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Micro- and macrovascular complications are a major burden for individuals with diabetes and can already arise in a prediabetic state. To allocate effective treatments and to possibly prevent these complications, identification of those at risk is essential. This study aimed to build machine learning (ML) models that predict the risk of developing a micro- or macrovascular complication in individuals with prediabetes or diabetes. In this study, we used electronic health records from Israel that contain information about demographics, biomarkers, medications, and disease codes; span from 2003 to 2013; and were queried to identify individuals with prediabetes or diabetes in 2008. Subsequently, we aimed to predict which of these individuals developed a micro- or macrovascular complication within the next 5 years. We included 3 microvascular complications: retinopathy, nephropathy, and neuropathy. In addition, we considered 3 macrovascular complications: peripheral vascular disease (PVD), cerebrovascular disease (CeVD), and cardiovascular disease (CVD). Complications were identified via disease codes, and, for nephropathy, the estimated glomerular filtration rate and albuminuria were considered additionally. Inclusion criteria were complete information on age and sex and on disease codes (or measurements of estimated glomerular filtration rate and albuminuria for nephropathy) until 2013 to account for patient dropout. Exclusion criteria for predicting a complication were diagnosis of this specific complication before or in 2008. In total, 105 predictors from demographics, biomarkers, medications, and disease codes were used to build the ML models. We compared 2 ML models: logistic regression and gradient-boosted decision trees (GBDTs). To explain the predictions of the GBDTs, we calculated Shapley additive explanations values. Overall, 13,904 and 4259 individuals with prediabetes and diabetes, respectively, were identified in our underlying data set. For individuals with prediabetes, the areas under the receiver operating characteristic curve for logistic regression and GBDTs were, respectively, 0.657 and 0.681 (retinopathy), 0.807 and 0.815 (nephropathy), 0.727 and 0.706 (neuropathy), 0.730 and 0.727 (PVD), 0.687 and 0.693 (CeVD), and 0.707 and 0.705 (CVD); for individuals with diabetes, the areas under the receiver operating characteristic curve were, respectively, 0.673 and 0.726 (retinopathy), 0.763 and 0.775 (nephropathy), 0.745 and 0.771 (neuropathy), 0.698 and 0.715 (PVD), 0.651 and 0.646 (CeVD), and 0.686 and 0.680 (CVD). Overall, the prediction performance is comparable for logistic regression and GBDTs. The Shapley additive explanations values showed that increased levels of blood glucose, glycated hemoglobin, and serum creatinine are risk factors for microvascular complications. Age and hypertension were associated with an elevated risk for macrovascular complications. Our ML models allow for an identification of individuals with prediabetes or diabetes who are at increased risk of developing micro- or macrovascular complications. The prediction performance varied across complications and target populations but was in an acceptable range for most prediction tasks.

Aims/hypothesis Ectopic lipids are fuel stores in non-adipose tissues (skeletal muscle [intramyocellular lipids; IMCL], liver [intrahepatocellular lipids; IHCL] and heart [intracardiomyocellular lipids; ICCL]). IMCL can be depleted by physical activity. Preliminary data suggest that aerobic exercise increases IHCL. Data on exercise-induced changes on ICCL is scarce. Increased IMCL and IHCL have been related to insulin resistance in skeletal muscles and liver, whereas this has not been documented in the heart. The aim of this study was to assess the acute effect of aerobic exercise on the flexibility of IMCL, IHCL and ICCL in insulin-sensitive participants in relation to fat availability, insulin sensitivity and exercise capacity. Methods Healthy physically active men were included. V ⋅ O 2 max was assessed by spiroergometry and insulin sensitivity was calculated using the HOMA index. Visceral and subcutaneous fat were separately quantified by MRI. Following a standardised dietary fat load over 3 days, IMCL, IHCL and ICCL were measured using MR spectroscopy before and after a 2 h exercise session at 50–60% of V ⋅ O 2 max . Metabolites were measured during exercise. Results Ten men (age 28.9 ± 6.4 years, mean ± SD; V ⋅ O 2 max 56.3 ± 6.4 ml kg −1  min −1 ; BMI 22.75 ± 1.4 kg/m 2 ) were recruited. A 2 h exercise session resulted in a significant decrease in IMCL (−17 ± 22%, p  = 0.008) and ICCL (−17 ± 14%, p  = 0.002) and increase in IHCL (42 ± 29%, p  = 0.004). No significant correlations were found between the relative changes in ectopic lipids, fat availability, insulin sensitivity, exercise capacity or changes of metabolites during exercise. Conclusions/interpretation In this group, physical exercise decreased ICCL and IMCL but increased IHCL. Fat availability, insulin sensitivity, exercise capacity and metabolites during exercise are not the only factors affecting ectopic lipids during exercise.

