Xin Tan,1,2 Xiangyu Wang,1,2 Shuai Xu,1,2 Yiyao Zeng,1,2 Ge Zhang,3 Anchen Xu,1,2 Yufeng Jiang,1,2 Hezi Jiang,1,2 Yahui Song,4 Jili Fan,5 Yangjun Fu,6 Xiaohong Bo,5 Huimin Fan,1,4 Yafeng Zhou1,2 

1Department of Cardiology, the Fourth Affiliated Hospital of Soochow University, Suzhou Dushu Lake Hospital, Medical Center of Soochow University, Suzhou, 215000, People’s Republic of China; 2Department of Hypertension, the Fourth Affiliated Hospital of Soochow University, Institute for Hypertension, Soochow University, Suzhou, 215000, People’s Republic of China; 3Department of Cardiology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, People’s Republic of China; 4Center of Translational Medicine and Clinical Laboratory, the Fourth Affiliated Hospital to Soochow University, Suzhou Dushu Lake Hospital, Suzhou, 215028, People’s Republic of China; 5Department of Cardiovascular Disease, Taihe County People’s Hospital, Fuyang, 236600, People’s Republic of China; 6Department of Neurology, the Third People’s Hospital of Hefei, Hefei, 230041, People’s Republic of China

Correspondence: Huimin Fan, Email fhm_sunshine@163.com Yafeng Zhou, Email Dryafengzhou@163.com

Background: Acute myocardial infarction (AMI) is a critical cardiovascular event characterized by sudden coronary blood flow interruption, leading to myocardial ischemia and necrosis. Despite advances in acute therapeutic measures, understanding the metabolic damage related to AMI, particularly through specific protein expressions, remains limited. This study utilized Olink cardiovascular metabolomics technology to explore cardiovascular metabolism-related protein biomarkers associated with AMI, aiming to address the clinical need for early diagnosis and targeted therapy.
Methods: This study utilized Olink cardiovascular metabolomics technology to analyze 92 cardiovascular metabolism-related proteins in coronary blood samples from 20 AMI patients and 10 healthy controls. Differentially expressed proteins were identified using statistical t-tests, followed by functional enrichment analysis (GO and KEGG) and protein-protein interaction network construction. Five core proteins were validated in plasma samples from an additional 125 AMI patients and 120 healthy controls via enzyme-linked immunosorbent assay. To evaluate diagnostic performance, receiver operating characteristic curves were generated using GEO-related datasets, and Mendelian randomization analysis was employed to investigate the causal relationship between core proteins and AMI risk.
Results: The study identified 32 proteins with significantly altered expression levels between AMI patients and healthy controls. Among these, five core proteins—PCOLCE, FCN2, REG1A, DEFA1, and CRTAC1—were significantly associated with key biological processes such as metabolism, collagen formation, and the PI3K/AKT signaling pathway. These proteins showed strong correlations with clinical indicators, including BMI, LVEF, NT-proBNP, CK-MB, and cTnT. FCN2 and DEFA1 were further validated as having a causal relationship with AMI risk, indicating their potential as diagnostic biomarkers.
Conclusion: The identified core proteins PCOLCE, FCN2, REG1A, DEFA1, and CRTAC1 are potential biomarkers for the early diagnosis and risk assessment of AMI. These findings suggest that these proteins could serve as targets for future therapeutic interventions aimed at mitigating cardiovascular metabolic damage in AMI.

Keywords: acute myocardial infarction, cardiovascular metabolomics, Olink proteomics, biomarkers