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Healthy lifestyle behaviors are essential in the treatment of type 2 diabetes, and meal registration is therefore important. Manual meal registration is cumbersome and could be automated using continuous glucose monitoring (CGM). If such an algorithm is based on patient-reported meals, potential errors might be induced. Thus, the aim was to investigate potential errors in patient-reported mealtimes and the effect on automatic meal detection. Two healthcare professionals (HCPs) reported the mealtimes of the 18 included patients based on the patients’ CGM data to assess the agreement between HCP- and patient-reported mealtimes. A developed meal detection algorithm based on detecting the post-prandial glucose response using cross-correlation was used to assess the impact of errors in patient reported meals. The results showed poor disagreement between HCP- and patient reported meals and that the meal detection algorithm had a moderately better performance on the HCP-reported meals. Therefore, the possibility of errors in patient-reported mealtimes should be considered in the development of meal detection algorithms. However, more research is needed to confirm the results of this study. [ABSTRACT FROM AUTHOR] more...

The adherence to oral antidiabetic drugs (OADs) among people with type 2 diabetes (T2D) is suboptimal. However, new OADs have been marketed within the last 10 years. As these new drugs differ in mechanism of action, treatment complexity, and side effects, they may influence adherence. Thus, the aim of this study was to assess the adherence to newer second‐line OADs, defined as drugs marketed in 2012–2022, among people with T2D. A systematic review was performed in CINAHL, Cochrane Trials, Embase, PubMed, PsycINFO, and Scopus. Articles were included if they were original research of adherence to newer second‐line OADs and reported objective adherence quantification. The quality of the articles was assessed using JBI's critical appraisal tools. The overall findings were reported according to the preferred reporting items for systematic reviews and meta‐analyses (PRISMA) guidelines and summarized in a narrative synthesis. All seven included articles were European retrospective cohort studies investigating alogliptin, canagliflozin, dapagliflozin, empagliflozin, and unspecified types of SGLT2i. Treatment discontinuation and medication possession ratio (MPR) were the most frequently reported adherence quantification measures. Within the first 12 months of treatment, 29%–44% of subjects on SGLT2i discontinued the treatment. In terms of MPR, 61.7%–94.9% of subjects on either alogliptin, canagliflozin, dapagliflozin, empagliflozin or an unspecified SGLT2i were adherent. The two investigated adherence quantification measures, treatment discontinuation and MPR, suggest that adherence to the newer second‐line OADs may be better than that of older OADs. However, a study directly comparing older and newer OADs should be done to verify this. [ABSTRACT FROM AUTHOR] more...

Background: There has been an increasing interest in the use of digital health lifestyle interventions for people with prediabetes, as these interventions may offer a scalable approach to preventing type 2 diabetes. Previous systematic reviews on digital health lifestyle interventions for people with prediabetes had limitations, such as a narrow focus on certain types of interventions, a lack of statistical pooling, and no broader subgroup analysis of intervention characteristics. The identified limitations observed in previous systematic reviews substantiate the necessity of conducting a comprehensive review to address these gaps within the field. This will enable a comprehensive understanding of the effectiveness of digital health lifestyle interventions for people with prediabetes. Objective: The objective of this systematic review, meta-analysis, and meta-regression is to systematically investigate the effectiveness of digital health lifestyle interventions on prediabetes-related outcomes in comparison with any comparator without a digital component among adults with prediabetes. Methods: This systematic review will include randomized controlled trials that investigate the effectiveness of digital health lifestyle interventions on adults (aged 18 years or older) with prediabetes and compare the digital interventions with nondigital interventions. The primary outcome will be change in body weight (kg). Secondary outcomes include, among others, change in glycemic status, markers of cardiometabolic health, feasibility outcomes, and incidence of type 2 diabetes. Embase, PubMed, CINAHL, and CENTRAL (Cochrane Central Register of Controlled Trials) will be systematically searched. The data items to be extracted include study characteristics, participant characteristics, intervention characteristics, and relevant outcomes. To estimate the overall effect size, a meta-analysis will be conducted using the mean difference. Additionally, if feasible, meta-regression on study, intervention, and participant characteristics will be performed. The Cochrane risk of bias tool will be applied to assess study quality, and the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach will be used to assess the certainty of evidence. Results: The results are projected to yield an overall estimate of the effectiveness of digital health lifestyle interventions on adults with prediabetes and elucidate the characteristics that contribute to their effectiveness. Conclusions: The insights gained from this study may help clarify the potential of digital health lifestyle interventions for people with prediabetes and guide the decision-making regarding future intervention components. Trial Registration: PROSPERO CRD42023426919; http://tinyurl.com/d3enrw9j International Registered Report Identifier (IRRID): PRR1-10.2196/50340 [ABSTRACT FROM AUTHOR] more...

Background: Multidisciplinary Teams (MDTs) has been suggested as an intervention to overcome some of the complexities experienced by people with diabetes and comorbidities in terms of diagnosis and treatment. However, evidence concerning MDTs within the diabetes field remains sparse. Objective: This review aims to identify and map available evidence on key characteristics of MDTs in the context of diagnosis and treatment in people with diabetes and comorbidities. Methods: This review followed the PRISMA-ScR guidelines. Databases PubMed, EMBASE, and CINAHL were systematically searched for studies assessing any type of MDT within the context of diagnosis and treatment in adult people (≥ 18 years) with diabetes and comorbidities/complications. Data extraction included details on study characteristics, MDT interventions, digital health solutions, and key findings. Results: Overall, 19 studies were included. Generally, the MDTs were characterized by high heterogeneity. Four overall components characterized the MDTs: Both medical specialists and healthcare professionals (HCPs) of different team sizes were represented; interventions spanned elements of medication, assessment, nutrition, education, self-monitoring, and treatment adjustment; digital health solutions were integrated in 58% of the studies; MDTs were carried out in both primary and secondary healthcare settings with varying frequencies. Generally, the effectiveness of the MDTs was positive across different outcomes. Conclusions: MDTs are characterized by high diversity in their outline yet seem to be effective and cost-effective in the context of diagnosis and treatment of people with diabetes and comorbidities. Future research should investigate the cross-sectorial collaboration to reduce care fragmentation and enhance care coordination. [ABSTRACT FROM AUTHOR] more...

