Adherence to newer second-line oral antidiabetic drugs among people with type 2 diabetes-A systematic review

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.

The Effectiveness of Digital Health Lifestyle Interventions on People With Prediabetes: Protocol for a Systematic Review, Meta-Analysis, and Meta-Regression

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.

Identification of individuals with diabetes who are eligible for continuous glucose monitoring forecasting

BACKGROUND AND OBJECTIVES

Predicting glucose levels in individuals with diabetes offers potential improvements in glucose control. However, not all patients exhibit predictable glucose dynamics, which may lead to ineffective treatment strategies. We sought to investigate the efficacy of a 7-day blinded screening test in identifying diabetes patients suitable for glucose forecasting.

METHODS

Participants with type 1 diabetes (T1D) were stratified into high and low initial error groups based on screening results (eligible and non-eligible). Long-term glucose predictions (30/60 min lead time) were evaluated among 334 individuals who underwent continuous glucose monitoring (CGM) over a total of 64,460,560 min.

RESULTS

A strong correlation was observed between screening accuracy and long-term mean absolute relative difference (MARD) (0.661-0.736; p < 0.001), suggesting significant predictability between screening and long-term errors. Group analysis revealed a notable reduction in predictions falling within zone D of the Clark Error Grid by a factor of three and in zone C by a factor of two.

CONCLUSIONS

The identification of eligible patients for glucose prediction through screening represents a practical and effective strategy. Implementation of this approach could lead to a decrease in adverse glucose predictions.

Prediction of pancreatic cancer risk in patients with new-onset diabetes using a machine learning approach based on routine biochemical parameters

OBJECTIVE

To develop a machine-learning model that can predict the risk of pancreatic ductal adenocarcinoma (PDAC) in people with new-onset diabetes (NOD).

METHODS

From a population-based sample of individuals with NOD aged >50 years, patients with pancreatic cancer-related diabetes (PCRD), defined as NOD followed by a PDAC diagnosis within 3 years, were included (n = 716). These PCRD patients were randomly matched in a 1:1 ratio with individuals having NOD. Data from Danish national health registries were used to develop a random forest model to distinguish PCRD from Type 2 diabetes. The model was based on age, gender, and parameters derived from feature engineering on trajectories of routine biochemical variables. Model performance was evaluated using receiver operating characteristic curves (ROC) and relative risk scores.

RESULTS

The most discriminative model included 20 features and achieved a ROC-AUC of 0.78 (CI:0.75-0.83). Compared to the general NOD population, the relative risk for PCRD was 20-fold increase for the 1 % of patients predicted by the model to have the highest cancer risk (3-year cancer risk of 12 % and sensitivity of 20 %). Age was the most discriminative single feature, followed by the rate of change in haemoglobin A1c and the latest plasma triglyceride level. When the prediction model was restricted to patients with PDAC diagnosed six months after diabetes diagnosis, the ROC-AUC was 0.74 (CI:0.69-0.79).

CONCLUSION

In a population-based setting, a machine-learning model utilising information on age, sex and trajectories of routine biochemical variables demonstrated good discriminative ability between PCRD and Type 2 diabetes.

Development and Validation of a Machine Learning Model to Predict Weekly Risk of Hypoglycemia in Patients with Type 1 Diabetes Based on Continuous Glucose Monitoring

AIM

The aim of this study was to develop and validate a prediction model based on CGM data to identify a week-to-week risk profile of excessive hypoglycemia.

METHODS

We analyzed, trained, and internally tested two prediction models using CGM data from 205 type 1 diabetes patients with long-term CGM monitoring. A binary classification approach (XGBoost) combined with feature engineering deployed on the CGM signals was utilized to predict excessive hypoglycemia risk defined by two targets (TBR > 4% and the upper TBR 90th percentile limit) of time below range (TBR) the following week. The models were validated in two independent cohorts with a total of 253 additional patients.

RESULTS

A total of 61,470 weeks of CGM data were included in the analysis. The XGBoost models had a ROC-AUC of 0.83-0.87 (95% confidence interval [CI]; 0.83-0.88) in the test dataset. The external validation showed ROC-AUCs of 0.81-0.90. The most discriminative features included the low blood glucose index (LBGI), the glycemic risk assessment diabetes equation (GRADE), hypoglycemia, the TBR, waveform length, the CV and mean glucose during the previous week. This highlights that the pattern of hypoglycemia combined with glucose variability during the past week contains information on the risk of future hypoglycemia.

CONCLUSION

Prediction models based on real-world CGM data can be used to predict the risk of hypoglycemia in the forthcoming week. The models showed good performance in both the internal and external validation cohorts.

Association between Glucose-lowering Treatments and Risk of Diabetic Retinopathy in People with Type 2 Diabetes: A Nationwide Cohort Study

INTRODUCTION

Glycaemic variability is possibly linked to the development of diabetic retinopathy, and newer second-line glucose-lowering treatments in type 2 diabetes might reduce glycaemic variability.

AIM

This study aimed to investigate whether newer second-line glucose-lowering treatments are associated with an alternative risk of developing diabetic retinopathy in people with type 2 diabetes.

METHODS

A nationwide cohort of people with type 2 diabetes on second-line glucose-lowering treatment regimens in 2008-2018 was extracted from the Danish National Patient Registry. Adjusted time to diabetic retinopathy was estimated with a Cox Proportional Hazards model. The model was adjusted for age, sex, diabetes duration, alcohol abuse, treatment start year, education, income, history of late-diabetic complications, history of non-fatal major adverse cardiovascular events, history of chronic kidney disease, and history of hypoglycaemic episodes.

RESULTS

Treatment regimens of metformin + basal insulin (HR: 3.15, 95% CI: 2.42-4.10) and metformin + glucagon-like peptide-1 receptor agonist (GLP-1-RA, HR: 1.46, 95% CI: 1.09-1.96) were associated with an increased risk of diabetic retinopathy compared with metformin + dipeptidyl peptidase-4 inhibitors (DPP-4i). Treatment with metformin + sodium-glucose cotransporter-2 inhibitor (SGLT2i, HR: 0.77, 95% CI: 0.28-2.11) was associated with the numerically lowest risk of diabetic retinopathy compared with all regimens investigated.

