changes in GRI and other indices in 45 pediatric patients 6 months after the initiation of an HCL system. In this study, the mean GRI score decreased significantly from 35.66 ± 17.46 at baseline to 22.83 ± 9.08 at 6 months (p < 0.01), postulating that its incorporation with other parameters such as TIR may aid in a more comprehensive assessment of the glycemic profile. The Czech group of Santova et al. [19] analyzed data from 512 users of different HCL systems (Medtronic MiniMed 780G® (Medtronic, Northridge, CA, USA), Tandem t: slim X2® (Tandem, San Diego, CA, USA) or AndroidAPS ‘do it yourself’ system) for more than 12 months, assessing different parameters of glycemic control. This study showed that patients with MiniMed 780G® had lower rates of hypoglycemia and a significantly lower GRI than users of the other two systems. This can be explained, according to the authors, by the settings and algorithms used by the different systems, since the MiniMed 780G® configuration, which only allows users to set the insulin/carbohydrate ratio, target blood glucose and insulin duration, would reduce the potential for insulin overdose when hyperglycemia is corrected by the user. The authors conclude that these data could help individualize the use of each of the systems. Castorani’s Italian group [20] evaluated the long-term effect of early technology initiation in 54 children under the age of 4 with T1D. The 24 subjects who started CSII earlier showed better HbA1c, TIR, and GRI values at 9 years of follow-up, supporting the use of technology from the onset of T1D.

3.3. Evidence of GRI in Other Types of Diabetes Mellitus

Recently, studies on other types of diabetes besides T1D and T2D have begun to appear, such as the one by Citro F. et al. [21], where 45 pregnant women with diabetes treated with insulin were studied. This study showed that women with adverse outcomes had significantly higher total TAR (26 [12–32] % vs. 10 [4–23] %, p = 0.018) and TAR > 250 (6 [2–15] % vs. 1 [0–4] %, p = 0.004), whereas TAR1 was comparable between the two groups. Accordingly, third trimester GRI was higher in women with adverse neonatal outcomes (38 [18–49] % vs. 18 [10–31] %, p = 0.013) and, at logistic regression, slightly but significantly increased the risk of adverse neonatal outcomes (1.044 [1.004–1.086], p = 0.024). Therefore, the authors concluded that the level of hyperglycemia should always be assessed during pregnancy and that GRI, emphasizing extreme hyperglycemia, may be a novel comprehensive tool for assessing the risk of adverse neonatal outcomes.
Another study compared 447 adult patients with T1D and Latent Autoimmune Diabetes in Adults (LADA) (83.9% vs. 16.1%), and isCGM, concluding that LADA presented better control according to some glycemic parameters and a lower GRI [22]. Other studies have also been published in specific populations—one involving 37 patients with T2D and isCGM in a hospital setting [23] and another study involving patients with T1D and CSII with end-stage renal disease and hemodialysis [24]—which already included GRI among the metrics studied to evaluate results.
Also, a paper by Zong et al. [25] examines the GRI in the main topic of remission of T1D. The aim of this work was to define remission status using the GRI and other CGM metrics. They studied 140 patients with T1D, who were classified into four groups—new onset, in remission, post-remission, and non-remission—and all groups of patients except those in remission were referred to as “non-remitting”. The results of the study showed that patients in remission had better glucose control measured as a higher TIR and lower GRI than non-remitters. Furthermore, GRI correlated strongly with HbA1c (R = 0.62, p < 0.001) and was sufficient to distinguish patients in remission from non-remitters. Therefore, they conclude that GRI added to CGM metrics helps to increase our understanding of the different patterns of glycemic fluctuation in T1D and may be useful to provide individual-specific management and control strategies.

