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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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).

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.

The impact of prior exposure to hypoglycaemia on the inflammatory response to a subsequent hypoglycaemic episode.

BACKGROUND: Hypoglycaemia has been shown to induce a systemic pro-inflammatory response, which may be driven, in part, by the adrenaline response. Prior exposure to hypoglycaemia attenuates counterregulatory hormone responses to subsequent hypoglycaemia, but whether this effect can be extrapolated to the pro-inflammatory response is unclear. Therefore, we investigated the effect of antecedent hypoglycaemia on inflammatory responses to subsequent hypoglycaemia in humans. METHODS: Healthy participants (n = 32) were recruited and randomised to two 2-h episodes of either hypoglycaemia or normoglycaemia on day 1, followed by a hyperinsulinaemic hypoglycaemic (2.8 ± 0.1 mmol/L) glucose clamp on day 2. During normoglycaemia and hypoglycaemia, and after 24 h, 72 h and 1 week, blood was drawn to determine circulating immune cell composition, phenotype and function, and 93 circulating inflammatory proteins including hs-CRP. RESULTS: In the group undergoing antecedent hypoglycaemia, the adrenaline response to next-day hypoglycaemia was lower compared to the control group (1.45 ± 1.24 vs 2.68 ± 1.41 nmol/l). In both groups, day 2 hypoglycaemia increased absolute numbers of circulating immune cells, of which lymphocytes and monocytes remained elevated for the whole week. Also, the proportion of pro-inflammatory CD16+-monocytes increased during hypoglycaemia. After ex vivo stimulation, monocytes released more TNF-α and IL-1ß, and less IL-10 in response to hypoglycaemia, whereas levels of 19 circulating inflammatory proteins, including hs-CRP, increased for up to 1 week after the hypoglycaemic event. Most of the inflammatory responses were similar in the two groups, except the persistent pro-inflammatory protein changes were partly blunted in the group exposed to antecedent hypoglycaemia. We did not find a correlation between the adrenaline response and the inflammatory responses during hypoglycaemia. CONCLUSION: Hypoglycaemia induces an acute and persistent pro-inflammatory response at multiple levels that occurs largely, but not completely, independent of prior exposure to hypoglycaemia. Clinical Trial information Clinicaltrials.gov no. NCT03976271 (registered 5 June 2019).

Counterregulatory hormone and symptom responses to hypoglycaemia in people with type 1 diabetes, insulin-treated type 2 diabetes or without diabetes: the Hypo-RESOLVE hypoglycaemic clamp study.

AIM: 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. MATERIALS: 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. RESULTS: 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). CONCLUSION: 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.

Statistical Packages and Algorithms for the Analysis of Continuous Glucose Monitoring Data: A Systematic Review.

BACKGROUND: Continuous glucose monitoring (CGM) measures glucose levels every 1 to 15 minutes and is widely used in clinical and research contexts. Statistical packages and algorithms reduce the time-consuming and error-prone process of manually calculating CGM metrics and contribute to standardizing CGM metrics defined by international consensus. The aim of this systematic review is to summarize existing data on (1) statistical packages for retrospective CGM data analysis and (2) statistical algorithms for retrospective CGM analysis not available in these statistical packages. METHODS: A systematic literature search in PubMed and EMBASE was conducted on September 19, 2023. We also searched Google Scholar and Google Search until October 12, 2023 as sources of gray literature and performed reference checks of the included literature. Articles in English and Danish were included. This systematic review is registered with PROSPERO (CRD42022378163). RESULTS: A total of 8731 references were screened and 46 references were included. We identified 23 statistical packages for the analysis of CGM data. The statistical packages could calculate many metrics of the 2022 CGM consensus and non-consensus CGM metrics, and 22/23 (96%) statistical packages were freely available. Also, 23 statistical algorithms were identified. The statistical algorithms could be divided into three groups based on content: (1) CGM data reduction (eg, clustering of CGM data), (2) composite CGM outcomes, and (3) other CGM metrics. CONCLUSION: This systematic review provides detailed tabular and textual up-to-date descriptions of the contents of statistical packages and statistical algorithms for retrospective analysis of CGM data.

The effect of insulin analogs in people with type 1 diabetes at increased risk of severe hypoglycemia.

Type 1 diabetes is characterized by insulin deficiency, and treatment is to supply insulin mimicking the physiological endogenous insulin secretion. Since its discovery, insulin therapy has evolved, and since the 1990s, an increasing number of insulin analogs with various pharmacokinetic and pharmacodynamic profiles have become available. Despite the improvement of insulin therapy, hypoglycemia remains the main side effect and is a daily concern for many people with diabetes and their families. A proportion of people with type 1 diabetes are at increased risk of hypoglycemia and experience recurring episodes. When designing insulin trials, this group of people is most often excluded in order to reduce the risk of adverse study outcomes, even though it may be the group that may benefit the most from treatment with new insulins. The results of the phase III trials, therefore, underestimate the clinical impact and pharmacoeconomic effect of the implementation of new insulins in the broader type 1 diabetes population. This paper reviews the four insulin trials that include people at increased risk of hypoglycemia. In general, the studies confirm the results from phase III trials in terms of similar reduction and maintenance of HbA1c, as well as relative rate reductions of hypoglycemia. However, the absolute treatment differences in the reduction of hypoglycemia are even greater in the trials, including people at high risk of hypoglycemia. This emphasizes the importance of including people at high risk of hypoglycemia to assess the full clinical and pharmacoeconomic benefit of new insulins.