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

Efficacy and safety of continuous glucose monitoring on glycaemic control in patients with chronic pancreatitis and insulin-treated diabetes: A randomised, open-label, crossover trial

AIMS

Continuous glucose monitoring (CGM) improves glycaemic control and reduces hypoglycaemia in type 1 and 2 diabetes, but its role in managing diabetes in chronic pancreatitis is unknown. We aimed to investigate the effect of CGM compared to self-monitoring of blood glucose (SMBG) on hypoglycaemia and glycaemic control in patients with chronic pancreatitis and insulin-treated diabetes.

MATERIALS AND METHODS

In a randomised, open-label, crossover trial, 30 participants with chronic pancreatitis and insulin-treated diabetes were randomised to 50 days of CGM or SMBG, separated by a 20-day washout period. The primary endpoint was time in level 2 hypoglycaemia (<3.0 mmol/L). Secondary endpoints included additional CGM metrics, HbA1c, daily insulin dose, questionnaires, and safety outcomes.

RESULTS

Twenty-nine participants completed the trial (mean age 64.4 ± 8.8 years; 22 men [75.9%]). There was a numerical reduction in time spent in level 2 hypoglycaemia with CGM compared to SMBG (mean difference -0.36%, 95% confidence interval (CI) -0.78% to 0.06%; p = 0.09). CGM improved time in range (3.9-10.0 mmol/L; mean difference 7.46%, 95% CI 1.67% to 13.25%; p = 0.01), reduced time above range (>10.0 mmol/L; mean difference -6.55%, 95% CI -12.59% to -0.51%; p = 0.04), and reduced time below range (<3.9 mmol/L; mean difference -0.91%, 95% CI -1.79% to -0.03%; p = 0.04) compared to SMBG. No differences were observed for the safety endpoints.

CONCLUSIONS

In patients with chronic pancreatitis and insulin-treated diabetes, CGM increased time in range and reduced time above and below range. These findings highlight the potential of CGM in improving glycaemic control.

State of the Art and Consensus Statements by Healthcare Providers, Patients, and Caregivers on Continuous Glucose Monitoring in Liver Glycogen Storage Diseases

Continuous glucose monitoring (CGM) is increasingly used although not officially registered for the management of people living with liver glycogen storage diseases (GSDs). The aims of this study were twofold: (a) to investigate the current experiences of healthcare providers (HCPs), patients, and caregivers using CGM to monitor glucose concentrations in liver GSDs, and (b) to formulate consensus statements. Two web-based questionnaires were distributed, one for HCPs and one for patients and/or their caregivers. The questionnaires collected data on demographics and epidemiology, current use of CGM, and opinions and statements about CGM in GSDs. For the statements, respondents rated their agreement on a 5-point Likert scale, and the consensus level was set at 75%. One Hundred Fourteen HCPs (including 87 physicians and 26 dietitians) from 28 countries responded, representing care of approximately 3800 liver GSD patients. Additionally, 148 GSD patients and/or their caregivers from 21 countries responded, mainly representing GSD Ia (n = 50), GSD Ib (n = 56), GSD III (n = 14), and GSD IX (n = 18). The median age to consider starting to use CGM was 6 and 2 months for HCPs and GSD families, respectively. Out of 16 statements common to the two questionnaires, HCPs and patients/caregivers reached consensus on 12 statements in both groups. Use of CGM is considered standard of care by both HCPs and GSD families, but reimbursement of CGM devices is challenging. Compared to diabetes mellitus, CGM should be applied differently in liver GSDs. Consensus guidelines are warranted on the use of CGM in liver GSDs, both in routine healthcare and in clinical trials.

The challenge of assessing impaired awareness of hypoglycaemia in diabetes in the era of continuous glucose monitoring: A narrative review of evidence and translation into clinical practice

Iatrogenic hypoglycaemia remains a major barrier in diabetes care. Over time, and with repeated hypoglycaemic episodes, the physiological responses to hypoglycaemia can become blunted, resulting in impaired awareness of hypoglycaemia (IAH). In IAH, the onset of cognitive dysfunction precedes the onset of autonomic symptoms, often preventing appropriate self-treatment, thus increasing the frequency of severe hypoglycaemia (SH). Historically, IAH has been assessed with questionnaires, such as the Gold and Clarke scores, which were developed in the 1990s. A stepwise change in diabetes management in the last few decades has been the deployment of continuous glucose monitoring (CGM). CGM allows people with diabetes to set alarms that can warn them of hypoglycaemia or even impending hypoglycaemia, thus providing a degree of 'technological' awareness. This creates a challenge in assessing awareness status, as people may be alerted to low-sensor glucose events before they experience any symptoms. CGM also allows the introduction of new measures of hypoglycaemia exposure such as time below range, which might complement traditional methods of risk assessment. These changes in the field prompt a need for reassessment of the measures of IAH. This narrative review evaluates the current epidemiology of SH and IAH, explores different measures of IAH, and evaluates the relationship between CGM metrics, IAH and SH. We conclude that a clinical approach involving traditional questionnaires, or newer updated alternatives such as the Hypo A-Q awareness scale, combined with CGM metrics and clinical assessment of human factors is recommended in the absence of a clearly superior measure.

Clinical trial protocol for continuous glucose monitoring in critical care at Hospital Clinic of Barcelona (CGM-UCI23)

BACKGROUND

Hyperglycaemia is common in intensive care units (ICUs), with a prevalence of up to 86.2%, increasing mortality. Technology has evolved towards continuous glucose monitoring (CGM), and its use in ICUs began especially during the coronavirus pandemic (COVID-19). Various studies have evaluated the reliability of CGM, indicating that it is safe for use in critically ill patients.

AIM

The aim of this study was to compare the use of CGM with point-of-care glucose (POC-G) testing in ICU. Specific objectives include evaluating the glycaemic control, the frequency of POC-G measurements, the incidence of hyperglycaemia, hypoglycaemia and morbidity and mortality at 90 days.

STUDY DESIGN

An experimental, controlled and randomized clinical trial with a single-blind design will be conducted at Hospital Clinic of Barcelona (HCB). A sample size of 376 participants will be recruited and randomly assigned to two groups: an experimental group, where glycaemic management will be based on CGM; and a control group, where glucose will be managed through POC-G testing, with a blinded CGM.

RESULTS

The primary variable considered will be time in range (TIR), with secondary outcomes including, time above range (TAR), time below range (TBR), number of POC-G measurements, incidence of hyperglycaemia and hypoglycaemia, and mortality. Hypothesis testing will use the Kolmogorov-Smirnov test to assess data normality, with appropriate statistical tests applied, considering a p-value <.05.

RELEVANCE TO CLINICAL PRACTICE

The results obtained will help us understand the impact of CGM on critically ill patients. CGM could potentially reduce the workload of nurses and improve the efficiency of decision-making by the ICU team, enabling early identification and treatment of glucose complications, thereby enhancing safety. Patient safety, a reduction in patient fingerstick and a decreased care burden are the criteria that add value to this research.

MiniMed 780G system performance in older users with type 1 diabetes: Real-world evidence and the case for stricter glycaemic targets

AIMS

Large-scale studies on the effectiveness of automated insulin delivery (AID) systems in older people with type 1 diabetes are still limited. A multinational, retrospective, real-world study was conducted to examine the performance of the MiniMed™ 780G advanced hybrid closed-loop system in users with type 1 diabetes aged ≥56 years compared with those aged 16-55 years.

MATERIALS AND METHODS

Data from 35 366 MiniMed™ 780G system users aged 16-55 years and 7415 users aged ≥56 years were included. The main outcome was time in range 70-180 mg/dL (TIR); other continuous glucose monitoring (CGM) metrics were also assessed.

RESULTS

Across all users, mean TIR was 77.1% for users aged ≥56 years and 73.1% for those aged 16-55 years (Δ4.0, 95% confidence interval [CI]: 3.8-4.2, p <0.0001). In users employing the optimal system settings (i.e., Glucose Target: 100 mg/dL; active insulin time: 2 h), mean TIR was 81.9% in older and 79.7% in younger users (Δ2.2, 95% CI: 1.5-2.9, p <0.0001). Across all users, mean time below range <70 mg/dL (TBR70) was 1.5% in older and 2.1% in younger users. In older users, TIR and TBR70 remained consistent over 12 months.

CONCLUSIONS

This real-world analysis demonstrated that older MiniMed™ 780G system users with type 1 diabetes can achieve a TIR >70% without increasing hypoglycaemia risk. Users employing optimal settings showed the best outcomes. The system performed as well as or better than in younger users. These findings support the case that more stringent TIR targets can be achieved safely.

