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Presynaptic Dopaminergic Imaging Characterizes Patients with REM Sleep Behavior Disorder Due to Synucleinopathy
Arnaldi D, Mattioli P, Raffa S, Pardini M, Massa F, Iranzo A, Perissinotti A, Niñerola-Baizán A et al.. Annals of neurology, 2024-03-11
Topic: Brain proteins, microglia and lab models
Objective To apply a machine learning analysis to clinical and presynaptic dopaminergic imaging data of patients with rapid eye movement (REM) sleep behavior disorder (RBD) to predict the development of Parkinson disease (PD) and dementia with Lewy bodies (DLB). Methods In this multicenter study of the International RBD study group, 173 patients (mean age 70.5 ± 6.3 years, 70.5% males) with polysomnography-confirmed RBD who eventually phenoconverted to overt alpha-synucleinopathy (RBD due to synucleinopathy) were enrolled, and underwent baseline presynaptic dopaminergic imaging and clinical assessment, including motor, cognitive, olfaction, and constipation evaluation. For comparison, 232 RBD non-phenoconvertor patients (67.6 ± 7.1 years, 78.4% males) and 160 controls (68.2 ± 7.2 years, 53.1% males) were enrolled. Imaging and clinical features were analyzed by machine learning to determine predictors of phenoconversion. Results Machine learning analysis showed that clinical data alone poorly predicted phenoconversion. Presynaptic dopaminergic imaging significantly improved the prediction, especially in combination with clinical data, with 77% sensitivity and 85% specificity in differentiating RBD due to synucleinopathy from non phenoconverted RBD patients, and 85% sensitivity and 86% specificity in discriminating PD-converters from DLB-converters. Quantification of presynaptic dopaminergic imaging showed that an empirical z-score cutoff of -1.0 at the most affected hemisphere putamen characterized RBD due to synucleinopathy patients, while a cutoff of -1.0 at the most affected hemisphere putamen/caudate ratio characterized PD-converters. Interpretation Clinical data alone poorly predicted phenoconversion in RBD due to synucleinopathy patients. Conversely, presynaptic dopaminergic imaging allows a good prediction of forthcoming phenoconversion diagnosis. This finding may be used in designing future disease-modifying trials. ANN NEUROL 2024;95:1178-1192.
Key findings
- Results Machine learning analysis showed that clinical data alone poorly predicted phenoconversion.
- Presynaptic dopaminergic imaging significantly improved the prediction, especially in combination with clinical data, with 77% sensitivity and 85% specificity in differentiating RBD due to synucleinopathy from non phenoconverted RBD patients, and 85% sensitivity and 86% specificity in discriminating PD-converters from DLB-converters.
- Quantification of presynaptic dopaminergic imaging showed that an empirical z-score cutoff of -1.0 at the most affected hemisphere putamen characterized RBD due to synucleinopathy patients, while a cutoff of -1.0 at the most affected hemisphere putamen/caudate ratio characterized PD-converters.
Why it matters
This multicenter study found that presynaptic dopaminergic imaging, especially combined with clinical data, improved prediction of which patients with REM sleep behavior disorder would develop Parkinson disease or dementia with Lewy bodies. Clinical data alone poorly predicted this conversion.
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Study details
- Participants173
- Effect size70.5%; 78.4%; 53.1%
- Evidence strengthLow
How this study scored 58/100
Published in a leading specialty journal. Scored zero for study design and randomised / blinded because the record does not state it. We have no data on senior author's track record, so that was left out: it earned 56 of the 96 points we could judge it on, which is 58 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 9 / 10 | Tier 2 — leading specialty journal (Annals of neurology) |
| Journal's citation record | 3 / 4 | Annals of Neurology: journal h-index 437 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2024 — within 3 year(s) of the newest study here |
| Sample size | 2 / 4 | 173 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 4 / 7 | Cited 50 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 12.5 citations a year over 4 year(s) since 2024 |
| Impact for its field and year | 4 / 6 | Cited 11.7× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 2 other studies in this library |
| Still being cited | 3 / 4 | Cited 50 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | 2 / 3 | University of Oxford: institution h-index 2,205 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 35 institution(s) in 9 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 38466158) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 56 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 56 of the 96 points we could judge it on is 58 out of 100. |
| Total | 58 / 100 | — |
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Read the original
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Arnaldi, D., Mattioli, P., Raffa, S., Pardini, M., Massa, F., Iranzo, A., et al. (2024). Presynaptic Dopaminergic Imaging Characterizes Patients with REM Sleep Behavior Disorder Due to Synucleinopathy. Annals of neurology. https://doi.org/10.1002/ana.26902
Vancouver
Arnaldi D, Mattioli P, Raffa S, Pardini M, Massa F, Iranzo A, et al. Presynaptic Dopaminergic Imaging Characterizes Patients with REM Sleep Behavior Disorder Due to Synucleinopathy. Annals of neurology. 2024. doi:10.1002/ana.26902. PMID: 38466158.
BibTeX
@article{arnaldi2024-38466158,
author = {Arnaldi, D. and Mattioli, P. and Raffa, S. and Pardini, M. and Massa, F. and Iranzo, A. and Perissinotti, A. and Niñerola-Baizán, A. and others},
title = {{Presynaptic Dopaminergic Imaging Characterizes Patients with REM Sleep Behavior Disorder Due to Synucleinopathy}},
journal = {Annals of neurology},
year = {2024},
doi = {10.1002/ana.26902},
pmid = {38466158},
url = {https://doi.org/10.1002/ana.26902}
}
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