细胞免疫与脂代谢相关炎症指标在IVIG无应答川崎病预测中的研究进展
Research Advances in Predicting Intravenous Immunoglobulin Non-Response in Kawasaki Disease Using Cellular Immunity and Lipid Metabolism-Associated Inflammatory Markers
摘要: 静脉注射免疫球蛋白(IVIG)无应答是川崎病(KD)临床管理中的重点与难点，与冠状动脉病变的发生风险密切相关。早期识别IVIG无应答高危患儿对优化治疗策略、改善预后具有重要意义。近年来，基于常规实验室检测的复合型炎症衍生指标因其简便、经济、可动态监测等优势而受到广泛关注。本章从细胞免疫与脂代谢两个关键维度上，系统综述了相关炎症指标在预测IVIG无应答川崎病中的研究进展，旨在为临床早期识别IVIG无应答高危患儿及后续研究提供参考。同时，本文对不同指标的证据强度、临床可及性和可重复性进行分层评价，强调对证据不足的新兴指标应审慎解读。
Abstract: Intravenous Immunoglobulin (IVIG) resistance presents a significant clinical challenge in the management of Kawasaki Disease (KD), strongly correlating with an increased risk of coronary artery lesions. The early identification of patients at high risk for IVIG resistance is crucial for refining treatment protocols and enhancing long-term outcomes. In recent years, composite inflammatory biomarkers derived from routine laboratory tests have garnered considerable interest owing to their simplicity, cost-effectiveness, and suitability for dynamic monitoring. This chapter provides a systematic review of the research advancements in these inflammatory indicators as predictors of IVIG resistance in KD, focusing on two critical aspects: cellular immunity and lipid metabolism. The objective is to offer a valuable resource for the early clinical identification of high-risk patients and to guide future studies in this field. The review also stratifies the strength of evidence, clinical feasibility, and reproducibility of different markers, emphasizing that emerging indicators with limited direct evidence should be interpreted cautiously.
文章引用：夏海伊, 易岂建. 细胞免疫与脂代谢相关炎症指标在IVIG无应答川崎病预测中的研究进展[J]. 临床医学进展, 2026, 16(6): 69-79. https://doi.org/10.12677/acm.2026.1662196

参考文献

[1] Jone, P., Tremoulet, A., Choueiter, N., Dominguez, S.R., Harahsheh, A.S., Mitani, Y., et al. (2024) Update on Diagnosis and Management of Kawasaki Disease: A Scientific Statement from the American Heart Association. Circulation, 150, e481-e500. [Google Scholar] [CrossRef] [PubMed]
[2] Burns, J.C. (2024) The Etiologies of Kawasaki Disease. Journal of Clinical Investigation, 134, e176938. [Google Scholar] [CrossRef] [PubMed]
[3] Barman, P., Pilania, R.K., Cv, G., Thangaraj, A., Arora, M. and Singh, S. (2024) Treatment Intensification in Kawasaki Disease—Current Perspectives. Expert Review of Clinical Immunology, 20, 1179-1191. [Google Scholar] [CrossRef] [PubMed]
[4] Han, J.W. (2018) Factors Predicting Resistance to Intravenous Immunoglobulin and Coronary Complications in Kawasaki Disease: IVIG Resistance in Kawasaki Disease. Korean Circulation Journal, 48, 86-88. [Google Scholar] [CrossRef] [PubMed]
[5] Wallace, C.A., French, J.W., Kahn, S.J. and Sherry, D.D. (2000) Initial Intravenous Gammaglobulin Treatment Failure in Kawasaki Disease. Pediatrics, 105, e78-e78. [Google Scholar] [CrossRef] [PubMed]
[6] Duignan, S.M., Brennan, K., Connolly, E., Watson, A., Noone, D., Dunne, E., et al. (2025) Single Nucleotide Polymorphisms and Their Association with Coronary Artery Aneurysms and IVIG Resistance in Kawasaki Disease in Ireland. Pediatric Cardiology, 47, 2018-2028. [Google Scholar] [CrossRef] [PubMed]
[7] Choi, H.Y. and Han, M.Y. (2025) Nationwide Validation of Japanese Initial Intravenous Immunoglobulin (IVIG) Resistance Scores in Korean Children with Kawasaki Disease. Kawasaki Disease, 3, e16. [Google Scholar] [CrossRef] 
[8] Lam, J.Y., Song, M., Kim, G., Shimizu, C., Bainto, E., Tremoulet, A.H., et al. (2023) Intravenous Immunoglobulin Resistance in Kawasaki Disease Patients: Prediction Using Clinical Data. Pediatric Research, 95, 692-697. [Google Scholar] [CrossRef] [PubMed]
[9] Ahn, J.G. and Kang, I. (2026) Multiomics Approaches in Kawasaki Disease: Insights into Pathogenesis and Emerging Directions for Diagnosis and Treatment. Clinical and Experimental Pediatrics, 69, 197-210. [Google Scholar] [CrossRef] 
[10] Han, X. and Qi, H. (2025) Pyroptosis in Kawasaki Disease: From Mechanisms to Targeted Interventions. Frontiers in Immunology, 16, Article 1566985. [Google Scholar] [CrossRef] [PubMed]
[11] Wang, C., Wu, Q., Chen, J., Wang, J. and Li, D. (2025) Mechanistic Role of Pyroptosis in Kawasaki Disease: An Integrative Bioinformatics Analysis of Immune Dysregulation, Machine Learning-Based Biomarker Discovery, WGCNA, and Drug Repurposing Insights. PLOS ONE, 20, e0323597. [Google Scholar] [CrossRef] [PubMed]
[12] 朱艳, 梁子辉, 任韫卓, 等. NLRP3炎症小体介导的炎症反应参与糖尿病导致的肾损伤和脂代谢异常[J]. 中国病理生理杂志, 2020, 36(1): 53-58.
