From Molecules to Machines: An Integrative Framework Linking Molecular Pathogenesis, Multi-Factorial Risk, Risk...

Area of Science:

• Cardiology
• Genetics
• Pharmacology

Background:

• QT prolongation is a critical predictor of torsades de pointes and sudden cardiac death.
• Risk factors include heritable, pharmacologic, metabolic, and nutritional triggers, often studied in isolation.
• Understanding the interplay of these factors is essential for comprehensive risk assessment.

Purpose of the Study:

• To synthesize current research on the molecular pathogenesis, risks, clinical stratification, therapy, and artificial intelligence (AI) prediction of QT prolongation.
• To provide an integrated perspective on managing QT prolongation by considering diverse contributing factors.

Main Methods:

• Integrative review of existing literature on QT prolongation.
• Analysis of molecular mechanisms, genetic factors (including dual-function channel mutations), and post-translational modifications.
• Evaluation of acquired risks (drug-gene-metabolic interactions), metabolic factors (insulin resistance, NAFLD, adiposity), and nutritional exposures.
• Assessment of clinical stratification tools and AI-based prediction models.

Main Results:

• Congenital Long QT Syndrome (LQTS) involves complex genetic and molecular defects beyond classic genes.
• Drug-gene-metabolic interactions, insulin resistance, NAFLD, adiposity, and specific nutritional exposures significantly amplify acquired risk.
• QTc prolongation alone is insufficient; T-wave morphology, genotype, and electromechanical window dynamics improve prognostic value.
• Nonpenetrant LQTS poses significant event risk.
• Machine learning, particularly deep learning, shows superior performance in predicting risk and differentiating congenital from acquired QT prolongation on ECG compared to traditional clinical scores.

Conclusions:

• Precision management of QT prolongation necessitates integrated strategies encompassing genetic, metabolic, nutritional, and AI-driven predictive approaches.
• Validated therapeutic alternatives like genotype-targeted mexiletine and left cardiac sympathetic denervation exist.
• Further prospective studies are required to validate AI tools for clinical decision-making in QT prolongation management.