Association between glycaemia risk index (GRI) and diabetic retinopathy in type 2 diabetes: A cohort study.

AIM: To investigate the association between a new composite metric, glycaemia risk index (GRI), and incident diabetic retinopathy (DR). METHODS: A total of 1204 adults with type 2 diabetes without DR at baseline were included between 2005 and 2019 from a single centre in Shanghai, China. GRI was obtained from continuous glucose monitoring data at baseline. Cox proportion hazard regression analysis was used to assess the association between GRI and the risk of incident DR. RESULTS: During a median follow-up of 8.4 years, 301 patients developed DR. The multivariable-adjusted hazard ratios (HRs) for incident DR across ascending GRI quartiles (≤14 [reference], 15 ~ 28, 29 ~ 47 and > 47) were 1.00, 1.05 (95% CI 0.74-1.48), 1.33 (95% confidence interval [CI] 0.96-1.84) and 1.53 (95% CI 1.11-2.11), respectively. For each 1-SD increase in GRI, the risk of DR was increased by 20% (HR 1.20, 95% CI 1.07-1.33) after adjustment for confounders. CONCLUSIONS: In patients with type 2 diabetes, higher GRI is associated with an increased risk of incident DR. GRI has the potential to be a valuable clinical measure, which needs to be further explored in future studies.

Proof-of-concept Application of Continuous Glucose Monitoring Data Analytics to Identify Diabetes Glucotypes.

Background: In this proof-of-concept study, we evaluated if monogenic diabetes resulting from mutations of the HNF-1α gene (HNF1A-MODY) has a distinctive continuous glucose monitoring (CGM) glucotype, in comparison to type 1 diabetes (T1D). Methods: Using CGM data from 5 subjects with HNF1A-MODY and 115 subjects with T1D, we calculated multiple glucose metrics, including measures of within- and between-day variability (such as coefficient variation for each hour [CVb_1h]). Results: The MODY and T1D cohorts had minimum CVb_1h of 11.3 ± 4.4 and 18.0 ± 4.9, respectively (P = .02) and maximum CVb_1h of 33.9 ± 5.0 and 50.3 ± 10, respectively (P < .001). All subjects with HNF1A-MODY had a minimum %CVb_1h ≤ 17.3% and maximum %CVb_1h ≤ 37.1%. In contrast, only 12 of 115 subjects with T1D had both a minimum and maximum %CVb_1h below these thresholds (P < .001). Conclusion: HNF1A- MODY is characterized by a low hourly, between-day glucose variability. CGM-derived glucose metrics may have potential applicability for screening for atypical diabetes phenotypes in the T1D population.

A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings.

BACKGROUND: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. METHODS: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. RESULTS: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. CONCLUSION: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

Continuous glucose monitoring metrics (Mean Glucose, time above range and time in range) are superior to glycated haemoglobin for assessment of therapeutic efficacy.

AIM: To evaluate continuous glucose monitoring (CGM) metrics for use as alternatives to glycated haemoglobin (HbA1c) to evaluate therapeutic efficacy. METHODS: We re-analysed correlations among CGM metrics from studies involving 545 people with type 1 diabetes (T1D), 5910 people with type 2 diabetes (T2D) and 98 people with T1D during pregnancy and the postpartum period. RESULTS: Three CGM metrics, interstitial fluid Mean Glucose level, proportion of time above range (%TAR) and proportion of time in range (%TIR), were correlated with HbA1c and provided metrics that can be used to evaluate therapeutic efficacy. Mean Glucose showed the highest correlation with %TAR (r = 0.98 in T1D, 0.97 in T2D) but weaker correlations with %TIR (r = -0.92 in T1D, -0.83 in T2D) or with HbA1c (r = 0.78 in T1D). %TAR and %TIR were highly correlated (r = -0.96 in T1D, -0.91 in T2D). After 6 months of use of real-time CGM by people with T1D, changes in Mean Glucose level were more highly correlated with changes in %TAR (r = 0.95) than with changes in %TIR (r = -0.85) or with changes in HbA1c level (r = 0.52). These metrics can be combined with metrics of hypoglycaemia and/or glycaemic variability to provide a more comprehensive assessment of overall quality of glycaemic control. CONCLUSION: The CGM metrics %TAR and %TIR show much higher correlations with Mean Glucose than with HbA1c and provide sensitive indicators of efficacy. Mean glucose may be the best metric and shows consistently higher correlations with %TAR than with %TIR.

