Comment on: Association between depression and anxiety with skin and musculoskeletal clinical phenotypes in systemic lupus erythematosus: reply
Notice bibliographique
Résumé
Dear Editor, We thank Dr Tecer and colleagues for their interest in and commentary on our article [1], ‘Association between depression and anxiety with skin and musculoskeletal clinical phenotypes in systemic lupus erythematosus’. We agree with the authors’ point that developing an understanding of the epidemiology, risk factors and pathophysiology of depression and anxiety in patients with SLE is a critical step in optimizing care. Tecer et al. bring up several important points regarding the potential contributions of autoantibodies, medications (glucocorticoids and opioids), and chronic comorbidities and habits to depression and anxiety in SLE. We agree that these factors likely play a role in the pathophysiology of depression and anxiety for many patients. In addition to the factors identified in our article and by Tecer et al., a variety of psychosocial, clinical and sociodemographic factors have been found to be associated with depression or anxiety in SLE in observational studies, including fatigue severity [2], disease activity and active disease [3, 4], cognitive impairment [5] marital relationship quality [2], psychological reserve [6] and financial strain [7]. The variety of findings in this literature reflects differing study designs, populations and methodologies and also reflects the heterogeneity of depression and anxiety in SLE. Depression and anxiety symptoms are common in the general population and may or may not be attributable to neuropsychiatric symptoms of SLE [8]. Given the variety of different potential contributors to depression and anxiety in SLE, we did not intend to build an explanatory model incorporating all potential variables suggested as being found in the literature, as our sample size was not adequate for that type of analysis. Instead, we aimed to perform a more focussed study, evaluating easily accessible and patient data that could be measured as part of routine care. We did not exclude patients using antidepressant medication or with a history of depression and anxiety, as we were interested in capturing clinically significant anxiety or depression (as defined by validated cut-offs) whether or not patients had been formally diagnosed or treated for a depressive or anxiety disorder. In our cohort, fibromyalgia is diagnosed based on ACR 1990 classification criteria, which involves the presence of chronic (>3 months) widespread pain as well as the need for at least 11 out of 18 possible tender points [9]. We also agree with Dr Tecer and colleagues on their comment regarding the importance of antiphospholipid antibodies and their association with different neuropsychiatric syndromes (NPSLE). Nevertheless, we and others have shown different NPSLE syndromes often occur regardless of the status of different antibodies, including antiphospholipid [10]. Our study aims were as follows: (i) to replicate other studies on the prevalence of depression and anxiety in our SLE cohort using measures and cut-offs with evidence in SLE; and (ii) to explore the relationship between clinical phenotypes of SLE and common sociodemographic factors with depression and anxiety in SLE. We hope that our results regarding the prevalence of anxiety (34%) and depression (27%) in our cohort will add to the literature regarding the burden of mood and anxiety symptoms in SLE. We also hope that our findings that SLE patients with musculoskeletal and skin involvement, shorter disease duration, history of fibromyalgia and unemployment are more likely to exhibit depression or anxiety will be helpful in informing future larger prospective studies in this area. Ultimately, convergent evidence from large cohort studies, intervention trials and basic science and translational research will allow us to gain a deeper understanding of anxiety and depression in SLE. Z.T. is supported by funding from the Arthritis Society, the Canadian Institutes of Health Research, Lupus Research Alliance, and the Department of Medicine, University of Toronto. Z.T.’s laboratory is also supported by donations from the Kathi and Peter Kaiser family, the Lou and Marissa Rocca family and the Bozzo family. K.B. is supported by funding from the University of Toronto Department of Psychiatry’s Excellence Fund and the Labatt Family Network’s Catalyst Fund. Funding: No specific funding was received from any bodies in the public, commercial or not-for-profit sectors to carry out the work described in this manuscript. Disclosure statement: The authors have declared no conflicts of interest.
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 | 0,006 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,062 | 0,046 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,009 |
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 ».