CONCLUSION

Findings from this study indicate that basal insulin and GLP-1-RA are suboptimal second- line choices for people with type 2 diabetes at risk of developing diabetic retinopathy. However, many other considerations concerning the choice of second-line glucose-lowering treatment for type 2 diabetes patients should be taken into account.

A Three-Step Data-Driven Methodology to Assess Adherence to Basal Insulin Therapy in Patients With Insulin-Treated Type 2 Diabetes

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.

Quantification of insulin adherence in adults with insulin-treated type 2 diabetes: A systematic review

AIMS

This systematic review aims to identify current methods used for the assessment of insulin adherence in adults with insulin-treated type 2 diabetes. The primary goal is to offer recommendations for clinical practice to improve quantification of adherence.

METHODS

The review was conducted in accordance with PRISMA 2020 and registered at PROSPERO (CRD42022334134). PubMed, Embase, CINAHL, and PsycINFO were searched on 15 November 2022 and included three blocks: Type 2 diabetes, insulin, and adherence. We considered primary full-text studies describing an assessment method and a threshold for assessment of insulin adherence in adults with insulin-treated type 2 diabetes.

RESULTS

A final sample of 50 studies were included. Identified methods fell into four categories: self-report, pharmacy claims, inulin count, and data from an insulin pen device. Commonly reported methods included: The Morisky Medication Adherence Scale, the (adjusted) Medication Possession Ratio, and the Proportions of Days Covered. A threshold of <80% was used to define non-adherence in nearly half of the studies. Yet, several thresholds were reported.

CONCLUSIONS

Most available methods for assessing insulin adherence in adults with insulin-treated type 2 diabetes are severely limited in providing in-depth insights into timing, dosing size, injection patterns, and adherence behavior. However, recognizing diverse types of non-adherence is crucial, as they denote unique behavioral entities requiring targeted intervention. Employing insulin injection data (e.g., from a smart insulin pen cap) to underlie an assessment method is a potential new approach to objectively assess insulin timing and dosing adherence in adults with insulin-treated type 2 diabetes.

Prediction of Hypoglycemia From Continuous Glucose Monitoring in Insulin-Treated Patients With Type 2 Diabetes Using Transfer Learning on Type 1 Diabetes Data: A Deep Transfer Learning Approach

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.

Personalized Prediction of Change in Fasting Blood Glucose Following Basal Insulin Adjustment in People With Type 2 Diabetes: A Proof-of-Concept Study

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 HbA1c 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.

Risk of pancreatic cancer in people with new-onset diabetes: A Danish nationwide population-based cohort study

BACKGROUND

New onset diabetes (NOD) in people 50 years or older may indicate underlying pancreatic ductal adenocarcinoma (PDAC). The cumulative incidence of PDAC among people with NOD remains uncertain on a population-based level.

METHODS

This was a nationwide population-based retrospective cohort study based on the Danish national health registries. We investigated the 3-year cumulative incidence of PDAC in people 50 years or older with NOD. We further characterised people with pancreatic cancer-related diabetes (PCRD) in relation to demographic and clinical characteristics, including trajectories of routine biochemical parameters, using people with type 2 diabetes (T2D) as a comparator group.

RESULTS

During a 21-year observation period, we identified 353,970 people with NOD. Among them, 2105 people were subsequently diagnosed with pancreatic cancer within 3 years (0.59%, 95% CI [0.57-0.62%]). People with PCRD were older than people with T2D at diabetes diagnosis (median age 70.9 vs. 66.0 years (P < 0.001) and had a higher burden of comorbidities (P = 0.007) and more prescriptions of medications used to treat cardiovascular diseases (all P < 0.001). Distinct trajectories of HbA1c and plasma triglycerides were observed in PCRD vs. T2D, with group differences observed for up to three years prior to NOD diagnosis for HbA1c and up to two years for plasma triglyceride levels.

CONCLUSIONS

The 3-year cumulative incidence of PDAC is approximately 0.6% among people 50 years or older with NOD in a nationwide population-based setting. Compared to T2D, people with PCRD are characterised by distinct demographic and clinical profiles, including distinctive trajectories of plasma HbA1c and triglyceride levels.

The multidisciplinary team in diagnosing and treatment of patients with diabetes and comorbidities: A scoping review

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.

Time for Using Machine Learning for Dose Guidance in Titration of People With Type 2 Diabetes? A Systematic Review of Basal Insulin Dose Guidance

BACKGROUND

Real-world studies of people with type 2 diabetes (T2D) have shown insufficient dose adjustment during basal insulin titration in clinical practice leading to suboptimal treatment. Thus, 60% of people with T2D treated with insulin do not reach glycemic targets. This emphasizes a need for methods supporting efficient and individualized basal insulin titration of people with T2D. However, no systematic review of basal insulin dose guidance for people with T2D has been found.

OBJECTIVE

To provide an overview of basal insulin dose guidance methods that support titration of people with T2D and categorize these methods by characteristics, effect, and user experience.

METHODS

The review was conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Studies about basal insulin dose guidance, including adults with T2D on basal insulin analogs published before September 7, 2022, were included. Joanna Briggs Institute critical appraisal checklists were applied to assess risk of bias.

RESULTS

In total, 35 studies were included, and three categories of dose guidance were identified: paper-based titration algorithms, telehealth solutions, and mathematical models. Heterogeneous reporting of glycemic outcomes challenged comparison of effect between the three categories. Few studies assessed user experience.

CONCLUSIONS

Studies mainly used titration algorithms to titrate basal insulin as telehealth or in paper format, except for studies using mathematical models. A numerically larger proportion of participants seemed to reach target using telehealth solutions compared to paper-based titration algorithms. Exploring capabilities of machine learning may provide insights that could pioneer future research while focusing on holistic development.