Retinal Microperimetry as a Novel Tool for Early Detection of Subclinical Cognitive Dysfunction and Brain Damage in Type 1 Diabetes: A Pilot Study

CONTEXT

Retinal microperimetry (MPR) is a non-invasive method that measures retinal light sensitivity (RS) and gaze fixation stability (GFS). MPR has been described as a marker of cognitive impairment in people with Type 2 diabetes, but it has never been assessed in people with Type 1 diabetes (T1D). Our group described subclinical cognitive alterations, structural brain differences, and increased levels of light chain neurofilament (NfL) in people with T1D and impaired awareness of hypoglycaemia.

OBJECTIVE

To measure RS and GFS using MPR in individuals with T1D and evaluate its correlation with neuropsychological assessment, plasma NfL levels and CGM-derived glucometric parameters.

SECONDARY OBJECTIVES

to evaluate the possible differences of RS and GFS in people with T1D depending on hypoglycaemia awareness.

DESIGN, SETTING AND PARTICIPANTS

Pilot observational study, people with T1D without clinical cognitive impairment, moderate-severe retinopathy or glaucoma. MPR was performed with MAIA3.

RESULTS

A total of 30 subjects were studied: 40% women, age 58 ± 11 years; T1D duration 31 ± 9 years, mild retinopathy 33%. RS was 27.5 dB (26.1-28.3) and GFS(%) 97.6% (93.5%-99.5%). We found a correlation between RS and memory alteration tests (p = 0.016) and between GFS(%) and a composite of attention and executive neuropsychological tests (p = 0.025). An inverse correlation between GFS and time below range was found. No correlation was found with NfL.

CONCLUSION

This first exploratory study in people with T1D supports the potential utility of MPR as a screening tool for subclinical neurocognitive alterations in this population.

Predicting Time in Range Without Hypoglycaemia Using a Risk Calculator for Intermittently Scanned CGM in Type 1 Diabetes

PURPOSE

To investigate the impact of clinical and socio-economic factors on glycaemic control and construct statistical models to predict optimal glycaemic control (OGC) after implementing intermittently scanned continuous glucose monitoring (isCGM) systems.

METHODS

This retrospective study included 1072 type 1 diabetes patients (49.0% female) from three centres using isCGM systems. Clinical data and net income from the census tract were collected for each individual. OGC was defined as time in range > 70%, with time below 70 mg/dL < 4%. The sample was randomly split in two equal parts. Logistic regression models to predict OGC were developed in one of the samples, and the best model was selected using the Akaike information criterion and adjusted for Pearson's and Hosmer-Lemeshow's statistics. Model reliability was assessed via external validation in the second sample and internal validation using bootstrap resampling.

RESULTS

Out of 2314 models explored, the most effective predictor model included annual net income per person, sex, age, diabetes duration, pre-isCGM HbA1c, insulin dose/kg, and the interaction between sex and HbA1c. When applied to the validation cohort, this model demonstrated 72.6% specificity, 67.3% sensitivity, and an area under the curve (AUC) of 0.736. The AUC through bootstrap resampling was 0.756. Overall, the model's validity in the external cohort was 80.4%.

CONCLUSIONS

Clinical and socio-economic factors significantly influence OGC in type 1 diabetes. The application of statistical models offers a reliable means of predicting the likelihood of achieving OGC following isCGM system implementation.

The interdependence of targets for continuous glucose monitoring outcomes in type 1 diabetes with automated insulin delivery

AIM

The aim was to determine the interdependence of targets for glucose management indicator (GMI), time within the ranges of 70-180 mg/dL (TIR) and 70-140 mg/dL (time in tight glucose range [TITR]), time above 180 mg/dL (TA180) and 250 mg/dL (TA250) and time below 70 mg/dL (TB70) and 54 mg/dL (TB54) and its implications for setting targets in automated insulin delivery (AID).

MATERIALS AND METHODS

Real-world data from individuals with type 1 diabetes using the 780G system were used to calculate the receiver operating characteristic curves and establish interdependent targets for time in ranges based on several GMI benchmarks. Correlation, regression and principal component analysis were used to determine their association and dimensionality.

RESULTS

In individuals aged >15 years (n = 41 692), a GMI <6.5% required targets of >81%, >58%, <15% and <1.9% for TIR, TITR, TA180 and TA250, respectively, with high sensitivity, specificity and accuracy (>90%), whereas these values were poor for time in hypoglycaemia and GMI, which had a modest correlation (-0.21 to -0.43). Two dimensions emerged: (1) GMI, TIR, TITR, TA180 and TA250, and (2) TB70 and TB54, explaining 95% of total variability. GMI (or TIR) and TB70 explained >81% of the variability in the remaining continuous glucose monitoring (CGM) metrics, providing accurate predictions. Individuals aged ≤15 years (n = 14 459) showed similar results.

CONCLUSION

We developed a methodology to establish interdependent CGM targets for therapies with CGM data outputs. In AID with the 780G system, a GMI <7% requires time in ranges close to consensus targets. Targets for GMI, TIR, TITR, TA180 and TA250 could be reduced to targets for GMI or TIR, whereas targets for time in hypoglycaemia are not inherently tied to GMI/TIR targets.

Generating real-world evidence on diabetes technology using the CareLink Personal data management system

AIM

To establish trust in real-world evidence (RWE) derived from CareLink Personal (CP), Medtronic's data management system for MiniMed system users, we show that this database and its analyses strictly adhere to the principles of RWE.

METHODS

The methodology is applicable to all MiniMed iterations. We described every step from raw data to predefined outcomes. In addition, we showed CP's fitness-for-research by the below metrics (using last year's MiniMed 780G system data as a case study): representative population, relevant endpoints, appropriate granularity, high data completeness, high data representativity and consistency in results.

RESULTS

The process from raw data to outcomes has been validated, and metrics/logics adhere to established definitions. Over 95% of users have a CP account; with 96% providing consent, this allows the use of >91% of the census population. There is no rationale for an over-representation of a specific phenotype among users not included. CP includes >50 endpoints, including 'International Consensus on Time in Range' based metrics. Data are recorded at 5-min intervals (maximum 288 per day), and on average there were 263 data points per person per day. Ninety-nine per cent of uploads were automated. For the last year, only 1 in 6 users had a data gap >1 day, and 1 in 50 had a gap >1 week. The time in range from in-silico studies was similar to that of real-world studies from different geographies and with ever growing populations.

CONCLUSION

RWE from CP adheres to the principles of RWE and can serve as robust evidence on the performance and safety of MiniMed systems.

Performance of Continuous Glucose Monitoring System Among Patients With Acute Ischaemic Stroke Treated With Mechanical Thrombectomy

AIMS

Glucose metabolism abnormalities are prevalent in acute ischaemic stroke (AIS) patients and are associated with poor prognosis. The continuous glucose monitoring (CGM) system can provide detailed information on glucose levels and glycaemic excursions. This study aimed to evaluate the feasibility and accuracy of CGM application in the acute phase of AIS patients.

METHODS

This single-centre, prospective, and observational study consecutively enrolled patients with AIS with anterior circulation large vessel occlusion (AC-LVO) and received mechanical thrombectomy (MT) within 24 h of symptom onset. A user-retrospectively calibrated iPro2 CGM system was implanted right before the MT procedure started and removed on the fifth day after MT or at discharge. Fingertip glucose was measured as a reference. Accuracy evaluation included the Bland-Altman plot (with a proportion of CGM values within 15/15, 20/20 and 30/30), the absolute relative difference (ARD) and error grid analysis (EGA). The safety and glucose profiles were also evaluated.

RESULTS

Of the 183 patients screened, 141 were included, with a median monitoring duration of 4.49 days. Compared to reference measurements, 3097 CGM readings were matched with a mean bias of -4.16 mg/dL. The proportions of sensor readings meeting the 15/15, 20/20 and 30/30 criteria were 64.55%, 76.07% and 87.21%, respectively. The overall mean and median ARD were 14.60% ± 14.62% and 9.77% (4.15, 20.00). EGA showed that 98.97%, 99.42% and 99.06% values fall within clinically accurate zones in Clarke, Parkes and continuous glucose EGA, respectively.

CONCLUSION

The CGM system was feasible, safe and accurate for in-hospital use among AIS patients who received MT.

