博客

Abstract

This study analyzed the influence of pain perception, functional capacity, quality of life (QoL), and disease activity on the mental health of patients with Rheumatoid Arthritis (RA). A quantitative cross-sectional cohort study was conducted with 461 adults (M = 65.10; SD = 12.00), of whom 415 were women. Participants completed a sociodemographic and health conditions questionnaire, the Health Assessment Questionnaire Disability Index (HAQ-DI), the 12-item Short Form Health Survey (SF-12), and the Clinical Disease Activity Index (CDAI). Regression analyses revealed that CDAI (β = -0.066), fatigue (β = -0.143), joint pain (β = -0.079), HAQ-DI (β = 0.093), physical SF-12 (β = -0.222), poor sleep quality (β = -0.065), stress (β = -0.197), anxiety (β = -0.087), and depression (β = -0.312) significantly predicted mental health outcomes. Therefore, RA may negatively affect patients’ mental health, being associated with stress, poor sleep, depression, and anxiety, which in turn may exacerbate pain perception. Incorporating QoL assessments into routine clinical consultations may provide a more comprehensive approach to care, addressing both physical and emotional dimensions of health in patients with RA.

Abstract

This study analyzed the influence of pain perception, functional capacity, quality of life (QoL), and disease activity on the mental health of patients with Rheumatoid Arthritis (RA). A quantitative cross-sectional cohort study was conducted with 461 adults (M = 65.10; SD = 12.00), of whom 415 were women. Participants completed a sociodemographic and health conditions questionnaire, the Health Assessment Questionnaire Disability Index (HAQ-DI), the 12-item Short Form Health Survey (SF-12), and the Clinical Disease Activity Index (CDAI). Regression analyses revealed that CDAI (β = -0.066), fatigue (β = -0.143), joint pain (β = -0.079), HAQ-DI (β = 0.093), physical SF-12 (β = -0.222), poor sleep quality (β = -0.065), stress (β = -0.197), anxiety (β = -0.087), and depression (β = -0.312) significantly predicted mental health outcomes. Therefore, RA may negatively affect patients’ mental health, being associated with stress, poor sleep, depression, and anxiety, which in turn may exacerbate pain perception. Incorporating QoL assessments into routine clinical consultations may provide a more comprehensive approach to care, addressing both physical and emotional dimensions of health in patients with RA.

Abstract

This study analyzed the influence of pain perception, functional capacity, quality of life (QoL), and disease activity on the mental health of patients with Rheumatoid Arthritis (RA). A quantitative cross-sectional cohort study was conducted with 461 adults (M = 65.10; SD = 12.00), of whom 415 were women. Participants completed a sociodemographic and health conditions questionnaire, the Health Assessment Questionnaire Disability Index (HAQ-DI), the 12-item Short Form Health Survey (SF-12), and the Clinical Disease Activity Index (CDAI). Regression analyses revealed that CDAI (β = -0.066), fatigue (β = -0.143), joint pain (β = -0.079), HAQ-DI (β = 0.093), physical SF-12 (β = -0.222), poor sleep quality (β = -0.065), stress (β = -0.197), anxiety (β = -0.087), and depression (β = -0.312) significantly predicted mental health outcomes. Therefore, RA may negatively affect patients’ mental health, being associated with stress, poor sleep, depression, and anxiety, which in turn may exacerbate pain perception. Incorporating QoL assessments into routine clinical consultations may provide a more comprehensive approach to care, addressing both physical and emotional dimensions of health in patients with RA.