Bedtime Prediction of Nocturnal Hypoglycemia in Insulin-Treated Type 2 Diabetes Patients

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.

Impaired postural control in diabetes-a predictor of falls?

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.

The Diabetes teleMonitoring of patients in insulin Therapy (DiaMonT) trial: study protocol for a randomized controlled trial

BACKGROUND

The effect of telemedicine solutions in diabetes remains inconclusive. However, telemedicine studies have shown a positive trend in regards to glycemic control. The telemedicine interventions that facilitate adjustment of medication seems to improve glycemic control more effectively. Hence, it is recommended that future telemedicine studies for patients with diabetes include patient-specific suggestions for changes in medicine. Hence, the aim of the trial is to explore the effect of telemonitoring in patients with type 2 diabetes (T2D) on insulin therapy.

METHODS

The trial is an open-label randomized controlled trial with a trial period of 3 months conducted in two sites in Denmark. Patients with T2D on insulin therapy will be randomized (1:1) to a telemonitoring group (intervention) or a usual care group (control). The telemonitoring group will use a continuous glucose monitor (CGM), an insulin pen, an activity tracker, and smartphone applications throughout the trial. Hospital staff will monitor the telemonitoring group and contact the subjects by telephone repeatedly throughout the trial period. The usual care group will use a blinded CGM the first and last 20 days of the trial and will use a blinded insulin pen for the entire period. The primary endpoint will be changed from baseline in CGM time in range (3.9-10.0 mmol/L) 3 months after randomization. Secondary endpoints include change from baseline in glycated hemoglobin (HbA1c), total daily dose, time above range, and time below range 3 months after randomization. Exploratory endpoints include health-related quality of life, diabetes-related quality of life, etc. DISCUSSION: The DiaMonT trial will test a telemonitoring setup including various devices. Such a setup may be criticized, because it is impossible to determine which element(s) add to the potential effect. However, it is not possible and counterproductive to test the elements individually, since it is the full telemedicine setup that is being evaluated. The DiaMonT trial is the first Danish trial to explore the effect of telemonitoring on patients on insulin therapy. Thus, the DiaMonT trial has the potential to form the basis for the implementation of telemedicine for patients with T2D in Denmark.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04981808. Registered on 8 June 2021.

Long-term glucose-lowering effect of intermittently scanned continuous glucose monitoring for type 1 diabetes patients in poor glycaemic control from Region North Denmark: An observational real-world cohort study

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.

Risk of heart failure following short-term non-steroidal anti-inflammatory drug use in patients with type 2 diabetes mellitus

Background

Fluid retention is a known but underappreciated side-effect of non-steroidal anti-inflammatory drug (NSAID) use. As type 2 diabetes mellitus (T2DM) has been linked to both subclinical cardiomyopathy and a decline in kidney function, short-term NSAID use could lead to subsequently development of heart failure (HF) due to aberrations in fluid balances.

Purpose

We investigated associations between short-term NSAID use and the risk of HF in a nationwide cohort of patients with T2DM.

Methods

Using nationwide Danish registers, we identified patients diagnosed with T2DM during 1998–2018. Follow-up began 120 days after first-time T2DM diagnosis among patients without prior heart failure or a rheumatological diagnosis indicating long-term NSAID use.

To describe use of NSAID among patients with T2DM, we reported proportions of patients claiming at least 1, 2, 3 or 4 prescriptions of NSAID within one year of start of follow-up. We investigated associations between use of NSAIDs (celecoxib, diclofenac, ibuprofen and naproxen) and new-onset HF hospitalizations using a case-crossover design with 28-day exposure windows and reported odds ratios (OR) with 95% confidence intervals (CI). The case-crossover design uses each individual as his or her own control making it suitable to study the effect of short-term exposure on immediate events while mitigating unmeasured confounding. Sensitivity analyses using exposure windows of 14 and 42 days were performed as well.

Results

A total of 334,950 patients with T2DM was included (47.7% female, median age of 61 [interquartile range 50–70]). Celecoxib and naproxen were rarely used; on the contrary, prescriptions of diclofenac and ibuprofen were claimed at least once within one year from the beginning of follow-up by 4.9% and 15.5% of patients, respectively–0.9% and 2.7% claimed at least four prescriptions (Figure 1).

The risk of new-onset HF hospitalization was increased following use of diclofenac or ibuprofen with corresponding ORs of 1.3 (95% CI 1.0 to 1.7) and 1.3 (95% CI 1.1 to 1.5) using 28-day exposure windows. An increased risk following use of celecoxib or naproxen was not found (Figure 2).

Conclusion

NSAIDs diclofenac and ibuprofen were both widely used and associated with an increased risk of new-onset HF hospitalization in patients with T2DM. This suggests a previously unknown and serious, clinically relevant concern of NSAID use in patients with T2DM.

Funding Acknowledgement

Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Ib Mogens Kristiansens Almene FondHelsefonden

Cardiovascular risk following cannabinoid treatment for patients with chronic pain

Background

Treatment with medical cannabis for chronic pain is in popular demand, and a rising number of countries allow physicians to prescribe medical cannabis for pain management. However, data on drug-safety is scarce. Studies have showed a risk of cardiovascular side effects following use of recreational cannabis warranting further investigations into the safety of prescribing medical cannabis.

Purpose

We investigated risk of new-onset arrhythmias (tachy- or bradyarrhythmia and conduction disorders), acute coronary syndrome (ACS) and heart failure (HF) following use of prescribed medical cannabis compared with no use in a nationwide cohort of patients with chronic pain.