Quality of Glycemic Control: Assessment Using Relationships Between Metrics for Safety and Efficacy.

Numerous methods have been proposed as measures of quality of glycemic control resulting in confusion regarding the best choice of metric to use by clinicians and researchers. Some methods use a single metric such as HbA1c, Mean Glucose, %Time In Range (%TIR), or Coefficient of Variation (%CV). Others use a combination of up to seven metrics, for example, Q-Score, Comprehensive Glucose Pentagon (CGP), and Personal Glycemic State (PGS). A recently proposed Composite continuous Glucose monitoring index utilizes three metrics: %TIR, Time Below Range (%TBR), and standard deviation (SD) of glucose. This review proposes that only two metrics can be sufficient when monitoring an individual patient or when comparing two or more forms of management interventions. These two metrics comprise (1) a measure of efficacy such as Mean Glucose, HbA1c, %TIR, or %Time Above Range (%TAR) and (2) a measure of safety based on risk of hypoglycemia such as %TBR, Low Blood Glucose Index (LBGI), or frequency of specified types of hypoglycemic events per patient year. By analysis of the two-dimensional graphical and statistical relationships between metrics for safety and efficacy and by testing identity versus nonidentity of these relationships, one can improve sensitivity for detection of the effects of medications and of other therapeutic interventions, avoid the need for arbitrary scoring systems for glucose values falling within versus outside the target range, and offer the advantage of conceptual and practical simplicity.

American Association of Clinical Endocrinology Clinical Practice Guideline: The Use of Advanced Technology in the Management of Persons With Diabetes Mellitus.

OBJECTIVE: To provide evidence-based recommendations regarding the use of advanced technology in the management of persons with diabetes mellitus to clinicians, diabetes-care teams, health care professionals, and other stakeholders. METHODS: The American Association of Clinical Endocrinology (AACE) conducted literature searches for relevant articles published from 2012 to 2021. A task force of medical experts developed evidence-based guideline recommendations based on a review of clinical evidence, expertise, and informal consensus, according to established AACE protocol for guideline development. MAIN OUTCOME MEASURES: Primary outcomes of interest included hemoglobin A1C, rates and severity of hypoglycemia, time in range, time above range, and time below range. RESULTS: This guideline includes 37 evidence-based clinical practice recommendations for advanced diabetes technology and contains 357 citations that inform the evidence base. RECOMMENDATIONS: Evidence-based recommendations were developed regarding the efficacy and safety of devices for the management of persons with diabetes mellitus, metrics used to aide with the assessment of advanced diabetes technology, and standards for the implementation of this technology. CONCLUSIONS: Advanced diabetes technology can assist persons with diabetes to safely and effectively achieve glycemic targets, improve quality of life, add greater convenience, potentially reduce burden of care, and offer a personalized approach to self-management. Furthermore, diabetes technology can improve the efficiency and effectiveness of clinical decision-making. Successful integration of these technologies into care requires knowledge about the functionality of devices in this rapidly changing field. This information will allow health care professionals to provide necessary education and training to persons accessing these treatments and have the required expertise to interpret data and make appropriate treatment adjustments.

Insulin Metrics: Need for Development of Consensus Standards for Reporting of Insulin Dosing Data.

The advent of connected insulin pens will generate an avalanche of digital insulin data, especially in the context of prandial- and multiple daily injection-insulin regimens. There is a need for the diabetes community to develop standards for such data, analogous to what has been achieved using the ambulatory glucose profile and associated metrics for glucose, permitting harmonization of data reporting for multiple devices and facilitating integration of glucose, insulin, food intake, and physical activity data. Several studies have estimated the timing of meals by analyses of glucose excursions but using diverse criteria. There is need for uniform criteria for multiple types of insulin boluses, including premeal, perimeal, delayed, missed, and correction boluses to facilitate research studies and patient care. This article contains a first preliminary proposal for standards regarding reporting of insulin dosing data. Clinical usage of these reports will require sensitive communication between health care providers and patients.

