Effect of climatic environment on immunological features of rheumatoid arthritis

Kondo, Yuya; Abe, Saori; Toko, Hirofumi; Hirota, Tomoya; Takahashi, Hiroyuki; Shimizu, Masaru; Noma, Hisashi; Tsuboi, Hiroto; Matsumoto, Isao; Inaba, Toshiro; Sumida, Takayuki

doi:10.1038/s41598-022-27153-3

Cited by 7 publications

(4 citation statements)

References 42 publications

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“…We found that most FRGs were differentially expressed between the ferroptosis clusters and that patients in cluster 1 had lower ferroptosis scores. Considering that immune disorder is the key feature of RA, there are abnormal levels of immune cells in blood and synovium in patients [ 30 ]. Therefore, we compared the infiltration of immune cells in the two clusters.…”

Section: Discussionmentioning

confidence: 99%

Ferroptosis-Related Molecular Clusters and Diagnostic Model in Rheumatoid Arthritis

Xie

Chen

2023

IJMS

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Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by synovitis, joint damage and deformity. A newly described type of cell death, ferroptosis, has an important role in the pathogenesis of RA. However, the heterogeneity of ferroptosis and its association with the immune microenvironment in RA remain unknown. Synovial tissue samples from 154 RA patients and 32 healthy controls (HCs) were obtained from the Gene Expression Omnibus database. Twelve of twenty-six ferroptosis-related genes (FRGs) were differentially expressed between RA patients and HCs. Furthermore, the patterns of correlation among the FRGs were significantly different between the RA and HC groups. RA patients were classified into two distinct ferroptosis-related clusters, of which cluster 1 had a higher abundance of activated immune cells and a corresponding lower ferroptosis score. Enrichment analysis suggested that tumor necrosis factor-α signaling via nuclear factor-κB was upregulated in cluster 1. RA patients in cluster 1 responded better to anti-tumor necrosis factor (anti-TNF) therapy, which was verified by the GSE 198520 dataset. A diagnostic model to identify RA subtypes and immunity was constructed and verified, in which the area under the curve values in the training (70%) and validation (30%) cohorts were 0.849 and 0.810, respectively. This study demonstrated that there were two ferroptosis clusters in RA synovium that exhibited distinct immune profiles and ferroptosis sensitivity. Additionally, a gene scoring system was constructed to classify individual RA patients.

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Section: Discussionmentioning

confidence: 99%

Ferroptosis-Related Molecular Clusters and Diagnostic Model in Rheumatoid Arthritis

Xie

Chen

2023

IJMS

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“…Studies have shown that severe sun exposure is directly related to LN, although the mechanism has not been elucidated to date [ 44 ]. Similarly, studies about air pressure and rheumatic autoimmune diseases are increasing in recent years [ 45 , 46 ], but the clear role in LN needs further discussion as well.…”

Section: Discussionmentioning

confidence: 99%

Lupus nephritis or not? A simple and clinically friendly machine learning pipeline to help diagnosis of lupus nephritis

Wang

Wang³

et al. 2023

Inflamm. Res.

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Objective Diagnosis of lupus nephritis (LN) is a complex process, which usually requires renal biopsy. We aim to establish a machine learning pipeline to help diagnosis of LN. Methods A cohort of 681 systemic lupus erythematosus (SLE) patients without LN and 786 SLE patients with LN was established, and a total of 95 clinical, laboratory data and 17 meteorological indicators were collected. After tenfold cross-validation, the patients were divided into training set and test set. The features selected by collective feature selection method of mutual information (MI) and multisurf were used to construct the models of logistic regression, decision tree, random forest, naive Bayes, support vector machine (SVM), light gradient boosting (LGB), extreme gradient boosting (XGB), and artificial neural network (ANN), the models were compared and verified in post-analysis. Results Collective feature selection method screens out antistreptolysin (ASO), retinol binding protein (RBP), lupus anticoagulant 1 (LA1), LA2, proteinuria and other features, and the hyperparameter optimized XGB (ROC: AUC = 0.995; PRC: AUC = 1.000, APS = 1.000; balance accuracy: 0.990) has the best performance, followed by LGB (ROC: AUC = 0.992; PRC: AUC = 0.997, APS = 0.977; balance accuracy: 0.957). The worst performance is naive Bayes model (ROC: AUC = 0.799; PRC: AUC = 0.822, APS = 0.823; balance accuracy: 0.693). In the composite feature importance bar plots, ASO, RF, Up/Ucr, and other features play important roles in LN. Conclusion We developed and validated a new and simple machine learning pathway for diagnosis of LN, especially the XGB model based on ASO, LA1, LA2, proteinuria, and other features screened out by collective feature selection. Supplementary Information The online version contains supplementary material available at 10.1007/s00011-023-01755-7.

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“…This came with upregulation of Xylitol and L-pyroglutamic acid in the microbiome, underling the association between gut, environment and immune system (43). In addition, a recent, but arguably small, population study highlighted the possibility that climatic environment, such as air temperature and pressure, might have an effect on the proportion of T and B cell subpopulations, possibly triggering autoimmunity in RA (44). The potential link between seasonality and the activity of RA is a subject of increasing interest within the medical community, particularly concerning the role of vitamin D (45).…”

Section: Weather and Seasonalitymentioning

confidence: 99%

Environment and arthritis

Ruzzon,

Adami

2024

Clinical and Experimental Rheumatology

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The present narrative review explores the multifactorial aetiology of rheumatoid arthritis (RA) and other immunemediated inflammatory disorders (IMI-Ds), emphasising the significant role of various environmental factors in disease development and exacerbation. Key modifiable environmental factors such as cigarette smoking and air pollution are identified as major contributors to RA. We will also focus on the influence of weather, seasonality, and particularly vitamin D levels, on RA activity, suggesting potential for seasonal management and supplementation to mitigate disease severity. The emerging role of diet and the gut microbiome in RA pathogenesis and progression is discussed as well, with dietary interventions and specific nutrients like omega-3 fatty acids offering protective benefits against inflammation. Despite the mounting evidence around these factors, further research is needed, to better understand the clinical impacts on RA, including well-designed randomised clinical trials.

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Effect of climatic environment on immunological features of rheumatoid arthritis

Cited by 7 publications

References 42 publications

Ferroptosis-Related Molecular Clusters and Diagnostic Model in Rheumatoid Arthritis

Ferroptosis-Related Molecular Clusters and Diagnostic Model in Rheumatoid Arthritis

Lupus nephritis or not? A simple and clinically friendly machine learning pipeline to help diagnosis of lupus nephritis

Environment and arthritis

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