Methods

Using nationwide Danish registers, a cohort of patients with chronic pain and without prior history of arrhythmias, ACS, HF or prescribed medical cannabis (cannabinoid, cannabidiol or dronabinol) use were followed from 2018–2021. Any patient from the cohort initiating first-time treatment with medical cannabis was identified and matched 1:10 to corresponding controls within the cohort using incidence density sampling. Matching parameters were age group, sex, and chronic pain diagnosis. Follow-up was initiated at the date of the first claimed prescription of medical cannabis or the corresponding date among controls. We reported 180-day standardized absolute risks (AR) with 95% confidence intervals (CI) and risk ratios (RR) from fitted multivariable logistic regression models comparing patients exposed to medical cannabis with patients not exposed. Separate analyses for each chronic pain group were conducted as well.

Results

Among 1.6 million patients with chronic pain, 4,562 patients claimed at least one prescription of medical cannabis (exposed) and were each matched to 10 controls (non-exposed). Exposed and non-exposed patients were identical in relation to matching parameters; however, exposed patients were slightly more comorbid, and a larger proportion was concomitantly treated with other pain medication (Table). The risk of new-onset arrhythmia was elevated among exposed patients with 180-day AR of 0.71% (95% CI 0.47%–0.94%) compared with 0.43% (95% CI 0.37%–0.49%) yielding a RR of 1.64 (95% CI 1.04–2.23). The risk of new-onset ACS and HF was not increased comparing exposed to non-exposed with corresponding 180-day ARs of 0.13% (95% CI 0.03%-0.23%) vs 0.11% (95% CI 0.08%–0.14% and 0.13% (95% CI 0.03%–0.24%) vs 0.14% (95% CI 0.11%–0.17% (corresponding RRs of 1.2 [95% CI 0.3–2.1] and 0.9 [95% CI 0.2–1.7]) (Figure). Subgroup analyses of each chronic pain group yielded similar results.

Conclusion

In a nationwide cohort of patients with chronic pain, use of medical cannabis was associated with a 64% risk increase of arrhythmias compared with no use. This poses a potential health concern and is vital knowledge for any physician prescribing medical cannabis. Use of medical cannabis was not associated with an elevated risk of ACS or HF.

Funding Acknowledgement

Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Ib Mogens Kristiansens Almene FondHelsefonden

Incidence, hospitalization and mortality and their changes over time in people with a first ever diabetic foot ulcer

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.

The pattern of incident fractures according to fracture site in people with T1D

Higher incidences of fractures are seen in people with type 1 diabetes (T1D), but knowledge on different fracture sites is sparse. We found a higher incidence mainly for distal fracture sites in people with T1D compared to controls. It must be further studied which fractures attributed to the higher incidence rates (IRs) at specific sites.

INTRODUCTION

People with T1D have a higher incidence of fractures compared to the general population. However, sparse knowledge exists on the incidence rates of individual fracture sites. Therefore, we examined the incidence of various fracture sites in people with newly treated T1D compared to matched controls.

METHODS

All people from the UK Clinical Practice Research Datalink GOLD (1987-2017), of all ages with a T1D diagnosis code (n = 6381), were included. People with T1D were matched by year of birth, sex, and practice to controls (n = 6381). Fracture IRs and incidence rate ratios (IRRs) were calculated. Analyses were stratified by fracture site and sex.

RESULTS

The IR of all fractures was significantly higher in people with T1D compared to controls (IRR: 1.39 (CI95%: 1.24-1.55)). Compared to controls, the IRR for people with T1D was higher for several fracture sites including carpal (IRR: 1.41 (CI95%: 1.14-1.75)), clavicle (IRR: 2.10 (CI95%: 1.18-3.74)), foot (IRR: 1.70 (CI95%: 1.23-2.36)), humerus (IRR: 1.46 (CI95%: 1.04-2.05)), and tibia/fibula (IRR: 1.67 CI95%: 1.08-2.59)). In women with T1D, higher IRs were seen at the ankle (IRR: 2.25 (CI95%: 1.10-4.56)) and foot (IRR: 2.11 (CI95%: 1.27-3.50)), whereas in men with T1D, higher IRs were seen for carpal (IRR: 1.45 (CI95%: 1.14-1.86)), clavicle (IRR: 2.13 (CI95%: 1.13-4.02)), and humerus (IRR: 1.77 (CI95%: 1.10-2.83)) fractures.

CONCLUSION

The incidence of carpal, clavicle, foot, humerus, and tibia/fibula fractures was higher in newly treated T1D, but there was no difference at other fracture sites compared to controls. Therefore, the higher incidence of fractures in newly treated people with T1D has been found mainly for distal fracture sites.

Optimal Data Collection Period for Continuous Glucose Monitoring to Assess Long-Term Glycemic Control: Revisited

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.

Use of Personal Continuous Glucose Monitoring Device Is Associated With Reduced Risk of Hypoglycemia in a 16-Week Clinical Trial of People With Type 1 Diabetes Using Continuous Subcutaneous Insulin Infusion

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.

Effect of Newer Long-Acting Insulins on Hypoglycemia and Fracture Risk Among People with Diabetes: A Systematic Review

PURPOSE OF REVIEW

To investigate the effect of newer long-acting insulins on the risk of hypoglycemic episodes and fractures in people with diabetes.

RECENT FINDINGS

Hypoglycemic episodes are the critical limiting factor in glycemic management due to a deteriorating effect on quality of life. Hypoglycemia may in severe cases lead to unconsciousness and thus fractures. Newer long-acting insulins may result in more stable blood glucose levels, less hypoglycemic episodes, and reduced risk of fractures. Use of insulin increases risk of hypoglycemic episodes, and hypoglycemic episodes increase risk of fractures plausible due to falls. Newer ultra-long-acting insulins reduce risk of hypoglycemic episodes compared to older alternatives, and they are thus promising for reducing fracture risk. However, more studies are needed to determine whether these new insulins reduce risk of fractures.

Usage and risk with phosphodiesterase type 5 inhibitors in male patients with chronic ischemic heart disease on oral organic nitrates

Background

Combining oral organic nitrates (OON) with phosphodiesterase type 5 (PDE5) inhibitors is contraindicated. Growing and liberal use of PDE5 inhibitors for erectile dysfunction among patients with ischemic heart disease (IHD) could pose serious health consequences especially among patients with IHD on OON.