Continuous measurement of interstitial glycaemia in professional female UCI world tour cyclists undertaking a 9-day cycle training camp

Nine cyclists (age: 26 ± 5 years, height: 168 ± 5 cm and mass 58.5 ± 4.5 kg) were observed using continuous glucose monitoring devices throughout a training camp. Interstitial glucose [iG] data were captured via the Abbott libre sense biosensor (Abbott Laboratories) and paired with the Supersapiens software (TT1 Products Inc.). [iG] data were split into time ranges, that is, overall (24-hourly), day-time (06:00-23:59), night-time (00:00-05:59) and exercise. [iG] data were stratified into percentage of time, below range ([TBR] < 70 mg/dl), in range ([TIR] 70-140 mg/dl) and above range ([TAR] ≥ 141 mg/dl). Differences in diurnal and nocturnal data were analysed via repeated measures analysis of variance and paired t-tests where appropriate. p-value of ≤0.05 was accepted as significant. Riders spent an average of 3 ± 1% TAR, 93 ± 2% TIR and 8 ± 3% TBR. Mean 24 h [iG] was 93 ± 2 mg/dl with a coefficient of variation (CV) of 18 ± 1%. Mean (day: 95 ± 3 vs. night: 86 ± 3 mg/dl and p < 0.001) and CV (day: 18 ± 1 vs. night: 9 ± 1% and p < 0.001) in [iG] were higher during the day-time hours. TAR was greater during the day (day: 3 ± 1 vs. night: 0 ± 0% and p < 0.001) but TBR and TIR were similar. Glucose levels below the clinical range may have implications for those without diabetes and warrants further investigation.

Metabolic dysfunction-associated steatotic liver disease is associated with worse time in ranges in type 1 diabetes

AIM

To investigate the relationship between continuous glucose monitoring (CGM)-derived glucometrics and metabolic dysfunction-associated steatotic liver disease (MASLD) in type 1 diabetes (T1D).

METHODS

A cross-sectional study collecting data on anthropometrics, glucometrics and MASLD in adults with T1D using a CGM device was conducted. MASLD was assessed by abdominal ultrasound and the presence of at least one cardiometabolic criterion. Backward multivariable logistic regression models were applied to define variables independently associated with MASLD.

RESULTS

A total of 302 consecutive participants were included (median age 49 [34-61] years, male sex 58%, median diabetes duration 29 [17-38] years, mean time in range [TIR] 55% ± 16%). MASLD was present in 17% of cases, and 32% had metabolic syndrome (MetS). MetS was significantly more prevalent in the MASLD group (65% vs. 25%, P < .0001). TIR (P = .038) and time below range (TBR) (P = .032) were lower and time above range (TAR) was higher (P = .006), whereas HbA1c did not reach significance (P = .068). No differences were found for the glycaemia risk index. TIR (P = .028), TAR (P = .007), TBR (P = .036), waist circumference (P < .001) and systolic blood pressure (P = .029) were independently associated with MASLD, while sex, age, aspartate aminotransferase/alanine aminotransferase ratio, gamma-glutamyl transferase, high-density lipoprotein cholesterol and triglycerides were not.

CONCLUSIONS

TIR, TAR, TBR, waist circumference and systolic blood pressure were independently associated with MASLD.

Decreased complexity of glucose time series index associated with adverse pregnancy outcomes in gestational diabetes mellitus

AIM

We investigated the relationship between the complexity of the glucose time series index (CGI) during pregnancy and adverse pregnancy outcomes in women with gestational diabetes mellitus (GDM).

MATERIALS AND METHODS

In this retrospective cohort study, 388 singleton pregnant women with GDM underwent continuous glucose monitoring (CGM) at a median of 26.86 gestational weeks. CGI was calculated using refined composite multiscale entropy based on CGM data. The participants were categorized into tertiles according to their baseline CGI (CGI <2.32, 2.32-3.10, ≥3.10). Logistic regression was used to assess the association between CGI and composite adverse outcomes or large for gestational age (LGA). The discrimination performance of CGI was estimated using receiver operating characteristic analysis.

RESULTS

Of the 388 participants, 71 (18.3%) had LGA infants and 63 (16.2%) had composite adverse outcomes. After adjustments were made for confounders, compared with those with a high CGI (CGI ≥3.10), participants with a low CGI (CGI <2.32) had a higher risk of composite adverse outcomes (odds ratio: 12.10, 95% confidence interval: 4.41-33.18) and LGA (odds ratio: 12.68, 95% confidence interval: 4.04-39.75). According to the receiver operating characteristic analysis, CGI was significantly better than glycated haemoglobin and conventional CGM indicators for the prediction of adverse pregnancy outcomes (all p < .05).

CONCLUSION

A lower CGI during pregnancy was associated with composite adverse outcomes and LGA. CGI, a novel glucose homeostasis predictor, seems to be superior to conventional glucose indicators for the prediction of adverse pregnancy outcomes in women with GDM.

Evaluation of time in tight range and the glycaemia risk index in adults with type 1 diabetes using an advanced hybrid closed loop system: A 1-year real-world assessment

AIM

To assess the long-term glycaemic outcomes, with additional metrics, in adults with type 1 diabetes (T1D) using the Tandem t:slim X2 with Control-IQ technology advanced hybrid closed-loop (AHCL) system.

METHODS

This was a single-centre, retrospective study involving 56 T1D patients who transitioned to the Tandem t:slim X2 with Control-IQ system. The primary and secondary endpoints consisted of variations in time in tight range (TiTR; 70-140 mg/dL) and the glycaemia risk index (GRI), respectively. Additional standardized continuous glucose monitoring (CGM) metrics, mean sensor glucose, coefficient of variation, the glucose management indicator (GMI), HbA1c and insulin daily dose, were also evaluated. Variables were measured at baseline and at 15 days, 3 months, 6 months and 1 year after Tandem t:slim X2 Control-IQ initiation. Glucose outcomes are expressed as mean (standard deviation).

RESULTS

Use of Tandem t:slim X2 with Control-IQ over 1 year was associated with an increase in mean TiTR, from 38.11% (17.05%) to 43.10% (13.20%) (P = .059), and with a decline in the GRI, from 41.03 (25.48) to 28.55 (16.27) (P = .008). CGM metrics, including time in range and time above range, showed consistent improvements. Mean sensor glucose, the GMI and HbA1c decreased significantly over time. After an initial increase, insulin daily dose remained stable throughout the 12 months.

CONCLUSIONS

The results highlight the sustained effectiveness of Tandem t:slim X2 with Control-IQ in improving glycaemic outcomes over 1 year and support the use of this technology for the management of T1D.

Inpatient diabetes management

Diabetes mellitus is currently approaching epidemic proportions and disproportionately affects patients in the hospital setting. In the United States, individuals living with diabetes represent over 17 million emergency department visits and 8 million admissions annually. The management of these patients in the hospital setting is complex and differs considerably from the outpatient setting. All patients with hyperglycemia should be screened for diabetes, as in-hospital hyperglycemia portends a greater risk for morbidity, mortality, admission to an intensive care unit, and increased hospital length of stay. However, the definition of hyperglycemia, glycemic targets, and strategies to manage hyperglycemia in the inpatient setting can vary greatly depending on the population considered. Moreover, the presenting illness, changing nutritional status, and concurrent hospital medications often necessitate thoughtful consideration to adjustments of home diabetes regimens and/or the initiation of new insulin doses. This review article will examine core concepts and emerging new literature surrounding inpatient diabetes management, including glycemic targets, insulin dosing strategies, noninsulin medications, new diabetes technologies, inpatient diabetes management teams, and discharge planning strategies, to optimize patient safety and satisfaction, clinical outcomes, and even hospital financial health.

Cross-sectional association of continuous glucose monitoring-derived metrics with cerebral small vessel disease in older adults with type 2 diabetes

AIM

To examine cross-sectional associations between continuous glucose monitoring (CGM)-derived metrics and cerebral small vessel disease (SVD) in older adults with type 2 diabetes.

MATERIALS AND METHODS

In total, 80 patients with type 2 diabetes aged ≥70 years were analysed. Participants underwent CGM for 14 days. From the CGM data, we derived mean sensor glucose, percentage glucose coefficient of variation, mean amplitude of glucose excursion, time in range (TIR, 70-180 mg/dl), time above range (TAR) and time below range metrics, glycaemia risk index and high/low blood glucose index. The presence of cerebral SVD, including lacunes, microbleeds, enlarged perivascular spaces and white matter hyperintensities, was assessed, and the total number of these findings comprised the total cerebral SVD score (0-4). Ordinal logistic regression analyses were performed to examine the association of CGM-derived metrics with the total SVD score.

RESULTS

The median SVD score was 1 (interquartile range 0-2). Higher hyperglycaemic metrics, including mean sensor glucose, TAR >180 mg/dl, TAR >250 mg/dl, and high blood glucose index and glycaemia risk index, were associated with a higher total SVD score. In contrast, a higher TIR (per 10% increase) was associated with a lower total SVD score (odds ratio 0.73, 95% confidence interval 0.56-0.95). Glycated haemoglobin, percentage glucose coefficient of variation, mean amplitude of glucose excursions, time below range and low blood glucose index were not associated with total cerebral SVD scores.

