Diagnostic and prognostic value of α-synuclein seed amplification assay kinetic measures in Parkinson's disease: a longitudinal cohort study
Notice bibliographique
Résumé
BACKGROUND: α-synuclein seed amplification assay (SAA) positivity has been proposed as a diagnostic biomarker for Parkinson's disease. However, studies of the prognostic value of this biomarker have been limited to small, single-centre studies over short follow-up periods. We aimed to assess the diagnostic and prognostic value of quantitative CSF α-synuclein SAA kinetic measures in Parkinson's disease. METHODS: In this longitudinal cohort study, we collected and analysed data from participants with Parkinson's disease, progressive supranuclear palsy, and healthy controls enrolled in three cohorts: the UK parkinsonism cohort, the Parkinson's Progression Markers Initiative (PPMI) international observational study, and the Tübingen Parkinson's disease cohort. Baseline CSF α-synuclein SAA data and longitudinal clinical data were collected between Jan 1, 2005, and Nov 1, 2023. The following seeding kinetic measures were calculated from the α-synuclein SAA curve for each SAA-positive sample: time to threshold (TTT) for a positive SAA result; maximum Thioflavin T fluorescence during the reaction time (MaxThT); and area under the fluorescence curve during the reaction time (AUC). We compared seeding kinetic measures between sporadic Parkinson's disease and progressive supranuclear palsy, and between sporadic Parkinson's disease and monogenic Parkinson's disease. We used time-to-event analyses to assess the ability of α-synuclein SAA kinetic measures to predict an unfavourable outcome in Parkinson's disease, adjusting for sex, age, and disease duration at SAA testing. FINDINGS: We analysed data from 1631 participants: newly generated data from the UK parkinsonism cohort (Parkinson's disease, n=66; progressive supranuclear palsy, n=52; controls, n=9) and previously generated data from the PPMI (Parkinson's disease, n=1036; controls, n=239) and Tübingen (Parkinson's disease, n=229) cohorts. In the UK parkinsonism cohort, α-synuclein SAA was positive in 63 (96%) of 66 Parkinson's disease samples and eight (15%) of 52 progressive supranuclear palsy samples, with six (75%) of eight positive progressive supranuclear palsy samples having distinct low and slow seeding kinetics (low MaxThT and high TTT) as a marker of Lewy body co-pathology. TTT was faster in GBA1-associated Parkinson's disease compared with sporadic Parkinson's disease in both the PPMI (p=0·04) and Tübingen (p=0·01) cohorts. In the PPMI cohort, after excluding individuals who had an unfavourable outcome at the time of baseline SAA testing, an unfavourable outcome was observed in 593 (73%) of 810 participants with α-synuclein SAA-positive Parkinson's disease during a median follow-up period of 4·5 years (IQR 2-9). TTT at baseline predicted only cognitive decline (Montreal Cognitive Assessment score ≤21) as a component of an unfavourable outcome in Parkinson's disease in both the PPMI (n=824, hazard ratio [HR] 2·36 [95% CI 1·60-3·46], p=0·001) and Tübingen (n=135, 2·17 [1·07-4·41], p=0·03) cohorts. TTT also predicted cognitive decline in a subgroup of participants with Parkinson's disease in the PPMI cohort who were Alzheimer's disease biomarker negative (n=355, HR 1·80 [95% CI 1·03-3·18], p=0·04). INTERPRETATION: Assessing α-synuclein SAA kinetic measures might aid in the diagnostic differentiation of Parkinson's disease from progressive supranuclear palsy with Lewy body co-pathology. Furthermore, faster seeding kinetics are found in GBA1-Parkinson's disease and predict cognitive decline in Parkinson's disease independently of Alzheimer's disease co-pathology. FUNDING: Medical Research Council, PSP Association. COPYRIGHT: © 2025 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».