AIMC Topic: Diabetes Mellitus, Type 2
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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,...
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 ...
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...
Type 2 Diabetes in Taiwan: Unmasking Influential Factors Through Advanced Predictive Modeling.
Journal of diabetes research
May 27, 2025
Type 2 diabetes (T2D) is influenced by lifestyle, genetics, and environmental conditions. By utilizing machine learning techniques, we can enhance the precision of T2D risk prediction by analyzing the complex interactions among these variables. This...
Continuous glucose monitoring combined with artificial intelligence: redefining the pathway for prediabetes management.
Frontiers in endocrinology
May 26, 2025
Prediabetes represents an early stage of glucose metabolism disorder with significant public health implications. Although traditional lifestyle interventions have demonstrated some efficacy in preventing the progression to type 2 diabetes, their lim...
Resting-State Functional MRI Reveals Altered Seed-Based Connectivity in Diabetic Osteoporosis Patients.
Clinical interventions in aging
May 21, 2025
BACKGROUND: Diabetic osteoporosis (DOP) can cause abnormal brain neural activity, but its mechanism is still unclear. This study aims to further explore the abnormal functional connectivity between different brain regions based on the team's previous...
Exploring potential diagnostic markers and therapeutic targets for type 2 diabetes mellitus with major depressive disorder through bioinformatics and in vivo experiments.
Scientific reports
May 15, 2025
Type 2 diabetes mellitus (T2DM) and Major depressive disorder (MDD) act as risk factors for each other, and the comorbidity of both significantly increases the all-cause mortality rate. Therefore, studying the diagnosis and treatment of diabetes with...
Machine Learning Models for Predicting Type 2 Diabetes Complications in Malaysia.
Asia-Pacific journal of public health
Apr 18, 2025
This study aimed to develop machine learning (ML) models to predict diabetic complications in patients with type 2 diabetes (T2D) in Malaysia. Data from the Malaysian National Diabetes Registry and Death Register were used to develop predictive model...
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