3.4. GRI and Its Relationship with Other Parameters

Regarding the relationship of GRI with other glycemic parameters, the Italian group of Piona et al. [26] conducted a multicenter study of 1067 children and adolescents with four different types of treatment: isCGM and MDI, real time (rt-) CGM and MDI, rtCGM and CSII, and HCL system. They concluded that the GRI was positively correlated with mean glucose, standard deviation, CV and HbA1c, and that the group with the lowest GRI value was the HCL system (30.8) (best control), compared to the isCGM and MDI groups which had the highest GRI value (68.4). The South Korean group of Kim et al. [27] tried to compare GRI and TIR by analyzing 524 90-day glucose continuous scanning tracings of 194 adult patients with T1D and 2 treated with insulin. As results of the study, they observed that GRI was strongly correlated not only with TIR (R = −0.974), but also with the CV (R = 0.683). To identify whether GRI differed as a function of % time in hypoglycemia even with similar TIR, CGM tracings were grouped according to TIR (50% to <60%, 60% to <70%, 70% to <80%, and ≥80%). In each TIR group, the GRI increased as the TBR increased significantly. They also observed that as TBR improved, GRI improved significantly (p = 0.003), while TIR did not (p = 0.704). In the hyperglycemia range, they observed that both TIR and GRI improved as TAR improved (p < 0.001 for both). Therefore, they concluded that GRI reflects hypoglycemia better than TIR, and that it is a useful tool for assessing the quality of glycemic control in clinical practice.
Simultaneously to this last work, another article [28] tried to assess this association, in which the effect of glycemic variability measured as CV on the relationship between the GRI and other parameters, especially TIR, was evaluated. A cross-sectional study was conducted with 202 adult and pediatric patients, showing a strong negative correlation between GRI and TIR (R = −0.917), with differences when dividing patients with low glycemic variability (CV < 36%) (R = −0.974) compared to those with higher variability (CV ≥ 36%) (R = −0.885). In addition, GRI correlated strongly and positively with HbA1c, mean glucose, glucose standard deviation, TAR > 250, and TBR < 54, and overall correlated significantly with all metrics analyzed, unlike TIR, which did not correlate significantly with hypoglycemia-related metrics such as CHypo or TBR 54–70. These data are consistent with the original GRI article [11], which showed significant correlations between GRI and the other variables in patients from clinical trials with rtCGM, as well as with previous publications on the influence of glycemic variability on parameters such as TIR and HbA1c [29,30]. Therefore, this was the first study to demonstrate the influence of glycemic variability on the relationship of GRI with other glycemic parameters, as well as to demonstrate, in line with previous publications, significant correlations between GRI and other glycemic parameters in the pediatric and adult population with T1D and isCGM in real clinical practice. Recently, another real-life study with 447 adult users of isCGM and T1D confirms our results [31].
Finally, a study conducted by the French group of Picard S. et al. [32] attempted to analyze in 136 patients with T1D the GRI threshold that could identify patients who achieved treatment targets (those of the time-in-range consensus) [9]. This study found that a GRI of 26 presented very good specificity (92%) and a negative predictive value (93%) to identify those who need further intensive support with HCL.

3.5. GRI and Chronic Complications

GRI has also been related to chronic complications of diabetes by different authors [33,34,35,36,37,38,39]. A cohort study [33] of 1204 patients with T2D followed for an average of 8.4 years in which GRI was calculated retrospectively from CGM data showed a relationship between the risk of diabetic retinopathy and a higher GRI, with a 20% increased risk (Hazard ratio 1.2) for each increase in GRI percentile. Other studies [34,35,36], have shown a strong association between GRI and indicators of diabetic nephropathy, such as albuminuria (especially macroalbuminuria) or the albumin/creatinine ratio, in patients with T1D and T2D. Two other studies [37,38], one conducted in 862 adults diagnosed with T2D, and another involving 165 patients with T1D, demonstrated an association between GRI and incidence of peripheral and autonomic neuropathy, respectively. Another study [39] even tried to relate GRI to specific cognitive domains in patients with T2D by using neuropsychological scales, without finding a relationship between test results and metrics such as GRI or TIR, although a relationship was observed between a high TBR < 45 and TAR > 250 and worse cognitive functions (memory, visuospatial ability, and executive function).

