Is It Time to Move Beyond TIR to TITR? Real-World Data from Over 20,000 Users of Continuous Glucose Monitoring in Patients with Type 1 and Type 2 Diabetes

The growing use of continuous glucose monitoring (CGM) has been supported by expert consensus and clinical guidelines on glycemic management in diabetes with time in range (TIR 70-180 mg/dL) representing a key CGM-derived glucose metric. Time in tight range (TITR) has also been proposed for clinical use, spanning largely normal glucose levels of 70-140 mg/dL. However, keeping such narrow glucose ranges can be challenging, and understanding the factors modulating TITR can help achieve these tight glycemic targets. Our real-life study aimed to evaluate the relationship between average glucose (AG) and TIR/TITR in a large cohort (n = 22,006) of CGM users, divided into four groups: self-identified as having type 1 diabetes (T1D) treated with insulin using multiple daily injections (MDI) or pumps; type 2 diabetes (T2D) on MDI or insulin pumps; T2D on basal insulin only; and T2D not on insulin treatment. The T2D groups, regardless of treatment type, displayed the highest TIR and TITR values, associated with lowest glycemic variability measured as glucose coefficient of variation (CV; 23-30%). The T1D group showed the lowest TIR and TITR, associated with the highest CVs (36-38%). Overall, higher CV was associated with lower TIR and TITR for AG values below 180 and 140 mg/dL, respectively, with the reverse holding true for AG values above these thresholds. The discordance between AG and TIR/TITR was less pronounced in T2D compared with T1D, attributed to lower CV in the former group. It was also observed that TITR has advantages over TIR for assessing glycemia status and progress toward more stringent A1C, particularly when approaching normal glucose levels. The data detail how CV affects the AG relationship with TIR/TITR, which has implications for CGM interpretation. In many instances TITR, rather than TIR, may be preferable to employ once AG falls below 140 mg/dL and near-normal glucose levels are required clinically.

Time in Range, Time in Tight Range, and Average Glucose Relationships Are Modulated by Glycemic Variability: Identification of a Glucose Distribution Model Connecting Glycemic Parameters Using Real-World Data

Background: Time in range (TIR), time in tight range (TITR), and average glucose (AG) are used to adjust glycemic therapies in diabetes. However, TIR/TITR and AG can show a disconnect, which may create management difficulties. We aimed to understand the factors influencing the relationships between these glycemic markers. Materials and Methods: Real-world glucose data were collected from self-identified diabetes type 1 and type 2 diabetes (T1D and T2D) individuals using flash continuous glucose monitoring (FCGM). The effects of glycemic variability, assessed as glucose coefficient of variation (CV), on the relationship between AG and TIR/TITR were investigated together with the best-fit glucose distribution model that addresses these relationships. Results: Of 29,164 FCGM users (16,367 T1D, 11,061 T2D, and 1736 others), 38,259 glucose readings/individual were available. Comparing low and high CV tertiles, TIR at AG of 150 mg/dL varied from 80% ± 5.6% to 62% ± 6.8%, respectively (P < 0.001), while TITR at AG of 130 mg/dL varied from 65% ± 7.5% to 49% ± 7.0%, respectively (P < 0.001). In contrast, higher CV was associated with increased TIR and TITR at AG levels outside the upper limit of these ranges. Gamma distribution was superior to six other models at explaining AG and TIR/TITR interactions and demonstrated nonlinear interplay between these metrics. Conclusions: The gamma model accurately predicts interactions between CGM-derived glycemic metrics and reveals that glycemic variability can significantly influence the relationship between AG and TIR with opposing effects according to AG levels. Our findings potentially help with clinical diabetes management, particularly when AG and TIR appear mismatched.

Safety and Functional Integrity of Continuous Glucose Monitoring Sensors When Used During Radiologic Procedures Under High Exposure Conditions

BACKGROUND

We investigated wearable components of the Abbott Diabetes Care FreeStyle Libre® (continuous glucose monitoring [CGM 1), FreeStyle Libre® 2 (CGM 2), and FreeStyle Libre® 3 (CGM 3) systems in simulated diagnostic radiologic procedures.

METHODS

Sensors were loaded with simulated glucose data and exposed to X-ray scanning, computed tomography (CT), and magnetic resonance imaging (MRI) to simulate radiotherapeutic procedures. The exposure settings were representative of maximum in clinical settings. After the simulations, bench tests were used to assess data integrity and responsiveness of sensors to various concentrations of aqueous glucose.

RESULTS

All sensors passed all acceptance criteria following each session of X-ray, CT, and MRI exposures. During the 3 T MRI simulation, the displacement forces for the CGM 1, CGM 2, and CGM 3 sensors were 0.132, 0.109, and 0.063 N, respectively, which are more than 100× smaller than the force of 15.97 N required to dislodge the sensor from the body. Data stored in the sensors prior to the exposures remained intact.

CONCLUSION

The sensors maintained functionality following a series of high exposure conditions in both X-ray and CT scanning systems, and the sensors were easily visible and identifiable when scanned using clinically relevant scanning parameters. Therefore, patients can continue to wear and use their sensors during and after imaging. The nonclinical MRI testing demonstrated that the sensors can be worn under the specified MRI conditions.

Real-life 24-week changes in glycemic parameters among European users of flash glucose monitoring with type 1 and 2 diabetes and different levels of glycemic control

AIM

To evaluate real-life changes of glycemic parameters among flash glucose monitoring (FLASH) users who do not meet glycemic targets.

METHODS

De-identified data were obtained between 2014 and 2021 from patients using FLASH uninterrupted for a 24-week period. Glycemic parameters during first and last sensor use were examined in four identifiable groups: type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM) on basal-bolus insulin, T2DM on basal insulin, and T2DM without insulin treatment. Within each group, subgroup analyses were performed in persons with initial suboptimal glycemic regulation (time in range (TIR; 3.9-10 mmol/L) <70%, time above range (TAR; >10 mmol/L) >25%, or time below range (TBR; <3.9 mmol/L) >4%).

