Risk factors at admission of in-hospital dysglycemia, mortality, and readmissions in patients with type 2 diabetes and pneumonia.

AIMS: In-hospital dysglycemia is associated with adverse outcomes. Identifying patients at risk of in-hospital dysglycemia early on admission may improve patient outcomes. METHODS: We analysed 117 inpatients admitted with pneumonia and type 2 diabetes monitored by continuous glucose monitoring. We assessed potential risk factors for in-hospital dysglycemia and adverse clinical outcomes. RESULTS: Time in range (3.9-10.0 mmol/l) decreased by 2.9 %-points [95 % CI 0.7-5.0] per 5 mmol/mol [2.6 %] increase in admission haemoglobin A1c, 16.2 %-points if admission diabetes therapy included insulin therapy [95 % CI 2.9-29.5], and 2.4 %-points [95 % CI 0.3-4.6] per increase in the Charlson Comorbidity Index (CCI) (integer, as a measure of severity and amount of comorbidities). Thirty-day readmission rate increased with an IRR of 1.24 [95 % CI 1.06-1.45] per increase in CCI. In-hospital mortality risk increased with an OR of 1.41 [95 % CI 1.07-1.87] per increase in Early Warning Score (EWS) (integer, as a measure of acute illness) at admission. CONCLUSIONS: Dysglycemia among hospitalised patients with pneumonia and type 2 diabetes was associated with high haemoglobin A1c, insulin treatment before admission, and the amount and severity of comorbidities (i.e., CCI). Thirty-day readmission rate increased with high CCI. The risk of in-hospital mortality increased with the degree of acute illness (i.e., high EWS) at admission. Clinical outcomes were independent of chronic glycemic status, i.e. HbA1c, and in-hospital glycemic status.

Estimating risk of consequences following hypoglycaemia exposure using the Hypo-RESOLVE cohort: a secondary analysis of pooled data from insulin clinical trials.

AIMS/HYPOTHESIS: Whether hypoglycaemia increases the risk of other adverse outcomes in diabetes remains controversial, especially for hypoglycaemia episodes not requiring assistance from another person. An objective of the Hypoglycaemia REdefining SOLutions for better liVEs (Hypo-RESOLVE) project was to create and use a dataset of pooled clinical trials in people with type 1 or type 2 diabetes to examine the association of exposure to all hypoglycaemia episodes across the range of severity with incident event outcomes: death, CVD, neuropathy, kidney disease, retinal disorders and depression. We also examined the change in continuous outcomes that occurred following a hypoglycaemia episode: change in eGFR, HbA1c, blood glucose, blood glucose variability and weight. METHODS: Data from 84 trials with 39,373 participants were pooled. For event outcomes, time-updated Cox regression models adjusted for age, sex, diabetes duration and HbA1c were fitted to assess association between: (1) outcome and cumulative exposure to hypoglycaemia episodes; and (2) outcomes where an acute effect might be expected (i.e. death, acute CVD, retinal disorders) and any hypoglycaemia exposure within the last 10 days. Exposures to any hypoglycaemia episode and to episodes of given severity (levels 1, 2 and 3) were examined. Further adjustment was then made for a wider set of potential confounders. The within-person change in continuous outcomes was also summarised (median of 40.4 weeks for type 1 diabetes and 26 weeks for type 2 diabetes). Analyses were conducted separately by type of diabetes. RESULTS: The maximally adjusted association analysis for type 1 diabetes found that cumulative exposure to hypoglycaemia episodes of any level was associated with higher risks of neuropathy, kidney disease, retinal disorders and depression, with risk ratios ranging from 1.55 (p=0.002) to 2.81 (p=0.002). Associations of a similar direction were found when level 1 episodes were examined separately but were significant for depression only. For type 2 diabetes cumulative exposure to hypoglycaemia episodes of any level was associated with higher risks of death, acute CVD, kidney disease, retinal disorders and depression, with risk ratios ranging from 2.35 (p<0.0001) to 3.00 (p<0.0001). These associations remained significant when level 1 episodes were examined separately. There was evidence of an association between hypoglycaemia episodes of any kind in the previous 10 days and death, acute CVD and retinal disorders in both type 1 and type 2 diabetes, with rate ratios ranging from 1.32 (p=0.017) to 2.68 (p<0.0001). These associations varied in magnitude and significance when examined separately by hypoglycaemia level. Within the range of hypoglycaemia defined by levels 1, 2 and 3, we could not find any evidence of a threshold at which risk of these consequences suddenly became pronounced. CONCLUSIONS/INTERPRETATION: These data are consistent with hypoglycaemia being associated with an increased risk of adverse events across several body systems in diabetes. These associations are not confined to severe hypoglycaemia requiring assistance.