3.6. GRI and Quality of Life

Regarding the relationship of GRI with quality-of-life parameters, only one study by Marigliano’s Italian group [40] assessed satisfaction with CGM in a cohort of 210 pediatric patients with T1D using a CGM satisfaction questionnaire (CGM-SAT). This cross-sectional study showed that satisfaction with CGM was significantly related to TIR and negatively related to GRI.
The quality of life of patients with diabetes and GRI was assessed in this work [41], which studied the relationship between the GRI and psychosocial aspects through questionnaires on quality of life (Diabetes Quality of Life Questionnaire (DQoL)), DM-related stress (Diabetes Distress Scale (DDS)), perception of hypoglycemia (Clarke’s Test), treatment satisfaction (Visual Analogue Scale (VAS)), and knowledge (Diabetes Knowledge Questionnaire 2 (DKQ2)) in a cohort of 92 adult patients with T1D, treated with isCGM and MDI or CSII (21.7%). In this study, patients with a TIR > 70% and GRI < 40 showed better VAS and DDS scores, with no differences between the two metrics, and a correlation between worse GRI (>40) and TIR (<70%) scores and lower quality of life, higher diabetes-related stress and lower treatment satisfaction, with no differences in knowledge scores. Poorer metabolic control, as defined by GRI, correlated significantly with worse VAS (R = −0.209), DQoL (R = 0.205), and DDS (R = 0.205) scores. No differences were observed in the knowledge scale. The parameter that was related to a greater deterioration in quality of life, stress related to DM, and lower satisfaction with treatment was CHyper, is therefore a metric to be taken into account in the future to assess quality of life in these patients.

4. Discussion

Despite its recent appearance, the GRI has aroused great interest in the scientific community dedicated to the care of patients with diabetes, with a significant amount of evidence generated around the use of this parameter in different population subgroups (pediatrics, adults, CSII or T2D), its association with chronic complications such as retinopathy or diabetic nephropathy, its relationship with other parameters such as TIR or glycemic variability, and another main topics such as quality of life or diabetes remission.
It has proven to be a better parameter than TIR for assessing the hypoglycemia component, which makes it especially useful in populations such as pediatric patients or those treated with HCL or CSII systems. It also assesses extreme values better than other metrics, being also useful for pregnant women with insulin-treated diabetes. The fact that it is a single, easy-to-calculate, actionable parameter saves time and effort for professionals, especially for those with less experience in interpreting CGM data. Also, it allows doctors to prioritize the care of patients with poorer glycemic control, as well as to guide specific treatments based on the results of its CHypo and CHyper components, which makes the GRI a parameter with enormous projection in the era of big data.

5. Future Directions

Despite the evidence generated in recent years, there are still some issues to be resolved today such as finding standardized values of GRI for different population subgroups such as T1D or T2D, which can be taken as a reference by professionals, as with the GRI and the rest of the CGM metrics, in the search for adequate glycemic control. On the other hand, it is necessary to systematically incorporate GRI values, as well as its hypo- and hyperglycemia components and its graphic representation, into the AGP report, allowing us to use it more quickly. It is also necessary to study it in new groups of patients, such as women with gestational or pre-gestational diabetes using CSII, or in frail/elderly patients, to validate this parameter in new, specific population groups.
Other metrics that are now widely supported, such as time in tight range (TITR), have been studied as better assessors than time in range when studying populations with ambitious glycemic targets, such as those with HCL systems [42]. However, at present, there are no comparative studies between GRI and TITR, which may be an interesting point of investigation for future research.