RESULTS

Data were obtained from 1,909 persons with T1DM and 1,813 persons with T2DM (1,499 basal-bolus insulin, 189 basal insulin, and 125 non-insulin users). In most of the performed analyses, both overall and in the various subgroups, significant improvements were observed in virtually all predefined primary (TIR) and secondary endpoints (eHbA1c, TAR, TBR and glucose variability).

CONCLUSIONS

24-weeks FLASH use in real life by persons with T1DM and T2DM with suboptimal glycemic regulation is associated with improvement of glycemic parameters, irrespective of pre-use regulation or treatment modality.

Personalized Glycated Hemoglobin in Diabetes Management: Closing the Gap with Glucose Management Indicator

Glycated hemoglobin (HbA1c) has played a central role in the management of diabetes since the end of the landmark Diabetes Control and Complications Trial 30 years ago. However, it is known to be subject to distortions related to altered red blood cell (RBC) properties, including changes in cellular lifespan. On occasion, the distortion of HbA1c is associated with a clinical pathological condition affecting RBCs, however, the more frequent scenario is related to interindividual RBC variations that alter HbA1c-average glucose relationship. Clinically, these variations can potentially lead to over- or underestimating glucose exposure of the individual to the extent that may put the person at excess risk of over- or undertreatment. Furthermore, the variable association between HbA1c and glucose levels across different groups of people may become an unintentional driver of inequitable health care delivery, outcomes, and incentives. The subclinical effects within the normal expected physiological range of RBCs can be large enough to alter clinical interpretation of HbA1c and addressing this will help with individualized care and decision making. This review describes a new glycemic measure, personalized HbA1c (pA1c), that may address the clinical inaccuracies of HbA1c by taking into account interindividual variability in RBC glucose uptake and lifespan. Therefore, pA1c represents a more sophisticated understanding of glucose-HbA1c relationship at an individual level. Future use of pA1c, after adequate clinical validation, has the potential to refine glycemic management and the diagnostic criteria in diabetes.

Evaluation of continuous glucose monitoring-derived person-specific HbA1c in the presence and absence of complications in type 1 diabetes

AIM

To evaluate the accuracy of a novel kinetic model at predicting HbA1c in a real-world setting and to understand and explore the role of diabetes complications in altering the glucose-HbA1c relationship and the mechanisms involved.

MATERIALS AND METHODS

Deidentified HbA1c and continuous glucose monitoring values were collected from 93 individuals with type 1 diabetes. Person-specific kinetic variables were used, including red blood cell (RBC) glucose uptake and lifespan, to characterize the relationship between glucose levels and HbA1c. The resulting calculated HbA1c (cHbA1c) was compared with glucose management indicator (GMI) for prospective agreement with laboratory HbA1c.

RESULTS

The cohort (42 men and 51 women) had a median age (IQR) of 61 (43, 72) years and a diabetes duration of 21 (10, 33) years. A total of 24 459 days of continuous glucose monitoring (CGM) data were available and 357 laboratory HbA1c were used to assess the average glucose-HbA1c relationship. cHbA1c had a superior correlation with laboratory HbA1c compared with GMI with a mean absolute deviation of 1.7 and 6.7 mmol/mol, r²  = 0.85 and 0.44, respectively. The fraction within 10% of absolute relative deviation from laboratory HbA1c was 93% for cHbA1c and 63% for GMI. Macrovascular disease had no effect on the model's accuracy, whereas microvascular complications resulted in a trend towards higher HbA1c, secondary to increased RBC glucose uptake.

CONCLUSIONS

cHbA1c, which takes into account RBC glucose uptake and lifespan, accurately reflects laboratory HbA1c in a real-world setting and can aid in the management of individuals with diabetes.

Real world hypoglycaemia related to glucose variability and Flash glucose scan frequency assessed from global FreeStyle Libre data

AIM

Flash glucose monitoring provides a range of glucose metrics. In the current study, we aim to identify those that indicate that glycaemic targets can be consistently met and contrast the total (t-CV) and within-day coefficient of variation (wd-CV) to guide the assessment of glucose variability and hypoglycaemia exposure.

METHODS

De-identified data from Flash readers were collected. The readers were sorted into 10 equally sized groups of scan frequency followed by quartiles of estimated A1c (eA1c). A similar grouping was performed for the total coefficient of variation (t-CV) and within-day coefficient of variation (wd-CV). In addition, analysis of the association of time below 54 mg/dl and glucose variability measured by t-CV and wd-CV was performed.

RESULTS

The dataset included 1 002 946 readers. Readers sorted by 10 equal groups of scan rate and quartiles by eA1c, t-CV and wd-CV represented 25 074 readers per group. The association of lower eA1c with higher time in range and reduced time above range was clear. The correlation of eA1c quartiles and time below range was not consistent. An association between glucose variability and hypoglycaemia was found. Both wd-CV and t-CV were associated with time below range. For achieving the consensus target of <1% time below 54 mg/dl, the associated wd-CV and t-CV values were 33.5% and 39.5%, respectively.

CONCLUSIONS

The type of CV reported by the different continuous glucose monitoring systems should be acknowledged. CV <36% might not be adequate to ensure low hypoglycaemia exposure. To our knowledge, the majority of continuous glucose monitoring reports the t-CV. Appropriate thresholds should be used to identify patients that would probably meet time below range targets (t-CV <40% or wd-CV <34%).

Glycaemic measures for 8914 adult FreeStyle Libre users during routine care, segmented by age group and observed changes during the COVID-19 pandemic

AIM

To evaluate the impact of the stay-at-home policy on different glucose metrics for time in range (%TIR 3.9-10 mmol/L), time below range (%TBR < 3.9 mmol/L) and time above range (%TAR > 10 mmol/L) for UK adult FreeStyle Libre (FSL) users within four defined age groups and on observed changes during the coronavirus disease 2019 (COVID-19) pandemic.

