Antidepressants and REM Behavior Disorder
Notice bibliographique
Résumé
This was not an industry supported study. The authors have indicated no financial conflicts of interest. We thank Drs. Kolla and Mansukhani for their interest in our article and for the opportunity to provide further clarifications.1 Regarding their suggestion of “antidepressants-as-depression-proxy,” it should first be noted that our study in no way tested whether depression is a risk factor or prodromal marker for synucleinopathies. This would require population-based studies, which in fact do suggest that this is the case.2 Rather, the hypothesis we tested (and found evidence against) spoke only to the relationship between antidepressants and REM behavior disorder (RBD). We found that antidepressants had no direct connection to RBD, but simply represented the presence of another prodromal marker (i.e. depression itself). If this were true, patients with RBD taking antidepressants should have had a higher risk of neurodegeneration, since they now had two prodromal markers of disease (i.e. RBD and depression). However, the observed risk was significantly lower.3 Note that our analysis was adjusted for age, so the younger age in antidepressant users would not explain the difference in disease risk (furthermore, this age difference would actually argue for the central hypothesis of antidepressants as symptom triggers of synucleinopathy; if antidepressants trigger early clinical presentation of an otherwise-latent synucleinopathy, these patients would be younger). We absolutely agree that the temporal relationship between RBD symptom onset and antidepressant use can often be murky. This was the main motivation for choosing all those taking antidepressants at baseline for the primary analysis. This minimized confounding from inaccurate RBD onset history. Regarding presentation history, all patients in our study had a complaint of dream-enactment behavior (and all had PSG confirming diagnosis); we had no asymptomatic patients diagnosed only on PSG. With regards to an antidepressant-neuroprotective hypothesis, this would certainly be an exciting possibility. Supporting evidence for this hypothesis would likely have to start with findings from population-based studies. We are unaware of any studies linking antidepressants to a lower risk of neuro-degenerative disease. Moreover, considering that persons with depression and anxiety are the primary recipients of antidepressants, but have a higher risk of neurodegenerative disease, this possibility may be less likely. The relationship between antidepressants and RBD is complex, and our study is only one piece of evidence pointing towards a potential explanation. No doubt there are additional complexities not yet understood. Moreover, our study can speak only to the predominant mechanism behind antidepressants and RBD; there may be exceptions, including patients for whom antidepressants only marked depression, or who have a “pure” pharmacologic non-neurodegenerative RBD. We greatly encourage further work on this fascinating area of sleep neurology.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,045 | 0,023 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,003 |
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 source (Gemma direct ou Codex distillé), 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 ».