Obesity often develops in childhood. In children (6 to <12 years of age) with obesity, treatment with liraglutide for 56 weeks plus lifestyle interventions induced a greater mean change in BMI than lifestyle interventions alone.

Aims/hypothesis COMBINE 2 assessed the efficacy and safety of once-weekly IcoSema (a combination therapy of basal insulin icodec and semaglutide) vs once-weekly semaglutide (a glucagon-like peptide-1 analogue) 1.0 mg in individuals with type 2 diabetes inadequately managed with GLP-1 receptor agonist (GLP-1 RA) therapy, with or without additional oral glucose-lowering medications. Methods This 52 week, randomised, multicentre, open-label, parallel group, Phase IIIa trial was conducted across 121 sites in 13 countries/regions. Adults with type 2 diabetes (HbA 1c 53.0–85.8 mmol/mol [7.0–10.0%]) receiving GLP-1 RA therapy with or without additional oral glucose-lowering medications were randomly assigned 1:1 to once-weekly IcoSema or once-weekly semaglutide 1.0 mg. The primary endpoint was change in HbA 1c from baseline to week 52; superiority of IcoSema to semaglutide 1.0 mg was assessed. Secondary endpoints included change in fasting plasma glucose and body weight (baseline to week 52), and combined clinically significant (level 2; <3.0 mmol/l) or severe (level 3; associated with severe cognitive impairment requiring external assistance for recovery) hypoglycaemia (baseline to week 57). Results Overall, 683 participants were randomised using a Randomisation and Trial Supply Management system to IcoSema ( n =342) or semaglutide 1.0 mg ( n =341). Mean ± SD baseline characteristics were as follows: HbA 1c 64.0±8.2 mmol/mol (8.0±0.7%); diabetes duration 12.6±6.9 years; and BMI 31.1±4.7 kg/m 2 . From baseline to week 52, mean change in HbA 1c was −14.7 mmol/mol (−1.35%-points) in the IcoSema group and −9.88 mmol/mol (−0.90%-points) in the semaglutide group; the estimated treatment difference (ETD) was –4.85 (95% CI −6.13, −3.57) mmol/mol (−0.44 [95% CI −0.56, −0.33]%-points), confirming superiority of IcoSema to semaglutide ( p <0.0001). The estimated mean change in fasting plasma glucose from baseline to week 52 was statistically significantly reduced with IcoSema vs semaglutide (−2.48 mmol/l vs −1.41 mmol/l, respectively; ETD −1.07 [95% CI −1.37, −0.76] mmol; p <0.0001). Mean change in body weight from baseline to week 52 was statistically significantly different between groups: +0.84 kg for IcoSema vs −3.70 kg for semaglutide (ETD 4.54 kg [95% CI 3.84, 5.23]; p <0.0001). There was no statistically significant difference in the rate of combined clinically significant or severe hypoglycaemia between IcoSema and semaglutide (0.042 vs 0.036 episodes per person-year of exposure; estimated rate ratio 1.20 [95% CI 0.53, 2.69]; p =0.66). The proportion of participants experiencing gastrointestinal adverse events was similar between treatment groups (IcoSema 31.4%; semaglutide 34.4%). Conclusions/interpretation In people living with type 2 diabetes inadequately managed with GLP-1 RA therapy, with or without additional oral glucose-lowering medications, switching to once-weekly IcoSema in comparison with once-weekly semaglutide 1.0 mg demonstrated superiority in HbA 1c reduction, similar rates of clinically significant or severe hypoglycaemia, and similar frequency of gastrointestinal adverse events. However, weight change from baseline to week 52 was statistically significantly in favour of semaglutide 1.0 mg. Trial registration ClinicalTrials.gov NCT05259033 Funding This trial was funded by Novo Nordisk Graphical Abstract

