Endocrinology

Prediction of Cardiovascular and Renal Complications of Diabetes by a multi-Polygenic Risk Score in Different Ethnic Groups

We developed a multi-Polygenic risk score (multiPRS) to predict the risk of nephropathy, stroke, and myocardial infarction in people with type 2 diabetes of European descent. The underrepresentation of non-European populations remains a major challenge in genomics research. Objective: To evaluate the ability of our multiPRS model to accurately predict these complications in patients of African and...

Assessing Large Language Model Utility and Limitations in Diabetes Education: A Cross-Sectional Study of Patient Interactions and Specialist Evaluations

To assess the value of an AI-powered conversational agent in supporting diabetes self-management among adults with diabetic retinopathy and limited educational backgrounds. In this cross-sectional study, 51 adults with Type□II diabetes and diabetic retinopathy participated in moderated Q-and-A sessions with ChatGPT. Non-English-speaking and visually impaired participants interacted through trained...

Barriers and Facilitators to the Implementation of Artificial Intelligence Enabled Diabetes Interventions in Lower-Middle-Income Countries: A Systematic Review Protocol

Diabetes represents an emerging global health crisis, with lower-middle-income countries experiencing a fast growth in prevalence. Diabetes care in th...

Artificial Intelligence-Driven Innovations in Diabetes Care and Monitoring

This study explores Artificial Intelligence (AI)’s transformative role in diabetes care and monitoring, focusing on innovations that optimize patient ...

Enhancing Fairness in Diabetes Prediction Systems through Smart User Interface Design

Artificial intelligence (AI) in chronic disease prediction often exhibits algorithmic biases, hindering equitable healthcare delivery. This study aims...

Daily Rhythms in Blood Glucose: Time-of-Day Forecasts in Type 2 Diabetes

Accurate and interpretable forecasting of blood glucose levels is critical for effective manage- ment of Type 2 diabetes. While complex machine learni...

Application of Machine Learning Approaches to Develop Predictive Models for Diabetes and Hypertension among Bangladesh Adults

With rapid urbanization, lifestyle changes, and an aging population, non-communicable diseases (NCDs), including hypertension and diabetes, pose signi...

Preserving Privacy, Increasing Accessibility, and Reducing Cost: An On-Device Artificial Intelligence Model for Medical Transcription and Note Generation

Clinical documentation represents a significant burden for healthcare providers, with physicians spending up to 2 hours daily on administrative tasks....

Hybrid Fuzzy Logic and Logistic Regression Model with Recursive Feature Elimination for Enhanced Prediction and Clinical Decision Support in Type 2 Diabetes Mellitus Among Adults Aged 35 to 45

This paper presents a hybrid model combining fuzzy logic, recursive feature elimination (RFE), and logistic regression to predict type 2 diabetes mell...

CausalDRIFT: Causal Dimensionality Reduction via Inference of Feature Treatments for Robust Healthcare Machine Learning

High-dimensional medical datasets present challenges in feature selection, where traditional methods often prioritize spurious correlations over causa...

Empowering digital health management with on-device large language models for glucose prediction

Long-term management of chronic diseases such as diabetes is increasingly based on wearable technologies, particularly continuous glucose monitoring (...

Myocardial Native T1 Mapping in the German National Cohort (NAKO): Associations with Age, Sex, and Cardiometabolic Risk Factors

In cardiovascular magnetic resonance (CMR), myocardial native T1 mapping enables quantitative, non-invasive tissue characterization and is sensitive t...

Development of an AI-enabled predictive model to identify the ‘sick child’ at a pediatric telemedicine and medication delivery service in Haiti

One of the most difficult challenges in pediatric telemedicine is to accurately discriminate between the ‘sick’ and ‘not sick’ child, especially in re...

Genetic Profiling and Early Detection of Type 2 Diabetes Subtypes through Sex-Stratified GWAS and Explainable AI

Type 2 diabetes (T2D) is a complex and clinically heterogeneous disease. Although clustering approaches have defined clinical subtypes, their genetic ...

Urinary steroid metabolome shows adrenal, gonadal, and neuroactive steroid dysregulation in adolescents with depression

Steroid hormone profiles in affective disorders suggest hypothalamic– pituitary–adrenal (HPA) axis dysregulation and may reveal novel therapeutic targ...

Genomics reveals eleven obesity endotypes with distinct biological and phenotypic signatures

Obesity, a leading global risk factor for cardiometabolic conditions, arises from multifaceted and biologically complex mechanisms1,2. To elucidate th...

Serum Exosomal Multi-Omic Signatures Stratify Glucose Tolerance in Cystic Fibrosis and Reveal Partial Therapeutic Reprogramming by CFTR Modulators

Cystic fibrosis-related diabetes (CFRD) affects up to 60% of adults with CF and contributes to poorer clinical outcomes, including accelerated lung de...

MedAdhereAI: An Interpretable Machine Learning Pipeline for Predicting Medication Non-Adherence in Chronic Disease Patients Using Real-World Refill Data

Medication non-adherence remains a significant challenge in managing chronic conditions like diabetes and hypertension, leading to increased morbidity...

AI-based Hepatic Steatosis Detection and Integrated Hepatic Assessment from Cardiac CT Attenuation Scans Enhances All-cause Mortality Risk Stratification: A Multi-center Study

Hepatic steatosis (HS) is a common cardiometabolic risk factor frequently present but under-diagnosed in patients with suspected or known coronary art...

A Data-Driven Approach to Polycystic Ovary Syndrome Diagnosis: Evaluating Machine Learning Models

PCOS is recognized as a major health concern affecting women around the world. Early detection and treatment of PCOS significantly reduce implications...