Cross-sectional study and bioinformatics analysis to reveal the correlations of osteoporosis in patients with Parkinson's disease

Abstract

Objectives: Osteoporosis and Parkinson's disease (PD) are both aging-related diseases. PD patients with comorbid osteoporosis are vulnerable to the risk of fracture, which leads to a serious public health burden to the whole society. Therefore, this study sought to reveal the clinical and genetic correlations between PD and osteoporosis based on a cross-sectional study and bioinformatics analysis. Methods: A cross-sectional study of 95 PD patients and 99 healthy controls was conducted. Ordinal logistic regression analysis was utilized to investigate the clinical correlations between PD and osteoporosis. Two microarray datasets (GSE20292, GSE35958) including PD, osteoporosis and normal control samples were retrieved from the GEO database for GO analysis, KEGG pathway analysis and PPI network. Results: PD patients had lower 25(OH)VitD, FN BMD, BMD and T-score of the LS and TH, as well as poorer bone mass diagnosis, yet higher PINP compared to healthy controls. Both age and UPDRS II score of PD patients were adversely correlated with BMD of LS and TH. PD diagnosis acted as an independent risk factor of osteoporosis, and PD patients had approximately double risk for osteoporosis. Bioinformatics analysis further revealed that SNAP25, AQP4, SV2B, KCND3, and ABCA2 had important diagnostic value and risk prediction value for both PD and osteoporosis. Conclusions: PD diagnosis can be used as an independent risk factor for osteoporosis. Moreover, SNAP25, AQP4, SV2B, KCND3 and ABCA2 as the top 5 hub genes have important diagnostic and risk predictive value for both PD and osteoporosis.

Keywords

• Parkinson's disease
• Osteoporosis
• Bone mineral density
• Risk
• Bioinformatics analysis