[13] 杨晓茹, 陈宇浩, 郝慧芳. CD36在炎症反应和脂质代谢中的作用[J]. 生命科学, 2019, 31(11): 1192-1199.
[14] 曹洁玮, 刘艳, 安占军, 等. 阿托伐他汀钙对冠心病合并糖尿病患者脂代谢、炎症指标及心室重构的影响[J]. 中国医学创新, 2017, 14(5): 14-17.
[15] Xu, Y., Yuan, Y., Mou, L., Hui, L., Zhang, X., Yao, X., et al. (2024) scRNA + TCR-seq Reveals the Pivotal Role of Dual Receptor T Lymphocytes in the Pathogenesis of Kawasaki Disease and during IVIG Treatment. Frontiers in Immunology, 15, Article 1457687. [Google Scholar] [CrossRef] [PubMed]
[16] Atici, A.E., Noval Rivas, M. and Arditi, M. (2024) The Central Role of Interleukin-1 Signalling in the Pathogenesis of Kawasaki Disease Vasculitis: Path to Translation. Canadian Journal of Cardiology, 40, 2305-2320. [Google Scholar] [CrossRef] [PubMed]
[17] 梅超生, 闵轩. 血常规衍生复合炎症指标在冠心病患者中研究进展[J]. 中国心血管病研究, 2023, 21(12): 1145-1150.
[18] Taha, S.I., Samaan, S.F., Ibrahim, R.A., Moustafa, N.M., El-Sehsah, E.M. and Youssef, M.K. (2022) Can Complete Blood Count Picture Tell Us More about the Activity of Rheumatological Diseases? Clinical Medicine Insights: Arthritis and Musculoskeletal Disorders, 15, Article 11795441221089182. [Google Scholar] [CrossRef] [PubMed]
[19] Islam, M.M., Satici, M.O. and Eroglu, S.E. (2024) Unraveling the Clinical Significance and Prognostic Value of the Neutrophil-to-Lymphocyte Ratio, Platelet-to-Lymphocyte Ratio, Systemic Immune-Inflammation Index, Systemic Inflammation Response Index, and Delta Neutrophil Index. Turkish Journal of Emergency Medicine, 24, 8-19. [Google Scholar] [CrossRef] [PubMed]
[20] Cheng, Y. and Chen, B. (2026) Predictors of Intravenous Immunoglobulin Non‐Responsiveness in Children with Kawasaki Disease. Immunity, Inflammation and Disease, 14, e70354. [Google Scholar] [CrossRef] 
[21] 李灿灿, 朱雪萍, 孙文强, 等. 6月龄以下婴儿川崎病的临床特征分析[J]. 临床儿科杂志, 2026, 44(3): 209-216.