Aims/hypothesis The majority of hybrid closed-loop systems still require carbohydrate counting (CC) but the evidence for its justification remains limited. Here, we evaluated glucose control with simplified meal announcement (SMA) vs CC in youth and young adults with type 1 diabetes using the mylife CamAPS FX system. Methods We conducted a two-centre, randomised crossover, non-inferiority trial in two University Hospitals in Switzerland in 46 participants (aged 12–20 years) with type 1 diabetes using multiple daily injections ( n =35), sensor-augmented pump ( n =4) or hybrid closed-loop ( n =7) therapy before enrolment. Participants underwent two 3 month periods with the mylife CamAPS FX system (YpsoPump, Dexcom G6) to compare SMA (individualised carbohydrate meal sizes) with CC, in a randomly assigned order using computer-generated sequences. The primary endpoint was the proportion of time glucose was in target range (3.9–10.0 mmol/l) with a non-inferiority margin of 5 percentage points. Secondary endpoints were other sensor glucose and insulin metrics, usability and safety endpoints. Results Forty-three participants (18 women and girls) completed the trial. In the intention-to-treat analysis, time in range (mean±SD) was 69.9±12.4% with SMA and 70.7±13.0% with CC (estimated mean difference −0.6 percentage points [95% CI −2.4, 1.1], demonstrating non-inferiority). Time <3.9 mmol/l (median [IQR] 1.8 [1.2–2.2]% vs 1.9 [1.6–2.5]%) and >10.0 mmol/l (28.2±12.6% vs 27.2±13.4%) was similar between periods. Total daily insulin dose was higher with SMA (54.0±14.7 U vs 51.7±12.1 U, p =0.037). Three participants experienced serious adverse events, none of which were intervention-related. Conclusions/interpretation Glucose control using the CamAPS FX algorithm with SMA was non-inferior to its use with CC in youth and young adults with type 1 diabetes. Trial registration ClinicalTrials.gov NCT05481034. Funding The study was supported by the Swiss Diabetes Foundation and by a YTCR grant from the Bangerter-Rhyner Foundation and the Swiss Academy of Medical Sciences. Dexcom and Ypsomed provided product support. Graphical Abstract

Aims/hypothesis To investigate exercise-related fuel metabolism in intermittent high-intensity (IHE) and continuous moderate intensity (CONT) exercise in individuals with type 1 diabetes mellitus. Methods In a prospective randomised open-label cross-over trial twelve male individuals with well-controlled type 1 diabetes underwent a 90 min iso-energetic cycling session at 50% maximal oxygen consumption ( V ⋅ O 2 max ), with (IHE) or without (CONT) interspersed 10 s sprints every 10 min without insulin adaptation. Euglycaemia was maintained using oral 13 C-labelled glucose. 13 C Magnetic resonance spectroscopy (MRS) served to quantify hepatocellular and intramyocellular glycogen. Measurements of glucose kinetics (stable isotopes), hormones and metabolites complemented the investigation. Results Glucose and insulin levels were comparable between interventions. Exogenous glucose requirements during the last 30 min of exercise were significantly lower in IHE ( p  = 0.02). Hepatic glucose output did not differ significantly between interventions, but glucose disposal was significantly lower in IHE ( p  < 0.05). There was no significant difference in glycogen consumption. Growth hormone, catecholamine and lactate levels were significantly higher in IHE ( p  < 0.05). Conclusions/interpretation IHE in individuals with type 1 diabetes without insulin adaptation reduced exogenous glucose requirements compared with CONT. The difference was not related to increased hepatic glucose output, nor to enhanced muscle glycogen utilisation, but to decreased glucose uptake. The lower glucose disposal in IHE implies a shift towards consumption of alternative substrates. These findings indicate a high flexibility of exercise-related fuel metabolism in type 1 diabetes, and point towards a novel and potentially beneficial role of IHE in these individuals. Trial registration : ClinicalTrials.gov NCT02068638 Funding : Swiss National Science Foundation (grant number 320030_149321/) and R&A Scherbarth Foundation (Switzerland).

