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Updated: Jun 23, 2026

Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
Published on: January 7, 2014
Unraveling Peripheral Neuropathy in Parkinsonism from Acquired to Genetic Forms: A Review with Diagnostic Framework
Cristina Moreno-Lopez1,2,3, Pietro Antenucci4, João Moura5,6,7
1Neurology Department, Ramón y Cajal Universitary Hospital, Madrid, Spain.
Background:
The coexistence of parkinsonism and peripheral neuropathy (PN) is more frequent than traditionally assumed and impacts patients' quality of life. Despite this, PN is often overlooked or misattributed to non-motor symptoms of Parkinson's disease (PD), resulting in missed diagnoses in clinical practice.
Objectives:
This review aims to summarize current evidence on the etiologies linking parkinsonism and PN and provide a diagnostic algorithm to support clinical decision-making.
Methods:
Narrative review of studies on parkinsonism and PN, limited to 2000-2025 to capture recent advances.
Results:
Several factors have been proposed to explain the connection between parkinsonism and PN. Long-term levodopa treatment has been associated with PN, due to metabolic disturbances, including vitamin B12 depletion and increased homocysteine levels. Small fiber neuropathy is commonly observed in PD and may precede motor symptom onset. Certain pathogenic variants, known to cause parkinsonian syndromes, have also been linked to peripheral nerve involvement (PRKN, DJ1, PINK1, GBA, and ATP13A2), while some inherited neuropathies (FIG4, LRSAM1, SLC25A46, DNAJB2, and AARS) are characterized by overlapping parkinsonian features. Moreover, a wide range of acquired conditions expands the differential diagnosis. This growing body of evidence highlights the emerging role of peripheral nervous system involvement in parkinsonism and underscores the importance of its systematic assessment.
Conclusions:
PN is a relevant yet underrecognized comorbidity in patients with parkinsonism. Early identification is crucial for enhancing diagnostic accuracy and therapeutic outcomes. The diagnostic algorithm proposed offers a structured approach for clinicians, facilitating comprehensive evaluation and personalized management in this complex clinical context.
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