Blood-Based Immune Profiling Combined with Machine Learning Discriminates Psoriatic Arthritis from Psoriasis Patients

Psoriasis (Pso) is a chronic inflammatory skin disease, and up to 30% of Pso patients develop psoriatic arthritis (PsA), which can lead to irreversible joint damage. Early detection of PsA in Pso patients is crucial for timely treatment but difficult for dermatologists to implement. We, therefore, aimed to find disease-specific immune profiles, discriminating Pso from PsA patients, possibly facilitating the correct identification of Pso patients in need of referral to a rheumatology clinic. The phenotypes of peripheral blood immune cells of consecutive Pso and PsA patients were analyzed, and disease-specific immune profiles were identified via a machine learning approach. This approach resulted in a random forest classification model capable of distinguishing PsA from Pso (mean AUC = 0.95). Key PsA-classifying cell subsets selected included increased proportions of differentiated CD4+CD196+CD183-CD194+ and CD4+CD196-CD183-CD194+ T-cells and reduced proportions of CD196+ and CD197+ monocytes, memory CD4+ and CD8+ T-cell subsets and CD4+ regulatory T-cells. Within PsA, joint scores showed an association with memory CD8+CD45RA-CD197- effector T-cells and CD197+ monocytes. To conclude, through the integration of in-depth flow cytometry and machine learning, we identified an immune cell profile discriminating PsA from Pso. This immune profile may aid in timely diagnosing PsA in Pso

Comparison of physical activity among different subsets of patients with knee or hip osteoarthritis and the general population

Contains fulltext : 217317.pdf (Publisher’s version ) (Open Access

Short fatigue questionnaire: Screening for severe fatigue.

Contains fulltext : 226147.pdf (Publisher’s version ) (Open Access

Blood-Based Immune Profiling Combined with Machine Learning Discriminates Psoriatic Arthritis from Psoriasis Patients

Psoriasis (Pso) is a chronic inflammatory skin disease, and up to 30% of Pso patients develop psoriatic arthritis (PsA), which can lead to irreversible joint damage. Early detection of PsA in Pso patients is crucial for timely treatment but difficult for dermatologists to implement. We, therefore, aimed to find disease-specific immune profiles, discriminating Pso from PsA patients, possibly facilitating the correct identification of Pso patients in need of referral to a rheumatology clinic. The phenotypes of peripheral blood immune cells of consecutive Pso and PsA patients were analyzed, and disease-specific immune profiles were identified via a machine learning approach. This approach resulted in a random forest classification model capable of distinguishing PsA from Pso (mean AUC = 0.95). Key PsA-classifying cell subsets selected included increased proportions of differentiated CD4+CD196+CD183-CD194+ and CD4+CD196-CD183-CD194+ T-cells and reduced proportions of CD196+ and CD197+ monocytes, memory CD4+ and CD8+ T-cell subsets and CD4+ regulatory T-cells. Within PsA, joint scores showed an association with memory CD8+CD45RACD197- effector T-cells and CD197+ monocytes. To conclude, through the integration of in-depth flow cytometry and machine learning, we identified an immune cell profile discriminating PsA from Pso. This immune profile may aid in timely diagnosing PsA in Pso

Disease-related and psychosocial factors associated with depressive symptoms in patients with systemic sclerosis, including fear of progression and appearance self-esteem.

Contains fulltext : 110938.pdf (publisher's version ) (Open Access)OBJECTIVE: The prevalence of depressive symptoms is high in patients with systemic sclerosis (SSc, scleroderma). This study was conducted to determine which disease-related and psychosocial factors are associated with depressive symptoms, independent of sociodemographic factors. METHODS: In total, 215 patients with SSc completed questionnaires on sociodemographics, physical functioning (HAQ-DI), pain (VAS), fatigue (CIS), psychosocial characteristics (CISS, ICQ, PRQ, ASE, FoP-Q-SF) and depressive symptoms (CES-D). Disease characteristics (disease duration, disease subtype, modified Rodnan Skin Score) were collected. Hierarchical linear regression analyses were conducted to assess associations with depressive symptoms. RESULTS: The mean CES-D score was 12.9 (SD=9.7) and the prevalence of patients scoring>= 16 and>=19 were 32.1% and 25.1%, respectively. The variance explained by sociodemographics and disease characteristics was negligible (R(2)</=.09). Fatigue and pain were independently associated with depressive symptoms (R(2) change=.35). After adding psychological factors (R(2) change=.21), satisfaction with social support, emotion-focused coping and helplessness were also significantly associated with depressive symptoms. Higher fear of progression was associated with more depressive symptoms (P</=.01), and appearance self-esteem showed a marginally significant association (P=.08). CONCLUSION: Depressive symptoms were common in the present sample of patients with SSc and were independently associated with pain, fatigue, social support, emotion-focused coping, helplessness and fear of progression. Results suggest that, in addition to assessment of disease characteristics, attention should be given also to psychosocial factors found to be associated with depressive symptoms. For the development and trialling of psychological interventions, fear of progression could be an important target.01 maart 20126 p