6. Conclusions

The Glycemia Risk Index is a new glycemic parameter that aims to summarize the overall quality of a given patient’s glycemic control using a single figure and its two components (hypo- and hyperglycemia). Its usefulness lies in the fact that it is a single, easy-to-calculate, actionable parameter, which saves professionals time and effort and allows them to prioritize the care of patients with poorer glycemic control, as well as to guide specific treatments based on the results of its hypoglycemia and hyperglycemia components. It has proven to be a better parameter than TIR for assessing the hypoglycemia component and extreme values of glycaemia, which makes it especially useful in populations such as pediatric patients or those treated with CSII, and has been related to different chronic complications, as well as to quality of life in patients with diabetes.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Magliano, D.J.; Boyko, E.J.; IDF Diabetes Atlas 10th Edition Scientific Committee. IDF DIABETES ATLAS [Internet], 10th ed.; International Diabetes Federation: Brussels, Belgium, 2021. Available online: http://www.ncbi.nlm.nih.gov/books/NBK581934/ (accessed on 27 December 2023).
  2. Libman, I.; Haynes, A.; Lyons, S.; Pradeep, P.; Rwagasor, E.; Tung, J.Y.L.; Jefferies, C.A.; Oram, R.A.; Dabelea, D.; Craig, M.E. ISPAD Clinical Practice Consensus Guidelines 2022: Definition, epidemiology, and classification of diabetes in children and adolescents. Pediatr. Diabetes 2022, 23, 1160–1174. [Google Scholar] [CrossRef] [PubMed]
  3. American Diabetes Association Professional Practice Committee. 2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes-2024. Diabetes Care 2024, 47 (Suppl. S1), S20–S42. [Google Scholar] [CrossRef] [PubMed]
  4. American Diabetes Association Professional Practice Committee. 7. Diabetes Technology: Standards of Care in Diabetes-2024. Diabetes Care 2024, 47 (Suppl. S1), S126–S144. [Google Scholar] [CrossRef]
  5. Holt, R.I.G.; DeVries, J.H.; Hess-Fischl, A.; Hirsch, I.B.; Kirkman, M.S.; Klupa, T.; Ludwig, B.; Nørgaard, K.; Pettus, J.; Renard, E.; 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). Diabetes Care 2021, 44, 2589–2625. [Google Scholar] [CrossRef] [PubMed]
  6. Karter, A.J.; Parker, M.M.; Moffet, H.H.; Gilliam, L.K.; Dlott, R. Association of Real-time Continuous Glucose Monitoring With Glycemic Control and Acute Metabolic Events Among Patients With Insulin-Treated Diabetes. JAMA 2021, 325, 2273–2284. [Google Scholar] [CrossRef] [PubMed]
  7. American Diabetes Association Professional Practice Committee. 6. Glycemic Goals and Hypoglycemia: Standards of Care in Diabetes-2024. Diabetes Care 2024, 47 (Suppl. S1), S111–S125. [Google Scholar] [CrossRef]
  8. Bergenstal, R.M.; Ahmann, A.J.; Bailey, T.; Beck, R.W.; Bissen, J.; Buckingham, B.; Deeb, L.; Dolin, R.H.; Garg, S.K.; Goland, R.; et al. Recommendations for standardizing glucose reporting and analysis to optimize clinical decision making in diabetes: The ambulatory glucose profile. J. Diabetes Sci. Technol. 2013, 7, 562–578. [Google Scholar] [CrossRef]
