To explore the relationship between impaired awareness of hypoglycemia (IAH) or severe hypoglycemia (SH), and health status and cognition in adults with type 1 diabetes (T1D).

T1D adults attending a tertiary diabetes service were recruited into this cross-sectional study. People screening positive for severe anxiety or depression were not included. Hypoglycemia awareness status was assessed using the full-scale and factor 1 of the Minimally Modified Clarke Hypoglycemia Survey (MMCHS; ≥4 and ≥2 = IAH); and data collected on health status (Diabetes Health Profile: barriers to activity, BA; psychological distress, PD; disinhibited eating); global cognition (Montreal Cognitive Assessment); and executive functions (EF; INECO Frontal Screening, IFS). A score of reduced awareness in item 3 and/or 4 of the MMCHS defined experience of ≥1 SH in past 6-12 months.

In 165 T1D adults, prevalences of SH, IAH by MMCHS full-scale and factor 1 were 35%, 13% and 28%. Participants with IAH by factor 1 had higher scores for PD (p = 0.008). Participants with SH and IAH (full-scale or factor 1) had higher BA scores (all p < 0.05) but no impairment of global cognition or EF. Participant Z-score IFS was lower than in non-diabetic individuals (p < 0.001).

In our cohort, executive dysfunction in T1D was not associated with SH or IAH. IAH was associated with PD, and both SH and IAH were related to behavioral dysfunction.

The relationship between the percent coefficient of variation (%CV) and the risk of severe hypoglycemia (SH) or non-severe hypoglycemia (NSH) in patients with type 1 diabetes (T1D) remains to be elucidated.

The Effect of Intermittent-Scanning Continuous Glucose Monitoring to Glycemic Control Including Hypoglycemia and Quality of Life of Patients with Type 1 Diabetes Mellitus (ISCHIA) study was a crossover, randomized, controlled trial for hypoglycemia prevention in patients with T1D using multiple daily injections (MDIs). Blinded continuous glucose monitoring (CGM) data of 93 patients obtained during the Control period (84 days) were used for the post hoc analysis. The receiver operating characteristics (ROC) curves were analyzed to determine the discrimination thresholds of %CV corresponding to the low blood glucose index (LBGI) > 5 and LBGI ≥ 2.5, and the occurrence of SH.

The %CV corresponding to LBGI > 5 and LBGI ≥ 2.5 was 42.2% and 37.0%, respectively. The episodes of SH were observed in three patients, and the %CV corresponding to the occurrence of SH was 40.7%.

The identification of the discrimination threshold of %CV associated with the risk of SH or NSH in patients with T1D is needed.

Poor data availability and accessibility characterizing some research areas in biomedicine are still limiting potentialities for increasing knowledge and boosting technological advancement. This phenomenon also characterizes the field of diabetes research, in which glycemic data may serve as a basis for different applications. To overcome this limitation, this review aims to provide a comprehensive analysis of the publicly available data sets related to dynamic glycemic data.

Search was performed in four different sources, namely scientific journals, Google, a comprehensive registry of clinical trials and two electronic databases. Retrieved data sets were analyzed in terms of their main characteristics and on the typology of data provided.

Twenty-five data sets were identified including data from challenge tests (5 of 25) or data from Continuous Glucose Monitoring (CGM, 20 of 25). As for the data sets including challenge tests, all of them were freely downloadable; most of them (80%) related only to oral glucose tolerance test (OGTT) with standard duration (2 h), but varying for timing and number of collected blood samples, and variables collected in addition to glucose levels (with insulin levels being the most common); the remaining 20% of them also included intravenous glucose tolerance test (IVGTT) data. As for the data sets related to CGM, 7 of 20 were freely downloadable, whereas the remaining 13 were downloadable upon completion of a request form.

This review provided an overview of the readily usable data sets, thus representing a step forward in fostering data access in diabetes field.

In 2022, the Centers for Medicare & Medicaid Services released proposed changes to Medicare's continuous glucose monitoring (CGM) coverage policy, making individuals with a history of problematic hypoglycaemia eligible for CGM coverage, irrespective of insulin use. This study estimated the burden of hypoglycaemia in Medicare Advantage beneficiaries with noninsulin-treated type 2 diabetes (T2D).

We retrospectively analysed US healthcare claims data using Optum's deidentified Clinformatics® database. Noninsulin-treated beneficiaries were identified in the 16 years from January 2007 to March 2023. Hypoglycaemia-related encounters (HREs) were those accompanied by a hypoglycaemia-specific ICD-9/10 diagnosis code in any position on the claim or the first or second position. HREs following the first claim related to T2D were reported by setting (ambulatory or inpatient/emergency department [ED]).

HREs were identified in 689,853 (21.4%) of 3,229,695 noninsulin-treated Medicare Advantage beneficiaries, of whom 82.9% (n = 571,581) had ≥1 HRE in an ambulatory location and 26.8% (n = 184,833) in an ED/inpatient location. Use of sulfonylurea (odds ratio [OR]: 4.33 confidence interval [CI: 4.27-4.38]), evidence of end-stage kidney disease (OR: 2.87 [CI: 2.79-2.94]), hypertension (OR: 3.09 [CI: 3.04-3.15]) and retinopathy (OR: 2.94 [CI: 2.82-3.07]) were the strongest predictors of an HRE (p < 0.001).

These findings show that HREs are prevalent in noninsulin-treated diabetes and identify a large number of patients who may benefit from CGM. Because >80% of HREs occur in the ambulatory setting and >70% occur in patients not taking sulfonylureas, primary care providers should be aware of the latest eligibility criteria for Medicare's coverage of CGM and not restrict this technology to their sulfonylurea-treated patients.

Older adults with type 1 diabetes are at risk for serious hypoglycemia. Automated insulin delivery can reduce risk but has not been sufficiently evaluated in this population.

We conducted a multicenter, randomized crossover trial in adults older than or equal to 65 years of age with type 1 diabetes. Participants completed three 12-week periods of using hybrid closed loop, predictive low-glucose suspend, and sensor-augmented pump insulin delivery in a randomized order. The primary outcome was the percentage of time with continuous glucose monitoring glucose values less than 70 mg/dl.

Eighty-two participants between 65 and 86 years of age were randomly assigned: 45% were female; the baseline mean (±SD) glycated hemoglobin level was 7.2±0.9%; and the baseline percentage of time with glucose values less than 70 mg/dl was 2.49±1.78%. In the sensor-augmented pump, hybrid closed-loop, and predictive low-glucose suspend periods, percentages of time with glucose less than 70 mg/dl were 2.57±1.54%, 1.58±0.95%, and 1.67±0.96%, respectively. Compared with the sensor-augmented pump results, the mean difference with the hybrid closed-loop system was -1.05 percentage points (95% confidence interval [CI], -1.48 to -0.73 percentage points; P<0.001) and with the predictive low-glucose suspend system it was -0.93 percentage points (95% CI, -1.27 to -0.66 percentage points; P<0.001). Comparing a hybrid closed-loop system with a sensor-augmented pump, time in the range 70 to 180 mg/dl changed by 8.9 percentage points (95% CI, 7.4 to 10.4 percentage points) and the glycated hemoglobin level changed by 0.2 percentage points (95% CI, -0.3 to -0.1 percentage points). Serious adverse events were uncommon. Severe hypoglycemia occurred in 4% or less of participants; there were two hospitalizations for diabetic ketoacidosis.

