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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]

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]

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Background: Continuous glucose monitoring (CGM) is a powerful tool to be considered both in clinical practice and clinical trials. However, CGM has been criticized for being inaccurate for many reasons including a physiological delay. This study sought to investigate the current delay issue and propose a simple post-processing procedure., Method: More than a million hours of the Dexcom G4 CGM from 472 subjects investigated in a state-of-the-art clinical trial were analyzed by time shifting the CGM measurements and comparing them to plasma glucose (PG) measurements. The resultant CGM measurements were then assessed in relation to real-world clinical research endpoints., Results: A CGM time shift of -9 minutes was optimal and reduced mean absolute relative difference (MARD) statistically significantly with 1.0% point. The MARD reduction resulted in better clinical research endpoints of hypoglycemia and postprandial glucose increments., Conclusions: The delay in CGM is still an issue. The delay in this study was identified to be 9 minutes compared to PG. With a simple post-processing approach of time shifting the CGM measurements with -9 minutes, it was possible to obtain a statistically significantly lower MARD and subsequently obtain clinical research endpoints of improved validity.

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.

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.

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.