  9. Battelino, T.; Danne, T.; Bergenstal, R.M.; Amiel, S.A.; Beck, R.; Biester, T.; Bosi, E.; Buckingham, B.A.; Cefalu, W.T.; Close, K.L.; et al. Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range. Diabetes Care 2019, 42, 1593–1603. [Google Scholar] [CrossRef]
  10. Rodbard, D. Metrics to Evaluate Quality of Glycemic Control: Comparison of Time in Target, Hypoglycemic, and Hyperglycemic Ranges with “Risk Indices”. Diabetes Technol. Ther. 2018, 20, 325–334. [Google Scholar] [CrossRef]
  11. Klonoff, D.C.; Wang, J.; Rodbard, D.; Kohn, M.A.; Li, C.; Liepmann, D.; Kerr, D.; Ahn, D.; Peters, A.L.; Umpierrez, G.E.; 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, 1226–1242. [Google Scholar] [CrossRef]
  12. Benhamou, P.-Y.; Adenis, A.; Tourki, Y.; Pou, S.; Madrolle, S.; Franc, S.; Kariyawasam, D.; Beltrand, J.; Klonoff, D.C.; Charpentier, G. Efficacy of a Hybrid Closed-Loop Solution in Patients with Excessive Time in Hypoglycaemia: A Post Hoc Analysis of Trials With DBLG1 System. J. Diabetes Sci. Technol. 2022, 18, 372–379. [Google Scholar] [CrossRef] [PubMed]
  13. Lee, M.H.; Vogrin, S.; Jones, T.W.; O’Neal, D.N. Hybrid Closed-Loop Versus Manual Insulin Delivery in Adults with Type 1 Diabetes: A Post Hoc Analysis Using the Glycemia Risk Index. J. Diabetes Sci. Technol. 2024, 18, 764–770. [Google Scholar] [CrossRef] [PubMed]
  14. Lee, M.H.; Gooley, J.; Obeyesekere, V.; Lu, J.; Paldus, B.; Hendrieckx, C.; MacIsaac, R.J.; McAuley, S.A.; Speight, J.; Vogrin, S.; et al. Hybrid Closed Loop in Adults with Type 1 Diabetes and Severely Impaired Hypoglycemia Awareness. J. Diabetes Sci. Technol. 2024, 19322968241245627. [Google Scholar] [CrossRef]
  15. Resmini, E.; Zarra, E.; Dotti, S.; Rotondi, G.; Cornaghi, A.V.; Madaschi, S.; Cimino, E.; Massari, G.; Pezzaioli, L.C.; Buoso, C.; et al. Impact on Glycemia Risk Index and other metrics in type 1 adult patients switching to Advanced Hybrid Closed-Loop systems: A one-year real-life experience. Eur. J. Med. Res. 2024, 29, 365. [Google Scholar] [CrossRef]
  16. Passanisi, S.; Salzano, G.; Bombaci, B.; Minuto, N.; Bassi, M.; Bonfanti, R.; Scialabba, F.; Mozzillo, E.; Di Candia, F.; Monti, S.; et al. Sustained Effectiveness of an Advanced Hybrid Closed Loop System in a Cohort of Children and Adolescents with Type 1 Diabetes: A 1-Year Real-World Study. Diabetes Care 2024, 47, 1084–1091. [Google Scholar] [CrossRef]
  17. Díaz-Soto, G.; Pérez-López, P.; Férnandez-Velasco, P.; Nieto de la Marca, M.O.; Delgado, E.; Del Amo, S.; de Luis, D.; Bahillo-Curieses, P. Glycemia Risk Index Assessment in a Pediatric and Adult Patient Cohort with Type 1 Diabetes Mellitus. J. Diabetes Sci. Technol. 2024, 18, 1063–1069. [Google Scholar] [CrossRef]
  18. Eviz, E.; Mutlu, G.Y.; Karakus, K.E.; Can, E.; Gokce, T.; Muradoglu, S.; Hatun, S. The Advanced Hybrid Closed Loop Improves Glycemia Risk Index, Continuous Glucose Monitoring Index, and Time in Range in Children with Type 1 Diabetes: Real-World Data from a Single Center Study. Diabetes Technol. Ther. 2023, 25, 689–696. [Google Scholar] [CrossRef] [PubMed]