Purpose

We hypothesize that concomitant prescription of OON and PDE5 inhibitors is prevalent and has increased in recent years, and further that possible co-exposure could be associated with an increased risk of ischemic stroke, myocardial infarction (MI) or acute coronary angiography (CAG).

Methods

During 2000–2018, we included all male patients with history of IHD between 18 and 85 years of age from nationwide Danish health registers. Patients with a history of pulmonary hypertension were excluded and not followed up afterwards if they developed the condition during follow-up. From this cohort, we identified an OON treated subgroup defined by two consecutively redeemed prescriptions of OON within 180 days from each other. Further, to become a case or control, patients had to redeem a prescription of OON within 180 days prior to the event or corresponding date among controls.

Temporal trends during 2001–2018 of PDE5 inhibitor use were calculated among all male patients with IHD and the subgroup on OON. Among OON treated patients, we examined associations between PDE5 inhibitor use and risk of ischemic stroke, MI or CAG using a case-crossover design where each individual serves as his/her own control thereby controlling for time-invariant confounding. The case-crossover design compares an individual's exposure in an index period just before the event occurred to a reference period prior to the index period. We investigated periods of varying length (7, 14, 21 and 28 days). To account for possible temporal trends in the use of PDE5 inhibitors, we also conducted a case-time-control analysis using a control group matched on age and calendar year.

Results

We identified 249,541 male patients with IHD (median age 65 years [IQR 56–73]), and a subgroup of 42,073 (17%) on OON treatment (median age 70 years [IQR 62–77]). From 2001 to 2018, the use of PDE5 inhibitors saw a 6-fold increase among all male IHD patients and a 10-fold rise in the subgroup on OON (Figure 1). The risk of ischemic stroke, MI or CAG following exposure to PDE5 inhibitors was not increased in the OON subgroup in neither the case-crossover nor the case-time-control analyses (Figure 2).

Conclusions

The use of PDE5 inhibitors has increased 6-fold since 2001 among male patients with IHD, and 10-fold among patients on OON–notwithstanding an established absolute contraindication. However, we did not find any evidence of an increased risk of ischemic stroke, MI or acute CAG following exposure to PDE5 inhibitors in the OON subgroup. This suggests that patients on OON are adequately informed and comply with the recommended pause in OON medication prior to PDE5 inhibitor use.

Funding Acknowledgement

Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Ib Mogens Kristiansens Almene FondandHelsefonden

Gastrointestinal bleeding risk following concomitant treatment with oral glucocorticoids in patients with atrial fibrillation on direct-acting oral anticoagulants

Background

Oral glucocorticoids and direct-acting oral anticoagulants (DOAC) have both been associated with a risk of gastrointestinal (GI) bleeding. However, drug safety, especially regarding the risk of bleeding, in relation to concomitant treatment with oral glucocorticoids and DOACs is insufficiently explored.

Purpose

We aimed to investigate the short-term risk of GI bleeding in patients with atrial fibrillation (AF) following concomitant treatment with DOACs and oral glucocorticoids.

Methods

Register-based, retrospective and nationwide Danish study including patients with AF and on DOAC treatment during 2012–2018. Patients were defined as exposed to oral glucocorticoids from the date of a redeemed prescription and 60 days forward. We associated concomitant treatment with GI bleeding and reported hazard ratios (HR) via a nested case-control design and standardized 60-day absolute risk adjusted for comorbidities using a cohort design. In both analyses, exposed were compared to non-exposed controls matched on age, sex, calendar year, follow-up time and DOAC agent.

Results

We included 98,376 patients (age [interquartile range]: 75 [68– 82], 44% females) with AF on DOAC treatment. The use of oral glucocorticoids among included patients was widespread with 16% redeeming at least one prescription within three years, 4% redeeming at least five (Figure 1A). Lung disease was the most frequent indication (Figure 1B). Concomitant treatment with DOACs and oral glucocorticoids was associated with an increased incidence of GI bleeding (total n=4,946) compared with only DOAC treatment, including a dose-response trend (&lt;20mg daily dose, HR [95% confidence interval (CI)]: 1.64 [1.38–1.95]; ≥20mg daily dose, HR [95% CI]: 2.29 [1.90–2.77]). Likewise, the standardized 60-day absolute risk of GI bleeding from first oral glucocorticoid exposure was increased compared with non-exposed (Figure 2).

Conclusion

Caution should be exercised when prescribing even short-term oral glucocorticoid treatment for DOAC treated patients, most notably in high doses and for patients with elevated bleeding risk.

Funding Acknowledgement

Type of funding sources: Foundation. Main funding source(s): Ib Mogens Kristiansens Almene FondandHelsefonden

Penalty weighted glucose prediction models could lead to better clinically usage

BACKGROUND AND OBJECTIVE

Numerous attempts to predict glucose value from continuous glucose monitors (CGM) have been published. However, there is a lack of proper analysis and modeling of penalty for errors in different glycemic ranges. The aim of this study was to investigate the potential for developing glucose prediction models with focus on the clinical aspects.

METHODS

We developed and compared six different models to test which approach were best suited for predicting glucose levels at different lead times between 10 and 60 min. The models were: last observation carried forward, linear extrapolation, ensemble methods using LSBoost and bagging, neural networks, one without error-weights and one with error-weights. The modeling and test were based on 225 type 1 diabetes patients with 315,000 h of CGM data.

RESULTS

Results show that it is possible to predict glucose levels based on CGM with a reasonable accuracy and precision with a 30-min prediction lead time. A comparison of different methods shows that there are improvements on performance gained from using more advanced machine learning algorithms (MARD 10.26-10.79 @ 30-min lead time) compared to a simple modeling (MARD 10.75-12.97 @ 30-min lead time). Moreover, the proposed use of error weights could lead to better clinical performance of these models, which is an important factor for real usage. E.g., the percentages in the C-zone of the consensus error grid without error-weights (0.57-0.68%) vs including error-weights (0.28%).