Summary: New evidence points toward that impaired postural control judged by center of pressure measures during quiet stance is a predictor of falls in people with type 1 and type 2 diabetes—even in occurrence of well-known risk factors for falls. Introduction/aim: People with type 1 diabetes (T1D) and type 2 diabetes (T2D) are at risk of falling, but the association with impaired postural control is unclear. Therefore, the aim was to investigate postural control by measuring the center of pressure (CoP) during quiet standing and to estimate the prevalence ratio (PR) of falls and the fear of falling among people with diabetes compared to controls. Methods: In a cross-sectional study, participants with T1D (n = 111) and T2D (n = 106) and controls without diabetes (n = 328) were included. Study procedures consisted of handgrip strength (HGS), vibration perception threshold (VPT), orthostatism, visual acuity, and postural control during quiet stance measured by CoPArea (degree of body sway) and CoPVelocity (speed of the body sway) with "eyes open," "eyes closed" in combination with executive function tasks. A history of previous falls and fear of falling was collected by a questionnaire. CoPArea and CoPVelocity measurements were analyzed by using a multiple linear regression model. The PR of falls and the fear of falling were estimated by a Poisson regression model. Age, sex, BMI, previous falls, alcohol use, drug, HGS, VPT, orthostatism, episodes of hypoglycemia, and visual acuity were covariates in multiple adjusted analyses. Results: Significantly larger mean CoPArea measures were observed for participants with T1D (p = 0.022) and T2D (0.002), whereas mean CoPVelocity measures were only increased in participants with T2D (p = 0.027) vs. controls. Additionally, T1D and T2D participants had higher PRs for falls (p = 0.044, p = 0.014) and fear of falling (p = 0.006, p < 0.001) in the crude analyses, but the PRs reduced significantly when adjusted for mean CoPArea and mean CoPVelocity, respectively. Furthermore, multiple adjusted PRs were significantly higher than crude the analyses. Conclusion: Impaired postural control during quiet stance was seen in T1D and T2D compared with controls even in the occurrence of well-known risk factors. and correlated well with a higher prevalence of falls. [ABSTRACT FROM AUTHOR] more...

Background: Lowering glucose levels is a complex task for patients with type 1 diabetes, and they often lack contact with health care professionals. Intermittently scanned continuous glucose monitoring (isCGM) has the potential to aid them with blood glucose management at home. The aim of this study was to investigate the long-term effect of isCGM on HbA1c in type 1 diabetes patients with poor glycaemic control in a region-wide real-world setting. Methods: All patients with type 1 diabetes receiving an isCGM due to poor glycaemic control (≥70 mmol/mol [≥8.6%]) in the period of 2020–21 in Region North Denmark ("T1D-CGM") were compared with all type 1 diabetes patients without isCGM ("T1D-NOCGM") in the same period. A multiple linear regression model adjusted for age, sex, diabetes duration and use of continuous subcutaneous insulin infusion was constructed to estimate the difference in change from baseline HbA1c between the two groups and within subgroups of T1D-CGM. Results: A total of 2,527 patients (T1D-CGM: 897; T1D-NOCGM: 1,630) were included in the study. The estimated adjusted difference in change from baseline HbA1c between T1D-CGM vs T1D-NOCGM was -5.68 mmol/mol (95% CI: (-6.69 to -4.67 mmol/mol; p<0.0001)). Older patients using isCGM dropped less in HbA1c. Conclusions: Our results indicate that patients with type 1 diabetes in poor glycaemic control from Region North Denmark in general benefit from using isCGM with a sustained 24-month improvement in HbA1c, but the effect on HbA1c may be less pronounced for older patients. [ABSTRACT FROM AUTHOR] more...

Aims: A diabetic foot ulcer (DFU) is a severe condition associated with morbidity and mortality. Population‐based studies are rare and limited by access to reliable data. Without this data, efforts in primary prevention cannot be evaluated. Therefore, we examined the incidence and changes over time for the first DFU in people with diabetes. We also examined hospitalization and all‐cause mortality and their changes over time. Methods: From the UK primary care CPRD GOLD database (2007–2017), we identified 129,624 people with diabetes by a prescription for insulin or a non‐insulin anti‐diabetic drug. DFUs were identified using Read codes and expressed as incidence rates (IRs). Changes over time were described using Poisson and logistic regression and expressed as incidence rate ratios (IRRs) and odds ratios (ORs) respectively. Results: The mean IR of first registered DFUs was 2.5 [95% CI: 2.1–2.9] per 1000 person‐years for people with type 2 diabetes and 1.6 [1.3–1.9] per 1000 person‐years for people with type 1. The IRs declined for people with type 2 diabetes (IRR per year: 0.97 [0.96–0.99]), while no changes were observed for people with type 1 diabetes (IRR per year: 0.96 [0.89–1.04]). Average hospitalization and 1‐year mortality risk for people with type 2 diabetes were 8.2% [SD: 4.7] and 11.7% [SD: 2.2] respectively. Both declined over time (OR: 0.89 [0.84, 0.94] and 0.94 [0.89, 0.99]). Conclusion: The decline in all IRs, hospitalizations and mortality in people with type 2 diabetes suggests that prevention and care of the first DFU has improved for this group in primary care in the UK. [ABSTRACT FROM AUTHOR] more...

