Application of the movement disorder society prodromal Parkinson's disease research criteria in 2 independent prospective cohorts

Padovani, Alessandro; Heinzel, Sebastian; Suenkel, Ulrike; Lerche, Stefanie; Brockmann, Kathrin; Röeben, Benjamin; Schaeffer, Eva; Wurster, Isabel; Yılmaz, Rezzak; Liepelt‐Scarfone, Inga; Thaler, Anna‐Katharina von; Metzger, Florian; Eschweiler, Gerhard W.; Postuma, Ron; Maetzler, Walter; Berg, Daniela

doi:10.1002/mds.27035

Cited by 81 publications

(87 citation statements)

References 43 publications

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“…For this purpose, Cox regression was performed, controlling for age at first visit and sex (male, female) entered as an initial block, before entering the gait characteristics in block 2. 28 accommodate for unequal proportion of females between the groups, estimated means were calculated assuming 50% of males and females in both groups. After considering each gait characteristic individually, we computed both backward and forward conditional models to examine whether a combination of gait characteristics could better predict conversion to PD.…”

Section: Defining the Predictive Value Of First Visit Gaitmentioning

confidence: 99%

Gait analysis with wearables predicts conversion to Parkinson disease

Din

Elshehabi

Galna

et al. 2019

Annals of Neurology

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164

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Objective: Quantification of gait with wearable technology is promising; recent cross-sectional studies showed that gait characteristics are potential prodromal markers for Parkinson disease (PD). The aim of this longitudinal prospective observational study was to establish gait impairments and trajectories in the prodromal phase of PD, identifying which gait characteristics are potentially early diagnostic markers of PD. Methods: The 696 healthy controls (mean age = 63 AE 7 years) recruited in the Tubingen Evaluation of Risk Factors for Early Detection of Neurodegeneration study were included. Assessments were performed longitudinally 4 times at 2-year intervals, and people who converted to PD were identified. Participants were asked to walk at different speeds under single and dual tasking, with a wearable device placed on the lower back; 14 validated clinically relevant gait characteristics were quantified. Cox regression was used to examine whether gait at first visit could predict time to PD conversion after controlling for age and sex. Random effects linear mixed models (RELMs) were used to establish longitudinal trajectories of gait and model the latency between impaired gait and PD diagnosis. Results: Sixteen participants were diagnosed with PD on average 4.5 years after first visit (converters; PDC). Higher step time variability and asymmetry of all gait characteristics were associated with a shorter time to PD diagnosis. RELMs indicated that gait (lower pace) deviates from that of non-PDC approximately 4 years prior to diagnosis. Interpretation: Together with other prodromal markers, quantitative gait characteristics can play an important role in identifying prodromal PD and progression within this phase.

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Section: Defining the Predictive Value Of First Visit Gaitmentioning

confidence: 99%

Gait analysis with wearables predicts conversion to Parkinson disease

Din

Elshehabi

Galna

et al. 2019

Annals of Neurology

Self Cite

164

116

View full text Add to dashboard Cite

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“…A recent study in a large cohort of leucine‐rich repeat kinase 2 (LRRK2) to whom the MDS prodromal PD criteria was applied showed 92% diagnostic specificity and 80% sensitivity . Conversely, in cohort studies in elderly populations, although specificity was very high, sensitivity was estimated at only 14%, 18%, and 54% .…”

Section: Discussionmentioning

confidence: 99%

Evolution and clustering of prodromal parkinsonian features in GBA1 carriers

et al. 2019

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Background Five to 25% of patients with PD carry glucocerebrosidase gene mutations, and 10% to 30% of glucocerebrosidase carriers will develop PD by age 80. Stratification of PD risk in glucocerebrosidase carriers provides an opportunity to target disease‐modifying therapies. Objective Cross‐sectional and longitudinal survey of prodromal PD signs among glucocerebrosidase carriers. Design Prospective assessment of 82 glucocerebrosidase mutation carriers and 35 controls over 4 to 5 years for prodromal clinical PD features. Results At all time points, olfactory (measured using University of Pennsylvania Smell Identification Test) and cognitive (Montreal Cognitive Assessment) function and the International Parkinson and Movement Disorder Society UPDRS parts II and III scores were significantly worse amongst glucocerebrosidase mutation carriers. Progression to microsmia (odds ratio: 8.5; 95% confidence interval: 2.6–28.2; P < 0.05) and mild cognitive impairment (odds ratio: 4.2; 95% confidence interval: 1.1–16.6; P < 0.05) were more rapid compared to controls. Those with worse olfaction also had worse cognition (OR, 1.5; 95% CI: 0.0–2.8; P < 0.05) and depression (OR, 1.3; 95% CI: 0.6–2.8; P < 0.05). No participants reached the MDS prodromal PD diagnostic criteria before PD diagnosis. One participant developed PD. He did not fulfill the International Parkinson and Movement Disorder Society prodromal PD criteria before diagnosis. Conclusion Assessment of individual and clustered PD prodromal features may serve as a useful tool to identify high‐risk subjects for conversion to PD. As a result of the low conversion rate in our glucocerebrosidase mutation carriers to date, prospective validation is needed in larger cohorts to establish the profile of these features in PD convertors. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

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“…As more data become available, statistical manipulation of multimodal models will allow for weighting of the different biomarkers (according to their test characteristics) to yield “risk scores.” This approach has been applied in multimodal risk scores proposed for identification of prodromal PD . Their application to date has been informative . However, it is also clear that additional biomarkers will need to be incorporated to optimize application of these models, particularly outside of the research setting .…”

Section: Approach To Biomarker Developmentmentioning

confidence: 99%

Parkinson's Disease Biomarkers: Where Are We and Where Do We Go Next?

Chahine

Stern

2017

Movement Disord Clin Pract

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Background Objective measures of Parkinson's disease (PD) are needed for purposes of diagnosis and prognostication, as well as identification of those at risk of PD. In this qualitative review, we provide an overview of the current state of the field of PD biomarker development, delineate challenges, and discuss how the field is evolving. Methods A search of PubMed was conducted for articles pertaining to objective biomarkers for PD. Articles were selected based on relevance and methodology; where available, meta‐analyses, systematic reviews, and comprehensive qualitative review articles were preferentially referenced. Results There are several potential sources of objective PD biomarkers including biofluids, peripheral tissue, imaging, genetics, and technology based objective motor testing. Approaches to biomarker identification include the candidate biomarker approach and unbiased discovery methods, each of which has advantages and disadvantages. Several emerging techniques hold promise in each of these areas. Advances in technology and bioinformatics, and the increasing availability of biobanks, are expected to facilitate future PD biomarker development. Conclusions The field of objective biomarkers for PD has made great progress but much remains to be done in translating putative biomarkers into tools useful in the clinic and for research. Multimodal biomarker platforms have the potential to capitalize on the utility and strengths of individual biomarkers. Rigorous methodology and standards for replication of findings will be key to meaningful progress in the field.

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Application of the movement disorder society prodromal Parkinson's disease research criteria in 2 independent prospective cohorts

Cited by 81 publications

References 43 publications

Gait analysis with wearables predicts conversion to Parkinson disease

Gait analysis with wearables predicts conversion to Parkinson disease

Evolution and clustering of prodromal parkinsonian features in GBA1 carriers

Parkinson's Disease Biomarkers: Where Are We and Where Do We Go Next?

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