METHODS

Data were extracted from 8914 LibreView de-identified user accounts for adult users aged 18 years or older with 5 or more days of sensor readings in each month from January to June 2020. Age-group categories were based on self-reported age on LibreView accounts (18-25, 26-49, 50-64 and ≥65 years).

RESULTS

In January, prior to the COVID-19 pandemic, the 65 years or older age group had the highest %TIR (57.9%), while the 18-25 years age group had the lowest (51.2%) (P < .001). Within each age group, TIR increased during the analysed months, by 1.7% (26-49 years) to 3.1% (≥65 years) (P < .001 in all cases). %TBR was significantly reduced only in the 26-49 years age group, whereas %TAR was reduced by 1.5% (26-49 years) to 3.0% (≥65 years) (P < .001 in both cases). The proportion of adults achieving both of the more than 70% TIR and less than 4% TBR targets increased from 11.7% to 15.9% for those aged 65 years or older (P < .001) and from 6.0% to 9.1% for those aged 18-25 years (P < .05). Mean daily glucose-sensor scan rates were at least 12 per day and remained stable across the analysis period.

CONCLUSIONS

Our data show the baseline glucose metrics for FSL users in the UK across different age groups under usual care. During lockdown in the UK, the proportion of adults achieving TIR consensus targets increased among FSL users.

Interindividual variability in average glucose-glycated haemoglobin relationship in type 1 diabetes and implications for clinical practice

AIM

Glycated haemoglobin (HbA1c) can fail to reflect average glucose levels, potentially compromising management decisions. We analysed variability in the relationship between mean glucose and HbA1c in individuals with diabetes.

MATERIALS AND METHODS

Three months of continuous glucose monitoring and HbA1c data were obtained from 216 individuals with type 1 diabetes. Universal red blood cell glucose transporter-1 Michaelis constant K_M and individualized apparent glycation ratio (AGR) were calculated and compared across age, racial and gender groups.

RESULTS

The mean age (range) was 30 years (8-72) with 94 younger than 19 years, 78 between 19 and 50 years, and 44 were >50 years. The group contained 120 women and 96 men with 106 white and 110 black individuals. The determined K_M value was 464 mg/dl and AGR was (mean ± SD) 72.1 ± 7 ml/g. AGR, which correlated with red blood cell lifespan marker, was highest in those aged >50 years at 75.4 ± 6.9 ml/g, decreasing to 73.2 ± 7.8 ml/g in 19-50 years, with a further drop to 71.0 ± 5.8 ml/g in the youngest group (p <0 .05). AGR differed between white and black groups (69.9 ± 5.8 and 74.2 ± 7.1 ml/g, respectively; p < .001). In contrast, AGR values were similar in men and women (71.5 ± 7.5 and 72.5 ± 6.6 ml/g, respectively; p = .27). Interestingly, interindividual AGR variation within each group was at least four-fold higher than average for between-group variation.

CONCLUSIONS

In this type 1 diabetes cohort, ethnicity and age, but not gender, alter the HbA1c-glucose relationship with even larger interindividual variations found within each group than between groups. Clinical application of personalized HbA1c-glucose relationships has the potential to optimize glycaemic care in the population with diabetes.

Frequency of flash glucose monitoring and glucose metrics: real-world observational data from Saudi Arabia

BACKGROUND

This real-world data study analyzed glucose metrics from FreeStyle Libre^® flash glucose monitoring in relation to scanning frequency, time in range (TIR) and estimated A1c (eA1c) in Saudi Arabia.

METHODS

Anonymized reader data were analyzed according to scanning frequency quartiles, eA1c categories (<7%,≥7%‒≤9% or>9%) and TIR categories (<50%,≥50%‒≤70% or>70%). Sensors, grouped by reader, were required to have≥120 h of operation. Differences in scanning frequency, eA1c, TIR, time in hypoglycemia and hyperglycemia, and glucose variability (standard deviation [SD] and coefficient of variation [CV]) were analyzed between groups.

RESULTS

6097 readers, 35,747 sensors, and 40 million automatic glucose measurements were analyzed. Patients in the highest scanning frequency quartile (Q4, mean 32.0 scans/day) had lower eA1c (8.47%), greater TIR (46.4%) and lower glucose variation (SD 75.0 mg/dL, CV 38.2%) compared to the lowest quartile (Q1, mean 5.2 scans/day; eA1c 9.77%, TIR 32.8%, SD 94.9 mg/dL, CV 41.3%). Lower eA1c and higher TIR were associated with greater scanning frequency, lower glucose variability and less time in hyperglycemia.

CONCLUSIONS

Higher scanning frequency in flash glucose users from Saudi Arabia is associated with lower eA1c, higher TIR, lower glucose variability and less time in hypoglycemia or hyperglycemia.

Addressing shortfalls of laboratory HbA1c using a model that incorporates red cell lifespan

Laboratory HbA_1c does not always predict diabetes complications and our aim was to establish a glycaemic measure that better reflects intracellular glucose exposure in organs susceptible to complications. Six months of continuous glucose monitoring data and concurrent laboratory HbA_1c were evaluated from 51 type 1 diabetes (T1D) and 80 type 2 diabetes (T2D) patients. Red blood cell (RBC) lifespan was estimated using a kinetic model of glucose and HbA_1c, allowing the calculation of person-specific adjusted HbA_1c (aHbA_1c). Median (IQR) RBC lifespan was 100 (86–102) and 100 (83–101) days in T1D and T2D, respectively. The median (IQR) absolute difference between aHbA_1c and laboratory HbA_1c was 3.9 (3.0–14.3) mmol/mol [0.4 (0.3–1.3%)] in T1D and 5.3 (4.1–22.5) mmol/mol [0.5 (0.4–2.0%)] in T2D. aHbA_1c and laboratory HbA_1c showed clinically relevant differences. This suggests that the widely used measurement of HbA_1c can underestimate or overestimate diabetes complication risks, which may have future clinical implications.