Objective: Postpancreatitis diabetes mellitus (PPDM) is a type of secondary diabetes that requires special considerations for management. The main objective was to examine prescription patterns of glucose-lowering therapy among adults with PPDM compared with type 1 and type 2 diabetes.Research Design and Methods: In a Danish nationwide population-based cohort study, we identified all individuals with adult-onset diabetes in the period 2000-2018 and categorized them as having type 1 diabetes, type 2 diabetes, or PPDM. We ascertained diabetes incidence rates, clinical and demographic characteristics, and classifications and prescription patterns of glucose-lowering therapy and compared these parameters across diabetes subgroups.Results: Among 398,456 adults with new-onset diabetes, 5,879 (1.5%) had PPDM, 9,252 (2.3%) type 1 diabetes, and the remaining type 2 diabetes (96.2%). The incidence rate of PPDM was 7.9 (95% CI 7.7-8.1) per 100,000 person-years versus 12.5 (95% CI 12.2-12.7) for type 1 diabetes (incidence rate ratio 0.6 [95% CI 0.6-0.7]; P < 0.001). A sizeable proportion of patients with PPDM were classified as having type 2 diabetes (44.9%) and prescribed sulfonylureas (25.2%) and incretin-based therapies (18.0%) that can potentially be harmful in PPDM. In contrast, 35.0% of patients never received biguanides, which are associated with a survival benefit in PPDM. Increased insulin requirements were observed for patients with PPDM compared with type 2 diabetes (hazard ratio 3.10 [95% CI 2.96-3.23]; P < 0.001) in particular for PPDM associated with chronic pancreatitis (hazard ratio 4.30 [95% CI 4.01-4.56]; P < 0.001).Conclusions: PPDM is a common type of secondary diabetes in adults but is often misclassified and treated as type 2 diabetes, although PPDM requires special considerations for management. [ABSTRACT FROM AUTHOR] more...

Aims: To evaluate the risk of severe hypoglycaemia for patients with Type 1 diabetes (T1D) when exposed to insulin regimens including human insulin only or insulin analogues. Methods: A total of 19 896 patients with T1D were extracted from the Danish National Patient Register. Of these, 6379 T1D patients experiencing 1 of more severe hypoglycaemic episodes (total of 17 242 episodes) were matched 1:1 with T1D patients without severe hypoglycaemia. A logistic regression model with last insulin regimen used as exposure was constructed to analyse the effect on severe hypoglycaemia. Results: People on a basal–bolus regimen with insulin analogues had a reduced risk of severe hypoglycaemia of 39% (odds ratio: 0.61, 95% confidence interval: 0.54–0.68) compared to patients on a basal–bolus human insulin only regimen. Furthermore, patients on a premixed regimen containing an insulin analogue had a 58% (odds ratio: 0.42, 95% confidence interval: 0.36–0.49) reduced risk of severe hypoglycaemia compared to patients on premixed human insulin only. Conclusion: This study indicates that use of a basal–bolus insulin regimen with an insulin analogue is safer with respect to severe hypoglycaemia in patients with T1D than the use of a basal–bolus human insulin only regimen. [ABSTRACT FROM AUTHOR] more...

The vast number of antihyperglycemic medications and growing amount of evidence make clinical decision making difficult. The aim of this study was to investigate the safety of antihyperglycemic dual and triple therapies for type 2 diabetes management with respect to major adverse cardiovascular events, severe hypoglycemia, and all-cause mortality in a real-life clinical setting. RESEARCH DESIGN AND METHODS Cox regression models were constructed to analyze 20 years of data from the Danish National Patient Registry with respect to effect of the antihyperglycemic therapies on the three end points. RESULTS A total of 66,807 people with type 2 diabetes were treated with metformin (MET) plus a combination of second- and third-line therapies. People on MET plus sulfonylurea (SU) had the highest risk of all end points, except for severe hypoglycemia, for which people on MET plus basal insulin (BASAL) had a higher risk. The lowest risk of major adverse cardiovascular events was seen for people on a regimen including a glucagon-like peptide 1 (GLP-1) receptor agonist. People treated with MET, GLP-1, and BASAL had a lower risk of all three end points than people treated with MET and BASAL, especially for severe hypoglycemia. The lowest risk of all three end points was, in general, seen for people treated with MET, sodium–glucose cotransporter 2 inhibitor, and GLP-1. CONCLUSIONS Findings from this study do not support SU as the second-line treatment choice for patients with type 2 diabetes. Moreover, the results indicate that adding a GLP-1 in people treated with MET and BASAL could be considered, especially if those people suffer from severe hypoglycemia. [ABSTRACT FROM AUTHOR] more...

Background: The purpose of this study was to evaluate the performance of a new continuous glucose monitoring (CGM) calibration algorithm and to compare it with the Guardian® REAL-Time (RT) (Medtronic Diabetes, Northridge, CA) calibration algorithm in hypoglycemia. Subjects and Methods: CGM data were obtained from 10 type 1 diabetes patients undergoing insulin-induced hypoglycemia. Data were obtained in two separate sessions using the Guardian RT CGM device. Data from the same CGM sensor were calibrated by two different algorithms: the Guardian RT algorithm and a new calibration algorithm. The accuracy of the two algorithms was compared using four performance metrics. Results: The median (mean) of absolute relative deviation in the whole range of plasma glucose was 20.2% (32.1%) for the Guardian RT calibration and 17.4% (25.9%) for the new calibration algorithm. The mean (SD) sample-based sensitivity for the hypoglycemic threshold of 70 mg/dL was 31% (33%) for the Guardian RT algorithm and 70% (33%) for the new algorithm. The mean (SD) sample-based specificity at the same hypoglycemic threshold was 95% (8%) for the Guardian RT algorithm and 90% (16%) for the new calibration algorithm. The sensitivity of the event-based hypoglycemia detection for the hypoglycemic threshold of 70 mg/dL was 61% for the Guardian RT calibration and 89% for the new calibration algorithm. Application of the new calibration caused one false-positive instance for the event-based hypoglycemia detection, whereas the Guardian RT caused no false-positive instances. The overestimation of plasma glucose by CGM was corrected from 33.2 mg/dL in the Guardian RT algorithm to 21.9 mg/dL in the new calibration algorithm. Conclusions: The results suggest that the new algorithm may reduce the inaccuracy of Guardian RT CGM system within the hypoglycemic range; however, data from a larger number of patients are required to compare the clinical reliability of the two algorithms. [ABSTRACT FROM AUTHOR] more...