In older adults with type 1 diabetes, automated insulin delivery decreased hypoglycemia compared with sensor-augmented pump delivery. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases and others; ClinicalTrials.gov number: NCT04016662.).

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

The population of older adults with diabetes mellitus is growing but heterogeneous. Because geriatric syndromes, comorbidity or multimorbidity, the complexity of glucose dynamics, and socioeconomic conditions are associated with the risk of severe hypoglycemia and mortality, these factors should be considered in individualized diabetes treatment. Because cognitive impairment and frailty have similar etiologies and risk factors, a common strategy can be implemented to address them through optimal glycemic control, management of vascular risk factors, diet, exercise, social participation, and support. To prevent frailty or sarcopenia, optimal energy intake, adequate protein and vitamin intake, and resistance or multi-component exercise are recommended. For hypoglycemic drug therapy, it is important to reduce hypoglycemia, to use sodium glucose cotransporter-2 (SGLT2) inhibitors and glucagon-like peptide-1 (GLP-1) receptor agonists, taking into account the benefits for cardiovascular disease and the risk of adverse effects, and to simplify treatment to address poor adherence. Glycemic control goals for older adults with diabetes should be set according to three categories, based on cognitive function and activities of daily living, using the Dementia Assessment Sheet for Community-based Integrated Care System 8-items. This categorization can be used to determine treatment strategies for diabetes when combined with the Comprehensive Geriatric Assessment (CGA). Based on the CGA, frailty prevention, treatment simplification, and social participation or services should be implemented for patients in Category II and above. Measures against hypoglycemia and for the prevention of cardiovascular disease and chronic kidney disease should also be promoted. Treatment based on categorization and CGA by multidisciplinary professionals would be an individualized treatment for older adults with diabetes. Geriatr Gerontol Int 2024; 24: 1257-1268.

Older adults may be less comfortable with continuous glucose monitoring (CGM) technology or require additional education to support use. The Virtual Diabetes Specialty Clinic study provided the opportunity to understand glycemic outcomes and support needed for older versus younger adults living with diabetes and using CGM.

Prospective, virtual study of adults with type 1 diabetes (T1D, N = 160) or type 2 diabetes (T2D, N = 74) using basal-bolus insulin injections or insulin pump therapy. Remote CGM diabetes education (3 scheduled visits over 1 month) was provided by Certified Diabetes Care and Education Specialists with additional visits as needed. CGM-measured glycemic metrics, HbA1c and visit duration were evaluated by age (<40, 40-64 and ≥65 years).

Median CGM use was ≥95% in all age groups. From baseline to 6 months, time 70 to 180 mg/dL improved from 45% ± 22 to 57% ± 16%; 50 ± 25 to 65 ± 18%; and 60 ± 28 to 69% ± 18% in the <40, 40-64, and ≥65-year groups, respectively (<40 vs 40-64 years P = 0.006). Corresponding values for HbA1c were 8.0% ± 1.6 to 7.3% ± 1.0%; 7.9 ± 1.6 to 7.0 ± 1.0%; and 7.4 ± 1.4 to 7.1% ± 0.9% (all P > 0.05). Visit duration was 41 min longer for ages ≥65 versus <40 years (P = 0.001).

Adults with diabetes experience glycemic benefit after remote CGM use training, but training time for those >65 years is longer compared with younger adults. Addressing individual training-related needs, including needs that may vary by age, should be considered.

The Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) study demonstrated continuous glucose monitoring (CGM) reduced hypoglycemia over 6 months among older adults with type 1 diabetes (T1D) compared with blood glucose monitoring (BGM). We explored heterogeneous treatment effects of CGM on hypoglycemia by formulating a data-driven decision rule that selects an intervention (ie, CGM vs BGM) to minimize percentage of time <70 mg/dL for each individual WISDM participant.

The precision medicine analyses used data from participants with complete data (n = 194 older adults, including those who received CGM [n = 100] and BGM [n = 94] in the trial). Policy tree and decision list algorithms were fit with 14 baseline demographic, clinical, and laboratory measures. The primary outcome was CGM-measured percentage of time spent in hypoglycemic range (<70 mg/dL), and the decision rule assigned participants to a subgroup reflecting the treatment estimated to minimize this outcome across all follow-up visits.

The optimal decision rule was found to be a decision list with 3 steps. The first step moved WISDM participants with baseline time-below range >1.35% and no detectable C-peptide levels to the CGM subgroup (n = 139), and the second step moved WISDM participants with a baseline time-below range of >6.45% to the CGM subgroup (n = 18). The remaining participants (n = 37) were left in the BGM subgroup. Compared with the BGM subgroup (n = 37; 19%), the group for whom CGM minimized hypoglycemia (n = 157; 81%) had more baseline hypoglycemia, a lower proportion of detectable C-peptide, higher glycemic variability, longer disease duration, and higher proportion of insulin pump use.

The decision rule underscores the benefits of CGM for older adults to reduce hypoglycemia. Diagnostic CGM and laboratory markers may inform decision-making surrounding therapeutic CGM and identify older adults for whom CGM may be a critical intervention to reduce hypoglycemia.

Hypoglycemia will inevitably occur. Being prepared and implementing a treatment plan should help to restore euglycemia and resolve hypoglycemia symptoms. The plan comprises fast-acting carbohydrates and, importantly, ready-to-use glucagon for self-administration when carbohydrates are not working or for third-party administration when the affected person is unwilling or unable to swallow (e.g., unconscious or in a coma).

The objective of the Hypoglycaemia REdefining SOLutions for better liVES (Hypo-RESOLVE) project is to use a dataset of pooled clinical trials across pharmaceutical and device companies in people with type 1 or type 2 diabetes to examine factors associated with incident hypoglycaemia events and to quantify the prediction of these events.

Data from 90 trials with 46,254 participants were pooled. Analyses were done for type 1 and type 2 diabetes separately. Poisson mixed models, adjusted for age, sex, diabetes duration and trial identifier were fitted to assess the association of clinical variables with hypoglycaemia event counts. Tree-based gradient-boosting algorithms (XGBoost) were fitted using training data and their predictive performance in terms of area under the receiver operating characteristic curve (AUC) evaluated on test data. Baseline models including age, sex and diabetes duration were compared with models that further included a score of hypoglycaemia in the first 6 weeks from study entry, and full models that included further clinical variables. The relative predictive importance of each covariate was assessed using XGBoost's importance procedure. Prediction across the entire trial duration for each trial (mean of 34.8 weeks for type 1 diabetes and 25.3 weeks for type 2 diabetes) was assessed.

For both type 1 and type 2 diabetes, variables associated with more frequent hypoglycaemia included female sex, white ethnicity, longer diabetes duration, treatment with human as opposed to analogue-only insulin, higher glucose variability, higher score for hypoglycaemia across the 6 week baseline period, lower BP, lower lipid levels and treatment with psychoactive drugs. Prediction of any hypoglycaemia event of any severity was greater than prediction of hypoglycaemia requiring assistance (level 3 hypoglycaemia), for which events were sparser. For prediction of level 1 or worse hypoglycaemia during the whole follow-up period, the AUC was 0.835 (95% CI 0.826, 0.844) in type 1 diabetes and 0.840 (95% CI 0.831, 0.848) in type 2 diabetes. For level 3 hypoglycaemia, the AUC was lower at 0.689 (95% CI 0.667, 0.712) for type 1 diabetes and 0.705 (95% CI 0.662, 0.748) for type 2 diabetes. Compared with the baseline models, almost all the improvement in prediction could be captured by the individual's hypoglycaemia history, glucose variability and blood glucose over a 6 week baseline period.