  19. Santova, A.; Plachy, L.; Neuman, V.; Pavlikova, M.; Petruzelkova, L.; Konecna, P.; Venhacova, P.; Skvor, J.; Pomahacova, R.; Neumann, D.; et al. Are all HCL systems the same? long term outcomes of three HCL systems in children with type 1 diabetes: Real-life registry-based study. Front. Endocrinol. 2023, 14, 1283181. [Google Scholar] [CrossRef]
  20. Castorani, V.; Favalli, V.; Rigamonti, A.; Frontino, G.; Di Tonno, R.; Morotti, E.; Sandullo, F.; Scialabba, F.; Arrigoni, F.; Dionisi, B.; et al. A comparative study using insulin pump therapy and continuous glucose monitoring in newly diagnosed very young children with type 1 diabetes: It is possible to bend the curve of HbA1c. Acta Diabetol. 2023, 60, 1719–1726. [Google Scholar] [CrossRef]
  21. Citro, F.; Bianchi, C.; Belcari, T.; Galleano, F.; Venturi, C.; Battini, L.; Marchetti, P.; Bertolotto, A.; Aragona, M. Predicting the Risk of Adverse Neonatal Outcomes in Women with Insulin-Treated Diabetes: Is It Time for a Pregnancy-Specific Glycemic Risk Index (GRI)? J. Diabetes Sci. Technol. 2024, 19322968241289957. [Google Scholar] [CrossRef]
  22. Herranz-Antolín, S.; Cotón-Batres, C.; López-Virgos, M.C.; Esteban-Monge, V.; Álvarez-de Frutos, V.; Torralba, M. Factors associated with Glycemia Risk Index in a cohort of patients with type 1 Diabetes Mellitus and Latent Autoimmune Diabetes in Adults (LADA). Endocrine 2024, 86, 574–583. [Google Scholar] [CrossRef]
  23. Rivas-Montenegro, A.; Añez-Ramos, R.; Galdón-Sanz Pastor, A.; González-Albarrán, O. Continuous Glucose Monitoring in Hospitalized Patients with Type 2 Diabetes: A Step Forward in Inpatient Glycemic Control. Endocr. Pract. 2025, 31, 564–570. [Google Scholar] [CrossRef] [PubMed]
  24. Ayers, A.T.; Ho, C.N.; Hussain, S.; Klonoff, D.C. Comparison of the glycemia risk index and time in range in a case series of using automated insulin delivery to improve glycemic control in people with type 1 diabetes and end stage kidney disease on hemodialysis. Diabetes Res. Clin. Pract. 2025, 219, 111940. [Google Scholar] [CrossRef]
  25. Zhong, T.; He, B.; Li, X.; Lei, K.; Tang, R.; Zhao, B.; Li, X. Glycaemia risk index uncovers distinct glycaemic variability patterns associated with remission status in type 1 diabetes. Diabetologia 2024, 67, 42–51. [Google Scholar] [CrossRef] [PubMed]
  26. Piona, C.; Marigliano, M.; Roncarà, C.; Mozzillo, E.; Di Candia, F.; Zanfardino, A.; Iafusco, D.; Maltoni, G.; Zucchini, S.; Piccinno, E.; et al. Glycemia Risk Index as a Novel Metric to Evaluate the Safety of Glycemic Control in Children and Adolescents with Type 1 Diabetes: An Observational, Multicenter, Real-Life Cohort Study. Diabetes Technol. Ther. 2023, 25, 507–512. [Google Scholar] [CrossRef]
  27. Kim, J.Y.; Yoo, J.H.; Kim, J.H. Comparison of glycemic risk index (GRI) with time in range for assessing glycemic quality. Diabetes Technol. Ther. 2023, 25, 883–892. [Google Scholar] [CrossRef]
  28. Pérez-López, P.; Férnandez-Velasco, P.; Bahillo-Curieses, P.; de Luis, D.; Díaz-Soto, G. Impact of glucose variability on the assessment of the glycemia risk index (GRI) and classic glycemic metrics. Endocrine 2023, 82, 560–568. [Google Scholar] [CrossRef] [PubMed]