Abstract

This study analyzed the influence of pain perception, functional capacity, quality of life (QoL), and disease activity on the mental health of patients with Rheumatoid Arthritis (RA). A quantitative cross-sectional cohort study was conducted with 461 adults (M = 65.10; SD = 12.00), of whom 415 were women. Participants completed a sociodemographic and health conditions questionnaire, the Health Assessment Questionnaire Disability Index (HAQ-DI), the 12-item Short Form Health Survey (SF-12), and the Clinical Disease Activity Index (CDAI). Regression analyses revealed that CDAI (β = -0.066), fatigue (β = -0.143), joint pain (β = -0.079), HAQ-DI (β = 0.093), physical SF-12 (β = -0.222), poor sleep quality (β = -0.065), stress (β = -0.197), anxiety (β = -0.087), and depression (β = -0.312) significantly predicted mental health outcomes. Therefore, RA may negatively affect patients’ mental health, being associated with stress, poor sleep, depression, and anxiety, which in turn may exacerbate pain perception. Incorporating QoL assessments into routine clinical consultations may provide a more comprehensive approach to care, addressing both physical and emotional dimensions of health in patients with RA.

Abstract

This study analyzed the influence of pain perception, functional capacity, quality of life (QoL), and disease activity on the mental health of patients with Rheumatoid Arthritis (RA). A quantitative cross-sectional cohort study was conducted with 461 adults (M = 65.10; SD = 12.00), of whom 415 were women. Participants completed a sociodemographic and health conditions questionnaire, the Health Assessment Questionnaire Disability Index (HAQ-DI), the 12-item Short Form Health Survey (SF-12), and the Clinical Disease Activity Index (CDAI). Regression analyses revealed that CDAI (β = -0.066), fatigue (β = -0.143), joint pain (β = -0.079), HAQ-DI (β = 0.093), physical SF-12 (β = -0.222), poor sleep quality (β = -0.065), stress (β = -0.197), anxiety (β = -0.087), and depression (β = -0.312) significantly predicted mental health outcomes. Therefore, RA may negatively affect patients’ mental health, being associated with stress, poor sleep, depression, and anxiety, which in turn may exacerbate pain perception. Incorporating QoL assessments into routine clinical consultations may provide a more comprehensive approach to care, addressing both physical and emotional dimensions of health in patients with RA.

Abstract

Rheumatoid arthritis (RA) is a heterogeneous disease with variable symptoms, prognosis, and treatment response, necessitating refined patient classification. We applied multimodal deep learning and clustering to identify distinct RA phenotypes using baseline clinical data from 1,387 patients in the Leiden Rheumatology clinic. Four Joint Involvement Patterns (JIP) emerged: foot-predominant arthritis, seropositive oligoarticular disease, seronegative hand arthritis, and polyarthritis. Findings were validated in clinical trial data (n = 307) and an independent secondary care cohort (n = 515). Clusters showed high stability and significant differences in remission rates (P = 0.007) and methotrexate failure (P < 0.001). JIP-hand patients had superior outcomes (particularly in ACPA-positive patients) versus JIP-foot (HR:0.37, P < 0.001) and JIP-poly (HR:0.33, P = 0.005), independent of baseline disease activity and clinical markers. Synovial histology analysis (n = 194) revealed distinct inflammatory patterns across clusters, hinting at different underlying biological mechanisms. These validated RA phenotypes based on joint involvement patterns may enable targeted research into disease mechanisms and personalized treatment strategies.

Abstract

Rheumatoid arthritis (RA) is associated with an increased risk of brain diseases, yet the underlying biological mechanisms remain poorly understood. Using data from the UK Biobank, including blood-based biomarkers, brain imaging, and diagnostic records, we investigated the roles of immunometabolic and brain structure alterations in the link between RA and brain diseases. The effects of antirheumatic treatments were evaluated using Cox proportional hazards models. To ensure a clear temporal sequence, only individuals with RA diagnosed before baseline were included. The cohort comprised 2534 prevalent RA cases (mean age: 59.80 ± 6.99 years; 70.84% women), with a mean disease duration of 5.54 years (SD = 3.57). RA was significantly associated with depression (HR = 1.659 [95% CI: 1.444–1.905]), sleep disturbances (HR = 1.673 [1.379–2.029]), and dementia (HR = 1.466 [1.225–1.755]). Mediation analyses revealed that immunometabolic alterations (indirect effect = 0.0099, P < 0.001) and reduced white matter integrity (FA; indirect effect = 0.012, P = 0.002) mediated these associations. Notably, treatment with slow-acting antirheumatic drugs (SAARDs) was linked to a reduced risk of anxiety (P = 0.006). These findings indicate that immunometabolic alterations in RA may lead to a decrease in white matter integrity, thereby increasing susceptibility to brain diseases. They highlight the importance of managing systemic immunometabolic alterations to preserve brain health in RA patients.