Although hypoglycaemia rates show large variation according to sociodemographic and clinical characteristics and treatment history, looking at a 6 week period of hypoglycaemia events and glucose measurements predicts future hypoglycaemia risk.

Up to one-third of older adults with type 1 diabetes experience impaired awareness of hypoglycemia (IAH), yet the factors associated with IAH remain underexplored in older adults.

This post hoc analysis evaluated the clinical and glycemic correlates of IAH in adults ≥60 years old with type 1 diabetes in the WISDM study. IAH and normal awareness of hypoglycemia (NAH) were defined by a Clarke score of ≥4 or <4, respectively. Demographic, clinical, and glycemic metrics were compared in those with IAH and NAH at baseline and in whom IAH did or did not improve over 26 weeks, using descriptive statistics and a multiple logistic regression variable selection procedure.

Of the 199 participants (age 68.1 ± 5.7 years, 52% female), 30.6% had IAH. At baseline, participants with IAH had a longer diabetes duration and greater daytime hypoglycemia and glycemic variability, and more participants had nondetectable C-peptide levels than those with NAH. Logistic regression associated longer diabetes duration (odds ratio [OR] 1.03, 95% CI 1.01-1.05; P = 0.008) and greater daytime hypoglycemia (OR 1.31, 95% CI, 1.15-1.51; P < 0.0001) with a greater odds of IAH. A similar modeling procedure identified less daytime hypoglycemia (OR per additional percentage point 0.55, 95% CI 0.32-0.94; P = 0.029) and shorter diabetes duration (OR per additional year 0.96, 95% CI 0.91-1.004; P = 0.07) as predictors of restored awareness at 26 weeks, although the effect size for diabetes duration was not statistically significant.

In older adults with type 1 diabetes, longer diabetes duration and greater daytime hypoglycemia are drivers of IAH. Dedicated research can personalize IAH management.

Numerous studies have demonstrated the clinical value of continuous glucose monitoring (CGM) in type 1 diabetes (T1D) and type 2 diabetes (T2D) populations. However, the eligibility criteria for CGM coverage required by the Centers for Medicare & Medicaid Services (CMS) ignore the conclusive evidence that supports CGM use in various diabetes populations that are currently deemed ineligible. In an earlier article, we discussed the limitations and inconsistencies of the agency's CGM eligibility criteria relative to current scientific evidence and proposed practice solutions to address this issue and improve the safety and care of Medicare beneficiaries with diabetes. Although Medicaid is administered through CMS, there is no consistent Medicaid policy for CGM coverage in the United States. This article presents a rationale for modifying and standardizing Medicaid CGM coverage eligibility across the United States.

The older population is increasing worldwide and up to 30% of older adults have diabetes. Older adults with diabetes are at risk of glucose-related acute and chronic complications. Recently, mostly in type 1 diabetes (T1D), continuous glucose monitoring (CGM) devices have proven beneficial in improving time in range (TIR glucose, 70-180 mg/dL or glucose 3.9-10 mmol/L), glycated hemoglobin (HbA1c), and in lowering hypoglycemia (time below range [TBR] glucose <70 mg/dL or glucose <3.9 mmol/L). The international consensus group formulated CGM glycemic targets relating to older adults with diabetes based on very limited data. Their recommendations, based on expert opinion, were aimed at mitigating hypoglycemia in all older adults. However, older adults with diabetes are a heterogeneous group, ranging from healthy to very complex frail individuals based on chronological, biological, and functional aging. Recent clinical trial and real-world data, mostly from healthy older adults with T1D, demonstrated that older adults often achieve CGM targets, including TIR recommended for non-vulnerable groups, but less often meet the recommended TBR <1%. Existing data also support that hypoglycemia avoidance may be more strongly related to minimization of glucose variability (coefficient of variation [CV]) rather than lower TIR. Very limited data are available for glucose goals in older adults adjusted for the complexity of their health status. Herein, we review the bidirectional associations between glucose and health status in older adults with diabetes; use of diabetes technologies, and their impact on glucose control; discuss current guidelines; and propose a new set of CGM targets for older adults with insulin-treated diabetes that are individualized for health and living status.

The sympathetic nervous and hormonal counterregulatory responses to hypoglycaemia differ between people with type 1 and type 2 diabetes and may change along the course of diabetes, but have not been directly compared. We aimed to compare counterregulatory hormone and symptom responses to hypoglycaemia between people with type 1 diabetes, insulin-treated type 2 diabetes and controls without diabetes, using a standardised hyperinsulinaemic-hypoglycaemic clamp.

We included 47 people with type 1 diabetes, 15 with insulin-treated type 2 diabetes, and 32 controls without diabetes. Controls were matched according to age and sex to the people with type 1 diabetes or with type 2 diabetes. All participants underwent a hyperinsulinaemic-euglycaemic-(5.2 ± 0.4 mmol/L)-hypoglycaemic-(2.8 ± 0.13 mmol/L)-clamp.

The glucagon response was lower in people with type 1 diabetes (9.4 ± 0.8 pmol/L, 8.0 [7.0-10.0]) compared to type 2 diabetes (23.7 ± 3.7 pmol/L, 18.0 [12.0-28.0], p < 0.001) and controls (30.6 ± 4.7, 25.5 [17.8-35.8] pmol/L, p < 0.001). The adrenaline response was lower in type 1 diabetes (1.7 ± 0.2, 1.6 [1.3-5.2] nmol/L) compared to type 2 diabetes (3.4 ± 0.7, 2.6 [1.3-5.2] nmol/L, p = 0.001) and controls (2.7 ± 0.4, 2.8 [1.4-3.9] nmol/L, p = 0.012). Growth hormone was lower in people with type 2 diabetes than in type 1 diabetes, at baseline (3.4 ± 1.6 vs 7.7 ± 1.3 mU/L, p = 0.042) and during hypoglycaemia (24.7 ± 7.1 vs 62.4 ± 5.8 mU/L, p = 0.001). People with 1 diabetes had lower overall symptom responses than people with type 2 diabetes (45.3 ± 2.7 vs 58.7 ± 6.4, p = 0.018), driven by a lower neuroglycopenic score (27.4 ± 1.8 vs 36.7 ± 4.2, p = 0.012).

Acute counterregulatory hormone and symptom responses to experimental hypoglycaemia are lower in people with type 1 diabetes than in those with long-standing insulin-treated type 2 diabetes and controls.

Severe hypoglycemia (SH) in older adults (OAs) with type 1 diabetes is associated with profound morbidity and mortality, yet its etiology can be complex and multifactorial. Enhanced tools to identify OAs who are at high risk for SH are needed. This study used machine learning to identify characteristics that distinguish those with and without recent SH, selecting from a range of demographic and clinical, behavioral and lifestyle, and neurocognitive characteristics, along with continuous glucose monitoring (CGM) measures.

Data from a case-control study involving OAs recruited from the T1D Exchange Clinical Network were analyzed. The random forest machine learning algorithm was used to elucidate the characteristics associated with case versus control status and their relative importance. Models with successively rich characteristic sets were examined to systematically incorporate each domain of possible risk characteristics.

Data from 191 OAs with type 1 diabetes (47.1% female, 92.1% non-Hispanic white) were analyzed. Across models, hypoglycemia unawareness was the top characteristic associated with SH history. For the model with the richest input data, the most important characteristics, in descending order, were hypoglycemia unawareness, hypoglycemia fear, coefficient of variation from CGM, % time blood glucose below 70 mg/dL, and trail making test B score.