CONCLUSIONS

The results point toward that using error weighting in the training of the models could lead to better clinical performance.

Glucose-Lowering Therapy in Patients With Postpancreatitis Diabetes Mellitus: A Nationwide Population-Based Cohort Study

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.

Smoking is Associated With Increased Risk of Not Achieving Glycemic Target, Increased Glycemic Variability, and Increased Risk of Hypoglycemia for People With Type 1 Diabetes

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.

Short-term prediction of future continuous glucose monitoring readings in type 1 diabetes: Development and validation of a neural network regression model

BACKGROUND AND OBJECTIVE

CGM systems are still subject to a time-delay, which especially during rapid changes causes clinically significant difference between the CGM and the actual BG level. This study had the aim of exploring the potential of developing and validating a model for prediction of future CGM measurements in order to overcome the time-delay.

METHODS

An artificial neural network regression (NN) approach were used to predict CGM values with a lead-time of 15 min. The NN were trained and internally validated on 23 million minutes of CGM and externally validated on 2 million minutes of CGM. The validation included data from 278 type 1 diabetes patients using three different CGM sensors. The NN performance were compared with three alternative methods, linear extrapolation, spline extrapolation and last observation carried forward.

RESULTS

The internal validation yielded a RMSE of 9.1 mg/dL, a MARD of 4.2 % and 99.9 % of predictions were in the A + B zone of the consensus error grid. The external validation yielded a RMSE of 5.9-11.3 mg/dL, a MARD of 3.2-5.4 % and 99.9-100 % of predictions were in the A + B zone of the consensus error grid. The NN performed better on all parameters compared to the two alternative methods.

CONCLUSIONS

We proposed and validated a NN glucose prediction model that is potential simple to use and implement. The model only needs input from a CGM system in order to facilitate glucose prediction with a lead time of 15 min. The approach yielded good results for both internal and external validation.

Twenty years with diabetes and amputations: a retrospective population-based cohort study

AIM

To investigate the trends in non-traumatic lower limb amputation in people with and without diabetes.

METHODS

From the Danish National Patient Register, all people with either type 1 or type 2 diabetes (n = 462 743) as well as a group of people without diabetes from the general population (n = 1 388 886) were identified and separated into three groups based on diabetes type. Among these, 17 265 amputations were identified between 1997 and 2017 and stratified into trans-femoral amputations, trans-tibial amputations and amputations below the ankle using surgical codes. Annual changes were described using least-squares linear regression.

RESULTS

The yearly mean decrease in incidence rate of amputation per 1000 person-years was -0.032 [95% CI: -0.062, -0.001], -0.022 [-0.032, -0.012] and -0.006 [-0.009, -0.003] for trans-femoral amputation, -0.072 [-0.093, -0.052], -0.090 [-0.102, -0.078] and -0.015 [-0.016, -0.013] for trans-tibial amputation, and -0.055 [-0.080, -0.020], -0.075 [-0.090, -0.060] and -0.011 [-0.014, -0.007] for amputation below the ankle in people with type 1 diabetes, people with type 2 diabetes and people without diabetes, respectively.

CONCLUSIONS

Over recent decades, the incidence of amputation has decreased significantly in people with diabetes and in the general population without diabetes.

Simple Post-Processing of Continuous Glucose Monitoring Measurements Improves Endpoints in Clinical Trials

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.

Cognitive impairment in elderly people with prediabetes or diabetes: A cross-sectional study of the NHANES population

AIM

To investigate the cognitive function in people without diabetes, with prediabetes and with diabetes.

METHODS/DESIGN

The study design used was a cross-sectional analysis of data in people above 60 years registered in NHANES from 2011 to 2014.Three assessments were used to characterize cognitive function: (a) CERAD Word Learning subtest assessing immediate and delayed learning ability, (b) The Animal Fluency test assesing categorical verbal fluency, and (c) The Digit Symbol Substitution test assessing processing speed, sustained attention, and working memory.

RESULTS

(A) Memory recall (-0.19, [-0.34; -0.039], p = 0.014) and Delayed memory recall decline was associated with diabetes (-0.285, [-0.503; -0.067], p = 0.01), but not in an adjusted analysis. (B) Animal Fluency score decline was associated with diabetes (-1.185, [-1.688; -0.682], p < 0.001). (C) Digit Symbol score decline was associated with diabetes (-6.897, [-8.491; -5.302], p < 0.001). Prediabetes was not associated with cognitive function.

CONCLUSIONS

This study demonstrates an association between cognitive dysfunction and diabetes. Results may also indicate that cognitive decline is not yet present in people with mild impairments of glucose homeostasis.

Associations between smoking, glucose metabolism and lipid levels: A cross-sectional study

AIMS

The aim of this study was to investigate glucose profiles assessed by oral glucose tolerance tests (OGTT), fasting glucose, and lipid profiles among smokers, ex-smokers and never-smokers.

MATERIALS AND METHODS

The study design used was a cross-sectional analysis of data from several years of the NHANES (National Health and Nutrition Examination Survey) from 2005 to 2014. A total of 12,460 participants with measures of OGTT, triglycerides, LDL-cholesterol and HDL-cholesterol were included for the data analysis. Outcomes were all assessed in an unadjusted and in an adjusted gender analysis. A GLM model was used to assess 2-hour OGTT, fasting plasma glucose, difference between fasting plasma glucose and OGTT, HbA1c, HDL-cholesterol, LDL-cholesterol, and triglyceride in relation to current smoking, ex-smoking and never smoking. The effects were adjusted with covariates: gender, BMI, age, alcohol usage, educational level and ethnicity.

