AIMC Topic: Diabetes Mellitus, Type 2

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Multi-omics analysis of plasma and CSF in spontaneous diabetic cynomolgus monkeys: Unravelling and validating the key molecular markers that predict the preclinical pathological formation of Alzheimer's disease.

Computers in biology and medicine
Alzheimer's disease (AD) biomarkers (Aβ42 or Tau 181) have high diagnostic performance. However, when they are altered, it indicates that irreversible pathology has developed in the brain. Therefore, there is a lack of early prediction or monitoring ...

Conditional universal differential equations capture population dynamics and interindividual variation in c-peptide production.

NPJ systems biology and applications
Universal differential equations (UDEs) are an emerging approach in biomedical systems biology, integrating physiology-driven mathematical models with machine learning for data-driven model discovery in areas where knowledge of the underlying physiol...

A multimodal deep learning architecture for predicting interstitial glucose for effective type 2 diabetes management.

Scientific reports
The accurate prediction of blood glucose is critical for the effective management of diabetes. Modern continuous glucose monitoring (CGM) technology enables real-time acquisition of interstitial glucose concentrations, which can be calibrated against...

Transfer learning prediction of type 2 diabetes with unpaired clinical and genetic data.

Scientific reports
The prevalence of type 2 diabetes mellitus (T2DM) in Korea has risen in recent years, yet many cases remain undiagnosed. Advanced artificial intelligence models using multi-modal data have shown promise in disease prediction, but two major challenges...

Machine learning driven diabetes care using predictive-prescriptive analytics for personalized medication prescription.

Scientific reports
The increasing prevalence of type 2 diabetes (T2D) is a significant health concern worldwide. Effective and personalized treatment strategies are essential for improving patient outcomes and reducing healthcare costs. Machine learning (ML) has the po...

AI-Driven segmentation and morphogeometric profiling of epicardial adipose tissue in type 2 diabetes.

Cardiovascular diabetology
BACKGROUND: Epicardial adipose tissue (EAT) is associated with cardiometabolic risk in type 2 diabetes (T2D), but its spatial distribution and structural alterations remain understudied. We aim to develop a shape-aware, AI-based method for automated ...

Predicting postprandial glucose excursions to personalize dietary interventions for type-2 diabetes management.

Scientific reports
Elevated postprandial glucose levels present a global epidemic and a major challenge in type-2 diabetes (T2D) management. A key barrier to developing effective dietary interventions for T2D management is the wide inter-individual variation in glycemi...

Multidomain Behavioral Change Digital Coaching for Chronic Disease Management in Patients With Type 2 Diabetes: Framework Development and Preliminary Evaluation.

JMIR formative research
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
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...