Machine learning may augment risk stratification for OAs by identifying key characteristics associated with SH. Prospective studies are needed to identify the predictive performance of these risk characteristics.

Hypoglycemia is a particular problem in people with diabetes while it can also occur in other clinical circumstances. Hypoglycemia unawareness describes a condition in which autonomic and neuroglycopenic symptoms of hypoglycemia decrease and hence are hardly perceivable. A failure to recognize hypoglycemia in time can lead to unconsciousness, seizure, and even death. The risk factors include intensive glycemic control, prior episodes of severe hypoglycemia, long duration of diabetes, alcohol consumption, exercise, renal failure, and sepsis. The pathophysiological mechanisms are manifold, but mainly concern altered brain glucose sensing, cerebral adaptations, and an impaired hormonal counterregulation with an attenuated release of glucagon, epinephrine, growth hormone, and other hormones, as well as impaired autonomous and neuroglycopenic symptoms. Physiologically, this counterregulatory response causes blood glucose levels to rise. The impaired hormonal counterregulatory response to recurrent hypoglycemia can lead to a vicious cycle of frequent and poorly recognized hypoglycemic episodes. There is a shift in glycemic threshold to trigger hormonal counterregulation, resulting in hypoglycemia-associated autonomic failure and leading to the clinical syndrome of hypoglycemia unawareness. This clinical syndrome represents a particularly great challenge in diabetes treatment and, thus, prevention of hypoglycemia is crucial in diabetes management. This mini-review provides an overview of hypoglycemia and the associated severe complication of impaired hypoglycemia awareness and its symptoms, pathophysiology, risk factors, consequences, as well as therapeutic strategies.

Advances in the development of innovative medical devices and telehealth technologies create the potential to improve the quality and efficiency of diabetes care through collecting, aggregating, and interpreting relevant health data in ways that facilitate more informed decisions among all stakeholder groups. Although many medical societies publish guidelines for utilizing these technologies in clinical practice, we believe that the methodologies used for the selection and grading of the evidence should be revised. In this article, we discuss the strengths and limitations of the various types of research commonly used for evidence selection and grading and present recommendations for modifying the process to more effectively address the rapid pace of device and technology innovation and new product development.

Insulin lispro 100 units/mL Jr KwikPen is the first prefilled, disposable, half-unit insulin pen that delivers 0.5-30 units in increments of 0.5 units for the treatment of patients with diabetes. This study describes the profile of patients in Spain who initiated insulin therapy with Jr KwikPen in a real-world setting.

This retrospective, observational study based on IQVIA's electronic medical records database included patients with Type 1 (T1D) or Type 2 (T2D) diabetes who initiated therapy with Jr KwikPen between May 2018 and December 2020. Sociodemographic, clinical, and treatment characteristics at treatment initiation were analysed descriptively.

A total of 416 patients were included. The main characteristics of the T1D/T2D groups (N = 326/90), respectively were as follows: female sex, 61.7%/65.6%; mean age (standard deviation [SD]), 32.5 (20.7)/55.5 (16.6) years; body mass index, 20.9 (4.2)/25.2 (4.6) kg/m2 (N = 239/77); HbA1c, 7.8 (1.7)%/8.0 (1.5)% (N = 141/64); and presence of diabetes-associated comorbidities, 27.9%/64.4%. Only 32.8% of patients with T1D were < 18 years old. Among Jr KwikPen users, 12.3% (T1D/T2D, 7.7%/28.9%) were ≥ 65 years old, 17.1% patients were newly diagnosed, and 3.8% were pregnant women. The mean (SD) total insulin dose pre-index for T1D/T2D was 43.1 (23.6) and 40.7 (21.6) UI/day, respectively. The mean (SD) insulin dose at the start of Jr KwikPen use was 26.63 (16.56) and 22.58 (13.59) UI/day for T1D/T2D, respectively. Jr KwikPen was first prescribed mainly by endocrinologists (58.7%) or paediatricians (22.6%).

The profile of patients who initiated therapy with Jr KwikPen in routine practice was broad with many patients being adults. Most of these patients had T1D, inadequate glycemic control, and multiple associated comorbidities. These results suggest that Jr KwikPen is prescribed in patients who may benefit from finer insulin dose adjustments, namely children, adolescents, adults, older individuals, or pregnant women with T1D or T2D.

To evaluate the discriminant and convergent validities of the Hypoglycemia Awareness Questionnaire Impaired Awareness (HypoA-Q IA) subscale and establish a diagnostic threshold for the classification of impaired awareness of hypoglycemia (IAH) in adults with type 1 diabetes (T1D).

Twenty-one adults with T1D (male, 48%; median age, 36 years; and T1D duration, 21 years) completed the HypoA-Q IA subscale, Clarke, and hypoglycemia severity (HYPO) scores, continuous glucose monitoring, and hyperinsulinemic hypoglycemic clamp testing. Those with IAH defined by a Clarke score of ≥4 (n = 10) and who experienced severely problematic hypoglycemia and/or marked glycemic lability started automated insulin delivery as part of an 18-month intervention study with the 6-monthly paired assessment of the HypoA-Q IA subscale, Clarke score, HYPO score and continuous glucose monitoring, and hypoglycemic clamp testing at baseline and 6 and 18 months.

The HypoA-Q IA subscale discriminated between those with and without IAH defined by the Clarke score (W = 110.5; P <.001). During intervention, the HypoA-Q IA subscale demonstrated convergent validity via significant relationships with the Clarke (r = 0.72; P <.001) and HYPO (r = 0.60; P <.001) scores; hypoglycemia exposure below 70 (r = 0.53; P <.01), 60 (r = 0.50; P <.01), and 54 (r = 0.48; P <.01) mg/dL; and autonomic symptom (r = -0.53; P <.05), epinephrine (r = -0.68; P <.001), and pancreatic polypeptide (r = -0.52; P <.05) responses to insulin-induced hypoglycemia. The receiver operating characteristic curve analysis revealed that the HypoA-Q IA subscale was an excellent predictor of an abnormal symptom response to insulin-induced hypoglycemia (area under the curve, 0.86) with a score of 12, which was the optimal threshold for IAH classification (sensitivity, 83%; specificity, 80%).

These findings support the validity of the HypoA-Q IA subscale and propose a HypoA-Q IA diagnostic threshold to identify IAH in both clinical and research settings.

Older adults with type 1 diabetes are at high risk for cognitive impairment, yet the usefulness of common cognitive screening instruments has not been evaluated in this population.

A total of 201 adults ≥60 years of age with type 1 diabetes completed a battery of neuropsychological measures and the Montreal Cognitive Assessment (MoCA). Receiver operating characteristic (ROC) curves and Youden indices were used to evaluate overall screening test performance and to select an optimal MoCA cutoff score for detecting low cognitive performance, as defined as two or more neuropsychological test performances ≥1.5 SD below demographically corrected normative data.

The ROC area under the curve (AUC) was 0.745 (P < 0.001). The publisher-recommended cutoff score of <26 resulted in sensitivity of 60.4% and specificity of 71.4%, whereas a cutoff score of <27 resulted in sensitivity of 75.0% and specificity of 61.0%. The Youden indices for these cutoff scores were 0.318 and 0.360, respectively. Minimally acceptable sensitivity (i.e., >0.80) was obtained when using a cutoff score of <28, whereas >0.80 specificity was obtained with a cutoff score of <25.

