Comparing the Two Prodromal Parkinson's Disease Research Criteria-Lessons for Future Studies

Comparison of different risk scores for Parkinson disease in a population-based 10-year study

BACKGROUND AND PURPOSE

Different algorithms aiming to identify individuals at risk of Parkinson disease (PD) have been proposed. Comparative studies of these scores and their recent updates in the general elder population are needed.

METHODS

We have previously applied the "basic" PREDICT-PD algorithm, designed for remote screening, and the original and updated Movement Disorder Society (MDS) criteria for prodromal PD to the longitudinal population-based Bruneck study cohort. We have now additionally employed the "enhanced" PREDICT-PD algorithm (which includes motor assessment, olfaction, probable rapid eye movement sleep behaviour disorder status, pesticide exposure, and diabetes as additional factors). Risk scores were calculated based on comprehensive baseline assessments (2005) in 574 subjects aged 55-94 years (290 females), and cases of incident PD were identified at 5-year (n = 11) and 10-year follow-up (n = 9). We analysed the association of the different log-transformed risk scores with incident PD at follow-up (calculated per 1-SD unit change).

RESULTS

The enhanced PREDICT-PD algorithm was associated with incident PD over 10-years of follow-up, yielding higher odds for incident PD (odds ratio [OR] = 4.61, 95% confidence interval [CI] = 2.68-7.93, p < 0.001) compared with the basic PREDICT-PD score (OR = 2.38, 95% CI = 1.49-3.79, p < 0.001). The updated MDS prodromal criteria yielded a numerically higher OR of 7.13 (95% CI = 3.49-14.54, p < 0.001) in comparison with the original criteria as well as the enhanced PREDICT-PD algorithm, with overlapping 95% CIs.

CONCLUSIONS

The enhanced PREDICT-PD algorithm was significantly associated with incident PD. The consistent performance of both the enhanced PREDICT-PD algorithm and the updated MDS prodromal criteria compared to their original versions supports their use in PD risk screening.

Bayesian network modeling of risk and prodromal markers of Parkinson's disease

Parkinson's disease (PD) is characterized by a long prodromal phase with a multitude of markers indicating an increased PD risk prior to clinical diagnosis based on motor symptoms. Current PD prediction models do not consider interdependencies of single predictors, lack differentiation by subtypes of prodromal PD, and may be limited and potentially biased by confounding factors, unspecific assessment methods and restricted access to comprehensive marker data of prospective cohorts. We used prospective data of 18 established risk and prodromal markers of PD in 1178 healthy, PD-free individuals and 24 incident PD cases collected longitudinally in the Tübingen evaluation of Risk factors for Early detection of NeuroDegeneration (TREND) study at 4 visits over up to 10 years. We employed artificial intelligence (AI) to learn and quantify PD marker interdependencies via a Bayesian network (BN) with probabilistic confidence estimation using bootstrapping. The BN was employed to generate a synthetic cohort and individual marker profiles. Robust interdependencies were observed for BN edges from age to subthreshold parkinsonism and urinary dysfunction, sex to substantia nigra hyperechogenicity, depression, non-smoking and to constipation; depression to symptomatic hypotension and excessive daytime somnolence; solvent exposure to cognitive deficits and to physical inactivity; and non-smoking to physical inactivity. Conversion to PD was interdependent with prior subthreshold parkinsonism, sex and substantia nigra hyperechogenicity. Several additional interdependencies with lower probabilistic confidence were identified. Synthetic subjects generated via the BN based representation of the TREND study were realistic as assessed through multiple comparison approaches of real and synthetic data. Altogether our work demonstrates the potential of modern AI approaches (specifically BNs) both for modelling and understanding interdependencies between PD risk and prodromal markers, which are so far not accounted for in PD prediction models, as well as for generating realistic synthetic data.

Risk disclosure in prodromal Parkinson's disease - A survey of neurologists

INTRODUCTION

In the absence of a disease-modifying treatment and prognostic uncertainty, ethics of risk disclosure in prodromal Parkinson's disease (PD) is challenging. Previous studies highlighted several facets of these challenges from the perspective of involved parties. However, to date, the view of neurologists who may encounter individuals with prodromal PD remained unrepresented. Moreover, cross-cultural differences intrinsic to the ethics of risk disclosure are yet to be elucidated. Therefore, we investigated the attitude of neurologists toward risk disclosure in prodromal PD.

METHODS

In this observational study, Turkish neurologists were invited to fill out a questionnaire evaluating their stance on risk disclosure regarding an individual with polysomnography-confirmed REM sleep behavior disorder, which is the strongest risk factor for PD.

RESULTS

More than 90% of the participating 222 neurologists were familiar with prodromal PD. While 15.3% stated that the risk should be disclosed in any case, 6.8% chose no disclosure. The remaining 77.9% favored disclosure only under certain circumstances, the plurality of which was the individual's consent to know about the risk. After reminding the potential neuroprotective effects of exercise and diet, neurologists who chose the option of "no disclosure" decreased to 3.2% (McNemar's test p = 0.008). No significant differences among the neurologists were found regarding sex, academic title, or field of interest.

CONCLUSION

The majority of the neurologists found it appropriate to disclose the risk of future PD only if the individual expresses a desire to know. Also, recognition of the impact of lifestyle factors on PD is important in prognostic counseling.