Abstract
Objective: To determine nonmotor signs (NMS) and evaluate the utility of several diagnostic tools in patients with de novo Parkinson disease (PD).
Methods: This is a large single-center study of the DeNoPa cohort, including frequency-matched healthy controls. This study covers motor signs, NMS, and a combination of diagnostic tests including olfactory testing, transcranial sonography of substantia nigra (TCS), and polysomnography (PSG). We report the frequency and characteristics of NMS and the outcomes of nonmotor tests at the time of diagnosis.
Results: Cross-sectional analyses of baseline investigations identified significant differences in the NMS Questionnaire (NMSQuest) and the Scopa-AUT Gastrointestinal score in 159 drug-naïve PD patients vs 110 controls. In addition, patients with PD showed reduced olfactory function, hyperechogenicity on TCS, and higher frequency of REM sleep behavior disorder (RBD). In exploring predictive markers, we found that the combination of several investigations, i.e., the NMSQuest, Scopa-AUT Gastrointestinal score, and Smell Identification Test reached an area under the receiver operating characteristic curve (AUC) of 0.913 (95% confidence interval [CI] 0.878-0.948). With the addition of serumcholesterol andmean heart rate values, the AUC value reached 0.919 (95% CI 886-0.953) when TCS and PSG were added, the AUC increased to 0.963 (95% CI 0.943-0.982).
Conclusions: We show feasibility and utility of standardized data acquisition in a large, single-center cohort of patients with de novo PD and matched healthy controls. The baseline results from our prospective investigations reached a value of .0.9 sensitivity and specificity for biological markers when we added routine laboratory investigations and quantified nonmotor features including sleep.
| Original language | English |
|---|---|
| Journal | Neurology |
| Volume | 81 |
| Issue number | 14 |
| Pages (from-to) | 1226-1234 |
| Number of pages | 9 |
| ISSN | 0028-3878 |
| DOIs | |
| Publication status | Published - 01.10.2013 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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SDG 10 Reduced Inequalities
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