Real-life hypoglycaemia partially blunts the inflammatory response to experimental hypoglycaemia in people with type 1 diabetes.

AIM: To determine whether recent repeated exposure to real-life hypoglycaemia affects the pro-inflammatory response during a hypoglycemia episode. MATERIALS AND METHODS: This was a post hoc analysis of a hyperinsulinaemic normoglycaemic-hypoglycaemic clamp study, involving 40 participants with type 1 diabetes. Glucose levels 1 week before the clamp were monitored using a Freestyle Libre 1. Blood was drawn during normoglycaemia and hypoglycaemia, and 24 hours after resolution of hypoglycaemia for measurements of inflammatory responses and counterregulatory hormone levels. We determined the relationship between the frequency and duration of spontaneous hypoglycaemia, and time below range (TBR) and the inflammatory response to experimental hypoglycaemia. RESULTS: On average, participants experienced 0.79 (0.43, 1.14) hypoglycaemia episodes per day, with a duration of 78 (47, 110) minutes and TBR of 5.5% (2.8%, 8.5%). TBR and hypoglycaemia frequency were inversely associated with the increase in circulating granulocyte and lymphocyte counts during experimental hypoglycaemia (P < .05 for all). A protein network consisting of DNER, IF-R, uPA, Flt3L, FGF-5 and TWEAK was negatively associated with hypoglycaemia frequency (P < .05), but not with the adrenaline response. Neither other counterregulatory hormones, nor hypoglycaemia awareness status, was associated with any of the inflammatory parameters markers. CONCLUSIONS: Repeated exposure to spontaneous hypoglycaemia is associated with blunted effects of subsequent experimental hypoglycaemia on circulating immune cells and the number of inflammatory proteins.

Healthcare professionals' competencies and confidence in managing hospitalized patients with type 2 diabetes.

AIMS: In hospitals, 15%-20% of patients have diabetes. Therefore, all healthcare professionals (HCPs) must have a basic knowledge of in-hospital diabetes management. This survey assessed the knowledge of diabetes among HCPs in Denmark. METHODS: A 27-item questionnaire was developed and reviewed independently before the survey was distributed. The questionnaire contained seven baseline questions on the HCPs' current workplace, educational level, usual shift routines and years of experience, 18 multiple-choice questions and 2 cases. RESULTS: A total of 252 completed questionnaires were returned by 133 (52.8%) physicians, 101 (40.1%) nurses and 18 (7.1%) healthcare assistants. HCPs answered 50% of the questions correctly. Having experience from endocrinological departments increased the correct response score (0%-100%) by 6.2% points (95% CI 0.3-12.1) (p = 0.039) and 3.1% points (95% CI 1.5-4.7) for every increase in confidence level on a scale from 1 to 10 (p < 0.001). HCPs scored 8 out of 10 on a confidence level scale on average. In a fictive case, 50% of HCPs administered the correct bolus insulin dose. Hyperglycaemia (>10.0 mmol/L) and hypoglycaemia (<3.9 mmol/L) were correctly identified by around 40% of HCPs. Hypoglycaemia was rated more important than hyperglycaemia by most HCPs. CONCLUSION: Significant gaps in identifying hypo- and hyperglycaemia and correct administration of bolus insulin have been identified, which could be targeted in future education for HCPs. HCPs answered 50% of questions related to in-hospital diabetes management correctly. Experience from endocrinological departments and self-rated confidence levels are associated with HCPs' in-hospital diabetes competencies.

Hyperglycaemia in people with diabetes and chronic kidney disease.

