Metabolic Syndrome

The association between triglyceride glucose-frailty index and cardiometabolic multimorbidity among Chinese middle-aged and older adults: a national prospective cohort study.

BACKGROUND: Cardiometabolic multimorbidity (CMM) poses a growing global health burden, yet few studies have combined the Triglyceride-Glucose (TyG) index, which reflects metabolic dysfunction, with the Frailty Index (FI), which captures physiological reserve and aging-related vulnerability, to assess CMM risk. Given their complementary biological information, this study examines whether a composit...

Identification of MMP14 and FOS as key regulators of vascular smooth muscle cell senescence in diabetic kidney disease: A combined bioinformatics and experimental study.

OBJECTIVE: Diabetic kidney disease (DKD) is a leading microvascular complication of diabetes in which vascular smooth muscle cell (VSMC) senescence plays a pivotal pathogenic role. This study aimed to identify key genes regulating VSMC senescence in DKD through integrated bioinformatics and machine learning analysis, construct a diagnostic nomogram prediction model, screen candidate therapeutic co...

Integrated plasma proteomics and metabolomics reveal immunometabolic pathways and predictive signatures for age-related eye diseases.

BACKGROUND AND AIMS: Age-related eye diseases (AREDs) share aging as a major risk factor, but the systemic molecular changes preceding disease onset r...

Tracing the evolution of cardiology: Pioneering discoveries and paradigm shifts in cardiovascular care.

Cardiology has undergone an impressive transformation from the initial anatomical observations of William Harvey to the current data-driven precision ...

A novel serum metabolite classifier for identifying Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) integrating metabolomics and machine learning.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) presents a growing global health burden, while reliable non-invasive biom...

Using machine learning to identify the most important predictors of fatty liver index in healthy young Taiwanese men.

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide. While many factors have been associated with NAFLD, their...

A pilot study on the impact of large language model assistance on the evaluation of complex medical living kidney donor candidates.

BACKGROUND: Large language models (LLMs) are undergoing exploration as clinical decision support tools. However, their role in complex, high-stakes tr...

Machine Learning Unveils Dietary Antioxidants as Influential Factors for Diabetes-Cancer Comorbidity: Insights From National Health and Nutrition Examination Survey.

This study aims to assess the predictive value of dietary antioxidants in diabetes-cancer comorbidity using interpretable machine learning (ML) models...

1H NMR-Based Plasma Metabolomics and Machine Learning Reveal Distinct Metabolic Signatures of Breast Cancer in Nigerian Women.

Untargeted 1H NMR metabolomics offers a noninvasive means to identify biomarkers in breast cancer (BC) patients; however, metabolic signatures specifi...

Predicting primary aldosteronism: risk stratification-based and machine learning-based models.

AIMS: Primary aldosteronism (PA) screening is difficult in an unselected population of hypertensive patients, as it can be challenging. We report new ...

Identifying Actionable Glucose Patterns in Type 1 and Type 2 Diabetes Using Continuous Glucose Monitoring and Algorithmic Pattern Recognition.

BACKGROUND: Continuous glucose monitoring (CGM) provides real-time glucose data, aiding diabetes management. Identifying glucose patterns is difficult...

An interpretable machine learning model for predicting stroke in patients with hypertension: insights from the SPRINT.

Stroke is a leading cause of mortality worldwide, with hypertension being its most significant risk factor. However, few studies have specifically dev...

Prognostic value of stress hyperglycemia ratio, hemoglobin glycation index, and glycemic variability for postoperative atrial fibrillation: a machine learning-based prediction model.

Machine learning prediction of CKD progression in hyperglycemic elderly adults: a prospective community cohort study.

Hyperglycemia is a major risk factor for chronic kidney disease (CKD). This multicenter prospective study developed and validated a machine learning (...

Adapting the REVEAL Lite 2 score to the pediatric population through machine learning: insights from the REHIPED registry for pediatric pulmonary hypertension.

INTRODUCTION AND OBJECTIVES: Risk scores for pulmonary hypertension (PH) have been proven useful in adults. However, no risk score has been validated ...

Empowering Patients and Clinicians: LLMs in Hypertension Care: A Scoping Review.

Large language models have emerged as potential tools to support hypertension care, including diagnosis, treatment decision-making, and patient educat...

Leveraging routine clinical data for dementia risk prediction using machine learning.

BackgroundEarly diagnosis of dementia is essential for enabling timely interventions that may slow disease progression, improve patient outcomes, and ...

Unsupervised Agglomerative Cluster Phenotyping of Young Patients With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention.

BACKGROUND: Young patients with acute coronary syndrome (ACS) exhibit diverse demographic, clinical and angiographic characteristics. We hypothesized ...

Screening of metabolic-related biomarkers linking intervertebral disc degeneration and type 2 diabetes based on comprehensive bioinformatics analysis and machine learning.

BACKGROUND: Intervertebral disc degeneration (IVDD) is a prominent etiology of lower back pain. Type 2 diabetes (T2D), the most prevalent metabolic di...

Comparative evaluation of obesity-related indices for predicting incident hypertension: evidence from Chinese and UK longitudinal cohorts with machine learning interpretation.