自身免疫性脑炎（autoimmune encephalitis， AE）是一组由免疫介导的中枢神经系统炎症性疾病，临床表现复杂多样，为早期诊断与风险评估带来挑战。特异性自身抗体检测在AE的分型和确诊中具有核心作用，但在抗体检测受限或抗体阴性（30%~50%的AE为抗体阴性AE）的临床情境下，仅依赖抗体检测往往难以满足AE诊疗需求。近年来，围绕血液和脑脊液可溶性生物标志物、影像学改变及遗传易感性等方面，非抗体类生物标志物研究迅速发展。这些研究不仅丰富了AE的病理生物学认识，也为临床提供了潜在的辅助诊断、疗效评估及个体化治疗依据。然而，目前非抗体标志物的验证仍有限，检测标准尚未统一，其临床应用仍面临诸多挑战。未来仍需依托大规模、前瞻性队列，结合人工智能等技术，系统探索非抗体标志物的动态变化及多指标联合评估在AE早期诊断、疗效监测和预后评估中的价值，以推动AE精准诊疗的临床转化。

Autoimmune encephalitis (AE) is a group of immune-mediated inflammatory diseases of the central nervous system, characterized by diverse and complex clinical manifestations, posing challenges for early diagnosis and risk assessment. Specific autoantibody testing plays a central role in the classification and diagnosis of AE. However, in clinical scenarios where antibody testing is limited or antibodies are negative (30%-50% of AE cases are antibody-negative AE), relying solely on antibody detection is often insufficient to meet the diagnostic and treatment needs of AE. In recent years, research on non-antibody biomarkers, including soluble biomarkers in blood and cerebrospinal fluid, imaging changes, and genetic susceptibility, has rapidly advanced. These studies not only deepen the understanding of AE pathobiology but also provide a potential basis for auxiliary diagnosis, treatment response evaluation, and individualized treatment in clinical practice. Nevertheless, the validation of non-antibody biomarkers remains limited, detection standards are not yet unified, and their clinical application still faces multiple challenges. Future research should leverage large-scale prospective cohorts, integrate artificial intelligence and other advanced technologies, and systematically investigate the value of dynamic changes in non-antibody biomarkers and multi-marker combined assessment in early diagnosis, treatment monito-ring, and prognosis evaluation of AE, thereby promoting the clinical translation of precision diagnosis and treatment for AE.