César Eduardo Ceja Tovar

Chronic diseases in internal medicine have traditionally been approached as isolated entities; however, emerging evidence supports their interpretation as interconnected processes sustained by metabolic–inflammatory networks. This review aimed to analyze and integrate current high-impact evidence on immunometabolism, chronic inflammation, and systemic dysregulation in order to propose a systems-based framework applicable to clinical practice and medical education. A structured narrative review was conducted using 20 peer-reviewed references indexed in major biomedical databases. The analysis focused on key mechanisms including metaflammation, insulin resistance, adipose tissue inflammation, vascular dysfunction, gut microbiota interactions, skeletal muscle signaling, and ageing-related inflammatory changes. The results demonstrated a consistent convergence across mechanistic, biological, and clinical domains, highlighting chronic inflammation as a central driver linking obesity, type 2 diabetes mellitus, atherosclerosis, and multimorbidity. Furthermore, the evidence revealed that these processes are dynamically influenced by lifestyle factors, environmental exposures, and ageing. The integration of these findings supports a network-based model of disease that transcends traditional organ-specific approaches. This framework offers potential benefits in clinical reasoning, risk stratification, preventive strategies, and medical education, particularly in regions with a high burden of chronic disease such as Mexico, Colombia, and Ecuador. In conclusion, metabolic–inflammatory networks represent a fundamental organizing principle in chronic disease, providing a coherent and clinically relevant perspective for modern internal medicine.

Cardiovascular diseases (CVDs) remain the leading cause of death worldwide, yet conventional predictive models often fail to capture the complex interplay of genetic and physiological risk factors in diverse populations. This study evaluated a hybrid predictive model integrating polygenic risk scores (PRS), artificial intelligence (AI) applied to electrocardiography (ECG), and traditional clinical variables to enhance cardiovascular risk prediction across Latin American cohorts from Mexico, Colombia, and Ecuador. A total of 6,450 participants aged 30 to 75 years were analyzed. PRS were derived from genome-wide association data, and ECGs were processed using deep convolutional neural networks. Model performance was assessed using AUROC, F1-score, and calibration metrics, with interpretability achieved through SHAP (Shapley Additive Explanations) analysis. The hybrid model demonstrated superior predictive accuracy (AUROC 0.91; F1-score 0.87; calibration slope 0.97) compared with the clinical (0.72), PRS (0.78), and AI-ECG (0.86) models. The most influential predictors were PRS, systolic blood pressure, age, and AI-derived electrical age. Subgroup analyses revealed consistent performance across ancestries (Mestizo 0.91; Amerindian 0.89; European 0.93; Afro-descendant 0.87) and countries, confirming the model’s generalizability and fairness. These findings demonstrate that integrating genetic and AI-based physiological data significantly improves cardiovascular risk assessment, enabling early, equitable, and personalized prevention. The proposed hybrid framework provides a scalable foundation for implementing AI-driven genomic precision medicine in multi-ancestry populations, marking an essential step toward reducing cardiovascular disparities in Latin America.