Flash Glucose Monitoring in the Netherlands: Increased monitoring frequency is associated with improvement of glycemic parameters

AIMS

To evaluate the association between Flash Glucose Monitoring (FLASH) frequency and glycemic parameters during real-life circumstances in the Netherlands.

METHODS

Obtained glucose readings were de-identified and uploaded to a dedicated database when FLASH reading devices were connected to internet. Data between September 2014 and March 2020, comprising 16,331 analyzable readers (163,762 sensors) were analyzed. Scan rate per reader was determined and each reader was sorted into 20 equally sized rank ordered groups (n = 817 each).

RESULTS

Users performed a median of 11.5 [IQR 7.7-16.7] scans per day. Those in the lowest and highest ventiles scanned on average 3.7 and 40.0 times per day and had an eHbA1c of 8.6% (71 mmol/mol) and 6.9% (52 mmol/mol), respectively. Increasing scan rates were associated with more time in target range (3.9-10 mmol/L), less time in hyperglycemia (>10 mmol/L), and a lower standard deviation of glucose. An eHbA1c of 7.0% (53 mmol/mol) translated in approximately 65% time in target range, 30% time in hyperglycemia and 5% time in hypoglycemia (<3.9 mmol/L).

CONCLUSIONS

These outcomes among Dutch FLASH users suggest that with higher scan rate glycemic control improves.

Personal Glycation Factors and Calculated Hemoglobin A1c for Diabetes Management: Real-World Data from the Diabetes Prospective Follow-up (DPV) Registry

Background: Glycated hemoglobin A1c (HbA1c) is a key biomarker in the glycemic management of individuals with diabetes, but the relationship with glucose levels can be variable. A recent kinetic model has described a calculated HbA1c (cHbA1c) that is individual specific. Our aim was to validate the routine clinical use of this glucose metric in younger individuals with diabetes under real-life settings. Materials and Methods: We retrieved HbA1c and glucose data from the German-Austrian-Swiss-Luxembourgian diabetes follow-up (DPV) registry, which covers pediatric individuals with type 1 diabetes (T1D). The new glycemic measure, cHbA1c, uses two individual parameters identified by data sections that contain continuous glucose data between two laboratory HbA1c measurements. The cHbA1c was prospectively validated using longitudinal HbA1c data. Results: Continuous glucose monitoring data from 352 T1D individuals in 13 clinics were analyzed together with HbA1c that ranged between 4.9% and 10.6%. In the prospective analysis, absolute deviations of estimated HbA1c (eHbA1c), glucose management indicator (GMI), and cHbA1c compared with laboratory HbA1c were (median [interquartile range]): 1.01 (0.50, 1.75), 0.46 (0.21, 084) and 0.26 (0.12, 0.46), giving an average bias of 0.6, 0.4 and 0.0, respectively, in National Glycohemoglobin Standardization Program (NGSP) % unit. For eHbA1c and GMI only 25% and 54% of subjects were within ±0.5% of laboratory HbA1c values, whereas 82% of cHbA1c were within ±0.5% of laboratory HbA1c results. Conclusions: Our data show the superior performance of cHbA1c compared with eHbA1c and GMI at reflecting laboratory HbA1c. These data indicate that cHbA1c can be potentially used instead in laboratory HbA1c, at least in younger individuals with T1D.

Accurate prediction of HbA1c by continuous glucose monitoring using a kinetic model with patient-specific parameters for red blood cell lifespan and glucose uptake

BACKGROUND

A recent kinetic model proposed a new individualized glycaemic marker, calculated HbA1c (cHbA1c), based on kinetic parameters and glucose levels that are specific to each person. The aims of the current work were to validate the accuracy of this glucose metric for clinical use and evaluate data requirements for the estimation of personal kinetic factors.

METHODS

We retrieved HbA1c and glucose data from a group of 51 Japanese T1D patients under sensor-augmented pump (SAP) therapy. Two patient-specific kinetic parameters were identified by data sections, defined as continuous glucose data between two laboratory HbA1c measurements. The cHbA1c was prospectively validated employing subsequent HbA1c data that were not originally used to determine personal kinetic parameters.

RESULTS

Compared to estimated HbA1c (eHbA1c) and glucose management indicator (GMI), cHbA1c showed clinically relevant accuracy improvement, with 20% or more within ±0.5% (±5.5 mmol/mol) of laboratory HbA1c. The mean absolute deviation of the cHbA1c calculation was 0.11% (1.2 mmol/mol), substantially less than for eHbA1c and GMI at 0.54% (5.9 mmol/mol) and 0.47% (5.1 mmol/mol), respectively.

CONCLUSION

Our study shows superior performance of cHbA1c compared with eHbA1c and GMI at reflecting laboratory HbA1c, making it a credible glucose metric for routine clinical use.

A Kinetic Model for Glucose Levels and Hemoglobin A1c Provides a Novel Tool for Individualized Diabetes Management

BACKGROUND

Regular assessment of glycated hemoglobin (HbA1c) is central to the management of patients with diabetes. Estimated HbA1c (eHbA1c) from continuous glucose monitoring (CGM) has been proposed as a measure that reflects laboratory HbA1c. However, discrepancies between the two markers are common, limiting the clinical use of eHbA1c. Therefore, developing a glycemic maker that better reflects laboratory HbA1c will be highly relevant in diabetes management.

METHODS

Using CGM data from two previous clinical studies in 120 individuals with diabetes, we derived a novel kinetic model that takes into account red blood cell (RBC) turnover, cross-membrane glucose transport, and hemoglobin glycation processes to individualize the relationship between glucose levels and HbA1c.

RESULTS

Using CGM data and two laboratory HbA1c measurements, kinetic rate constants for RBC glycation and turnover were calculated. These rate constants were used to project future HbA1c, creating a new individualized glycemic marker, termed calculated HbA1c (cHbA1c). In contrast to eHbA1c, the new glycemic marker cHbA1c gave an accurate estimation of laboratory HbA1c across individuals. The model and data demonstrated a non-linear relationship between laboratory HbA1c and steady-state glucose and also showed that glycation status is modulated by age.

