Construction and Validation of an Early Diagnosis Model for Atherosclerosis Based on Lactate Metabolism-related Genes.

Atherosclerosis (AS), a chronic inflammatory disease characterized by arterial plaque formation, remains a leading global cause of cardiovascular mortality; however, the molecular pathways that contribute to AS, particularly the role of lactate metabolism-related genes (LMRGs), remain poorly understood. This study aims to identify novel biomarkers and diagnostic models for early AS diagnosis by examining LMRGs. The AS datasets GSE100927, GSE40231, and GSE28829 were acquired from the Gene Expression Omnibus (GEO) database. Using bioinformatics approaches, including differential gene expression, functional enrichment, gene set enrichment analysis, random forest algorithm, Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, and immune infiltration profiling, we analyzed integrated AS datasets, developed a diagnostic model using key LMRGs, and validated this model through quantitative polymerase chain reaction (qPCR) using clinical AS samples. We identified 15 differentially expressed LMRGs (LMRDEGs) and developed a LASSO regression model with five genes predictive of AS. The diagnostic model achieved high accuracy (area under the curve (AUC) > 0.9). Functional enrichment indicated that LMRDEGs play roles in lactate metabolism, small molecule catabolism, and pathways such as HIF-1 signaling and glycolysis/gluconeogenesis. Immune-cell infiltration analysis further indicated notable immune-cell variations across risk categories, with activated B cells, CD4+ T cells, and natural killer T cells exhibiting strong positive correlations. Clinical validation via qPCR confirmed significant expression differences for two genes (DISC1 and PIK3C2A), achieving AUCs of 0.730 and 0.758 and supporting the model's clinical relevance. This study demonstrates that LMRGs are critically involved in the pathogenesis of AS, providing a novel molecular framework for early diagnosis and mechanistic insight. Functional analyses underscore the role of lactate-driven metabolic reprogramming in linking immune inflammation to plaque instability. Furthermore, immune infiltration analysis indicates that LMRGs may regulate immune cell recruitment, further supporting the "metabolism-inflammation" cycle concept in AS. Clinical validation confirms the differential expression and diagnostic value of DISC1 and PIK3C2A, reinforcing their relevance in human AS pathology. This study highlights potential diagnostic biomarkers for early-stage AS. The results also imply that lactate metabolism-related pathways are intertwined with inflammatory and immune responses, offering new insights into AS mechanisms.