Assessment and treatment of hyperglycaemia in people with diabetes and chronic kidney disease (CKD) are challenging. In advanced CKD HbA1c can be unreliable, and treatment adjustments should be supported by other glucose measurements (e.g., continuous glucose monitoring (CGM) or blood glucose measurements). Glucose-lowering treatments should be evaluated based on CKD and an individualised assessment of risk factors especially hypoglycaemia. This review aims at providing an overview of the options for glycaemic monitoring and glucose-lowering treatments in people with diabetes and CKD.

Glucagon-like-peptide-1 receptor agonists versus dipeptidyl peptidase-4 inhibitors and cardiovascular outcomes in diabetes in relation to achieved glycemic control. A Danish nationwide study.

AIM: To compare the cardiovascular preventive effect associated with glucagon-like-peptide-1 receptor agonists (GLP-1 RA) versus dipeptidyl peptidase-4 inhibitors (DPP-4i) according to the achieved target level of glycated hemoglobin (HbA1c). METHODS: We used retrospective Danish registries to include type 2 diabetes patients already in metformin treatment initiating GLP-1 RA or DPP-4i between 2007 and 2021. Patients were included 6 months after GLP-1 RA or DPP-4i initiation. The last available HbA1c measurement before inclusion was collected. The achieved HbA1c level was categorized according to a target level below or above 53 mmol/mol (7%). The primary outcome was a composite of nonfatal myocardial infarction, nonfatal stroke, and all-cause death. We used a multivariable Cox proportional hazard model to estimate the effect of HbA1c levels on the outcome among GLP-1 RA users compared to DPP-4i users. RESULTS: The study included 13 634 GLP-1 RA users (median age 56.9, interquartile range [IQR]: 48.5-65.5; 53% males) and 39 839 DPP-4i users (median age 63.4, IQR: 54.6-71.8; 61% males). The number of GLP-1 RA and DPP-4i users according to achieved HbA1c levels were as follows: HbA1c ≤ 53 mmol/mol (≤7.0%): 3026 (22%) versus 4824 (12%); HbA1c > 53 mmol/mol (>7.0%): 6577 (48%) versus 17 508 (44%); missing HbA1c: 4031 (30%) versus 17 507 (44%). During a median follow-up of 5 years (IQR: 2.6-5.0), 954 GLP-1 RA users experienced the primary outcome compared to 7093 DPP-4i users. The 5-year risk (95% confidence interval [CI]) of the outcome associated with GLP1-RA versus DPP-4i according to HbA1c categories was as follows: HbA1c ≤ 53 mmol/mol: 10.3% (8.2-12.3) versus 24.3% (22.7-25.8); HbA1c > 53 mmol/mol: 16.0% (14.3-17.6) versus 21.1% (20.3-21.9); missing HbA1c: 17.1% (15.7-18.5) versus 25.6% (24.9-26.3). The preventive effect associated with GLP-1 RA versus DPP-4i was significantly enhanced when achieving lower HbA1c levels: HbA1c ≤ 53 mmol/mol: 0.65 (0.52-0.80); HbA1c > 53 mmol/mol: 0.92 (0.83-1.03); missing HbA1c: 0.92 (0.84-1.02) (p value for interaction <.001). CONCLUSION: GLP-1 RA use was associated with a lower rate of major adverse cardiovascular outcomes. The association was stronger in patients achieving the target glycemic level and weaker in patients not achieving the target glycemic level, suggestive of an interaction between achieved HbA1c level and GLP-1 RA.

Association between recent exposure to continuous glucose monitoring-recorded hypoglycaemia and counterregulatory and symptom responses to subsequent controlled hypoglycaemia in people with type 1 diabetes.