CONCLUSION

Our kinetic model offers mechanistic insights into the relationship between glucose levels and glycated hemoglobin. Therefore, the new glycemic marker does not only accurately reflect laboratory HbA1c but also provides novel concepts to explain the mechanisms for the mismatch between HbA1c and average glucose in some individuals, which has implications for future clinical management.

Real-world flash glucose monitoring in Brazil: can sensors make a difference in diabetes management in developing countries?

BACKGROUND

New technologies are changing diabetes treatment and contributing better outcomes in developed countries. To our knowledge, no previous studies have investigated the comparative effect of sensor-based monitoring on glycemic markers in developing countries like Brazil. The present study aims to evaluate the use of intermittent Continuous Glucose Measurements (iCGM) in a developing country, Brazil, regarding (i) frequency of glucose scans, (ii) its association with glycemic markers and (iii) comparison with these findings to those observed in global population data.

METHODS

Glucose results were de-identified and uploaded to a dedicated database when Freestyle Libre™ readers were connected to an internet-ready computer. Data between September 2014 and Dec 2018, comprising 688,640 readers and 7,329,052 sensors worldwide, were analysed (including 17,691 readers and 147,166 sensors from Brazil). Scan rate per reader was determined and each reader was sorted into 20 equally-sized rank ordered groups, categorised by scan frequency. Glucose parameters were calculated for each group, including estimated A1c, time above, below and within range identified as 70-180 mg/dL.

RESULTS

In Brazil, reader users performed an average of 14 scans per day, while around the world, reader users performed an average of 12 scans per day (p < 0.01). In Brazil dataset, those in the lowest and in the highest groups scanned on average 3.6 and 43.1 times per day had an estimated A1c of 7.56% (59 mmol/mol) and 6.71% (50 mmol/mol), respectively (p < 0.01). Worldwide, the lowest group and the highest groups scanned 3.4 times/day and 37.8 times/day and had an eA1c of 8.14% (65 mmol/mol) and 6.70% (50 mmol/mol), respectively (p < 0.01). For the scan groups in both populations, the time spent above 180 mg/dL decreased as the scan frequency increased. In both Brazil and around the world, as scan frequency increased, time in range (TIR) increased. In Brazil, TIR increased from 14.15 to 16.62 h/day (p < 0.01). Worldwide, TIR increased from 12.06 to 16.97 h/day (p < 0.01).

CONCLUSIONS

We conclude that Brazilian users have a high frequency of scans, more frequent than global data. Similarly to the world findings, increased scan frequency is associated with better glycemic control.

Real-world flash glucose monitoring patterns and associations between self-monitoring frequency and glycaemic measures: A European analysis of over 60 million glucose tests

AIMS

Randomised controlled trials demonstrate that using flash glucose monitoring improves glycaemic control but it is unclear whether this applies outside trial conditions. We investigated glucose testing patterns in users worldwide under real life settings to establish testing frequency and association with glycaemic parameters.

METHODS

Glucose results were de-identified and uploaded onto a dedicated database once readers were connected to an internet-ready computer. Data between September 2014 and May 2016, comprising 50,831 readers and 279,446 sensors worldwide, were analysed. Scan rate per reader was determined and each reader was sorted into twenty equally-sized rank-ordered groups, categorised by scan frequency. Glucose parameters were calculated for each group, including estimated HbA_1c, time above, below and within range identified as 3.9-10.0 mmol/L.

RESULTS

Users performed a mean of 16.3 scans/day [median (IQR): 14 (10-20)] with 86.4 million hours of readings and 63.8 million scans. Estimated HbA_1c gradually reduced from 8.0% to 6.7% (64 to 50 mmol/mol) as scan rate increased from lowest to highest scan groups (4.4 and 48.1 scans/day, respectively; p < .001). Simultaneously, time below 3.9, 3.1 and 2.5 mmol/L decreased by 15%, 40% and 49%, respectively (all p < .001). Time above 10.0 mmol/L decreased from 10.4 to 5.7 h/day (44%, p < .001) while time in range increased from 12.0 to 16.8 h/day (40%, p < .001). These patterns were consistent across different countries.

CONCLUSIONS

In real-world conditions, flash glucose monitoring allows frequent glucose checks with higher rates of scanning linked to improved glycaemic markers, including increased time in range and reduced time in hyper and hypoglycaemia.

Individuals with Type 1 and Type 2 Diabetes Mellitus Trade Increased Hyperglycemia for Decreased Hypoglycemia When Glycemic Variability is not Improved

INTRODUCTION

Glycemic variability refers to oscillations in blood glucose within a day and differences in blood glucose at the same time on different days. Glycemic variability is linked to hypoglycemia and hyperglycemia. The relationship among these three important metrics is examined here, specifically to show how reduction in both hypo- and hyperglycemia risk is dependent on changes in variability.

METHODS

To understand the importance of glycemic variability in the simultaneous reduction of hypoglycemia and hyperglycemia risk, we introduce the glycemic risk plot-estimated HbA1c % (eA1c) vs. minutes below 70 mg/dl (MB70) with constant variability contours for predicting post-intervention risks in the absence of a change in glycemic variability.

RESULTS

The glycemic risk plot illustrates that individuals who do not reduce glycemic variability improve one of the two metrics (hypoglycemia risk or hyperglycemia risk) at the cost of the other. It is important to reduce variability to improve both risks. These results were confirmed by data collected in a randomized controlled trial consisting of individuals with type 1 and type 2 diabetes on insulin therapy. For type 1, a total of 28 individuals out of 35 (80%) showed improvement in at least one of the risks (hypo and/or hyper) during the 100-day course of the study. Seven individuals (20%) showed improvement in both. Similar data were observed for type 2 where a total of 36 individuals out of 43 (84%) showed improvement in at least one risk and 8 individuals (19%) showed improvement in both. All individuals in the study who showed improvement in both hypoglycemia and hyperglycemia risk also showed a reduction in variability.