  29. Díaz-Soto, G.; Bahíllo-Curieses, M.P.; Jimenez, R.; Nieto, M.d.l.O.; Gomez, E.; Torres, B.; Gomez, J.J.L.; de Luis, D. The relationship between glycosylated hemoglobin, time-in-range and glycemic variability in type 1 diabetes patients under flash glucose monitoring. Endocrinol. Diabetes Nutrición (Engl. Ed.) 2021, 68, 465–471. [Google Scholar] [CrossRef]
  30. Lu, J.; Ma, X.; Zhang, L.; Mo, Y.; Lu, W.; Zhu, W.; Bao, Y.; Jia, W.; Zhou, J. Glycemic variability modifies the relationship between time in range and hemoglobin A1c estimated from continuous glucose monitoring: A preliminary study. Diabetes Res. Clin. Pract. 2020, 161, 108032. [Google Scholar] [CrossRef]
  31. Herranz-Antolín, S.; Coton-Batres, C.; López-Virgos, M.C.; Esteban-Monge, V.; Álvarez-de Frutos, V.; Pekarek, L.; Torralba, M. Glycemic Risk Index in a Cohort of Patients with Type 1 Diabetes Mellitus Stratified by the Coefficient of Variation: A Real-Life Study. Diabetes Technol. Ther. 2024, 26, 960–967. [Google Scholar] [CrossRef]
  32. Picard, S.; Courbebaisse, B.; Dupont, J.; Amiot-Chapoutot, F.; Lecornet-Sokol, E.; Personeni, E.; Mougel, F.; Bouché, C.; Giroud, F.; Lablanche, S.; et al. Glycemia Risk Index (GRI) and international glucose targets before and 6 months after initiation of hybrid closed loop system in the CIRDIA, a French multisite out-of-hospital center. Diabetes Metab. 2025, 51, 101617. [Google Scholar] [CrossRef] [PubMed]
  33. Wang, Y.; Lu, J.; Ni, J.; Wang, M.; Shen, Y.; Lu, W.; Zhu, W.; Bao, Y.; Rodbard, D.; Vigersky, R.A.; et al. Association between glycaemia risk index (GRI) and diabetic retinopathy in type 2 diabetes: A cohort study. Diabetes Obes. Metab. 2023, 25, 2457–2463. [Google Scholar] [CrossRef] [PubMed]
  34. Takagi, A.; Kusunoki, Y.; Ohigashi, M.; Osugi, K.; Inoue, C.; Inoue, M.; Tsunoda, T.; Kadoya, M.; Konishi, K.; Katsuno, T.; et al. Association between continuous glucose monitoring-derived glycemic control indices and urinary biomarkers of diabetic kidney disease: Hyogo Diabetes Hypoglycemia Cognition Complications study. Acta Diabetol. 2024, 61, 413–423. [Google Scholar] [CrossRef] [PubMed]
  35. Yoo, J.H.; Kim, J.Y.; Kim, J.H. Association Between Continuous Glucose Monitoring-Derived Glycemia Risk Index and Albuminuria in Type 2 Diabetes. Diabetes Technol. Ther. 2023, 25, 726–735. [Google Scholar] [CrossRef]
  36. Kim, J.Y.; Yoo, J.H.; Kim, N.H.; Kim, J.H. Glycemia Risk Index is Associated with Risk of Albuminuria Among Individuals with Type 1 Diabetes. J. Diabetes Sci. Technol. 2025, 19322968241310850. [Google Scholar] [CrossRef]
  37. Tang, Y.; Zhang, P.; Li, L.; Li, J. Diabetic Peripheral Neuropathy and Glycemia Risk Index in Type 2 Diabetes: A Cross-Sectional Study. Diabetes Metab. Syndr. Obes. 2024, 17, 4191–4198. [Google Scholar] [CrossRef]
  38. Jun, J.E.; Lee, Y.B.; Kim, J.H. Association of Continuous Glucose Monitoring-Derived Glycemia Risk Index with Cardiovascular Autonomic Neuropathy in Patients with Type 1 Diabetes Mellitus: A Cross-sectional Study. J. Diabetes Sci. Technol. 2024, 19322968241288579. [Google Scholar] [CrossRef]