Standardizing Reporting of Glucose and Insulin Data for Patients on Multiple Daily Injections Using Connected Insulin Pens and Continuous Glucose Monitoring.

Background: Recent development and availability of several connected insulin pens with digital memory are likely to expand the availability of glucose and insulin metrics that previously had been available only for the much smaller number of people using insulin pumps. It would be highly desirable to standardize data presentations to avoid the chaotic emergence of multiple formats that might reduce the clinical utility of connected pens. Methods: We reviewed the literature and analyzed data displays from multiple blood glucose monitoring, continuous glucose monitoring (CGM), insulin pump, and automated insulin delivery systems, and methods for combination of glucose and insulin data. We examined multiple forms of presentation and now propose a prototype for a standardized method for data analysis and display, focusing on the content and format of a one-page dashboard summary for patients on multiple daily injection (MDI) insulin regimens. Results: We propose the following metrics to be included in the one-page report: (A) glucose metrics: simplified glucose distribution in the form of a stacked bar chart showing percentages of time below-, above-, or within-target ranges overall and (optionally) by date, by time of day, or day of the week; (B) insulin metrics: types and doses, and timing of basal and bolus insulin; (C) an enhanced ambulatory glucose profile or "AGP+" showing glucose data points and/or distributions (10th to 90th percentiles), dosages and timing of basal and bolus insulins and (optionally) graphical display of risk of hypoglycemia and hyperglycemia; and (D) user experience regarding technology usage, frequency of alerts for hypo- and hyperglycemia, and information regarding lifestyle, meals, exercise, and sleep, if available; and (E) clinical insights and interpretation. Conclusion: We propose a prototype for a dashboard summary report of glucose, insulin, meals, and activity data intended for providers and patients on MDI using connected pens and CGM. Our goal is to stimulate development of a standardized approach.

Glucose Variability and Time in Range in Type 2 Diabetes Treated with U-500R by Pump or Injection: CGM Findings from the VIVID Study.

Background: The EValuating U-500R Infusion Versus Injection in Type 2 Diabetes Mellitus (VIVID) study compared two methods of U-500R insulin delivery, continuous subcutaneous insulin infusion (CSII) and multiple daily injection (MDI), for 26 weeks in people with type 2 diabetes (T2D) requiring high doses of insulin. To assess glycemic variability (GV) and time in range (TIR), a subset of participants performed masked continuous glucose monitoring (CGM). Methods: VIVID participants were adults who had insulin requirements of >200 but ≤600 U/day and A1C 7.5% to 12%. Participants performed masked CGM for seven consecutive days on each of three occasions: before weeks 0 (baseline), 14, and 26. The primary objective was to compare GV between CSII and MDI groups, based on change from baseline of within-day standard deviation (SDw) of CGM glucose. Results: Of 54 participants enrolled, 41 with evaluable data were analyzed (17 and 24 in CSII and MDI groups, respectively). The CSII group had a significantly greater reduction from baseline in mean SDw of glucose (45.0 to 38.2 mg/dL [-8.1 mg/dL]) compared with the MDI group (47.0 to 45.8 [-0.4 mg/dL]; P = 0.047). TIR 70-180 mg/dL glucose increased significantly from baseline in the CSII group only, from 59.8% to 73.1% (change +12.9%, P < 0.05), but was not significantly different between groups. There were no significant between-group differences in the endpoint mean glucose or A1C. Conclusions: In the VIVID CGM substudy of U-500R in people with T2D requiring high doses of insulin, participants using CSII significantly reduced GV compared with MDI. CSII also significantly increased TIR with no difference between groups.

The Ambulatory Glucose Profile: Opportunities for Enhancement.