Continuous glucose monitoring (CGM) is a new technology with the potential to detect hypoglycemia in people with Type 1 diabetes. However, the inaccuracy of the device in the hypoglycemic range is unfortunately too large. The aim of this study was to develop an information and communication technology system for improving hypoglycemia detection in CGM. The system was developed as an Android application with a build-in pattern classification algorithm. The algorithm processes features from CGM and typed in data from the patient, then warns the patient about incoming hypoglycemia. The system improved the detection of hypoglycemic events by 29%, with only one 1 false alert compared to CGM alone. Furthermore, the algorithm increased the average lead-time by 14 minutes. These findings indicate that it is possible to improve the hypoglycemia detection with an information and communication technology system, but that the system must be validated on a larger dataset. [ABSTRACT FROM AUTHOR] more...

Background: Hypoglycemia is a potentially fatal condition. Continuous glucose monitoring (CGM) has the potential to detect hypoglycemia in real time and thereby reduce time in hypoglycemia and avoid any further decline in blood glucose level. However, CGM is inaccurate and shows a substantial number of cases in which the hypoglycemic event is not detected by the CGM. The aim of this study was to develop a pattern classification model to optimize real-time hypoglycemia detection. Materials and Methods: Features such as time since last insulin injection and linear regression, kurtosis, and skewness of the CGM signal in different time intervals were extracted from data of 10 male subjects experiencing 17 insulin-induced hypoglycemic events in an experimental setting. Nondiscriminative features were eliminated with SEPCOR and forward selection. The feature combinations were used in a Support Vector Machine model and the performance assessed by samplebased sensitivity and specificity and event-based sensitivity and number of false-positives. Results: The best model was composed by using seven features and was able to detect 17 of 17 hypoglycemic events with one false-positive compared with 12 of 17 hypoglycemic events with zero false-positives for the CGM alone. Lead-time was 14 min and 0 min for the model and the CGM alone, respectively. Conclusions: This optimized real-time hypoglycemia detection provides a unique approach for the diabetes patient to reduce time in hypoglycemia and learn about patterns in glucose excursions. Although these results are promising, the model needs to be validated on CGM data from patients with spontaneous hypoglycemic events. [ABSTRACT FROM AUTHOR] more...

Background: Chronic obstructive pulmonary disease (COPD) affects millions of people worldwide. A complication of COPD is exacerbations that result in increased utilization of healthcare services, readmissions to the hospital, and a decline in health-related quality of life. Home telehealth has been shown both to improve health-related quality of life and to reduce admission rates. Using clinical data from a home telemonitoring group, this study sought to investigate the clinical impact of telemonitoring. Subjects and Methods: Fifty-seven subjects with COPD were included in a 4-month telemonitoring project. Differences between the clinical parameters during the first and last months of participation in the project were tested for significance, and the levels for the first month versus the difference were tested for correlation. Results: Significant declines were observed in prescriptions for antibiotics and steroids (p =0.03), clinical consultations (p =0.05), mean systolic blood pressure (p <0.001), standard deviation of systolic blood pressure (p =0.03), and mean diastolic blood pressure (p =0.02). No significant differences were observed for mean of oxygen saturation (p =0.77), standard deviation of oxygen saturation (p =0.36), mean of forced expiratory volume in 1 s (p =0.17), mean of forced vital capacity (p =0.29), mean of pulse rate (p= 0.78), standard deviation of pulse rate (p =0.57), and standard deviation of diastolic blood pressure (p =0.27). Conclusions: The results suggest that telemonitoring improves the condition of the patient by lowering the blood pressure, the number of prescribed antibiotics and steroids, and the number of clinical consultations. [ABSTRACT FROM AUTHOR] more...

In this study, we investigated which predictors from people with type 1 diabetes at initiation of intensive treatment that increase the risk of not achieving glycemic target. Data from a clinical trial with type 1 diabetes people (n=460) were used in a logistic regression model to analyze the effect of the predictors on achievement of glycemic target. Results indicate that age, smoking, glycated hemoglobin, 1,5-anhydroglucitol and fluctuation from continuous glucose monitoring are predictors of achievement of glycemic target, which can be used in an algorithm to predict people who fail to achieve glycemic target. [ABSTRACT FROM AUTHOR] more...

Automation of organizing and analyzing NHANES data can provide easier access to data and potentially reducing risk of introducing bias. This study investigates the potential for developing a software for this purpose. MATLAB R2016b was used for transforming and analyzing data from the NHANES. The software was tested successful by analyzing the association between smoking and glucose metabolism in the general population. [ABSTRACT FROM AUTHOR] more...

Aims: Real-world evidence of the safety of insulin degludec compared with insulin glargine U100 is sparse. This study sought to investigate the risk of major cardiovascular events, severe hypoglycaemia, and all-cause mortality after initiation of degludec or glargine U100 in the population of Denmark.Materials and Methods: All Danish people with diabetes initiating treatment on degludec (n=5159) or glargine (n=4041) in 2016 to 2017 were included in the study. The effect of insulin treatment on the endpoints of major cardiovascular events, severe hypoglycaemia, and all-cause mortality was analysed with Cox proportional hazard models. The models were adjusted for age, sex, diabetes duration, diabetes type, highest completed education, and annual income. The model of severe hypoglycaemia was also adjusted for severe hypoglycaemia prior to baseline. The model of mortality was also adjusted for history of alcohol abuse, use of antidepressants, use of opioids, and use of anxiolytics. Lastly, the models of major cardiovascular events and mortality were also adjusted for Charlson comorbidity index.Results: Use of degludec resulted in an almost twofold decrease in risk of death (hazard rate [HR]: 0.54, 95% CI: 0.44-0.65) compared with use of glargine. No statistically significant risk changes were found for major cardiovascular events (HR: 0.86, 95% CI: 0.62-1.19) and severe hypoglycaemia (HR: 1.13, 95% CI: 0.66-1.93). The proportion of cause of death due to malignant neoplasm of pancreas was almost doubled for glargine compared with degludec.Conclusions: These results indicate that insulin degludec has a safer profile with respect to all-cause mortality as compared with insulin glargine U100. [ABSTRACT FROM AUTHOR] more...

