Primary Hyperhidrosis and Sensitive Skin: Exploring the Link with Predictive Machine Learning-Based Classification Models

October 2024 | Volume 23 | Issue 10 | 882 | Copyright © October 2024


Published online September 30, 2024

doi:10.36849/JDD.8461

Erika T. McCormick MDa, Joung Min Choi MSb, Sara Abdel Azim MSc, Cleo Whiting BSa, Lisa Pieretti MBAc, Liqing Zhang PhDb, Adam Friedman MD FAADa

aDepartment of Dermatology, George Washington University School of Medicine and Health Sciences, Washington, DC
bDepartment of Computer Science, Virginia Tech, Blacksburg, VA
cInternational Hyperhidrosis Society, Pipersville, PA

Abstract
Background: Primary hyperhidrosis (PHH) is a disorder of excessive sweating caused by aberrant cholinergic signaling. Sensitive skin (SS) is a condition of subjective cutaneous hyperreactivity to innocuous stimuli, impacting 40% to 70% of the population. SS is exacerbated by sweat, stress, and heat, suggesting that cholinergic stimulation may contribute to SS flares.
Objective: To survey PHH sufferers to assess hyperhidrosis (HH) and SS symptom burden.
Methods: An International Review Board (IRB)-exempt survey was disseminated by the International Hyperhidrosis Society. A predictive classification model for SS was built using random forest machine learning algorithms.
Results: Of the 637 respondents with PHH, 89% reported SS; and there was a significant association between HH and SS severity scores. Importantly, SS occurred on body sites affected and unaffected by HH. Predictive modeling designated Sensitive Scale-10 (SS-10), a validated questionnaire to gauge SS severity, to be the most helpful in predicting SS in this cohort.
Limitations: Self-reported data.
Conclusion: These data are the first to propose and support a relationship between SS and HH. SS occurred with greatest frequency at HH-afflicted body sites, but also occurred on unaffected sites, suggesting that sweat is not the sole causative link. Future work can explore cholinergic signaling as a potential link between these conditions. Screening HH patients for SS may be warranted.

J Drugs Dermatol. 2024;23(10):882-888. doi:10.36849/JDD.8461

INTRODUCTION

Primary hyperhidrosis (PHH) is the most common disease of the eccrine gland, and is characterized by excessive sweating due to elevated sympathetic stimulation, severely impacting the quality of life.1 PHH is diagnosed clinically in patients with excessive sweat on one or more eccrine-rich body regions that occurs symmetrically at least once weekly for over 6 months, that is absent during sleep, and that causes impairment in daily activities.2

For physiologic sweating, stimuli (ie, warm environment, physical activity, stress) trigger sympathetic signaling, activating the eccrine sweat gland, which in response generates retrograde negative feedback to the hypothalamus.2 In PHH, despite having a normal size, number, and histologic appearance of eccrine glands,3 imbalanced sympathetic signaling results in amplified efferent signaling and overstimulation of cholinergic receptors on sweat glands.2,4

Sensitive skin (SS) is also a condition of neurosensory dysfunction, resulting in cutaneous hyperreactivity to otherwise innocuous stimuli that cannot be attributed to an underlying skin disease. SS is common, with an estimated global prevalence between 40% to 70% of the population, and manifests as sensations of itching, burning, tightness, or stinging.5-9 While SS pathophysiology remains poorly understood, sweat, stress, and hot temperatures are known exacerbants of SS, suggesting that cholinergic stimulation may contribute to symptom flares.10,11 Sweat is also a known skin irritant, causing an inappropriate activation of cutaneous inflammation.12

Given our hypothesis that aberrant cholinergic stimulation contributes to the pathophysiology of both PHH and SS, this study was conducted to assess a potential relationship between these 2 conditions. The purpose of this study was threefold: 1) to provide updated epidemiologic trends for PHH by surveying a large population of HH sufferers, 2) to evaluate the frequency, quality, and location of SS in patients with PHH, and 3) to assess the predictive capacity of machine learning classification models in predicting SS.
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