The ambulatory glucose profile (AGP) and the frequency distribution for glucose by ranges are well established as standard methods for display, analysis, and interpretation of glucose data arising from self-monitoring, continuous glucose monitoring, and automated insulin delivery systems. In this review, we consider several refinements that may further improve the utility of the AGP. These include (1) display of the AGP together with information regarding dietary intake, medication administration (e.g., insulin), glucose lowering (pharmacodynamic) activity of medications, and physical activity measured by accelerometers or heart rate; (2) display of average time below range (%TBR), time above range (%TAR), and time in range (%TIR) by time of day to indicate timing of hypoglycemic and hyperglycemic episodes; (3) detailed analysis of postprandial excursions for each of the major meals after synchronizing by onset of meals and adjusting for the premeal glucose levels, enabling comparisons of magnitude, shape, and patterns; (4) methods to characterize distinct patterns on different days of the week; (5) display of glucose on a nonlinear scale to improve the balance between deviations associated with hypoglycemia versus hyperglycemia; (6) use of time scales other than midnight-to-midnight to facilitate analysis of time segments of particular interest (e.g., overnight); (7) options to display individual glucose values to assist in the identification of dates and times of outliers and episodes of hypoglycemia and hyperglycemia; and (8) methods to compare AGPs obtained from different individuals or groups receiving alternative interventions in terms of therapy or technology. These refinements, individually or collectively, can potentially further enhance the effectiveness of the AGP for assessment of glucose levels, patterns, and variability. We discuss several questions regarding implementation and optimization of these methods.

Managing New-Onset Type 1 Diabetes During the COVID-19 Pandemic: Challenges and Opportunities.

Background: The current COVID-19 pandemic provides an incentive to expand considerably the use of telemedicine for high-risk patients with diabetes, and especially for the management of type 1 diabetes (T1D). Telemedicine and digital medicine also offer critically important approaches to improve access, efficacy, efficiency, and cost-effectiveness of medical care for people with diabetes. Methods: Two case reports are presented where telemedicine was used effectively and safely after day 1 in person patient education. These aspects of the management of new-onset T1D patients (adult and pediatric) included ongoing diabetes education of the patient and family digitally. The patients used continuous glucose monitoring with commercially available analysis software (Dexcom Clarity and Glooko) to generate ambulatory glucose profiles and interpretive summary reports. The adult subject used multiple daily insulin injections; the pediatric patient used an insulin pump. The subjects were managed using a combination of e-mail, Internet via Zoom, and telephone calls. Results: These two cases show the feasibility and effectiveness of use of telemedicine in applications in which we had not used it previously: new-onset diabetes education and insulin dosage management. Conclusions: The present case reports illustrate how telemedicine can be used safely and effectively for new-onset T1D training and education for both pediatric and adult patients and their families. The COVID-19 pandemic has acutely stimulated the expansion of the use of telemedicine and digital medicine. We conclude that telemedicine is an effective approach for the management of patients with new-onset T1D.

Glucose Time In Range, Time Above Range, and Time Below Range Depend on Mean or Median Glucose or HbA1c, Glucose Coefficient of Variation, and Shape of the Glucose Distribution.

Background: Examine the expected relationships between time in range (%TIR), time above range (%TAR), and time below range (%TBR) with median glucose (or %HbA1c) and %coefficient of variation (%CV) of glucose for various shapes of the glucose distribution. Methods: We considered several thresholds defining hypoglycemia and hyperglycemia and examined wide ranges of median glucose and %CV using three models for the glucose distribution: gaussian, log-gaussian, and a modified log-gaussian distribution. Results: There is a linear relationship between %TIR and median glucose for any specified %CV when median glucose is well removed from the threshold for hypoglycemia. %TIR reaches a peak when median glucose is close to 120 mg/dL and declines both at higher and lower median glucose values. There is a nearly linear relationship for %TAR and median glucose for a wider range of glucose (80-220 mg/dL). Risk of hypoglycemia is minimal when %CV is below 20%, but rises exponentially as %CV increases or as median glucose decreases. Similar results were obtained for a wide range of possible shapes of glucose distribution. These simulations are consistent with results from clinical studies. Conclusion: Both %TIR and %TAR are approximately linearly related to mean and median glucose (or %HbA1c). %TAR provides linearity over a wider range than %TIR. Risk of hypoglycemia (%TBR) is critically dependent on both glycemic variability (%CV) and mean or median glucose. These relationships support the use of %TIR, %TAR, and %TBR as metrics of quality of glycemic control for clinical, research, and regulatory purposes.