The MoCA has modest overall performance (AUC 0.745) as a cognitive screening instrument in older adults with type 1 diabetes. The standard cutoff score of <26/30 may not adequately detect individuals with neuropsychological testing-defined abnormal cognition. The optimal MoCA cutoff score (based on the Youden index) was <27/30. A score of <28 resulted in acceptable sensitivity but was accompanied by low specificity (42%). Future studies with a more diverse population are needed to confirm these findings.

Objective: Older adults are generally less proficient in technology use compared with younger adults. Data on telemedicine use during the COVID-19 pandemic in older persons with type 1 diabetes (T1D) and the association of telemedicine with the use of diabetes-related technology are limited. We evaluated care delivery to older adults compared with younger adults with T1D in a prepandemic and pandemic period. Methods: Data from electronic health records were evaluated for visit types (in-person, phone, and video) from two sequential 12-month intervals: prepandemic (April 2019-March 2020) and pandemic (April 2020-March 2021). Results: Data from 2,832 unique adults with T1D were evaluated in two age cohorts: younger (40-64 years) and older (≥65 years). Half of each group used continuous glucose monitoring (CGM), whereas 54% of the younger and 37% of the older cohort used pump therapy (p < 0.001). During the pandemic compared with the prepandemic period, visit frequency increased in both the younger (0.65 vs. 0.76 visits/patient/quarter; p < 0.01) and older (0.72 vs. 0.80 visits/patient/quarter; p < 0.01) cohorts. During the pandemic, older adults used more phone visits compared with younger adults (48% vs. 32%; p = 0.001). Patients using either pump therapy or CGM were more likely to use video visits compared with phone visits in both younger (41% vs. 24%; p < 0.001) and older cohorts (53% vs. 42%; p < 0.001). Conclusions: Adults using diabetes-related technologies, independent of age, accessed more video visits than those not using devices. Telemedicine visits appeared to maintain continuity of care for younger and older adults with T1D, supporting the future of a hybrid-care model.

Diabetes mellitus is a chronic metabolic health condition affecting millions globally. Diabetes is a growing concern among aging societies, with its prevalence increasing among those aged 65 and above. Enabling disease self-management via relevant education is part of high-quality care to improve health outcomes and minimize complications for individuals living with diabetes. Successful diabetes self-management education (DSME) programs usually require tailoring for the intended audience; however, there is limited literature about the preferences of older persons in Western countries concerning DSME. As such, a broad overview of DSME for older persons was an identified need. To map the available evidence on DSME for persons aged 65 years and older in Western countries, the JBI methodology for conducting and reporting scoping reviews was used. In this scoping review, we considered all studies about DSME for older persons with T1D and T2D in Western countries where lifestyles, risks, prevention, treatment of diabetes, and approaches to self-management and DSME are similar (e.g., North America, Western and Northern Europe and Australasia). Systematic keyword and subject heading searches were conducted in 10 databases (e.g., MEDLINE, JBI EBP) to identify relevant English language papers published from 2000 to 2022. Titles and abstracts were screened to select eligible papers for full-text reading. Full-text screening was done by four independent reviewers to select studies for the final analysis. The review identified 2,397 studies, of which 1,250 full texts were screened for eligibility. Of the final 44 papers included in the review, only one included participants' understanding of DSME. The education programs differed in their context, design, delivery mode, theoretical underpinnings, and duration. Type of research designs, outcome measures used to determine the effectiveness of DSME, and knowledge gaps were also detailed. Overall, most interventions were effective and improved clinical and behavioural outcomes. Many of the programs led to improvements in clinical outcomes and participants' quality of life; however, the content needs to be adapted to older persons according to their culture, different degrees of health literacy, preference of education (e.g., individualized or group), preference of setting, degree of frailty and independence, and comorbidities. Few studies included the voices of older persons in the design, implementation, and evaluation of DSME programs. Such experiential knowledge is vital in developing educational programs to ensure alignment with this population's preferred learning styles, literacy levels, culture, and needs-such an approach could manifest more substantive, sustained results.

The number of older adults with type 1 diabetes (T1D) is increasing due to an overall increase in life expectancy and improvement in diabetes management and treatment of complications. They are a heterogeneous cohort due to the dynamic process of aging and the presence of comorbidities and diabetes-related complications. A high risk for hypoglycemia unawareness and severe hypoglycemia has been described. Periodic assessment of health status and adjustment of glycemic goals to mitigate hypoglycemia is imperative. Continuous glucose monitoring, insulin pump, and hybrid closed-loop systems are promising tools to improve glycemic control and mitigate hypoglycemia in this age group.

The adoption of CGM results in vast amounts of data, but their interpretation is still more art than exact science. The International Consensus on Time in Range (TIR) proposed the widely accepted TIR system of metrics, which we now take forward by introducing a finite and fixed set of clinically-similar clusters (CSCs), such that the TIR metrics of the daily CGM profiles within a cluster are homogeneous.

CSC definition and validation used 204,710 daily CGM profiles in health, type 1 and type 2 diabetes (T1D, T2D), on different treatments. The CSCs were defined using 23,916 daily CGM profiles (Training set), and the final fixed set of CSCs was obtained using another 37,758 profiles (Validation set). The Testing set (143,036 profiles) was used to establish the robustness and generalizability of the CSCs.

The final set of CSCs contains 32 clusters. Any daily CGM profile was classifiable to a single CSC which approximated common glycemic metrics of the daily CGM profile, as evidenced by regression analyses with 0 intercept (R-squares≥0.83, e.g., correlation≥0.91), for all TIR and several other metrics. The CSCs distinguished CGM profiles in health, T2D, and T1D on different treatments, and allowed tracking of the daily changes in a person's glycemic control over time.

Daily CGM profiles can be classified into one of 32 prefixed CSCs, which enables a host of applications, e.g. tabulated data interpretation and algorithmic approaches to treatment, database indexing, pattern recognition, and tracking disease progression.

Older adults with type 1 diabetes (T1D) may have an elevated risk of developing Alzheimer disease and related dementia. Higher intraindividual cognitive variability (IICV) has been proposed as a novel risk factor of Alzheimer disease and related dementia. Here, we examined the association between cross-domain IICV measured using the Montreal Cognitive Assessment (MoCA) and cognitive impairment measured using traditional neuropsychological tests in older individuals with T1D. Participants with T1D (N=201) completed both the MoCA and a battery of traditional neuropsychological tests. Participants with cognitive impairment, determined using traditional tests, had significantly higher IICV scores and significantly lower total MoCA scores ( P <0.001). However, the effect of the total score was greater than that of the IICV score on the likelihood of cognitive impairment (total odds ratio=3.50, IICV odds ratio=2.03, P <0.001). The MoCA total score performed better than the MoCA IICV score in identifying T1D individuals classified with cognitive impairment.

The effect of continuous glucose monitoring on the risk of severe hypoglycaemia and ketoacidosis in patients with diabetes is unclear. We investigated whether rates of acute diabetes complications are lower with continuous glucose monitoring, compared with blood glucose monitoring, and which metrics predict its risk in young patients with type 1 diabetes.

