Dataset Information

Merging Clinical and Imaging Biomarkers to Tackle Parkinson's Disease.

ABSTRACT:

Background

In Parkinson's disease, biomarkers represent tools that are potentially suitable for either clinical or research settings and are useful in predicting onset, confirming diagnosis, detecting progression, and evaluating response to potential disease-modifying treatments. The range of available biomarkers in Parkinson's disease is fast expanding and includes an increasing amount of laboratory, clinical, and imaging data. Indeed, the latter 2 represent the cornerstones of the diagnostic criteria for Parkinson's disease recently proposed by the International Parkinson and Movement Disorders Society Task Force on the definition of Parkinson's disease.

Methods and results

In this review, we describe current knowledge and emerging findings on clinical (with emphasis on nonmotor symptoms) and imaging biomarkers for Parkinson's disease, with a focus on prodromal, diagnostic, and middle/advanced phases.

Conclusion

An increasing body of evidence suggests that merging clinical and imaging biomarkers through disease stages may be the best, fastest track to tackle Parkinson's disease.

SUBMITTER: Picillo M

PROVIDER: S-EPMC6174371 | biostudies-literature | 2017 Sep-Oct

REPOSITORIES: biostudies-literature

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Publications

Merging Clinical and Imaging Biomarkers to Tackle Parkinson's Disease.

Picillo Marina M Barone Paolo P Pellecchia Maria Teresa MT

Movement disorders clinical practice 20170821 5

<h4>Background</h4>In Parkinson's disease, biomarkers represent tools that are potentially suitable for either clinical or research settings and are useful in predicting onset, confirming diagnosis, detecting progression, and evaluating response to potential disease-modifying treatments. The range of available biomarkers in Parkinson's disease is fast expanding and includes an increasing amount of laboratory, clinical, and imaging data. Indeed, the latter 2 represent the cornerstones of the diag ...[more]

PMID: 30363377

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Project description:Unexplained weight changes that occur in Parkinson's disease (PD), are often neglected and remain a poorly understood non-motor feature in patients with PD. A specific 'Park-weight' phenotype with low body weight has been described, and our aim was to evaluate the clinical and prognostic trajectories and biomarkers of weight variability in PD. We evaluated body weight-related biomarkers in 405 de novo PD patients and 187 healthy controls (HC) over a 5-year follow-up period from the PPMI database. Body-weight variability was defined as intra-individual variability in body weight between visits. PD patients were categorized as weight losers, gainers, or patients with stable weight. The differential progression of motor and non-motor clinical variables between groups was explored using linear mixed-effects models. Finally, we estimated longitudinal changes in weight as a function of baseline and longitudinal striatal presynaptic dopaminergic transporter imaging. PD patients presented a greater weight variability compared to HC (p = 0.003). Patients who developed weight loss had lower CSF amyloid-beta 1-42 (p = 0.009) at baseline. In addition, patients with weight loss showed a faster cognitive decline (p = 0.001), whereas patients with weight gain showed a slower motor progression (p = 0.001), compared to patients with stable weight. Baseline right striatal denervation was a predictor of weight variability in both PD patients and HC (p < 0.001). Similarly, weight variability in PD patients was associated with the progression of right striatal denervation (p < 0.001). Weight variability and specifically weight loss are more frequent in PD compared to HC, and are associated with specific motor, non-motor and cognitive progression patterns. A greater CSF amyloid burden was present at baseline in patients with subsequent weight loss. Presynaptic dopaminergic imaging in the right striatum may serve as a predictor of future weight changes in PD and HC.

Dataset Information

Merging Clinical and Imaging Biomarkers to Tackle Parkinson's Disease.

Background

Methods and results

Conclusion

Publications

Merging Clinical and Imaging Biomarkers to Tackle Parkinson's Disease.

Similar Datasets

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