Background: The study aimed to determine the relationship between basal insulin adherence and glycemic control evaluated by time in range (TIR) in people with insulin-treated type 2 diabetes (T2D), using data from both continuous glucose monitors (CGM) and connected insulin pens. Furthermore, the study aimed to determine the best basal insulin adherence metric., Methods: CGM data and basal insulin data were collected from 106 insulin-treated people (aged ≥18 years) with T2D. Three different adherence metrics were employed (dose deviation, dose deviation ≤20%, and a traditional metric) and a three-step methodology was used to measure insulin adherence level. The coefficient of determination (R 2 ), based on a univariate linear regression analysis, was used to determine the relationship between each adherence metric and TIR., Results: A statistically significant relationship was observed between TIR and adherence quantified as the dose deviation ≤20% metric (R 2 = 0.67, P = .006). Neither the relationship between the dose deviation metric and TIR (R 2 = 0.43, P = .08) nor the relationship between the traditional metric and TIR (R 2 = 0.35, P =.23) was found to be statistically significant., Conclusions: Our study indicates a relationship between basal insulin adherence and TIR in people with insulin-treated T2D. This seems to underscore the role of basal insulin adherence for optimal glycemic outcomes and utilizing TIR as a clinical marker. Furthermore, the results suggest that the magnitude of deviation from the recommended basal insulin dose impacts glycemic control, indicating dose deviation ≤20% as a more accurate metric for quantifying adherence., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: M.H.J. is an employee of, and holds stock in, Novo Nordisk A/S and has received consultant fees from Abbott Laboratories. P.V. is head of research at Steno Diabetes Center North Denmark, funded by an unrestricted grant from the Novo Nordisk Foundation. Apart from that, we declare that no conflicts of interest are associated with this publication and that Novo Nordisk A/S did not influence the research or its presentation. more...

Background: Digital health lifestyle interventions (DHLI) may offer scalable solutions to manage prediabetes in clinical practice; however, their effectiveness on people with prediabetes has not been systematically investigated and reviewed. Hence, in this systematic review, meta-analysis, and meta-regression the effectiveness of DHLI on prediabetes-related outcomes was investigated., Methods: Four databases were searched to identify randomized controlled trials investigating the effectiveness of DHLI on adults with prediabetes published before 23 February 2024. The primary outcome was the change in body weight, with secondary outcomes including, among others, glycemic status, body composition, and feasibility outcomes. Meta-analyses were conducted to provide overall effect estimates of outcomes. In addition, meta-regressions on the primary outcome were conducted. The study quality was assessed using the Cochrane Risk of Bias tool, and the certainty of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach., Results: A total of 33 studies were included (n = 14 398). The study duration ranged from 3 to 60 months. The digital interventions varied from in-person meetings combined with pedometers and telephone calls to fully digital interventions. The overall estimated treatment difference in change in body weight favored the intervention (mean difference: -1.74 kg; 95% confidence interval: -2.37, -1.11; P < .01) with moderate certainty. Statistically significant overall effect estimates favoring the intervention were also found for secondary outcomes with very low to moderate certainty., Conclusion: Digital health lifestyle interventions can result in statistically significant change in body weight and other secondary outcomes among people with prediabetes., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: KF, MHJ, and BJvS is full-time employee and owns shares at Novo Nordisk A/S. No conflicts of interest were declared by the remaining authors. more...

Background and Objective: The aim of this study was to develop and validate explainable prediction models based on continuous glucose monitoring (CGM) and baseline data to identify a week-to-week risk of CGM key metrics (hyperglycemia, hypoglycemia, glycemic variability). By having a weekly prediction of CGM key metrics, it is possible for the patient or health care personnel to take immediate preemptive action., Methods: We analyzed, trained, and internally tested three prediction models (Logistic regression, XGBoost, and TabNet) using CGM data from 187 type 1 diabetes patients with long-term CGM monitoring. A binary classification approach combined with feature engineering deployed on the CGM signals was used to predict hyperglycemia, hypoglycemia, and glycemic variability based on consensus targets (time above range ≥5%, time below range ≥4%, coefficient of variation ≥36%). The models were validated in two independent cohorts with a total of 223 additional patients of varying ages., Results: A total of 46 593 weeks of CGM data were included in the analysis. For the best model (XGBoost), the area under the receiver operating characteristic curve (ROC-AUC) was 0.9 [95% confidence interval (CI) = 0.89-0.91], 0.89 [95% CI = 0.88-0.9], and 0.8 [95% CI = 0.79-0.81] for predicting hyperglycemia, hypoglycemia, and glycemic variability in the interval validation, respectively. The validation test showed good generalizability of the models with ROC-AUC of 0.88 to 0.95, 0.84 to 0.89, and 0.80 to 0.82 for predicting the glycemic outcomes., Conclusion: Prediction models based on real-world CGM data can be used to predict the risk of unstable glycemic control in the forthcoming week. The models showed good performance in both internal and external validation cohorts., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: The research was funded by i-SENS, Inc (Seoul, South Korea) and SLC’s involvement with the company did not influence the design, implementation, or interpretation of the study. SLC have received research funding from i-SENS, Inc (Seoul, South Korea), which manufactures some of the product types discussed in this paper. However, the study was conducted independently, and the authors declare that their involvement with i-SENS, Inc (Seoul, South Korea) did not influence the findings or conclusions of the study. more...

