Evidence map›Paper›PMID 39204204›Full record

ArticlePharmaceuticals (Basel, Switzerland)2024

Comprehensive Analysis of Drug-Induced Parkinson-like Events.

Mami Kikegawa, Hideko Sone, Yoshihiro Uesawa

Abstract read
In one paragraph

Article in Pharmaceuticals (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Mami KikegawaDepartment of Medical Molecular Informatics, Meiji Pharmaceutical University, 2-522-1 Noshio, Kiyose 204-8588, Japan.
Hideko SoneEnvironmental Health and Prevention Research Unit, Department of Health Science, Graduate School of Pharmaceutical Sciences, Yokohama University of Pharmacy, Yokohama 245-0066, Japan.ORCID 0000-0001-7162-6455
Yoshihiro UesawaDepartment of Medical Molecular Informatics, Meiji Pharmaceutical University, 2-522-1 Noshio, Kiyose 204-8588, Japan.ORCID 0000-0002-5773-991X

Funding

Japan Society for the Promotion of Science 21K06653Japan Society for the Promotion of Science 22K06707
6 · The paper itself

Abstract

Specific drugs are well known to have the capacity to induce Parkinson-like symptoms. Parkinson-like events are side effects that may persist for an extended period even after drug administration is discontinued. Although these events can be triggered by various drugs, the mechanisms underlying their diverse symptoms remain largely unclear. To investigate this, we used the Japanese Adverse Drug Event Reporting Database, which is maintained by the Pharmaceuticals and Medical Devices Agency, to analyze the risk factors associated with Parkinson-like events along with the associated drug trends and characteristics. Our findings indicate that similar to Parkinson's disease, age-related differences affect the onset of these reported events, with older individuals being more susceptible. Hierarchical clustering and principal component analysis revealed that the mechanisms triggering these Parkinson-like events are consistent across reports, suggesting a common underlying cause. However, even with a consistent mechanism, the side effects can vary depending on the site of action. These insights underline the importance of the swift identification of the drugs suspected of causing these events and the implementation of measures to reduce their side effects.

Indexed as

hierarchical clusteringJapanese Adverse Drug Event Report databaseParkinson-like eventsprincipal component analysisspontaneous reporting systemvolcano plot

Identifiers

PMID39204204
PMCPMC11359003

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.