AIM: Experimental hypoglycaemia blunts the counterregulatory hormone and symptom responses to a subsequent episode of hypoglycaemia. In this study, we aimed to assess the associations between antecedent exposure and continuous glucose monitoring (CGM)-recorded hypoglycaemia during a 1-week period and the counterregulatory responses to subsequent experimental hypoglycaemia in people with type 1 diabetes. MATERIALS AND METHODS: Forty-two people with type 1 diabetes (20 females, mean ± SD glycated haemoglobin 7.8% ± 1.0%, diabetes duration median (interquartile range) 22.0 (10.5-34.9) years, 29 CGM users, and 19 with impaired awareness of hypoglycaemia) wore an open intermittently scanned CGM for 1 week to detect hypoglycaemic exposure before a standardized hyperinsulinaemic-hypoglycaemic [2.8 ± 0.1 mmol/L (50.2 ± 2.3 mg/dl)] glucose clamp. Symptom responses and counterregulatory hormones were measured during the clamp. The study is part of the HypoRESOLVE project. RESULTS: CGM-recorded hypoglycaemia in the week before the clamp was negatively associated with adrenaline response [ß -0.09, 95% CI (-0.16, -0.02) nmol/L, p = .014], after adjusting for CGM use, awareness of hypoglycaemia, glycated haemoglobin and total daily insulin dose. This was driven by level 2 hypoglycaemia [<3.0 mmol/L (54 mg/dl)] [ß -0.21, 95% CI (-0.41, -0.01) nmol/L, p = .034]. CGM-recorded hypoglycaemia was negatively associated with total, autonomic, and neuroglycopenic symptom responses, but these associations were lost after adjusting for potential confounders. CONCLUSIONS: Recent exposure to CGM-detected hypoglycaemia was independently associated with an attenuated adrenaline response to experimental hypoglycaemia in people with type 1 diabetes.

Risk factors and prediction of hypoglycaemia using the Hypo-RESOLVE cohort: a secondary analysis of pooled data from insulin clinical trials.

AIMS/HYPOTHESIS: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS/INTERPRETATION: 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.

Summary of clinical investigation plan for The DIATEC trial: in-hospital diabetes management by a diabetes team and continuous glucose monitoring or point of care glucose testing - a randomised controlled trial.

BACKGROUND: Worldwide, up to 20 % of hospitalised patients have diabetes mellitus. In-hospital dysglycaemia increases patient mortality, morbidity, and length of hospital stay. Improved in-hospital diabetes management strategies are needed. The DIATEC trial investigates the effects of an in-hospital diabetes team and operational insulin titration algorithms based on either continuous glucose monitoring (CGM) data or standard point-of-care (POC) glucose testing. METHODS: This is a two-armed, two-site, prospective randomised open-label blinded endpoint (PROBE) trial. We recruit non-critically ill hospitalised general medical and orthopaedic patients with type 2 diabetes treated with basal, prandial, and correctional insulin (N = 166). In both arms, patients are monitored by POC glucose testing and diabetes management is done by ward nurses guided by in-hospital diabetes teams. In one of the arms, patients are monitored in addition to POC glucose testing by telemetric CGM viewed by the in-hospital diabetes teams only. The in-hospital diabetes teams have operational algorithms to titrate insulin in both arms. Outcomes are in-hospital glycaemic and clinical outcomes. DISCUSSION: The DIATEC trial will show the glycaemic and clinical effects of in-hospital CGM handled by in-hospital diabetes teams with access to operational insulin titration algorithms in non-critically ill patients with type 2 diabetes. The DIATEC trial seeks to identify which hospitalised patients will benefit from CGM and in-hospital diabetes teams compared to POC glucose testing. This is essential information to optimise the use of healthcare resources before broadly implementing in-hospital CGM and diabetes teams. TRIAL REGISTRATION: Prospectively registered at ClinicalTrials.gov with identification number NCT05803473 on March 27th 2023.

Associations of clinical, psychological, and sociodemographic characteristics and ecological momentary assessment completion in the 10-week Hypo-METRICS study: Hypoglycaemia MEasurements ThResholds and ImpaCtS.

