AIMC Topic: Diabetes Mellitus, Type 2
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Multidomain Behavioral Change Digital Coaching for Chronic Disease Management in Patients With Type 2 Diabetes: Framework Development and Preliminary Evaluation.
JMIR formative research
Jul 9, 2025
BACKGROUND: Unhealthy lifestyle behaviors have been identified as a major cause of numerous health issues, with a steady global increase in their prevalence. Addressing this challenge requires comprehensive behavioral changes to promote the adoption ...
Unsupervised learning using EHR and census data to identify distinct subphenotypes of newly diagnosed hypertension patients.
PloS one
Jul 9, 2025
BACKGROUND: Hypertension (HTN) is a complex condition with significant heterogeneity in presentation and treatment response. Identifying distinct subphenotypes of HTN may improve our understanding of its underlying mechanisms and guide more precise t...
Deep learning-based dipeptidyl peptidase IV inhibitor screening, experimental validation, and GaMD/LiGaMD analysis.
BMC biology
Jul 1, 2025
BACKGROUND: Dipeptidyl peptidase-4 (DPP4) is considered a crucial enzyme in type 2 diabetes (T2D) treatment, targeted by inhibitors due to its role in cleaving glucagon-like peptide-1 (GLP-1). In this study, a novel DPP4 inhibitor screening strategy ...
Integrated analysis of shared gene expression signatures and immune microenvironment heterogeneity in type 2 diabetes mellitus and colorectal cancer.
Scientific reports
Jul 1, 2025
Emerging evidence suggests a bidirectional relationship between colorectal cancer (CRC) and type 2 diabetes mellitus (T2DM), yet the shared molecular mechanisms and prognostic biomarkers remain poorly characterized. This study aimed to identify novel...
Therapeutic horizons in metabolic dysfunction-associated steatohepatitis.
The Journal of clinical investigation
Jul 1, 2025
Metabolic dysfunction-associated steatohepatitis (MASH), the progressive inflammatory form of MASLD, is now a leading cause of chronic liver disease worldwide. Driven by obesity and type 2 diabetes, MASH significantly increases the risk of cirrhosis,...
In-silico guided identification and studies of potential FFAR4 agonists for type 2 diabetes mellitus therapy.
Expert opinion on drug discovery
Jun 28, 2025
BACKGROUND: The activation of free fatty acid receptor 4 (FFAR4) enhances insulin sensitivity and glucose uptake while mitigating inflammation. It is a promising therapeutic approach for managing type 2 diabetes mellitus (T2DM).
A Responsible Framework for Assessing, Selecting, and Explaining Machine Learning Models in Cardiovascular Disease Outcomes Among People With Type 2 Diabetes: Methodology and Validation Study.
JMIR medical informatics
Jun 27, 2025
BACKGROUND: Building machine learning models that are interpretable, explainable, and fair is critical for their trustworthiness in clinical practice. Interpretability, which refers to how easily a human can comprehend the mechanism by which a model ...
Association of urinary metal elements with sarcopenia and glucose metabolism abnormalities: Insights from NHANES data using machine learning approaches.
Ecotoxicology and environmental safety
Jun 5, 2025
BACKGROUND: Sarcopenia, a condition marked by the decline of skeletal muscle mass and function, is prevalent in the elderly and closely linked to abnormal glucose metabolism, particularly type 2 diabetes. Hyperglycemia can increase the formation of a...
Case-control study combined with machine learning techniques to identify key genetic variations in GSK3B that affect susceptibility to diabetic kidney diseases.
BMC endocrine disorders
Jun 5, 2025
The role of genetic susceptibility in early warning and precise treatment of diabetic kidney disease (DKD) requires further investigation. A case-control study was conducted to evaluate the predictive effect of GSK3B genetic polymorphisms on the susc...
Improving ACS prediction in T2DM patients by addressing false records in electronic medical records using propensity score.
Scientific reports
May 28, 2025
Our study aims to improve the prediction performance of machine learning (ML) models by addressing false records (i.e., false positive, false negative, or missingness) in binary categorical variables in electronic medical records (EMRs) using propens...
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