CONCLUSIONS

The hyperglycaemia metrics and TIR, derived from CGM, were associated with cerebral SVD in older adults with type 2 diabetes.

Time In Tight Range in children and adolescents with type 1 diabetes: A cross-sectional observational single centre study evaluating efficacy of new advanced technologies

INTRODUCTION

Early and tight glycaemic control is crucial to prevent long-term complications of Type 1 Diabetes (T1D). The aim of our study was to compare glucose metrics, including Time In Tight Range (TITR), in a real-world setting.

METHODS

We performed a single-centre cross-sectional study in 534 children and adolescents with T1D. Participants were divided into four groups (multiple daily injections + real-time Continuous glucose monitoring (CGM), multiple daily injections + intermittently scanned CGM, sensor augmented pump (SAP), and Advanced Hybrid Closed-Loop (AHCL). Demographical and clinical data were collected and analysed.

RESULTS

The group with AHCL showed significantly higher Time In Range (TIR) (71.31% ± 10.88) than SAP (57.82% ± 14.98; p < 0.001), MDI + rtCGM (54.56% ± 17.04; p < 0.001) and MDI + isCGM (52.17% ± 19.36; p < 0.001) groups with a lower Time Above Range (p < 0.001). The group with AHCL also showed lower Time Below Range than MDI + isCGM and SAP groups (p < 0.01). The overall TITR was 37% ± 14 with 19% of participants who reached a TITR ≥50% with a mean TIR of 81%. AHCL had significantly higher TITR (45.46% ± 11.77) than SAP (36.25% ± 13.53; p < 0.001), MDI + rtCGM (34.03% ± 13.89; p < 0.001) and MDI + isCGM (33.37% ± 15.84; p < 0.001) groups with a lower Coefficient of Variation (p < 0.001).

CONCLUSIONS

Our study indicates that AHCL ensures a better glycaemic control with an improvement in both TIR and TITR, along with a reduction in CV. Implementation of automated insulin delivery systems should be considered in the treatment of children and adolescents with T1D.

Efficacy of information and communication technology interventions for the management of diabetes mellitus: An umbrella review and evidence map

INTRODUCTION

We validated the quality of evidence and potential benefits of information and communication technology interventions on diabetes-related health outcomes.

METHODS

We systematically searched PubMed/MEDLINE, Embase, Google Scholar, and CINAHL and manually searched the reference lists of the retrieved review articles from each database's inception to October 2022. Randomized controlled trials were included to determine the benefits of information and communication technology interventions on diabetes outcomes.

RESULTS

Ten meta-analyses of randomized controlled trials were included, with 37 unique outcomes encompassing 379 studies and >70,000 participants across 47 countries and six continents. Information and communication technology intervention was associated with reduced HbA1c levels in patients with type 1 (moderate certainty), type 2 (moderate certainty), and gestational diabetes (low certainty) and showed potential benefits for type 2 diabetes, demonstrating a reduction in systolic blood pressure (high certainty), low-density lipoprotein cholesterol (low certainty), and body weight (low certainty), whereas those for gestational diabetes demonstrated a reduction in fasting (low certainty) and 2-h postprandial blood glucose levels (low certainty).

CONCLUSION

This umbrella review and evidence map revealed varying evidence on the potential benefits of information and communication technology interventions for diabetes-related outcomes. Our results demonstrate these interventions to be novel treatment options for policymakers and physicians to establish personalized health strategies.

Glucose sensor with predictive alarm for hypoglycaemia: Improved glycaemic control in adolescents with type 1 diabetes

AIM

Hypoglycaemic events are linked to microvascular and macrovascular complications in people with type 1 diabetes. We aimed to evaluate the efficacy of glucose sensor [real-time continuous glucose monitoring (RT-CGM)] with predictive alarm (PA) in reducing the time spent below the range (%TBR <70 mg/dl) in a group of adolescents with type 1 diabetes (AwD).

MATERIALS AND METHODS

This was a crossover, monocentric and randomized study. RT-CGM was set with Alarm on Threshold (AoT) at 70 mg/dl) or PA for hypoglycaemia (20 m before threshold). Twenty AwD were enrolled and randomized to either a PA/AoT or AoT/PA treatment sequence, in a 1:1 ratio. The two groups (PA vs. AoT) were compared using two-way repeated measures ANOVA taking account of the carryover effect.

RESULTS

AwD using PA for hypoglycaemia spent less time in severe hypoglycaemia (%TBR2 <54 mg/dl; 0.32 ± 0.31 vs. 0.91 ± 0.90; p < .02) and hypoglycaemia (%TBR <70 mg/dl; 1.68 ± 1.06 vs. 2.90 ± 2.05; p < .02), with better glycaemia risk index (51.3 ± 11.0 vs. 61.5 ± 12.6; p ≤ .01).

CONCLUSION

The use of RT-CGM with PA for hypoglycaemia technology in AwD using multiple daily insulin injection treatment could significantly reduce the risk of having hypoglycaemic events resulting in an improved quality of glucose control.

CLINICAL TRIAL REGISTRATION NUMBER

NCT05574023.

The MiniMed 780G automated insulin delivery system adapts to substantial changes in daily routine: Lessons from real world users during Ramadan

AIM

To report on the effectiveness and safety of the MiniMed 780G automated insulin delivery system in real-world users during the month of Ramadan.

MATERIALS AND METHODS

CareLink Personal data were extracted from MiniMed 780G system users from the Gulf region. Users were included if they had ≥10 days of sensor glucose data during the month of Ramadan 2022 as well as in the month before and after. For the main analysis, continuous glucose monitoring endpoints were aggregated per month and were reported by time of day (daytime: 05.31-18.00 h, and night-time). Additional analyses were performed to study the pace at which the algorithm adapts.

RESULTS

Glycaemic control was well kept in the 449 included users (mean sensor glucose = 152.6 ± 18.7 mg/dl, glucose management indicator = 7.0 ± 0.4%, time in range = 70.7 ± 11.0%, time below 70 mg/dl = 2.3 ± 2.3%). Albeit some metrics differed from the month before (p < .0001 for all), absolute differences were very small and considered clinically irrelevant. During Ramadan, there was no increased risk of hypoglycaemia during daytime (time below 70 mg/dl = 2.3 ± 2.4%), time in range was highest during daytime (80.0 ± 10.7%, night: 60.4 ± 15.3%), while time above 180 mg/dl was highest during night-time (37.3 ± 16.3%, day: 17.7 ± 10.7%). The algorithm adapted immediately upon lifestyle change.

CONCLUSION

The MiniMed 780G automated insulin delivery system is effective, safe and fast in adapting to the substantial changes that occur in the lifestyle of people with type 1 diabetes during Ramadan.

The role of real-time continuous glucose monitoring in diabetes management and how it should link to integrated personalized diabetes management

Diabetes is a complex metabolic condition that demands tailored, individualized approaches for effective management. Real-time continuous glucose monitoring (rtCGM) systems have improved in terms of design, usability and accuracy over the years and play a pivotal role in the delivery of integrated personalized diabetes management (iPDM). iPDM is a comprehensive multidisciplinary approach that combines individualized care strategies utilizing technologies and interventions and encourages the active involvement of the person with diabetes in the care provided. The use of stand-alone rtCGM and its integration with other diabetes technologies, such as hybrid automated insulin delivery, have enabled improved glycaemic and quality of life outcomes for people with diabetes. As the uptake of rtCGM and associated technologies is increasing and becoming the standard of care for people with diabetes, it is important that efforts are focused on associated goals such as reducing health inequalities in terms of access, aligning structured education with rtCGM usage, choosing the right technology based on needs and preferences, and minimizing burden while aiming for optimal glucose outcomes. Utilizing rtCGM in other settings than outpatients and in diabetes cohorts beyond type 1 and type 2 diabetes needs further exploration. This review aims to provide an overview of the role of rtCGM and how best to link rtCGM to iPDM, highlighting its role in enhancing personalized treatment strategies.

Cardio-renal-metabolic disease in primary care setting

In the primary care setting providers have more tools available than ever before to impact positively obesity, diabetes, and their complications, such as renal and cardiac diseases. It is important to recognise what is available for treatment taking into account diabetes heterogeneity. For those who develop type 2 diabetes (T2DM), effective treatments are available that for the first time have shown a benefit in reducing mortality and macrovascular complications, in addition to the well-established benefits of glucose control in reducing microvascular complications. Some of the newer medications for treating hyperglycaemia have also a positive impact in reducing heart failure (HF). Technological advances have also contributed to improving the quality of care in patients with diabetes. The use of technology, such as continuous glucose monitoring systems (CGM), has improved significantly glucose and glycated haemoglobin A1c (HbA1c) values, while limiting the frequency of hypoglycaemia. Other technological support derives from the use of predictive algorithms that need to be refined to help predict those subjects who are at great risk of developing the disease and/or its complications, or who may require care by other specialists. In this review we also provide recommendations for the optimal use of the new medications; sodium-glucose co-transporter-2 inhibitors (SGLT2i) and Glucagon-like peptide-receptor agonists 1 (GLP1RA) in the primary care setting considering the relevance of these drugs for the management of T2DM also in its early stage.