INTRODUCTION: Reporting of hypoglycaemia and its impact in clinical studies is often retrospective and subject to recall bias. We developed the Hypo-METRICS app to measure the daily physical, psychological, and social impact of hypoglycaemia in adults with type 1 and insulin-treated type 2 diabetes in real-time using ecological momentary assessment (EMA). To help assess its utility, we aimed to determine Hypo-METRICS app completion rates and factors associated with completion. METHODS: Adults with diabetes recruited into the Hypo-METRICS study were given validated patient-reported outcome measures (PROMs) at baseline. Over 10 weeks, they wore a blinded continuous glucose monitor (CGM), and were asked to complete three daily EMAs about hypoglycaemia and aspects of daily functioning, and two weekly sleep and productivity PROMs on the bespoke Hypo-METRICS app. We conducted linear regression to determine factors associated with app engagement, assessed by EMA and PROM completion rates and CGM metrics. RESULTS: In 602 participants (55% men; 54% type 2 diabetes; median(IQR) age 56 (45-66) years; diabetes duration 19 (11-27) years; HbA1c 57 (51-65) mmol/mol), median(IQR) overall app completion rate was 91 (84-96)%, ranging from 90 (81-96)%, 89 (80-94)% and 94(87-97)% for morning, afternoon and evening check-ins, respectively. Older age, routine CGM use, greater time below 3.0 mmol/L, and active sensor time were positively associated with app completion. DISCUSSION: High app completion across all app domains and participant characteristics indicates the Hypo-METRICS app is an acceptable research tool for collecting detailed data on hypoglycaemia frequency and impact in real-time.

Frequency of hypoglycaemia with basal insulin treatments in adults with type 1 diabetes treated with basal-bolus insulin regimens in treat-to-target trials: A narrative review.

AIM: To summarise, in a narrative review, published data on hypoglycaemia occurrence with basal insulin therapy in adults with type 1 diabetes treated with basal-bolus insulin regimens in treat-to-target randomised controlled trials. METHODS: Data were included from 21 eligible trials, which mainly used self-measured blood glucose or plasma glucose to detect hypoglycaemia. RESULTS: All-day self-measured blood glucose or plasma glucose level 2 (glucose threshold of 3.1 or 3.0 mmol/L) and level 3 (severe, requiring assistance) hypoglycaemic events were reported, respectively, by a range of 69.0%-97.5% and 0%-13.4% adults when receiving basal-bolus insulin therapy, with rates of 10.6-68.1 and 0.0-0.4 events per patient-year of exposure, respectively. Hypoglycaemia rates measured using continuous glucose monitoring (three studies) were numerically, yet consistently, higher than with either other method, except when limiting to symptomatic events. Nocturnal hypoglycaemia rates were generally less than 30% of the equivalent all-day rates. CONCLUSIONS: Differences across the studies in design (e.g., titration targets) and participant characteristics hindered comparison of hypoglycaemia rates by insulin formulation. Consequently, few trends were identified by insulin formulation, study methodology or individuals' characteristics, suggesting that further research is required to identify treatment strategies that facilitate development of individualised recommendations to lower hypoglycaemia risk. These findings are useful to understand hypoglycaemia risk with available basal insulin therapies when used in a multiple daily injection regimen, as well as to provide context for the results of ongoing and future clinical trials, including those for two once-weekly basal insulins, insulin icodec and basal insulin Fc.

Hemostatic Effects of Exercise-Related Hypoglycemia in Male Persons With Type 1 Diabetes.

CONTEXT: People with type 1 diabetes (T1D) are at increased risk of thrombosis, however, the underlying mechanisms remain unclear. Hypoglycemia induced at rest can induce coagulation activation, but little is known about the hemostatic effects of exercise-related hypoglycemia in people with T1D. OBJECTIVE: We compared hemostatic profiles of individuals with T1D with healthy controls and explored hemostatic effects of hypoglycemia, induced with or without exercise, in participants with T1D. METHODS: Thrombelastography (TEG) was used for a baseline hemostatic comparison between fifteen men with T1D and matched healthy controls. In addition, the participants with T1D underwent two euglycemic-hypoglycemic clamp days in a randomized, crossover fashion. Hypoglycemia was induced with the participants at rest (Hypo-rest) or during exercise (Hypo-exercise). TEG provides data on the rate of coagulation activation (R-time), the rate of clot formation (K-time, α-Angle), the maximum clot amplitude (MA), the functional fibrinogen contribution to the clot strength (MA-FF) and the fibrinolysis (LY-30). RESULTS: The T1D group exhibited shorter R-time and K-time and a greater α-Angle compared to the controls. During the clamp experiments, Hypo-exercise induced an increased clot strength (MA) with a mean difference from baseline of 2.77 mm [95% confidence interval 2.04; 3.51] accompanied with a decreased fibrinolysis (LY-30) of -0.45 percentage points [-0.60; -0.29]. Hypo-rest resulted in increased functional fibrinogen (MA-FF) of 0.74 mm [0.13; 1.36] along with an increased fibrinolysis (LY-30) of 0.54 percentage points [0.11; 0.98]. CONCLUSION: Individuals with T1D exhibit a hypercoagulable hemostatic profile compared to healthy controls and exercise-related hypoglycemia may increase the susceptibility to thrombosis via both procoagulant and antifibrinolytic effects.