CONCLUSION

Therapy changes intended to improve an individual's hypoglycemia or hyperglycemia risk often result in the reduction of one risk at the expense of another. It is important to improve glucose variability to reduce both risks or at least maintain one risk while reducing the other.

FUNDING

Abbott Diabetes Care.

Ambulatory glucose profile analysis of the juvenile diabetes research foundation continuous glucose monitoring dataset-Applications to the pediatric diabetes population

BACKGROUND

Increased continuous glucose monitor (CGM) use presents both the benefit and burden of increased data for clinicians to rapidly analyze. The ambulatory glucose profile (AGP) is an evolving a universal software report for CGM data analysis.

OBJECTIVES/HYPOTHESES

We utilized the Juvenile Diabetes Research Foundation-CGM dataset to evaluate the AGP across a broad spectrum of patients to show how AGP can be used clinically to assist with CGM-related decision making. We hypothesized that AGP metrics would be different across age and HbA1c strata.

SUBJECTS

AGPs were generated from the JDRF-CGM trial dataset for all periods during which there were ≥10 days of CGM coverage in the 2 weeks adjacent to an HbA1c measurement yielding 1101 AGPs for 393 unique subjects.

METHODS

AGPs were stratified by age group (8-14, 15-24, and ≥25 years) and HbA1c (within or above target for age) and compared for between group differences in AGP metrics via two-factor ANOVA. Glycemic differences between time periods were analyzed via segmented regression analysis.

RESULTS

Glucose exposure (average and estimated A1c) and variability (standard deviation and interquartile range) were different between the low and high HbA1c levels. Within a given HbA1c level all age groups were significantly different from each other with older patients having lower averages with less variability than younger patients.

CONCLUSIONS

AGP analysis of the JDRF-CGM data highlights significant differences in glycemic profiles between pediatric and adult age groups and between well and less well-controlled patient populations.

Development of the Likelihood of Low Glucose (LLG) algorithm for evaluating risk of hypoglycemia: a new approach for using continuous glucose data to guide therapeutic decision making

The objective was to develop an analysis methodology for generating diabetes therapy decision guidance using continuous glucose (CG) data. The novel Likelihood of Low Glucose (LLG) methodology, which exploits the relationship between glucose median, glucose variability, and hypoglycemia risk, is mathematically based and can be implemented in computer software. Using JDRF Continuous Glucose Monitoring Clinical Trial data, CG values for all participants were divided into 4-week periods starting at the first available sensor reading. The safety and sensitivity performance regarding hypoglycemia guidance "stoplights" were compared between the LLG method and one based on 10th percentile (P10) values. Examining 13 932 hypoglycemia guidance outputs, the safety performance of the LLG method ranged from 0.5% to 5.4% incorrect "green" indicators, compared with 0.9% to 6.0% for P10 value of 110 mg/dL. Guidance with lower P10 values yielded higher rates of incorrect indicators, such as 11.7% to 38% at 80 mg/dL. When evaluated only for periods of higher glucose (median above 155 mg/dL), the safety performance of the LLG method was superior to the P10 method. Sensitivity performance of correct "red" indicators of the LLG method had an in sample rate of 88.3% and an out of sample rate of 59.6%, comparable with the P10 method up to about 80 mg/dL. To aid in therapeutic decision making, we developed an algorithm-supported report that graphically highlights low glucose risk and increased variability. When tested with clinical data, the proposed method demonstrated equivalent or superior safety and sensitivity performance.

Impact of glucose measurement processing delays on clinical accuracy and relevance

BACKGROUND

In a hospital setting, glucose is often measured from venous blood in the clinical laboratory. However, laboratory glucose measurements are typically not available in real time. In practice, turn-around times for laboratory measurements can be minutes to hours. This analysis assesses the impact of turn-around time on the effective clinical accuracy of laboratory measurements.

METHODS

Data obtained from an earlier study with 58 subjects with type 1 diabetes mellitus (T1DM) were used for this analysis. In the study, glucose measurements using a YSI glucose analyzer were obtained from venous blood samples every 15 min while the subjects were at the health care facility. To simulate delayed laboratory results, each YSI glucose value from a subject was paired with one from a later time point (from the same subject) separated by 15, 30, 45, and 60 min. To assess the clinical accuracy of a delayed YSI result relative to a real-time result, the percentage of YSI pairs that meet the International Organization for Standardization (ISO) 15197:2003(E) standard for glucose measurement accuracy (±15 mg/dl for blood glucose < 75 mg/dl, ±20% for blood glucose ≥ 75 mg/dl) was calculated.

RESULTS

It was observed that delays of 15 min or more reduce clinical accuracy below the ISO 15197:2003(E) recommendation of 95%. The accuracy was less than 65% for delays of 60 min.

CONCLUSION

This analysis suggests that processing delays in glucose measurements reduce the clinical relevance of results in patients with T1DM and may similarly degrade the clinical value of measurements in other patient populations.

Trabecular bone structure of the calcaneus: comparison of MR imaging at 3.0 and 1.5 T with micro-CT as the standard of reference

PURPOSE

To investigate in vitro the calcaneal trabecular bone structure in elderly human donors with high spatial resolution magnetic resonance (MR) imaging at 3.0 T and 1.5 T, to quantitatively compare MR measures of bone microarchitecture with those from micro-computed tomography (CT), and to compare the performance of 3.0-T MR imaging with that of 1.5-T MR imaging in differentiating donors with spinal fractures from those without spinal fractures.