Background and Aims: Glycemic control is crucial for people with type 2 diabetes. However, only about half achieve the advocated HbA1c target of ≤7%. Identifying those who will probably struggle to reach this target may be valuable as they require additional support. Thus, the aim of this study was to develop a model to predict people with type 2 diabetes not achieving HbA1c target after initiating fast-acting insulin., Methods: Data from a randomized controlled trial (NCT01819129) of participants with type 2 diabetes initiating fast-acting insulin were used. Data included demographics, clinical laboratory values, self-monitored blood glucose (SMBG), health-related quality of life (SF-36), and body measurements. A logistic regression was developed to predict HbA1c target nonachievers. A potential of 196 features was input for a forward feature selection. To assess the performance, a 20-repeated stratified 5-fold cross-validation with area under the receiver operating characteristics curve (AUROC) was used., Results: Out of the 467 included participants, 98 (21%) did not achieve HbA1c target of ≤7%. The forward selection identified 7 features: baseline HbA1c (%), mean postprandial SMBG at all meals 3 consecutive days before baseline (mmol/L), sex, no ketones in urine, baseline albumin (g/dL), baseline low-density lipoprotein cholesterol (mmol/L), and traces of protein in urine. The model had an AUROC of 0.745 [95% CI = 0.734, 0.756]., Conclusions: The model was able to predict those who did not achieve HbA1c target with promising performance, potentially enabling early identification of people with type 2 diabetes who require additional support to reach glycemic control., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. more...

Background: Comorbidities such as cardiovascular disease (CVD) and diabetic kidney disease (DKD) are major burdens of type 1 diabetes (T1D). Predicting people at high risk of developing comorbidities would enable early intervention. This study aimed to develop models incorporating socioeconomic status (SES) to predict CVD, DKD, and mortality in adults with T1D to improve early identification of comorbidities., Methods: Nationwide Danish registry data were used. Logistic regression models were developed to predict the development of CVD, DKD, and mortality within five years of T1D diagnosis. Features included age, sex, personal income, and education. Performance was evaluated by five-fold cross-validation with area under the receiver operating characteristic curve (AUROC) and the precision-recall area under the curve (PR-AUC). The importance of SES was assessed from feature importance plots., Results: Of the 6572 included adults (≥21 years) with T1D, 379 (6%) developed CVD, 668 (10%) developed DKD, and 921 (14%) died within the five-year follow-up. The AUROC (±SD) was 0.79 (±0.03) for CVD, 0.61 (±0.03) for DKD, and 0.87 (±0.01) for mortality. The PR-AUC was 0.18 (±0.01), 0.15 (±0.03), and 0.49 (±0.02), respectively. Based on feature importance plots, SES was the most important feature in the DKD model but had minimal impact on models for CVD and mortality., Conclusions: The developed models showed good performance for predicting CVD and mortality, suggesting they could help in the early identification of these outcomes in individuals with T1D. The importance of SES in individual prediction within diabetes remains uncertain., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. more...

Background and Aims: Hypoglycemia may lead to anxiety, poor adherence, and hypoglycemia unawareness and is especially a threat during the night in patients with insulin-treated type 2 diabetes (T2D). It would therefore be beneficial to warn patients at risk of hypoglycemia at bedtime so they can react accordingly and avoid the episode. Hence, the aim of the present study was to develop a model for predicting nocturnal hypoglycemia., Methods: Continuous glucose monitoring (CGM), mealtime, and insulin data were collected from 67 insulin-treated patients with T2D (NCT01819129). Data were structured into 24-hour periods and labeled as nocturnal hypoglycemia or not depending on whether 15 consecutive minutes were spent below 3.0 mmol/L (54 mg/dL) during the following night. Each period was divided into "last night," "morning," "day," and "evening" for feature extraction purposes, and 72 potential features were extracted for every period. A five-fold cross-validation was used to select features by forward selection and for training and validating a model based on logistic regression., Results: The prediction model was based on 30 patients with 60/496 periods resulting in nocturnal hypoglycemia. Forward selection revealed that the best features were based on CGM and involved the last value and mean value during the evening, as well as the relative difference in maximum value during the day between the present period and previous periods. The model obtained a mean area under the receiver operating characteristics curve (AUC) of 0.82 with an accuracy of 0.79., Conclusions: The model was able to predict nocturnal hypoglycemia with an acceptable accuracy and could therefore prevent such cases., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. more...

Background: While health care providers (HCPs) are generally aware of the challenges concerning insulin adherence in adults with insulin-treated type 2 diabetes (T2D), data guiding identification of insulin nonadherence and understanding of injection patterns have been limited. Hence, the aim of this study was to examine detailed injection data and provide methods for assessing different aspects of basal insulin adherence., Method: Basal insulin data recorded by a connected insulin pen and prescribed doses were collected from 103 insulin-treated patients (aged ≥18 years) with T2D from an ongoing clinical trial (NCT04981808). We categorized the data and analyzed distributions of correct doses, increased doses, reduced doses, and missed doses to quantify adherence. We developed a three-step model evaluating three aspects of adherence (overall adherence, adherence distribution, and dose deviation) offering HCPs a comprehensive assessment approach., Results: We used data from a connected insulin pen to exemplify the use of the three-step model to evaluate overall, adherence, adherence distribution, and dose deviation using patient cases., Conclusion: The methodology provides HCPs with detailed access to previously limited clinical data on insulin administration, making it possible to identify specific nonadherence behavior which will guide patient-HCP discussions and potentially provide valuable insights for tailoring the most appropriate forms of support., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: MHJ is an employee of, and holds stock in, Novo Nordisk A/S and PV is head of research at Steno Diabetes Center North Denmark funded by an unrestricted grant from the Novo Nordisk Foundation. Apart from that, we declare that no conflicts of interest are associated with this publication and that Novo Nordisk A/S did not influence the research or its presentation. more...

