Increased Production of the Soluble Tumor-Associated Antigens CA19-9, CA125, and CA15-3 in Rheumatoid Arthritis

Some tumor-associated antigens (TAAs) are expressed on inflammatory cells. We previously detected carcinoembryonic antigen (CEA; CD66) in the rheumatoid (RA) synovium. The production of CEA, CA19-9, CA125, and CA15.3, may be increased in patients with RA, scleroderma, lupus, and SjÖgren's syndrome (SS). Some of these TAAs contain sialylated carbohydrate motifs and they are involved in tumor-associated cell adhesion and metastasis. We assessed levels of TAAs in the sera of RA patients and healthy subjects. Serum TAA levels were correlated with disease markers including serum rheumatoid factor (RF), C-reactive protein (CRP), and anti-CCP antibody levels, DAS28, age disease duration. TAAs including CEA, CA15-3, CA72-4, CA125, and CA19-9, and neuron-specific enolase (NSE) were assessed by immunoassay in the sera of 75 patients with RA and 50 age- and sex-matched healthy controls. Normal upper limits for these TAAs were 3.4 Μg/L, 25 kU/L, 6.9 kU/L, 35 kU/L, 34 kU/L, and 16.3 Μg/L, respectively. There were significantly more RA patients showing abnormally high levels of CA125 (10.8% versus 7.1%), CA19-9 (8.1% versus 0%), and CA15-3 (17.6% versus 14.3%) in comparison to controls ( P < 0.05). The mean absolute serum levels of CA125 (23.9 ± 1.8 versus 16.8 ± 2.2 kU/L) and CA19-9 (14.2 ± 1.2 versus 10.5 ± 1.6 kU/L) were also significantly higher in RA compared to controls ( P < 0.05). Among RA patients, serum CEA showed significant correlation with RF ( r = 0.270; P < 0.05). None of the assessed TAAs showed any correlation with CRP, anti-CCP, DAS28, age or disease duration. The concentration of some TAAs may be elevated in the sera of patients with established RA in comparison to healthy subjects. CEA, CA19-9, CA125, and CA15-3 contain carbohydrate motifs and thus they may be involved in synovitis-associated adhesive events. Furthermore, some TAAs, such as CEA, may also correlate with prognostic factors, such as serum RF levels.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73224/1/annals.1422.037.pd

A review of spatial causal inference methods for environmental and epidemiological applications

The scientific rigor and computational methods of causal inference have had great impacts on many disciplines, but have only recently begun to take hold in spatial applications. Spatial casual inference poses analytic challenges due to complex correlation structures and interference between the treatment at one location and the outcomes at others. In this paper, we review the current literature on spatial causal inference and identify areas of future work. We first discuss methods that exploit spatial structure to account for unmeasured confounding variables. We then discuss causal analysis in the presence of spatial interference including several common assumptions used to reduce the complexity of the interference patterns under consideration. These methods are extended to the spatiotemporal case where we compare and contrast the potential outcomes framework with Granger causality, and to geostatistical analyses involving spatial random fields of treatments and responses. The methods are introduced in the context of observational environmental and epidemiological studies, and are compared using both a simulation study and analysis of the effect of ambient air pollution on COVID-19 mortality rate. Code to implement many of the methods using the popular Bayesian software OpenBUGS is provided