MATERIALS AND METHODS

The study was performed in line with institutional and legislative requirements; all donors had dedicated their body for educational and research purposes prior to death. Sagittal MR images of 49 human calcaneus cadaveric specimens were obtained (mean age of donors, 79.5 years +/- 11 [standard deviation]; 26 male donors, 23 female donors). After the spatial coregistering of images acquired at 3.0-T and 1.5-T MR imaging, the signal-to-noise-ratios and structural parameters obtained at each magnetic field strength were compared in corresponding sections. Micro-CT was performed on calcaneus cores obtained from corresponding regions in 40 cadaveric specimens. Vertebral deformities of the thoracic and lumbar spine were radiographically classified by using the spinal fracture index. Diagnostic performance of the structural parameters in differentiating donors with vertebral fractures from those without was assessed by using receiver operator characteristic (ROC) analysis, including area under the ROC curve (A(z)).

RESULTS

Correlations between structural parameters at 3.0-T MR imaging and those at micro-CT were significantly higher (P < .05) than correlations between structural parameters at 1.5-T MR imaging and those at micro-CT (trabecular thickness, r = 0.76 at 3.0 T vs r = 0.57 at 1.5 T). Trabecular dimensions were amplified at 3.0 T because of increasing susceptibility artifacts. Also, higher ROC values were found for structural parameters at 3.0 T than at 1.5 T, but differences were not significant (trabecular thickness, A(z) = 0.75 at 3.0 T vs A(z) = 0.66 at 1.5 T, P > .05).

CONCLUSION

MR imaging at 3.0 T provided a better measure of the trabecular bone structure than did MR imaging at 1.5 T. There was a trend for better differentiation of donors with from those without osteoporotic vertebral fractures at 3.0 T than at 1.5 T.

MR imaging findings in the follow-up of patients with different stages of knee osteoarthritis and the correlation with clinical symptoms

OBJECTIVE

To assess the rate of cartilage loss, the change in bone marrow edema pattern and internal joint derangement at 1.5-T MRI in patients with knee osteoarthritis and to correlate these findings with the clinical Western Ontario and McMaster University Osteoarthitis (WOMAC) score.

METHODS

Forty subjects (mean age 57.7+/-15 years; 16 females and 24 males) were recruited: 6 healthy volunteers (OA0), 17 patients with mild osteoarthritis (OA1) and 17 with severe osteoarthritis (OA2) based on the Kellgren-Lawrence scale. MR scans, radiographs and WOMAC scores were obtained at baseline, first follow-up (1.4+/-0.67 years; n=40) and second follow-up (2.4+/-0.4 years; n=26). Cartilage morphology, bone marrow edema (BME), meniscal and ligamentous pathology were assessed on MR images and quantified by two radiologists in consensus.

RESULTS

Full-thickness cartilage lesions were observed in 12/17 OA2 at baseline, in 13/17 at the first follow-up and in 7/10 at the second follow-up. Cartilage loss was found in eight patients at the first follow-up and five at the second follow-up. BME was observed in 23/40 patients at baseline, in 22/40 at the first follow-up and in 12/26 at the second follow-up. Changes in BME were visualized in 19/22 and 4/13 patients at the first and second follow-up, respectively. Changes in WOMAC scores over time did not correlate significantly with the amount of cartilage loss and the change in BME (P>0.05).

CONCLUSION

MRI is well suited to monitor the progression of OA in the longitudinal follow-up since it shows cartilage defects, BME and internal joint derangement, pathologies that are not visualized by radiographs. The lack of significant correlation between MRI findings and clinical findings is not unexpected, has been previously described and may in part be due to the fact that patients get more accustomed to their pain as the knee progressively degenerates.

A study of the relationship between molecular biomarkers of joint degeneration and the magnetic resonance-measured characteristics of cartilage in 16 symptomatic knees

We used quantitative magnetic resonance (MR) imaging to determine if relationships exist between proposed molecular biomarkers for degenerative joint disease (DJD) and structural characteristics of articular cartilage. Subjects were eight male and eight female volunteers diagnosed with osteoarthritis. Magnetic resonance images of the symptomatic knee were taken and blood samples were drawn. Concentrations of serum cartilage oligomeric matrix protein (COMP) and cleaved collagen neoepitope were compared to cartilage volume and cartilage T2, respectively, in four compartments of the tibiofemoral joint. A significant, negative correlation was found between serum COMP and medial tibia volume in the male subject group (rho=-.738, P=.037). A significant, positive correlation (rho=.881, P=.0039) was found between serum COMP and lateral femur volume in the female subject group. In both groups, positive correlations were found between serum C2C and cartilage T2, which were significant in two compartments of the male group (rho=.714, P=.047; rho=.738, P=.037) and similarly strong, but not statistically significant (rho=.750, P=.052), in one compartment of the female group. We identify strong and biologically relevant correlations between two proposed molecular biomarkers for DJD and MR measures of symptomatic knees of a small number of arthritic patients. Our findings support the hypothesis that cartilage molecular biomarkers reflect the molecular processes of cartilage degeneration and loss.

A pilot, two-year longitudinal study of the interrelationship between trabecular bone and articular cartilage in the osteoarthritic knee

OBJECTIVE

To examine the relationship between structural changes of trabecular bone and cartilage, in patients with varying degrees of osteoarthritis (OA) over 2 years, using magnetic resonance imaging.

METHODS

High-resolution, axial images were acquired for assessing trabecular bone structure, using a 3-D fast gradient-echo sequence. High-resolution, fat-suppressed, sagittal images were acquired for assessing cartilage structure, using a 3-D spoiled gradient-echo sequence. In a subset of the patients, sagittal images were acquired for measuring T(2) relaxation time, using a 2-D dual-echo spin echo sequence.

RESULTS

A large variation in bone and cartilage parameters is evident among individual subjects in each group, however, group-specific means demonstrate decreasing trends (in bone and cartilage parameters) in osteoarthritic subjects (especially in mild OA subjects). The mean T(2) increased significantly (P<0.05) between the baseline and follow-up exams for all cartilage compartments except the lateral tibia. A positive relationship was established between cartilage changes and localized bone changes closest to the joint line, while a negative relationship was established between cartilage changes and global bone changes farthest from the joint line.