In this population-based cohort study, patients were identified from 511 diabetes centres across Austria, Germany, Luxembourg, and Switzerland participating in the Diabetes Prospective Follow-up initiative. We included people with type 1 diabetes aged 1·5-25·0 years, with a diabetes duration of more than 1 year, who had been treated between Jan 1, 2014, and June 30, 2021, and had an observation time of longer than 120 days in the most recent treatment year. Severe hypoglycaemia and ketoacidosis rates during the most recent treatment year were examined in people using continuous glucose monitoring and in those using blood glucose monitoring. Adjustments of statistical models included age, sex, diabetes duration, migration background, insulin therapy (pump or injections), and treatment period. Rates of severe hypoglycaemia and diabetic ketoacidosis were evaluated by several continuous glucose monitoring metrics, including percentage of time below target glucose range (<3·9 mmol/L), glycaemic variability (measured as the coefficient of variation), and mean sensor glucose.

Of 32 117 people with type 1 diabetes (median age 16·8 years [IQR 13·3-18·1], 17 056 [53·1%] males), 10 883 used continuous glucose monitoring (median 289 days per year), and 21 234 used blood glucose monitoring. People using continuous glucose monitoring had lower rates of severe hypoglycaemia than those using blood glucose monitoring (6·74 [95% CI 5·90-7·69] per 100 patient-years vs 8·84 [8·09-9·66] per 100 patient-years; incidence rate ratio 0·76 [95% CI 0·64-0·91]; p=0·0017) and diabetic ketoacidosis (3·72 [3·32-4·18] per 100 patient-years vs 7·29 [6·83-7·78] per 100 patient-years; 0·51 [0·44-0·59]; p<0·0001). Severe hypoglycaemia rates increased with percentage of time below target glucose range (incidence rate ratio 1·69 [95% CI 1·18-2·43]; p=0·0024, for 4·0-7·9% vs <4·0% and 2·38 [1·51-3·76]; p<0·0001, for ≥8·0% vs <4·0%) and glycaemic variability (coefficient of variation ≥36% vs <36%; incidence rate ratio 1·52 [95% CI 1·06-2·17]; p=0·022). Diabetic ketoacidosis rates increased with mean sensor glucose (incidence rate ratio 1·77 [95% CI 0·89-3·51], p=0·13, for 8·3-9·9 mmol/L vs <8·3 mmol/L; 3·56 [1·83-6·93], p<0·0001, for 10·0-11·6 mmol/L vs <8·3 mmol/L; and 8·66 [4·48-16·75], p<0·0001, for ≥11·7 mmol/L vs <8·3 mmol/L).

These findings provide evidence that continuous glucose monitoring can reduce severe hypoglycaemia and ketoacidosis risk in young people with type 1 diabetes on insulin therapy. Continuous glucose monitoring metrics might help to identify those at risk for acute diabetes complications.

German Center for Diabetes Research, German Federal Ministry of Education and Research, German Diabetes Association, and Robert Koch Institute.

Data of the diabetes mellitus patients is essential in the study of diabetes management, especially when employing the data-driven machine learning methods into the management. To promote and facilitate the research in diabetes management, we have developed the ShanghaiT1DM and ShanghaiT2DM Datasets and made them publicly available for research purposes. This paper describes the datasets, which was acquired on Type 1 (n = 12) and Type 2 (n = 100) diabetic patients in Shanghai, China. The acquisition has been made in real-life conditions. The datasets contain the clinical characteristics, laboratory measurements and medications of the patients. Moreover, the continuous glucose monitoring readings with 3 to 14 days as a period together with the daily dietary information are also provided. The datasets can contribute to the development of data-driven algorithms/models and diabetes monitoring/managing technologies.

Individuals with type 1 diabetes represent a population with important vulnerabilities to dynamic physiological, behavioral, and psychological interactions, as well as cognitive processes. Ecological momentary assessment (EMA), a methodological approach used to study intraindividual variation over time, has only recently been used to deliver cognitive assessments in daily life, and many methodological questions remain. The Glycemic Variability and Fluctuations in Cognitive Status in Adults with Type 1 Diabetes (GluCog) study uses EMA to deliver cognitive and self-report measures while simultaneously collecting passive interstitial glucose in adults with type 1 diabetes.

We aimed to report the results of an EMA optimization pilot and how these data were used to refine the study design of the GluCog study. An optimization pilot was designed to determine whether low-frequency EMA (3 EMAs per day) over more days or high-frequency EMA (6 EMAs per day) for fewer days would result in a better EMA completion rate and capture more hypoglycemia episodes. The secondary aim was to reduce the number of cognitive EMA tasks from 6 to 3.

Baseline cognitive tasks and psychological questionnaires were completed by all the participants (N=20), followed by EMA delivery of brief cognitive and self-report measures for 15 days while wearing a blinded continuous glucose monitor. These data were coded for the presence of hypoglycemia (<70 mg/dL) within 60 minutes of each EMA. The participants were randomized into group A (n=10 for group A and B; starting with 3 EMAs per day for 10 days and then switching to 6 EMAs per day for an additional 5 days) or group B (N=10; starting with 6 EMAs per day for 5 days and then switching to 3 EMAs per day for an additional 10 days).

A paired samples 2-tailed t test found no significant difference in the completion rate between the 2 schedules (t17=1.16; P=.26; Cohen dz=0.27), with both schedules producing >80% EMA completion. However, more hypoglycemia episodes were captured during the schedule with the 3 EMAs per day than during the schedule with 6 EMAs per day.

The results from this EMA optimization pilot guided key design decisions regarding the EMA frequency and study duration for the main GluCog study. The present report responds to the urgent need for systematic and detailed information on EMA study designs, particularly those using cognitive assessments coupled with physiological measures. Given the complexity of EMA studies, choosing the right instruments and assessment schedules is an important aspect of study design and subsequent data interpretation.

Background: We investigated the potential associations between race/ethnicity and adherence to prescribed glucose monitoring in a sample of Medicare beneficiaries with diabetes and how adherence to the method used impacted diabetes-related inpatient hospitalizations and associated costs among beneficiaries with intensive insulin-treated diabetes. Methods: This 12-month retrospective analysis utilized Centers for Medicare & Medicaid Services data to identify Medicare beneficiaries who used intensive insulin therapy from January through December 2018 and classified them into four groups: (1) persons using real-time continuous glucose monitoring (rtCGM), (2) persons using any method of blood glucose monitoring (BGM) who followed prescribed use patterns (adherent), (3) persons who were prescribed BGM but were nonadherent in its use, and (4) no record of any form of BGM. Analyses compared these groups and the role that comorbidities (Charlson Comorbidity Index [CCI]), and race/ethnicity played on group assignment, diabetes-related inpatient hospitalizations, and costs. Results: Among the 1,329,061 persons assessed, 38.14% had no record of glucose monitoring and 35.42% were BGM nonadherent. Similarly, among the 629,514 beneficiaries with a CCI risk score of ≥2, 466,646 (74.13%) were either nonadherent to BGM or had no monitoring record. The percentage of White (3.65%) rtCGM adherent beneficiaries was significantly larger than Black (1.58%) and Hispanic (1.28%) beneficiaries, both P < 0.0001. Hospitalizations and costs were higher for Black and Hispanic beneficiaries versus Whites within the risk score ≥ 2 group regardless of glucose monitoring method. Conclusions: Race is associated with increased hospitalizations and costs associated with diabetes care and absence of any form of BGM was associated with higher rates of comorbidities. Persons of color were less likely to use rtCGM despite Medicare coverage. New initiatives that promote diabetes self-management education and support services are needed to improve utilization of glucose monitoring within the Medicare diabetes population.