Introduction The COVID‐19 pandemic has forced rapid reconsideration as to the way in which health care is delivered. One potential means to provide care while avoiding unnecessary person‐to‐person contact is to offer remote services (telemedicine). This study aimed to (1) gather real‐time information on the use and perception of telemedicine in people living with type 1 diabetes and (2) assess the challenges, such as restricted access to health care and/or medical supplies. Methods An anonymous questionnaire was widely distributed between 24 March and 5 May 2020 using an open‐access web‐based platform. Data were analysed descriptively, and results were stratified according to age, sex and HbA1c. Results There were 7477 survey responses from individuals in 89 countries. Globally, 30% reported that the pandemic had affected their healthcare access due to cancelled physical appointments with their healthcare providers. Thirty‐two per cent reported no fundamental change in their medical follow‐up during this period, with 9% stating that no personal contact was established with their doctors over the duration of the study. Twenty‐eight per cent received remote care through telephone (72%) or video‐calls (28%). Of these, 86% found remote appointments useful and 75% plan to have remote appointments in the future. Glucose control, indicated by HbA1c, was positively associated with positive perception of telemedicine. In males, 45% of respondents with an HbA1c > 9% rated telemedicine not useful compared to those with lower HbA1c, while 20% of females with an HbA1c > 9% rated it not useful (χ2 = 14.2, P = .0016). Conclusion Remote appointments have largely been perceived as positive in people with type 1 diabetes with the majority (75%) stating that they would consider remote appointments beyond the pandemic. Age and level of education do not appear to influence perception of telemedicine, whereas poor glucose control, particularly in males, seems to negatively affect perception. This study aimed to gather information on the use and perception of telemedicine in people living with type 1 diabetes during the COVID‐19 pandemic. An anonymous questionnaire was widely distributed. Data were analysed descriptively and the mean population responses were summarised and stratified based on country, age, sex, health status and HbA1c. Age and level of education do not appear to influence perception of telemedicine, whereas poor glucose control, particularly in males, seems to negatively affect perception.

Hypoglycemia is a frequent and acute complication in type 1 diabetes mellitus (T1DM) and is associated with a higher risk of car mishaps. Currently, hypoglycemia can be detected and signaled through flash glucose monitoring or continuous glucose monitoring devices, which require manual and visual interaction, thereby removing the focus of attention from the driving task. Hypoglycemia causes a decrease in attention, thereby challenging the safety of using such devices behind the wheel. Here, we present an investigation of a hands-free technology-a voice warning that can potentially be delivered via an in-vehicle voice assistant. This study aims to investigate the feasibility of an in-vehicle voice warning for hypoglycemia, evaluating both its effectiveness and user perception. We designed a voice warning and evaluated it in 3 studies. In all studies, participants received a voice warning while driving. Study 0 (n=10) assessed the feasibility of using a voice warning with healthy participants driving in a simulator. Study 1 (n=18) assessed the voice warning in participants with T1DM. Study 2 (n=20) assessed the voice warning in participants with T1DM undergoing hypoglycemia while driving in a real car. We measured participants' self-reported perception of the voice warning (with a user experience scale in study 0 and with acceptance, alliance, and trust scales in studies 1 and 2) and compliance behavior (whether they stopped the car and reaction time). In addition, we assessed technology affinity and collected the participants' verbal feedback. Technology affinity was similar across studies and approximately 70% of the maximal value. Perception measure of the voice warning was approximately 62% to 78% in the simulated driving and 34% to 56% in real-world driving. Perception correlated with technology affinity on specific constructs (eg, Affinity for Technology Interaction score and intention to use, optimism and performance expectancy, behavioral intention, Session Alliance Inventory score, innovativeness and hedonic motivation, and negative correlations between discomfort and behavioral intention and discomfort and competence trust; all P<.05). Compliance was 100% in all studies, whereas reaction time was higher in study 1 (mean 23, SD 5.2 seconds) than in study 0 (mean 12.6, SD 5.7 seconds) and study 2 (mean 14.6, SD 4.3 seconds). Finally, verbal feedback showed that the participants preferred the voice warning to be less verbose and interactive. This is the first study to investigate the feasibility of an in-vehicle voice warning for hypoglycemia. Drivers find such an implementation useful and effective in a simulated environment, but improvements are needed in the real-world driving context. This study is a kickoff for the use of in-vehicle voice assistants for digital health interventions.

