Developing an AI-Based clinical decision support system for basal insulin titration in type 2 diabetes in primary Care: A Mixed-Methods evaluation using heuristic Analysis, user Feedback, and eye tracking.

Background And Aim: The progressive nature of type 2 diabetes often, in time, necessitates basal insulin therapy to achieve glycemic targets. However, despite standardized titration algorithms, many people remain poorly controlled after initiating insulin therapy, leading to suboptimal glycemic control and complications. Both healthcare professionals and people with type 2 diabetes have expressed the need for novel tools to aid in this process.

Impact of TCF7L2 rs7903146 on clinical presentation and risk of complications in patients with type 2 diabetes.

Aims: TCF7L2 rs7903146 is the most impactful single genetic risk variant for type 2 diabetes. However, its role on disease progression, complications and mortality among people with type 2 diabetes at diagnosis remains unclear.

Materials And Methods: We assessed the per allele impact of the rs7903146 T-allele on clinical characteristics and complication risk in 9231 individuals with type 2 diabetes at diagnosis and over a 10-year follow-up period.

YKL-40, cardiovascular events, and mortality in individuals recently diagnosed with type 2 diabetes: A Danish cohort study.

Aims: We investigated the association of the inflammatory biomarker YKL-40 with cardiovascular events (CVEs) and mortality in individuals with type 2 diabetes.

Methods: We followed 11,346 individuals recently diagnosed with type 2 diabetes for up to 14 years. Baseline YKL-40 levels (measured in 9,010 individuals) were grouped into percentiles (0-33 %, 34-66 %, 67-90 %, and 91-100 %) and analyzed continuously (per 1 SD log increment), with comparisons to CRP (measured in 9,644 individuals).

Modeling the fasting blood glucose response to basal insulin adjustment in type 2 diabetes: An explainable machine learning approach on real-world data.

Introduction: Optimal basal insulin titration for people with type 2 diabetes is vital to effectively reducing the risk of complications. However, a sizeable proportion of people (30-50 %) remain in suboptimal glycemic control six months post-initiation of basal insulin. This indicates a clear need for novel titration methods that account for individual patient variability in real-world settings.

Elevated risk of infection in individuals with hyperinsulinaemic type 2 diabetes: a Danish 12 year cohort study.

Aims/hypothesis: A better understanding of the mechanisms underlying an elevated infection risk in individuals with type 2 diabetes is needed to guide risk stratification and prevention. We investigated the risk of infection in subgroups of individuals with type 2 diabetes according to indices of insulin sensitivity and beta cell function.

Methods: We classified 7265 individuals with recently diagnosed type 2 diabetes (median duration 1.

Identifying the Relationship Between CGM Time in Range and Basal Insulin Adherence in People With Type 2 Diabetes.

Background: The study aimed to determine the relationship between basal insulin adherence and glycemic control evaluated by time in range (TIR) in people with insulin-treated type 2 diabetes (T2D), using data from both continuous glucose monitors (CGM) and connected insulin pens. Furthermore, the study aimed to determine the best basal insulin adherence metric.

Methods: CGM data and basal insulin data were collected from 106 insulin-treated people (aged ≥18 years) with T2D.

The intelligent diabetes telemonitoring using decision support to treat patients on insulin therapy (DiaTRUST) trial: study protocol for a randomized controlled trial.

Background: Diabetes affects 10.5% of adults globally, with type 2 diabetes accounting for 90-95% of cases. Achieving optimal glycemic control is crucial yet challenging, particularly with insulin therapy, where 30-50% of patients fail to meet treatment targets.

Incidence of Graves' Disease with Validation and Completeness of the Diagnosis for Registry Extracts in the Danish National Patient Register.

Purpose: Graves' disease (GD) is one of the most common causes of thyrotoxicosis. It has been proposed to identify incident GD by using the GD-specific code, E05.0, of the 10th revision of the International Classification of Disease (ICD-10) in the Danish National Patient Register (DNPR).

The Danish Centre for Strategic Research in Type 2 Diabetes (DD2) Project Cohort and Biobank from 2010 Through 2023-A Cohort Profile Update.

Purpose: This paper provides an overview of the Danish Centre for Strategic Research in Type 2 Diabetes (DD2) cohort and biobank, including baseline characteristics of participants enrolled up to 2023, and post-enrollment rates of cardiovascular disease outcomes and mortality.

Methods: Since 2010, the DD2 project has enrolled individuals with type 2 diabetes mellitus (T2DM) recently diagnosed by general practitioners and by hospital-based clinicians across Denmark. Data from questionnaires, clinical examinations, and biological samples are collected at enrollment.

Prediction of People With Type 2 Diabetes Not Achieving HbA1c Target After Initiation of Fast-Acting Insulin Therapy: Using Machine Learning Framework on Clinical Trial Data.

Background And Aims: Glycemic control is crucial for people with type 2 diabetes. However, only about half achieve the advocated HbA1c target of ≤7%. Identifying those who will probably struggle to reach this target may be valuable as they require additional support.

