Beyond pain: Using Unsupervised Machine Learning to Identify Phenotypic Clusters of Small Fiber Neuropathy

Background and ObjectivesSmall fiber neuropathy (SFN) is characterized by dysfunction and loss of peripheral unmyelinated and thinly myelinated nerve fibers, resulting in a phenotype that includes varying combinations of somatosensory and dysautonomia symptoms, which can be profoundly disabling and lead to decreased quality of life. Treatment aimed mainly at pain reduction, which may not target the underlying pathophysiology, is frequently ineffective. Another impediment to the effective management of SFN may be the significant between-patient heterogeneity. Accordingly, we launched this study to gain insights into the symptomatic variability of SFN and determine if SFN patients can be sub-grouped based on clinical characteristics. MethodsTo characterize the phenotype and investigate how patients with SFN differ from those with large fiber involvement, 105 patients with skin-biopsy proven SFN and 45 with mixed fiber neuropathy (MFN) were recruited. Using unsupervised machine learning, SFN patients were clustered based upon symptom concurrence and severity. Demographics, clinical data, symptoms, and skin biopsy- and laboratory findings were compared between the groups. ResultsMFN- as compared to SFN patients, were more likely to be male, older, had a lower intraepidermal nerve fiber density at the ankle and more frequent abnormal immunofixation. Beyond these differences, symptom prevalence and intensities were similar in the two cohorts. SFN patients comprised three distinct phenotypic clusters, which differed significantly in symptom severity, co-occurrence, localization, and skin biopsy findings. Only one subgroup, constituting about 20% of the patient population, was characterized by intense neuropathic pain, which was always associated with several other SFN symptoms of similarly high intensities. A pauci-symptomatic cluster comprised patients who experienced few SFN symptoms, generally of low to moderate intensity. The largest cluster was characterized by intense fatigue, myalgias and subjective weakness, but lower intensities of burning pain and paresthesia. DiscussionThis data-driven study introduces a new approach to subgrouping patients with SFN. Considering both neuropathic pain and pernicious symptoms beyond pain, we identified three clusters, which may be related to distinct pathophysiological mechanisms. Although additional validation will be required, our findings represent a step towards stratified treatment approaches and, ultimately, personalized treatment.