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.

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.

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.

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.

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.

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.

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.

Bone parameters in T1D and T2D assessed by DXA and HR-pQCT - A cross-sectional study: The DIAFALL study.

INTRODUCTION/AIM: People with type 1 diabetes (T1D) and type 2 diabetes (T2D) have an increased risk of fractures due to skeletal fragility. We aimed to compare areal bone mineral density (aBMD), volumetric BMD (vBMD), cortical and trabecular measures, and bone strength parameters in participants with diabetes vs. controls. METHODS: In a cross-sectional study, we included adult participants with T1D (n = 111, MA = 52.9 years), T2D (n = 106, MA = 62.1 years) and controls (n = 328, MA = 57.7 years). The study comprised of DXA scans and HR-pQCT scans, biochemistry, handgrip strength (HGS), Timed Up and GO (TUG), vibration perception threshold (VPT), questionnaires, medical histories, alcohol use, and previous fractures. Group comparisons were performed after adjustment for sex, age, BMI, diabetes duration, HbA1c, alcohol, smoking, previous fractures, postmenopausal, HGS, TUG, and VPT. RESULTS: We found decreased aBMD in participants with T1D at the femoral neck (p = 0.028), whereas T2D had significantly higher aBMD at peripheral sites (legs, arms, p < 0.01) vs. controls. In T1D we found higher vBMD (p < 0.001), cortical vBMD (p < 0.001), cortical area (p = 0.002) and thickness (p < 0.001), lower cortical porosity(p = 0.008), higher stiffness (p = 0.002) and failure load (p = 0.003) at radius and higher vBMD (p = 0.003), cortical vBMD(p < 0.001), bone stiffness (p = 0.023) and failure load(p = 0.044) at the tibia than controls. In T2D we found higher vBMD (p < 0.001), cortical vBMD (p < 0.001), trabecular vBMD (p < 0.001), cortical area (p < 0.001) and thickness (p < 0.001), trabecular number (p = 0.024), lower separation (p = 0.010), higher stiffness (p < 0.001) and failure load (p < 0.001) at the radius and higher total vBMD (p < 0.001), cortical vBMD (p < 0.011), trabecular vBMD (p = 0.001), cortical area (p = 0.002) and thickness (p = 0.021), lower trabecular separation (p = 0.039), higher stiffness (p < 0.001) and failure load (p = 0.034) at tibia compared with controls. CONCLUSION: aBMD measures were as expected lower in T1D and higher in T2D than controls. Favorable bone microarchitecture and strength parameters were seen at the tibia and radius for T1D and T2D.

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.

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.

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.

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.

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.

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.

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 (HbA1c), 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 HbA1c (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 HbA1c, which adds to the evidence about the real-world use of CGM, where device type is not prespecified, and users are not CGM naïve.

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.