Adaptive bolus calculators for people with type 1 diabetes: A systematic review

AIM

To conduct a systematic review of studies assessing adaptive insulin bolus calculators for people with type 1 diabetes (T1D).

METHODS

Electronic databases (Medline, Embase and Web of Science) were systematically searched from date of inception to 13 October 2022 for single-arm or randomized controlled studies assessing adaptive bolus calculators only, in children or adults with T1D on multiple daily injections or insulin pumps with glycaemic outcomes reported. The Clinicaltrials.gov registry was searched for recently completed studies evaluating decision support in T1D. The quality of extracted studies was assessed using the Standard Quality Assessment criteria and the Cochrane Risk of Bias assessment tool.

RESULTS

Six studies were identified. Extracted data were synthesized in a descriptive review because of heterogeneity. All the studies were small feasibility studies or were not suitably powered, and all were deemed to be at a high risk of performance and detection bias because they were unblinded. Overall, these studies did not show a significant glycaemic improvement. Two studies showed a reduction in postprandial time below range or an incremental change in blood glucose concentration; however, these were in controlled environments over a short duration.

CONCLUSIONS

There are limited clinical trials evaluating adaptive bolus calculators. Although results from small trials or in-silico data are promising, further studies are required to support personalized and adaptive management of T1D.

Twelve-month results of the ADAPT randomized controlled trial: Reproducibility and sustainability of advanced hybrid closed-loop therapy outcomes versus conventional therapy in adults with type 1 diabetes

AIMS

To reassess the 6-month efficacy and to assess the 12-month sustained efficacy of the MiniMed™ 780G advanced hybrid closed-loop automated insulin delivery (AID) system compared to multiple daily injections plus intermittently scanned glucose monitoring (MDI+isCGM) in people with type 1 diabetes not meeting glucose targets.

METHODS

The ADAPT study was a prospective, multicentre, open-label, randomized control trial in people with type 1 diabetes, with a glycated haemoglobin (HbA1c) concentration of at least 8.0% (64 mmol/mol), on MDI+isCGM therapy. After a 6-month study phase, participants randomized at baseline to MDI+isCGM switched to AID (SWITCH) while the others continued AID therapy (SUSTAIN) for an additional 6 months. The primary endpoint of this continuation phase was the within-group change in mean HbA1c between 6 and 12 months, with superiority in the SWITCH group and noninferiority in the SUSTAIN group (ClinicalTrials.gov: NCT04235504).

RESULTS

A total of 39 SWITCH and 36 SUSTAIN participants entered the continuation phase. In the SWITCH group, HbA1c was significantly decreased by -1.4% (95% confidence interval [CI] -1.7% to -1.1%; P < 0.001) from a mean ± SD of 8.9% ± 0.8% (73.9 ± 8.6 mmol/mol) at 6 months to 7.5% ± 0.6% (58.5 ± 6.9 mmol/mol) at 12 months. Mean HbA1c increased by 0.1% (95% CI -0.05% to +0.25%), from 7.3% ± 0.6% (56.5 ± 6.7 mmol/mol) to 7.4% ± 0.8% (57.7 ± 9.1 mmol/mol) in the SUSTAIN group, meeting noninferiority criteria. Three severe hypoglycaemia events occurred in two SWITCH participants during the continuation phase.

CONCLUSION

ADAPT study phase glycaemic improvements were reproduced and sustained in the continuation phase, supporting the early adoption of AID therapy in people with type 1 diabetes not meeting glucose targets on MDI therapy.

Changes in the glycaemia risk index and its association with other continuous glucose monitoring metrics after initiation of an automated insulin delivery system in adults with type 1 diabetes

AIM

To evaluate the glycaemia risk index (GRI) and its association with other continuous glucose monitoring (CGM) metrics after initiation of an automated insulin delivery (AID) system in patients with type 1 diabetes (T1D).

MATERIALS AND METHODS

Up to 90 days of CGM data before and after initiation of an AID system from 185 CGM users with T1D were collected. GRI and other CGM metrics were calculated using cgmanalysis R software and were analysed for 24 hours, for both night-time and daytime. GRI values were assigned to five GRI zones: zone A (0-20), B (21-40), C (41-60), D (61-80) and E (81-100).

RESULTS

Compared with baseline, GRI and its components decreased significantly after AID initiation (GRI: 48.7 ± 21.8 vs. 29 ± 13; hypoglycaemia component: 2.7 ± 2.8 vs. 1.6 ± 1.7; hyperglycaemia component: 25.3 ± 14.5 vs. 15 ± 8.5; P < .001 for all). The GRI was inversely correlated with time in range before (r = -0.962) and after (r = -0.961) AID initiation (P < .001 for both). GRI was correlated with time above range (before: r = 0.906; after = 0.910; P < .001 for both), but not with time below range (P > .05). All CGM metrics improved after AID initiation during 24 hours, for both daytime and night-time (P < .001 for all). Metrics improved significantly more during night-time than daytime (P < .01).

CONCLUSIONS

GRI was highly correlated with various CGM metrics above, but not below target range, both before and after AID initiation.

Characterizing Associations of QTc Interval with Nocturnal Glycemic Control in Children with Type 1 Diabetes

An association between QT prolongation (Bazett's corrected QT interval, QTcB) of 7 milliseconds and nocturnal hypoglycemia, compared with euglycemia, has been observed in children with type 1 diabetes (T1D). The objective of this pharmacometric analysis was to understand this association and other sources of QTc variability quantitatively. Data originate from a prospective observational study (25 cardiac healthy children with T1D, aged 8.1-17.6 years) with continuous subcutaneous glucose and electrocardiogram measurements for 5 consecutive nights. Mixed-effect modeling was used to compare QTcB with individual heart-rate correction (QTcI). Covariate models accounting for circadian variation, age, and sex were evaluated, followed by an investigation of glucose-QTc relationships (with univariable and combined adjusted analysis). Factors potentially modifying sensitivity to QTc lengthening were explored. Random inter-individual variability was reduced in the QTcI versus QTcB model (±12.6 vs 14.1 milliseconds), and was further reduced in the adjusted covariate model (±9.7 milliseconds), accounting for the significantly (P < .01) shortened QTc in adolescent boys (-14.6 milliseconds), circadian variation (amplitude, 19.2 milliseconds; shift, 2.9 hours), and linear glucose-QTc relationship (delay rate, 0.56-h ; slope, 0.76 milliseconds [95%CI 0.67- 0.85 milliseconds] per 1 mmol/L decrease in glucose). Differing sensitivity was suggested to depend upon hemoglobin A1c (HbA1c), T1D duration, and time spent in nocturnal hypoglycemia. In conclusion, a clinically mild association of QTc prolongation with nocturnal hypoglycemia was confirmed and quantified in this pharmacometric analysis, and the longest QTc interval was around 03:00 a.m. The characterized delayed association with glucose highlights the relevance of both the extent and the duration of hypoglycemia. Further clinical studies are warranted to investigate whether these factors contribute to increased risk of hypoglycemia-associated cardiac arrhythmia in children with T1D.

A pregnancy-specific Glucose management indicator derived from continuous glucose monitoring in pregnant women with type 1 diabetes

OBJECTIVE

Glucose management indicator (GMI) is a core metric derived from continuous glucose monitoring (CGM) and is widely used to evaluate glucose control in patients with diabetes. No study has explored the pregnancy-specific GMI. This study aimed to derive a best-fitting model to calculate GMI from mean blood glucose (MBG) obtained from CGM among pregnant women with type 1 diabetes mellitus (T1DM).

METHODS

A total of 272 CGM data and corresponding laboratory HbA1c from 98 pregnant women with T1DM in the CARNATION study were analysed in this study. Continuous glucose monitoring data were collected to calculate MBG, time-in-range (TIR), and glycaemic variability parameters. The relationships between the MBG and HbA1c during pregnancy and postpartum were explored. Mix-effect regression analysis with polynomial terms and cross-validation method was conducted to investigate the best-fitting model to calculate GMI from MBG obtained by CGM.