RESULTS

The OGTT results was lower for the group smoking (-10.1 [-13.2; -7.1], p < 0.001), and no effect was observed from ex-smoking (-2.7 [-5.7; 0.8], p = 0.08). Fasting glucose was not different for smokers (-0.2 [-1.6; 1.2], p = 0.80) or ex-smokers (0.1 [-1.3; 1.5], p = 0.90). For smokers', triglycerides (1.2 [1.1; 1.3], p < 0.001), LDL-cholesterol (7.7 [6.0; 9.3], p < 0.001) were increased and HDL-cholesterol was decreased (-2.1 [-2.8; -1.5], p < 0.001).

CONCLUSIONS

Although this study is cross-sectional and cannot, by the same nature of the design, prove a cause-effect relationship, the present results indicate that cigarette smoking may be associated with factors that are adversely related to the metabolic syndrome. But the evidence from our results are not unanimous pointing in the same direction as 2-hour OGTT measurements are considerably lower in participants smoking.

Increased Risk of Falls, Fall-related Injuries and Fractures in People with Type 1 and Type 2 Diabetes - A Nationwide Cohort Study

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.

Sodium Glucose Cotransporter-2 Inhibitor Treatment and the Risk of Diabetic Ketoacidosis in Denmark: A Retrospective Cohort Study of Five Years of Use

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.

A Matlab Tool for Organizing and Analyzing NHANES Data

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.

Towards Prediction of Type 1 Diabetes Patients Who Fail to Achieve Glycemic Target

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.

Risk of Major Adverse Cardiovascular Events, Severe Hypoglycemia, and All-Cause Mortality for Widely Used Antihyperglycemic Dual and Triple Therapies for Type 2 Diabetes Management: A Cohort Study of All Danish Users

OBJECTIVE

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.

Association of severe hypoglycemia with mortality for people with diabetes mellitus during a 20-year follow-up in Denmark: a cohort study

AIMS

Severe hypoglycemia has a significant deteriorating effect on quality of life of the individual and has been associated with increased mortality. The aim of this study was to investigate the mortality among people with type 1 and type 2 diabetes suffering from severe hypoglycemia in Denmark in the last two decades.

METHODS

People diagnosed with type 1 (n = 44,033) and type 2 diabetes (n = 333,581) were extracted from the complete population of Denmark from 1996 to 2017 via ICD-10 diabetes codes and ATC diabetes medication codes. People suffering from severe hypoglycemia (type 1 diabetes n = 8808, type 2 diabetes n = 5605) as identified from ICD-10 codes were then matched 1:1 by year of birth, gender and year of diabetes diagnosis with those without severe hypoglycemia. Cox proportional hazards models were constructed to analyze the effect of severe hypoglycemia on mortality.

RESULTS

For both people with type 1 (HR 1.11, CI 95% 1.06 to 1.17) and type 2 diabetes (HR 1.77, CI 95% 1.67 to 1.87) suffering from hypoglycemia, an increased mortality risk was observed, compared to people without severe hypoglycemia. An investigation of the death causes did not indicate an association between the severe hypoglycemic episodes and death.

CONCLUSION

In this study, severe hypoglycemic episodes increased the mortality risk for people with type 1 and type 2 diabetes. The risk was higher among people with type 2 diabetes. Whether severe hypoglycemia is a symptom of other underlying illnesses increasing mortality risk or a risk factor itself needs further investigation.

Association of insulin regimens with severe hypoglycaemia in patients with type 1 diabetes: A Danish case-control study

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.

Diabetes and fractures: new evidence of atypical femoral fractures?

Patients with diabetes have an increased risk of fractures. In this study, subtrochanteric and femoral shaft fractures were increased in patients with type 1 diabetes compared with the general population. In the light of this, more evidence points towards an association between diabetes and atypical femoral fractures.

INTRODUCTION

Patients with diabetes have an increased risk of femoral fractures, but little is known about the risk of atypical femoral fractures (AFFs). The aim of this study was to identify the risk of subtrochanteric and femoral shaft (ST/FS) fractures and estimate the risk of AFFs in patients with type 1 (T1D) and type 2 diabetes (T2D).

METHODS

From the nationwide Danish National Patient Register, we identified patients with T1D (n = 19,896), T2D (n = 312,188), and sex- and aged-matched controls without diabetes (n = 996,252) from the general population and all ST/FS fractures (n = 7509). Data were analyzed using a Cox proportional-hazards model and the incidence rate and rate ratio of ST/FS fractures were estimated.

RESULTS

The incidence rate of ST/FS fractures in T1D was 52.14 events per 100,000 person years and 73.21 per 100,000 person years in T2D. T1D was associated with an increased risk of ST/FS (HR 2.07 (95% CI 1.68-2.56)), whereas T2D was not (HR 0.99 (95% CI 0.94-1.10)). Previous ST/FS fractures were associated with an increased risk of subsequent ST/FS fractures (HR 6.95 (95% CI 6.00-8.05)) and the use of bisphosphonates with an increased risk of ST/FS fractures (HR 1.72 (95% CI 1.54-1.91)).

CONCLUSION

Patients with T1D have a higher risk of ST/FS fractures compared with sex- and age-matched controls. Since a proportion of ST/FS fractures are classified as AFFs, this could point towards the fact that AFFs also are increased in patients with T1D, but not T2D.

Risk of major cardiovascular events, severe hypoglycaemia, and all-cause mortality for users of insulin degludec versus insulin glargine U100-A Danish cohort study

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.

Hypoglycaemia and type 1 diabetes are associated with an increased risk of fractures

People with diabetes have an increased risk of fractures, and in this study, the effect of hypoglycaemia and insulin on this risk was investigated. Type 1 diabetes and hypoglycaemia did increase the fracture risk, and prevention of hypoglycaemia is thus an important focus area in the prevention of fractures.

INTRODUCTION

Studies have shown that type 1 diabetes (T1D) and type 2 diabetes (T2D) are associated with increased risk of fractures. Especially, subjects with T1D have an increased risk of fractures. The purpose of this study was to investigate the association of T1D, hypoglycaemia and insulin on fracture risk.