In the century since the discovery of insulin, diabetes has changed from an early death sentence to a manageable chronic disease. This change in longevity and duration of diabetes coupled with significant advances in therapeutic options for patients has fundamentally changed the landscape of diabetes management, particularly in patients with type 1 diabetes mellitus. However, hypoglycemia remains a major barrier to achieving optimal glycemic control. Current understanding of the mechanisms of hypoglycemia has expanded to include not only counter-regulatory hormonal responses but also direct changes in brain glucose, fuel sensing, and utilization, as well as changes in neural networks that modulate behavior, mood, and cognition. Different strategies to prevent and treat hypoglycemia have been developed, including educational strategies, new insulin formulations, delivery devices, novel technologies, and pharmacologic targets. This review article will discuss current literature contributing to our understanding of the myriad of factors that lead to the development of clinically meaningful hypoglycemia and review established and novel therapies for the prevention and treatment of hypoglycemia.

Older adults with type 1 diabetes are recommended modified glucose targets. However, data on the effects of diabetes technology in older age are scarce. We assessed older adults established on sensor-augmented insulin pump therapy during clinical trial run-in and compared their continuous glucose monitoring (CGM) profiles with consensus recommendations. We aimed to provide insight into the applicability of currently recommended CGM-based targets while accounting for current Diabetes UK guidelines.

In this analysis, adults aged 60 years or older with type 1 diabetes with a duration of at least 10 years and entering the Older Adult Closed Loop (ORACL) trial were studied. The trial was done at two tertiary hospitals in Australia. Individuals who were independent with diabetes self-management, as well as those receiving caregiver assistance for their diabetes management, were eligible for inclusion. Participants underwent baseline clinical assessment, which included medical history and examination, testing for frailty, functional ability, cognitive functioning, psychosocial wellbeing, and subjective sleep quality; fasting venous blood samples were collected for C-peptide, glucose, and glycated haemoglobin A_1c measurement. Sensor-augmented pumps, carbohydrate-counting education, and diabetes education were provided to participants by diabetes nurse educators, dietitians, and endocrinologists experienced in type 1 diabetes clinical care. CGM data were subsequently collected for 2 weeks during sensor-augmented pump therapy. The ORACL trial is registered with the Australian New Zealand Clinical Trial Registry, ACTRN12619000515190.

Our analysis included all 30 participants who completed the ORACL trial run-in-19 (63%) women and 11 (37%) men (mean age 67 years [SD 5], median diabetes duration 38 years [IQR 20-47], and insulin total daily dose 0·55 units [0·41-0·66] per kg bodyweight). Ten (33%) of 30 participants had impaired hypoglycaemia awareness and six (20%) were pre-frail; none were frail. The median CGM time in range 3·9-10·0 mmol/L was 71% (IQR 64-79). The time spent with glucose above 10·0 mmol/L was 27% (18-35) and above 13·9 mmol/L was 3·9% (2·4-10·2). The time with glucose below 3·9 mmol/L was 2·0% (1·2-3·1) and the time below 3·0 mmol/L was 0·2% (0·1-0·4). Only two (7%) of 30 participants met all CGM-based consensus recommendations modified for older adults. Time in hypoglycaemia was lower among the 16 participants with predictive low-glucose alerts enabled than among the 14 participants not using predictive low-glucose alerts (median difference -1·1 percentage points [95% CI -2·0 to -0·1]; p=0·038). This difference was even greater overnight (-2·3 percentage points [-3·2 to -1·0]; p=0·0018). One serious adverse event occurred (elective cardiac stent).

Using sensor-augmented pumps after multidisciplinary education, this group of older adults without frailty achieved a time in range far exceeding minimum consensus recommendations. However, the current stringent hypoglycaemia recommendations for all older adults were not met. Predictive low alerts could reduce hypoglycaemia, particularly overnight. Investigation into the effectiveness of CGM-based targets that consider frailty, functional status, and diabetes therapies for older adults is warranted.

JDRF and Diabetes Australia.

Many new technologies have been developed over the past decade, and these have substantially changed the way diabetes is managed. Continuous glucose monitoring is now the standard of care for many people living with diabetes, and among its numerous benefits, it has been shown to improve glycaemic outcomes and enhance quality of life. Older adults carry a high burden of diabetes and have a high risk of hypo-glycaemia and hypo-glycaemic unawareness, and continuous glucose monitoring can help to improve glycaemic management in this vulnerable population. Unfortunately, only a few trials have evaluated the effectiveness of continuous glucose monitoring in older adults. Certainly, the implementation of continuous glucose monitoring in older adults can come with many challenges, including logistical, educational and reimbursement barriers. This article will discuss the benefits of continuous glucose monitoring in older adults with diabetes, the clinical studies that support its use and the barriers to its optimal implementation in this population.

The aim of this study was to investigate the factors associated with hypoglycemia and severe hypoglycemia (SH) in individuals with type 1 diabetes mellitus (T1D) in Brazil.

This multicenter, cross-sectional study was conducted between August 2011 and August 2014 across 10 Brazilian cities. The data were obtained from 1,760 individuals with T1D. Sociodemographic and clinical characteristics related to hypoglycemic events in the previous 4 weeks were evaluated.

Of 1,760 individuals evaluated, 1,319 (74.9%) reported at least one episode of hypoglycemia in the previous 4 weeks. The main factors associated with hypoglycemia were lower hemoglobin A1c (HbA1c) level, better adherence to self-monitoring of blood glucose (SMBG), and higher education level. Episodes of SH were reported by 251 (19%) individuals who, compared with subjects with nonsevere hypoglycemia, received lower doses of prandial insulin and higher doses of basal insulin, in addition to reporting less frequent use of long-acting basal insulin analogs. The percentage of SH episodes was evenly distributed across all ranges of HbA1c levels, and there were no correlations between the mean number of nonsevere or severe hypoglycemic events and HbA1c values. Higher alcohol consumption and more frequent hospitalizations were independently associated with SH.

Although individuals presenting with hypoglycemia had lower HbA1c values than those not presenting hypoglycemia, there were no correlations between the number of nonsevere hypoglycemia or SH and HbA1c values. Also, the frequency of SH was evenly distributed across all ranges of HbA1c values. Better adherence to SMBG and higher education level were associated with hypoglycemia, while alcohol consumption, higher doses of basal insulin, and more frequent hospitalizations in the previous year were associated with SH.

This report describes how a Conditional Generative Adversarial Network (CGAN) was used to synthesize realistic continuous glucose monitoring systems (CGM) from healthy individuals and individuals with type 1 diabetes over a range of different HbA1c levels. The results showed that even though the CGAN generated data, did not perfectly reflect real world CGM, many of the important features were captured and reflected in the synthetic signals. It is briefly discussed how heterogenous data sources constitutes a challenge for comparison of predictive CGM models. Therefore 40,000 CGM days were generated by the trained CGAN, equivalent to 940,000 hours of synthetic CGM measurements. These data have been made available in a public database, which can be used as a reference in future studies.

Delayed gastric emptying is a substantial challenge for people with diabetes, affecting quality of life and blood glucose regulation. The complication is underdiagnosed, and current diagnostic tests are expensive or time consuming or have modest accuracy. The assessment of glycemic variations has potential use in gastroparesis screening. The aim of this study was to investigate the differences in glycemic variability between type 1 diabetes patients with gastroparesis and without a diagnosis of gastroparesis and the potential for using a classification model to differentiate between groups.

