Age and gender differences in non-motor symptoms in people with Parkinson’s disease - Maas, Bart R.; Göttgens, Irene; Tijsse Klasen, Hermina P. S.; Kapelle, Willanka M.; Radder, Danique L. M.; Bloem, Bastiaan R.; Post, Bart; de Vries, Nienke M.; Darweesh, Sirwan K. L. (Frontiers in Neurology, 2024)
Background Non-motor symptoms of Parkinson’s disease (PD) are highly prevalent and heterogenic. Previous studies aimed to gain more insight on this heterogeneity by investigating age and gender differences in non-motor symptom severity, but findings were inconsistent. Furthermore, besides examining the single effects of age and gender, the interaction between them in relation to non-motor functioning has -as far as we know- not been investigated before. Objectives To investigate the association of age and gender identity -as well as the interaction between age and gender identity- with non-motor symptoms and their impact on quality of life. Methods We combined three large and independent studies. This approach resulted in a total number of unique participants of 1,509. We used linear regression models to assess the association of age and gender identity, and their interaction, with non-motor symptoms and their impact on quality of life. Results Older people with PD generally had worse cognitive functioning, worse autonomic functioning and worse quality of life. Women with PD generally experienced more anxiety, worse autonomic functioning and worse quality of life compared to men with PD, whereas men with PD generally had worse cognitive functioning. In interaction analyses by age and gender identity, depressive symptoms and anxiety were disproportionally worse with increasing age in women compared to men. Conclusion Our findings indicate that both age and gender -as well as their interaction- are differentially associated with non-motor symptoms of PD. Both research and clinical practice should pay more attention to demographic subgroups differences and possible different treatment approaches with respect to age and gender. We showed how combining datasets is of added value in this kind of analyses and encourage others to use similar approaches.