  39. Dong, S.; Wang, L.; Zhao, C.; Zhang, R.; Gao, Z.; Jiang, L.; Guo, Y.; Zhou, H.; Xu, S. Relationship between key continuous glucose monitoring-derived metrics and specific cognitive domains in patients with type 2 diabetes mellitus. BMC Neurol. 2023, 23, 200. [Google Scholar] [CrossRef]
  40. Marigliano, M.; Pertile, R.; Mozzillo, E.; Troncone, A.; Maffeis, C.; Morotti, E.; Di Candia, F.; Fedi, L.; Iafusco, D.; Zanfardino, A.; et al. Satisfaction with continuous glucose monitoring is positively correlated with time in range in children with type 1 diabetes. Diabetes Res. Clin. Pract. 2023, 204, 110895. [Google Scholar] [CrossRef]
  41. Díaz-Soto, G.; Pérez-López, P.; Férnandez-Velasco, P.; Bahillo-Curieses, P.; de la ONieto de la Marca, M.; Jimenez, R.; de Luis, D. Quality of life, diabetes-related stress and treatment satisfaction are correlated with glycemia risk index (GRI), time in range and hypoglycemia/hyperglycemia components in type 1 diabetes. Endocrine 2024, 86, 186–193. [Google Scholar] [CrossRef]
  42. Bahillo-Curieses, P.; Fernández Velasco, P.; Pérez-López, P.; Vidueira Martínez, A.M.; Nieto de la Marca, M.O.; Díaz-Soto, G. Utility of time in tight range (TITR) in evaluating metabolic control in pediatric and adult patients with type 1 diabetes in treatment with advanced hybrid closed-loop systems. Endocrine 2024, 86, 539–545. [Google Scholar] [CrossRef] [PubMed]
Diabetology 06 00049 g001
Figure 1. Glycemia Risk Index calculation formula.
Figure 1. Glycemia Risk Index calculation formula.
Diabetology 06 00049 g001
Diabetology 06 00049 g002
Figure 2. Graphical representation of the glycemia risk index (GRI grid). The vertical axis shows the hyperglycemia component, and the horizontal axis shows the hypoglycemia component. The results in percentiles are divided into zones (A–E) from the best (Pc: 0–20) to the worst (Pc: 80–100) glycemic control.
Figure 2. Graphical representation of the glycemia risk index (GRI grid). The vertical axis shows the hyperglycemia component, and the horizontal axis shows the hypoglycemia component. The results in percentiles are divided into zones (A–E) from the best (Pc: 0–20) to the worst (Pc: 80–100) glycemic control.
Diabetology 06 00049 g002
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Diaz Soto, G.; Pérez López, P.; Fernández Velasco, P.; Bahillo Curieses, P. Glycemia Risk Index: A New Metric to Rule Them All? Diabetology 2025, 6, 49. https://doi.org/10.3390/diabetology6060049

AMA Style

Diaz Soto G, Pérez López P, Fernández Velasco P, Bahillo Curieses P. Glycemia Risk Index: A New Metric to Rule Them All? Diabetology. 2025; 6(6):49. https://doi.org/10.3390/diabetology6060049

Chicago/Turabian Style

Diaz Soto, Gonzalo, Paloma Pérez López, Pablo Fernández Velasco, and Pilar Bahillo Curieses. 2025. "Glycemia Risk Index: A New Metric to Rule Them All?" Diabetology 6, no. 6: 49. https://doi.org/10.3390/diabetology6060049

APA Style

Diaz Soto, G., Pérez López, P., Fernández Velasco, P., & Bahillo Curieses, P. (2025). Glycemia Risk Index: A New Metric to Rule Them All? Diabetology, 6(6), 49. https://doi.org/10.3390/diabetology6060049

Article Metrics

Back to TopTop