Hongmei Tan, Xuan Deng, Jingzi ZhangBao, Lei Zhou, Wenqing Wu, Haiqing Li, Yuxin Li, Yuxin Fan, Zhouzhou Wang, Yiqin Xiao, Chongbo Zhao, Ming Guan, Chao Quan, Haoqin Jiang
{"title":"在中国队列中,kflc指数区分多发性硬化与抗髓鞘少突胶质细胞糖蛋白和水通道蛋白4疾病。","authors":"Hongmei Tan, Xuan Deng, Jingzi ZhangBao, Lei Zhou, Wenqing Wu, Haiqing Li, Yuxin Li, Yuxin Fan, Zhouzhou Wang, Yiqin Xiao, Chongbo Zhao, Ming Guan, Chao Quan, Haoqin Jiang","doi":"10.1136/jnnp-2025-335953","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Kappa free light chain (KFLC) index has emerged as a diagnostic biomarker for multiple sclerosis (MS). This study aims to evaluate the diagnostic accuracy of the KFLC-index in Chinese patients with MS, and its capacity to discriminate MS from myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) and neuromyelitis optica spectrum disorders with aquaporin-4 antibody (AQP4+NMOSD).</p><p><strong>Methods: </strong>428 patients tested for KFLC-index were enrolled in the study, including 130 patients with MS, 41 with MOGAD, 25 with AQP4+NMOSD, 123 with other inflammatory or infectious neurological disorders (OIND) and 109 with non-inflammatory neurological disorders (NIND). Their oligoclonal band (OCB) results and clinical data were reviewed.</p><p><strong>Results: </strong>KFLC-index was significantly higher in MS (20.1 (0.9-388.9)) compared with MOGAD (4.8 (0.8-56.1), p=0.003), AQP4+NMOSD (4.5 (1.5-46.4), p=0.011), OIND (2.9 (0.6-238.7), p<0.001) and NIND (1.8 (0.6-110.7), p<0.001). The optimal cut-off value for the KFLC-index to identify MS from the non-selective controls was 8.3, with an accuracy comparable to that of OCB (area under the curve 0.84 vs 0.81, p=0.249). The optimal cut-off values for differentiating MS from MOGAD and AQP4+NMOSD were 18.5 and 12.1, with performance similar to OCB (p=0.756 and 0.064). Combination of KFLC-index and OCB outperformed OCB alone in differentiating MS from non-selective controls and MOGAD (p<0.001 and p=0.044). Female (p=0.009) and higher cerebrospinal fluid leucocyte count (p<0.001) were associated with higher KFLC-index in MS.</p><p><strong>Conclusion: </strong>KFLC-index is a valuable diagnostic tool for differentiating MS from other inflammatory demyelinating diseases.</p>","PeriodicalId":16418,"journal":{"name":"Journal of Neurology, Neurosurgery, and Psychiatry","volume":" ","pages":""},"PeriodicalIF":8.7000,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"KFLC-index distinguishes multiple sclerosis from anti-myelin oligodendrocyte glycoprotein and aquaporin 4 diseases in a Chinese cohort.\",\"authors\":\"Hongmei Tan, Xuan Deng, Jingzi ZhangBao, Lei Zhou, Wenqing Wu, Haiqing Li, Yuxin Li, Yuxin Fan, Zhouzhou Wang, Yiqin Xiao, Chongbo Zhao, Ming Guan, Chao Quan, Haoqin Jiang\",\"doi\":\"10.1136/jnnp-2025-335953\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Kappa free light chain (KFLC) index has emerged as a diagnostic biomarker for multiple sclerosis (MS). This study aims to evaluate the diagnostic accuracy of the KFLC-index in Chinese patients with MS, and its capacity to discriminate MS from myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) and neuromyelitis optica spectrum disorders with aquaporin-4 antibody (AQP4+NMOSD).</p><p><strong>Methods: </strong>428 patients tested for KFLC-index were enrolled in the study, including 130 patients with MS, 41 with MOGAD, 25 with AQP4+NMOSD, 123 with other inflammatory or infectious neurological disorders (OIND) and 109 with non-inflammatory neurological disorders (NIND). Their oligoclonal band (OCB) results and clinical data were reviewed.