Hypoglycemia threatens cognitive function and driving safety. Previous research investigated in-vehicle voice assistants as hypoglycemia warnings. However, they could startle drivers. To address this, we combine voice warnings with ambient LEDs. The study assesses the effect of in-vehicle multimodal warning on emotional reaction and technology acceptance among drivers with type 1 diabetes. Two studies were conducted, one in simulated driving and the other in real-world driving. A quasi-experimental design included 2 independent variables (blood glucose phase and warning modality) and 1 main dependent variable (emotional reaction). Blood glucose was manipulated via intravenous catheters, and warning modality was manipulated by combining a tablet voice warning app and LEDs. Emotional reaction was measured physiologically via skin conductance response and subjectively with the Affective Slider and tested with a mixed-effect linear model. Secondary outcomes included self-reported technology acceptance. Participants were recruited from Bern University Hospital, Switzerland. The simulated and real-world driving studies involved 9 and 10 participants with type 1 diabetes, respectively. Both studies showed significant results in self-reported emotional reactions (P<.001). In simulated driving, neither warning modality nor blood glucose phase significantly affected self-reported arousal, but in real-world driving, both did (F =4.3; P<.05 and F =4.1; P=.03). Warning modality affected self-reported valence in simulated driving (F =3.9; P<.05), while blood glucose phase affected it in real-world driving (F =9.3; P<.001). Skin conductance response did not yield significant results neither in the simulated driving study (modality: F =2.46; P=.09, blood glucose phase: F =0.3; P=.74), nor in the real-world driving study (modality: F =0.8; P=.47, blood glucose phase: F =0.7; P=.5). In both simulated and real-world driving studies, the voice+LED warning modality was the most effective (simulated: mean 3.38, SD 1.06 and real-world: mean 3.5, SD 0.71) and urgent (simulated: mean 3.12, SD 0.64 and real-world: mean 3.6, SD 0.52). Annoyance varied across settings. The standard warning modality was the least effective (simulated: mean 2.25, SD 1.16 and real-world: mean 3.3, SD 1.06) and urgent (simulated: mean 1.88, SD 1.55 and real-world: mean 2.6, SD 1.26) and the most annoying (simulated: mean 2.25, SD 1.16 and real-world: mean 1.7, SD 0.95). In terms of preference, the voice warning modality outperformed the standard warning modality. In simulated driving, the voice+LED warning modality (mean rank 1.5, SD rank 0.82) was preferred over the voice (mean rank 2.2, SD rank 0.6) and standard (mean rank 2.4, SD rank 0.81) warning modalities, while in real-world driving, the voice+LED and voice warning modalities were equally preferred (mean rank 1.8, SD rank 0.79) to the standard warning modality (mean rank 2.4, SD rank 0.84). Despite the mixed results, this paper highlights the potential of implementing voice assistant-based health warnings in cars and advocates for multimodal alerts to enhance hypoglycemia management while driving. ClinicalTrials.gov NCT05183191; https://classic.clinicaltrials.gov/ct2/show/NCT05183191, ClinicalTrials.gov NCT05308095; https://classic.clinicaltrials.gov/ct2/show/NCT05308095.

Background:To provide effective care for inpatients with COVID-19, clinical practitioners need systems that monitor patient health and subsequently allow for risk scoring. Existing approaches for risk scoring in patients with COVID-19 focus primarily on intensive care units (ICUs) with specialized medical measurement devices but not on hospital general wards.Objective:In this paper, we aim to develop a risk score for inpatients with COVID-19 in general wards based on consumer-grade wearables (smartwatches).Methods:Patients wore consumer-grade wearables to record physiological measurements, such as the heart rate (HR), heart rate variability (HRV), and respiration frequency (RF). Based on Bayesian survival analysis, we validated the association between these measurements and patient outcomes (ie, discharge or ICU admission). To build our risk score, we generated a low-dimensional representation of the physiological features. Subsequently, a pooled ordinal regression with time-dependent covariates inferred the probability of either hospital discharge or ICU admission. We evaluated the predictive performance of our developed system for risk scoring in a single-center, prospective study based on 40 inpatients with COVID-19 in a general ward of a tertiary referral center in Switzerland.Results:First, Bayesian survival analysis showed that physiological measurements from consumer-grade wearables are significantly associated with patient outcomes (ie, discharge or ICU admission). Second, our risk score achieved a time-dependent area under the receiver operating characteristic curve (AUROC) of 0.73-0.90 based on leave-one-subject-out cross-validation.Conclusions:Our results demonstrate the effectiveness of consumer-grade wearables for risk scoring in inpatients with COVID-19. Due to their low cost and ease of use, consumer-grade wearables could enable a scalable monitoring system.Trial Registration:Clinicaltrials.gov NCT04357834; https://www.clinicaltrials.gov/ct2/show/NCT04357834