Continuous glucose monitoring (CGM) from 425 patients with diabetes was included in the analytic cohort, including 16 patients with a diagnosis of gastroparesis and 409 without a known gastroparesis diagnosis. Sixteen features (9 daytime features and 7 nighttime features) describing glucose dynamics were extracted to assess differences between patients with and without a diagnosis of gastroparesis. A logistic regression model was trained using forward selection and cross-validation.

In total, 3 features were included in the model utilizing forward selection of features and cross-validation: mean absolute glucose (MAG), span, and standard deviation during the night. The Receiver operating characteristic (ROC) AUC for the classification model was 0.76.

Gastroparesis seems to have an impact on glucose variability, especially during the night. Moreover, CGM could possibly be used as a part of the screening process for delayed gastric emptying, but more studies are needed to determine a realistic accuracy.

A significant proportion of individuals with diabetes have suboptimal glycemic management. Studies have shown that persistent hyperglycemia significantly increases the risks for both acute and long-term microvascular and macrovascular complications of diabetes. A key contributor to suboptimal glycemic management is therapeutic inertia in which clinicians delay intensifying therapy when patients are not meeting their glycemic goals. During the past five years, an increasing number of individuals with type 1 diabetes (T1D) and insulin-treated type 2 diabetes (T2D) have adopted use of continuous glucose monitoring (CGM) for daily measurement of glucose levels. As demonstrated in numerous clinical trials and real-world observational studies, use of CGM improves glycated hemoglobin (HbA1c) and reduces the occurrence and severity of hypoglycemia. However, for primary care clinicians who are unfamiliar with using CGM, integrating this technology into clinical practice can be daunting. In this article, we discuss the benefits and rationale for using CGM compared with traditional blood glucose monitoring (BGM), review the evidence supporting the clinical value of CGM in patients with T1D and T2D, and describe how use of CGM in primary care can facilitate appropriate and more timely therapy adjustments.

As continuous glucose monitoring (CGM) sensors generate ever increasing amounts of CGM data, the need for methods to simplify the storage and analysis of this data becomes increasingly important. Lobo et al. developed a classifier of daily CGM profiles as an initial step in addressing this need. The classifier has several important applications including, but not limited to, data compression, data encryption, and indexing of databases. While the classifier has already successfully classified 99.0% of the 42,595 daily CGM profiles in a Test Set, this work presents an external validation using an external validation set (EVal Set) derived from 8 publicly available data sets. The Test Set and the EVal Set differ in terms of (but not limited to) demographics, data collection time periods, and data collection geographies. The classifier successfully classified 98.2% of the 137,030 daily CGM profiles in the EVal Set. Furthermore, each of the 483 distinct groups of classified daily CGM profiles from the EVal Set retains the same clinical characteristics as the corresponding group from the Test Set, as desired. Finally, the set of unclassified daily CGM profiles from the EVal Set retains the same statistical characteristics as the set of unclassified daily CGM profiles from the Test Set, as desired. These results establish the robustness and generalizability of the classifier: the performance of the classifier is unchanged despite the marked differences between the Test Set and the EVal Set.

Adults with type 1 diabetes (T1D) are aging successfully. The impact of diabetes duration on clinical and functional status as people age with T1D is not well known.

We performed a cross-sectional study of older adults (age ≥65 years) with T1D.

We evaluated 165 older adults, mean age 70 ± 10 years. After adjustment for age, sex, and A1C, longer duration of T1D, ≥50 years, was associated with a higher likelihood of depression (odds ratio [OR] 2.8; P = 0.008), hypoglycemia unawareness (OR 2.6; P = 0.01), lower scores on 6-Minute Walk Test (OR 0.99; P = 0.01) and the Physical Component Summary (PCS) of Short Form-36 (SF-36) (OR 0.96; P = 0.02), and greater daily medication use (OR 1.1; P = 0.004) compared with those with duration <50 years.

In older adults with T1D, duration of diabetes impacts clinical and functional status, independent of age and glycemic control, and should be considered in development of management strategies for safety and success.

To assess the efficacy and safety of closed-loop insulin delivery compared with sensor-augmented pump therapy among older adults with type 1 diabetes.

This open-label, randomized (1:1), crossover trial compared 4 months of closed-loop versus sensor-augmented pump therapy. Eligible adults were aged ≥60 years, with type 1 diabetes (duration ≥10 years), using an insulin pump. The primary outcome was continuous glucose monitoring (CGM) time in range (TIR; 3.9-10.0 mmol/L).

There were 30 participants (mean age 67 [SD 5] years), with median type 1 diabetes duration of 38 years (interquartile range [IQR] 20-47), randomized (n = 15 to each sequence); all completed the trial. The mean TIR was 75.2% (SD 6.3) during the closed-loop stage and 69.0% (9.1) during the sensor-augmented pump stage (difference of 6.2 percentage points [95% CI 4.4 to 8.0]; P < 0.0001). All prespecified CGM metrics favored closed loop over the sensor-augmented pump; benefits were greatest overnight. Closed loop reduced CGM time <3.9 mmol/L during 24 h/day by 0.5 percentage points (95% CI 0.3 to 1.1; P = 0.0005) and overnight by 0.8 percentage points (0.4 to 1.1; P < 0.0001) compared with sensor-augmented pump. There was no significant difference in HbA1c between closed-loop versus sensor-augmented pump stages (7.3% [IQR, 7.1-7.5] (56 mmol/mol [54-59]) vs. 7.5% [7.1-7.9] (59 mmol/mol [54-62]), respectively; P = 0.13). Three severe hypoglycemia events occurred during the closed-loop stage and two occurred during the sensor-augmented pump stage; no hypoglycemic events required hospitalization. One episode of diabetic ketoacidosis occurred during the sensor-augmented pump stage; no serious adverse events occurred during the closed-loop stage.

Closed-loop therapy is an effective treatment option for older adults with long-duration type 1 diabetes, and no safety issues were identified. These older adults had higher TIR accompanied by less time below range during closed loop than during sensor-augmented pump therapy. Of particular clinical importance, closed loop reduced the time spent in hypoglycemic range overnight.

To describe glycaemic control and diabetes management in adults with type 1 diabetes (T1DM), in a real-life global setting.

Study of Adults' GlycEmia (SAGE) was a multinational, multicentre, single visit, noninterventional, cross-sectional study in adult patients with T1DM. Data were collected at a single visit, analysed according to predefined age groups (26-44, 45-64 and ≥65 years) and reported across different regions. The primary endpoint was the proportion of participants achieving HbA1c  less than 7.0 % in each age group. Secondary endpoints included incidence of hypoglycaemia, severe hypoglycaemia and severe hyperglycaemia leading to diabetic ketoacidosis (DKA) and therapeutic management of T1DM.

Of 3903 included participants, 3858 (98.8%) were eligible for the study. Overall, 24.3% (95% confidence interval [CI]: 22.9-25.6) of participants achieved the glycaemic target of HbA1c  less than 7.0 %, with more participants achieving this target in the 26-44 years group (27.6% [95% CI: 25.5-29.8]). Target achievement was highest in Eastern and Western Europe, and lowest in the Middle East. The incidence of hypoglycaemia and of severe hyperglycaemia leading to DKA tended to decrease with age, and varied across regions. Age and regional differences were observed in therapeutic management, including types of device/insulin usage, frequency of insulin dose adjustment and technology usage.

Glycaemic control remains poor in adults with T1DM globally. Several areas of treatment may be optimised to improve outcomes, including supporting patient self-management of insulin therapy, increasing use of technologies such as CGM, and greater provision of healthcare support.