Abstract
Peripheral neuropathy (PN) is a common neurological complication associated with substantial morbidity, disability, and reduced quality of life. Although numerous studies have identified risk factors for PN, findings remain heterogeneous across diabetic and non-diabetic populations, and comprehensive evidence synthesizing these determinants, particularly in the Indonesian context, is still limited. This study aims to identify and compare the major risk factors associated with peripheral neuropathy in diabetic and non-diabetic populations through a systematic literature review. A systematic review was conducted in accordance with PRISMA guidelines. Literature searches were performed in PubMed and Google Scholar using keywords related to peripheral neuropathy and risk factors. Articles published between 2020 and 2025 in English or Indonesian were screened through identification, eligibility assessment, and inclusion stages. Twelve studies meeting the predefined criteria were included and analyzed qualitatively. The most consistently reported risk factors were poor glycemic control and advanced age. Additional determinants were grouped into metabolic factors (hypertension, dyslipidemia, obesity), lifestyle factors (physical inactivity and smoking), clinical factors (longer diabetes duration and male sex), treatment-related factors (taxane-based chemotherapy, oxaliplatin, and beta-blockers), and sociodemographic factors (race, low educational level, and urban residence). These findings indicate that PN is a multifactorial condition influenced by metabolic, clinical, therapeutic, lifestyle, and social determinants. Peripheral neuropathy is associated with a broad range of modifiable and non-modifiable risk factors, with poor glycemic control and advanced age emerging as the most consistent determinants. Early identification of these factors may support targeted screening, prevention strategies, and risk reduction interventions in clinical practice and public health settings. Future longitudinal studies are needed to clarify causal relationships and improve risk prediction models.