Integrated In Vivo Optical Coherence Tomography and Metabolomics in a Rabbit Model Identify Specific Metabolic Dysregulation Underlying In-Stent Neoatherosclerosis

Background In-stent neoatherosclerosis (ISNA) leads to late stent thrombosis (LST) and very late stent thrombosis (VLST), which are major limitations of drug-eluting stent (DES) therapy. However, the metabolic drivers of ISNA progression remain poorly defined. This study aimed to identify specific metabolic molecular dysregulation underlying ISNA. Methods Thirty-two New Zealand White rabbits underwent DES implantation in the iliac artery. Surviving animals were randomized to receive either an 8-week high-cholesterol diet (HCD; 1% cholesterol, n = 22) or a standard diet (Control, n = 6). Serum samples were collected at baseline and 8 weeks post-diet for untargeted metabolomic analysis. Optical coherence tomography (OCT) and histological examination were utilized to classify ISNA progression. Molecular docking simulations elucidated structural interactions between potential biomarkers and their target receptors. Results Untargeted metabolomics identified 36883 metabolic peaks, with 276 metabolites annotated. Significant metabolic dysregulation was observed 8 weeks post-diet compared to baseline (paired t-test, p  < 0.05). Specifically, 136 metabolites were altered in ISNA progressors (16/22 HCD-fed rabbits), 76 in non-progressors (6/22 HCD-fed rabbits), and 36 in controls. Pathway enrichment analysis linked ISNA-associated metabolites to 10 dysregulated pathways. Notably, 11 metabolites (i.e., 9-trans-palmitelaidic acid, biliverdin, choline, cystine, histidine, L-proline, L-tryptophan, methionine, myristic acid, palmitoleic acid, and pipecolinic acid) exhibited specific changing trends in the ISNA group diverged from both non-ISNA group and Control group. Molecular docking revealed high-affinity binding of choline to Slc5a7 (ΔG = -3.3 kcal/mol). Conclusions This study provides comprehensive insights into ISNA-specific metabolic signatures, highlight the potential of personalized prevention strategies targeting individual metabolic profiles.