Google Internet searches related to inflammatory arthritis: An observational study using Google Trends data.

OBJECTIVE: The Internet has transformed how patients access health information. We examined Google search engine data to understand which aspects of health are most often searched for in combination with inflammatory arthritis (IA). METHODS: Using Google Trends data (2011-2022) we determined the relative popularity of searches for 'patient symptoms' (pain, fatigue, stiffness, mood, work) and 'treat-to-target' (disease-modifying drugs, steroids, swelling, inflammation) health domains made with rheumatoid arthritis (RA), psoriatic arthritis (PsA), and axial spondyloarthritis (AxSpA) in the UK/USA. Google Trends normalises searches by popularity over time and region, generating 0-100 scale relative search volumes (RSV; 100 represents the time-point with most searches). Up to five search term combinations can be compared. RESULTS: In all IA forms, pain was the most popular patient symptom domain. UK/USA searches for pain gave mean RSVs of 58/79, 34/51, and 39/63 with RA, PsA, and AxSpA; mean UK/USA RSVs for other patient symptom domains ranged 2-7/2-8. Methotrexate was the most popular treat-to-target search term with RA/PsA in the UK (mean 28/21) and USA (mean 63/33). For AxSpA, inflammation was most popular (mean UK/USA 9/34). Searches for pain were substantially more popular than searches for methotrexate in RA and PsA, and inflammation in AxSpA. Searches increased over time. CONCLUSIONS: Pain is the most popular search term used with IA in Google searches in the UK/USA, supporting surveys/qualitative studies highlighting the importance of improving pain to patients with IA. Routine pain assessments should be embedded within treat-to-target strategies to ensure patient perspectives are considered.

Deconvolution of whole blood transcriptomics identifies changes in immune cell composition in patients with systemic lupus erythematosus (SLE) treated with mycophenolate mofetil.

BACKGROUND: Systemic lupus erythematosus (SLE) is a clinically and biologically heterogeneous autoimmune disease. We explored whether the deconvolution of whole blood transcriptomic data could identify differences in predicted immune cell frequency between active SLE patients, and whether these differences are associated with clinical features and/or medication use. METHODS: Patients with active SLE (BILAG-2004 Index) enrolled in the BILAG-Biologics Registry (BILAG-BR), prior to change in therapy, were studied as part of the MASTERPLANS Stratified Medicine consortium. Whole blood RNA-sequencing (RNA-seq) was conducted at enrolment into the registry. Data were deconvoluted using CIBERSORTx. Predicted immune cell frequencies were compared between active and inactive disease in the nine BILAG-2004 domains and according to immunosuppressant use (current and past). RESULTS: Predicted cell frequency varied between 109 patients. Patients currently, or previously, exposed to mycophenolate mofetil (MMF) had fewer inactivated macrophages (0.435% vs 1.391%, p = 0.001), naïve CD4 T cells (0.961% vs 2.251%, p = 0.002), and regulatory T cells (1.858% vs 3.574%, p = 0.007), as well as a higher proportion of memory activated CD4 T cells (1.826% vs 1.113%, p = 0.015), compared to patients never exposed to MMF. These differences remained statistically significant after adjusting for age, gender, ethnicity, disease duration, renal disease, and corticosteroid use. There were 2607 differentially expressed genes (DEGs) in patients exposed to MMF with over-representation of pathways relating to eosinophil function and erythrocyte development and function. Within CD4 + T cells, there were fewer predicted DEGs related to MMF exposure. No significant differences were observed for the other conventional immunosuppressants nor between patients according disease activity in any of the nine organ domains. CONCLUSION: MMF has a significant and persisting effect on the whole blood transcriptomic signature in patients with SLE. This highlights the need to adequately adjust for background medication use in future studies using whole blood transcriptomics.