Uncovering Hidden Profiles: From Pain-Centric to Multi-Symptom Small Fiber Neuropathy

揭开隐藏的疾病面纱:从以疼痛为中心的小纤维神经病变到多症状小纤维神经病变

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Abstract

BACKGROUND AND PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

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