Evidence map›Paper›PMID 36793590›Full record

ArticleClinical parkinsonism & related disorders2023

Updated Parkinson's disease motor subtypes classification and correlation to cerebrospinal homovanillic acid and 5-hydroxyindoleacetic acid levels.

Christopher Adams, Jessika Suescun, Anam Haque, Kelly Block, Shivika Chandra, Timothy M Ellmore, Mya C Schiess

Abstract read
In one paragraph

Article in Clinical parkinsonism & related disorders, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

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0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

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  18. Speech Characteristics Across Motor Subtypes of Parkinson's Disease.International journal of language & communication disorders
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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Christopher AdamsDepartment of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, United States.
Jessika SuescunDepartment of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, United States.
Anam HaqueDepartment of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, United States.
Kelly BlockDepartment of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, United States.
Shivika ChandraDepartment of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, United States.
Timothy M EllmoreDepartment of Psychology, The City College of New York, New York City, United States.
Mya C SchiessDepartment of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Motor classifications of Parkinson's Disease (PD) have been widely used. This paper aims to update a subtype classification using the MDS-UPDRS-III and determine if cerebrospinal neurotransmitter profiles (HVA and 5-HIAA) differ between these subtypes in a cohort from the Parkinson's Progression Marker Initiative (PPMI). Methods: UPDRS and MDS-UPDRS scores were collected for 20 PD patients. Akinetic-rigid (AR), Tremor-dominant (TD), and Mixed (MX) subtypes were calculated using a formula derived from UPDRS, and a new ratio was developed for subtyping patients with the MDS-UPDRS. This new formula was subsequently applied to 95 PD patients from the PPMI dataset, and subtyping was correlated to neurotransmitter levels. Data were analyzed using receiver operating characteristic models and ANOVA. Results: Compared to previous UPDRS classifications, the new MDS-UPDRS TD/AR ratios produced significant areas under the curve (AUC) for each subtype. The optimal sensitivity and specificity cutoff scores were ≥0.82 for TD, ≤0.71 for AR, and >0.71 and <0.82 for Mixed. Analysis of variance showed that the AR group had significantly lower HVA and 5-HIAA levels than the TD and HC groups. A logistic model using neurotransmitter levels and MDS-UPDRS-III could predict the subtype classification. Conclusions: This MDS-UPDRS motor classification system provides a method to transition from the original UPDRS to the new MDS-UPDRS. It is a reliable and quantifiable subtyping tool for monitoring disease progression. The TD subtype is associated with lower motor scores and higher HVA levels, while the AR subtype is associated with higher motor scores and lower 5-HIAA levels.

Identifiers

PMID36793590
PMCPMC9922918

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.