Murin Peyton J, Schulze Nicholas F, Gao Vivian D, Valtcheva Manouela V, Geisler Stefanie
Department of Neurology, Saint Louis University, Saint Louis, Missouri, USA.
Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA.
Eur J Neurol. 2025 Aug;32(8):e70321. doi: 10.1111/ene.70321.
Small fiber neuropathy (SFN), resulting from dysfunction and loss of peripheral unmyelinated and thinly myelinated nerve fibers, is traditionally characterized by distal skin pain and sensory loss. Patients often report additional symptoms, including muscle cramps, myalgias, fatigue, subjective weakness, and neuropathic itch, but the prevalence and intensity of these symptoms remain poorly defined. Current treatments-focused primarily on pain-are often ineffective, possibly due to substantial between-patient heterogeneity. This study aimed to characterize the symptomatic variability of SFN and determine whether patients can be stratified into clinically meaningful subgroups.
Demographic, clinical, and laboratory data were analyzed from patients with skin biopsy-confirmed SFN (n = 203) and healthy controls (n = 30). SFN patients were clustered based on clinical features using unsupervised machine learning.
Fatigue-not neuropathic pain-was the most prevalent and intense symptom among patients with SFN. Muscle symptoms were as common and severe as neuropathic pain, while neuropathic itch was rare. Three distinct phenotypic clusters were identified: (1) an "algesic" group (20%) with severe neuropathic pain and co-occurring intense symptoms; (2) a "myalgic" group (60%) with prominent fatigue, myalgias, and subjective weakness but milder neuropathic pain; and (3) a "pauci-symptomatic" group with few, mild to moderate symptoms. Clusters differed by symptom profiles, disease duration, intraepidermal nerve fiber density, sex, and body mass index.
Fatigue and muscle symptoms are highly prevalent in SFN and may rival pain in clinical importance. Identifying patient subgroups provides a foundation for stratified treatment approaches and improved disease management.
小纤维神经病变（SFN）是由外周无髓鞘和薄髓鞘神经纤维功能障碍及丧失所致，传统上其特征为远端皮肤疼痛和感觉丧失。患者常报告其他症状，包括肌肉痉挛、肌痛、疲劳、主观虚弱和神经性瘙痒，但这些症状的患病率和严重程度仍未明确界定。目前主要针对疼痛的治疗往往无效，可能是由于患者间存在显著异质性。本研究旨在描述SFN的症状变异性，并确定患者是否可分为具有临床意义的亚组。
对经皮肤活检确诊为SFN的患者（n = 203）和健康对照者（n = 30）的人口统计学、临床和实验室数据进行分析。使用无监督机器学习根据临床特征对SFN患者进行聚类。
疲劳而非神经性疼痛是SFN患者中最普遍且严重的症状。肌肉症状与神经性疼痛同样常见且严重，而神经性瘙痒则很少见。确定了三个不同的表型聚类：（1）“疼痛型”组（约20%），伴有严重神经性疼痛和同时出现的强烈症状；（2）“肌痛型”组（约60%），有明显的疲劳、肌痛和主观虚弱，但神经性疼痛较轻；（3）“症状轻微型”组，症状少且为轻至中度。各聚类在症状谱、病程、表皮内神经纤维密度、性别和体重指数方面存在差异。
疲劳和肌肉症状在SFN中非常普遍，在临床重要性上可能与疼痛相当。识别患者亚组为分层治疗方法和改善疾病管理提供了基础。