METHODS

A cohort study with T1D subjects (n = 19,896) and T2D subjects (n = 312,188) matched with subjects from the general populated (n = 996,252) and a nested case-control study with T1D subjects with fracture (n = 895) as cases and T1D subjects without (n = 2685) as controls were conducted based on subjects from the Danish National Patient Registry (DNPR).

RESULTS

T1D (HR = 2.47, 95% CI 2.37 to 2.59), age (HR = 1.05, 95% CI 1.05 to 1.05), previous fracture (HR = 1.95, 95% CI 1.92 to 1.99) and being female (HR = 2.06, 95% CI 2.04 to 2.09) increased the risk of fractures. Also, T2D (HR = 1.14, 95% CI 1.11 to 1.18) increased the risk of proximal upper arm and shoulder fractures. T1D (HR = 2.41, 95% CI 2.20 to 2.65) increased the risk of hip and femoral region fractures. Hypoglycaemia (OR = 1.58, 95% CI 1.27 to 1.97) increased the risk of fractures, whereas insulin use did not change the risk.

CONCLUSIONS

Hypoglycaemic episodes are associated with increased fracture risk, and the frequency of hypoglycaemic episodes leading to hospital admission was above 16% for T1D subjects. Prevention of hypoglycaemia is thus an important focus area in the prevention of fractures.

A systematic study of the isothermal crystallization of the mono-alcohol n-butanol monitored by dielectric spectroscopy

Isothermal crystallization of the mono-hydroxyl alcohol n-butanol was studied with dielectric spectroscopy in real time. The crystallization was carried out using two different sample cells at 15 temperatures between 120 K and 134 K. Crystallization is characterized by a decrease of the dielectric intensity. In addition, a shift in relaxation times to shorter times was observed during the crystallization process for all studied temperatures. The two different sample environments induced quite different crystallization behaviors, consistent and reproducible over all studied temperatures. An explanation for the difference was proposed on the background of an Avrami analysis and a Maxwell-Wagner analysis. Both types of analysis suggest that the morphology of the crystal growth changes from a higher dimension to a lower at a point during the crystallization. More generally, we conclude that a microscopic interpretation of crystallization measurements requires multiple probes, sample cells, and protocols.

Heuristic evaluation of a telehealth system from the Danish TeleCare North Trial

OBJECTIVE

The aim was to evaluate the usability of the design of the telehealth system, named Telekit, developed for the Danish TeleCare North Trial, early into the design process in order to assess potential problems and limitations which could hinder its successful implementation.

METHODS

Five experts, including one who pilot-tested the Telekit system, individually evaluated its usability and its compliance with Jakob Nielsen's ten usability heuristics for interaction design. Usability problems were categorised according to Rolf Molich's severity classification.

RESULTS

The five experts identified a total of 152 problems in the Telekit system, each identifying 22-40 problems. 86 (57%) out of the 152 problems were identified only once. All heuristics were used, but the three most frequently used were: "Match between system and the real world" (32%), "Consistency and standards" (13%) and "Aesthetic and minimalist design" (13%). The most widely used classifications were: "Improvement" (40%) and "Minor problem" (43%).

CONCLUSION

Heuristic evaluation was an effective method for uncovering and identifying problems with the system. The consistent finding of particular usability problems confirms that the development of a telehealth system should pay particular attention to user aspects. The most serious problem was the inability of the system to inform users of how to perform measurements correctly and to "speak the users' language". The problems found in the heuristic evaluation have led to several significant changes in the telehealth system. We suggest that heuristic evaluation always be followed by user tests to evaluate the design of telehealth systems.

Accuracy evaluation of a new real-time continuous glucose monitoring algorithm in hypoglycemia

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.

Evaluation of an Algorithm for Retrospective Hypoglycemia Detection Using Professional Continuous Glucose Monitoring Data

BACKGROUND

People with type 1 diabetes (T1D) are unable to produce insulin and thus rely on exogenous supply to lower their blood glucose. Studies have shown that intensive insulin therapy reduces the risk of late-diabetic complications by lowering average blood glucose. However, the therapy leads to increased incidence of hypoglycemia. Although inaccurate, professional continuous glucose monitoring (PCGM) can be used to identify hypoglycemic events, which can be useful for adjusting glucose-regulating factors. New pattern classification approaches based on identifying hypoglycemic events through retrospective analysis of PCGM data have shown promising results. The aim of this study was to evaluate a new pattern classification approach by comparing the performance with a newly developed PCGM calibration algorithm.

METHODS

Ten male subjects with T1D were recruited and monitored with PCGM and self-monitoring blood glucose during insulin-induced hypoglycemia. A total of 19 hypoglycemic events occurred during the sessions.

RESULTS

The pattern classification algorithm detected 19/19 hypoglycemic events with 1 false positive, while the PCGM with the new calibration algorithm detected 17/19 events with 2 false positives.

CONCLUSIONS

We can conclude that even after the introduction of new calibration algorithms, the pattern classification approach is still a valuable addition for improving retrospective hypoglycemia detection using PCGM.

The dynamics of genetic control in the cell: the good and bad of being late

The expression of genes in the cell is controlled by a complex interaction network involving proteins, RNA and DNA. The molecular events associated with the nodes of such a network take place on a variety of time scales, and thus cannot be regarded as instantaneous. In many cases, the cell is robust with respect to the delay in gene expression control, behaving as if it were instantaneous. However, there are specific cases in which delay gives rise to temporal oscillations. This is the case, for example, of the expression of tumour-suppressor protein p53, of protein Hes1, involved in the differentiation of stem cells, of NFkB and Wnt, in which case delay arises implicitly from the structure of the associated network. By means of delay rate equations, we study the kinetics of small regulatory networks, emphasizing the role of delay in an evolutionary context. These models suggest that oscillations are a typical outcome of the dynamics of regulatory networks, and evolution has to work to avoid them when not required (and not vice versa).

Real-time hypoglycemia detection from continuous glucose monitoring data of subjects with type 1 diabetes

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 sample-based 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.