Mapping the research landscape of Mild Cognitive Impairment in Parkinson's disease: a bibliometric and visualization analysis.

Background: Mild Cognitive Impairment in Parkinson's disease, a common non-motor symptom of Parkinson's disease, significantly impacts individuals' quality of life and predicts dementia risk, underscoring its clinical research importance. This study aimed to characterize the global bibliometric landscape and identify research hotspots, knowledge gaps, and future trends in the PD-MCI field over the past two decades using bibliometric and visualization methods.

Methods: Literature related to PD-MCI published between 2005 and 2024 was retrieved from the Web of Science Core Collection database. Tools such as CiteSpace and VOSviewer were employed for visual analysis of annual publication volume, country/institutional distribution, author collaborations, journal co-citations, and keyword co-occurrence and bursts, constructing knowledge maps.

Results: Mild cognitive impairment in Parkinson's disease research demonstrated significant growth, with sustained increases in annual publication volume and citation frequency. The United States dominated the field, while research output from countries like China grew rapidly. Research hotspots evolved from early explorations of molecular mechanisms toward clinical and translational studies focusing on neuroimaging, biomarkers, application of MDS diagnostic criteria, non-motor symptoms, and cognitive subtypes. Recently, machine learning, multi-omics integration, neuroinflammation, and mitochondrial function have emerged as new frontiers.

Conclusion: Mild cognitive impairment in Parkinson's disease research has progressed from basic mechanism exploration to a multidisciplinary, integrated clinical-basic stage, following an evolutionary path of "pathological mechanism - clinical phenotype - biomarker - intervention strategy." Future research should focus on unifying diagnostic criteria, deepening understanding of multifactorial pathological mechanisms, developing precise biomarker combinations, and exploring individualized intervention strategies to achieve early warning and disease modification for PD-MCI.