RESULTS

The pregnant women had a mean age of 28.9 ± 3.8 years, with a diabetes duration of 8.8 ± 6.2 years and a mean body mass index (BMI) of 21.1 ± 2.5 kg/m2 . The HbA1c levels were 6.1 ± 1.0% and 6.4 ± 1.0% during pregnancy and at postpartum (p = 0.024). The MBG levels were lower during pregnancy than those at postpartum (6.5 ± 1.1 mmol/L vs. 7.1 ± 1.5 mmol/L, p = 0.008). After adjusting the confounders of haemoglobin (Hb), BMI, trimesters, disease duration, mean amplitude of glycaemic excursions and CV%, we developed a pregnancy-specific GMI-MBG equation: GMI for pregnancy (%) = 0.84-0.28* [Trimester] + 0.08 * [ BMI in kg/m2 ] + 0.01 * [Hb in g/mL] + 0.50 * [MBG in mmol/L].

CONCLUSIONS

We derived a pregnancy-specific GMI equation, which should be recommended for antenatal clinical care.

CLINICAL TRIAL REGISTRY NUMBER

ChiCTR1900025955.

Association of thyroid stimulating hormone and time in range with risk of diabetic retinopathy in euthyroid type 2 diabetes

AIMS

Diabetic retinopathy (DR) can occur even in well-controlled type 2 diabetes, suggesting residual risks of DR in this population. In particular, we investigated the combined effect of thyroid function and glycaemic control assessed by an emerging metric, time in range (TIR) with DR.

MATERIALS AND METHODS

In this cross-sectional study, a total of 2740 euthyroid patients with type 2 diabetes were included. Thyroid indicators, including thyroid-stimulating hormone (TSH), free triiodothyronine, free thyroxine, thyroid peroxidase antibody and thyroglobulin antibody, were measured. TIR was measured using continuous glucose monitoring data.

RESULTS

Overall, the multivariable-adjusted odds ratios (ORs) for DR across ascending tertiles of TSH were 1.00 (reference), 1.06 (95% confidence interval [CI] 0.85-1.32), and 1.48 (95% CI 1.19-1.85). Even in well-controlled participants who achieved a TIR target of >70% (n = 1449), the prevalence of DR was 23.8%, which was significantly related to TSH (OR = 1.54, 95% CI 1.12-2.12, highest vs. lowest TSH tertile). Participants were then classified into 6 groups by the joint categories of TIR (>70%, ≤70%) and TSH (tertiles), and the multivariable-adjusted ORs for DR were highest in TIR ≤70% and the highest TSH tertile group (OR = 1.96, 95% CI 1.41-2.71) when compared with the TIR >70% and the lowest TSH tertile group.

CONCLUSIONS

In type 2 diabetic patients with well-controlled glycaemic status, higher TSH within the normal range was associated with an increased risk of DR. The combination of suboptimal TSH and TIR further increased the risk of DR.

Impact on glucometric variables and quality of life of the advanced hybrid closed-loop system in pediatric and adolescent type 1 diabetes

BACKGROUND

In recent years, technological advances in the field of diabetes have revolutionized the management, prognosis, and quality of life of diabetes patients and their environment. The aim of our study was to evaluate the impact of implementing the MiniMed 780G closed-loop system in a pediatric and adolescent population previously treated with a continuous subcutaneous insulin infusion pump and intermittent glucose monitoring.

METHODS

Data were collected from 28 patients with type 1 diabetes aged 6 to 17 years, with a follow-up of 6 months. We included both glucometric and quality of life variables, as well as quality of life in primary caregivers. Metabolic control variables were assessed at baseline (before system change) and at different cutoff points after initiation of the closed-loop system (48 hours, 7 days, 14 days, 21 days, 1 month, 3 months, 6 months).

RESULTS

Time in range 70-180 mg/dL increased from 59.44% at baseline to 74.29% in the first 48 hours after automation of the new system, and this improvement was maintained at the other cutoff points, as was time in hyperglycemia 180-250 mg/dL (24.44% at baseline to 18.96% at 48 hours) and >250 mg/dL (11.71% at baseline to 3.82% at 48 hours).

CONCLUSIONS

Our study showed an improvement in time in range and in all time spent in hyperglycemia from the first 48 hours after the automation of the system, which was maintained at 6 months.

Nocturnal hypoglycemia is underdiagnosed in older people with insulin-treated type 2 diabetes: The HYPOAGE observational study

BACKGROUND

There is a lack of real-life data regarding the frequency and predictive factors of hypoglycemia in older patients with type 2 diabetes (T2D). This study aimed to determine the frequency and predictors of hypoglycemia in older patients with insulin-treated T2D.

METHODS

This prospective multicenter study included 155 insulin-treated T2D patients aged 75 years and older with ≥2 self-monitoring of blood glucose (SMBG) daily controls. Participants underwent a geriatric and diabetic assessment and received ambulatory blinded continuous glucose monitoring (CGM) for 28 consecutive days with FreeStyle Libre Pro® sensor. Study population (n = 141) has >70% CGM active time. Multivariable logistic regressions were used to identify factors associated with SMBG confirmed hypoglycemia (≥70 mg/dL) and with nocturnal level 2 time below range (glucose concentration <54 mg/dL during ≥15 consecutive min between 0.00 and 6.00 am).

RESULTS

The mean age of the 141 analyzed patients was 81.5 ± 5.3 years and 56.7% were male. The mean baseline HbA_1c was 7.9% ± 1.0%. After geriatric assessment, 102 participants (72.3%) were considered as complex and 39 (27.7%) as healthy. The primary endpoint (confirmed SMBG <70 mg/dL) occurred in 37.6% patients. In multivariable analysis, the risk of SMBG-confirmed hypoglycemia was positively associated with a longer duration of diabetes (OR (+1 year) =1.04, (1.00-1.08), p = 0.04) and glycemic variability assessed by CGM (CV %) (OR (+1%) = 1.12, [1.05-1.19], p = <0.001). Nighty-two patients (65.2%) experienced nocturnal time in hypoglycemia (i.e., <54 mg/dL during ≥15 consecutive min between midnight and 6 a.m.). In multivariable analyses, cognitive impairment (OR: 9.31 [2.59-33.4]), heart failure (OR: 4.81 [1;48-15.6]), and depressive disorder (OR: 0.19 [0.06-0.53]) were associated with nocturnal time in hypoglycemia.

CONCLUSION

Nocturnal hypoglycemia is very common and largely underdiagnosed in older patients with insulin-treated T2D. CGM is a promising tool to better identify hypoglycemia and adapt diabetes management in this population.

Real-world performance of the MiniMed 780G advanced hybrid closed loop system in Latin America: Substantial improvement in glycaemic control with each technology iteration of the MiniMed automated insulin delivery system

AIM

We studied real-world performance of MiniMed (MM) 780G system users from Argentina, Brazil, Colombia and Chile (geographical analysis), and the effect of each technology iteration of the MM system on glycaemic control (technology iteration analysis).

MATERIALS AND METHODS

CareLink data from August 2020 to September 2022 were extracted. Endpoints included continuous glucose monitoring metrics. For the geographical analysis, aggregated endpoints for MM780G system users were calculated. For the technology iteration analysis, MM780G system user outcomes were compared with outcomes when the same individuals were still using the MM640G or MM670G system.

RESULTS

On average, 1025 MM780G system users from the geographical analysis were followed for 136 (SD 135) days, spent 91.5 (14.3)% in advanced hybrid closed loop, showed a glucose management indicator (GMI) of 6.7 (0.3)%, a time in range between 70 and 180 mg/dl (TIR) of 76.5 (9.0)%, and a time below range 70 mg/dl (TBR) of 2.7 (2.1)%. The percentage of users reaching targets of GMI <7%, TIR >70% and TBR <4% was 80.8%, 78.1% and 80.1%, respectively. The technology iteration analysis on users transitioning from MM640G to MM780G system (N = 381) showed 0.4% decrease in GMI (7.1% to 6.7%, p < .0001), 10.7% increase in TIR (65.9% to 76.6%, p < .0001), while TBR remained. The percentage of insulin delivered automatically increased as well (47.5%-57.7%, p < .0001). Users transitioning from MM670G system (N = 78) showed a similar but less pronounced pattern.

CONCLUSIONS

Real-world Latin American MM780G users on average showed good glucose control, achieving international targets. Glycaemic control increased with every technology iteration of the MM system, providing more automation each time.

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.

Analysis of real-world capillary blood glucose data to help reduce HbA1c and hypoglycaemia in type 1 diabetes: Evidence in favour of using the percentage of readings in target and coefficient of variation

AIMS

To examine real-world capillary blood glucose (CBG) data according to HbA_1c to define proportions of CBG readings at different HbA_1c levels, and evaluate patterns in CBG measurements to suggest areas to focus on with regard to self-management.

METHODS

A retrospective analysis stratified 682 adults with type 1 diabetes split into quartiles based on their HbA_1c . The proportions of results in different CBG ranges and associations with HbA_1c were evaluated. Patterns in readings following episodes of hyperglycaemia and hypoglycaemia were examined, using glucose to next glucose reading table (G2G).

