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

Area of Science:

• Neurology
• Clinical Medicine
• Biostatistics

Background:

• Small fiber neuropathy (SFN) involves nerve fiber dysfunction, typically causing pain and sensory loss.
• Other symptoms like fatigue, muscle pain, and weakness are common but poorly understood.
• Patient heterogeneity complicates effective treatment for SFN.

Purpose of the Study:

• To characterize the full spectrum of symptoms in SFN.
• To investigate the prevalence and intensity of non-pain symptoms.
• To identify distinct patient subgroups within SFN for stratified care.

Main Methods:

• Analysis of demographic, clinical, and laboratory data from 203 SFN patients and 30 controls.
• Skin biopsy confirmed SFN diagnosis.
• Unsupervised machine learning used for clinical feature-based patient clustering.

Main Results:

• Fatigue was the most prevalent and intense symptom, surpassing neuropathic pain.
• Muscle symptoms (myalgias, cramps) were as common as pain.
• Three clusters emerged: 'algesic' (pain-dominant), 'myalgic' (fatigue/muscle-dominant), and 'pauci-symptomatic'.

Conclusions:

• Fatigue and muscle symptoms are highly significant in SFN, potentially as important as pain.
• Identifying patient subgroups is crucial for developing targeted SFN therapies.
• Stratified treatment approaches can improve SFN disease management.