{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T13:35:57Z","timestamp":1791380157535,"version":"4.3.3"},"reference-count":54,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>Patients with systemic lupus erythematosus (SLE) frequently experience poor sleep quality. This cross-sectional study aimed to identify distinct sleep disturbance profiles in SLE patients and examine their associations with demographic, disease-related, and psychosocial factors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A total of 331 patients with SLE were included. Latent profile analysis (LPA) was conducted using the tidyLPA package. Logistic regression models were constructed to assess associations between the identified sleep disturbance clusters and physical and psychological outcomes, based on factors significantly influencing the LPA results. The physical and psychological outcomes were estimated using the Hospital Anxiety and Depression Scale (HADS) and the Fatigue Severity Scale (FSS). Sleep clusters were analyzed through multivariate logistic regression.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Three distinct sleep disturbance profiles were identified: Cluster 1 (severe sleep disturbance) (n = 42), Cluster 2 (moderate sleep disturbance) (n = 174), and Cluster 3 (mild sleep disturbance) (n = 115). LPA yielded an entropy value of 0.996 for the three-cluster model. The mean total Pittsburgh Sleep Quality Index (PSQI) score for the SLE samples was 7.59 \u00b1 3.44. Among the various sleep quality domains, sleep latency and subjective sleep quality were the most significantly affected in SLE patients. The analysis revealed that disease duration, severity of fatigue, use of calcium supplements, impaired renal function, anxiety, and depression were all significant factors influencing cluster membership.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study identified three distinct patterns of sleep disturbance among SLE patients. Cluster 1 (severe sleep disturbance) was characterized by prolonged sleep latency despite high sleep efficiency and subjective sleep quality scores. Cluster 2 (moderate sleep disturbance) exhibited longer sleep duration than Cluster 1, while Cluster 3 (mild sleep disturbance) had the lowest scores across all sleep quality domains. These findings suggest that sleep disturbance profiling may facilitate personalized sleep management strategies for patients with SLE.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fimmu.2025.1626597","type":"journal-article","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T06:21:11Z","timestamp":1760509271000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Associations of sleep disturbances in systemic lupus erythematosus with physical and psychological outcomes: a cross-sectional latent profile 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