RESULTS

CBG readings in the target range (3.9-10 mmol/L) increase by ~10% across each CBG quartile (31% in the highest versus 63% in the lowest quartile, p < 0.05). The novel G2G table helps the treatment-based interpretation of data. Hypoglycaemia is often preceded by hyperglycaemia, and vice-versa, and is twice as likely in the highest HbA_1c quartile. Re-testing within 30 min of hypoglycaemia is associated with less hypoglycaemia, 1.6% versus 7.2%, p < 0.001, and also reduces subsequent hyperglycaemia and further hypoglycaemia in the proceeding 24 h. The coefficient of variation, but not standard deviation, is highly associated with hypoglycaemia, r = 0.71, and a CV ≤ 36% equates to 3.3% of CBG readings in the hypoglycaemic range.

CONCLUSIONS

HbA_1c <58 mmol/mol (7.5%) is achievable even when only ~60% of CBG readings are between 3.9-10 mmol/L. Examining readings subsequent to out-of-range readings suggests useful behaviours which people with type 1 diabetes could be supported to adhere to, both in a clinic and structured education programmes, thereby decreasing the risk of hypoglycaemia whilst also reducing hyperglycaemia and improving HbA_1c .

Effects of intermittently scanned continuous glucose monitoring in adult type 1 diabetes patients with suboptimal glycaemic control: A multi-centre randomized controlled trial

AIMS

To investigate whether intermittently scanned continuous glucose monitoring without alarms (intermittently scanned CGM (isCGM)) improves glycaemic control over capillary blood glucose monitoring (BGM) among adult type 1 diabetes mellitus (T1DM) patients with suboptimal control.

MATERIALS AND METHODS

Adults with T1DM and HbA1c between 7% and 10% were 1:1 randomized to use isCGM or BGM for 24 weeks. The primary outcome was the change in HbA1c levels after intervention. The secondary outcomes were the changes in sensor-derived metrics.

RESULTS

A total of 104 adults with T1DM (34.2 ± 12.2 years; M/F, 38/66) were randomized to the isCGM group (n = 54) and the BGM group (n = 50). After 24 weeks, HbA1c significantly decreased in the isCGM group (8.1 ± 0.7% to 7.5 ± 1.0%) and the BGM group (8.0 ± 0.8% to 7.7 ± 1.0%) with between-group differences of 0.3% (95% coefficient intervals, 0.0%-0.6%; P = 0.04). The percentage of HbA1c reduction over 1.0% and 1.5% was significantly higher in the isCGM group with adjusted odds ratios of 2.5 (95% CI: 1.1-5.5; P = 0.03) and 3.2 (95% CI: 1.1-9.0; P = 0.03). Mean time-in-range 70-180 mg/dl (TIR) in the isCGM group significantly increased (from 58.5 ± 13.0% to 63.0 ± 12.6%), whereas mean TIR was similar in the BGM group (from 58.0 ± 14.6% to 57.5 ± 14.5%). Time spent in hyperglycemia reduced more in the isCGM group and time spent in hypoglycemia did not change significantly in both groups.

CONCLUSIONS

Among adult T1DM patients with suboptimal glycaemic control, compared with BGM, isCGM use resulted in a statistically significant improvement in glycaemic control after 24-week intervention.

TRIAL REGISTRATION

Clinicaltrials.gov Identifier (NCT03522870).

Professional continuous glucose monitoring in patients with diabetes mellitus: A systematic review and meta-analysis

AIM

To evaluate the effect on glucose control of professional continuous glucose monitoring (p-CGM)-based care as compared with standard care in the management of patients with type 1 and type 2 diabetes.

MATERIALS AND METHODS

The PubMed database was searched comprehensively to identify prospective or retrospective studies evaluating p-CGM as a diagnostic tool for subsequent implementation of lifestyle and/or medication changes and reporting glycated haemoglobin (HbA1c) as an outcome measure.

RESULTS

We found 872 articles, 22 of which were included in the meta-analysis. Overall, the use of p-CGM was associated with greater HbA1c reduction from baseline (-0.28%, 95% confidence interval [CI] -0.36% to -0.21%, I²  = 0%, P < 0.00001) than usual care, irrespective of type of diabetes, length of follow-up, frequency of continuous glucose monitoring (CGM) use and duration of CGM recording. In the few studies describing CGM-derived glucose metrics, p-CGM showed a beneficial effect on change in time in range from baseline (5.59%, 95% CI 0.12 to 11.06, I²  = 0%, P = 0.05) and a neutral effect on change in time below the target range from baseline (-0.11%, 95% CI -1.76% to 1.55%, I²  = 33%, P = 0.90).

CONCLUSIONS

In patients with type 1 and type 2 diabetes, p-CGM-driven care is superior to usual care in improving glucose control without increasing hypoglycaemia.

Sex differences in body composition in youth with type 1 diabetes and its predictive value in cardiovascular disease risk assessment

BACKGROUND

Women with type 1 diabetes (T1D) are more susceptible than men to cardiovascular disease (CVD). Signs of increased risk may already appear among adolescent girls.

OBJECTIVES

We explored the contribution of body composition to the development of CVD risk factors among youth with T1D.

METHODS

One hundred and eighty nine subjects with T1D (mean age 15.3 ± 5.1 years, 55% boys) followed between January 2018-January 2022 were included in this observational study. Sociodemographic and clinical data were extracted from medical files. Body composition was measured by bioelectrical impedance analysis, and muscle-to-fat ratio (MFR) z-scores were calculated. Logistic regression model assessed the association between body composition (MFR z-scores) and evidence of CVD risk factors.

RESULTS

Females were characterised by higher median BMI z-scores (0.47 vs. 0.04, p = 0.012), higher fat and truncal fat percentage levels (p ≤ 0.001) and lower median MFR z-scores (-0.64 vs. -0.25, p ≤ 0.001), higher median triglyceride (TG) levels (71 vs. 61 mg/dl, p = 0.05), longer disease duration to initiation of insulin pump therapy (p = 0.041), and more time spent in marked hypoglycemia (1 vs. 0.2%, p = 0.007) than males. Males' MFR z-scores were associated with several diabetes-related parameters (age at diagnosis, CGM metrics, HbA1c and insulin dose), while the females'' MFR z-scores were linked to the atherogenic dyslipidemia index (TG:HDL ratio). The odds for CVD risk factors were doubled for every 1 SD decrease in MFR z-score (OR = 0.50, CI [0.30-0.84], p = 0.009) and also increased with age (OR = 1.07, CI [1.004-1.148], p = 0.038).

CONCLUSIONS

Body composition measurement has a predictive value in CVD risk assessment in youth with T1D, with unique characteristics and influences in each sex.

Long-term effectiveness of advanced hybrid closed loop in children and adolescents with type 1 diabetes

BACKGROUND

Advanced hybrid closed loop (AHCL) systems are the newest tool to improve metabolic control in type 1 diabetes (T1D). Long-term glycemic control of children and adolescents with T1D switching to MiniMed™ 780G in a real clinical setting was evaluated.

METHODS

Time in range (TIR) and in different glucose ranges, glycemic variability indexes, HbA1c and basal-bolus insulin distribution were evaluated in 44 subjects (mean age 14.2 ± 4.0 years, 22 males) during manual mode period, first 14 days (A14d) and first month after auto-mode activation (A1M), first 14 days after 3 months (A3M) and 6 months (A6M) in auto-mode.

RESULTS

Mean TIR at A14d was 76.3 ± 9.6% versus 69.3 ± 12.6% in manual mode (p < 0.001), and this improvement was maintained over 6 months. Subjects with TIR >70% and >80% in manual mode were 45% and 23%, respectively, and increased to 80% (p = 0.041) and 41% (p = 0.007) at A14d. Basal-bolus distribution changed in favor of bolus, and auto-correction boluses inversely correlated with TIR. HbA1c was 7.2 ± 0.7% (55 mmol/mol) at baseline and significantly improved after 3 months (6.7 ± 0.5%, 50 mmol/mol, p < 0.001) and 6 months (6.6 ± 0.5%, 49 mmol/mol, p < 0.001). TIR was higher in individuals >13 years at all time periods (p < 0.001). Glycemic target <120 mg/dl was associated with better TIR.

CONCLUSIONS

AHCL MiniMed™ 780G allowed rapid and sustained improvement of glycemic control in young T1D patients, reaching recommended TIR. Teenagers showed good technology adherence with optimal TIR, maintained better over time compared to younger children. Stricter settings were associated with better metabolic control, without increase in severe hypoglycemia occurrence.

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%).