Background: Hypoglycemia is common in insulin-treated type 2 diabetes (T2D) patients, which can lead to decreased quality of life or premature death. Deep learning models offer promise of accurate predictions, but data scarcity poses a challenge. This study aims to develop a deep learning model utilizing transfer learning to predict hypoglycemia., Methods: Continuous glucose monitoring (CGM) data from 226 patients with type 1 diabetes (T1D) and 180 patients with T2D were utilized. Data were structured into one-hour samples and labeled as hypoglycemia or not depending on whether three consecutive CGM values were below 3.9 [mmol/L] (70 mg/dL) one hour after the sample. A convolutional neural network (CNN) was pre-trained with the T1D data set and subsequently fitted using a T2D data set, all while being optimized toward maximizing the area under the receiver operating characteristics curve (AUC) value, and it was externally validated on a separate T2D data set., Results: The developed model was externally validated with 334 711 one-hour CGM samples, of which 15 695 (4.69%) were labeled as hypoglycemic. The model achieved an AUC of 0.941 and a positive predictive value of 40.49% at a specificity of 95% and a sensitivity of 69.16%., Conclusions: The transfer learned CNN model showed promising performance in predicting hypoglycemic episodes and with slightly better results than a non-transfer learned CNN model., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: M.H.J. is employed at Novo Nordisk A/S more...

Aims: For people with type 2 diabetes treated with basal insulin, suboptimal glycemic control due to clinical inertia is a common issue. Determining the optimal basal insulin dose can be difficult, as it varies between individuals. Thus, insulin titration can be slow and cautious which may lead to treatment fatigue and non-adherence. A model that predicts changes in fasting blood glucose (FBG) after adjusting basal insulin dose may lead to more optimal titration, reducing some of these challenges., Objective: To predict the change in FBG following adjustment of basal insulin in people with type 2 diabetes using a machine learning framework., Methods: A multiple linear regression model was developed based on 786 adults with type 2 diabetes. Data were divided into training (80%) and testing (20%) sets using a ranking approach. Forward feature selection and fivefold cross-validation were used to select features., Results: Participants had a mean age of approximately 59 years, a mean duration of diabetes of 12 years, and a mean HbA 1c at screening of 65 mmol/mol (8.1%). Chosen features were FBG at week 2, basal insulin dose adjustment from week 2 to 7, trial site, hemoglobin level, and alkaline phosphatase level. The model achieved a relative absolute error of 0.67, a Pearson correlation coefficient of 0.74, and a coefficient of determination of 0.55., Conclusions: A model using FBG, insulin doses, and blood samples can predict a five-week change in FBG after adjusting the basal insulin dose in people with type 2 diabetes. Implementation of such a model can potentially help optimize titration and improve glycemic control., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Author P.V. is the head of research at the Steno Diabetes Center North Denmark, funded by the Novo Nordisk Foundation. Author M.H.J holds Novo Nordisk shares. more...

Background and Objective: It is not clear how the short-term continuous glucose monitoring (CGM) sampling time could influence the bias in estimating long-term glycemic control. A large bias could, in the worst case, lead to incorrect classification of patients achieving glycemic targets, nonoptimal treatment, and false conclusions about the effect of new treatments. This study sought to investigate the relation between sampling time and bias in the estimates., Methods: We included a total of 329 type 1 patients (age 14-86 years) with long-term CGM (90 days) data from three studies. The analysis calculated the bias from estimating long-term glycemic control based on short-term sampling. Time in range (TIR), time above range (TAR), time below range (TBR), correlation, and glycemic target classification accuracy were assessed., Results: A sampling time of ten days is associated with a high bias of 10% to 47%, which can be reduced to 4.9% to 26.4% if a sampling time of 30 days is used ( P < .001). Correct classification of patients archiving glycemic targets can also be improved from 81.5% to 91.9 to 90% to 95.2%., Conclusions: Our results suggest that the proposed 10-14 day CGM sampling time may be associated with a high correlation with three-month CGM. However, these estimates are subject to large intersubject bias, which is clinically relevant. Clinicians and researchers should consider using assessments of longer durations of CGM data if possible, especially when assessing time in hypoglycemia or while testing a new treatment. more...

Aims: Continuous glucose monitoring (CGM) has the potential to promote diabetes self-management at home with a better glycemic control as outcome. Investigation of the effect of CGM has typically been carried out based on randomized controlled trials with prespecified CGM devices on CGM-naïve participants. The aim of this study was to investigate the effect on glycemic control in people using their personal CGM before and during the trial., Materials and Methods: Data from the Onset 5 trial of 472 people with type 1 diabetes using either their personal CGM ( n  = 117) or no CGM ( n  = 355) and continuous subcutaneous insulin infusion in a 16-week treatment period were extracted. Change from baseline in glycated hemoglobin A1c (HbA 1c ), number of hypoglycemic episodes, and CGM metrics at the end of treatment were analyzed with analysis of variance repeated-measures models., Results: Use of personal CGM compared with no CGM was associated with a reduction in risk of documented symptomatic hypoglycemia (event rate ratio: 0.82; 95% CI: 0.69-0.97) and asymptomatic hypoglycemia (event rate ratio: 0.72; 95% CI: 0.53-0.97), reduced time spent in hypoglycemia ( P  = .0070), and less glycemic variability ( P  = .0043) without a statistically significant increase in HbA 1c ( P  = .2028)., Conclusions: Results indicate that use of personal CGM compared with no CGM in a population of type 1 diabetes is associated with a safer glycemic control without a statistically significantly deteriorated effect on HbA 1c , which adds to the evidence about the real-world use of CGM, where device type is not prespecified, and users are not CGM naïve. more...