Abstract

Rheumatoid arthritis (RA) is a heterogeneous disease with variable symptoms, prognosis, and treatment response, necessitating refined patient classification. We applied multimodal deep learning and clustering to identify distinct RA phenotypes using baseline clinical data from 1,387 patients in the Leiden Rheumatology clinic. Four Joint Involvement Patterns (JIP) emerged: foot-predominant arthritis, seropositive oligoarticular disease, seronegative hand arthritis, and polyarthritis. Findings were validated in clinical trial data (n = 307) and an independent secondary care cohort (n = 515). Clusters showed high stability and significant differences in remission rates (P = 0.007) and methotrexate failure (P < 0.001). JIP-hand patients had superior outcomes (particularly in ACPA-positive patients) versus JIP-foot (HR:0.37, P < 0.001) and JIP-poly (HR:0.33, P = 0.005), independent of baseline disease activity and clinical markers. Synovial histology analysis (n = 194) revealed distinct inflammatory patterns across clusters, hinting at different underlying biological mechanisms. These validated RA phenotypes based on joint involvement patterns may enable targeted research into disease mechanisms and personalized treatment strategies.

Abstract

Rheumatoid arthritis (RA) is a heterogeneous disease with variable symptoms, prognosis, and treatment response, necessitating refined patient classification. We applied multimodal deep learning and clustering to identify distinct RA phenotypes using baseline clinical data from 1,387 patients in the Leiden Rheumatology clinic. Four Joint Involvement Patterns (JIP) emerged: foot-predominant arthritis, seropositive oligoarticular disease, seronegative hand arthritis, and polyarthritis. Findings were validated in clinical trial data (n = 307) and an independent secondary care cohort (n = 515). Clusters showed high stability and significant differences in remission rates (P = 0.007) and methotrexate failure (P < 0.001). JIP-hand patients had superior outcomes (particularly in ACPA-positive patients) versus JIP-foot (HR:0.37, P < 0.001) and JIP-poly (HR:0.33, P = 0.005), independent of baseline disease activity and clinical markers. Synovial histology analysis (n = 194) revealed distinct inflammatory patterns across clusters, hinting at different underlying biological mechanisms. These validated RA phenotypes based on joint involvement patterns may enable targeted research into disease mechanisms and personalized treatment strategies.

Abstract

Rheumatoid arthritis (RA) is a heterogeneous disease with variable symptoms, prognosis, and treatment response, necessitating refined patient classification. We applied multimodal deep learning and clustering to identify distinct RA phenotypes using baseline clinical data from 1,387 patients in the Leiden Rheumatology clinic. Four Joint Involvement Patterns (JIP) emerged: foot-predominant arthritis, seropositive oligoarticular disease, seronegative hand arthritis, and polyarthritis. Findings were validated in clinical trial data (n = 307) and an independent secondary care cohort (n = 515). Clusters showed high stability and significant differences in remission rates (P = 0.007) and methotrexate failure (P < 0.001). JIP-hand patients had superior outcomes (particularly in ACPA-positive patients) versus JIP-foot (HR:0.37, P < 0.001) and JIP-poly (HR:0.33, P = 0.005), independent of baseline disease activity and clinical markers. Synovial histology analysis (n = 194) revealed distinct inflammatory patterns across clusters, hinting at different underlying biological mechanisms. These validated RA phenotypes based on joint involvement patterns may enable targeted research into disease mechanisms and personalized treatment strategies.