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Background: The incidence of depression in patients with systemic lupus erythematosus (SLE) is high and leads to a lower quality of life than that in undepressed SLE patients and healthy individuals. The causes of SLE depression are still unclear. Methods: A total of 94 SLE patients were involved in this study. A series of questionnaires (Hospital Depression Scale, Social Support Rate Scale and so on) were applied. Flow cytometry was used to test the different stages and types of T cells and B cells in peripheral blood mononuclear cells. Univariate and multivariate analyses were conducted to explore the key contributors to depression in SLE. Support Vector Machine (SVM) learning was applied to form the prediction model. Results: Depressed SLE patients showed lower objective support, severer fatigue, worse sleep quality and higher percentages of ASC%PBMC, ASC%CD19+, MAIT, TEM%Th, TEMRA%Th, CD45RA+CD27-Th, TEMRA%CD8 than non-depressed patients. A learning-based SVM model combining objective and patient-reported variables showed that fatigue, objective support, ASC%CD19+, TEM%Th and TEMRA%CD8 were the main contributing factors to depression in SLE. With the SVM model, the weight of TEM%Th was 0.17, which is the highest among objective variables, and the weight of fatigue was 0.137, which was the highest among variables of patients' reported outcomes. Conclusions: Both patient-reported factors and immunological factors could be involved in the occurrence and development of depression in SLE. Scientists can explore the mechanism of depression in SLE or other psychological diseases from the above perspective. [ABSTRACT FROM AUTHOR]

Background: The incidence of depression in patients with systemic lupus erythematosus (SLE) is high and leads to a lower quality of life than that in undepressed SLE patients and healthy individuals. The causes of SLE depression are still unclear. Methods: A total of 94 SLE patients were involved in this study. A series of questionnaires (Hospital Depression Scale, Social Support Rate Scale and so on) were applied. Flow cytometry was used to test the different stages and types of T cells and B cells in peripheral blood mononuclear cells. Univariate and multivariate analyses were conducted to explore the key contributors to depression in SLE. Support Vector Machine (SVM) learning was applied to form the prediction model. Results: Depressed SLE patients showed lower objective support, severer fatigue, worse sleep quality and higher percentages of ASC%PBMC, ASC%CD19+, MAIT, TEM%Th, TEMRA%Th, CD45RA+CD27-Th, TEMRA%CD8 than non-depressed patients. A learning-based SVM model combining objective and patient-reported variables showed that fatigue, objective support, ASC%CD19+, TEM%Th and TEMRA%CD8 were the main contributing factors to depression in SLE. With the SVM model, the weight of TEM%Th was 0.17, which is the highest among objective variables, and the weight of fatigue was 0.137, which was the highest among variables of patients’ reported outcomes. Conclusions: Both patient-reported factors and immunological factors could be involved in the occurrence and development of depression in SLE. Scientists can explore the mechanism of depression in SLE or other psychological diseases from the above perspective.