OBJECTIVES
Osteoporosis and fragility fractures are clinically important complications of primary Sjögren's syndrome (pSS) that may accelerate functional decline and excess mortality. In practice, osteoporosis risk is often assessed using general-population tools that do not incorporate disease activity, glucocorticoid exposure or inflammation-related bone remodelling. We aimed to develop and externally validate a prediction model for DXA-defined osteoporosis in pSS using routinely available clinical and laboratory indicators.
METHODS
This retrospective cohort study included 1,000 patients with pSS from Longhua Hospital, randomly split into training and internal validation sets (7:3), and an independent external validation cohort of 266 patients from Shanghai Seventh People's Hospital. Candidate predictors were screened by univariable analysis, multivariable logistic regression and LASSO. Logistic regression was compared with seven supervised machine-learning algorithms. Performance was evaluated by area under the receiver operating characteristic curve (AUC), calibration and decision curve analysis.
RESULTS
The final logistic regression model retained seven predictors: sex, age, current glucocorticoid use, EULAR Sjögren's Syndrome Disease Activity Index score, 25-hydroxyvitamin D, procollagen type 1 N-terminal propeptide and β-C-terminal telopeptide of type I collagen. AUCs were 0.820, 0.807 and 0.787 in the training, internal validation and external validation cohorts, respectively, with good calibration. Machine-learning models achieved higher training AUCs but showed poorer transportability. A freely accessible web-based calculator was developed for point-of-care use.
CONCLUSIONS
A transparent, externally validated seven-variable model provides individualised DXA-defined osteoporosis risk estimation in pSS and may help clinicians prioritise bone density testing during routine visits.
Rheumatoid Arthritis (RA) is a common autoimmune disease
with complex pathogenesis and high prevalence, severely affecting patients’ quality of
life. Multi-omics analysis has emerged as a powerful tool in RA research, providing insights
into cell heterogeneity, genetic mechanisms, and immune microenvironment characteristics.
This research looks into important genes linked to rheumatoid arthritis(RA)
using single-cell data and Mendelian randomization analysis, while also uncovering the
characteristics of the immune microenvironment and its mechanisms linking to these
key genes.
Gene expression, eQTL, and GWAS data were collected. Analyses included
single-cell data processing (quality control, dimensionality reduction, clustering, cell annotation,
and cell subset contribution assessment), subgroup non-negative matrix factorization,
Mendelian randomization, co-localization analysis, immune infiltration analysis,
and GSEA/GSVA.
This study analyzed 95,036 single-cell samples and found that alterations in B cells are closely associated with disease progression. Further analysis identified seven distinct B cell subpopulations, with immune responses and the tumor microenvironment exerting significant influence on their dynamics. Mendelian randomization analysis revealed key genes, CD83 and CRIP2, that are linked to the risk of RA. Subsequent investigation demonstrated strong associations between these genes and immune cell populations. GSEA and GSVA analyses showed that CD83 is involved in pathways related to allograft rejection and antigen processing, while CRIP2 is associated with interactions of extracellular matrix receptors and the IL-17 signaling pathway. Additionally, immunometabolism pathway analysis highlighted potential therapeutic targets and underlying mechanisms.
This study analyzed 95,036 single-cell samples and found that alterations in B
cells are closely associated with disease progression. Further analysis identified seven
distinct B cell subpopulations, with immune responses and the tumor microenvironment
exerting significant influence on their dynamics. Mendelian randomization analysis revealed
key genes, CD83 and CRIP2, that are linked to the risk of RA. Subsequent investigation
demonstrated strong associations between these genes and immune cell populations.
GSEA and GSVA analyses showed that CD83 is involved in pathways related to
allograft rejection and antigen processing, while CRIP2 is associated with interactions
of extracellular matrix receptors and the IL-17 signaling pathway. Additionally, immunometabolism
pathway analysis highlighted potential therapeutic targets and underlying
mechanisms.
The findings suggest that B cells play a major role in the RA immune microenvironment
and identify CD83 and CRIP2 as potential biomarkers and therapeutic
targets. CD83 may contribute to RA through immune regulation, antigen presentation,
and inflammatory signaling, whereas CRIP2 may be involved in metal ion homeostasis,
cellular stress responses, and immune-related pathological processes. Immune infiltration
analyses further indicate that these genes are closely associated with immune-cell
activity in RA. Overall, the study provides new insight into RA pathogenesis and supports
the potential clinical relevance of CD83 and CRIP2, although further experimental
and clinical validation is required.
This study offers valuable understanding about RA pathogenesis and identifies
probable diagnostic biomarkers as well as therapeutic targets (CD83 and CRIP2),
which could improve our awareness of the illness and help with formulating more successful
care plans.
Shi-Kai Chen, Hanyu Wang, Cheng Wang et al.· Current Medicinal Chemistry· 0 citations
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