CONCLUSION

This study quantifies the changes in bone and cartilage structural parameters over time, and demonstrates a longitudinal relationship between the morphological changes in bone and cartilage structure in patients with varying degrees of OA. Although a large variation of bone and cartilage changes is apparent among subjects, significant trends are evident in a relatively small sample size, with a short follow-up duration.

Computer-aided quantification of focal cartilage lesions of osteoarthritic knee using MRI

Noninvasive assessment of articular cartilage using magnetic resonance imaging (MRI) has gained popularity in the diagnosis of osteoarthritis (OA), a condition that affects 20 million Americans. Focal cartilage lesions, a defect found in roughly 19% of the OA population, currently can only be evaluated with confidence using minimally invasive arthroscopy. This article presents a computer-aided procedure using MRI to quantify focal cartilage lesions and aims to support clinical practices of diagnosis and monitoring of lesion progress. Upon a local minima search for identifying focal lesions, the proposed gradient peak method outlines lesion boundaries and then generates morphological properties, such as lesion volume and lesion area. The procedure was evaluated using simulated and in vivo data. First, a simulated lesion was created and analyzed, and the results were compared with the exact solutions. Second, an in vivo evaluation was carried out on seven human knees in which nine focal lesions were identified and quantified. Three of the subjects had follow-up analyses, at either 1 or 2 years. Finally, in an attempt to characterize local biochemical changes underlying focal lesions, MR-derived T2 values of defective cartilage within the lesion boundaries were examined and compared with the values of adjacent cartilage compartments.

Rates of glucose change measured by blood glucose meter and the GlucoWatch Biographer during day, night, and around mealtimes

OBJECTIVE

The purpose of this study was to characterize the distribution of the rate of change of blood glucose for a diabetic population.

RESEARCH DESIGN AND METHODS

The study population consisted of 124 adults with type 1 or type 2 diabetes requiring insulin. Study participants applied a GlucoWatch Biographer during the day at home for 5 consecutive days and took finger-prick blood glucose measurements hourly. Subjects kept a diary of meals. The Biographer frequently and automatically measured glucose up to three times per hour for up 12 h. Rates of glucose change were calculated for both Biographer and blood glucose measurements. Rates of glucose change during a separate study of 134 subjects were determined for daytime and nighttime use.

RESULTS

-Mean (+/-SD) rates of change of glucose of -0.36 +/- 0.95 and 0.36 +/- 0.99 mg. dl(-1). min(-1) were found before and after lunch using blood glucose data and -0.31 +/- 1.23 and 0.43 +/- 1.26 using Biographer data. For both types of diabetes, rates of glucose change exceeded 2 mg. dl(-1). min(-1) before and after meals approximately 10% of the time. Periprandial glucose patterns showed some significant differences between type 1 and type 2 diabetic subjects. Glucose levels changed more gradually at night than during the day.

CONCLUSIONS

Glucose values were almost equally unstable before and after meals. Glycemic instability around dinner was different in type 1 and type 2 diabetes. The GlucoWatch Biographer was found to be effective in tracking trends in glucose levels and yielded similar results as obtained by finger-prick blood samples.

T2 relaxation time of cartilage at MR imaging: comparison with severity of knee osteoarthritis

PURPOSE

To evaluate differences in T2 values in femoral and tibial cartilage at magnetic resonance (MR) imaging in patients with varying degrees of osteoarthritis (OA) compared with healthy subjects and to develop a mapping and display method based on calculation of T2 z scores for visual grading and assessment of cartilage heterogeneity in patients with OA.

MATERIALS AND METHODS

Knee cartilage was evaluated in 55 subjects who were categorized with radiography as healthy (n = 7) or as having mild OA (n = 20) or severe OA (n = 28). Cartilage regions were determined with manual segmentation of an MR image acquired with spoiled gradients and fat suppression. The segmentation was applied to a map of T2 relaxation time and was analyzed in four knee cartilage compartments (ie, the medial and lateral tibia and femur). Differences between cartilage compartment T2 values and subject groups were analyzed with analysis of covariance. Correlations of cartilage T2 values with clinically reported symptoms and cartilage thickness and volume were examined. Cartilage T2 values were converted to z scores per voxel on the basis of normal population values in the same cartilage compartment to better interpret cartilage heterogeneity and variation from normal.

RESULTS

Healthy subjects had mean T2 values of 32.1-35.0 msec, while patients with mild and severe OA had mean T2 values of 34.4-41.0 msec. All cartilage compartments except the lateral tibia showed significant (P <.05) increases in T2 relaxation time between healthy and diseased knees; however, no significant difference was found between patients with mild and severe OA. Correlation of T2 values with clinical symptoms and cartilage morphology was found predominantly in medial compartments.

CONCLUSION

Femoral and medial tibial cartilage T2 values increase with the severity of OA.

Stress wave velocities in bovine patellar tendon

The velocity of longitudinal stress waves in an elastic body is given by the square root of the ratio of its elastic modulus to its density. In tendinous and ligamentous tissue, the elastic modulus increases with strain and with strain rate. Therefore, it was postulated that stress wave velocity would also increase with increasing strain and strain rate. The purpose of this study was to determine the velocity of stress waves in tendinous tissue as a function of strain and to compare these values to those predicted using the elastic modulus derived from quasi-static testing. Five bovine patellar tendons were harvested and potted as bone-tendon-bone specimens. Quasi-static mechanical properties were determined in tension at a deformation rate of 100 mm/s. Impact loading was employed to determine wave velocity at various strain levels, achieved by preloading the tendon. Following impact, there was a measurable delay in force transmission across the specimen and this delay decreased with increasing tendon strain. The wave velocities at tendon strains of 0.0075, 0.015, and 0.0225 were determined to be 260 +/- 52 m/s, 360 +/- 71 m/s, and 461 +/- 94 m/s, respectively. These velocities were significantly (p < 0.01) faster than those predicted using elastic moduli derived from the quasi-static tests by 52, 45, and 41 percent, respectively. This study has documented that stress wave velocity in patellar tendon increases with increasing strain and is underestimated with a modulus estimated from quasi-static testing.