</p><p><strong>Results: </strong>KFLC-index was significantly higher in MS (20.1 (0.9-388.9)) compared with MOGAD (4.8 (0.8-56.1), p=0.003), AQP4+NMOSD (4.5 (1.5-46.4), p=0.011), OIND (2.9 (0.6-238.7), p<0.001) and NIND (1.8 (0.6-110.7), p<0.001). The optimal cut-off value for the KFLC-index to identify MS from the non-selective controls was 8.3, with an accuracy comparable to that of OCB (area under the curve 0.84 vs 0.81, p=0.249). The optimal cut-off values for differentiating MS from MOGAD and AQP4+NMOSD were 18.5 and 12.1, with performance similar to OCB (p=0.756 and 0.064). Combination of KFLC-index and OCB outperformed OCB alone in differentiating MS from non-selective controls and MOGAD (p<0.001 and p=0.044). Female (p=0.009) and higher cerebrospinal fluid leucocyte count (p<0.001) were associated with higher KFLC-index in MS.</p><p><strong>Conclusion: </strong>KFLC-index is a valuable diagnostic tool for differentiating MS from other inflammatory demyelinating diseases.</p>\",\"PeriodicalId\":16418,\"journal\":{\"name\":\"Journal of Neurology, Neurosurgery, and Psychiatry\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":8.7000,\"publicationDate\":\"2025-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Neurology, Neurosurgery, and Psychiatry\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1136/jnnp-2025-335953\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Neurology, Neurosurgery, and Psychiatry","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1136/jnnp-2025-335953","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
KFLC-index distinguishes multiple sclerosis from anti-myelin oligodendrocyte glycoprotein and aquaporin 4 diseases in a Chinese cohort.
Background: Kappa free light chain (KFLC) index has emerged as a diagnostic biomarker for multiple sclerosis (MS). This study aims to evaluate the diagnostic accuracy of the KFLC-index in Chinese patients with MS, and its capacity to discriminate MS from myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) and neuromyelitis optica spectrum disorders with aquaporin-4 antibody (AQP4+NMOSD).
Methods: 428 patients tested for KFLC-index were enrolled in the study, including 130 patients with MS, 41 with MOGAD, 25 with AQP4+NMOSD, 123 with other inflammatory or infectious neurological disorders (OIND) and 109 with non-inflammatory neurological disorders (NIND). Their oligoclonal band (OCB) results and clinical data were reviewed.
Results: KFLC-index was significantly higher in MS (20.1 (0.9-388.9)) compared with MOGAD (4.8 (0.8-56.1), p=0.003), AQP4+NMOSD (4.5 (1.5-46.4), p=0.011), OIND (2.9 (0.6-238.7), p<0.001) and NIND (1.8 (0.6-110.7), p<0.001). The optimal cut-off value for the KFLC-index to identify MS from the non-selective controls was 8.3, with an accuracy comparable to that of OCB (area under the curve 0.84 vs 0.81, p=0.249). The optimal cut-off values for differentiating MS from MOGAD and AQP4+NMOSD were 18.5 and 12.1, with performance similar to OCB (p=0.756 and 0.064). Combination of KFLC-index and OCB outperformed OCB alone in differentiating MS from non-selective controls and MOGAD (p<0.001 and p=0.044). Female (p=0.009) and higher cerebrospinal fluid leucocyte count (p<0.001) were associated with higher KFLC-index in MS.
Conclusion: KFLC-index is a valuable diagnostic tool for differentiating MS from other inflammatory demyelinating diseases.
期刊介绍:
The Journal of Neurology, Neurosurgery & Psychiatry (JNNP) aspires to publish groundbreaking and cutting-edge research worldwide. Covering the entire spectrum of neurological sciences, the journal focuses on common disorders like stroke, multiple sclerosis, Parkinson’s disease, epilepsy, peripheral neuropathy, subarachnoid haemorrhage, and neuropsychiatry, while also addressing complex challenges such as ALS. With early online publication, regular podcasts, and an extensive archive collection boasting the longest half-life in clinical neuroscience journals, JNNP aims to be a trailblazer in the field.