Accuracy of continuous glucose monitoring during exercise-related hypoglycemia in individuals with type 1 diabetes.

Background: Hypoglycemia is common in individuals with type 1 diabetes, especially during exercise. We investigated the accuracy of two different continuous glucose monitoring systems during exercise-related hypoglycemia in an experimental setting. Materials and methods: Fifteen individuals with type 1 diabetes participated in two separate euglycemic-hypoglycemic clamp days (Clamp-exercise and Clamp-rest) including five phases: 1) baseline euglycemia, 2) plasma glucose (PG) decline ± exercise, 3) 15-minute hypoglycemia ± exercise, 4) 45-minute hypoglycemia, and 5) recovery euglycemia. Interstitial PG levels were measured every five minutes, using Dexcom G6 (DG6) and FreeStyle Libre 1 (FSL1). Yellow Springs Instruments 2900 was used as PG reference method, enabling mean absolute relative difference (MARD) assessment for each phase and Clarke error grid analysis for each day. Results: Exercise had a negative effect on FSL1 accuracy in phase 2 and 3 compared to rest (ΔMARD = +5.3 percentage points [(95% CI): 1.6, 9.1] and +13.5 percentage points [6.4, 20.5], respectively). In contrast, exercise had a positive effect on DG6 accuracy during phase 2 and 4 compared to rest (ΔMARD = -6.2 percentage points [-11.2, -1.2] and -8.4 percentage points [-12.4, -4.3], respectively). Clarke error grid analysis showed a decrease in clinically acceptable treatment decisions during Clamp-exercise for FSL1 while a contrary increase was observed for DG6. Conclusion: Physical exercise had clinically relevant impact on the accuracy of the investigated continuous glucose monitoring systems and their ability to accurately detect hypoglycemia.

The effect of bolus advisors on glycaemic parameters in adults with diabetes on intensive insulin therapy: A systematic review with meta-analysis.

AIM: To conduct a systematic review with meta-analysis to provide a comprehensive synthesis of randomized controlled trials (RCTs) and prospective cohort studies investigating the effects of currently available bolus advisors on glycaemic parameters in adults with diabetes. MATERIALS AND METHODS: An electronic search of PubMed, Embase, CINAHL, Cochrane Library and ClinicalTrials.gov was conducted in December 2022. The risk of bias was assessed using the revised Cochrane Risk of Bias tool. (Standardized) mean difference (MD) was selected to determine the difference in continuous outcomes between the groups. A random-effects model meta-analysis and meta-regression were performed. This systematic review was registered on PROSPERO (CRD42022374588). RESULTS: A total of 18 RCTs involving 1645 adults (50% females) with a median glycated haemoglobin (HbA1c) concentration of 8.45% (7.95%-9.30%) were included. The majority of participants had type 1 diabetes (N = 1510, 92%) and were on multiple daily injections (N = 1173, 71%). Twelve of the 18 trials had low risk of bias. The meta-analysis of 10 studies with available data on HbA1c showed that the use of a bolus advisor modestly reduced HbA1c compared to standard treatment (MD -011%, 95% confidence interval -0.22 to -0.01; I2 = 0%). This effect was accompanied by small improvements in low blood glucose index and treatment satisfaction, but not with reductions in hypoglycaemic events or changes in other secondary outcomes. CONCLUSION: Use of a bolus advisor is associated with slightly better glucose control and treatment satisfaction in people with diabetes on intensive insulin treatment. Future studies should investigate whether personalizing bolus advisors using artificial intelligence technology can enhance these effects.