Background: The prevalence of smoking and diabetes is increasing in many developing countries. The aim of this study was to investigate the association of smoking with inadequate glycemic control and glycemic variability with continuous glucose monitoring (CGM) data in people with type 1 diabetes., Methods: Forty-nine smokers and 320 nonsmokers were obtained from the Novo Nordisk Onset 5 trial. After 16 weeks of treatment with continuous subcutaneous insulin infusion, risk of not achieving glycemic target and glycemic variability from six CGM measures was investigated. Analyzes were carried out with logistic regression models (glycemic target) and general linear models (glycemic variability). Finally, CGM median profiles were examined for the identification of daily glucose excursions., Results: A 4.7-fold (95% confidence interval: 1.5-15.4) increased risk of not achieving glycemic target was observed for smokers compared with nonsmokers. Increased time in hyperglycemia, decreased time in range, increased time in hypoglycemia (very low interstitial glucose), and increased fluctuation were observed for smokers compared with nonsmokers from CGM measures. CGM measures of coefficient of variation and time in hypoglycemia were not statistically significantly different. Examination of CGM median profiles revealed that risk of morning hypoglycemia is increased for smokers., Conclusions: In conclusion, smoking is associated with inadequate glycemic control and increased glycemic variability for people with type 1 diabetes with especially risk of morning hypoglycemia. It is important for clinicians to know that if the patient has type 1 diabetes and is smoking, a preemptive action to treat high glycated hemoglobin levels should not necessarily be treatment intensification due to the risk of hypoglycemia. more...

Introduction: People with diabetes could have an increased risk of falls as they show more complications, morbidity and use of medication compared to the general population. This study aimed to estimate the risk of falls and to identify risk factors associated with falls in people with diabetes. The second aim was to estimate fall-related injuries, such as lesions and fractures, including their anatomic localization in people with diabetes compared with the general population., Methods: From the Danish National Patient Register, we identified people with Type 1 Diabetes (T1D) (n=12,975) Type 2 Diabetes (T2D) (n=407,009). The cohort was divided into two groups, with respective control groups matched on age and sex (1:1). All episodes of people hospitalized with a first fall from 1996 to 2017 were analyzed using a Cox proportional-hazards model. Risk factors such as age, sex, diabetic complications, a history of alcohol abuse and the use of medication were included in an adjusted analysis. The incidence rate, incidence rate difference and incidence rate ratio (IRR) of falls and the anatomic localization of fall-related injuries as lesions and fractures were identified., Results and Discussion: The cumulative incidence, of falls requiring hospital treatment, was 13.3% in T1D, 11.9% in T2D. In the adjusted analysis, T1D and T2D were associated with a higher risk of falls [T1D, Hazard Ratio (HR): 1.33 (95% CI: 1.25 - 1.43), T2D, HR: 1.19 (95% CI:1.16 - 1.22), respectively]. Women [group 1, HR 1.21 (CI:95%:1.13 - 1.29), group 2, HR 1.61 (CI:95%:1.58-1.64)], aged >65 years [groups 1, HR 1.52 (CI:95%:1.39 - 1.61), group 2, HR 1.32 (CI:95%:1.58-1.64)], use of selective serotonin receptor inhibitors (SSRI) [group 1, HR 1.35 (CI:95%:1.1.30 - 1.40), group 2, HR 1.32 (CI:95%:1.27-1.38)], opioids [group 1, HR 1.15 (CI:95%:1.12 - 1.19), group 2, HR 1.09 (CI:95%:1.05-1.12)] and a history of alcohol abuse [group 1, HR 1.77 (CI:95%:1.17 - 2.15), group 2, HR 1.88 (CI:95%:1.65-2.15)] were significantly associated with an increased risk of falls in both groups. The IRR of fall-related injuries as hip, radius, humerus and skull/facial fractures were higher in people with T2D than controls [IRR 1.02 (CI:95%:1.01-1.04), IRR 1.39 (CI:95%: 1.18-1.61), IRR 1.24 (CI:95%: 1.12-1.37) and IRR 1.15 (CI:95%:1.07-1.24)]. People with T1D had a higher IRR of hip fractures than controls [IRR: 1.11 (CI:95%:1.02 - 1.23)]., Conclusion: People with diabetes have an increased risk of first fall and a higher incidence of fall- related injuries, including fractures. Advanced aging and sex are non-modifiable risk factors, whereas diabetes, the use of SSRIs and opioids and alcohol abuse could be potentially modifiable risk factors for falls. Gaining information on risk factors for falls could guide the management of diabetes treatment, i.e., choice of drugs, which enables us to improve treatment, particularly in people with a high risk of falls and fractures associated with high mortality., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.) more...

Background: Sodium-glucose cotransporter 2 inhibitors (SGLT2i) have been associated with increased risk of diabetic ketoacidosis (DKA) in both people with type 1 and type 2 diabetes mellitus. Few studies using data from high-quality registries exist that attempt to determine the real- world impact of the increasing use of this drug., Objective: The aim of this study was to investigate the incidence and risk of DKA in connection with SGLT2i treatment in Denmark between 2013-2017., Methods: A nationwide retrospective cohort of people with type 2 diabetes mellitus using SGLT2i or glucagon-like peptide-1 receptor agonists (GLP1-RA) was established and analysed using both Cox-proportional hazard regression and Kaplan-Meier survival analysis., Results: The 37,058 individuals included in the cohort, were made up of SGLT2i (10,923), GLP1- RA (18,849), SGLT2i+insulin (2,069), and GLP1-RA+insulin (10,178) users. The incidence rate (IR) of DKA was 0.84 (95% CI 0.49-1.44) and 0.53 (95% CI 0.36-0.77) for the SGLT2i and GLP1-RA groups, respectively. There was no statistically significant increase in the risk for DKA with SGLT2i use (HR 1.02, 95% CI, 0.44-2.36). However, for the SGLT2i+insulin and GLP1- RA+insulin groups, IRs were 3.47 (95% CI 1.92-6.27) and 0.97 (95% CI 0.68-1.37) respectively, and the risk was statistically significantly higher (HR 5.42, 95% CI 2.16-13.56)., Conclusion: We observed no significant increase in the risk of DKA for SGLT2i users compared to GLP1-RA. However, a significantly higher IR of DKA was observed with concomitant insulin use, and the risk of DKA was considerably higher for the SGLT2 group using insulin., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.) more...