[22] Lim, Y.T., Kwon, J.E. and Kim, Y.H. (2025) Clinical Utility of CBC-Derived Inflammatory Markers for Predicting IVIG Resistance: Findings from a Korean Infant Kawasaki Disease Cohort. Pediatric Rheumatology, 23, Article No. 116. [Google Scholar] [CrossRef] 
[23] Liu, Z., Huang, X. and Zhao, S. (2024) Diagnostic Accuracy of Neutrophil Lymphocyte Ratio and Platelet Lymphocyte Ratio for IVIG-Resistance Kawasaki Disease: An Updated Meta-Analysis. Journal of Public Health & Environment, 7, 1-21. [Google Scholar] [CrossRef] 
[24] Wu, Y., Huang, Y., Wu, Y., Sun, J., Xie, Q. and Yin, G. (2025) Systemic Immune-Inflammation Index as a Versatile Biomarker in Autoimmune Disorders: Insights from Rheumatoid Arthritis, Lupus, and Spondyloarthritis. Frontiers in Immunology, 16, Article 1621209. [Google Scholar] [CrossRef] [PubMed]
[25] Yi, C., Zhou, Y., Guo, J., Chen, J. and She, X. (2024) Novel Predictors of Intravenous Immunoglobulin Resistance in Patients with Kawasaki Disease: A Retrospective Study. Frontiers in Immunology, 15, Article 1399150. [Google Scholar] [CrossRef] [PubMed]
[26] Masuda, H., Matsubayashi, J. and Ae, R. (2025) Low Platelet Count, Immunoglobulin Resistance, and Coronary Artery Involvement in Kawasaki Disease. Pediatric Research. [Google Scholar] [CrossRef] 
[27] Ye, Z., Hu, T., Wang, J., Xiao, R., Liao, X., Liu, M., et al. (2022) Systemic Immune-Inflammation Index as a Potential Biomarker of Cardiovascular Diseases: A Systematic Review and Meta-Analysis. Frontiers in Cardiovascular Medicine, 9, Article 933913. [Google Scholar] [CrossRef] [PubMed]
[28] Bolos, O.C., Sorca, B., Rusu, L. and Tapalaga, G. (2024) Comparative Assessment of the qSOFA, SII, dNLR, and OISS Infection Severity Scores in Diabetic versus Non-Diabetic Patients with Odontogenic Infections. Biomedicines, 12, Article 2712. [Google Scholar] [CrossRef] [PubMed]
[29] Akdogan, M.R. (2024) A Potential Biomarker of Disease Activity in Systemic Lupus Erythematosus, Systemic Immune-Inflammation Index. Northern Clinics of Istanbul, 11, 115-119. [Google Scholar] [CrossRef] [PubMed]
[30] Liu, B., Wang, J., Li, Y., Li, K. and Zhang, Q. (2023) The Association between Systemic Immune-Inflammation Index and Rheumatoid Arthritis: Evidence from NHANES 1999-2018. Arthritis Research & Therapy, 25, Article No. 34. [Google Scholar] [CrossRef] [PubMed]
[31] Tian, B., Yang, Y., Yang, C., Yan, L., Ding, Z., Liu, H., et al. (2022) Systemic Immune-Inflammation Index Predicts Prognosis of Cancer Immunotherapy: Systemic Review and Meta-analysis. Immunotherapy, 14, 1481-1496. [Google Scholar] [CrossRef] [PubMed]
[32] Yorulmaz, A., Hayran, Y., Akpinar, U., et al. (2020) Systemic Immune-Inflammation Index (SII) Predicts Increased Severity in Psoriasis and Psoriatic Arthritis. Current Health Sciences Journal, 46, 352-357.
[33] Dong, M., Shi, Y., Yang, J., Zhou, Q., Lian, Y., Wang, D., et al. (2020) Prognostic and Clinicopathological Significance of Systemic Immune-Inflammation Index in Colorectal Cancer: A Meta-Analysis. Therapeutic Advances in Medical Oncology, 12, Article 1758835920937425. [Google Scholar] [CrossRef] [PubMed]
[34] Kim, Y., Choi, H., Jung, S.M., Song, J.J., Park, Y. and Lee, S. (2019) Systemic Immune‐Inflammation Index Could Estimate the Cross-Sectional High Activity and the Poor Outcomes in Immunosuppressive Drug‐Naïve Patients with Antineutrophil Cytoplasmic Antibody‐associated Vasculitis. Nephrology, 24, 711-717. [Google Scholar] [CrossRef] [PubMed]