Associations between use of diabetes technology and diabetes distress: a Danish cross-sectional survey of adults with type 1 diabetes.

INTRODUCTION: The study aimed to investigate independent and combined associations between insulin delivery method (insulin pump therapy (IPT) vs multiple daily injections (MDI)), glucose monitoring method (intermittently scanned continuous glucose monitoring (isCGM) and real-time continuous glucose monitoring (rtCGM) vs blood glucose metre (BGM)) and diabetes distress (DD) in adults with type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: We combined data from two Danish questionnaire-based surveys, the Steno Tech Survey (n=1591) and the Type 1 Diabetes Distress Scale (T1-DDS) validation survey (n=4205), in which individuals aged ≥18 years with T1D were invited to participate. The 28-item T1-DDS was used to measure DD and DD scores were categorised as little or no distress (score <2.0), moderate distress (2.0-2.9) and high distress (score ≥3.0). Associations between insulin delivery, glucose monitoring methods and DD were assessed using linear regression. RESULTS: Among 2068 adults with T1D who responded to one of the surveys, the use of IPT was associated with a lower total T1-DDS score (-0.09, 95% CI 0.16 to -0.03) compared with MDI and adjusted for glucose monitoring method. The use of CGM was associated with a higher total T1-DDS score (0.11, 95% CI 0.05 to 0.18) compared with BGM and adjusted for the insulin delivery method. IPT was still associated with a lower T1-DDS score, regardless of being combined with BGM (-0.17, 95% CI -0.28 to -0.06) or CGM (-0.13, 95% CI -0.21 to -0.05), compared with MDI with CGM. No association was found between the type of CGM (isCGM vs rtCGM) and DD among either IPT or MDI users when restricting analysis to individuals using CGM. CONCLUSIONS: Among Danish adults with T1D, the use of IPT was associated with lower levels of DD, while CGM use was associated with higher levels of DD. DD should be addressed when introducing people with T1D to diabetes technology, CGM in particular. TRIAL REGISTRATION NUMBER: NCT04311164 (Results).

Associations Between Hypoglycemia Awareness Status and Symptoms of Hypoglycemia Among Adults with Type 1 or Insulin-Treated Type 2 Diabetes Using the Hypo-METRICS Smartphone Application.

Introduction: This study examined associations between hypoglycemia awareness status and hypoglycemia symptoms reported in real-time using the novel Hypoglycaemia-MEasurement, ThResholds and ImpaCtS (Hypo-METRICS) smartphone application (app) among adults with insulin-treated type 1 (T1D) or type 2 diabetes (T2D). Methods: Adults who experienced at least one hypoglycemic episode in the previous 3 months were recruited to the Hypo-METRICS study. They prospectively reported hypoglycemia episodes using the app for 10 weeks. Any of eight hypoglycemia symptoms were considered present if intensity was rated between "A little bit" to "Very much" and absent if rated "Not at all." Associations between hypoglycemia awareness (as defined by Gold score) and hypoglycemia symptoms were modeled using mixed-effects binary logistic regression, adjusting for glucose monitoring method and diabetes duration. Results: Of 531 participants (48% T1D, 52% T2D), 45% were women, 91% white, and 59% used Flash or continuous glucose monitoring. Impaired awareness of hypoglycemia (IAH) was associated with lower odds of reporting autonomic symptoms than normal awareness of hypoglycemia (NAH) (T1D odds ratio [OR] 0.43 [95% confidence interval {CI} 0.25-0.73], P = 0.002); T2D OR 0.51 [95% CI 0.26-0.99], P = 0.048), with no differences in neuroglycopenic symptoms. In T1D, relative to NAH, IAH was associated with higher odds of reporting autonomic symptoms at a glucose concentration <54 than >70 mg/dL (OR 2.18 [95% CI 1.21-3.94], P = 0.010). Conclusion: The Hypo-METRICS app is sensitive to differences in hypoglycemia symptoms according to hypoglycemia awareness in both diabetes types. Given its high ecological validity and low recall bias, the app may be a useful tool in research and clinical settings. The clinical trial registration number is NCT04304963.

