In silico exploration of potential associations between aspartame and cardiometabolic disease networks: a network...

Aspartame is a widely used artificial sweetener, and concerns regarding its potential cardiometabolic effects have attracted increasing attention. However, the molecular mechanisms linking aspartame exposure to cardiometabolic diseases (CMD) remain incompletely understood. This study aimed to systematically explore potential molecular associations between aspartame and multiple CMD using an integrated computational approach. Potential protein targets of aspartame were predicted using the Comparative Toxicogenomics Database, ChEMBL, and SwissTargetPrediction. CMD-related targets were retrieved from GeneCards. Overlapping targets were used to construct protein-protein interaction networks and identify hub genes and enriched pathways. Functional enrichment analysis was performed to characterize biological processes and signaling pathways. Molecular docking and molecular dynamics (MD) simulations were conducted to evaluate the binding interactions and structural stability between aspartame and core targets. A total of 45 overlapping targets were identified between aspartame and CMD. Functional enrichment analyses revealed that these targets were primarily involved in inflammation, lipid metabolism, insulin resistance, and the renin-angiotensin-aldosterone system (RAAS) signaling pathways. Network analysis highlighted AKT1, IL - 6, AGT, TNF, INS, and STAT3 as central hub genes. Notably, AGT ranked among the top targets. Molecular docking and three independent 200 ns MD simulations suggested stable binding interactions between aspartame and AGT, supported by favorable binding free energies (average ΔG =  - 20.79 ± 3.97 kcal/mol), dominated by van der Waals forces. This integrative in silico study provides a systems-level overview of potential molecular associations between aspartame and cardiometabolic disease networks. The findings offer plausible, hypothesis-generating mechanistic insights, particularly involving RAAS-related pathways, and lay the groundwork for future experimental validation. Importantly, this computational study does not establish causality and should not be interpreted as evidence of clinical effects.