Effect of divergent continuous glucose monitoring technologies on glycaemic control in type 1 diabetes mellitus: A systematic review and meta-analysis of randomised controlled trials

AIMS

We aimed to conduct a systematic review and meta-analysis of randomised controlled clinical trials (RCTs) assessing separately and together the effect of the three distinct categories of continuous glucose monitoring (CGM) systems (adjunctive, non-adjunctive and intermittently-scanned CGM [isCGM]), compared with traditional capillary glucose monitoring, on HbA1c and CGM metrics.

METHODS

PubMed, Web of Science, Scopus and Cochrane Central register of clinical trials were searched. Inclusion criteria were as follows: randomised controlled trials; participants with type 1 diabetes of any age and insulin regimen; investigating CGM and isCGM compared with traditional capillary glucose monitoring; and reporting glycaemic outcomes of HbA1c and/or time-in-range (TIR). Glycaemic outcomes were extracted post-intervention and expressed as mean differences and 95%CIs between treatment and comparator groups. Results were pooled using a random-effects meta-analysis. Risk of bias was assessed using the Cochrane Rob2 tool.

RESULTS

This systematic review was conducted between January and April 2021; it included 22 RCTs (15 adjunctive, 5 non-adjunctive, and 2 isCGM)). The overall analysis of the pooled three categories showed a statistically significant absolute improvement in HbA1c percentage points (mean difference (95% CI): -0.22% [-0.31 to -0.14], I²  = 79%) for intervention compared with comparator and was strongest for adjunctive CGM (-0.26% [-0.36, -0.16]). Overall TIR (absolute change) increased by 5.4% (3.5 to 7.2), I²  = 71% for CGM intervention compared with comparator and was strongest with non-adjunctive CGM (6.0% [2.3, 9.7]).

CONCLUSIONS

For individuals with T1D, use of CGM was beneficial for impacting glycaemic outcomes including HbA1c, TIR and time-below-range (TBR). Glycaemic improvement appeared greater for TIR for newer non-adjunctive CGM technology.

Real-world prospective observational single-centre study: Hybrid closed loop improves HbA1c, time-in-range and quality of life for children, young people and their carers

Hybrid closed-loop (HCL) systems are characterised by integrating continuous glucose monitoring (CGM) with insulin pumps which automate insulin delivery via specific algorithms and user-initiated insulin delivery. The aim of the study was to evaluate the effectiveness of HCLs on Hba1c, time-in-range (TIR), time in hypoglycaemia, fear of hypoglycaemia, sleep and quality of life measure in children and young people (CYP) with T1D and their carers. Data on HbA1c, TIR and hypoglycaemia frequency were reviewed at baseline prior to starting HCL and 3 months after commencement. As part of clinical care, all patients and carers were provided with key education on the use of the HCL system by trained diabetes healthcare professionals. CYP aged 12 years and above independently completed the validated Hypoglycaemia Fear Survey (HFS). Parents of patients <12 were asked to complete a modified version of the HFS-Parent (HFS-P) survey. There were 39 CYP (22 men) with T1D included with a mean age of 11.8 ± 4.4 at commencement of HCL. Median duration of diabetes was 3.8 years (interquartile range 1.3-6.0). There were 55% of patients who were prepubertal at the time of HCL commencement. 91% were on the Control-IQ system and 9% on the CamAPS FX system. HCL use demonstrated significant improvements at 3 months in the following: HbA1c in mmol/mol (63.0 vs. 56.6, p = 0.03), TIR (50.5 vs. 67.0, p = 0.001) and time in hypoglycaemia (4.3% vs. 2.8%, p = 0.004). HFS scores showed improved behaviour (34.0 vs. 27.5.9, p = 0.02) and worry (40.2 vs. 31.6, p = 0.03), and HFS-P scores also showed improved behaviour (p < 0.001) and worry (p = 0.01). Our study shows that HCL at 3 months improves glucose control, diabetes management and quality of life measures such as fear and worry of hypoglycaemia for CYP and carers.

Choosing the duration of continuous glucose monitoring for reliable assessment of time in range: A new analytical approach to overcome the limitations of correlation-based methods

AIMS

Reliable estimation of the time spent in different glycaemic ranges (time-in-ranges) requires sufficiently long continuous glucose monitoring. In a 2019 paper (Battelino 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), an international panel of experts suggested using a correlation-based approach to obtain the minimum number of days for reliable time-in-ranges estimates. More recently (in Camerlingo et al., Design of clinical trials to assess diabetes treatment: minimum duration of continuous glucose monitoring data to estimate time-in-ranges with the desired precision. Diabetes Obes Metab. 2021;23:2446-2454) we presented a mathematical equation linking the number of monitoring days to the uncertainty around time-in-ranges estimates. In this work, we compare these two approaches, mainly focusing on time spent in (70-180) mg/dL range (TIR).

METHODS

The first 100 and 150 days of data were extracted from study A (148 subjects, ~180 days), and the first 100, 150, 200, 250 and 300 days of data from study B (45 subjects, ~365 days). For each of these data windows, the minimum monitoring duration was computed using correlation-based and equation-based approaches. The suggestions were compared for the windows of different durations extracted from the same study, and for the windows of equal duration extracted from different studies.

RESULTS

When changing the dataset duration, the correlation-based approach produces inconsistent results, ranging from 23 to 64 days, for TIR. The equation-based approach was found to be robust versus this issue, as it is affected only by the characteristics of the population being monitored. Indeed, to grant a confidence interval of 5% around TIR, it suggests 18 days for windows from study A, and 17 days for windows from study B. Similar considerations hold for other time-in-ranges.

CONCLUSIONS

The equation-based approach offers advantages for the design of clinical trials having time-in-ranges as final end points, with focus on trial duration.

Impact of visit-to-visit fasting plasma glucose variability on the development of diabetes: The mediation by insulin resistance

BACKGROUND

Little is known about the risk of diabetes due to higher glycemic variability and the underlying mechanisms. We aimed to examine the association of visit-to-visit variability (VVV) in fasting plasma glucose (FPG) with incident diabetes in Chinese adults and whether the association was mediated by changes in insulin resistance (IR).

METHODS

We included 1856 community residents without a history of diabetes and having attended 3 examinations in 2008, 2009, and 2013 respectively. The SD, the average successive variability (ASV), the coefficient of variation (CV), and the variability independent of the mean (VIM) of three recorded FPG measurements were calculated for each participant, and SD, ASV, CV, and VIM were used as a measure of VVV in FPG. Incident diabetes was defined according to the 1999 World Health Organization criteria. IR was evaluated using the homeostatic model assessment (HOMA).

RESULTS

A total of 153 (8.2%) participants developed incident diabetes at the third visit. Compared with the lowest tertile (0-5.83 mg/dl) of FPG-SD, the highest tertile (9.55-74.17 mg/dl) was associated with a 148% increased risk of diabetes (odds ratio [OR], 2.48; 95% confidence interval [CI], 1.36-4.49), after adjustment for covariates including mean FPG at 3 visits. Mediation analyses suggested that changes in IR (ΔHOMA-IR) might mediate 17.3% of the association between increased FPG-SD and elevated diabetes risk. Similar results were found for FPG-CV, FPG-ASV, and FPG-VIM.

CONCLUSIONS

The VVV in FPG was significantly associated with risks of diabetes in Chinese adults, which was partially mediated by changes in IR.

Standardized hospital management of pediatric diabetic ketoacidosis reduces frequency of low blood glucose episodes

OBJECTIVE

In patients treated for DKA, decrease the rate of visits experiencing one or more BG < 80 mg/dl by 10% within 24 months.

RESEARCH DESIGN AND METHODS

Plan-do-study-act cycles tested interventions linked to key drivers including: standardized DKA guidelines incorporating a two-bag fluid system, efficient ordering process, and care team education. Inclusion criterion: treatment for DKA with a bicarbonate value (HCO₃ ) <15 mEq/L.

PRIMARY OUTCOME

the percent of patient visits experiencing a BG < 80 mg/dl while undergoing treatment for DKA. Process measures included: order panel and order set utilization rates. Balancing measures included: emergency department and hospital lengths of stay, time to acidosis resolution (time to HCO₃  ≥ 17 mEq/L), and admission rates. Outcomes were analyzed using statistical process control charts.

RESULTS

From January 2017 through May 2021, our institution treated 288 different patients during 557 visits for suspected DKA. Following our interventions, the overall percent of patient visits for DKA with a BG < 80 mg/dl improved from 32% to 5%. The team did see small improvements in emergency department and hospital lengths of stay; otherwise, there was no significant change in our balancing measures.

CONCLUSIONS

Use of quality improvement methodology and standardized DKA management resulted in a significant reduction of BG < 80 mg/dl in patients treated for DKA.