Lu ZY, Li YF, Bao L, Wang XT, Wu H, Xu YF, Wang Y, Chen Y. Assessing residual cardiovascular risk and vulnerable plaques via non-traditional lipids: From biomarkers to novel targets for precision therapy. World J Cardiol 2026; 18(8): 123626 [DOI: 10.4330/wjc.123626]
Corresponding Author of This Article
Yan Chen, Department of Emergency and Critical Care Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, No. 1 Lijiang Road, High-tech Zone, Suzhou 215000, Jiangsu Province, China. chenyandoc@njmu.edu.cn
Research Domain of This Article
Cardiac & Cardiovascular Systems
Article-Type of This Article
review-article
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Lu ZY, Li YF, Bao L, Wang XT, Wu H, Xu YF, Wang Y, Chen Y. Assessing residual cardiovascular risk and vulnerable plaques via non-traditional lipids: From biomarkers to novel targets for precision therapy. World J Cardiol 2026; 18(8): 123626 [DOI: 10.4330/wjc.123626]
World J Cardiol. Aug 26, 2026; 18(8): 123626 Published online Aug 26, 2026. doi: 10.4330/wjc.123626
Assessing residual cardiovascular risk and vulnerable plaques via non-traditional lipids: From biomarkers to novel targets for precision therapy
Zi-Yi Lu, Ya-Fei Li, Lei Bao, Xu-Tong Wang, Hui Wu, Yin-Fei Xu, Yi Wang, Yan Chen
Zi-Yi Lu, Xu-Tong Wang, Hui Wu, Yin-Fei Xu, Yi Wang, Yan Chen, Department of Emergency and Critical Care Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou 215000, Jiangsu Province, China
Zi-Yi Lu, Department of Emergency and Critical Care Medicine, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, Jiangsu Province, China
Ya-Fei Li, Department of Cardiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou 215000, Jiangsu Province, China
Lei Bao, Department of Emergency Medicine, Nanjing First Hospital, Nanjing 210009, Jiangsu Province, China
Yan Chen, Department of Emergency Management, School of Health Policy and Management, Nanjing Medical University, Nanjing 211166, Jiangsu Province, China
Author contributions: Lu ZY conceptualized the review, wrote the original draft, and prepared the figures; Wang XT and Wu H performed the literature search and prepared the figures; Xu YF and Wang Y reviewed and edited the manuscript; Chen Y, Li YF, and Bao L conceptualized and supervised the work and critically revised the manuscript; all authors have read and approved the final manuscript.
AI contribution statement: The authors take full responsibility and accountability for all content of this manuscript, including any portions for which AI tools were used as assistive technologies. All AI-assisted outputs were carefully reviewed, validated, and approved by the authors. AI tools were not used to generate original scientific data, perform independent scientific analyses, or draw scientific conclusions.
Supported by National Science and Technology Major Project, No. 2023ZD0503902.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Yan Chen, Department of Emergency and Critical Care Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, No. 1 Lijiang Road, High-tech Zone, Suzhou 215000, Jiangsu Province, China. chenyandoc@njmu.edu.cn
Received: May 25, 2026 Revised: July 6, 2026 Accepted: August 20, 2026 Published online: August 26, 2026 Processing time: 94 Days and 19.2 Hours
Core Tip
Core Tip: Traditional lipids may underestimate residual cardiovascular risk and do not fully identify biologically vulnerable plaques. This review critically evaluates lipoprotein(a), low-density lipoprotein particle number, small dense low-density lipoprotein cholesterol, non-high-density lipoprotein cholesterol, apolipoproteins E and A1, remnant cholesterol, and polyunsaturated fatty acids. It distinguishes guideline-endorsed measures from investigational biomarkers, summarizes clinically relevant thresholds and therapeutic evidence, and highlights assay standardization, unit conversion, and reference-interval limitations. Integration with plaque imaging and artificial intelligence may improve individualized risk stratification and precision prevention.
