ArticleMedicine2025
Bidirectional Mendelian randomization reveals causal immune cell phenotypes in rheumatoid arthritis.
Article in Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by persistent inflammation in synovial joints and influenced by genetic and environmental factors. The immune system, comprising various cells that either exacerbate or reduce inflammation, plays a central role in RA pathogenesis. Despite extensive research, the causal relationships between immune cell phenotypes and RA remain unclear. We employed a two-sample Mendelian randomization (MR) approach to investigate the causal associations between 731 immune cell phenotypes and RA. Genetic variants, carefully selected based on rigorous criteria, served as instrumental variables to ensure analytical validity. Data on RA were derived from the FinnGen database, whereas immune cell phenotype data were obtained from the genome-wide association studies catalog. We employed 5 MR methods, including inverse variance weighted and MR Egger, to ensure robust causal inference. We assessed for pleiotropy and heterogeneity and adjusted findings using the False Discovery Rate. After False Discovery Rate adjustment (threshold < 0.05), inverse variance weighted analysis revealed potential causal relationships between 4 immune cell types and RA: CD62L- dendritic cells %dendritic cells (P = 3.88E-05; 95% confidence interval [CI] = 1.056), CD19 on IgD+ CD38- naive cells (P = 1.75E-04; 95% CI = 0.969), CD45RA on TD CD8br (P = 5.59E-04; 95% CI = 0.919), and HLA DR on CD33- HLA DR+ (P = 8.13E-05; 95% CI = 1.422). In reverse Mendelian studies, specific immune cell phenotypes were found to be associated with RA risk and progression: the percentage of memory B cells among lymphocytes (P = 2.74E-04; 95% CI = 0.861), IgD+ CD24+ cells among lymphocytes (P = 6.93E-04; 95% CI = 0.867), CD4+ CD8dim cells among lymphocytes (P = 2.92E-04; 95% CI = 0.802), CD4+ CD8dim cells among leukocytes (P = 4.37E-04; 95% CI = 0.814), and CD24 expression on IgD+CD24+ cells (P = 1.05E-04; 95% CI = 0.857). These results identify immune cell phenotypes closely linked to RA susceptibility and progression. The findings suggest that specific immune cell phenotypes are not only influenced by RA but may also contribute to its development and progression. These results offer new insights into the immunological underpinnings of RA and highlight potential targets for therapeutic intervention. Future research should focus on validating these causal relationships in diverse populations and exploring the molecular mechanisms involved.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.