Publishing type：Research paper (scientific journal)  

DOI： 10.3390/jcm15020883

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Publishing type：Research paper (scientific journal)  

<h4>Background</h4>Thymic stromal lymphopoietin (TSLP) is a master regulator of type 2 immune responses; however, the associations between serum TSLP and the characteristics of adult asthma have not been fully clarified. The aim of this study was to determine the characteristics of adult asthma with high serum TSLP and explore TSLP's association with the late-onset eosinophilic asthma phenotype.<h4>Methods</h4>Baseline data of the TNH-Azma study (a real-world observational cohort study conducted in Japan on 1344 patients with asthma from 30 hospitals) was used and serum cytokines were measured. Patients were stratified into quartile groups based on the baseline serum TSLP levels, and their clinical characteristics were compared. Multivariable regression analyses were used to determine clinical variables associated with serum TSLP and cytokines associated with the late-onset eosinophilic asthma phenotype.<h4>Results</h4>Patients with TSLP-high asthma were older, late-onset, eosinophilic, and less atopic; had a higher BMI; more smoking history; and more asthma-COPD overlap, sleep apnea syndrome (SAS), hypertension, and heart disease. They also exhibited lower lung function with worse asthma symptoms and were more frequently on oral corticosteroids. Multivariable regression analyses adjusted for age and sex demonstrated that a high TSLP level was positively associated with later asthma onset, longer asthma duration, hypertension, higher blood eosinophils, BMI, smoking history, use of biologics, SAS, and high Fres, and was negatively associated with pollinosis. Among the serum cytokines, TSLP exhibited the strongest association with late-onset, eosinophilic asthma.<h4>Conclusion</h4>High serum TSLP is a distinctive feature of late-onset, long-duration, eosinophilic asthma. Patients with asthma with this feature may be a unique target population for specific asthma therapy.

DOI： 10.1111/all.70109

PubMed

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Language：English   Publishing type：Research paper (scientific journal)  

<jats:title>ABSTRACT</jats:title><jats:sec><jats:title>Objective</jats:title><jats:p>Although smoking, an established cardiovascular disease (CVD) risk, is a modifier of body mass index (BMI), studies are still lacking addressing a potential association between smoking and BMI or BMI‐related risk factors in Asia. The aim of this study was to investigate the association between smoking and BMI or CVD risk factors in a working‐age population in Japan.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>Eight thousand eight hundred thirty‐six male and seven thousand three hundred seventy female health checkup examinees in a single center without self‐reported heart disease or stroke were analyzed by multiple regression analysis for an association between the smoking status (never, current, or past) or the number of cigarettes consumed (never (0/day), mild (&lt; 10/day), moderate (10–19/day) or heavy (≧ 20/day)) and age‐adjusted BMI. In subgroups of 3430 males and 2407 females, the predictive risk score of CVD from the Hisayama study except for the smoking item was calculated and compared among groups with different smoking habits.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>In the analysis of the association between smoking and BMI, age‐adjusted BMI was significantly higher in heavy smokers than never, mild, or moderate smokers in males (<jats:italic>p</jats:italic> &lt; 0.01 vs. never, <jats:italic>p</jats:italic> &lt; 0.05 vs. mild, <jats:italic>p</jats:italic> &lt; 0.01 vs. moderate). Past smokers exhibited a higher CVD risk score than never or current smokers (<jats:italic>p</jats:italic> &lt; 0.01 vs. never, <jats:italic>p</jats:italic> &lt; 0.01 vs. current). Even though the score of the smoking item was excluded from the calculation, heavy smokers showed a significantly higher CVD risk score than the other three groups in males (<jats:italic>p</jats:italic> &lt; 0.01 vs. never, <jats:italic>p</jats:italic> &lt; 0.05 vs. mild, <jats:italic>p</jats:italic> &lt; 0.01 vs. moderate). This was accompanied by the deterioration of CVD risk factors, including diabetes, dyslipidemia, proteinuria, and lack of exercise habit.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Smoking status or the number of cigarettes was associated with age‐adjusted BMI or CVD risk factors in Japanese males.</jats:p></jats:sec>

DOI： 10.1002/osp4.70089

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Publishing type：Research paper (scientific journal)  

<jats:sec><jats:title>Objective</jats:title><jats:p>Rheumatoid arthritis (RA) is classified into seropositive (SP-RA) and seronegative (SN-RA) types, reflecting distinct immunological profiles. This study aimed to identify the T cell phenotypes associated with each type, thereby enhancing our understanding of their unique pathophysiological mechanisms.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We analyzed peripheral blood T cells from 50 participants, including 16 patients with untreated SP-RA, 17 patients with SN-RA, and 17 healthy controls, utilizing 25 T cell markers. For initial analysis, a dataset was established through manual T cell subset gating analysis. For advanced analysis, two distinct datasets derived from a self-organizing map algorithm, FlowSOM, were used: one encompassing all CD3+ T cells and another focusing on activated T cell subsets. Subsequently, these datasets were rigorously analyzed using adaptive least absolute shrinkage and selection operator in conjunction with leave-one-out cross-validation. This approach enhanced analysis robustness, identifying T cell clusters consistently discriminative between SP-RA and SN-RA.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Our analysis revealed significant differences in T cell subsets between RA patients and healthy controls, including elevated levels of activated T cells (CD3+, CD4+, CD8+) and helper subsets (Th1, Th17, Th17.1, and Tph cells). The Tph/Treg ratio was markedly higher in SP-RA, underscoring an effector-dominant immune imbalance. FlowSOM-based clustering identified 44 unique T cell clusters, six of which were selected as discriminative T cell clusters (D-TCLs) for distinguishing SP-RA from SN-RA. TCL21, an activated Th1-type Tph-like cell, was strongly associated with SP-RA’s aggressive profile, while TCL02, a central memory CD4+ T cell subset, displayed ICOS+, CTLA-4low+, PD-1low+, and CXCR3+, providing insights into immune memory mechanisms. Additionally, TCL31 and TCL35, both CD4−CD8− T cells, exhibited unique phenotypes: CD161+ for TCL31 and HLA-DR+CD38+TIM-3+ for TCL35, suggesting distinct pro-inflammatory roles. Support vector machine analysis (bootstrap n = 1000) validated the D-TCLs’ discriminative power, achieving an accuracy of 86.2%, sensitivity of 85.7%, and specificity of 80.9%.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>This study advances our understanding of immunological distinctions between SP-RA and SN-RA, identifying key T cell phenotypes as potential targets for SP-RA disease progression. These findings provide a basis for studies on targeted therapeutic strategies tailored to modulate the markers and improve treatment for SP-RA.</jats:p></jats:sec>

DOI： 10.3389/fimmu.2025.1491041

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Publishing type：Research paper (scientific journal)  

DOI： 10.1016/s2213-2600(25)00127-4

PubMed

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Total pages：x, 183p   Language：Japanese

CiNii Research

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Event date： 2025.8

Language：English   Presentation type：Poster presentation  

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