[35] Huang, T., Peng, Q., Zhang, Y., Zhu, Z. and Fan, X. (2024) The Systemic Immune-Inflammation Index (SII) and Coronary Artery Lesions in Kawasaki Disease. Clinical and Experimental Medicine, 24, Article No. 4. [Google Scholar] [CrossRef] [PubMed]
[36] Deng, L., Wang, T., Duan, Y., Liu, B., Jiang, J., Liu, D., et al. (2024) Neutrophil Percentage-to-Albumin Ratio Is a Potential Marker of Intravenous Immunoglobulin Resistance in Kawasaki Disease. Scientific Reports, 14, Article No. 15232. [Google Scholar] [CrossRef] [PubMed]
[37] Du, Q., Zhang, X., Sun, H., Bi, Z., Li, X., Wei, X., et al. (2025) Value of the Neutrophil Percentage-to-Albumin Ratio in Predicting Intravenous Immunoglobulin Resistance in Kawasaki Disease: A Retrospective Cohort Study. Italian Journal of Pediatrics, 51, Article No. 330. [Google Scholar] [CrossRef] 
[38] Zhang, C., Wang, L., Fan, Q. and Pan, Y. (2025) Polymorphism-Driven Immune Disruptions in Kawasaki Disease across Populations: Decoding the Role of T and B-Cells. Frontiers in Immunology, 16, Article 1640024. [Google Scholar] [CrossRef] 
[39] Niu, M., Lin, Z., Zhang, F., Chen, Y., Zhang, L., Pan, J., et al. (2026) A Diagnostic Prediction Model Was Established Based on the Clinical Characteristics of Multicenter Children with Kawasaki Disease in Xinjiang. Frontiers in Cardiovascular Medicine, 12, Article 1608572. [Google Scholar] [CrossRef] 
[40] Kong, W., Gao, L., Hu, J., Xu, Z., Zhang, Q., Wang, Y., et al. (2025) Efficacy Analysis of a 12-Cytokine Panel for the Diagnosis of Kawasaki Disease and Prediction of Intravenous Immunoglobulin Resistance. Journal of Inflammation Research, 18, 15973-15983. [Google Scholar] [CrossRef] 
[41] 王萌, 张瑄, 邢淑华, 等. 血清趋化素水平与川崎病患儿急性期冠状动脉损害的相关研究[J]. 中国临床医生杂志, 2023, 51(11): 1361-1364.
[42] Wang, H., Chen, S., Zhang, C. and Huang, J. (2025) IL-10 and IL-2R as Combined Predictors of Intravenous Immunoglobulin Resistance in Kawasaki Disease: A Retrospective Cohort Study. Frontiers in Immunology, 16, Article 1646502. [Google Scholar] [CrossRef] 
[43] 杨雁华, 汤建民, 宿东升, 等. 冠心病患者CCTA斑块特征、血流储备分数及其与脂代谢、炎症反应指标的关系分析[J]. 临床放射学杂志, 2024, 43(11): 1903-1907.
[44] 赵星星, 贾永平, 范春雨, 等. 阿托伐他汀联合依折麦布对冠心病患者脂代谢指标及炎症反应的影响[J]. 中国药物与临床, 2019, 19(4): 590-592.
[45] 柳春霞, 朱琳, 李雅菁. 冠心病患者颈动脉斑块易损性与脂代谢、炎症反应、蛋白酶活性的相关性研究[J]. 海南医学院学报, 2018, 24(12): 1147-1150.
[46] Zhang, H., Cai, J., Zhang, R., Shuai, S., Tang, M., Ju, R., et al. (2024) The Role of Serum Lipid in Predicting Coronary Artery Lesions and Intravenous Immunoglobulin Resistance in Kawasaki Disease: A Cohort Study. Journal of International Medical Research, 52, Article 03000605241252115. [Google Scholar] [CrossRef] [PubMed]
[47] Wang, X., Han, L., Jiang, J., Fan, Z., Hua, Y., He, L., et al. (2025) Alterations in Bile Acid Metabolites Associated with Pathogenicity and IVIG Resistance in Kawasaki Disease. Frontiers in Cardiovascular Medicine, 12, Article 1549900. [Google Scholar] [CrossRef] [PubMed]
[48] Li, L., Xu, X., Guo, Y., Yan, Y., Li, M., Zhao, H., et al. (2025) C-Reactive Protein to High Density Lipoprotein Cholesterol Ratio for Predicting Coronary Artery Lesions, and Intravenous Immunoglobulin Resistance in Kawasaki Disease. Pediatric Rheumatology, 23, Article No. 108. [Google Scholar] [CrossRef] 
[49] Gao, Y., Peng, L., Liu, J., et al. (2025) Establishment and Validation of a Nomogram for Predicting Intravenous Immunoglobulin Resistance and Coronary Artery Lesions Involvement in Kawasaki Disease: A Retrospective Study. Clinical Rheumatology, 44, 799-809.