Biosynthetic Human Insulin and Insulin Analogs.

BACKGROUND: Biosynthetic human insulins and analogs have replaced animal insulins and permitted structural modifications to alter the rate of absorption, duration of action, improve reproducibility of effects, and modulate relative efficacy in various target tissues. Several forms of rapidly acting insulins nearly achieve rapid pharmacokinetics and pharmacodynamics similar to first-phase insulin release. There is need for even faster-acting analogs to mimic normal physiology and improve control of postprandial glycemic excursions. Two biosynthetic insulin analogs have sufficiently long duration of action for use as once-daily basal insulins; controversy persists regarding their respective risks of hypoglycemia and relative glycemic variability. RESULTS: Basal-bolus therapy and insulin pump therapy, including closed-loop automated insulin delivery, require rapid-acting insulin analogs. The longer acting insulins can provide stable, reproducible basal insulin with reduced rates of hypoglycemia, particularly nocturnal hypoglycemia, greater efficacy in reducing mean glucose and glucose variability while increasing time in glucose target range. Inhalable human insulin provides very rapid action. Premixture of rapid-acting analogs with protamine has been useful for some patients with type 2 diabetes. An insulin analog with preferential efficacy at the liver has been developed and tested clinically but not marketed. Current research is aimed at developing even faster-acting insulin analogs. Long-acting basal insulins coformulated with GLP-1 receptor agonists or with a rapidly acting insulin analog have valuable clinical applications. Excipients, chaperones, local heating of the infusion site, and hyaluronidase have also been used to accelerate the absorption of insulin analogs. CONCLUSIONS: Biosynthetic human insulins have radically revolutionized management of both type 1 and type 2 diabetes worldwide. The ability to manipulate the structure and formulation of insulin provides for more physiologic pharmacokinetics and pharmacodynamics, enabling improved glycemic control, reduced risk of hypoglycemia, and reduced rates of long-term complications.

Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range.

Improvements in sensor accuracy, greater convenience and ease of use, and expanding reimbursement have led to growing adoption of continuous glucose monitoring (CGM). However, successful utilization of CGM technology in routine clinical practice remains relatively low. This may be due in part to the lack of clear and agreed-upon glycemic targets that both diabetes teams and people with diabetes can work toward. Although unified recommendations for use of key CGM metrics have been established in three separate peer-reviewed articles, formal adoption by diabetes professional organizations and guidance in the practical application of these metrics in clinical practice have been lacking. In February 2019, the Advanced Technologies & Treatments for Diabetes (ATTD) Congress convened an international panel of physicians, researchers, and individuals with diabetes who are expert in CGM technologies to address this issue. This article summarizes the ATTD consensus recommendations for relevant aspects of CGM data utilization and reporting among the various diabetes populations.

The Relationships Between Time in Range, Hyperglycemia Metrics, and HbA1c.

BACKGROUND: As the use of continuous glucose monitoring (CGM) increases, there is a need to better understand key metrics of time in range 70-180 mg/dL (TIR70-180) and hyperglycemia and how they relate to hemoglobin A1c (A1C). METHODS: Analyses were conducted utilizing datasets from four randomized trials encompassing 545 adults with type 1 diabetes (T1D) who had central-laboratory measurements of A1C. CGM metrics were calculated and compared with each other and A1C cross-sectionally and longitudinally. RESULTS: Correlations among CGM metrics (TIR70-180, time >180 mg/dL, time >250 mg/dL, mean glucose, area under the curve above 180 mg/dL, high blood glucose index, and time in range 70-140 mg/dL) were typically 0.90 or greater. Correlations of each metric with A1C were lower (absolute values 0.66-0.71 at baseline and 0.73-0.78 at month 6). For a given TIR70-180 percentage, there was a wide range of possible A1C levels that could be associated with that TIR70-180 level. On average, a TIR70-180 of 70% and 50% corresponded with an A1C of approximately 7% and 8%, respectively. There also was considerable spread of change in A1C for a given change in TIR70-180, and vice versa. An increase in TIR70-180 of 10% (2.4 hours per day) corresponded to a decrease in A1C of 0.6%, on average. CONCLUSIONS: In T1D, CGM measures reflecting hyperglycemia (including TIR and mean glucose) are highly correlated with each other but only moderately correlated with A1C. For a given TIR or change in TIR there is a wide range of possible corresponding A1C values.

