Abstract Gut microbiome differences between people with Parkinson’s disease (PD) and control subjects without Parkinsonism are widely reported, but potential alterations related to PD with mild cognitive impairment (MCI) have yet to be comprehensively explored. We compared gut microbial features of PD with MCI ( n = 58) to cognitively unimpaired PD ( n = 60) and control subjects ( n = 90) with normal cognition. Our results did not support a specific microbiome signature related to MCI in PD.
Abstract Introduction While there is an interest in defining longitudinal change in people with chronic illness like Parkinson’s disease (PD), statistical analysis of longitudinal data is not straightforward for clinical researchers. Here, we aim to demonstrate how the choice of statistical method may influence research outcomes, (e.g., progression in apathy), specifically the size of longitudinal effect estimates, in a cohort. Methods In this retrospective longitudinal analysis of 802 people with typical Parkinson’s disease in the Luxembourg Parkinson's study, we compared the mean apathy scores at visit 1 and visit 8 by means of the paired two-sided t-test. Additionally, we analysed the relationship between the visit numbers and the apathy score using linear regression and longitudinal two-level mixed effects models. Results Mixed effects models were the only method able to detect progression of apathy over time. While the effects estimated for the group comparison and the linear regression were smaller with high p -values (+ 1.016/ 7 years, p = 0.107, -0.056/ 7 years, p = 0.897, respectively), effect estimates for the mixed effects models were positive with a very small p -value, indicating a significant increase in apathy symptoms by + 2.345/ 7 years ( p < 0.001). Conclusion The inappropriate use of paired t-tests and linear regression to analyse longitudinal data can lead to underpowered analyses and an underestimation of longitudinal change. While mixed effects models are not without limitations and need to be altered to model the time sequence between the exposure and the outcome, they are worth considering for longitudinal data analyses. In case this is not possible, limitations of the analytical approach need to be discussed and taken into account in the interpretation.