[50] Wang, S., Luo, G., Ji, Z. and Pan, S. (2026) TREM-1-Associated Neutrophil Extracellular Trap Formation Is Linked to IVIG Resistance in Kawasaki Disease: A Convergent Transcriptomic and Prospective Validation Study. Journal of Clinical Immunology. [Google Scholar] [CrossRef] 
[51] Xia, Y., Huang, Y., Gong, M., Liu, W., Meng, Y., Wu, H., et al. (2025) A Machine Learning-Based Model to Predict Intravenous Immunoglobulin Resistance in Kawasaki Disease. iScience, 28, Article ID: 112004. [Google Scholar] [CrossRef] [PubMed]
[52] Yi, C., Xue, D., Chen, J., Guo, J., Zhou, Y., Hu, Y., et al. (2025) Predictive Value of Novel Nutritional Inflammation Indexes in IVIG-Unresponsive Kawasaki Disease: A Retrospective Study. Frontiers in Nutrition, 12, Article 1651750. [Google Scholar] [CrossRef] 
[53] Zhang, J., Huang, H., Xu, L., Wang, S., Gao, Y., Zhuo, W., et al. (2024) Knowledge Framework of Intravenous Immunoglobulin Resistance in the Field of Kawasaki Disease: A Bibliometric Analysis (1997-2023). Immunity, Inflammation and Disease, 12, e1277. [Google Scholar] [CrossRef] [PubMed]
[54] Li, J., Tian, Y., Zhu, Y., Zhou, T., Li, J., Ding, K., et al. (2020) A Multicenter Random Forest Model for Effective Prognosis Prediction in Collaborative Clinical Research Network. Artificial Intelligence in Medicine, 103, Article ID: 101814. [Google Scholar] [CrossRef] [PubMed]
[55] Wang, S., Lv, H. and Liu, z. (2025) Abstract 4366024: Development and Validation of an Interpretable Prediction Model for IVIG-Resistant Kawasaki Disease: A Multicenter Cohort Study. Circulation, 152, A4366024. [Google Scholar] [CrossRef] 
[56] Cheon, E.J., Kim, G.B. and Park, S. (2025) Predictive Modeling of Consecutive Intravenous Immunoglobulin Treatment Resistance in Kawasaki Disease: A Nationwide Study. Scientific Reports, 15, Article No. 903. [Google Scholar] [CrossRef] [PubMed]
[57] Li, X., Zhou, Z., Fan, J., Zhao, L., Xu, R., Li, D., et al. (2025) A Machine Learning Algorithm to Predict Treatment Effectiveness for Kawasaki Disease in China: A Retrospective Model Development and Validation Study. Frontiers in Immunology, 16, Article 1629600. [Google Scholar] [CrossRef] 
[58] Shi, T., Wang, F., An, X., Wang, Z., Xu, Y., Niu, L., et al. (2026) Development and Validation of a Novel Interpretable Machine Learning Model Integrating Immune-Inflammatory Indicators for Intravenous Immunoglobulin Resistance in Kawasaki Disease. Translational Pediatrics, 15, 74-74. [Google Scholar] [CrossRef] 
[59] Deng, L., Zhao, J., Wang, T., Liu, B., Jiang, J., Jia, P., et al. (2024) Construction and Validation of Predictive Models for Intravenous Immunoglobulin-Resistant Kawasaki Disease Using an Interpretable Machine Learning Approach. Clinical and Experimental Pediatrics, 67, 405-414. [Google Scholar] [CrossRef] [PubMed]
[60] Zhang, C., Zhao, Y., Liu, Y., Liu, C., Wei, Y., Xu, N., et al. (2026) Machine Learning Prediction Models for Intravenous Immunoglobulin Resistance in Kawasaki Disease: A Meta-Analysis. BMC Medical Informatics and Decision Making. [Google Scholar] [CrossRef] 
[61] Huang, H., He, Y., Lv, H., et al. (2026) Temporal Drift in a Kawasaki Disease IVIG Resistance Prediction Model and Adaptive Updating with Half-Life Weighting. SSRN.
[62] Nakaoka, H., Hirono, K., Hara, A., Tsuboi, K., Ibuki, K., ozawa, s., et al. (2025) Abstract 4359772: A First-in-Class Ev-Mirna Diagnostic System for Early Identification of IVIG-Resistant Kawasaki Disease. Circulation, 152, A4359772. [Google Scholar] [CrossRef] 
[63] Wu, Y., Liu, P., Zhou, Y., Yang, Y., Li, S., Yin, W., et al. (2024) Combination of S100A12/TLR2 Signaling Molecules and Clinical Indicators in a New Predictive Model for IVIG-Resistant Kawasaki Disease. Scientific Reports, 14, Article No. 7261. [Google Scholar] [CrossRef] [PubMed]

为你推荐