Glucose Variability: A Review of Clinical Applications and Research Developments.

Glycemic variability (GV) is a major consideration when evaluating quality of glycemic control. GV increases progressively from prediabetes through advanced T2D and is still higher in T1D. GV is correlated with risk of hypoglycemia. The most popular metrics for GV are the %Coefficient of Variation (%CV) and standard deviation (SD). The %CV is correlated with risk of hypoglycemia. Graphical display of glucose by date, time of day, and day of the week, and display of simplified glucose distributions showing % of time in several ranges, provide clinically useful indicators of GV. SD is highly correlated with most other measures of GV, including interquartile range, mean amplitude of glycemic excursion, mean of daily differences, and average daily risk range. Some metrics are sensitive to the frequency, periodicity, and complexity of glycemic fluctuations, including Fourier analysis, periodograms, frequency spectrum, multiscale entropy (MSE), and Glucose Variability Percentage (GVP). Fourier analysis indicates progressive changes from normal subjects to children and adults with T1D, and from prediabetes to T2D. The GVP identifies novel characteristics for children, adolescents, and adults with type 1 diabetes and for adults with type 2. GVP also demonstrated small rapid glycemic fluctuations in people with T1D when using a dual-hormone closed-loop control. MSE demonstrated systematic changes from normal subjects to people with T2D at various stages of duration, intensity of therapy, and quality of glycemic control. We describe new metrics to characterize postprandial excursions, day-to-day stability of glucose patterns, and systematic changes of patterns by day of the week. Metrics for GV should be interpreted in terms of percentiles and z-scores relative to identified reference populations. There is a need for large accessible databases for reference populations to provide a basis for automated interpretation of GV and other features of continuous glucose monitoring records.

Metrics to Evaluate Quality of Glycemic Control: Comparison of Time in Target, Hypoglycemic, and Hyperglycemic Ranges with "Risk Indices".

OBJECTIVE: We sought to cross validate several metrics for quality of glycemic control, hypoglycemia, and hyperglycemia. RESEARCH DESIGN AND METHODS: We analyzed the mathematical properties of several metrics for overall glycemic control, and for hypo- and hyperglycemia, to evaluate their similarities, differences, and interrelationships. We used linear regression to describe interrelationships and examined correlations between metrics within three conceptual groups. RESULTS: There were consistently high correlations between %Time in range (%TIR) and previously described risk indices (M100, Blood Glucose Risk Index [BGRI], Glycemic Risk Assessment Diabetes Equation [GRADE], Index of Glycemic Control [IGC]), and with J-Index (J). There were also high correlations among %Hypoglycemia, Low Blood Glucose Index (LBGI), percentage of GRADE attributable to hypoglycemia (GRADE%Hypoglycemia), and Hypoglycemia Index, but negligible correlation with J. There were high correlations of percentage of time in hyperglycemic range (%Hyperglycemia) with High Blood Glucose Index (HBGI), percentage of GRADE attributable to hyperglycemia (GRADE%Hyperglycemia), Hyperglycemia Index, and J. %TIR is highly negatively correlated with %Hyperglycemia but very weakly correlated with %Hypoglycemia. By adjusting the parameters used in IGC, Hypoglycemia Index, Hyperglycemia Index, or in MR, one can more closely approximate the properties of BGRI, LBGI, or HBGI, and of GRADE, GRADE%Hypoglycemia, or GRADE%Hyperglycemia. CONCLUSIONS: Simple readily understandable criteria such as %TIR, %Hypoglycemia, and %Hyperglycemia are highly correlated with and appear to be as informative as "risk indices." The J-Index is sensitive to hyperglycemia but insensitive to hypoglycemia.