Effect of climatic environment on immunological features of rheumatoid arthritis

Abstract

The aim of this study was to clarify the effect of climatic environment on the immunological features of rheumatoid arthritis (RA). Blood samples were collected from patients with RA and healthy controls (HCs), matched by age and sex, living in two locations, Tsukuba and Karuizawa, which differ in their altitude and average air temperature and atmospheric pressure. Analysis of peripheral blood mononuclear cells (PBMCs) revealed that the proportion of T and B cell subpopulations in HCs and RA patients were significantly different between two sites. Inverse probability weighting adjustment with propensity scores was used to control for potential confounding factors. The results revealed that, in comparison with RA patients in Tsukuba, those in Karuizawa showed a significant increase in cTh1, cTfh1, and Tph cells, and significant decrease in cTh17, cTh17.1, and CD8+ Treg in T cell subpopulations, and a significant increase in DNB, DN1, DN2, and class-switched memory B cells, and a significant decrease in unswitched memory B, naïve B cells, and ABCs in B cell subpopulations. Our results suggest the possibility that climatic environment might have an effect on immune cell proportion and function, and be related to the pathogenic mechanism of RA.

Figure 1

Differences in the proportion of subpopulations in peripheral blood T cells between RA patients and HCs from two distinct geographic locations. The proportion of T cell subpopulations in PBMC collected from RA patients and HCs in two hospitals was analyzed using flow cytometry. Histograms show percentage of cTh1 cells, cTh17 cells, cTh17.1 cells, cTfh1 cells, cTfh2 cells, cTfh17 cells, and cTph cells in memory CD4⁺ T cells, cTreg cells in naïve and memory CD4⁺ T cells, CD8⁺ T cells, and CD8⁺ Treg cells in CD8⁺ T cells. Data are presented as mean ± SEM, and circle and square shows samples collected from Tsukuba and Karuizawa, respectively. Unpaired t-test was performed to analyze statistical interactions between blood samples collected from HCs and RA patients in Tsukuba or Karuizawa. Statistical significance was defined as *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001.

Figure 2

Differences in the proportion of subpopulations of peripheral blood B cells between RA patients and HCs from two distinct geographic locations. The proportion of subpopulation in B cells in PBMC collected from RA patients and HC in two hospitals was analyzed by flow cytometry. Histograms show percentage of class switched memory B cells, unswitched memory B cells, memory B cells, and double negative (DN) B cells in CD19⁺CD20⁺ cells, DN1 cells and DN2 cells in DNB cells, plasmablast in CD19⁺CD20⁻ cells, ABC and Breg cells in CD19⁺ cells. Data are presented as mean ± SEM. Unpaired t-test was performed to analyze a statistical interaction between blood samples collected from HC or RA patients in Tsukuba or Karuizawa. Statistical significance was defined as *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001.

Figure 3

Comparison of subpopulations of peripheral blood T and B cells in RA patients after adjustment with propensity score weighting. Inverse probability weighting adjustments with propensity scores were performed to control for biases caused by the imbalance of potential confounding factors in comparison of subpopulation in T cells (A) and B cells (B) of PBMC collected from RA patients in two hospitals. The graph shows the mean difference with 95% confidence intervals for each subpopulation after IPW adjustment. Positive or negative numbers represent increase or decrease of the subpopulations in the patients in Karuizawa, respectively. Statistical significance was defined as *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001.

Figure 4

Comparison of subpopulations of peripheral blood T and B cells in HCs after adjustment with propensity score weighting. Inverse probability weighting adjustments with propensity scores were performed to control for biases caused by the imbalance of potential confounding factors in comparison of subpopulation in T cells (A) and B cells (B) of PBMC collected from HCs in two hospitals. The graph shows the mean difference with 95% confidence intervals for each subpopulation after IPW adjustment. Positive or negative numbers represent increase or decrease of the subpopulations in the patients in Karuizawa, respectively. Statistical significance was defined as *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001.

Figure 5

Correlation between T cell or B cell subpopulations and disease activity in RA patients. Correlation between disease activity of RA and proportion of the subpopulations in T cells (A) and B cells (B) in PBMCs collected from Tsukuba and Karuizawa were analyzed. Correlation analysis was performed using Spearman rank correlation coefficient.