Metabolic effect of adrenaline infusion in people with type 1 diabetes and healthy individuals.

AIMS/HYPOTHESIS: As a result of early loss of the glucagon response, adrenaline is the primary counter-regulatory hormone in type 1 diabetes. Diminished adrenaline responses to hypoglycaemia due to counter-regulatory failure are common in type 1 diabetes, and are probably induced by exposure to recurrent hypoglycaemia, however, the metabolic effects of adrenaline have received less research attention, and also there is conflicting evidence regarding adrenaline sensitivity in type 1 diabetes. Thus, we aimed to investigate the metabolic response to adrenaline and explore whether it is modified by prior exposure to hypoglycaemia. METHODS: Eighteen participants with type 1 diabetes and nine healthy participants underwent a three-step ascending adrenaline infusion during a hyperinsulinaemic-euglycaemic clamp. Continuous glucose monitoring data obtained during the week before the study day were used to assess the extent of hypoglycaemia exposure. RESULTS: While glucose responses during the clamp were similar between people with type 1 diabetes and healthy participants, plasma concentrations of NEFAs and glycerol only increased in the group with type 1 diabetes (p<0.001). Metabolomics revealed an increase in the most common NEFAs (p<0.01). Other metabolic responses were generally similar between participants with type 1 diabetes and healthy participants. Exposure to hypoglycaemia was negatively associated with the NEFA response; however, this was not statistically significant. CONCLUSIONS/INTERPRETATION: In conclusion, individuals with type 1 diabetes respond with increased lipolysis to adrenaline compared with healthy participants by mobilising the abundant NEFAs in plasma, whereas other metabolic responses were similar. This may suggest that the metabolic sensitivity to adrenaline is altered in a pathway-specific manner in type 1 diabetes. TRIAL REGISTRATION: ClinicalTrials.gov NCT05095259.

A Comparison of the Rates of Clock-Based Nocturnal Hypoglycemia and Hypoglycemia While Asleep Among People Living with Diabetes: Findings from the Hypo-METRICS Study.

Introduction: Nocturnal hypoglycemia is generally calculated between 00:00 and 06:00. However, those hours may not accurately reflect sleeping patterns and it is unknown whether this leads to bias. We therefore compared hypoglycemia rates while asleep with those of clock-based nocturnal hypoglycemia in adults with type 1 diabetes (T1D) or insulin-treated type 2 diabetes (T2D). Methods: Participants from the Hypo-METRICS study wore a blinded continuous glucose monitor and a Fitbit Charge 4 activity monitor for 10 weeks. They recorded details of episodes of hypoglycemia using a smartphone app. Sensor-detected hypoglycemia (SDH) and person-reported hypoglycemia (PRH) were categorized as nocturnal (00:00-06:00 h) versus diurnal and while asleep versus awake defined by Fitbit sleeping intervals. Paired-sample Wilcoxon tests were used to examine the differences in hypoglycemia rates. Results: A total of 574 participants [47% T1D, 45% women, 89% white, median (interquartile range) age 56 (45-66) years, and hemoglobin A1c 7.3% (6.8-8.0)] were included. Median sleep duration was 6.1 h (5.2-6.8), bedtime and waking time ∼23:30 and 07:30, respectively. There were higher median weekly rates of SDH and PRH while asleep than clock-based nocturnal SDH and PRH among people with T1D, especially for SDH <70 mg/dL (1.7 vs. 1.4, P < 0.001). Higher weekly rates of SDH while asleep than nocturnal SDH were found among people with T2D, especially for SDH <70 mg/dL (0.8 vs. 0.7, P < 0.001). Conclusion: Using 00:00 to 06:00 as a proxy for sleeping hours may underestimate hypoglycemia while asleep. Future hypoglycemia research should consider the use of sleep trackers to record sleep and reflect hypoglycemia while asleep more accurately. The trial registration number is NCT04304963.