Phenotyping autonomic neuropathy using principal component analysis

Steven Lawrence, Bridget R. Mueller, Patrick Kwon, Jessica Robinson-Papp

Research output: Contribution to journalArticlepeer-review


To identify autonomic neuropathy (AN) phenotypes, we used principal component analysis on data from participants (N = 209) who underwent standardized autonomic testing including quantitative sudomotor axon reflex testing, and heart rate and blood pressure at rest and during tilt, Valsalva, and standardized deep breathing. The analysis identified seven clusters: 1) normal, 2) hyperadrenergic features without AN, 3) mild AN with hyperadrenergic features, 4) moderate AN, 5) mild AN with hypoadrenergic features, 6) borderline AN with hypoadrenergic features, 7) mild balanced deficits across parasympathetic, sympathetic and sudomotor domains. These findings demonstrate a complex relationship between adrenergic and other aspects of autonomic function.

Original languageEnglish
Article number103056
JournalAutonomic Neuroscience: Basic and Clinical
StatePublished - Mar 2023


  • Autonomic function tests
  • Autonomic neuropathy
  • Phenotyping
  • Physiology
  • Principal component analysis (PCA)


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