Fracture risk revisited: Bone mineral density T-score and fracture risk in type 2 diabetes.

Aim: To study the association between femoral neck (FN) bone mineral density (BMD) T-score and fracture risk in individuals with and without type 2 diabetes (T2D).

Materials And Methods: We performed a single-centre retrospective cohort study using the Danish National Health Service. BMD of the FN was measured by dual-energy X-ray absorptiometry.

The association of type 2 diabetes-related characteristics with fracture risk at different sites.

Aim: To determine the association of diabetes-related characteristics with fractures at different sites in individuals with type 2 diabetes (T2D).

Materials And Methods: We conducted a cohort study using the Clinical Practice Research Datalink (CPRD) GOLD. Patients aged over 30 years with T2D were identified within the CPRD.

Clinical features of fallers and non-fallers: finding best-performing combinations of physical performance measurements to discriminate physical impairments between fallers and non-fallers among older adults with and without osteoporosis.

Unlabelled: Is osteoporosis related to worst outcomes after fall accidents? After a fall accident, there were no differences in walking and balance between individuals with/without osteoporosis. Gains in fat tissue, higher pain, and difficulty to walk were related to previous falls, regardless of osteoporosis.

Purpose: Impairments are expected after an accidental fall in the older age; whoever, it is still unclear if patients suffering from osteoporosis are in higher risks of fall accidents and if such accidents would cause worst outcomes compared with older adults without osteoporosis.

Discordance Between Mealtimes Reported by Trial Participants with Type 2 Diabetes and Healthcare Professionals.

Healthy lifestyle behaviors are essential in the treatment of type 2 diabetes, and meal registration is therefore important. Manual meal registration is cumbersome and could be automated using continuous glucose monitoring (CGM). If such an algorithm is based on patient-reported meals, potential errors might be induced.

Detailed Insights into Basal Insulin Adherence in People with Type 2 Diabetes Using a Data-Driven Assessment Method.

Introduction: Basal insulin non-adherence is a challenge in people with type 2 diabetes (T2D).

Methods: Using injection data recorded by a connected insulin pen, we employed a novel three-step methodology to assess three aspects of adherence (overall adherence, adherence distribution, and dose deviation) in individuals with insulin-treated T2D undergoing telemonitoring.

Results: Among participants, 52% were considered overall adherent.

Intelligent Diabetes Telemonitoring for Patients on Insulin Therapy: A Randomized Controlled Trial Protocol.

Protocol for a three-arm randomized controlled trial investigating telemonitoring with decision support in insulin-treated people with type 2 diabetes.

Development of a Basal Insulin Titration System to Support Healthcare Providers in Treatment of People with Type 2 Diabetes: Preliminary Results.

The study aims to develop a system for insulin titration in type 2 diabetes to assist primary healthcare providers. Usability testing with end-users is ongoing.

From Stability to Variability: Classification of Healthy Individuals, Prediabetes, and Type 2 Diabetes Using Glycemic Variability Indices from Continuous Glucose Monitoring Data.

This study aims to investigate the continuum of glucose control from normoglycemia to dysglycemia (HbA1c ≥ 5.7%/39 mmol/mol) using metrics derived from continuous glucose monitoring (CGM). In addition, we aim to develop a machine learning-based classification model to classify dysglycemia based on observed patterns.

Bone microarchitecture and strength assessed by HRpQCT in individuals with type 2 diabetes and prediabetes: the Maastricht study.

Type 2 diabetes (T2D) is a prevalent disease and has been associated with an increased fracture risk despite normal or even higher areal BMD. The aim of this study was to estimate the association between glucose metabolism status (GMS) and measurements of glycemic control with HRpQCT parameters of bone microarchitecture and strength. Participants of the Maastricht study who underwent an HRpQCT scan at the distal radius and tibia were included.

Declining Incidence of Major Lower-Extremity Amputations in the Northern Danish Region Between 2016 and 2021: The Impact of Diabetes and Preventive Vascular Procedures.

Background: Preventing lower-extremity amputations (LEAs) is pivotal. In the present study, we aimed to examine the recent trends in nontraumatic LEAs seen in the Northern Danish Region.

Methods: Using data from the regional Business Intelligence unit, we identified all nontraumatic LEAs (n = 689) performed in people above 50 years of age in the Northern Danish Region between January 2016 and December 2021 (approximately 600,000 inhabitants).

Machine Learning-Driven Prediction of Comorbidities and Mortality in Adults With Type 1 Diabetes.

Background: Comorbidities such as cardiovascular disease (CVD) and diabetic kidney disease (DKD) are major burdens of type 1 diabetes (T1D). Predicting people at high risk of developing comorbidities would enable early intervention. This study aimed to develop models incorporating socioeconomic status (SES) to predict CVD, DKD, and mortality in adults with T1D to improve early identification of comorbidities.